Modeling an AI jobs transition

Most discussions about AI and employment begin with a simple question: Which jobs can AI perform?

That is an important question, but it is not enough to predict what will happen to workers. A technology may be capable of completing many of an occupation’s tasks without eliminating the occupation itself. This can be true for a number of reasons. Humans may remain essential in roles because customers want human interaction, institutions require human accountability, or the work involves physical presence and judgment. Or lower costs may increase demand enough that employment in an industry expands rather than contracts even as AI makes workers more productive.

OpenAI’s AI Jobs Transition Framework offers a new way to think about these possibilities. Instead of treating technical exposure as a forecast of displacement, it asks three questions:

• Can AI perform a meaningful share of the occupation’s tasks?

• Is a person still central to delivering, supervising, or taking responsibility for the work?

• If AI lowers the cost of the service, will demand grow enough to absorb the productivity gains?

Applied across 921 occupations covering approximately 148 million U.S. jobs, the framework produces a more varied picture of the labor-market transition. Around 18 percent of jobs face relatively high automation risk, 24 percent are likely to reorganize, 12 percent could grow with AI, and 46 percent show less immediate change.

These categories are not predictions that a particular percentage of jobs will disappear. They are a map of where different kinds of change may emerge first.

Four different paths for work

Jobs at higher automation risk: This could include occupations such as data-entry clerks, telemarketers, proofreaders, and some bookkeeping or administrative roles. Much of the work involves processing, checking, or communicating standardized information—tasks that AI can increasingly perform without a person directly delivering the final service. Demand for these activities may not grow enough to offset the resulting productivity gains.

These jobs deserve close monitoring, particularly where employment is geographically concentrated or workers have limited pathways into adjacent roles. But even here, technical capability does not translate automatically or immediately into displacement. Employers still need to redesign workflows, integrate AI with existing systems, manage errors and regulatory risks, and determine whether automation is actually cheaper than continuing to employ people.

Jobs that will reorganize: A second and larger group is likely to reorganize rather than disappear. Lawyers, accountants, software developers, financial analysts, and teachers are plausible examples. AI can already draft contracts, summarize evidence, prepare financial analysis, write code, and create lesson materials. But clients and institutions still need people to exercise judgment, take responsibility for decisions, understand unusual cases, and build relationships with colleagues, students, or customers.

AI may allow each lawyer to review more documents, each developer to produce more code, or each teacher to prepare more individualized materials. That could reduce the amount of labor required for some activities, but the occupations themselves remain recognizably human. The central issue becomes how the jobs are redesigned: which tasks are delegated to AI, which remain with workers, and whether entry-level roles and career pathways continue to provide opportunities to learn.

Jobs that grow with AI: These are jobs where AI may lower costs enough to create more demand.  Examples include tutors, mental-health counselors, personal financial advisers, and some health-care professionals These services are currently expensive or difficult to access for many people. In these fields, greater productivity does not necessarily mean fewer workers. More affordable tutoring could lead more families to purchase it. Lower-cost financial advice could make personalized guidance available to households that cannot currently afford an adviser. AI-supported clinicians might provide more follow-up and ongoing care. If demand expands enough, total employment could rise even as each worker becomes more productive.

Jobs with less immediate change: This includes many jobs whose central tasks remain physical and must be performed in a particular place, such as electricians, plumbers, roofers, construction laborers, and many food-service workers. AI may still help with scheduling, estimates, training, inventory, or customer communication, but it cannot currently install wiring, repair a pipe, replace a roof, or prepare and serve a meal.

These occupations will not be untouched by AI. Their administrative and managerial tasks may change, and advances in robotics could eventually broaden automation. But for now, their core work has relatively low exposure to language-based AI, making large near-term changes less likely.

Early evidence does not show a simple displacement pattern

AI’s early effects are likely to appear through changing tasks, workflows, and skill requirements before they appear as the wholesale disappearance of occupations. Indeed, currently, actual AI usage is already higher in occupations identified as facing greater automation risk. ChatGPT use is roughly three times as prevalent in the most at-risk occupations as it is across the workforce more broadly.

Yet recent unemployment changes do not line up neatly with technical exposure. Since the first quarter of 2024, unemployment has risen more in some less-exposed occupations than in the occupations the framework classifies as facing the greatest AI risk. This does not prove that AI is having no labor-market effect, however. Occupational unemployment reflects many influences, and some changes may appear first in hiring, entry-level opportunities, wages, or the composition of work rather than in layoffs.

A better map for policy

AI does not determine one inevitable future of work. Technical capability matters, but so do institutions, consumer demand, business decisions, regulation, and the enduring value of human participation. The transition framework proposed in this paper suggests that different occupations will require different policy interventions.

• Jobs facing high automation risk may call for early-warning systems, targeted adjustment assistance, and locally designed transition plans.

• Jobs likely to reorganize may require updated training, professional standards, staffing rules, and clearer expectations about human oversight.

• Jobs with the potential to grow may benefit from policies that encourage adoption, expand access, and prepare more workers to enter the field.

Across all categories, policymakers need better real-time information. Traditional labor statistics are valuable, but they often move too slowly to capture changes in tasks, employer expectations, and technology use. Combining employment data with measures of AI capability and actual adoption can provide a more timely picture of where pressure is accumulating.