In Nevada, a summer of weak international tourism has stunted hiring and weakened the local economy. But a data-center construction boom tied to the fast-growing artificial intelligence industry has helped to soften the blow.
In the Washington D.C., area, federal job cuts and the longest government shutdown on record have threatened to push the regional economy into a recession. But there, too, A.I.-related investments are helping to offset the damage.
And in North Dakota, low oil prices have idled drilling rigs and dented state revenues, but A.I. data centers are helping to fill the gap.
The U.S. economy in 2025 is split in two: Everything tied to artificial intelligence is booming. Just about everything else is not.
A.I. developers and chipmakers are raking in hundreds of billions of dollars in investments. Data centers the size of theme parks are sprouting around the country. Utilities are racing to build new power plants and to bring old ones out of retirement to meet surging electrical demand. Workers with the right skills — from the developers building A.I. models to the electricians wiring the facilities that run them — are commanding premium salaries.
In the rest of the economy, the picture is different. Unemployment has risen, hiring has slowed and industries including manufacturing and home building are cutting jobs. Consumer sentiment has slumped amid high prices. The public sector has been weighed down by budget cuts and federal layoffs. Tariffs, and the uncertainty surrounding them, have been a drag on international trade and led to slower investment by many companies.
“It’s a two-track economy,” said Mark Muro, an economist at the Brookings Institution who has studied the effect of A.I. on local economies. “This A.I. gold rush is generating all the excitement and papering over a drift in the rest of the economy.”
By one measure, investments in computer equipment and software accounted for more than 90 percent of growth in gross domestic product in the first half of the year. And while economists caution against taking those numbers at face value — if it weren’t for A.I., some of those dollars would have flowed elsewhere — they say there is no doubt that A.I. investments help explain the economy’s surprising resilience this year.
But reliance on A.I. as a source of growth poses a question for the economy: What happens if the gold rush stops?
That threat is most obvious in the stock market, which has set record after record in recent months largely on the strength of a handful of A.I.-focused companies. Seven companies, including Amazon, Microsoft and Alphabet, the parent company of Google, now make up well over a third of the value of the S&P 500 index. Just one of the so-called Magnificent Seven, Nvidia, which makes the chips that power many of the most advanced large language models, recently, though briefly, topped $5 trillion in market value.
Such lofty valuations are predicated on assumptions that recent rapid growth will continue for years, which some investors have warned could prove unrealistic. Even Sam Altman, the chief executive of OpenAI, said in August that he thought investors were too excited about A.I. A strong quarterly earnings report from Nvidia on Wednesday failed to fully calm those doubts.
A bursting of the bubble — whenever it happens — could have real-world implications. Consumer spending in recent quarters has been increasingly driven by high-income households, who have continued to shop even as many lower-income families have pulled back. But if the stock market stumbles, wealthy households, too, might pare down their spending.
“If spending growth is being dominated by households that have benefited significantly from the performance of A.I.-related names in the stock market, then an equity sell-off could be really quite painful for the economy,” said Aditya Bhave, a U.S. economist at Bank of America. “That does create a degree of fragility.”
Lower-income households haven’t seen the benefit of the stock-market run-up. But they could still be hurt by its reversal. If wealthy Americans spend less on restaurant meals, vacations and luxury goods, that could lead to job losses in the service sector.
“If you see a pullback in spending in leisure, in hospitality, in that top cohort, that has knock-on effects,” said Michael Reid, an economist at RBC Capital Markets. “That’s where I wonder about a downward spiral in the labor market.”
Ripple Effects
For now, the boom — and its lift to the broader economy — shows little sign of letting up. U.S. companies spent more than $60 billion on computer equipment in the second quarter, up 45 percent from a year earlier. They spent another $10 billion on data center construction, up 35 percent. Artificial intelligence probably accounts for most of that growth, economists and industry experts say.
Those investments are rippling outward, propping up pieces of the economy outside the traditional technology sector. Caterpillar, typically associated with its bulldozers and backhoes, has seen a surge in sales of its turbines and power-generation equipment, which are used in data centers. Johnson Controls, another industrial conglomerate, has benefited from demand for its cooling and fire-suppression systems. Eaton Corp., which makes power-management systems, has a growing backlog of projects waiting for its equipment.
The companies are betting on that growth to continue. Eaton is investing more than $1 billion to expand its manufacturing capacity to meet soaring demand, and this month it said it was spending $9.5 billion to acquire Boyd Thermal, which makes cooling equipment used in data centers.
“We are in the very early innings of the A.I. build out,” Paulo Ruiz, the chief executive officer of Eaton, said in an interview.
The two-track nature of the economy is especially clear in the construction industry, which has been battered by high interest rates and tariffs. Nonresidential construction spending fell in August, continuing its downward trend. Home building is well below its pandemic-era peak.
Yet even as the construction industry’s momentum has stalled, data center construction has swelled. That has also fueled the construction of infrastructure required to power the data centers and distribute information. The American Cement Association released a report in June that estimated that A.I. data centers would use approximately one million metric tons of cement over the next three years.
Data center construction is “the only real driver of nonresidential construction spending growth in America,” said Anirban Basu, the chief economist at Associated Builders and Contractors, a trade group.
“If one is looking for what’s growing nonresidential spending, it’s data centers and attendant investment in power generation and distribution,” he said.
The surge has also buoyed the contractors who work on the components of data centers. Associated Builders and Contractors recently said that about one in seven of its members were under contract to work on data centers.
Chuck Goodrich, the chief executive of Gaylor Electric, an electrical contractor based in Indianapolis, said 50 to 60 percent of his business was now tied to data center construction. He estimated Gaylor Electric’s revenue would increase about 30 percent this year, to $1 billion, and grow another 20 percent next year, largely because of the frenzy around data centers.
“From a biblical perspective, we happen to be living the life of abundance,” he said.
Uneven Effects
That abundance isn’t reaching everyone, however, or everywhere. The companies developing leading A.I. models, like OpenAI and Google, are mostly headquartered in the San Francisco area, and their highest paid employees are concentrated in the large “superstar” cities that have been the winners in the 21st century economy.
Investments in A.I. infrastructure are far more spread-out, but the benefits are much less certain. Some of the largest data centers are being built in rural areas, where land is cheap and jobs have been disappearing for decades. Local leaders in many communities have embraced data centers and other A.I. infrastructure as a way to diversify their economies and get a toehold in a burgeoning industry.
But data centers voraciously consume water and electricity, which can drive up costs for residents. And while they create jobs during construction, they employ relatively few people once they are operational, turning the sprawling campuses into humming ghost towns. Microsoft said, for instance, that its data centers employ about 50 people per building over a 24-hour period.
Many big data-center companies, known as “hyperscalers,” have won generous tax incentives from local communities, limiting their benefit to residents. And while local leaders argue the projects will attract other technology companies, it isn’t clear that will actually happen, said Mr. Muro, the Brookings economist.
“Some places have gotten confused by a lot of messaging from the hyperscalers that this is the beginning of a regional technology economy,” he said. Instead, he added, “These become just massive buildings with a couple hundred jobs, which aren’t terrible, but aren’t going to really move the dial.”
Still, many economists and industry experts say the infrastructure-building phase of the A.I. boom has room to run. Even as data center capacity expands at a record pace, demand is growing just as quickly, and companies are reporting yearslong backlogs. The demand for infrastructure is likely to keep growing, said Paul Ashworth, the chief North America economist at Capital Economics.
“It’s really only just beginning to get going,” he said. “The stock market may be close to a bubble,” he added, but there is little sign that the industry has bought too many chips or built too many data centers.
For these investments to pay off, however, artificial intelligence will need to fulfill its promise not just as a useful tool, but as a transformational technology that leads to huge increases in productivity.
If that doesn’t happen?
“A lot of the investment that has been put in place might turn out to be unjustified,” said Mr. Bhave, the Bank of America economist.
Ben Casselman is the chief economics correspondent for The Times. He has reported on the economy for nearly 20 years.
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