If you connect your AI to Glean, it gives you all the information that you need to do your work, and that results in AI consuming far fewer tokens compared to if you unleash AI onto your systems directly. That's because with Glean, AI ends up performing fewer operations.

When you stop using the agent, all the productivity benefit goes away... but the added maintenance costs don't!

When Anthropic launched Opus 4.5 in November 2025, the bigger, more expensive model was actually cheaper to use.

Sora 每天烧掉大约 100 万美元的推理成本，活跃用户从峰值的 100 万跌到不足 50 万。

For instance, a recent analysis by Epoch AI of the total training cost of AI models estimated that energy was a marginal part of total cost of AI training and experimentation (less than 6% in the case of all 4 frontier AI models analyzed), and a recent analysis by Dwarkesh Patel and Romeo Dean estimated that power generation represents roughly 7% of a datacenter’s cost.

Hans Moravec argued in 1976 that computers were still millions of times too weak to exhibit intelligence. He suggested an analogy: artificial intelligence requires computer power in the same way that aircraft require horsepower. Below a certain threshold, it's impossible, but, as power increases, eventually it could become easy.[79] With regard to computer vision, Moravec estimated that simply matching the edge and motion detection capabilities of human retina in real time would require a general-purpose computer capable of 109 operations/second (1000 MIPS).[80] As of 2011, practical computer vision applications require 10,000 to 1,000,000 MIPS. By comparison, the fastest supercomputer in 1976, Cray-1 (retailing at $5 million to $8 million), was only capable of around 80 to 130 MIPS, and a typical desktop computer at the time achieved less than 1 MIPS.