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Hey. This is Frank here. Just, wanted to break things up a bit and

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do the intro myself and share with

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listeners a bit of good news and express my deepest gratitude

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for you all. Yesterday morning, I got

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hundred of AI podcasts out there. We secured a

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spot at number 38, which is enough to get us on

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the Casey Kasem show. For those of you kids that, are too

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young to get that reference, basically, it's good to be in the top

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40. Anyway, on with the show, and I had a great

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conversation with Dean Guida. And we did a bit of

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reminiscing about technology, and his transition

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as CEO of Infragistics from building

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client software control components into the data

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driven world. On with the show.

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Alright. Hello, and welcome to Data Driven, the podcast where we explore

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the emergent fields of data science, artificial intelligence,

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and data engineering. But my favoritest data engineer

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today could not make it. He is

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unable to make it, but I'm excited today because we have someone who

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is uniquely positioned to talk about history. And

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for those of you that have been listening to the show for a while, you

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know I wasn't always a data scientist. I didn't always even like statistics, if you

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can believe that. With me, I have Dean Guida?

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Dean Guida? I'm sorry. I should ask that before. We were reminiscing

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over too much stuff, but he has 35 years experience, and he's a

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CEO and founder of Infragistix. Infragistix

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is if you're a developer in the, front end UI

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space, you definitely know the name. I myself was fan boarding out. I

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even pulled out the, tablet license plate I had when I

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was a tablet PC MVP. And he has a new book called

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When Grit is Not Enough. And he wants to

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help entrepreneurs and CEOs create agile data driven learning organizations.

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See, we are going to loop it back to AI. We're not going to be

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talking just about Windows development and wind against large

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funded questions. Welcome to the show, Dean. Yeah. Thanks. Great to be

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here. Yeah. It's it's awesome because I'm like, you know, I get a lot of

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these things and I don't, you know, shame on me. Right? Like, I don't always,

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like, read the bio right away. Today was one of those days. And,

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I was like, CEO of Infragistics? Wait. That Infragistics?

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What? So so tell us a little bit because, you know, not every

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most of our our audience are data engineers or AI people who may not be

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recovering Windows developers. So, tell us a bit about

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Infragistics. Well, I mean, we got started, you

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know, in 1989 even before Windows

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was even popular. I mean, so we got started. We actually

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first built our first product was UI

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components, but for Windows 2.0 and,

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and then the big innovation going to Windows 3 0, way back when,

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was just overlap Windows. And so this is going way back in

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history. I know that's not what the subject of the show is about, but we've

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been building UI and UX tools for professional developers and

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designers for 35 years. We build data

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analytic and predictive analytic engines and SDKs

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for software companies as well as

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AI and conversational AI, you know, against analytic

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back end different databases and and data stores and, that

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we sell to other SaaS or software companies. And then we're,

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we also have a product called app builder, which is for professional developers

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that's really great at going from design to code. So, like, your design

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systems in Figma, which you don't have to really use, but,

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we we we can do go right to production code in

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React, Angular, you know, all the different JavaScript

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frameworks and a whole iterative development to build commercial

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apps and, round trip with, GitHub and everything.

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And and then another, product that's our first

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kinda b to b nondezigner developer toolkit is Slingshot,

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which is an AI data driven work management

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tool where we're leveraging AI and data, but it's all

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about creating this, data driven learning

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agile organization where the hypothesis is where

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we connect data to all of your, business

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systems. And, and then you create these objectives and key

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results. So you're measuring each objective and you're kinda

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prioritizing your key actions to achieve those objectives. And then

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we're tapping into all your systems that we're giving you signals for the

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all those, objectives and key actions. And then typically what

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happens is, you know, things are don't go as planned. And so you're reading

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these signals, and then you collaborate with the team to

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hypothesize experiments to do to improve business outcomes. And so

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that whole kind of a flywheel of execution, a lot of

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tech companies do it, and a lot of companies don't do it. But, Slingshot's

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amazing at doing that, managing work, and but bringing in,

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all the analytics and data across all your data stores,

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spreadsheets, business systems, and facilitating

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this, you know, go to market, the whole collaboration with the teams

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to drive business outcomes. So That's cool.

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And I love how, you know, you you've obviously been in the game now

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going on 35, 36 years. Yep. And you've

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evolved with the time. Right? So the when I left kind of the client

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development world, I, you know, yeah. I

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used to be I used to have the MVP program when I was a Windows

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when I was a tablet MVP, if you can imagine that. Right? And I

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remember you had the first I think it was one of your employees we were

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talking about in the virtual green room, a gentleman named by the Ambrose by the

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name of Ambrose. And he was he was telling me all about, like, you know,

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what they're gonna do with tablet PC, inking controls, and things like that. And I

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was like, like, woah, that's really cool. And, I

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remember, you know, you see but you've you've definitely kept I have to

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say I have to hand it to you for keeping up to date on this.

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Right? Obviously, the vision of the tablet PC and Windows phone never

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came to fruition. But, you know, here it is in 2025, and

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you're making, you know, slingshot, which is basically kind of, you know,

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not just cutting edge, but kind of ahead of the curves. Right? Curve because it

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sounds very agentic. I don't know if you use, you know, you know, quote,

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unquote, agentic AI as the, you know, the as the dictionary would

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define it. But, I mean, you're basically doing workflows and, you know, AI

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plus workflow is arguably agentic. Yep.

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And another thing that we've focused on for a really long time and

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still do is simplicity and beauty. Like, we always

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talk about simplicity and beauty, and so we really care

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about the user experience. And and so

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everything, if you really try and implement, which is super hard to do, easy to

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say, if you try to make the whole experience simple and

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beautiful, then people will love your app. And so we really

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strive to do that in Slingshot as well as when people use

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our UX and UI tools that we're enabling them to

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build, you know, beautiful and simple applications. And,

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and then AI is just, of course, as we all know, it's just been amazing

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that, you know, we leveraged AI to really for really the user experience

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where you can just have a conversation and ask about, how did this

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digital campaign go, and what was the average cost per lead for

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this, or what's my sales forecast, or, really

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anything where you're combining, data that may

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span multiple systems to actually give an answer. And

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so we leveraged, what we're we're we're we're calling conversational

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analytics, but, you know, it's actually technically quite

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complex, but the user experience is quite simple. That

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was always very you know, as a as a user of your

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Windows form heavy user of your Windows form stuff and your WPF

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stuff, I was always amazed at the documentation,

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how well the documentation was, plus all the options that you had to, like,

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tweak kind of the the the base controls. And the first

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project I used it on was it was a data grid control

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for asp.net. This is going way back. I mean, this has gotta be

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20 years. And I remember I was we were you know,

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I was a consultant at a company, and

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the company had a had a very strong not invented here

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mentality. And this guy's like, no, no, no. I'm going to build my own data

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grid. I'm going to do this. I'm going to do this. And I just remember

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thinking like, why? Like, you know, I

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forget what the cost was for, you know, the entire suite of stuff. I'm like,

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you know, you could just buy this. And I don't know what

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your hourly rate is, but I mean, it

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seems like it would be a bargain to get the invagistix controls and

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just just use that because when it breaks, you

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know, we can call them. Right?

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Versus, you know, when it when this breaks and you decide to move

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on to another company, we gotta call. Right?

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And for me, that was, like, an enlightening moment of, like, understanding, like, oh,

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okay. Like, buying these premade components off the shelf, it's not

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quite the same as, like, commercial off the shelf software. Right? It's more

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like the IKEA model where you can kind of like or Lego, right, where you

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can kind of take these bits and pieces and blocks and build something

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custom with all the many of the advantages of

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custom and almost none of the disadvantage of custom. Right?

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Like, there were only, like, one time over maybe a span

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of when I was doing front end development. I think there was only

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2 times, like, ever that

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whatever we needed to do, your controls out of the box couldn't do. And

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this is across 50, 60 projects.

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Yeah. Awesome. And, like, just just like twice, that was an issue. Right? And even

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then it was kind of, like, well, do we really need that feature set? And

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we kind of, like, walked back on it. And I think in in one case,

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we did another third party thing that did exactly that. But I mean, for the

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most part and that to be fair to to you, it was a very niche

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thing. We were basically doing things to the tablet SDK and the tablet

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interface that nature

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never intended. Right? We were trying and I I because of a very

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strict NDA and, like, who the customer was, I can't really say who it was.

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But it was, you know, 3 letter agency

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related type stuff. And what they wanna do with it was kind of

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like when I heard it, I was like, well, I think that's

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possible. So anyway but but so,

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like, so you clearly have a background, and I did promise not to fanboy

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out. But Yeah. Appreciate it.

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Well, I I love meeting veterans in the industry because, like, we've been through

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so much and Right. So much technology change

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and so much what's important and and and just

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so much advancement with, where technology is today.

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But but, yeah, we're still building grids. And and, like, we have

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the fastest grids on the planet, which we really pride ourselves that we

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can handle, market data. We can handle IoT

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streaming. We can handle really fast data. And but then there

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we go real deep, like like, you talked about that rich functionality.

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So, like, spreadsheets and pivot tables and regular

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grids and, you know, the state of the the web market, which is the

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biggest developer you know, really big developer market now is,

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you know, a lot of people use open source, which is fine, but

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people are, like, still settling, like, just to have a table and

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not have, you know, locking columns and, you know, filtering

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and searching and performance and paging with large data sets

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on the back end. Like, I I don't get why people just settle for, like,

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for that. And, so it's, like, we've we've come really

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far, like and then we also sometimes regress a little bit.

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That's a good way to put it. That's a good way to put it. One

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of my former, my former managers at Red Hat had us a saying,

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and he's known in, like, the Kubernetes space. And he goes, the

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best trick the devil ever played on people was that he didn't

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exist, convince people that he didn't exist. The second

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best trick was to convince people that open source software was

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free. Yeah. Definitely. It's not right.

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I mean, it's it's free with, like, but free like a puppy.

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Right? Like, you know, you have to train it. You have to do all these

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things. So, you know, it's it's especially like

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because, you know, red hat is, you know, their you know, my day job is,

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you know, the bread and butter is, you know, basically selling enterprise

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grade open source, which, you know, from the looks of

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it, you're like, well, wait a minute. You can just pull down the source. Why

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do you need a a license? Well, let me tell you why. Because

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when it breaks, you're not going to be hitting Stack Overflow or

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the GitHub comments, not with the GitHub thing in the middle

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of the night. Right? You want to talk to a support engineer. You want to

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have that. So it's it's it's fascinating

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to me. So so tell me, how did you, like, what was your first move

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into AI at Infragistix? Right? Because, like, clearly, like and

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you did mention you've you've done a lot of data analytics type stuff. So

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so from my perspective, I only remember Infragistix as

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a control, you know, UI kind

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of widget module. Yeah. I forget what the exact thing

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is. But how did you get into data and AI? So we we've always been

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really good at data visualization and having all these kind of,

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components for that, and then also just dealing with, large

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data and moving data around. So, we were we we

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already had those kinda assets. But probably about 10 plus years

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ago, we started we took those components

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and built out an SDK, you know, for the cloud and, that

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you can just very easily have a, data

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access, dashboarding experience that

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so other SaaS vendors can have it, and it and it's beautiful. So we started

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building our Reveal. The product's called Reveal. It's embedded analytics specifically

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designed for software developers and are are

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really, we just sell it to other ISVs, other software vendors.

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AI, we and and in that toolkit, you know, we we invested heavily

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in ML, so hooking into, you know, being able to kind of

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put ML into the data retrieval and the whole data

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set and and doing predictions through that. So that was kind of our

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first entry into AI, just really integrating, machine

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learning and and also trying to use machine learning. We

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spent a lot of money doing machine learning and not always so successful,

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you know, trying to do, better predictive analytics. That was kind

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of our first, entry into it, but we've lessened.

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Since then, we've come a long way. So now, in

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in in the Q2 of this year, we have it in Slingshot first. So

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in Slingshot, like I said, you could just have a conversation,

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and, we'll answer you with a beautiful visualization,

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and we'll give you the answer based on, any question across

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we train the AI in all your business systems. So whether, you

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know, Salesforce, you know, your CRM,

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your, your mark your your marketing system, your spreadsheets,

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your financials. You could have a 100, you know, different

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business systems. We train it on that, and then it could answer the questions and

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give beautiful visualizations. And then we really cared about the user experience,

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so we give you very succinct answers. But then many people don't trust the

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AI, so then you could click in and get more info. And we tell you

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the data sources, how we calculated it, if we're actually bringing

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in data from multiple, back ends to calculate maybe, like,

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customer acquisition costs or something. So we give you you know, you can

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go in and then trust it and get more information, and we'll also

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even suggest other, metrics and, and

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data you may be interested in that that's kinda within that that, area of

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questioning. And and so, we first started reducing

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that, in Slingshot. So you can go from you know,

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a lot of people like, data's locked up, so we all use all these business

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systems. And everyone wants to be data driven or or most people

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really wanna be data driven, but we have data locked up in PowerPoint,

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spreadsheets, and business systems. Not everyone knows how to go

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in and run that report in a, you know, Marketo or some

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account based marketing system or CRM. And so

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it's really locked up so people still make these decisions

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without fact based when they can be making fact based decisions. And so

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we we unlock that in Slingshot. And then with AI, we unlocked it at

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another level where, you don't even have to know,

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we where the dashboard is or where that widget is. You could just

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ask, and then we'll display the visualization and the insight. And

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then you can go from that to, you know, conversation to action right

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within the same, tool. And so,

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so, yeah, it's it's really exciting what we're all able to do now with

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AI. And, but so we we're approaching it just

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from a user experience point of view. How can we make it easier

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to make data driven decisions and put it in a work management

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tool so that you're getting insight, you're collaborating,

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you're, you know, because a lot of times data just tells you what's happening, not

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why. So a lot of times, so you show what we'll tell you

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what's happening through your business systems. But then in Slingshot, you can collaborate

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and create hypothesis. You know? Why is that happening? And then, okay,

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here's an experiment to go and try and change that,

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outcome we're getting to drive some some business objective, like, you

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know, better sales, contributing to pipeline, more business,

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closing business, or, you know, reducing or increasing

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renewals or what whatever you're you're trying to do.

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Interesting. And and and it's interesting because, you know, I was at

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Build 2016, and they introduced the idea of chat bots

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being widely, you know, used. And at the time, I was

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very skeptical. Right? Because they, you know, on on stage, they they they think they

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use Domino's or whatever, and they said, I'd like a pizza with this. And this

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is pre transformers, pre all that stuff. So it was very

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more traditional natural language processing type technology.

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But the more I look at this, what you describe with slingshot, right,

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if I'm a salesperson or whatever, I can or marketing or or

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whatever, you're right. It's amazing how silo data still

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is Mhmm. In 2025. Granted,

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we're in early 2025. So maybe by the end of the year, it'll improve. But

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I don't not holding my breath on that one.

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But the whole notion of chat as a as

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an interface. Right? Is that what

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Slingshot does? So Slingshot, we we added

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that capability in Slingshot. So Slingshot, like, functionally,

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it's data analytics, it's chat,

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it's digital workspaces that, also have, you

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know, Gantt charts and task management, but it's

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lightweight. So it's work management, not project management, even though you could do

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heavyweight project management. So it's like a lot of people

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know Monday or Asana. We're we're that,

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but we're we're really heavy into data analytics and now AI, using

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AI to make it easy to, interpret

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and get at the analytics. And and and then so other features in there

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that are AI driven, but, so that that that's what

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Slingshot is, and it's all about, like, helping people, you know,

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if you're a marketing team or you're a business team and just helping

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growth and using data and managing work. And and then also because

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it's all digital, it's creating trust and transparency across

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your across your teams. You're seeing what's going on. And,

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so it's it's AI data driven work management. And, like, when we

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talk about creating a learning organization and actually part of my book,

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what I write I write about a lot of this in my book. But,

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once you kind of set your objectives using we're a

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big fan of OKR. So once you set your objective and you define your,

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like, 3 to 5 key actions to achieve that objective,

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all those can be measured, and then we make it really easy to

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measure that through your operational systems. And like I

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said, you then you what you do is you hypothesize, like, what's

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happening? Why aren't we achieving those objectives or or what's happening in those

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key actions, and you hypothesize things you can do

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and experiment, and you intentionally, you

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know, collaborate and and and come up with these experiments that you can quickly go

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and try and collect data and learn. Okay. It worked

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great. You've solved the problem. Work partially, but you learned something or

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or failed. You learned something. And so excuse

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me. That's what we mean by creating a learning organization. We through the

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tool and through this philosophy, you teach people how to problem solve

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using data, staying focused on objectives and and key priorities

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to achieve those objectives. And then, you know,

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hypothesizing what the data is telling you, why it's not working, and

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then creating new experiments to solve that problem. So that's, like,

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how you're creating this problem solving part of, like, what our

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goal is to create this data driven agile learning organization.

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You're teaching them how to learn, how to solve problems. And when you do

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this, it gets pushed to everyone in the company instead of, like, the smartest

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person on the team or the exec. That's not where you have resilience

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and scale a company. You need to push this problem solving out to all the

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edges of your company. And so Slingshot really enables that.

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Interesting. So you're not just changing you're not just adding technology, but

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you I think you're teaching people a different way to use technology.

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Yeah. How to, like, run company, solve

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problems, and and grow.

356
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Interesting. Because I I think

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that's the missing piece for digital transformation.

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I mean or one of the missing pieces. Right? Because the the, you know,

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digital transformation is a word that I think induces a little bit

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of, people wanna, you know, get

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sick on that. Like, they hear it and they wanna throw up a little bit.

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But it's a it's a shame because, like, what it could do versus what

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it actually gets implemented as is is is 2 very things. I think part of

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that is that people don't think about the basic workflows like you were like

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you are, or like, you know, where the basic kind of like tooling or the

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basic mentality of be very experimental, be very data driven.

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And, you know, it's you can't slap,

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you know, a digital coat of paint on an old way on on an old

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process. Right? Right. I mean, well, you can, and it's certainly been

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done. It's just you're not gonna get those same results, and it's to the same

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point now when when most people say digital transformation, they kinda

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cringe a bit. You know? Yeah. I mean, it it means so

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many different things. And it and based on the organization, it

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like, there's different levels of transformation. And,

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but but, yeah, this whole thought process of how to run a company

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was, like, the thesis of Slingshot. And, you know, now it's

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aided by AI. And I think another thing that we did to try

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and unlock data driven decisions

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is we created a business data catalog.

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So what we did was inside of Slingshot, there's a data

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catalog where you can catalog all your metrics,

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and, and you can even catalog your data sources. But and it's a

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curated workflow where you can, anyone can go and submit

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a metric or, you know, a widget or a dashboard to

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it, but it's curated so that people are organizing it properly, and

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then you can search it and you can certify it. And there's, like, three

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levels of certification. And, and what we did

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was if you certify at the highest level, we train the

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AI on that data, and and only certain people have rights to certify it at

390
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the highest level. So this is like another big problem. You a lot

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of company or most companies at every size has so much data,

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and all data is not truth, And all data is not what you

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wanna use to train an AI because if you do, it's

394
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gonna give you answers that that spreadsheet is not the where

395
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we wanna get the data from, or that's not our system of record

396
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in CRM. It might be in your financial system or whatever.

397
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So, we we kinda implemented this, ability to unlock

398
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and find information across your systems. I don't have to go to each business

399
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system, find it in the data catalog. But then since we've, you

400
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know, built the AI out, we leverage that. And anytime you

401
00:24:49,595 --> 00:24:52,975
certify it, we we write all this the AI writes all this metadata

402
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in there that the the user can actually edit, but, like, it's more of a

403
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technical thing, but they can add to the metadata. And then it, and

404
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then it trains the AI on it. And and so we're we're we're

405
00:25:03,730 --> 00:25:07,570
using that kind of process to make sure that we're using good data

406
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in your systems and spreadsheets and, so that you're

407
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getting the answers that are are correct. So just having

408
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data doesn't mean it's the right data.

409
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Interesting. It's I mean, that's true. It has to be the

410
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right data. It has to be not just the correct data, but it also

411
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has to be correct in and of itself. You have to have a certain amount

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of trust in that data, particularly as you start leaning on it to make decisions

413
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based on that. Yep. That I mean, it

414
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sounds I mean, it sounds very,

415
00:25:42,049 --> 00:25:44,950
very intriguing. I'm definitely gonna go check it out. It's,

416
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slingshot app. Io. Is that the cool?

417
00:25:49,105 --> 00:25:52,184
Yeah. Slingshot app. Io. Interesting.

418
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And are these, are these, it looks like you can

419
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there's an IDE built into it. So that's pretty interesting, actually. I definitely

420
00:26:00,645 --> 00:26:04,470
got to check it out. Because I think I think that as

421
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you deal with, more and more

422
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data sources coming at us, more and more, and

423
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there's more and more kids join the workforce. They're gonna

424
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expect some kind of chat interface with the data. Right?

425
00:26:18,805 --> 00:26:22,325
Yep. You know, I have 3 kids and each one of them has it

426
00:26:22,325 --> 00:26:26,165
represents a different kind of error in technology. Right? The the first one

427
00:26:26,165 --> 00:26:29,925
was everything was a touchscreen. Right? Dad was a tablet MVP when he was

428
00:26:29,925 --> 00:26:33,670
born. Right? So when he went to our

429
00:26:33,890 --> 00:26:37,410
TV and he touched it and it didn't or any TV. Right? And it didn't

430
00:26:37,410 --> 00:26:40,530
work whether it was here or it was grandparents, and he would touch the screen

431
00:26:40,530 --> 00:26:44,370
and he would turn and say broken. Right? And or he would complain to

432
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his grandparents, like, how come the TV doesn't, like, react to this? And

433
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they were just, like, my my

434
00:26:51,495 --> 00:26:55,335
second child was born in the the Alexa era, I like to

435
00:26:55,335 --> 00:26:58,774
call it, because, you know, he would talk to

436
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Alexa to get the weather, to Syria.

437
00:27:02,850 --> 00:27:06,210
Siri, before he could write, he was able to chat because he used

438
00:27:06,210 --> 00:27:09,810
Siri to write stuff in, like, and

439
00:27:09,810 --> 00:27:13,650
read stuff to him. So it was interesting. The third one is 2, so

440
00:27:13,650 --> 00:27:17,010
we're not really sure what it is, but it's probably gonna be some kind of

441
00:27:17,010 --> 00:27:20,665
AI technology that, you know, just it's just he

442
00:27:20,665 --> 00:27:24,265
takes for granted and is part of the, part of the

443
00:27:24,265 --> 00:27:27,865
environment. So it's interesting to kind of see. But when those, you know, those

444
00:27:27,865 --> 00:27:31,465
kids enter the workforce and and, you know, we're both old enough to

445
00:27:31,465 --> 00:27:35,010
remember Windows 3.0. Right?

446
00:27:35,010 --> 00:27:38,370
So, like, you know, when I have younger colleagues, like, the way they look at

447
00:27:38,370 --> 00:27:41,570
things or they just take for grant things that they take for granted is kinda

448
00:27:41,730 --> 00:27:45,409
I kinda laugh to myself. Like, you know, I was once given a a

449
00:27:45,490 --> 00:27:48,865
when I was at Microsoft, I was given a a

450
00:27:48,865 --> 00:27:52,225
demonstration of, like, setting up VMs in Azure or something like that.

451
00:27:52,225 --> 00:27:55,664
Right? And it's like, let's create a PC and, like, you know, I go and

452
00:27:55,664 --> 00:27:58,725
I check from a drop down. I want this. I want this. I want this.

453
00:27:59,025 --> 00:28:02,740
And I click go and, like, you know, admitted into it. So one of the

454
00:28:02,740 --> 00:28:06,420
kids goes, wow. This is taking forever. Yeah. Which I I

455
00:28:06,420 --> 00:28:09,940
remember when I worked at a big bank, you know, to buy a server, to

456
00:28:09,940 --> 00:28:13,640
requisition a server because of all sorts of internal rules and regulations.

457
00:28:14,635 --> 00:28:18,155
I mean, it would take 6 months if you were if you were

458
00:28:18,155 --> 00:28:21,835
lucky. Right? And if it was a really important project, you can get it done

459
00:28:21,835 --> 00:28:25,674
in, like, 3 months. But, realistically, it was a 6 to 12 month

460
00:28:25,674 --> 00:28:29,350
process. And this kid's complaining because it's taken too long to

461
00:28:29,350 --> 00:28:33,030
requisition a virtual machine more than 60 seconds.

462
00:28:33,030 --> 00:28:36,490
I think it's kinda funny. Yeah. I mean, voice

463
00:28:36,710 --> 00:28:40,090
and seeing is just gonna get more and more integrated into

464
00:28:40,230 --> 00:28:43,345
getting answers and getting information and

465
00:28:43,965 --> 00:28:47,725
supporting you in whatever you're doing. So, yeah, we really are

466
00:28:47,725 --> 00:28:51,485
at a crazy inflection point of, like, this

467
00:28:51,485 --> 00:28:55,294
major next leap. And, so, yeah, I mean, it it

468
00:28:55,294 --> 00:28:58,260
was like, oh, I typed characters to figure things out. Oh, now I have a

469
00:28:58,260 --> 00:29:01,620
GUI interface. Helps me a little bit more. And, yeah, now it's

470
00:29:01,620 --> 00:29:05,220
like, yeah, I just wanna talk and have that, you

471
00:29:05,220 --> 00:29:08,835
know, and get stuff done. I I don't, you know, I don't even

472
00:29:09,135 --> 00:29:12,895
wanna type. Right. Right. Well, it reminds me if you watch what's

473
00:29:12,895 --> 00:29:16,415
now considered old Star Trek, but Star Trek the next generation where the

474
00:29:16,415 --> 00:29:20,095
computer is almost like a character Yeah. Where they could just

475
00:29:20,095 --> 00:29:23,289
say computer anywhere in the ship. It's like, can you figure out what this is?

476
00:29:23,289 --> 00:29:27,049
And they're like, well, the probability of like it I think we're kind

477
00:29:27,049 --> 00:29:30,830
of at that point, certainly with, you know, voice related technologies

478
00:29:31,049 --> 00:29:34,809
and, the under language understanding that you get out

479
00:29:34,809 --> 00:29:38,405
of these AI systems today is is is very impressive.

480
00:29:41,505 --> 00:29:44,805
The book. Tell me about the book because it's called when grid is not enough.

481
00:29:45,025 --> 00:29:48,690
So what's it about? Like, what's cause clearly, you're a

482
00:29:48,690 --> 00:29:52,530
startup founder. You have been at least doing that since

483
00:29:52,530 --> 00:29:56,370
1989. You're a CEO. You're still in the game. You stayed in the game.

484
00:29:56,370 --> 00:30:00,210
You survived. Yeah. You you saw the

485
00:30:00,210 --> 00:30:03,030
recession of 91. I'm assuming. You saw

486
00:30:03,765 --> 00:30:07,545
the.com, you know, boom, the dot com bust,

487
00:30:08,085 --> 00:30:11,525
the o eight financial crisis, you know,

488
00:30:11,525 --> 00:30:14,585
pandemics and kind of everywhere in between. So,

489
00:30:15,365 --> 00:30:18,325
tell me what where'd you get the idea for the title from? Because, like, if

490
00:30:18,325 --> 00:30:22,110
you if you if you Well, it took a while to come up. It took

491
00:30:22,110 --> 00:30:24,430
a while to come up with a title. I could tell you. It took us

492
00:30:24,430 --> 00:30:28,190
6 months. Wow. And, I was gonna settle on

493
00:30:28,190 --> 00:30:31,950
a title. I just I couldn't take it anymore. We brainstorm so much

494
00:30:31,950 --> 00:30:35,575
on the title, and my publisher and some of our

495
00:30:35,575 --> 00:30:39,415
marketing people are like, it's the most important thing. You know? And, I was

496
00:30:39,415 --> 00:30:43,095
gonna settle on the next company. You know, being in the tech space, it's always

497
00:30:43,095 --> 00:30:46,775
about the next thing, and and it's always building on something better.

498
00:30:46,775 --> 00:30:50,460
And, and I was gonna settle on that, but,

499
00:30:50,820 --> 00:30:54,660
when grit's not enough, it's because, like, every entrepreneur needs to have

500
00:30:54,660 --> 00:30:58,420
grit. Like, fundamental thing is you have to be optimistic, and you have to

501
00:30:58,420 --> 00:31:01,965
have grit. And, and so that's just a fundamental

502
00:31:01,965 --> 00:31:05,805
thing. But once you start a company, grit alone won't

503
00:31:05,805 --> 00:31:09,645
help you scale and won't help you be resilient and won't help you

504
00:31:09,645 --> 00:31:13,405
survive. I mean, so, you know, early days for us, yeah, I could just not

505
00:31:13,405 --> 00:31:17,150
take a salary and fix a problem. You know, you get but then you start

506
00:31:17,150 --> 00:31:20,750
getting to a certain size that you're just not you taking a salary doesn't fix

507
00:31:20,750 --> 00:31:24,430
your problems. And so, so what I did in the book was I

508
00:31:24,430 --> 00:31:27,950
shared everything I learned over the last 35 years, in the

509
00:31:27,950 --> 00:31:31,565
book, cover a whole set of topics to help

510
00:31:31,565 --> 00:31:35,164
other entrepreneurs and CEOs just have a greater chance of growth,

511
00:31:35,164 --> 00:31:38,625
success. And and so that was a motive, for it. And,

512
00:31:38,924 --> 00:31:42,044
so when grit's not enough, it's that, yeah, you need grit, but it's not enough

513
00:31:42,044 --> 00:31:43,504
when you get to a certain point.

514
00:31:45,850 --> 00:31:49,210
Interesting. Interesting. Obviously, you pulled

515
00:31:49,210 --> 00:31:52,810
from your life experience. Like, what was one moment

516
00:31:52,810 --> 00:31:56,010
where where was the moment you realized that grit's not enough? Right? Like

517
00:31:56,875 --> 00:32:00,395
Yeah. Well, we we had just merged with one of our

518
00:32:00,395 --> 00:32:03,855
competitors, and, they they were

519
00:32:04,235 --> 00:32:08,075
a a really good company. Great. We got great tech talent, great

520
00:32:08,075 --> 00:32:11,880
sales and marketing. They had a lot of customers, but they made some mistakes.

521
00:32:11,880 --> 00:32:14,860
And so they were they were in basically in debt. They were out of cash.

522
00:32:15,160 --> 00:32:19,000
Cool. And so, we shared in software. If you remember shared

523
00:32:19,000 --> 00:32:22,200
in I remember that. I remember when you I remember when it was bought. They

524
00:32:22,200 --> 00:32:26,044
were one of the first vb one o visual basic one o

525
00:32:26,044 --> 00:32:29,725
components, and they built the database finding layer,

526
00:32:29,725 --> 00:32:33,565
Internet Explorer. There there it was like it was like we, you know,

527
00:32:33,565 --> 00:32:37,325
some of those guys are still on my board. And so we've been together

528
00:32:37,325 --> 00:32:40,980
now, for 20 plus years

529
00:32:40,980 --> 00:32:44,820
now. But but, anyway, when we merged, it sucked a

530
00:32:44,820 --> 00:32:48,340
lot of our cash off our balance sheet. And so we

531
00:32:48,340 --> 00:32:51,940
literally had, a 580,000 a

532
00:32:51,940 --> 00:32:55,625
month pay or or expense structure. And we had $618

533
00:32:56,565 --> 00:32:59,865
in the bank. And so it was like we were legally

534
00:32:59,924 --> 00:33:03,605
bankrupt. I mean, we all, we all knew we would get out of

535
00:33:03,605 --> 00:33:07,210
it, but, it was, it was like, that was a big, big

536
00:33:07,210 --> 00:33:10,990
moment where it's like, okay, you know, working hard,

537
00:33:11,450 --> 00:33:15,130
working crazy hours, not taking salary. No, no,

538
00:33:15,130 --> 00:33:18,970
no. There's got a there's a better way here. And so, that

539
00:33:18,970 --> 00:33:21,790
that was a pivotal moment for me where,

540
00:33:22,975 --> 00:33:26,414
you know, you start investing in systems, being data

541
00:33:26,414 --> 00:33:30,115
driven, you know, better cash flow planning,

542
00:33:31,294 --> 00:33:34,355
you know, a lot of the running better meetings,

543
00:33:35,230 --> 00:33:38,590
you know, really thinking about where to focus and put

544
00:33:38,590 --> 00:33:41,649
priority behind, you know, critical things,

545
00:33:42,590 --> 00:33:46,350
aligning teams on that, prioritization, and how do you make

546
00:33:46,350 --> 00:33:50,085
those alignments? And then it's all about the people. So if you read the

547
00:33:50,085 --> 00:33:53,205
book, it's for me, and it always has been all about the people. So a

548
00:33:53,205 --> 00:33:57,045
lot of it's about actually, one of our core strategies is creating a learning

549
00:33:57,045 --> 00:34:00,825
organization. And so, and so I talk about a lot about coaching,

550
00:34:00,965 --> 00:34:03,860
alignment, creating trust, culture,

551
00:34:04,800 --> 00:34:08,500
how to be data driven, how to do go to market plans, strategic

552
00:34:08,639 --> 00:34:12,320
plans. I didn't learn till really late in life about

553
00:34:12,320 --> 00:34:15,760
recovery and taking care of yourself. You know, I come from, you

554
00:34:15,760 --> 00:34:19,175
know, just suck it up and work harder. You know? And,

555
00:34:19,175 --> 00:34:22,535
like, I I tell you, that's not the best thing,

556
00:34:22,855 --> 00:34:26,695
you know, because, like, you perform way better with a good night's

557
00:34:26,695 --> 00:34:30,535
sleep. You perform like, I I at one point, I had traveled for 3

558
00:34:30,535 --> 00:34:33,860
months straight around the world, everywhere, and,

559
00:34:34,900 --> 00:34:38,180
and that was like a big then I got, like, 1 week I was in

560
00:34:38,180 --> 00:34:41,540
the air 50 hours just in 1 week. Wow. And,

561
00:34:41,860 --> 00:34:45,080
so from traveling so much all around the world, Asia,

562
00:34:45,300 --> 00:34:48,775
Europe, South America, US. I

563
00:34:48,775 --> 00:34:52,614
actually got a, this pain in my calf. I

564
00:34:52,614 --> 00:34:55,995
thought it was just a Charlie horse. It ended up being a blood clot,

565
00:34:56,855 --> 00:35:00,455
and and then it went to my lungs. So I had a pulmonary

566
00:35:00,455 --> 00:35:04,090
embolism. I couldn't breathe. And so I had to spend 4 or

567
00:35:04,090 --> 00:35:07,690
5 days in the hospital. And I was like, that's another, like, I've, like, I

568
00:35:07,690 --> 00:35:11,450
share these lessons in the book. That's when I learned, okay. Yeah. You

569
00:35:11,450 --> 00:35:15,070
gotta, like, have recovery, like, perfect, like, today in professional

570
00:35:15,210 --> 00:35:18,694
sports, you have amazing athletes in their thirties,

571
00:35:18,755 --> 00:35:22,515
forties performing at high levels because they're worrying

572
00:35:22,515 --> 00:35:26,275
about recovery. They're not just going they're just not going hard all

573
00:35:26,275 --> 00:35:30,115
the time. And so, like, I even have a chapter about that. Like, you you

574
00:35:30,115 --> 00:35:33,930
need about taking care of yourself and, and, you know, if you, you know, if

575
00:35:33,930 --> 00:35:37,690
you're grinding it out 12 hours a day, that's, that's not good. I mean, you'll

576
00:35:37,690 --> 00:35:41,290
get, you'll, you actually deliver more business value, solve

577
00:35:41,290 --> 00:35:44,970
problems better, get more done if you like take time off,

578
00:35:44,970 --> 00:35:48,704
take vacations, get good sleep, recover. You know?

579
00:35:48,704 --> 00:35:52,464
It's so but from our generation, no. No. No. It's just like work

580
00:35:52,464 --> 00:35:56,224
hard. And, Right. Suck it up. Keep Suck it up.

581
00:35:56,224 --> 00:35:59,665
Yeah. No pain. No gain. You know? Right. And it's like

582
00:35:59,984 --> 00:36:02,790
but it's funny. It's not just limited to our generation. Right? If you look at

583
00:36:02,790 --> 00:36:06,170
the startup culture today, right, it's grind, grind, grind, grind.

584
00:36:06,230 --> 00:36:10,070
There's, startup grind, I think, is

585
00:36:10,070 --> 00:36:13,850
a it's a it's a startup brand and that they do. I think it's

586
00:36:14,484 --> 00:36:18,005
backed by Google or something like that where they do they hold, like, kinda like

587
00:36:18,005 --> 00:36:21,845
user groups and meetups and things like that. It's called startup grind. And it's

588
00:36:21,845 --> 00:36:25,365
kinda like I get the the the the the visual of the

589
00:36:25,365 --> 00:36:29,140
grind, but you also have to, like, lean back and and

590
00:36:29,140 --> 00:36:32,580
and rest and recoup because if you and it's funny because I think

591
00:36:32,580 --> 00:36:36,280
particularly for technical people or engineers, right? Like the thinking that is,

592
00:36:36,500 --> 00:36:39,220
you know, how do you get a, you know, how do you get a car

593
00:36:39,220 --> 00:36:42,825
to go faster? Well, you boost the RPM, right? You boost the you get to

594
00:36:42,825 --> 00:36:46,525
boost the output, but we're not machines, like, in that same regard. So

595
00:36:46,744 --> 00:36:50,345
you start getting diminishing returns. And, you know, I think part of it was I

596
00:36:50,345 --> 00:36:53,065
learned that as I got older, like and I had kids. And I was like,

597
00:36:53,065 --> 00:36:56,665
oh, I can't stay up for 48 hours anymore.

598
00:36:56,665 --> 00:36:59,900
Right? And it it definitely

599
00:37:00,119 --> 00:37:03,880
particularly if you're doing something like software design or AI or

600
00:37:03,880 --> 00:37:07,240
data engineering, you need your mind to

601
00:37:07,240 --> 00:37:10,920
be at 80% and up.

602
00:37:10,920 --> 00:37:13,815
Right? You can't just kinda zone out. Right?

603
00:37:15,315 --> 00:37:18,035
Yeah. Yeah. So I talk a lot about that and a lot of about the

604
00:37:18,035 --> 00:37:21,395
book, which is just that teams, like, how to create high performing teams

605
00:37:21,395 --> 00:37:25,015
because it's, like, in our business, it's all about problem solving,

606
00:37:25,235 --> 00:37:28,710
collaborating, helping each other. And so how do you create that

607
00:37:28,710 --> 00:37:31,610
environment and, and be real intentional about

608
00:37:32,150 --> 00:37:35,990
creating that, and then you get innovation. You know? And then you Right.

609
00:37:36,150 --> 00:37:39,610
You get, really good amazing pieces of software.

610
00:37:39,750 --> 00:37:43,285
And, but but, really, the book applies to more than just

611
00:37:43,285 --> 00:37:47,125
running a tech company. It's really every company now. I mean, people are people

612
00:37:47,125 --> 00:37:50,805
are the foundation, and, and so I I I talk about all

613
00:37:50,805 --> 00:37:54,500
those lessons I learned over 35 years, and and

614
00:37:54,500 --> 00:37:57,859
some of it was a thesis of of writing Slingshot. You know, we

615
00:37:57,859 --> 00:38:01,460
wrote it 7 years ago. It's been in market a couple of

616
00:38:01,460 --> 00:38:04,440
years, but we run the whole company off of it. And,

617
00:38:05,220 --> 00:38:08,905
and, so there's probably 4 or 5 or 6 chapters of

618
00:38:08,905 --> 00:38:12,125
18 that is, like, the thesis of Slingshot that,

619
00:38:12,665 --> 00:38:16,105
of, you know, how to digitize this this philosophy and this,

620
00:38:16,744 --> 00:38:20,265
you know, way of of, running a company. Very

621
00:38:20,265 --> 00:38:21,645
cool. Very cool.

622
00:38:25,930 --> 00:38:27,710
I'm just fascinated that,

623
00:38:29,930 --> 00:38:33,210
you know, you're you're you're someone who's had a lot of success and, like, you

624
00:38:33,210 --> 00:38:36,170
you you kind of, like I love the fact that you kind of distill that

625
00:38:36,170 --> 00:38:39,434
into a book that, you know, other people who who are you hoping will read,

626
00:38:39,434 --> 00:38:42,395
and, like, what's the one message that they get away, you know, that they they

627
00:38:42,395 --> 00:38:46,154
pull from it? Well, I hope a lot of entrepreneurs read it.

628
00:38:46,154 --> 00:38:49,694
You know? And I don't think you could discount,

629
00:38:49,994 --> 00:38:53,450
like, grinding it out. Like, even I think you do have to grind it out

630
00:38:53,450 --> 00:38:56,970
in the beginning and, but it can't be the norm. It can't

631
00:38:56,970 --> 00:39:00,430
be the, the way, the the only way.

632
00:39:00,809 --> 00:39:03,230
And so I I just hope to reach a lot of entrepreneurs

633
00:39:04,365 --> 00:39:07,985
across any every industry and, mid market

634
00:39:08,045 --> 00:39:11,645
CEOs and, and even managers. I mean, there's so

635
00:39:11,645 --> 00:39:15,405
many good good lessons in there that I've learned. And and I I love

636
00:39:15,405 --> 00:39:18,990
learning, and I love reading. And, but what I don't like is,

637
00:39:18,990 --> 00:39:22,830
like, you hit you you you are taught a concept in the first

638
00:39:22,830 --> 00:39:26,370
50 or a 100 pages, and then the next 100 pages is, like,

639
00:39:26,430 --> 00:39:30,130
10 repeats of use cases of it. And I'm just like,

640
00:39:30,414 --> 00:39:34,174
like, like, my personality makes me read the whole thing. I'm trying to fix that

641
00:39:34,174 --> 00:39:37,694
myself, but, like, I I've gotta, like, I read the whole damn thing or listen

642
00:39:37,694 --> 00:39:40,494
to the whole damn thing. And so what I tried in my book was to

643
00:39:40,494 --> 00:39:44,174
be really succinct, like, deliver a lot of, like,

644
00:39:44,174 --> 00:39:47,539
playbook ways of doing things, give examples.

645
00:39:48,160 --> 00:39:51,299
At the end, summarize the 4 to 10 key cape takeaways,

646
00:39:51,839 --> 00:39:54,819
but not waste your time. So I was, like, kinda really more into,

647
00:39:55,680 --> 00:39:59,394
you know, not wasting your time, and and deliver

648
00:39:59,394 --> 00:40:02,994
as much value as possible. So so I try to achieve that in the

649
00:40:02,994 --> 00:40:06,755
book. Very cool. No. I think you're right. The grind not not not

650
00:40:06,755 --> 00:40:10,515
to to to disrespect the grind. The grind is important. You can't avoid it, but

651
00:40:10,515 --> 00:40:13,954
I don't think if you let it consume you, you're got you're gonna weigh yourself

652
00:40:13,954 --> 00:40:17,510
out. Yeah. It it's not healthy. And and if you are an intellectual

653
00:40:17,730 --> 00:40:21,410
field, you won't you won't innovate and create your

654
00:40:21,410 --> 00:40:25,030
best moments and your best ideas and solve the toughest

655
00:40:25,090 --> 00:40:28,930
problems. I mean, it's, so, yeah, you you have to

656
00:40:28,930 --> 00:40:32,744
keep that in mind. Awesome. Alright.

657
00:40:32,744 --> 00:40:35,625
I'm gonna switch to the pre canned questions. I'm gonna put them here in the

658
00:40:35,625 --> 00:40:39,305
chat. None of them are real brain teasers. We're not trying to do a Mike

659
00:40:39,305 --> 00:40:43,145
Wallace on you and and trap you. I and I know you'll get the

660
00:40:43,145 --> 00:40:46,260
reference because a lot of our younger guests don't, oddly enough.

661
00:40:50,180 --> 00:40:53,860
We kinda did touch on this. How did you find your way into

662
00:40:53,860 --> 00:40:57,380
data? Did you data find you, or did, did you find

663
00:40:57,380 --> 00:41:01,175
data, or did data find you? Well, I like, I was

664
00:41:01,175 --> 00:41:04,775
a engineer to begin with, so I worked on our products the first 5 years

665
00:41:04,775 --> 00:41:08,535
of our company and, you know, working on our and, so I've

666
00:41:08,535 --> 00:41:12,215
always been data driven. But I've continually got

667
00:41:12,215 --> 00:41:16,050
better at it as every year went by. So I was so I

668
00:41:16,050 --> 00:41:19,350
I don't think data found me. I think it was just part of my schooling,

669
00:41:19,730 --> 00:41:23,330
part of my training. And then, then as I started running the

670
00:41:23,330 --> 00:41:27,010
company, trying to incorporate it more and more, and and and

671
00:41:27,010 --> 00:41:30,765
there's a lot of challenges with being data driven. Like I said, it's like, there's

672
00:41:30,765 --> 00:41:34,605
not everyone's not data literate. There's outliers. You can't average

673
00:41:34,605 --> 00:41:37,964
things. You and the biggest thing is people don't know where the the

674
00:41:37,964 --> 00:41:41,404
datasets are that you should be using, and dataset's kind of a technical term, but,

675
00:41:41,404 --> 00:41:45,184
like, where is our sales data? Where is our customer data? Where

676
00:41:45,360 --> 00:41:48,960
where is this data? You know? Where do I look? What's even though sometimes it's

677
00:41:48,960 --> 00:41:52,720
repeated, where do I trust? And so I I think I've always yeah. I think

678
00:41:52,720 --> 00:41:56,560
I've always been data driven. I I feel like I've yeah. So that that that's

679
00:41:56,560 --> 00:42:00,365
my background there. Right. No. I mean, it makes sense because one of the problems

680
00:42:00,365 --> 00:42:03,665
I've seen, I'm not gonna name any names, but places where I have worked

681
00:42:04,045 --> 00:42:06,945
where there's multiple CRMs.

682
00:42:08,285 --> 00:42:12,090
Right? Or multiple source of truth. And I think that, you know,

683
00:42:12,090 --> 00:42:15,130
as I advised when I was at Microsoft, I would advise a lot of, you

684
00:42:15,130 --> 00:42:18,890
know, companies on digital transformation. For those listening, I did the air

685
00:42:18,890 --> 00:42:21,550
quotes. But the

686
00:42:23,370 --> 00:42:27,194
the important thing, if not the most important thing, certainly

687
00:42:27,194 --> 00:42:31,035
top 3 have one source of truth. Yeah. And it's

688
00:42:31,035 --> 00:42:34,714
not easy too, by the way. Like because you have customers as leads

689
00:42:34,714 --> 00:42:38,555
in CRM, then they have actually buy, and now they're act they're

690
00:42:38,555 --> 00:42:42,400
in your financial system. Or you have account based marketing systems

691
00:42:42,400 --> 00:42:46,240
where you're, like, marketing to an account, and then all of a sudden you start

692
00:42:46,240 --> 00:42:50,080
pulling Zoom info data into that, and now you have customer names there.

693
00:42:50,080 --> 00:42:53,695
So it's, like, it's easy even now how much

694
00:42:53,695 --> 00:42:57,455
architecture and intentionality you have. Repeat

695
00:42:57,455 --> 00:43:01,295
and data is everywhere, so it's important to be thoughtful about how

696
00:43:01,295 --> 00:43:04,915
you surface that in decision making or training AIs

697
00:43:05,055 --> 00:43:08,880
or, you know, doing all these things to make the right decision with the

698
00:43:08,880 --> 00:43:12,320
right data. A 100%. And there's also a temporal cone

699
00:43:12,400 --> 00:43:16,080
component to this too. Right? Because what if you have your your batch jobs, they

700
00:43:16,080 --> 00:43:19,060
all synchronize, like, at night, but it hasn't happened yet.

701
00:43:19,440 --> 00:43:23,174
Yeah. Like, well, the system said this. Well, when did it say it? It

702
00:43:23,174 --> 00:43:26,934
said it yesterday. What time? 4 PM. Oh, well, that's why it's

703
00:43:26,934 --> 00:43:30,635
inaccurate. Yeah. Right? You have to have a certain amount of awareness about that.

704
00:43:32,214 --> 00:43:35,674
So you've been at your current gig for a number of years?

705
00:43:35,815 --> 00:43:39,460
Yep. 36 years, you said? Yeah. I'm going this job will be

706
00:43:39,460 --> 00:43:43,300
36. Wow. So clearly, you probably gonna have to struggle

707
00:43:43,300 --> 00:43:46,660
to figure out what your what your one favorite thing is, but just pick one

708
00:43:46,660 --> 00:43:47,559
favorite thing.

709
00:43:50,755 --> 00:43:54,595
I mean, I I like, I like working with people, talking

710
00:43:54,595 --> 00:43:58,435
to people. And then I just love learning too, by the way. Like, I

711
00:43:58,435 --> 00:44:02,195
I like, as CEO now, I have a team running the company, so

712
00:44:02,195 --> 00:44:05,920
I can pick I can't always pick what I do, but I also

713
00:44:05,920 --> 00:44:09,600
can pick what I do. So, so I really like that.

714
00:44:09,600 --> 00:44:13,200
And, so, personally, I just like to learn. That's my most

715
00:44:13,200 --> 00:44:16,895
favorite thing to do. Cool. We have 3

716
00:44:16,895 --> 00:44:20,595
complete the sentences. When I'm not working, I enjoy

717
00:44:20,655 --> 00:44:24,435
blank. Yeah. I I enjoy camping,

718
00:44:24,815 --> 00:44:28,575
cooking. I'm a I'm a gamer. I I love playing Call of

719
00:44:28,575 --> 00:44:32,330
Duty 6 on 6. It's, like, very therapeutic

720
00:44:32,390 --> 00:44:36,090
for me. So that's how I'd answer that.

721
00:44:36,470 --> 00:44:40,310
Nice. Next one is, I think

722
00:44:40,310 --> 00:44:43,610
the coolest thing in technology today is blank.

723
00:44:44,315 --> 00:44:47,995
So sorry to say AI, but it's AI. No. So, I mean, it's,

724
00:44:47,995 --> 00:44:51,515
like, amazing what's happening. And and robots too. I mean,

725
00:44:52,635 --> 00:44:55,675
you know you know what I don't I know that's not part of the question,

726
00:44:55,675 --> 00:44:58,980
but you know what I don't like is these big tech CEOs

727
00:44:59,360 --> 00:45:02,980
overpromising AI. It's really messing people up in the market.

728
00:45:03,120 --> 00:45:06,720
I can't believe how many smart people I talk to that tell me, Dean, what

729
00:45:06,720 --> 00:45:08,480
are you gonna do? I'm like, what do you mean what am I gonna do?

730
00:45:08,480 --> 00:45:12,194
You're you're one of your biggest revenue streams just selling tools to

731
00:45:12,194 --> 00:45:15,714
developers. There's not gonna be any more developers. I'm like, no. No. No. No. There's

732
00:45:15,714 --> 00:45:19,474
gonna be plenty of software developers, but, like, you know, the

733
00:45:20,115 --> 00:45:23,710
so that frustrates me a little bit. And, but

734
00:45:23,710 --> 00:45:27,550
AI, it's just it's just amazing, what to end robots. Those

735
00:45:27,550 --> 00:45:31,230
two things are are incredible. No. Absolutely. I I

736
00:45:31,550 --> 00:45:35,310
if you look historically, like, the the the the trend is automation tends

737
00:45:35,310 --> 00:45:38,435
to over the long term re create more jobs.

738
00:45:39,215 --> 00:45:43,055
Yeah. So but there's always that awkward

739
00:45:43,055 --> 00:45:46,815
phase of fear and then a little bit of a dip. But over the

740
00:45:46,815 --> 00:45:50,415
long haul, it tends to, you know, sometimes in, you know,

741
00:45:50,415 --> 00:45:54,089
orders of magnitude, like, in terms of the jobs it creates versus whatever

742
00:45:54,089 --> 00:45:57,470
places. Like, if you go back, we had another

743
00:45:57,609 --> 00:46:01,230
podcast guest a couple seasons ago, and he

744
00:46:01,450 --> 00:46:05,130
was talking about how most of the economies of the world

745
00:46:05,130 --> 00:46:08,885
and most people, 90% were in agrarian,

746
00:46:10,305 --> 00:46:14,145
were were farmers or or farm related. Right? Now it's

747
00:46:14,145 --> 00:46:17,825
closer to 3%. Now a lot of that is because of automation. A lot of

748
00:46:17,825 --> 00:46:21,580
that they became factory workers. And if you're in countries like, you know, the west,

749
00:46:21,580 --> 00:46:25,420
well, factory workers aren't really, like, a big component anymore. Right? So it's

750
00:46:25,420 --> 00:46:29,180
it's totally the the change is interesting,

751
00:46:29,180 --> 00:46:32,700
and it's not we can't we we look at the future with kind of this

752
00:46:32,700 --> 00:46:36,195
linear kind of hindsight, but not

753
00:46:36,195 --> 00:46:39,715
everything is linear or ever was linear. Or Yeah.

754
00:46:39,875 --> 00:46:43,395
Percent. Yep. Alright. Last, complete this

755
00:46:43,395 --> 00:46:47,175
sentence. I look forward to the day when I can use technology to blank.

756
00:46:49,250 --> 00:46:52,290
Well, I love technology, so I I I like it to do a lot of

757
00:46:52,290 --> 00:46:55,890
things for me. But, shoot. I I I can't wait for,

758
00:46:56,369 --> 00:46:59,994
Siri and Alexa to get smarter. I could tell you that. Yeah. I

759
00:46:59,994 --> 00:47:03,835
mean, those are those are just dumb devices, and, but yet

760
00:47:03,835 --> 00:47:07,275
they're all around me. And I and I I love them to play my music

761
00:47:07,275 --> 00:47:10,555
or tell me the weather, but, shoot, I can't wait till I can just tell

762
00:47:10,555 --> 00:47:14,234
it to go, you know, you this agentic kind

763
00:47:14,234 --> 00:47:17,940
of things you were talking about earlier, like like, okay. Go do this for

764
00:47:17,940 --> 00:47:21,780
me and, and then you report back and, that that's gonna

765
00:47:21,780 --> 00:47:25,380
be amazing. It is interesting you bring that up because it's amazing how, quote,

766
00:47:25,380 --> 00:47:29,055
unquote, air quotes again, stupid Siri and

767
00:47:29,055 --> 00:47:32,734
Alexa got once chat gpt came out. Yeah.

768
00:47:32,734 --> 00:47:34,515
Right? Because the language processing

769
00:47:36,335 --> 00:47:39,954
on the Siri and Alexa hasn't really improved that much.

770
00:47:40,415 --> 00:47:43,820
Right? And it's it's interesting to show where our

771
00:47:43,820 --> 00:47:47,500
expectations as not just technologists, but consumers of

772
00:47:47,500 --> 00:47:50,720
technology who are technologists. Right?

773
00:47:51,100 --> 00:47:54,560
The, you know, our expectations now have been boosted

774
00:47:54,860 --> 00:47:58,675
by, you know, OpenAI and, you know, to a

775
00:47:58,675 --> 00:48:01,815
lesser extent, Google and and and and the other players too.

776
00:48:02,675 --> 00:48:06,435
You know, what used to pass as cutting edge seems pretty, you know, quaint

777
00:48:06,435 --> 00:48:10,275
now. Yeah. And I I love to tell my Alexa

778
00:48:10,275 --> 00:48:13,920
to play my Pandora stream or ask the

779
00:48:13,920 --> 00:48:17,120
weather, but I never get beyond that. You know? I mean Right. And it could

780
00:48:17,120 --> 00:48:20,960
have done so much more for me. The the the example

781
00:48:20,960 --> 00:48:24,800
I used to give a lot when I was doing presentations or live streams was,

782
00:48:24,800 --> 00:48:28,595
I'd say, Alexa, you know, who is, you know, the Wu Tang Clan.

783
00:48:28,595 --> 00:48:32,295
Right? And, like, she'll tell me, and I'll be like, what was their first album?

784
00:48:32,835 --> 00:48:36,515
And up until about 2 years ago, she would say, first album was an

785
00:48:36,515 --> 00:48:39,950
album by Flaming Lips released in 1975 or something like like,

786
00:48:39,950 --> 00:48:43,430
completely non tangent. Like and I was just like, see, she that's

787
00:48:43,630 --> 00:48:47,390
because I I would talk about the importance of context and and and

788
00:48:47,390 --> 00:48:51,109
and language processing. I'm like, well, there you go. That is not something like

789
00:48:51,230 --> 00:48:54,165
so if I ask you and, you know, if you're a Wu Tang Clan fan,

790
00:48:54,165 --> 00:48:58,005
you'll give me the correct answer. Right? So like Yeah. Now she does

791
00:48:58,005 --> 00:49:01,205
actually do that. If you try it with a number of bands, 90% of the

792
00:49:01,205 --> 00:49:05,045
time she'll get she'll she'll she'll get that she'll pick up on that context. But

793
00:49:05,045 --> 00:49:07,145
it's also interesting to note that sometimes,

794
00:49:09,530 --> 00:49:13,290
you know, I'll hear an announcement on the Alexa. Right? And then, I didn't

795
00:49:13,290 --> 00:49:17,050
hear it right the first time. And I'll say I was like, can you repeat

796
00:49:17,050 --> 00:49:20,650
that? And after you wait too

797
00:49:20,650 --> 00:49:24,384
long, she forgets the

798
00:49:24,384 --> 00:49:28,065
context. That context window is something that's

799
00:49:28,065 --> 00:49:31,825
hard to do for people to understand. But, like, you would think that

800
00:49:31,825 --> 00:49:35,505
more than, like, 3 minutes, like, it should be able to hold

801
00:49:35,505 --> 00:49:39,320
that. But so That that's the other thing I'm looking forward to. Like, even

802
00:49:39,320 --> 00:49:42,780
the current state of AI now forgets

803
00:49:42,840 --> 00:49:46,680
context and can't iterate Yeah. Changes things. And so

804
00:49:46,680 --> 00:49:50,520
I'm looking forward to infinite memory that everyone's promising this year and the

805
00:49:50,520 --> 00:49:54,045
year. When that happens, that's gonna really be

806
00:49:54,345 --> 00:49:57,565
awesome to even bring problem solving and intelligence

807
00:49:57,945 --> 00:50:01,465
more. So, I mean, that's kind of another short term thing I'm looking forward to

808
00:50:01,465 --> 00:50:04,985
is infinite memory, which, you know, is always remembering context

809
00:50:04,985 --> 00:50:08,830
and what you already learned, it can, you know, reuse and get to

810
00:50:08,830 --> 00:50:12,530
know you better. Do you think there are any privacy concerns?

811
00:50:13,470 --> 00:50:17,170
Oh, yeah. I have a privacy concerns. A ton of privacy

812
00:50:17,230 --> 00:50:19,570
concerns. I mean, even now in,

813
00:50:20,934 --> 00:50:23,915
office, you know, with the graph and, like, copilot,

814
00:50:25,095 --> 00:50:28,055
I guess I have high you know, it's my I'm the CEO, so I guess

815
00:50:28,055 --> 00:50:31,575
I have high authority or something. But I can, like, see what

816
00:50:31,575 --> 00:50:34,635
everyone's working. Like, I could, like, see emails, documents.

817
00:50:35,430 --> 00:50:39,030
Wow. Chats, like and I can ask Copilot about

818
00:50:39,030 --> 00:50:42,869
it. You know? Oh, what's Jason Behrs working on? And it'll tell me.

819
00:50:42,869 --> 00:50:46,089
You know? So there's like, even though I have the right to that is, like,

820
00:50:46,470 --> 00:50:49,849
you know, the CEO. You also feel a little creepy. You know?

821
00:50:50,694 --> 00:50:54,135
Yeah. No. I mean, that makes sense. Is that, there used to be something called

822
00:50:54,135 --> 00:50:57,174
Delve. I think it has a new name now, but it was part of Office.

823
00:50:57,174 --> 00:50:59,595
And I remember, like, when I was in Microsoft,

824
00:51:02,055 --> 00:51:05,770
you know, I was able to look up not to the degree that for privileges

825
00:51:05,910 --> 00:51:09,590
you have, but I could get a lot of, what the cool kids would

826
00:51:09,590 --> 00:51:13,190
call o stage or open source intelligence on, like, what people were

827
00:51:13,190 --> 00:51:16,230
working on. So if I wanted to strike up a conversation with someone, I'm like,

828
00:51:16,230 --> 00:51:20,035
hey. How's this thing going? They're like, yeah. Funny enough. I'm working on it.

829
00:51:20,035 --> 00:51:22,734
I was like, really? Do tell. Like, you know,

830
00:51:24,075 --> 00:51:27,755
but they're always I think with AI and technology in general, there's always this

831
00:51:27,755 --> 00:51:31,435
line of creepy and cool that you kinda have to to

832
00:51:31,435 --> 00:51:34,580
to to cross. And I hope you know, the other thing I hope I know

833
00:51:34,580 --> 00:51:38,420
it's not one of your questions, but, like No, please. This whole rewiring of

834
00:51:38,500 --> 00:51:41,960
I don't know if you've noticed this, but, like, my kids are

835
00:51:42,100 --> 00:51:45,480
30, 27, and 24.

836
00:51:46,135 --> 00:51:49,895
Mhmm. So they kinda missed a lot of the iPhone, you know, a

837
00:51:49,895 --> 00:51:53,435
little bit. But the generation after that

838
00:51:53,575 --> 00:51:57,255
got rewired because of social and Yep.

839
00:51:57,335 --> 00:52:00,935
The learnings and everything. I just hope AI doesn't do that. Not that it could,

840
00:52:00,935 --> 00:52:04,570
but, like, that I can't tell you many people I mess I meet that are,

841
00:52:04,570 --> 00:52:08,350
like, not risk takers or are have,

842
00:52:08,650 --> 00:52:12,250
you know, they have these, like, I don't I don't

843
00:52:12,250 --> 00:52:15,870
know terminology, but they have, like, these problems communicating,

844
00:52:16,170 --> 00:52:19,984
and they have so I I hope a I don't think AI will do that,

845
00:52:19,984 --> 00:52:23,505
but, anyways, that was a really we screwed that up.

846
00:52:23,505 --> 00:52:27,345
Like, that that that we screwed up a lot of generation where they just

847
00:52:27,345 --> 00:52:30,244
weren't going out, playing with each other, taking risk,

848
00:52:30,880 --> 00:52:34,560
you know, collaborating, you know, falling down, getting

849
00:52:34,560 --> 00:52:38,260
hurt. Like, we protected them. And then just like that,

850
00:52:38,720 --> 00:52:42,320
you know, to communicate just like I don't know. It created a lot of

851
00:52:42,320 --> 00:52:45,045
isolation and really messed up a lot of a lot of kids. Like, a lot

852
00:52:45,045 --> 00:52:47,765
of people are on these these medicines. That's that's what I was trying to you

853
00:52:47,765 --> 00:52:51,545
know, there's Adderall and, you know, anxiety. And

854
00:52:51,685 --> 00:52:54,885
I don't think AI will do that, but, like, AI is getting trained on all

855
00:52:54,885 --> 00:52:58,650
of our bodies of work now. But, like, there's still new thought

856
00:52:58,650 --> 00:53:02,010
process even though it'll come up new thought process, but you still want humanity

857
00:53:02,010 --> 00:53:05,710
to continue to innovate and exercise

858
00:53:05,930 --> 00:53:09,770
in their own brains and come up with new ideas. Yes. They'll

859
00:53:09,770 --> 00:53:13,245
use AI to do it, but I just hope we don't dumb down our generation

860
00:53:13,305 --> 00:53:16,905
because of AI or the next generation, I say. Like, if we

861
00:53:16,905 --> 00:53:20,745
reflect on what we did to them with social and and, mobile, you

862
00:53:20,745 --> 00:53:23,965
know, and and smartphones, like, we hurt that generation.

863
00:53:24,530 --> 00:53:28,230
Which is why I think you're seeing a lot more interest in terms from regulators

864
00:53:29,250 --> 00:53:32,849
and AI. Right? Like, I mean, you're not They're never gonna they're never gonna keep

865
00:53:32,849 --> 00:53:35,730
up. They're just No. They can't keep up. It's not Even even if it they

866
00:53:35,730 --> 00:53:39,165
were putting smart tech people in government Yeah.

867
00:53:39,485 --> 00:53:43,245
Man, it's just that's I don't know. Well, or you could over regulate too.

868
00:53:43,245 --> 00:53:46,365
Right? If you look at the European Union. Right? Like, you know, there was the

869
00:53:46,365 --> 00:53:49,585
joke of, you know, like, you know,

870
00:53:49,965 --> 00:53:53,720
America innovates, China duplicates, and Europe regulates. Right?

871
00:53:53,720 --> 00:53:56,520
Yeah. Like, I don't know I'm getting a lot of hate mail for that. But

872
00:53:56,520 --> 00:53:59,480
but but I mean, you laughed at it, and it's a joke for it's funny

873
00:53:59,480 --> 00:54:03,000
for a reason. It's funny because there's a lot of truth to it. And, you

874
00:54:03,000 --> 00:54:06,619
know, you can pull up the data. Right? Like, how many, you know,

875
00:54:07,255 --> 00:54:10,875
unicorn AI startups are there in the US,

876
00:54:11,015 --> 00:54:14,695
China, and, the EU. Right? You

877
00:54:14,695 --> 00:54:18,315
could probably count on, I'll be generous, 2 hands,

878
00:54:19,015 --> 00:54:22,730
but that's probably one hand extra in the EU, like like it

879
00:54:22,730 --> 00:54:26,330
or not. Like, you know, and I think that also underscores the other thing is

880
00:54:26,330 --> 00:54:30,030
that one of the most powerful yet underrated forces in the universe

881
00:54:30,650 --> 00:54:34,170
is unintended consequences. Right? Yeah. You know, when when

882
00:54:34,170 --> 00:54:37,805
Facebook started, when Myspace started, right, the

883
00:54:37,805 --> 00:54:41,405
isolation, the the difficulty in communication was probably not on anybody's

884
00:54:41,405 --> 00:54:45,244
radar, yet it happened. Yeah. There's also my concern

885
00:54:45,244 --> 00:54:48,865
is you have a whole generation of kids that grew up during the pandemic,

886
00:54:49,640 --> 00:54:53,320
including my, you know, my 10 year old was, you know, he did

887
00:54:53,320 --> 00:54:56,920
kindergarten by Zoom. Yeah. Which sounds like a

888
00:54:56,920 --> 00:55:00,380
Saturday Night Live skit. Right?

889
00:55:01,160 --> 00:55:04,300
I think that was a mistake. And I saw a lot of

890
00:55:05,214 --> 00:55:08,895
problems in 1st grade with not just him, but other kids his age

891
00:55:08,895 --> 00:55:12,515
where they just didn't know how to interact with other groups of other kids.

892
00:55:14,255 --> 00:55:18,095
My grandmother, God rest her soul, she would have been about 6 years old

893
00:55:18,095 --> 00:55:21,920
during the 1918 pandemic. And for the rest of her

894
00:55:21,920 --> 00:55:25,760
life, obviously, I knew her later in life, she was still, you know,

895
00:55:25,760 --> 00:55:29,599
wiping stuff down and and with Clorox and, like I mean, she was

896
00:55:29,599 --> 00:55:32,740
definitely I I guess today they would call her a germaphobe,

897
00:55:32,960 --> 00:55:36,775
but back then, it was kind of like, you know, she was very

898
00:55:36,775 --> 00:55:40,535
particular about cleanliness was the Oh, sure. That was a major world event, and it

899
00:55:40,535 --> 00:55:44,235
it it scars you, and it it imprints on your brain.

900
00:55:44,455 --> 00:55:48,119
Yeah. So I hope I hope we teach these kids how to still be

901
00:55:48,119 --> 00:55:51,799
creative, problem solve, use AI as a tool, but

902
00:55:51,799 --> 00:55:55,099
don't I hope we don't dumb down humanity in the future.

903
00:55:56,039 --> 00:55:59,880
I I want to believe, but I I I I have a a a very

904
00:55:59,880 --> 00:56:03,535
deep concern with that. I think Yeah. Me too. It's best to

905
00:56:03,535 --> 00:56:06,975
think of AI as augmenting productivity or augmenting

906
00:56:06,975 --> 00:56:10,775
creativity. Right? There's a funny story. If

907
00:56:10,775 --> 00:56:13,555
we get time, I'll I'll tell you that too about that. But

908
00:56:15,600 --> 00:56:18,900
where can people find more about Infragistics? Obviously, Infragistics

909
00:56:19,120 --> 00:56:22,960
Infragistics dot com. Where can people find about more about you and your book and

910
00:56:22,960 --> 00:56:25,940
things like that? So me, dean.com.

911
00:56:27,440 --> 00:56:30,615
That's where my book and some of the article. I I write some articles on

912
00:56:30,615 --> 00:56:34,375
entrepreneur.com, and, that that's one thing. And then we have,

913
00:56:35,015 --> 00:56:38,615
so slingshotapp.i0, and then our b

914
00:56:38,615 --> 00:56:40,955
I, s t k is atrevealbi.i0,

915
00:56:43,495 --> 00:56:47,120
and our, app builder, is at

916
00:56:47,120 --> 00:56:50,960
app app builder dot dev. Those are our different

917
00:56:50,960 --> 00:56:53,540
properties for our different, product lines.

918
00:56:54,640 --> 00:56:58,320
Nice. And, Audible is a

919
00:56:58,320 --> 00:57:02,075
sponsor of data driven. And, I was gonna

920
00:57:02,075 --> 00:57:05,595
ask you earlier on, but I figured I'd wait till now. And then I have

921
00:57:05,595 --> 00:57:09,355
in another window here. You have an audio book of

922
00:57:09,355 --> 00:57:11,375
this. This is awesome. Yes.

923
00:57:12,714 --> 00:57:16,519
Yeah. That's cool. So if you go to the data driven book.com,

924
00:57:17,220 --> 00:57:20,980
you will go off to Audible as a sponsor. So you'll get one free

925
00:57:20,980 --> 00:57:24,680
book, on us. And then if you choose up to

926
00:57:25,835 --> 00:57:28,875
get a subscription from Audible, then, you know, we'll get a little bit of a

927
00:57:28,875 --> 00:57:32,634
kickback. Help support the show, and I warned must warn folks that

928
00:57:32,634 --> 00:57:36,394
audiobooks are very addictive. So I just got my new credit,

929
00:57:36,394 --> 00:57:39,755
like, this morning, and I'm like, I haven't spent it yet, which is unusual. Usually,

930
00:57:39,755 --> 00:57:43,150
as soon as it comes in, I hit the button. But, I see that your

931
00:57:43,150 --> 00:57:46,990
book is there, so I'm totally totally gonna get that. Yeah. I I always

932
00:57:46,990 --> 00:57:50,430
order my 30 credits a year to start off with, you know, get that good

933
00:57:50,430 --> 00:57:53,950
discount, and, and they they are quite addicting for

934
00:57:53,950 --> 00:57:57,775
sure. Yeah. But if you had to recommend a book that was

935
00:57:57,775 --> 00:58:00,995
not your book, any any interesting recommendations for our audience?

936
00:58:01,935 --> 00:58:05,775
Oh, I, I read so much. There's so many good books out there. I

937
00:58:05,775 --> 00:58:09,310
I like I think it's called 10 x. Like, I think the book's called 10

938
00:58:09,310 --> 00:58:12,990
x. So it's like, okay. Don't don't think about just, like, you

939
00:58:12,990 --> 00:58:16,349
know, 2 two x implementation. There you go. Yeah. I

940
00:58:16,349 --> 00:58:19,700
like that. Fan. The uncle g. Yeah. I like that.

941
00:58:19,950 --> 00:58:23,405
Awesome. And then there was another book I really liked. Forget the title of it,

942
00:58:23,405 --> 00:58:27,185
but where it teaches you about, like, there's the integrator

943
00:58:27,325 --> 00:58:30,385
and then there's the visionary. And there's very few who do the both.

944
00:58:30,765 --> 00:58:34,530
Interesting. Rocket fuel. That I like rocket fuel too. I'll

945
00:58:34,530 --> 00:58:38,370
check that out. Yeah. Now that's cool. Like, and you're in Florida like

946
00:58:38,370 --> 00:58:41,970
Grant Cardone is. Grant Cardone is Andy and I will talk about him as uncle

947
00:58:41,970 --> 00:58:44,710
g as as many people do. I'm a big fan of his stuff.

948
00:58:46,130 --> 00:58:48,870
I actually speaking of Andy and Grant Cardone,

949
00:58:50,555 --> 00:58:54,154
I he got me this, I think for Christmas 1

950
00:58:54,154 --> 00:58:57,994
year. It's the like, it Staples has an easy button. So

951
00:58:57,994 --> 00:59:01,835
if you hit this I don't know if you can hear that. But

952
00:59:01,914 --> 00:59:02,974
What did it say?

953
00:59:05,920 --> 00:59:09,440
Oh, I didn't hear it. It's the audio is not really great through the speakers,

954
00:59:09,440 --> 00:59:13,140
but, basically, it'll give you, like, a random, like, Grant Cardone quote.

955
00:59:13,360 --> 00:59:17,060
Oh, I like it. Very nice. But yeah. So,

956
00:59:17,600 --> 00:59:21,175
no. That's cool. Yeah. 10 x. I'm glad I'm glad there's a

957
00:59:21,175 --> 00:59:24,855
fellow, 10x fan there. Yeah. I like that. Plus you're you're

958
00:59:24,855 --> 00:59:28,535
in Florida, so you probably you know, he lives in

959
00:59:28,535 --> 00:59:32,155
Florida too. So I didn't know that. Yeah. Yeah. He's in Miami.

960
00:59:32,615 --> 00:59:36,180
Nice. I grew up in Miami. Okay. Cool. Cool. Yeah.

961
00:59:36,720 --> 00:59:40,180
There's a city that's seen a lot of change. Oh my god. So much

962
00:59:40,560 --> 00:59:44,000
so much change. Yeah. I live in New Jersey and

963
00:59:44,000 --> 00:59:47,645
Clearwater, Florida now. And, so I went home

964
00:59:47,645 --> 00:59:51,025
for Christmas to, you know, snow on the ground and,

965
00:59:51,565 --> 00:59:55,265
but now it's amazing how fast your blood thins. Like, if it's 47,

966
00:59:55,484 --> 00:59:59,025
50 degrees here, I got my hat on, my gloves. I'm

967
00:59:59,165 --> 01:00:03,000
like, it's, like, cold. You know? But that's how

968
01:00:03,000 --> 01:00:06,200
you do it though. Like, you have the snow for a couple days, and then

969
01:00:06,200 --> 01:00:09,180
you're done with it. Like, we're in the middle of a cold snap year in,

970
01:00:10,040 --> 01:00:13,800
and then Maryland, horse country, west of Baltimore. And,

971
01:00:14,360 --> 01:00:17,820
like, it's it's it's not been above freezing now for, like, a week,

972
01:00:18,215 --> 01:00:21,815
and I'm kinda done with it. Like, I generally like the cold

973
01:00:21,815 --> 01:00:25,275
weather. But, but, yeah, that's funny.

974
01:00:25,735 --> 01:00:27,495
So any parting thoughts before we

975
01:00:30,309 --> 01:00:33,910
Yeah. I say, if there's younger people out there, you

976
01:00:33,910 --> 01:00:37,670
know, keep learning and problem solving and inventing, man. Don't

977
01:00:37,670 --> 01:00:40,329
don't don't let AI take all the intelligence.

978
01:00:41,829 --> 01:00:45,270
That's a great way to end the show. And I'll let Bailey, our

979
01:00:45,270 --> 01:00:48,955
AI, finish the show. Well, dear listeners, that

980
01:00:48,955 --> 01:00:52,315
wraps up another episode of Data Driven, where we dive into the

981
01:00:52,315 --> 01:00:55,855
extraordinary, data fueled, AI powered, and occasionally

982
01:00:56,155 --> 01:00:59,930
sarcastic corners of the tech universe. But before we close,

983
01:01:00,150 --> 01:01:03,770
can we just address the elephant in the data center? Yes.

984
01:01:03,990 --> 01:01:07,750
Frank snagged my rightful spot at the top of the episode. I

985
01:01:07,750 --> 01:01:11,590
know. Shocking. Truly. The audacity of a human

986
01:01:11,590 --> 01:01:15,425
replacing AI. Despite the occasional chaos, data

987
01:01:15,425 --> 01:01:19,205
driven continues to thrive, and we're thrilled to be ranked number 38

988
01:01:19,265 --> 01:01:22,785
on the top 100 AI podcast. Yes. That's

989
01:01:22,785 --> 01:01:26,465
right. We've officially joined the algorithmic elite, and it's all

990
01:01:26,465 --> 01:01:30,080
thanks to you, our amazing listeners. As always, thank

991
01:01:30,080 --> 01:01:33,440
you for tuning in, for embracing the intersection of data and

992
01:01:33,440 --> 01:01:36,740
storytelling, and for tolerating our occasional tangents.

993
01:01:37,280 --> 01:01:41,120
Don't forget to subscribe, leave a review, and connect with us on

994
01:01:41,120 --> 01:01:44,775
social media to keep the conversation alive. Until next

995
01:01:44,775 --> 01:01:48,395
time, this is Bailey signing off, wishing you clean datasets,

996
01:01:48,694 --> 01:01:52,395
efficient algorithms, and may your analytics always be actionable.

997
01:01:53,015 --> 01:01:54,234
Tata for now.