Sure, Claude. Sure.

I’ve been involved in shutting down a lot of software in my career. It’s like documentation: not the most exciting thing, just part of the job.

This one turned out to be different.

My hypothesis was that there were a small number of users on really old devices, and that this was why they appeared “funny” in our telemetry. So… get the device impact analysis, double-check our support statements, figure out how many of these users would lose access to the service, etc. etc.

Here’s where it got interesting: I had Claude writing the queries for me, and as I added more tables to enrich the segmentation it said, “This isn’t old devices. This is organized credential sharing or piracy.”

Sure, Claude. Sure. I know my space. This is just old devices.

That was two weeks ago.

A lot of what I do as a PM is use my network to navigate the organization. I know I don’t know the answer to everything, even in my area of subject-matter expertise. Assumptions are always dangerous. Besides, it’s fun to share examples of AI getting it wrong.

So I asked around. Peers. People I remembered talking to a couple of years ago. VPs. Strangers I found in the corporate phone book.

This morning, a contact I’d been introduced to by another contact added a contact from our anti-piracy group to a Teams chat.

A minute later he started a video call with me.

I was wrong to be skeptical of Claude’s hypothesis. It noticed something I didn’t, something I would never have noticed.

But Claude couldn’t navigate the organization to validate that idea, let alone take action. It didn’t know who to trust, who to call, or whether its own inference was credible. That part was mine.

That’s the partnership I’m enjoying: not AI replacing expertise, but AI being just credible enough that it makes me question my own assumptions.