From the time I began work on AI in 2010 to now, the amount of training data that goes into frontier AI models has grown by a staggering 1 trillion times—from roughly 10¹⁴ flops for early systems to over 10²⁶ flops for today's largest models.
令人惊讶的是：AI训练数据的增长速度令人难以置信。从2010年到2026年，AI模型的训练数据量增长了1万亿倍，这是一个天文数字般的增长，远超大多数人的想象。这种指数级增长是AI发展的核心驱动力，也是为什么AI进步如此迅速的原因。
From the time I began work on AI in 2010 to now, the amount of training data that goes into frontier AI models has grown by a staggering 1 trillion times—from roughly 10¹⁴ flops for early systems to over 10²⁶ flops for today's largest models.
令人惊讶的是：AI训练数据量在短短16年间增长了1万亿倍，这是一个难以想象的指数级增长。这种计算能力的爆炸式发展远超人类直觉，解释了为什么AI进步如此迅速且难以预测。大多数人无法真正理解这种指数级增长意味着什么，这也是为什么许多专家对AI发展速度预测失败的原因。
GPT-3.5 — the model that powered the original ChatGPT — could complete tasks that took a human programmer about 30 seconds.
从 GPT-3.5 的 30 秒到 Claude Opus 4.6 的 12 小时，两年内增长了 1440 倍。从 GPT-2 到 GPT-5，任务难度增长了 5400 倍。这个进步速度在人类技术史上几乎没有先例——工业革命历经百年实现劳动效率数十倍提升，而 AI 在五年内实现了数千倍的某种意义上的「认知效率」提升。令人不安的是，这条曲线目前没有任何放缓的迹象。
a logistic curve is a poor fit because we haven't seen any evidence of the exponential growth in time horizon slowing down.
METR 明确指出：截至 2026 年初，时间地平线的指数增长没有任何放缓迹象——这意味着 S 曲线的「饱和阶段」尚未到来。对 AI 进展持怀疑态度者常援引「进步将减速」的论点，但这个数据点直接挑战了这一叙事。指数增长持续意味着每隔固定时间，AI 能独立完成的任务复杂度就翻倍——而这个倍增周期，根据历史数据，大约是 6-7 个月。
Global AI computing capacity is doubling every 7 months
Epoch AI 的相关研究显示全球 AI 算力每 7 个月翻倍——比摩尔定律（18-24 个月）快了 3 倍以上。在这个速度下，Google 今天 25% 的市场份额意味着：如果竞争对手没能跟上这个扩张节奏，算力差距不会缩小，只会以指数级扩大。算力竞赛正在进入「赢家通吃」的临界点。
destructive potential weapons over history, you see a very similar spike, very similar exponential curve as we've seen over and over again today
for - greal acceleration - exponential growth of destructiveness of weapons (in joules)
Trevor Bedford. (2022, January 28). Omicron viruses can be divided into two major groups, referred to as PANGO lineages BA.1 and BA.2 or @nextstrain clades 21K and 21L. The vast majority of globally sequenced Omicron have been 21K (~630k) compared a small minority of 21L (~18k), but 21L is gaining ground. 1/15 [Tweet]. @trvrb. https://twitter.com/trvrb/status/1487105396879679488
Ciccione, L., Sablé-Meyer, M., & Dehaene, S. (2021). Analyzing the misperception of exponential growth in graphs. PsyArXiv. https://doi.org/10.31234/osf.io/dah3x
Since about 70% of water delivered from the Colorado River goes to growing crops, not to people in cities, the next step will likely be to demand large-scale reductions for farmers and ranchers across millions of acres of land, forcing wrenching choices about which crops to grow and for whom — an omen that many of America’s food-generating regions might ultimately have to shift someplace else as the climate warms.
Deep Concept: The US Government, in the 1960's/70's provided a crystal ball glimpse into the future by defining climate change (man-made global warming) as a national security concern. Various reports warned of "exponential" growth (population) and related man-made factors (technology etc.) that would contribute to climate change and specifically discussed the possibility of irreconcilable damage to "finite" natural resources.
IZA – Institute of Labor Economics. ‘COVID-19 and the Labor Market’. Accessed 6 October 2020. https://covid-19.iza.org/publications/dp13664/.
Exponential-Growth Prediction Bias and Compliance with Safety Measures in the Times of COVID-19. COVID-19 and the Labor Market. (n.d.). IZA – Institute of Labor Economics. Retrieved August 5, 2020, from https://covid-19.iza.org/publications/dp13257/
Zimmer, C. (2020, June 1). How You Should Read Coronavirus Studies, or Any Science Paper. The New York Times. https://www.nytimes.com/article/how-to-read-a-science-study-coronavirus.html
Mbopi-Keou, F.-X., Pondi, J.-E., & Sosso, M. A. (2020). COVID-19 in Cameroon: A crucial equation to resolve. The Lancet Infectious Diseases, 0(0). https://doi.org/10.1016/S1473-3099(20)30373-X