Prefer video?

Every market mania eventually gets compared to the last one. And right now, the comparison everyone keeps reaching for is the dot-com crash.

The logic is intuitive. A transformative technology captures the public imagination. Capital floods in. Valuations detach from reality. Then the music stops. We saw it with the internet in 2000, and critics argue we’re watching the same movie play out with AI. But how well does the analogy actually hold up when you look at the numbers?

At Veridion, we spend our time tracking what businesses are actually doing, not what markets think they’ll do. So we decided to look at this question through the lens of real adoption data, financial structure, and historical precedent.

The short answer: the dot-com parallel is useful, but it breaks down in important ways. The risks are real, but they look very different from the ones that triggered the crash in 2000.

The parallels are hard to ignore

Both eras share the same origin story. A breakthrough technology captures public attention, and the market flips from curiosity to mania almost overnight. For the internet, that tipping point was the Netscape IPO in 1995. For AI, it was the release of ChatGPT in late 2022.

What follows the tipping point is always the same: an infrastructure arms race. In the late ’90s, companies raced to lay fiber optic cable. Today, they’re racing to build data centers and stockpile GPUs. The logic in both cases is identical. If this technology is going to change the world, whoever builds the pipes will own the future.

The company comparisons are equally striking. Cisco was the backbone supplier of the internet era. If you wanted to get online, you bought their routers. Nvidia occupies that same position today. And both companies used a similar playbook to accelerate growth: investing in the startups that would, in turn, buy their hardware. Cisco funneled money to internet companies who spent it on Cisco routers. Nvidia is funneling money into AI companies who spend it on Nvidia chips.

This creates a circular economy that looks impressive on paper but carries a structural fragility. When the dot-com startups collapsed, Cisco’s revenue engine collapsed with them. The stock dropped nearly 80% and took three decades to recover.

Where the comparison breaks down

Here’s where things get interesting. Despite the surface-level similarities, the financial architecture of the AI boom is fundamentally different from the dot-com era.

Start with the quality of the companies involved. In 2000, the market was propped up by thousands of small, speculative firms with no revenue, no product, and no clear path to profitability. Their entire value proposition was attention: can we get people to look at a screen? The crash happened because, for most of them, the answer turned out to be no.

The companies at the center of the AI boom are nothing like that. Google, Microsoft, Meta, Apple, Amazon, Nvidia, and Tesla are among the most profitable businesses in human history. They generate real revenue at enormous scale. The dot-com bubble was spread thin across thousands of fragile startups. The AI concentration sits in a small number of companies with deep balance sheets.

The market structure has changed, too. Since 2000, total global market capitalization has grown from around $33 trillion to $159 trillion, but the number of listed companies has only increased by about 20%. The value didn’t spread out. It consolidated upward. And a major structural force that didn’t exist during the dot-com era is now holding those valuations in place: passive investing. Somewhere between 40% and 45% of all equities are now held by ETFs and index funds. Every 401(k) contribution automatically buys into the S&P 500 without asking whether any individual stock is overpriced. That creates a persistent upward pressure on large-cap valuations that the dot-com market simply didn’t have.

There’s also a difference in where the risk lives. In 2000, weak regulation allowed companies to IPO with nothing more than a pitch deck. Retail investors absorbed the losses when those companies failed. Today, the IPO window is much narrower, which means the speculative risk is concentrated in private markets and on the balance sheets of Big Tech rather than in the portfolios of everyday investors.

The technology is actually working

The most important difference between 2000 and now might be the simplest one: AI works.

The dot-com era was full of companies solving problems that didn’t exist yet for customers who didn’t care. AI is already generating measurable economic value. Google’s CEO has said that 25% of all new code at the company is now written by AI. McKinsey reports that 78% of organizations have adopted AI in at least one business function.

Our data at Veridion adds another dimension to this picture. We found that AI adoption in non-tech industries in 2025 was four times higher than the previous four years combined. That’s not hype. That’s a technology crossing over from early adopters into the mainstream economy. A recent survey of small businesses reinforces the point: 91% of those using AI reported revenue growth, compared to a much flatter trend among non-users. When a three-person company can add the equivalent of a fourth employee for $20 a month, the value proposition isn’t theoretical.

So where’s the actual risk?

If the companies are profitable, the technology works, and adoption is accelerating, what could go wrong?

The risk isn’t a sudden crash. It’s a slow suffocation.

Current AI valuations don’t just assume continued growth. They assume extraordinary growth sustained over a long period. For Nvidia to justify its current stock price, and for OpenAI to survive its burn rate, global GDP would need to grow at 3.5% to 4.5% annually for the better part of a decade. Historically, that almost never happens. (Alexandra Tofan ran the math on this using the Buffett Indicator and IMF projections, and the numbers are sobering: even under optimistic assumptions, convergence takes roughly 28 years.)

The underlying cash flow is also more fragile than it appears. Microsoft pays OpenAI. OpenAI pays Nvidia. Nvidia reinvests in AI startups and cloud providers. It’s a closed loop that works beautifully as long as end-user adoption keeps expanding. But if AI doesn’t generate hundreds of billions in new economic value fast enough to justify the capital being spent on it, the loop breaks.

The saving grace is that even in the worst case, this doesn’t play out like 2000. The companies at the center of the AI boom aren’t going to zero. Google, Microsoft, and Meta have the financial depth to absorb a correction without existential consequences.

The more likely downside scenario isn’t a dramatic collapse. It’s a drawn-out period of stagnation where stock prices slowly deflate as reality fails to keep pace with the expectations baked into current valuations.

The long view

There is one last parallel with the dot-com era worth considering, and it’s actually an optimistic one.

The fiber optic cables laid during the ’90s infrastructure binge were a financial catastrophe. An estimated 90% of them went dark after the crash. But they weren’t wasted. That excess capacity became the cheap, abundant backbone that made YouTube, Netflix, and the modern internet possible two decades later.

The data centers being built today may follow the same trajectory. In the near term, they might represent financial overreach. But in the long term, they’re laying the physical foundation for whatever comes next, whether that’s general intelligence, embodied AI, or applications we haven’t imagined yet.

The bubble may deflate. Valuations may correct. But if history is any guide, the infrastructure will outlast the speculation. The dot-com crash didn’t kill the internet. It cleared the ground for the internet to actually become what everyone had prematurely promised it would be.

The AI boom might end the same way: not with a bang or a bust, but with the quiet realization that the most important thing being built right now isn’t a stock portfolio. It’s a foundation.