If the AI bubble is already bursting, who will truly remain?

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This article source: GeLong Chengbei Xu Gong

Data support: Gougu Big Data

AI bubbles are becoming the most divisive consensus in global markets. Dalio says the bubble is already very high, while Huang Renxun says the opportunity has just begun; one sees an overheated capital market, the other sees the beginning of a productivity revolution.

The real question is not whether AI has a bubble, but what remains after the bubble bursts. The internet bubble in 2000 caused the Nasdaq to plummet, companies to go bankrupt, and wealth to evaporate, but it also left behind infrastructure like submarine cables, broadband networks, and cloud computing, which ultimately supported Amazon, Netflix, YouTube, and mobile internet.

Today’s AI is standing in a similar position. On one side, hundreds of billions are poured into data centers, power, liquid cooling, optical modules, and GPUs; on the other side, application revenue has not yet fully materialized, creating a huge gap. The bubble clearly exists, but the underlying productivity is not inflated. When token costs plummet and intelligence begins to be called upon like water and electricity, AI will no longer be just a chat tool, but will enter real workflows in coding, healthcare, finance, law, manufacturing, and scientific research. The market will weed out shell companies and PPT entrepreneurs, but will not reverse the AI+ trend. Bubbles will burst, but industries will remain. Below, enjoy:

In recent days, market volatility has been intense, and “AI bubble theory” is everywhere.

Ray Dalio, founder of Bridgewater Associates, said: “There is a bubble in the AI market, and it’s relatively high.”

Nvidia CEO Huang Renxun said: “There are huge opportunities in AI, and demand for computing power has just begun to explode.”

Who should we believe?

Both of them are right.

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 when facing disruptive advanced productive forces. It is not purely a negative term.

In the long run, this is an inevitable phenomenon at the emergence of advanced productivity.

Many people compare the current situation to the internet bubble of 2000, and are worried. That bubble indeed caused the Nasdaq to crash nearly 78%, evaporating over 5 trillion dollars in wealth.

But twenty years later, which industry can be separated from the internet? Today, the value of the internet industry has far surpassed that of the bubble era.

The AI bubble, at least on the surface, appears similar. The bubble in the capital market cannot stop almost all industries from actively being empowered by AI.

AI+ is the trend. Just as all industries now rely on the internet, in the future, all industries will rely on AI.

01 The “IQ Tax” that innovation must pay

In the era when just having a company name ending in .com could list and raise funds, from 1995 to 2000, the Nasdaq soared nearly 600%. Then, a financial storm lasted two and a half years.

Those well-known names back then, like software company MicroStrategy, due to accounting scandals and overhype, plummeted 62% in one day; Pets.com (selling dog food online), Webvan (pioneer in fresh e-commerce) went bankrupt outright… Amid panic, almost everyone accused the internet of being a scam.

However, the physical infrastructure accumulated through excessive speculation often nurtures the next generation of giants at very low costs. The bubble burst not because of internet technology itself, but because the physical infrastructure’s pace of construction couldn’t keep up with market demand.

For example, the once-dominant telecom companies (like WorldCom, Global Crossing), invested heavily in global submarine cables and dense wavelength division multiplexing networks, which led to their bankruptcy, but these cheap “information highways” became the perfect foundation for the rise of Netflix, Zoom, and mobile internet later.

Without the frantic pre-investment in telecom infrastructure around 2000, there would be no explosion of video streaming with YouTube, nor subsequent cloud infrastructure.

The most typical example is Amazon. Its stock price soared from a high of $107 in 1999 to a low of $7 in 2001, a drop of over 90%. But it survived because its underlying business logic—“rebuilding retail through the internet”—aligned with the direction of advanced productivity.

This is the classic Amara’s Law: overestimating the short-term impact of a new technology while severely underestimating its long-term influence. In the early stages of technological revolutions, speculative capital’s frenzy inevitably leads to overinvestment and bubbles. This is the IQ tax that innovation must pay. But once the bubble bursts, what remains is a more resilient and unstoppable advanced productivity.

02 Why are enterprise AI expenditures not decreasing but increasing?

Returning to 2026, the AI industry’s bubble looks even bigger.

Only five major cloud service providers—Amazon, Google, Meta, Microsoft, and Oracle—are expected to spend $690 billion on capital expenditures in 2026, with total AI infrastructure investment projected to reach $5.3 trillion by 2030. Of this, only about 25% is spent on GPUs; the remaining 75% is invested in physical infrastructure: liquid cooling systems, power transmission, network switches, optical modules, and land.

In terms of revenue, all top pure AI companies like OpenAI, Anthropic, Cohere, Mistral, Perplexity combined are expected to generate no more than $40 billion in 2026.

Spending nearly $7 trillion on infrastructure while only earning a few hundred billion in applications—what is this if not a bubble?

It’s not a simple conclusion to draw. There’s a key point that cannot be ignored:

In March 2023, when OpenAI released GPT-4, the mixed cost per million tokens input was about $30.

By April 2025, with model architecture optimizations and inference computing power improvements, the cost for models of similar intelligence level plummeted to $0.1–$0.15 per million tokens.

According to Stanford University’s “AI Index Report” and TokenCost data: AI inference costs have fallen over 99.7% in the past two years.

Following traditional linear thinking, with costs dropping so sharply, enterprise AI spending should decrease. But in reality, enterprise cloud AI 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 bot, but enters a new era of intelligent agents and multimodal enhanced retrieval. Companies begin to let AI agents run thousands of tasks automatically—coding, scanning millions of legal contracts, simulating biological experiments.

Cheap tokens unlock vast amounts of long-tail demand that was previously constrained by costs and unable to be commercialized.

This can be compared to Nvidia in 2026 and Cisco, the network hardware giant in 2000, whose ecosystems are very similar, but their financial health is worlds apart.

(Comparison of Nvidia and Cisco’s core finances)

This precisely confirms the “Jevons Paradox” in economics: technological progress increases energy efficiency, which does not reduce energy consumption but, due to lower costs, actually increases demand.

Even after the so-called “DeepSeek moment” early last year, the market quickly woke up in the following months: the more optimized the algorithms, the lower the threshold for enterprises to adopt AI, and total computing power consumption actually increased exponentially.

Because of this, AI is gradually embedding into almost all traditional industries. Just as the past twenty years saw all industries embracing Internet+, from SaaS software to biomedicine, and advanced manufacturing driven by embodied intelligence, today, in 2026, nearly every industry is adopting AI+. No one is asking “Should we use AI?” but rather “Are our data properly cleaned? Are API call limits sufficient? Is the RAG architecture optimal?”

Currently, the AI industry does have bubbles. But for enterprises, if you don’t embrace the bubble, you will be crushed by the times. The internet era of the past twenty years has already proven this.

03 Deep market evolution: from infrastructure to application

We are undoubtedly at a critical node in the technology lifecycle: on the verge of Gartner’s “Trough of Disillusionment,” or the turning point in the “Technological Revolution and Financial Capital” theory.

The AI bubble is already bursting, but many people haven’t realized it. A few new players, just by writing dozens of pages of PPTs and wrapping OpenAI’s API, can raise funds. Now, as the tide recedes, these companies with no moat and only concepts are dying in large numbers.

This is market self-purification and a sign of bubble bursting. But it’s only superficial. The deeper logic of the market is undergoing three profound evolutions:

First, the shift from CapEx to OpEx value

Currently, most of the money is earned by those selling the tools—Nvidia, TSMC, and those selling optical modules and server liquid cooling equipment—taking the lion’s share of the benefits. But as computing power becomes more “infrastructure-like,” like water and electricity, true excess profits will gradually shift to the application layer. That is, AI-native companies that can use ultra-low-cost tokens to truly solve industry pain points and reshape business processes (OpEx optimization).

Second, valuation multiple compression and performance digestion

High valuations for AI infrastructure do not necessarily mean a collapse. In many cases, rapid profit growth of companies will gradually digest high valuations “over time.” As long as cloud giants’ revenue growth keeps pace with depreciation of capital expenditures, this game of musical chairs can evolve into an unprecedented industry upgrade.

For example, global auto manufacturers and chip giants, by introducing end-to-end AI twin technology, shortened the R&D-to-production cycle of new products by 35%, and improved overall equipment efficiency by 18%.

Similarly, in finance, by 2026, quantitative trading, risk control, and credit assessment are fully dominated by multimodal agents. AI not only processes macro expectations in microsecond timestamps but also deeply participates in every micro-level asset pricing.

In highly knowledge-dependent industries like law, healthcare, and auditing, AI has also evolved from “junior assistant” to “partner-level expert.”

Over 1 billion active users of ChatGPT, Gemini, Claude—many of them use these as substitutes for high-intensity daily mental work. Including you and me. All of this is happening in reality, visible to everyone.

04 Final thoughts

Looking back at the grand history of technology, Schumpeter’s “creative destruction” is always ongoing.

Capital markets are always impatient, eager to turn 1 dollar invested today into 10 dollars tomorrow. When nearly $7 trillion in infrastructure investments cannot be quickly converted into application profits, a brutal reshuffle is inevitable. It will eliminate those speculative shell companies that rely only on PPTs, leaving behind those with real technology and practical scenarios.

After the reshuffle, those low-cost, massive computing centers and highly optimized models will serve countless industries at extremely low prices.

Since 2000, humanity has entered a digital era where all industries depend on the internet. Today, we are also irreversibly heading toward an era where all industries are dominated and empowered by AI.

Amid the noise of bubbles, the underlying productive energy remains completely genuine.