eng manager

AI can automate 30-40% of tactical management tasks: breaking down epics into user stories (tools like Jira AI, Linear AI), generating sprint reports, analyzing velocity metrics, and drafting technical documentation. However, the core 60-70% remains human: one-on-ones, performance coaching, conflict resolution, hiring decisions, architecture trade-offs, and translating ambiguous business requirements into technical vision. Your experience level means you handle more strategic, less automatable work.

AI progress in software development is rapid (GitHub Copilot, Cursor, Devin, etc.), which indirectly affects management by potentially reducing team sizes and changing skill requirements. However, AI advancement in actual management capabilities—understanding team dynamics, making personnel decisions, navigating organizational politics—is much slower. Management AI tools remain assistive rather than autonomous. The 12-year timeline to highly capable management AI gives you time to adapt.

IT/software companies are aggressively adopting AI coding tools (80%+ of tech companies now use GitHub Copilot or similar), but adoption of AI for management functions is much slower and more cautious. Companies recognize that people management requires human judgment, especially in mid-sized organizations where relationships and culture are critical. Expect gradual adoption of AI planning assistants over 3-5 years, not wholesale replacement.

Engineering management is heavily weighted toward uniquely human capabilities: building trust and psychological safety, mentoring junior engineers, navigating interpersonal conflicts, making ethical trade-offs between speed and quality, reading room dynamics in meetings, and providing emotional support during crunch times. Your role requires empathy, political savvy, and the ability to inspire—areas where AI has minimal capability. The 'long-term vision' aspect requires creative strategic thinking that AI cannot replicate.

Your skills are highly transferable across multiple dimensions: (1) Technical leadership roles in adjacent industries (fintech, healthcare tech, SaaS), (2) Product management (technical PMs are in high demand), (3) Engineering director/VP roles, (4) Technical program management, (5) Consulting/advisory for startups. Your 12 years of experience and ability to bridge technical and business contexts makes you valuable across many contexts. Mid-sized company experience is particularly transferable to startups scaling up.

Demand for engineering managers remains strong despite tech industry volatility. LinkedIn shows 50,000+ active EM postings in the US, with median salaries increasing 4-6% annually. However, there's a shift toward 'player-coach' roles where managers also contribute code, and some companies are flattening hierarchies. Mid-sized companies (your segment) particularly value experienced managers who can scale teams. The market is competitive but healthy for those with proven track records.