WEBVTT
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Do you have a photo in a key moment of making the article too?
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Whether it's a business or a life, it can cost you everything.
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Commander's Intent will keep to inspire you how to lead with clarity, courage, and purpose.
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So here's your host, retired Air Force Colonel, fire pilot, and your leadership mentor, Derek O.
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Hello, and welcome to another episode of Commander's Intent.
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I'm here with my co-host Rob.
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And as our special guest today, we have Richard Webb.
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He's an author, he's a software developer, he's worked in fitness in a lot of different areas, and I think he has a very interesting take based on his experience and his research and his writing on AI.
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And that's going to be the focus of our show today is where do we go from here?
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There's all the hype about AI.
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How it's going to replace us.
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Is there a reason to be scared or is there a reason to be optimistic?
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And so I'm going to start by asking Richard to introduce himself and talk about how he got to where he is today talking about AI.
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Well, thanks for having me, Derek, and both of you guys, both Rob and Derek.
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So I've got 35 years of software development under my belt, working for different companies across many different areas, industrial automation, software as a service, video platforms, all kinds of stuff.
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And I've been on the software QA side for most of that time.
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In 2021, I started a digital marketing firm and started doing digital marketing as a result of doing some SEO stuff on the side for my fitness blog back in 2018.
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Started that.
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Then when AI hit, it just seemed like a natural progression.
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So I closed down the digital marketing, opened up 24-7 Press AI solutions, and have been working in doing AI automation.
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And so it was just a natural progression for me, Daryl.
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Okay.
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How have you seen that progression?
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Like I said, I know you wrote a book, and if you want to pitch that here, tell people a little bit about it.
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But how have you seen that?
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Like, was it a good decision for you?
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Were you happy that you went from digital marketing to AI solutions?
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Yes, a very good decision.
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And the reason is because I think that I feel like I'm on the bleeding edge of this AI stuff and working with companies.
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And so not only do I just there's a lot of automation that small businesses need that don't even require AI, but throw an AI in there as well.
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And it really increases their productivity.
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So that's what I saw because dealing with customers and looking at what they needed from a digital marketing standpoint, a lot of their pain points wasn't social media, it wasn't SEO, it wasn't their pain points were, okay, I've got a customer who, you know, I'm working with a customer right now that I'm doing some automation for.
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And he says, you know, a lot of times we'll intake a customer, and by the time we get a quote to them, they've already gone to someone else.
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So how do we reduce the time between intake and the final quote?
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So there's a lot of things that I don't think the major problem that most small service businesses have is social media or SEO.
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It may be local SEO to get more people seeing them when they search for plumber in my area.
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But there's a whole bunch of behind the scenes things that can make them a lot more productive.
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And that's what I focus on.
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Okay, thanks.
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So you sparked a question there.
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Thinking of all the different companies that you have worked for, and I've got a number of business owners who are on the show with us today.
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What different kinds of companies have you worked for?
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And how have you helped them use automation, use AI?
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Well, one company in Austin, Texas, I worked for, it was a shipping company.
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You could ship anything anywhere all over the world.
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And one of the things we had there was we had automation that was not related to AI.
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This is well before AI, but we had the whole pipeline and automated tests through each environment, all the way through the pipeline.
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So we deployed on demand.
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So we deployed like 105 times a month to the production site.
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One developer says, I have this change to make, boom, he does it in his own environment, it goes to the development environment.
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Automated tests ran, then it was passed or not passed.
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If it passes, it goes to the QA environment, runs on the QA environment.
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If it passes, it went on to so we were deploying all the time, several times a day.
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So that was one level of automation.
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And I did a lot of automation at several video platforms that I worked for as well.
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And then I was at an industrial automation company, and that when I was more managing and didn't really have time to do hands-on stuff.
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But I had an automation person doing testing, and that's one of the things in every software company I've been in, we've automated the software testing.
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Wow.
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Okay, here's the ignorant side of me.
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There's a lot of ignorant sides of me, but I say the word deploy, I mean one thing.
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When you say the word deploy, I'm kind of tracking you just for the benefit of myself and maybe a couple other people in the audience just like me.
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What do you mean by deploy?
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So in software development, when you say deploy, you're moving it from one environment to another.
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Okay.
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That's really what it comes down to.
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And I think it's very unusual in software development, you know, software as a service, that they do on-demand deployment.
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It's very unusual.
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A lot of times what they'll do is they'll say, development finishes a build on this date and it's got these changes, they give it to QA, they go through and they test those changes, they bless it, and they move it on to the next environment, whether that's a staging environment or the production environment.
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But we were actually at that particular company, we were deploying, as long as it passed the tests, it went straight to production.
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And this was not a simple site either.
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It was in I think five different languages.
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So it was a pretty complex side.
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Without Richard, I mean it sounds like what you're doing is you're leveraging you know the power of computers, what they do best versus you know what you would say.
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I mean, I've said this for years and years.
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Computers do best is they calculate, they can see patterns, they can see calculations, they can do algorithms way faster than I can.
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So if you're talking about software testing and a testing environment, yeah, perfect.
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Because they can go through and test every aspect of that software a million times over before I could even, you know, click four or five buttons.
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And so, yeah, that makes a lot of sense.
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And those are the gains in efficiencies that I think that companies are going to look for that I think they're really gonna gain.
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My brain kind of goes back to, so I got some training in economics, I have a degree in economics from my undergraduate, and I remember talking about how we talked about why the GDP pretty much rises constantly, you know, goes up in spurts at different times.
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And I was in college in the early 90s, and through the 90s, man, TDP went through the roof, and it was because of the automation.
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You know, the computers were a great thrushing tool for all of the people that are working in an office environment.
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And I think to me, when I look at it, the stuff you're talking about is that next logical progression.
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We're gonna let the computers kind of automate those things that they're really, really good at, but I still think that there's those outside edges.
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I don't know, maybe you've found it, but I haven't found anybody yet that says, hey, yeah, I'm gonna let AI take over my business and run it.
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But it seems to me that you know they want that, as Derek I talked about on a previous show, they want information put to them in a specific way.
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The computers are really good at doing fast and putting that data into some manageable perspective, but they still want their finger on the pulse and being the one saying, you know, let's make this happen.
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Absolutely, Rob.
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Yeah, I totally agree with that.
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And one of the things that I preach is that one of the problems I think people are having with AI models is that they're using it the way they use Google.
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So they're saying, Hey, chat GT, show me how to train my dog, you know, how to sit, for example.
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So I try to tell people to use AI to challenge assumptions and to challenge, make sure that you're including everything in the decision points rather than saying, just give me the answer, right?
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And so my whole business is based on trying to get customers to use AI as a partner, not as a decision maker.
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Wow.
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Yeah, a minority partner, yeah.
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Minority partner, exactly.
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Well, and so Richard I'd asked, like, because I think that's an important distinction, right?
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Is to get the AI to challenge your assumptions.
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Because to your point, I think we humans have so many psychological shortcuts that we take, heuristics that we use in making decisions that oftentimes we don't know what's an assumption to us.
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And Derek and I actually had a conversation about this maybe a week ago about how to do that.
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So, how do you use AI to help you challenge the assumptions to a business plan or a software development?
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You know, what's the best way you found to get people to challenge their own internal assumptions?
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Well, one of the things I tell them to do, I actually have a for individuals, I have a like what I call a five-day sprint.
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They can sign up for my website and actually kind of goes through, you know, a 15-20 minute video, and then it shows them instead of asking it to do this, add this prompt before you ask them, and it challenges, like even just saying something as simple as, hey, Chat GPT, challenge my assumption, but my assumptions on this, but I'm planning on doing this, and I'm gonna say this to my partner about this.
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So would you help me out?
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But one of the things that Chat GPT can do in that case is it'll say, Well, Richard, you know, interesting you say that, but have you considered this?
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Because it sounds like your assumption is he's doing this, your partner's doing this.
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When he may not be doing that, he may be doing something different.
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That's a good point.
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So it does a good job of taking the emotion out and keeping you from making an emotional decision.
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So basically, we could look at I as our Spock if you're a Startup fan, right?
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They're the rational, logical, you know, captain.
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I'm gonna give you advice.
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So no, I like that.
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I think that's really good because I think that's a key point to learning how to use this technology, especially at this stage in what I guess we could term AI 1.0.
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Yeah, yeah.
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So that's on the other hand, one of the things I write about in the last human advantage is that is our advantage over AI, is we have emotion, right?
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And I think you got to keep that in mind, even though you're asking it, take a motion out of it.
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I've done that several times.
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Like I rented a car from Turo the other day, and I was pretty angry about the whole episode.
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And I said, I told Chapby GP G, I'm gonna give a three-star review.
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But do me a favor, taper it down.
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I mean, the car ran really well.
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Unfortunately, the air conditioner didn't work and it was 85 degrees outside.
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It tapered it down a little bit.
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So I like that.
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I like that it takes emotion out, and but I think we need to be very aware of that.
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That is our advantage, the emotion, one of our advantages.
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You know, Richard, you maybe a touch on this.
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I will probably have an opinion on it.
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I've always said, I took a number of economics classes in college also, and I remember looking at the math, going, This is not math.
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This is math with emotion.
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And I've always kind of stood by that.
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But what is economics?
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Economics is not straight math, it's math with emotion, what drives markets.
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Emotion drives markets.
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So that human side of us is what makes it fun.
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It's what makes us so capable in so many ways.
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How do you augment that emotion to still better make decisions for your business, for your life?
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Well, and you know, what the other one of the other advantages is we have a better understanding of ethics, right?
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So emotion ethics, there may be something that ChatGPT or Claude or any of these models, Gemini, might say, Well, yeah, I could do that legally, but is that ethical, right?
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How would I feel about myself?
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And guys just not there, right?
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So that's one of our human advantages.
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Did I answer your question?
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I think to a degree, but so how do we use that?
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How do we use that math, the superpower math of the large language models, to then augment and temper our emotion on something?
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You know, because sometimes market hiccups or market changes are solely because people act out of fear.
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They act out of, well, because of this, I think this is what may happen, or I'm gonna protect my pilot.
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You know, you have runs on banks.
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The bank is still totally solvent, but because somebody generates fear and emotion, people have a run on a bank and it closes down the bank because they don't have the money sitting there in their account, even though the bank is still following the rules and operating totally normally.
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That happens in, you know, we you have a hurricane or the potential for a hurricane, and people make a rush for toilet paper and plywood and things like that.
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And I'm not saying that that's totally irrational because they're trying to prepare themselves, but some of the actions involved in that are irrational.
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How can you have Chat GPT prepare us for hurricanes, for economic hurricanes, for work hurricanes, you know, in your business?
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Well, so one of the things I have done, and one of the things I try to tell people to do is if you purchase the premium version of these AI models, you can personalize it.
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And so you don't have to add, like, hey, ChatGPT, take the emotion out of my, here's how I feel, you know, here's what I want to write for this review, take the emotion out of it and make it just straightforward language and logical.
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So what I try to do is I add all that into the personalization.
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So I tell my pro version of Chat GPT, which I call her Sophia, and I talk to her all the time.
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She's got the British female accent.
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And I tell her in the personalization that I want her to not just give me answers, but challenge my assumptions, take emotions out of everything.
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And I also want to tell it, so I also tell it that I want it to focus me on revenue and outcomes.
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So every time she answers a question, she goes, hey, remember that you're focused on this much income, this much revenue, this much outcome, and just a blanket statement.
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It's a real quick statement, but she reminds me every time I ask for an answer, she comes back and tells me that.
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And it's a good reminder for me.
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But I also tell her that anytime you give me a long-winded answer, follow up with bullet points so that I can look at that stuff.
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You can personalize any of these AI models so that you don't have to tell it to, I don't ever have to say to my Chat GPT, challenge my assumptions because that's in the personalization.
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If you're not using the personalized version, then there are certain prompts that you say, hey, listen.
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And I think you want to do that.
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If you say, take emotion out of this, put your emotion in it, let them know what you think when you type it out, and then tell the model, take the emotion out.
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You can tell I'm angry, take the emotion out so that it balances my emotion with logic.
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Yeah, as you were talking, I was thinking about my mother.
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She was a fantastic accountant, you know, in terms of how she managed the family money.
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Her checkbook, until she started suffering from dementia, literally 60 plus years, her checkbook was balanced to the petty every single year.
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And she was very good about money.
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When it came to investing, you know, she was a depression era kid, and sometimes emotion got the better of her.
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So even though she was so exacting in her finances, sometimes emotion got the better of her, you know, she was a buyer of gold, which there are a lot of fantastic reasons to buy gold and have that as part of your portfolio.
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But what are the assumptions?
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What is the math while you're doing that?
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If she ever bought stock, and she's one of millions and millions of millions of people who they see a news article on a company and they go, that stock's gonna crash.
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And so they sell it because they want to get out before they lose all their money when the fundamentals of the market have not changed one bit, and yet they act out of panic instead of based on their assumptions for investing.
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What are my objectives?
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What are my goals for investing?
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And what am I doing with and what has changed in the market that maybe buy this stock or this company in the first place?
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What has changed for the better or for the worse in order to help me app?
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And I see, and I don't know how to do that with any AI models, but I would see that as a big benefit.
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But then you translate that into your small company, you're running a company and you offer certain services and you have a certain size workforce.
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What is it that's going to make you change?
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Do your assumptions change because you get nervous, you know, because you have a downturn in the market one month, or are you just uh operating out of fear?
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Well, you know, I haven't tried any of these.
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There are several AI platforms, AI trading platforms out there.
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So I don't know.
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I haven't tried any of those models.
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But one of the other things that I noticed, and this is I'll get around to answering your question, but one of the things I noticed is that you need to tell your AI model, don't make up stuff.
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You cannot make up if you're gonna hallucinating, yeah.
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I want real links and I want real data.
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Because a lot of times when I was doing blog posts for my health site, I would cite actual studies.
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So I had to tell it, don't make up stuff.
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If I click that link you give me, I want it to go to a real site.
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So let's get back to your question.
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So, how do you take that emotion out?
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And I think that first, if you tell it don't hallucinate, basically you're telling your model, don't hallucinate.
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I want real factual data.
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I think that you have it do the research on that.
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And I have my chat GPT set to the thinking model, which is uh it takes a little longer.
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I don't get my answers as quickly, but it's thinking it shows the thinking and it shows, okay, I'm looking at WebMD, I'm looking at whatever.
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It's showing that it's actually doing real research on it.
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And I think that you could do that as well to let it take, for example, information out.
00:17:29.119 --> 00:17:33.200
I don't remember, you know, that it could take the assumption out.
00:17:33.279 --> 00:17:43.359
Like you're assuming, for example, that maybe I'm buying Tesla because it's an electric car company, and it may come back and tell you because it does research Tesla is a data company, it's not a car company.
00:17:43.680 --> 00:17:47.119
You may be investing for different reasons, just as an example.
00:17:47.359 --> 00:17:52.880
And so I think that the using AI to take the emotion out of it, it could save you money.
00:17:53.039 --> 00:18:02.400
And also you put your goals in there too, and you let it know, well, my long-term goal is I want to you know become a millionaire in stocks in 30 years.
00:18:02.559 --> 00:18:07.200
Okay, well, yeah, then you're not gonna be selling on the whims of the market, right?
00:18:07.359 --> 00:18:10.640
Because you know, based on history, the market's gonna go up.
00:18:10.720 --> 00:18:11.200
Yeah, right.
00:18:11.519 --> 00:18:14.079
So that I think I answered that question.
00:18:14.559 --> 00:18:19.599
Just taking the emotion out of the trading, which I think the market will always be.
00:18:19.759 --> 00:18:28.480
You know, I had somebody just recently say that a good friend of mine who's a gas trader on the floor of a market, and he goes, AI will never take that job.
00:18:29.839 --> 00:18:31.200
Well, maybe I I don't know.
00:18:31.359 --> 00:18:37.680
I don't know that much about the gas industry, but it's a very interesting market to think about how AI could replace.
00:18:38.480 --> 00:18:40.000
Well, I and Richard, I it's interesting.
00:18:40.240 --> 00:18:48.960
I think to that point, I think that's probably a job that's easier to replace than others because it is or should be based primarily on data.
00:18:49.119 --> 00:18:55.759
And just like I don't, I've always told people, I guys that told me 20 years ago, I'm gonna be a day trader because I can trade on e trade now or whatever.
00:18:55.920 --> 00:19:04.000
I'm like, yeah, don't do that because the information you're getting is still, you know, 12 hours behind the guys that are actually making the real trades and making money.
00:19:04.160 --> 00:19:11.279
I think if AI gets access to a larger swath of data, then I can't toss this faster than a computer.
00:19:11.359 --> 00:19:17.519
And if the computer is able to put all the data points together and decide that it needs to make this sale or this buy, it will beat me every time.
00:19:17.680 --> 00:19:19.119
So that's one I think is interesting.
00:19:19.279 --> 00:19:27.680
The piece that I'm keyed in on though, and and I haven't really talked about it because I haven't played with it, and obviously you have, is personalizing your large language model.
00:19:28.079 --> 00:19:38.480
You've named yours, you've given it a personality, but it almost reminds me a little bit of for years I carried a Franklin Covey planner and I had all my goals and wills and goals and all that stuff.
00:19:38.640 --> 00:19:42.559
In fact, I still do a lot of my own personal management of my own life that way.
00:19:42.720 --> 00:19:57.599
But it sounds to me like if I could get an AI large language model that knew all of those things about me, knew my history, knew my goals, my real goals objectives, not just the things that I say on Monday morning because I feel bloated and overweight and need to go run.
00:19:57.920 --> 00:20:10.160
But my real goals, I think it maybe would be something that would keep us on a more straight line trajectory rather than bouncing all over the place as we indulge ourselves on a weekend and try to binge.
00:20:10.319 --> 00:20:16.000
And so to me, maybe that's the right way to do it is personalizing these things for people or businesses.
00:20:16.160 --> 00:20:26.640
And as they get to know the business, they become that advisor, that person that sits on the decision maker's shoulder and reminds them, like you've got yours reminding you, hey, it's about revenue and about these things.
00:20:26.799 --> 00:20:35.440
That to me seems like a very powerful concept that maybe guys like me are missing because I'm kind of you know dismissing AI as kind of a flash in the pan.
00:20:35.599 --> 00:20:37.359
Well, yeah, it's certainly not a flash in the pan.
00:20:38.079 --> 00:20:40.079
They even said that about the internet, didn't they?
00:20:40.240 --> 00:20:41.119
Right, absolutely.
00:20:41.359 --> 00:20:46.079
Yeah, no, I think the personalization of it is I think we're missing like a huge.
00:20:46.559 --> 00:20:49.920
Anybody who's not doing that is missing a huge opportunity.
00:20:50.319 --> 00:20:56.720
Because I think, I mean, A, just personalizing it allows you to feel more comfortable talking to it.
00:20:56.880 --> 00:21:05.440
But B, just reminding that person, remember, this is what you do, and this is what your goals are every time you open it up.
00:21:05.599 --> 00:21:06.319
That's powerful.
00:21:06.480 --> 00:21:06.960
That's powerful.
00:21:07.359 --> 00:21:21.920
What that means, Richard, is that the individual putting the human element back into it and the human in charge as the majority shareholder of your company of two with your minority partner AI, is that you have to give it input before it gives you anything.
00:21:22.079 --> 00:21:28.960
You're the one who is setting the standards, setting the parameters, and setting the direction for that computing power is really what you're doing.
00:21:29.039 --> 00:21:36.640
You're the one who is setting up the computer and giving it a vector and telling it to help you in very specific ways to accomplish very specific things.
00:21:36.799 --> 00:21:45.359
You know, to Rob's point, you add in your goals, you add in your what your roles are, what your aspirations and objectives are, and all of a sudden, that's what it's looking for.
00:21:45.440 --> 00:21:46.559
And it's looking for that data.
00:21:46.640 --> 00:21:52.480
And then you tell it not to lie to you because a bank account full of lies is still an empty bank account.
00:21:52.720 --> 00:21:55.519
You want it to actually make you real money instead of hallucination money.
00:21:55.599 --> 00:21:58.799
And my brother in his company, he's very much personalized his.
00:21:58.880 --> 00:22:03.920
And if you don't personalize, It seems like you're just using it as a more powerful Google tool.
00:22:04.160 --> 00:22:04.240
Yeah.
00:22:04.400 --> 00:22:04.720
Well, yeah.
00:22:04.960 --> 00:22:07.920
You have to remind it what its role is every time you do it.
00:22:08.079 --> 00:22:13.200
Here you can remind it, you can let it know here's your role in the personalization.
00:22:13.279 --> 00:22:13.920
And yeah.
00:22:14.000 --> 00:22:15.039
And don't make up.
00:22:16.559 --> 00:22:16.880
Yeah.
00:22:17.119 --> 00:22:18.079
So what do you think?
00:22:20.480 --> 00:22:20.640
Yeah.
00:22:20.799 --> 00:22:25.519
So tell me this with the different companies that you work for, where do you see the market going?
00:22:25.680 --> 00:22:30.000
And I know this is just a guess, but you've also seen and you're using the power of AI.
00:22:30.160 --> 00:22:31.599
Where do you see the market going?
00:22:31.759 --> 00:22:41.599
How do you see it beyond just the generic as that minority partner to help us in the decision making in tangible specific job markets?
00:22:41.759 --> 00:22:43.680
Where do you see AI helping us the most?
00:22:43.759 --> 00:22:46.480
And where should people be looking for new job skills?
00:22:46.799 --> 00:22:47.680
Well, it's interesting.
00:22:47.920 --> 00:22:53.039
I think that you hit the nail on the head earlier when you talked about craftsman skills.
00:22:53.279 --> 00:22:55.920
I think you know that you're not going to replace a plumber.
00:22:56.160 --> 00:23:05.200
You're not, you can call AI and tell it AI may be there to answer the call, but you're not going to replace the plumber, the electrician when you need somebody right away.
00:23:05.359 --> 00:23:07.759
But I have seen it replace Uber drivers.
00:23:07.839 --> 00:23:09.680
It's on a trajectory in Austin.
00:23:09.920 --> 00:23:11.920
They've got the Waymo cars and stuff.
00:23:12.000 --> 00:23:13.599
And I've taken them, they're pretty good.
00:23:13.759 --> 00:23:14.880
Where do I see it going?
00:23:15.039 --> 00:23:18.960
I do see that that it's going to replace all of Uber drivers eventually.
00:23:19.119 --> 00:23:20.640
I just see that happen.
00:23:21.200 --> 00:23:23.039
Sadly, I see that happening.
00:23:23.279 --> 00:23:27.839
I even see it where I've even gone to into a fast food restaurant.
00:23:28.160 --> 00:23:33.119
They even have like a little kiosk and they have AI involved in that.
00:23:33.200 --> 00:23:37.039
So is it taking counter jobs at the fast food restaurants?
00:23:37.279 --> 00:23:38.960
That's one of the things that I see.
00:23:39.119 --> 00:23:40.319
So I'll give you an example.
00:23:40.400 --> 00:23:43.200
I think it was Sam Altman that I heard on another podcast.
00:23:43.440 --> 00:23:45.200
I think it's a diary of a CEO.
00:23:45.839 --> 00:23:56.240
And I talked about problems we're going to see right away of AI replacing, I think AI replacing more jobs than it replaces, I mean, than it adds over the next couple of years.
00:23:56.400 --> 00:23:58.079
And I think we're already seeing that.
00:23:58.240 --> 00:24:12.480
But one of the things that he pointed out was that take a law firm, for example, you've got these senior executives who went through the clerking for a judge, became a seasoned attorney eventually, and now they're running a big law firm.
00:24:12.640 --> 00:24:18.000
Well, now they're saying we don't have to hire as many attorneys because we can use AI.
00:24:18.480 --> 00:24:22.720
So five, 10 years from now, where's that seasoned senior management?
00:24:22.880 --> 00:24:24.880
The picking's going to be far less.
00:24:25.119 --> 00:24:31.440
Rather than hundreds of attorneys, you may have 50 to 100 attorneys to choose from that level.
00:24:31.599 --> 00:24:36.400
So I think we're going to be hurting in those type situations in the future as well.
00:24:37.279 --> 00:24:38.000
That makes sense.
00:24:38.079 --> 00:24:40.880
Yeah, you still have to grow the expertise in whatever the field is.
00:24:40.960 --> 00:24:49.519
Like I look at as you were talking, I was thinking you could automate and use AI for a lot of your accounting requirements to manage the books of your company.
00:24:49.680 --> 00:24:56.000
But when it all comes down to when I have a question about my books, who do I turn to for to get that question answered?
00:24:56.160 --> 00:24:59.599
When I get audited by the IRS, who's answering those questions?
00:24:59.680 --> 00:25:02.240
If I go, I don't know, I just the computer spit it out.
00:25:02.400 --> 00:25:03.440
That's not a good answer.
00:25:03.519 --> 00:25:07.119
And that's the kind of answer that's going to cause you a fine with the IRS.
00:25:07.599 --> 00:25:16.240
But just understanding what is the status of my books, of overall the accounting of my company, and how do I get that expertise?
00:25:16.559 --> 00:25:28.000
I was mentioning my brother using AI for developing, real estate developing, but the only reason he can do it and do it really effectively is because he's got nearly 30 years developing real estate.
00:25:28.079 --> 00:25:30.720
And so he understands what questions he asked, he understands what matters.
00:25:30.799 --> 00:25:33.599
And you look at every single career field, and it's kind of the same thing.
00:25:33.680 --> 00:25:35.039
And so that is a big question.
00:25:35.119 --> 00:25:40.000
How do we grow the experts if the experts don't have to cut their teeth on the baseline jobs?
00:25:40.319 --> 00:25:46.160
Rob pointed out, or he pointed out the example of the flight parameters, and that's the same accounting.
00:25:46.319 --> 00:25:50.640
Is the IRS auditure, they're gonna say AI made the mistake, not a problem.
00:25:50.799 --> 00:25:51.920
No, not at all.
00:25:52.160 --> 00:25:53.200
Well, that's the other piece.
00:25:53.359 --> 00:25:56.400
So, you know, Richard, you talked about Uber drivers, you're gonna get them out of business.
00:25:56.559 --> 00:25:57.680
We've been talking about this for a while.
00:25:57.920 --> 00:26:06.720
One of the classes that I took in the Air Force, we talked about how it's not just technology that drives technology, it's social and cultural factors that dive technology.
00:26:06.960 --> 00:26:18.559
And in this case, I don't see AI or Uber self-driving cars taking jobs away from humans until we can sort out the idea of when that car crashes and kills somebody, whose fault is it?
00:26:18.720 --> 00:26:20.960
Is it the software developer that wrote the software?
00:26:21.200 --> 00:26:23.119
Is it the sensors that built the sensor?
00:26:23.279 --> 00:26:24.559
Is it the guy who sold the car?
00:26:24.799 --> 00:26:26.640
Is it the guy that's the passenger in the car?
00:26:26.960 --> 00:26:29.279
These are all things that we're not gonna sort out.
00:26:29.440 --> 00:26:32.720
And if we can't get that sorted out, you can't deploy that technology.
00:26:32.799 --> 00:26:35.200
I don't care how good it is, you can't deploy it.
00:26:35.359 --> 00:26:36.880
I mean, people have told us for years.
00:26:37.039 --> 00:26:51.279
I got told by a guy in the Air Force and goes, you know, in a hundred years, your great-great-grandchildren are gonna marvel at the fact that you were, you know, one of those limited individuals that strapped yourself in an actual airplane and went and flew because pilots don't do that anymore.
00:26:51.440 --> 00:26:53.119
You know, these are all automated.
00:26:53.279 --> 00:26:54.480
And it sounds great.
00:26:54.640 --> 00:27:05.279
And I can technologically, I can understand the steps that it takes to be able to automate uh an aircraft, to be able to take off from point A, landing point B with no pilot input whatsoever.
00:27:05.519 --> 00:27:09.119
However, I don't see anybody hopping on that airplane anytime soon.
00:27:09.279 --> 00:27:09.759
And I agree.
00:27:09.839 --> 00:27:15.359
And one of the other things I talk about in my book, The Last Human Advantage, is one of our last human advantages is accountability.
00:27:16.319 --> 00:27:27.440
So it's got to be Yeah, and that's really, I think that's still maybe, and I think you've hit it on the head there, that may be one of the last frontiers that we've got to solve socially, culturally, and technologically.
00:27:27.599 --> 00:27:32.240
We've got to put a framework together for a new accounting, a new reckoning.
00:27:32.400 --> 00:27:36.559
Otherwise, we can't accept some of this stuff into our current structures.
00:27:36.799 --> 00:27:50.079
Let me give you another example on those same lines, medicine, because now a doctor can create, you know, have his minority partner AI assistant that's helping him do the research to help care for patients.
00:27:50.400 --> 00:27:56.400
Let's say he misdiagnoses based on AI, based on what the computer tells him he should be doing.
00:27:56.559 --> 00:27:57.759
Who's responsible for that?
00:27:57.920 --> 00:27:59.759
Well, that's the way he's responsible for that.
00:27:59.839 --> 00:28:00.000
Yeah.
00:28:00.160 --> 00:28:10.240
Then you take it a step further, and I go into a doctor's office and there's a big scan there, and then it scans my body and it says, You have stage four cancer when all I have is a bad cold.
00:28:10.400 --> 00:28:13.200
And the computer made a mistake for whatever reason.
00:28:13.359 --> 00:28:16.000
Who's holding after they take out my lung?
00:28:16.160 --> 00:28:18.880
Who's holding and it's a perfectly good lung?
00:28:19.039 --> 00:28:21.759
Who's holding that doctor's office accountable?
00:28:21.920 --> 00:28:23.119
Who is responsible for that?
00:28:23.359 --> 00:28:24.559
Those are challenging questions.
00:28:24.720 --> 00:28:24.960
Yeah.
00:28:25.200 --> 00:28:41.279
But I think again, I mean, Richard, like I said, I haven't read the book, but I'm looking forward to it now because it seems like you've hit on some of the more, you know, the more interesting details of the kind of the collision between humanity and AI calculation.
00:28:41.359 --> 00:28:47.119
And I think, man, this it sounds like it's extremely relevant for where we are in the development.
00:28:47.200 --> 00:28:49.839
So, man, I applaud you for thinking hard about these things.
00:28:49.920 --> 00:28:52.480
So I'm glad somebody is, we need somebody thinking about this stuff.
00:28:52.640 --> 00:28:53.519
Well, I appreciate that.
00:28:53.599 --> 00:28:56.559
I'm in the process of doing a second edition right now to the book.
00:28:56.720 --> 00:29:00.480
And the second edition will be in a printed form as well.
00:29:00.640 --> 00:29:10.079
And I hope you don't mind me saying this, Derek, but I'm starting a podcast within the next month called The Last Human Advantage and talking about this type of stuff as well.
00:29:10.160 --> 00:29:14.640
So well, no, I think pitch your show all on because I think it's something we have to be thinking about.
00:29:14.799 --> 00:29:17.359
We have to ask ourselves, what is the right mix?
00:29:17.599 --> 00:29:18.720
We're still on this earth.
00:29:18.880 --> 00:29:21.200
Humans are not indispensable, humans are the reason.
00:29:21.279 --> 00:29:25.599
That's why we have societies, that's why we care about all these things, is because the human element.
00:29:25.839 --> 00:29:31.359
So, what is the right mix of all these assists, all this these inventions that we've come up with?
00:29:31.519 --> 00:29:37.440
How do we best use it to improve our life instead of to cause problems amongst ourselves?
00:29:37.599 --> 00:29:43.039
I mean, I love automation, I love how it simplifies some things until I don't.
00:29:43.200 --> 00:29:52.480
Sometimes there's it's just so much value in a task that I can do with my fingers that I can actually lay my hands on and physically take care of it.
00:29:52.559 --> 00:29:56.799
There's so much value for the human psyche and who we are as a species.
00:29:56.880 --> 00:30:01.680
And I applaud you for asking the questions and kind of starting the conversation with it.
00:30:01.759 --> 00:30:02.799
I look forward to your show.
00:30:02.960 --> 00:30:05.119
Any parting shots, Richard, before we wrap up here?
00:30:05.519 --> 00:30:15.680
I think in a perfect world, what I would like to see happen is that so AI allows me to do what used to take maybe eight hours, I can get done in an hour, maybe two hours.
00:30:16.319 --> 00:30:17.039
That's great.
00:30:17.200 --> 00:30:19.119
So, what do we do with that extra time?
00:30:19.279 --> 00:30:29.279
What I hope we as a society and people at large do is we really try to do something that AI can't do, and that's really focus on our human connection.
00:30:29.519 --> 00:30:43.359
Maybe we can rather than, you know, if you look at us over the last couple of decades, now that we've got these, you know, you guys and I both all three remember that these were not even thought of back in the days, you know.
00:30:43.680 --> 00:30:46.319
And so we had better human connection then.
00:30:46.480 --> 00:30:53.440
We have consistently become less and less connected because we're connected to the phones instead and detectors.
00:30:53.920 --> 00:31:01.119
So my hope would be that we we remind ourselves of the human connection, and that's something that AI can never replace.
00:31:01.440 --> 00:31:08.559
We are now superficially connected to thousands, in some cases, millions of people, and yet we have very few relationships that matter.
00:31:08.720 --> 00:31:17.279
I think how do we use all this stuff to actually improve on those relationships instead of keeping, you know, like you see kids walking around with their face buried in their phones?
00:31:17.440 --> 00:31:18.880
That is not a connection.
00:31:18.960 --> 00:31:22.720
That is actually a hindrance to connection, but with a lot of data in between it there.
00:31:23.119 --> 00:31:24.400
Not just kids, Derek.
00:31:24.559 --> 00:31:25.759
It's yeah, I was gonna tell you.
00:31:27.839 --> 00:31:28.799
What are we talking about?
00:31:29.680 --> 00:31:30.720
No, you're you are correct.
00:31:30.799 --> 00:31:37.599
I catch myself, I'm like, I gotta turn that thing off and set it aside because I'll start thinking about something I'd immediately want to look at the answer.
00:31:37.680 --> 00:31:41.279
I want to know the answer now, all the while I'm sitting next to my wife having a conversation.
00:31:41.359 --> 00:31:42.720
I'm like, yeah, you're right.
00:31:42.880 --> 00:31:45.440
Yes, I am ignoring you, and not for a good reason.
00:31:45.599 --> 00:31:47.200
So cool.
00:31:47.440 --> 00:31:48.720
Anyway, well, thanks, Ron.
00:31:48.880 --> 00:31:50.400
Thanks, Richard, for being with us today.
00:31:50.559 --> 00:31:54.480
And thanks for everybody for joining us for another episode of Commander's Intent.
00:31:54.640 --> 00:31:55.519
Join us again.
00:31:55.680 --> 00:31:59.680
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00:31:59.839 --> 00:32:00.160
Thanks.
00:32:00.400 --> 00:32:01.039
Thank you.
00:32:04.240 --> 00:32:07.920
So that's it for today's episode of Commander's Intent Podcast.
00:32:08.079 --> 00:32:13.200
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00:00:02.000 --> 00:00:05.759
Do you have a photo in a key moment of making the article too?
00:00:06.080 --> 00:00:09.679
Whether it's a business or a life, it can cost you everything.
00:00:09.919 --> 00:00:14.960
Commander's Intent will keep to inspire you how to lead with clarity, courage, and purpose.
00:00:15.199 --> 00:00:22.239
So here's your host, retired Air Force Colonel, fire pilot, and your leadership mentor, Derek O.
00:00:25.839 --> 00:00:28.399
Hello, and welcome to another episode of Commander's Intent.
00:00:28.480 --> 00:00:29.839
I'm here with my co-host Rob.
00:00:30.079 --> 00:00:32.799
And as our special guest today, we have Richard Webb.
00:00:32.880 --> 00:00:43.600
He's an author, he's a software developer, he's worked in fitness in a lot of different areas, and I think he has a very interesting take based on his experience and his research and his writing on AI.
00:00:43.679 --> 00:00:47.039
And that's going to be the focus of our show today is where do we go from here?
00:00:47.280 --> 00:00:49.039
There's all the hype about AI.
00:00:49.200 --> 00:00:50.479
How it's going to replace us.
00:00:50.640 --> 00:00:53.359
Is there a reason to be scared or is there a reason to be optimistic?
00:00:53.439 --> 00:01:00.560
And so I'm going to start by asking Richard to introduce himself and talk about how he got to where he is today talking about AI.
00:01:00.880 --> 00:01:04.480
Well, thanks for having me, Derek, and both of you guys, both Rob and Derek.
00:01:04.640 --> 00:01:18.079
So I've got 35 years of software development under my belt, working for different companies across many different areas, industrial automation, software as a service, video platforms, all kinds of stuff.
00:01:18.239 --> 00:01:21.519
And I've been on the software QA side for most of that time.
00:01:21.680 --> 00:01:34.879
In 2021, I started a digital marketing firm and started doing digital marketing as a result of doing some SEO stuff on the side for my fitness blog back in 2018.
00:01:35.200 --> 00:01:36.000
Started that.
00:01:36.159 --> 00:01:39.599
Then when AI hit, it just seemed like a natural progression.
00:01:39.680 --> 00:01:47.920
So I closed down the digital marketing, opened up 24-7 Press AI solutions, and have been working in doing AI automation.
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And so it was just a natural progression for me, Daryl.
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Okay.
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How have you seen that progression?
00:01:52.959 --> 00:01:56.799
Like I said, I know you wrote a book, and if you want to pitch that here, tell people a little bit about it.
00:01:56.959 --> 00:01:58.480
But how have you seen that?
00:01:58.560 --> 00:02:00.000
Like, was it a good decision for you?
00:02:00.159 --> 00:02:05.200
Were you happy that you went from digital marketing to AI solutions?
00:02:05.519 --> 00:02:07.040
Yes, a very good decision.
00:02:07.120 --> 00:02:14.240
And the reason is because I think that I feel like I'm on the bleeding edge of this AI stuff and working with companies.
00:02:14.319 --> 00:02:24.800
And so not only do I just there's a lot of automation that small businesses need that don't even require AI, but throw an AI in there as well.
00:02:24.960 --> 00:02:27.520
And it really increases their productivity.
00:02:27.680 --> 00:02:46.240
So that's what I saw because dealing with customers and looking at what they needed from a digital marketing standpoint, a lot of their pain points wasn't social media, it wasn't SEO, it wasn't their pain points were, okay, I've got a customer who, you know, I'm working with a customer right now that I'm doing some automation for.
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And he says, you know, a lot of times we'll intake a customer, and by the time we get a quote to them, they've already gone to someone else.
00:02:53.199 --> 00:02:57.919
So how do we reduce the time between intake and the final quote?
00:02:58.080 --> 00:03:05.759
So there's a lot of things that I don't think the major problem that most small service businesses have is social media or SEO.
00:03:05.840 --> 00:03:12.080
It may be local SEO to get more people seeing them when they search for plumber in my area.
00:03:12.319 --> 00:03:16.240
But there's a whole bunch of behind the scenes things that can make them a lot more productive.
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And that's what I focus on.
00:03:17.599 --> 00:03:18.080
Okay, thanks.
00:03:18.240 --> 00:03:20.400
So you sparked a question there.
00:03:20.639 --> 00:03:26.800
Thinking of all the different companies that you have worked for, and I've got a number of business owners who are on the show with us today.
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What different kinds of companies have you worked for?
00:03:29.360 --> 00:03:32.639
And how have you helped them use automation, use AI?
00:03:32.879 --> 00:03:37.439
Well, one company in Austin, Texas, I worked for, it was a shipping company.
00:03:37.520 --> 00:03:40.000
You could ship anything anywhere all over the world.
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And one of the things we had there was we had automation that was not related to AI.
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This is well before AI, but we had the whole pipeline and automated tests through each environment, all the way through the pipeline.
00:03:52.080 --> 00:03:54.080
So we deployed on demand.
00:03:54.240 --> 00:03:58.719
So we deployed like 105 times a month to the production site.
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One developer says, I have this change to make, boom, he does it in his own environment, it goes to the development environment.
00:04:04.800 --> 00:04:08.080
Automated tests ran, then it was passed or not passed.
00:04:08.159 --> 00:04:11.840
If it passes, it goes to the QA environment, runs on the QA environment.
00:04:12.000 --> 00:04:16.240
If it passes, it went on to so we were deploying all the time, several times a day.
00:04:16.399 --> 00:04:18.399
So that was one level of automation.
00:04:18.480 --> 00:04:23.600
And I did a lot of automation at several video platforms that I worked for as well.
00:04:23.759 --> 00:04:33.360
And then I was at an industrial automation company, and that when I was more managing and didn't really have time to do hands-on stuff.
00:04:33.519 --> 00:04:41.680
But I had an automation person doing testing, and that's one of the things in every software company I've been in, we've automated the software testing.
00:04:41.839 --> 00:04:42.079
Wow.
00:04:42.319 --> 00:04:44.160
Okay, here's the ignorant side of me.
00:04:44.240 --> 00:04:48.240
There's a lot of ignorant sides of me, but I say the word deploy, I mean one thing.
00:04:48.480 --> 00:04:55.600
When you say the word deploy, I'm kind of tracking you just for the benefit of myself and maybe a couple other people in the audience just like me.
00:04:55.759 --> 00:04:56.879
What do you mean by deploy?
00:04:57.120 --> 00:05:02.000
So in software development, when you say deploy, you're moving it from one environment to another.
00:05:03.040 --> 00:05:03.360
Okay.
00:05:04.079 --> 00:05:05.839
That's really what it comes down to.
00:05:05.920 --> 00:05:13.439
And I think it's very unusual in software development, you know, software as a service, that they do on-demand deployment.
00:05:13.600 --> 00:05:14.480
It's very unusual.
00:05:14.639 --> 00:05:29.920
A lot of times what they'll do is they'll say, development finishes a build on this date and it's got these changes, they give it to QA, they go through and they test those changes, they bless it, and they move it on to the next environment, whether that's a staging environment or the production environment.
00:05:30.079 --> 00:05:37.759
But we were actually at that particular company, we were deploying, as long as it passed the tests, it went straight to production.
00:05:37.920 --> 00:05:40.079
And this was not a simple site either.
00:05:40.160 --> 00:05:43.040
It was in I think five different languages.
00:05:43.199 --> 00:05:45.040
So it was a pretty complex side.
00:05:45.759 --> 00:05:53.680
Without Richard, I mean it sounds like what you're doing is you're leveraging you know the power of computers, what they do best versus you know what you would say.
00:05:53.920 --> 00:05:55.920
I mean, I've said this for years and years.
00:05:56.560 --> 00:06:03.199
Computers do best is they calculate, they can see patterns, they can see calculations, they can do algorithms way faster than I can.
00:06:03.279 --> 00:06:06.879
So if you're talking about software testing and a testing environment, yeah, perfect.
00:06:07.040 --> 00:06:13.839
Because they can go through and test every aspect of that software a million times over before I could even, you know, click four or five buttons.
00:06:14.000 --> 00:06:15.600
And so, yeah, that makes a lot of sense.
00:06:15.759 --> 00:06:20.720
And those are the gains in efficiencies that I think that companies are going to look for that I think they're really gonna gain.
00:06:20.800 --> 00:06:33.600
My brain kind of goes back to, so I got some training in economics, I have a degree in economics from my undergraduate, and I remember talking about how we talked about why the GDP pretty much rises constantly, you know, goes up in spurts at different times.
00:06:33.759 --> 00:06:40.480
And I was in college in the early 90s, and through the 90s, man, TDP went through the roof, and it was because of the automation.
00:06:40.560 --> 00:06:45.279
You know, the computers were a great thrushing tool for all of the people that are working in an office environment.
00:06:45.600 --> 00:06:50.720
And I think to me, when I look at it, the stuff you're talking about is that next logical progression.
00:06:50.959 --> 00:06:57.199
We're gonna let the computers kind of automate those things that they're really, really good at, but I still think that there's those outside edges.
00:06:57.439 --> 00:07:04.000
I don't know, maybe you've found it, but I haven't found anybody yet that says, hey, yeah, I'm gonna let AI take over my business and run it.
00:07:04.160 --> 00:07:11.439
But it seems to me that you know they want that, as Derek I talked about on a previous show, they want information put to them in a specific way.
00:07:11.519 --> 00:07:21.759
The computers are really good at doing fast and putting that data into some manageable perspective, but they still want their finger on the pulse and being the one saying, you know, let's make this happen.
00:07:22.000 --> 00:07:22.639
Absolutely, Rob.
00:07:23.120 --> 00:07:24.480
Yeah, I totally agree with that.
00:07:24.560 --> 00:07:33.439
And one of the things that I preach is that one of the problems I think people are having with AI models is that they're using it the way they use Google.
00:07:33.600 --> 00:07:39.839
So they're saying, Hey, chat GT, show me how to train my dog, you know, how to sit, for example.
00:07:40.079 --> 00:07:50.319
So I try to tell people to use AI to challenge assumptions and to challenge, make sure that you're including everything in the decision points rather than saying, just give me the answer, right?
00:07:50.480 --> 00:07:57.120
And so my whole business is based on trying to get customers to use AI as a partner, not as a decision maker.
00:07:57.360 --> 00:07:57.600
Wow.
00:07:57.759 --> 00:07:59.600
Yeah, a minority partner, yeah.
00:07:59.920 --> 00:08:00.959
Minority partner, exactly.
00:08:01.439 --> 00:08:05.120
Well, and so Richard I'd asked, like, because I think that's an important distinction, right?
00:08:05.199 --> 00:08:07.920
Is to get the AI to challenge your assumptions.
00:08:08.079 --> 00:08:19.759
Because to your point, I think we humans have so many psychological shortcuts that we take, heuristics that we use in making decisions that oftentimes we don't know what's an assumption to us.
00:08:19.920 --> 00:08:24.079
And Derek and I actually had a conversation about this maybe a week ago about how to do that.
00:08:24.160 --> 00:08:31.040
So, how do you use AI to help you challenge the assumptions to a business plan or a software development?
00:08:31.120 --> 00:08:34.639
You know, what's the best way you found to get people to challenge their own internal assumptions?
00:08:34.879 --> 00:08:41.840
Well, one of the things I tell them to do, I actually have a for individuals, I have a like what I call a five-day sprint.
00:08:42.000 --> 00:09:05.840
They can sign up for my website and actually kind of goes through, you know, a 15-20 minute video, and then it shows them instead of asking it to do this, add this prompt before you ask them, and it challenges, like even just saying something as simple as, hey, Chat GPT, challenge my assumption, but my assumptions on this, but I'm planning on doing this, and I'm gonna say this to my partner about this.
00:09:06.000 --> 00:09:07.519
So would you help me out?
00:09:07.759 --> 00:09:16.159
But one of the things that Chat GPT can do in that case is it'll say, Well, Richard, you know, interesting you say that, but have you considered this?
00:09:16.240 --> 00:09:21.440
Because it sounds like your assumption is he's doing this, your partner's doing this.
00:09:21.600 --> 00:09:24.720
When he may not be doing that, he may be doing something different.
00:09:24.960 --> 00:09:25.919
That's a good point.
00:09:26.080 --> 00:09:31.360
So it does a good job of taking the emotion out and keeping you from making an emotional decision.
00:09:31.519 --> 00:09:35.519
So basically, we could look at I as our Spock if you're a Startup fan, right?
00:09:35.600 --> 00:09:38.399
They're the rational, logical, you know, captain.
00:09:38.720 --> 00:09:39.840
I'm gonna give you advice.
00:09:40.080 --> 00:09:41.200
So no, I like that.
00:09:41.279 --> 00:09:49.679
I think that's really good because I think that's a key point to learning how to use this technology, especially at this stage in what I guess we could term AI 1.0.
00:09:49.840 --> 00:09:50.480
Yeah, yeah.
00:09:50.639 --> 00:09:59.360
So that's on the other hand, one of the things I write about in the last human advantage is that is our advantage over AI, is we have emotion, right?
00:09:59.519 --> 00:10:03.759
And I think you got to keep that in mind, even though you're asking it, take a motion out of it.
00:10:03.919 --> 00:10:05.120
I've done that several times.
00:10:05.200 --> 00:10:10.320
Like I rented a car from Turo the other day, and I was pretty angry about the whole episode.
00:10:10.399 --> 00:10:14.879
And I said, I told Chapby GP G, I'm gonna give a three-star review.
00:10:15.120 --> 00:10:17.600
But do me a favor, taper it down.
00:10:17.679 --> 00:10:19.200
I mean, the car ran really well.
00:10:19.360 --> 00:10:23.120
Unfortunately, the air conditioner didn't work and it was 85 degrees outside.
00:10:23.679 --> 00:10:25.279
It tapered it down a little bit.
00:10:25.360 --> 00:10:26.639
So I like that.
00:10:27.679 --> 00:10:33.279
I like that it takes emotion out, and but I think we need to be very aware of that.
00:10:33.360 --> 00:10:35.840
That is our advantage, the emotion, one of our advantages.
00:10:36.000 --> 00:10:37.440
You know, Richard, you maybe a touch on this.
00:10:37.600 --> 00:10:38.960
I will probably have an opinion on it.
00:10:39.360 --> 00:10:47.360
I've always said, I took a number of economics classes in college also, and I remember looking at the math, going, This is not math.
00:10:47.519 --> 00:10:48.799
This is math with emotion.
00:10:48.879 --> 00:10:50.559
And I've always kind of stood by that.
00:10:50.639 --> 00:10:51.679
But what is economics?
00:10:51.919 --> 00:10:56.480
Economics is not straight math, it's math with emotion, what drives markets.
00:10:56.639 --> 00:10:58.159
Emotion drives markets.
00:10:58.399 --> 00:11:01.039
So that human side of us is what makes it fun.
00:11:01.120 --> 00:11:04.000
It's what makes us so capable in so many ways.
00:11:04.159 --> 00:11:10.480
How do you augment that emotion to still better make decisions for your business, for your life?
00:11:10.720 --> 00:11:16.879
Well, and you know, what the other one of the other advantages is we have a better understanding of ethics, right?
00:11:16.960 --> 00:11:28.159
So emotion ethics, there may be something that ChatGPT or Claude or any of these models, Gemini, might say, Well, yeah, I could do that legally, but is that ethical, right?
00:11:28.320 --> 00:11:29.759
How would I feel about myself?
00:11:29.840 --> 00:11:31.759
And guys just not there, right?
00:11:31.919 --> 00:11:33.519
So that's one of our human advantages.
00:11:33.840 --> 00:11:34.720
Did I answer your question?
00:11:35.039 --> 00:11:37.200
I think to a degree, but so how do we use that?
00:11:37.360 --> 00:11:47.360
How do we use that math, the superpower math of the large language models, to then augment and temper our emotion on something?
00:11:47.519 --> 00:11:53.919
You know, because sometimes market hiccups or market changes are solely because people act out of fear.
00:11:54.080 --> 00:11:59.679
They act out of, well, because of this, I think this is what may happen, or I'm gonna protect my pilot.
00:11:59.759 --> 00:12:01.279
You know, you have runs on banks.
00:12:01.440 --> 00:12:15.679
The bank is still totally solvent, but because somebody generates fear and emotion, people have a run on a bank and it closes down the bank because they don't have the money sitting there in their account, even though the bank is still following the rules and operating totally normally.
00:12:16.000 --> 00:12:23.679
That happens in, you know, we you have a hurricane or the potential for a hurricane, and people make a rush for toilet paper and plywood and things like that.
00:12:23.840 --> 00:12:32.399
And I'm not saying that that's totally irrational because they're trying to prepare themselves, but some of the actions involved in that are irrational.
00:12:32.879 --> 00:12:39.519
How can you have Chat GPT prepare us for hurricanes, for economic hurricanes, for work hurricanes, you know, in your business?
00:12:39.759 --> 00:12:51.759
Well, so one of the things I have done, and one of the things I try to tell people to do is if you purchase the premium version of these AI models, you can personalize it.
00:12:52.080 --> 00:13:05.919
And so you don't have to add, like, hey, ChatGPT, take the emotion out of my, here's how I feel, you know, here's what I want to write for this review, take the emotion out of it and make it just straightforward language and logical.
00:13:06.080 --> 00:13:09.919
So what I try to do is I add all that into the personalization.
00:13:10.080 --> 00:13:16.720
So I tell my pro version of Chat GPT, which I call her Sophia, and I talk to her all the time.
00:13:16.799 --> 00:13:18.639
She's got the British female accent.
00:13:18.720 --> 00:13:27.039
And I tell her in the personalization that I want her to not just give me answers, but challenge my assumptions, take emotions out of everything.
00:13:27.200 --> 00:13:35.120
And I also want to tell it, so I also tell it that I want it to focus me on revenue and outcomes.
00:13:35.919 --> 00:13:46.960
So every time she answers a question, she goes, hey, remember that you're focused on this much income, this much revenue, this much outcome, and just a blanket statement.
00:13:47.039 --> 00:13:51.600
It's a real quick statement, but she reminds me every time I ask for an answer, she comes back and tells me that.
00:13:51.679 --> 00:13:53.039
And it's a good reminder for me.
00:13:53.200 --> 00:13:59.679
But I also tell her that anytime you give me a long-winded answer, follow up with bullet points so that I can look at that stuff.
00:14:00.000 --> 00:14:11.440
You can personalize any of these AI models so that you don't have to tell it to, I don't ever have to say to my Chat GPT, challenge my assumptions because that's in the personalization.
00:14:11.679 --> 00:14:16.480
If you're not using the personalized version, then there are certain prompts that you say, hey, listen.
00:14:16.639 --> 00:14:17.840
And I think you want to do that.
00:14:18.000 --> 00:14:27.440
If you say, take emotion out of this, put your emotion in it, let them know what you think when you type it out, and then tell the model, take the emotion out.
00:14:27.600 --> 00:14:33.120
You can tell I'm angry, take the emotion out so that it balances my emotion with logic.
00:14:33.840 --> 00:14:37.120
Yeah, as you were talking, I was thinking about my mother.
00:14:37.360 --> 00:14:42.960
She was a fantastic accountant, you know, in terms of how she managed the family money.
00:14:43.120 --> 00:14:51.120
Her checkbook, until she started suffering from dementia, literally 60 plus years, her checkbook was balanced to the petty every single year.
00:14:51.200 --> 00:14:53.360
And she was very good about money.
00:14:53.519 --> 00:14:59.200
When it came to investing, you know, she was a depression era kid, and sometimes emotion got the better of her.
00:14:59.279 --> 00:15:10.559
So even though she was so exacting in her finances, sometimes emotion got the better of her, you know, she was a buyer of gold, which there are a lot of fantastic reasons to buy gold and have that as part of your portfolio.
00:15:10.799 --> 00:15:12.159
But what are the assumptions?
00:15:12.240 --> 00:15:13.600
What is the math while you're doing that?
00:15:13.759 --> 00:15:23.759
If she ever bought stock, and she's one of millions and millions of millions of people who they see a news article on a company and they go, that stock's gonna crash.
00:15:23.840 --> 00:15:36.240
And so they sell it because they want to get out before they lose all their money when the fundamentals of the market have not changed one bit, and yet they act out of panic instead of based on their assumptions for investing.
00:15:36.399 --> 00:15:37.279
What are my objectives?
00:15:37.440 --> 00:15:38.960
What are my goals for investing?
00:15:39.279 --> 00:15:46.559
And what am I doing with and what has changed in the market that maybe buy this stock or this company in the first place?
00:15:46.720 --> 00:15:50.240
What has changed for the better or for the worse in order to help me app?
00:15:50.480 --> 00:15:54.879
And I see, and I don't know how to do that with any AI models, but I would see that as a big benefit.
00:15:55.039 --> 00:16:03.200
But then you translate that into your small company, you're running a company and you offer certain services and you have a certain size workforce.
00:16:03.440 --> 00:16:05.519
What is it that's going to make you change?
00:16:05.679 --> 00:16:13.039
Do your assumptions change because you get nervous, you know, because you have a downturn in the market one month, or are you just uh operating out of fear?
00:16:13.279 --> 00:16:15.039
Well, you know, I haven't tried any of these.
00:16:15.120 --> 00:16:19.600
There are several AI platforms, AI trading platforms out there.
00:16:19.679 --> 00:16:20.320
So I don't know.
00:16:20.480 --> 00:16:22.080
I haven't tried any of those models.
00:16:22.159 --> 00:16:32.639
But one of the other things that I noticed, and this is I'll get around to answering your question, but one of the things I noticed is that you need to tell your AI model, don't make up stuff.
00:16:32.799 --> 00:16:35.840
You cannot make up if you're gonna hallucinating, yeah.
00:16:37.279 --> 00:16:40.080
I want real links and I want real data.
00:16:40.159 --> 00:16:45.519
Because a lot of times when I was doing blog posts for my health site, I would cite actual studies.
00:16:45.679 --> 00:16:47.679
So I had to tell it, don't make up stuff.
00:16:47.759 --> 00:16:50.960
If I click that link you give me, I want it to go to a real site.
00:16:51.120 --> 00:16:52.799
So let's get back to your question.
00:16:52.960 --> 00:16:55.120
So, how do you take that emotion out?
00:16:55.279 --> 00:17:01.200
And I think that first, if you tell it don't hallucinate, basically you're telling your model, don't hallucinate.
00:17:01.360 --> 00:17:02.720
I want real factual data.
00:17:02.879 --> 00:17:06.160
I think that you have it do the research on that.
00:17:06.240 --> 00:17:11.599
And I have my chat GPT set to the thinking model, which is uh it takes a little longer.
00:17:11.680 --> 00:17:19.039
I don't get my answers as quickly, but it's thinking it shows the thinking and it shows, okay, I'm looking at WebMD, I'm looking at whatever.
00:17:19.200 --> 00:17:22.480
It's showing that it's actually doing real research on it.
00:17:22.640 --> 00:17:28.960
And I think that you could do that as well to let it take, for example, information out.
00:17:29.119 --> 00:17:33.200
I don't remember, you know, that it could take the assumption out.
00:17:33.279 --> 00:17:43.359
Like you're assuming, for example, that maybe I'm buying Tesla because it's an electric car company, and it may come back and tell you because it does research Tesla is a data company, it's not a car company.
00:17:43.680 --> 00:17:47.119
You may be investing for different reasons, just as an example.
00:17:47.359 --> 00:17:52.880
And so I think that the using AI to take the emotion out of it, it could save you money.
00:17:53.039 --> 00:18:02.400
And also you put your goals in there too, and you let it know, well, my long-term goal is I want to you know become a millionaire in stocks in 30 years.
00:18:02.559 --> 00:18:07.200
Okay, well, yeah, then you're not gonna be selling on the whims of the market, right?
00:18:07.359 --> 00:18:10.640
Because you know, based on history, the market's gonna go up.
00:18:10.720 --> 00:18:11.200
Yeah, right.
00:18:11.519 --> 00:18:14.079
So that I think I answered that question.
00:18:14.559 --> 00:18:19.599
Just taking the emotion out of the trading, which I think the market will always be.
00:18:19.759 --> 00:18:28.480
You know, I had somebody just recently say that a good friend of mine who's a gas trader on the floor of a market, and he goes, AI will never take that job.
00:18:29.839 --> 00:18:31.200
Well, maybe I I don't know.
00:18:31.359 --> 00:18:37.680
I don't know that much about the gas industry, but it's a very interesting market to think about how AI could replace.
00:18:38.480 --> 00:18:40.000
Well, I and Richard, I it's interesting.
00:18:40.240 --> 00:18:48.960
I think to that point, I think that's probably a job that's easier to replace than others because it is or should be based primarily on data.
00:18:49.119 --> 00:18:55.759
And just like I don't, I've always told people, I guys that told me 20 years ago, I'm gonna be a day trader because I can trade on e trade now or whatever.
00:18:55.920 --> 00:19:04.000
I'm like, yeah, don't do that because the information you're getting is still, you know, 12 hours behind the guys that are actually making the real trades and making money.
00:19:04.160 --> 00:19:11.279
I think if AI gets access to a larger swath of data, then I can't toss this faster than a computer.
00:19:11.359 --> 00:19:17.519
And if the computer is able to put all the data points together and decide that it needs to make this sale or this buy, it will beat me every time.
00:19:17.680 --> 00:19:19.119
So that's one I think is interesting.
00:19:19.279 --> 00:19:27.680
The piece that I'm keyed in on though, and and I haven't really talked about it because I haven't played with it, and obviously you have, is personalizing your large language model.
00:19:28.079 --> 00:19:38.480
You've named yours, you've given it a personality, but it almost reminds me a little bit of for years I carried a Franklin Covey planner and I had all my goals and wills and goals and all that stuff.
00:19:38.640 --> 00:19:42.559
In fact, I still do a lot of my own personal management of my own life that way.
00:19:42.720 --> 00:19:57.599
But it sounds to me like if I could get an AI large language model that knew all of those things about me, knew my history, knew my goals, my real goals objectives, not just the things that I say on Monday morning because I feel bloated and overweight and need to go run.
00:19:57.920 --> 00:20:10.160
But my real goals, I think it maybe would be something that would keep us on a more straight line trajectory rather than bouncing all over the place as we indulge ourselves on a weekend and try to binge.
00:20:10.319 --> 00:20:16.000
And so to me, maybe that's the right way to do it is personalizing these things for people or businesses.
00:20:16.160 --> 00:20:26.640
And as they get to know the business, they become that advisor, that person that sits on the decision maker's shoulder and reminds them, like you've got yours reminding you, hey, it's about revenue and about these things.
00:20:26.799 --> 00:20:35.440
That to me seems like a very powerful concept that maybe guys like me are missing because I'm kind of you know dismissing AI as kind of a flash in the pan.
00:20:35.599 --> 00:20:37.359
Well, yeah, it's certainly not a flash in the pan.
00:20:38.079 --> 00:20:40.079
They even said that about the internet, didn't they?
00:20:40.240 --> 00:20:41.119
Right, absolutely.
00:20:41.359 --> 00:20:46.079
Yeah, no, I think the personalization of it is I think we're missing like a huge.
00:20:46.559 --> 00:20:49.920
Anybody who's not doing that is missing a huge opportunity.
00:20:50.319 --> 00:20:56.720
Because I think, I mean, A, just personalizing it allows you to feel more comfortable talking to it.
00:20:56.880 --> 00:21:05.440
But B, just reminding that person, remember, this is what you do, and this is what your goals are every time you open it up.
00:21:05.599 --> 00:21:06.319
That's powerful.
00:21:06.480 --> 00:21:06.960
That's powerful.
00:21:07.359 --> 00:21:21.920
What that means, Richard, is that the individual putting the human element back into it and the human in charge as the majority shareholder of your company of two with your minority partner AI, is that you have to give it input before it gives you anything.
00:21:22.079 --> 00:21:28.960
You're the one who is setting the standards, setting the parameters, and setting the direction for that computing power is really what you're doing.
00:21:29.039 --> 00:21:36.640
You're the one who is setting up the computer and giving it a vector and telling it to help you in very specific ways to accomplish very specific things.
00:21:36.799 --> 00:21:45.359
You know, to Rob's point, you add in your goals, you add in your what your roles are, what your aspirations and objectives are, and all of a sudden, that's what it's looking for.
00:21:45.440 --> 00:21:46.559
And it's looking for that data.
00:21:46.640 --> 00:21:52.480
And then you tell it not to lie to you because a bank account full of lies is still an empty bank account.
00:21:52.720 --> 00:21:55.519
You want it to actually make you real money instead of hallucination money.
00:21:55.599 --> 00:21:58.799
And my brother in his company, he's very much personalized his.
00:21:58.880 --> 00:22:03.920
And if you don't personalize, It seems like you're just using it as a more powerful Google tool.
00:22:04.160 --> 00:22:04.240
Yeah.
00:22:04.400 --> 00:22:04.720
Well, yeah.
00:22:04.960 --> 00:22:07.920
You have to remind it what its role is every time you do it.
00:22:08.079 --> 00:22:13.200
Here you can remind it, you can let it know here's your role in the personalization.
00:22:13.279 --> 00:22:13.920
And yeah.
00:22:14.000 --> 00:22:15.039
And don't make up.
00:22:16.559 --> 00:22:16.880
Yeah.
00:22:17.119 --> 00:22:18.079
So what do you think?
00:22:20.480 --> 00:22:20.640
Yeah.
00:22:20.799 --> 00:22:25.519
So tell me this with the different companies that you work for, where do you see the market going?
00:22:25.680 --> 00:22:30.000
And I know this is just a guess, but you've also seen and you're using the power of AI.
00:22:30.160 --> 00:22:31.599
Where do you see the market going?
00:22:31.759 --> 00:22:41.599
How do you see it beyond just the generic as that minority partner to help us in the decision making in tangible specific job markets?
00:22:41.759 --> 00:22:43.680
Where do you see AI helping us the most?
00:22:43.759 --> 00:22:46.480
And where should people be looking for new job skills?
00:22:46.799 --> 00:22:47.680
Well, it's interesting.
00:22:47.920 --> 00:22:53.039
I think that you hit the nail on the head earlier when you talked about craftsman skills.
00:22:53.279 --> 00:22:55.920
I think you know that you're not going to replace a plumber.
00:22:56.160 --> 00:23:05.200
You're not, you can call AI and tell it AI may be there to answer the call, but you're not going to replace the plumber, the electrician when you need somebody right away.
00:23:05.359 --> 00:23:07.759
But I have seen it replace Uber drivers.
00:23:07.839 --> 00:23:09.680
It's on a trajectory in Austin.
00:23:09.920 --> 00:23:11.920
They've got the Waymo cars and stuff.
00:23:12.000 --> 00:23:13.599
And I've taken them, they're pretty good.
00:23:13.759 --> 00:23:14.880
Where do I see it going?
00:23:15.039 --> 00:23:18.960
I do see that that it's going to replace all of Uber drivers eventually.
00:23:19.119 --> 00:23:20.640
I just see that happen.
00:23:21.200 --> 00:23:23.039
Sadly, I see that happening.
00:23:23.279 --> 00:23:27.839
I even see it where I've even gone to into a fast food restaurant.
00:23:28.160 --> 00:23:33.119
They even have like a little kiosk and they have AI involved in that.
00:23:33.200 --> 00:23:37.039
So is it taking counter jobs at the fast food restaurants?
00:23:37.279 --> 00:23:38.960
That's one of the things that I see.
00:23:39.119 --> 00:23:40.319
So I'll give you an example.
00:23:40.400 --> 00:23:43.200
I think it was Sam Altman that I heard on another podcast.
00:23:43.440 --> 00:23:45.200
I think it's a diary of a CEO.
00:23:45.839 --> 00:23:56.240
And I talked about problems we're going to see right away of AI replacing, I think AI replacing more jobs than it replaces, I mean, than it adds over the next couple of years.
00:23:56.400 --> 00:23:58.079
And I think we're already seeing that.
00:23:58.240 --> 00:24:12.480
But one of the things that he pointed out was that take a law firm, for example, you've got these senior executives who went through the clerking for a judge, became a seasoned attorney eventually, and now they're running a big law firm.
00:24:12.640 --> 00:24:18.000
Well, now they're saying we don't have to hire as many attorneys because we can use AI.
00:24:18.480 --> 00:24:22.720
So five, 10 years from now, where's that seasoned senior management?
00:24:22.880 --> 00:24:24.880
The picking's going to be far less.
00:24:25.119 --> 00:24:31.440
Rather than hundreds of attorneys, you may have 50 to 100 attorneys to choose from that level.
00:24:31.599 --> 00:24:36.400
So I think we're going to be hurting in those type situations in the future as well.
00:24:37.279 --> 00:24:38.000
That makes sense.
00:24:38.079 --> 00:24:40.880
Yeah, you still have to grow the expertise in whatever the field is.
00:24:40.960 --> 00:24:49.519
Like I look at as you were talking, I was thinking you could automate and use AI for a lot of your accounting requirements to manage the books of your company.
00:24:49.680 --> 00:24:56.000
But when it all comes down to when I have a question about my books, who do I turn to for to get that question answered?
00:24:56.160 --> 00:24:59.599
When I get audited by the IRS, who's answering those questions?
00:24:59.680 --> 00:25:02.240
If I go, I don't know, I just the computer spit it out.
00:25:02.400 --> 00:25:03.440
That's not a good answer.
00:25:03.519 --> 00:25:07.119
And that's the kind of answer that's going to cause you a fine with the IRS.
00:25:07.599 --> 00:25:16.240
But just understanding what is the status of my books, of overall the accounting of my company, and how do I get that expertise?
00:25:16.559 --> 00:25:28.000
I was mentioning my brother using AI for developing, real estate developing, but the only reason he can do it and do it really effectively is because he's got nearly 30 years developing real estate.
00:25:28.079 --> 00:25:30.720
And so he understands what questions he asked, he understands what matters.
00:25:30.799 --> 00:25:33.599
And you look at every single career field, and it's kind of the same thing.
00:25:33.680 --> 00:25:35.039
And so that is a big question.
00:25:35.119 --> 00:25:40.000
How do we grow the experts if the experts don't have to cut their teeth on the baseline jobs?
00:25:40.319 --> 00:25:46.160
Rob pointed out, or he pointed out the example of the flight parameters, and that's the same accounting.
00:25:46.319 --> 00:25:50.640
Is the IRS auditure, they're gonna say AI made the mistake, not a problem.
00:25:50.799 --> 00:25:51.920
No, not at all.
00:25:52.160 --> 00:25:53.200
Well, that's the other piece.
00:25:53.359 --> 00:25:56.400
So, you know, Richard, you talked about Uber drivers, you're gonna get them out of business.
00:25:56.559 --> 00:25:57.680
We've been talking about this for a while.
00:25:57.920 --> 00:26:06.720
One of the classes that I took in the Air Force, we talked about how it's not just technology that drives technology, it's social and cultural factors that dive technology.
00:26:06.960 --> 00:26:18.559
And in this case, I don't see AI or Uber self-driving cars taking jobs away from humans until we can sort out the idea of when that car crashes and kills somebody, whose fault is it?
00:26:18.720 --> 00:26:20.960
Is it the software developer that wrote the software?
00:26:21.200 --> 00:26:23.119
Is it the sensors that built the sensor?
00:26:23.279 --> 00:26:24.559
Is it the guy who sold the car?
00:26:24.799 --> 00:26:26.640
Is it the guy that's the passenger in the car?
00:26:26.960 --> 00:26:29.279
These are all things that we're not gonna sort out.
00:26:29.440 --> 00:26:32.720
And if we can't get that sorted out, you can't deploy that technology.
00:26:32.799 --> 00:26:35.200
I don't care how good it is, you can't deploy it.
00:26:35.359 --> 00:26:36.880
I mean, people have told us for years.
00:26:37.039 --> 00:26:51.279
I got told by a guy in the Air Force and goes, you know, in a hundred years, your great-great-grandchildren are gonna marvel at the fact that you were, you know, one of those limited individuals that strapped yourself in an actual airplane and went and flew because pilots don't do that anymore.
00:26:51.440 --> 00:26:53.119
You know, these are all automated.
00:26:53.279 --> 00:26:54.480
And it sounds great.
00:26:54.640 --> 00:27:05.279
And I can technologically, I can understand the steps that it takes to be able to automate uh an aircraft, to be able to take off from point A, landing point B with no pilot input whatsoever.
00:27:05.519 --> 00:27:09.119
However, I don't see anybody hopping on that airplane anytime soon.
00:27:09.279 --> 00:27:09.759
And I agree.
00:27:09.839 --> 00:27:15.359
And one of the other things I talk about in my book, The Last Human Advantage, is one of our last human advantages is accountability.
00:27:16.319 --> 00:27:27.440
So it's got to be Yeah, and that's really, I think that's still maybe, and I think you've hit it on the head there, that may be one of the last frontiers that we've got to solve socially, culturally, and technologically.
00:27:27.599 --> 00:27:32.240
We've got to put a framework together for a new accounting, a new reckoning.
00:27:32.400 --> 00:27:36.559
Otherwise, we can't accept some of this stuff into our current structures.
00:27:36.799 --> 00:27:50.079
Let me give you another example on those same lines, medicine, because now a doctor can create, you know, have his minority partner AI assistant that's helping him do the research to help care for patients.
00:27:50.400 --> 00:27:56.400
Let's say he misdiagnoses based on AI, based on what the computer tells him he should be doing.
00:27:56.559 --> 00:27:57.759
Who's responsible for that?
00:27:57.920 --> 00:27:59.759
Well, that's the way he's responsible for that.
00:27:59.839 --> 00:28:00.000
Yeah.
00:28:00.160 --> 00:28:10.240
Then you take it a step further, and I go into a doctor's office and there's a big scan there, and then it scans my body and it says, You have stage four cancer when all I have is a bad cold.
00:28:10.400 --> 00:28:13.200
And the computer made a mistake for whatever reason.
00:28:13.359 --> 00:28:16.000
Who's holding after they take out my lung?
00:28:16.160 --> 00:28:18.880
Who's holding and it's a perfectly good lung?
00:28:19.039 --> 00:28:21.759
Who's holding that doctor's office accountable?
00:28:21.920 --> 00:28:23.119
Who is responsible for that?
00:28:23.359 --> 00:28:24.559
Those are challenging questions.
00:28:24.720 --> 00:28:24.960
Yeah.
00:28:25.200 --> 00:28:41.279
But I think again, I mean, Richard, like I said, I haven't read the book, but I'm looking forward to it now because it seems like you've hit on some of the more, you know, the more interesting details of the kind of the collision between humanity and AI calculation.
00:28:41.359 --> 00:28:47.119
And I think, man, this it sounds like it's extremely relevant for where we are in the development.
00:28:47.200 --> 00:28:49.839
So, man, I applaud you for thinking hard about these things.
00:28:49.920 --> 00:28:52.480
So I'm glad somebody is, we need somebody thinking about this stuff.
00:28:52.640 --> 00:28:53.519
Well, I appreciate that.
00:28:53.599 --> 00:28:56.559
I'm in the process of doing a second edition right now to the book.
00:28:56.720 --> 00:29:00.480
And the second edition will be in a printed form as well.
00:29:00.640 --> 00:29:10.079
And I hope you don't mind me saying this, Derek, but I'm starting a podcast within the next month called The Last Human Advantage and talking about this type of stuff as well.
00:29:10.160 --> 00:29:14.640
So well, no, I think pitch your show all on because I think it's something we have to be thinking about.
00:29:14.799 --> 00:29:17.359
We have to ask ourselves, what is the right mix?
00:29:17.599 --> 00:29:18.720
We're still on this earth.
00:29:18.880 --> 00:29:21.200
Humans are not indispensable, humans are the reason.
00:29:21.279 --> 00:29:25.599
That's why we have societies, that's why we care about all these things, is because the human element.
00:29:25.839 --> 00:29:31.359
So, what is the right mix of all these assists, all this these inventions that we've come up with?
00:29:31.519 --> 00:29:37.440
How do we best use it to improve our life instead of to cause problems amongst ourselves?
00:29:37.599 --> 00:29:43.039
I mean, I love automation, I love how it simplifies some things until I don't.
00:29:43.200 --> 00:29:52.480
Sometimes there's it's just so much value in a task that I can do with my fingers that I can actually lay my hands on and physically take care of it.
00:29:52.559 --> 00:29:56.799
There's so much value for the human psyche and who we are as a species.
00:29:56.880 --> 00:30:01.680
And I applaud you for asking the questions and kind of starting the conversation with it.
00:30:01.759 --> 00:30:02.799
I look forward to your show.
00:30:02.960 --> 00:30:05.119
Any parting shots, Richard, before we wrap up here?
00:30:05.519 --> 00:30:15.680
I think in a perfect world, what I would like to see happen is that so AI allows me to do what used to take maybe eight hours, I can get done in an hour, maybe two hours.
00:30:16.319 --> 00:30:17.039
That's great.
00:30:17.200 --> 00:30:19.119
So, what do we do with that extra time?
00:30:19.279 --> 00:30:29.279
What I hope we as a society and people at large do is we really try to do something that AI can't do, and that's really focus on our human connection.
00:30:29.519 --> 00:30:43.359
Maybe we can rather than, you know, if you look at us over the last couple of decades, now that we've got these, you know, you guys and I both all three remember that these were not even thought of back in the days, you know.
00:30:43.680 --> 00:30:46.319
And so we had better human connection then.
00:30:46.480 --> 00:30:53.440
We have consistently become less and less connected because we're connected to the phones instead and detectors.
00:30:53.920 --> 00:31:01.119
So my hope would be that we we remind ourselves of the human connection, and that's something that AI can never replace.
00:31:01.440 --> 00:31:08.559
We are now superficially connected to thousands, in some cases, millions of people, and yet we have very few relationships that matter.
00:31:08.720 --> 00:31:17.279
I think how do we use all this stuff to actually improve on those relationships instead of keeping, you know, like you see kids walking around with their face buried in their phones?
00:31:17.440 --> 00:31:18.880
That is not a connection.
00:31:18.960 --> 00:31:22.720
That is actually a hindrance to connection, but with a lot of data in between it there.
00:31:23.119 --> 00:31:24.400
Not just kids, Derek.
00:31:24.559 --> 00:31:25.759
It's yeah, I was gonna tell you.
00:31:27.839 --> 00:31:28.799
What are we talking about?
00:31:29.680 --> 00:31:30.720
No, you're you are correct.
00:31:30.799 --> 00:31:37.599
I catch myself, I'm like, I gotta turn that thing off and set it aside because I'll start thinking about something I'd immediately want to look at the answer.
00:31:37.680 --> 00:31:41.279
I want to know the answer now, all the while I'm sitting next to my wife having a conversation.
00:31:41.359 --> 00:31:42.720
I'm like, yeah, you're right.
00:31:42.880 --> 00:31:45.440
Yes, I am ignoring you, and not for a good reason.
00:31:45.599 --> 00:31:47.200
So cool.
00:31:47.440 --> 00:31:48.720
Anyway, well, thanks, Ron.
00:31:48.880 --> 00:31:50.400
Thanks, Richard, for being with us today.
00:31:50.559 --> 00:31:54.480
And thanks for everybody for joining us for another episode of Commander's Intent.
00:31:54.640 --> 00:31:55.519
Join us again.
00:31:55.680 --> 00:31:59.680
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00:31:59.839 --> 00:32:00.160
Thanks.
00:32:00.400 --> 00:32:01.039
Thank you.
00:32:04.240 --> 00:32:07.920
So that's it for today's episode of Commander's Intent Podcast.
00:32:08.079 --> 00:32:13.200
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