The traditional drug-centric trial model is failing. The next evolution is trials designed to validate the *decision-making process* itself, using platforms to assign the best therapy to heterogeneous patient groups, rather than testing one drug on a narrow population.

While AI holds long-term promise for molecule discovery, its most significant near-term impact in biotech is operational. The key benefits today are faster clinical trial recruitment and more efficient regulatory submissions. The revolutionary science of AI-driven drug design is still in its earliest stages.

By continuously measuring a drug's effect on the body (pharmacodynamics), the wearable device provides a real-time view of a patient's phenotype. This granular data can revolutionize clinical trial design, safety monitoring, and drug dosing, moving beyond static genomic data to understand real-world drug response.

By using big data for continuous, real-time post-market surveillance, the FDA can identify safety signals almost instantly. This robust safety net after a drug is launched paradoxically allows the agency to lower the evidence threshold required for initial approval, accelerating access to new cures.

The FDA now allows a single, well-designed pivotal trial instead of the traditional two. This reform significantly cuts costs by $100M-$300M and shortens development timelines, enabling companies to test twice as many potential drugs with the same capital.

While most focus on AI for drug discovery, Recursion is building an AI stack for clinical development, where 70% of costs lie. By using real-world data to pinpoint patient locations and causal AI to predict responders, they are improving trial enrollment rates by 1.5x. This demonstrates a holistic, end-to-end AI strategy that addresses bottlenecks across the entire value chain, not just the initial stages.

AI will create jobs in unexpected places. As AI accelerates the discovery of new drugs and medical treatments, the bottleneck will shift to human-centric validation. This will lead to significant job growth in the biomedical sector, particularly in roles related to managing and conducting clinical trials.

An FDA-style regulatory model would force AI companies to make a quantitative safety case for their models before deployment. This shifts the burden of proof from regulators to creators, creating powerful financial incentives for labs to invest heavily in safety research, much like pharmaceutical companies invest in clinical trials.

The FDA's current leadership appears to be raising the bar for approvals based on single-arm studies. Especially in slowly progressing diseases with variable endpoints, the agency now requires an effect so dramatic it's akin to a parachute's benefit—unmistakable and not subject to interpretation against historical data.