The bottleneck for AI in drug discovery is not the algorithm but the lack of high-quality, large-scale biological data. New platforms are needed to generate this necessary "substrate" for AI models to learn from, challenging the narrative that better models alone are the solution.

The next inflection point will come from clever data generation strategies optimized for AI models, not human analysis. This "black box data" approach—like pooled screening with sequencing readouts—is vastly more scalable and creates a powerful, proprietary moat for companies.

AI's potential in drug discovery is contingent on having a robust "data factory" to generate massive, high-quality biological datasets. Najat Khan emphasizes that the combination of this data infrastructure, AI, supercomputing, and human expertise is what creates a true competitive advantage, not the algorithm alone.

The progress of AI in predicting cancer treatment is stalled not by algorithms, but by the data used to train them. Relying solely on static genetic data is insufficient. The critical missing piece is functional, contextual data showing how patient cells actually respond to drugs.

Current medical AI relies on electronic health records, which capture only infrequent snapshots of a person's health. The next breakthrough will come from companies that create new, continuous data rails by engaging with patients longitudinally, generating proprietary "N-of-one" datasets for superior models.

The bottleneck for AI in drug development isn't the sophistication of the models but the absence of large-scale, high-quality biological data sets. Without comprehensive data on how drugs interact within complex human systems, even the best AI models cannot make accurate predictions.

The competitive advantage in pharma isn't the sophistication of an AI algorithm, which is often a commodity built on third-party models. The true differentiator is the quality, relevance, and end-to-end consistency of the proprietary data used to train and validate these models. Poor data invalidates even the best analytics.

Frontier AI models excel in medicine less because of their encyclopedic knowledge and more because of their ability to integrate huge amounts of context. They can synthesize a patient's entire medical history with the latest research—a task difficult for any single human. This highlights that the key to unlocking AI's value is feeding it comprehensive data, as context is the primary driver of superhuman performance.

Beyond analyzing existing datasets, a significant benefit of AI is its ability to create higher-quality data in the first place. For instance, accurate, automated transcription of doctors' notes improves the richness and reliability of clinical data, creating a virtuous cycle for future analysis.