AI Bubble Fears Intensify as Tech Giants Pour Trillions Into Infrastructure Without Matching Returns

The Most Worrying Bits from Bloomberg's Worrisome AI Bubble Q&A with Jason Furman

Which data points qualify as true recession indicators? The yield curve, a comparison of short- and long-term interest rates, was scary recently, but does not suggest super high recession fears at the moment. The Sahm Rule raises alarms when there's a sudden relative spike in unemployment, and it hasn't technically been triggered yet despite incrementally worsening employment in recent months. But how does the Bloomberg Rule work? That's the one that lays out how many times a Bloomberg Q&A article with a normie economist can contain variations on the word "worry"? The one from Thursday with Jason Furman, which is specifically about an AI bubble, contains a deeply troubling 14 worries. I don't want to be alarmist, but I'm not liking the data, folks. Jason Furman is as normal as economists get: He's a Harvard professor. He was chairman of the White House Council of Economic Advisers under president Barack Obama. In October he was on the podcast of conservative New York Times opinion columnist Ross Douthat to talk about this same topic, and that interview only contained one "worry." So why the spike? Furman says "I'm more worried about the financial valuation bubble than I am a technological bubble" in his first answer. This seemingly gets at some kind of granular distinction -- as if the tech might be fantastic, but the companies can be overvalued anyway, and the second thing is the real problem. But what he says next sort of makes it sound like we should all worry about both equally: "To justify financial valuations, you basically need two things: the technology works really, really well, and you can make a profit from that. The two threats to valuations are that we hit diminishing returns and a lot of the different scaling laws that have applied to date don't apply in the future. Moreover, I don't know that every scaling law translates economically. Every time your microchip in your computer gets two times as fast, you don't write Word documents two times as fast or respond to emails two times as fast. In fact, a lot of that is almost like excess capacity that is building up in our computers, and that could be what happens in AI, even if it follows the law." That arguably describes one of the biggest AI stories of the year. When OpenAI released the GPT-5 model in August, whether or not it was a worthwhile incremental step or not, ChatGPT users clearly didn't see enough of an upside to balance out the fact that they didn't enjoy talking to it. OpenAI upgraded the model people were using as a friendship substitute, and it didn't suddenly get exponentially warmer and more insightful. It arguably just had "excess capacity." If you're still confused as to where the line is between a tech bubble and a valuation bubble, don't worry because Bloomberg's interviewer Shirin Ghaffary says she is too. Furman elaborates a bit, saying that separately from valuations, there are also "hundreds of billions of dollars a year being spent on data centers, energy and the like," and that this is "an actual, real activity." He compares this to internet infrastructure being built out during the dot com craze. But he continues: The thing that would worry me is if it just ended up not working and adding to productivity. Right now, we're seeing AI mostly on the demand side of our economy. He later adds: We do not have a US economy that is firing on all cylinders. We have a US economy that is firing on one cylinder right now. These are two important things to keep in mind about how normie economists see AI right now. Saying AI is on the demand side may feel silly -- how much AI do you demand on a day-to-day basis? If you're like me, zero to very little. But that's not the demand he's talking about. Think of the global economy as one giant, worryingly empty Home Depot. AI being on the demand side means it's one giant, voracious customer in the global Home Depot buying enough drills, cement bags, and ladders to keep it in business for the time being. But AI can't just be the only big-spending customer in the global Home Depot forever, and what it's going to build with all that stuff has to drive enough economic activity to drive other customers -- in fact more customers than ever -- into the global Home Depot so they can build things too. If we go back to that ChatGPT incident from this year, while that is a big part of why people use consumer-grade AI, it turns out not to be a particularly good example of the type of use case Furman thinks could drive growth. He also downplays the idea that AI is going to clobber employment in pursuit of efficiency, nor that this is a major risk or ever could be ("At every point in time that people have thought that in the past about this employment question, they've been wrong," he says). Instead, Furman's crystal ball contains a very murky image of what AI is supposed to do to keep the economy afloat: "People in the wild are just slow and kind of complicated and figure out one use case this year and the next use case the next year, and want to test it eight different ways before they deploy it. Different businesses, different industries, different sectors will figure this out at different times, as opposed to you waking up one day and there's a big bang. I should say that is a best guess, with an extreme caveat that anything could happen." Your mileage may vary on how reassuring this is but my interpretation of what Furman is saying is, basically, AI will be genuinely useful at as-yet unknown times, to as-yet unknown people. That's not a totally unconvincing prediction. The really worrying part, though, is that it has to be true.

Fears grow of AI bubble - and here are the pressure points that could burst it

Sky News' science and tchnology reporter Tom Clarke explains why. The market seems to be content, for now at least, to keep betting big on AI. While the value of some companies integral to the AI boom like Nvidia, Oracle and Coreweave have seen their value fall since the highs of the mid-2025, the US stockmarket remains dominated by investment in AI. Of the S&P500 index of leading companies 75% of returns are thanks to 41 AI stocks. The "magnificent seven" of big tech companies, Nvidia, Microsoft, Amazon, Google, Meta, Apple and Tesla, account for 37% of the S&P's performance. Such dominance, based almost exclusively on building one kind of AI - Large Language Models is sustaining fears of an AI bubble. Nonsense, according to the AI titans. "We are long, long away from that," Jensen Huang, CEO of AI chip-maker Nvidia and the world's first $5trn company, told Sky News last month. Not everyone shares that confidence. Too much confidence in one way of making AI, which so far hasn't delivered profits anywhere close to the level of spending, must be testing the nerve of investors wondering where their returns will be. The consequences of the bubble bursting, could be dire. "If a few venture capitalists get wiped out, nobody's gonna be really that sad," said Gary Marcus, AI scientist and emeritus professor at New York University. But with a large part of US economic growth this year down to investment in AI, the "blast radius", could be much greater, said Marcus. "In the worst case, what happens is the whole economy falls apart, basically. Banks aren't liquid, we have bailouts, and taxpayers have to pay for it." By one estimate Microsoft, Amazon, Google Meta and Oracle are expected to spend around $1trn on AI by 2026. Open AI, maker of the first breakthrough Large Language Model ChatGPT, is committing to spend $1.4trn over the coming three years. But what are investors in those companies getting in return for their investment? So far, not very much. Take OpenAI, it's expected to make little more than $20bn in profit in 2025. A lot of money, but nothing like enough to sustain spending of $1.4trn. The size of the AI boom - or bubble depending on your view - comes down to the way it's being built. Computer cities The AI revolution came in early 2023 when OpenAI released ChatGPT4. The AI represented a mind-blowing improvement in natural language, computer coding and image generation ability that grew almost entirely out of one advance: Scale GPT-4 required 3,000 to 10,000 times more computer power - or compute - than its predecessor GPT-2. To make it smarter, it was trained on far more data. GPT-2 was trained on 1.5 billion "parameters" compared perhaps 1.8 trillion for GPT-4 - essentially all the text, image and video data on the internet. The leap in performance was so great, "Artificial General Intelligence" or AGI that rivals humans on most tasks, would come from simply repeating that trick. And that's what's been happening. Demand for frontline GPU chips to train AI soared - and hence the share price of Nvidia which makes them doing the same. The bulldozers then moved in to build the next generation of mega-data centres to run the chips and make the next generations of AI. And they moved fast. Stargate, announced in January by Donald Trump, Open AI's Sam Altman and other partners, already has two vast data centre buildings in operation. By mid-2026 the complex in central Texas is expected to cover an area the size of Manhattan's Central Park. And already, it's beginning to look like small fry. Meta's $27bn Hyperion data centre being built in Louisiana is closer to the size of Manhattan itself. The data centre is expected to consume twice as much power as the nearby city of New Orleans. The rampant increase in power demand is putting a major squeeze on America's power grid with some data centres having to wait years for grid connections. A problem for some, but not, say optimists, firms like Microsoft, Meta and Google, with such deep pockets they can build their own power stations. Once these vast AI brains are built and switched on however, will they print money? Stale Chips Unlike other expensive infrastructure like roads, rail or power networks, AI data centres are expected to need constant upgrades. Investors have good estimates for "depreciation curves" of various types of infrastructure asset. But not so for cutting-edge purpose-built AI data centres which barely existed five years ago. Nvidia, the leading maker of AI chips, has been releasing new, more powerful processors every year or so. It claims their latest chips will run for three to six years. But there are doubts. Fund manager Michael Burry, immortalised in the movie The Big Short, for predicting America's sub-prime crash, recently announced he was betting against AI stocks. His reasoning, that AI chips will need replacing every three years and given competition with rivals for the latest chips, perhaps faster than that. Cooling, switching and wiring systems of data centres also wears down over time and is likely to need replacing within 10 years. A few months ago, the Economist magazine estimated that if AI chips alone lose their edge every three years, it would reduce the combined value of the 5 big tech companies by $780bn. If depreciation rates were two years, that number goes up to $1.6trn. Factor in that depreciation and it further widens the already colossal gap between their AI spending and likely revenues. By one estimate, the big tech will need to see $2trn in profit by 2030 to justify their AI costs. Are people buying it? And then there's the question of where the profits are to justify the massive AI investments. AI adoption is undoubtedly on the rise. You only have to skim your social media to witness the rise of AI-generated text, images and videos. Read more from Sky News: Epstein victims react to partial release of files Fears Palestine Action hunger striker will die in prison Kids are using it for homework, their parents for research, or help composing letters and reports. But beyond casual use and fantastical cat videos, are people actually profiting from it - and therefore likely to pay enough for it to satisfy trillion-dollar investments? There's early signs current AI could revolutionise some markets, like software and drug development, creative industries and online shopping, And by some measures, the future looks promising, OpenAI claims to have 800 million "weekly active users" across its products, double what it was in February. However, only 5% of those are paying subscribers. And when you look at adoption by businesses - where the real money is for Big Tech - things don't look much better. According to the US census bureau at the start of 2025, 8-12% of companies said they are starting to use AI to produce goods and services. For larger companies - with more money to spend on AI perhaps - adoption grew to 14% in June but has fallen to 12% in recent months. According to analysis by McKinsey the vast majority of companies are still in the pilot stage of AI rollout or looking at how to scale their use. In a way, this makes total sense. Generative AI is a new technology, with even the companies building still trying to figure out what it's best for. But how long will shareholders be prepared to wait before profits come even close to paying off the investments they've made? Especially, when confidence in the idea that current AI models will only get better is beginning to falter. Is scaling failing? Large Language Models are undoubtedly improving. According to industry "benchmarks", technical tests that evaluate AI's ability to perform complex maths, coding or research tasks show performance is tracking the scale of computing power being added. Currently doubling every six months or so. But on real-world tasks, the evidence is less strong. LLMs work by making statistical predictions of what answers should be based on their training data, without actually understanding what that data actually "means." They struggle with tasks that involve understanding how the world works and learning from it. Their architecture doesn't have any kind of long-term memory allowing them to learn what types of data is important and what's not. Something that human brains do without having to be told. For that reason, while they make huge improvements on certain tasks, they consistently make the same kind of mistakes, and fail at the same kind of tasks. "Is the belief that if you just 100x the scale, everything would be transformed? I don't think that's true," Ilya Sutskever, the co-founder of OpenAI told the Dwarkesh Podcast last month. The AI scientist who helped pioneer ChatGPT, before leaving OpenAI predicted, "it's back to the age of research again, just with big computers". Will those who've taken big bets with AI be satisfied with modest future improvements, while they wait for potential customers to figure out how to make AI work for them? "It's really just a scaling hypothesis, a guess that this might work. It's not really working," said Prof Marcus, "So you're spending trillions of dollars, profits are negligible and depreciation is high. It does not make sense. And so then it's a question of when the market realises that."

The AI Bubble Question: Promise, Pressure, and the Fear of a Burst

Bill Gates and Demis Hassabis warn of correction; Friar sees momentum Artificial intelligence (AI), just like any emerging technology, has polarised the market ever since its inception. In 2023, the talks were about whether it was a buzzword or had real applications. In 2024, concerns were raised about irresponsible development and expansion of a potentially dangerous tech. The industry has been able to alleviate some of these fears while solidifying its position in the market. However, throughout 2025, AI faced its harshest criticism yet -- is it a bubble that is about to burst? The answer might be more nuanced than what the general discourse suggests. What Is a Bubble? A bubble has a transient existence, growing in size quickly and doomed to burst and disappear forever. In economics, it has a similar meaning, referring to innovation or market trends that have high potential and quickly gain investors' faith and wallets, only to eventually wipe out wealth from the market after the burst. The high investment in the space results in the mushrooming of new companies that can raise a large amount of funds due to this investor excitement, often before they generate revenue or even have a product in the market. This results in the existing players in the market raising more money to expand aggressively and retain their market share. The external funding keeps flowing in, and the industry inflates to an unimaginable size. However, then comes the fall. All this money being poured in results in high valuations for the companies. But this valuation was reached purely on the basis of investors, not revenue generation. The investors begin to realise that due to the large number of companies operating in this space, many neither have a scalable product nor a large enough user base to ever generate enough revenue to have any return on investment (ROI). The final nail in the coffin arrives as the investment money dries up while the companies find themselves in debt due to most of their expenses going towards expansion and scaling the product, not towards revenue generation efforts. Many companies begin shutting down in quick succession, disappearing with the investment money, creating a sizable hole in the market. As was the case with the dotcom bubble, if the hole is big enough, it can also trigger a global recession. Why Is AI Being Called a Bubble? If the naysayers are to be believed, AI has already reached a stage where it is not justifying the investor money being poured into it. The concerns have largely been driven by record spending on infrastructure, sky-high valuations, and rapidly expanding startup funding. Over the past 12 months, companies across the AI ecosystem have committed unprecedented capital to expand computing and model capacity. One of the most striking examples is OpenAI's planned $1.4 trillion (roughly Rs. 126 lakh crore) spend on compute and infrastructure over the next eight years, a figure far outstripping the company's current revenues, which are said to reach a little over $13 billion (roughly Rs. 1.16 lakh crore) in 2025. Deals struck this year also illustrate this scale. Nvidia invested $100 billion (roughly Rs. 8.8 lakh crore) into OpenAI, building on its existing stake and creating what some analysts see as "circular financing" where chip vendors are also investors in the companies buying their products. Tech giants have also made massive commitments. Oracle agreed to a $300 billion (26.8 lakh crore) data centre deal with OpenAI, and discussions are underway for Amazon to invest more than $10 billion (roughly Rs. 89,555 crore) in the ChatGPT maker, potentially pushing its valuation above $500 billion (roughly Rs. 44.7 lakh crore). Beyond individual companies, large joint ventures and infrastructure projects are underway. The Stargate Project, a multibillion-dollar collaboration involving OpenAI, Oracle and SoftBank, is building large AI datacenters with an initial commitment of about $100 billion (roughly Rs. 8.8 lakh crore), aiming for far more in the coming years. Spending on infrastructure has also soared outside the AI developer ecosystem. According to Reuters, AI-related financing deals, particularly for data centres, surged to $125 billion (roughly Rs. 11.1 lakh crore) in 2025, up from $15 billion (roughly Rs. 1.34 lakh crore) in 2024, highlighting how much capital is chasing perceived future demand. These commitments have coincided with equally striking valuations. Nvidia became the world's most valuable company in 2025, driven by demand for its AI chips, and briefly topped a market cap above $5 trillion (roughly Rs. 447 lakh crore). Investors are now asking whether such figures reflect real earnings potential or simply hype around future AI dominance. AI Bubble: Room for Debate It is easy to dismiss the rise of AI as a bubble based on these numbers. However, several experts and industry stalwarts believe that such an opinion is perhaps oversimplified. A Tech in Asia analysis describes the industry as a two-layered advancement, and it is important to distinguish between the two. The publication says there is a foundation layer to AI that comprises data centres, cloud servers, and other infrastructure for the technology, as well as the large language models themselves. The second layer is the valuation or the economics of it all, which is defined by the investor enthusiasm and the external money being poured into the industry. In a conversation with Bloomberg, Jason Furman, economist and former US Chairman of the Council of Economic Advisers, said, "I'm more worried about the financial valuation bubble than I am a technological bubble." The implication here is that the technology, over the last three years, has proven itself to be transformative and a crucial commodity in both enterprise and consumer space. And there is enough evidence to point out that with further advancement, more critical applications will be built. But even in the worst-case scenario, the infrastructure will not depreciate, and it can always be repurposed for a proven existing tech stack. The valuation game is where concerns rise, as we have explained above. OpenAI CEO Sam Altman himself has acknowledged bubble talk, noting that "when bubbles happen, smart people get overexcited about a kernel of truth." This comment was aimed at the mushrooming AI startups, not the industry as a whole. But it caused some confusion among individuals. Later, the company CFO, Sarah Friar, in an interview, provided clarification and told CNBC, "We still feel that the AI era is upon us, and we're leading the path. As we've come out of the gate, we're seeing, actually, acceleration in Plus and Pro subscriptions. That's a good sign; people are seeing a lot of value. And we're seeing a lot of momentum in the enterprise, great momentum with developers." Similar sentiments have been echoed by others. Bill Gates told CNBC that many AI companies are overvalued and only a fraction will succeed, urging investors to prepare for corrections. Google DeepMind's Demis Hassabis has also highlighted that many early-stage AI startups are raising tens of billions in valuation without substantial operations, predicting a likely market adjustment. Bridgewater Associates' Greg Jensen went further, warning that Big Tech's reliance on external capital to fund AI expansion "is dangerous" and that there is a "reasonable probability" of finding an AI bubble as spending outpaces internal cash generation. Should You Be Concerned? If you are a retail or institutional investor who owns stocks or equity in an AI company, there are concerns about long-term benefits. Additionally, no one knows the right time to exit, as the numbers can double next week or fall to zero. With so much uncertainty, investment discipline and a long-term vision are important. If you are a founder or a newly created AI startup, and you have raised massive funds before focusing on your product-market fit, the bubble might affect you. However, as long as you have a product in the market, a loyal user base, and a long-term vision to scale the product and expand the revenue streams, you might be safeguarded from any market crash in the future. Tech professionals working in AI companies might also be at risk if the company they're working for fails at sustainable revenue generation. Individuals should tread carefully when joining a new AI startup, no matter the pay package and perks. However, those working in larger companies such as Amazon, Google, Meta, Nvidia, or even OpenAI should not be impacted. Finally, end consumers really do not have anything to worry about, apart from possible price hikes on their AI subscriptions in the near future if the bubble bursts. As the market adjusts and the smaller players are removed from the scene, the remaining players will feel the pressure for profitability, and the easiest way for them to do that is by increasing the prices of their products and services. AI companies might be offering these for peanuts as market capture remains a priority, but individuals should be wary before they make any long-term commitments. To conclude, the AI market valuation bubble is not a "if it will happen" question, but rather a "when will it happen and who will be impacted" question. The technology is here to stay, but some of the companies might not be.