AI: A doomsday scenario lurking in the future

Global capital markets have committed an unprecedented volume of capital to AI infrastructure since 2023, intensifying through 2025-26 and drawing comparisons to railway mania and the dot-com telecom overbuild. Cumulative 2022-26 investment estimates range from roughly $1.8 trillion (Goldman Sachs) to $2.5-2.6 trillion in total 2026 AI spending (Gartner), with infrastructure alone accounting for around $1.4 trillion. The four largest hyperscalers - Amazon, Alphabet, Microsoft and Meta - are guiding towards roughly $725 billion in combined 2026 capex, up 77 per cent year-on-year; including Oracle, the group approaches $800 billion. Goldman projects hyperscaler capex could reach $5.3 trillion between 2025 and 2030. Capital intensity is extreme: 2026 capex runs at roughly 86 per cent of revenue for Oracle, 54 per cent for Meta, and 46-47 per cent for Microsoft and Alphabet.

Realised returns lag far behind this spending. Only about 21 per cent of S&P 500 companies report measurable financial benefits from AI (Morgan Stanley), and a widely cited MIT study found that 95 per cent of generative AI pilots show no measurable profit impact. Sequoia’s David Cahn estimates an annual $600 billion revenue gap between hyperscaler infrastructure spending and actual AI ecosystem sales - a divergence already exceeding the gap seen before the 2001 telecom collapse. Combined revenue of leading AI developers, including OpenAI and Anthropic, likely totals under $35 billion, a small fraction of hyperscaler capex. Gartner projects that over 40 per cent of agentic AI projects will be cancelled by 2027 due to unclear returns. The downstream supply chain carries uneven exposure. Chipmakers such as Nvidia, TSMC, Broadcom and Micron remain supply-constrained rather than speculative, though they are still fully exposed as direct “capex receivers”. Power utilities face the risk that stranded generation investment gets socialised onto ratepayers. The most vulnerable tier is smaller, thinly capitalised cooling, packaging and construction firms, which McKinsey warns face genuine stranded-asset risk if capex decelerates.

A defining 2026 feature is the accelerating shift from cash to debt financing. Alphabet reported its first negative free cash flow quarter since its 2004 IPO. Incremental debt as a share of hyperscaler capex rose from about 9 per cent in FY2024 to roughly 32 per cent by mid-2026, pushing aggregate hyperscaler debt to about $700 billion, with up to $1.5 trillion in additional issuance projected. This burden is unevenly distributed: Microsoft, Alphabet, Meta and Amazon retain strong investment-grade ratings and are supplementing debt with equity, including Alphabet’s record $84.75 billion raise in June 2026. Oracle, rated just two notches above sub-investment grade, with capex at 86 per cent of revenue and concentrated exposure to OpenAI, represents the most plausible point of initial stress, with its credit default swap cost having more than tripled since September 2025. Over $1 trillion in anticipated AI debt is expected from private credit, prompting Oliver Wyman comparisons to 2008’s hidden housing-risk exposure. Most analysts treat a severe crisis as a tail risk rather than a base case. Prediction markets price an end-2026 “bubble burst” at roughly 18 per cent probability, with a moderate 20-30 per cent valuation correction seen as more likely. A typical failure pathway would begin with a monetisation shortfall or interest-rate shock. Because AI market value is concentrated in a few firms, distress at a highly exposed counterparty such as Oracle - especially if tied to an OpenAI setback - could trigger credit-spread contagion across the broader hyperscaler bond market. Illustrative severe scenarios describe unemployment above 10 per cent and equity markets falling nearly 40 per cent by 2028, with S&P Global estimating a full bust could eliminate 2.5 million jobs.

A separate risk involves a sudden efficiency breakthrough rendering current hardware-intensive infrastructure obsolete, echoing unused fibre-optic capacity after the dot-com bust. Insurers carry dual exposure: as underwriters of novel, hard-to-price data centre risk, with sites costing up to $20 billion and often located in hail- and tornado-prone regions, and, more significantly, as creditors financing the buildout through private credit and structured securities - an arrangement Oliver Wyman likens directly to pre-2008 mortgage exposure. Other sectors face varied risks: utilities from stranded assets, commercial real estate from concentrated hyperscaler tenancy, and automakers and electronics manufacturers from rising component costs as AI absorbs semiconductor and memory supply, with memory prices having more than doubled over the past year. Moody’s models a theoretical 40 per cent AI valuation correction as reducing household wealth and consumer spending, especially among high-net-worth households, with spillovers into luxury goods, real estate and advertising-dependent media.

The US government’s capacity to intervene is more constrained than in 2008 or 2020. Federal interest payments are projected to reach $1 trillion in FY2026, already exceeding defence and Medicare spending, while the government must refinance roughly $9 trillion in maturing debt in 2026 alone. The Dallas Fed finds hyperscaler bond issuance is already crowding out other investment-grade borrowers. Any rescue would likely have to come through Federal Reserve monetary action rather than fiscal intervention, but this is itself constrained if a credit event coincides with elevated inflation - and much AI-related private credit exposure sits with non-bank entities lacking the Fed’s established emergency-lending relationships. India’s exposure runs through distinct channels. Its IT/BPO sector, employing 10-15 million people, is already experiencing disruption from AI’s ordinary productivity effects: gross IT hiring fell from roughly 230,000 to 170,000 annually, TCS reported its first revenue decline in years, and Cognizant announced 12,000-15,000 layoffs, mostly in India. This reflects AI’s demonstrated capability rather than infrastructure spending, meaning a capex slowdown would not reverse it. Separately, foreign portfolio investors withdrew record capital from Indian equities in early 2026 amid global reallocation towards AI-exposed stocks, though a deepened domestic institutional base has absorbed much of the selling. In sum, the AI investment cycle represents an unprecedented capital commitment with returns still unproven and financing increasingly reliant on debt. While most analysts see a severe crisis as a tail risk, the fiscal and monetary tools available to contain one in the US and exposed economies such as India are more limited than during previous major financial disruptions.

The writer is a retired IAS officer, IIT Kharagpur engineer, and infrastructure leader. He writes on economics, technology policy and AI investment risks; Views presented are personal.