Grid-Level Thinking Wasn't the Plan

It started with a phone call.

Me and a friend I trust—someone sharp, skeptical, and deep in the tech trenches—got into it about AI. Not the buzzwords. The real stuff:
LLMs. Vector math. Probability engines. All of it.

The question underneath it all was simple:

Is this actually useful right now?

The hype says yes.
The reality says “depends.”

There are entire categories of work that AI isn’t ready for.
Not even close.
I wouldn’t trust it—supervised or not—to write a real QA strategy, build a system of record, or handle nuance in high-stakes production logic. That’s not where we are.

And yet…

I use these tools every day.
To think faster.
To reframe messes.
To clarify systems.
To offload the mechanical parts of strategy so I can focus on what matters.

It’s not magic.
It’s just… utility.
It’s a new kind of infrastructure that’s only useful when you wire it into your real workflows with intent.

That’s where this series came from.
Not hype. Not fear.
But the same place every good system design comes from:

Trying to make something work better, in the real world.

We’re not at some clean inflection point.
This isn’t the internet all over again.
We’re probably a decade early on half of what people are claiming.

But it’s also foolish to ignore it.

To walk everywhere just because the car isn’t perfect?
That’s not noble. That’s inefficient.
The world is already wired for motion—you just need to learn to drive in this new system.

So no, I’m not evangelizing.

I’m experimenting.
I’m watching for signal in the noise.
I’m trying to figure out where this new current can make my work—not just faster, but better.

And I wrote this series to start that conversation out loud.

This post closes the Grid-Level Thinking series.

Read all four posts in the collection here.