Will AI create or take away more job opportunities?

There's no single answer — but most major forecasts lean toward net job creation, with significant caveats

This is one of the most debated questions in economics right now, and credible organizations have produced genuinely different numbers. Here's what the evidence shows:

The optimistic case: Net positive job creation

The World Economic Forum's Future of Jobs Report 2025 — based on surveys of over 1,000 employers representing 14 million workers across 55 economies — is the most-cited source here. It projects that job creation and destruction due to structural labour-market transformation will amount to 22% of today's total jobs, with the creation of new jobs equivalent to 14% of today's total employment, amounting to 170 million jobs, offset by displacement of 8% (92 million) of current jobs, resulting in net growth of 7% of total employment, or 78 million jobs.

McKinsey's newer research tells a similar story for the US specifically: AI and automation will cut demand for about 36 million U.S. jobs by 2035 while growth elsewhere creates about 41 million. The gains come largely from unexpected places — rising living standards accounting for roughly eighteen million jobs as households and firms purchase more services, professionalize informal work, and increase demand for management, hospitality, and personal services.

Goldman Sachs is also relatively reassuring: AI could displace 6-7% of the US workforce if widely adopted, but the impact is likely to be transitory as new job opportunities created by the technology ultimately put people to work in other capacities. They note if current AI use cases were expanded across the economy, just 2.5% of US employment would be at risk of displacement.

The catch: creation and destruction aren't evenly distributed

Even the optimistic reports flag a serious mismatch problem. McKinsey found that only one in seven displaced workers has a direct path into a growing job needing little retraining and paying at least as much, while nearly half face an "unpaved" path blocked by large skill gaps or credential requirements — and about 85% of growing jobs require a credential.

The WEF data shows a similar pattern: the jobs disappearing (clerical, administrative, data entry) are different from the jobs appearing (AI specialists, green-energy roles, care work, farming). Technology-related roles like big data specialists and AI/ML specialists are the fastest-growing in percentage terms, while clerical and secretarial workers, postal clerks, bank tellers, and data entry clerks are expected to see the largest declines.

More cautious/negative forecasts

Not everyone agrees. Forrester predicts 6.1% of US jobs will be lost by 2030 due to AI and automation — about 10.4 million jobs, and notes AI's influence is already strongly influencing 20% of jobs, nearly four times the 2023 forecast.

Real-time layoff tracking shows AI-attributed cuts accelerating sharply: from January to August 2026, employers cited AI for 116,175 cuts, about 22% of total cuts and the top stated reason for the year — already more than double the figure for all of 2025. However, analysts caution this is a messy metric, since this is a self-reported, incentive-loaded metric closer to investor-relations language than a measurement of causation — some cuts labelled AI are ordinary cost-cutting in a flattering wrapper.

Public sentiment also skews pessimistic: a BMG poll showed 55% of employees think more jobs will be lost to AI than created by it, though this reflects sentiment rather than data-driven forecasting.

Who gets hurt most right now

The clearest real-world signal is generational: younger tech workers appear to be disproportionately affected, with unemployment among 20- to 30-year-olds in tech-exposed occupations rising by almost 3 percentage points since the start of 2025. The Yale Budget Lab and others frame this as "small in aggregate, severe at the entry point" — meaning the overall job count may hold up while specific groups (new graduates, routine knowledge workers) bear concentrated pain.

Bottom line

Most major institutional forecasts (WEF, McKinsey, Goldman Sachs, OECD) lean toward net job creation or roughly neutral outcomes over the next 5-10 years, but with three important caveats: (1) the jobs created often require different skills and credentials than the jobs lost, creating painful transition gaps; (2) early-career and routine white-collar workers face disproportionate near-term disruption; and (3) real-time layoff data suggests disruption is accelerating faster in 2026 than earlier models predicted. The honest answer is that aggregate numbers may look fine while millions of individual workers experience real displacement — making "will AI create more jobs than it destroys" less important than "will displaced workers be able to reach the new jobs."