Most people still use AI like a better search box, but the real shift is AI agents, systems that decide the next action, not just the next word.
I explain the difference between prompts and agents using ARR, show what’s happening “under the hood” with four roles, and map how agents adapt through an OODA loop when workflows break.
I also cover why agents fail in real life: they amplify vague thinking and bad processes, so you need a GPS check before automating anything.
The opportunity isn’t broad intelligence; it’s narrow, specific agents that solve repeated, hated tasks, as output gets cheap and judgment, taste, and standards become more valuable.
