Mayank Agrawal

founder, proof of human · YC S23

I'm an entrepreneur building Proof of Human, the global identity network for the AI era. Earlier I studied how people make decisions at Princeton with Jon Cohen, Tom Griffiths, and Nathaniel Daw — and ran distance at Swarthmore before that.

As AI agents flood every login, form, and survey, the internet needs a way to tell people from machines. Proof of Human confirms there's a real person behind an account, a click, or a response.

Proof of Human is backed by Y Combinator and angels from Anthropic, Google DeepMind, TikTok, Vercel, Cursor, SpaceX, and more.

proof of human research

• Proof of Human: creating the invisible Turing Test for the internet
• Benchmarking Bot Detection Systems Against Modern AI Agents
• Benchmarking Leading AI Agents Against CAPTCHAs
• AI Capability isn't Humanness

minds, machines, and markets

• Oxford and the Philosopher-CEO
• CAPTCHAs can still detect AI agents
• The Integrity Crisis in Online Research
• Omaha as judgment day for AGI
• Proof of Human as a research agenda, not a product feature
• Why I Turned Down a Tenure-Track Professorship
• Guilty until proven human: a brief history of the Turing Test
• Three is more than two
• The Cartel
• The Most Interesting Fund in the World

further research

• Process Matters more than Output for Distinguishing Humans from Machines — arXiv, preprint
• Maxim Consequentialism for Bounded Agents — PhilPapers, preprint
• Stress, Intertemporal Choice, and Mitigation Behavior During the COVID-19 Pandemic — JEP: General
• A Computational Model of Unintentional Mind Wandering in Focused Attention Meditation
• Using Large-Scale Experiments and Machine Learning to Discover Theories of Human Decision-Making — Science
• The Temporal Dynamics of Opportunity Costs — Psych Review
• Scaling up Psychology via Scientific Regret Minimization — PNAS
• Pyglmnet: Python Implementation of Elastic-Net Regularized GLMs — JOSS
• Using Machine Learning to Guide Cognitive Modeling: A Case Study in Moral Reasoning — CogSci
• Predicting Beta Bursts from Local Field Potentials to Improve Closed-Loop DBS Paradigms in Parkinson's Patients — IEEE