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Technology jobs did not disappear in 2025 so much as change shape. AI made routine digital work faster to produce, while raising the value of people who can design systems, check outputs, secure data, connect tools and take responsibility for results. That shift affects software, data, cybersecurity, cloud infrastructure and more—not only jobs with “AI” in the title.
The evidence points to two trends at once: employers became more selective overall, yet forecasts still show growth in several technical specialties. The figures below distinguish global employer expectations from U.S. occupational projections; neither should be mistaken for a count of jobs created or lost during 2025.
The short answer: tasks changed faster than occupations
AI’s clearest near-term effect on technology work is a shift in the unit of work. A developer may spend less time writing boilerplate and more time defining requirements, reviewing generated code, testing security and handling production failures. An analyst may generate a first-pass report faster but still need to check data quality and explain what the numbers mean.
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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →That is not the same as saying every job is safe, or that AI has already caused a wave of technology employment growth. A task can be automated without eliminating the occupation that contains it. Whether automation reduces hiring, increases output from the same team or helps a company take on more work depends on adoption, error costs, regulation, demand and management choices.
The market also became more selective. Indeed’s 2025 report describes heavier applicant flows and changing employer expectations, drawing on hiring-trend data and a survey of more than 1,000 technology workers conducted May 22–June 10, 2025. That is evidence about hiring conditions and sentiment, not a census of all U.S. tech jobs. Indeed’s tech talent report
For a global view, the World Economic Forum’s Future of Jobs Report 2025 surveyed more than 1,000 employers representing over 14 million workers across 55 economies. Its findings are employer expectations, primarily looking toward 2030—not measured outcomes for 2025. WEF report digest
Technology roles with strong growth signals
These job families reflect a mix of global forecasts and U.S. projections. Job titles vary between employers, and a title alone does not establish that a role is new or growing in every location.
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AI and machine learning
AI work spans more than training large models. Employers need machine-learning engineers, AI engineers, applied scientists and people who evaluate model quality. They also need data and ML platform engineers to make systems usable in production, AI product managers to connect technical capability to user needs, and specialists in responsible AI, governance and model risk.
WEF ranked AI and machine-learning specialists among the fastest-growing occupations in its outlook, projecting demand growth of 40%, or about one million jobs, over its modeled period. That is a global employer forecast, not a record of jobs added in 2025 or a guarantee for a particular specialty. WEF jobs outlook
“Prompt engineer” is not automatically a durable standalone career path. The ability to give models useful instructions and context is increasingly relevant inside broader engineering, product, research and operations jobs. A stronger bet is to pair that skill with a field—such as software, data, security or healthcare—where you can judge the output.
Data engineering and analytics
AI systems are only useful when the data they rely on is accessible, accurate and governed. That supports demand for data engineers, analytics engineers, data scientists, business-intelligence analysts, warehouse and platform specialists, and professionals focused on data quality and governance.
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WEF projected a 30–35% increase in demand for several data-related roles in its modeled outlook, amounting to approximately 1.4 million positions. Treat that as a forecast based on employer responses, not as a verified tally of openings. WEF jobs outlook
In the U.S., the Bureau of Labor Statistics’ 2024–34 projections put data scientists among the occupations expected to grow quickly, at 33.5%. This is a projection across an occupational category, not a promise that every data job will expand at that rate. BLS 2024–34 projections overview
Cybersecurity
More connected services and AI-enabled workflows create more systems, identities and data to protect. Relevant roles include security analysts, cloud- and application-security engineers, identity and access-management specialists, security architects, detection and response engineers, and governance, risk and compliance professionals.
WEF reported a global shortage of approximately three million cybersecurity professionals and projected a 31% increase in demand for information-security analysts in its outlook. These are global figures and forecasts, not U.S. job counts. Separately, BLS projected 28.5% growth in U.S. information-security-analyst employment from 2024 to 2034. The measures and time periods differ, but both point to a strong demand signal. WEF jobs outlook · BLS 2024–34 projections overview
Cybersecurity is not recession-proof: hiring can still vary with company budgets, sector and experience level. But security work is difficult to remove from systems where errors, breaches or regulatory failures carry serious costs.
Cloud, platform and infrastructure engineering
AI services depend on more than models. They need compute, storage, networking, reliable data pipelines, observability, access controls and cost management. That makes cloud engineers, site-reliability engineers, platform engineers, DevOps and DevSecOps specialists, infrastructure-as-code experts, database architects and AI-infrastructure specialists relevant to the shift. Demand for data-center and computing-infrastructure work also matters behind the software layer.
The practical opportunity is often not to build a new model but to make systems dependable: deploy workloads, manage GPU capacity and cloud costs, monitor performance, secure access and respond when services fail. BLS’s 2024–34 industry projections show growth in software publishers and computing-infrastructure, data-processing and web-hosting services, among other areas. BLS industry and occupational projections
Software and application development
Software development remains a large U.S. occupation. BLS projected developer employment to grow 17.9% from 2023 to 2033, reaching 1,995,700 jobs in 2033. The agency also noted that generative AI could affect programming and other core tasks. These projections cover an occupation over a decade; they do not show how every level, specialty or region will fare. BLS on AI and employment projections
The role is broadening from producing code to owning the system around it: clarifying requirements, choosing tools and models, reviewing generated code, testing behavior and security, managing dependencies, observing production systems, maintaining data and model pipelines, and explaining trade-offs to stakeholders. AI can increase a developer’s output, but it also increases the amount of output that needs competent review.
Work under pressure: tasks, not whole professions
Tasks that are routine, clearly specified and easy to check are more exposed to automation. Examples include boilerplate code generation, basic test creation, routine documentation, simple data transformations, repetitive reports, low-complexity support responses, manual data entry and straightforward configuration. Similar pressure can reach manual QA, routine web production, basic analytics and some junior data or content operations.
Exposure is not the same as elimination. Automation is harder when internal data is poor or inaccessible, the work is security-sensitive or regulated, a mistake is expensive, systems are difficult to integrate, or a human must approve and explain the result. A company may also choose not to adopt a tool, or may use productivity gains to handle more demand instead of reducing headcount.
WEF identified data-entry, clerical and secretarial occupations, along with certain teller-related roles, among the fastest-declining in its employer outlook. Those broad categories should not be read as a direct forecast that all technology workers doing repetitive tasks will lose their jobs. WEF report digest
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsOne unsettled risk is the entry-level experience ladder. If AI handles routine assignments that once helped beginners learn, new workers may find it harder to get that first practical experience. That is a plausible concern, not a settled universal outcome. Employers and teams will need deliberate ways to teach fundamentals, review work and give junior staff progressively harder responsibilities.
Skills employers want: technical foundations plus judgment
WEF identified AI and big data, networks and cybersecurity, and technological literacy as the fastest-growing skill groups in its 2025 outlook. It also estimated that 39% of workers’ existing skill sets may be transformed or become outdated during 2025–2030. That is a forecast about skills, not a claim that 39% of workers or jobs will disappear. WEF report digest
Rank #4
Technical foundations
- Build and query: Python, SQL, programming fundamentals, version control, testing and deployment.
- Understand systems: APIs, distributed systems, Linux, networking, cloud architecture and observability.
- Handle data responsibly: data modeling, data quality, pipelines, privacy and governance.
- Secure what you build: secure development, identity and access management, threat awareness and compliance.
- Operate AI systems: machine-learning basics, model evaluation and monitoring, plus infrastructure-as-code where relevant.
Not everyone needs to become a machine-learning researcher. A software engineer, security analyst or IT professional may gain more from applying AI carefully in their existing field than from abandoning it for an ML specialization.
Practical AI fluency
Using AI well means more than writing a prompt. It means breaking work into suitable tasks, giving the system relevant context and constraints, checking outputs for factual errors and insecure code, comparing alternatives, building repeatable workflows, protecting confidential information and knowing when the tool should not be used. Track whether a workflow improves quality, time or cost—not just whether it produces an answer.
Analytical thinking, communication and domain knowledge
WEF reported that analytical thinking remained the most sought-after core skill, followed by resilience, flexibility and agility, leadership and social influence. These capabilities complement technical work: analytical thinking catches plausible but wrong output; communication turns engineering into business value; domain knowledge supplies context models may not reliably infer; and leadership helps teams reorganize work responsibly. WEF report digest
PwC’s 2025 AI Jobs Barometer reported a 56% wage premium for U.S. workers with advanced AI skills in its analysis. This is an association in labor-market data, not proof that learning AI alone causes higher pay or a guarantee for an individual. Skills, experience, sector and job selection can all affect earnings. PwC U.S. AI Jobs Barometer
Is software engineering still a good career?
It can be, but no occupation-wide growth projection guarantees a smooth path for every developer. The BLS projection of 17.9% U.S. growth from 2023 to 2033 is a favorable long-term signal; it is not evidence that hiring was easy in 2025 or that every employer needs the same skills.
Developers are better positioned when they can do more than produce code: translate ambiguous needs into requirements, design maintainable systems, evaluate AI output, test edge cases, protect data and services, debug production behavior and explain trade-offs. People whose experience is limited to repetitive implementation may face more competition as those tasks become cheaper. The sensible response is to deepen engineering judgment, not to assume either that AI will do all programming or that programming work will remain unchanged.
What to learn next, by starting point
If you are a student or changing careers
- Choose a job family—software, data, cloud, IT support or security—instead of vaguely deciding to “learn AI.”
- Build the relevant foundation: programming and SQL for software or data; networking and systems for IT and security.
- Learn one cloud platform after you can explain the underlying concepts.
- Practice secure, responsible AI use, including verification and data protection.
- Build two or three projects that can be demonstrated and explained. Include architecture, tests, limitations and deployment where appropriate.
- Seek applied experience through internships, open source, freelance work or projects for a real organization.
A portfolio is useful when it shows decisions and execution, not just a polished demo. Explain what you built, what failed, how you tested it and where you used AI assistance.
Best Value
If you are already a developer
Prioritize reviewing and testing AI-generated code, system design, security, data and observability. Learn how agent or automation workflows fit into your team’s development process, but keep ownership of the behavior and risks of the software. A 2025 study of professional developers grouped AI-era capabilities into generative-AI use, core software engineering, adjacent engineering and adjacent nonengineering skills—a useful reminder that success requires more than tool familiarity. Study of professional developers’ AI-era skills
If you work in IT or infrastructure
Build depth in cloud operations, identity and security, automation, incident response, cost controls and data-platform literacy. AI infrastructure is an option, but strong networking, systems and operational fundamentals transfer across vendors and workloads.
If you work in data or security
Data professionals should strengthen SQL or Python, modeling, quality controls, governance and the ability to communicate findings. Security professionals should deepen networking, cloud and identity skills, detection and response, and practical lab experience. AI literacy matters in both fields, but it should support—not replace—your core discipline.
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Start with a workflow problem, not a tool purchase. Set measures for quality, cycle time, reliability and total cost; train staff; establish human review where errors are expensive; and make security and privacy requirements explicit. Avoid treating “AI” as a proven explanation for layoffs when restructuring may have other causes. Update job descriptions around outcomes and judgment rather than a task list that automation has already changed.
Degrees, certifications and portfolios
A computer-science degree can provide fundamentals, internships, structured recruiting access and a useful route into research-heavy or regulated work. It is not the only way into software, cloud, data, support engineering, QA automation or security. Requirements vary by employer, seniority, geography and role; some regulated, research or immigration contexts may impose additional constraints.
WEF’s employer findings indicate growing emphasis on upskilling, reskilling and hiring for new skills, with skills-based hiring becoming more prominent in some sectors. That does not mean credentials have become irrelevant; it means employers may weigh demonstrated capability alongside formal education. WEF region, economy and industry insights
Certifications can structure learning and signal familiarity, especially in entry-level IT, cloud and security paths. They do not prove that you can build, troubleshoot, explain or secure a real system. Pair a credential with a lab, deployed project, incident exercise or documented troubleshooting example. Likewise, a portfolio should show sound choices and limitations, not just a list of tools.
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What remains uncertain
Three questions are still open: whether productivity gains will create enough new demand to offset reduced need for some routine work; how employers will preserve entry-level learning opportunities; and which AI-specific titles will persist once their responsibilities become standard parts of other jobs. Regulation, security requirements and the reliability of organizational data will also shape where automation is practical.
Forecasts help map possible directions, but they are not hiring guarantees. WEF’s global outlook, BLS’s U.S. occupational projections, Indeed’s hiring survey and PwC’s wage analysis answer different questions. Read each in its own context rather than treating them as one definitive count of jobs gained or lost in 2025.
The durable strategy
Technology careers are shifting from producing routine digital artifacts to owning the systems, decisions and risks around them. Build strong fundamentals in a job family, learn to use AI productively and verify its work, then add domain knowledge, security awareness and communication. That combination is more durable than chasing a job title or tool trend alone.
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