Artificial Intelligence Clones

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Large language models, trained on personal data, may soon be able to mimic individual personalities. These "AI clones' or "AI agents' have the potential to transform how people search for matches in contexts ranging from marriage to employment. 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. An individual's personality is modeled as a point in k-dimensional Euclidean space, and an individual's AI clone is modeled as a noisy approximation of that personality. I compare two search regimes: In the "in-person regime,' each person randomly meets some number of individuals and matches to the most compatible among them; in the "AI representation regime,' 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 yields a better expected match than search over infinite AI clones. Moreover, when personality is sufficiently high-dimensional, simply meeting two people in person is more effective than search on an AI platform, regardless of the size of its candidate pool.