Topos Institute

Berkeley, CA, USA

established19 peopleFounded 2019

Endorsed by:

Topos Institute is a mission-driven nonprofit research institute headquartered in Berkeley, California, dedicated to shaping technology for public benefit by advancing mathematical sciences of connection and integration. Founded in 2019 by Brendan Fong, David Spivak, and Valeria de Paiva, the institute draws on category theory, topos theory, and type theory to develop foundational insights into complex systems and collective sense-making. Its work spans fundamental mathematical research, translational software development (including the AlgebraicJulia ecosystem and CatColab collaborative modeling platform), societal engagement on issues like public health and emerging technology risks, and institution-building as a model for public-interest research organizations.

Funding Details

Annual Budget: $1,901,519
Monthly Burn Rate: $158,460
Current Runway: -
Funding Goal: -
Funding Raised to Date: $6,447,927
Fiscal Sponsor: -

Theory of Change

Topos Institute believes that the mathematical sciences of connection and integration, particularly category theory, provide essential tools for understanding and designing complex systems that benefit humanity. Their theory of change operates through a vertical integration model: fundamental mathematical research produces new insights into how systems compose and interact, which are then translated into open-source computational tools accessible to working scientists and policymakers. These tools enable collaborative modeling and collective sense-making across domains like public health, climate, and AI safety. By developing rigorous mathematical frameworks for compositionality, formal verification, and systems specification, they aim to make complex technological systems (including AI) more transparent, auditable, and safe. Their work on categorical systems theory for AI safety, funded by ARIA's Safeguarded AI programme, directly targets the challenge of building mathematical foundations for verifying safety properties of autonomous AI systems.