The Governance of Artificial Intelligence

I expect AI to rapidly transform our world over the next decade. It’s the most important technological development of this century, and it needs attention. If you’re interested in working in this area, I’m always available for conversations. Reach out.

Papers

Position: Strategic Resilience Requires the Targeted Maintenance of Human Technical Capacities (2026)

As AI systems are deployed across critical infrastructural, decision-making, and economic domains, two trends compound to create a structural risk: humans lose proficiency in tasks delegated to AI (deskilling), and AI develops capabilities which exceed current human expertise (supersession). Existing scholarship on resilience to advanced AI does not address this risk as a strategic problem, potentially resulting in human disempowerment vis-à-vis AI. In this paper, we address the technical requirements behind strategic resilience—the ability of a human society to maintain power over critical systems relative to advanced AI—and propose one productive avenue for future research: a Strategic Human Capacity Reserve (SHCR). The SHCR requires further study along three critical dimensions: threat modeling, skill measurement, and policy development.

Government AI Use as a Monitoring Primitive: A Public Document Pilot Study (2026)

Governments are important actors in frontier AI governance, but many facts about their adoption and use of AI systems are difficult to observe directly. Procurement disclosures and official statements are useful, but can also be delayed, selective, and better suited to measuring formal adoption than actual day-to-day use. We propose a complementary monitoring primitive: measuring traces of language-model assistance in public government documents. The approach is lightweight, externally reproducible, and based on revealed behavior rather than stated intent. In a pilot study of ten public document streams from U.S. and PRC government-related sources, we find that, while 2021 baselines are consistently near zero, by 2026, four of our ten sources show statistically significant signs of AI-assisted writing. In our sample, the U.S. signal concentrates in publications downstream of policy work; the PRC signal concentrates closer to it. We close by discussing how this signal could complement existing instruments for monitoring government AI adoption, and where it falls short.

Classes

I designed and taught a rigorous “Governance of Artificial Intelligence” class at Harvard (Spring 2026). My syllabus has been featured on the 80,000 hours policy upskilling recommendation page. You can find the reading list here; I’m happy to share a more detailed syllabus (including research assignments) on request.