AI has not been shown to shrink engineering teams across the economy. The evidence points to a more specific shift: U.S. coder employment is still growing more slowly than before 2022, and junior software-developer vacancies have fallen relative to senior vacancies in one large study of job postings. Neither result measures the size of engineering teams at individual companies. The clearest answer is that hiring signals and some work requirements are changing, but the overall effect on team size remains unsettled.
What does the evidence say about engineering employment?
There is no single measure that answers whether AI has made engineering teams smaller. Employment statistics count people in occupational categories; job-posting data count vacancies; forecasts estimate future employment. None directly tracks team sizes at named employers, and the measures should not be treated as interchangeable.
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| Evidence | Measure and period | Finding | What it can tell you |
|---|---|---|---|
| Federal Reserve staff paper, March 2026 | U.S. coder employment, comparing the period after ChatGPT’s introduction with the pre-2022 trend | Employment continued to grow, but at a substantially slower pace than before 2022. | An aggregate change in coder employment growth; not a count of engineering teams or proof that AI caused the change. |
| Bureau of Labor Statistics (BLS), 2025 projections article | U.S. software developers, 2023–2033 | Projected employment growth of 17.9%, compared with 4.0% for all occupations. | A forecast that accounts for multiple labor-market factors, not a realized outcome or an AI-only estimate. |
| BLS, 2025–2035 occupational projections | U.S. software developers and computer programmers | Software developers: 1,717.8 thousand jobs in 2025 and 1,892.6 thousand in 2035, a projected 10.2% increase. Computer programmers: 110.8 thousand in 2025 and 102.7 thousand in 2035, a projected 7.3% decline. | Forecasts for two distinct occupational categories; the different results illustrate why job titles and projection periods matter. |
The Federal Reserve paper links occupational data to Current Population Survey data and reports that its industry-level control did not explain the slowdown in coder employment growth. It is preliminary staff research, and its authors note that the views are theirs. It does not isolate the effect of AI adoption at particular companies.
The two BLS projections are not contradictory: they cover different date ranges and use different occupational categories. “Software developer” and “computer programmer” are not interchangeable labels, and neither forecast establishes what will happen to a particular team.
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Are junior software-engineering jobs disappearing?
A June 2026 working paper by Samuel Westby, Alicia Sasser Modestino, and Peiran Cheng examines near-universe U.S. online vacancy data from Lightcast. Using event-study and difference-in-differences methods, it finds a 14–15% relative decline in junior versus senior software-developer vacancies after ChatGPT’s public release, compared with related technical occupations. The paper reports that a similar decline was absent in mechanical engineering.
That percentage is a change in the junior-to-senior mix of vacancies in the study’s data—not a 14–15% fall in all junior jobs, total software employment, or engineering-team headcount. The authors find that experience requirements rose mainly because employers asked for more experience within the same job titles, rather than because postings shifted to a different mix of titles. The paper’s findings describe a pattern in postings; they do not show that AI alone caused every change.
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What changed in the junior postings?
In the remaining junior vacancies, the paper identifies greater emphasis on problem solving, communication, and attention to detail, rather than a shift toward AI-specific skills. Its authors summarize the implication this way: “The results show how generative AI redefines entry-level work by raising the bar for what counts as a qualified junior hire.” That is the paper’s interpretation of its posting analysis, not a universal checklist for employers.
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Which parts of software work can AI affect?
BLS describes AI as a potential aid for developers working on coding, testing, documentation, data quality, and user stories. It also points to possible demand for developers who build AI-based business solutions, maintain AI systems, and support more complex data infrastructure. Those are ways AI may change the work and demand for particular capabilities; they do not imply that every developer will use AI in the same way or that every team will add staff.
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DORA and Google’s 2025 report draws on more than 100 hours of qualitative research and responses from nearly 5,000 technology professionals around the world. It calls AI “an amplifier,” saying it can magnify both high-performing organizations’ strengths and struggling organizations’ dysfunctions. Treat that as the report’s organizational framing, not a measured productivity guarantee or a formula for staffing.
In practice, assistance with generating or documenting code is only one part of delivering software. Teams still need to decide what to build, understand existing systems, evaluate changes, and maintain what ships. Whether AI makes those workflows more effective depends partly on the organization’s ability to integrate it into sound development and review practices.
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Does AI exposure prove that AI caused job losses?
No. The UK Department for Science, Innovation and Technology’s January 2026 assessment says that hiring patterns in some AI-exposed occupations are suggestive, but do not establish causality. An exposure measure describes whether tasks could be assisted or replaced; it does not establish that employers deployed AI in a way that reduced labor needs. Exposure can also overlap with interest-rate sensitivity, business cycles, and sector-specific shocks. The assessment notes that alternative analyses have found no significant employment effects to date.
That caution matters when interpreting any employment or vacancy trend. A slowdown after a technology release is not, by itself, evidence that the technology caused it. The Federal Reserve paper’s industry control and the IZA paper’s comparisons add useful analysis, but their findings still have different outcomes and limits: coder employment growth in one case, and the relative seniority mix of vacancies in the other.
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What should software engineers focus on now?
The available evidence supports preparation for changing expectations, not a guarantee that any one skill will secure a job. For people entering the field, the junior-posting study suggests that employers in its sample valued problem solving, communication, attention to detail, and experience alongside technical qualifications. For developers at any level, the BLS task descriptions point to the value of being able to test, document, and maintain software—not just produce code.
- Build fundamentals: learn to reason about behavior, edge cases, and how a change fits into a larger system.
- Practice verification: be able to review code and tests critically, including work generated with AI assistance.
- Develop communication: explain trade-offs, clarify requirements, and make technical work understandable to teammates.
- Understand the full lifecycle: connect implementation with testing, documentation, data quality, deployment, and maintenance.
These are evidence-informed priorities, not a forecast that every employer will hire for the same profile. The UK AI Labour Market Survey 2025 offers relevant context about skills needs in the UK AI sector, but it should not be generalized to all software-engineering teams: 97% of surveyed respondents identified at least one skills gap, 57% of surveyed businesses reported a technical gap, and 30% reported a non-technical gap. The survey also found that 88% of surveyed organizations used on-the-job training rather than structured education and training programs.
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