josh

Chai Discovery is building a computational design suite that uses diffusion models and scaling laws to achieve high hit rates (15% vs 0.1% prior) and enable de novo molecular design, turning drug discovery into an engineering discipline with rapid iteration cycles.

Data centers require not just GPUs but transformers, switchgear, steel, optical links — industrial capacity that China is subsidizing ($295B fund). Latency matters today but may matter less for agentic workloads; the window to build US industrial capacity (Giga Energy, solid-state transformers) is now, before supply chains cement overseas. Hyperscalers' deep pockets can fund energy infrastructure and next-gen tech.

Applying the bitter lesson — scaling compute, data, and model simplicity — to protein design has yielded step-function improvements in antibody binding rates, suggesting drug discovery follows the same scaling dynamics as language models.

Partnering with demanding pharma customers forces model rigor and generalization across diverse targets, creating a more reliable path to impact than full-stack drug development where limited targets risk overfitting.

Shortages of transformers, switchgear, and power electronics create opportunity for US manufacturers (e.g., Giga Energy) to build at scale with hyperscaler financing. Data centers' willingness to pay for speed and advanced tech (solid-state transformers, optical links) funds domestic production that wouldn't be viable otherwise.

Pharma companies like Eli Lilly and Novartis are rigorous validators but adopt AI quickly once models prove effective, driven by patent cliffs and competitive pressure to maintain pipelines; adoption upscales rapidly post-validation.

As models reach sufficient accuracy to design testable molecules, the resulting lab data — both successes and failures — becomes high-quality training data for next-gen models, mirroring the RLHF takeoff that accelerated LLM capabilities.