Hypothetica is designing standards-based decentralization of experimental science. AI is already formulating hypotheses, designing experiments, and analyzing data — what's missing is execution in the physical world, a body for the mind of the AI-scientist. We are developing scalable, general-purpose approaches to disaggregate experimental workflows into programmable, interoperable elements.
Networked, decentralized science demands interoperability — not just reproducible end results, but composable processes: standard ways to describe experiments and invoke their operations, so discrete elements assemble into integrated workflows. This lands at a moment of exploding complexity in biological measurement — frontier platforms producing spatially resolved, high-dimensional, dynamic readouts down to single cells. New metrology is needed to make those results trustworthy and networkable.
Founded by Marc Salit, who brings 40+ years in metrology and standards-development innovation — bringing the rigor of physical-science precision measurement to biology at the dawn of the genomics era. Hypothetica builds on this experience to create the network that brings experimental science within reach of AI.