Stock prices imply AI coding gains worth 3.6% of GDP, economists estimate

The GDP figure rises to 6.5% when faster software engineering also speeds up research and development. The authors say the effect on productivity and GDP had more than doubled by mid-2026, as coding agents such as Claude Code and Codex spread.

The numbers are market expectations, not measured output. The method gives a real-time, forward-looking reading, but it depends on how closely investors' beliefs match what happens. For scale, the authors note that earlier GDP forecasts range from about one percent over a decade to scenarios with growth rates many times higher.

The authors reason that if AI makes software engineers more productive, firms that spend more of their payroll on engineers should gain more profit, and stock prices should react more strongly to AI news. They run a two-stage regression: first each firm's sensitivity to an AI stock index, then how that sensitivity varies with software engineering payroll share, controlling for industry and firm size. A model then converts the slope into a productivity gain.

The baseline slope is 1.24 and stays similar when the authors add controls for firms' exposure to generative AI (1.28), other AI indexes, standard financial ratios and finer industry groupings. Year-by-year regressions from 2022 through 2025 are stable. A coefficient-stability test suggests unobserved differences between firms do not explain the pattern.

To get from software engineering productivity to GDP, the authors apply a modified Hulten's theorem and include software-producing firms, whose costs also fall. Adding a task-based model with an explicit AI sector and a full input-output network changes the result only moderately. Adding an R&D sector roughly doubles the GDP effect.

The paper contrasts its approach with studies that start from occupation-level AI exposure. Those face the problem that large task-level gains often shrink at the firm level, for example more coding activity but smaller increases in projects and releases. Because stock prices already reflect investors' beliefs about how tasks combine into jobs, the authors say they sidestep that aggregation step.