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.
> 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.
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.
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.
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.
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.
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.
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.
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.
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
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.
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.
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.
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.
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…
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'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.
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.
Easy, the problem was never writing code. The problem is and always has been finishing the job and shipping it and driving user adoption. AI has done nothing to help that part and so the rate of released and successfully marketed apps stays the same.
The caveat here is to say it hasn't helped with this YET. It's very possible that one or more people/companies come up with a way to have AI handle this process whether it's from a purely autonomous approach like ralph looping until done, deploying and then buying ads or posting about it or from an AI CEO approach of managing the human or hiring humans to do some of those tasks or from a handholding den mother approach of motivating the human to complete all the necessary steps.
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.
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.
> 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.
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.
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.
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.
They exist. Go look at any "I built this in a weekend with Cursor" post — there are hundreds. The problem is most of them ship broken and stay broken. Auth that doesn't actually check anything, API keys in the frontend, falls over with 5 concurrent users.
The quantity is there. Nobody's asking "does this thing actually work" before hitting deploy. That's the real gap.
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 ".
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.
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.
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.
Other comments have pointed out that packages on PyPi might not be the best metric and posted other countering evidence like spikes in GitHub contributions or mobile app submissions or even mobile app revenue. However I think open source package numbers are still worth watching as an inverse measure of AI adoption.
That is, I expect the numbers (at least the frequency of downloads, if not the number of new packages) to go down over time as AI makes generating functionality easier than hunting down and adding a dependency.
The number of new packages could still go up as people may still open-source their generated code, for street cred if not actual utility. But it's not clear how much of those incentives apply if the code is not very generally useful and the effort put into is minimal.
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.
- 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.
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.
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.
My take on AI-apps is that now its possible to build apps that we kind of wanted to build, but never did because it was too inconvenient.
I'm stealing this idea from this paragraph [1]:
> The book points out that the major value in a flying car (as with supersonic) would not be in taking the same trips you do now, only a bit faster. Instead, it would be in taking the trips you don’t take now, because they’re too inconvenient.
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.
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.
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.
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.
They definitely exist. I have a little media server at home and was looking for iOS clients for it. Turns out there are dozens of apps, and new ones popping up every day because of AI. The authors are using AI all over the place. I think we’re seeing the apps in niches like this: there’s a gap where not much software exists (or maybe it just sucks), and is also an interest and “easy” side project with AI for the dev. Doesn’t have to be a massive scalable SASS, but seeing it a lot in the homelab space
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It is incredibly easy now to get an idea to the prototype stageYup. 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.
> 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.
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.
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.
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 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.
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 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.
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.
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...
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.
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.
Personally, I see the paid or adware software market shrinking, not growing, as a testament to the success of LLMs in coding.
It's like looking at tire sales to wonder about where the EV cars are.
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…
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.
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.
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.
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.
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.
The caveat here is to say it hasn't helped with this YET. It's very possible that one or more people/companies come up with a way to have AI handle this process whether it's from a purely autonomous approach like ralph looping until done, deploying and then buying ads or posting about it or from an AI CEO approach of managing the human or hiring humans to do some of those tasks or from a handholding den mother approach of motivating the human to complete all the necessary steps.
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.
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.
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.
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.
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).
The quantity is there. Nobody's asking "does this thing actually work" before hitting deploy. That's the real gap.
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".
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.
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.
That is, I expect the numbers (at least the frequency of downloads, if not the number of new packages) to go down over time as AI makes generating functionality easier than hunting down and adding a dependency.
The number of new packages could still go up as people may still open-source their generated code, for street cred if not actual utility. But it's not clear how much of those incentives apply if the code is not very generally useful and the effort put into is minimal.
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.
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.
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'm stealing this idea from this paragraph [1]: > The book points out that the major value in a flying car (as with supersonic) would not be in taking the same trips you do now, only a bit faster. Instead, it would be in taking the trips you don’t take now, because they’re too inconvenient.
[1]: https://blog.rootsofprogress.org/where-is-my-flying-car
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.
- 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.
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.