Why "AI for science" is undervaluing the bottleneck it is best placed to fix
Most AI-for-science investment chases discovery. The higher-leverage use is making the experimental record reproducible and machine-readable.
Discovery captures attention; infrastructure captures compounding returns. Funding agencies and platforms that systematize how experiments are recorded, indexed, and replicated will create more cumulative value than the next foundation model trained on papers.