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The last time @GavinSBaker and I sat down to record a podcast, it was almost exactly a year ago and we started with the immediate question: is AI a bubble? Gavin's answer focused on utilization and returns (and perhaps unsurprisingly, his answer was: No). Unlike the dark fiber of 2000, there were, and still are, no idle GPUs. The largest buyers of compute were funding the buildout from some of the strongest balance sheets in the world, and their AI investment was already producing real returns.
I sat down with Gavin again last week, and to chart just exactly where we are in the cycle. He has spent the summer asking operators for one quantitative measure in their business that is getting worse and has yet to find one. At the same time, public AI stocks have gone through meaningful drawdowns.
Still, if you look at the way people are using AI today, there’s a good chance that demand diffusion has barely begun. AI revenue rests on fewer than 10m heavy users, against roughly 1.5b knowledge workers. At the most AI-native startups, token spend is approaching or exceeding 10% of human compensation, which suggests that there’s a long way to go before even the earliest adopters fully integrate agentic capabilities. Within Atreides, Gavin said token consumption rose 100x from March through August, and even further once the team started using products like GrokBot. As Gavin put it, once people begin approving automations, token consumption starts to feel “sort of endless.”
This dynamic would be reason enough alone to believe that we’re massively undersupplied at the moment. But there are other reasons on the supply side too, namely that there are a near-unbounded number of potential winners in the space:
- Frontier labs can keep winning because on the highest-value tasks, marginal improvements in intelligence are worth far more than marginal differences in price.
- Open models illustrate that most work doesn’t require frontier performance, creating a much larger market for intelligence that is cheaper and customizable.
- Nvidia, hyperscalers, neoclouds, and inference providers can simultaneously win because every additional token still requires physical compute, even if models become more efficient.
- Enterprises can win as proprietary data, workflows, and institutional knowledge become more valuable.
- Application companies can win by capturing services budgets and owning the customer relationship.
This doesn’t suggest everyone will be successful, but it does mean the market itself is positive-sum and we will look back on zero-sum thinking as far too limiting. Better models create better products, better products create more users, more users create more token demand, and more demand supports continued investment in models and infrastructure.
Check out the whole conversation below:
@a16z
I sat down with Gavin again last week, and to chart just exactly where we are in the cycle. He has spent the summer asking operators for one quantitative measure in their business that is getting worse and has yet to find one. At the same time, public AI stocks have gone through meaningful drawdowns.
Still, if you look at the way people are using AI today, there’s a good chance that demand diffusion has barely begun. AI revenue rests on fewer than 10m heavy users, against roughly 1.5b knowledge workers. At the most AI-native startups, token spend is approaching or exceeding 10% of human compensation, which suggests that there’s a long way to go before even the earliest adopters fully integrate agentic capabilities. Within Atreides, Gavin said token consumption rose 100x from March through August, and even further once the team started using products like GrokBot. As Gavin put it, once people begin approving automations, token consumption starts to feel “sort of endless.”
This dynamic would be reason enough alone to believe that we’re massively undersupplied at the moment. But there are other reasons on the supply side too, namely that there are a near-unbounded number of potential winners in the space:
- Frontier labs can keep winning because on the highest-value tasks, marginal improvements in intelligence are worth far more than marginal differences in price.
- Open models illustrate that most work doesn’t require frontier performance, creating a much larger market for intelligence that is cheaper and customizable.
- Nvidia, hyperscalers, neoclouds, and inference providers can simultaneously win because every additional token still requires physical compute, even if models become more efficient.
- Enterprises can win as proprietary data, workflows, and institutional knowledge become more valuable.
- Application companies can win by capturing services budgets and owning the customer relationship.
This doesn’t suggest everyone will be successful, but it does mean the market itself is positive-sum and we will look back on zero-sum thinking as far too limiting. Better models create better products, better products create more users, more users create more token demand, and more demand supports continued investment in models and infrastructure.
Check out the whole conversation below:
@a16z
2:32
Gavin Baker and a16z's David George on the state of the AI boom:
The future doesn't have to be winner-take-all. Labs, open-source, applications, and the clouds can all capture value.
Demand for intelligence is still dramatically underestimated. Today's power users number in the millions and will grow to hundreds of millions. Gavin and David argue a compute shortage is a more real risk than an AI bubble, and building through it is an opportunity to reindustrialize America.
In this episode, they get into why compute investments pay back so fast, what the data center backlash gets wrong, the case for putting compute in orbit, why enterprises will run several models at once, and how Nvidia ended up at the center of the entire supply chain.
00:00 Intro
01:06 The bear case Gavin couldn't find
05:50 Why a lab would cut its own revenue 75%
08:05 What LPs get wrong about a crash
10:50 Microsoft slowed its capex and regrets it
14:33 The engineers spending 100x the median
17:35 Why 23-year-olds use AI better than Gavin
21:45 How much copper 500M AI users need
23:00 Stop promising to cure cancer
26:00 America's richest county is full of data centers
30:48 Who gets priced out of compute
33:05 The age of Elon and Jensen
34:25 Orbital data centers
44:40 Asteroid mining
48:12 Why Microsoft doesn't need a frontier model
54:02 Who becomes the abstraction layer
55:40 Everyone wanted a deity, Cursor wanted a product
1:00:25 Never take shots at Jensen
1:07:40 What happens when the chip doesn't work
1:12:10 What chip deals reveal about customer demand
YouTube: youtube.com/watch?v=FGC4of…
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