AI’s overlooked $97 billion contribution to the US economy

First-quarter 2025 GDP was down an annualized 0.5%. Labor productivity growth ticked up a respectable but hardly transformative 2.3% in 2024, following a few lean years of gains and losses. Is AI overhyped?

Only if you look exclusively at GDP. Our research, with Felix Eggers, widens the lens and finds that Americans already enjoyed roughly $97 billion in “consumer surplus” from generative AI tools in 2024 alone. Consumer surplus—the difference between the maximum a consumer is willing to pay for a good or service and its actual price—is a more direct measure of economic well-being than GDP. 

Generative AI’s $97 billion in consumer surplus dwarfs the roughly $7 billion in U.S. revenue recorded by OpenAI, Microsoft, Anthropic and Google from their generative AI offerings last year. It doesn’t appear in GDP because most of the benefit accrues to users rather than the companies.

Economists have heard this story before. Personal computers failed to improve measured productivity significantly for nearly two decades after they were introduced to office desks. As Robert Solow famously quipped in 1987, “You can see the computer age everywhere but in the productivity statistics.” ChatGPT reached 100 million users in two months, yet productivity still behaves as if it were 2015—when the AI chatbot didn’t even exist.

There are structural reasons for the lag. Translating a flashy demo into organization-wide workflows requires new software, retraining and—most crucially—an overhaul of management practices. 

In the short run, many firms pay twice: first for the AI software and then for employees to learn how to use it. Payoffs often come later, through complementary investments such as redesigned supply chains or revised legal processes. The costs are counted today; many benefits arrive tomorrow, leading to a productivity J-curve.

The larger issue is conceptual. GDP captures the value of most things bought and sold. But with few exceptions, free goods are invisible in the GDP numbers, even if they make consumers better off. 

When a consumer takes advantage of a free-tier chatbot or image generator, no market transaction occurs, so the benefits that users derive—saving an hour drafting a brief, automating a birthday-party invitation, tutoring a child in algebra—don’t get tallied. That mismeasurement grows when people replace a costly service like stock photos with a free alternative like Bing Image Creator or Google’s ImageFX.

To bridge the gap, we developed a measure, GDP-B (B for benefits), in our forthcoming paper with Erwin Diewert, Mr. Eggers and Kevin Fox. Rather than asking what people pay for a good, we ask what they would need to be paid to give it up.

In late 2024, a nationally representative survey of U.S. adults revealed that 40% were regular users of generative AI. Our own survey found that their average valuation to forgo these tools for one month is $98. Multiply that by 82 million users and 12 months, and the $97 billion surplus surfaces.

William Nordhaus calculated that, in the 20th century, 97% of welfare gains from major innovations accrued to consumers, not firms. Our early AI estimates fit that pattern. While the consumer benefits are already piling up, we believe that measured GDP and productivity will improve as well. History shows that once complementary infrastructure matures, the numbers climb.

Tyler Cowen forecasts a 0.5% annual boost to U.S. productivity, while a report by the National Academies puts the figure at more than 1% and Goldman Sachs at 1.5%. Even if the skeptics prove right and the officially measured GDP gains top out under 1%, we would be wrong to call AI a disappointment. Life may improve far faster than the spreadsheets imply, especially for lower-income households, which gain most, relative to their baseline earnings, from free tools.

As more digital goods become available free, measuring benefits as well as costs will become increasingly important. The absence of evidence in GDP isn’t evidence of absence in real life. AI’s value proposition already sits in millions of browser tabs and smartphone keyboards. Our statistical mirrors haven’t caught the reflection. The productivity revolution is brewing beneath the surface, but the welfare revolution is already on tap.

Mr. Collis is an assistant professor at Carnegie Mellon University’s Heinz College of Information Systems and Public Policy. Mr. Brynjolfsson is a professor at Stanford and co-chairman of Workhelix, a company that assesses machine-learning opportunities.