# Artificial Intelligence Clones

arXiv: https://arxiv.org/abs/2501.16996 · HTML: https://mlsanity.com/paper/2501.16996/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2025.

## Built on by (most cited first, 5 of 5)

- [Magentic Marketplace: An Open-Source Environment for Studying Agentic Markets](https://mlsanity.com/paper/2510.25779/) (2025)
- [An Economy of AI Agents](https://mlsanity.com/paper/2509.01063/) (2025)
- [From Product Search to Preference Articulation: The Economics of Agentic Commerce](https://mlsanity.com/paper/2608.08395/) (2026)
- [LLM Biases](https://mlsanity.com/paper/2604.26960/) (2026)
- [Et Tu, Brute? Economic Misalignment in Personal AI Agents](https://mlsanity.com/paper/2609.24927/) (2026)

## Abstract (arXiv)

Large language models, trained on personal data, are increasingly 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 representations 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 representation is modeled as a noisy approximation of that personality. I compare two search regimes: Under in person search, each person randomly meets some number of individuals and matches to the most compatible among them; under AI-mediated search, individuals match to the person with the most compatible AI representation. I show that a finite number of in-person encounters yields a better expected match than search over infinite AI representations. 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.