• Kevin Hassett, a senior Trump adviser and potential Fed chair candidate, argues AI is creating a "huge positive supply shock" that pushes U.S. underlying productivity growth to 2.5%-3% annually.
  • He cites recent strong GDP growth alongside weaker hiring and higher unemployment as evidence that AI is boosting output per worker, allowing firms to produce more with fewer new employees.
  • The productivity surge, which Hassett says affects about 85% of the economy, could enable the Federal Reserve to tolerate lower unemployment and stronger growth without aggressive interest rate hikes.

Kevin Hassett, a senior economic adviser to former President Donald Trump and one of several finalists for the Federal Reserve chair role, has been making waves in policy circles with a bold claim: artificial intelligence is driving a productivity revolution that could reshape America's economic landscape. In recent public and private remarks, Hassett has argued that U.S. underlying productivity growth has accelerated to 2.5%-3% annually because of AI, comparing the current moment to the late-1990s tech boom that transformed the economy.

"What we're seeing is a massive productivity shift across most of the U.S. economy," Hassett said in comments that have circulated among policymakers and market participants. "Around 85% of the economy is experiencing this transformation." His assessment comes as the Trump administration has promoted aggressive AI development, eased regulations, and encouraged large data-center and AI-infrastructure investments, including initiatives like the "Stargate" project that represent one of the largest private capital investment cycles in recent memory.

Hassett frames AI as a "huge positive supply shock" that pushes output capacity up and price pressures down, allowing what he calls "strong growth without needing high interest rates." This perspective marks a notable shift from earlier concerns among some economists that AI-driven investment booms might actually fuel inflation through data-center energy demand and capital expenditure surges. Instead, Hassett emphasizes the deflationary, capacity-expanding effects that he believes mirror Alan Greenspan's interpretation of the 1990s productivity miracle.

The economic data provides some support for his thesis. Recent GDP figures show robust growth even as hiring has weakened and unemployment has ticked higher—what Hassett describes as "mixed signals" in the job market. According to people familiar with his thinking, this pattern suggests firms are temporarily producing more with fewer new workers because AI has raised worker efficiency. Research cited in policy discussions shows notable declines in employment for young entry-level coders since AI coding tools became widespread, though Hassett maintains this displacement will be temporary as new spending channels create fresh opportunities.

"We're in a quiet time for hiring, especially for new graduates," Hassett acknowledged in one exchange, according to sources who heard the remarks. "But this is part of the adjustment process as companies integrate these powerful new tools." His comments represent a rare explicit acknowledgment from within the Trump camp that AI can replace entry-level jobs, a concern more frequently voiced by academics and tech critics than by pro-AI policymakers.

Market participants are watching closely how this productivity narrative might influence monetary policy. Hassett's 2.5%-3% productivity estimate, if accepted by the Federal Reserve, could support arguments for keeping interest rates lower for longer even amid strong growth. "The Fed should recognize this positive supply shock," he has said in private discussions, according to people familiar with the matter. This stance aligns with the administration's broader pro-trade, pro-tech agenda, which includes efforts to finalize a trade agreement with India that could further integrate AI development across borders.

Not all economists are convinced by the precision of Hassett's numbers. Many agree AI has the potential to substantially raise productivity but caution about pinpointing a precise trend rate so early, noting measurement lags and sectoral unevenness. Still, if trend productivity truly stabilizes near 2.5%-3%, that would represent a significant step-up from the post-2008 productivity stagnation, with implications for higher sustainable real wages, fiscal capacity, and equity valuations.

As the debate continues, Hassett's remarks add fuel to ongoing discussions about AI's distributional effects—gains in aggregate productivity versus concentrated job losses in specific occupations. The Trump administration has rejected the idea of federal bailouts for AI firms, emphasizing market-driven investment rather than public backstops, even as it promotes the technology's development. For now, the productivity question remains central to understanding whether America is entering another transformative era like the 1990s, or facing a more complex adjustment with winners and losers spread unevenly across the workforce.