Google DeepMind has invested heavily in scientific AI for years, and it paid off in 2024 when Demis Hassabis and John Jumper, the company’s CEO and director, won the Nobel Prize in chemistry for AlphaFold, a specialized system that can predict the three-dimensional structure of a protein.
Now its competitors are working to catch up. In October 2025, OpenAI launched a team devoted to AI for science, and Anthropic announced several Claude features geared toward the biological sciences around the same time. OpenAI in particular has called building an autonomous researcher its “North Star.” It just announced GPT‑Rosalind, the first in a planned series of specialized scientific models. Google released its own AI co-scientist tool last February.
Under the hood, many of these AI-for-science systems are in fact multiple specialized AI agents working in concert. Google’s co-scientist uses a supervisor agent, a generation agent, and a ranking agent, among several others, in order to generate potential hypotheses and research plans in response to a goal provided by a human scientist. More recently, researchers at Stanford’s AI for Science Lab, led by James Zou, devised a “virtual lab” made up of agents that took on the roles of specialists in different scientific fields. They found that their system could design new antibody fragments that bind to SARS-CoV-2, the virus that causes covid.
