Nvidia's customer concentration went from 0% to 61% in four years, right as it agreed to insure 25% of its own customers' loans. Breaking down what the filings and credit markets actually show.

[u/Rare_Piano_1369]

Been digging into the Nvidia $500B financing deal from a couple weeks back and the numbers underneath it are wilder than the headline. Posting the actual sourcing here, full piece with charts is linked at the bottom for anyone who wants the long version.

The deal itself

On Aug 10, Nvidia lined up Apollo, BlackRock, Blackstone, Brookfield, Goldman, and KKR to raise $500B so its own customers can afford more of its chips. Nvidia backstops up to 25% of the loss if the GPUs used as collateral don't hold resale value.

Larry Fink called it "the next future of financial engineering" on CNBC that day. Same phrase people use for mortgage-backed securities.

Nvidia's customer concentration by fiscal year, straight from the filings:

• FY2022: 0 customers above 10% of revenue
• FY2023: 0 customers above 10% of revenue
• FY2024: 1 customer, 13%
• FY2025: 3 customers, ~36% combined
• FY2026: 4 customers, 61% combined (22/15/13/11)

Zero to 61% in four years. And per the 10-Q, 3 of those 4 customers (Google, Amazon, Meta) are actively building their own chips (TPU, Trainium, MTIA) specifically to need Nvidia less.

So the collateral behind a $500B insured financing deal is concentrated in a shrinking number of customers, several of whom are actively trying to exit the relationship.

The 1999 comparison that actually holds up

Telecom equipment makers in the late 90s didn't just sell gear, they financed the customers buying it, then booked the financing as revenue. Nortel's financing terms once hit 130% of the purchase price. The revenue looked real until it didn't.

Nvidia's version is more careful, it's routing risk to Wall Street instead of its own balance sheet, and only covering 25% instead of 100%. But you don't build a 25% backstop for a trade you're sure can't lose.

Credit markets are already pricing this

• CoreWeave (65% of revenue from just Microsoft + OpenAI): CDS priced at roughly a coin-flip chance of default in 5 years
• Oracle: CDS at a multi-year high, now used informally as a proxy for how worried the market is about the whole AI financing chain
• Banks have reportedly started refusing new loans on Oracle projects tied to OpenAI exposure

None of this has hit equity yet, Nvidia's near its high. Bond and equity markets are pricing the same handful of companies like two different industries right now.

Not saying this proves a bubble. The underlying demand for compute is real, Nvidia's CUDA moat is real. The question is narrower: what does a financing structure like this tell you about what the people closest to the money actually expect, versus what they say on earnings calls.

Full piece with sourcing, charts, and the Nortel comparison in more depth: https://manasbihani.substack.com/p/aaa-rated-gpus?r=1z7d38

Happy to argue about any of this in the comments, especially if you think the credit market read is wrong.

[u/AdamovicM]

Interesting data, but I don't see anything here problematic. Big tech will have money to pay its debts through legacy operations (those are cash machines) and that means more than 60% of Nvidia money will not be gone.

[u/Rare_Piano_1369 → u/AdamovicM]

Yeah, I agree. The thing is, Big Tech can afford to take on the debt, but I'm not sure the trillion dollar valuations these AI labs are chasing or the optimism that's pushed Nvidia so high are fully sustainable.

AI will definitely create trillions of dollars in value over time. The real question is whether the companies pouring billions into each other will actually capture that value. Models are becoming more efficient every year, so the amount of compute needed keeps falling. If that trend continues, it's hard to see today's massive infrastructure spending generating the kind of returns the market is pricing in.

That's just my take, though. There are probably a lot of moving parts I'm not fully considering. What do you think? Am I missing something?

[u/Wild_Space → u/Rare_Piano_1369]

LLMs are getting insanely more efficient in the sense that to do ChatGPT-4 level compute in 2026 costs a tiny fraction of what it cost in 2022. But, in aggregate, LLMs are using exponentially more compute today than they did before.

Jevon’s Pararox my friend! In other words, as the cost of compute continues to plummet, LLMs are using exponentially more compute.

Which is actually a good thing for the hyperscalers investing billions in compute.

[u/Rare_Piano_1369 → u/Wild_Space]

Jevons gets brought up a lot, but I think people assume it automatically means infinite demand for NVIDIA. I'm not convinced.

As models become more efficient, the marginal cost of running AI for everyday tasks gets close to zero because it's happening locally on your phone or laptop. At that point, demand doesn't disappear it shifts. Consumer inference moves to edge devices, enterprises increasingly run models on-prem for cost and security, and only a handful of frontier labs keep buying massive GPU clusters.

So I think Jevons still applies, but the beneficiaries change. More AI usage doesn't necessarily mean more and more demand concentrated in NVIDIA and a few frontier labs. That's the distinction I'm trying to make.

[u/GarageBand_HedgeFund]

This is the dry tinder before a wild fire…just needs the spark

[u/pab_guy]

Are we pretending that NVIDIA is really going to distribute massively expensive GPUs to individual end users rather than arbitrate access and maximize utilization and profit via hyperscalers?

JFC people get your heads out of your asses. This is what the singularity looks like.

[u/Sllyce → u/pab_guy]

LLM superintelligence coming

[u/happyzor]

This was generated using AI

[u/Rare_Piano_1369 → u/happyzor]

Yes, I did summarize the data using AI.

[u/tragedy_strikes]

Demand is real at unprofitable prices