Source: Gelong Chengbei Xugong
Data Support: GOGU Big Data
The AI bubble has become the most divisive consensus in the global market. Ray Dalio says the bubble is already high, while Jensen Huang says the opportunity is just beginning; one sees overheating in the capital markets, the other sees the dawn of a productivity revolution.
The real issue isn't whether there is an AI bubble, but what remains after it bursts. The 2000 dot-com bubble caused the Nasdaq to crash, companies to fail, and fortunes to evaporate, but it also left behind the physical infrastructure of undersea cables, broadband networks, and cloud computing, which ultimately supported Amazon, Netflix, YouTube, and the mobile internet.
Today's AI is in a similar position. On one side, hundreds of billions of dollars are pouring into data centers, power, liquid cooling, optical modules, and GPUs; on the other, there is a huge gap with application revenues yet to be fully realized. A bubble clearly exists, but the underlying productivity gains are not illusory. When token costs plummet and intelligence begins to be invoked like water and electricity, AI will no longer be just a chat tool but will enter real workflows in code, healthcare, finance, law, manufacturing, and research. The market will wash away shell companies and PPT-driven startups, but it will not reverse the direction of AI+. The bubble will burst, but the industry will remain. Below, Enjoy:
In recent days, the market has experienced intense volatility, and talk of an "AI bubble" is rampant.
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Bridgewater founder Ray Dalio said: The AI market has a bubble, and the level is "relatively high."
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Nvidia CEO Jensen Huang said: AI presents a huge opportunity, and demand for computing power is just beginning to explode.
Whom should we believe?
They are both correct.
Does the AI industry have a bubble? It certainly does.
But bubbles in the tech sector are often the only way society can pay homage to disruptive, advanced productive forces. They are not purely pejorative.
In the long run, this is an inevitable phenomenon at the dawn of an advanced productive force.
Many people compare today's situation to the 2000 internet bubble, feeling anxious. That bubble did indeed cause the Nasdaq to plummet nearly 78%, wiping out over $5 trillion in wealth.
But twenty years later, which industry can function without the internet? Today, the value of the internet industry far exceeds its value during the bubble period.
At least on the surface, the AI bubble is similar. The bubble in the capital market cannot stop almost every industry in society from actively being empowered by AI.
AI+ is the overarching trend. Just as no industry today can function without the internet, no industry will be able to function without AI in the future.
01 The "IQ Tax" That Innovation Must Pay
In the era when any company with .com in its name could go public and raise money, the Nasdaq surged nearly 600% between 1995 and 2000. This was followed by a two-and-a-half-year financial storm.
Illustrious names of that time: software company MicroStrategy, due to accounting scandals and overblown claims, plunged 62% in a single day; Pets.com (selling pet food online), Webvan (the pioneer of online grocery delivery) simply went out of business... In the panic, almost everyone declared the internet a scam.
However, the physical infrastructure that remains from the overinvestment of speculative capital often nourishes the supergiants of the next era at extremely low cost. The reason bubbles burst is not the technology itself, but the fact that the pace of physical infrastructure construction couldn't keep up with market expectations.
For example, the global undersea cables and dense wave-division multiplexing networks laid down by heavily investing telecom companies (like WorldCom, Global Crossing) at their peak, though they bankrupted themselves, later became the perfect breeding ground for the rise of Netflix, Zoom, and the mobile internet as cheap "information superhighways."
Without the frenzied over-investment in telecom infrastructure globally around 2000, there would have been no later video streaming explosion on YouTube, let alone the cloud computing infrastructure.
The most typical example is Amazon. Its stock price fell from a 1999 high of $107 all the way to $7 in 2001, a drop of over 90%. But it survived because its underlying business logic, "reconstructing retail with the network," aligned with the direction of advanced productive forces.
This is the classic Amara's Law: we tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run. At the beginning of a technological revolution, the frenzy of speculative capital inevitably leads to overinvestment, forming bubbles. This is the IQ tax that innovation must pay. But when the bubble dissipates, what remains is an even more unshakable advanced productive force.
02 Why Are Corporate AI Spending Rising Instead of Falling?
Coming back to 2026, the bubble in the AI industry seems even bigger.
Just the five major cloud service providers—Amazon, Google, Meta, Microsoft, and Oracle—are expected to have capital expenditures of $690 billion in 2026, with total AI infrastructure investment expected to reach $5.3 trillion by 2030. Only about 25% of this buys GPUs; the remaining 75% is being poured into physical infrastructure: liquid cooling systems, power transmission, network switches, optical modules, and land.
In terms of revenue, the combined total revenue of all leading pure-play AI vendors like OpenAI, Anthropic, Cohere, Mistral, Perplexity in 2026 is expected to be no more than $40 billion.
The foundational layer is investing nearly $700 billion, while the application layer is generating a few hundred billion. This severe asymmetry—isn't that a bubble?
We shouldn't jump to that conclusion so simplistically. One key point cannot be ignored:
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In March 2023, when OpenAI released GPT-4, the mixed cost per million input tokens was about $30.
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By April 2025, with optimized model architectures and improved inference computing power, the price per million tokens for models of comparable intelligence had plummeted to $0.1–$0.15.
According to Stanford University's AI Index Report and data from TokenCost: AI inference costs have fallen by over 99.7% in the last two years.
Following traditional linear thinking, if costs plummet, corporate AI spending should decrease. But the reality is that enterprise AI cloud spending tripled between 2024 and 2025.
Why?
Because when the marginal cost of "intelligence" approaches zero, AI is no longer just a simple text summarizer or chat companion but enters a new era of agents and multimodal enhanced retrieval. Enterprises are starting to let AI agents automatically run thousands of tasks, write code, scan millions of legal contracts, simulate biology experiments.
Cheap tokens unlock a massive amount of long-tail demand that was previously uncommercializable due to cost constraints.
We can also see this by comparing Nvidia in 2026 with Cisco, the network hardware king in 2000. Their ecosystem positions are extremely similar, but the underlying financial health is worlds apart.
(A hardcore financial comparison between Nvidia and Cisco)
This precisely illustrates the economic "Jevons Paradox": technological progress improves the efficiency of energy (or intelligence) use, but instead of reducing consumption, the lower cost leads to even greater demand.
Even after experiencing the so-called "DeepSeek moment" early last year, the market quickly sobered up in the following months: the more optimized the algorithms, the lower the barrier for enterprises to adopt AI, and the final total computing power consumption actually rises exponentially.
It is precisely because of this that AI can gradually embed itself into almost every old industry. Just as in the past twenty years every industry embraced "Internet+". From SaaS software to biopharmaceuticals, to advanced manufacturing robots driven by embodied intelligence, in 2026, almost every industry is embracing AI+. No one discusses "whether we should use AI," but rather worries "Is our data cleaned? Do we have enough API call quotas? Is our RAG architecture optimal?"
Currently, the AI industry indeed has a bubble. But for enterprises, if you don't embrace the bubble, you will be crushed by the times. This was already proven during the internet era of the last twenty years.
03 Deep Market Evolution: From Infrastructure to Application
Currently, we are undoubtedly at a critical juncture in the technology lifecycle: on the eve of the "Trough of Disillusionment" on the Gartner Hype Cycle, or at the inflection point described in the theory of "Technological Revolutions and Financial Capital."
The AI bubble is already bursting, but many haven't realized it. Just a few years ago, newcomers could write a few dozen pages of PPT, wrap an OpenAI API, and raise money. Now, the tide is receding, and these companies with no moat, only concepts, are dying in large numbers.
This is the market purifying itself, a manifestation of the bubble bursting. But this is only the surface. The deep logic of the market is undergoing three profound evolutions:
First, value transfer from CapEx to OpEx
Currently, the money is being made by the shovel sellers—Nvidia, TSMC, and those selling optical modules and server liquid cooling equipment—are taking most of the profits. But as computing power gradually becomes "infrastructural," like water and electricity, true supernormal profits will gradually shift to the application layer. That is, to those AI-native enterprises that can use extremely low-cost tokens to truly solve pain points in vertical industries and reshape business processes (OpEx optimization).
Second, valuation multiple compression and earnings digestion
The market's high valuation for AI infrastructure doesn't necessarily mean a crash is imminent. In many cases, the rapid growth of corporate profits can gradually digest the high valuation by "trading time for space." As long as the revenue growth rate of cloud computing giants keeps pace with the depreciation rate of their capital expenditures, this game of hot potato can evolve into an unprecedented industrial upgrade.
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For example, global auto manufacturing giants and chip giants, by introducing end-to-end AI twin technology, have shortened the R&D-to-mass-production cycle for new products by 35%, and improved overall equipment effectiveness by 18%.
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In the financial industry, quantitative trading, risk control, and credit assessment in 2026 are fully dominated by multimodal Agents. AI is not only processing macro expectations at microsecond-level timestamps but also deeply participating in every micro-level asset pricing.
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In industries like law, healthcare, and auditing that highly rely on senior expertise, AI has already completed the transformation from "junior assistant" to "partner-level expert."
Among the over 1 billion active users of ChatGPT, Gemini, and Claude, a significant portion use them as replacement tools for high-intensity daily mental labor. Including you and me. All the above are real things happening, visible to everyone.
04 Conclusion
Looking back at the grand history of technology, the "creative destruction" proposed by Schumpeter is always in play.
The capital market is always impatient, hoping that $1 invested today will earn $10 tomorrow. When nearly $700 billion in infrastructure investment cannot be fully converted into profits on the application side in the short term, the market is bound to face a brutal shakeout. It will eliminate those opportunistic shell companies surviving only on PPT presentations and leave behind those with real technical depth and viable application scenarios.
After this shakeout, those cheap yet massive computing centers and highly optimized model algorithms will serve all industries at extremely low cost.
After 2000, humanity entered a digital era where no industry could function without the internet. Today, we are also irreversibly heading towards an intelligent, flourishing era where all industries are led and empowered by AI.
Amidst the clamor of the bubble, the underlying productive force has no水分 (moisture) at all.
