The User Side of AI Model Lifecycles: Evidence from the Keep4o Movement
2026-08-17 • Human-Computer Interaction
Human-Computer InteractionComputers and Society
AI summaryⓘ
The authors studied how people reacted after a new AI model version, called GPT-4o, was introduced and users wanted the old version kept. They analyzed many online posts and found that users' concerns were not just about accessing the model but about their experiences, how the model changed, and how decisions were managed over time. The study shows that simply replacing one AI model version with another doesn’t always meet user needs or expectations. The authors argue that managing AI systems after they’re released should pay close attention to user value and experiences. This helps better understand the real impact of model updates and improve future decision-making.
AI model lifecyclepost-deploymentuser experienceGPT-4omodel versioningcontent analysisuser valuemodel governanceinteractional value
Authors
Yiwen Wu
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.