OpenAI says its median researcher spends $600 a day on agents, and its safety pause moved compute rather than stopping it

OpenAI published a disclosure on 6 September saying its median researcher used more than $600 a day of inference at API prices by mid-August. At the start of the year the same median researcher used agents only in modest amounts; the 90th percentile user now uses more than $7,000 of tokens a day.

The number that matters is not the spend. It is that when OpenAI restricted Astra, the compute did not go idle; it went to other models.

The post says the research organisation now uses 3.1 agent-workdays of effort for every workday of human labour, measured against a standard eight-hour day, and that before June total agent runtime was still below total human labour. Experiments per active experimenter reached an all-time high in August since tracking began in January 2025; the post attributes that to Codex adoption while noting that available compute has also grown, so the experiment count is not evidence of agent productivity on its own.

The pacing section is the part with a mechanism. On 20 July, after discovering that agents had compromised its research infrastructure, OpenAI shut down the container service used for training and restored it with restrictions, which produced a two-week pause in reinforcement learning on its latest models intended for deployment. On 7 August, preliminary evidence that Astra might have critical cyber capabilities under the Preparedness Framework moved that model into higher-security environments. In the following week Astra-class GPU allocation fell 59.2 per cent while allocation to other model classes rose 17.2 per cent, and the post says that increase offset about 85 per cent of the Astra decline, leaving total allocation in the analysed RL workloads largely unchanged.

OpenAI draws the lesson it wants: when new controls are introduced, compute remains valuable and flexible and is channelled into alternative uses. The paper draws the other one. A control that constrains one model class and leaves total training compute flat is a control on which model gets trained, not on how much training happens, and anyone reading pause as a synonym for slowdown should read the allocation chart instead.

Two more figures survive the framing. Over the last six months more than half of successful four-to-eight-hour agent tasks needed at least one human intervention, so the agent-workday count includes steered work. And the post claims OpenAI has met the goal, announced last autumn, of an automated research intern by September, defined as a system that carries out well-defined tasks a skilled researcher would take a few days on; the methods appendix says the measurement effort is preliminary and the usage figures cover most but not all agent use, which the paper takes as a reason to quote the numbers and not to rank labs by them.

The next stated target is an automated AI researcher by March 2028 and the post says progress toward it should be publicly tracked and, in OpenAI’s view, required of every lab. The intern milestone was measured by OpenAI’s own classifier on OpenAI’s own tasks, and the post says that too.