AI Bubble Threatens Economy as Tech Giants Pour $700B Into Infrastructure With No Clear Returns

A.I. Is Lifting Markets and the Economy and Raising Risks for Both

Ben Casselman is the chief economics correspondent. Joe Rennison covers financial markets. There is an adage in the financial world: The stock market is not the economy. Except that, right now, maybe it is. Major stock indexes have set record after record in recent years, fueled by seemingly boundless appetite among investors for anything connected to artificial intelligence. The total value of the U.S. stock market has more than doubled over the past decade to over $75 trillion, roughly two and a half times the annual output of the entire U.S. economy, itself a record ratio. Much of that value exists only on paper, bets on future profits that may or may not materialize in the years ahead. But the boom is supporting real economic activity today, pumping trillions of dollars of investment into semiconductor factories, data centers, power plants and transmission lines. And the wealth it is creating is helping to drive consumer spending, particularly among affluent Americans, whose appreciating stock portfolios make them more willing to shell out for luxury vacations, pricey electronics and meals at high-end restaurants. That spending and investment have helped carry the U.S. economy through a tumultuous period of inflation, tariffs and geopolitical uncertainty. But it also creates a vulnerability: If investor confidence in A.I. falters, the economic activity built atop it could come crashing down. Bank of America's monthly global fund-manager survey for July reported a bursting of the A.I. bubble as the key risk to financial markets. It's also now the key risk to the economy. "The thing that has been holding everything up is the A.I. story," said Torsten Slok, the chief economist at Apollo Global Management. A.I. related stocks account for roughly half of the rise in the S&P 500 this year, said Adam Turnquist, the chief technical strategist for LPL Financial, adding that growth in the economy is also increasingly dependent on A.I. infrastructure spending. "It's becoming one big A.I. trade," he said. Stock markets have wavered in recent weeks in a sign of investor worry over so much dependence on one sector's ability to continue outperforming expectations. But this isn't the first instance of investors' flinching during the A.I. run-up. Several times over the past few years stock prices have dropped steeply in response to a piece of bad news, only to rebound within days. Even many analysts who believe an A.I. bubble is still inflating are reluctant to predict that this is the moment it pops. Declines in stock prices, even drastic ones, don't necessarily have much impact beyond the world of finance -- hence the adage about the market not being the economy. On "Black Monday" in 1987, the Dow Jones industrial average fell more than 20 percent, still the record for a one-day decline. Yet broader measures like the unemployment rate and gross domestic product barely wobbled. What could make this time different is the sheer scale of the stock market. Economic research has found that for every $100 investors gain in their stock portfolios, they spend about $3 more on goods and services, a phenomenon known as the wealth effect. The wealth effect also operates in reverse: When stock prices fall, investors become less willing to spend. At its present valuation, a 30 percent decline in the stock market could lead to a nearly $700 billion pullback in consumer spending. That could be enough to set off a recession on its own, or at least come close. "Times when the market seems like it's highest are times when that wealth effect can have the biggest bite," said Gabriel Chodorow-Reich, a Harvard economist who has studied the wealth effect. The other distinguishing factor of the current market is how concentrated it is in a handful of companies. The Magnificent Seven group of companies of Meta, Alphabet, Amazon, Apple, Tesla, Nvidia and Microsoft account for roughly a quarter of the value of all publicly listed stocks in the United States. Because so much real-world activity is also being driven by the A.I. boom, a stumble by one of the big A.I. companies could have much larger economic ripples. "It would spill over to the rest of the economy," said Roger Aliaga-Diaz, the chief economist for the Americas for Vanguard. Mr. Aliaga-Diaz and other economists described one scenario for how that could happen. If companies find that their A.I. investments aren't paying off as quickly as they hoped, they might pull back their spending, forcing the A.I. labs and their suppliers to trim their growth projections. Such a disappointment could incite a market sell-off, which would make it more difficult or more expensive for companies to raise the capital they need to fund the A.I. build out. That, in turn, could lead companies to delay or cancel plans to build data centers, power plants and related infrastructure, giving way to layoffs in the construction industry. And at the same time, the drop in the market would push wealthy consumers to pare their spending, leading to wider job losses and, ultimately, a recession. "The consumer spending growth is not nearly what A.I. growth is, but it is such a large portion of the economy," said Kristina Hooper, the chief global strategist at Man Group. "And growth there is being driven by high-income consumers who are tied to the fortunes of the stock market, and A.I. being such a big part of that." A turn in those fortunes is not inevitable. Many of the biggest A.I. players have already raised vast sums of capital, which could allow them to keep spending even if new sources of cash dry up. And companies won't necessarily stop using and paying for A.I. tools just because the market enthusiasm for them dims. A bursting of the A.I. bubble could also free up cash for other investments that have struggled to attract capital in recent years. That is, at least in part, what happened in the 2000s, when the end of the dot-com boom led investors to redeploy cash to housing and other sectors. The economy did experience a recession in 2001, but it was brief and relatively mild. One factor that helped insulate the economy in 2001, however, was that the Federal Reserve cut interest rates aggressively, which helped contain the crisis mostly to the tech sector. This time around, inflation has been elevated for five years, which might leave policymakers more reluctant to cut rates. "Monetary policy has a huge role in determining whether a decline in investment demand in one sector spreads more broadly or not," Mr. Chodorow-Reich said. "The fact that monetary policy is a little bit more constrained weighs in on that." It could be years before policymakers are forced to deal with such a scenario. Economists are notoriously bad at identifying bubbles, and even worse at predicting when they pop. Alan Greenspan, the former Fed chairman, warned of the risks of "irrational exuberance" in the markets in 1996. The dot-com bubble didn't burst for over three more years. "I am of the view that it is very hard to predict recessions or recession timing," Mr. Chodorow-Reich said. But he and many other economists think the risks of a market-induced recession are real and growing. That is partly because investors are betting on such spectacular growth from the A.I. companies that even a fairly mild disappointment -- an earnings release that falls short of expectations, a project that takes longer to pay off than initially assumed -- could lead them to re-evaluate their assumptions and produce a steep drop in the markets. "If investors are told, well, actually, instead of five to seven years, it's going to be 15 to 20 years to pay off, that's a very big difference," said Ryan Cummings, the chief of staff for the Stanford Institute for Economic Policy Research, who recently published an article warning about the risks of an A.I. bubble. "Right now, the burden of proof is on the skeptics," he added. "Once you have this slow trickle of disappointing information, then the burden starts to be on the optimists."

The AI Bubble Is No Ordinary Bubble

Tech companies need to generate huge revenues fast, or the economy could be in trouble. The American stock market is booming, thanks to artificial intelligence. Tech giants are borrowing billions to acquire AI talent, purchase chips and hardware, and construct data centers. And market watchers are starting to get worried. They see financiers bulldozing giant piles of money to private AI start-ups with no realistic path to profitability, tech companies reliant on other tech companies for revenue growth, and non-tech businesses without a lot to show for their AI investments. The value of AI-linked firms has climbed $27 trillion in the past three years -- an astonishing amount, equivalent to 36 percent of the value of the entire U.S. stock market today. Although future earnings could justify those valuations, as Dominic Wilson and Vickie Chang of Goldman Sachs argued in a note to clients, the profit expectations require Panglossian optimism. No less an authority than Sam Altman is arguing that we are in an AI bubble. The International Monetary Fund is citing it as a significant risk to financial stability and warning about what might happen when it bursts: diminished investment, tighter credit, reduced consumption, disrupted trade flows. That's pretty much what happens when any bubble pops, as a Dutch tulip obsessive could have told you in 1637 or a bitcoin evangelist could have told you in 2011, 2013, 2014, 2018, or 2022. Yet the AI bubble is no ordinary bubble. Hyper-rich corporations are stoking it, rather than kitchen-table investors. They're blowing it up when credit is fairly expensive, not dirt cheap. That might make the bubble less fragile and longer lasting than those of the past. But it won't make it any less painful when it pops. The dot-com bubble of the late 1990s and the housing bubble of the late aughts were remarkably broad-based compared with the AI bubble today. Uncle Ted got a hulking desktop computer, opened a newfangled E*TRADE account, and started day-trading shares in Apple and Pets.com. The share of American households owning equities climbed 13 percentage points from 1995 to 2001, during which time more than 2,752 firms went public (far more than the 730 operating businesses that have IPOed in the last six years). A half decade later, Aunt Linda bought a condo with nothing down, flipped it, and mortgaged three new investment properties in the Phoenix suburbs, sight unseen. The homeownership rate rose 5 percentage points as the housing bubble inflated; 40 percent of mortgages issued at its height went to investment or vacation properties. In both cases, regular people were staking their savings on what seemed like a winning bet: the internet changing everything, housing prices never going down. In both cases, cheap credit fueled the irrational exuberance. Low interest rates let venture capitalists fund nonsense web businesses and let banks provide junk loans to borrowers with terrible credit. In both cases, rising interest rates popped the bubble. Today, Uncle Ted and Aunt Linda aren't really getting in on the AI frenzy. The share of Americans who own stocks has held steady. Household debt has grown, but it has fallen relative to disposable income and GDP. Everybody seems to know someone who was personally burned by the dot-com collapse and the housing crisis. How many people know someone who's staking it all on OpenAI and Anthropic today? (The companies aren't public, after all.) Indeed, how many people know someone whose livelihood has been directly affected by the AI frenzy at all? The insularity of the AI bubble isn't the only thing that makes it unusual, and we should probably think about it as two overlapping bubbles instead of just one. AI is driving tremendous spending on capital expenditures -- physical infrastructure, software development. And it is driving a tremendous run-up in company valuations. Digital innovations don't tend to require a ton of labor or heavy equipment. The companies offering them tend to be capital-light, like marketers and insurers, rather than capital-intensive, like airlines and hotel chains. But AI is different. A start-up might need thousands of times as much computing power to train an algorithm as it would need to develop a conventional software product. To get that power, Silicon Valley is building 1,500 data centers in the United States and counting, and purchasing a mammoth quantity of semiconductor chips. (The Wall Street Journal calls these chips "the 21st century's most important market.") Amazon, Microsoft, Alphabet, and Meta alone are spending more than $700 billion on the build-out this year. AI-infrastructure investment is responsible for essentially all American GDP growth at the moment. Without it, in other words, we might be in a recession. The boom is lifting the value of farmland, driving up the cost of construction employment, and flushing huge sums to water and electrical utilities. Still, tech companies are the main beneficiaries: NVIDIA is selling chips to Meta; Amazon is selling cloud-computing capacity to OpenAI. This surge in revenue, and the excitement about the money to be made when AI starts bolstering productivity and profits, is driving up valuations. The Magnificent Seven -- Alphabet, Amazon, Apple, Meta, Microsoft, Nvidia, and Tesla -- now account for one-third of the value of the Standard & Poor's 500. OpenAI is worth more than Eli Lilly, JPMorgan Chase, Visa, Costco, Exxon Mobil, Wells Fargo, CVS Health, McDonald's, and Boeing. Tech companies are going to need to start generating huge revenues and huge profits to justify these valuations. OpenAI needs to spin up roughly $100 billion in free cash flow by 2030, according to calculations by Harrison Rolfes of PitchBook. Analysts expect it will lose $10 billion to $30 billion that year. If it does -- or if more communities ban data centers, or if non-tech companies prove reticent about purchasing AI software, or if China develops AI models that do not require so much computing power -- we could be in for a massive correction. The AI economy is a trillion-dollar ouroboros of buying and selling, investment and equity staking, all happening between San Francisco and San Jose. Big Tech is advancing money to AI start-ups to buy cloud services from Big Tech, which is using the revenue to run new AI models ... you get the idea. What happens to one firm could happen to all of them. Tech companies are also going to need to start generating even bigger revenues and profits to pay off their debts. In the early days of AI, venture capitalists, wealthy individuals, and Big Tech firms used hard cash to invest in the new technology. But the build-out has proven so expensive that Silicon Valley has turned to corporate bonds and private credit, meaning loans made by entities other than banks. Thus, though the AI boom has happened while interest rates are fairly high, it still involves a lot of leverage. The deals are complicated, opaque, and structured so as to be invisible on traditional balance sheets, making the "ultimate distribution of risk less transparent," Stijn Van Nieuwerburgh of Columbia Business School has found. Already, lenders are getting queasy -- "buy-side indigestion," as Morningstar describes the market's recent reticence to meet Silicon Valley's demands. When the bubble bursts, Uncle Ted and Aunt Linda will be affected, even if they were never the ones stoking the frenzy. Their retirement plans and pensions are invested in Silicon Valley stocks that could drop; their small businesses are reliant on access to credit that might get throttled. But, hey, there's always the possibility that AI might steal their job before any of that happens.

AI critic Ed Zitron warns OpenAI collapse could crash markets

Ed Zitron, a technology critic and newsletter author, is warning that OpenAI's failure would function as a market-shaking collapse comparable to the fall of Lehman Brothers, arguing that the entire AI industry's financial architecture depends on a single company continuing to exist. In a post published Wednesday on his newsletter Where's Your Ed At, Zitron contends that OpenAI is "one of the largest liabilities in recent economic history" and that without it, the justification for trillions of dollars in capital expenditure across the technology sector evaporates. "Should it fail, the reverberations would mark a turning point -- the AI era's Lehman Brothers moment, closing one chapter of economic history and violently opening the next," he wrote. Zitron argues the AI bubble is not grounded in measurable returns but in what he calls "cult-like psychosis" infecting wealthy investors and institutions. He traces the current spending cycle to the November 2022 launch of ChatGPT, which he says gave a struggling tech industry a narrative to justify massive infrastructure investments. He also argues that Anthropic, OpenAI's closest competitor, only exists because of the mythology surrounding OpenAI, and faces the same underlying financial pressures. Central to his argument is OpenAI's financial exposure. The company intends to spend more than $50 billion on compute this year and has made roughly $748 billion in performance obligations to Microsoft $MSFT, Amazon $AMZN, and Oracle $ORCL, according to his analysis. OpenAI is also carrying the weight of a $122 billion funding round that has not fully closed, with SoftBank Group contributing $30 billion in tranches -- the third of which is due October 1, 2026. Zitron contends that a payment stoppage to infrastructure partners such as Oracle and CoreWeave would leave those companies without the cash flow needed to meet their own debt commitments. Oracle, he notes, has committed more than $340 billion to build data center capacity for OpenAI as part of a $300 billion compute contract, and has seen its credit rating cut to the lowest investment-grade level by S&P Global $SPGI -- with OpenAI named as a key credit risk in the agency's own language. OpenAI submitted a confidential IPO filing with the Securities and Exchange Commission last month at an $852 billion valuation, with Goldman Sachs $GS and Morgan Stanley $MS leading the process. The company is leaning toward delaying its public offering until 2027, after advisers warned that a $1 trillion valuation -- which CEO Sam Altman has called a minimum -- may not be achievable in current market conditions. OpenAI posted a net loss of $38.5 billion in 2025 on $13.07 billion in revenue. Zitron's newsletter closes with a stark prediction: "I believe that once OpenAI collapses it'll have a violent, punishing effect on the entire stock market, a precursor to a much greater drawdown as everybody accepts that the AI bubble has burst."

The AI honeymoon appears over amid stock sell-off

The artificial intelligence spending spree sparked by the release of ChatGPT 3.5 in November 2022 has been unprecedented in its scale. Global AI infrastructure spending is expected to reach $758 billion by 2029, according to the International Data Corporation. That's more than double the $300 billion spent in 2025. "The AI bubble isn't a result of any actual return on investment," frequent AI critic Ed Zitron wrote in a recent scathing takedown of the industry. He called the outsized AI spending and the economic bubble it has inflated "the greatest capital misallocation in history." ChatGPT parent company OpenAI has already declared that it intends to spend more than $852 billion by the end of 2030. It intends to spend $50 billion or more on compute power just this year, which, according to Zitron's math, is more than 50% of all global AI compute spend. For comparison, Chipotle's entire market cap is about $42 billion. But Chipotle also generated about $11 billion in revenue last year, compared to OpenAI's 13.1 billion in revenue. That incongruity was behind some of the tech sell-off Wall Street witnessed last week. Big tech AI spending runs into investor resistance Last week, investors made major pullbacks in chip stocks, leading to a broader sell-off in tech stocks. Information technology was the worst-performing group in the S&P 500 Index last week, falling 1.6%. Meanwhile, the tech-heavy Nasdaq 100 Index also lost 4.1%. Investor concern about runaway AI spending with little return on investment is behind the selloff, analysts told Bloomberg this week. "Investors are getting to the point where they're uncomfortable with how much money is being spent, and they're worried about a bubble," said Jake Seltz, portfolio manager at Allspring Global Investments. "Ultimately, we need to see a re-acceleration in revenue." OpenAI has the most popular consumer-facing AI product, with ChatGPT boasting more than 900 million weekly users. But even as the company's revenue has jumped from $2 billion in 2023 to a projected $29 billion in 2026, the company is also losing market share and money on every new user that utilizes its platform. OpenAI lost about $5 billion in 2024; it expects to lose about $14 billion this year, but outside watchers estimate it will lose as much as $36.6 billion in 2026. "OpenAI's collapse will be a direct result of its loss-laden economics - its doomed, loss-making subscriptions, its pathetic advertising revenue, and API costs that became a 'huge issue' for its enterprise customers - and the fact that outside of the hype, AI lacks measurable ROI," Zitron says. But OpenAI is still a private company, so it doesn't have to worry about retail investor sentiment just yet. The same can't be said for some of its biggest backers. Alphabet is scheduled to report second-quarter earnings on Wednesday, July 22, and investors are selling heading into the print. The stock rallied on Monday, July 20, climbing more than 2.5% at last check, but the stock dropped more than 6% over the previous two sessions last week. Microsoft is coming off its worst month since 2000, according to Bloomberg, and the stock has lost nearly 20% year to date. "At some point, earnings are being questioned so much that you can't put as high of a multiple" on these stocks, Todd Ahlsten, chief investment officer at Parnassus Investments, told Bloomberg. "There's going to be a lot more focus on cloud gross margins, pricing, what kind of AI revenue is being generated per dollar of compute." Amazon, Alphabet, Meta and Oracle are known as hyperscalers due to the hundreds of billions they've committed to building out AI and AI infrastructure. But that spending has decimated their free cash flow, with 2026 estimates for the group dropping to nothing from $300 million in 2024. Robert Way / Getty Images Investors urged to be more careful about AI stocks Investors piling into AI-related stocks helped drive the stock market to all-time highs in June, and while there are still companies with attractive balance sheets, there are fewer than there used to be. "At some point, earnings are being questioned so much that you can't put as a high of a multiple" on these stocks, said Todd Ahlsten, chief investment officer at Parnassus Investments. "There's going to be a lot more focus on cloud gross margins, pricing, what kind of AI revenue is being generated per dollar of compute." One of the most glaring signs that investors are beginning to tire of the unfettered spending on AI is Apple. Apple has avoided large capital expenditures on AI, instead opting to partner with model providers to power its AI services, and has been the best Magnificent 7 performer by a wide margin with a 23% gain so far in 2026. "The existence of OpenAI justified an era of mania and opulence. Hyperscalers, bereft of new hypergrowth ideas, were able to point at the fact that ChatGPT had 'the fastest growing user base of all time' and the Microsoft 'supercomputer' that built it and tell their investors that if they didn't invest, they'd be left behind, with Amazon, Meta, and Google announcing their own nebulous 'supercomputers' in 2023," Zitron said. "This is the underlying greed that has driven this wasteful, reckless and destructive era - the belief that there will be another OpenAI and, as I've said, the chance to become the next OpenAI's landlord," Zitron said. "And like any great investment bubble, the more money that piled in, the greater the fear of missing out, the more dollars that can be justified in turn, and the more complex and deranged the mythology becomes." The Arena Media Brands, LLC THESTREET is a registered trademark of TheStreet, Inc. This story was originally published July 21, 2026 at 11:33 AM.

Ed Zitron predicts an OpenAI collapse

The release of ChatGPT 3.5 in November 2022 was an indelible moment in the modern history of Silicon Valley tech. Over the coming months, all of the SV big whigs would let the media and people know that the future had arrived, and the world would not be the same now that generative artificial intelligence had arrived. The spending spree that has ensued since that moment has been unprecedented in its scale, though the return on that investment hasn't matched the hype. While AI use and adoption rates are increasing among the general public, there is a significant and persistent cohort that is firmly anti-AI everything, from its use to the infrastructure it needs to operate. But that opposition hasn't slowed anything. Global AI infrastructure spending is expected to reach $758 billion by 2029, according to the International Data Corporation. That's more than double the $300 billion spent in 2025. That is part of the problem, according to a new analysis from Ed Zitron, the frequent AI tech critic who has been following this story since the beginning. "The AI bubble isn't a result of any actual return on investment," Zitron declared in his latest scathing takedown. "Whether that be in purely monetary terms, like revenue or profitability, productivity gains, or anything tangible or measurable. Rather, it's an episode of cult-like psychosis that infected the brains of some of the most powerful and wealthy individuals and institutions, where the powerful mythology of a company inspired - and been used to inspire - the greatest capital misallocation in history. " Zitron has the facts and figures to back up his argument. OpenAI is spending too much money to ever be profitable OpenAI has already declared that it intends to spend over $852 billion by the end of 2030. About $750 billion of that total is tied to the remaining performance obligations of its partners and investors, Microsoft, Amazon and Oracle. It intends to spend $50 billion or more on compute power just this year, which, according to Zitron's math, is more than 50% of all global AI compute spend. Since OpenAI doesn't generate nearly enough revenue to cover that, it can only afford to pay that amount thanks to its latest $122 billion funding round, of which it has received at least $50 billion. At this point of the argument, AI boosters could point out that OpenAI is simply one cog in a vast AI industry, so even if the company is financially imprudent, that doesn't automatically mean the rest of the industry is the same. Zitron's argument: OpenAI is by far the biggest cog. In fact, according to him, "OpenAI is also the reason Anthropic exists - not just because multiple founders came from the company, but because both Google and Amazon both agreed to give it a total of $6 billion in 2023 as a means of "competing" with Microsoft's new obsession, which allowed both to justify spending further hundreds of billions of dollars "to make sure they didn't miss out on AI." "The launch of ChatGPT in November 2022 came at the perfect time for a tech industry that had run out of ideas and was flirting with a prolonged depression," Zitron said. " The IPO market had collapsed, interest hikes killed the Zero Interest Free era dead, pandemic-era overhiring began to unwind with some of the worst layoffs in the history of the industry." For the first time in its history, the modern tech industry was about to have to "cut its cloth in accordance with its means - something which it has historically been loath to do," according to Zitron. But OpenAI's emergence changed all of that. "The existence of OpenAI justified an era of mania and opulence. Hyperscalers, bereft of new hypergrowth ideas, were able to point at the fact that ChatGPT had 'the fastest growing userbase of all time' and the Microsoft 'supercomputer' that built it and tell their investors that if they didn't invest, they'd be left behind, with Amazon, Meta, and Google announcing their own nebulous 'supercomputers' in 2023," Zitron said. Carlos Rodrigues / Getty Images OpenAI inflated the AI bubble almost by itself By the fourth quarter of 2023, global venture capital funding had dropped to its lowest level since the third quarter of 2016, Zitron reported. And even if it hadn't, venture capital by itself couldn't have backed OpenAI or Anthropic with the money necessary to build their infrastructure, Zitron said. Still, the tens of billions in private investments from the tech giants of the world proved to be more than enough not only to get OpenAI off the ground, but also to inflate the AI bubble to where we see it now. "This is the underlying greed that has driven this wasteful, reckless and destructive era - the belief that there will be another OpenAI and, as I've said, the chance to become the next OpenAI's landlord," Zitron said. "And like any great investment bubble, the more money that piled in, the greater the fear of missing out, the more dollars that can be justified in turn, and the more complex and deranged the mythology becomes." According to Zitron's sources who are familiar with their infrastructures, OpenAI, Anthropic, Microsoft, Google and Amazon have done their best to "obfuscate the actual underlying costs of their operations," and the media and public have been "more than willing to accept whatever convenient myths might sustain their dreams." Despite this, Zitron says the AI industry can't survive without OpenAI under any circumstances. It's the company with the most money, the most infrastructure, the most attention and the most AI talent, so if it collapses, it won't be until after AI data center debt and venture capital funding have been exhausted. But once the hyperscalers stop spending money, banks that are already "choking on data center debt will see that a vast amount of capital is leaving the market and underwrite deals as such." "This will mean, at some point, that both OpenAI and Anthropic will be walking around with their hands out saying "money please!" at precisely the moment that everybody will be cutting back," Zitron said. OpenAI needs to keep growing to pay its bills and at some point the company will "run out of real dollars to pay people, likely at exactly the time that it's hardest to find ore of them." And unfortunately for OpenAI, its free user base is quickly becoming a massive money-losing proposition. OpenAI expects to generate $2.4 billion in advertising revenue this year, with that number inexplicably jumping to $102 billion by 2030. For reference, Meta generated about $196 billion in advertising revenue in 2025 and Meta's free user base is in the billions. "OpenAI's collapse will be a direct result of its loss-laden economics - its doomed, loss-making subscriptions, its pathetic advertising revenue, and API costs that became a "huge issue" for its enterprise customers - and the fact that outside of the hype, AI lacks measurable ROI," Zitron says. The Arena Media Brands, LLC THESTREET is a registered trademark of TheStreet, Inc. This story was originally published July 17, 2026 at 6:03 PM.