AI has accelerated progress in mathematical proofs but lags significantly in drug discovery and experimental biology, according to a Google-MIT study. Scientists report that AI has shifted bottlenecks toward physical experimentation and data collection, while many spend substantial time verifying AI outputs. Technical and practical hurdles—including automation limitations, data scarcity, and the complexity of real-world experiments—explain why AI hasn't yet transformed experimental sciences despite government emphasis on its potential.