Comment by denn-gubsky

In my experience, AI solves the implementation part well - if you know explicitly what should be implemented. If your engineering understanding is fuzzy, the implementation will be fuzzy too.

Engineering and architectural skills matter more now than before. Previously, you had time for rethinking and rearchitecting while coding. That timeline is collapsing - your ideas become real in hours, the bad ones included.

Verification matters more than implementation. When you ship 20 PRs to verify in a single day, every day, all month, how can you be sure the ideas are right, and the implementation is correct? You need ways to challenge your own system: integration tests, runtime tests, and load tests that make you confident in what you're building.

Three skills become non-negotiable in this AI era (until tomorrow, when the answer probably changes again):

1. System Architecture. Build correct framing for your ideas with room for future extensions. Requirements should be complete, precise, and extensible. Project documentation grows with the code and stays current.

2. Organizing AI agentic teams into verifiable flows. Self-evolving specialized agents that verify each other's output until you're confident the code matches the requirements you actually had.

3. Verification, verification, verification. Integration tests, runtime tests, load tests, experiments. The code an AI produces has to be confirmed correct across every dimension that matters.