The life sciences industry has spent the better part of three years chasing model performance. Bigger foundation models, more parameters, better fine-tuning pipelines. The implicit assumption has been that if you get the algorithms right, the rest follows. DNAnexus CEO Thomas Laur theorizes that the primary obstacle to AI-driven drug discovery is not model quality or algorithmic sophistication, but rather the inability to get scientific data into a state that AI systems can actually consume. The evidence supports him.

Faster drug discovery, optimized drug design, even programmable therapeutics: AI is impacting R&D. Data and development bottlenecks remain.