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Welcome to another exciting episode of Data Driven.

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Today, we have a truly unique guest, legendary theme park

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experience creator, Jeff Thatcher. With decades of

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expertise under his belt, Jeff has crafted groundbreaking

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exhibitions worldwide, from immersive museum tours to innovative

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AI enhanced live experiences. In this episode, we'll

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dive into how Jeff is blending creativity with cutting edge technology

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to create stories that don't just entertain but also connect emotionally.

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From talking paintings to college football heroics, Jeff's work

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shows how AI isn't just a tool, it's a bridge to imagination.

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And a quick note for parents, while we don't use any profanity,

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there's a discussion about Saint Nick that might not align with what the little

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ones believe. So fasten your seatbelts for an episode

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that redefines storytelling in the age of AI.

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Well, hello, and welcome back to Driven, the podcast where we explore the

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emergent fields of data science, artificial

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intelligence, and, of course, data engineering. With me this time,

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is my most favoritest data engineer in the world, Andy Leonard.

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How's it going, Andy? It's going pretty well, Frank. How are you?

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Good. Good. Good. We're recording this just before Thanksgiving, which is gonna

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be what I like to call the time of madness. Because 2

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of my 2 of my kids have birthdays in December and, obviously

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yeah. But today, I'm

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excited. I really have an interesting guest here.

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And he is, he's been

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told to me by his PR person. We had a little bit of banter about

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this in the, in in the thing. But I would say it's true. Legendary

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theme park experience creator, Jeff Thatcher. And for the last

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4 decades, Jeff has been pushing the boundaries of live experiences.

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With his company, Creative Principles, Jeff has designed ground

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beak breaking, exhibitions around the world from,

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doing some work with the largest zoo in the United Arab Emirates

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to designing something that is called a mobile

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reverse vending machines, that managed to gather

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13,000 donations in one season to creating an immersive

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tour at the, Ozark Ozark Historic Mill

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and more. But now Jeff is exploring the many ways that

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artificial intelligence can elevate the live experience and designs.

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That could mean museum visitors forming an emotional connection with a

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holocaust victim or, visitors forming

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a relationship with an indentured field worker. Individuals could ask

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these characters or museum subjects questions,

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that a curator never would have thought of, and receive

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thoughtful detailed answers thanks to the power of

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AI. So welcome to the show, Jeff. I'm definitely,

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very curious about this, and I also like the the your your description of let's

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not let the fun police ruin AI because I can totally

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see that happening. Amen, brother.

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So so tell us about You know, I was just so it was funny. I

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was just watching all the people complain about the new Coca

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Cola, holiday magic commercial that was produced in AI.

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And everybody's all this backlash about using artificial intelligence to create

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this commercial. And I'm like, wait a second. Don't they know that Santa Claus is

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fake? And you're complaining about using artificial intelligence to create

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a commercial about someone that's not real? Or talking polar

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bears? What's the problem? Right. Right. It's

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all fake. It's okay. We love it. It's storytelling. It's

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alright. You know? It's okay to believe in the magic.

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But how can you complain about using artificial intelligence to

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create something that's in and of itself not

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real. So and Oh, that's a good point. And if they you know,

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it's one of those things where if Coca Cola hadn't done something kinda cutting

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edge, they would have been just labeled another big company that's not

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using AI and being cutting edge. Right? Like, it's kinda like the they're in a

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no win situation with I'm starting I'm starting to think

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honestly that we should just stop calling things AI because, like, we

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just opened. And, you know, listen, I'm not trying to, like, overstate it. We

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actually did our research on this because, you know, if you look at

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AI in live experiences or AI in museums

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or attractions, the very first use of it was in 2017 in

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Brazil where IB IBM and Ogilvy with Watson

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went down to Brazil, and they allowed people to actually talk to

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paintings and ask paintings, you know, these paintings as artwork. You could ask the

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artwork a question. You know? I remember that. I remember there was a

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Salvador Dali. Yeah. Well, that's that's a different project. That's a that's

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a different project. Okay. Yeah. That's a different one. So 2017 was in Brazil

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with Watson. And then Salvador Dali was 2019 in

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Saint Petersburg, Florida, the saint the Salvador Dali Museum, where you can

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actually talk And they updated again, I think, in 2021, where you can

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actually talk to Salvador Dali. And then there was

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the Shoah Foundation did a AI experience where you could actually talk

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and ask, you know, like personal questions of Holocaust survivors, like, you know,

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how do you still have joy? And what was it like when you were

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a kid? And, you know, just you could ask it anything. Like one one

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of these Holocaust survivors, you could ask him or her anything you wanted. And

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then the National World War 2 Museum did this something very similar to the

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show a foundation where you could, you know, talk to a USO dancer and talk

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to a pilot and just ask them questions. But what we did

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at the College Football Hall of Fame in 2024 was we actually made the guest

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the star of the experience. And no one had ever done that before Where, you

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know, the guest comes in. You know, they they stand at a kiosk. Their their

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picture is taken 5 times. They answer questions

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like, Frank, what's your favorite football team? College football team. That was

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not a rhetorical question. I actually wanna know. Oh, I actually

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don't follow college football, so I'll, I don't know. Fordham Rams.

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I'll give a shout out to you. Rams. There you go. What about you, Andy?

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Do you have a favorite college football team? Sure. We'll go with the Virginia Tech

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Hokies. There you go. The Hokies. And then you ask what the

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least favorite team is. Most people say Alabama or Ohio State or one

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of the big ones, but, you know, your least favorite team. And and then, you

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know, you know, what what do you what kind of football food do you like?

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And how do people describe your personality? So it kinda gets your coaching. So, yeah,

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basically, you know, the kiosk asks you a bunch of

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questions. You upload your pictures. It all goes up into the cloud, all processes.

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And then as you go to 19

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different exhibits around the hall of fame, you become part of the exhibition.

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So when you go, for example, to the coaches exhibit, and

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there there's an actual coaches exhibit that talks about, you know,

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Lavelle Edwards and Lou Paterno and all these great coaches and Newt

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Rockne. You get to actually there's a creates a mini

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mockumentary about your life as the best coach that never was. When you go to

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the cheerleading exhibit, you see That's really cool. Yeah. When you see the

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TCU cheerleading outfit, you see the, you know, Notre Dame

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mascot, you you then become you get to pose with your favorite team's

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mascot. You get to see yourself in a cheerleading outfit. You get to see

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yourself in the weight room. You get to see yourself as a flyover pilot. You

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get to see yourself posing with the Heisman. You get to see yourself

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in the exhibition. And, honestly, it's never been done before.

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But our challenge has been that everybody just is like, oh, yeah. AI.

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Whatever. You know? You know what I mean? Because it's like

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because, you know, we actually had a reporter from Wired come in, and

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she was like, man, you guys totally undersold this. And we're like, no. We really

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didn't. We told you exactly what it was, but we have this

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this filter over us with AI where so many people are talking about

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it, and so much of it is hype. And so much of it is beyond

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hype. It's unbelievable. Like, I was just looking at the new, oh,

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it's a Google thing where it turns anything into a podcast. Like

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Notebook l m, actually. Yes. Thank you. Notebook l m. Talking about that,

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before you joined. Yeah. Yeah. That's amazing. Right? So you so you have these

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two issues going on. You have one where every single company in the world is

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labeling what they're doing as AI, and it's not really true. And then

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you've got the other hand where people are talking about and doing things that are

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groundbreaking with AI, like we did at the College Football Hall of Fame in Atlanta.

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And everybody's like, whatever. You know what I mean? Because they

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don't interesting dichotomy. You're right. Like, I I now that you pointed out, like, you

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know, the whole thing of, like, you know, people do not

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realize that, like, you know, obviously, there are no polar bears looking, you

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know, cracking open, you know, glass bottles of Coca Cola with

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their, you know, with their paws. But, like, you know, that was CGI. Why was

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that, like, you know, praised and became a cultural icon? And then this

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is, you know, universally panned. Now I will say from from

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from looking at it, there are scenes in there where it definitely looks

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uncanny valley when you have the people, but that's to be expected right

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now. You know, you know, I am not a data

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scientist like Andy. Right? I I when it

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comes to programming, I'm I'm not a, you know, I'm not

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an AI expert. I don't consider I mean, I use AI, the

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groundbreaking work with AI, but the people that did the programming and did the

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heavy engineering prompting were a great partner in New Zealand called Hypercinema.

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But I have to say, what what we always forget with new

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technology is that it's really about the story. It's about telling a

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great story, especially on the creative side. You know, I

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mean, you know, people clients sometimes

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will will like, oh, what if people say this was a waste of money? Like,

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you know, like, oh, I'm not I'm not sure we could spend you know, $2,000,000

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on an LED tower in a lobby because then they'll say, oh, you're just wasting

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money. And I say, well, it's it's just like the movies. How

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often do you walk out of a movie and say, well, that was a big

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waste of money and time And it's bad. You never say

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that when you walk out of a great movie. You never walk out of a

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great movie and say, oh, that was a waste of time and money because it

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was a great story. But when you walk out of Michael Bay's Pearl Harbor, you're

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like, yeah, the effects were great, but what a waste of

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time. What a waste of money. Right? Because it

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was it was. It wasn't a great story. So, you know and

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technology, specmology. You know, it's like people forget. I mean, animatronics,

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we think are amazing, and we think Walt Disney invented them. That's not true. They're

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actually invented Marie Antoinette and the Silver Swan. 17th

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century, 18th century, we were making animatronics.

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The holographic experiences that you see at Disney and Universal Studios

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and all these things. The 19th century Pepper's ghost that was

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invented in the UK by an illusionist.

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3 d is great, and the new liminal

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space 3 d LED screen is amazing because it gives you

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10 times more projection and 10 times more depth of field and it's better on

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the sides. So, you know, technology keeps improving. But, you know,

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3 d has been around since, what, the 19 forties. It's but how many

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of us have seen really lousy 3 d movies

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or 4 d cinema or 5 d cinema or there's actually a theater in

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China that says it's a 14 d cinema which is just ridiculous and

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stupid. Right. But I know 14 d. But

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it all is about, are you just gonna use it to tell what what are

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you gonna do with it? Right? What are you gonna do with that technology? What

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are you gonna do with that data? You guys talk about data. Right? Right. How

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often do people take data and just do horrible things with it? You know,

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how do people take it? You gotta tell a great story. You gotta

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use it in a way that puts people in

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the story. And, you know, the analogy that we use all the

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time is I I don't know how if you guys are into theme parks, but,

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you know, most of us have been to a theme park where they have the

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old time photo studio in the western themed land, right, where you go

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in and you put on the cowboy hat and the boots and the vest and

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the gun and your wife or your girlfriend,

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sometimes she's a salon girl. If you wanna be more stereotypical or she'll be

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a cowgirl. Hopefully, they're not there at the same time. Well, hopefully,

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you're posing together. Right? Yeah. But, you know. Oh, I see. Yeah.

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Definitely. With with your spouse

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or and or your yes. Okay. So you're, you know, you're posing

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in this you're posing in this, you know, you're posing in this outfit.

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Right? And it's fun, and it's an experience to do that. Well, what

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we've done at the College Football Hall of Fame is essentially allow any guest

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to pose as a football player, as a coach, as a cheerleader, as

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a marching band, as a historic fan in the 19 twenties, as a historic fan

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in the 19 eighties, as a flyover pilot. But we've

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done that for 764 different teams. Now if

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you were to actually go acquire

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764 football uniforms for all sizes,

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different genders. And then the same thing for

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cheerleading costumes, uniforms, marching band uniforms. The warehouse

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would be the size of that warehouse in Indiana Jones. Right? There's a lost ark

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at the end of the movie. It's totally impossible. So all we

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did with AI is something that we've been doing forever in the theme park industry,

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which was we put you in the story. We immersed you in the story. We've

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just been able to do it with AI with the scale and personalization that's not

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possible anywhere else. And

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that truly is as a experience designer as a

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storyteller. That's what excites me about AI is being able to

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tell great stories and to put people in the

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story. And I have to tell you that is something that isn't

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fake. That is something that is very real. When you see

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someone walk onto the football field, it's like

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the College Football Hall of Fame has this massive football field at the the finale

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experience. Right? Well, it's not a complete it's like about the size of

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a of a basketball court. Right? But it's a football field. Right? It's got the

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green grass and it's got a big jumbotron at the end of it and it's

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got a goalpost and you go in there and you go into the football field

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to actually, you know, play football, like throw, catch, kick and things like

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that. But when you see people walking in

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to that football field and they see a video play

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of themselves, it's a recruiting hype video with them in a uniform that comes over

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and says, ladies and gentlemen, please welcome Andy Lender to the Ohio

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State Buckeyes. You know, you know, we're really excited about Andy's potential

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as a wide receiver for, you know, when they see them,

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their reaction is not fake. Their reaction is real. Their

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reaction is to put their hands in the sky, to pump their

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fists, because now they're part of their favorite

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team. They're part of their college football experience. They are seeing

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themselves in the story. And when you see yourself in the story,

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it feels very real to you. And so that, to me,

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is the exciting part about AI is in the future. You know,

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when you start seeing this, the video tools that are coming from Meta

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and, you know, you know, OpenAI and

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everything else coming down the road. I believe that

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you and I, that everyone, all the guests, all

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the consumers will demand to be part of the

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story because if they're not, it just won't feel the

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same. So when you go ride Pirates of the Caribbean in a decade,

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you'll be in the ride. You'll be actually

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in the ride with Jack Sparrow. You'll be one of the pirates

250
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on the ride. You'll see yourself in the ride.

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And it'll be very interesting to see if the same thing happens in Hollywood, where,

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no, I actually wanna be in the movie. I I, you know,

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when, you know, when I go watch in 10 years, you

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know, imagine redoing Rudy with AI.

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And when the Notre Dame coach, Dan Devine, is giving

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that great pep talk in the locker room, you know? And

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I just love that scene, you know, where he's like in there at the locker

258
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room and Rudy's in there and all the other players around it. And he's like,

259
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he's like, gentlemen, this is our house. And no

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one, and I mean no one comes into our house and pushes us

261
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around. But in the future, you could

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be one of the players kneeling next to Rudy.

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That could be me. I could totally see that. I mean, you kinda get

264
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glimpses of that now. Right? I mean, there's a lot

265
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of music generation. Right? Like, I could easily see where

266
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I want I'm having a bad day. I need some inspirational songs

267
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that speak to me, and I want it in the style of pit bull. And

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I want this type of, like, you know, this type of song about what I'm

269
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working on. Yeah. Right? Yeah. You can kinda do that now with with a tool

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like YuDio. And there's 100 of them. Right? Like

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but, you know, the whole idea of, you know, pop music where

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everyone listens to the same thing. I mean, it's,

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you know, it might just be customized, like, not just a customized

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playlist, but customized songs on your playlist. Like, it's not

275
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it's not that outrageous. I mean, like, when you say that, I'm like, I

276
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could see that being a thing. Right? Because Yeah.

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Yeah. Cinema is a 100 ish years old. I mean,

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it's time for you know, it's it's due for disruption of some sort right

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now. You know, we you know, I don't and if and

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and literally sorry. I've just got get passionate about this. No, please. I love

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Scott Wiener if senator Scott Wiener of California would have been alive in

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1910 or whatever it was, you know what I mean? He would have been saying,

283
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oh, yes. This silent cinema, we need to make sure it's safe for everyone. You

284
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know what I mean? Right. Right. Right. Right. Let's kill this silent

285
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cinema industry before it even gets off the ground because, you know, it needs to

286
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be safe for everyone. It's On police. Right? The you

287
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know, I mean, just just let us tell stories.

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Let us use this technology to tell stories. And, you know, listen, there's

289
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there is a danger. I'll give you a very real example

290
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that happened to us 3 weeks ago with music. So 3 weeks ago and

291
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I know, this is just a a podcast. You're not gonna see

292
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this. But so Well, they may. Oh, so this

293
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so we were we we had a a a small gig. It was a

294
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2 day consulting project with the the Yosemite Mountain Sugar Pine

295
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Railroad, which is 5 minutes outside of Yosemite National Park in

296
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in the Sierra Nevada Forest. Right? Mhmm. And it's a a logging

297
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train, and guests get to ride on it and go through the forest and come

298
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back in. It's a great train, great people. And they they they brought

299
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us in to say, hey. Would you look at look at our operation and just

300
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give us some advice and give us some tips? And it was

301
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like, you know, it was we had a great time. Right?

302
00:18:41,051 --> 00:18:44,753
And they they gave us this book, The Whistle Blow Knows More,

303
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as part of our research. Now if I would

304
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have just gone into AI, gone into ChachiPT or

305
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whatever Gemini and just said, hey, could you please summarize the book, Whistleblows

306
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No More by Hank Johnston? I would have

307
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learned about it, but I wouldn't have gotten the inspiration

308
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Right. That I needed to really tell a great story. You

309
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still have to, as a creator, and I think the same is true as

310
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a researcher, right, or as a data scientist, you still have to

311
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do the hard work and read between the lines. Because if you

312
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simply rely on AI to summarize something for you, you will lose

313
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the inspiration. Because in this book, You know, I

314
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started going through the pages and I'm going through it and I'm looking at it.

315
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And of course, I didn't have time in 2 days to read everything, but I

316
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was skimming through it. Right. And as I was skimming through the

317
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pages, I found a poem. Called The Whistle Blows No

318
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More. Right? And it's a a really

319
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powerful poem that talks about essentially the whistles

320
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stopping because the logging industry ended

321
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after clearcutting 35,000 acres, you know what I mean,

322
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around the around the Sierra Nevadas. They, you know,

323
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they the Great Depression was hitting. They saw the writing on the wall,

324
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and they, you know, they shut it down

325
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and completely liquidated everything and sold everything for

326
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scrap. Wow. Including the 54 mile

327
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log flume that went from the Sierra Nevadas down to Madera, California.

328
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This is just amazing, the things that happened. And now when you're there and

329
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you look at the mill pond, it's just so beautiful and pristine. It's kind of

330
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hard to believe that the whole thing was clear cut, and was

331
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just a major industrial operation in

332
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1931. And of course, I can't find the poem right now.

333
00:20:38,085 --> 00:20:41,845
But just in skimming through it, I found this wonderful poem. And

334
00:20:41,845 --> 00:20:45,684
so we took that poem. And we took it into A.

335
00:20:45,684 --> 00:20:49,525
I. And we created a song out of it. Oh, very cool.

336
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A folk song. Right. And then we played the folk

337
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song, for the owner of the railroad.

338
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She got a tear in her eye. Wow. You

339
00:21:02,020 --> 00:21:05,855
know? And, and this was even before

340
00:21:05,855 --> 00:21:09,695
version 4 of Suno came out, which was pretty amazing. Version 4

341
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came out a couple days ago. But but

342
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so, you know, when it comes to AI, and I believe this is true with

343
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any technology, you have to be very careful and

344
00:21:20,880 --> 00:21:23,940
guard yourself that you still have the discipline to do the research

345
00:21:24,799 --> 00:21:28,559
and to do the legwork and to do the hard work to get the

346
00:21:28,559 --> 00:21:31,059
inspiration you need to tell a powerful story.

347
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But then you can use that AI. You can use that technology. Because,

348
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honestly, it was a 2 day consulting project.

349
00:21:39,625 --> 00:21:42,845
I would have never had time, money, or resources

350
00:21:43,225 --> 00:21:46,985
Right. To compose a folk song based on this

351
00:21:46,985 --> 00:21:50,340
poem in that amount of time.

352
00:21:50,640 --> 00:21:54,480
Yeah. So the automation is you

353
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you you were just relying on the automation that has

354
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gotten I and I'd I'd say it's gotten mixed reviews. I

355
00:22:01,680 --> 00:22:05,435
think there are a number of people who look at stuff and kinda focus on

356
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the things that where AI misses.

357
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You know, and it does. It it misses the hallucinations and the

358
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things like that. Mhmm. And they kinda build their narrative,

359
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off of that. And we find that across all sorts of

360
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fields. I mean, I I see that in data warehousing,

361
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and and, you know, before AI even showed up. But it's

362
00:22:28,950 --> 00:22:32,645
it's amazing to to not just we get to

363
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see you, and we do have video, by the way, that we do post, that

364
00:22:36,405 --> 00:22:38,025
people can see you as well.

365
00:22:40,005 --> 00:22:43,765
But people don't need to see you to hear it in your in your

366
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voice, Jeff, your your passion for what you're talking

367
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about. And that passion is really, about telling

368
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the story and delighting your consulting

369
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clients, and you found a way to automate

370
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some of that using AI. And it's that's, like,

371
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excitement on the excitement of the normal passion that you

372
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bring to your job. And I think that's awesome. But I

373
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like that. I like I like I like the idea that where you're going with

374
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this is that, you know, the time no

375
00:23:16,100 --> 00:23:19,780
one I'm not a musical person, really, so I'm not gonna compose a

376
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song. But I can't. I'm not a musical person either. I have no idea how

377
00:23:23,460 --> 00:23:26,680
to compose a song. Use AI to adjust it and create songs

378
00:23:27,460 --> 00:23:31,125
based on things that I like as a listener. Right? So if you watch

379
00:23:31,125 --> 00:23:34,405
any of my live streams, the intro song that sings, you know, Frank's World on

380
00:23:34,405 --> 00:23:38,085
the stream and all that, that that kinda like that, you know, Europop or

381
00:23:38,085 --> 00:23:41,065
late nineties, early 2000 type of thing. That's AI.

382
00:23:41,685 --> 00:23:44,259
Right? I never would have had that. I just would have used some kind of

383
00:23:44,259 --> 00:23:48,100
stock thing or whatever or or or sound effects. So the ability to do

384
00:23:48,100 --> 00:23:51,539
that, the ability to to to kind of also tie it to a much larger

385
00:23:51,539 --> 00:23:55,000
story, I brought tears to that ladies eye. I think that's very,

386
00:23:55,144 --> 00:23:58,985
very powerful. I think I think when you get down to it, that these are

387
00:23:58,985 --> 00:24:02,625
gonna become storytelling tools that, you know,

388
00:24:02,825 --> 00:24:06,585
now we look at it as controversial. But in maybe 10 years,

389
00:24:06,585 --> 00:24:10,429
it'll just be just part of the toolkit. You know? Yeah. But but the

390
00:24:10,429 --> 00:24:14,130
other point that I think is just as important is if I wouldn't have actually

391
00:24:14,830 --> 00:24:18,610
thumbed through the real book. Yes. Yeah.

392
00:24:19,470 --> 00:24:23,235
And actually, you know, taking the time to look at

393
00:24:23,235 --> 00:24:26,434
it and to read it, I would have missed that poem. Because if I would

394
00:24:26,434 --> 00:24:30,195
have just said, hey, ChatJPT, can you summarize The Whistle Blows No

395
00:24:30,195 --> 00:24:33,955
More by Hank Johnson? It wouldn't have told me about the poem. No. I wouldn't

396
00:24:33,955 --> 00:24:37,730
have got it. And, you know, you know, again, it's not just

397
00:24:37,730 --> 00:24:39,510
about AI. One of my,

398
00:24:42,610 --> 00:24:45,250
I'm gonna probably nerd out on you guys a little bit, which is kind of

399
00:24:45,250 --> 00:24:48,710
ironic since you are both nerdy data scientists, but

400
00:24:50,305 --> 00:24:53,905
but I'm fascinated by the NASA report about the

401
00:24:53,905 --> 00:24:57,665
Columbia shuttle disaster. Mhmm. Because in that in

402
00:24:57,665 --> 00:25:01,425
that report, it says that the endemic use of and I'm

403
00:25:01,425 --> 00:25:05,240
I'm paraphrasing, but the endemic use of PowerPoint. No.

404
00:25:05,240 --> 00:25:08,940
Yeah. It's right. Led to the engineers

405
00:25:10,039 --> 00:25:13,639
not understanding the importance of what was being

406
00:25:13,639 --> 00:25:17,320
said. Because if you if you actually write a report, if you

407
00:25:17,320 --> 00:25:20,955
actually, as an engineer, write a report and

408
00:25:20,955 --> 00:25:24,635
make somebody read that report, you can't miss what's

409
00:25:24,635 --> 00:25:28,475
between the lines because you read all the lines. Right? It is there, and it

410
00:25:28,475 --> 00:25:31,995
would be stated very, very clearly. You know what I mean? You know, this is

411
00:25:31,995 --> 00:25:35,549
a danger. You know what I mean? And this needs to be

412
00:25:35,549 --> 00:25:39,330
addressed. Well, people put the engineers were were going straight to the PowerPoint,

413
00:25:39,950 --> 00:25:43,549
skipping the writing part. Mhmm. Right. And going and we've all been

414
00:25:43,549 --> 00:25:47,390
guilty of that. Right? Of, oh, I have a presentation due. And you just build

415
00:25:47,390 --> 00:25:50,795
the presentation at PowerPoint rather than doing the harder

416
00:25:50,795 --> 00:25:54,475
work of actually writing it yourself. And then once you have it

417
00:25:54,475 --> 00:25:58,235
written, translated into a PowerPoint. But the

418
00:25:58,235 --> 00:26:01,695
actual committee that studied that shuttle Columbia

419
00:26:02,280 --> 00:26:05,660
tragic loss that cost, I don't know, was it 7 lives or something.

420
00:26:06,040 --> 00:26:09,640
Right? They blamed, and I'm quoting, it was the

421
00:26:09,640 --> 00:26:13,480
endemic use of PowerPoint by the engineers. And we have to be careful.

422
00:26:13,480 --> 00:26:17,045
We have to watch ourselves as creatives that we

423
00:26:17,045 --> 00:26:19,785
don't lose our edge and lose our creativity

424
00:26:21,045 --> 00:26:24,485
through the endemic use of AI. We we have to make sure we still do

425
00:26:24,485 --> 00:26:28,325
the hard work. And again, I recently watched a

426
00:26:28,325 --> 00:26:32,120
talk about AI from actually one of my, we'll call it a spiritual leader,

427
00:26:32,120 --> 00:26:35,100
somebody in my church, and we don't you're gonna get the details of it, but,

428
00:26:36,760 --> 00:26:40,600
and they talked about, you know, yes, use

429
00:26:40,600 --> 00:26:44,115
AI, but don't let it act upon you. Make sure

430
00:26:44,115 --> 00:26:47,795
you're using it. Make sure it doesn't use you. Right?

431
00:26:47,795 --> 00:26:51,395
Right. Because Yeah. We can let it use

432
00:26:51,395 --> 00:26:55,235
us. We can let it, you know, because it's like, where does

433
00:26:55,235 --> 00:26:58,850
that inspiration come from? Right? I mean, if you believe in inspiration.

434
00:26:58,910 --> 00:27:02,270
I believe in inspiration. I do. And I

435
00:27:02,270 --> 00:27:05,470
believe that, you know, not to get spiritual on you guys, but I believe that

436
00:27:05,470 --> 00:27:09,310
God inspires us. I do. Yeah. And I believe we get inspiration. And I

437
00:27:09,310 --> 00:27:13,145
believe he cares about our work. Right? In the same way he cares about a

438
00:27:13,145 --> 00:27:16,985
farmer, you know, providing food for his family. I I think he cares about

439
00:27:16,985 --> 00:27:20,765
our work. And and so I believe in inspiration.

440
00:27:20,985 --> 00:27:24,605
I believe in that spark, that light that comes. Mhmm. Right? And

441
00:27:25,065 --> 00:27:28,830
and, you know, this this guy

442
00:27:28,830 --> 00:27:31,230
that gave this talk was amazing. He basically said at the end of it, he

443
00:27:31,230 --> 00:27:34,990
said, always remember, it is an algorithm. It does not

444
00:27:34,990 --> 00:27:38,590
like you. It does not care about you. It doesn't even know you

445
00:27:38,590 --> 00:27:41,945
exist. Right? Yeah. It's a tool. That's

446
00:27:41,945 --> 00:27:45,164
powerful. You know? And I personally hate

447
00:27:45,625 --> 00:27:49,404
hate hate hate hate hate all the branding around AI

448
00:27:49,625 --> 00:27:53,465
because it's all humanistic. I hate what Open Eye Eye is doing with, like, oh,

449
00:27:53,465 --> 00:27:56,910
hey. How's I tell that stupid chatty p t

450
00:27:56,970 --> 00:28:00,650
whatever I have the upgraded one or whatever all the time. Would you

451
00:28:00,650 --> 00:28:04,090
please stop asking me questions? Would you please shut up? Would you please

452
00:28:04,090 --> 00:28:07,690
stop referring like, I want it to be like Star Trek.

453
00:28:07,690 --> 00:28:11,125
Computer. You know what I mean? Tell me this, and then it shuts

454
00:28:11,125 --> 00:28:14,804
up. Right? Don't act like a human on

455
00:28:14,884 --> 00:28:18,565
I don't want you you are an algorithm. You are a tool. Right.

456
00:28:18,565 --> 00:28:22,004
Right? I don't wanna treat you like a human being. We already live in a

457
00:28:22,004 --> 00:28:25,610
society where people treat their pets like human beings, and they

458
00:28:25,610 --> 00:28:29,370
shouldn't. Right? Sorry. But I don't wanna offend anybody here. It's sort of

459
00:28:29,370 --> 00:28:33,049
controversial. But, you know My dogs won't hear it. I'm on headphones. Well,

460
00:28:33,049 --> 00:28:36,730
listen. I'm sure you love your dogs. Right? But hopefully not as much as

461
00:28:36,730 --> 00:28:40,335
your children. Right? No. It's Yeah. Right? You

462
00:28:40,335 --> 00:28:44,175
know? I mean, we have a sickness in our

463
00:28:44,175 --> 00:28:47,935
country, in our world where we we personify things that should not

464
00:28:47,935 --> 00:28:51,375
be personified, and AI should not be

465
00:28:51,375 --> 00:28:55,020
personified. It shouldn't. It's a tool. And so

466
00:28:55,100 --> 00:28:58,700
dangerous to personify these things. It is. What was the

467
00:28:58,940 --> 00:29:02,700
was it 2 years ago now? There was some guy who was saying that,

468
00:29:03,180 --> 00:29:07,020
he was he was fired from Google, but he was saying that he was

469
00:29:07,020 --> 00:29:10,684
dealing with a sentient. He said that he thought it was Ascension. It was

470
00:29:10,684 --> 00:29:14,304
alive, and it was like, not really.

471
00:29:14,605 --> 00:29:18,365
I mean, it was just It's an algorithm. It's Mac. Yeah. Well, if you asked

472
00:29:18,365 --> 00:29:21,644
it, like, why are you and even some of the transcripts of the conversations while

473
00:29:21,644 --> 00:29:25,200
they you could be led down the path thinking it was

474
00:29:25,200 --> 00:29:27,920
alive, it would say, I'm sad because I can't go to the pub with my

475
00:29:27,920 --> 00:29:31,200
friends or something like that. Like, it's a computer. It doesn't go to the pub

476
00:29:31,200 --> 00:29:33,920
with your friends. Right? Yeah. And for someone that would say to me, like, oh,

477
00:29:33,920 --> 00:29:37,680
well, you're using AI to generate emotions in a live experience, like a museum or

478
00:29:37,680 --> 00:29:41,505
a show. I'm like, I use theatrical lighting to create

479
00:29:41,505 --> 00:29:44,725
emotion. I use I use

480
00:29:44,945 --> 00:29:48,465
music to create emotion. I use all kinds of tricks and

481
00:29:48,465 --> 00:29:52,145
scenery that are fake to create emotion. There's nothing wrong with

482
00:29:52,145 --> 00:29:55,350
that. Yeah. Right? Yeah. It's a tool.

483
00:29:56,130 --> 00:29:59,590
To me, AI is a tool like animatronics and Pepper's

484
00:29:59,650 --> 00:30:03,250
ghost and theatrical lighting and music and everything

485
00:30:03,250 --> 00:30:06,950
else. It's a tool to basically create to tell to tell a great story.

486
00:30:07,010 --> 00:30:10,605
But it is important that that story is a human story

487
00:30:10,905 --> 00:30:14,585
and and a powerful story that's that's, you know, and there are good stories and

488
00:30:14,585 --> 00:30:18,365
bad stories, and they're great storytellers and lousy storytellers. So

489
00:30:18,905 --> 00:30:22,125
and it's not easy to tell a great story, but it's important

490
00:30:23,029 --> 00:30:25,750
Well, I always like to use the example of when you're using AI, it's kind

491
00:30:25,750 --> 00:30:28,730
of like, you know, the Aliens, the the second movie.

492
00:30:29,350 --> 00:30:33,110
Yeah. Arguably, the last good Aliens movie, but that's Oh,

493
00:30:33,110 --> 00:30:35,450
100% agree with you on that one. Alright. Good.

494
00:30:36,595 --> 00:30:39,635
And, you know, we're at the end where she has the,

495
00:30:40,275 --> 00:30:43,955
the exoskeleton. Right? Yeah. The exoskeleton was

496
00:30:43,955 --> 00:30:47,495
originally built to lift heavy things. Right? But, you know, it's kind of like,

497
00:30:47,795 --> 00:30:50,855
that's how I see AI. Right? Like, without a person in there,

498
00:30:51,790 --> 00:30:55,550
it's it's it's just a tool. Right? It's just this lump of metal

499
00:30:55,550 --> 00:30:59,230
in robotics. But, like, with a person in there, that person could do more,

500
00:30:59,230 --> 00:31:02,210
whether it's, you know,

501
00:31:02,830 --> 00:31:06,535
create a song out of a poem that never really existed or you know,

502
00:31:06,535 --> 00:31:10,295
but but there still has to be a person driving it. And without without

503
00:31:10,295 --> 00:31:13,895
a person driving it, it's it's it comes across as very

504
00:31:13,895 --> 00:31:17,495
mechanical. It does. And in fact, I think and again, I'm not

505
00:31:18,130 --> 00:31:21,490
you know, things are moving so fast with AI, but I I believe there was

506
00:31:21,490 --> 00:31:25,169
some research done in Japan about 6 to 12 months

507
00:31:25,169 --> 00:31:28,929
ago where they had, the AI write a

508
00:31:28,929 --> 00:31:32,470
haiku, and they had people write a haiku, and then they had

509
00:31:33,265 --> 00:31:37,105
people and AI collaborate to create a haiku. And it

510
00:31:37,105 --> 00:31:40,705
was the the writing that was generated by a

511
00:31:40,705 --> 00:31:44,304
combination of both that scored the highest among

512
00:31:44,304 --> 00:31:44,804
consumers.

513
00:31:48,000 --> 00:31:51,540
That doesn't surprise me at all. No. I was gonna say the same reaction.

514
00:31:52,160 --> 00:31:55,520
Yeah. The collaboration the collaborative, aspect of

515
00:31:55,520 --> 00:31:59,360
it is, I think, the strength. I think it's you know, when you

516
00:31:59,360 --> 00:32:02,794
use an LLM to draft an

517
00:32:02,794 --> 00:32:06,475
outline of some topic, and then you take it from

518
00:32:06,475 --> 00:32:10,235
there or vice versa. You draft an outline and you hand it to

519
00:32:10,235 --> 00:32:13,455
the LLM and say, you know, can you flesh this out?

520
00:32:14,220 --> 00:32:17,900
Fill in the blanks. And I I've done both. I've

521
00:32:17,900 --> 00:32:21,340
had, you know, 5 minutes I needed to respond to a

522
00:32:21,340 --> 00:32:25,020
consulting client, and I've written 4 sentences because I

523
00:32:25,020 --> 00:32:28,645
knew it needed to be longer than this, and it needed some finesse.

524
00:32:28,645 --> 00:32:30,905
I I'm an engineer, and it shows.

525
00:32:32,485 --> 00:32:36,165
So I've handed it to the LLM and said, hey. Make this, you know,

526
00:32:36,165 --> 00:32:39,305
nice. Make this sound way nicer and expand it.

527
00:32:39,525 --> 00:32:43,370
And a couple of times when I've done that, it's been so good.

528
00:32:43,370 --> 00:32:46,890
I just copied and pasted it and sent it to the client. Now at the

529
00:32:46,890 --> 00:32:50,730
same time, I've handed, you know, code, you know, in

530
00:32:50,730 --> 00:32:54,255
one language. It's to translate this to another language. And,

531
00:32:54,875 --> 00:32:58,715
it was my first exercise that I that I did that with it. I thought

532
00:32:58,715 --> 00:33:02,554
it worked because it didn't fail. And I learned

533
00:33:02,554 --> 00:33:06,315
a very important lesson from that that not failing is not the same

534
00:33:06,315 --> 00:33:09,810
as succeeding. Mhmm. The code compiled, and it

535
00:33:09,810 --> 00:33:12,770
executed, but it did not do what I wanted it to do. It looked like

536
00:33:12,770 --> 00:33:16,530
it did, but it didn't. So the creative aspect of it,

537
00:33:16,530 --> 00:33:20,290
though, I'm I'm fascinated to to hear you talk about, you know, how

538
00:33:20,290 --> 00:33:23,590
you can do that. And it the story you told specifically about,

539
00:33:24,185 --> 00:33:28,025
not having the resources or the time to, you know,

540
00:33:28,025 --> 00:33:31,865
to turn that that poem into a song, that's such an

541
00:33:31,865 --> 00:33:35,705
accelerator. And that's where I keep hearing the stories over and over again,

542
00:33:35,705 --> 00:33:38,205
whether it's in language, whether it's in art.

543
00:33:39,360 --> 00:33:43,060
It just keeps repeating itself that people are able,

544
00:33:44,080 --> 00:33:47,840
through percussive prompting, you know,

545
00:33:47,840 --> 00:33:50,960
you just keep beating on it until it gives you the answer that you want

546
00:33:50,960 --> 00:33:54,260
or need. But because it can do so much so fast,

547
00:33:54,935 --> 00:33:58,615
it can fail 3 or 4 times before it gives you

548
00:33:58,615 --> 00:34:02,295
the song or the paragraphs or or what have you

549
00:34:02,295 --> 00:34:05,495
because it it is an accelerator. So

550
00:34:06,055 --> 00:34:09,435
There's no doubt. And, you know, I I was having a

551
00:34:09,840 --> 00:34:13,680
healthy debate with the chair of the illustration department of the Savannah College

552
00:34:13,680 --> 00:34:17,440
of Art and Design, and 2 of our kids went to SCAD, and we

553
00:34:17,440 --> 00:34:21,280
live here in Savannah. You know, and and her initial reaction

554
00:34:21,280 --> 00:34:24,824
was, oh, AI is gonna take my job. And I said, no. I said, it'll

555
00:34:24,824 --> 00:34:27,965
take your job if you don't learn AI. Right.

556
00:34:29,145 --> 00:34:32,984
But, you know, our designers and illustrators, we're a family creative firm, so they're

557
00:34:33,065 --> 00:34:36,800
my daughters are illustrators and designers. They use they're

558
00:34:36,800 --> 00:34:40,500
using it today. We're designing our Christmas card. Nice. Now, the ultimate

559
00:34:40,800 --> 00:34:44,320
design of the Christmas card is definitely gonna have a human touch. It was written

560
00:34:44,320 --> 00:34:47,840
by my son and I, who's our creative writer. So we we wrote it. And

561
00:34:47,840 --> 00:34:51,514
we wrote it without the help of AI. We did. We just wrote it

562
00:34:51,514 --> 00:34:55,275
ourselves. And then, what we

563
00:34:55,275 --> 00:34:58,634
didn't do and probably should have is pumped it back into AI and said, hey.

564
00:34:58,634 --> 00:35:02,474
Would you analyze this as a Christmas card and offer any suggested edits? Right? Because

565
00:35:02,474 --> 00:35:06,120
that that is one benefit of AI is you can get an instant critic on

566
00:35:06,120 --> 00:35:09,800
your work. Yep. And, but, you know, we did a

567
00:35:09,800 --> 00:35:13,000
couple of you know, as as the writers in the firm, we did a couple

568
00:35:13,000 --> 00:35:15,720
of we did some creative direction and some mood boards of what we wanted the

569
00:35:15,720 --> 00:35:18,440
Christmas card to be like, but then we gave it to our designer and illustrator.

570
00:35:18,440 --> 00:35:22,015
And, you know, and then she ran with it. And, you know Jeez. And she

571
00:35:22,015 --> 00:35:25,395
did use AI as part of it, but, you know, she added her own little,

572
00:35:25,775 --> 00:35:28,995
you know, touch to it from an illustrator perspective. And, again,

573
00:35:29,535 --> 00:35:33,235
you know, I feel like a broken record here, but I keep emphasizing,

574
00:35:34,900 --> 00:35:38,580
you know, illustrators, you know, over the last, you

575
00:35:38,580 --> 00:35:42,420
know, probably 15 years, they've been doing

576
00:35:42,420 --> 00:35:45,620
something called a mash up, where they don't actually like, if they need to put

577
00:35:45,620 --> 00:35:49,460
a rock in an illustration and concept art, they don't actually draw

578
00:35:49,460 --> 00:35:52,964
the rock. They go to Google and say rock. You know what I

579
00:35:52,964 --> 00:35:56,805
mean? Images. They grab a rock they like, put it in, paint

580
00:35:56,805 --> 00:36:00,405
over it, fundamentally transform it from a copyright perspective. They

581
00:36:00,405 --> 00:36:04,109
fundamentally transform it, and then they're fine. Right? You know, I I loved

582
00:36:04,109 --> 00:36:07,730
a couple of things. That's one that you just said, and then I'll go back

583
00:36:07,790 --> 00:36:11,230
to something you mentioned earlier. I love that your family is working with you.

584
00:36:11,230 --> 00:36:15,069
That that's a huge blessing, and I know because my mine

585
00:36:15,069 --> 00:36:18,535
work with me. And you mentioned earlier about that. Nice, isn't

586
00:36:18,535 --> 00:36:22,295
it? It is. And then you mentioned earlier about the spiritual aspect of that.

587
00:36:22,295 --> 00:36:26,135
Both Frank and I are believers, so we we track. You know,

588
00:36:26,135 --> 00:36:29,835
we we get what you're saying, and that all kinda ties together.

589
00:36:31,100 --> 00:36:34,860
And my my children are older than Frank. So Frank's getting there. They've he's

590
00:36:34,860 --> 00:36:38,700
got a teenager, a couple of teenagers, I think. No. I have a

591
00:36:38,700 --> 00:36:42,540
preteen. Preteen? A teenager, preteen, and a toddler. And

592
00:36:42,540 --> 00:36:46,275
so mine are a toddler. Oh my goodness. I'm a glider. There you go.

593
00:36:46,515 --> 00:36:50,355
Yeah. And so I've got 2 children from my first marriage, and they are

594
00:36:50,355 --> 00:36:54,194
the moms of my grandkids. And then I've got 3 children from my second

595
00:36:54,194 --> 00:36:58,035
marriage, and they the youngest one is now 17. So I've kinda

596
00:36:58,035 --> 00:37:01,690
done the the family, almost the family 2 point o thing,

597
00:37:01,690 --> 00:37:05,370
but, it's super cool because there's they

598
00:37:05,370 --> 00:37:09,050
never say things like half brother, half sister, or anything. Like, they're

599
00:37:09,050 --> 00:37:12,730
all brother, sister. And they they mostly get along. They're

600
00:37:12,730 --> 00:37:16,325
different. I mean, gosh, they're 2 sets. You know,

601
00:37:16,325 --> 00:37:20,165
they in in between themselves, you know, same

602
00:37:20,165 --> 00:37:23,925
mom and dad, they they don't always get along either. But it's it's

603
00:37:23,925 --> 00:37:27,560
fascinating to to see what

604
00:37:27,940 --> 00:37:31,700
I'll say this about it. I think the spiritual, dimension that you bring to

605
00:37:31,700 --> 00:37:34,840
it brings a purpose beyond just

606
00:37:35,460 --> 00:37:39,220
something. I don't wanna, I don't wanna poo poo it, but I also wanna make

607
00:37:39,220 --> 00:37:42,605
the distinction. It's like if you're just in this for doing money,

608
00:37:43,065 --> 00:37:46,585
for making money, it's not the same as if you're in it

609
00:37:46,585 --> 00:37:50,425
for fulfilling some purpose beyond money. Do you wanna

610
00:37:50,425 --> 00:37:53,645
make money? Well, yeah, you kinda got to. But

611
00:37:54,069 --> 00:37:57,910
is it all about the money? No. And the

612
00:37:57,910 --> 00:38:01,750
best and I would say the people who make probably the most

613
00:38:01,750 --> 00:38:05,130
money in general, you know, 80 20 rule applies,

614
00:38:05,670 --> 00:38:09,285
are the people who are driven by passion, driven

615
00:38:09,285 --> 00:38:13,045
by some purpose that's beyond that. And I

616
00:38:13,045 --> 00:38:16,325
know that's true of me, and I know, you know, I know it's true of

617
00:38:16,325 --> 00:38:20,165
Frank. It sounds like there's that dimension, in your life and work as

618
00:38:20,165 --> 00:38:23,930
well. Yeah. Agreed. Yeah.

619
00:38:24,470 --> 00:38:28,170
Or I'm sorry. I should I should say amen. Yeah. There you go.

620
00:38:28,390 --> 00:38:31,830
Well, I I think that there's an interesting thing here because I think one of

621
00:38:31,830 --> 00:38:35,505
the things that I think has shocked a lot of people it's been

622
00:38:35,505 --> 00:38:39,265
about 2 years since chat gpt was released. Right? I think

623
00:38:39,265 --> 00:38:42,805
almost to the week almost to the day, but definitely to the week. Mhmm.

624
00:38:43,424 --> 00:38:46,005
And I think that there was a certain smugness

625
00:38:48,500 --> 00:38:52,339
in the creative class that prior to

626
00:38:52,339 --> 00:38:56,020
generative AI, that they would have been the last jobs on earth.

627
00:38:56,020 --> 00:38:59,859
Right? Like, the most ridiculous things you'll ever have heard Mhmm. Would never

628
00:38:59,859 --> 00:39:03,545
be replaced. And then suddenly, I think 2 years ago, you

629
00:39:03,545 --> 00:39:07,385
haven't had this big bag moment of, oh, wait a minute. You

630
00:39:07,385 --> 00:39:10,744
know? And I when I say creative class, I don't No. It's just artists. Oh,

631
00:39:10,744 --> 00:39:14,425
no. There are people that are very worried in the creative class for sure. Yeah.

632
00:39:14,425 --> 00:39:18,049
Yeah. And it's it's it's one of those things where,

633
00:39:18,910 --> 00:39:21,470
you know, the whole idea of, like, you know, the low skilled jobs will be

634
00:39:21,470 --> 00:39:25,069
automated away first. Right? That was that was the the the mantra of of

635
00:39:25,069 --> 00:39:28,450
kinda AI people up until about 2 years ago this week.

636
00:39:29,735 --> 00:39:32,155
But I think it really shook people

637
00:39:33,415 --> 00:39:37,095
to the core to what does it mean to be human. Right? Because no one's

638
00:39:37,095 --> 00:39:39,995
really figured that. No one really has a good solid

639
00:39:41,900 --> 00:39:45,580
mathematical foundation of what does it mean to be creative? What does it mean to

640
00:39:45,580 --> 00:39:49,260
be human? Right? I think. Right? This is just

641
00:39:49,260 --> 00:39:52,940
me. And one of the things that people could

642
00:39:53,020 --> 00:39:56,160
would always point to whether it was, you know,

643
00:39:56,655 --> 00:40:00,255
weaving machines in the 1800 or, you know,

644
00:40:00,255 --> 00:40:03,935
factory assembly lines in the 1900 and, you know,

645
00:40:03,935 --> 00:40:07,455
whatever we have going now in terms of automation, was the idea that,

646
00:40:07,455 --> 00:40:09,955
well, a machine can never be creative.

647
00:40:11,910 --> 00:40:15,190
And I think 2 years ago, I think we're still struggling to get our head

648
00:40:15,190 --> 00:40:18,630
around that. Right? And then when you start throwing in other things

649
00:40:18,630 --> 00:40:22,390
like, well, you know, the notion of sentience. Right? The notion of a

650
00:40:22,390 --> 00:40:25,994
consciousness and things like that, then it really gets muddled. And again

651
00:40:25,994 --> 00:40:28,974
like you said before with our prevalence

652
00:40:29,835 --> 00:40:32,174
to anthropomorphize things

653
00:40:33,434 --> 00:40:36,815
and certainly I think today's culture generally

654
00:40:38,820 --> 00:40:41,960
that's really muddied the waters

655
00:40:42,900 --> 00:40:45,860
in terms of what what does what does it mean to be human? What does

656
00:40:45,860 --> 00:40:48,900
it mean to be creative? And what does it mean? And I think you're you're

657
00:40:48,900 --> 00:40:52,580
seeing people kind of go into 2 camps or maybe more

658
00:40:52,580 --> 00:40:56,395
than 2. But the 2 obvious ones are extremes on the spectrum of I'm

659
00:40:56,395 --> 00:40:59,775
never touching AI. If you use AI, it's cheating

660
00:41:00,474 --> 00:41:04,315
versus there's people that use AI, but don't have

661
00:41:04,315 --> 00:41:08,130
any soul to it. Right? Like the example of you actually reading the book. Right?

662
00:41:08,350 --> 00:41:11,550
Yeah. I've have AI. I'm guilty of it. I have AI. I have no book

663
00:41:11,550 --> 00:41:15,310
l m. I'll give it a PDF. It summarizes like a 300 page book for

664
00:41:15,310 --> 00:41:19,150
me. I can listen while picking up the little one from daycare. I can listen

665
00:41:19,150 --> 00:41:22,575
to their summary of the book. I've done it just as an experiment. And there

666
00:41:22,655 --> 00:41:26,495
and there's as much value in that as reading cliff notes of of mice

667
00:41:26,495 --> 00:41:29,715
and men. And I I mean, here's the thing. I mean, this is

668
00:41:30,575 --> 00:41:33,775
you know, at the time, I didn't realize it was a sign that I actually

669
00:41:33,775 --> 00:41:37,510
had a career in writing and and and, you know, storytelling,

670
00:41:37,510 --> 00:41:41,110
but I would show up at my high school on the

671
00:41:41,110 --> 00:41:44,870
day morning of the paper was due that day, and I would

672
00:41:44,870 --> 00:41:48,630
sit and I'd interview my classmates about the book report

673
00:41:48,630 --> 00:41:52,285
that was due. And I'd quickly write up a 2 page report on yellow

674
00:41:52,285 --> 00:41:56,125
notepad because we didn't have typewriters and, you know, that stuff. And it's and I

675
00:41:56,125 --> 00:41:59,645
turned it in and get a, you know, a b, whatever. And I didn't

676
00:41:59,645 --> 00:42:03,340
care. It's a long story, but I didn't really have a parent that was

677
00:42:03,340 --> 00:42:06,380
caring about my grades, and so I didn't care about grades. And you know what

678
00:42:06,380 --> 00:42:09,040
I mean? So Mhmm. You know,

679
00:42:10,380 --> 00:42:14,060
I would just interview people. Did I get less out of that as a human

680
00:42:14,060 --> 00:42:17,755
being? Absolutely. You know what I mean? Yeah. Every cliff note I

681
00:42:17,755 --> 00:42:21,595
I I didn't read this is true story. I didn't read one book in high

682
00:42:21,595 --> 00:42:24,735
school. Oh, wow. Not one book.

683
00:42:26,235 --> 00:42:29,780
Am I a better person because of that? No. I

684
00:42:29,780 --> 00:42:33,480
probably should. I should have read books, but you know what I mean? But

685
00:42:33,700 --> 00:42:36,660
you know, and and that and that is the worry. But you know, going back

686
00:42:36,660 --> 00:42:40,420
to your point, I think there's another reason why the

687
00:42:40,420 --> 00:42:43,915
creative class are so worried. And it's because

688
00:42:43,915 --> 00:42:47,135
AI democratizes creativity. I believe

689
00:42:47,674 --> 00:42:51,515
that every single person is creative. You guys are engineers, but I think you're

690
00:42:51,515 --> 00:42:55,035
creative. I think every every child is

691
00:42:55,035 --> 00:42:58,750
creative in our public education system pounds it

692
00:42:58,750 --> 00:43:02,589
out of them. Our culture pounds creativity out of children. Right?

693
00:43:02,589 --> 00:43:06,369
They're right here. Yeah. Everybody is creative. Everyone.

694
00:43:06,910 --> 00:43:09,490
Right? And what AI allows

695
00:43:10,835 --> 00:43:14,275
us to do is to if you have an

696
00:43:14,275 --> 00:43:18,055
idea, you can then now use AI

697
00:43:18,515 --> 00:43:21,954
to bring that idea to life, and that

698
00:43:21,954 --> 00:43:25,740
scares people who have been the gatekeepers of that

699
00:43:25,740 --> 00:43:29,260
creativity it scares people who write

700
00:43:29,260 --> 00:43:32,780
music and produce produce music that I can create a folk song without

701
00:43:32,780 --> 00:43:35,760
them it scares it scares writers

702
00:43:36,140 --> 00:43:39,585
right That a CEO can write a book

703
00:43:40,045 --> 00:43:43,484
without their help. And and and I

704
00:43:43,484 --> 00:43:45,744
believe it should actually

705
00:43:47,644 --> 00:43:50,944
to me, AI will have in the long term this kind of

706
00:43:54,630 --> 00:43:58,010
sifting effect. And I'll give you one example

707
00:43:58,470 --> 00:44:02,250
because I've never been a fan of the

708
00:44:02,869 --> 00:44:06,710
brand consultants who make 1,000,000 of dollars coming up with brand

709
00:44:06,710 --> 00:44:10,095
purpose, mission, and vision Because I just think it's so

710
00:44:10,095 --> 00:44:13,635
obvious. Right? I I I like really you paid

711
00:44:13,695 --> 00:44:17,055
$1,000,000 for that? For somebody to come in and tell you what your purpose and

712
00:44:17,055 --> 00:44:20,675
your mission and vision is? You know what I mean? And then they're so

713
00:44:21,880 --> 00:44:25,640
long winded that it's very difficult for people to make

714
00:44:25,640 --> 00:44:28,940
decisions. I I am into simplicity when it comes to storytelling.

715
00:44:29,320 --> 00:44:32,380
Right? The best stories are just a few words long.

716
00:44:33,320 --> 00:44:36,860
Again, I had nothing to do with it. But at Animal Kingdom,

717
00:44:37,925 --> 00:44:41,385
the story there or the theme there is the intrinsic value of nature.

718
00:44:42,885 --> 00:44:46,585
Very simple. Mhmm. If that's the story, then

719
00:44:47,285 --> 00:44:50,645
and if you and if you stay true to that story, then all

720
00:44:50,645 --> 00:44:54,370
decisions become relatively easy. You know, and the guy that

721
00:44:54,370 --> 00:44:57,670
was the imagineer over at Joe Rodey talks about it. He's like, okay,

722
00:44:58,130 --> 00:45:01,890
you have a door in a zoo theme

723
00:45:01,890 --> 00:45:05,430
park about the intrinsic value of nature. Is that is the door steel

724
00:45:05,570 --> 00:45:09,185
or is it wood? Wood.

725
00:45:09,185 --> 00:45:13,025
Obviously. Right? You know? It allows you to make decisions if you

726
00:45:13,025 --> 00:45:16,625
keep it simple. I think when you when you, make

727
00:45:16,625 --> 00:45:20,465
things complicated with all this brand mission vision stuff, it's amazing. But but you

728
00:45:20,465 --> 00:45:23,940
can go into chat gpt now, and you could basically say, hey, could you give

729
00:45:23,940 --> 00:45:27,380
me the mission vision of the I mean, this, you know, this train, for example,

730
00:45:27,380 --> 00:45:31,060
did not have a brand position and and brand purpose and mission and

731
00:45:31,060 --> 00:45:34,660
vision. I just asked ChatGPT to do it for us, and it it nailed

732
00:45:34,660 --> 00:45:37,880
it. It nailed it. Wow. So

733
00:45:38,715 --> 00:45:42,475
if you are if you are a creative in a

734
00:45:42,475 --> 00:45:46,235
industry that is a bunch

735
00:45:46,235 --> 00:45:50,075
of already, and again, bias

736
00:45:50,075 --> 00:45:53,515
here. I think a lot of brand consulting is smoke and mirrors and

737
00:45:53,515 --> 00:45:56,850
just stupid and ridiculous, but they

738
00:45:57,150 --> 00:46:00,110
convince people to pay them a lot of money to do it. So if you're

739
00:46:00,110 --> 00:46:03,950
in an industry where, you know, it's a

740
00:46:03,950 --> 00:46:07,790
bunch of smoke and mirrors, well, you're exposed, and AI is gonna

741
00:46:07,790 --> 00:46:11,305
expose you. There's there's there's just ignore the man behind the

742
00:46:11,305 --> 00:46:15,145
curtain, so to speak moment. Exactly. Exactly. So

743
00:46:15,145 --> 00:46:18,985
listen, if you're not good, if you're not talented, I mean, you can

744
00:46:18,985 --> 00:46:22,825
certainly try to use AI to, you know, beef up your talents. But the bottom

745
00:46:22,825 --> 00:46:25,800
line is, it is gonna have a sifting effect.

746
00:46:27,700 --> 00:46:31,300
Because I think if one creative

747
00:46:31,300 --> 00:46:34,740
firm is only using AI to generate ideas and another firm is

748
00:46:34,740 --> 00:46:38,339
using their own inspiration and sparks with AI to enhance

749
00:46:38,339 --> 00:46:41,995
those inspirations, very human inspirations and sparks. I

750
00:46:41,995 --> 00:46:44,735
believe the latter agency is gonna win.

751
00:46:45,355 --> 00:46:48,895
Yeah. Just like the haikus. Just like the haikus.

752
00:46:49,195 --> 00:46:52,955
So, I think it will

753
00:46:52,955 --> 00:46:56,690
have you know, that kind of effect on the industry. And,

754
00:46:56,690 --> 00:47:00,530
again, it will also and this is totally true. We have no idea where it's

755
00:47:00,530 --> 00:47:04,369
gonna take us. We can guess. Yeah. I mean, one of my

756
00:47:04,369 --> 00:47:08,215
favorite story about technology not, you know, not really

757
00:47:08,215 --> 00:47:11,915
knowing where it's gonna take us is I worked on the Iron Bridge,

758
00:47:15,095 --> 00:47:18,535
Iron Bridge in the UK, Coalbrookdale. It's where the blast

759
00:47:18,535 --> 00:47:22,360
furnace was invented. Like the coke

760
00:47:22,360 --> 00:47:25,740
fired blast furnace, right? Was in Iron Bridge, UK.

761
00:47:26,040 --> 00:47:29,720
And Abraham Darby, and this was I think, oh I'm gonna get it

762
00:47:29,720 --> 00:47:33,375
wrong, like 1807 I think. Or maybe it was

763
00:47:33,375 --> 00:47:37,215
in 7th, so I can't. I'm getting to get the date

764
00:47:37,215 --> 00:47:40,655
wrong. But it was Abraham Darby and all he was trying to

765
00:47:40,655 --> 00:47:43,955
do was make a cheaper iron pot.

766
00:47:44,575 --> 00:47:48,130
That's it. He was simply trying to find a better way to make an iron

767
00:47:48,130 --> 00:47:51,890
pot. Yeah. That's it. That's all he was trying to

768
00:47:51,890 --> 00:47:54,870
do was make an iron pot. Invented the blast furnace,

769
00:47:56,050 --> 00:47:59,645
and then because of that blast furnace, they were able to

770
00:47:59,745 --> 00:48:03,405
make, steam engines because the iron. Right?

771
00:48:03,465 --> 00:48:06,845
You because you couldn't make a steam engine without, you know,

772
00:48:07,065 --> 00:48:10,825
without the ability to make it with iron. You know? So iron bridges, iron

773
00:48:10,825 --> 00:48:14,300
buildings, iron, you know, you know, iron steam

774
00:48:14,300 --> 00:48:17,740
engines, railroads, everything in the industrial

775
00:48:17,740 --> 00:48:21,580
revolution was born out of that blast furnace. And all the guy was trying to

776
00:48:21,580 --> 00:48:25,285
do was make a better pot. So, you know,

777
00:48:25,285 --> 00:48:29,045
we always we never know exactly where this technology is going to take

778
00:48:29,045 --> 00:48:32,885
us. That's true. I mean, we have prognosticators who think they

779
00:48:32,885 --> 00:48:36,645
know. And I Yeah. Think I know in certain areas where

780
00:48:36,645 --> 00:48:40,284
it's gonna take us. But at the bottom line, I don't know

781
00:48:40,284 --> 00:48:43,976
exactly where it's gonna take us because somebody, some human being is

782
00:48:43,976 --> 00:48:47,668
gonna come up with some really creative way to use AI that

783
00:48:47,668 --> 00:48:51,355
none of us have thought of. Well, let me go back to, like, the nineties.

784
00:48:51,355 --> 00:48:55,055
Right? Like, where, when the Internet was first kinda

785
00:48:55,115 --> 00:48:58,954
starting to become of its own, like, the the graphical browser was new. Who would

786
00:48:58,954 --> 00:49:02,795
have thought that would have replaced going to the mall? Who would have thought that

787
00:49:02,795 --> 00:49:06,590
you'd be able to, you know, interact with people on

788
00:49:06,590 --> 00:49:10,270
a device like this? Right? It just didn't

789
00:49:10,270 --> 00:49:13,790
seem possible. Right? And it just that had all

790
00:49:13,790 --> 00:49:16,450
these let alone you know, what's the joke about,

791
00:49:18,925 --> 00:49:22,525
I mentioned in the last episode where it was like, don't talk to strangers. Don't

792
00:49:22,525 --> 00:49:26,204
get it. Don't talk to strangers on the Internet, and don't get into

793
00:49:26,204 --> 00:49:29,920
strange people's cars. And what do you do when you call an Uber? You

794
00:49:29,920 --> 00:49:32,660
do exactly that. So true.

795
00:49:34,400 --> 00:49:38,160
So true. Yeah. Well, the potential is I I agree with you, Jeff. The

796
00:49:38,160 --> 00:49:41,680
potential is just limitless. And like you, I'm kind

797
00:49:41,680 --> 00:49:44,985
of kinda waiting to see what all comes of this.

798
00:49:45,365 --> 00:49:48,985
And we get to interview lots of guests,

799
00:49:49,525 --> 00:49:53,045
doing work, or some of them doing work on the literal cutting

800
00:49:53,045 --> 00:49:55,865
edge. We've actually had a few

801
00:49:56,910 --> 00:50:00,369
guests that were CEOs, of companies,

802
00:50:00,589 --> 00:50:04,430
and they said, you can't release this until we send you an email

803
00:50:04,430 --> 00:50:07,830
saying that you can't, because they were telling us basically Oh, yeah.

804
00:50:08,109 --> 00:50:11,890
Trade secrets. And so hearing that, that's,

805
00:50:12,085 --> 00:50:15,465
of course, that's that's cool and shiny from a podcast

806
00:50:15,525 --> 00:50:19,285
perspective. Don't get me wrong. We, you know, certainly love it when

807
00:50:19,285 --> 00:50:23,045
that happens. I I love hearing people talk about

808
00:50:23,045 --> 00:50:25,705
very pragmatic, very practical, very applied,

809
00:50:26,790 --> 00:50:30,550
things because and it you know, I wasn't always an engineer. I started

810
00:50:30,550 --> 00:50:34,070
as a farmer. And, you know, grew up on a a small

811
00:50:34,070 --> 00:50:37,510
farm, not too far from where I live now. I live in

812
00:50:37,510 --> 00:50:40,890
Farmville, Virginia. And, I mean, you mentioned Savannah.

813
00:50:40,950 --> 00:50:44,685
Beautiful, beautiful place there. Yeah. Very pretty there. I

814
00:50:44,685 --> 00:50:47,905
I lived in Jacksonville for a few years, and,

815
00:50:48,685 --> 00:50:52,525
we would often drive from from Jack's back up to Virginia where the rest

816
00:50:52,525 --> 00:50:56,305
of our family was. And we'd come through Savannah. We'd jump off

817
00:50:56,470 --> 00:51:00,230
and go through and see just just drive through. We didn't I

818
00:51:00,230 --> 00:51:03,670
mean, I think there was the is that where the fountain of youth is

819
00:51:03,670 --> 00:51:07,270
attraction in Savannah? That's Saint Augustine. Saint oh,

820
00:51:07,270 --> 00:51:10,915
okay. Saint Augustine. There's there's something like that there. I've I've

821
00:51:10,994 --> 00:51:14,355
There's a there's a fancy there's a nearly nice fountain in the middle of town,

822
00:51:14,355 --> 00:51:17,875
if memory serves. Maybe that's where we left. Forsyth, yeah.

823
00:51:17,875 --> 00:51:21,395
Forsyth Park has a beautiful fountain. Yeah. So we'd stop there. But just

824
00:51:21,795 --> 00:51:25,349
it was just relaxing to get off of this, you know,

825
00:51:25,349 --> 00:51:29,049
more efficient, if you wanna call it that, highway

826
00:51:29,670 --> 00:51:33,510
and and drive through this town very slow, you know, slow

827
00:51:33,510 --> 00:51:37,035
way down, quarter or third of the speed. And just

828
00:51:37,395 --> 00:51:41,235
it was just so peaceful just, you know, driving through there and and getting

829
00:51:41,235 --> 00:51:44,995
that, I don't know, a renewal of the soul almost because, you know,

830
00:51:44,995 --> 00:51:48,215
it's a long drive, from Jacksonville to Virginia.

831
00:51:48,435 --> 00:51:51,790
And I just the the application

832
00:51:52,090 --> 00:51:55,530
of the I guess it's

833
00:51:55,610 --> 00:51:59,450
instead of humanizing AI, and I'm kinda changing the subject here

834
00:51:59,450 --> 00:52:03,210
midway, but instead of humanizing or or, you know, doing

835
00:52:03,210 --> 00:52:06,994
the anthropomorphic thing, letting it

836
00:52:07,535 --> 00:52:11,375
enhance our work, letting it assist us in

837
00:52:11,375 --> 00:52:14,194
being creative, letting letting it help

838
00:52:14,734 --> 00:52:18,275
improve our life and work and efficiency

839
00:52:18,895 --> 00:52:22,510
and just the amount of work, you know, we're able to crank out

840
00:52:22,810 --> 00:52:26,650
on this. That's I think that's more of a thing. Is

841
00:52:26,650 --> 00:52:30,010
is it gonna cost people jobs? I I agree with you, although I hadn't heard

842
00:52:30,010 --> 00:52:33,630
it characterized that way before that if you're

843
00:52:34,255 --> 00:52:38,015
phoning it in, you're faking it and, you know, mail and then

844
00:52:38,015 --> 00:52:41,635
sending out invoices, then maybe, probably.

845
00:52:42,095 --> 00:52:45,215
As as it gets better. I mean, there's no doubt it will take I mean,

846
00:52:45,215 --> 00:52:48,869
but here's the thing. That's that's life. I mean, my first job was at

847
00:52:48,869 --> 00:52:52,550
the Salt Lake Tribune and Deseret News Newspaper Agency. It was a joint operating

848
00:52:52,550 --> 00:52:55,990
agreement company, the newspaper agency in Salt Lake City, Utah. That was my first

849
00:52:55,990 --> 00:52:59,635
job. And and I remember there were, you know

850
00:52:59,795 --> 00:53:01,875
and then I and then I went to the Hamilton Journal News. And at the

851
00:53:01,875 --> 00:53:05,475
Hamilton Journal News, they weren't advances, the Salt Lake Tribune and Deseret News. At the

852
00:53:05,475 --> 00:53:08,855
Hamilton Journal News, we still had a union shop

853
00:53:09,075 --> 00:53:12,695
where somebody who was a union, right, a union employee

854
00:53:12,995 --> 00:53:16,670
would paste up the newspaper on big pay stub boards,

855
00:53:16,670 --> 00:53:20,510
like the headlines, the pictures. You know, and I'd stand there

856
00:53:20,510 --> 00:53:24,109
with my and I still have it actually on the shelf, my pica and points

857
00:53:24,109 --> 00:53:27,915
wheel, you know, to to to crop pictures. And, you know, and I'd

858
00:53:27,915 --> 00:53:31,355
stand there with the union member, and I was not allowed to touch that pay

859
00:53:31,355 --> 00:53:34,795
stub board because I was not in the union. Right? If I tried to move

860
00:53:34,875 --> 00:53:38,155
Wow. If I tried to put my finger on that board and move it, you

861
00:53:38,155 --> 00:53:41,775
know, a point or a pika this way or just you know,

862
00:53:42,930 --> 00:53:46,530
I was yelled at. Right? I mean Wow. And then they would take that they

863
00:53:46,530 --> 00:53:50,309
take that big pay stub board, and they would take it into the next room,

864
00:53:50,369 --> 00:53:54,210
and they would photograph it. Right? And then they would, you know,

865
00:53:54,210 --> 00:53:57,905
after they photograph it, they'd add, you know, the the acetate to it,

866
00:53:57,905 --> 00:54:01,345
and they take it and and print the newspaper from it. Right? Wow. That was

867
00:54:01,345 --> 00:54:05,185
a very real thing. That was my my first career out of college

868
00:54:05,185 --> 00:54:08,625
was in journalism. And, those jobs are

869
00:54:08,625 --> 00:54:11,685
gone. Yeah. Never to come back. Never.

870
00:54:12,410 --> 00:54:14,830
You know? And

871
00:54:16,090 --> 00:54:19,730
that's okay. You know what I mean? Because some jobs come up. I mean, the

872
00:54:19,770 --> 00:54:23,530
Exactly. The college football the college football hall of fame job we did. Right? We

873
00:54:23,530 --> 00:54:27,195
had over a 100 people working on it, like, a 112 people working on it.

874
00:54:27,435 --> 00:54:30,875
Right? And we probably had at at one point, probably about

875
00:54:30,875 --> 00:54:34,575
15 people doing prompting. Right? Oh, wow.

876
00:54:35,115 --> 00:54:38,335
It it's work. It's a lot of work. And the creativity,

877
00:54:39,035 --> 00:54:42,635
you know, there's a lot of creativity that goes into prompting a

878
00:54:42,635 --> 00:54:46,450
lot. And I have to tell you. And there's a lot of failure. One thing

879
00:54:46,450 --> 00:54:49,910
that we've learned that was not even possible was

880
00:54:50,049 --> 00:54:53,890
AI is not ready to do face painting and body painting of fans in a

881
00:54:53,890 --> 00:54:57,645
college football. It's just not ready. We tried it. Oh,

882
00:54:57,645 --> 00:55:01,485
my gosh. We've spent so much time trying to get the prompts right

883
00:55:01,485 --> 00:55:05,245
to do body paint and face paint, and it just didn't

884
00:55:05,245 --> 00:55:08,859
work. It just didn't work. And then this was probably

885
00:55:08,859 --> 00:55:12,480
from a a nerdy perspective. What you guys might appreciate the most.

886
00:55:13,100 --> 00:55:16,480
How do you come up with the right colors for

887
00:55:16,619 --> 00:55:20,300
764 different college football teams when you can't use

888
00:55:20,300 --> 00:55:23,945
hex codes, can't use pms colors, you have to accurately describe

889
00:55:23,945 --> 00:55:27,305
in English the difference between Clemson's orange, the University of

890
00:55:27,305 --> 00:55:30,905
Tennessee's orange, Auburn's orange, Florida's orange, and the

891
00:55:30,905 --> 00:55:34,585
University of Texas is orange. And if you put burnt orange for

892
00:55:34,585 --> 00:55:38,150
Texas, you get somebody on you get

893
00:55:38,369 --> 00:55:41,970
someone charred coming at you get you get, like, on

894
00:55:41,970 --> 00:55:44,930
fire, like flame charred. Oh, man. Look at the little girl about the burnt If

895
00:55:44,930 --> 00:55:48,609
you put pumpkin orange for Clemson, you'll get a hallucination where the person's

896
00:55:48,609 --> 00:55:52,150
holding a pumpkin wearing a marching band uniform. Right. So

897
00:55:53,085 --> 00:55:56,545
how do you describe accurately in English 764

898
00:55:57,325 --> 00:56:01,005
different colors? That in and of itself was a

899
00:56:01,005 --> 00:56:04,845
huge challenge that human beings solved. The computers didn't solve that.

900
00:56:04,845 --> 00:56:08,330
Right? AI did not solve that. We had to solve that

901
00:56:08,330 --> 00:56:12,170
issue. Wow. So there's a lot of work. Do you end up

902
00:56:12,170 --> 00:56:15,770
do using hex codes? I'm curious. Oh, no. No. You can't use

903
00:56:15,770 --> 00:56:19,465
hex codes with AI. No. We we we had to come

904
00:56:19,465 --> 00:56:23,225
up with a solution using English to describe the colors that

905
00:56:23,225 --> 00:56:26,905
would match accurately. Doubt. What

906
00:56:26,905 --> 00:56:30,685
we ended up doing is we created a library of colors.

907
00:56:31,580 --> 00:56:35,340
And then and then we would say, okay, this orange

908
00:56:35,340 --> 00:56:39,020
works for these 3 teams. This orange works for this one

909
00:56:39,020 --> 00:56:42,860
team. This orange works for these 2 teams. This blue works

910
00:56:42,860 --> 00:56:46,215
for these 4 teams. This purple works for this. So we

911
00:56:46,455 --> 00:56:49,975
literally created a you know, so it's not

912
00:56:49,975 --> 00:56:53,735
perfect, but it's it's it's close enough

913
00:56:53,735 --> 00:56:57,015
that we you know, nobody's gonna be yelling at us for, like, you got my

914
00:56:57,015 --> 00:57:00,809
orange wrong. Right? But we we literally

915
00:57:00,869 --> 00:57:04,089
just we created essentially a color wheel in AI,

916
00:57:04,869 --> 00:57:08,309
and then we then matched. And there were a few

917
00:57:08,309 --> 00:57:12,069
outliers where it was like, oh, you know, there were some where it was

918
00:57:12,069 --> 00:57:15,585
like they had a custom color just for them. But most of the colors, we

919
00:57:15,585 --> 00:57:19,025
were able to apply it to, you know, as, you know, a certain number of

920
00:57:19,025 --> 00:57:22,785
teams. And so that that's that's essentially how

921
00:57:22,785 --> 00:57:26,465
we solve that problem. But then there were there were other problems as well. So

922
00:57:26,465 --> 00:57:30,089
I I have no idea. I I have no idea how to solve that problem,

923
00:57:30,089 --> 00:57:33,930
but I would my first thought was, send it an image, like,

924
00:57:33,930 --> 00:57:37,690
of a color palette and say, you know, a number, the

925
00:57:37,690 --> 00:57:41,295
the colors that you know, there's gotta be some subset of some of the hired

926
00:57:41,295 --> 00:57:44,895
you, Andy. Maybe you coulda helped us. I don't know. I I don't know. I

927
00:57:44,895 --> 00:57:48,255
don't know if that woulda worked or not, but I had the same issues when

928
00:57:48,255 --> 00:57:51,375
I've you know, it it seems like the AIs that I work with and I

929
00:57:51,375 --> 00:57:54,840
work with a handful, of LLMs, and I'm

930
00:57:54,840 --> 00:57:58,380
asking it to generate images. And I have the worst,

931
00:57:58,760 --> 00:58:02,600
the worst experiences ever with that. Frank, on the other

932
00:58:02,600 --> 00:58:06,360
hand, Frank is a naturally gifted graphic artist. He

933
00:58:06,360 --> 00:58:09,100
is an artist Thank you. Who went to school,

934
00:58:09,905 --> 00:58:13,505
to learn computer programming languages, and he's gotten

935
00:58:13,505 --> 00:58:17,265
into AI. And he's the he's the data science person in this

936
00:58:17,265 --> 00:58:20,085
team. I credit my parents with that because when

937
00:58:21,440 --> 00:58:24,960
they were, like, there were really only a handful of careers,

938
00:58:24,960 --> 00:58:28,320
doctor, lawyer, engineer, or, the

939
00:58:28,320 --> 00:58:32,080
military. Yeah. They wanted you to get a real job.

940
00:58:32,080 --> 00:58:35,520
That was how it was. Then there were only, like, 3 type 4 types of

941
00:58:35,520 --> 00:58:39,244
jobs, doctor, lawyer. And now you're like, look, mom and dad.

942
00:58:39,244 --> 00:58:43,005
I'm a podcast host. That's right. Right. Right. Well, when I switched to

943
00:58:43,005 --> 00:58:46,684
computer science, when I switched to computer science, that was a

944
00:58:46,684 --> 00:58:49,484
big that was a big thing. Because originally, I went to college to be a

945
00:58:49,484 --> 00:58:53,180
chemical engineer. And then I had to convince my parents that this

946
00:58:53,180 --> 00:58:57,020
was a legitimate engineering discipline with

947
00:58:57,020 --> 00:59:00,160
a monetary upside. And that was,

948
00:59:01,660 --> 00:59:05,339
you know, today, you you how could well, okay. Maybe with AI,

949
00:59:05,339 --> 00:59:08,835
but, like, maybe programming isn't necessarily have

950
00:59:08,835 --> 00:59:12,675
the the the bright future it once did. But, you know, it's hard to

951
00:59:12,675 --> 00:59:16,515
imagine, I think, kids today, like, well, how could how could computer

952
00:59:16,515 --> 00:59:19,430
science not be considered an engineering discipline? And,

953
00:59:20,690 --> 00:59:24,290
you know, the 1990 something, it was not

954
00:59:24,290 --> 00:59:27,990
immediately obvious. Yeah. Yeah. But it's you know,

955
00:59:28,210 --> 00:59:32,050
I I have the prompting. I I have just the

956
00:59:32,050 --> 00:59:35,885
worst luck, prompting for image type stuff. So

957
00:59:35,885 --> 00:59:38,845
I've come up with a couple of hacks to do it, but my number one

958
00:59:38,845 --> 00:59:42,385
hack is to email Frank. Email is your own. Message

959
00:59:42,445 --> 00:59:46,180
message Frank. I said, Frank, I need something that looks like this. I

960
00:59:46,180 --> 00:59:50,020
won't convey this image. And, you know, he's got Frank's Yeah. You

961
00:59:50,020 --> 00:59:53,780
know, his his free time is spiky. And so when he

962
00:59:53,780 --> 00:59:56,980
gets the next free time, he thinks of me. He'll he'll build something or send

963
00:59:56,980 --> 01:00:00,684
me a song. He's done all my intro videos and all of that stuff,

964
01:00:00,684 --> 01:00:04,525
and they're all awesome. But he has that natural gift already, and I don't. I

965
01:00:04,525 --> 01:00:07,964
still don't color in the lines. Yeah. You know? It's a so it's not my

966
01:00:07,964 --> 01:00:11,645
idea. There's a there's a gift to prompting. There is. There's a talent to it.

967
01:00:11,645 --> 01:00:15,490
And, you know, I tell every young person who's in college who

968
01:00:15,490 --> 01:00:19,329
asks me for advice, I'm saying, learn learn prompting. Yeah. That's

969
01:00:19,329 --> 01:00:22,930
that's the if you and, again, it's it's a combination of skills. I

970
01:00:22,930 --> 01:00:26,690
mean, you know, again, I've I've got Discord popping over here right now

971
01:00:26,690 --> 01:00:30,235
on Midjourney as I'm watching, you know, my kids working on our

972
01:00:30,235 --> 01:00:33,995
Christmas card. They're coming up with different ideas, and, you know, you know

973
01:00:34,235 --> 01:00:37,995
That is so cool. It is cool. I mean, you know, and they'll some

974
01:00:37,995 --> 01:00:41,515
ideas, actually. But I mean no. I mean and they'll end up

975
01:00:41,515 --> 01:00:45,290
painting, you know, painting over it, you know, and and customizing it to

976
01:00:45,290 --> 01:00:48,830
a certain degree. But that's, you know, that's,

977
01:00:49,770 --> 01:00:53,210
you know, that's it's just it's a

978
01:00:53,210 --> 01:00:56,265
natural evolution. I I you know, people who are like,

979
01:00:56,664 --> 01:01:00,265
oh, again, I get it. We have to

980
01:01:00,265 --> 01:01:03,244
guard ourselves. We cannot get lazy,

981
01:01:03,785 --> 01:01:07,545
right? We have to make sure that we still Don't be like

982
01:01:07,545 --> 01:01:10,810
me in high school. Read the books, Right? Don't don't just

983
01:01:11,510 --> 01:01:15,270
go with the the cliff notes. But there again, you

984
01:01:15,270 --> 01:01:18,950
know, there's always been ways to cheat. Always.

985
01:01:18,950 --> 01:01:22,790
Yeah. Yeah. And Shorten road. Technology makes it easier to cheat, but there's

986
01:01:22,790 --> 01:01:26,494
always been ways to cheat. Always. Sure. I mean, you

987
01:01:26,494 --> 01:01:30,255
know, so we just have to make sure that we

988
01:01:30,255 --> 01:01:34,095
don't cheat. We have to have the discipline to actually, you know,

989
01:01:34,095 --> 01:01:37,855
read, write, look for that spark of inspiration. And,

990
01:01:37,855 --> 01:01:41,690
again, at the end of the day, people ask me all the time, well,

991
01:01:41,690 --> 01:01:45,470
oh, what's the secret to your creativity? How come you're so creative? And I

992
01:01:46,410 --> 01:01:49,850
think there's a much longer conversation, but I

993
01:01:49,850 --> 01:01:53,630
think a lot of it is just about making connections. And it's about having stimulus

994
01:01:53,690 --> 01:01:57,394
coming in. It's a simple formula. You guys appreciate formulas.

995
01:01:57,615 --> 01:02:01,234
You're computer scientists and math people.

996
01:02:02,095 --> 01:02:05,775
In order to get something out, you have to put something in, right?

997
01:02:05,775 --> 01:02:08,335
In order to have an idea come out of your brain, you have to have

998
01:02:08,335 --> 01:02:12,160
stimulus come into your brain. So the more stimulus you put in,

999
01:02:12,380 --> 01:02:16,059
the better opportunity you have to have great ideas coming out. And if you

1000
01:02:16,059 --> 01:02:19,900
want a creative company, you know, make sure you surround

1001
01:02:19,900 --> 01:02:23,440
your employees with a lot of stimulus. And that includes things like food

1002
01:02:23,740 --> 01:02:26,565
and great design and music

1003
01:02:27,585 --> 01:02:31,045
and and encouraging trips. You know what I mean? A lot of our ideas

1004
01:02:31,745 --> 01:02:35,105
come from simply what we call hashtag research, which

1005
01:02:35,105 --> 01:02:38,625
is we go places. We see things. We do

1006
01:02:38,625 --> 01:02:42,200
stuff Because we're in the experience business, and so

1007
01:02:42,200 --> 01:02:45,840
we want to take part in experiences. We wanna

1008
01:02:45,840 --> 01:02:48,980
go look at stuff. And so, you

1009
01:02:49,520 --> 01:02:53,360
know, AI is yet one more point of stimulus that can

1010
01:02:53,360 --> 01:02:57,184
come in. We just need to be careful that we don't let it,

1011
01:02:58,444 --> 01:03:01,885
act upon us. We need to be the one that

1012
01:03:01,885 --> 01:03:05,645
uses it and and commands it to to Yeah. To

1013
01:03:05,645 --> 01:03:09,359
work for us rather than have it command us. I love that point.

1014
01:03:10,079 --> 01:03:13,599
And it's a it's a great I think it's a great

1015
01:03:13,599 --> 01:03:17,359
summary of of of a lot of the points that we've

1016
01:03:17,359 --> 01:03:20,960
talked about on the episode. So, Jeff, if people wanna learn more

1017
01:03:20,960 --> 01:03:24,684
about you personally and your business, where

1018
01:03:24,684 --> 01:03:28,045
can they find out more about you and the business? Well, thanks,

1019
01:03:28,285 --> 01:03:31,325
Andy and Frank. It's been great to be on and talking with you. I just

1020
01:03:31,325 --> 01:03:35,005
love I mean, we could go on for hours and hours talking about this stuff.

1021
01:03:35,005 --> 01:03:38,610
Well, it's very inspirational, which is Same. Yeah. It's

1022
01:03:38,690 --> 01:03:42,290
it's exciting. It really is. I you know, it's it it is. 1 of 1

1023
01:03:42,290 --> 01:03:46,130
of the yeah. 1 of the sorry. One last thing.

1024
01:03:46,130 --> 01:03:49,890
I mean, just just like Okay. You know, like, one of

1025
01:03:49,890 --> 01:03:53,430
the projects we're working right now is talking to a fire sprinkler. Right?

1026
01:03:53,585 --> 01:03:56,005
You know, and, you know,

1027
01:03:57,184 --> 01:04:00,865
but have the sprinkler talk to you about engineering from a fire

1028
01:04:00,865 --> 01:04:03,924
sprinkler's perspective. But just something like that, you know,

1029
01:04:05,184 --> 01:04:08,710
we it's just fun. I mean, do we all have that real time

1030
01:04:08,710 --> 01:04:11,990
conversation with a Spire sprinkler? Are you kidding me? Or a lithium ion battery to

1031
01:04:11,990 --> 01:04:15,109
talk about thermal runways. There's so many things you can do with it. Anyway, I

1032
01:04:15,109 --> 01:04:18,855
digress. If you wanna get a hold of us, honestly, you know, just Google

1033
01:04:18,855 --> 01:04:22,695
Creative Principles, creativeprinciples.com. LinkedIn's an easy way

1034
01:04:22,695 --> 01:04:26,535
to find us. You know, Jeff Thatcher with a g. You know, Thatcher like Margaret,

1035
01:04:26,535 --> 01:04:29,895
the prime minister. And if you're old enough, you know who she is. But, you

1036
01:04:29,895 --> 01:04:33,495
know, but that that's the that's the best way to find

1037
01:04:33,495 --> 01:04:37,300
us. Just find us online. And, yeah. It was

1038
01:04:37,300 --> 01:04:40,980
great talking. I really appreciate the time. Oh, same. Excellent. Same

1039
01:04:40,980 --> 01:04:44,600
here. And we'll let the nice British lady finish the show.

1040
01:04:44,660 --> 01:04:48,295
And that's a wrap for this episode of Data Driven. A massive

1041
01:04:48,295 --> 01:04:51,735
thank you to Jeff Thatcher for taking us on a journey through the world of

1042
01:04:51,735 --> 01:04:55,095
immersive experiences and the role AI plays in creating

1043
01:04:55,095 --> 01:04:58,455
unforgettable stories. From theme parks to football

1044
01:04:58,455 --> 01:05:01,995
fields, Jeff reminded us how technology can spark creativity

1045
01:05:02,360 --> 01:05:05,960
and amplify the human connection when used wisely. If you

1046
01:05:05,960 --> 01:05:09,720
enjoyed today's episode, don't forget to subscribe, leave us a

1047
01:05:09,720 --> 01:05:13,061
review, and share it with your network. And as always,

1048
01:05:13,281 --> 01:05:16,961
stay curious, stay data driven and maybe keep an eye out for

1049
01:05:16,961 --> 01:05:20,741
where you might show up in your own favourite story someday. Cheers

1050
01:05:20,881 --> 01:05:21,381
everyone.