Sayash Kapoor

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Email: sayashk@princeton.edu
AI Snake Oil Book Cover


My AI Snake Oil book with Arvind Narayanan was named one of Nature's 10 best books of 2024. The book looks critically at what AI can and cannot do. We're continuing that work through AI as Normal Technology, a newsletter with over 85,000 readers. Subscribe here.

I am an incoming assistant professor at the UC Berkeley School of Information, starting in July 2027. I completed my computer science Ph.D. requirements at Princeton University in August 2026, working at the Center for Information Technology Policy, where I was a Porter Ogden Jacobus Fellow. Previously, I was a senior fellow at Mozilla and a Laurance S. Rockefeller Graduate Prize Fellow at Princeton University.

I co-authored AI Snake Oil with Arvind Narayanan, named one of Nature's 10 best books of 2024. I have been included in the inaugural TIME100 AI list and have received the Privacy Papers for Policymakers Award, the 2026 IP3 Information Policy Award, and the Jacobus Fellowship, Princeton University's highest honor for a graduate student.

Prospective Ph.D. applicants: If you are interested in working with me at Berkeley, please fill out this form.

The science of AI evaluation

I study how to evaluate AI agents and frontier systems by building large-scale systems to conduct evaluations at scale.

AI as Normal Technology

With Arvind Narayanan, I write the AI as Normal Technology newsletter, and we are working on our next book on the topic before I join Berkeley.

Evidence-based AI policy

I work on AI policy grounded in evidence about model openness, evaluation access, transparency, and accountability.

  • Open foundation models:
    Science (2024): governance considerations for open foundation models.
    ICML oral (2024): societal impacts of open foundation models.
  • FMTI: A longitudinal effort to measure transparency across leading foundation model developers: 2025 (TMLR 2026) · 2024 (TMLR 2025) · 2023 (TMLR 2025)
  • Accountability and input:
    FLARE-AI (ICML 2026): interoperable AI flaw reporting.
    ASAP RFI on AI-based science.
    New Jersey Assembly: reducing harm from deepfakes.
    Congressional Forum: accountability in predictive AI.
AI-based science

I study how AI-based science fails to reproduce and what practices, benchmarks, and reporting standards can make computational research more credible.