chipmakers, in anticipation of the handbags that will now remain on the shelves. The trillion-dollar question for investors in South Korea is when the price of memory will peak. Bulls argue that data-centre construction and robotics mean demand for memory will outstrip supply into the 2030s. But the increasingly common view is that the same thing will happen to memory chips as has happened to every commodity in every commodity cycle in history—that new supply will, within a year or two, cause prices and profits to fall. Despite their vertiginous rise, Samsung and SK Hynix have on average traded only at single-digit multiples of forward earnings during the past year, suggesting that investors think their profits are at or near their highest point. Perhaps it is inevitable that the first country to taste the riches promised by AI should be the first to have them taken away. No industry can survive if all the rewards are concentrated in one corner of the supply chain. Yet the dangers facing South Korea’s memory giants are also lurking elsewhere in the AI landscape. First is the dreaded “commoditisation”. The sales of less advanced memory chips by SK Hynix and Samsung are threatened by the rising market share of Chinese competitors (one of which went public this week to great fanfare). So too are America’s model-makers, which face a potentially existential threat from Chinese open-source models. Second is the risk that supposedly ironclad spending commitments from customers turn out to be no such thing. Both South Korean firms have made much of their “long-term agreements” with buyers, arguing that pre-payment and price certainty gives them visibility on profits for years to come. Yet judicious investors have begun to fear that customers will find ways of wriggling out of deals when it is advantageous for them to do so. Investors also worry how airtight lease agreements for data centres would prove if model-makers tried to get out of them in a bust. The piles of debt tied to Meta’s giant off-balance-sheet data centre have drawn particular scrutiny. Nvidia has played an important role in stabilising this precarious situation. Jensen Huang, its leather-jacketed chief executive, has something to say on
every debate: last week he published an influential letter in defence of open- source models. But his cheque-book is more important. Mr Huang has constructed a vest web of financial ties that keep the party going. This week it was reported that his company is in talks to guarantee $250bn of commitments from OpenAI, maker of ChatGPT, to lease a data centre. More remarkable still is the vague “$500bn” partnership with SK Hynix it announced over the weekend, and how little that seemed to matter to investors this week. Like a central banker meddling in markets, the more Mr Huang intervenes the more nervous investors become. The eventual bust will be all the worse for it. ■ This article was downloaded by zlibrary from https://www.economist.com/business/2026/07/29/south-koreas-stock-market-boom-is- collapsing-spectacularly
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Finance & economics | When 11 zeros is child’s play AI revenues are growing fast, but not fast enough The returns on trillions of dollars of spending are deeply uncertain Jul 30th 2026 YOU AIN’T seen nothing yet. Last year America’s biggest technology companies, including Amazon, Google and Microsoft, spent $450bn on infrastructure, much of it to power artificial intelligence. This was just an amuse-bouche. For the main course they will this year spend $900bn on chips, data centres, power and so forth, with a $1.4trn pudding to follow in 2027. To fund this feast they have borrowed more than $400bn this year. The AI capex boom is fast becoming the largest investment surge in history (see chart 1).
If superintelligence is in reach, building football fields’ worth of compute could also be history’s most valuable capital-allocation exercise. And yet capital spending can still generate disappointing returns for investors. Since peaking in June, the share prices of the biggest AI firms have fallen by 20%, as worries have mounted that flows of capital from one tech firm to another, rather than genuine demand from end-users, have been propping up the industry, while South Korea’s benchmark index, dominated by Samsung Electronics and SK Hynix, two big chipmakers, has dropped by almost 40%. After Meta reported second-quarter earnings on July 29th, its shares shed more than 7%, even as Mark Zuckerberg, its boss, defended its spending on AI, which has eaten deeply into free cashflow. A rough calculation finds that covering AI capex through identifiable AI income requires revenue on the order of $2.5trn per year, more than tech’s entire combined revenue today (and far higher than what would have been needed a year or so ago, when capex plans were more modest). Only a small minority of consumers seem willing to pay for personal AI subscriptions, so the real money will have to be made from selling to enterprises. Traders might use Microsoft’s Copilot to create better financial models, for instance, while schools could teach children with models from Google. For now, though, sales in the trillions are a long way off.
For AI revenues to soar, more firms will have to use AI (what economists call an increase at the “extensive margin”) and use it more deeply (the “intensive margin”). At the extensive margin, roughly 20% of American firms used AI “in any…business functions” in the previous fortnight, according to the Census Bureau’s latest two-weekly survey. In Britain, official figures say about a third of firms claim to use AI, though the question is put differently. That indicates rapid technological diffusion by any standard. After all, four years ago no one used large language models. Yet those numbers may have recently levelled off. In May economists at the Census Bureau reported that “AI use remained relatively steady in many sectors over the last six months.” A survey by Jon Hartley of the University of Texas at Austin and others has found that about 33% of people now use AI at work, down from a peak of 46% in mid-2025. If, say, a third of firms across the OECD club of mostly rich countries adopt AI, then to generate $2.5trn of AI revenues the firms would have to spend about $100,000 a year on average. Is that plausible? Perhaps, though for now few firms treat AI as a core technology, which limits how much they are willing to spend on it. According to a survey by the European Central Bank, in late 2025 only a tenth of euro-area companies using AI reported doing so “intensively”. Researchers at the Bundesbank find that about half of German firms using AI do so for 5% of working hours or less. Ivan Yotzov of the Bank of England and his colleagues estimate that the average American executive uses AI for 1.7 hours a week—enough time to create a decent PowerPoint presentation, but not much more. For these dilettantes, free or ultra-cheap AI models are often good enough. Official data from Britain suggest that close to half of businesses using AI do not pay for it, presumably making do with the free tier of an American model or an open-source Chinese one. Ramp, a fintech firm, finds that a fraction of firms spend thousands of dollars a month per employee on AI. The median firm’s monthly spending per worker in June, however, was $10.66. Intuit, a software firm which tracks small and medium-sized businesses in America, Britain and Canada, reports that about one in ten has paid for a dedicated AI tool.
Total AI spending can only be guessed at, because data sources are murky and the picture is changing fast (see chart 2). Exponential View, a consultancy, counts $175bn of generative-AI revenue, on an annualised basis, in June. In a recent paper Anton Korinek of Anthropic and Patrick McKelvey of the Bank of Canada estimate total “AI services” revenue. Adapting their methodology, we reckon this is currently around $220bn (again annualised). Ramp’s data imply that 2-3% of business spending now goes on AI, pointing to $170bn a year. All these complex calculations roughly tally with a much simpler one: adding up the AI revenue of the firms selling most of the AI. Anthropic pulls in perhaps $75bn, annualised; OpenAI makes tens of billions; Google, via its AI model Gemini, and Microsoft probably get a bit less. SpaceX may have a few billion dollars’ worth of revenue from enterprise AI this year. Meta also makes a few bucks from AI. Add this up and you land at roughly $150bn a year. Revenue is rising extraordinarily fast. But perhaps not fast enough for investors, as calculations by Phurichai Rungcharoenkitkul of the Bank for International Settlements suggest. The crux is that AI usage, as proxied by the consumption of tokens, is probably growing even more quickly than revenue. This may be because more people are flitting from one free model
to the next, rather than paying. In Mr Rungcharoenkitkul’s analysis, this has an important implication: at least part of the AI capex splurge represents zero-sum competition. Firms are using some of the extra compute they are building not to expand the paying market, but to poach customers from rivals. The paper implies that perhaps one-third of the investment, or even more, is unlikely to make a satisfactory return. That said, revenues may soon grow even more quickly, if two conditions are met. The first is that AI boosts productivity markedly. For now, there is little evidence that AI is transforming businesses. Few firms are saving money by replacing workers with bots. According to Mr Yotzov’s study, nine in ten executives report no impact of AI on their firm’s productivity over the past three years. If that were to change, though, more firms would see reason to devote greater resources to the technology. They would also be happier to absorb price rises, juicing revenue further. The second is that AI adds to “intangible capital”. For firms to make the most of AI they cannot simply pay for a chatbot, but must instead rework their processes, including their staff and their use of data, from top to bottom. The historical evidence suggests that for every $1 of investment in computer hardware, companies have made $5-10 of these intangible