AI持续扩展需不断实现效率翻倍，否则成本将难以承受。
Continued AI Scaling Requires Repeated Efficiency Doublings
- 将算力视为逻辑算力，强调硬件与算法协同提升效率
- 效率停滞会导致运行成本飙升，仅靠规模扩张难以为继
- 类摩尔定律的周期性效率提升是可持续发展的关键
本文认为，人工智能的持续扩展需要反复实现效率翻倍。经典的AI缩放定律仍具实用性，因它们使进展可预测，尽管存在收益递减。但这些定律中的算力变量应理解为逻辑算力，而非单一物理实现的记录。因此实际负担取决于物理资源实现该算力的效率。在此解读下，收益递减意味着运营负担上升，而不仅是曲线变平。持续进步要求在硬件、算法和系统方面反复取得进展，以使额外的逻辑算力在可接受成本内实现。相关类比是摩尔定律，更应被视作对周期性效率提升的组织性预期，而非数学定理。当前人工智能尚无统一的效率提升节奏，但近期证据表明趋势至少类似摩尔定律，有时甚至更快。因此本文主张：若要维持AI扩展的活跃性，重复的效率翻倍并非可选，而是必需的。
原文摘要 · Abstract (English)
This paper argues that continued AI scaling requires repeated efficiency doublings. Classical AI scaling laws remain useful because they make progress predictable despite diminishing returns, but the compute variable in those laws is best read as logical compute, not as a record of one fixed physical implementation. Practical burden therefore depends on the efficiency with which physical resources realize that compute. Under that interpretation, diminishing returns mean rising operational burden, not merely a flatter curve. Sustained progress then requires recurrent gains in hardware, algorithms, and systems that keep additional logical compute feasible at acceptable cost. The relevant analogy is Moore's Law, understood less as a theorem than as an organizing expectation of repeated efficiency improvement. AI does not yet have a single agreed cadence for such gains, but recent evidence suggests trends that are at least Moore-like and sometimes faster. The paper's claim is therefore simple: if AI scaling is to remain active, repeated efficiency doublings are not optional. They are required.
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