More than twenty years after the memory of the internet bubble has yet to fade, Wall Street instinctively feels tense about this wave of AI frenzy, with warnings of "this is just a new round of tech bubble" visible everywhere in the market. Amid this rising sentiment, Dan Loeb, founder of Third Point, which manages approximately $24 billion in funds, stood on the entirely opposite side. The media reported heavily on his latest statements on May 30, 2026: concerns about an "AI bubble" are grossly exaggerated; right now, we're only on the surface of this wave of AI investments, and the real cycle is still in its early stages. Loeb directly compared the current situation with the internet bubble around the year 2000, emphasizing that the internet startups of that time relied on speculative capital and lacked mature business models, while today's large tech companies are a completely different breed with abundant cash flow and strong profitability. The numbers cited by Loeb are equally striking – it is reported that Alphabet, Microsoft, Amazon, and Meta have collectively invested over $700 billion in AI infrastructure this year, which could rise to over $1 trillion next year. In his view, this money is not “flushed down the toilet,” but rather a prelude to securing a timeline for a yet-to-be-fully-revealed revenue curve. Therefore, for him, this seemingly long-standing AI frenzy has just begun to truly gain momentum.

AI Frenzy under the Shadow of Bubbles

The instinct of the market to reflect this wave of AI enthusiasm in relation to the internet bubble of 2000 comes from the latter's collapse, which became the psychological baseline for all subsequent tech bull markets: back then, many internet companies lacking mature business models were pushed to the skies by high valuations and blooming stories, only to be slammed back to earth when the bubble burst, leaving investors with vast stretches of capital ruins. In the twenty-plus years since, any signs of steeply rising valuations, explosive investments, and business models still under verification will reflexively evoke memories of 2000. Today, as AI-related assets and large tech companies’ stock prices rise rapidly, naturally reigniting anxieties about “history repeating itself.”

This anxiety is directly projected onto the current massive capital expenditures: the larger the number, the tenser the emotions. Investors continuously monitor the AI investment plans announced by Alphabet, Microsoft, Amazon, and Meta, while internally comparing them to the "burning cash for growth" narrative of the past. Their risk appetite oscillates between excitement and fear, and the instinct for hedging drives them to label the unknown with the familiar tag of "bubble." Within this narrative framework, any optimism appears glaring, whereas Loeb publicly states that the concerns surrounding the AI bubble are severely exaggerated. He believes this wave is still on the surface; the current high investments do not represent a repeat of the speculative frenzy of 2000, but are long-term investments laid down by profitable, cash-rich tech giants for future returns. This contrarian judgment provides him with a unique position in this AI frenzy.

Loeb Bets on AI: Aligning $24 Billion

On Wall Street, Dan Loeb is not just a commentator expressing opinions on television; behind him stands Third Point, a hedge fund managing approximately $24 billion (according to a single source). This means that when he stands up for AI in public, he is not speaking for just himself, but for a hefty financial ledger. The position choices, risk appetites, and public statements of Third Point are seen by peers as indicators of direction, and his attitude toward this wave of AI naturally gets interpreted as a stance backed by real capital.

In a recent program discussing AI investments, Loeb made a simple yet clear judgment: "At this stage, we are only touching the surface of this wave of AI investments." He focused on those figures repeatedly discussed by the market: the total investment by Alphabet, Microsoft, Amazon, and Meta in AI infrastructure this year has reached "over $700 billion," and he further anticipates that next year it could exceed $1 trillion (according to a single source). While most people describe these expenditures as "burning cash" or even "replaying the 2000 internet bubble," Loeb emphasizes that these huge capital expenditures are not "flushed down the toilet," but are long-term investments aimed at actual returns. In his view, this is not a short-term speculation but a long-term capital race dominated by cash flow machines, targeting tangible returns.

$700 Billion Invested in Computing Power: Giants Bet on Returns

During the program, when Loeb revealed those jaw-dropping numbers, he was very specific – according to a single source, he cited data that Alphabet, Microsoft, Amazon, and Meta, the global tech giants, have collectively invested over $700 billion in AI infrastructure this year, and he expects that relevant capital expenditures next year could surpass $1 trillion. More importantly, this $700 billion is not scattered across countless early-stage projects telling stories, but is concentrated on data centers, computing clusters, and underlying architectures, decided by a few profitable companies with ample cash flow.

Loeb repeatedly emphasizes a comparison: back then, it was the unprofitable startups reliant on speculative capital that burned cash during the internet bubble; today, it is the global tech behemoths with proven profitability leading the wave of AI investments. This means that each AI capital expenditure is more about reallocating existing massive cash flows to next-generation infrastructure rather than gambling for survival. For Loeb, these numbers do not represent "burned" expenses, but rather prepayments for future cash flows, with $700 billion seeming like a collective betting button pressed by the giants to lock in the next stage's yield instead of a bubble frenzy destined to replay the old collapse script.

Old Shadows of the Internet Bubble: Who's Paying This Time?

In the internet bubble of 2000, the protagonists were startups that had yet to grasp profitable models: website traffic was considered an "asset," and equity financing and speculative capital flowed in endlessly, with almost no one seriously calculating payback periods. After the bubble burst, stock prices were slashed repeatedly, and bankruptcies became everyday vocabulary. The true payers of that lavish feast were the investors caught at high positions and the public funds that took over, with risks widely spread yet without corresponding cash flows to support them.

Loeb emphasizes that the current AI cycle’s financials look completely different. The over $700 billion (according to a single source) splashed down chiefly by companies like Alphabet, Microsoft, Amazon, and Meta are already highly profitable and cash-rich. They are not relying on storytelling for funding but are using their income statements to pave a wider "data highway." From a financial structure perspective, this transformed from "speculative startups risking other people's money" to "profit machines using their own money to expand capacity," which appears to weaken the bubble narrative. But no matter how the books are rewritten, one premise cannot be changed: as long as the speed and scale of AI commercialization do not meet today’s implied market expectations, these massive capital expenditures will revert at some future point through declining profit margins and valuation reassessments, borne uniformly by the shareholders of the giants in stock prices.

From Bubble Worries to the Test of a Super Cycle

In Loeb's narrative, this wave of AI frenzy is still just the "prologue": the giants have already poured over $700 billion into AI infrastructure this year, with expectations of hitting $1 trillion next year, and this money is not “flushed down the toilet,” but rather a necessary input for the next generation of profit engines. He uses this logic to counter the notion of an "AI bubble," but the lessons of the 2000 internet bubble loom large – once valuations and capital expenditures disconnect from real profits, corrections can only be completed through a bubble burst. Today's difference is that Alphabet, Microsoft, Amazon, and Meta come packaged with strong profitability and cash flow, betting their own money on the future, but that does not automatically equate to a risk-free "new paradigm." For investors, Loeb's optimism feels more like a hypothesis waiting to be validated: what needs to be monitored next is whether the giants' AI capital expenditures can sustain over long timelines without being forcibly braked by profitability pressures, whether the profit margins and cash flow structures will collapse under depreciation and computing costs, or whether they will be offset or even elevated by new businesses. Each time valuation fluctuates, market sentiment will ultimately reveal whether it believes more in "bubble bursting" or "super cycle," and Loeb’s claim that "the wave has yet to peak" will ultimately be answered not by any words but by these numbers.

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