As AI agents’ scientific prowess grows, some scientists worry about getting scooped not just by human rivals, but also by bots. Credit: Getty
As AI agents’ ability to do science autonomously continues to improve, scientists are facing a once-unthinkable question: will I get scooped by a bot?
At least twice in the past five weeks, researchers have said that they had been working for an extended period on a particular research question, only to learn that artificial-intelligence companies had either answered the question before them or announced results before they were ready to do the same. In response, researchers who are worried that AI tools might scrape their unpublished work and scoop them are limiting their use of such tools.
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Nevertheless, some AI researchers, although not all, say that AI agents are increasingly likely to reach conclusions before scientists can do so. Researchers are “going to be scooped, but not because they’ve uploaded a manuscript”, says Geoffrey Irving, chief scientist at Resolution, an AI-safety research organization in Berkeley, California. “They’re going to get scooped because the AIs are very good at solving problems, and they’re going to get better and better.”
Controversial findings
On 7 September, Tristan Buckmaster, a mathematician at New York University in New York City, posted online that he and a collaborator had made progress on the Navier–Stokes problem, a long-standing open question in fluid dynamics. According to Buckmaster’s account, he and Levent Alpöge, a mathematician at the AI company Anthropic, based in San Francisco, California, whose work with Buckmaster was a personal project, had been pursuing the problem for a year. Buckmaster’s 7 September post also included a paper with the solution to a simpler version of the problem. The next day, 8 September, OpenAI, also in San Francisco, announced that its agents had solved the puzzle — a timing that Buckmaster has publicly questioned.
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Buckmaster raised the possibility that information that he and Alpöge had uploaded to an OpenAI tool could have been used to train the company’s models. OpenAI disputed this scenario. The company said in a statement that an investigation had confirmed that Buckmaster’s “prompts” in the two months before the 8 September announcement “could not have influenced the system in any way, including through training”. The statement also said that OpenAI’s researchers and agents “did not see any of their [Buckmaster and Alpöge’s] work through any means until they released it publicly”.
Two weeks later, Anthropic announced that agents running on its Claude large language model (LLM) had discovered that certain viruses have a pattern of repeated DNA segments similar to the pattern seen in CRISPR gene-editing systems. After the announcement, Mario Rodríguez Mestre, a PhD student in computational biology at the University of Copenhagen, told The New York Times that he has been studying the same DNA patterns for several years, often using Claude, but has not published the work. Mestre also raised the possibility that information his team had uploaded to Anthropic’s tools could have been incorporated into Claude’s training data. Anthropic told the Times that its model was “not trained on any user transcripts”.
Lack of trust
Despite these reassurances, some scientists are now re-evaluating how they use AI. Sandra Laurentino, a reproductive epigeneticist at the University of Münster in Germany, says she no longer trusts AI systems. She now uses AI only to check why her code fails, and before doing so changes all of the parameter and variable names to generic labels such as “group A has feature X” to avoid giving the system information about her experiments. “I am quite careful when it comes to AI,” she says, but the recent controversy has “made me even more paranoid”.
The AI discoveries have also made Samuel Mehr, an auditory cognitive scientist at the University of Auckland in New Zealand, even more hesitant to use commercial AI models than he already was. He has created an AI-use policy for his laboratory that forbids students from uploading protected information to commercial LLMs and warns against using the models for any part of the research process. “I think it’s incredibly risky to hand out your intellectual property to third parties when you don’t know what they’re going to do with it,” he says.