Almost everything in the AI economy is a bet about the future.
When Nvidia reports its quarterly earnings, its backlog — orders planned but not yet filled — matters nearly as much as revenue. Anthropic and OpenAI’s IPO chatter and “valuations” are bets on what they’ll earn as much as decades from now. Companies like Coreweave finance data centers before they have tenants. Everyone is pushing capital through the same bottleneck, roughly 2% of GDP a year, on a simple premise: demand for AI compute is close to infinite, so either businesses will pay more for smarter models, or they’ll use so much of them that it won’t even matter.
Jensen Huang, CEO of Nvidia, calls that the “two exponentials” driving the price of AI compute; models are growing more complex, and more people and agents use them. Either way, the idea is that the labs capture that surplus and send it back through the ecosystem to cover their debts. There’s just one problem: as impressive as the new model releases are, they don’t seem to be causing sustained spikes in the price of AI compute—in fact, the AI token is getting cheaper, fast.
