Multiply the ratio out. OpenAI converts the time an agent spends on a task into standard eight-hour units, so 3.1 agent-workdays against an eight-hour human day is 24.8 hours of agent runtime per human day [4]. Researchers run several agents at once, which is why the figure describes how long agents were busy rather than what came back finished [3]. Runtime is the easiest thing in this stack to instrument, which is presumably why it is the number in the headline.
OpenAI makes the same point at greater length. Faster code and more experiments do not make the research process 3.1 times faster, because research also includes deciding what to pursue, designing experiments, analyzing results, allocating compute, catching failures and applying safety controls, and speeding up one stage can move the waiting line somewhere else [15]. The report adds that code output and experiment counts are easier to measure than their relationship to research progress, and that the least automatable tasks grow as a share of human work as automation improves [16].
The phase data agrees. Using Epoch AI's six-phase taxonomy of Decide, Design, Build, Run, Analyze and Communicate, OpenAI found activity rose in all six between January and August, while agents still did relatively little of the work of deciding what research to pursue [7]. Build got cheaper while Decide stayed where it was.
The intervention rate was produced by another model judging agent performance, over a period when success rates were improving [8]. For that number to transfer, the grader's notion of success has to match what your reviewers would accept, and your task mix has to sit in the same four-to-eight-hour band. OpenAI's own definition of the automated research intern is a well-defined task that would take a skilled person several days, with a human still in charge [9], which is a harder band than the one where humans were still stepping in.
Then the allocation side. In the week after the Astra access change, Astra-class GPU allocation fell 59.2%, allocation to other models rose 17.2%, and that rise covered roughly 85% of the drop [13]. If both percentages are measured against each pool's own prior week, the non-Astra pool was about 2.9 times the size of the Astra pool: 0.592 x 0.85 / 0.172 = 2.93 [14]. The work moved because there was somewhere roughly three times as large to move it into. Compute also rose significantly as experiment counts grew [18], and Astra's persistent agents let researchers hand off multi-day assignments, which adds to what one person has to track [19].
The published metrics are runtime, code volume, experiment counts, compute and spend; a completed-task count is not among them [22]. That leaves the relocation of the constraint well evidenced by OpenAI's own qualification, its July and August incidents and the intervention rate, and the size of the net gain unmeasured.
