Most of us on some level felt confident that AI would completely revolutionise our society. The singularity made sense to me, at least. It hasn't worked out like that, but rather than accept the crushing reality of our mistake and take a second to re-evaluate, we've decided to LARP out the future we promised ourselves by crowbar-and-vaselineing AI into every crack and crevice we can find and loudly proclaiming progress.
It's like some 90s kid with a Nintendo power glove who's convinced themselves that this makes them a hacker. This is not the singularity.
I personally can't wait for this to end, and for everyone to collectively get back to waiting for whatever the next golden ticket that's supposed to solve all of our problems turns out to be.
I just don't understand how someone could think we're going back to a pre-LLM world. Have you not used one since Opus 4.5 came out?
Especially as somebody that's no longer in the software profession and so doesn't have 40 hours a week to just sit there and crank out code, agentic AI is a technological leap on the level of search engines, bulletin boards or compilers.
You can literally just open Claude Code, say:
- "Add these domains with these mailboxes to an SMTP server running on my homelab, forward all incoming and outgoing via mxroute"
- "Port this unmaintained app from 2017 to modern Android, and then test it on an emulator and sign it with my real key for distribution via Google Play"
- "get my GitHub Actions runner on Linux, Windows, macOS and Android, ensuring that it supports all available architectures from the last decade and a half, then check your outputs in a loop until it works"
and it just spits it out while you do the dishes.
Someone the other day talked about "AI psychosis psychosis" and it absolutely stuck with me. The people who deny this is a big deal are in la-la land.
We could literally hit a brick wall in terms of model intelligence now and it would still be a game changer.
> - "Add these domains with these mailboxes to an SMTP server running on my homelab, forward all incoming and outgoing via mxroute"
I laughed reading this. Have you actually tried it? I recently tried to get Claude (Opus) to give me a setup for a simple SMTP relay with Postfix. I had to constantly fight it not to include completely unnecessary components and configurations like Dovecot or just override my requirements. Even Claude is nothing but a semi-competent tutorial clicker.
To someone who never did anything like this themselves the result might seem fantastic. They might not battle Claude on details like I did because "hey, it works, doesn't it".
Don't get me wrong, I agree in general that LLMs are a significant milestone. But I would like to add, especially because of your tone, that perception of how much of a leap it is, is very likely a matter of individual competence. So your enthusiasm might say more about yourself than about the tool.
"To someone who never did anything like this themselves the result might seem fantastic ..."
I've been "doing this" for over 30 years now and I consider these LLM results to be fantastic.
If one has a deep, conceptual understanding of tools, systems and protocols and if one has the ability to carefully provide careful, concise specifications, these LLM tools are like being granted superpowers.
In fact ...
My next project upon which to bring LLM tools to bear is a fully local, solar-powered, self-hosted LLM toolchain.
Not only do I intend to never work without these tools again, I intend to do so without the ongoing environmental externalities that they produce.
> If one has a deep, conceptual understanding of tools, systems and protocols and if one has the ability to carefully provide careful,
concise specifications
Most people already don't have the ability to do this and their use of LLMs atrophies the little they had. "AI natives" will be completely doomed.
However, I never asked for LLMs nor did my consumer nor professional behavior send market signals that I would use them. They were thrust upon me apropos of nothing.
Now that I find them useful, the least I can do is stop making the problem worse.
As a rule of thumb the energy use to produce a silicon wafer is roughly equal to the energy it will use in its lifetime. For GPU and VRAM this is considerable. To equip yourself with the capability to run an LLM locally is making an upfront investment in the production of extremely toxic and resource intensive products.
Why buy a personal EV with 600 mile range that sits idle 95% of the time when you can take a bus (aka the cloud) that maximizes the resource utilization?
One of the most wild parts of the "Is AI useful" arguments is how different people are getting so astoundingly different results from it. One guy will claim that he can one-shot "Port this unmaintained app from 2017 to modern Android," go do the dishes, and come back to a working application. And the next guy will say, "I can't get it to do anything groundbreaking and have to micro-babysit it every step of the way to the point where it is more work." Assuming both people are truthful and operating in good faith, how can this be explained? How can simply writing a prompt in English be a "skill issue?"
I'm personally much more towards the "I struggle to get it to work well" side, but there have been so many anecdotes from people who are earnestly saying it does all their work for them and that they hit enter, go to bed, and wake up to working software. I'm trying to imagine a hammer or band saw that produces such wildly different results in the hands of different users.
I'm the GP that got Claude to port the app, set up the SMTP server etc. etc.
And I do see stuff on that level of complexity, multiple times a week. I'm not a full-time software dev any more, but did learn the craft at a really good company before AI came along - and I've been doing basic sysadmin stuff by hand, almost exclusively on the terminal, for almost a decade now.
A lot of pithy responses to your comments that contain some pretty bad-faith assumptions about AI "power users", but to answer your question I think part of it genuinely does come down to a "skill issue" - or at least familiarity.
AIs really do have huge blind spots, and there are things it can't do well. But on the flip side, there are modes of operation that do get good results and if you keep it in this "zone" you can get amazing results.
Note the "Working with unfamiliar data or systems" section - it doesn't stop the models from making wrong assumptions but it does get them to test these assumptions with simple experiments and course-correct before human eyes ever see the result.
Setting up things like CLAUDE.md files and skills so that it doesn't make the same mistakes over and over is a big help too. The model doesn't literally get smarter, but it knows how to avoid pitfalls its fallen into in the past.
If you want to have a chat about it earnestly, my email's in my profile.
My guess is they do something like "Hey dingus, make my app send emails" and then Claude or GPT just goes off and does a thing that makes it look superficially like it sends emails but it really only sends email exactly on Tuesdays and if the subject line is "send email to user". And there's no tests, so who knows if the next LLM can fix the bugs without introducing new ones.
For the last several years (since GPT 4o) I have written several apps in use by people, making money, entirely with AI/LLMs (me neither writing nor reviewing the code in any meaningful fashion - other than high level architecture, schemas, etc.) and - yes, in a few hours it can do things that would take a normal human weeks (if ever!). But left unconstrained, it will just pile more and more garbage on the pile.
Fable MAX is the thing that gets me much closer to "just send emails" but even then it doesn't look in my specs directory for the spec, it just goes off and does a Jurassic Park-style "It's a Ruby-on-Rails system! I know this!" and disregards all the other ways we send email and writes its own thing. And often I'll go "Where's the button to do X" and it will say "You're right! Nobody asked for it, so this page is an orphan!"
I happen to use Superpower's (https://github.com/obra/superpowers) "brainstorm -> spec -> plan" workflow in e.g. Fable MAX (for anything non-trivial) then I have Fable send to GPT 5.6 Sol XHIGH for execution, with Fable (either the original, or a separate one, depending on the criticality of the task and the blast radius) being the critical reviewer.
Even then, though, I need to continually guide it against the norms and conventions of the codebase/app, because it makes a ton of assumptions.
It doesn't surprise me that if people don't take a fairly rigorous approach to AI-software development then they'll end up with a mess. Even if you buy into "just re-write it" (I happen to think that's where we're ending up) - we aren't there yet and without e.g. a strong test suite, re-writing it is just as likely to create more bugs than it is to fix the existing ones.
> Assuming both people are truthful and operating in good faith, how can this be explained?
Quite simply, difference in skill.
The sysadmin that has configured a few applications in their career would likely get annoyed at how many mistakes an LLM makes.
The manager who barely knows their way around a shell, uses an LLM to configure an application in less than the three business days it will take them, and will claim they are 10x more productive now.
Once again, LLMs are effectively great at triggering the Dunning-Kruger effect. The least you know, the more productive you feel.
that’s perhaps a bit harsh, and I’m late (intentionally) to all this as I’ve worried about personal atrophy, security, and feeding the shoggoth
but also with my instructions it will churn on prompts for hours. It makes use of my local rags by domain (which it also helped me build and maintain). Most recent
example: it’s been on my list to set up headscale for like 5 years now. I know how to do it, I’ve read the docs and kept putting it off, partially cuz Tailscale works and has a generous free tier, and partially because I didn’t want to strand assets that I’d somewhat shortsightedly hardened to only accept ssh from my Tailnet.
claude did it all in 3 hours, while I worked on other stuff, prompting for `op` access when it needed keys, and logging each usage, with minimal involvement from me. All with more documentation, roll back points, and “learnings” propagated to the appropriate rags than I could have created in a weekend session. It made mistakes (some that I likely would have made, like region settings) then reasoned about it, corrected and tested, all transparently, all git backed, all posted about on my Mattermost server as it went, so I can easily search it the way I would of an extremely detailed teammate did it.
I don’t know what to make of it all, but letting it churn through my backlog has been eye-opening.
"it must be some sort of weird badge of honor to be so profoundly incommunicative that you can't get the ai that desperately wants to sycophantically pump you up about all your brilliant ideas to be that useless. that is genuinely harder to do than simply using the technology even passably."
Definitely incomprehensible that someone might have a different take than you about the 'goodness' of your prompt outcome...
I mostly view AI as raising me up one more level of abstraction in coding. It still requires babysitting and verification if you want anything vaguely maintainable that's not pulling in a ton of irrelevant libraries, but now I am able to give a clear spec for a ~50-100 line task and it'll get it 90% right, and it's a small enough chunk I can validate it well.
Claude though is still slow enough that I think it works out to maybe a 50% improvement at my time to do a task, but sacrificing deep knowledge and learning. I worry that I'll never develop the same skills and intuition that senior engineers developed a decade ago because I'm outsourcing a lot of the planning and thinking.
Here's the thing. I've been an AI denier and skeptic since the beginning. I've softened. This is my reality on the ground:
I am tech lead on a front end project that is typescript with react and everything else as bog standard as possible. In the last year the backend devs have been pressured to be full stack, so have been submitting AI PRs to my repo with Claude. The PRs mostly 'work', but invariably have major problems that wouldn't make it through a code review in the before times.
Front end JS on a CRUD app is the literal best scenario for AI. Professional senior devs are using it, and it can kind of work, but still not really.
I've given it the old college try myself recently, and it is at the same time astonishing and disappointing. Yeah it can do a lot, but there's always something wrong with it. The 'something' is not always a bug; it can be a design issue, performance issue, unused code, duplicate code, etc, thus many people don't notice the problems. But I think for those of us who prided themselves on quality, the stuff doesn't work well enough to use. For those who leaned more towards speed, they're overawed. I am concerned for my career, yes.
It’s not going to go back to pre LLM times, it’s also not going to go to a generative ai utopia where we can turn off our brains and a model will understand every nuance of human existence without some from of defined guidance.
Just like block chain still exists, it just didn’t revolutionize decentralization. It created a new tool that can be implemented when you want the benefits of what the blockchain can do, same thing will happen with language models.
I think the biggest realization to me of this whole thing is that most people don't want to turn off their brains. I think we, very naively, thought that some hypothetical life of zero-thought pure-leisure was not only achievable, but desirable.
And it's just... not. People want to be challenged, they want to read human material, they want to listen to human music. Sure those like crappy Spotify fake artists might make some coin for Spotify. But are people going out to those non-existent tours, or are they instead listening to Confessions II and praying nightly that Madonna tours again?
And, Madonna, if you're reading this: please go on tour. Please.
Right? It feels akin to people who oppose social welfare and things like UBI because “people just don’t want to work.” The reality seems more like “people want to work, they mostly want to have a say in what that work is.” How many people working service jobs didn’t have access to higher ed, or couldn’t afford a guitar, or accidentally wound up taking care of a family before they could research algae, etc etc.
Personally, I’d be knitting and crocheting things for other people with most of my day if I didn’t have to worry about money, or I’d help my neighbor with the website she wants for her business venture, or I’d teach kids how to code. These are things I’d like to do, that I can’t readily do while having the overhead of a full time job unless I run myself ragged and tbh, I’d rather not.
I think the sentiment comes from people that just don’t find pleasure from working/mastery. The incentives have changed so much with llm-enabled development, but not which ones people respond to.
Biologically, life is an effort-minimizing machine. Our brains have a whole system of habit-forming to minimize the enormous amount of energy it takes to think.
Whatever people say, give them a way to think less, and they will necessarily prefer it unless they literally are a Buddhist monk, seeking to be aware over every action and thought.
They want to be challenged in such a way that they perceive to be enriching to their life. The reason people use AI at work and don't blink, but then won't listen to AI artists on Spotify, is that they see one as enriching, and the other as menial.
Optimizing the menial is the end-goal, optimizing fun and humanity and expression is not desirable. It's just not, and we're seeing that over and over again.
Yeah and no... I have been a developer for 20 years and most of my time isn't spent writing code. Most of my time is spent monitoring production, being in meetings, doing design work, doing code/spec reviews, creating tickets for infra/IT to deploy stuff...
So yeah I love AI and it really helps my personal projects. But my actual work? A little, I can work on some nice-to-have PR in the background during my meetings. I might be 20% more effective coding? Mostly because I do more of these low-hanging fruit PR like bug fixes and refactoring. The hard stuff still require thinking. But that's not even half of my time. It's fun, but not world-changing.
> I just don't understand how someone could think we're going back to a pre-LLM world.
That's not the problem, the problem is that for every hour of dev saved society will lose 10 hours on fake data, fake bug reports, automated scams, needlessly convoluted email, etc.
Because having a slave do whatever you say can also enable some pretty remarkable workflows, but that doesn't stop it from being too morally and economically expensive to become ubiquitous AND sustainable. LLM usage certainly doesn't involve sapient beings and so doesn't carry the same moral reprehensibility as slavery, but in real terms, it's probably even more of a financial quandary.
Every time there's a new model This Changes Everything. Every single time.
Every time someone points out a limitation of LLMs it's always the same refrain: "Have you tried the latest? Because they're so much better now!". Except this happens with every single model generation. Every time the new models are so good that it's a seismic shift, a step change, a paradigm shift. It's like the finest curve asymptoting ever upwards, a few billions of training cost at a time.
But- to where? AI keeps getting so groundbreakingly better and we're still stuck in the same old world, except now you can...
... port your unmaintained app from 2017 to modern Android? Why was your app unmaintained? What is the societal and technological upheaval that will come from automating such low-stakes tasks? For such huge costs?
But it is now possible for a very non technical user to vibe code Grand Theft Auto II for fun. In two years they'll be able to vibe code GTA V for fun.
Likewise with many other things - people who don't know how to code can produce large custom programs which do what they needed. They're probably not amazingly well written nor maintainable by a human with limited time on their hands but that also doesn't matter at all for nearly all use cases.
That should completely destroy the software industry and the software development profession, but this hasn't happened yet far as I can tell. Maybe we're not quite there yet?
I've only found this guy trying to code GTA 6 before it's released (not II):
Yes it's a useful tool. What does that have to do with the singularity, recursive ASI, fully automated economies or whatever is being hyped as the radical transformation of society?
The issue is not capabilities (and never was). The issue is reliability.
And that’s assuming you really need to do these type of tasks using an LLM. As GP said, and as you’ve proven, most people are trying to use LLM as an hammer for everything that looks like a nail, without verifying first if it’s a screw or something else.
1. Everything you said is more about revolutionizing IT though. IT is more easily testable than other things in real life.
2. The gap between the singularity and really strong automation is huge. It's basically that between 1 million and infinity, or even better, that joke about the difference between 1 million and 1 billion being roughly 1 billion.
#1 is what's so crazy to me that people have trouble understanding. No one is saying that we're going back to the pre-LLM world; it's just that the world isn't honestly that different than before. Most people's lives are pretty much the same. Their power bills are higher, but that's not all that different from when gas starts costing more because of a new oil war. The government has some new ways to spy and target weapons, but that's pretty abstract to most people (and it's not like they were having trouble doing those things before). Some new tech companies pop up and have a bunch of money, and some existing ones have less, but nobody outside of the tech industry who isn't already a huge outlier in terms of how much they pay attention to stock prices really cares.
If this is the singularity, consider me extremely underwhelmed. My job my have changed, but to the rest of the world, it's just more of the same.
LLMs are very good at programming. This is clear. Software engineering will look very different in 2030 than it did in 2020. There are also opportunities now for people to use code to solve problems that weren't previously economical because hiring a programmer was expensive.
Will AI function the same way in all domains? That is the current narrative.
No revolution revolutionizes in the way planned or hyped. Instead, once triggered, it emerges mostly unplanned. It unfolds as the product of human action but not of human design.
Individuals will use LLMs as they see fit in their work. They'll make a lot of mistakes because they don't see their own work clearly and because they don't really understand the outcomes others care about. Executives who make their money with their mouths will, of course, endlessly open their mouths, even as they don't understand the tech because they never really use any tech beyond email, text, and phones. And so on.
Nobody knows how LLMs will ultimately shake out. But even five minutes of using Claude makes it hard to imagine all white-collar work will continue as in the past.
Even as a teenager the singularity didn't make sense to me. Why would AI capabilities suddenly improve by an astronomical amount? It's still going to be running on hardware and software that we (humans) design. Even if AI reached parity with human brains it would still need to improve efficiency by orders of magnitude to reach parity with human society's output. And only then could it start to outpace the improvements that humans are making.
Even if the AI singularity is possible, the acceleration would take a long time. Possibly a lifetime.
The singularity is the tipping point where the AI can improve itself faster than humans can comprehend, so the software at least wouldn't be human-designed anymore.
It requires a level of self-driven intent and access to their own code LLMs aren't capable of on their own. They might be one component of such a future system though, we just don't know yet. Even then it would probably take quite a while to reach that tipping point. GP is just wrong, talking as if we'd already reached it.
We won't know how close we are to takeoff until takeoff. My main critique of AI is that takeoff is dangerous, and we're basically running towards the cliff as fast as we can. Will you grow wings before you fall to your death? That's basically the bet we're making, if you believe in takeoff.
Silver lining is that if it goes bad we probably won't live long (as a species) to say "I told you so"
Probably not though. AI is already designing its own chips, optimizing its own GPU kernels, suggesting the next experiments to run and writing its own harnesses... recursive self improvement is here already. It's just a question of speed, time and how far it goes.
The thing i never got about the singularity is that its just one concept that was suggested by a few notable people over time. There is so many different ways AI can go, some which we wouldn't have foreseen.
> Most of us on some level felt confident that AI would completely revolutionise our society.
Seriously? Maybe I’m just getting old, but having lived through the dot-com hype in the 1990s, the XML hype in the 2000s, and the cloud hype in the 2010s, this has been an utterly predictable hype cycle around AI. This does not mean that AI is completely useless. It will impact the world but not in the ways the snake oil salesmen are claiming. This is very similar to the internet. The best bubbles are built on a kernel of truth that is then blown way out of proportion and wrapped in layers of snake oil fabrication.
> I personally can't wait for this to end, and for everyone to collectively get back to waiting for whatever the next golden ticket that's supposed to solve all of our problems turns out to be.
So, you’re committed to falling for the next one, too, eh?
The history of the UK's railway mania in the 1840s is worth researching, because it's uncannily similar, with a stock bubble, fraudsters and chancers, huge hype, cutthroat competition, boardroom intrigue and drama, miles and miles of unnecessary infrastructure in the wrong places, and eventually a technology that became useful, but could have been developed more intelligently.
I recommend the book/audiobook „ Boom and Bust
A Global History of Financial Bubbles“, it does a great job covering various bubbles, with historical context, and going through the details of regulatory and economic factors
And manias are so foundational to the relatively young field of AI that they made the term “AI winter”. We should brace for the third (I think) AI winter soon
When the current mania ends that will be more like a tech/software winter than just AI. I expect the public to have a complete distrust for tech companies and politics to push for strict regulations
The paradox of the cutbacks after the railway industry corrected is that more or less every track they got rid of would now be useful. The Beeching cut damage done to East Sussex, for example, is devastating and unfixable.
Manias are required for humans to develop a new technology. It’s just part of the human condition.
Tilting at windmills over the stupidity of it is just something I’m trying to get over and not care about at a personal level. Nothing I can do about it either way!
"most" is a hell of a claim, yea. "they're going to be useful sometimes and will stick around in some form" has been rather clear from early on (they're Transformer++, of course they'd be useful sometimes), but every session I spend with one still turns into lengthy deeply-misleading wild goose chases until I learn enough to force them down the right path[1]. quality/size of the model hasn't mattered for that, it always happens at some points (and it's just luck whether or not it's a critical point), and I see no reason to expect that it will change.
>From memory, the last time I was given a presentation on it, by actual Snowflake staff, they reported that ideal configuration results in something like ~92% accuracy due to the complexity of data at a large business ...
it's exactly this problem. nobody has solved it. lots of companies are trying to sell that they have solved it.
when I don't care about the end result that might be fine (I have used wordpress too), but frequently it ends up that they helped find relevant docs (they're quite good at finding that from vague descriptions, and it's very easy to verify) and I would have been quicker if I had simply read those docs in full. and then I'd have a more complete understanding of the space too. my biggest worry for personal use is that LLMs will write those docs in the future, and be riddled with mistakes :\
1: ...and then they're quite good code dictation tools. people who think "writing code isn't the bottleneck" just haven't spent enough time in well-understood spaces or rebuilt enough wheels. it absolutely can be a several-multiples bottleneck in day-to-day work, or when re-doing something you've done before and fully understand.
> having lived through the dot-com hype in the 1990s
Having not lived through this, I'm actually curious as to how similar it was to the current hype cycle. Was there a general sense that something singularity-like was approaching?
I don't and wouldn't claim that AI is completely useless, I personally use it for at least a couple hours out of each working day and feel that it boosts my productivity significantly. My point is that the kind of benefits we're seeing from AI are not the ones we expected, and the difference between expectation and reality doesn't seem to have sunk in for most. I regularly hear friends talk of the post-scarcity future which is just around the corner, something like the culture Minds from Ian M. Banks, and this pseudoreligious narrative seems to be what the big AI players are deliberately selling. What I'm actually seeing is something more akin to Mr Meeseeks from Rick and Morty.
> So, you’re committed to falling for the next one, too, eh?
Haha, no. The point was that I've seen better things in the long winters between hype cycles than in any hype cycle.
If you read the media from 1998 - 1999, there was than overarching message that EVERYTHING was changing. EVERY business needed to be on the Internet right now. Any business that wasn’t investing in the internet was going to be slaughtered. Brick and mortar stores were viewed as a negative on your balance sheet. All commerce would be e-commerce in just a couple of years. Startups with no revenue and no prospects for profitability for at least a decade were IPOing for hundreds of millions of dollars, sometimes with 2x-3x the valuation of brick and mortar peers in the same segment who had established brands going back decades. Whenever anyone questioned the lunacy, everyone responded that, “This time it’s different because the Internet changes everything. None of the old rules apply.”
And then the crash happened and we learned that all the old rules still did apply, and while the Internet was hugely transformative, the simplistic idea that brick and mortar was bad and e-commerce was the only way forward was fundamentally wrong. Yea, surely some segments did change (Amazon killing Borders and Waldenbooks, Netflix crushing Blockbuster, etc.), but investors ultimately look at profit even if they’re willing to ignore it for a while to grab market share.
So, I suspect that AI is going to play out similarly. Right now everyone is frothy, screaming that anyone not token maxing will die. Yes, AI is going to be impactful, and, yes, it’s not going away. But the hype is surely overblown. When was the singularity supposed to arrive? Three years ago, Altman was saying it was going to be 2025, right?
> Was there a general sense that something singularity-like was approaching?
To a degree. There was breathless expectation that this was a turning point for commerce, government and society and that everyone had to stake their claim now. Everything was going to change.
What was it going to change into? Beyond "$OLDTHING but on the Internet!!!", nobody really knew. Those first steps were just webbifying existing processes, stumbling along, refining what was reasonably successful and dumping that which wasn't.
It was like a black hole; from afar we could vaguely see an event horizon, but the people falling into it had no idea what was happening or where they were in the process.
> Having not lived through this, I'm actually curious as to how similar it was to the current hype cycle. Was there a general sense that something singularity-like was approaching?
A singularity? No.
One of the early driving obsessions was a coalition dethroning Microsoft (which both did and didn't happen). A battle over closed-vs-open. That story was everywhere.
The main thing I notice is that there's a bit of a difference in the way value is created. Even in the UK the arrival of the web created a lot of early adopter winners — ISPs, design agencies, small dev shops like the one I worked for. And it generated a fair chunk of early money, changed the direction of state monopoly telecom etc.
Early well-bounded successes in 96-98 earned proper money, generated a bunch of press, generated new work. Because they had to: you didn't get access to huge pots of cash to grow with, until you had a product that was on the market and selling. The VCs didn't know you existed.
At the beginning it was small tech industry hustle and actual product creation, and a lot of companies really had the small software company playbook and ran that. You had a lot of PC resellers and systems builders who suddenly found themselves as qualified as anyone to build websites or run ISPs.
The problem after that was everyone's eyes got bigger than their bellies, the projects got too ambitious too soon, the VC money arrived even in the UK, and there were a lot of failures. I perceive the moment the firm I worked for got VC money to be the beginning of the end. I thought my bosses were altogether crazy to take the deal.
We basically were not ready to scale fast — the dot com era really scaled brand new companies in terms of number of employees and scope at a level that was at that time sort of unprecedented except maybe in the semiconductor industry.
Towards the pets.com IPO crash end of things, that is when it started to get unreal in a way that you'd recognise now — people talking about entirely new business models, and you had a running joke that if you just put "… on the internet" at the end of your elevator pitch people showered you with cash. The most insane of these being CueCat: a barcode scanner, but on the internet.
But did we think it would fuck everything up everywhere all at once? No. It was going to make us money and make it easier for everyone to buy and sell things, but it was going to create jobs (and it did for a while). It took until well into the early 2000s for the impact on retail to be truly felt.
Nowadays, companies scale enormously before they make any money, VC cash gets handed over to PhD students who only have the concepts of a plan, freemium tiers do the marketing and everyone is trying to follow the loss-leading market-share growth strategy that Amazon arguably pioneered.
The AI boom is perhaps the peak of that Amazon growth culture, "winner-takes-all" thinking, which was always insane.
This hype cycle is much more like the NFT hype cycle — it's just that it has a more easily understood product. Impossible to see Beeple's Everydays as anything other than a harbinger, IMO.
Each new iteration of the hype cycle feels "like its not hype" because the tech is more promising in new ways, but the hype comes from human perception. New tech has to fight a lot of real world and project management issues, and development morphs and slows. We don't know exactly what we have until it faces these issues.
It was the same thing with blockchain, who here remembers the peak delusion of IoT + smart contracts replacing human judicial systems and crypto making central banks useless?
LLMs are of course a form of progress, the same way blockchain was, but it has been baffling and cringy nonetheless to see people with a strong technical background pretending that at some point in the future the development of complex applications was going to be reduced to a few prompt lines written in English.
Once the AI fad dies and the next hype appears I told myself I'm going to jump on it to take financial advantage of it. At this point I believe that the technological cycle of hype is an almost entirely social/economic phenomenon and there is no reason not to make money from it given that people insist on this kind of irrational behavior.
It'd be different from today's state of affairs in that I wouldn't need to spend the majority of my waking hours reading stuff I don't care about and building things I don't believe need to exist in order to pay for a house I don't own. Healthcare would probably be more reliable and accessible too.
At this point though, I no longer believe this is anyone's future except the most fortunate of the fortunate, most of whom essentially live this way already.
It seems quite premature to say that. We're 3 to 4 years into the LLM revolution and the rate of progress is still impressive. The recursive self-improvement aspect that is necessary for the actual singularity is something we're only really starting to get into this year.
If the singularity is 5 years from now, that is still much sooner than most people (including me) previously expected it to happen.
> We're 3 to 4 years into the LLM revolution and the rate of progress is still impressive.
The “Attention Is All You Need” paper came out in 2017 and OpenAI released GPT-1 in 2018. ChatGPT was released in late 2022 but that was not the beginning of LLMs. Transformers and generative AI go back even further.
Can you pinpoint the revolution by pinpointing moments of invention and release? Revolution takes time because it’s more about uptake, downstream effects, and feedback.
Mobile phones at that time were 800g and cost $4000. Using your logic you could say we are almost 20 years into the age of LLMs because Cleverbot was released in 2008.
The first prototype of a cellphone was functional in the early 70s. Before that you had walkie talkies and farther back wireless communications.
We're just shy of 10 years since attention is all you need and just a few years past the first commercially viable LLMs. So in cellphones we are something like early 90s.
What they're saying is that mobile phone tech didn't scale at the same rate as LLMs. How fast it takes a technology to unfold into full adoption varies depending on the industry and on what's blocking development at its inception.
Mobile phones were severely constrained by hardware that made them impractically expensive and unwieldy, and also by the time it took to set up wireless networks at a large enough scale. That's why it took mobile phones a few decades to really get going, and this is going to be different for every other technology.
LLMs, for instance, were not as hardware-constrained - we have a lot more compute now, but there wasn't a hardware paradigm shift or anything, things were already pretty good a few years ago - now it's just about scale. LLMs were also always relatively affordable - there wasn't a period where they cost $10/token, so we don't get the same kind of affordability scaling as with phones. They also utilize existing internet infrastructure and don't need to be rolled out in some new worldwide grid.
I think this is off. LLMs are severely constrained by hardware and we've seen at least an order of magnitude improvement in flops since ChatGPT launched. Over the next couple of decades we are likely to see that level of improvement in memory capacity. There's likely at least a few step changes left.
Regardless, the point of the GP that we saw revolutionary changes in society within a few years of technology introduction is off. It took decades for the full impact of the pc, internet, or mobile to be seen. We're still in a very early phase of figuring out how to use LLMs.
Define "everyone". Google has an AI result before the search results. People do use that. People also still use search results when they're dissatisfied with the AI answer.
> As for LLMs... outside of a few precious professions you could live your life and not notice they are there.
And old folks said the same thing about the internet and smart phones. Fads that would pass. While the younger generations lived an entirely different lifestyle.
I’m personally seeing the same. I resisted AI being a “thing” for a long while, but since I’m around a lot of teens and young adults it was a choice of burying my head in the sand or deciding to take a look.
They are going to change society on a scale far beyond smart phones. In ways many will not see as positives, but change never goes the way one may expect.
Surely you jest. Since people here are fans of LLMs, the Google summary describes said publication as: "The Daily Mail is widely regarded as a sensationalist tabloid with low factual credibility and a strong right-leaning bias."
I was there in the 1990s too and we were deluged with headlines trumpeting how everyone, young and old, was rushing headlong onto the Information Superhighway as the dotcom bubble kept inflating.
It's clear that while LLM progress is continuing, it's at something like a linear rate, or slower.
The singularity requires exponential improvement. That's not happening. Maybe we will develop something that sets that off, but there's no indication of that right now.
Yet. If the exponential improvement had already started, the singularity would already have happened or happen very, very soon.
The logic has always been that the AI would have to have significant tools and agency to do self-improvement for the singularity to occur. This is exactly the thing that a bunch of the AI labs are working on hard right now.
As a vocal advocate for the use of machine intelligence in certain contexts and a skeptic of claims they are useless or always hallucinate or whatever...
It hasn't been even close to even the recent claims! It's been like three straight years where it was supposed to utterly transform society any minute now.
I think we more or less have a fragile consensus that in like, computer programming and really very little else, that on a good day you're probably going to come out ahead with the AI assist. That seems relatively uncontroversial now. But it also seems like success with AI is largely about working hard to get good at it, as a first order concern. It is not at all obvious that blind, uncritical use by anyone is a net win.
It's pretty unclear, sone might say dubious, that anyone has made serious, aboveboard money net of debt and equity, other than the hardware vendors. There's some pretty serious revenue, but it's a drop in the proverbial bucket against the outlay. There like a two trillion dollar balance sheet hole in the US alone where investment into AI has gone.
While I personally don't agree with them, multiple S-tier machine learning researchers think we've got our wheels stuck in the mud, that autoregressive decoder architectures on a tokin-suffix pre-train is tapped out as a paradigm.
It's ok to say "AI is starting to get useful in pretty durable ways" and not sound like an Anthropic shareholder/employee, i.e. completely full of shit.
Uncritical use of today's AI beats all of the optimized use of yesterday's AI by far. I think people like the idea that their success is a result of their efforts, but this is just not the truth.
I don't think anyone's disputing that the models are better than a year ago (though clearly all this, recursive self improvement stuff is utterly hypothetical and that's being generous, most of the progress has been on cost and fit and finish stuff). When it's on the plan, I'll use Fable for some stuff. If I'm paying for Opus? It's 4.5 or 4.6 which were dramatically better aligned and token efficient in the trace at a capability gap that's "you win some and lose some".
That can be true while it also being the case that to someone who has no idea how this stuff works under the hood, it's basically Dunning Kreuger in a box. The next person to go /u/PhdInEverything on me with Fable is getting an education in the history of hardware support for mixed precision training or something. Fable is a masterclass in refusal to ground and a dozen other alignment catastrophes.
So yeah, the models are still getting a little better, but from here out I think it's rapidly becoming a skill game.
Right, and there are two parallel tracks. First is the “every crack and crevice” part - “ summarize with AI”, “re write with AI”, “help me write”, “analyze with AI”, basically useless features being splattered everywhere in the name of incorporating AI while annoying the F out of everyone.
The second is the AI race / existential risk / pretending we’re Oppenheimer track which I think is the larpiest, basically pretending that LLM chatbots are some kind of super weapon and role playing accordingly.
None of this means they’re useful, AI coding, for one, obviously is a valuable tool (though there remains a lot to sort out). But the usefulness is unfortunately being overshadowed by, as you say, people not resetting expectations and instead wanting to play terminator or neuromamcer or whatever (I’m sure there are more relevant sci fi).
I think people think things will go faster than they do. The AI revolution will probably be a bit like the industrial revolution which took a couple of centuries to go from Newcomen's steam engine of 1712 to mass produced cars and the like. With AI, Turing came up with his test in 1950 and it's taken about 75 years to get to something that looks like passing. Give it time.
> Most of us on some level felt confident that AI would completely revolutionise our society.
I don't want to sound like a jerk, so please let me know if I do - I don't understand why you claim that "most of us" felt that way. I ignored "AI" until mid 2024. Then I tried it and was very unimpressed. I tried again in 2026. Seemed useful, then very quickly I realized that it's something that looks great until you look closer.
I was never impressed by a text generator. A talking computer might be the most boring thing I could imagine. I never understood why people got so obsessed by it and I certainly don't understand why it would "change society". Society is composed of people. What would change society is something that would allow people to think less, feel more clearly, express themselves more freely, and suffer less. Of course, things that accomplish these results exist, but they require a lot of effort. I cannot fathom how a technology that doesn't lead to these results could revolutionize anything. In fact, every technology, that I witnessed arising since late 90s has made things worse precisely because it has not done anything to address the human condition, instead praying on it.
This part toward the end of the article resonated with me:
> If you’re being asked to review huge volumes of terrible AI code, just assume that the organisation is going to burn you out and fire you. You will not convince the person drowning you in 2000 line PRs to stop. Start looking for a new job as if you have already been fired. I have seen this happen many times now
I suspect we will see this phenomenon more and more as organizations more widely adopt agentic development.
I don’t buy it - if you’re an engineering leader and this is happening it’s your responsibility to get out there and fix the problem through education, process improvements, improved automated quality checks, better planning. Sticking your fingers in your ears and shouting “no!” is not a sustainable strategy. Figure out what would allow your throughput to double, triple etc and go organise it. Focus on bottlenecks and the most difficult pieces. I’m speaking from active experience here.
Thankfully it's not happening in my organization because we happen to have a sane, incremental strategy. But it's telling that you perceive any criticism of AI code reviewer burnout as "sticking your fingers in your ears". Perhaps you might benefit from a closer read of the article.
« it's telling that you perceive any criticism of AI code reviewer burnout as "sticking your fingers in your ears" »
Go back and re-read the quote from your comment - which I responded to: “ You will not convince the person drowning you in 2000 line PRs to stop. Start looking for a new job as if you have already been fired.”
I’m not overreacting to some nuanced take here. I’m not sure how to characterise quitting as a response to over-enthusiasm / slop as anything other than the career equivalent of sticking your fingers in your ears. I’ve been there, I’ve had the arguments, I have little sympathy for staff/principal engineers or above who react like this - their job is to fix the organisation, put a yoke around the beast of AI and find a way to tap the benefits and use the problems (like those 2000 line PRs) as a golden signal for where you need to educate and improve process and planning.
Again - I’m not selling anything here, I just have found it an interesting challenge over the last year and one where I’ve been happy to make progress organisationally despite the frustrating moments. If someone’s response to a problem like this is to throw their hands up in despair or to engage in whatever the logical opposite of AI-hype is (which the article resorts to repeatedly) then yes I’d worry about being laid off. If you’re in an organisation where you don’t have the agency to fix problems then absolutely - start looking for a new job.
I think the author means if it happens regularly, and asking people to stop doesn't work or marks you as a dangerous dissident. Obviously, it is wrong to quit in response to one person doing something stupid
One of the key things I've needed to educate people on is that code contributions are no longer valuable in and of themselves. Someone who produces a large volume of bad code does not have high throughput that needs to be allowed, they have low throughput that needs to be corrected. High throughput in software is now almost entirely about having the right ideas and analysis.
This sounds to me like a demand that engineers fully submit to their future as reverse centaurs [0]. "Submit to the slop tsunami, become a glorified and burnt out button pusher!"
My F100 corporation has answered this problem by removing code review entirely. Thankfully it's not Boeing or anything important. Yes I am looking desperately for a new job.
Be sure to short the shit out of that company after you leave, assuming something in your employment contract doesn't prevent it (doubtful). Especially if they are software development focused.
> All of the AI projects we have observed as a team are failing. Every single one – we have seen 0% success in a year and a half, not only amongst projects we have been asked to participate in, but even within projects that we have observed in passing while doing totally unrelated work.
That's got to be hyperbole, which blows out their credibility. They chose to say 'AI' rather than, for example, LLM, or Transformer model, or Diffusion model. This means they are including a huge swathe of things dating back to Expert Systems in their claim.
And who hasn't seen productivity gains from more established AI technology - at least things like semantic search? Who hasn't seen diffusion models generating content in roles that might have done the work by hand before? Who hasn't seen some kind of regression algorithm (even using linear regression in a supervised context counts as AI - so you can absolutely do AI even in tools like Excel) improve operation productivity?
Even if they narrowed it to the Transformer model LLMs which re-ignited recent public interest in AI, less ambitious projects to give them to engineering staff to automate easy but boring tasks in the background generally have been a success. More ambitious ones that are beyond what you'd reasonably expect the models to be able to do - for sure, those tend to fail. For most of these, the failure is predictable in advance, while some are at the boundary of what's possible, and so it is harder to predict (these are rationally genuine R&D projects).
If you read the footnote, they follow up to say they've rejected 100% of the AI projects brought to them.
Go to their home page and one of their consulting selling points is recovering struggling projects.
One of their front-page selling points is that they use "ancient techniques" from books written prior to the year 2000, because presumably everything newer than that is bad?
> For non-executive management who might be struggling to deliver things that feel beyond their control, we have ancient techniques (see: books written between 1986 and 1999) to turn your team into the envy of the organisation, and we can drop in directly to get your team the resources it needs to save a struggling project.
This is entirely a selection bias issue that they've created for themselves: Advertise a consulting service for saving failing projects to companies that don't have internal expertise to handle it, then write blog posts that 100% of the projects you see are failing. Also refuse to help them, to guarantee they can't be converted to successful projects to keep the success number at 0%.
> One of their front-page selling points is that they use "ancient techniques" from books written prior to the year 2000, because presumably everything newer than that is bad?
I get what you are saying here.
At the same time, was reading Deming's last book [0] and there is a great line that I have never seen in any recent business books:
"The organizational chart is usually a pyramid. The main benefit of this style of chart is to clarify who reports to whom. Notably absent from this chart: the customer."
I've also worked at firms filled with very smart people who had no concept (or were not incentivized) to think about the Theory of Constraints, Total Quality Management or that the firm is supposed to work as a team and not a collection of fiefdoms.
What I'm saying is: just because a book was written before 2000 doesn't make it irrelevant.
There’s some selection bias involved especially based on their marking, but you’re misreading what they are saying.
> even within projects that we have observed in passing while doing totally unrelated work.
The kind of companies with failing projects seem to be very bad at using AI. That’s different from the normal mix of success and failures at most large companies.
Companies with projects that are doing well don't try to hire consultants who specialized in saving failing projects.
No company with a good AI strategy is going to go to the Hermit Tech website where text written in Ye Olde Font explains that they'll use ancient techniques from the 80s and 90s to make your software work and thinks, "These are the right people for our AI job!"
These people are trying to carve out a niche for themselves as being anti-modern, anti-AI, and being contrarian consultants that you can bring in when you want some external consultants to agree with you in a very specific way.
I think you are right on target. I’ve seen this pattern:
Company does x. X sucks, and is largely derided as being a bad idea, but it’s important-employee-bobs baby. No one wants to be the guy that tells bob that x is killing the company in some small or large way, because bob can get them fired or is protected by someone who can, so you hire consultants xyz who specialize in “transforming productivity through x management” and they come in and “transform” x into something less lovecraftian, or just explain why it’s bad in a way that makes bob look like a genius.
> Companies with projects that are doing well don't try to hire consultants who specialized in saving failing projects.
Large companies where every project is going well are rare.
I agree companies with good AI strategies are unlikely to use these people, but excluding failures is just as much a bias as excluding winners. AI lets you shoot your sold in the foot even faster shouldn’t be surprising, but it’s still something to consider.
Tale as old as time. Revolution has been merchandised. The action and the reaction, they all have suppliers, which often turn out to be the same entities supplying to both sides.
Perhaps I should have specified Anti-AI. Its just the thing that upsets me the most about your everyday liberal professional, how much they hate AI just because Trump is pro AI
I think "your everyday liberal professional" is more likely to be anti-AI because it is being advertised as a direct threat to their livelihoods, rather than for any more abstract reason. The latter includes both political allegiance stuff and the reasons those professionals would likely cite, like its energy requirements or the economic damage if the bubble bursts.
Whether or not you think it likely that this threat will materialize is beside the point. Workers never like it when someone is advertising that they will put you out of work.
No, because most people in say, California, which is very pro-tech, are perfectly fine with AI. But people in the Northeast are angry at it. It’s entirely cultural.
That observation[0] isn't particularly useful for arguing that it's a political allegiance thing. A lower fraction of voters in the pro-tech part of California (SFO, roughly) voted for Trump than, say, in NYC. Your observation cuts against the argument that political allegiance is the main driver, not in favor of it.
[0]- Assuming it's true, which in my experience it isn't especially, but this isn't the best argument.
You have no idea, you must not know that they are afraid of Waymos out in the northeast, they think they are evil or something, I'm not even joking.
Anyway, I didn't say it was a political thing I said it was a cultural thing. The average liberal is not someone who thinks primarily in political terms, they are just going about their lives unreflectively and they genuinely believe in the moral weight of their feelings, which are their primary guide. Now, I'm not saying that human life is not generally composed of its daily concerns, which tend to rule us, but for the average liberal politics is no different from acting on basic urges; like a mouse chasing the next piece of cheese in a maze, they cannot see that no turn will ever lead to their escape; that so long as they continue to chase the very next thing, they are trapped in constraints that have been laid out for them in advance. Like the conservatives of the 80s, there is always one more moral panic, one more thing that you're supposed to be upset about, one more cause to fight for. There is no stopping to think why this is something that is supposed to upset me, why is this something that needs to change. They are not able to go just one step beyond common opinion and wonder why there are so few avenues for them in the first place, they are not able to question why the maximum content of their decisions is good or evil, just or unjust, left or right. Yes any decision is binary, but one must be able to choose that for themselves, be able to consider the decision to leave the maze, even if they are, in the end, no more powerful than all the other rats, and there might not be any cheese outside of it; and there might be no end to the maze, and even very far from what you thought was the exit lay the bodies of those who attempted to leave before.
But anyway this is what I mean by primarily cultural. Liberals "drink matcha," go to running clubs, obsess over "consent" (they now have a term "weaponized consent," how absurd! that they are just realizing now that consent is always given within certain relations of power...) Whatever politics they have is on the same plane, they are doing it because its something to be done. Someone who thinks primarily in terms of politics, that is, in terms of the social dynamics of power, would never call themselves a liberal.
> Its just the thing that upsets me the most about your everyday liberal professional, how much they hate AI just because Trump is pro AI
That's what I'm refuting here -- that people here dislike AI just to be contrary to Trump. This is implausible.
And trying to lecture a life long New Englander who currently lives within the MBTA zone on what the culture is like in the northeast is maybe not the best look. We aren't afraid of Waymos here; the only people I've met who weren't jealous of them were taxi or Uber drivers. That supports my hypothesis, not yours.
I'm not sure why my experience differs from yours. When I was in the northeast recently I got into heated arguments with people, one person said they "hated Waymo" and refused to elaborate (but also said they used AI everyday for work), another said flat out that technology is evil, so I asked them if poor people should go without electricity. Then they restricted their argument to AI is evil, and that was a more nuanced (if not trite) debate, which they eventually conceded. But not without some gnawing and thrashing!
But I don't think that hating AI or technology or whatever to be contrary to Trump is a genuine political statement, its just reaction. It involves politics in the same way that everything we do involves politics, but it is not "consciously" political in the same way that a baby demanding care from an overworked mother is also not consciously political. Baby wants milk, your average liberal wants to feel self-righteous (they would honestly do better with the milk). Someone can claim anything they want is political, it does not mean they are doing it for genuinely political reasons, they are probably just doing it because they fear the censure of their friends and the dissolution of their social life. Not engaging with something because you are afraid of how it could harm you personally, consciously or no, is not an objective way of making a decision that effects society more broadly. Like, a baby will just die if it doesn't get milk, you won't necessarily die because some of your friends stop talking to you.
If you were in NYC, some of this may be a Boston/NYC difference. NYC is much more anti-Waymo (which I interpret mostly as an extension of being much more anti-car in general). There are also substantially different cultures between the two re: art and finance, which may have reflected on the AI questions.
I don't think you can decouple Trump from himself in this way without a concrete explanation. If some factor causes one to dislike something just because Trump likes it, then that same factor would influence voting. We have numbers for voting. When there are lots of other plausible explanations than Trump, the onus would be on you to demonstrate why more people are taking reverse-psychological cues from Trump in a place where more people also voted for him. Gesturing at "culture" doesn't do that.
I’m not sure I understand your point. Its not the case that more people voted for Trump in a place where more people are taking reverse psychological cues from him, but precisely the opposite case, the more people take reverse cues the less vote for him, which implies their positions are on account of reaction and not because they are genuinely reflecting on their social and political condition, which would elevate them to conscious political actors rather than people following a trend because they are afraid of going against it.
You are simply incorrect about that. More people voted for Trump in NYC (the typical exemplar of "the northeast") than in SFO (the exemplar of the part of California that is more pro-AI than NYC).
I think that absolute numbers in specific cities don't tell the full story. NYC also has far fewer people with college degrees than SFO, which would correlate more strongly with Trump supporters. I think that if you polled those who voted against Trump in NYC now, and asked them about their opinions on AI, you'd find they diverge significantly from those who did vote for him, whereas if you asked the same question to Trump voters and non Trump voters in SFO they would answer pretty much the same. SFO is also in California which does not have the same cultural relationship with technology as NYC or much of the Northeast, although the culture I'm describing stretches basically all over the east coast.
it's not surprising as LLM's will generally fail you when you yourself don't know what you want. it's surprising how many people and organizations just blunder around scribbling code without clear goals in mind - for such people LLM's are a net negative as they will just end up with even more scribbles and no hope to make sense of it all.
> Supposedly 70% of software projects failed even before AI.
Given the way LLMs are marketed, we should expect a vastly more favorable project success rate, not simply a close parity, and definitely not a decreased rate.
Even if we consider that the number of attempts will rise if development becomes faster, we're told to expect a greater number of projects should also succeed because they're easier. They're supposed to justify the higher costs.
> Given the way LLMs are marketed, we should expect a vastly more favorable project success rate, not simply a close parity, and definitely not a decreased rate.
> Even if we consider that the number of attempts will rise if development becomes faster, we're told to expect a greater number of projects should also succeed because they're easier.
I'm not sure I follow why you would think this. Prior to AI was the reason most projects failed because generating the code was too slow or too labor intensive? That would be surprising to me if it was true.
Generating code is not, and never was, a bottleneck to projects. Understanding the problem, designing a solution, organizing resources, testing, and customer buy-in and moving targets have always represented the real issues.
Now, with LLMs, we're supposed to be able to paper over some or all of these problems.
They're supposed to be able to understand and intuitively fill the assumptions present in vague requirements, then produce working applications. They're supposed to shrink the timelines from years and months to weeks and days. They're supposed to replace human expertise with a reliable service.
Why else would I hire an LLM service over a team of humans?
> They're supposed to be able to understand and intuitively fill the assumptions present in vague requirements, then produce working applications. They're supposed to shrink the timelines from years and months to weeks and days. They're supposed to replace human expertise with a reliable service.
But none of those sound like things that would change how likely any given project is to succeed. Shrinking timelines from years and months to weeks and days just means that you can fail faster and try again sooner (hopefully with lest costs), but nothing about that implies your rate of success is going to get better.
I think the point is that using an LLM is not really going to shrink project timelines by that degree.
Most large software projects I've worked on in a professional environment, "writing the code" was one of the fastest and smallest parts of the project. Maybe 10% of the overall calendar time of any given project was "designing and typing in software." The bulk of time spent tends to be working with customers, researching and refining requirements, reviewing the requirements with execs and leads to get their buy-in and support for staffing the project, performing various non-engineering reviews like legal and privacy, aligning timeframes among multiple contributing teams, seeking approval from Director X to move on to the next project phase, waiting for Key Team Member Y to get back from vacation, conducting QA and field testing, soaking the software through various rollout stages like internal, dogfood, internal staging, external beta, and then incremental percentages in prod, while watching and waiting for any regressions or failures. On a project that takes 6 months from idea to 100% in-production, writing the code might take a few weeks of that.
So why are we so focused on a tool that speeds up the smallest part of this process?
The article is a fun read. There's a certain amount of hyperbole and subtext which may perhaps be unfamiliar to some readers.
But the point is fair. Where are the huge AI wins? Why haven't Anthropic and OpenAI produced a regular series of case studies where Company X did Fun AI Thing and incredible success ensued?
Why do these conversations always end up mentioning chat bots?
Why do c-suites seem to be full of people chasing trends instead of leading?
Does anyone remember what customers are, or are corporations aiming for Platonic abstraction, where business activities become ends in themselves, with no useful output?
Yeah, and how and what someone jokes about will often tell you something about what and how they think. I don't think they'll literally just ignore everything after that point, but I do expect that they are going to tend towards an old-school approach.
To be completely fair, most software projects fail, AI or not, and we've known that since before the 2000s. I'm not saying everything post-2000s is bad, but just that the core is unchanged: most software projects fail, you should expect failure, and there is no magic silver-bullet process to either anticipate or prevent failure. Agile (big A) is certainly not that process.
I mean, that line regarding ancient techniques is very obviously written as satirical. I read it as „we are serious professionals, not young trend following folks“
The prominent "Business Owners" link on this company's website is broken. "Not Found".
Perhaps this will get downvoted but I personally take with a grain of salt anything written/stated by a company that can't even get the most basic functions (like running a simple website) correct.
And the even bigger irony here is that the author has a ranty blog post in which he claims he saved his employer $500,000 by clicking a button. "[It] is fucking wild that an inefficiency that took me five minutes to solve in a GUI configuration panel was allowed to persist," he wrote.
I don't think this is surprising at all. I'm pretty sure I've saved my employers similar amounts in the past, because what happens is something gets configured months or years ago, the dev leaves, everyone forgets and assumes things are how they are.
I've had to delete some really silly code that would slow things down or just force waits as a result of either dealing with legacy APIs or some other arcane reason. Not without testing or making sure it wasn't there for a reason mind you, but these inefficiencies can sometimes be hidden big problems.
The point isn't that this stuff happens. It's that a person who is trying to sell consulting services with ranty "this industry is a sham" and "it's a disgrace that it was even possible" blog posts has a website with just 5 major navigation links...and one of them is 404.
> And who hasn't seen productivity gains from more established AI technology
You have to be very careful about claiming productivity gains. There may have been some instances of gains in a specific part of a workflow but does it slow down others or result in overall gains is yet to be empirically measured and validated. We’re seeing metrics like more lines of code, better unit tests, documentation, faster PRs etc. but the actual gains of businesses are still a question mark. Do more PRs lead to faster features being shipped or does it lead to slower reviews or bug ridden code that breaks user experience? I’ve see a lot of companies tout their metrics around more code being shipped but the same companies aren’t talking about how that translates to an actual dollar amount.
These things have been "good" for a while now. And yet, companies like Amazon and Microsoft aren't showing significant improvement in their most visible products.
If it's not obviously showing for the all-in, AI-selling companies, I simply don't expect serious improvement for everyone else.
They're undeniably neat tools, but so far there's no observable evidence that they're transformative.
Amazon and Microsoft have shown considerable decay in their most visible products. Maybe it’s AI, maybe it’s just momentum, maybe it’s something else. But they AI clearly isn’t helping them and they have near infinite access(compared to me anyway), so why should I expect it to help me?
I think there's a big difference between individual employees using AI tools to boost their productivity - with things like Claude Code and Codex - and "AI projects" where companies build custom software on top of LLMs.
The former is easy to get right. Any software engineer (at least provided they aren't actively resisting the technology )can get useful results out of Claude Code these days.
The latter is really hard. LLMs are a strange beast to build software on, and most of the obvious projects - like the internal chatbots described in this article - are easy to have over-promise and under-deliver.
Look, if they have data and it says 0%, and you have vibes that say that can’t be true, who should we believe?
Do you work with lots of companies and see large AI success stories?
Or do you just vibe that you personally find AI useful so it must also be a business success?
Look, I honestly don’t care, but I think “it must be false” is also unsubstantiated hyperbole. If an agency says they see no AI success, I see no particular reason to believe they’re lying.
They’re not saying AI can’t be a success. They’re saying they haven’t seen it. That matches my experience too. Proven AI success stories seem… vague, when you dig into the details, in my personal experience.
In general I find their submissions tend towards extreme grandiosity. I find I really appreciate people who have some nuance about the world, can see some duality, and the many many many submissions here are (I admit) often quite fun and enjoyable, but spoken much more from a bully pulpit perspective, with a zeal and self certainty that I find rarely coincides with truth-seeking.
That's a completely different person, they (ludic and smileplease) just both happen to be publishing on mataroa.blog. This is a blogging website anybody can sign up for, basically Medium but for nerds.
I get what you are saying, and while I'll say it is definitely not 0%, I have seen very little in the way of useful software that is primarily generated. The vast majority just does not go the distance for whatever reason. I could explain many reasons, but I am getting really tired of explaining myself. If the tools were as great as everyone says, we'd be going through a software Renaissance, but we're not. I would argue a software dark ages since it feels like things are getting worse and I find bugs in what was historically very long running and stable applications. But, whatever. I think the author is clearly talking about modern AI, I don't think they need to be explicit about models.
The AI mania has been really fascinating to witness because on the surface it’s surprising that so many otherwise intelligent people have fallen into it. But I suppose intelligence as a concept is multifaceted and doesn’t include wisdom. I also wonder if it has to do with personality, where the people whose personality best suits leadership roles are more susceptible to this psychosis. I think there is also an archetype of “nerd” who believes they are smarter than they are and has all sorts of surface beliefs about AI from sci-fi that makes them susceptible.
It’s also a bit of a dilemma; if your boss has AI mania, and you buying into it or not is the difference between being promoted, keeping your job, passed up or even fired, the rational course of action for self-preservation is to also buy into the mania.
You can't get a fad like this with a true believer rate of 0. Too unstable. You have to have some true believers in the mix to see this situation arise, preferably widely distributed throughout the population.
Something I think about quite often with HN is there are probably people on this forum that have friends / family who work at AI companies or are highly monetarily invested in AI themselves.
I don't think everyone on this forum is as objective as we'd like to think. I know I'm not. A large part of why I dislike AI is because I view it as making my life, personally, worse. It has completely fucked up the career I've been building for going on two decades
And all I hear is "adapt or die" from assholes who are chugging the AI koolaid by the bucketfull
It's the bell curve meme every time - very, very often stupid people end up being "wiser" than moderately intelligent people.
Meanwhile the really intelligent people end up with beliefs surprisingly close directionally to the stupid people who've barely thought about an issue, just fleshed out with 100x the detail.
I feel though that if you picked a random sample of members of the public and asked them about the probability of the singularity you would get blank looks from most people on the left tail even if you explained it.
Whereas if you had the statement "ChatGPT can be useful for some things, but it isn't going to fundamentally change the world" you would get a lot of people on both tails agreeing and the people who disagreed would be clustered in the middle.
Maybe that's just my bias speaking though and actually I'm the midwit
The bellcurve meme is essentially that the people on the left use their intuition, the people in the middle discount intuition for pure rationality, and the people on the right are intelligent enough to understand that their intuition is correct.
It's a very interesting technology, so it leaves some shockwaves while it's impacting the world. Over time this will be absorbed and the insanity will die out, but it's quite transformative, just like cars and the internet were (to name a few).
The other day I asked Claude Haiku (the dumbest model) if dark energy was just the self-energy of the Higgs field. I know barely anything about quantum physics. I just wanted some example of AI writing style - something like "You're absolutely right! That's a key insight into a load-bearing ..."
Instead it spat back a bunch of physics related stuff, alleged problems with the idea ("there are 30 orders of magnitude difference between that and dark energy, and why the Higgs field and not any of the other fields, and did you know about axions which are somehow relevant, and ..."). I humored it by saying "well the Higgs field is the only one with a nonzero vacuum expectation value" and it told me I'm absolutely right, that's a key insight, etc etc etc. Then I asked it to tell me more about axions and it said some stuff about CP violation, instantons, but then it made some connections to dark energy and to the Higgs field and said I was clever to bring that up.
Now from my point of view I had to deliberately keep in mind that I was talking to a Markov chain and that I was myself acting as a Markov chain, and it's likely that none of these statements actually meant anything in physics. I can very easily see how someone who didn't realize that would be captured and then write a blog post proclaiming themselves to have made a discovery in physics.
> Checking out a parallel copy of our Go repository and telling the AI to rewrite the whole thing in Zig while I work on something else just so I can keep my job.
> Was it just sales fluff? The answer was a lot more interesting.... Executives at their customers were saying absurd things about achieving 100x productivity, and this meant that if any executive at the vendor said that these gains were not plausible, it would undermine the credibility of the customer’s executive, be perceived as an attack (or heresy), and possibly result in an enterprise contract cancellation.
This post feels like its correct but also doesn't align with my own personal usage of claude for writing advanced sql and python code. I haven't with my own eyes seen an AI chatbot actually deliver a user experience that lets them query data with natural language BUT I have personally experienced writing extremely advanced queries using natural language and it is absolutely able to get close to (by my estimate) 80-90% of the way there.
There surely are companies out there using AI in such a way that is actually advancing them above and beyond their competition. They are probably quietly doing it rather than announcing it loudly.
The whole point of the article is about the big corps with hordes of management and people in them. My argument is that they have always been that way. Before AI it was "data science and analytics" or (as the author says) "blockchain".
Maybe maybe not. In large organisations for each person creating value with LLMs there are probably more destroying value. Bob used to only waste one person time with LLMs he can waste the entire organisations time.
Anyone can use an LLM make a bad ideas sound like a good idea. I imagine this will lead to insane amounts of productivity loss as the entire organisation ends up pivoting to follow the bad idea of a mediocre VP etc.
Don't agree with the hyperbole in the article but I think the broad strokes are there. I'm the same as you, AI has excelled at small tasks and daily chores. Like writing a complex query in seconds that used to take an hour to work out.
Doing feature development seems to take just as long or productivity gains are modest (where I work at least) and I think the reason is touched on in the article. Most orgs are just terminally bad at building software.
When executives say "AI", they basically seem to mean LLMs right now.
AI that just, does something, like extract text from scanned documents, is apparently boring. They don't seem interested in bragging about boring things.
The companies tracking AI usage don't seem to be tracking usage of that sort of thing either.
Their company does data projects. That plus context makes me think they’re talking about internal work process automation type of work, although it also seems like they’re talking about conversational interfaces (chatbots).
I completely buy the “emperor’s new clothes” argument for work process automation. I’m surprised they don’t address AI-assisted engineering, which seems to be going positively for a lot of folks (although I have doubts about its sustainability). I disagree about the success of chatbots, if the problem is narrowly-defined and chosen properly. My previous company built a conversational interface to a vector database and saw good results. (Although, arguably, the vector database was the real magic, and a traditional UI would have been faster and more accurate.)
In general, I think OP is more right than wrong, though, particularly about the AI mania and unrealistic expectations sweeping the C-suite.
Do yo have links handy for AI-assisted engineering going positively? The case I have on my mind of it going negatively is this recent Ford case [1] It's not that I believe it couldn't go positively, of course.
[1] Ford rehires human engineers after AI fails to match quality checks
That Ford story was really misleading. It wasn't about modern LLMs, and the way it was reported implied that Ford had fired and then hired people but if you read closer that wasn't necessarily the case at all - it sounded more like they were re-hiring people who had retired because they needed expertise that had left the company.
> Over the last three years, Ford says it has hired 350 veteran engineers, many of them former employees and others from suppliers, to help address seemingly intractable quality woes that have cost the automaker billions. [...]
> “We had been relying more and more on automated quality systems” and not getting the desired results, Galhotra said. “We brought back technical specialists” and “they hunt for failure points before a part ever reaches the plant floor.”
I think it's not clear from the article why they left (e.g. could be anything from retirement to going to work at another firm/contracting to being fired to switching careers), and likely it's going to be a mix, plus not all were previous Ford employees. Similarly the "AI" isn't clearly defined (but like you I would be surprised if it were LLMs). I suspect though why the article exists (and a possible source of your downvotes) is signalling against "AI", which if Ford wants more expert employees (given their issues), is something Ford wants to present.
> That Ford story was really misleading. It wasn't about modern LLMs, and the way it was reported implied that Ford had fired and then hired people but if you read closer that wasn't necessarily the case at all - it sounded more like they were re-hiring people who had retired because they needed expertise that had left the company.
Oh come on, Simon. Are your glasses so rose coloured they are opaque? They fired, and then re-hired because the AI couldn't do the job. They had to re-hire the quality folks. So, no, it wasn't LLMs, it was quality control -- which is a much more well established domain for automated systems. LLMs are a complete shitshow compared to industrial process automation.
Side question -- do you worry about being so pro-LLM when the promises of LLMs are so clearly falling short?
Show me evidence that they had previously fired any of the people who were hired.
Your comment is exactly the misinterpretation I'm pushing back against here.
The time period covered by this story starts three years ago! What kind of LLMs do you think they were using back then?
> Side question -- do you worry about being so pro-LLM when the promises of LLMs are so clearly falling short
I think my record is looking pretty good here. I was early to the "LLMs are useful for writing code" thing, especially with the code interpreter pattern (both write and then execute code in a loop) which I now realize was our first hint at coding agents.
As @simonw said, the Ford example isn’t a good one.
As for AI-assisted engineering going well, I think the jury is still out. Here on HN and with the engineers I know, you see people claiming multiples of productivity on coding tasks. But you also see people complaining about drowning in slop PRs.
I think there’s a lot of confounding factors to these reports. The type of work matters a lot: bug fixing good, prototyping good, big legacy codebases not so much, but maybe good for increased understanding. The type of automation matters: aggressive autocomplete good, vibe coding bad, dark factory (vibe coding with fancy harnesses and auto-“correcting” eval loops) questionable.
And then finally, the perennial mistake our industry makes, which is to value speed of creation over maintenance costs. Personally, I think this is where AI-assisted engineering is going to fall down really hard, but the jury’s still out on that one.
Anyway, there’s a really big spread in experiences with AI, that I think chalk up more to all this context rather than religion and belief. OP didn’t address it at all, which I think is a big gap in their essay, but I do think think they describe the executive-level mania pretty well.
> As for AI-assisted engineering going well, I think the jury is still out.
Anecdotally, AI-assisted engineering has helped me flesh out ideas or to learn extremely complicated APIs faster than trying to understand the docs (which usually are labyrinthine). MS COM ones, for example. I can go read the docs but it's easier to get a quick idea of what I need to do if I ask Claude to provide me an example of doing something specific with it, because MS's code samples (particularly their full ones in, say, the windows Desktop SDK repo) have always been annoying for me to wade through because I have to filter out a bunch of noise. I can't (and won't) try to guestimate "productivity" improvements though, but as an assistant AI has (somewhat) helped. I still do all the engineering work though. Along with it giving me tips on using more modern language features for languages like C++.
The Microsoft API docs are a special case where i'd say you pretty much need LLMs nowadays because after a bunch of document format conversions over the years they degraded massively.
If you can find some MSDN CDs/DVDs from the early 2000s, the content is much better (and you can clearly see that the current docs are often missing descriptions and names for method arguments or even entire paragraphs).
> I disagree about the success of chatbots, if the problem is narrowly-defined and chosen properly.
If you can narrow the problem down, then you could design a much better interface for it than a text box and free form text (unless that's the better solution).
As for as AI assisted engineering goes, the thing is that after some time with a project, you already have much of the workflows and routines nailed down as scripts and other various combinations of tooling. And unless it's spaghetti code, you will have various snippets you can copy from for new code. The one thing I've observed about AI projects is that there's often little technical design coherence about them. It's always a kitchen sink of technologies and practices.
> If you can narrow the problem down, then you could design a much better interface for it than a text box and free form text (unless that's the better solution).
Yes, I agree, in that the chatbot we built probably would have worked just as well with a traditional UI, and would have been done a lot faster. But it would have been a lot less sexy (actually important for the bottom line!) and there are future directions that could take advantage of the conversational interface that’s potentially better than a traditional UI.
On the down side, good chatbots are really frikkin difficult to write. These things (LLMs) are not reliable at scale. The basic functionality came together in weeks. Getting it to behave consistently and obey guardrails took months, and even then we had to accept a low level of failed conversations.
> But it would have been a lot less sexy (actually important for the bottom line!
That’s what the author have been saying. They do for nice demos which sell the illusion of having Jarvis in text format, but the usefulness is not really proven. And that may be important business wise. But as far as end users is concerned, there’s not a lot of productivity boost.
They run a consulting company called "Hermit Tech". Their websites has an olde style font.
They boast about "ancient techniques" from books written prior to the year 2000
> For non-executive management who might be struggling to deliver things that feel beyond their control, we have ancient techniques (see: books written between 1986 and 1999) to turn your team into the envy of the organisation, and we can drop in directly to get your team the resources it needs to save a struggling project.
So yeah, of course these people hate AI and everything about it.
No serious company is reaching out to these people for help with their AI project.
That has ever been the case. As soon as it works reliably, it's not "AI" any more. Take spellcheckers or collaborative filtering as examples, but there are lots more. Hofstadter in G.E.B. said it well:
> There is a related “Theorem” about progress in AI: once some mental function is programmed, people soon cease to consider it as an essential ingredient of “real thinking”. The ineluctable core of intelligence is always in that next thing which hasn’t yet been programmed. This “Theorem” was first proposed to me by Larry Tesler, so I call it Tesler’s Theorem: “AI is whatever hasn’t been done yet.”
In this moment, the opposite is happening. Everything is getting called "AI", whether it uses LLMs to prompt LLMs about how to prompt LLMs, uses "conventional" machine learning, or just looks mysterious enough that they can expect the market to not ask questions.
I am reminded of "game AI", which for the most part has historically been just giant decision trees, encoded one way or another, because if you hook up any sort of real AI to a game entity or collection of game entities that does any sort of learning or training, even simple 1980s-era reinforcement learning, it turns out the game entities will roflstomp the human players, and the human players aren't interested in paying for that experience. We've been calling those collections of if statements and for loops "AI" for a long time, though, because who wants to hear about how deliberately stupid their opponents are?
Sure, or maybe the actual applications of “AI” are small and unobtrusive, like the dictation of doctor’s notes example, and it’s not actually the massive revolution it’s claimed to be.
I struggle to reconcile this attitude with, among other things, the massive number of exploits being discovered and the pretty straightforward utility for coding. Have you used codex recently?
Perhaps, but I think what people mean by intelligence is something that learns and adapts. If LLMs couldn’t do in context learning I don’t think people would think of them as AI, more as a kind of queryable database via natural language. There are other algorithms that learn and adapt, but in much more narrow circumstances that most people won’t obviously interact with.
Right, and my point is that now that is the bar. If you'd told me in 2008 that markov chains would be smart enough to one-shot even a trivial video game from scratch I would certainly have called that AI even if you had to do some major rigamarole to get it to work.
Because AI meant "make computers smart like people".
We taught the computer to spell check, but the computer still didn't feel smart like a person, just smart like a machine so that obviously wasn't AI. We taught it to do algebra, same thing. With LLMs though, now it really does feel like an artificial human, so this time it really is AI.
Right, but I remember when spellcheck first started to be a thing, and people were like "Wow proofreaders are out of a job, ai is so cool", but then it became normal.
Just this past week, I have had to answer two surveys, from different sources, asking how I use AI in my work. Both had a "choose all that apply" checkbox, and, funnily enough, neither allowed me to select zero options. "This question is mandatory". :)
>Reading this while working for a division that pivoted to provide interfaces for agentic workflows, only to discover that only ten users had ever touched the products we made for agents, only to pivot again to support for agentic workflows, which has a lot of competition because every company has to do something agentic now and there's only like four things you can do in that space, is bracing.
>– An editor of this essay
The most popular form of AI psychosis in a nutshell, yea. "AI will understand this for us, so we can be groundbreaking and get rich if we just try" is only possible to believe when you have no idea what you or the rest of your field is doing, and have only attempted to learn about it from "AI", which has spent a lot of your money convincing you that you're brilliant and revolutionary.
It's exactly like surrounding yourself with relentless ego-boosting yes-men. You lose touch with reality. Just take a brief look at any super-wealthy person, they're obviously insane but everyone around them thrives on convincing them that they're not.
---
>For the latter customer-facing applications, I have rarely had a pleasant experience as a consumer, with perhaps the exception of live transcription during medical appointments...
These have been abject failures, personally. They've made major mistakes in summaries, and added non-existent symptoms multiple times. Medical transcription seems an especially dangerous place to put them.
I have, in two wildly different companies, on opposite sides of the globe, witnessed leadership go all in on using LLMs to write external documents that would have significant legal and financial consequences for any inaccurate statements or numbers included. There's always a "we'll have our experts read and fact check everything before we send it out," but I have a strongly held view that people whose expertise is not in editing/fact checking tend to be far worse and sloppier at it than they are at writing things correctly the first time.
You are basically calling out the fact that the Emperor has no clothes. Many said this before. While it is a true statement, it is not going to help. Because, as you rightly said, it is a mania - like the tulip mania of 17th century or the manias of many forms today. The mania continues to evolve and flourish through it's peak and then go down. For that matter, there is hardly anything that is not a mania. Think of agile processes, timesheets, LoC based productivity, ...
The corporate mindset keeps going through different mania at different times. It could be initiated by some consulting gurus (processes), or some security nerds (strap yourself down until you can't move), or peer pressure (fear of missing out), or presentation goals (show that you are a AI-powered and modern company).
We can't remove or stop manias. Infact that is not the goal. The music should go on and the dance should go on. Everyone is in this dance - customers, businesses, supply chains, governments, thinkers and philosophers. It's a world-wide dance. So it's OK. The music track won't last forever. It will change and dance will change.
This mania can also be called "herd behavior" or "crowd psychology". There was that book a while ago about the intelligence of crowds, but far more common is the stupidity and insanity of crowds. Business and politics are mainly driven by it, but the tech industry routinely falls to hype, trends, and decades-long mistakes that seem obvious in hindsight.
You all are missing the larger context. Not everyone here is equal. There are certain people who control economic organizations, set their goals and priorities, generate hype and fluff to increase sales, push everyone to be more productive, dont do the actual programming work. And then there is everyone else. This is how global capitalism is structured.
For what it’s worth „Extraordinary Popular Delusions and the Madness of Crowds“, while entertaining, isn’t considered a reliable source as it bases most of its story telling on satirical descriptions and reporting of past events and doesn’t do a good job of digging into the actual historical context and dynamics
It's super hard to time the market. I would say the sanest approach is to have cash on hand, choose AI adjacent companies with solid fundamentals and buy their shares once the bubble pops. It's very hard to make money being an outright bear, but somewhat easier if you're an early phase/post recovery bull.
I love this author's article about saving half a million dollars with a click from a while back. Nikhil, if you're reading this, I have a decent war story about a similar situation (I was lucky enough that there were two such things, so I saved a full million a year and was still denied a $15k raise) and so I was really entertained at your post from a few years back. There are actually a lot of lessons to be learned about corporate politics there, about how you can save someone a million a year in perpetuity and promise to do that again next year (which I could have!) and see them still refuse to pay you a single extra cent.
> The world’s organisations have been captured by people in the throes of frothing excitement, and saner people who now live in a state of constant commingled fear and frustration.
This reminds me of "Youth without God" by Ödön von Horváth:
> Perhaps Anne’s mother was right. Perhaps men are mad now—those who aren’t mad haven’t the courage to put their maniacal fellows into strait-jackets.
Everyone knows the ivory towers are full of people who shouldnt be there. Its marvelous to see them crumble even if you arent in the chain gang below.
Nothing will improve until things get bad enough. You need enough greedy yes men doing quailty control on airplanes to escalate.
It took me decades to understand the use of and need for escalation in big organisations.
I didnt understand seemingly unproductive strict job descriptions either. Hilarious situations with 10 people doing nothing at all their entire shift (really nothing) while i had work todo on my own that really required 5 people. A few days later someone showed up to tell me the qualty was below average. LOL
Now i know i should do only half a shift worth of work. When they come complaint about it i say: very good, write it down, make the official report.
Then i hear nothing and a year later its two people with work for 5 scheduled. I tell them to slow down but we still do 2.5 shifts because they dont understand how escalation works.
The nummers now show we are 5 times as productive which isnt good for the company. The beurocracy is slow to adapt and all it has is numbers.
For many years i tried to do all of the work but that means nothing is wrong. The numbers say all is fine most of the time. Someone grinning at how much work i did isnt going to get recorded or processed.
If it looks like an unattended LLM can do a better job it means you dont know what you are talking about. If you fire everyone who noticed you might buy time but reality will catch up.
It reminds me of when they first put computers in trains in NL, they ran on windows 3.11 and no one trusted it to do anything. The solution was to give it all the data so that it could display a nice overview but it didnt control anything. Lots of trains drove around with a blue screen of death or a boot error. If there was a problem it was slightly harder to diagnose but it wouldnt drive if [say] a door was open. If the gui said a door was open you could just ignore it. On its own it means someone has to replace a sensor. If it also didnt move anymore the message is a real issue.
I imagine LLMs are wonderful for that kind of thing.
> Employees don’t use internal chatbots because companies tend to have low-quality documentation and an LLM is not psychic – it can only know things that have been written down and made accessible.
Then later:
> In one extreme case, I have seen an executive confess that they had never even used ChatGPT or any AI tool in their life, immediately after producing a technical strategy for an organisation with $2B+ in revenue which was entirely centered around AI.
And:
> In fact, we have been forced to opt out of every sale where the lead has expressed anything beyond the most fleeting curiosity in the use of AI in their business. I don’t mean that we’ve heard that they’re interested in AI and elected to drop the contract on moral grounds. I mean that, over the course of the engagement, these people have exhibited a pattern of behavior that has made it near-impossible to sell to them without incurring reputational and legal risk, and are furthermore crafting management environments that I can only describe as cultish, ineffective, and “please dear God, do not let it be on earth as it is on LinkedIn”.
And:
> with one client, we uncovered that staff were totally unaware they had been given licenses for AI tooling, which cast into doubt all productivity claims.
Huh, that's interesting. Sometimes that happens because HN's AI text detector kicks in, I've seen it against my comments that quoted from AI-generated text in the past.
I don't see any hints of LLM writing in this article though.
I think AI is only part of the problem. The Multi-crisis ahead, makes it even less of a proposition to be in charge right now- and AI offers a "responsibility" cope out for already stressed to the limit human systems who have no solutions for the problems, because there often arent any.
Take climate change- you have torrential rainfalls, sweping away whole city-parts in mountanous regions, in some enormous russian roulett. And it doesnt even factor into building evaluations because then it would basically reduce the prevalent pension scheme to cinders.
You have dry months in europe now, where some thrown cigarett butt could ignite a firestorm- and the obvious solution is to remove the dangerous greenery from the burbs. Nobody does it though.
And that is just the plain sight visible layer of this shit cake. If i was some missguided fool into heroics and leadership and signed up for a little more then i could take and fake- i would long for some magic box that lowers the burden too. Those up there are human after all.
We have villages near cities, whos native populations get displaced due to the price hikes and then we have "settlements" which is roughly equivalent to suburbs for those who are more well todo - while the less wealthy are going into the old buildings the even less wealthy natives have vacated.
As someone who uses LLMS for coding, ideas validation and research, I think the article is biased against AI forctge wrong reasons.
If you know what you are looking for and know what “a solution looks like”, AI is amazing at distilling ideas. If you have no clue, the AI will return “clueless” solutions.
It is just like before the AI: there are people who know how to search the web, read and understand documentation and so on. And then there are peole who are incapable.
AI is naking the latter category fail incredibly fast. Really, nothing new under the sun: garbage in, garbage out.
The range of ai approaches ranges from inappropriately betting on it to wholly ignoring it both of which are dumb.
It helps to think of AI as analogous to previous game changers like electricity, computers, internet. If your business is bad or you have no game plan beyond "use internet" your are fucked. But at the same time almost every business has some angle in which these technologies are valuable and necessary and ignoring them is also death.
Ignoring it is hard to distinguish from waiting to see how other businesses successfully use it, then copying them, which will then be framed as adopting best practices.
It's often the difference between people saying "we are taking a wait and see approach"(wise) and saying "it's spicy auto complete that's totally useless" (dumb)
My hunch is that AI tools are probabilistically good enough to deceive most casual users in the beginning, so beliefs are built on these foundations. LLMs handle simple tasks reasonably well so their perceived 'intelligence' is easily convincing for many users (often managers and executives).
However, this illusion immediately falls apart during actual stress-testing like writing code for a complex, battle-tested production environments. Non-technical folks can effortlessly spin up simple web apps or tweak a frontend so now they conclude that software engineers are becoming redundant.
And even if there are obvious examples of all of this backfiring no one believes it...
There are a lot of simple web apps out there that haven't been built because of the cost and many more that have been built but cost a lot to maintain. AI seems to work on those and fill in a pretty large space.
Probably true. But isn't this a failure of software engineering? We should have plenty of simple ways to build simple apps by now.
Maybe you should be able to go down to your local web app shop, give a couple simple requirements, and get back an app in a week. So why can't you? Well there's actually a good reason for that - the customer would never be satisfied by the result, and building a satisfactory result requires many iterations back and forth. Rapid feedback is essential to software development.
Okay, an alternative is to give the customer an app builder so they can make their own app. We've done this many times in history, it always takes the customer time to make the app, the results are always mediocre, but it does work, why do we keep doing this and then stopping it? I'm not really sure. Why isn't there a visual basic today (apart from Roblox) or even a Frontpage (apart from Squarespace)? My best guess is the customer tires of working on the app or never had time for it in the first place. Why won't AI suffer the same fate? Really no idea.
AI doesn't suffer the same fate because the customer can input that feedback in near realtime. I built a very quick database site for my car club to track a car meet we had. Registrations, Money, Cars coming, hotels, t-shirts. It was probably a mess of code. It worked though and didn't take long to get setup. This would never have been an app and would have been done with spreadsheets if we had to pay a developer for a month to get things right.
I could not disagree more. You must have used the better models by now. Even if you only lightly use them for a question from time to time, it must have become clear that these things are pretty damn good and aren't going away?
When doing hard things I need to be 100% in the loop, but I don't have to do anything menial anymore. Imagine being in some CIA control room with 10 emplyees, and you're directing the team. You're still on top of things, just not bogged down with bulky minutiae.
“Infidelity does not consist in believing, or in disbelieving; it consists in professing to believe what he does not believe. It is impossible to calculate the moral mischief, if I may so express it, that mental lying has produced in society. When a man has so far corrupted and prostituted the chastity of his mind, as to subscribe his professional belief to things he does not believe, he has prepared himself for the commission of every other crime.”
― Thomas Paine, The Age of Reason
The author doesn't distinguish whether these projects failed because of technology and execution, or failed because product market-fit. They simply blame AI being involved.
AI will only help if you use rapid iteration to cheaply/quickly produce ideas. All the normal project failure modes still exist.. Blaming AI because AI is dumb.
I only read up to point 3 because the hyperbole and frothing fervour was overwhelming.
Broadly I don't think this is quite so true, quite such a mortal threat.
What I see are that there are a lot of extremely fake humans, who want and need cover. Who have absurd ridiculous (and often dastardly or sinister) plans. Who want to do things, a-priori. But could never get away with their actions, in any just clear reasoned normal rules of society.
And AI is this new circuit breaker. It's innovative permission to move ridiculously fast and break everything, right now. Take the perhaps old IBM slide and flip it upside down,
> A computer can never be held accountable.
> Therefore a computer must never make a management decision
The people "using" AI today to "make decisions" are using it because AI cannot be held accountable therefore that is the cover for their decisions.
This is is all such a resounding PKD nightmare, a reality bring invaded by Fake Humans. It was that was already, just gobs of nonsense, the worst liars spreading the most ridiculous memetic caltrap everywhere: Bullshit Asymmetry Principle weaponized against reason to ever higher degrees, Fox News terrormongering advanced and advanced, Hastert Rule obstructionist politics by wicked pedophile protectors and system ruiners and monsters. AI is a rapid accelerant for burning down reality, for propagating the disreality that the fake humans require for existence. Un-people truly from some other dimension, who've worked and worked to get away with their twisted anti- reality over us all.
AI can and does help with a lot of decision making, in good ways. It's an incredibly tool. It can comb through incredible amounts of data. But it's primary use in "decision making" seems to be in deflecting responsibility, in making hideous choices no human system could reasonably make. In concocting fabulations. Both of management design, and endless fuel nightmare disreality slop video to dislodge any last bits of real reality still clinging on (hello ai faked campaign videos!).
The "frothing excitement" here is the frothing excitement to destroy society, to be and bring out the most wicked brutal careless world that can be brought upon us, to raise up the Theil-istic/(Octavia) Butler-ian nightmare neofuedalim. It is to escape accountability, to give cover for sin savagery and sabotage.
(Regarding the article, I do think it's worth tempering ones read of this article by reading the authors previous work on AI. Which to me exposes their baises and in my view makes them so vastly unreliable & overdramatic a narrator as to be near worthless. Their other submissions are less greviously clearly full of it, but also tend towards ridiculous over-grandiosity. https://news.ycombinator.com/item?id=48002795 )
It should be possible to exploit a rich person's poor decision making to make them poorer and myself richer. But I don't see how. Why not?
It sounds like the writer of this article could benefit from starting another brand which is the same product but with AI. That brand would make all the delusional AI chatbot sales and absorb the reputational damage when the chatbot destroys companies.
Honestly, this is close to my experience within my org. It is not 0%, but it is very close. It seems like you're just uncomfortable with the author's experience conflicting with yours.
Perhaps it is the wording. For my org, I think people are getting value out of AI use supplementally, but what I would see as "AI projects" are definitely dead in the water.
I want to frame my comment here carefully to talk about the narratives, specifically, and accept that we need to stipulate on face value that this is true (it certainly rings true from people I know in real life):
This specific narrative reads like every narrative explanation of the Republican party in the period from the second half of the first Trump administration until now.
Again, I am talking only about the narratives here. But the pervasive narrative around Trump is that privately everyone hates his guts, comes away with a worse impression when they work with him closely, dreads him opening his mouth, thinks his decisions are insane, and they are telling journalists that, at every opportunity, and have done since the 2018 midterms. And yet in public we have committee hearings where any number of irrational topics become a sort of terror zone here rational people have to assert irrational beliefs or pretzel logic to be able to continue; where ordinary absurdities have become cult-like witness-bearing that everyone polices everyone else on. It's like the Hunger Games of litmus tests.
For example the desire for career advancement is a common thread in both of these narratives, even though this is being promoted further up into the kafkaesque absurdity.
Both of these things remind me of other things, like somehow everyone in US politics seemingly believes in God and somehow nobody in an international football (soccer) team is gay. Even though these are not plausible.
honestly just seems like selection bias right? 0% success rate with ai projects, really? based on some of their other about us material it seems like they probably only attract companies that aren't motivated to adopt ai successfully.
I read the entire post, and I think this company has no expertise at all. So I'd rather they just used AI writing instead.At least Frontier model AI doesn't make such overblown claims.
They proudly claim that every AI project they've observed over the past year and a half had a 0% success rate, and that they've rejected all AI implementation work. While this is evidence that the market is crazy, at its core, it's a painful confession that they have no engineering expertise to implement and control modern AI architectures like RAG, Agentic Workflow, and context window optimization to meet business requirements. I find it fascinating how they're packaging that. It's basically saying, 'We're behind the times.'
There are already products that have achieved results by using AI as part of their development process, yet lumping all different types of AI usage into a single failure category is not only inaccurate but also misleading.
Same goes for the Snowflake Cortex anecdote. Even a freelancer like me can explain technical limitations and distinguish between what's possible and what's not, especially when clients are eager.
There's no engineering analysis in this entire post about why AI fails. No mention of technical bottlenecks like vector DB retrieval quality degradation or prompt injection failures.
I've also worked on RAG for a specific company. For internal knowledge chatbots, it often fails depending on document collection rates and chunking. But none of that is mentioned.
So I understand that AI projects and related things are bad. But there's no analysis of why.
For example, regarding Snowflake, I'm not sure, but did they discuss accuracy in terms of what query set or what ground truth they were using? You're consultants, aren't you?
Honestly, I don't understand why people are excited about this. I'd rather they just used AI. TIt's not about whether human writing is good or bad. It's that this kind of writing feels like a deception of the reader.
When making overgeneralizations, there's a basic minimum standard required.
Saying that making token usage a KPI makes it hard for employees to report is just an 'obvious' fact that's already appeared in far too many essays. Wake up. You're 'consultants.' Consultants are supposed to provide metrics and directions, but all you're doing is shouting into an echo chamber and asking for agreement.
If a significant portion of corporate AI investments are shoddy, you could at least propose specific metrics like document collection rates or user evaluation scores using the very skills you claim to have. I really don't get it.
Just use AI. I wish the OP had used AI. Let me be realistic.
The article is not a technical overview of AI's failures, it's about the organizational failures that lead to things like token leaderboards. I would argue that this article goes far beyond the "obvious" fact of token leaderboards and into the deeper problems faced by both vendors and companies trying to measure the efficacy of AI.
If this were genuinely a consultant's writing, they would need to present a modeling hypothesis for why failure occurs in organizations and support it with metrics. But claiming their own observation of 0% is not a hypothesis.
Of course, they can write it on a personal blog. That's not inherently bad.
But when they're rejecting client requests while offering no opinion on how to model the metrics that would define success—that's the problem. This type of writing is just an illusionary piece that claims to offer insight. It's a bad form of human writing. I like to call this style the 'Rhonda Byrne Secret type.'
At the very least, a consultant should provide modeling of their hypothesis and actionable advice.
Explaining everything with a single reason—that people are afraid of being laid off if they don't adopt AI—is just a cop-out. There's no hypothesis, no reasoning behind why that claim holds. It's trying to explain everything with just that one assertion. I'm not even sure if that holds up organizationally
The OP offers no logical reasoning about how decisions are made or why they break down.
A proper piece of writing would have looked like this:
Why did the client want to adopt LLMs?
What problems arose during the client's process of adopting LLMs?
How does this connect to the organization's internal issues and disrupt its pipeline?
You need at least that level of description. This is just an emotional post that seems to say 'I wish LLMs would fail.
If they had really explained organizational dynamics, they would have talked about the asymmetry of expected costs.
Professional managers and mid-level managers prioritize career defense above all else, so they're extremely risk-averse. They would have explained the costs of adopting AI versus the costs of failing to adopt AI.
If they reject AI adoption, shareholders or the board would say they're 'failing to read the trend.' If they adopt AI and fail, they can package it as 'early market entry costs'—a defensible risk.
Providing that kind of defensive logic to decision-makers is exactly what consultants do.
It's described as being held at gunpoint, but it's really just a prisoner's dilemma. In a market where competitors and vendors are all lying and saying 'we've achieved 100x productivity with AI,' the moment someone tells the truth and says 'we don't need AI for our business,' they become a political target both inside and outside the organization. In the end, everyone knows it's useless, but they approve budgets for 'fake AI projects' anyway. I can offer that kind of cheap insight too—it's a Nash equilibrium where the system has been driven to a race to the bottom.
Honestly, it's just shouting into an echo chamber. They call it organizational failure, but it's really just 'come on, my side.' A consultant who does that is untrustworthy. It means they have no metrics.
Same goes for their 0% claim. It's overgeneralization, isn't it?
So why did they fail, what went wrong, and how should these AI projects be analyzed—and by what metrics? They skip all that and just throw out a single KPI. What insight does that offer?
Honestly, there are a lot of people on HN who just shout that AI is useless because their own jobs are at risk. That's an undeniable fact. In my country, Korea, we say 'like a pheasant hiding its head in the ground.' It means denying reality and lacking self-awareness.
I've also seen many failed AI projects due to a lack of proper evaluation of AI capabilities. I don't want to deny that premise.
But if you're going to make that argument, you need to describe why they failed. There's none of that here, just this:
'We're going to opt out because it looks like AI projects will fail.'
They package it as being cautious, but when you peel back the wrapper, it really means:
'We can't keep up with the AI trend. And we can't tell what works and what doesn't.'
Sure, AI has a bubble. No one can deny that. But separate from that, it is useful. Do you really think millions of consumers would stick around for something useless?
Social problems are arising, and people are losing jobs because of it. Let's think the other way around. It doesn't make sense for people to lose jobs over something that's truly useless and wrong.
People are losing jobs because it's useful and valuable. So wouldn't it be more accurate to say that the problem is 'over-application' rather than the thing itself being bad? In other words, we could say that it's being valued more than it's worth. But this argument just says it's bad, period, with no nuance. I can't trust someone who talks like that.
AI writing, if you give it a red-team prompt, can at least refine the surface of counterarguments to your claim. But this—from overgeneralization to extremes—is a textbook example of bad human writing
(i'm more chatty because it's my "down-day". Hope you don't mind)
I don't think you're upset because AI is useful and valuable. Are you upset because you can't describe how the bubble will burst?
Some (more) quantitative types saying that the economics are deflationary. So NVIDIA have already won.
But there are some that claim that the bubble has nothing to do with NVIDIA's monopoly on compute (21.C steam engine/steel). That the bubble is in the OX layer. (You?). If Anthropic (or Palantir replacement) fail to do what? Then the bubble will burst.
This is the more interesting but I will have to sleep on it.
I think AI is useful and has value, but what I find problematic is the illogical behavior that people display while claiming to be engaging in thoughtful writing.
Rather than saying NVIDIA will be the ultimate winner, I think Apple could be the final winner as a hedge against semiconductor stocks, at least for now.
In that sense, I'm not sure. You can't time a bubble, so it's just a bubble.
But I do think NVIDIA will remain powerful because of its CUDA ecosystem—though the future is uncertain.
[Bro I was hoping you'd rip into your own OX idea here.. then we could have a convo here or offline.
I wouldn't say I get frustrated by illogical writing--esoecially ones that only _seem_ illogical---, but more that people like us who might know better don't point out precisely where that illogic is after thinking carefully about it.
]
It's possible to time a burst _if_ you see the pin, but anything you do _when_ you see it is technical illegal?
So your OX idea feels like just the sort of thing that could open our eyes new kinds of pins.
Further, I'm not totally closed to the idea that actively trying to pop the Anthropic bubble is moral, indie of how quixotic that sounds. Even if it might be a good case study for deinstitionalizing research math.. even if that Napoleon quote that's trending..
I would also never bet against Jensen taking some ideas from "the Apple playbook". Especially when multiple people have already pointed it out. Though this is tangential. Less tangential---
Refinement of OX framework would help us think about how NVIDIA can or cannot play the Apple game. Certainly, if given calibrated riskiness and timing, Apple would move against Anthropic. You know what I mean?
Clicking the footnote for the weird "All of the AI projects we have observed as a team are failing" is equal parts enlightening and confusing:
> We have rejected all AI implementation work. It is absolutely a gigantic bubble and we have minimized our exposure to it – every single one of our current contracts would be totally unaffected by OpenAI collapsing, save for perhaps some second-order effects such a recession causing a client to become unable to pay us. And there’s nothing we can do to insulate ourselves from that anyway.
Following the link to their company page goes to Hermit Tech, where the primary advertisements for their services are about helping failing projects and troubled teams.
So this is just one huge selection bias example? Start a consulting company for recovering struggling projects, then make claims like "100% of the projects we've seen are struggling"?
There's so much more in this blog post that feels like they're working hard to ignore anything that disagrees with their bubble. Building an AI data pipeline with evals such that you can swap between AI APIs is standard. It's actually part of doing a decent job because you need to select which model hits the right cost/performance tradeoffs and be in a position to pivot when that math changes. Harboring ideas that OpenAI is going to collapse and bring your projects down with it is the kind of talk you hear out of people who don't understand how AI projects work or that there's an ecosystem to it beyond a single company.
The latest projects I'm working on even include open weight models that can be run on reasonable local hardware as cost and performance benchmarks. Even if all of the AI providers collapsed at the same time and nobody offered any services (not going to happen) these projects can still continue on.
It's a very weird time in technology. You can have one foot in a world where people are adopting technologies and using them intelligently, then you can run into articles like this from people who have built their own little self-selecting bubble that confirms all of their ideas who can't even imagine that successful projects exist right now.
What do you expect from a person other than the generalizations they see from the experiences they have had...? What 'evidence' could possibly be given other than extensive anecdotes?
The article presents hypothesis as fact with insufficient science. I have no problem discussing speculation, but if the author wants to promote their claims to any more than that I would want to see more journalism.
AI may have made a few mistakes, e.g. suggesting the US abduct a sitting president (Claude), and maybe went too far with the whole diarrhea-vomit machine thing, but you can't say it doesn't write a clean python script. Maybe if we can just get AI to view humanity as a software problem... and give Palantir the chance it's been waiting for.
One thing is certain; it's here to stay. Now we just need to centralize it into the hands of a small, highly organized group of wise and motivated people, and give it the control it needs to follow through on things, without human oversight and accountability getting in the way.
We should focus on the positive side.*
*The positive side being the classified private versions where the models aren't hindered by guardrails, ethics, moralizing, etc and function as genuine force multipliers.
That's a great insight. I appreciate you sharing this perspective directly. But honestly, it's not diarrhea, it's not vomit, it's a lean mean machine—AI has become load-bearing and you're right to question that.
I think it's more accurate satire than insight, but you know, maybe people really don't see it. I suppose to some, it might not even make sense. Give it a few months though, when it might qualify as insight, or prescience.
All I can say on the bearing of loads is that you forgot the emdash and preceding 'not' statements.
PS: the diarrhea machine was a reference to the Active Denial style tech they used to make Maduro's soldiers shit themselves.
I'm no fan of narco, but with all the prejudicial executions of boaters and such, I really think Claude overstepped with the Venezuela plot. You just don't want too many historically unprecedented events happening all at once. The tyranny should be spaced. As I said, a little more sanding down of the harness, AI & Palantir should be ready to permanently solve our problems,
Most of us on some level felt confident that AI would completely revolutionise our society. The singularity made sense to me, at least. It hasn't worked out like that, but rather than accept the crushing reality of our mistake and take a second to re-evaluate, we've decided to LARP out the future we promised ourselves by crowbar-and-vaselineing AI into every crack and crevice we can find and loudly proclaiming progress.
It's like some 90s kid with a Nintendo power glove who's convinced themselves that this makes them a hacker. This is not the singularity.
I personally can't wait for this to end, and for everyone to collectively get back to waiting for whatever the next golden ticket that's supposed to solve all of our problems turns out to be.
I just don't understand how someone could think we're going back to a pre-LLM world. Have you not used one since Opus 4.5 came out?
Especially as somebody that's no longer in the software profession and so doesn't have 40 hours a week to just sit there and crank out code, agentic AI is a technological leap on the level of search engines, bulletin boards or compilers.
You can literally just open Claude Code, say:
- "Add these domains with these mailboxes to an SMTP server running on my homelab, forward all incoming and outgoing via mxroute"
- "Port this unmaintained app from 2017 to modern Android, and then test it on an emulator and sign it with my real key for distribution via Google Play"
- "get my GitHub Actions runner on Linux, Windows, macOS and Android, ensuring that it supports all available architectures from the last decade and a half, then check your outputs in a loop until it works"
and it just spits it out while you do the dishes.
Someone the other day talked about "AI psychosis psychosis" and it absolutely stuck with me. The people who deny this is a big deal are in la-la land.
We could literally hit a brick wall in terms of model intelligence now and it would still be a game changer.
> - "Add these domains with these mailboxes to an SMTP server running on my homelab, forward all incoming and outgoing via mxroute"
I laughed reading this. Have you actually tried it? I recently tried to get Claude (Opus) to give me a setup for a simple SMTP relay with Postfix. I had to constantly fight it not to include completely unnecessary components and configurations like Dovecot or just override my requirements. Even Claude is nothing but a semi-competent tutorial clicker.
To someone who never did anything like this themselves the result might seem fantastic. They might not battle Claude on details like I did because "hey, it works, doesn't it".
Don't get me wrong, I agree in general that LLMs are a significant milestone. But I would like to add, especially because of your tone, that perception of how much of a leap it is, is very likely a matter of individual competence. So your enthusiasm might say more about yourself than about the tool.
"To someone who never did anything like this themselves the result might seem fantastic ..."
I've been "doing this" for over 30 years now and I consider these LLM results to be fantastic.
If one has a deep, conceptual understanding of tools, systems and protocols and if one has the ability to carefully provide careful, concise specifications, these LLM tools are like being granted superpowers.
In fact ...
My next project upon which to bring LLM tools to bear is a fully local, solar-powered, self-hosted LLM toolchain.
Not only do I intend to never work without these tools again, I intend to do so without the ongoing environmental externalities that they produce.
> If one has a deep, conceptual understanding of tools, systems and protocols and if one has the ability to carefully provide careful, concise specifications
Most people already don't have the ability to do this and their use of LLMs atrophies the little they had. "AI natives" will be completely doomed.
Umm, hate to break it to you, but by the time you load up an open weights model, the environmental externality has already happened
This is a fair point - and well taken.
However, I never asked for LLMs nor did my consumer nor professional behavior send market signals that I would use them. They were thrust upon me apropos of nothing.
Now that I find them useful, the least I can do is stop making the problem worse.
As a rule of thumb the energy use to produce a silicon wafer is roughly equal to the energy it will use in its lifetime. For GPU and VRAM this is considerable. To equip yourself with the capability to run an LLM locally is making an upfront investment in the production of extremely toxic and resource intensive products.
Why buy a personal EV with 600 mile range that sits idle 95% of the time when you can take a bus (aka the cloud) that maximizes the resource utilization?
But you do plan on continuing to work, not have the AI bootstrap itself into a machine god and do all possible work for humanity instead.
[flagged]
One of the most wild parts of the "Is AI useful" arguments is how different people are getting so astoundingly different results from it. One guy will claim that he can one-shot "Port this unmaintained app from 2017 to modern Android," go do the dishes, and come back to a working application. And the next guy will say, "I can't get it to do anything groundbreaking and have to micro-babysit it every step of the way to the point where it is more work." Assuming both people are truthful and operating in good faith, how can this be explained? How can simply writing a prompt in English be a "skill issue?"
I'm personally much more towards the "I struggle to get it to work well" side, but there have been so many anecdotes from people who are earnestly saying it does all their work for them and that they hit enter, go to bed, and wake up to working software. I'm trying to imagine a hammer or band saw that produces such wildly different results in the hands of different users.
I'm the GP that got Claude to port the app, set up the SMTP server etc. etc. And I do see stuff on that level of complexity, multiple times a week. I'm not a full-time software dev any more, but did learn the craft at a really good company before AI came along - and I've been doing basic sysadmin stuff by hand, almost exclusively on the terminal, for almost a decade now.
A lot of pithy responses to your comments that contain some pretty bad-faith assumptions about AI "power users", but to answer your question I think part of it genuinely does come down to a "skill issue" - or at least familiarity.
AIs really do have huge blind spots, and there are things it can't do well. But on the flip side, there are modes of operation that do get good results and if you keep it in this "zone" you can get amazing results.
For example, here is my CLAUDE.md that's global to all projects: https://github.com/EspoTek/.claude/blob/master/CLAUDE.md
Note the "Working with unfamiliar data or systems" section - it doesn't stop the models from making wrong assumptions but it does get them to test these assumptions with simple experiments and course-correct before human eyes ever see the result.
Setting up things like CLAUDE.md files and skills so that it doesn't make the same mistakes over and over is a big help too. The model doesn't literally get smarter, but it knows how to avoid pitfalls its fallen into in the past.
If you want to have a chat about it earnestly, my email's in my profile.
I think the answer to your question is the simple and unfortunate one: groupthink.
So many businesses are pushing AI hard. People with varying levels of job (in)security want to be seen as "leading the charge" into a post-AI era.
LLMs can do some really impressive things, don't get me wrong. But I regularly find massive blunders in the code it produces.
And that actually makes sense if you understand how LLMs operate and the quality of software on GitHub.
My guess is they do something like "Hey dingus, make my app send emails" and then Claude or GPT just goes off and does a thing that makes it look superficially like it sends emails but it really only sends email exactly on Tuesdays and if the subject line is "send email to user". And there's no tests, so who knows if the next LLM can fix the bugs without introducing new ones.
For the last several years (since GPT 4o) I have written several apps in use by people, making money, entirely with AI/LLMs (me neither writing nor reviewing the code in any meaningful fashion - other than high level architecture, schemas, etc.) and - yes, in a few hours it can do things that would take a normal human weeks (if ever!). But left unconstrained, it will just pile more and more garbage on the pile.
Fable MAX is the thing that gets me much closer to "just send emails" but even then it doesn't look in my specs directory for the spec, it just goes off and does a Jurassic Park-style "It's a Ruby-on-Rails system! I know this!" and disregards all the other ways we send email and writes its own thing. And often I'll go "Where's the button to do X" and it will say "You're right! Nobody asked for it, so this page is an orphan!"
I happen to use Superpower's (https://github.com/obra/superpowers) "brainstorm -> spec -> plan" workflow in e.g. Fable MAX (for anything non-trivial) then I have Fable send to GPT 5.6 Sol XHIGH for execution, with Fable (either the original, or a separate one, depending on the criticality of the task and the blast radius) being the critical reviewer.
Even then, though, I need to continually guide it against the norms and conventions of the codebase/app, because it makes a ton of assumptions.
It doesn't surprise me that if people don't take a fairly rigorous approach to AI-software development then they'll end up with a mess. Even if you buy into "just re-write it" (I happen to think that's where we're ending up) - we aren't there yet and without e.g. a strong test suite, re-writing it is just as likely to create more bugs than it is to fix the existing ones.
> Assuming both people are truthful and operating in good faith, how can this be explained?
Quite simply, difference in skill.
The sysadmin that has configured a few applications in their career would likely get annoyed at how many mistakes an LLM makes.
The manager who barely knows their way around a shell, uses an LLM to configure an application in less than the three business days it will take them, and will claim they are 10x more productive now.
Once again, LLMs are effectively great at triggering the Dunning-Kruger effect. The least you know, the more productive you feel.
The most interesting part of the Dunning-Kruger paper was the "skilled and aware of it" subgroup.
Try to be aware that mentioning Dunning-Kruger is recursive.
>"I'm trying to imagine a hammer or band saw that produces such wildly different results in the hands of different users.
r/DiWHY
that’s perhaps a bit harsh, and I’m late (intentionally) to all this as I’ve worried about personal atrophy, security, and feeding the shoggoth
but also with my instructions it will churn on prompts for hours. It makes use of my local rags by domain (which it also helped me build and maintain). Most recent example: it’s been on my list to set up headscale for like 5 years now. I know how to do it, I’ve read the docs and kept putting it off, partially cuz Tailscale works and has a generous free tier, and partially because I didn’t want to strand assets that I’d somewhat shortsightedly hardened to only accept ssh from my Tailnet.
claude did it all in 3 hours, while I worked on other stuff, prompting for `op` access when it needed keys, and logging each usage, with minimal involvement from me. All with more documentation, roll back points, and “learnings” propagated to the appropriate rags than I could have created in a weekend session. It made mistakes (some that I likely would have made, like region settings) then reasoned about it, corrected and tested, all transparently, all git backed, all posted about on my Mattermost server as it went, so I can easily search it the way I would of an extremely detailed teammate did it.
I don’t know what to make of it all, but letting it churn through my backlog has been eye-opening.
"it must be some sort of weird badge of honor to be so profoundly incommunicative that you can't get the ai that desperately wants to sycophantically pump you up about all your brilliant ideas to be that useless. that is genuinely harder to do than simply using the technology even passably."
Definitely incomprehensible that someone might have a different take than you about the 'goodness' of your prompt outcome...
I mostly view AI as raising me up one more level of abstraction in coding. It still requires babysitting and verification if you want anything vaguely maintainable that's not pulling in a ton of irrelevant libraries, but now I am able to give a clear spec for a ~50-100 line task and it'll get it 90% right, and it's a small enough chunk I can validate it well.
Claude though is still slow enough that I think it works out to maybe a 50% improvement at my time to do a task, but sacrificing deep knowledge and learning. I worry that I'll never develop the same skills and intuition that senior engineers developed a decade ago because I'm outsourcing a lot of the planning and thinking.
Here's the thing. I've been an AI denier and skeptic since the beginning. I've softened. This is my reality on the ground:
I am tech lead on a front end project that is typescript with react and everything else as bog standard as possible. In the last year the backend devs have been pressured to be full stack, so have been submitting AI PRs to my repo with Claude. The PRs mostly 'work', but invariably have major problems that wouldn't make it through a code review in the before times.
Front end JS on a CRUD app is the literal best scenario for AI. Professional senior devs are using it, and it can kind of work, but still not really.
I've given it the old college try myself recently, and it is at the same time astonishing and disappointing. Yeah it can do a lot, but there's always something wrong with it. The 'something' is not always a bug; it can be a design issue, performance issue, unused code, duplicate code, etc, thus many people don't notice the problems. But I think for those of us who prided themselves on quality, the stuff doesn't work well enough to use. For those who leaned more towards speed, they're overawed. I am concerned for my career, yes.
It’s not going to go back to pre LLM times, it’s also not going to go to a generative ai utopia where we can turn off our brains and a model will understand every nuance of human existence without some from of defined guidance.
Just like block chain still exists, it just didn’t revolutionize decentralization. It created a new tool that can be implemented when you want the benefits of what the blockchain can do, same thing will happen with language models.
I think the biggest realization to me of this whole thing is that most people don't want to turn off their brains. I think we, very naively, thought that some hypothetical life of zero-thought pure-leisure was not only achievable, but desirable.
And it's just... not. People want to be challenged, they want to read human material, they want to listen to human music. Sure those like crappy Spotify fake artists might make some coin for Spotify. But are people going out to those non-existent tours, or are they instead listening to Confessions II and praying nightly that Madonna tours again?
And, Madonna, if you're reading this: please go on tour. Please.
Right? It feels akin to people who oppose social welfare and things like UBI because “people just don’t want to work.” The reality seems more like “people want to work, they mostly want to have a say in what that work is.” How many people working service jobs didn’t have access to higher ed, or couldn’t afford a guitar, or accidentally wound up taking care of a family before they could research algae, etc etc.
Personally, I’d be knitting and crocheting things for other people with most of my day if I didn’t have to worry about money, or I’d help my neighbor with the website she wants for her business venture, or I’d teach kids how to code. These are things I’d like to do, that I can’t readily do while having the overhead of a full time job unless I run myself ragged and tbh, I’d rather not.
I think the sentiment comes from people that just don’t find pleasure from working/mastery. The incentives have changed so much with llm-enabled development, but not which ones people respond to.
I miss the code mines
People say they want to get challenged.
> most people don't want to turn off their brains
Biologically, life is an effort-minimizing machine. Our brains have a whole system of habit-forming to minimize the enormous amount of energy it takes to think.
Whatever people say, give them a way to think less, and they will necessarily prefer it unless they literally are a Buddhist monk, seeking to be aware over every action and thought.
They want to be challenged in such a way that they perceive to be enriching to their life. The reason people use AI at work and don't blink, but then won't listen to AI artists on Spotify, is that they see one as enriching, and the other as menial.
Optimizing the menial is the end-goal, optimizing fun and humanity and expression is not desirable. It's just not, and we're seeing that over and over again.
> I think we, very naively, thought that some hypothetical life of zero-thought pure-leisure was not only achievable, but desirable.
May I recommend Aldous Huxley's "Brave New World" to you?
May I recommend "Look to Windward" to you?
How did you get that impression?
Yeah and no... I have been a developer for 20 years and most of my time isn't spent writing code. Most of my time is spent monitoring production, being in meetings, doing design work, doing code/spec reviews, creating tickets for infra/IT to deploy stuff...
So yeah I love AI and it really helps my personal projects. But my actual work? A little, I can work on some nice-to-have PR in the background during my meetings. I might be 20% more effective coding? Mostly because I do more of these low-hanging fruit PR like bug fixes and refactoring. The hard stuff still require thinking. But that's not even half of my time. It's fun, but not world-changing.
> I just don't understand how someone could think we're going back to a pre-LLM world.
That's not the problem, the problem is that for every hour of dev saved society will lose 10 hours on fake data, fake bug reports, automated scams, needlessly convoluted email, etc.
Because having a slave do whatever you say can also enable some pretty remarkable workflows, but that doesn't stop it from being too morally and economically expensive to become ubiquitous AND sustainable. LLM usage certainly doesn't involve sapient beings and so doesn't carry the same moral reprehensibility as slavery, but in real terms, it's probably even more of a financial quandary.
Nothing contradicts AI psychosis psychosis by comparing a computer program to slavery
>> Have you not used one since Opus 4.5 came out?
Every time there's a new model This Changes Everything. Every single time.
Every time someone points out a limitation of LLMs it's always the same refrain: "Have you tried the latest? Because they're so much better now!". Except this happens with every single model generation. Every time the new models are so good that it's a seismic shift, a step change, a paradigm shift. It's like the finest curve asymptoting ever upwards, a few billions of training cost at a time.
But- to where? AI keeps getting so groundbreakingly better and we're still stuck in the same old world, except now you can...
... port your unmaintained app from 2017 to modern Android? Why was your app unmaintained? What is the societal and technological upheaval that will come from automating such low-stakes tasks? For such huge costs?
But it is now possible for a very non technical user to vibe code Grand Theft Auto II for fun. In two years they'll be able to vibe code GTA V for fun.
Likewise with many other things - people who don't know how to code can produce large custom programs which do what they needed. They're probably not amazingly well written nor maintainable by a human with limited time on their hands but that also doesn't matter at all for nearly all use cases.
That should completely destroy the software industry and the software development profession, but this hasn't happened yet far as I can tell. Maybe we're not quite there yet?
I've only found this guy trying to code GTA 6 before it's released (not II):
https://mashable.com/tech/ai-startup-founder-attempts-to-vib...
Is that the one you meant? I wonder why he didn't try to code GTA 7. I mean, why not?
Yes it's a useful tool. What does that have to do with the singularity, recursive ASI, fully automated economies or whatever is being hyped as the radical transformation of society?
The issue is not capabilities (and never was). The issue is reliability.
And that’s assuming you really need to do these type of tasks using an LLM. As GP said, and as you’ve proven, most people are trying to use LLM as an hammer for everything that looks like a nail, without verifying first if it’s a screw or something else.
1. Everything you said is more about revolutionizing IT though. IT is more easily testable than other things in real life.
2. The gap between the singularity and really strong automation is huge. It's basically that between 1 million and infinity, or even better, that joke about the difference between 1 million and 1 billion being roughly 1 billion.
#1 is what's so crazy to me that people have trouble understanding. No one is saying that we're going back to the pre-LLM world; it's just that the world isn't honestly that different than before. Most people's lives are pretty much the same. Their power bills are higher, but that's not all that different from when gas starts costing more because of a new oil war. The government has some new ways to spy and target weapons, but that's pretty abstract to most people (and it's not like they were having trouble doing those things before). Some new tech companies pop up and have a bunch of money, and some existing ones have less, but nobody outside of the tech industry who isn't already a huge outlier in terms of how much they pay attention to stock prices really cares.
If this is the singularity, consider me extremely underwhelmed. My job my have changed, but to the rest of the world, it's just more of the same.
> and sign it with my real key
That's the kind of irresponsible foolishness that makes it hard for me to take you seriously.
LLMs are very good at programming. This is clear. Software engineering will look very different in 2030 than it did in 2020. There are also opportunities now for people to use code to solve problems that weren't previously economical because hiring a programmer was expensive.
Will AI function the same way in all domains? That is the current narrative.
No revolution revolutionizes in the way planned or hyped. Instead, once triggered, it emerges mostly unplanned. It unfolds as the product of human action but not of human design.
Individuals will use LLMs as they see fit in their work. They'll make a lot of mistakes because they don't see their own work clearly and because they don't really understand the outcomes others care about. Executives who make their money with their mouths will, of course, endlessly open their mouths, even as they don't understand the tech because they never really use any tech beyond email, text, and phones. And so on.
Nobody knows how LLMs will ultimately shake out. But even five minutes of using Claude makes it hard to imagine all white-collar work will continue as in the past.
Even as a teenager the singularity didn't make sense to me. Why would AI capabilities suddenly improve by an astronomical amount? It's still going to be running on hardware and software that we (humans) design. Even if AI reached parity with human brains it would still need to improve efficiency by orders of magnitude to reach parity with human society's output. And only then could it start to outpace the improvements that humans are making.
Even if the AI singularity is possible, the acceleration would take a long time. Possibly a lifetime.
The singularity is the tipping point where the AI can improve itself faster than humans can comprehend, so the software at least wouldn't be human-designed anymore.
It requires a level of self-driven intent and access to their own code LLMs aren't capable of on their own. They might be one component of such a future system though, we just don't know yet. Even then it would probably take quite a while to reach that tipping point. GP is just wrong, talking as if we'd already reached it.
We won't know how close we are to takeoff until takeoff. My main critique of AI is that takeoff is dangerous, and we're basically running towards the cliff as fast as we can. Will you grow wings before you fall to your death? That's basically the bet we're making, if you believe in takeoff.
Silver lining is that if it goes bad we probably won't live long (as a species) to say "I told you so"
Probably not though. AI is already designing its own chips, optimizing its own GPU kernels, suggesting the next experiments to run and writing its own harnesses... recursive self improvement is here already. It's just a question of speed, time and how far it goes.
The thing i never got about the singularity is that its just one concept that was suggested by a few notable people over time. There is so many different ways AI can go, some which we wouldn't have foreseen.
> Most of us on some level felt confident that AI would completely revolutionise our society.
Seriously? Maybe I’m just getting old, but having lived through the dot-com hype in the 1990s, the XML hype in the 2000s, and the cloud hype in the 2010s, this has been an utterly predictable hype cycle around AI. This does not mean that AI is completely useless. It will impact the world but not in the ways the snake oil salesmen are claiming. This is very similar to the internet. The best bubbles are built on a kernel of truth that is then blown way out of proportion and wrapped in layers of snake oil fabrication.
> I personally can't wait for this to end, and for everyone to collectively get back to waiting for whatever the next golden ticket that's supposed to solve all of our problems turns out to be.
So, you’re committed to falling for the next one, too, eh?
Manias go back a long way.
The history of the UK's railway mania in the 1840s is worth researching, because it's uncannily similar, with a stock bubble, fraudsters and chancers, huge hype, cutthroat competition, boardroom intrigue and drama, miles and miles of unnecessary infrastructure in the wrong places, and eventually a technology that became useful, but could have been developed more intelligently.
I recommend the book/audiobook „ Boom and Bust A Global History of Financial Bubbles“, it does a great job covering various bubbles, with historical context, and going through the details of regulatory and economic factors
https://www.cambridge.org/core/books/boom-and-bust/D09C2E3BE...
And manias are so foundational to the relatively young field of AI that they made the term “AI winter”. We should brace for the third (I think) AI winter soon
When the current mania ends that will be more like a tech/software winter than just AI. I expect the public to have a complete distrust for tech companies and politics to push for strict regulations
I would like to see that happen. We've really let tech companies get out of control
The paradox of the cutbacks after the railway industry corrected is that more or less every track they got rid of would now be useful. The Beeching cut damage done to East Sussex, for example, is devastating and unfixable.
And railways are now mostly state-funded infrastructure. Seems plausible to me that the same will happen with LLMs.
Manias are required for humans to develop a new technology. It’s just part of the human condition.
Tilting at windmills over the stupidity of it is just something I’m trying to get over and not care about at a personal level. Nothing I can do about it either way!
"most" is a hell of a claim, yea. "they're going to be useful sometimes and will stick around in some form" has been rather clear from early on (they're Transformer++, of course they'd be useful sometimes), but every session I spend with one still turns into lengthy deeply-misleading wild goose chases until I learn enough to force them down the right path[1]. quality/size of the model hasn't mattered for that, it always happens at some points (and it's just luck whether or not it's a critical point), and I see no reason to expect that it will change.
>From memory, the last time I was given a presentation on it, by actual Snowflake staff, they reported that ideal configuration results in something like ~92% accuracy due to the complexity of data at a large business ...
it's exactly this problem. nobody has solved it. lots of companies are trying to sell that they have solved it.
when I don't care about the end result that might be fine (I have used wordpress too), but frequently it ends up that they helped find relevant docs (they're quite good at finding that from vague descriptions, and it's very easy to verify) and I would have been quicker if I had simply read those docs in full. and then I'd have a more complete understanding of the space too. my biggest worry for personal use is that LLMs will write those docs in the future, and be riddled with mistakes :\
1: ...and then they're quite good code dictation tools. people who think "writing code isn't the bottleneck" just haven't spent enough time in well-understood spaces or rebuilt enough wheels. it absolutely can be a several-multiples bottleneck in day-to-day work, or when re-doing something you've done before and fully understand.
> having lived through the dot-com hype in the 1990s
Having not lived through this, I'm actually curious as to how similar it was to the current hype cycle. Was there a general sense that something singularity-like was approaching?
I don't and wouldn't claim that AI is completely useless, I personally use it for at least a couple hours out of each working day and feel that it boosts my productivity significantly. My point is that the kind of benefits we're seeing from AI are not the ones we expected, and the difference between expectation and reality doesn't seem to have sunk in for most. I regularly hear friends talk of the post-scarcity future which is just around the corner, something like the culture Minds from Ian M. Banks, and this pseudoreligious narrative seems to be what the big AI players are deliberately selling. What I'm actually seeing is something more akin to Mr Meeseeks from Rick and Morty.
> So, you’re committed to falling for the next one, too, eh?
Haha, no. The point was that I've seen better things in the long winters between hype cycles than in any hype cycle.
If you read the media from 1998 - 1999, there was than overarching message that EVERYTHING was changing. EVERY business needed to be on the Internet right now. Any business that wasn’t investing in the internet was going to be slaughtered. Brick and mortar stores were viewed as a negative on your balance sheet. All commerce would be e-commerce in just a couple of years. Startups with no revenue and no prospects for profitability for at least a decade were IPOing for hundreds of millions of dollars, sometimes with 2x-3x the valuation of brick and mortar peers in the same segment who had established brands going back decades. Whenever anyone questioned the lunacy, everyone responded that, “This time it’s different because the Internet changes everything. None of the old rules apply.”
And then the crash happened and we learned that all the old rules still did apply, and while the Internet was hugely transformative, the simplistic idea that brick and mortar was bad and e-commerce was the only way forward was fundamentally wrong. Yea, surely some segments did change (Amazon killing Borders and Waldenbooks, Netflix crushing Blockbuster, etc.), but investors ultimately look at profit even if they’re willing to ignore it for a while to grab market share.
So, I suspect that AI is going to play out similarly. Right now everyone is frothy, screaming that anyone not token maxing will die. Yes, AI is going to be impactful, and, yes, it’s not going away. But the hype is surely overblown. When was the singularity supposed to arrive? Three years ago, Altman was saying it was going to be 2025, right?
> Was there a general sense that something singularity-like was approaching?
To a degree. There was breathless expectation that this was a turning point for commerce, government and society and that everyone had to stake their claim now. Everything was going to change.
What was it going to change into? Beyond "$OLDTHING but on the Internet!!!", nobody really knew. Those first steps were just webbifying existing processes, stumbling along, refining what was reasonably successful and dumping that which wasn't.
It was like a black hole; from afar we could vaguely see an event horizon, but the people falling into it had no idea what was happening or where they were in the process.
Exactly. If I could upvote you twice, I would.
> Having not lived through this, I'm actually curious as to how similar it was to the current hype cycle. Was there a general sense that something singularity-like was approaching?
A singularity? No.
One of the early driving obsessions was a coalition dethroning Microsoft (which both did and didn't happen). A battle over closed-vs-open. That story was everywhere.
The main thing I notice is that there's a bit of a difference in the way value is created. Even in the UK the arrival of the web created a lot of early adopter winners — ISPs, design agencies, small dev shops like the one I worked for. And it generated a fair chunk of early money, changed the direction of state monopoly telecom etc.
Early well-bounded successes in 96-98 earned proper money, generated a bunch of press, generated new work. Because they had to: you didn't get access to huge pots of cash to grow with, until you had a product that was on the market and selling. The VCs didn't know you existed.
At the beginning it was small tech industry hustle and actual product creation, and a lot of companies really had the small software company playbook and ran that. You had a lot of PC resellers and systems builders who suddenly found themselves as qualified as anyone to build websites or run ISPs.
The problem after that was everyone's eyes got bigger than their bellies, the projects got too ambitious too soon, the VC money arrived even in the UK, and there were a lot of failures. I perceive the moment the firm I worked for got VC money to be the beginning of the end. I thought my bosses were altogether crazy to take the deal.
We basically were not ready to scale fast — the dot com era really scaled brand new companies in terms of number of employees and scope at a level that was at that time sort of unprecedented except maybe in the semiconductor industry.
Towards the pets.com IPO crash end of things, that is when it started to get unreal in a way that you'd recognise now — people talking about entirely new business models, and you had a running joke that if you just put "… on the internet" at the end of your elevator pitch people showered you with cash. The most insane of these being CueCat: a barcode scanner, but on the internet.
But did we think it would fuck everything up everywhere all at once? No. It was going to make us money and make it easier for everyone to buy and sell things, but it was going to create jobs (and it did for a while). It took until well into the early 2000s for the impact on retail to be truly felt.
Nowadays, companies scale enormously before they make any money, VC cash gets handed over to PhD students who only have the concepts of a plan, freemium tiers do the marketing and everyone is trying to follow the loss-leading market-share growth strategy that Amazon arguably pioneered.
The AI boom is perhaps the peak of that Amazon growth culture, "winner-takes-all" thinking, which was always insane.
This hype cycle is much more like the NFT hype cycle — it's just that it has a more easily understood product. Impossible to see Beeple's Everydays as anything other than a harbinger, IMO.
Yea, I forgot a big one: blockchain.
Each new iteration of the hype cycle feels "like its not hype" because the tech is more promising in new ways, but the hype comes from human perception. New tech has to fight a lot of real world and project management issues, and development morphs and slows. We don't know exactly what we have until it faces these issues.
It was the same thing with blockchain, who here remembers the peak delusion of IoT + smart contracts replacing human judicial systems and crypto making central banks useless?
LLMs are of course a form of progress, the same way blockchain was, but it has been baffling and cringy nonetheless to see people with a strong technical background pretending that at some point in the future the development of complex applications was going to be reduced to a few prompt lines written in English.
Once the AI fad dies and the next hype appears I told myself I'm going to jump on it to take financial advantage of it. At this point I believe that the technological cycle of hype is an almost entirely social/economic phenomenon and there is no reason not to make money from it given that people insist on this kind of irrational behavior.
I totally agree with all your points except welcoming the singularity.
How do you imagine your or your children's lives in the singularity?
What do you do there for a living, or which hobbies do you pursue in the best case it frees you and everyone around to pursuit nothing but hobbies?
What do you do in the worst case it pushes everyone to underground strongholds to fight machines for the survival of the humankind?
And if there's some middle ground, how is it different from today's state of affairs?
I'd play games, read books and do drugs.
It'd be different from today's state of affairs in that I wouldn't need to spend the majority of my waking hours reading stuff I don't care about and building things I don't believe need to exist in order to pay for a house I don't own. Healthcare would probably be more reliable and accessible too.
At this point though, I no longer believe this is anyone's future except the most fortunate of the fortunate, most of whom essentially live this way already.
> which hobbies do you pursue in the best case it frees you and everyone around to pursuit nothing but hobbies?
I could absolutely fill my days with stuff, no problem
> It hasn't worked out like that
It seems quite premature to say that. We're 3 to 4 years into the LLM revolution and the rate of progress is still impressive. The recursive self-improvement aspect that is necessary for the actual singularity is something we're only really starting to get into this year.
If the singularity is 5 years from now, that is still much sooner than most people (including me) previously expected it to happen.
> We're 3 to 4 years into the LLM revolution and the rate of progress is still impressive.
The “Attention Is All You Need” paper came out in 2017 and OpenAI released GPT-1 in 2018. ChatGPT was released in late 2022 but that was not the beginning of LLMs. Transformers and generative AI go back even further.
Can you pinpoint the revolution by pinpointing moments of invention and release? Revolution takes time because it’s more about uptake, downstream effects, and feedback.
The revolution is way overblown.
> It seems quite premature to say that. We're 3 to 4 years into the LLM revolution and the rate of progress is still impressive.
When we were 3 to 4 years into mobile phone revolution it was already abundantly clear that it's changing the way people leave.
Ditto when mobile phones became smartphones, and for many other tech items we got in the past decades.
As for LLMs... outside of a few precious professions you could live your life and not notice they are there.
This isn't accurate. The first mobile phone came out in 1983. It took at least a decade before they started to become common.
Mobile phones at that time were 800g and cost $4000. Using your logic you could say we are almost 20 years into the age of LLMs because Cleverbot was released in 2008.
The first prototype of a cellphone was functional in the early 70s. Before that you had walkie talkies and farther back wireless communications.
We're just shy of 10 years since attention is all you need and just a few years past the first commercially viable LLMs. So in cellphones we are something like early 90s.
What they're saying is that mobile phone tech didn't scale at the same rate as LLMs. How fast it takes a technology to unfold into full adoption varies depending on the industry and on what's blocking development at its inception.
Mobile phones were severely constrained by hardware that made them impractically expensive and unwieldy, and also by the time it took to set up wireless networks at a large enough scale. That's why it took mobile phones a few decades to really get going, and this is going to be different for every other technology.
LLMs, for instance, were not as hardware-constrained - we have a lot more compute now, but there wasn't a hardware paradigm shift or anything, things were already pretty good a few years ago - now it's just about scale. LLMs were also always relatively affordable - there wasn't a period where they cost $10/token, so we don't get the same kind of affordability scaling as with phones. They also utilize existing internet infrastructure and don't need to be rolled out in some new worldwide grid.
I think this is off. LLMs are severely constrained by hardware and we've seen at least an order of magnitude improvement in flops since ChatGPT launched. Over the next couple of decades we are likely to see that level of improvement in memory capacity. There's likely at least a few step changes left.
Regardless, the point of the GP that we saw revolutionary changes in society within a few years of technology introduction is off. It took decades for the full impact of the pc, internet, or mobile to be seen. We're still in a very early phase of figuring out how to use LLMs.
Everyone uses chatgpt instead of Google now, but at least half of that is on Google.
Define "everyone". Google has an AI result before the search results. People do use that. People also still use search results when they're dissatisfied with the AI answer.
> As for LLMs... outside of a few precious professions you could live your life and not notice they are there.
And old folks said the same thing about the internet and smart phones. Fads that would pass. While the younger generations lived an entirely different lifestyle.
I’m personally seeing the same. I resisted AI being a “thing” for a long while, but since I’m around a lot of teens and young adults it was a choice of burying my head in the sand or deciding to take a look.
They are going to change society on a scale far beyond smart phones. In ways many will not see as positives, but change never goes the way one may expect.
> "And old folks said the same thing about the internet and smart phones."
Ironic how LLM promoters like to hallucinate citations, much like the tools they espouse.
"Internet 'may be just a passing fad as millions give up on it'" - Daily Mail, December 2000
I recall seeing similar sentiment in other publications throughout the late 90s.
https://www.reddit.com/r/retrocomputing/comments/1m6i4ui/art...
(Apologies that this was the best image host I could find for it)
Surely you jest. Since people here are fans of LLMs, the Google summary describes said publication as: "The Daily Mail is widely regarded as a sensationalist tabloid with low factual credibility and a strong right-leaning bias."
I was there in the 1990s too and we were deluged with headlines trumpeting how everyone, young and old, was rushing headlong onto the Information Superhighway as the dotcom bubble kept inflating.
It's clear that while LLM progress is continuing, it's at something like a linear rate, or slower.
The singularity requires exponential improvement. That's not happening. Maybe we will develop something that sets that off, but there's no indication of that right now.
Yet. If the exponential improvement had already started, the singularity would already have happened or happen very, very soon.
The logic has always been that the AI would have to have significant tools and agency to do self-improvement for the singularity to occur. This is exactly the thing that a bunch of the AI labs are working on hard right now.
Sorry, but labs working hard doesn't make non-polynomial problems suddenly polynomial.
As a vocal advocate for the use of machine intelligence in certain contexts and a skeptic of claims they are useless or always hallucinate or whatever...
It hasn't been even close to even the recent claims! It's been like three straight years where it was supposed to utterly transform society any minute now.
I think we more or less have a fragile consensus that in like, computer programming and really very little else, that on a good day you're probably going to come out ahead with the AI assist. That seems relatively uncontroversial now. But it also seems like success with AI is largely about working hard to get good at it, as a first order concern. It is not at all obvious that blind, uncritical use by anyone is a net win.
It's pretty unclear, sone might say dubious, that anyone has made serious, aboveboard money net of debt and equity, other than the hardware vendors. There's some pretty serious revenue, but it's a drop in the proverbial bucket against the outlay. There like a two trillion dollar balance sheet hole in the US alone where investment into AI has gone.
While I personally don't agree with them, multiple S-tier machine learning researchers think we've got our wheels stuck in the mud, that autoregressive decoder architectures on a tokin-suffix pre-train is tapped out as a paradigm.
It's ok to say "AI is starting to get useful in pretty durable ways" and not sound like an Anthropic shareholder/employee, i.e. completely full of shit.
Uncritical use of today's AI beats all of the optimized use of yesterday's AI by far. I think people like the idea that their success is a result of their efforts, but this is just not the truth.
I don't think anyone's disputing that the models are better than a year ago (though clearly all this, recursive self improvement stuff is utterly hypothetical and that's being generous, most of the progress has been on cost and fit and finish stuff). When it's on the plan, I'll use Fable for some stuff. If I'm paying for Opus? It's 4.5 or 4.6 which were dramatically better aligned and token efficient in the trace at a capability gap that's "you win some and lose some".
That can be true while it also being the case that to someone who has no idea how this stuff works under the hood, it's basically Dunning Kreuger in a box. The next person to go /u/PhdInEverything on me with Fable is getting an education in the history of hardware support for mixed precision training or something. Fable is a masterclass in refusal to ground and a dozen other alignment catastrophes.
So yeah, the models are still getting a little better, but from here out I think it's rapidly becoming a skill game.
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Right, and there are two parallel tracks. First is the “every crack and crevice” part - “ summarize with AI”, “re write with AI”, “help me write”, “analyze with AI”, basically useless features being splattered everywhere in the name of incorporating AI while annoying the F out of everyone.
The second is the AI race / existential risk / pretending we’re Oppenheimer track which I think is the larpiest, basically pretending that LLM chatbots are some kind of super weapon and role playing accordingly.
None of this means they’re useful, AI coding, for one, obviously is a valuable tool (though there remains a lot to sort out). But the usefulness is unfortunately being overshadowed by, as you say, people not resetting expectations and instead wanting to play terminator or neuromamcer or whatever (I’m sure there are more relevant sci fi).
I think people think things will go faster than they do. The AI revolution will probably be a bit like the industrial revolution which took a couple of centuries to go from Newcomen's steam engine of 1712 to mass produced cars and the like. With AI, Turing came up with his test in 1950 and it's taken about 75 years to get to something that looks like passing. Give it time.
> Most of us on some level felt confident that AI would completely revolutionise our society.
I don't want to sound like a jerk, so please let me know if I do - I don't understand why you claim that "most of us" felt that way. I ignored "AI" until mid 2024. Then I tried it and was very unimpressed. I tried again in 2026. Seemed useful, then very quickly I realized that it's something that looks great until you look closer.
I was never impressed by a text generator. A talking computer might be the most boring thing I could imagine. I never understood why people got so obsessed by it and I certainly don't understand why it would "change society". Society is composed of people. What would change society is something that would allow people to think less, feel more clearly, express themselves more freely, and suffer less. Of course, things that accomplish these results exist, but they require a lot of effort. I cannot fathom how a technology that doesn't lead to these results could revolutionize anything. In fact, every technology, that I witnessed arising since late 90s has made things worse precisely because it has not done anything to address the human condition, instead praying on it.
We are still in time for the "revolution" ... but it will take longer and won't look like than most thought.
We still need a bubble pop to drive out all nonsense ideas and paths.
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This part toward the end of the article resonated with me:
> If you’re being asked to review huge volumes of terrible AI code, just assume that the organisation is going to burn you out and fire you. You will not convince the person drowning you in 2000 line PRs to stop. Start looking for a new job as if you have already been fired. I have seen this happen many times now
I suspect we will see this phenomenon more and more as organizations more widely adopt agentic development.
I don’t buy it - if you’re an engineering leader and this is happening it’s your responsibility to get out there and fix the problem through education, process improvements, improved automated quality checks, better planning. Sticking your fingers in your ears and shouting “no!” is not a sustainable strategy. Figure out what would allow your throughput to double, triple etc and go organise it. Focus on bottlenecks and the most difficult pieces. I’m speaking from active experience here.
Thankfully it's not happening in my organization because we happen to have a sane, incremental strategy. But it's telling that you perceive any criticism of AI code reviewer burnout as "sticking your fingers in your ears". Perhaps you might benefit from a closer read of the article.
« it's telling that you perceive any criticism of AI code reviewer burnout as "sticking your fingers in your ears" »
Go back and re-read the quote from your comment - which I responded to: “ You will not convince the person drowning you in 2000 line PRs to stop. Start looking for a new job as if you have already been fired.”
I’m not overreacting to some nuanced take here. I’m not sure how to characterise quitting as a response to over-enthusiasm / slop as anything other than the career equivalent of sticking your fingers in your ears. I’ve been there, I’ve had the arguments, I have little sympathy for staff/principal engineers or above who react like this - their job is to fix the organisation, put a yoke around the beast of AI and find a way to tap the benefits and use the problems (like those 2000 line PRs) as a golden signal for where you need to educate and improve process and planning.
Again - I’m not selling anything here, I just have found it an interesting challenge over the last year and one where I’ve been happy to make progress organisationally despite the frustrating moments. If someone’s response to a problem like this is to throw their hands up in despair or to engage in whatever the logical opposite of AI-hype is (which the article resorts to repeatedly) then yes I’d worry about being laid off. If you’re in an organisation where you don’t have the agency to fix problems then absolutely - start looking for a new job.
I think the author means if it happens regularly, and asking people to stop doesn't work or marks you as a dangerous dissident. Obviously, it is wrong to quit in response to one person doing something stupid
One of the key things I've needed to educate people on is that code contributions are no longer valuable in and of themselves. Someone who produces a large volume of bad code does not have high throughput that needs to be allowed, they have low throughput that needs to be corrected. High throughput in software is now almost entirely about having the right ideas and analysis.
This sounds to me like a demand that engineers fully submit to their future as reverse centaurs [0]. "Submit to the slop tsunami, become a glorified and burnt out button pusher!"
[0] https://pluralistic.net/2025/12/05/pop-that-bubble/#u-washin...
[1] https://pluralistic.net/2026/01/05/fisher-price-steering-whe...
I'm in this quote and I don't like it.
This is overly dramatic and not true.
This exact situation occurred to me. I blocked all PRs with “request changes”. Most engineers don’t like AI slop, most take pride in their work.
The occasional AI slop engineer can be contained by rejecting their PRs, having team discussions around norms, bringing up feedback on retros, etc.
My F100 corporation has answered this problem by removing code review entirely. Thankfully it's not Boeing or anything important. Yes I am looking desperately for a new job.
Be sure to short the shit out of that company after you leave, assuming something in your employment contract doesn't prevent it (doubtful). Especially if they are software development focused.
> All of the AI projects we have observed as a team are failing. Every single one – we have seen 0% success in a year and a half, not only amongst projects we have been asked to participate in, but even within projects that we have observed in passing while doing totally unrelated work.
That's got to be hyperbole, which blows out their credibility. They chose to say 'AI' rather than, for example, LLM, or Transformer model, or Diffusion model. This means they are including a huge swathe of things dating back to Expert Systems in their claim.
And who hasn't seen productivity gains from more established AI technology - at least things like semantic search? Who hasn't seen diffusion models generating content in roles that might have done the work by hand before? Who hasn't seen some kind of regression algorithm (even using linear regression in a supervised context counts as AI - so you can absolutely do AI even in tools like Excel) improve operation productivity?
Even if they narrowed it to the Transformer model LLMs which re-ignited recent public interest in AI, less ambitious projects to give them to engineering staff to automate easy but boring tasks in the background generally have been a success. More ambitious ones that are beyond what you'd reasonably expect the models to be able to do - for sure, those tend to fail. For most of these, the failure is predictable in advance, while some are at the boundary of what's possible, and so it is harder to predict (these are rationally genuine R&D projects).
If you read the footnote, they follow up to say they've rejected 100% of the AI projects brought to them.
Go to their home page and one of their consulting selling points is recovering struggling projects.
One of their front-page selling points is that they use "ancient techniques" from books written prior to the year 2000, because presumably everything newer than that is bad?
> For non-executive management who might be struggling to deliver things that feel beyond their control, we have ancient techniques (see: books written between 1986 and 1999) to turn your team into the envy of the organisation, and we can drop in directly to get your team the resources it needs to save a struggling project.
This is entirely a selection bias issue that they've created for themselves: Advertise a consulting service for saving failing projects to companies that don't have internal expertise to handle it, then write blog posts that 100% of the projects you see are failing. Also refuse to help them, to guarantee they can't be converted to successful projects to keep the success number at 0%.
> One of their front-page selling points is that they use "ancient techniques" from books written prior to the year 2000, because presumably everything newer than that is bad?
I get what you are saying here.
At the same time, was reading Deming's last book [0] and there is a great line that I have never seen in any recent business books:
"The organizational chart is usually a pyramid. The main benefit of this style of chart is to clarify who reports to whom. Notably absent from this chart: the customer."
I've also worked at firms filled with very smart people who had no concept (or were not incentivized) to think about the Theory of Constraints, Total Quality Management or that the firm is supposed to work as a team and not a collection of fiefdoms.
What I'm saying is: just because a book was written before 2000 doesn't make it irrelevant.
0 - https://amzn.to/4waEjA2
There’s some selection bias involved especially based on their marking, but you’re misreading what they are saying.
> even within projects that we have observed in passing while doing totally unrelated work.
The kind of companies with failing projects seem to be very bad at using AI. That’s different from the normal mix of success and failures at most large companies.
Companies with projects that are doing well don't try to hire consultants who specialized in saving failing projects.
No company with a good AI strategy is going to go to the Hermit Tech website where text written in Ye Olde Font explains that they'll use ancient techniques from the 80s and 90s to make your software work and thinks, "These are the right people for our AI job!"
These people are trying to carve out a niche for themselves as being anti-modern, anti-AI, and being contrarian consultants that you can bring in when you want some external consultants to agree with you in a very specific way.
I think you are right on target. I’ve seen this pattern:
Company does x. X sucks, and is largely derided as being a bad idea, but it’s important-employee-bobs baby. No one wants to be the guy that tells bob that x is killing the company in some small or large way, because bob can get them fired or is protected by someone who can, so you hire consultants xyz who specialize in “transforming productivity through x management” and they come in and “transform” x into something less lovecraftian, or just explain why it’s bad in a way that makes bob look like a genius.
> Companies with projects that are doing well don't try to hire consultants who specialized in saving failing projects.
Large companies where every project is going well are rare.
I agree companies with good AI strategies are unlikely to use these people, but excluding failures is just as much a bias as excluding winners. AI lets you shoot your sold in the foot even faster shouldn’t be surprising, but it’s still something to consider.
Christ they are profiting off of reaction, they're like Trump but even worse since at least Trump is not stupid enough to be anti-technology.
Tale as old as time. Revolution has been merchandised. The action and the reaction, they all have suppliers, which often turn out to be the same entities supplying to both sides.
Check out some of Trump's positions on renewable energy.
Perhaps I should have specified Anti-AI. Its just the thing that upsets me the most about your everyday liberal professional, how much they hate AI just because Trump is pro AI
I think "your everyday liberal professional" is more likely to be anti-AI because it is being advertised as a direct threat to their livelihoods, rather than for any more abstract reason. The latter includes both political allegiance stuff and the reasons those professionals would likely cite, like its energy requirements or the economic damage if the bubble bursts.
Whether or not you think it likely that this threat will materialize is beside the point. Workers never like it when someone is advertising that they will put you out of work.
No, because most people in say, California, which is very pro-tech, are perfectly fine with AI. But people in the Northeast are angry at it. It’s entirely cultural.
That observation[0] isn't particularly useful for arguing that it's a political allegiance thing. A lower fraction of voters in the pro-tech part of California (SFO, roughly) voted for Trump than, say, in NYC. Your observation cuts against the argument that political allegiance is the main driver, not in favor of it.
[0]- Assuming it's true, which in my experience it isn't especially, but this isn't the best argument.
You have no idea, you must not know that they are afraid of Waymos out in the northeast, they think they are evil or something, I'm not even joking.
Anyway, I didn't say it was a political thing I said it was a cultural thing. The average liberal is not someone who thinks primarily in political terms, they are just going about their lives unreflectively and they genuinely believe in the moral weight of their feelings, which are their primary guide. Now, I'm not saying that human life is not generally composed of its daily concerns, which tend to rule us, but for the average liberal politics is no different from acting on basic urges; like a mouse chasing the next piece of cheese in a maze, they cannot see that no turn will ever lead to their escape; that so long as they continue to chase the very next thing, they are trapped in constraints that have been laid out for them in advance. Like the conservatives of the 80s, there is always one more moral panic, one more thing that you're supposed to be upset about, one more cause to fight for. There is no stopping to think why this is something that is supposed to upset me, why is this something that needs to change. They are not able to go just one step beyond common opinion and wonder why there are so few avenues for them in the first place, they are not able to question why the maximum content of their decisions is good or evil, just or unjust, left or right. Yes any decision is binary, but one must be able to choose that for themselves, be able to consider the decision to leave the maze, even if they are, in the end, no more powerful than all the other rats, and there might not be any cheese outside of it; and there might be no end to the maze, and even very far from what you thought was the exit lay the bodies of those who attempted to leave before.
But anyway this is what I mean by primarily cultural. Liberals "drink matcha," go to running clubs, obsess over "consent" (they now have a term "weaponized consent," how absurd! that they are just realizing now that consent is always given within certain relations of power...) Whatever politics they have is on the same plane, they are doing it because its something to be done. Someone who thinks primarily in terms of politics, that is, in terms of the social dynamics of power, would never call themselves a liberal.
You did claim that it was political:
> Its just the thing that upsets me the most about your everyday liberal professional, how much they hate AI just because Trump is pro AI
That's what I'm refuting here -- that people here dislike AI just to be contrary to Trump. This is implausible.
And trying to lecture a life long New Englander who currently lives within the MBTA zone on what the culture is like in the northeast is maybe not the best look. We aren't afraid of Waymos here; the only people I've met who weren't jealous of them were taxi or Uber drivers. That supports my hypothesis, not yours.
I'm not sure why my experience differs from yours. When I was in the northeast recently I got into heated arguments with people, one person said they "hated Waymo" and refused to elaborate (but also said they used AI everyday for work), another said flat out that technology is evil, so I asked them if poor people should go without electricity. Then they restricted their argument to AI is evil, and that was a more nuanced (if not trite) debate, which they eventually conceded. But not without some gnawing and thrashing!
But I don't think that hating AI or technology or whatever to be contrary to Trump is a genuine political statement, its just reaction. It involves politics in the same way that everything we do involves politics, but it is not "consciously" political in the same way that a baby demanding care from an overworked mother is also not consciously political. Baby wants milk, your average liberal wants to feel self-righteous (they would honestly do better with the milk). Someone can claim anything they want is political, it does not mean they are doing it for genuinely political reasons, they are probably just doing it because they fear the censure of their friends and the dissolution of their social life. Not engaging with something because you are afraid of how it could harm you personally, consciously or no, is not an objective way of making a decision that effects society more broadly. Like, a baby will just die if it doesn't get milk, you won't necessarily die because some of your friends stop talking to you.
If you were in NYC, some of this may be a Boston/NYC difference. NYC is much more anti-Waymo (which I interpret mostly as an extension of being much more anti-car in general). There are also substantially different cultures between the two re: art and finance, which may have reflected on the AI questions.
I don't think you can decouple Trump from himself in this way without a concrete explanation. If some factor causes one to dislike something just because Trump likes it, then that same factor would influence voting. We have numbers for voting. When there are lots of other plausible explanations than Trump, the onus would be on you to demonstrate why more people are taking reverse-psychological cues from Trump in a place where more people also voted for him. Gesturing at "culture" doesn't do that.
I’m not sure I understand your point. Its not the case that more people voted for Trump in a place where more people are taking reverse psychological cues from him, but precisely the opposite case, the more people take reverse cues the less vote for him, which implies their positions are on account of reaction and not because they are genuinely reflecting on their social and political condition, which would elevate them to conscious political actors rather than people following a trend because they are afraid of going against it.
You are simply incorrect about that. More people voted for Trump in NYC (the typical exemplar of "the northeast") than in SFO (the exemplar of the part of California that is more pro-AI than NYC).
I think that absolute numbers in specific cities don't tell the full story. NYC also has far fewer people with college degrees than SFO, which would correlate more strongly with Trump supporters. I think that if you polled those who voted against Trump in NYC now, and asked them about their opinions on AI, you'd find they diverge significantly from those who did vote for him, whereas if you asked the same question to Trump voters and non Trump voters in SFO they would answer pretty much the same. SFO is also in California which does not have the same cultural relationship with technology as NYC or much of the Northeast, although the culture I'm describing stretches basically all over the east coast.
it's not surprising as LLM's will generally fail you when you yourself don't know what you want. it's surprising how many people and organizations just blunder around scribbling code without clear goals in mind - for such people LLM's are a net negative as they will just end up with even more scribbles and no hope to make sense of it all.
Cheaper than consultants!
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Supposedly 70% of software projects failed even before AI. So are AI projects better or worse than that?
Also like people have asked about the 70% failure rate, how do you define failure/success.
https://medium.com/@trienpont/why-do-over-70-of-software-pro...
> Supposedly 70% of software projects failed even before AI.
Given the way LLMs are marketed, we should expect a vastly more favorable project success rate, not simply a close parity, and definitely not a decreased rate.
Even if we consider that the number of attempts will rise if development becomes faster, we're told to expect a greater number of projects should also succeed because they're easier. They're supposed to justify the higher costs.
It's reasonable to ask if that's the case.
> Given the way LLMs are marketed, we should expect a vastly more favorable project success rate, not simply a close parity, and definitely not a decreased rate. > Even if we consider that the number of attempts will rise if development becomes faster, we're told to expect a greater number of projects should also succeed because they're easier.
I'm not sure I follow why you would think this. Prior to AI was the reason most projects failed because generating the code was too slow or too labor intensive? That would be surprising to me if it was true.
Generating code is not, and never was, a bottleneck to projects. Understanding the problem, designing a solution, organizing resources, testing, and customer buy-in and moving targets have always represented the real issues.
Now, with LLMs, we're supposed to be able to paper over some or all of these problems.
They're supposed to be able to understand and intuitively fill the assumptions present in vague requirements, then produce working applications. They're supposed to shrink the timelines from years and months to weeks and days. They're supposed to replace human expertise with a reliable service.
Why else would I hire an LLM service over a team of humans?
> They're supposed to be able to understand and intuitively fill the assumptions present in vague requirements, then produce working applications. They're supposed to shrink the timelines from years and months to weeks and days. They're supposed to replace human expertise with a reliable service.
But none of those sound like things that would change how likely any given project is to succeed. Shrinking timelines from years and months to weeks and days just means that you can fail faster and try again sooner (hopefully with lest costs), but nothing about that implies your rate of success is going to get better.
I think the point is that using an LLM is not really going to shrink project timelines by that degree.
Most large software projects I've worked on in a professional environment, "writing the code" was one of the fastest and smallest parts of the project. Maybe 10% of the overall calendar time of any given project was "designing and typing in software." The bulk of time spent tends to be working with customers, researching and refining requirements, reviewing the requirements with execs and leads to get their buy-in and support for staffing the project, performing various non-engineering reviews like legal and privacy, aligning timeframes among multiple contributing teams, seeking approval from Director X to move on to the next project phase, waiting for Key Team Member Y to get back from vacation, conducting QA and field testing, soaking the software through various rollout stages like internal, dogfood, internal staging, external beta, and then incremental percentages in prod, while watching and waiting for any regressions or failures. On a project that takes 6 months from idea to 100% in-production, writing the code might take a few weeks of that.
So why are we so focused on a tool that speeds up the smallest part of this process?
Faster and cheaper.
Yeah, the "ancient techniques" thing is a joke.
The article is a fun read. There's a certain amount of hyperbole and subtext which may perhaps be unfamiliar to some readers.
But the point is fair. Where are the huge AI wins? Why haven't Anthropic and OpenAI produced a regular series of case studies where Company X did Fun AI Thing and incredible success ensued?
Why do these conversations always end up mentioning chat bots?
Why do c-suites seem to be full of people chasing trends instead of leading?
Does anyone remember what customers are, or are corporations aiming for Platonic abstraction, where business activities become ends in themselves, with no useful output?
> Why haven't Anthropic and OpenAI produced a regular series of case studies where Company X did Fun AI Thing and incredible success ensued?
Even better, why aren't AI labs guaranteeing success if a certain formula is followed and asking for perpetual royalties for successful projects?
Yeah, and how and what someone jokes about will often tell you something about what and how they think. I don't think they'll literally just ignore everything after that point, but I do expect that they are going to tend towards an old-school approach.
To be completely fair, most software projects fail, AI or not, and we've known that since before the 2000s. I'm not saying everything post-2000s is bad, but just that the core is unchanged: most software projects fail, you should expect failure, and there is no magic silver-bullet process to either anticipate or prevent failure. Agile (big A) is certainly not that process.
I mean, that line regarding ancient techniques is very obviously written as satirical. I read it as „we are serious professionals, not young trend following folks“
The prominent "Business Owners" link on this company's website is broken. "Not Found".
Perhaps this will get downvoted but I personally take with a grain of salt anything written/stated by a company that can't even get the most basic functions (like running a simple website) correct.
And the even bigger irony here is that the author has a ranty blog post in which he claims he saved his employer $500,000 by clicking a button. "[It] is fucking wild that an inefficiency that took me five minutes to solve in a GUI configuration panel was allowed to persist," he wrote.
https://ludic.mataroa.blog/blog/i-accidentally-saved-half-a-...
I don't think this is surprising at all. I'm pretty sure I've saved my employers similar amounts in the past, because what happens is something gets configured months or years ago, the dev leaves, everyone forgets and assumes things are how they are.
I've had to delete some really silly code that would slow things down or just force waits as a result of either dealing with legacy APIs or some other arcane reason. Not without testing or making sure it wasn't there for a reason mind you, but these inefficiencies can sometimes be hidden big problems.
The point isn't that this stuff happens. It's that a person who is trying to sell consulting services with ranty "this industry is a sham" and "it's a disgrace that it was even possible" blog posts has a website with just 5 major navigation links...and one of them is 404.
2 brain cells, have you tried reading below the fold of https://hermit-tech.com/business-owners
Soy un loser
Hilarious coming from a guy who whined that he wouldn't get a $30,000 raise because he clicked a button.
https://ludic.mataroa.blog/blog/i-accidentally-saved-half-a-...
Never a good sign when a "consulting" company has time to play petulant child.
This is an interesting strategy for trying to impress potential customers.
Most consultants try to impress you by talking about the great companies they've worked for.
This blog post screams, "Look how dumb my past coworkers were!" from top to bottom, then expects us to be impressed with their experience?
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> And who hasn't seen productivity gains from more established AI technology
You have to be very careful about claiming productivity gains. There may have been some instances of gains in a specific part of a workflow but does it slow down others or result in overall gains is yet to be empirically measured and validated. We’re seeing metrics like more lines of code, better unit tests, documentation, faster PRs etc. but the actual gains of businesses are still a question mark. Do more PRs lead to faster features being shipped or does it lead to slower reviews or bug ridden code that breaks user experience? I’ve see a lot of companies tout their metrics around more code being shipped but the same companies aren’t talking about how that translates to an actual dollar amount.
These things have been "good" for a while now. And yet, companies like Amazon and Microsoft aren't showing significant improvement in their most visible products.
If it's not obviously showing for the all-in, AI-selling companies, I simply don't expect serious improvement for everyone else.
They're undeniably neat tools, but so far there's no observable evidence that they're transformative.
Amazon and Microsoft have shown considerable decay in their most visible products. Maybe it’s AI, maybe it’s just momentum, maybe it’s something else. But they AI clearly isn’t helping them and they have near infinite access(compared to me anyway), so why should I expect it to help me?
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He does employ a lot of hyperbole. Some of his previous blog posts have titles like "I Will Fucking Dropkick You If You Use That Spreadsheet" (https://ludic.mataroa.blog/blog/i-will-fucking-dropkick-you-...) and "I Will Fucking Piledrive You If You Mention AI Again" (https://ludic.mataroa.blog/blog/i-will-fucking-piledrive-you...).
He's not a big fan of polite, corporate speech, I suspect.
But I like his style and he often has some insightful points that go against some popular industry practices.
Yes, from the first link:
"This is unreasonable.
"Do I seem like a reasonable person?"
I think there's a big difference between individual employees using AI tools to boost their productivity - with things like Claude Code and Codex - and "AI projects" where companies build custom software on top of LLMs.
The former is easy to get right. Any software engineer (at least provided they aren't actively resisting the technology )can get useful results out of Claude Code these days.
The latter is really hard. LLMs are a strange beast to build software on, and most of the obvious projects - like the internal chatbots described in this article - are easy to have over-promise and under-deliver.
Look, if they have data and it says 0%, and you have vibes that say that can’t be true, who should we believe?
Do you work with lots of companies and see large AI success stories?
Or do you just vibe that you personally find AI useful so it must also be a business success?
Look, I honestly don’t care, but I think “it must be false” is also unsubstantiated hyperbole. If an agency says they see no AI success, I see no particular reason to believe they’re lying.
They’re not saying AI can’t be a success. They’re saying they haven’t seen it. That matches my experience too. Proven AI success stories seem… vague, when you dig into the details, in my personal experience.
It doesn’t seem surprising to me.
...especially considering someone has to justify their token spend...
>semantic search
I'm doing fundraising for my tf-idf startup. It's named after a very big number!
Outside of Google, has semantic search succeeded anywhere else?
Their previous article on AI shows a pretty strident ham-handedness. https://news.ycombinator.com/item?id=48002795
In general I find their submissions tend towards extreme grandiosity. I find I really appreciate people who have some nuance about the world, can see some duality, and the many many many submissions here are (I admit) often quite fun and enjoyable, but spoken much more from a bully pulpit perspective, with a zeal and self certainty that I find rarely coincides with truth-seeking.
That's a completely different person, they (ludic and smileplease) just both happen to be publishing on mataroa.blog. This is a blogging website anybody can sign up for, basically Medium but for nerds.
I get what you are saying, and while I'll say it is definitely not 0%, I have seen very little in the way of useful software that is primarily generated. The vast majority just does not go the distance for whatever reason. I could explain many reasons, but I am getting really tired of explaining myself. If the tools were as great as everyone says, we'd be going through a software Renaissance, but we're not. I would argue a software dark ages since it feels like things are getting worse and I find bugs in what was historically very long running and stable applications. But, whatever. I think the author is clearly talking about modern AI, I don't think they need to be explicit about models.
I would love to learn about and from the details of their projects.
zero percent of statistics online are made up!
[citation needed]
https://hermit-tech.com/business-owners
The AI mania has been really fascinating to witness because on the surface it’s surprising that so many otherwise intelligent people have fallen into it. But I suppose intelligence as a concept is multifaceted and doesn’t include wisdom. I also wonder if it has to do with personality, where the people whose personality best suits leadership roles are more susceptible to this psychosis. I think there is also an archetype of “nerd” who believes they are smarter than they are and has all sorts of surface beliefs about AI from sci-fi that makes them susceptible.
It’s also a bit of a dilemma; if your boss has AI mania, and you buying into it or not is the difference between being promoted, keeping your job, passed up or even fired, the rational course of action for self-preservation is to also buy into the mania.
Yeah but that doesn't explain the people on anonymous forums talking like they have a brainworm.
You can't get a fad like this with a true believer rate of 0. Too unstable. You have to have some true believers in the mix to see this situation arise, preferably widely distributed throughout the population.
Something I think about quite often with HN is there are probably people on this forum that have friends / family who work at AI companies or are highly monetarily invested in AI themselves.
I don't think everyone on this forum is as objective as we'd like to think. I know I'm not. A large part of why I dislike AI is because I view it as making my life, personally, worse. It has completely fucked up the career I've been building for going on two decades
And all I hear is "adapt or die" from assholes who are chugging the AI koolaid by the bucketfull
FWIW I have multiple friends in the AI world and was very close to joining it full time, and still feel exactly like you
It's the bell curve meme every time - very, very often stupid people end up being "wiser" than moderately intelligent people.
Meanwhile the really intelligent people end up with beliefs surprisingly close directionally to the stupid people who've barely thought about an issue, just fleshed out with 100x the detail.
A little knowledge is a dangerous thing
I've seen both variants of this where the midwit is enthusiastic about the impending singularity and where the midwit is saying it's all a bubble.
Everyone's just portraying a strawman of the other side with new formats.
Yes true, I can see that actually.
I feel though that if you picked a random sample of members of the public and asked them about the probability of the singularity you would get blank looks from most people on the left tail even if you explained it.
Whereas if you had the statement "ChatGPT can be useful for some things, but it isn't going to fundamentally change the world" you would get a lot of people on both tails agreeing and the people who disagreed would be clustered in the middle.
Maybe that's just my bias speaking though and actually I'm the midwit
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The bellcurve meme is essentially that the people on the left use their intuition, the people in the middle discount intuition for pure rationality, and the people on the right are intelligent enough to understand that their intuition is correct.
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It's a very interesting technology, so it leaves some shockwaves while it's impacting the world. Over time this will be absorbed and the insanity will die out, but it's quite transformative, just like cars and the internet were (to name a few).
And the quantum NFT blockchain metaverse .com (don't register that domain, I call dibs)
The other day I asked Claude Haiku (the dumbest model) if dark energy was just the self-energy of the Higgs field. I know barely anything about quantum physics. I just wanted some example of AI writing style - something like "You're absolutely right! That's a key insight into a load-bearing ..."
Instead it spat back a bunch of physics related stuff, alleged problems with the idea ("there are 30 orders of magnitude difference between that and dark energy, and why the Higgs field and not any of the other fields, and did you know about axions which are somehow relevant, and ..."). I humored it by saying "well the Higgs field is the only one with a nonzero vacuum expectation value" and it told me I'm absolutely right, that's a key insight, etc etc etc. Then I asked it to tell me more about axions and it said some stuff about CP violation, instantons, but then it made some connections to dark energy and to the Higgs field and said I was clever to bring that up.
Now from my point of view I had to deliberately keep in mind that I was talking to a Markov chain and that I was myself acting as a Markov chain, and it's likely that none of these statements actually meant anything in physics. I can very easily see how someone who didn't realize that would be captured and then write a blog post proclaiming themselves to have made a discovery in physics.
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> Checking out a parallel copy of our Go repository and telling the AI to rewrite the whole thing in Zig while I work on something else just so I can keep my job.
> Was it just sales fluff? The answer was a lot more interesting.... Executives at their customers were saying absurd things about achieving 100x productivity, and this meant that if any executive at the vendor said that these gains were not plausible, it would undermine the credibility of the customer’s executive, be perceived as an attack (or heresy), and possibly result in an enterprise contract cancellation.
A lot of excellent anecdotes here.
This post feels like its correct but also doesn't align with my own personal usage of claude for writing advanced sql and python code. I haven't with my own eyes seen an AI chatbot actually deliver a user experience that lets them query data with natural language BUT I have personally experienced writing extremely advanced queries using natural language and it is absolutely able to get close to (by my estimate) 80-90% of the way there.
There surely are companies out there using AI in such a way that is actually advancing them above and beyond their competition. They are probably quietly doing it rather than announcing it loudly.
The whole point of the article is about the big corps with hordes of management and people in them. My argument is that they have always been that way. Before AI it was "data science and analytics" or (as the author says) "blockchain".
Maybe maybe not. In large organisations for each person creating value with LLMs there are probably more destroying value. Bob used to only waste one person time with LLMs he can waste the entire organisations time.
Anyone can use an LLM make a bad ideas sound like a good idea. I imagine this will lead to insane amounts of productivity loss as the entire organisation ends up pivoting to follow the bad idea of a mediocre VP etc.
Don't agree with the hyperbole in the article but I think the broad strokes are there. I'm the same as you, AI has excelled at small tasks and daily chores. Like writing a complex query in seconds that used to take an hour to work out.
Doing feature development seems to take just as long or productivity gains are modest (where I work at least) and I think the reason is touched on in the article. Most orgs are just terminally bad at building software.
When executives say "AI", they basically seem to mean LLMs right now.
AI that just, does something, like extract text from scanned documents, is apparently boring. They don't seem interested in bragging about boring things.
The companies tracking AI usage don't seem to be tracking usage of that sort of thing either.
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> All of the AI projects we have observed as a team are failing. Every single one – we have seen 0% success in a year and a half,
What is an "AI project"? The post doesn't define it.
Is it writing some software from scratch? Using an LLM chatbot by non-coders, either internally or externally? Or something else entirely?
Some examples would really help.
Their company does data projects. That plus context makes me think they’re talking about internal work process automation type of work, although it also seems like they’re talking about conversational interfaces (chatbots).
I completely buy the “emperor’s new clothes” argument for work process automation. I’m surprised they don’t address AI-assisted engineering, which seems to be going positively for a lot of folks (although I have doubts about its sustainability). I disagree about the success of chatbots, if the problem is narrowly-defined and chosen properly. My previous company built a conversational interface to a vector database and saw good results. (Although, arguably, the vector database was the real magic, and a traditional UI would have been faster and more accurate.)
In general, I think OP is more right than wrong, though, particularly about the AI mania and unrealistic expectations sweeping the C-suite.
Do yo have links handy for AI-assisted engineering going positively? The case I have on my mind of it going negatively is this recent Ford case [1] It's not that I believe it couldn't go positively, of course.
[1] Ford rehires human engineers after AI fails to match quality checks
https://www.bbc.com/news/articles/cgrkd41n2v9o
That Ford story was really misleading. It wasn't about modern LLMs, and the way it was reported implied that Ford had fired and then hired people but if you read closer that wasn't necessarily the case at all - it sounded more like they were re-hiring people who had retired because they needed expertise that had left the company.
You need to get through the Bloomberg paywall: https://www.bloomberg.com/news/articles/2026-06-25/ford-has-...
> Over the last three years, Ford says it has hired 350 veteran engineers, many of them former employees and others from suppliers, to help address seemingly intractable quality woes that have cost the automaker billions. [...]
> “We had been relying more and more on automated quality systems” and not getting the desired results, Galhotra said. “We brought back technical specialists” and “they hunt for failure points before a part ever reaches the plant floor.”
(I made these points on the HN thread about it 3 weeks ago and got voted down and I'm still salty about it https://news.ycombinator.com/item?id=48674446#48675045 )
I think it's not clear from the article why they left (e.g. could be anything from retirement to going to work at another firm/contracting to being fired to switching careers), and likely it's going to be a mix, plus not all were previous Ford employees. Similarly the "AI" isn't clearly defined (but like you I would be surprised if it were LLMs). I suspect though why the article exists (and a possible source of your downvotes) is signalling against "AI", which if Ford wants more expert employees (given their issues), is something Ford wants to present.
> That Ford story was really misleading. It wasn't about modern LLMs, and the way it was reported implied that Ford had fired and then hired people but if you read closer that wasn't necessarily the case at all - it sounded more like they were re-hiring people who had retired because they needed expertise that had left the company.
Oh come on, Simon. Are your glasses so rose coloured they are opaque? They fired, and then re-hired because the AI couldn't do the job. They had to re-hire the quality folks. So, no, it wasn't LLMs, it was quality control -- which is a much more well established domain for automated systems. LLMs are a complete shitshow compared to industrial process automation.
Side question -- do you worry about being so pro-LLM when the promises of LLMs are so clearly falling short?
Show me evidence that they had previously fired any of the people who were hired.
Your comment is exactly the misinterpretation I'm pushing back against here.
The time period covered by this story starts three years ago! What kind of LLMs do you think they were using back then?
> Side question -- do you worry about being so pro-LLM when the promises of LLMs are so clearly falling short
I think my record is looking pretty good here. I was early to the "LLMs are useful for writing code" thing, especially with the code interpreter pattern (both write and then execute code in a loop) which I now realize was our first hint at coding agents.
A couple of years ago I was one of the few people talking about what a natural fit LLMs were for the commandline - https://simonwillison.net/2024/Jun/17/cli-language-models/ With hindsight maybe I should have doubled-down on that!
I've also written plenty about the weaknesses of these models - in terms of security in particular - which has aged well.
As @simonw said, the Ford example isn’t a good one.
As for AI-assisted engineering going well, I think the jury is still out. Here on HN and with the engineers I know, you see people claiming multiples of productivity on coding tasks. But you also see people complaining about drowning in slop PRs.
I think there’s a lot of confounding factors to these reports. The type of work matters a lot: bug fixing good, prototyping good, big legacy codebases not so much, but maybe good for increased understanding. The type of automation matters: aggressive autocomplete good, vibe coding bad, dark factory (vibe coding with fancy harnesses and auto-“correcting” eval loops) questionable.
And then finally, the perennial mistake our industry makes, which is to value speed of creation over maintenance costs. Personally, I think this is where AI-assisted engineering is going to fall down really hard, but the jury’s still out on that one.
Anyway, there’s a really big spread in experiences with AI, that I think chalk up more to all this context rather than religion and belief. OP didn’t address it at all, which I think is a big gap in their essay, but I do think think they describe the executive-level mania pretty well.
> As for AI-assisted engineering going well, I think the jury is still out.
Anecdotally, AI-assisted engineering has helped me flesh out ideas or to learn extremely complicated APIs faster than trying to understand the docs (which usually are labyrinthine). MS COM ones, for example. I can go read the docs but it's easier to get a quick idea of what I need to do if I ask Claude to provide me an example of doing something specific with it, because MS's code samples (particularly their full ones in, say, the windows Desktop SDK repo) have always been annoying for me to wade through because I have to filter out a bunch of noise. I can't (and won't) try to guestimate "productivity" improvements though, but as an assistant AI has (somewhat) helped. I still do all the engineering work though. Along with it giving me tips on using more modern language features for languages like C++.
The Microsoft API docs are a special case where i'd say you pretty much need LLMs nowadays because after a bunch of document format conversions over the years they degraded massively.
If you can find some MSDN CDs/DVDs from the early 2000s, the content is much better (and you can clearly see that the current docs are often missing descriptions and names for method arguments or even entire paragraphs).
What places would you suggest where I can get digital copies of that? I don't have a physical CD/DVD reader so... :)
LLMs are good at natural language search. They're bad at everything else.
The Ford case is not about AI coding. It's about computer vision processes that went wrong. This was less about AI and more about Ford being dumb.
> I disagree about the success of chatbots, if the problem is narrowly-defined and chosen properly.
If you can narrow the problem down, then you could design a much better interface for it than a text box and free form text (unless that's the better solution).
As for as AI assisted engineering goes, the thing is that after some time with a project, you already have much of the workflows and routines nailed down as scripts and other various combinations of tooling. And unless it's spaghetti code, you will have various snippets you can copy from for new code. The one thing I've observed about AI projects is that there's often little technical design coherence about them. It's always a kitchen sink of technologies and practices.
> If you can narrow the problem down, then you could design a much better interface for it than a text box and free form text (unless that's the better solution).
Yes, I agree, in that the chatbot we built probably would have worked just as well with a traditional UI, and would have been done a lot faster. But it would have been a lot less sexy (actually important for the bottom line!) and there are future directions that could take advantage of the conversational interface that’s potentially better than a traditional UI.
On the down side, good chatbots are really frikkin difficult to write. These things (LLMs) are not reliable at scale. The basic functionality came together in weeks. Getting it to behave consistently and obey guardrails took months, and even then we had to accept a low level of failed conversations.
> But it would have been a lot less sexy (actually important for the bottom line!
That’s what the author have been saying. They do for nice demos which sell the illusion of having Jarvis in text format, but the usefulness is not really proven. And that may be important business wise. But as far as end users is concerned, there’s not a lot of productivity boost.
They run a consulting company called "Hermit Tech". Their websites has an olde style font.
They boast about "ancient techniques" from books written prior to the year 2000
> For non-executive management who might be struggling to deliver things that feel beyond their control, we have ancient techniques (see: books written between 1986 and 1999) to turn your team into the envy of the organisation, and we can drop in directly to get your team the resources it needs to save a struggling project.
So yeah, of course these people hate AI and everything about it.
No serious company is reaching out to these people for help with their AI project.
i feel like the useful applications of AI get silently integrated into workflows.
the ill-conceived moonshots by and for a non-technical audience get labelled as "AI projects/initiatives" and they fail.
That has ever been the case. As soon as it works reliably, it's not "AI" any more. Take spellcheckers or collaborative filtering as examples, but there are lots more. Hofstadter in G.E.B. said it well:
> There is a related “Theorem” about progress in AI: once some mental function is programmed, people soon cease to consider it as an essential ingredient of “real thinking”. The ineluctable core of intelligence is always in that next thing which hasn’t yet been programmed. This “Theorem” was first proposed to me by Larry Tesler, so I call it Tesler’s Theorem: “AI is whatever hasn’t been done yet.”
In this moment, the opposite is happening. Everything is getting called "AI", whether it uses LLMs to prompt LLMs about how to prompt LLMs, uses "conventional" machine learning, or just looks mysterious enough that they can expect the market to not ask questions.
I am reminded of "game AI", which for the most part has historically been just giant decision trees, encoded one way or another, because if you hook up any sort of real AI to a game entity or collection of game entities that does any sort of learning or training, even simple 1980s-era reinforcement learning, it turns out the game entities will roflstomp the human players, and the human players aren't interested in paying for that experience. We've been calling those collections of if statements and for loops "AI" for a long time, though, because who wants to hear about how deliberately stupid their opponents are?
Sure, or maybe the actual applications of “AI” are small and unobtrusive, like the dictation of doctor’s notes example, and it’s not actually the massive revolution it’s claimed to be.
I struggle to reconcile this attitude with, among other things, the massive number of exploits being discovered and the pretty straightforward utility for coding. Have you used codex recently?
Perhaps, but I think what people mean by intelligence is something that learns and adapts. If LLMs couldn’t do in context learning I don’t think people would think of them as AI, more as a kind of queryable database via natural language. There are other algorithms that learn and adapt, but in much more narrow circumstances that most people won’t obviously interact with.
Right, and my point is that now that is the bar. If you'd told me in 2008 that markov chains would be smart enough to one-shot even a trivial video game from scratch I would certainly have called that AI even if you had to do some major rigamarole to get it to work.
Because AI meant "make computers smart like people".
We taught the computer to spell check, but the computer still didn't feel smart like a person, just smart like a machine so that obviously wasn't AI. We taught it to do algebra, same thing. With LLMs though, now it really does feel like an artificial human, so this time it really is AI.
Right, but I remember when spellcheck first started to be a thing, and people were like "Wow proofreaders are out of a job, ai is so cool", but then it became normal.
Just this past week, I have had to answer two surveys, from different sources, asking how I use AI in my work. Both had a "choose all that apply" checkbox, and, funnily enough, neither allowed me to select zero options. "This question is mandatory". :)
Apparently, there is confabulating AI autofill browser extension. Would be nice to seamlessly bullshit them back.
>Reading this while working for a division that pivoted to provide interfaces for agentic workflows, only to discover that only ten users had ever touched the products we made for agents, only to pivot again to support for agentic workflows, which has a lot of competition because every company has to do something agentic now and there's only like four things you can do in that space, is bracing.
>– An editor of this essay
The most popular form of AI psychosis in a nutshell, yea. "AI will understand this for us, so we can be groundbreaking and get rich if we just try" is only possible to believe when you have no idea what you or the rest of your field is doing, and have only attempted to learn about it from "AI", which has spent a lot of your money convincing you that you're brilliant and revolutionary.
It's exactly like surrounding yourself with relentless ego-boosting yes-men. You lose touch with reality. Just take a brief look at any super-wealthy person, they're obviously insane but everyone around them thrives on convincing them that they're not.
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>For the latter customer-facing applications, I have rarely had a pleasant experience as a consumer, with perhaps the exception of live transcription during medical appointments...
These have been abject failures, personally. They've made major mistakes in summaries, and added non-existent symptoms multiple times. Medical transcription seems an especially dangerous place to put them.
> Are companies actually seeing massive productivity gains from their AI adoption? Does any of this sordid affair make sense?
It makes perfect sense for the shovel sellers (nvidia, anthropic)
I have, in two wildly different companies, on opposite sides of the globe, witnessed leadership go all in on using LLMs to write external documents that would have significant legal and financial consequences for any inaccurate statements or numbers included. There's always a "we'll have our experts read and fact check everything before we send it out," but I have a strongly held view that people whose expertise is not in editing/fact checking tend to be far worse and sloppier at it than they are at writing things correctly the first time.
You are basically calling out the fact that the Emperor has no clothes. Many said this before. While it is a true statement, it is not going to help. Because, as you rightly said, it is a mania - like the tulip mania of 17th century or the manias of many forms today. The mania continues to evolve and flourish through it's peak and then go down. For that matter, there is hardly anything that is not a mania. Think of agile processes, timesheets, LoC based productivity, ...
The corporate mindset keeps going through different mania at different times. It could be initiated by some consulting gurus (processes), or some security nerds (strap yourself down until you can't move), or peer pressure (fear of missing out), or presentation goals (show that you are a AI-powered and modern company).
We can't remove or stop manias. Infact that is not the goal. The music should go on and the dance should go on. Everyone is in this dance - customers, businesses, supply chains, governments, thinkers and philosophers. It's a world-wide dance. So it's OK. The music track won't last forever. It will change and dance will change.
This mania can also be called "herd behavior" or "crowd psychology". There was that book a while ago about the intelligence of crowds, but far more common is the stupidity and insanity of crowds. Business and politics are mainly driven by it, but the tech industry routinely falls to hype, trends, and decades-long mistakes that seem obvious in hindsight.
You all are missing the larger context. Not everyone here is equal. There are certain people who control economic organizations, set their goals and priorities, generate hype and fluff to increase sales, push everyone to be more productive, dont do the actual programming work. And then there is everyone else. This is how global capitalism is structured.
For what it’s worth „Extraordinary Popular Delusions and the Madness of Crowds“, while entertaining, isn’t considered a reliable source as it bases most of its story telling on satirical descriptions and reporting of past events and doesn’t do a good job of digging into the actual historical context and dynamics
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The right question should be: How can I make money by exploiting other people's mania?
It's super hard to time the market. I would say the sanest approach is to have cash on hand, choose AI adjacent companies with solid fundamentals and buy their shares once the bubble pops. It's very hard to make money being an outright bear, but somewhat easier if you're an early phase/post recovery bull.
Small discussion yesterday (43 points, 7 comments) https://news.ycombinator.com/item?id=48956153
I love this author's article about saving half a million dollars with a click from a while back. Nikhil, if you're reading this, I have a decent war story about a similar situation (I was lucky enough that there were two such things, so I saved a full million a year and was still denied a $15k raise) and so I was really entertained at your post from a few years back. There are actually a lot of lessons to be learned about corporate politics there, about how you can save someone a million a year in perpetuity and promise to do that again next year (which I could have!) and see them still refuse to pay you a single extra cent.
Incredible skill issue selecting for profit instead of fun. Why does everyone need to make money?? What about like... shareware? Remember shareware?
You mean the software that marketed trials of itself in the hopes you'd pay for it?
okay, beerware, then.
> The world’s organisations have been captured by people in the throes of frothing excitement, and saner people who now live in a state of constant commingled fear and frustration.
This reminds me of "Youth without God" by Ödön von Horváth:
> Perhaps Anne’s mother was right. Perhaps men are mad now—those who aren’t mad haven’t the courage to put their maniacal fellows into strait-jackets.
Everyone knows the ivory towers are full of people who shouldnt be there. Its marvelous to see them crumble even if you arent in the chain gang below.
Nothing will improve until things get bad enough. You need enough greedy yes men doing quailty control on airplanes to escalate.
It took me decades to understand the use of and need for escalation in big organisations.
I didnt understand seemingly unproductive strict job descriptions either. Hilarious situations with 10 people doing nothing at all their entire shift (really nothing) while i had work todo on my own that really required 5 people. A few days later someone showed up to tell me the qualty was below average. LOL
Now i know i should do only half a shift worth of work. When they come complaint about it i say: very good, write it down, make the official report.
Then i hear nothing and a year later its two people with work for 5 scheduled. I tell them to slow down but we still do 2.5 shifts because they dont understand how escalation works.
The nummers now show we are 5 times as productive which isnt good for the company. The beurocracy is slow to adapt and all it has is numbers.
For many years i tried to do all of the work but that means nothing is wrong. The numbers say all is fine most of the time. Someone grinning at how much work i did isnt going to get recorded or processed.
If it looks like an unattended LLM can do a better job it means you dont know what you are talking about. If you fire everyone who noticed you might buy time but reality will catch up.
It reminds me of when they first put computers in trains in NL, they ran on windows 3.11 and no one trusted it to do anything. The solution was to give it all the data so that it could display a nice overview but it didnt control anything. Lots of trains drove around with a blue screen of death or a boot error. If there was a problem it was slightly harder to diagnose but it wouldnt drive if [say] a door was open. If the gui said a door was open you could just ignore it. On its own it means someone has to replace a sensor. If it also didnt move anymore the message is a real issue.
I imagine LLMs are wonderful for that kind of thing.
Dupe:
https://news.ycombinator.com/item?id=48955929
https://news.ycombinator.com/item?id=48956153
This is excellent.
> Employees don’t use internal chatbots because companies tend to have low-quality documentation and an LLM is not psychic – it can only know things that have been written down and made accessible.
Then later:
> In one extreme case, I have seen an executive confess that they had never even used ChatGPT or any AI tool in their life, immediately after producing a technical strategy for an organisation with $2B+ in revenue which was entirely centered around AI.
And:
> In fact, we have been forced to opt out of every sale where the lead has expressed anything beyond the most fleeting curiosity in the use of AI in their business. I don’t mean that we’ve heard that they’re interested in AI and elected to drop the contract on moral grounds. I mean that, over the course of the engagement, these people have exhibited a pattern of behavior that has made it near-impossible to sell to them without incurring reputational and legal risk, and are furthermore crafting management environments that I can only describe as cultish, ineffective, and “please dear God, do not let it be on earth as it is on LinkedIn”.
And:
> with one client, we uncovered that staff were totally unaware they had been given licenses for AI tooling, which cast into doubt all productivity claims.
For some reason this comment was shadowbanned.
Huh, that's interesting. Sometimes that happens because HN's AI text detector kicks in, I've seen it against my comments that quoted from AI-generated text in the past.
I don't see any hints of LLM writing in this article though.
Dupe https://news.ycombinator.com/item?id=48962963 (33 points 10 comments)
I think AI is only part of the problem. The Multi-crisis ahead, makes it even less of a proposition to be in charge right now- and AI offers a "responsibility" cope out for already stressed to the limit human systems who have no solutions for the problems, because there often arent any.
Take climate change- you have torrential rainfalls, sweping away whole city-parts in mountanous regions, in some enormous russian roulett. And it doesnt even factor into building evaluations because then it would basically reduce the prevalent pension scheme to cinders.
You have dry months in europe now, where some thrown cigarett butt could ignite a firestorm- and the obvious solution is to remove the dangerous greenery from the burbs. Nobody does it though.
And that is just the plain sight visible layer of this shit cake. If i was some missguided fool into heroics and leadership and signed up for a little more then i could take and fake- i would long for some magic box that lowers the burden too. Those up there are human after all.
Does Europe even have burbs?
Russian roulette was a good pun. I don't know if it was intended.
We have villages near cities, whos native populations get displaced due to the price hikes and then we have "settlements" which is roughly equivalent to suburbs for those who are more well todo - while the less wealthy are going into the old buildings the even less wealthy natives have vacated.
As someone who uses LLMS for coding, ideas validation and research, I think the article is biased against AI forctge wrong reasons.
If you know what you are looking for and know what “a solution looks like”, AI is amazing at distilling ideas. If you have no clue, the AI will return “clueless” solutions.
It is just like before the AI: there are people who know how to search the web, read and understand documentation and so on. And then there are peole who are incapable.
AI is naking the latter category fail incredibly fast. Really, nothing new under the sun: garbage in, garbage out.
Dupe https://news.ycombinator.com/item?id=48956153
The range of ai approaches ranges from inappropriately betting on it to wholly ignoring it both of which are dumb.
It helps to think of AI as analogous to previous game changers like electricity, computers, internet. If your business is bad or you have no game plan beyond "use internet" your are fucked. But at the same time almost every business has some angle in which these technologies are valuable and necessary and ignoring them is also death.
Ignoring it is hard to distinguish from waiting to see how other businesses successfully use it, then copying them, which will then be framed as adopting best practices.
It's often the difference between people saying "we are taking a wait and see approach"(wise) and saying "it's spicy auto complete that's totally useless" (dumb)
My hunch is that AI tools are probabilistically good enough to deceive most casual users in the beginning, so beliefs are built on these foundations. LLMs handle simple tasks reasonably well so their perceived 'intelligence' is easily convincing for many users (often managers and executives).
However, this illusion immediately falls apart during actual stress-testing like writing code for a complex, battle-tested production environments. Non-technical folks can effortlessly spin up simple web apps or tweak a frontend so now they conclude that software engineers are becoming redundant.
And even if there are obvious examples of all of this backfiring no one believes it...
There are a lot of simple web apps out there that haven't been built because of the cost and many more that have been built but cost a lot to maintain. AI seems to work on those and fill in a pretty large space.
Probably true. But isn't this a failure of software engineering? We should have plenty of simple ways to build simple apps by now.
Maybe you should be able to go down to your local web app shop, give a couple simple requirements, and get back an app in a week. So why can't you? Well there's actually a good reason for that - the customer would never be satisfied by the result, and building a satisfactory result requires many iterations back and forth. Rapid feedback is essential to software development.
Okay, an alternative is to give the customer an app builder so they can make their own app. We've done this many times in history, it always takes the customer time to make the app, the results are always mediocre, but it does work, why do we keep doing this and then stopping it? I'm not really sure. Why isn't there a visual basic today (apart from Roblox) or even a Frontpage (apart from Squarespace)? My best guess is the customer tires of working on the app or never had time for it in the first place. Why won't AI suffer the same fate? Really no idea.
AI doesn't suffer the same fate because the customer can input that feedback in near realtime. I built a very quick database site for my car club to track a car meet we had. Registrations, Money, Cars coming, hotels, t-shirts. It was probably a mess of code. It worked though and didn't take long to get setup. This would never have been an app and would have been done with spreadsheets if we had to pay a developer for a month to get things right.
I could not disagree more. You must have used the better models by now. Even if you only lightly use them for a question from time to time, it must have become clear that these things are pretty damn good and aren't going away?
When doing hard things I need to be 100% in the loop, but I don't have to do anything menial anymore. Imagine being in some CIA control room with 10 emplyees, and you're directing the team. You're still on top of things, just not bogged down with bulky minutiae.
Ai Mania is making evangelists and non-believers alike sound like they are taking sides in a cult.
I mean, that's what it looks like when it is sane vs manic people and the observer cannot quite tell which side is which.
“Infidelity does not consist in believing, or in disbelieving; it consists in professing to believe what he does not believe. It is impossible to calculate the moral mischief, if I may so express it, that mental lying has produced in society. When a man has so far corrupted and prostituted the chastity of his mind, as to subscribe his professional belief to things he does not believe, he has prepared himself for the commission of every other crime.” ― Thomas Paine, The Age of Reason
The author doesn't distinguish whether these projects failed because of technology and execution, or failed because product market-fit. They simply blame AI being involved.
AI will only help if you use rapid iteration to cheaply/quickly produce ideas. All the normal project failure modes still exist.. Blaming AI because AI is dumb.
I only read up to point 3 because the hyperbole and frothing fervour was overwhelming.
Seems like this consultant needs a consultant to help them adapt to modern technology.
Interrasting article :D. Chef's kiss :D.
Broadly I don't think this is quite so true, quite such a mortal threat.
What I see are that there are a lot of extremely fake humans, who want and need cover. Who have absurd ridiculous (and often dastardly or sinister) plans. Who want to do things, a-priori. But could never get away with their actions, in any just clear reasoned normal rules of society.
And AI is this new circuit breaker. It's innovative permission to move ridiculously fast and break everything, right now. Take the perhaps old IBM slide and flip it upside down,
> A computer can never be held accountable.
> Therefore a computer must never make a management decision
https://simonwillison.net/2025/Feb/3/a-computer-can-never-be...
The people "using" AI today to "make decisions" are using it because AI cannot be held accountable therefore that is the cover for their decisions.
This is is all such a resounding PKD nightmare, a reality bring invaded by Fake Humans. It was that was already, just gobs of nonsense, the worst liars spreading the most ridiculous memetic caltrap everywhere: Bullshit Asymmetry Principle weaponized against reason to ever higher degrees, Fox News terrormongering advanced and advanced, Hastert Rule obstructionist politics by wicked pedophile protectors and system ruiners and monsters. AI is a rapid accelerant for burning down reality, for propagating the disreality that the fake humans require for existence. Un-people truly from some other dimension, who've worked and worked to get away with their twisted anti- reality over us all.
AI can and does help with a lot of decision making, in good ways. It's an incredibly tool. It can comb through incredible amounts of data. But it's primary use in "decision making" seems to be in deflecting responsibility, in making hideous choices no human system could reasonably make. In concocting fabulations. Both of management design, and endless fuel nightmare disreality slop video to dislodge any last bits of real reality still clinging on (hello ai faked campaign videos!).
The "frothing excitement" here is the frothing excitement to destroy society, to be and bring out the most wicked brutal careless world that can be brought upon us, to raise up the Theil-istic/(Octavia) Butler-ian nightmare neofuedalim. It is to escape accountability, to give cover for sin savagery and sabotage.
(Regarding the article, I do think it's worth tempering ones read of this article by reading the authors previous work on AI. Which to me exposes their baises and in my view makes them so vastly unreliable & overdramatic a narrator as to be near worthless. Their other submissions are less greviously clearly full of it, but also tend towards ridiculous over-grandiosity. https://news.ycombinator.com/item?id=48002795 )
It should be possible to exploit a rich person's poor decision making to make them poorer and myself richer. But I don't see how. Why not?
It sounds like the writer of this article could benefit from starting another brand which is the same product but with AI. That brand would make all the delusional AI chatbot sales and absorb the reputational damage when the chatbot destroys companies.
It seems that the writer has standards other than “getting richer”.
The writer appears to run a company whose job is extracting money from other companies whose executives fucked things up.
Fair point as far as there being low success rate in some ways and over-enthusiasm. But 0% success is a dishonest exaggeration.
He's ramped his own AI spite up to a manic level.
Honestly, this is close to my experience within my org. It is not 0%, but it is very close. It seems like you're just uncomfortable with the author's experience conflicting with yours.
Perhaps it is the wording. For my org, I think people are getting value out of AI use supplementally, but what I would see as "AI projects" are definitely dead in the water.
I want to frame my comment here carefully to talk about the narratives, specifically, and accept that we need to stipulate on face value that this is true (it certainly rings true from people I know in real life):
This specific narrative reads like every narrative explanation of the Republican party in the period from the second half of the first Trump administration until now.
Again, I am talking only about the narratives here. But the pervasive narrative around Trump is that privately everyone hates his guts, comes away with a worse impression when they work with him closely, dreads him opening his mouth, thinks his decisions are insane, and they are telling journalists that, at every opportunity, and have done since the 2018 midterms. And yet in public we have committee hearings where any number of irrational topics become a sort of terror zone here rational people have to assert irrational beliefs or pretzel logic to be able to continue; where ordinary absurdities have become cult-like witness-bearing that everyone polices everyone else on. It's like the Hunger Games of litmus tests.
For example the desire for career advancement is a common thread in both of these narratives, even though this is being promoted further up into the kafkaesque absurdity. Both of these things remind me of other things, like somehow everyone in US politics seemingly believes in God and somehow nobody in an international football (soccer) team is gay. Even though these are not plausible.
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I flagged your comment for adding zero additional information to the discussion. Let's see if it also sticks around.
still is a shit article.
So you say.
honestly just seems like selection bias right? 0% success rate with ai projects, really? based on some of their other about us material it seems like they probably only attract companies that aren't motivated to adopt ai successfully.
If you read the article, the go over a case where they introduce AI into their pitch and were met with results they were not comfortable with.
I read the entire post, and I think this company has no expertise at all. So I'd rather they just used AI writing instead.At least Frontier model AI doesn't make such overblown claims.
They proudly claim that every AI project they've observed over the past year and a half had a 0% success rate, and that they've rejected all AI implementation work. While this is evidence that the market is crazy, at its core, it's a painful confession that they have no engineering expertise to implement and control modern AI architectures like RAG, Agentic Workflow, and context window optimization to meet business requirements. I find it fascinating how they're packaging that. It's basically saying, 'We're behind the times.'
There are already products that have achieved results by using AI as part of their development process, yet lumping all different types of AI usage into a single failure category is not only inaccurate but also misleading.
Same goes for the Snowflake Cortex anecdote. Even a freelancer like me can explain technical limitations and distinguish between what's possible and what's not, especially when clients are eager.
There's no engineering analysis in this entire post about why AI fails. No mention of technical bottlenecks like vector DB retrieval quality degradation or prompt injection failures.
I've also worked on RAG for a specific company. For internal knowledge chatbots, it often fails depending on document collection rates and chunking. But none of that is mentioned.
So I understand that AI projects and related things are bad. But there's no analysis of why.
For example, regarding Snowflake, I'm not sure, but did they discuss accuracy in terms of what query set or what ground truth they were using? You're consultants, aren't you?
Honestly, I don't understand why people are excited about this. I'd rather they just used AI. TIt's not about whether human writing is good or bad. It's that this kind of writing feels like a deception of the reader.
When making overgeneralizations, there's a basic minimum standard required.
Saying that making token usage a KPI makes it hard for employees to report is just an 'obvious' fact that's already appeared in far too many essays. Wake up. You're 'consultants.' Consultants are supposed to provide metrics and directions, but all you're doing is shouting into an echo chamber and asking for agreement.
If a significant portion of corporate AI investments are shoddy, you could at least propose specific metrics like document collection rates or user evaluation scores using the very skills you claim to have. I really don't get it.
Just use AI. I wish the OP had used AI. Let me be realistic.
The article is not a technical overview of AI's failures, it's about the organizational failures that lead to things like token leaderboards. I would argue that this article goes far beyond the "obvious" fact of token leaderboards and into the deeper problems faced by both vendors and companies trying to measure the efficacy of AI.
How would AI writing help in any way?
If this were genuinely a consultant's writing, they would need to present a modeling hypothesis for why failure occurs in organizations and support it with metrics. But claiming their own observation of 0% is not a hypothesis.
Of course, they can write it on a personal blog. That's not inherently bad.
But when they're rejecting client requests while offering no opinion on how to model the metrics that would define success—that's the problem. This type of writing is just an illusionary piece that claims to offer insight. It's a bad form of human writing. I like to call this style the 'Rhonda Byrne Secret type.'
At the very least, a consultant should provide modeling of their hypothesis and actionable advice.
Explaining everything with a single reason—that people are afraid of being laid off if they don't adopt AI—is just a cop-out. There's no hypothesis, no reasoning behind why that claim holds. It's trying to explain everything with just that one assertion. I'm not even sure if that holds up organizationally
The OP offers no logical reasoning about how decisions are made or why they break down.
A proper piece of writing would have looked like this:
Why did the client want to adopt LLMs?
What problems arose during the client's process of adopting LLMs?
How does this connect to the organization's internal issues and disrupt its pipeline?
You need at least that level of description. This is just an emotional post that seems to say 'I wish LLMs would fail.
If they had really explained organizational dynamics, they would have talked about the asymmetry of expected costs.
Professional managers and mid-level managers prioritize career defense above all else, so they're extremely risk-averse. They would have explained the costs of adopting AI versus the costs of failing to adopt AI.
If they reject AI adoption, shareholders or the board would say they're 'failing to read the trend.' If they adopt AI and fail, they can package it as 'early market entry costs'—a defensible risk.
Providing that kind of defensive logic to decision-makers is exactly what consultants do.
It's described as being held at gunpoint, but it's really just a prisoner's dilemma. In a market where competitors and vendors are all lying and saying 'we've achieved 100x productivity with AI,' the moment someone tells the truth and says 'we don't need AI for our business,' they become a political target both inside and outside the organization. In the end, everyone knows it's useless, but they approve budgets for 'fake AI projects' anyway. I can offer that kind of cheap insight too—it's a Nash equilibrium where the system has been driven to a race to the bottom.
And I'm not even a consultant
Honestly, it's just shouting into an echo chamber. They call it organizational failure, but it's really just 'come on, my side.' A consultant who does that is untrustworthy. It means they have no metrics.
Same goes for their 0% claim. It's overgeneralization, isn't it?
So why did they fail, what went wrong, and how should these AI projects be analyzed—and by what metrics? They skip all that and just throw out a single KPI. What insight does that offer?
Honestly, there are a lot of people on HN who just shout that AI is useless because their own jobs are at risk. That's an undeniable fact. In my country, Korea, we say 'like a pheasant hiding its head in the ground.' It means denying reality and lacking self-awareness.
I've also seen many failed AI projects due to a lack of proper evaluation of AI capabilities. I don't want to deny that premise.
But if you're going to make that argument, you need to describe why they failed. There's none of that here, just this:
'We're going to opt out because it looks like AI projects will fail.'
They package it as being cautious, but when you peel back the wrapper, it really means:
'We can't keep up with the AI trend. And we can't tell what works and what doesn't.'
Sure, AI has a bubble. No one can deny that. But separate from that, it is useful. Do you really think millions of consumers would stick around for something useless?
Social problems are arising, and people are losing jobs because of it. Let's think the other way around. It doesn't make sense for people to lose jobs over something that's truly useless and wrong.
People are losing jobs because it's useful and valuable. So wouldn't it be more accurate to say that the problem is 'over-application' rather than the thing itself being bad? In other words, we could say that it's being valued more than it's worth. But this argument just says it's bad, period, with no nuance. I can't trust someone who talks like that.
AI writing, if you give it a red-team prompt, can at least refine the surface of counterarguments to your claim. But this—from overgeneralization to extremes—is a textbook example of bad human writing
(i'm more chatty because it's my "down-day". Hope you don't mind)
I don't think you're upset because AI is useful and valuable. Are you upset because you can't describe how the bubble will burst?
Some (more) quantitative types saying that the economics are deflationary. So NVIDIA have already won.
But there are some that claim that the bubble has nothing to do with NVIDIA's monopoly on compute (21.C steam engine/steel). That the bubble is in the OX layer. (You?). If Anthropic (or Palantir replacement) fail to do what? Then the bubble will burst.
This is the more interesting but I will have to sleep on it.
I think AI is useful and has value, but what I find problematic is the illogical behavior that people display while claiming to be engaging in thoughtful writing.
Rather than saying NVIDIA will be the ultimate winner, I think Apple could be the final winner as a hedge against semiconductor stocks, at least for now.
In that sense, I'm not sure. You can't time a bubble, so it's just a bubble.
But I do think NVIDIA will remain powerful because of its CUDA ecosystem—though the future is uncertain.
[Bro I was hoping you'd rip into your own OX idea here.. then we could have a convo here or offline.
I wouldn't say I get frustrated by illogical writing--esoecially ones that only _seem_ illogical---, but more that people like us who might know better don't point out precisely where that illogic is after thinking carefully about it. ]
It's possible to time a burst _if_ you see the pin, but anything you do _when_ you see it is technical illegal?
So your OX idea feels like just the sort of thing that could open our eyes new kinds of pins.
Further, I'm not totally closed to the idea that actively trying to pop the Anthropic bubble is moral, indie of how quixotic that sounds. Even if it might be a good case study for deinstitionalizing research math.. even if that Napoleon quote that's trending..
I would also never bet against Jensen taking some ideas from "the Apple playbook". Especially when multiple people have already pointed it out. Though this is tangential. Less tangential---
Refinement of OX framework would help us think about how NVIDIA can or cannot play the Apple game. Certainly, if given calibrated riskiness and timing, Apple would move against Anthropic. You know what I mean?
You can contact me anytime via my emai
Clicking the footnote for the weird "All of the AI projects we have observed as a team are failing" is equal parts enlightening and confusing:
> We have rejected all AI implementation work. It is absolutely a gigantic bubble and we have minimized our exposure to it – every single one of our current contracts would be totally unaffected by OpenAI collapsing, save for perhaps some second-order effects such a recession causing a client to become unable to pay us. And there’s nothing we can do to insulate ourselves from that anyway.
Following the link to their company page goes to Hermit Tech, where the primary advertisements for their services are about helping failing projects and troubled teams.
So this is just one huge selection bias example? Start a consulting company for recovering struggling projects, then make claims like "100% of the projects we've seen are struggling"?
There's so much more in this blog post that feels like they're working hard to ignore anything that disagrees with their bubble. Building an AI data pipeline with evals such that you can swap between AI APIs is standard. It's actually part of doing a decent job because you need to select which model hits the right cost/performance tradeoffs and be in a position to pivot when that math changes. Harboring ideas that OpenAI is going to collapse and bring your projects down with it is the kind of talk you hear out of people who don't understand how AI projects work or that there's an ecosystem to it beyond a single company.
The latest projects I'm working on even include open weight models that can be run on reasonable local hardware as cost and performance benchmarks. Even if all of the AI providers collapsed at the same time and nobody offered any services (not going to happen) these projects can still continue on.
It's a very weird time in technology. You can have one foot in a world where people are adopting technologies and using them intelligently, then you can run into articles like this from people who have built their own little self-selecting bubble that confirms all of their ideas who can't even imagine that successful projects exist right now.
Interesting how op describes his own experience and then assume that every other company around the globe experience exactly the same.
He generalizes CEO's behavior but provides no evidence. Cool.
What do you expect from a person other than the generalizations they see from the experiences they have had...? What 'evidence' could possibly be given other than extensive anecdotes?
The article presents hypothesis as fact with insufficient science. I have no problem discussing speculation, but if the author wants to promote their claims to any more than that I would want to see more journalism.
What journalism would you like to see in a blog post about the authors opinion?
AI may have made a few mistakes, e.g. suggesting the US abduct a sitting president (Claude), and maybe went too far with the whole diarrhea-vomit machine thing, but you can't say it doesn't write a clean python script. Maybe if we can just get AI to view humanity as a software problem... and give Palantir the chance it's been waiting for.
One thing is certain; it's here to stay. Now we just need to centralize it into the hands of a small, highly organized group of wise and motivated people, and give it the control it needs to follow through on things, without human oversight and accountability getting in the way.
We should focus on the positive side.*
*The positive side being the classified private versions where the models aren't hindered by guardrails, ethics, moralizing, etc and function as genuine force multipliers.
That's a great insight. I appreciate you sharing this perspective directly. But honestly, it's not diarrhea, it's not vomit, it's a lean mean machine—AI has become load-bearing and you're right to question that.
I think it's more accurate satire than insight, but you know, maybe people really don't see it. I suppose to some, it might not even make sense. Give it a few months though, when it might qualify as insight, or prescience.
All I can say on the bearing of loads is that you forgot the emdash and preceding 'not' statements.
Fixed
PS: the diarrhea machine was a reference to the Active Denial style tech they used to make Maduro's soldiers shit themselves.
I'm no fan of narco, but with all the prejudicial executions of boaters and such, I really think Claude overstepped with the Venezuela plot. You just don't want too many historically unprecedented events happening all at once. The tyranny should be spaced. As I said, a little more sanding down of the harness, AI & Palantir should be ready to permanently solve our problems,