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Evidence-backed analysis of how AI automation affects Software Engineers. Scores derived from published research — McKinsey, BLS, Stack Overflow, and industry data.
Automation Risk
Defensive Strength
Estimated Runway
4–6 YearsMarket Intelligence
41% of all code written in 2025 is AI-generated (Stack Overflow, 49K developers). Traditional programmer employment declined 27.5% between 2023–2025 per US BLS data. Mid-level coding tasks (boilerplate, unit tests, bug fixes) are now largely AI-assisted. Demand is bifurcating: AI-fluent engineers commanding premiums; traditional stack developers seeing reduced demand.
Source: Based on Stack Overflow Developer Survey 2025, BLS Occupational Outlook 2025, GitHub Copilot adoption data, and METR July 2025 RCT findings on AI-assisted development.
Task Breakdown — Time Allocation vs. Vulnerability
Highest Exposure Areas
Hands-On Technical Execution
41% of code written in 2025 is AI-generated. The defensible technical work is system architecture, novel problem-solving, and integration of AI tools — not execution of known patterns. Standard technical execution is being absorbed at an accelerating rate.
Meetings / Coordination / Scheduling
Calendar AI and agentic scheduling tools already handle meeting coordination. The coordination value that remains human is the nuanced political navigation — and that erodes as AI gains organisational context.
Writing / Summarising / Documentation
GPT-5 Deep Research and Claude already produce publication-quality reports, emails, and documentation. By 2027, AI writing assistants will handle first-draft creation for virtually all standard business documents with minimal human input.
Strongest Defenses
Hands-On Technical Execution
41% of code written in 2025 is AI-generated. The defensible technical work is system architecture, novel problem-solving, and integration of AI tools — not execution of known patterns. Standard technical execution is being absorbed at an accelerating rate.
Decision-Making Under Uncertainty
This remains one of the most defensible task categories — AI struggles with genuine novelty and accountability. The erosion condition: as AI decision-support tools become standard, the bar for what counts as 'genuine uncertainty' rises, and roles that mostly execute defined playbooks lose this protection.
Domain Specialist Judgement
Deep domain expertise is the most durable protection — but it degrades when AI is trained on sufficient domain-specific data to match pattern recognition. The erosion condition: the more codifiable your expertise, the faster this protection erodes. Truly novel, context-dependent judgement remains human-critical.
This is the average. What about you?
The average Software Engineer scores 42/100 risk. But your specific role, environment, and task allocation could be higher or lower. Get your personalised score in ~10 minutes.