The original title is about career strategy in the AI era - specializing vs being versatile. Let me rewrite this as a punchy headline.

Short summary

A personal reflection on whether professionals should specialize deeply or become versatile generalists in the AI era. The author argues for a dynamic balance: AI automates routine tasks and raises the bar for strategic thinking, but deep specialization remains essential for complex problem-solving that AI cannot handle. Practical examples include using RAG and prompt engineering to accelerate ERP production planning, while still needing deep PostgreSQL knowledge to diagnose WAL bloat.

• •AI automates routine tasks, increasing demand for strategic and complex thinking
• •Deep specialization remains critical for problems AI can't solve (e.g., WAL bloat debugging)
• •Career success in the AI era requires balancing breadth with depth, not choosing one

Generated with AI, which can make mistakes.