> If this tech is as amazing as you say it is, I'll be able to pick it up and become productive on a timescale of my choosing not yours.
Broadly speaking, I think this is a wise assessment. There are opportunities for productivity gains right now, but it I don't think it's a knockout for anyone using the tech, and I think that onboarding might be challenging for some people in the tech's current state.
It is safe to assume that the tech will continue to improve in both ways: productivity gains will increase, onboarding will get easier. I think it will also become easier to choose a particular suite of products to use too. Waiting is not a bad idea.
What I get a bit annoyed is companies forcing AI tools, getting usage metrics and actively hunting the engineers that don't use the tool "enough", I've never seen anything like it for a technically optional tool. Even in the past, aside from technical limitaions, you were not required to use enough of a tool.
It just sounds like a giant scheme to burn through tokens and give money to the AI corps, and tech directors are falling for it immediately.
> I've never seen anything like it for a technically optional tool
Cloud had a very similar vibe when it was really running advertising to CIO/CTOs hard. Everything had to be jammed into the cloud, even if it made absolutely no sense for it to be run there.
This seems to come pretty frequently from visionless tech execs. They need to justify their existence to their boss, and thus try to show how innovative and/or cost cutting they can be.
That and microservices in lieu of a monolith. Or how about being the odd one out a few years ago when suggesting a MPA instead of a SPA when it made sense. I like to think where at the point of everybody is rebuilding their portfolio website with Angular 1 but this time it's Claude Code and a SaaS instead.
Easier to justify monthly costs than big capital asks (even if your infra is depreciated at a normal rate) is where I think many saw (incorrectly) cost savings. It’s also a bit of these execs mortgaging the future, banking on either being out of their role when the real cost comes due or that people will have incredibly short memories (not a wild assumption).
I think this is the result of a c-suite that has never actually done the work of the businesses they're running. The MBAificiation of management. Of course they constantly do brain-dead shit, they literally don't have a clue how anything actually works in "their" own business.
There weren't really any failure states for the ZIRP "lifestyle CEO". If you remember old black and white movies about pigeons from psych 101 it's been that level of conditioning for how many years now?
If your CEO doesn't look like a taxi dispatcher he's just moving his wings around waiting for a food pellet.
I was trying to think of a way to word this exact argument. I think it’s especially easy when your business technology is not your primary means of revenue generation. Having these execs understand how things work is significantly less critical in these scenarios, so it becomes much easier to hire for alternative characteristics (golf game, pedigree, gender, whatever).
> Cloud had a very similar vibe when it was really running advertising to CIO/CTOs hard. Everything had to be jammed into the cloud, even if it made absolutely no sense for it to be run there.
100% accurate - some of us are old enough to have lived through a few of the mini-revolutions in between the mega-revolutions of Internet/Web in the 1990s and now AI/LLM in the 2020s.
We are in the "stupid phase" of adoption still. C-level people have to follow the herd and they are being evaluated on keeping up with everyone else. Idiotic mandates are a way to cause things to happen short-term even though everyone knows long-term it will have to be re-done.
Consultants gonna make a looooooooot of money this coming decade.
I think the feel bad moment here is how impersonal everything got in 3 decades. During the dotcom bust you still had to meet and talk to people to get interviews started.
Now you can make a perfectly tailored resume, apply to 50 jobs in a day, and it's not unexpected to not get any response from those in 2 weeks. You don't know if it's your resume, the company, or the economy. And no one wants to admit the latter two are problems.
Not to mention the utter disrespect these days. There's no decorum in many of these "professional" settings, when normally you want your interview process to show off your best face.
It's using a bad tool to try to aim at something reasonable-ish: Developers not taking advantage of the tools in places where it's very easy to get use out of them. I have coworkers like that: One spent 3 days researching a bug that Claude found in 10 minutes by pointing it at the logs in the time window and the codebase. And he didn't even find the bug, when Claude nailed it in one.
But is this something that is best done top to bottom, with a big report, counting tokens? Hell no. This is something that is better found, and tackled at the team level. But execs in many places like easy, visible metrics, whether they are actually helping or not. And that's how you find people playing JIRA games and such. My worse example was a VP has decided that looking at the burndown charts from each team under them, and using their shape as a reasonable metric is a good idea.
It's all natural signs of a total lack of trust, and thinking you can solve all of this from the top.
The thing is we’ve always had people who spend more time on their tooling or learn different tools and perform better.
I’ve seen people use notepad and I’ve seen people who are so good at vim that they look like they’re on editing code directly with their mind.
Your particular example is extreme and my guess is the coworker is just not great at debugging. I use Claude all the time for finding bugs, but it fails fairly frequently though. I think there’s probably advantage to having some people who don’t use it that often, so you have someone to turn to when it fails.
I’m definitely not exercising my debugging skills as much as I used to and I’m fairly confident they’ve atrophied.
That, and an objective comparison measuring time saving should include all time that went into learning, configuring, maintaining the tool.
And ideally a sample large enough to capture any wasted time from dead ends in other tasks where the tool may actually fail to solve the problem.
I’ve definitely lost a couple hours here and there from when it felt like I was right on the verge of CC fixing something but never actually got there and finally had to just do it myself anyway.
> But execs in many places like easy, visible metrics, whether they are actually helping or not.
Most execs didn't get where they were by being truly helpful and adding value to the company. They played the game long enough to know that politics trumps accomplishments. The rest from there is the ability to weave a good story (be it slightly or completely exaggerated).
It's not even about trust. It's about incentives in a structure that is dog-eat-dog. Rugged individualism in a corporate structure is a self defeating prophecy. But it's inevitable when executives extract from the company instead of rising the tides for all ships. And shareholders reward it.
> t just sounds like a giant scheme to burn through tokens and give money to the AI corps, and tech directors are falling for it immediately.
This is exactly what's happening. The top 5 or 6 companies in the s&p 500 are running a very sophisticated marketing/pressure campaign to convince every c-suite down stream that they need to force AI on their entire organization or die. It's working great. CEOs don't get fired for following the herd.
> No of course there isn't enough capital for all of this. Having said that, there is enough capital to do this for a at least a little while longer. -- Gil Luria (Managing Director and Analyst at D.A. Davidson)
Elon Musk is planning to put his AI company into the SpaceX IPO, and accelerate getting it into the major indices, effectively making pension funds, banks and individual investors his bag holders.
Patric Boyle has a video on this in case you care for the details.
I guess it's possible for the top companies to have spent so much already, that now the best move is to convince the next tier to do the same otherwise those competitiors may pull ahead without such a financial handicap.
>I've never seen anything like it for a technically optional tool
If you broaden the comparison (only a little bit) it looks suspiciously like employees being forced to train their own replacement (be that other employees, or factory automation), a regular occurrence.
Yeah this is the thing I think many don't want to see. Imagine a bunch of farm laborers being trained to use a tractor/reaper early on its development. Certainly they'd think it's cool and convenient, because it is. But if it works out, then most of those farm laborers are now obsolete, and a handful of them can now replace the rest. And indeed this is why agricultural employment went from the majority of jobs to a footnote.
Tech directors, CEOs, managers, etc tend to be people with a certain personality and ( learned behaviors/thinking ) just like "technical people".
Yes, they tend to be incredible gullible to certain things, over-simplistic and over-confident but also very "agile" when it comes to sweep their failures under the rug and move on to keep their own neck in one piece. At this point in time even the median CEO knows AI has been way overhyped and they over invested to a point of absolute financial insanity.
The first line of defense about the pressure to deliver is to mandate their minions to use it as much as possible.
We spent a fortune on this over-rated Michelin star reservation, and now you kids are going to absolutely enjoy it, like it or not goddammit!
What is there to learn? If anything developers are still the one's training and enhancing the models by giving them more feedback cycles and what works and what doesn't.
I'm encouraging my folks to try it pretty hard because A) I've personally seen the productivity gains and B) using it is at first deeply weird/uncomfortable. Sometimes you've got to convince people to push through that kind of thing.
They measured 16 developers and called it a "study"? That is amusing. Not to mention it was conducted almost a year ago, the tools have already changed dramatically.
So just run a new study this year. I do think the tools have improved, but it should show up empirically. The only people for whom the urgency of "right now" is present is for the C-suite and investor class who are fighting to make sure they survive, but it might also be a crisis of their own making. Don't confuse your identity as a worker with the identity of the capitalist class.
> It's like "A study found that parachutes were no more effective than empty backpacks at protecting jumpers from aircraft."
Are you under the impression that we don't bother to empirically prove things that seem obvious, like the safety benefits of parachutes? You don't think parachute manufacturers test their designs and quantify their performance?
There are no randomized controlled trials that parachutes save lives.
This is repeatedly used as an example in the medical community about the limits of randomized controlled trials. This isn't some impression - your impression that such evidence exists is wrong.
There might be some parachute company tests about effective of velocity, etc., but there are no human trials.
> There are no randomized controlled trials that parachutes save lives.
It's a good thing "randomized controlled trials" aren't the only kind of empirical evidence, then.
We know the limits of how fast a human can safely land. Parachute manufactures have to prove that their designs meet the minimum performance specifications to achieve a safe speed. This proof is not invalidated by the fact that it doesn't include throwing some poor bastard with a placebo parachute out of an airplane to demonstrate that he dies on impact.
Also, the answer to your original question is yes. There are numerous studies showing that multiple monitors improve productivity.
I think my employer should buy me a laptop and possibly a monitor or two to help my productivity because I subjectively feel they'd be helpful, and I have the market power to insist on tools that I subjectively feel are helpful. If my CEO announced that monitors are super important and everyone will be tracked on monitor space usage going forwards, I would still want to see evidence that this is going to accomplish something.
Your CEO likewise subjectively feels all of their employees using AI will be helpful, and has the market power to insist that their employees use them.
When engineers demand evidence that AI is productive, but not that having laptops and monitors are productive, it screams confirmation bias. "I'm right, you're wrong" as a default prior.
I wouldn't call it confirmation bias, but you're right that is my prior. If an executive and a line worker disagree about whether a tool is useful, I assume unless presented with evidence to the contrary that the executive is wrong.
I would emphasize that I don't think there's anything particularly wrong with the converse either. If an executive is just absolutely convinced that dual monitors are a scam and nobody needs more than their laptop screen, they can run their company that way, and I'm sure there are many successful companies with that philosophy.
I'm just using Copilot CLI for mindless stuff and set it to the premium models to meet the quota, as long as they can't see the prompts I think I should be fine
I am seeing this at my work right now. They are about to start using token consumption as _part_ of the performance review process. Obviously this is a coarse and problematic proxy for productivity.
OTOH, it’s an attempt to address a real problem. There are people who are in fact falling behind (I’m talking literally editing code in notepad), and we can either let them get PIPped eventually, or try to bring them along. There is a real “activation energy” required to learning new tools, and some people need an excuse/permission. Not saying that token count is a GOOD signal, but I haven’t heard many better ideas
It's really insane what is happening. My wife manages 70 software developers. Her boss mandated that managers replace 50% of the staff with AI within a year. And, she's scrambling trying to figure out if any of the tools actually work and annoying her team because she keeps pushing AI on them. Unsurprisingly it's only slowed things down and put her in a terrible position.
Brutal. But probably all too common. One of my clients has very suddenly gone all-in on agentic AI and they're in this crazy hurry. (Probably the most annoying part is they want to automate stuff that I built a POC for using GPT-4o, two years ago - at the time they saw no use for it, but now they're all-in on the hype.)
This started literally two weeks ago and a couple of days ago I talked to one of the admin people who wanted an update on the progress I'd made with sanding off some of the rough edges of the very rough implementation that the managing partner had put in place (he bought a Mac Mini, put OpenClaw on it, then gave it admin access to a whole pile of stuff!) I said I needed a couple more days. "Okay," she said, "but I need this quickly, because we're firing people next week."
They have literally gone from no agentic AI, to discovering OpenClaw, to firing people, in a two-week time span.
When economists say that the predicted job losses as a result of AI have not yet shown up in the data, I'm genuinely befuddled. Either we don't have long to wait to start seeing them, or there's something wrong with the data, because you can't tell me what I just described above is an isolated phenomenon.
I also have to say: I've always enjoyed working with this client, but this experience has been a huge turnoff on a number of different levels.
> Probably the most annoying part is they want to automate stuff that I built a POC for using GPT-4o, two years ago - at the time they saw no use for it, but now they're all-in on the hype.
I’d have guessed the most annoying part would be that you’re assisting them in a hare brained scheme to terminate some people’s employment.
For a non-tech case of this, my wife worked at a place that fired like 80% of their writers in anticipation of huge speed-ups they expected from LLMs, a couple years ago.
They had to hire a bunch of them back less than two months later. The speed-ups were approximately nil and making the editors edit AI slop all day long had them all close to quitting.
They didn't even wait to see if there were any actual benefits, they just blindly fired a bunch of people based on marketing lies. I can only assume they're the same sorts who fall for Nigerian Prince scams.
Maybe what they really want her to do is get rid of 50% of her staff and the AI is just an excuse? In that case she should focus on "who can we do without?" rather than "how can we replace people with AI?"
The assumption that those managers have is that it’s easy to replace tech guys because AI is advancing, and a crap like that nonsense.
Funny enough, I got laid off last month, yes I’m a tech guy, now they apparently regret it because they are now scrambling to find a replacement to do the tech tasks!
TBH, I’m happy I got laid off because I’m finally building something I wanted to use.
> I've never seen anything like it for a technically optional tool.
It has often been the case for technologies though, like “now we’re doing everything in $language and $technology”. If you see LLM coding as a technology in that vein, it’s not a completely new phenomenon, although it does affect developers differently.
Language and technology are normal, but we are talking about a metered code editor, nobody have asked me to "use X hours of IntellijIDEA" in the past, or "use git enough or be fired". Tools are never required to be used when they are not needed
Well the tech or the language had some feature to it that lead you to using it. By definition LLM coding doesn’t. It is like the job requirement turned into “ask jeff to write all your code and if you don’t we won’t hire you.”
Technologies were imposed by management whether it made sense or not. Like, “all data exchange formats now have to use XML”, or “all applications must be J2EE now”, because it was the new hot thing. “You” weren’t making that choice, management imposed it. That’s the parallel I’m drawing.
> That was usually only the case where everyone had to be using it or it didn’t work.
Absolutely not, a lot was done just because it was pushed as the current fashion and advertised to be solving problems that either weren’t applicable to the concrete use case or that it didn’t actually solve.
You’re not understanding what I’m saying. If you as a company want to do OOP, having one guy writing everything in imperative style hinders everyone else because their code has to interact with his.
AI isn’t like this because the final output is the same as hand coding.
This is largely due to the age old fact that corporations rarely make decisions based on actual data, introspection, and good judgment. Usually the decision is made first and then the justifications are invented afterwards.
In this case, every executive is terrified of being "left out" in the AI race. As we saw with the mass layoffs across companies, most of CEO decision making is just adhering to herd behavior. So it is literally better for execs to have shoveled a shit ton of money into 'strategic' AI initiatives and have them fail than potentially deal with the potentially remote chance of some other exec or company succeeding with 'AI enabled transformation'.
What makes it even more fun is that nobody really has a good understanding of how to measure the ROI of AI. Hence we have people burning a lot of money due to FOMO and no easy way of measuring the outcome, which is usually how the foundations for good Ponzi schemes are laid.
This is unlikely to end well. However, as usual, it's us, the common plebs, who will suffer regardless of outcome.
We're doing that in my office, forced Cursor use. A good chunk of the "edited by AI" lines in my history were just auto-completing about the same as a traditional intellisense-alike would do (and actually Cursor doesn't seem to supply that, which is frequently annoying and wastes my time, in particular when I need to make sure it hasn't hallucinated a method or property on an object it should be able to "see" the definition of, which it does constantly; IDK maybe there's a setting somewhere, but I don't have to fiddle with settings in vanilla VSCode to get that...)
It's actually kinda useful in some cases, but the UI is terrible and it needs to integrate much better with existing tools that are superior to it for specific purposes, before I'll be happy using it. I'd say the productivity gains are a wash, for me, so far. Plus it's entirely too memory-hungry, I'd just come to accept that a text editor takes a couple GB now (SIGH), and here it comes taking way more than that.
As an employee of a big tech company doing this, it's all fear mongering. We are being told that if everyone doesn't use these tools, our competitors will wipe the floor with us because they are using them and will ship features 10x faster. But many engineers are suspicious as well.
I’m at a big tech company top and essentially no one is a true believer more than 3 or 4 levels down from the top. We’re all just trying to keep our use metrics high enough to not get noticed.
But for those top layers, I’ve never seen so much FOMO in all my life. We’re a very slow moving company but they act like we’ve got 2 weeks to go “AI first” or we’re dead in the water. I’ve never seen such a successful hype cycle. I’m pretty sure it’s the bots that are accelerating it so far behind a normal hype cycle.
It's baffling, to be honest. I'm at a fintech that is currently pushing very hard at this, but in the same breath talking about how we're not a pure software play. I just don't understand where they're coming from.
Sadly, the quote on markets and solvency rings true here. Tech (among an increasing number of sector) is being hit hard by layoffs over this. Nothing is steady right now.
my company (mid-size, publicly traded) is mandating [x] hours spent on AI per week. i have no idea how they're planning on measuring this, and as far as i can tell, neither does management.
suppose it's better than counting lines of code, though.
My uncle leads IT support teams, the org is measuring AI use in writing reports and tickets. The org has very poorly structured and obsolete processes (he's trying to straighten them up as he goes), AI will probably amplify the lack of structure, by making it easier for the work to _look_ as if someone carefully reviewed the issues and followed procedure.
A friend is a team lead in an org that's mandating vibecoding via "Devin", a lesser known player an "architect" chose after shallow review. The company also has endemic process issues and simply can't do deployments reliably, it's behind the times in methodology in every other respect. Higher ups are placing their trust in a B-list agentic tool instead of fixing the problems.
Anyway, I wouldn't be caught dead working at either of those two shops even before the AI rollout, but this is what's going on in the IT underworld.
I hate the AI assistants for ticket-writing. The beneficial use there would be to prompt for possibly-useful information that's not present, or call out ambiguity and let the writer decide how to resolve any of that. Coaching, basically. Suggesting actual text to include, for people who aren't already excellent at ticket-writing, just leads to noisier tickets that take more work to understand ("did they really mean this, or did the LLM just prompt them to include it and they thought sure, I guess that's good?")
[EDIT] Oh and much of your post rings true for my org. They operate at a fraction the speed they could because of organizational dysfunction and failure to use what's already available to them as far as processes and tech, but are rushing toward LLMs, LOL. Yeah, guys, the slowness has nothing to do with how fast code is written, and I'm suuuuure you'll do a great job of integrating those tools effectively when you're failing at the basics....
Lots of organizations don't want to accept that their velocity issues are quality issues. It's often a view held by an old guard that was there when the business experienced growth by adding features, while not having to bear any maintenance burden. The people who remain are either also oblivious to this, or simply have stopped caring.
LLM-generated code hits all the right notes, it's done fast, in great volumes, and it even features what the naysayers were asking for. Each PR has 20 pages of documentation and adds some bulk to the stuff in the tests folder, that can sit there looking pretty. How wonderful! Hell, you can even do now that "code review" that some nerd was always complaining about, just ask the bot to review it and hit that merge button.
Then you ask the bot to generate the commands again for the deploy (what CI pipeline?) and bam! New features customers will love. And maybe data corruption.
A firm that is led by people who can envision, very clearly, revenue-generating and cost-reduction projects - wins. Writing code by hand is absolutely irrelevant. Who fucking cares. The former is what matters.
Code generation acceleration only matters when those pre-requisites are met. How did Apple go from the verge of bankruptcy to where it is today?
All Im seeing is most people are not smart at all - no wonder they are so impressed by LLMs! They can't think straight. I only see this become even worse over-time. Perhaps this is the stated goal.
Well before offices were computerized at all some of the manual processes turned out to be more effective than after full computerization was completely accomplished. Which was sometimes decades later so nobody could tell which workflows it actually applied to, or wouldn't believe it anyway by the 21st century.
It was truly quite rare to have such well-honed manual processes though, the "average" place had a lot of elements that were far from perfect but still benefited after the computerization dust had settled. Then at the opposite end of the spectrum were companies where everything was an absolute shitshow, maybe since the beginning.
That's kind of where Conway's Law comes from, if you benchmark against a manual shitshow that has built up over the years, and replace it with a computerized version, you get a shitshow on steroids. The only other choice would have been to spend the appropriate number of years manually undoing the shitshow before making any really bold moves.
Now AI can really take things to a whole 'nother level, not just on steroids but possibly violating Conway's Law . . . squared.
The only thing I've mandated for engineers is that folks give it a try occasionally, as models, best practices, and tooling improves.
I'm currently tracking exactly two numeric metrics: total MAUs (to track the aforementioned), and total DAUs (to gauge adoption and rightsize seat-licensed contracts.)
Why do you care so much? If these are really revolutionary tools that vastly optimize work, why bother forcing people to "try new models and best practices"?
If the benefit is there people will use it or get left behind, there's no sense having a mandate that people resentfully try the new tooling.
Imagine you had a developer who writes Java using vim. It sounds insane but they are just as productive as everyone else. Then you mandate they have to try IntelliJ every quarter, just to see if maybe they like it now. You're just going to piss them off and reduce their productivity by mandating their workflow.
FWIW in the face of these kind of mandates I have been using tokens but ignoring the output. So it's costing my employer money and they have a warped metric of whether the tool is actually useful.
>If these are really revolutionary tools that vastly optimize work, why bother forcing people to "try new models and best practices"?
"If the colleges were better, if they really had it, you would need to
get the police at the gates to keep order in the inrushing multitude.
See in college how we thwart the natural love of learning by leaving
the natural method of teaching what each wishes to learn, and insisting
that you shall learn what you have no taste or capacity for. The
college, which should be a place of delightful labor, is made odious
and unhealthy, and the young men are tempted to frivolous amusements to
rally their jaded spirits. I would have the studies elective.
Scholarship is to be created not by compulsion, but by awakening a pure
interest in knowledge. The wise instructor accomplishes this by
opening to his pupils precisely the attractions the study has for
himself. The marking is a system for schools, not for the college; for
boys, not for men; and it is an ungracious work to put on a professor."
> Why do you care so much? If these are really revolutionary tools that vastly optimize work, why bother forcing people to "try new models and best practices"?
If AI makes an employee 10X more productive they get a slight pay raise maybe, but the company makes substantially more money or gets substantially more output. So there is a large difference in incentives.
This is true, though I believe savvy employees have leverage to ensure they participate in a larger share of that upside. As you can see from other comments, lots of people will just drag their heels and not give it a good-faith attempt, so it'll often average out in the way you predict.
Are you budgeting time to allow people to properly evaluate LLMs and possibly struggle with them? This is not the sort of new tool whose utility is universally immediately obvious to all builders and craftsmen out there. Are you willing to pay down the likely debt of some individual contributors never clicking with this, or being outright resentful to towards the technology or the mandates? There is a LOT of self-selecting bias from LLM proponents assuming everybody else is willing or able to travel the same path as them.
> Are you budgeting time to allow people to properly evaluate LLMs and possibly struggle with them?
Great question. That is absolutely the goal. My take is that building with LLMs - at least with the current popular harnesses like Claude Code - is a skill on its own, and people need time to develop that skill and also to figure out where these tools might fit into their workflows.
> Are you willing to pay down the likely debt of some individual contributors never clicking with this or being outright resentful to towards the technology or the mandates?
I'll be honest as I have been elsewhere in the thread: A few years from now, I don't know what the state of the technology or its adoption will be, or what expectations of software engineers at large will be.
But for the foreseeable future, yes, absolutely, I'm willing to give engineers the time and space to develop familiarity and comfort with the tools, as long as they're engaging in good faith.
edit: oops, didn't mean to dodge the last part of your question (re: resentment): I genuinely don't know the answer to how I'll handle that, but I'm also sure it'll happen. Hopefully I'll still be in a position to speak publicly about how one can deal with those challenges.
edit 2: also, thank you for the thoughtful questions and dialogue.
> FWIW in the face of these kind of mandates I have been using tokens but ignoring the output. So it's costing my employer money and they have a warped metric of whether the tool is actually useful.
What you're actually doing here, from my POV, is incentivizing your employer to use more invasive metrics when they tried to stay hands-off and mandate the absolute bare minimum of "uh, give it a shot and see if you think it's useful right now."
The analytics that Claude Enterprise exposes are far more intrusive than I would want to be subjected to as an engineer, so I rolled out a compromise. I don't even track who the active users are, currently.
But maybe you're right, and there are enough people sabotaging the metrics out of spite, that there's a reason they provide the other data.
I hope that the engineers in my org are more mature than that, and would be willing to just say "I'm not currently using it", but thanks for giving me something to think about.
> mandate the absolute bare minimum of "uh, give it a shot and see if you think it's useful right now."
That’s not the bare minimum, though. The bare minimum is: “if you are meeting or exceeding your job expectations, great work, keep using the tools that are working for you.”
To a productive employee, merely saying “just try out AI, it might help” feels like the boss saying “just try out astrology or visit a psychic for a reading. You might find it interesting.”
When the CEO, CTO and Director are all saying "everyone has to use AI" I think it's pretty naive to think people will speak out openly. The bare minimum would be making the tools available and letting people do their jobs.
Go ahead and spend more time collecting more granular metrics spying on your employees. Apparently there aren't more valuable things for you to do than micromanaging individual developers.
I think one side of the issues folks are having is that combined with the mandate to use these tools, there is also an expectation or assumption that the developers will instantly get X% more productive. Like, "you must use this tool and you will be twice as productive".
Where I work there as certainly been that kind of discussions, "we need to use AI for this, because no offense but you are simply not fast enough". And this from people who do not understand software development and has never worked with it. They have only read the online stuff about 20X speeds and FOMO. (And my workplace is generally quite laid back and reasonable. I am sure many other places are much more aggressively steered.)
If you have accurate metrics to gauge developer productivity then use them.
But you don’t because if you did you’d be a billionaire.
What you have is metrics that can measure developer busyness. If you use those metrics all you’ll do is run your good devs off and keep the ones who can’t find new jobs.
So you’ll have to do what anyone who manages software teams has always done and trust your line managers to manage your devs.
When it comes to people wasting tokens, most people aren’t gonna to do it with the intent to fuck your metrics. But if you tell people you are measuring something they will find a way in increase that metric whether it results in anything productive or not.
Making the tools available is one thing, but saying you're mandating their use at any level sounds like micro management to me. How would you feel if one of your subordinates started telling you how to do your job? I'm sure you would be mightily pissed off about it.
I don't think telling people what their job is counts as micro management. Part of their job right now is staying abreast of technological developments and experimenting with new ways of working.
Re: some of them being upset about it- probably. Some people are also upset about being required to use Jira. I personally dislike using Okta.
It is micromanagement. If the job are not being done, the best way is to investigate what current practices are blocking people from doing it (the answer is probably meetings and bad communication). The worst way is to present a tool as a silver bullet for tasks you’re not doing and not accountable for.
Where am I presenting the tool as a silver bullet? You seem to be confusing me with someone else in this thread, or making the mistake of turning this into a polarized conversation of "AI is a panacea" vs "AI is worthless".
I engaged in the thread in good faith, and am transparent about what I'm doing and why. I also clarified that part of the job in my org is experimenting with these tools.
Have you ever monitored and encouraged the use of a particular text editor or IDE? If you had an employee whose manager thought was a high performer, but you noticed they used notepad would you encourage that they regularly give vim a try?
The reason we force people to use Jira is because it only works if everyone uses it.
AI doesn’t work like that. If it does enhance productivity 50% then use will spread and the expectations of your line managers will naturally go up and the holdouts won’t be able to keep up.
Or only the exceptional ones will. And in that case why do you care how they do it?
> The reason we force people to use Jira is because it only works if everyone uses it.
In my experience, AI out of the box is at first a useless gimmick - until someone starts seriously playing with it and defines a skill file for integrating it with some internal tool. And another person starts playing with it and figures out that AI is pretty good at using another internal tool but only if the tool runs in --silent=1 mode by default, so as not to confuse AI with too much logging output. And a third person figures out that it's actively dangerous to let AI some some other internal tool - but hey, there's a safer alternative, and which happens to perform better too. And pretty soon you end up with an ecosystem of business-specific scripts and .md files and skills and MCPs that's actually helpful 85%+ of the time. But the only way to get there is to get devs and power users tinkering with it.
In my experience all the md files just pollute the context and make it less likely to do what I want it to do. I’m at a huge org with thousands of power users doing all of this and I haven’t seen anything resembling the results you’re seeing.
But even assuming this is the case, you don’t create enthusiastic power users with threats (implicit or explicit) and metric tracking. The only thing that does is force people to do the minimum to keep their job.
The complaint in the thread is that management is forcing AI tooling usage. If part of your job is to experiment with these tools, then like any experiment, the correct way is to share the findings with a report detailing the methodology and findings. But no one is doing that AFAIK. It’s all superlatives.
>Part of their job right now is staying abreast of technological developments and experimenting with new ways of working.
Not necessarily. A carpenter has a job to make things. Not to use specific tools and keep up with the latest tools used for repairs. It can be suggested, but telling a caprenter what tool to definitely falls under micromanagement.
>Some people are also upset about being required to use Jira
Jira's job is to report metrics to management. That implicit to the job. Telling people how to perform their tickets is micromanagement. The whole goal of a non-junior employee is to trust they can estimate and accomplish their task.
I'll cross that bridge when I get there. No one who works for me has refused to be paid to try out a new technology when I ensure the time is set aside for them to do so.
I was thinking about this but sometimes its hard to distinguish between what the marketing of OpenAI, Anthropic and other companies say and what actually all the companies are doing throughout the world. Any thoughts on that?
For example even the layoffs, nowadays seem to be because of AI or so they said but just a year or two ago there were quite some layoffs and people said "its because of the high demand in COVID and now its over, or Ukraine or inflation" but then that ignores that exactly during that time earlier were there many layofs but it was super easy "Oh COVID and supply chain!" and earlier maybe something else.
Surely there are also economic booms but when did the whole world jsut suddenly started seriously listening to public statements of companies (and that jsut a few with no real income, just money from VCs) and nobody shows us the real data of whats actually happening? E.g. the companies saying they fired 10K due to AI, how much did they actually now direct their budget to AI? How many products are actually being build? Is the productivity the same? Are the customers thinking that support is suddenly amazing or actually it has seriously dropepd in quality? Or no change at all? Is it a company like KFC, your local hardwore chain store, financial isntitutions, truck manufactures or anothet AI company with funding using another AIs company with other funding using now one more AIs company products up to the power suppliers?
For me it seems that its definitely impacting things and a cool technology to be more productive (for example it helps me a lot daily but its not like my life really changed) but the other things I haven't seen yet.
Another point each actual AI generated app is either something akin to a toilet game or not really working (like the C compiler). So where are the amazing enterprise complicated apps fully built via agents? In banks, in government, in apps that respect GDPR and actually are secure but proudly build only or mostly with agents? The only ones, not even secure, are other AI apps to do AI stuff but its whole value it says is to be more productive in the "real" economy but it still hasn't done it yet anywhere. People still struggle with Word or AWS infra or debugging why some specific user cant log in with their custom auth provider at some esoteric region with their laws and audits and GDPR variant.
So one side says its basically a tool from God and they never have created more stuff but on the other hand the other group analyzing blood work, delivering food, writing reports, etc uses it a bit or not at all but all the 95% of problems they had are there with some new ones. Also I'm afraid most of them just write now their email better or with more volume, but no real work is getting done.
So yeah maybe my confusion simply lies in that fact that I have a real job and nobody can keep up with all the slop and shit generated online anymore. I'm open to feedback or learn.
It also seems like skills with particular tech (prompt engineering, harnesses, mixture of experts set ups) doesn't always necessarily pay off when there's a sea change. Hard to predict what you'll want in a few years anyway, right?
> (prompt engineering, harnesses, mixture of experts set ups)
Prompt engineering as a specific skill got blown out of proportion on LinkedIn and podcasts. The core idea that you need to write decent prompts if you want decent output is true, but the idea that it was an expert-level skill that only some people could master was always a lie. Most of it is common sense about having to put your content into the prompt and not expecting the LLM to read your mind.
Harnesses isn’t really a skill you learn. It’s how you get th LLM to interact with something. It’s also not as hard as the LinkedIn posts imply.
Mixture of Experts isn’t a skill you learn at all. It’s a model architecture, not something you do. At most it’s worth understanding if you’re picking models to run on your own hardware but for everything else you don’t even need to think about this phrase.
I think all of this influencer and podcast hype is giving the wrong impression about how hard and complicated LLMs are. The people doing the best with them aren’t studying all of these “skills”, they’re just using the tools and learning what they’re capable of.
In my experience (and this may be confirmation bias on my part), casting a wide net and trying out new tech, while you maintain depth in the area relevant at the time, makes you ready for what's coming, even when you don't know what that may be.
Curiosity is good and helps with your personal development, for sure.
OTOH, tfa specifically said:
> I feel the same way about the current crop of AI tools. I've tried a bunch of them. Some are good. Most are a bit shit. Few are useful to me as they are now. I'm utterly content to wait until their hype has been realised.
So, it's not like he's being deliberate ignorant, rather simply deliberately slow-walking his journey.
Past the sea change: half the reason those prompt and harness solutions seem to work are LLM-lies, the testing is gassing you about how it works and the efficacy, defaulting to ‘yes’.
If you test specific features of those solutions over time you see very inconsistent results, lots of lies, and seemingly stable solutions that one-shot well but suddenly experience behaviour changes due to tweaks on the backend. Tuesdays awesome agent stack that finally works is loading totally different on Thursday, and debugging is “oh, sorry, it’s better now” even when it isn’t. Compression, lies, and external hosting are a bad combo.
Sometimes I imagine a world where computers executed programs the same way each time. You could write some code once and run it a whole calendar month later with a predictable outcome. What a dream, we can hope I guess.
People are doing toy projects and praising them, while some are testing them in real world situations and not findings them that useful. But the former is labelling the latter as luddites and telling them they will be left behind.
As someone on the intersection of both (I've built a lot of vibe coded toy projects and lead a vibe coding initiative at work), they're both right and both wrong.
For a single dev team, vibe coding is great. Write specs, write plans, write code. I know what the project wants and needs because I'm the target market.
At work, I haven't written more than a few lines of code since December. But I work with other people vibe coding this same project. Lots of changing requirements and rapid iteration. Lots of mistakes were made by everyone involved. Lots of tech debt. Sure, we built something in 2 mos that would have otherwise taken us 6 mos, but now I'm fixing the mess that we caused.
I think the critical difference is the attitude towards our situation. My boss said to fix the AI harness so we can vibe code more confidently and freely. But other bosses might cut their losses and ban vibe coding. Who's right? I dunno. In both cases I'd just do what my boss wants me to do. But it's not that I don't want to be left behind. I don't want to lose my job. There's a difference.
Even two or three years ago I had ideas for projects but I could see the models were not ergonomic for my uses. I decided to wait for better models and sure enough the agentic models showed up which are much easier to use.
Next thing I'm waiting on is building a new server for a powerful locally hosted LLM in 5 years. No need to go through the headaches and cost of doing it now with models that may not be powerful enough.
All of these occulted skills, that we literally can't explain why they work are akin to gamblers superstitions. If I write something this way, it works. Its like a gambler who think they order in which the push the buttons on the slot machine makes a difference.
Kind of weird tools also incorporate addictive gambling game's UX design. They're literally allowing you to multiply your output: 3x, 4x, 5x (run it 5 times for a better shot at a working prompt). You're being played by billionaires who are selling you a slot machine as a thinking machine.
> All of these occulted skills, that we literally can't explain why they work are akin to gamblers superstitions.
Yes, it's hard to see how, at this moment in time, "Anybody can write code with an LLM" is so different from "Anybody can make money in the stock market."
The underlying mechanisms are completely different, of course, and the putative goal of the LLM purveyors is to make it where anybody really can write code with an LLM.
I'm typically a nay-sayer and a perfectionist, but many not-so-great things become and stay popular, and this may fall into that category.
> Kind of weird tools also incorporate addictive gambling game's UX design.
It's unclear it started out this way, but since it's obviously going this way, it is certainly prudent to ask if some of this is by design. It would presumably be more worrisome if there were only a single vendor, but even with multiple vendors, it might be lucrative for them to design things so that "true insider knowledge" of how to make good prompts is a sought-after skill.
Why? Because all the folks involved have created a technology in search for a problem to solve. That never, ever works. Steve Jobs of all people left this piece of wisdom behind. Its amazing how few actually apply it.
The internet was never this - its origins go back to the need to able to transmit data - darpa. And this is what we still do now...
The only thing worse than the overuse of AIs is the ever present handwringing and finger-pointing of people who wrongly believe they are infallible AI detectors.
There really isn't anything special to using AI anyways it's not rocket science. Sometimes I will use AI to write me some tailwind tags, sometimes I will use AI to write me a static site for a custom report.
Most of my AI usage comes from doing things I don't enjoy doing like making a series of small tweaks to a function or block of code. Honestly, I just levelled the playing field with vim users and its nothing to write home about
I almost entirely agree with the author's assessment of new technology. Yet that statement rubbed me the wrong way.
Sometimes it is better to get into things early because it will grow more complex as time goes on, so it will be easier to pick up early in its development. Consider the Web. In the early days, it was just HTML. That was easy to learn. From there on, it was simply a matter of picking up new skills as the environment changed. I'm not sure how I would deal with picking up web development if I started today.
This isn't a good example - people were completing 6-month bootcamps and getting $100k offers to do web development not too long ago, decades after the web and HTML took off. After a few years they were making as much as anyone who learned HTML and Web 1.0 back in the 90s.
Are the bootcampers better developers? Probably not. But they still were employable and paid relatively the same.
I think this applies a bit less to the AI sphere, which has the purported goal of making things easier and more automated over time. 90% of the time if you have an AI question you can just... ask the LLM itself.
Remember all the hoopla over how people needed be a "prompt engineer" a couple years back? A lot of that alchemy is basically totally obsolete.
Think about the hoops you had to jump through with early GenAI diffusion models: tons of positive prompt suffixes (“4K, OCTANE RENDER, HYPERREALISTIC TURBO HD FINAL CHALLENGERS SPECIAL EDITION”) bordering on magical incantations, samplers (Euler vs. DPM), latent upscalers, CFG scales, denoising strengths for img2img, masking workflows, etc.
And now? The vast majority of people can mostly just describe desired image in natural language, and any decent SOTA model can handle the vast majority of use cases (gpt-image-1.5, Seedream 4, Nano-banana).
Even when you’re running things locally, it’s still significantly easier than it used to be a few years ago, with options like Flux and Qwen which can handle natural language along with a nice intuitive frontend such as InvokeAI instead of the heavily node-based ComfyUI. (which I still love but understand it's not for everybody).
Things will never be easier long term, you will just be expected to get more done. And if you dont spend time learning the tools, you will get less done than your competition.
"It will grow more complex" is never a good reason to get into things early. It's just your mind playing FOMO tricks on you.
Many developers who picked up the web in the early years struggle with (front-end) web development today. It doesn't matter if they fetched jQuery or MooTools from some CDN as it was done in the mid 00s. Once the tooling became too complicated and ever changing they couldn't keep up as front-end dilettante. It required to commit as professionals.
If you started today, you'd simply learn the hard way, as it's always been done: get a few books or register for a course. Carve some time every day for theory and practice. All the while prioritizing what matters the most to get stuff done quickly right now, with little fluff. You will not learn Grunt, Bower, and a large array of historic tech. You'll go straight for what's relevant today. That applies to abstractions, frameworks, and tooling, but also to the fundamentals. You'll probably learn ES6+ and TS, not JS WAT. A lot of the early stuff seems like an utter waste of time in retrospect.
This is true for all tech. If you knew nothing about LLMs by the end of this year, you could find a course that teaches you all the latest relevant tricks in 5 to 10 hours for 10 bucks.
No, this thread and sub-discussion is about specifically early web fundamentals. The web is special in this sense, it's intentionally long-lived warts and all. So the fundamentals pay outsized dividends. The rube goldberg machine that is modern JS dev still spits out an index.html result.
Being a good professional developer means getting the primitives and the data model not horribly pointed in the wrong direction. So it's extremely helpful to be aware of those primitives. And the argument "nobody is better off knowing assembly as a primitive" doesn't hold because as-said the web is literally still html files. It's right there in the source.
The discussion is centered around the idea that "adopting early" provides some future proofing in a rapidly evolving (and largely non-standard) terrain. I share the FA's position that it does not.
> The web is special in this sense, it's intentionally long-lived warts and all. So the fundamentals pay outsized dividends.
Fundamentals pay dividends, but what makes you think that what you learn as an early adopter are fundamentals? Fundamentals are knowledge that is deemed intemporal, not "just discovered".
The historical web and its simplicity are as available to anyone today as it was back then. People can still learn HTML today and make table-based layouts. HTML is still HTML, whether you learned it then or today. But if back then you intended to become a professional front-end developer, you would still have to contend with the tremendous difficulties that some seem to have forgotten out of nostalgia. You'd soon have to also learn CSS in its early and buggy drafts, then (mostly non-standard) JavaScript (Netscape and IE6) and the multiple browser bugs that required all kinds of hacks and shims. Then you'd have to keep up with the cycles of changing front-end tools and practices, as efforts to put some sense into the madness were moved there. Much in all that knowledge went nowhere since it was not always part of a progression, but rather a set of competing cycles.
Fundamentals are indisputably relevant, but they're knowledge that emerges as victorious after all the fluff of uncertainty has been left behind. Front-end development is only now settling into that phase. With LLMs we're still figuring out where we're going.
This sounds exactly right. I'm someone who learned the web back when IE6 was something we wished everyone was on, and also someone who learned the fundamentals of the web and CS in general enough to try writing a book about it to teach everyone else.
Picking up the web early didn't help with the latter. I spent most of my early time memorizing tips and tricks that only applied to old browsers. I didn't pick up the fundamentals till I went back to school for CS and took a networking class.
web fundamentals and web development fundamentals are different.
How HTML, CSS and javascript come together is extremely relevant to developers 20 years ago and today.
I do support and agree with the parent comment, see the discussion, but I do credit getting into web development when it was raw and open paid dividends for me. Todays ecosystem is opaque in comparison. You don't think there's more friction today?
HTML CSS and JavaScript are just a small subset of web development.
And yes understanding them is still relevant. But when I started I was spending more time memorizing the the quirks of IE6 than I was learning how JavaScript, CSS, and HTML come together.
I think it you start directly in react you don’t learn the layer below it sure. But there’s no reason you have to start leaning react. There’s nothing inherent about starting today that forces you to start directly with React. You could start building a static webpage. And if you did that it would be easier and more fundamental than if you did that same thing 20 years ago because you can ignore most of the non-standard browser quirks.
You're right, fundamentals are distilled, so to think they are free just by getting in early is likely backwards. And earning one's professional chops doesn't stop or start based on when you enter.
Web dev definitely is nostalgic. I miss the early days but I also conveniently erased ie6, binding data to HTML, the need for backbone and jQuery to do anything. hmmm yeah doesn't matter when you start, it's all a grind if you dig deep enough.
> Once the tooling became too complicated and ever changing they couldn't keep up as front-end dilettante. It required to commit as professionals.
The best professionals did not fall for insanity of the modern front-end dilettante and continued hacking shit without that insanitity.
> You will not learn Grunt, Bower, and a large array of historic tech. You'll go straight for what's relevant today.
which will be outdated "tomorrow" just like grunt/bower... are looked at today
> A lot of the early stuff seems like an utter waste of time in retrospect.
This cannot be further from the truth, if you learned Javascript early, like really learned it, that mastery gets you far today. The best front-end devs I know are basically Javascript developers, everything else is "tech du jour" that comes and goes and the less of it you invest in the better off you'll be in the long-run.
> If you knew nothing about LLMs by the end of this year, you could find a course that teaches you all the latest relevant tricks in 5 to 10 hours for 10 bucks.
Hard disagree with this unless you are doing simple CRUD-like stuff
> The best professionals did not fall for insanity of the modern front-end dilettante and continued hacking shit without that insanitity.
"Front-end professional" and "no tooling" have been exclusive propositions since the early 2010s. You either learned to use tools or you were out of the loop.
> which will be outdated "tomorrow" just like grunt/bower... are looked at today
Not really. Historically, the main problem with front-end development has not been change, but the pace of it. That's how it ties in with the current discussion regarding the (now) ever-changing terrain of LLM-assisted coding. Front-end development is still changing today, but it's coalescing and congealing more than it's revolving. The chasms between transitions are narrowing. If you observe how long Webpack lasted and familiarity with it carried over to using Vite, it's somewhat safe to expect that the latter will last even longer and that its replacement will be a near copy. Someone putting time to learn front-end skills today might reap the benefits of that investment longer.
> if you learned Javascript early, like really learned it, that mastery gets you far today.
I did. I got a copy of the Rhino book 4th ed. and read it cover to cover. I would not advise to learn JS today with historical references. JS was not designed like most other languages. It was hastily put together to get things done and it had a lot of "interesting", but ultimately undesirable, artifacts. It only slowly turned into a more sensible standard after-the-fact. Yes, there are some parts that are still in its core identity, but a lot in the implementation has changed. Efforts like "Javascript: The Good Parts", further standardization, and TS helped to slowly turn it into what we know today. You don't need to travel back in time for that mastery. Get a modern copy of the Rhino book and you'll be as good as the best of them.
Yeah, I still get use out of XMLHttpRequest to this day good thing I got in early and variable hoisting isn't gonna get me! /s
A lot of snark aside there's a bit of a false dichotomy (I think) here at work. Whenever or wherever your jumping in point is into $something it will always pay dividends to learn the fundamentals of that $something well and unless you interact with older iterations on that $something then you'll never have to bother learning the equivalent of Grunt, Gulp, Stylus, Nunjuncks and so on for that $something.
With that being said it's also good to put aside time once a year to check out a good recommended (and usually paid) course from an established professional aimed at busy professionals.
As for LLMs I feel it's slowly becoming a thing big enough where people will have to consider where to focus their energy starting with 2027. Kinda like some people branched from web development into backend, frontend and UI/UX a good while back. Do you want to get good at using Claude Code or do you want to integrate gen AI features at work for coworkers to use or customers/users? It's still early days just like when NodeJS started gaining a lot of traction and people were making fun of leftpad.
The web/html is a great analogy. I too am in no rush to be hyper effective with LLMs. In fact i want to deliberately slow down because ai-native coding is so exhausting.
That said, your point about the leverage of learning html and web in the early days compared to now rings true. pre-compiled isomorphic typescript apps are completely unrecognizable from the early days of index.html
Counter point. It’s always advantageous to learn and grow as things evolve. This way you have an active role and maybe a say in how it will evolve. And maybe you could contribute towards that evolution (despite poor execution openclaw showed what LLMs could be doing)
> There are a 16,000 new lives being born every hour. They're all starting with a fairly blank slate.
Not long ago we were ridiculing genZ for not knowing why save icon looks like a floppy disk.
I’ve learned a lot of stuff that don’t really benefits me right now, but now and then I encounter a situation that made me happy that I did. It may never happens for some, but at the time, I was probably happy learning it.
But there’s some stuff that I don’t bother explore in depth because my time is finite and I don’t really need it. And anything LLM tooling is probably easier than a random JS framework. Vim’s documentation is probably longer than cursor’s.
I agree with this point. There is absolutely a 'left behind' gap that is under-explored.
My last job was a cable technician - making house calls to fix wifi, satellite tv, phone issues. Mostly elderly residents. The majority of them all were computer and phone illiterate. They were slow adopters to the fast-moving technology and many of them did not know how to operate their devices after we (UI/UX/hardware/software engineer 'we') removed them.
I wonder if this also has contributed to the elderly lonliness problem - sure its probably mostly related to physical companionship, acceptance of aging, etc, but the world that they knew (in general and the technological world they grew up in) is no longer recognizable.
But maybe it doesn’t matter that much to them. I don’t know how to skin a rabbit, but that knowledge could be handy in some situations. But I don’t see myself being in that situation other than accidentally.
My mother has a phone, but only use it to call. She has never needed a computer even though I spent my teenage years glued to one. But I have like 1 percent of a skill in cooking.
Exactly. We look at older people and think “oh, look at those poor souls. They don’t know X and Y technologies and they keep doing things the old way! They must feel so left behind.” Nothing is further from the truth. My whole life I’ve lived in neighborhoods full of people 20+ years older than me and not once did I have a neighbor or friend who I thought was overwhelmed with the pace of modern life and upset about how different the world was becoming from what they are used to. This is a trope. People are resilient and adaptive, and as you get older you learn how to embrace new things that actually help and reject new things that don’t. As I get older, I find myself just not caring about a lot of things that younger people care about and not doing a lot of things they do. I don’t use social media, I still pay for things with cash and checks, I don’t understand or care about the Kardashians or reality tv. My phone is 8 years old. I listen to prog rock and new wave music, and I probably couldn’t name a single popular musical performer today (besides Taylor Swift because I have a daughter). I don’t feel even slightly “left behind” or “obsolete.”
But it's so easy to try something like Claude Code. It's not like you need to get up to speed. There is no learning curve*, that's the nature of AI. Just start using it and you'll see why it has attracted so much hype.
*I should qualify that "using" CC in the strict sense has no learning curve, but really getting the most out of it may take some time as you see its limitations. But it's not learning tech in the traditional sense.
I've been playing with it on weekends for the last few months. 9 out of 10 projects, it's failed.
Projects as simple as "set up a tmux/vim binding so I can write prompts in one pane and run claude in the other". Fails.
I've been coding for over 20 years.
If there is no learning curve, why doesn't it work for me? You can't say I'm not using it right, because if that was true, then all I need to do is climb the learning curve to fix that, the curve that you say doesn't exist.
It doesn't work if you're treating it like a peer engineer. It only works if you treat it like you're a customer with no concern with how it works behind the scenes.
That's what's being asked of me in my last two jobs. Vibe code it, if it's bad just throw it away and regenerate it because it's "cheap". The only thing that matters is that you can quickly generate visible changes and ship it to market.
Out of frustration I asked upper management (in my current job), if you want me to use AI like that then I'll do it. But when it inevitably fails, who is responsible? If there's no risk to me, I will AI generate everything starting today, but if I have to take on the risk I won't be able to do this.
Their response was that AI generates the code, I'm responsible for reviewing it and making sure it's risk free. I can see that they're already looking for contractors (with no skin in the game) that are more than willing to run the AI agents and ship vibe code, so I'm at a loss on what to do.
I've used Claude Code to do everything from vibe-code personal apps including a terminal on top of libghostty to building my perfect desktop environment on NixOS (I'd never used Nix until then).
I'm not sure why it isn't working for you. Maybe your expectation is a perfect one-shot or else it has zero value, and nothing in between?
But my advice is to switch gears and see the "plan file" as the deliverable that you're polishing over implementation. It's planning and research and specification that tends to be the hard part, not yoloing solutions live to see if they'll work -- we do the latter all the time to avoid 10min of planning.
So, try brainstorming the issue with Claude Code, talk it through so it's on the same page as you, ensure it's done research (web search, docs) to weigh the best solutions, and then enter plan mode so it generates a markdown plan file.
From there you can read/review,tweak the plan file. Or have it implement it. Or you implement it. But the idea is that an LLM is useful at this intermediate planning stage without tacking on additional responsibilities.
I think by "no learning curve" they are referring to how you can get value from it without doing the research you'd need to use a conventional tool. But there is a learning curve to getting better results.
I learned my plan file workflow just from Claude Code having "Plan Mode" that spits out a plan file, and it was obvious to me from there, but there are people who don't know it exists nor what the value of it is, yet it's the centerpiece of my workflow. I also think it's the right way to use AI: the plan/prompt is the thing you're building and polishing, not skipping past it to an underspecified implementation. Because once you're done with the plan, then the impl is trivial and repeatable from that plan, even if you wanted to do the impl yourself.
I'm way past the point of arguing anything here, just trying to help.
> So, try brainstorming the issue with Claude Code, talk it through so it's on the same page as you, ensure it's done research (web search, docs) to weigh the best solutions, and then enter plan mode so it generates a markdown plan file.
From there you can read/review,tweak the plan file. Or have it implement it. Or you implement it.
This is exactly the workflow that works very well for me in Cursor (although I don't use their Plan Mode - I do my version of it). If you know the codebase well this can increase your speed/productivity quite a bit. Not trying to convince naysayers of this, their minds are already made up. Just wanted to chime in that this workflow does actually work very well (been using it for over 6 months).
The first time I saw something like this in action was in a video about agentic blabla features in VS Code on the official VS Code YouTube channel. Pretty much write a complete and detailed specification, fire away and hope for the best. The workflow kinda clicked for me then but I still find a hard time adjusting to this potential new reality where slowly it won't make sense to generally write code "by hand" and only intervene to make pinpoint changes after reviewing a lot of code.
I've been reading a book about the history of math and at some points in the beginning the author pointed out how some fields undergo a radical change within due to some discovery (e.g. quantum theory in physics) and the practitioners in that field inevitably go through this transformation where the generations before and after can't really relate to each other anymore. I'm paraphrasing quite a bit though so I'll just recommend people check out the book if they're interested: The History of Mathematics by Jacqueline Stedall
I haven't written code by hand since December when Claude Opus 4.5 came out. It was clear that the inflection point arrived where it's at least as good as I am at implementing a plan. But not only that: it had good ideas like making impossible states impossible with a smart union type without being told and without me deeply modeling the domain in my head to derive a system invariant I could encode like that.
It was depressing watching all of this unfold over the last few years, but now I'm taking on more projects and delivering more features/value than ever before. That was the reason I got into software anyways, to make good software that people like to use.
> the generations before and after can't really relate to each other anymore
Yeah, good point. In some ways it's already crazy to me that we used to write code by hand. Especially all the chore work, like migrating/refactoring, that's trivial for even a dumb LLM to do. It kinda feels like a liability now when I'm writing code, kinda like how it feels when the syntax highlighting or type-checker breaks in the editor and isn't giving you live feedback, so you're surprised when it compiles and runs on the first try.
I remember having a hard time imagining what it was like for my dad to stub out his software program on paper until his scheduled appointment with the university punch card machine. And then sure being happy that I could just click a Run button in my editor to run my program.
Did it not work after the first try and you gave up? Did it not produce any usable code that you could hand tweak or build off of? I want to understand your definition of "failed" here.
What's your definition of "working"? Do you consider it working, when you have to put more effort into prompting back-and-forth than writing it the old way?
I honestly think the people who love Claude were not super proficient coders. That's the only thing I can think of to explain why writing gobs of English and then code reviewing in a loop could be easier than just coding yourself.
> There is no learning curve*, that's the nature of AI.
There isn't? Then why is it that whenever devs have tried it and not achieved useful results, they're told that they just haven't learned how to use it right?
You aren't holding it wrong, the truth is AI is a mixed bag, leaning towards a liability.
If people really counted all the time they spend coddling the AI, trying again, then trying again and again and again to get a useful output, then having to clean up that output, they would see that the supposed efficiency gains are near zero if not negative. The only people it really helps are people who were not good at coding to begin with, and they will be the ones producing the absolute worst slop because they don't know the difference between good and bad code. AI is constantly trying to introduce bugs into my codebase, and I see it happening in real-time with AI code completion. So, no you aren't "holding it wrong", the other people are no different than the crypto-bro's who were pushing blockchain into everything and hoping it would stick.
Imagine you are a JS dev and github comes out with a new search feature that's really good. it lets you use natural language to find open source projects really easily. So whenever you have a new project you check to see if something similar exists. And instead of starting from scratch you start from that and tweak it to fit what you want to do.
If you were the type of person who makes tiny toy apps, or you worked on lots of small already been done stuff, you'd love doing this. It would speed you up so much.
But if you worked on a big application with millions of users that had evolved into it's own snowflake through time and use, you'd get very little from it.
I think I probably could benefit from looking at existing open source solutions and modifying them a lot of the time, and I kinda started out doing that at first. But eventually you realize that even though starting with something can save you time, it can also cost you a ton of time so it's frequently a wash or a net negative.
Nothing you described in this comment is only achievable with "AI". I've been able to search for and find open source projects since forever, and fork them and extend them, long before an LLM was a glimmer in Sam Altman's beady eye.
No it’s not at all. AI just makes finding it faster. But that’s my point AI isn’t that different from what you could already do before. Most of us didn’t do things that way before, so maybe programming like that is just a bad idea.
I've started "racing" Claude when I have a somewhat simple task that I think it should be able to handle. I spend a few minutes writing out detailed instructions, which I already knew because I had to do initial discovery around the problem domain to understand what the goal was supposed to be. It took a while to be thorough enough writing it down for Claude, which is time I did not need to spend if I had just started writing the code myself - I'm sure the AI-bro's aren't considering the time it takes just to write down instructions to Claude vs just start coding.
So then Claude starts discecting the instructions. I start writing some code.
After a while Claude is done, and I've written about two or three dozen lines of code. Claude is way off, so I have to think about why and then write more instructions for it to follow. Then I continue coding.
After a while Claude is done, and I've written about three dozen more lines of code. Claude is closer this time, but still not right. Round 3 of thinking about how Claude got it wrong and what to tell it to do now. Then I continue coding.
After a while Claude is done (yet again), and I've written a lot more code and tested it and it's working as needed. The output Claude came up with is just a little bit off, so I have it rework the output a little bit and tell it to run again.
I downloaded the resulting code Claude wrote and compared it to my solution, and I will take my solution every single time. Claude wrote a bloated monstrosity.
This is my experience with "AI", and I'm honestly not loving it.
It does sometimes save me time converting code from one language to another (when it works), or implementing simple things based on existing code (when it works), and a few other tasks (when it works), but overall I end up asking myself over and over "Is this really how developers want the future to be?"
I'm skeptical that these LLM-based coding tools will ever get good enough to not make me feel ill about wasting my time typing instructions to them to produce code that is bloated and mostly not reusable.
Because the AI bros hyping it up are incapable of admitting that the hype is overblown. That would mean they have nothing to sell you, so of course they aren't going to say that.
I gave Claude Code with Sonnet 4.6* a try a few weeks ago. I pointed it at a hobby project with less than 1kloc of C (about 26,500 characters) across ~10 modules and asked it to summarize what the project does. It used about $0.50 worth of tokens and gave a summary that was part spot on and part hallucinated. I then asked it how to solve a simple bug with an easy solution. It identified the right place to make the fix but its entire suggested solution was a one-liner invoking a hallucinated library method.
I use LLMs pretty regularly, so I'm familiar with the kinds of tasks they work well on and where they fall flat. I'm sure I could get at least some utility from Claude Code if I had an unlimited budget, but the voracious appetite for tokens even on a trivially small project -- combined with a worse answer than a curated-context chatbot prompt -- makes its value proposition very dubious. For now, at least.
* I considered trying Opus, but the fundamental issue of it eating through tokens meant, for me, that even if it worked much better, the cost would dramatically outweigh the benefit.
I think working with the technology gives you powerful intuitions that improve your skill and lead to better outcomes, but you don't really notice that that's what's happening. Personally speaking - and I suspect this is true of most people in general - I have very poor recollections of what it was like to be really bad/new at things that I am now very skilled at.
If you have try teaching someone something from the absolute ground up, you will quickly realize that a huge number of things you now believe are "standard assumptions" or "obvious" or "intuitive" are actually the result of a lot of learning you forgot you did.
I think it comes down to your own personality, appetite, and also how external factors like hype might impact you (resent, annoyance, curiosity, excitement).
Then what is the point? If what I'm doing can be done by Claude, as operated by someone who "doesn't need to get up to speed", then I really need to look at another career.
I've let tech pass me by many times, then the tech that passed me which I was never in a position to use got replaced by the next big tech innovation. I've found that you can climb aboard the train at anytime since everything new is a lot easier to get started on than learning C and having to manually allocate memory.
Some might be getting into AI in order to sell AI. As OpenClaw has shown, there is opportunity in this space to be a trailblazer. There are no doubt companies that are not tech-aligned that someone could help set up local LLMs for…
For me though, I'm dabbling in AI because it fascinates me. Bitcoin was like, I don't know, Herbalife? —never interesting to me at all.
Mistakes are less costly in the beginning and the knowledge gained from them is more valuable.
Over-sharing on social media. Secret / IP leaks with LLMs. That kind of thing.
I agree:
FOMO is an all-in mindset. Author admits to dabbling out of curiosity and realizing the time is not right for him personally. I think that's a strong call.
I think it was challenging 2 or 3 years ago. I plunged a year ago and it already was quite easy to use mainstream tools. I could run some local models with Ollama by just installing it. I could use coding assistance in VSCode. Connecting with http API to use AI within applications you build was also easy for local models or cloud.
There are loads of BS tools out there of course but I don’t use that many tools.
Broadly speaking I agree. But the reality for many SWEs is that if they don't learn new AI tools they'll get let go. It's use AI or be replaced by AI (or, more accurately, be replaced by someone using Ai) for many folks.
I think it's a luxury to be able to ignore a trend like AI. crypto was fine to ignore because it didn't really replace anyone, but Ai is a different beast
I think they want to sell that perception, but the biggest thing the tech execs want in their SWEs is fear.
Fear that makes us stay in our jobs, even when raises and bonuses stagnate, because the market is scary with all the "AI layoffs" (which have largely been regular downsizing with the AI label slapped on).
Fear that makes us use the LLMs and then put in extra hours when we don't see the 10x productivity gains that they expect.
Fear that makes us erode our own skills and become dependent on these gatekeepers to maintain even a base level of productivity.
So much of the advertising and discussion around AI is based around fear. It's inevitable. It will take your job. Render you useless. It will render humanity useless. You better get in now, or you'll both lose your job and could end up in a virtual hell (https://en.wikipedia.org/wiki/Roko%27s_basilisk).
I think you're right that it is fear. But it's kind of a self fulfilling fear. Teams are requiring employees to use AI, probably because of this fear. Maybe it isn't directly taking my job, but it is necessary for me to use Ai to keep my job.
That certainly could be true for some teams. Personally I think AI has directly improved my productivity. There's certainly sharp edges and we definitely shot ourselves in the foot more than a few times in ways we wouldn't have without AI, but by and large we've built an internal app where no human wrote a single line of code. I don't think we'd have been able to build it in 2 months without AI. We could have gone faster but the bottleneck was product and user testing, not code.
Sure, in the same way I can always quit being an engineer and become a farmer. It can work for some, but not everyone.
FWIW I do client work on the side. Full time client work has always been more draining than just having a regular job IME. Maybe I just can't find the right clients, but that's not something I have to worry about when I work for a company.
Oh. Well personally I don't mind using AI, and I use AI when I do client work as well (they know and some clients who use AI apps like lovable expect it).
But I know not everyone is in my shoes which is where my comment is coming from.
The early adopters started years ago and they've seen improvements over time that they started attributing them to their own skill. They tell you that if you didn't spend years prompting the AI, it will be difficult to catch up.
However, the exact opposite is happening. As the models get better, the need for the perfect prompt starts waning. Prompt engineering is a skill that is obsoleting faster than handwriting code.
I personally started using codex in march and honestly, the hardest part was finding and setting up the sandbox. (I use limactl with qemu and kvm). Meanwhile the agentic coding part just works.
i thought so too. but now we are onboarding project managers in non-tech fields to Claude Code and they are crushing it. on a terminal. vs code. the first thirty min is the hard part, after that the feedback loop kicks in. they ask for what they really want, they get it.
The thing is, this post is hitting a straw man. ngmi culture was deeply toxic and pervasive in crypto. I think the people who are really into LLMs are having a blast.
I'm definitely having a blast, but I agree with the author. You're not going to get left behind, the "getting left behind" rhetoric was just cryptocurrency pump-and-dumpers. It's fine to wait and not engage if you don't want to.
I had an heavy ai user on my team say that “those who learn how ti use the tools wont get fired, those who dont are gone”. I used it to generate a bunch of cfn and it worked fine from an example and a couple line prompt, doesnt seem that hard to learn to me.
Now reviewing the 1k lines it generated and making sure its secure, thats going to take me longer than writing it by hand.
Yeah, I think this is it. If you don't learn to use them, you'll be much slower than people who do, but also they're not really that hard to learn, so it's not super urgent.
I'm still confused about the things I'll be slower in though, and I'm being sincere with that confusion. If it's "boilerplate", then I haven't done enough research or pick a library which has little to none of that, or I'm not using the template(s) built into whatever framework I am using.
For example, in one of the projects I'm working on, I'm using the VSA pattern. I have the list of 50 to 75 features I need to implement and what "categories" they slot into, I have all of the frameworks and libraries picked out, and I have built out "feature templates" with all of the boilerplate setup (I'm reusing these over multiple projects going forward). for each of the features all I need to do is
'ftr new {FEATURE_TYPE} {FEATURE_NAME} {OUTPUT_FOLDER}'
and then plug int the domain specific business logic.
I'll most likely use Claude/Codex/Whatever to write out some of my tests, but the majority of the 'boilerplate' is already done and I'm just sorting out the pieces that matter / can't be automated.
Am I missing something huge with these tools?
Don't get me wrong, for doing reverse engineering they're great helpers and I've made a tonne of progress on projects that had been languishing.
I find that can write features 5-10x faster with these tools than by hand, at a comparable level of quality (though it hasn't been long enough for me to judge what'll happen in a year).
Would you be able to give an example of a feature? For my example, I need to query an ancient undocumented database , pull back a pile of data, do some validations on it, and then show it to the user or pass it along with another processing step. The human part is researching the database and the data living in it, and implementing the validation(s) while talking to a business user, everything else can be templated.
Oh yes, this is what LLMs excel at. Introspecting a database, either the schema or the live data, running a few checks to see whether all the data had the same shape (or how many different shapes it has), writing validations to catch edge cases, they do this extremely quickly and pretty accurately, whereas it would have taken me hours of trawling.
Then I can look at the output and say things like "what if the data is lowercase?" or anything else I suspect they may have missed. A few rounds of these and I have a pretty good feel for the quality of the resulting checks, while taking a few minutes of my attention/tens of minutes of wallclock time to do.
If I don't have experience with the underlying framework/language/thing being modified, it makes it quite difficult to trust the actual review. In this example, I haven't worked heavily with Cloudformation, so I can't call b.s if it leaves a database instance exposed to the wider public internet rather than in my company's private VPC.
You can ask the agent to check that it doesn't leave a database instance exposed to the public, and present you with proof for you to check (references to the code and the relevant Cloudformation documentation). Then repeat this for all the things you'd normally want to check for in a code review.
In that case I'm just moving the reading of the documentation from reading it as I'm writing the yaml to when I'm doing a code review. Not saying it isn't helpful to have a pair researcher, just seems like I'm moving things around .
You're not actually doing engineering if you're just vibe-coding, reviewing, and testing all the way down. What the hell is that? Just a weird simulacrum of software development that will break apart in unpredictable ways. Security consultants are going to have very lucrative careers in the coming years.
The llm person having a blast is compelled to push everyone to see what they see. If they have a leadership role at their company, then the getting-left-behind drum does get banged in the form of "ai native company transformation" initiatives.
Lots, and not just online. I run into them regularly in my office, and so do my friends and family in tech. One of my coworkers is now spending all his time writing SKILLs, he's convinced that we'll never need to solve operational issues again if we have the right SKILLs.
I'm not worried about being left behind technologically, but I am worried about being left behind after every company on the planet decides we need N years experience in AI to be employable.
Ok, here is the risk of being left behind - if we have moderately fast take-off, the 1-2 years required to upskill in AI might mean you find yourself unemployable when your role gets axed.
I don’t think folks are taking seriously the possible worlds at the P(0.25) tail of likelihood.
You do not get to pick up this stuff “on a timescale of my choosing”, in the worlds where the capability exponential keeps going for another 5-10 years.
I’m sure the author simply doesn’t buy that premise, but IMO it’s poor epistemics to refuse to even engage with the very obvious open question of why this time might be different.
But they have engaged with it, and made an assessment about it's current utility.
We have no reason to believe that they won't keep an eye on this.
Little to nothing about AI tools so far suggests that that one can't just as easily pick the skills later. Tools that will get "exponentially better" will almost certainly be unrecognizable to someone desperately engaging with them now, for not other reason the sake of "having 1-2 years of experience"
Someone might reasonably choose to to bet on the upside. That doesn't imply that everyone else ought to fearfully hedge.
Eh, I'm not super worried. After all, Every six months or so, the latest model changes everything and the former model was complete garbage. It's not just a new model—it's a new paradigm shifting the landscape of agentic development.
One area where it may end up leaving you behind is if you’re looking for a job right now. There are a lot of companies putting vibe coding in their job requirements. The more companies that do this the harder it will be to find employment if you’re not adopting this tool/workflow.
I don't know, I kind of wonder if this applies to all technologies equally.
for example, (dodging the whole full-self-driving controversy) tesla cars have had advanced safety features like traffic aware cruise control and autosteer for over a decade.
so, buying into safety early...
for other technologies, there's sort of the rugpull effect. The people who get in early enjoy something with little drama vs the late adopters. ask people who bought into sonos early vs late, probably more exampless of this.
so getting technology the founders envisioned, vs later enshittified versions.
Even if it reaches the end state of AGI, e.g. AI that's smarter and more capable than 90% of humans, there'll still be a huge learning curve to using it well, as anyone who's tried managing very smart humans can attest.
somehow the ai bros are saying creating .md files is the real ingenuity, and couldn't be learned in say half a day. There's absolutely no rush to keep up with the latest code producing tools especially when they're all "pay to play".
We've seen multiple ideas/products get quickly absorbed into frontier models, OSS, or well-funded startups. The cycle from "interesting idea" to "commoditized feature" is getting very short. Personally, there were three of these in the last year.
And even if your product is genuinely great, distribution is becoming the real bottleneck. Discovery via prompting or search is limited, and paid acquisition is increasingly expensive.
One alternative is to loop between build and kill, letting usage emerge organically rather than trying to force distribution.
This is the central thing that changes in a person with age. When you are born, the only thing you do is pick up new things. Literally nothing else. When you're young, picking up new things is how you improve your social position. It's what you do to even be talked to in the first place. It's what you do to get a girl/boyfriend, or be the best student in class, or to be the best (or worst even) employee at your first job ...
Once you have a good social position, or at least one you're happy with, you stop doing this, and you grow ever more irritated at others doing it ... because it's your social position that they're coming after. And they're younger, more motivated and hungrier. More than that, a decent chunk of these people want a better social position, even if that means taking yours.
Sure, but this is mostly irrelevant because software dev is one of the youngest fields. Something like 70% of professional devs are under 35 depending on which survey you look at. And the number of CS graduates exploded over the last decade.
The only reason the numbers aren’t even more tilted is because people stopped hiring juniors 2 years ago. But they’re out there, and if there’s a new technology around that makes them vastly more productive than the seniors today, there’s nothing stopping companies from hiring them.
No. It just assumes there is no utility in the underlying tech, someone who believes vaccines don't work could make the same argument. Most people trust Morgan-Stanley when it comes to financial instruments more than some bozo on the internet.
You do have to drag stubborn people, kicking and screaming, into the future or they will continue using old tech. The article is framed in the past tense, "someone tried", "the crypto grift was". As if it's not currently swallowing the world. I guess he is so maximally sensible that he self-assess faster than MS and realizes bitcoin just isn't for him every time.
He has a strange hyper-specific definition of utility and productivity, (wrote my MSc, had fun) don't count.
Feels like a false equivalency. It's just my experience, but I've completely ignored crypto and the metaverse, and I don't get the sense I'm missing out on much.
In contrast, LLMs in their current state have (for me) dramatically reduced the distance between an idea and a working implementation, which has been legitimately transformative in my software dev life. Transformative for the better? Time will tell I suppose, but I'm really enjoying it so far.
As a freelancer I do a bit of everything, and I’ve seen places where LLM breezes through and gets me what I want quickly, and times where using an LLM was a complete waste of time.
For sure. The more specialized or obscure of things you have to do, the less LLMs help you.
Building a simple marketing website? Probably don’t waste your time - an LLM will probably be faster.
Designing a new SLAM algorithm? Probably LLMs will spin around in circles helplessly. That being said, that was my experience several years ago… maybe state of the art has changed in the computer vision space.
> The more specialized or obscure of things you have to do, the less LLMs help you.
I've been impressed by how this isn't quite true. A lot of my coding life is spent in the popular languages, which the LLMs obviously excel at.
But a random dates-to-the-80s robotics language (Karel)? I unfortunately have to use it sometimes, and Claude ingested a 100s of pages long PDF manual for the language and now it's better at it than I am. It doesn't even have a compiler to test against, and still it rarely makes mistakes.
I think the trick with a lot of these LLMs is just figuring out the best techniques for using them. Fortunately a lot of people are working all the time to figure this out.
Agreed. This sentiment you are replying to is a common one and is just people self-aggrandizing. No, almost nobody is working on code novel enough to be difficult for an LLM. All code projects build on things LLM's understand very well.
Even if your architectural idea is completely unique... a never before seen magnum opus, the building blocks are still legos.
Specialized is probably not the word I'd use, because llms are generally useful to understand more specialized / obscure topics. For example I've never randomly heard people talking about the dicom standard, llms have no trouble with it.
I think there is a sweet spot for the training(?) on these LLMs where there is basically only "professional" level documentation and chatter, without the layman stuff being picked up from reddit and github/etc.
I was looking at trying to remember/figure out some obscure hardware communication protocol to figure out enumeration of a hardware bus on some servers. Feeding codex a few RFC URLs and other such information, plus telling it to search the internet resulted in extremely rapid progress vs. having to wade through 500 pages of technical jargon and specification documents.
I'm sure if I was extending the spec to a 3.0 version in hardware or something it would not be useful, but for someone who just needs to understand the basics to get some quick tooling stood up it was close to magic.
The standard for obscurity is different for LLMs, something can be very widespread and public without the average person knowing about it. DICOM is used at practically every hospital in the world, there's whole websites dedicated to browsing the documentation, companies employ people solely for DICOM work, there's popular maintained libraries for several different languages, etc, so the LLM has an enormous amount of it in its training data.
The question relevant for LLMs would be "how many high quality results would I get if I googled something related to this", and for DICOM the answer is "many". As long the that is the case LLMs will not have trouble answering questions about it either.
One tendency I've noticed is that LLMs struggle with creativity. If you give them a language with extremely powerful and expressive features, they'll often fail to use them to simplify other problems the way a good programmer does. Wolfram is a language essentially designed around that.
I wasn't able to replicate in my own testing though. Do you know if it also fails for "mathematica" code? There's much more text online about that.
> Building a simple marketing website? Probably don’t waste your time - an LLM will probably be faster.
This is actually where I would be most reluctant to use an LLM. Your website represents your product, and you probably don’t want to give it the scent of homogenized AI slop. People can tell.
They can tell if you let it use whatever CSS it wants (Claude will nearly always make a purple or blue website with gross rainbow gradients). They can also tell if you let it write your marketing copy.
If you decide on your own brand colors and wording, there’s very little left about the code that can’t be done instantly by an LLM (at least on a marketing website).
I would say they are not changing but evolving and you evolve with them.
For example: gemini became a lot better in a lot more tasks. How do I know? because i also have very basic benchmarks or lets say "things which haven't worked" are my benchmark.
Some subscriptions offer "unlimited tokens" for certain models. i.e. GitHub co-pilot can be unlimited for GPT-4o and GPT-4.1 (and, actually, GPT-5 mini!). So: I spent some time with those models to see what level of scaffolding and breaking things down (hand holding) was required to get them to complete a task.
Why would I do that? Well, I wanted to understand more deeply how differences in my prompting might impact the outcomes of the model. I also wanted to get generally better at writing prompts. And of course, improving at controlling context and seeing how models can go off the rails. Just by being better at understanding these patterns, I feel more confident in general at when and how to use LLMs in my daily work.
I think, in general, understanding not only that earlier models are weaker, but also _how_ they are weaker, is useful in its own right. It gives you an extra tool to use.
I will say, the biggest findings for "weaknesses" I've found are in training data. If you're keeping your libraries up-to-date, and you're using newer methods or functionality from those libraries, AI will constantly fail to identify with those new things. For example, Zod v4 came out recently and the older models absolutely fail to understand that it uses some different syntax and methods under the hood. Jest now supports `using` syntax for its spyOn method, and models just can't figure it out. Even with system prompts and telling them directly, the existing training data is just too overpowering.
Honestly I think this is the primary explanation for why there is so much disagreement on if LLMs are useful or not. If you leave out the more motivated arguments in particular.
> In contrast, LLMs in their current state have (for me) dramatically reduced the distance between an idea and a working implementation, which has been legitimately transformative in my software dev life.
Feels like a false dichotomy.
Have I become faster with LLMs? Yes, maybe. Is it 10x or 1000x or 10,000x? Definitely not. I think actually in the past I would have leaned more on senior developers, books, stack overflow etc. but now I can be much more independent and proactive.
LLM-based tools are a wide spectrum, and to argue that the whole spectrum is worth exploring because one sliver of it has definite utility is a bit wonky. Kind of like saying $SHITCOIN is worth investing in because $BITCOIN mooned as a speculative asset:
- I’m bullish on LLMs chat interfaces replacing StackOverflow and O’Reilly
- I could not be more bearish on Agents automating software engineering
Feel like we’re back at Adobe Dreameaver release and everyone is claiming that web development jobs are dead.
>Feel like we’re back at Adobe Dreameaver release and everyone is claiming that web development jobs are dead
I truly believe so much of the anti-AI sentiment is the same as the Luddites.
They're often used as a meme now, but they were very real people, faced with a real and present risk to their livelihoods. They acted out of fear, but not just irrational fear.
AI is the same: it's unquestionably (to anyone evaluating it fairly) a huge boost to productivity ... and also, unquestionably, a threat to programmer jobs.
Maybe the OP is right about waiting, but to me whenever new tech is disrupting jobs, that seems like the best time to learn it. If you don't, it's not just FOMO as the author suggests ... it's failing to keep up on the skills that keep you employed.
> it's failing to keep up on the skills that keep you employed.
I judge "failing to keep up" by my ability to "catch up". Right now if I search for paying courses on AI-assisted coding, I get a royal bunch for anything between 3$ to about 25$. These are distilled and converging observations by people who have had more time playing around with these toys than me. Most are less than 10 hours (usually 3 to 5). I also find countless free ones on YouTube popping up every week that can catch me up to a decent bouquet of current practices in an hour or two. They all also more or less need to be updated to relevancy after a few months (e.g. I've recently deleted my numerous bookmarks on MCP).
Don't get me wrong, LLM-assisted coding is disruptive, but when practice becomes obsolete after a few months it's not really what's keeping you employed. If after you've spent much time and effort to live near that edge, the gap that truly separates you from me in any meaningful way can be covered in a few hours to catch up, you're not really leaving me behind.
I have found that maximising AI coding is a skill on its own. There is a lot of context switching. There is making sure agents are running in loops. Keeping the quality high is also important, as they often take shortcuts. And finally you need an somewhat of an architectural vision to ensure agents don’t just work in a single file.
This is all very tiring and difficult. You can be significantly better than other people at this skill.
This is not an argument for its revolutionary utility. Balancing rocks on the beach is very tiring and difficult for some people, and you can be significantly better at it. Not really bringing anything to the immediate conversation with that insight.
The burden of proof lies with he who makes grand claims. My counterargument in the face of your lack of evidence is: “Where are all the improvements to my daily life? Where are the disrupting geniuses who go-to market 100x faster than their Luddite counterparts?”
To paraphrase another analogy that I enjoyed, it’s a bit like when 3d printing became a thing and hype con artists claimed that no one would buy anything anymore, you could just 3d print it.
You don’t need 100x productivity to be disruptive. In business 10% gain can be quite enormous. My senior engineers are estimating 25-50% gains. That is a far cry from your 100,000% gain, but very real and meaningful.
AI is the same: it's unquestionably (to anyone evaluating it fairly) a huge boost to productivity .
And yet, the only research that tries to evaluate this in a controlled, scientific way does not actually show this. Critics then say those studies aren’t valid because of X, Y or Z but don’t provide anything stronger than anecdotes in rebuttal.
It’s ridiculous double standard and poisons any reasonable discussion to assert something is a fact and anyone who disagrees is a hysterical Luddite based on no actual evidence.
The question isn’t if you’ve improved. It’s if the path you took to getting to your current improvement could have been shortcut with the benefit of hindsight. Given the number of dead ends we’ve traversed, the answer almost certainly is yes.
Crypto and the Metaverse were solutions in search of a problem. LLMs kind of felt like that until tooling arrived that enabled doing a lot more than copying + pasting chat conversations.
Sure, maybe crypto changed some lives, but an entire industry? I think ALL of software dev is going under a transformation and I think we're past the point of "wait it out" IMO.
Or I'm wrong, but right I'm being paid to develop a new skill professionally. Maybe the skill ends up not being useful - ok, back to writing code the old way then.
> In contrast, LLMs in their current state have (for me) dramatically reduced the distance between an idea and a working implementation, which has been legitimately transformative in my software dev life.
I can't really agree. I've never seen anything from an LLM that I would consider even helpful, never mind transformative.
You're right, you are using it wrong. An LLM can read code faster than you can, write code faster than you can, and knows more things than you do. By "you" I mean you, me, and anyone with a biological brain.
Where LLMs are behind humans is depth of insight. Doing anything non-trivial requires insight.
The key to effectively using LLMs is to provide the insight yourself, then let the LLM do the grunt work. Kind of like paint by numbers. In your case, I would recommend some combination of defining the API of the library you want yourself manually, thinking through how you would implement it and writing down the broad strokes of the process for the LLM, and collecting reference materials like a format spec, any docs, the code that's creating these packets, and so on.
> An LLM can read code faster than you can, write code faster than you can, and knows more things than you do.
I don't agree. It can't write code at all, it can only copy things it's already seen. But, if that is true, why can't it solve my problem?
> The key to effectively using LLMs is to provide the insight yourself, then let the LLM do the grunt work
Okay, so how do I do that? Remember, I want to do ZERO TYPING. I do not want to type a single character that is not code. I already know what I want the code to do, I just want it typed in.
I just don't think AI can ever solve a problem I have.
When you write a library the first step is always designing it. LLMs dont get rid of that step, they get rid of the next step where you implement your design.
Is this really "additional"? do you not do design docs/adrs/rfcs etc and talk about them with your team? do you take any notes or write out your design/plan in some way even for yourself?
If I'm writing a library to work with a binary format, there is very little English in my head required, let alone written English.
That is a heavily symbolic exercise. I will "read" the spec, but I will not pronounce it in literal audible English in my head (I'm a better reader than that.)
I write Haskell tho so maybe I'm biased. I do not have an inner narrative when programming ever.
I’m not part of any team, I work on my projects alone. I rarely write long-form design documents; usually I either just start coding or write very vague notes that only make sense when combined with what’s in my head.
It's clearly a textbook example of survivorship bias.
In the 90s the same argument was directed at this new thing called the internet, and those who placed a bet on it being a fad ended up being forgotten by history.
It's rather obvious that this AI thing is a transformative event in world history, perhaps more critical than the advent of the internet. Take a look at traffic to established sites such as Stack Overflow to get a glimpse of the radical impact. Even in social media we started to see the dead internet theory put to practice in real time.
In the 90s was also the dotcom boom, and the vast majority of those who placed an all-in bet on it being everything lost it all in the dotcom bust and also "ended up being forgotten by history". Some of those bets were prescient but too early but many of those bets never made any sense. The dotcom bust was worse than the software industry crash we're experiencing now.
"It's rather obvious that this AI thing is a transformative event in world history" perhaps but it's not at all obvious how it's going to shake out or which bets are sensible.
> In the 90s was also the dotcom boom, and the vast majority of those who placed an all-in bet on it being everything lost it all in the dotcom bust and also "ended up being forgotten by history".
I think you are missing the point, and also the very site you're posting on.
Look at the top 50 list of most valuable companies in the world. Over half of the total market value reported today is attributed to companies which were either dotcom startups or whose growth was driven by the dotcom growth period. Dismissing the advent of the internet as anything short of revolutionary is disingenuous, no matter how many zombo.com companies failed.
LLMs have the exact same transformative impact on humanity.
> LLMs have the exact same transformative impact on humanity.
But this is begging the question.
Yes, we can see that the internet was radically transformative.
But you are arguing that this somehow proves that LLMs are too, when there's wildly insufficient evidence—either on where LLMs are going in themselves, or in the comparison—to credibly make that claim.
> It's rather obvious that this AI thing is a transformative event in world history, perhaps more critical than the advent of the internet. Take a look at traffic to established sites such as Stack Overflow to get a glimpse of the radical impact. Even in social media we started to see the dead internet theory put to practice in real time.
It's worth noting that SO was declining well before ChatGPT launched. It seems more likely that the decline of SO was more driven by Google ranking changes to prioritise websites that served Google ads. Certainly I remember having to go down a few results to get SO results for a while, even when the top results were just copypasta from SO.
> It's worth noting that SO was declining well before ChatGPT launched. It seems more likely that the decline of SO was more driven by Google ranking changes to prioritise websites that served Google ads.
I don't think that's it. SO was the go-to page for troubleshooting, whose traffic was not exactly originating from web search. Also, the LLM-correlated drop in traffic is also reported by search engines. Stack Overflow just so happens to be a specialized service with a very specialized audience whose demand is perfectly dominated by LLM chatbots.
Internet is something new. By definition llm coding isn’t doing anything you couldn’t have done already. Once the agents aren’t writing a human syntax based language but are spitting out opaque functions in binary machine code, then they are doing something new and compelling imo because there are real performance gains with that.
No, this is wrong. AI has drastically shortened the time and effort between idea and implementation. The upshot being, that not only do you get things done faster, but things you wouldn't otherwise countenance doing are now within reach.
So where is all the new tech years into ai? Turns out that wasn’t the limiting factor. The limiting factor is still what it was 10,000 years ago in business: accumulating capital to start and finding a fit in the market to last.
As someone who does monitor /new, Show HN is overwhelmed with copycat low effort AI rubbish, not innovative AI generated tech that is interesting or useful. How many AI powered coloring book generators and AI powered anime-ification sites does the world really need?
> In the 90s the same argument was directed at this new thing called the internet, and those who placed a bet on it being a fad ended up being forgotten by history.
Allow me to introduce you to the dot-com boom, where everyone who bet on the internet went broke.
> In the 90s the same argument was directed at this new thing called the internet, and those who placed a bet on it being a fad ended up being forgotten by history.
Almost all people are "forgotten" by history.
In any case, people who were not even born yet in the 1990s are using the internet today, very successfully, so clearly you can wait.
There's value in being early - in the right thing.
- If you'd invested in Bitcoin in 2016, you'd have made a 200x return
- If you'd specialized in neural networks before the transformer paper, you'd be one of the most sought-after specialists right now
- If you'd started making mobile games when the iPhone was released, you could have built the first Candy Crush
Of course, you could just as well have
- become an ActionScript specialist as it was clearly the future of interactive web design
- specialized in Blackberry app development as one of the first mobile computing platforms
- made major investments in NFTs (any time, really...)
Bottom line - if you want to have a chance at outsized returns, but are also willing to accept the risks of dead ends, be early. If you want a smooth, mid-level return, wait it out...
There may be financial value in being early (if you're lucky), but there are other values in waiting.
My goal in life is not to maximize financial return, it's to maximize my impact on things I care about. I try to stay comfortable enough financially to have the luxury to make the decisions that allow me to keep doing things I care about when the opportunities come along.
Deciding whether something new is the right path for me usually takes a little time to assess where it's headed and what the impacts may be.
> My goal in life is not to maximize financial return, it's to maximize my impact on things I care about.
In the vast majority of cases, financial returns help maximize your impact on the things you care about. Arguably in most cases it's more effective for you to provide the financing and direction but not be directly involved. That's why the EA guys are off beng quants.
The only real exceptions are things that specifically require you personally, like investing time with your family, or developing yourself in some way.
I knew this canned rebuttal was coming and almost addressed it in my previous comment.
I've not found this to be true at all, for a variety of reasons. One of my moral principles that extreme wealth accumulation by any individual is ultimately harmful to society, even for those who start with altruistic values. Money is power, and power corrupts.
Also, the further from my immediate circle I focus my impact on, the less certainty I have that my impact is achieving what I want it to. I've worked on global projects, and looking back at them those are the projects I'm least certain moved the needle in the direction I wanted them to. Not because they didn't achieve their goals, but because I'm not sure the goals at the outset actually had the long term impact I wanted them to. In fact, it's often due to precisely what we're talking about in this thread: sometimes new things come along and change everything.
The butterfly effect is just as real with altruism as it is with anything else.
Being a quant is inherently accumulating and growing someone's wealth for them, even if it's not your own.
If there were a way to be a true Robin Hood and only extract wealth from the wealthy and redistribute that to poor, I'd call that a noble cause, although finance is not my field (nor is crime, for that matter) so it's not for me.
My chosen wealth multiplier is working at a community-owned cooperative, building the wealth for others directly.
Not sure about this because many charities are designed to spend their income, rather than hoard it. A big part of choosing which charity to donate to is, or should be, how effective they are in spending what you give them.
I mean, I'm not arguing that if you can find a way to make a large amount of money in an ethical way without enriching yourself or the wealthy further and then find a way to accurately evaluate charities to maximize impact, that you shouldn't do that. But there are several very difficult problems embedded in that path, and I could easily sees just solving all of those problems becoming a full-time job by itself.
I also, candidly, haven't ever seen anyone successfully do that.
There is no moral excellence but which you invent for yourself. But given the first principle or goal of 'having the most impact', maximizing money is often quite useful.
> The only real exceptions are things that specifically require you personally, like investing time with your family, or developing yourself in some way.
So, the things that matter the most for most people?
Studies pretty consistently show that happiness caps off at relatively modest wealth.
I want to cure lung cancer, therefore as an Effective Altruist™ I maximize my income by selling cigarettes to children outside playgrounds. The money will go towards research in my will, and in the meantime the incidence of lung cancer in teenagers will incentivize the free market to find a cure!
People don't become quants because they are EAs, they become EAs to justify to themselves why they became quants.
> Arguably in most cases it's more effective for you to provide the financing and direction but not be directly involved. That's why the EA guys are off beng quants.
The EA guys aren't the final word on ethics or a fulfilling life.
Ursula K. Le Guin wrote that one might, rather than seeking to always better one's life, instead seek to share the burden others are holding.
Making a bunch of money to turn around and spend on mosquito nets might seem to be making the world better, but on the other hand it also normalizes and enshrines the systems of oppression and injustice that created a world where someone can make 300,000$ a year typing "that didn't work, try again" into claude while someone else watches another family member die of malaria because they couldn't afford meds.
Impact only has meaning per a chosen framework to measure within. For example, if I apply my ethical system to measure the impact of an EA, they have essentially no impact, since all they do is perpetuate a system that is the root of the problems they're trying to solve.
IMO the only way you'd be rich with bitcoin would be if you forget about your coins and reminded of it years later or if you were a hardcore "fix the money fix the world hodl" believer.
Otherwise you would most likely have sold during one of huge crashes or values, attempted trade and lost it all, invested into the new shitcoin NFT or whatever or just got hacked along the way.
indeed. even if I didn't lose them along the way or have them stolen via hack, I would have cashed out back when they were outlandishly priced at $100. I never would have held on to them this long.
But even $100 would have been nice given you could still pop them out for free on a standard PC back then with mining software.
You would probably have sold early for a relatively small amount too.
I bought 10ish BTC at some point for almost nothing, sold them for a low 4-digit amount thinking they were stupid anyway. I still think they were stupid but it turns out they could have paid off my house easily. Oh well.
Question is whether you sold all or just a segment. If you bought 100 btc early on, sold 99 of them, you'd still have something worth a deposit for a house or a car now, which is still life-changing.
Or bought btc early on and forgot about it. Then haphazardly attenpting to find it on some old HD when the price skyrocketed.. There was a guy who combed through a trash landfill to recover a HD that presumably had dozens of millions in BTC
To be fair, I remember being about ten years old and berating my friend who just told me that he had something called "Rebel Assault" on a CD how this was completely impossible as CDs could only store music and how he was a complete idiot for believing otherwise... :-)
I distinctly remember staring at my check/bank/debit card laying on the desk in front of my keyboard, all the info punched in to buy $500 of bitcoin for something like $0.29 a coin.
Didn't pull the trigger. I just tell myself I'd have sold them when they doubled in price or they'd have been hacked in one of the mt. gox attacks and I'd have lost them anyways.
Same. there's no chance I'd have the riches associated with those coins. Or at least that's how I manage to sleep at night.
There was a local food delivery service at the time that accepted bitcoin. Can you imagine looking back on life and realizing you spent the equivalent of $1M on a burrito?
I think these ideas are similar to long-term relationships. Identify when it's clear it's worth your time, like the author, and commit appropriately, and then when it's time to move on move on.
AngularJS, Backbone, Knockout, YUI, were all a wave of pretty groundbreaking frontend technology. It was absolutely worth experimenting with and committing to once they had some uptake, but probably not before then unless you wanted to work on the teams building them. Time went on, they had years of longevity that overlapped with the next wave of Vue, React, and the rest, and those became worth investing in long-term. Along the way, fundamentals in underlying web technologies were crucial, programming, logic, networking, markup, design.
Actionscript was totally worth investing in, until it ran it's course, and then other things came along and you would have adapted your game programming and engine programming skills yo a different platform.
> If you'd invested in Bitcoin in 2016, you'd have made a 200x return
Except you would've probably sold it at any of 1.5x, 2x, 4x, or 10x points. That's what people keep missing about this whole "early bitcoin". You couldn't tell it will 2x at 1.5x, you couldn't tell it will 4x at 2x, and so on.
> - If you'd specialized in neural networks before the transformer paper, you'd be one of the most sought-after specialists right now
> - If you'd started making mobile games when the iPhone was released, you could have built the first Candy Crush
I disagree:
Concerning the first point: how neural networks are today is very different from how they were in former days. So, the knowledge of neural networks from the past does only very partially transfer to modern neural networks, and clearly does not make you a very sought-after specialist right now.
Concerning your second point: the success of mobile games is very marketing-centric. While it is plausible that being early in mobile games development when the iPhone was released might have opened doors in the game industry for you, I seriously doubt whether having this skill would have made you rich.
There's also the problem that there is currently a huge transformer monoculture in the AI space. Everything gets better if you throw transformers at the problem.
If you had worked on anything other than a transformer based architecture post 2016, such as Mamba or RWKV, you would have wasted your time.
Mamba 3 is the third iteration and somehow I doubt that it will catch on.
> I’m saying someone pursuing that portfolio will probably end up underperforming an index.
This holds if you consider "underperforming" to be a comparison of expected values.
On the other hand, if you consider "probability of getting a really huge payoff" to be the measure by which the investments are compared, the index fund is the one that looses in the comparison.
Would you be comfortable using this same logic to invest most of your net worth in lottery tickets/betting on black in a casino? If not, I'd be curious to hear what is different in that for you.
> Would you be comfortable using this same logic to invest most of your net worth in lottery tickets/betting on black in a casino?
I wouldn't "invest" in lottery tickets because for these p is far too small (exception: if I found a loophole in the system of the lottery, which has been found for some lotteries). For casinos, there is additionally the very important aspect that the casino will scam you (if you start winning money (for example by having found some clever strategy that gives you an advantage), the security will escort you out of the building and ban you from entering the casino again).
So, to give an explanation of the differences:
- Because "the typical run" for such an investment will be loosing, you should never invest your whole net worth (or a significant fraction thereof) into such an investment. The advice that I personally often give is to use index funds or stock investments for generating the money for investments that are much more risky, but have huge possible payouts.
- You should only do such an "early investment" if you have a significant information advantage over the average person. Such an advantage is plausible, for example, if you are deeply interested in technology topics
- Lottery tickets have an insanely small p (as defined in my comment). You only do "early investments" into topics where the p is still small, but not absurdly bad. The difference is that for lottery tickets the p is basically well-known. On the other hand, for "early investments", people can only estimate the p. Because of your information advantage from the previous point, you can estimate the p much better than other people, which gains you a strong advantage in picking the right "early investments" to choose.
But be aware that this is a strategy for risk-affine people. If you aren't, you better stay, for example, with index funds.
If you’re paying a fair price for the risk, sure. Most of the examples you gave seemed to be in deep speculative territory to the point that they don’t very much resemble anything economic.
Crucially those are all investments. Just like creating AI or buying data centers to run AI is an investment. Whereas merely using AI feels more like being in the general population of consumers. The shape of the outlay for it is un-investment-like. A monetary investment is a big charge up front, not a monthly fee. A skills investment looks like effort spent learning, but I mean how difficult is it to type reasonably precise English? Conclusion: you're a customer, not an investor, so you can start any time.
Some of those things involve a bit of money as a gamble; others require some time learning tooling that can be repurposed (mobile game developers can obviously do mobile apps), and others would be dedicating years of your life into something that may be a dead end.
There's really no need to be "early" for something like this. I've seen coworkers who've never used agentic coding pick it up and know everything that need to in ~2 days. The people who really dive in and are running orchestrated agents overnight are in general not building much of real value.
to be fair a friend of mine made one of the first flashlight apps on iOs and made a tidy sum out of it.
I think the question really is about how well you hit your timings. You can have held bitcoin but sold it went it hit $5k or less. You can have a technical advantage in a given field but somehow waste it (dead startup, serious illness) and lose your timing. Nobody knows what the right timings are, but I think the OP is pushing for a more consistent risk investment strategy and setting up the timings to raise the floor significantly at the cost of losing some of the best possible ceilings.
I get what you are saying, but as someone who was coding mobile apps in 2003 (iPhone was released in 2007), I have to give a few details.
By the time iPhone was released, it was already too late for small companies. When you developed apps and games for MS PocketPC and Blackberry, you charged $20 per app, and any average quality product would make money.
In 2007, there were only 2 kinds of success stories:
1. Companies that were able to throw a lot of money around (Your example of Candy Crush).
2. Some rare flukes of someone getting rich with some app.
So what I was trying to say: The golden days were really before the release of the iPhone.
In all fairness I was an actionscript specialist, and up until 2010 or so I was making REAL good money doing contract flash work -
Luckily I was also doing frontend work alongside, so when the time came to transition to html+css+javascript, it wasn’t much of a move at all, it was just putting down AS and focusing fully on JS
It's a horrifying feeling facing the possibility that the career I spent so much time and money to get into is fading away. Sure, LLMs are not there yet, and they might not ever quite get there. But will companies start hiring again? If productivity has gone up, and it seems like it has, then no.
So, a decade of hanging by a thread, getting by and doubling down on CS, hoping that the job market sees an uptick? Or trying to switch careers?
I went to get a flat tire fixed yesterday and the whole time I was envious of the cheerful guy working on my car. A flat tire is a flat tire, no matter whether a recession is going on or whether LLMs are causing chaos in white collar work. If I had no debt and a little bit saved up I might just content myself with a humble moat like that.
Anecdata, but the few people I know who were looking to switch gigs all had multiple offers within a few weeks. One thing they all had in common was taking a very targeted approach with their search and leveraging their networks. Not spamming thousands of resumes into the ether.
I'm looking for jobs after my PhD and I notice the same things. I only get interviews and advance in processes when I target the right jobs and companies. Sadly, not too much network to leverage.
Just finished a search - agree. The resume process is fundamentally broken, but a strong network makes it irrelevant. Lean on connections - there's a ton of opportunity out there.
so you either are well connected or you starve, got it
guess it's death and destitute for introverts
edit: please explain the downvotes, i'm curious why you think i'm wrong
if what op says it's true, that today only networking works, then it easily follows that if for some reasons you do not have a network then you don't get hired
fwiw this is how it's always been. You don't have to be some kind of extreme extrovert, but people who are good at what they do tend to enjoy working with other people who are good at what they do, and when they see someone they recognize as "I'd work with this guy again" they put in the minimal effort to stay in contact - a hello text every few months, a cup of coffee every few years, that type of thing. Thats all it takes. If you're not doing that, yeah, you're in for a world of hurt when you go job hunting.
I'm currently involved in the hiring process in our company, selecting engineers for my team. If someone applies who has the programming language we ask for in their CV, they get a first interview. If they can read code, and write VERY basic code, they will get through at least the first 2 rounds without any issues.
If people put down the AI, and actually learn how to write a `for` loop, they would be more hire-able than 50% of candidates.
> "Guess it's death [...] for introverts"
There is a meritocracy somewhere in our capitalist system. Not everyone participates, but it exists.
> One thing they all had in common was taking a very targeted approach with their search and leveraging their networks
Right, so they applied to a couple of jobs and it worked for them?
I'm sorry, do you understand how uncommon and rare that is? sure, if their domain was REALLY niche and the jobs weren't publicly advertised, then i could see how that would work. but the experience is VASTLY different outside such niche cases
They applied to a couple of jobs where they were certain the fit would be good, and didn't mindlessly spam their resume to some bot. They got in touch with the right people, and worked it out from there. Because they had done their homework, the path was easier for them.
What if you weren't in the industry for several years or more? New grads are completely walled off from the industry because very few employers are willing to give them a shot anymore, even at minimum wage and no other demands from this most desperate worker segment. The few connections I scraped together weren't helpful because barely anyone is hiring juniors anymore. Having one of the resumes on top of someone's pile is only helpful if they ever get to using it. If you're at the lower end of the market, you have to be very exceptional or very lucky. Otherwise, you're done for.
There is infinite amount of software to be made. Desires and wants never get satisfied. There will simply be more software, more features, more supported platforms, more bug checks, more tests, more CI/CD, more docs, more websites, more services, more more more. Once we solve something, we have a million new desires that we want to solve. There will be plenty of work in software, up until the time when really all knowledge work can be replaced. At that point all bets are off.
Do you think all software demands are completely elastic? I don't share your confidence, it feels like it's applying the desired conclusion to the premise. For sure, newer technology prompts new solutions and new tools to use with it, but some industries are much less flexible than others. If your business serves a certain number of clients - for example, companies that work in a specific industry that's growth-constrained - then making software twice as easy to make isn't going to double the number of their clients. That business you work for will either have to look at expanding into a different market, or, more realistically, lay off all the unneeded workers. There's a lot of businesses that work like that. What are they going to grow into?
Competing software companies will offer more and more advanced software that handles more use cases, faster, better, with more features, with more compatibility, with more flexibility, with more integrations, with more ease of use, with more etc etc. They will propose software solutions for things that are currently not economical to provide. And other competing software companies will have to match that or do even more.
> A flat tire is a flat tire, no matter whether a recession is going on or whether LLMs are causing chaos in white collar work.
There’s really not much stopping changing tires from being automated away. Further standardization of tires or wage increases would probably do the trick.
There’s still plenty of software to be created. You’ll probably have to learn some ML tricks or whatever, but there’s nothing going away, just changing as software has always done.
I wouldn't be so sure. I have been impressed by several things that seem to be complex but a way to be automated was found. Sure a no controlled environment is not conducive to automation, but who said a tow truck wouldn't be a part of the process? Washing a car has been automated with the precursor that the car is brought to the controlled environment first.
I have even replaced car tires before and yet still have this opinion.
This is the funniest possible answer to "Sounds like you've never changed a tire. Or at least not outside of a very controlled environment."
"Oh, you think I've never changed a tire? Well here is my abstract high level understanding of the steps to changing a tire! And have you considered the quintessential controlled environment for putting tires onto cars?"
The "edge cases" make a simple task like this more difficult. What if the nuts are stripped? What if the terrain under/around the car is uneven or not solid ground? What if it's raining or snowing or hailing? What if the driver of the car is irrationally upset and kicks your tire-changing robot over? What if a tire change was requested, but it's clear (to a human) that there is more work that needs to be done?
Every new successful tool doesn't start by trying to meet every need or edge case. They perfect the main case, and then edge cases in priority of likelihood.
Car washes are automated even though they haven't answered the edge cases of how to wash your car when your car is rolled on its side or a terrorist is actively blowing up the equipment. They simply only operate when your car is right side up (and other conditions, like in neutral, wipers off, and a driver who is willing to not exit the vehicle) and when there aren't active bombings on the building. And other "edge" cases.
Just because there is a possibility for something to not work, doesn't make it useless. Automated tire replacements could start with very rigid cases where they are applicable, and expact the scope slowly to allow more cases, like a bent wheel or poor weather.
Then you see a mechanic for the 5% of cases where it's weird? If you think AI is replacing 100% of software engineers anytime soon, idk what to tell you.
I don't think it's a psychologically positive self identification to see yourself merely as a gatekeeper and toll extractor rent seeker who only makes a living by withholding agency and skill from others.
I know many jobs are about giving partial access to secrets or insider knowledge etc but I simply can't see myself accepting that this is my value proposition.
No, let the pie grow. Let more people be able to do more things. Use the new capabilities to do even more. See how you can provide genuine value in the new environment. I know it isn't easy. There are many unknowns. But at least aspirationally I see that as the only positive way forward.
The same thing has happened to many jobs. 100 years ago being a photographer was a difficult skill. They must have felt a rug pull when compact cameras became mainstream and they were no longer called to take all family pictures. Surely the codex writers felt a rug pull when printing became widespread. Typesetters when people could use word processors on their PC with font settings. Prop designers and practical effects people when movies switched to vfx. Etc etc.
> I don't think it's a psychologically positive self identification to see yourself merely as a gatekeeper and toll extractor rent seeker who only makes a living by withholding agency and skill from others.
That's an incredibly uncharitable reading of the parent comment. At no point in history prior to maybe this year could you argue that working in software was gatekeeping, toll extracting, or rent seeking. Being a highly skilled craftsperson creating software for those who can't or don't want to is a very psychologically positive self identification. Lamenting that the industry is moving away from highly skilled craftspeople is also perfectly valid, even if you believe that it is somehow good for society, which is yet to become clear.
They complained about the skill leveling where now lower skilled people can also do what needed higher skill before. You toiled to learn the craft, now there is a fast track to those results. That's what the rug pull is.
Yes, producing software was value. (It of course still is as of today, we are talking about what may be coming). My plead is to continue searching for ways to contribute value. Don't resign to a feeling that the only way to hold on is if you try to stop others from knowing about or being able to use the skill leveling tech. This makes one bitter and negative. Embrace it, aspire to be happy about it.
Its like getting scooped in science. In research, I always try to reframe it to be happy that science has progressed. Let me try to learn from it and pivot my research to some area where I can contribute something. Sulking about having been scooped does not lead to positive change and devalues ones own self-image.
The problem is that we don't live in a society where the benefits of new technology benefit all.
We're about to pull the rug underneath all knowledge workers. This will disrupt wage earners lives. This will disrupt the economy.
You might feel great about when things become cheaper but remember that when things are cheap it's only because costs are low and when costs are low the revenues are low and when revenues are low salaries are low too. Keep in mind that one party's cost is other party's revenue.
The economy is ultimately one large circle where the money needs to go around. You might think of yourself a winner as long as someone else's salary drops to zero and you still get to keep your income but eventually it will be you whose income will also be disrupted.
Just something to keep in mind.
And also we're going to just not rug pull on the individual knowledge workers but businesses too. Any software company with a software product will quickly find themselves in a situation where their software is worth zero.
Also this comment about gatekeeping is absolutely stupid. It's like saying trained doctors and medical schools are gatekeeping people from doctoring. It would be so much better if anyone could just doctor away, maybe with some tool assistance. So much fantastically better and cheaper? Right! Just lay off those expensive doctors and hire doctor-prompters for a fraction of the price.
> We're about to pull the rug underneath all knowledge workers. This will disrupt wage earners lives. This will disrupt the economy.
to tie back to the actually article, if you believe a rug pull is imminent then you got to get off the rug. Idk, you have to make a decision because we're certainly at a fork in the road. There's no guarantee waiting will result in a better outcome nor one saying it will be a worse outcome. There's going to be winners and losers always and lot of it is really just luck in timing. I guess, in reality, the careers we've built come down to a flip of a coin; stay on the rug, get off the rug.
/i'm thinking of buying a welding truck and getting in to that, then hire a welder and rinse repeat until i have a welding business. There's plenty of pipe fence in my neck of the woods and i see "welder wanted" all over the place so there's opportuntiy too.
Good luck to you and your welding business. Personally I'm getting to a point where I'm just "too old" (and grumpy) to start over, so I guess for me it's going to be a retirement to some LOCO that I can afford.
You can make a living, if: you have a way to modify your behavior in a way such that it compels another human being to reciprocate and modify their behavior in a way that you find beneficial for your life. All of money and economics in the end boils down to this. If you no longer have any kind of behavior that your neighbors and community see as valuable enough to modify their behavior to benefit you and keep you around, then we will be in trouble.
> We're about to pull the rug underneath all knowledge workers. This will disrupt wage earners lives. This will disrupt the economy.
It will put and end to the middle class entirely, but that’s the intent.
The reality is a lot of people who were formerly middle or upper middle class, and even some lower class populations will face steep, irreversible “status adjustment”.
I’m not talking about “we used to be able to take vacations and now we can’t”. I’m talking about “we used to be highly paid professionals now we’re viciously competing for low paid day labor (gig work) to hopefully be able to afford the cheap cuts this week”.
I doubt software development will stay as "low skilled prompting", or that it is even low skilled prompting right now. Productive LLM usage goes beyond typing in better prompts and involves things like improving guardrails (eg type definitions and tests), context (docs and "skills" and MCP servers), and management strategy (instructing specialized agents together). It seems natural that there will be high skill AI coding to differentiate engineers, at least until superintelligent AGI emerges and kills us all.
If you think any programming task at hand one must have at least some reasonable grasp of formalism, boolean logic, predicate logic, then understanding the software developing concepts, your APIs frameworks, language constructs etc and finally the domain knowledge.Most of this goes away when changing from coding to prompting.
I was just doing some computer graphics work myself doing Signed Distance Fields and Claude just literally regurgitated code that I could just adopt (since it works) without understanding any of the math involved.
I'd say that prompting is at least two orders of magnitude easier than coding.
I think looking at what the web did to the journalism industry as a model to what's happening to the software dev industry is worth while. Journalism didn't go away but it did completely change. Many old school journalists just couldn't adapt and left the industry, many papers died too.
Digital products such as "photoshop" have had value because people need a tool like that and there's only a limited number of competition, i.e. scarcity. The scarcity exists because of the cost. I.e. the cost of creating "photoshop"creates limit for how many "photoshops" exist. When you bring down the cost you'll have more "photoshops" when you have more "photoshops" as the volume increases the value decreases. Imagine if you can just tell claude "write me photoshop", go take a dump and come back 30 mins later to a running photoshop. You wouldn't now pay 200USD for a license, now would you? You'd pay 0USD.
If you now create a tool that can (or promises it) can obliterate the costs, it means essentially anyone can produce "photoshop". And when anyone can do it it will be done over and over and at which point they're worth zero and you can't give them away.
The same thing has happened to media publishing, print media -> web, computer games etc.
Then the problem is that when your product is worth zero you can no longer make a business by creating your product, so in order to survive you must look into alternative revenue streams such as ads, data mining etc. None of which are a benefit to to the product itself.
Whether you like it or not but the society is built on certain social constructs and agreements.
Do you think it's fair that when the society moves underneath, the capitalistic system moves its tectonic plates it's the individual who has to bear the cost of that?
Abd let's be clear only software devs are just sucking it up. You think lawyers and doctors would allow themselves to be laid off en masse and be replaced with trainees who just prompt the computer?
Also what will happen when high wage earners start loosing their discretionary income. The whole service sector for starters will be shaken.
Just imagine some big tech company laying off 10k engineers. Making 0.3m per year. That's 3b dollars that disappear from the incomes and thus from the economy and just stays in the pockets of the capital holders.
i made this point a few comments up but i think what's happening to the software dev industry is what happened to the journalism industry when the web really came into its own and everyone was now a journalist. There were even books written by tech people about how great "creative destruction" is heh now the shoe is on the other foot. How many "old dinosaurs" did web development and software dev in general put out of business? My neck is on the line too but even i have to chuckle at that a little bit.
yep you're absolutely right. the value in journalism and journalistic output was based on the scarcity, i.e. the cost of publishing reduced the amount of available content. With web the costs were obliterated so the content exploded and the value of any individual piece dropped to essentially zero. When it's worth zero your revenues are zero and zero revenues you can't really pay for any journalism.
So then you have no choice but to seek alternative revenue streams (ads, data mining) and in fact this becomes the thing, since the original thing no longer produces a revenue.
LLMs have lowered the bar for the unskilled person to create shit software. I have used Opus 4.6 on a number of projects, and it still spits out buggy, and sometimes, flat out broken code. I was actually surprised when it completely hallucinated the names of query params for an HTTP request in my code, when in the prompt I had explicitly given it the exact names it needed to use. I thought these frontier models were supposed to be game changing.
The query parameter issue is a pattern I see a lot. The model has thousands of examples of "how HTTP requests usually look" from its training process. When your input data conflicts with the pattern, the training data takes precedence.
Interestingly, the model doesn't "know" that it's ignoring you. From its perspective, it has retrieved a "meaningful" pattern—virtual parameter names that probably fit common conventions it saw during training. Your actual request simply... wasn't documented.
> LLMs have lowered the bar for the unskilled person to create shit software.
So? Demand the source code. Run your own AI to review the quality of the code base. The contracting company doesn't want to do it? Fine, find one that will.
I've worked in tech since the late 90's and recently became an apprentice potter. My work in pottery is so much more fulfilling than any tech work I've done. I wish I had started sooner.
I'm still working in tech, and likely will forever in a much reduced capacity. But pottery is my life now.
I'm maybe less pessimistic, but I expect in the next decade or two, I'm going to spend a lot of time fixing apps that people who were moderately technical vibe-coded years before. They seemed to work well enough until they didn't, and at some point, they became central to the business.
MS Access and so many more "you won't need a programmer again" dev tools over the decades blazed the trail.
Green is always greener on the other side. You can always work on that, get an apprenticeship and work on it. Why don't you do it? Because you know not everything's so rosy. These are hard jobs, not super well paid.
> A flat tire is a flat tire, no matter whether a recession is going on or whether LLMs are causing chaos in white collar work. If I had no debt and a little bit saved up I might just content myself with a humble moat like that.
Bad idea. Automotive repair is barely a moat, because you don't need that much training to work those jobs. There's a lot of people who want to do it. And cars are definitely susceptible to recessions - if fewer people are buying cars, if there's a shift to transit, if your locality builds more pedestrian-friendly infrastructure, if businesses that use work vehicles are forced to close, then your demand drops and everyone already in the field is forced to compete with one another.
For moats, look for things that are complex (not everyone can do it), licensed and always needed.
>Automotive repair is barely a moat, because you don't need that much training to work those jobs.
You need to study for 3 years to be a car mechanic. And even then you'll need baby sitting for a while in the auto-shop because no one trusts the new guy fresh out of school, with good reason.
>There's a lot of people who want to do it.
No there isn't. What do you mean by lot of people? Automotive repair is a blue collar job with intense physical strain, you're exposed to chemicals you shouldn't be, there will be hearing loss involved no matter how much protection you have and it doesn't even pay all that well, considering the risks involved and the amount of training you need. And no, it's not because the market is saturated with car mechanics, it's because auto repair shops have a lot of pressure to be cheap. Job listings are full of car mechanic openings, you'll never be unemployed.
All the "if's" presented are solved by relocation and not even that much of it, except for this:
>if fewer people are buying cars
Then the skills are easily transferable to other vehicles. But less people are buying cars already, they use uber, which involves a car. A car that needs 10x more repair time than the car you drive daily.
So yeah, auto repair is a good moat. It's complex, not everyone can do it, it's not licensed in most cases (unless you work for a brand or a niche) but there's reputation involved and it's always needed. It just doesn't pay all that well, specially not compared to what I see on HN's monthly whoshiring.
The vast majority of tires that need to be repaired or replaced (and the processes to do so) haven't changed much if at all, though. And there are entire franchises that pretty much only do tires. Same with many other manual labor tasks.
These are predictable jobs with very few variables that there is still no sign of automation replacing any time soon. They often don't suck as bad as people think. One of the most enjoyable jobs I had was on an assembly line, because my mind was mostly free to wander. It was almost like meditation.
> most people want a white collar job and send their kids to college
Part of the reason for my prior comment is the clear fact that a not-insignificant percentage of white collar jobs are being massively devalued at the moment, which means many people who thought they'd be able to send their kids to college with income from such jobs won't.
Considering that the field of robotics is so far behind LLMs in terms of clear value outside of niche industrial applications, I think manual labor is about due for a resurgence. There may be some major rebalancing happening. The big question for laborers will be - as it has always been - what can I do that sucks the least but also allows me to pay for a decent life? Answers will vary.
I'm not sure how long this state of robotics will last. Dexterity is improving very fast. Robots are getting cheaper and cheaper.
But also, a lot of the manual labor is quite expensive and only affordable as long as there are white collar workers who can pay for fancy bathroom remodelings and landscaping and so on. I don't know how a big deluge of reskilled pipefitters and HVAC technicians will be able to find work. Will everyone just pay each other to do a bunch of handy work for each other?
This is the point missed by many. The trades are in high demand, right now, because of a labor shortage and demand from upper-middle class individuals without any DIY skills. A generation or two of pushing kids into college, and an almost disparaging view of 'getting your hands dirty' has built this perfect storm.
However, besides a few trades that use unions/licensure/apprenticeship as an artificial supply limit, most trades are only limited by a willingness to do the work. A few decades ago, trade work was much less expensive, because supply was higher and many did their own DIY, which limited what prices the market would tolerate.
I think the bigger issue is not that LLMs are taking away developer jobs, but the current geopolitical crisis (the collapse of the US empire and the end of the neoliberal era) is leading towards an imminent economic catastrophe, and that would be enough to pop not only the AI bubble, but an even bigger "IT bubble" that has been proliferating since the 90s.
Programmers (and other white collar jobs) were able to luxuriously coast along the ZIRP era because capital (replenished twice via quantitative easing) was cheap and plentiful, and because the elites at the top had to pump huge amounts money to create a shared fantasy of the "technological future" that validates the neoliberal era. Now that the reality of the actual "physical economy" (the economy of making tangible things) has clawed back at us because of that forbidden three-letter word (war), we all realize that doubling and tripling oil prices were actually dictating our lives rather than some "Skynet AI" crap, and thus our fantasy simulacra of "virtual" play-things have now come to an end. Oh and we all found out that most of SaaS was actually bullshit anyway. In fact, if it could be completely replaced by AI then it was already pretty bullshit in the first place.
So, for smart STEM people uninterested in programming and only looking for a stable career, I think they would be better off by just doing engineering work that's a bit more tangible, like robotics, manufacturing, shipbuilding, construction, etc. (Or anything related to war, but only if you're able to stomach what you're doing.) If you don't like to sit all day for a salary, then niche blue collar work can also be a good option, since general-purpose robotics (Physical AI?) is still too far away because of many, many issues that's just too long to explain here. I still think if you like programming then you should stick to it in the long run - there will be a very cold winter because of the combination of LLMs, AI bubble pop, and general economic depression, but for those who survive this era there will be an opportunity because of the shortage of skilled programmers (since no-one bothered to hire juniors after the pop, no one will grow to become seniors themselves!) Computing will still be with us forever, just not in a way that investors thought that it's going to "engulf the world".
I would say if all of your doomastrophizing comes to light (the myriad of collapses and depressions and winters in your post), then there is no opportunity for anyone anywhere, and we should all stock up on bullets and cigarettes while we can.
Yup, absolutely! People before us already lived through the Great Depression, people already lived through two World Wars, and despite all that there were still some technological advancements made and some good opportunities amongst the chaos! There's obviously going to be pains from transitioning into a new world order, but humanity will still keep on living. Just don't expect the next world to be the same as before!
I think it's important to know and practice your passion, even if you have to work on something different to pay the bills. You can only be good at something if you really like it, and you never know what opportunity you'll stumble onto if you're ready for it.
I actually think the opposite approach might be the most optimal one, at least from a monetary perspective. That is, be on the cutting-edge of something, but be willing to bail out at the moment its future starts seeming questionable. Or even more specifically, maximize your foothold in it while minimizing your downside.
Bitcoin is a good example: if you bought it 15 years ago and held it, you're probably quite wealthy by now. Even if you sold it 5 years ago, you would have made a ton of money. But if you quit your job and started a cryptocurrency company circa 2020, because you thought crypto would eat the entire economic system, you probably wasted a lot of time and opportunities. Too much invested, too much risked.
AI is another one. If you were using AI to create content in the months/years before it really blew up, you had a competitive advantage, and it might have really grown your business/website/etc. But if you're now starting an AI company that helps people generate content about something, you're a bit late. The cat is out of the bag, and people know what AI-speak is. The early-adopter advantage isn't there anymore.
How do you know what not to invest in then? Bitcoin wasn't the only cryptocurrency fifteen years ago. It wasn't even the first digital currency. If you hop on every bandwagon, you'll go broke far more then you become wealthy. Just look at all the people who poured money into NFTs or digital real estate in the metaverse or Dogecoin or whatever.
It's easy to say "well of course I would have invested in Google in 1999" but there was nothing in 1999 to say that Google was going to be as big as it was. Why not Lycos or Dogpile or AskJeeves?
How many people dedicated their careers to Flash, only to have it die at the hands of Steve Jobs and HTML5? It's not just about bailing out: lots of folks had to start over because taking advantage of the opportunity means actually investing real time and money. "As a tulip bulb producer, I would have simply stopped producing tulip bulbs when it started to seem questionable." https://en.wikipedia.org/wiki/Tulip_mania
I don't think you're understanding my point. Hopping on the bandwagon is explicitly not "maximizing your foothold while minimizing your downside," nor is it early-stage almost by definition. An early stage technology with an uncertain future cannot have a bandwagon.
I think the logical thing to do is to invest a minor amount of time/money across a broad spectrum of new promising tech. If you had been aware of and bought $500 of Bitcoin in 2010, you'd be a billionaire today. The early people involved with NFTs also did very well.
The Flash example is specifically the opposite of my point. Flash was a lucrative skill for a period of time, but at a certain point it became very clear that it didn't have a future.
> That is, be on the cutting-edge of something, but be willing to bail out at the moment its future starts seeming questionable
Counterpoint, I sold all my Bitcoin in 2011 when Mt Gox got hacked and the price plummeted 80%. Would have done it again after their 2014 hack too if I had any left.
> Bitcoin is a good example: if you bought it 15 years ago and held it, you're probably quite wealthy by now
But you just said bail the moment it's future starts to be questionable. If you follow that you would have never held it for 15 years.
> be on the cutting-edge of something, but be willing to bail out at the moment its future starts seeming questionable
The problem is this leaves you undifferentiated from every hype chaser in Silicon Valley. Our world is littered with folks who went to coding school, traded Bitcoin, did something in the metaverse and blogged about AI. That jack-of-all-trades knowledge can be useful. But only if you’re making unlikely connections. Having the same cutting-edge familiarity as every tech journalist doesn’t that make.
Better: develop deep knowledge and expertise in something. Anything. Not only does this give you some ability to recognize what expertise looks like from afar, it also lets you dip into new topics and have a chance at seeing something everyone else hasn’t already. That, in turn, gives you the ability to be a meaningful first mover.
Maybe - but that's also a style of life definition, isn't it. I did not care at all about Bitcoin, and I'm still pretty fine. I even think, sitting Bitcoin out was better for me, because who (vrosdly speaking) cares about it today (besides speculating with it)?
Except that strategy gets you killed through a thousand paper cuts.
What would have you done when the Bitcoin fork happened 50/50? Would you have gone int ICOs? Which ones? Etc…
There’s simply too many “new things”, so by trying to get exposure to them you’ll be massively in the red.
Let’s say you get into 1000 “new things”, and you strike it lucky and hit BTC. You’d had to buy BTC in early 2013, hold it over the whole period and sold at the historical maximum for you to be at break even.
If instead of buying 1000 “new things”, you’ve put your money into the S&P you’d be at +250% by the same time.
Yeah it looks reasonable when you only pick success stories for your examples.
If you sold the farm to get in early in the Metaverse, you're totally hosed now because that was a dead end. The idea of digital real estate was as terrible then as it is now.
But IMO the most fruitful thing for an engineering org to do RIGHT NOW is learn the tools well enough to see where they can be best applied.
Claude Code and its ilk can turn "maybe one day" internal projects into live features after a single hour of work. You really, honestly, and truly are missing out if you're not looking for valuable things like that!
You're only missing out if that's what you want to do. Not every software developer is interested in creating new software projects from scratch in an hour, or at all. It's totally find to do software development as a job, and then close your laptop and not see it until Monday. Learn the tools that suit when when you need them.
> You're only missing out if that's what you want to do.
Who writes software and doesn't have a list of "I'll fix this one day" issues as long as their arm?
This is honestly one of the things I enjoy most at the moment. There's whole classes of issues where I know the fix is probably pretty simple but I wouldn't have had time to sort it previously. Now I can just point claude at it and have a PR 5mins later. It's really nice when you can tell users "just deployed a fix for your thing" rather than "I've made a ticket for your request" your issue is on the never-ending backlog pile and might get fixed in 5 years time if you're lucky.
Claude code makes it so easy to do things the "right way" that it also makes it really easy for you to let scope creep get out of hand. I have a personal project that I haven't deployed yet that in some ways is way overengineered for its purpose. It's hard to blame the tool though, it's always telling me I'm making it more complicated than it needs to be but I don't listen
I've felt this recently. I've often been bad about scope creep. CC makes it so easy.
On the other hand, I can see these tools getting good enough that scope creep doesn't even matter.
ATM I usually get stuck around the review/verification stage. As in, my code works, I have tested that it works, but it is failing CI or someone left a PR comment. And for each comment I'll have to make sure it makes sense, make the change, test again, and get CI passing again.
> Claude Code and its ilk can turn "maybe one day" internal projects into live features after a single hour of work. You really, honestly, and truly are missing out if you're not looking for valuable things like that!
You're right, it's possible. But you might be both overestimating the ease of onboarding and underestimating the variety of tasks and constraints devs are responsible for.
I've seen Claude knock out trivial stuff with a sufficiently good spec. But I've also seen it utterly choke on a bad spec or a hard task. I think these outcomes are pretty broadly established. So is the expectation that the tech will get better. Waiting isn't unwise.
Waiting may not be “unwise” but acting now may be optimal. Even though tooling may be much better in 12 months, if it can improve quality or time now, that’s a net benefit.
Bikers in the Tour de France used to not wear helmets. They were seen as uncouth (“why jump on the bandwagon?”). Helmets today are way better than they were then. But if the utility provided is greater than the cost, of course it makes sense to act sooner.
I’m not explicitly arguing for investing in AI or other newfangled tech, I’m arguing that the premise of waiting may be “sounded” but also “leaves money on the table”, or in some cases, lives.
The author talks about vaccines as a counter example but doesn’t really address the cost/benefit in any detail.
If you want to see the potential value of Claude, pick a somewhat isolated function in your code and tell Claude "write unit tests for this". Watch it write them, compile your code, validate the test, fix the ones it did something dumb on, and iterate. Laugh as you get 10 awesome tests and 2 really dumb ones you have to go in and clean up by hand for 5 minutes of work. Half a day of iterating later you'll have finished the extremely tedious test writing that would have taken you a day or two without Claude.
When doing a code review, tell Claude "review commit xyz". It finds things humans are not finding in reviews at this point.
Not sure about what you want to do for some design? Use Claude as a rubber duck - tell it "I'm deciding between implementing this with A or B and here's why... is there any thing I'm missing that would make the decision more clear, or any potential solution I've missed?" It has the context of your existing code, which can be incredibly helpful for stuff like this.
Opus 4.6 is good enough that it's undeniable that it's going to change the industry at this point. It's just a question of how big the change will be. The earlier models were shit. Everyone I worked with spurned them, including me. Everyone around me using Opus 4.6 is saying "well, shit, this is real now" with varying degrees of excitement and unease.
AI is not some magic pill. A lot of software development is not writing code, it's requirements gathering and design. That part doesn't magically go away, in fact it becomes way more important. But AI is speeding up the writing code part of things - if, and only if, you put down a good plan first.
People will structure their code where AI can churn on it (specs in markdown, and TDD all over again), it's useful enough to be worth it. We're going to get segmented into engineers who can use it and engineers who can't.
For this technology I'd not wait. Start learning it.
We're all going to be more focused on being architects instead of developers.
Agree with the message, coding since 1986, I have learned to not suffer from FOMO and wait for the dust to settle.
Ironically one might even get projects to fix the mess left behind, as the magpies focus their attention into something else.
In the case of AI, the fallacy is thinking that even if ridding the wave, everyone is allowed to stay around, now that the team can deliver more with less people.
Maybe rushing out to the AI frontline won't bring in the interests that one is hoping for.
EDIT: To make the point even clearer, with SaaS and iPaaS products, serverless, managed clouds, many projects now require a team that is rather small, versus having to develop everything from scratch on-prem. AI based development reduces even further the team size.
I am increasingly feeling okay with the idea of being left out. The worst parts of working professionally in a software development team have been amplified by LLMs. Ridiculously large PRs, strong opinions doubled down due to being LLM-"confirmed", bigger expectations coming from above, exceptionally unwarranted confidence in the change or approach the LLM has come up with.
I am dying inside when I make a comment and receive a response that has clearly been prompted toward my comment and possibly filtered in the voice of the responder if not copied and pasted directly. Particularly when it's wrong. And it often is wrong because the human using them doesn't know how to ask the right questions.
Fortunately, most of the fundamental technological infrastructure is well in place at this point (networking, operating systems, ...). Low skilled engineers vibe coding features for some fundamentally pointless SaaS is OK with me.
On the otherhand, when Cloud Computing started to come in, I knew a bunch of sysadmins. Some were in the "it'll never take off" camp and no doubt they know it now, kicking and screaming.
But the curious early adopters were the ones best positioned to be leading the charge on "cloud migration" when the business finally pulled the trigger.
Similarly with mobile dev. As a Java dev at the time that Android came along, I didn't keep abreast of it - I can always get into it later. Suddenly the job ads were "Android Dev. Must have 3 years experience".
Sometimes, even just from self-interest, it's easier to get in on the ground floor when the surface area of things to learn is smaller than it is to wait too long before checking something out.
> But the curious early adopters were the ones best positioned to be leading the charge on "cloud migration" when the business finally pulled the trigger.
lol no. There's nothing actually different about managing VMs in EC2 versus managing physical servers in a datacenter. It's all the same skills, and anyone who is competent in one can pick up the other with zero adjustment.
Maybe in a greenfield environment, but this I disagree with your assessment in an enterprise setting. Have you worked with orgs that have on-prem + cloud and are migrating? It is a massive pain and regardless of whether it's easy, people and orgs don't want to do it.
> On the other[ ]hand, when Cloud Computing started to come in, I knew a bunch of sysadmins. Some were in the "it'll never take off" camp and no doubt they know it now, kicking and screaming.
> But the curious early adopters were the ones best positioned to be leading the charge on "cloud migration" when the business finally pulled the trigger.
From a technological perspective, these sysadmins were right: in nearly all cases (exception: you have a low average load, but it is essential that the servers can handle huge spikes in the load), buying cloud services is much more expensive overall than using your own servers.
The reason cloud computing took of is that many managers believed much more in the marketing claims of the cloud providers than in the technological expertise of their sysadmins.
Yeah, most companies still should not be using cloud for stuff. I think a lot of people here who work for startups don't get that, because cloud makes a ton of sense if you're a new business that has a hundred other higher priorities than spending capital on infrastructure and sysadmins. But for most companies? They are better off with on prem.
I'm not sure why you're downvoted, but this is the right take IMO. I hate cheating and lying in general, but in any job posting you have to separate what are the actual requirement in term of knowledge versus what can be realistically learned on the job / doing a prototype in a weekend.
Of course don't fraud by like pretending you're a statistician when you have absolutely no mathematical background, but also don't take at face value the "Must have {x} years of experience in {y} tech" requirement when you know you have the necessary work experience to have a good grasp on it in a few weekend prototypes, and you also know that the job doesn't actually require deep expertise of that particular tech.
I did the same for my first React.js job, and I didn't feel bad because 1) I was honest about it and did not sold myself as a React expert, and 2) I had 10 years of front-end development, and I understood web dev enough to not be baffled by hooks and the difference between shallow copy vs. deep copy of a data structure, so passing technical test was good enough for it.
Employers check your work history. You'd better be able to back up having the amount of experience they require with paid, value-delivering work at past employers, or they'll pass.
I was an engineering manager an for decades and I did first technical phone screening of a very large number of candidates. I couldn't really assess someone's design and programming skills. So I would pick a particular area they were promoting and go as deep as I could on it, both on the technology and their project experience using it. I weeded out lots of candidates, saving my engineers' interview time for more suitable candidates. And my teams were good at giving me feedback when I let someone unqualified e slip through...
I've never actually encountered one who didn't. Just like I've never been able to actually quit on Tuesday and walk into my next role on Thursday the way Hackernews told me "any halfway decent developer" should be able to do. They tend to ask about this sort of thing in interviews too and if you prove not to have the required background, you are considered weak and filtered out.
Anyways, checking happens often enough that the risk of being considered a liar and a fraud for claiming experience you don't have is high.
But here's the thing: learning Android dev is nothing like "learning" to use an LLM.
Obviously there are tons of tools and systems building up around LLMs, and I don't intend to minimize that, but at the end of the day, an LLM is more analogous to a tool such as an IDE than a programming language. And I've never seen a job posting that dictated one must have X number of years in Y IDE; if they exist, they're rare, and it's hardly a massive hill to climb.
Sure, there's a continuum with regards to the difficulty of picking up a tool, e.g. learning a new editor is probably easier than learning, say, git. But learning git still has nothing on learning a whole tech stack.
I was very against LLM-assisted programming, but over time my position has softened, and Claude Code has become a regular part of my workflow. I've begun expanding out into the ancilary tools that interact with LLMs, and it's...not at all difficult to pick up. It's nothing like, say, learning iOS development. It's more like learning how to configure Neovim.
In fact, isn't this precisely one of the primary value propositions of LLMs -- that non-technical people can pick up these tools with ease and start doing technical work that they don't understand? If non-technical folks can pick up Claude Code, why would it be even _kind_ of difficult for a developer to?
So, I'm with the post author here: what is there to get left behind _from_?
Not quite on topic but as an engineering manager responsible for IDE development, explaining to recruiters and candidates I wanted engineers who developed IDEs, not just used them. Unfortunately, that message couldn't get through so I saw many resumes claiming, say 5 years of Eclpse experience, but I would later determine they knew nothing of the internals of an IDE.
Presumably, people now claim 3 years of machine learning experience but via ChatGPT prompting.
For me, it's beyond doubt these tools are an essential skill in any SWE's toolkit. By which I mean, knowing their capabilities, how they're valuable and when to use them (and when not to).
As with any other skill, if you can't do something, it can be frustrating to peers. I don't want collegeues wasting time doing things that are automatable.
I'm not suggesting anyone should be cranking out 10k LOC in a week with these tools, but if you haven't yet done things like sent one in an agentic loop to produce a minimal reprex of a bug, or pin down a performance regression by testing code on different branches, then you could potentially be hampering the productivity of the team. These are examples of things where I now have a higher expectation of precision because it's so much easier to do more thorough analysis automatically.
There's always caveats, but I think the point stands that people generally like working with other people who are working as productively as possible.
That's a reasonable strategy. I don't think spreading FOMO is good. But pragmatically, I enjoy working with the latest crop of AI models regarding all sorts of computer tasks, including coding but many other sysadmin stuff and knowledge organization.
I didn't pick them up until last November and I don't think I missed out on much. Earlier models needed tricks and scaffolding that are no longer needed. All those prompting techniques are pretty obsolete. In these 3-4 months I got up to speed very well, I don't think 2 years of additional experience with dumber AI would have given me much.
For now, I see value in figuring out how to work with the current AI. But next year even this experience may be useless. It's like, by the time you figure out the workarounds, the new model doesn't need those workarounds.
Just as in image generation maybe a year ago you needed five loras and controlnet and negative prompts etc to not have weird hands, today you just no longer get weird hands with the best models.
Long term the only skill we will need is to communicate our wants and requirements succinctly and to provide enough informational context. But over time we have to ask why this role will remain robust. Where do these requirements come from, do they simply form in our heads? Or are they deduced from other information, such that the AI can also deduce it from there?
This really hinges on what you mean by "didn't use git".
If you were using bzr or svn, that's one thing.
If you were saving multiple copies of files ("foo.old.didntwork" and the like), then I'd submit that you're making the point for the AI supporters. I consulted with a couple developers at the local university as recently as a couple years ago who were still doing the copy files method and were struggling, when git was right there ready to help.
Was "when it first came out" a confusing limiter, there? I guess it seems like the article was making the point that jumping on git the day it came out could've just meant learning the betamax of repos, and that it was better to wait a bit to see what mature tech looked like, rather than waiting until 202X to realize that saving multiple file versions was suboptimal.
I don't understand how this, at all, makes "the point" for anyone.
I don't understand the rush to be "the first". Facebook isn't the first social media, Google isn't the first search engine, iPhone is not the first smart phone, Microsoft is not the first OS, the list goes on.
Clearly there's an advantage for being an early adopter, but the advantage is often overblown, and the cost to get it is often underestimated.
“There are a 16,000 new lives being born every hour. They're all starting with a fairly blank slate. Are you genuinely saying that they'll all be left behind because they didn't learn your technology in utero?”
> keep and/or increase my current compensation, I have to be competitive in the software development market
Author’s point is that competitiveness can come in many forms. Having the same AI proficiency as everyone else isn’t differentiating. (And it isn’t table stakes.)
Isn't that what is implied by the toughest job market yet for junior level candidates? Author is very confident that the answer to his question is "no".
I think the implication is that even though the technological landscape is evolving, it's not as if people born in the 60's couldn't foray into computer science because they arrived too late to study the ENIAC first.
Not really, cause they will have lots of years to catch up to Only whatever is in vogue at the moment while adopting what surrounds them growing up. They won't have to unlearn and let go of their own pride or sunk cost into specializing on the wrong thing.
The mistake is that 1 every N waves of hype are in fact monumental shifts and it makes sense to embrace as soon as possible. Also being early to the right thing can have massive implications in appreciating the shift before the general public, which is upstream from making smart resource allocation (investments, career choices). I have a friend that went to super early OpenAI as a designer. He has more equity than most AI researchers there and made a 0.001% amount of wealth. Being early does very much matter in the right conditions.
> If this tech is as amazing as you say it is, I'll be able to pick it up and become productive on a timescale of my choosing not yours.
In contrast to the current top comment [1], I don't think this is a wise assessment. I'm already seeing companies in my network stall hiring, and in fact start firing. I think if you're not trying to take advantage of this technology today then there may not be a place for you tomorrow.
I find it hard to empathise with people who can't get value out of AI. It feels like they must be in a completely different bubble to me. I trust their experience, but in my own experience, it has made things possible in a matter of hours that I would never have even bothered to try.
Besides the individual contributor angle, where AI can make you code at Nx the rate of before (where N is say... between 0.5 and 10), I think the ownership class are really starting to see it differently from ICs. I initially thought: "wow, this tool makes me twice as productive, that's great". But that extra value doesn't accrue to individuals, it accrues to business owners. And the business owners I'm observing are thinking: "wow, this tool is a new paradigm making many people twice as productive. How far can we push this?"
The business owners I know who have been successful historically are seeing a 2x improvement and are completely unsatisfied. It's shattered their perspective on what is possible, and they're rebuilding their understanding of business from first principles with the new information. I think this is what the people who emerge as winners tomorrow are doing today. The game has changed.
Speaking as an IC who is both more productive than last year, but simultaneously more worried.
> I find it hard to empathise with people who can't get value out of AI. It feels like they must be in a completely different bubble to me.
I think it depends on why you do programming. I like programming for its own sake. I enjoy understanding a complex system, figuring out how to make change to it, how to express that change within the language and existing code structure, how to effectively test it, etc. I actively like doing these things. It's fun and that keeps me motivated.
With AI I just type in an English sentence, wait a few minutes, and it does the thing, and then I stare out the window and think about all the things I could be doing with my life that I enjoy more than what just happened. I find my productivity is way down this year since the AI push at work, because I'm just not motivated to work. This isn't the job I signed up for. It's boring now.
The money's nice, I guess. But the joy is gone. Maybe I should go find more joy in another career, even if it pays less.
Oh, I agree entirely. The new paradigm is entirely unsatisfying to me too. It's not the same work that I trained my entire life to get good at, and the new work is not as fun. I trained to get good at this work because I just loved it since I was first introduced to it at ~10. I would have, and was, doing it for free for years.
Unfortunately that doesn't change my outlook on where all this is headed.
Perhaps, then, you can actually empathize with people who don't get value from it :) I used to enjoy the work, now I don't, so I'm posting on HN and daydreaming about other careers, instead of doing something useful.
> I just struggle to see how people cannot use it to get more done.
To be blunt about it, there's a decent chance I'll be quitting this job later this year, largely because of the AI push. I just hate these tools and I do not want to work this way. Losing an employee is a pretty big cost to the company. I guess the AI stuff is probably worth it to them, but there's a downside to it, too.
Yeah I agree with you, and I think a lot of people feel the same. It's totally different now and it's not what I signed up for. Maybe I'll get used to it, idk.
I agree with your take. It was obvious to my VP. It's not obvious to the rest of the enterprise (yet).
But I think it's just a matter of when not if.
My current guess at my slow fortune 500 is ~1-2 years before we see real employment impact.
Startups are happening now at least with my anecdotal conversations. Right now the discussion is more just slower growth than actually doing layoffs. That coin will flip at some point.
> Slashing labor costs while maintaining the status quo is still a big productivity gain.
Maybe I didn't express myself properly, but I think we agree, at least on this point?
Besides this effect, of enabling smaller teams to produce the same results, I think there is a larger effect coming where fundamentally different structures produce the same or better results as last year. I just don't think we've completely figured out what that looks like yet.
In general, a good strategy is just staying a little bit behind. Let the new fads play themselves out. Some have staying power. Bitcoin never did turn into a usable currency, just another speculator's toy. Luckily I am - so far - in a position where I can watch the AI thing from the sidelines to see how it plays out.
I'm healthily skeptical of new technology. Meaning I'm not the early adopter. But I've also found over the years I don't get left behind. I become curious at the time things are stabilising. Maybe on the cusp where there's still a lot of pushback but there's also clear value. Crypto in 2014-2017. AI in 2023-2024. You don't have to feel FOMO but if you're a technologist, if you have a healthy desire to evolve, change and learn then you'll naturally pick things up. I went from total crypto skepticism in 2014 to investing most of what I had. I went from total AI skepticism to doing RAG for the Quran and agentic tech for the small web. I think there's value in staying true to who you are but also naturally discovering and learning on your own timeline.
My experience so far tells me that the default path with AI tooling is that it lets us create without learning. So the author is right in that they can pay for a seat in this revolution whenever they want.
A practitioner with more experience maybe a few percentage points more productive, but the median - grab subscription, get tool, prompt, will be mostly good enough.
I’m in senior management and my take is - this is one of the very few times in our industry when the companies are actually both encouraging and investing in L&D at an unprecedented rate. When do you last remember a company being comfortable throwing 10,000$+ a year for you to learn a new skill and experiment at your own pace? This is why I’m encouraging my engineers so much - it’s free training! Take it while it lasts!
Sadly, I'm still disagreeing while crypto kiddies are driving past me in lambo's. If its the future of money, yes we'll get there eventually, but like every technology shift, there's a lot of money to be made in the transition, not after. *
* I sold all crypto a few years ago and I'm a happier person :D
Alternatively, there's money to be lost in the transition. The vast majority of "crypto investors" did not walk away from it any richer. Some folks have gotten lucky, but it's just that: their thesis about the future of money was evidently wrong, they just happened to get the timing right. Getting lucky for the wrong reasons is not a good investment strategy.
Meanwhile, the main category of people who have consistently gotten rich off the "crypto revolution" were various scammers and pump-and-dumpers who have since moved on to meme stocks, AI content farming, and so on.
But I wouldn't use crypto as a benchmark because AI has more substance. We can debate if it's going to change the world, but you can build some new types of businesses and services if you have near-perfect natural language comprehension on the cheap.
When you see people making money from something you did not get involved in, just remember: Someone will always be luckier than you, wealthier than you, etc. It doesn't matter. Measure yourself by your own life satisfaction, not by how others are doing.
That is why I agree with the sentiment as well. I use AI a little. Not too much. And I'm as swamped with work as ever because my focus is on legacy stacks, where AI is really not strong.
Others are pointing out that many also lost money, but I think we can say something even stronger, which is that the people that got rich only did so by taking the money of the people who lost money on it. If it's the future of money, you'll be able to buy in at a stable valuation, neither winning nor losing in the transition -- that's kind of a key property of money.
> Sadly, I'm still disagreeing while crypto kiddies are driving past me in lambo's.
I know a few people who got wealthy by being early to crypto. None of them had the correct reasoning at the time: They thought BTC was going to become a common way to pay for things or that “the flippening” was going to see worldwide currency replaced with BTC. They thought they’d be kings in a new economy but instead they’re just moderately wealthy with a large tax bill they’re determined to dodge.
I know far more people who lost money on crypto, though. Some were even briefly crypto-rich but failed to sell before the crash or did things like double down on the altcoin bubble.
The second group had gone quiet about their crypto while the few people in the first group gloat and evangelize (because continued evangelization is necessary to keep their portfolios pumped). This creates an intense survivorship bias where it appears like all the crypto kiddies are wealthy, but a quiet mass of people who played with crypto are most definitely not.
Solid piece, very wise. AI is fun but where I find it useful with code is first writing more thorough comments and then writing the base of your tests.
Writing the actual code that's efficient is iffy at times and you better know the language well or you'll get yourself in trouble. I've watched AI make my code more complex and harder to read. I've seen it put an import in a loop. It's removed the walrus operator because it doesn't seem to understand it. It's used older libraries or built-ins that are no longer supported. It's still fun and does save me some time with certain things but I don't want to vibe code much because it removes the joy out of what you're doing.
At some point you commit the time to learn what you need to. I like to think of the analogy to SEO. The veterans in the industry are not who they are because they were at the front of the line. It’s because they have the 15 years of experience under their belt.
> I wrote my MSc on The Metaverse. Learning to built VR stuff was fun, but a complete waste of time. There was precisely zero utility in having gotten in early.
Wonderful life lesson on hype cycles. I am curious if hype literacy will join media literacy in academia.
One hidden premise of this is "AI tools are not useful now, even if they might be in the future." For example:
> Few are useful to me as they are now.
Except current AI tools are extremely useful and I think you're missing something if you don't see that. This is one of the main differences between LLMs and cryptocurrency; cryptocurrencies were the "next big thing", always promising more utility down the road. Whereas LLMs are already extremely useful; I'm using them to prototype software faster, Terrance Tao is using them to formalize proofs faster, my mom's using them to do administrative work faster.
Not only is he not being left behind, he might even have the liberty to abandon ship.
So many humans are uploading their code into Github for the AIs to train on, and thus digging their own graves whereby they'll eventually be outsmarted by AI.
On the other side of the coin, don't the AIs have to be weary about training on prior output in order to maintain the purity of their sources? The dogfooding for them will end up becoming overwhelming.
Perhaps innovation should stay private or used to extract micropayments.
Managing themselves well, hardware tinkerers have a solid future, because robots aren't as effective yet in the real-world. I think that is the next golden age, actually.
> For every HTML 2.0 you might have tried, you were just as likely to have got stuck in the dead-end of Flash.
i'll just say, and i understand this is not the point of the article at all, but for all its faults, if you got in on flash as earl as html 2.0 and you were staring at an upcoming dead-end of flash in say, 2009, you also knew or had been exposed at that time to plenty of javascript, e4x and what were essentially entirely clientside SPAs, providing you a sort of bizarro view of the future of react in a couple of years. honestly, not a bad offramp even if flash itself didn't make it.
Crypto was interesting to think through and it was clear very early on how many flaws it has. It basically just moved the goal post to level deeper and it was quite an eye opener how few people even understood the major flaw of crypto: You can only do crypto savely with anything on blockchain and it has not solved any real issue off blockchain (which means you can literlay just send crypto to each other and thats it).
But AI is a beast.
Its A LOT to learn. RAG, LLMs, Architecture, tooling, ecosystem, frameworks, approaches, terms etc. and this will not go away.
Its clear today already and it was clear with GPT-3 that this is the next thing and in comparison to other 'next things' its the next thing in the perfect environment: The internet allows for fast communication and we never have been as fast and flexible and global scaled manufactoring than today.
Which means whatever the internet killed and changed, will happen / is happening a lot faster with ai.
And tbh. if someone gets fired in the AI future, it will always be the person who knows less about AI and knows less about how to leverage than the other person.
For me personally, i just enjoy the whole new frontier of approaches, technologies and progress.
But i would recommend EVERYONE to regularly spend time with this technology. Play around regularly. You don't need to use it but you will not gain any gut knowledge of models vs. models and it will be A LOT to learn when it crosses the the line for whatever you do.
That hasn’t been my observation and seems unlikely to be true. I suspect AI is a wicked learning environment. Narrow experience may hinder rather than help
AI/LLMs are not getting stupid in something they are already good in (or very seldom if).
So i see it more as an evolving thing not a wicked thing.
Newest claude release made context better, for this information I already need to know claude and know what a context is and what the problem with context length is.
I'm following AI for a few years now and def not have the feeling at all its wicked.
Many companies usually want to compare themselves to Apple and at the same time say they are disruptors and innovators but Apple is probably the best company at being okey with being left behind. Many think about them as experts in products but for me they always been best att copy what others are doing and refine it, maybe not neccassary better technical but always seen the market fit better then others. Like poker, the later you need to take your decision the more information you have.
I heard from a senior leader at Amazon that "Today, I am choosing how I fail". This has echoed in my head for many years.
At any moment, you are failing at thousands of things that you may not even know about, and that is the gist of what I took away from it. The thing is that you have to be OK when you intentionally choose to not invest in something as regret is ultimately a poison.
The other thing is this: you are not obligated to bring people with you and you have a choice of free association.
I've noticed 2 very obvious trends. The high-performers who previously wrote the most PRs are now using AI the most aggressively and the number PRs they produce has almost doubled. The other trend is that large teams are being split into smaller teams that work on new products. I hope the trend is more projects and less grunt coding. (I know the ai coders are not yet L6 but I suspect they will achieve L3-L5 this year).
This is definitely appearing in the AI tool space and industry as a whole. I feel like companies are pushing AI tools into the workplace because they are worried that a competitor is going to do something that revolutionizes productivity and credit is going to be given to the AI tool as the factor.
There's a tough timing call that comes into play with this because there is no ability to predict, only to look in hindsight, it's more of a hedge to be in and say I bought a lottery ticket and had a chance during the AI boom than it is to not engage.
I believe the saying is "you never win if you never play."
Some people are comfortable living on the edge, but it isn't for most.
I was developing in the metaverse space, and the problems we were facing led me to learn about the state of AI image generation (2018), and where the world was headed.
People assume the thing you are focused on is the thing that must win in the end, but that just means you are too focused on your little part of the world to take in the bigger picture of what's out there.
Prior to working in the metaverse (really a form of volumetric video, but I won't go into details), I was working in telehealth (2014). I did some research in augmented reality (2009), and lots of other areas of interest as well.
Some people would say I wasted my time on these, but there was a mass of secondary learnings which I value every day.
The answer was quite the opposite. I wanted to see if the technology lived up to the hype. The answer, unsurprisingly was no. If only Zuck had listened to me :-)
People don't believe in that anymore. If your gut instinct was wrong, you're not only bound to it for all time, but you'd best angrily double down at every opportunity. God forbid you "flip flop" or consider new information, or whatever.
This is one of those posts I would like look back on in a year or two. I am usually a late adopter with everything. This time is think its different. I am seeing what AI can do with my own eyes. I am creating new things at light speed and figuring out this all works. I don't think you want to be late to the party on this one.
Not advocating for crypto here, but the ROI evaluations here are a bit incongruous.
The risk of getting in early on crypto is you lose a little money. The risk of not is missing out on money. You can't simply replay that later, the way that you could invest the time to catch up on how git works.
Crypto isn't bad because it failed to make early adopters rich — it did make them rich. It's bad because it has horrible externalities in scams, war crimes / sanctions evasion, organised crime — which most of those early adopters were well aware of.
And thus it is reasonable to not use LLMs. And it's not only the training process that's problematic. There are so many individual LLM users who waste electricity on tasks that they would have solved by thinking for themselves just a few years ago.
For whatever reason the folks on here refuse to understand that for a big chunk of this planet people get paid in inflationary currencies that they used to have to immediately convert to dollars and then stuff under a mattress because the local banking system is corrupt. In that environment cryptocurrency is a god send.
This is fine so long as you don't confuse stubbornness for caution. As technologies lose favor, and others suggested you expand your toolset, don't post about your frustration while you're standing in the unemployment line.
I would not have started my article with "I could get into Bitcoin anytime, why the rush". That is not the killer first example you think it is. It's been ~17 years of proof so far that you would've made a ton of money by simply mindlessly buying $200 of bitcoin every month after lower risk contributions are made, and just holding onto it.
I mean if you did that you'd have contributed ~$38K USD by 2026 and had ~1.5B USD now if you started in 2010. BTC being so cheap back then dominates the whole process so to demonstrate my point more if you had heard about it all those years and were nervous about trying it and decided to wait until 2016, you'd still need to just put in $24K overall to come out with ~$450K by 2026.
That's not biting your finger nails over the price changes, the hype cycles, the price drop scares. You just set and forget a $200 recurring buy a month and put your energy elsewhere and pocket half a million for basically no effort
And if anything is possible in hindsight, then why in hindsight would you write an article acting like bitcoin was a bad decision to be an early adopter for
I remember working on a few early tools for VRML towards the end of the 90's... It was cool, but far from great... was remembered from the mention of VR in the article.
That said, my only regret with Bitcoin was deleting my early wallets when I realized the coins were only worth $.25 ... if I'd had any inkling what they'd be worth someday, I'd probably have just bought $1000 worth back then and zipped it up until closer to today. I'm truly curious how many bitcoins were similarly deleted from existence.
Adoption of a new technology has always sorted itself into buckets by early adopters, mainstream adopters and late adopters. I think this post is just demonstrating the mindset of the latter.
It's more about job seeking than anything. If you jump on a fad early, and it turns out to be the winner, when you're looking for work you can say you have X years of experience with it, which will be a few more than most of the other candidates.
It also shows a passion for learning and improvement, something hiring managers are often looking for signals of.
But of course it's a trade off. This rewards people who don't have family or other obligations, who have time to learn all the new fads so they can be early on the winners.
I had this with Rust. I always saw the huge hype, especially some years ago, and it was hugely off-putting. Ridiculous projects like rewriting famously full coverage branch tested projects like SQLite in Rust, or rewriting the GNU coreutils, and always spamming "blazing fast" and "written in Rust (crab emoji)" was very, very hostile to a C++ developer.
When I eventually got around to using Rust, I was hooked, and now I don't use C++ anymore if I can choose Rust instead. The hype was not completely unjustified, but it was also misplaced, and to this day I disagree with most of those hype projects.
It was no issue to silently pick up Rust, write some code that solves problems, and enjoy it as a very very good language. I don't feel a need to personally contact C or C++ project maintainers and curse at them for not using Rust.
I do the same with AI. I'm not going around screaming at people who dare to write code by hand, going "Claude will replace you", or "I could vibe code this for 10 bucks". I silently write my code, I use AI where I find it brings value, and that's it.
Recognize these tools for what they are: Just tools. They have use-cases, tradeoffs, and a massive community of incompetent idiots who like it ONLY because they don't know better, not because they understand the actual value. And then there's the normal, every day engineers, who use tools because, and ONLY because, they solve a problem.
My advice: Don't be an idiot. It's not the solution for all problems. It can be good without being the solution to a problems. It can be useful without replacing skill. It can add value without replacing you. You don't have to pick a side.
While you can certainly take a wait and watch approach on many things you also have to take a strategic bet on some otherwise you will never be at the forefront of any field.
I'm reminded of the parable of the Chinese farmer (a quick Google search if you aren't familiar) when I see this sentiment. Is going all in on crypto good or bad? Maybe so, maybe not. We'll see. Is going all in on AI-assisted development good or bad? Maybe so, maybe not. We'll see.
All I know is, I've always enjoyed building things. And I enjoy building things with AI-assisted tools too, so I'll continue doing it.
> There are a 16,000 new lives being born every hour. They're all starting with a fairly blank slate. Are you genuinely saying that they'll all be left behind because they didn't learn your technology in utero?
> No. That's obviously nonsense.
That's does not obviously follow, I do worry about the ever increasing proportion of humanity who are no longer 'economically viable' and this includes people who are not yet born.
I don't think the "craftsman" self-identification is going to work for software engineers anymore. The tool capabilities are too dynamic, you have to be some sort of opportunistic pirate/entrepreneur. Sure you can jump in and get up to speed on some aspect of the toolchain later on, but the identity shift is the hard and slow part that I think it's wise to get started on ASAP.
If this is a real strategic shift, some will adapt and some won't or can't.
From Thomas Kuhn's Structure of Scientific Revolutions:
"a new scientific truth does not triumph by convincing its opponents... but rather because its opponents eventually die, and a new generation grows up that is familiar with it"
The biggest issue is - you will be left behind, in the end. This is the race you cannot win. You can try as much as you like, spend free time trying to catch up, and you, most likely, will lose. If you play this game, you've already lost.
I am actually surprised by people willingly trying to be more productive, like... machines. And then crying when machines are proven to be better at being machines than meatbags.
> If this tech is as amazing as you say it is, I'll be able to pick it up and become productive on a timescale of my choosing not yours.
I saw meme in X the other day, which roughly says that one does not have to learn if she learns slow enough in the age AI. I guess the undertone is that AI evolves faster than one can learn about the tricks of using it.
There’s a lot of truth to this post. I’m very pro AI, and I believe everyone should get comfortable with it because it’s not just the future, it’s already the present. If you want to stay competitive in today’s workforce, AI is going to be part of your toolkit.
But on the other hand... I also only learned git when I needed it at a new job... So we can pump the breaks a bit.
Actually, given the path their world is on (in the USA anyway), it's true. Falling economic opportunity, worsening physical environment, social atomization, increased indebtedness, etc.
This is in an excellent characterization of the kind of marketing tactic I see all over social media right now and that I find absolutely disgusting.
The keyword here is fear. Despite faux-positive veneer, the messaging around certain technologies (especially GenAI) is clearly designed to induce anxiety and fear, rather than inspire genuine optimism or pique curiosity. This is significant, because fear is one of the most powerful tools to shut down rational thinking.
The subliminal (although not very subtle) message there is something very primitive. "If you don't join our group, you will soon starve to death." This is radically different from how most transformative technologies were promoted in the past.
I think AI is not quite the same as crypto when it comes to FOMO. At the peak of the craze you could not write on HN that 'crypto is nonsense' unless you wanted to be modded down to oblivion, to be shadow banned forever. I exaggerate, but not much.
With AI people are able to say 'this is nonsense' without people getting the pitchforks out.
As for myself, I don't have the bandwidth to learn how to do clever things with AI. I know you just have to write a prompt and it all happens by magic, but I have been burned quite badly.
First off, my elderly father got tricked out of all of his money and my mother's savings, which were intended for my niece, when she comes of age. It was an AI chatbot that did the deed. So no inheritance for me, cheers AI, didn't need it anyway!
Then there was the time I wanted to tidy up the fonts list on my Ubuntu computer. I just wanted to remove Urdu, Hebrew and however many other fonts that don't have any use for me. So I asked Google and just copied and pasted the Gemini suggestion. Gemini specified command line options so that you could not review the changes, but the text said 'use this as you can review changes'. I thought the '-y' looked off, but I just wanted to do some drawing and was not really thinking. So I typed in the AI suggestion. It then began to remove all the fonts and the window manager, and the apps. It might as well have suggested 'sudo rm -fr /'.
This was my wakeup call. I am sure an AI evangelist could blame me for being stupid, which I freely admit to. However, as a clueless idiot, I have been copying and pasting from Stack Overflow for aeons, to never be tricked into destroying all my work.
My compromise is to allow some fun with cat pictures, featuring my uncle's cat, with Google Banana. This allows me to have a toe in the water.
Recently I went on a course with lots of people with few of them being great intellects. I was amazed at how popular AI was with people that have no background in coding. They have collectively outsourced their critical thinking to AI.
I did not feel the FOMO. However, I am old enough to remember when Word came out. I was at university at the time and some of my coursemates were using it. I had genuine FOMO then. What is this Word tool? I was intimidated that I had this to learn on top of my studies. In time I did fire up Word, to find that there was nothing to learn of note, apart from 'styles', which few use to this day, preferring to highlight text and making it bold or biglier. I haven't used a word processor in decades, however, it was a useful tool for a long time.
Looking back, I could have skipped learning how to use a word processor, to stick to vi, latex and ghostscript until email became the way. But, for its time, it was the tool. AI is a bit like that, for some disciplines, you can choose to do it the hard way, using your own brain, or use the new tools. However, I have been badly burned, so I am waiting it out.
The irony is that if LLMs live up to their potential then the value of software development as a skill is going to plummet, at least as far as something to do for others. I say it's ironic because obviously the people most interested in using LLMs for software development are software developers, and most are not working independently. It'd be like if we were all proactively getting involved in training our own replacements.
I was highly skeptical of this happening not that long ago, but I have to say that it seems increasingly likely. LLMs are still quite mediocre at esoteric stuff, but most software development work isn't esoteric. There's the viable argument that software development largely isn't about writing code, but the ability to write code is what justifies software developer salaries, because there's a large barrier to entry there that most just can't overcome. The 80/20 law seems to apply to everything, certainly here - 80% of your salary is justified from 20% of what you spend your time doing.
It's quite impossible to imagine what this will do to the overall market, because while this sounds highly negative for software developers, we're also talking about a future where going independent will be way easier than ever before, because one of the main barriers for fully independent development is gaps in your skillset. Those gaps may not be especially difficult, but they're just outside your domain. And LLMs do a terrific job of passably filling them in.
It'd be interesting if the entire domain of internet and software tech plummets in overall value due to excessive and trivialized competition. That'd probably be a highly disruptive but ultimately positive direction for society.
Ive been using AI/LLMs for 3 years non-stop and feel like I've barely scratched the surface of learning how to wield them to their full potential. I can’t imagine the mindset of thinking these tools don’t take extreme dedication and skill to master.
I started working in AI/ML about ten years ago. Reasonably early. Today, professionally and financially I'm doing about as well as a typical programmer. I find the field interesting so I have no regrets but I tend to agree with OP.
I agree with the conclusion but not with the premise. The conclusion is, "I don't have to be an early adopter," but the premise seems to be "there is zero utility in getting in on anything early."
> Might I be 7% more effective if I'd suffered through the early years? Maybe. But so what? I could just as easily have wasted my time learning something which never took off.
I prefer to move slower. I've accepted that I'm not going to create some unicorn startup (that was never an aspiration). As an employee at a company, my goal is to focus my time learning the things that are relevant to my job and that will be useful for 10 years, not 10 weeks.
Chasing every new tech will lead to burnout and disillusionment at some point.
AI probably isn't going away in the same way NFTs largely did, and I use it to some degree. However, I don't see a lot of value of being on the bleeding edge of AI, as the shape it takes for those skills that will be used for the next 10 years are still forming. Trying to keep up now means constantly adapting how I work, where more time is spent keeping up on the changes in AI than actually doing something useful with it.
After the bubble pops, I think we'll start to see a much more clear picture of what the landscape of AI will look like long-term. Who are the winners, who are the losers, and what tools rise to the top after the hype is gone. I'll go deeper at that time.
Right now, the only thing I'm allowed to use at work is Copilot, so I just use that and don't bother messing around with much more in my free time.
It’s a personal choice, and both early and late(er) can be valid rational choices if it’s you who is making the choice and not just following a crowd (or even a single person).
FOMO is making me feel like I should mess around with openclaw but I can’t see any use cases that I can’t accomplish with other tools. What should I do based on this article?
Because the planetary alignments won't allow for another launch any time soon... which I guess has a natural correspondence in that the macroeconomic conditions (read: bubble) mean that now is a great time to play around with things while they're basically giving it away to get you hooked.
It is said that major providers more than break even on what they're charging.
But at the same time that's not the point of capitalism, is it? The point is to charge close to the value you're providing.
My lunch money is approximately $10 and I often blow through as much in Claude tokens generously provided by the company which hired me. But I'm not getting $10 value from those tokens, but much more.
The cost of entry to this market is extremely high. Should Anthropic win and become an almost monopoly, it is bound to keep increasing prices to the point, where the value it's providing matches the cost.
That's the endgame of every AI company out there. It's worth using these tools now, while there's still competition and moats weren't established.
Luckily the models were built on copyrighted materials so hopefully the big players won't have strong legal standing to kill off model destilling. Then, with models like current deepseek or kimi k2.5, you are perhapy 1/2 years behind in their capabilities at the fraction of the cost. For real inference costs, look at prices on openrouter. For hobby, I wasn't able to burn trough more than $5 in a month.
This is where I landed too. At least in the js frameworks "wars" it was reading the docs and prototyping angular and react for free. To try and keep up with the latest AI tools you're expected to spend ~$1k / year.
My worry is are two or even three companies at the top enough for healthy competition? You're not limited to one product - arguably it's better to have multiple subscriptions.
If they decide to increase pricing in lockstep just like FAANG applied RTO policies, we as consumers are going to get the short end of the stick.
> But at the same time that's not the point of capitalism, is it? The point is to charge close to the value you're providing.
The question is how large the value delta will be from open models. And I’m not sure if the cost of entry is really “extremely” high comparatively if as you project the market will be so profitable. Surely investors will see a chance to get a larger piece of that profit. While model training is costly, there is a ceiling imposed by the training material being limited (at least for text). LLMs also don’t have a network-effect moat like social media has, or a web search moat like Google has, or a chip technology moat like TSMC has. It’s unclear if a significant moat will emerge.
sure you can pick up any tool whenever you want, but from your employer's perspective AI is the best force multiplier since slavery, everything between it still required humans with leverage, the question is if your boss will need you at all by then
As long as it's not coupled with calls to tax and regulate those who do get in early and reap benefits from doing so, this is good and healthy.
(I'm not the earliest adopter of crypto and AI by any means. I only rode up crypto a couple of times for 2X and 3X kinda gains on my investment, and I only started using Claude last year.)
> For every HTML 2.0 you might have tried, you were just as likely to have got stuck in the dead-end of Flash.
Like a lot of tech Flash had its moment in the sun and then faded away, but that “moment” lasted a decade, and plenty of people got their start because of or built successful businesses around it. Did they have to pivot as Flash waned? Sure, but change is part of life.
I’m sorry but I find the take expressed in this piece to be absolutely miserable and uninspiring.
But, hey, congratulations on the 20:20 hindsight, I suppose.
I'm upvoting because it's useful to see and debate this viewpoint — shared by many engineers I know
I do think it's a bad take though. Not all new trends are the same: the metaverse was an obvious flop and crypto hasn't found practical applications. AI isn't like those because it's already practically changed the way I get my job done.
It takes time to learn skills, and getting started earlier will means more time to use them in your working life.
The way I see it is - AI still makes mistakes, and I have to know how things work at some point anyway. So I'd rather spend my time actually understanding fundamentals (in my case, CSS at the moment), than trying to keep up with the frequently changing AI tools and models.
Once the tools and models stabilize more (as well as the pricing model), there's less risk in me learning something that is no longer relevant.
Except when I choose to wait on learning how to use AI tools effectively, I get told I am going to be "left behind".
I use AI not for what the various fraudsters hype it for. It utterly fails coding what i want each time. But it is a great tool to create things and not be censored/hindered by collaboration with humans. It liberates the lone nutters of the world to make music, create movies, do things, which society desperately tried to stop them from. Just because new things are frightening and money shies away from risk like the devil from holy water. Look at all those sequels. Each sequel in existence murdered a dozen artists hopes to create something new.
A ton of it is garbage. But 1/1000 gets to realize a vision, that could never be marketed, never be discovered and is something new entirely. For that i love it and thank those who made it..
As someone further down the road in my career, I would argue that waiting is your prerogative but you do so at your own peril.
I made these kind of mistakes early in my career, stuck it out with PHP for far too long ignoring all the changes with frontend design trends, react, etc. I was using jQuery far too late in my career and it really hurt me during interviews. What I was doing was seen as dated and it made ageism far worse for me.
Showing a portfolio website that was using tables instead of divs.
I had to rapidly skill up and it takes longer than you think when you stick too long with what works for you.
If AI truly is a nothing-burger than guess what? Nothing lost and perhaps you learned some adjacent tech that will help you later. My advice is to NEVER stop learning in this field.
Learning is your true superpower. Without that skill, you are a cog that will be easily replaced. AI has revealed to me who among my colleagues is curious, and a continuous learner. Those virtues have proven over the course of my 25+ year career in technology to be what keeps you relevant and marketable.
I think the point isn't "wait forever and never learn" but simply "you don't have to be at the forefront of the wave" - because the real ones will lift everything, and you can come it a bit later.
It is easy NOW to look back and see the optimal path for a web developer, but was that obvious from the start? How many killer technologies lie unused today?
You're trying to make the point using BitCoin, but in the early 2000s I had just over 14,000 of them, so I can quite clearly see a point in getting in early.
(Assuming you meant early 2010s, since Bitcoin didn't exist in the early 2000s)
This underscores a main counter point: dipping your toes and casting a wide net often has a low cost, since back then you could mine (and even purchase) Bitcoin relatively inexpensively. If it hadn't worked out, then it wouldn't have hurt much at all.
The argument about bitcoin was against it being "the future of currency". There is no point, whatsoever, to "getting in early" to that.
If everyone had been talking about it like the casino that it actually is, then sure - some people made some good bets, and a lot of people made bad ones trying to get in early. Imagine being the person who sold all your bitcoin for whatever other stupid memecoin, to "get in early"?
It's not a real counter-argument, it's just "I had a lot of dumb luck on this one specific thing, aren't you silly for not guessing as well as me".
I’m suprised nobody has mentioned that Claude is the realization of all this blockchain work - an internet computer you rent time from where the computation is measured in tokens :)
I understand and empathize with his sentiment, but I think he is missing the point. Using AI effectively as an engineer requires a paradigm shift in terms of how you work. You cannot approach your work as you did in the past, and use AI and expect it to be a big improvement. In fact, if you do that you will likely be disappointed, and worse off. Shifting your paradigm is one of the hardest things you can do, even more so if you have been in the field for a while, but it is also the most rewarding, and opens up many new possibilities. It's not about being left behind, as much as it is about limiting yourself unnecessarily, by staying in your comfort zone.
Say what you want but the layoffs of people who don’t use these tools getting replaced with those who can have and continue to happen; I miss manual coding just as much as the next man but this seems like a hot take
Okay, this text was pretty good. Refreshing to read something that doesn't seem written by AI too (would be ironic given the contents).
The only scenario where I think it pays off to be on top of the hype is of you are chasing money sloshing around the latest hype. You know, the hustle culture thing. If that's not your thing, waiting until things are established (if they ever get there) is harmless.
And yeah, AI as it is now is at best moderately useful. I use it on a daily basis, but could do without it with little harm.
> Refreshing to read something that doesn't seem written by AI too (would be ironic given the contents).
As much as I dislike the idea of not writing/checking code I am responsible for, it was a surprise to me seeing a few "anti/limited AI in coding" articles that don't pass an LLM detector. (I know those are not perfect but not much else one can do).
That's really the big difference - if you're a startup or founder (or apparently even an billionaire CEOwner) you gotta chase the hype because your customers are the VCs/shareholders and if you are NOT chasing hype they're going to have serious doubts about you.
As an employee (perhaps even a highly stock-option compensated one) the equation is very different. Perhaps you're aligned if you're an employee of a startup/AI obsessed company. But for the vast majority they're not.
So? Economy is entertainment. When crypto was hype, billions were made and burned from building whatever entertaining thing around that. Now it's AI's turn. Billions will be made and burned. Economy is just a fun game. Let's have fun. The idea that everything needs to be "useful" is highly subjective. What is truly useful? Is it food? Shelter? Medicine?
Anyone obsessively insisting others adopt $tech with threats that you'll be obsolete, left behind, whatever, are just selling you something. If anything, they should be trying to keep it a secret, so that they stay among the elite few who get outsized benefits from $tech while everyone else plays in the mud.
i’ve said this before, but the “left behind” narrative is FUD nonsense. as an llm avoider i’ve never felt further _ahead_ than now. all of my peers who never bothered to learn their tools (which gave tangible benefits) have opted into deskilling themselves further.
it’s readily apparent who has bought into the llm hype and who hasn’t
People mostly lose their jobs and suffer through no failure they were positioned to avoid, and often through no fault of their own at all. Spending your career chasing fads out of fear of being abandoned to penury is at least as limiting as mere conservatism towards new technologies. The risk is real and the fear is valid but that isn't the solution, no individual action you can take is the solution.
It's a problem of motivation, all right? Now if I work my ass off and Initech ships a few extra units, I don't see another dime, so where's the motivation? And here's something else, Bob: I have eight different bosses right now.
I’m running a solo saas. In the last six months, I’ve added about $300k in ARR. There’s zero chance I could have done this without AI. My velocity just keeps going up, month after month.
It amazes me that companies are developing proprietary IP with somebody else's cloud-based AI that ingests and learns from everything that they type and it generates.
These companies are paying for the privilege of having their IP stolen.
This reasoning is solid and applies equally to AI. I do not need it crammed into every service and forced on me thank you very much "If this tech is as amazing as you say it is, I'll be able to pick it up and become productive on a timescale of my choosing not yours."
Getting early into any technology only makes sense if you are building your business on top of it. Or you are making money from it in some way. Other than that it makes sense for the rest of us to wait.
Of course those that believe that AI will convert into AGI and destroy society as we know it won't be convinced.
General idea is true, except for this particular technology.
When AI will be easy to pick up and guide, guess what, there will be no need for a programmer to pick it up. AI will be using itself, Claude Manager driving Claude programmers.
So leverage AI while you still can provide value doing so.
> If this tech is as amazing as you say it is, I'll be able to pick it up and become productive on a timescale of my choosing not yours.
I mean... yeah? It's obviously true. However people use LLM coding today not because they're "afraid of being left behind" or "investing into a new tech" or whatever abstract reasoning. It's because they're already reaping the benefit right away. It takes just a few hours to go through like 80% of the learning curve.
Too late for what? Could no one start a viable internet business in 2005, or were they all taken in 1998? Is it impossible to learn machine learning today, if you weren't jumping into Tensorflow in 2015? Do you think it's impossible to learn OpenClaw today, if you weren't playing with it six months ago, and do you think there might not be a successor that "wins" and is easier to learn and use six months from now, or will I have "gotten there too late" to possibly leverage or learn agents?
I just don't understand what it is you think anyone will be too late for, unless this is just self-justification and snide ego-boosting.
Do you think no one can create anything alone ever again? Or can they only do it by adopting the bleeding edge?
> It's called low-hanging fruits.
1 in a million ideas are 1 in a million, and they don't require being a bleeding edge adopter of anything. Do you think no one can create a better version of a first-try service? Is the agentic world now closed because someone built a mediocre version of it?
For a start-up based board, this point-of-view just feels so sad and myopic.
The behaviors of NFT advocates and AI advocates are shockingly similar.
Remember how NFTs were supposed to be the future or art ownership, and all it amounted to was awful pictures of bored apes and ahegao lamas? The NFT bros proudly displayed their shitty art - not because it was good, but because it signaled their allegiance.
Now go on to any pro-AI blog. Look at the images. They've stopped trying to edit out the AI errors - they proudly display images with garbled text and bad anatomy. Just like before, it signals allegiance to AI consumption.
Even the last sentence of your post is the same sentiment as "have fun staying poor" was for the crypto bros.
> The behaviors of NFT advocates and AI advocates are shockingly similar.
It’s the same people every time. Stupid, gullible idiots.
Remember when HN was obsessed with the room temp superconductivity fraud a few years back? Remember the zealous indignation at anyone suggest skepticism? The empty attempts to downplay their rabid stupidity afterwards?
The thing is, Bitcoin, at least before cryptocurrencies were picked up by "tech bros", was originally a way to disconnect from the corrupt, centralized banking system.
LLMs, at the moment, are all about giving up your own brain and becoming fully dependent on a subscription-based online service.
> IMO it reads a little desperate and very much like the hype bros but from opposite side.
I would coin it Chill Guys, and I claim on average they are more often correct than hype bros. We all know the old saying that nobody ever was fired for selecting an established solution/technology. Being chill, staying safe, it's more beneficial in real business. Just wait until the hype has normalized and then start dabbling with the new, when all others starting doing it also. You may not be the frontrunner of a new wave, but you can still ride it well. You are only really left behind when everyone changed, and your income starts drying up, until then, it's just business as usual.
And always remember: when ChatGPT started its hype, many were predicting Googles death and the end of Web searches. Now, 3(?) years later, Google is still around, the search is still going strong, and Gemini is seriously competing with ChatGPT. Hypes come fast and die fast.
I find the hivemind terribly oppressing at times. AI tools are great, but in the end it seems to me that the results matter most. However we seem to go from hype to hype, again and again. It's all so tiresome. Why can't we just respect individual choices and focus less on the tools and more on the results?
Not going to lie, I’d rather be poor. Not destitute - I’ve been poor but not destitute and I’d rather not go desperate - but poor? As in (because “poor” is very imprecise and can imply anything between utter poverty to “not owning three homes”) like having a low paying job but still enough to pay rent?
I’d rather be that than do AI assisted software development. Genuinely the only thing stopping me now is that there’s actually way more skill and qualifications in most low-paying jobs than a typical software developer imagines, and acquiring those takes time and money itself. But by now I know multiple people who made the jump even before the latest madness, and they’re all happier. Some still code, but don’t even publish. Some are like “I haven’t used a proper computer in _months_ this is great.” All work hard jobs at odd hours. None regret.
Honestly, those who lost money did so because they were aiming for profits, not for The Network State, or for having non-seizable money etc. On the other hand, for those who got in early because they were online and curious at the time, with a small amount of money back then has made them billionaires today, an extremely low risk. If they arrived a bit later, millionaires, still with a really soft risk.
Then of course, there are MANY problems starting with BTC https://blog.dshr.org/2025/09/the-gaslit-asset-class.html but they generally aren't what most people talk about, and today, even if the complexity is insane, the documentation sparse, and the code quality questionable, Lightning is actually the only truly scalable solution we have for micropayments and payments of various amounts, generally not large ones. It has some absurd aspects, but that hardly matters. It works.
More generally, we don't have a crystal ball; where we can, we diversify, certainly limiting risk but also taking a bit of a gamble; where we can't, we choose to watch and see how it goes, knowing that we'll pay for it in terms of returns.
As things stand for me personally, given the level of IT obscenity in the traditional banking world, which in 2026 still doesn't have decent APIs open to retail customers, or at least decent export functions, shameful websites, a push towards completely unacceptable mobile apps, absurd limits etc etc etc, well, the worst CEx is less bad than the best bank. I don't trust either, but I distrust the CEx less than the bank, and given Wikileaks, Francesca Albanese, the protesters in Canada, the various private individuals illegally "sanctioned" by the EU Commission and so on, I'd say it's madness to rely on banks for anything more than the bare minimum.
Most people today know nothing of this and don't weigh it up, but they will, and they'll pay for this delay with their lack of attention.
I think this is a carry over from the early 2000s boom and bust mindset. That if you jump in early enough on nearly any technology, you'll become a billionaire. So hop on board our burning platform!
In general, we as a society have not adjusted to technology. We've gone through to much change to have any stable base lines. So we're going to float in insanity for a while until things finally settle down. Probably 2 wars, a famine, and several periods of resource scarcity away still, but we'll get there one day...
I love the Cryptocurrency analogy. The LLM hype monkeys are the same people that were screaming that NFTs/Digital Art was going to replace all the traditional art in gallaries in 10 years. They are literally the same people, and they are all addicted to money and hype. Ignore them..
Did handmade Swiss watch movements lose all demand when Asia started mass manufacturing watches? No. There is always going to be more demand for quality over slop. Its the same reason that handmade clothes are worth 100x more than clothes at a department store.
This is all by design too, these billionaires selling thinking machines are trying to make us all dependent on their fountain of tokens. Don't fall for it. Just like how maps apps made everyone reliant on Google/Apple for your ability to navigate around your own city, these billionaires want to do the same think with your ability to think, build, plan and even learn/read.
Don't fall for this scam, unlike other hype cycles like NFTs and Crypto this will actually damage more than just your bank account, it will fry your brain if you become over reliant on it.
Take a second and consider why these LLM tool companies design their products like slot machines. They put multipliers in there UIs (run this x3,x4,x5) times so that you inevitably treat the thing like a slot machine. And it is like a slot machine, you have no way to control the results its quite random, in the case of llms they just have a better payout percentage, at the cost of making your brain become dopamine and structurally dependent on their output. They convince people there is some occulted art in the formation of a prompt, like a gambler who thinks if they press buttons in a certain order they'll get better results or many other gambling superstitions.
If you're writing software please take a moment to breath, and ask yourself if its really that useful to have piles of code where you have little idea how things work, even if they do. Billionaires will sell you on the idea that this doesn't matter because the llm, that you conveniently have to pay them to use, will always be able to fix that bug.
Don't fall for the ruling classes trick, they want you reliant on this thing so they can tell you that your input isn't as valuable, and therefor your salary and skills are not as valuable. We have to stop this now.
It costs about 60-80k to run a 1T param model at your house.. which is the only size model that's going to get anywhere close to a foundation model's capability.
The kid who showed his work in detail in math class is doing better in life 9/10 times than the kids that only knew how to use a calculator. Now consider how well the people who think you just need to know how to yell at the calculator are going to do?
When Maps apps came around, people totally lost the brain muscle for being able to navigate. Using LLMs is no different, people over reliant on these tools are simply ngmi. They are going to be totally reliant on their favorite billionaire being willing to sell them competency via their thinking machines.
I would caution everyone to consider if the Billionaires who are screaming that you're going to be left behind, laid off and redundant if you don't (pay them to) use their brain nerfing machine, whether or not they have your best interest at heart.
Firstly, you can run the LLMs on your own machine. So I find the proprietary/moat narrative weak.
Secondly, I find that correct usage of LLMs can accelerate learning. My brother used an LLM to generate flash cards for a driver's license test. I use LLMs to digest a ton of text and debug issues that would have been impossible to find (I would have given up) Have it generate, explain, review, compare code or general writing.
It is like having access to wise old man in every field. They may have inferior reasoning capability, and their memory may falter, but they have seen everything in their corpus and are great at pointing you to external references. And you can delegate them to busywork.
> Firstly, you can run the LLMs on your own machine. So I find the proprietary/moat narrative weak.
You cannot run useful models on consumer hardware, sorry this is wrong and will always be the case. Atleast for 10 years until GPUs with 48GB vRAM depreciate. This is a limitation of llm architecture. You cannot post train a <1T Param model to a place where it competes with frontier model capability. If you think you 70b param models (which still require 5k in GPUs) are useful, you are being dishonest with yourself.
It costs about $60-80k to run a 1T param model at your house like Kimi 2.5 .. which is the only size model that's going to get anywhere close to a foundation model's capability. Nobody is going to spend close to 100k to run a mediocre open source model as opposed to spending $200.00 a month. Its a ridiculous notion.
I run Qwen-3.5 based LLMs in the 20-35 parameter range on my laptop's iGPU and it works great for my use case, which includes coding, search, reasoning, and general tasks. Gemma3 is good too.
There are ones that are distilled with better reasoning models or abliterated for whatever you need, and the multimodal features work... fine.
Just started running local LLMs this week, and it is pretty much overkill for what anyone in my family needs. All it really lacks is some tools for it to use, which I am putting together now.
To be fair, the best model I have used is claude sonnet. I don't really know what I am missing with opus.
I think that framing your observations in terms of "Billionaires who are screaming" about a "brain nerfing machine" doesn't help think about the issues clearly or contribute to a healthy discussion.
What is too late? If you want to use cryptocurrency as a medium of exchange, go ahead, it’s right there!
There’s this irony to the FOMO in crypto, which is people argue the “sensible” thing (it’s the future of money) to create FOMO for the insensible thing (it’s a lottery ticket). You’re right it’s too late to buy a lottery ticket, but the vision wasn’t a lottery, it was a medium of exchange!
AI assisted coding is the same way. I use it every day, but if I decided to stop and wait a year, I could still pick it up, probably more easily when the tools are better.
In fact, people who wait might do better than me because their mental model won’t be locked into a way of interacting that will be out of date in six months.
Wouldn’t it be ironic if all the early adopters were the losers because they liked the hacky nature of it? This happened to a lot of early computer adopters, low level programmers, etc.
> Wouldn’t it be ironic if all the early adopters were the losers because they liked the hacky nature of it? This happened to a lot of early computer adopters, low level programmers, etc.
It sounds like you're just trying to delay the inevitable then. Manager's may be automated before Software Engineers! Or at the very least otherwise be made redundant.
You make it seem like AI coding has already "totally changed" our jobs. This is exactly the FOMO the article talks about ("until its too late"). It hasn't. I'm still using the same workflows without AI tools, and so are most of my teammates.
Crypto didn't "win", the technology is there but people are mostly gambling, or doing shady stuff. Shall I mention NFTs? It didn't change the life of the average joe, nor business. It's a niche.
Many people are still coding without AI and doing perfectly fine. When you design serious things, coding is not where most time is spent anyway. Maybe it'll become unavoidable at some point, by that time the experience will be refined and it'll be easier to learn.
Point is, it's never too late. If you don't need to be cutting edge on a new tech, it may not make sense to put the extra effort of early birds. If you put that effort, you better not do it for free.
He is rejecting the framing of get in now before it's "too late". If it is so useful then we will be able to pick it up when it is more polished rather than learning to use some half polished turd that will be obsolete in 6 months.
I'm not sure Bitcoin won.. it just continues being a ponzi scheme that you can make money in.
You can also accept that certain things and be happy in life either way. Don't need to chase get rich schemes. Some are more privileged than others in being able to do this.
Bitcoin won? I don't think it did. Main use is still scams, circumventing sanctions, and grifting. My SEPA instant money transfer does everything bitcoin promised without the trash, and bad people surrounding bitcoin.
You should be able to pick up tools and learn on need. There are better version control systems than git - always have been, but git won despite being worse. (Git was massively better than what was popular before it won) if you can't learn git quick then you shouldn't program at all - there are much harder tools you will need to know and many are company or project specific
That is the one I know best but there are other options that people I tend to trust say are good. There are more options than I have time to give them an honest evaluation.
Just last year I migrated to Git a major code base that was still stuck in SVN. It's not even a legacy project, just a laggard. For some colleagues, this was their first time using Git on the job.
There are large projects still using CVS. Not to say everyone should, but git is only a tool. It isn't essential, and there are alternate ways to achive the same ends with different tools.
There are real productivity gains by using these tools right now. Instead of doing 1x your normal work, you can do 5x while still maintaining quality. This is like an accountant sticking to pen and paper because calculators are big and clunky.
Also, if your AI has a 20% error rate, you're not holding it right. You need to spend more time keeping it on rails - unit tests, integration tests, e2e tests, local dev + browser use, preview deployments, staging environments, phased rollouts, AI PR reviews, rolling releases. The error rate will be much closer to 0%.
That wasn't what the comment you responded to was referring to. I guess it makes sense since you are kind of like an LLM with how you respond to input.
> I feel the same way about the current crop of AI tools. I've tried a bunch of them. Some are good. Most are a bit shit. Few are useful to me as they are now. [...] If this tech is as amazing as you say it is, I'll be able to pick it up and become productive on a timescale of my choosing not yours.
I think the point the author is making is not that it's all useless, but against the very overly simplistic idea the plot of Amount of AI vs Productivity in All Situations is a hockey stick chart.
Being told to be excited about something when clearly all they're saying is "it works sometimes, other times not so much. I'll keep checking and when it's good enough for me I'll get on board" is aggravating.
I remember when React was the hotness and I was still using jQuery, I didn't learn it immediatley, maybe a couple years later is when I finally started to use React. I believe this delayed my chance in getting a job especially around that time when hiring was good eg. 2016 or so.
With vibe-coding it just sucks the joy out of it. I can't feel happy if I can just say "make this" and it comes out. I enjoy the process... which yeah you can say it's "dumb/waste of time" to bother with typing out code with your hands. For me it isn't about just "here's the running code", I like architecting it, deciding how it goes together which yeah you can do that with prompts.
Idk I'm fortunate right now using tools like Cursor/Windsurf/Copilot is not mandatory. I think in the long run though I will get out of working in software professionally for a company.
I do use AI though, every time I search something and read Google's AI summary, which you'd argue it would be faster to just use a built in thing that types for you vs. copy paste.
Which again... what is there to be proud of if you can just ask this magic box to produce something and claim it as your own. "I made this".
Even design can be done with AI too (mechanical/3D design) then you put it into a 3D printer, where is the passion/personality...
Anyway yeah, my own thoughts, I'm a luddite or whatever
It's funny too you'll hear people say they reverse-engineered something (not related to today's post) and you're like "wow that's impressive" but it turns out AI did it.
I mean people use IDA for decompilation instead of reading assembly directly, and that's counted as as valid reverse engineering. Why shouldn't throwing it an LLM count as well? I think reverse engineering is one of the fields where "anything goes", it doesn't matter how you get to the answer as long as you get there.
I do agree that the notion of difficulty needs to be recalibrated though, seemingly impressive RE tasks can now be done trivially with LLMs.
> If this tech is as amazing as you say it is, I'll be able to pick it up and become productive on a timescale of my choosing not yours.
Broadly speaking, I think this is a wise assessment. There are opportunities for productivity gains right now, but it I don't think it's a knockout for anyone using the tech, and I think that onboarding might be challenging for some people in the tech's current state.
It is safe to assume that the tech will continue to improve in both ways: productivity gains will increase, onboarding will get easier. I think it will also become easier to choose a particular suite of products to use too. Waiting is not a bad idea.
What I get a bit annoyed is companies forcing AI tools, getting usage metrics and actively hunting the engineers that don't use the tool "enough", I've never seen anything like it for a technically optional tool. Even in the past, aside from technical limitaions, you were not required to use enough of a tool.
It just sounds like a giant scheme to burn through tokens and give money to the AI corps, and tech directors are falling for it immediately.
> I've never seen anything like it for a technically optional tool
Cloud had a very similar vibe when it was really running advertising to CIO/CTOs hard. Everything had to be jammed into the cloud, even if it made absolutely no sense for it to be run there.
This seems to come pretty frequently from visionless tech execs. They need to justify their existence to their boss, and thus try to show how innovative and/or cost cutting they can be.
That and microservices in lieu of a monolith. Or how about being the odd one out a few years ago when suggesting a MPA instead of a SPA when it made sense. I like to think where at the point of everybody is rebuilding their portfolio website with Angular 1 but this time it's Claude Code and a SaaS instead.
I agree with you but man the absurdity of aggressively pushing cloud or AI adoption as a cost cutting move is off the charts.
Easier to justify monthly costs than big capital asks (even if your infra is depreciated at a normal rate) is where I think many saw (incorrectly) cost savings. It’s also a bit of these execs mortgaging the future, banking on either being out of their role when the real cost comes due or that people will have incredibly short memories (not a wild assumption).
I think this is the result of a c-suite that has never actually done the work of the businesses they're running. The MBAificiation of management. Of course they constantly do brain-dead shit, they literally don't have a clue how anything actually works in "their" own business.
They've been vibe-driving businesses long before we've started vibe-coding software.
There weren't really any failure states for the ZIRP "lifestyle CEO". If you remember old black and white movies about pigeons from psych 101 it's been that level of conditioning for how many years now?
If your CEO doesn't look like a taxi dispatcher he's just moving his wings around waiting for a food pellet.
I was trying to think of a way to word this exact argument. I think it’s especially easy when your business technology is not your primary means of revenue generation. Having these execs understand how things work is significantly less critical in these scenarios, so it becomes much easier to hire for alternative characteristics (golf game, pedigree, gender, whatever).
“Cloud” seems like a better comparison than stuff like cryptocurrency. AI seems totally over-hyped but with some obvious sensible use-cases.
> Cloud had a very similar vibe when it was really running advertising to CIO/CTOs hard. Everything had to be jammed into the cloud, even if it made absolutely no sense for it to be run there.
100% accurate - some of us are old enough to have lived through a few of the mini-revolutions in between the mega-revolutions of Internet/Web in the 1990s and now AI/LLM in the 2020s.
We are in the "stupid phase" of adoption still. C-level people have to follow the herd and they are being evaluated on keeping up with everyone else. Idiotic mandates are a way to cause things to happen short-term even though everyone knows long-term it will have to be re-done.
Consultants gonna make a looooooooot of money this coming decade.
I think the feel bad moment here is how impersonal everything got in 3 decades. During the dotcom bust you still had to meet and talk to people to get interviews started.
Now you can make a perfectly tailored resume, apply to 50 jobs in a day, and it's not unexpected to not get any response from those in 2 weeks. You don't know if it's your resume, the company, or the economy. And no one wants to admit the latter two are problems.
Not to mention the utter disrespect these days. There's no decorum in many of these "professional" settings, when normally you want your interview process to show off your best face.
"And no one wants to admit the latter two are problems."
Im working on building something to address this. That's all I'll say lol.
It's using a bad tool to try to aim at something reasonable-ish: Developers not taking advantage of the tools in places where it's very easy to get use out of them. I have coworkers like that: One spent 3 days researching a bug that Claude found in 10 minutes by pointing it at the logs in the time window and the codebase. And he didn't even find the bug, when Claude nailed it in one.
But is this something that is best done top to bottom, with a big report, counting tokens? Hell no. This is something that is better found, and tackled at the team level. But execs in many places like easy, visible metrics, whether they are actually helping or not. And that's how you find people playing JIRA games and such. My worse example was a VP has decided that looking at the burndown charts from each team under them, and using their shape as a reasonable metric is a good idea.
It's all natural signs of a total lack of trust, and thinking you can solve all of this from the top.
The thing is we’ve always had people who spend more time on their tooling or learn different tools and perform better.
I’ve seen people use notepad and I’ve seen people who are so good at vim that they look like they’re on editing code directly with their mind.
Your particular example is extreme and my guess is the coworker is just not great at debugging. I use Claude all the time for finding bugs, but it fails fairly frequently though. I think there’s probably advantage to having some people who don’t use it that often, so you have someone to turn to when it fails.
I’m definitely not exercising my debugging skills as much as I used to and I’m fairly confident they’ve atrophied.
That, and an objective comparison measuring time saving should include all time that went into learning, configuring, maintaining the tool.
And ideally a sample large enough to capture any wasted time from dead ends in other tasks where the tool may actually fail to solve the problem.
I’ve definitely lost a couple hours here and there from when it felt like I was right on the verge of CC fixing something but never actually got there and finally had to just do it myself anyway.
> But execs in many places like easy, visible metrics, whether they are actually helping or not.
Most execs didn't get where they were by being truly helpful and adding value to the company. They played the game long enough to know that politics trumps accomplishments. The rest from there is the ability to weave a good story (be it slightly or completely exaggerated).
It's not even about trust. It's about incentives in a structure that is dog-eat-dog. Rugged individualism in a corporate structure is a self defeating prophecy. But it's inevitable when executives extract from the company instead of rising the tides for all ships. And shareholders reward it.
There are countless other stories about the AI's spouting complete bullshit. This easily wastes as much time as they save.
> t just sounds like a giant scheme to burn through tokens and give money to the AI corps, and tech directors are falling for it immediately.
This is exactly what's happening. The top 5 or 6 companies in the s&p 500 are running a very sophisticated marketing/pressure campaign to convince every c-suite down stream that they need to force AI on their entire organization or die. It's working great. CEOs don't get fired for following the herd.
~40-50% of the S&P500 rely on this continuing.
S&P 500 Concentration Approaching 50% - https://news.ycombinator.com/item?id=47384002 - March 2026
> No of course there isn't enough capital for all of this. Having said that, there is enough capital to do this for a at least a little while longer. -- Gil Luria (Managing Director and Analyst at D.A. Davidson)
OpenAI Needs a Trillion Dollars in the Next Four Years - https://news.ycombinator.com/item?id=45394071 - September 2025 (8 comments)
Elon Musk is planning to put his AI company into the SpaceX IPO, and accelerate getting it into the major indices, effectively making pension funds, banks and individual investors his bag holders.
Patric Boyle has a video on this in case you care for the details.
I guess it's possible for the top companies to have spent so much already, that now the best move is to convince the next tier to do the same otherwise those competitiors may pull ahead without such a financial handicap.
>I've never seen anything like it for a technically optional tool
If you broaden the comparison (only a little bit) it looks suspiciously like employees being forced to train their own replacement (be that other employees, or factory automation), a regular occurrence.
Yeah this is the thing I think many don't want to see. Imagine a bunch of farm laborers being trained to use a tractor/reaper early on its development. Certainly they'd think it's cool and convenient, because it is. But if it works out, then most of those farm laborers are now obsolete, and a handful of them can now replace the rest. And indeed this is why agricultural employment went from the majority of jobs to a footnote.
Tech directors, CEOs, managers, etc tend to be people with a certain personality and ( learned behaviors/thinking ) just like "technical people".
Yes, they tend to be incredible gullible to certain things, over-simplistic and over-confident but also very "agile" when it comes to sweep their failures under the rug and move on to keep their own neck in one piece. At this point in time even the median CEO knows AI has been way overhyped and they over invested to a point of absolute financial insanity.
The first line of defense about the pressure to deliver is to mandate their minions to use it as much as possible.
We spent a fortune on this over-rated Michelin star reservation, and now you kids are going to absolutely enjoy it, like it or not goddammit!
Yeah, I found this strange as well, if the tech is so amazing why do developers need to be forced to use it?
Maybe there's a positive externality: your individual learning percolates to others and benefits the firm as a whole.
What is there to learn? If anything developers are still the one's training and enhancing the models by giving them more feedback cycles and what works and what doesn't.
I'm encouraging my folks to try it pretty hard because A) I've personally seen the productivity gains and B) using it is at first deeply weird/uncomfortable. Sometimes you've got to convince people to push through that kind of thing.
How you objectively measuring success?
93% of Developers Use AI Coding Tools. Productivity Hasn't Moved. - https://philippdubach.com/posts/93-of-developers-use-ai-codi... - March 4th, 2026
They measured 16 developers and called it a "study"? That is amusing. Not to mention it was conducted almost a year ago, the tools have already changed dramatically.
Depending on the effect size a sample size of 16 can be plenty.
> Not to mention it was conducted almost a year ago, the tools have already changed dramatically.
There is no point at which this argument will not be made. Therefore, it is a useless argument.
So just run a new study this year. I do think the tools have improved, but it should show up empirically. The only people for whom the urgency of "right now" is present is for the C-suite and investor class who are fighting to make sure they survive, but it might also be a crisis of their own making. Don't confuse your identity as a worker with the identity of the capitalist class.
You should be able to just develop software on your cellphone, right?
Do you have an empirical study to support that your employer should buy you a laptop and possibly a monitor or two to help your productivity?
If there's no study, why should we believe it?
It's like "A study found that parachutes were no more effective than empty backpacks at protecting jumpers from aircraft."
https://www.npr.org/sections/health-shots/2018/12/22/6790830...
> It's like "A study found that parachutes were no more effective than empty backpacks at protecting jumpers from aircraft."
Are you under the impression that we don't bother to empirically prove things that seem obvious, like the safety benefits of parachutes? You don't think parachute manufacturers test their designs and quantify their performance?
There are no randomized controlled trials that parachutes save lives.
This is repeatedly used as an example in the medical community about the limits of randomized controlled trials. This isn't some impression - your impression that such evidence exists is wrong.
There might be some parachute company tests about effective of velocity, etc., but there are no human trials.
Why? Because that would be unethical.
> There are no randomized controlled trials that parachutes save lives.
It's a good thing "randomized controlled trials" aren't the only kind of empirical evidence, then.
We know the limits of how fast a human can safely land. Parachute manufactures have to prove that their designs meet the minimum performance specifications to achieve a safe speed. This proof is not invalidated by the fact that it doesn't include throwing some poor bastard with a placebo parachute out of an airplane to demonstrate that he dies on impact.
Also, the answer to your original question is yes. There are numerous studies showing that multiple monitors improve productivity.
> Oh, there's one important detail here. The drop in the study was about 2 feet total, because the biplane and helicopter were parked.
I don't think that's making the argument you think it is.
That is exactly why I posted it.
I think my employer should buy me a laptop and possibly a monitor or two to help my productivity because I subjectively feel they'd be helpful, and I have the market power to insist on tools that I subjectively feel are helpful. If my CEO announced that monitors are super important and everyone will be tracked on monitor space usage going forwards, I would still want to see evidence that this is going to accomplish something.
Your CEO likewise subjectively feels all of their employees using AI will be helpful, and has the market power to insist that their employees use them.
When engineers demand evidence that AI is productive, but not that having laptops and monitors are productive, it screams confirmation bias. "I'm right, you're wrong" as a default prior.
Sounds like it would be pretty productive for employees to unionize and replace their CEO with an LLM.
I wouldn't call it confirmation bias, but you're right that is my prior. If an executive and a line worker disagree about whether a tool is useful, I assume unless presented with evidence to the contrary that the executive is wrong.
I would emphasize that I don't think there's anything particularly wrong with the converse either. If an executive is just absolutely convinced that dual monitors are a scam and nobody needs more than their laptop screen, they can run their company that way, and I'm sure there are many successful companies with that philosophy.
> Not to mention it was conducted almost a year ago
false. The article is from 4th of March 2026, less than a month ago.
From the first sentence of the article proper: "A study published in July 2025".
I'm just using Copilot CLI for mindless stuff and set it to the premium models to meet the quota, as long as they can't see the prompts I think I should be fine
You're not going to get fired. Don't worry about it :)
Almost 3 years too late for that warning.
Same, but with Opus 4.6.
I am seeing this at my work right now. They are about to start using token consumption as _part_ of the performance review process. Obviously this is a coarse and problematic proxy for productivity.
OTOH, it’s an attempt to address a real problem. There are people who are in fact falling behind (I’m talking literally editing code in notepad), and we can either let them get PIPped eventually, or try to bring them along. There is a real “activation energy” required to learning new tools, and some people need an excuse/permission. Not saying that token count is a GOOD signal, but I haven’t heard many better ideas
It's really insane what is happening. My wife manages 70 software developers. Her boss mandated that managers replace 50% of the staff with AI within a year. And, she's scrambling trying to figure out if any of the tools actually work and annoying her team because she keeps pushing AI on them. Unsurprisingly it's only slowed things down and put her in a terrible position.
Brutal. But probably all too common. One of my clients has very suddenly gone all-in on agentic AI and they're in this crazy hurry. (Probably the most annoying part is they want to automate stuff that I built a POC for using GPT-4o, two years ago - at the time they saw no use for it, but now they're all-in on the hype.)
This started literally two weeks ago and a couple of days ago I talked to one of the admin people who wanted an update on the progress I'd made with sanding off some of the rough edges of the very rough implementation that the managing partner had put in place (he bought a Mac Mini, put OpenClaw on it, then gave it admin access to a whole pile of stuff!) I said I needed a couple more days. "Okay," she said, "but I need this quickly, because we're firing people next week."
They have literally gone from no agentic AI, to discovering OpenClaw, to firing people, in a two-week time span.
When economists say that the predicted job losses as a result of AI have not yet shown up in the data, I'm genuinely befuddled. Either we don't have long to wait to start seeing them, or there's something wrong with the data, because you can't tell me what I just described above is an isolated phenomenon.
I also have to say: I've always enjoyed working with this client, but this experience has been a huge turnoff on a number of different levels.
> Probably the most annoying part is they want to automate stuff that I built a POC for using GPT-4o, two years ago - at the time they saw no use for it, but now they're all-in on the hype.
I’d have guessed the most annoying part would be that you’re assisting them in a hare brained scheme to terminate some people’s employment.
For a non-tech case of this, my wife worked at a place that fired like 80% of their writers in anticipation of huge speed-ups they expected from LLMs, a couple years ago.
They had to hire a bunch of them back less than two months later. The speed-ups were approximately nil and making the editors edit AI slop all day long had them all close to quitting.
They didn't even wait to see if there were any actual benefits, they just blindly fired a bunch of people based on marketing lies. I can only assume they're the same sorts who fall for Nigerian Prince scams.
Maybe what they really want her to do is get rid of 50% of her staff and the AI is just an excuse? In that case she should focus on "who can we do without?" rather than "how can we replace people with AI?"
I'm sure part of this mandatw implied "if you can't show us the numbers we want, you're part of the 50%". And the incentives are set.
The assumption that those managers have is that it’s easy to replace tech guys because AI is advancing, and a crap like that nonsense.
Funny enough, I got laid off last month, yes I’m a tech guy, now they apparently regret it because they are now scrambling to find a replacement to do the tech tasks!
TBH, I’m happy I got laid off because I’m finally building something I wanted to use.
> Her boss mandated that managers replace 50% of the staff with AI within a year
I bet we could replace nearly all the CEOs in the country with chatgpt controlling a ceo@thatcompany.com email and nobody would notice.
We’d probably get better outcomes too.
For society, yeah, since the AI training corpus is more normal people than sociopaths. Shareholders would be mad, I bet.
> Shareholders would be mad, I bet.
But think of how much profits will improve by not paying $tens of millions to employ a CEO!
They perks and dread of middle management...
> I've never seen anything like it for a technically optional tool.
It has often been the case for technologies though, like “now we’re doing everything in $language and $technology”. If you see LLM coding as a technology in that vein, it’s not a completely new phenomenon, although it does affect developers differently.
Language and technology are normal, but we are talking about a metered code editor, nobody have asked me to "use X hours of IntellijIDEA" in the past, or "use git enough or be fired". Tools are never required to be used when they are not needed
Well the tech or the language had some feature to it that lead you to using it. By definition LLM coding doesn’t. It is like the job requirement turned into “ask jeff to write all your code and if you don’t we won’t hire you.”
Technologies were imposed by management whether it made sense or not. Like, “all data exchange formats now have to use XML”, or “all applications must be J2EE now”, because it was the new hot thing. “You” weren’t making that choice, management imposed it. That’s the parallel I’m drawing.
That was usually only the case where everyone had to be using it or it didn’t work.
One person hand coding and one person having Claude still results in the same output that is compatible with each other.
This is more like mandating that you use vim. I’ve never some something like that before in 20+ years.
> That was usually only the case where everyone had to be using it or it didn’t work.
Absolutely not, a lot was done just because it was pushed as the current fashion and advertised to be solving problems that either weren’t applicable to the concrete use case or that it didn’t actually solve.
You’re not understanding what I’m saying. If you as a company want to do OOP, having one guy writing everything in imperative style hinders everyone else because their code has to interact with his.
AI isn’t like this because the final output is the same as hand coding.
This is largely due to the age old fact that corporations rarely make decisions based on actual data, introspection, and good judgment. Usually the decision is made first and then the justifications are invented afterwards.
In this case, every executive is terrified of being "left out" in the AI race. As we saw with the mass layoffs across companies, most of CEO decision making is just adhering to herd behavior. So it is literally better for execs to have shoveled a shit ton of money into 'strategic' AI initiatives and have them fail than potentially deal with the potentially remote chance of some other exec or company succeeding with 'AI enabled transformation'.
What makes it even more fun is that nobody really has a good understanding of how to measure the ROI of AI. Hence we have people burning a lot of money due to FOMO and no easy way of measuring the outcome, which is usually how the foundations for good Ponzi schemes are laid.
This is unlikely to end well. However, as usual, it's us, the common plebs, who will suffer regardless of outcome.
We're doing that in my office, forced Cursor use. A good chunk of the "edited by AI" lines in my history were just auto-completing about the same as a traditional intellisense-alike would do (and actually Cursor doesn't seem to supply that, which is frequently annoying and wastes my time, in particular when I need to make sure it hasn't hallucinated a method or property on an object it should be able to "see" the definition of, which it does constantly; IDK maybe there's a setting somewhere, but I don't have to fiddle with settings in vanilla VSCode to get that...)
It's actually kinda useful in some cases, but the UI is terrible and it needs to integrate much better with existing tools that are superior to it for specific purposes, before I'll be happy using it. I'd say the productivity gains are a wash, for me, so far. Plus it's entirely too memory-hungry, I'd just come to accept that a text editor takes a couple GB now (SIGH), and here it comes taking way more than that.
A certain YC company fired a few employees for not using AI, the CEO bragged about it on X and incidentally it was a crypto company.
Yes it's very weird - why is my CEO being so nosy about my text editor all of a sudden? Stay in your lane, buddy.
As an employee of a big tech company doing this, it's all fear mongering. We are being told that if everyone doesn't use these tools, our competitors will wipe the floor with us because they are using them and will ship features 10x faster. But many engineers are suspicious as well.
I’m at a big tech company top and essentially no one is a true believer more than 3 or 4 levels down from the top. We’re all just trying to keep our use metrics high enough to not get noticed.
But for those top layers, I’ve never seen so much FOMO in all my life. We’re a very slow moving company but they act like we’ve got 2 weeks to go “AI first” or we’re dead in the water. I’ve never seen such a successful hype cycle. I’m pretty sure it’s the bots that are accelerating it so far behind a normal hype cycle.
Maybe AI is really good for vibe-coding bots that amplify FOMO?
It’s really good at spitting out prose that is frequently good enough to pass as human and bypass spam filters.
It's baffling, to be honest. I'm at a fintech that is currently pushing very hard at this, but in the same breath talking about how we're not a pure software play. I just don't understand where they're coming from.
irrational exuberance
Hook line and sinker..
Right so you are going to be left behind whilst the ground keeps shifting under you, given the models are non-deterministic and continuously changing?
There was a big rush of prompt engineers. Where are they now? Nobody even referse to 'prompt engineering' anymore.
The best thing to do is wait for steady-state. Whats going on is insane... a slow implosion of the code base.
Sadly, the quote on markets and solvency rings true here. Tech (among an increasing number of sector) is being hit hard by layoffs over this. Nothing is steady right now.
I don't doubt you, but I'm out of the loop.
Who does this?
my company (mid-size, publicly traded) is mandating [x] hours spent on AI per week. i have no idea how they're planning on measuring this, and as far as i can tell, neither does management.
suppose it's better than counting lines of code, though.
My uncle leads IT support teams, the org is measuring AI use in writing reports and tickets. The org has very poorly structured and obsolete processes (he's trying to straighten them up as he goes), AI will probably amplify the lack of structure, by making it easier for the work to _look_ as if someone carefully reviewed the issues and followed procedure.
A friend is a team lead in an org that's mandating vibecoding via "Devin", a lesser known player an "architect" chose after shallow review. The company also has endemic process issues and simply can't do deployments reliably, it's behind the times in methodology in every other respect. Higher ups are placing their trust in a B-list agentic tool instead of fixing the problems.
Anyway, I wouldn't be caught dead working at either of those two shops even before the AI rollout, but this is what's going on in the IT underworld.
I hate the AI assistants for ticket-writing. The beneficial use there would be to prompt for possibly-useful information that's not present, or call out ambiguity and let the writer decide how to resolve any of that. Coaching, basically. Suggesting actual text to include, for people who aren't already excellent at ticket-writing, just leads to noisier tickets that take more work to understand ("did they really mean this, or did the LLM just prompt them to include it and they thought sure, I guess that's good?")
[EDIT] Oh and much of your post rings true for my org. They operate at a fraction the speed they could because of organizational dysfunction and failure to use what's already available to them as far as processes and tech, but are rushing toward LLMs, LOL. Yeah, guys, the slowness has nothing to do with how fast code is written, and I'm suuuuure you'll do a great job of integrating those tools effectively when you're failing at the basics....
Lots of organizations don't want to accept that their velocity issues are quality issues. It's often a view held by an old guard that was there when the business experienced growth by adding features, while not having to bear any maintenance burden. The people who remain are either also oblivious to this, or simply have stopped caring.
LLM-generated code hits all the right notes, it's done fast, in great volumes, and it even features what the naysayers were asking for. Each PR has 20 pages of documentation and adds some bulk to the stuff in the tests folder, that can sit there looking pretty. How wonderful! Hell, you can even do now that "code review" that some nerd was always complaining about, just ask the bot to review it and hit that merge button.
Then you ask the bot to generate the commands again for the deploy (what CI pipeline?) and bam! New features customers will love. And maybe data corruption.
I dont get the craziness at all.
A firm that is led by people who can envision, very clearly, revenue-generating and cost-reduction projects - wins. Writing code by hand is absolutely irrelevant. Who fucking cares. The former is what matters.
Code generation acceleration only matters when those pre-requisites are met. How did Apple go from the verge of bankruptcy to where it is today?
All Im seeing is most people are not smart at all - no wonder they are so impressed by LLMs! They can't think straight. I only see this become even worse over-time. Perhaps this is the stated goal.
Well before offices were computerized at all some of the manual processes turned out to be more effective than after full computerization was completely accomplished. Which was sometimes decades later so nobody could tell which workflows it actually applied to, or wouldn't believe it anyway by the 21st century.
It was truly quite rare to have such well-honed manual processes though, the "average" place had a lot of elements that were far from perfect but still benefited after the computerization dust had settled. Then at the opposite end of the spectrum were companies where everything was an absolute shitshow, maybe since the beginning.
That's kind of where Conway's Law comes from, if you benchmark against a manual shitshow that has built up over the years, and replace it with a computerized version, you get a shitshow on steroids. The only other choice would have been to spend the appropriate number of years manually undoing the shitshow before making any really bold moves.
Now AI can really take things to a whole 'nother level, not just on steroids but possibly violating Conway's Law . . . squared.
The only thing I've mandated for engineers is that folks give it a try occasionally, as models, best practices, and tooling improves.
I'm currently tracking exactly two numeric metrics: total MAUs (to track the aforementioned), and total DAUs (to gauge adoption and rightsize seat-licensed contracts.)
Why do you care so much? If these are really revolutionary tools that vastly optimize work, why bother forcing people to "try new models and best practices"?
If the benefit is there people will use it or get left behind, there's no sense having a mandate that people resentfully try the new tooling.
Imagine you had a developer who writes Java using vim. It sounds insane but they are just as productive as everyone else. Then you mandate they have to try IntelliJ every quarter, just to see if maybe they like it now. You're just going to piss them off and reduce their productivity by mandating their workflow.
FWIW in the face of these kind of mandates I have been using tokens but ignoring the output. So it's costing my employer money and they have a warped metric of whether the tool is actually useful.
>If these are really revolutionary tools that vastly optimize work, why bother forcing people to "try new models and best practices"?
"If the colleges were better, if they really had it, you would need to get the police at the gates to keep order in the inrushing multitude. See in college how we thwart the natural love of learning by leaving the natural method of teaching what each wishes to learn, and insisting that you shall learn what you have no taste or capacity for. The college, which should be a place of delightful labor, is made odious and unhealthy, and the young men are tempted to frivolous amusements to rally their jaded spirits. I would have the studies elective. Scholarship is to be created not by compulsion, but by awakening a pure interest in knowledge. The wise instructor accomplishes this by opening to his pupils precisely the attractions the study has for himself. The marking is a system for schools, not for the college; for boys, not for men; and it is an ungracious work to put on a professor."
-- Ralph Waldo Emerson
> Why do you care so much? If these are really revolutionary tools that vastly optimize work, why bother forcing people to "try new models and best practices"?
If AI makes an employee 10X more productive they get a slight pay raise maybe, but the company makes substantially more money or gets substantially more output. So there is a large difference in incentives.
This is true, though I believe savvy employees have leverage to ensure they participate in a larger share of that upside. As you can see from other comments, lots of people will just drag their heels and not give it a good-faith attempt, so it'll often average out in the way you predict.
Are you budgeting time to allow people to properly evaluate LLMs and possibly struggle with them? This is not the sort of new tool whose utility is universally immediately obvious to all builders and craftsmen out there. Are you willing to pay down the likely debt of some individual contributors never clicking with this, or being outright resentful to towards the technology or the mandates? There is a LOT of self-selecting bias from LLM proponents assuming everybody else is willing or able to travel the same path as them.
> Are you budgeting time to allow people to properly evaluate LLMs and possibly struggle with them?
Great question. That is absolutely the goal. My take is that building with LLMs - at least with the current popular harnesses like Claude Code - is a skill on its own, and people need time to develop that skill and also to figure out where these tools might fit into their workflows.
> Are you willing to pay down the likely debt of some individual contributors never clicking with this or being outright resentful to towards the technology or the mandates?
I'll be honest as I have been elsewhere in the thread: A few years from now, I don't know what the state of the technology or its adoption will be, or what expectations of software engineers at large will be.
But for the foreseeable future, yes, absolutely, I'm willing to give engineers the time and space to develop familiarity and comfort with the tools, as long as they're engaging in good faith.
edit: oops, didn't mean to dodge the last part of your question (re: resentment): I genuinely don't know the answer to how I'll handle that, but I'm also sure it'll happen. Hopefully I'll still be in a position to speak publicly about how one can deal with those challenges.
edit 2: also, thank you for the thoughtful questions and dialogue.
> FWIW in the face of these kind of mandates I have been using tokens but ignoring the output. So it's costing my employer money and they have a warped metric of whether the tool is actually useful.
What you're actually doing here, from my POV, is incentivizing your employer to use more invasive metrics when they tried to stay hands-off and mandate the absolute bare minimum of "uh, give it a shot and see if you think it's useful right now."
The analytics that Claude Enterprise exposes are far more intrusive than I would want to be subjected to as an engineer, so I rolled out a compromise. I don't even track who the active users are, currently.
But maybe you're right, and there are enough people sabotaging the metrics out of spite, that there's a reason they provide the other data.
I hope that the engineers in my org are more mature than that, and would be willing to just say "I'm not currently using it", but thanks for giving me something to think about.
> mandate the absolute bare minimum of "uh, give it a shot and see if you think it's useful right now."
That’s not the bare minimum, though. The bare minimum is: “if you are meeting or exceeding your job expectations, great work, keep using the tools that are working for you.”
To a productive employee, merely saying “just try out AI, it might help” feels like the boss saying “just try out astrology or visit a psychic for a reading. You might find it interesting.”
When the CEO, CTO and Director are all saying "everyone has to use AI" I think it's pretty naive to think people will speak out openly. The bare minimum would be making the tools available and letting people do their jobs.
Go ahead and spend more time collecting more granular metrics spying on your employees. Apparently there aren't more valuable things for you to do than micromanaging individual developers.
I think one side of the issues folks are having is that combined with the mandate to use these tools, there is also an expectation or assumption that the developers will instantly get X% more productive. Like, "you must use this tool and you will be twice as productive".
Where I work there as certainly been that kind of discussions, "we need to use AI for this, because no offense but you are simply not fast enough". And this from people who do not understand software development and has never worked with it. They have only read the online stuff about 20X speeds and FOMO. (And my workplace is generally quite laid back and reasonable. I am sure many other places are much more aggressively steered.)
>more invasive metrics
If you have accurate metrics to gauge developer productivity then use them.
But you don’t because if you did you’d be a billionaire.
What you have is metrics that can measure developer busyness. If you use those metrics all you’ll do is run your good devs off and keep the ones who can’t find new jobs.
So you’ll have to do what anyone who manages software teams has always done and trust your line managers to manage your devs.
When it comes to people wasting tokens, most people aren’t gonna to do it with the intent to fuck your metrics. But if you tell people you are measuring something they will find a way in increase that metric whether it results in anything productive or not.
Making the tools available is one thing, but saying you're mandating their use at any level sounds like micro management to me. How would you feel if one of your subordinates started telling you how to do your job? I'm sure you would be mightily pissed off about it.
I don't think telling people what their job is counts as micro management. Part of their job right now is staying abreast of technological developments and experimenting with new ways of working.
Re: some of them being upset about it- probably. Some people are also upset about being required to use Jira. I personally dislike using Okta.
It is micromanagement. If the job are not being done, the best way is to investigate what current practices are blocking people from doing it (the answer is probably meetings and bad communication). The worst way is to present a tool as a silver bullet for tasks you’re not doing and not accountable for.
Where am I presenting the tool as a silver bullet? You seem to be confusing me with someone else in this thread, or making the mistake of turning this into a polarized conversation of "AI is a panacea" vs "AI is worthless".
I engaged in the thread in good faith, and am transparent about what I'm doing and why. I also clarified that part of the job in my org is experimenting with these tools.
Have you ever monitored and encouraged the use of a particular text editor or IDE? If you had an employee whose manager thought was a high performer, but you noticed they used notepad would you encourage that they regularly give vim a try?
The reason we force people to use Jira is because it only works if everyone uses it.
AI doesn’t work like that. If it does enhance productivity 50% then use will spread and the expectations of your line managers will naturally go up and the holdouts won’t be able to keep up.
Or only the exceptional ones will. And in that case why do you care how they do it?
> The reason we force people to use Jira is because it only works if everyone uses it.
In my experience, AI out of the box is at first a useless gimmick - until someone starts seriously playing with it and defines a skill file for integrating it with some internal tool. And another person starts playing with it and figures out that AI is pretty good at using another internal tool but only if the tool runs in --silent=1 mode by default, so as not to confuse AI with too much logging output. And a third person figures out that it's actively dangerous to let AI some some other internal tool - but hey, there's a safer alternative, and which happens to perform better too. And pretty soon you end up with an ecosystem of business-specific scripts and .md files and skills and MCPs that's actually helpful 85%+ of the time. But the only way to get there is to get devs and power users tinkering with it.
In my experience all the md files just pollute the context and make it less likely to do what I want it to do. I’m at a huge org with thousands of power users doing all of this and I haven’t seen anything resembling the results you’re seeing.
But even assuming this is the case, you don’t create enthusiastic power users with threats (implicit or explicit) and metric tracking. The only thing that does is force people to do the minimum to keep their job.
The complaint in the thread is that management is forcing AI tooling usage. If part of your job is to experiment with these tools, then like any experiment, the correct way is to share the findings with a report detailing the methodology and findings. But no one is doing that AFAIK. It’s all superlatives.
>Part of their job right now is staying abreast of technological developments and experimenting with new ways of working.
Not necessarily. A carpenter has a job to make things. Not to use specific tools and keep up with the latest tools used for repairs. It can be suggested, but telling a caprenter what tool to definitely falls under micromanagement.
>Some people are also upset about being required to use Jira
Jira's job is to report metrics to management. That implicit to the job. Telling people how to perform their tickets is micromanagement. The whole goal of a non-junior employee is to trust they can estimate and accomplish their task.
Whats your plan for when someone flatly refuses?
I'll cross that bridge when I get there. No one who works for me has refused to be paid to try out a new technology when I ensure the time is set aside for them to do so.
I was thinking about this but sometimes its hard to distinguish between what the marketing of OpenAI, Anthropic and other companies say and what actually all the companies are doing throughout the world. Any thoughts on that?
For example even the layoffs, nowadays seem to be because of AI or so they said but just a year or two ago there were quite some layoffs and people said "its because of the high demand in COVID and now its over, or Ukraine or inflation" but then that ignores that exactly during that time earlier were there many layofs but it was super easy "Oh COVID and supply chain!" and earlier maybe something else.
Surely there are also economic booms but when did the whole world jsut suddenly started seriously listening to public statements of companies (and that jsut a few with no real income, just money from VCs) and nobody shows us the real data of whats actually happening? E.g. the companies saying they fired 10K due to AI, how much did they actually now direct their budget to AI? How many products are actually being build? Is the productivity the same? Are the customers thinking that support is suddenly amazing or actually it has seriously dropepd in quality? Or no change at all? Is it a company like KFC, your local hardwore chain store, financial isntitutions, truck manufactures or anothet AI company with funding using another AIs company with other funding using now one more AIs company products up to the power suppliers?
For me it seems that its definitely impacting things and a cool technology to be more productive (for example it helps me a lot daily but its not like my life really changed) but the other things I haven't seen yet.
Another point each actual AI generated app is either something akin to a toilet game or not really working (like the C compiler). So where are the amazing enterprise complicated apps fully built via agents? In banks, in government, in apps that respect GDPR and actually are secure but proudly build only or mostly with agents? The only ones, not even secure, are other AI apps to do AI stuff but its whole value it says is to be more productive in the "real" economy but it still hasn't done it yet anywhere. People still struggle with Word or AWS infra or debugging why some specific user cant log in with their custom auth provider at some esoteric region with their laws and audits and GDPR variant.
So one side says its basically a tool from God and they never have created more stuff but on the other hand the other group analyzing blood work, delivering food, writing reports, etc uses it a bit or not at all but all the 95% of problems they had are there with some new ones. Also I'm afraid most of them just write now their email better or with more volume, but no real work is getting done.
So yeah maybe my confusion simply lies in that fact that I have a real job and nobody can keep up with all the slop and shit generated online anymore. I'm open to feedback or learn.
I've also never seen an optional tool become a step change like this
Even moving from assembly language to compiled languages was not as much of a step change.
It also seems like skills with particular tech (prompt engineering, harnesses, mixture of experts set ups) doesn't always necessarily pay off when there's a sea change. Hard to predict what you'll want in a few years anyway, right?
> (prompt engineering, harnesses, mixture of experts set ups)
Prompt engineering as a specific skill got blown out of proportion on LinkedIn and podcasts. The core idea that you need to write decent prompts if you want decent output is true, but the idea that it was an expert-level skill that only some people could master was always a lie. Most of it is common sense about having to put your content into the prompt and not expecting the LLM to read your mind.
Harnesses isn’t really a skill you learn. It’s how you get th LLM to interact with something. It’s also not as hard as the LinkedIn posts imply.
Mixture of Experts isn’t a skill you learn at all. It’s a model architecture, not something you do. At most it’s worth understanding if you’re picking models to run on your own hardware but for everything else you don’t even need to think about this phrase.
I think all of this influencer and podcast hype is giving the wrong impression about how hard and complicated LLMs are. The people doing the best with them aren’t studying all of these “skills”, they’re just using the tools and learning what they’re capable of.
> It’s also not as hard as the LinkedIn posts imply.
Keeping in mind the LinkedIn posters/audience (marketers/recruiters), it probably was quite hard for most of them.
In my experience (and this may be confirmation bias on my part), casting a wide net and trying out new tech, while you maintain depth in the area relevant at the time, makes you ready for what's coming, even when you don't know what that may be.
Curiosity is good and helps with your personal development, for sure.
OTOH, tfa specifically said:
> I feel the same way about the current crop of AI tools. I've tried a bunch of them. Some are good. Most are a bit shit. Few are useful to me as they are now. I'm utterly content to wait until their hype has been realised.
So, it's not like he's being deliberate ignorant, rather simply deliberately slow-walking his journey.
Past the sea change: half the reason those prompt and harness solutions seem to work are LLM-lies, the testing is gassing you about how it works and the efficacy, defaulting to ‘yes’.
If you test specific features of those solutions over time you see very inconsistent results, lots of lies, and seemingly stable solutions that one-shot well but suddenly experience behaviour changes due to tweaks on the backend. Tuesdays awesome agent stack that finally works is loading totally different on Thursday, and debugging is “oh, sorry, it’s better now” even when it isn’t. Compression, lies, and external hosting are a bad combo.
Sometimes I imagine a world where computers executed programs the same way each time. You could write some code once and run it a whole calendar month later with a predictable outcome. What a dream, we can hope I guess.
People are doing toy projects and praising them, while some are testing them in real world situations and not findings them that useful. But the former is labelling the latter as luddites and telling them they will be left behind.
As someone on the intersection of both (I've built a lot of vibe coded toy projects and lead a vibe coding initiative at work), they're both right and both wrong.
For a single dev team, vibe coding is great. Write specs, write plans, write code. I know what the project wants and needs because I'm the target market.
At work, I haven't written more than a few lines of code since December. But I work with other people vibe coding this same project. Lots of changing requirements and rapid iteration. Lots of mistakes were made by everyone involved. Lots of tech debt. Sure, we built something in 2 mos that would have otherwise taken us 6 mos, but now I'm fixing the mess that we caused.
I think the critical difference is the attitude towards our situation. My boss said to fix the AI harness so we can vibe code more confidently and freely. But other bosses might cut their losses and ban vibe coding. Who's right? I dunno. In both cases I'd just do what my boss wants me to do. But it's not that I don't want to be left behind. I don't want to lose my job. There's a difference.
> Sure, we built something in 2 mos that would have otherwise taken us 6 mos, but now I'm fixing the mess that we caused.
You didn't actually build it in 2 months.
Even if it takes me a month to get us to fix (likely a week tbh), then it took us 3 months to build.
A mere 2x productivity improvement sounds like something you could achieve by introducing new tools that are predictable (i.e.: Not AI).
Even two or three years ago I had ideas for projects but I could see the models were not ergonomic for my uses. I decided to wait for better models and sure enough the agentic models showed up which are much easier to use.
Next thing I'm waiting on is building a new server for a powerful locally hosted LLM in 5 years. No need to go through the headaches and cost of doing it now with models that may not be powerful enough.
Agreed! Investing lightly at this stage seems smart if your time/attention budget is tight.
All of these occulted skills, that we literally can't explain why they work are akin to gamblers superstitions. If I write something this way, it works. Its like a gambler who think they order in which the push the buttons on the slot machine makes a difference.
Kind of weird tools also incorporate addictive gambling game's UX design. They're literally allowing you to multiply your output: 3x, 4x, 5x (run it 5 times for a better shot at a working prompt). You're being played by billionaires who are selling you a slot machine as a thinking machine.
> All of these occulted skills, that we literally can't explain why they work are akin to gamblers superstitions.
Yes, it's hard to see how, at this moment in time, "Anybody can write code with an LLM" is so different from "Anybody can make money in the stock market."
The underlying mechanisms are completely different, of course, and the putative goal of the LLM purveyors is to make it where anybody really can write code with an LLM.
I'm typically a nay-sayer and a perfectionist, but many not-so-great things become and stay popular, and this may fall into that category.
> Kind of weird tools also incorporate addictive gambling game's UX design.
It's unclear it started out this way, but since it's obviously going this way, it is certainly prudent to ask if some of this is by design. It would presumably be more worrisome if there were only a single vendor, but even with multiple vendors, it might be lucrative for them to design things so that "true insider knowledge" of how to make good prompts is a sought-after skill.
Broadly speaking, LLMs are destined to fail.
Why? Because all the folks involved have created a technology in search for a problem to solve. That never, ever works. Steve Jobs of all people left this piece of wisdom behind. Its amazing how few actually apply it.
The internet was never this - its origins go back to the need to able to transmit data - darpa. And this is what we still do now...
Can you stop using them? https://news.ycombinator.com/item?id=47462767
I dont use them.
The only thing worse than the overuse of AIs is the ever present handwringing and finger-pointing of people who wrongly believe they are infallible AI detectors.
lol its comical. truly comical
There are a few examples of technologies that only found their application later, such as the glue in post-it notes.
And to be fair, Steve Jobs was a master of taking things that had been invented elsewhere, and making them work well enough to foster a demand.
But your point stands. Who made the most money, Xerox PARC, or Apple?
There really isn't anything special to using AI anyways it's not rocket science. Sometimes I will use AI to write me some tailwind tags, sometimes I will use AI to write me a static site for a custom report.
Most of my AI usage comes from doing things I don't enjoy doing like making a series of small tweaks to a function or block of code. Honestly, I just levelled the playing field with vim users and its nothing to write home about
> There really isn't anything special to using AI anyways it's not rocket science
Could have fooled me, the way some people manage to confuse themselves with it
I almost entirely agree with the author's assessment of new technology. Yet that statement rubbed me the wrong way.
Sometimes it is better to get into things early because it will grow more complex as time goes on, so it will be easier to pick up early in its development. Consider the Web. In the early days, it was just HTML. That was easy to learn. From there on, it was simply a matter of picking up new skills as the environment changed. I'm not sure how I would deal with picking up web development if I started today.
This isn't a good example - people were completing 6-month bootcamps and getting $100k offers to do web development not too long ago, decades after the web and HTML took off. After a few years they were making as much as anyone who learned HTML and Web 1.0 back in the 90s.
Are the bootcampers better developers? Probably not. But they still were employable and paid relatively the same.
I think this applies a bit less to the AI sphere, which has the purported goal of making things easier and more automated over time. 90% of the time if you have an AI question you can just... ask the LLM itself.
Remember all the hoopla over how people needed be a "prompt engineer" a couple years back? A lot of that alchemy is basically totally obsolete.
Think about the hoops you had to jump through with early GenAI diffusion models: tons of positive prompt suffixes (“4K, OCTANE RENDER, HYPERREALISTIC TURBO HD FINAL CHALLENGERS SPECIAL EDITION”) bordering on magical incantations, samplers (Euler vs. DPM), latent upscalers, CFG scales, denoising strengths for img2img, masking workflows, etc.
And now? The vast majority of people can mostly just describe desired image in natural language, and any decent SOTA model can handle the vast majority of use cases (gpt-image-1.5, Seedream 4, Nano-banana).
Even when you’re running things locally, it’s still significantly easier than it used to be a few years ago, with options like Flux and Qwen which can handle natural language along with a nice intuitive frontend such as InvokeAI instead of the heavily node-based ComfyUI. (which I still love but understand it's not for everybody).
Things will never be easier long term, you will just be expected to get more done. And if you dont spend time learning the tools, you will get less done than your competition.
The goal posts will move.
"It will grow more complex" is never a good reason to get into things early. It's just your mind playing FOMO tricks on you.
Many developers who picked up the web in the early years struggle with (front-end) web development today. It doesn't matter if they fetched jQuery or MooTools from some CDN as it was done in the mid 00s. Once the tooling became too complicated and ever changing they couldn't keep up as front-end dilettante. It required to commit as professionals.
If you started today, you'd simply learn the hard way, as it's always been done: get a few books or register for a course. Carve some time every day for theory and practice. All the while prioritizing what matters the most to get stuff done quickly right now, with little fluff. You will not learn Grunt, Bower, and a large array of historic tech. You'll go straight for what's relevant today. That applies to abstractions, frameworks, and tooling, but also to the fundamentals. You'll probably learn ES6+ and TS, not JS WAT. A lot of the early stuff seems like an utter waste of time in retrospect.
This is true for all tech. If you knew nothing about LLMs by the end of this year, you could find a course that teaches you all the latest relevant tricks in 5 to 10 hours for 10 bucks.
No, this thread and sub-discussion is about specifically early web fundamentals. The web is special in this sense, it's intentionally long-lived warts and all. So the fundamentals pay outsized dividends. The rube goldberg machine that is modern JS dev still spits out an index.html result.
Being a good professional developer means getting the primitives and the data model not horribly pointed in the wrong direction. So it's extremely helpful to be aware of those primitives. And the argument "nobody is better off knowing assembly as a primitive" doesn't hold because as-said the web is literally still html files. It's right there in the source.
The discussion is centered around the idea that "adopting early" provides some future proofing in a rapidly evolving (and largely non-standard) terrain. I share the FA's position that it does not.
> The web is special in this sense, it's intentionally long-lived warts and all. So the fundamentals pay outsized dividends.
Fundamentals pay dividends, but what makes you think that what you learn as an early adopter are fundamentals? Fundamentals are knowledge that is deemed intemporal, not "just discovered".
The historical web and its simplicity are as available to anyone today as it was back then. People can still learn HTML today and make table-based layouts. HTML is still HTML, whether you learned it then or today. But if back then you intended to become a professional front-end developer, you would still have to contend with the tremendous difficulties that some seem to have forgotten out of nostalgia. You'd soon have to also learn CSS in its early and buggy drafts, then (mostly non-standard) JavaScript (Netscape and IE6) and the multiple browser bugs that required all kinds of hacks and shims. Then you'd have to keep up with the cycles of changing front-end tools and practices, as efforts to put some sense into the madness were moved there. Much in all that knowledge went nowhere since it was not always part of a progression, but rather a set of competing cycles.
Fundamentals are indisputably relevant, but they're knowledge that emerges as victorious after all the fluff of uncertainty has been left behind. Front-end development is only now settling into that phase. With LLMs we're still figuring out where we're going.
This sounds exactly right. I'm someone who learned the web back when IE6 was something we wished everyone was on, and also someone who learned the fundamentals of the web and CS in general enough to try writing a book about it to teach everyone else.
Picking up the web early didn't help with the latter. I spent most of my early time memorizing tips and tricks that only applied to old browsers. I didn't pick up the fundamentals till I went back to school for CS and took a networking class.
web fundamentals and web development fundamentals are different.
How HTML, CSS and javascript come together is extremely relevant to developers 20 years ago and today.
I do support and agree with the parent comment, see the discussion, but I do credit getting into web development when it was raw and open paid dividends for me. Todays ecosystem is opaque in comparison. You don't think there's more friction today?
HTML CSS and JavaScript are just a small subset of web development.
And yes understanding them is still relevant. But when I started I was spending more time memorizing the the quirks of IE6 than I was learning how JavaScript, CSS, and HTML come together.
I think it you start directly in react you don’t learn the layer below it sure. But there’s no reason you have to start leaning react. There’s nothing inherent about starting today that forces you to start directly with React. You could start building a static webpage. And if you did that it would be easier and more fundamental than if you did that same thing 20 years ago because you can ignore most of the non-standard browser quirks.
Good points and thoughtful reply.
You're right, fundamentals are distilled, so to think they are free just by getting in early is likely backwards. And earning one's professional chops doesn't stop or start based on when you enter.
Web dev definitely is nostalgic. I miss the early days but I also conveniently erased ie6, binding data to HTML, the need for backbone and jQuery to do anything. hmmm yeah doesn't matter when you start, it's all a grind if you dig deep enough.
> I also conveniently erased ie6
Also known as PTSD-induced amnesia, haha. We all tried to forget.
> Once the tooling became too complicated and ever changing they couldn't keep up as front-end dilettante. It required to commit as professionals.
The best professionals did not fall for insanity of the modern front-end dilettante and continued hacking shit without that insanitity.
> You will not learn Grunt, Bower, and a large array of historic tech. You'll go straight for what's relevant today.
which will be outdated "tomorrow" just like grunt/bower... are looked at today
> A lot of the early stuff seems like an utter waste of time in retrospect.
This cannot be further from the truth, if you learned Javascript early, like really learned it, that mastery gets you far today. The best front-end devs I know are basically Javascript developers, everything else is "tech du jour" that comes and goes and the less of it you invest in the better off you'll be in the long-run.
> If you knew nothing about LLMs by the end of this year, you could find a course that teaches you all the latest relevant tricks in 5 to 10 hours for 10 bucks.
Hard disagree with this unless you are doing simple CRUD-like stuff
> The best professionals did not fall for insanity of the modern front-end dilettante and continued hacking shit without that insanitity.
"Front-end professional" and "no tooling" have been exclusive propositions since the early 2010s. You either learned to use tools or you were out of the loop.
> which will be outdated "tomorrow" just like grunt/bower... are looked at today
Not really. Historically, the main problem with front-end development has not been change, but the pace of it. That's how it ties in with the current discussion regarding the (now) ever-changing terrain of LLM-assisted coding. Front-end development is still changing today, but it's coalescing and congealing more than it's revolving. The chasms between transitions are narrowing. If you observe how long Webpack lasted and familiarity with it carried over to using Vite, it's somewhat safe to expect that the latter will last even longer and that its replacement will be a near copy. Someone putting time to learn front-end skills today might reap the benefits of that investment longer.
> if you learned Javascript early, like really learned it, that mastery gets you far today.
I did. I got a copy of the Rhino book 4th ed. and read it cover to cover. I would not advise to learn JS today with historical references. JS was not designed like most other languages. It was hastily put together to get things done and it had a lot of "interesting", but ultimately undesirable, artifacts. It only slowly turned into a more sensible standard after-the-fact. Yes, there are some parts that are still in its core identity, but a lot in the implementation has changed. Efforts like "Javascript: The Good Parts", further standardization, and TS helped to slowly turn it into what we know today. You don't need to travel back in time for that mastery. Get a modern copy of the Rhino book and you'll be as good as the best of them.
Yeah, I still get use out of XMLHttpRequest to this day good thing I got in early and variable hoisting isn't gonna get me! /s
A lot of snark aside there's a bit of a false dichotomy (I think) here at work. Whenever or wherever your jumping in point is into $something it will always pay dividends to learn the fundamentals of that $something well and unless you interact with older iterations on that $something then you'll never have to bother learning the equivalent of Grunt, Gulp, Stylus, Nunjuncks and so on for that $something.
With that being said it's also good to put aside time once a year to check out a good recommended (and usually paid) course from an established professional aimed at busy professionals.
As for LLMs I feel it's slowly becoming a thing big enough where people will have to consider where to focus their energy starting with 2027. Kinda like some people branched from web development into backend, frontend and UI/UX a good while back. Do you want to get good at using Claude Code or do you want to integrate gen AI features at work for coworkers to use or customers/users? It's still early days just like when NodeJS started gaining a lot of traction and people were making fun of leftpad.
And yet, at some point most web developers will have picked it up after the "raw html" era -- that point has probably come, even.
The web/html is a great analogy. I too am in no rush to be hyper effective with LLMs. In fact i want to deliberately slow down because ai-native coding is so exhausting.
That said, your point about the leverage of learning html and web in the early days compared to now rings true. pre-compiled isomorphic typescript apps are completely unrecognizable from the early days of index.html
Counter point. It’s always advantageous to learn and grow as things evolve. This way you have an active role and maybe a say in how it will evolve. And maybe you could contribute towards that evolution (despite poor execution openclaw showed what LLMs could be doing)
> There are a 16,000 new lives being born every hour. They're all starting with a fairly blank slate.
Not long ago we were ridiculing genZ for not knowing why save icon looks like a floppy disk.
Do you want to feel like that in next 5-10 years?
The counterpoint is that you will learn jank.
If you started early webdev, you learned lots of tricks, that dont benefit a modern webdev. E.g soap, long polling, the JsonP workaround... and so on
Many of the Llm frameworks will be seen simular. Mcp is already kinda heading in the obsolete direction imo, as skills took over
I’ve learned a lot of stuff that don’t really benefits me right now, but now and then I encounter a situation that made me happy that I did. It may never happens for some, but at the time, I was probably happy learning it.
But there’s some stuff that I don’t bother explore in depth because my time is finite and I don’t really need it. And anything LLM tooling is probably easier than a random JS framework. Vim’s documentation is probably longer than cursor’s.
I agree with this point. There is absolutely a 'left behind' gap that is under-explored.
My last job was a cable technician - making house calls to fix wifi, satellite tv, phone issues. Mostly elderly residents. The majority of them all were computer and phone illiterate. They were slow adopters to the fast-moving technology and many of them did not know how to operate their devices after we (UI/UX/hardware/software engineer 'we') removed them.
I wonder if this also has contributed to the elderly lonliness problem - sure its probably mostly related to physical companionship, acceptance of aging, etc, but the world that they knew (in general and the technological world they grew up in) is no longer recognizable.
But maybe it doesn’t matter that much to them. I don’t know how to skin a rabbit, but that knowledge could be handy in some situations. But I don’t see myself being in that situation other than accidentally.
My mother has a phone, but only use it to call. She has never needed a computer even though I spent my teenage years glued to one. But I have like 1 percent of a skill in cooking.
Exactly. We look at older people and think “oh, look at those poor souls. They don’t know X and Y technologies and they keep doing things the old way! They must feel so left behind.” Nothing is further from the truth. My whole life I’ve lived in neighborhoods full of people 20+ years older than me and not once did I have a neighbor or friend who I thought was overwhelmed with the pace of modern life and upset about how different the world was becoming from what they are used to. This is a trope. People are resilient and adaptive, and as you get older you learn how to embrace new things that actually help and reject new things that don’t. As I get older, I find myself just not caring about a lot of things that younger people care about and not doing a lot of things they do. I don’t use social media, I still pay for things with cash and checks, I don’t understand or care about the Kardashians or reality tv. My phone is 8 years old. I listen to prog rock and new wave music, and I probably couldn’t name a single popular musical performer today (besides Taylor Swift because I have a daughter). I don’t feel even slightly “left behind” or “obsolete.”
But it's so easy to try something like Claude Code. It's not like you need to get up to speed. There is no learning curve*, that's the nature of AI. Just start using it and you'll see why it has attracted so much hype.
*I should qualify that "using" CC in the strict sense has no learning curve, but really getting the most out of it may take some time as you see its limitations. But it's not learning tech in the traditional sense.
I've been playing with it on weekends for the last few months. 9 out of 10 projects, it's failed.
Projects as simple as "set up a tmux/vim binding so I can write prompts in one pane and run claude in the other". Fails.
I've been coding for over 20 years.
If there is no learning curve, why doesn't it work for me? You can't say I'm not using it right, because if that was true, then all I need to do is climb the learning curve to fix that, the curve that you say doesn't exist.
It doesn't work if you're treating it like a peer engineer. It only works if you treat it like you're a customer with no concern with how it works behind the scenes.
That's what's being asked of me in my last two jobs. Vibe code it, if it's bad just throw it away and regenerate it because it's "cheap". The only thing that matters is that you can quickly generate visible changes and ship it to market.
Out of frustration I asked upper management (in my current job), if you want me to use AI like that then I'll do it. But when it inevitably fails, who is responsible? If there's no risk to me, I will AI generate everything starting today, but if I have to take on the risk I won't be able to do this.
Their response was that AI generates the code, I'm responsible for reviewing it and making sure it's risk free. I can see that they're already looking for contractors (with no skin in the game) that are more than willing to run the AI agents and ship vibe code, so I'm at a loss on what to do.
I've used Claude Code to do everything from vibe-code personal apps including a terminal on top of libghostty to building my perfect desktop environment on NixOS (I'd never used Nix until then).
I'm not sure why it isn't working for you. Maybe your expectation is a perfect one-shot or else it has zero value, and nothing in between?
But my advice is to switch gears and see the "plan file" as the deliverable that you're polishing over implementation. It's planning and research and specification that tends to be the hard part, not yoloing solutions live to see if they'll work -- we do the latter all the time to avoid 10min of planning.
So, try brainstorming the issue with Claude Code, talk it through so it's on the same page as you, ensure it's done research (web search, docs) to weigh the best solutions, and then enter plan mode so it generates a markdown plan file.
From there you can read/review,tweak the plan file. Or have it implement it. Or you implement it. But the idea is that an LLM is useful at this intermediate planning stage without tacking on additional responsibilities.
I think by "no learning curve" they are referring to how you can get value from it without doing the research you'd need to use a conventional tool. But there is a learning curve to getting better results.
I learned my plan file workflow just from Claude Code having "Plan Mode" that spits out a plan file, and it was obvious to me from there, but there are people who don't know it exists nor what the value of it is, yet it's the centerpiece of my workflow. I also think it's the right way to use AI: the plan/prompt is the thing you're building and polishing, not skipping past it to an underspecified implementation. Because once you're done with the plan, then the impl is trivial and repeatable from that plan, even if you wanted to do the impl yourself.
I'm way past the point of arguing anything here, just trying to help.
> So, try brainstorming the issue with Claude Code, talk it through so it's on the same page as you, ensure it's done research (web search, docs) to weigh the best solutions, and then enter plan mode so it generates a markdown plan file. From there you can read/review,tweak the plan file. Or have it implement it. Or you implement it.
This is exactly the workflow that works very well for me in Cursor (although I don't use their Plan Mode - I do my version of it). If you know the codebase well this can increase your speed/productivity quite a bit. Not trying to convince naysayers of this, their minds are already made up. Just wanted to chime in that this workflow does actually work very well (been using it for over 6 months).
The first time I saw something like this in action was in a video about agentic blabla features in VS Code on the official VS Code YouTube channel. Pretty much write a complete and detailed specification, fire away and hope for the best. The workflow kinda clicked for me then but I still find a hard time adjusting to this potential new reality where slowly it won't make sense to generally write code "by hand" and only intervene to make pinpoint changes after reviewing a lot of code.
I've been reading a book about the history of math and at some points in the beginning the author pointed out how some fields undergo a radical change within due to some discovery (e.g. quantum theory in physics) and the practitioners in that field inevitably go through this transformation where the generations before and after can't really relate to each other anymore. I'm paraphrasing quite a bit though so I'll just recommend people check out the book if they're interested: The History of Mathematics by Jacqueline Stedall
And the aforementioned VS Code video, if I remember correctly: https://youtu.be/dutyOc_cAEU?si=ulK3MaYN7_CPO76k
I haven't written code by hand since December when Claude Opus 4.5 came out. It was clear that the inflection point arrived where it's at least as good as I am at implementing a plan. But not only that: it had good ideas like making impossible states impossible with a smart union type without being told and without me deeply modeling the domain in my head to derive a system invariant I could encode like that.
It was depressing watching all of this unfold over the last few years, but now I'm taking on more projects and delivering more features/value than ever before. That was the reason I got into software anyways, to make good software that people like to use.
> the generations before and after can't really relate to each other anymore
Yeah, good point. In some ways it's already crazy to me that we used to write code by hand. Especially all the chore work, like migrating/refactoring, that's trivial for even a dumb LLM to do. It kinda feels like a liability now when I'm writing code, kinda like how it feels when the syntax highlighting or type-checker breaks in the editor and isn't giving you live feedback, so you're surprised when it compiles and runs on the first try.
I remember having a hard time imagining what it was like for my dad to stub out his software program on paper until his scheduled appointment with the university punch card machine. And then sure being happy that I could just click a Run button in my editor to run my program.
Did it not work after the first try and you gave up? Did it not produce any usable code that you could hand tweak or build off of? I want to understand your definition of "failed" here.
What's your definition of "working"? Do you consider it working, when you have to put more effort into prompting back-and-forth than writing it the old way?
I honestly think the people who love Claude were not super proficient coders. That's the only thing I can think of to explain why writing gobs of English and then code reviewing in a loop could be easier than just coding yourself.
There definitely is a learning curve. Not sure what you're doing. Are you trying to one-shot it?
I think a decent place to start is: given a small web app, give it a bug report and ask it what causes the bug.
> If there is no learning curve, why doesn't it work for me?
Because LLMs are not actually good at programming, despite the hype.
I think they are better than a lot of people though, which is where their fans come from.
Failing 9 out of 10 times for such simple tasks is indeed puzzling. I have no idea what you're doing to achieve that but I'm impressed.
> There is no learning curve*, that's the nature of AI.
There isn't? Then why is it that whenever devs have tried it and not achieved useful results, they're told that they just haven't learned how to use it right?
“You're holding it wrong.” is the most common response I get, when I talk about problems I had with LLM-assisted coding.
You aren't holding it wrong, the truth is AI is a mixed bag, leaning towards a liability.
If people really counted all the time they spend coddling the AI, trying again, then trying again and again and again to get a useful output, then having to clean up that output, they would see that the supposed efficiency gains are near zero if not negative. The only people it really helps are people who were not good at coding to begin with, and they will be the ones producing the absolute worst slop because they don't know the difference between good and bad code. AI is constantly trying to introduce bugs into my codebase, and I see it happening in real-time with AI code completion. So, no you aren't "holding it wrong", the other people are no different than the crypto-bro's who were pushing blockchain into everything and hoping it would stick.
Imagine you are a JS dev and github comes out with a new search feature that's really good. it lets you use natural language to find open source projects really easily. So whenever you have a new project you check to see if something similar exists. And instead of starting from scratch you start from that and tweak it to fit what you want to do.
If you were the type of person who makes tiny toy apps, or you worked on lots of small already been done stuff, you'd love doing this. It would speed you up so much.
But if you worked on a big application with millions of users that had evolved into it's own snowflake through time and use, you'd get very little from it.
I think I probably could benefit from looking at existing open source solutions and modifying them a lot of the time, and I kinda started out doing that at first. But eventually you realize that even though starting with something can save you time, it can also cost you a ton of time so it's frequently a wash or a net negative.
Nothing you described in this comment is only achievable with "AI". I've been able to search for and find open source projects since forever, and fork them and extend them, long before an LLM was a glimmer in Sam Altman's beady eye.
No it’s not at all. AI just makes finding it faster. But that’s my point AI isn’t that different from what you could already do before. Most of us didn’t do things that way before, so maybe programming like that is just a bad idea.
> If people really counted [...]
Exactly. I counted and reported my results in a previous thread [0].
[0] https://news.ycombinator.com/item?id=47272913
I've started "racing" Claude when I have a somewhat simple task that I think it should be able to handle. I spend a few minutes writing out detailed instructions, which I already knew because I had to do initial discovery around the problem domain to understand what the goal was supposed to be. It took a while to be thorough enough writing it down for Claude, which is time I did not need to spend if I had just started writing the code myself - I'm sure the AI-bro's aren't considering the time it takes just to write down instructions to Claude vs just start coding.
So then Claude starts discecting the instructions. I start writing some code.
After a while Claude is done, and I've written about two or three dozen lines of code. Claude is way off, so I have to think about why and then write more instructions for it to follow. Then I continue coding.
After a while Claude is done, and I've written about three dozen more lines of code. Claude is closer this time, but still not right. Round 3 of thinking about how Claude got it wrong and what to tell it to do now. Then I continue coding.
After a while Claude is done (yet again), and I've written a lot more code and tested it and it's working as needed. The output Claude came up with is just a little bit off, so I have it rework the output a little bit and tell it to run again.
I downloaded the resulting code Claude wrote and compared it to my solution, and I will take my solution every single time. Claude wrote a bloated monstrosity.
This is my experience with "AI", and I'm honestly not loving it.
It does sometimes save me time converting code from one language to another (when it works), or implementing simple things based on existing code (when it works), and a few other tasks (when it works), but overall I end up asking myself over and over "Is this really how developers want the future to be?"
I'm skeptical that these LLM-based coding tools will ever get good enough to not make me feel ill about wasting my time typing instructions to them to produce code that is bloated and mostly not reusable.
I've done the racing thing too. Or I just reject its suggestions, do it better, and have it review and tell me why I did better.
And writing those instructions when I race it..it's more cognitive effort for me than coding!
Interesting stuff. Thx for sharing!
Because the AI bros hyping it up are incapable of admitting that the hype is overblown. That would mean they have nothing to sell you, so of course they aren't going to say that.
I gave Claude Code with Sonnet 4.6* a try a few weeks ago. I pointed it at a hobby project with less than 1kloc of C (about 26,500 characters) across ~10 modules and asked it to summarize what the project does. It used about $0.50 worth of tokens and gave a summary that was part spot on and part hallucinated. I then asked it how to solve a simple bug with an easy solution. It identified the right place to make the fix but its entire suggested solution was a one-liner invoking a hallucinated library method.
I use LLMs pretty regularly, so I'm familiar with the kinds of tasks they work well on and where they fall flat. I'm sure I could get at least some utility from Claude Code if I had an unlimited budget, but the voracious appetite for tokens even on a trivially small project -- combined with a worse answer than a curated-context chatbot prompt -- makes its value proposition very dubious. For now, at least.
* I considered trying Opus, but the fundamental issue of it eating through tokens meant, for me, that even if it worked much better, the cost would dramatically outweigh the benefit.
I think working with the technology gives you powerful intuitions that improve your skill and lead to better outcomes, but you don't really notice that that's what's happening. Personally speaking - and I suspect this is true of most people in general - I have very poor recollections of what it was like to be really bad/new at things that I am now very skilled at.
If you have try teaching someone something from the absolute ground up, you will quickly realize that a huge number of things you now believe are "standard assumptions" or "obvious" or "intuitive" are actually the result of a lot of learning you forgot you did.
It’s only “easy to try” if you’re okay with using proprietary software and having to rely on an evil megacorporation that engages in cyber-warfare.
Not to mention sucking on a monthly subscription tit that will go up in price by an order of magnitude once the market is captured.
I think it comes down to your own personality, appetite, and also how external factors like hype might impact you (resent, annoyance, curiosity, excitement).
There's no learning curve if you don't care about token spend.
Then what is the point? If what I'm doing can be done by Claude, as operated by someone who "doesn't need to get up to speed", then I really need to look at another career.
I tried it. Either I don't know how to use it, or it just doesn't work.
I've let tech pass me by many times, then the tech that passed me which I was never in a position to use got replaced by the next big tech innovation. I've found that you can climb aboard the train at anytime since everything new is a lot easier to get started on than learning C and having to manually allocate memory.
Some might be getting into AI in order to sell AI. As OpenClaw has shown, there is opportunity in this space to be a trailblazer. There are no doubt companies that are not tech-aligned that someone could help set up local LLMs for…
For me though, I'm dabbling in AI because it fascinates me. Bitcoin was like, I don't know, Herbalife? —never interesting to me at all.
Counterpoint:
Mistakes are less costly in the beginning and the knowledge gained from them is more valuable.
Over-sharing on social media. Secret / IP leaks with LLMs. That kind of thing.
I agree:
FOMO is an all-in mindset. Author admits to dabbling out of curiosity and realizing the time is not right for him personally. I think that's a strong call.
I think it was challenging 2 or 3 years ago. I plunged a year ago and it already was quite easy to use mainstream tools. I could run some local models with Ollama by just installing it. I could use coding assistance in VSCode. Connecting with http API to use AI within applications you build was also easy for local models or cloud.
There are loads of BS tools out there of course but I don’t use that many tools.
Broadly speaking I agree. But the reality for many SWEs is that if they don't learn new AI tools they'll get let go. It's use AI or be replaced by AI (or, more accurately, be replaced by someone using Ai) for many folks.
I think it's a luxury to be able to ignore a trend like AI. crypto was fine to ignore because it didn't really replace anyone, but Ai is a different beast
> It's use AI or be replaced by AI
I think they want to sell that perception, but the biggest thing the tech execs want in their SWEs is fear.
Fear that makes us stay in our jobs, even when raises and bonuses stagnate, because the market is scary with all the "AI layoffs" (which have largely been regular downsizing with the AI label slapped on).
Fear that makes us use the LLMs and then put in extra hours when we don't see the 10x productivity gains that they expect.
Fear that makes us erode our own skills and become dependent on these gatekeepers to maintain even a base level of productivity.
So much of the advertising and discussion around AI is based around fear. It's inevitable. It will take your job. Render you useless. It will render humanity useless. You better get in now, or you'll both lose your job and could end up in a virtual hell (https://en.wikipedia.org/wiki/Roko%27s_basilisk).
I think you're right that it is fear. But it's kind of a self fulfilling fear. Teams are requiring employees to use AI, probably because of this fear. Maybe it isn't directly taking my job, but it is necessary for me to use Ai to keep my job.
I agree that it's self-fulfilling. I just feel that it's being used more as a tool for compliance than for direct productivity.
That certainly could be true for some teams. Personally I think AI has directly improved my productivity. There's certainly sharp edges and we definitely shot ourselves in the foot more than a few times in ways we wouldn't have without AI, but by and large we've built an internal app where no human wrote a single line of code. I don't think we'd have been able to build it in 2 months without AI. We could have gone faster but the bottleneck was product and user testing, not code.
You can always hang your shingle and work for clients directly.
Sure, in the same way I can always quit being an engineer and become a farmer. It can work for some, but not everyone.
FWIW I do client work on the side. Full time client work has always been more draining than just having a regular job IME. Maybe I just can't find the right clients, but that's not something I have to worry about when I work for a company.
I wasn’t talking to everyone I was talking to you. And becoming a contractor is a lot more viable than becoming a farmer.
Client work is hard, but you have to decide if the freedom to work the way you want is worth it.
Oh. Well personally I don't mind using AI, and I use AI when I do client work as well (they know and some clients who use AI apps like lovable expect it).
But I know not everyone is in my shoes which is where my comment is coming from.
I think this is particularly evident with AI.
The early adopters started years ago and they've seen improvements over time that they started attributing them to their own skill. They tell you that if you didn't spend years prompting the AI, it will be difficult to catch up.
However, the exact opposite is happening. As the models get better, the need for the perfect prompt starts waning. Prompt engineering is a skill that is obsoleting faster than handwriting code.
I personally started using codex in march and honestly, the hardest part was finding and setting up the sandbox. (I use limactl with qemu and kvm). Meanwhile the agentic coding part just works.
This is true in my experience.
I waited until it seemed good enough to use without having to spend most of my time keeping up with the latest magical incantations.
Now I have multiple Claude instances running and producing almost all of my commits at work.
Yes, with a lot of time spent planning and validating.
i thought so too. but now we are onboarding project managers in non-tech fields to Claude Code and they are crushing it. on a terminal. vs code. the first thirty min is the hard part, after that the feedback loop kicks in. they ask for what they really want, they get it.
The thing is, this post is hitting a straw man. ngmi culture was deeply toxic and pervasive in crypto. I think the people who are really into LLMs are having a blast.
I'm definitely having a blast, but I agree with the author. You're not going to get left behind, the "getting left behind" rhetoric was just cryptocurrency pump-and-dumpers. It's fine to wait and not engage if you don't want to.
I agree with you, which is why I think it's a straw man. How many real devs are actually banging the "you're getting left behind!" drums?
I had an heavy ai user on my team say that “those who learn how ti use the tools wont get fired, those who dont are gone”. I used it to generate a bunch of cfn and it worked fine from an example and a couple line prompt, doesnt seem that hard to learn to me.
Now reviewing the 1k lines it generated and making sure its secure, thats going to take me longer than writing it by hand.
Yeah, I think this is it. If you don't learn to use them, you'll be much slower than people who do, but also they're not really that hard to learn, so it's not super urgent.
I'm still confused about the things I'll be slower in though, and I'm being sincere with that confusion. If it's "boilerplate", then I haven't done enough research or pick a library which has little to none of that, or I'm not using the template(s) built into whatever framework I am using.
For example, in one of the projects I'm working on, I'm using the VSA pattern. I have the list of 50 to 75 features I need to implement and what "categories" they slot into, I have all of the frameworks and libraries picked out, and I have built out "feature templates" with all of the boilerplate setup (I'm reusing these over multiple projects going forward). for each of the features all I need to do is
'ftr new {FEATURE_TYPE} {FEATURE_NAME} {OUTPUT_FOLDER}'
and then plug int the domain specific business logic.
I'll most likely use Claude/Codex/Whatever to write out some of my tests, but the majority of the 'boilerplate' is already done and I'm just sorting out the pieces that matter / can't be automated.
Am I missing something huge with these tools?
Don't get me wrong, for doing reverse engineering they're great helpers and I've made a tonne of progress on projects that had been languishing.
I find that can write features 5-10x faster with these tools than by hand, at a comparable level of quality (though it hasn't been long enough for me to judge what'll happen in a year).
Would you be able to give an example of a feature? For my example, I need to query an ancient undocumented database , pull back a pile of data, do some validations on it, and then show it to the user or pass it along with another processing step. The human part is researching the database and the data living in it, and implementing the validation(s) while talking to a business user, everything else can be templated.
Oh yes, this is what LLMs excel at. Introspecting a database, either the schema or the live data, running a few checks to see whether all the data had the same shape (or how many different shapes it has), writing validations to catch edge cases, they do this extremely quickly and pretty accurately, whereas it would have taken me hours of trawling.
Then I can look at the output and say things like "what if the data is lowercase?" or anything else I suspect they may have missed. A few rounds of these and I have a pretty good feel for the quality of the resulting checks, while taking a few minutes of my attention/tens of minutes of wallclock time to do.
I have a more detailed example here: https://www.stavros.io/posts/how-i-write-software-with-llms/
I'd share all my plans but I once found that the LLM used my actual phone number as test data, so I don't share those any more, just in case.
>Now reviewing the 1k lines it generated and making sure its secure, thats going to take me longer than writing it by hand.
Then you still need to learn how to use the tools to speed up reviewing the code.
If I don't have experience with the underlying framework/language/thing being modified, it makes it quite difficult to trust the actual review. In this example, I haven't worked heavily with Cloudformation, so I can't call b.s if it leaves a database instance exposed to the wider public internet rather than in my company's private VPC.
You can ask the agent to check that it doesn't leave a database instance exposed to the public, and present you with proof for you to check (references to the code and the relevant Cloudformation documentation). Then repeat this for all the things you'd normally want to check for in a code review.
In that case I'm just moving the reading of the documentation from reading it as I'm writing the yaml to when I'm doing a code review. Not saying it isn't helpful to have a pair researcher, just seems like I'm moving things around .
You're not actually doing engineering if you're just vibe-coding, reviewing, and testing all the way down. What the hell is that? Just a weird simulacrum of software development that will break apart in unpredictable ways. Security consultants are going to have very lucrative careers in the coming years.
It can be implicit though.
The llm person having a blast is compelled to push everyone to see what they see. If they have a leadership role at their company, then the getting-left-behind drum does get banged in the form of "ai native company transformation" initiatives.
I have personally heard people say this at work. It's not a strawman, there really is a message of "you'll be left behind" out there.
> How many real devs are actually banging the "you're getting left behind!" drums?
My CEO/CTO :)
I don't know for devs, but that's the message we get from upper management.
Lots, and not just online. I run into them regularly in my office, and so do my friends and family in tech. One of my coworkers is now spending all his time writing SKILLs, he's convinced that we'll never need to solve operational issues again if we have the right SKILLs.
You're using a no-true-Scotsman to accuse the author of a strawman.
Consider that.
Devs don't make hiring and firing decisions.
I think FOMO-aligned ai stuff is fairly common on HN, doesn't mean it's always deliberately manipulative.
The executives are, not the devs.
I'm not worried about being left behind technologically, but I am worried about being left behind after every company on the planet decides we need N years experience in AI to be employable.
I already have 30 years of experience in LLMs, if you believe my CV, so I'm not worried.
Ok, here is the risk of being left behind - if we have moderately fast take-off, the 1-2 years required to upskill in AI might mean you find yourself unemployable when your role gets axed.
I don’t think folks are taking seriously the possible worlds at the P(0.25) tail of likelihood.
You do not get to pick up this stuff “on a timescale of my choosing”, in the worlds where the capability exponential keeps going for another 5-10 years.
I’m sure the author simply doesn’t buy that premise, but IMO it’s poor epistemics to refuse to even engage with the very obvious open question of why this time might be different.
But they have engaged with it, and made an assessment about it's current utility.
We have no reason to believe that they won't keep an eye on this.
Little to nothing about AI tools so far suggests that that one can't just as easily pick the skills later. Tools that will get "exponentially better" will almost certainly be unrecognizable to someone desperately engaging with them now, for not other reason the sake of "having 1-2 years of experience"
Someone might reasonably choose to to bet on the upside. That doesn't imply that everyone else ought to fearfully hedge.
Eh, I'm not super worried. After all, Every six months or so, the latest model changes everything and the former model was complete garbage. It's not just a new model—it's a new paradigm shifting the landscape of agentic development.
I don't think there's such a thing as a "fast take-off" where human experience with 2026-era LLM coding remains economically relevant.
One area where it may end up leaving you behind is if you’re looking for a job right now. There are a lot of companies putting vibe coding in their job requirements. The more companies that do this the harder it will be to find employment if you’re not adopting this tool/workflow.
I don't know, I kind of wonder if this applies to all technologies equally.
for example, (dodging the whole full-self-driving controversy) tesla cars have had advanced safety features like traffic aware cruise control and autosteer for over a decade.
so, buying into safety early...
for other technologies, there's sort of the rugpull effect. The people who get in early enjoy something with little drama vs the late adopters. ask people who bought into sonos early vs late, probably more exampless of this.
so getting technology the founders envisioned, vs later enshittified versions.
Even if it reaches the end state of AGI, e.g. AI that's smarter and more capable than 90% of humans, there'll still be a huge learning curve to using it well, as anyone who's tried managing very smart humans can attest.
somehow the ai bros are saying creating .md files is the real ingenuity, and couldn't be learned in say half a day. There's absolutely no rush to keep up with the latest code producing tools especially when they're all "pay to play".
We've seen multiple ideas/products get quickly absorbed into frontier models, OSS, or well-funded startups. The cycle from "interesting idea" to "commoditized feature" is getting very short. Personally, there were three of these in the last year.
And even if your product is genuinely great, distribution is becoming the real bottleneck. Discovery via prompting or search is limited, and paid acquisition is increasingly expensive.
One alternative is to loop between build and kill, letting usage emerge organically rather than trying to force distribution.
This is the central thing that changes in a person with age. When you are born, the only thing you do is pick up new things. Literally nothing else. When you're young, picking up new things is how you improve your social position. It's what you do to even be talked to in the first place. It's what you do to get a girl/boyfriend, or be the best student in class, or to be the best (or worst even) employee at your first job ...
Once you have a good social position, or at least one you're happy with, you stop doing this, and you grow ever more irritated at others doing it ... because it's your social position that they're coming after. And they're younger, more motivated and hungrier. More than that, a decent chunk of these people want a better social position, even if that means taking yours.
Sure, but this is mostly irrelevant because software dev is one of the youngest fields. Something like 70% of professional devs are under 35 depending on which survey you look at. And the number of CS graduates exploded over the last decade.
The only reason the numbers aren’t even more tilted is because people stopped hiring juniors 2 years ago. But they’re out there, and if there’s a new technology around that makes them vastly more productive than the seniors today, there’s nothing stopping companies from hiring them.
No. It just assumes there is no utility in the underlying tech, someone who believes vaccines don't work could make the same argument. Most people trust Morgan-Stanley when it comes to financial instruments more than some bozo on the internet.
You do have to drag stubborn people, kicking and screaming, into the future or they will continue using old tech. The article is framed in the past tense, "someone tried", "the crypto grift was". As if it's not currently swallowing the world. I guess he is so maximally sensible that he self-assess faster than MS and realizes bitcoin just isn't for him every time.
He has a strange hyper-specific definition of utility and productivity, (wrote my MSc, had fun) don't count.
Feels like a false equivalency. It's just my experience, but I've completely ignored crypto and the metaverse, and I don't get the sense I'm missing out on much. In contrast, LLMs in their current state have (for me) dramatically reduced the distance between an idea and a working implementation, which has been legitimately transformative in my software dev life. Transformative for the better? Time will tell I suppose, but I'm really enjoying it so far.
This depends very much on your line of work.
As a freelancer I do a bit of everything, and I’ve seen places where LLM breezes through and gets me what I want quickly, and times where using an LLM was a complete waste of time.
For sure. The more specialized or obscure of things you have to do, the less LLMs help you.
Building a simple marketing website? Probably don’t waste your time - an LLM will probably be faster.
Designing a new SLAM algorithm? Probably LLMs will spin around in circles helplessly. That being said, that was my experience several years ago… maybe state of the art has changed in the computer vision space.
> The more specialized or obscure of things you have to do, the less LLMs help you.
I've been impressed by how this isn't quite true. A lot of my coding life is spent in the popular languages, which the LLMs obviously excel at.
But a random dates-to-the-80s robotics language (Karel)? I unfortunately have to use it sometimes, and Claude ingested a 100s of pages long PDF manual for the language and now it's better at it than I am. It doesn't even have a compiler to test against, and still it rarely makes mistakes.
I think the trick with a lot of these LLMs is just figuring out the best techniques for using them. Fortunately a lot of people are working all the time to figure this out.
Agreed. This sentiment you are replying to is a common one and is just people self-aggrandizing. No, almost nobody is working on code novel enough to be difficult for an LLM. All code projects build on things LLM's understand very well.
Even if your architectural idea is completely unique... a never before seen magnum opus, the building blocks are still legos.
the building blocks never were the hard part tho
Specialized is probably not the word I'd use, because llms are generally useful to understand more specialized / obscure topics. For example I've never randomly heard people talking about the dicom standard, llms have no trouble with it.
I think there is a sweet spot for the training(?) on these LLMs where there is basically only "professional" level documentation and chatter, without the layman stuff being picked up from reddit and github/etc.
I was looking at trying to remember/figure out some obscure hardware communication protocol to figure out enumeration of a hardware bus on some servers. Feeding codex a few RFC URLs and other such information, plus telling it to search the internet resulted in extremely rapid progress vs. having to wade through 500 pages of technical jargon and specification documents.
I'm sure if I was extending the spec to a 3.0 version in hardware or something it would not be useful, but for someone who just needs to understand the basics to get some quick tooling stood up it was close to magic.
The standard for obscurity is different for LLMs, something can be very widespread and public without the average person knowing about it. DICOM is used at practically every hospital in the world, there's whole websites dedicated to browsing the documentation, companies employ people solely for DICOM work, there's popular maintained libraries for several different languages, etc, so the LLM has an enormous amount of it in its training data.
The question relevant for LLMs would be "how many high quality results would I get if I googled something related to this", and for DICOM the answer is "many". As long the that is the case LLMs will not have trouble answering questions about it either.
> llms are generally useful to understand more specialized / obscure topics
A very simple kind of query that in my experiences causes problems to many current LLMs is:
"Write {something obscure} in the Wolfram programming language."
One tendency I've noticed is that LLMs struggle with creativity. If you give them a language with extremely powerful and expressive features, they'll often fail to use them to simplify other problems the way a good programmer does. Wolfram is a language essentially designed around that.
I wasn't able to replicate in my own testing though. Do you know if it also fails for "mathematica" code? There's much more text online about that.
> Do you know if it also fails for "mathematica" code?
My experience concerning using "Mathematica" instead of "Wolfram" in AI tasks is similar.
Several years ago is ancient with the rate of advancement that LLMs have had recently
> Building a simple marketing website? Probably don’t waste your time - an LLM will probably be faster.
This is actually where I would be most reluctant to use an LLM. Your website represents your product, and you probably don’t want to give it the scent of homogenized AI slop. People can tell.
They can tell if you let it use whatever CSS it wants (Claude will nearly always make a purple or blue website with gross rainbow gradients). They can also tell if you let it write your marketing copy.
If you decide on your own brand colors and wording, there’s very little left about the code that can’t be done instantly by an LLM (at least on a marketing website).
But this learning is also value.
Without playing around with it, you wouldn't know when to use an LLM and when not.
But the models change every 3-6 months. What's the use of learning what they can and can't do when what they can and can't do changes so frequently?
I would say they are not changing but evolving and you evolve with them.
For example: gemini became a lot better in a lot more tasks. How do I know? because i also have very basic benchmarks or lets say "things which haven't worked" are my benchmark.
Some subscriptions offer "unlimited tokens" for certain models. i.e. GitHub co-pilot can be unlimited for GPT-4o and GPT-4.1 (and, actually, GPT-5 mini!). So: I spent some time with those models to see what level of scaffolding and breaking things down (hand holding) was required to get them to complete a task.
Why would I do that? Well, I wanted to understand more deeply how differences in my prompting might impact the outcomes of the model. I also wanted to get generally better at writing prompts. And of course, improving at controlling context and seeing how models can go off the rails. Just by being better at understanding these patterns, I feel more confident in general at when and how to use LLMs in my daily work.
I think, in general, understanding not only that earlier models are weaker, but also _how_ they are weaker, is useful in its own right. It gives you an extra tool to use.
I will say, the biggest findings for "weaknesses" I've found are in training data. If you're keeping your libraries up-to-date, and you're using newer methods or functionality from those libraries, AI will constantly fail to identify with those new things. For example, Zod v4 came out recently and the older models absolutely fail to understand that it uses some different syntax and methods under the hood. Jest now supports `using` syntax for its spyOn method, and models just can't figure it out. Even with system prompts and telling them directly, the existing training data is just too overpowering.
If you had to hold on for dear life during the ~2014-2017 JavaScript framework chaos, then 3-6 months is peanuts.
This is an industry that requires continuous learning.
Honestly I think this is the primary explanation for why there is so much disagreement on if LLMs are useful or not. If you leave out the more motivated arguments in particular.
> In contrast, LLMs in their current state have (for me) dramatically reduced the distance between an idea and a working implementation, which has been legitimately transformative in my software dev life.
Feels like a false dichotomy.
Have I become faster with LLMs? Yes, maybe. Is it 10x or 1000x or 10,000x? Definitely not. I think actually in the past I would have leaned more on senior developers, books, stack overflow etc. but now I can be much more independent and proactive.
LLM-based tools are a wide spectrum, and to argue that the whole spectrum is worth exploring because one sliver of it has definite utility is a bit wonky. Kind of like saying $SHITCOIN is worth investing in because $BITCOIN mooned as a speculative asset:
Feel like we’re back at Adobe Dreameaver release and everyone is claiming that web development jobs are dead.>Feel like we’re back at Adobe Dreameaver release and everyone is claiming that web development jobs are dead
I truly believe so much of the anti-AI sentiment is the same as the Luddites.
They're often used as a meme now, but they were very real people, faced with a real and present risk to their livelihoods. They acted out of fear, but not just irrational fear.
AI is the same: it's unquestionably (to anyone evaluating it fairly) a huge boost to productivity ... and also, unquestionably, a threat to programmer jobs.
Maybe the OP is right about waiting, but to me whenever new tech is disrupting jobs, that seems like the best time to learn it. If you don't, it's not just FOMO as the author suggests ... it's failing to keep up on the skills that keep you employed.
> it's failing to keep up on the skills that keep you employed.
I judge "failing to keep up" by my ability to "catch up". Right now if I search for paying courses on AI-assisted coding, I get a royal bunch for anything between 3$ to about 25$. These are distilled and converging observations by people who have had more time playing around with these toys than me. Most are less than 10 hours (usually 3 to 5). I also find countless free ones on YouTube popping up every week that can catch me up to a decent bouquet of current practices in an hour or two. They all also more or less need to be updated to relevancy after a few months (e.g. I've recently deleted my numerous bookmarks on MCP).
Don't get me wrong, LLM-assisted coding is disruptive, but when practice becomes obsolete after a few months it's not really what's keeping you employed. If after you've spent much time and effort to live near that edge, the gap that truly separates you from me in any meaningful way can be covered in a few hours to catch up, you're not really leaving me behind.
I have found that maximising AI coding is a skill on its own. There is a lot of context switching. There is making sure agents are running in loops. Keeping the quality high is also important, as they often take shortcuts. And finally you need an somewhat of an architectural vision to ensure agents don’t just work in a single file.
This is all very tiring and difficult. You can be significantly better than other people at this skill.
This is not an argument for its revolutionary utility. Balancing rocks on the beach is very tiring and difficult for some people, and you can be significantly better at it. Not really bringing anything to the immediate conversation with that insight.
The burden of proof lies with he who makes grand claims. My counterargument in the face of your lack of evidence is: “Where are all the improvements to my daily life? Where are the disrupting geniuses who go-to market 100x faster than their Luddite counterparts?”
To paraphrase another analogy that I enjoyed, it’s a bit like when 3d printing became a thing and hype con artists claimed that no one would buy anything anymore, you could just 3d print it.
You don’t need 100x productivity to be disruptive. In business 10% gain can be quite enormous. My senior engineers are estimating 25-50% gains. That is a far cry from your 100,000% gain, but very real and meaningful.
This is a completely different claim than the commenter made that I was responding to
The last study that came out on this showed that engineers were significantly overestimating their own productivity gains.
If a stat like that is not accurately measured, it's useless.
It’s ridiculous double standard and poisons any reasonable discussion to assert something is a fact and anyone who disagrees is a hysterical Luddite based on no actual evidence.
> Have I become faster with LLMs? Yes, maybe.
The question isn’t if you’ve improved. It’s if the path you took to getting to your current improvement could have been shortcut with the benefit of hindsight. Given the number of dead ends we’ve traversed, the answer almost certainly is yes.
Crypto and the Metaverse were solutions in search of a problem. LLMs kind of felt like that until tooling arrived that enabled doing a lot more than copying + pasting chat conversations.
Sure, maybe crypto changed some lives, but an entire industry? I think ALL of software dev is going under a transformation and I think we're past the point of "wait it out" IMO.
Or I'm wrong, but right I'm being paid to develop a new skill professionally. Maybe the skill ends up not being useful - ok, back to writing code the old way then.
> In contrast, LLMs in their current state have (for me) dramatically reduced the distance between an idea and a working implementation
It may have reduced the time to an implementation, based on my experiences I sincerely doubt the veracity of applying the adjective "working".
> In contrast, LLMs in their current state have (for me) dramatically reduced the distance between an idea and a working implementation, which has been legitimately transformative in my software dev life.
I can't really agree. I've never seen anything from an LLM that I would consider even helpful, never mind transformative.
How are you supposed to use them?
Is this seriously so? Have you never seen anything helpful from an LLM? That seems such a black and white statement that I get confused.
I am conservative regarding AI driven coding but I still see tremendous value.
It makes me want to ask you: do you ever see helpful things from your colleagues at all?
> Is this seriously so? Have you never seen anything helpful from an LLM?
No, not at all. I may be using it wrong.
I put in "write me a library that decodes network packets in <format I'm working with>" and it had no idea where to start.
What part of it is it supposed to do? I don't want to do any more typing than I have to.
You're right, you are using it wrong. An LLM can read code faster than you can, write code faster than you can, and knows more things than you do. By "you" I mean you, me, and anyone with a biological brain.
Where LLMs are behind humans is depth of insight. Doing anything non-trivial requires insight.
The key to effectively using LLMs is to provide the insight yourself, then let the LLM do the grunt work. Kind of like paint by numbers. In your case, I would recommend some combination of defining the API of the library you want yourself manually, thinking through how you would implement it and writing down the broad strokes of the process for the LLM, and collecting reference materials like a format spec, any docs, the code that's creating these packets, and so on.
> An LLM can read code faster than you can, write code faster than you can, and knows more things than you do.
I don't agree. It can't write code at all, it can only copy things it's already seen. But, if that is true, why can't it solve my problem?
> The key to effectively using LLMs is to provide the insight yourself, then let the LLM do the grunt work
Okay, so how do I do that? Remember, I want to do ZERO TYPING. I do not want to type a single character that is not code. I already know what I want the code to do, I just want it typed in.
I just don't think AI can ever solve a problem I have.
Well, if you ask it to write a library at the start, it's likely it will not do that well. Start small, spoon feed some examples.
If you have to put in this much effort, why not just write it yourself?
some people suck ass at programming so they'd rather use English
When you write a library the first step is always designing it. LLMs dont get rid of that step, they get rid of the next step where you implement your design.
They also added an additional step where you have to explain your design using vague natural language.
Is this really "additional"? do you not do design docs/adrs/rfcs etc and talk about them with your team? do you take any notes or write out your design/plan in some way even for yourself?
Why can't you just pass any of those to an AI?
If I'm writing a library to work with a binary format, there is very little English in my head required, let alone written English.
That is a heavily symbolic exercise. I will "read" the spec, but I will not pronounce it in literal audible English in my head (I'm a better reader than that.)
I write Haskell tho so maybe I'm biased. I do not have an inner narrative when programming ever.
I’m not part of any team, I work on my projects alone. I rarely write long-form design documents; usually I either just start coding or write very vague notes that only make sense when combined with what’s in my head.
So I have to do a lot of typing? Because the typing is the bit I don't want to do.
Actually writing code is the fun and easy bit.
> Feels like a false equivalency.
It's clearly a textbook example of survivorship bias.
In the 90s the same argument was directed at this new thing called the internet, and those who placed a bet on it being a fad ended up being forgotten by history.
It's rather obvious that this AI thing is a transformative event in world history, perhaps more critical than the advent of the internet. Take a look at traffic to established sites such as Stack Overflow to get a glimpse of the radical impact. Even in social media we started to see the dead internet theory put to practice in real time.
And coding is the lowest of low hanging fruits.
In the 90s was also the dotcom boom, and the vast majority of those who placed an all-in bet on it being everything lost it all in the dotcom bust and also "ended up being forgotten by history". Some of those bets were prescient but too early but many of those bets never made any sense. The dotcom bust was worse than the software industry crash we're experiencing now.
"It's rather obvious that this AI thing is a transformative event in world history" perhaps but it's not at all obvious how it's going to shake out or which bets are sensible.
> In the 90s was also the dotcom boom, and the vast majority of those who placed an all-in bet on it being everything lost it all in the dotcom bust and also "ended up being forgotten by history".
I think you are missing the point, and also the very site you're posting on.
Look at the top 50 list of most valuable companies in the world. Over half of the total market value reported today is attributed to companies which were either dotcom startups or whose growth was driven by the dotcom growth period. Dismissing the advent of the internet as anything short of revolutionary is disingenuous, no matter how many zombo.com companies failed.
LLMs have the exact same transformative impact on humanity.
> LLMs have the exact same transformative impact on humanity.
But this is begging the question.
Yes, we can see that the internet was radically transformative.
But you are arguing that this somehow proves that LLMs are too, when there's wildly insufficient evidence—either on where LLMs are going in themselves, or in the comparison—to credibly make that claim.
> It's rather obvious that this AI thing is a transformative event in world history, perhaps more critical than the advent of the internet. Take a look at traffic to established sites such as Stack Overflow to get a glimpse of the radical impact. Even in social media we started to see the dead internet theory put to practice in real time.
It's worth noting that SO was declining well before ChatGPT launched. It seems more likely that the decline of SO was more driven by Google ranking changes to prioritise websites that served Google ads. Certainly I remember having to go down a few results to get SO results for a while, even when the top results were just copypasta from SO.
> It's worth noting that SO was declining well before ChatGPT launched. It seems more likely that the decline of SO was more driven by Google ranking changes to prioritise websites that served Google ads.
I don't think that's it. SO was the go-to page for troubleshooting, whose traffic was not exactly originating from web search. Also, the LLM-correlated drop in traffic is also reported by search engines. Stack Overflow just so happens to be a specialized service with a very specialized audience whose demand is perfectly dominated by LLM chatbots.
I mean the first decline happened well before ChatGPT so it can't just be that.
Internet is something new. By definition llm coding isn’t doing anything you couldn’t have done already. Once the agents aren’t writing a human syntax based language but are spitting out opaque functions in binary machine code, then they are doing something new and compelling imo because there are real performance gains with that.
No, this is wrong. AI has drastically shortened the time and effort between idea and implementation. The upshot being, that not only do you get things done faster, but things you wouldn't otherwise countenance doing are now within reach.
So where is all the new tech years into ai? Turns out that wasn’t the limiting factor. The limiting factor is still what it was 10,000 years ago in business: accumulating capital to start and finding a fit in the market to last.
Wait. Which one is it? Is Show HN overwhelmed with too much new stuff because of AI? Or is there no new stuff? I am so confused!
As someone who does monitor /new, Show HN is overwhelmed with copycat low effort AI rubbish, not innovative AI generated tech that is interesting or useful. How many AI powered coloring book generators and AI powered anime-ification sites does the world really need?
> In the 90s the same argument was directed at this new thing called the internet, and those who placed a bet on it being a fad ended up being forgotten by history.
Allow me to introduce you to the dot-com boom, where everyone who bet on the internet went broke.
The Google founders did ok.
Google raised only a single large round of institutional funding before going public, with the investors contributing $12.5 million
I can be much more specific about “everyone” if you’d like.
> In the 90s the same argument was directed at this new thing called the internet, and those who placed a bet on it being a fad ended up being forgotten by history.
Almost all people are "forgotten" by history.
In any case, people who were not even born yet in the 1990s are using the internet today, very successfully, so clearly you can wait.
> Transformative for the better? Time will tell I suppose
That's the point of the blog post. If you can't even say right now whether it's for the better, then there's no reason to rush in.
I read OP as saying it is transformative, at least for them. Whether it's transformative for society is left to be decided.
And conversely if it is, then there is no point to getting in early since the whole point is to externalize knowledge and experience
There's value in being early - in the right thing.
- If you'd invested in Bitcoin in 2016, you'd have made a 200x return
- If you'd specialized in neural networks before the transformer paper, you'd be one of the most sought-after specialists right now
- If you'd started making mobile games when the iPhone was released, you could have built the first Candy Crush
Of course, you could just as well have
- become an ActionScript specialist as it was clearly the future of interactive web design
- specialized in Blackberry app development as one of the first mobile computing platforms
- made major investments in NFTs (any time, really...)
Bottom line - if you want to have a chance at outsized returns, but are also willing to accept the risks of dead ends, be early. If you want a smooth, mid-level return, wait it out...
There may be financial value in being early (if you're lucky), but there are other values in waiting.
My goal in life is not to maximize financial return, it's to maximize my impact on things I care about. I try to stay comfortable enough financially to have the luxury to make the decisions that allow me to keep doing things I care about when the opportunities come along.
Deciding whether something new is the right path for me usually takes a little time to assess where it's headed and what the impacts may be.
> My goal in life is not to maximize financial return, it's to maximize my impact on things I care about.
In the vast majority of cases, financial returns help maximize your impact on the things you care about. Arguably in most cases it's more effective for you to provide the financing and direction but not be directly involved. That's why the EA guys are off beng quants.
The only real exceptions are things that specifically require you personally, like investing time with your family, or developing yourself in some way.
I knew this canned rebuttal was coming and almost addressed it in my previous comment.
I've not found this to be true at all, for a variety of reasons. One of my moral principles that extreme wealth accumulation by any individual is ultimately harmful to society, even for those who start with altruistic values. Money is power, and power corrupts.
Also, the further from my immediate circle I focus my impact on, the less certainty I have that my impact is achieving what I want it to. I've worked on global projects, and looking back at them those are the projects I'm least certain moved the needle in the direction I wanted them to. Not because they didn't achieve their goals, but because I'm not sure the goals at the outset actually had the long term impact I wanted them to. In fact, it's often due to precisely what we're talking about in this thread: sometimes new things come along and change everything.
The butterfly effect is just as real with altruism as it is with anything else.
But you're not supposed to accumulate the wealth, you're supposed to forward it to your elected causes.
Being a quant is inherently accumulating and growing someone's wealth for them, even if it's not your own.
If there were a way to be a true Robin Hood and only extract wealth from the wealthy and redistribute that to poor, I'd call that a noble cause, although finance is not my field (nor is crime, for that matter) so it's not for me.
My chosen wealth multiplier is working at a community-owned cooperative, building the wealth for others directly.
Not sure about this because many charities are designed to spend their income, rather than hoard it. A big part of choosing which charity to donate to is, or should be, how effective they are in spending what you give them.
I mean, I'm not arguing that if you can find a way to make a large amount of money in an ethical way without enriching yourself or the wealthy further and then find a way to accurately evaluate charities to maximize impact, that you shouldn't do that. But there are several very difficult problems embedded in that path, and I could easily sees just solving all of those problems becoming a full-time job by itself.
I also, candidly, haven't ever seen anyone successfully do that.
I didn't realize maximizing money is the way to achieve moral excellence. It's interesting how Puritanical the EA folks are
There is no moral excellence but which you invent for yourself. But given the first principle or goal of 'having the most impact', maximizing money is often quite useful.
Or, utilitarianism
> The only real exceptions are things that specifically require you personally, like investing time with your family, or developing yourself in some way.
So, the things that matter the most for most people?
Studies pretty consistently show that happiness caps off at relatively modest wealth.
That's not their stated goal. Their stated goal is to maximize impact, not their own happiness.
Impact is nebulous. For example, Zuckerberg has had impact but it’s been almost entirely negative. The world is a worse place for him having existed.
It being signed doesn't make it nebulous.
> That's why the EA guys are off beng quants.
Or in prison for fraud.
I want to cure lung cancer, therefore as an Effective Altruist™ I maximize my income by selling cigarettes to children outside playgrounds. The money will go towards research in my will, and in the meantime the incidence of lung cancer in teenagers will incentivize the free market to find a cure!
People don't become quants because they are EAs, they become EAs to justify to themselves why they became quants.
Being a quant is not that interesting and if you're not redirecting the money you're not really an EA, are you?
Your first paragraph is just a standard response to utilitarianism, although a poor one because it doesn't consider EV.
Nonetheless I'm not quite sure why merely mentioning EA draws out all these irrelevant replies about it. It was incidental, not an endorsement of EA.
> Arguably in most cases it's more effective for you to provide the financing and direction but not be directly involved. That's why the EA guys are off beng quants.
The EA guys aren't the final word on ethics or a fulfilling life.
Ursula K. Le Guin wrote that one might, rather than seeking to always better one's life, instead seek to share the burden others are holding.
Making a bunch of money to turn around and spend on mosquito nets might seem to be making the world better, but on the other hand it also normalizes and enshrines the systems of oppression and injustice that created a world where someone can make 300,000$ a year typing "that didn't work, try again" into claude while someone else watches another family member die of malaria because they couldn't afford meds.
Nobody is asking about ethics or a fulfilling life. We are talking about maximum _impact_.
Impact only has meaning per a chosen framework to measure within. For example, if I apply my ethical system to measure the impact of an EA, they have essentially no impact, since all they do is perpetuate a system that is the root of the problems they're trying to solve.
A friend told me about bitcoin in early 2010, back when the coins were effectively free. I laughed at the idea and called it stupid.
I still think it's stupid, but I'd be a whole lot richer if I went along with it at the time!
IMO the only way you'd be rich with bitcoin would be if you forget about your coins and reminded of it years later or if you were a hardcore "fix the money fix the world hodl" believer.
Otherwise you would most likely have sold during one of huge crashes or values, attempted trade and lost it all, invested into the new shitcoin NFT or whatever or just got hacked along the way.
indeed. even if I didn't lose them along the way or have them stolen via hack, I would have cashed out back when they were outlandishly priced at $100. I never would have held on to them this long.
But even $100 would have been nice given you could still pop them out for free on a standard PC back then with mining software.
You would probably have sold early for a relatively small amount too.
I bought 10ish BTC at some point for almost nothing, sold them for a low 4-digit amount thinking they were stupid anyway. I still think they were stupid but it turns out they could have paid off my house easily. Oh well.
Question is whether you sold all or just a segment. If you bought 100 btc early on, sold 99 of them, you'd still have something worth a deposit for a house or a car now, which is still life-changing.
Or bought btc early on and forgot about it. Then haphazardly attenpting to find it on some old HD when the price skyrocketed.. There was a guy who combed through a trash landfill to recover a HD that presumably had dozens of millions in BTC
To be fair, I remember being about ten years old and berating my friend who just told me that he had something called "Rebel Assault" on a CD how this was completely impossible as CDs could only store music and how he was a complete idiot for believing otherwise... :-)
I distinctly remember staring at my check/bank/debit card laying on the desk in front of my keyboard, all the info punched in to buy $500 of bitcoin for something like $0.29 a coin.
Didn't pull the trigger. I just tell myself I'd have sold them when they doubled in price or they'd have been hacked in one of the mt. gox attacks and I'd have lost them anyways.
Today it would be about 120m. Oh well.
Same. there's no chance I'd have the riches associated with those coins. Or at least that's how I manage to sleep at night.
There was a local food delivery service at the time that accepted bitcoin. Can you imagine looking back on life and realizing you spent the equivalent of $1M on a burrito?
My roommates and I literally bought a pizza with our stash of bitcoins. So yes, we fully understand how this feels.
> Or at least that's how I manage to sleep at night.
Admirable self-reflection.
Wow, shots fired here for me.
I was ahead of the game with my intimidate expertise in ActionaScript and Silverlight! I made 3D engines in browsers well before WebGL was a spec.
It was quite profitable for a few years, then poof. Dead end lol
Shockwave director studio even!?
I think these ideas are similar to long-term relationships. Identify when it's clear it's worth your time, like the author, and commit appropriately, and then when it's time to move on move on.
AngularJS, Backbone, Knockout, YUI, were all a wave of pretty groundbreaking frontend technology. It was absolutely worth experimenting with and committing to once they had some uptake, but probably not before then unless you wanted to work on the teams building them. Time went on, they had years of longevity that overlapped with the next wave of Vue, React, and the rest, and those became worth investing in long-term. Along the way, fundamentals in underlying web technologies were crucial, programming, logic, networking, markup, design.
Actionscript was totally worth investing in, until it ran it's course, and then other things came along and you would have adapted your game programming and engine programming skills yo a different platform.
> If you'd invested in Bitcoin in 2016, you'd have made a 200x return
Except you would've probably sold it at any of 1.5x, 2x, 4x, or 10x points. That's what people keep missing about this whole "early bitcoin". You couldn't tell it will 2x at 1.5x, you couldn't tell it will 4x at 2x, and so on.
> - If you'd specialized in neural networks before the transformer paper, you'd be one of the most sought-after specialists right now
> - If you'd started making mobile games when the iPhone was released, you could have built the first Candy Crush
I disagree:
Concerning the first point: how neural networks are today is very different from how they were in former days. So, the knowledge of neural networks from the past does only very partially transfer to modern neural networks, and clearly does not make you a very sought-after specialist right now.
Concerning your second point: the success of mobile games is very marketing-centric. While it is plausible that being early in mobile games development when the iPhone was released might have opened doors in the game industry for you, I seriously doubt whether having this skill would have made you rich.
I can confirm your response to the point you can first
There's also the problem that there is currently a huge transformer monoculture in the AI space. Everything gets better if you throw transformers at the problem.
If you had worked on anything other than a transformer based architecture post 2016, such as Mamba or RWKV, you would have wasted your time.
Mamba 3 is the third iteration and somehow I doubt that it will catch on.
Almost everyone chasing those returns would be better off investing in index funds.
Index funds give you something like the expected value over a huge class of possible investments.
What you aim for if you want to invest early is rather a probability distribution of
- get rich with a small (but nevertheless realistic) probability p
- get something between little, nothing, and loosing a little bit with probability 1-p
This is a very different offering than the profit probability distribution that index funds give you.
> What you aim for if you want to invest early
Sure. I’m saying someone pursuing that portfolio will probably end up underperforming an index. Most new early-stage VCs do.
> get something between little, nothing, and loosing a little
Broadly speaking, when your investment outcomes don’t differentiate between anything and zero, you’re mostly going to get zero.
> I’m saying someone pursuing that portfolio will probably end up underperforming an index.
This holds if you consider "underperforming" to be a comparison of expected values.
On the other hand, if you consider "probability of getting a really huge payoff" to be the measure by which the investments are compared, the index fund is the one that looses in the comparison.
> if you consider "probability of getting a really huge payoff" to be the measure by which the investments are compared
That’s gambling. You’re truncating the curve below the top. It’s a terrific strategy for middlemen. Its expected value is lower than index investing.
Would you be comfortable using this same logic to invest most of your net worth in lottery tickets/betting on black in a casino? If not, I'd be curious to hear what is different in that for you.
> Would you be comfortable using this same logic to invest most of your net worth in lottery tickets/betting on black in a casino?
I wouldn't "invest" in lottery tickets because for these p is far too small (exception: if I found a loophole in the system of the lottery, which has been found for some lotteries). For casinos, there is additionally the very important aspect that the casino will scam you (if you start winning money (for example by having found some clever strategy that gives you an advantage), the security will escort you out of the building and ban you from entering the casino again).
So, to give an explanation of the differences:
- Because "the typical run" for such an investment will be loosing, you should never invest your whole net worth (or a significant fraction thereof) into such an investment. The advice that I personally often give is to use index funds or stock investments for generating the money for investments that are much more risky, but have huge possible payouts.
- You should only do such an "early investment" if you have a significant information advantage over the average person. Such an advantage is plausible, for example, if you are deeply interested in technology topics
- Lottery tickets have an insanely small p (as defined in my comment). You only do "early investments" into topics where the p is still small, but not absurdly bad. The difference is that for lottery tickets the p is basically well-known. On the other hand, for "early investments", people can only estimate the p. Because of your information advantage from the previous point, you can estimate the p much better than other people, which gains you a strong advantage in picking the right "early investments" to choose.
But be aware that this is a strategy for risk-affine people. If you aren't, you better stay, for example, with index funds.
> this is a strategy for risk-affine people
If you’re paying a fair price for the risk, sure. Most of the examples you gave seemed to be in deep speculative territory to the point that they don’t very much resemble anything economic.
Sure, you could take calculated risks for predictable returns over a long enough time scale.
Or you could take what's in the box!!
Only if it’s leveraged.
Crucially those are all investments. Just like creating AI or buying data centers to run AI is an investment. Whereas merely using AI feels more like being in the general population of consumers. The shape of the outlay for it is un-investment-like. A monetary investment is a big charge up front, not a monthly fee. A skills investment looks like effort spent learning, but I mean how difficult is it to type reasonably precise English? Conclusion: you're a customer, not an investor, so you can start any time.
Some of those things involve a bit of money as a gamble; others require some time learning tooling that can be repurposed (mobile game developers can obviously do mobile apps), and others would be dedicating years of your life into something that may be a dead end.
There's really no need to be "early" for something like this. I've seen coworkers who've never used agentic coding pick it up and know everything that need to in ~2 days. The people who really dive in and are running orchestrated agents overnight are in general not building much of real value.
None of those examples are useful.
1. 2016 was years after Bitcoin was developed. So you could still make 200x returns without being early adopter.
2. Is this even true? I'd bet scraping experts or people who can fine tune LLMs have easier time finding a job than classical ML academics.
3. Candy Crash was released when iPhone was on its 5th iteration.
If anything, you just added to OPs points. Being an early adopter gives limited advantage.
to be fair a friend of mine made one of the first flashlight apps on iOs and made a tidy sum out of it.
I think the question really is about how well you hit your timings. You can have held bitcoin but sold it went it hit $5k or less. You can have a technical advantage in a given field but somehow waste it (dead startup, serious illness) and lose your timing. Nobody knows what the right timings are, but I think the OP is pushing for a more consistent risk investment strategy and setting up the timings to raise the floor significantly at the cost of losing some of the best possible ceilings.
I should've started registering a bunch of two and three character and generic .com domains in the early 90s when registration was free.
I get what you are saying, but as someone who was coding mobile apps in 2003 (iPhone was released in 2007), I have to give a few details.
By the time iPhone was released, it was already too late for small companies. When you developed apps and games for MS PocketPC and Blackberry, you charged $20 per app, and any average quality product would make money.
In 2007, there were only 2 kinds of success stories:
1. Companies that were able to throw a lot of money around (Your example of Candy Crush).
2. Some rare flukes of someone getting rich with some app.
So what I was trying to say: The golden days were really before the release of the iPhone.
In all fairness I was an actionscript specialist, and up until 2010 or so I was making REAL good money doing contract flash work -
Luckily I was also doing frontend work alongside, so when the time came to transition to html+css+javascript, it wasn’t much of a move at all, it was just putting down AS and focusing fully on JS
It's a horrifying feeling facing the possibility that the career I spent so much time and money to get into is fading away. Sure, LLMs are not there yet, and they might not ever quite get there. But will companies start hiring again? If productivity has gone up, and it seems like it has, then no.
So, a decade of hanging by a thread, getting by and doubling down on CS, hoping that the job market sees an uptick? Or trying to switch careers?
I went to get a flat tire fixed yesterday and the whole time I was envious of the cheerful guy working on my car. A flat tire is a flat tire, no matter whether a recession is going on or whether LLMs are causing chaos in white collar work. If I had no debt and a little bit saved up I might just content myself with a humble moat like that.
> But will companies start hiring again?
Anecdata, but the few people I know who were looking to switch gigs all had multiple offers within a few weeks. One thing they all had in common was taking a very targeted approach with their search and leveraging their networks. Not spamming thousands of resumes into the ether.
I'm looking for jobs after my PhD and I notice the same things. I only get interviews and advance in processes when I target the right jobs and companies. Sadly, not too much network to leverage.
> leveraging their networks
Just finished a search - agree. The resume process is fundamentally broken, but a strong network makes it irrelevant. Lean on connections - there's a ton of opportunity out there.
so you either are well connected or you starve, got it
guess it's death and destitute for introverts
edit: please explain the downvotes, i'm curious why you think i'm wrong
if what op says it's true, that today only networking works, then it easily follows that if for some reasons you do not have a network then you don't get hired
fwiw this is how it's always been. You don't have to be some kind of extreme extrovert, but people who are good at what they do tend to enjoy working with other people who are good at what they do, and when they see someone they recognize as "I'd work with this guy again" they put in the minimal effort to stay in contact - a hello text every few months, a cup of coffee every few years, that type of thing. Thats all it takes. If you're not doing that, yeah, you're in for a world of hurt when you go job hunting.
>a hello text every few months, a cup of coffee every few years
sounds like hell to me
Selecting where to apply and who you contact for it is also very important.
I'm currently involved in the hiring process in our company, selecting engineers for my team. If someone applies who has the programming language we ask for in their CV, they get a first interview. If they can read code, and write VERY basic code, they will get through at least the first 2 rounds without any issues.
If people put down the AI, and actually learn how to write a `for` loop, they would be more hire-able than 50% of candidates.
> "Guess it's death [...] for introverts"
There is a meritocracy somewhere in our capitalist system. Not everyone participates, but it exists.
OP was saying the only way to get hired is through work connections
of course if the process doesn't involve networking then we don't have a problem, we agree on that
> OP was saying the only way to get hired is through work connections
That is not at all what I said. Please do not misrepresent.
I said they took a targeted approach *and* exercised their networks. Those are two separate things.
> One thing they all had in common was taking a very targeted approach with their search and leveraging their networks
Right, so they applied to a couple of jobs and it worked for them?
I'm sorry, do you understand how uncommon and rare that is? sure, if their domain was REALLY niche and the jobs weren't publicly advertised, then i could see how that would work. but the experience is VASTLY different outside such niche cases
They applied to a couple of jobs where they were certain the fit would be good, and didn't mindlessly spam their resume to some bot. They got in touch with the right people, and worked it out from there. Because they had done their homework, the path was easier for them.
yes, that's exactly what you said before. you're not engaging with what i'm saying at all.
Seems like that is the case, yes.
Bro I'm an introvert and I have work connections. It isn't hard.
> I have work connections.
> It isn't hard.
you're not an introvert then
Anyone who has been in the industry for several years or more should have people they can reach out to. That’s not being well connected.
This is really just a reversion to how things used to work, relying on human connections. People seemed to manage to get jobs 30 years ago just fine
What if you weren't in the industry for several years or more? New grads are completely walled off from the industry because very few employers are willing to give them a shot anymore, even at minimum wage and no other demands from this most desperate worker segment. The few connections I scraped together weren't helpful because barely anyone is hiring juniors anymore. Having one of the resumes on top of someone's pile is only helpful if they ever get to using it. If you're at the lower end of the market, you have to be very exceptional or very lucky. Otherwise, you're done for.
There is infinite amount of software to be made. Desires and wants never get satisfied. There will simply be more software, more features, more supported platforms, more bug checks, more tests, more CI/CD, more docs, more websites, more services, more more more. Once we solve something, we have a million new desires that we want to solve. There will be plenty of work in software, up until the time when really all knowledge work can be replaced. At that point all bets are off.
Do you think all software demands are completely elastic? I don't share your confidence, it feels like it's applying the desired conclusion to the premise. For sure, newer technology prompts new solutions and new tools to use with it, but some industries are much less flexible than others. If your business serves a certain number of clients - for example, companies that work in a specific industry that's growth-constrained - then making software twice as easy to make isn't going to double the number of their clients. That business you work for will either have to look at expanding into a different market, or, more realistically, lay off all the unneeded workers. There's a lot of businesses that work like that. What are they going to grow into?
Competing software companies will offer more and more advanced software that handles more use cases, faster, better, with more features, with more compatibility, with more flexibility, with more integrations, with more ease of use, with more etc etc. They will propose software solutions for things that are currently not economical to provide. And other competing software companies will have to match that or do even more.
I'm also being on Jevon's paradox in the case of software.
https://en.wikipedia.org/wiki/Jevons_paradox
> A flat tire is a flat tire, no matter whether a recession is going on or whether LLMs are causing chaos in white collar work.
There’s really not much stopping changing tires from being automated away. Further standardization of tires or wage increases would probably do the trick.
There’s still plenty of software to be created. You’ll probably have to learn some ML tricks or whatever, but there’s nothing going away, just changing as software has always done.
There’s really not much stopping changing tires from being automated away.
Sounds like you've never changed a tire. Or at least not outside of a very controlled environment.
I wouldn't be so sure. I have been impressed by several things that seem to be complex but a way to be automated was found. Sure a no controlled environment is not conducive to automation, but who said a tow truck wouldn't be a part of the process? Washing a car has been automated with the precursor that the car is brought to the controlled environment first.
I have even replaced car tires before and yet still have this opinion.
https://www.pitproauto.com/ is working on that!
Are most cars not just undoing the lock, the bolts, switching the tire and redoing the bolts and locks?
How are these put on in the first place on an assembly line?
This is the funniest possible answer to "Sounds like you've never changed a tire. Or at least not outside of a very controlled environment."
"Oh, you think I've never changed a tire? Well here is my abstract high level understanding of the steps to changing a tire! And have you considered the quintessential controlled environment for putting tires onto cars?"
We all change tires with a jack and a spare? It's like a simple skill? I don't get the point your making then?
The tire is the black rubber that's mounted to the wheel. Tire shops don't give you a new wheel when you get a flat tire.
The "edge cases" make a simple task like this more difficult. What if the nuts are stripped? What if the terrain under/around the car is uneven or not solid ground? What if it's raining or snowing or hailing? What if the driver of the car is irrationally upset and kicks your tire-changing robot over? What if a tire change was requested, but it's clear (to a human) that there is more work that needs to be done?
Every new successful tool doesn't start by trying to meet every need or edge case. They perfect the main case, and then edge cases in priority of likelihood.
Car washes are automated even though they haven't answered the edge cases of how to wash your car when your car is rolled on its side or a terrorist is actively blowing up the equipment. They simply only operate when your car is right side up (and other conditions, like in neutral, wipers off, and a driver who is willing to not exit the vehicle) and when there aren't active bombings on the building. And other "edge" cases.
Just because there is a possibility for something to not work, doesn't make it useless. Automated tire replacements could start with very rigid cases where they are applicable, and expact the scope slowly to allow more cases, like a bent wheel or poor weather.
Then you see a mechanic for the 5% of cases where it's weird? If you think AI is replacing 100% of software engineers anytime soon, idk what to tell you.
I cannot but agree. It's a massive skill leveling where software development is transforming from high skilled coding to low skilled prompting.
For an old dog like myself it feels an unjust rug pull.
I don't think it's a psychologically positive self identification to see yourself merely as a gatekeeper and toll extractor rent seeker who only makes a living by withholding agency and skill from others.
I know many jobs are about giving partial access to secrets or insider knowledge etc but I simply can't see myself accepting that this is my value proposition.
No, let the pie grow. Let more people be able to do more things. Use the new capabilities to do even more. See how you can provide genuine value in the new environment. I know it isn't easy. There are many unknowns. But at least aspirationally I see that as the only positive way forward.
The same thing has happened to many jobs. 100 years ago being a photographer was a difficult skill. They must have felt a rug pull when compact cameras became mainstream and they were no longer called to take all family pictures. Surely the codex writers felt a rug pull when printing became widespread. Typesetters when people could use word processors on their PC with font settings. Prop designers and practical effects people when movies switched to vfx. Etc etc.
> I don't think it's a psychologically positive self identification to see yourself merely as a gatekeeper and toll extractor rent seeker who only makes a living by withholding agency and skill from others.
That's an incredibly uncharitable reading of the parent comment. At no point in history prior to maybe this year could you argue that working in software was gatekeeping, toll extracting, or rent seeking. Being a highly skilled craftsperson creating software for those who can't or don't want to is a very psychologically positive self identification. Lamenting that the industry is moving away from highly skilled craftspeople is also perfectly valid, even if you believe that it is somehow good for society, which is yet to become clear.
They complained about the skill leveling where now lower skilled people can also do what needed higher skill before. You toiled to learn the craft, now there is a fast track to those results. That's what the rug pull is.
Yes, producing software was value. (It of course still is as of today, we are talking about what may be coming). My plead is to continue searching for ways to contribute value. Don't resign to a feeling that the only way to hold on is if you try to stop others from knowing about or being able to use the skill leveling tech. This makes one bitter and negative. Embrace it, aspire to be happy about it.
Its like getting scooped in science. In research, I always try to reframe it to be happy that science has progressed. Let me try to learn from it and pivot my research to some area where I can contribute something. Sulking about having been scooped does not lead to positive change and devalues ones own self-image.
The problem is that we don't live in a society where the benefits of new technology benefit all.
We're about to pull the rug underneath all knowledge workers. This will disrupt wage earners lives. This will disrupt the economy.
You might feel great about when things become cheaper but remember that when things are cheap it's only because costs are low and when costs are low the revenues are low and when revenues are low salaries are low too. Keep in mind that one party's cost is other party's revenue.
The economy is ultimately one large circle where the money needs to go around. You might think of yourself a winner as long as someone else's salary drops to zero and you still get to keep your income but eventually it will be you whose income will also be disrupted.
Just something to keep in mind.
And also we're going to just not rug pull on the individual knowledge workers but businesses too. Any software company with a software product will quickly find themselves in a situation where their software is worth zero.
Also this comment about gatekeeping is absolutely stupid. It's like saying trained doctors and medical schools are gatekeeping people from doctoring. It would be so much better if anyone could just doctor away, maybe with some tool assistance. So much fantastically better and cheaper? Right! Just lay off those expensive doctors and hire doctor-prompters for a fraction of the price.
> We're about to pull the rug underneath all knowledge workers. This will disrupt wage earners lives. This will disrupt the economy.
to tie back to the actually article, if you believe a rug pull is imminent then you got to get off the rug. Idk, you have to make a decision because we're certainly at a fork in the road. There's no guarantee waiting will result in a better outcome nor one saying it will be a worse outcome. There's going to be winners and losers always and lot of it is really just luck in timing. I guess, in reality, the careers we've built come down to a flip of a coin; stay on the rug, get off the rug.
/i'm thinking of buying a welding truck and getting in to that, then hire a welder and rinse repeat until i have a welding business. There's plenty of pipe fence in my neck of the woods and i see "welder wanted" all over the place so there's opportuntiy too.
Good luck to you and your welding business. Personally I'm getting to a point where I'm just "too old" (and grumpy) to start over, so I guess for me it's going to be a retirement to some LOCO that I can afford.
You can make a living, if: you have a way to modify your behavior in a way such that it compels another human being to reciprocate and modify their behavior in a way that you find beneficial for your life. All of money and economics in the end boils down to this. If you no longer have any kind of behavior that your neighbors and community see as valuable enough to modify their behavior to benefit you and keep you around, then we will be in trouble.
> We're about to pull the rug underneath all knowledge workers. This will disrupt wage earners lives. This will disrupt the economy.
It will put and end to the middle class entirely, but that’s the intent.
The reality is a lot of people who were formerly middle or upper middle class, and even some lower class populations will face steep, irreversible “status adjustment”.
I’m not talking about “we used to be able to take vacations and now we can’t”. I’m talking about “we used to be highly paid professionals now we’re viciously competing for low paid day labor (gig work) to hopefully be able to afford the cheap cuts this week”.
I'm too old to be competing for day labour jobs, but not old enough or rich enough to retire
So I'm extremely bitter about this potential direction
I doubt software development will stay as "low skilled prompting", or that it is even low skilled prompting right now. Productive LLM usage goes beyond typing in better prompts and involves things like improving guardrails (eg type definitions and tests), context (docs and "skills" and MCP servers), and management strategy (instructing specialized agents together). It seems natural that there will be high skill AI coding to differentiate engineers, at least until superintelligent AGI emerges and kills us all.
Oh I cannot agree with this at all.
If you think any programming task at hand one must have at least some reasonable grasp of formalism, boolean logic, predicate logic, then understanding the software developing concepts, your APIs frameworks, language constructs etc and finally the domain knowledge.Most of this goes away when changing from coding to prompting.
I was just doing some computer graphics work myself doing Signed Distance Fields and Claude just literally regurgitated code that I could just adopt (since it works) without understanding any of the math involved.
I'd say that prompting is at least two orders of magnitude easier than coding.
I think looking at what the web did to the journalism industry as a model to what's happening to the software dev industry is worth while. Journalism didn't go away but it did completely change. Many old school journalists just couldn't adapt and left the industry, many papers died too.
Many things only have value because of scarcity.
Digital products such as "photoshop" have had value because people need a tool like that and there's only a limited number of competition, i.e. scarcity. The scarcity exists because of the cost. I.e. the cost of creating "photoshop"creates limit for how many "photoshops" exist. When you bring down the cost you'll have more "photoshops" when you have more "photoshops" as the volume increases the value decreases. Imagine if you can just tell claude "write me photoshop", go take a dump and come back 30 mins later to a running photoshop. You wouldn't now pay 200USD for a license, now would you? You'd pay 0USD.
If you now create a tool that can (or promises it) can obliterate the costs, it means essentially anyone can produce "photoshop". And when anyone can do it it will be done over and over and at which point they're worth zero and you can't give them away.
The same thing has happened to media publishing, print media -> web, computer games etc.
Then the problem is that when your product is worth zero you can no longer make a business by creating your product, so in order to survive you must look into alternative revenue streams such as ads, data mining etc. None of which are a benefit to to the product itself.
And what is left of journalism of low effort high volume clickbait garbage
Boy I can't wait for the equivalent of low effort high volume clickbait to take over software. Yay!
Why unjust? Who promised you that the way software is made will stay static?
Our software industry has specialized, for decades, in "rug pulling" / changing / "disrupting" other industries on a massive scale.
I find it pretty ironic when engineers make these statements in that context.
Whether you like it or not but the society is built on certain social constructs and agreements.
Do you think it's fair that when the society moves underneath, the capitalistic system moves its tectonic plates it's the individual who has to bear the cost of that?
Abd let's be clear only software devs are just sucking it up. You think lawyers and doctors would allow themselves to be laid off en masse and be replaced with trainees who just prompt the computer?
Also what will happen when high wage earners start loosing their discretionary income. The whole service sector for starters will be shaken.
Just imagine some big tech company laying off 10k engineers. Making 0.3m per year. That's 3b dollars that disappear from the incomes and thus from the economy and just stays in the pockets of the capital holders.
i made this point a few comments up but i think what's happening to the software dev industry is what happened to the journalism industry when the web really came into its own and everyone was now a journalist. There were even books written by tech people about how great "creative destruction" is heh now the shoe is on the other foot. How many "old dinosaurs" did web development and software dev in general put out of business? My neck is on the line too but even i have to chuckle at that a little bit.
yep you're absolutely right. the value in journalism and journalistic output was based on the scarcity, i.e. the cost of publishing reduced the amount of available content. With web the costs were obliterated so the content exploded and the value of any individual piece dropped to essentially zero. When it's worth zero your revenues are zero and zero revenues you can't really pay for any journalism.
So then you have no choice but to seek alternative revenue streams (ads, data mining) and in fact this becomes the thing, since the original thing no longer produces a revenue.
I'm not seeing you calling it unjust that journalism has been disrupted. Why do you think software development is different?
LLMs have lowered the bar for the unskilled person to create shit software. I have used Opus 4.6 on a number of projects, and it still spits out buggy, and sometimes, flat out broken code. I was actually surprised when it completely hallucinated the names of query params for an HTTP request in my code, when in the prompt I had explicitly given it the exact names it needed to use. I thought these frontier models were supposed to be game changing.
The query parameter issue is a pattern I see a lot. The model has thousands of examples of "how HTTP requests usually look" from its training process. When your input data conflicts with the pattern, the training data takes precedence.
Interestingly, the model doesn't "know" that it's ignoring you. From its perspective, it has retrieved a "meaningful" pattern—virtual parameter names that probably fit common conventions it saw during training. Your actual request simply... wasn't documented.
> LLMs have lowered the bar for the unskilled person to create shit software.
So? Demand the source code. Run your own AI to review the quality of the code base. The contracting company doesn't want to do it? Fine, find one that will.
Add another layer of jank to review the original jank? That doesn't sound like a very helpful solution. But the companies selling AI will love it!
I've worked in tech since the late 90's and recently became an apprentice potter. My work in pottery is so much more fulfilling than any tech work I've done. I wish I had started sooner.
I'm still working in tech, and likely will forever in a much reduced capacity. But pottery is my life now.
I'm maybe less pessimistic, but I expect in the next decade or two, I'm going to spend a lot of time fixing apps that people who were moderately technical vibe-coded years before. They seemed to work well enough until they didn't, and at some point, they became central to the business.
MS Access and so many more "you won't need a programmer again" dev tools over the decades blazed the trail.
Green is always greener on the other side. You can always work on that, get an apprenticeship and work on it. Why don't you do it? Because you know not everything's so rosy. These are hard jobs, not super well paid.
But well, I feel like you too.
> A flat tire is a flat tire, no matter whether a recession is going on or whether LLMs are causing chaos in white collar work. If I had no debt and a little bit saved up I might just content myself with a humble moat like that.
Bad idea. Automotive repair is barely a moat, because you don't need that much training to work those jobs. There's a lot of people who want to do it. And cars are definitely susceptible to recessions - if fewer people are buying cars, if there's a shift to transit, if your locality builds more pedestrian-friendly infrastructure, if businesses that use work vehicles are forced to close, then your demand drops and everyone already in the field is forced to compete with one another.
For moats, look for things that are complex (not everyone can do it), licensed and always needed.
Electricians are on a rip right now with all the data center build outs and will likely always be in demand.
>Automotive repair is barely a moat, because you don't need that much training to work those jobs.
You need to study for 3 years to be a car mechanic. And even then you'll need baby sitting for a while in the auto-shop because no one trusts the new guy fresh out of school, with good reason.
>There's a lot of people who want to do it.
No there isn't. What do you mean by lot of people? Automotive repair is a blue collar job with intense physical strain, you're exposed to chemicals you shouldn't be, there will be hearing loss involved no matter how much protection you have and it doesn't even pay all that well, considering the risks involved and the amount of training you need. And no, it's not because the market is saturated with car mechanics, it's because auto repair shops have a lot of pressure to be cheap. Job listings are full of car mechanic openings, you'll never be unemployed.
All the "if's" presented are solved by relocation and not even that much of it, except for this:
>if fewer people are buying cars
Then the skills are easily transferable to other vehicles. But less people are buying cars already, they use uber, which involves a car. A car that needs 10x more repair time than the car you drive daily.
So yeah, auto repair is a good moat. It's complex, not everyone can do it, it's not licensed in most cases (unless you work for a brand or a niche) but there's reputation involved and it's always needed. It just doesn't pay all that well, specially not compared to what I see on HN's monthly whoshiring.
You must be joking that being a car mechanic is anything like it was 20 years ago.
The vast majority of tires that need to be repaired or replaced (and the processes to do so) haven't changed much if at all, though. And there are entire franchises that pretty much only do tires. Same with many other manual labor tasks.
These are predictable jobs with very few variables that there is still no sign of automation replacing any time soon. They often don't suck as bad as people think. One of the most enjoyable jobs I had was on an assembly line, because my mind was mostly free to wander. It was almost like meditation.
Hm, why don't you just go do it then? Seems strange to spread this knowledge and create competition for yourself.
Theres a reason most people want a white collar job and send their kids to college instead of to such manual jobs.
> most people want a white collar job and send their kids to college
Part of the reason for my prior comment is the clear fact that a not-insignificant percentage of white collar jobs are being massively devalued at the moment, which means many people who thought they'd be able to send their kids to college with income from such jobs won't.
Considering that the field of robotics is so far behind LLMs in terms of clear value outside of niche industrial applications, I think manual labor is about due for a resurgence. There may be some major rebalancing happening. The big question for laborers will be - as it has always been - what can I do that sucks the least but also allows me to pay for a decent life? Answers will vary.
I'm not sure how long this state of robotics will last. Dexterity is improving very fast. Robots are getting cheaper and cheaper.
But also, a lot of the manual labor is quite expensive and only affordable as long as there are white collar workers who can pay for fancy bathroom remodelings and landscaping and so on. I don't know how a big deluge of reskilled pipefitters and HVAC technicians will be able to find work. Will everyone just pay each other to do a bunch of handy work for each other?
This is the point missed by many. The trades are in high demand, right now, because of a labor shortage and demand from upper-middle class individuals without any DIY skills. A generation or two of pushing kids into college, and an almost disparaging view of 'getting your hands dirty' has built this perfect storm.
However, besides a few trades that use unions/licensure/apprenticeship as an artificial supply limit, most trades are only limited by a willingness to do the work. A few decades ago, trade work was much less expensive, because supply was higher and many did their own DIY, which limited what prices the market would tolerate.
Money.
I think the bigger issue is not that LLMs are taking away developer jobs, but the current geopolitical crisis (the collapse of the US empire and the end of the neoliberal era) is leading towards an imminent economic catastrophe, and that would be enough to pop not only the AI bubble, but an even bigger "IT bubble" that has been proliferating since the 90s.
Programmers (and other white collar jobs) were able to luxuriously coast along the ZIRP era because capital (replenished twice via quantitative easing) was cheap and plentiful, and because the elites at the top had to pump huge amounts money to create a shared fantasy of the "technological future" that validates the neoliberal era. Now that the reality of the actual "physical economy" (the economy of making tangible things) has clawed back at us because of that forbidden three-letter word (war), we all realize that doubling and tripling oil prices were actually dictating our lives rather than some "Skynet AI" crap, and thus our fantasy simulacra of "virtual" play-things have now come to an end. Oh and we all found out that most of SaaS was actually bullshit anyway. In fact, if it could be completely replaced by AI then it was already pretty bullshit in the first place.
So, for smart STEM people uninterested in programming and only looking for a stable career, I think they would be better off by just doing engineering work that's a bit more tangible, like robotics, manufacturing, shipbuilding, construction, etc. (Or anything related to war, but only if you're able to stomach what you're doing.) If you don't like to sit all day for a salary, then niche blue collar work can also be a good option, since general-purpose robotics (Physical AI?) is still too far away because of many, many issues that's just too long to explain here. I still think if you like programming then you should stick to it in the long run - there will be a very cold winter because of the combination of LLMs, AI bubble pop, and general economic depression, but for those who survive this era there will be an opportunity because of the shortage of skilled programmers (since no-one bothered to hire juniors after the pop, no one will grow to become seniors themselves!) Computing will still be with us forever, just not in a way that investors thought that it's going to "engulf the world".
I would say if all of your doomastrophizing comes to light (the myriad of collapses and depressions and winters in your post), then there is no opportunity for anyone anywhere, and we should all stock up on bullets and cigarettes while we can.
But something tells me you won't do that.
Yup, absolutely! People before us already lived through the Great Depression, people already lived through two World Wars, and despite all that there were still some technological advancements made and some good opportunities amongst the chaos! There's obviously going to be pains from transitioning into a new world order, but humanity will still keep on living. Just don't expect the next world to be the same as before!
I think it's important to know and practice your passion, even if you have to work on something different to pay the bills. You can only be good at something if you really like it, and you never know what opportunity you'll stumble onto if you're ready for it.
I actually think the opposite approach might be the most optimal one, at least from a monetary perspective. That is, be on the cutting-edge of something, but be willing to bail out at the moment its future starts seeming questionable. Or even more specifically, maximize your foothold in it while minimizing your downside.
Bitcoin is a good example: if you bought it 15 years ago and held it, you're probably quite wealthy by now. Even if you sold it 5 years ago, you would have made a ton of money. But if you quit your job and started a cryptocurrency company circa 2020, because you thought crypto would eat the entire economic system, you probably wasted a lot of time and opportunities. Too much invested, too much risked.
AI is another one. If you were using AI to create content in the months/years before it really blew up, you had a competitive advantage, and it might have really grown your business/website/etc. But if you're now starting an AI company that helps people generate content about something, you're a bit late. The cat is out of the bag, and people know what AI-speak is. The early-adopter advantage isn't there anymore.
How do you know what not to invest in then? Bitcoin wasn't the only cryptocurrency fifteen years ago. It wasn't even the first digital currency. If you hop on every bandwagon, you'll go broke far more then you become wealthy. Just look at all the people who poured money into NFTs or digital real estate in the metaverse or Dogecoin or whatever.
It's easy to say "well of course I would have invested in Google in 1999" but there was nothing in 1999 to say that Google was going to be as big as it was. Why not Lycos or Dogpile or AskJeeves?
How many people dedicated their careers to Flash, only to have it die at the hands of Steve Jobs and HTML5? It's not just about bailing out: lots of folks had to start over because taking advantage of the opportunity means actually investing real time and money. "As a tulip bulb producer, I would have simply stopped producing tulip bulbs when it started to seem questionable." https://en.wikipedia.org/wiki/Tulip_mania
I don't think you're understanding my point. Hopping on the bandwagon is explicitly not "maximizing your foothold while minimizing your downside," nor is it early-stage almost by definition. An early stage technology with an uncertain future cannot have a bandwagon.
I think the logical thing to do is to invest a minor amount of time/money across a broad spectrum of new promising tech. If you had been aware of and bought $500 of Bitcoin in 2010, you'd be a billionaire today. The early people involved with NFTs also did very well.
The Flash example is specifically the opposite of my point. Flash was a lucrative skill for a period of time, but at a certain point it became very clear that it didn't have a future.
What is the broad spectrum of new promising tech today? How much time and money should you invest in each one?
This question is easy to answer in hindsight but it's not trivial to answer in the moment. I like your mindset though
If applying that advice were as easy as it sounds, there would be many more billionaires in the world.
If only Mt Gox didn't vaporize all of my bitcoin in the early 2010's :(
I didn’t say it was easy, I was just thinking theoretically, as a strategy that was the opposite of the OP.
Ah yeah, the beauty of hindsight knowledge.
> That is, be on the cutting-edge of something, but be willing to bail out at the moment its future starts seeming questionable
Counterpoint, I sold all my Bitcoin in 2011 when Mt Gox got hacked and the price plummeted 80%. Would have done it again after their 2014 hack too if I had any left.
> Bitcoin is a good example: if you bought it 15 years ago and held it, you're probably quite wealthy by now
But you just said bail the moment it's future starts to be questionable. If you follow that you would have never held it for 15 years.
But you can't be on the cutting-edge of everything, and there are opportunity costs.
> be on the cutting-edge of something, but be willing to bail out at the moment its future starts seeming questionable
The problem is this leaves you undifferentiated from every hype chaser in Silicon Valley. Our world is littered with folks who went to coding school, traded Bitcoin, did something in the metaverse and blogged about AI. That jack-of-all-trades knowledge can be useful. But only if you’re making unlikely connections. Having the same cutting-edge familiarity as every tech journalist doesn’t that make.
Better: develop deep knowledge and expertise in something. Anything. Not only does this give you some ability to recognize what expertise looks like from afar, it also lets you dip into new topics and have a chance at seeing something everyone else hasn’t already. That, in turn, gives you the ability to be a meaningful first mover.
> Having the same cutting-edge familiarity as every tech journalist doesn’t that make.
You are giving too much credit to tech journalists. How many of them truly understand Bitcoin or AI?
Maybe - but that's also a style of life definition, isn't it. I did not care at all about Bitcoin, and I'm still pretty fine. I even think, sitting Bitcoin out was better for me, because who (vrosdly speaking) cares about it today (besides speculating with it)?
If you bought into NFTs when they were hot you would have lost money. Not every new tech is worth investing immediately.
When they were hot is not when they were new.
Except that strategy gets you killed through a thousand paper cuts.
What would have you done when the Bitcoin fork happened 50/50? Would you have gone int ICOs? Which ones? Etc…
There’s simply too many “new things”, so by trying to get exposure to them you’ll be massively in the red.
Let’s say you get into 1000 “new things”, and you strike it lucky and hit BTC. You’d had to buy BTC in early 2013, hold it over the whole period and sold at the historical maximum for you to be at break even.
If instead of buying 1000 “new things”, you’ve put your money into the S&P you’d be at +250% by the same time.
Yeah it looks reasonable when you only pick success stories for your examples.
If you sold the farm to get in early in the Metaverse, you're totally hosed now because that was a dead end. The idea of digital real estate was as terrible then as it is now.
I specifically wrote this:
> Or even more specifically, maximize your foothold in it while minimizing your downside.
A lot of people feel this way.
But IMO the most fruitful thing for an engineering org to do RIGHT NOW is learn the tools well enough to see where they can be best applied.
Claude Code and its ilk can turn "maybe one day" internal projects into live features after a single hour of work. You really, honestly, and truly are missing out if you're not looking for valuable things like that!
You're only missing out if that's what you want to do. Not every software developer is interested in creating new software projects from scratch in an hour, or at all. It's totally find to do software development as a job, and then close your laptop and not see it until Monday. Learn the tools that suit when when you need them.
> You're only missing out if that's what you want to do.
Who writes software and doesn't have a list of "I'll fix this one day" issues as long as their arm?
This is honestly one of the things I enjoy most at the moment. There's whole classes of issues where I know the fix is probably pretty simple but I wouldn't have had time to sort it previously. Now I can just point claude at it and have a PR 5mins later. It's really nice when you can tell users "just deployed a fix for your thing" rather than "I've made a ticket for your request" your issue is on the never-ending backlog pile and might get fixed in 5 years time if you're lucky.
Claude code makes it so easy to do things the "right way" that it also makes it really easy for you to let scope creep get out of hand. I have a personal project that I haven't deployed yet that in some ways is way overengineered for its purpose. It's hard to blame the tool though, it's always telling me I'm making it more complicated than it needs to be but I don't listen
I've felt this recently. I've often been bad about scope creep. CC makes it so easy.
On the other hand, I can see these tools getting good enough that scope creep doesn't even matter.
ATM I usually get stuck around the review/verification stage. As in, my code works, I have tested that it works, but it is failing CI or someone left a PR comment. And for each comment I'll have to make sure it makes sense, make the change, test again, and get CI passing again.
> Claude Code and its ilk can turn "maybe one day" internal projects into live features after a single hour of work. You really, honestly, and truly are missing out if you're not looking for valuable things like that!
You're right, it's possible. But you might be both overestimating the ease of onboarding and underestimating the variety of tasks and constraints devs are responsible for.
I've seen Claude knock out trivial stuff with a sufficiently good spec. But I've also seen it utterly choke on a bad spec or a hard task. I think these outcomes are pretty broadly established. So is the expectation that the tech will get better. Waiting isn't unwise.
Waiting may not be “unwise” but acting now may be optimal. Even though tooling may be much better in 12 months, if it can improve quality or time now, that’s a net benefit.
Bikers in the Tour de France used to not wear helmets. They were seen as uncouth (“why jump on the bandwagon?”). Helmets today are way better than they were then. But if the utility provided is greater than the cost, of course it makes sense to act sooner.
I’m not explicitly arguing for investing in AI or other newfangled tech, I’m arguing that the premise of waiting may be “sounded” but also “leaves money on the table”, or in some cases, lives.
The author talks about vaccines as a counter example but doesn’t really address the cost/benefit in any detail.
If you want to see the potential value of Claude, pick a somewhat isolated function in your code and tell Claude "write unit tests for this". Watch it write them, compile your code, validate the test, fix the ones it did something dumb on, and iterate. Laugh as you get 10 awesome tests and 2 really dumb ones you have to go in and clean up by hand for 5 minutes of work. Half a day of iterating later you'll have finished the extremely tedious test writing that would have taken you a day or two without Claude.
When doing a code review, tell Claude "review commit xyz". It finds things humans are not finding in reviews at this point.
Not sure about what you want to do for some design? Use Claude as a rubber duck - tell it "I'm deciding between implementing this with A or B and here's why... is there any thing I'm missing that would make the decision more clear, or any potential solution I've missed?" It has the context of your existing code, which can be incredibly helpful for stuff like this.
Opus 4.6 is good enough that it's undeniable that it's going to change the industry at this point. It's just a question of how big the change will be. The earlier models were shit. Everyone I worked with spurned them, including me. Everyone around me using Opus 4.6 is saying "well, shit, this is real now" with varying degrees of excitement and unease.
AI is not some magic pill. A lot of software development is not writing code, it's requirements gathering and design. That part doesn't magically go away, in fact it becomes way more important. But AI is speeding up the writing code part of things - if, and only if, you put down a good plan first.
People will structure their code where AI can churn on it (specs in markdown, and TDD all over again), it's useful enough to be worth it. We're going to get segmented into engineers who can use it and engineers who can't.
For this technology I'd not wait. Start learning it.
We're all going to be more focused on being architects instead of developers.
Agree with the message, coding since 1986, I have learned to not suffer from FOMO and wait for the dust to settle.
Ironically one might even get projects to fix the mess left behind, as the magpies focus their attention into something else.
In the case of AI, the fallacy is thinking that even if ridding the wave, everyone is allowed to stay around, now that the team can deliver more with less people.
Maybe rushing out to the AI frontline won't bring in the interests that one is hoping for.
EDIT: To make the point even clearer, with SaaS and iPaaS products, serverless, managed clouds, many projects now require a team that is rather small, versus having to develop everything from scratch on-prem. AI based development reduces even further the team size.
I am increasingly feeling okay with the idea of being left out. The worst parts of working professionally in a software development team have been amplified by LLMs. Ridiculously large PRs, strong opinions doubled down due to being LLM-"confirmed", bigger expectations coming from above, exceptionally unwarranted confidence in the change or approach the LLM has come up with.
I am dying inside when I make a comment and receive a response that has clearly been prompted toward my comment and possibly filtered in the voice of the responder if not copied and pasted directly. Particularly when it's wrong. And it often is wrong because the human using them doesn't know how to ask the right questions.
Fortunately, most of the fundamental technological infrastructure is well in place at this point (networking, operating systems, ...). Low skilled engineers vibe coding features for some fundamentally pointless SaaS is OK with me.
Preach
On the otherhand, when Cloud Computing started to come in, I knew a bunch of sysadmins. Some were in the "it'll never take off" camp and no doubt they know it now, kicking and screaming.
But the curious early adopters were the ones best positioned to be leading the charge on "cloud migration" when the business finally pulled the trigger.
Similarly with mobile dev. As a Java dev at the time that Android came along, I didn't keep abreast of it - I can always get into it later. Suddenly the job ads were "Android Dev. Must have 3 years experience".
Sometimes, even just from self-interest, it's easier to get in on the ground floor when the surface area of things to learn is smaller than it is to wait too long before checking something out.
> But the curious early adopters were the ones best positioned to be leading the charge on "cloud migration" when the business finally pulled the trigger.
lol no. There's nothing actually different about managing VMs in EC2 versus managing physical servers in a datacenter. It's all the same skills, and anyone who is competent in one can pick up the other with zero adjustment.
Maybe in a greenfield environment, but this I disagree with your assessment in an enterprise setting. Have you worked with orgs that have on-prem + cloud and are migrating? It is a massive pain and regardless of whether it's easy, people and orgs don't want to do it.
> On the other[ ]hand, when Cloud Computing started to come in, I knew a bunch of sysadmins. Some were in the "it'll never take off" camp and no doubt they know it now, kicking and screaming.
> But the curious early adopters were the ones best positioned to be leading the charge on "cloud migration" when the business finally pulled the trigger.
From a technological perspective, these sysadmins were right: in nearly all cases (exception: you have a low average load, but it is essential that the servers can handle huge spikes in the load), buying cloud services is much more expensive overall than using your own servers.
The reason cloud computing took of is that many managers believed much more in the marketing claims of the cloud providers than in the technological expertise of their sysadmins.
Yeah, most companies still should not be using cloud for stuff. I think a lot of people here who work for startups don't get that, because cloud makes a ton of sense if you're a new business that has a hundred other higher priorities than spending capital on infrastructure and sysadmins. But for most companies? They are better off with on prem.
> Suddenly the job ads were "Android Dev. Must have 3 years experience".
So just read up on it and say you do. They don't really need 3 years experience, so you don't really need to have it.
I'm not sure why you're downvoted, but this is the right take IMO. I hate cheating and lying in general, but in any job posting you have to separate what are the actual requirement in term of knowledge versus what can be realistically learned on the job / doing a prototype in a weekend.
Of course don't fraud by like pretending you're a statistician when you have absolutely no mathematical background, but also don't take at face value the "Must have {x} years of experience in {y} tech" requirement when you know you have the necessary work experience to have a good grasp on it in a few weekend prototypes, and you also know that the job doesn't actually require deep expertise of that particular tech.
I did the same for my first React.js job, and I didn't feel bad because 1) I was honest about it and did not sold myself as a React expert, and 2) I had 10 years of front-end development, and I understood web dev enough to not be baffled by hooks and the difference between shallow copy vs. deep copy of a data structure, so passing technical test was good enough for it.
Employers check your work history. You'd better be able to back up having the amount of experience they require with paid, value-delivering work at past employers, or they'll pass.
I was an engineering manager an for decades and I did first technical phone screening of a very large number of candidates. I couldn't really assess someone's design and programming skills. So I would pick a particular area they were promoting and go as deep as I could on it, both on the technology and their project experience using it. I weeded out lots of candidates, saving my engineers' interview time for more suitable candidates. And my teams were good at giving me feedback when I let someone unqualified e slip through...
Many actually do not check anything.
I've never actually encountered one who didn't. Just like I've never been able to actually quit on Tuesday and walk into my next role on Thursday the way Hackernews told me "any halfway decent developer" should be able to do. They tend to ask about this sort of thing in interviews too and if you prove not to have the required background, you are considered weak and filtered out.
Anyways, checking happens often enough that the risk of being considered a liar and a fraud for claiming experience you don't have is high.
But here's the thing: learning Android dev is nothing like "learning" to use an LLM.
Obviously there are tons of tools and systems building up around LLMs, and I don't intend to minimize that, but at the end of the day, an LLM is more analogous to a tool such as an IDE than a programming language. And I've never seen a job posting that dictated one must have X number of years in Y IDE; if they exist, they're rare, and it's hardly a massive hill to climb.
Sure, there's a continuum with regards to the difficulty of picking up a tool, e.g. learning a new editor is probably easier than learning, say, git. But learning git still has nothing on learning a whole tech stack.
I was very against LLM-assisted programming, but over time my position has softened, and Claude Code has become a regular part of my workflow. I've begun expanding out into the ancilary tools that interact with LLMs, and it's...not at all difficult to pick up. It's nothing like, say, learning iOS development. It's more like learning how to configure Neovim.
In fact, isn't this precisely one of the primary value propositions of LLMs -- that non-technical people can pick up these tools with ease and start doing technical work that they don't understand? If non-technical folks can pick up Claude Code, why would it be even _kind_ of difficult for a developer to?
So, I'm with the post author here: what is there to get left behind _from_?
"must have X number of years in Y IDE"
Not quite on topic but as an engineering manager responsible for IDE development, explaining to recruiters and candidates I wanted engineers who developed IDEs, not just used them. Unfortunately, that message couldn't get through so I saw many resumes claiming, say 5 years of Eclpse experience, but I would later determine they knew nothing of the internals of an IDE.
Presumably, people now claim 3 years of machine learning experience but via ChatGPT prompting.
For me, it's beyond doubt these tools are an essential skill in any SWE's toolkit. By which I mean, knowing their capabilities, how they're valuable and when to use them (and when not to).
As with any other skill, if you can't do something, it can be frustrating to peers. I don't want collegeues wasting time doing things that are automatable.
I'm not suggesting anyone should be cranking out 10k LOC in a week with these tools, but if you haven't yet done things like sent one in an agentic loop to produce a minimal reprex of a bug, or pin down a performance regression by testing code on different branches, then you could potentially be hampering the productivity of the team. These are examples of things where I now have a higher expectation of precision because it's so much easier to do more thorough analysis automatically.
There's always caveats, but I think the point stands that people generally like working with other people who are working as productively as possible.
That's a reasonable strategy. I don't think spreading FOMO is good. But pragmatically, I enjoy working with the latest crop of AI models regarding all sorts of computer tasks, including coding but many other sysadmin stuff and knowledge organization.
I didn't pick them up until last November and I don't think I missed out on much. Earlier models needed tricks and scaffolding that are no longer needed. All those prompting techniques are pretty obsolete. In these 3-4 months I got up to speed very well, I don't think 2 years of additional experience with dumber AI would have given me much.
For now, I see value in figuring out how to work with the current AI. But next year even this experience may be useless. It's like, by the time you figure out the workarounds, the new model doesn't need those workarounds.
Just as in image generation maybe a year ago you needed five loras and controlnet and negative prompts etc to not have weird hands, today you just no longer get weird hands with the best models.
Long term the only skill we will need is to communicate our wants and requirements succinctly and to provide enough informational context. But over time we have to ask why this role will remain robust. Where do these requirements come from, do they simply form in our heads? Or are they deduced from other information, such that the AI can also deduce it from there?
>I didn't use Git when it first came out.
This really hinges on what you mean by "didn't use git".
If you were using bzr or svn, that's one thing.
If you were saving multiple copies of files ("foo.old.didntwork" and the like), then I'd submit that you're making the point for the AI supporters. I consulted with a couple developers at the local university as recently as a couple years ago who were still doing the copy files method and were struggling, when git was right there ready to help.
Git may have an arcane interface, but if you learn it, you can be sure what exactly will happen when you run a Git command. Unlike with LLMs.
Pretty sure he meant he didn't use Git.
I'm still stuck with TFS and SVN in my day jobs but use Git on and off on side projects. I really wish all my clients would just switch to Git.
Git is old and busted, jj is the new hotness.
Was "when it first came out" a confusing limiter, there? I guess it seems like the article was making the point that jumping on git the day it came out could've just meant learning the betamax of repos, and that it was better to wait a bit to see what mature tech looked like, rather than waiting until 202X to realize that saving multiple file versions was suboptimal.
I don't understand how this, at all, makes "the point" for anyone.
Using Git when it came out would have probably meant to use Cogito, which has been dead for such a long time by now.
Or have bet on Mercurial. Which is also close to dead. Or darcs, which has been big in certain environments and now practically extinct.
I don't understand the rush to be "the first". Facebook isn't the first social media, Google isn't the first search engine, iPhone is not the first smart phone, Microsoft is not the first OS, the list goes on.
Clearly there's an advantage for being an early adopter, but the advantage is often overblown, and the cost to get it is often underestimated.
The Programmer's Prayer
Nothing is happening. And if it is, it's just hype.
And if it isn't, it only works on toy problems. And if it doesn't, I'll learn it when it stabilizes.
And if I can't, the gains all go to owners anyway. And if they don't, it's just managers chasing metrics.
And if it isn't, well I'm a real programmer. And if I'm not, then neither are you.
“There are a 16,000 new lives being born every hour. They're all starting with a fairly blank slate. Are you genuinely saying that they'll all be left behind because they didn't learn your technology in utero?”
This is a great framing.
I'm less convinced.
To keep and/or increase my current compensation, I have to be competitive in the software development market.
(Whether I need AI to remain competitive is another matter.)
The 16,000 new babies will be competing in different markets.
Oh, and of those 16,000 babies, many are born in far less fortunate circumstances, they're already far behind their cohort. :/
> keep and/or increase my current compensation, I have to be competitive in the software development market
Author’s point is that competitiveness can come in many forms. Having the same AI proficiency as everyone else isn’t differentiating. (And it isn’t table stakes.)
Isn't that what is implied by the toughest job market yet for junior level candidates? Author is very confident that the answer to his question is "no".
I think the implication is that even though the technological landscape is evolving, it's not as if people born in the 60's couldn't foray into computer science because they arrived too late to study the ENIAC first.
Not really, cause they will have lots of years to catch up to Only whatever is in vogue at the moment while adopting what surrounds them growing up. They won't have to unlearn and let go of their own pride or sunk cost into specializing on the wrong thing.
I think the framing just doesn't help at all.
The mistake is that 1 every N waves of hype are in fact monumental shifts and it makes sense to embrace as soon as possible. Also being early to the right thing can have massive implications in appreciating the shift before the general public, which is upstream from making smart resource allocation (investments, career choices). I have a friend that went to super early OpenAI as a designer. He has more equity than most AI researchers there and made a 0.001% amount of wealth. Being early does very much matter in the right conditions.
> If this tech is as amazing as you say it is, I'll be able to pick it up and become productive on a timescale of my choosing not yours.
In contrast to the current top comment [1], I don't think this is a wise assessment. I'm already seeing companies in my network stall hiring, and in fact start firing. I think if you're not trying to take advantage of this technology today then there may not be a place for you tomorrow.
I find it hard to empathise with people who can't get value out of AI. It feels like they must be in a completely different bubble to me. I trust their experience, but in my own experience, it has made things possible in a matter of hours that I would never have even bothered to try.
Besides the individual contributor angle, where AI can make you code at Nx the rate of before (where N is say... between 0.5 and 10), I think the ownership class are really starting to see it differently from ICs. I initially thought: "wow, this tool makes me twice as productive, that's great". But that extra value doesn't accrue to individuals, it accrues to business owners. And the business owners I'm observing are thinking: "wow, this tool is a new paradigm making many people twice as productive. How far can we push this?"
The business owners I know who have been successful historically are seeing a 2x improvement and are completely unsatisfied. It's shattered their perspective on what is possible, and they're rebuilding their understanding of business from first principles with the new information. I think this is what the people who emerge as winners tomorrow are doing today. The game has changed.
Speaking as an IC who is both more productive than last year, but simultaneously more worried.
[1] https://news.ycombinator.com/item?id=47454614
> I find it hard to empathise with people who can't get value out of AI. It feels like they must be in a completely different bubble to me.
I think it depends on why you do programming. I like programming for its own sake. I enjoy understanding a complex system, figuring out how to make change to it, how to express that change within the language and existing code structure, how to effectively test it, etc. I actively like doing these things. It's fun and that keeps me motivated.
With AI I just type in an English sentence, wait a few minutes, and it does the thing, and then I stare out the window and think about all the things I could be doing with my life that I enjoy more than what just happened. I find my productivity is way down this year since the AI push at work, because I'm just not motivated to work. This isn't the job I signed up for. It's boring now.
The money's nice, I guess. But the joy is gone. Maybe I should go find more joy in another career, even if it pays less.
Oh, I agree entirely. The new paradigm is entirely unsatisfying to me too. It's not the same work that I trained my entire life to get good at, and the new work is not as fun. I trained to get good at this work because I just loved it since I was first introduced to it at ~10. I would have, and was, doing it for free for years.
Unfortunately that doesn't change my outlook on where all this is headed.
Perhaps, then, you can actually empathize with people who don't get value from it :) I used to enjoy the work, now I don't, so I'm posting on HN and daydreaming about other careers, instead of doing something useful.
Yeah, maybe empathise was the wrong word. I certainly empathise with the feelings, I just struggle to see how people cannot use it to get more done.
I'm also daydreaming about other careers instead of doing something useful.
> I just struggle to see how people cannot use it to get more done.
To be blunt about it, there's a decent chance I'll be quitting this job later this year, largely because of the AI push. I just hate these tools and I do not want to work this way. Losing an employee is a pretty big cost to the company. I guess the AI stuff is probably worth it to them, but there's a downside to it, too.
Yeah I agree with you, and I think a lot of people feel the same. It's totally different now and it's not what I signed up for. Maybe I'll get used to it, idk.
I hope everything works out well for you.
I agree with your take. It was obvious to my VP. It's not obvious to the rest of the enterprise (yet).
But I think it's just a matter of when not if.
My current guess at my slow fortune 500 is ~1-2 years before we see real employment impact.
Startups are happening now at least with my anecdotal conversations. Right now the discussion is more just slower growth than actually doing layoffs. That coin will flip at some point.
> I find it hard to empathise with people who can't get value out of AI
> But that extra value doesn't accrue to individuals, it accrues to business owners.
What is value?
Is a 2X faster lumberjack 2X as valuable? Sure
Is a 2X faster programmer 2X as valuable? At what, fixing bugs? Adding features? That's not how the "ownership class" would define value.
Productivity is a measure of efficiency, not growth. Slashing labor costs while maintaining the status quo is still a big productivity gain.
> Slashing labor costs while maintaining the status quo is still a big productivity gain.
Maybe I didn't express myself properly, but I think we agree, at least on this point?
Besides this effect, of enabling smaller teams to produce the same results, I think there is a larger effect coming where fundamentally different structures produce the same or better results as last year. I just don't think we've completely figured out what that looks like yet.
> between 0.5 and 10
Hopefully not too many people are "enhanced" to the tune of 0.5x!
That was deliberate. Some people report that it slows them down - fine.
In general, a good strategy is just staying a little bit behind. Let the new fads play themselves out. Some have staying power. Bitcoin never did turn into a usable currency, just another speculator's toy. Luckily I am - so far - in a position where I can watch the AI thing from the sidelines to see how it plays out.
I'm healthily skeptical of new technology. Meaning I'm not the early adopter. But I've also found over the years I don't get left behind. I become curious at the time things are stabilising. Maybe on the cusp where there's still a lot of pushback but there's also clear value. Crypto in 2014-2017. AI in 2023-2024. You don't have to feel FOMO but if you're a technologist, if you have a healthy desire to evolve, change and learn then you'll naturally pick things up. I went from total crypto skepticism in 2014 to investing most of what I had. I went from total AI skepticism to doing RAG for the Quran and agentic tech for the small web. I think there's value in staying true to who you are but also naturally discovering and learning on your own timeline.
My experience so far tells me that the default path with AI tooling is that it lets us create without learning. So the author is right in that they can pay for a seat in this revolution whenever they want.
A practitioner with more experience maybe a few percentage points more productive, but the median - grab subscription, get tool, prompt, will be mostly good enough.
I think this is true at the solo developer scale, but I suspect experience will be much more evident when working with a team.
I make my money cleaning up all the stupid fads. The tail end of the curve is profitable.
I’m in senior management and my take is - this is one of the very few times in our industry when the companies are actually both encouraging and investing in L&D at an unprecedented rate. When do you last remember a company being comfortable throwing 10,000$+ a year for you to learn a new skill and experiment at your own pace? This is why I’m encouraging my engineers so much - it’s free training! Take it while it lasts!
I agree with the sentiment of this article.
Sadly, I'm still disagreeing while crypto kiddies are driving past me in lambo's. If its the future of money, yes we'll get there eventually, but like every technology shift, there's a lot of money to be made in the transition, not after. *
* I sold all crypto a few years ago and I'm a happier person :D
Alternatively, there's money to be lost in the transition. The vast majority of "crypto investors" did not walk away from it any richer. Some folks have gotten lucky, but it's just that: their thesis about the future of money was evidently wrong, they just happened to get the timing right. Getting lucky for the wrong reasons is not a good investment strategy.
Meanwhile, the main category of people who have consistently gotten rich off the "crypto revolution" were various scammers and pump-and-dumpers who have since moved on to meme stocks, AI content farming, and so on.
But I wouldn't use crypto as a benchmark because AI has more substance. We can debate if it's going to change the world, but you can build some new types of businesses and services if you have near-perfect natural language comprehension on the cheap.
When you see people making money from something you did not get involved in, just remember: Someone will always be luckier than you, wealthier than you, etc. It doesn't matter. Measure yourself by your own life satisfaction, not by how others are doing.
That is why I agree with the sentiment as well. I use AI a little. Not too much. And I'm as swamped with work as ever because my focus is on legacy stacks, where AI is really not strong.
Others are pointing out that many also lost money, but I think we can say something even stronger, which is that the people that got rich only did so by taking the money of the people who lost money on it. If it's the future of money, you'll be able to buy in at a stable valuation, neither winning nor losing in the transition -- that's kind of a key property of money.
> Sadly, I'm still disagreeing while crypto kiddies are driving past me in lambo's.
I know a few people who got wealthy by being early to crypto. None of them had the correct reasoning at the time: They thought BTC was going to become a common way to pay for things or that “the flippening” was going to see worldwide currency replaced with BTC. They thought they’d be kings in a new economy but instead they’re just moderately wealthy with a large tax bill they’re determined to dodge.
I know far more people who lost money on crypto, though. Some were even briefly crypto-rich but failed to sell before the crash or did things like double down on the altcoin bubble.
The second group had gone quiet about their crypto while the few people in the first group gloat and evangelize (because continued evangelization is necessary to keep their portfolios pumped). This creates an intense survivorship bias where it appears like all the crypto kiddies are wealthy, but a quiet mass of people who played with crypto are most definitely not.
Solid piece, very wise. AI is fun but where I find it useful with code is first writing more thorough comments and then writing the base of your tests.
Writing the actual code that's efficient is iffy at times and you better know the language well or you'll get yourself in trouble. I've watched AI make my code more complex and harder to read. I've seen it put an import in a loop. It's removed the walrus operator because it doesn't seem to understand it. It's used older libraries or built-ins that are no longer supported. It's still fun and does save me some time with certain things but I don't want to vibe code much because it removes the joy out of what you're doing.
At some point you commit the time to learn what you need to. I like to think of the analogy to SEO. The veterans in the industry are not who they are because they were at the front of the line. It’s because they have the 15 years of experience under their belt.
> I wrote my MSc on The Metaverse. Learning to built VR stuff was fun, but a complete waste of time. There was precisely zero utility in having gotten in early.
Wonderful life lesson on hype cycles. I am curious if hype literacy will join media literacy in academia.
One hidden premise of this is "AI tools are not useful now, even if they might be in the future." For example:
> Few are useful to me as they are now.
Except current AI tools are extremely useful and I think you're missing something if you don't see that. This is one of the main differences between LLMs and cryptocurrency; cryptocurrencies were the "next big thing", always promising more utility down the road. Whereas LLMs are already extremely useful; I'm using them to prototype software faster, Terrance Tao is using them to formalize proofs faster, my mom's using them to do administrative work faster.
Cool. But every time I use them I get a wrong answer faster.
I know, I know. I'm prompting it wrong. I'm using the wrong model. I need to pull the slot-machine arm just one more time.
I know I'm not as clever as Terrance Tao - so I'll wait until the machines are useful to someone like me.
Not only is he not being left behind, he might even have the liberty to abandon ship.
So many humans are uploading their code into Github for the AIs to train on, and thus digging their own graves whereby they'll eventually be outsmarted by AI.
On the other side of the coin, don't the AIs have to be weary about training on prior output in order to maintain the purity of their sources? The dogfooding for them will end up becoming overwhelming.
Perhaps innovation should stay private or used to extract micropayments.
Managing themselves well, hardware tinkerers have a solid future, because robots aren't as effective yet in the real-world. I think that is the next golden age, actually.
> For every HTML 2.0 you might have tried, you were just as likely to have got stuck in the dead-end of Flash.
i'll just say, and i understand this is not the point of the article at all, but for all its faults, if you got in on flash as earl as html 2.0 and you were staring at an upcoming dead-end of flash in say, 2009, you also knew or had been exposed at that time to plenty of javascript, e4x and what were essentially entirely clientside SPAs, providing you a sort of bizarro view of the future of react in a couple of years. honestly, not a bad offramp even if flash itself didn't make it.
>There are a 16,000 new lives being born every hour. They're all starting with a fairly blank slate.
No, they are not.
Crypto was interesting to think through and it was clear very early on how many flaws it has. It basically just moved the goal post to level deeper and it was quite an eye opener how few people even understood the major flaw of crypto: You can only do crypto savely with anything on blockchain and it has not solved any real issue off blockchain (which means you can literlay just send crypto to each other and thats it).
But AI is a beast.
Its A LOT to learn. RAG, LLMs, Architecture, tooling, ecosystem, frameworks, approaches, terms etc. and this will not go away.
Its clear today already and it was clear with GPT-3 that this is the next thing and in comparison to other 'next things' its the next thing in the perfect environment: The internet allows for fast communication and we never have been as fast and flexible and global scaled manufactoring than today.
Which means whatever the internet killed and changed, will happen / is happening a lot faster with ai.
And tbh. if someone gets fired in the AI future, it will always be the person who knows less about AI and knows less about how to leverage than the other person.
For me personally, i just enjoy the whole new frontier of approaches, technologies and progress.
But i would recommend EVERYONE to regularly spend time with this technology. Play around regularly. You don't need to use it but you will not gain any gut knowledge of models vs. models and it will be A LOT to learn when it crosses the the line for whatever you do.
That hasn’t been my observation and seems unlikely to be true. I suspect AI is a wicked learning environment. Narrow experience may hinder rather than help
Plenty of frameworks and tools still exist.
AI/LLMs are not getting stupid in something they are already good in (or very seldom if).
So i see it more as an evolving thing not a wicked thing.
Newest claude release made context better, for this information I already need to know claude and know what a context is and what the problem with context length is.
I'm following AI for a few years now and def not have the feeling at all its wicked.
Many companies usually want to compare themselves to Apple and at the same time say they are disruptors and innovators but Apple is probably the best company at being okey with being left behind. Many think about them as experts in products but for me they always been best att copy what others are doing and refine it, maybe not neccassary better technical but always seen the market fit better then others. Like poker, the later you need to take your decision the more information you have.
I heard from a senior leader at Amazon that "Today, I am choosing how I fail". This has echoed in my head for many years.
At any moment, you are failing at thousands of things that you may not even know about, and that is the gist of what I took away from it. The thing is that you have to be OK when you intentionally choose to not invest in something as regret is ultimately a poison.
The other thing is this: you are not obligated to bring people with you and you have a choice of free association.
I've noticed 2 very obvious trends. The high-performers who previously wrote the most PRs are now using AI the most aggressively and the number PRs they produce has almost doubled. The other trend is that large teams are being split into smaller teams that work on new products. I hope the trend is more projects and less grunt coding. (I know the ai coders are not yet L6 but I suspect they will achieve L3-L5 this year).
This is definitely appearing in the AI tool space and industry as a whole. I feel like companies are pushing AI tools into the workplace because they are worried that a competitor is going to do something that revolutionizes productivity and credit is going to be given to the AI tool as the factor.
There's a tough timing call that comes into play with this because there is no ability to predict, only to look in hindsight, it's more of a hedge to be in and say I bought a lottery ticket and had a chance during the AI boom than it is to not engage.
I believe the saying is "you never win if you never play."
Some people are comfortable living on the edge, but it isn't for most.
I was developing in the metaverse space, and the problems we were facing led me to learn about the state of AI image generation (2018), and where the world was headed.
People assume the thing you are focused on is the thing that must win in the end, but that just means you are too focused on your little part of the world to take in the bigger picture of what's out there.
Prior to working in the metaverse (really a form of volumetric video, but I won't go into details), I was working in telehealth (2014). I did some research in augmented reality (2009), and lots of other areas of interest as well.
Some people would say I wasted my time on these, but there was a mass of secondary learnings which I value every day.
Guy who's ok with being left behind (crypto, AI) did a MSc on the metaverse. Sounds like he tried to go with the hype once, got burned.
You're welcome to read my MSc and see if I was "going with the hype" https://arxiv.org/abs/2304.10542
The answer was quite the opposite. I wanted to see if the technology lived up to the hype. The answer, unsurprisingly was no. If only Zuck had listened to me :-)
Wanna comment on why you'd be spending your time and energy doing free work for facebook?
Some might call that learning from our mistakes.
People don't believe in that anymore. If your gut instinct was wrong, you're not only bound to it for all time, but you'd best angrily double down at every opportunity. God forbid you "flip flop" or consider new information, or whatever.
Fortunately the LLM will always see that your gut instinct was right all along.
This is one of those posts I would like look back on in a year or two. I am usually a late adopter with everything. This time is think its different. I am seeing what AI can do with my own eyes. I am creating new things at light speed and figuring out this all works. I don't think you want to be late to the party on this one.
Not advocating for crypto here, but the ROI evaluations here are a bit incongruous.
The risk of getting in early on crypto is you lose a little money. The risk of not is missing out on money. You can't simply replay that later, the way that you could invest the time to catch up on how git works.
Crypto isn't bad because it failed to make early adopters rich — it did make them rich. It's bad because it has horrible externalities in scams, war crimes / sanctions evasion, organised crime — which most of those early adopters were well aware of.
You can make pretty much the same arguments for LLMs. Pollution, corruption, and war are all things I'm keen to avoid.
And thus it is reasonable to not use LLMs. And it's not only the training process that's problematic. There are so many individual LLM users who waste electricity on tasks that they would have solved by thinking for themselves just a few years ago.
I think you can actually replace crypto with LLMs/Diffusion models in this sentence and it will still hold true
Not to defend crypto, but this all applies to fiat too
For whatever reason the folks on here refuse to understand that for a big chunk of this planet people get paid in inflationary currencies that they used to have to immediately convert to dollars and then stuff under a mattress because the local banking system is corrupt. In that environment cryptocurrency is a god send.
All those apply to AI too, no?
The way early adopters got rich was by scamming others, so not sure I see your differentiation there
This is fine so long as you don't confuse stubbornness for caution. As technologies lose favor, and others suggested you expand your toolset, don't post about your frustration while you're standing in the unemployment line.
I would not have started my article with "I could get into Bitcoin anytime, why the rush". That is not the killer first example you think it is. It's been ~17 years of proof so far that you would've made a ton of money by simply mindlessly buying $200 of bitcoin every month after lower risk contributions are made, and just holding onto it.
I mean if you did that you'd have contributed ~$38K USD by 2026 and had ~1.5B USD now if you started in 2010. BTC being so cheap back then dominates the whole process so to demonstrate my point more if you had heard about it all those years and were nervous about trying it and decided to wait until 2016, you'd still need to just put in $24K overall to come out with ~$450K by 2026.
That's not biting your finger nails over the price changes, the hype cycles, the price drop scares. You just set and forget a $200 recurring buy a month and put your energy elsewhere and pocket half a million for basically no effort
And if anything is possible in hindsight, then why in hindsight would you write an article acting like bitcoin was a bad decision to be an early adopter for
I remember working on a few early tools for VRML towards the end of the 90's... It was cool, but far from great... was remembered from the mention of VR in the article.
That said, my only regret with Bitcoin was deleting my early wallets when I realized the coins were only worth $.25 ... if I'd had any inkling what they'd be worth someday, I'd probably have just bought $1000 worth back then and zipped it up until closer to today. I'm truly curious how many bitcoins were similarly deleted from existence.
Adoption of a new technology has always sorted itself into buckets by early adopters, mainstream adopters and late adopters. I think this post is just demonstrating the mindset of the latter.
It's more about job seeking than anything. If you jump on a fad early, and it turns out to be the winner, when you're looking for work you can say you have X years of experience with it, which will be a few more than most of the other candidates.
It also shows a passion for learning and improvement, something hiring managers are often looking for signals of.
But of course it's a trade off. This rewards people who don't have family or other obligations, who have time to learn all the new fads so they can be early on the winners.
I had this with Rust. I always saw the huge hype, especially some years ago, and it was hugely off-putting. Ridiculous projects like rewriting famously full coverage branch tested projects like SQLite in Rust, or rewriting the GNU coreutils, and always spamming "blazing fast" and "written in Rust (crab emoji)" was very, very hostile to a C++ developer.
When I eventually got around to using Rust, I was hooked, and now I don't use C++ anymore if I can choose Rust instead. The hype was not completely unjustified, but it was also misplaced, and to this day I disagree with most of those hype projects.
It was no issue to silently pick up Rust, write some code that solves problems, and enjoy it as a very very good language. I don't feel a need to personally contact C or C++ project maintainers and curse at them for not using Rust.
I do the same with AI. I'm not going around screaming at people who dare to write code by hand, going "Claude will replace you", or "I could vibe code this for 10 bucks". I silently write my code, I use AI where I find it brings value, and that's it.
Recognize these tools for what they are: Just tools. They have use-cases, tradeoffs, and a massive community of incompetent idiots who like it ONLY because they don't know better, not because they understand the actual value. And then there's the normal, every day engineers, who use tools because, and ONLY because, they solve a problem.
My advice: Don't be an idiot. It's not the solution for all problems. It can be good without being the solution to a problems. It can be useful without replacing skill. It can add value without replacing you. You don't have to pick a side.
Devs who never mentored or never had to delegate/explain the work to be done to someone else, might be in for a rough first few weeks/months.
It is a skill, but not a special AI specific skill.
While you can certainly take a wait and watch approach on many things you also have to take a strategic bet on some otherwise you will never be at the forefront of any field.
I'm reminded of the parable of the Chinese farmer (a quick Google search if you aren't familiar) when I see this sentiment. Is going all in on crypto good or bad? Maybe so, maybe not. We'll see. Is going all in on AI-assisted development good or bad? Maybe so, maybe not. We'll see.
All I know is, I've always enjoyed building things. And I enjoy building things with AI-assisted tools too, so I'll continue doing it.
> There are a 16,000 new lives being born every hour. They're all starting with a fairly blank slate. Are you genuinely saying that they'll all be left behind because they didn't learn your technology in utero? > No. That's obviously nonsense.
That's does not obviously follow, I do worry about the ever increasing proportion of humanity who are no longer 'economically viable' and this includes people who are not yet born.
I don't think the "craftsman" self-identification is going to work for software engineers anymore. The tool capabilities are too dynamic, you have to be some sort of opportunistic pirate/entrepreneur. Sure you can jump in and get up to speed on some aspect of the toolchain later on, but the identity shift is the hard and slow part that I think it's wise to get started on ASAP.
If this is a real strategic shift, some will adapt and some won't or can't.
From Thomas Kuhn's Structure of Scientific Revolutions:
"a new scientific truth does not triumph by convincing its opponents... but rather because its opponents eventually die, and a new generation grows up that is familiar with it"
The biggest issue is - you will be left behind, in the end. This is the race you cannot win. You can try as much as you like, spend free time trying to catch up, and you, most likely, will lose. If you play this game, you've already lost.
I am actually surprised by people willingly trying to be more productive, like... machines. And then crying when machines are proven to be better at being machines than meatbags.
> If this tech is as amazing as you say it is, I'll be able to pick it up and become productive on a timescale of my choosing not yours.
I saw meme in X the other day, which roughly says that one does not have to learn if she learns slow enough in the age AI. I guess the undertone is that AI evolves faster than one can learn about the tricks of using it.
There’s a lot of truth to this post. I’m very pro AI, and I believe everyone should get comfortable with it because it’s not just the future, it’s already the present. If you want to stay competitive in today’s workforce, AI is going to be part of your toolkit.
But on the other hand... I also only learned git when I needed it at a new job... So we can pump the breaks a bit.
The last comment is ridiculous. Newborns have a literal lifetime to catch up—rather, they will learn what they need when they need it.
Actually, given the path their world is on (in the USA anyway), it's true. Falling economic opportunity, worsening physical environment, social atomization, increased indebtedness, etc.
You mean in Canada:
https://nationalpost.com/news/canada/canada-falls-in-the-wor...
https://financialpost.com/news/canada-manufacturing-longest-...
https://thehub.ca/2026/02/02/canadas-spiralling-debt-problem...
> weaponisation of FOMO
This is in an excellent characterization of the kind of marketing tactic I see all over social media right now and that I find absolutely disgusting.
The keyword here is fear. Despite faux-positive veneer, the messaging around certain technologies (especially GenAI) is clearly designed to induce anxiety and fear, rather than inspire genuine optimism or pique curiosity. This is significant, because fear is one of the most powerful tools to shut down rational thinking.
The subliminal (although not very subtle) message there is something very primitive. "If you don't join our group, you will soon starve to death." This is radically different from how most transformative technologies were promoted in the past.
I think AI is not quite the same as crypto when it comes to FOMO. At the peak of the craze you could not write on HN that 'crypto is nonsense' unless you wanted to be modded down to oblivion, to be shadow banned forever. I exaggerate, but not much.
With AI people are able to say 'this is nonsense' without people getting the pitchforks out.
As for myself, I don't have the bandwidth to learn how to do clever things with AI. I know you just have to write a prompt and it all happens by magic, but I have been burned quite badly.
First off, my elderly father got tricked out of all of his money and my mother's savings, which were intended for my niece, when she comes of age. It was an AI chatbot that did the deed. So no inheritance for me, cheers AI, didn't need it anyway!
Then there was the time I wanted to tidy up the fonts list on my Ubuntu computer. I just wanted to remove Urdu, Hebrew and however many other fonts that don't have any use for me. So I asked Google and just copied and pasted the Gemini suggestion. Gemini specified command line options so that you could not review the changes, but the text said 'use this as you can review changes'. I thought the '-y' looked off, but I just wanted to do some drawing and was not really thinking. So I typed in the AI suggestion. It then began to remove all the fonts and the window manager, and the apps. It might as well have suggested 'sudo rm -fr /'.
This was my wakeup call. I am sure an AI evangelist could blame me for being stupid, which I freely admit to. However, as a clueless idiot, I have been copying and pasting from Stack Overflow for aeons, to never be tricked into destroying all my work.
My compromise is to allow some fun with cat pictures, featuring my uncle's cat, with Google Banana. This allows me to have a toe in the water.
Recently I went on a course with lots of people with few of them being great intellects. I was amazed at how popular AI was with people that have no background in coding. They have collectively outsourced their critical thinking to AI.
I did not feel the FOMO. However, I am old enough to remember when Word came out. I was at university at the time and some of my coursemates were using it. I had genuine FOMO then. What is this Word tool? I was intimidated that I had this to learn on top of my studies. In time I did fire up Word, to find that there was nothing to learn of note, apart from 'styles', which few use to this day, preferring to highlight text and making it bold or biglier. I haven't used a word processor in decades, however, it was a useful tool for a long time.
Looking back, I could have skipped learning how to use a word processor, to stick to vi, latex and ghostscript until email became the way. But, for its time, it was the tool. AI is a bit like that, for some disciplines, you can choose to do it the hard way, using your own brain, or use the new tools. However, I have been badly burned, so I am waiting it out.
The irony is that if LLMs live up to their potential then the value of software development as a skill is going to plummet, at least as far as something to do for others. I say it's ironic because obviously the people most interested in using LLMs for software development are software developers, and most are not working independently. It'd be like if we were all proactively getting involved in training our own replacements.
I was highly skeptical of this happening not that long ago, but I have to say that it seems increasingly likely. LLMs are still quite mediocre at esoteric stuff, but most software development work isn't esoteric. There's the viable argument that software development largely isn't about writing code, but the ability to write code is what justifies software developer salaries, because there's a large barrier to entry there that most just can't overcome. The 80/20 law seems to apply to everything, certainly here - 80% of your salary is justified from 20% of what you spend your time doing.
It's quite impossible to imagine what this will do to the overall market, because while this sounds highly negative for software developers, we're also talking about a future where going independent will be way easier than ever before, because one of the main barriers for fully independent development is gaps in your skillset. Those gaps may not be especially difficult, but they're just outside your domain. And LLMs do a terrific job of passably filling them in.
It'd be interesting if the entire domain of internet and software tech plummets in overall value due to excessive and trivialized competition. That'd probably be a highly disruptive but ultimately positive direction for society.
Ive been using AI/LLMs for 3 years non-stop and feel like I've barely scratched the surface of learning how to wield them to their full potential. I can’t imagine the mindset of thinking these tools don’t take extreme dedication and skill to master.
WordStar for DOS was great! A lot better then my hand-writing. But still, I get the point. :-)
I was a WordPerfect guy, personally.
I started working in AI/ML about ten years ago. Reasonably early. Today, professionally and financially I'm doing about as well as a typical programmer. I find the field interesting so I have no regrets but I tend to agree with OP.
> what's the point of "getting in early"?
Ask this to the guy who deliver a pizza for a few bitcoins in the early stage
I agree with the conclusion but not with the premise. The conclusion is, "I don't have to be an early adopter," but the premise seems to be "there is zero utility in getting in on anything early."
That's not quite what they write:
> Might I be 7% more effective if I'd suffered through the early years? Maybe. But so what? I could just as easily have wasted my time learning something which never took off.
I prefer to move slower. I've accepted that I'm not going to create some unicorn startup (that was never an aspiration). As an employee at a company, my goal is to focus my time learning the things that are relevant to my job and that will be useful for 10 years, not 10 weeks.
Chasing every new tech will lead to burnout and disillusionment at some point.
AI probably isn't going away in the same way NFTs largely did, and I use it to some degree. However, I don't see a lot of value of being on the bleeding edge of AI, as the shape it takes for those skills that will be used for the next 10 years are still forming. Trying to keep up now means constantly adapting how I work, where more time is spent keeping up on the changes in AI than actually doing something useful with it.
After the bubble pops, I think we'll start to see a much more clear picture of what the landscape of AI will look like long-term. Who are the winners, who are the losers, and what tools rise to the top after the hype is gone. I'll go deeper at that time.
Right now, the only thing I'm allowed to use at work is Copilot, so I just use that and don't bother messing around with much more in my free time.
Same situation here, Copilot the only available option. But I get the feeling of things moving ahead...
It’s a personal choice, and both early and late(er) can be valid rational choices if it’s you who is making the choice and not just following a crowd (or even a single person).
FOMO is making me feel like I should mess around with openclaw but I can’t see any use cases that I can’t accomplish with other tools. What should I do based on this article?
Don't just do something, stand there!
Like investing in index funds, a big part of it is psychology of the individual as Jeeves would say.
Find a way to scratch the FOMO itch without taking on too much risk.
I recommend going to the pub with some friends and having a chat about anything that isn't work.
Nice blog btw
Why do you need a reason to try anything?
Just go out and prove how useless it is. If, during your testing, you find that it has no good use case, toss it.
Waiting for others to validate a tech for you is a mistake IMO.
Especially when people pushing it are trying to capture your attention, it’s good to be deliberate about the tech that you introduce.
Why launch Voyager-1 if, in X years, no matter how far it flies, we’ll catch up to it and overtake it using a new version?
Because the planetary alignments won't allow for another launch any time soon... which I guess has a natural correspondence in that the macroeconomic conditions (read: bubble) mean that now is a great time to play around with things while they're basically giving it away to get you hooked.
Without lessons learned from Voyager-1 the successors would never be built.
How else can you be in the right place and right time to discover a problem to solve that can’t be seen from afar?
the main value being created is by selling courses and convincing people they're late and need to catch up
To me the main question is the long term pricing.
It is said that major providers more than break even on what they're charging.
But at the same time that's not the point of capitalism, is it? The point is to charge close to the value you're providing.
My lunch money is approximately $10 and I often blow through as much in Claude tokens generously provided by the company which hired me. But I'm not getting $10 value from those tokens, but much more.
The cost of entry to this market is extremely high. Should Anthropic win and become an almost monopoly, it is bound to keep increasing prices to the point, where the value it's providing matches the cost.
That's the endgame of every AI company out there. It's worth using these tools now, while there's still competition and moats weren't established.
Luckily the models were built on copyrighted materials so hopefully the big players won't have strong legal standing to kill off model destilling. Then, with models like current deepseek or kimi k2.5, you are perhapy 1/2 years behind in their capabilities at the fraction of the cost. For real inference costs, look at prices on openrouter. For hobby, I wasn't able to burn trough more than $5 in a month.
This is where I landed too. At least in the js frameworks "wars" it was reading the docs and prototyping angular and react for free. To try and keep up with the latest AI tools you're expected to spend ~$1k / year.
I don't think there's a universe where Google stops competing here, though. The second Anthropic gets greedy, there will be alternatives.
My worry is are two or even three companies at the top enough for healthy competition? You're not limited to one product - arguably it's better to have multiple subscriptions.
If they decide to increase pricing in lockstep just like FAANG applied RTO policies, we as consumers are going to get the short end of the stick.
> But at the same time that's not the point of capitalism, is it? The point is to charge close to the value you're providing.
The question is how large the value delta will be from open models. And I’m not sure if the cost of entry is really “extremely” high comparatively if as you project the market will be so profitable. Surely investors will see a chance to get a larger piece of that profit. While model training is costly, there is a ceiling imposed by the training material being limited (at least for text). LLMs also don’t have a network-effect moat like social media has, or a web search moat like Google has, or a chip technology moat like TSMC has. It’s unclear if a significant moat will emerge.
NVIDIA Jensen predicted today that soon engineers will spend $20k in tokens every month.
sure you can pick up any tool whenever you want, but from your employer's perspective AI is the best force multiplier since slavery, everything between it still required humans with leverage, the question is if your boss will need you at all by then
> What is there to be left behind from?
Employment?
As long as it's not coupled with calls to tax and regulate those who do get in early and reap benefits from doing so, this is good and healthy.
(I'm not the earliest adopter of crypto and AI by any means. I only rode up crypto a couple of times for 2X and 3X kinda gains on my investment, and I only started using Claude last year.)
I mean, whatever, man.
This line, as one example:
> For every HTML 2.0 you might have tried, you were just as likely to have got stuck in the dead-end of Flash.
Like a lot of tech Flash had its moment in the sun and then faded away, but that “moment” lasted a decade, and plenty of people got their start because of or built successful businesses around it. Did they have to pivot as Flash waned? Sure, but change is part of life.
I’m sorry but I find the take expressed in this piece to be absolutely miserable and uninspiring.
But, hey, congratulations on the 20:20 hindsight, I suppose.
I'm upvoting because it's useful to see and debate this viewpoint — shared by many engineers I know
I do think it's a bad take though. Not all new trends are the same: the metaverse was an obvious flop and crypto hasn't found practical applications. AI isn't like those because it's already practically changed the way I get my job done.
It takes time to learn skills, and getting started earlier will means more time to use them in your working life.
The way I see it is - AI still makes mistakes, and I have to know how things work at some point anyway. So I'd rather spend my time actually understanding fundamentals (in my case, CSS at the moment), than trying to keep up with the frequently changing AI tools and models.
Once the tools and models stabilize more (as well as the pricing model), there's less risk in me learning something that is no longer relevant.
Except when I choose to wait on learning how to use AI tools effectively, I get told I am going to be "left behind".
I use AI not for what the various fraudsters hype it for. It utterly fails coding what i want each time. But it is a great tool to create things and not be censored/hindered by collaboration with humans. It liberates the lone nutters of the world to make music, create movies, do things, which society desperately tried to stop them from. Just because new things are frightening and money shies away from risk like the devil from holy water. Look at all those sequels. Each sequel in existence murdered a dozen artists hopes to create something new.
A ton of it is garbage. But 1/1000 gets to realize a vision, that could never be marketed, never be discovered and is something new entirely. For that i love it and thank those who made it..
As someone further down the road in my career, I would argue that waiting is your prerogative but you do so at your own peril.
I made these kind of mistakes early in my career, stuck it out with PHP for far too long ignoring all the changes with frontend design trends, react, etc. I was using jQuery far too late in my career and it really hurt me during interviews. What I was doing was seen as dated and it made ageism far worse for me.
Showing a portfolio website that was using tables instead of divs.
I had to rapidly skill up and it takes longer than you think when you stick too long with what works for you.
If AI truly is a nothing-burger than guess what? Nothing lost and perhaps you learned some adjacent tech that will help you later. My advice is to NEVER stop learning in this field.
Learning is your true superpower. Without that skill, you are a cog that will be easily replaced. AI has revealed to me who among my colleagues is curious, and a continuous learner. Those virtues have proven over the course of my 25+ year career in technology to be what keeps you relevant and marketable.
I think the point isn't "wait forever and never learn" but simply "you don't have to be at the forefront of the wave" - because the real ones will lift everything, and you can come it a bit later.
It is easy NOW to look back and see the optimal path for a web developer, but was that obvious from the start? How many killer technologies lie unused today?
> I'm OK being left behind, thanks!
> It is 100% OK to wait and see if something is actually useful.
> I took part in a vaccine trial
> Getting Jabbed With EXPERIMENTAL SCIENCE!
This is such a weird article. The author presents so many contradictory anecdotal experiences against the author's own conclusion.
I think he used it as a counter-example where it made sense to him to be early. But you don't always have to be early, is his point, I think.
>what's the point of "getting in early"?
You're trying to make the point using BitCoin, but in the early 2000s I had just over 14,000 of them, so I can quite clearly see a point in getting in early.
(Assuming you meant early 2010s, since Bitcoin didn't exist in the early 2000s)
This underscores a main counter point: dipping your toes and casting a wide net often has a low cost, since back then you could mine (and even purchase) Bitcoin relatively inexpensively. If it hadn't worked out, then it wouldn't have hurt much at all.
The bitcoin white paper was released in 2008.
The argument about bitcoin was against it being "the future of currency". There is no point, whatsoever, to "getting in early" to that.
If everyone had been talking about it like the casino that it actually is, then sure - some people made some good bets, and a lot of people made bad ones trying to get in early. Imagine being the person who sold all your bitcoin for whatever other stupid memecoin, to "get in early"?
It's not a real counter-argument, it's just "I had a lot of dumb luck on this one specific thing, aren't you silly for not guessing as well as me".
I’m suprised nobody has mentioned that Claude is the realization of all this blockchain work - an internet computer you rent time from where the computation is measured in tokens :)
You just described time-sharing. Cryptocurrency mining is much different, in that you can commit computation resources and never get paid.
Indeed it’s much more similar to time sharing than cloud compute.
But time sharing was never as global and accessible. It was a group in a company or in a university sharing a specific machine.
The currency discussion is different too. Time sharing was just a local fee. Now people are talking about the tokens themselves can do for them.
HN is generally sleeping on the next big tech that is coming for running this stuff locally. Which is quite performant and will soon be in everything.
I understand and empathize with his sentiment, but I think he is missing the point. Using AI effectively as an engineer requires a paradigm shift in terms of how you work. You cannot approach your work as you did in the past, and use AI and expect it to be a big improvement. In fact, if you do that you will likely be disappointed, and worse off. Shifting your paradigm is one of the hardest things you can do, even more so if you have been in the field for a while, but it is also the most rewarding, and opens up many new possibilities. It's not about being left behind, as much as it is about limiting yourself unnecessarily, by staying in your comfort zone.
Say what you want but the layoffs of people who don’t use these tools getting replaced with those who can have and continue to happen; I miss manual coding just as much as the next man but this seems like a hot take
His ignorance is my first-mover opportunity!
Okay, this text was pretty good. Refreshing to read something that doesn't seem written by AI too (would be ironic given the contents).
The only scenario where I think it pays off to be on top of the hype is of you are chasing money sloshing around the latest hype. You know, the hustle culture thing. If that's not your thing, waiting until things are established (if they ever get there) is harmless.
And yeah, AI as it is now is at best moderately useful. I use it on a daily basis, but could do without it with little harm.
> Refreshing to read something that doesn't seem written by AI too (would be ironic given the contents).
As much as I dislike the idea of not writing/checking code I am responsible for, it was a surprise to me seeing a few "anti/limited AI in coding" articles that don't pass an LLM detector. (I know those are not perfect but not much else one can do).
That's really the big difference - if you're a startup or founder (or apparently even an billionaire CEOwner) you gotta chase the hype because your customers are the VCs/shareholders and if you are NOT chasing hype they're going to have serious doubts about you.
As an employee (perhaps even a highly stock-option compensated one) the equation is very different. Perhaps you're aligned if you're an employee of a startup/AI obsessed company. But for the vast majority they're not.
So? Economy is entertainment. When crypto was hype, billions were made and burned from building whatever entertaining thing around that. Now it's AI's turn. Billions will be made and burned. Economy is just a fun game. Let's have fun. The idea that everything needs to be "useful" is highly subjective. What is truly useful? Is it food? Shelter? Medicine?
Anyone obsessively insisting others adopt $tech with threats that you'll be obsolete, left behind, whatever, are just selling you something. If anything, they should be trying to keep it a secret, so that they stay among the elite few who get outsized benefits from $tech while everyone else plays in the mud.
i’ve said this before, but the “left behind” narrative is FUD nonsense. as an llm avoider i’ve never felt further _ahead_ than now. all of my peers who never bothered to learn their tools (which gave tangible benefits) have opted into deskilling themselves further.
it’s readily apparent who has bought into the llm hype and who hasn’t
When did ignorance become a virtue? Or is it the contrarian montra?
I see you haven't spent much time on Twitter the past 10 years :-)
<insert no body quits twitter rick and Morty meme>
This isn't Twitter though.
some people are not ok, some people lose their jobs and suffer because they are too complacent and it's too uncomfortable to adapt.
This is the lazy guy path, is not the wise one.
People mostly lose their jobs and suffer through no failure they were positioned to avoid, and often through no fault of their own at all. Spending your career chasing fads out of fear of being abandoned to penury is at least as limiting as mere conservatism towards new technologies. The risk is real and the fear is valid but that isn't the solution, no individual action you can take is the solution.
It's a problem of motivation, all right? Now if I work my ass off and Initech ships a few extra units, I don't see another dime, so where's the motivation? And here's something else, Bob: I have eight different bosses right now.
I’m running a solo saas. In the last six months, I’ve added about $300k in ARR. There’s zero chance I could have done this without AI. My velocity just keeps going up, month after month.
Wouldn't play that game with LLM's
It is a far better investment to attend to the eternities rather than the times.
It amazes me that companies are developing proprietary IP with somebody else's cloud-based AI that ingests and learns from everything that they type and it generates.
These companies are paying for the privilege of having their IP stolen.
This reasoning is solid and applies equally to AI. I do not need it crammed into every service and forced on me thank you very much "If this tech is as amazing as you say it is, I'll be able to pick it up and become productive on a timescale of my choosing not yours."
Getting early into any technology only makes sense if you are building your business on top of it. Or you are making money from it in some way. Other than that it makes sense for the rest of us to wait.
Of course those that believe that AI will convert into AGI and destroy society as we know it won't be convinced.
General idea is true, except for this particular technology.
When AI will be easy to pick up and guide, guess what, there will be no need for a programmer to pick it up. AI will be using itself, Claude Manager driving Claude programmers.
So leverage AI while you still can provide value doing so.
It's literally a "use it or lose it situation".
Whenever I hear "It's never too late to do X", I can't help but think "Well in this case, there is no harm in waiting a bit longer, is there?".
I'm glad I missed: GraphQL, Kubernetes, Microservices, the Metaverse.
I'm glad I jumped early on: Linux, Python, virtualization, cloud, nodejs, Solana.
I wish I'd gotten into Rust and LLMs earlier.
> If this tech is as amazing as you say it is, I'll be able to pick it up and become productive on a timescale of my choosing not yours.
I mean... yeah? It's obviously true. However people use LLM coding today not because they're "afraid of being left behind" or "investing into a new tech" or whatever abstract reasoning. It's because they're already reaping the benefit right away. It takes just a few hours to go through like 80% of the learning curve.
Comparing these tools to the crypto or NFTs hype is so out of touch with reality.
This is more on the scale of the invention of the press, the telegraph, or the internet itself.
"I'm ok being left behind, I will join this Internet thing when it really becomes useful"...
Ok... you do you. Hope you don't get there too late.
> Hope you don't get there too late.
Too late for what? Could no one start a viable internet business in 2005, or were they all taken in 1998? Is it impossible to learn machine learning today, if you weren't jumping into Tensorflow in 2015? Do you think it's impossible to learn OpenClaw today, if you weren't playing with it six months ago, and do you think there might not be a successor that "wins" and is easier to learn and use six months from now, or will I have "gotten there too late" to possibly leverage or learn agents?
I just don't understand what it is you think anyone will be too late for, unless this is just self-justification and snide ego-boosting.
In 1998 or 2005 two persons could single-handedly start a Google or a Facebook. Not possible anymore today in Internet.
But in AI a single person created OpenClaw.
It's called low-hanging fruits.
> But in AI a single person created OpenClaw.
Do you think no one can create anything alone ever again? Or can they only do it by adopting the bleeding edge?
> It's called low-hanging fruits.
1 in a million ideas are 1 in a million, and they don't require being a bleeding edge adopter of anything. Do you think no one can create a better version of a first-try service? Is the agentic world now closed because someone built a mediocre version of it?
For a start-up based board, this point-of-view just feels so sad and myopic.
The behaviors of NFT advocates and AI advocates are shockingly similar.
Remember how NFTs were supposed to be the future or art ownership, and all it amounted to was awful pictures of bored apes and ahegao lamas? The NFT bros proudly displayed their shitty art - not because it was good, but because it signaled their allegiance.
Now go on to any pro-AI blog. Look at the images. They've stopped trying to edit out the AI errors - they proudly display images with garbled text and bad anatomy. Just like before, it signals allegiance to AI consumption.
Even the last sentence of your post is the same sentiment as "have fun staying poor" was for the crypto bros.
> The behaviors of NFT advocates and AI advocates are shockingly similar.
It’s the same people every time. Stupid, gullible idiots.
Remember when HN was obsessed with the room temp superconductivity fraud a few years back? Remember the zealous indignation at anyone suggest skepticism? The empty attempts to downplay their rabid stupidity afterwards?
The thing is, Bitcoin, at least before cryptocurrencies were picked up by "tech bros", was originally a way to disconnect from the corrupt, centralized banking system.
LLMs, at the moment, are all about giving up your own brain and becoming fully dependent on a subscription-based online service.
Its interesting to see this author's historical takes about AI.
IMO it reads a little desperate and very much like the hype bros but from opposite side. Take a look at the articles if you don't believe.
https://shkspr.mobi/blog/tag/ai/
- I'm OK being left behind, thanks!
- Unstructured Data and the Joy of having Something Else think for you
- This time is different
- How close are we to a vision for 2010?
- AI is a NAND Maximiser
- Reputation Scores for GitHub Accounts
- Agentic AI is brilliant because I loath my family
- Stop crawling my HTML you dickheads - use the API!
- Removing "/Subtype /Watermark" images from a PDF using Linux
- LLMs are still surprisingly bad at some simple tasks
- Books will soon be obsolete in school
- Winners don't use ChatGPT
- Grinding down open source maintainers with AI
- Why do people have such dramatically different experiences using AI?
- Large Language Models and Pareidolia
- How to Dismantle Knowledge of an Atomic Bomb
- GitHub's Copilot lies about its own documentation. So why would I trust it with my code?
- LLMs are good for coding because your documentation is shit
> IMO it reads a little desperate and very much like the hype bros but from opposite side.
I would coin it Chill Guys, and I claim on average they are more often correct than hype bros. We all know the old saying that nobody ever was fired for selecting an established solution/technology. Being chill, staying safe, it's more beneficial in real business. Just wait until the hype has normalized and then start dabbling with the new, when all others starting doing it also. You may not be the frontrunner of a new wave, but you can still ride it well. You are only really left behind when everyone changed, and your income starts drying up, until then, it's just business as usual.
And always remember: when ChatGPT started its hype, many were predicting Googles death and the end of Web searches. Now, 3(?) years later, Google is still around, the search is still going strong, and Gemini is seriously competing with ChatGPT. Hypes come fast and die fast.
this particular person seems to be more wrong than right though.
Why not do fads in moderation? I've always done this.
Don't convert it all to xhtml, just start closing your <tags>
Don't go full UML code generation mode, just use it to illustrate core business logic.
Don't buy an iPhone, get an n8.
Don't be a cryptobro, just get one, set it aside.
Don't go full TDD, but use it for bug fixes.
Don't go full knowledge graph/org mode, just have a captain's log.md and an assets/ folder.
Don't go full microservices, create modular monoloths.
Don't go full agent teams, just use normal cli.
etc. etc.
I find the hivemind terribly oppressing at times. AI tools are great, but in the end it seems to me that the results matter most. However we seem to go from hype to hype, again and again. It's all so tiresome. Why can't we just respect individual choices and focus less on the tools and more on the results?
> Have fun being poor
Not going to lie, I’d rather be poor. Not destitute - I’ve been poor but not destitute and I’d rather not go desperate - but poor? As in (because “poor” is very imprecise and can imply anything between utter poverty to “not owning three homes”) like having a low paying job but still enough to pay rent?
I’d rather be that than do AI assisted software development. Genuinely the only thing stopping me now is that there’s actually way more skill and qualifications in most low-paying jobs than a typical software developer imagines, and acquiring those takes time and money itself. But by now I know multiple people who made the jump even before the latest madness, and they’re all happier. Some still code, but don’t even publish. Some are like “I haven’t used a proper computer in _months_ this is great.” All work hard jobs at odd hours. None regret.
Honestly, those who lost money did so because they were aiming for profits, not for The Network State, or for having non-seizable money etc. On the other hand, for those who got in early because they were online and curious at the time, with a small amount of money back then has made them billionaires today, an extremely low risk. If they arrived a bit later, millionaires, still with a really soft risk.
Then of course, there are MANY problems starting with BTC https://blog.dshr.org/2025/09/the-gaslit-asset-class.html but they generally aren't what most people talk about, and today, even if the complexity is insane, the documentation sparse, and the code quality questionable, Lightning is actually the only truly scalable solution we have for micropayments and payments of various amounts, generally not large ones. It has some absurd aspects, but that hardly matters. It works.
More generally, we don't have a crystal ball; where we can, we diversify, certainly limiting risk but also taking a bit of a gamble; where we can't, we choose to watch and see how it goes, knowing that we'll pay for it in terms of returns.
As things stand for me personally, given the level of IT obscenity in the traditional banking world, which in 2026 still doesn't have decent APIs open to retail customers, or at least decent export functions, shameful websites, a push towards completely unacceptable mobile apps, absurd limits etc etc etc, well, the worst CEx is less bad than the best bank. I don't trust either, but I distrust the CEx less than the bank, and given Wikileaks, Francesca Albanese, the protesters in Canada, the various private individuals illegally "sanctioned" by the EU Commission and so on, I'd say it's madness to rely on banks for anything more than the bare minimum.
Most people today know nothing of this and don't weigh it up, but they will, and they'll pay for this delay with their lack of attention.
I think this is a carry over from the early 2000s boom and bust mindset. That if you jump in early enough on nearly any technology, you'll become a billionaire. So hop on board our burning platform!
In general, we as a society have not adjusted to technology. We've gone through to much change to have any stable base lines. So we're going to float in insanity for a while until things finally settle down. Probably 2 wars, a famine, and several periods of resource scarcity away still, but we'll get there one day...
I love the Cryptocurrency analogy. The LLM hype monkeys are the same people that were screaming that NFTs/Digital Art was going to replace all the traditional art in gallaries in 10 years. They are literally the same people, and they are all addicted to money and hype. Ignore them..
Did handmade Swiss watch movements lose all demand when Asia started mass manufacturing watches? No. There is always going to be more demand for quality over slop. Its the same reason that handmade clothes are worth 100x more than clothes at a department store.
This is all by design too, these billionaires selling thinking machines are trying to make us all dependent on their fountain of tokens. Don't fall for it. Just like how maps apps made everyone reliant on Google/Apple for your ability to navigate around your own city, these billionaires want to do the same think with your ability to think, build, plan and even learn/read.
Don't fall for this scam, unlike other hype cycles like NFTs and Crypto this will actually damage more than just your bank account, it will fry your brain if you become over reliant on it.
Take a second and consider why these LLM tool companies design their products like slot machines. They put multipliers in there UIs (run this x3,x4,x5) times so that you inevitably treat the thing like a slot machine. And it is like a slot machine, you have no way to control the results its quite random, in the case of llms they just have a better payout percentage, at the cost of making your brain become dopamine and structurally dependent on their output. They convince people there is some occulted art in the formation of a prompt, like a gambler who thinks if they press buttons in a certain order they'll get better results or many other gambling superstitions.
If you're writing software please take a moment to breath, and ask yourself if its really that useful to have piles of code where you have little idea how things work, even if they do. Billionaires will sell you on the idea that this doesn't matter because the llm, that you conveniently have to pay them to use, will always be able to fix that bug.
Don't fall for the ruling classes trick, they want you reliant on this thing so they can tell you that your input isn't as valuable, and therefor your salary and skills are not as valuable. We have to stop this now.
You can run the LLMs on your own machine
It costs about 60-80k to run a 1T param model at your house.. which is the only size model that's going to get anywhere close to a foundation model's capability.
I don't really need a foundation model's capability
There's a shocking amount of overlap between AI & Crypto/NFT bros:
https://mystudentfailedtheirmid.substack.com/p/theres-a-shoc...
The kid who showed his work in detail in math class is doing better in life 9/10 times than the kids that only knew how to use a calculator. Now consider how well the people who think you just need to know how to yell at the calculator are going to do?
When Maps apps came around, people totally lost the brain muscle for being able to navigate. Using LLMs is no different, people over reliant on these tools are simply ngmi. They are going to be totally reliant on their favorite billionaire being willing to sell them competency via their thinking machines.
I would caution everyone to consider if the Billionaires who are screaming that you're going to be left behind, laid off and redundant if you don't (pay them to) use their brain nerfing machine, whether or not they have your best interest at heart.
You're not going to be left behind.
https://arxiv.org/abs/2506.08872
Firstly, you can run the LLMs on your own machine. So I find the proprietary/moat narrative weak.
Secondly, I find that correct usage of LLMs can accelerate learning. My brother used an LLM to generate flash cards for a driver's license test. I use LLMs to digest a ton of text and debug issues that would have been impossible to find (I would have given up) Have it generate, explain, review, compare code or general writing.
It is like having access to wise old man in every field. They may have inferior reasoning capability, and their memory may falter, but they have seen everything in their corpus and are great at pointing you to external references. And you can delegate them to busywork.
> Firstly, you can run the LLMs on your own machine. So I find the proprietary/moat narrative weak.
You cannot run useful models on consumer hardware, sorry this is wrong and will always be the case. Atleast for 10 years until GPUs with 48GB vRAM depreciate. This is a limitation of llm architecture. You cannot post train a <1T Param model to a place where it competes with frontier model capability. If you think you 70b param models (which still require 5k in GPUs) are useful, you are being dishonest with yourself.
It costs about $60-80k to run a 1T param model at your house like Kimi 2.5 .. which is the only size model that's going to get anywhere close to a foundation model's capability. Nobody is going to spend close to 100k to run a mediocre open source model as opposed to spending $200.00 a month. Its a ridiculous notion.
I run Qwen-3.5 based LLMs in the 20-35 parameter range on my laptop's iGPU and it works great for my use case, which includes coding, search, reasoning, and general tasks. Gemma3 is good too.
There are ones that are distilled with better reasoning models or abliterated for whatever you need, and the multimodal features work... fine.
Just started running local LLMs this week, and it is pretty much overkill for what anyone in my family needs. All it really lacks is some tools for it to use, which I am putting together now.
To be fair, the best model I have used is claude sonnet. I don't really know what I am missing with opus.
Who the hell downvotes this lol
I think that framing your observations in terms of "Billionaires who are screaming" about a "brain nerfing machine" doesn't help think about the issues clearly or contribute to a healthy discussion.
Had another 2-hour throwaway session today with an LLM.
These only really happen in mature codebases tied up in complex business requirements.
The last few times I’ve tried LLMs with this codebase it has not been fruitful.
Weird because it’s impressive in other areas, especially tech with no real users lmao
Have fun writing code yourself /s
Tldr; dude makes wrong choices one after the other and copes with it by being ok with being left behind. "I wrote my Msc on The Metaverse", ....
This would hit harder if Bitcoin didn't win and AI coding didn't completely change our jobs.
Why not simply evaluate things instead of ignoring them until its too late?
Sure, we don't have infinity time, but the fact that OP mentions these two things, means the pattern showed up enough.
What is too late? If you want to use cryptocurrency as a medium of exchange, go ahead, it’s right there!
There’s this irony to the FOMO in crypto, which is people argue the “sensible” thing (it’s the future of money) to create FOMO for the insensible thing (it’s a lottery ticket). You’re right it’s too late to buy a lottery ticket, but the vision wasn’t a lottery, it was a medium of exchange!
AI assisted coding is the same way. I use it every day, but if I decided to stop and wait a year, I could still pick it up, probably more easily when the tools are better.
In fact, people who wait might do better than me because their mental model won’t be locked into a way of interacting that will be out of date in six months.
Wouldn’t it be ironic if all the early adopters were the losers because they liked the hacky nature of it? This happened to a lot of early computer adopters, low level programmers, etc.
> Wouldn’t it be ironic if all the early adopters were the losers because they liked the hacky nature of it? This happened to a lot of early computer adopters, low level programmers, etc.
Relevant XKCD: 598
I never said Crypto. I said bitcoin.
And with AI... I am genuinely afraid my job will be automated. I'm trying to become a manager before we are relegated to minimum wage workers.
It sounds like you're just trying to delay the inevitable then. Manager's may be automated before Software Engineers! Or at the very least otherwise be made redundant.
https://fortune.com/2025/03/04/amazon-ceo-andy-jassy-middle-...
But what are you too late for bitcoin for? Go buy some!
You make it seem like AI coding has already "totally changed" our jobs. This is exactly the FOMO the article talks about ("until its too late"). It hasn't. I'm still using the same workflows without AI tools, and so are most of my teammates.
To be fair, I would have talked like you in January 2026. But things have changed since Feb.
Crypto didn't "win", the technology is there but people are mostly gambling, or doing shady stuff. Shall I mention NFTs? It didn't change the life of the average joe, nor business. It's a niche.
Many people are still coding without AI and doing perfectly fine. When you design serious things, coding is not where most time is spent anyway. Maybe it'll become unavoidable at some point, by that time the experience will be refined and it'll be easier to learn.
Point is, it's never too late. If you don't need to be cutting edge on a new tech, it may not make sense to put the extra effort of early birds. If you put that effort, you better not do it for free.
"if bitcoin didn't win?" It didn't win. It's still useless.
You can use it at steak n shake
must be nice to always live under stable currencies.
Where do you live?
At one point I lived in the Soviet Union but that's not really relevant to my point.
I don't understand your point. You said it would be nice to live..... what does that have to do with bitcoin? What's your point? Make a point.
My point is your opinion is sheltered and lacks life experience.
I understand your point. And I agree on people without life changing fluctuations in currency will hardly understand that side of bitcoin value.
I've had friends in Venezuela for the past 20 years. They hold dollars, not bitcoin. You're both talking out of your ass.
Your opinion on my opinion is not really relevant to bitcoin.
He is rejecting the framing of get in now before it's "too late". If it is so useful then we will be able to pick it up when it is more polished rather than learning to use some half polished turd that will be obsolete in 6 months.
What did bitcoin win? Seems to me like it's mainly used as speculative asset instead of using it to pay goods and services.
I'm not sure Bitcoin won.. it just continues being a ponzi scheme that you can make money in.
You can also accept that certain things and be happy in life either way. Don't need to chase get rich schemes. Some are more privileged than others in being able to do this.
Bitcoin won? I don't think it did. Main use is still scams, circumventing sanctions, and grifting. My SEPA instant money transfer does everything bitcoin promised without the trash, and bad people surrounding bitcoin.
daily reminder your have yet to start paying for using LLMs, the current paid tiers are just to lure you in.
It also has changed nothing in the way i do stuff. Checked it out, not for me, thanks but no thanks
(Sick and tired of hearing how you made an ui in a couple of hours by directing the code when it still takes me the same couple of hours of coding.)
But they didn’t have to think as hard, so they win
Oh wait…
A great blog article for 2023. In 2026 I think the wait is over..
> I didn't use Git when it first came out. Once it was stable and jobs began demanding it, I picked it up.
What jobs aren’t requiring usage of these tools by now?
Most are, implicitly at least. But at the time, there was definitely a period of transition with many shops continuing to use subversion for a while.
And TFS, ClearCase, Mercurial, Plastic, Perforce, Fossil, CVS, RCS, ....
Then there are those still using folders with timestamps.
You should be able to pick up tools and learn on need. There are better version control systems than git - always have been, but git won despite being worse. (Git was massively better than what was popular before it won) if you can't learn git quick then you shouldn't program at all - there are much harder tools you will need to know and many are company or project specific
Mercurial?
That is the one I know best but there are other options that people I tend to trust say are good. There are more options than I have time to give them an honest evaluation.
Just last year I migrated to Git a major code base that was still stuck in SVN. It's not even a legacy project, just a laggard. For some colleagues, this was their first time using Git on the job.
There are large projects still using CVS. Not to say everyone should, but git is only a tool. It isn't essential, and there are alternate ways to achive the same ends with different tools.
There are real productivity gains by using these tools right now. Instead of doing 1x your normal work, you can do 5x while still maintaining quality. This is like an accountant sticking to pen and paper because calculators are big and clunky.
In your analogy that calculator would only produce a correct answer 80% of the time, and plausible looking but incorrect ones the other 20%.
If that were the case I’d hire pen guy.
What's the error rate of the pen guy?
Also, if your AI has a 20% error rate, you're not holding it right. You need to spend more time keeping it on rails - unit tests, integration tests, e2e tests, local dev + browser use, preview deployments, staging environments, phased rollouts, AI PR reviews, rolling releases. The error rate will be much closer to 0%.
How does a phased rollout improve LLM error rates exactly?
Error rate here is the rate of shipping bugs to customers.
That wasn't what the comment you responded to was referring to. I guess it makes sense since you are kind of like an LLM with how you respond to input.
More like “Producing 80% of the correct answer” and the remaining 20% with some nudging and tweaking. Still extremely valuable.
> I feel the same way about the current crop of AI tools. I've tried a bunch of them. Some are good. Most are a bit shit. Few are useful to me as they are now. [...] If this tech is as amazing as you say it is, I'll be able to pick it up and become productive on a timescale of my choosing not yours.
I think the point the author is making is not that it's all useless, but against the very overly simplistic idea the plot of Amount of AI vs Productivity in All Situations is a hockey stick chart.
Being told to be excited about something when clearly all they're saying is "it works sometimes, other times not so much. I'll keep checking and when it's good enough for me I'll get on board" is aggravating.
> Instead of doing 1x your normal work, you can do 5x while still maintaining quality.
Yet my pay stays the same, all my coworkers get fired, and Sam Altman gets all of their paychecks. Hrm.
To be honest, I would rather spend 5x effort while doing my normal work, because salary won't grow.
> Instead of doing 1x your normal work, you can do 5x while still maintaining quality.
That's a gross overestimate. 2x I would maybe believe.
Someone has to sign off your work and unless it's hard to write but easy to read, this is where the bottleneck currently lies.
Everything is relative. If your systems aren't adapted to AI development, it will be much lower.
What if the calculator had randomness built into it?
If this is about vibe-coding.
I remember when React was the hotness and I was still using jQuery, I didn't learn it immediatley, maybe a couple years later is when I finally started to use React. I believe this delayed my chance in getting a job especially around that time when hiring was good eg. 2016 or so.
With vibe-coding it just sucks the joy out of it. I can't feel happy if I can just say "make this" and it comes out. I enjoy the process... which yeah you can say it's "dumb/waste of time" to bother with typing out code with your hands. For me it isn't about just "here's the running code", I like architecting it, deciding how it goes together which yeah you can do that with prompts.
Idk I'm fortunate right now using tools like Cursor/Windsurf/Copilot is not mandatory. I think in the long run though I will get out of working in software professionally for a company.
I do use AI though, every time I search something and read Google's AI summary, which you'd argue it would be faster to just use a built in thing that types for you vs. copy paste.
Which again... what is there to be proud of if you can just ask this magic box to produce something and claim it as your own. "I made this".
Even design can be done with AI too (mechanical/3D design) then you put it into a 3D printer, where is the passion/personality...
Anyway yeah, my own thoughts, I'm a luddite or whatever
It's funny too you'll hear people say they reverse-engineered something (not related to today's post) and you're like "wow that's impressive" but it turns out AI did it.
I mean people use IDA for decompilation instead of reading assembly directly, and that's counted as as valid reverse engineering. Why shouldn't throwing it an LLM count as well? I think reverse engineering is one of the fields where "anything goes", it doesn't matter how you get to the answer as long as you get there.
I do agree that the notion of difficulty needs to be recalibrated though, seemingly impressive RE tasks can now be done trivially with LLMs.
I wonder what the distinction is, where you draw the line
Is using an LLM the same as writing JavaScript over Assembler? idk
I guess it's the same argument of doing math yourself vs. using a calculator, gets the job done
But yeah it goes back to my perspective of why be a carpenter/furniture maker when a 3D printer can just spit one out
Why milk the cow when you can just buy the milk