Morgan Stanley: Compared with previous technological revolutions, AI is spreading faster, but there are "no signs of overwhelming job losses yet."

Is artificial intelligence the engine of a productivity revolution or a disruptor of the job market?

Morgan Stanley’s Chief Economist Seth B Carpenter gives a cautious and evidence-based judgment in his latest research: AI is the sixth major wave of innovation since the Industrial Revolution, and its spread is much faster than before, but current employment data has yet to show signs of any systemic impact.

Carpenter points out that despite rapid leaps in AI's computing power and ability, and continued increase in enterprise adoption rates, overall labor market indicators exhibit an "unusually high degree of stability." Key indicators such as employment growth, unemployment rate, job vacancies, and quit rate all do not show a significant weakness in high-AI-exposure industries compared to low-exposure industries. At the same time, AI's productivity boost is already starting to appear—industries with high AI exposure have stronger labor productivity growth, mainly driven by accelerated output, not layoffs or reduced hours.

However, Morgan Stanley also warns that AI is spreading much faster than any previous technological revolution in history, which means adjustment dynamics will be greatly compressed. The largest productivity dividends have not yet been realized; the disappearance of some jobs and hiring contractions are almost unavoidable, and policy response capacity will be the key variable determining the depth and duration of employment shocks.

Historical precedent: Every technological revolution triggered employment panic

Carpenter positions AI as the sixth major wave of innovation, following mechanization, electrification, mass production, automation, and the IT revolution. He notes that every one of these revolutions triggered concerns about mass unemployment, but the end result without exception was: productivity rises, work tasks are reorganized, total labor demand expands.

These technologies did eliminate some tasks and jobs, but the more common effect was to change the composition of work, not eliminate work itself. Carpenter believes AI follows the same logic—it reduces the cost of specific tasks, but whether it can eliminate entire professions remains unknown.

He particularly points out a cognitive misperception: many people understand AI as "doing the same output with fewer people," but the same mechanism also means "the same number of people can create much more output." The two statements are mathematically equivalent, but Morgan Stanley leans towards the latter being more likely.

Current data: Productivity improvements driven by output, not layoffs

Based on current data, Carpenter believes there is reason to be cautiously optimistic. At the labor market level, employment growth, unemployment rate, job vacancies, and quit rate show no systematic divergence between high-AI-exposure and low-AI-exposure industries.

The rise in youth unemployment rate is often cited as evidence of AI’s impact on jobs, but Carpenter points out that if cyclical factors behind the general slowdown in US hiring are removed, the excess rise in youth unemployment is only slightly higher than what historical cycles suggest, and does not constitute a structural anomaly.

In terms of productivity, AI's effect is already starting to show up in the data. Labor productivity growth is faster in industries with high AI exposure, but crucially, this growth mainly comes from accelerated expansion in output, not reductions in work hours or personnel. Carpenter stresses that this distinction is critical—it shows AI currently plays more of a "booster" than a "replacement" role.

Biggest risk: The spread exceeds the economy’s capacity to adapt

Although early data is reassuring, Carpenter makes it clear that future trends remain highly uncertain. Unlike previous technological revolutions which unfolded slowly over decades, AI’s adoption speed has greatly compressed the adjustment cycle, which is the most significant structural difference of this wave of innovation.

He presents a scenario worthy of vigilance: If companies quickly realize AI's productivity benefits in the short term, and this effect spreads widely across the economy, unemployment rates may jump similarly to an economic recession—at least until the labor market reaches equilibrium.

However, Carpenter lists several buffering mechanisms: productivity-driven income growth will support overall demand; rising wealth effects will sustain consumption; new tasks and roles will emerge within companies to absorb replaced labor; cyclic employment slowdowns and resulting deflationary pressures will spur monetary policy easing; and if monetary policy space is exhausted, automatic stabilizers and discretionary fiscal tools can help smooth income gaps during the transition. He believes these buffers will make AI-driven unemployment shocks "smaller, shorter, and more manageable."

Infrastructure bottleneck: Over three-quarters of capital expenditure has yet to be deployed

Carpenter also points out that the real speed of AI's spread will be constrained by the progress of physical infrastructure building. Morgan Stanley strategists previously estimated that from 2025 to 2028, total capital expenditure on data centers and related infrastructure will exceed $3 trillion, but only about one-quarter has been deployed so far.

This means AI's biggest impact on productivity and the job market is still largely "in the future." The pace of infrastructure construction will directly determine how quickly AI capacity penetrates the real economy, thus affecting the timing window for job market adjustments.

Carpenter concludes that Morgan Stanley will continue to track the speed of AI’s spread, labor market evolution, and policy responses. "History shows that productivity ultimately prevails, but not everyone in society can share the gains equally. Early evidence is encouraging, but the story is still being written."

Risk warnings and disclaimerThe market has risks; investment requires caution. This article does not constitute individual investment advice, nor does it take into account individual users' specific investment objectives, financial situations, or needs. Users should consider whether any opinions, viewpoints or conclusions in this article are appropriate for their particular circumstances. Invest at your own risk.