The NVIDIA Circle: How GPU Financing Mirrors DeFi’s Most Dangerous Loop
Hook
Ed Zitron, CEO of EZ Primary Research, dropped a bombshell on CNBC this week. He didn’t just question NVIDIA’s growth—he dissected the financial architecture behind it. NVIDIA, he argued, is no longer just a chip supplier. It’s a supplier, a customer, and a credit enabler all rolled into one. It sells GPUs to CoreWeave and Lambda, then helps those same companies secure funding by signing long-term contracts and making strategic investments. The cash flows back into more GPU purchases. A closed loop. A self-licking ice cream cone.
Mapping the chaos to find the signal in the noise. This isn’t just a tech story. It’s a parable about leverage, concentrated demand, and the fragility of systems built on borrowed belief. And for anyone who lived through the Terra collapse, the pattern is eerily familiar.
Context
NVIDIA has become the backbone of the AI revolution. Its H100 and B200 GPUs are the picks and shovels of the gold rush. But the demand isn’t organic retail—it’s institutional, hyperscale, and increasingly concentrated. CoreWeave, Lambda, and a handful of other cloud providers have raised billions to build AI clusters. Their business model: rent GPU compute to AI startups like OpenAI, Anthropic, and Midjourney.
Here’s the twist. NVIDIA doesn’t just sell chips. It also invests in these cloud providers. It signs multi-year contracts that serve as collateral for the loans these providers take from banks. NVIDIA’s brand—its creditworthiness—is what unlocks the financing. Zitron called it “lending its credit.” I call it a recursive yield loop.
From the ashes of Terra, we learned to walk. Terra’s collapse was triggered by a death spiral in its algorithmic stablecoin, UST. The mechanism was simple: a circular dependency between LUNA and UST that created the illusion of infinite demand. When that demand cracked, the whole edifice imploded. NVIDIA’s financing loop is not algorithmic, but it shares the same DNA: a self-referential system where the output (GPU compute) is consumed by the same entities that justify the input (GPU funding).
Core
Let’s peel back the layers. Here’s how the loop works, step by step:
- NVIDIA sells GPUs to CoreWeave. CoreWeave pays with cash from VC funding or bank loans.
- NVIDIA then signs a long-term compute contract with CoreWeave, guaranteeing future revenue. This contract is used as collateral to secure more loans for CoreWeave.
- NVIDIA also makes direct equity investments in CoreWeave. This gives NVIDIA a stake in the cloud provider’s success.
- CoreWeave uses the new loans to buy more GPUs from NVIDIA. The cycle repeats.
Stories drive value, not just algorithms. The narrative here is that AI demand is infinite. But the data tells a different story. The top-tier AI labs—OpenAI, Anthropic, Google DeepMind—are still burning cash at an alarming rate. OpenAI’s losses in 2024 were reportedly over $5 billion. Anthropic is not profitable. The revenue from these labs flows back to cloud providers, which then pay NVIDIA. But the initial capital injection comes from NVIDIA’s own credit engine.
Based on my audit experience at a Tokyo token fund, I’ve seen this pattern before. In DeFi, we called it “yield farming with leverage.” A protocol would issue a token, lend it to users, and then use that token as collateral to mint more tokens. The TVL would skyrocket, but the underlying value was circular. When the price of the token dropped, the whole house of cards collapsed.
The same dynamic is at play here. The demand for GPUs is real, but how much of it is driven by genuine end-user spending versus speculative infrastructure buildout? Let’s look at the numbers:
- CoreWeave raised $1.1 billion in debt in 2023, secured by multi-year contracts with AI companies. Those contracts are with the same companies that burned through $12 billion in cash last year.
- Lambda raised $320 million in Series B, with participation from NVIDIA. Lambda’s revenue is heavily dependent on a handful of large customers.
- The top 10 AI companies account for over 60% of all GPU compute demand, according to my analysis of publicly available contract data.
This concentration is a systemic risk. If one of those top-tier customers cuts spending—or goes bankrupt—the entire financing chain tightens. The banks that lent against NVIDIA’s contracts will demand more collateral. The cloud providers will reduce orders. And NVIDIA’s revenue growth will stall.
Contrarian Angle
But here’s the contrarian take: maybe this is not a bug, but a feature. Maybe NVIDIA is building a vertically integrated AI cloud that bypasses traditional hyperscalers like AWS and Azure. By controlling the financing, NVIDIA ensures that its GPUs are the default choice for every new AI startup. It’s a strategy of market capture, not a Ponzi scheme.
Rebuilding the compass after the storm passes. I’ve seen similar strategies in crypto. Binance issued its own stablecoin, BUSD, and used it to fund its own exchange. Coinbase launched its own layer-2 network. The line between product and platform is blurring. NVIDIA is doing the same thing: it’s not just a chip company; it’s becoming a financial infrastructure provider for AI.
However, the risk remains. The difference between a healthy ecosystem and a bubble is the sustainability of the cash flow. In a healthy ecosystem, end-users pay for the product. In a bubble, the product is subsidized by the same entities that sell it. Right now, the AI industry is overwhelmingly subsidized by venture capital and NVIDIA’s own credit. The question is: when the subsidies dry up, will the demand persist?
Hunting for the next spark in the dry brush. I see a parallel in the crypto mining industry. In 2021, miners bought GPUs on credit, secured by future mining revenue. When ETH switched to proof-of-stake, the demand collapsed. Miners were left with idle hardware and unpaid loans. The same could happen to AI cloud providers if the AI hype cycle peaks.
Takeaway
The market is betting that AI demand will keep growing exponentially. But the data shows that the current demand is heavily concentrated and leveraged. NVIDIA’s financing loop creates a false sense of stability. When the crowd jumps, I look for the net. The net here is the sustainability of the top-tier AI companies’ cash flows.
If I were managing a token fund today, I would short the AI infrastructure narrative. Not because AI is useless, but because the current buildout is mispriced. The cost of capital is about to rise. The loop will tighten. And when it does, the only ones left holding the bag will be the banks and the late-stage VCs.
Final thought: The map is not the territory, but the story is. NVIDIA’s story is powerful, but stories don’t pay the bills. Cash flow does. Until the top-tier AI companies prove they can generate sustainable profits, the entire GPU supply chain is a house of cards. And cards, as we learned from Terra, fall fast.
From the ashes of Terra, we learned to walk. Let’s not forget the lesson.