OpenAI is getting closer to one of its milestone goals: systems that can function at the same level as research interns.<\/p>\n
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On an episode of the “Unsupervised Learning” podcast on Thursday, OpenAI’s chief scientist Jakub Pachocki said recent\u00a0breakthroughs in coding<\/a>, advances in math research capabilities, and progress in physics suggest AI is on track to handle increasingly complex, multi-step technical work with less human oversight.<\/p>\n “I definitely see this as a signal that something here is on track,” he said.<\/p>\n He said the key measure is how long a model can work mostly autonomously.<\/p>\n “The way I would distinguish a research intern<\/a> from a full automated researcher is the span of time that we would have it work mostly autonomously,” Pachocki said, pointing to longer task horizons as the key metric of progress.<\/p>\n At a company livestream in October, Pachocki outlined OpenAI’s internal goal of building an “AI research intern<\/a>” by September 2026, followed by a fully autonomous AI researcher by March 2028.<\/p>\n In an X post following the livestream, OpenAI CEO Sam Altman said OpenAI “may totally fail” at the goal, but that it was important to be transparent given the potential impact.<\/p>\n ‘Explosive growth of coding tools’<\/p>\n Pachocki said the company is already seeing fast progress in the kinds of tasks that matter for that goal, pointing to coding agents like Codex,<\/a> which he said are now handling much of the company’s programming work.<\/p>\n He also pointed to math benchmarks as a “north star” for improving model reasoning, since they are easy to verify.<\/p>\n “We’ve seen this explosive growth of coding tools,” Pachocki said. “For most people, the act of programming has changed quite a bit.”<\/p>\n He added that the near-term challenge is moving toward systems that can tackle specific technical tasks<\/a> with more autonomy, use more compute, and work for longer stretches of time.<\/p>\n “For more specific technical ideas, like I have this particular idea how to improve the models, how to run this evaluation differently, I think we have the pieces that we mostly just need to put together,” he said.<\/p>\n Still, Pachocki was clear that AI isn’t ready to operate independently at the level of a full researcher.<\/p>\n