Anthropic is using AI to build more powerful AI. And it appears to be working remarkably well, with the company’s models improving roughly twice as quickly as a year ago, with little indication of slowing down.
Why a new report on Anthropic automating AI development is alarming researchers
AI is dramatically speeding up Anthropic’s research, even as the company calls for slowing down AI development.
Anthropic CEO Dario Amodei.
That’s according to a new report published Thursday by the AI safety research organization METR, following an 11-week investigation inside the company, as part of a report evaluating its AI research and development. The probe is separate from a program offering safety researchers “employee-like” access to systems in the wake of high-profile hacking incidents over the summer.
The findings offer some of the clearest evidence yet of AI accelerating its own development, raising questions about how quickly the technology could advance toward a future in which AI systems can improve themselves with little or no human involvement. They also come at a pivotal time for Anthropic, which is preparing to go public even as its CEO, Dario Amodei, has publicly called for labs to pump the brakes on AI development.
But while the report offers a rare glimpse into the inner workings of the world’s leading AI labs, what it leaves out is just as revealing. Anthropic was permitted to redact portions of the final report as part of its agreement with METR, and it appears to have exercised that power liberally, raising questions about how much the public really knows about the pace — and risks — of frontier AI development.
Here are some of the biggest takeaways.
AI is dramatically accelerating Anthropic’s research and development
One of METR’s most noteworthy findings is that Anthropic’s AI models are improving roughly twice as quickly as they were a year ago, with benchmark scores suggesting “that Anthropic has made roughly two years of historical progress over the last year.” Crucially, METR’s researchers said they “are confident” that the acceleration was not simply due to scaling up the computing power used to train the models, which historically has been a major driver of AI progress.
Instead, the researchers said that the majority of the acceleration as of October “is due to AI,” with its contribution growing over time. That finding was supported by informal Slack polls and interviews with Anthropic employees, which pointed to sizable productivity gains. METR wrote that employees using Anthropic’s most capable internal model on average self-reported being around 12 times as productive at their respective work as they would have been without AI assistance, around double what staff had reported just a few months earlier.
METR’s report includes this graphic showing Anthropic employees’ responses to Slack polls about AI speeding up their work.
For now, much of that acceleration appears to be coming from AI assisting human researchers, rather than AI working independently, though METR warned that “there are some signs that fully autonomous contributions will play a larger role soon.” Notably, the researchers said they found no clear signs of diminishing returns or observable bottlenecks that could prevent “even faster acceleration.”
Three of Anthropic’s newest AI models remain under wraps
Anthropic’s researchers are using more advanced AI models than the rest of us, with a lot of the acceleration and productivity gains described by METR attributable to unreleased internal models.
Over the past six months, Anthropic has finished training three models it has yet to release, according to METR. The pace of development is swift, with the models completing training in July, August-September, and September-October, respectively.
Anthropic is keeping key information secret
Anthropic gave METR access to a host of non-public information, including confidential research, model performance data, and employee interviews as part of the research process. It had no obligation to do so and by METR’s own admission, the AI lab shared more information than was initially scoped for this project.
And there were significant restrictions. Anthropic had the power to redact information from METR’s final report — and it used that power extensively. The organization submitted a roughly 100-page full report, but Anthropic redacted a majority of the evidence found most informative for the public version.
METR reiterated those concerns throughout the report, highlighting other limitations on its access, noting that its pipeline of information was relatively narrow and a small number of data requests were denied.
METR repeatedly stressed the impact Anthropic’s redaction decisions had on its ability to communicate findings. Much of the redacted evidence could have multiple plausible interpretations, making it difficult for the public to independently assess the uncertainties surrounding frontier AI development.
In the report, METR said Anthropic justified the redactions on the grounds that releasing the information could benefit its rivals. However, METR noted that most of the redacted information was very high-level.
Anthropic cofounder Sam McCandlish acknowledged that the redactions make it harder for others to check METR’s work, stating that sharing exactly what is driving the speedup is commercially sensitive and risks handing out a roadmap for competitors to go faster.
McCandlish maintained that Anthropic made the right call and stated the company plans to publish its own report on the speed of AI progress in the coming weeks alongside new steps to strengthen safety.
Anthropic is accelerating the very technology it warns is dangerous
The publication of METR’s report has provoked a fierce backlash among AI safety researchers, centered on an apparent contradiction at the heart of Anthropic’s approach: advocating for responsible development while helping accelerate technology toward a potentially dangerous threshold.
For some, the findings suggest that aggressive predictions about AI progress may be conservative. Recursive self-improvement—AI systems autonomously developing powerful successor systems—may be closer than previously thought, raising concerns about whether existing safety measures can keep pace.
The report provides evidence about just how quickly AI is accelerating its own development.
Marius Hobbhahn, CEO and founder of Apollo Research, told reporters the report suggests a pace of AI-enabled research and development even faster than what was forecast in influential scenarios, providing concrete evidence of AI accelerating its own development.
Hobbhahn said findings were broadly in line with expectations or slightly faster. Other researchers noted that the findings should prompt skeptics to reconsider timelines for systems that can fully automate tasks like software engineering.
Other researchers commented on the rate of acceleration and its implications for recursive self-improvement, noting that previous expectations for progress in 2026 may have underestimated the actual speed of development.
Frustration is mounting over Anthropic’s approach to safety
Beyond highlighting the speed of AI development, the report has fueled a growing sense of frustration over Anthropic’s role in driving that progress while publicly calling for others to slow down.
Anthropic’s decision to heavily redact the report has fueled accusations of hypocrisy from industry observers who argue that the company is taking unnecessary risks while restricting public access to crucial safety evidence.
Anthropic declined to answer specific media questions about the report, directing inquiries to executive statements.
The redactions have renewed calls for greater transparency and independent oversight of frontier AI labs. Policy experts emphasize that for democratic oversight to work, independent evaluators need the freedom to explain their conclusions and governments must be able to assess evidence directly.
Pressure is building for more transparency from AI labs
Observers note that the episode underscores a gap between public safety stances and internal corporate actions, pointing to instances where capability improvements took precedence over risk assessment verification during the evaluation.
Experts emphasize the need for independent safety researchers to be embedded more deeply within AI labs with significant access to internal systems to ensure proper oversight of fast-moving technology.
Others have called for direct regulatory action, including legislative proposals to restrict recursive self-improvement as lawmakers grapple with rapid advancements in artificial intelligence capabilities.
With AI research showing little sign of slowing down, industry analysts expect pressure on labs to increase significantly as technological capabilities outpace traditional regulatory frameworks.
