# Undermining Mental Proof: How AI Can Make Cooperation Harder by Making Thinking Easier

arXiv: https://arxiv.org/abs/2407.14452 · HTML: https://mlsanity.com/paper/2407.14452/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2024.

## Built on by (most cited first, 2 of 2)

- [Scientific production in the era of Large Language Models](https://mlsanity.com/paper/2601.13187/) (2026)
- [Academic journals' AI policies fail to curb the surge in AI-assisted academic writing](https://mlsanity.com/paper/2512.06705/) (2025)

## Abstract (arXiv)

Large language models and other highly capable AI systems ease the burdens of deciding what to say or do, but this very ease can undermine the effectiveness of our actions in social contexts. We explain this apparent tension by introducing the integrative theoretical concept of "mental proof," which occurs when observable actions are used to certify unobservable mental facts. From hiring to dating, mental proofs enable people to credibly communicate values, intentions, states of knowledge, and other private features of their minds to one another in low-trust environments where honesty cannot be easily enforced. Drawing on results from economics, theoretical biology, and computer science, we describe the core theoretical mechanisms that enable people to effect mental proofs. An analysis of these mechanisms clarifies when and how artificial intelligence can make low-trust cooperation harder despite making thinking easier.