At a glance
WHAT IT’S REALLY ABOUT
AI adoption is exploding faster than power, chips, and data centers
- Gavin Baker and David George argue that AI demand is accelerating across labs, open source, and applications even as public AI equities have drawn down, making the real risk persistent compute undersupply rather than a near-term bubble pop.
- They contend the AI buildout has unusually strong economics—often sub‑one‑year paybacks—supported by prepayments, spot-market monetization, extended useful lives, and sophisticated project financing.
- They describe a demand-side story still in its infancy: token spend is concentrated among a small set of heavy users today, but diffusion to 1.5B knowledge workers plus agentic automation could multiply consumption dramatically.
- They warn that regulation and local opposition to data centers could create ‘compute inequality’ and slow societal benefits, while emphasizing data centers’ role in reindustrializing parts of the U.S. through jobs and tax bases.
- They explore ‘future capacity’ bets—especially SpaceX orbital compute enabled by Starship reusability—and predict a multi-model enterprise future where the key battle is who becomes the abstraction layer for intelligence across organizations.
IDEAS WORTH REMEMBERING
5 ideasAI is more likely to be supply-constrained than overbuilt through the late 2020s.
They argue the market is still early in real adoption (tens of millions of heavy users vs ~1.5B knowledge workers), while physical constraints (power, chips, copper, permitting, politics) limit buildouts—creating a sustained shortage dynamic rather than an imminent glut.
Public-market AI financials may be unusually volatile because revenue is partly a compute-allocation choice.
Unlike prior tech waves where serving more users didn’t force a direct infrastructure trade-off, frontier labs can materially swing reported revenue by reallocating scarce power/compute between inference (monetized) and training/research (future capability).
Compute infrastructure can justify massive capital deployment because payback periods are exceptionally short.
They cite disclosures and unit-economics heuristics suggesting gigawatt-scale deployments can pay back in under a year, aided by customer prepayments, spot pricing, and improving utilization/asset life—plus the ability to finance large portions of the build.
Autonomous agents imply potentially ‘endless’ token consumption, not just incremental productivity gains.
As companies move from “reactive” copilots (summaries, assist) toward autonomous agents that plan and execute workflows, token usage becomes closer to an open-ended operational input—driving much higher ongoing demand.
The AI industry’s biggest near-term risk is political/regulatory friction, not technical feasibility.
They claim data centers can dramatically increase local tax revenue and jobs, and that some popular critiques (especially water use) are overstated; they also warn that blocking buildouts could produce ‘compute inequality’ where only large firms/wealthy users can afford frontier access.
WORDS WORTH SAVING
5 quotesWhen the history of the 21st century is written, you know, there was like the Victorian age. I think this will be like the age of Elon and Jensen because they are fundamentally altering the fabric of human society and civilization.
— Gavin Baker
This is not an or thing, it's an and thing, right? Like, this is an and thing.
— David George
I just, in my career as an investor, there haven't been that many opportunities where you have companies that could deploy tens, hundreds of billions of dollars and get sub one year paybacks.
— Gavin Baker
The only person who can, the only group that can tell the AI, AI industry's truth is the AI industry. They need to just start telling the truth.
— Gavin Baker
An increasing fraction of the world's compute is gonna be in orbit.
— Gavin Baker
High quality AI-generated summary created from speaker-labeled transcript.
