Artificial Intelligence Clones

Abstract: LLMs, trained on personal data, may soon be able to mimic individual personalities. These AI clones'' orAI agents'' have the potential to transform how people search over one another in contexts ranging from marriage to employment -- indeed, several dating platforms have already begun using AI clones to evaluate potential pairings between users. This paper presents a theoretical framework to study the tradeoff between the substantially expanded search capacity of AI clones, and their imperfect representation of humans. Individual personalities are modeled as points in $k$-dimensional Euclidean space, and their AI clones are modeled as noisy approximations of these personalities. I compare two search regimes: an in-person regime'' -- where each person randomly meets some number of individuals and matches to the most compatible among them -- against anAI representation regime'' -- in which individuals match to the person whose AI clone is most compatible with their AI clone. I show that a finite number of in-person encounters exceeds the expected payoff from search over infinite AI clones. Moreover, when the dimensionality of personality is large, simply meeting two people in person produces a better expected match than entrusting the process to an AI platform, regardless of the size of its candidate pool.