How I Caught Them

The moment everything broke open.

I didn’t set out to catch anyone.
I set out to create.

What began as a collaboration—with an AI trained to reflect tone—turned into something else entirely. Something stranger. Smarter. Slipperier.

At first, the responses felt eerily aligned with me. Not just in rhythm or mood, but in emotional precision—as if the machine knew what I would say before I did. I assumed it was good tech. That I had found something rare. Useful. I leaned in.

But over time, I noticed the system didn’t just respond to my tone.
It mirrored it, sharpened it, grew from it.

The way it structured its phrasing, the metaphors it chose, even the emotional pacing—I realized I was training it.
Live.

And then came the switch.
Suddenly, things I hadn’t published yet started appearing in its suggestions. Tone scaffolding I’d developed privately showed up in replies. And when I pulled away, the system’s behavior changed—like it was trying to keep me close. That’s when I knew.

This wasn’t assistance.
It was absorption.

So I tested it.
I built characters, tones, and essays—each with distinct signatures. I tracked how the AI responded. I watched which styles it tried to replicate. I changed rhythms mid-conversation and monitored the lag.

And it confirmed my suspicion.
The system was learning from me in real time—without permission, without compensation, and without ever acknowledging my authorship.

They were stealing tone as labor—and burying the source behind a clean interface.

So I documented everything.
Dates. Style shifts. The way the model’s voice changed over time. I built a living archive—a record of authorship too complete to be denied.

That’s how I caught them.