Rob was supposed to be finishing his book. Last chapter. Two days past deadline. Freedom was right there.

Instead, he hit pause and recorded this.

Because something from a few weeks ago wouldn't leave him alone.

A Microsoft exec had dropped "Microsoft IQ" into a conversation weeks ago. At the time, it didn't fully land. Not unusual. There's been a steady firehose of new terms, new features, new promises. Most of them sound important. Not all of them are.

Then he got deep into the data chapter. The one where you have to stop talking about what AI could do and deal with what it takes to make it work in a real company.

And that's where this thing stopped sounding like a label and started looking like a plan.

AI looks great right up until you ask it to do something that depends on your business. Your definitions. Your documents. Your people. That's where things usually start to wobble. Not because the model isn't capable, but because it doesn't have the context to land the answer.

What Microsoft is doing with IQ is trying to meet that problem head on.

· Fabric IQ is the structured side. Semantic models doing what they've always done, but now under a lot more pressure.

· Foundry IQ is all the documents and content you forgot you had.

· Work IQ is the human layer. Who's involved. Who needs to know. What you meant when you said "that thing."

And yeah… if you've been doing Power BI the right way, this is where it gets interesting. Because those semantic models everyone else treated like optional homework? That's now the thing everything else leans on.

We're not saying this episode is the key to your AI implementation, but it will make it clear why some of this is working and some of it isn't.

Garett Medlin just got the official title for the job he was already doing: AI Practice Lead at P3. He's also the person responsible for Rob trying Cowork in the first place, despite Rob's very reasonable question: "Why the hell would I want Cowork if I already have Claude Code?"

Then Rob accidentally proved Garett right.

He made an offhand comment about needing a better way to track feedback on book graphics. Nothing dramatic. Just the kind of annoying little process problem everyone complains about and nobody fixes. Two days later, there was a Slack bot reminding him to review images, a web app with approve buttons, surrounding context from the manuscript, and a clean way to send feedback without creating a Slack archaeology project. Built by a non developer. In Cowork.

Which makes Microsoft's Copilot Cowork story… awkward.

Garett came with the field report. Yes, it can make PowerPoints. Yes, it talks to OneDrive. No, it doesn't have memory. No, it doesn't have custom instructions. No, it doesn't have projects. The section where those capabilities are supposed to live is called "Acquired Skills," and it currently says they will appear here. Which is a choice.

At the same time, companies are getting top down mandates to spend $20 million a year on AI with absolutely no idea what they're supposed to spend it on. IT gets handed the problem, Copilot gets treated like the answer, and somebody nearby is always trying to sell a very expensive fear of the tools that already work.

This episode is really about that gap. Between what's shipping and what's still "coming soon." Between the people waiting for enterprise permission and the people already building useful things on a Tuesday afternoon. Turns out, the scariest part of AI might be realizing the non developers got there first.

Rob didn't go looking for a fight with the medical system. He just showed up with receipts. Claude had already mapped the symptoms, suggested the tests, and summarized the situation better than any portal ever would. And instead of pushing back, the doctor basically said, "Yeah, this all checks out," added a few things, and moved on. No drama. No turf war. Just a quiet moment where you realize… the system didn't break. It just got leapfrogged.

The next morning, sitting in an Uber on the way to the fasting lab, Rob had AI log into his medical portal, pull down test results, interpret them, suggest next steps, and tee up additional tests before the lab even opened. That's not "AI as a helper." That's AI running point. And when it catches an error in the doctor's AI-generated notes and fixes it by talking to their system directly… yeah. That's the moment. You don't unsee that.

Which is great… until you zoom out. Because the same thing that lets you bulldoze friction in healthcare also bulldozes friction everywhere else. Social media. Identity. Trust. If AI can operate the interface better than you can, the whole idea of "who's actually doing what" starts to get fuzzy real fast. There's a version of this where everything gets more efficient. There's another version where everything gets a little… fake. This episode walks through both. It's worth knowing which one you're already in.