用能量与算力推演文明认知上限，揭示当前算力远未达物理极限。
The Cognitive Kardashev Scale: Quantifying the Material Envelope of Civilisational Computation
- 以能量、算力效率和分配比例构建文明计算能力上限模型。
- 现有能源可支撑的机器认知远超人类实际使用量。
- 算力瓶颈将取决于未来工程选择，适合关注技术战略的读者。
文明能进行多少计算？本文借鉴卡达舍夫的文明等级划分，提出衡量每级文明所能支撑的机器认知规模。假设文明拥有一定总功率，其中仅部分可用于计算，而每焦耳能量所能换取的计算量取决于当前硬件效率。三者乘积即为机器思维的理论上限。为使结果直观，以人脑处理速率作为参照单位（非等同于意识）。通过与当今超级计算机和AI加速器对比，得出两个意外结论：即使在现有能源条件下，机器认知潜力也远超人类实际使用，说明物理容量并非制约因素；未来十年，是能源还是硬件效率成为瓶颈，取决于尚未做出的工程选择。至于谁将获得这种认知能力——这是政治经济问题，本文的校准结果仅为该讨论提供输入。
原文摘要 · Abstract (English)
How much thinking can a civilisation do? Kardashev ranked civilisations by the energy they command. This paper borrows his ladder and asks how much machine cognition each rung could support. The arithmetic is deliberately simple. A civilisation has some total power. Only a fraction of that can be spared for computing, and each joule spent buys computation at whatever efficiency the hardware of the day has reached. The product of the three sets a ceiling on machine thought. To keep the resulting quantities intelligible, I express them in units of the human brain's own processing rate, as a rough yardstick rather than a claim about minds. Calibrating the ceiling against present-day supercomputers and AI accelerators led me to two conclusions I did not expect at the outset. Even today's energy supply could support far more machine cognition than humanity actually uses, so physical capacity is not what binds. And whether energy or hardware efficiency becomes the constraint over the coming decade turns on engineering choices that have not yet been made. On the question that may matter most, who gets access to the cognition it describes, the scale is silent. That is a matter of political economy, and the calibration is offered as an input to that debate.
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