Rodney Sullivan, Executive Director of the Mayo Center of Asset Management at the Darden School of Business (University of Virginia), has published an analysis on the emergence of a productivity boom in the United States, which he mainly attributes to the adoption of artificial intelligence in work processes.
In the fourth quarter of 2025, the real GDP growth in the United States is estimated at 4.2%, driven by a significant increase in domestic private investment. This performance is accompanied by a structural characteristic: it is not based on strong job creation. The workforce is growing slowly due to demographic dynamics, a slowdown in migration flows, and the constraints of returning to the labor market post-COVID. As a result, production is increasing faster than the labor input, which precisely defines an increase in productivity.
Technology companies have invested around an additional $180 billion compared to the previous year in software and research and development. Unlike previous waves of IT investment, such as the Internet revolution of the 1990s, which mainly improved information flow, current AI systems directly enhance cognitive and operational tasks: drafting initial versions, code generation, logistical optimization, customer support. Economically, AI functions as a form of evolving work, allowing a given worker to supervise and deploy much larger production than before.
Rodney Sullivan highlights that the increase in productivity has not yet led to widespread labor market tensions. Layoffs remain contained, and unemployment benefit claims are moderate. Workers are shifting towards supervisory, coordination, and judgment functions, while machines take care of routine tasks. The transition is smooth: machines complement human work rather than massively replacing it, at least at this stage.
This dynamic has constructive macroeconomic implications. Growth driven by productivity alleviates inflationary pressures by limiting unit labor costs, allowing real incomes to increase without constraining the Federal Reserve to raise rates.
When productivity growth outpaces wage growth, unit labor costs moderate—this is the most benign form of disinflation: prices stabilize because supply becomes more efficient, not because demand collapses. For companies, this mechanism supports margin expansion through operational leverage, especially in non-labor-intensive sectors.
Sullivan notes that productivity gains are more sustainable than those from temporary pricing power and are more widely distributed across sectors. This provides a stronger foundation for stock valuations compared to a technology boom focused on large market capitalizations. Productivity gains related to AI are expected to increasingly benefit companies that utilize the technology, not just those that create it, arguing for a broader performance beyond the narrow circle of large technology stocks.
Sullivan identifies an counterintuitive tension: strong GDP growth coexists with a more challenging career entry market. When companies can increase production through technology rather than by increasing their workforce, recruitment becomes more selective. Entry-level positions that served as learning grounds for tasks like drafting, data cleaning, basic analysis, are precisely where AI excels. The premium now goes to profiles capable of exercising judgment, integrating information, and supervising automated productions.
Sullivan emphasizes, “These are the first steps of an AI-driven growth regime. Not as a science fiction scenario, nor as an employment apocalypse, but, for now, as a set of tools enabling companies to extract more value from the workers they already have. The productivity gains emerging today suggest that AI has crossed this threshold, moving from promise to production.”
