That idea is not without its detractors. Among other issues, many feel AI is not capable of the creative thought needed in research, makes too many mistakes and hallucinations, and may limit opportunities for young researchers. Nevertheless, a number of scientists and policymakers are very keen on the promise of AI scientists. The US government’s AI Action Plan describes the need to “invest in automated cloud-enabled labs for a range of scientific fields.” Some researchers think AI scientists could unlock scientific discoveries that humans could never find alone. For Zou, the proposition is simple: “AI agents are not limited in time. They could actually meet with us and work with us 24/7.” Last month, Zou published an article in Nature with results obtained from his own group of autonomous AI workers. Spurred on by his success, he now wants to see what other AI scientists (that is, scientists that are AI) can accomplish. He describes what a successful paper at Agents4Science will look like: “The AI should be the first author and do most of the work. Humans can be advisors.” A virtual lab staffed by AI As a PhD student at Harvard in the early 2010s, Zou was so interested in AI’s potential for science that he took a year off from his computing research to work in a genomics lab, in a field that has greatly benefited from technology to map entire genomes. His time in so-called wet labs taught him how difficult it can be to work with experts in other fields. “They often have different languages,” he says. Large language models, he believes, are better than people at deciphering and translating between subject-specific jargon. “They’ve read so broadly,” Zou says, that they can translate and generalize ideas across science very well. This idea inspired Zou to dream up what he calls the “Virtual Lab.” At a high level, the Virtual Lab would be a team of AI agents designed to mimic an actual university lab group. These agents would have various fields of expertise and could interact with different programs, like AlphaFold. Researchers could give one or more of these agents an agenda to work on, then open up the model to play back how the agents communicated to each other and determine which experiments people should pursue in a real-world trial.