OpenAI and Nvidia sign a circular deal

Nvidia and OpenAI announced a letter of intent under which Nvidia would invest up to $100 billion in OpenAI, staged against the deployment of at least 10 gigawatts of Nvidia systems.

Read that structure carefully, because it is the interesting part.

the circularity#

Nvidia invests in OpenAI. OpenAI uses the money to buy Nvidia systems. The purchase is recognized as Nvidia revenue. The investment is staged against deployment milestones.

This is not fraud and it is not unusual in capital-intensive industries — vendor financing has been standard in telecom, aviation, and semiconductor equipment for decades. A supplier finances a customer's purchase because the supplier has the balance sheet and wants the volume.

It does deserve scrutiny for a specific reason: it makes the demand signal less informative. When a supplier funds its customer's purchases, revenue growth no longer cleanly indicates independent market demand. Some portion of it is the supplier's own capital cycling through.

Analysts have been tracking a widening set of these arrangements across the AI sector — investments in customers, prepayments, equity stakes in companies that are also large purchasers. Individually each is defensible. Collectively they make the sector's growth figures harder to interpret.

the gigawatt as a unit#

Note what is being measured. Not chips, not dollars, not FLOPs. Gigawatts.

Ten gigawatts is on the order of the electricity consumption of a large metropolitan area. It is roughly ten large nuclear reactors' worth of continuous generation.

The industry has converged on power as the natural unit because power is the binding constraint. You can order chips. You cannot order a substation and have it next quarter.

The consequences flow outward: electricity prices in datacenter-heavy regions, grid interconnection queues, transmission buildout, and the political economy of who pays for it. Several US utility regulators are now handling rate cases that are effectively about whether residential customers subsidize datacenter connections.

That fight is going to define a lot of the next five years and it is being had in public utility commission hearings that nobody in tech reads.

what it means for you#

If you are building on AI APIs, the practical questions are:

Is my provider's capacity growing? Rate limits and availability during demand spikes are the observable symptom. Announcements like this are a positive signal for capacity.

Am I exposed to a single provider's economics? If the financing environment tightens, pricing changes. Multi-provider capability is cheap insurance and you should have built it anyway for reliability reasons.

Are my costs actually falling? Per-token prices have fallen consistently. Per task costs have not fallen as much, because reasoning models consume more tokens. Measure the thing you pay for.

the honest uncertainty#

Nobody knows whether the capex cycle is correctly sized. The bull case is that inference demand compounds and every gigawatt gets used. The bear case is that efficiency improvements outrun demand and a lot of concrete is stranded.

Both are held sincerely by smart people with access to the same information. That is what genuine uncertainty looks like, and anyone expressing confidence in either direction is telling you about their position, not about the world.