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AI is changing IT employment, but current evidence does not show that it is eliminating technology work across the board. Employers cited artificial intelligence in announcements covering 87,714 U.S. job cuts through May 2026, according to Challenger, Gray & Christmas. Technology companies had announced 139,156 cuts through June.
At the same time, the U.S. Bureau of Labor Statistics projects software-developer, quality-assurance and tester employment to grow 15% from 2024 through 2034, with about 129,200 openings a year. The clearer conclusion is not “AI is taking all the tech jobs,” but that companies are changing the tasks, skills and staffing levels they expect from IT workers.
The layoff numbers need careful interpretation
The headline figures describe announced job cuts and employers’ stated reasons. They do not prove that AI directly replaced the same number of workers, nor do they measure the net size of the U.S. IT workforce.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteChallenger reported:
- 87,714 U.S. announced cuts attributed to AI through May 2026.
- 54,836 AI-attributed cuts during all of 2025.
- 123,653 announced technology-sector cuts through May 2026.
- 139,156 announced technology-sector cuts through June 2026, up 83% from the comparable period in 2025.
- 45,849 total U.S. announced cuts in June, down 53% from May, while technology remained the leading sector.
These are useful indicators of restructuring activity, but they are not interchangeable measures. “Technology layoffs” includes cuts for many reasons. “AI-attributed layoffs” refers to cuts employers connected to AI in announcements. Neither category equals verified permanent job destruction. Actual employment also depends on new hiring, internal transfers, attrition, contracting decisions and the creation of roles in other specialties.
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Challenger is an outplacement and workforce-transition firm that tracks announced reductions. Its data can show what companies are publicly saying about planned cuts; it cannot independently establish that every affected position was automated.
Is AI causing layoffs—or providing a convenient explanation?
Both possibilities can exist at the same time. AI can genuinely reduce the labor required for work such as routine coding, ticket triage or first-draft documentation. But a company may also cite an “AI transformation” while dealing with weaker demand, post-pandemic over-hiring, acquisitions, outsourcing, cost pressure or a broader organizational redesign.
A company can cut one group while hiring another. A restructuring may reduce general application-development or support roles while increasing hiring for AI engineering, data infrastructure, cloud platforms, cybersecurity, technical implementation and sales. Spending may also move from labor-intensive projects to model development, computing capacity and automated systems.
A useful way to judge whether a reduction is genuinely AI-driven is to examine four signals:
- Announcement language: Did the employer explicitly mention AI, automation or an AI-first reorganization?
- Role pattern: Were repetitive, standardized or easily codified tasks disproportionately affected?
- Replacement evidence: Did the company describe deploying AI tools or setting automation targets?
- Offsetting hiring: Is it adding workers in AI, infrastructure, security, data or integration roles?
Even when all four signals appear, they do not establish that AI caused every individual termination. Layoffs often have multiple causes.
IT work is changing task by task
The most immediate impact is likely to be on task bundles rather than entire occupations. An AI system may handle part of a job while leaving humans responsible for design, judgment, validation and accountability.
| Task area | Likely near-term change | Human work that remains important |
|---|---|---|
| Boilerplate coding and basic scripting | More automated code generation | Architecture, review, integration, debugging and maintenance |
| Routine quality assurance | Faster test-case and test-data generation | Test strategy, edge cases, release decisions and risk assessment |
| Help-desk triage | More self-service and automated classification | Complex diagnosis, escalations, empathy and accountability |
| Documentation and release notes | Faster first drafts | Accuracy, governance, institutional context and approval |
| Monitoring and alert review | Automated summaries and prioritization | Incident command, remediation and production responsibility |
| Routine data work | More automated cleaning and query generation | Data architecture, quality controls, privacy and interpretation |
| First-pass security analysis | More automated triage | Threat hunting, incident response and risk decisions |
Work is most exposed when it is repetitive, clearly specified and easy to check. Work is harder to displace when it involves ambiguous requirements, legacy integration, high consequences or responsibility for an outcome.
Which IT roles face the most pressure?
Routine application maintenance, basic website and interface production, entry-level testing, repetitive technical support, data administration, internal reporting and low-complexity scripting may see the greatest productivity pressure. That does not mean every job in these categories disappears. It means one worker, assisted by software, may handle more volume, or employers may hire fewer people for the same project.
Entry-level workers may feel this shift first. Junior roles often include implementation, documentation, simple bug fixes and test preparation—the types of assignments AI tools can accelerate. If those tasks shrink, employers may raise expectations for new hires and provide fewer traditional apprenticeship assignments.
That creates a difficult trade-off. Experienced developers can become more productive with AI, but organizations still need a pipeline of people who learn how systems work. Eliminating junior hiring too aggressively can create a future shortage of experienced engineers.
Why software jobs can grow while hiring becomes harder
The BLS projects 15% growth for software developers, quality-assurance analysts and testers from 2024 to 2034, along with about 129,200 annual openings. The BLS says demand is supported by AI, automation, robotics, the Internet of Things and cybersecurity. The occupation’s May 2024 national median pay was $133,080, though that figure is not an entry-level salary and should not be treated as a guarantee.
Earlier BLS analysis projected software-developer employment to grow 17.9% from 2023 to 2033 and information-security-analyst employment to grow 32.7% over the same period. Those figures come from a different projection vintage and should not be mixed with the newer 2024–34 estimate as though they were one forecast. Both are projections, not promises for an individual worker.
There is no contradiction between positive long-term occupational growth and layoffs today:
- Productivity: Fewer developers may be needed for a particular project if each can produce more.
- Demand: Lower software-production costs can encourage companies to build more software.
- Reallocation: Hiring can move from routine development to AI systems, cloud, data and security.
- Timing: Short-term restructuring may occur before new projects and roles appear.
- Distribution: Growth can benefit some employers, regions, specialties and seniority levels more than others.
In practice, developers may face higher expectations rather than immediate replacement: faster delivery, broader ownership, stronger testing, secure integration and the ability to operate systems in production.
Specialties likely to gain importance
AI systems require more than prompts or generated code. They need data pipelines, compute, deployment, monitoring, security controls and human oversight. Areas likely to benefit from that work include:
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- AI and machine-learning engineering.
- Data engineering and database architecture.
- Cloud, platform and infrastructure engineering.
- AI infrastructure and compute operations.
- Cybersecurity and identity management.
- Model evaluation, monitoring and governance.
- Privacy, compliance and audit.
- Systems integration and implementation.
- Product management for AI-enabled software.
- Human-in-the-loop quality assurance.
- Specialized engineering in regulated or technically complex industries.
The BLS has identified AI systems, cloud infrastructure and data infrastructure as demand drivers, while its earlier projections showed particularly strong growth for information-security analysts. No specialty is completely protected: the advantage belongs to workers who combine technical ability with judgment, domain knowledge and responsibility for results.
Global expectations point to transformation, not a simple collapse
The World Economic Forum’s Future of Jobs Report 2025 expects AI and machine-learning specialists, big-data specialists, and software and applications developers to rank among the fastest-growing jobs through 2030. It also estimates that 39% of workers’ existing skill sets could be transformed or become outdated between 2025 and 2030.
Those figures reflect global employer expectations combined with labor-market data. They are not a direct forecast of U.S. IT layoffs. Their significance is the direction of change: workers may remain employed while the skills attached to their jobs change substantially.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What IT workers should do next
The strongest response is not to abandon core IT knowledge for a short-lived tool credential. It is to become effective at using AI inside a durable specialty.
- Use AI in your existing field. A systems administrator can evaluate automation for incident workflows; a developer can use it for code exploration and tests; a security analyst can apply it to triage while validating findings.
- Prove verification skills. Show how you test generated code, detect hallucinations, protect secrets, review dependencies and measure error rates.
- Strengthen fundamentals. Networking, databases, operating systems, version control, software design and security remain necessary for reviewing automated output.
- Learn deployment and operations. APIs, cloud platforms, data handling, observability and incident response make AI work useful in production.
- Build domain expertise. Finance, health care, government, manufacturing and other regulated fields require context, controls and accountability that general-purpose tools cannot supply alone.
- Create a real portfolio. Demonstrate a working system, deployment process, tests, monitoring and a written explanation of trade-offs—not only a chatbot demo.
- Track internal opportunities. Review job postings for new responsibilities involving AI evaluation, governance, platform engineering, data quality and security.
- Measure your contribution. Document improvements in cycle time, defect rates, reliability, security findings or customer outcomes.
- Avoid single-vendor dependence. Learn transferable concepts so that your value does not disappear when a model, platform or product changes.
Training resources from Microsoft Learn, AWS Skill Builder, Google Cloud Skills Boost and CompTIA can help, but a course is most useful when it leads to hands-on work and evidence relevant to a target role. Be skeptical of expensive programs focused only on prompting or guaranteed employment.
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- Author: Bungay Stanier, Michael.
- Publisher: Page Two
- Pages: 244
- Publication Date: 2016-02-29
- Edition: 1
What responsible employers should measure and disclose
Employers should identify the tasks changing before eliminating whole roles. Faster generation is not the same as successful automation. Management should measure rework, defects, security incidents, reliability, customer outcomes and total operating cost.
Companies should also establish rules for confidential data, code ownership, auditability and model use. High-risk systems need human review and clear accountability. Reskilling may cost more in the short term than layoffs, but it can preserve institutional knowledge and reduce the risk of losing future senior talent.
When announcing reductions, companies should distinguish among direct automation, broader cost reduction, mergers, demand changes and spending reallocation. Useful questions include:
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- Which tasks were automated?
- How many positions were eliminated specifically because of that automation?
- How many AI, infrastructure, data or security roles were added?
- How many affected workers were offered retraining or internal placement?
- Were the cuts also related to demand, acquisitions, outsourcing or cost targets?
The bottom line for workers and the economy
AI is already changing who gets hired, what developers and support staff are expected to do, and how many workers companies believe they need for a given amount of output. The layoff data shows real disruption, especially in technology, but it does not establish that AI alone caused every cut or that U.S. IT employment is broadly collapsing.
The more defensible description is uneven displacement and reallocation. Routine, measurable work is under pressure. Systems judgment, security, infrastructure, integration, governance and domain expertise are becoming more valuable. The people best positioned for the transition will not merely know how to operate an AI tool; they will know when its output is wrong, how to make it reliable and how to connect it to a real business outcome.
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