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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsSoftware engineers still need to understand how software works. As AI tools generate more code, the job puts greater weight on defining requirements, supplying context, evaluating generated code, designing systems, and taking responsibility for testing, security, and behavior.
Why software engineering skills are shifting—not disappearing
Code-generating AI can reduce the amount of code a developer writes by hand, but generated code still has to fit a real system and meet real requirements. A U.S. federal workshop report says engineers using code-generating large language models may spend less time writing code and more time understanding and reasoning about it. That is a shift in effort, not evidence that programming fundamentals or engineering roles are obsolete. The 2024 Leadership in Software Engineering & AI Engineering workshop report frames the change as a need for stronger reasoning and judgment.
A 2025 qualitative study based on 21 developers experienced in AI-supported work groups relevant capabilities into four areas: generative-AI use, core software engineering, adjacent engineering, and adjacent non-engineering skills. It offers a useful map, not a representative estimate of what every developer does. The occupational-profile study by Kam and colleagues emphasizes that foundational engineering ability remains central.
Keep the foundations that let you judge code
AI can produce plausible code that is incomplete, incorrect, or mismatched to the system around it. The engineer needs enough knowledge to read unfamiliar code, trace behavior, find defects, and decide whether a proposed change is maintainable.
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- Programming and code comprehension: Read and explain code in the languages and frameworks relevant to your work, including code you did not write.
- Debugging and testing: Reproduce failures, isolate causes, write meaningful tests, and check that a fix does not break neighboring behavior.
- Data structures, algorithms, and design patterns: Understand the choices behind an implementation so you can assess performance, complexity, and fit.
- Requirements engineering: Turn an ambiguous request into observable behavior, constraints, and acceptance criteria before asking AI to implement it.
- System familiarity: Learn the existing architecture, interfaces, dependencies, and operational assumptions before changing a component.
The 2025 study specifically highlights foundational programming, data structures, algorithms, design patterns, and debugging for junior developers. Its small, qualitative sample does not establish a universal curriculum, but it supports a practical principle: stronger foundations make it easier to identify when an AI-generated answer is unsuitable.
Use AI as a workflow tool, then verify its output
Effective AI use is more than writing a clever prompt. Engineers need to describe the goal, constraints, examples, and relevant codebase context clearly; divide work into manageable pieces; and evaluate the result against the system’s actual needs. The NITRD workshop report notes that different prompts can produce different code and describes prompt engineering as a natural-language programming technique that can apply across development stages. It does not make prompting a substitute for engineering competence.
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- Specify the task: State the behavior you want, relevant constraints, and how success can be observed. Include only context that is appropriate to share under your organization’s policies.
- Keep the change reviewable: Break large requests into smaller changes with clear boundaries. Ask for an explanation or tests when those will help you review the result.
- Check the implementation: Compare generated code with the requirements, existing interfaces, tests, and operational constraints. Run the relevant checks rather than relying on an explanation alone.
- Investigate uncertainty: Look up unfamiliar APIs or assumptions, seek another source of evidence, or reject the output when you cannot establish that it is correct.
- Own the result: Review, revise, test, and maintain the code as part of the software system—not as an isolated AI response.
This skill is best understood as a generation-and-evaluation loop: use AI to help produce or explore an implementation, then apply engineering judgment to decide what should ship.
Develop systems thinking and risk judgment
Software behavior emerges from components interacting with dependencies, data, users, and operations. Engineers need to reason beyond whether a code snippet runs: will the design remain reliable under failure, protect sensitive information, and behave safely for the people affected by it?
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- Systems thinking: Trace how a change affects other components, data flows, integrations, and production operations.
- Problem detection: Look for edge cases, hidden assumptions, failure modes, and behavior the request did not spell out.
- Design judgment: Choose an approach that balances functionality with reliability, maintainability, performance, and cost.
- Security and privacy awareness: Review permissions, input handling, data exposure, dependency risks, and the impact of errors.
- AI and ethical awareness: When a product includes AI or ML, understand enough about uncertainty and limitations to assess its behavior and consider who may be harmed by it.
The NITRD report says engineers will need “a firm grasp of probabilistic reasoning to deal with uncertainty; an increased capacity to detect problems and make informed design decisions; strong systems thinking skills; and a keen awareness of the ethics of AI.” It also warns that AI tools can obscure trade-offs between functionality and safety or security. The level of scrutiny should therefore match the consequences of failure.
Keep communication and product understanding in the job
Engineering work depends on understanding what users need, clarifying shared requirements, explaining trade-offs, and coordinating changes with teammates. AI can help with implementation, but it cannot make an underspecified product decision safe by itself. Engineers still need to surface unanswered questions and communicate why a design meets—or fails to meet—the intended need.
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A 2024 online survey commissioned by GitHub and conducted by Wakefield Research polled 2,000 non-student, non-manager employees at enterprises with more than 1,000 employees: 500 each in the United States, Brazil, Germany, and India. Fielded from February 26 to March 18, it found that more than 97% reported using AI coding tools at work at some point; that measures any-point use, not regular use. Among respondents in the U.S. and Germany, 47% said they used time saved with AI for collaboration and system design. These are respondents’ reported experiences, not proof that AI caused a particular productivity gain. GitHub’s survey summary describes the findings.
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You do not need to become an AI/ML specialist simply because AI tools are becoming common. Build broad engineering competence first, then deepen specialized knowledge where your role requires it.
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- If you are early in your career: Focus on programming, debugging, testing, data structures, algorithms, and reading existing code. Practice using AI to explore solutions, but make yourself explain and verify each change.
- If you are an experienced engineer: Strengthen requirements work, architecture, integration, operational reasoning, and the ability to review changes across a system—not only within one file.
- If you work on consequential or sensitive systems: Give extra attention to security, privacy, reliability, safety, and documenting trade-offs. The costs of an unnoticed defect vary by application.
- If you build AI-enabled products: Learn enough about AI/ML behavior, uncertainty, evaluation, and ethical impact to assess the system being shipped. The needed depth depends on your responsibilities.
Organizational practices matter alongside individual skills. DORA’s 2025 report draws on more than 100 hours of qualitative data and survey responses from nearly 5,000 technology professionals worldwide. It characterizes AI as an amplifier of high-performing organizations’ strengths and struggling organizations’ dysfunctions. The landing page does not provide detailed skill-by-skill results, but its central point is that tools do not compensate automatically for weak delivery practices. DORA’s 2025 report summarizes that conclusion.
What current evidence can—and cannot—tell you
Available findings document changing workflows and identify capabilities that researchers and respondents consider important; they do not settle long-term employment effects or rank a single career path as best. The NITRD publication is a workshop synthesis, the GitHub results are a commissioned survey of a defined enterprise sample, and the 2025 skills profile is based on 21 experienced developers. Treat them as evidence for learning priorities, not as guarantees about every team or the future of engineering.
Security is one area where a public finding underscores the importance of the skill: Gartner’s July 2024 abstract says 75% of surveyed software engineering leaders rated application security highly important and identifies applying AI/ML to applications as the most significant skills gap. The abstract does not expose the full study context, so this should be read as Gartner’s reported finding, not a universal measurement. Gartner’s public abstract contains the available details.
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