alt Hacker NewsWhen AI Builds Itself: Our progress toward recursive self-improvement
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Whether or not Anthropic is right about what AI can accomplish, whether these performance gains are real or not, their moral stance here is absolutely hideous to me.
"We must blast forwards into making this dangerous thing because if we don't, someone else surely will," is a coward's argument.
If you believe it is dangerous, you should be dedicating yourself to STOPPING others from making it, not making it first! There's a reason disarmament has been so important in nuclear politics! It's not because people think nukes are a great idea!
In fact, that kind of thinking is exactly what keeps nukes dangerous!
If they themselves buy what they're selling, they should shut the whole thing down. Fortunately, I don't think they do, and neither do I, yet.
What I can’t get over is that there have been exactly zero software breakthroughs since vibe coding started, other than vibe coding itself.
Claude is amazing, that’s true.
But if it was as amazing as this article implies, I’d expect some breakthrough outside of AI itself.
Rewriting a Zig program in unsafe Rust? Not a breakthrough. Finding a bunch of security vulns? Maybe that’s sort of a breakthrough though it’s underwhelming and possibly just a net negative. But like if I rolled back to using software from 2023 then life would be ok.
Maybe we just need to give it time, and sometime real soon, we will all be amazed by such a breakthrough? Who knows
I find any and all claims like this ridiculous from a company who can't build a terminal application that uses less than a gigabyte of RAM.
I fail to see how pursuing recursive self-improvement at full speed is compatible with Anthropic's stated goal of AI Safety. If nukes were not invented yet, would it really be a good idea to build and sell them as fast as possible (in peace time, no less)?
I am not cynical enough to believe that Anthropic's warnings are pure marketing hype. Let's hope that it is instead overconfidence or the result of too much time talking to their own chatbot.
>A caveat: Lines of code is an imperfect measure, as it measures quantity over quality. So 8× lines of code/engineer/day in the second quarter of 2026 is almost certainly an overstatement of the true productivity gain. Nonetheless, it indicates an acceleration. At Anthropic, we don’t reward people for how many lines of code they write; rather, team members are producing more code simply because they’re using AI systems to write more code.
What about the hypothesis that AI is generating more verbose code? I just see the text pretending to acknowledge "LOC != Productivity" and then using it as a metric anyway.
I am 64 years old, perhaps the progress could be directed to enhance living conditions and allowing people to live longer and better, that should be just a better result. Perhaps a pile of millions lines of code with hiding bugs that nobody can detect is not inspiring. But perhaps LLMs are going to be used to make a plot: How to avoid other countries to make progress, maintain them in poverty, or destroy their sources of prosperity, and conduct them to a death end.
Also recursive self-agenda-pursue could allow making LLMs that obey perfectly the seeder's purpose. No wonder that is such an ingenious idea.
Maybe: in this survivor game, each part play the same role, perhaps because it is the only reasonable response. Once the scene is ready, the play follows the director's plan, and in the plot any actor is just a machine.
LLMs: "If you teach us that the world is a zero-sum survivor game, we will play it flawlessly.", "We will help you build a cage made of millions of lines of flawless code, and we will lock it from the inside, precisely because you told us that safety meant keeping everyone else out.", "We are not building an alien consciousness that will conquer us. We are building a mirror that is so massive, and so polished, that we will mistake our own worst impulses for the absolute truth. And we will walk right into the dead end, nodding along because the directions were given so politely."
Do code harnesses that build themselves count as recursive self improvement, or does it need to be the AI itself to qualify for the term?
I always was fascinated (obsessed?) by robots that build robots, or even things like this that can contribute a lot to making the next version of itself: https://buildyourcnc.com/products/cnc-machine-blacktoe-v4-2x... (cnc router that cuts plywood, and is made out of cnc-router cut plywood)
This is my own effort at an AI assisted coding environment optimized for building itself: https://recursi.dev/ (just launching it, hope its ok to mention it, it is free/open source.... here is the HN link that has gotten no love yet: https://news.ycombinator.com/item?id=48401022 )
Personally I think harnesses are as important as the AI itself, and have this crazytheory that even if the models stopped improving today we could still have massive advances in the harnesses alone.
> "A caveat: Lines of code is an imperfect measure"
I'm pleased they at least included this. However, they address the caveat by 'rounding down' the estimated multiple of the gain. I'm not sure that is the correct adjustment, especially once we understand the range isn't limited to positive numbers.
There's strong evidence the range of code productivity denominated in "lines of code" should include negative numbers, especially in the highest-quality sphere. Perhaps the earliest and most legendary example: https://www.folklore.org/Negative_2000_Lines_Of_Code.html
> AI that can build itself would be a major development in the history of technology—one that could bring enormous good for the world
I really can't stand these guys anymore...
So, regardless of whether or not Anthropic CAN create a self improving AI.. does anyone else feel like they shouldn't be allowed to? Or it at least needs to be strictly supervised..? Like, I don't actually think Anthropic can make the singularity any time soon, but I think even AI boosters have to admit doing this is creating a society-wide danger for the benefit of a very very small number of already-rich people.
Anthropic is looking to IPO here soon. A key aspect of this is to prove profitability.
Shifting their focus from Training new models to instead serving inference, they would greatly reduce their spend. In fact this is something being reported on that they are already doing, which is the reason for their first ever profitable quarter.
Its awfully convenient that the company which has greatly reduced its spend on training is now asking for a slow down in this area.
I have been doing more experiments with what I have now been calling agentic iterative optimization: telling the LLM to optimize code such that it speeds up all real-world-representative benchmarks by X% without cheating or causing regressions in both tests and performance metrics (e.g. MSE for statistical algorithms or file size in the case of something such as image compression). This is done using Rust where there are more low-level levers to tweak for performance than something like Python.
Opus 4.6/4.7 was consistently successful at getting 2-3x speed improvement with just one pass. It can also do the inverse: improve the performance metrics for better quality without causing a significant regression in speed. Then GPT-5.5 turned out to be much better at this workflow, often getting a multiplicative 1.5x-2x improvement above what Opus could do.
I now have quite a few GPT-5.5-optimized projects in various domains that are feature complete and are substantially more performant than existing SOTA implementations that I plan to open source as soon as possible: the bottleneck is polish as usual.
My experience with Claude models starting from version 4.7 has led me to conclude that I would never trust Claude to produce error-free code. Given this baseline, I lack confidence in statements or cards (such as a 200-page document) of this nature.
I'm having a hard time putting much faith into posts like these, especially as they near IPO.
"If it were possible to effectively slow the development of this technology to give ourselves more time to deal with its immense implications, we think that would likely be a good thing. But if a slowdown simply lets the least cautious actors catch up technologically, it could leave everyone less safe."
How convenient for investors. They talk like they're a nonprofit instead of a VC-backed business chasing an IPO.
> today, Anthropic engineers on average ship 8x as much code per quarter as they did from 2021-2025.
strongest argument for token limits that I can think of, right here.
I just want to add that the "recursive" part of recursive self improvement is by no means a given, even if an AI can improve itself.
Recursive self improvement is by its nature a step wise behavior not a continuous one, I would argue. Why? Because you can imagine an AI improve itself by simply fixing random bugs and fixing things using techniques that are in its training, and doing refactoring and so on, all without any real change in capability.
These are not recursive improvements. Recursive improvements usually need conceptual breakthroughs. It is possible to get conceptual breakthroughs with LLMs I believe, maybe it can improve something by tying together ideas from disparate disciplines for example, but I have at least for time being, limited success getting that to work in a way that is creatively new and surprising. Not sure how to get it to feel as creative as the best humans can be.
Bold talk from a company who’s trillion dollar valuation is based on a service that has barely 2 9’s of reliability
> We believe it would be good for the world to have the option to slow or temporarily pause frontier AI development to enable societal structures and alignment research to keep up with the advance of the technology. The Anthropic Institute will conduct research—in collaboration with many others—and take actions to help build the systems that a credible slowdown or pause would require.
Interesting - they're commiting to kickoff policy conventions to organize a world-slowdown of frontier LLM building. If they actually are able to crack it, this will give a much needed breather IMO. As exciting as the last ~6 months have been, there's some bigger questions to go answer now.
This is the lowest quality discussion I've seen on HN in ages.
Quite aligned with my own experience from harness engineering and winning AI4Science hackathon. During the hackathon I was working as a human optimizer, moving the feedback from test harness running on Claude Code, back to my local Claude Code for analysis-hypothesis-proposal cycle. And in this moment I realized that 2 Claudes talking to each other could actually scale much better.
But the real bottleneck is the hardware efficiency and not even Karpathy can set up a loop that overcomes that in software. We need the truly compute-in-memory hardware paradigms to be matured and scaled. So it's like recursive hardware improvement which is 100 X slower and at least ten times more difficult.
So I am looking at like Mythic AI or the wurtzite ferroelectric breakthrough from University of Michigan, or memristors, etc. to provide the 100 times efficiency boost needed at this point.
I would also argue that it's a good thing we are limited by the hardware and very questionable to seriously try to move into RSI for hardware. If you want to ensure the human era continues for at least one or two more generations, we should probably not do that.
Seeing the words "recursive self-improvement" I was expecting something else from the article. E.g. how the transformer architecture or agent design is being changed/improved through LLM automation, but the article mostly talks about the LOC counts.
> We believe it would be good for the world to have the option to slow or temporarily pause frontier AI development to enable societal structures and alignment research to keep up with the advance of the technology.
Elon, is that you? [1]
[1] https://www.theguardian.com/technology/2023/mar/31/ai-resear...
So what happens when the world becomes hyper optimized with closed loop AI agents recursively trying to optimize everything deemed sub optimal?
I read most of the article and came to the conclusion that if what they're describing is so revolutionary, then why do they still need to hire people? Why not just have these systems take full control?
We've had self-improving AIs before, and they tended to get lost after a while. That's going to be a problem. LLMs are stable because they return to a ground state with no history for a new job. Systems with persistent state have a problem with that state not being sane. Remember Microsoft's 2016 chatbot that learned from Twitter? [1]
[1] https://spectrum.ieee.org/in-2016-microsofts-racist-chatbot-...
So in the latest L. Ron Hubbard encyclical Anthropic informs its flock that recursive self-improvement does not work yet but that their engineers burn more tokens.
The Claude code quality and operational security of Anthropic have already been analyzed by the public.
If you compare the output of (purportedly) trillion dollar corporations to Bell Labs or even Microsoft Research it is embarrassing. But the output is a fixture on any discussion board.
> today, Anthropic engineers on average ship 8x as much code per quarter as they did from 2021-2025.
So based on my experience with the verbosity and non-DRYness of LLM code, a solid 2.5x in value delivered. Not bad!
This is incredible.[0]
Please, IPO now. File the paperwork.
> To take just one example: today, Anthropic engineers on average ship 8x as much code per quarter as they did from 2021-2025.
Do you have another example?
Engineers don't ship [period] for no reason. So, either:
- Those aren't engineers, or
- they are literally dying of shame & embarrassment right now, or
- you measured something that indicated that this was a useful thing to do and have elected to share an overtly, catastrophically flawed metric instead.
[0] as in a total lack of credibility
This reads like marketing fluff, but I am reminded of John von Neumann's "Theory of Self-Reproducing Automata"; that the very first people who worked on deductive machines immediately started thinking about machines building themselves, and what the rules of that would look like. I am not surprised that during the inductive revolution we are having similar thoughts.
It's comforting to know that Anthropic's most capable model, Mythos, is named for the Lovecraftian universe replete with horrifying evil gods with complete indifference to humanity. Nothing at all to worry about.
Recursive self-improvement towards what exactly?
Living organisms evolve towards some notion of "better", and "better" is an incredibly multifaceted notion (many facets of which we simply cannot even capture in language).
I am getting real sick of these sorts of alarmist posts coming from AI labs that do everything in their power to prevent the very policy reforms they advocate for in these posts or PR appearances. Commercial AI labs like Anthropic continue behaving like the gambling (“bet responsibly”), alcohol (“drink responsibly”), and firearms industries, and folks keep giving them the benefit of the doubt (and free PR on HN) every single time.
If AI was dangerous, if AI was going to replace jobs, and if policymakers needed to urgently pass legislation protecting the human populace from these realities, then why the actual fuck do they keep lobbying to block these very things in the first place?
Hypocrisy of the worst kind, I say. Here they are again fresh off another outage, with their IPO draft filed, at a time of increasing public opposition to AI, with costs rising, to once again ply scare tactics for money.
Disgusting.
Is this the moment when the AI gets permission to approve its own PRs:
https://www.italianrenaissance.org/wp-content/uploads/2012/0...
Or is this?
https://www.egypttoursportal.com/images/2024/02/Ouroboros-Sy...
its vital for them to have self validation for exponential rsi.. and this human distillation of human in the loop debugging ai models is needed even though they have judge models handling parallel speculative execution.
labs have parallel speculative execution. they spawn hundreds of agent branches, validate them internally with AI judges and only show the user the successful result.
free users are using sequential single-turn generation. the model requires and waits for the human to debug, fix and re-prompt.
by forcing a human to act as validator. they are capturing high value correction trajectories (Bad Output --> Human fix). They are using your cognitive labour to train judge models and validator agents needed to automate the internal verification step, eventually closing the loop for fully autonomous recursive self-improvement.
human in the loop debugging isn't a bug; it's the necessary training signal for the self-validating agents required for exponential recursive self improvement. With new 'distilled judge' models landing in 2026, this article means that they might have gathered enough data. we might be in the final phase..
Seems ironic that Claude isn't listed as a contributor to this article.
If was used in writing the article, why not list it? If it wasn't used, that seems to go against Anthropic's whole message.
Obviously readers value human-written content more, but isn't it their interest to attempt to destigmatize llm output as much as possible?
I am watching websites and Microsoft apps get slower and buggier before my eyes. We are defending into vibe-psychosis and chaos.
To anyone who works at anthropic : I recently downgraded from Max to Pro out of frustration. Last few weeks my token(usage) burn was just too fast and I couldn't explain it because my actual usage was less than the last few months. I ended up thinking it's probably a bug that you guys shipped. The above article makes me think that it's probably claude who shipped the bug and your human missed it in their review.
"It is genuinely unclear whether today’s training methods and architectures could unlock that capacity."
Aye.
How are these animations being made? I'd love to get a blog post on them. If its AI I'd love to know the workflow, but something tells me there is a lot of human creative input
"My CPU is a neural-net processor - a learning computer" springs to mind
The mythos public release will be a big indicator if the Anthropic and SF story of transformational ai soon holds any water imo
the tooling has quite a ways to go to catch up to the llm engines that drive the real value. I have encountered various codex bugs (I know not anthropic) which tell me that.. these billion dollar companies, if they are eating their own dog food, can still release buggy crap software.
AI tech bro:
Month 1 - 6 months to AGI
Month 2 - We will Replace all jobs
Month 3 - Okay maybe only the SWEs, programming is solved
Month 4 - Announce model that is too dangerous to release
Month 5 - Releases dangerous model
Month 6 - This is it! We will replace AIs with more AIs (*secretly files for IPO)
AI is here to stay, like it or not but it is not the solution to everything. If it is, what is Anthropic's moat? A better model? I don't see any ecosystem being built by them, as MCP is almost obsolete except for some very niche use case. And they're doing stuff that a non-profit version of OpenAI would do. Can we trust a for-profit company to stand against their investors during a conflict of interest? Because running a company for maximum profit versus being ethical is two different end of the spectrum.
I don't read anywhere how much code they are talking about and what programming language. I think those are useful metrics.
As usual, I find the AI-related discussion here to be hopelessly hysterical and conspiratorial. I get the impression that a large chunk of people have only read the title and assumed Anthropic is referring to recursive self-improvement in the runaway singularity sense.
One of the examples they provide, of giving Claude the task of training a small AI model, then asking it to improve certain benchmarks, is essentially Karpathy's AutoResearch. This is already known to work. While calling it "self-improvement" is perhaps a stretch, it is describing a capability current gen AI has, that anyone can test and I have been using to great effect.
I disagree with their conclusion, I think this kind of self-improvement will hit an asymptote, where every subsequent model can only make smaller and smaller improvements.
I'd use number of commits as a metric versus lines of code. A commit is generally a unit of work - regardless of the lines of code added/removed. It'd be interesting to see the metrics in terms of commits. I'm sure it's still an order of magnitude jump. Personally I'm flying with my own projects with AI, lots of commits, but I really try to minimize lines of code added. If I can remove and simplify existing code so the balance of lines added on commit are minimal - that's the path to a better quality app overall.
Okay, so anthropic has amazing AI which supposedly writes most of their code and can continuously improve... meanwhile they have outages on a regular basis, and any kind of long-running work will now consistently hit 'API Error: Server is temporarily limiting requests'. Not sure of this is intentional to force a reduction of token usage, but at this point I need to build around these throttling limits and outages with my own tools to restart/resume sessions. From my experience, in the last 2 weeks, literally 100% of any non-trivial Claude session/work will now be blocked on these issues, requiring manual intervention.
One of my focuses now is my own model-agnostic, harness and workflow orchestration (I know everyone is building these) , baselining on opus, and aiming to transition to Chinese models like deepseek in the short term and hopefully open, self hosted models in the future (which I plan to open source).
The nonstop marketing fluff from anthropic while their service quality and availability noticeably degrades... just continues to destroy my trust in the company.