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No. Current evidence does not support the claim that AI will take all the code or all the jobs. AI can automate or assist with particular tasks, including parts of software development, but task exposure is not the same as a whole job disappearing. Recent evidence shows mixed effects: the International Labour Organization (ILO) finds large-scale displacement limited so far, a U.S. study finds slower coder-employment growth after 2022, and the U.S. Bureau of Labor Statistics (BLS) projects software-developer employment to grow through 2033.
Why AI exposure does not mean a job disappears
An occupation is made up of different tasks. AI may be able to draft code, explain a function, or help test software without being able to take responsibility for the entire development process. A worker may spend less time on some tasks while still doing work that requires human judgment, coordination, context, or accountability.
The ILO’s May 2025 update assessed nearly 30,000 tasks using expert input and AI predictions. It estimated that one in four workers globally is in an occupation with some degree of generative-AI exposure. The ILO’s conclusion was that “most jobs will be transformed rather than made redundant.” That estimate describes exposure to AI within occupations; it is not a forecast that one in four jobs will be eliminated.
The same distinction applies to the ILO’s exposure scores. Its mean automation score was 0.29 in 2025, compared with 0.30 in 2023; the standard deviation was 0.14 in 2025, compared with 0.30 in 2023. These are measures from the ILO’s exposure methodology, not percentages of jobs automated or lost.
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What recent evidence says about AI and jobs
Different studies measure different things. A retrospective analysis of a recent employment trend, a government projection, and an employer-based global forecast should not be read as competing estimates of one shared outcome.
| Evidence | Scope and measure | What it says—and does not say |
|---|---|---|
| ILO review, June 1, 2026 | Empirical evidence from experiments, firm data, platform studies, and worker and firm surveys in Australia, Denmark, Germany, Korea, Kuwait, the United Kingdom, and the United States. | Large-scale job displacement remains limited so far. Reported time savings have not yet translated into higher measured output, earnings, or employment. This describes evidence available at publication, not a guarantee about future effects. |
| Federal Reserve working paper, March 2026 | Retrospective analysis of U.S. coding-intensive occupations using O*NET and Current Population Survey data after ChatGPT’s introduction in November 2022. | Coder employment continued to grow, but more slowly than before 2022. The authors estimate annual growth was about 3% lower after controlling for industry-level shocks. This is not a finding that AI eliminated 3% of coder jobs. |
| BLS article, March 11, 2025 | U.S. occupational projections from 2023 to 2033. | Software-developer employment is projected to grow 17.9%, compared with 4.0% for all occupations. A projection is not proof that AI caused growth, nor a guarantee that every developer will benefit. |
What the U.S. software-developer outlook means
The BLS projects software-developer employment to rise from 1,692,100 jobs in 2023 to 1,995,700 in 2033—an increase of 303,700, or 17.9%. For all occupations, it projects 4.0% growth over the same period. These are U.S. projections, not observed outcomes.
The BLS says AI can help developers develop, test, and document code, and describes programming as an activity in which AI may augment worker effort and increase productivity. It also points to possible demand for workers who develop AI-based business solutions and maintain AI systems. Its projection does not isolate AI as the cause of the expected employment change.
The Federal Reserve paper and BLS projection are not direct contradictions. The Federal Reserve paper looks backward at coder-employment growth since 2022 and estimates a slowdown relative to earlier growth; BLS projects employment for the broader software-developer occupation over 2023–2033. Their populations, time periods, and methods differ, and the Federal Reserve authors caution that measurement, aggregate labor demand, prices, and occupational task mix complicate interpretation. They say short- and long-run employment effects remain empirical questions.
What the global jobs forecasts do—and do not—count
The World Economic Forum’s Future of Jobs Report 2025 estimates that 170 million jobs will be created and 92 million displaced by 2030, for net growth of 78 million. These are expectations based on employer surveys combined with ILO employment data, not observed outcomes. The report covers selected roles and a subset of workers, rather than total global employment.
Those totals are not an AI-only forecast. The WEF’s outlook includes several forces: technology, demographic shifts, economic uncertainty, the green transition, and geoeconomic fragmentation. Software and applications developers are among the roles the report expects to grow, but the overall totals should not be presented as jobs created or lost because of AI alone.
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What coding-AI usage statistics actually measure
Anthropic’s April 2025 analysis classified 500,000 coding-related interactions on its products. It classified 79% of Claude Code conversations as automation and 21% as augmentation; for Claude.ai conversations, 49% were classified as automation. In this analysis, automation meant AI directly performing tasks, while augmentation meant collaboration between the user and AI.
Those percentages describe sampled conversations on Anthropic products. They are not shares of all code written, developer time, jobs, or employers using AI. Product usage can illustrate how people work with a particular tool, but it cannot by itself establish economy-wide employment effects.
Anthropic’s January 2026 Economic Index examined a sample of Claude.ai conversations from November 2025, predominantly using Claude Sonnet 4.5. For tasks associated with high-school-level prompts, it estimated a speedup factor of 9; for college-level prompts, a factor of 12. Estimated successful completion rates were 70% for tasks requiring less than a high-school education and 66% for college-level tasks. These estimates come from Anthropic’s conversation sample and methodology; they are not direct workplace time-and-motion measurements or evidence of equivalent productivity gains across the economy.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What remains uncertain
Whether AI ultimately reduces employment, changes the mix of jobs, or increases demand for some kinds of work will depend on more than what a model can do in a demonstration. Adoption, the way organizations redesign tasks, demand for software and other products, and how productivity gains are distributed can all affect the result. The ILO’s June 2026 review reports uneven productivity gains and identifies potential risks involving younger workers, inequality, worker autonomy, coordination, and job quality.
The evidence available today therefore supports a narrower conclusion than either “AI will take all the jobs” or “AI will have no effect.” Some tasks are being automated or assisted, and some employment indicators may change; the scale and distribution of longer-term effects remain unsettled.
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