AI vendor circular finance: Ponzi scheme & house of cards?

 

·         It’s like a big shop owner gives $1000 to one of his top clients and asks him to buy goods from his shop for $1000

The U.S. markets have been hemorrhaging for several days. The Nasdaq is down 11% from its October high, Nvidia has shed 14% year-to-date, and Palantir is nursing a 9% drop. The headlines scream “AI bubble burst,” but the truth cuts deeper. What we’re witnessing isn’t a mere correction—it’s the slow-motion unraveling of a trillion-dollar illusion built on circular vendor financing, off-balance-sheet debt bombs, and desperate pleas for government salvation, all while China races ahead with electricity costs at a quarter of America’s. This isn’t just a tech story. It’s the return of 2008’s ghosts—synthetic CDOs, shadow banking, and “too big to fail” rhetoric—only this time, the toxic asset isn’t subprime mortgages. It’s GPUs.

The GPU Ponzi: A Shopkeeper’s $1,000 Gift

Imagine a shop owner handing a customer $1,000 and saying, “Go ahead, spend it all here." The customer buys $1,000 worth of goods. The owner books it as revenue. The customer walks away with the inventory. Everyone wins—except reality. That $1,000 never left the system. It was never a real demand. It was a vicious loop.

The Circular Financing Engine: Money In, Money Out—Back to the Same Pockets

At the heart of the AI frenzy lies a brilliant but perilous structure: vendor financing masquerading as equity investment. OpenAI doesn't just raise capital—it funnels it straight back to its "investors" through massive infrastructure commitments, creating a closed-loop ecosystem that inflates valuations without generating true free cash flow.

·         Microsoft: Pours billions into OpenAI, and then bills it for Azure cloud compute—turning "investment" into immediate revenue.

·         NVIDIA: "Invests" $100 billion, only to sell OpenAI the exact GPUs that funding buys, securing risk-free returns.

·         Oracle: Signs a $300 billion cloud deal while taking equity, ensuring dependency.

·         AMD and others: Swap hardware for stock options, locking in future spend

This isn't traditional venture capital; it's circular vendor finance. Every dollar "invested" recycles as revenue for the supplier, propping up balance sheets on both sides. OpenAI's projected $1.4 trillion in eight-year compute commitments? Largely pre-allocated to these partners, leaving headline valuations ($500–$1T IPO rumors) built on obligations, not organic growth. Smaller players like xAI, Anthropic, Mistral, and Figure AI are ensnared in similar traps, all feeding the same nexus. NVIDIA reigns supreme, collecting cash upfront for chips (growing ~45% CAGR) regardless of who "wins" the AI race.

Why It's Genius for Investors—and a Gilded Cage for OpenAI

Benefits for the "Pick-and-Shovel" Sellers:

·         Zero Net Risk: Capital returns as locked-in sales.

·         Double-Dip Profits: Equity upside + operational margins.

·         Ecosystem Lock-In: OpenAI can't switch providers without unraveling its model.

·         Accounting Sleight-of-Hand: Investments as assets; spend as revenue.

For OpenAI, it's a dependency disguised as a partnership. CEO Sam Altman projects $20 billion in annualized revenue in 2025, scaling to hundreds of billions by 2030. But with $1 trillion+ in infra CAPEX looming, much of that growth is spoken for. Fail to hit milestones? The house of cards collapses.

The AI Infrastructure Crunch: $3 Trillion by 2028, But Who's Paying?

AI isn't magic—it's computing power housed in massive data centers that process and store petabytes of data. These beasts demand cutting-edge semiconductor chips (Nvidia's GPUs) and oceans of cheap electricity. The scale? Mind-boggling.

Morgan Stanley's latest estimates paint a stark picture: Tech giants will need nearly $3 trillion in global data center investments through 2028 to fuel the AI boom. That's $1.6 trillion on hardware like chips and servers, plus $1.3 trillion on real estate, construction, and maintenance. Of that, hyperscalers (Microsoft, Google, Meta, and Amazon) can cover about $1.4 trillion from cash flows—but the remaining $1.5 trillion gap? It falls to debt markets, private credit, and creative financing.

This isn't pocket change; it's a macro event rivaling the entire S&P 500's annual capex (~$950 billion in 2024). By 2028, annual AI infra spend could hit $900 billion—more than the EU's defense budget. Revenues from generative AI are being projected to balloon from $45 billion in 2024 to $1 trillion by 2028. But until then, it's all upfront capex with long payback periods, sparking fears of a "debt bomb" if adoption slows or energy costs spike.

Creative Financing: SPVs and Off-Balance-Sheet Magic

How are Big Tech firms bridging this gap without tanking their credit ratings? Enter Special Purpose Vehicles (SPVs)**—ring-fenced entities that raise debt and equity for specific projects, keeping the burden off corporate balance sheets. It's financial engineering at its finest: Tech giants get the assets, financiers get steady "rent" payments, and investors get yield-hungry assets.

Take Meta's blockbuster example: They're building a **$30 billion "Hyperion" data center in rural Louisiana, set to go live by 2029. Here's how it works:

·         Total Financing: $30B -For 4 million sq. ft. facility; largest private capital deal ever

·         Debt-$27B-Raised via bonds (maturing 2049, fully amortizing, +225 bps over Treasuries, A+ rated). Led by PIMCO as anchor; other investors via 144A private placement.

·         Equity: $2.5B-Meta contributes ~$6B total via 20% ownership stake; Blue Owl Capital takes 80%.

·         Meta's Role-Operator/Tenant | Pays rent to SPV for usage; no direct debt on Meta's books. Morgan Stanley structured the deal.

Echoes of the Dot-Com/Telecom Bubble: History Rhymes with Vendor-Financed Hype

This mirrors the late-1990s telecom crash, where equipment giants like Lucent and Cisco financed customer buildouts. Revenues ballooned, stocks soared—until defaults cascaded. Today's AI version is amplified:

·         Sky-High Valuations on Future Promises: S&P 500 at bubble P/Es; AI stocks lead the charge.

·         Ponzi Scheme-Like Dynamics: New capital pays for old commitments, sustaining the illusion of growth.

OpenAI: Too Big to Fail?

OpenAI's scale ($1T+ infra asks) sparked systemic failure risk and bailout fears.

OpenAI CFO Sarah Friar floated government "backstops" at a November 5, 2025, WSJ conference to de-risk $1T+ chip/data center builds, and compared it to energy/telecom PPPs. Backlash was swift:

·         Friar clarified on LinkedIn: Not seeking subsidies, just policy talks for private funding.

·         Trump AI Czar David Sacks tweeted November 6: "No federal bailout for AI... If one fails, others take its place. Call it deregulation, not backstop."

·         Altman followed on October 6: "We do not want government guarantees for datacenters... Taxpayers shouldn't bail out bad decisions." He endorsed public-owned AI infra for national benefit, not private gain.

·         Yet the damage lingers. Industry pushes for PPPs to counter China (leading in cheap energy/alternatives), but critics see Big Tech rent-seeking.

Why might it work?

·         Off-Balance Sheet - SPV borrows, not Meta—preserves credit rating and enables more borrowing elsewhere.

·         Meta raised separate $30B in corporate bonds for other needs, doubling effective leverage without showing it.

This structure is spreading like wildfire—Elon Musk's xAI used a similar lease-not-own model for $20 billion in chip financing. Private credit could fund over half the $1.5T gap, per Morgan Stanley. But it's opaque: High leverage (90%+ debt) hides risks, and if AI revenues flop, "rent" payments could strain cash flows.

Why Stocks Are Crashing: Bubble Fears Meet Funding Reality

This combo—sky-high capex, SPV sleight-of-hand, and bailout whispers—has investors spooked. AI stocks drove 75% of S&P gains since ChatGPT's launch, with Nvidia hitting $5T valuation (8% of the index). But Palantir trades at 700x earnings; Nvidia's data center sales are 88% of revenue. A Bain report warns AI firms need $2T annual revenue by 2030, but could fall $800B short.

Add macro woes: U.S. chip export bans to China hammered Nvidia; consumer sentiment at 50.3 with 4.7% inflation expectations. "Big Short" Michael Burry resurfaced, tweeting warnings of an AI "tulip mania" bubble. Even optimists like AlphaSense's Sarah Hoffman see a "market correction," not a catastrophe—yet.

The Bottom Line: Healthy Reset or Dot-Com 2.0?

More valuation reset than burst—tech's 36% S&P weight (higher than dot-com peak) demands a breather. If AI delivers (e.g., Nvidia earnings Nov. 19), it rebounds. But unresolved funding gaps, energy crunches, and political pushback could drag it into 2026 territory. As Reuters quips: AI can be both bubble *and* breakthrough. Investors: Diversify beyond the Mag 7. The party's not over—but the tab just arrived.

But let's cut through the noise: This isn't some random correction—it's the market waking up to the trillion-dollar elephant in the room. AI's backbone—data centers gobbling up semiconductors, electricity, and endless capital—is creaking under the weight of hype-fueled spending. With 70-75% of the S&P 500's 2025 rally driven by AI stocks, any whiff of a funding crunch sends shockwaves. And right now, OpenAI's Sam Altman is at the epicenter, publicly begging for government intervention that Wall Street sees as a desperate "bailout" signal.

Broader Risks: Bubble Burst, Energy Crunch, and Geopolitical Lag

The AI ecosystem devours power—data centers need cheap, abundant energy. China excels here; the U.S. lags without scaled green alternatives. Add macro headwinds:

·         Weak labor, subprime delinquencies, inflation.

·         Trump-era policy volatility.

If hype deflates or OpenAI stumbles, who holds the bag? Not NVIDIA (paid in cash). Everyone else: investors, partners, markets.

Conclusion: A Ponzi in Silicon Valley Clothing?

OpenAI's circular finance is financial engineering at its peak—vertical control without ownership, antitrust-proof. But it's fragile. Valuations assume eternal growth; reality demands profits from pre-committed spend. Like dot-com excesses, the music may stop in 2026, triggering a crisis amid overvalued AI/crypto stocks.

This is AI’s complex & circular vendor financing in a nutshell.

Nvidia pours $100 billion into OpenAI through non-voting equity and leases. OpenAI turns around and spends nearly all of it on Nvidia GPUs. Nvidia books full revenue. Oracle signs a $300 billion cloud deal with OpenAI, then buys Nvidia chips to power it. CoreWeave takes $3 billion from Nvidia, spends $7.5 billion on its GPUs, and signs a $22 billion contract with OpenAI. Elon Musk’s xAI gets $2 billion from Nvidia and locks into five-year GPU lease payments. The money never escapes the circle. It’s not growth. It’s motion. Nvidia’s exposure now exceeds $400 billion—nearly three times its annual revenue. OpenAI lost $4.7 billion in the first half of 2025 despite $4.3 billion in revenue. And under U.S. accounting rules, none of this circular cash is treated as a loan, an advance, or a gift. It’s pure, unadulterated revenue. This isn’t innovation; it’s financial engineering dressed as progress.

The SPV Mirage: $60 Billion in Debt, $30 Billion on the Books

Meta doesn’t want $30 billion in debt weighing down its balance sheet. So it builds the largest data center in history—Hyperion—through a special purpose vehicle. The SPV raises $27 billion in debt and $2.5 billion in equity. Meta owns just 20%. The debt never touches Meta’s books. Then Meta raises another $30 billion in corporate bonds for “other purposes.” Total leverage: $60 billion; Reported leverage: $30 billion. This is shadow banking reborn. Private credit is funding half of the $1.5 trillion AI infrastructure gap. If AI revenue falls short—and analysts warn of an $800 billion shortfall by 2030—these SPVs become dominoes. One default triggers the next. The accounting fix is simple: if a company controls the asset through long-term leases or operational dominance, the debt should be consolidated. But under current rules, it isn’t. The emperor has no clothes—and the market is finally noticing.

The White House Letter: OpenAI’s Cry for Help

On October 27, 2025, OpenAI’s Chief Global Affairs Officer, Chris Lehane, sent an official letter to the White House. It wasn’t subtle. Expand the CHIPS Act to cover data centers. Issue grants. Offer low-interest loans. Provide loan guarantees to “AI infrastructure manufacturers.” This wasn’t a vision for public AI labs. This was a plea to de-risk $1.4 trillion in private compute commitments. Just days later, CFO Sarah Friar floated “government backstops” at a Wall Street Journal conference. Sam Altman walked it back: “We don’t want guarantees for private data centers.” But the letter exists. It’s public. And Wall Street heard one thing loud and clear: *too big to fail is back.* Trump’s AI Czar, David Sacks, fired back: “No federal bailout for AI; Deregulation, not backstop.” But the damage was done. The market smelled desperation.

The Energy Abyss: China Runs on 25-Cent Power

AI isn’t code. It’s electricity. By 2030, U.S. data centers will consume 425 terawatt-hours—8 to 12% of national demand—up from 4% today. That’s more power than California uses in a year. Household bills in AI-heavy states like Virginia and Ohio are rising $15 to $50 a month. Utilities are building $5 billion gas plants just to keep the lights on for Meta and Microsoft.

China? Its data centers will use 277 terawatt-hours—only 6% of demand. Industrial electricity costs $0.05 to $0.07 per kilowatt-hour. America pays $0.12 to $0.20. China’s grid has an 80 to 100% reserve margin. America’s is stretched at 15 to 20%. China isn’t just building AI. It’s powering it like America powered the internet in the 1990s—cheap, fast, and at scale. The U.S. is debating zoning laws while Beijing deploys coal, nuclear, and renewables in lockstep.

The Tech Cliff: When Generative AI Becomes Obsolete

Generative AI—the foundation of ChatGPT, Gemini, and Claude—runs on brute-force scaling: more data, more chips, and more power. But by 2030, that model could be dead. Neuromorphic chips mimic the human brain and use 1,000 times less energy. Federated learning trains models on edge devices, slashing data center needs by 50 to 70%. Quantum hybrids solve complex problems without trillion-parameter models. One breakthrough—a “DeepSeek moment” where a Chinese model outperforms GPT-5 on a tenth of the compute—and $3 trillion in GPU infrastructure becomes scrap metal. The empire built on scale collapses under the weight of efficiency.

The 2030 Reckoning

This isn’t a drill. By 2030, AI could add $15.7 trillion to global GDP. Or it could trigger a financial crisis worse than 2008. The risks are stacking: circular finance is ready to collapse, SPVs hiding trillions in leverage, a government being asked to backstop private greed, an energy grid that can't keep up, and a technological foundation that may not survive the decade. Seventy-five percent of the S&P 500’s gains since 2022 have been AI-driven. Nvidia trades at 60 times forward earnings. OpenAI is valued at $500 billion to $1 trillion—on commitments, not profits. This is dot-com 2.0. This is Telecom 2000. This is CDO 2008. The music is still playing. But the tab is due.

The Way Out

The fix isn’t complicated. Force circular vendor cash to be treated as loans or contra-revenue. Mandate SPV consolidation when operational control exists. Build a national AI power grid—through public-private partnerships (PPP), not bailouts. And pivot from scale to efficiency before the world does it for us.

AI isn't fake, but its financial foundation is built on sand.

The empire is cracking.  Wall Street is under stress amid fresh revelations that OpenAI, the poster child of the AI boom, is entangled in a web of circular financing that increasingly resembles a Ponzi scheme. With the S&P 500 trading around 7,000—implying a trailing-twelve-month (TTM) P/E of 31 on Q2 2025 EPS of $223—the market is deep in bubble territory (P/E >30). Analysts are projecting 2025–2026 EPS at $234–$259, suggesting fair-value levels of 5,273–5,834 even under optimistic 20–25x multiples. Yet valuations soar on hype, not fundamentals, as AI's "perpetual motion" funding model raises alarms of an impending crash.