Generative Models Erode Human Temporal Learning Through Market Selection

生成式模型通过市场选择机制侵蚀人类的时间性学习

Abstract: We argue that modern generative models create structural risks for knowledge and cultural production at current, sub-AGI capability levels. We define Human Temporal Learning (HTL) as path-dependent knowledge accumulation through sustained engagement with problems over time.

摘要： 我们认为，在当前的次通用人工智能（sub-AGI）能力水平下，现代生成式模型对知识和文化生产构成了结构性风险。我们将“人类时间性学习”（Human Temporal Learning, HTL）定义为通过长期持续参与问题解决而实现的路径依赖型知识积累。

Generative outputs increasingly resemble HTL-intensive work in surface features, so verifying whether a given output reflects genuine human learning grows costly relative to its expected benefit. Once verification loses economic justification, evaluators reward outputs regardless of production mode, and producers who invested years of learning compete on price against outputs that cost almost nothing to generate.

生成式模型的输出在表面特征上越来越像高强度 HTL 的产物，因此，验证某一输出是否反映了真正的人类学习，其成本相对于预期收益而言变得越来越高。一旦验证过程失去了经济合理性，评估者就会不加区分地奖励所有输出，而那些投入多年学习的生产者，将不得不与几乎零成本生成的输出进行价格竞争。

We call this pathway value collapse and formalize it through a costly-inspection framework. Cross-domain evidence from academic publishing, legal practice, content platforms, and software security maps onto four stages of verification erosion.

我们将这一路径称为“价值坍塌”（value collapse），并通过一个“昂贵检查框架”（costly-inspection framework）对其进行了形式化描述。来自学术出版、法律实践、内容平台和软件安全等跨领域证据，映射出了验证侵蚀的四个阶段。

Alignment success is orthogonal. Better-aligned models narrow observable gaps between human and AI outputs, making source verification harder and intensifying competitive pressure against HTL-intensive work even when individual AI outputs improve.

对齐（Alignment）的成功与此问题正交。表现更好的对齐模型缩小了人类产出与 AI 产出之间可观察到的差距，这使得来源验证变得更加困难，并加剧了对高强度 HTL 工作的竞争压力，即便单个 AI 的输出质量在不断提升。