推理算力扩张正重塑AI治理模式，影响模型开放性与行业格局。
Inference Scaling Reshapes AI Governance
- 聚焦推理阶段算力扩展，改变模型部署与训练逻辑
- 大幅降低对开源权重依赖，削弱当前算力阈值监管有效性
- 适合关注AI治理、产业模式变革的政策制定者与研究者
从提升预训练算力转向扩大推理算力，可能深刻影响AI治理。其影响取决于新增推理算力主要用于外部部署还是实验室内的复杂训练流程。若在部署阶段快速扩展推理算力，则会降低开源权重的重要性（也降低保护封闭模型权重的需求），减弱首个类人水平模型的影响，改变前沿AI的商业模式，减少对高功耗数据中心的需求，并破坏当前基于训练算力阈值的治理范式。若推理算力用于训练过程，则可能带来更复杂的效应，从复兴预训练算力扩展到通过迭代蒸馏与放大实现递归自我改进。
原文摘要 · Abstract (English)
The shift from scaling up the pre-training compute of AI systems to scaling up their inference compute may have profound effects on AI governance. The nature of these effects depends crucially on whether this new inference compute will primarily be used during external deployment or as part of a more complex training programme within the lab. Rapid scaling of inference-at-deployment would: lower the importance of open-weight models (and of securing the weights of closed models), reduce the impact of the first human-level models, change the business model for frontier AI, reduce the need for power-intense data centres, and derail the current paradigm of AI governance via training compute thresholds. Rapid scaling of inference-during-training would have more ambiguous effects that range from a revitalisation of pre-training scaling to a form of recursive self-improvement via iterated distillation and amplification.
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