Today, everyone is sharing this Anthropic article: https://anthropic.com/institute/recursive-self-improvement… But only those who actually visit the official website can feel the “creepiness” of that animation — the recursion has begun.

When AI builds itself

Source: https://www.anthropic.com/institute/recursive-self-improvement For most of AI’s history, humans drove every step in its development cycle. But at Anthropic, we are delegating a growing share of AI development to AI systems themselves, which is speeding up our work.

Taken far enough, and given enough compute, that trend points to an AI system capable of fully autonomously designing and developing its own successor. This is called recursive self-improvement. We are not there yet, and recursive self-improvement is not inevitable. But it could come sooner than most institutions are prepared for.

Using public benchmarks and previously unreported data from within Anthropic, The Anthropic Institute (https://www.anthropic.com/institute) is showing that AI is already accelerating the development of AI systems. To take just one example: today, Anthropic engineers on average ship 8x as much code per quarter as they did from 2021-2025.

The technical trends discussed in this piece suggest that AI systems are going to become much more capable in coming years. These trends have huge implications. AI that can build itself would be a major development in the history of technology—one that could bring enormous good for the world (https://www.darioamodei.com/essay/machines-of-loving-grace) in science, healthcare, and beyond. But full recursive self-improvement also might increase the risks (https://www.darioamodei.com/essay/the-adolescence-of-technology) of humans losing control over AI systems. If systems are capable of fully building their own successors, the ways we secure them, monitor them, and shape their behavior all grow much more important.

2021–2023 Building the first Claude In the early days, work at Anthropic looked like work at any other tech company: people writing code and docs on laptops.

2023–2025 Chatbots People used early chatbots to help with parts of the process, like generating short code snippets and copying the output into text editors.

2025–2026 Coding agents As the agents became more capable, they were able to write and edit code on their own, sometimes entire files.

Today Autonomous agents Agents can now run code themselves and delegate hours of work to other agents.

20XX? Closing the loop In the future, agents could become capable enough to build and train models themselves. If this happens, future versions of Claude could be continuously improved by Claude itself.

Evidence from the outside world

The rate at which AI models improve is accelerating. The length of tasks that they can reliably complete on their own (https://metr.org/time-horizons/) has been doubling roughly every four months, up from an earlier trend of doubling (https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/) every seven months. In March 2024, Claude Opus 3 could complete software tasks that take humans about four minutes to complete. A year later, Claude Sonnet 3.7 managed tasks that took about an hour and a half. A year after that, Claude Opus 4.6 managed 12-hour tasks.1 If this trend holds, tasks that take a skilled person days could come into range this year. In 2027, AI systems could be capable of tasks that take a person weeks.

The same pattern appears on coding and research benchmarks. Benchmarks measure the performance of models in a given domain, and they’re “saturated” when models achieve close to 100% performance.2 SWE-bench (https://www.swebench.com/) is a standard test of real-world software engineering: it hands a model an actual open-source codebase and a real bug report, and asks it to write a code change that fixes the issue and passes the project’s own tests. Models have gone from scoring in the low single digits to saturating the benchmark in two years.

CORE-Bench (https://arxiv.org/abs/2409.11363) tests whether a model can reproduce existing research, a prerequisite for them to conduct original research. It gives an AI model the code and data behind a published paper, and asks it to rerun everything and confirm it can replicate the paper’s results. AI systems went from succeeding at reproducing the results roughly 20% of the time in 2024 to saturating the benchmark fifteen months later. METR, which runs the benchmark measuring how well models can complete long-duration tasks, found (https://x.com/METR_Evals/status/2052896621760004602) that Claude Mythos Preview could work for “at least” 16 hours and was “at the upper end of what [METR] can measure without new tasks.”

Public benchmarks say a lot about the capabilities of these systems. But they can’t reveal the impact AI systems are having on speeding up AI development itself. For that, we need direct evidence from within AI companies like Anthropic.

Evidence from within Anthropic

Building a frontier model takes two broad categories of work. There is engineering: writing the code, standing up the infrastructure, and overseeing the model training. And there is research: deciding what experiments to run, interpreting what comes back, and figuring out which ideas to try next.

Across both engineering and research, the picture is consistent. In engineering, Claude can be handed an underspecified problem and figure out how to solve it; humans supply the goal, but they no longer need to supply the method. In research, Claude can already match or outperform skilled humans at executing a well-specified experiment. However, large performance gaps persist when it comes to Claude exercising judgement in choosing goals in both engineering and research. That’s the gap between AI today and a future system that could autonomously design its own successor.

It’s common for employees at Anthropic to receive more open-ended and important tasks as they gain more experience. Early on, they execute a task someone else specified, like, “The export button isn’t working, please fix it.” With experience, they’re handed a goal and design the approach themselves, such as, “Investigate why the network slows down under heavy load.” At the most senior levels, they are deciding which problems are worth working on at all: “What should the team build next quarter?” We can use internal Anthropic data to see how far Claude has come in being able to handle these different kinds of tasks.

Claude writes a significant proportion of Anthropic’s code. As of May 2026, more than 80% of the code we merge into Anthropic’s codebase was authored by Claude.3 Before Claude Code launched in research preview in February 2025, this number was in the low single digits. That shift also shows up in the amount of output per engineer. Lines of code merged per engineer per day stayed constant through Anthropic’s first four years (2021-2024), then began to climb upward in 2025 when Claude began to run code rather than just suggesting it for an engineer to copy and paste. The slope steepened again in 2026 when models began to work autonomously over longer time horizons. These two inflection points are shown in the chart below. In the second quarter of 2026, the typical engineer was merging 8× as much code per day as they were in 2024.4 This is because much of the code is written by Claude, with the engineer directing and reviewing, rather than typing it themselves.

Bar graph showing code contributed per person, per quarter, starting in Q2 2021 and ending in Q2 2026. The graph notes the release dates of eight different models: Claude 1, Claude 2, Claude 3, Claude 4, Claude Code, Claude Sonnet 4.5, Claude Opus 4.5, Claude Mythos Preview (internal access), and Claude Mythos Preview. 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.

The increase in lines of code written lines up with subjective impressions of large productivity increases. In a March 2026 poll of 130 employees from across Anthropic research teams, the median respondent estimated that they produced around 4x as much output with Mythos Preview as they would have without access to any AI models, on the kinds of projects they would have been working on regardless.5 We expect that the true degree of uplift in March was somewhat lower.6 Nevertheless, we find the overall claim plausible, and in line with our other observations: a significant fraction of Anthropic technical staff is accomplishing their core work multiple times faster than they could without AI assistance.

We also see evidence that people at Anthropic are using Claude to do work that simply wouldn’t have happened otherwise, like building exploratory tooling and addressing long-deferred cleanup. For example, in April 2026, Claude shipped over 800 fixes that reduced a class of API errors by a factor of one thousand. The engineer overseeing Claude estimated that a human would have taken four years to complete this work; solving other people’s bugs is slow and painstaking, and humans struggle to hold that much unfamiliar context in their head at once.

I started leaning hard into Claudifying about a year ago. That’s been a crazy adventure and it’s now been ~5 months since I last wrote any code myself.

Anthropic employee*

The code that Claude writes is “good” and improving. “Good code” means two things: it works, and it is written in a manner that allows another engineer to understand it and build upon it. On the first criterion, the evidence is clear. The rate at which Anthropic staff correct, redirect, or take over mid-task from Claude has been falling steadily for a year, including on the most complex and open-ended tasks. This means problems with no clear specification, where the engineer isn’t sure what the answer looks like. This is evident in Claude’s success rate over time on tasks of different difficulties, as shown in the graph below. Claude writes code that works.

Line graph showing the Claude Code session success rate on four different types of tasks—trivial tasks, routine tasks, substantial tasks, and open-ended problems—with six different models: Claude Sonnet 4.5, Claude Opus 4.5, Claude Opus 4.6, Mythos Preview (internal access), Mythos Preview, and Claude Opus 4.7. How to read this: Session success is determined by a Claude judge; a session is deemed successful if the Claude Code agent clearly succeeded at the user’s tasks without requiring corrections. Changes in workloads can lead to short-term fluctuations in success rates. On the most open-ended tasks, Claude’s success rate reached 76% in May 2026, up 50 percentage points in six months. To give an example of tasks in this difficulty tier, a routine upgrade began crashing tens of thousands of training jobs. An engineer pointed Claude at the live incident with little more than some text content and cluster access. Working through the running jobs and testing one environment setting at a time, Claude isolated the single obscure debugging flag that was triggering the crash, reproduced it reliably, and confirmed a fix. In about two hours, Claude delivered what would normally be two to three days of work.

The second criterion is writing code that another engineer can understand and build on. Here the gap between humans and AI persists, but is closing fast. There isn’t full consensus among staff at Anthropic, but many believe that the Claude-written code was still worse in quality than human-written code at Anthropic in late 2025, and is roughly at parity today. We expect it to be better within the year.

This has changed the way that Anthropic now reviews its own code. Proposed changes to our codebase are now read by an automated Claude reviewer that looks for bugs, security flaws, and other defects before it can merge. Using this tool, we ran a retrospective analysis, and found that an automated Claude review of every change to our codebase would have caught roughly a third of the bugs behind past incidents on claude.ai (http://claude.ai/) before they ever reached production. The engineers who wrote that code are among the best in the world at building these systems. Claude is now catching the mistakes that they missed.

Claude-written code was somewhat worse than human-written code at Anthropic in late 2025, is roughly at parity today, and we expect it to be strictly better within the year.

Claude is good at running experiments to hit a goal that someone else has set. Every time Anthropic releases a model, we run the same test: we give Claude some code that trains a small AI model, and ask it to make that code run as fast as possible while still passing the same correctness checks. The goal and the success metrics are fixed in advance, so Claude’s job is to find speedups by rewriting the code, running it, timing it, and repeating. It’s a miniature version of an experimental research loop. In May 2025, Claude Opus 4 (https://www-cdn.anthropic.com/6d8a8055020700718b0c49369f60816ba2a7c285.pdf) averaged a ~3x speedup over the starting code. By April 2026, Claude Mythos Preview (https://www-cdn.anthropic.com/8b8380204f74670be75e81c820ca8dda846ab289.pdf) was achieving ~52x. For calibration, a skilled human researcher would need four to eight hours to reach 4x.7 In this part of the research workflow—optimizing steps within a clearly defined experiment—Claude has gone from super helpful to superhuman in under a year.

The shape of stuff today is roughly ‘humans have ideas, and the models are able to implement, test and evaluate them an [order of magnitude] faster than before.’

Claude is getting better at proposing its own experiments. In April 2026, Anthropic published (https://alignment.anthropic.com/2026/automated-w2s-researcher/) the first demonstration of Claude running an open-ended research project end to end. Claude-powered agents were given an open problem in AI safety—roughly, can a weaker model reliably supervise a stronger one?—and were left to solve it. This involved proposing hypotheses, testing them, sharing findings with parallel agents, and iterating. The task has a clear performance “floor” and “ceiling”: the floor is how well the weak supervisor would do on its own; the ceiling is how the strong model does when trained on correct answers. Two human researchers, over about a week, recovered roughly 23% of that gap; the agents recovered 97% over 800 cumulative hours and used roughly $18,000 in compute. There are some caveats to this work; the result didn’t transfer cleanly to production-scale models, and humans still chose the problem and created the scoring rubric. But within those bounds, the agents designed every experiment themselves. Direction-setting was the only meaningful role a human played.

Claude did all of this with pretty minimal help from me over the course of 1-2 days. I think if [a junior colleague] came back to me with results like this in the same span of time, I would be mildly impressed. The future is now.

Claude is getting better at steering research sessions towards research findings. We examined real Claude Code sessions (between January and March 2026) where Anthropic researchers were working with Claude on an open-ended investigative problem, like figuring out why a training run kept crashing, or why a model scored poorly on a benchmark. In each case, we found a moment where the researcher took a detour: they pursued a direction that sent the session sideways before it eventually got back on track. We then sh