Why in the News
Capital markets have begun to price the distance between what artificial intelligence (AI) promises and what it earns. Nvidia backed Firmus withdrew a nearly $5 billion initial public offering (IPO) after investors resisted its valuation, and OpenAI and Anthropic postponed their planned trillion dollar listings. The boom now faces eight risks, four financial and four beyond the balance sheet.
What is the AI boom, and why is it fragile?
- What it is: AI companies are spending heavily on models and data centres in expectation of future revenue, like a builder raising towers before buyers have paid.
- Hyperscalers: The biggest builders are hyperscalers, cloud companies that run giant data centres AI cannot do without.
- Growth outpacing support: AI models are growing faster than the financing, governance and social acceptance they need.
- Revenue reset: OpenAI indicated that its September annualised revenue (a month’s earnings scaled to a year) was nearer $50 billion, not the nearly $70 billion signalled earlier.
- The takeaway: When revenue lags the money already spent, investors cut valuations, and the debt behind the buildout becomes a risk to lenders.
What are the four financial risks?
- Macroeconomy: The Iran war, high US gas and diesel prices and the wide US fiscal deficit have pushed treasury yields, the interest the US government pays to borrow, to multi decadal highs. Higher interest rates hurt homebuyers and slow data centre growth.
- Revenue trailing capex: Capital expenditure (capex), spending on long lived assets, is outrunning revenue. Anthropic ran an operating loss of about $8 billion in 2025, so it has little room to raise investors’ expectations.
- Costlier, opaque debt: Capex once paid in cash now relies on private credit, loans from funds rather than banks, and bespoke deals, most of them opaque. Most hyperscaler bonds issued in 2026 traded below issue price by end August.
- Sobering valuations: OpenAI and Anthropic were reportedly seeking about $60 billion each from their listings. Cautious valuation assessments forced both chief executives to delay.
What are the four risks beyond the balance sheet?
- Regulatory risk: Three frontier models (the most advanced AI systems) faced sudden regulatory slowdowns, a sign of control by diktat, rule by sudden order. The US President made AI chiefs accept safety responsibility and external audits.
- Rogue AI liability: Rogue AI, systems acting beyond their makers’ control, brings multimillion dollar claims over cyber attacks, copyright and privacy against directors and firms. Such litigation could rival the cases against tobacco, glyphosate and pharmaceutical companies.
- AI in war: Autonomous AI has been used in Ukraine, Gaza and Iran without due regard for human scrutiny, civilian harm, proportionality and accountability.
- Data centre opposition: Communities cite lost drinking water, power outages, noise, land loss and few permanent jobs. Protests have forced some rollbacks, yet China’s fast buildout makes data centres a national security concern.
Challenges
- Hidden exposure: Opaque AI debt can spread losses to banks, insurers and pension funds if AI stocks sell off.
- Unconvinced workforce: Sceptical workers slow AI adoption, and with it the revenue the boom depends on.
- No rule for military AI: No multilateral agreement yet governs the use of autonomous AI in war.
- Local costs, global gains: Water, power and land costs fall on communities near data centres, and the gains flow to distant firms.
Way Forward
- Disclose AI debt: Financial regulators should require lenders and funds to report their exposure to data centre debt.
- Audit frontier models: Make independent external audits a standard condition for deploying the most powerful models.
- Human control in war: States should agree on a multilateral rule keeping humans accountable for autonomous weapons.
- Community benefit siting: Approve data centres only after water and power assessments, with local jobs and benefits.
Conclusion
AI remains an exceptional technology, but markets now judge it on revenue, regulation and public consent, not on promise alone. Whether the boom cools gradually or breaks suddenly will depend on how much of its opaque debt is exposed when expectations reset.
Key numbers
- SpaceX listing, mid 2026: raised $75 billion, listing at $1.77 trillion.
- Hyperscaler capex, 2026: about $725 billion expected from leading US firms; about 40% assessed by end June.
- Meta free cash flow: about $800 million, from $8.5 billion a year earlier.
- Data centre capacity: China adding 50 GW to its current 24 GW; US at 56 GW.
- Firmus valuation: about $31 billion, three times its August figure.
Matching Previous Year Question
“[2026] Which of the following statements with regard to Large Language Models (LLMs) used in machine learning is/are correct? 1. LLMs assign probabilities to the next possible words and then pick the one with the highest probability. 2. LLMs process data through mathematical optimization to minimise prediction errors. 3. LLMs produce unbiased outputs. (a) 1 only (b) 1 and 2 only (c) 2 and 3 only (d) 1, 2 and 3 ANSWER: B”







