AI for Science: AI Scientists -- Tools, Co-authors, or Founders?

We have crossed an inflection point: AI has moved from passive tool to active agent that closes the loop on hypothesis generation, experimental design, and execution. Nations and corporations are investing at unprecedented scale, e.g., the U.S. Genesis Mission mobilizing 17 national laboratories, and a recent Nature study confirms that AI-augmented research is accelerating in adoption. The AI Scientist is no longer a vision; it is here. The question is no longer whether AI Scientists will reshape science, but how—and in particular, where AI sits on the spectrum from tool to co-author to founder. This distinction carries concrete consequences for authorship, credit, funding, and ethical oversight, yet these roles already coexist across domains without shared definitions to distinguish them. As a tool, AlphaFold predicts protein structures that biologists interpret and experimentally validate; GNoME screens hundreds of thousands of candidate crystals for thermodynamic stability while materials scientists choose which to synthesize. In each case, scientists retain full authority; the model accelerates search but does not set the agenda. As a co-author, AI autonomously executes substantial research steps within human-defined problem spaces: Coscientist uses large language models to plan chemical syntheses and drive robotic execution, CuspAI generates synthesizable materials candidates up to 10× faster, AlphaProof solves Olympiad problems at gold-medal level, and A-Lab combines target selection with robotic synthesis to realize novel compounds in a 17-day closed-loop campaign. At the far end, AI approaches founder: FutureHouse’s Kosmos identified and pursued questions without human guidance, Sakana’s AI Scientist autonomously generates ideas, designs experiments, and writes papers, and Lila Sciences has built “AI Science Factories”: autonomous labs integrating generative AI with robotics that generate hypotheses, execute experiments, and iterate across biology, chemistry, and materials science. These examples span a wide spectrum of autonomy, yet all fall under the umbrella of “AI Scientists.” Without shared definitions and meaningful benchmarks, we cannot separate marketing from milestones. Our workshop aims to fill this gap by bringing together ML researchers, domain scientists, experimentalists, policymakers, and industry practitioners to define clearer taxonomies, propose evaluation standards, and initiate governance dialogue for AI-driven discovery. Workshop attendees will leave with: (1) a shared vocabulary and taxonomy for discussing AI Scientist systems across domains; (2) concrete evaluation criteria for assessing whether AI contributions constitute tool use, co-authorship, or independent discovery; (3) draft principles for attribution, accountability, and governance that can inform institutional policies; and (4) connections across the AI and domain science communities to advance responsible development.