{"id":9505,"date":"2025-11-17T06:36:40","date_gmt":"2025-11-17T06:36:40","guid":{"rendered":"https:\/\/myra.ac.in\/blog\/?p=9505"},"modified":"2025-11-21T08:05:51","modified_gmt":"2025-11-21T08:05:51","slug":"betting-against-the-machine-burrys-billion-dollar-ai-put","status":"publish","type":"post","link":"https:\/\/myra.ac.in\/blog\/betting-against-the-machine-burrys-billion-dollar-ai-put\/","title":{"rendered":"Betting Against the Machine: Burry\u2019s Billion-Dollar AI Put"},"content":{"rendered":"\n

So,<\/em> <\/em>renowned<\/em> <\/em>investor<\/em> <\/em>and<\/em> <\/em>Hedge<\/em> <\/em>fund<\/em> <\/em>manager<\/em> <\/em>of<\/em> <\/em>the<\/em> <\/em>\u2018Big<\/em> <\/em>Short\u2019<\/em> <\/em>fame,<\/em> <\/em>Michael<\/em> <\/em>Burry,<\/em> <\/em>has<\/em> <\/em>made<\/em> <\/em>a<\/em> billion-<\/em>dollar bet against AI\u2026<\/em> <\/em>will<\/em> <\/em>he be proven<\/em> <\/em>right?<\/em><\/p>\n\n\n\n

When the $500 billion Project Stargate was announced in January of this year, it elicited largely positive feedback from many tech pundits and industry leaders. After all, ChatGPT went from something no one had heard of to hundreds of millions of users in under three years \u2013a growth trend that only promised to continue for years to come, both in terms of users and usage.<\/p>\n\n\n

\n
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As expected, leaders with a vested interest in the project, such as Microsoft\u2019s Satya Nadella and OpenAI\u2019s Sam Altman, went all-in with their support. Other AI experts, such as Robert Scoble, noted that unless companies decide to channel all their data into a single pipeline, there isn\u2019t enough data in the world to keep the chips busy at this scale. Most conservative geopolitical analysts in the US had mixed feelings about the project. On the one hand, they were elated that the US was unambiguously claiming its position as the global leader in AI; on the other, they worried about who was actually going to foot the bill for all the data centers. Elon Musk was perhaps the only big name who criticized Project Stargate for its scale and opacity\u2014especially at a time when the US government was underfunded. Some experts in India remarked that, regardless of whether the entire sum is spent, the project signaled a major US commitment to making AI the next internet, aimed at rapid adoption; others raised concerns around data sovereignty and autonomy and alluded to various ways data can be weaponized against other countries.<\/p>\n\n\n\n

Fast-forward nine months, and the general sentiment appears to be shifting in the opposite direction. What began as little more than internet murmur planting doubts about the overhyped profitability of LLMs has now reached the corridors of high finance, which are abuzz with fears that the AI cartel has overplayed its hand\u2014and may end up dragging the entire market down with it.<\/p>\n\n\n\n

Many analysts, investors and intellectuals have raised concerns regarding the current state of affairs in the AI space. The following are some of the major ones.<\/p>\n\n\n\n

1.<\/strong>  <\/strong>The<\/strong> <\/strong>Core<\/strong> <\/strong>Offerings:<\/strong> <\/strong>The core offering put forth by leading AI companies is a massive productivity boost\u2014think 50\u00d7 or even 100\u00d7. The immediate sales pitch is that any organization can become far more efficient than it ever dreamed possible. From a longer-term perspective, however, the ambitions are far loftier: Sam Altman of OpenAI has repeatedly stated that building AGI is the ultimate goal; Mark Zuckerberg at Meta has set his sights on an even more ambitious target\u2014creating the world\u2019s first superintelligent machine. Other major players have<\/p>\n\n\n\n

articulated similar visions, though Alphabet (Google) has chosen a different path, pursuing quantum computing as its route to AGI. Unfortunately, AI companies are now facing serious questions on both fronts.<\/p>\n\n\n\n

A new MIT study<\/a> released in August 2025 rattled many tech-enthusiast investors. It revealed that, despite more than $40 billion invested in enterprise AI applications, over 95% of those projects have yielded zero financial returns\u2014many have actually proved net-negative for the companies involved. In the United States alone, 42% of companies abandoned AI initiatives in 2025, up from just 17% the previous year. Managers who bet heavily on \u201cdisruptive AI\u201d now admit they are seeing little to no real disruption. In a widely discussed article<\/a> titled \u201cAI- Generated \u2018Workslop\u2019 Is Destroying Productivity\u201d, researchers from Stanford and Harvard highlight a now well-understood reality: LLM outputs remain superficial and lack the creative spark needed to solve complex problems meaningfully. In a recent interview, JP Morgan Chase CIO Lori Beer shared<\/a> that the company has restricted employee use of ChatGPT. The near-term efficiency narrative is taking a severe beating. Worse still, corporate AI evangelists continue to insist that the pursuit of AGI or superintelligence is being held back solely by insufficient data- center scale\u2014and that everything will fall into place once larger facilities come online. That claim has become a point of intense debate, especially as every successive leap in model size delivers sharply diminishing returns.<\/p>\n\n\n\n

2.  A Rocky Road to Profits:<\/strong><\/p>\n\n\n\n

Even if AI offerings eventually live up to the hype, a fundamental question remains: can these companies ever turn a profit large enough to justify their astronomical valuations?<\/p>\n\n\n

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\"\"<\/figure>\n<\/div>\n\n\n

OpenAI alone expects to burn through $115 billion in cash over the next four years while projecting revenues of $200 billion by 2030<\/a>. Yet investors are deeply worried\u2014ChatGPT remains heavily unprofitable<\/a> even for users paying $200 a month for the premium plan. In a recent interview, OpenAI CFO Sarah Friar claimed that many customers would gladly pay $2,000 a month for full capabilities. Independent analyses from consultants and academics paint a far less rosy picture, however. For starters, there iswidespread agreement that never before has so much capital been poured into a single technology with suchan unproven track record of generating profits. A full- blown spending frenzy is underway. When asked about the possibility of an AI bubble, Mark Zuckerberg acknowledged the risk but stressed the far greater danger of missing the wave by failing to \u201cinvest enough\u201d. Sam Altman has gone further, declaring that he wants to see trillions\u2014yes, trillions\u2014 of dollars invested in AI infrastructure over the coming decade. So how exactly will investors get their money back? No clear answer has emerged.<\/p>\n\n\n\n

Against a backdrop of underperforming enterprise projects, massive capital outlays, and uncertain revenue models, analysts foresee a foggy future. Bain & Company estimates that the leading AI players\u2014OpenAI, Meta, Google, and NVIDIA\u2014will need combined earnings of roughly<\/p>\n\n\n\n

$2 trillion by 2030 simply to fund the computing power required to meet the projected demand<\/a>. Their current combined annual revenue is just over $650 billion. If McKinsey\u2019s \u201ccontinued momentum\u201d scenario (published in its August 2025 quarterly<\/a>) proves accurate and total AI<\/p>\n\n\n\n

investment reaches $7.2 trillion by the end of the decade, the industry will be under even greater pressure to deliver returns. All of this ignores intensifying competition from China and<\/p>\n\n\n\n

the skyrocketing cost of electricity needed to keep the data centers running.<\/p>\n\n\n\n

3.Inflated Profits: <\/strong>Investor Michael Burry\u2014famous for \u201cThe Big Short\u201d\u2014has recently accused<\/a> major AI-related companies of artificially inflating earnings through questionable accounting practices. His primary charge centers on the treatment of depreciation, particularly for semiconductor chips. Burry argues that hyperscale AI players, including Meta, Oracle, and Palantir, are significantly understating depreciation expense by extending the useful life of GPUs and custom accelerators to five or six years, when a more realistic period would be two to three years. This practice materially boosts reported earnings in the short term. To back his thesis, Burry has taken large put positions totaling more than $1.5 billion against these companies (and related stocks). Although markets have taken notice of both the allegations and his bets, there has\u2014so far\u2014been no widespread domino effect or forced restatements. This is largely because accounting standards still grant companies considerable latitude in determining the useful life of depreciable assets.<\/p>\n\n\n\n

4.Circular Deal Making: <\/strong>Over the past year, leading AI companies have woven an intricate web of circular deals among themselves. At the center stand two dominant players: NVIDIA\u2014the world\u2019s most valuable company, currently valued at over $4 trillion\u2014and OpenAI. NVIDIA recently agreed to co-invest<\/a> roughly $100 billion in OpenAI\u2019s massive new data-center build- outs. In return, OpenAI has committed to equipping those facilities almost exclusively with NVIDIA chips\u2014a textbook example of circular deal-making. Similar arrangements are proliferating: OpenAI has signed a comparable contract with AMD under which it will purchase large volumes of AMD chips while AMD becomes one of OpenAI\u2019s largest shareholders. This tightly interconnected network of cross-investments, revenue guarantees, and equity swaps now encompasses virtually every major name in the AI ecosystem, including Oracle, CoreWeave, Microsoft, NScale, Anthropic, Meta, xAI, and others.<\/p>\n\n\n\n

Investors have always distrusted circular deals. As the old saying goes, money that merely sloshes around a closed loop eventually comes back to the same pockets\u2014often after creating the illusion of explosive growth. The result is a self-reinforcing ecosystem that looks impressive on paper but raises serious questions about long-term sustainability and genuine economic value.<\/p>\n\n\n\n

What worries the market most is the lack of independent, organic demand. This tangled web of cross-investments, revenue commitments, and equity swaps has dramatically increased systemic risk across the entire AI sector: the failure of one major player could quickly cascade into multiple others. In short, many of these companies now appear too interconnected to stand\u2014or fall\u2014alone. As one recent article<\/a> in The Register pointedly observed, AI giants are effectively using investor capital to fund their own customers, who then turn around and spend that money back on the original suppliers. Unlike traditional vendor-financing arrangements, however, these customers are not yet seeing meaningful financial upside. Perhaps this explains why Sam Altman, during a recent investor call, cautiously floated the idea of a federal backstop for OpenAI\u2019s massive investments\u2014essentially a pre-emptive government lifeline. CFO Sarah Friar later walked back the suggestion, insisting Altman was not asking for a bailout but rather for some form of federal loan guarantees that would \u201cde-risk\u201d the sector as a whole. Whatever the wording, such remarks do little to inspire traditional investor confidence.<\/p>\n\n\n\n

Will Michael Burry\u2019s massive AI short positions pay off? No one can predict the future with certainty. What does seem inevitable, however, is that some form of correction\u2014whether orderly or chaotic\u2014is now looming over the AI sector.<\/p>\n\n\n\n

The top five U.S. technology companies, all deeply committed to AI hyperscaling, now command a combined market capitalization exceeding $17 trillion\u2014roughly 35\u201340% of the entire U.S. stock market. The federal government in general, and the second Trump administration in particular, has placed an enormous bet that these AI hyperscalers will deliver transformative economic growth for the country. Yet that wager is being made while the sword of Damocles\u2014an unsustainable national debt\u2014continues to hang precariously overhead. – Prof. S Abhijith<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"

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