HOW HAS AI CHANGED THE CREATIVE INDUSTRY?

Hello, Yann! How are you?

Hello, Anders. I’m great, thank you.

You can see it’s nice and sunny here in California.

It’s February, but it’s twenty-two degrees centigrade, so I can’t complain. Most of my team is on the East

Coast, and they’re suffering snowstorms right now, so I feel pretty snug.

And, and tell me, Leftfield Labs, what is that?

Yeah, so maybe just a, a little bit of background.

So this is Leftfield Labs, so we are a creative tech company.

That’s what we say, we’re a creative tech company.

What we actually are is a design business that uses new and emerging

technologies to give our clients a competitive advantage.

And so a lot of what we do is for a lot of B2B companies, um, and we help them accelerate the

adoption of new and emerging technologies.

So they will come to us with… You’ve probably heard the term

FOMO. With FOMO, for an acronym, they’ll say: “I’ve heard

about XR, I’ve heard about AR, I’ve heard about blockchain, I’ve heard about

AI,” obviously. And, uh, we will help

them, um, either adopt those technologies, or we’ll

help them create brand experiences,

products, platforms that leverage those,

um, those technologies and give them a competitive

advantage. Sometimes the answer isn’t a new

technology, it’s just good design, uh, but in most

cases, that’s our sweet spot. We, we like to say that we make these big bets that they take

real. So we don’t really talk about them, and I realize the irony ’cause

I’m talking to you right now, but we don’t really talk that much about it.

It’s more we take those technologies, we implement them, and we create measurable

impact for our clients.

So it’s not like a normal

agency as such?

I mean, there’s some parts of it that… My background is, is in agency. Um, and so

there’s some parts of it that you would recognize in the sense that,

um, we still pitch a lot of creative.

Uh, we have members of the team that have transferable skills

that you would see in agencies. So we have content

creators, we have 3D designers, and things like that, but it’s the output that’s very, very different.

And then it’s also the fact that, um, we operate

more like a software company than a traditional agency.

A traditional agency will tend to go a bit more waterfall, you know, from a

project management standpoint, so they will, you know, very much

do this phase, then the next phase, then the next phase.

Um, we operate a lot more in an agile work stream.

So clients will come with

a challenge, a, uh… Sometimes it’s very ambiguous, so it’s not a very well-defined thing.

It might be something like, “We’re realizing that our customers are going to a competitor,” or it might be something like, “There is a new business on the

market that is creating an existential threat for us.” And so we will show up, we’ll analyze their business, we’ll understand what their challenges are, and then in many cases, we

will, you know, give them, uh, access to new

growth, new customers, or sometimes completely a whole new

business, uh, through experience design,

through, you know, operation design, and then also everything that we do with new

and emerging technologies.

And, and what’s your role in this?

So I lead creative, um, and strategy. Um, so really my role is to help, um,

help the client define, as articulately as we can, what the challenge is, come up withhow to create a solution that is on brand but is also gonna be from aperformance standpoint, deliver the results that they want, so kind of a bit like

what a consultancy would do. And then I have a team of creative technologists, so people who are one foot in creativity and, um, you know, it could be visual creativity, it could be, uh, experiential architecture, it could be product design, and then one foot in

engineering, in most cases. So they might be software

developers, uh, they might be software architects, um, you

know, writing TDDs, technical design documents, et

cetera. So we’re, like,

creating the in-between, between brands that are looking to step

into-… a brave new world of new and emerging technology, and

then the Silicon Valley-style firms themselves who

develop, you know, large language models or other technologies.

And we tend to be the people in between who make these– you know, who help these

businesses capitalize on these new innovations.

And what kind of clients are you working with?

Um, so most of our clients are in the B2B space.

Yeah, but- We are beginning to-

Can you name drop?

Okay. Uh, I’ll name drop a couple. So I would say our longest

client has been Google. Um, with Google, we do

everything from helping them build some of their more

high-tech, uh, websites and online experiences. We’ve helped them develop, uh,

products. We were involved in, very early in Project

Tango, which is now, uh, part of their AR, so

augmented reality platform. Um, but we also

do things for Google I/O, which is their big, um, annual

event. And so if you go to googleio.com, you’ll see there’s a fun

little puzzle. Many cases, it uses AI tools

in there, but they’re, like, game-based, and they’re designed to engage

developers. Um, other clients that we have

are Salesforce, which is, um, you’ve already heard, the CRM

company, and we’re helping them, uh,

market, but also create proof points for what they call

the agentic enterprise, which is AIs that

aren’t just chatbots, they’re AIs that can perform tasks

on behalf of organizations at scale, and

obviously, on top of what they mostly do, which is cloud-based CRM

tools and platforms. We do work

for, uh, Meta. We did a lot of work for

them in XR, so the Meta glasses, which were

released this year, um, also project code-named

Orion, which is now public, which is XR glasses, which have

the Llama model built in, the AI model.

So you can speak to AI, and it’s contextual.

So where you look with the glasses, the AI knows that you’re looking at it,

and so that opens up plenty of new, exciting experiences and

opportunities to interact with the world.

Uh, we’ve done a lot of work for Meta,

for, uh, Amazon. For in automotive space,

we helped companies like Ford and Toyota adopt these new

technologies, find new, exciting experiences to do

in-vehicle. We helped them launch the

Mach-E, which is their first ground-up EV.

We designed the, uh, center stack.

Now, we were one of the partners, many partners were involved in this, but we

helped them design, like, some hero features for it, so things like that.

I can go on forever, but it’s mostly…

I would say the common threads

are companies that are at an inflection point because

technology, market, and their core business is all

beco- becoming shaken, and they need a partner to help

them define a vision and then build the

first real proof points for it.

So that’s, of course, one of the reasons why I wanted to bring you

here to Tønsberg Film Festival, uh, is, of course,

how you kind of are in, in the middle of this, this

big… where, where, where it happens, basically, you know,

because the things that happen in Silicon Valley, they affect the whole

world, of course, and, um-

… after a few years, uh, changes that happen there often tends

to arrive here somewhat later. So it’s really cool,

so to hear, you know, what’s, what’s the atmosphere? What’s the vibe?

What’s the trend? What’s the zeitgeist in, in these big

corporate e-environments?

That’s such a big question. So, so I’m– you can probably hear

from my accent, I’m French and British, so I didn’t grew up in the US.

So, um, I’ve only been here just over ten years

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now, and

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it’s been through many changes. I mean, you’re right.

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Like, I would say, uh, California as

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a whole is the fourth largest economy, and if you look at the pie

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of where it creates most value, it is, you know,

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big tech by far. Um, and one of the interesting things

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is, I don’t live in San Francisco. Most of my clients do, and I’m

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regularly over there in the, in the Bay Area,

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but I am in the space where a lot of the content that I grew up with, the

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movies, you know… I, I live not far from the beach, where I remember

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seeing, you know, people in red swimsuits running in slow motion.

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You know, it sort of defines my childhood in some extents, a lot of the content

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that came from here. And so you’ve got two

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big worlds: one which is, in some respects,

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defining a lot of culture for the world, and one that’s

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defining a lot of tech and

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innovation, and they’re coexisting in

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this, you know, desert strip of land and creating all

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this wealth. What’s interesting is that

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they are ki- kind of odd bedfellows in some ways.

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Um, Silicon Valley has this, you know, belief that

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technology will save the world,

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um, that what you need is just some brilliant

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engineers and the right tools, and any problem is

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solvable. And Hollywood, um,

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is more like the legacy, um,

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you know, mega industry here, where it’s being a little

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bit frazzled by all this, a bit more than frazzled,

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and I’ll give you some, some practical examples. So we do…

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we, we do build, uh, AI post-production pipelines

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for our clients. Um, so that’s where both the worlds that we operate in

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come together. So brands will come to us and

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say, “We have a lot of content that needs to be

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brand accurate, that we spent hundreds of thousands of dollars, if not

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millions, on, um, but they’re fairly repetitive.” So

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think-… um, graphics

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or, um, content that is, I would call it, through the

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line, if you call it in marketing. So maybe not the above the line adverts

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you see at the Super Bowl, and not the stuff that is like a

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podcast or like a training video, but the bits in

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between. And we will build complete pipelines that

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can then allow them to generate their own ads and

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content on the fly with AI content completely, and

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it’s on brand, it’s approved. So,

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you know, um, because of NDAs, I can’t discuss which companies, but I can– I think

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I can say that you have to imagine that most,

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uh, companies currently in the US who are B2B, have something

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similar or are exploring it. Um, so I

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think maybe five, five years

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ago, there was the realization that these tools are

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here, and yes, they caught a lot of attention,

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but are they actual real business tools?

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You know, it looked a bit like a bad acid trip with most of it.

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Um, so the credibility of it was not quite

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there as a business tool. Um,

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over the past five years, the models have improved

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dramatically. The top hat tools that you

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sit on top of it and the pipelines

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have also improved, which means that creatives have more

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control over those tools. The

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legislation, um, copyright, etc., is

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beginning to catch up, although that’s trailing

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behind. So I would say,

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depending on who you speak to, there’s either acceptance

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that these are the tools that are here to stay, or

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there is still some malaise, I would say, with the more traditional Hollywood,

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which is saying, well… And you know, we had film

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directors here recently. We had a big event to do with Hollywood and AI here at

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the office. Private event, phones down, so I can’t discuss who attended, but I can

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talk about the headlines. So some directors were

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committed to using these technologies.

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So they might be working for a big studio, like a Universal or a Disney,

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etc., and they are building, uh, AI labs

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where they’re beginning to test and iterate on content.

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There are actually some movies right now in the theaters that have

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sequences that were generated with AI and then

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composited in post-production. They’re not vocal

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about it, but they are currently in theaters right

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now. There are some which, um, where they

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were vocal. If you, you know the movie, The Brutalist, um, that

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came out recently, the act- the,

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the, the, um, the biopic,

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basically, of the architect. The architect was Hungarian,

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and he couldn’t nail his accent when he was speaking Hungarian,

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so they used AI and trained it on his voice so he

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speaks perfect Hungarian. So those are

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examples where Hollywood is beginning to adopt these technologies, and they feel it

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improves their art form and the authenticity of the content that they

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create.

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Yeah, I, I think, uh-

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But at the same time, this… Yeah.

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Yeah, let’s, let’s get back to the sort of the, that,

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s- you know-

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Yeah

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… core film storytelling. I, I was just going a bit more around-

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Yeah

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-in terms of, uh,

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so in of, in the whole business world.

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So what I’ve understood is-

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Yeah

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-you know, is AI a bubble or not, you know?

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Is, is it affecting the work market?

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How, how is kind of the whole business around this AI, not just film, but, you

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know, in all the creative industries and the… You know, what do you see there?

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So

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I, so I’m not a– I’m not gonna give any, any, uh, advice on what

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shares to buy or anything like that.

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I’m not a financial expert, but I would say the bubble question is a question that

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obviously we talk a lot about. So,

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yes, there’s a bubble, and no, there isn’t a bubble.

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So h- here’s what I mean by that. So, yes, there is a bubble in the

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sense that if you look at the investment made

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and the returns,

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not all businesses that have heavily invested with AI

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are going to make it on the other side, and I would compare this

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to the beginning of the internet. How many of you are still

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using AOL, right? Um, so there are

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going to be- there is going to be a change of guards, where the investment has been

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so high that there is… It’s gonna be incredibly difficult for a

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trillion-dollar company to recoup that

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investment. Um, so you will see some

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change, and there’s gonna be- it’s gonna be a bumpy road, for sure.

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But, uh, yeah, because like in terms of, of s- software, uh, you

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know, using AI to make code, uh-

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Yeah

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… there’s been studies saying that, for instance, uh, some developers

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claim that they are twenty percent faster now that they can get the AI

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to, to code things. Uh, but b- when they’re measured, they’re actually

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twenty percent slower. Or, you know, they get into a

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dangerous territory when the AI just makes so much code that they can’t

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revise it, or it becomes so complicated that they don’t, they don’t know…

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They’re shipping code that they don’t know what, what is, basically, you know?

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And, uh-

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Yeah

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… so, so it’s like-

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There’s, you’re basically saying…

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I mean, remember, we are, I would say, the

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emergence of LLMs is only a few years in.

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It is the fastest adopted piece of technology in the

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history of mankind. There is nothing that has had this

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level of adoption rate. So I think let’s just put that down.

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There is no precedent for this, right?

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The, um, the internet, the wheel, fire, etc.,

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took

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tens of thousands of years for it to adopt this.

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Within a matter of weeks, was hundreds of millions of users.

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So I-… at the same time, it’s an emerging

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technology, and if we look back a little bit about

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how AI was built, if you go to the origin stories,

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um, a lot of these weren’t designed to do what they’re doing now.

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So the large language model had very different

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applications when it was started, right?

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For example, Google

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used a very early version of an LLM

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to predict what the next word was going to be in your search

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box. So when you typed dog, it would predict

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food. It, it wasn’t a- it wasn’t

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ana- analyzing other people’s names or, or other people’s

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searches. It was predicting what it thought your next word was going to

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1. That’s like at the very beginning, that was the beginning of

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a large language model. It’s not

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intelligence in the sense that it ha- is fully context

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aware. It is just a very sophisticated mathematical

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tool that predicts the next word, and then the

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rationale was, well, maybe I can use that to

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predict

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in, in a, in a translation context.

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So I’m speaking to you in English, you might be

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speaking to me in Mandarin. You can’t translate word for word,

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you wanna translate whole sentences to be able to capture the

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meaning. So large language models were then used for

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translation purposes, and then someone said, “Well, hold on, wait a

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00:19:39.576 –> 00:19:43.116

minute. Maybe I can build something that

347

00:19:43.156 –> 00:19:47.096

appears intelligent and that is predicting answers to

348

00:19:47.136 –> 00:19:50.176

a question.” And the answer was yes.

349

00:19:50.196 –> 00:19:53.736

If you give it enough data, it can respond with

350

00:19:54.556 –> 00:19:58.476

a computational answer to your question, and then

351

00:19:58.516 –> 00:20:02.256

you keep going down, and you keep feeding it more data, and you keep refining the

352

00:20:02.316 –> 00:20:06.136

model, and eventually you end up with something that can predict the next

353

00:20:06.716 –> 00:20:10.696

two thousand pages of code. But the origin

354

00:20:10.816 –> 00:20:14.396

story was a prediction tool, not a coding

355

00:20:14.496 –> 00:20:18.136

tool. So you’re going to see some weird

356

00:20:18.176 –> 00:20:21.096

results, you know? But that’s not to say

357

00:20:22.376 –> 00:20:22.696

that

358

00:20:23.756 –> 00:20:27.446

using this methodology, whether it’s LLMs or growing into

359

00:20:27.476 –> 00:20:30.306

something like neural nets, if those

360

00:20:30.336 –> 00:20:32.585

tools… That, that’s not in question.

361

00:20:32.616 –> 00:20:36.566

I think anybody here, there’s no one in here who’s, like, doubting that this

362

00:20:36.616 –> 00:20:40.276

is a fad and somehow we’ll retor- return to the way things

363

00:20:40.336 –> 00:20:43.416

were. I think it’s consensus now, um,

364

00:20:44.256 –> 00:20:48.176

that this is well afoot. Whether this technology or that technology

365

00:20:48.216 –> 00:20:51.856

becomes dominant, whether LLMs lead to AGI,

366

00:20:51.876 –> 00:20:55.696

artificial general intelligence, is a whole different story,

367

00:20:55.736 –> 00:20:55.936

but

368

00:20:56.836 –> 00:20:59.116

I think everybody’s adamant that this is happening, this is here to

369

00:20:59.176 –> 00:21:01.116

stay.

370

00:21:01.216 –> 00:21:04.016

And, uh, I guess some people are panicking.

371

00:21:06.316 –> 00:21:06.596

Yeah,

372

00:21:07.576 –> 00:21:11.376

uh, and I, you know, I, I was panicking a bit, if I’m quite honest,

373

00:21:11.456 –> 00:21:13.796

about a decade ago. Um-

374

00:21:14.376 –> 00:21:14.756

So, yeah.

375

00:21:14.836 –> 00:21:15.356

I ran-

376

00:21:15.516 –> 00:21:16.656

So, so, so, uh, go… Let me-

377

00:21:16.666 –> 00:21:17.476

Yeah. You want me to talk a bit about that?

378

00:21:17.536 –> 00:21:18.916

Yeah, take me through your-

379

00:21:18.925 –> 00:21:18.925

Yeah

380

00:21:18.925 –> 00:21:22.696

… your, uh, career, which is quite interesting.

381

00:21:22.705 –> 00:21:25.596

Well, I don’t know if it’s interesting, but, uh, but thank you for saying that.

382

00:21:25.656 –> 00:21:28.396

I mean, I had a very classic,

383

00:21:28.496 –> 00:21:32.096

um… I, I studied m- media, and we studied

384

00:21:32.176 –> 00:21:36.116

together, which was amazing. Um, so I studied the, the film, film,

385

00:21:36.156 –> 00:21:39.776

video, graphic design portion, and then, um,

386

00:21:40.136 –> 00:21:43.756

the other part of my, uh, course was computer science.

387

00:21:43.816 –> 00:21:44.365

And, um,

388

00:21:45.376 –> 00:21:49.266

the rationale was that if I compare engineering with design and

389

00:21:49.356 –> 00:21:53.056

creativity, there might be a path for an interesting career

390

00:21:53.066 –> 00:21:56.746

somewhere in there, but I hadn’t really figured out what that was going to be.

391

00:21:56.816 –> 00:22:00.516

Um, and so I- that’s always been mixing the two.

392

00:22:00.616 –> 00:22:03.156

It’s a left brain, right brain type of approach.

393

00:22:03.276 –> 00:22:07.156

Um, can I apply engineering to design, and can I buy ar-

394

00:22:07.196 –> 00:22:10.296

can I apply artistic flair to technology and

395

00:22:10.316 –> 00:22:14.296

engineering? And over time, I kind of created a, a path for myself.

396

00:22:14.356 –> 00:22:17.796

I did, uh, traditional advertising for a while.

397

00:22:17.856 –> 00:22:21.826

I wanted to do film also and video. We, we did a short film together, and I

398

00:22:21.856 –> 00:22:25.516

used a lot of technology to do post-production and all sorts of things.

399

00:22:25.556 –> 00:22:28.986

And then over time, I, I realized that

400

00:22:29.196 –> 00:22:32.756

where I got excited about was where

401

00:22:32.816 –> 00:22:36.686

it’s in the real world. It’s where people, and

402

00:22:36.716 –> 00:22:40.316

technologies, and brands, and experiences all come together in the physical

403

00:22:40.396 –> 00:22:41.296

world. I felt

404

00:22:42.176 –> 00:22:45.916

like being able to create a transformational moment for someone in the

405

00:22:46.016 –> 00:22:49.976

physical world, to me, was very exciting and interesting,

406

00:22:49.996 –> 00:22:53.596

and so that led me down the path of… I did automotive for many years.

407

00:22:53.676 –> 00:22:57.456

I did, um, experience design for, like, big

408

00:22:57.496 –> 00:23:01.396

auto show spaces, um, product,

409

00:23:01.456 –> 00:23:05.216

you know, um, so apps and those types of technologies,

410

00:23:05.376 –> 00:23:08.956

XR, VR. So I really migrated, but applying the same principles

411

00:23:09.016 –> 00:23:12.496

of art to engineering and engineering to art.

412

00:23:12.596 –> 00:23:13.016

Um-

413

00:23:14.156 –> 00:23:17.906

And, and what’s, what’s your, what’s your, what’s your design philosophy in that?

414

00:23:18.856 –> 00:23:22.356

What’s your, what’s your main, your core thing that you try to

415

00:23:22.396 –> 00:23:24.996

infuse in these projects?

416

00:23:25.996 –> 00:23:29.856

I mean, I’m very much a people first.

417

00:23:29.976 –> 00:23:30.296

Um,

418

00:23:32.336 –> 00:23:36.236

I think for me, it’s, can I create something that is

419

00:23:36.296 –> 00:23:38.796

genuinely useful, genuinely

420

00:23:38.816 –> 00:23:40.926

delightful, um,

421

00:23:42.136 –> 00:23:44.536

and a net positive using new and emerging

422

00:23:44.576 –> 00:23:48.056

technologies? I mean, so I would say

423

00:23:48.096 –> 00:23:51.956

philosophically, but the… If I had one headline, I, I want the world to be

424

00:23:51.976 –> 00:23:55.556

more creative, a more creative place everywhere we go, I

425

00:23:55.576 –> 00:23:59.056

think. Um, in the world that we live

426

00:23:59.156 –> 00:24:02.556

today, we all know when we’re unhappy.

427

00:24:02.565 –> 00:24:06.156

Most of us, even if we don’t consider ourselves creative people,

428

00:24:06.256 –> 00:24:10.236

you’re more unhappy if you consume more content

429

00:24:10.276 –> 00:24:13.788

and experiences-… and you tend to be happier if you have the

430

00:24:13.828 –> 00:24:17.768

opportunity to be creative. And it doesn’t have to be art, anything

431

00:24:17.828 –> 00:24:21.548

is creative, but it just has– you’ve contributed something.

432

00:24:21.568 –> 00:24:24.948

I think we’re inherently creative creatures.

433

00:24:24.968 –> 00:24:25.348

And so

434

00:24:27.088 –> 00:24:30.288

ten years ago, um, I ran a program with Ford.

435

00:24:31.208 –> 00:24:34.878

It was a, um, a research program to try and

436

00:24:34.928 –> 00:24:38.708

identify what would be the in-car experience

437

00:24:38.848 –> 00:24:41.148

when cars became fully autonomous.

438

00:24:41.188 –> 00:24:44.948

So some of that work became Ford’s autonomous program.

439

00:24:44.968 –> 00:24:48.868

But I, I pitched to them that we should do a parallel program where we

440

00:24:48.968 –> 00:24:49.948

also research

441

00:24:51.428 –> 00:24:54.708

how will AI transform the discipline of

442

00:24:54.828 –> 00:24:56.588

design, research and design,

443

00:24:57.468 –> 00:25:00.848

thinking that it would just be a little bit of interesting PR.

444

00:25:00.948 –> 00:25:04.488

Um, and so we, we went through a four D process: uh,

445

00:25:04.498 –> 00:25:06.648

discover, design, develop, deploy.

446

00:25:06.668 –> 00:25:10.188

And at each of those steps, we had the latest in AI

447

00:25:10.308 –> 00:25:13.948

tools and algorithms and technologies, even like research

448

00:25:14.008 –> 00:25:16.788

papers that we had applied to some of that work.

449

00:25:16.828 –> 00:25:20.018

And at the end of the workstream, I was a little bit shocked, um,

450

00:25:20.108 –> 00:25:23.248

because I realized that roughly one-third

451

00:25:24.068 –> 00:25:26.748

of the work that I had built Ford for

452

00:25:27.968 –> 00:25:31.848

was– could be replaced by AI. And

453

00:25:31.928 –> 00:25:34.808

what I mean replaced, not

454

00:25:34.888 –> 00:25:38.728

automated, but certainly so heavily disrupted

455

00:25:39.348 –> 00:25:43.248

that it was difficult to charge the same amounts for those

456

00:25:43.328 –> 00:25:47.168

portions of work. And so that made me

457

00:25:47.288 –> 00:25:51.208

quite anxious about, selfishly, my career, but

458

00:25:51.228 –> 00:25:55.208

also the industry as a whole. An industry where people like us, who are creative

459

00:25:55.288 –> 00:25:58.368

people, who don’t really fit very well anywhere else,

460

00:25:59.488 –> 00:26:03.108

what’s our value if what we do is being

461

00:26:03.168 –> 00:26:06.318

eroded and being changed and being handed over to

462

00:26:06.318 –> 00:26:10.138

technologies? So I did a talk. I was invited at South

463

00:26:10.168 –> 00:26:14.028

by Southwest. I thought it was gonna be a small session, but like a thousand people

464

00:26:14.108 –> 00:26:17.808

showed up, so clearly, I wasn’t the only one with that

465

00:26:17.888 –> 00:26:18.628

anxiety.

466

00:26:19.828 –> 00:26:23.168

Um, and so and I’ve been, I’ve been on a mission since, to

467

00:26:24.048 –> 00:26:27.208

basically lean into the technology and not

468

00:26:27.328 –> 00:26:31.088

just, um… My, my goal is to

469

00:26:31.108 –> 00:26:35.048

not be on the receiving end of the technology, but to lean in

470

00:26:35.108 –> 00:26:35.568

enough

471

00:26:36.408 –> 00:26:39.308

that we become the architects of this technology.

472

00:26:39.368 –> 00:26:43.308

We influence how it should show up and how it should elevate

473

00:26:43.368 –> 00:26:47.148

what we do, um, instead of being in this position where we feel like we’re in

474

00:26:47.268 –> 00:26:49.528

competition with the technology.

475

00:26:49.588 –> 00:26:52.688

Um, so I’ve done a, a bunch of projects here.

476

00:26:52.708 –> 00:26:54.768

I’ve done a bunch of talks. I have a podcast.

477

00:26:54.778 –> 00:26:58.668

It’s only five months old, um, but where I interview people who have

478

00:26:58.708 –> 00:27:02.028

been disrupted, so in some cases, completely lost their revenue

479

00:27:02.108 –> 00:27:05.188

stream, but found new ways to adopt the

480

00:27:05.228 –> 00:27:08.568

technology, um, to create new revenues and still be

481

00:27:08.608 –> 00:27:09.728

creative.

482

00:27:09.768 –> 00:27:12.088

Great. And so, so either change or die,

483

00:27:12.128 –> 00:27:14.368

basically?

484

00:27:14.448 –> 00:27:18.168

So I don’t know if I agree that it’s adapt or die.

485

00:27:18.248 –> 00:27:22.128

I think, yes, there will be for

486

00:27:22.148 –> 00:27:25.768

sure, um, and there already is some

487

00:27:25.788 –> 00:27:29.398

things on our, on our… You know, when we, we

488

00:27:29.548 –> 00:27:33.028

bill hourly on our projects, there are some

489

00:27:33.128 –> 00:27:36.048

items on our services that just don’t exist anymore.

490

00:27:36.128 –> 00:27:39.638

Um, um, or they’re so heavily

491

00:27:39.708 –> 00:27:43.148

disrupted that they don’t really justify a full-time role

492

00:27:43.188 –> 00:27:46.088

anymore. However, at the same

493

00:27:46.208 –> 00:27:48.338

time, um,

494

00:27:49.328 –> 00:27:52.588

it is– we are seeing a productivity increase.

495

00:27:52.628 –> 00:27:55.648

So I know there’s… You, you mentioned the controversy around

496

00:27:56.048 –> 00:27:59.967

development, and a lot of developers are, you know, spreading the word

497

00:28:00.108 –> 00:28:03.348

that AI is creating a lot of noise. True.

498

00:28:03.388 –> 00:28:07.168

But if you look at, uh, tools that are just a few weeks old now, like Cloud

499

00:28:07.268 –> 00:28:10.238

Code, it’s a fantastic tool. It’s an agentic

500

00:28:10.288 –> 00:28:14.208

coder, uh, which means it doesn’t just write code, it can write

501

00:28:14.288 –> 00:28:17.708

across multiple platforms, it can do some backhand work.

502

00:28:17.768 –> 00:28:21.098

So it’s coming. Um, the–

503

00:28:21.608 –> 00:28:25.388

but we haven’t really seen a lot of layoffs on our side, on the

504

00:28:25.468 –> 00:28:29.008

developer side. Um, and the reason is because we pick

505

00:28:29.108 –> 00:28:32.768

developers who are also architects, um, they need

506

00:28:33.388 –> 00:28:36.688

to know what efficient code looks like, so that they can

507

00:28:36.848 –> 00:28:40.818

evaluate what the AI is doing. There are some areas

508

00:28:40.848 –> 00:28:44.748

that AI just doesn’t code in yet. Um, there

509

00:28:44.758 –> 00:28:48.588

are some areas that, for security reasons, uh, we don’t

510

00:28:48.628 –> 00:28:52.008

let AI code. Um, and then there are

511

00:28:52.108 –> 00:28:55.918

also, um, you know, being able to

512

00:28:55.948 –> 00:28:59.748

design an application that exists in the real world, you need to be

513

00:28:59.908 –> 00:29:03.348

context aware in a way that currently AI can’t.

514

00:29:03.528 –> 00:29:07.328

Um, for example, we helped to develop some of these XR

515

00:29:07.368 –> 00:29:10.548

glasses and experiences that have AI, plus

516

00:29:10.988 –> 00:29:14.548

context-aware content, so, uh, digital content

517

00:29:14.708 –> 00:29:18.658

overlaid on the real world. You, you need to understand how

518

00:29:18.708 –> 00:29:22.568

those things work. The AI needs to be able to interpret

519

00:29:23.348 –> 00:29:27.048

that right now you’re in the car, what’s more important

520

00:29:27.608 –> 00:29:31.508

is eyes on the road and not a text message

521

00:29:31.628 –> 00:29:35.428

with a picture that would take over your screen real estate.

522

00:29:35.438 –> 00:29:39.378

So things like that, that are obvious to us as humans,

523

00:29:39.428 –> 00:29:42.278

are not necessarily obvious to AI.

524

00:29:42.368 –> 00:29:46.088

So, um, increasingly, we’re finding that our

525

00:29:46.128 –> 00:29:49.568

developers are coding in specific areas that AI

526

00:29:49.588 –> 00:29:53.568

doesn’t work into or becoming… taking the ten thousand foot

527

00:29:53.608 –> 00:29:56.728

view. So this narrative that it’s adapt or

528

00:29:56.808 –> 00:30:00.668

die, is– has some truth. You do need to adapt, you

529

00:30:00.788 –> 00:30:03.357

do need to lean in. Um,

530

00:30:04.228 –> 00:30:08.208

and there are some roles that are disappearing, and a lot of post-production

531

00:30:08.668 –> 00:30:12.248

is going to move to AI, for sure.

532

00:30:12.368 –> 00:30:16.212

Um, but at the same time-… we’re also finding

533

00:30:16.912 –> 00:30:19.552

that especially in the content space, um,

534

00:30:20.572 –> 00:30:24.312

if you look at the work that, uh, through-the-line agencies are doing and

535

00:30:24.392 –> 00:30:28.232

content companies are doing, there is a clear fork in the

536

00:30:28.272 –> 00:30:32.112

road. You’re either embracing AI in your content, and that’s a

537

00:30:32.172 –> 00:30:36.152

certain type of content, so the big blockbuster movies, I think

538

00:30:36.172 –> 00:30:38.772

there is no doubt in my mind that they’re gonna adopt those technologies.

539

00:30:38.812 –> 00:30:42.652

Why should you pay $350,000 for the next Spider-Man movie,

540

00:30:42.712 –> 00:30:46.312

three, $350 million for the next Spider-Man movie when you can do it

541

00:30:46.432 –> 00:30:49.912

for less than 100? I think that’s going to be a clear

542

00:30:49.992 –> 00:30:53.172

area. But at the same time, content like

543

00:30:53.232 –> 00:30:56.472

this, content where people want to interface with

544

00:30:56.552 –> 00:31:00.432

people, be it in the real world or over content, those area

545

00:31:00.492 –> 00:31:04.472

heres are right now booming, and one of the areas that for us has been

546

00:31:04.712 –> 00:31:08.592

massive growth has been our work in experiences and experiential marketing,

547

00:31:09.292 –> 00:31:13.152

so where people meet in person. So it could be anything

548

00:31:13.252 –> 00:31:16.132

from themed entertainment to big

549

00:31:16.292 –> 00:31:19.292

conferences to sponsorship at sporting

550

00:31:19.372 –> 00:31:21.912

events. That’s becoming more

551

00:31:21.992 –> 00:31:25.312

important, in part as a

552

00:31:25.392 –> 00:31:29.372

by-product of the pandemic, where we took it away from each other

553

00:31:29.452 –> 00:31:32.722

for a short while, and now we’ve realized how valuable that

554

00:31:32.772 –> 00:31:36.392

1. But also in this world where AI is taking

555

00:31:36.472 –> 00:31:39.952

over a lot of screen-based content, a lot of screen-based work,

556

00:31:40.852 –> 00:31:44.812

the more valuable interactions are the ones that we do in person that are more

557

00:31:44.832 –> 00:31:48.432

authentic. So, so it’s not

558

00:31:48.472 –> 00:31:52.452

necessarily die. I think there will be some shedding, but

559

00:31:52.492 –> 00:31:55.342

at the same time, there is an opportunity, um,

560

00:31:56.212 –> 00:31:59.892

but you have to lean into it, uh, and you have to do it now.

561

00:32:01.252 –> 00:32:04.992

So, uh, I know you have to leave, uh, soon, but, uh,

562

00:32:05.512 –> 00:32:09.352

so maybe if you can return to, to the film part.

563

00:32:09.412 –> 00:32:10.132

And, uh-

564

00:32:10.142 –> 00:32:10.162

Mm-hmm.

565

00:32:10.192 –> 00:32:13.992

… I saw a video on this, uh, photographer, a- and on

566

00:32:14.032 –> 00:32:17.852

his website, uh, he had, uh, you

567

00:32:17.892 –> 00:32:21.832

know, “I- I’m a photographer that, uh, captures

568

00:32:21.972 –> 00:32:25.052

photons of events that actually

569

00:32:25.092 –> 00:32:28.992

happened.” Now, which I th- thought was a really good positioning,

570

00:32:29.012 –> 00:32:32.392

sort of on the other hand, you know, because,

571

00:32:32.532 –> 00:32:36.272

uh, you know, we’re kind of all blown away with these clips you see

572

00:32:36.352 –> 00:32:40.182

on Facebook or whatever. Uh, but

573

00:32:40.252 –> 00:32:41.232

then I’ve

574

00:32:42.852 –> 00:32:46.542

s- there’s very few examples where I sort of see something that’s

575

00:32:46.612 –> 00:32:50.442

AI, where, where kind of that’s not on the front of your mind. It kind of…

576

00:32:50.452 –> 00:32:54.272

it, it flavours the experience, and most of the

577

00:32:54.312 –> 00:32:58.112

time, it’s, it’s just so much, it’s just basically s**t, you know?

578

00:32:58.132 –> 00:33:00.152

It’s like, I would never pay to watch

579

00:33:01.092 –> 00:33:05.052

some AI slop, you know. It just- it doesn’t- because it’s

580

00:33:05.112 –> 00:33:05.322

like,

581

00:33:06.312 –> 00:33:08.932

if you, if you watch like, uh, an animation,

582

00:33:09.972 –> 00:33:11.312

you have this kind of-

583

00:33:11.322 –> 00:33:11.322

Mm

584

00:33:11.332 –> 00:33:15.132

… you have, like, an agreed with the creator that this is a,

585

00:33:15.232 –> 00:33:16.832

a, uh, what do you call it? Uh,

586

00:33:18.032 –> 00:33:19.352

a suspension of disbelief.

587

00:33:19.392 –> 00:33:20.432

Yeah, suspended dis-

588

00:33:20.492 –> 00:33:20.612

Yeah.

589

00:33:20.672 –> 00:33:20.932

Yeah.

590

00:33:20.952 –> 00:33:24.832

We have sort of an agreed agreement that this is not real, this is animation, and

591

00:33:24.932 –> 00:33:27.952

people can’t fly and… but, but we accept it, and we buy it.

592

00:33:27.972 –> 00:33:28.812

And I, I find that-

593

00:33:29.092 –> 00:33:29.292

Mm-hmm

594

00:33:29.532 –> 00:33:33.512

… because AI is so close to the real thing, it’s kind of- it’s, it’s in a

595

00:33:33.532 –> 00:33:36.521

way, it’s kind of like it’s lying. It’s, it’s, it’s

596

00:33:36.572 –> 00:33:39.012

pretending, but it’s so close that we-

597

00:33:39.212 –> 00:33:39.222

Well

598

00:33:39.222 –> 00:33:41.402

… s- we’re, it, it, it makes us, uh…

599

00:33:41.412 –> 00:33:45.152

it tricks us, and, and nobody likes being tricked

600

00:33:45.492 –> 00:33:47.212

or fooled or, you know-

601

00:33:47.252 –> 00:33:49.152

I, I think you’ve hit the nail on the head, right?

602

00:33:49.212 –> 00:33:53.032

It’s- but that’s true of every piece of content,

603

00:33:53.092 –> 00:33:56.192

right? I, I- especially if you’re in the space of advertising and

604

00:33:56.232 –> 00:34:00.072

marketing. I’ve always believed strongly that there’s a clear

605

00:34:00.132 –> 00:34:03.952

line between convincing someone and

606

00:34:04.032 –> 00:34:07.992

coercing someone. To me, convincing someone

607

00:34:08.053 –> 00:34:11.932

is we both have the same information, and then I convince you to

608

00:34:12.033 –> 00:34:13.241

see the world the way I do.

609

00:34:14.252 –> 00:34:17.592

Coercing someone is, I hold back some of that

610

00:34:17.672 –> 00:34:20.792

information, and I convince you to see my point of

611

00:34:20.832 –> 00:34:23.493

view. It’s in those, in this latter

612

00:34:23.973 –> 00:34:27.752

example, that you feel robbed, you feel cheated,

613

00:34:27.812 –> 00:34:31.732

right? I was sold a car, but he didn’t tell me there was a, a leak in

614

00:34:31.792 –> 00:34:34.572

the, uh, in the, in the, um, in the gearbox, right?

615

00:34:34.752 –> 00:34:37.392

Uh, y- I, I was coerced into buying it.

616

00:34:37.432 –> 00:34:40.432

And I think the same is true for anything in the creative

617

00:34:40.513 –> 00:34:44.292

space. Um, if you are a photographer and you generate a

618

00:34:44.392 –> 00:34:47.993

spectacular image of an animal, and then you submit it to a

619

00:34:48.033 –> 00:34:51.752

competition for wildlife, you’ve coerced your audience.

620

00:34:51.772 –> 00:34:55.352

The trust is broken, and I do think that that’s a, that’s a clear

621

00:34:55.572 –> 00:34:58.232

line that can’t be crossed. At the same

622

00:34:58.292 –> 00:35:02.192

time, you know, we go watch Titanic

623

00:35:02.572 –> 00:35:05.652

knowing that we’re not filming, we’re not seeing the actual boat.

624

00:35:06.352 –> 00:35:09.992

We go see Marvel movies by the millions,

625

00:35:10.002 –> 00:35:12.532

knowing that we’re not seeing something real.

626

00:35:12.592 –> 00:35:16.502

So then the question becomes: Do I need to know

627

00:35:16.532 –> 00:35:20.292

that they spent $250 million and three years

628

00:35:21.112 –> 00:35:23.772

for the story to have more meaning? Perhaps.

629

00:35:23.812 –> 00:35:27.152

Maybe that was true also in the, in the ’60s with movies like

630

00:35:27.192 –> 00:35:30.772

Cleopatra, you know, that in today’s dollars would have been half a billion

631

00:35:30.832 –> 00:35:34.772

dollar. So maybe there’s some value in that, but at

632

00:35:34.812 –> 00:35:37.432

the same time, I think it’s gonna weed out, um,

633

00:35:38.752 –> 00:35:41.552

a lot of those types of movies that we went to see because they were

634

00:35:41.592 –> 00:35:45.562

expensive. You know, maybe that’s not such a bad thing. Maybe it’ll…

635

00:35:45.592 –> 00:35:49.172

if everybody can generate big spectacle movies, well, maybe that

636

00:35:49.312 –> 00:35:52.882

means that we’re gonna have to focus back on what is

637

00:35:52.952 –> 00:35:56.912

true and authentic, and that’s story, because who knows

638

00:35:56.952 –> 00:36:00.212

how it was made, um, and does that actually matter?

639

00:36:00.252 –> 00:36:03.972

Isn’t it, like, the connection we make with the characters, how it makes me feel,

640

00:36:04.012 –> 00:36:06.012

and how I come out of it that really matters?

641

00:36:07.992 –> 00:36:11.392

So why haven’t I, why haven’t I seen anything that really, that moves me

642

00:36:11.752 –> 00:36:15.312

and, uh, that’s sort of AI-made?

643

00:36:15.372 –> 00:36:18.892

Well, if you’ve been in movie, to movies in the past six

644

00:36:18.952 –> 00:36:22.777

months-… I guarantee you that es- I mean, I’m not

645

00:36:22.808 –> 00:36:26.648

talking like niche, you know, French, you know, nouvelle

646

00:36:26.668 –> 00:36:29.598

vague style movie. I’m talking like big budget movies.

647

00:36:29.648 –> 00:36:33.448

I guarantee you that there were parts, full sequences in some of

648

00:36:33.488 –> 00:36:36.588

them, that would’ve been generated, and you probably didn’t see

649

00:36:36.628 –> 00:36:38.488

1. Um, so

650

00:36:39.968 –> 00:36:43.338

yes, you know, maybe you were coerced into something,

651

00:36:43.388 –> 00:36:47.308

thinking that they used a Flame suite and multiple hours of

652

00:36:47.448 –> 00:36:51.328

3D modeling. Maybe some of us in the room will be pissed off and say, “Hey,

653

00:36:51.448 –> 00:36:55.268

I wish they’d told me this was an AI sequence.” But when you’re talking

654

00:36:55.348 –> 00:36:59.288

about storytelling and sustaining disbelief, I feel like

655

00:36:59.328 –> 00:37:02.968

most of audiences just want to believe that it’s

656

00:37:03.008 –> 00:37:06.658

real for the short few hours that they’re spending with you in the

657

00:37:06.668 –> 00:37:10.008

theater. Who cares if it was a miniature 3D

658

00:37:10.088 –> 00:37:12.788

animation or AI generation, right?

659

00:37:12.828 –> 00:37:16.588

It’s, it’s if you’re showing me something and telling me this is real when it was

660

00:37:16.648 –> 00:37:19.708

not, that’s when the trust is broken for me.

661

00:37:19.768 –> 00:37:23.708

And obviously, if it’s poor, if it’s badly done, and the character’s got

662

00:37:23.808 –> 00:37:27.348

seven fingers and one eye that’s droopy, yeah, I

663

00:37:27.408 –> 00:37:30.888

mean, that’s just- that’s just poor use of the tools.

664

00:37:30.988 –> 00:37:34.508

Um, but that’s true for 3D animation, you know, uh,

665

00:37:34.608 –> 00:37:38.138

also. So but I would tell you that the, the tools right

666

00:37:38.228 –> 00:37:40.258

now, you know, there’s been some…

667

00:37:40.288 –> 00:37:43.818

If you look at a lot of the models right now, there’s open source models that

668

00:37:43.968 –> 00:37:47.068

anybody can download, you can put on your own machines.

669

00:37:47.168 –> 00:37:49.748

You can train the model and make it your own.

670

00:37:49.788 –> 00:37:53.708

You know, if you think of like Wan, for example, Wan 2.2, I think, or 2.3

671

00:37:53.768 –> 00:37:56.368

now. Download it, put it on your machine.

672

00:37:56.428 –> 00:38:00.388

If you’re a DOP, train your footage on it, and then you’ll be able to generate

673

00:38:00.448 –> 00:38:04.348

content that looks like your work, that you own, um, that you’re not feeding

674

00:38:04.428 –> 00:38:08.318

back to a, a company in Silicon Valley that can then generate off of your

675

00:38:08.348 –> 00:38:12.118

work. You become effectively the author, um,

676

00:38:12.528 –> 00:38:16.148

of work that looks like the way you like to shoot, and I’m seeing

677

00:38:16.188 –> 00:38:18.448

directors here in LA that are beginning to do this.

678

00:38:18.468 –> 00:38:22.268

And so they’re- it’s not yet used for,

679

00:38:22.368 –> 00:38:22.658

um,

680

00:38:23.548 –> 00:38:27.208

you know, movies that it’s maybe just a couple scenes, it’s not for full movies

681

00:38:27.288 –> 00:38:30.908

yet, but certainly in pre-production, people are doing

682

00:38:30.928 –> 00:38:34.308

complete pre-production movies in AI that look

683

00:38:34.368 –> 00:38:37.818

fantastic, that look very close to the finished thing, and they’re doing it on

684

00:38:37.868 –> 00:38:41.248

models that they’ve trained themselves on their own footage.

685

00:38:41.308 –> 00:38:45.177

So that’s a whole new area of, um, of

686

00:38:45.228 –> 00:38:46.668

opportunity for creative people.

687

00:38:48.848 –> 00:38:51.368

Instead of asking, what will AI do next?

688

00:38:51.408 –> 00:38:55.388

I believe we should be asking: How are we going to use AI to

689

00:38:55.428 –> 00:38:58.658

create the future that we want? Join us for

690

00:38:58.668 –> 00:38:59.928

breakfast.

691

00:39:01.808 –> 00:39:04.078

So to finish off, uh,

692

00:39:05.148 –> 00:39:08.988

going back to your, uh, YouTube channel on, uh, For

693

00:39:09.048 –> 00:39:12.658

Breakfast, where you’ve interviewed, uh…

694

00:39:12.688 –> 00:39:16.548

Well, who, who have you, who have you had on there, and, and what’s

695

00:39:16.628 –> 00:39:17.318

your, you know,

696

00:39:18.288 –> 00:39:20.998

biggest takeaways from, from those conversations that you can,

697

00:39:21.188 –> 00:39:23.868

uh, ha- hand us, hand off

698

00:39:23.908 –> 00:39:26.388

to?

699

00:39:26.488 –> 00:39:29.768

So top line, I’ve had,

700

00:39:29.868 –> 00:39:33.308

um, a famous car designer who,

701

00:39:33.388 –> 00:39:36.528

uh, designed the Mustang, the new Mustang

702

00:39:36.588 –> 00:39:40.328

recently. I’ve had a music producer who had a

703

00:39:40.388 –> 00:39:43.307

fantastic Malibu house, um, doing lo-fi

704

00:39:43.368 –> 00:39:46.838

music.

705

00:39:46.888 –> 00:39:50.168

Today, we’re talking to Jeremy Dalme, a successful music

706

00:39:50.228 –> 00:39:53.628

entrepreneur who saw his record label be completely

707

00:39:53.668 –> 00:39:56.988

disrupted by AI and who decided to

708

00:39:57.048 –> 00:40:00.658

reinvent the business of music and

709

00:40:00.768 –> 00:40:04.408

sponsorship with his new company, Acrylic.

710

00:40:04.468 –> 00:40:05.188

Join us for

711

00:40:05.208 –> 00:40:09.428

breakfast.

712

00:40:09.508 –> 00:40:13.048

For several months, we were doing about $40,000

713

00:40:13.188 –> 00:40:17.108

in, uh, just streaming revenue, um, which for an indie label

714

00:40:17.148 –> 00:40:19.328

that was established a year before, is just-

715

00:40:19.408 –> 00:40:19.828

Insane

716

00:40:20.108 –> 00:40:20.117

… insane.

717

00:40:20.128 –> 00:40:20.198

Yeah.

718

00:40:20.288 –> 00:40:24.207

Um, and because of that, our, our distribution partner gave us a half- a

719

00:40:24.248 –> 00:40:26.848

little over half a million dollar advance.

720

00:40:26.868 –> 00:40:30.848

The problem is that just three months after we h- we got

721

00:40:30.948 –> 00:40:34.308

that advance in 2022, AI music was

722

00:40:34.328 –> 00:40:38.028

announced, and the playlists that we

723

00:40:38.048 –> 00:40:41.988

depended on grew in size, and we started looking at

724

00:40:42.028 –> 00:40:45.928

these playlists and be like, “Why are we losing 75% of our revenue right now?”

725

00:40:45.988 –> 00:40:48.088

And we couldn’t recognize any of these artists.

726

00:40:48.108 –> 00:40:51.548

We didn’t really know where they were coming from, but still, the result was the

727

00:40:51.608 –> 00:40:52.068

same.

728

00:40:52.088 –> 00:40:52.158

Mm.

729

00:40:52.168 –> 00:40:55.348

Producers coming to my house saying, “Jeremy, what are we gonna do?

730

00:40:55.388 –> 00:40:59.128

I’ve gotta drive an Uber now.” And me thinking, “Well, uh, uh, we’re the label,

731

00:40:59.148 –> 00:41:02.808

like, I, I’m in bed with you. Like, I don’t know what we’re gonna

732

00:41:02.848 –> 00:41:06.608

do.” Um, and that was really the moment, like, the

733

00:41:06.668 –> 00:41:10.268

seminal moment where, where the, the idea behind

734

00:41:10.308 –> 00:41:14.208

Acrylic really was born. Like, when we were really back against the wall, and I

735

00:41:14.268 –> 00:41:17.988

was like: Well, what’s the difference between millions of emerging artists

736

00:41:18.408 –> 00:41:18.528

and-

737

00:41:18.648 –> 00:41:18.828

Yeah

738

00:41:18.848 –> 00:41:19.338

… AI robots?

739

00:41:19.388 –> 00:41:22.928

How… So how did that, how did that

740

00:41:23.168 –> 00:41:26.588

feel, that moment? Because obviously, you came out the other

741

00:41:26.728 –> 00:41:27.188

side,

742

00:41:28.088 –> 00:41:30.768

but did it take you by surprise?

743

00:41:31.788 –> 00:41:34.878

I, I recall the moment very, very clearly and

744

00:41:34.988 –> 00:41:35.848

vividly.

745

00:41:35.868 –> 00:41:39.058

Like, when you got the advance, where- di- did- was

746

00:41:39.128 –> 00:41:42.808

there, uh, something in the back of your head saying, “This

747

00:41:42.908 –> 00:41:46.768

is sort of sunsetting,” or, “This is the beginning of growth?” How were

748

00:41:46.828 –> 00:41:47.408

you seeing-

749

00:41:47.468 –> 00:41:49.308

Oh, yeah. Uh, when I got, when we got the advance-

750

00:41:49.348 –> 00:41:49.468

Yeah

751

00:41:49.478 –> 00:41:53.398

… it was beautiful. I mean, we had an, uh, an, an in-house artist, a

752

00:41:53.468 –> 00:41:53.998

designer-

753

00:41:54.048 –> 00:41:54.058

Yeah

754

00:41:54.058 –> 00:41:57.858

… who was making all of our cover art, because a lot of these lo-fi guys, like,

755

00:41:57.868 –> 00:42:01.518

they made amazing music, but not all of them, you know, were into

756

00:42:01.568 –> 00:42:03.258

art and, and their artwork and whatever.

757

00:42:03.258 –> 00:42:03.268

Yeah.

758

00:42:03.278 –> 00:42:07.228

And we wanted to have these incredible covers, and so we kind of created a

759

00:42:07.288 –> 00:42:11.138

world where we could make this beautiful art and have beautiful visual art and

760

00:42:11.188 –> 00:42:11.808

videos.

761

00:42:11.828 –> 00:42:11.928

Mm.

762

00:42:11.948 –> 00:42:15.788

So like, no, no, those two years were some of the best of my life,

763

00:42:15.848 –> 00:42:19.608

if not the best to this point, where I felt like I had creative freedom.

764

00:42:19.668 –> 00:42:22.098

I didn’t have a boss, you know, telling me, “You gotta do this.”

765

00:42:22.108 –> 00:42:23.268

But, but could you see a trajectory?

766

00:42:23.288 –> 00:42:24.168

Could you go, “Uh-

767

00:42:24.324 –> 00:42:26.253

… this is it. This is gonna keep going up?

768

00:42:26.284 –> 00:42:26.804

Oh, yeah!

769

00:42:26.884 –> 00:42:27.724

I have a-

770

00:42:27.744 –> 00:42:28.484

Oh, 100%.

771

00:42:28.504 –> 00:42:29.384

And so when that

772

00:42:30.424 –> 00:42:33.644

playlist started to transform, was it a shock?

773

00:42:33.664 –> 00:42:34.274

It was a shock.

774

00:42:34.344 –> 00:42:34.784

Complete shock?

775

00:42:34.804 –> 00:42:38.784

It was a complete shock. The announce of AI music, and the fact that the

776

00:42:38.824 –> 00:42:41.744

world was saying… And, and this- and not just the world, like, people were

777

00:42:41.824 –> 00:42:45.604

actually telling me, “Jeremy, like, your

778

00:42:45.644 –> 00:42:49.304

music, I can make it with a button now.” And I’d look at them and

779

00:42:49.344 –> 00:42:53.184

be, “Really? You can go talk to Paraguayan harpists

780

00:42:53.244 –> 00:42:57.024

and say, ‘Hey, let’s pair you with cool producers in LA-

781

00:42:57.064 –> 00:42:57.144

Mm

782

00:42:57.204 –> 00:43:00.814

… to make this music that no one has made before with these instruments that are,

783

00:43:00.844 –> 00:43:04.784

you know, hundreds of years old. And, you know, be in the front page of the

784

00:43:04.804 –> 00:43:08.084

Asuncion, Paraguay, um, arts section of their, their

785

00:43:08.164 –> 00:43:09.974

main, you know, newspaper-

786

00:43:10.144 –> 00:43:10.154

Mm-hmm

787

00:43:10.154 –> 00:43:13.584

… you know, and create opportunities for all these, these people around the

788

00:43:13.624 –> 00:43:13.814

world.’

789

00:43:13.814 –> 00:43:13.814

Right.

790

00:43:13.824 –> 00:43:17.524

Like, you can do that with robots?” And I refused to believe it, Jan.

791

00:43:17.584 –> 00:43:18.304

I refused.

792

00:43:18.324 –> 00:43:21.344

Did you… I mean, I, I know the first time I saw

793

00:43:22.224 –> 00:43:25.904

a piece of AI video, you know, I, I studied film, I

794

00:43:25.984 –> 00:43:28.384

studied media. I wanted to become a director.

795

00:43:28.424 –> 00:43:31.764

I did some commercials, some t- and I thought this was a career path for me.

796

00:43:31.804 –> 00:43:32.274

Yeah.

797

00:43:32.324 –> 00:43:35.274

And when I saw the first ones, I thought,

798

00:43:35.344 –> 00:43:38.904

really, um, it’s going to completely

799

00:43:38.944 –> 00:43:42.934

devalue. Even if I felt, as an artist, it didn’t

800

00:43:43.004 –> 00:43:46.464

have the depth, and I missed some of the humanness-

801

00:43:46.684 –> 00:43:47.164

Mm-hmm

802

00:43:48.024 –> 00:43:51.784

… I was concerned that it was just going to devalue it as a whole

803

00:43:52.264 –> 00:43:55.584

because it would muddy the water. You may have that one nice

804

00:43:56.184 –> 00:43:59.684

jewel of a human-crafted track, but if you have

805

00:43:59.944 –> 00:44:03.854

20,000 others that are 80%, 90%,

806

00:44:04.344 –> 00:44:07.414

to the person who’s just got their headphones at Starbucks and doing some work,

807

00:44:08.104 –> 00:44:10.404

it’s, it’s, it’s a nuance, the difference.

808

00:44:10.444 –> 00:44:11.104

Yeah.

809

00:44:11.124 –> 00:44:12.724

It’s just gonna demolish the industry.

810

00:44:12.734 –> 00:44:12.744

Yeah.

811

00:44:12.764 –> 00:44:14.664

Did you think this is the end of music?

812

00:44:15.524 –> 00:44:19.504

I th- I, I, I mean, the idea did cross my mind, but that is

813

00:44:19.584 –> 00:44:21.984

when I came up with the idea for Acrylic.

814

00:44:22.204 –> 00:44:22.254

Yeah.

815

00:44:22.254 –> 00:44:24.664

And, and, and I, it, it is really at that point-

816

00:44:24.704 –> 00:44:24.804

Yeah

817

00:44:24.814 –> 00:44:28.304

… because we were all desperate. I remember we were running out of money.

818

00:44:28.424 –> 00:44:32.344

Um, we owed our distributor on the advance, and we

819

00:44:32.364 –> 00:44:35.984

were running out of options. And that’s when I, I literally, I was in, in my, uh,

on my desk, and I got up.

Yeah.

822

00:44:38.304 –> 00:44:41.344

I have a board behind me, um, a, a whiteboard.

823

00:44:41.384 –> 00:44:41.724

Yeah.

824

00:44:41.744 –> 00:44:45.714

And I put a line through it, and I put, “Millions of emerging artists,” on the

825

00:44:45.784 –> 00:44:46.264

left-

826

00:44:46.324 –> 00:44:46.404

Yeah

827

00:44:46.484 –> 00:44:50.424

… “and AI robots” on the right. And I asked myself,

828

00:44:50.434 –> 00:44:52.544

“What is the difference between the two?

829

00:44:52.644 –> 00:44:56.244

How do we fight this?” And it came to me, and I was like, “Hold

830

00:44:56.284 –> 00:44:58.624

1. Hyperlocal-

831

00:44:58.664 –> 00:44:58.744

Mm

832

00:44:58.844 –> 00:45:02.404

… hyper-engaged fan bases. AI robots don’t have fans,

833

00:45:03.304 –> 00:45:06.164

so no one cares about their music,

834

00:45:07.044 –> 00:45:10.084

but we can prove that people care about ours.

835

00:45:10.124 –> 00:45:11.324

Maybe it’s only 100 fans-

836

00:45:11.364 –> 00:45:11.564

Mm-hmm

837

00:45:12.344 –> 00:45:16.184

… but those 100 fans are human, so we better bet on

838

00:45:16.244 –> 00:45:20.004

human IP because this war is not over.” And

839

00:45:20.064 –> 00:45:23.084

that’s when, really, things changed.

840

00:45:23.144 –> 00:45:26.884

That’s when I had the idea for the Acrylic platform.

841

00:45:26.984 –> 00:45:30.964

Um, and then, you know, things grew from there.

842

00:45:31.044 –> 00:45:32.624

I’ve had, um,

843

00:45:34.244 –> 00:45:37.924

you know, uh, who else have I had? I’ve had business leaders in software

844

00:45:38.304 –> 00:45:41.604

who… But I would say the common thread between all of them

845

00:45:42.324 –> 00:45:43.584

is they are people who were

846

00:45:44.804 –> 00:45:48.664

very successful, um, very committed to their career

847

00:45:48.784 –> 00:45:52.764

path, and then there was an inflection point in their career

848

00:45:52.804 –> 00:45:56.664

where AI stepped in and either made it very difficult

849

00:45:56.764 –> 00:45:59.864

or impossible for them to continue their business as it was.

850

00:45:59.884 –> 00:46:02.054

That’s the common thread, and it’s not a…

851

00:46:02.144 –> 00:46:04.264

I hope it’s not a depressing channel.

852

00:46:04.304 –> 00:46:08.224

The idea- it’s, they’re all showing what they did

853

00:46:08.234 –> 00:46:11.364

to come out the other side. Um, so for example, the car

854

00:46:11.424 –> 00:46:15.134

designer, now instead of spending

855

00:46:15.244 –> 00:46:18.864

hours rendering a sketch to try and convince his clients

856

00:46:19.284 –> 00:46:22.564

that this is the right design, he will

857

00:46:23.364 –> 00:46:26.724

do some rapid sketches and then use AI to

858

00:46:26.784 –> 00:46:30.584

create 20,000 variations, and then

859

00:46:30.644 –> 00:46:34.244

using his artistic flair and craft, he will

860

00:46:34.324 –> 00:46:38.184

select those that are most valuable and then immediately generate them

861

00:46:38.264 –> 00:46:42.224

into 3D models, into fake

862

00:46:42.364 –> 00:46:45.944

TV commercials. So you can design your car and then see it in a

863

00:46:46.024 –> 00:46:48.124

commercial or just driving on the street.

864

00:46:49.284 –> 00:46:52.524

And, uh, but, but what really, what really began to transform the

865

00:46:52.564 –> 00:46:56.444

process, uh, for me and my team was the,

866

00:46:56.504 –> 00:47:00.184

the rise of these sort of rapid 3D modeling tools, like, like

867

00:47:00.224 –> 00:47:03.514

Blender and, uh, Maya and, um, and,

868

00:47:04.424 –> 00:47:08.124

um, now Gravity Sketch, which is, which is the ability to, to

869

00:47:08.204 –> 00:47:12.004

spacely sketch in s- uh, sketch spatially with virtual

870

00:47:12.064 –> 00:47:15.604

reality and augmented reality. And, um, and so a

871

00:47:15.624 –> 00:47:17.944

designer could go from sketching on paper

872

00:47:18.784 –> 00:47:22.524

to, to building a model, um, but now they can sketch

873

00:47:22.544 –> 00:47:23.704

directly in 3D.

874

00:47:23.724 –> 00:47:23.794

Right.

875

00:47:23.804 –> 00:47:27.784

And so, so, um, that, that sort of translation step

876

00:47:28.184 –> 00:47:32.034

from 2D to 3D is now irrelevant. You’re just, you’re just going

877

00:47:32.084 –> 00:47:36.034

straight to 3D. And so, uh, and, and in doing so, we began to bypass a lot

878

00:47:36.044 –> 00:47:39.994

of the traditional workflows with clay models and

879

00:47:40.504 –> 00:47:44.344

working with digital sculptors, and, and, uh, the designers themselves were

880

00:47:44.364 –> 00:47:48.184

beginning to do more and more of their own sculpting and sending data

881

00:47:48.204 –> 00:47:51.184

directly to, to milling machines and, and,

882

00:47:51.324 –> 00:47:55.274

um, uh, and, and only working with the clay modelers in, in sort

883

00:47:55.304 –> 00:47:56.744

of more of the finishing stages.

884

00:47:56.784 –> 00:47:57.034

Right.

885

00:47:57.064 –> 00:47:58.304

Yeah.

886

00:47:58.404 –> 00:47:59.814

And so it means that from

887

00:48:01.024 –> 00:48:04.884

the challenge that he had as a creative was, “The

888

00:48:04.944 –> 00:48:07.584

vision I have in my head, how do I convince

889

00:48:08.384 –> 00:48:12.244

the accountants and the marketers in this automotive group

890

00:48:12.304 –> 00:48:16.024

to see it the way I do?” And so the designers that

891

00:48:16.164 –> 00:48:18.784

succeeded there were people who could sketch great.

892

00:48:19.284 –> 00:48:22.224

Wasn’t always the best idea, but they were the people who sketched the

893

00:48:22.244 –> 00:48:26.024

best. And now using AI, you don’t have to be the best

894

00:48:26.044 –> 00:48:29.322

sketch artist. You have to have the best taste-…

895

00:48:29.352 –> 00:48:31.632

you have to know what great looks like.

896

00:48:31.672 –> 00:48:35.202

You have to know when there’s a, a real white space that no one else is

897

00:48:35.232 –> 00:48:39.212

going, and you need to, you know, drill deeper on that one.

898

00:48:39.252 –> 00:48:43.122

And so the tools are here to accelerate, and I

899

00:48:43.192 –> 00:48:46.872

think used well, they are an opportunity to make the world more

900

00:48:46.912 –> 00:48:47.422

creative.

901

00:48:48.572 –> 00:48:52.502

I mean, at the same time, there is a place also where

902

00:48:52.532 –> 00:48:55.812

those accountants and marketers can generate their own cars

903

00:48:55.852 –> 00:48:59.632

themselves, and so far, it, it doesn’t

904

00:48:59.752 –> 00:49:03.612

sound as though they’re doing as good a job at figuring out what

905

00:49:03.672 –> 00:49:07.432

the right, you know, design is and what is culturally relevant and what is

906

00:49:07.472 –> 00:49:10.332

a, you know, a beautiful design and why.

907

00:49:10.392 –> 00:49:10.502

So,

908

00:49:11.492 –> 00:49:14.252

but that’s one example.

909

00:49:15.412 –> 00:49:18.952

So, so, uh, having good taste and knowing your,

910

00:49:19.012 –> 00:49:21.712

uh, uh, art history still

911

00:49:21.792 –> 00:49:23.512

matters?

912

00:49:24.672 –> 00:49:24.932

Yeah.

913

00:49:25.892 –> 00:49:29.382

And then also, you know, I, um, I was reading

914

00:49:29.412 –> 00:49:33.282

recently about the lost generation, which I think you and I are both a

915

00:49:33.332 –> 00:49:36.712

part of, which they call them Xennials.

916

00:49:36.772 –> 00:49:40.252

So we are- we tend to be born, I believe it’s

917

00:49:40.262 –> 00:49:43.512

’78 to ’85, something like

918

00:49:43.572 –> 00:49:47.252

that. It’s a generation that was born without the

919

00:49:47.312 –> 00:49:50.692

Internet. We know what a dial-up

920

00:49:50.872 –> 00:49:54.592

telephone is. We remembered having to walk to the

921

00:49:54.652 –> 00:49:57.712

television to change station, but at the same

922

00:49:57.772 –> 00:50:01.612

time, our education and our career

923

00:50:01.672 –> 00:50:04.942

meant that we used the Internet and had to adapt to new

924

00:50:04.972 –> 00:50:08.772

technologies. So we’re not quite Gen Xers, who

925

00:50:09.592 –> 00:50:11.512

learned about the Internet later in life.

926

00:50:11.532 –> 00:50:15.272

We’re not millennials, who were born with social media and the Internet.

927

00:50:15.292 –> 00:50:17.412

We’re somewhere in between, and so

928

00:50:18.432 –> 00:50:22.052

I think that makes us very well-suited to helping

929

00:50:22.532 –> 00:50:26.512

adapt to change. But if I look at my kids and the Gen

930

00:50:26.592 –> 00:50:29.932

Alpha, the generation coming before the

931

00:50:29.972 –> 00:50:33.952

millennials, they were born with AI, they were born with social

932

00:50:34.012 –> 00:50:36.892

media. They have no hang-up about it.

933

00:50:36.972 –> 00:50:40.472

Uh, they’re generating memes. They’re using it to express

934

00:50:40.512 –> 00:50:44.092

themselves, um, and they have no qualms about it

935

00:50:44.632 –> 00:50:48.082

because they haven’t experienced a world where

936

00:50:49.112 –> 00:50:52.612

a Disney movie was hand-drawn. They’ve seen them as

937

00:50:52.812 –> 00:50:55.112

3D characters all their lives, and so

938

00:50:56.092 –> 00:50:59.632

I think we might be the generation that feels

939

00:50:59.972 –> 00:51:03.772

this anxiety the most, um, because we remember a

940

00:51:03.872 –> 00:51:07.812

world where it didn’t exist. Uh, but at the same time, we’re

941

00:51:07.852 –> 00:51:10.812

probably the generation also that will help make the

942

00:51:10.872 –> 00:51:13.832

transition because we have a foot in before and

943

00:51:13.952 –> 00:51:17.572

after. But I… My, my gut tells me that the next

944

00:51:17.612 –> 00:51:20.972

generation coming isn’t gonna have the same qualms about

945

00:51:21.032 –> 00:51:23.972

it, um, and will adopt it freely.

946

00:51:25.092 –> 00:51:26.592

It’s hard to know what that means for

947

00:51:27.572 –> 00:51:30.832

society or where value is

948

00:51:30.872 –> 00:51:34.842

created, where purpose lies, what will- what

949

00:51:34.972 –> 00:51:38.852

would it mean to be creative if you’re co-creating together

950

00:51:38.952 –> 00:51:42.852

with AI? But at the same time,

951

00:51:42.932 –> 00:51:46.921

I, I know that the hang-ups we have, um, is ours and

952

00:51:46.932 –> 00:51:48.352

not necessarily the next generations.

953

00:51:49.772 –> 00:51:53.632

Yeah. Okay, so Jan, uh, I know you have to shoot, so, uh, thank you so much for,

954

00:51:53.732 –> 00:51:56.992

uh, being here in Tønsberg, uh, with us for this

955

00:51:57.032 –> 00:51:58.212

chat.

956

00:51:58.312 –> 00:52:01.932

Yeah, I mean, my pleasure. I, I wish I could be there in person.

958

00:52:03.432 –> 00:52:07.201

I, it- I’ve been to a few film festivals, and I, I love the energy and

959

00:52:07.272 –> 00:52:11.252

the, the passion that people have for storytelling, and I would say

960

00:52:12.292 –> 00:52:16.272

to the people in the room who are worried about AI, focus

961

00:52:16.332 –> 00:52:19.332

on that. The storytelling still matters.

How we connect with each other still matters, um,

whatever the tools show up. And my podcast is designed to be a platform for creatives to talk to each

other, to exchange ideas, and to, you

know, adapt to the change. So, you know, feel free to reach out.

I’m sure Anders will give you my contact details.

969

Excellent. Thank you, Jan.

All the best. Thank you.