WEBVTT

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Hey there, welcome to Data Democracy. This is

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a podcast where we explore ways to make data

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and AI more accessible to everyone. I'm super

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excited to have our dear friend Matt Layton today

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on the podcast. Matt Layton in the house. Matt

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is the Senior Vice President of Data Insights

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and Intelligence at LegalShield. He also advises

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startups consulting on the side. He's also involved

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in community service and leadership. He was the

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vice mayor and councilman of Ada, Oklahoma. He's

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also a rancher who took me ranching one day and

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it did not go well. Let's just say I was more

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of a hazard than help. Matt was my boss for a

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long time and I've just learned so much from

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him. Matt's like John McClane of leaders. He's

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always got your back. If you mess anything up,

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he just absorbs it and takes care of it. He says

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you're not learning if you don't make mistakes.

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All he asks is don't make the same mistake twice.

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Matt's been such a solid support system for me,

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for the podcast. I'm just thrilled to have him

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here with me. Matt, welcome. Hey man, thanks

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for having me. We've been trying to put this

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together for a really long time now. I've been

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jealous watching all your other episodes and

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I haven't been on yet, so finally. I was so excited.

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Let's do this. Yeah, let's start with your story.

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How did you get where you are? What's your story?

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Hey, well, you know what? I think at the end

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of the day, my story is, you know, mid... 40s,

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and I still don't know what I want to do when

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I grow up, right? I'm interested in a whole lot

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of different things, love to learn, and probably

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spread myself way too thin, but that's a good

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way to go through life, right? And like you kind

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of mentioned, I'm in data analytics now, never...

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never thought or intended that's where I would

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end up right I you know went to college and I've

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got a political science degree I was going to

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go to law school got sick of school between you

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know graduating undergrad and finally you know

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going to law school and I said the heck with

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that went to work full -time at a you know, a

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little company called Prepaid Legal Services

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at the time, you know, LegalShield, PPLSI now,

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and, you know, kind of stayed there ever since.

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And, you know, as I learned about that business,

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I kind of just moved through the ranks there.

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I like to say. I've sat in just about every seat

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at that company, right? And I don't know if that's

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a positive or a negative, right? Other folks

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either wanted me or the current folks wanted

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to get rid of me. But anyway, it's helped me

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create a knowledge base that I think probably

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meshes well with my love of learning and always

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looking for new. And that kind of just lands

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me right in. analytics business insights you

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know i i know the business i love digging through

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the through the data i've always said i think

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the answer is there i mean if you have enough

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data the answer is there right you you just have

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to you know have the smarts and the tools to

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mine it mine it out and you know You kind of

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mentioned politics. Again, I had a political

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science undergrad, so I kind of toyed in politics

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there, was a local city councilman. Now I like

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to say I'm up to here with civic pride, right?

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I mean, I've always thought if I'm going to do

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that again, the next one's going to come with

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a check, because the last one certainly didn't.

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And again, I've always loved the outdoors. There's

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no cattle ranchers in my family, but I thought,

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you know what, I can give this a shot. So, I

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mean, I like to say I'm a pretend rancher. I've

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been doing that for, well, 15 years now, right?

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It's, you know, it's probably a better tax write

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-off than it is a profit center. But, you know,

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it gets me out of the office. It gets me out

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from behind the computer screen. You know, it

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gets me out into the real. real, you know, world

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at times instead of sitting here looking at Excel

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spreadsheets all day. And you know what? I will

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say I have had worse ranch hands come out to

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feed hay with me. But, you know, there is a proper

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way to carry a hay bale and, you know, Mitty's

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show just about ever weighs other than the proper

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way. But hey, again, you mentioned it early on.

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Mistakes are fine, just don't make the same one

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twice. I think Mitty taught himself not to care.

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So, you know, all good. Yep, I never showed up

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on the ranch again. Just the ones. No, yeah,

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I think that's a great story, right? So that

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feeds right off. And you've been, like you said,

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you've been at... every rank at LegalShield and

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you've been a veteran of LegalShield. I think

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there's so much to say about understanding, gaining

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a deep understanding of our company, its business,

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especially at LegalShield. It's 50 years old

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and we've been digging into data that never ends

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and there's just so much. Average tenure of employees

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around It's around about three to four years.

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You and I are kind of anomalies, right? So you're

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in a different league. I just put myself in there

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as well. You know what I said? I think I'm going

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on again. Me and you both, we do a lot of random

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stuff on the side, right? So we do a lot of different

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things. But as far as working at LegalShield,

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prepaid legal services, I think I'm going on

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my 27th year now. There you go. Of course, I

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started when I was in elementary school, paying

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no attention to the white beard. I always joke,

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it's like, I don't know if that's a good thing

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or this is just the result of a string of bad

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decisions that have caused me to be here 27 years.

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But I, you know, love it, love the company, love

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what I do. Most of all, I love the teams I've

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got to work with and kind of building teams.

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go on and do better, better, better and bigger

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and better things. And that's probably the coolest,

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coolest reason that you would, you know, want

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to build and run what I call high performing

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teams. Yeah, no, absolutely. I never thought

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so either. Like that I would stay at a company

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for a really long time and it's been about eight

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years for now, but yeah, definitely. 27 is awesome.

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But like you said, I mean, we both kind of do

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things on the side to keep us out of the bubble

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and keep us in the loop with outside stuff that's

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going on. Yeah, yeah. So if you were to, and

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I know we talked about it, if you were to kind

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of make the case for people staying longer at

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a firm, what would those be? I think I lost you

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there. Oh, can you hear me now? Yeah, I can.

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Okay. Now, if we were to make a case for people

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staying at a company longer, what would those

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merits be? First of all, I think you have to

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enjoy the people you're there with. I think I...

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...to the people that you have to be with every

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single day, right? I mean, if you don't get along

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and like the folks you're working with, I don't

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think the company itself or the mission or the

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vision matters, right? I think that's probably

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the number one reason that anyone would want

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to stay, right? Because, I mean, of course, you

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can say money. I want more money. I'm not getting

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paid enough. Or, you know, the job duties are

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boring. Or I don't see a path to advancement.

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Or, you know, I just don't believe in what the

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company is, you know, based on or what the company's

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goals are. And all those things are valid, right?

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But at the end of the day, if you like the people

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you're working with, you're probably more interested

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in getting up in the morning and going you know

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hanging out with your buddies right i think that's

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the kind of the team that we build right you

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know mistakes are okay no one no one's getting

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in trouble for screwing something up again you

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know as long as you learn from it right and don't

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screw the same Enjoy the people you're working

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with. You look forward to going hanging out with

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them every single day. Because again, if you

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enjoy the folks you're working with, I think

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everything else lines up, right? You're going

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to get the pace going to come, right? You're

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going to enjoy the projects you're working on.

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You're going to find value in the output of the

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team. Even if maybe you're not, you know, at

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least initially fully engaged in maybe what the

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company mission is. Yeah, 100%. I think working

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with people you like makes things a lot, you

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know, a lot exciting and a lot more fun. Sweet.

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I think we need to tell the audience about LegalShield

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because we're kind of the insiders. So tell us

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what's LegalShield and what do we do? Why don't

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many tell us about LegalShield? I think if I

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meet somebody and I don't have a lot of time,

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I just say LegalShield is, we're like a... We're

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the Netflix of legal services. I know I'll get

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into trouble for it, but the basic premise is

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a subscription company for legal services and

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identity theft services. And we're the OGs. We've

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been around for 53 years now. This was before

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subscription model was cool. So we've been at

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it for a long time. Yeah, and I think you're

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spot on, right? Whether you say Netflix, whether

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you say Uber, whatever it is. So really what

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we do is we connect, again, exactly like Netflix,

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Uber, any of these other models. I don't even

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have to talk about legal or law, right? We connect

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a person with a want or a need to someone who

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can solve that issue or answer those questions,

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right? And we just do that for the legal industry.

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A lot of times, who knows, do you even know a

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lawyer, right? Or are you afraid of how much

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of one costs? Or who do I call? Or my buddy's

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a real estate lawyer, but I've got a bankruptcy

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issue. I don't even know what to do, right? So

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again, we kind of take that education and investigation

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out of the equation. And we give you access either

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through a mobile app, through a telephone, through

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the web to, you know, contact a law firm that

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has experts in the legal issues that you're going

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to need assistance with. And it's as easy as,

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you know, dialing a phone or pushing a button

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on your app and we'll immediately connect you

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to a lawyer who can answer the questions that

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you have, right? Again. I just always explain

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it as simple as, you know, we connect a person

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with a need to someone who can solve the issue.

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Yeah, absolutely. And again, I mean, founded

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in 1973, I think many kind of hit on it. OGs

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of the subscription model business. I mean, that's

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fully in vogue today. Everyone wants to, you

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know, to be in the subscription model business.

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You know, we've been doing it and perfecting

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it for, you know 53 54 years now yeah it's it's

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it's amazing and especially the the cost we're

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able to bring the cost uh down so much and you

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you've worked with the provider services uh division

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so much How are we able to get the cost down

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to like $30 to $60 a month instead of $300 for

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the hour? Sure. Why do we say it's like less

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than a cup of coffee a day is kind of the tagline.

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But then on the other side is like, okay, if

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some of these best lawyers are out there charging

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$300, $400, $500, $600 an hour, the question

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comes back to us. well, hell, what does a $30

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a month lawyer look like? And is that who I want

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solving my issues? But you hit exactly on it,

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right? It's the efficiency and the model that

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we've built. And I really say it's our competitive

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advantage. It's the moat around our business

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is our network of provider law firms that we've

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built throughout North America, the United States

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and Canada. almost 40 years, average 10 year

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with us. Again, these are full service law firms

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throughout the United States and Canada. And

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the model that we pay them on is really interesting,

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right? They don't get paid based upon the services

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they provide. It's a fully capitated model. You

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have an area of responsibility. Every single

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month, we're going to pay you per active member

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or per active customer within your area. Regardless,

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I mean, Mitty could be a member. He could never

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use the service, but we're going to pay his provider

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law firm every single month that Mitty remains

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active. And that's kind of the power in numbers

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that we've created. And really, it not only aligns

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our company. but also the provider law firms

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because retention matters, right? Mitty, you've

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been deep into retention. In a subscription model

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business, it's, you know, cost per acquisition,

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lifetime value, right? How much do I have to

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pay to, you know, create a new customer and maintain

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that customer? How much is that customer going

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to pay us over the life of the membership? directly

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aligned with the providers who provide our services

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and the company in order to maintain high customer

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satisfaction rates. We know when we meet or exceed

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customers' expectations, they stay with us longer,

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right? And then honestly, the longer they stay

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with us, the less they use the service. And that's

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really when our provider offers begin making

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a lot of money. Might have to put a little more

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effort in up front to like, all right, Middy,

00:16:58.080 --> 00:17:00.259
you made a good choice. We're probably going

00:17:00.259 --> 00:17:02.620
to go above and beyond the first one or two times

00:17:02.620 --> 00:17:06.759
you call and you need any assistance. But at

00:17:06.759 --> 00:17:08.700
that point, you're sold, right? I mean, we do

00:17:08.700 --> 00:17:12.000
a lot of net promoter scoring. I mean, Middy's

00:17:12.000 --> 00:17:14.180
a promoter at that point. If we meet or exceed

00:17:14.180 --> 00:17:16.920
his expectations on the first or the second time

00:17:16.920 --> 00:17:19.539
he uses the service, he's a member for life.

00:17:19.720 --> 00:17:23.009
He's most likely not going to cancel. And then

00:17:23.009 --> 00:17:26.009
again, that's where the snowball of the number

00:17:26.009 --> 00:17:28.769
of active members that we can maintain because

00:17:28.769 --> 00:17:31.089
we're meeting our exceeding customer satisfaction

00:17:31.089 --> 00:17:34.210
rates. You know, we all make a whole lot of money

00:17:34.210 --> 00:17:36.829
there. And again, we've been doing this for 50

00:17:36.829 --> 00:17:39.430
plus years. So I think we're getting pretty,

00:17:39.470 --> 00:17:43.220
pretty good at it. Yeah, I think the company

00:17:43.220 --> 00:17:45.200
has nailed two things, right? Like you said,

00:17:45.319 --> 00:17:49.200
economy of scale, the number of people, and also

00:17:49.200 --> 00:17:54.039
aligning incentives with everybody's incentives

00:17:54.039 --> 00:17:57.099
together. So I think that those are key things

00:17:57.099 --> 00:18:06.940
that the company has nailed. Can you hear me?

00:18:08.750 --> 00:18:10.569
I think you kind of froze up on me, but hey,

00:18:10.630 --> 00:18:14.150
and again, it's really that capitated model and

00:18:14.150 --> 00:18:22.509
that... I couldn't tell if I lost you again.

00:18:22.970 --> 00:18:25.849
I don't know what's going on. I don't either.

00:18:27.130 --> 00:18:36.109
My internet seems to be fine. Is Zoom weird today?

00:18:39.789 --> 00:18:42.930
I don't know. Let's keep going and we'll cut

00:18:42.930 --> 00:18:47.009
it out. So, again, this is why I say our provider

00:18:47.009 --> 00:18:50.269
law firms and our capitated model is that, you

00:18:50.269 --> 00:18:53.029
know, competitive moat, right, that we've built

00:18:53.029 --> 00:18:56.890
around the business. Who can start this from

00:18:56.890 --> 00:19:00.970
scratch today, right? What startup can build

00:19:00.970 --> 00:19:04.950
a network of provider law firms across the country,

00:19:05.109 --> 00:19:10.069
you know, pay? based on active subscribers and

00:19:10.069 --> 00:19:14.170
run the business, right? I mean, you can't start

00:19:14.170 --> 00:19:17.630
that today, right? So again, we don't want to

00:19:17.630 --> 00:19:19.970
sit here and rest on our laurels, right? And

00:19:19.970 --> 00:19:23.450
just let a new startup with AI or something sneak

00:19:23.450 --> 00:19:27.789
up and take over the world. But again, I think

00:19:27.789 --> 00:19:29.650
we're going to see someone coming from a long

00:19:29.650 --> 00:19:34.430
ways off just because of the... network of provider

00:19:34.430 --> 00:19:38.470
law firms that we've built over 50 plus years

00:19:38.470 --> 00:19:43.049
now. Yeah, the model only works once you have

00:19:43.049 --> 00:19:47.569
massive traction and that's hard to reach. Yeah,

00:19:47.589 --> 00:19:52.430
and hey, you're a data geek, data genius. Tell

00:19:52.430 --> 00:19:56.829
me, what do you think, do we think people are

00:19:56.829 --> 00:20:00.529
going to, either today or quickly in the future?

00:20:01.109 --> 00:20:03.150
Are they going to get to the point where they're

00:20:03.150 --> 00:20:07.369
going to trust and believe in AI giving them

00:20:07.369 --> 00:20:13.509
legal advice, right? At what point does that

00:20:13.509 --> 00:20:17.069
tip over, right? I would say today that probably

00:20:17.069 --> 00:20:20.529
some people are going to ask some basic questions,

00:20:20.609 --> 00:20:24.569
but I don't know how important of a decision

00:20:24.569 --> 00:20:27.130
someone is going to make today if they talk to

00:20:27.130 --> 00:20:30.940
an AI lawyer, right? But there is a... There

00:20:30.940 --> 00:20:33.180
is a point into the future, right? Especially

00:20:33.180 --> 00:20:36.960
as, you know, AIs gets, right? I saw something

00:20:36.960 --> 00:20:41.759
the other day. The AIs are taking IQ tests and

00:20:41.759 --> 00:20:43.839
they're, you know, getting up close to genius

00:20:43.839 --> 00:20:50.059
level. So at some point it probably works, but

00:20:50.059 --> 00:20:52.680
I don't know when that tipping point or if there

00:20:52.680 --> 00:20:55.279
will be, right? If I'm making, you know, really

00:20:55.279 --> 00:20:59.670
important life decisions that are based on. you

00:20:59.670 --> 00:21:04.450
know, legal opinions from lawyers, am I ever

00:21:04.450 --> 00:21:06.210
going to be comfortable talking to a machine

00:21:06.210 --> 00:21:08.690
as opposed to, you know, sitting across from

00:21:08.690 --> 00:21:14.769
a real person? That's the one challenge I see

00:21:14.769 --> 00:21:17.609
potentially in the future of the business that,

00:21:17.630 --> 00:21:20.049
you know, we're kind of employed with today.

00:21:21.190 --> 00:21:26.690
When do we employ AI more deeper? And if we don't,

00:21:27.039 --> 00:21:29.160
Is that where someone's going to sneak up on

00:21:29.160 --> 00:21:34.519
us and take over? Yeah, that's the billion -dollar

00:21:34.519 --> 00:21:40.279
question for companies and legal services. I

00:21:40.279 --> 00:21:47.039
feel some of the lower -hanging or low -effort

00:21:47.039 --> 00:21:49.220
things will be automated and people will start

00:21:49.220 --> 00:21:52.579
trusting it if it's standard enough. So I'm trying

00:21:52.579 --> 00:21:57.859
to start a company now. There, it's all the same

00:21:57.859 --> 00:22:00.700
thing. It's the same forms, same this thing.

00:22:00.759 --> 00:22:03.299
I just don't know what it is. So for that, I

00:22:03.299 --> 00:22:07.579
could probably rely on AI to just tell me what

00:22:07.579 --> 00:22:12.140
these mean. So if it's like a very standard traffic

00:22:12.140 --> 00:22:16.400
ticket, I need to just do it. Those could be

00:22:16.400 --> 00:22:20.839
potentially automated, but legal issues are so

00:22:20.839 --> 00:22:25.579
complicated most of the times that... Even lawyers

00:22:25.579 --> 00:22:31.799
have to be up to speed on new laws and stuff

00:22:31.799 --> 00:22:37.500
like that. Even further, it's up to interpretation.

00:22:38.299 --> 00:22:42.140
Especially a lot of these trials, it's what lawyer

00:22:42.140 --> 00:22:47.680
is arguing the best. So it's like, are you going

00:22:47.680 --> 00:22:51.180
to have two AI machines in the room arguing with

00:22:51.180 --> 00:22:54.950
each other? I 100 % agree with your point, right?

00:22:55.049 --> 00:22:58.490
If I'm filling out forms, maybe the AI can ask

00:22:58.490 --> 00:23:02.009
me a set of questions that pull out the answers,

00:23:02.150 --> 00:23:04.789
help me fill out the form. But I think at the

00:23:04.789 --> 00:23:10.910
end of the day, if I've got a multimillion dollar

00:23:10.910 --> 00:23:16.369
estate and I'm doing a will and a trust, I don't

00:23:16.369 --> 00:23:20.509
know what AI has got to prove to me before I

00:23:20.509 --> 00:23:22.660
would be comfortable just... totally agreeing

00:23:22.660 --> 00:23:26.140
with what it said, hit print and go, right? Yeah.

00:23:26.500 --> 00:23:32.380
And large language models need to have seen the

00:23:32.380 --> 00:23:36.079
situation enough to be able to answer, but every

00:23:36.079 --> 00:23:39.000
situation is so different that it's hard. And

00:23:39.000 --> 00:23:40.960
you never know when it's making stuff up and

00:23:40.960 --> 00:23:45.900
when it's not. I mean, I'm in the industry. I

00:23:45.900 --> 00:23:49.390
work on AI, but... There are limitations to it

00:23:49.390 --> 00:23:53.690
and we need to address those. It's really cool,

00:23:53.829 --> 00:23:57.869
the technology, but there are things that I don't

00:23:57.869 --> 00:24:04.029
think we can use AI for everything at this point.

00:24:04.369 --> 00:24:08.569
We always hear the guys talk about AI is not

00:24:08.569 --> 00:24:11.130
going to take your job. The guy using AI is going

00:24:11.130 --> 00:24:17.980
to take your job. I think at some point, All

00:24:17.980 --> 00:24:20.500
of this is going to be solved, right? So at what

00:24:20.500 --> 00:24:23.799
point in the future, and again, you've created

00:24:23.799 --> 00:24:28.299
lots of different AI models that answer a lot

00:24:28.299 --> 00:24:31.759
of different questions. At what point in the

00:24:31.759 --> 00:24:33.799
future do you think that we're going to solve

00:24:33.799 --> 00:24:36.519
that hallucination issue, right? Because again,

00:24:36.700 --> 00:24:41.259
you can ask the AI something today and it's confident

00:24:41.259 --> 00:24:44.839
and it's going to tell you X, Y, Z, and it could

00:24:44.839 --> 00:24:50.000
be talking. You could ask it about how to start

00:24:50.000 --> 00:24:51.720
a fire and it's going to tell you how to make

00:24:51.720 --> 00:24:53.940
an ice cube, right? But it's going to be confident

00:24:53.940 --> 00:24:58.980
and lay it all out, right? But I think at some

00:24:58.980 --> 00:25:03.660
point, again, as we keep investing more and more,

00:25:03.880 --> 00:25:06.039
right? And again, I think this is probably even

00:25:06.039 --> 00:25:09.539
a bigger question of who's going to eventually

00:25:09.539 --> 00:25:12.900
rule the economies and maybe even the world,

00:25:12.940 --> 00:25:16.460
right? China and the US, probably the two main

00:25:16.460 --> 00:25:18.880
players, they're going to keep investing in this.

00:25:18.940 --> 00:25:21.539
They're going to keep perfecting it. And whoever

00:25:21.539 --> 00:25:27.099
figures it out the fastest first is probably

00:25:27.099 --> 00:25:30.559
going to win, right? So I think there's a point

00:25:30.559 --> 00:25:33.019
in time in the future that this probably gets

00:25:33.019 --> 00:25:35.900
solved. I just don't know how far we are away

00:25:35.900 --> 00:25:40.220
from that. Yeah, yeah. I think the trick is to...

00:25:42.349 --> 00:25:46.690
build smaller, more niche models for specific

00:25:46.690 --> 00:25:51.609
use cases. Because right now, the foundational

00:25:51.609 --> 00:25:55.390
models from OpenAI or Anthropic, anybody, any

00:25:55.390 --> 00:26:00.069
of these foundational models that are famous

00:26:00.069 --> 00:26:02.809
are basically trying to do everything all at

00:26:02.809 --> 00:26:07.009
once. It's the whole internet, it's an aggregation

00:26:07.009 --> 00:26:13.950
of the entire human intellect. I think as we

00:26:13.950 --> 00:26:18.849
build smaller models for specific use cases,

00:26:19.029 --> 00:26:24.509
if I'm able to train a large language model just

00:26:24.509 --> 00:26:31.329
to fill out traffic tickets, I can get that to

00:26:31.329 --> 00:26:37.789
be like 95, 98 % accurate versus asking ChatGPT

00:26:37.789 --> 00:26:44.289
today. Retrieval augmented generation, RAG, is

00:26:44.289 --> 00:26:47.829
a technique that you upload a lot of text or

00:26:47.829 --> 00:26:51.349
files or resources and say, just look at this

00:26:51.349 --> 00:26:54.650
and nothing else. Look at this and answer from

00:26:54.650 --> 00:26:58.430
it. And that'll give us a lot of accuracy, but

00:26:58.430 --> 00:27:01.329
not flexibility. So it's a balance between flexibility

00:27:01.329 --> 00:27:04.589
and accuracy. So I think that's a good way to

00:27:04.589 --> 00:27:09.049
approach it for smaller companies that are wanting

00:27:09.049 --> 00:27:13.660
to build niche. tools that are solving real problems.

00:27:13.880 --> 00:27:19.460
I honestly think that's where we can actually

00:27:19.460 --> 00:27:26.359
make real impact from AI. AI models, solving

00:27:26.359 --> 00:27:28.759
real problems. This is really cool. This is like

00:27:28.759 --> 00:27:31.519
the foundational models are like the internet.

00:27:31.619 --> 00:27:34.819
It's everything for everybody. But really, you

00:27:34.819 --> 00:27:38.000
need to build apps on top of it to solve specific

00:27:38.000 --> 00:27:40.279
problems. And that's where I think there's going

00:27:40.279 --> 00:27:45.079
to be a lot of startups come up that will solve

00:27:45.079 --> 00:27:48.180
specific problems. Do you think the foundational

00:27:48.180 --> 00:27:51.099
models at some point in the future will kind

00:27:51.099 --> 00:27:54.130
of... attach on to the idea you're talking about

00:27:54.130 --> 00:27:57.369
and instead of just trying to answer a question

00:27:57.369 --> 00:28:02.190
over the entire soup of the online intellect

00:28:02.190 --> 00:28:05.329
right they're going to start grabbing up all

00:28:05.329 --> 00:28:08.730
these niche models that are built very specifically

00:28:08.730 --> 00:28:12.430
but are very accurate and very smart and then

00:28:12.430 --> 00:28:15.470
stitch those together in order to go back and

00:28:15.470 --> 00:28:19.269
just do what they attempted to in the beginning,

00:28:19.490 --> 00:28:23.269
you know, one ring to serve them all, right?

00:28:23.430 --> 00:28:26.309
But it's made up of all these niche models that

00:28:26.309 --> 00:28:29.369
have proven themselves individually. And then

00:28:29.369 --> 00:28:32.109
maybe once, you know, those can all be put together,

00:28:32.150 --> 00:28:36.970
it wins, right? Maybe that's how we solve the

00:28:36.970 --> 00:28:40.420
hallucination issue. And again. Hey, man, I am

00:28:40.420 --> 00:28:43.799
way outside my area of expertise. I'm a political

00:28:43.799 --> 00:28:46.680
scientist here, so I'm just pretending to know

00:28:46.680 --> 00:28:50.420
what I'm talking about. But I think you're right.

00:28:50.539 --> 00:28:54.319
It's what's the models best tuned for, right?

00:28:54.460 --> 00:28:58.859
And are the computers smart enough, strong enough,

00:28:58.940 --> 00:29:00.859
have enough power, right? Because we've seen,

00:29:00.980 --> 00:29:04.059
I think, even OpenAI talked about whenever they...

00:29:04.380 --> 00:29:07.000
They pushed out this new image generator and

00:29:07.000 --> 00:29:09.380
everyone was making the cartoons and all this

00:29:09.380 --> 00:29:12.619
stuff. I think Altman came out and said their

00:29:12.619 --> 00:29:15.680
processors were literally melting in the machines

00:29:15.680 --> 00:29:19.000
and they had to turn it all off, right? So, you

00:29:19.000 --> 00:29:22.900
know, maybe the functionality that we're building

00:29:22.900 --> 00:29:26.259
into some of these models is... far ahead currently

00:29:26.259 --> 00:29:29.279
of a lot of the hardware that we've even got

00:29:29.279 --> 00:29:31.420
that we're trying to run it over, right? So it

00:29:31.420 --> 00:29:35.539
seems like it's a race on both, right? The hardware

00:29:35.539 --> 00:29:38.859
that we're building to run it and kind of the

00:29:38.859 --> 00:29:41.579
software that we're developing that's getting

00:29:41.579 --> 00:29:44.819
smarter and smarter. Yeah, it's definitely both

00:29:44.819 --> 00:29:52.299
sides. You mentioned about, hey, R is, say, for

00:29:52.299 --> 00:29:54.900
example, OpenAI going to build all of these little

00:29:54.900 --> 00:29:58.980
niche models as well. I was thinking about it,

00:29:59.059 --> 00:30:05.180
and I think we have an analogy to this in the

00:30:05.180 --> 00:30:09.440
mobile era, right? Apple and Google could have

00:30:09.440 --> 00:30:12.180
built all the apps. They knew how to do it, but

00:30:12.180 --> 00:30:17.440
it wasn't in their... I think it wasn't their

00:30:17.440 --> 00:30:21.690
expertise to... build all these apps, Uber and

00:30:21.690 --> 00:30:26.410
even LegalShield app. So we are the experts in

00:30:26.410 --> 00:30:30.589
LegalShield's use case. So we build an app for

00:30:30.589 --> 00:30:34.990
it versus Apple doing a half good job at it.

00:30:35.269 --> 00:30:38.589
So I think that's where I think there will be

00:30:38.589 --> 00:30:42.450
a lot of startups that come in and build AI models

00:30:42.450 --> 00:30:46.869
on top of the foundational models. And then OpenAI

00:30:46.869 --> 00:30:49.559
can... Take a 30 % cut like Apple is doing today,

00:30:49.619 --> 00:30:52.299
and then that's how they win, and that's how

00:30:52.299 --> 00:30:55.480
the others win. Or is that how someone beats

00:30:55.480 --> 00:30:59.339
OpenAI or Grok or Gemini or something, right?

00:30:59.440 --> 00:31:02.059
Somebody with really deep pockets comes in and

00:31:02.059 --> 00:31:04.680
just starts buying up all these niche models,

00:31:05.000 --> 00:31:08.519
puts them together, and now all of a sudden I've

00:31:08.519 --> 00:31:12.480
got the super AI, right? Yeah. yeah there are

00:31:12.480 --> 00:31:15.559
super apps now wechat and china and they kind

00:31:15.559 --> 00:31:19.440
of do everything all all at once so uh it is

00:31:19.440 --> 00:31:23.119
possible so again it's uh and i wonder did wechat

00:31:23.119 --> 00:31:25.640
build it all themselves or did they just start

00:31:25.640 --> 00:31:28.220
buying smaller companies and you know piecing

00:31:28.220 --> 00:31:30.960
them together that'd be interesting yeah i don't

00:31:30.960 --> 00:31:34.200
know so it's a good good question to ask i think

00:31:34.200 --> 00:31:39.880
yeah tencent may own i don't know Don't they

00:31:39.880 --> 00:31:46.920
own TikTok? Yeah. That's the same company, I

00:31:46.920 --> 00:31:50.859
think. It's super interesting. We don't know

00:31:50.859 --> 00:31:56.579
what's going to happen, but it's fun. It is fun.

00:31:56.740 --> 00:31:58.619
Again, it's kind of back to what we were talking

00:31:58.619 --> 00:32:02.680
about earlier. Again, LegalShield, the company

00:32:02.680 --> 00:32:06.200
that we're working for, we've got 53 years of

00:32:06.200 --> 00:32:11.099
data. right and you know not only until about

00:32:11.099 --> 00:32:14.940
10 or 15 years ago did we ever even get serious

00:32:14.940 --> 00:32:18.500
about mining that information what's that data

00:32:18.500 --> 00:32:21.730
telling us How do we run the business from a,

00:32:21.730 --> 00:32:25.509
you know, data direction instead of just a feeling

00:32:25.509 --> 00:32:29.289
or a thought, right? Because again, and I'm not

00:32:29.289 --> 00:32:33.529
knocking either method over the other because,

00:32:33.670 --> 00:32:37.250
you know, up until 2010 -ish, right, we were

00:32:37.250 --> 00:32:40.730
founder -led, founder -driven, right? The guy

00:32:40.730 --> 00:32:44.369
that started the company in 1973, he was the

00:32:44.369 --> 00:32:48.960
CEO, he was the leader. charismatic leader. He

00:32:48.960 --> 00:32:53.480
was driving the direction of the company. And

00:32:53.480 --> 00:32:56.059
again, when you're in something from the very,

00:32:56.140 --> 00:33:01.160
very, very beginning, you might miss some nuances

00:33:01.160 --> 00:33:05.859
of the data, but you've built something. It works.

00:33:05.880 --> 00:33:09.039
You've been successful. So I think intuitively,

00:33:09.240 --> 00:33:12.670
you kind of know. what's happening in the business,

00:33:13.170 --> 00:33:16.710
how to move, how to make decisions. And it was

00:33:16.710 --> 00:33:21.130
really only after he stepped down did we really

00:33:21.130 --> 00:33:24.509
begin diving into all the data that we had. Again,

00:33:24.750 --> 00:33:28.430
you know, 50 plus years of information. And again,

00:33:28.529 --> 00:33:31.430
and we had all that data, right? Because again,

00:33:31.589 --> 00:33:35.589
the software and the systems that we used to

00:33:35.589 --> 00:33:38.089
run the company were all, you know, in -house

00:33:38.089 --> 00:33:41.559
built. Right. So nothing was outsourced. All

00:33:41.559 --> 00:33:44.900
the data maintained on site. And at that point,

00:33:44.920 --> 00:33:47.980
we kept everything right. Regardless of whether

00:33:47.980 --> 00:33:50.960
we used it or thought we might use it or if it

00:33:50.960 --> 00:33:53.460
was good data or if it was bad data. I mean,

00:33:53.480 --> 00:33:57.039
we kept it all. So it was all there. So we really

00:33:57.039 --> 00:34:00.579
had an interesting playground. And probably,

00:34:00.720 --> 00:34:04.059
you know, a little bit before MIDI, you started,

00:34:04.220 --> 00:34:07.779
we started going down this direction. But really,

00:34:07.819 --> 00:34:11.289
when you. When you joined the team, I mean, we

00:34:11.289 --> 00:34:16.250
kind of exploded in the intelligence, right?

00:34:16.429 --> 00:34:19.309
And I like to say we create actionable intelligence,

00:34:19.670 --> 00:34:23.449
right? We're not an analytics team that just

00:34:23.449 --> 00:34:27.110
creates spreadsheets and just blasts out data

00:34:27.110 --> 00:34:30.889
to the entire company, right? One, who cares?

00:34:31.090 --> 00:34:33.829
And two, we ain't got time to read spreadsheets,

00:34:33.829 --> 00:34:38.340
but what's actionable? Do some analysis, find

00:34:38.340 --> 00:34:40.579
your data, have the data to back up what you're

00:34:40.579 --> 00:34:44.460
saying. But the power, right, or the value is

00:34:44.460 --> 00:34:48.159
in the analysis. And how can we apply this to

00:34:48.159 --> 00:34:51.440
the goals of the company, right, or the direction

00:34:51.440 --> 00:34:56.480
that we're going in? And, you know, not to pat

00:34:56.480 --> 00:34:59.260
myself and you on the back, but, you know, I

00:34:59.260 --> 00:35:01.320
think we got really, really good at that. And

00:35:01.320 --> 00:35:04.960
we learned a lot of things. specifically around

00:35:04.960 --> 00:35:08.760
the subscription model business and, you know,

00:35:08.780 --> 00:35:11.820
retention of those subscriptions. Because, you

00:35:11.820 --> 00:35:13.659
know, at the end of the day, if you're paying,

00:35:13.820 --> 00:35:18.019
you know, $400 to acquire a new member and they're

00:35:18.019 --> 00:35:20.860
paying a $30 monthly subscription, you know,

00:35:20.880 --> 00:35:24.440
do the math, right? They have to stay around

00:35:24.440 --> 00:35:27.300
quite a while before you begin to make, you know,

00:35:27.320 --> 00:35:31.800
any profit or any real revenue on that person.

00:35:32.920 --> 00:35:35.440
Membership retention is extremely important.

00:35:35.760 --> 00:35:39.219
And you've been, I think, one of the pioneers

00:35:39.219 --> 00:35:41.559
on, you know, how we've gotten as good as we've

00:35:41.559 --> 00:35:45.300
gotten on, you know, retaining members, lowering

00:35:45.300 --> 00:35:49.599
churn, increasing member satisfaction. And again,

00:35:49.699 --> 00:35:52.119
all of that's come from data. And as we've gotten

00:35:52.119 --> 00:35:57.000
smarter, you've gotten extremely versed and really

00:35:57.000 --> 00:36:01.400
good at building these AI models, these chat.

00:36:01.869 --> 00:36:04.809
chat models that are pulling out a lot of these

00:36:04.809 --> 00:36:08.789
insights that, you know, I never would have gotten

00:36:08.789 --> 00:36:11.829
to or it would have taken me three months of

00:36:11.829 --> 00:36:14.849
just only working on that data to even find half

00:36:14.849 --> 00:36:17.030
of what, you know, some of the new insights that

00:36:17.030 --> 00:36:19.989
we've been churning out. So, you know, kind of

00:36:19.989 --> 00:36:23.469
taking it from the very, very top, you know.

00:36:24.170 --> 00:36:26.769
artificial general intelligence, open AI, just

00:36:26.769 --> 00:36:29.210
down to what we're doing every single day. You

00:36:29.210 --> 00:36:31.469
know, we're head and shoulders above where we

00:36:31.469 --> 00:36:34.750
would have been if I still just had a team of

00:36:34.750 --> 00:36:38.570
analysts, you know, chiseling data out of an

00:36:38.570 --> 00:36:41.489
Excel spreadsheet. Yeah, appreciate that, Ryan.

00:36:41.570 --> 00:36:44.530
Yeah, definitely. It's a really strong team,

00:36:44.530 --> 00:36:47.730
right? Like shout out to Kevin Hardcastle and

00:36:47.730 --> 00:36:52.510
Sam Harris, OGs of retention and yeah. Vanessa

00:36:52.510 --> 00:36:57.489
Madden -Glass, all the good people. It's really,

00:36:57.750 --> 00:37:01.030
like you said, I think it comes down to data

00:37:01.030 --> 00:37:04.530
and technology in service of business to provide

00:37:04.530 --> 00:37:08.329
actionable intelligence versus just, there's

00:37:08.329 --> 00:37:12.409
a lot of teams that get bogged down in building

00:37:12.409 --> 00:37:18.849
the best. technology for the use case the the

00:37:18.849 --> 00:37:23.050
technology is so complicated and so fascinating

00:37:23.050 --> 00:37:27.829
that people lose sight of the actual use case

00:37:27.829 --> 00:37:31.090
for it yeah and hey i think you're hitting on

00:37:31.090 --> 00:37:36.010
an extremely important point that i don't hear

00:37:36.010 --> 00:37:38.889
talked about enough and i'd talk i try to talk

00:37:38.889 --> 00:37:42.039
about it every chance i get right and it's exactly

00:37:42.039 --> 00:37:44.420
what you're talking about that marriage between

00:37:44.420 --> 00:37:50.760
technology and the business right when i'm out

00:37:50.760 --> 00:37:54.119
looking for a new analyst or a team member or

00:37:54.119 --> 00:37:58.039
i've got an opening on the team you know my inbox

00:37:58.039 --> 00:38:03.199
is flooded with you know data engineers statisticians

00:38:03.199 --> 00:38:06.679
you know i like the code i'm all about technology

00:38:07.639 --> 00:38:09.940
Or I'll get a whole list of folks that, you know,

00:38:09.960 --> 00:38:13.739
I've got an MBA. I love the business, business

00:38:13.739 --> 00:38:17.440
administration. You know, I know front and back

00:38:17.440 --> 00:38:20.300
the textbook on, you know, how to run a business,

00:38:20.440 --> 00:38:23.159
how to do meetings, how to project manage all

00:38:23.159 --> 00:38:28.320
this stuff. But really, I call them the unicorns

00:38:28.320 --> 00:38:33.780
of, you know, analysts is how can we find someone

00:38:33.780 --> 00:38:37.420
that's, you know, probably not. the top or the

00:38:37.420 --> 00:38:40.679
best at both, but can do both, understand both

00:38:40.679 --> 00:38:43.340
and, you know, like both, right? Because again,

00:38:43.500 --> 00:38:48.719
for us to do our job well, we've got to be versed

00:38:48.719 --> 00:38:51.079
in the technology. We've got to understand the

00:38:51.079 --> 00:38:53.199
data. We've got to know what it's telling us.

00:38:53.460 --> 00:38:56.340
And we also have to understand the business objectives,

00:38:56.619 --> 00:38:59.300
what it is that we're trying to accomplish. You

00:38:59.300 --> 00:39:01.480
know, what are the sales targets? What are the

00:39:01.480 --> 00:39:05.320
churn targets? What are their cancellation targets?

00:39:06.000 --> 00:39:09.579
And how that data speaks to us in a way that

00:39:09.579 --> 00:39:12.400
we can implement what we learn into the business

00:39:12.400 --> 00:39:16.440
to meet the objectives. That's kind of the person

00:39:16.440 --> 00:39:19.659
that I don't see that's being trained out there,

00:39:19.780 --> 00:39:21.719
right? I mean, someone can go to business school

00:39:21.719 --> 00:39:24.559
and then maybe, you know, get a computer science

00:39:24.559 --> 00:39:30.619
degree. But, you know, there's not a good way,

00:39:30.659 --> 00:39:33.980
at least today, that I see in, you know, education.

00:39:34.920 --> 00:39:37.599
that's creating that person that can do both.

00:39:37.920 --> 00:39:40.519
And again, I think that's what a lot of companies

00:39:40.519 --> 00:39:43.179
have trouble with or fail because then we end

00:39:43.179 --> 00:39:45.420
up siloed ourselves, right? We've got the tech

00:39:45.420 --> 00:39:48.800
geeks and nerds over here and we've got the business

00:39:48.800 --> 00:39:52.239
MBAs with their ties over here. And these suckers

00:39:52.239 --> 00:39:54.159
can't even talk to each other, right? Or they're

00:39:54.159 --> 00:39:57.219
talking past each other. There's like, oh, they're...

00:39:57.760 --> 00:40:00.079
I can't work with them. They don't understand

00:40:00.079 --> 00:40:01.800
the technology, right? And then the business

00:40:01.800 --> 00:40:04.400
guys go, they don't even know what it is that

00:40:04.400 --> 00:40:06.679
we're in business for. They're talking about,

00:40:06.739 --> 00:40:10.880
you know, pipelines and moving data and blah,

00:40:11.000 --> 00:40:13.039
blah, blah, blah, right? But again, you've got

00:40:13.039 --> 00:40:15.800
to find, if you can't find enough of those people

00:40:15.800 --> 00:40:18.500
to build out the team, then you need to find

00:40:18.500 --> 00:40:20.860
a handful of really good, right, to help bridge

00:40:20.860 --> 00:40:24.559
that divide. And I think that's where. That's

00:40:24.559 --> 00:40:27.340
where you create a really high -performing, powerful

00:40:27.340 --> 00:40:32.280
team is when you have a core group of folks that

00:40:32.280 --> 00:40:36.559
understand technology, data, and how the business

00:40:36.559 --> 00:40:41.460
is run. And those people are hard to find, right?

00:40:41.539 --> 00:40:44.840
And a lot of times, I think we kind of err on

00:40:44.840 --> 00:40:48.219
building or creating that person, right? So,

00:40:48.219 --> 00:40:51.179
I mean, I think when you're interviewing or talking

00:40:51.179 --> 00:40:54.760
to a person, you can kind of see. Are they leaning

00:40:54.760 --> 00:40:58.360
tech data? Are they, you know, a business acumen

00:40:58.360 --> 00:41:04.539
type person? And I found it's probably, I mean,

00:41:04.539 --> 00:41:06.800
as long as the person's willing, I think it's

00:41:06.800 --> 00:41:10.860
easy to like teach the opposite side that they're

00:41:10.860 --> 00:41:15.400
not good at if they're willing to do it, right?

00:41:15.500 --> 00:41:18.360
Not everyone's willing to do it, right? I've

00:41:18.360 --> 00:41:21.650
seen data engineers that I... I don't think they

00:41:21.650 --> 00:41:24.230
care nor want to know anything about the business,

00:41:24.429 --> 00:41:26.289
right? And the same way with business people.

00:41:26.469 --> 00:41:30.010
I don't care about the data or any of the technology.

00:41:30.610 --> 00:41:33.750
I just got to meet my sales quota, right? So

00:41:33.750 --> 00:41:38.570
again, it's a special person that can do both

00:41:38.570 --> 00:41:42.170
of those things and turn the data into value

00:41:42.170 --> 00:41:47.030
for the business. Yeah, 100%. I think you got

00:41:47.030 --> 00:41:50.619
to get it all together. Honestly, I think the

00:41:50.619 --> 00:41:53.539
technology piece is being solved by technology

00:41:53.539 --> 00:41:58.960
a lot of times. I could chat with ChatGPT and

00:41:58.960 --> 00:42:03.679
understand and figure out technical stuff that

00:42:03.679 --> 00:42:07.360
I'm not super good at. But I think as long as

00:42:07.360 --> 00:42:11.719
the person is willing to go find out the answer,

00:42:11.920 --> 00:42:15.460
I think we can get there. But yeah, it definitely

00:42:15.460 --> 00:42:19.360
has to be a balance. So that just brings up another

00:42:19.360 --> 00:42:21.739
point. And I've got a question for you. And this

00:42:21.739 --> 00:42:26.079
is probably heresy for me to even mention this,

00:42:26.320 --> 00:42:29.739
right? But one of the things that I kind of run

00:42:29.739 --> 00:42:34.039
into is, you know, technology is great. All these

00:42:34.039 --> 00:42:37.760
softwares and services and systems that, you

00:42:37.760 --> 00:42:42.039
know, generate dashboards and data and reports.

00:42:42.219 --> 00:42:45.579
So, you know, we use Tableau a lot. Great tool.

00:42:46.360 --> 00:42:49.519
Wonderful. Saves my guys a whole lot of time.

00:42:49.659 --> 00:42:53.980
We're not building Excel spreadsheets and dashboards

00:42:53.980 --> 00:42:57.840
in Excel. It's automated. We've built the logic.

00:42:58.219 --> 00:43:02.239
Updates daily. Beautiful. The answers are always

00:43:02.239 --> 00:43:11.300
there. But then at some point, do you lose the

00:43:11.300 --> 00:43:16.039
knowledge and the detailed understanding of what...

00:43:16.230 --> 00:43:19.230
even that data is, right? And where is that data

00:43:19.230 --> 00:43:21.690
coming from that's generating that dashboard?

00:43:23.070 --> 00:43:26.869
And if something's a little bit off, how long

00:43:26.869 --> 00:43:29.449
do you leave it kind of monitoring this dashboard

00:43:29.449 --> 00:43:32.989
before you decide there is a problem to where

00:43:32.989 --> 00:43:36.230
if you were like intimately in the data on a

00:43:36.230 --> 00:43:39.690
daily basis, you would know like immediately

00:43:39.690 --> 00:43:43.489
that something was off. So I struggle with this

00:43:43.489 --> 00:43:47.670
problem, right? Because Our output is so much

00:43:47.670 --> 00:43:52.150
better. The data is almost immediate. Everyone

00:43:52.150 --> 00:43:58.289
can see it. People can self -serve. My guys can

00:43:58.289 --> 00:44:01.510
probably be working on bigger and better problems

00:44:01.510 --> 00:44:04.090
as opposed to creating some of these reports.

00:44:04.789 --> 00:44:10.030
But then I also worry, and I see it, myself included,

00:44:10.349 --> 00:44:15.380
I'm losing a lot of the fundamental. intelligence

00:44:15.380 --> 00:44:19.380
and education of what some of that data is. So

00:44:19.380 --> 00:44:23.860
I guess it's a trade -off, but at what point

00:44:23.860 --> 00:44:29.000
are we so far removed from the base data that

00:44:29.000 --> 00:44:32.840
no one can explain the business anymore? I do

00:44:32.840 --> 00:44:37.320
worry about that. Yeah, no, 100%. It's definitely

00:44:37.320 --> 00:44:40.599
a trade -off and it's like, oh, nobody on the

00:44:40.599 --> 00:44:46.630
team knows. If something breaks, so we've kind

00:44:46.630 --> 00:44:51.789
of built on top of foundation. If nobody takes

00:44:51.789 --> 00:44:55.210
care of the foundation, maybe it's rotting and

00:44:55.210 --> 00:44:58.250
nobody is looking at it and it's becoming a,

00:44:58.250 --> 00:45:02.550
you know, building. Or, and it's not even, maybe

00:45:02.550 --> 00:45:05.769
no one is looking at it, right? Three or four

00:45:05.769 --> 00:45:10.230
people ago have built it. So I don't even understand

00:45:10.230 --> 00:45:13.480
enough to know that. It is rotting, right? Or

00:45:13.480 --> 00:45:16.659
there is a hole. I don't even understand the

00:45:16.659 --> 00:45:19.239
logic that was used to build this because I don't

00:45:19.239 --> 00:45:21.679
understand the raw data because the guy that

00:45:21.679 --> 00:45:26.900
was in my seat six iterations ago built it. I'm

00:45:26.900 --> 00:45:29.239
just making sure that I get a successful run

00:45:29.239 --> 00:45:32.719
every morning. I don't know what's running anymore.

00:45:33.860 --> 00:45:38.500
That does worry me. It's the same thing with...

00:45:39.119 --> 00:45:41.860
computers and software, right? So you have the

00:45:41.860 --> 00:45:45.059
assembly language, which is what really computers

00:45:45.059 --> 00:45:48.420
understand. And then you have C, which is a higher

00:45:48.420 --> 00:45:52.219
level language, which kind of makes it easier

00:45:52.219 --> 00:45:55.880
for humans to understand, but it translates it

00:45:55.880 --> 00:45:58.239
to assembly language. And then you have all these

00:45:58.239 --> 00:46:00.619
other languages, Python, Java, all those things.

00:46:00.760 --> 00:46:04.280
Now we have AI writing code, people are vibe

00:46:04.280 --> 00:46:08.280
coding. So they just tell AI what to do, but

00:46:09.710 --> 00:46:13.530
The thing is, it is still going down into assembly

00:46:13.530 --> 00:46:16.829
language. It's getting converted into ones and

00:46:16.829 --> 00:46:19.610
zeros, and that's how really computer understands

00:46:19.610 --> 00:46:23.409
it. But something fundamentally breaks, nobody,

00:46:23.650 --> 00:46:27.070
10 levels above, nobody knows how to fix it.

00:46:27.889 --> 00:46:33.500
I think, I feel like... Companies need to be

00:46:33.500 --> 00:46:37.500
aware of it and probably have one or two people

00:46:37.500 --> 00:46:42.159
who are experts in that, value them and not just

00:46:42.159 --> 00:46:46.059
go after the shiny things. And like you said,

00:46:46.139 --> 00:46:48.500
the new things that we can build on top of the

00:46:48.500 --> 00:46:51.440
foundational things, that's cool and important

00:46:51.440 --> 00:46:57.699
to be more efficient, but really don't underestimate

00:46:57.699 --> 00:47:01.829
the value of the people who actually the one

00:47:01.829 --> 00:47:04.670
or two people who actually are experts in the

00:47:04.670 --> 00:47:10.550
ground level. Yeah, no, I call that your institutional

00:47:10.550 --> 00:47:13.530
knowledge base, right? You've really got to protect

00:47:13.530 --> 00:47:18.349
that core. And again, I always lean back to folks

00:47:18.349 --> 00:47:21.030
instead of the technology, right? It's great

00:47:21.030 --> 00:47:24.289
that the tech stack's working and it's built

00:47:24.289 --> 00:47:27.550
all these layers and it's wonderful. But again,

00:47:27.670 --> 00:47:30.550
I think you need to protect. a core group of

00:47:30.550 --> 00:47:34.250
folks that fully understand that. Maybe they've

00:47:34.250 --> 00:47:36.889
been here since day one and they built the first

00:47:36.889 --> 00:47:39.849
line of code on which everything else was built.

00:47:40.829 --> 00:47:44.150
I think those people are invaluable to an organization

00:47:44.150 --> 00:47:48.269
because they're the only people that's got that

00:47:48.269 --> 00:47:51.530
chain of custody all the way back to the beginning

00:47:51.530 --> 00:47:54.929
and fully understand what it is that the machines

00:47:54.929 --> 00:47:58.760
are doing. They're the Hercules that are holding

00:47:58.760 --> 00:48:02.840
everything together. I mean, the other side of...

00:48:02.840 --> 00:48:06.320
Because again, just like we talked about earlier,

00:48:06.599 --> 00:48:10.440
if and when we turn all this over to AI, the

00:48:10.440 --> 00:48:13.219
AI is confident, right? Whether it's right or

00:48:13.219 --> 00:48:16.219
wrong, it's going to tell you exactly what it

00:48:16.219 --> 00:48:20.360
thinks and believes. We're going to read it.

00:48:20.420 --> 00:48:22.300
We're going to start repeating it and telling

00:48:22.300 --> 00:48:26.630
everyone else. And then... I mean, it gets down

00:48:26.630 --> 00:48:28.869
to a point that it's like truth is relative,

00:48:29.030 --> 00:48:32.349
right? I mean, whatever most of the people believe,

00:48:32.610 --> 00:48:35.730
that's now the truth, right? Right or wrong,

00:48:35.929 --> 00:48:40.469
that's the way it is. And that's, it's dangerous.

00:48:41.670 --> 00:48:46.550
It is dangerous. And it's, yeah. I think that

00:48:46.550 --> 00:48:50.369
the other side of the argument is, hey, have

00:48:50.369 --> 00:48:55.670
really good documentation for everything. is

00:48:55.670 --> 00:48:58.630
the other side, but it's hard to document it.

00:48:58.949 --> 00:49:02.769
Have you worked or been in a company or seen

00:49:02.769 --> 00:49:05.429
any documentation that you considered really

00:49:05.429 --> 00:49:09.070
good documentation? I've seen good documentation.

00:49:10.929 --> 00:49:13.829
I don't know that I've been in a situation where

00:49:13.829 --> 00:49:17.050
I'm like, oh my goodness, this documents all

00:49:17.050 --> 00:49:21.190
the way down to day zero. This is wonderful.

00:49:22.800 --> 00:49:25.820
I completely agree. Documentation is good. It's

00:49:25.820 --> 00:49:29.500
hard to do that correctly. Yeah, no, 100%. It's

00:49:29.500 --> 00:49:33.260
hard to scale that culture of documentation.

00:49:33.320 --> 00:49:37.280
And even then, like a person at level 10, it's

00:49:37.280 --> 00:49:39.940
really hard for them to really, even if the documentation

00:49:39.940 --> 00:49:44.539
is present, it's hard for them to really understand,

00:49:44.699 --> 00:49:48.340
read the document and get to the core of the

00:49:48.340 --> 00:49:51.900
problems. Sure, right? So it's like... the documentation's

00:49:51.900 --> 00:49:54.679
written all encompassing and to know it all,

00:49:54.820 --> 00:49:56.880
but then how do you write it in a way that a

00:49:56.880 --> 00:50:00.599
brand new person can understand, right? I have

00:50:00.599 --> 00:50:04.280
this issue daily again, because I've been with

00:50:04.280 --> 00:50:08.099
this company for a long time and I catch myself

00:50:08.099 --> 00:50:11.420
speaking to brand new to where it immediately

00:50:11.420 --> 00:50:14.300
clicks in my brain is like, we're not even speaking

00:50:14.300 --> 00:50:18.849
the same language. I think, I mean, I think I

00:50:18.849 --> 00:50:20.849
know exactly what I'm talking about. And, you

00:50:20.849 --> 00:50:24.389
know, to an extent I do. They think they know

00:50:24.389 --> 00:50:26.909
exactly what they're talking about. And to a

00:50:26.909 --> 00:50:28.869
different extent, they know what they're talking

00:50:28.869 --> 00:50:32.030
about. But we're not communicating because the

00:50:32.030 --> 00:50:34.969
language is so different. I know things that

00:50:34.969 --> 00:50:37.809
they don't. They're bringing in new things that

00:50:37.809 --> 00:50:41.210
I've probably never, never heard of. So, again,

00:50:41.389 --> 00:50:43.869
even even if you've got the best documentation.

00:50:45.469 --> 00:50:48.829
Who? who is the audience and you know is you

00:50:48.829 --> 00:50:51.409
have to have different level you have to have

00:50:51.409 --> 00:50:54.489
different trends trend translation layers depending

00:50:54.489 --> 00:50:57.329
on who it is that's even look looking at it right

00:50:57.329 --> 00:51:01.369
yeah i mean i think the i think you mentioned

00:51:01.369 --> 00:51:08.530
it like the core of the uh solution if you will

00:51:08.530 --> 00:51:15.090
is to protect the folks that are they they're

00:51:15.090 --> 00:51:18.210
from day zero and understand everything it's

00:51:18.210 --> 00:51:21.090
it's hard to everybody is looking at the new

00:51:21.090 --> 00:51:27.170
cool things that those uh core people might feel

00:51:27.170 --> 00:51:30.070
undermined but yeah it's as leaders it's our

00:51:30.070 --> 00:51:32.949
job to make sure that they're protected and taken

00:51:32.949 --> 00:51:36.570
care of hey but you know what The brand new person

00:51:36.570 --> 00:51:40.489
may come in and know the best way to change everything,

00:51:40.710 --> 00:51:43.190
right? We may have been doing it wrong all along,

00:51:43.409 --> 00:51:46.769
and this new person's got the greatest and best

00:51:46.769 --> 00:51:51.409
idea, right? But until or unless they understand

00:51:51.409 --> 00:51:54.630
everything that came before them, I don't think

00:51:54.630 --> 00:51:57.429
it's possible for them to improve upon a system

00:51:57.429 --> 00:52:02.070
that they don't even understand, right? Theoretically,

00:52:02.090 --> 00:52:05.769
high level, you may have them. the best new programming

00:52:05.769 --> 00:52:09.429
language and i i can build this app and lay it

00:52:09.429 --> 00:52:12.409
on top and it's just going to be beautiful but

00:52:12.409 --> 00:52:17.170
i don't i do not think it can work unless there's

00:52:17.170 --> 00:52:20.449
humility on both sides right the old person and

00:52:20.449 --> 00:52:24.130
the new the old person's got to explain what's

00:52:24.130 --> 00:52:26.349
went on and understand that there probably are

00:52:26.349 --> 00:52:29.070
better ways to do it and the new person's got

00:52:29.070 --> 00:52:32.099
to understand that hey this This may not be the

00:52:32.099 --> 00:52:34.079
best way, but it's been working for a really

00:52:34.079 --> 00:52:37.599
long time. And the more I understand, the better

00:52:37.599 --> 00:52:41.980
I can improve it, right? And a lot of times you

00:52:41.980 --> 00:52:44.519
just have an ego clash and nothing gets done

00:52:44.519 --> 00:52:47.360
at all, right? Or things get done and they just

00:52:47.360 --> 00:52:50.619
start breaking because no one took the time to

00:52:50.619 --> 00:52:54.320
explain to the old and the new guard. There was

00:52:54.320 --> 00:52:56.639
no meeting other minds in order to create something

00:52:56.639 --> 00:53:02.530
better. Yeah, it's always the... I know the solution

00:53:02.530 --> 00:53:07.369
mindset versus know -it -all mindset versus learn

00:53:07.369 --> 00:53:16.510
-it -all mindset, right? It's hard. Yeah, I mean,

00:53:16.550 --> 00:53:22.909
we can talk about this for forever, right? So

00:53:22.909 --> 00:53:29.710
let's go back to kind of... Data at LegalShield

00:53:29.710 --> 00:53:34.250
and you've pioneered the system of closed loop

00:53:34.250 --> 00:53:40.269
infrastructure and data that leads into cool

00:53:40.269 --> 00:53:44.269
stuff like Consumer Stress Index. And let's dig

00:53:44.269 --> 00:53:50.090
into that. Sure. So kind of talk about the Consumer

00:53:50.090 --> 00:53:54.150
Stress Legal Index or the CSLI, right? So on

00:53:54.150 --> 00:54:00.420
any given month. We service 150 ,000 requests

00:54:00.420 --> 00:54:03.360
for legal service, right? Again, that ebbs and

00:54:03.360 --> 00:54:08.920
flows due to the month. But on average, we get

00:54:08.920 --> 00:54:12.880
150 ,000 requests for service a month. And we've

00:54:12.880 --> 00:54:15.500
been doing that again. We keep talking about

00:54:15.500 --> 00:54:19.420
it a long time, 50 plus years. So then we got

00:54:19.420 --> 00:54:23.599
to thinking, again, there's got to be some truth

00:54:23.599 --> 00:54:27.510
that lives in this data, right? I mean, back

00:54:27.510 --> 00:54:32.429
to January 2002, we've got 35 million individual

00:54:32.429 --> 00:54:37.650
requests for legal service. So it's like, what

00:54:37.650 --> 00:54:40.610
can that tell us? First, we started looking at

00:54:40.610 --> 00:54:43.190
it in terms of our business, right? What can

00:54:43.190 --> 00:54:45.969
we learn from this data to improve our service,

00:54:46.289 --> 00:54:50.030
improve sales rates, improve membership retention?

00:54:50.429 --> 00:54:53.230
And we got a lot of really good information out

00:54:53.230 --> 00:54:56.630
of that, right? What age are the people? What

00:54:56.630 --> 00:54:59.769
legal services are they calling about? What areas

00:54:59.769 --> 00:55:03.929
of law can we then train our attorneys and lawyers

00:55:03.929 --> 00:55:09.769
to talk to a customer where they're at, right?

00:55:09.849 --> 00:55:12.670
You probably talk to a 20 -year -old about a

00:55:12.670 --> 00:55:16.170
traffic ticket different than you talk to a 75

00:55:16.170 --> 00:55:18.710
-year -old about updating their wheel, right?

00:55:18.789 --> 00:55:21.980
And again, We like to say they don't teach customer

00:55:21.980 --> 00:55:27.260
service in law school. And it's true. And again

00:55:27.260 --> 00:55:30.280
and again and again, that's what we hear from

00:55:30.280 --> 00:55:35.039
our customers is the lawyer was rude. They didn't

00:55:35.039 --> 00:55:38.260
listen. You know, they were short with me. They

00:55:38.260 --> 00:55:41.800
didn't take my issues seriously. Rarely do we

00:55:41.800 --> 00:55:44.900
get, you know, complaints from our customers

00:55:44.900 --> 00:55:48.070
that. The lawyer didn't know the law. They were

00:55:48.070 --> 00:55:50.989
a terrible lawyer. It's all of these bedside

00:55:50.989 --> 00:55:54.650
manner, customer service type issues. So we've

00:55:54.650 --> 00:56:00.050
really began focusing on how do we improve those?

00:56:00.250 --> 00:56:02.469
And we talked about it a little bit earlier.

00:56:02.909 --> 00:56:09.050
We implemented net promoter scoring, NPS scores.

00:56:09.469 --> 00:56:13.429
So every time any of our customers access legal

00:56:13.429 --> 00:56:17.340
services, They get a survey and we generate a

00:56:17.340 --> 00:56:21.219
net promoter score for that person. This is kind

00:56:21.219 --> 00:56:23.940
of that closed loop feedback system you were

00:56:23.940 --> 00:56:26.900
talking about. And again, as far as I know, it's

00:56:26.900 --> 00:56:30.920
unique in the legal industry and the kind of

00:56:30.920 --> 00:56:33.079
the legal services industry to have this. So

00:56:33.079 --> 00:56:35.940
our customer access is legal services. They get

00:56:35.940 --> 00:56:42.239
their issue resolved or not. They talk to a lawyer.

00:56:42.880 --> 00:56:46.639
We surveyed that member. That survey then generates

00:56:46.639 --> 00:56:50.639
a net promoter score. They can leave comments

00:56:50.639 --> 00:56:53.760
if they like. We then grade that net promoter

00:56:53.760 --> 00:56:56.719
score based upon the strengths and weaknesses

00:56:56.719 --> 00:57:01.179
at a law firm level and even an individual lawyer

00:57:01.179 --> 00:57:06.500
level. We grade individually, right? So every

00:57:06.500 --> 00:57:10.820
lawyer that's working in a month, we... We identify

00:57:10.820 --> 00:57:12.780
what their strengths and weaknesses are, right?

00:57:12.940 --> 00:57:15.340
You know, we applaud them for their strengths.

00:57:15.800 --> 00:57:18.679
Yeah, let's go. And we have a training system

00:57:18.679 --> 00:57:23.659
for those specific weaknesses. Again, not identified

00:57:23.659 --> 00:57:26.960
by the law firm, not identified by their partner,

00:57:27.139 --> 00:57:29.659
not identified by LegalShield where we're just

00:57:29.659 --> 00:57:32.800
picking on people. We're telling them what their

00:57:32.800 --> 00:57:36.699
weaknesses are. as identified by the customers

00:57:36.699 --> 00:57:39.519
or the clients that they've consulted with right

00:57:39.519 --> 00:57:42.900
we're not making any determinations here the

00:57:42.900 --> 00:57:46.559
actual people are saying what they felt and thought

00:57:46.559 --> 00:57:49.619
about their interactions with a with a lawyer

00:57:49.619 --> 00:57:52.880
and we're rolling that back to that to that lawyer

00:57:52.880 --> 00:57:55.539
through you know we're reporting and telling

00:57:55.539 --> 00:58:00.400
them and then we even have a lms system to where

00:58:00.400 --> 00:58:04.500
we create specific trainings right we're not

00:58:04.500 --> 00:58:07.760
buying trainings off the shelf right again based

00:58:07.760 --> 00:58:12.659
on 50 plus years of data we we know the types

00:58:12.659 --> 00:58:16.340
of training content you know length delivery

00:58:16.340 --> 00:58:19.719
method we know those trainings that you know

00:58:19.719 --> 00:58:23.320
create the best outcome when lawyers view them

00:58:23.320 --> 00:58:25.920
right and then again we measure before before

00:58:25.920 --> 00:58:29.239
and after you had all these nps score surveys

00:58:29.239 --> 00:58:33.239
you were you were low in you know listening attentively

00:58:33.239 --> 00:58:36.500
well here's a couple trainings we're going to

00:58:36.500 --> 00:58:39.619
push out to you that's you know designed to design

00:58:39.619 --> 00:58:42.719
for lawyers just like just just like you who've

00:58:42.719 --> 00:58:45.980
taken these before whose scores in those areas

00:58:45.980 --> 00:58:48.940
have have improved we drip out one or two trainings

00:58:48.940 --> 00:58:51.780
to you you take them We're measuring your NPS

00:58:51.780 --> 00:58:54.360
scores after, you know, and, you know, overall,

00:58:54.579 --> 00:58:57.440
right, again, you got some bad eggs out there

00:58:57.440 --> 00:58:59.599
that's probably never going to improve. But again,

00:58:59.760 --> 00:59:02.980
the average overall, we see improvements from

00:59:02.980 --> 00:59:05.760
before the training to after the training. These

00:59:05.760 --> 00:59:09.019
scores go up in these areas. NPS scores go up.

00:59:09.380 --> 00:59:13.059
We know that, you know, the NPS score is a leading

00:59:13.059 --> 00:59:15.659
indicator of future membership retention rates.

00:59:16.329 --> 00:59:19.090
As retention rates goes up, right, the active

00:59:19.090 --> 00:59:22.510
customer base goes up, the law firm gets paid

00:59:22.510 --> 00:59:25.789
more money because it's based upon a capitated

00:59:25.789 --> 00:59:29.750
model. Again, everyone is aligned on, you know,

00:59:29.750 --> 00:59:33.409
where are the weaknesses? What can we do to improve?

00:59:34.190 --> 00:59:38.170
Improve, and we all make more money, right? That's

00:59:38.170 --> 00:59:41.210
really the power, I think, of the closed -loop

00:59:41.210 --> 00:59:46.960
feedback system that we've built. um strictly

00:59:46.960 --> 00:59:49.579
focused on continuous improvement and again at

00:59:49.579 --> 00:59:52.599
the end of the day it's all aligned we all make

00:59:52.599 --> 00:59:56.179
more money in this in this company when everyone

00:59:56.179 --> 01:00:00.019
is looking at the data you know what are the

01:00:00.019 --> 01:00:02.780
actual insights how do we apply it to the business

01:00:02.780 --> 01:00:06.860
and how do we improve yeah i think i think that

01:00:06.860 --> 01:00:09.900
that's the key part right so uh what the what

01:00:10.269 --> 01:00:12.969
the company and you and the teams have nailed

01:00:12.969 --> 01:00:16.909
is the alignments of incentives. I think it's

01:00:16.909 --> 01:00:22.530
an easy proposition. Hey, we've churned through

01:00:22.530 --> 01:00:26.250
the numbers and look what we could do to improve

01:00:26.250 --> 01:00:30.989
your bottom line. Or it's easy just to pick on

01:00:30.989 --> 01:00:33.409
a lawyer and say, you suck. All your members

01:00:33.409 --> 01:00:36.690
hate you. You're terrible. But again, we're kind

01:00:36.690 --> 01:00:39.769
of looking at it from the carrot. version instead

01:00:39.769 --> 01:00:43.230
of the stick right i mean okay you've got you

01:00:43.230 --> 01:00:46.710
know lower lower scores here we have a system

01:00:46.710 --> 01:00:49.570
that's been proven to improve that score right

01:00:49.570 --> 01:00:52.329
watch some of these trainings think about what's

01:00:52.329 --> 01:00:56.250
going on and improve right so we're fully focused

01:00:56.250 --> 01:01:00.050
on increasing that net promoter score as opposed

01:01:00.050 --> 01:01:02.989
to just firing people you know giving them bad

01:01:02.989 --> 01:01:05.670
scores and saying you're terrible improve right

01:01:05.670 --> 01:01:08.610
that's not what we've done we've created the

01:01:08.610 --> 01:01:10.829
improvement. We're helping them improve. And

01:01:10.829 --> 01:01:13.730
again, I'll just say, like overall, you look

01:01:13.730 --> 01:01:16.869
at Clio, I think the average NPS for the legal

01:01:16.869 --> 01:01:19.150
industry is, you know, depending on, you know,

01:01:19.150 --> 01:01:21.650
when you look, it's anywhere from, you know,

01:01:21.650 --> 01:01:26.329
27 to 30, 32, depending on the report. You know,

01:01:26.329 --> 01:01:31.809
on average, every single month, our NPS score

01:01:31.809 --> 01:01:35.690
is between, you know, 54 and 58, right? Double,

01:01:35.809 --> 01:01:40.349
again, double. the NPS score of the legal industry.

01:01:40.530 --> 01:01:43.230
And again, I think it's because we're focusing

01:01:43.230 --> 01:01:46.849
on these customer service related aspects, right?

01:01:47.010 --> 01:01:49.269
Meeting or exceeding the members' expectations

01:01:49.269 --> 01:01:53.250
instead of just like, we're just spitting law

01:01:53.250 --> 01:01:56.889
out at you, move on, churn to the next one, you

01:01:56.889 --> 01:01:59.929
know, billable hours, right? Again, our model's

01:01:59.929 --> 01:02:03.070
not based upon. average billable hours right

01:02:03.070 --> 01:02:05.170
we're going to bonus people because you know

01:02:05.170 --> 01:02:08.869
oh my goodness you you build 48 hours today there's

01:02:08.869 --> 01:02:14.670
only 20 we're we're not even in that model and

01:02:14.670 --> 01:02:17.369
i think it shows again because you know our customers

01:02:17.369 --> 01:02:21.349
our members have double the net promoter score

01:02:21.349 --> 01:02:24.630
of the legal industry as a as a whole and i think

01:02:24.630 --> 01:02:28.170
that's telling due to the way that you know I

01:02:28.170 --> 01:02:31.429
think we've revolutionized the practice of law.

01:02:32.269 --> 01:02:35.570
I love it. I love it. Yeah, I mean, I think it's

01:02:35.570 --> 01:02:41.570
the, like you said, carrots versus stick approach.

01:02:41.750 --> 01:02:46.190
And it's not a... It's a culture of learning

01:02:46.190 --> 01:02:48.489
and improvement versus culture of fear. Oh, I'm

01:02:48.489 --> 01:02:51.050
going to get fired by legal shield if I don't

01:02:51.050 --> 01:02:52.929
improve. I think that's never going to happen.

01:02:53.590 --> 01:02:57.409
Lawyers are, like you said earlier, our lawyers

01:02:57.409 --> 01:03:02.769
are really solid law firms. We don't doubt their

01:03:02.769 --> 01:03:07.469
ability to understand and implement the law,

01:03:07.610 --> 01:03:12.250
but it's just the bedside manners and some stuff

01:03:12.250 --> 01:03:15.130
that... Customers are telling us that we're just

01:03:15.130 --> 01:03:18.289
relaying the message over to the lawyers. And

01:03:18.289 --> 01:03:21.889
you know what? Hey, maybe the way we provide

01:03:21.889 --> 01:03:24.590
service isn't for all lawyers, right? Again,

01:03:25.809 --> 01:03:28.929
it's that customer service aspect, it's that

01:03:28.929 --> 01:03:33.969
bedside manner that we've shown, not only that

01:03:33.969 --> 01:03:36.550
we think, that we've proven time and time again,

01:03:36.650 --> 01:03:40.599
increases the entire... you know, interaction

01:03:40.599 --> 01:03:46.219
between a lawyer and a member. If I'm your lawyer

01:03:46.219 --> 01:03:48.820
and you think I'm listening and, you know, and

01:03:48.820 --> 01:03:52.980
I'm consoling and I'm really understanding what

01:03:52.980 --> 01:03:55.019
you're saying, I'm letting you have your piece,

01:03:55.360 --> 01:03:58.500
I'm, you know, we're feeling it, right? We're

01:03:58.500 --> 01:04:02.619
jiving. Even if I, you know, lose your case or

01:04:02.619 --> 01:04:05.820
I don't have good information for you or I can't

01:04:05.820 --> 01:04:08.400
think of a way to help you solve it the way you

01:04:08.889 --> 01:04:12.670
You want it solved? You still feel better at

01:04:12.670 --> 01:04:15.130
the end of that interaction than if I cut you

01:04:15.130 --> 01:04:18.409
off, if I was short. I just said, no, no, you

01:04:18.409 --> 01:04:21.090
don't have any defense. You're screwed. Move

01:04:21.090 --> 01:04:26.230
on, right? So we see great scores and comments

01:04:26.230 --> 01:04:30.010
from members who lost their freaking case, right?

01:04:30.389 --> 01:04:33.170
But they had a good interaction with the lawyer.

01:04:33.250 --> 01:04:37.199
The lawyer cared. The lawyer listened. The customer

01:04:37.199 --> 01:04:39.239
thought the lawyer did everything they could.

01:04:39.400 --> 01:04:42.059
It was just, you know, bad, bad set of facts.

01:04:42.179 --> 01:04:46.500
Right. I lost, I lost, I lost this one, but you

01:04:46.500 --> 01:04:49.900
know, by God, they were on my side. Yeah. I mean,

01:04:49.900 --> 01:04:52.599
this may be a pro tip to lawyers everywhere,

01:04:52.719 --> 01:04:56.099
right? Because the large language models can

01:04:56.099 --> 01:04:58.519
nail the customer service aspect really well.

01:04:58.989 --> 01:05:04.210
Is that how people, AI wins over bad lawyers

01:05:04.210 --> 01:05:10.230
with bad bedside manners? Because the AI systems

01:05:10.230 --> 01:05:14.070
will make people feel heard and listened to and

01:05:14.070 --> 01:05:18.710
all that. So I think it's really key. And it's

01:05:18.710 --> 01:05:22.230
the case everywhere, right? So even as data people

01:05:22.230 --> 01:05:26.909
or lawyers or any person, even for doctors, I

01:05:26.909 --> 01:05:30.559
think... Picking up on soft skills is going to

01:05:30.559 --> 01:05:34.559
be key going forward in the age of AI and as

01:05:34.559 --> 01:05:39.300
technology improves. Yeah, it is for sure. But

01:05:39.300 --> 01:05:42.179
hey, I think I kind of went off on a tangent

01:05:42.179 --> 01:05:45.980
there. We kind of started talking about the stress

01:05:45.980 --> 01:05:49.099
index, and then you mentioned closed -loop systems,

01:05:49.119 --> 01:05:53.019
so I kind of thought I needed to explain kind

01:05:53.019 --> 01:05:55.900
of that service model first, but kind of back

01:05:55.900 --> 01:06:00.019
to the... consumer stress legal index again 30

01:06:00.019 --> 01:06:03.980
35 plus million requests since the year 2000

01:06:03.980 --> 01:06:09.079
150 000 requests per month um we got to thinking

01:06:09.079 --> 01:06:12.500
there's got to be value in that data right it

01:06:12.500 --> 01:06:15.320
has to tell us something this is a large enough

01:06:15.320 --> 01:06:18.159
data set right so maybe it not only tells us

01:06:18.159 --> 01:06:20.780
something about our business but about the the

01:06:20.780 --> 01:06:23.400
world at large, right, or the United States,

01:06:23.500 --> 01:06:26.219
the North American economy. And that's really

01:06:26.219 --> 01:06:28.860
when we kind of stepped out and we began thinking,

01:06:29.059 --> 01:06:34.980
you know, are there any indicators in our data

01:06:34.980 --> 01:06:38.880
that can talk about any of the financial economic

01:06:38.880 --> 01:06:41.960
conditions of the, you know, North American household?

01:06:42.420 --> 01:06:45.840
And we did a lot of tests in the beginning. And

01:06:45.840 --> 01:06:48.139
what's interesting, we kind of landed on the

01:06:49.960 --> 01:06:52.360
University of Michigan does a consumer sentiment

01:06:52.360 --> 01:06:56.079
index and the conference board does consumer

01:06:56.079 --> 01:06:58.440
confidence index. And both of those are kind

01:06:58.440 --> 01:07:03.079
of held up as like the high most important reading

01:07:03.079 --> 01:07:06.480
in kind of what the consumer is feeling out in

01:07:06.480 --> 01:07:10.519
the market. And then how that talks to and impacts

01:07:10.519 --> 01:07:13.900
the overall economy. And what was really interesting

01:07:13.900 --> 01:07:17.650
when we, again, we did a study of our data. all

01:07:17.650 --> 01:07:21.449
the way back to 2002, right? So that's what,

01:07:21.489 --> 01:07:25.510
23 years now? We saw an extremely high correlation

01:07:25.510 --> 01:07:28.150
with the conference board's consumer confidence

01:07:28.150 --> 01:07:34.190
index, even up to a, we're a 45 to 60 day leading

01:07:34.190 --> 01:07:37.349
indicator of what that consumer confidence index

01:07:37.349 --> 01:07:41.750
is going to read, right? Extremely powerful.

01:07:41.949 --> 01:07:48.230
So, and then really what that says is, We like

01:07:48.230 --> 01:07:52.849
to say we've got hard data, right? Folks calling

01:07:52.849 --> 01:07:56.969
in, requesting services for specific issues that

01:07:56.969 --> 01:08:00.389
we've shown there's a high correlation to economic

01:08:00.389 --> 01:08:04.550
stress and economic confidence out in the market.

01:08:04.750 --> 01:08:08.329
We've got hard data versus what I call soft data,

01:08:08.489 --> 01:08:12.170
which is, you know, survey responses, sentiment

01:08:12.170 --> 01:08:16.359
data. So we're not going out and asking people

01:08:16.359 --> 01:08:19.939
what they think and how they feel about things.

01:08:20.140 --> 01:08:23.880
We're measuring, you know, specific actions and

01:08:23.880 --> 01:08:27.100
then extrapolating that onto the economy and

01:08:27.100 --> 01:08:30.399
saying, we're seeing consumer financial stress,

01:08:30.439 --> 01:08:33.479
you know, go up. It's getting worse or it's going

01:08:33.479 --> 01:08:39.920
down. Things are getting better. Yeah, I think

01:08:39.920 --> 01:08:46.010
this is how legal shields, proprietary data can

01:08:46.010 --> 01:08:48.090
be tied to what's happening in the world, right?

01:08:48.189 --> 01:08:51.350
Sure, because I like to say folks don't call

01:08:51.350 --> 01:08:53.329
lawyers for a good time. They call because they've

01:08:53.329 --> 01:08:56.090
got a problem, right? And when we're seeing issues

01:08:56.090 --> 01:09:00.630
like calls for bankruptcy increase, calls for

01:09:00.630 --> 01:09:03.850
foreclosure increase, consumer finance issues,

01:09:04.369 --> 01:09:07.890
billing disputes, collections, over the last

01:09:07.890 --> 01:09:11.890
three quarters, we've seen a slow but consistent

01:09:11.890 --> 01:09:15.479
increase. And the Consumer Stress Index, which

01:09:15.479 --> 01:09:18.500
tells us that, you know, stress is increasing,

01:09:18.819 --> 01:09:21.300
right? Folks are continuing to, you know, feel

01:09:21.300 --> 01:09:25.079
squeezed by increasing prices, higher interest

01:09:25.079 --> 01:09:30.699
rates. You know, we've had high inflation for,

01:09:30.779 --> 01:09:34.600
you know, multiple years now. That compounded

01:09:34.600 --> 01:09:37.479
upon itself is really squeezing the household

01:09:37.479 --> 01:09:41.640
budgets. And we're seeing that again daily. and

01:09:41.640 --> 01:09:43.840
the requests for legal assistance that we're

01:09:43.840 --> 01:09:46.439
receiving for a lot of those, you know, kitchen

01:09:46.439 --> 01:09:51.420
table, consumer finance issues. Yeah. I think

01:09:51.420 --> 01:09:54.380
it's, it's, it's really interesting. And also

01:09:54.380 --> 01:09:57.720
I think, I think we need to clarify, maybe make

01:09:57.720 --> 01:10:01.020
sure we know we inherently understand, but when

01:10:01.020 --> 01:10:03.140
we're talking about churning all these data,

01:10:03.239 --> 01:10:08.500
looking at what, what customers are talking about,

01:10:08.579 --> 01:10:14.180
we really, We really have high -level data and

01:10:14.180 --> 01:10:16.199
insights around it, but not anything related

01:10:16.199 --> 01:10:19.739
to client attorney privilege where we don't,

01:10:19.739 --> 01:10:22.260
as LegalShield folks, we don't have access to

01:10:22.260 --> 01:10:25.539
it or we don't look at it. Sure, right. We only

01:10:25.539 --> 01:10:30.199
look in aggregate at the highest level. The law

01:10:30.199 --> 01:10:33.539
firms themselves attach what we call an area

01:10:33.539 --> 01:10:37.550
of law. to a specific request. So we don't know

01:10:37.550 --> 01:10:40.489
any of the details or the substance of any of

01:10:40.489 --> 01:10:44.689
these requests. We only know the category or

01:10:44.689 --> 01:10:48.710
the area of law that the law firm assigned to

01:10:48.710 --> 01:10:53.670
this specific request. You've been really humble

01:10:53.670 --> 01:10:56.069
not to bring these up, but you've been killing

01:10:56.069 --> 01:11:01.010
it with doing a media spree with Forbes and Wall

01:11:01.010 --> 01:11:07.329
Street Journal. CNBC or a host of other things.

01:11:07.529 --> 01:11:11.189
I think you were brought in as an expert to talk

01:11:11.189 --> 01:11:15.390
about how, like what you said, how our Legal

01:11:15.390 --> 01:11:17.770
Shields data relates to what's happening in the

01:11:17.770 --> 01:11:21.350
world. What are some of the insights that we're

01:11:21.350 --> 01:11:25.079
seeing from the data? Sure. And it's just kind

01:11:25.079 --> 01:11:26.880
of what we've been talking about. You know, you

01:11:26.880 --> 01:11:29.020
kind of mentioned, oh, we're getting some media

01:11:29.020 --> 01:11:31.420
attention. Yeah, I like to say this is about

01:11:31.420 --> 01:11:34.460
a 10 -year overnight success, right? We've been

01:11:34.460 --> 01:11:37.960
doing this index for a long time. You know, it

01:11:37.960 --> 01:11:40.079
almost feels like no one was paying attention

01:11:40.079 --> 01:11:42.760
to it. You know, maybe they were, maybe they

01:11:42.760 --> 01:11:46.600
weren't. But, you know, certainly recently, kind

01:11:46.600 --> 01:11:49.520
of beginning with the, you know, election in

01:11:49.520 --> 01:11:53.460
November through current. We've been lucky enough

01:11:53.460 --> 01:11:56.880
to probably over the last 10 plus years kind

01:11:56.880 --> 01:12:00.880
of prove that our data is legitimate. It does

01:12:00.880 --> 01:12:05.039
have these leading indications and it is valuable.

01:12:05.199 --> 01:12:11.199
It's up to the moment of the economic of the

01:12:11.199 --> 01:12:15.180
American household. And again, what we're seeing.

01:12:16.270 --> 01:12:18.630
The Consumer Stress Legal Index is kind of the

01:12:18.630 --> 01:12:22.270
high overarching index, and it's composed of

01:12:22.270 --> 01:12:26.430
several sub -indices, the Bankruptcy Index, Foreclosure

01:12:26.430 --> 01:12:30.550
Index, Consumer Finance Index. Those three roll

01:12:30.550 --> 01:12:34.010
up to the Consumer Stress Legal Index. And we're

01:12:34.010 --> 01:12:36.970
seeing, you know, increased bankruptcies right

01:12:36.970 --> 01:12:41.529
now. So when the pandemic hit, we had a lot of

01:12:41.529 --> 01:12:44.359
government in. intervention that kind of stopped

01:12:44.359 --> 01:12:47.399
payments on some bills, pushed out a lot of money

01:12:47.399 --> 01:12:50.279
to a whole lot of folks. So we saw bankruptcy

01:12:50.279 --> 01:12:52.899
filings, as reported by the U .S. government,

01:12:53.039 --> 01:12:57.279
go down significantly. And so did our index.

01:12:57.859 --> 01:13:00.859
But what we're seeing now is that bankruptcy

01:13:00.859 --> 01:13:05.399
index is back up to and moving above those pre

01:13:05.399 --> 01:13:08.560
-pandemic bankruptcy levels. And what's really

01:13:08.560 --> 01:13:13.479
key here is that our index is a two -quarter

01:13:13.479 --> 01:13:17.800
up to a six -month leading indicator of U .S.

01:13:17.800 --> 01:13:22.239
bankruptcy filings, with almost a 0 .96 positive

01:13:22.239 --> 01:13:26.819
correlation, which you just don't find that out

01:13:26.819 --> 01:13:29.260
in the real world with data, right? But when

01:13:29.260 --> 01:13:32.479
we look at the U .S. reported bankruptcy filings

01:13:32.479 --> 01:13:35.840
and line that up with our index, again, we're

01:13:35.840 --> 01:13:38.199
a two -quarter leading indicator, which leads

01:13:38.199 --> 01:13:42.350
us to believe through summer into the fall, we're

01:13:42.350 --> 01:13:45.289
going to continue seeing increased bankruptcy

01:13:45.289 --> 01:13:48.550
filings, right? And again, we talk about bankruptcy.

01:13:48.550 --> 01:13:51.250
I mean, folks just don't wake up one morning

01:13:51.250 --> 01:13:53.930
and start thinking about bankruptcy, that this

01:13:53.930 --> 01:13:58.289
is a process, right? I've had prolonged what

01:13:58.289 --> 01:14:00.430
we're going to call consumer financial stress

01:14:00.430 --> 01:14:03.109
over a long period of time. And that's, it has

01:14:03.109 --> 01:14:06.010
been showing up for probably a year now. Again,

01:14:06.210 --> 01:14:09.590
billing disputes. collections issues, consumer

01:14:09.590 --> 01:14:13.310
finance increases in requests for service. As

01:14:13.310 --> 01:14:16.409
those have been increasing, we can go all the

01:14:16.409 --> 01:14:19.710
way back to when we began increasing the interest

01:14:19.710 --> 01:14:23.029
rates that have been so low for so long, we can

01:14:23.029 --> 01:14:26.350
see consumer financial stress begin to creep

01:14:26.350 --> 01:14:30.210
up. Again, it's because I think we saw reports

01:14:30.210 --> 01:14:35.590
that The delinquency rate on paying the mortgages

01:14:35.590 --> 01:14:39.029
are going up, right? Revolving credit card debt

01:14:39.029 --> 01:14:42.210
is as high or higher than it's ever been before.

01:14:42.550 --> 01:14:47.189
And as interest rates remain high, those monthly

01:14:47.189 --> 01:14:49.829
payments are taking a bigger and a bigger and

01:14:49.829 --> 01:14:53.489
a bigger bite out of whatever salary comes in,

01:14:53.569 --> 01:14:56.750
right? We like to say, when we talk to our lawyers,

01:14:56.869 --> 01:15:00.289
they tell us, folks are concerned. They don't

01:15:00.289 --> 01:15:02.189
have any money left at the end of the month.

01:15:02.270 --> 01:15:04.630
Right. But I still have a stack of bills. So,

01:15:04.689 --> 01:15:07.350
you know, which bills should I pay? Which ones

01:15:07.350 --> 01:15:09.609
can I push to the side? What's going to happen

01:15:09.609 --> 01:15:13.210
if I don't pay? Right. We've all of these questions

01:15:13.210 --> 01:15:16.390
kind of lead up to now we see the bankruptcy

01:15:16.390 --> 01:15:19.010
index increasing. But we've been reporting this

01:15:19.010 --> 01:15:21.850
all along. It's like, hey, stress, stress is

01:15:21.850 --> 01:15:25.050
rising. Folks are having having issues. But,

01:15:25.130 --> 01:15:26.890
you know, once we see that bankruptcy number

01:15:26.890 --> 01:15:30.729
tick up again. higher than what we saw pre -pandemic

01:15:30.729 --> 01:15:34.430
before all the government intervention that it's

01:15:34.430 --> 01:15:38.850
telling yeah it's yeah it's hard and it's kind

01:15:38.850 --> 01:15:42.989
of depressing at times that you know we we know

01:15:42.989 --> 01:15:46.149
some of these we look at this data and okay this

01:15:46.149 --> 01:15:49.909
is going to lead to something bad or people are

01:15:49.909 --> 01:15:53.170
hurting and we know that out of any of these

01:15:53.170 --> 01:15:58.649
means extreme uh indexes can can report on those

01:15:58.649 --> 01:16:02.250
are all usually uh lagging indicators well and

01:16:02.250 --> 01:16:06.329
then we like to i mean again i may be pontificating

01:16:06.329 --> 01:16:09.489
a little bit here but i think we we try to stay

01:16:09.489 --> 01:16:13.170
out of narratives and politics and positions

01:16:13.170 --> 01:16:16.130
because again depending on where you turn tune

01:16:16.130 --> 01:16:19.130
in to get your to get your news oh the economy's

01:16:19.130 --> 01:16:22.449
going great oh or oh my goodness It's blowing

01:16:22.449 --> 01:16:25.109
up. The tariffs are going to increase prices

01:16:25.109 --> 01:16:27.409
through the roof. No one can afford anything.

01:16:27.750 --> 01:16:29.250
And then the other side is like, oh, this is

01:16:29.250 --> 01:16:32.329
the greatest thing we can do. Bring back. I mean,

01:16:32.369 --> 01:16:35.510
I'm not even commenting on any of that stuff.

01:16:35.510 --> 01:16:38.770
Right. Again, all that we're commenting on is

01:16:38.770 --> 01:16:43.289
every single day we, you know, field requests

01:16:43.289 --> 01:16:47.750
for help from people with issues. And then we're

01:16:47.750 --> 01:16:52.010
categorizing those requests. And again, over

01:16:52.010 --> 01:16:56.449
20 plus years now, based upon the request we

01:16:56.449 --> 01:17:00.430
see, we know where that leads, right? And we

01:17:00.430 --> 01:17:03.930
can foresee what those mean, right? So again,

01:17:04.090 --> 01:17:07.970
that doesn't care what color, red, blue, what

01:17:07.970 --> 01:17:10.550
your affiliations are, has nothing to do with

01:17:10.550 --> 01:17:14.529
this. We've got 25 plus years of data that says

01:17:14.529 --> 01:17:18.229
when we see X happen, we know that Y is extremely

01:17:18.229 --> 01:17:21.880
likely. And right now, we're saying stress has

01:17:21.880 --> 01:17:25.319
been high for a while now. Our bankruptcy index

01:17:25.319 --> 01:17:28.159
is a two -quarter leading indicator of filings.

01:17:28.479 --> 01:17:32.380
We expect bankruptcy filings to continue to increase

01:17:32.380 --> 01:17:36.500
through summer into the fall. Yeah, yeah. I think,

01:17:36.500 --> 01:17:40.539
yeah, to your point, I think we, regardless of

01:17:40.539 --> 01:17:43.159
what caused these things, we're just looking

01:17:43.159 --> 01:17:45.199
at the data and saying, okay, this is happening,

01:17:45.300 --> 01:17:49.750
so this is going to lead to... X, Y, Z. Yep.

01:17:51.289 --> 01:17:54.449
Makes a lot of sense. And then just real quick,

01:17:54.710 --> 01:17:58.869
they don't get quite as much play, but other

01:17:58.869 --> 01:18:01.829
than the Consumer Stress Legal Index and kind

01:18:01.829 --> 01:18:04.310
of those sub -indices, bankruptcy, foreclosure,

01:18:04.310 --> 01:18:07.310
consumer finance, we also have a Housing Sales

01:18:07.310 --> 01:18:10.229
Index and a Housing Construction Index. And again,

01:18:10.350 --> 01:18:14.250
they track extremely closely and they're a 30

01:18:14.250 --> 01:18:17.920
to a 45 -day leading indicator of... housing

01:18:17.920 --> 01:18:22.640
starts and existing home sales, right? And again,

01:18:22.739 --> 01:18:25.779
so we're seeing kind of flat to maybe even a

01:18:25.779 --> 01:18:28.520
little bit lower on both of those indices. We

01:18:28.520 --> 01:18:31.479
can kind of go back to the increasing consumer

01:18:31.479 --> 01:18:34.579
stress and kind of lay those over, right, folks?

01:18:34.779 --> 01:18:37.180
A lot of people just aren't looking to, you know,

01:18:37.199 --> 01:18:40.340
upgrade their house, add to their monthly obligations

01:18:40.340 --> 01:18:43.220
in terms of bills, right? And then on the housing

01:18:43.220 --> 01:18:46.659
construction side, As these tariffs begin to

01:18:46.659 --> 01:18:48.819
pile up, especially when we're talking about

01:18:48.819 --> 01:18:52.560
Canada and Mexico, where we get a lot of lumber

01:18:52.560 --> 01:18:58.340
and a lot of steel and a lot of sheet rock, as

01:18:58.340 --> 01:19:01.600
those tariffs are layered onto those construction

01:19:01.600 --> 01:19:05.260
materials, builders are having difficulties.

01:19:05.340 --> 01:19:09.340
Should I pay this much more to build a house

01:19:09.340 --> 01:19:11.890
that may or may not be sold because... Consumer

01:19:11.890 --> 01:19:14.550
financial stress is up and, you know, delinquency

01:19:14.550 --> 01:19:17.390
rates on mortgages are already high. You know,

01:19:17.390 --> 01:19:20.909
can I afford to invest this much more into building

01:19:20.909 --> 01:19:23.789
a house that may or may not be purchased, right?

01:19:23.890 --> 01:19:27.470
So again, it's kind of a trickle down, right?

01:19:27.569 --> 01:19:31.329
When the average American consumer household

01:19:31.329 --> 01:19:34.569
is feeling stressed over a long period of time,

01:19:34.729 --> 01:19:38.310
all of these other, you know, the housing industry

01:19:38.310 --> 01:19:42.000
certainly takes the hit on that too. Yeah, no,

01:19:42.079 --> 01:19:45.739
I think it's all, it's really cool that you're

01:19:45.739 --> 01:19:49.659
able to kind of build these indexes that are

01:19:49.659 --> 01:19:52.500
kind of related to each other. I mean, in the

01:19:52.500 --> 01:19:55.340
economy, everything is connected to each other,

01:19:55.420 --> 01:19:57.800
right? So it's really cool to see that interplay

01:19:57.800 --> 01:20:01.859
as a data person. But I mean, the results are

01:20:01.859 --> 01:20:06.659
kind of bleak sometimes. The data is cool in

01:20:06.659 --> 01:20:10.140
and of itself and building an index that correlates

01:20:10.140 --> 01:20:12.060
with something out in the real world and then

01:20:12.060 --> 01:20:14.640
moves. And then you can say, oh my goodness,

01:20:14.680 --> 01:20:17.079
this is a leading indicator. That's really super

01:20:17.079 --> 01:20:20.260
cool, right? But then when you can use that data,

01:20:20.260 --> 01:20:23.460
again, every single day we talk about actionable

01:20:23.460 --> 01:20:26.579
intelligence. The data and index is cool, but

01:20:26.579 --> 01:20:29.739
so what? What the hell? Who cares, right? Until

01:20:29.739 --> 01:20:32.520
or unless you can apply it. to something in the

01:20:32.520 --> 01:20:35.359
real world. And I think that's really why a lot

01:20:35.359 --> 01:20:38.720
of the kind of the major financial news outlets

01:20:38.720 --> 01:20:41.039
and medias are finally starting to see us and

01:20:41.039 --> 01:20:44.260
want to talk to us because we have created valuable

01:20:44.260 --> 01:20:47.140
data and we are talking about how it applies

01:20:47.140 --> 01:20:52.680
and how it's affecting the real world. Yeah,

01:20:52.760 --> 01:20:58.000
100%. I think, yeah, if we don't impact either

01:20:58.000 --> 01:21:00.199
the business or outside world, we're just...

01:21:00.909 --> 01:21:03.409
playing with toys in our garage it may be really

01:21:03.409 --> 01:21:06.829
cool toys but if it's not that that's not what

01:21:06.829 --> 01:21:09.510
we're getting paid for to build cool toys right

01:21:09.510 --> 01:21:12.270
yeah it's fun and it's cool but who cares at

01:21:12.270 --> 01:21:14.409
the end of the day right you have to apply it

01:21:14.409 --> 01:21:20.029
to something right right 100 percent um cool

01:21:20.029 --> 01:21:26.750
um i think we can probably talk about your um

01:21:26.750 --> 01:21:36.659
i think your career as a councilman. We've got

01:21:36.659 --> 01:21:39.739
a really cool story about the election campaign

01:21:39.739 --> 01:21:43.100
and how you did that. Please tell us about that.

01:21:43.840 --> 01:21:49.760
Sure. Again, I'm kind of an analyst on the side,

01:21:50.000 --> 01:21:55.420
always interested in politics. Can I run for

01:21:55.420 --> 01:21:59.279
a local election? How do I use data to do this?

01:22:00.460 --> 01:22:03.020
Heck, I don't even know what the year was anymore.

01:22:04.199 --> 01:22:10.079
04, 05, 2003, maybe. I'm not sure what it was.

01:22:10.460 --> 01:22:13.340
So I decided I was going to run for city council

01:22:13.340 --> 01:22:17.640
of the local small city we lived in, Ada, Oklahoma.

01:22:20.380 --> 01:22:25.159
Ran for the first election. Most folks didn't

01:22:25.159 --> 01:22:30.850
know who I was. Ran against an incumbent, got

01:22:30.850 --> 01:22:35.689
my butt beat. I was upset. Was just going to

01:22:35.689 --> 01:22:40.029
throw it all in. But the lucky thing was the

01:22:40.029 --> 01:22:44.430
elections lined up to where you had to run for

01:22:44.430 --> 01:22:48.390
a specific ward within where you lived. And there

01:22:48.390 --> 01:22:51.149
was an at -large seat, which anyone in the entire

01:22:51.149 --> 01:22:54.310
city could run for this at -large seat. So it

01:22:54.310 --> 01:22:56.840
just lined up to the fact that the... At -large

01:22:56.840 --> 01:22:59.300
seat was up for election, and that's what I ran

01:22:59.300 --> 01:23:02.600
for and lost. The very next election cycle, the

01:23:02.600 --> 01:23:06.979
next year, my ward was up for election. So luckily,

01:23:07.159 --> 01:23:09.859
I stopped feeling sorry for myself shortly thereafter

01:23:09.859 --> 01:23:13.779
I lost, and I just rolled the campaign over.

01:23:13.979 --> 01:23:17.239
I was like, this is what I want to do. I can't

01:23:17.239 --> 01:23:20.579
let one loss beat me. So I immediately began

01:23:20.579 --> 01:23:24.899
running for the seat for the ward that I lived

01:23:24.899 --> 01:23:29.050
in. just rolled the campaign over. And that's

01:23:29.050 --> 01:23:32.789
when I really got serious about, I don't see

01:23:32.789 --> 01:23:35.310
a whole lot of people out campaigning other than,

01:23:35.390 --> 01:23:38.090
you know, they're putting yard signs up and then

01:23:38.090 --> 01:23:41.810
they may go to the rotary meeting, you know,

01:23:41.810 --> 01:23:45.289
in the morning or whatever, but that's it. So

01:23:45.289 --> 01:23:50.069
I got really interested in, and at this time,

01:23:50.409 --> 01:23:54.920
I don't know that. Instagram was that big. There

01:23:54.920 --> 01:23:57.119
wasn't a whole lot of TikTok stuff. It was mainly

01:23:57.119 --> 01:24:01.760
Facebook was the main medium of social media.

01:24:02.239 --> 01:24:05.060
So I got really interested in and began researching

01:24:05.060 --> 01:24:09.720
Facebook ads, right? And again, no one else,

01:24:09.720 --> 01:24:13.000
at least at that point in time, I haven't looked

01:24:13.000 --> 01:24:15.840
at it recently, but I can geofence my Facebook

01:24:15.840 --> 01:24:19.279
ad to the city that I was in, right? So Ada,

01:24:19.359 --> 01:24:22.960
Oklahoma. And at this time, no... I think there

01:24:22.960 --> 01:24:25.479
was one other guy that had like an oil business

01:24:25.479 --> 01:24:28.500
that was running some Facebook ads in the area.

01:24:28.739 --> 01:24:32.199
So I got on Facebook, you know, built up a couple

01:24:32.199 --> 01:24:35.960
of small, of small ads. I had no competition.

01:24:36.399 --> 01:24:39.800
So I was paying like half a cent per impression.

01:24:39.819 --> 01:24:43.359
I mean, basically free for the, for the volume

01:24:43.359 --> 01:24:46.159
of people. I mean, I think eight is probably

01:24:46.159 --> 01:24:52.170
30 ,000 people on a good day. I'm not sure what

01:24:52.170 --> 01:24:57.130
the population is today, but I was paying per

01:24:57.130 --> 01:25:01.550
impression. I had zero competition. I finally

01:25:01.550 --> 01:25:05.569
called the other guy that was running ads, and

01:25:05.569 --> 01:25:09.010
we kind of talked about how we could adjust our

01:25:09.010 --> 01:25:11.489
metrics to where we weren't competing or bidding

01:25:11.489 --> 01:25:14.430
against each other. So again, basically free

01:25:14.430 --> 01:25:18.819
ads. I did the yard signs, you know, I walked

01:25:18.819 --> 01:25:21.859
door to door. But I think the biggest key was

01:25:21.859 --> 01:25:26.100
no one else was running those Facebook ads targeted

01:25:26.100 --> 01:25:30.960
to my area. So I was just basically just blanketed

01:25:30.960 --> 01:25:34.199
everyone that logged into the social media app

01:25:34.199 --> 01:25:40.699
with my city council ads. So again, use data.

01:25:41.000 --> 01:25:43.159
I went to the... You know, the state election

01:25:43.159 --> 01:25:46.619
board, they they will give you the full download

01:25:46.619 --> 01:25:50.359
of like the entire voter history, voter registration.

01:25:51.300 --> 01:25:54.560
When's the last time someone someone voted? When

01:25:54.560 --> 01:25:57.819
did they register? So, again, because, you know,

01:25:57.819 --> 01:26:01.340
I didn't feel like I could walk and talk to every

01:26:01.340 --> 01:26:04.479
single person or I didn't want to spend so much

01:26:04.479 --> 01:26:07.840
money to mail every single person on this on

01:26:07.840 --> 01:26:09.880
this list, especially those who weren't going

01:26:09.880 --> 01:26:14.149
to vote. You know, then I began sorting the list,

01:26:14.270 --> 01:26:17.449
right? Who's most likely to vote? And then I

01:26:17.449 --> 01:26:20.010
created a walking and a mailing list on those

01:26:20.010 --> 01:26:22.210
people that, you know, I knew had an extremely

01:26:22.210 --> 01:26:25.550
high likelihood to vote in that election. Also

01:26:25.550 --> 01:26:27.989
kind of, you know, tweak some of the targeting

01:26:27.989 --> 01:26:31.810
on the Facebook ads to go after these specific

01:26:31.810 --> 01:26:37.199
people. So, hey, long, long story short. The

01:26:37.199 --> 01:26:39.880
person I was running against, I think they'd

01:26:39.880 --> 01:26:42.600
been on the council for 30 -something years.

01:26:43.220 --> 01:26:45.979
I'd talked to a lot of, you know, elders in the

01:26:45.979 --> 01:26:48.159
city. They're like, hey, man, we're happy you're

01:26:48.159 --> 01:26:50.960
running, but you don't have a shot. No one's

01:26:50.960 --> 01:26:54.680
ever beaten her. Like the president of the bank

01:26:54.680 --> 01:26:57.380
had just run against this individual and lost.

01:26:58.039 --> 01:27:00.739
So everyone's like, hey, you know, good luck,

01:27:00.859 --> 01:27:03.840
but you don't have a chance. So, hey, long story

01:27:03.840 --> 01:27:09.079
short. To this day, again, off -cycle election.

01:27:09.300 --> 01:27:12.220
There was no presidential election. There was

01:27:12.220 --> 01:27:17.960
no other races on the ballot except me. There

01:27:17.960 --> 01:27:21.340
was two city council elections. To this day,

01:27:21.439 --> 01:27:28.579
largest turnout on an off -cycle Ada City election.

01:27:29.850 --> 01:27:32.130
I think I probably got the largest amount of

01:27:32.130 --> 01:27:34.310
votes for a single candidate, you know, more

01:27:34.310 --> 01:27:38.890
than doubled the votes of my opponent, who supposedly

01:27:38.890 --> 01:27:42.449
could not be beat, right? And again, I don't

01:27:42.449 --> 01:27:44.909
think it had anything to do with I was going

01:27:44.909 --> 01:27:49.609
to be a better councilman than this person. I

01:27:49.609 --> 01:27:51.890
don't think probably, you know, that many more

01:27:51.890 --> 01:27:55.670
people liked me than liked them. But again, it's

01:27:55.670 --> 01:28:00.710
the power of using data. Having a goal in mind,

01:28:00.850 --> 01:28:04.770
collecting the information, analyzing it, creating

01:28:04.770 --> 01:28:07.630
actionable intelligence, applying it towards

01:28:07.630 --> 01:28:13.170
a goal, and just going through the process. I

01:28:13.170 --> 01:28:16.949
mean, really fun, and it probably cemented in

01:28:16.949 --> 01:28:21.270
my mind the power of data and what someone can

01:28:21.270 --> 01:28:23.930
do with it if they can harness it, right? If

01:28:23.930 --> 01:28:26.289
you can figure it out, and if you can harness

01:28:26.289 --> 01:28:30.420
what you have. Anything is possible at that point.

01:28:30.800 --> 01:28:34.979
Yeah, you're kind of the OG of data and politics,

01:28:35.279 --> 01:28:37.560
especially social media. Yeah, I wouldn't say

01:28:37.560 --> 01:28:42.060
that. Maybe social media, political campaigning,

01:28:42.119 --> 01:28:44.260
right? And you know what? Probably, you know,

01:28:44.279 --> 01:28:47.420
it was really fun, loved every second I was on

01:28:47.420 --> 01:28:49.680
the council, but I probably realized that, you

01:28:49.680 --> 01:28:52.600
know, I like the campaigning and the strategy

01:28:52.600 --> 01:28:58.489
better than the... governing, right? Again, wouldn't

01:28:58.489 --> 01:29:03.750
trade it for a second, but I was super pumped

01:29:03.750 --> 01:29:07.170
when I won and it was exciting. And then a day

01:29:07.170 --> 01:29:10.430
or two later, I'm like, what have I done? What

01:29:10.430 --> 01:29:13.550
have I gotten myself into? It's two entirely

01:29:13.550 --> 01:29:17.729
different job descriptions. It is, right? And

01:29:17.729 --> 01:29:22.270
honestly, politicians get a bad... bad rap and

01:29:22.270 --> 01:29:24.989
a bad name and you know a lot of times deservedly

01:29:24.989 --> 01:29:29.310
so but kind of that campaigning politician that's

01:29:29.310 --> 01:29:31.689
a total you have to have a totally different

01:29:31.689 --> 01:29:35.210
mindset from campaigning and selling yourself

01:29:35.210 --> 01:29:38.810
to actually governing right because this big

01:29:38.810 --> 01:29:42.010
ego person over here that says i'm the best i'm

01:29:42.010 --> 01:29:45.609
better vote for me that's not always the best

01:29:45.609 --> 01:29:49.569
way to govern and you know lead yeah how do you

01:29:49.569 --> 01:29:53.949
switch The big ego, big charisma to a servant

01:29:53.949 --> 01:29:57.630
leadership mindset. Absolutely. And again, I

01:29:57.630 --> 01:30:00.109
think that's why a lot of politicians get a bad

01:30:00.109 --> 01:30:03.350
rap is they never make that transition, right?

01:30:03.470 --> 01:30:07.289
They stay in this, I'm the best, big ego, look

01:30:07.289 --> 01:30:11.689
at me. And they don't move over to, I'm here

01:30:11.689 --> 01:30:17.390
for you, right? I'm serving you. Yeah, not everybody

01:30:17.390 --> 01:30:20.960
can get that. But I think your story is, Maybe

01:30:20.960 --> 01:30:24.460
a lesson for me and everybody in the data space

01:30:24.460 --> 01:30:28.800
is, like you said, how do you... I think you

01:30:28.800 --> 01:30:36.020
bring it all together, right? So you went in

01:30:36.020 --> 01:30:40.979
and deeply understood what the... issues were

01:30:40.979 --> 01:30:43.500
you looked at the data gathered the data understood

01:30:43.500 --> 01:30:46.460
the data and then implemented created actionable

01:30:46.460 --> 01:30:50.260
insights from it and then implemented it so it's

01:30:50.260 --> 01:30:53.880
it's all of those aspects have to come together

01:30:53.880 --> 01:30:58.899
to make a business subjective business goal achievable

01:30:58.899 --> 01:31:01.979
or you know in in politics or business or any

01:31:01.979 --> 01:31:06.609
anywhere And any one of those on themselves would

01:31:06.609 --> 01:31:09.029
fail, right? I mean, if I'm just really good

01:31:09.029 --> 01:31:12.810
and love data, great. I mean, I make myself happy,

01:31:12.850 --> 01:31:15.869
but what do you improve, right? So you've got

01:31:15.869 --> 01:31:19.369
to take it one step at a time. What can the data

01:31:19.369 --> 01:31:23.289
do, right? How can I use the data to do X or

01:31:23.289 --> 01:31:25.869
Y, right? That's the question we should all be

01:31:25.869 --> 01:31:28.750
asking ourselves is, what can I learn from the

01:31:28.750 --> 01:31:32.359
data? How can I implement it? results cannot

01:31:32.359 --> 01:31:36.340
create. Yeah, yeah. That's really cool. And then

01:31:36.340 --> 01:31:39.199
I mean, fast forward from like, like you were

01:31:39.199 --> 01:31:44.920
saying 2004, five to now, a lot of people have

01:31:44.920 --> 01:31:47.359
our data savvy, a lot of political campaigns

01:31:47.359 --> 01:31:51.739
are have figured it out. But we have a new era

01:31:51.739 --> 01:31:58.439
of AI is have you? Do you have any opinions on

01:31:58.439 --> 01:32:02.340
what? What? kind of role AI would play? I know

01:32:02.340 --> 01:32:06.479
we've heard about AI being used to doctor videos

01:32:06.479 --> 01:32:09.199
and stuff like that, but is there a productive

01:32:09.199 --> 01:32:14.300
way AI can play a role in politics? Sure, I'll

01:32:14.300 --> 01:32:17.600
just say supplement, right? None of what we just

01:32:17.600 --> 01:32:22.359
talked about is replaced by AI, right? AI can

01:32:22.359 --> 01:32:25.380
only make it better or make you better at it.

01:32:25.439 --> 01:32:30.260
So at the end of the day, It would be interesting,

01:32:30.359 --> 01:32:34.279
I guess, if AI put themselves in to run for office

01:32:34.279 --> 01:32:37.220
and they could actually win, which they may be

01:32:37.220 --> 01:32:41.640
able to, right? But again, I think it's all about

01:32:41.640 --> 01:32:45.899
supplementation, not replacement. And again,

01:32:46.000 --> 01:32:48.260
you're working on a whole lot of projects. It's

01:32:48.260 --> 01:32:52.159
kind of on just that specific idea. in and of

01:32:52.159 --> 01:32:56.479
itself right how can we use ai to improve a process

01:32:56.479 --> 01:33:00.939
to improve learning not to replace you know education

01:33:00.939 --> 01:33:04.380
and teachers same way with same way with politics

01:33:04.380 --> 01:33:08.520
if i don't have a map built in my head that says

01:33:08.520 --> 01:33:12.779
i want to win this office right so then i build

01:33:12.779 --> 01:33:16.449
the plan back to i've got this data What can

01:33:16.449 --> 01:33:18.930
I learn from the data? Who can I target? Who

01:33:18.930 --> 01:33:21.470
do I talk to? How do I get out the vote in order

01:33:21.470 --> 01:33:24.430
to win? And what does that data tell me about

01:33:24.430 --> 01:33:26.470
each of those things that I can implement to

01:33:26.470 --> 01:33:30.670
make those things happen? AI is going to be worthless,

01:33:30.810 --> 01:33:33.569
right? I mean, I guess I could go to chat GBT

01:33:33.569 --> 01:33:36.390
and say, I want to win a local election. Give

01:33:36.390 --> 01:33:38.750
me the steps. Right. And you know what? It'll

01:33:38.750 --> 01:33:43.329
probably build you a high level process. But

01:33:43.329 --> 01:33:46.220
again, you still got to understand. not only

01:33:46.220 --> 01:33:49.500
the area that you're running in, right? Who are

01:33:49.500 --> 01:33:52.640
the people? Who are the audience? How do I talk

01:33:52.640 --> 01:33:55.180
to those people? How do I talk to different people,

01:33:55.460 --> 01:33:58.399
right? I've got a singular message, but, you

01:33:58.399 --> 01:34:01.460
know, I'm probably going to talk to a businessman,

01:34:01.840 --> 01:34:05.479
tech PhD middie. I'm probably going to talk to

01:34:05.479 --> 01:34:08.119
you different than I talk to the, you know, garbage

01:34:08.119 --> 01:34:10.939
man, right? Or the guy that comes to replace

01:34:10.939 --> 01:34:13.479
the shingles on my house after. you know, the

01:34:13.479 --> 01:34:16.039
two tornadoes go through the local community.

01:34:16.199 --> 01:34:18.859
I mean, you've got to understand your audience

01:34:18.859 --> 01:34:21.899
and talk to them differently. And I don't think

01:34:21.899 --> 01:34:25.680
AI is there yet, right? So again, AI supplements

01:34:25.680 --> 01:34:29.760
what we do. It doesn't replace any of the hard

01:34:29.760 --> 01:34:33.399
work to actually, you know, win an election and

01:34:33.399 --> 01:34:36.539
then, you know, govern appropriately. Yeah, no,

01:34:36.640 --> 01:34:42.970
100%. And I really feel that it... It will, and

01:34:42.970 --> 01:34:46.590
it is making each of those steps a little more

01:34:46.590 --> 01:34:49.909
easier, a little more efficient, but you cannot

01:34:49.909 --> 01:34:52.590
skip over those steps and say, oh, let AI take

01:34:52.590 --> 01:34:55.670
care of it. That's not happening or will happen

01:34:55.670 --> 01:34:58.270
in the real world for a long time. And I think

01:34:58.270 --> 01:35:01.170
you hit it right. What it probably does is it

01:35:01.170 --> 01:35:04.229
shortens the learning curve, right? Because a

01:35:04.229 --> 01:35:07.489
brand new person who's not studied, who may not

01:35:07.489 --> 01:35:09.810
know it all, may not know all the right steps

01:35:09.810 --> 01:35:13.979
to get from, you know, A to B, right? And I don't

01:35:13.979 --> 01:35:15.479
even know what they are. So it's going to be

01:35:15.479 --> 01:35:17.859
hard to get where I'm going without a map, right?

01:35:17.939 --> 01:35:20.880
To me, and I use AI a lot for this, is like,

01:35:20.939 --> 01:35:24.340
what is the map, right? I'm here today. This

01:35:24.340 --> 01:35:28.800
is my goal. I want to do this. Kind of what are

01:35:28.800 --> 01:35:31.279
the steps in between, right? I may know half

01:35:31.279 --> 01:35:34.039
of them. I may know three quarter of them, but

01:35:34.039 --> 01:35:36.760
there's probably one that I'm forgetting about

01:35:36.760 --> 01:35:39.460
that might just make it all possible, right?

01:35:39.539 --> 01:35:44.140
So again, I think using AI is all about supplementation.

01:35:44.439 --> 01:35:48.260
Yeah. I mean, I feel like to add to your point,

01:35:48.319 --> 01:35:53.100
AI is like a supercharged. So you've got your

01:35:53.100 --> 01:35:57.020
specialists and generalists. AI is kind of a

01:35:57.020 --> 01:36:02.810
really good generalist that can get you to. 80

01:36:02.810 --> 01:36:06.930
in any step but in order to get those 20 additional

01:36:06.930 --> 01:36:10.449
20 you have to be uh you have to have people

01:36:10.449 --> 01:36:12.829
who can design and we were talking like you you

01:36:12.829 --> 01:36:18.550
mentioned about us discussing about um supplementing

01:36:18.550 --> 01:36:22.250
uh human experiences with ai designing ai for

01:36:22.250 --> 01:36:26.289
augmented human experiences versus just replacing

01:36:26.289 --> 01:36:29.689
it i think that's where i think a lot of uh a

01:36:29.689 --> 01:36:35.409
lot of money is put into, hey, let's just replace

01:36:35.409 --> 01:36:37.729
all developers. Let's just replace all lawyers.

01:36:37.829 --> 01:36:41.369
Let's just replace all teachers. I think that's

01:36:41.369 --> 01:36:43.850
the, in my opinion, it's the wrong way of doing

01:36:43.850 --> 01:36:47.010
it because I wrote an article the other day.

01:36:47.050 --> 01:36:51.010
It's like, we've got, even before AI, we've got

01:36:51.010 --> 01:36:54.489
all these automated tools with, say, in customer

01:36:54.489 --> 01:36:58.289
care, we're closing down on more tickets than

01:36:58.289 --> 01:37:03.130
ever. customer care tickets but everybody is

01:37:03.130 --> 01:37:07.590
frustrated we're we're closing uh health in healthcare

01:37:07.590 --> 01:37:11.350
people automated systems of people have automated

01:37:11.350 --> 01:37:15.670
a bunch of processes but patients are frustrated

01:37:15.670 --> 01:37:18.369
they don't feel like they're they're it's a human

01:37:18.369 --> 01:37:22.180
experience anymore so if we really have to I

01:37:22.180 --> 01:37:26.859
mean, I hope there are some investors and VCs

01:37:26.859 --> 01:37:30.840
and people who are thinking about investing in

01:37:30.840 --> 01:37:34.579
technology that will not just replace, but even

01:37:34.579 --> 01:37:40.060
with EVs, we saw that, yes, there are early adopters

01:37:40.060 --> 01:37:45.369
that went all in on EVs. Over the last couple

01:37:45.369 --> 01:37:47.970
of years or a year or two, we were seeing that

01:37:47.970 --> 01:37:51.350
everybody that wants to buy an EV, which is 7%,

01:37:51.350 --> 01:37:55.050
8%, have got an EV. And then the market's kind

01:37:55.050 --> 01:37:58.750
of saturated now because people want that middle

01:37:58.750 --> 01:38:01.229
ground. A lot of times hybrids are taken off

01:38:01.229 --> 01:38:07.510
a lot more than EVs. So people need to be thinking

01:38:07.510 --> 01:38:12.390
of those transitional times where... Maybe it's

01:38:12.390 --> 01:38:14.649
not even a transition time. It's how it's always

01:38:14.649 --> 01:38:18.850
going to be that AI is going to augment human

01:38:18.850 --> 01:38:21.250
experience. Sure. And I kind of think of it,

01:38:21.270 --> 01:38:23.909
and I just kind of had this thought as you were

01:38:23.909 --> 01:38:27.109
talking, I kind of think of AI in terms of a

01:38:27.109 --> 01:38:31.890
mentor, right? Kind of back to the political

01:38:31.890 --> 01:38:35.449
discussion, right? Maybe I don't know anyone

01:38:35.449 --> 01:38:37.850
that's ran for office before. Maybe I don't know

01:38:37.850 --> 01:38:40.819
any current office holders who... could impart

01:38:40.819 --> 01:38:43.439
all this wisdom on me right or could counsel

01:38:43.439 --> 01:38:46.300
me or could talk to me or help me learn if i

01:38:46.300 --> 01:38:50.079
don't have that person i think ai is a you know

01:38:50.079 --> 01:38:53.060
a decent replacement right i don't i don't i

01:38:53.060 --> 01:38:55.899
don't think they can replace the discussion we're

01:38:55.899 --> 01:38:59.000
having to like make new thoughts shoot off in

01:38:59.000 --> 01:39:02.159
my head but it could certainly you know prove

01:39:02.159 --> 01:39:05.760
and be used as a as a mentor to help me learn

01:39:05.760 --> 01:39:08.579
kind of hey go down this path right it'll tell

01:39:08.579 --> 01:39:10.760
me all these things i was like oh i hadn't thought

01:39:10.760 --> 01:39:13.260
about that i need to go research and think about

01:39:13.260 --> 01:39:18.199
that one yeah it's a it's a great it's unbelievably

01:39:18.199 --> 01:39:22.960
great research partner right i don't have to

01:39:22.960 --> 01:39:26.399
be an expert in anything to start learning and

01:39:26.399 --> 01:39:33.159
getting to 70 60 70 there It's immediately, so

01:39:33.159 --> 01:39:35.260
I've got that in the palm of my hands. I don't

01:39:35.260 --> 01:39:40.199
even have to go read up on what others have written

01:39:40.199 --> 01:39:42.739
on it. It just gives me the answer. So that's

01:39:42.739 --> 01:39:45.340
really powerful. So I think AI will generate

01:39:45.340 --> 01:39:49.659
a lot more generalists. Somebody was saying there

01:39:49.659 --> 01:39:53.279
will be billion -dollar businesses with just

01:39:53.279 --> 01:39:56.770
one person running it. It's entirely possible

01:39:56.770 --> 01:39:59.909
because they can streamline each part of those

01:39:59.909 --> 01:40:02.369
steps. Like you were saying, each part of those

01:40:02.369 --> 01:40:06.850
process can be made super efficient, but you

01:40:06.850 --> 01:40:10.109
cannot replace all of it. So instead of maybe

01:40:10.109 --> 01:40:14.430
having to have like three, four people that can

01:40:14.430 --> 01:40:17.270
have one person do it, but that one person have

01:40:17.270 --> 01:40:22.090
to really be dedicated to use AI and have to

01:40:22.090 --> 01:40:26.689
know. how to leverage AI for all of those different

01:40:26.689 --> 01:40:33.470
aspects. It's interesting times. I don't know.

01:40:33.989 --> 01:40:39.930
Are there any AI tools that are your favorites?

01:40:42.779 --> 01:40:46.520
are your go -tos for every day? I was an early

01:40:46.520 --> 01:40:50.600
chat GPT guy and I've just stuck with it, right?

01:40:50.720 --> 01:40:53.319
I mean, occasionally I'll play with a lot of

01:40:53.319 --> 01:40:57.739
the other tools, but it's almost like I've invested

01:40:57.739 --> 01:41:00.800
a lot of time into kind of learning how to talk

01:41:00.800 --> 01:41:04.159
to the open AI model. And, you know, now it kind

01:41:04.159 --> 01:41:08.659
of knows what I'm asking and I know how to phrase

01:41:08.659 --> 01:41:12.319
things to. get the output that I'm looking for.

01:41:12.699 --> 01:41:17.859
So I just kind of stick to it. Yeah, I like it.

01:41:17.960 --> 01:41:21.140
I think we're living in a... Have you seen the

01:41:21.140 --> 01:41:26.279
movie Her? Yes. You can also talk to it now.

01:41:26.920 --> 01:41:30.800
And it remembers everything. So it's eerie, but

01:41:30.800 --> 01:41:35.039
also cool. It's eerie, but helpful. I go back

01:41:35.039 --> 01:41:37.420
and forth on whether I should have it remember

01:41:37.420 --> 01:41:39.800
all the... things that i've talked to it about

01:41:39.800 --> 01:41:43.640
but i also get frustrated when it's like i've

01:41:43.640 --> 01:41:46.180
asked this before and you know we've had this

01:41:46.180 --> 01:41:48.859
discussion and now you're back to not knowing

01:41:48.859 --> 01:41:51.739
anything right so i go back and flip that memory

01:41:51.739 --> 01:41:55.739
back on yeah yeah i mean i've turned it off i'm

01:41:55.739 --> 01:41:59.979
i'm a little uh scared of it remembering everything

01:41:59.979 --> 01:42:05.720
also i think i feel another point is if i'm okay

01:42:07.300 --> 01:42:09.319
saying the same things again and again because

01:42:09.319 --> 01:42:14.399
it'll start hallucinating if I have a big like

01:42:14.399 --> 01:42:18.079
a thousand chats going on and it needs to sift

01:42:18.079 --> 01:42:20.520
through all of it. It'll start hallucinating

01:42:20.520 --> 01:42:25.640
and the results are kind of less accurate at

01:42:25.640 --> 01:42:28.500
that point. I agree. I agree with that. What

01:42:28.500 --> 01:42:31.640
I found is I think there's a new feature on there

01:42:31.640 --> 01:42:34.800
where you can break your discussions up into

01:42:34.800 --> 01:42:39.060
different projects. Yeah. And it can like and

01:42:39.060 --> 01:42:42.399
it seems to do better keeping into the train

01:42:42.399 --> 01:42:45.020
of thought and understanding everything instead

01:42:45.020 --> 01:42:48.260
of like it having to sift through every single

01:42:48.260 --> 01:42:51.220
thing you've ever talked about. It seems to focus

01:42:51.220 --> 01:42:54.840
in on the output of that specific project. And

01:42:54.840 --> 01:42:58.500
I think that's I think that's helped it compartmentalize

01:42:58.500 --> 01:43:01.739
and know what the response is ought to be. Yeah,

01:43:01.779 --> 01:43:04.659
100%. Yeah, I think I've been using that as well.

01:43:04.659 --> 01:43:07.699
Create those specific projects that, hey, I want

01:43:07.699 --> 01:43:10.140
you to remember these things and nothing else.

01:43:10.680 --> 01:43:13.619
So that's really helpful that it knows about

01:43:13.619 --> 01:43:17.060
the specific things I wanted to know. Yeah, that's

01:43:17.060 --> 01:43:24.079
really cool. Cool. So we do have this question

01:43:24.079 --> 01:43:26.659
that we ask everybody. If money and resources

01:43:26.659 --> 01:43:30.109
were not constraints, what kind of... uh data

01:43:30.109 --> 01:43:34.890
or ai models would you wish to have um access

01:43:34.890 --> 01:43:38.130
to or you want to build if there's if you had

01:43:38.130 --> 01:43:40.069
a magic wand and you had access to everything

01:43:40.069 --> 01:43:46.989
goodness what kind of answers do you do you get

01:43:46.989 --> 01:43:50.729
on that one right hey i think i would say just

01:43:50.729 --> 01:43:54.210
probably we've talked about all the things right

01:43:54.210 --> 01:44:01.229
but just a better model at the things that we've

01:44:01.229 --> 01:44:04.590
talked about here, right? So, I mean, that may

01:44:04.590 --> 01:44:07.310
get us, I mean, we may be talking about, you

01:44:07.310 --> 01:44:11.829
know, AGI, right? How do we create a model that,

01:44:11.869 --> 01:44:17.310
you know, with all appropriate security mechanisms

01:44:17.310 --> 01:44:21.569
in place, right? I mean, you kind of... It gets

01:44:21.569 --> 01:44:25.010
difficult for a model to know everything it needs

01:44:25.010 --> 01:44:28.329
to know about me that can converse with me and

01:44:28.329 --> 01:44:31.350
understand and like answer my questions and really

01:44:31.350 --> 01:44:36.329
be a sidekick, assistant type thing, but then

01:44:36.329 --> 01:44:40.170
also not share that information out to others,

01:44:40.310 --> 01:44:44.869
right? So that's probably if I could get a self

01:44:44.869 --> 01:44:49.600
-contained AGI model. but I was fully confident

01:44:49.600 --> 01:44:52.579
that it could know everything and there was no

01:44:52.579 --> 01:44:55.319
security issues about that data leaking out,

01:44:55.420 --> 01:45:01.380
basically create an AGI super assistant. That

01:45:01.380 --> 01:45:05.520
would be it, right? Because again, I'm multiple

01:45:05.520 --> 01:45:09.520
times more productive today based upon the AI

01:45:09.520 --> 01:45:12.659
models that are out there now. If that was like

01:45:12.659 --> 01:45:16.960
10x or 100x, and I didn't have to worry about

01:45:16.960 --> 01:45:21.140
any security concerns or leaking my data, that's

01:45:21.140 --> 01:45:24.159
probably exactly where I would go first. Yeah,

01:45:24.239 --> 01:45:28.680
you would store all the information about your

01:45:28.680 --> 01:45:33.159
own life there, and so it can be just accessed

01:45:33.159 --> 01:45:35.680
immediately. All of it, right? So maybe it's

01:45:35.680 --> 01:45:40.789
like... I forget what it's called in cosmology,

01:45:40.970 --> 01:45:42.970
but they're like, where are all the aliens at,

01:45:43.050 --> 01:45:45.029
right? And it's like, well, we're not going to

01:45:45.029 --> 01:45:47.250
find them because they've already uploaded themselves.

01:45:48.409 --> 01:45:52.050
So maybe it even gets to the point to where,

01:45:52.149 --> 01:45:54.829
you know, if I could download my consciousness

01:45:54.829 --> 01:46:00.949
every night into this AGI model, I wouldn't have

01:46:00.949 --> 01:46:03.369
to tell it anything. It would know all that I

01:46:03.369 --> 01:46:06.090
know. And it's not going to forget and it's not

01:46:06.090 --> 01:46:09.130
going to procrastinate. And then I can just,

01:46:09.149 --> 01:46:11.649
we can both have our list of, you know, tasks

01:46:11.649 --> 01:46:16.270
for the day. We may a thousand X our productivity.

01:46:17.069 --> 01:46:19.470
Yeah. Yeah. There's no, there's no stopping.

01:46:21.689 --> 01:46:25.130
That's awesome. Yeah. Thank you so much, Matt.

01:46:25.310 --> 01:46:28.710
Appreciate it. It was always, it's always a good

01:46:28.710 --> 01:46:31.729
hang. No, thanks for having me on. We need to

01:46:31.729 --> 01:46:33.819
do this again soon. Absolutely.