Brian

I struggle with this.

I teach AI to professionals all the time.

And I struggle with what do they need to know today?

And how is that different than what I would have taught or did teach a year ago, six months ago, two years ago, three years ago?

I've been doing AI and teaching AI since, well, three years now.

So the generative AI side, not the machine learning side of AI, but the generative AI side of things.

Yesterday was episode 650 of the Daily AI Show.

I've been on over 90% of those shows live.

So that means I've spoken well over probably 600 hours in the last three years live about AI in conversations with the rest of the DOS crew.

I say all to say, I'm in it.

I'm in the bubble.

I'm here.

And yet this conundrum gets me.

It's called the liquid literacy conundrum.

Over the last six weeks, the center of gravity shifted.

People spent 2024 learning how to talk to one model, now they manage systems where models talk to each other.

Problems still matter, but they increasingly hide inside workflows, agent routers, tool pools, and multi-step automation.

That shift rakes the normal way professionals build competence because the surface area you have to learn keeps changing faster than most teams can train, document, and standardize.

So here's the conundrum.

If AI skills now behave like a liquid, always taking the shape of the latest interface model or agent framework, what should you actually invest in?