You’re staring at another AI vendor pitch. The numbers are extreme—30x revenue multiples, billion-dollar valuations for companies that have barely celebrated their second birthday. Meanwhile, 95% of AI pilots are failing. So what’s the real story? Are we watching history’s greatest con, or are we just too early to the party?
Here’s the thing about technology cycles—history has seen this movie before. Multiple times. Genuine transformation and speculative bubbles happen at the same time. It’s a pattern.
So in this article we’re going to give you a framework for understanding what’s happening with AI. You’ll learn how to spot bubble conditions using actual metrics, not vibes. You’ll see how the dot-com crash and railway mania played out—spoiler: most companies died but the technology transformed everything. And you’ll walk away with practical tools to monitor bubble indicators in your own context.
This analysis is part of our comprehensive examination of the AI bubble debate, exploring the paradox of 95% enterprise AI failure alongside record AI-native company growth.
What Is the AI Bubble and How Do We Identify It Using Rigorous Metrics?
Let’s look at the data: 80% of U.S. stock gains in 2025 came from AI companies. Americans are holding a record share of their wealth in equities, and most of those trades are AI-related. Meta, Amazon, and Microsoft have become the biggest issuers of debt—a classic late-cycle bubble sign.
So what is a bubble? It’s when asset prices shoot way above long-term trends because of speculation, not fundamentals.
Investment firm GMO has been spotting bubbles for years. They’ve found over 300 historical bubbles using a quantitative approach called the 2-sigma methodology. When prices rise more than two standard deviations above their long-term trends (adjusted for inflation), you’re in bubble territory. It’s not guesswork—it’s statistical deviation.
The current AI market ticks all the boxes. The U.S. market’s CAPE ratio (cyclically adjusted price-to-earnings) sits at 40. That’s close to the dot-com peak of 44, and way above the historical average of 16-17. U.S. stocks now represent over 70% of the MSCI World index. AI companies drive most of that concentration.
AI company price-to-sales ratios are running at multiples that exceed historical tech sector norms by 2+ standard deviations. When you see these deviations across multiple metrics at once, you’re looking at bubble conditions.
Economist Ruchir Sharma uses four criteria—the “four O’s”: overinvestment, overvaluation, over-ownership, and over-leverage. The AI surge checks every box.
Now here’s the thing about bubble conditions—they don’t tell you when the crash happens. They don’t even guarantee a crash. Bubbles can persist for years. What they tell you is that investor behaviour has disconnected from economic reality.
AI and tech spending in the U.S. has surged at a rate comparable to past bubbles. Roughly 60% of U.S. economic growth in 2025 has been driven by AI.
Sharma put it bluntly: “This big bet on AI better work out for America—because if it doesn’t work out, then I think there’s a lot of trouble for this country ahead.”
You can monitor this yourself. Track six indicators: investment levels, data centre construction timelines, adoption rates, price levels, competition, and public trust in technology. When these indicators move together—all rising or all falling—you’re seeing bubble dynamics.
Bubbles measure market conditions, not technology merit. That distinction matters.
How Did the Dot-com Bubble Combine 90% Company Failures With Internet Transformation?
The dot-com crash wasn’t one event. It was a convergence of factors that exposed how late-1990s tech companies actually operated.
The Federal Reserve raised interest rates repeatedly through 1999 and 2000, climbing from around 4.7% in early 1999 to 6.5% by May 2000. When Japan’s economy tipped into recession in March 2000, global markets panicked. Money fled risky assets.
But that just accelerated what was already coming. Most dot-com companies had flawed business models.
Look at the numbers. Commerce One hit a $21 billion valuation despite minimal revenue. TheGlobe.com saw its stock jump 606% on day one to $63.50, despite having no revenue beyond venture funding. Pets.com burned through $300 million in 268 days before bankruptcy.
Companies were valued on website traffic and growth metrics instead of cash flow and profitability. “Eyeballs” and “land grab” became valuation metrics. Until they weren’t.
Most internet companies couldn’t justify their valuations. That caused the crash.
But here’s what happened next. Technology was real. Valuations were not. Most investors lost money. Society gained enormously. All these things happened at the same time.
Much of what seemed like wasteful overinvestment became infrastructure later.
What Does Railway Mania Teach Us About Infrastructure Bubbles and Long-Term Value?
If you think the dot-com bubble was bad, take a look at 1840s Britain. Railway Mania makes AI look rational.
Speculative frenzy drove massive capital into railway companies. Investors poured money into anything with “railway” in its name—sound familiar? Over 90% of those companies went bankrupt. Investors were wiped out.
Yet railways revolutionised civilisation.
The pattern repeats: infrastructure overbuilding during bubbles precedes transformative use by decades. The bankrupt companies left physical rail networks that others used profitably. First-mover investors got destroyed. Long-term societal benefit was enormous.
Current AI infrastructure spending—trillions—mirrors railway overbuilding. You can see the pattern: massive capital deployment, speculative valuations, inevitable consolidation, long-term transformation.
The lesson here is straightforward—distinguish between investor returns (often terrible in bubbles) and technology impact (can be profound). They’re not the same. You can acknowledge the bubble while pursuing selective AI investments in high-value use cases.
How Do Current AI Valuations Compare to Historical Technology Bubble Peaks?
The comparison to the dot-com bubble has become obvious.
Global corporate AI investment hit $252.3 billion in 2024, per Stanford research. The sector has grown thirteenfold since 2014.
America’s biggest tech companies—Amazon, Google, Meta, and Microsoft—pledged to spend a record $320 billion on capex in 2025, mostly for AI infrastructure.
OpenAI is valued at roughly $500 billion despite launching ChatGPT just two years ago.
Cursor, an AI coding assistant, raised $2.3 billion at a $29.3 billion valuation—nearly triple its June valuation. The company crossed $1 billion in annualised revenue.
Unlike the dot-com era, major AI players are generating actual revenue. Microsoft’s Azure grew 39% year-over-year to an $86 billion run rate. OpenAI projects $20 billion in annualised revenue by end of 2025, up from around $6 billion at the start of the year.
But here’s the problem: Microsoft, Meta, Tesla, Amazon, and Google invested about $560 billion in AI infrastructure over two years, but brought in just $35 billion in AI-related revenue combined.
That’s a 16:1 investment-to-revenue ratio.
A recent MIT study found that 95% of AI pilots fail to yield meaningful results, despite more than $40 billion in generative AI investment.
Even Sam Altman admits it: “Are we in a phase where investors as a whole are overexcited about AI? My opinion is yes. Is AI the most important thing to happen in a very long time? My opinion is also yes.”
Both things are true.
Why Has Venture Capital Concentration in AI Risen From 23% to 65% in Two Years?
ChatGPT launched in December 2022. That single event revived the deflating 2022 bear market and demonstrated consumer AI viability at scale. It triggered fear-of-missing-out among investors that hasn’t stopped.
The data tells the story: venture capital allocation to AI/ML companies surged from 23% in 2023 to over 65% by 2025. That’s not gradual. That’s panic.
Why? Winner-take-all perception. Nobody wants to miss “the next Google.” Everyone remembers the investors who passed on Facebook. The belief that AI will consolidate into a few dominant players is driving massive early-stage overinvestment.
Hyperscalers validated the category. When Amazon, Google, Microsoft, and Meta collectively spend hundreds of billions on AI infrastructure, it signals market conviction. Or creates the appearance of it.
Look closer at the spending patterns. Microsoft invests in OpenAI. OpenAI spends on Microsoft Azure. Meta builds AI models. Meta builds data centres to run those models. These are circular investment patterns—self-reinforcing capital flows that raise bubble concerns. As explored in our AI bubble debate, this infrastructure spending both reflects and reinforces valuation dynamics.
It’s a prisoner’s dilemma. Hyperscalers can’t stop spending even while recognising oversupply risk. If you stop and your competitor doesn’t, you lose. If everyone stops, someone will cheat. So everyone keeps spending.
Extreme capital concentration is a late-stage bubble characteristic. We saw similar VC concentration during the dot-com peak in 1999-2000. It didn’t end well.
How Does Dark Fibre Repurposing Illustrate Infrastructure Overbuilding Preceding Utilisation?
During the dot-com boom, telecommunications companies laid more than 80 million miles of fibre optic cables across the U.S., driven by WorldCom‘s wildly inflated claim that internet traffic was doubling every 100 days—far beyond the actual annual rate.
Companies like Global Crossing, Level 3, and Qwest raced to build massive networks to capture demand that never came.
The result? Overcapacity. Four years after the bubble burst, 85% to 95% of the fibre laid in the 1990s remained unused. It earned the nickname “dark fibre.”
Corning‘s stock crashed from nearly $100 in 2000 to about $1 by 2002. Ciena‘s revenue fell from $1.6 billion to $300 million almost overnight, with its stock plunging 98%.
For years, dark fibre was proof of bubble irrationality. Wasteful overinvestment. Money down the drain.
Then broadband happened. Cloud computing happened. Streaming video happened. AI data centres happened.
All built on that “wasteful” infrastructure. Dark fibre got lit up. Overcapacity became essential capacity.
The current AI infrastructure boom follows the same pattern. Meta CEO Mark Zuckerberg announced plans for an AI data centre “so large it could cover a significant part of Manhattan.” The Stargate Project aims to develop a $500 billion nationwide network of AI data centres, backed by OpenAI, SoftBank, Oracle, and MGX.
Amazon is devoting $100 billion to data centres in 2025. Meta will spend over $600 billion in three years. Microsoft planned to spend $80 billion in 2025. Google will devote $75 billion in 2025.
Is this wasteful overinvestment or infrastructure buildout? Both. The same infrastructure that bankrupts first-movers enables second-wave transformation.
Pattern recognition: what looks like bubble waste during buildout can become infrastructure for subsequent technology waves. Overbuilding benefits future use but doesn’t protect initial investors from losses during bubble correction.
The question for you: are you a first-mover investor or a second-wave user? The strategy is different.
Can Bubbles and Paradigm Shifts Coexist—What Does Historical Evidence Show?
Yes.
GMO’s analysis of 300+ historical bubbles shows all extreme valuation events eventually return to trend—this is mean reversion. But many of those bubbles represented genuine technological transformation. The bubble corrects. The transformation persists.
Sharma suggests the AI boom could be a “good bubble” that ultimately boosts productivity—like past tech manias that overshot but left valuable infrastructure.
Transformative technologies create legitimate excitement about future potential. Early successes—like ChatGPT—validate the technology, triggering fear of missing out. The difficulty in predicting winners leads to overinvestment across many competitors. The technology significance is real. The speculation about timing, scale, and which companies win is excessive.
History shows this repeatedly. Over 90% of dot-com companies failed, yet the internet transformed the global economy. Over 90% of railway companies went bankrupt, yet railways revolutionised civilisation.
Take electricity as another example. When factories first got electric power, they replaced gas lamps with electric bulbs. Productivity barely budged. It took decades before manufacturers redesigned entire factories around electric motors and assembly lines. That’s when productivity exploded. The transformation was real, but took far longer than early champions predicted.
The internet did change the world, but not as quickly as early champions promised. The fibre-optic cables of the 1990s eventually became useful infrastructure, but much sat unused while demand caught up with supply.
Even transformative technologies can’t escape economics.
For you, the implication is clear—you can acknowledge bubble risk while pursuing selective AI investments in high-value use cases. Bubble conditions don’t invalidate AI’s transformative potential. They do suggest many current investments will fail.
Framework for you: technology merit (often real) exists separately from investment timing and valuation (often poor). Recognise both at once. For a comprehensive understanding of how these historical patterns relate to broader AI bubble dynamics and contemporary challenges, see our complete analysis.
FAQ Section
Are we definitely in an AI bubble right now?
Using GMO’s 2-sigma methodology, current AI valuations show bubble characteristics: price-to-sales ratios 2+ standard deviations above norms, CAPE ratio at 40 (near dot-com peak), and 65%+ VC concentration. The AI boom exhibits all four bubble signs: overinvestment, overvaluation, over-ownership, and over-leverage. However, “bubble” describes market conditions, not inevitable crash timing—bubbles can persist for years.
Does an AI bubble mean the technology isn’t transformative?
No. Historical evidence from 300+ bubbles shows transformative technology and speculative bubbles routinely coexist. The internet was revolutionary and AI will be too, but that doesn’t mean companies with valuations based on those themes were or are good investments. Railway Mania bankrupted investors yet revolutionised civilisation. Bubble conditions suggest poor investment returns for many, not invalid technology.
What caused the dot-com bubble to burst in 2000?
The Federal Reserve raised interest rates from around 4.7% in early 1999 to 6.5% by May 2000. Economic recession began in Japan in March 2000, triggering global market fears. But the root cause was that most dot-com companies had flawed business models and couldn’t justify valuations with actual results. Companies running out of cash triggered the collapse.
How long did it take for dot-com infrastructure to become useful?
Years after the bubble burst, most fibre laid in the 1990s remained unused. Infrastructure overbuilding often precedes productive use by decades. Broadband expansion in the 2000s, cloud computing in the 2010s, and AI data centres in the 2020s gradually consumed capacity that seemed wasteful in 2000. The pattern: overbuilding → bankruptcy → eventual use.
Should you avoid AI investments if we’re in a bubble?
Not necessarily. Unlike many dot-com companies that had no revenue, major AI players are generating substantial income. Microsoft’s Azure grew 39% year-over-year to an $86 billion run rate. However, pilot success rates remain low. Focus on high-value use cases with measurable ROI. Bubble conditions warrant caution about timing and valuation, not blanket avoidance.
What’s the difference between a bubble and a crash?
A “bubble” describes market conditions where valuations deviate significantly from norms—specifically, 2+ standard deviations from long-term trends in GMO’s framework. A “crash” describes an outcome—rapid price collapse. Bubbles can deflate gradually rather than crash. GMO’s research shows all 2-sigma events eventually mean-revert, but timing and severity vary widely.
Are current AI capex levels sustainable long-term?
America’s biggest tech companies pledged to spend a record $320 billion on capex in 2025. Historical patterns show infrastructure overbuilding often precedes eventual use, but not without bankruptcies and consolidation. Prisoner’s dilemma dynamics mean hyperscalers can’t stop spending even while recognising bubble risk. If one stops and competitors continue, they lose market position.
How can you monitor bubble indicators in your own context?
Track these six indicators: investment levels, data centre construction timelines, adoption rates, price levels, competition, and public trust in technology. Monitor whether AI investments remain in balance with potential revenues. Watch valuation multiples for AI vendors you work with. Track vendor burn rates and path to profitability. Monitor your own AI pilot ROI timelines. Rising indicators together suggest increasing bubble risk.
What happens to AI technology if the bubble pops?
Historical precedent suggests company failures and consolidation. Equipment suppliers and infrastructure builders typically suffer the worst losses—stock prices can drop 95%+ as happened with dot-com telecommunications firms. Yet technology development continues. Infrastructure persists and gets repurposed despite company failures. Transformation still occurs but takes longer than early predictions. Reduced funding slows development but doesn’t stop it.
Why do genuine paradigm shifts attract speculative bubbles?
Transformative technologies create legitimate excitement about future potential. Early successes validate the technology, triggering fear of missing out. Difficulty in predicting ultimate winners leads to overinvestment across many competitors. The technology significance is real—the speculation about timing, scale, and which companies win is excessive. This pattern repeats across railway mania, electricity, dot-com, and now AI.
Can I distinguish hype from reality in AI claims?
Sam Altman admits: “Are we in a phase where investors as a whole are overexcited about AI? My opinion is yes.” Look at the numbers: major tech companies invested $560 billion in AI infrastructure but brought in just $35 billion in AI-related revenue. Look for specific quantifiable claims versus vague “transformation” language. Check if vendors show path to profitability or just revenue growth. Ask for customer references with measured ROI. Compare valuation multiples to historical tech company norms.
What role does mean reversion play in technology bubbles?
GMO’s analysis of 300+ bubbles shows all 2-sigma valuation events eventually return to historical trend lines—this is mean reversion. Timing varies from months to years, but reversion is statistically reliable. For AI, this suggests current extreme valuations will eventually normalise, though technology transformation can persist even as prices correct. The bubble corrects. The transformation continues. Both happen.
