Raw Data with Rob Collie

There's a new word for what your team is doing with the AI you bought them. You're not going to love it.
"Botsitting." Babysitting, but for bots. Journalists are suddenly all over it, and not one of the AI people Rob asked had ever heard of it. New name, familiar burn. Turns out mandating AI usage is a great way to pay good money for software that makes everybody slower.
Then the flip side. Rob's spent years telling anyone who'd sit still that conversational data changes how we work. A recent experiment made him realize he'd been underselling his own argument.
So one half of AI is wearing people out, and the other half is hooking people who never cared about data in their lives. We're still early, folks. And apparently, we're going to need some new vocabulary.
Listen to the latest episode of Raw Data. Turns out AI gets a lot more interesting once you see what people actually do with it.

For most of tech history, the new thing belonged to the new people.
Then AI showed up and ruined a perfectly good pattern.
Because the smartest model on the planet can know darn near everything and still have no idea how your company works. It doesn't know which rules matter, which ones everyone ignores, why that ugly spreadsheet still exists, or that the "temporary" workaround from 2019 is now apparently infrastructure.
Jon Perl, Tim Rodman, and Trent McKinster join Rob for a conversation about Fair Game that pretty quickly becomes a conversation about who has the upper hand here. And it might just be the crafters. The people who spent years poking at problems, pulling things apart, building better ways to do the work, and collecting the kind of business context you can't download with a model.
From there, things get wonderfully nerdy. Custom AI. Semantic models getting their long overdue victory lap. Whether SaaS is about to get picked apart one annoying subscription at a time. And the possibility that we've been thinking about the AI skills gap completely backwards.
Maybe experience isn't the thing AI replaces.
Maybe it's the thing AI has been waiting for.

There have always been people who can't help themselves. Give them a clunky process, a messy spreadsheet, or a problem nobody owns, and they're already halfway to building something better. Rob calls them crafters. They were valuable before AI. They're becoming indispensable because AI rewards exactly the way they've always worked.
While everyone else is trying to "become AI native," crafters are doing something much less glamorous. They're making one workflow better. Then another. Pedro Rossello from Aderant is one of those people. Along with Rob and P3's own David "Oz" Osorio, he makes the case that the biggest AI wins don't come from chasing the newest thing. They come from solving real business problems with better tools and better judgment.
The funny thing is, those wins don't always look like AI success stories. They look like fewer headaches, smarter decisions, and work that simply gets done better than it did last week. That's the crafter's edge. It isn't flashy. It isn't loud. But it's probably what separates the companies still talking about AI from the ones already putting it to work.

AI can write code faster than most of us can describe the problem. That's the good news.
The catch is that building software has never just been about writing code. It's about making decisions today that won't come back to haunt you six months from now. In this episode, Rob and Justin unpack what happened after Justin and Kellan built a real application with AI. The code came together quickly. The architecture, the maintenance, and the "why on earth did we do it this way?" moments took a little longer.
Along the way they discovered something that applies far beyond software development. AI can help you build almost anything, but it still depends on people to define the patterns, document the rules, and recognize when today's shortcut quietly becomes tomorrow's technical debt. Sometimes the smallest notes left behind become the biggest lessons later.
If you're building with AI, or simply trying to understand where human judgment still matters, this episode is worth the time. It turns out the future has a funny way of leaving breadcrumbs.

AI headlines have already moved on to the deep end. Most businesses haven't.
Every week brings another headline about what's next for AI. Build your own model. Train your own LLM. Customize everything. It's exciting, unless you're one of the thousands of companies still trying to answer a much simpler question: where does AI actually fit into the work we do every day?
Here's the thing. AI has become everyone's priority and almost nobody's job. Leadership knows it matters but the real work still lives inside thousands of everyday workflows, where tribal knowledge, context, and experience drive the decisions. That's the gap, and it's a very different problem than the one the headlines are chasing.
That's the conversation Rob and Justin have this week. Sparked by Satya Nadella's comment that every company should eventually have its own LLM, they make the case that the industry is getting ahead of itself. As Rob puts it, "Everyone's sitting poolside and Satya's talking about the deep end." Most organizations don't need a custom model. They need AI that understands their business, their data, and their workflows. That's where the biggest wins are happening today, and it's exactly where companies should be focused before they start worrying about building their own LLM.
If AI has started to feel like an arms race you somehow missed, this episode is a welcome reminder that the biggest opportunities are still waiting in the shallow end.