U.S. Employment Change by Occupation Type
U.S. Employment Change by Occupation Type
Experts expect blue-collar employment to be 4.3% above its January 2025 level by 2030 — the strongest of the three groups.
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Our Analysis
How do we interpret these results
However, the median expert predicts 2% growth in white-collar jobs between January 2025 and December 2030. This is significantly slower than the pre-existing trend, which predicts 6.8% growth. 25% of experts predict more than 5% white-collar job gain (close to baseline), meaning over 75% of experts predict slower growth than recent trends. Moreover, 25% of experts predict more than 4% white-collar job loss by 2030.
Forecasts by group
Median forecasts from each group
| group | 2027 | 2030 |
|---|---|---|
| Experts | 3 pp | 4.3 pp |
| Superforecasters | 3.8 pp | 6 pp |
| Public | 3 pp | 4.5 pp |
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* Denotes non-overlapping bootstrapped confidence intervals between group aggregates.
Rationale analysis
The main lines of reasoning driving the spread in estimates
White-collar jobs
Most high-forecast respondents (those predicting more growth) believe white-collar employment will adapt rather than collapse: “Revolutionary technologies rarely deliver the job losses that people fear,” writes one, and another, “AI adoption can engender task-level displacement, but the broader digital ecosystem…generates complementary roles that absorb displaced workers and create net employment gains.” Low-forecast respondents (those predicting more losses) frequently express the belief that AI's speed and cognitive focus make this disruption different: “I am most pessimistic about white-collar jobs due to two developments - the tech sector becoming an increasingly bigger proportion of the white collar sector and AI systems making sufficient progress to replace a small but significant proportion of jobs in the tech sector.”
Blue-collar jobs
Forecasters broadly agree blue-collar jobs will be the most resilient to automation. High-forecast respondents particularly emphasize physical complexity barriers: “Blue collar jobs involving complex physical manipulation will likely be among the last job categories to be fully automated by AI. Humans excel at intricate physical tasks requiring dexterity, problem-solving in unpredictable environments, and adaptability.” Even among forecasters who foresee long-term losses, many acknowledge that “blue-collar occupations are likely resilient to AI trends short of advanced post-AGI robotics.” Infrastructure investment is commonly thought to be another factor bolstering blue-collar work: “There will be significant investment as part of the AI revolution (data centers, research, new types of companies being spun out), which will create new other jobs across the spectrum.”
Service-sector jobs
Views diverge on service sector outcomes. Some emphasize that “people have a stronger intrinsic preference for many of these roles to be done by humans,” and that there are demographic tailwinds: “Pink collar will increase because of longevity and old-age care.” Others focus on automation vulnerability, particularly in customer service.
Demand elasticity
Experts are split on whether AI productivity gains will increase or decrease employment. High-forecast respondents invoke economic theory: “A correct prediction depends upon predicting the effects of the Jevons Paradox (where price declines lead to increased purchases) as well as wealth effects (where efficiency boosts overall wealth increasing consumption).” Several low-forecast respondents argue elasticity won't offset displacement: “Even for software, elasticity is probably not high enough to increase the number of SWEs if the number needed to produce software falls by 1000x.”
Recent trends
Low-forecast respondents often emphasize current evidence: “From Intel to Microsoft, many top executives and management staff were laid off to make room for other investments at the organization. Google laid off 10% of its managerial staff last December.” Another notes: “In October 2024, S&P Global claimed that one in every four American workers that lost their jobs last year had worked in professional and business services.”
Rationale examples
High-quality rationales from forecasters across the range
High-forecast respondents
Rather than decreasing it’s highly likely that this area of labor will simply adapt. Historically, human labor patterns have experienced quite radical transformations over time, even in established sectors. Emerging technologies, rather than sucking people out of the labor market of white-collar work, are more likely to make them work differently and lead to new white-collar roles that can capitalize on this transition.
The easiest trap on forecasting is to overestimate the short-term impact and underestimate the long-term impact. While AI is going to revolutionize the world, and especially the workforce, it won’t happen overnight. Forecasting in 1995 that by 2000 e-commerce would represent more than 5% of total sales would [have been] a big overestimate. But by 2010 it would be low balling. It is similar for impact on jobs.
Low-forecast respondents
Reports of white-collar jobs shrinking haven’t just come to light this month but have been a topic of conversation for months now. In October 2024, S&P Global claimed that one in every four American workers that lost their jobs last year had worked in professional and business services and that in September, 497,000 professional and business services jobs were eliminated. From Intel to Microsoft, many top executives and management staff were laid off to make room for other investments at the organization. Google laid off 10% of its managerial staff last December. More recently, companies like HP and Block are also looking at large-scale layoffs in their organization.
Software engineering is already being hugely impacted and this is only accelerating…[although] it will take much longer for more experienced roles to be eliminated. The only way that we may see an increase in white collar jobs is through new roles and industries enabled by AI. We have seen this before through the industrial revolution and the invention of computers for example, but these occurred over decades, giving people time to learn new skills. AI is coming much much faster.