Acknowledging the "garbage in, garbage out" principle, Haya heavily invests in generating high-quality, layered, and paired multi-omic data from the same biological material. This curated input is considered the most critical component for building effective AI models to unlock new biology.

The power of AI for Novonesis isn't the algorithm itself, but its application to a massive, well-structured proprietary dataset. Their organized library of 100,000 strains allows AI to rapidly predict protein shapes and accelerate R&D in ways competitors cannot match.

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.

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.

Despite the buzz, a clinical development expert cautions that AI's impact in drug development is limited. The primary bottleneck isn't the algorithms but the lack of sufficient, high-quality human biological data that can be translated into reliable predictions, as animal models often fail to provide it.

The primary value of AI in bioprocessing is not just automating tasks, but analyzing process data to predict outcomes. This requires a fundamental shift in capital equipment design, focusing on integrating more sensors and methods to collect far more granular data than is standard today.

The key advantage for AI biotech isn't the model itself, but generating massive, proprietary datasets ("science tokens") via automated labs. This novel data, which doesn't exist publicly, is crucial for training superior models and achieving true scientific intelligence.

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.