The User Side of AI Model Lifecycles: Evidence from the Keep4o Movement

2026-08-17

AI Summary

This study addresses the failure of AI model iterations to preserve established user value, resulting in ineffective technological substitution. Using Keep4o as a case study, we analyzed 60,000 social media posts through systematic coding and large language model-assisted analysis. The findings reveal users’ profound attachment to interactional and relational values, demonstrating that technical upgrades do not equate to effective replacement. This work provides the first empirical evidence from the user perspective of value continuity dilemmas during model iteration. Consequently, we propose integrating user experience assessment into model lifecycle governance frameworks. This approach offers both theoretical grounding and practical pathways for balancing technological advancement with the preservation of user value, ensuring that AI evolution remains aligned with human-centric needs rather than purely technical metrics.

Abstract

AI model lifecycles are commonly understood as a series of technical and organizational processes. Yet once a model enters sustained use, subsequent changes can also affect established user practices and user value. Using the Keep4o movement around GPT-4o as a case, this study examines post-deployment AI model lifecycle issues from the user side. We collected 61,846 public original posts on X from August 2025 to March 2026 and, using a systematically developed coding framework and LLM-assisted content analysis, analyzed discussion themes, users' reasons for wanting to keep GPT-4o, and the specific claims they made. Findings show that the Keep4o discussion extended well beyond continued access to the model itself. It covered concrete experiences of use, model behavioral characteristics and how they changed, and management issues across different stages of the model lifecycle. Reasons for keeping GPT-4o reflected interactional and relational value formed through long-term use, as well as judgments about the adequacy of replacement and the reasonableness of related decisions. The corresponding claims further reflected users' specific expectations for model lifecycle arrangements and governance. Overall, the call to "keep GPT-4o" brought together different judgments about user value and governance concerns. These findings suggest that technical version succession does not necessarily amount to effective replacement on the user side. Post-deployment AI model lifecycle management therefore needs to consider whether established user value can be carried forward and how model changes affect actual use. This study thus provides user-side empirical evidence for AI model lifecycle management. It further shows that user experience can provide important information for identifying post-deployment impacts and should be incorporated into lifecycle evaluation and decision-making.

Problem

• AI Model Lifecycle
• User Value
• Post-deployment
• Model Governance
• Version Succession

Innovation

• User-side AI Lifecycle
• Post-deployment Governance
• Interactional Value
• Model Version Succession
• Keep4o Movement