Mapping AI

Reducing existential risk from powerful AI systems will require technical work and governance responses to be coordinated. People working on those interventions need a current picture of the field and the world models of the entities shaping it. Relevant information is scattered across organization websites, public statements, policy documents, staff announcements, informal living knowledge, and research outputs. Mapping AI turns that material into structured records about the people and organizations working on AI policy, their stated and perceived views, and their relationships to each other.

Safety researchers, funders, and policy teams can use the map to find related work, identify potential collaborators, track changes in stances, and support crosspartisan coalition-building. The map also gives new entrants a public starting point for understanding who is working on a problem and where a proposed intervention fits.

Many AI-policy trackers are paywalled or built for organizations with dedicated government-affairs capacity. Mapping AI provides a public, open-source alternative that lets safety researchers, academics, policymakers, and civil society groups access and contribute structured information about the AI governance landscape.

Mapping AI is an open stakeholder-mapping tool for the U.S. AI policy landscape. The database includes 3000+ entities and relationships between them, covering funding streams and collaborations, alongside measures such as each actor's regulatory stance, AGI timelines, levels of x-risk, and threat-model focus. A BlueDot rapid grant helped support our launch in May.

With support from the first grant, we hosted our first community workshop (a “Mapping Party”) in San Francisco, created a community Discord, launched publicly with a thread on X that drew millions of page views, and were featured on NPR Marketplace shortly after. In June we worked with a volunteer to add a crowdsourced verification site where contributors can check individual claims.

After the launch, we received inbound from dozens of people and orgs, like academics, think tanks, AI safety groups, civil society orgs, and forecasters, several asking for API or direct data access. Many of our new priorities to scale the tool have come out of the feedback we received. What those organizations tell us is that the map is useful as long as the data stays rich and current (which may also involve future design choices that narrow its scope). However, data about a landscape that changes weekly cannot sustainably be maintained by hand, by our volunteers or a host org’s FTEs.

As the tool continues to grow, the co-leads don't have the full-time capacity to maintain it long-term. We have worked on this nearly full-time since February alongside our day jobs, and we're hoping to free up more time to think deeply about the policy and governance issues the tool surfaces. We also believe the tool is more effective when, in addition to being a strategic awareness resource used and built by the public, it can be hosted by an organization with capacity to maintain and scale it. A non-negotiable is that the tool remains fully open-source and publicly accessible. This grant funds the costs in building an automated data seeding/verification layer and additional features that would allow the tool to be easily maintained by any host organization: agents that review crowdsourced submissions, read new sources, check claims against the database, recheck old entries on a schedule, and seed new organizations, people, and policy issues as they emerge. We would build the verification scripts and features around the adopting organization's needs and timeline, and run the system alongside them until the handover.