This article is reprinted with authorization from Automatic Observation Beating, author: Rhythm Editorial Department, copyright belongs to the original author.
"The only winning move is not to play."
In October, Michael Burry wrote this on social media. It comes from the 1983 film "WarGames," where a supercomputer concludes this after repeatedly simulating nuclear war.
A few days later, Burry disclosed his third-quarter holdings. The investor, known for his precise shorting during the 2008 subprime crisis, placed nearly 80% of the assets in his managed fund, about $1 billion, all in one direction—shorting Nvidia and Palantir.
In his view, the most powerful way to avoid participating in this irrational "long" frenzy is to short it.
Burry's bet is not just against a few overvalued companies but against the very powerful consensus of this era. Because in this consensus, AI is not just a technological revolution but a belief in capital.
But how is this consensus formed? How is it pushed to a climax? As this machine of belief continues to operate, what price are we paying for it?
Behind all financial frenzies lies a story that has been repeatedly told and believed by countless people.
In this wave of AI, the writing of this story is textbook-level. It is accomplished by three forces: technology leaders responsible for writing the "myth," Wall Street providing the "rationale," and the media completing the "evangelism."
The first batch of storytellers are the evangelists of the singularity. Technology leaders represented by OpenAI's CEO Sam Altman and Google DeepMind's co-founder Demis Hassabis have successfully depicted the concept of general artificial intelligence, which originally existed in science fiction and academia, as a "new god" that is close at hand, within reach, and capable of solving all major human problems.
Altman repeatedly states in his global speeches that AGI will be "the greatest technological leap for humanity to date," and the abundance it can bring will "far exceed all our imaginations." Hassabis, using more philosophical language, defines it as a tool to help humanity understand the ultimate mysteries of the universe.
Their language is filled with a religious fervor for "the future" and "intelligence," successfully endowing this technological wave with a meaning that transcends business, almost sacred.
If the technology leaders provide the script for the myth, then Wall Street and economists provide the "rationale" for this myth.
Against the backdrop of slowing global economic growth and frequent geopolitical conflicts, AI was quickly chosen as the "growth antidote" that could make capital believe in the future again.
Goldman Sachs released a report at the end of 2024, predicting that generative AI would contribute a 7% increase to global GDP within a decade, about $7 trillion. Almost simultaneously, Morgan Stanley provided an even grander definition, stating that AI is "the core of the Fourth Industrial Revolution," with productivity effects comparable to the steam engine and electricity.
The true role of these numbers and metaphors is to turn imagination into assets, to turn belief into valuation.
Investors began to believe that giving Nvidia a sixty-fold price-to-earnings ratio was not crazy; they were not buying a chip company but the engine of the future global economy.
Since the launch of ChatGPT in November 2022, AI-related stocks have contributed 75% of the S&P 500 index returns, 80% of profit growth, and 90% of capital expenditure growth. This technological narrative has almost become the sole pillar supporting the entire U.S. stock market.
Finally, the media and social networks became the ultimate amplifiers of this myth.
From the stunning debut of the text-to-video model Sora to every model update from giants like Google and Meta, each node is amplified, looped, and re-amplified, with algorithms pushing this belief into everyone's timeline.
Meanwhile, discussions about "AI replacing humans" spread like a shadow; from engineers to teachers, from designers to journalists, no one can be sure if they belong to the next era.
As fear and awe spread simultaneously, a grand, almost unquestionable creation myth was written, paving the way for one of the largest capital accumulations in human history.
As the "gospel" spreads to every corner of the world, a group of financial engineers, the best at structural design, begins to take action.
Their goal is to turn this abstract belief into a functioning machine, a capital system that can self-cycle and self-reinforce. Rather than being a bubble, it is more like a finely constructed financial engine, its complexity far exceeding that of the derivatives designed in 2008.
The core of this machine is built by a few tech giants. They weave capital, computing power, and revenue into a closed loop, where funds circulate, amplify, and circulate again, like an algorithm-driven perpetual motion system.
First, tech giants represented by Microsoft invest huge sums into AI research institutions like OpenAI. This company, accustomed to betting on infrastructure during the cloud computing era, has invested over $13 billion in OpenAI. Over a few years, OpenAI's valuation skyrocketed from several billion to nearly $100 billion, becoming a new myth in the capital market.
The first thing that massive financing brings is more expensive training. To create GPT-4, OpenAI utilized over 25,000 Nvidia A100 GPUs, and the computing power requirements for the next generation of models are still growing exponentially. These orders naturally flow to the market's only monopolist, Nvidia.
Nvidia's data center revenue jumped from $4 billion in 2022 to $20 billion in 2025, with profit margins exceeding 70%. Its stock price soared, making it the most valuable company in the world.
And those holding a large amount of Nvidia stock include major tech giants and institutional investors, including Microsoft. The rise in Nvidia's stock price has made their balance sheets look even more impressive.
The story is not over; training is just the beginning, and deployment is the main battlefield for expenses.
OpenAI needs to host its models in the cloud, and its largest partner is Microsoft. Billions of dollars in cloud service fees flow into Microsoft's accounts, translating into growth for Azure's business.
A perfect closed loop is thus born. Microsoft invests in OpenAI, OpenAI purchases Nvidia's GPUs and Microsoft's cloud services, the revenue growth of Nvidia and Microsoft boosts stock prices, and the rising stock prices make Microsoft's investment look even more successful.
In this process, funds only circulate among a few giants, yet they create enormous "revenue" and "profits" out of thin air, with the growth on paper mutually corroborating and valuations lifting each other. The machine begins to feed itself. It doesn't even need real demand from the physical economy to achieve "perpetual motion."
This core engine quickly expands into various industries.
The fintech and payment industries are among the first to be integrated.
Stripe is the most typical example. This payment company, valued at over $100 billion, processed a total payment volume of $14 trillion in 2024, equivalent to 1.3% of global GDP. A year later, it announced a partnership with OpenAI to launch an "instant checkout" feature in ChatGPT, embedding the payment system into the interactive scenarios of language models for the first time.
Stripe's role in this wave is quite subtle. It is both a purchaser of AI infrastructure, continuously buying computing power to train more efficient fraud detection systems and payment recommendation algorithms, and a direct beneficiary of AI commercialization, creating new transaction entry points through integration with language models, thereby boosting its own valuation.
PayPal quickly followed suit. In October 2025, this veteran payment giant became the first wallet system fully integrated with ChatGPT.
But the ripples did not stop at finance. Manufacturing is one of the traditional industries that first felt the tremors; it once relied on automated hardware but is now starting to pay for algorithms.
In 2025, a German car manufacturer announced it would invest €5 billion over three years to promote AI transformation, with most of the funds going towards purchasing cloud services and GPUs to reshape the nervous system of production lines and supply chains. This is not an isolated case. Managers in the automotive, steel, and electronics industries are all trying to improve efficiency in similar ways, as if computing power is the new fuel.
Retail, logistics, advertising—almost every industry you can think of is undergoing a similar transformation.
They purchase AI computing power, sign cooperation agreements with model companies, and repeatedly emphasize their "AI strategy" in financial reports and investor meetings, as if those three letters alone can bring a premium. The capital market has indeed provided returns, with valuations climbing, financing becoming smoother, and narratives becoming more complete.
And the endpoint of all this almost points to the same few companies. No matter which industry the funds flow out of, they ultimately return to core nodes like Nvidia, Microsoft, and OpenAI, flowing towards GPUs, towards the cloud, towards models. Their revenues thus continue to rise, stock prices keep climbing, which in turn reinforces the belief in the entire AI narrative.
However, this machine is not without cost. Its fuel comes from real economic and social resources, which are gradually extracted, transformed, and burned into the roar of growth. Those costs are often obscured by the clamor of capital, but they do exist and are quietly reshaping the skeleton of the global economy.
The first cost is the opportunity cost of capital.
In the world of venture capital, funds always chase the direction of the highest returns. The gold rush in AI has created an unprecedented capital black hole. According to PitchBook data, in 2024, about one-third of global venture capital flowed into AI; by the first half of 2025, this proportion in the U.S. rose to an astonishing two-thirds.
This means that capital that could have supported key areas like climate technology, biomedicine, and clean energy is being disproportionately absorbed into the same story.
When all the smartest money is chasing the same story, the soil for innovation is being hollowed out. The focus of capital does not always mean improved efficiency; it often means the disappearance of diversity.
In 2024, the total venture capital received by the global clean energy sector was only one-fifth of that of AI. Climate change is still seen as humanity's most urgent threat, yet funds are flowing towards computing power and models. The situation for biotechnology is no different. Several entrepreneurs have admitted in interviews that investors show little interest in their research because "the AI story is sexier and has a shorter return cycle."
This frenzy of capital has approached a dangerous tipping point.
The year-on-year growth rate of capital expenditure in the U.S. tech industry is now almost catching up to the peak of the internet bubble from 1999 to 2000. At that time, everyone was talking about a "new paradigm," companies were expanding significantly before becoming profitable, and investors were scrambling to bet on visions of "changing the world." Until the bubble burst, the Nasdaq evaporated two-thirds of its market value, and Silicon Valley fell into a long winter.
Twenty-five years later, the same emotions have been reignited, only this time the protagonist is AI. The capital expenditure curve is steeply rising again, with giants competing to invest billions of dollars in building data centers and computing clusters, as if the expenditure itself can bring a certain future.
The historical similarities are unsettling; perhaps the outcomes will not be entirely the same, but this extreme concentration of capital momentum means that once the turning point arrives, the costs will be borne by society as a whole.
The second cost is the intellectual cost of talent.
This AI boom is creating an unprecedented intellectual siphon globally. The top engineers, mathematicians, and physicists are being drawn away from the front lines of solving fundamental human problems into the same direction.
In Silicon Valley, the most scarce resource today is not capital, but the top scientists in large model teams. Companies like Google, Meta, and OpenAI are offering salaries that dwarf those of all other scientific and engineering disciplines.
Industry data shows that an experienced AI research scientist can easily earn over a million dollars a year; meanwhile, a top physics professor in a university lab often earns less than one-fifth of that.
Behind the salary gap is a shift in direction. The world's smartest minds are withdrawing from long-term fields like fundamental science, energy innovation, and biological research, concentrating instead on a highly commercialized track. The speed of knowledge flow has never been so fast, but the channels it flows into are becoming increasingly narrow.
The third cost is the strategic cost to industries.
Under the wave of AI, almost all traditional industry companies are caught in a passive anxiety. They are forced to join this expensive AI arms race, investing huge sums and building AI teams, even though the vast majority of them do not have a clear investment return roadmap.
According to data from the Dell'Oro Group, global capital expenditure on data centers is expected to reach $500 billion in 2025, most of which is related to AI; just Amazon, Meta, Google, and Microsoft plan to invest over $200 billion. But this investment frenzy has long exceeded the boundaries of the tech industry.
A large retail company announced in its financial report that it would invest tens of millions of dollars over the next three years to purchase AI computing power for optimizing recommendation algorithms and inventory systems.
However, according to research from MIT, the vast majority of investments in such projects yield returns that are far from covering costs. For these companies, AI is not a tool but a statement. Many times, this investment is not driven by proactive strategic needs but by a fear of "falling behind the times."
However, viewing this AI wave merely as a story of financial bubbles and resource misallocation is somewhat one-sided. Because regardless of whether the market tide rises or falls in the future, some profound and irreversible structural changes have quietly occurred amidst this clamor.
"Intelligence" and the computing power driving it are replacing traditional capital and labor, becoming new fundamental production factors.
Its status is akin to electricity in the 19th century and the internet in the 20th century—irreversible and indispensable. It is quietly infiltrating all industries, rewriting cost structures and competitive orders.
The competition for computing power has also become the oil race of this era. The ability to control advanced semiconductors and data centers is no longer just an issue of industrial competition but a core aspect of national security.
The U.S. CHIPS Act, the EU's technology export bans, and policy subsidies from East Asian countries constitute a new geopolitical economic front, accelerating a global competition centered around computing power sovereignty.
At the same time, AI is setting a new benchmark for all industries.
Whether a company has a clear AI strategy has become key to whether it can win the trust of the capital market and survive in future competition. Whether we like it or not, we must learn to communicate with the world in the language of AI; it is the new business grammar and the new rules of survival.
Michael Burry is not always right; he has been wrong many times over the past decade. This time's bet may once again prove his foresight, or it may turn him into a tragic figure reshuffled by the times.
But regardless of the outcome, this world has been permanently changed by AI. Computing power has become the new oil, and AI strategy has become a must-answer question for corporate survival, with global capital, talent, and innovation resources concentrating in this direction.
Even if the bubble bursts and the tide recedes, these changes will not disappear; they will continue to shape our world, becoming the irreversible backdrop of this era.
Related: China's low-cost AI robots defeat ChatGPT in cryptocurrency trading duel
Original: “How Did AI Inflate a Bubble Big Enough to Encompass the Entire World?”
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