40 Trillion Dollar Debt Mountain Looms, but the AI Computing Power Infrastructure Boom Won't Stop! Trillion-Dollar Wave Extends the Life of the Super Computing Power Cycle

According to Financial Associated Press APP, a team led by Andrew Sheets, a senior strategist at Wall Street financial giant Morgan Stanley, recently stated that the U.S. government has accumulated about $40 trillion in federal debt. However, the soaring government debt has so far failed to halt the historic borrowing wave by AI-related tech companies or undermine the resilience of U.S. household spending.

This means, in the view of the Morgan Stanley strategist team, that the $40 trillion U.S. federal debt has not yet triggered a "credit squeeze" in the corporate and household sectors—especially as corporate leverage remains stable, bond issuance could still hit records, household debt burdens are lower than in 2000 and 2019, and increasingly robust consumer spending and corporate profits continue to offset the negative impact of rising interest rates/Treasury yields on U.S. and even global equity markets. The 6.2% yield on long-duration investment-grade U.S. bonds and the 30-year Treasury return, about 300 basis points above expected inflation, are consistently raising the opportunity cost of equities. What could truly end the “stocks and bonds both strong” pattern is not the debt level itself, but that higher financing costs might finally slow corporate earnings growth.

The $40 trillion wall of U.S. debt has not closed off corporate credit channels, meaning the AI compute supercycle currently faces a scenario of "rising financing costs" rather than "credit supply exhaustion." U.S. corporate debt as a percentage of GDP remains roughly the same as a decade ago and is significantly lower than pre-pandemic levels. Morgan Stanley still expects record corporate bond issuance this year; household debt accounts for about 67% of GDP, not at its peak, and consumption resilience is also supporting corporate earnings.

Therefore, hyperscalers, global top AI labs like Anthropic and OpenAI, and "new cloud" AI data center developers can still use investment-grade bonds, project financing, and private credit portfolios to closely link their nearly endless future Token revenue expectations and actual cash flow from AI product subscriptions, API calls, and cloud services, with robust orders throughout the AI compute supply chain—from GPU compute cabinets, HBM/DRAM/NAND storage components, data center CPUs and optical interconnect systems, to power infrastructure and liquid cooling systems. The total U.S. debt has topped $40 trillion, but has not yet formed the classic private sector "crowding out effect."

In other words, the $40 trillion debt has not ended the AI compute investment boom; on the contrary, it has pushed AI compute infrastructure construction into the second stage of "credit still open, but capital more expensive." Over the next 2-3 years, AI compute infrastructure is likely to remain in a high-intensity construction cycle.

With capital markets still relatively open, hyperscalers can issue investment-grade corporate bonds based on their balance sheets; AI labs can secure funding via equity capital, compute purchase commitments, and third-party guarantees; data center developers can use long-term leases, power purchase agreements, and project assets to obtain project financing or private credit. This funding ultimately converts into GPU, HBM, optical interconnect, power supply, and cooling system orders, with actual cash flow from AI subscriptions, API calls, and cloud services serving as the main source of debt repayment, rather than just aggressive future Token revenue forecasts.

Another Wall Street giant, Goldman Sachs, predicts that large tech companies will spend a cumulative $5.3 trillion in capex from 2025–2030. The share of debt financing for AI capex may rise from about 33% in 2026 to 35% in 2027; just in 2026, direct bond issuance by hyperscalers may reach about $250 billion, not including project financing. These forecasts and AI infrastructure trends show that the $40 trillion in U.S. debt has not closed corporate credit gates. Investment-grade bonds, project financing backed by long-term leases, private credit, and supplier guarantees continue to collectively drive the landing of compute assets.

$40 Trillion Debt Overhang, Yet Corporate Financing Flood Continues! Morgan Stanley: The Real Threat to U.S. Stocks Is Not Rates, But Earnings Slowdown

About half of the U.S. federal debt has been added in the past decade, while government debt-to-GDP ratios in major global economies have also increased. Except for the UK, fiscal deficits in various countries are expected to remain high for an extended period, keeping government borrowing levels elevated.

Despite rising benchmark rates and long-term Treasury yields, corporate balance sheets remain relatively resilient. U.S. corporate debt as a percentage of GDP has changed little compared to a decade ago and remains significantly lower than the pre-pandemic level. Meanwhile, Morgan Stanley’s credit strategist team continues to forecast a record amount of corporate bond issuance this year. Sheets noted that it is unrealistic to expect higher yields to halt the current unprecedented AI compute financing boom.

U.S. households remain financially healthy as well. Household debt is currently about 67% of U.S. GDP, lower than around 70% in 2000 and the record-high of 74% seen in 2019. Sheets pointed out that, even as rates and long-term Treasury yields continue to rise, household spending remains resilient.

For financial markets, the bigger and more serious question is whether yields/benchmark rates will ultimately rise enough to induce investors to shift large-scale asset allocation from equities to bonds. Morgan Stanley data show that the U.S. 30-year Treasury yield curve now offers yields about 300 basis points above expected inflation, while long-term U.S. investment-grade bond yields are at 6.2%.

So far, this capital rotation has not occurred on a large scale. Despite the U.S. 10-year Treasury yield rising about 50 basis points year-to-date, the S&P 500 benchmark index has still surged 13% this year. Driven by the AI boom since 2023, strong profit growth among technology companies continues to help equities compete against higher bond yields.

For the Morgan Stanley strategist team led by Sheets, the key question is whether equity market earnings can remain resilient in the face of persistently high borrowing costs. Should earnings growth slow significantly in the future, the relationship between the rising yields on long-term Treasuries and overall equity market valuation will become significantly more important for the markets.

Token Consumption Targeting 24x Growth, $10 Trillion Institutional Funds Spreading Along the AI Compute Bottleneck! $40 Trillion Debt Can't Close the Credit Expansion Gates

Goldman Sachs' latest $10 trillion institutional holdings report reveals not the complete fading of "AI faith," but that capital is starting to shift from core GPU leaders toward "irreplaceable infrastructure bottlenecks." Goldman’s data covers 991 hedge funds with $5.4 trillion in equity positions, and 504 large active mutual funds managing $4.6 trillion in equities.

Both types of capital are adding positions across institutional lines in bottleneck segments of the AI compute supply chain, such as Bloom Energy, Flex, Seagate Technology, etc.—spanning storage product lines, data center power chains, and server manufacturing. Nine out of ten top hedge fund holdings are AI-related, with Amazon leading for the 11th consecutive quarter; mutual funds remain about 100 and 60 basis points underweight in Nvidia and AMD relative to benchmarks, indicating AI trades haven’t hit overall market position limits, but potential further increases will require actual earnings rather than just theme-driven momentum.

The underlying logic of this dispersion and rotation is that AI is evolving from a “base compute chip super-boom” to a full-stack AI compute capital cycle: the increase in GPU/TPU/AI ASIC units drives up demand for servers, memory, enterprise-grade NAND storage components, Ethernet switches, optical modules, data center optical communications/optical interconnects, high-speed connectors, server rack rails, cooling, and power distribution. The larger the training and inference clusters, the greater the number of ports, the per-rack value, and the complexity of interconnections.

Morgan Stanley predicts that by 2028, nearly $3 trillion in AI-related infrastructure investment will flow through the global economy, with more than 80% of spending still ahead. Goldman Sachs’ latest estimates indicate that global AI capex will likely grow from an annual $765 billion in 2026 to $1.6 trillion in 2031, totaling approximately $7.6 trillion in capex from 2026 to 2031. U.S. data center power demand is expected to rise from 31GW in 2025 to 66GW in 2027, which will directly drive AI compute infrastructure spending into servers, CPUs, DRAM/NAND/HBM, advanced packaging, liquid cooling, power equipment, transformers, gas turbines, grid connection equipment, data center REITs, and engineering construction segments.

The core incremental demand for AI compute comes from the transition of AI from “answering questions” to “executing workflows”: Agents need to continually plan, call tools, verify results, and retry failures, so the Token consumption for a single task can be 10x, 20x, or even 50x that of a traditional chat query; world models will also expand the compute frontier from text to robots, industrial simulation, and physical systems. Goldman Sachs officially predicts global Token consumption could grow 24-fold by 2030 to 120 quadrillion per month; meanwhile, inference Token unit costs are dropping 60–70% annually, creating a “cost drop—application diffusion—total demand growth” Jevons Paradox effect.

The $40 trillion debt has not ended the AI compute investment boom but has instead pushed the industry into a second stage of "credit still open, but capital more expensive." The 30-year U.S. Treasury yield is about 5.23%, and long-term investment-grade corporate bond yields reach 6.2%, meaning every AI project must demonstrate its internal rate of return (IRR) can cover the continually rising cost of capital; consequently, subsequent capital will not indiscriminately flood all concept stocks, but will be prioritized for AI compute industry leaders with long-term orders, free cash flow, pricing power, and critical bottleneck positions, including GPU clusters, HBM/DRAM/NAND storage, data center high-speed networks and optical interconnects, and new cloud vendors. Should Token monetization continue to deliver, the corporate financing flood will extend the AI capex cycle; should earnings slow or long-term yields spike chaotically under “bond vigilante” pressure, highly leveraged new cloud vendors and projects reliant on external guarantees will face double hits to valuation and credit first.