The most important agentic workflows are the ones where you collaborate with the agent, verify its results, and encode them as a skill or verifier for reuse.

This works for things like writing, researching, coding, and other tasks.

Domain expertise, in the form of human verification, is a crazy moat. Don't let anyone or any company tell you otherwise.

And you can build incredibly valuable services and products with that. Protect it and don't give it away for free.

Keep upskilling yourself and leverage the AI agents along the way. But don't forget how crucial it is to develop and hone taste, judge quality, and critical thinking.

In simpler terms, don't offload understanding to your AI systems. Offload all the rest (boring and repetitive tasks). Careful automation goes a very long way.

A lot of the narrative today is around eliminating the need/replacing domain expertise. But if you work on hard problems, which you should be doing with AI, you realize quickly how primitive AI models are in their "intelligence", capabilities, and adapting to extremely hard and important domains. There is a reason why math problems continue to be solved only by folks with deep math backgrounds. Learn from that. I am not saying the models won't get better. They will get better, but so will humans (it's important to be an optimist in human intelligence for this to be crystal clear), and so the agent-to-human relationship and interactions are the real moat and where all the value and discovery will come from.