We interview 2026 primary candidates about their theory of change and diagnosis of failure points to inform strategy for the 2028 presidential election.

A pilot proposal for structured transparency between frontier labs and government statistical agencies.

Today’s corporate structures don’t hold AI megacorps accountable for their effects on the public. We’re exploring new institutional forms, such as an enforceable public fiduciary, that bind firms to account for the public interest through both internal governance and verifiable external checks.

Different institutional forms are suited to different problem spaces. The equivalent principle applied to AI governance include an IAEA-style scientific body for verifying compute and training claims, a NTSB-style bureau for investigating AI incidents across companies, and a FRO-style entity for funding shared safety infrastructure like evals and interpretability tools.

A public map of who shapes U.S. AI policy, what they believe, and how they are connected.

A systematic, causal map of how power concentrates across multiple world models.

Introducing the Compact Institute.

Sophia Wang, Anushree Chaudhuri

How does power concentrate across multiple world models?

Anushree Chaudhuri, Sophia Wang

A shared data commons for the government, labs, and the public

Anushree Chaudhuri

Part 1: The rise of planetary-scale technology companies, and their stakeholders

Sophia Wang