AI Is Already Changing Data Jobs: Why You Must Create More Value Now with Rob Collie

Speaker A: Welcome to the Future Finance show, where we talk about treasury management on bars.

Speaker B: One of the things that all of us need to internalize as data professionals and bi professionals. It's an uncomfortable truth, but it's something that we need to just start trying to get comfortable with is that whatever our existing job is today, like, it's kind of already gone. In the same way that, like, if something happened at the sun right at this moment, we wouldn't know for eight minutes it's already gone. Moment has happened, but it's taking a while to work its way through the system to us. Now. That doesn't mean that we're going to be left without jobs, but if you sit still, you might.

Speaker A: Future Finance is brought to you by qflow AI, the strategic finance platform. Platform solving the toughest part of Planning and analysis. B2B Revenue align Sales, marketing and finance. Seamlessly speed up decision making and lock in accountability with qflow AI.

Speaker C: Welcome to Future Finance. Uh, I'm dialing in today from home. My, My building was hit by lightning the other day, and the Internet's been down for like six days or something now, so I'm really out of sorts. And I'm surrounded by video games, too, which makes it really hard to do podcasts, so.

Speaker A: Likely story. Sure. You just want to be at home with your video games. It's all right, Glenn.

Speaker C: Well, I'm here today with my esteemed co host, Mr. Paul Barnhurst, the FPA guy himself, and our guest, Rob Collie. Really looking forward to, uh, to diving in today.

Speaker A: Excited to have you. Welcome to the show, Rob.

Speaker B: Thank you very much. So did your Internet become super Internet after being hit by lightning or.

Speaker C: I think that's if it gets bit by a spider, I don't know. I see. Yeah.

Speaker A: You weren't in the building when a guy hit, were you?

Speaker C: I was not. I was at the one, uh, Stream Splash conference last week. And, uh, I came in and noticed

Speaker A: as long as you're safe, I can make fun. If something had happened, then I would feel bad.

Speaker C: Yeah, uh, no, everybody's fine. Apparently some, a couple of computers and some keyboards and our switch, our Internet switch and some wiring around it and some signs because. Got knocked out too. But, um, yeah, pretty good.

Speaker A: Pretty good jolt, then fried a few things. He was like, all right, well, we're glad everybody's safe and just. We'll start here by giving a little bit of background about Rob and then we'll jump into things. I have to start before I even read his bio to say I'm really excited for this interview. I've listened to several of his podcasts a while back and I even have a copy of here. We were talking about it before his book Power Pivot and Power Bi. What was this from 2013? Is that when it was 15?

Speaker B: 20. 15, 15.

Speaker A: Yeah, I picked it up in early 17 when I first learned about Power Query and Power Pivot. It got me through a project along with There's a. Ken, ah, Pulse's book on Power Query. You two were bought about the same time?

Speaker B: Yeah, that's right.

Speaker A: Yeah.

Speaker B: Uh, and I promised myself that was the last book I would ever, ever, ever write. And I recently discovered that that was a lie.

Speaker A: Yeah, we're gonna. We'll have to bring that up at some point. I did see that on LinkedIn. Uh, a little bit about it. You shared a little bit of the. I think it was the, uh, back jacket.

Speaker B: Indeed, indeed. Yes.

Speaker A: So let me get a little bit of Rob's background and then we'll jump into the question.

Speaker C: So.

Speaker A: Rob Cawley is the founder and CEO of P3 Adaptive. You can see it on his shirt there if you're watching online, a national Microsoft analytics and AI consulting firm. During his 13 years at Microsoft, Rob led the BI focused capabilities in Excel and was subsequently one of the founding engineers on Power BI. He then left Microsoft to found P3 Adaptive. Rob is a sought after speaker and author of the number one selling Power BI book, Power BI and Power Pivot, which you just saw there a minute ago. Rob is relentlessly committed to making transformative data solutions accessible to organizations of all sizes. He is widely regarded as one of the leading authorities on Microsoft Power Pivot and Power BI technology and a thought leader within the business intelligence industry. He also hosts the Raw Data with Rob Collie podcast and is an active voice in the data and AI community. And as he mentioned earlier, he's getting ready to release another book, which he swore he wouldn't. I think a lot of people have done that. Glenn, did you swear you wouldn't after your first.

Speaker C: No, I was hooked. So I'm what, three books in four years? I think it is.

Speaker B: That's a different experience than that.

Speaker C: My fingers. Yeah.

Speaker B: Uh, I love writing, but boy, the commitment, the slog and what it does to my life is the thing that ultimately the first couple of chapters are. Uh, that's great.

Speaker A: Yeah. I haven't been able to bring myself to write a book yet. I've had a few people try to convince me and I say I go back and forth. I did. I've talked about it, but I'm not quite there yet. So we'll see. If I, uh, become one of those, uh, book authors like you two, I just like to be different. It allows me to stand out.

Speaker C: And you can't read, so that would be a.

Speaker A: Well, based on the way I read the bio. Thanks. Thanks, Derek. La.

Speaker B: Speaking of which, when we run into people, my wife has been telling people, like, yeah, we've been really busy lately. Like, I've been doing this. I had to go in for surgery and then. And Rob's been finishing a book. And it sounds like Rob's been finishing reading a book. Right. Like, it's like reading a book is, like a huge challenge, you know, and so I'm. Obviously, I have to jump in and say, just be clear, I'm writing a book.

Speaker A: It's funny, when you said that, uh, the first thought that came to mind was almost like, I had to go in for surgery, but Rob was busy reading a book.

Speaker B: Yeah. One word at a time. I'm still sounding it out. You know, you and me both.

Speaker A: No. Okay. In all seriousness, so love to ask you a question. I know you were one of the founding engineers of Power bi. Obviously, you worked on Power Pivot. Would love to learn a little bit about that experience, how it came about. Just tell us a little bit of that journey.

Speaker B: A lot of credit. Really, kind of. Really. In a lot of ways, like, all the credit goes to Amir Nets. You know, you hear the term visionary used a lot, you know, and it's thrown around a lot. Amir, uh, Nets at Microsoft is a true. A true visionary in every sense of the word. And so he had basically been watching the price of memory, like, literally been watching the price of RAM drop over the years, and was waiting for his moment to do this new thing. So he had been the architect behind the original SQL Server analysis services, predates the DAX and Tabular model and all that kind of stuff. And that technology was the market leader in its space, but it was really, really, really hard to use. It was not. Like, I tried to learn it and I couldn't. I couldn't learn. I couldn't. How to write formulas. I couldn't write a formula in mdx.

Speaker A: Like, uh, I said that was mdx. That's what I thought. I've played with MDX a little bit in Excel. Okay.

Speaker C: It was just.

Speaker B: It was just. I. I don't think it was beyond my intellectual capacity. It was, but it was definitely beyond my combination of intellectual capacity and enthusiasm. I'm, like, trying to write an if. And I'm 15 minutes into a tutorial from someone on how to write an if. And I'm like, wait, if. An if is this hard? I'm, I'm out. So he had been waiting to essentially do a version two, like a reboot of analysis services that wasn't so hard. And, and also one that took advantage of end memory in memory storage technology, which again, which is why he was watching the RAM prices and they reached this critical threshold where like they were cheap enough RAM was cheap enough that enough data would be able to compress and fit into main memory on a computer. That he said, okay, now we actually have a viable product that we could go and build. And the in memory component was really important because the Excel crowd, which is who he was targeting, he wanted the Excel crowd, the, the highest end of the Excel community, to be able to build these new versions of these at the time were called OLAP cubes. OLAP models, you know, data models.

Speaker A: Yep. Pivot formulas, the cube formulas from. Yeah.

Speaker B: So we know today that this was a just an absolutely raging success. Power BI has taken over and the idea that we now have millions and millions of people worldwide building dimensional semantic models and writing reusable DAX calculations, like it's, it's kind of breathtaking how we take it, almost take it for granted now. It's sort of like just part of the, you know, like the baseline of our world that was not at all a guaranteed outcome. I mean this was crazy. This was a crazy, ambitious, crazy project that, you know, and I'd worked on a lot of version 1 stuff at Microsoft and most of it never amounted to anything. Well, there's never version two or, you know, the other famous thing about Microsoft is it's version three before it's good. Right. And so I was fully expecting it to be one of those things, but you know, version one was a, was a world beater. Even just still when it was just in Excel as Power Pivot. I mean it blew my hair back, you know, been there for such a, an ambitious project that not only delivered but actually like over delivered, like it over delivered. I think relative to even our like ambitious expectations of it was really kind of life changing. And to watch these guys come because, uh, a lot of people who were working on this project had been part of the first one, had been part of the original mdx, you know, OLAP project. Watching them retrace their steps and unmake mistakes that they had made that had made the prior version too complicated, too stubborn, you know, was Also like a really fascinating like sociological experience, you know. And what we ended up with is just, I, I just, you know, we, I don't know. A couple years ago we did, uh, a podcast we titled the Software hall of Fame. It was basically the podcast. It's like, okay, so Power Bi belongs. What else? Visical Can Excel clearly belonged and things like that. But I mean, to be there at the formation of something that if there was a Software hall of Fame, it would be in it, um, was quite an experience for sure.

Speaker A: Glenn, what do you think? Should we create a Software hall of Fame? That could be pretty cool with the old boxes, the original micro people come in and.

Speaker C: Oh yeah, yeah, you know, we could charge.

Speaker A: I don't know what we could charge per ticket, but that could be a new business.

Speaker B: Not very much. Yeah, turns out, uh, you're right next

Speaker C: to the Rock and Roll hall of Fame.

Speaker B: We put up a website with the episode this Offer hall of Fame and it gets, you know, like five visitors, you know.

Speaker A: Yeah, exactly.

Speaker C: As you're going through and talking about all that, I'm thinking great new products, doing really cool things at Microsoft. You're finding Fortune, I don't know what they were. Fortune 10 company or however big Microsoft was at that time. And uh, you're sitting on top of the world and then you say, I think I'm gonna go do my own thing. That seems like an interesting time to pivot. Can you tell us a little bit about what kind of led to. You're doing great work at Microsoft and it's probably pretty, it's a reliable position and you're probably on the upswing there, but, uh, you get the call to go start, start P3, to go start your own thing. How did, uh, how did all that

Speaker B: go about the real story here is that in. You're right, Microsoft, very. It's only thing I'd ever known. I worked there straight out of college. I had a very good reputation there. So like I could move around, I could get new jobs, et cetera. So like I was safe and being paid well and on um, a good trajectory and all that kind of stuff. So basically in 2009, I had gotten divorced in Seattle and my ex wife moved to Cleveland and took my children, our children with her and the courts in Seattle didn't really see my side of things. Right. So like I had a choice to make. Like I could either be with my children or I could continue my Microsoft career. This was before remote work, like, just wasn't an option, you know. And so, uh, in 2009, I moved to Cleveland and said, okay, now what? And. But it wasn't even then the idea of starting a company around Power Pivot wasn't. That wasn't the idea. I didn't. Again, I, at the time, I still thought PowerPivot wasn't out yet, wasn't even released. Uh, I wasn't even in beta when I moved. And I still. They let me stay on for a while, so I still had like, not, not a full year, but I stayed on for like another nine months as a Microsoft employee. And, um, that gave me time to play with it, like to be able to play with the product to. The analogy I use is that if you spend all day building race cars, you don't learn how to drive them. And this is why a lot of people who work on Power Bi aren't as good at it as people in the community. And same thing is true of Excel. And so I just come to play with it. And I started a blog. The blog. The whole point of the blog at the time was just to be like my digital resume to get me some sort of job in the middle of the 2008, 2009 financial, you know, recession, depression, whatever you want to call it, meltdown, whatever you want to call it in Cleveland, Ohio. Not exactly the hotbed. Uh, I used to do, I would do. But you know that Cleveland's kind of like the Silicon Valley of the Midwest. And people would go, really? I go, no. And you know, I just needed a job. I didn't have high hopes for what I was going to do, but somewhere in the course of writing the blog, I was like, oh my God. I mean, I felt, I actually remember the night that the electricity hit me. Not lightning, but there is sort of an origin story moment where I wrote this formula in Power Pivot that I. Then I remembered I had happened to built this exact same formula via consultant with the old school technology. And I remember the old. That version of the formula had taken like two weeks. Real time in the, in the real world, it taken two weeks to get the formula right. And I got it right in like 30 minutes or less. And I. It's just this like, it was like this virtual lightning bolt. Like, oh my God, the world is going to change. And, uh, from that point forward, all idea of getting some sort of boring, you know, it job in Cleveland was gone. And so, you know, at that point I knew my career was going to be, uh, at the forefront of this new thing. That's where it really. So, like, I needed The. And by the time. It was really funny, by the time I got really excited about this, Microsoft came to me and said, hey, you know, we have some, actually some really cool remote positions for you. That if, if they'd offered them to me six, seven months before, I'd have been thrilled. I'd have taken them. But instead, I'm like, no, I'm too excited about this other thing. I turned down going back, you know, so I could say, like, hey, I left Microsoft to found this company. And it, you know, all the important parts of it. That's still the true. That's still true. But it does leave out the fact it makes me sound more courageous and more entrepreneurial than what I actually am. Like, this. This one found me. You know, I didn't. I didn't go looking for it.

Speaker C: Isn't it great, though, when it just feels like the stars aligned and you ended up doing exactly what you were supposed to? And now this many years on later, clearly that, uh, you made the right, right choice on that. I wonder, though, are you now, because the, the data and analytics and what's been the core of your work, that's not going to go anywhere because of AI. It's going to only become more important. Are you seeing that same sort of holy cow moment around AI with what you guys are doing today? Or how is. How does this shift feel compared to what you were seeing in those. In those early days?

Speaker B: We were joking earlier about swearing I was never going to write another book. And even the first book felt like it. Almost like I owed it to the world. Like, I didn't. Of course, writing a book benefits you professionally. Of course it does. But I'm not the kind of person who can finish writing one. For that reason. I need to have an emotional driver as well as some sort of professional incentive. If I don't have an emotional driver, it's just. I'm just gonna. That project's gonna drag for years. It's never gonna finish.

Speaker A: I've had a few projects like that.

Speaker B: Yeah, well, I do, too. Right. You know, so. But I felt like what you're describing, Glenn, I think you're really picking up on exactly the kind of feeling I was having. Then there's this like, like, like, uh, you know, these silly things. It's almost like my whole career had been building to that moment, and. And people needed to know the things that were in my head, and I knew that other people were going to be able to do this. There wasn't something special about me. It was just that I had kind of like an early glimpse and yeah, I am feeling that again today. And uh, this, this book that I'm know releasing soon, I didn't go into this process of, of diving deep into AI so that I could write a book. You know, I just really started a little over a year ago, like really diving really super deep into AI because not. I needed to know what our company was going to look like. Like it was, it, it was my responsibility as the leader of this company to plot us a course. And you know, I agree with you that data and analytics is just going to become more important. And at the same time, AI is making it easier and easier and faster and faster. And I think like one of the things that all of us need to internalize as data professionals and uh, bi professionals. It's an uncomfortable truth, but it's something that we need to just start trying to get comfortable with is that whatever our existing job is today, like, it's kind of already gone. In the same way that like, if something happened at the sun right at this moment, we wouldn't know for eight minutes, you know, like it's already gone. Moment has happened, but it's taking a while to work its way through the system to us now that doesn't mean that we're going to be left without jobs, but if you sit still, you might, you know. So the reason for this adventure that I went on over the past year plus was to figure out what our new jobs were. And there's, it turns out there's a tremendous amount of opportunity. The new jobs for people who are wired like us, who are in this data and analytics space, the new jobs are probably better than our old jobs and there's going to be even m more demand for it than our old jobs. So it's very exciting, but you kind of have to first internalize that. The place you're standing at the moment is the tsunami flood zone. You can't sit still. And then again, in the course of all of this, this process, I'm like, oh my God, the things that I just learned about AI, no one's explaining them as simply as they could be. If I'd had the book that I just wrote at the beginning of this process, I wouldn't have needed to go on a year deep dive of understanding like what, what, what AI is all about. And so I was like, oh God. I literally, uh, felt it like now I owe another book and told my wife about it. She's very, she was very unhappy because she remembers what, what it's like when I'm writing a book, I'm not, I'm not, I'm not a great husband when I'm writing a book, like I'm kind of, she's like a book widow, you know. So it was not, it's not the most pleasant of realization but it is very pleasant to be done with it.

Speaker C: Well, and actually while you're talking about it, let's be sure that everybody knows the name and, and sounds like we have a pretty good idea of what it's about but maybe a little bit more detail about what it's about and uh, release date and where people can find it and all that.

Speaker B: I uh, appreciate it. So it's called Fair Game. Um, that's the main title and it's a very visual title. So the A and the I are highlighted in Fair Game. But the subtitle is Customizing AI to your business is easier than you think. And it just takes people on the uh, it's really, it's not, it's not aimed at the same technical level that my power BI books were. You know, like the power BI books were aimed at the formula writers. This is aimed at explaining, you know, I, I think it's, it's, I wrote it at a level that is relevant to a technical audience but also relevant to a business, just business leader, business thinker, audience because they need to understand it too. And in the same way that we've, we all kind of understand software. Like we have an intrinsic, always have an intrinsic understanding of what's possible with traditional software. And we've grown up with CPU style computing. We know more about it. Like if you were going to write a book about what software could do, It'd be a 400 page book before you knew it. And you just don't understand how much you know about software. It's crazy. AI LLMs are for the first time in our like really in humanity's history. It's the second kind of computing. It's a new kind of computing and it's brand new. And it turns out that it's much easier to understand than I anticipated it would be. But just understanding how to like the fact that like, like you don't need to be an AI expert, like able to develop LLMs, able to like be a researcher, like you don't have to be able to someone who works in anthropic in order to build an agent that helps your business. And guess what folks, it just comes back to traditional data, traditional information and traditional software. It's all the stuff that you wrap around this magic, I call it the magic Lego brick, the LLM, um, the stuff that you wrap around it is all stuff that we understand. It's stuff that we all grew up with. We all understand it. Whether, whether we're technical or just as a business leader, we understand what those things are like. And understanding that like an AI system, a customized system for your company is really just, you know, the air quotes for just, is just us taking normal regular information and wrapping it around the LLM. It's kind of a mind blowing moment when you start to, to piece that story together.

Speaker C: And Rob, you're really preaching to the choir here because one of the things, I can be a pedantic SOB at times, right Paul?

Speaker A: I, I will vouch for that. Rob. I will uh, go on record.

Speaker C: You're spot on with the direction of the book because I always tell people like, look, you don't have to be a data scientist, you don't have to be a machine learning engineer. You don't, like you said, you don't have to be qualified to work as an AI engineer at Anthropic. But if you're going to be using these tools, it's not like, you know, your phone or this camera or microphone that are passive kind of just pass through. I'm not, I'm not offloading decisions or at least decision support to them. If I've got this magic black box that's going to be answering things and looped into all of my work processes, it's incumbent on me as a leader or even as someone who's using the technology to have pretty solid understanding of what's happening under the hood so that you know where its limitations are, where to apply it and where not to and all that. I think, I think from everything you're saying, it sounds like the book will resonate really well. And it's, it's much needed so look very much looking forward to that.

Speaker B: I've been doing it like testing it on civilians, you know, giving it to people like, you know, like who, who are not in the tech industry and engaging their reaction. And um, you know, it's, it's been, it's been, they've been coming back to me and saying like, hey, like, like even, like the 70 year old woman who has never worked in tech in her life and when she, and when her webcam needs fixing, she needs to call someone. You know, she read it and said that uh, she did a party and someone was talking. She's like, she said actually no that's not actually quite right. This.

Speaker A: She.

Speaker B: She started correcting someone a little bit about AI uh, at a social gathering and she told me about that. I was just, I was so. I was like one of the most heartwarming moments of my ever feel like

Speaker A: your go to market teams and finance speak different languages. This misalignment is a breeding ground for failure, impairing the predictive power of forecasts and delaying decisions that drive efficient growth. It's not for lack of trying, but getting all the data in one place doesn't mean you've gotten everyone on the same page. Meet qflow AI, the strategic finance platform. Purpose built to solve the toughest part of planning and analysis, B2B revenue. QFlow quickly integrates key data from your go to market stack and accounting platform, then handles all the data prep and normalization under the hood. It automatically assembles your go to market stats, makes segmented scenario planning a breeze, and closes the planning loop. Create airtight alignment, improve decision latency, and ensure accountability across the teams. Grady, you gotta love those kind of. Speaking of fun moments. So I saw your post where when you started to see Cowork and how big of a deal it was, how you believed in it. I believe you went out because Cowork at first was only on the Mac and bought your whole family Macs. As a former Microsoft employee, that's almost treason. But talk about that experience. What, what made you go out and do that? What was that kind of whole adventure like?

Speaker B: Yeah, uh, I'm talking to you on Mac right now. This is, this is a. I'm, I am a. I'm on a Mac on this podcast, you know, do we have

Speaker A: to hang up on him, Glenn?

Speaker C: No. 1999, I was the only finance.

Speaker A: I, I figured that.

Speaker C: M listening. By the way. He's not happy.

Speaker B: He's, um. Yeah, we need to, we need to talk, Glenn, because I even. I have uh, the recent convert. I have some, I have some, I have some complaints about the, about the Mac platform. But like, I started using Cowork to, to train my own editor for the book and I ended up with an editor that I call Eddie. And you know, Eddie, you know, it's a very original name and Eddie is amazing. In fact, Eddie is the first word in the book because the first sentence, the first title in the book is Eddie didn't write this book because he didn't. But I was getting such incredible value even just in the early going, uh, uh, so they did release it on the PC Cowork. It just didn't work very well. It was constantly, constantly crashing and dying and, and it worked just long enough for me to see the promise of it. Like the ability for me to provide durable instruction to, uh, an editing partner saying, okay, here's what my voice is. Here's what I'm trying to do. Here's what I'm going for. Like, almost should be my editor. Not some stuffy business voice, you know, editor. Like if I, if I, if I got a real editor at a real, um, like major publisher, they would try to remove all of the risk from the book. They would try to take all of the tone and the voice and everything out of the book. Which is really the reason one of the things people really liked about my previous books, but it's different. And one thing that's safe is to conform. So you give us all this pressure to conform. If you had a real, real editor. But honestly, even a real editor wouldn't have had time to do what I had Eddie do. I could send Eddie three paragraphs and ask him what he thinks. I'd be like, this is, you see what I'm trying to do here, Eddie? But it's not kind of coming out right. What is it that's not working. And I could go back and forth with, with Eddie five times in a row like that. Just, just micro focused on crafting these three paragraphs, which is not something that you could ever, ever, ever do with a human editor. Like you could maybe send a chapter at a time. You get like one bulk review and one back and forth. And eventually one party or the other just gets exhausted and gives up. And so you end up with what you end up with. You know, it's really, really, really funny. And kind of eye, uh, opening that at the end of the project, the end of the book, I went back and had Claude assemble all of our conversations. It's over. Over 300 conversations. 300 separate, like 308 conversations between me and my editor and cowork. And the, the size of those conversations is 10 times the size of the book. And 7x of that is Eddie. Because, you know, I would say things like, hey, Eddie, I just finished a bunch of, a bunch of edits. Please review them. So like, I would contribute just a tiny little bit and then Eddie would give me all kinds of feedback. So of course it makes sense that Eddie said a lot more in those conversations than me. But even my interaction with Eddie is three times the volume of what I wrote in this book. Like, I wrote like a 675 page book for Eddie and a 225 page book for everyone else. And so like when I realized that I could be getting that experience on the Mac without this, these batch scripts that I was writing that were like trying to clean things out and like make Cowork work, I mean, I just, I just, okay, I'm just gonna go get a Mac and, and having it work, having cowork just work seamlessly without, I mean, without having that, that crash dynamic in it. In those early days, this is back like in March, I think. I mean it was, it was really striking. Like the reason people bought PCs was to. Was the spreadsheet.

Speaker A: That's why I have a PC still.

Speaker B: Yeah, I mean in the 1980s, even like spreadsheet was the reason to have a PC, like other than games. Right. Like the reason how the PC in business was, was the spreadsheet. And it's funny if you go back and look at the ads for the PCs in the 1980s when they're trying to convince people to buy PCs for business for the first time, all of it is spreadsheets. It's all charts.

Speaker A: It's all the guy in the elevator, all of it. The elevator copilot.

Speaker B: Recently they did, didn't they? And I had that same kind of feeling like that cowork was the reason I got a Mac and I don't change platforms. I'm the last person in the world who would change from PC to Mac. Not because I, uh, so much of a PC style where is more just like I don't change foundational parts of my life. It messes me up, you know, like it's an inconvenience. I don't, I don't play around with tech for the, for the fun of it, but I just roll right over that, that, that Mac obstacle. Like, yep, absolutely, we're all going to get Macs.

Speaker C: You know, when I first got my Mac, I was running because Excel, obviously on a PC is still much better than a Mac. So I was just running a Windows environment. Like I was using Bootcamp back in the day or whatever. But then once I became a cfo, I wasn't as much of a hardcore. Ah, pizza. Yeah. So, uh, yeah, but that's um, the shifting, switching costs on anything. I mean, if I had to go from my iPhone to an Android at this point, I would lose my mind. It's like I've got to learn a whole other operating system. I couldn't imagine what's happening.

Speaker B: And that's what I did. Like my first few days was The Mac. We're also asking Claude, hey, how do I do this thing I used to do on Windows? I could see that.

Speaker A: So you also had your daughter on your podcast recently about how she used Claude Cowork to help her do a job search. Talk a little bit about that.

Speaker B: That's the third Mac in our family.

Speaker C: Right.

Speaker B: It was right in the middle of, like. In fact, I think I was still on the PC at the moment when I. When I started helping her with her job hunt. Yeah, like the. When I started a covert project to help her with her job hunt. Everything from resume, you know, revisions to cover letters, to literally also even giving the Chrome extension the ability to go and start browsing the web for her and finding jobs that she wasn't finding. Like, it was finding dozens of jobs. And she was sitting there, like, really diligently searching. Like, she's. She's got a high, high motor.

Speaker C: She's.

Speaker B: She's not lazy.

Speaker A: She's.

Speaker B: She's driven. And this thing, you know, cowork, once it knew what her background was and understood like, oh, she's pivoting from med school to this and all that kind of stuff was like a strategic counselor. I mean, we recorded that podcast on her second day with her Mac, and she was already, like. I could just tell, right? She's, you know, she's got the. She's got the itch, you know, and so John Punt's still in progress because it's. It's hard, but, you know, for the next few months, she's going to intern with us, and she's going to be here learning about AI and doing AI stuff with us and, like, shadowing some of our projects and helping us with the back office and things like that. She's downstairs right now because she's visiting for the next six weeks. He's downstairs with her, with her Mac. You know, I'm sure there's a. There's some sort of cloud window open at the moment.

Speaker A: No, that's awesome. Well, we're gonna. We're gonna excuse Glenn and we're gonna wrap up here in a minute because he has to run to the airport. We don't want his wife to come down and hang up on us. So we'll, uh, we'll let him go. But we. I have one more question to ask, and then we'll do our fun little AI section. I'll make sure to do yours for you, Glenn.

Speaker B: Thanks, Glenn, it's a pleasure.

Speaker C: Rob, great meeting you.

Speaker B: Likewise. Have a good trip to the airport.

Speaker C: Nice.

Speaker A: Drive safe. Talk to you later, Glenn. No, that's great that, you know, she's going to be interning with you. Sounds like that'll be a really good experience for her. You know, the one last kind of question I want to ask before we move into our kind of fun AI section is how are you using AI in your company and how do you think it's going to change your business? You mentioned how it's already kind of changed it, but. And how you had to figure out what that looks like in the future. So maybe talk a little bit about that, of how you see it impacting your business. Data analytics kind of what, what you've learned that uh, you see at least right now as those changes. Obviously it could be different in two years, three years with the rate things change these days.

Speaker B: At, uh, a high level, there's at least two ways. So one is if you think of your, your existing job and Data bi, fpa, there's a lot of technical work that you perform in that space and all of that gets just tremendously faster, which is mostly a good thing. I think. It's kind of like it does represent, I believe, the end of hourly billing. Any business that's going to charge by the hour to perform work like that is not going to have enough work.

Speaker A: Yeah, you make less money on, uh, it, uh, unless you, unless you can double your rate, which odds are, you can't.

Speaker B: The longest chapter in my new book is the one about data. And it starts off talking about Power BI semantic models. And you know, I think you've probably, you know, I've watched with kind of delight for many years, there's many BI companies haven't had a semantic model like Power BI does. And they've gotten away with it. They've gotten away with not having one. So like, Tableau has had one, but they never really emphasized it. You know, they were always just write some SQL over some data, make a new rectangle of data, make a dashboard. That was their, that's their, their process. Well, everyone, even Tableau, every single vendor, is now all in on semantic models. Because if you want AI to work, uh, at your company, it needs to be, it needs to have access to all that structured data.

Speaker A: It needs that context that comes with that semantic model.

Speaker B: That's right. And companies that haven't adopted Power BI or, uh, companies that have adopted Power BI but not really built good semantic models, their first step in a lot of AI scenarios is going to be building good semantic models. The world's need for semantic models. Power BI semantic Models. And by the way, like 99% of the world's semantic models today are power bi semantic models. There are new competitive standards springing up. Tableau's got one now and they're sharing it with a bunch of other companies and things like that, but they're still starting. The world's installed footprint of, uh, semantic models is right in our wheelhouse, you know. And so the realization, you know, ultimately led to me writing the book was that AI really is a data problem. It's about getting the right information to the LLM, um, at the right time. So all of us in data are so well positioned to be the people who actually make AI a real thing. And so, and I talk about sort of like there's this, on the one hand, there's this absolute necessity that we, the data people move sort of, let's call it upstream in the value chain. We need to be able to create more value for the business than what we have been. At the same time, we're being given this opportunity in the form of AI in which to do that.

Speaker A: Pretty amazing what you, what you can do. We're going to move into our fun AI section. And in honor of Glenn leaving us, I'm going to give you two options. We each take different approaches to this. We'll do one question, but you can choose the approach. So my approach is one of two things. So what we've done is we've fed an AI your bio information from the Internet, the questions we were going to ask and ask that to come up with 25 kind of fun, personal, a little bit of quirky questions we should ask you. We use different models all the time. I don't remember which model we used here for these ones.

Speaker B: It was a few weeks ago.

Speaker A: So option one, I can use a random number generator, or you can pick a number between 1 and 25. So I have human in the loop. No human in the loop. Glenn's approach was I go out to an AI and I ask it what the best question is to ask, and I ask that question. So you want to use my approach or Glenn's?

Speaker B: Oh, I think that the pretzel self folding of asking another AI what the best question is out of the 25 AI generated questions. But here's the key thing though. Will the new AI have the context that the original one had when they were making the questions?

Speaker A: No, they just get to like the questions and they get to pick from the questions.

Speaker B: But a random number generator is also dumb. So, you know, it has no, it has no Context either. Do the AI thing.

Speaker A: All right, so I did that, and the first one came back. You'll ask question three, which we're going to skip because we covered it. But I'll, uh, I'll read the question. So you know what it is? It says question 3. Family Tech Pay patron. You bought Max for your whole family to use Claude Cowork. What was the f. First family reaction and the funniest outcome so far. So you could answer that if you wanted, but we spent quite a bit on Claude cowork. Or I can go to the second one because I said. Give me one more question. Thirteen, office mascot. If P3 adaptive had an official mascot, what would it be and what would its job description read?

Speaker B: Well, okay, so I think the mascot for our company, it would have to be named something to do with Venn. We might call it. We might call this mascot Venny, not Vinnie Venn. Because we have this principle internally we call good human and good business. And the. And is important. It's the, it's the intersection.

Speaker A: And that Venn diagram.

Speaker B: Yeah, the Venn. The overlapping Venn diagram. We have, we have an emoji in Slack that is the Venn Venn overlapping circles. And what we have found is that when we align the company's interests with the employees interests and we do a good job of it, that's when we're most successful. So we definitely have a good human ethos at our company. But the finding the places where that overlaps with good business is the. Is the key. Because if you're just, if you're just nothing but good human, you go out of business. And if you're nothing but good business, you don't take care of the people. And also I think eventually you go out of business. But I don't see most companies treating this as like an engineering discipline the way that, the way that we do. And it's a lot of work, and it's also takes constant maintenance. It's not something that you solve, and it just stays solved. One of the reasons why we have the word adaptive in the name of our company, it would definitely be you sort of imagine some cartoon version of a Venn diagram, uh, running around like going, hey, kids, like a, uh, maybe like someone like at a mascot, like at a football game or a basketball game, but really nerdy because it's a Venn diagram and I like it.

Speaker A: I'm looking forward to, uh, seeing this mascot come alive and, you know, see it at some kind of event someday. That pretty much wraps up where we're at. So I was just going to kind of wrap us up here and let you go.

Speaker B: Can I give you one last little plug?

Speaker A: You can.

Speaker B: So we're offering on our website, our new website for the book Fair Gamebook AI we're offering a bundle where a pre order bundle where you get the hardcover, the ebook and the audiobook all at release for one one price. It's cheaper than buying all three separately from m Amazon. And if you pre order, you get access to the first, I think the first four chapters immediately. Like even today. Yeah, I would really, really encourage you to check out, uh, if you're interested in the book, don't get it from Amazon, get it from us. Better deal to get it through fair gamebook A.I.

Speaker A: perfect. Well, awesome. Well, thank you so much for joining, Rob. Appreciate you, uh, joining us for the uh, episode today and good luck with the new uh, book and continue to enjoy your Mac. Thanks for listening to the future finance show and thanks to our sponsor, qflow.AI if you enjoyed this episode, please leave a rating and review on your podcast platform of choice. And may your robot overlords be with you. Sa.