On 8 October, OpenAI told its investors that its annualised revenue was about $50 billion. The figure that had been doing the rounds was $68 billion.

That is a lot of rounding.

Nvidia, Oracle and CoreWeave fell the same day. Within six sessions the Philadelphia semiconductor index had shed $1.6 trillion of value. On 22 October, Meta beat its revenue estimates, raised the floor of its capex guidance to $135 billion, and still fell about 7%, helping drag the Nasdaq to its worst day since June.

So the bubble people are back on television, the boom people are back on LinkedIn, and both are mostly arguing about share prices. (I build AI products for a living, so feel free to discount my optimism accordingly.) The more useful question is who is paying for this boom, and who pays if it breaks. The list includes a lot of people who have never bought a GPU.

“Is AI a bubble?” is three questions wearing one coat

1. Are people actually using AI and paying for it? Yes. OpenAI went from $13 billion of revenue in 2025 to a run rate of around $50 billion, even after the correction.
2. Do the companies building the models make money? Not yet. OpenAI expects to spend roughly $600 billion on compute through 2030. Its inference costs reportedly quadrupled in 2025, and its adjusted gross margin slipped from 40% to 33%.
3. Is the physical build-out (chips, data centres, power) being financed sensibly? Increasingly, no.

The third is where the damage would start. It is also where it would spread from.

The money has started moving in circles

In October 2025, OpenAI agreed to buy six gigawatts of AMD’s GPUs. As part of the deal, AMD gave OpenAI warrants for up to 160 million AMD shares, vesting as OpenAI deploys the chips and as AMD’s share price hits agreed targets.

Read that slowly. The customer is paid in the supplier’s stock for buying from the supplier, and that stock is worth more if the market believes the customer will keep buying. It’s a restaurant paying you in restaurant shares to eat there, with a bonus if the shares rise because people saw you eating there.

Nvidia’s version is quieter. It sells chips to CoreWeave, owns a stake in CoreWeave, and has agreed to buy any CoreWeave capacity nobody else wants, all the way to April 2032. When a supplier guarantees its own customer’s demand, that demand looks sturdier than it is.

The loans will outlive the chips

CoreWeave carries about $50 billion of debt against roughly $13 billion of expected revenue this year. In mid-October its credit default swaps implied about a 63% chance of default within five years, double the odds of a year earlier, and its April bonds were trading at 79 cents on the dollar.

A GPU is state of the art for two, maybe three years. The building around it, and much of the debt that paid for both, lasts far longer. Funding AI chips with long-dated debt is a bit like taking a fifteen-year loan on a smartphone. It works fine for as long as someone keeps paying you rent on the phone.

Gartner now says about 80% of these GPU-rental “neoclouds” could shut down by 2030. Gartner has been wrong before. Bond markets have too, though less often, and right now they agree with Gartner.

You are already paying for the boom

Bubbles are usually discussed as if the only people exposed are the ones holding the shares. This one sends invoices to people who don’t.

Start with memory. Chipmakers have moved capacity to the high-bandwidth memory that AI servers need, and conventional DRAM prices rose 55% to 60% in a single quarter around the turn of the year. In India, average smartphone prices rose 16% in the first half of 2026, according to Counterpoint. IDC says shipments of phones under $100 fell 74% year on year in the second quarter. The cheapest phones in the country are quietly disappearing, partly because the memory that went into them is now in AI servers on another continent.

None of this appears on anyone’s AI capex slide.

Then power. On PJM, the grid that serves 13 US states and Washington DC, capacity costs hit a record $16.4 billion for 2027 to 2028, and almost all of the projected growth in peak demand comes from data centres. Some households there are seeing bills rise 1.5% to 5% this year, and NRDC projects an extra $70 a month for an average family by 2028. Closer to home, Telangana is offering data centres power at generation cost and “continuous water supply” for cooling, in the same month Hyderabad is running a 150 MGD water deficit.

Workers are funding the capex too

Announced tech layoffs reached 196,349 this year as of 23 October, according to TrueUp. Amazon has announced around 16,800 cuts, Meta around 8,700, Microsoft around 4,800. Meta cut staff in the same year it raised capex to as much as $145 billion.

Companies rarely say “we’re letting you go so we can buy GPUs”, and only about a quarter of the layoff announcements that gave a reason mentioned AI at all. But the capex has to be paid for from somewhere, and payroll is the line a CFO can move fastest. (I’m aware that’s a cynical reading. I’ve also sat in budget meetings.)

The data centres don’t hire many people back. A large one employs somewhere between 50 and 300 people once it’s running. The construction crews move on to the next site, and while they’re there they’re competing with every other project in the region for electricians. And the squeeze lands hardest at the entry level, where firms are hiring fewer juniors because AI now does junior work.

Geopolitics is propping this up, and will decide how it comes down

This build-out is happening during a war. The Strait of Hormuz has been effectively closed to commercial traffic since late February. Brent was around $114 this week, and India paid an extra $22.5 billion for fuel imports between March and August. Iranian strikes took Qatar’s Ras Laffan complex offline in March, and with it about a third of the world’s helium, which chip fabs use to cool EUV lithography machines. Helium prices have risen 40% to 100%.

Against that backdrop, ING estimates AI investment accounts for roughly a third of US GDP growth this year, offsetting the drag from the energy crunch. So if the AI financing layer cracks, it will crack in an economy that is already absorbing an oil shock. That combination is the scenario I’d actually lose sleep over.

The race framing also changes how any bust would play out. Governments now treat AI as strategic. Chips have been under US export controls for years, and in June a US export-control directive forced Anthropic to switch off two of its frontier models for every customer for close to three weeks. Gulf states are being pressed to choose between American and Chinese technology stacks, and the war is pushing them to hedge with both. Washington took roughly 10% of Intel in 2025. In November 2025, OpenAI’s CFO spoke publicly about a government “backstop” for chip financing, before the company walked the remark back.

When something is declared strategic, governments rarely let it fail tidily.

Fibre ran this experiment under different rules

In the late 1990s, telecom carriers borrowed heavily to lay fibre on the belief that internet traffic was doubling every 100 days. Andrew Odlyzko, then at AT&T Labs, measured it and found growth of 75% to 150% a year. That was extraordinary growth. It just wasn’t the growth they had borrowed against. By mid-2002, the FDIC estimated that only 2% to 5% of US fibre was lit, telecom made up about a quarter of all corporate bond defaults by value, and Global Crossing and WorldCom had gone under. A decade later, that same fibre carried YouTube, Netflix and most of the cloud.

Nobody bailed out Global Crossing. The lenders took the loss, and everyone after them got cheap bandwidth. This time the losses are more likely to be spread around: some to lenders, some to taxpayers, and some to ordinary people through the price of phones, power and jobs. The infrastructure will still survive.

The bill just gets posted to more addresses.

For buyers in India and the Gulf, a correction is mostly good news, with conditions

If you buy AI rather than build it, a repricing in the financing layer would mostly help you. Compute gets cheaper and models get commoditised.

The catch is vendor mortality. Many AI products are thin layers on rented GPUs, funded by investors who assumed the money would keep arriving. Some of the tools you sign up for this year won’t exist in 2028, and your workflows and data will be inside them when they go.

India is exposed in quieter ways too: through electronics prices, through an oil bill the war has already inflated, and through US technology budgets that pay for Indian IT services and GCC hiring. Google’s $15 billion AI hub is going up in Visakhapatnam and Telangana wants 8 to 10 gigawatts of data centres, so we are inside this build-out with everyone else.

I’m betting on a restructuring within 18 months, possibly with a government in the room

Within 18 months, at least one major neocloud or data-centre financing vehicle will restructure its debt, get absorbed by a larger player, or renegotiate its tenant contracts. Given everything above, I now think there’s a fair chance a government helps arrange it. Meanwhile, AI usage will keep rising and the price of AI output will keep falling, while memory and power stay expensive for the rest of us.

I’ll be wrong if neocloud debt gets refinanced at investment-grade rates, if AI revenue keeps pace with capex through 2027, or if memory and electricity prices fall back during 2027. I’ll come back to this in April 2028 and say which it was.

If you’re signing an AI contract this quarter, ask the vendor three questions. Who pays for your GPUs? What happens to my data if you stop operating? Can I leave within 90 days? And if you’re approving a data centre in Telangana, ask the developer how much water and power it will draw in May, and get the answer in writing.

This is analysis, not investment advice.

Sources

• Quartz: OpenAI corrects annualized revenue figure to $50 billion (8 Oct 2026)
• Reuters via Investing.com: CoreWeave’s slump puts spotlight on debt behind AI boom (15 Oct 2026)
• Stocktwits via Yahoo Finance: Meta’s capex hits tech stocks (22 Oct 2026)
• Reuters via Yahoo Finance: OpenAI expects compute spend of around $600 billion through 2030 (20 Feb 2026)
• AMD 8-K: AMD and OpenAI announce strategic partnership to deploy 6 gigawatts of AMD GPUs (6 Oct 2025)
• FDIC Bank Trends: telecommunications industry (June 2002)
• TechFinitive: The infrastructure nobody wanted is the infrastructure everyone uses (Aug 2026)
• DatacenterDynamics: Google confirms $15bn data center project in Andhra Pradesh
• Deccan Chronicle: Telangana’s data-centre push faces power, water test (9 Oct 2026)
• Electronics For You: AI memory boom is making smartphones more expensive (12 Oct 2026)
• The Register: AI chip frenzy to wallop DRAM prices (6 Jan 2026)
• Canary Media: PJM capacity costs hit record (Dec 2025)
• TechRepublic: Tech layoffs 2026 (23 Oct 2026)
• Chmura: The real bottleneck for data centers is people (Aug 2026)
• Straits Daily Brief: Strait of Hormuz, 23 Oct 2026
• Free Press Journal: India’s fossil fuel import bill rises by $22.5 billion (Aug 2026)
• Tom’s Hardware: The global helium shortage is a direct threat to chipmaking (Mar 2026)
• AI Weekly: ING says AI investment is a third of US GDP growth in 2026 (Aug 2026)
• Anthropic: statement on Fable 5 and Mythos 5 access (June 2026)
• Asia Times: US-China race is on for the Gulf’s AI future (Oct 2026)
• The Register: OpenAI’s Altman and Friar walk back remarks about federal loan guarantees (Nov 2025)
• etcentric: U.S. is taking 10 percent Intel stake (Aug 2025)
• Telangana Today: Hyderabad faces 150 MGD water deficit (12 Oct 2026)

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