Confidence is not the destination
A prediction can look precise while leaving the central scientific question unresolved. The useful question is whether it helps a team choose an experiment that will clarify a mechanism, distinguish competing hypotheses, or reveal an important limitation.
Design for information
An experiment can be valuable even when the result is negative. If it rules out an assumption that shaped a program, it can prevent repeated work and redirect attention toward a more plausible explanation. The goal is learning that changes a decision.
Keep the loop connected
Our approach to AI-driven discovery begins with this relationship: models suggest, experiments challenge, and the resulting evidence informs the next question. Progress depends on the quality of that exchange, not simply the number of predictions produced.
All perspectives