On September 29, 2026, U.S. President Donald Trump met at the White House with leaders of the artificial intelligence (AI) industry, including Mark Zuckerberg (Meta) and Elon Musk (SpaceX). At the meeting the participants highlighted AI’s transformative potential — from personalized medical research and treatments to large productivity gains — while the administration framed winning the AI race as central to future growth.
At the same time, private-sector investment in AI has surged to such an extent that it is already affecting how the U.S. government finances its debt and is adding to macroeconomic pressures that could slow growth in the short and medium term.
Macro pressures: inflation, deficits and debt
Consumers feel inflation more than other macro indicators. The article identifies three primary upward pressures on prices: fuel-price increases linked to tensions around the Hormuz Strait and U.S.–Iran relations; persistently high food costs partially tied to climate-related production issues; and rising electricity prices driven in part by AI’s large energy demands.
Key macro figures highlighted are:
- budget deficit around 6 percent of GDP;
- public debt at roughly 123 percent of GDP;
- 10-year Treasury yield at 5.23 percent;
- annual government interest payments equal to about 3.5 percent of GDP.
Although nominal GDP growth offsets some effects, these indicators nonetheless represent a heavy fiscal burden.
Policy response: promises and market operations
U.S. officials have combined a message of short-term sacrifice with active market measures. Policymakers argue that accepting a difficult near-term period is worthwhile if the U.S. secures leadership in AI and reaps later productivity gains. Practically, Treasury and Federal Reserve actions include modifying bond issuance and purchases — selling or refraining from issuing certain long-term paper while operating on the short end of the yield curve — to manage the unusually high 10-year yield.
Private-sector crowding out on the bond market
The AI investment boom has driven major technology firms to seek large amounts of long-term financing in bond markets. As Hold Alapkezelő founder Szabó László noted, investment in AI today is comparable, as a share of GDP, to large-scale historical infrastructure efforts: he cited roughly 3 percent of GDP allocated to AI development versus 2.5 percent of GDP historically directed to major rail projects. If the U.S. GDP is approximately $30 trillion and companies spend around $1 trillion on AI, that is a very large investment wave.
Analysts warn that the largest tech firms — Google, Microsoft, Meta, Amazon, Oracle, Anthropic and OpenAI — are competing with the U.S. government for bond investors. Beschenbacher Kornél of SPB Befektetési Zrt. observes global upward pressure on yields and notes that AI companies, pricing in strong growth, can offer higher returns and are thus less rate-sensitive.
In practice, the biggest "hyperscalers" tend to promise 5.5–6 percent on long-term debt, infrastructure builders may offer 6.5–7 percent, and lower-rated industry players can attract investors with 7–9.5 percent yields on international markets. The result is an inversion of the textbook case: private borrowers are crowding out the sovereign on the bond market.
Sectoral spillovers and risks
Higher yields and redirected capital are squeezing other sectors: real estate investments become less attractive, renewable-energy projects face funding headwinds, and many software firms also struggle to secure financing. Cybersecurity companies may fare relatively better because growing AI deployment increases security demand.
At the same time, AI investments represent potential future growth: winning the AI race could generate substantial productivity improvements that offset short-term costs.
Effects on consumption and the global reserve system
Higher yields make saving relatively more attractive than consuming, which could dampen household spending and slow growth. The dollar’s role as the principal global reserve currency has also declined — from about 70 percent of global reserves two decades ago to roughly 56 percent now — reducing the automatic external demand for U.S. paper. Market participants are reallocating into gold, cautiously into crypto, and diversifying reserves more broadly — a ‘‘balkanization’’ of reserve portfolios.
Political and geopolitical risks
The article flags geopolitical risks tied to political instability. If President Trump becomes politically constrained after midterm elections, the risk of bolder, riskier foreign-policy moves could grow. Trade tensions and tariff measures also risk weakening the U.S. position internationally, sometimes strengthening rivals like China by demonstrating perceived unreliability as a partner. In turn, inability to secure energy suppliers or to stabilize regions could undermine both the dollar’s role in global oil trade and U.S. influence.
Possible policy outcomes
Traditional responses to large public debt are rapid growth, fiscal consolidation, or tolerating higher inflation to erode real debt burdens. Each option carries risks, particularly when deficits are large, growth is shaky and interest costs are significant.
AI is likely a genuine transformational technology rather than a speculative bubble akin to the dot-com era. Still, the sector’s scale means even modest investor disappointment could destroy hundreds of billions of dollars. It remains uncertain whether first movers, the highest-quality providers, or lower-cost followers (for example abroad) will ultimately prevail — and whether the long-term gains from AI will fully compensate for the current fiscal strains and market distortions.
