Analyse · Physique appliquée
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.
Prof. Daniel Okafor2 mai 20263 min de lecture