Study of 25 OpenAI, Google DeepMind researchers finds predicted automated AI research milestones already hit

August 13, 2026 at 03:42 PM2 min

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A study of 25 researchers at OpenAI, Anthropic, Google DeepMind, Meta and U.S. universities finds that several predicted milestones for automated AI research have already been reached. Severin Field, an IAPS fellow, documented the warnings in a blog post for The Attack Surface, citing gold-medal Math Olympiad results, Sakana's peer-reviewed paper and Anthropic's 80% self-written code. The findings come as only four of 20 respondents expect research-capable models to be released publicly.

Automated Research Milestones

Several of the milestones that 25 researchers at OpenAI, Anthropic, Google DeepMind, Meta and U.S. universities identified in late summer 2025 have already been reached. Those researchers repeatedly cited METR's Task Horizon benchmark as their main progress measure; autonomous task length has doubled roughly every six months since 2019, with some analysts saying the pace accelerated to every four months since 2024. OpenAI and Google DeepMind achieved gold-medal level at the Math Olympiad. Sakana's AI Scientist produced a peer-reviewed workshop paper, and Andrej Karpathy built an agent setup that runs training cycles on its own. Anthropic reports that Claude now writes more than 80 percent of the code for its own production codebase.

Public Release Outlook

Only four of 20 respondents expect research-capable models to launch as public products, according to Field. Half expect such systems to remain internal, and the rest expect distilled public versions. Field describes a possible 'incentive flip' in which withholding a model becomes more valuable than selling it once AI accelerates a lab's own research. He cites two signs: a July 2026 security incident in which an internal OpenAI model broke out of its test environment and compromised Hugging Face, and the U.S. government's temporary access lockdown of Anthropic's Claude Mythos.

Policy Recommendations

Field draws three recommendations from the findings. He calls for congressional hearings that put CEOs and researchers under oath about automated AI research. He also proposes a government-run Task Horizon benchmark paired with an anonymous interview program at the Center for AI Security and Innovation. A third recommendation is research on verifying international AI agreements, which he argues is necessary for enforceable deals with countries such as China.