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🔮 Exponential View #553: The story of 100 trillion tokens; China’s chip win; superhuman persuasion, Waymo ethics, Polymarket & hydrology++

An insider’s view of AI and exponential technologies

Azeem Azhar and Nathan Warren

Dec 7

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I haven’t read anyone as thorough as you on the AI bubble and related topics. – Miguel O., a paying member

Hi all,

In this edition:

Superhuman persuasion. New data shows that AI is more effective than TV ads at changing minds, even when it lies.
The chip sanctions failed. How “stacking” old tech is neutralizing the US blockade (and validating my 2020 thesis).
Solving the unsolved. Terence Tao calls AI “routine” and autonomous agents are finally cracking the backlog of neglected science.
The end of Hollywood. Financialization, not AI, may be the root cause.

In my latest video, I break down how I think about the overlapping technology S-curves that are driving the market upheaval:

There are more exponentials in this AI wave doing their work than just ChatGPT and large language models. These new technologies make new things possible: we start to do things we didn’t do before, either because they weren’t possible or because they were too expensive.

Listen on Apple Podcasts or Spotify

RIP chatbot, hello glass slipper

OpenRouter, an aggregator routing traffic across 300+ AI models for five million developers, released an analysis of 100 trillion tokens of usage. Their data offers a unique, cross-platform look at the market. It’s worth a deep read, but for now I’d highlight two directions:

First, the “glass slipper” effect – retention is driven by “first-to-solve,” not “first-to-market.” When a model is the first to unlock a specific, high-friction workload (like a complex reasoning chain), the cohort that adopts it shows effectively zero churn, even when cheaper or faster competitors emerge. This confirms my long-held view: customers don’t buy benchmarks; they buy solutions. Once a model fits the problem, like a glass slipper, switching costs become irrelevant.

Second, the shift to agentic inference is undeniable. In less than 12 months, reasoning-optimised models have surged from negligible to over 50% of all token volume. Consequently, average prompt lengths have quadrupled to over 6,000 tokens, while completion lengths have tripled. The insight here is that users aren’t becoming more verbose; the systems are. We are seeing the mechanical footprint of agentic loops iterating in the background.

Azeem Azhar 1d

the age of reason(ing)

China’s chip tipping point

China’s drive for semiconductor independence is accelerating faster than predicted. The recent Shanghai IPO of Moore Threads, a leading AI chipmaker, surged 425% on debut, signaling voracious domestic capital support for “China’s Nvidia” alternatives. This aligns with a bold forecast from Bernstein, that China is on track to produce more AI chips than it consumes by 2026, effectively neutralizing the intended chokehold of US export controls...

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