AI has solved Navier-Stokes. What would it take for AI to make similar advances in life sciences? In our latest preprint, led by @AdaFang_, we argue that, while fields like mathematics and programming can cheaply evaluate many AI-generated candidates at scale, "closing the loop" of hypothesis ➡️ experiment ➡️ revised hypothesis remains a central bottleneck in AI-driven biomedical discovery. We outline a vision for autonomous AI scientists that: 🧠 Reason over scientific knowledge, multimodal data, competing hypotheses, and uncertainty across multi-loop campaigns lasting days to months. 🔬 Learn under sparse and delayed feedback, combining soft verifiers (e.g., simulations, predictive models, and biological world models) with hard verifiers such as wet lab assays, robotic experiments, organoids, and clinical studies. 🎯 Allocate limited experimental budgets to tests with the greatest expected information gain. We suggest that achieving this vision will require advances in long-horizon reasoning, process-based evaluation, uncertainty-aware exploration, self-driving laboratories, and human-on-the-loop oversight. Read our paper here: preprints.org/manuscript/202… More details below from @marinkazitnik. 👇🏽