A $1T investment needs to produce on the order of $100B in yearly earnings to be a good investment.
Global GDP is about $100T.
So one way for things to work out for the AI companies would be if AI raises GDP by 1% and the AI companies capture 10% of the created value.
Right now much of the direct monetization occurs via OpenAI and Anthropic, who together have around $30B in annualized revenue. They are burning cash like crazy, though admittedly have potentially sustainable unit economics (gross margins around 40-60% before revenue share).
However, they need to spend a huge chunk of revenue on training. OpenAI spent something like $9b on training against around $13-14b in rev in 2025 (different from annualized rev) according to The Information. Anthropic's mix is supposed to be similar. Also implies a lot (maybe majority) of their compute spend is training.
If scaling laws falter, what happens to training spending? What happens to competitive degree of differentiation given Chinese open source models are a few months behind frontier? Then what happens to margins? It is very fragile.
Google search revenue for example was over $200B in 2025. This revenue will be tightly coupled to the quality of their AI models in the future.
In most countries, AI answers are the central aspect of Google now. Not the ranked pages.
With respect to Google, I'd also wonder about the economics of AI search vs traditional.
https://youtu.be/VQKMoT-6XSg?t=4605
Now it is at 20%.
That would mean earnings of ~ $1.1T would be required on that investment annually, so maybe on $2T of revenue, capturing 2% of the global GDP - so I'd estimate that GDP would need to go up more like 5-10% to justify this.
[1] https://substackcdn.com/image/fetch/$s_!Gf2t!,f_auto,q_auto:...
For call-me pricing to work, you have to ensure that any sort of public sticker price is not a suitable alternative. You can not have a sticker price, make the sticker price so high essentially nobody will buy it or by finding a feature like oauth that makes the public version infeasible for businesses.
And then you also have to maintain enough of a monopoly / oligarchy to sustain that level of pricing.
I don't think either of those two conditions will apply in the future.
AI providers now have a sticker price that provides basically all functionality, almost completely eliminating the opportunity for extremely high-margin b2b. They've decided a small slice of a large pie is bigger than large piece of a smaller pie. I suspect that's true and will continue to be true in the future.
An oligarchy is difficult to sustain with more than 3 global players. Right now we seem to have 3 frontier models for coding that can and will charge more than commodity prices. However there are open source non-frontier models that you can use for inference costs only and even if those don't keep up it seems likely there will be enough non-frontier models available that their pricing will also be at the commodity level. Those cheaper models will provide significant downward pressure on frontier pricing.
Will AI increase the rate of GDP growth by 0.5% or so over 20 years?
We have not seen iterative AI use for example.
The use case, where you tell the model "Solve this task. Then solve it again. Keep the better solution, then solve it again. On and on. Tomorrow, show me the best solution.".
And also not the "Run a company on your own" use case.
Those might make people and companies use models full-time. The price of that will be way different from current subscription prices. The TCO of a single instance of a SOTA model is on the order of $100k per year.