Raw Data with Rob Collie

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.

For about two years, we've all been reaching for the biggest hammer on the wall because someone else was paying for the nails. If you were on a subscription, you grabbed the biggest, baddest model on the menu and used the crap out of it. Two hundred dollars a month for work that would have cost thousands on the meter. It rounded to free.
Then a new model showed up for roughly fifteen minutes.
It wasn't covered by anyone's subscription. It was priced by the token. And Rob immediately saw something much bigger than a product launch. The migration everyone assumed would be painful, moving millions of people away from all you can eat subscriptions, suddenly had a simple answer. Just make the newest, smartest model a premium experience. Checkmate. The buffet doesn't disappear. You just have to decide whether the lobster is worth paying for.
Justin made the exact mistake he told himself he wouldn't make. He tried it anyway. He handed the model a sprawling request to audit an entire codebase and walked away. It came back with nearly twenty legitimate findings, from accessibility improvements to a legal disclosure that referred to the company as a corporation instead of an LLC. More importantly, it handled a level of independent work he wouldn't have trusted another model to do. His reaction afterward said everything: "I wish I hadn't tried it." Because once you've seen what the next generation can do, you can't unsee it. But if using it costs six or seven thousand dollars a month for one developer, "always use the best model" stops being a habit and starts becoming a business decision.
Whether you're building with AI every day or just trying to make sense of where it's all headed, this conversation is a good reminder that the technology isn't the only thing changing. The business model is too. Give it a listen and see where Rob and Justin think it all leads.

For years, Rob had a pretty good system.
When a new technology showed up, he didn't immediately declare it the next big thing. He wanted to understand why it mattered first. Sometimes that meant jumping in early, like he did with Power BI. Other times, it meant waiting until the signal was stronger than the hype.
 
AI was different.
 
It was the first technology that made Rob question whether his usual approach was enough.
 
That's where Fair Game begins.
 
In this special episode, Rob shares the foreword from the audiobook, along with his introduction to Eddie, the AI collaborator that helped shape the book from first draft to finished manuscript. More importantly, he tells the story behind the story. How someone who never considered himself an AI evangelist ended up writing a book about it, why fear became an unexpectedly good teacher, and why he came away convinced that AI success has far less to do with the models themselves than most people think.
If you've been hearing Rob talk about Fair Game over the past several months, this is your first chance to hear how it all comes together. It's not Chapter One. It's the reason there had to be a Chapter One.
 
Also in this episode:
Fair Game Preorders