Are We Watching an AI Bubble About to Burst?

Neon “AI” shattering over red falling stock charts; headline questions an AI bubble.

Welcome to FreeAstroScience.com, where we break down complex topics into clear, accessible insights. Today, we're tackling a question that's sending shockwaves through global markets: Is artificial intelligence driving us toward the next great financial bubble?

This article was written exclusively for you, our dear readers. In recent days, stock markets from Tokyo to New York have experienced dramatic selloffs, with technology companies losing hundreds of billions in value. The catalyst? A famous investor's massive bet against AI giants, combined with growing concerns that valuations have soared far beyond what companies can actually deliver. We'll explore what's really happening, why it matters to everyday investors, and what history teaches us about market bubbles. Stay with us through the entire article to understand this critical moment in financial history—because as the saying goes, "the sleep of reason breeds monsters.

What Just Happened to Global Markets?

Markets don't usually panic without reason. So when Wall Street's tech-heavy Nasdaq 100 dropped 2% on Tuesday, November 5th, 2025, followed by even steeper declines across Asia, investors started asking serious questions.

Japan's Nikkei 225 plunged 2.5%, while South Korea's Kospi fell 2.85%. Taiwan Semiconductor Manufacturing Company declined 3%, and Samsung Electronics and SK Hynix saw devastating drops of 8% and 9% respectively. The selloff wiped approximately $500 billion from technology stocks in just two days.

What triggered this mass exodus? The answer lies in a combination of sky-high valuations, warnings from Wall Street's biggest names, and one investor's contrarian bet that's making headlines worldwide.

Who Is Michael Burry and Why Does His Bet Matter?

If you've seen the film "The Big Short," you already know Michael Burry. He's the hedge fund manager portrayed by Christian Bale who correctly predicted the 2008 housing market collapse and made a fortune betting against it.

Now Burry is back with another big short. Regulatory filings revealed that his firm, Scion Asset Management, purchased put options—essentially bets that stock prices will fall—worth $912 million on Palantir Technologies and $187.6 million on Nvidia. That's a combined $1.1 billion wager that two of the hottest AI stocks will tumble.

Burry didn't stay silent about his reasoning. In a cryptic post on X, he wrote: "Sometimes, we see bubbles. Sometimes, there is something to do about it. Sometimes, the only winning move is not to play".

His timing couldn't have been more dramatic. The disclosure came just as Palantir reported third-quarter revenue of $1.2 billion—up 63% and beating Wall Street expectations. Yet despite these stellar results, Palantir shares fell 8% on Tuesday and continued declining another 3% in after-hours trading.[6][1]

Why Palantir CEO Fired Back

Palantir CEO Alex Karp didn't take Burry's bet lying down. During a CNBC interview, Karp called the short position "batshit crazy," questioning why anyone would bet against companies "making all the money".[1][6]

"The idea that chips and ontology is what you want to short is bats--- crazy," Karp said, arguing that Burry was essentially "putting a short on AI".[1]

But here's the uncomfortable truth for Karp: Burry's trades may already be underwater. Market expert Jon Najarian estimates that Nvidia would need to fall another 7% and Palantir another percentage point for Burry's positions to reach breakeven. "There's no way he isn't significantly in the red on both positions," Najarian stated.[1]

This raises an interesting question. If Burry is likely losing money on these bets, why do markets care so much?

The Valuation Problem Nobody Can Ignore

Let's talk numbers. Palantir was trading at more than 300 times its projected 2025 earnings as of early November. To put that in perspective, the broader S&P 500's forward price-to-earnings ratio stands at approximately 23—already elevated compared to the historical median of 18.

What does this mean in plain language? Investors are paying $300 for every dollar of profit they expect Palantir to generate next year. That's an extraordinary premium, reflecting enormous faith in future growth. By comparison, the "Magnificent 7" tech giants—Alphabet, Amazon, Apple, Broadcom, Meta, Microsoft, and Nvidia—trade at an average forward P/E ratio of just 35.

Nvidia, which recently became the first company ever to cross a $5 trillion market valuation, isn't immune to these concerns either. The chipmaker's value now exceeds the entire GDP of India, Japan, or the United Kingdom.

Wall Street analysts at D.A. Davidson, Goldman Sachs, and other firms have raised red flags about Palantir's valuation despite acknowledging the company's strong fundamentals. Even supporters worry that the stock price has gotten ahead of reality.

"Despite the strong results, several Wall Street analysts said they worry Palantir's stock could be overvalued after a torrid rally this year," Investopedia reported. The stock had already surged approximately 150% in 2025 before the recent selloff.

When Wall Street's Elite Sound the Alarm

Markets took an additional hit when two of Wall Street's most powerful CEOs issued stark warnings at the Global Financial Leaders' Investment Summit in Hong Kong.

Goldman Sachs CEO David Solomon told the audience: "It's likely there'll be a 10% to 20% drawdown in equity markets sometime in the next 12 to 24 months". He explained that market pullbacks allow investors to reassess and that such declines are normal even during long-term bull markets.

Morgan Stanley CEO Ted Pick echoed these concerns, suggesting investors should "welcome the possibility that there would be drawdowns, 10 to 15% drawdowns that are not driven by some sort of macro cliff effect".

These aren't fringe pessimists. These are the leaders of two of the world's largest investment banks, speaking publicly about their expectations for significant market corrections. Their warnings carry weight because they have access to data and client flows that few others can see.

Morgan Stanley's chief investment officer, Lisa Shalett, went further, issuing a note advising clients to "consider taking profits in high-beta, small/micro-cap, speculative and unprofitable equities and redeploying to large-cap core and quality stocks".[18]

The Concentration Risk That Should Worry Everyone

Here's a fact that should give every investor pause: technology stocks contributed more than 90% of the S&P 500's total return in October 2025, with the "Magnificent 7" stocks alone accounting for 80%.

The Magnificent Seven now constitute 37% of the S&P 500's total market capitalization—up from just 12% in 2015. Between 2015 and 2024, these seven companies delivered a combined return of 698%, compared to 178% for the broader S&P 500.

This extreme concentration creates a dangerous dynamic. When such a small number of companies drive the entire market's gains, investors face what's called "concentration risk." Your portfolio might look diversified on paper, holding hundreds of stocks through index funds, but in reality, your returns depend heavily on just a handful of names.

As Louis Navellier, founder of Navellier and Associates, put it: "There is fear of an AI correction, and if it comes, it will sweep the rest of the market with it due to the heavyweight of the leading names".

Historical Echoes of Concentration

We've seen this story before. During the dot-com bubble, concentration reached extreme levels as investors piled into internet stocks regardless of whether those companies had viable business models. When the bubble burst in 2000, the Nasdaq plummeted from 5,048 to 1,139 by October 2002—a devastating 77% decline.

The period that followed is now known as the "lost decade" for investors. Those who bought at the peak waited years just to break even.

Current concentration levels actually exceed those seen during the dot-com bubble. The top 10 companies in the S&P 500 now account for approximately 40% of the index's total market capitalization, compared to 27% at the peak of the tech bubble in 1999-2000.

How Is This Different From the Dot-Com Bubble?

Before we succumb to panic, we need to examine what makes today's AI boom different from the dot-com mania of the late 1990s. The differences matter because they help us assess whether we're heading for a similar crash.

Real Revenue vs. Empty Promises

During the dot-com era, companies went public with little more than a website and a dream. Pets.com, Webvan, and countless others burned through investor cash without ever finding sustainable business models. Many had minimal revenue and no path to profitability.

Today's AI leaders tell a starkly different story. Nvidia reported revenues that soared from $11 billion in fiscal 2020 to an anticipated $285 billion in the coming fiscal year. Microsoft, Meta, Amazon, and Alphabet are all expected to increase their total capital expenditures by 34% to approximately $440 billion over the next year.[12]

These aren't speculative bets—they're massive, profitable companies making strategic investments. CEO Jensen Huang recently disclosed that Nvidia has secured more than $500 billion in orders for its AI chips through the end of 2026. "I think we are probably the first technology company in history to have visibility into half a trillion dollars [in revenue]," Huang said.[13][32]

The Profitability Question

But here's where things get complicated. While companies like Nvidia are profitable, many AI firms struggle to turn their technology into sustainable revenue.[33][34]

Bain & Company research reveals a sobering reality: by 2030, $2 trillion in annual revenue will be needed to fund the computing power required to meet anticipated AI demand. Even accounting for AI-related savings, the world is still $800 billion short.[33]

Data center operators face particularly harsh economics. Investor Harris Kupperman calculates that AI facilities coming online in 2025 will face roughly $40 billion in annual depreciation costs—primarily from expensive GPUs that become obsolete quickly—while generating only $15-20 billion in revenue at current usage rates.[34]

OpenAI's ChatGPT, despite attracting roughly 700 million weekly active users, generates subscription and API revenue running at a $12-13 billion annual pace. That's impressive growth, but the company reportedly loses money on each interaction due to the enormous computational costs.[34]

The Circular Spending Problem

One of the most concerning aspects of the current AI boom is what analysts call "circular financing". The same firms funding AI development are often the ones selling to each other.[20]

Tony Yoseloff, managing partner at Davidson Kempner Capital Management, described it as a "prisoner's dilemma" in an October 2025 podcast with Goldman Sachs. Tech giants feel compelled to invest massive sums in AI infrastructure because their competitors are doing the same. "You have to invest in it because your peers are investing in it," Yoseloff explained, "and so if you're left behind you're not going to have the stronger competitive position".[20]

This creates a feedback loop where capital flows in one direction regardless of actual returns. When companies make investments primarily to avoid being left behind rather than based on clear return-on-investment calculations, that's a warning sign.

What Psychology Tells Us About Bubbles

Markets aren't just about numbers and balance sheets. They're about human behavior, emotions, and the cognitive biases that drive us to make irrational decisions.

Herd Behavior and FOMO

One of the most powerful forces creating bubbles is herd behavior—the tendency to mimic what everyone else is doing. When investors see others making money on AI stocks, they assume those people must know something they don't. This creates a feedback loop where rising prices attract more buyers, which pushes prices even higher.

This behavior intensifies as the bubble grows. People who initially stayed on the sidelines begin to feel they're missing out on easy profits. Fear of missing out, or FOMO, drives late-stage buying when prices are already stretched.

Overconfidence and the Illusion of Control

Bubbles also feed on overconfidence. As prices continue rising, even poorly informed investment decisions appear brilliant. This success reinforces the belief that investors possess special insight or skill, encouraging them to take on more risk than prudent.

The "illusion of control" plays a role too. Investors start believing they can time their exit perfectly—that they'll sell before the crash hits. Of course, everyone thinks they'll be the one to get out in time. In reality, very few do.

Narrative-Driven Investing

Perhaps most importantly, bubbles are driven by compelling stories. During the dot-com era, the narrative was that the internet would revolutionize everything and traditional business rules no longer applied. "This time is different," became the mantra.[39][26][36]

Today's narrative centers on artificial intelligence as a transformational technology that will reshape every industry. That narrative isn't wrong—AI likely will transform our world. The question is whether current stock prices accurately reflect the timeline and magnitude of that transformation, or whether they've raced ahead of reality.

What Should Investors Actually Do?

So you're sitting there looking at your portfolio, wondering whether you should panic, sell everything, or just ignore the noise. Let's talk practical strategies based on what actually works.

Don't Try to Time the Market

First, the most important lesson: trying to time market tops and bottoms is extraordinarily difficult, even for professionals. Studies consistently show that investors who try to jump in and out of the market typically underperform those who stay invested long-term.

If you're young—say, under 50—with decades until retirement, you have time to recover from market corrections. The worst move you can make is to sell during a panic and lock in losses.

Diversification Is Your Friend

If you're concerned about AI exposure, the simplest solution is diversification. Here are specific strategies:

Switch to equal-weight funds: Most index funds weight companies by market capitalization, meaning the biggest companies have the largest impact on your returns. Equal-weight funds give each company the same weighting, reducing concentration risk. The Vanguard S&P 500 ETF, for example, is 8% invested in Nvidia and 7.4% in Microsoft—that's massive exposure to just two companies.

Increase small-cap exposure: Smaller companies have less reliance on AI hype and have been neglected by investors for years. They may perform better if mega-cap tech stocks stumble.

Consider value investing: Value funds naturally avoid expensive areas of the market, providing a counterbalance to growth-oriented tech stocks. Financials, consumer staples, and industrials typically trade at more reasonable valuations.

Look beyond the U.S.: American markets are at the epicenter of AI mania. European and UK stock markets offer lower valuations and less concentration risk. Some funds focused on international markets have only 27% exposure to U.S. stocks, roughly half what you'd get with a global tracker.

Build Your Cash Position

Warren Buffett and Berkshire Hathaway are currently stashing record amounts of cash instead of investing. That's not because Buffett is pessimistic about the long term—it's because he recognizes that high valuations today mean lower returns tomorrow.

Having cash available serves two purposes:

First, it provides a cushion so you don't have to sell investments at depressed prices during a correction. Life doesn't stop during bear markets. You still need money for emergencies, and the worst time to sell stocks is when they're down.

Second, cash positions you to buy quality assets "on sale" if a serious correction occurs. The investors who prosper during crashes are those with dry powder to deploy when everyone else is panicking.

Review and Rebalance Regularly

Falko Hoernicke, a senior portfolio manager for US Bank, recommends reviewing and rebalancing holdings semiannually or quarterly. As certain positions grow to outsized portions of your portfolio, trim them back to your target allocations. This forces you to "sell high" automatically, locking in gains before potential corrections.

Focus on Quality and Fundamentals

Morgan Stanley's Lisa Shalett advises taking profits in "high-beta, small/micro-cap, speculative and unprofitable equities" and redeploying to "large-cap core and quality stocks".

What does "quality" mean? Companies with strong current earnings, solid balance sheets, stable cash flows, and proven business models. When markets get choppy, quality names with real profitability tend to hold up better than speculative plays.

What Are the Warning Signs to Watch?

If you want to monitor whether AI bubble fears are justified, keep your eyes on these specific indicators:

Slowing AI Investment

Capital expenditures related to AI have been a main driver of the stock rally in recent years. If mega-cap tech firms start pulling back on their ambitious spending plans, that could signal waning confidence in near-term returns. So far, announcements continue pointing upward—but watch for any cracks in that consensus.

Nvidia's Cash Conversion Ratio

BCA Research suggests monitoring Nvidia's ability to convert earnings into actual cash. If the company starts showing deteriorating cash conversion, it could indicate that accounting profits aren't translating into real financial strength.

Weakening Economic Data

Market corrections rarely occur in isolation. They typically coincide with or precede broader economic weakness. Keep an eye on employment numbers, consumer spending, and manufacturing data. If the economy shows sustained weakness, it could accelerate any pullback in stock prices.

Margin Debt and Leverage

When investors borrow heavily to buy stocks, it amplifies both gains and losses. Rising margin debt often precedes market corrections because leveraged positions unwind rapidly when prices start falling. Data on margin debt is publicly available and worth monitoring.

The Bigger Picture: Is AI Really a Bubble?

Here's the truth that gets lost in the noise: AI is genuinely transformational technology. The question isn't whether AI will reshape industries—it almost certainly will. The question is whether current valuations accurately reflect the timeline and profitability of that transformation.

Bubbles form when asset prices disconnect from fundamental value, driven by speculation and emotion rather than cold analysis. Current AI valuations show some bubble characteristics: extremely rapid funding, stretched valuations, herd behavior, and compelling narratives that justify any price.

But they also show important differences from classic bubbles: real revenue, actual profitability among leaders, tangible applications, and broad enterprise adoption. Many AI companies, particularly Nvidia, are making enormous amounts of money right now, not in some hypothetical future.

One analysis comparing AI to the dot-com bubble concluded that "one can credibly argue that AI is partly a bubble but also a legitimate tech supercycle". That seems about right. There's genuine substance beneath the hype, but prices have likely gotten ahead of reality.

The unwinding of such situations doesn't always mean a catastrophic crash. Sometimes it means a period of consolidation where prices move sideways for months or years while fundamentals catch up. Other times it means sharp but temporary corrections that shake out speculative excess before the longer-term trend resumes.

What we can say with confidence is that extreme concentration in a handful of stocks creates fragility. When 80% of market returns come from just seven companies, the system becomes vulnerable to any stumble by those leaders. That's not a sustainable situation.

Conclusion: Staying Rational When Markets Go Mad

We're living through one of the most exciting technological revolutions in history. Artificial intelligence will likely deliver transformative benefits across every sector of the economy. The investments being made today could power decades of innovation and productivity growth.

But excitement and sound investment strategy aren't always the same thing. Markets have a long history of overshooting in both directions—getting too optimistic during booms and too pessimistic during busts.

Michael Burry's $1.1 billion bet against AI giants, while currently underwater, serves as a valuable reminder that even the hottest trends eventually face reality checks. The warnings from Goldman Sachs and Morgan Stanley CEOs about 10-20% corrections reinforce the same message.

Does this mean you should sell all your tech stocks tomorrow? Almost certainly not. But it does mean taking a hard look at your portfolio's concentration risk, ensuring adequate diversification, building cash reserves, and focusing on quality companies with real earnings rather than speculative promises.

The bubble question ultimately comes down to time horizon. Over the next 12-24 months, current valuations look stretched and vulnerable to correction. Over the next 10-20 years, AI will likely justify much of the enthusiasm—though probably not at the exact companies or valuations people expect today.

Stay disciplined, stay diversified, and remember that the most successful investors are those who keep their heads when everyone else is losing theirs. Thank you for taking this journey with us through the complexities of market dynamics and bubble psychology. We invite you to return to FreeAstroScience.com for more insights that make complex topics accessible to everyone. Keep your mind actively engaged—because as we said at the beginning, the sleep of reason breeds monsters.

Are We Watching an AI Bubble About to Burst?

What Just Happened to Global Markets?