AI co-scientists reshape research workflows

AI research assistants are beginning to take on tasks once handled largely by human scientists, from scanning literature and comparing hypotheses to designing experiments and analyzing data. Google’s Co-Scientist uses multiple AI agents that pursue different lines of reasoning, critique ideas and test them against published evidence before converging on possible solutions.

At the Whitehead Institute for Biomedical Research, biochemist Anna Pertl used Co-Scientist to search for ways to target MYC, a cancer-linked protein that has long resisted drug-development efforts. After researchers corrected misunderstandings in the system’s framing, Co-Scientist reviewed more than 700 scientific papers and generated 108 possible strategies, ultimately proposing a counterintuitive approach: gluing molecular condensates together rather than dissolving them.

Similar platforms from Google, Anthropic, OpenAI, FutureHouse, Phylo, Huawei Technologies and Sakana AI are being tested across biology and other fields. Researchers have used these systems to identify drug combinations, explore tissue regeneration and reproduce theories about bacterial DNA transfer from public data.

The growing role of AI shifts more weight onto human judgment. Scientists still need to ask productive questions, catch errors, assess whether an idea is meaningful and verify claims experimentally. The same tools that promise faster discovery could also make training harder if students have fewer chances to build expertise through hands-on work.