Reassessing Singapore’s Sovereign Exposure to the American AI Bubble

I believe a debt-financed capital loop is inflating an American AI bubble to a scale the underlying cash flow does not support.  I believe that bubble sits inside an economy that no longer carries the fiscal reserves it once had to absorb a correction.  I believe the same debt trajectory is pushing Treasury yields to levels the market itself is beginning to call structural rather than cyclical.  I believe China’s chip constraints, intended to slow its AI progress, instead forced a more efficient architecture that may prove the more durable advantage.  I am making the case that Singapore’s sovereign funds should reassess their exposure accordingly.

A circular financing loop, estimated at over US$800 billion, links Nvidia, AI laboratories, and cloud providers in a structure that books related-party spending as independent revenue.  OpenAI alone has committed US$1.15 trillion through 2035 against a projected US$14 billion loss in 2026.  Nearly 40 per cent of the S&P 500’s market capitalisation is now under direct AI influence.  Lucent, Nortel, 1929, and 2008 each followed the identical pattern: concentrated leverage mistaken for collateral, collapsing once independent cash flow failed to materialise.  Federal debt has crossed US$40 trillion, and the Federal Reserve raised rates in September 2026 rather than cutting them, removing the monetary cushion that absorbed the 2001 dot-com correction.  The 10-year Treasury yield crossed 5 per cent the same month, and every Treasury intervention attempted in 2026 produced only temporary relief before yields resumed climbing.  Chip sanctions forced Chinese laboratories toward architectures requiring a fraction of the compute of Western models, at a fraction of the cost.  US data centres already consume as much electricity as Ireland’s entire national grid, with household electricity bills already rising to fund the buildout.  The top 1 per cent of US households now hold 31.7 per cent of national wealth, concealing fragility beneath resilient-looking aggregate consumer data.  Each section below closes with a specific implication for Singapore’s funds.

Part One: The Capital Loop Feeding an Unsustainable Bubble

Nvidia invests billions into AI laboratories such as OpenAI and Anthropic.  Those laboratories sign compute contracts with cloud providers, including Microsoft, Oracle, and Amazon Web Services.  Those providers spend a large share of that revenue buying chips back from Nvidia.  Cash leaves Nvidia’s balance sheet as an investment.  It returns as revenue, having toured through two or three other balance sheets along the way.  Analysts have identified over US$800 billion moving through this loop, and deals of this scale fuel circularity concerns.

OpenAI has committed US$1.15 trillion across seven vendors through 2035, while projecting a US$14 billion loss for 2026, nearly triple the prior year’s loss.  Big Tech tripled annual AI capital spending from US$150 billion in 2023 to over US$500 billion in 2026.  Nearly 40 per cent of the S&P 500’s market capitalisation is under direct AI influence, a concentration ratio without precedent outside the dot-com peak.  Forward price-to-earnings on the index is 22.85 times, within a fraction of the 2020 peak of 23.11.

Revenue booked from a related-party compute contract is not the same as revenue earned from an independent customer with an independent reason to keep paying.  When one node in this loop misses a covenant or writes down an asset, the loss does not stay contained to that balance sheet.  It removes demand from every counterparty whose revenue projections assumed the spending would continue uninterrupted.

Temasek Holdings reported a net portfolio value of S$518 billion, US$401 billion, for the year ended 31^st March 2026.  The United States accounts for 26 per cent of that portfolio.  AI-related exposure is 6 per cent and is committed to rise to 10 to 15 per cent by 2031.  Temasek Holdings holds stakes in both OpenAI and Anthropic, and was, per Reuters, in active discussions to increase its OpenAI position further.  Chief Executive of Temasek Global Investments Chia Song Hwee has told reporters overvaluation in AI is “unavoidable” and that nobody, Temasek Holdings included, can predict when a correction arrives.  Temasek Holdings has declined to disclose specific stakes or their effect on overall performance, leaving me unable to independently assess how concentrated this exposure has become beneath the reported 6 per cent figure.  I believe a portfolio review distinguishing genuine end-user revenue from circular vendor-financed revenue should precede any further allocation toward the stated target.

Part Two: The Historical Parallel, and the Write-Down Cascade

Lucent Technologies and Nortel Networks ran an almost identical loop during the dot-com era, lending customers money to buy their equipment and booking the proceeds as revenue on both sides.  When real demand failed to match financed demand, both collapsed in the same downturn.  Lucent’s market value fell from over US$258 billion at its 2000 peak to a fraction of that within two years.  Nortel filed for bankruptcy in 2009.

The 1929 crash carried the same architecture in a different sector: margin-financed speculation, concentrated in a narrow set of favoured stocks, collapsing once credit tightened.  The 2008 crisis ran the identical script through mortgage-backed securities.  Lehman Brothers held US$639 billion in assets at its collapse, and the resulting deleveraging wiped out an estimated US$19.2 trillion in US household wealth by 2011.  Every one of these episodes shares the same signature: concentrated leverage, mistaken for collateral, until the underlying cash flow failed to materialise.

MAS has built genuinely sophisticated AI governance infrastructure.  The MindForge AI Risk Management Toolkit, released in April 2026, was developed with 24 institutions, including DBS, OCBC, UOB, GIC, and Temasek, alongside Nvidia, AWS, Google Cloud, and Microsoft.  Director Alan Lim of MAS’s Financial Infrastructure and AI Office described it as moving institutions “from theory to practice.”  This framework governs operational AI risk, how banks deploy AI internally, not the separate question of what happens to Singapore’s banking balance sheet if the AI sector itself suffers a credit event.  GIC led Anthropic’s US$30 billion Series G round, valuing the company at roughly US$380 billion.  GIC took a leading role in financing a private AI company at a valuation built on projected revenue rather than demonstrated cash flow.  I believe MAS’s stress-testing framework for DBS, OCBC, and UOB should explicitly model an AI-sector credit event as a distinct scenario, separate from MindForge’s operational governance work.

Part Three: Why America Cannot Absorb This the Way It Absorbed the Dot-Com Crash

The dot-com correction in 2001 hit an economy with far more fiscal room.  Federal debt was below 60 per cent of GDP, and the government ran a surplus as recently as fiscal year 2000.  That room no longer exists.  National debt has crossed US$40 trillion.  The Congressional Budget Office projects debt held by the public rising from 101 per cent of GDP this year to 120 per cent by 2036.  Net interest reached US$963 billion over ten months of fiscal 2026, US$3.18 billion a day.

The Committee for a Responsible Federal Budget estimates that if yields stay just 80 basis points above baseline, annual interest payments reach US$2.7 trillion by decade’s end, exceeding combined Medicare and Social Security spending.  The Federal Reserve raised rates on 16^th September 2026, its first hike since 2023, because inflation had run above target for years and the central bank had no room to accommodate a fresh shock stacked on its own.

Singapore held US$280 billion in US Treasury securities as of April 2026, according to US Treasury International Capital data, separate from GIC’s estimated US$936 billion to US$1.16 trillion and Temasek Holdings’ S$518 billion portfolio, both carrying further undisclosed US exposure.  Singapore’s Ministry of Finance will not disclose GIC’s exact assets, because doing so would “amount to publishing the full size of Singapore’s financial reserves,” what it calls Singapore’s “war chest.”  Then MAS Managing Director Ravi Menon described the reserves’ three functions in 2019: a crisis buffer, an income stream, and a foundation for exchange-rate confidence.  A Treasury market seizing up around an AI-sector credit event does not merely threaten the market value of these holdings.  It threatens the function they exist to perform during a crisis.  GIC’s 20-year annualised real return fell to 3.4 per cent as of March 2026, a six-year low.  I believe MAS and MOF should explicitly model a US Treasury market dysfunction scenario tied to an AI-sector credit event, before market conditions force the question.

Part Four: The Structural Yield Problem

The 10-year Treasury yield crossed 5 per cent in September 2026.  The 30-year yield reached its highest level since 2001 the month before, with auction demand weaker than dealers expected.  The Treasury Borrowing Advisory Committee flagged a US$1.45 trillion funding shortfall for fiscal 2027 to 2028, a warning from the dealers who underwrite these auctions themselves.

Treasury Secretary Scott Kenneth Homer Bessent attempted three separate interventions across 2026: a joint yen operation with Japan structured to avoid selling Treasuries outright; a bond buyback funded by selling short-dated debt; and a described strategy of trading on “asymmetric information,” a hedge fund framing applied to sovereign debt stewardship.  Every intervention produced a temporary rally followed by a return to, or a breach of, the prior yield level within weeks, itself evidence that this is structural rather than cyclical.

I want to credit GIC before I criticise it.  Its 2025/26 annual report confirmed increased allocations to “commodities, gold, and infrastructure” specifically “to enhance inflation resilience,” and it overhauled its entire investment framework this year, replacing a 65/35 equities-bonds reference portfolio it had run for thirteen years with a Strategic Portfolio built around growth, uncertain rate navigation, and inflation resilience.  GIC’s stated reasoning names “a changing world order, rising fiscal risks, and advances in artificial intelligence,” the same three forces I have argued throughout this paper.

Here is the contradiction I believe needs resolving.  GIC’s equities allocation rose to 51 per cent in the year to March 2025, up from 46 per cent the year before, even as its report acknowledged high valuations “provide a challenging backdrop for forward returns.”  GIC has confirmed it “invested selectively” in AI, in companies it believes have “enduring value beyond short-term market enthusiasm,” an acknowledgement that it distinguishes durable value from speculative enthusiasm and has chosen to increase exposure regardless.  Global SWF, an independent tracker, has noted GIC’s reporting “has grown more opaque, now lacking specific asset class mix disclosures” that earlier reports provided.  I cannot verify what share of the 51 per cent equity allocation carries AI concentration, nor how the real assets increase compares in scale to the simultaneous equity increase.  I believe GIC and MAS should resolve this contradiction directly, and should, at minimum, internally restore the granular disclosure that Global SWF notes is no longer available.

Part Five: Why China’s Chip Constraints Handed It the Efficiency Advantage

US chip sanctions, intended to slow Chinese AI progress, instead forced a different kind of innovation.  DeepSeek released its R1 model in January 2025, matching OpenAI’s o1 on multiple benchmarks at a reported training cost under US$6 million, using export-controlled H800 chips.  Nvidia lost approximately US$589 billion in market capitalisation on 27^th January 2025, the largest single-day value loss in stock market history.

DeepSeek’s V3 model cost US$5.58 million to train, a 5.5 per cent fraction of GPT-4’s reported cost.  By 2026, this pattern spread across Alibaba’s Qwen, Moonshot AI’s Kimi, and Zhipu AI’s GLM.  GLM-5, released in February 2026, became the first frontier-scale model trained entirely on Huawei’s Ascend 910B chips, without a single Nvidia GPU.  DeepSeek founder Liang Wen Feng described the underlying constraint in a July 2024 interview: “We have to consume twice the computing power to achieve the same results ...  Our goal is to continuously close these gaps.”  His team closed them through architecture, sparse Mixture-of-Experts routing, FP8 low-precision training, aggressive context caching, cutting inference costs by 90 to 97 per cent against comparable Western models, not through acquiring more hardware.  In a world where US data centre electricity demand is projected to reach 325 to 580 terawatt-hours by 2028, an architecture requiring a tenth of the compute for equivalent output is the actual efficiency frontier, not a footnote to it.

Singapore’s National AI Strategy update, unveiled by Minister for Digital Development and Information Josephine Teo Li Min at ATxSummit on 20^th May 2026, names “efficient AI computing” as a stated priority.  Yet Singapore committed over S$1 billion in AI research funding, S$150 million to the Enterprise Compute Initiative, and S$37 billion under RIE2030, while OpenAI committed S$300 million to its first overseas Applied AI Lab here and Nvidia opened its second Asia-Pacific research lab here.  There is a stated commitment to efficiency, alongside deepening operational dependence on the same Nvidia supply chain already exposed to the rare earth export controls.  Singapore’s published strategy does not yet specify how efficiency translates into a concrete hedge against this dependency, as opposed to simply meaning lower costs within an unchanged hardware relationship.  I have also looked at AiRTS Pte.  Ltd., a Singapore-founded company building a patented enterprise product called GenAI Twin, as an example of genuine domestic AI intellectual property.  AiRTS operates at the application layer, addressing hallucination and output consistency in deployed models, not at the foundational training layer where DeepSeek’s efficiency gains are.  It is evidence of a capable domestic ecosystem.  It is not a substitute for the hardware diversification I believe the national compute strategy needs.

Part Six: The Domestic Precedent

Singapore imposed a de facto data centre moratorium from 2019 to 2022, after data centres reached 7 per cent of national electricity consumption, projected to climb to 12 per cent by 2030 without intervention.  Rather than simply lifting the ban, Singapore rebuilt the approval framework around efficiency as the primary gate.  The Data Centre Call for Application 2, launched in December 2025, requires every approved facility to achieve a Power Usage Effectiveness of 1.25 at full load, at least 50 per cent green power sourcing, and liquid cooling for at least 60 per cent of IT load, standards that significantly outpace regional benchmarks in Hong Kong, Japan, and South Korea.

This standard has not repelled capital.  Amazon Web Services pledged S$12 billion under this framework.  Google committed US$5 billion.  Jurong Island will host a 700-megawatt low-carbon data centre park.  Singtel’s Nxera unit is building a 58-megawatt facility backed by a US$476 million green loan.  Keppel plans to double its power capacity by 2030.

I believe any Singapore-linked data centre or compute cluster development outside our borders, in Johor, in Indonesia, or in any regional partnership our capital participates in, should be held to the same PUE and green energy standard we already enforce domestically.  A Singapore-backed facility built to a looser regional standard carries higher exposure to both rare earth cost inflation and water and power constraints, for a lower return on the same capital, than a facility built to our proven standard.  The domestic policy already exists.  I believe we should simply apply it consistently to where our capital builds next.

Part Seven: Unmanaged Data Centre Growth is a Quantified American Risk

PJM Interconnection’s capacity auction clearing price rose from US$28.92 per megawatt-day in the 2024/25 delivery year to US$329.17 for 2026/27, a factor of more than ten.  Data centres accounted for 63 per cent of that increase, translating to roughly US$9.3 billion passed to ratepayers.  New Jersey residents saw bills jump 17 to 20 per cent in a single year.  Virginia’s Dominion Energy implemented its first base-rate increase since 1992, adding US$8.51 a month to a typical household bill, in a state where data centres already account for 40 per cent of total electricity consumption.  Utilities requested US$18.6 billion in rate increases in the first half of 2026 alone, on pace to exceed 2025’s record US$29 billion.  Seven major technology companies signed a voluntary Ratepayer Protection Pledge in March 2026, committing to cover their grid costs.  The pledge carries no legal weight and cannot override existing tariff structures.

I believe Singapore should present this specific, quantified American contrast, not the general principle of sustainability, in every regional and international forum where data centre policy is discussed.  Our DC-CFA2 framework mandates efficiency before approval is granted.  America’s experience shows precisely what happens without that mandatory gate.

Part Eight: The Hollowed-Out Middle Class is a Financial Risk to Any AI-Exposure Model

The top 1 per cent of US households held 31.7 per cent of national wealth in the third quarter of 2025, the highest share since the Federal Reserve began tracking this in 1989.  That 1 per cent held US$55 trillion, close to the combined wealth of the bottom 90 per cent.  Wage growth for higher-income households reached 3 per cent in December 2025, against 1.1 per cent for lower-income households.  Moody’s Analytics found the top 10 per cent of households drove 49.2 to 49.7 per cent of consumer spending by mid-2025, though this figure has been disputed; the Bureau of Economic Analysis’s data show the top 10 per cent by disposable income responsible for only about 20 per cent of spending across 2004 to 2022.  I believe this dispute itself is evidence worth acting on: if credentialed economists working from the same Federal Reserve data cannot agree on how concentrated spending has become, the aggregate consumer spending figure is less reliable as a signal of broad economic health than most headline coverage suggests.  The wealth concentration data carries no comparable dispute, sourced directly from the Federal Reserve’s Distributional Financial Accounts: the top 1 per cent held 29.2 per cent of aggregate wealth in Q4 2025, against 5.3 per cent for the entire bottom half.

I believe any Singapore-based fund or institution modelling US consumer demand resilience should explicitly disaggregate spending data by wealth decile rather than relying on the aggregate figure, since that aggregate materially overstates broad-based economic health in an economy this concentrated, and since the same equity concentration directly transmits into this same consumer spending pattern through the wealth effect.

My Conclusion

I have traced one argument across eight parts.  A circular capital loop, an absent fiscal cushion, a structural yield problem, and a hollowed consumer base sit underneath an American AI bubble our sovereign funds are increasing exposure to, even as our central bank’s risk framework does not yet cover the specific scenario that exposure creates.  China’s constrained path produced a structurally more efficient alternative.  I believe Singapore’s institutions should reassess this exposure with the data, not with the assumption that scale alone protects against a correction nobody, including Temasek Holdings’ and GIC’s leadership, claims to be able to time.

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Terence Nunis | Executive Chairman, Equinox Zenith | Author, The 1% Playbook: The Billionaire Cheat Code