Every engineering leader is hearing the same advice right now: retrain the whole team as AI engineers before it is too late. That advice is going to waste a lot of money, and I want to walk through why.
The advice conflates two different skills. Using AI to build software faster is one skill. Building software out of AI is a different skill. They sound like the same thing, and they are not close.
Push your whole team toward the specialist track and most of them will not want it, the organization does not need twenty of them, and you will trade away depth you already own for shallow coverage of a skill the market only needs from a few. A great database engineer who uses AI well is worth more to you than a mediocre AI engineer, and that stays true for as long as I can see.
But the opposite mistake is just as common. Some leaders read the hype fatigue correctly and conclude nobody needs to change. That fails too, because somebody has to own the AI systems your company is about to depend on, and if nobody inside owns them, you will rent that judgment forever.
The sorting is not complicated. The power user track is for everyone, full stop. The specialist track is for the one or two people who were already experimenting before anyone asked them to. That last part matters more than seniority or title. The engineers who should own your AI systems announce themselves by what they do on a slow Wednesday. You are not assigning the track. You are noticing it.
I run a firm that sells engineering enablement, so weigh my incentive. But notice that the advice cuts against the bigger engagement. The expensive version of this is retraining everyone. The correct version is cheaper.
The power user track is not free just because it is short. Without shared conventions and real review discipline, an AI-accelerated team ships AI-accelerated defects, and you will not see it in the velocity numbers until it is in production. Measure defect rates alongside throughput or you are measuring half the story.
And your specialists need an actual system to build, not a title change. A specialist with no production AI system to own is just an enthusiast with a new business card.
The fear underneath all of this is real. The panic version says your craft is obsolete. I think the opposite is happening. When the first draft of everything gets cheap, judgment becomes the bottleneck, and judgment is the part of your craft that took the longest to build. The engineers in trouble are not the ones who skip the AI specialist track. They are the ones who refuse the power user track and keep typing everything by hand out of principle.
List your engineers. Mark who is using AI tools daily, occasionally, and not at all.
Then answer one question about yourself. Do you know, today, who owns AI systems in your organization? If a name did not come to mind, that is the gap, and it is not solved by sending everyone to a course.