Chasing new skills, going back to basics and pushing for collective action: how software engineers are adapting to AI

2026-07-12 · Source: AI (artificial intelligence) | The Guardian · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Fundamental Awareness, long

Summary

Software engineering, a leading profession in 2022 with 1.5 million US practitioners earning over \$200,000 annually, faces significant disruption from artificial intelligence. Since ChatGPT's 2022 release, over 600,000 US tech workers have been laid off, and computer science graduate unemployment rose to 7% in 2024, with underemployment exceeding 19%. Companies like Google now report 75% of their code is AI-generated, shifting engineer roles from writing to reviewing code. This has led to anxiety among professionals, with some, like Matt, actively trying to preserve traditional coding skills. Others, such as George Dover, successfully adapted by learning to rigorously evaluate AI-generated code, securing an AI-oriented software engineering job after 400 applications. Experts emphasize that while direct coding skills may diminish, the ability to define problems, design systems, and critically assess AI output is becoming paramount. This shift is also impacting education, with computer science program enrollments declining 8.1% for undergraduates and 14% for graduates in the 2025-2026 school year, prompting some engineers to seek collective action and support through organizations like "What We Will."

Key takeaway

For software engineers navigating the AI-driven transformation, your career stability now hinges on adapting your skillset. You should prioritize developing expertise in critically evaluating AI-generated code, identifying vulnerabilities, and understanding system design rather than solely focusing on writing code from scratch. This shift ensures your continued relevance and value, mitigating the risk of skill obsolescence as AI increasingly handles code generation. Consider joining professional groups to advocate for industry standards and support.

Key insights

AI is rapidly transforming software engineering, shifting value from coding to evaluating AI-generated code and system design.

Principles

• Coding skill value is diminishing.
• AI augments, not replaces, engineers.
• Critical evaluation of AI code is paramount.

Method

Generate code with AI, then rigorously evaluate it for errors, redundancies, unusual decisions, bugs, and visual glitches to understand its strengths and limitations.

In practice

• Rigorously test AI-generated code.
• Prioritize problem definition and system design.
• Engage in collective action for industry standards.

Topics

• Software Engineering
• AI Impact
• Code Generation
• Skill Transformation
• Workforce Adaptation
• Tech Layoffs
• Collective Action

Best for: CTO, VP of Engineering/Data, AI Engineer, Software Engineer, Director of AI/ML, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI (artificial intelligence) | The Guardian.