Interview Questions for Jamez Zou, Stanford

James Zou’s (Stanford) responses to interview questions from Tal Behar, Precision World Medicine ConferenceQ1.
When your virtual AI lab started generating and debating its own hypotheses, what was one idea or research direction it proposed that genuinely surprised you, and what did that moment change in how you think about discovery?
A1. The virtual lab AI agents made several decisions, such as choosing to design nanobodies rather than the more common antibodies, that surprised me initially but turned out to work well. Most of the decisions and work done by the virtual lab are quite human-like, actually, but much faster. For example, one virtual lab research meeting might take less than a minute, and the agents typically run five meetings in parallel to discuss each topic so they can explore a broader set of ideas. That efficiency will change what discoveries are possible.Q2.
Where does this agentic AI approach still break today, or make you pause before acting on its output, and where does it already outperform how human teams typically reason or explore the problem space?
A2. The current AI agents are better at leveraging and combining existing tools than at creating entirely new tools from scratch. For example, agents can adapt AlphaFold and combine it with other models to create new computational pipelines for drug discovery—I call this combinatorial creativity. It’s still open how to make truly creative AI agents that can come up with entirely novel yet feasible ideas.Q3.
Looking ahead a few years, what does meaningful “human judgment” look like in an AI-native lab, and what is the first concrete proof point that would convince skeptics that this model can close the loop from discovery to development?A3. Imagine each human researcher being a mini-PI, supported by a customized virtual lab of AI agents. The human manager would delegate tasks to the agents, review critical steps and give feedback. The agents would be running tirelessly, analyzing data, synthesizing literature knowledge, drafting documentation, etc. The technology is getting close to the point where this is feasible. This would require changing human habits and workflows, which would take longer than getting the technology ready.