Goldman Says Both AI Bulls and Bears Are Overestimating the Economic Impact of Big...

The economic impact of the artificial intelligence investment boom is being overstated by both its biggest supporters and its most skeptical critics, according to Goldman Sachs, which says that the surge in spending by Big Tech is neither contributing as much to U.S. economic growth nor crowding out as much investment elsewhere as market narratives suggest.

The debate has intensified as the second-quarter earnings season shows little sign that major technology companies are preparing to slow their AI spending. Companies including Microsoft, Alphabet, Amazon and Meta Platforms continue to commit enormous sums to data centers, advanced chips, networking equipment and other infrastructure needed to develop and deploy AI systems.

For AI optimists, the spending is a powerful new source of economic growth. The construction of data centers, purchases of semiconductors and expansion of electricity infrastructure are now being cited as evidence that AI has become a major contributor to U.S. GDP.

Bearish investors, meanwhile, believe that the AI investment boom is absorbing capital and resources that could otherwise support other parts of the economy. They worry that technology companies are concentrating an unusually large share of corporate investment in a sector whose eventual financial returns remain uncertain.

Goldman Sachs economists say both interpretations go too far.

"While media reports and market commentary often claim that AI is making a very large contribution to US GDP growth but also crowding out a great deal of other activity, our analysis in prior work and above suggests that both claims are exaggerated," Goldman U.S. economist Jessica Rindels wrote in a note on Tuesday.

The distinction matters because the amount companies spend on AI infrastructure does not translate directly into an equivalent contribution to domestic economic output.

A significant portion of the equipment being purchased by U.S. technology companies is manufactured overseas. Imports therefore reduce the amount of domestic value added captured in GDP calculations, even when U.S. companies are spending heavily on the equipment.

That means the headline figures for AI capital expenditure can give the impression of a larger direct economic contribution than the national accounts ultimately record.

Goldman's analysis also takes into account the indirect effects of the investment boom, including the resources required to support rapidly expanding data-center capacity.

"We estimate that accounting for the indirect effects of AI-roughly $50bn of incremental crowding-out in 2026 from the three channels above, positive stock market wealth effects on consumer spending, and the hit to real income and consumer spending from higher electricity and other prices-would shave about 0.1pp off of the impact on 2026 GDP growth," Rindels said.

The estimate points to a more complicated economic transmission mechanism than simply treating AI investment as an additional source of growth.

Data centers require enormous amounts of electricity, while the expansion of AI infrastructure is increasing demand for power generation, transmission equipment, and other resources. Higher demand can put upward pressure on electricity and other prices, affecting households and businesses outside the technology sector.

At the same time, the concentration of capital in AI does not necessarily mean that investment elsewhere is being displaced on a one-for-one basis. Technology companies are drawing on substantial cash flows and capital-market resources to finance their AI programs, while the broader U.S. economy remains capable of supporting investment in other sectors.

There is also a wealth effect working in the opposite direction. A sustained rise in technology stocks and other assets linked to the AI boom can increase household wealth and support consumer spending, partially offsetting some of the negative effects of higher infrastructure and energy costs.

This helps explain why Goldman does not see AI investment as either an enormous standalone boost to GDP or an investment vacuum that is starving the rest of the economy of capital.

The distinction could become increasingly essential for investors. Big Tech's capital expenditure plans are now large enough to influence demand across semiconductor manufacturing, construction, power generation, utilities, networking equipment and data-center infrastructure. But the ultimate economic payoff will depend on how efficiently those investments translate into revenue and productivity gains.

For companies such as Microsoft, Amazon, Alphabet and Meta, the central question is therefore shifting from how much they are willing to spend to how much economic and financial output that spending ultimately produces.

The enormous cost of training and running AI models has also made infrastructure efficiency increasingly important. Companies are investing in increasingly powerful processors and specialized systems while seeking to improve utilization rates and reduce the amount of electricity required for each unit of computing.

Goldman's assessment suggests that the AI boom should not be judged simply by the size of corporate capital-expenditure budgets. A large investment number can coexist with a relatively modest direct contribution to GDP when much of the underlying equipment is imported, while the broader economic effects can spread through electricity prices, construction, labor demand, financial markets and consumer spending.

The same analysis also challenges the bearish argument that AI spending is necessarily crowding out a comparable amount of investment elsewhere.

In other words, the AI boom is economically significant, but its impact is more nuanced than the more polarized market debate suggests. The investment surge is generating activity across multiple industries, while its direct contribution to measured U.S. output is constrained by the structure of the supply chain and the imported content of much of the infrastructure.