#276 - Bob Bonniol

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What can you say it's Bob Bonnie
all really cool talk about AI

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Finally someone who really is
into it even more than I am and

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and super smart guy.
Obviously Bob has written his

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own code.
He's a very smart computer guy.

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So fun talk.
We got into it pretty heavy here

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on on this episode, which is
episode #276.

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So I really hope you enjoy it.
It was fun and I look forward to

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doing it again with Bob because
we only got we only got so far

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into it.
So enjoy.

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Thank you for joining me today
for Gears of Gear episode number

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276.
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Pulse Series.
You've probably already seen it

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and therefore I'm going to drive
you crazy by telling you again

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that I went to see this week
David Gilmour from Pink Floyd

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fame, of course, and designed by
Mark Brickman, of course, who

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we've had on the podcast
recently.

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I believe it was episode 271 if
I remember correctly.

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And so Bob or I'm sorry, Mark
invited me to see the show.

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And sorry, I was reading about
today's guest, Bob Barniel.

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Mark invited me to see the show
along with John Wiseman from PRG

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of course, and hung out with
them, spent some great time with

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them.
Also Jerry Harris from, you

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know, the founder of PRG, of
course he invited or he, he was

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there as well.
And I got to spend some time

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with him, some quality time with
Jerry, which is always fun.

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Sorry, I'm just trying to get
this light out of my eyes.

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There's a LED friggin video
light thing in my eyes and it's

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driving me crazy.
So anyways, got to spend some

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time with Jerry, got to see the
show, love the show.

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I'm not going to talk too much
about it because I've posted

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about it already a whole bunch.
And if you want to see some

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pictures or videos, you can go
to I think either my Instagram

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or the geez of gear Instagram.
I can't remember which it is,

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but there's some reels and you
know, it's just a fabulous show.

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It's it's so well designed, it's
so unique and different.

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And I just really enjoyed myself
and enjoyed spending time with

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some some great friends as well.
And it's always fun to pop into

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New York for 24 hours as well.
So I would say if you get a

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chance to see it, go see it.
But unless you're in New York,

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you're you're not going to see
it 'cause the tour is very

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short.
It was only four cities.

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It's in the fourth city now, New
York.

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And I believe there are two more
shows, if I remember correctly,

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maybe tonight and tomorrow night
or tonight and Sunday, something

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like that.
Maybe even just one show this

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weekend.
But yeah, what is today?

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Today is the 8th.
So I think there's a show

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tonight and a show Sunday and
that's it.

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And then it's over.
And so I hate to say it and I

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don't want to be the one to say
it, but this could be the last

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chance you get to see David
Gilmour or Pink Floyd or David

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Gilmour doing Pink Floyd or any
of that.

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And also, it's really worth
seeing this design.

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So if you have, if you're in New
York, if you have any ability to

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get tickets, if, if you actually
by the time you hear this, it's

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over.
So I apologize, forget all that

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stuff.
So anyways, it was great.

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Watch it on YouTube.
I guess So what else do I want

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to talk about?
I'm not going to do a big intro

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today because we are actually
going to talk about AI today.

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And I've been looking forward to
this one because there are a few

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people in the industry I think
who are as geeked out as I am on

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AI and today's guest is one of
them.

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Bob Bonnie.
All of course, is an Emmy

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award-winning director,
production designer and

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producer.
He's known for his

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implementation of extensive
media and interactive features

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in his productions and
installations.

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His practice spans the world of
theatre, broadcast, opera

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installations and architecture.
In 2023, he widely deployed new

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interactive technology and AI to
elevate live concert production

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with designs for Billy Joel,
Blake Shelton, Megan Thee

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Stallion, and the One Night
Houston Festival.

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In the course of his career,
he's worked from Broadway to

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Beijing, helping clients from
Disney, NBCUABC, Live Nation,

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AEG, Marvel Studios, and
Lucasfilms.

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Gather, gathers and dazzles.
Dazzles audiences of millions.

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Speaking today is challenging
for me.

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We had the pleasure of having
Bob on the podcast in February

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of 2023.
You can check that episode out.

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It's #183 and that is available
on Spotify, on YouTube, on lots

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of places, or on our website,
geezerofgear.com.

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So please join me and welcome
Bob Bonnie all again here to

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episode #276.
Bob Marcel, how are you?

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What's?
Happening.

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I am lovely, man I'm I'm good.
It's a beautiful fall day here

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in Seattle.
Yeah.

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And I've been living in Los
Angeles for the last two plus

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months.
So I just got back home after my

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residency in Los Angeles and.
What was that?

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Can you talk about it?
Yeah, there, there.

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You know, there's a variety of
projects that I have going on in

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LA, but primarily I was there
because my daughter was doing

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two months of training and ACT
lighting on becoming an MA3

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support.
Oh, very cool.

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So she's a programmer.
She's a budding programmer,

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let's put it that way.
Yeah.

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And had had become quite
proficient on the ETC consoles

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through the course of her
education.

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Yeah.
Just not surprising.

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ETC kind of has that
marketplace.

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They do, yeah.
Sewn up, but she was quite eager

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to learn about MA.
She's been on site with me a few

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times and she's always been
particularly blown away with,

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with Felix Peralta and his
proficiency with MA.

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Yeah.
So I, you know, I reached out to

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Ben Saltzman at ACT Lighting and
said, hey, do you guys have any

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opportunities for training or
for that?

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And he's like, it's funny you
mention it.

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We are actively looking for
people to put through this

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program, you know, to come, you
know, sort of serve an

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internship at ACTS.
And of course that my daughter

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Izzy, she got to learn about Zac
tracks.

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She got to go through all of the
training on MA 3, but then go

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yet deeper and learn how to
debug the new software and learn

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oh wow.
You know, so she learned it at a

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level that's not just a
programming level, but at a deep

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functionality level.
So we've just, I was primarily

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there to make sure that my, my
daughter was successful.

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You know, let, let's put it this
way, any of any of our listeners

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who are parents know that, you
know, sometimes your kids have

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a, a, a variably successful
relationship with like living in

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the world, you know, yes.
How old is she?

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She's 20.
Oh, OK, yeah, my son's 20, so I

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trust me.
I get it, you know, just keeping

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him from killing himself by
doing something dumb.

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Is is, you know, most of my goal
in life.

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I, I worried less about that
with Izzy than than than, you

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know, just her, you know, a, you
know, call me conservative about

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this, but setting my daughter
free in Los Angeles, where she's

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ever been before.
Yeah.

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Maybe a little harrowing, yeah,
but but I also wanted her to be

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free to really focus on what she
was up to.

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And of course, I have infinite
reasons to be in Los Angeles.

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Yeah.
You know, both projects going

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on, but also developing
developing new stuff.

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Too, I did something similar and
I, I, because again, I guess one

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of the differences between a
young man and a young woman is

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you're a little less nervous
about sending away a young man.

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And my son went to London by
himself for six months and, and

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I didn't worry about him at all.
I just, I got, I dropped him off

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with his bags and stuff at an
Airbnb and said, see you later,

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you know, and that was that,
right?

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And then went over to visit when
he was racing or whatever.

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So yeah, I mean, I get it, I get
it.

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But it's really cool that you
did that with her and, and

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helped to make sure she was
successful.

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And obviously, it's nice when
you have connections in the

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industry that you can sort of
help some of those things along

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or or at least, yeah, so good
for you.

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Good fathering.
Just doing my best.

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You know, back when I was a
production designer for

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Nickelback, I remember talking
to to Chad's mom, the lead

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singer Chad.
I was talking to his mom and we

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were talking about being parents
and, and, and you know, I have a

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girl, obviously she had a boy
and I and she said, well, I'll

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tell you Bob, you know, when
you're raising a boy, you only

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have to worry about one dude.
When you're raising a girl, you

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have to worry about every dude
in town.

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Yeah, I heard, I heard, I heard
a slightly more colorful version

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of that same story.
There is that there I can tell

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you that that is not a quote it.
Was.

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Yeah, it was.
A slightly different verb going.

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On.
But I think that gets the point

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across.
Exactly.

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Exactly.
No.

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And I get it, trust me.
But I mean, you know, again,

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with boys, I think really like,
you know, again, my son likes to

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go fast in everything that he
does, right?

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So like.
Course he does if.

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He's pushing a shopping cart
through the store.

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He's riding it like a skateboard
or a scooter or something as

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fast as he possibly can and
probably racing his body or

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something.
And you know, just bad things

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tend to happen.
You know, it's like funny story,

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when I was designing my, the
house that I used to live in pre

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divorce, when I was designing
the pool on the back of it, I

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had a guy with CAD and you know,
he's designing all here's the

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waterfall and this is going to
be the hot tub over here.

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And here's where we're going to,
you know, whatever.

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And I said that distance right
there between the house and the

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edge of the pool.
How far is that?

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And he said it's 8 feet.
That's pretty standard.

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And I said, we've got the room.
Can you move that double like 12

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to 16 feet?
And he goes, I mean, we can, but

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won't it look a little strange
that far from the house?

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And then you got to build a
bigger screen if you build a

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screen over it.
And I said, yeah, but that

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upstairs window, that's his
bedroom.

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00:13:42,840 --> 00:13:45,960
And I guarantee you we're.
Measuring the trajectory of the.

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00:13:46,040 --> 00:13:48,760
Yeah, yeah.
If it's too far, he's not even

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going to try it, right?
But 8 feet, he's going to be

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jumping, you know, he's going to
jump from there to the pool.

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It's going to happen.
So I'd rather not inspire that

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to happen right the minute I
leave.

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But anyway, I appreciate you
coming back on again.

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It's been a year and a half and
lots of things have happened in

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00:14:06,320 --> 00:14:08,400
the last year and a half.
God forbid.

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I mean, our industry went that
shit crazy and now it's crazy in

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00:14:11,720 --> 00:14:15,000
a different way.
And you know, I was talking to

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00:14:15,800 --> 00:14:19,720
an account exec from one of the
big companies the other day and

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00:14:20,120 --> 00:14:23,920
he said, you know, I went from
the crazy busiest year in my

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00:14:23,920 --> 00:14:27,880
career and, and biggest, most
successful year in my career to

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00:14:28,440 --> 00:14:31,160
like this weird year where it
was incredibly busy, but they

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00:14:31,160 --> 00:14:34,040
were all smaller deals.
And, and so I was still just as

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00:14:34,040 --> 00:14:35,600
busy, but I didn't make any
money.

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00:14:36,200 --> 00:14:39,000
And you know, it's just like
weird, it's weird.

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Our industry's weird, but you're
staying busy.

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It's a, it's an up and downs
thing.

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I'm lucky in that I've got
professional ADD, you know, and

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plenty of our colleagues are
very focused folks like they're,

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00:14:53,240 --> 00:14:55,720
you know, they, they either
focus on their their concert

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00:14:55,720 --> 00:14:59,680
career or they focus on doing
more brand event type stuff from

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00:14:59,680 --> 00:15:05,560
maybe a focus in architecture.
But because I am like stay

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00:15:05,560 --> 00:15:10,920
focused on much for very long.
The net benefit of that is that

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00:15:11,080 --> 00:15:14,880
you know what mode we've we
built a a business it's about

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00:15:15,160 --> 00:15:17,400
playing in all three sandboxes.
Yeah.

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00:15:17,640 --> 00:15:22,440
So as businesses seem to have
their cyclical ups and downs,

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00:15:22,440 --> 00:15:24,600
usually there's an offset so.
There's overlaps.

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00:15:24,880 --> 00:15:28,400
Having a good concert year, we
can focus on the corporate work.

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00:15:29,200 --> 00:15:32,360
If the corporates are pulling
back on spending and there's

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00:15:32,360 --> 00:15:36,440
less work there, we can look at
architecture and music and you

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00:15:36,440 --> 00:15:40,480
know, between the three
verticals, we we seem to find

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00:15:40,480 --> 00:15:43,240
enough to buy the groceries.
Was that on purpose, or was that

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00:15:43,360 --> 00:15:47,080
just entirely by accident?
You know, if you look back at

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00:15:47,080 --> 00:15:50,440
the history of the company and
the like across my career and

254
00:15:50,440 --> 00:15:54,760
Colleen, my my wife's career,
you might infer that we were

255
00:15:54,760 --> 00:15:57,080
very smart about how we did
that.

256
00:15:57,080 --> 00:15:59,440
But.
But in fact, you know, I'm, I'm

257
00:15:59,440 --> 00:16:02,840
here to say it was a little bit
of a happy accident, you know?

258
00:16:03,280 --> 00:16:07,680
And to that end, I tell people
just starting out, say yes to

259
00:16:07,680 --> 00:16:10,120
everything.
Yeah, try everything.

260
00:16:10,120 --> 00:16:12,400
Go work in fashion.
Yeah.

261
00:16:12,400 --> 00:16:14,440
Do fashion shows.
Go work in business.

262
00:16:14,440 --> 00:16:18,440
Go do corporate events, go do
theater, right?

263
00:16:18,440 --> 00:16:21,040
Go go see what architecture
stuff is like.

264
00:16:21,040 --> 00:16:25,000
Because I, I think, you know, so
many of the, the best

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00:16:25,000 --> 00:16:29,360
practitioners at what we do have
had that diversity of

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00:16:29,360 --> 00:16:30,920
experience.
And I think in the beginning of

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00:16:30,920 --> 00:16:36,240
your career you are well served
to not worry about as much about

268
00:16:36,680 --> 00:16:41,680
what pays more or maybe what you
think you're interested in and

269
00:16:41,680 --> 00:16:43,800
just to get that diversity of
experience.

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00:16:43,800 --> 00:16:47,560
Now, I think as a career
proceeds, what you really ought

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00:16:47,560 --> 00:16:50,480
to be doing is saying no a lot
more because.

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00:16:50,480 --> 00:16:52,960
Then of course you.
Determine what your focus should

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00:16:52,960 --> 00:16:57,640
be and and you know where where
you are best able to contribute

274
00:16:57,640 --> 00:16:59,120
to things.
Well, and so many people don't

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00:16:59,120 --> 00:17:02,320
understand that it's generally
harder to say no, especially in

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00:17:02,320 --> 00:17:04,680
business.
Like what business people have

277
00:17:04,680 --> 00:17:07,839
the hardest time learning to do
is to say no what to say no to,

278
00:17:07,839 --> 00:17:11,280
not what to say yes to.
But it's funny, you know, when

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00:17:11,280 --> 00:17:14,319
you talk about the, the multiple
different disciplines and, and

280
00:17:14,319 --> 00:17:17,960
that kind of thing.
So I went and saw David Gilmour

281
00:17:17,960 --> 00:17:19,880
in New York this week.
And isn't, isn't.

282
00:17:20,720 --> 00:17:23,440
That show, I, I get to see it at
the Hollywood Bowl.

283
00:17:23,720 --> 00:17:24,720
Yeah.
Oh, cool.

284
00:17:24,720 --> 00:17:28,240
So, but I was going to say that,
you know, Mark had taken

285
00:17:28,240 --> 00:17:31,640
basically a couple of years, I
think 8 or 10 years off of

286
00:17:31,640 --> 00:17:35,880
lighting and became an A very
good artist.

287
00:17:35,880 --> 00:17:38,000
Like he's he's become an artist.
Yeah.

288
00:17:38,000 --> 00:17:46,000
And and so seeing that show, you
could see very much art blending

289
00:17:46,000 --> 00:17:52,520
with a, you know, classic rock
show and so well, like, it was

290
00:17:52,520 --> 00:17:56,560
such a beautiful show.
It was just so unique and and so

291
00:17:56,560 --> 00:17:59,680
cool.
And I mean, it had some aspects

292
00:17:59,680 --> 00:18:02,520
of some old Pink Floyd shows
with the big lasers and stuff.

293
00:18:02,920 --> 00:18:06,440
So much fog, more fog than I
think I could ever remember

294
00:18:06,440 --> 00:18:11,000
anyone using but just an
incredible show.

295
00:18:11,000 --> 00:18:13,360
I loved it.
I this morning again I went and

296
00:18:13,360 --> 00:18:16,760
looked back at the pictures from
the show 2 days ago and and I

297
00:18:16,760 --> 00:18:19,360
was just like wow you know what
a beautiful show.

298
00:18:19,680 --> 00:18:25,040
I think that Mark is in a really
fortunate position there.

299
00:18:25,040 --> 00:18:28,280
I mean, it's, it's, it's evident
and, and, and it's true.

300
00:18:28,640 --> 00:18:34,680
I think that the David Gilmour
regards Mark as a, as a, a

301
00:18:34,680 --> 00:18:38,560
trusted and and equal
collaborator, yeah.

302
00:18:38,680 --> 00:18:41,320
In artistic freedom, so.
Few examples of that.

303
00:18:41,320 --> 00:18:45,000
I mean, OK, you know, Steve
Cohen with Billy Joel, Billy

304
00:18:45,000 --> 00:18:47,440
Williams with you too.
You know, there there's some

305
00:18:47,720 --> 00:18:53,960
some very rare examples where
the artist has has not just

306
00:18:53,960 --> 00:18:58,680
given permission, but has fully
embraced like a A and what is

307
00:18:58,680 --> 00:19:04,000
usually a long collaboration
with with a person in

308
00:19:04,000 --> 00:19:06,880
particular.
So you know, Mark's got that

309
00:19:06,880 --> 00:19:10,880
with David and and Mark having
had his art sabbatical, as you

310
00:19:10,880 --> 00:19:13,880
say, you know, he was he came to
the store.

311
00:19:13,880 --> 00:19:18,360
And what I love about Mark is
he's so performative, right,

312
00:19:18,520 --> 00:19:22,760
Like like he's he in as much as
he is.

313
00:19:23,360 --> 00:19:27,160
You know, he's like jamming with
the band, right?

314
00:19:27,880 --> 00:19:29,920
There's not a lick of time code
going on there.

315
00:19:30,360 --> 00:19:32,880
Well, and I don't know if you
heard his podcast, but he talked

316
00:19:32,880 --> 00:19:35,040
about that.
He said if I'm going to do this

317
00:19:35,040 --> 00:19:38,560
and if this is going to be my
last David Gilmour show because

318
00:19:38,560 --> 00:19:42,240
I don't know, because you know,
David's up there.

319
00:19:42,240 --> 00:19:44,680
I think he's turning 80 this
year or something.

320
00:19:45,200 --> 00:19:48,720
And he said, so I don't know if
this is the last one.

321
00:19:48,720 --> 00:19:51,960
If it is, I want to, I want to
control the lights.

322
00:19:51,960 --> 00:19:57,560
I want to actually operate the
lights and I want to make, you

323
00:19:57,560 --> 00:19:59,360
know, create art with the
lights.

324
00:19:59,640 --> 00:20:01,480
Yeah.
And, and he did.

325
00:20:01,560 --> 00:20:05,200
I mean, he really did.
It's, it's such a very, you

326
00:20:05,200 --> 00:20:08,160
know, it's not like it's
outrageous technology or it's

327
00:20:08,160 --> 00:20:11,280
just an outrageous lighting
package or anything.

328
00:20:11,480 --> 00:20:14,160
It's just really cool use of
light.

329
00:20:14,320 --> 00:20:17,480
It is, is.
And so I like it.

330
00:20:17,600 --> 00:20:19,520
I think it's really cool.
But you know, it's a.

331
00:20:19,560 --> 00:20:21,800
It's just interesting that you
talk about.

332
00:20:22,200 --> 00:20:25,080
The different disciplines and
saying yes, because I think when

333
00:20:25,080 --> 00:20:27,840
you like, if you grow up a
rock'n'roll designer and you

334
00:20:27,840 --> 00:20:31,200
stay a rock'n'roll designer your
entire life, you're kind of

335
00:20:31,200 --> 00:20:34,360
boxed in a little bit, right?
But when you start pulling in

336
00:20:34,360 --> 00:20:37,520
theater, or you start pulling in
art, or you start pulling in

337
00:20:37,520 --> 00:20:41,560
different things is when it gets
interesting, I think.

338
00:20:42,360 --> 00:20:45,600
Well, and I think that that was
a that was a differentiated for

339
00:20:45,600 --> 00:20:46,840
me in the beginning of my
career.

340
00:20:47,200 --> 00:20:50,120
Yeah, I grew up in Rhode Island.
Yeah, I was coming up in the

341
00:20:50,120 --> 00:20:56,000
scene in Providence and I was, I
was mainly, you know, I was kind

342
00:20:56,000 --> 00:20:58,760
of, I was very wishy washy about
what I was studying either in

343
00:20:58,760 --> 00:21:01,200
the high school or college.
I was kind of half of my time

344
00:21:01,200 --> 00:21:03,560
with Tech.
Like I was, I was working to

345
00:21:03,560 --> 00:21:07,360
learn computer programming,
which has since informed so much

346
00:21:07,360 --> 00:21:09,840
of everything about what I've
done, which I think we're going

347
00:21:09,840 --> 00:21:15,800
to get to.
But then simultaneously, I got a

348
00:21:15,800 --> 00:21:20,320
job at Trinity Repertory Company
in Providence, which is very

349
00:21:20,320 --> 00:21:24,320
well regarded regional theater
as a spotlight operator.

350
00:21:24,320 --> 00:21:27,320
And then and then that's where I
learned to become an

351
00:21:27,320 --> 00:21:29,640
electrician.
That's where I met my wife.

352
00:21:29,920 --> 00:21:31,400
Who?
It took me 12 years.

353
00:21:31,560 --> 00:21:34,520
To go on a date with me.
But you know, when I met Colleen

354
00:21:34,520 --> 00:21:37,920
at Trinity, she had just come
off the road with the original

355
00:21:37,920 --> 00:21:40,880
Joshua Tree tour.
She had been on the lighting

356
00:21:40,880 --> 00:21:43,880
crew for you too.
She had been out with

357
00:21:44,080 --> 00:21:48,200
Springsteen and had worked with
Jeff Rabbits for a while, and I

358
00:21:48,200 --> 00:21:51,240
thought she was amazing, you
know, but I would say, but that

359
00:21:51,240 --> 00:21:53,680
was a theater and we were doing
incredible theater.

360
00:21:53,680 --> 00:21:55,080
We were.
And it was a very theatrical.

361
00:21:55,280 --> 00:21:58,680
I also met Anne Militello there
who came into design shows.

362
00:21:58,680 --> 00:22:02,080
I met a whole bunch of other
great L DS and I got steeped in

363
00:22:02,080 --> 00:22:04,960
this theatrical tradition of
what lighting look like.

364
00:22:05,240 --> 00:22:13,240
Now at the same time I'm also
working, I found a job at a club

365
00:22:13,240 --> 00:22:16,560
in Providence called The Living
Room, which was the one of the

366
00:22:16,560 --> 00:22:20,640
big concert clubs in Providence
where basically for the

367
00:22:20,640 --> 00:22:23,720
compensation of as much beer as
I could drink and maybe if I

368
00:22:23,720 --> 00:22:28,040
were lucky I'd get a 20 or you
know, you know, 20 bucks a

369
00:22:28,040 --> 00:22:32,960
night, 40 bucks a night.
I was running, you know, an old

370
00:22:32,960 --> 00:22:37,560
Leprechaun LP 1002 scene preset
board and 24 park cans.

371
00:22:38,240 --> 00:22:41,520
And I was doing bands and I was
learning how to busk, you know,

372
00:22:41,920 --> 00:22:45,520
when I was, I was learning how
to be performative with

373
00:22:45,520 --> 00:22:48,280
lighting, with music.
So I was doing music from the

374
00:22:48,280 --> 00:22:49,840
beginning.
I was also doing theater from

375
00:22:49,840 --> 00:22:51,800
the beginning.
And I had then I also stumbled

376
00:22:51,800 --> 00:22:55,520
into the local corporate market.
All of this is an electrician,

377
00:22:56,120 --> 00:22:58,760
you know, but also then very
quickly I was bringing the tech

378
00:22:58,760 --> 00:23:02,840
stuff that, you know, like I
was, I was, I'd worked out all

379
00:23:02,840 --> 00:23:06,160
kinds of, you know, I was
fascinated by graphics and

380
00:23:06,160 --> 00:23:09,120
computers.
So one of the first thing that I

381
00:23:09,120 --> 00:23:12,360
did at the living room, for
instance, is I went out and I

382
00:23:12,400 --> 00:23:16,680
like, I found an old ruined GE
light valve, which is an

383
00:23:16,960 --> 00:23:21,800
improbably gigantic, incredibly
stupid projector to work with,

384
00:23:21,800 --> 00:23:26,400
but it, it had been abandoned
and unusable by an AV company in

385
00:23:26,400 --> 00:23:28,520
Massachusetts.
I went and got this thing and I

386
00:23:28,520 --> 00:23:33,360
would bring it to the living
room and I was doing, I was, I

387
00:23:33,360 --> 00:23:38,920
was literally using like a video
toaster or an Amiga computer to

388
00:23:38,920 --> 00:23:44,760
like, do I I'm not going to
insult Joshua White by saying I

389
00:23:44,760 --> 00:23:47,920
was doing liquid light shows.
Yeah, but I was doing like, I

390
00:23:47,920 --> 00:23:51,840
was doing like psychedelic
projected texture on these bands

391
00:23:52,720 --> 00:23:56,760
at, you know, by like fiddling
around with the computer at the

392
00:23:56,760 --> 00:24:01,120
same time I was doing the the
lighting so so you could see all

393
00:24:01,120 --> 00:24:04,760
the pieces that now I've become
known for.

394
00:24:04,920 --> 00:24:07,360
Yeah, at that point.
But I'm, I can't tell you that

395
00:24:07,360 --> 00:24:09,080
there was a conscious decision.
Yeah.

396
00:24:09,080 --> 00:24:11,080
Oh, I know what I want to be and
what I want to do.

397
00:24:11,080 --> 00:24:14,480
I want to combine all these
things and I, I was just a like

398
00:24:14,480 --> 00:24:19,800
a 21 year old kid, like saying,
you know, man, my, my beer money

399
00:24:19,800 --> 00:24:23,680
jobs sure are cool.
Yeah, well, I mean, it's, you

400
00:24:23,680 --> 00:24:27,040
know, those lucky accidents and
and things that happen like

401
00:24:27,040 --> 00:24:29,720
that, you just go, hey, this
actually turned out really well.

402
00:24:29,720 --> 00:24:32,320
You know, like a lot of those
things like verilite, you know,

403
00:24:32,320 --> 00:24:36,760
I remember the the story that I
heard on my podcast where, you

404
00:24:36,760 --> 00:24:39,920
know, the the light wasn't
supposed to be on while it

405
00:24:39,920 --> 00:24:41,280
moved.
That was just and.

406
00:24:41,400 --> 00:24:43,400
Then they to.
Reposition the fixture.

407
00:24:43,760 --> 00:24:46,040
The shutter was supposed to be
closed in the middle and and

408
00:24:46,040 --> 00:24:47,520
there was a glitch where it
stayed.

409
00:24:47,520 --> 00:24:49,960
Open moved and everyone lost
their mind, right?

410
00:24:50,160 --> 00:24:53,280
Yeah, and went well, do that
again, you know, and suddenly,

411
00:24:53,320 --> 00:24:56,920
you know, the look that became
so famous happened.

412
00:24:56,920 --> 00:24:59,520
So I like that kind of stuff
when that.

413
00:24:59,520 --> 00:25:00,960
Happens.
I adore that anecdote.

414
00:25:00,960 --> 00:25:04,960
Yeah, sometimes mistakes are
are, you know, the best part of

415
00:25:04,960 --> 00:25:07,960
the show.
But you know, the cool thing,

416
00:25:07,960 --> 00:25:10,640
one of the cool things, not to
go on and on about this, this

417
00:25:10,640 --> 00:25:13,560
Brickman show, but one of the
cool things about it is I've

418
00:25:13,560 --> 00:25:17,160
seen the same song multiple
times on video and stuff from

419
00:25:17,160 --> 00:25:19,960
this show, and every time it
looks completely different

420
00:25:19,960 --> 00:25:23,120
because it is because he's
performing it, you know?

421
00:25:24,120 --> 00:25:27,280
Among the things he's doing,
he's got, he's got that Corel

422
00:25:27,280 --> 00:25:30,760
paint thing, which is feeding
the VPU on the MA, which is

423
00:25:31,200 --> 00:25:33,120
letting him literally be
rhythmic.

424
00:25:33,480 --> 00:25:40,160
Yeah, and and, and and motion
based and emotional based in the

425
00:25:40,160 --> 00:25:42,720
way he's running the lighting
and pieces of the show.

426
00:25:43,240 --> 00:25:45,680
And I just think that that's
it's just phenomenal.

427
00:25:45,720 --> 00:25:46,920
You know, I've heard busting.
On.

428
00:25:46,920 --> 00:25:50,040
Acid talking about that show and
how different it is from

429
00:25:50,040 --> 00:25:52,200
anything else.
And that's Mark.

430
00:25:52,240 --> 00:25:56,640
Mark thinks without constraints.
He doesn't worry if something

431
00:25:56,640 --> 00:25:59,560
doesn't exist or if a workflow
hasn't been determined.

432
00:25:59,880 --> 00:26:02,800
He's going to, he's going to,
he's going to get it figured out

433
00:26:02,800 --> 00:26:05,520
and he's going to be, he's going
to put it to work and he's going

434
00:26:05,520 --> 00:26:09,640
to put his, his fingerprints on
it.

435
00:26:09,680 --> 00:26:12,440
Yeah.
You know, you see that show and

436
00:26:12,440 --> 00:26:15,040
you can't.
Well, the, the very first time I

437
00:26:15,040 --> 00:26:20,160
ever met Mark, it's a story I've
never shared, but we, I was with

438
00:26:20,160 --> 00:26:25,520
Martin and we were creating our
largest, most expensive booth

439
00:26:25,520 --> 00:26:29,040
and trade show at LDI ever,
which was in Miami Beach.

440
00:26:29,520 --> 00:26:33,800
And we hired Mark Brickman to
design the show on our booth.

441
00:26:34,320 --> 00:26:40,600
And so Brickman like took him
forever to come up with the

442
00:26:40,600 --> 00:26:42,840
design.
And I'm not mad at him for this.

443
00:26:42,840 --> 00:26:46,600
This is just how he operates.
We put walls around him and you

444
00:26:46,600 --> 00:26:51,200
can't do that with Mark.
And so he came to us with a

445
00:26:51,200 --> 00:26:54,680
design that had like a half
$1,000,000 in video on it.

446
00:26:55,040 --> 00:26:58,440
And our whole budget, including
the party was like a half

447
00:26:58,440 --> 00:27:02,400
$1,000,000, right?
And so we were like, Mark, this

448
00:27:02,400 --> 00:27:05,120
ain't going to work.
And he went, wow, I can't do it

449
00:27:05,120 --> 00:27:06,800
then.
You people are too small

450
00:27:06,800 --> 00:27:12,520
thinking and, and so, you know,
in a panic, you know, we, we, I

451
00:27:12,520 --> 00:27:16,400
called Peter Morse and said,
Peter, you were really our first

452
00:27:16,400 --> 00:27:18,240
choice.
Can you come do this with us?

453
00:27:18,240 --> 00:27:22,280
You know, and, and Peter did an
incredible job and I don't know

454
00:27:22,280 --> 00:27:26,240
if you were there in 1994 or
whatever with the.

455
00:27:26,240 --> 00:27:27,360
Bicycles.
It was there.

456
00:27:27,520 --> 00:27:30,120
It was one of my, it wasn't the
first.

457
00:27:30,160 --> 00:27:32,160
It was, I think it was my second
LDI and everything.

458
00:27:32,160 --> 00:27:33,600
OK.
Yeah, yeah.

459
00:27:33,600 --> 00:27:38,640
I mean, it, it was, it was like
legendary the the show that

460
00:27:38,640 --> 00:27:42,280
Peter did and how hard it was
for us to pull it off with pals

461
00:27:42,280 --> 00:27:46,200
that were like overheating and
just things failing left and

462
00:27:46,200 --> 00:27:49,640
right because it was a hot box
of a room there, you know, with

463
00:27:49,640 --> 00:27:53,040
no ventilation and stuff.
And but, but it worked out

464
00:27:53,040 --> 00:27:55,360
really, really cool.
But that was my first experience

465
00:27:55,360 --> 00:27:57,720
with Mark.
And what I learned was Mark

466
00:27:57,720 --> 00:28:01,560
needs to be with bands the size
of Pink Floyd.

467
00:28:01,560 --> 00:28:04,720
Like you can't.
But then the next thing I saw

468
00:28:04,720 --> 00:28:08,520
him do was matchbox 20 with
those incredible with the motors

469
00:28:08,520 --> 00:28:10,960
and the and the rig moving so
quickly and stuff.

470
00:28:10,960 --> 00:28:14,200
It was just incredible.
I was like, wow, you know, and

471
00:28:14,200 --> 00:28:15,840
he was the first guy that I saw
do that.

472
00:28:15,840 --> 00:28:19,360
So it was just so cool.
But anyways, moving on.

473
00:28:19,360 --> 00:28:22,200
So one of the reasons I wanted
you to do this, and I'm glad you

474
00:28:22,200 --> 00:28:27,000
finally agreed to it, is because
when I think of who the two

475
00:28:27,400 --> 00:28:31,920
probably geekiest AI talking
guys are in the industry, it's

476
00:28:31,920 --> 00:28:34,200
me and you.
And I'm not much of A geeky guy

477
00:28:34,200 --> 00:28:38,000
'cause I'm not that smart.
But I do love AI, and I do love

478
00:28:38,000 --> 00:28:40,920
talking about it.
And I've certainly tried a lot

479
00:28:40,920 --> 00:28:44,640
of AI stuff and have
experimented an awful lot.

480
00:28:44,640 --> 00:28:46,720
And I actually want to share
something with you real quick

481
00:28:46,720 --> 00:28:49,760
before I forget.
Hang on one SEC.

482
00:28:49,760 --> 00:28:52,200
Let me figure out how to do
this.

483
00:28:52,200 --> 00:28:54,440
This is only going to take me
one second.

484
00:28:55,080 --> 00:28:59,840
Here we go to share audio Share
a tab instead.

485
00:28:59,840 --> 00:29:03,600
Well, how do I do that?
It's not going to let me share

486
00:29:03,600 --> 00:29:05,920
audio you.
Suck.

487
00:29:06,600 --> 00:29:10,120
Hang on a second, let me figure
this out.

488
00:29:11,920 --> 00:29:17,120
Oh, Oh yeah, I don't think it's
going to let me share audio.

489
00:29:17,120 --> 00:29:23,880
This ain't fun.
Well, I'm gonna do it without

490
00:29:23,880 --> 00:29:25,760
sharing audio.
That's what I'm gonna do.

491
00:29:26,040 --> 00:29:29,480
All right, well, I'll just, I'll
use my mind's ear, as it were.

492
00:29:29,840 --> 00:29:37,880
Well, it's your basic.
I've seen some wild stuff.

493
00:29:38,400 --> 00:29:39,920
It's no good without the audio.
It's.

494
00:29:40,400 --> 00:29:42,360
Like, you know, a magician I can
kind of have.

495
00:29:42,440 --> 00:29:46,440
The audio A new AI tech too.
He's impressing that in 823

496
00:29:46,680 --> 00:29:50,120
using all the crazy.
Interactive tech and AI to make

497
00:29:50,120 --> 00:29:52,840
live concerts.
Absolutely mind blowing.

498
00:29:53,720 --> 00:29:57,440
We're talking Billy Joel, Blake
Shelton, Megan Lee Stallion.

499
00:29:57,680 --> 00:30:01,360
Bob's been behind the scenes
making their shows pop and get

500
00:30:01,360 --> 00:30:04,640
this, he even rocked the one
night Houston festival.

501
00:30:05,280 --> 00:30:08,240
It's insane how he takes all
these high tech gadgets and

502
00:30:08,240 --> 00:30:11,720
turns concerts into these
immersive experiences.

503
00:30:19,240 --> 00:30:20,920
Dude kind of looks like check
out what Bob.

504
00:30:20,920 --> 00:30:23,920
'S been up to and learn a ton
about AI as well on Geezers of

505
00:30:23,920 --> 00:30:30,240
Gear episode number 276.
So that that was created on a on

506
00:30:30,240 --> 00:30:34,400
an AI video app called NVIDIA in
about 5 minutes.

507
00:30:34,400 --> 00:30:38,240
And all I did was I took your
bio and copied and pasted it

508
00:30:38,240 --> 00:30:41,160
into the the script for the
video.

509
00:30:41,360 --> 00:30:44,800
Yeah, and it so do you use
NVIDIA at all?

510
00:30:45,480 --> 00:30:47,400
No, no, I don't use NVIDIA at
all.

511
00:30:47,400 --> 00:30:51,360
I use a little bit of a
different tool set for the video

512
00:30:51,720 --> 00:30:54,600
that I do, but.
Yeah, well, NVIDIA, if you're

513
00:30:54,600 --> 00:30:56,960
looking to do a quick little
marketing video or something

514
00:30:56,960 --> 00:30:59,800
like that, it's just really
super easy to use.

515
00:31:00,200 --> 00:31:05,880
And again, you can just paste a
document into it and it'll look

516
00:31:05,880 --> 00:31:07,920
through the document and create
a video based on the.

517
00:31:08,440 --> 00:31:12,000
But what it used to do is it
used to just use stock video and

518
00:31:12,000 --> 00:31:15,240
stock images and stuff.
So it was OK, you could do a

519
00:31:15,400 --> 00:31:19,520
kind of a cool video, but it was
just like guys shaking hands and

520
00:31:19,520 --> 00:31:22,720
like just boring stuff.
Now you can say OK, like that

521
00:31:22,720 --> 00:31:27,680
one I said I wanted animated.
And, you know, it's about this

522
00:31:27,680 --> 00:31:30,000
Geezers of Gear episode number
276.

523
00:31:30,360 --> 00:31:34,240
And then I put your bio in there
and it came up with that in

524
00:31:34,240 --> 00:31:37,000
like, you know, 3 or 4 minutes
probably.

525
00:31:37,280 --> 00:31:42,720
So if you can do generative
video now and you can do it

526
00:31:42,720 --> 00:31:45,840
animated, you can do it
lifelike, you can do it in all

527
00:31:45,840 --> 00:31:48,000
kinds of different ways.
It can be fun, it can be

528
00:31:48,000 --> 00:31:54,040
serious, it can be, you know, so
it's actually a tool that I use

529
00:31:54,040 --> 00:31:59,000
quite often just for creating
little market blurbs and things

530
00:31:59,040 --> 00:32:01,640
like that.
But yeah.

531
00:32:02,520 --> 00:32:05,600
I like it, it was good.
Yeah, yeah.

532
00:32:05,640 --> 00:32:08,360
I mean, it was just something
funny that I thought I'd, I

533
00:32:08,360 --> 00:32:11,640
thought I'd show you just using
one of the apps.

534
00:32:11,880 --> 00:32:16,080
So, you know, I want to talk
about some of the things I use

535
00:32:16,080 --> 00:32:18,640
on a daily basis, which will be
totally different than some of

536
00:32:18,640 --> 00:32:21,920
the things you use, of course,
because you're using them for to

537
00:32:21,920 --> 00:32:25,000
generate probably content and to
generate.

538
00:32:26,680 --> 00:32:29,560
Yeah.
So give me give me some examples

539
00:32:29,560 --> 00:32:34,240
of of ways that you've managed
to incorporate AAI into your

540
00:32:34,240 --> 00:32:36,400
workflow.
Well, there's a few different

541
00:32:36,400 --> 00:32:37,920
ways and there's a few
different.

542
00:32:38,000 --> 00:32:43,000
Of course, when we when we say
AI, that's a that's a broad

543
00:32:43,000 --> 00:32:43,960
topic, right?
Yeah.

544
00:32:43,960 --> 00:32:49,600
I mean, you know, for the
record, I started, I, I attended

545
00:32:49,600 --> 00:32:55,440
a computer camp when I was a
teenager at MIT.

546
00:32:55,800 --> 00:33:00,440
Yeah.
And I remember that when we went

547
00:33:00,440 --> 00:33:03,680
there, you know, it was, it was
very cool.

548
00:33:03,680 --> 00:33:06,160
But they said, OK, what we're
going to do is by the end of the

549
00:33:06,160 --> 00:33:10,200
Cam, you're all going to have
written a computer program.

550
00:33:11,040 --> 00:33:14,440
All right?
And out of the, you know, out of

551
00:33:14,440 --> 00:33:17,520
the 100 kids there, 99 were
like, I want to write a computer

552
00:33:17,520 --> 00:33:18,920
game.
Oh, OK.

553
00:33:19,120 --> 00:33:22,520
Which is predictable and cool
right now from the time this is

554
00:33:22,520 --> 00:33:27,480
like 1980 3 maybe?
Something like that.

555
00:33:27,840 --> 00:33:30,840
I was in high.
School at the time I did this, I

556
00:33:30,840 --> 00:33:37,560
did this and but what I wanted
to do was I wanted to write a

557
00:33:37,560 --> 00:33:41,800
program that would teach the
computer to talk to me, you

558
00:33:41,800 --> 00:33:44,440
know, and not necessarily
verbalize, but I wanted to be

559
00:33:44,440 --> 00:33:47,680
able to type, hi, how are you?
And have the computer go, Oh,

560
00:33:47,680 --> 00:33:49,800
I'm I'm having a good day or
whatever.

561
00:33:49,800 --> 00:33:52,240
Like, I wanted to be able to
have a free form conversation

562
00:33:52,240 --> 00:33:56,880
with a computer, which the
instructors were both, you know,

563
00:33:56,880 --> 00:33:58,080
they were like, oh, wow, that's
cool.

564
00:33:58,080 --> 00:34:01,560
And they were also like, you're
really screwing up our Lesson

565
00:34:01,560 --> 00:34:03,520
plan here because you're
supposed to want to make a

566
00:34:03,520 --> 00:34:05,720
computer game, right?
Yeah, but.

567
00:34:06,040 --> 00:34:10,360
You know, but but what I did was
at that time in the course at

568
00:34:10,360 --> 00:34:14,239
camp, which was a month long
thing that I did and you know, I

569
00:34:14,239 --> 00:34:17,719
think it was a freshman in high
school or something was I wrote,

570
00:34:17,960 --> 00:34:21,600
you know, a piece of code that
was effectively a chatbot.

571
00:34:22,120 --> 00:34:25,320
OK.
Yeah, it was a very limited chat

572
00:34:25,320 --> 00:34:29,520
bot because at the time all you
could do was maybe guess at what

573
00:34:29,520 --> 00:34:33,000
people would say to this thing.
And then you could come up with

574
00:34:33,520 --> 00:34:35,760
a range of things it could say
in response.

575
00:34:35,760 --> 00:34:38,280
So you could only have a very
rudimentary discussion with the

576
00:34:38,280 --> 00:34:40,120
singing.
And it only had so many options

577
00:34:40,120 --> 00:34:45,000
as as to how it could reply.
But so now we look at modern day

578
00:34:45,320 --> 00:34:51,760
large language models.
So ChatGPT anthropics clawed

579
00:34:53,320 --> 00:34:59,440
Google's Gemini.
These are the the, the, the, you

580
00:34:59,480 --> 00:35:03,080
know, the offspring of that chat
bot thinking that goes all the

581
00:35:03,080 --> 00:35:06,160
way back to that.
So I've been noodling with this

582
00:35:06,160 --> 00:35:08,200
early on.
And, and even if we go back to

583
00:35:08,200 --> 00:35:13,960
2010 when mode, when we put our
interactive screens in Microsoft

584
00:35:13,960 --> 00:35:18,720
headquarters in Studios A
through D, we were using machine

585
00:35:18,720 --> 00:35:22,920
learning to allow the screens to
be different every day.

586
00:35:22,920 --> 00:35:27,360
And to to how they responded to.
These were screens that you

587
00:35:27,360 --> 00:35:30,920
could gesturally play with if
you were in the right spots in

588
00:35:30,920 --> 00:35:34,000
the lobby and you could audit,
you could play with them by

589
00:35:34,120 --> 00:35:38,280
through your voice.
And we came up with, you know,

590
00:35:38,280 --> 00:35:42,240
machine learning things that
allowed the code to put together

591
00:35:42,240 --> 00:35:44,880
different graphic looks on the
spot.

592
00:35:44,880 --> 00:35:47,080
And this is 2010.
Wow.

593
00:35:47,320 --> 00:35:49,600
Yeah, that was very.
Early and this and it's been a

594
00:35:49,600 --> 00:35:52,080
hallmark of a lot of the work
we've done, especially on the

595
00:35:52,080 --> 00:35:56,600
permanent installation side of
creating what we dream to be

596
00:35:56,600 --> 00:36:00,920
semi autonomous art pieces, you
know, art pieces that would that

597
00:36:00,920 --> 00:36:06,040
would think for themselves or do
what do do do things because of

598
00:36:06,040 --> 00:36:07,200
environmental.
That's interesting.

599
00:36:07,280 --> 00:36:08,440
Yeah.
What the?

600
00:36:08,440 --> 00:36:11,400
Weather was what the traffic's
like around the building.

601
00:36:11,880 --> 00:36:14,160
You know how many people are
moving through a lobby and then

602
00:36:14,160 --> 00:36:17,720
suddenly this LED sculpture was
behaving differently, showing

603
00:36:17,720 --> 00:36:19,400
different colors, doing
different patterns.

604
00:36:19,400 --> 00:36:24,200
We repeated that in the in an
installation at a Salesforce

605
00:36:24,200 --> 00:36:27,320
building up here.
We repeated it in a bunch of

606
00:36:27,320 --> 00:36:30,760
different installations.
And then in 2018, when we

607
00:36:30,760 --> 00:36:34,800
designed GM World, which is a
visitor center in the lobby of

608
00:36:34,800 --> 00:36:38,640
General Motors Global
headquarters, where we had 17

609
00:36:38,640 --> 00:36:42,760
moving LED screens, we came up
with a thing called the

610
00:36:42,760 --> 00:36:47,720
Interactive Content Engine,
which would basically give an

611
00:36:47,720 --> 00:36:52,080
input from the GM people, but
also environmental input and

612
00:36:52,120 --> 00:36:55,920
computer vision input.
It would put together new pieces

613
00:36:55,920 --> 00:36:58,240
of content every day for those
screens.

614
00:36:58,480 --> 00:37:01,680
And that came from the chief
marketing officer for GM, Tim

615
00:37:01,680 --> 00:37:06,200
Mahoney saying, Bobby, how can
we make sure that people's

616
00:37:06,200 --> 00:37:09,560
experience of this area is
different this Tuesday at 9:00

617
00:37:09,560 --> 00:37:13,880
AM from next Tuesday at 9 AMI
don't have a budget to pay an

618
00:37:13,880 --> 00:37:17,720
agency to be constantly making
new content.

619
00:37:17,800 --> 00:37:19,280
Yeah.
And I was like, well, I think we

620
00:37:19,280 --> 00:37:21,240
have an answer.
And that was because we have

621
00:37:21,240 --> 00:37:24,680
been doing all of this work.
So we built the interactive

622
00:37:24,680 --> 00:37:29,560
content engine which which which
does this, it comes up with

623
00:37:29,560 --> 00:37:35,360
whole basically GM is making new
content everyday, like terabytes

624
00:37:35,360 --> 00:37:37,640
of it.
So we were having all of that go

625
00:37:37,640 --> 00:37:43,080
into a bucket and we had one
machine learning piece of code

626
00:37:43,360 --> 00:37:46,680
that would look at every piece
of content that that GM

627
00:37:46,680 --> 00:37:50,600
contributed.
So an ad for, you know, an ad

628
00:37:50,600 --> 00:37:57,400
for Chevy Silverado, training
video, marketing videos,

629
00:37:57,440 --> 00:37:59,280
etcetera.
It would go through all of these

630
00:37:59,280 --> 00:38:02,800
videos and it would look at the
videos and it would tag those

631
00:38:02,800 --> 00:38:06,200
videos.
So we put meta tags on it to say

632
00:38:06,200 --> 00:38:09,120
this piece of content features a
Chevy Silverado.

633
00:38:09,640 --> 00:38:13,640
And I can see that it has this
trim package and I can see that

634
00:38:13,640 --> 00:38:17,400
the the truck is on the beach
and there appears to be a family

635
00:38:17,400 --> 00:38:20,920
around it of a mother and a dad
and some kids.

636
00:38:21,240 --> 00:38:24,680
And so it would then Add all
this metadata to those clips.

637
00:38:25,000 --> 00:38:29,080
So all of the media going into
the bucket now had a way to, you

638
00:38:29,080 --> 00:38:33,440
had a way to identify, you know,
through metadata, what it was.

639
00:38:33,640 --> 00:38:36,160
Then we built a different
machine learning program and

640
00:38:36,160 --> 00:38:39,440
this was the interactive content
engine, which on a given day,

641
00:38:39,440 --> 00:38:45,280
let's say it's February, it's
truck month, the Chinese

642
00:38:45,280 --> 00:38:48,920
manufacturing representatives
from the factory that GMs in

643
00:38:48,920 --> 00:38:53,720
Shanghai are visiting and
there's there's more traffic

644
00:38:53,720 --> 00:38:55,640
around the Renaissance Center
than usual.

645
00:38:55,960 --> 00:38:58,760
The interactive content engine
would know all of these things.

646
00:38:58,760 --> 00:39:01,280
It would know the stuff about
who was visiting because GM

647
00:39:01,280 --> 00:39:05,120
would tell it, but it would know
what the weather was.

648
00:39:05,120 --> 00:39:06,440
It would know what the traffic
was.

649
00:39:06,440 --> 00:39:09,480
It would know things like it's
truck month and then it would go

650
00:39:09,480 --> 00:39:13,080
into the media buckets and say,
oh, I've got a clip of a Chevy

651
00:39:13,080 --> 00:39:14,920
Silverado.
Oh, it's on the beach.

652
00:39:15,160 --> 00:39:17,480
That's good because it's cold
outside.

653
00:39:17,480 --> 00:39:21,240
So we want to put nice warm
images on the screen.

654
00:39:21,520 --> 00:39:25,920
And I can also use it because
the Silverados are drive trains

655
00:39:25,920 --> 00:39:29,760
are manufactured in, you know,
and I'm making this up, by the

656
00:39:29,760 --> 00:39:32,160
way.
This is I don't do, you know,

657
00:39:32,240 --> 00:39:35,520
not actual content, but they're
writing engines in China.

658
00:39:35,800 --> 00:39:38,280
They probably aren't.
But yeah, you know, it it would

659
00:39:38,280 --> 00:39:42,120
know, oh, these pieces of that
truck are made in the Chinese

660
00:39:42,120 --> 00:39:44,840
plant.
So the Chinese contingents going

661
00:39:44,840 --> 00:39:47,320
to be happy to see that.
And then it would put together a

662
00:39:47,320 --> 00:39:50,400
whole content sequence based
around that.

663
00:39:50,400 --> 00:39:54,920
And it does that, and it and as
the more it does it, the more it

664
00:39:54,920 --> 00:39:57,480
can build its model out and it
gets better.

665
00:39:57,480 --> 00:40:01,040
So it was learning.
So we were doing that in 2018,

666
00:40:01,040 --> 00:40:04,520
which is, which is, you know,
relatively speaking, like

667
00:40:04,520 --> 00:40:07,480
centuries before we were all
talking about what we're talking

668
00:40:07,480 --> 00:40:10,120
about now.
So we're not, this isn't

669
00:40:10,120 --> 00:40:12,040
something that we've come lately
to.

670
00:40:12,040 --> 00:40:13,960
It's kind of been something
we've been doing from the

671
00:40:13,960 --> 00:40:16,560
beginning.
Now would the would the new

672
00:40:16,560 --> 00:40:19,280
tools that have came come out
with the chat, you know, on the

673
00:40:19,280 --> 00:40:22,640
large language model side, just
to give you an example like we

674
00:40:22,640 --> 00:40:25,120
use.
ChatGPT affords you the

675
00:40:25,120 --> 00:40:28,000
opportunity to create custom
GPTS.

676
00:40:28,000 --> 00:40:31,920
They call it some other people
call these agents right, which

677
00:40:31,920 --> 00:40:35,480
is basically you can train the
version of the model to become

678
00:40:35,480 --> 00:40:39,200
specifically knowledgeable about
something you would like it to

679
00:40:39,200 --> 00:40:42,200
be specifically knowledgeable
about by feeding it a lot of

680
00:40:42,200 --> 00:40:47,520
information, which it then adds
to what what open AI is already

681
00:40:47,520 --> 00:40:50,240
trained it on and gives it
better context.

682
00:40:50,240 --> 00:40:55,720
So we, for instance, at Mode,
we've been working to train a

683
00:40:55,800 --> 00:41:01,800
custom GPT into being an
experiential strategist, right?

684
00:41:01,800 --> 00:41:05,440
So it helps us with our
corporate world like we can.

685
00:41:05,800 --> 00:41:09,520
You know, we feed it all the
news about brand activations and

686
00:41:09,520 --> 00:41:14,200
what the big agencies are up to
and what installations were

687
00:41:14,200 --> 00:41:19,440
South by Southwest this year and
what you know what, what popular

688
00:41:19,760 --> 00:41:24,280
installations out there and the
news around them and data around

689
00:41:24,280 --> 00:41:26,160
it and stuff.
We've been working to build this

690
00:41:26,160 --> 00:41:31,880
custom GPT, which has now become
quite an expert at, you know, so

691
00:41:31,920 --> 00:41:36,200
now I can come up to it and say,
hey, I'm going to do, I'm going

692
00:41:36,200 --> 00:41:39,040
to do a big engagement for a
tech company.

693
00:41:39,360 --> 00:41:43,120
What would be a great
interactive, immersive,

694
00:41:43,120 --> 00:41:49,360
temporary engagement to show
clients or to show guests the

695
00:41:49,360 --> 00:41:57,920
power of using, you know, using
a particular software suite to

696
00:41:57,920 --> 00:41:59,960
get this done in their lives?
Yeah.

697
00:42:00,160 --> 00:42:03,720
And this custom GPT will then
think about it, and it'll go

698
00:42:03,720 --> 00:42:05,160
into all the training we've
given it.

699
00:42:05,360 --> 00:42:09,200
And it'll say, here's some great
ideas you should set up, you

700
00:42:09,200 --> 00:42:13,040
know, the high tech TP on the
ground and you should like, you

701
00:42:13,040 --> 00:42:16,600
know, put these, put these
installations in it and these

702
00:42:16,600 --> 00:42:19,800
engagement points in it.
And here's the strategic reasons

703
00:42:19,800 --> 00:42:23,600
why that is good.
Here's stuff I know from metrics

704
00:42:23,600 --> 00:42:26,800
on other activations,
corporations have done that have

705
00:42:26,800 --> 00:42:30,080
been successful or not and why
this might be a good idea to do

706
00:42:30,080 --> 00:42:32,360
it.
So in one way we're using it.

707
00:42:33,280 --> 00:42:35,840
We're using artificial
intelligence in our work as a

708
00:42:35,840 --> 00:42:42,680
creative agency to help us with
coming up with new ideas and

709
00:42:42,680 --> 00:42:46,840
strategy stuff, in particular in
that corporate activation

710
00:42:46,840 --> 00:42:48,560
market.
So that's one way we're using AI

711
00:42:49,000 --> 00:42:51,280
one second.
Go back up one second.

712
00:42:51,280 --> 00:42:54,360
So like, I know what a custom
GPT is.

713
00:42:54,360 --> 00:42:57,720
I know how a custom GPGPT comes
about.

714
00:42:57,720 --> 00:43:04,960
But can you explain like just a
very simple custom GPT that

715
00:43:04,960 --> 00:43:08,480
somebody might build to do
something and and what that

716
00:43:08,480 --> 00:43:11,320
would entail?
Definitely, definitely.

717
00:43:11,320 --> 00:43:16,440
So you know, let's say that you
run, let's say that you run a

718
00:43:17,320 --> 00:43:20,440
major lighting distributing
company or distributorship,

719
00:43:20,440 --> 00:43:23,440
let's say, let's say you're
you're Ben Saltzman and Act

720
00:43:23,440 --> 00:43:25,360
Lighting.
Or let's say you're, you know,

721
00:43:25,440 --> 00:43:28,160
you're Colin Waters at TMB or
something like that.

722
00:43:28,240 --> 00:43:31,480
Yeah.
And you've got a lot of people

723
00:43:31,480 --> 00:43:34,720
on your staff, right?
You're managing a lot of

724
00:43:34,720 --> 00:43:36,800
employees.
But you're from show business,

725
00:43:36,800 --> 00:43:38,600
right?
You're not necessarily the best

726
00:43:38,600 --> 00:43:39,880
HR guy, right?
Yeah.

727
00:43:40,320 --> 00:43:44,160
And but you've got employees who
want to know, is it appropriate

728
00:43:44,160 --> 00:43:49,960
for me to tell my coworker at
work, wow, those jeans make your

729
00:43:49,960 --> 00:43:52,240
butt look good today or, you
know, whatever.

730
00:43:52,240 --> 00:43:53,760
Right.
Like or whatever.

731
00:43:53,760 --> 00:43:54,200
Right.
Yeah.

732
00:43:54,680 --> 00:43:57,160
You could phrase.
I could totally see Ben saying

733
00:43:57,160 --> 00:43:59,280
that.
Yeah, but I could not see that.

734
00:43:59,560 --> 00:44:01,800
Exactly.
But I could see other people

735
00:44:01,800 --> 00:44:03,240
maybe.
Of course, of course.

736
00:44:03,240 --> 00:44:06,880
I think I might have said that
in my career 100 times or so.

737
00:44:07,320 --> 00:44:08,480
You've been around a while.
I'm going.

738
00:44:08,680 --> 00:44:11,480
To be controversial about it,
let's say that you are running a

739
00:44:11,480 --> 00:44:14,840
company and you want, you want
your employees to be able to

740
00:44:14,840 --> 00:44:18,360
find out like can I take a
personal day or what are the

741
00:44:18,520 --> 00:44:22,760
rules about if I'm late to work,
right?

742
00:44:22,800 --> 00:44:24,400
Or.
And you don't want them to have

743
00:44:24,400 --> 00:44:27,840
to go find an employee manual
that you gave them when they got

744
00:44:27,840 --> 00:44:30,840
hired that they probably stuck
in a drawer and they don't know.

745
00:44:31,000 --> 00:44:35,920
So for instance, you could take
your employee manual and you

746
00:44:35,920 --> 00:44:41,520
could take your HR guidelines
and you could train a custom GPT

747
00:44:41,520 --> 00:44:45,520
on that and make that into a
chat bot that exists on your

748
00:44:45,520 --> 00:44:48,000
company's Internet.
So it's a great application.

749
00:44:48,000 --> 00:44:53,000
To know, hey what is my
company's policy on PTO and they

750
00:44:53,000 --> 00:44:56,640
can engage with the chat bot and
say how many, how many days off

751
00:44:56,640 --> 00:45:01,000
have I allowed in a given year
and what for and that and now

752
00:45:01,000 --> 00:45:06,760
that Custom GPT is like your
ultimate unbiased HR

753
00:45:06,760 --> 00:45:08,240
representative.
Right.

754
00:45:08,360 --> 00:45:11,840
Yep.
Who your employees have 24 hour

755
00:45:11,840 --> 00:45:15,480
immediate access to.
Yep, that's a good example of.

756
00:45:15,800 --> 00:45:19,720
It is, yeah.
That's a great rather low tech

757
00:45:20,440 --> 00:45:23,840
example of why and how you would
create a custom GPT.

758
00:45:24,120 --> 00:45:26,720
So thanks.
Thanks for going through that

759
00:45:26,720 --> 00:45:28,600
one.
So where were you going next

760
00:45:28,600 --> 00:45:29,960
that you were on to something
when I.

761
00:45:29,960 --> 00:45:31,040
Stopped.
So there's another.

762
00:45:31,160 --> 00:45:35,560
So that's one kind of AI which
are these, which are these kind

763
00:45:35,560 --> 00:45:39,720
of very intelligent chat bots.
The other kind of AI I think

764
00:45:39,720 --> 00:45:42,200
that a lot of people are seeing
and talking about is the

765
00:45:42,200 --> 00:45:45,800
generative art AI.
So things are creating the

766
00:45:45,800 --> 00:45:52,040
pictures like Dolly or Mid
journey or programs like that,

767
00:45:52,480 --> 00:46:00,280
you know, and now also the ones
that create video like Runways

768
00:46:00,280 --> 00:46:05,200
Gen. 3, the one, the one that
you mentioned that you just

769
00:46:05,200 --> 00:46:07,560
used.
There's a spectrum of these

770
00:46:07,560 --> 00:46:11,720
these apps as well.
And what these are is these are

771
00:46:12,680 --> 00:46:17,920
these are, these are diffusion
based transformer apps.

772
00:46:17,920 --> 00:46:21,280
And I don't want to, I don't
want to make people's eyes roll

773
00:46:21,280 --> 00:46:22,800
into the back of their heads,
right.

774
00:46:22,840 --> 00:46:27,000
But basically what these apps do
is they've been trained on, that

775
00:46:27,000 --> 00:46:33,320
is to say they have been exposed
to a vast volume of, of, of

776
00:46:33,320 --> 00:46:37,560
visual information, pictures,
videos, etcetera.

777
00:46:38,120 --> 00:46:41,360
And then what they do, and I,
and I want to be careful because

778
00:46:41,360 --> 00:46:43,560
I want to make sure that people
really understand this.

779
00:46:44,120 --> 00:46:47,920
They don't absorb all that and
it sits in the database.

780
00:46:47,920 --> 00:46:51,080
And then, and I think this is a
a misapprehension people have

781
00:46:51,400 --> 00:46:54,400
that like they then go into mid
journey and they say, give me a

782
00:46:54,400 --> 00:47:00,600
picture, you know, of, you know,
a lady walking down the street

783
00:47:01,200 --> 00:47:05,560
in the start, you know, you
know, as if it were a painting

784
00:47:05,560 --> 00:47:08,120
made by Monet.
Yeah.

785
00:47:08,800 --> 00:47:13,560
Those programs then don't go
look at that moment at Monet's

786
00:47:13,560 --> 00:47:17,400
art.
And they don't then like sample

787
00:47:17,400 --> 00:47:21,440
that and put paste together a
collage of existing stuff.

788
00:47:22,480 --> 00:47:25,000
Here's what happens.
And it and it's, and it's much

789
00:47:25,000 --> 00:47:28,720
in the same way like people who
go to art school, the programs

790
00:47:28,720 --> 00:47:31,920
are trained on the materials.
So the materials end up

791
00:47:31,920 --> 00:47:36,840
resulting in a database that
contains no pictorial imagery

792
00:47:36,840 --> 00:47:39,960
whatsoever.
It is a, it is a literally a

793
00:47:40,040 --> 00:47:45,240
four dimensional hang on there.
It is A4 dimensional

794
00:47:47,520 --> 00:47:52,800
mathematical space that
basically has a bunch of data

795
00:47:53,320 --> 00:47:59,160
about composition, line weight,
color, aesthetics, styles.

796
00:47:59,160 --> 00:48:01,480
There's no pictures in it
whatsoever, whatsoever.

797
00:48:01,640 --> 00:48:05,560
If you were to look at it, if
you could look at it, because

798
00:48:05,560 --> 00:48:09,200
looking at a latent space
database has problems of its

799
00:48:09,200 --> 00:48:12,960
own, it would look like a
massive table of numbers.

800
00:48:13,040 --> 00:48:16,280
Yeah.
So basically when you enter a

801
00:48:16,280 --> 00:48:20,480
prompt into a program like Mid
Journey, which is a very popular

802
00:48:22,920 --> 00:48:27,600
image generator, it looks at
what you've written, right?

803
00:48:27,600 --> 00:48:30,520
Yeah.
I want a picture of a blue

804
00:48:30,520 --> 00:48:35,200
painted picture of a crazy
purple haired, you know,

805
00:48:35,200 --> 00:48:42,440
entertainment designer, you
know, sitting in a room full of

806
00:48:42,440 --> 00:48:47,120
clouds, right?
It turns that verbiage into

807
00:48:47,120 --> 00:48:51,600
tokens, right?
It translates what you've

808
00:48:51,600 --> 00:48:55,560
written into tokens.
Now each of those tokens, it

809
00:48:55,840 --> 00:49:02,760
relates to a specific thing, a
color, a descriptive term, male,

810
00:49:02,760 --> 00:49:04,760
female, you know, etcetera,
etcetera.

811
00:49:04,760 --> 00:49:09,440
And then then the program takes
those tokens which basically

812
00:49:09,440 --> 00:49:15,560
give it numbers, data points, it
goes off into its latent space

813
00:49:15,560 --> 00:49:18,800
database which has been
generated from the training and

814
00:49:18,800 --> 00:49:25,600
it matches the tokens to areas
of the numerical matrix.

815
00:49:25,880 --> 00:49:28,600
Yeah.
And so it understands that

816
00:49:28,600 --> 00:49:31,920
bananas are yellow when it
understands that clouds are

817
00:49:31,920 --> 00:49:34,840
puffy and white.
And it understands that, you

818
00:49:34,840 --> 00:49:38,360
know, hair looks like this and
etcetera, etcetera.

819
00:49:38,360 --> 00:49:44,720
And it goes into that space, and
then the app begins with noise,

820
00:49:45,240 --> 00:49:47,840
right?
It begins with literally what

821
00:49:47,840 --> 00:49:50,480
you might call static if you
were looking at it.

822
00:49:51,400 --> 00:49:55,680
And then it sharpens it and
sharpens it and sharpens it

823
00:49:55,880 --> 00:49:59,680
until an image begins to appear
based off the tokens and based

824
00:49:59,680 --> 00:50:03,680
off the data it's gotten from
its latent space database.

825
00:50:04,000 --> 00:50:07,480
Now, the best ones, the best
versions of these programs are

826
00:50:07,480 --> 00:50:10,880
actually 2 AIS.
One AI is doing this work.

827
00:50:11,480 --> 00:50:15,320
The other AI is sitting there
serving as a real time critic

828
00:50:15,640 --> 00:50:19,560
and going that's not a banana.
Yeah, I don't know about that.

829
00:50:19,880 --> 00:50:22,760
Right.
And and basically it's help, one

830
00:50:22,760 --> 00:50:25,880
AI is helping the other AI to
tune its results.

831
00:50:26,160 --> 00:50:29,960
And that fuzzy static picture
gets clearer and clearer and

832
00:50:29,960 --> 00:50:34,840
clearer and clearer until it
matches the verbal request, the

833
00:50:34,840 --> 00:50:37,160
typed request you'd give it.
Yeah.

834
00:50:37,240 --> 00:50:41,160
So it is not sampling.
It is not copying.

835
00:50:41,680 --> 00:50:46,480
It is not directly going out,
scraping the Internet and

836
00:50:47,040 --> 00:50:51,480
putting a picture together.
It has learned about.

837
00:50:51,600 --> 00:50:55,480
It's learned about, you know, a
Monet painting.

838
00:50:55,800 --> 00:50:58,920
Monet paintings are soft,
they're impressionistic.

839
00:50:58,920 --> 00:51:02,600
They're painterly.
They usually have a more pastel

840
00:51:02,600 --> 00:51:05,640
palette, right?
And so now the computer program

841
00:51:05,640 --> 00:51:10,160
knows what pastel means.
It knows what warm means.

842
00:51:10,760 --> 00:51:13,560
It knows what soft and painterly
means.

843
00:51:13,720 --> 00:51:17,240
And instead of sharp and
graphic, right.

844
00:51:17,920 --> 00:51:21,240
And so when you prompted to give
you something like that, it

845
00:51:21,240 --> 00:51:24,760
doesn't go find a Monet painting
and bend it and twist it into

846
00:51:24,760 --> 00:51:26,240
something you want.
Yeah.

847
00:51:26,480 --> 00:51:29,040
It makes, that's such a great
point.

848
00:51:29,440 --> 00:51:31,880
Completely custom picture.
Yeah.

849
00:51:32,240 --> 00:51:37,000
That that references the
aesthetic, the composition, the

850
00:51:37,000 --> 00:51:39,480
style.
It's just like me going to art

851
00:51:39,480 --> 00:51:42,880
school with which I did I, you
know, I attended RISD for a

852
00:51:42,880 --> 00:51:44,520
while, Rhode Island School of
Design.

853
00:51:45,440 --> 00:51:49,040
When I went to RISD, I showed up
and the first thing they did was

854
00:51:49,040 --> 00:51:54,160
teach us art history and we
learned about the great masters

855
00:51:54,160 --> 00:51:57,400
and we looked at their work and
we went into the museum and they

856
00:51:57,400 --> 00:52:00,800
would make us try to recreate
their work, right?

857
00:52:01,240 --> 00:52:02,720
They would.
And at the same time, they're

858
00:52:02,720 --> 00:52:06,160
teaching us techniques.
You know, they would teach us

859
00:52:06,160 --> 00:52:09,400
the techniques of these artists
so that we could do things like

860
00:52:09,400 --> 00:52:13,000
what they did, having looked at
what they've done before and

861
00:52:13,000 --> 00:52:18,400
been inspired by that, and then
go out and, and then they taught

862
00:52:18,400 --> 00:52:21,080
us how to synthesize all these
things together to come up with

863
00:52:21,080 --> 00:52:24,560
our own individual style.
You know, the way these apps

864
00:52:24,560 --> 00:52:27,360
work is a lot like that.
You know, they get exposed to

865
00:52:27,360 --> 00:52:30,120
things.
They get taught why those things

866
00:52:30,120 --> 00:52:33,720
happened and how they work, and
then they get taught on how to

867
00:52:33,720 --> 00:52:36,680
create things like it.
Well, you know, it's such an

868
00:52:36,680 --> 00:52:41,040
amazing point because this is
one of the greatest fears.

869
00:52:41,040 --> 00:52:44,800
You know, it is just the the
fact that it's going to, you

870
00:52:44,800 --> 00:52:48,480
know, copy music, it's going to
take people's likelihood and do

871
00:52:48,480 --> 00:52:50,040
things to it.
It's going to do this.

872
00:52:50,040 --> 00:52:54,480
It's going to all of this copy
replica stuff is such a big

873
00:52:54,480 --> 00:52:57,240
concern, but it's no different
than, you know, with music.

874
00:52:57,240 --> 00:52:59,280
There's only so many notes in
music.

875
00:52:59,320 --> 00:53:02,160
And so yes, there's a good
chance the song's going to have

876
00:53:02,160 --> 00:53:06,440
an A in it and it might have AG
or AB minor or something, right.

877
00:53:06,960 --> 00:53:10,160
And but that doesn't mean you're
copying another song that's got

878
00:53:10,160 --> 00:53:14,240
AB minor and an A in it.
You know, it's and no different

879
00:53:14,240 --> 00:53:17,520
than than lighting design.
You know, you're going to use a

880
00:53:17,520 --> 00:53:19,720
similar color of red.
You're going to use a similar

881
00:53:19,720 --> 00:53:23,240
color of blue, perhaps doesn't
mean you copied, you know, John

882
00:53:23,240 --> 00:53:26,680
Featherstone or, or Cosmo Wilson
or Mark Rickman.

883
00:53:26,680 --> 00:53:29,440
You know it.
It just means that you're doing

884
00:53:29,440 --> 00:53:31,160
a design.
And when you do that design,

885
00:53:31,160 --> 00:53:33,520
you're taking all those
experiences, all the other

886
00:53:33,520 --> 00:53:35,360
things you've seen and learned
over the years.

887
00:53:35,640 --> 00:53:38,680
And you know, you're, you're
fitting them together in a way

888
00:53:38,680 --> 00:53:40,840
that matches this particular
performance, right.

889
00:53:41,240 --> 00:53:43,600
And so I think AI does a lot of
that as well.

890
00:53:43,600 --> 00:53:47,440
And, and you know, the way you
just explained it is incredible.

891
00:53:47,440 --> 00:53:50,200
Like, I don't know that I've
ever, I've seen 1000 YouTube

892
00:53:50,200 --> 00:53:52,800
videos and I don't think I've
seen anybody explain it quite as

893
00:53:52,800 --> 00:53:56,440
well as you did just now,
Especially the two AIS.

894
00:53:56,720 --> 00:53:59,480
You know, when you're saying 1
is basically creating it and one

895
00:53:59,480 --> 00:54:03,640
is critiquing it.
So how do things like 3 arms or

896
00:54:03,640 --> 00:54:06,680
three eyeballs happen?
Then what?

897
00:54:06,680 --> 00:54:10,840
Is that you know AI doesn't
understand context, right?

898
00:54:10,840 --> 00:54:15,400
Like it doesn't, it has, it can
tell you that it no, like it has

899
00:54:15,440 --> 00:54:18,040
a general sense of what a hand
looks like.

900
00:54:18,720 --> 00:54:21,640
You know, it's a big BLOB with
some sticks coming off of it.

901
00:54:21,640 --> 00:54:25,440
Kind of like it knows from a
compositional standpoint what a

902
00:54:25,440 --> 00:54:28,360
hand looks like.
It doesn't understand because no

903
00:54:28,360 --> 00:54:32,200
one that you know that it's not
the AIS aren't trained with

904
00:54:32,200 --> 00:54:35,120
context.
And This is why there's always

905
00:54:35,120 --> 00:54:38,560
going to be a place for artists
in this, because artists and

906
00:54:38,560 --> 00:54:43,000
creatives, I mean, part of what
makes up me anyway creative is

907
00:54:43,040 --> 00:54:46,200
that I have the, you know, a ton
of books behind me, right?

908
00:54:46,200 --> 00:54:47,960
If you could see the office
around me.

909
00:54:47,960 --> 00:54:49,960
Yeah.
All books.

910
00:54:50,160 --> 00:54:50,880
Yeah.
Right.

911
00:54:50,880 --> 00:54:54,600
And a lot of what I bring to my
creativity is context.

912
00:54:54,640 --> 00:54:57,320
Yeah.
And the AIS don't understand

913
00:54:57,320 --> 00:54:58,840
context.
So yes, it's true.

914
00:54:58,840 --> 00:55:02,480
Like the classic thing of like,
you know, you can tell an AI

915
00:55:02,480 --> 00:55:04,680
image because everybody's got 7
fingers.

916
00:55:04,760 --> 00:55:07,600
Yeah, they got the wrong number
of fingers and toes.

917
00:55:08,760 --> 00:55:12,200
The AIS don't they understand
that they're making, they're

918
00:55:12,200 --> 00:55:17,240
going to, they're going to
sharpen a diffuse thing into a

919
00:55:17,240 --> 00:55:21,400
picture of something that they
that it's been told is the shape

920
00:55:21,400 --> 00:55:24,040
of a human.
But the AIS, you know, that

921
00:55:24,040 --> 00:55:29,280
these creative AIS haven't been
trained in deeply in human

922
00:55:29,280 --> 00:55:32,200
anatomy.
Yeah, you know, they don't know.

923
00:55:32,200 --> 00:55:36,160
So there's, there's like so much
that they don't know about yet,

924
00:55:37,200 --> 00:55:41,480
you know, and I say yet quite on
purpose, because, you know, with

925
00:55:41,520 --> 00:55:44,960
every time they refine these
models and train these models,

926
00:55:44,960 --> 00:55:47,840
for instance, the whole
phenomenon of too many fingers

927
00:55:47,840 --> 00:55:50,920
on hands has gets, has been
reduced and reduced and reduced

928
00:55:50,920 --> 00:55:54,440
has come because because it
became something that people

929
00:55:54,440 --> 00:55:56,640
noticed.
And so the people training the

930
00:55:56,640 --> 00:56:03,360
models were like, oh, I can fix
that now, you know, and and

931
00:56:03,360 --> 00:56:07,320
that's the thing, you can just
like I talked about training the

932
00:56:07,320 --> 00:56:10,640
large language model to become
an expert in experiential

933
00:56:10,640 --> 00:56:14,920
strategy, you can train a
graphics model to be to get much

934
00:56:14,920 --> 00:56:17,440
better at a specific thing you
want it to be much better at.

935
00:56:17,440 --> 00:56:22,040
For instance, when we did Blake
Shelton and Blake the back to

936
00:56:22,040 --> 00:56:27,040
the Honky Tonk tour, we wanted
to build a big virtual piece of

937
00:56:27,040 --> 00:56:30,600
scenery in the video that was
kind of supposed to be like

938
00:56:30,600 --> 00:56:34,760
Blake's barn on his property.
That was a honky tonk and that

939
00:56:34,840 --> 00:56:38,080
one of the things in there would
be all of these honky tonk

940
00:56:38,240 --> 00:56:44,320
posters of Blake, right?
Like Blake plays, you know, the

941
00:56:44,360 --> 00:56:47,520
the the Riverfront Bar and Grill
and Blake plays this and Blake

942
00:56:47,520 --> 00:56:51,920
plays that.
Well, you know, and AI doesn't

943
00:56:51,920 --> 00:56:53,680
really know what Blake looks
like.

944
00:56:53,680 --> 00:56:58,560
Like it probably has seen Blake
a few times in the materials

945
00:56:58,560 --> 00:57:00,840
it's been given.
But you're going to get hit or

946
00:57:00,840 --> 00:57:02,920
miss results out of it for sure,
right?

947
00:57:04,040 --> 00:57:09,840
So one thing we did was we used
a technique called Laura LORA,

948
00:57:10,280 --> 00:57:15,360
which is a way to give a
generative image AI the ability

949
00:57:15,360 --> 00:57:20,640
to gain extra knowledge about a
specific way something looks or

950
00:57:20,640 --> 00:57:24,400
feels.
We trained a version of Stable

951
00:57:24,400 --> 00:57:29,000
Diffusion, which is a different
generative AI art thing, to be

952
00:57:29,000 --> 00:57:32,320
an expert at making pictures of
Blake.

953
00:57:33,360 --> 00:57:34,480
Oh, interesting.
Added.

954
00:57:34,680 --> 00:57:38,000
All of these pictures of Blake
from his, from his younger days

955
00:57:38,000 --> 00:57:41,320
to his older days in all kinds
of different, like we, we had

956
00:57:41,320 --> 00:57:43,800
access to all the photographs
that had ever been, you know,

957
00:57:43,840 --> 00:57:48,040
that Blake's people had.
And we trained this AI to be

958
00:57:48,240 --> 00:57:50,480
really good at making images of
Blake.

959
00:57:50,920 --> 00:57:54,040
It was already pretty good at
making images of stuff that

960
00:57:54,040 --> 00:57:55,880
would look like honky tonk
posters.

961
00:57:56,120 --> 00:58:02,160
So now we could prompt it make a
series of Honky tonk concert

962
00:58:02,160 --> 00:58:06,120
show advertisement poster
posters featuring Blake Shelton,

963
00:58:06,360 --> 00:58:09,000
and it made hundreds of them for
us to choose.

964
00:58:09,000 --> 00:58:10,800
From Oh, that's awesome, so
that.

965
00:58:10,800 --> 00:58:13,800
Was the way that we trained
Stable Diffusion to get better

966
00:58:13,800 --> 00:58:17,240
at that we, you know.
But don't a lot of these models

967
00:58:17,240 --> 00:58:21,680
don't a lot of these like things
like Dolly and, and different

968
00:58:22,520 --> 00:58:26,920
image generation models or video
generation models, aren't they

969
00:58:27,120 --> 00:58:32,400
really afraid of like the sort
of anti plagiarism and, and you

970
00:58:32,400 --> 00:58:34,360
know, so aren't they avoiding
that?

971
00:58:34,360 --> 00:58:38,120
Like, aren't they purposely not
allowing you to say, give me a

972
00:58:38,120 --> 00:58:40,840
picture of Taylor Swift with a
mustache or whatever?

973
00:58:40,840 --> 00:58:44,400
100% and I think that's that's
probably from a.

974
00:58:44,800 --> 00:58:47,560
It's judicious of them to do
that, of course.

975
00:58:47,560 --> 00:58:49,920
Yeah.
You know, being accessible to a

976
00:58:49,920 --> 00:58:54,520
larger public, there are risks
of misinformation, Yeah, you

977
00:58:54,520 --> 00:58:56,040
know, and.
And the.

978
00:58:56,560 --> 00:58:59,400
You know, the kind of the bad
things that can happen if.

979
00:58:59,480 --> 00:59:00,880
You fake?
Yeah.

980
00:59:02,280 --> 00:59:06,800
Which is why we trained Stable
Diffusion is an open source

981
00:59:06,800 --> 00:59:10,240
model.
That is not, it's not, you know,

982
00:59:10,240 --> 00:59:13,720
I have that model here in the
studio on our own computers.

983
00:59:13,960 --> 00:59:15,920
It's not accessible to the
public.

984
00:59:15,920 --> 00:59:18,520
OK.
We train this model on Blake.

985
00:59:18,520 --> 00:59:21,200
It's not out in the world.
You can't just you.

986
00:59:21,200 --> 00:59:23,360
Can't go to stable.
You can't download Stable

987
00:59:23,360 --> 00:59:27,040
Diffusion now and it's, you
know, and we've made it an

988
00:59:27,040 --> 00:59:29,760
expert on Blake.
We've made our own version in

989
00:59:29,760 --> 00:59:33,680
our server closet.
Very good at making Blade Blake

990
00:59:33,680 --> 00:59:36,080
pictures.
That's cool, really sad out into

991
00:59:36,080 --> 00:59:38,480
the universe.
So you could do Blake, You could

992
00:59:38,480 --> 00:59:43,560
do Blake Shelton songs, you
know, deep fakes from your

993
00:59:43,560 --> 00:59:44,200
computers.
Yeah.

994
00:59:44,640 --> 00:59:47,840
We can, and intentionally, like
we've been granted the

995
00:59:47,840 --> 00:59:50,160
permission by by Blake to do
that.

996
00:59:50,240 --> 00:59:52,560
Yeah.
And now we've created something

997
00:59:52,560 --> 00:59:56,320
that is like with anything and
is for us it's a highly, you

998
00:59:56,320 --> 00:59:58,920
know, that's a piece of
proprietary technology we.

999
00:59:58,960 --> 01:00:02,400
Have.
And our agreements with with

1000
01:00:02,400 --> 01:00:06,120
Blake Shelton and with Blake
Shelton's management is that's

1001
01:00:06,120 --> 01:00:07,840
not publicly accessible.
Yeah.

1002
01:00:07,840 --> 01:00:09,160
Of course.
Yeah, of course.

1003
01:00:09,280 --> 01:00:14,000
And in fact, they own they own
the that.

1004
01:00:14,160 --> 01:00:15,680
All the image rights and
everything.

1005
01:00:15,680 --> 01:00:19,080
Else that version of it.
So yeah, if you ask, for

1006
01:00:19,080 --> 01:00:22,040
instance, mid journey to give
you a picture of someone famous,

1007
01:00:22,040 --> 01:00:25,240
yeah, they've trained it to.
Yeah, trust me, I've tried.

1008
01:00:25,280 --> 01:00:27,240
Seen this true of Dolly, Same as
true of many.

1009
01:00:28,320 --> 01:00:32,040
They've set up guardrails to
make sure that can't happen.

1010
01:00:32,040 --> 01:00:37,920
Yeah, but if you have to know
how to code, you have to know

1011
01:00:37,920 --> 01:00:42,000
how to.
It's not easy to grab one of

1012
01:00:42,000 --> 01:00:48,080
these open source AIS like Flux,
yeah, or Stable Diffusion and

1013
01:00:48,080 --> 01:00:51,520
just use it like I have computer
programming.

1014
01:00:51,640 --> 01:00:53,600
Yeah, yeah, yeah, yeah.
Experience.

1015
01:00:53,600 --> 01:00:56,280
So for me, you know, and even
though I had to puzzle out a

1016
01:00:56,280 --> 01:00:59,800
bunch of stuff and yeah,
afternoon, I mean Python And you

1017
01:00:59,800 --> 01:01:02,880
have to, you have to, it's not.
Yeah, mid Journey and mid

1018
01:01:02,880 --> 01:01:06,360
Journey and ChatGPT and all of
these are end user types of

1019
01:01:06,360 --> 01:01:10,560
programs that a knucklehead like
me can go on to and, you know,

1020
01:01:11,040 --> 01:01:15,600
figure out how to do some, some
prompt engineering and get some

1021
01:01:15,600 --> 01:01:17,720
results out of it, get some
pretty good results out of it,

1022
01:01:17,720 --> 01:01:21,560
which I have.
But no, I mean, I've tried all

1023
01:01:21,560 --> 01:01:23,920
of those things that that you're
talking about, like getting

1024
01:01:23,920 --> 01:01:27,960
images of, of specific people,
specific places, specific

1025
01:01:27,960 --> 01:01:30,840
whatever, and it doesn't do so
well at most of those things.

1026
01:01:31,160 --> 01:01:35,040
So music blows my mind, though
the music ones blow my mind.

1027
01:01:35,320 --> 01:01:39,360
Like I've, I've written some
really good songs on AI and just

1028
01:01:39,360 --> 01:01:41,720
gone, whoa, this is nuts, you
know?

1029
01:01:41,720 --> 01:01:42,840
Yeah.
And then now there are

1030
01:01:42,840 --> 01:01:45,360
guardrails showing up on that
because of course.

1031
01:01:45,600 --> 01:01:48,320
Of course you know the.
The, the record labels and the

1032
01:01:48,320 --> 01:01:52,680
people that control the rights
to music are, are, are, are

1033
01:01:52,680 --> 01:01:55,480
legally well equipped as we
know.

1034
01:01:56,320 --> 01:01:58,840
And they've, they've you know,
and so now you can't.

1035
01:01:58,840 --> 01:02:01,480
It used to be that there were
some of these opens, there was

1036
01:02:01,480 --> 01:02:04,480
some of these music AIS you
could go and you could say I

1037
01:02:04,480 --> 01:02:10,280
want a song of like a like
Kendrick Lamar singing, you

1038
01:02:10,280 --> 01:02:12,800
know, song.
By The Beatles, right?

1039
01:02:12,800 --> 01:02:14,920
And it would do it and it would
blow your mind.

1040
01:02:14,920 --> 01:02:17,320
It would be like, I never
thought of the Eye, you know,

1041
01:02:17,320 --> 01:02:21,040
And it, and I think it's really
cool, but the industry clamped

1042
01:02:21,040 --> 01:02:23,920
down on it immediately.
And the reason is because they

1043
01:02:23,920 --> 01:02:25,360
don't want the public doing
this.

1044
01:02:25,360 --> 01:02:28,120
And I think there is still a
bunch of great tools out there

1045
01:02:28,120 --> 01:02:31,400
that if you are musically
inclined or a musician, you can

1046
01:02:31,400 --> 01:02:33,560
still use these AI tools to
create music.

1047
01:02:33,560 --> 01:02:39,320
And it's super cool, but you
can't do that that thing.

1048
01:02:40,040 --> 01:02:44,120
But I'll tell you who can is the
labels themselves.

1049
01:02:44,440 --> 01:02:47,320
The reason, you know, the reason
is that the the the record

1050
01:02:47,320 --> 01:02:52,240
labels and the same goes for the
major any of the major media

1051
01:02:52,240 --> 01:02:57,840
companies on the planet.
You know they're going to be and

1052
01:02:57,840 --> 01:02:59,880
some of them already very much
are.

1053
01:03:00,120 --> 01:03:06,320
They are training their own
versions of these A highs so

1054
01:03:07,320 --> 01:03:10,320
they can have the ability.
So they can sign an artist to

1055
01:03:10,320 --> 01:03:14,360
four albums and do 12.
Yeah, yeah.

1056
01:03:14,360 --> 01:03:18,440
I mean, they own the the, the
likeness of the artist or

1057
01:03:18,440 --> 01:03:22,400
whatever.
And, and yeah, I mean, that's

1058
01:03:22,400 --> 01:03:24,240
scary.
That's definitely some scary

1059
01:03:24,240 --> 01:03:27,200
stuff right there.
It is scary, but, but I think

1060
01:03:27,200 --> 01:03:29,280
there were a lot of things that
were a lot of people were scared

1061
01:03:29,320 --> 01:03:32,120
of a there are a lot.
The big overwhelming,

1062
01:03:32,120 --> 01:03:34,880
overarching scary thing that a
lot of people thought about was

1063
01:03:34,880 --> 01:03:37,240
like, oh, are we headed into
Terminator times?

1064
01:03:37,280 --> 01:03:40,560
Is there going to be an
artificial general intelligence

1065
01:03:40,840 --> 01:03:44,320
that basically decides that
that, that humans are optional

1066
01:03:44,320 --> 01:03:46,000
and probably should be
eliminated?

1067
01:03:46,400 --> 01:03:51,520
We're not close to that.
I'm here to tell you, really

1068
01:03:52,240 --> 01:03:57,760
smart sounding chat bots are
nowhere near being an artificial

1069
01:03:57,760 --> 01:04:02,040
general intelligence, right?
It's a different, it's a whole

1070
01:04:02,040 --> 01:04:04,000
different, I know from
programming.

1071
01:04:04,000 --> 01:04:07,640
But did you hear Ray Kurzweil?
I think what where was he?

1072
01:04:07,640 --> 01:04:10,960
Was it on Joe Rogan?
Or maybe it was, I can't

1073
01:04:10,960 --> 01:04:16,840
remember which podcast it was
on, but he says 2029.

1074
01:04:18,160 --> 01:04:21,000
I don't think it's quite that
close.

1075
01:04:21,000 --> 01:04:25,760
I think, look, the kind of code
that results in an AGI depends

1076
01:04:25,760 --> 01:04:31,400
on a very different structure.
Yeah, you know, and the chat bot

1077
01:04:31,400 --> 01:04:35,240
is a basically is basically what
it's really good at is guessing

1078
01:04:35,240 --> 01:04:39,240
the next word that you want.
Yeah, right.

1079
01:04:39,240 --> 01:04:41,040
That's.
When you get right down to the

1080
01:04:41,040 --> 01:04:42,480
root of what it's doing, that's
what it's.

1081
01:04:42,880 --> 01:04:45,680
Yeah, right.
You have to build a very

1082
01:04:45,680 --> 01:04:51,000
different code base to be just
good at everything, right?

1083
01:04:51,280 --> 01:04:54,880
It it's a real neural network,
right?

1084
01:04:54,960 --> 01:04:59,040
The, the large language models
are not, they are neural

1085
01:04:59,040 --> 01:05:01,040
networks, but they're a
different kind of neural

1086
01:05:01,040 --> 01:05:03,720
network.
I think we're, I think we are

1087
01:05:03,720 --> 01:05:06,720
going to see AGI.
Yeah, I think we, you and I

1088
01:05:06,720 --> 01:05:09,760
might see it in our lifetime.
Like maybe.

1089
01:05:09,760 --> 01:05:11,640
Somewhere, I think we
definitely.

1090
01:05:11,720 --> 01:05:15,200
Would probably.
I don't think it's that far off.

1091
01:05:15,200 --> 01:05:17,560
I mean, when you look at how
fast things have happened over

1092
01:05:17,560 --> 01:05:19,960
the last, what is it 18 months
or something?

1093
01:05:20,120 --> 01:05:23,800
We've gone from pretty much no
one knowing not no one but the

1094
01:05:23,800 --> 01:05:28,480
normal human walking the street
knowing nothing about AI to now

1095
01:05:28,480 --> 01:05:31,680
they've got it on their iPhone
or or Google phone or whatever,

1096
01:05:31,680 --> 01:05:35,800
right?
And I think things are going to

1097
01:05:35,800 --> 01:05:38,440
continue moving very fast.
I think the low hanging fruit

1098
01:05:38,440 --> 01:05:40,760
with some of the stuff we've
been seeing, the translator

1099
01:05:40,760 --> 01:05:47,200
models and the the web coding
models and you know, personal

1100
01:05:47,200 --> 01:05:50,480
assistant types of models and
things like that, writing is

1101
01:05:50,480 --> 01:05:52,800
obviously gotten pretty good
already.

1102
01:05:52,920 --> 01:05:55,200
Oh, yeah, yeah.
And I think that, you know, I

1103
01:05:55,200 --> 01:05:58,720
think, you know, I don't think
that we need to talk about

1104
01:05:58,720 --> 01:06:01,400
politics or anything, but I can
tell you that the incoming

1105
01:06:01,400 --> 01:06:06,480
administration has a very
sympathetic viewpoint in terms

1106
01:06:06,480 --> 01:06:14,040
of deregulating AI and allowing
the tech companies to go as fast

1107
01:06:14,040 --> 01:06:16,200
as they want in the directions
they want to go.

1108
01:06:16,440 --> 01:06:17,800
Yeah.
I mean that that.

1109
01:06:17,800 --> 01:06:20,920
May be, which is fine.
I don't have a Yeah, you know, I

1110
01:06:20,920 --> 01:06:22,680
don't have a horse in that race.
I.

1111
01:06:22,680 --> 01:06:27,080
Think The funny thing is Bob
Elon is very much the opposite

1112
01:06:27,080 --> 01:06:29,760
of that.
So I would think Elon in, in

1113
01:06:29,760 --> 01:06:36,080
other people's ears is going to
be because Elon is, is very, I

1114
01:06:36,080 --> 01:06:40,680
wouldn't say afraid, but nervous
of where things are going and

1115
01:06:40,680 --> 01:06:44,240
how quickly they're going.
Marcel Elon says that.

1116
01:06:44,320 --> 01:06:47,120
Yeah, that's true.
But then he spins up Grok.

1117
01:06:47,280 --> 01:06:50,880
Yeah, which is an open source
large language model.

1118
01:06:50,880 --> 01:06:53,040
Yeah, and freely distributed it
to the world.

1119
01:06:53,040 --> 01:06:56,200
The Chinese now have Grok.
Yeah.

1120
01:06:56,480 --> 01:06:59,360
And what they're going to do
with it may or may not align

1121
01:06:59,360 --> 01:07:00,920
with whatever sympathies Elon
has.

1122
01:07:00,920 --> 01:07:06,600
So one is saying one thing, but
at the same time, he's building

1123
01:07:07,040 --> 01:07:11,440
the world's biggest training
data centers that exist, and

1124
01:07:11,600 --> 01:07:13,920
he's freely distributing the
result.

1125
01:07:14,120 --> 01:07:16,680
Well, he also says that by the
end of the year he's going to

1126
01:07:16,680 --> 01:07:23,440
have GPT 44-O power.
December I think is when he

1127
01:07:23,760 --> 01:07:27,080
announces Grok 3.
I believe it is and.

1128
01:07:27,360 --> 01:07:29,880
Like he has, he's definitely
made huge strides.

1129
01:07:30,320 --> 01:07:33,040
What I can tell you is a
training a big large language

1130
01:07:33,040 --> 01:07:37,720
model like GPT 4 is hugely
expensive in terms of times and

1131
01:07:37,880 --> 01:07:44,040
time and resources.
But also, it's not like Elon

1132
01:07:44,040 --> 01:07:49,960
Bolt believes in this very this,
this, this work philosophy of

1133
01:07:49,960 --> 01:07:53,280
the of intensity and that that,
you know, they look, there are

1134
01:07:53,280 --> 01:07:56,560
things in his companies that
that I admire a great deal.

1135
01:07:56,560 --> 01:07:59,000
Like I I am a huge fan of
SpaceX.

1136
01:07:59,000 --> 01:08:01,200
Yeah.
And I think that because SpaceX

1137
01:08:01,200 --> 01:08:04,440
is vertically integrated, and I
think because of Elon's

1138
01:08:04,440 --> 01:08:08,520
philosophy of how to tackle
engineering, it has afforded

1139
01:08:08,520 --> 01:08:11,240
them this huge advantage.
Yeah, but it's one thing.

1140
01:08:11,240 --> 01:08:15,680
But I also know this about Elon.
He says things like are gonna be

1141
01:08:15,880 --> 01:08:18,200
something by something.
Oh yeah.

1142
01:08:18,680 --> 01:08:20,200
And they're that.
Never happens.

1143
01:08:20,399 --> 01:08:21,840
Yeah, that never happened, you
know.

1144
01:08:21,920 --> 01:08:23,880
I.
Have well, it's like I have AI

1145
01:08:23,880 --> 01:08:27,960
have a friend who has a deposit
on the the whatever the the

1146
01:08:27,960 --> 01:08:30,200
sports car is called.
Yeah, exactly.

1147
01:08:30,200 --> 01:08:33,520
The Roadster I think it's called
or whatever, but I have a friend

1148
01:08:33,520 --> 01:08:37,240
who's had a deposit on that for
like 3 years and is like, are we

1149
01:08:37,240 --> 01:08:40,279
ever going to see this party?
You know, and look, we'll see

1150
01:08:40,279 --> 01:08:43,680
about what he has by December.
In terms of Grok, Yeah, Yeah.

1151
01:08:43,920 --> 01:08:46,640
But I want to circle back to
that fear factor.

1152
01:08:46,640 --> 01:08:48,880
I mean, artificial intelligence
is one thing.

1153
01:08:48,880 --> 01:08:51,520
I don't think that that's what
we need to be worrying about

1154
01:08:51,520 --> 01:08:54,760
immediately right now.
But but one another thing that

1155
01:08:54,760 --> 01:08:59,040
people worry about is that that
AI is going to put people out of

1156
01:08:59,040 --> 01:09:02,600
work.
Now look, AI is going to change

1157
01:09:02,600 --> 01:09:06,600
the nature of the workforce.
And there already has been huge

1158
01:09:06,600 --> 01:09:10,880
impacts in journalism, in
particular if you're a

1159
01:09:10,920 --> 01:09:15,760
professional writer, like there
used to be a decent living to be

1160
01:09:15,760 --> 01:09:20,640
made, for instance, in
aggregating news about a special

1161
01:09:20,640 --> 01:09:22,359
topic.
Like, let's say you were a tech

1162
01:09:22,359 --> 01:09:23,720
journalist.
Yeah.

1163
01:09:23,720 --> 01:09:27,760
And you write reviews about tech
stuff like, you know, like

1164
01:09:27,760 --> 01:09:31,240
phones or like gaming consoles
and stuff like that.

1165
01:09:31,240 --> 01:09:32,880
Forget it.
That job's gone.

1166
01:09:32,960 --> 01:09:36,040
That job is gone.
Yeah, there are AIS who are

1167
01:09:36,040 --> 01:09:38,920
just, they can much, they can
look at everything on the web

1168
01:09:38,920 --> 01:09:41,800
instantly.
They can and they can summarize

1169
01:09:41,800 --> 01:09:46,120
it and repackage it.
So you know, so you know.

1170
01:09:47,240 --> 01:09:51,120
Have you used notebook LM yet?
Notebook LM Yeah, I've tried.

1171
01:09:51,120 --> 01:09:53,640
Yeah, I've worked with Notebook,
with the with Google.

1172
01:09:53,920 --> 01:09:56,800
It's pretty freakish and cool.
I love it.

1173
01:09:57,040 --> 01:09:58,640
I love it.
Here's.

1174
01:09:58,640 --> 01:10:04,080
Another, you know, and the job
of the paralegal is to go in the

1175
01:10:04,080 --> 01:10:08,200
most part, or a big part of that
job was go find existing

1176
01:10:08,200 --> 01:10:12,480
information, find the important
parts of it that relate to this

1177
01:10:12,480 --> 01:10:16,920
particular case, summarize it,
organize it, put it together,

1178
01:10:16,920 --> 01:10:19,600
give it to the lawyer.
That job's gone.

1179
01:10:19,760 --> 01:10:22,040
Yeah, it's gone.
Well, you know, some low end

1180
01:10:22,040 --> 01:10:26,760
lawyers, some lawyers who do
like contract law or, you know,

1181
01:10:26,760 --> 01:10:29,880
very basic, like, I mean, if you
got to do a lease for an

1182
01:10:29,880 --> 01:10:32,280
apartment that you're trying to
rent out, are you going to go to

1183
01:10:32,280 --> 01:10:34,800
a lawyer?
You're going to go to ChatGPT?

1184
01:10:34,800 --> 01:10:39,280
I mean, it's so easy to get
those documents done now a. 100%

1185
01:10:39,280 --> 01:10:41,960
like, and then this goes like
for instance, we do a lot of

1186
01:10:41,960 --> 01:10:44,280
work with big corporations,
right?

1187
01:10:44,280 --> 01:10:47,400
Like work with GM.
To work with GM, you have to

1188
01:10:47,400 --> 01:10:50,280
jump through a lot of hoops.
You have to have, you have to

1189
01:10:50,280 --> 01:10:53,440
have compliance documentation.
You have to have all kinds of

1190
01:10:53,440 --> 01:10:57,240
documentation for yourself as a
company, your emergency response

1191
01:10:57,240 --> 01:10:59,120
documentation.
Because for instance, GM wants

1192
01:10:59,120 --> 01:11:03,000
to know that if there is a
hurricane or an, or where I am

1193
01:11:03,000 --> 01:11:07,800
an earthquake, what, what are
our, what are our emergency

1194
01:11:07,800 --> 01:11:10,560
response procedures to make sure
that we're still going to be

1195
01:11:10,560 --> 01:11:12,760
able to do business the next
day.

1196
01:11:13,360 --> 01:11:15,280
Interesting.
This our GM account right?

1197
01:11:15,280 --> 01:11:18,760
So for instance, so there's a
lot of documentation you have to

1198
01:11:18,760 --> 01:11:21,440
write as a company if you're
doing business with people like

1199
01:11:21,440 --> 01:11:22,240
that.
Yeah.

1200
01:11:22,720 --> 01:11:25,960
That kind of stuff, like, would
you, we've been able to use

1201
01:11:25,960 --> 01:11:31,400
ChatGPT to do emergency response
plans, compliance plans, you

1202
01:11:31,400 --> 01:11:35,080
know, computer security plans,
you know, Yeah.

1203
01:11:35,080 --> 01:11:39,560
Me too, I that simplifies so
much of my day so.

1204
01:11:39,880 --> 01:11:43,360
But there's also been a big,
there's been a big within the

1205
01:11:43,360 --> 01:11:46,560
art community, in the arts
community, there's been this

1206
01:11:46,640 --> 01:11:50,120
backlash of like this is going
to put artists out of work.

1207
01:11:50,720 --> 01:11:53,440
I don't agree.
It's another tool.

1208
01:11:53,680 --> 01:11:59,640
It's another thing we can use.
The effects industry did not put

1209
01:12:00,400 --> 01:12:03,680
regular movie making people out
of work.

1210
01:12:03,880 --> 01:12:06,120
It just became part of the
workflow.

1211
01:12:07,320 --> 01:12:10,440
You know I want.
To give the flip side of that,

1212
01:12:10,440 --> 01:12:13,000
because like, you know, what
about like Tyler Perry who

1213
01:12:13,000 --> 01:12:16,880
cancelled, what was it an 800,
1000 square foot studio or

1214
01:12:16,880 --> 01:12:21,880
something that was being built?
And you know, because he just

1215
01:12:21,880 --> 01:12:24,880
said we're not going to need
studios anymore.

1216
01:12:24,880 --> 01:12:25,920
We're just not going to need
them.

1217
01:12:26,000 --> 01:12:30,720
Tyler Perry personal opinion.
I think that was calculated much

1218
01:12:30,720 --> 01:12:36,200
more off of that was an excuse
all of the big networks and film

1219
01:12:36,200 --> 01:12:40,760
studios and most probably
streamers stopped buying

1220
01:12:40,760 --> 01:12:42,240
content.
Yeah.

1221
01:12:42,840 --> 01:12:45,080
Right, because they're writing
content down because they're

1222
01:12:45,080 --> 01:12:49,480
trying to trying to drive share,
share price up by reducing debt.

1223
01:12:49,680 --> 01:12:53,520
Here's the fact of the matter.
It is there's a huge, huge,

1224
01:12:56,160 --> 01:13:00,400
there's a huge fall off in
production of television and

1225
01:13:00,400 --> 01:13:02,960
film right now.
And none of it's got anything to

1226
01:13:02,960 --> 01:13:05,800
do with AI.
Yeah, it's got to do with media

1227
01:13:05,800 --> 01:13:10,280
companies trying to figure out
how to monetize their content in

1228
01:13:10,280 --> 01:13:13,760
the age of streaming.
I think consumption consumption

1229
01:13:13,800 --> 01:13:16,040
was changed.
Wasn't about to build a billion

1230
01:13:16,040 --> 01:13:21,640
dollar film studio to sit empty.
Not because AI had replaced it,

1231
01:13:21,640 --> 01:13:24,040
but because there's no market
for that shit.

1232
01:13:24,040 --> 01:13:25,760
Well, do.
You know, do you know, would you

1233
01:13:25,760 --> 01:13:30,680
know Glenn Rogeman from AED in
Nether, Belgium?

1234
01:13:31,560 --> 01:13:32,960
No.
He'd kill me for saying

1235
01:13:32,960 --> 01:13:39,200
Netherlands because he has a, he
has a lighting sound, all kinds

1236
01:13:39,200 --> 01:13:41,640
of companies, but he's got a, a
studio as well.

1237
01:13:42,120 --> 01:13:45,320
And I talked to him about this
and I said, does that make you

1238
01:13:45,320 --> 01:13:47,720
nervous at all?
And he said, not really, you

1239
01:13:47,720 --> 01:13:50,800
know, we're going to stay busy.
But the problem is here's what

1240
01:13:50,800 --> 01:13:53,440
he says.
Like, you know, I know I paid a

1241
01:13:53,440 --> 01:13:56,000
lot of attention to Sora.
I haven't followed some of the

1242
01:13:56,000 --> 01:13:58,200
newer ones that have come out,
but Sora was doing some

1243
01:13:58,200 --> 01:14:00,520
incredible stuff, like really
incredible stuff.

1244
01:14:01,000 --> 01:14:04,240
And but he said, here's what's
going to happen.

1245
01:14:04,240 --> 01:14:07,520
Movies are going to get to here.
They're going to get really

1246
01:14:07,520 --> 01:14:10,560
good.
And but he said then what's

1247
01:14:10,560 --> 01:14:14,040
going to happen is all of this
content is going to be right

1248
01:14:14,040 --> 01:14:15,880
here.
It's all going to be really

1249
01:14:15,880 --> 01:14:18,240
good.
And so really good is going to

1250
01:14:18,240 --> 01:14:24,040
become vanilla, and everything
will become sort of ubiquitous

1251
01:14:24,040 --> 01:14:27,640
and boring.
And he just thinks that, you

1252
01:14:27,640 --> 01:14:32,360
know, the nuances and the good
and bad combined together is

1253
01:14:32,360 --> 01:14:37,960
what makes, you know, film
discovery or movie discovery,

1254
01:14:37,960 --> 01:14:41,480
show discovery so good.
It's not like everything is an

1255
01:14:41,480 --> 01:14:43,320
A.
Well, what about some A pluses

1256
01:14:43,320 --> 01:14:46,000
and some B minuses and whatever?
You know, you need some of that

1257
01:14:46,000 --> 01:14:47,800
stuff, right?
But well.

1258
01:14:48,680 --> 01:14:52,280
I, I, I'm of, I'm of various
opinions about that.

1259
01:14:52,360 --> 01:14:57,720
I think that that I think that
misunderstands and maybe skips

1260
01:14:57,720 --> 01:15:01,080
over the idea that the AI should
get better, right?

1261
01:15:01,400 --> 01:15:04,360
Like they're going to feed
context into these AIS.

1262
01:15:04,360 --> 01:15:07,680
The AIS are going like the
generative AIS are going to be

1263
01:15:07,680 --> 01:15:12,240
taught that everything being
perfect leads to homogenization,

1264
01:15:12,280 --> 01:15:14,280
right?
So they will introduce little

1265
01:15:14,520 --> 01:15:15,440
human.
Point.

1266
01:15:16,640 --> 01:15:19,720
You know, just to appeal to us.
So they also haven't yet even

1267
01:15:19,720 --> 01:15:24,640
begun to train these AIS to
produce stuff that is targeted

1268
01:15:24,640 --> 01:15:27,880
to demographic psychographics
and audience segmentation.

1269
01:15:27,880 --> 01:15:28,720
Right.
Good point.

1270
01:15:28,720 --> 01:15:30,080
Yeah.
And they're going to get really

1271
01:15:30,080 --> 01:15:33,280
good at it.
And in the culture has become

1272
01:15:33,280 --> 01:15:36,400
short attention span theater.
You know, I know that my

1273
01:15:36,400 --> 01:15:40,720
daughter, she gets, she does
most of her media consumption

1274
01:15:40,720 --> 01:15:44,240
not in this, not in a, in a, in
a movie theater.

1275
01:15:44,360 --> 01:15:45,760
Yeah, my son too.
And.

1276
01:15:45,760 --> 01:15:50,800
Only barely in any long form,
anything she sees on TV, Yeah,

1277
01:15:50,800 --> 01:15:55,040
she mainly looks at TikTok,
Instagram stories and stuff like

1278
01:15:55,040 --> 01:15:56,000
that.
Yeah.

1279
01:15:56,000 --> 01:15:59,720
And you know, or maybe like half
hour episodic stuff, right?

1280
01:15:59,720 --> 01:16:03,080
Yeah, Yeah.
So I mean, look, I agree.

1281
01:16:04,880 --> 01:16:09,120
I just want to say the AIS, yes,
what your friend said, I think

1282
01:16:09,120 --> 01:16:12,000
that there's some value in it,
but but I think it skips by the

1283
01:16:12,000 --> 01:16:16,840
idea that they're going to teach
the AIS to to get better at not.

1284
01:16:16,840 --> 01:16:19,440
Being that's better, Yeah,
that's true all the time.

1285
01:16:19,440 --> 01:16:22,640
We didn't discuss that, yeah.
And then the other part, this is

1286
01:16:22,640 --> 01:16:25,560
kind of a three prong thing.
The other part of it is I once

1287
01:16:25,560 --> 01:16:28,320
had an executive producer told
me, and this is a guy who's made

1288
01:16:28,320 --> 01:16:31,240
billions of dollars off
audiences.

1289
01:16:31,240 --> 01:16:39,520
He, he said, Bobby, never
underestimate the, the, the, you

1290
01:16:39,520 --> 01:16:42,720
know, the, the appetites of the
general public, which is to say

1291
01:16:43,080 --> 01:16:48,600
they don't really care.
You know, most of mainstream

1292
01:16:48,600 --> 01:16:52,200
culture, they don't give a shit
about quality.

1293
01:16:52,480 --> 01:16:57,840
You know, they look at video in
bad MPEG 4 on their phone all

1294
01:16:57,840 --> 01:17:02,480
day in little bits and pieces
and they they aren't necessarily

1295
01:17:02,480 --> 01:17:06,200
captivated or captured in a
conscious way by art.

1296
01:17:06,240 --> 01:17:08,520
So.
Troy, I heard, I heard Justine

1297
01:17:08,520 --> 01:17:12,920
Bateman who is the president of
of Screen Actors Guild, she

1298
01:17:12,920 --> 01:17:14,600
said.
I think it was in a Variety

1299
01:17:14,600 --> 01:17:18,480
article this week that there was
going to be a mass cultural

1300
01:17:18,480 --> 01:17:23,720
backlash against AI produced
content 'cause people would get

1301
01:17:23,720 --> 01:17:29,240
tired of it not being great or,
or being just vanilla.

1302
01:17:29,360 --> 01:17:31,360
Yeah, They're not.
No, they're not.

1303
01:17:31,640 --> 01:17:34,840
No, I think a lot of the
audience is perfectly happy with

1304
01:17:34,840 --> 01:17:36,680
vanilla.
And I am not.

1305
01:17:37,120 --> 01:17:41,720
I I am an artist.
I want people to be inspired and

1306
01:17:41,720 --> 01:17:44,760
to feel deeper feelings and to
go deeper with things.

1307
01:17:44,840 --> 01:17:49,120
Yeah, but I think a lot of the
mainstream culture, they just

1308
01:17:49,120 --> 01:17:52,440
want to hit a dopamine when they
want to hit a dopamine.

1309
01:17:53,040 --> 01:17:55,240
Yeah.
And they're and they they don't

1310
01:17:55,240 --> 01:17:59,480
much they don't get attached.
They they don't think to the

1311
01:17:59,480 --> 01:18:03,800
extent of like, wait a minute,
this all looks a little bit just

1312
01:18:03,800 --> 01:18:07,800
too homogeneously.
AII don't want to watch anything

1313
01:18:07,800 --> 01:18:09,560
made by.
Yeah, I saw one of those things

1314
01:18:09,560 --> 01:18:13,600
today.
I saw, I saw a video that had a

1315
01:18:14,240 --> 01:18:16,320
notebook LM.
You know how you can do the

1316
01:18:16,320 --> 01:18:20,280
podcast thing with notebook LM
And so it had the notebook.

1317
01:18:21,080 --> 01:18:24,120
It's actually AI was going to
show it to you, but boring.

1318
01:18:24,400 --> 01:18:28,960
But it's it's a a video about
the Jeff Bezos.

1319
01:18:29,240 --> 01:18:31,600
What's it called PI0?
Have you seen this thing yet?

1320
01:18:31,600 --> 01:18:37,080
Jeff Bezos backed it.
So Pi 0 is basically giving

1321
01:18:37,080 --> 01:18:41,000
robots brains that you can tell
a robot what you want it to do.

1322
01:18:41,000 --> 01:18:44,760
And the video shows like folding
clothes and lot of house chores,

1323
01:18:44,760 --> 01:18:47,000
right?
Household chores, breaking and

1324
01:18:47,080 --> 01:18:50,280
folding clothes and cleaning up
and doing things like that.

1325
01:18:50,560 --> 01:18:55,920
But I mean, Bezos round I think
headed up around of like 1/2 a

1326
01:18:55,920 --> 01:18:59,920
billion dollars in funding or
something for this, this π zero

1327
01:18:59,920 --> 01:19:03,800
thing.
But the, the video is a video of

1328
01:19:03,800 --> 01:19:07,040
this robot doing its thing.
And the audio in the background

1329
01:19:07,040 --> 01:19:12,960
is an LM notebook, LM podcast.
And there's a few commenters who

1330
01:19:12,960 --> 01:19:17,000
have like 1 follower who are
like, how dare you insult me

1331
01:19:17,000 --> 01:19:20,960
with an AI generated podcast.
Really.

1332
01:19:21,480 --> 01:19:23,160
You know, are you serious right
now?

1333
01:19:23,400 --> 01:19:26,440
I think a lot of people would be
perfectly happy to listen to the

1334
01:19:26,480 --> 01:19:28,960
Notebook LM podcast.
Their voices are great.

1335
01:19:29,160 --> 01:19:32,240
I actually find that they they
have a very interesting dynamic,

1336
01:19:32,240 --> 01:19:38,000
those I love it, you know, P
Here's what you know a lot of

1337
01:19:38,000 --> 01:19:40,800
audiences are perfectly happy
with good enough.

1338
01:19:41,080 --> 01:19:42,320
Yeah.
Right.

1339
01:19:42,600 --> 01:19:48,000
And I and I and I am constantly
seeking to help escalate

1340
01:19:48,000 --> 01:19:52,240
audiences to a point where they
can see something extraordinary

1341
01:19:52,240 --> 01:19:54,960
and really be moved by it.
That's where I am as an artist.

1342
01:19:55,320 --> 01:19:59,040
But but but but as a
professional designer and

1343
01:19:59,040 --> 01:20:02,520
creative and strategist, who
advises?

1344
01:20:03,080 --> 01:20:07,360
On these topics and who's I
spend a lot of time thinking

1345
01:20:07,360 --> 01:20:10,280
about audience segmentation and
demographics.

1346
01:20:10,320 --> 01:20:13,720
Yeah, what do audiences want,
right.

1347
01:20:14,120 --> 01:20:17,920
And I could tell you that a lot
of audiences are perfectly happy

1348
01:20:17,920 --> 01:20:21,600
with good enough.
Now there are, there is an

1349
01:20:21,600 --> 01:20:27,240
audience segment, there is a
demographic that appreciates,

1350
01:20:27,240 --> 01:20:32,800
handcrafted, they appreciate and
they want and in this era of AI

1351
01:20:32,800 --> 01:20:38,680
they are going to seek out real
human created art just kind of

1352
01:20:38,680 --> 01:20:40,680
like at the independent film
movement.

1353
01:20:40,680 --> 01:20:41,720
Right.
Yeah, yeah, like.

1354
01:20:41,800 --> 01:20:43,560
People.
People have an appreciation for

1355
01:20:43,560 --> 01:20:48,040
smaller stories told more
intimately in an intimate way.

1356
01:20:48,240 --> 01:20:52,920
There's always going to be a
place for real human handcrafted

1357
01:20:52,920 --> 01:20:55,600
art.
And by the way, that's probably

1358
01:20:55,600 --> 01:20:58,960
in the marketplace going to
escalate to a fairly expensive

1359
01:20:58,960 --> 01:21:00,560
place where people are going to
be.

1360
01:21:00,640 --> 01:21:04,600
Those people who want that are
going to be willing to pay more.

1361
01:21:04,920 --> 01:21:07,840
To connect, yeah.
You know, absolutely going to go

1362
01:21:07,840 --> 01:21:09,840
out of existence.
And here's the other thing I

1363
01:21:09,840 --> 01:21:13,680
have to say, Mode Studios, since
we started widely deploying AI,

1364
01:21:14,120 --> 01:21:18,880
we haven't laid people off.
We've hired more people.

1365
01:21:18,880 --> 01:21:23,080
We have more designers working
here then now that we're two

1366
01:21:23,080 --> 01:21:26,880
years into deploying AI in all
kinds of places than we did

1367
01:21:26,880 --> 01:21:30,320
before.
So it seems to me that like the

1368
01:21:30,360 --> 01:21:34,760
evidence I have in hand isn't
that it's eliminating jobs, it's

1369
01:21:34,760 --> 01:21:37,760
changing jobs.
You know, with the with the

1370
01:21:37,800 --> 01:21:42,360
advent of things like follow me
and Robos, you know, robospots,

1371
01:21:43,080 --> 01:21:48,680
we no longer have rigs full of
stagehands in spot baskets, you

1372
01:21:48,680 --> 01:21:53,440
know, move.
That job has changed because of

1373
01:21:53,440 --> 01:21:55,840
technology, but it hasn't gone
away.

1374
01:21:56,000 --> 01:22:00,360
Moving lights didn't get rid of
follow spots entirely, right?

1375
01:22:00,760 --> 01:22:03,800
Yeah.
You know, things, technology

1376
01:22:03,800 --> 01:22:07,840
changes the way things are made.
It changes the way that that

1377
01:22:07,840 --> 01:22:12,440
vocations happen and it changes
what kind of careers are

1378
01:22:12,440 --> 01:22:16,440
possible, you know, But the fact
of the matter is that right now

1379
01:22:16,440 --> 01:22:20,440
in the marketplace, AI, you
know, we are searching for

1380
01:22:20,440 --> 01:22:23,560
people with AI talents.
We also want those people to be

1381
01:22:23,560 --> 01:22:29,040
talented in using software like
Unreal Engine or 3D software or

1382
01:22:29,040 --> 01:22:32,440
other complimentary tool sets
that allow us to put all these

1383
01:22:32,440 --> 01:22:36,520
pieces together and make great.
Contact but going back to like

1384
01:22:36,520 --> 01:22:42,520
this whole fear thing and you
know, like the common thing in

1385
01:22:42,520 --> 01:22:45,040
our industry is just like I
don't want anything to do with

1386
01:22:45,040 --> 01:22:47,000
this AI.
It's coming from my job and it's

1387
01:22:47,000 --> 01:22:51,520
going to take away my job and
you know, I used to my my go to

1388
01:22:51,520 --> 01:22:55,160
statement on that used to be you
know, no, it's really not.

1389
01:22:55,160 --> 01:22:58,840
It's going to provide you tools
to make your job better and to

1390
01:22:58,840 --> 01:23:03,200
make you more functional and
more efficient.

1391
01:23:03,200 --> 01:23:07,240
And and so that at the sort of
lowest level, that is what it's

1392
01:23:07,240 --> 01:23:09,920
going to do.
It's, it's today, you know, like

1393
01:23:10,520 --> 01:23:14,640
I had Kadena on the podcast and
we talked about AI at one point

1394
01:23:14,640 --> 01:23:18,440
and, you know, he grabbed the
tool that I had shared with him

1395
01:23:18,440 --> 01:23:22,640
and went out and started using
it to create JavaScript for him

1396
01:23:23,080 --> 01:23:25,040
on some of the things that he
was doing and it.

1397
01:23:25,040 --> 01:23:27,160
Very good at creating code.
Correct.

1398
01:23:27,160 --> 01:23:31,840
Yeah, so, so you know that I
think at the very lowest level,

1399
01:23:31,840 --> 01:23:33,200
that's the stuff that it's going
to do.

1400
01:23:33,200 --> 01:23:35,320
It's going to help you.
But when you look at these

1401
01:23:35,320 --> 01:23:41,000
robots, you know, both Elon's
robot, this, this Pi 01, there's

1402
01:23:41,000 --> 01:23:44,200
a Chinese one that I read about
that's incredible and very close

1403
01:23:44,200 --> 01:23:47,120
to launching right now.
And you know, these things are

1404
01:23:47,120 --> 01:23:51,600
twenty, 30,000 bucks and they
can be housekeepers, they can

1405
01:23:51,600 --> 01:23:56,120
be, you know, they can work in
your shop doing tedious things.

1406
01:23:56,520 --> 01:24:00,320
I'm sure at some point, you
know, they'll be able to work on

1407
01:24:00,320 --> 01:24:03,760
lighting consoles or or sound
consoles or whatever it is.

1408
01:24:04,160 --> 01:24:07,960
Cuz I used to say, you know, the
people who are always gonna be

1409
01:24:07,960 --> 01:24:10,760
safe no matter what are the
plumbers and the, you know, the

1410
01:24:10,760 --> 01:24:14,600
people who do physical jobs.
Yeah, I don't think that so much

1411
01:24:14,640 --> 01:24:16,400
anymore.
Like I think that's agree with.

1412
01:24:16,680 --> 01:24:17,920
You.
I think that, I think there will

1413
01:24:17,920 --> 01:24:20,280
be a massive deployment of
automation.

1414
01:24:20,560 --> 01:24:21,880
Yeah.
Of robotics, yeah.

1415
01:24:21,880 --> 01:24:23,360
I think it is gonna change
things.

1416
01:24:23,800 --> 01:24:27,400
I think before we see robots
behind lighting desks, the first

1417
01:24:27,400 --> 01:24:32,440
thing we're going to see is
lighting desks deploying more AI

1418
01:24:32,440 --> 01:24:36,480
functionality.
How you approach a console to

1419
01:24:36,480 --> 01:24:40,840
create effects, for instance,
Yeah, or even like you is like,

1420
01:24:41,120 --> 01:24:44,680
you know, I know because I'm.
I've been talking with people at

1421
01:24:44,680 --> 01:24:48,440
various of the companies that
that, that are making lighting

1422
01:24:48,440 --> 01:24:49,400
console.
Yeah, me too.

1423
01:24:49,480 --> 01:24:53,560
They're already working on being
able to have a prompting model

1424
01:24:53,560 --> 01:24:54,760
for lighting.
Yeah.

1425
01:24:54,760 --> 01:24:58,560
Like, so rather than saying, oh,
I want group 5 at 50 in Group,

1426
01:24:58,960 --> 01:25:03,440
you know, eight at 75 in Group
1000, and you're working towards

1427
01:25:03,440 --> 01:25:08,160
making, you know, a blue
mysterious night time queue,

1428
01:25:08,360 --> 01:25:10,440
you're going to be able to
prompt the console.

1429
01:25:10,440 --> 01:25:13,080
You're going to be able to say,
you know, create a blue

1430
01:25:13,080 --> 01:25:16,200
mysterious night time Exactly.
Focus mainly on stage, right?

1431
01:25:16,360 --> 01:25:18,840
Speaking in English going to be
able to do that.

1432
01:25:18,840 --> 01:25:19,760
Right.
Yeah, yeah.

1433
01:25:19,840 --> 01:25:21,520
Look, there's going to be an
impact.

1434
01:25:22,600 --> 01:25:26,160
I, you know, and I, I think, you
know, we're going to see it.

1435
01:25:26,400 --> 01:25:28,760
We're going to see AI deployed
in a whole bunch of

1436
01:25:28,760 --> 01:25:31,120
technological ways.
Yeah.

1437
01:25:31,760 --> 01:25:33,920
This is inevitable.
And I mean, I think that I

1438
01:25:33,920 --> 01:25:35,440
understand the people pushing
back.

1439
01:25:35,440 --> 01:25:38,160
Can I understand the fear?
Change is hard.

1440
01:25:38,360 --> 01:25:42,520
Humans are not wired to be
constantly adapting.

1441
01:25:42,520 --> 01:25:48,360
We crave finding cycles, finding
rhythms of things we can do.

1442
01:25:48,360 --> 01:25:51,480
You know, we're, we're going to
plant the seeds, take care of

1443
01:25:51,480 --> 01:25:55,200
the crops, harvest the seeds,
make the food, put the, you

1444
01:25:55,200 --> 01:25:57,720
know, like we, we have certain
things that are genetically

1445
01:25:57,720 --> 01:26:01,800
coded in US and behaviorally
coded in US and holy crap, we

1446
01:26:01,800 --> 01:26:05,160
are in a hockey.
Stick curve big time, yeah.

1447
01:26:05,160 --> 01:26:08,640
Technological change.
That's really uncomfortable,

1448
01:26:08,640 --> 01:26:11,920
right?
But at the same time, like I'm

1449
01:26:11,920 --> 01:26:16,240
so excited about it because of
the control that it gives the

1450
01:26:16,240 --> 01:26:19,080
humans.
So like for example, you know, I

1451
01:26:19,080 --> 01:26:21,760
don't remember what it was, but
last week there was something

1452
01:26:21,760 --> 01:26:24,480
bothering me health wise.
You know, I just turned 60, so

1453
01:26:24,480 --> 01:26:26,040
there's always something
bothering me.

1454
01:26:26,480 --> 01:26:30,840
And you know, I would have
normally gone to the doctor and

1455
01:26:30,840 --> 01:26:32,840
said, here's what's happening.
Bah, Bah, Bah.

1456
01:26:32,840 --> 01:26:34,640
What should I do?
Should I take a different

1457
01:26:34,640 --> 01:26:36,120
vitamin?
Should I do this?

1458
01:26:36,120 --> 01:26:38,320
Like what, what is it that's
causing this problem?

1459
01:26:38,720 --> 01:26:43,960
And instead I use ChatGPT.
And within 1015 minutes of

1460
01:26:43,960 --> 01:26:47,160
prompting and, and going back
and forth a little bit, I had

1461
01:26:47,160 --> 01:26:49,280
the answers and they were really
good answers.

1462
01:26:49,280 --> 01:26:52,800
And it worked, you know?
And so some people will say

1463
01:26:52,800 --> 01:26:54,680
you're a maniac, that's
dangerous.

1464
01:26:54,680 --> 01:26:58,360
You know, how dare you trust
your health to what the hell do

1465
01:26:58,360 --> 01:27:00,960
you think doctors have been
doing for the last 20 years?

1466
01:27:01,160 --> 01:27:03,000
The same thing they.
Go into Google.

1467
01:27:03,240 --> 01:27:06,920
They either know things or they
or they go to their bookshelf.

1468
01:27:06,920 --> 01:27:08,520
Yeah.
Like, and they like.

1469
01:27:08,520 --> 01:27:11,280
Oh.
Judging by your symptoms, yeah.

1470
01:27:11,560 --> 01:27:15,960
I I I'm pretty sure that you
have a violent case of herpes,

1471
01:27:15,960 --> 01:27:18,600
Marcel.
No, but I mean, all we're

1472
01:27:18,600 --> 01:27:22,240
dealing with, if we do it
ourselves with Chachi, it's just

1473
01:27:22,240 --> 01:27:25,280
a larger language model than
what the doctor's been using,

1474
01:27:25,280 --> 01:27:26,240
you know well.
Exactly.

1475
01:27:26,240 --> 01:27:28,760
I'm like just mentioned that
they're really good at code.

1476
01:27:29,040 --> 01:27:31,840
What?
What AIS are really good as, as

1477
01:27:31,840 --> 01:27:33,880
anything.
That's a question of deriving a

1478
01:27:33,880 --> 01:27:36,240
result from aggregated
information.

1479
01:27:36,240 --> 01:27:38,280
Correct.
Super good at yeah.

1480
01:27:38,280 --> 01:27:39,000
Right.
Correct.

1481
01:27:39,280 --> 01:27:41,480
So the nature of jobs is going
to change.

1482
01:27:41,480 --> 01:27:45,120
You know, is the lighting
programming job in danger?

1483
01:27:45,280 --> 01:27:50,280
It's in danger of changing.
Ultimately, we might move the

1484
01:27:50,400 --> 01:27:53,720
ultimately, and I'm talking, you
know, probably maybe a decade

1485
01:27:53,720 --> 01:27:57,960
out, we might be at a place
where, you know, a production

1486
01:27:57,960 --> 01:28:02,800
designer is now sitting next to
a director and prompting the

1487
01:28:02,800 --> 01:28:07,360
media servers and prompting the
light just verbally talking to

1488
01:28:07,360 --> 01:28:08,720
his interface.
Yeah.

1489
01:28:09,000 --> 01:28:11,640
And there aren't as many
programmers involved.

1490
01:28:11,640 --> 01:28:14,120
Yeah, I think.
That that might happen, yeah.

1491
01:28:15,800 --> 01:28:18,800
It's hard to know.
It's hard to know where it's

1492
01:28:18,840 --> 01:28:23,160
going.
I can tell you that right now it

1493
01:28:23,160 --> 01:28:26,440
hasn't changed a whole lot.
It is going to change things

1494
01:28:26,520 --> 01:28:28,680
moving forward.
It's even going to change them

1495
01:28:28,680 --> 01:28:31,360
dramatically.
We talked about at the beginning

1496
01:28:31,360 --> 01:28:34,040
of the episode about how my
daughter just went and got deep

1497
01:28:34,040 --> 01:28:36,080
training in MA Three.
Yeah.

1498
01:28:36,160 --> 01:28:39,760
You know, judging by the
crawling pace of lighting

1499
01:28:39,840 --> 01:28:42,160
console innovation, correct.
Yeah, correct.

1500
01:28:43,000 --> 01:28:47,400
We might be also here, you know
our industry is small enough

1501
01:28:47,400 --> 01:28:49,840
where it's relationship based
and all of those things.

1502
01:28:49,840 --> 01:28:52,400
I don't think anybody's going to
want to be the first console

1503
01:28:52,400 --> 01:28:56,240
that program programs itself,
you know, because that's a scary

1504
01:28:56,240 --> 01:28:59,280
thought because you're going to
be boycotted and cancelled and

1505
01:28:59,280 --> 01:29:01,680
all of those things when you're
the first one coming out.

1506
01:29:02,200 --> 01:29:07,080
There is that you have to get by
that resistance to really bring.

1507
01:29:07,080 --> 01:29:10,120
Into there will be resistance.
Time there are going to be

1508
01:29:10,120 --> 01:29:13,680
people who are super interested
in that and who are even if

1509
01:29:13,680 --> 01:29:17,520
they're not people working at
the, you know, ETC or MA

1510
01:29:17,520 --> 01:29:19,800
lighting or stuff.
There are people who are

1511
01:29:19,960 --> 01:29:23,480
interested in I've seen a
product, I get assaulted with it

1512
01:29:23,480 --> 01:29:26,360
all the time on my social feeds.
Are these guys that have made a

1513
01:29:26,360 --> 01:29:28,840
little box?
Yeah, that was the one that got

1514
01:29:28,840 --> 01:29:31,920
blasted at LDI last year, Yeah.
And I think they did get

1515
01:29:31,920 --> 01:29:33,160
blasted.
But you know what?

1516
01:29:33,800 --> 01:29:36,000
That company is going
gangbusters.

1517
01:29:36,000 --> 01:29:37,560
Yeah.
They are selling lots of those

1518
01:29:37,560 --> 01:29:39,920
little boxes to little clubs.
Yeah.

1519
01:29:40,080 --> 01:29:43,240
Does it make sense, you know, to
DJs and to people playing in

1520
01:29:43,240 --> 01:29:47,880
certain things who just want
some basic, you know, generative

1521
01:29:47,880 --> 01:29:51,960
lighting to happen on tempo and,
yeah, you know, moves of the

1522
01:29:51,960 --> 01:29:54,480
thing?
There is a marketplace for it.

1523
01:29:54,680 --> 01:29:57,320
So one other thing.
Stopping, you know, trying to

1524
01:29:57,320 --> 01:30:00,920
stop technical innovation from
changing what we know is like

1525
01:30:00,920 --> 01:30:04,800
trying to is like going to the
beach with a mop and trying to

1526
01:30:04,800 --> 01:30:06,440
keep the waves.
It's just it's.

1527
01:30:06,600 --> 01:30:07,920
You're right.
No, you're right.

1528
01:30:07,920 --> 01:30:11,440
There's no stopping this thing.
Like you either get on the train

1529
01:30:11,440 --> 01:30:14,240
or it runs over you.
And we decided to get on the

1530
01:30:14,240 --> 01:30:17,640
train and, and I have had, I
have had discussions with people

1531
01:30:18,080 --> 01:30:22,680
who literally have come at me
and said you're a vile, you are,

1532
01:30:22,880 --> 01:30:25,440
you are architecting the end of
art.

1533
01:30:25,600 --> 01:30:28,840
Well, while that may be true, I
and I happen to be with you on

1534
01:30:28,840 --> 01:30:31,840
the AI thing.
Like circumstances beyond my

1535
01:30:31,840 --> 01:30:34,400
control and time and the advance
of technology.

1536
01:30:34,600 --> 01:30:37,120
I've always been an artist
interested in deploying

1537
01:30:37,120 --> 01:30:39,240
technology since the very
beginning.

1538
01:30:39,240 --> 01:30:43,920
Yeah, since the very beginning,
I and I can't ignore this.

1539
01:30:43,920 --> 01:30:47,520
I want to, I am curious.
I want to understand this stuff.

1540
01:30:47,520 --> 01:30:49,600
I want to understand how it
works in the process.

1541
01:30:49,600 --> 01:30:50,800
Well, I want to understand how
I.

1542
01:30:50,800 --> 01:30:53,960
Remember how bad, how hard
people fought against time code,

1543
01:30:53,960 --> 01:30:57,920
how hard people fought against,
you know, programming cues and

1544
01:30:57,920 --> 01:30:59,920
then running them in sequence
and things like that.

1545
01:30:59,920 --> 01:31:04,680
Like, you know, advancements
always tend to have people

1546
01:31:04,680 --> 01:31:06,040
fighting on the other side,
right?

1547
01:31:06,040 --> 01:31:08,760
It you run into to resistance
always.

1548
01:31:09,120 --> 01:31:11,280
But one of the things I want to
spend a few minutes talking

1549
01:31:11,280 --> 01:31:14,840
about because I know I don't
have you all day is this this

1550
01:31:14,840 --> 01:31:19,840
term prompt engineering.
And so you know, I know that

1551
01:31:19,920 --> 01:31:24,400
from my own experience when you
take the time to learn how to

1552
01:31:24,400 --> 01:31:27,720
and you can go out and Google
and there's 1000 YouTube videos

1553
01:31:27,720 --> 01:31:31,520
and probably 100,000 TikTok
videos and stuff on how to do

1554
01:31:31,520 --> 01:31:33,720
the best prompt engineering and
all that stuff.

1555
01:31:34,120 --> 01:31:37,360
But one of the things I've
discovered recently, and I'm

1556
01:31:37,360 --> 01:31:41,240
sure you're very aware of it, is
anthropic.

1557
01:31:41,840 --> 01:31:46,640
Claude writes prompts.
So you can basically tell Claude

1558
01:31:46,640 --> 01:31:52,160
to write a prompt for ChatGPT to
do blah blah blah whatever it

1559
01:31:52,160 --> 01:31:55,600
is, and you speak to it in
English, it writes the prompt in

1560
01:31:55,600 --> 01:32:00,920
prompt world and then you copy
and paste it into ChatGPT and it

1561
01:32:00,920 --> 01:32:04,880
works amazingly well.
Yeah, and in fact, you should

1562
01:32:04,880 --> 01:32:09,440
know that if you're using Dolly
3, which is Open a Eyes image

1563
01:32:09,440 --> 01:32:15,200
generator, when you prompt Dolly
three, it sends your prompt to

1564
01:32:15,200 --> 01:32:18,600
ChatGPT for ChatGPT to rewrite
it.

1565
01:32:19,120 --> 01:32:20,520
Really.
Better prompt.

1566
01:32:20,520 --> 01:32:22,720
Oh, that's interesting.
And this is all behind the

1567
01:32:22,720 --> 01:32:23,480
curtain.
Yeah.

1568
01:32:23,480 --> 01:32:26,240
You don't see it.
And then ChatGPT feeds the

1569
01:32:26,240 --> 01:32:28,600
better prompt.
Back to Dolly three.

1570
01:32:28,760 --> 01:32:31,680
Yeah, You know, Yeah.
And the, you know, and I know

1571
01:32:31,680 --> 01:32:36,400
some people who, who deploy this
in a way I use, you know, the

1572
01:32:36,400 --> 01:32:38,680
tools I use, like I'm a big mid
journey user.

1573
01:32:38,680 --> 01:32:42,280
My workflow is I tend to make
images in mid journey and then

1574
01:32:42,280 --> 01:32:47,080
if I need to then move to video,
I'll take anchor images that,

1575
01:32:47,200 --> 01:32:49,880
that, that are like the look in
the feel, the aesthetic in the

1576
01:32:49,880 --> 01:32:51,400
composition.
I want to make them in mid

1577
01:32:51,400 --> 01:32:54,440
journey.
And then I go into Runways Gen.

1578
01:32:54,440 --> 01:33:00,040
3, which is a fantastic video
creation tool.

1579
01:33:00,280 --> 01:33:03,120
Yeah.
And I will use an image prompt.

1580
01:33:03,120 --> 01:33:05,520
I will use the picture I made in
mid Journey.

1581
01:33:05,520 --> 01:33:08,680
I will put it into Runway.
I will add a little bit of

1582
01:33:08,680 --> 01:33:10,800
detail.
And then Runway makes a piece of

1583
01:33:10,800 --> 01:33:11,760
video.
Yeah.

1584
01:33:11,960 --> 01:33:15,280
The thing about prompt
engineering and like and like

1585
01:33:15,280 --> 01:33:20,720
using AI to help you prompt
other AI is a the models move

1586
01:33:20,720 --> 01:33:22,360
really fast, right?
Yeah.

1587
01:33:22,360 --> 01:33:26,960
And how you prompted, like for
instance, in Mid Journey version

1588
01:33:26,960 --> 01:33:33,120
4, you benefited from making
really long prompts, right?

1589
01:33:33,120 --> 01:33:35,960
Like, like the more description
you gave it, the better.

1590
01:33:36,480 --> 01:33:40,480
When Mid Journey went to version
5, because of the way the model

1591
01:33:40,480 --> 01:33:43,240
worked, it preferred you to be
very direct.

1592
01:33:43,520 --> 01:33:46,520
Like it wanted you to deliver
like a couple of sentences and

1593
01:33:46,520 --> 01:33:50,640
then it filled in the gaps.
You know, version 6 works in a

1594
01:33:50,640 --> 01:33:55,000
very different way, which is
kind of structured more in a way

1595
01:33:55,520 --> 01:34:00,000
of like version six wants you to
work in a in a very specific

1596
01:34:00,320 --> 01:34:04,440
like it wants to you to start
with style, tell it about

1597
01:34:04,440 --> 01:34:09,760
subject, give it detail about
settings, then information about

1598
01:34:09,760 --> 01:34:13,880
composition, then information
maybe about exterior forces like

1599
01:34:13,880 --> 01:34:15,320
lighting.
Yeah.

1600
01:34:15,520 --> 01:34:22,320
And then add additional info
like commands about aspect ratio

1601
01:34:22,320 --> 01:34:25,440
and stuff like that.
OK now it has format it wants

1602
01:34:25,440 --> 01:34:30,640
you to follow and if you feed it
a ChatGPT prompt into version 6

1603
01:34:30,640 --> 01:34:33,000
and mid journey your results
might be hit or.

1604
01:34:33,000 --> 01:34:37,840
Miss, you're going to get crap.
ChatGPT has not caught up with

1605
01:34:38,680 --> 01:34:43,440
the format that mid journey.
Prefers, right, So when we're

1606
01:34:43,440 --> 01:34:47,720
talking about prompt prompt
engineering, so like ChatGPT, if

1607
01:34:47,720 --> 01:34:51,720
you go to it and you just say,
write me a blog article about,

1608
01:34:51,800 --> 01:34:56,680
you know, the live events
lighting industry, you're going

1609
01:34:56,680 --> 01:34:58,680
to get shit right.
You're going to just get.

1610
01:34:58,680 --> 01:35:03,440
You're going to need to do, and
I do use ChatGPT to help me

1611
01:35:03,440 --> 01:35:06,600
write things, Yeah.
But usually I start with

1612
01:35:06,600 --> 01:35:11,560
something like, you know, if I
and then I'll feed it to ChatGPT

1613
01:35:11,560 --> 01:35:13,280
to help me.
That's me too.

1614
01:35:13,520 --> 01:35:15,280
Polish it and.
Or vice versa.

1615
01:35:15,280 --> 01:35:18,680
Or I'll do it the opposite way.
Out of ChatGPT and then I go

1616
01:35:18,680 --> 01:35:20,880
back and that's.
What I do and then yeah, yeah,

1617
01:35:20,880 --> 01:35:21,400
that's what.
I do.

1618
01:35:21,400 --> 01:35:24,600
My work with the large language
models is very back and forth

1619
01:35:24,600 --> 01:35:27,320
iterative.
Yeah, like if you just say give

1620
01:35:27,320 --> 01:35:31,320
me a brilliant article, you
know, about the current state of

1621
01:35:31,320 --> 01:35:34,080
the lighting industry, you're
going to get garbage.

1622
01:35:34,320 --> 01:35:37,680
Well, you know, it's like I had
people tell me that Grok in the

1623
01:35:37,680 --> 01:35:43,560
fun mode on Grok, it wrote a lot
better things than ChatGPT where

1624
01:35:43,560 --> 01:35:46,600
you could actually copy and
paste right out of Grok and and

1625
01:35:46,600 --> 01:35:48,920
use that article.
I don't think so.

1626
01:35:48,920 --> 01:35:54,320
Like Grok, Grok tries to use AI
humor and it's just so stupid.

1627
01:35:54,360 --> 01:35:58,080
You know, it's just like it,
it's nonsensical, right?

1628
01:35:58,080 --> 01:35:59,960
It just doesn't work, doesn't
work.

1629
01:36:00,080 --> 01:36:04,080
It makes me throw up, not laugh,
but but yeah.

1630
01:36:04,080 --> 01:36:10,720
So I mean, you know, the thing
about AI, I guess in closing is

1631
01:36:10,720 --> 01:36:14,680
that that I say to friends all
the time, just start using it

1632
01:36:14,680 --> 01:36:17,800
like find if you want, like I
tell people and I'm telling

1633
01:36:17,800 --> 01:36:19,600
people again here.
And I'm sure you would do the

1634
01:36:19,600 --> 01:36:23,480
same if you come up to me at LDI
or you send me an e-mail or a

1635
01:36:23,480 --> 01:36:26,640
text message and say, what are
you using for spreadsheets?

1636
01:36:26,640 --> 01:36:29,240
What are you using for writing
articles?

1637
01:36:29,240 --> 01:36:31,680
What are you using for images?
What are you using for this or

1638
01:36:31,680 --> 01:36:33,720
that?
I'm happy to share that

1639
01:36:33,720 --> 01:36:36,240
information with you, you know,
Yeah.

1640
01:36:36,240 --> 01:36:39,600
I mean, it's it's like, like
people ask me, you know, it's

1641
01:36:39,600 --> 01:36:41,640
just you and Sarah doing the
podcast.

1642
01:36:41,640 --> 01:36:45,240
How does Sarah do so many posts?
Like she's always posting

1643
01:36:45,440 --> 01:36:48,920
YouTube Shorts and Instagram
reels and does she not sleep?

1644
01:36:49,400 --> 01:36:51,520
And the bottom line is she
sleeps really well.

1645
01:36:51,520 --> 01:36:54,000
She's probably sleeping right
now while we're recording this.

1646
01:36:54,400 --> 01:36:58,760
And but we use AI apps to do
some of those things.

1647
01:36:58,760 --> 01:37:01,640
So like there's, there's one
called Opus clips that we use

1648
01:37:01,640 --> 01:37:06,360
pretty religiously, which takes
a YouTube video of our podcast

1649
01:37:06,720 --> 01:37:09,080
and it grabs like 20 or 30 clips
out of it.

1650
01:37:09,080 --> 01:37:11,560
And then you look at them and
you find the best ones.

1651
01:37:11,560 --> 01:37:15,040
But it's looking for things that
might become viral, things that

1652
01:37:15,040 --> 01:37:18,440
make a lot of sense, things
that, you know, it thinks people

1653
01:37:18,440 --> 01:37:22,080
are going to relate to and, and
it works pretty well.

1654
01:37:22,080 --> 01:37:25,880
And, you know, so there's a lot
of really logical, really good

1655
01:37:25,880 --> 01:37:29,440
things that I use on a daily
basis that I think people would

1656
01:37:29,440 --> 01:37:33,360
benefit from and at least get
comfortable with it so that when

1657
01:37:33,360 --> 01:37:37,640
it moves a little deeper into
your world, you know, you're not

1658
01:37:37,640 --> 01:37:40,320
afraid of it.
That's my advice to people too.

1659
01:37:40,320 --> 01:37:43,640
It's, it's like, look, if you're
a professional technician, if

1660
01:37:43,640 --> 01:37:46,480
you're a, if you're a designer,
if you're a creative director or

1661
01:37:46,480 --> 01:37:50,880
creative producer, you can make
the global decision, do I want

1662
01:37:50,880 --> 01:37:54,480
to use this stuff or not?
And I honor that, whatever your

1663
01:37:54,480 --> 01:37:57,360
choices are.
But the fact is that this

1664
01:37:57,360 --> 01:37:59,920
technology is coming.
It's not coming.

1665
01:37:59,920 --> 01:38:02,480
It's here.
If there's only going to be more

1666
01:38:02,480 --> 01:38:04,600
of it, it's only going to be
more pervasive.

1667
01:38:04,920 --> 01:38:08,360
Yeah, you can, if you're
interested in participating in

1668
01:38:08,360 --> 01:38:10,920
this industry and being a
professional in this industry,

1669
01:38:11,320 --> 01:38:15,600
choose to learn about it and use
it or not.

1670
01:38:16,040 --> 01:38:18,120
Yeah.
And, and again, I honor whatever

1671
01:38:18,120 --> 01:38:21,160
decision you want to make.
I've always had the

1672
01:38:21,160 --> 01:38:24,640
predisposition to want to figure
out how to use new technology

1673
01:38:24,640 --> 01:38:27,240
and what I do.
I was one of the first people

1674
01:38:27,240 --> 01:38:30,680
that figured out media servers
and using media servers.

1675
01:38:31,200 --> 01:38:34,480
You know, I was, I, I, you know,
I've been along for the ride in

1676
01:38:34,480 --> 01:38:38,240
terms of like, you know, moving
lights coming into the picture

1677
01:38:38,240 --> 01:38:41,680
and then, and then, you know,
and then the advent of LED and

1678
01:38:41,760 --> 01:38:44,880
now this diversity of fixture
types that do all these kind of

1679
01:38:44,880 --> 01:38:48,000
different things.
I've always been an artist that

1680
01:38:48,000 --> 01:38:50,720
wanted to incorporate technology
into my design.

1681
01:38:50,720 --> 01:38:53,040
So I'm curious about AI.
Yeah.

1682
01:38:53,120 --> 01:38:55,920
And since the beginning and,
and, and as you heard in the

1683
01:38:55,920 --> 01:38:59,960
podcast, you know, we've been
deploying it really since like

1684
01:39:00,400 --> 01:39:01,080
2000.
That's.

1685
01:39:01,080 --> 01:39:02,680
Crazy.
Yeah, I didn't know that.

1686
01:39:02,680 --> 01:39:07,400
That's such a cool story.
So I, you know, I'm not, I'm not

1687
01:39:07,400 --> 01:39:08,920
slamming the brakes.
Yeah.

1688
01:39:09,040 --> 01:39:11,600
And keeping up with it.
I spend a lot of time educating

1689
01:39:11,600 --> 01:39:14,440
myself on it.
We've certainly been able to do

1690
01:39:14,440 --> 01:39:17,160
things we've never been able to
do as a creative agency.

1691
01:39:17,160 --> 01:39:20,040
We've been able to swing way
above our weight because we've

1692
01:39:20,040 --> 01:39:23,560
been using using AI to help us
put together creative pitches,

1693
01:39:24,040 --> 01:39:27,480
to help us put strategy stuff
together to give our work

1694
01:39:27,520 --> 01:39:31,160
underpinnings that are stronger.
We've been able to compete with

1695
01:39:31,400 --> 01:39:35,400
agencies with way bigger
resources than ours because we

1696
01:39:35,400 --> 01:39:36,720
have.
That makes so much sense.

1697
01:39:36,720 --> 01:39:38,320
Listen to us as a small
business.

1698
01:39:38,320 --> 01:39:40,880
It's your superpower.
It's your superpower.

1699
01:39:41,120 --> 01:39:43,280
I'm interested in it.
I'm curious about it.

1700
01:39:43,280 --> 01:39:46,920
My feeling on it is if I'm
talking to people that are new

1701
01:39:46,920 --> 01:39:49,640
or just coming into the
business, learn about it.

1702
01:39:49,800 --> 01:39:52,160
You can choose.
Whether or not you want to use

1703
01:39:52,160 --> 01:39:53,840
it.
But learn about it.

1704
01:39:53,840 --> 01:39:57,120
Learn how it is used.
Learn how you can deploy it.

1705
01:39:57,200 --> 01:39:59,520
Learn how you can deploy it to
sell yourself.

1706
01:39:59,520 --> 01:40:01,960
Sell your work.
Improve your work.

1707
01:40:02,040 --> 01:40:06,160
Yeah, be more nimble.
Save time, right?

1708
01:40:06,160 --> 01:40:08,760
Save money.
Well, there's a part of your job

1709
01:40:08,760 --> 01:40:13,280
that you hate doing and there's
a really good chance that

1710
01:40:13,320 --> 01:40:18,040
there's AI tools already in
place that will enable you to do

1711
01:40:18,040 --> 01:40:23,040
less of that, whatever that is.
So, you know, that's, that's the

1712
01:40:23,040 --> 01:40:27,240
bottom line for me is, is AI,
like when I saw the, the clothes

1713
01:40:27,240 --> 01:40:32,640
folding thing from, from Bezos
yesterday, I was like, oh,

1714
01:40:32,640 --> 01:40:35,320
really?
It can fold your clothes for

1715
01:40:35,320 --> 01:40:36,880
you.
I want one of those, you know,

1716
01:40:37,360 --> 01:40:40,680
that's perfect.
So are you doing anything at LDI

1717
01:40:40,680 --> 01:40:44,840
as far as like are you doing any
any speeches or talks or

1718
01:40:44,840 --> 01:40:48,040
anything on on AI stuff?
I will be I will be talking on

1719
01:40:48,040 --> 01:40:49,320
the 10th.
OK.

1720
01:40:49,640 --> 01:40:51,680
Along with some of my friends
from Tate.

1721
01:40:51,880 --> 01:40:54,760
OK about placemaking.
We're gonna have a panel that's

1722
01:40:54,760 --> 01:41:00,040
part of the the DSE, part of
LDI, the typical signage people.

1723
01:41:00,040 --> 01:41:02,800
Yeah, we're gonna be talking
about interactivity and

1724
01:41:02,800 --> 01:41:07,360
technology used in creating
immersive experiences for

1725
01:41:07,360 --> 01:41:09,760
permanent installation, so I'll
be talking about that.

1726
01:41:09,760 --> 01:41:13,840
I'm also apparently one of the
judges for Battle of the Busk.

1727
01:41:14,240 --> 01:41:16,720
Fun.
So I'll be, I will be there as

1728
01:41:16,720 --> 01:41:18,920
well.
And then I'll be walking the

1729
01:41:18,920 --> 01:41:21,120
floor and.
So there's no AI?

1730
01:41:21,200 --> 01:41:23,640
There's no AI talks or anything
that surprises me.

1731
01:41:23,680 --> 01:41:26,960
I thought they'd do something.
I think there is the there is

1732
01:41:26,960 --> 01:41:30,960
the X live part of.
LDI yeah, yeah.

1733
01:41:31,200 --> 01:41:36,280
Where they I think they are
having and Jake Pinholster, OK

1734
01:41:36,560 --> 01:41:38,440
is leading that this time
around.

1735
01:41:38,440 --> 01:41:43,960
And I think that Jake does have
several panels about AI and how

1736
01:41:44,320 --> 01:41:45,680
it's being used.
Cool.

1737
01:41:45,800 --> 01:41:47,400
I'm not talking about it this
year.

1738
01:41:47,400 --> 01:41:51,760
I've been, I was, I've been, I
was too busy this year to commit

1739
01:41:51,840 --> 01:41:56,640
to, yeah, to doing a lot of
stuff at LDI, but I will

1740
01:41:56,640 --> 01:41:59,080
certainly be there.
I will be talking on the 10th

1741
01:41:59,080 --> 01:42:02,840
and I'll be around and about if
anybody wants to talk to me

1742
01:42:02,920 --> 01:42:03,840
one-on-one.
So.

1743
01:42:03,840 --> 01:42:06,720
Yeah, buy him a beer at the
Circle Bar and he'll tell you

1744
01:42:06,720 --> 01:42:08,160
whatever you want to know about
AII.

1745
01:42:08,760 --> 01:42:11,160
Don't know, you know, having
been having been clean and

1746
01:42:11,160 --> 01:42:12,920
sober.
For about don't buy him a beer,

1747
01:42:12,920 --> 01:42:13,680
buy him a.
Coffee.

1748
01:42:14,200 --> 01:42:17,600
I don't know that I'll share the
beer, but we have fizzy water

1749
01:42:17,600 --> 01:42:20,280
with a slice of There you go.
There you go.

1750
01:42:20,440 --> 01:42:23,680
Well, that's probably about
$27.00 at the Circle bar, so.

1751
01:42:24,280 --> 01:42:26,480
That's the problem.
That's a that's a nice fee.

1752
01:42:27,280 --> 01:42:29,240
All right, Bob, thank you so
much for doing this.

1753
01:42:29,240 --> 01:42:32,840
I appreciate all your time.
You're you're certainly very,

1754
01:42:32,840 --> 01:42:35,480
very up on this.
And I'd actually love to buy you

1755
01:42:35,480 --> 01:42:40,200
that soda water with a, with a
lime and, and have a deeper talk

1756
01:42:40,200 --> 01:42:43,440
because I'm not a technical guy,
but I'm really an AI guy.

1757
01:42:43,440 --> 01:42:46,080
I love the tools that it's
providing me.

1758
01:42:46,080 --> 01:42:51,040
So I I play with them every day.
Thanks.