The article argues that AI’s success in molecular design won’t cure most diseases without a deeper understanding of human biology and disease mechanisms. It calls for large-scale, causal biological measurements and better links between cellular data and clinical outcomes before AI can deliver truly transformative medicines.

• AI drug discovery has focused on molecule design, but the real bottleneck is upstream: most diseases lack a validated biological target to hit.
• 90% of clinical trials fail because they target the wrong biological mechanism, not because molecule design is too slow.
• Genuinely new drug targets have dropped from ~100 in 2015 to ~30 in 2024, while 38 targets each now have 50+ drug programs piling onto the same well-known locks.
• Fixing this requires massive causal/perturbational biology data (billions more measured cell states), not just better AI models, since current cell atlases barely scratch the surface.