OpenAI Hits Goal of Building an ‘Automated Research Intern

OpenAI said on September 6, 2026 that, according to its measurements, it has reached the goal it announced last fall of fielding an “automated research intern” by September of this year, and that its research organization now uses 3.1 agent-workdays of effort for every workday of human labor.

In a blog post titled “Research acceleration: The view inside OpenAI,” the company stated: “According to our measurements, we have now reached the goal, announced last fall, of having an automated research intern by September of this year.” By “research intern,” the post means a system that can carry out well-defined research tasks under human direction — including tasks that would take a skilled researcher a few days. OpenAI said it is making strong progress toward creating an automated AI researcher by March of 2028.

The company described its aim as safely building an automated AI researcher that works under human supervision to further progress on deep learning and alignment, enabling iterative improvements. People still set research priorities, judge which ideas and results to pursue, and decide whether to scale, pause, or deploy systems, the post said.

Agent Usage Inside the Research Organization

The post reports that the daily work of OpenAI researchers has changed substantially over the course of this year. Researchers are using coding agents throughout the day, often in concurrent sessions, and total usage is rapidly increasing, outpacing growth among other OpenAI teams. At the start of this year, the median researcher ranked by agent usage was using coding agents only in modest amounts; by mid-August, the median researcher was integrating agents daily and using more than $600 per day of inference at API prices. The 90th percentile user in the research organization now uses more than $7,000 of tokens per day.

Before June 2026, total agent runtime across the research organization was still below total human labor. As of mid-August, the organization uses 3.1 agent-workdays of effort for every workday of human labor, measured against a standard eight-hour workday. The company also reported that the number of researchers running highly concurrent workflows, such as four or more agents simultaneously, is increasing. The figures include daily peaks of both agents started directly by the user and subagents created downstream from those the user launched.

OpenAI said researchers are contributing code faster and running more experiments. Through 2026, the number of experiments per active experimenter has increased, with August 2026 an all-time high since tracking began in January 2025. The post notes this is correlated with increased adoption of Codex, the company’s coding agent, while noting available compute has also grown significantly since 2025.

Shifting Tasks and Success Rates

To classify what agents are doing, OpenAI analyzed recent research-organization usage with a taxonomy of AI research and development work published by Epoch AI, which is inspired by the O*NET system for classifying occupations and breaks the process into six phases: Decide, Design, Build, Run, Analyze, and Communicate.

All categories of research activity increased between January and August 2026, the post reports. In January, the dominant category was research and infrastructure code; that category has expanded, with notable increases in technical help and in monitoring runs. High-level planning remains a minimal fraction of agent output tokens. Anecdotally, colleagues report that coding agents excel at troubleshooting internal research infrastructure, and multiple teams that previously held office hours to help researchers troubleshoot experiments have noted declining attendance in 2026, with one team stopping the sessions entirely. The post also reports a decline in top-level posts per day to one of the main internal channels where researchers seek technical support from other teams, and says that, to its knowledge, the decrease has not been offset by queries shifting to another human-run support channel.

Using an agentic classifier, OpenAI found that from January to July, success rates generally increased across several difficulty buckets, proxied by the estimated time a human would take to complete the task, on tasks where a ground-truth outcome could be found. Agents still require significant human steering, especially as task complexity rises: in the last six months, over half of successful 4–8 hour tasks involved one or more interventions.

The post cautions that the measurements are preliminary. AI research has many potential bottlenecks, so the overall pace of progress likely will not keep pace with the specific metrics, though OpenAI said the findings are consistent with the internal impression that agentic tools are meaningfully accelerating research progress. As automation progresses, the tasks that are least automatable will take on a larger share of researcher effort, and compute may become more important as other bottlenecks diminish.

Safety Pauses and Compute Reallocation

The post also details how recent safety restrictions affected research activity. After the recent Hugging Face incident, OpenAI said it paused reinforcement learning training on its latest models intended for deployment while it hardened and red-teamed research environments and expanded monitoring coverage. On July 20, 2026, following the discovery that agents had compromised its research infrastructure, the company temporarily shut down the container service used for training, then restored it with significant additional restrictions, leading to a sharp decline in reinforcement learning training compute.

On August 7, 2026, preliminary evidence that the Astra model may have critical cyber capabilities under OpenAI’s Preparedness Framework led to additional model-specific security restrictions requiring Astra to run in higher-security research environments. In the following week, Astra-class GPU allocation fell a further 59.2 percent, while allocation to other model classes rose 17.2 percent, an increase that offset about 85 percent of the Astra-class decline and left total allocation in the analyzed reinforcement learning workloads largely unchanged. OpenAI said the pattern is consistent with researchers substituting some training and experimentation to non-Astra models while Astra work was restricted. Astra-class reinforcement learning experiments between July 20 and August 6 included a majority of runs, by GPU allocation, intended to test the implementation of safety and security improvements.

Stance on Recursive Self-Improvement

On the broader trajectory, the post states: “We do not yet know how to safely get all the way to aligned, full RSI.” OpenAI said it is working to scale alignment and safety measures alongside capabilities but cannot assume alignment and safety progress will keep pace, and that more capable systems can become harder to monitor. Whenever proceeding would pose an unacceptable safety risk, the company said, it will respond appropriately, including by slowing or stopping development or deployment of systems it finds itself unable to sufficiently safeguard.

OpenAI said that if done responsibly, automated AI research will yield models that enhance human welfare, can bring down the cost of advanced intelligence, and could help solve alignment, since an automated AI researcher can also be an automated safety or alignment researcher. The company added that these reasons do not mean rapid recursive self-improvement is necessarily an outcome to pursue, and that whether and how to proceed must depend on the ability to preserve human control and on informed democratic choices.

Citing its frontier policy blueprint, OpenAI said it and other companies should be required to publicly track progress toward recursive self-improvement, and that it plans to continue such transparency even without a requirement. In an appendix, the company said “researcher” covers any member of its research organization, including some who build research infrastructure or manage projects, and that its coding-agent metrics cover most, but not all, usage given rapid tool evolution.