The economic payoff from artificial intelligence remains contested, with major Wall Street institutions questioning whether the technology’s rapid corporate adoption has translated into measurable gains for the U.S. economy.
Analysts at Goldman Sachs wrote that the impact of AI on U.S. gross domestic product in 2025 was “basically zero,” arguing that large language models, chatbots, and related systems did not materially contribute to the country’s officially recorded 2.2% GDP growth. The assessment, led by Goldman economist Joseph Briggs, challenges the narrative that AI is already functioning as a broad-based growth engine.
The claim lands at a sensitive moment when equity markets have poured capital into AI infrastructure providers and application-layer firms, and valuations across parts of the technology sector imply expectations of substantial productivity gains. If the measurable macroeconomic impact remains negligible, questions about a potential AI-driven market bubble are likely to intensify.
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Goldman’s position highlights a distinction between capital spending and realized productivity. While technology companies are committing hundreds of billions of dollars to AI infrastructure, those outlays do not automatically translate into immediate gains in output per worker or overall GDP growth.
Other financial institutions, including Morgan Stanley and JPMorgan Chase, have expressed similar caution. Analysts at those firms have noted that much of the near-term economic benefit from AI-related investment may accrue to manufacturing economies in Asia rather than the United States.
Massive data center expansion plans from Amazon, Google, and Microsoft require advanced semiconductors, servers, cooling systems, and networking hardware. Analysts estimate that roughly three-quarters of projected Big Tech capital expenditures could directly support GDP growth in Taiwan and other Asian technology manufacturing hubs, where much of the hardware supply chain is concentrated.
This geographic distribution complicates measurement. When U.S. firms import high-value chips and components, the domestic GDP effect may be muted even if corporate revenues and market capitalizations rise.
President Donald Trump has argued that AI investments are supporting U.S. economic growth. At the same time, successive administrations have sought to reduce reliance on semiconductor production in Asia through domestic manufacturing incentives and export controls.
Yet reshoring complex chip fabrication ecosystems remains capital-intensive and time-consuming. Despite federal initiatives, a substantial share of advanced semiconductor manufacturing capacity remains abroad, limiting the immediate domestic multiplier effect of AI infrastructure spending.
State-level regulatory initiatives aimed at governing AI development have also drawn scrutiny from some industry voices, who argue that excessive constraints could dampen investment. Others counter that regulatory clarity is necessary to sustain long-term growth and public trust.
Bubble concerns and productivity skepticism
The number of investors warning of a potential AI bubble appears to be rising. Corporate executives have acknowledged that AI is not an automatic productivity accelerant and that integrating large language models into workflows requires retraining, process redesign, and ongoing oversight.
A recurring economic concern centers on labor substitution. Some observers argue that replacing human workers with software-based systems could have second-order effects if displaced employees reduce consumption and tax contributions. While such outcomes remain speculative, they underscore the importance of distinguishing between firm-level efficiency gains and economy-wide income distribution.
Analyst Joseph Politano has suggested that AI’s macroeconomic contribution, while meaningful, has been overstated. He estimated that chatbots and large language models accounted for roughly 0.2 percentage points of last year’s 2.2% GDP growth — a fraction of headline expansion but not insignificant. However, because much of the supporting infrastructure is imported, isolating AI’s net contribution within national accounts remains challenging.
Joe Brusuelas, a tax advisor and economist, said AI’s economic effects may require future revisions as data improves. He described the current debate as an attempt to interpret incomplete signals, with analysts “trying to peer through the fog to understand what is driving growth.”
Short-term lag, long-term potential
Historically, general-purpose technologies — from electricity to the internet — have exhibited productivity lags. Significant investment often precedes measurable gains, as complementary innovations and organizational changes take time to diffuse across industries.
AI may follow a similar trajectory. Early spending is heavily concentrated in infrastructure and experimentation, while widespread productivity effects depend on integration into healthcare, finance, manufacturing, logistics, and professional services. If AI tools remain primarily assistive rather than transformative, macroeconomic gains could stay modest in the near term.
The tension between soaring equity valuations and muted GDP impact reflects this timing mismatch. Markets price expected future cash flows, while GDP measures realized output within a specific period.
What the data is suggesting is that AI’s direct contribution to official U.S. growth statistics in 2025 was limited. Thus, 2026 is expected to redefine the trajectory – erasing the bubble concern.