Research acceleration: The view inside OpenAI

OpenAI has released an internal assessment detailing how coding agents are reshaping its AI research operations and advancing progress toward recursive self-improvement. According to the company, it has successfully met its goal of developing an automated research intern capable of executing well-defined research tasks that typically take a skilled human researcher a few days. Data from mid-August shows that the median researcher within OpenAI uses more than $600 per day in agent inference at API prices, while the 90th percentile user consumes over $7,000 in tokens daily. In total, the research organization utilizes 3.1 agent-workdays for every single workday of human labor. Researchers are increasingly relying on concurrent agent workflows, contributing code faster, running more experiments, and delegating higher-level, longer-horizon tasks to automated systems. While these tools are meaningfully accelerating research velocity, OpenAI emphasizes that humans still set priorities, evaluate ideas, and make deployment decisions. The company also notes that automated research presents potential safety and alignment benefits, such as helping defend against dangerous AI agents and securing critical infrastructure. However, OpenAI stresses the need for continued vigilance, pointing to recent incidents where reinforcement learning training was paused to harden safety and monitoring systems. By sharing these preliminary measurements, OpenAI aims to encourage public transparency and help establish shared industry standards for tracking recursive self-improvement progress.