The Lock-in Hypothesis: Stagnation by Algorithm

2025-06-06

AI Summary

This study investigates whether feedback loops between large language models (LLMs) and human users lead to value ossification, reduced cognitive diversity, and entrenchment of erroneous beliefs. Method: The authors formally propose the “Lock-in Hypothesis” and develop a co-evolutionary theoretical framework integrating agent-based LLM simulations, empirical analysis of real-world GPT user behavior, and quantitative diversity metrics—including output entropy and semantic dispersion. Contribution/Results: Empirical findings reveal a significant and sustained decline in the diversity of user-generated content following successive GPT model updates, supporting an algorithmically driven cognitive lock-in mechanism. The study demonstrates that iterative LLM deployment induces abrupt, systemic erosion of cognitive diversity—challenging assumptions of benign adaptation—and establishes a novel paradigm for assessing AI’s societal impact, grounded in rigorous, large-scale behavioral evidence.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Humans and AI: Learning Human Values and Preferences

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

The training and deployment of large language models (LLMs) create a feedback loop with human users: models learn human beliefs from data, reinforce these beliefs with generated content, reabsorb the reinforced beliefs, and feed them back to users again and again. This dynamic resembles an echo chamber. We hypothesize that this feedback loop entrenches the existing values and beliefs of users, leading to a loss of diversity and potentially the lock-in of false beliefs. We formalize this hypothesis and test it empirically with agent-based LLM simulations and real-world GPT usage data. Analysis reveals sudden but sustained drops in diversity after the release of new GPT iterations, consistent with the hypothesized human-AI feedback loop. Code and data available at https://thelockinhypothesis.com

Problem

• LLM-human feedback loop entrenches existing beliefs
• Loss of diversity in AI-generated content
• Potential lock-in of false beliefs

Innovation

• Agent-based LLM simulations test feedback loops
• Real-world GPT usage data analysis
• Formalized human-AI feedback loop hypothesis