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President Donald Trump’s drive to make the United States the world’s AI leader could deliver faster innovation and geopolitical leverage, but it is also concentrating growth in expensive data centers and a narrow group of technology stocks. A Los Angeles Times analysis published Oct. 25, 2026, describes a plausible route from an AI-market correction to broader economic stress: high valuations fall, construction spending slows, borrowing costs remain high and government has less room to respond. That is a risk scenario, not a prediction that a crash is imminent. The same analysis notes that voters already view the economy poorly, making the stakes higher as the 2026 midterm elections approach.
What is Trump’s AI strategy, and why does it matter to the economy?
Trump’s approach prioritizes speed, U.S. dominance and a race toward superintelligence over negotiating new guardrails for advanced systems. He has also supported a large data-center buildout. That combination links national technology policy to construction, energy demand, chip supply, financial markets and the federal budget.
Speed over new international guardrails
The Los Angeles Times analysis says Trump has dismissed warnings about catastrophic AI risks and rejected calls to negotiate advanced-model safeguards with Chinese President Xi Jinping. The strategic objective is to stay ahead, but a faster race can increase spending before companies or regulators have resolved reliability, security and profitability problems.
A buildout with wider economic effects
Data centers require chips, servers, networking equipment, electricity, metals, land and financing. Their demand can support growth and technological capacity, while also competing with other users for scarce components and power. Bipartisan public opposition to some projects reflects those local costs, even when the national policy goal is AI leadership.
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Could an AI-stock bubble hurt the wider economy?
Yes, because the market’s exposure is unusually concentrated. The Los Angeles Times reported that companies dependent on AI made up roughly 70% of the Nasdaq 100’s weight in 2026. A sharp repricing would therefore affect more than a small technology niche, although the size and timing of any decline remain uncertain.
What the warnings actually say
Harvard economist Kenneth Rogoff said a stock-market decline of 30% or more was plausible and could produce recession and widespread financial distress in an economy dependent on AI. He described that as a scenario, not a forecast that such a fall is underway. Ray Dalio warned that AI-stock prices appeared detached from fundamentals, while Citadel founder Ken Griffin said AI was “almost certainly” contributing to a bubble.
High valuations become more dangerous when investors, lenders and businesses depend on continued appreciation to justify new capacity. If expectations change, companies can delay projects, reduce hiring and cut orders at the same time. That synchronized pullback is the channel through which a market correction could reach suppliers and communities far beyond the largest AI firms.
How much recent U.S. growth depends on data centers?
Independent analyses reported by the Los Angeles Times found that data-center infrastructure spending accounted for nearly all recent U.S. growth during the preceding winter and spring. Without that spending, growth would have been only a fraction of a percentage point. The finding does not mean every dollar of growth came from AI, but it shows how little momentum could remain if construction slows abruptly.
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Construction spending boosts measured output immediately through building activity and equipment purchases. The longer-term payoff depends on whether the resulting computing capacity produces enough productivity and revenue to justify its cost. If projects continue while demand or returns disappoint, the economy can be left with heavy debt and underused facilities rather than a durable productivity lift.
Which costs could make the AI buildout more fragile?
Several pressures can raise the cost of building and financing data centers at the same time:
- Tariffs: Duties can increase prices for chips, servers, networking gear, metals and electrical components.
- Labor constraints: Visa restrictions can make it harder or more expensive to hire specialized workers needed for construction and operations.
- Deficits and Treasury yields: Large federal borrowing can push up long-term rates, increasing the cost of financing projects and refinancing existing debt.
- Debt-service pressure: Higher interest rates leave highly leveraged companies and households with less cash for investment or consumption.
- Energy and grid competition: Rapidly expanding facilities can consume power and transmission capacity that other users also need.
These factors reinforce one another. A project that looked viable with cheap financing and low equipment costs can become marginal when tariffs rise, rates stay high and construction takes longer.
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Are companies finding profitable uses for AI?
Adoption is uneven, and operational friction can undermine the investment case. The Los Angeles Times reported that Microsoft, Meta and Walmart were scaling back some AI use because employees spent time correcting errors, cleaning poor-quality output and dealing with security issues.
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Delft University of Technology economist Servaas Storm said frontier AI laboratories lacked a credible path to profitability. That assessment does not deny that AI can create valuable products; it questions whether current spending levels can be supported by near-term cash flow. If businesses require extensive human checking, the productivity gains may arrive more slowly than investors expect.
Why are voters worried while AI investment surges?
Public sentiment is already weak. A Fox News poll cited by the Los Angeles Times found that 77% of Americans rated the economy fair or poor. A CNBC survey put approval of Trump’s handling of the economy at 34%; 35% of Republicans in that survey disapproved.
The same report said inflation had reached 3.7% under Trump, compared with 2.9% at the end of Joe Biden’s presidency and a 9% peak in 2022. Those figures describe different points in time and do not by themselves establish why prices moved. They do show why households may be sensitive to a policy that appears to prioritize a capital-intensive technology race while borrowing costs and everyday prices remain concerns.
Is Trump’s AI gamble making a recession more likely?
It could increase vulnerability, but the available evidence does not establish that a recession is inevitable or provide a reliable date. The risk comes from the interaction of concentrated stock valuations, growth tied to construction, expensive financing, limited fiscal room and uncertain AI returns.
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Rogoff warned that Trump could “easily run into a financial crisis before his term is up,” while Stanford finance professor Darrell Duffie stressed that “financial crises, by their nature, are virtually impossible to predict.” Duffie’s point is crucial: analysts can identify exposures and transmission channels without knowing whether, or when, they will combine into a crisis.
What should observers watch before the midterm vote?
The most informative signals will be whether the AI expansion is broadening the economy or becoming more dependent on ever-higher spending:
- AI companies’ earnings guidance and evidence that customers are achieving measurable productivity gains.
- Data-center cancellations, delays or sharply reduced capital-expenditure plans.
- Changes in long-term Treasury yields and the interest expense carried by heavily indebted firms.
- Orders for chips, servers and electrical equipment, which can reveal whether the buildout is accelerating or cooling.
- Credit-market stress, layoffs among suppliers and weaker construction activity outside the largest technology companies.
- Progress on power and transmission projects, along with community resistance that could slow permitting.
Trump’s strategy offers a clear promise: move faster than rivals and capture the economic benefits of advanced AI. Its vulnerability is equally clear. If market expectations outrun business results while debt, tariffs and interest rates raise the cost of expansion, the same concentration that made AI a growth engine could amplify a downturn.
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Can an AI boom raise GDP even if many AI companies lose money?
Yes. Building data centers and buying equipment add to economic output before the technology generates profits. Sustained gains, however, require those investments to produce enough productivity, revenue and tax receipts to cover operating and financing costs.
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What would show that AI-market problems are spreading to households?
Look for simultaneous layoffs among technology suppliers, tighter business lending, weaker construction employment and rising household delinquencies. A falling stock index alone would indicate financial-market stress, not necessarily an economy-wide crisis.
Are poor economic polls proof that a recession is coming?
No. Polls measure perceptions and approval, while recession determinations rely on production, employment, income and spending data. Persistently negative sentiment can still affect elections and consumer decisions even when output continues to grow.
Which policy trade-off is at the center of the AI debate?
Moving quickly may strengthen U.S. technological and geopolitical position, but fewer guardrails can leave more safety, security and financial risks unresolved. Slower, better-coordinated investment could reduce those risks while potentially giving competitors more time to catch up.
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