Topic
AI
4 posts filed under this topic.
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The model is not the molecule
A better predictor is not a better drug. The gap between model performance and biological consequence is where most AI-biotech claims quietly break.
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Evaluation is the product
As model weights commoditize, the durable asset in AI biotech is the loop that tells you whether a prediction was any good.
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Data moats in AI biotech are wet, not digital
Public datasets are a starting point, not a fortress. The durable advantage in AI biotech is operational: assays, automation, and the discipline to keep provenance.
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What I mean when I say “AI for biology”
A small taxonomy, because prediction tools, design tools, and lab automation are solving very different problems.