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There is no good evidence that AI has universally shrunk engineering teams. The clearer early signal is a change in the mix of software jobs employers post, especially fewer junior openings relative to senior ones. Employment data, forecasts and job postings measure different things, so none alone tells you how many engineers a particular company needs.
What the employment evidence actually says
A March 2026 Federal Reserve staff paper finds that U.S. coder employment continued to grow after ChatGPT’s introduction, but at a substantially slower pace than before 2022. Its authors report that differences in industry exposure to slowing industries did not explain the deceleration. The study links occupational data to the Current Population Survey; it examines coders in aggregate, not engineering-team headcounts at named employers. It is preliminary staff research, and its findings do not establish that AI caused the slowdown. Read the Federal Reserve paper.
Official U.S. projections also depend on the occupation definition and the forecast window. The Bureau of Labor Statistics says AI may assist developers with coding, testing, documentation, data quality and user stories, while demand may grow for people who build AI-based business solutions or maintain AI systems. Its 2025 article projected developer employment growth of 17.9% from 2023 to 2033, versus 4.0% for all occupations. Those are forecasts shaped by multiple labor-market factors, not a count of jobs already created or a prediction for every employer. See the BLS discussion of AI in its projections.
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A separate BLS forecast, covering 2025–35, distinguishes software developers from computer programmers:
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| Occupation | 2025 employment | 2035 employment | Projected change, 2025–35 |
|---|---|---|---|
| Software developers | 1,717.8 thousand | 1,892.6 thousand | 10.2% growth |
| Computer programmers | 110.8 thousand | 102.7 thousand | 7.3% decline |
These BLS figures are projections, not realized outcomes and not an estimate of AI’s causal effect. The different directions also show why “software jobs” is too broad a category for a precise claim. See the BLS 2025–35 occupational projections.
Why junior hiring may be changing first
A June 2026 IZA discussion paper reports a 14–15% relative decline in junior versus senior software developer vacancies in its U.S. online-posting data after ChatGPT’s public release. The comparison is a change in the junior-to-senior vacancy mix, benchmarked against related technical occupations; it does not mean junior vacancies fell 14–15% in absolute terms, that total software employment fell, or that teams became smaller by that amount. The paper reports no comparable decline in mechanical engineering vacancies.
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The authors also find that rising experience requirements largely reflect employers asking for more experience within the same job titles, rather than a shift toward a different mix of titles. In the remaining junior postings, signals shifted toward problem solving, communication and attention to detail—not specifically toward AI skills. These are patterns in the paper’s posting sample, not a universal hiring checklist. Its conclusion is that generative AI raises the bar for what employers count as a qualified junior hire. Read IZA Discussion Paper No. 18723.
That distinction matters to people entering the field: fewer relative openings can make the first job harder to land even while overall employment is stable or growing. Vacancy data captures advertised opportunities, not filled jobs, layoffs or the size of engineering teams already at work.
AI changes tasks, but productivity depends on the work around it
AI can assist with parts of development without eliminating the need to decide what software should do, check whether it works, integrate it safely and maintain it. BLS identifies coding, testing, documentation and related tasks as areas where AI may augment developers; it also points to work building AI solutions and maintaining AI systems. The effect on a team depends on which tasks it performs, how much output it can usefully deliver and what review and reliability requirements apply.
DORA and Google’s 2025 report characterizes AI as an “amplifier”: it can magnify both organizational strengths and dysfunctions. That is a useful way to think about why adopting a coding assistant does not automatically translate into fewer engineers. If requirements are unclear, tests are weak or review is overloaded, faster code generation can create more work downstream. The report draws on more than 100 hours of qualitative research and responses from nearly 5,000 technology professionals worldwide; it is not a universal causal estimate of productivity or an ideal team size. Read the DORA 2025 report.
What the UK evidence can—and cannot—tell us
The UK government’s January 2026 assessment finds that job postings declined more sharply in some AI-exposed occupations, but says this does not establish AI as the cause. An exposure measure indicates that tasks could be assisted or replaced; it does not show whether employers deployed AI in a labor-reducing way. Exposure also overlaps with interest-rate sensitivity, business cycles and sector shocks, and the assessment notes alternative analyses that found no significant employment effects to date. This is a useful caution against treating a correlation between AI exposure and hiring as proof of displacement. Read the UK government assessment.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsA separate UK AI Labour Market Survey 2025, commissioned by the Department for Science, Innovation and Technology and conducted by Gardiner & Theobald, reports that 97% of surveyed respondents identified at least one AI labor-market skills gap, while 57% of surveyed businesses reported a technical gap and 30% a non-technical gap. It also says 88% of surveyed organizations relied on on-the-job training rather than structured education and training programs. These are survey results about the UK AI labor market, not percentages for software engineers generally or proof that a particular skill is in demand at every engineering employer. Read the survey executive summary.
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What software engineers should focus on
The evidence does not support a guaranteed formula for staying employed, but it does point to durable capabilities that help engineers produce and verify useful software:
- Strengthen fundamentals. Understand data structures, system behavior, debugging and testing well enough to spot when generated code is wrong or incomplete.
- Practice problem definition. Turn an ambiguous request into clear requirements, trade-offs and testable outcomes before reaching for implementation.
- Build communication and review skills. Explain decisions, ask precise questions and make code understandable to teammates; these appeared among the skills emphasized in junior postings examined by IZA.
- Learn to evaluate and maintain software. AI can produce code, but someone still has to assess security, correctness, data quality, integration and long-term support.
- Use AI with judgment. Treat its output as a proposal to inspect, test and revise, rather than as verified work. The benefit depends on the quality of the surrounding development and review process.
For employers, the corresponding lesson is to measure outcomes across the full delivery process—not just how quickly code is drafted—and to make entry-level work teachable through mentoring, review and meaningful ownership. Neither the vacancy evidence nor the forecasts establish that junior engineers are no longer needed.
So, did AI shrink engineering teams?
The evidence available here does not establish a broad, economy-wide reduction in engineering-team size caused by AI. It does show slower growth in aggregate U.S. coder employment after 2022 in preliminary Federal Reserve research, a relative shift away from junior U.S. software vacancies in an IZA working paper, and occupational forecasts that vary by job category. The most defensible answer is that hiring signals and the work expected of engineers are changing; whether a given company adds, holds or cuts team members depends on its workload, organization and labor market.
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