AI Bubble Fears: “Big Short” Investor Bets Against Tech Stocks, Shaking Markets

In a dramatic turn of events, Michael Burry – the legendary trader who inspired The Big Short – has set his sights on what he suspects is a new bubble: artificial intelligence. As tech shares falter, Burry’s bearish wager against high-flying AI stocks has intensified debate on whether the AI boom has outrun reality. This comprehensive analysis examines recent market trends, the skepticism around AI valuations, and what it all means for tech, finance, and innovation. We’ll also explore lessons from past bubbles and how investors can navigate the turbulence.

Michael Burry, famous for predicting the 2008 housing crash, has revealed massive bets against two of the AI sector’s darlings: chipmaker Nvidia and software firm Palantir. In a recent regulatory filing, Burry’s firm Scion Asset Management disclosed put options (a bet that a stock will fall) on both companies, with a notional value of just over $1.1 billion. Specifically, Scion purchased roughly $187.6 million in puts on Nvidia and $912 million in puts on Palantir, signaling a bold wager that these high-flying stocks could come down to earth. The move echoes Burry’s contrarian style – much like his famous bet against subprime mortgages – but this time the target is an apparent “AI bubble.”

Burry’s actions did not occur in a vacuum. Just days before the filing became public, he reappeared on social media (after a two-year hiatus) to post a cryptic warning: “Sometimes, we see bubbles. Sometimes, there is something to do about it. Sometimes, the only winning move is not to play.” . This ominous message – a nod to the 1983 film WarGames, where an AI realizes that nuclear war has no winners – underscored Burry’s belief that the current market frenzy around AI could end in tears. He even updated his X (formerly Twitter) profile with pop-culture hints, positioning himself as a lone Jedi fighting an AI “Empire,” and posted charts highlighting slowing growth and excessive spending in tech – all suggesting that the AI boom might be built on shaky foundations.

The revelation of Burry’s short positions sent ripples through the stock market. On the day his bet became news, technology stocks saw a sharp sell-off. Palantir’s stock plunged nearly 8% – its worst one-day drop since August – and Nvidia’s shares tumbled about 3.9% :contentReference[oaicite:6]{index=6}. The shockwaves spread to other tech giants as well; industry heavyweights like Oracle, Microsoft, and Apple all traded down amid a broader flight from risk:contentReference[oaicite:7]{index=7}. It appears that Burry’s bearish stance, combined with already jittery market sentiment, spooked investors into rethinking the lofty valuations of AI-focused companies.

Palantir’s CEO, Alex Karp, was quick to hit back at Burry’s bet. In a CNBC interview, Karp blasted the idea of shorting Palantir and Nvidia as “bats**t crazy”, arguing that those two companies are among the few actually “making all the money” in the AI sector. He even suggested that short sellers like Burry might be engaging in “market manipulation” by stoking fears around AI stocks. Nvidia’s CEO Jensen Huang also defended the long-term prospects of AI, telling media that the sector is “a long, long way” from any kind of collapse and implying that current investments are justified. These responses highlight a widening split between AI true believers and skeptics.

It’s worth noting that while Burry’s moves grab headlines – and clearly can move markets – his track record on public calls is not infallible. Famously, he tweeted “Sell” in January 2023, just before tech stocks rallied, only to admit a couple of months later, “I was wrong to say sell”. That humbling episode reminds investors to consider Burry’s warning but also to do their own homework rather than blindly follow any single guru. With that context, let’s examine why Burry and others suspect an AI bubble, and what the data shows about the current tech rally.

Ever since generative AI burst into the public consciousness (thanks in large part to OpenAI’s ChatGPT in late 2022), tech stocks have been on a tear. Investors poured into anything remotely related to artificial intelligence, fueling a spectacular rally in 2023-2025. The “Magnificent Seven” – a group of mega-cap tech stocks including Apple, Microsoft, Alphabet (Google), Amazon, Meta (Facebook), Tesla, and Nvidia – saw their market values skyrocket on AI optimism. By mid-2023, Nvidia even briefly joined the trillion-dollar valuation club as its stock price tripled on frenzied demand for AI chips. Software names like Palantir similarly enjoyed surging stock prices as they repositioned themselves as AI leaders.

This AI euphoria had very real impacts on the market. Since the launch of ChatGPT in November 2022, AI-related stocks (primarily those same tech giants) have accounted for an estimated 75% of the S&P 500’s gains, contributed about 80% of earnings growth among index companies, and drove roughly 90% of growth in capital spending🔗 . In other words, the stock market’s strength became overwhelmingly concentrated in a few AI-focused winners. This narrow leadership can be a warning sign: when so much of the market’s health rests on a handful of high-fliers, any stumble by those names can drag everything down.

At the same time, valuations for many AI-exposed companies have reached stratospheric levels. Nvidia, for example, recently traded at price-to-earnings multiples reminiscent of the dot-com era, as investors effectively bet that its torrid growth will continue for years. Startups have been adding “AI” to their pitches to attract venture capital at eye-popping valuations, often despite minimal revenues. In one eyebrow-raising case, an AI startup founded by a prominent ex-OpenAI executive raised $2 billion at a $10 billion valuation – all before even explaining what its product would be🔗. Such anecdotes evoke memories of the late 1990s, when mere association with the Internet could send a stock soaring.

With tech valuations so high, a growing chorus of investors and analysts are asking a sobering question: is this AI gold rush setting up for a crash? Aside from Michael Burry, many market watchers see patterns that are worryingly similar to past bubbles:

• Massive Spending vs. Meager Returns: Corporations are projected to spend roughly $400 billion this year on AI infrastructure – an unprecedented investment boom. Yet by one estimate, consumers are spending only about $12 billion annually on AI-driven services🔗. The gulf between vision and reality is enormous. Companies are essentially plowing Apollo-program levels of money into AI (the spending in 2025 alone rivals what it cost to put a man on the moon, in real dollars) while returns so far are barely a rounding error in the economy. This imbalance isn’t sustainable indefinitely.
• Circular Financing and Incestuous Investments: The AI sector has developed a tight-knit web of interdependent deals. For instance, OpenAI has major investments from Microsoft and reportedly from Nvidia, while OpenAI itself is taking stakes in chipmakers and committing huge sums to cloud contracts. Nvidia invests in certain AI startups that also buy its chips. Such circular arrangements – where companies are simultaneously each other’s investors, suppliers, and customers – are raising eyebrows🔗. Some liken it to the late stages of the dot-com boom, when telecoms and tech firms bolstered each other’s lofty projections by trading assets and contracts, only to all collapse together when reality set in.
• Unsustainable Infrastructure Buildup: The rush to build AI capabilities has triggered an arms race in data centers and chip purchases. Tech giants are spending unprecedented sums on GPU-powered supercomputers and cloud infrastructure to support AI – so much so that AI-related capital expenditures contributed over 1.1% of U.S. GDP growth in the first half of 2025🔗. However, skeptics question whether even the largest companies can generate enough new revenue to justify this spending spree:contentReference. If the promised AI-driven efficiencies and new businesses don’t materialize soon, these investments could become expensive boondoggles. Already, there are signs of strain – for example, cloud-computing growth at Amazon, Microsoft, and Google has actually slowed in recent quarters despite the AI hype, implying that AI hasn’t yet reignited their core businesses as hoped.
• Questionable Profitability and ROI: Many organizations are struggling to turn AI experiments into profits. A recent MIT study of large firms found that 95% of companies reported essentially no return on their AI investments, despite spending $30–40 billion on more than 300 AI initiatives🔗 . This suggests that for all the excitement, most corporate AI projects haven’t yet cracked the code on monetization. Such poor returns echo the early 2000s, when companies invested in online ventures or big IT overhauls that didn’t pay off until much later, if at all.
• Market Sentiment and Momentum Investing: In frothy times, investors often chase stocks simply because they are going up, not due to fundamentals – a hallmark of bubble behavior. Analysts note that recent stock price surges for AI names have been driven more by momentum and FOMO (fear of missing out) than by traditional metrics like earnings growth. When valuations disconnect from reality in this way, a minor piece of bad news can spark a big correction as the spell breaks.

These signs don’t guarantee an imminent crash, but they do indicate a fragile situation. Even the Bank of England recently warned that if investor enthusiasm for AI were to suddenly sour, it could trigger a “sharp market correction” with potentially material spillover risks to the broader financial system🔗. Similarly, a Bank of America fund manager survey in October found that 54% of managers believe AI stocks are in a bubble (versus 38% who disagree)🔗. When a majority of savvy investors smell smoke, it’s worth investigating where the fire might be.

The surge of skepticism from figures like Burry (and others) could mark a turning point for the tech industry. If the AI bubble thesis is correct and valuations deflate, Silicon Valley may face a period of painful adjustment. Easy funding for AI startups might dry up, forcing a shakeout where only the strongest players or truly viable business models survive. We’ve seen this pattern before: after the dot-com bubble burst in 2000, a lot of fluffy internet startups vanished, but the truly valuable companies (e.g. Amazon, eBay) eventually came back stronger. Similarly, a correction in AI could refocus innovators and investors on solving real problems and achieving sustainable results rather than chasing hype.

In the short term, however, a deflating AI hype bubble could slow down some research and deployment of AI technologies. Companies might become more cautious in adopting experimental AI projects, and boards could demand clearer ROI on tech investments. Hiring sprees for AI talent might cool off, tempering the war for machine-learning engineers. On the flip side, proven AI applications – say in healthcare diagnostics, supply chain optimization, or enterprise software – would likely continue to receive support, albeit with greater scrutiny. In other words, the industry may shift from a “growth at any cost” mindset back to an emphasis on pragmatism and efficiency.

It’s also worth noting that not everyone agrees an AI reckoning is at hand. Many tech leaders remain optimistic that we are in the early innings of a decade-long “AI supercycle.” For example, Lisa Su, CEO of chipmaker AMD, has pushed back on bubble fears, arguing that AI’s transformative potential justifies significant investment and could drive growth for years🔗. Joseph Briggs, an economist at Goldman Sachs, similarly opined that the wave of multibillion-dollar AI investments is sustainable in the big picture – though he cautions that it’s uncertain which companies will emerge as the ultimate winners🔗. These views suggest that while valuations may be debated, the consensus is that AI as a technology is very real; it’s the timeline and profitability that are in question.

A significant pullback in AI-related stocks would have ramifications far beyond Silicon Valley. Given the outsized role that tech giants play in global stock indices, any correction in these names can drag down entire markets. We already saw a glimpse of this when Burry’s disclosure helped knock down the Nasdaq and S&P 500 for a few days. If an “AI bubble” were truly to pop – say, due to a major earnings disappointment from an AI bellwether, or a broader crisis of confidence – it could potentially shave trillions off equity values in a short span. Investors heavily concentrated in tech could see substantial portfolio losses.

However, unlike the housing bubble of 2008, an AI stock bust is unlikely to trigger a systemic banking crisis because, as the International Monetary Fund’s chief economist pointed out, the AI investment surge isn’t being financed by heavy debt leverage🔗. In a dot-com-like scenario, the pain would be mostly felt by equity investors and tech sector employees (through stock price declines and possibly layoffs), rather than collapsing the credit system. Still, the broader economy wouldn’t be unscathed. If tech companies pull back on spending, that means less business for suppliers, fewer capital expenditures, and possibly slower innovation diffusion to other industries. On the upside, if AI truly boosts productivity in the long run, any short-term market pullback might just be a hiccup on the way to genuine economic gains.

Interestingly, some veterans note that a bubble pop can even have silver linings. Amazon founder Jeff Bezos described the current AI boom as “kind of an industrial bubble” – suggesting that while many projects will fail, the frenzy is pouring money into exploration, and when the dust settles, society will benefit from the breakthroughs that do pan out🔗 . In Bezos’s view, an “industrial bubble” (one centered on investment in new technology) isn’t as dangerous as a financial bubble, because the end result can be new infrastructure and knowledge that drive progress. That perspective is a good reminder that short-term market outcomes and long-term societal outcomes aren’t always aligned – a bust can weed out excess while still leaving behind a transformative legacy.

History doesn’t repeat, but it often rhymes. The AI mania of the mid-2020s has parallels with previous tech booms and busts, from the dot-com crash to the cryptocurrency frenzy. Understanding these patterns can help investors and observers make sense of what might come next.

• Dot-Com Bubble (1995–2000): Just as AI is touted as “the future” today, the internet was seen as a world-changing technology in the late ’90s – and it was. Investors bid up anything with a “.com” in its name, often ignoring fundamentals like revenue or profit. The bubble burst spectacularly in 2000, wiping out $5 trillion in market value. Yet, out of the ashes, the internet did indeed change the world. Companies like Amazon, Cisco, and eBay survived and eventually thrived, but only after valuations were slashed to realistic levels. The lesson: transformative technology can have wildly over-optimistic timelines baked into stock prices. When those timelines prove too optimistic, a crash ensues – but the technology itself continues to develop.
• Housing Bubble (2003–2008): This wasn’t a tech bubble, but it’s one Michael Burry knows well. The housing market euphoria was driven by the belief that prices could only go up. Complex financial engineering (subprime mortgages bundled into CDOs) hid risks, much like today some complex corporate deal-making might be obscuring the real economic viability of AI ventures:contentReference[oaicite:31]{index=31}. When reality struck, the downturn spread through the financial system. The key lesson here is about leverage and interconnectedness – two factors that fortunately are less severe in the equity-driven AI boom, but the “contagion” scenario could play out if a few major AI players falter together:contentReference[oaicite:32]{index=32}.
• Cryptocurrency and Blockchain Craze (2016–2021): The rise of Bitcoin and other cryptocurrencies was accompanied by grand claims of a financial revolution. Many projects raised huge sums via ICOs (initial coin offerings) without viable products, similar to how some AI startups are raising billions on promise alone. That bubble partially burst in 2018, then peaked again in 2021 before a sharp decline in 2022. Today, crypto is still around but far from its hype peak, and regulators stepped in after scandals (e.g., the FTX collapse) exposed weak governance. AI could follow a similar trajectory: periods of over-exuberance followed by retrenchment and increased oversight, but ultimately the core technology finds its footing in a more regulated, mature form.

Each cycle reminds us that early excitement about a revolutionary technology often morphs into over-exuberance. As investors chase the next big thing, valuations and expectations detach from reality. Eventually, a tipping point is reached – a big player fails, growth disappoints, or liquidity dries up – and the bubble deflates. However, after a period of consolidation and learning, the true promise of the technology can take shape. AI likely will be no exception: a correction now could be followed by a steadier, more sustainable growth phase for the industry in years to come.

“How can I avoid getting burned by the next bubble?” This question is top-of-mind for many investors watching the AI frenzy with equal parts fear and greed. While no strategy guarantees safety, here are some prudent steps to consider for navigating the hype cycles:

• Diversify Across Sectors: Ensure your portfolio isn’t over-concentrated in one theme (even one as exciting as AI). A well-diversified mix of asset classes and sectors can cushion the blow if tech stocks suddenly stumble. Remember, during the dot-com bust, traditional sectors like consumer goods and utilities held up much better than tech.
• Focus on Fundamentals: When evaluating tech companies, look at their revenues, earnings, cash flows, and real adoption metrics for their AI products. Companies trading on pure hype with little substance are the most vulnerable in a downturn. It’s telling that even some AI bulls are now separating “good ideas from bad ideas”🔗 – you should too.
• Avoid Timing the Market: It’s notoriously difficult to predict exactly when a bubble will pop. Michael Burry himself was early in his housing short bets and endured pain before being proven right. If you have long-term conviction in a stock, consider scaling in gradually rather than all at once, and don’t try to day-trade around volatile news.
• Use Hedging and Stops Wisely: For those with significant tech exposure, hedging instruments (like put options, much like Burry is using) can provide insurance against downturns. Setting stop-loss orders on highly speculative positions is another way to limit downside – though be careful, as sharp dips can trigger stops in fast markets.
• Don’t Believe All the Hype: Finally, maintain a healthy skepticism. In bubble phases, promotional language runs rampant – every startup is “revolutionary,” and every new product is “game-changing.” Do your own research or consult unbiased experts. Often, what sounds too good to be true (e.g., an AI that will solve everything overnight) is tempered by significant challenges. Keeping an independent, value-oriented mindset can help you sidestep the froth.

By staying disciplined and informed, you can participate in technological revolutions like AI without risking your financial well-being. It’s possible to be excited about the future of AI and invest in it, while still guarding against the excesses of market mania.

No one has a crystal ball, but we can outline a few plausible scenarios for how the AI-stock saga might unfold in the coming months and years. The outcomes range from a soft landing to a hard pop, with very different implications:

• Soft Landing – Hype Moderates, Growth Continues: In this optimistic scenario, the AI sector experiences a gradual reality check but no violent crash. Perhaps corporate earnings in 2026 start to justify some of the 2023–25 optimism as AI begins boosting productivity. Stock valuations drift down to more reasonable levels via slower gains rather than sudden drops. The market leadership broadens beyond just AI megacaps, as other sectors catch up (indeed, some forecasts suggest the growth gap between the “Magnificent Seven” and the rest of the S&P 500 will narrow next year. Innovation continues steadily, and while a few high-profile AI startups might fail, the overall industry isn’t derailed. This outcome would resemble the mid-2010s post-social media boom, where tech valuations cooled but the sector kept advancing at a sustainable pace.
• Bubble Bursts – Sharp Correction in Tech: Here, the warning signs culminate in a full-blown selloff. It could be triggered by a major AI company missing earnings expectations or issuing cautious guidance, thereby pricking the confidence bubble. Alternatively, a negative catalyst like stricter regulation on AI data usage or an incident raising ethical concerns could sour sentiment. In this scenario, stocks like Nvidia, Palantir, and other high-P/E names might plunge 30-50% or more, as happened to many tech stocks in 2000-2002. Broader indexes would likely fall into a correction or bear market. We might see some consolidation – for instance, cash-rich giants acquiring struggling AI startups on the cheap. The crucial question would be whether the financial damage stays contained to equity markets. Given the lack of debt leverage noted earlier, a burst AI bubble might hurt portfolios but not freeze credit. After the shakeout, the truly valuable AI applications (similar to how Amazon and Google emerged from the dot-com bust) would pick up the pieces and lead the next phase of growth.
• Muddling Through – Volatile but No Clear Resolution: It’s also possible that we won’t get a definitive “pop” or a gentle cooling, but rather an extended period of ups and downs. Markets could swing between AI excitement and fear depending on the news cycle: one quarter a big AI breakthrough or revenue beat ignites another rally, the next quarter a flop or macroeconomic issue (like rising interest rates) deflates valuations again. This choppy environment could persist as investors essentially “feel out” the true potential of AI over time. Such a pattern occurred in the biotech sector in past decades, where breakthroughs would cause surges, followed by crashes when drugs failed or hype receded. For investors, this would mean brace for volatility: large swings that require patience and a strong stomach, but not necessarily a final catastrophic crash.

Beyond the market outcomes, there’s the broader economic context to consider. One notable effect of the current AI boom is that it has been a significant driver of economic activity – to the point that AI-related spending has propped up GDP growth in 2025:contentReference[oaicite:35]{index=35}. If that spending pulls back, we might see a drag on growth in sectors like hardware, electronics, and even commercial construction (data centers have been a big source of demand for real estate and electricity). On the other hand, if AI begins delivering productivity gains, it could help counteract inflation and labor shortages by making businesses more efficient. Policymakers are watching closely: central banks and governments are aware that bubbles can misallocate capital, but they also don’t want to squelch innovation that could boost the economy’s long-term health.

Another factor on the horizon is regulation. Thus far, AI companies have had a relatively free hand, but as the technology becomes more critical (and potentially more feared in terms of job displacement or misinformation), regulatory frameworks will tighten. Just as the post-dotcom era saw increased oversight of accounting and corporate governance (e.g., Sarbanes-Oxley Act) and the post-crypto hype saw targeted rules for digital assets, a post-AI-bubble landscape may bring new rules around data privacy, AI transparency, and antitrust in the tech sector. Such regulation could either be a headwind (increasing compliance costs and limiting some business models) or a stabilizer that actually encourages more sustainable growth by removing the wild-west element.

Michael Burry’s bet against the AI champions is a bold pronouncement that the emperor has no clothes – or at least not as much as everyone believes. Whether he will be proven right or early (or wrong) remains to be seen. What’s clear is that the current landscape features both tremendous promise and peril: artificial intelligence is almost certainly a transformative force that will reshape industries and societies, but the feverish excitement around it may have overshot what can realistically be achieved in the short term.

For investors and innovators, the key takeaway is to stay level-headed amid the excitement. Booms and busts are cyclical companions to technological revolutions. By learning from history, diversifying risk, and focusing on real value creation, one can participate in groundbreaking advancements like AI without being blindsided by market excesses. Skepticism from voices like Burry’s serves as a healthy counterbalance in a time of exuberance – a reminder that financial gravity still exists. At the same time, optimism from visionaries serves to remind us that beyond the market volatility, real innovation is happening that could drive growth and progress for years to come.

In the end, reconciling these perspectives will likely yield the wisest course: believe in the transformative potential of AI, but temper that belief with due diligence and realistic expectations. The truth of this AI moment probably lies somewhere in the middle – not purely bubble nor pure gold rush, but a bit of both. As we navigate this new frontier, keeping one foot in the world of data and fundamentals and the other in the world of imagination and possibility will be the best way to thrive, come what may in the markets.

Michael Burry is a hedge fund manager best known for predicting the 2008 housing market crash. He famously made a fortune by shorting subprime mortgage securities ahead of the 2008 crisis, a story told in Michael Lewis’s book The Big Short (and the movie based on it)

That successful contrarian bet made Burry a legend in investing circles. Ever since, when Burry takes a big short position – like he has now with AI stocks – it draws a lot of attention because of his history.

Palantir’s CEO Alex Karp publicly blasted Michael Burry’s bet against his company. He called the idea of shorting Palantir and Nvidia “bats**t crazy,” arguing that those two firms are actually the ones “making all the money” in AI. Karp even hinted that Burry’s move might amount to market manipulation, suggesting the famous short-seller could be stirring fear to benefit his position

According to Scion Asset Management’s SEC filing, Michael Burry wagered roughly $1.1 billion in total via put options against the AI high-flyers. This consisted of about $912 million in puts on Palantir and approximately $188 million in puts on Nvidia, based on the notional values disclosed. It’s a massive short position targeting two of the most prominent AI-focused stocks.

Palantir reported strong quarterly results (beating Wall Street estimates and raising guidance), yet its stock still dropped around 8–9% in one day. The pullback came because investors were jittery about sky-high valuations and took profits amid broader AI bubble fears. In Palantir’s case, the stock had surged over 170% this year and was trading at a very rich forward P/E (over 200), so even good news led some to sell, especially after Michael Burry’s big short bet spooked the market.

Alex Karp suggested that short-sellers betting against AI companies might be trying to manipulate the market narrative. In his comments, Karp implied that Michael Burry’s very public short position against Palantir and Nvidia could be a tactic to drive down prices for personal gain, rather than a genuine long-term call Essentially, Karp believes skeptics like Burry are unfairly undermining the value of AI firms by stoking bubble fears.

Before the pullback, Palantir’s stock was on an enormous rally in 2023–2025. Year-to-date, it had climbed roughly 170%+ as of early November, driven by the company’s positioning as an AI leader and a string of upbeat results. This huge run-up put Palantir at lofty valuation levels (one reason Burry targeted it). The stock’s strong performance set the stage for volatility – when bad news or skepticism hit, a highly run-up stock can swing down sharply.

Burry sees signs of an “AI bubble” – he believes the surge in AI-related stock prices has been fueled by hype and unsustainably high valuations. By taking large short positions against Nvidia and Palantir, Burry is effectively betting that the optimism around AI has overshot reality and that these stock prices could come crashing back down to earth. In short, he’s skeptical that the current AI frenzy can continue without a major correction, so he’s positioning to profit if these popular AI stocks falter.