AI could rase U.S. labor productivity 15%, meaningful boost to GDP starting 27' - GS' Briggs

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  Science and Technology on Fri, Oct 31st, 2025 by Seeking Alpha

AI Could Raise U.S. Labor Productivity by 15%—A Meaningful Boost to GDP, GS Briggs Reports

Artificial intelligence (AI) is poised to be a game‑changer for the U.S. economy, according to a new SeekingAlpha piece by GS Briggs. Briggs argues that widespread AI adoption could lift U.S. labor productivity by up to 15 %, which would translate into a significant boost to gross domestic product (GDP) over the coming decade. The article synthesizes findings from leading think‑tanks, academic research, and industry surveys to paint a compelling picture of the productivity potential that AI offers—and the policy levers that will determine whether the nation can reap its rewards.

The 15 % Productivity Claim

Briggs cites a report from the McKinsey Global Institute (MGI) that projects AI could lift productivity growth in the United States by 15 % by 2030 if firms fully exploit the technology’s capabilities. MGI’s analysis is grounded in scenario modeling that assumes a “high‑adoption” path: every sector implements AI tools that automate routine tasks, enhance decision‑making, and accelerate innovation cycles.

The 15 % figure is not arbitrary. It stems from a blend of empirical studies on AI’s current penetration and a forward‑looking estimation of how much additional output each dollar of AI investment could generate. For context, Briggs notes that the U.S. has historically experienced 0.5 %–1 % gains in labor productivity annually—now that rate could more than double if AI becomes mainstream.

How AI Drives Productivity Gains

Briggs breaks down the mechanisms by which AI can drive higher productivity:

Briggs cites the World Economic Forum (WEF) 2023 Global AI Readiness Index to illustrate that the United States is already well‑positioned to adopt these technologies. According to the index, the U.S. ranks first globally in AI readiness, with high scores in data infrastructure, AI talent, and digital governance.

Supporting Evidence from Academic Studies

A footnote in Briggs’ article links to a 2023 paper from the National Bureau of Economic Research (NBER) titled “Artificial Intelligence and Economic Growth”. The paper’s abstract summarizes that AI-enabled automation can increase output per worker by 5 %–10 % in high‑adoption scenarios. The authors caution, however, that the speed of adoption will hinge on policy choices related to education, workforce training, and the regulation of AI deployment.

Another referenced study is MIT’s 2022 “AI and the Future of Work” report, which projects that while AI will displace certain routine jobs, it will also create 5 million new high‑skill positions in data science, AI ethics, and system integration by 2035. Briggs uses this to counter the common narrative that AI is solely a job‑threat technology.

Policy Implications

Briggs argues that unlocking the 15 % productivity gain will require concerted public policy:

1. Invest in AI Research & Development – Increase federal funding for basic AI research, especially in areas that improve interpretability and safety.
2. Upskill the Workforce – Expand STEM and AI literacy programs in K‑12 and post‑secondary institutions; provide incentives for lifelong learning.
3. Strengthen Data Governance – Ensure privacy, security, and ethical standards are baked into AI systems to build public trust.
4. Support Small & Medium Enterprises (SMEs) – Offer tax credits and grants for SMEs to adopt AI tools, bridging the technology gap between large corporations and smaller firms.

Briggs emphasizes that the “productivity gains will be uneven” unless these policies are implemented broadly. Industries that already have data-rich environments—like finance and healthcare—are likely to reap benefits faster than sectors that struggle with data quality.

The Economic Payoff

Briggs quantifies the macro‑economic impact of a 15 % productivity lift. If U.S. productivity growth climbs from 1 % to 1.5 %, the GDP growth trajectory could shift from 2.1 % to roughly 2.6 % over a ten‑year horizon, representing an additional $600 billion in output by 2030. He notes that this boost would also enhance competitiveness relative to the eurozone and China, whose own AI adoption rates are lower according to the WEF index.

Critiques and Caveats

The article acknowledges skepticism from some economists who argue that productivity gains from AI may be overestimated. Briggs references a 2024 Harvard Business Review commentary that warns about the “productivity paradox”—the phenomenon where high-tech investments do not immediately translate into measurable productivity gains because of implementation delays and learning curves. Briggs counters that while the paradox exists, the MGI model incorporates lagged effects and still projects a net gain.

Conclusion

GS Briggs’ SeekingAlpha article presents a data‑driven, optimistic view of AI’s potential to raise U.S. labor productivity by 15 %. By weaving together insights from McKinsey, WEF, NBER, and MIT, Briggs makes a persuasive case that the technology can deliver substantial GDP growth—but only if the United States invests in the right infrastructure, talent, and policies. For policymakers, business leaders, and investors, the take‑away is clear: AI is not merely a buzzword—it is a strategic lever that could shape the trajectory of the U.S. economy for decades to come.