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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsAI is not yet shown to be causing mass software-engineering job cuts. In Karat’s 2025 survey, 85% of engineering leaders said they expect headcount to stay flat or grow over the next three years. At the same time, many respondents believe AI is widening the gap between stronger and weaker engineers. Those are leaders’ expectations and assessments—not verified employment outcomes or proof that AI caused layoffs.
What the survey says about engineering jobs
Karat’s 2025–2026 AI Workforce Transformation Report surveyed 400 engineering leaders, including 300 senior vice presidents and C-suite executives, across the United States, India, and China. GeekWire covered the findings on December 10, 2025. The survey reflects this group’s views; it is not a census of employers or a longitudinal record of hiring and layoffs.
Karat reports that 85% of respondents expect engineering headcount to remain flat or increase over the next three years. That is a forecast, not evidence that layoffs have not happened or that staffing will ultimately follow the forecast. The findings do not identify the causes of any layoffs.
Why leaders think AI may widen the performance gap
Karat reports an average 34% productivity increase attributed to AI. It also says 59% of surveyed leaders believe weak engineers deliver net zero or negative value in the AI era, while 73% believe a strong engineer is worth at least three times their total compensation. These are company-reported estimates and respondent views, not independent measurements of productivity, employee value, or AI’s effect on jobs.
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Karat describes AI as a multiplier: engineers with strong foundations and skill using AI tools may produce more, rather than the technology automatically making all engineers equally effective. The reviewed findings do not establish a causal link between AI use and any individual’s output or employment.
Where engineers are using AI—and what leaders want next
In Karat’s survey, 83% of leaders cited code generation and 61% cited testing, quality assurance, and code review as common day-to-day engineering uses. A majority identified agentic AI or autonomous engineering agents as having the highest return on investment.
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The skills leaders want to add or assess include familiarity with agentic AI, using AI for coding, integrating third-party AI APIs, prompt engineering, and evaluating and mitigating AI-related risks. Karat also says problem-solving, communication, and product sense remain foundational.
The hiring mismatch: AI skills matter, but assessments lag
The survey points to a gap between the skills leaders say they need and how they evaluate candidates. Karat reports that 62% of respondents do not allow candidates to use AI in interviews; 30% rank updating technical assessments for AI skills as a top priority, and 25% prioritize training interviewers. GeekWire reports that nearly 70% of leaders plan to strengthen AI capabilities through strategic hiring. These figures describe this survey sample, not all employers.
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Karat’s report also promotes NextGen, its AI-enabled engineering talent evaluation service. The company describes a realistic development environment with an integrated AI assistant, expert interviewers, and project-based questions. That commercial context is relevant when interpreting the report’s emphasis on measuring AI-ready talent; it does not by itself invalidate the survey findings.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What this means for engineers’ career planning
The survey is a signal about what surveyed leaders value, not a reliable forecast of any one engineer’s job security or compensation. For an engineer deciding what to develop, the reported priorities point toward combining durable engineering judgment with practical AI fluency: using tools for coding and testing, working with APIs and agents, and checking the quality and risks of AI-generated output. Communication, problem-solving, and product sense still matter in the report’s account.
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For readers assessing broader career or income risk, keep the distinction clear: a predicted stable or growing workforce can coexist with layoffs at particular firms, role changes, and higher expectations for remaining staff. This survey does not measure those outcomes or determine what they mean for an individual worker.
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