推理型AI的算力消耗不受限，仅靠效率提升无法实现可持续发展。
Efficiency Will Not Lead to Sustainable Reasoning AI
- 传统效率提升已逼近物理极限，推理AI仍持续依赖指数级算力增长。
- 当前推理模型性能不再受数据量限制，而是随算力投入无限扩展。
- 需在技术和政策层面设定明确算力上限，推动可持续发展。
AI研究正转向复杂问题求解，模型不仅追求模式识别，更注重多步推理能力。历史上，全球计算能耗因持续的能效提升和需求自然饱和而趋于稳定。但随着能效改进接近物理极限，新兴推理型AI缺乏类似的饱和点：性能不再受限于训练数据量，而是随训练与推理阶段的算力投入呈指数级增长。本文指出，仅靠效率提升无法实现可持续的推理型AI，并探讨了在系统优化与治理中嵌入明确限制的研究与政策方向。
原文摘要 · Abstract (English)
AI research is increasingly moving toward complex problem solving, where models are optimized not only for pattern recognition but for multi-step reasoning. Historically, computing's global energy footprint has been stabilized by sustained efficiency gains and natural saturation thresholds in demand. But as efficiency improvements are approaching physical limits, emerging reasoning AI lacks comparable saturation points: performance is no longer limited by the amount of available training data but continues to scale with exponential compute investments in both training and inference. This paper argues that efficiency alone will not lead to sustainable reasoning AI and discusses research and policy directions to embed explicit limits into the optimization and governance of such systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。