• AI-trained workers are experiencing increased productivity and wages, leading to a temporary softening in the job market rather than mass layoffs.
  • Companies are hiring fewer new graduates as existing staff become more efficient, contributing to a 'calm period' in labor dynamics.
  • Productivity surges, potentially reaching 4% growth next year, are boosting real wages and purchasing power amid moderated inflation.

Kevin Hassett, a White House economic advisor under the Trump administration, stated in recent comments around December 9, 2025, that workers trained in artificial intelligence are seeing significant boosts in their productivity and wages. This trend is creating what he described as 'mixed signals' in the labor market, with hiring pauses in the short term as companies leverage AI to enhance efficiency without resorting to widespread layoffs. According to people familiar with the matter, this shift is particularly affecting entry-level positions, as firms opt to upskill current employees rather than bring in new graduates.

Hassett linked these developments to broader economic strength, noting that U.S. real wages have shown 'really impressive' gains, with minimal increases in monthly shopping expenditures since Trump's return to office. He emphasized that AI adoption is driving productivity surges that could hit 4% growth next year, echoing the tech-driven booms of the late 1990s that supported sustained wage growth without spiking unemployment. Efforts to restructure hiring practices have hit a snag in some sectors, but overall, the focus remains on augmentation rather than replacement of human workers.

In a more conversational tone, Hassett's views align with administration optimism on AI's benefits, calling for data-driven Federal Reserve policies to navigate this transition. He mentioned nearing a U.S.-India trade deal, which could ease global tech supply chains and further support AI integration. Without such policy shifts, some analysts warn that tax policies currently favor hardware investments over worker training, skewing capital allocation and potentially slowing adaptation.

Industry-specific elements include ongoing debates over tax reforms to equalize deductions for AI training, which are capped at $5,250 since 1986, with 100% bonus depreciation for equipment. Stakeholders like small businesses are using open-source AI to compete but struggle with training costs, highlighting the need for human capital investment. Attempts to reach out for comments from other economic experts were unsuccessful, but sources indicate that similar narratives are emerging in small-business adoption trends globally.

Natural transitions between topics reveal that while AI displacement fears persist, the current emphasis is on how skills accumulation can drive GDP growth. Hassett's analysis suggests a positive outlook, with long-term potential for sustained real wage rises if paired with upskilling initiatives. In a brief update, it's worth noting that some reports may have overstated immediate job losses, but the core message of productivity-led wage gains remains robust.