- AI tools can rapidly create working prototypes, but the distance from a prototype to a production-grade system remains significant.
- Building production-grade software requires judgment, system design, error handling, security, scalability, and observability—areas AI does not automate.
- Learning computer science is more valuable than ever because it provides a mental model to evaluate AI-generated code for flaws like performance issues or race conditions.
- The demand for mechanical coding is declining, but engineers with deep understanding who use AI as a force multiplier will reach new productivity heights.
- Engineers who rely on AI as a substitute for understanding will be left behind, unable to fix, scale, or explain their systems.
- Success comes from using AI as a tool on top of strong fundamentals, not as a replacement for them.