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.

Contrary to the belief that AI requires perfect, clean data, the biggest opportunity lies in building technology that can find signals in messy, diverse data sets across different modalities and organisms. The tech should solve the data problem, not wait for it to be solved.

The primary bottleneck for creating powerful foundation models in biology is the lack of clean, large-scale experimental data—orders of magnitude less than what's available for LLMs. This creates a major opportunity for "data foundries" that use robotic labs to generate high-quality biological data at scale.

When automating lab processes, the primary challenge is not adapting to new scientific methods but scaling the infrastructure to handle the massive, 24/7 flow of data from instruments and process logs. This requires a robust data management strategy from the outset.

Early AI models advanced by scraping web text and code. The next revolution, especially in "AI for science," requires overcoming a major hurdle: consolidating and formatting the world's vast but fragmented scientific data across disciplines like chemistry and materials science for model training.

The primary obstacle to creating sophisticated AI models of cells isn't the AI itself, but the data. Existing datasets often perturb only one cellular variable at a time, failing to capture the complex interactions that arise from simultaneous changes. New platforms are needed to generate this multi-dimensional data.

Early efforts like the Human Cell Atlas were criticized as mere data collection ("stamp collecting"). However, the rise of LLMs provided the key to unlock this data's value, transforming vast, unstructured biological datasets into systems that generate scientific insights and move biology from discovery to engineering.

A critical weakness of current AI models is their inefficient learning process. They require exponentially more experience—sometimes 100,000 times more data than a human encounters in a lifetime—to acquire their skills. This highlights a key difference from human cognition and a major hurdle for developing more advanced, human-like AI.

Human perception is limited to 2D plus time, but cellular data from advanced microscopy is 5-dimensional (3D space, time, and color). The solution isn't to accelerate human analysis but to build AI models that can natively perceive and reason within these higher dimensions, much like AI for self-driving cars handles 3D plus time.