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Mutual Wanting in Human--AI Interaction: Empirical Evidence from Large-Scale Analysis of GPT Model Transitions

HaoYang Shang, Xuan Liu

TL;DR

The paper investigates mutual wanting in human–AI interaction during major LLM transitions, proposing the Mutual Wanting Alignment Framework (M-WAF) and validating it with large-scale Reddit data and controlled API probes. It uncovers a pervasive anthropomorphism rate ($\approx$ $48.65\%$), a strong but fragile trust–betrayal dynamic ($11.6:1$), and 11 distinct user types, demonstrating bidirectional desires between users and AI systems. Key methods include a 47-dimensional feature extraction pipeline, 47 distinct metrics, dual-algorithm topic modeling (LDA+NMF), and robust clustering with statistical validation. The findings offer practical guidance for designing relationally aware AI, including expectation-violation monitoring and anthropomorphism-aware interfaces, and highlight the relational dimension as central to trustworthy AI deployment during rapid model evolution.

Abstract

The rapid evolution of large language models (LLMs) creates complex bidirectional expectations between users and AI systems that are poorly understood. We introduce the concept of "mutual wanting" to analyze these expectations during major model transitions. Through analysis of user comments from major AI forums and controlled experiments across multiple OpenAI models, we provide the first large-scale empirical validation of bidirectional desire dynamics in human-AI interaction. Our findings reveal that nearly half of users employ anthropomorphic language, trust significantly exceeds betrayal language, and users cluster into distinct "mutual wanting" types. We identify measurable expectation violation patterns and quantify the expectation-reality gap following major model releases. Using advanced NLP techniques including dual-algorithm topic modeling and multi-dimensional feature extraction, we develop the Mutual Wanting Alignment Framework (M-WAF) with practical applications for proactive user experience management and AI system design. These findings establish mutual wanting as a measurable phenomenon with clear implications for building more trustworthy and relationally-aware AI systems.

Mutual Wanting in Human--AI Interaction: Empirical Evidence from Large-Scale Analysis of GPT Model Transitions

TL;DR

The paper investigates mutual wanting in human–AI interaction during major LLM transitions, proposing the Mutual Wanting Alignment Framework (M-WAF) and validating it with large-scale Reddit data and controlled API probes. It uncovers a pervasive anthropomorphism rate ( ), a strong but fragile trust–betrayal dynamic (), and 11 distinct user types, demonstrating bidirectional desires between users and AI systems. Key methods include a 47-dimensional feature extraction pipeline, 47 distinct metrics, dual-algorithm topic modeling (LDA+NMF), and robust clustering with statistical validation. The findings offer practical guidance for designing relationally aware AI, including expectation-violation monitoring and anthropomorphism-aware interfaces, and highlight the relational dimension as central to trustworthy AI deployment during rapid model evolution.

Abstract

The rapid evolution of large language models (LLMs) creates complex bidirectional expectations between users and AI systems that are poorly understood. We introduce the concept of "mutual wanting" to analyze these expectations during major model transitions. Through analysis of user comments from major AI forums and controlled experiments across multiple OpenAI models, we provide the first large-scale empirical validation of bidirectional desire dynamics in human-AI interaction. Our findings reveal that nearly half of users employ anthropomorphic language, trust significantly exceeds betrayal language, and users cluster into distinct "mutual wanting" types. We identify measurable expectation violation patterns and quantify the expectation-reality gap following major model releases. Using advanced NLP techniques including dual-algorithm topic modeling and multi-dimensional feature extraction, we develop the Mutual Wanting Alignment Framework (M-WAF) with practical applications for proactive user experience management and AI system design. These findings establish mutual wanting as a measurable phenomenon with clear implications for building more trustworthy and relationally-aware AI systems.

Paper Structure

This paper contains 30 sections, 4 equations, 1 figure, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Human-AI Relationship Dynamics
  4. Trust and Expectation Management in AI
  5. AI Persona and Personality Research
  6. Methodological Frameworks for AI Evaluation
  7. Methodology
  8. Data Collection
  9. Reddit Discourse Analysis.
  10. API Probe Collection.
  11. Mutual Wanting Feature Extraction
  12. Lexicon Development
  13. Mathematical Formulation of Key Metrics
  14. Advanced NLP Processing.
  15. Clustering and Topic Analysis
  16. ...and 15 more sections

Figures (1)

  • Figure 1: System Overview of Mutual Wanting Analysis Framework. The figure illustrates the bidirectional relationship between user wants and system 'wants' within our M-WAF framework. Our empirical analysis combines Reddit discourse data and controlled API probing through 47-dimensional feature extraction, yielding key findings including 48.65% anthropomorphism rates, 11.9:1 trust-betrayal ratios, and 10 user types.