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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesAI is changing technology work, but the evidence does not show that it has caused a general “purge” of tech workers. Some signs of pressure are emerging, particularly in coding and early-career hiring. At the same time, employment projections remain positive for software developers and computer occupations overall. Your risk depends on the work you do, your experience, your employer and your local job market—not simply on whether your occupation is described as “AI-exposed.”
Is AI taking tech jobs?
AI use is becoming part of work, but using a tool is not the same as replacing a worker. In its 2026 report on U.S. households, the Federal Reserve said one in four surveyed U.S. workers had used generative AI at work in the prior month. Among those users, 81% reported that it saved time, 52% that it improved work quality and 55% that it let them do new tasks. The survey describes 2025; these results measure workers’ reported experiences, not jobs eliminated.
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Labor-market studies measure different things—tool use, hiring, job postings, employment and future projections. They can point to emerging pressure without proving that AI caused a particular layoff. The distinctions matter:
| Evidence | Finding | What it can and cannot tell you |
|---|---|---|
| Workplace use, United States | One in four surveyed workers used generative AI at work in the prior month; user-reported benefits included time savings, better quality and new tasks. | Shows reported use and perceived effects, not the number of jobs replaced. Federal Reserve, 2026 report describing 2025. |
| Firm and industry job postings, United States | A Federal Reserve analysis found no overall reduction in postings at firms or industries with higher AI adoption. | An aggregate result does not rule out unusually difficult job searches in particular occupations. Federal Reserve, March 27, 2026. |
| Coder employment, United States | Employment growth slowed sharply relative to the pre-2022 pace but continued. | Suggests an occupation-specific change; it does not establish that AI alone caused individual job losses. Crane and Soto, Federal Reserve discussion paper, March 2026. |
| Early-career hiring, United States | A Census working paper finds a discontinuous decline in early-career job gains and backfill hires around ChatGPT’s release, relative to older workers in the same industries. | The paper also discusses earlier trend differences and other explanations. A relative change is not proof that AI caused each missing hire. U.S. Census Bureau Center for Economic Studies, 2026. |
| Projected employment, United States | Software developer employment is projected to grow 17.9%, and computer occupations overall 11.7%, from 2023 to 2033. | These are projections, not a guarantee of openings for a specific role or location. U.S. Bureau of Labor Statistics, 2025. |
Taken together, the findings support a more precise conclusion than “AI is taking tech jobs”: some groups and activities may be under pressure, but the available evidence does not establish a broad, AI-caused technology layoff wave.
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Will AI replace software developers?
The evidence points to changing tasks and a slower employment-growth pattern for coders—not the disappearance of software development as a job category. Federal Reserve authors Leland D. Crane and Paul E. Soto report that coder employment growth decelerated sharply after ChatGPT’s release compared with growth before 2022, while coder employment continued to grow. They interpret their analysis as evidence of an occupation-specific shock, but their March 2026 discussion paper is preliminary, represents the authors’ views and does not prove that AI alone caused the change.
The Bureau of Labor Statistics takes a broader, forward-looking view. Its 2025 projections put software developer employment growth at 17.9% between 2023 and 2033, and growth across all computer occupations at 11.7% over the same period. BLS explains that AI can change the tasks workers perform while also supporting demand for developers and data-infrastructure roles. These U.S. projections describe expected occupation-level employment, not a promise that every specialty, employer or region will expand.
For an individual developer, the relevant question is therefore not just whether AI can generate code. It is which parts of the role an employer can automate, which it chooses to automate, and what work still needs a person to define, verify and maintain. AI exposure measures whether tasks appear suitable for AI; they do not show that an employer has deployed it in a way that reduces staffing.
Are entry-level tech jobs disappearing?
Early-career hiring is a particular area to watch, but “disappearing” is stronger than the evidence warrants. The 2026 Census working paper reports a discontinuous decline in early-career job gains and backfill hires around ChatGPT’s release, compared with older workers in the same industries. Its analysis also considers differences in earlier trends and other possible explanations, so the timing does not establish that AI caused every change.
A separate U.S. study summarized in the UK government’s 2026 review reported a 13% decline in employment among early-career workers in highly AI-exposed occupations, while less-exposed roles remained stable or grew. That is a reported study result, not a count of jobs verified as directly replaced by AI. It should not be combined with the Census result as if the two measured the same outcome.
For someone starting out, fewer entry-level openings or fewer replacement hires can matter even if total employment in a field remains positive. Track openings for the specific role and region you want, and distinguish a tougher search from evidence that an entire occupation is vanishing.
What does the UK evidence show?
The UK government’s January 28, 2026 review summarizes several findings about British job adverts and AI exposure. These figures concern the UK and are not interchangeable with U.S. employment or hiring statistics.
- The review reports a 38% fall in adverts from 2022 to 2025 for high-exposure occupations, compared with a 21% fall for low-exposure occupations. Those figures summarize a McKinsey analysis; they are not a government-produced primary statistic and do not prove AI caused the difference.
- The review also summarizes an analysis in which a one-standard-deviation increase in AI exposure was associated with 3.9% lower job-posting volume. Posting levels later returned to their original levels after about 20 months. The review cautions that this association does not establish causality.
The review’s central qualification is: “First, exposure is not adoption.” It warns that task-based exposure measures do not establish actual workplace deployment that reduces labor. Business cycles, interest rates and sector-specific shocks can also influence hiring and postings.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can I protect my tech career from AI?
No credential or job title guarantees safety. The following steps are practical ways to prepare, not interventions proven by the cited labor studies to prevent job loss.
- Map your work by task. List recurring activities in your role, then separate routine, well-specified work from tasks that depend on context, judgment, collaboration or accountability. Exposure at the task level is more useful than assuming every duty in a job will change in the same way.
- Learn approved AI tools and verify their output. Ask your employer which tools are permitted, especially when handling company, customer or personal data. Practice checking generated code, summaries or analysis for errors, security issues and missing context; do not treat a fluent answer as a verified one.
- Make your impact visible. Keep a record of outcomes you can explain—such as a process improved, a system made more reliable or a customer problem resolved—and the domain knowledge that helped you deliver them. Do not claim that AI produced results you cannot substantiate.
- Build adjacent skills from your existing strengths. A developer might deepen skills in testing, system design or a business domain; a support specialist might strengthen automation and incident analysis. Choose learning that fits real work in your target market rather than collecting credentials on the assumption that one will guarantee security.
- Keep a current portfolio and professional network. Maintain examples of work you are allowed to share, explain your contribution clearly and stay in touch with peers and former colleagues. Respect confidentiality and ownership rules when presenting projects.
- Prepare for a search before you need one. Keep your résumé current, identify roles and locations you would consider, and review openings periodically. For training or job-search services, compare the cost and time required with independently verifiable outcomes and the relevance to your location and target role.
How to judge your own risk
Use evidence close to the decision you are making, rather than treating a national average as a prediction about your job. Compare the following:
- Exposure versus deployment: Are tasks merely considered suitable for AI, or has your employer changed the workflow and staffing?
- Career stage: Are openings and backfill hires changing for entry-level workers, experienced hires or both?
- Projected demand versus current openings: Long-range occupation projections and today’s local vacancies answer different questions. Compare figures from the same geography and period.
- Transferable skills: Could your experience apply in another industry or role if hiring weakens in your current niche?
- Training trade-offs: What evidence supports the proposed course or credential, and are its cost and time justified for the roles you want?
Company announcements about AI, an occupation’s exposure score or a coincident layoff can each be relevant signals. None alone verifies that AI replaced a particular worker or caused a particular employment change. Federal Reserve authors studying firm job postings described their own analysis as part of “an active monitoring process for signs of labor market impacts in the months and years to come.” That is a useful frame for workers too: watch for concrete changes in your role and target market, and adapt without treating an uncertain forecast as a verdict.
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