❯ OpenAI says automated research interns are deployed, with median daily researcher inference usage exceeding $600 at API list prices

Research usageOpenAI disclosed on September 6 that researchers’ median daily agent inference usage, valued at API list prices, exceeded $600, while the 90th percentile exceeded $7,000. The company says it met its September target for an automated research intern. Its next milestone is an automated researcher by March 2028. Internal progress report.

Division of workBy mid-August, each human workday corresponded to 3.1 agent workdays. That measures machine runtime; people still lead research direction, experiment selection and interpretation. The company describes interns that handle well-defined tasks previously requiring several days, allowing researchers to advance more work simultaneously. Parallel experimentation has expanded without removing supervision.

Capability and oversightIn a separate essay that day, Chief Scientist Jakub Pachocki called for extreme caution, expressing concern that alignment and monitoring lag capability gains and proposing coordinated slowing when necessary. Expanded machine use and demands for stronger constraints are coming from the same lab. The essay supports conditional slowing; it does not announce a training halt. Signed essay.

More hardwareHardware deployment is expanding too. OpenAI President Greg Brockman confirmed that Astra was trained using more than 100,000 GPUs. Jensen Huang separately discussed another 400,000 GPUs coming online and expressed his view that AGI has arrived. Coming online does not mean a donation. Each advance in research automation can also consume more machine time for experiments, checks and reruns. Brockman interview; Huang’s remarks.

Budget changesInference is becoming part of the lab’s research budget. Running more experiments and identifying useful directions are different abilities. Automation expands the former faster, placing more weight on researcher judgment for the latter. As daily usage rises, teams need to count the machine and human costs per useful result, rather than simply the amount of code produced.

▪ SIGNALResearchers are consuming hundreds of dollars of inference resources a day, extending AI development costs from training clusters into everyday experiments.