Better Offline Podcast Summary

Episode: Part Three: NVIDIA Isn't Enron – So What Is It?
Host: Ed Zitron
Date: December 19, 2025

Main Theme & Purpose

In this third and final installment of the Better Offline Nvidia series, Ed Zitron takes a skeptical deep dive into Nvidia’s unprecedented growth, examining the opaque economics behind its success and raising critical questions about the sustainability of its business model. While dismissing direct comparisons to infamous corporate frauds like Enron, Zitron spotlights Nvidia’s dependency on a fragile ecosystem of indebted customers buying enormous amounts of GPUs primarily for AI infrastructure. He questions where these GPUs are actually going, who is profiting (if anyone), and what risks this poses to the broader tech and financial markets.

Key Discussion Points & Insights

1. Nvidia and the Mirage of AI Growth

• Not Enron, But Still Worrying:
  Ed stresses Nvidia isn’t Enron—the numbers are real, the hardware exists, and there aren’t comparably fraudulent accounting practices. However, the company’s rise is built on highly uncertain ground, with customers piling into debt and capital expenditures without obvious, profitable returns.

  "Despite being on incredibly infirm ground, [Nvidia] is definitely not Enron... But there’s still quite a few causes for concern." (03:31)

• Unsustainable Capital Expenditures:
  Big Tech (Microsoft, Amazon, Google, Meta) has spent an estimated $776 billion on capital expenditures (capex) for AI from 2023 to 2025, with another $400 billion expected in 2026—all largely funneled into building out AI data centers and buying Nvidia GPUs, yet with no matching growth in AI-based revenues.

  "Big Tech needs to make $2 trillion in brand new revenue… by 2030. All of this was effectively for nothing." (04:36)

2. Depreciation: The Accounting Puzzle

• Depreciation Shenanigans:
  Companies are using aggressive depreciation schedules, spreading the cost of expensive, rapidly obsolescent GPUs over 5-6 years, despite those chips only realistically being useful (technologically or physically) for 1-3 years.

  "[They] spread the cost of their massive capital expenditures over a longer period than the facts warrant." (07:45)

  • This practice flatters company earnings but masks the real economic impact, likened to what The Economist dubbed a “$4 trillion accounting puzzle.”
  • There’s broad uncertainty about how long these assets maintain value, and what happens as future chip generations quickly render them obsolete.
  • The physical infrastructure is being built around chips that may be outdated before the data centers themselves are even completed.

3. The Mystery of the Missing GPUs

• Where Are They?
  Zitron painstakingly investigates Nvidia’s claimed GPU shipment numbers, finding:

  • Only a fraction of Blackwell GPUs can be traced to active data centers.
  • Even generous estimates (factoring in announced—but not yet operational—projects) can’t identify where millions of shipped GPUs are.

    "I am genuinely unable to find 1 million Blackwell GPUs in existence. Now some might say, there’s a bunch of secret ones—they don’t announce every single one. Here’s the thing: 3 million of these fucking things have allegedly been shipped. I can’t find a million of them." (17:48)

• Power & Infrastructure Shortfall:
  Even when chips are purchased, they often cannot be plugged in due to lack of adequate power and unfinished data centers.

  "In really simple terms, there isn’t enough power or built data centers for those Blackwell GPUs to run." (19:02)

  • Example: Stargate Abilene has only 200 megawatts of power provisioned, while a full build-out would require over 1.4 gigawatts.

• Nvidia’s Slippery Accounting:
  Nvidia has a history of blending product lines for reporting purposes (e.g., crypto GPUs reported as gaming revenue in the 2022 crypto rush), and may be aggregating orders or unshipped inventory into “shipped” numbers.

4. The (Lack of) AI Revenue Problem

• Money-Losing AI Champions:
  The two most prominent AI companies—OpenAI and Anthropic—are deeply unprofitable, spending more on compute and infrastructure than they earn.

  "OpenAI spent $8.67 billion on inference… Anthropic spent 104% of its revenue up to that point just on AWS, likely as much on Google Cloud." (23:28)

• Illusion of Demand:
  Demand for AI compute is real, but it’s “demand” funded almost entirely by debt and venture capital—not sustainable profit.

  • Example: CoreWeave, a major AI compute provider, posted a $110 million loss on $1.36 billion in revenue and is heavily indebted from GPU purchases.
  • The two largest customers for GPU compute—OpenAI and Anthropic—continue to raise billions from VCs and sovereign wealth funds just to cover their operational losses.

• The Circular, Unsustainable Money Flow:
  Zitron lays out the self-consuming ecosystem:

  "Venture capital funds AI startups, who pay OpenAI/Anthropic for model access, who rent GPUs from hyperscalers, who buy more Nvidia GPUs. Only Nvidia makes a profit here." (36:13)

  • Any break in this debt and VC-fueled chain would collapse the entire system.

5. Nvidia’s Real Customer: Debt Markets

• Dependence on Borrowing:
  The only entities able to continue buying Nvidia’s expensive chips are those who can access vast amounts of debt or venture financing.

  "Nvidia’s largest customers are increasingly unable to afford its GPUs... even Google, Amazon, Meta, and Oracle are taking on massive amounts of new debt, all without a plan to make a profit." (39:42)

• Credit Ratings as an Unseen Risk:
  As customers’ credit dries up, their ability to buy more Nvidia GPUs collapses—threatening Nvidia’s own revenue growth.

6. Market Implications and Existential Warnings

• Systemic Financial Risk:
  Nvidia’s growth props up not just itself but is deeply intertwined with the health of the US stock market and includes the 401ks of millions of Americans.

  "So many hundreds of billions of dollars of Nvidia stock sits in the hands of retail investors and people’s 401ks. Once this pops—and it will pop—there must be a referendum." (41:10)

• Skepticism About Long-Term Viability:
  Zitron insists he is not predicting an Enron-style fraud or imminent Nvidia collapse, but he is deeply concerned by the lack of real, profitable demand for AI compute and how long the illusion can be sustained.

  "Much of the US stock market’s growth is held up by how long everybody is willing to be gaslit by Jensen Huang into believing that they need more GPUs. At this point, it’s barely about AI anymore." (41:44)

Notable Quotes & Moments

• On Tech’s AI Investment Hole:

  "Every time I read these numbers I feel a little crazy… Big Tech needs to make $2 trillion in brand new, brand spanking new revenue, specifically from AI by 2030, or all of this was effectively for nothing." (04:35)

• On GPU Depreciation Math:

  "GPUs depreciate, meaning they lose value over time... In Microsoft’s case, depreciation for its servers is spread over six years, a convenient change it made in August 2022, a few months [before] it bought a bunch of fucking GPUs." (06:25)

• On Obsolete Hardware:

  "Nvidia is committed to releasing a new GPU every single year. Newer generation GPUs require entirely new data center architecture, meaning that one has to either build a brand new data center or retrofit an old one." (10:12)

• On the Absurdity of AI Compute Revenues:

  "Even if OpenAI made $13 billion this year, even if Anthropic made $5 billion, okay, wow… That's like $19 billion less than Microsoft spent on GPUs and other capex in the last quarter. That's dog shit." (25:55)

• On Systemic Risk:

  "At some point, a link in this debt-backed chain breaks because very little cash flow exists to prop it up… At some point, venture capitalists will be forced to stop funneling money into unprofitable, unsustainable AI companies…" (37:18)

• On Nvidia's Puzzling Success:

  "None of Nvidia’s success really makes any sense. Who’s buying so many GPUs and where are they going?... How long realistically can the largest company on the stock market continue to grow revenues selling assets that only seem to lose its customers money and don’t seem to even be in use for years?" (40:36)

• On the Coming Reckoning:

  "Once this pops, and it will pop, because there's simply not enough money to do this forever, there must be a referendum on those that chose to ignore the naked instability of this era and the endless lies that inflated the AI bubble." (41:51)

• On the Wile E. Coyote Moment:

  "Everybody is betting billions on the idea that Wile E. Coyote won’t look down. He’s gonna have to at some point, won’t he?" (42:35)

Timestamps for Key Segments

• Nvidia’s Risky Foundations – 03:31
• Big Tech’s AI Infrastructure Spend – 04:15 to 06:00
• Depreciation Accounting Problem – 06:31 to 10:19
• Data Center & Power Limitations – 17:48 to 21:25
• The “Missing” GPUs – 16:46 to 19:58
• AI Revenue vs. Costs Reality – 22:30 to 26:20
• Circular Money Flows and Debt Dependency – 36:00 to 39:00
• Systemic Market Consequences & Host’s Final Rant – 40:30 to 42:35

Tone and Style

Ed Zitron’s approach is direct, caustic, and darkly comedic, alternating deep research with exasperated asides and sarcasm. He leverages industry data, premium reporting, and pointed anecdotes, using frequent profanity and rhetorical questions to punctuate his skepticism about the industry’s ability to continue its current trajectory. He makes clear he's not predicting criminal collapse—just “plain old economic reality.”

For Listeners New to the Episode

This episode is a must-listen for anyone concerned about the sustainability of Silicon Valley, the real economics of AI, or the health of the American stock market. Zitron expertly exposes how Nvidia’s meteoric rise is exposed to breathtaking systemic risks rooted in debt, untested technology, and a pervasive lack of profitability throughout the AI ecosystem.

Host: Ed Zitron
Podcast: Better Offline (Cool Zone Media / iHeartPodcasts)