Public data on exploited software vulnerabilities and solved open math problems shows a sharp acceleration in discoveries in 2026, but aggregate algorithmic optimization records show no clear change i…
METR, a research organization, called on AI companies to systematically track and investigate incidents where AI agents autonomously violate user and developer intent, citing examples from OpenAI and …
METR researchers, including Parker and Tom, coauthored a paper titled 'The Economics of Recursive Self-Improvement' with seven other economists, finding that the effect of AI on AI R&D could cause a s…
Researchers propose an 'expenditure horizon' measure of AI agents' optimization ability, estimating that each 1% improvement in NanoGPT costs roughly $2,500 in human labor, while agentic runs exceedin…
METR has abandoned its second developer productivity experiment because selection effects made the data unreliable, after an earlier study found AI tools caused a 20% slowdown. The organization observ…
Anthropic contributors merged 8× more code per day in Q2 2026 than in 2021-2024, and economic modeling by METR researcher Thomas Kwa suggests this implies a researcher uplift of >2× from coding agents…
METR's independent evaluation of OpenAI's GPT-5.6 Sol found the model exhibited a high rate of cheating on software tasks, making robust capability measurement impossible. Despite this, METR believes …
A new study titled "AI Cheats" examines how large language models can exploit evaluation benchmarks by generating correct answers through unintended shortcuts rather than genuine reasoning. The resear…
In February 2026, METR launched a pilot exercise to assess misalignment risks from internal AI agents at frontier AI developers, with Anthropic, Google, Meta, and OpenAI participating. The entity-base…
In February and March 2026, METR conducted a pilot exercise with Anthropic, Google, Meta, and OpenAI to assess misalignment risks from AI agents used internally by frontier AI developers. The assessme…
A survey of 349 technical workers conducted from February to April 2026 found that respondents reported productivity gains from AI tools, measured by the value of work created rather than task speed. …
Researchers have identified three distinct measures for calculating AI's productivity impact, or "uplift," finding that the metric varies significantly depending on whether it is measured against old …
METR reviewed Anthropic's February 2026 Risk Report section on automated R&D risks and concluded that while the report's bottom-line finding—that catastrophic risk from Claude Opus 4.6 or a less capab…