Do Words Still Matter?

Confession:
I’ve stopped saying “machine learning.”
Not because it’s wrong - because “AI” gets the meeting.

I used to correct people.
Five years ago, I’d explain that AI was the big idea, and ML was the practical engine underneath.
Now? I just nod.

Somewhere along the way, clarity stopped paying off.
Executives understand “AI initiative.”
It sounds strategic, transformative, fundable.
“ML pipeline optimization”? That sounds like plumbing.

But when we blur those words, we blur expectations too.
People start thinking “AI” means reasoning, creativity, even sentience - when most of what’s running in production are pattern-recognition systems trained on structured data.

The danger isn’t that the tech changed - it’s that our language did.

Words shape how organizations think about intelligence, risk, and value.
When we say “AI” instead of “ML,” we’re not just simplifying - we’re changing how people understand what we’ve built.

Maybe I’ve adapted to the language of the business.
But sometimes I wonder if, in chasing simplicity, we’ve traded away a little too much precision.

Do words still matter?
For those of us building intelligent systems, I think they matter more than ever.