The $10 Trillion AI Boom: Why It’s a Bubble — And How It Might Burst

“This time is different” are four of the most dangerous words in finance. They usually show up right before everything goes off a cliff, Forbes reports.

But when you compare today’s AI mania to the Dot-Com bubble of 2000, there is something genuinely different going on. We’re not just replaying the old script — we’re living through a new kind of bubble.

Back then, we had a valuation bubble: stock prices completely disconnected from reality.
Today, we’re likely watching a capacity bubble: trillions being poured into AI infrastructure that may not yet have the real-world demand to justify it.

And at the center of it all is Nvidia.

In 2000, Cisco briefly became the most valuable company on the planet, worth over $500 billion, priced for decades of near-perfect growth.

Fast-forward to 2025, and Nvidia has smashed through the $4 trillion mark as the undisputed king of AI chips. It’s easy to assume:

“Huge market cap = same bubble.”

But the numbers tell a more nuanced story:

• Valuation:

  • Cisco at the peak: about 200x earnings;

  • Nvidia now: about 38x forward earnings.

• Growth:

  • Cisco: ~55% revenue CAGR from 1997–2000;

  • Nvidia: ~70% CAGR over the last three years — off a much bigger base.

• Cash flow:

  • Cisco: around $1.3 billion per quarter;

  • Nvidia: roughly $25 billion per quarter.

• Customer quality:

  • Cisco: lots of fragile dot-com startups that later died;

  • Nvidia: selling to cash-rich Big Tech giants and hyperscalers.

So Cisco’s bubble was mostly about overpriced stock.
Nvidia’s risk is more about whether demand for all this AI compute actually sticks.

The core problem isn’t that companies are spending on AI. It’s how much they’re spending versus how little revenue AI is actually generating so far.

The Capacity (the spend):

• Microsoft, Google, Meta and others are together shelling out well over $200 billion a year on AI data centers and chips.
• OpenAI alone is reportedly committing more than $1.2 trillion over the coming years.

The Utility (the payoff):

• Sequoia estimates AI needs about $600 billion in new annual revenue to justify this level of investment.
• OpenAI right now? Around a $20 billion run rate.
• About 800 million weekly users, but only 5–10% actually pay ($20–$200/month). The rest use it free.

In other words, we’re building a global AI supergrid on the hope that the monetization catches up later.

Another big difference from 2000: how the bubble is funded.

During the dot-com era, companies like Cisco lent money to shaky startups so those startups could… buy Cisco gear. When the startups collapsed, the loans and demand vaporized.

A. The “Safe” Giants

• Microsoft, Google, Amazon and other hyperscalers are mostly funding AI buildout with operating cash flow.
• If AI flops, it hurts — but it doesn’t kill them. They’re not going to zero.

B. The Risky Middle Layer: Neo-Clouds

Here’s where it gets dicey.

Companies like CoreWeave and Lambda Labs:

• Borrow heavily to buy massive amounts of GPUs;
• Lease compute time to AI startups and enterprises;
• Operate on thin margins, volatile demand, and rapidly depreciating hardware.

A lot of their debt is secured by the GPUs themselves. But GPUs lose value fast as each new generation wipes out the last.

If AI compute prices fall because there’s too much capacity?

• Rental rates drop;
• The value of the GPUs used as collateral plunges;
• Lenders get nervous;
• Companies may be forced to fire-sale hardware, pushing prices even lower.

That’s a classic negative feedback loop.

It gets weirder:

• Nvidia is reportedly investing tens of billions into AI players like OpenAI.
• Those same companies then spend that money on… Nvidia chips.
• AMD has given OpenAI warrants for up to a 10% stake at essentially zero cost, helping OpenAI finance multi-gigawatt chip buys with AMD’s own soaring equity as leverage.

When the supplier and the customer start blurring into the same balance sheet, you’re deep in bubble territory.

You don’t need a sci-fi scenario. Just a few disappointments.

If AI productivity gains don’t live up to the hype:

• Enterprises cut back on AI spending;
• Hyperscalers slash capex;
• Compute prices fall;
• Neo-clouds and heavily leveraged GPU buyers struggle to service debt;
• Forced selling hits the market;
• Orders for new chips drop sharply.

The trigger could be almost anything:

• A big earnings miss from a major chipmaker or cloud provider;
• A nasty surprise in private credit tied to data centers or GPUs;
• A geopolitical shock that disrupts supply chains or demand.

Once confidence cracks, the unwinding can move quickly — especially when a lot of the same players are on both sides of the trade.

So… Is This a Bubble? Probably yes. But it’s not the Dot-Com bubble 2.0.

• 2000: Bubble of Junk
  Investors were throwing money at companies with no revenue, no plan, and often no real product. Many disappeared entirely.
• 2025: Bubble of Anticipation
  Today’s investors believe they’re funding what might be the biggest infrastructure buildout in human history. The problem is, they’re doing it before the economics and real-world utility are fully proven.

The tech is real. The use cases are growing. But the current pace and scale of spending may be detached from what customers are actually willing — and able — to pay for in the near term.

That’s how a $10 trillion AI boom quietly turns into a very expensive lesson in overbuilding.