Scaling laws describe how language model capabilities grow with compute and data, but say nothing about how long a model matters once released. We introduce time-to-peak and lifespan as measures of model obsolescence and use them to characterize the scientific adoption trajectories of 62 LLMs across more than 108k citing papers (2019-2025), separating active adoption from background citation to recover per-model trajectories that citation counts cannot resolve. We find that a model's longevity is shaped more by when it was released than by its characteristics: release year predicts time-to-peak and lifespan more strongly than architecture, openness, or scale. LLM adoption follows an inverted-U curve (rising after release, peaking, and then declining), but this pattern is rapidly compressing. Each successive release year is associated with a 27% shorter time-to-peak and a 23% shorter lifespan ($p < 0.001$), robust to minimum-age thresholds and controls for model size. These adoption-side dynamics are invisible to scaling laws and suggest that specialization on any single model may be a depreciating investment, with costs falling on reproducibility and migration.

翻译：标度定律描述了语言模型能力如何随计算量和数据规模增长，但未揭示模型发布后能持续发挥作用的时间。我们引入"达到峰值时间"和"生命周期"作为模型过时程度的度量指标，利用它们刻画了62个LLM在超过10.8万篇引用论文（2019-2025年）中的科学采纳轨迹。通过将主动采纳与背景引用分离，我们恢复了引用数量无法分辨的每个模型的采纳轨迹。研究发现，模型寿命受其发布时间的塑造程度大于模型自身特征：发布年份对峰值时间和生命周期的预测能力强于架构、开放性或规模。LLM的采纳呈倒U型曲线（发布后上升、达到顶峰、随后下降），但这一模式正在迅速压缩。每连续一个发布年份，峰值时间缩短27%，生命周期缩短23%（p < 0.001），这一结论在设定不同最小年龄阈值和控制模型规模后依然稳健。这些采纳侧动态对标度定律而言不可见，并表明对任何单一模型的专业化投资可能正在贬值，其代价落在可重复性和迁移性上。