The AI boom is threatened by an epidemic of unreliable numbers
Investors can no longer put their trust in data
THE Financial Times wrote on Oct 8 that OpenAI’s revenue was nearing US$50 billion. A massive number, though massively lower than the US$70 billion reported by Axios the week before.
Tech stocks fell. Journalists were not on top of their figures, cried investors. Tech stocks rose. If investors could be swayed so easily, they were not on top of their investments, cried Hock Tan. The boss of chipmaker Broadcom compared markets to a casino, reported Bloomberg.
Tech stocks fell, and have continued to do so. Markets may have been flying. But when it comes to the numbers that matter, they have been flying blind. Radical change has always been hard to count.
The merger wave of the 1980s left corporate balance-sheets full of goodwill, that problematic wedge between the book value of a company’s assets and what they were bought for.
Measuring other intangible assets such as brands and patents was an even bigger head-scratcher in the 1990s and 2000s. The rise of stock-based compensation in the 2010s further obscured the picture of corporate health, particularly for tech firms.
Every investment boom brings with it a rogues’ gallery of acronyms, adjusted numbers and valuation techniques, sometimes designed to elucidate, and other times, to obscure. One problem today is that the reported revenues of OpenAI and Anthropic are not really revenues at all. Rather, they are riffs on a famously vague Silicon Valley metric: ARR.
The “A” is for “annual”, in the sense of bearing some relationship to the Gregorian calendar (sales in the past month are multiplied by 12).
The first “R” traditionally stands for “recurring”, which means only that the company hopes they will continue. And “revenue”, the second “R”? Anthropic includes sales of its models made through cloud providers such as Amazon, whereas OpenAI does so only after subtracting its partners’ cut.
Until the pair eventually list, investors are trapped in a strange interregnum where the faintest suggestion of news about numbers that are themselves merely suggestive shakes the entire market.
On Oct 14, Anthropic reportedly told investors that it had revenues of US$16.8 billion in the third quarter and that, on its own adjusted measure, it was profitable.
Still, it remained unclear how much the firm’s sales had grown since July, when its ARR was US$65 billion. New numbers are good for little when investors do not know what to compare them with.
Wacky financials deserve wacky valuations. Coatue, an investment firm, reckons that the labs should be worth some multiple of their pre-training Ebitda (that is, their earnings before interest, tax, depreciation and amortisation – and before the cost of training their models, an enormous expense).
This might remind investors of the oil industry (where Ebitdax, earnings before exploration, is common); or private equity (where inflating Ebitda in order to borrow more has been raised to something of an art form); or even WeWork, the office-leasing company whose creative financial reporting (“community-adjusted Ebitda”) earned it a deserved place in the pantheon of bad accounting.
Perhaps they could live even more dangerously. How about price-to-token-price ratio? Or existentially-adjusted earnings per share? Public markets offer more clarity. But not much more. In 2025, the accounting question of the day was whether big tech firms were writing down the value of their computing assets too slowly, thus overstating their earnings.
This issue was not so much resolved as replaced by two bigger ones. How leveraged are the giants? The US$2.4 trillion of off-balance-sheet commitments made by Amazon, Google, Meta and Microsoft exceeds what they have borrowed from bond markets.
And how to measure their return on this capital? In April, Microsoft said its AI business had an “annual revenue run rate” of US$37 billion. In July, Amazon put its ARRR at US$25 billion. Despite the additional “R”, these figures, too, are black boxes.
Yet, perhaps nothing matters quite as much to the AI trade than where this revenue is actually coming from. Researchers at Goldman Sachs said if the cloud giants are to earn a return between 10 and 20 per cent on what they invest in data centres in 2026 and 2027, and AI developers are to earn a 30 per cent margin, some US$1 trillion of annual revenue is required.
That means the value of productivity gains companies extract from the technology should far exceed that. Therein lies yet another measurement problem. Ask a boss how he measures his returns from spending on the technology, and prepare to be met with a blank stare.
ARRrr-gh!
Even the most basic measures of corporate America’s vitality have been rendered unintelligible. The spending of tech giants on data centres immediately lifts the profits of semiconductor and industrial firms, but flows through their own financial statements far more slowly as depreciation, inflating aggregate earnings in the short term.
One-off adjustments for public firms’ stakes in private labs fatten them further. In the second quarter, the unadjusted earnings growth of the S&P 500 index was an astonishing 50 per cent. Exclude the write-ups of private investments by Alphabet and Amazon, and it falls to a still-remarkable 32 per cent.
Measuring the impact of AI on the economy has become similarly fraught. Soaring prices for memory chips mean that inflation has been incorrectly overstated for some software products.
One widely discussed report by Epoch AI, a research firm, suggests that gross domestic product growth in America was undercounted by a full 0.3 percentage points in the past year because of the way Nvidia’s cross-border sales are measured.
At the same time, America’s data-collection bodies have been starved of funding and are declining in competence.
Another casualty of deregulation is the accounting watchdog that has inspected firms since the Enron scandal in the 2000s, and whose budget has been slashed. The big four audit firms were given top marks in its recent review, one last set of numbers that might prove too good to be true.
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