Unlike LLMs, parameter count is a misleading metric for AI models in structural biology. These models have fewer than a billion parameters but are more computationally expensive to run due to cubic operations that model pairwise interactions, making inference cost the key bottleneck.

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 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.

Numenos AI found that unifying biological data without traditional borders, such as incorporating mouse data or cancer data for dermatological diseases, surprisingly increases the predictive accuracy of their models. This challenges the siloed approach to traditional research.

Unlike language models trained on existing internet data, Biohub's biological models require data that doesn't exist yet. Their strategy pairs a frontier AI lab with a "frontier biology" effort to invent new imaging and measurement tools, creating proprietary data streams to fuel their models.

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.

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.

While acknowledging the power of Large Language Models (LLMs) for linear biological data like protein sequences, CZI's strategy recognizes that biological processes are highly multidimensional and non-linear. The organization is focused on developing new types of AI that can accurately model this complexity, moving beyond the one-dimensional, sequential nature of language-based models.

While petabytes of observational DNA sequence data exist, it's insufficient for the next wave of AI. The key to creating powerful, functional models is generating causal data—from experiments that systematically test function—which is a current data bottleneck.