Anthropic details its progress toward recursive self-improvement, and its implications, and says 80%+ of the code merged into its codebase is authored by Claude
Our progress toward recursive self-improvement, and its implications. — For most of AI's history, humans drove every step in its development cycle.
Anthropic
Context & Ripple Effects
Anthropic’s prior coverage positioned Claude Code as an early leader in AI coding, though pull-request approval data indicated OpenAI’s Codex had narrowed the gap. Anthropic had also described multi-agent research workflows as outperforming single-agent approaches in its internal evaluations.
This report moves the discussion from selling coding assistance to using Claude inside Anthropic’s own development loop. It also gives concrete operational context to the company’s earlier public discussion of automated AI R&D and systems contributing to their successors.
First-order effects
Anthropic can shift a larger share of routine implementation work in its own codebase to Claude, with human engineers increasingly responsible for review, architecture, evaluation, and deployment decisions rather than drafting every change.
Claude’s reported contribution rate becomes a prominent proof point for Anthropic’s coding product and for its claim that AI can accelerate the work of building AI.
Second-order effects
The claim raises the competitive bar for OpenAI and other coding-model providers: benchmark performance alone is less compelling if rivals can demonstrate sustained use in their own production development processes.
Customers evaluating AI coding tools may place more weight on governance around AI-authored changes—review workflows, testing, traceability, and accountability—rather than on code generation capability alone.
Third-order effects
If internally deployed coding agents reliably improve the systems and infrastructure used to train and operate them, AI development could become more iterative and increasingly constrained by evaluation, compute, and safety controls rather than solely by engineering headcount.
The same feedback loop strengthens the case for the tougher AI-safety rules Anthropic is pursuing, since the relevant policy question shifts from model outputs alone to how quickly increasingly capable systems can be deployed in development pipelines.
The trend: AI coding is evolving from a developer-assistance market into automated R&D infrastructure, where the strategic advantage comes from safely integrating agents into the software-production loop.
Our internal data shows Claude is accelerating AI development—a possible path to recursive self-improvement, or AI autonomously building a more capable successor. It's happening faster than we thought, and the implications deserve greater attention. https://www.anthropic.com/...
None of this guarantees recursive self-improvement is on the horizon. It's not yet clear that Claude is capable of research judgment—of choosing the right problems to work on. But if these trends continue, AI systems designing and building their own successors is plausible. This
AI research is a series of next-step decisions. We looked at sessions where a human researcher took a wrong turn, showed Claude the session up to that point, and asked it what to do next. Mythos Preview improved on humans 64% of the time—up from 22% in 2024. [image]
Each time we release a model, we run the same test: give it code that trains a small AI model, ask the new model to speed it up. It takes a skilled human 4-8 hours to reach 4x faster. In May 2024, Claude Opus 4 averaged a ~3x speedup. This April, Mythos Preview achieved ~52x.
The speedup isn't just in volume. On open-ended coding problems where answers are unclear, Claude's success rate is now 76%—a 50 point jump in just 6 months. Many engineers also say Claude's code quality is now on par with human code; we expect it to be better within the year. [i…
Anthropic - Our internal data shows Claude is accelerating AI development—a possible path to recursive self-improvement, or AI autonomously building a more capable successor.
SITUATION EXPLAINED: Anthropic just published data on how fast AI itself is accelerating AI development. Here are the numbers: • Claude's success rate on open-ended coding problems: 76% • On a standard AI training speedup task: Opus 4 got 3X in May 2024. Mythos Preview got [video…
great article on RSI by anthropic, i'm a bit curious about this plot i don't think they clarify if people used opus 4.7 when it was release, i'm guessing no, but then it's a bit missleading to put it here imo? i actually think the 4 week trailing average make it hard to see the […
This paragraph reads a bit defeatist. And “training runs are far easier to conceal than missile silos” seems overstated. How tractable of a technical problem is it to create a solution to verify training runs aren't happening? My view is its could be done with a few hundred [imag…
Kudos to Anthropic for writing about this! I'm confused that even in their most aggressive scenarios humans still “play a substantially diminished role in [AI] development.” Does Anthropic really think that humans will stay relevant indefinitely? [image]
my main takeaway from this post is how unsurprising model capabilities + state of AI R&D automation is vs. expectations based only on public evidence. the internal-external gap is smaller than you might think. https://x.com/...
We are in the recursive age of AI. Faster inference => faster AI development. Our take on this from the hardware perspective: https://www.cerebras.ai/...
We just published internal data on how much of Claude's development is already being done by Claude: - Over 80% of all code merged into our codebase is now written by Claude - It's been months since many researchers at Anthropic hand-wrote code - The typical Anthropic engineer
Could a global slowdown in frontier AI model development really happen? In the midst of progress towards RSI, Anthropic here floats the idea. Demis Hassabis has been floating the same idea, including when he spoke at Stanford a few weeks ago. This would have seemed totally
recursive self improvement is right around the corner anthropic is willing to slow down, but only if everyone else slows dow nice position to be in when you're in the lead 🤔
Holy moly, Anthropic is getting very serious about recursive self-improvement! One word: acceleration. Insane blog article. Tl;dr: •We are close to an AI capable of fully autonomously designing and building its own successor •They stress this isn't here yet and isn't [image]
Anthropic says Recursive Self Improvement is approaching faster than they expected. Quoting from the blog: 'What should we do? 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 [i…
“As of May 2026, more than 80% of the code we merge into Anthropic's codebase was authored by Claude” — Matches independent measures. There is no sign this is slowing down (which doesn't mean there aren't organizational challenges to absorbing this much productivity) www.anthr…
they always want to sound measured but I'm going to go on record and say this is obviously going to be the case — www.anthropic.com/institute/ re... [image]
We are at the stage of labs declaring very near future RSI. Claude writes 80% of Anthropic's code now. They expect Claude written code to be better than human written code, generally, before the end of this year. They're aware of possible oil shocks slowing progress. They dou…
Interesting blog from Anthropic about RSI, AI & coding, ofcourse from their perspective but worth reading, — https://www.anthropic.com/... #ai #rsi #coding #anthropic
This was a really good read. A lot of people are going to focus on specific numbers mentioned in the blog post like “we estimate these 800 API fixes would have taken 4 years”, but I recommend reading the whole thing holistically as a write up of how software development processe…