On Wednesday, Anthropic announced that its molecular biology lab had made a discovery after running agents for 21 hours.
The system, composed of 950 agents, scanned millions of DNA sequences and flagged a repeating pattern around a known enzyme. Anthropic stated this pattern had not been catalogued before and described it as reminiscent of the findings that led to CRISPR technology.
The biological reaction
These claims have angered some biologists. A viral post from a researcher, subsequently endorsed by the chair and CEO of Eli Lilly, argued that finding a cluster of genes is often the easy part. The hard part, where real discoveries come from, is figuring out what the system actually does.
The agents helped with laboratory grunt work, but a discovery is not the result. Even if AI finds a pattern in biological data that would be difficult for human eyes alone to perceive, the result may not constitute a breakthrough for science. What is novel for AI may be routine, unsurprising, or simply not consequential to a biologist.
Questions about originality
Muddying the issue further, Mario Rodríguez Mestre, a biologist at the University of Copenhagen, said over the weekend that his team had already discovered this particular pattern. The New York Times reported this account.
Mestre, who regularly chatted with Claude in his work, wondered whether Anthropic’s team had learned from his conversations. Anthropic denies this, but Mestre says he is stopping all use of Claude anyway.
How companies frame their work
Part of the problem is that AI companies are not presenting their systems simply as tools scientists can use, like microscopes or supercomputers. They are insisting that the AI systems are making discoveries themselves.
To some, that approach is incompatible with how science actually works, with new knowledge more typically emerging from collaboration and an ever-growing arsenal of tools. It is also making people more skeptical of genuine progress when it happens.
Whittling 200,000 candidates down to a few worth exploring is no small feat. It is legitimate scientific work. The fact that a general-purpose chatbot could do that work is notable, even if humans helped steer it and ultimately ran the experiments. But once the standard is whether Claude itself made a discovery, all that becomes evidence for one side or the other in a debate that has only two answers: breakthrough or bust.
Shifting the goalposts
Once we are judging AI by whether it has made a discovery, it is also tempting to shift the goalposts even after it really does seem to notch a win. Earlier this month, OpenAI said its own team agents had cracked a million-dollar problem in mathematics.
But a couple of weeks later, nearly every AI skeptic was sharing an article asking whether it was the math problem that really mattered. The piece did not argue that OpenAI’s solution was wrong. Instead, it argued that the particular result may not be the one mathematicians care most about.
Throw in the accusation by a mathematician that the models may have used some of his work without credit, and people are left thinking either OpenAI cheated or the solution was not important anyway. Or both.
What it means
Lucas Harrington, the biologist who wrote the post critiquing Anthropic’s announcement, suggested that AI companies should set the bar high now. He said this is necessary so that when an AI actually discovers a fundamentally new biological mechanism, everyone appreciates how big a deal it is.
As OpenAI’s Sam Altman and Anthropic’s Dario Amodei race to one-up each other, raising the bar for scientific breakthroughs by AI might be the last thing on their minds.
