The Discovery Coordination Problem in Biology
A category thesis on why better biological models are not enough by themselves, and why discovery programs need operating loops that preserve objectives, evidence, decisions, and redesign logic across cycles.
What is inside
The paper frames the next bottleneck in AI-enabled biology as coordination: converting objectives, model outputs, assays, interpretation, and redesign into a repeatable program-level loop.
It introduces the discovery latency stack, program memory types, and the idea of a discovery decision package as the customer-facing output of a biological optimization program.
Who should read it
- Partners evaluating AI-guided discovery workflows.
- Investors assessing platform biology narratives.
- Teams trying to shorten assay-to-redesign cycle time.
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