¶ What a Chief Innovation Officer does
I don't. I'm not chaotic for the sake of being chaotic, and I'm not in the. And I'm not chaotic for the sake of breaking something or causing harm to others. Right. We're chaotic because our brains work differently. And this is now the age of the chaotic brain. We can now take these cool, crazy ideas, get angry at it for. For a half an hour like you said, and then have Claude code come in here for 40
minutes and then fix it, and then you're on to the next thing. If that sounds like your brain on a good day, then you're in the right place. Welcome to Data Driven. Hello and welcome back to Data Driven, the podcast where we explore the emerging industry and field that is AI data and of course, all the stuff that underpins it. Normally, my most favorite data engineer in the world would be with me, but however, today he is not. However, I did do have a different Andy.
Welcome to the show, Andy. Is it Butcher or Betcher? It is Betcher. Betcher. Okay, well, welcome to the show. You are a Chief innovation officer at. double track. So tell me a little bit. What is a. What does this Chief Innovation Officer do? Well, I will tell you. My favorite way to describe a Chief Innovation Officer is the one person you call when you're stuck and you need to move your top or bottom line and don't quite know how. Got it right.
So I will come into an organization and ask some of the crazy questions, propose some of the crazy approaches, throw a bunch at the wall, see what sticks, and then the old overused trope of go fast, fail fast. We will try a bunch of things as quickly as we can with all of the new awesome technologies that we've never had before in our careers as you, and probably twice as better as this Andy, your normal Andy that you have on your podcasts.
As you guys have talked about, we have not had this type of technology available to us ever in our careers. And, Frank, I've been doing this for 32 years, right? I have done a lot of fun stuff in my day, but not like I have in the last couple years. I mean, heck, Frank, much less than last six months. So taking all of this wonderful crazy brain that we all have and applying it with some really cool technology, that's how I see a Chief Innovation Officer is. Is just. That's the best way I can
describe it, man. I think that's a good way to put it because you see a lot of companies, they really struggle with. They know they want to use AI, particularly in the software space. Right. They will know they want to use AI, they know they want to do all these things, but they don't really know how. Right. And it's a different mindset, I think, you know, in the virtual green room, you said, you know, you, you, you, you don't
want to be responsible for running things. Right. You want to be responsible for trying things, is basically what you said. Yep. And I think that's a. Put it. Because we live in such a. No one, no one knows how this is going to play out. Like, honestly, like, everyone thinks they know how it'll play out or, or whatever. But, I mean, I was able to, you know, put together,
literally I had, I had a car accident in December. Literally that day I started a new project on GitHub and just started Claude code, just chewing away at something, and Now I'm like 80,000 lines of code later, and it's only been maybe three months. Yeah. Right. And, you know, 80,000 lines of code is not a trivial amount of work. Right. And it's an
idea that I would not have. Yeah. I mean, theoretically I could have, you know, raised the money, found the money, paid people to do it, but I wasn't, I wasn't going to do it. Like, realistically, I wasn't going to do it. But now, now I am the precipice of having this product, you know, that's out there, that helps podcasters. I built it for the
needs that Andy, Candace and I have for the different shows that we have. And, and, you know, basically product, you know, production line for podcasts is basically what it is. And recently had to change the domain name because somebody else had something very similar. So, but, but, but again, that was not a lot of work comparatively. I just told, you know, Claude, like, hey, look, this is too similar. This is the new branding. And it went, it did it in about, you know, I think I was salty
about it for like an hour. Yep. And then, you know, Claude had it fixed in 40 minutes. So, you know, I was, I was mad longer than it took to implement the fix, which if that's not, if that is not a metaphor for our time, because, you know, maybe a part of me was stuck in the old ways, like, oh, my God, I have to change the domain. Oh, my God, I have to change the code base. I have to do this. The AI doesn't really care that much. Right. To it. It's just finding a replace. Yeah.
It's crazy. So I will say, what was it? Probably 2006, 2007 was my last kind of career pivot, right? And I was moving from a primarily Microsoft driven developer and then vb, C net, SQL Data, like all the things as I was pivoting that into what would turn into a 13 year Salesforce career when I stopped, when I stepped in Salesforce and I was looking, comparing, contrasting, walk in saying, well, heck, out
of the 100% of time. Or you know, Frank, as you and I know as nerds, we've got about 120, 130% of time. You know, families might disagree with that, but that's what we do. If you look at the 100% of time, you used to spend 100% of the time standing things up and dealing with domain name security and stuff. And when I looked at Salesforce, it was like, well, heck, I can take 80% of that too it off the plate because the platform handles it and the rest 20%. I can
use my crazy brain to actually like do something cool. And that went for
¶ The age of chaotic creativity
a bunch of years. And now we are in the world of AI and the same thing's taking place again right now. It's with all of the other technologies plus all the new ones, right? To your point of making that application to help you and your cohorts there with, with the podcasting, you can now walk in with the idea, excuse me, post production, and edit that one out there or leave it in for comedic effect, whatever, right? So you can look at this stuff
and like really stand some things up. But my wife pointed out, you get you Frank. You've seen on the Internet the stupid little like, you know, nine box D and D role character matrices, right? And my wife sticks me in the center column all the way to the right, which is the chaotic neutral, right? I, I, I, I don't, I'm not chaotic for the sake of being chaotic. And I'm not in the in, I'm not the chaotic for the sake
of breaking something or causing harm to others, right? We're chaotic because our brains work differently. And this is now the age of the chaotic brain. We can now take these cool, crazy ideas, get angry at it for it for a half an hour like you said, and then have Claude code come in here for 40 minutes and then fix it and then you're on to the next thing, which both addresses our wonderful chaotic nature and our crazy ADHD brains which are jumping around like a pair of, you know,
like a whole bunch of popcorn kernels and a popcorn popper. So, you know, I similarly, but with my clients and also with all of Our side projects that we all have, right? You know, to try to apply this technology and bake it into our brains. You know, same type
of thing, 80, 90, 100,000 lines of code. You're typing stuff in. You're putting what would have been six to eight months of a team doing test frameworks, which is one thing, by the way, Frank, if you have not tied a test framework into your work with your applications or anybody listening to this podcast, the. The power and the resiliency that you're getting through Claude code and, you know, pick your tool. I'm just
picking on Claude because, I mean, my opinion, about four months ago, they. They went into a different ballpark. They're not even in the same ballpark as everybody else right now. So you take all the crazy, awesome stuff you've got going with cloud code right now and how it helps you through things and all this, tell it to go pick on what you're not thinking of or put a test framework in, or have it do security auditing.
Have it. Go find, you know, find. And I can give you a list here, Frank, of the different security organizations that put out really good white papers on the. On the methodologies they go through that all apps should do. And you point Claude at it, you say, hey, either it's Claude code by itself or you pick up co worker dispatch. Now, that will tie into the browsers and browses you to go through and look through stuff and say, hey, I need
you to compile all. All of this and then apply that good logic in here. So it's not just ideation, it's also a level of fortification. Now, I will also say this for all of the old grizzled gray hairs that are listening to the podcast, because, Frank, you and I both, I think we've been around the block a couple times, and I know one of the biggest things I get talking around this stuff is, well, you know, I've been doing this stuff for a while. There's a lot of things that aren't spoken. There's
a lot of experience we bring into play. Well, absolutely there is. We have to teach our tools to help us go through that. That's why we use our crazy brains and do it. Like I mentioned that testing framework, right? So, man, it is an exciting time. What was the craziest thing, Frank, when you were dinking through your app, like, what was the one, like, bang, aha. Moment that came up that you could not have done otherwise? Oh, God, there's so many. But the
¶ Using AI for planning discussions
first one was really I think testing framework, no one really enjoys testing. No necessary evil, but no it's necessary evil. For me it was Planning mode, right? Because for me, because it would be, you know, it would go through and be like have you thought about this? Like I think it just said that and we can go into and it basically suggests
we go into planning mode and discuss it. And I, I find myself having my, this discussion with an AI that you know is approximates a pretty reasonable conversation one would have with a junior to to mid level architect, right? You talking through these problems. I love Planning mode, right. I what I'll do in my projects because now with my, my ada I like to say I have Schrodinger's adhd, right. Because it's both, you know, I don't have it
diagnosed, right. So it's adhd ish. So I can have it when I need it and I don't have it, I don't need it is I have like four different project ideas going on. Maybe, maybe five. Right. And it's kind of like I also have my co host on Impact Quantum is also very, very neurodiverse and she leans into that and it really is kind of the superpower if you don't, you know, especially in the age we live in now, right. Because you can have these ideas and
you know, as long as you can prioritize them. And I find, you know I basically created out of a Claude project a project manager, right. So have a VP of project engineering. Each one of the
¶ Choosing the name Show Dog
project ideas have kind of their PM that manages that project and I kind of talk to them and it sounds weird but I mean I converse with them one way or the other and they come up with ideas. And you know, part of it was ideating on the name change, right. Originally I called it Podsy because it was going to call it Podzi McPaderson, right. But I had to change it so it kind of like had the whole list of things and you know, it helped me ideate the ideas and I gave it the name of the other.
So ultimately I landed on Show Dog. Okay, but, but it basically kind of helped me walk through it, walk through the branding chain. So you can tell it to act like a marketing manager, act like this and it will kind of, for lack of better term switch hats and it'll do that. And for me that was amazing, right? And I can kind of have them all meet together and then I basically one thing I discovered is you just output your conversation, the ideas that you have
into report and markdown Right. Which you can read at your leisure and other bots can read. Yep. So you go through and you kind of have this, like, very productive, you know, session of like an hour or two, and they basically, you. You plan out with the different bots and different Personas. You, you, you write everything down. It's like the olden days, right, where, you know, people would come up with a Project Action Memo and things like that. And,
you know, everybody acronym TPS report type of thing. Right. Except useful. And, you know, you can basically put Claude on dangerous mode. Right. And go out, Go out to lunch, do something else. Do something like, you know, hang out with the family. Right. And a few hours later you come back to it and it's done, right? It's checked in, it's done. I also always have it kind of do like a. Like a change log and like, write a daily report, like, what'd you do today? And I can look back and
like, when I feel like I'm not making progress, I can look. Well, you know, 10,000 lines got written today, right? I mean, this is just you. Basically everyone has effectively, like a 10,000, you know, team of developers, right? Because, I mean, how long would it take to write 10,000 lines of code? Right? It would take, you know, if you needed to do them a day, you would need to have at least a thousand developers. And that's being generous, but then being able to, on a dime,
say, that's not what I meant. Pivot. Yes. With no attitude or very little attitude. With very little attitude. Right, Right. Yeah. It's that. That perhaps was one of the. One of the biggest things, like when. When the Anthrotic. The Anthropic app came out, and then all of a sudden they turned on Claude code. And then I was able to hook my IDE into the cloud versions of it, so I can be sitting, you know, in a restaurant and, you know, and on my way to the bathroom and back. Not
in the bathroom because it's weird, right? But on the way to the bathroom and back. You can just type something into your phone now and. And just get the idea out of your head, because I know, Frank, I know about you, but a lot of my thoughts don't happen when I'm just sitting here. Right. When I'm sitting here, I've got. I. I don't know if you can see off camera over here, but I got like 10 monitors in front of me and three
computers. Right. When I'm here, I'm locked into productivity mode and I'm doing things And I'm being distracted by IMs and all this other stuff when I'm out walking around the block or I'm driving my kids somewhere or driving my grandkids somewhere or my wife and I are out. That's where the ideas happen, when your brain is free. Right. I do a lot of off roading. If you read anything, read anything about my bio. Anybody who's
listening, right? Like there's, oh yeah, he knows data and AI stuff. Oh yeah, and don't get him started on jeeps because he'll monopolize the conversation with off roading and jeeps all day and forget about data and AI, you know, save for the fun space of when all of those intersections, right. When I can finally figure out how to make money making that intersection happen, then come talk to me too. Right? But that's the time. All that fun
¶ Brainstorming and organizing ideas
time is when the cr. The cool thoughts are coming out and what do you have in front of you? Oh, I need to remember to do this. Or you take a voice memo and forgot you made the voice memo. Right. So I can just go quick, go pop open, you know, a claw dispatch a cloud dispatch and say, hey, go do these Google searches, put this crazy hair braid idea back together. Notify me here when you're done at the end of the day and then ask me if you have any questions along the way. And
then I put my phone back down and boom. You know, Bob's your uncle, goes for three hours and does stuff and ask me some questions. Like it's, it's like a digital assistant, an offshore team that, you know, I, you know, again, I, I pay the high end for, for Claude because I heard burning out limits, right. And it costs more to buy more than just to buy the top. Right? Right. Incredibly good marketing strategy and product strategy
from Anthropic. Right? Right. Like when have we ever had this ability, especially as neurospicy individuals, being able to go dive in and just have all this crazy insanity. 80% of it drops off. But the 20 that sticks, Frank, is pretty damn cool. I mean it is amazing. It is. I think it's a time. This really is a great time to be alive. Everyone's talking about, worried about, you know, we're recording this day after Easter, right. And we had
a friend over for Easter who is very much an old school developer. And I'm like, you know, like you don't understand understand. He's, you know, not really dived into AI and I'm like, you know, you really need to take a Look at this, right? And he's in between jobs right now. And I'm like, you really need to look into this, right? Because there's no, you know, there's no avoiding it now, right? Everybody and their cousin is. Is doing something with AI. Everybody. And their dog
is an expert in AI now. Right? But, you know, one, I was talking to someone who was a former Microsoft evangelist and teammate and coworker and friend. Still a friend, not a former friend, but, you know, she's like, AI has basically made every developer a manager now, right? Because you have. Everybody has a team if they so choose to treat it like a team. Yep. Right. It's a threat and an opportunity. It's a threat for those who don't really seize on it,
but it's an opportunity for those that do. Right? Like, I mean, you know, you mentioned Salesforce. Salesforce is probably the poster child for SaaS success, right? They have a. A building in San Francisco. You know, they have a tower, right? You go back to, like, New York, right? There was the Woolworth Tower, right. They changed retail, right? And there was the Empire State Building, the Chrysler Building. All these things. You know, to think that a SAS company could have a tower
named after them, right. Is phenomenal. And, you know, is every idea I have gonna be like, you know, is there going to be like a, you know, a show dog tower in Baltimore? Well, probably not, but, you know, maybe. Maybe, right? Like, you know, I mean, but, you know, the fact that I have kind of these five active projects in my mind right now and they wouldn't. They would stay in the back of their cocktail napkin in the past. Now they went
on the whiteboard and I kind of basically type into the. Into Claude what was on the whiteboard or, you know, whatever the voice notes have.
¶ Using tech for learning
I don't think people realize, like, just. Just how much power is at your fingertips or your voice at this point, right. I drive my kids around all the time, right? I'm taxi dad. And I'll, you know, I'll see like a new research paper dropped. And, you know, I don't. I don't have time to read them all, right? But I do have time to drop them in a notebook lm and then have a podcast made out of it that I can listen to. So that way, the first time I sit through and read, it's not my first
time exposed to the material, right? So, like, I noticed I get a lot more out of actually reading the. Not the physical, but like the electronic, you know, reading it. I get a lot More out of it because I've kind of been prepped and I heard lecture notes and things like that, that, that the AI created. It's just, it's a phenomenal time to learn, for sure. And, and I think now, particularly in the last six months, it's a phenomenal time to build. Yes. Although I will say I've got two
phrases I'm going to pop here. Right? So the first one is more serious. The second one is a little silly, right? So the first one here is as I talk to individuals, you mentioned your friend who's between jobs right now, right? And you did the exact right thing. Hey, you know what? You got time. Go look at this. Make your own decisions, use your own critical thinking. Like if this is something you could use or something you can reference or something speak to, you
know, all that kind of thing, right? So the first one is I talk to individuals and I'm like, you know, AI is not coming for anybody's job at this point. Not yet. Right? That is a. Even, even the craziest super thinkers, right, are like, you know, six months, 12 months. No, there's no AI coming for your job. Right? However, people that use AI are coming for the jobs that people that don't, Right? And that same thing goes for companies. I've got myself a nice little sweet
battery of customers. I've got a bigger network that I talk to. Like. Like, that's the one thing. And anybo. Anybody in my network who's listening to this is probably going to snicker a little bit and say, oh, Andy says that to me a bunch. It's like, you know, companies. I mean, the AI is not
¶ Companies adopting AI strategies
coming for companies, but companies who are leveraging AI are going to come for the companies that don't. And that's actually, you know, Frank, to an earlier point you made, and I'll get to my silly thing in a second here, but a point you made earlier was that, you know, all companies are like, we need to use AI. You know, that was like three years ago when I, when I went out on my own and, and really took all of my data backbone and my data history and brought it in and then
said, I know these tools are coming. I need to get brain centered around to be able to talk to people about this. You know, company after company after company are coming in and saying, we need to use AI. The board's pushing us to use AI. My CEO is pushing, we need to use this stuff. I have no idea what to do. I bought a bunch of stuff and it didn't actually work and it's sitting on the shelf. And Now I've spent $15,000 a month on AI stuff sitting on the shelf
and now they get a bad taste for it in their mouths. Like, no, you're doing the right thing, but you need to think differently. These are tools for ideation. There's no easy button, right? You don't. I just put a post on LinkedIn a little bit ago. But you don't treat an AI agent. An AI agent you should treat like an employee, right? You need to onboard it. You need to feed it information. You need to work with it. You need to understand that it's not
perfect. You need to be able to put it in situations where it can fail and support. Support it and bring it back and work together, you know, so that's what I talk with companies a lot about. I've got a whole methodology and framework which, Frank, I'm not going to bother you guys with today, right? That's a whole different podcast for a different day. But, but really that's how I talk to individuals and companies today. You have to play
with this stuff. And then. So the silly one, Frank here is, is the Spider man reference, right? It's the. With great power comes great responsibility, right? You mentioned it. There's no better time right now to be able to learn something, be able to, to, to have Notebook LLM, go tear apart a large, a large document and share it to you. This is one piece I am very, very, very, very fervent about here.
And this is. I want to. I'm going to preface this with everybody listening, that this is not a political statement, this is not a religious statement, this is not an anything statement. But, but critical thinking is now more important than ever because these tools are so fast that they will summarize information in the way that you. That if you worked with it enough, it'll summarize in the way that, the way that you think. But you still have to use your
critical thinking skills to be able to pull this stuff apart. You still need to use your critical thinking skills to question it and then go back and look for more information. But I'll tell you, even that, you know, slightly funny, slightly weird statement I just made there, Frank, still, I have never been more excited in my career other than, you know, when I was 20 and making cool things, right? Like, you know, staying up for four
days in a row making cool things. Now I stay up for four days in a row with a lot of coffee and make cool things, but that's right. There's a really so good. You're right, Frank. It's a cool time to be alive. These technologies have never been here. I really thank you for inviting me on and talking about this stuff. My, my, my, my biggest, nastiest appliance is my Jeep Wrangler. That's where all of my cash and time goes. So I actually have used AI to actually
help try to troubleshoot that sucker too. It's amazing, really. Yeah, Tell me about that. Because I've had mixed results with like, AI troubleshooting. Right. So like, we had a, you know, we're on well and septic, right. So we had like a well, tank leak and, and AI kind of got that completely wrong. But the other day, actually for the, for the clothes dryer, like, I, I basically said, I need this type of replacement screw. What does it use?
And it was like, well, tell me the model and make a model. I told him, make a model. And it was like, well, try this. And I bought a, I was at Home Depot and I bought a package of those screws and it was magically it worked. Right. So it does seem to be kind of a, I think, I think goes back to what you said, right. Like think about critical thinking. Right. Had I followed its advice with the plumbing, I probably would have done a disastrous work.
But with the, you know, for, you know, like $5 for a package of screws. Right. It was the, the risk reward was, was pre, like, you know, if it's the wrong one, I go back and I return it. Yeah. Right. These days I'll spend more in gasoline, probably to drive back to the store. Unfortunately, yes, right now that is the case. Yes. Then,
¶ Classic car ownership struggles
yeah, Then, then it would actually just be just, well, I have a box screws I'm not going to use. But I do, I do think though, like, so, like, how do you find it? Help you with, with, with your Jeep Wringer? Because I am, I am also, I'm kind of, I wouldn't say disgruntled. I would say a, I, I, I'm a car guy, right. I like, I like big Chevys and I cannot lie. And you know, I, about 10 years ago I had this, I guess you could call it a midlife crisis, I suppose. But I bought a 76 El Dorado
and convertible and that thing. Car was beautiful. But I really overestimated my abilities and underestimated the cost of owning and was kind of like, well, I had my fun with a classic car, so. But it's, and it was funny, right? Like when you have the money, you don't have the time. When you have the time, you don't really have the money. Right. So I found myself, you know, when I got laid off. When I, When I got laid off, I found myself with time and I was like, you know,
oh, God, but this is expensive. Yeah. My. My exploits into a. So there's kind of two ways I've done in car, right? Yeah. There is the it's broken, I'm 3,000 miles from home and I have to figure this out moment. Right? Right. So I've got a really good battery of friends who. We all have different levels of mechanic. None of us are mechanics. Right? Right. We. We are all weekend warrior mechanics at best. And we've only
been. We've only honed our skills over years of being stranded in places or looking at spending $4,000 in labor on something or giving it a whirl first. Right. You know, I look at how I spin over to AI with it is, you know, so the Chrysler, Chrysler can bus is behind the Jeep Wrangler, right. And it's got a pretty standard thing of codes and there's a lot of readers out there that read them. And, you know, it's. It's like you look at regular like corporate AI stuff. You
gotta ground it, right. You gotta ground it. You gotta ground the silly thing before you do anything, whether it's cars and you go give all of the codes that are possibly there. And you pointed at a bunch of good troubleshooting websites and forums and boards you use again, coworker dispatch to go have it dig through a couple things and say, hey, you know, I found some stuff over here. Go figure out what the
patterns are. You know, when you get down to things, there's always a bit of artist or artistic interpretation and good old physics that defeats an AI every day. Right. But at least gives you an idea to be able to go make that and I'll spin it over to your dryer comment to go make you buy three different bags of screws for a low cost of trial and error. And you find one works and you put the other two bags in the drawer that you'll probably never touch again except for 10 years
down the road when you need that one screw. Right? Right. So, you know, there are places it works. There's places it's not if I super pivot for a minute. Right. If I go take a. Take that same thing we talked about with how we ground and look at cars and use good critical thinking and spin it over into like how we talk with corporations and how to use AI and data stuff. When I left my previous organization, I did not have a job, right? I know I needed to
do something in here, and I was waiting. I'm a big guy that believes in fate. I'm a big guy that believes in, and in. You know,
¶ Taking time to rethink plans
when you put good juju out in the universe, like, like good things come back. So that's why, that's how I usually try to live things. And I took three months off and I'm like, all right, how the heck do I pivot this stuff around? You know, even three years ago, we're talking 20, 23. We all knew that AI wasn't brand new. It's been, it's been an academia for decades, right? We all know about grounding, we all know about all the things. Now it just became commercial.
So it's not brand spanking. I was thinking, well, how do I ground my conversations with organizations to be able to take what we just talked about with the car and apply it toward how they want to move or move or change their top or bottom line? Or I'm talking to a friend of mine and they're trying to troubleshoot a lawnmower, right? You know, you gotta ground the stuff. So I came up with, you know, every organization has eight types
of data. So we feed, you know, I've got a whole head, I got like a massive prompt that feeds that in. And then every, every one of those domains, every field to be relevant, reliable, revealing and reusable. Like every single data piece has to be that. And we feed that with a prompt over there. Then we feed some schemas and talk about stuff. But you know, again, with great power, with great power comes great responsibility. You have to be
responsible and be critical in what you do. And then you can fix your lawnmower, fix your Jeep Wrangler, or fix the fact that your support department can't figure out X, Y and Z and communicate it to the customers. Right? Like, that's the whole chaotic brain side of things. You know, chaotic doesn't mean messy. Chaotic means different or unpredictable. Right? Or unpredictable. Right. I. One of my superpowers is I can see commonalities across many different, distinctly different areas that
are not normally tieable. Right? So I don't know. That's my, kind of my personal walk in with this stuff. The tools are fantastic. But if you've got a, if you've got a crazy brain, Frank, man, you can do some cool stuff right now. Oh, exactly. All the things that were Would have been a headwind before now. Tailwinds, right? Like, in terms of what, you know, just. It would be impractical to. To build out something like this. Like, you know, whether
it's. Whether it's show dog, whether it's another tool. I have a command line tool I wrote like three, four years ago called Dingo that helps me write blogs, and it was a command line tool, right? So I'm not really, really the friendliest UI in the world, right? But I basically pointed Claude and I was like, here's the code base. I want this to be a web interface. And that's going to be another, you know, another SaaS. Maybe it'll be the Dingo Tower in downtown Baltimore, who knows? But the,
¶ Using AI to solve problems
The. The solving problems is what people pay for, right? And if I look at it this way, as a podcaster, as a blogger, as a content creator, as a marketer, as a technologist, right? I encounter a problem, I'll be like, you know, I should probably have AI see what it can do about this, right? Like, and that's how each one of these things was built, right?
As, you know, as we were kind of building out something and trying to organize all the content from the different blog posts and podcast episodes and video clips that we make from them, we really was like, there really should be a tool to do this. And in the olden days would be like, yeah, let me get something out on the whiteboard or notebook. And then now I'm like, wait a minute, I can just tell it to Claude. And Claude will build it, right? And that's. I mean, it's fascinating.
But you're right, you do have to exercise some common sense, right? And I don't think. I don't think everyone's kind of figured this out yet. I think very few people, to your point, like, the people that are going to be the next Mark Benioffs are going to be the ones who figure this out first. And I don't think people. I think people are still stuck in that mode of, oh, my God, AI is going to take my job. Funny story, last. Last springtime, actually, there
was a baby bird fell out of a nest, and my. I live in kind of a rural area now, and I'm like, I'm a city boy, right? Like, I have no idea what this. What to do. So I basically, you know, basically asked Chat GPT, like, what do I do? Like, what type of bird is this? Like, you know, and it told me and it gave me a list of resources. I called around and you know, the, the baby bird was saved because, you know, Chat GPT recognized what species was. And I said they're like, how did you know
to contact us? And I was like, oh, Chat GPT told me. Yeah, I was like. They were like, what? You know, it was a first for them. Which is pretty funny. Yeah, I mean, I mean, Frank, look, I don't know at this point, what, 15, 20 years ago we had the same, I mean, to be overly simplistic about this, right. 15, 20 years ago it was Google, right? How'd you know to call us? Well, I Googled it. Googled, yeah, yeah. Now, I mean the, the tongue in cheek thing is my Google fu is strong. Wrong. Right.
I can find things faster, better, quicker on the first page than other people because we don't, we, we, you and I know how to structure queries. It's going to be the same thing with LLMs and, and I'll even just say, you know, AI is a very broad term. Right, Frank, you and all of our listeners know that too, right? AI is a very broad term. We're talking specifically around like the, the LLM generative AI side of ChatGPT and Claude and all that kind of stuff. Right?
Like there's going to be a point where prompt generation and prompt engineering become second nature to the human. Just like Googling has become second nature for years. For the last 15 or 20 years. We're going to get to that point and then there's going to be the next generation or evolution beyond that.
You and I are able to build cool stuff with Claude code right now because we can at least half rear end prompt generate on the fly and can learn and see how it's reacting and go twist it and turn it and restart and give it queued messages to go to refine and tune it and respond to it. Right. We're learning in real time there. I mean, heck, there's college courses out about
prompt generation. You know, I saw that two and a half years ago. There were, there were college courses out there and it's a good base to get. But that's, that's, it's, that's, you know, a long way around the bush talking around Frank about, you know, prop generations and Googling. Right? So when you're talking about the baby bird thing, it doesn't surprise me. It makes me smile. It's like, yeah, it's because Frank knows how to ask a question. I know how to pick a picture and send
it to the AI. Well, it's true, like you mentioned 2023. Right. 2023. OpenAI looked untouchable. Oh, yeah. And look at the tool that we've spoken most about. Drops. Right, right. Chat TPT drops commercially in December of 22. And everyone stares at it like the baby bird that has fallen out of the tree sitting in your yard. And you're like, what do I do with this? Right. And not to get really queer, weird meta on your, on your reference there, but. But 23 was a wild year. 24 was
wilder. 25 started to be able to give us like, actual stories about people making businesses out of this. Hell, I've made a business out of it. 26 has no signs of slowing down. Right. Well, and, you know, I also think too, like, the dynamics of it. Right. You know, OpenAI looked untouchable for most of 2023. And here we are. The tool we've spoken most about has been Anthropics, you know, flagship Claude. Right.
Now, the other ones kind of also are roughly peers. But I mean, in terms of adoption and preference, like, I think it's. People don't realize, like, this is still no 1. No 1 has really dominated the space just yet. Right. This is a lot, like, reminds me, you're old enough to remember the browser wars, right? Like, you know, Internet Explorer was the underdog. This is before Internet Explorer became the punchline to a joke. Right? So just time can change a lot of things. It's. I don't know,
like, it'll be interesting to see how this pans out. I'm also excited about agentic stuff too. I don't. I have an open claw instance, but I kind of see the value, but I don't see what the rah rah hype is about. Oh, it's, it's, it's just starting, Garten. Right again. Yeah. The concept has been in academia,
¶ Challenges in commercializing academic concepts
right. It's the, the minds are spinning it to commercial purposes and there's a lot of trial and error. I think I saw there was a. I, I could pull it up. I got it in one of my favorites here, there's a Gartner article about like, 80% of all agentic projects are doomed to fail and sit on the shelf because they didn't have the right outcomes in mind when they did it or the wrong technology or, or, or, or, or. Right. If I pick back to something
you had just said ago a bit ago, you're, you're right. We're talking about Claude because you and I have, and people like us have really tied to Claude. And you know, this Isn't the, you know, after the Department of Defense, you know, the whole thing and the social before that. It's not that. Right. It's like you and I are using it because it speaks to us and we
can speak to it and we're getting some cool stuff done. Right. This could be, you know, Gemini might come back out in a massive leap forward in six months. Right. Or pick your next company that we don't know about out right now as a, as a, as a strategist and implementer in a space that is so rapidly changing and I always miss this
up. Is it, is it Boyle's law or Moore's law? I don't remember the name of the law, but it's the one that measured the progressive technology over years by saying the number of transistors would double on a chip every two years. You know, I, this is not an official statement. This is something I just say. But you know, basically it's running on a monthly cycle right now. What was taking a two year cycle is now taking a one month
if, if even that long cycle to jump. And one of the biggest challenges that I have been, I have, I have always got tripped up a little bit on and then always think about as a strategist, implementer is I cannot bind anything that I'm doing to one specific company, technology or model. Right.
¶ Adapting to evolving AI technology
You have to be able to abstract those concepts far enough to be able to say, I need an LLM here that does X, Y and Z and build it in a modular way where, you know, it might be GPT 5.2 right now. And then I'm going to swap in Sonnet for this and I'm going to swap in Llama for that. And
I'm going to do this and continue to grow. Being able to effectively articulate that as a business owner, Frank, to do stuff for yourself or, or as a customer, being able to say, hey, you know, you're gonna, I'm gonna charge you a bunch of money to go do this thing and we're gonna go play with this stuff and we're gonna build something really cool and it's gonna bring ROI and it's gonna move our. It's gonna, there's our
defined outcomes, here's how we're gonna get there. Oh, and it's on technology that's gonna literally shift every month. How do we plan for that? Like that, to me is one of the biggest challenges right now to be able to snap based on the technology we have. We're going to have about a 60 month. 60. Not 60 month, no 60 day build cycle before this
thing comes out all the way into your production environment. But on day 61, while it runs off and meets the outcomes that we had, we're going to be talking about version 2 already. Not because something is completely outdated, not working. It's because there's that much new that has come out in 60 days that
could make our outcomes even better. Better. Right. It's an odd balance and it's an, Honestly, it's a, it's a mind shift with companies that have been dealt with Salesforce for years or dealt with Microsoft Dynamics for years that they'll, they, they talk about, you know, quarterly or annual value improvement cycles. Frank, the stuff we're talking about is monthly. It's insane. Right? I mean that's, I can easily see it going even more
granular than monthly. Right? Yeah. I don't know when, but like it, you know, it. I, you know, sales software cycles used to be what, three years, then 18 months, then 12 months, then quarterly. I mean there's no, really no, I'm sure there's a practical upper limit, but I, I think we still got a lot more headroom. Oh, Frank. Frankly, the, the, the ultimate limiter is going to be the human ability to adapt to change.
Yeah. Yeah. That's what it is. Right. I mean, you're right. I mean I just put out a response to a massive RFP that I've got more change management people than I have technical people. I've never done that before, Frank. But that's where, right, it's, it's. I need to be able to change hearts and minds and the way people think and the way that people see outcomes rapidly enough in a corporate setting to be able to accommodate the change, not just put it
in and let you know, internal change management or governance. Right. It. Or business leaders take it like we actually need a fleet of change managers. That sounds absurd, but the more I think about it, the more I think that's smart. Right. At first blush you're like, well, that sounds kind of. Oh yeah, I see why I couldn't even finish the thought. The sentence in my head was like, oh yeah, that makes a lot of sense.
It's a crazy time, man. Yeah, it's like the old playbook, while not completely like useless, is definitely, definitely need some updates. You know, history is always the best teacher. Right, right. You know, now you just have to compress your history time scales. You don't compress your history lessons. You know, we both, I guarantee you, we've Both been through 36 month SAP implementations at different organizations. Right,
right. And even the change management. But once you intrude, entrench people in that, you know, I mean you. They're not changing. Lord, they're not changing. Right. But now you're taking things like I just built an account, an accounting, I'm not going to say I built accounting system because that's dumb, but built an accounting helper to be able to do things and trying to introduce that into a regulated or any type of industry that has hardcore rules that they
have to answer to, like you have to change hearts and minds. The tech is easy. The tech's the easy part. How change management is the hardest part. Yeah, it's a good way to put it because like, you know,
¶ AI tools and problem-solving in coding
one of the things I think that has been exposed through the use of, you know, AI generated code is that generating code isn't the only thing that software developers do. They solve problems, they have to talk through problems. Right. I spend probably most of my time interacting with any of these AI tools
trying to solve a problem. Right. Or when you do solve a problem that, you know the engineering discipline of, when you solve a problem, you have certain trade offs which trade off matters to you. Right. As kind of like some of them are hard, some of those are not hard, difficult, but like kind of just physical limitations versus some of those are mental limitations. Right. In terms of how you design a ui. Well, you know, if you call it this, then you. That's at the
expense of that. And then it's just a lot of trade offs in conversations, which is what I find myself looking at. Most of the interactions I've had with these tools have been mostly talking through the trade offs. Yeah, we used to, we did the same thing for years, Frank, just at a slower pace. Yeah, yeah, yeah. I mean this would be like a meeting you would have
every week or two now. Now you're having it in the half an hour that you're working with Claude and, and a, a business representative to be able to ground you on outcomes. Oh, shoot. Okay, now I got to think of this and this and this and now we've got identity and access we have to worry about. We got to worry about this UI piece and this ux and we have this ux. We have to
worry about this and that and this and that. You're literally making changes as fast as that into your, into your plan mode and Claude to put out something that somebody can poke with a stick. Right. And then they can poke it enough with A stick and it works just fine. Then like talking about your friend
¶ Limitations of AI and data security
the, the, I'll call it the again, older and haggard software architect who's built things very well as a living. Like eventually Claude is never going to replace those individuals at scale, right? All we're doing is getting the next alpha or the next beta or the next stable piece out there. And then if somebody needs to harden that, then you need to put real process around. How do I properly secure this? Because I mean Frank, the spoken and whispered in corners is
data, right? We're making these applications and you're a smart guy, I'm a pretty smart guy. Together we're smarter together, right? We don't automatically cover everything. Claude leaves a hole here and there, right. I was talking with this absolutely brilliant woman who was trying to pull an application to market and I eventually I talked with her for a good like six sixty days about
ideating through this thing. And it got to a point where we were collecting so much private information about an individual for a good reason, right. But it automatically puts a big old target on your back. And I said, you know, we're gonna have to alpha through this thing, get a workable, a workable prototype, get a couple people to trust us enough to put stuff in here and have it secured
and disconnected in a way where I'm not gonna risk their stuff. But then I'm going to need to put a security architect on this where to put these layers. We're going to spend a quarter million dollars to get sock to a compliance to make sure that we hold this thing right. You know, I've never gone from idea to a Sock2 conversation in 60 days and I ended up walking away from that because of the data. Chance was, was so big, but even, I mean, and that, that was, that was like gathering
will information and bills and things like that. Like, like really like potentially harmful information if somebody got that in the wrong hands. But you think about your company CRM, right? We talked about Salesforce a minute ago. We can go put up, we can go stand up an
¶ Data security and CRM setup
agentix CRM in a week to be able to have somebody log into it through a, you know, through a, you know, login through Google, Microsoft, sso, all that kind of stuff. But, and I know I'll stop, I'll get off my soapbox here in a moment because I know we're coming to the close of our time here, Frank, but the one of the, you know, we talked about change managers. A big thing I want our listeners to walk away with here is to think about that piece. The second
is data security. You know, just because you can doesn't mean you should. Right? There is. There is such a market in people's data and the tools that we have are so awesome, but they. We in our, in our, in our absolute happy path. Exuberance. To be able to solve a problem, you always should have assigned
tag to your wall that says, what about the data? Because the last thing you want to do is to have this awesome thing that solves a perfect problem and then somebody gets wiggled through an unsecured API and downloads your data. I don't care if it's your company's CRM, which is frankly, a CRM is pretty. I mean, you're assembling it for public information, for God's sakes. Right,
Right. You know, much less anything that has financial data data, or much less anything that has personal identifiable data or health data or health data. Dear God. Right? Like there is a whole bunch of stuff that we are barreling toward. And like, like I've got right at my eyeline up here, I have five pieces of paper that have immutable concerns that I always have because I can glance up over my monitors and I can see them and I can see. Well, how do I manage
my Outlook inbox? Right. I'm an inbox zero guide, Frank. Like, here's my inbox zero stuff. Then I've got what about the data? And then I've got a couple other things up here as well. Topics for a future thing. Andy's weird things on the wall. But, you know, it's just one more piece I wanted to talk about. Here is. Is, you know, again, with great power comes great responsibility. Just stop and think about what you're making. Right. Right. Yeah. All right, Frank. I'm off my box, bud.
No, no, I think it's important. It's. It's important. Sometimes soapboxes, you have to have those soapbox moments, particularly around data security, data quality, data provenance. I guess that's what the cool kids are calling it now. There needs to be. Sorry. I have docs and puppies who are wrestling in the background. It's that kind of day. Kids are off from school and all that. Appliances breaking down. Dogs and cats living together. Mass hysteria. Mass hysteria.
Where can folks find out? I'd love to have you back on the show because I think that we barely scratched the surface. Because an interesting perspective on, you know, not just how, because billions of YouTube videos, you know, tell you how to do it, but like the why, the what and the constraints and the issues that you'll come up with. I think this has been a very enlightening conversation.
Love to have you back on the show. And we'll have two Andy's on the show and you know, that'll be, that'll be interesting experience. And where can folks find out about, more about you and kind of what you're up to these days? I mean, frankly, the two biggest places is of course, my LinkedIn profile. Like I'll spout out a random thing here or there. You know, even a blind squirrel finds another every now and then on
LinkedIn. Right. Frank, what I'm really trying to do is my website, doubletrack.com I'm working with my marketing people, so I try not to do anything in a vacuum because they get angry at that. But I'll work with my marketing people to put out good, impactful tools, utilities, questionnaires, things like that. Like you said, Frank, there's, there's millions of people right now that have YouTube videos out there. Everyone's telling you how and how cool it is and how fast, but
no one's going to come to my website for that. They're going to come for
¶ Challenging assumptions and blind spots
the, the alternate point or the part that says, oh, nobody actually asked me that question before. That's what I'm trying to put out on my website. DoubleTrack.com is a bunch of those type of things. So, you know, if you want to go and just kind of challenge your own brain or, you know, even if you're talking to Claude, say, what am I missing missing? Ever done that before, Frank? Have you ever asked it like, what am I missing? What am I not thinking of? What are the holes? What is the anti
design pattern that I need to do? That's kind of my mode when I come into this is I'm always challenging myself and others about what are you not thinking about? What should you be thinking about? That's. So to answer your question, Frank looked at my LinkedIn, weird things come out there. Look at my website, more structured things come out there. I would absolutely love to come back on, on, on your show and talk about crazy stuff. I, you know, I
promise I won't get too crazy or too weird. I read a lot of weird books. So. Hey, man, when, when the going gets tough, when the going gets weird, the weird turn pro. Yes, they do. All right. And with that, we'll end the show. Okay.
