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. 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. 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. 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.

翻译：我们认为，在亚通用人工智能（sub-AGI）能力水平上，现代生成模型为知识与文化生产带来了结构性风险。我们将人类时序学习（Human Temporal Learning, HTL）定义为：通过长期持续解决问题而形成的路径依赖式知识积累。生成输出在表面特征上日益接近需要人类时序学习的作品，导致验证某输出是否反映真实人类学习的成本相对于其预期收益不断增长。一旦验证失去经济合理性，评估者将无论生产模式均给予奖励；而投入数年学习的生产者则被迫与几乎零成本的生成输出展开价格竞争。我们将这一路径称为价值崩溃（value collapse），并通过成本核查框架（costly-inspection framework）将其形式化。来自学术出版、法律实务、内容平台及软件安全等领域的跨域证据映射出验证侵蚀的四个阶段。对齐（alignment）的成功与否在其中作用正交——更好对齐的模型会缩小人类与人工智能输出之间的可观测差距，使来源验证愈发困难，从而在个体人工智能输出质量提升的同时，加剧对时序密集型工作的竞争压力。