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Notes -
AI & open source
What do you think about LLM's impact in software development?
Inspired by recent events in open source:
Bun's LLM-assisted rewrite
The Bun project was translated from the Zig programming language to Rust, mostly by LLM. This task usually takes months-to-years by hand (e.g. Rust to Zig rewrite), but with the LLM it took 11 days. Its success is debatable: the code isn't idiomatic Rust, and lines-of-code increased 67%, which suggests it will be harder to maintain, but it works (passes the thorough existing test suite), and the maintainer says it's going well in a blog post that provides more details.
In response, Zig's maintainer wrote a scathing blog post personally criticizing the Bun maintainer credited for the rewrite ("[he] was already writing slop well before he had access to LLMs"). Zig's maintainer is known for blunt strong opinions: he previously moved Zig off GitHub and the announcement was rewritten twice to be softer on GitHub employees.
Earlier, the Ladybird web browser was also rewritten in Rust mostly by LLM. This got less attention and controversy, probably because it was announced better, and the previous language was C++ so they got more sympathy (2-hour video on why C++ sucks).
Rsync's LLM-assisted bugs
Rsync's maintainer got accused of "vibe fuck[ing] up this software", and the discussion got very heated. He explained he has been using LLMs a lot because he's getting lots of vulnerability reports (see below). An AI-assisted analysis concludes the LLM did introduce bugs, although not more than the usual amount.
LLM slop contributions are overwhelming open source maintainers
Many projects are seeing a massive influx of low-quality contributions by inexperienced software developers and bots, heavily relying on AI. This has caused some to stop accepting any outside contributions, while others have added strict LLM contribution policies (see below).
Example: an LLM managed to implement an entire big feature, albeit incorrectly. The file headers also credit random people who it presumably stole code from, thankfully(/s) the LLM concluded there are no copyright violations.
Serious LLM-discovered vulnerabilities are also overwhelming maintainers
Among the slop contributions are actual vulnerabilities discovered with the help of the latest LLMs like Mythos. There's a vague background threat that open-source libraries will become too dangerous to use, but LLMs can also find closed-source vulnerabilities, so it's unlikely.
Cal.com has made their core codebase private and announced "open source is dead", but others point out this probably won't help them, and AFAIK this hasn't become a trend.
curl ended its bug bounty program because of an "explosion in AI slop reports", but still accepts reports and admits they have improved because LLMs have become good at finding vulnerabilities. Although Mythos only found one benign one, because curl is one of the most hardened codebases that exists. Still, legitimate (maybe-issue) reports have increased causing more work for the maintainers, so they're taking a vacation this month.
"Vulnerability Reports Are Not Special Anymore", written by a former security lead, explains that the recommended process to handle them has changed due to LLMs
Open-source LLM contribution policies
Due to the aforementioned slop contributions and/or general ideological reasons, many projects have banned LLM usage entirely, while others require it to be disclosed. Big list. Highlights:
Misc
Picrocrypt's maintainer quits open source because of LLMs: "I want to do something actually meaningful and innovative with my time and life, not command Claude like I'm speaking to a child who forgets something every ten minutes and makes an oopsies every twenty before breaking down every hour."
We mourn our craft: senior developer laments hand-writing code becoming obsolete.
The LLM Critics Are Right. I Use LLMs Anyway. This guy outright admits what most people quietly do: criticize LLMs while using them.
My opinions
For reference: I'm under 30 and have been writing code for most of my life. I've written open-source projects, although they're not popular enough to get LLM contributions. Today I work in formal methods (theoretical computer science). I've always expected my future to involve creating software.
I am sad and worried that software development is changing. I enjoy working with code directly and making it (opinionatedly) well-designed, writing prompts is less intuitive to me and it's harder to control the design. I worry if most of my time spent learning to code better has been wasted, or even my entire Computer Science career.
But software development is much more than writing code, and my specialization is much more than software development. Although I've intrinsically enjoyed it, to me, writing code has always ultimately been a means to an end. And now with LLMs I write code much faster and easier. I also still write lots of code directly, LLMs (even Fable/Sol) can't fully automate my work at least yet.
As I wrote before, in the long term I'm neither optimistic nor pessimistic about AI, but I think it's inevitable and most people should learn how to use it to their advantage. I think it should be possible for those who really despise LLMs to continue to do things manually, there should be a market for "hand-coded" software etc., but they will be at a disadvantage.
I share Linus's opinion that AI is a tool, clearly useful but can be misused or misapplied. I believe its future (at least near term) is for boring uncreative tasks. For example, right now LLMs excel at straightforward (not idiomatic) translation and research, but struggle with high-level design. They also suck at art (no good examples except shallow memes, this is my experience elsewhere) and creative writing. I believe AI's future shouldn't and probably won't be creating art, because humans intrinsically enjoy it and make it very specific to their personalities; but it will make tools that humans use for art.
Lastly, I believe that ideally everything digital would be free and its creators would be paid another way. This means I have no issue with LLMs training on open-source, except they also trained on copyleft (GPL) code, they should be open-source and open-weights themselves.
It will be strictly worse in all cases, for all users, all developers, all maintainer, and all licence holders for the foreseeable future unless there is a massive improvement in the tools. They are good enough to make things that are just bad enough to be an enormous pain in the ass, especially when people who think they know a lot more than they do are using them.
WITH THE MASSIVE EXCEPTION OF: It is wild to tell the robot to write me a little piece of shit that does some shitty little task where i don't care how it works and it doesn't need to touch anything. productivity app providers in shambles, etc etc.
There are definitely some stupid developers harming projects with LLMs by adding loads of unmaintainable spaghetti slop, but "strictly worse in all cases" is strictly wrong.
LLMs aren't capable of fully structuring a project on their own, but they're more than capable of implementing a well-encapsulated algorithm or throwaway script. Also, many real-world codebases are already spaghetti, but it's adequate especially for small projects: for example Undertale and Balatro, all of Undertale's dialogue is in one switch statement and Balatro has this monstrosity among many more bad design decisions. Would the median LLM-assisted developer write worse?
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If AI programming was so awful for users and developers, nobody would be paying tens of billions of dollars for it.
Statements by the utterly deranged! I guess lots of people might be getting emotional about self-driving cars, car accident lawyers for instance or race car enthusiasts... The same goes for AI programming but that's no excuse to let it cloud judgement.
Here's a fact that sounds fake but isn't: the best AI developer (Anthropic) had less revenue than Ebay last fiscal year.
Yes, it was roughly $5B in 2025 to Ebay's $11B, also interestingly the best guess for Anthropics revenue in June 2026 was also $5B. Things are moving quickly and Anthropic is projected to have ~$60B in revenue over 2026 with error bars as low as $45B and as high as $80B. Putting it in the class of Goldman Sachs, Intel and IBM as far as revenue is concerned. And well, the straight lines on graphs interpretation makes this year to year change pretty interesting.
Is that with GAAP accounting?
Of course not, but neither is the comparison to ebay. There aren't audited numbers so we're all estimating based on available information.
"You really think a startup would do that? Just go on the internet and tell lies about their revenue!?"
I'm not the one who originally tried to make some point about what the speculative revenue tells us about Anthropic.
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Going a little further back, in Jan 2024 Anthropic had $87 million ARR, by May 2026 they were at $47 billion ARR
Some have questioned this accounting, whether ARR is representative of realized revenue over the whole year. We shall have to wait for the IPO for more detailed accounting. But it seems likely that Anthropic is the fastest growing company in history.
Yeah, I am very interested in the IPO. OpenAI lost nearly 40 billion last year and may be undershooting its goals this year, which I don't think bodes well for the US frontier labs.
But Anthropic might not be fated to the same destiny as OpenAI - for one thing, it explicitly kept its hands away from the advertising cookie jar.
OpenAI may indeed be undershooting in ads. But do we really think the ad market for chatbots is a mere $5 billion? What does that even mean? At what point in time?
A lot of emotive language in the article...
If I go to the front page of Futurism, I find these article headlines about AI:
The only time when AI is good is when it's bad for US companies it seems.
If AI psychosis is so significant that it deserves a whole category of Futurism articles about it, surely that indicates there is some significant advertising power in AI, some ability to change minds? Only the technology hasn't been fully developed yet. And what about the google AI summary at the start of nearly all searches? There are ads above, below and within it. There are all kinds of effects going on, revenue shifting between Google Adsense and Search. I think it's a lot more complicated and a lot less one-sided than Futurism would have us think.
I am not OpenAI's top guy, I think the company is a negative force in the world and AI development is an extremely serious matter with the gravest possible implications for humanity. But this totally negative view that some outlets push is a distortion.
Case in point, the nearly 40 billion loss for OpenAI. That's true from a certain perspective but a good chunk of it is an accounting loss from the shift from non-profit to for-profit company, moving control around. That shift is reprehensible but means the 'true' financial loss is closer to $10-20 billion.
Where is the headline about AI disproving the Jacobian conjecture the other day? That's roughly Fields Medal worthy in mathematics, albeit of little practical relevance. These systems are extremely capable but that doesn't fit the narrative that the media wants. Journalists are extremely threatened by AI, as are media companies. Why would I want to look up articles and read them (let alone pay a j**rnalist!) when I can just ask an AI about OpenAI's losses? Why would a journalist want to get a real job when they could instead try to hyperstition an AI bubble into existence and stick it to their hated class enemies? Not all journos... but many.
I agree with Shrike on the other points, but to clarify a misunderstanding:
While very impressive, nobody is claiming that a counterexample to the Jacobian conjecture for N > 2 would have merited a Fields Medal.
Daniel Litt, probably one of the most AI-involved mathematicians right now, agrees that it would have been a notable accomplishment for a mathematician, but not anything close to a Fields Medal worthy achievement.
https://xcancel.com/littmath/status/2079091534159298769
Litt is almost certainly correct that "Fields Medal" is overselling it, but I think "very impressive" is still underselling it. This was roughly a top-50 open problem in mathematics, one famous for attracting flawed "proofs". A restricted version of the conjecture had been open for nearly a century before being proven. The full conjecture was open for over 80 years. One of the most successful 21st century number theorists had his math career derailed by spending his PhD on the Jacobian conjecture with unimpressive results.
I think my biggest reason for epistemic caution here is that this wasn't a case of "AI disproving the Jacobian conjecture", it's a case of "AI led by a Princeton math PhD disproving the Jacobian conjecture", and I haven't yet seen which half of that team was responsible for which ideas in the derivation. From a quick glance at Alpöge's dissertation and senior thesis, I guess he seems to be more of an Algebraic-geometry guy than an algebraic-Geometry guy, but he still seems to be comfortable with the sorts of algebraic geometry tools that were likely used to derive his example. It's possible that Fable did most of the heavy lifting and Alpöge's critical contribution was "being an Anthropic employee with unlimited Fable access", but it might not be a total coincidence that he's not a number theorist or a topologist or something.
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My personal belief has actually been that the online ad market is inflated, so chatbots not having a high ad ceiling wouldn't surprise me.
True, there was some weird accounting there. What strikes me is that they made $13 billion but spent $7.5 billion on cost of revenue - inference, most likely - and nearly $6 billion in advertising. Or in other words, their business was not profitable before you factor in R&D and training.
I do think this is true, at least partially - I think that journalists were souring on Silicon Valley before AI kicked off, so some of them are going to be biased against it. But I also do think that OpenAI (and Anthropic) are more than happy to dangle substantially false narratives to promote their businesses - and these narratives flatter the interests of investors. In particular, I think that people underappreciated the massive difference between software and AI - the cost of providing software is vastly lower than that of providing compute. But I think there was insufficient appreciation of that with AI, although I do think that is now changing.
This is relevant insofar as I absolutely think investors could be wrong.
souring? They've openly hated Silicon Valley for a very long time.
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