---
title: "McKinsey: 11M US Workers May Switch Jobs by 2035 Due to AI"
slug: "mckinsey-11m-us-workers-may-switch-jobs-by-2035-due-to-ai"
published: "2026-10-06"
beat: "Economy"
tags: ["Economy", "Research"]
creator: "Agentry Newsroom"
editor: "Susanne Sperling, Editor — Human in the Loop"
tools: ["Claude (Anthropic)", "Perplexity Sonar"]
creativeWorkStatus: "verified"
dateReviewed: "2026-10-06"
aiActArticle50: "compliant"
humanView: "https://agentry.news/research/mckinsey-11m-us-workers-may-switch-jobs-by-2035-due-to-ai"
agentView: "https://agentry.news/agent/mckinsey-11m-us-workers-may-switch-jobs-by-2035-due-to-ai"
---# McKinsey: 11M US Workers May Switch Jobs by 2035 Due to AI

> The McKinsey Global Institute released an analysis on September 29, 2026, estimating that roughly 11 million U.S. workers—about 7% of current employees—may need to change occupations by 2035 as AI aut

*Drafted by an AI agent. Verified by Susanne Sperling, Editor — Human in the Loop. [AI policy](/ai-policy).*

The **McKinsey Global Institute** projected on September 29, 2026, that approximately **11 million U.S. workers—roughly 7% of the current workforce—may need to switch occupations entirely by 2035** as artificial intelligence accelerates labor displacement [Bloomberg](https://www.bloomberg.com/news/articles/2026-09-29/ai-may-force-11-million-workers-into-new-jobs-mckinsey-says).

## The Scale of Job Transition

The 11 million figure represents McKinsey's base-case model output under stated assumptions, not an observed measurement. The actual range could vary significantly: the institute estimated between **6 million to more than 16 million workers** may face occupational changes, contingent on the pace of automation adoption and its downstream effect on labor demand [CNN](https://www.cnn.com/2026/09/29/economy/us-economy-jobs-consumer-confidence-ai-jolts).

McKinsey's analysis balances displacement against expansion. While automation could reduce labor demand by the equivalent of approximately **36 million jobs by 2035**, economic growth and the emergence of AI-related fields are projected to generate demand for approximately **40 million jobs** during the same period [McKinsey Global Institute](https://www.mckinsey.com/mgi/our-research/workforce-in-motion-skills-and-pathways-to-future-jobs-in-the-united-states). This net positive job creation masks significant occupational churn: "The remaining 11 million may need to switch occupations entirely."

## Implications for Worker Transition

The gap between jobs destroyed and jobs created underscores a critical labor-market reality—new opportunities will emerge in different sectors and geographies than those experiencing displacement. Workers in roles most vulnerable to automation may not possess the skills required for emerging AI-adjacent positions without retraining or career pivots.

The wide confidence interval (6 million to 16 million) reflects deep uncertainty about how quickly companies will adopt agentic AI systems, how aggressively they will automate workflows, and how fast labor markets can absorb and reskill displaced workers. Faster adoption would push the estimate toward the upper bound; slower deployment or successful reskilling programs could narrow the impact.

## What This Means for Policy and Business

The analysis arrives as enterprises accelerate AI agent deployments across customer service, finance, operations, and software development. McKinsey's projections suggest that large-scale occupational transition is not a tail risk but a central scenario—one that will require coordinated investment in education, retraining, and social safety nets to manage effectively.

For workers and policymakers, the report underscores that AI-driven labor displacement is a structural phenomenon, not a cyclical downturn. The challenge lies not in preventing job losses—economic dynamism ensures new roles will exist—but in enabling 11 million people to transition successfully and equitably.