AI stocks are no longer just rallying. They are rewriting market history. In the last one year, some AI-linked stocks have surged as much as 4,000%, while names like Nvidia, Palantir, Broadcom, Micron, Western Digital, Seagate and SanDisk have ridden the AI infrastructure boom to multi-year highs.

The money flow is just as extreme. AI-related venture capital touched $258.7 billion in 2025, making up 61% of all global VC funding, double its share from 2022. At the same time, Michael Burry, the investor who predicted the 2008 housing crash, is reportedly holding nearly $1.1 billion in short bets against Nvidia and Palantir.

That is why the dot-com bubble comparison is back. And to be fair, some parallels are hard to ignore: stretched valuations, investor euphoria, and a market willing to price the future aggressively. But calling AI “dot-com 2.0” without looking deeper is lazy analysis. The surface looks similar, but the underlying economics, revenue base and infrastructure demand are very different.

Let’s break down what the data actually says, where the AI stocks bubble risk is real, and where the AI boom may be structurally stronger than the dot-com era.

AI Bubble: Where the Bears Are Right

1. The Price Surges Are Almost Identical in Shape

Cisco stock surged roughly 1,000% from 1990 through its March 2000 peak, briefly becoming the world's most valuable company. Nvidia has surged approximately 2,000% from its October 2022 lows to a market cap that exceeded $3.3 trillion in early 2026. Palantir, trading at a trailing P/E of approximately 135-146x as of May 2026, is priced entirely on narrative premium for a company that had $5.2 billion in revenue over the last twelve months. Plot Nvidia stock's curve against Cisco's from 1995-2000 and the shape is almost indistinguishable.

Sources: Nasdaq-100 P/E at peak: INSEAD Knowledge, GuruFocus,VC share: IntuitionLabs/OECD data

2. Market Concentration Is at a 50-Year High

The Magnificent Seven (Apple, Nvidia, Microsoft, Amazon, Tesla, Alphabet, Meta) account for approximately 35-37% of the entire S&P 500. In late 2025, 30% of the S&P 500 was concentrated in just five companies, the greatest such concentration in half a century. For historical parallel, this eclipses the peak of the dot-com bubble in 2000, when the top 10 companies accounted for roughly 27% of the index and the top five made up just 17%

3. The Dot-Com Infrastructure Overbuilding Pattern Is Repeating

In the late 1990s, companies like Global Crossing and WorldCom borrowed massively to lay fiber-optic cable for an internet that arrived years later than projected. When debt came due before revenue did, they collapsed. Today's version: Morgan Stanley estimates Big Tech will collectively spend approximately $3 trillion on AI data centers through 2028, with their own cash flows covering only about half. 

Source: Company capex guidance via VanEck, OpenAI, NPR

4. Circular Financing: The House-of-Cards Risk

OpenAI, CoreWeave, Nvidia and Microsoft are increasingly tied together through circular financing, where the same companies act as customers, suppliers and investors to each other. OpenAI rents capacity from CoreWeave, CoreWeave is backed by Nvidia, Nvidia has committed to absorb unused capacity, and Microsoft holds a major stake in OpenAI.

The dot-com parallel is vendor financing. In the late 1990s, firms like Lucent, Nortel and Cisco financed telecom startups that used the money to buy their equipment, while companies like Global Crossing and Qwest swapped unused capacity to book inflated revenue. When real demand failed to catch up, the loop broke. 

As MIT economist and 2024 Nobel laureate Daron Acemoglu said, "The danger is that these kinds of deals eventually reveal a house of cards."

5. The Debt-Funded Cost Subsidy: The Risk Nobody Is Pricing Clearly

AI services look cheap today because their real costs are being absorbed by venture capital, debt and hyperscaler spending, not necessarily by sustainable unit economics. Former Fidelity manager George Noble estimated OpenAI was burning $15 million per day on Sora alone.

The dot-com parallel is the “Get Big Fast” subsidy model. Companies like Webvan and Pets.com used investor capital to sell products and shipping below cost, hoping scale would fix the economics. When funding dried up, prices had to reflect real costs, and demand collapsed.

AI faces a similar question: if companies eventually pass true compute costs to customers, will enterprises still pay two or three times more for the same tools?

6. The Shiller CAPE Is at Its Second-Highest Reading in 125 Years

The Shiller CAPE (cyclically adjusted price-to-earnings ratio) stood at approximately 44-45 at the peak of the dot-com bubble in 1999, the highest reading in modern history. As of May 16, 2026, it stands at approximately 41, the second-highest reading in 125 years of US stock market data. The historical median is 17.

7. AI-Washing: The .com Suffix Problem, Reborn

During the dot-com era, adding ".com" to any company name was sufficient to trigger a valuation premium regardless of fundamentals. A 2001 study published in the Journal of Finance found that 95 companies adding ".com," ".net," or "Internet" to their names in 1998 and 1999 saw an average abnormal stock return of 74% in the 10 days surrounding the announcement—even if their core business remained entirely unchanged. Today, AI-themed rebranding carries the same gravity. Supermicro surged over 1,000% before governance concerns prompted a dramatic reversal. SanDisk, Seagate, and Western Digital have surged on the thesis that AI will drive explosive storage demand, even as their core businesses remain fundamentally cyclical, driven by NAND and HDD supply-demand cycles that have nothing to do with AI timelines. Palantir, a data analytics platform founded in 2003, now trades at a trailing P/E of 135-146x primarily because it repackaged its government surveillance software as an AI platform.

Is the AI Bubble Really Similar to the Dot Com Bubble?

1. The Companies at the Center Are Massively Profitable

At the dot-com peak, 36% of large-cap tech companies were unprofitable. Barron’s March 2000 “Burning Up” story found 74% of 207 internet companies had negative cash flow, with 51 likely to run out of cash within a year.

Today, only around 20% of tech companies are unprofitable, while Nvidia reported $215.9 billion revenue and $120.1 billion net income in FY26, up 65% YoY.

Valuations are also less extreme: tech trades near 30x forward P/E today versus ~50x at the dot-com peak. Cisco hit a $555 billion market cap in 2000 with just $2.67 billion net income and a 200x+ P/E. Nvidia now earns more in one quarter than Cisco did in a full year then.

2. The Technology Is Already in Production (The Demand Side)

In 1999, the internet was genuinely transformative but commercially thin. Most businesses had digital brochures, not digital operations, and e-commerce accounted for just 0.6% of US retail sales. AI looks fundamentally different today because it is already commercially embedded. McKinsey notes 88% of organizations use AI in at least one function, enterprise GenAI spending rose 3.2x to $37 billion in 2025, and IBM estimates a $3.50 return for every $1 invested.

3. The Infrastructure Generates Revenue From Day One (The Supply Side)

During the dot-com boom, telecom companies spent over $500 billion laying 80 million miles of fiber-optic cable, but by 2002, up to 90% of it sat "dark" (unused) waiting for demand. Today’s infrastructure build-out is structurally different because data centers generate revenue immediately. Nvidia reported data center segment revenue of $197.3 billion for fiscal year 2026, and AWS, Azure, and Google Cloud all report that AI computing capacity is fully utilized as fast as it is provisioned.

4. The Core Companies Are Diversified

In 2000, many tech leaders were one-theme businesses. Cisco depended heavily on networking hardware, Yahoo on banner ads, and Sun Microsystems on servers for internet startups. When dot-com demand collapsed, there was little revenue cushion, leading to 80-90%+ stock crashes.

Today’s AI leaders are more diversified. Microsoft has Office, Azure and gaming; Alphabet has search ads; Amazon has retail and AWS; Meta has global social advertising. AI is a major growth bet, but not their only business. If AI ROI disappoints, they can cut capex. They are unlikely to face the same survival risk.

5. AI Is a National Security Asset. Dot-Com Was Not

Unlike the dot-com era, AI is now being treated as a national security asset. The US Intelligence Community’s 2026 threat assessment called AI a “defining technology for the 21st century,” while the Pentagon’s AI strategy aims to build an AI-first military. During the dot-com boom, internet companies relied mainly on private capital and consumer demand. Today, AI demand is also being driven by governments, defence budgets and the US-China technology race.

6. The Balance Sheet and Macro Context Is Entirely Different

The dot-com bubble burst into a tougher macro setup. The Fed raised rates from 4.75% to 6.5%, inflation was rising, and unemployment was near 4%, leaving cash-burning internet companies with little refinancing room.

Today’s AI leaders are financially stronger. Nvidia, Microsoft, Alphabet, Amazon and Meta generate enough operating cash flow to fund or service heavy AI spending. Only around 20% of tech companies are unprofitable today, versus 36% during dot-com. So even if AI valuations correct, the balance-sheet shock risk is much lower than in 2000.

What should Indian Investors Do?

For people investing in US stocks from India or thinking about it, it is worth remembering that AI is not a bubble to blindly avoid or a rally to blindly chase. It is a real technology cycle with real excess built into prices.

The risks are clear: stretched valuations, extreme capex, market concentration and AI pricing that may not fully reflect infrastructure costs. But this is not dot-com 2.0. Today’s AI leaders are profitable, cash-rich, diversified and backed by enterprise as well as government demand.

The smarter approach is selective exposure. Avoid weak AI narratives and companies rising only because of the “AI” tag. Focus on US companies with real earnings, pricing power, balance-sheet strength and long-term AI monetisation visibility. When the correction comes, hype will crack first. Cash flows will matter most.