AI模型生命周期的用户侧：来自Keep4o运动的证据
The User Side of AI Model Lifecycles: Evidence from the Keep4o Movement
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中文总结 AI 辅助
本研究以Keep4o运动为案例，通过分析X平台6万余条帖子，发现AI模型技术更迭未必是用户侧的有效替换，用户体验应纳入AI模型生命周期管理。
中文摘要 AI 辅助
AI模型生命周期通常被理解为一系列技术和组织流程。然而，一旦模型进入持续使用阶段，后续变更也会影响已确立的用户实践和用户价值。本研究以围绕GPT-4o展开的Keep4o运动为案例，从用户侧考察部署后AI模型生命周期的问题。我们收集了2025年8月至2026年3月期间X平台上的61846条公开原创帖子，采用系统开发的编码框架和大语言模型（LLM）辅助的内容分析方法，分析了讨论主题、用户希望保留GPT-4o的原因以及他们提出的具体主张。研究结果显示，Keep4o的讨论远不止于持续获取该模型本身，还涵盖了具体的使用体验、模型的行为特征及其变化，以及模型生命周期不同阶段的管理问题。保留GPT-4o的原因反映了长期使用形成的交互价值与关系价值，以及对替换的充分性和相关决策合理性的判断。对应的主张进一步体现了用户对模型生命周期安排和治理的具体期望。总体而言，“保留GPT-4o”的呼吁汇集了关于用户价值和治理关切的不同判断。这些发现表明，技术版本的更迭在用户侧未必等同于有效的替换，部署后AI模型生命周期管理因此需要考虑已确立的用户价值能否被传承，以及模型变更如何影响实际使用。本研究为AI模型生命周期管理提供了用户侧的实证证据，还表明用户体验可为识别部署后影响提供重要信息，应纳入生命周期评估与决策过程。
英文摘要
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.
