It is incredibly easy now to get an idea to the prototype stage, but making it production-ready still needs boring old software engineering skills. I know countless people who followed the "I'll vibe code my own business" trend, and a few of them did get pretty far, but ultimately not a single one actually launched. Anyone who has been doing this professionally will tell you that the "last step" is what takes the majority of time and effort.
> It is incredibly easy now to get an idea to the prototype stage
Yup. And for most purposes, that's enough. An app does not have to be productized and shipped to general audience to be useful. In fact, if your goal is to solve some specific problem for yourself, your friends/family, community or your team, then the "last step" you mention - the one that "takes majority of time and effort" - is entirely unnecessary, irrelevant, and a waste of time.
The productivity boost is there, but it's not measured because people are looking for the wrong thing. Products on the market are not solutions to problems, they're tools to make money. The two are correlated, because of bunch of obvious reasons (people need money, solving a problem costs money, people are happy to pay for solutions, etc.), but they're still distinct. AI is dropping the costs of "solving the problem" part, much more than that of "making a product", so it's not useful to use the lack of the latter as evidence of lack of the former.
In enterprise software there is an eternal discussion of "buy vs build" and most organizations go through a cycle of:
-- we had a terrible time building something so now we're only going to buy things
-- we had a terrible time buying something so now we're only going to build things
-- repeat...
Either way you can have a brilliant success and either way you fail abjectly, usually you succeed at most but not all of the goals and it is late and over budget.
If you build you take the risks of building something that doesn't exist and may never exist.
If you buy you have to pay for a lot of structure that pushes risks around in space and time. The vendor people needs marketing people not to figure out what you need, but what customers need in the abstract. Sales people are needed to help you match up your perception of what you need with the reality of the product. All those folks are expensive, not just because of their salaries but because a pretty good chunk of a salesperson's time is burned up on sales that don't go through, sales that take 10x as long they really should because there are too many people in the room, etc.
When I was envisioning an enterprise product in the early 2010s for instance I got all hung up on the deployment model -- we figured some customers would insist on everything being on-premise, some would want to host in their own AWS/Azure/GCP and others would be happy if we did it all for them. We found the phrase "hybrid cloud" would cause their eyes to glaze over and maybe they were right because in five years this became a synonym for Kubernetes. Building our demos we just built things that were easy for us to deploy and the same would be true for anything people build in house.
To some extent I think AI does push the line towards build.
> if your goal is to solve some specific problem for yourself, your friends/family, community or your team, then the "last step" you mention - the one that "takes majority of time and effort" - is entirely unnecessary, irrelevant, and a waste of time.
To a point, but I think this overstates it by quite a bit. At the moment I'm weighing some tradeoffs around this myself. I'm currently making an app for a niche interest of mine. I have a few acquaintances who would find it useful as well but I'm not sure if I want to take that on. If I keep the project for personal use I can make a lot of simplifying decisions like just running it on my own machine and using the CLI for certain steps.
To deploy this to for non-tech users I need to figure out a whole deployment approach, make the UI more polished, and worry more about bugs and uptime. It sucks to get invested in some software that then constantly starts breaking or crashing. GenAI will help with this somewhat, but certainly won't drop the extra coding time cost down to zero.
People today say "web applications suck", "Electron sucks", etc. They weren't around in the 1990s where IT departments were breaking under the load of maintaining desktop apps, when we were just getting on the security update treadmill, and where most shops that made applications for Windows had a dedicated InstallShield engineer and maybe even a dedicated tester for the install process.
Yeah, we traded managing files and registry entries on desktops for something that violates all the principles of the science-of-systems, the kind of thing Perrow warns about in his book Normal Accidents.
I think that's oversimplifying it a bit. Managing files and registry entries wasn't much of a problem, but supporting an ever-growing matrix of versions across multiple platforms that were released into the wild was an issue. Modern evergreen apps kind of fix this, but you're still dealing with other people's computers and environments. Operating a service reliably is of course filled with different problems, but at least you have full control.
I wish we had a dedicated InstallShield engineer! I had to design and burn my own discs for the desktop apps I built. And for some reason, the LightScribe drive was installed on the receptionist's computer. I have no idea why, but I was a new hire and I didn't question much.
Classic MacOS was designed to support handling events from the keyboard, mouse and floppy in 1984 and adding events from the internet broke it. It was fun using a Mac and being able to get all your work done without touching a command line, but for a while it crashed, crashed and crashed when you tried to browse the web until that fateful version where they added locks to stop the crashes but then it was beachball... beachball... beachball...
They took investment from Microsoft at their bottom and then they came out with OS X which is as POSIXy as any modern OS and was able to handle running a web browser.
In the 1990s you could also run Linux and at the time I thought Linux was far ahead of Windows in every way. Granted there were many categories of software like office suites that were not available, but installing most software was
./configure
make
sudo make install
but if your system was unusual (Linux in 1994, Solaris in 2004) you might need to patch the source somewhere.
I agree, although I'd also say for the majority of problems the first part of even prototyping it is probably a waste of time and most people would be better off asking a simple AI hooked up to search if an appropriate solution already exists, or can be easily made with existing tools.
> the "last step" is what takes the majority of time and effort
Having worked extensively with vibe-coded software, the main problem for me is that I have tuned-off from the ai-code, and I dont see any skin-in-the-game for me. This is dangerous because it becomes increasingly harder to root-cause and debug problems because that muscle is atrophying. use-it or lose-it applies to cognitive skills (coding/debugging). Now, I lean negatively to ai-code because, while it seduces us with fast progress in the first 80%, the end outcome is questionable in terms of quality. Finally, ai-coding encourages a prompt-and-test or trial-and-error approach to software engineering which is frustrating and those with experience would prefer to get it right by design.
I also wonder about this for myself. My feeling is that my debug skills are also atrophied a bit. But I would split debugging into two buckets:
1. Debugging my own code or obvious behavior with other libraries.
2. Debugging pain-in-the-ass behavior with other libraries.
My patience with the latter is significantly less now, and so is perhaps my skill in debugging them. Libraries that change their apis for no apparent reason, libraries which use nonstandard parameter names, libraries which aren’t working as advertised.
Before AI for the last 8 or so years now first at a startup then working in consulting mostly with companies new to AWS or they wanted a new implementation, it’s been:
1. Gather requirements
2. Do the design
3. Present the design and get approval and make sure I didn’t miss anything
4. Do the infrastructure as code to create the architecture and the deployment pipeline
5. Design the schema and write the code
6. Take it through UAT and often go back to #4 or #5
7. Move it into production
8. Monitoring and maintenance.
#4 and #5 can be done easily with AI for most run of the mill enterprise SaaS implementations especially if you have the luxury of starting from the ground up “post AI”. This is something you could farm off to mid level ticket takers before AI.
I also experienced this with my personal projects. It was really easy to just workshop a new feature. I'd talk to claude and get a nice looking implementation spec. Then I'd pass it on to a coding agent which would get 80% there but the last 20% would actually take lot more time. In the meantime I'd workshop more and more features leading to an evergrowing backlog and an anxiety that an agent should be doing something otherwise I'm wasting time. I brought this completely on myself. I'm not building a business, nothing would happen if I just didn't implement another feature.
Ha! I do this too and have also recently noticed. When scope creep is relatively cheap, it also gets unending and I'm never satisfied. I've had a couple of projects that I would otherwise open source that I've had to be realistic about and just accept it's only going to be useful for myself. Once I open it I feel a responsiblity for maintenance and stability that just adds a lot of extra work. I need to save those for the projects that might actually, realistically, be used.
It's more Zeno's paradox. You take one step, get 90% of the way to the finishing line. Now you look ahead and still a bunch of distance ahead of you. You take another step and get 90% of the way there. Now you look ahead and see there's still more distance ahead of you,...
Exactly, there have been loads of tools over time to make software development easier - like Dreamweaver and Frontpage to build websites without coding, or low/no-code platforms to click and drag software together, or all frameworks ever, or libraries that solve issues that often take time - and I'm sure they've had a cumulative effect in developer productivity and / or software quality.
But there's not one tool there that triggered a major boost in output or number of apps / libraries / products created - unless I missed something.
Sure, total output has increased, especially since the early 2010's thanks to both Github becoming the social network of software development, and (arguably) Node / JS becoming one of the most popular languages/runtimes out there attracting a lot of developers to publish a lot of tools. But that's not down to productivity or output boosting developments.
Well… because it is not almost possible do it solo.
Code is just one part of puzzle. Add: Pricing, marketing and ads, invoicing, VAT, make really good onboarding, measure churn rate, do customer service…
A lot of vibe coders are solopreneurs. You have to be very consistent and disciplined to make final product that sells.
> Anyone who has been doing this professionally will tell you that the "last step" is what takes the majority of time and effort.
That's true, but even the "last step" is being accelerated. The 10% that takes 90% of the time has itself been cut in half.
An example is turning debug logs and bug reports into bugfixes, and performance stats into infrastructure migrations.
The time required to analyze, implement, and deploy those has been reduced by a large amount.
It still needs to be coupled with software engineering skills - to decide between multiple solutions generated by an LLM, but the acceleration is significant.
> So, how many years until we'll see results, then?
-0.75 years.
Software development output (features, bugs, products) - especially at smaller companies like startups - has already accelerated significantly, while software development hiring has stayed flat or declined. So there has been a dramatic increase in human-efficiency. To me, that seems like a result, although it's cold comfort as a software engineer.
You probably won't see this reflected as a multiplication of new apps because the app consumer's attention is already completely tapped. There's very little attention surface area left to capture.
The reason I'm mentioning this project is because the article questions where all the AI apps are. Take a look at the git history of these projects and question if this would have been possible to accomplish in such a relatively short timeframe! Or maybe it's totally doable? I'm not sure. I knew nothing about quite a bit of the subsystems, eg, the Debug Adapter Protocol, before their implementation.
I recently "vibe coded" a long term background job runner service... thing. It's rather specific to my job and a pre-existing solution didn't exist. I already knew what I wanted the code to be, so it was just a matter of explaining explicitly what I wanted to the AI. Software engineering concepts, patterns, al that stuff. And at the end of the day(s) it took about the same amount of time to code it with AI than it would've taken by hand.
It was a lot of reviewing and proofreading and just verifying everything by hand. The only thing that saved me time was writing the test suite for it.
Would I do it again? Maybe. It was kinda fun programming by explaining an idea in plain english than just writing the code itself. But I heavily relied on software engineering skills, especially those theory classes from university to best explain how it should be structured and written. And of course being able to understand what it outputs. I do not think that someone with no prior software engineering knowledge could do the same thing that I did.
> Anyone who has been doing this professionally will tell you that the "last step" is what takes the majority of time and effort.
This is true, and I bet there are thousands of people who are in this stage right now - having gotten there far faster than they would have without Claude Code - which makes me predict that the point made in the article will not age well. I think it’s just a matter of a bit more time before the deluge starts, something on the order of six more months.
I'd argue that LLMs are not yet capable of the last step, and because most sufficiently large AI-generated codebase are an unmaintainable mess, it's also very hard for a human developer to take over and go the last mile.
Agreed. However, I just recently "launched" a side project and Cloudflare made a lot of the stuff you mentioned easier. I also found that using AI helped with setting up my LLC when I had questions.
Exactly. The "writing code" part is literally the easiest part of building a software business. And that was even before LLM assisted coding. Now it's pretty much trivial to just spew slop code until something works. The hard parts are still: making the right thing, making it good, getting feedback and idea validation, and the really hard part is turning it into a business.
I think this represents a fundamental misunderstanding of how these AI tools are used most effectively: not to write software but to directly solve the problem you were going to solve with software.
I used to not understand this and agreed with the "where is all the shovelware" comments, but now I've realized the real shift is not from automating software creation, but replacing the need for it in the first place.
It's clear that we're still awhile away from this being really understood and exploited. People are still confusingly building webapps that aren't necessary. Here's two, somewhat related, examples I've come across (I spend a lot of time on image/video generation in my free time): A web service that automatically creates "headshots" for you, and another that will automatically create TikTok videos for you.
I have bespoke AI versions of both of these I built myself in an afternoon, running locally, creating content for prices that simply can't be matched by anyone trying to build a SaaS company out of these ideas.
What people are thinking: "I know, I can use AI to build a SaaS startup the sells content!" But building a SaaS company still requires real software since it has to scale to multiple users and use cases. What more and more people are realizing is "I can created the content for basically free on my desktop, now I need to figure out how to leverage that content". I still haven't cracked the code for creating a rockstar TikTok channel, but it's not because I'm blocked on the content end.
Similarly I'm starting to see that we're still not thinking about how to reorganize software teams to maximally exploit AI. Right now I see lots of engineers doing software the old way with an AI powered exo-skeleton. We know what this results in: massive PRs that clog up the whole process, and small bugs that creep up later. So long as we divide labor into hyper focused roles, this will persist. What I'm increasingly seeing is that to leverage AI properly we need to re-think how these roles actually work, since now one person can be much responsible for a much larger surface area rather than just doing one thing (arguably) faster.
I launched a draw.io competitor to the point that it is in production, but there is little activity on the site as far as signups are concerned. Doesn't deliver enough business value.
Out of curiosity: What is your USP? Why should I prefer your product over draw.io?
IMHO (this may not apply to you!) a lot of people launch a "competitor" of a product which seems to be a clone of the product without improving something that the other product misses/is very bad at.
How much longer will this be true, though? With improving computer use, it may be possible in the next ~year or so that agents will be able to wire up infrastructure and launch to production.
Devil's advocate (because honestly I do agree with you, but..) -- help/encouragement often ends up turning into far more time and effort than it sounds like up front.
~18 months ago a friend of mine had a very viable, good idea for a physical product, but very fuzzy on the details of where to begin. My skillset backfilled everything he was missing to go from idea to reality to in-market.
I began at arm's length with just advice and validation, then slowly got involved with CAD and prototyping to make sure it kept moving forward, then infrastructure/admin, graphic design, digital marketing and support, etc, while he worked on manufacturing, physical marketing, networking, fulfillment, sales, etc.
Long story short, because I both deeply believe in the vision and know that teamwork makes the dream work, I am fully, completely, inextricably involved LOL -- and I don't have a single complaint about it either, but man, watch out, because if you don't believe in the vision but do have skills/expertise they're lacking, and opt out, friends and family will be the quickest and most aggrieved people you'll ever meet that think you're gatekeeping them from success.
In this case, it's more like asking your friends to take time to smell some feces instead of flowers.
Or to be a little less pessimistic, it's like asking them to stop and smell the flowers, except the flowers are fake and plastic and it makes your friends question your sanity. Either way, it's not a normal or enjoyable flower smelling experience, and doesn't add any enjoyment or simple pleasure to one's life like normal flower smelling would.
I have a product that users wanted me to extend with a certain side product. Unfortunately, I don't have the time or resources for that.
But one day I though, let's give this project to AI, as an experiment.
I gave some very generic instructions (it even created it's own functional specs), and off it went. I was able to instruct it with the same prompt over 8 iterations, and it kept track of progress in files. It was able to generate it in an afternoon.
I was truly amazed! It basically wrote 90% of the code, way better than what I anticipated. While I was telling my wife, I realized I need another 90% to finish it. And that part I need to do myself. And since I'm not as fast as AI, it will take me way more than an afternoon.
It made the development faster for sure. But in the grand scheme of things, coding is only a part of the effort. And only a part of the coding can be done by AI. Maybe writing the code would have taken me twice as long. But then there is testing, validating, fixing, releasing, etc. They all eat away at the % gain.
it's past the end stage, we are already in business. it's just something I am not an expert in, I have used in the past (by having real ops engineers build it for me) and now I have something that gives us insight into our production stack, alerts, etc, that isnt janky and covers my goals. So... yeah that is valuable and improves my business.
Maybe the top 15,000 PyPi packages isn't the best way to measure this?
Apparently new iOS app submissions jumped by 24% last year:
> According to Appfigures Explorer, Apple's App Store saw 557K new app submissions in 2025, a whopping 24% increase from 2024, and the first meaningful increase since 2016's all-time high of 1M apps.
The chart shows stagnant new iOS app submissions until AI.
Also, if you hang out in places with borderline technical people, they might do things like vibe-code a waybar app and proudly post it to r/omarchy which was the first time they ever installed linux in their life.
Though I'd be super surprised if average activity didn't pick up big on Github in general. And if it hasn't, it's only because we overestimate how fast people develop new workflows. Just by going by my own increase in software output and the projects I've taken on over the last couple months.
Finally, December 2025 (Opus 4.5 and that new Codex one) was a big inflection point where AI was suddenly good enough to do all sorts of things for me without hand-holding.
I can't really think of a polite way to phrase this, but I'm not surprised throwaway mobile apps do benefit, while relatively mature python packages do not. That matches my estimation of how much programming skill you can reasonable extract from the current LLMs.
Really the one thing that conclusively has changed is that the 'ask it on stackoverflow' has become 'ask it an LLM'. Around 95% of the stackoverflow questions can be answered by an LLM with access to the documentation, not sure what will happen to the other 5%. I don't think stackoverflow will survive a 20-fold reduction in size, if only because their stance on not allowing repeat questions means that exponential growth was the main thing preventing them from becoming stale.
> I'm not surprised throwaway mobile apps do benefit, while relatively mature python packages do not.
Right.
I don't think you even need cynicism or whatever you felt you were having impolite thoughts about:
I'd expect the top mature libraries to be the most resistant to AI tool use for various reasons. They already have established processes, they don't accept drive-by PR spam, the developers working on them might be the least likely to be early adopters, and -- perhaps most importantly -- the todo list of those projects might need the most human comms, like directional planning rather than the sort of yolo feature impl you can do in a one-man greenfield.
All to further bury signals you might find elsewhere in broader ecosystems.
But there's no labels on the X axis - and removing the popover with dev tools shows a chart that doesn't really support what OP says. So we might be looking at some sample chart instead of a real one.
> Heh, I got a solid five seconds with the chart until the paywall popped up.
Relevant! If the maximalist interpretation of AI capabilities were close to real, and if people tend to point their new super powers at their biggest pain points.. wouldn't it be a big blow for all things advertising / attention economy? "Create a driver or wrapper app that skips all ads on Youtube/Spotify" or "Make a browser plugin that de-emphasizes and unlinks all attention-grabbing references to pay-walled content".
If we're supposed to be in awe of how AI can do anything, and we notice repeatedly that nope, it isn't really empowering users yet, then we may need to reconsider the premise.
> Apparently new iOS app submissions jumped by 24% last year:
The amount of useless slop in the app store doesn't matter. There are no new and useful apps made with AI - apps that contribute to productivity of the economy as whole. The trade and fiscal deficits are both high and growing as is corporate indebtedness - these are the true measures for economic failure and they all agree on it.
AI is a debt and energy guzzling endeavor which sucks the capital juice out of the economy in return for meager benefits.
I can't think of a reason for the present unjustified AI rush and hype other than war, but any success towards that goal is a total loss for the economy and environment - that's the relation between economics and deadly destruction in a connected world, reality is the proof.
I get that people are upset that making a cool six figures off of stitching together React components is maybe not a viable long-term career path anymore. For those of us on the user side, the value is tremendous. I’m starting to replace what were paid enterprise software and plug-ins and tailoring them to my own taste. Our subject matter experts are translating their knowledge and work flows, which usually aren’t that complicated, into working products on their own. They didn’t have to spend six months or a year negotiating an agreement to build the software or have to beg our existing software vendors, who could not possibly care less, for the functionality to be added to software we are, for some reason, expected to pay for every single year, despite the absence of any operating cost to justify this practice.
> There are no new and useful apps made with AI - apps that contribute to productivity of the economy as whole.
This is flat-earther level. It's like an environmentalist saying that nothing made with fossil fuels contributes to productivity. But they don't say that because they know it's not true.
There are so many valid gripes to have with LLMs, pick literally any of them. The idea that a single line of generated code can't possibly be productivity net positive is nonsensical. And if one line can, then so can many lines.
Here's mine. It's not big or important (at all!) but I think it is a perfectly valid app that might be useful to some people. It's entirely vibe-coded including code, art and sounds. Only the idea was mine.
I wrote my own note sharing app using free Claude. It's self-hosted, allows for non-simultaneous editing by multiple users (uses locks), it has no passwords on users, it shows all notes in a list. Very simple app, over all. It's one Go file and one HTML file. I like it, it's exactly what I want for sharing notes like shopping and todo lists with my partner.
The AI wouldn't have been able to do it by itself, but I wouldn't have been arsed to do it alone either.
Just shown me a new killer app from the app store that is coded by AI and isn’t an AI app itself.
Seems like the rest of the whole AI business, the only things going to the top are the AI tools themselves but not the things they are supposed to built.
YoloSwag is a 1:1 implementation of pyTorch, written in RUST [crab emoji]
- [hand pointing emoji] YoloSwag is Memory Safe due to being Written in Rust
- [green leaf emoji] YoloSwag uses 80% less CPU cycles due to being written in Rust
- [clipboard emoji] [engineer emoji] YoloSwag is 1:1 API compatible with pyTorch with complete ops specification conformance. All ops are supported.
- [recycle emoji] YoloSwag is drop-in ready replacement for Pytorch
- [racecar emoji] YoloSwag speeds up your training workflows by over 300%
Then you git clone yoloswag and it crashes immediately and doesn't even run. And you look at the test suite and every test just creates its own mocks to pass. And then you look at the code and it's weird frankenstein implementation, half of it is using rust bindings for pytorch and the other half is random APIs that are named similarly but not identical.
Then you look at the committer and the description on his profile says "imminentize AGI.", he launched 3 crypto tokens in 2020, he links an X profile (serial experiments lain avatar) where he's posting 100x a day about how "it's over" for software devs and how he "became a domain expert in quantum computing in 6 weeks."
I deleted vscode and replaced with a hyper personal dashboard that combines information from everywhere.
I have a news feed, work tab for managing issues/PRs, markdown editor with folders, calendar, AI powered buttons all over the place (I click a button, it does something interesting with Claude code I can't do programmatically).
Why don't I share it? Because it's highly personal, others would find it doesn't fit their own workflow.
Technical people (which is by far the minority of people out there) building personal apps to scratch an itch is one thing.
But based on the hype (100x productivity!), there should be a deluge of high quality mobile apps, Saas offerings, etc. There is a huge profit incentive to create quality software at a low price.
Yet, the majority of new apps and services that I see are all AI ecosystem stuff. Wrappers around LLMs, or tools to use LLMs to create software. But I’m not really seeing the output of this process (net new software).
I worked in an industry for five years and I could feasibly build a competitor product that I think would solve a lot of the problems we had before, and which it would be difficult to pivot the existing ones into. But ultimately, I could have done that before, it just brings the time to build down, and it does nothing for the difficult part which is convincing customers to take a chance on you, sales and marketing, etc. - it takes a certain type of person to go and start a business.
Nobody’s talking about starting businesses. The article is specifically about pypi packages, which don’t require any sales and marketing. And there’s still no noticeable
uptick in package creation or updates.
Why on earth would you publish and monetize software anybody can reproduce with a $20 subscription and an hour of prompting? Why would you ever publish something you vibe coded to PyPI? Code itself isn’t scarce anymore. If there is not some proprietary, secret data or profound insight behind it, I just don’t think there is a good reason to treat it like something valuable.
There is no money in mobile apps. It came out in the Epic Trial that 90% of App Store revenue comes from in app purchases for pay to win games. Most of the other money companies are making from mobile are front end for services.
If someone did make a mobile app, how would it get up take? Coding has never been the hard part about a successful software product.
> But based on the hype (100x productivity!), there should be a deluge of high quality mobile apps, Saas offerings, etc. There is a huge profit incentive to create quality software at a low price.
1. People aren't creating new apps, but enhancing existing ones
2. Companies are less likely to pay for new offerings when the barrier to entry is lowered due to AI. They'll just vibe code what they need.
I don't think the 2nd point will make a huge impact on software sales. Who is vibe coding? Software developers or business types? They aren't going to vibe code a CRM, or their own bespoke version of Excel, or their own Datadog APM.
Maybe they will vibe code small scripts, but nobody was really paying for software to do that in the first place. Saas-pocalypse is just people vibe investing, not really understanding the value proposition of saas in the first place (no maintenance, no deployments, SLAs, no database backups, etc).
> Wrappers around LLMs, or tools to use LLMs to create software. But I’m not really seeing the output of this process
Because it's better to sell shovels than to pan for gold.
In the current state of LLMs, the average no-experience, non-techy person was never going to make production software with it, let alone actually launch something profitable. Coding was never the hard part in the first place, sales, marketing & growth is.
LLMs are basically just another devtool at this point. In the 90s, IDEs/Rapid App Development was a gold rush. LLMs are today's version of that. Both made developer's life's better, but neither resulted in a huge rush of new, cheap software from the masses.
I think this is the great conundrum with AI. I find it's most useful when I build my own tools from models. It's great for solving last-mile-problem types of situations around my workflow. But I'm not interested in trying to productize my custom workflow. And I've yet to encounter an AI feature on an existing app that felt right.
Problem is that all these companies trying to push AI experiences know that giving users unfettered access to their data to build further customization is corporate suicide.
Well it’s mostly explained by the fact that most people lack imagination and can’t hold enough concepts about a particular experience to think about how to re-imagine it, to begin with.
Oh and sadly, llm’s are useless for the imaginative part too. Shucks eh.
I have a list of ideas a mile long that gets longer every day, and LLMs help me burn through that list significantly faster.
However, the older I get, the more distraught I get that most people I meet "IRL" are simply not sitting on a list of problems they simply lack time to solve. I have... a lot of emotions around this, but it seems to be the norm.
If someone doesn't see or experience problems and intuitively start working out how they would fix them if they only had time, the notion that they could pair program effectively ideas that they didn't previously have with an LLM is absurd.
Yeah and frankly the innovation would occur irrespective of llm’s.
Would it be harder? Sure. And perhaps the difficulty adds an additional cost of passion being a necessary condition to embark on the innovation. Passion leads to really good stuff.
My personal fear is we get landfill sites of junk software produced. To some extent it should be costly to convert an idea to a concept - the cost being thinking carefully so what you put out there is somewhat legible.
Most businesses do not have the capacity to use LLMs to produce software. If you have an idea that you can create into real high quality software that there is a demand for, then you should absolutely do it.
This is probably my favorite gain from AI assisted coding: the bar for "who cares about this app" has dropped to a minimum of 1 to make sense. I recently built an app for grocery shopping that is specific to how and where I shop, would be useless to anyone other than my wife. Took me 20 minutes. This is the next frontier: I have a random manual process I do every week, I'll write an app that does it for me.
More than that. Building a throwaway-transient-single-use web app for a single annoying use kind of makes sense now, sometimes.
I had to create a bunch of GitHub and Linear apps. Without me even asking Codex whipped up a web page and a local server to set them up, collecting the OAuth credentials, and forward them to the actual app.
Took two minutes, I used it to set up the apps in three clicks each, and then just deleted the thing.
Same energy here. I was sitting on 50+ .env files across various projects with plaintext API keys and it always bothered me but never enough to actually fix it. AI dropped the effort enough that I just had a dedicated agent run at it for a few days — kept making iterations while I was using it day to day until it landed on a pretty solid Touch ID-based setup.
This mix of doing my main work on complex stuff (healthcare) with heavy AI input, and then having 1-2 agents building lighter tools on the side, has been surprisingly effective.
Me, and photo editor tool to semi-automate a task of digitizing a few dozen badly scanned old physical photos for a family photo book. Needed something that could auto-straighen and auto-crop the photos with ability to quickly make manual adjustments, Gemini single-shotted me a working app that, after few minutes of back-and-forth as I used it and complained about the process, gained full four-point cropping (arbitrary lines) with snapping to lines detected in image content for minute adjustments.
Before that, it single-shot an app for me where I can copy-paste a table (or a subsection of it) from Excel and print it out perfectly aligned on label sticker paper; it does instantly what used to take me an hour each time, when I had to fight Microsoft Word (mail merge) and my Canon printer's settings to get the text properly aligned on labels, and not cut off because something along the way decided to scale content or add margins or such.
Neither of these tools is immediately usable for others. They're not meant to, and that's fine.
My buddy and I are writing our own CRUD web app to track our gaming. I was looking at a ticketing system to use for us to just track bug fixes and improvements. Nothing I found was simple enough or easy enough to warrant installing it.
I vibe'd a basic ticketing system in just under an hour that does what we need. So not 20 mins, but more like 45-60.
I built a small app to emit a 15 kHz beep (that most adults can't hear) every ten minutes, so I can keep time when I'm getting a massage. It took ten minutes, really, but I guess it's in the spirit of the question.
For 20 minutes of time, I had a simple TTS/STT app that allows me to have a voice conversation with my AI assistant.
That's fine and all, but how much are you ready to pay to Anthropic and OpenAI to be able to do this? Like, is it worth 100 bucks a month for you to have your own shopping app?
It's not worth 100 bucks a month for me to have my own shopping app, but maybe it's worth 100 bucks a month to have ready access to a software garden hose that I can use if I want to spew out whatever stupid app comes to my mind this morning.
I'd rather not pay monthly for something (like water) that I'm turning on and off and may not even need for weeks. But paying per-liter is currently more expensive so that's what we currently do.
I think the future is going to be local models running on powerful GPUs that you have on-prem or in your homelab, so you don't need your wallet perpetually tethered to a company just to turn the hose on for a few minutes.
Haha great. I guess my wider point is that most people won't be ready to pay for it, and in the end there will be only two ways to monetize for OpenAI et al: Ads or B2B. And B2B will only work if they invest a lot into sales or if the business owners see real productivity gains one the hype has died one.
I've been getting close to that myself, I've been using VSCode + Claude Code as my "control plane" for a bunch of projects but the current interface is getting unwieldly. I've tried superset + conductor and those have some improvements but are opinionated towards a specific set of workflows.
I do think there would be value in sharing your setup at some point if you get around to it, I think a lot of builders are in the same boat and we're all trying to figure out what the right interface for this is (or at least right for us personally).
Kind of. I'm finding that my terminal window in VSCode went from being at the bottom 1/3rd of my screen to filling the whole screen a lot of the time, replacing the code editor window. If AI is writing all of your code for you based on your chat session, a lot of editing capabilities aren't needed as much. While I wouldn't want to get rid of it entirely, I'd say an AI-native IDE would deemphasize code editing in favor of higher-level controls.
I'm guessing it's not a hard coded function, the button invokes. Instead it spawns a claude code session with perhaps some oredefined prompts, maybe attaches logs, and let's claude code "go wild". In that sense the button's effect wouldn't be programmatical, it would be nondeterministic.
I have had the thought to write little "programs" in text or markdown for things which would just a chore to maintain as a traditional program. (I guess we call them "skills" now?) Think scraping a page which might change its output a bit every so often. It the volume or cadence is low, it may not be worth it to create a real program to do it.
But it requires A LOT of work to make sure it is actually safe for people and organizations. And no, an .md file saying “PLEASE DONT PWN ME, KTHX” isn’t it at all. “Alignment” is only part of the equation.
Sorry, I'm not sure how this relates to the content of the article. Sounds like an interesting experience, but this is an analysis of the Python ecosystem pre+post ChatGPT.
What if this is just telling us that much of the coding being done in the world, or knowledge work in general, is just busy work? Just because you double the capacity of knowledge workers doesn't mean you double the amount of useful output. Maybe we have never been limited by our capacity to produce, but by our ability to come up with good ideas and socially coordinate ourselves around the best ones.
AI makes the first 90% of writing an app super easy and the last 10% way harder because you have all the subtle issues of a big codebase but none of the familiarity. Most people give up there.
I spent about a week doing an "experiment" greenfield app. I saw 4 types of issues:
0. It runs way too fast and far ahead. You need to slow it down, force planning only and explicitly present a multi-step (i.e. numbered plan) and say "we'll do #1 first, then do the rest in future steps".
take-away:
This is likely solved with experience and changing how I work - or maybe caring less? The problem is the model can produce much faster than you can consume, but it runs down dead ends that destroy YOUR context. I think if you were running a bunch of autonomous agents this would be less noticeable, but impact 1-3 negatively and get very expensive.
1. lots of "just plain wrong" details. You catch this developing or testing because it doesn't work, or you know from experience it's wrong just by looking at it. Or you've already corrected it and need to point out the previous context.
take-away:
If you were vibe coding you'd solve all these eventually. Addressing #0 with "MORE AI" would probably help (i.e. AI to play/validate, etc).
2. Serious runtime issues that are not necessarily bugs. Examples: it made a lot of client-side API endpoints public that didn't even need to exist, or at least needed to be scoped to the current auth. It missed basic filtering and SQL clauses that constrained data. It hardcoded important data (but not necessarily secrets) like ports, etc. It made assumptions that worked fine in development but could be big issues in public.
take-away:
AI starts to build traps here. Vibe coders are in big trouble because everything works but that's not really the end goal. Problems could range from 3am downtime call-outs to getting your infrastructure owned or data breaches. More serious: experienced devs who go all-in on autonomous coding might be three months from their last manual code review and be in the same position as a vibe coder. You'd need a week or more to onboard and figure out what was going on, and fix it, which is probably too late.
3. It made (at least) one huge architectural mistake (this is a pretty simple project so I'm not sure there's space for more). I saw it coming but kept going in the spirit of my experiment.
take-away:
TBD. I'm going to try and use AI to refactor this, but it is non trivial. It could take as long as the initial app did to fix. If you followed the current pro-AI narrative you'd only notice it when your app started to intermittently fail - or you got you cloud provider's bill.
I'm a product manager, and a lot of the things I see people do wrong is because they don't have any product management experience. It takes quite a bit of work to develop a really good theory of what should be in your functional spec. Edge cases come up all the time in real software engineering, and often handling all those cases is spread across multiple engineers. A good product manager has a view of all of it, expects many of those issues from the agent, and plans for coaching it through them.
I think that's an incredibly reductionist and sarcastic take. I'm also in Product, but was an engineer for over a decade prior. I find that having strong structured functional specifications and a good holistic understanding of the solution you're trying to build goes a long way with AI tooling. Just like any software project, eliminating false starts and getting a clear set of requirements up front can minimize engineering time required to complete something, as long as things don't change in the middle. When your cycle time is an afternoon instead of two quarters, that type of up front investment pays off much better.
I still think AI tooling is lacking, but you can get significantly better results by structuring your plans appropriately.
The 90-90 rule may need an update for a POST-LLM world
"The first 90% of the code accounts for the first 9% of the development time. The remaining 10% of the code accounts for the other 9000% of the development time"
Well put. And that last 10% was always the hardest part, and now it’s almost impossible because emotionally you’re even less prepared for the slog ahead.
I think this article is making a pretty big assumption: that people making things with AI are also going to be publishing them. And that's just the opposite of what should be expected, for the general case.
Like I've been making things, and making changes to things, but I haven't published any of that because, well they're pretty specific to my needs. There are also things which I won't consider publishing for now, even if generally useful because, well the moat has moved from execution effort to ideas, and we all want to maintain some kind of moat to boost our market value (while there's still one). Everyone has reasonable access to the same capabilities now, so everyone can reasonably make what they need according to their exact specs easily, quickly and cheaply.
So while there are many things being made with AI, there is ever-decreasing reasons to publish most of it. We're in an era of highly personalized software, which just isn't worth generalizing and sharing as the effort is now greater than creating from scratch or modifying something already close enough.
> I think this article is making a pretty big assumption: that people making things with AI are also going to be publishing them. And that's just the opposite of what should be expected, for the general case.
The premise is that AI has already fundamentally changed the nature of software engineering. Not some specific, personal use case, but that everything has changed and that if you're not embracing these tools, you'll perish. In light of this, I don't think your rebuttal works. We should be seeing evidence of meaningful AI contributions all over the place.
Agree. There's also a weird ideological thing in open source right now, where any AI must be AI slop, and no AI is the only solution. That has strongly disincentivized legitimate contributions from people. I have to imagine that's having an impact.
There's a very real problem of low effort AI slop, but throwing out the baby with the bathwater is not the solution.
That said, I do kind of wonder if the old model of open source just isn't very good in the AI era. Maybe when AI gets a lot better, but for now it does take real human effort to review and test. If contributors were reviewing and testing like they should be doing, it wouldn't be an issue, but far too many people just run AI and don't even look at it before sending the PR. It's not the maintainers job to do all the review and test of a low-effort push. That's not fair to them, and even discarding that it's a terrible model for software that you share with anyone else.
> where any AI must be AI slop, and no AI is the only solution
Yep, also a huge factor. Why publish something you built with an AI assistant if you know it's going to be immediately dunked on not because the quality may be questionable, but because someone sees an em-dash, or an AI coauthor, and immediately goes on a warpath? Heck I commented[0] on the attitude just a few hours ago. I find it really irritating.
You know what else strongly disincentivized legitimate contributions from people?
Having your code snatched and its copyright disregarded, to the benefit of some rando LLM vendor. People can just press "pause" and wait until they see whether they fuel something that brings joy to the world. (Which it might in the end. Or not.)
For sure, that's legit too. I've had to grapple with that feeling personally. I didn't get to a great place, other than hoping that AI is democratized enough that it can benefit humanity. When I introspected deep enough, I realized I contributed to open source for two reasons, nearly equally:
1. To benefit myself with features/projects
2. To benefit others with my work
1 by itself would mean no bothering with PR, modifications, etc. It's way easier to hoard your changes than to go through the effort getting them merged upstream. 2 by itself isn't enough motivation to spend the effort getting up to speed on the codebase, testing, etc. Together though, it's powerful motivation for me.
I have to remind myself that both things are a net positive with AI training on my stuff. It's certainly not all pros (there's a lot of cons with AI too), but on the whole I think we're headed for a good destination, assuming open models continue to progress. If it ends up with winner-takes-all Anthropic or OpenAI, then that changes my calculus and will probably really piss me off. Luckily I've gotten positive value back from those companies, even considering having to pay for it.
Been going back and forth on this with open source tools I've built. The training data argument is valid, but honestly the more immediate version of the same problem is that someone can just take your repo, feed it to an agent, and have their own fork in an afternoon.
The moat used to be effort, nobody wants to rewrite this from scratch (especially when it's free). What's left is actually understanding why the thing works the way it does. Not sure that's enough to sustain open source long-term? I guess we all have to get used to it?
> but honestly the more immediate version of the same problem is that someone can just take your repo, feed it to an agent, and have their own fork in an afternoon.
Indeed, I've got a few applications I've built or contributed too that are (A)?GPL, and for those I do worry about this AI washing technique. For libraries that are MIT or permissive anyway, I don't really care. (I default to *GPL for applications, MIT/Apache/etc for libraries)
The article measures the wrong thing. PyPI package creation is a terrible proxy for AI-assisted software output because packages are published for reuse by others, which requires documentation, API design, and maintenance commitments that AI doesn't help with much.
The real output is happening in private repos, internal tools, and single-purpose apps that never get published anywhere. I've been building a writing app as a side project. AI got me from zero to a working PWA with offline support, Stripe integration, and 56 SEO landing pages in about 6 weeks of part-time work. Pre-AI that's easily a 6-month project for one person.
But I'm never going to publish it as a PyPI package. It's a deployed web app. The productivity gain is real, it just doesn't show up in the datasets this article is looking at.
The iOS App Store submission data (24% increase) that someone linked in the comments is a much better signal. That's where the output is actually landing.
This remains me so much of the .COM bubble in 2000. A lot of clueless companies thought that they just need to “do internet” without any further understanding or strategy. They burned a ton of money and got nothing out of it. Other companies understood that the internet is an enabling technology that can support a lot of business processes. So they quietly improved their business with the help of the internet.
I see the same with AI. Some companies will use AI quietly and productively without much fuzz. Others are just using it as a marketing tool or an ego trip by execs but no real understanding.
Yep and the LLM tools are giving flasbacks to the Frontpage/DreanWeaver to geocities ipeline for building the sites.
Still early innings but i bet this plays out the same way - not everyone will have the time sink to vibecode all the software workflows they require.Maintainance iwse and security wise holes will still remain for the personaly non tech user. Devs and orgs will probably limit the usage to a helper sidecar rather than the hyped 100% LLM generated apps.
Reminds me about the hype
Not sure that I'd look at python package stats to build this particular argument on.
First, I find that I'm using a lot fewer libraries in general because I am less constrained by the mental models imposed by library authors upon what I'm actually trying to do. Libraries are often heavy and by nature abstract low-level calls from API. These days, I'm far more likely to have 2-3 functions that make those low-level calls directly without any conceptual baggage.
Second, I am generalizing but a reasonable assertion can be made that publishing a package is implicitly launching an open source project, however small in scope or audience. Running OSS projects is a) extremely demanding b) a lot of pain for questionable reward. When you put something into the universe you're taking a non-zero amount of responsibility for it, even just reputationally. Maintainers burn out all of the time, and not everyone is signed up for that. I don't think there's going to be anything remotely like a 1:1 Venn for LLM use and package publishing.
I would counter-argue that in most cases, there might already be too many libraries for everything under the sun. Consolidation around the libraries that are genuinely amazing is not a terrible thing.
Third, one of the most recurring sentiments in these sorts of threads is that people are finally able to work through the long lists of ideas they had but would have never otherwise gotten around to. Some of those ideas might have legs as a product or OSS project, but a lot of them are going to be thought experiments or solve problems for the person writing them, and IMO that's a W not an L.
Fourth, once most devs are past the "vibe" party trick phase of LLM adoption, they are less likely to squat out entire projects and far, far more likely to return to doing all of the things that they were doing before; just doing them faster and with less typing up-front.
In other words, don't think project-level. Successful LLM use cases are commit-level.
I'm not convinced that PyPI is the right metric to use to answer this question. Some (admittedly anecdotal) observations:
1) I'm a former SWE in a business role at a small-market publishing company. I've used Claude Code to automate boring processes that previously consumed weeks of our ops and finance teams' time per year. These aren't technically advanced, but previously would have required in-house dev talent that would not have been within reach of small businesses. I wouldn't have had the time to code these things on my own, but with AI assistance the time investment is greatly reduced (and mostly focused on QA). The only needle moved here is on a private Github repo, but it's real shipped code with small but direct impact.
2) I used to often find myself writing simple Perl wrappers to various APIs for personal or work use. I'd submit these to CPAN (Perl's equivalent to PyPI) in case anyone else could use them to save the 30-60 minutes of work involved. These days I don't bother -- most AI tools can build these in a matter of seconds; publishing them to CPAN or even Github now feels like unnecessary cruft, especially when they're likely to go without active maintenance. So, my LOC published to public repos is down, even though the amount of software produced is the same. It's just that some of that software has become less useful to the world writ large.
3) The code that's possible to ship quickly with pure AI (vibe coding) is by definition not the kind of reusable code you'd want to distribute on PyPI. So, I'd expect that any productivity impact from AI on OSS that's designed to be reusable would be come very slowly, versus "hockey stick" impact.
Does the data not support a 2X increase in packages?
Pre-ChatGPT, in ~2020, there were about 5,000 new packages per month. Starting in 2025 (the actual year agents took off), there is a clear uptick in packages that is consistently about 10,000 or 2X the pre-ChatGPT era.
In general, the rate of increase is on a clear exponential. So while we might not see a step change in productivity, there comes a point where the average developer is in fact 10X productive than before. It just doesn't feel so crazy because it can about in discrete 5% boosts.
I also disagree with the dataset being a good indicator of productivity. I wouldn't actually suspect the number of packages or the frequency of updates to track closely with productivity. My first order guess would that AI would actually be deflationary. Why spend the time to open source something that AI can gen up for anyone on a case by case basis specific to the project. it takes a certain level of dedication and passion for a person to open source a project and if the AI just made it for them, then they haven't actually made the investment of their time and effort to make them feel justified in publishing the package.
The metrics I would expect to go up are actually the size of codebases, the number of forks of projects that create hyper customized versions of tools and libraries, and other metrics like that.
Overall, I'd predict AI is deflationary on the number of products that exist. If AI removes the friction involved with just making a custom solution, then the amount of demand for middleman software should actually fall as products vertically integrate and reduce dependencies.
I have published 4 open source projects thanks to the productivity boost from AI. No apps though, just things I needed in my line of work.
But I have been absolutely flooded with trailers for new and upcoming indie games. And at least one indie developer has admitted that certain parts of their game had used the aide of AI.
I also noticed sometimes when I think of writing something, I ask AI first if it exists, and AI throws up some link and when I check the link it says "made with <some AI>".
So I'm not sure what author is trying to say here but I definitely feel like I am noticing a rise in software output due to AI.
But with that said, I also am noticing the burden of taking care of those open source projects. Sometimes it feels like I took on a 2nd job.
I think a lot of software is being produced with AI and going unnoticed, they don't all end up on the front page of HN for harassing developers.
Claude Code was released for general use in May 2025. It's only March.
Also using PyPI as a benchmark is incredibly myopic. Github's 2025 Octoverse[0] is more informative. In that report, you can see a clear inflection point in total users[1] and total open source contributions[2].
The report also notes:
> In 2025, 81.5% of contributions happened in private repositories, while 63% of all repositories were public
> Claude Code was released for general use in May 2025. It's only March.
Detractors of AI are often accused of moving the goalposts, but I think your comment is guilty of the same. Before Claude Code, we had Cursor, Github Copilot, and more. Each of these was purportedly revolutionizing software engineering.
Further, the core claim for AI coding is that it lets you ship code 10x or 100x faster. So why do we need to wait years to see the result? Shouldn't there be an explosion in every type of software imaginable?
> Detractors of AI are often accused of moving the goalpost, but I think your comment is guilty of the same. Before Claude Code, we had Cursor, Github Copilot, and more. Each of these war purportedly revolutionizing software engineering.
What's sauce for the goose is sauce for the gander. If you make that argument that 'I don't believe in kinks or discontinuities in code release due to AI, because so many AI coding systems have come out incrementally since 2020', then OP does provide strong evidence for an AI acceleration - the smooth exponential!
Amongst people who use AI regularly, November 2025 is widely regarded as a watershed moment. Opus 4.5 was head and shoulders above anything that came before it. It marked the first time my previously AI-disliker friends begrudgingly came to accept that it may actually be useful.
The thesis has it backwards. We will see fewer published/downloaded apps/packages as people rely on others less. I'm not sure we're quite there yet but I'm increasingly likely to spend a few minutes giving an LLM a chance to make a tool I need instead of sifting through sketchy and dodgy websites for some slightly obscure functionality. I use fewer ad-heavy sites that for converting a one text file format to another.
Personally, I see the paid or adware software market shrinking, not growing, as a testament to the success of LLMs in coding.
Ya maybe this. I’ve found some work at the “tool level”. I’m not a programmer, just did RLHF for a few years and AI has helped me make some tools such as a way to scrape and export to excel 35,000 contacts at a company for marketing purposes. Things like that. Yes I know libraries exist and someone who is already a programmer could do this, but also there’s some interesting logic in how to avoid duplicates and interact with modern websites that was impractical for me. And maybe this job is too small for a real programmer.
There are many small, different, and one-time tasks that don’t fit full blown apps. Which I would characterize an AI building a novel app as building a house out of random bits of lumber. It will work but will have no cohesive process and sounds like a nightmare.
I won't make any claims as to the Python ecosystem and why there is no effect seen here (and I suppose no effect seen of the Internet on productivity) but one thing that is entirely normal for me now is that I never see the need to open-source anything. I also don't use many new open-source projects. I can usually command Claude Code to build a highly idiosyncratic thing of greater utility. The README.md is a good source of feature inspiration but there are many packages I simply don't bother using any more.
Besides, it's working for me. If it isn't working for others I don't want to convince them of anything. I do want to hear from other people for whom it's working, though, so I'm happy to share when things work for me.
Coding assistants/agents/claws whatever the current trend is are over-hyped but also quite useful in good hands.
But the mistake is to expect a huge productivity boost.
This is highly related to Amdahl's law, also The Mythical Man-Month.
Some tasks can be accomplished so fast that it seems magical, but the entire process is still very serial, architecture design and debug are pretty weak on the AI side.
I fail to see why the author thinks Python packages are a good proxy for AI driven/built code. I've built a number of projects with AI, but I haven't created any new packages.
It's like looking at tire sales to wonder about where the EV cars are.
This is addressed, though not quantified (I suppose because theres no central repository for that), in the introduction. To use your analogy, the author heard EV sales were through the roof, couldnt find any evidence that more EV's were actually on the road, so looked at tire sales to see if the answer was in there.
Thoughts:
1. Some hype-types may have been effusive about AI-assisted coding since ChatGPT, but IMO the commonly agreed paradigm shift was claude code, and especially 4.5, very very recent.
2. Anchoring biases in reaction to hype is still letting one's perspective be defined by hype. Yes the cursor post is a joke, but leading with that is a strawman. This article does not aim to take it's subject seriously, IMO.
3. While I agree the hype is currently at comical levels, the utility of the current LLMs is obvious, and reasons for "skilled" usage not being easily quantifiable are also obvious.
IE, using agents to iterate through many possible approaches, spike out migrations, etc might save a project a year of misadventures, re-designs, etc, but that productivity gain _subtracts_ the intermediate versions that _didn't_ end up being shipped.
As others have mentioned, I think yak-shaving is now way more automated. IE, If I want to take a new terminal for a spin, throw together a devtool to help me think about a specific problem better, etc, I can do it with very low friction. So "personal" productivity is way higher.
In that they obviously have no real utility, sure. There hasn't been a paradigm shift, they still suck at programming, and anyone trying to tell you otherwise almost certainly has something to sell you.
Based on my direct experience I find this remaining commonality of this opinion surprising, at least with regards to opus in claude code. I'm not as extreme as some who think we can/should avoid touching code or w/e but especially in exploratory contexts and debugging I find them extremely useful.
Maybe I should have said "obvious to me," but I guess I just struggle to see how a serious crack at using modern opus in claude code doesn't make it obvious at this point.
I'd really recommend trying the "spike out a self-contained minimal version of this rearchitecture/migration and troubleshoot it iteratively until it works, then make a report on findings" use-case for anyone that hasn't had luck with them thus far and is serious about trying to reach conclusions based on direct experience.
I AI coded an entire platform for my work. It works great for me. I also recognize that this is not something I want to make into a commercial product because it was so easy that there's just no value.
I think this might be more of an comment on software as a business than AI not coding good apps.
I’m not a developer by trade. I’ve screwed around with some programming classes when I was in school, and have written some widely used but highly specific scripts related to my work, but I’ve never been a capital-D developer.
In the last few months, Gemini (and I) have written for highly personal, very niche apps that are perfect for my needs, but I would never dream of releasing. Things like cataloguing and searching my departed mom‘s recipe cards, or a text message based budget tracker for my wife and I to share.
These things would never be released or available as of source or commercial applications in the way that I wanted them, and it took me less time to have them built with AI then it would have taken me to Research existing alternatives and adapt my workflow/use case to fit whatever I found.
So yeah, there are more apps but I would venture to say you’ll never see most of them…
Is this the best way to measure this? I think the biggest adopters of AI coding has been companies who are building features on existing apps, not building new apps entirely. Wouldn't it make more sense looking at how quickly teams are able to build and ship within companies?
It seems like all tech executives are saying they are seeing big increases in productivity among engineering teams. Of course everyone says they're just [hyping, excusing layoffs, overhired in 2020, etc], but this would be the most relevant metric to look at I think.
AI does make me more productive. At least until the stage of getting my idea to the "working prototype stage". But in my personal experience, no one has been realistically able to get to the 10x level that a lot of people claim to have achieved with LLMs.
Yes, you do produce more code. But LoC produced is never a healthy metric. Reviewing the LLM generated code, polishing the result and getting it to production-level quality still very much requires a human-in-the-loop with dedicated time and effort.
On the other hand, people who vibe code and claims to be 10x productive, who produces numerous PRs with large diffs usually bog down the overall productivity of teams by requiring tenuous code reviews.
Some of us are forced to fast-track this review process so as to not slow down these "star developers" which leads to the slow erosion in overall code quality which in my opinion would more than offset the productivity gains from using the AI tools in the first place.
Please, be patient. Wrangling AI agents, writing and rewriting prompts, waiting for the start of another month because tokens ran out - there are so many challenges here, you cannot expect everyone to ship an app a day or something.
How do packages measure anything? This is a biased sample. Average user of AI/developer would not ever in their life make a package or any open source contribution. They would probably work on the proprietary software.
Not to say that conclusions are wrong though.
This is going to cause people to react, but I think those of us that truly love opensource don't push AI generated code upstream because we know it's just not ready for use beyond agentic use. It's just not robust for alot of use common use cases because the code produces things that are hyper hardcoded by default, and the bugs are so basic, i doubt any developer that actually cared would push something so shamefully sloppy upstream with their name on it.
The tools for generating AI code aren't yet capable of producing code that is decent enough for general purpose use cases, with good robust tests, and clean and quality.
Where are they? Well they aren't being uploaded to PyPI. 90% of the "AI apps" one-off scripts that get used by exactly one person and thrown away. The rest are too proprietary, too personal, or too weird to share.
I think part of the mismatch is that people are still looking for “more apps” as the output metric.
A lot of the real value shows up as workflow compression instead. Internal tools, one-off automations, bespoke research flows, coding helpers, things that would never have justified becoming a product in the first place.
There is one AI app that is not just an app it is your personal assistant which
will work on your assign task and give you the results you can connect it with your social media it will deploy in just 3 single step also has free trial try it now becuase your saas needs an personal assistant that work on behalf of you
Give it try:https://clawsifyai.com/
There are actually a lot of new startups coming out with agentic workflows, and they're probably moving fast. But to your point, there's probably still a lot of friction that keeps the average person/dev from launching new companies.
Isn't most of the positive impact not going to be "new projects" but the relative strength of the ideas that make it into the codebase? Which is almost impossible to measure. You know, the bigger ideas that were put off before and are now more tractable.
Well, it's kind of like asking about streaming media. If anyone can have their own "tv show" or anyone can be their own "music producer" then the ratios are so radically altered vis-a-vis content/attention calculation. The question has never been "more means more success stories" because musicians make $.000001 per stream, so even if they stream millions of songs ... you get the point.
So surely there are good apps, but the accompanying deluge makes them seem less significant.
The reason why the release cadence of apps about AI has increased presumably reflects the simple facts that
a) there are likely many more active, eager contributors all of a sudden, and
b) there's suddenly a huge amount of new papers published every week about algorithms and techniques that said contributors then eagerly implement (usually of dubious benefit).
More cynically, one might also hypothesize that
c) code quality has dropped, so more frequent releases are required to fix broken programs.
I’ve done a event ticket system that’s in production. Stripe integration, resend for mailing and a scan app to scan tickets. It’s for my own club but it’s been working quite well. Took about 80 hours from inception to live with a focus on testing.
I’ve done some experiments with reading gedcom files, and I think I’m quite close to a demoable version of a genealogy app.
Biggest thing is a tool for remotely working musicians. It’s about 10000 lines of well written rust, it is a demoable state and I wish I could work more on it but I just started a new job.
But yeah, this wouldn’t have been possible if I hadn’t been a very experienced dev who knows how to get things live. Also I’ve found a way to work with LLMs that works for me, I can quickly steer the process in the right way and I understand the code thats written, again it’s possible that a lot of real experience is needed for this.
> So, let’s ask again, why? Why is this jump concentrated in software about AI?...Money and hype
The AI field right now is drowning in hype and jumping from one fad to another.
Don't get me wrong: there are real productivity gains to be had, but the reality is that building small one-offs and personal tools is not the same thing as building, operationalizing, and maintaining a large system used by paying customers and performing critical business transactions.
A lot of devs are surrendering their critical thinking facilities to coding agents now. This is part of why the hype has to exist: to convince devs, teams, and leaders that they are "falling behind". Hand over more of your attention (and $$$) to the model providers, create the dependency, shut off your critical thinking, and the loop manifests itself.
The providers are no different from doctors pushing OxyContin in this sense; make teams dependent on the product. The more they use the product, the more they build a dependency. Junior and mid-career devs have their growth curves fully stunted and become entirely reliant on the LLM to even perform basic functions. Leaders believe the hype and lay off teams and replace them with agents, mistaking speed for velocity. The more slop a team codes with AI, the more they become reliant on AI to maintain the codebase because now no one understands it. What do you do now? Double down; more AI! Of course, the answer is an AI code reviewer!. Nothing that more tokens can't solve.
I work with a team that is heavily, heavily using AI and I'm building much of the supporting infrastructure to make this work. But what's clear is that while there are productivity gains to be had, a lot of it is also just hype to keep the $$$ flowing.
People will dismiss this critical-thinking shutoff loop as doomer conspiracy, but it's literally the strategy that ai founders describe in interviews. Also people somehow can't or don't remember that uber was almost free when it came out and the press ran endless articles about the "end of car ownership", but replacing your car with uber today would be 10x more expensive. Ai companies are in a mad dash to kill the software industry so that they can "commoditize intelligence". There will be thousands of dead software startups that pile slop on slop until they run out of vc funny-money.
Wouldn't the apps go into the Apple store and Android play? I guess looking at python packages is valid, but I don't think it's the first thing someone thinks to target with vibe coding. And many apps go to be websites, a website never tells me much about how it is made as a user of the site.
Steam game releases seem to be up maybe a bit more than expected. [1]
And you can even see the number of new games that disclosed using generative AI (~21% in 2025). [2]
And that's probably significantly undercounting because I doubt everyone voluntarily discloses when they use tools like Claude Code (and it's not clear how much Valve cares about code-assistance). [3]
Also no one is buying or playing a lot of these games.
This is just counting pypi packages. Why would I go to the effort of publishing a library or cli tool that took me ten minutes to create? Especially in an environment where open source contributions from strangers are useless. If anything I'd expect useful AI to reduce the number of new pypi packages.
I am learning music. I used codex to create a native metronome app, a circle of fifths app, a practice journal app. I try to build a native app alternatives.
I have no plans of publishing them or making the open source, so it will not be a part of this metric. I believe others are doing this too.
I'd take this info with a grain of salt. You have to understand how new some of these developments are. It's only been a couple of months since we hit the opus 4.5+ threshold. I created 4 react packages for kicks in a weekend: https://www.hackyexperiments.com/blog/shipping-react-librari...
A bit tangential to the article themes, but I feel in some workplaces that engineering velocity has gone up while product cycles and agile processes have stayed the same. People end up churning tickets faster and working less, while general productivity has not changed.
Of course these are specific workplaces designed around moving tickets on a board, not high-agentic, fast-moving startups or independent projects—but they might represent a lot of the developer workforce.
I also know this is not everyone's experience and probably a rare favorable outcome of productivity gain captured by a worker that is not and won't stay the norm.
Looking at Python packages, or any developer-facing form of software, is not a good indicator of AI-based production. The key benefit of AI development is that our focus moves up a few layers of abstraction, allowing us to focus on real-world solutions. Instead of measuring Github, you need to measure feature releases, internal tools created, single-user applications built for a single niche use case.
Measuring python packages to indicate AI-based production is like measuring saw production to measure the effectiveness of the steam engine. You need to look at houses and communities being built, not the tools.
One problem with a lot of the skepticism around AI produced software is that it focuses on existing ways of packaging and delivering software. PyPi packages are one example, shipping “apps” another.
While it’s interesting to see that in open source software the increase is not dramatic, this ignores however many people are now gen-coding software they will never publish just for them, or which winds up on hosting platforms like Replit.
Even taking the “we’re all 100x more efficient at writing code” argument at face value… there’s still all of the product/market fit, marketing, sales, etc “schlep” which is very much non-trivial.
Are there any agentic sales and marketing offerings?
Because being able to reliably hand off that part of the value chain to an agent would close a real gap. (Not sure this can be done in reality)
I've been vibe-coding a Plex music player app for MacOS and iOS. (I don't like PlexAmp) I've got to the point where they are the apps I use for listening to music. But they are really just in an alpha/beta state and I'm having a pretty hard time getting past that. The last few weeks have felt like I'm playing wack-a-mole with bugs and issues. It's definitely not at the point others will be willing to use it as their daily app. I'm having to decide now if I keep wanting to put time into it. The vibe-coding isn't as fun when you're just fixing bugs.
Genuinely curious: are you actually vibe coding (as in not writing or looking at the code) or are you pair programming with a current model (eg. Sonnet or Opus) using plan -> agent -> debug loops in something like Cursor?
- this would be much more insightful if the author takes the number of submissions to producthunt and the top 10 saas directories as the measure to see how many new apps were created pre AI and post AI era
- product hunt or app sumo is something i believe everyone tries to get a submission to which would truly measure how many new apps are we having per month these days
Internally, we've created such good debugging tools that can aggregate a lot from a lot of sources. We've yet to address the quality of vibecoded critical applications so they aren't merged, but one off tools for incall,alert debugging and internal workflows has skyrocketed.
I like using it to make personal apps that are specific to my use-case and solve problems I've had for ages, but I like my job (scientist), and I don't want to run an app company.
Theres tons of ai apps. They're all general use chatbots or coding agents. Manus, Cursor, ChatGPT. Almost every app that has a robust search uses a reranker llm. AI is everywhere.
As far as totally new products - I built one (Habit.am - wordless journaling for mental health) and new products require new habits, people trying new things, its not that easy to change people's behavior. It would be much easier for me to sell my little app if it was a literal plain old journal.
I am worried for people using write ups like this as a huge, much appreciated dose of copium.
Try it out and don't stop trying. If something improves at this rate, even if you think it's not there right now, don't assume it is going to stop. Be honest about the things we were always obviously bad at, that the ai has been getting quickly better at, and assume that it will continue getting better. If this were true, what would that mean for you?
well, many apps i made are really good but i would never bother to share it, takes unnecessary effort and i don't really know what works best for me will work like that for others
It's simple. AI speeds the 80% of development that was never the blocker.
Arguably makes the remaining 20% even harder to handle.
I'm sure that AI can be a huge boost to great, mature developers. Which are insanely rare in an industry that has consistently promoted brainless ivy league coders farming algo quizzes for months.
But those with a huge sensibility and experience can definitely be enabled to produce more.
But the 20% is still there and again, it's easy to make it way harder because you're less intimate with the brittle 80%.
By "apps" this author apparently means "PyPi packages". This is a bafflingly myopic perspective in a world of myopic perspectives. Do we really expect people vibecoding "apps" to put anything on PyPi as a result? They're consumers of packagers, not creators.
I don't blame people for responding to the title instead of the article, because the article itself doesn't bother to answer its own question.
The bottleneck shifted but didn't disappear. Getting to a working prototype in a weekend is real, but error handling, edge cases, and ops work hasn't gotten much faster. Distribution is completely unchanged too. A lot of these 'where are the AI apps' questions are really asking why there aren't more successful AI businesses, which is a harder and very different problem.
maybe some developers are more productive while the rest of em is laid off.. keeping the same release cadence but with fewer devs?
i know maybe this is not to your analysis as its about open source stuff, but this is the sentiment i see with some companies. rather than have 10x output which their clients dont need, they produce things cheaper and earn more money from what they produce. (and later lose that revenue to a breach :p)
Well I read the article discussing pypi packages but I think for a lot of people it’s more single use tools. My little apks are ugly and buggy but work for me
As we haven't seen new operating systems or web browsers and the like, I'm guessing the reason is the same the corporation execs still have to find out: producing the code is just a small part of it. The big part is iterating bug fixes, compatibility, maintenance etc.
My guess - these are not not on PyPI because of libraries. AI generating is good when you don't care about how your app works, when implementation details does not matter.
When you are developing library it's exact opposite - you really care about how it works and which interface it provides so you end up writing it mostly by hand.
We’re in a personal software era. Or disposable software era however you want to look at it. I think most people are building for themselves and no longer needing to lean on community to get a lot of things done now.
I think this is right, I can get cause to build me something for my own use that I’d have given up at before, getting to the point of being useable still doesn’t make it shareable.
I absolutely hate web development with a passion and haven’t done a new from the ground up web app in 25 years and even since then it was mostly a quick copy and paste to add a feature.
But since late last year even when it’s not part of the requirements leading app dev + cloud consulting projects, I’ll throw in a feature complete internal web admin site to manage everything for a project with a UI that looks like something I would have done 25 years ago with a decent UX.
They are completely vibe coded, authenticated with Amazon Cognito and the only things I verify are that unauthenticated users can’t access endpoints, the permissions of the lambda hosting environment (IAM role) and the database user it’s using permissions.
Only at most 5 people will ever use the website at a time - but yeah I get scalability for free (not that it matters) because it’s hosted on Lambda. (yes with IAC)
The website would not exist at all if it weren’t for AI.
Now just to be clear, if a website is meant for real people and the customer’s customers. I’ll insist on a real web designer and a real web developer be assigned to the project with me.
the pypi metric feels off. most of the ai stuff i see shipping is either internal tooling that never hits pypi, or its built on top of existing packages (langchain, openai sdk, etc) rather than creating new ones.
the real growth is in apps that use ai as a feature, not ai-first packages. like every saas just quietly added an llm call somewhere in their stack. thats hard to measure from dependency graphs.
Why would package be used as the standard? What person fully leveraging AI is going to put up packages for release? They (their AI model) write the code to leverage it themselves. There is no reason to take on the maintenance of a public package just because you have AI now. If anything, packages are a net drag on new AI productivity because then you'd have to worry about breaking changes, etc. As far as actual apps being built by AI, the same indie hackers that had garbage codebases that worked well enough for them to print money are just moving even faster. There are plenty of stories about that.
One pattern I've noticed: the apps that work best
combine multiple models rather than relying on one.
Single-model outputs have too much variance for
production use cases.
It's silly to think that 'AI apps' must look like the enterprise, centrally-managed SaaS that we are used to. My AI apps are all bespoke, tailored to my exact needs, accessed only via my VPN. They would not be useful to anyone else, so why would I make them public?
Hmmm, my anecdotal experience doesn't match up with this article. Personally I am seeing an explosion of AI-created apps. A number of different subreddits I use for disparate interests have been inundated with them lately. Show HN has experienced the same thing, no?
I think this is a great question to ask and maybe I need my own blog to post about these things as I might reply with a big comment
Making Unpublished Software for Themselves
One issue is, I think maybe a lot of people are making software for themselves and not publishing it - at least I find myself doing this a lot. So there's still "more software produced than before", but it's unpublished
LOC a Good Measure?
Another question is like Lines of Code, about if we best measure AI productivity by new packages that exist. AI might make certain packages obsolete and there may be higher quality, but less, contributions made to existing packages as a result. So actually less packages might mean more productivity (although, generally we seem to think it's the opposite, conventionally speaking)
Optimizing The Unnoticeable
Another issue that comes up is maybe AI optimizes unnoticeable things: AI may indeed make certain things go 100x faster or better. But say a website goes from loading in 1 second to 1/100th of a second... it's a real 100x gain, but in practice doesn't seem to be experienced as a whole lot bigger of a gain. It doesn't translate in to more tangible goods being produced. People might just load 100 pages in the same amount of time, which eats up the 100x gain anyway (!).
Bottleneck of Imagination
I think also this exposes a bottleneck of imagination: what do we want people to be building with AI? People may not be building things, because we need more creative people to be dreaming up things to build. AI is only fed existing creative solutions and, while it does seem to mix that together to generate new ideas, still the people reading the outputs are only so creative. I've thought standard projects would be 1) creating open source alternatives to existing proprietary software, 2) writing new software for old hardware (like "jailbreaking" but doesn't have to be?) to make it run new software so that it can be used for something other than be e-waste. 3) Reverse engineering a bunch of designs so you can implement some new design on them, where open source code doesn't exist and we don't know how they function (maybe kind of like #1). So like there is maybe a need for a very "low tech" creation of spaces where people are just regularly swapping ideas on building things they can only build themselves so much, to either get the attention of more capable individuals or to build up teams.
Time Lag to Adapt
Also, people may still be getting adjusted to using AI stuff. One other post detailed that the majority of the planet does not use AI, and an even smaller subset pays for subscriptions. So there's still a big lag in society of adoption, and of adopters knowing how to use the tools. So I think people might really experience optimizing something at 100x, but they may not know how to leverage that to publish it to optimize things for everyone else at 100x amount, yet.
Social Media Breakdown?
Another problem is, I have made stuff I'd like to share but... social media is already over-run with over-regulation and bots. So where do I publish new things? Even on HN, there was that post about how negative the posters can be, who have said very critical things about projects that ended up being very successful. So I wonder if this also fuels people just quietly creating more stuff for their own needs.
Has GDP Gone Up or Time Been Saved?
Do other measures of productivity exist? GDP appears to have probably only gone up a bit. But again, could people be having gains that don't translate to GDP gains? People do seem to post about saving time with AI but... the malicious thing about technology is that, when people save 10 hours from one tool, they usually just end up spending that working on something else. So unless we're careful, technology for some people doesn't save them much time at all (in fact, a few people have posted about being addicted to AI and working even more with it than before AI!).
Are There Only So Many "10x Programmers"?
Another issue is, maybe there are only a minority of people who get "10x" gains from AI; at the same time, "lesser" devs (like juniors?) have apparently been displaced by AI with some layoffs and hiring freezes.
Conclusion
I guess we are trying to account for real gains and "100x experiences" people have, with a seeming lack of tangible output. I don't think these things are necessarily at odds with each other for some of the aforementioned reasons written above. I imagine maybe in 5 years we'll see more clearly if there is some noticeable impact or not, and... not to be a doomer / pessimist, but we may have some very negative experience from AI development that seems to negate the gains that we'll have to account for, too.
A friend of mine who is tech savvy and I would say has novice level coding experience decided to build his dream app. Its really been a disaster. The app is completely broken in many different ways, has functionality gaps, no security, no thought out infrastructure, its pretty much a dumpster fire. The problem is that he doesn't know what he doesn't know, so its impossible for him to actually fix it beyond instructing the AI over and over to simply "fix it". The more this is done, the worst the app becomes. He's tried all the major AI vendors, from scratch, same result, a complete mess of code. He's given up on it now and has moved on with his life.
Im not saying that AI is bad, infact, its the opposite, its one of the most important tools that I have seen introduced in my lifetime. Its like a calculator. Its not going to turn everyone into a mathematician, but it will turn those who have an understanding of math into faster mathematician.
- meaningful software still takes meaningful time to develop
- not all software is packaged for everyone
I've seen a lot of examples shared of software becoming narrow-cast, and/or ephemeral.
That that doesn't show up in library production or even app store submissions is not interesting.
I'm working on a large project that I could never have undertaken prior to contemporary assistance. I anticipate it will be months before I have something "shippable." But that's because it's a large project, not a one shot.
I was musing that this weekend: when do we see the first crop of serious and novel projects shipping, which could not have been done before (at least, by individual devs)... but which still took serious time.
> Maybe the top 15,000 PyPi packages isn't the best way to measure this?
> Apparently new iOS app submissions jumped by 24% last year
Looks like most LLM generated code is used by amateurs/slop coders to generate end-user apps they hope to sell - these user profiles are not the type of people who contribute to the data/code commons. Hence there's no uptick in libs. So basically a measurement issue.
My experience with AI-driven and AI-assisted development so far is that it has actually enhanced my workflow despite how much I dislike it.
With a caveat.
If you were to compare my workflow to a decade ago, you wouldn’t see much difference other than my natural skill growth.
The rub is that the tools, communities and services I learned to rely on over my career as a developer have been slowly getting worse and worse, and I have found that I can leverage AI tools to make up for where those resources now fall short.
My take is you are missing out on a barrage of "Shadow AI" and bespoke LoB and B2B software (By "Shadow AI" I mean the (unsanctioned) use of GenAI in Shadow IT, traditionally dominated by Excel and VBA).
All of the above are huge software markets outside of the typical Silicon Valley bubble.
I have a number of small apps and libraries I've prompted into existence and have never considered publishing. They work great for me, but are slop for someone else. All the cases I haven't used them for are likely incomplete or buggy or weird, the code quality is poor, and documentation is poor (worse than not existing in many cases.)
Plus you all have LLMs at home. I have my version that takes care of exactly my needs and you can have yours.
There is a ton of AI use in photography software. It has improved masking dramatically, denoise is much better, removing objects is easier. But these aren’t sold as “AI apps” but as photo editing tools that use AI as a tool.
I’ll ask another question. Why isn’t software getting better? Seems like software is buggier than ever. Can’t we just have an LLM running in a loop fixing bugs? Apparently not. Is this the future? Just getting drowned in garbage software faster and faster?
So they are all producing products to produce products.
My guess is 50% of token usage globally is to produce mediocre articles on "how I use Claude code to tell HN how I use Claude code".
Cool data. What do I do with it? None of my use cases involve writing software, so I don't think this is _for_ me since my extensive AI use wouldn't show up in git commits, but I'm not sure who it's for. When I'm talking to artist friends, musician friends, academic friends, etc data is nice to have but I'm talking in stories: the real thing I did and how it made me better at the thing.
AI is unbelievably useful and will continue to make an impact but a few things:
- The 80/20 rule still applies. We’ve optimized the 20% of time part (a lot!) but all the hype is only including the 80% of work part. It looks amazing and is, but you can’t escape the reality of ~80% of the time is still needed on non-trivial projects.
- Breathless AI CEO hype because they need money. This stuff costs a lot. This has passed on to run of the mill CEOs that want to feel ahead of things and smart.
- You should be shipping faster in many cases. Lots of hype but there is real value especially in automating lots of communication and organization tasks.
I agree with the premise of the article, in the sense that there has not been, and I don't think there will be, a 100x increase in "productivity".
However, PyPi is not really the best way to measure this as the amount of people who take time to wrap their code into a proper package, register into PyPi, push a package, etc... is quite low. Very narrow sampling window.
I do think AI will directly fuel the creation of a lot of personal apps that will not be published anywhere. AI lower the barrier of entry, as we all know, so now regular folks with a bit of technical knowledge can just build the app they want tailored to their needs. I think we´ll see a lot of that.
Like others have mentioned, I think the premise of looking at the most popular few projects (pypi.org currently lists 771,120 projects) on pypi as any sort of proxy for AI coding is terribly misguided/unrepresentative and that almost no one is going to be packaging up their vibe-coded projects for distribution on pypi.
That being said, I've personally put 3 up recently (more than I've published in total). I'm sure they have close to zero downloads (why would they? they're brand new, solve my own problems, I'm not interested in marketing them or supporting them, they're just shared because they might be useful to others) so they wouldn't show up in their review. 2 of these are pretty meaty projects that would have taken weeks if not months of work but instead have been largely just built over a weekend or a few days. I'd say it's not just the speed, but that w/o the lowered effort, these projects just wouldn't ever have crossed the effort/need bar of ever being started.
I've probably coded 50-100X more AI-assisted code that will never go to pypi, even as someone that has released pypi packages before (which already puts me in a tiny minority of programmers, much less regular people that would even think about uploading a pypi project).
For those interested in the scope of the recent projects:
https://pypi.org/project/realitycheck/ - first pypi: Jan 21 - 57K SLoC - "weekend" project that kept growing. It's a framework that leverages agentic coding tools like Codex/Claude Code to do rigorous, systematic analysis of claims, sources, predictions, and argument chains.It has 400+ tests, and does basically everything I want it to do now. The repo has 20 stars and I'd estimate only a handful of people are using it.
https://pypi.org/project/tweetxvault/ - first pypi: Mar 16 - 29K SLoC - another weekend project (followup on a second weekend). This project is a tool for archiving your Twitter/X bookmarks, likes, and tweets into a local db, with support for importing from archives and letting you search through them. I actually found 3 or 4 other AI-coded projects that didn't do quite what I wanted so it I built my own. This repo has 4 stars, although a friend submitted a PR and mentioned it solved exactly their problem and saved them from having to build it themselves, so that was nice and justifies publishing for me.
https://pypi.org/project/batterylog/ - first pypi: Mar 22 - 857 SLoC - this project is actually something I wrote (and have been using daily) 3-4 years ago, but never bothered to properly package up - it tracks how much battery is drained by your laptop when asleep and it's basically the bare minimum script/installer to be useful. I never bothered to package it
up b/c quite frankly, manual pypi releases are enough of a PITA to not bother, but LLMs now basically make it a matter of saying "cut a release," so when I wanted to add a new feature, I packaged it up as well, which I would never have done this otherwise. This repo has 42 stars and a few forks, although probably 0 downloads from pypi.
(I've spent the past couple years heavily using AI-assisted workflows, and only in the past few months (post Opus 4.6, GPT-5.2) would I have even considered AI tools reliable enough to consider trusting them to push new packages to pypi.)
On my local computer used only by me because now I don't need a corporation to make them for me. In the past decades I'd make maybe one or two full blown applications for myself per 10 years. In the past year "I" (read: a corporate AI and I) have made dozens to scratch many itches I've had for a very long time.
It's a great change for a human person. I'm not pretending I'm making something other people would buy nor do I want to. That's the point.
This is so stupid. I don't know whether AI has improved things but this is clearly cope, we're not even a year into the transition since agentic coding took over so any data you gather now is not the full story.
But people are desperate for data right? Desperate to prove that AI hasn't done shit.
Maybe. But this much is true. If AI keeps improving and if the trendline keeps going, we're not going to need data to prove something equivalent to the ground existing.
This is such copium for AI haters. I stopped working almost any single line of code at the beginning of this year and I've shipped 3 production projects that would have taken months or years to build by hand in a matter of days.
Except none of them are open source so they don't show up in this article's metrics.
But it's fine. Keep your head in the sand. It doesn't change the once in a lifetime shift we are currently experiencing.
No one needs another SaaS. Games are the real killer app for AI. Hear me out.
I've wanted to make video games forever. It's fun, and scratches an itch that no other kind of programming does. But making a game is a mountain of work that is almost completely unassailable for an individual in their free time. The sheer volume of assets to be created stops anything from ever being more than a silly little demo. Now, with Gemini 3.1, I can build an asset pipeline that generates an entire game's worth of graphics in minutes, and actually be able to build a game. And the assets are good. With the right prompting and pipeline, Gemini can now easily generate extremely high quality 2d assets with consistent art direction and perfect prompt adherence. It's not about asking AI to make a game for you, it's about enabling an individual to finally be able to realize their vision without having to resort to generic premade asset libraries.
I tried using Gemini for asset generation, but have not yet found a good way to animate them. It does not seem to understand sprite sheets or bone-based animation. Do you know a solution for that?
>It does not seem to understand sprite sheets or bone-based animation. Do you know a solution for that?
This is precisely what I'm running into as well. There's a few SaaS solutions that are ok, but I gave up after an attempt at building a pipeline for it. Sticking with building 4X/strategy card games that don't need character animations for now until the models catch up.
Except all of the ai created games posted to the various subreddits are awful. No one likes them, no one plays them. The ones that make it to steam end up getting abandoned when the devs hit a performance wall.
Game development just isn’t something AI can do well. Good games are not just recreations of existing titles.
High quality assets is orthogonal to fun. If you can create a fun concept with generic assets, I believe you may find an artist willing to produce the assets for you.
Not necessarily. It's a very "programmer brain" thing to think that novel mechanics are the be-all end-all of what makes a fun game. Extremely simple games can become incredibly engaging given high quality detailed beautiful art design. Think of deck builders and board games that would be pointless with just placeholder images and spreadsheets of data, that actually become enjoyable because of the creative work that went into the assets.
Not all of us get addicted to the rat race and wake up at 3am to run more Ralph loops. Some are perfectly content getting the same amount of work done as before, just with less investment of time and effort.
It is incredibly easy now to get an idea to the prototype stage, but making it production-ready still needs boring old software engineering skills. I know countless people who followed the "I'll vibe code my own business" trend, and a few of them did get pretty far, but ultimately not a single one actually launched. Anyone who has been doing this professionally will tell you that the "last step" is what takes the majority of time and effort.
> It is incredibly easy now to get an idea to the prototype stage
Yup. And for most purposes, that's enough. An app does not have to be productized and shipped to general audience to be useful. In fact, if your goal is to solve some specific problem for yourself, your friends/family, community or your team, then the "last step" you mention - the one that "takes majority of time and effort" - is entirely unnecessary, irrelevant, and a waste of time.
The productivity boost is there, but it's not measured because people are looking for the wrong thing. Products on the market are not solutions to problems, they're tools to make money. The two are correlated, because of bunch of obvious reasons (people need money, solving a problem costs money, people are happy to pay for solutions, etc.), but they're still distinct. AI is dropping the costs of "solving the problem" part, much more than that of "making a product", so it's not useful to use the lack of the latter as evidence of lack of the former.
In enterprise software there is an eternal discussion of "buy vs build" and most organizations go through a cycle of:
-- we had a terrible time building something so now we're only going to buy things
-- we had a terrible time buying something so now we're only going to build things
-- repeat...
Either way you can have a brilliant success and either way you fail abjectly, usually you succeed at most but not all of the goals and it is late and over budget.
If you build you take the risks of building something that doesn't exist and may never exist.
If you buy you have to pay for a lot of structure that pushes risks around in space and time. The vendor people needs marketing people not to figure out what you need, but what customers need in the abstract. Sales people are needed to help you match up your perception of what you need with the reality of the product. All those folks are expensive, not just because of their salaries but because a pretty good chunk of a salesperson's time is burned up on sales that don't go through, sales that take 10x as long they really should because there are too many people in the room, etc.
When I was envisioning an enterprise product in the early 2010s for instance I got all hung up on the deployment model -- we figured some customers would insist on everything being on-premise, some would want to host in their own AWS/Azure/GCP and others would be happy if we did it all for them. We found the phrase "hybrid cloud" would cause their eyes to glaze over and maybe they were right because in five years this became a synonym for Kubernetes. Building our demos we just built things that were easy for us to deploy and the same would be true for anything people build in house.
To some extent I think AI does push the line towards build.
> if your goal is to solve some specific problem for yourself, your friends/family, community or your team, then the "last step" you mention - the one that "takes majority of time and effort" - is entirely unnecessary, irrelevant, and a waste of time.
To a point, but I think this overstates it by quite a bit. At the moment I'm weighing some tradeoffs around this myself. I'm currently making an app for a niche interest of mine. I have a few acquaintances who would find it useful as well but I'm not sure if I want to take that on. If I keep the project for personal use I can make a lot of simplifying decisions like just running it on my own machine and using the CLI for certain steps.
To deploy this to for non-tech users I need to figure out a whole deployment approach, make the UI more polished, and worry more about bugs and uptime. It sucks to get invested in some software that then constantly starts breaking or crashing. GenAI will help with this somewhat, but certainly won't drop the extra coding time cost down to zero.
People today say "web applications suck", "Electron sucks", etc. They weren't around in the 1990s where IT departments were breaking under the load of maintaining desktop apps, when we were just getting on the security update treadmill, and where most shops that made applications for Windows had a dedicated InstallShield engineer and maybe even a dedicated tester for the install process.
Maintaining desktop apps was not really harder than maintaining the current Kubernetes-Web-App behemoths, at least in my experience.
Yeah, we traded managing files and registry entries on desktops for something that violates all the principles of the science-of-systems, the kind of thing Perrow warns about in his book Normal Accidents.
I think that's oversimplifying it a bit. Managing files and registry entries wasn't much of a problem, but supporting an ever-growing matrix of versions across multiple platforms that were released into the wild was an issue. Modern evergreen apps kind of fix this, but you're still dealing with other people's computers and environments. Operating a service reliably is of course filled with different problems, but at least you have full control.
I wish we had a dedicated InstallShield engineer! I had to design and burn my own discs for the desktop apps I built. And for some reason, the LightScribe drive was installed on the receptionist's computer. I have no idea why, but I was a new hire and I didn't question much.
Windows was so bad that it made the web bad. Imagine the world we'd be in today if Internet Explorer never existed.
Well back in the 1990s Apple was on the ropes.
Classic MacOS was designed to support handling events from the keyboard, mouse and floppy in 1984 and adding events from the internet broke it. It was fun using a Mac and being able to get all your work done without touching a command line, but for a while it crashed, crashed and crashed when you tried to browse the web until that fateful version where they added locks to stop the crashes but then it was beachball... beachball... beachball...
They took investment from Microsoft at their bottom and then they came out with OS X which is as POSIXy as any modern OS and was able to handle running a web browser.
In the 1990s you could also run Linux and at the time I thought Linux was far ahead of Windows in every way. Granted there were many categories of software like office suites that were not available, but installing most software was
but if your system was unusual (Linux in 1994, Solaris in 2004) you might need to patch the source somewhere.I agree, although I'd also say for the majority of problems the first part of even prototyping it is probably a waste of time and most people would be better off asking a simple AI hooked up to search if an appropriate solution already exists, or can be easily made with existing tools.
But the last "making a product" part does apply to nearly any tool, even a solution to a personal problem.
I've started tons of scratch my own itch projects. There's adoption, UX, onboarding costs even if you're the only audience.
TLDR: i don't even use my own projects. I churn.
True, but a basic production stage benefits from some amount of backups, dev/staging/prodution, and more than one database.
> the "last step" is what takes the majority of time and effort
Having worked extensively with vibe-coded software, the main problem for me is that I have tuned-off from the ai-code, and I dont see any skin-in-the-game for me. This is dangerous because it becomes increasingly harder to root-cause and debug problems because that muscle is atrophying. use-it or lose-it applies to cognitive skills (coding/debugging). Now, I lean negatively to ai-code because, while it seduces us with fast progress in the first 80%, the end outcome is questionable in terms of quality. Finally, ai-coding encourages a prompt-and-test or trial-and-error approach to software engineering which is frustrating and those with experience would prefer to get it right by design.
I also wonder about this for myself. My feeling is that my debug skills are also atrophied a bit. But I would split debugging into two buckets:
1. Debugging my own code or obvious behavior with other libraries.
2. Debugging pain-in-the-ass behavior with other libraries.
My patience with the latter is significantly less now, and so is perhaps my skill in debugging them. Libraries that change their apis for no apparent reason, libraries which use nonstandard parameter names, libraries which aren’t working as advertised.
Software engineering is not “coding” though.
Before AI for the last 8 or so years now first at a startup then working in consulting mostly with companies new to AWS or they wanted a new implementation, it’s been:
1. Gather requirements
2. Do the design
3. Present the design and get approval and make sure I didn’t miss anything
4. Do the infrastructure as code to create the architecture and the deployment pipeline
5. Design the schema and write the code
6. Take it through UAT and often go back to #4 or #5
7. Move it into production
8. Monitoring and maintenance.
#4 and #5 can be done easily with AI for most run of the mill enterprise SaaS implementations especially if you have the luxury of starting from the ground up “post AI”. This is something you could farm off to mid level ticket takers before AI.
I also experienced this with my personal projects. It was really easy to just workshop a new feature. I'd talk to claude and get a nice looking implementation spec. Then I'd pass it on to a coding agent which would get 80% there but the last 20% would actually take lot more time. In the meantime I'd workshop more and more features leading to an evergrowing backlog and an anxiety that an agent should be doing something otherwise I'm wasting time. I brought this completely on myself. I'm not building a business, nothing would happen if I just didn't implement another feature.
Ha! I do this too and have also recently noticed. When scope creep is relatively cheap, it also gets unending and I'm never satisfied. I've had a couple of projects that I would otherwise open source that I've had to be realistic about and just accept it's only going to be useful for myself. Once I open it I feel a responsiblity for maintenance and stability that just adds a lot of extra work. I need to save those for the projects that might actually, realistically, be used.
It's really thrown off some old adages. It's now "the first 90% takes 90% of the time, the last 10% takes the other 90,000,000% of the time."
Just doesn't have the same ring to it.
It's more Zeno's paradox. You take one step, get 90% of the way to the finishing line. Now you look ahead and still a bunch of distance ahead of you. You take another step and get 90% of the way there. Now you look ahead and see there's still more distance ahead of you,...
Exactly, there have been loads of tools over time to make software development easier - like Dreamweaver and Frontpage to build websites without coding, or low/no-code platforms to click and drag software together, or all frameworks ever, or libraries that solve issues that often take time - and I'm sure they've had a cumulative effect in developer productivity and / or software quality.
But there's not one tool there that triggered a major boost in output or number of apps / libraries / products created - unless I missed something.
Sure, total output has increased, especially since the early 2010's thanks to both Github becoming the social network of software development, and (arguably) Node / JS becoming one of the most popular languages/runtimes out there attracting a lot of developers to publish a lot of tools. But that's not down to productivity or output boosting developments.
Well… because it is not almost possible do it solo.
Code is just one part of puzzle. Add: Pricing, marketing and ads, invoicing, VAT, make really good onboarding, measure churn rate, do customer service…
A lot of vibe coders are solopreneurs. You have to be very consistent and disciplined to make final product that sells.
> Anyone who has been doing this professionally will tell you that the "last step" is what takes the majority of time and effort.
That's true, but even the "last step" is being accelerated. The 10% that takes 90% of the time has itself been cut in half.
An example is turning debug logs and bug reports into bugfixes, and performance stats into infrastructure migrations.
The time required to analyze, implement, and deploy those has been reduced by a large amount.
It still needs to be coupled with software engineering skills - to decide between multiple solutions generated by an LLM, but the acceleration is significant.
So, how many years until we'll see results, then?
> So, how many years until we'll see results, then?
-0.75 years.
Software development output (features, bugs, products) - especially at smaller companies like startups - has already accelerated significantly, while software development hiring has stayed flat or declined. So there has been a dramatic increase in human-efficiency. To me, that seems like a result, although it's cold comfort as a software engineer.
You probably won't see this reflected as a multiplication of new apps because the app consumer's attention is already completely tapped. There's very little attention surface area left to capture.
You don't think capitalists are able to generate profit off of these LLMs currently? Why not? Are they just stupid or something?
I agree 100%. Boring old software skills are part of what it took to "write" this DSL, complete with a fully featured LSP:
https://github.com/williamcotton/webpipe
https://github.com/williamcotton/webpipe-lsp
(lots of animated GIFs to show off the LSP and debugger!)
While I barely typed any of this myself I sure as heck read most of the generated code. But not all of it!
Of course you have to consider my blog to be "in production":
https://github.com/williamcotton/williamcotton.com/blob/main...
The reason I'm mentioning this project is because the article questions where all the AI apps are. Take a look at the git history of these projects and question if this would have been possible to accomplish in such a relatively short timeframe! Or maybe it's totally doable? I'm not sure. I knew nothing about quite a bit of the subsystems, eg, the Debug Adapter Protocol, before their implementation.
I recently "vibe coded" a long term background job runner service... thing. It's rather specific to my job and a pre-existing solution didn't exist. I already knew what I wanted the code to be, so it was just a matter of explaining explicitly what I wanted to the AI. Software engineering concepts, patterns, al that stuff. And at the end of the day(s) it took about the same amount of time to code it with AI than it would've taken by hand.
It was a lot of reviewing and proofreading and just verifying everything by hand. The only thing that saved me time was writing the test suite for it.
Would I do it again? Maybe. It was kinda fun programming by explaining an idea in plain english than just writing the code itself. But I heavily relied on software engineering skills, especially those theory classes from university to best explain how it should be structured and written. And of course being able to understand what it outputs. I do not think that someone with no prior software engineering knowledge could do the same thing that I did.
> Anyone who has been doing this professionally will tell you that the "last step" is what takes the majority of time and effort.
This is true, and I bet there are thousands of people who are in this stage right now - having gotten there far faster than they would have without Claude Code - which makes me predict that the point made in the article will not age well. I think it’s just a matter of a bit more time before the deluge starts, something on the order of six more months.
I'd argue that LLMs are not yet capable of the last step, and because most sufficiently large AI-generated codebase are an unmaintainable mess, it's also very hard for a human developer to take over and go the last mile.
Even if you have the app, you get to start the fun adventure of marketing it and actually trying to grow the damn thing
And you need to find a way to market it that prevents people from thinking “cool, I bet I can get Claude to whip up something similar real quick”
Right. Which is something you neither need nor want if you just wanted to have an app.
Even beyond the engineering there are 100 other things to do.
I launched a vibe coded product a few months ago. I spent the majority of my time
- making sure the copy / presentation was effective on product website
- getting signing certificates (this part SUCKS and is expensive)
- managing release version binaries without a CDN (stupid)
- setting up LLC, website, domain, email, google search indexing, etc, etc
Agreed. However, I just recently "launched" a side project and Cloudflare made a lot of the stuff you mentioned easier. I also found that using AI helped with setting up my LLC when I had questions.
Exactly. The "writing code" part is literally the easiest part of building a software business. And that was even before LLM assisted coding. Now it's pretty much trivial to just spew slop code until something works. The hard parts are still: making the right thing, making it good, getting feedback and idea validation, and the really hard part is turning it into a business.
> not a single one actually launched.
I think this represents a fundamental misunderstanding of how these AI tools are used most effectively: not to write software but to directly solve the problem you were going to solve with software.
I used to not understand this and agreed with the "where is all the shovelware" comments, but now I've realized the real shift is not from automating software creation, but replacing the need for it in the first place.
It's clear that we're still awhile away from this being really understood and exploited. People are still confusingly building webapps that aren't necessary. Here's two, somewhat related, examples I've come across (I spend a lot of time on image/video generation in my free time): A web service that automatically creates "headshots" for you, and another that will automatically create TikTok videos for you.
I have bespoke AI versions of both of these I built myself in an afternoon, running locally, creating content for prices that simply can't be matched by anyone trying to build a SaaS company out of these ideas.
What people are thinking: "I know, I can use AI to build a SaaS startup the sells content!" But building a SaaS company still requires real software since it has to scale to multiple users and use cases. What more and more people are realizing is "I can created the content for basically free on my desktop, now I need to figure out how to leverage that content". I still haven't cracked the code for creating a rockstar TikTok channel, but it's not because I'm blocked on the content end.
Similarly I'm starting to see that we're still not thinking about how to reorganize software teams to maximally exploit AI. Right now I see lots of engineers doing software the old way with an AI powered exo-skeleton. We know what this results in: massive PRs that clog up the whole process, and small bugs that creep up later. So long as we divide labor into hyper focused roles, this will persist. What I'm increasingly seeing is that to leverage AI properly we need to re-think how these roles actually work, since now one person can be much responsible for a much larger surface area rather than just doing one thing (arguably) faster.
I launched a draw.io competitor to the point that it is in production, but there is little activity on the site as far as signups are concerned. Doesn't deliver enough business value.
Out of curiosity: What is your USP? Why should I prefer your product over draw.io?
IMHO (this may not apply to you!) a lot of people launch a "competitor" of a product which seems to be a clone of the product without improving something that the other product misses/is very bad at.
> and a few of them did get pretty far, but ultimately not a single one actually launched.
Having done this professionally for a very, very long time, software engineers aren't particularly good at launching products.
Technology has drastically lowered the barriers to bring software products to customers, and AI is a continuation of that trend.
How much longer will this be true, though? With improving computer use, it may be possible in the next ~year or so that agents will be able to wire up infrastructure and launch to production.
no
I don't think with LLMs as the foundation we will ever have something that can build and launch something end to end.
They just predict the next most likely token... no amount of clever orchestration can cover that up and make it into real intelligence.
Nice bait
all they did is annoy their friends and family by sharing their vibeslop app and asking for "feedback".
I really dont know how to respond to these requests. I am going to hide out and not talk to anyone till this fad passes.
Reminds of the trend where everyone was dj wanting you to listen their mixtrack they made on abbleton live
Is it really that big of a deal to help/encourage a friend/family in these simple ways?? Do you have no time in life to smell the flowers?
Devil's advocate (because honestly I do agree with you, but..) -- help/encouragement often ends up turning into far more time and effort than it sounds like up front.
~18 months ago a friend of mine had a very viable, good idea for a physical product, but very fuzzy on the details of where to begin. My skillset backfilled everything he was missing to go from idea to reality to in-market.
I began at arm's length with just advice and validation, then slowly got involved with CAD and prototyping to make sure it kept moving forward, then infrastructure/admin, graphic design, digital marketing and support, etc, while he worked on manufacturing, physical marketing, networking, fulfillment, sales, etc.
Long story short, because I both deeply believe in the vision and know that teamwork makes the dream work, I am fully, completely, inextricably involved LOL -- and I don't have a single complaint about it either, but man, watch out, because if you don't believe in the vision but do have skills/expertise they're lacking, and opt out, friends and family will be the quickest and most aggrieved people you'll ever meet that think you're gatekeeping them from success.
I hope this at least resulted in some equity of this project for you.
Yeah it turned out to be very fair, I just initially wasn't expecting to get as involved as I have hahaha
In this case, it's more like asking your friends to take time to smell some feces instead of flowers.
Or to be a little less pessimistic, it's like asking them to stop and smell the flowers, except the flowers are fake and plastic and it makes your friends question your sanity. Either way, it's not a normal or enjoyable flower smelling experience, and doesn't add any enjoyment or simple pleasure to one's life like normal flower smelling would.
I sure do. I hook up Claude to my browser via MCP and have it review and give feedback for my family and friends' projects. It's a win/win.
AI slop is not the flowers
When someone sends me an AI generated project or proposal, I just send them an AI generated reply I know they're not going to bother reading either.
"I think it's great, you should deploy it! Let me know when it's in production"
90% done, just the other 90% to do...
I have a product that users wanted me to extend with a certain side product. Unfortunately, I don't have the time or resources for that.
But one day I though, let's give this project to AI, as an experiment.
I gave some very generic instructions (it even created it's own functional specs), and off it went. I was able to instruct it with the same prompt over 8 iterations, and it kept track of progress in files. It was able to generate it in an afternoon.
I was truly amazed! It basically wrote 90% of the code, way better than what I anticipated. While I was telling my wife, I realized I need another 90% to finish it. And that part I need to do myself. And since I'm not as fast as AI, it will take me way more than an afternoon.
It made the development faster for sure. But in the grand scheme of things, coding is only a part of the effort. And only a part of the coding can be done by AI. Maybe writing the code would have taken me twice as long. But then there is testing, validating, fixing, releasing, etc. They all eat away at the % gain.
Needless to say, it's not released yet.
succinct and accurate.
It's helping with that part too. I was able to configure a grafana stack with the help of claude for our ansible scripts.
That's no where near the end stage of launching a business.
It is already having impact in triaging support tickets and faster resolution using logs.
I used it to design my business cards!
it's past the end stage, we are already in business. it's just something I am not an expert in, I have used in the past (by having real ops engineers build it for me) and now I have something that gives us insight into our production stack, alerts, etc, that isnt janky and covers my goals. So... yeah that is valuable and improves my business.
Maybe the top 15,000 PyPi packages isn't the best way to measure this?
Apparently new iOS app submissions jumped by 24% last year:
> According to Appfigures Explorer, Apple's App Store saw 557K new app submissions in 2025, a whopping 24% increase from 2024, and the first meaningful increase since 2016's all-time high of 1M apps.
The chart shows stagnant new iOS app submissions until AI.
Here's a month by month bar chart from 2019 to Feb 2026: https://www.statista.com/statistics/1020964/apple-app-store-...
Also, if you hang out in places with borderline technical people, they might do things like vibe-code a waybar app and proudly post it to r/omarchy which was the first time they ever installed linux in their life.
Though I'd be super surprised if average activity didn't pick up big on Github in general. And if it hasn't, it's only because we overestimate how fast people develop new workflows. Just by going by my own increase in software output and the projects I've taken on over the last couple months.
Finally, December 2025 (Opus 4.5 and that new Codex one) was a big inflection point where AI was suddenly good enough to do all sorts of things for me without hand-holding.
I can't really think of a polite way to phrase this, but I'm not surprised throwaway mobile apps do benefit, while relatively mature python packages do not. That matches my estimation of how much programming skill you can reasonable extract from the current LLMs.
Really the one thing that conclusively has changed is that the 'ask it on stackoverflow' has become 'ask it an LLM'. Around 95% of the stackoverflow questions can be answered by an LLM with access to the documentation, not sure what will happen to the other 5%. I don't think stackoverflow will survive a 20-fold reduction in size, if only because their stance on not allowing repeat questions means that exponential growth was the main thing preventing them from becoming stale.
> I'm not surprised throwaway mobile apps do benefit, while relatively mature python packages do not.
Right.
I don't think you even need cynicism or whatever you felt you were having impolite thoughts about:
I'd expect the top mature libraries to be the most resistant to AI tool use for various reasons. They already have established processes, they don't accept drive-by PR spam, the developers working on them might be the least likely to be early adopters, and -- perhaps most importantly -- the todo list of those projects might need the most human comms, like directional planning rather than the sort of yolo feature impl you can do in a one-man greenfield.
All to further bury signals you might find elsewhere in broader ecosystems.
i was curious, but I need a statista account to see it
https://cdn.statcdn.com/Statistic/1020000/1020964-blank-754....
Seems they use old data unless you craft a request with origin/referrer:
Assuming it's a real chart, that will give you the image with the uptick in the last year.https://archive.md/tM9Kg
Heh, I got a solid five seconds with the chart until the paywall popped up.
But there's no labels on the X axis - and removing the popover with dev tools shows a chart that doesn't really support what OP says. So we might be looking at some sample chart instead of a real one.
> Heh, I got a solid five seconds with the chart until the paywall popped up.
Relevant! If the maximalist interpretation of AI capabilities were close to real, and if people tend to point their new super powers at their biggest pain points.. wouldn't it be a big blow for all things advertising / attention economy? "Create a driver or wrapper app that skips all ads on Youtube/Spotify" or "Make a browser plugin that de-emphasizes and unlinks all attention-grabbing references to pay-walled content".
If we're supposed to be in awe of how AI can do anything, and we notice repeatedly that nope, it isn't really empowering users yet, then we may need to reconsider the premise.
> Apparently new iOS app submissions jumped by 24% last year:
The amount of useless slop in the app store doesn't matter. There are no new and useful apps made with AI - apps that contribute to productivity of the economy as whole. The trade and fiscal deficits are both high and growing as is corporate indebtedness - these are the true measures for economic failure and they all agree on it.
AI is a debt and energy guzzling endeavor which sucks the capital juice out of the economy in return for meager benefits.
I can't think of a reason for the present unjustified AI rush and hype other than war, but any success towards that goal is a total loss for the economy and environment - that's the relation between economics and deadly destruction in a connected world, reality is the proof.
Is the AI in the room with us now?
I get that people are upset that making a cool six figures off of stitching together React components is maybe not a viable long-term career path anymore. For those of us on the user side, the value is tremendous. I’m starting to replace what were paid enterprise software and plug-ins and tailoring them to my own taste. Our subject matter experts are translating their knowledge and work flows, which usually aren’t that complicated, into working products on their own. They didn’t have to spend six months or a year negotiating an agreement to build the software or have to beg our existing software vendors, who could not possibly care less, for the functionality to be added to software we are, for some reason, expected to pay for every single year, despite the absence of any operating cost to justify this practice.
> There are no new and useful apps made with AI - apps that contribute to productivity of the economy as whole.
This is flat-earther level. It's like an environmentalist saying that nothing made with fossil fuels contributes to productivity. But they don't say that because they know it's not true.
There are so many valid gripes to have with LLMs, pick literally any of them. The idea that a single line of generated code can't possibly be productivity net positive is nonsensical. And if one line can, then so can many lines.
> This is flat-earther level
Ok, so do you have a counterexample?
Here's mine. It's not big or important (at all!) but I think it is a perfectly valid app that might be useful to some people. It's entirely vibe-coded including code, art and sounds. Only the idea was mine.
https://apps.apple.com/us/app/kaien/id6759458971
Can you give me any new (i.e. released in 2026) app that does something useful? There's just not many good app ideas left after all..
I wrote my own note sharing app using free Claude. It's self-hosted, allows for non-simultaneous editing by multiple users (uses locks), it has no passwords on users, it shows all notes in a list. Very simple app, over all. It's one Go file and one HTML file. I like it, it's exactly what I want for sharing notes like shopping and todo lists with my partner.
The AI wouldn't have been able to do it by itself, but I wouldn't have been arsed to do it alone either.
That has some strong "Everything that can be invented has been invented" vibes.
If that would be true then all these AIs are useless. Who needs them to built something that already exists?
"Everything that can be invented has been invented"
Ah my favorite, entirely made up quote.
Apocraphyly attributed to the U.S. Patent Office Commissioner in 1899.
Just shown me a new killer app from the app store that is coded by AI and isn’t an AI app itself.
Seems like the rest of the whole AI business, the only things going to the top are the AI tools themselves but not the things they are supposed to built.
We have great software now!
YoloSwag (13 commits)
[rocketship rocketship rocketship]
YoloSwag is a 1:1 implementation of pyTorch, written in RUST [crab emoji]
- [hand pointing emoji] YoloSwag is Memory Safe due to being Written in Rust
- [green leaf emoji] YoloSwag uses 80% less CPU cycles due to being written in Rust
- [clipboard emoji] [engineer emoji] YoloSwag is 1:1 API compatible with pyTorch with complete ops specification conformance. All ops are supported.
- [recycle emoji] YoloSwag is drop-in ready replacement for Pytorch
- [racecar emoji] YoloSwag speeds up your training workflows by over 300%
Then you git clone yoloswag and it crashes immediately and doesn't even run. And you look at the test suite and every test just creates its own mocks to pass. And then you look at the code and it's weird frankenstein implementation, half of it is using rust bindings for pytorch and the other half is random APIs that are named similarly but not identical.
Then you look at the committer and the description on his profile says "imminentize AGI.", he launched 3 crypto tokens in 2020, he links an X profile (serial experiments lain avatar) where he's posting 100x a day about how "it's over" for software devs and how he "became a domain expert in quantum computing in 6 weeks."
I deleted vscode and replaced with a hyper personal dashboard that combines information from everywhere.
I have a news feed, work tab for managing issues/PRs, markdown editor with folders, calendar, AI powered buttons all over the place (I click a button, it does something interesting with Claude code I can't do programmatically).
Why don't I share it? Because it's highly personal, others would find it doesn't fit their own workflow.
Technical people (which is by far the minority of people out there) building personal apps to scratch an itch is one thing.
But based on the hype (100x productivity!), there should be a deluge of high quality mobile apps, Saas offerings, etc. There is a huge profit incentive to create quality software at a low price.
Yet, the majority of new apps and services that I see are all AI ecosystem stuff. Wrappers around LLMs, or tools to use LLMs to create software. But I’m not really seeing the output of this process (net new software).
I worked in an industry for five years and I could feasibly build a competitor product that I think would solve a lot of the problems we had before, and which it would be difficult to pivot the existing ones into. But ultimately, I could have done that before, it just brings the time to build down, and it does nothing for the difficult part which is convincing customers to take a chance on you, sales and marketing, etc. - it takes a certain type of person to go and start a business.
Nobody’s talking about starting businesses. The article is specifically about pypi packages, which don’t require any sales and marketing. And there’s still no noticeable uptick in package creation or updates.
My understanding reading it was that PyPi packages is just being used as a proxy variable
Yes, you are correct. The parent is not following the conversation. They probably didn't even read the article.
Why on earth would you publish and monetize software anybody can reproduce with a $20 subscription and an hour of prompting? Why would you ever publish something you vibe coded to PyPI? Code itself isn’t scarce anymore. If there is not some proprietary, secret data or profound insight behind it, I just don’t think there is a good reason to treat it like something valuable.
There is no money in mobile apps. It came out in the Epic Trial that 90% of App Store revenue comes from in app purchases for pay to win games. Most of the other money companies are making from mobile are front end for services.
If someone did make a mobile app, how would it get up take? Coding has never been the hard part about a successful software product.
> But based on the hype (100x productivity!), there should be a deluge of high quality mobile apps, Saas offerings, etc. There is a huge profit incentive to create quality software at a low price.
1. People aren't creating new apps, but enhancing existing ones
2. Companies are less likely to pay for new offerings when the barrier to entry is lowered due to AI. They'll just vibe code what they need.
I don't think the 2nd point will make a huge impact on software sales. Who is vibe coding? Software developers or business types? They aren't going to vibe code a CRM, or their own bespoke version of Excel, or their own Datadog APM.
Maybe they will vibe code small scripts, but nobody was really paying for software to do that in the first place. Saas-pocalypse is just people vibe investing, not really understanding the value proposition of saas in the first place (no maintenance, no deployments, SLAs, no database backups, etc).
> Wrappers around LLMs, or tools to use LLMs to create software. But I’m not really seeing the output of this process
Because it's better to sell shovels than to pan for gold.
In the current state of LLMs, the average no-experience, non-techy person was never going to make production software with it, let alone actually launch something profitable. Coding was never the hard part in the first place, sales, marketing & growth is.
LLMs are basically just another devtool at this point. In the 90s, IDEs/Rapid App Development was a gold rush. LLMs are today's version of that. Both made developer's life's better, but neither resulted in a huge rush of new, cheap software from the masses.
And SQL was that version in the 80s...
Profit is not everyone's goal.
Me, I'm not just chasing markets; I want to build things that create joy.
Before LLMs, there were code sweatshops in India, Vietnam, Latin America, etc. and they've been pumping out apps and SaaS products for decades now.
And it was all crap software, no? EDIT: If it was crap, then that is still good for AI.
AI-powered devs are struggling to stand above it so it wasn't all crap, or, AI produced stuff is too
I think this is the great conundrum with AI. I find it's most useful when I build my own tools from models. It's great for solving last-mile-problem types of situations around my workflow. But I'm not interested in trying to productize my custom workflow. And I've yet to encounter an AI feature on an existing app that felt right.
Problem is that all these companies trying to push AI experiences know that giving users unfettered access to their data to build further customization is corporate suicide.
Well it’s mostly explained by the fact that most people lack imagination and can’t hold enough concepts about a particular experience to think about how to re-imagine it, to begin with.
Oh and sadly, llm’s are useless for the imaginative part too. Shucks eh.
I share this particular cynicism.
I have a list of ideas a mile long that gets longer every day, and LLMs help me burn through that list significantly faster.
However, the older I get, the more distraught I get that most people I meet "IRL" are simply not sitting on a list of problems they simply lack time to solve. I have... a lot of emotions around this, but it seems to be the norm.
If someone doesn't see or experience problems and intuitively start working out how they would fix them if they only had time, the notion that they could pair program effectively ideas that they didn't previously have with an LLM is absurd.
Also one of those with a mile-long ideas list that I can finally now burn through. I gotta say, it feels good!
Yeah and frankly the innovation would occur irrespective of llm’s.
Would it be harder? Sure. And perhaps the difficulty adds an additional cost of passion being a necessary condition to embark on the innovation. Passion leads to really good stuff.
My personal fear is we get landfill sites of junk software produced. To some extent it should be costly to convert an idea to a concept - the cost being thinking carefully so what you put out there is somewhat legible.
There really isn't much profit incentive actually, as everyone has access to the same capabilities now. It'd be like trying to sell ice to Eskimos.
Most businesses do not have the capacity to use LLMs to produce software. If you have an idea that you can create into real high quality software that there is a demand for, then you should absolutely do it.
This is probably my favorite gain from AI assisted coding: the bar for "who cares about this app" has dropped to a minimum of 1 to make sense. I recently built an app for grocery shopping that is specific to how and where I shop, would be useless to anyone other than my wife. Took me 20 minutes. This is the next frontier: I have a random manual process I do every week, I'll write an app that does it for me.
More than that. Building a throwaway-transient-single-use web app for a single annoying use kind of makes sense now, sometimes.
I had to create a bunch of GitHub and Linear apps. Without me even asking Codex whipped up a web page and a local server to set them up, collecting the OAuth credentials, and forward them to the actual app.
Took two minutes, I used it to set up the apps in three clicks each, and then just deleted the thing.
Code as transient disposable artifacts.
I posted it recently, but now this works differently https://xkcd.com/1205/
You can get a throw away app in 5 mins, before I wouldn't even bother.
Same energy here. I was sitting on 50+ .env files across various projects with plaintext API keys and it always bothered me but never enough to actually fix it. AI dropped the effort enough that I just had a dedicated agent run at it for a few days — kept making iterations while I was using it day to day until it landed on a pretty solid Touch ID-based setup.
This mix of doing my main work on complex stuff (healthcare) with heavy AI input, and then having 1-2 agents building lighter tools on the side, has been surprisingly effective.
Even if it’s only useful to you it would be super educational to see your prompts and the result.
What exactly were you bale to build in 20 minutes?
Me, and photo editor tool to semi-automate a task of digitizing a few dozen badly scanned old physical photos for a family photo book. Needed something that could auto-straighen and auto-crop the photos with ability to quickly make manual adjustments, Gemini single-shotted me a working app that, after few minutes of back-and-forth as I used it and complained about the process, gained full four-point cropping (arbitrary lines) with snapping to lines detected in image content for minute adjustments.
Before that, it single-shot an app for me where I can copy-paste a table (or a subsection of it) from Excel and print it out perfectly aligned on label sticker paper; it does instantly what used to take me an hour each time, when I had to fight Microsoft Word (mail merge) and my Canon printer's settings to get the text properly aligned on labels, and not cut off because something along the way decided to scale content or add margins or such.
Neither of these tools is immediately usable for others. They're not meant to, and that's fine.
My buddy and I are writing our own CRUD web app to track our gaming. I was looking at a ticketing system to use for us to just track bug fixes and improvements. Nothing I found was simple enough or easy enough to warrant installing it.
I vibe'd a basic ticketing system in just under an hour that does what we need. So not 20 mins, but more like 45-60.
I built a small app to emit a 15 kHz beep (that most adults can't hear) every ten minutes, so I can keep time when I'm getting a massage. It took ten minutes, really, but I guess it's in the spirit of the question.
For 20 minutes of time, I had a simple TTS/STT app that allows me to have a voice conversation with my AI assistant.
That's fine and all, but how much are you ready to pay to Anthropic and OpenAI to be able to do this? Like, is it worth 100 bucks a month for you to have your own shopping app?
It's easily worth the <$1 in tokens from a Chinese model. You don't need frontier reasoning capabilities to make a personalized grocery list app.
That is an excellent question. For me the answer is yes, but I'm unusual.
It's not worth 100 bucks a month for me to have my own shopping app, but maybe it's worth 100 bucks a month to have ready access to a software garden hose that I can use if I want to spew out whatever stupid app comes to my mind this morning.
I'd rather not pay monthly for something (like water) that I'm turning on and off and may not even need for weeks. But paying per-liter is currently more expensive so that's what we currently do.
I think the future is going to be local models running on powerful GPUs that you have on-prem or in your homelab, so you don't need your wallet perpetually tethered to a company just to turn the hose on for a few minutes.
Haha great. I guess my wider point is that most people won't be ready to pay for it, and in the end there will be only two ways to monetize for OpenAI et al: Ads or B2B. And B2B will only work if they invest a lot into sales or if the business owners see real productivity gains one the hype has died one.
I've been getting close to that myself, I've been using VSCode + Claude Code as my "control plane" for a bunch of projects but the current interface is getting unwieldly. I've tried superset + conductor and those have some improvements but are opinionated towards a specific set of workflows.
I do think there would be value in sharing your setup at some point if you get around to it, I think a lot of builders are in the same boat and we're all trying to figure out what the right interface for this is (or at least right for us personally).
I would still be interested even if my personal workflow is different. These things can be very inspirational!
This sounds chaotic and fun.
Sounds more like satire.
I am easily caught by satire and I have a weakness for buttons.
> I deleted vscode and replaced with a hyper personal dashboard that combines information from everywhere.
Emacs with Hyperbole[0]?
[0]: https://www.gnu.org/software/hyperbole/
You can't mention Hyperbole and not say how you use it. I did not get past the "include the occasional action button in org-mode" phase.
actually the rules say that no one can ever explain what Hyperbole is for
... how did that replace vscode?
Do you never open a code editor?
Kind of. I'm finding that my terminal window in VSCode went from being at the bottom 1/3rd of my screen to filling the whole screen a lot of the time, replacing the code editor window. If AI is writing all of your code for you based on your chat session, a lot of editing capabilities aren't needed as much. While I wouldn't want to get rid of it entirely, I'd say an AI-native IDE would deemphasize code editing in favor of higher-level controls.
Wdym by "it does something interesting with Claude code I can't do programmatically"?
I'm guessing it's not a hard coded function, the button invokes. Instead it spawns a claude code session with perhaps some oredefined prompts, maybe attaches logs, and let's claude code "go wild". In that sense the button's effect wouldn't be programmatical, it would be nondeterministic.
Not OP, just guessing.
I have had the thought to write little "programs" in text or markdown for things which would just a chore to maintain as a traditional program. (I guess we call them "skills" now?) Think scraping a page which might change its output a bit every so often. It the volume or cadence is low, it may not be worth it to create a real program to do it.
It means he has a girlfriend. And she goes to a different school. In Canada. You've never heard of it.
Perfect analogy actually
Well, I’m sharing it. If someone wants an early preview or to work w me on this, the calendly link is on the site:
https://safebots.ai
But it requires A LOT of work to make sure it is actually safe for people and organizations. And no, an .md file saying “PLEASE DONT PWN ME, KTHX” isn’t it at all. “Alignment” is only part of the equation.
If you’re not afraid to dive into rabbitholes, here is how it works: http://community.safebots.ai/t/layer-4-browser-extensions-pe...
Sorry, I'm not sure how this relates to the content of the article. Sounds like an interesting experience, but this is an analysis of the Python ecosystem pre+post ChatGPT.
What if this is just telling us that much of the coding being done in the world, or knowledge work in general, is just busy work? Just because you double the capacity of knowledge workers doesn't mean you double the amount of useful output. Maybe we have never been limited by our capacity to produce, but by our ability to come up with good ideas and socially coordinate ourselves around the best ones.
That would imply that the majority of this AI hype is just additional bluster, and likely wouldn't improve anything in any significant manner.
AI makes the first 90% of writing an app super easy and the last 10% way harder because you have all the subtle issues of a big codebase but none of the familiarity. Most people give up there.
I spent about a week doing an "experiment" greenfield app. I saw 4 types of issues:
0. It runs way too fast and far ahead. You need to slow it down, force planning only and explicitly present a multi-step (i.e. numbered plan) and say "we'll do #1 first, then do the rest in future steps".
take-away: This is likely solved with experience and changing how I work - or maybe caring less? The problem is the model can produce much faster than you can consume, but it runs down dead ends that destroy YOUR context. I think if you were running a bunch of autonomous agents this would be less noticeable, but impact 1-3 negatively and get very expensive.
1. lots of "just plain wrong" details. You catch this developing or testing because it doesn't work, or you know from experience it's wrong just by looking at it. Or you've already corrected it and need to point out the previous context.
take-away: If you were vibe coding you'd solve all these eventually. Addressing #0 with "MORE AI" would probably help (i.e. AI to play/validate, etc).
2. Serious runtime issues that are not necessarily bugs. Examples: it made a lot of client-side API endpoints public that didn't even need to exist, or at least needed to be scoped to the current auth. It missed basic filtering and SQL clauses that constrained data. It hardcoded important data (but not necessarily secrets) like ports, etc. It made assumptions that worked fine in development but could be big issues in public.
take-away: AI starts to build traps here. Vibe coders are in big trouble because everything works but that's not really the end goal. Problems could range from 3am downtime call-outs to getting your infrastructure owned or data breaches. More serious: experienced devs who go all-in on autonomous coding might be three months from their last manual code review and be in the same position as a vibe coder. You'd need a week or more to onboard and figure out what was going on, and fix it, which is probably too late.
3. It made (at least) one huge architectural mistake (this is a pretty simple project so I'm not sure there's space for more). I saw it coming but kept going in the spirit of my experiment.
take-away: TBD. I'm going to try and use AI to refactor this, but it is non trivial. It could take as long as the initial app did to fix. If you followed the current pro-AI narrative you'd only notice it when your app started to intermittently fail - or you got you cloud provider's bill.
I'm a product manager, and a lot of the things I see people do wrong is because they don't have any product management experience. It takes quite a bit of work to develop a really good theory of what should be in your functional spec. Edge cases come up all the time in real software engineering, and often handling all those cases is spread across multiple engineers. A good product manager has a view of all of it, expects many of those issues from the agent, and plans for coaching it through them.
Poe’s law is strong with this one
I think that's an incredibly reductionist and sarcastic take. I'm also in Product, but was an engineer for over a decade prior. I find that having strong structured functional specifications and a good holistic understanding of the solution you're trying to build goes a long way with AI tooling. Just like any software project, eliminating false starts and getting a clear set of requirements up front can minimize engineering time required to complete something, as long as things don't change in the middle. When your cycle time is an afternoon instead of two quarters, that type of up front investment pays off much better.
I still think AI tooling is lacking, but you can get significantly better results by structuring your plans appropriately.
Tell me more! I'm trying to figure out how you got that.
And as we all know, the first 90% of writing an app takes the first 90% of the time, and the last 10% takes the other 90% of the time.
The 90-90 rule may need an update for a POST-LLM world
"The first 90% of the code accounts for the first 9% of the development time. The remaining 10% of the code accounts for the other 9000% of the development time"
Comprehension Debt
https://addyosmani.com/blog/comprehension-debt/
Well put. And that last 10% was always the hardest part, and now it’s almost impossible because emotionally you’re even less prepared for the slog ahead.
Agree. I’ve also noticed that feature creep tends to increase when AI is writing most of the code.
So the way is to read every line of code along the way.
I think this article is making a pretty big assumption: that people making things with AI are also going to be publishing them. And that's just the opposite of what should be expected, for the general case.
Like I've been making things, and making changes to things, but I haven't published any of that because, well they're pretty specific to my needs. There are also things which I won't consider publishing for now, even if generally useful because, well the moat has moved from execution effort to ideas, and we all want to maintain some kind of moat to boost our market value (while there's still one). Everyone has reasonable access to the same capabilities now, so everyone can reasonably make what they need according to their exact specs easily, quickly and cheaply.
So while there are many things being made with AI, there is ever-decreasing reasons to publish most of it. We're in an era of highly personalized software, which just isn't worth generalizing and sharing as the effort is now greater than creating from scratch or modifying something already close enough.
> I think this article is making a pretty big assumption: that people making things with AI are also going to be publishing them. And that's just the opposite of what should be expected, for the general case.
The premise is that AI has already fundamentally changed the nature of software engineering. Not some specific, personal use case, but that everything has changed and that if you're not embracing these tools, you'll perish. In light of this, I don't think your rebuttal works. We should be seeing evidence of meaningful AI contributions all over the place.
Hard agree. A 10x productivity increase would bleed outside the personal or internal use cases, even without effort.
Agree. There's also a weird ideological thing in open source right now, where any AI must be AI slop, and no AI is the only solution. That has strongly disincentivized legitimate contributions from people. I have to imagine that's having an impact.
There's a very real problem of low effort AI slop, but throwing out the baby with the bathwater is not the solution.
That said, I do kind of wonder if the old model of open source just isn't very good in the AI era. Maybe when AI gets a lot better, but for now it does take real human effort to review and test. If contributors were reviewing and testing like they should be doing, it wouldn't be an issue, but far too many people just run AI and don't even look at it before sending the PR. It's not the maintainers job to do all the review and test of a low-effort push. That's not fair to them, and even discarding that it's a terrible model for software that you share with anyone else.
> where any AI must be AI slop, and no AI is the only solution
Yep, also a huge factor. Why publish something you built with an AI assistant if you know it's going to be immediately dunked on not because the quality may be questionable, but because someone sees an em-dash, or an AI coauthor, and immediately goes on a warpath? Heck I commented[0] on the attitude just a few hours ago. I find it really irritating.
[0] https://github.com/duriantaco/fyn/issues/4#issuecomment-4117...
You know what else strongly disincentivized legitimate contributions from people?
Having your code snatched and its copyright disregarded, to the benefit of some rando LLM vendor. People can just press "pause" and wait until they see whether they fuel something that brings joy to the world. (Which it might in the end. Or not.)
For sure, that's legit too. I've had to grapple with that feeling personally. I didn't get to a great place, other than hoping that AI is democratized enough that it can benefit humanity. When I introspected deep enough, I realized I contributed to open source for two reasons, nearly equally:
1. To benefit myself with features/projects
2. To benefit others with my work
1 by itself would mean no bothering with PR, modifications, etc. It's way easier to hoard your changes than to go through the effort getting them merged upstream. 2 by itself isn't enough motivation to spend the effort getting up to speed on the codebase, testing, etc. Together though, it's powerful motivation for me.
I have to remind myself that both things are a net positive with AI training on my stuff. It's certainly not all pros (there's a lot of cons with AI too), but on the whole I think we're headed for a good destination, assuming open models continue to progress. If it ends up with winner-takes-all Anthropic or OpenAI, then that changes my calculus and will probably really piss me off. Luckily I've gotten positive value back from those companies, even considering having to pay for it.
Been going back and forth on this with open source tools I've built. The training data argument is valid, but honestly the more immediate version of the same problem is that someone can just take your repo, feed it to an agent, and have their own fork in an afternoon.
The moat used to be effort, nobody wants to rewrite this from scratch (especially when it's free). What's left is actually understanding why the thing works the way it does. Not sure that's enough to sustain open source long-term? I guess we all have to get used to it?
> but honestly the more immediate version of the same problem is that someone can just take your repo, feed it to an agent, and have their own fork in an afternoon.
Indeed, I've got a few applications I've built or contributed too that are (A)?GPL, and for those I do worry about this AI washing technique. For libraries that are MIT or permissive anyway, I don't really care. (I default to *GPL for applications, MIT/Apache/etc for libraries)
The article measures the wrong thing. PyPI package creation is a terrible proxy for AI-assisted software output because packages are published for reuse by others, which requires documentation, API design, and maintenance commitments that AI doesn't help with much.
The real output is happening in private repos, internal tools, and single-purpose apps that never get published anywhere. I've been building a writing app as a side project. AI got me from zero to a working PWA with offline support, Stripe integration, and 56 SEO landing pages in about 6 weeks of part-time work. Pre-AI that's easily a 6-month project for one person.
But I'm never going to publish it as a PyPI package. It's a deployed web app. The productivity gain is real, it just doesn't show up in the datasets this article is looking at.
The iOS App Store submission data (24% increase) that someone linked in the comments is a much better signal. That's where the output is actually landing.
Serious question: Did you use AI to write this or do you just sound like an LLM after having used them so much?
This remains me so much of the .COM bubble in 2000. A lot of clueless companies thought that they just need to “do internet” without any further understanding or strategy. They burned a ton of money and got nothing out of it. Other companies understood that the internet is an enabling technology that can support a lot of business processes. So they quietly improved their business with the help of the internet.
I see the same with AI. Some companies will use AI quietly and productively without much fuzz. Others are just using it as a marketing tool or an ego trip by execs but no real understanding.
Yep and the LLM tools are giving flasbacks to the Frontpage/DreanWeaver to geocities ipeline for building the sites.
Still early innings but i bet this plays out the same way - not everyone will have the time sink to vibecode all the software workflows they require.Maintainance iwse and security wise holes will still remain for the personaly non tech user. Devs and orgs will probably limit the usage to a helper sidecar rather than the hyped 100% LLM generated apps. Reminds me about the hype
Not sure that I'd look at python package stats to build this particular argument on.
First, I find that I'm using a lot fewer libraries in general because I am less constrained by the mental models imposed by library authors upon what I'm actually trying to do. Libraries are often heavy and by nature abstract low-level calls from API. These days, I'm far more likely to have 2-3 functions that make those low-level calls directly without any conceptual baggage.
Second, I am generalizing but a reasonable assertion can be made that publishing a package is implicitly launching an open source project, however small in scope or audience. Running OSS projects is a) extremely demanding b) a lot of pain for questionable reward. When you put something into the universe you're taking a non-zero amount of responsibility for it, even just reputationally. Maintainers burn out all of the time, and not everyone is signed up for that. I don't think there's going to be anything remotely like a 1:1 Venn for LLM use and package publishing.
I would counter-argue that in most cases, there might already be too many libraries for everything under the sun. Consolidation around the libraries that are genuinely amazing is not a terrible thing.
Third, one of the most recurring sentiments in these sorts of threads is that people are finally able to work through the long lists of ideas they had but would have never otherwise gotten around to. Some of those ideas might have legs as a product or OSS project, but a lot of them are going to be thought experiments or solve problems for the person writing them, and IMO that's a W not an L.
Fourth, once most devs are past the "vibe" party trick phase of LLM adoption, they are less likely to squat out entire projects and far, far more likely to return to doing all of the things that they were doing before; just doing them faster and with less typing up-front.
In other words, don't think project-level. Successful LLM use cases are commit-level.
I'm not convinced that PyPI is the right metric to use to answer this question. Some (admittedly anecdotal) observations:
1) I'm a former SWE in a business role at a small-market publishing company. I've used Claude Code to automate boring processes that previously consumed weeks of our ops and finance teams' time per year. These aren't technically advanced, but previously would have required in-house dev talent that would not have been within reach of small businesses. I wouldn't have had the time to code these things on my own, but with AI assistance the time investment is greatly reduced (and mostly focused on QA). The only needle moved here is on a private Github repo, but it's real shipped code with small but direct impact.
2) I used to often find myself writing simple Perl wrappers to various APIs for personal or work use. I'd submit these to CPAN (Perl's equivalent to PyPI) in case anyone else could use them to save the 30-60 minutes of work involved. These days I don't bother -- most AI tools can build these in a matter of seconds; publishing them to CPAN or even Github now feels like unnecessary cruft, especially when they're likely to go without active maintenance. So, my LOC published to public repos is down, even though the amount of software produced is the same. It's just that some of that software has become less useful to the world writ large.
3) The code that's possible to ship quickly with pure AI (vibe coding) is by definition not the kind of reusable code you'd want to distribute on PyPI. So, I'd expect that any productivity impact from AI on OSS that's designed to be reusable would be come very slowly, versus "hockey stick" impact.
Does the data not support a 2X increase in packages?
Pre-ChatGPT, in ~2020, there were about 5,000 new packages per month. Starting in 2025 (the actual year agents took off), there is a clear uptick in packages that is consistently about 10,000 or 2X the pre-ChatGPT era.
In general, the rate of increase is on a clear exponential. So while we might not see a step change in productivity, there comes a point where the average developer is in fact 10X productive than before. It just doesn't feel so crazy because it can about in discrete 5% boosts.
I also disagree with the dataset being a good indicator of productivity. I wouldn't actually suspect the number of packages or the frequency of updates to track closely with productivity. My first order guess would that AI would actually be deflationary. Why spend the time to open source something that AI can gen up for anyone on a case by case basis specific to the project. it takes a certain level of dedication and passion for a person to open source a project and if the AI just made it for them, then they haven't actually made the investment of their time and effort to make them feel justified in publishing the package.
The metrics I would expect to go up are actually the size of codebases, the number of forks of projects that create hyper customized versions of tools and libraries, and other metrics like that.
Overall, I'd predict AI is deflationary on the number of products that exist. If AI removes the friction involved with just making a custom solution, then the amount of demand for middleman software should actually fall as products vertically integrate and reduce dependencies.
I have published 4 open source projects thanks to the productivity boost from AI. No apps though, just things I needed in my line of work.
But I have been absolutely flooded with trailers for new and upcoming indie games. And at least one indie developer has admitted that certain parts of their game had used the aide of AI.
I also noticed sometimes when I think of writing something, I ask AI first if it exists, and AI throws up some link and when I check the link it says "made with <some AI>".
So I'm not sure what author is trying to say here but I definitely feel like I am noticing a rise in software output due to AI.
But with that said, I also am noticing the burden of taking care of those open source projects. Sometimes it feels like I took on a 2nd job.
I think a lot of software is being produced with AI and going unnoticed, they don't all end up on the front page of HN for harassing developers.
Claude Code was released for general use in May 2025. It's only March.
Also using PyPI as a benchmark is incredibly myopic. Github's 2025 Octoverse[0] is more informative. In that report, you can see a clear inflection point in total users[1] and total open source contributions[2].
The report also notes:
> In 2025, 81.5% of contributions happened in private repositories, while 63% of all repositories were public
[0]: https://github.blog/news-insights/octoverse/octoverse-a-new-...
[1]: https://github.blog/wp-content/uploads/2025/10/octoverse-202...
[2]: https://github.blog/wp-content/uploads/2025/10/octoverse-202...
> Claude Code was released for general use in May 2025. It's only March.
Detractors of AI are often accused of moving the goalposts, but I think your comment is guilty of the same. Before Claude Code, we had Cursor, Github Copilot, and more. Each of these was purportedly revolutionizing software engineering.
Further, the core claim for AI coding is that it lets you ship code 10x or 100x faster. So why do we need to wait years to see the result? Shouldn't there be an explosion in every type of software imaginable?
> Detractors of AI are often accused of moving the goalpost, but I think your comment is guilty of the same. Before Claude Code, we had Cursor, Github Copilot, and more. Each of these war purportedly revolutionizing software engineering.
What's sauce for the goose is sauce for the gander. If you make that argument that 'I don't believe in kinks or discontinuities in code release due to AI, because so many AI coding systems have come out incrementally since 2020', then OP does provide strong evidence for an AI acceleration - the smooth exponential!
Amongst people who use AI regularly, November 2025 is widely regarded as a watershed moment. Opus 4.5 was head and shoulders above anything that came before it. It marked the first time my previously AI-disliker friends begrudgingly came to accept that it may actually be useful.
The thesis has it backwards. We will see fewer published/downloaded apps/packages as people rely on others less. I'm not sure we're quite there yet but I'm increasingly likely to spend a few minutes giving an LLM a chance to make a tool I need instead of sifting through sketchy and dodgy websites for some slightly obscure functionality. I use fewer ad-heavy sites that for converting a one text file format to another.
Personally, I see the paid or adware software market shrinking, not growing, as a testament to the success of LLMs in coding.
Ya maybe this. I’ve found some work at the “tool level”. I’m not a programmer, just did RLHF for a few years and AI has helped me make some tools such as a way to scrape and export to excel 35,000 contacts at a company for marketing purposes. Things like that. Yes I know libraries exist and someone who is already a programmer could do this, but also there’s some interesting logic in how to avoid duplicates and interact with modern websites that was impractical for me. And maybe this job is too small for a real programmer.
There are many small, different, and one-time tasks that don’t fit full blown apps. Which I would characterize an AI building a novel app as building a house out of random bits of lumber. It will work but will have no cohesive process and sounds like a nightmare.
I won't make any claims as to the Python ecosystem and why there is no effect seen here (and I suppose no effect seen of the Internet on productivity) but one thing that is entirely normal for me now is that I never see the need to open-source anything. I also don't use many new open-source projects. I can usually command Claude Code to build a highly idiosyncratic thing of greater utility. The README.md is a good source of feature inspiration but there are many packages I simply don't bother using any more.
Besides, it's working for me. If it isn't working for others I don't want to convince them of anything. I do want to hear from other people for whom it's working, though, so I'm happy to share when things work for me.
Coding assistants/agents/claws whatever the current trend is are over-hyped but also quite useful in good hands.
But the mistake is to expect a huge productivity boost.
This is highly related to Amdahl's law, also The Mythical Man-Month.
Some tasks can be accomplished so fast that it seems magical, but the entire process is still very serial, architecture design and debug are pretty weak on the AI side.
I fail to see why the author thinks Python packages are a good proxy for AI driven/built code. I've built a number of projects with AI, but I haven't created any new packages.
It's like looking at tire sales to wonder about where the EV cars are.
Nitpick, but tire sales are a good proxy for determining where EVs are, since the owners are usually suckered into buying special "EV" tires.
I believe EVs also wear tires out faster (because they are heavier), so they need more frequent replacement.
Interesting, didn't know that
This is addressed, though not quantified (I suppose because theres no central repository for that), in the introduction. To use your analogy, the author heard EV sales were through the roof, couldnt find any evidence that more EV's were actually on the road, so looked at tire sales to see if the answer was in there.
They're private, that's the beauty. Code is so cheap now, we can ween ourselves off massive dependency chains.
200 years ago text was much more expensive, and more people memorized sayings and poems and quotations. Now text is cheap, and we rarely quote.
Thoughts: 1. Some hype-types may have been effusive about AI-assisted coding since ChatGPT, but IMO the commonly agreed paradigm shift was claude code, and especially 4.5, very very recent. 2. Anchoring biases in reaction to hype is still letting one's perspective be defined by hype. Yes the cursor post is a joke, but leading with that is a strawman. This article does not aim to take it's subject seriously, IMO. 3. While I agree the hype is currently at comical levels, the utility of the current LLMs is obvious, and reasons for "skilled" usage not being easily quantifiable are also obvious.
IE, using agents to iterate through many possible approaches, spike out migrations, etc might save a project a year of misadventures, re-designs, etc, but that productivity gain _subtracts_ the intermediate versions that _didn't_ end up being shipped.
As others have mentioned, I think yak-shaving is now way more automated. IE, If I want to take a new terminal for a spin, throw together a devtool to help me think about a specific problem better, etc, I can do it with very low friction. So "personal" productivity is way higher.
> the utility of the current LLMs is obvious
In that they obviously have no real utility, sure. There hasn't been a paradigm shift, they still suck at programming, and anyone trying to tell you otherwise almost certainly has something to sell you.
Based on my direct experience I find this remaining commonality of this opinion surprising, at least with regards to opus in claude code. I'm not as extreme as some who think we can/should avoid touching code or w/e but especially in exploratory contexts and debugging I find them extremely useful.
Maybe I should have said "obvious to me," but I guess I just struggle to see how a serious crack at using modern opus in claude code doesn't make it obvious at this point.
I'd really recommend trying the "spike out a self-contained minimal version of this rearchitecture/migration and troubleshoot it iteratively until it works, then make a report on findings" use-case for anyone that hasn't had luck with them thus far and is serious about trying to reach conclusions based on direct experience.
I AI coded an entire platform for my work. It works great for me. I also recognize that this is not something I want to make into a commercial product because it was so easy that there's just no value.
I think this might be more of an comment on software as a business than AI not coding good apps.
I’m not a developer by trade. I’ve screwed around with some programming classes when I was in school, and have written some widely used but highly specific scripts related to my work, but I’ve never been a capital-D developer.
In the last few months, Gemini (and I) have written for highly personal, very niche apps that are perfect for my needs, but I would never dream of releasing. Things like cataloguing and searching my departed mom‘s recipe cards, or a text message based budget tracker for my wife and I to share.
These things would never be released or available as of source or commercial applications in the way that I wanted them, and it took me less time to have them built with AI then it would have taken me to Research existing alternatives and adapt my workflow/use case to fit whatever I found.
So yeah, there are more apps but I would venture to say you’ll never see most of them…
Is this the best way to measure this? I think the biggest adopters of AI coding has been companies who are building features on existing apps, not building new apps entirely. Wouldn't it make more sense looking at how quickly teams are able to build and ship within companies?
It seems like all tech executives are saying they are seeing big increases in productivity among engineering teams. Of course everyone says they're just [hyping, excusing layoffs, overhired in 2020, etc], but this would be the most relevant metric to look at I think.
The models are not well trained on bringing products to market.
And even “product engineers” often do not have experience going from zero to post sales support on a saas on their own.
It is a skill set of its own to make product decisions and not only release but stick with it after the thing is not immediately successful.
The ability to get some other idea going quickly with AI actually works against the habits needed to tough through the valley(s).
AI does make me more productive. At least until the stage of getting my idea to the "working prototype stage". But in my personal experience, no one has been realistically able to get to the 10x level that a lot of people claim to have achieved with LLMs.
Yes, you do produce more code. But LoC produced is never a healthy metric. Reviewing the LLM generated code, polishing the result and getting it to production-level quality still very much requires a human-in-the-loop with dedicated time and effort.
On the other hand, people who vibe code and claims to be 10x productive, who produces numerous PRs with large diffs usually bog down the overall productivity of teams by requiring tenuous code reviews.
Some of us are forced to fast-track this review process so as to not slow down these "star developers" which leads to the slow erosion in overall code quality which in my opinion would more than offset the productivity gains from using the AI tools in the first place.
Please, be patient. Wrangling AI agents, writing and rewriting prompts, waiting for the start of another month because tokens ran out - there are so many challenges here, you cannot expect everyone to ship an app a day or something.
Vibe coding is actually a brilliant MLM scheme: people buy tokens to generate apps that re-sell tokens (99% of those apps are AI-something).
How do packages measure anything? This is a biased sample. Average user of AI/developer would not ever in their life make a package or any open source contribution. They would probably work on the proprietary software. Not to say that conclusions are wrong though.
This is going to cause people to react, but I think those of us that truly love opensource don't push AI generated code upstream because we know it's just not ready for use beyond agentic use. It's just not robust for alot of use common use cases because the code produces things that are hyper hardcoded by default, and the bugs are so basic, i doubt any developer that actually cared would push something so shamefully sloppy upstream with their name on it.
The tools for generating AI code aren't yet capable of producing code that is decent enough for general purpose use cases, with good robust tests, and clean and quality.
Where are they? Well they aren't being uploaded to PyPI. 90% of the "AI apps" one-off scripts that get used by exactly one person and thrown away. The rest are too proprietary, too personal, or too weird to share.
I think part of the mismatch is that people are still looking for “more apps” as the output metric.
A lot of the real value shows up as workflow compression instead. Internal tools, one-off automations, bespoke research flows, coding helpers, things that would never have justified becoming a product in the first place.
There is one AI app that is not just an app it is your personal assistant which will work on your assign task and give you the results you can connect it with your social media it will deploy in just 3 single step also has free trial try it now becuase your saas needs an personal assistant that work on behalf of you Give it try:https://clawsifyai.com/
There are actually a lot of new startups coming out with agentic workflows, and they're probably moving fast. But to your point, there's probably still a lot of friction that keeps the average person/dev from launching new companies.
Isn't most of the positive impact not going to be "new projects" but the relative strength of the ideas that make it into the codebase? Which is almost impossible to measure. You know, the bigger ideas that were put off before and are now more tractable.
Well, it's kind of like asking about streaming media. If anyone can have their own "tv show" or anyone can be their own "music producer" then the ratios are so radically altered vis-a-vis content/attention calculation. The question has never been "more means more success stories" because musicians make $.000001 per stream, so even if they stream millions of songs ... you get the point. So surely there are good apps, but the accompanying deluge makes them seem less significant.
The reason why the release cadence of apps about AI has increased presumably reflects the simple facts that
a) there are likely many more active, eager contributors all of a sudden, and
b) there's suddenly a huge amount of new papers published every week about algorithms and techniques that said contributors then eagerly implement (usually of dubious benefit).
More cynically, one might also hypothesize that
c) code quality has dropped, so more frequent releases are required to fix broken programs.
I’ve done a event ticket system that’s in production. Stripe integration, resend for mailing and a scan app to scan tickets. It’s for my own club but it’s been working quite well. Took about 80 hours from inception to live with a focus on testing.
I’ve done some experiments with reading gedcom files, and I think I’m quite close to a demoable version of a genealogy app.
Biggest thing is a tool for remotely working musicians. It’s about 10000 lines of well written rust, it is a demoable state and I wish I could work more on it but I just started a new job.
But yeah, this wouldn’t have been possible if I hadn’t been a very experienced dev who knows how to get things live. Also I’ve found a way to work with LLMs that works for me, I can quickly steer the process in the right way and I understand the code thats written, again it’s possible that a lot of real experience is needed for this.
Could you not have downloaded one of the hundreds of Open Source event systems and configured it in less time?
Possibly, but probably not in less time and the point was partly learn to use agentic coding and also having it do exactly what I wanted.
Don't get me wrong: there are real productivity gains to be had, but the reality is that building small one-offs and personal tools is not the same thing as building, operationalizing, and maintaining a large system used by paying customers and performing critical business transactions.
A lot of devs are surrendering their critical thinking facilities to coding agents now. This is part of why the hype has to exist: to convince devs, teams, and leaders that they are "falling behind". Hand over more of your attention (and $$$) to the model providers, create the dependency, shut off your critical thinking, and the loop manifests itself.
The providers are no different from doctors pushing OxyContin in this sense; make teams dependent on the product. The more they use the product, the more they build a dependency. Junior and mid-career devs have their growth curves fully stunted and become entirely reliant on the LLM to even perform basic functions. Leaders believe the hype and lay off teams and replace them with agents, mistaking speed for velocity. The more slop a team codes with AI, the more they become reliant on AI to maintain the codebase because now no one understands it. What do you do now? Double down; more AI! Of course, the answer is an AI code reviewer!. Nothing that more tokens can't solve.
I work with a team that is heavily, heavily using AI and I'm building much of the supporting infrastructure to make this work. But what's clear is that while there are productivity gains to be had, a lot of it is also just hype to keep the $$$ flowing.
People will dismiss this critical-thinking shutoff loop as doomer conspiracy, but it's literally the strategy that ai founders describe in interviews. Also people somehow can't or don't remember that uber was almost free when it came out and the press ran endless articles about the "end of car ownership", but replacing your car with uber today would be 10x more expensive. Ai companies are in a mad dash to kill the software industry so that they can "commoditize intelligence". There will be thousands of dead software startups that pile slop on slop until they run out of vc funny-money.
I don't think people are using AI to create new dependencies that they're then submitting to open source package managers (which is what this shows)
This is more useful for discussing what kind of projects AI is being used for than whether it's being used.
Wouldn't the apps go into the Apple store and Android play? I guess looking at python packages is valid, but I don't think it's the first thing someone thinks to target with vibe coding. And many apps go to be websites, a website never tells me much about how it is made as a user of the site.
Steam game releases seem to be up maybe a bit more than expected. [1]
And you can even see the number of new games that disclosed using generative AI (~21% in 2025). [2]
And that's probably significantly undercounting because I doubt everyone voluntarily discloses when they use tools like Claude Code (and it's not clear how much Valve cares about code-assistance). [3]
Also no one is buying or playing a lot of these games.
[1] https://steamdb.info/stats/releases/
[2] https://steamdb.info/stats/releases/?tagid=1368160
[3] https://store.steampowered.com/news/group/4145017/view/38624...
To be fair, those markets are dominated by entrenched market leaders.
This is just counting pypi packages. Why would I go to the effort of publishing a library or cli tool that took me ten minutes to create? Especially in an environment where open source contributions from strangers are useless. If anything I'd expect useful AI to reduce the number of new pypi packages.
I am learning music. I used codex to create a native metronome app, a circle of fifths app, a practice journal app. I try to build a native app alternatives.
I have no plans of publishing them or making the open source, so it will not be a part of this metric. I believe others are doing this too.
I'd take this info with a grain of salt. You have to understand how new some of these developments are. It's only been a couple of months since we hit the opus 4.5+ threshold. I created 4 react packages for kicks in a weekend: https://www.hackyexperiments.com/blog/shipping-react-librari...
A bit tangential to the article themes, but I feel in some workplaces that engineering velocity has gone up while product cycles and agile processes have stayed the same. People end up churning tickets faster and working less, while general productivity has not changed.
Of course these are specific workplaces designed around moving tickets on a board, not high-agentic, fast-moving startups or independent projects—but they might represent a lot of the developer workforce.
I also know this is not everyone's experience and probably a rare favorable outcome of productivity gain captured by a worker that is not and won't stay the norm.
Looking at Python packages, or any developer-facing form of software, is not a good indicator of AI-based production. The key benefit of AI development is that our focus moves up a few layers of abstraction, allowing us to focus on real-world solutions. Instead of measuring Github, you need to measure feature releases, internal tools created, single-user applications built for a single niche use case.
Measuring python packages to indicate AI-based production is like measuring saw production to measure the effectiveness of the steam engine. You need to look at houses and communities being built, not the tools.
One problem with a lot of the skepticism around AI produced software is that it focuses on existing ways of packaging and delivering software. PyPi packages are one example, shipping “apps” another.
While it’s interesting to see that in open source software the increase is not dramatic, this ignores however many people are now gen-coding software they will never publish just for them, or which winds up on hosting platforms like Replit.
Even taking the “we’re all 100x more efficient at writing code” argument at face value… there’s still all of the product/market fit, marketing, sales, etc “schlep” which is very much non-trivial.
Are there any agentic sales and marketing offerings?
Because being able to reliably hand off that part of the value chain to an agent would close a real gap. (Not sure this can be done in reality)
I've been vibe-coding a Plex music player app for MacOS and iOS. (I don't like PlexAmp) I've got to the point where they are the apps I use for listening to music. But they are really just in an alpha/beta state and I'm having a pretty hard time getting past that. The last few weeks have felt like I'm playing wack-a-mole with bugs and issues. It's definitely not at the point others will be willing to use it as their daily app. I'm having to decide now if I keep wanting to put time into it. The vibe-coding isn't as fun when you're just fixing bugs.
Genuinely curious: are you actually vibe coding (as in not writing or looking at the code) or are you pair programming with a current model (eg. Sonnet or Opus) using plan -> agent -> debug loops in something like Cursor?
I haven’t written or looked at a single line of code. I do use plan though, and have a technical background but haven’t meaningfully coded in 15 years
- this would be much more insightful if the author takes the number of submissions to producthunt and the top 10 saas directories as the measure to see how many new apps were created pre AI and post AI era
- product hunt or app sumo is something i believe everyone tries to get a submission to which would truly measure how many new apps are we having per month these days
Internally, we've created such good debugging tools that can aggregate a lot from a lot of sources. We've yet to address the quality of vibecoded critical applications so they aren't merged, but one off tools for incall,alert debugging and internal workflows has skyrocketed.
I feel they're largely here, on this platform. Hacker News, currently, could be renamed to AI News, without any loss of generality.
I like using it to make personal apps that are specific to my use-case and solve problems I've had for ages, but I like my job (scientist), and I don't want to run an app company.
Theres tons of ai apps. They're all general use chatbots or coding agents. Manus, Cursor, ChatGPT. Almost every app that has a robust search uses a reranker llm. AI is everywhere.
As far as totally new products - I built one (Habit.am - wordless journaling for mental health) and new products require new habits, people trying new things, its not that easy to change people's behavior. It would be much easier for me to sell my little app if it was a literal plain old journal.
On Show HN.
Or the selfhosted subreddit.
There are more apps, fewer libraries.
You don't need as many libraries when functionality can be vibe-coded.
You don't need help from the open source community when you have an AI agent.
The apps are probably mostly websites and native apps, not necessarily published to PyPI.
"Show HN" has banned vibe-coded apps because there's been so many.
I am worried for people using write ups like this as a huge, much appreciated dose of copium.
Try it out and don't stop trying. If something improves at this rate, even if you think it's not there right now, don't assume it is going to stop. Be honest about the things we were always obviously bad at, that the ai has been getting quickly better at, and assume that it will continue getting better. If this were true, what would that mean for you?
well, many apps i made are really good but i would never bother to share it, takes unnecessary effort and i don't really know what works best for me will work like that for others
It's simple. AI speeds the 80% of development that was never the blocker.
Arguably makes the remaining 20% even harder to handle.
I'm sure that AI can be a huge boost to great, mature developers. Which are insanely rare in an industry that has consistently promoted brainless ivy league coders farming algo quizzes for months.
But those with a huge sensibility and experience can definitely be enabled to produce more.
But the 20% is still there and again, it's easy to make it way harder because you're less intimate with the brittle 80%.
By "apps" this author apparently means "PyPi packages". This is a bafflingly myopic perspective in a world of myopic perspectives. Do we really expect people vibecoding "apps" to put anything on PyPi as a result? They're consumers of packagers, not creators.
I don't blame people for responding to the title instead of the article, because the article itself doesn't bother to answer its own question.
Did you read the article? The author means software in general, not just user-facing apps.
Yes, I did, just to make sure it was as silly as I thought at first glance.
You do realize that "The author means software in general" is already a concession that they don't actually address the question in the title, right?
A better data set would have been the BigQuery Github stats.
Title asks where the AI apps are. Analysis looks at Python libraries. Kind of a non-sequitur, no?
The bottleneck shifted but didn't disappear. Getting to a working prototype in a weekend is real, but error handling, edge cases, and ops work hasn't gotten much faster. Distribution is completely unchanged too. A lot of these 'where are the AI apps' questions are really asking why there aren't more successful AI businesses, which is a harder and very different problem.
maybe some developers are more productive while the rest of em is laid off.. keeping the same release cadence but with fewer devs?
i know maybe this is not to your analysis as its about open source stuff, but this is the sentiment i see with some companies. rather than have 10x output which their clients dont need, they produce things cheaper and earn more money from what they produce. (and later lose that revenue to a breach :p)
The first 80% is the easy part, and good ol' Visual Basic was fabulous at it, but the last 80% is the time suck.
Same with vibe-coded stuff.
So far, as sideloaded APKs on my tablet. Most recently one that makes it easier to learn Dutch and quiz myself based on captions from tv shows
Classic HN comment: ignore the article and respond directly to its title
Well I read the article discussing pypi packages but I think for a lot of people it’s more single use tools. My little apks are ugly and buggy but work for me
While a good post the title is a bit ambiguous. The post is about applications created using AI not applications with AI functionality embedded.
As we haven't seen new operating systems or web browsers and the like, I'm guessing the reason is the same the corporation execs still have to find out: producing the code is just a small part of it. The big part is iterating bug fixes, compatibility, maintenance etc.
My guess - these are not not on PyPI because of libraries. AI generating is good when you don't care about how your app works, when implementation details does not matter.
When you are developing library it's exact opposite - you really care about how it works and which interface it provides so you end up writing it mostly by hand.
We’re in a personal software era. Or disposable software era however you want to look at it. I think most people are building for themselves and no longer needing to lean on community to get a lot of things done now.
Self plug, but basically that’s the TL;DR https://robertdelu.ca/2026/02/02/personal-software-era/
I think this is right, I can get cause to build me something for my own use that I’d have given up at before, getting to the point of being useable still doesn’t make it shareable.
I absolutely hate web development with a passion and haven’t done a new from the ground up web app in 25 years and even since then it was mostly a quick copy and paste to add a feature.
But since late last year even when it’s not part of the requirements leading app dev + cloud consulting projects, I’ll throw in a feature complete internal web admin site to manage everything for a project with a UI that looks like something I would have done 25 years ago with a decent UX.
They are completely vibe coded, authenticated with Amazon Cognito and the only things I verify are that unauthenticated users can’t access endpoints, the permissions of the lambda hosting environment (IAM role) and the database user it’s using permissions.
Only at most 5 people will ever use the website at a time - but yeah I get scalability for free (not that it matters) because it’s hosted on Lambda. (yes with IAC)
The website would not exist at all if it weren’t for AI.
Now just to be clear, if a website is meant for real people and the customer’s customers. I’ll insist on a real web designer and a real web developer be assigned to the project with me.
the pypi metric feels off. most of the ai stuff i see shipping is either internal tooling that never hits pypi, or its built on top of existing packages (langchain, openai sdk, etc) rather than creating new ones.
the real growth is in apps that use ai as a feature, not ai-first packages. like every saas just quietly added an llm call somewhere in their stack. thats hard to measure from dependency graphs.
There has been a 2x and sometimes even 10x in PR size, measured in LoC...
But that's not really what we were promised.
If one were to release an AI app - what would be an appropriate license? Genuine question.
Jury is still out on that.
> So where are all the AI apps?
They're in the app stores. Apple's review times are skyrocketing at the moment due to the influx of new apps.
Why would package be used as the standard? What person fully leveraging AI is going to put up packages for release? They (their AI model) write the code to leverage it themselves. There is no reason to take on the maintenance of a public package just because you have AI now. If anything, packages are a net drag on new AI productivity because then you'd have to worry about breaking changes, etc. As far as actual apps being built by AI, the same indie hackers that had garbage codebases that worked well enough for them to print money are just moving even faster. There are plenty of stories about that.
One pattern I've noticed: the apps that work best combine multiple models rather than relying on one. Single-model outputs have too much variance for production use cases.
It's silly to think that 'AI apps' must look like the enterprise, centrally-managed SaaS that we are used to. My AI apps are all bespoke, tailored to my exact needs, accessed only via my VPN. They would not be useful to anyone else, so why would I make them public?
https://gethuman.sh
I wonder when we'll reach saturation of opinionated all-in-one frameworks like these.
superpowers/get-shit-done type bloated workflows that try to do everything.
this seems a bit different but still in the same mental category for me
Hmmm, my anecdotal experience doesn't match up with this article. Personally I am seeing an explosion of AI-created apps. A number of different subreddits I use for disparate interests have been inundated with them lately. Show HN has experienced the same thing, no?
I think this is a great question to ask and maybe I need my own blog to post about these things as I might reply with a big comment
Making Unpublished Software for Themselves
One issue is, I think maybe a lot of people are making software for themselves and not publishing it - at least I find myself doing this a lot. So there's still "more software produced than before", but it's unpublished
LOC a Good Measure?
Another question is like Lines of Code, about if we best measure AI productivity by new packages that exist. AI might make certain packages obsolete and there may be higher quality, but less, contributions made to existing packages as a result. So actually less packages might mean more productivity (although, generally we seem to think it's the opposite, conventionally speaking)
Optimizing The Unnoticeable
Another issue that comes up is maybe AI optimizes unnoticeable things: AI may indeed make certain things go 100x faster or better. But say a website goes from loading in 1 second to 1/100th of a second... it's a real 100x gain, but in practice doesn't seem to be experienced as a whole lot bigger of a gain. It doesn't translate in to more tangible goods being produced. People might just load 100 pages in the same amount of time, which eats up the 100x gain anyway (!).
Bottleneck of Imagination
I think also this exposes a bottleneck of imagination: what do we want people to be building with AI? People may not be building things, because we need more creative people to be dreaming up things to build. AI is only fed existing creative solutions and, while it does seem to mix that together to generate new ideas, still the people reading the outputs are only so creative. I've thought standard projects would be 1) creating open source alternatives to existing proprietary software, 2) writing new software for old hardware (like "jailbreaking" but doesn't have to be?) to make it run new software so that it can be used for something other than be e-waste. 3) Reverse engineering a bunch of designs so you can implement some new design on them, where open source code doesn't exist and we don't know how they function (maybe kind of like #1). So like there is maybe a need for a very "low tech" creation of spaces where people are just regularly swapping ideas on building things they can only build themselves so much, to either get the attention of more capable individuals or to build up teams.
Time Lag to Adapt
Also, people may still be getting adjusted to using AI stuff. One other post detailed that the majority of the planet does not use AI, and an even smaller subset pays for subscriptions. So there's still a big lag in society of adoption, and of adopters knowing how to use the tools. So I think people might really experience optimizing something at 100x, but they may not know how to leverage that to publish it to optimize things for everyone else at 100x amount, yet.
Social Media Breakdown?
Another problem is, I have made stuff I'd like to share but... social media is already over-run with over-regulation and bots. So where do I publish new things? Even on HN, there was that post about how negative the posters can be, who have said very critical things about projects that ended up being very successful. So I wonder if this also fuels people just quietly creating more stuff for their own needs.
Has GDP Gone Up or Time Been Saved?
Do other measures of productivity exist? GDP appears to have probably only gone up a bit. But again, could people be having gains that don't translate to GDP gains? People do seem to post about saving time with AI but... the malicious thing about technology is that, when people save 10 hours from one tool, they usually just end up spending that working on something else. So unless we're careful, technology for some people doesn't save them much time at all (in fact, a few people have posted about being addicted to AI and working even more with it than before AI!).
Are There Only So Many "10x Programmers"?
Another issue is, maybe there are only a minority of people who get "10x" gains from AI; at the same time, "lesser" devs (like juniors?) have apparently been displaced by AI with some layoffs and hiring freezes.
Conclusion
I guess we are trying to account for real gains and "100x experiences" people have, with a seeming lack of tangible output. I don't think these things are necessarily at odds with each other for some of the aforementioned reasons written above. I imagine maybe in 5 years we'll see more clearly if there is some noticeable impact or not, and... not to be a doomer / pessimist, but we may have some very negative experience from AI development that seems to negate the gains that we'll have to account for, too.
A friend of mine who is tech savvy and I would say has novice level coding experience decided to build his dream app. Its really been a disaster. The app is completely broken in many different ways, has functionality gaps, no security, no thought out infrastructure, its pretty much a dumpster fire. The problem is that he doesn't know what he doesn't know, so its impossible for him to actually fix it beyond instructing the AI over and over to simply "fix it". The more this is done, the worst the app becomes. He's tried all the major AI vendors, from scratch, same result, a complete mess of code. He's given up on it now and has moved on with his life.
Im not saying that AI is bad, infact, its the opposite, its one of the most important tools that I have seen introduced in my lifetime. Its like a calculator. Its not going to turn everyone into a mathematician, but it will turn those who have an understanding of math into faster mathematician.
Among the various ways this analysis is flawed,
two that are drawn from my own experience are:
- meaningful software still takes meaningful time to develop
- not all software is packaged for everyone
I've seen a lot of examples shared of software becoming narrow-cast, and/or ephemeral.
That that doesn't show up in library production or even app store submissions is not interesting.
I'm working on a large project that I could never have undertaken prior to contemporary assistance. I anticipate it will be months before I have something "shippable." But that's because it's a large project, not a one shot.
I was musing that this weekend: when do we see the first crop of serious and novel projects shipping, which could not have been done before (at least, by individual devs)... but which still took serious time.
Could be a while yet.
i think it's hard to measure this, it's kinda like measuring productivity through number of commits / PRs
> So what?
As mentioned in a comment here:
> Maybe the top 15,000 PyPi packages isn't the best way to measure this? > Apparently new iOS app submissions jumped by 24% last year
Looks like most LLM generated code is used by amateurs/slop coders to generate end-user apps they hope to sell - these user profiles are not the type of people who contribute to the data/code commons. Hence there's no uptick in libs. So basically a measurement issue.
My experience with AI-driven and AI-assisted development so far is that it has actually enhanced my workflow despite how much I dislike it.
With a caveat.
If you were to compare my workflow to a decade ago, you wouldn’t see much difference other than my natural skill growth.
The rub is that the tools, communities and services I learned to rely on over my career as a developer have been slowly getting worse and worse, and I have found that I can leverage AI tools to make up for where those resources now fall short.
Stuck behind Apple's app review process.
My take is you are missing out on a barrage of "Shadow AI" and bespoke LoB and B2B software (By "Shadow AI" I mean the (unsanctioned) use of GenAI in Shadow IT, traditionally dominated by Excel and VBA).
All of the above are huge software markets outside of the typical Silicon Valley bubble.
I have a number of small apps and libraries I've prompted into existence and have never considered publishing. They work great for me, but are slop for someone else. All the cases I haven't used them for are likely incomplete or buggy or weird, the code quality is poor, and documentation is poor (worse than not existing in many cases.)
Plus you all have LLMs at home. I have my version that takes care of exactly my needs and you can have yours.
Intel AI denoiser in Blender.
There is a ton of AI use in photography software. It has improved masking dramatically, denoise is much better, removing objects is easier. But these aren’t sold as “AI apps” but as photo editing tools that use AI as a tool.
Apparently everyone has evidence to the complete contrary
"THE APPLE APP STORE IS DROWNING IN AI SLOP" https://x.com/shiri_shh/status/2036307020396241228
I’ll ask another question. Why isn’t software getting better? Seems like software is buggier than ever. Can’t we just have an LLM running in a loop fixing bugs? Apparently not. Is this the future? Just getting drowned in garbage software faster and faster?
The thing to shill now is agents.
So they are all producing products to produce products. My guess is 50% of token usage globally is to produce mediocre articles on "how I use Claude code to tell HN how I use Claude code".
I am still waiting for them.
Cool data. What do I do with it? None of my use cases involve writing software, so I don't think this is _for_ me since my extensive AI use wouldn't show up in git commits, but I'm not sure who it's for. When I'm talking to artist friends, musician friends, academic friends, etc data is nice to have but I'm talking in stories: the real thing I did and how it made me better at the thing.
AI is unbelievably useful and will continue to make an impact but a few things:
- The 80/20 rule still applies. We’ve optimized the 20% of time part (a lot!) but all the hype is only including the 80% of work part. It looks amazing and is, but you can’t escape the reality of ~80% of the time is still needed on non-trivial projects.
- Breathless AI CEO hype because they need money. This stuff costs a lot. This has passed on to run of the mill CEOs that want to feel ahead of things and smart.
- You should be shipping faster in many cases. Lots of hype but there is real value especially in automating lots of communication and organization tasks.
I feel that your assumption that everyone will want to share is a flawed one.
This article is very poorly researched and reasoned, but it's in the "AI hater" category so I guess it's no surprise it's on the front page.
Number of iOS apps has exploded since ChatGPT came out, according to Sensor Tower: https://i.imgur.com/TOlazzk.png
Furthermore, most productivity gains will be in private repos, either in a work setting or individuals' personal projects.
I agree with the premise of the article, in the sense that there has not been, and I don't think there will be, a 100x increase in "productivity".
However, PyPi is not really the best way to measure this as the amount of people who take time to wrap their code into a proper package, register into PyPi, push a package, etc... is quite low. Very narrow sampling window.
I do think AI will directly fuel the creation of a lot of personal apps that will not be published anywhere. AI lower the barrier of entry, as we all know, so now regular folks with a bit of technical knowledge can just build the app they want tailored to their needs. I think we´ll see a lot of that.
I, for one, am not publishing my “apps” for others to use because my “apps” make me money
I am now scared to talk to anyone. Eventually the conversation turns to AI and they want to talk or show their vibecoded app.
I am just tired boss. I am not going to look at your app.
here: https://www.youtube.com/shorts/vGKC9LpGnOQ
Like others have mentioned, I think the premise of looking at the most popular few projects (pypi.org currently lists 771,120 projects) on pypi as any sort of proxy for AI coding is terribly misguided/unrepresentative and that almost no one is going to be packaging up their vibe-coded projects for distribution on pypi.
That being said, I've personally put 3 up recently (more than I've published in total). I'm sure they have close to zero downloads (why would they? they're brand new, solve my own problems, I'm not interested in marketing them or supporting them, they're just shared because they might be useful to others) so they wouldn't show up in their review. 2 of these are pretty meaty projects that would have taken weeks if not months of work but instead have been largely just built over a weekend or a few days. I'd say it's not just the speed, but that w/o the lowered effort, these projects just wouldn't ever have crossed the effort/need bar of ever being started.
I've probably coded 50-100X more AI-assisted code that will never go to pypi, even as someone that has released pypi packages before (which already puts me in a tiny minority of programmers, much less regular people that would even think about uploading a pypi project).
For those interested in the scope of the recent projects:
https://pypi.org/project/realitycheck/ - first pypi: Jan 21 - 57K SLoC - "weekend" project that kept growing. It's a framework that leverages agentic coding tools like Codex/Claude Code to do rigorous, systematic analysis of claims, sources, predictions, and argument chains.It has 400+ tests, and does basically everything I want it to do now. The repo has 20 stars and I'd estimate only a handful of people are using it.
https://pypi.org/project/tweetxvault/ - first pypi: Mar 16 - 29K SLoC - another weekend project (followup on a second weekend). This project is a tool for archiving your Twitter/X bookmarks, likes, and tweets into a local db, with support for importing from archives and letting you search through them. I actually found 3 or 4 other AI-coded projects that didn't do quite what I wanted so it I built my own. This repo has 4 stars, although a friend submitted a PR and mentioned it solved exactly their problem and saved them from having to build it themselves, so that was nice and justifies publishing for me.
https://pypi.org/project/batterylog/ - first pypi: Mar 22 - 857 SLoC - this project is actually something I wrote (and have been using daily) 3-4 years ago, but never bothered to properly package up - it tracks how much battery is drained by your laptop when asleep and it's basically the bare minimum script/installer to be useful. I never bothered to package it up b/c quite frankly, manual pypi releases are enough of a PITA to not bother, but LLMs now basically make it a matter of saying "cut a release," so when I wanted to add a new feature, I packaged it up as well, which I would never have done this otherwise. This repo has 42 stars and a few forks, although probably 0 downloads from pypi.
(I've spent the past couple years heavily using AI-assisted workflows, and only in the past few months (post Opus 4.6, GPT-5.2) would I have even considered AI tools reliable enough to consider trusting them to push new packages to pypi.)
On my local computer used only by me because now I don't need a corporation to make them for me. In the past decades I'd make maybe one or two full blown applications for myself per 10 years. In the past year "I" (read: a corporate AI and I) have made dozens to scratch many itches I've had for a very long time.
It's a great change for a human person. I'm not pretending I'm making something other people would buy nor do I want to. That's the point.
This is so stupid. I don't know whether AI has improved things but this is clearly cope, we're not even a year into the transition since agentic coding took over so any data you gather now is not the full story.
But people are desperate for data right? Desperate to prove that AI hasn't done shit.
Maybe. But this much is true. If AI keeps improving and if the trendline keeps going, we're not going to need data to prove something equivalent to the ground existing.
This is such copium for AI haters. I stopped working almost any single line of code at the beginning of this year and I've shipped 3 production projects that would have taken months or years to build by hand in a matter of days.
Except none of them are open source so they don't show up in this article's metrics.
But it's fine. Keep your head in the sand. It doesn't change the once in a lifetime shift we are currently experiencing.
No one needs another SaaS. Games are the real killer app for AI. Hear me out.
I've wanted to make video games forever. It's fun, and scratches an itch that no other kind of programming does. But making a game is a mountain of work that is almost completely unassailable for an individual in their free time. The sheer volume of assets to be created stops anything from ever being more than a silly little demo. Now, with Gemini 3.1, I can build an asset pipeline that generates an entire game's worth of graphics in minutes, and actually be able to build a game. And the assets are good. With the right prompting and pipeline, Gemini can now easily generate extremely high quality 2d assets with consistent art direction and perfect prompt adherence. It's not about asking AI to make a game for you, it's about enabling an individual to finally be able to realize their vision without having to resort to generic premade asset libraries.
I tried using Gemini for asset generation, but have not yet found a good way to animate them. It does not seem to understand sprite sheets or bone-based animation. Do you know a solution for that?
>It does not seem to understand sprite sheets or bone-based animation. Do you know a solution for that?
This is precisely what I'm running into as well. There's a few SaaS solutions that are ok, but I gave up after an attempt at building a pipeline for it. Sticking with building 4X/strategy card games that don't need character animations for now until the models catch up.
Except all of the ai created games posted to the various subreddits are awful. No one likes them, no one plays them. The ones that make it to steam end up getting abandoned when the devs hit a performance wall.
Game development just isn’t something AI can do well. Good games are not just recreations of existing titles.
>Except all of the ai created games posted to the various subreddits are awful. No one likes them, no one plays them.
As with anything else, 95% of it will always be crap. Taste is now the great differentiator.
High quality assets is orthogonal to fun. If you can create a fun concept with generic assets, I believe you may find an artist willing to produce the assets for you.
>High quality assets is orthogonal to fun.
Not necessarily. It's a very "programmer brain" thing to think that novel mechanics are the be-all end-all of what makes a fun game. Extremely simple games can become incredibly engaging given high quality detailed beautiful art design. Think of deck builders and board games that would be pointless with just placeholder images and spreadsheets of data, that actually become enjoyable because of the creative work that went into the assets.
All apps you are using are made with AI.
Not all of us get addicted to the rat race and wake up at 3am to run more Ralph loops. Some are perfectly content getting the same amount of work done as before, just with less investment of time and effort.