Nvidia, CoreWeave, and Nebius: The Circular GPU Financing Scheme That Could Crash Your AI API Costs

If you're building a no-code app that calls OpenAI or Claude, here's something you probably haven't budgeted for: your API costs doubling or tripling because a financing scheme involving three companies you've barely heard of suddenly comes undone.

This isn't a hypothetical. Last week, a Financial Times investigation laid out in painstaking detail how Nvidia has been lending money to its own customers so they can buy Nvidia GPUs. The customers then use those GPUs as collateral to borrow even more money, which they also spend on Nvidia GPUs. The FT called it what it is: circular financing. And the circle now contains somewhere north of $30 billion.

The three names at the centre of this are Nvidia, CoreWeave, and Nebius. If you're a no-code builder who thinks this is just a Wall Street story, I'd ask you to reconsider. Every AI API call your app makes routes through GPU infrastructure that, in many cases, exists only because of this financing loop. When the loop breaks, your costs are going to feel it.

What actually is the circular financing?

Here's the simplified version. Nvidia invested roughly $2 billion into CoreWeave, a GPU cloud provider, giving it about a 7-9% equity stake. CoreWeave then used that capital, plus an additional $10.45 billion in debt secured against its existing Nvidia GPUs, to buy more Nvidia GPUs. CoreWeave's 2026 capital expenditure is expected to hit $35 billion.

Nvidia did the same thing with Nebius, another GPU cloud provider, investing about $2 billion there too. Nebius then signed a $27 billion deal with Meta. And Nvidia separately committed $100 billion in vendor financing to OpenAI.

The loop works like this: Nvidia gives money to CoreWeave and Nebius. They hand it straight back to Nvidia for GPUs. Nvidia books that as revenue. The GPU providers then lease the chips to AI companies, who use them to serve API calls. The revenue from those leases services the debt. Everyone's numbers look fantastic, as long as the GPUs hold their value and the AI demand keeps growing at its current rate.

The Financial Times didn't mince words about what happens if either assumption breaks. Neither did Tomasz Tunguz of Theory Ventures, who compared the whole arrangement to Lucent's vendor financing playbook during the telecom bubble. Lucent lent $8.1 billion to customers to buy Lucent equipment. When the bubble burst, between 33% and 80% of those loans went uncollected. Lucent's revenue collapsed 69% in three years. It never recovered.

How big is this house of cards?

Pretty big. CoreWeave alone carries $24.9 billion in debt. Its stock dropped roughly 45% in a single month late last year, wiping out over $30 billion in market value. The company forecasts $12.6 billion in revenue for 2026, which sounds impressive until you compare it to the $35 billion it plans to spend. That is not a gap you close with operational efficiency.

Nebius is smaller but no less precarious. It projects $3.4 billion in FY2026 revenue and needs another $6.3 billion in additional funding this year. Its customer concentration is extreme: that $27 billion Meta deal is doing a lot of heavy lifting.

And then there's the structural problem that nobody in Silicon Valley wants to talk about. GPUs depreciate. Fast. Nvidia releases new architectures roughly every 18-24 months, and each generation makes the previous one look slow and power-hungry. When you've borrowed billions using last year's GPUs as collateral and the market decides they're worth 40% less because Blackwell Ultra just shipped, your lenders get nervous. Nervous lenders mean higher interest rates on the next round of borrowing. Higher borrowing costs mean higher lease rates for GPU compute. Higher lease rates mean more expensive API calls.

That's the chain. It ends with you.

What happens when the music stops?

I don't think this ends in some dramatic collapse where all AI APIs go dark overnight. That's not how these things usually play out. What I do expect is a slower, messier repricing of GPU compute that ripples through every layer of the AI stack over 12-18 months.

Here's what that looks like from the perspective of someone running a no-code app:

OpenAI and Anthropic don't own their own GPU fleets at the scale they need. They lease from providers like CoreWeave and Microsoft Azure, who in turn depend on the GPU financing infrastructure. When those providers face margin pressure, they pass it on. When they pass it on, your API bill goes up.

OpenAI has already shown it's perfectly willing to raise prices. GPT-4.5 launched at a premium. The o-series models cost more per query than their predecessors. There is no reason to think this trend reverses, especially if the underlying compute gets more expensive.

The builders who are going to get hurt worst are the ones who've hard-coded their entire application around a single AI provider. I've seen Bubble apps with hundreds of OpenAI API calls per user session, no caching, no fallback models, no usage monitoring. Those apps are one price hike away from becoming unprofitable.

What no-code builders should do right now

You don't need to panic. You do need to get your house in order. Here's what I'd recommend:

Model diversity is not optional anymore. If your app only works with GPT-4o, you have a single-point-of-failure problem that extends far beyond supply chain risk. OpenAI could change its pricing, deprecate the model, or suffer an outage. Claude, Gemini, Mistral, and open-source models running on your own infrastructure are all viable alternatives for most use cases. The switching cost is mostly engineering time, and if you're on a no-code platform that abstracts model selection, it might be negligible.

Put usage caps in place. Not soft alerts. Hard caps. Every AI-powered feature in your app should have a rate limit and a cost ceiling per user, per day, per month. If a single user can rack up £50 in API costs without anyone noticing, your business model has a hole in it. This is basic operational discipline, but I'm constantly surprised by how many no-code builders skip it.

Cache aggressively where it makes sense. If five users ask your app the same question and get the same AI response, you're paying for four redundant API calls. Semantic caching isn't hard to implement and it can cut your API bill by 30-50% for common query patterns.

Use platforms that abstract the model layer. This is where I'll be direct about Stacker's approach. Platforms that handle model selection, rate limiting, and failover at the platform level protect builders from supply-chain shocks they didn't even know to worry about. When your no-code tool lets you swap from GPT to Claude with a dropdown instead of a rewrite, the GPU financing crisis becomes an inconvenience rather than an existential threat. Tools that lock you into one provider's API are technical debt you're taking on without realising it.

The takeaway

Nobody knows exactly when the GPU financing loop will unwind or how messy it will be. The smart money, including the Financial Times and analysts who covered the telecom bubble, is saying the risk is real and the parallels are uncomfortable.

For no-code builders, the right response isn't to stop building with AI. The opportunity is too big and the tools are too good. The right response is to build like the API pricing you depend on could change without warning. Because it probably will.

If your app would break if OpenAI raised prices by 50%, fix that now. Not next quarter. Not when you get around to it. Now. The music hasn't stopped yet. But the people closest to the speakers are starting to look nervous.