JPMorgan: AI is not a job killer, but a productivity multiplier; demand expansion is the key to employment

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Morgan Stanley points out that while the diffusion of AI is far faster than historical technological revolutions, the labor market still exhibits unusual stability, with AI currently acting more as an augmentor than a substitute. (Summary: Who says AI coins are FET? In the real machine economy, the only winner is USDC) (Background: Morgan Stanley: Tariff wars could cause tech stocks like TSMC to plummet by 20%, suggests taking profits first) The latest research from Morgan Stanley Chief Economist Seth B. Carpenter provides a sobering dose of reality to the collective anxiety currently surrounding AI. He positions artificial intelligence as the sixth major wave of innovation, following mechanization, electrification, mass production, automation, and the IT revolution, and points out a core contradiction: the diffusion speed of AI far exceeds any previous technological revolution in history, yet labor market indicators in major global economies show "unusual stability." From employment growth and unemployment rates to job openings and quit rates, these core data points have not shown systemic divergence between industries with high AI exposure and those with low AI exposure. In his research, Carpenter argues that current evidence better supports the thesis that "AI is an augmentor, not a substitute." Looking back at technological leaps since the Industrial Revolution, each wave has been accompanied by deep-seated fears of "machines replacing humans." The Luddites smashing looms in the early 19th century, the fear of automation in the 1960s, and the concerns over the disappearance of white-collar jobs during the early dot-com bubble of the 1990s were all proven by history to be overreactions. In the report, Carpenter emphasizes that while these technologies did replace certain specific tasks and positions, the more prevalent impact was the reshaping of the composition of work rather than the disappearance of work itself. Mechanization shifted agricultural labor into factories, electrification gave birth to a massive service sector, and the IT revolution fostered entirely new professions such as programmers and data analysts. After each technological transition, the total demand for labor did not shrink; instead, it continued to expand on a broader industrial base. He points out in the report that a frequently overlooked cognitive bias is that many people understand AI as "using fewer people to achieve the same output," but the same mechanism also implies that "the same number of people can create much more output." While the two formulations are mathematically equivalent, Morgan Stanley tends to believe the latter is more likely to become reality. Behind this is the expansion of aggregate demand driven by productivity gains—as the costs of goods and services fall, consumers' real purchasing power rises, which in turn generates new demand and stimulates employment. Based on existing data, Carpenter believes there is reason to remain cautiously optimistic. At the labor market level, indicators such as employment growth, unemployment rates, job openings, and quit rates have not shown systemic divergence between high-AI-exposure and low-AI-exposure industries. The rise in youth unemployment is often cited as evidence of AI's impact on jobs, but if the cyclical factors of the overall slowdown in U.S. hiring are excluded, the excess rise in youth unemployment is only slightly higher than what historical cyclical patterns would predict, and does not constitute a structural anomaly. At the productivity level, the effects of AI have begun to emerge in the data. Labor productivity growth is faster in high-AI-exposure industries, but the key point is: this growth stems primarily from the accelerated expansion of output, rather than from compressed working hours or staff reductions. This distinction is crucial—it indicates that AI currently plays more of an "augmentor" role than a "substitute" role. Companies are using AI tools to improve the productivity of existing employees rather than directly laying them off. Despite the reassuring early data, Carpenter clearly points out that future trends remain highly uncertain. Unlike previous technological revolutions that unfolded slowly over decades, the adoption speed of AI has significantly compressed the adjustment cycle—this is the most significant structural difference in this wave of innovation. He proposes a scenario that requires high vigilance: if companies quickly realize the productivity gains from AI in the short term, and this effect spreads widely across the entire economy, the unemployment rate could see a recession-like spike—at least until the labor market clears. This "flash-freeze" adjustment would pose severe challenges to social stability and distributive equity. However, Carpenter also lists multiple buffer mechanisms: income growth driven by productivity will support aggregate demand; rising wealth effects will sustain consumption; new tasks and roles will emerge within companies to absorb displaced labor; cyclical slowdowns in employment and the resulting deflationary pressure will trigger monetary policy easing; and if monetary policy space is exhausted, there are "automatic stabilizers" (such as unemployment benefits and progressive income taxes) and "discretionary fiscal tools" that can be deployed to help smooth the business cycle. He argues that the existence of these buffer mechanisms will make the unemployment shock driven by AI "smaller, shorter, and more controllable." Carpenter also notes that the actual diffusion speed

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