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Anthropic data highlights AI doomer concerns

By Investing.com2 min readInvesting.com
Anthropic data highlights AI doomer concernsAnthropic data highlights AI doomer concerns

Anthropic data highlights AI doomer concerns

Anthropic has released new measurements showing how much of its AI research is now being performed by AI itself, providing fresh data for a central question in the AI-doomer debate: whether increasingly capable AI could eventually accelerate the development of even more capable systems.

The company said Claude now “leads” 26% of Anthropic’s AI R&D work, up from less than 1% in February. Anthropic defines “leads” as completing most of a task end-to-end from a high-level prompt, with a human supervising. More than 90% of AI R&D is performed at or above the level where AI “collaborates” with humans.

The significance is less the 26% figure itself than the potential feedback loop: AI performs AI research, that research produces better AI, and better AI performs more AI research. This is a key component of the recursive-self-improvement scenario that has fueled concerns about the long-term risks of advanced AI.

Anthropic said it had roughly 30,000 AI agents simultaneously conducting research and engineering work on its most-used internal platform in August. More than 1 billion agent decisions were analyzed during the month, with about 0.002% blocked by online monitoring.

The company has not reached the most important threshold for the recursive-self-improvement scenario: fully autonomous AI R&D. Claude is not yet operating autonomously for any measured portion of Anthropic’s R&D, meaning humans remain in the loop.

Still, the pace of change is notable. The share of AI R&D classified as AI-led rose from below 1% to 26% in just six months.

Anthropic also found that about 12% of compute used for AI-driven AI R&D went toward safety work during one week in July, although the company cautioned that compute is an imperfect measure of safety investment.

The measurements do not show that recursive self-improvement has arrived. They do, however, provide a way to track how quickly AI is taking over the work of building future AI — a trend that could become increasingly important if the human role in the development loop continues to shrink.