AI In Film: Is There A Ghost In The Machine?

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Welcome back to nightmare logic the podcast that

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your mother warned you about I’m Christopher

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Smith here with Peter Sawyer Strap in as we rant

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about artificial intelligence and ask questions

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that we’ve all been asking how screwed our indie

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filmmakers really Hey Peter, how you doing man?

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Not too bad. How about you? Yeah, pretty good.

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What’s new? I have almost a beard. I haven’t

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done this in… Yeah, you do. You look slightly

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homeless. Good. No, this is… I call the beard…

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I heard a quote about beards as being the padded

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bra of masculinity and I stand by that. I think

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it’s just kind of a funny way to put it, but

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I’ve grown one once and that was 2000… 12 and

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I had red facial hair and I was like, oh, hell

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no. So I didn’t keep it very long. Now that I

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have some gray in my hair, this is more of an

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experiment to see like if it’s all gray, but

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I still see the fucking red, so. Yeah, yeah,

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totally makes sense. Did you watch anything good

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this past week? Yeah, I have, I guess two and

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a half things I wanna mention and I’ll start

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with a half thing because I’m not quite done

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with it, but. If you’re a Texas Chainsaw Massacre

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fan, you are probably well aware of Chain Reactions,

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a documentary about it with like five different

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speakers. And I’m four fifths of the way through.

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But the reason I thought to bring this up was

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last week we talked about titles and the documentary

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opens with Patton Oswald talking about the title

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of the Texas Chainsaw Massacre. That’s cool.

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And how he’s like. Because it was from one of

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his like stand -ups it’s footage of that and

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he’s basically saying he’s like, yeah You need

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a title that you can see in your head before

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you even see the movie And so he’s like the texas

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chainsaw massacre is it and the funny thing about

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it is it is and it’s but it’s also not true because

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The texas chainsaw massacre only one person gets

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massacred by a chainsaw But yeah, it’s a it’s

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a hell of a name um, and the it’s really insightful.

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I I highly recommend people watching this because

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every person so far has really interesting insight

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to the Texas Chainsaw Massacre, but also that

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kind of brand of horror movie that’s just primal

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and there’s no fat on the bones and how it pushed

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the limits and was really, Stephen King was like,

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this wasn’t even in this. Like guys, that’s how

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insane it was when I saw it. Wow. Yeah. You know,

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it’s also interesting cause it, I’m pretty sure

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it looks pretty low budge for the time, you know,

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like 16 millimeter film probably. Yeah, there

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was a theory, I think, that it was shot on 16

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and then presented as 35, which made it look

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even more grainy. Yeah, yeah. So it looked like

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you were watching something that was real. Yeah.

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You know, it’s interesting. My executive producer

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on a film that I’ve been working on for a long

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time, a documentary, she worked on the first

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Chainsaw Massacre as a… I think she’s like

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a production assistant or something. It was the

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first thing she did. Wow. In the 70s or whatever?

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Yeah, she’s older and I think that she lived

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in Texas at the time and you know, that was just

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like the local production. Yeah, I don’t doubt

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it. It’s a very cool documentary. Yeah, I want

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to check that out actually. I love any kind of

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making of behind -the -scenes kind of documentaries

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because I mean, I don’t know, I get all… First

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I like… learning how they did things, but I

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also get kind of stoked about making my own projects.

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So, yeah, definitely want to check that out.

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I’ve actually never seen any of the Texas Chainsaw

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Massacres. See the first one, just because that’s

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kind of a… It’s like a Night of the Living

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Dead or a, you know, psycho kind of exorcist.

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One of those, like… It’s canon. Yeah, yeah,

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for the genre, because it was such a shocking

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movie. Yeah, I’m going to check that out. I saw

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another movie that Peter recommended this week,

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which is shockingly embarrassed. Shockingly embarrassed?

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I’m embarrassed that I hadn’t seen it up until

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this point. That’s In the Mouth of Madness, classic

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John Carpenter. But man, I loved it. Thanks for

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recommending that. It’s like psychological horror

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and also has like a weird, I guess they’re like

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aliens. something, but it kind of like D -mini

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quality. It’s the old HP Lovecraft. Exactly.

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It’s Lovecraft. That’s a very good way to put

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it. Yeah. So I really enjoyed it. I, you know,

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I, I, interestingly enough, I felt it, it looked

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a little old. I mean, it came out in 95 and I

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was a little surprised by that because it almost

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looks like an eighties or, you know, early like

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eighties film in a way, the way that like cinematography

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was and, uh, and the creature. some of the creature

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effects, but mid -90s it turns out, so. A fun

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fact about that movie is John Carpenter wanted

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Metallica’s Enter Sandman to be the opening theme,

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and he couldn’t get it, so he just wrote the

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kind of like metal, hard rock intro. Was he already

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scoring his movies at that point? Yeah, I think

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he always did. I don’t know about his short films,

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but you know, Halloween I think is the big one

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that put it up. Put his scores on the map or

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the most familiar right when people are and he

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did he score other people’s films in addition

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to Making his own That’s a great question I’m

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not sure it wouldn’t surprise me. I feel like

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I should know the answer. I was just curious

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I mean neither here nor there but he was asked

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recently to do that and I believe he said yes,

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and I cannot remember who Asked him. That’s cool.

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That’s cool Well, the other thing I want to talk

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about real quick is Wait, wait, wait. I got my

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two other things. Oh, yeah, you’ve seen other

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things. OK. So if you know me, or I guess most

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of you probably don’t know me, I am not that

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well -versed in David Lynch. I had seen Dune

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as a little kid, and it didn’t vibe with me.

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I’ve seen Blue Velvet, and I’ve seen Eraserhead.

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And so I finally decided to watch Mulholland

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Drive. And I feel like I finally get the uber

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appeal of David Lynch. I don’t know if that’s

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seen as his best film, but I was entranced with

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it and highly recommend it. You should check

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out Straight Story. I’ve heard that’s really,

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really good, actually. I’ve heard that too. I’m

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curious to see a rated G version of a Lynch film.

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Yeah, I mean, I think it also doesn’t feel like

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one of his movies. Right. Yeah, I mean, I think

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it’s pretty straightforward. He told it pretty

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straightforward, I think, right? Right, hence

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the name. With Mulholland Drive, though, it made

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me realize with David Lynch, you just don’t know

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what’s coming. Every scene was a complete surprise

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to me watching that, which I found very exciting.

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Cool. Because it worked, but you just were like,

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oh, wow, this is interesting and different. Yeah.

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I’m not super familiar with his work beyond,

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I’ve seen, obviously I’ve seen, Twin Peaks, you

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know, and then I didn’t actually watch all of

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it I watched the first season part of the second

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season I think so I’m not a huge fan to be honest

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because I did try at one point starting Mulholland

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Drive and I turned it off cuz I mean I was a

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lot younger and I probably just wasn’t primed

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for it, you know, but I I don’t know. I mean

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I should get another shot, but I generally speaking

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don’t always sometimes like psychological types

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of films that jump around a lot and are, you

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know, somewhat disorienting. Sometimes that comes

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off as confusing to me and I don’t like when

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things feel confusing. Yeah, I feel like with

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Mulholland Drive, you could spend a lot of time

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analyzing it. And some people do. Yeah, that’s

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sad. I’m sure that’s the thing. But I mean, I

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just cheat and we’ll look up what someone else

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put put up there. And I’m like, oh, yeah, I guess

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I can see it because otherwise I’ll be thinking

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about those things for days. Right. And the last

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thing I wanted to mention is more of a deep cut

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that I was not aware of that I just kind of stumbled

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on on Tubi. And that is Alison’s birthday. And

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it’s a I think it was shot in seventy nine and

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made in eighty one. But it’s kind of it’s ahead

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of its time in that it’s there’s a couple of

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modern movies, one especially that either borrowed

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from it or just hit the same. themes and beats.

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I don’t even want to say too much about it because

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it’s it’s a it’s it’s one of those you want to

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go on blind but I will say it opens with kind

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of a seance with this girl Allison and something

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bad is going to happen on her 19th birthday so

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it sets the stage and then it kind of goes from

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there but there is a little bit of like getting

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to know the character and all that before it

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becomes a horror film but once it does I was

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just hooked. And so it’s a nice gem for people

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looking for something a little more obscure.

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Right, so go check that out. That was… Allison’s

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birthday. Allison’s birthday. On 2B. On 2B. One

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more thing I wanted to chat about real quick

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is that last night the Golden Globes happened,

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and a lot of people are talking today about how

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sinners essentially… Some people say got shut

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out of a lot of the things that they were nominated

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for with the exception of two awards One is for

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cinematic and box office achievement Which I

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think is a relatively new award and some people

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kind of consider it the Golden Globes consolation

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prize and the second one was for best score I

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believe which they didn’t even televise and so

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there’s a lot of people upset because they thought

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it deserved a lot more and I’m kind of curious

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I know we’ve both seen it and Probably a lot

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of you have out there as well. So what’s your

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take on that? Do you think that it deserved to

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win more or So I’m gonna preface this by saying

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I personally don’t care for award shows because

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at the end of the day our Films are totally subjective.

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One person may love a movie. The next person

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may hate it That said I would say sitters Should

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get those two awards. It was probably the best

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-made movie. I saw last year I thought it was

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a really strong score, so I agree with that.

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Do you know what the other categories were? Yeah,

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the one that people thought in particular that

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he was shut out from was, well, Ryan Kugler’s

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missing out on best screenplay and losing to

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one battle after the other. And yeah, what do

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you think about that as a writer? Do you think

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that it should have deserved? So as a writer,

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I feel The best way to know best screenplay is

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to actually read the screenplay, is not just

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see the film version. Yeah, I agree, 100%. Because

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it’s a different version of the story. Right.

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And then, you know, you’re looking at the language

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and all these things that don’t end up on the

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screen. Right. So I’ve always kind of been like

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best written or when people in comment sections

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are like, the story, you know, or the script

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sucked. I’m like, well, have you read the scripts?

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Because I don’t think those movies would get

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made if the script sucked. No, 100%. I mean,

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well. Depends, you know someone like a Ryan Coogler

00:11:39.629 –> 00:11:43.389
though might You know get stuff funded just on

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his track record, you know, but for people like

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us Yeah, well so and I still have not seen one

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battle after another so I can’t compare the two.

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Yes Or read the scripts presumably the I I was

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a fan of sinners I Ironically was not a fan of

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the vampire stuff, but I loved everything else

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about it for the most part. So I’m rooting for

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it, of course. Yeah, this might give me some

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haters, but I kind of thought it was a little

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bit muddled of a story. There were like elements

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I really loved of it and I still really like

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the movie overall, but I kind of felt like it

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could have been used another pass or two just

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in terms of some of the, I don’t know, elements

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of it felt a little disjointed to me. like that

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it wasn’t entirely cohesive. What I’ve heard

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is that the and I can see it is that the vampire

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element sort of feels tacked on because it has

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all this rich backstory and then it you know

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they tease kind of this supernatural darkness

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and then it’s that. I feel like it probably would

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have been a stronger story if it wasn’t vampires

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but it was the clan. Yeah well and you know obviously

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there’s trying to touch on some metaphor there

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and I think my problem was it should have been

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before he went back and just killed all the racists

00:13:04.340 –> 00:13:06.960
coming to get him. To me, that just kind of felt

00:13:06.960 –> 00:13:09.500
like a hat on a hat, like it almost didn’t need

00:13:09.500 –> 00:13:12.340
it. And it felt heavy handed in terms of the

00:13:12.340 –> 00:13:16.299
messaging of the theme. But I still enjoyed it.

00:13:16.320 –> 00:13:19.559
And that was a good segment in terms of it was

00:13:19.559 –> 00:13:23.340
fun to watch. Right. But yeah, I just thought

00:13:23.340 –> 00:13:25.059
that was interesting. And the reason Golden Globes

00:13:25.059 –> 00:13:27.340
are important to begin with is that it really

00:13:27.340 –> 00:13:30.159
sets up the Oscar race and really shows who like

00:13:30.159 –> 00:13:32.919
the front runners are. So, you know, for those

00:13:32.919 –> 00:13:36.139
out there who don’t know, the award season kind

00:13:36.139 –> 00:13:39.659
of starts a little earlier in last year in the

00:13:39.659 –> 00:13:42.200
fall. And, you know, there’s some smaller award

00:13:42.200 –> 00:13:45.679
shows and they kind of build up and certain films

00:13:45.679 –> 00:13:47.700
that start winning awards early might pick up

00:13:47.700 –> 00:13:49.779
steam and towards winning the Oscar. And it could

00:13:49.779 –> 00:13:52.259
totally change the race. So that’s the reason

00:13:52.259 –> 00:13:55.139
that Golden Globes are important to people because

00:13:55.139 –> 00:13:57.919
they tend to indicate which direction the Academy’s

00:13:57.919 –> 00:14:02.779
gonna vote. Interesting. A last thought I think

00:14:02.779 –> 00:14:06.700
is when a movie in the zeitgeist performs really

00:14:06.700 –> 00:14:09.379
well and it seems like you’re reading the room,

00:14:09.679 –> 00:14:13.000
everyone’s rooting for it, then people might

00:14:13.000 –> 00:14:16.000
automatically assume that that’s like the winner.

00:14:16.919 –> 00:14:20.019
Right. And Sinners came out, what, like late

00:14:20.019 –> 00:14:24.200
spring, early summer, I think? Yeah. So… If

00:14:24.200 –> 00:14:25.940
that’s already made up in someone’s mind that

00:14:25.940 –> 00:14:28.820
that’s the movie of the year, other movies can

00:14:28.820 –> 00:14:31.139
come along and kind of… And they might not

00:14:31.139 –> 00:14:35.860
be… They might experience it the same way because

00:14:35.860 –> 00:14:38.200
they already got one in their head. Yeah, I could

00:14:38.200 –> 00:14:40.899
see that. What’s also interesting actually is

00:14:40.899 –> 00:14:43.539
the meta -conversation around it, because now

00:14:43.539 –> 00:14:45.879
this talk of being shut out in the backlash,

00:14:46.759 –> 00:14:49.870
does that kind of persuade some people? to vote

00:14:49.870 –> 00:14:52.230
differently for the Oscars. I mean, it’s a different

00:14:52.230 –> 00:14:54.649
voting base, but maybe they’re like, oh, well,

00:14:54.909 –> 00:14:57.169
I want centers to win. And so therefore, I’ll

00:14:57.169 –> 00:14:59.830
change my vote, or thinking that somebody else

00:14:59.830 –> 00:15:03.269
would have voted for it. It’s tricky. But as

00:15:03.269 –> 00:15:04.990
you mentioned at the beginning of this whole

00:15:04.990 –> 00:15:08.049
segment is that it’s all so subjective. And some

00:15:08.049 –> 00:15:09.330
of these people are voting for their friends.

00:15:09.409 –> 00:15:11.289
Some of them haven’t seen all the movies in the

00:15:11.289 –> 00:15:15.470
categories. And traditionally, horror films aren’t.

00:15:15.480 –> 00:15:17.799
Even in the running for most of these things,

00:15:17.960 –> 00:15:20.259
you know, if it wasn’t Ryan Coogler It probably

00:15:20.259 –> 00:15:22.000
wouldn’t be you know, I mean it was artfully

00:15:22.000 –> 00:15:25.340
done in many ways So like that helps but there’s

00:15:25.340 –> 00:15:27.039
a lot of horror films that are I’d never get

00:15:27.039 –> 00:15:29.299
that kind of recognition But the interesting

00:15:29.299 –> 00:15:31.559
thing about sinners is like most of it I don’t

00:15:31.559 –> 00:15:34.279
feel like was horror if it didn’t have the vampire

00:15:34.279 –> 00:15:36.379
element. It wouldn’t right be considered It was

00:15:36.379 –> 00:15:39.299
it was like a good entry film for horrors like

00:15:39.299 –> 00:15:42.769
horror light, you know It may be more like the

00:15:42.769 –> 00:15:44.389
way to think about it is it’s a little bit more

00:15:44.389 –> 00:15:46.990
gothic or stylized or so let’s say I think it’s

00:15:46.990 –> 00:15:50.970
like a genre blend like it’s historical like

00:15:50.970 –> 00:15:54.009
Fiction would you is that say and then it’s action

00:15:54.009 –> 00:15:57.250
and then like historical fantasy or historical

00:15:57.250 –> 00:15:59.450
So it just kind of brings in all these those

00:15:59.450 –> 00:16:06.610
different elements. Yeah, totally Now we can

00:16:06.610 –> 00:16:09.029
dive into our main segment for the day which

00:16:09.500 –> 00:16:11.940
I know everyone’s stoked to talk about, which

00:16:11.940 –> 00:16:15.700
is artificial intelligence. And so today we’re

00:16:15.700 –> 00:16:18.220
basically going to get into an in -depth discussion

00:16:18.220 –> 00:16:23.039
about it and discuss a little bit of the recent

00:16:23.039 –> 00:16:26.460
events that have come up that have, you know,

00:16:26.580 –> 00:16:30.019
keeps stirring the debate around AI and its use,

00:16:30.159 –> 00:16:33.879
particularly in Hollywood. So to get into this,

00:16:34.000 –> 00:16:36.360
I thought maybe we could discuss the McDonald’s

00:16:36.360 –> 00:16:40.879
commercial that Is infamous now it was put out

00:16:40.879 –> 00:16:43.419
in the Netherlands right before Christmas and

00:16:43.419 –> 00:16:46.259
it was a Christmas commercial advertising the

00:16:46.259 –> 00:16:49.639
McDonald’s cafe and it was basically a series

00:16:49.639 –> 00:16:54.899
of a little vignettes of all the ways in which

00:16:54.899 –> 00:16:58.259
Christmas time could be terrible from like people

00:16:58.259 –> 00:17:00.779
their Christmas trees falling off their cars

00:17:00.779 –> 00:17:04.539
to You know destroying their cake mix batter

00:17:04.539 –> 00:17:07.940
mix or you know all of the like grumpy people

00:17:07.940 –> 00:17:11.180
you encounter, things like that. In fact, why

00:17:11.180 –> 00:17:13.299
don’t we go ahead and play that for you guys

00:17:13.299 –> 00:17:15.519
right now, just a little bit of audio. And this

00:17:15.519 –> 00:17:17.460
will also be available on the website for you

00:17:17.460 –> 00:17:19.740
to check out. And you can pause now and go watch

00:17:19.740 –> 00:17:22.119
it if you want. But we’re just going to play

00:17:22.119 –> 00:17:23.720
a little snippet of the audio so you can get

00:17:23.720 –> 00:17:44.670
a taste of it. Okay, so that was uh, yeah, it’s

00:17:44.670 –> 00:17:47.369
so ridiculous So, I mean I think I don’t know

00:17:47.369 –> 00:17:49.730
what do you think of it? Did you think it’s good?

00:17:50.210 –> 00:17:53.710
I mean is any of McDonald’s food actually artificial

00:17:53.710 –> 00:17:56.490
as well because it would then be on brand with

00:17:56.490 –> 00:18:03.450
their Bad joke I mean I Christopher had asked

00:18:03.450 –> 00:18:06.869
me if I had seen this, I couldn’t remember. Because

00:18:06.869 –> 00:18:09.890
you come across so much, I guess the term is

00:18:09.890 –> 00:18:12.910
AI slop, if you’re on the internet or if it finds

00:18:12.910 –> 00:18:17.369
its way in your algorithm. So I think I’d seen

00:18:17.369 –> 00:18:21.190
a close -up shot of the woman in it. I can’t

00:18:21.190 –> 00:18:24.269
remember what she’s doing. But I did re -watch

00:18:24.269 –> 00:18:27.029
it, and I was just like, yeah, it’s kind of forgettable.

00:18:28.109 –> 00:18:31.730
Forgettable. And for me, it. It’s cartoonish

00:18:31.730 –> 00:18:34.630
and everything looks just off like it’s it’s

00:18:34.630 –> 00:18:37.410
like looks kind of real, right? But there’s like

00:18:37.410 –> 00:18:39.450
it’s like looks like it’s gone through ten Instagram

00:18:39.450 –> 00:18:42.230
filters Yeah, you know and then like even that

00:18:42.230 –> 00:18:44.289
woman’s face. It’s just the way she grins It

00:18:44.289 –> 00:18:45.930
should kind of look like the Joker. I mean, yeah,

00:18:45.930 –> 00:18:48.210
I agree Yeah, I mean that was supposed to be

00:18:48.210 –> 00:18:50.650
I’m assuming her reflection in like a Christmas

00:18:50.650 –> 00:18:53.829
ball ornament on a tree But even still it just

00:18:53.829 –> 00:18:59.940
it felt so Like through the Palace through the

00:18:59.940 –> 00:19:02.779
like looking glass or whatever it is. So so what’s

00:19:02.779 –> 00:19:06.539
interesting too is It doesn’t like make you hungry

00:19:06.539 –> 00:19:09.660
or want to crave McDonald’s not at all yet Which

00:19:09.660 –> 00:19:11.720
is like the whole point of a commercial you have

00:19:11.720 –> 00:19:15.859
and just you know It’s just a negative messaging

00:19:15.859 –> 00:19:18.259
about Christmas, which you know, a lot of people

00:19:18.259 –> 00:19:20.160
love Christmas So it seems weird that you’re

00:19:20.160 –> 00:19:22.160
like, yeah, you hate Christmas to come get a

00:19:22.160 –> 00:19:25.400
coffee, you know so I mean that’s the You know

00:19:25.400 –> 00:19:27.140
not related to the AI but it just seems like

00:19:27.140 –> 00:19:29.819
a bad idea to begin with So it’s a bit, you know,

00:19:29.859 –> 00:19:31.960
when you watch it, it just feels so dystopian.

00:19:32.119 –> 00:19:34.240
It’s like, I mean, if it does anything right,

00:19:34.400 –> 00:19:36.180
it probably captures how a lot of us are feeling

00:19:36.180 –> 00:19:39.220
in the inside just about society right now. Yeah.

00:19:40.559 –> 00:19:44.519
But yeah, so the company that did that is called,

00:19:48.299 –> 00:19:50.099
well, they have a parent company, I’m forgetting

00:19:50.099 –> 00:19:51.650
the name of it, it’s like Sweet. something or

00:19:51.650 –> 00:19:54.950
other, but their subsidiary, which is their AI

00:19:54.950 –> 00:19:57.250
marketing company, is called The Gardening Club.

00:19:58.150 –> 00:20:00.890
And so they actually released a statement on

00:20:00.890 –> 00:20:04.329
it, which I’m going to read aspects of here because

00:20:04.329 –> 00:20:08.329
I think it brings up some interesting questions.

00:20:09.089 –> 00:20:12.970
And they basically said, yes, this ad is 100

00:20:12.970 –> 00:20:15.329
% AI, but it was built with the same care that

00:20:15.329 –> 00:20:18.750
we bring to any live action film. The brief was

00:20:18.750 –> 00:20:22.079
tightly prescribed. With clear mandate for every

00:20:22.079 –> 00:20:24.319
single scene so complete control of each shot

00:20:24.319 –> 00:20:26.259
was essential and pulled off by our team using

00:20:26.259 –> 00:20:29.220
proprietary workflows and Pulled off spectacularly.

00:20:29.220 –> 00:20:32.440
Well, this was not a prompt and pray process

00:20:32.440 –> 00:20:38.259
So they had a director it goes on to say that

00:20:38.259 –> 00:20:41.039
shaped every AI performance their look their

00:20:41.039 –> 00:20:46.299
energy their emotional presence They had An AI

00:20:46.299 –> 00:20:48.740
and a post team that spent seven intense weeks

00:20:48.740 –> 00:20:52.539
refining seven weeks. Refining every single frame,

00:20:52.960 –> 00:20:55.480
they’ve rebuilt shots, debated composition, shaped

00:20:55.480 –> 00:20:57.460
shadows, and turned emotional microbeats with

00:20:57.460 –> 00:21:01.099
precision all overseen by Sweet Shop, director

00:21:01.099 –> 00:21:04.240
of duo MAMA. And Sweet Shop is the parent company.

00:21:06.000 –> 00:21:08.119
And they said they had 10 or more specialists

00:21:08.119 –> 00:21:11.299
on each shot running through tightly engineered

00:21:11.299 –> 00:21:17.549
pipeline, yada, yada, yada. Read that because

00:21:17.549 –> 00:21:19.809
that was their defense once the internet was

00:21:19.809 –> 00:21:21.549
like this looks like crap. What the hell are

00:21:21.549 –> 00:21:26.309
you doing? What to me? It’s like Assuming this

00:21:26.309 –> 00:21:29.670
is all true what they say Like hey, where are

00:21:29.670 –> 00:21:32.230
the cost savings, right? I mean, I I mean it

00:21:32.230 –> 00:21:34.650
is expensive to have live -action but you know

00:21:34.650 –> 00:21:37.910
a lot of the budget spent in post and so maybe

00:21:37.910 –> 00:21:40.630
they’re just cutting out like 20 30 % of the

00:21:40.630 –> 00:21:42.309
budget if you’re cutting out the live -action

00:21:42.309 –> 00:21:47.140
stuff or 40 % I don’t know But if you, and also

00:21:47.140 –> 00:21:48.819
if you’re spending that much time, seven weeks,

00:21:49.000 –> 00:21:51.299
and that’s what you’re making out of AI, like

00:21:51.299 –> 00:21:53.819
that’s a kind of a statement about how not ready

00:21:53.819 –> 00:21:56.180
for prime time it is, you might say. I don’t

00:21:56.180 –> 00:21:57.960
know. What do you, any thoughts around that?

00:21:58.539 –> 00:22:00.440
Yeah. I mean, like if you’re going to spend seven

00:22:00.440 –> 00:22:04.180
weeks creating, I don’t know, is that like a

00:22:04.180 –> 00:22:08.779
32nd commercial, maybe a minute, you could easily

00:22:08.779 –> 00:22:12.440
do that with live action people. I mean, I know

00:22:12.440 –> 00:22:14.599
some of them are, some of the shots are like,

00:22:14.539 –> 00:22:17.319
kind of wild Christmas tree on fire type things.

00:22:18.359 –> 00:22:21.299
That’s true, because there are some potentially

00:22:21.299 –> 00:22:24.480
expensive set pieces, either closing down a road

00:22:24.480 –> 00:22:27.220
to drive through town or whatever. So you could

00:22:27.220 –> 00:22:30.819
have bigger ambitions with AI, I guess. But I

00:22:30.819 –> 00:22:33.559
look at companies like McDonald’s or Coca -Cola,

00:22:34.079 –> 00:22:36.319
these name brands that you’ve seen a million

00:22:36.319 –> 00:22:39.940
times. And I almost wonder why they even bother

00:22:39.940 –> 00:22:42.599
advertising, because everyone knows it’s there.

00:22:44.240 –> 00:22:47.240
I mean, I know they so related to no, but but

00:22:47.240 –> 00:22:49.819
I’m saying it has to do with marketing and commercials

00:22:49.819 –> 00:22:53.480
and This commercial really didn’t do anything

00:22:53.480 –> 00:22:55.880
except shit. I didn’t move the needle. It’s just

00:22:55.880 –> 00:22:57.980
yeah exactly Well, I mean what it did is it backfired

00:22:57.980 –> 00:23:00.259
and now everybody’s like thinks, you know, and

00:23:00.259 –> 00:23:02.339
so it backfired so bad In fact, it’s worth mentioning

00:23:02.339 –> 00:23:06.779
that McDonald’s corporate in the US distance

00:23:06.779 –> 00:23:08.839
itself and they said this was like a rogue commercial

00:23:08.839 –> 00:23:13.230
done by the like Netherlands McDonald’s or the

00:23:13.230 –> 00:23:15.670
European McDonald’s or whatever and they actually

00:23:15.670 –> 00:23:20.410
took it down. So I Guess they were testing the

00:23:20.410 –> 00:23:23.109
waters Yeah, I mean, I think that’s what a lot

00:23:23.109 –> 00:23:25.069
of people are doing right now, you know I think

00:23:25.069 –> 00:23:27.369
whenever there’s a new technology you get a certain

00:23:27.369 –> 00:23:29.849
segment of people who are like this is gonna

00:23:29.849 –> 00:23:31.809
be the next big thing and I want to get in early

00:23:31.809 –> 00:23:35.130
so I can capitalize off it and I think that’s

00:23:35.130 –> 00:23:36.930
what we’re seeing now and we’ll have more examples

00:23:36.930 –> 00:23:39.799
of that later in the conversation And, you know,

00:23:39.799 –> 00:23:42.920
they’re testing the waters and they’re brave

00:23:42.920 –> 00:23:45.880
souls because, you know, at the moment, there’s

00:23:45.880 –> 00:23:47.859
a lot of backlash against that, particularly

00:23:47.859 –> 00:23:52.519
when it goes wrong. So. Which brings up the next

00:23:52.519 –> 00:23:59.160
point that we can talk about here is that. Is

00:23:59.160 –> 00:24:01.880
really about the current state of AI in the industry,

00:24:01.880 –> 00:24:03.759
and I have a little bit of insight into this

00:24:03.759 –> 00:24:06.599
personally, just from my work, editing and doing

00:24:06.599 –> 00:24:09.960
cinematography for. commercials and other people’s

00:24:09.960 –> 00:24:15.599
productions and So jumping into that what I’ve

00:24:15.599 –> 00:24:18.099
seen personally is you know for almost two years

00:24:18.099 –> 00:24:22.539
now on documentaries and Branded content pieces

00:24:22.539 –> 00:24:26.000
that I do which are usually doc style a lot of

00:24:26.000 –> 00:24:30.380
times we when we’re editing Whether or not you

00:24:30.380 –> 00:24:32.779
know this this happens on every non scripted

00:24:32.779 –> 00:24:36.059
thing they’ll take we do like what we call Franken

00:24:36.059 –> 00:24:40.220
biting and essentially taking different parts

00:24:40.220 –> 00:24:42.500
of sentences and combining them together from

00:24:42.500 –> 00:24:45.460
different places to both cut out the fad of people

00:24:45.460 –> 00:24:47.700
rambling and stuff like that, but also sometimes

00:24:47.700 –> 00:24:51.099
to create new statements along the lines of what

00:24:51.099 –> 00:24:53.740
they were saying. You know, there is an ethical

00:24:53.740 –> 00:24:55.400
line there. You don’t want them to create them

00:24:55.400 –> 00:24:57.920
saying completely new things, but it’s totally

00:24:57.920 –> 00:25:00.119
been fair game for as long as I’ve worked in

00:25:00.119 –> 00:25:02.920
industry over 15 years to say, take someone saying

00:25:02.920 –> 00:25:07.329
like. something over here, pairing with something

00:25:07.329 –> 00:25:10.150
over here to get an entirely new sentence. And

00:25:10.150 –> 00:25:12.490
now what people are doing with AI is they’ll

00:25:12.490 –> 00:25:14.910
if if they can’t make something like that work.

00:25:14.990 –> 00:25:16.869
And there’s oftentimes where that won’t work

00:25:16.869 –> 00:25:21.210
and you can’t quite get the person to to to convey

00:25:21.210 –> 00:25:23.829
the thought that, you know, they were trying

00:25:23.829 –> 00:25:26.750
to through editing. So but you know, they’re

00:25:26.750 –> 00:25:28.390
going to are willing to say it and you know,

00:25:28.390 –> 00:25:29.750
you’re going to have another shoot with them.

00:25:30.029 –> 00:25:32.990
Sometimes we’re asked to go in and temp that

00:25:32.990 –> 00:25:37.299
thought. using AI and will feed, you know, the

00:25:37.299 –> 00:25:40.740
interview into a tool. I think I use 11 labs

00:25:40.740 –> 00:25:46.240
often to do this and it’ll spit out what we’d

00:25:46.240 –> 00:25:48.900
like them to say with their voice. And that doesn’t

00:25:48.900 –> 00:25:51.920
end up in the final piece ever, as far as I know.

00:25:51.920 –> 00:25:55.700
It shouldn’t unless they approve it or whatever.

00:25:56.119 –> 00:25:58.619
But we do that just as a placeholder because

00:25:58.619 –> 00:26:01.529
a lot of times they don’t want to hold up the

00:26:01.529 –> 00:26:04.009
editing process in order to tempt it in to see

00:26:04.009 –> 00:26:05.890
what it sounds like, because they might not even

00:26:05.890 –> 00:26:07.849
want it in the end anyways. But then when they

00:26:07.849 –> 00:26:09.789
kind of get the final thing, they might go back

00:26:09.789 –> 00:26:12.970
and ask a question that gets at that point. Or,

00:26:12.970 –> 00:26:15.869
you know, in the case of commercial branded content,

00:26:15.970 –> 00:26:18.130
they might even just ask them to say it because

00:26:18.130 –> 00:26:20.950
they’re typically getting paid in those scenarios

00:26:20.950 –> 00:26:22.990
where in documentaries you typically don’t pay.

00:26:23.900 –> 00:26:26.099
people for interviews because it’s unethical.

00:26:26.700 –> 00:26:29.440
So I’ve seen that. They’re just using a lot of

00:26:29.440 –> 00:26:32.519
VFX tools built into a lot of programs like DaVinci

00:26:32.519 –> 00:26:36.420
Resolve with Dorotascoping. Premiere has a lot

00:26:36.420 –> 00:26:40.359
of AI tools, particularly with cleaning up sound.

00:26:40.880 –> 00:26:44.519
And now I think they have tools to extend music

00:26:44.519 –> 00:26:46.779
cues to the length that you need, things like

00:26:46.779 –> 00:26:49.240
that, which… You know, are all things that

00:26:49.240 –> 00:26:52.000
we have been doing for a long time. We just do

00:26:52.000 –> 00:26:55.859
them manually. And, you know, the tools basically

00:26:55.859 –> 00:26:58.099
just speed up that process or sometimes maybe

00:26:58.099 –> 00:27:01.119
do a little bit better. The roto scoping in DaVinci

00:27:01.119 –> 00:27:03.160
Resolve is a really good example because that

00:27:03.160 –> 00:27:05.420
process usually you would in the past, you’d

00:27:05.420 –> 00:27:08.579
have to go frame by frame and cut out every single,

00:27:08.700 –> 00:27:11.380
say, like arm in front of a TV or something,

00:27:11.700 –> 00:27:14.240
you know, by hand. And now you can just kind

00:27:14.240 –> 00:27:16.099
of like do a few marks and have a track it and

00:27:16.099 –> 00:27:21.279
it’s done, you know. And so that’s AI is definitely

00:27:21.279 –> 00:27:25.500
working its way into these post tools. Then there’s,

00:27:25.500 –> 00:27:28.900
as I mentioned, music, not in addition to extending

00:27:28.900 –> 00:27:33.819
the music. There’s a lot of AI generated music

00:27:33.819 –> 00:27:36.240
out there. And I think that we’re going to start

00:27:36.240 –> 00:27:38.599
seeing that being used more. I personally haven’t

00:27:38.599 –> 00:27:41.339
seen it like, you know, in any of the productions

00:27:41.339 –> 00:27:44.579
I’ve worked on, but I can only imagine that particularly

00:27:44.579 –> 00:27:47.529
in things like reality and other commercial cheaper

00:27:47.529 –> 00:27:49.509
commercial content or social media that they’re

00:27:49.509 –> 00:27:52.170
probably starting to do AI generated songs and

00:27:52.170 –> 00:27:56.710
using them in in final pieces and Something else

00:27:56.710 –> 00:27:59.690
I’ve seen is people you know using generative

00:27:59.690 –> 00:28:03.529
AI mostly for images for decks and and pitch

00:28:03.529 –> 00:28:06.670
decks that is and essentially trying to convey

00:28:06.670 –> 00:28:11.690
the idea of a film to help sell it to a commissioner

00:28:11.690 –> 00:28:18.740
or studio or some kind of funder I’ve seen Well,

00:28:18.759 –> 00:28:22.400
I think that you you mentioned Peter the That

00:28:22.400 –> 00:28:25.279
film that used AI to create some of the art department

00:28:25.279 –> 00:28:27.559
assets. Yeah, I want to talk about that a little

00:28:27.559 –> 00:28:30.519
bit Yeah, so late night with the devil that came

00:28:30.519 –> 00:28:35.660
out like a year or two ago A movie I really enjoyed

00:28:35.660 –> 00:28:39.380
After watching it, you know, you go online you

00:28:39.380 –> 00:28:42.380
start thinking about it and you see reviews and

00:28:42.380 –> 00:28:46.470
a lot of people were attacking it for AI use.

00:28:46.490 –> 00:28:49.210
So I was like, oh shit, they stepped in it. And

00:28:49.210 –> 00:28:53.730
what I discovered is the art department used

00:28:53.730 –> 00:28:58.190
it, it was their choice to use it, in the capacity

00:28:58.190 –> 00:29:01.569
where the film is kind of like if you have a

00:29:01.569 –> 00:29:04.069
Johnny Carson comeback, you know, set in the

00:29:04.069 –> 00:29:07.509
70s and his comeback is on Halloween and he’s

00:29:07.509 –> 00:29:10.470
gonna have all these spooky guests on, they have

00:29:10.470 –> 00:29:14.180
like the Halloween TV cards that flash before

00:29:14.180 –> 00:29:17.500
commercials. That’s where the AI was implemented.

00:29:18.519 –> 00:29:22.480
I didn’t know watching it. Like it, I just assumed

00:29:22.480 –> 00:29:25.019
that, hey, maybe they figured it out on, I don’t

00:29:25.019 –> 00:29:29.660
know, like CGI. Yeah, like just some, some, uh,

00:29:29.700 –> 00:29:32.579
graphic designer did it, but they used AI and

00:29:32.579 –> 00:29:35.220
I don’t know to what extent, but it got a lot

00:29:35.220 –> 00:29:40.220
of backlash and it’s kind of, it’s just an interesting

00:29:40.220 –> 00:29:45.259
thing because I understand why they probably

00:29:45.259 –> 00:29:47.720
used it for that. Hey, maybe they were short

00:29:47.720 –> 00:29:52.559
on budget. Who knows, found themselves in a position

00:29:52.559 –> 00:29:56.000
like that. But on the other hand, if you’re the

00:29:56.000 –> 00:29:59.420
first one to do it, you might be getting that

00:29:59.420 –> 00:30:03.460
backlash. So it wasn’t worth it to use. Yeah,

00:30:03.759 –> 00:30:07.500
so essentially the art department used it in

00:30:07.500 –> 00:30:10.970
the assets that they created for the film. That’s

00:30:10.970 –> 00:30:14.609
something that I’ve actually, and the same thing

00:30:14.609 –> 00:30:17.309
happened in video games. The game that won Game

00:30:17.309 –> 00:30:21.410
of the Year this year at the Game Awards, they

00:30:21.410 –> 00:30:25.329
took back some of their trophies because it came

00:30:25.329 –> 00:30:28.529
out after the fact that they used AI -generated

00:30:28.529 –> 00:30:31.309
textures in some, not all, but some of the game.

00:30:32.190 –> 00:30:36.009
And they didn’t disclose that. So there was some…

00:30:35.920 –> 00:30:38.099
You know anger around that they’ve since gone

00:30:38.099 –> 00:30:40.160
back and replaced all those with totally human

00:30:40.160 –> 00:30:43.119
created ones But I think their excuse was that

00:30:43.119 –> 00:30:45.099
they were just you know moving fast and like

00:30:45.099 –> 00:30:47.119
they didn’t get a chance to swap them all out

00:30:47.119 –> 00:30:49.660
So but it is you know, it seems like a lot of

00:30:49.660 –> 00:30:52.500
times that when they use them AI in those ways

00:30:52.500 –> 00:30:55.759
It’s usually like a time -saving or cost -saving

00:30:55.759 –> 00:30:59.059
reason and the last thing I’ve seen is AI and

00:30:59.059 –> 00:31:02.680
social media, you know a lot of companies creating

00:31:02.680 –> 00:31:04.940
AI slop or AI videos whatever you want to call

00:31:04.940 –> 00:31:07.849
it and just so they don’t have to like do a full

00:31:07.849 –> 00:31:09.750
production. But when you’re doing social and

00:31:09.750 –> 00:31:11.430
you’re trying to post something every day, it’s

00:31:11.430 –> 00:31:14.390
a very tempting, quick way to get a lot of content

00:31:14.390 –> 00:31:17.829
out. So I think those are the ways that AI mostly

00:31:17.829 –> 00:31:21.289
appears at the moment. We aren’t quite yet. It

00:31:21.289 –> 00:31:23.950
seems like we’re going to get into this a little

00:31:23.950 –> 00:31:28.150
bit, but fully generated AI films and like real

00:31:28.150 –> 00:31:31.269
content, like premium content. We’re not there

00:31:31.269 –> 00:31:33.849
yet, but that doesn’t mean that a lot of people

00:31:33.849 –> 00:31:36.160
aren’t trying to make that happen. You know,

00:31:36.559 –> 00:31:38.980
and the first example of that that I would like

00:31:38.980 –> 00:31:43.200
to ask you about, Peter, is Tilly Norwood. So

00:31:43.200 –> 00:31:46.460
for those aren’t familiar, Tilly Norwood is a

00:31:46.460 –> 00:31:53.200
AI. Young female, so totally, totally made up

00:31:53.200 –> 00:31:58.779
by AI person that this company called Particle

00:31:58.779 –> 00:32:01.980
Six Group, and they have a new division called

00:32:01.980 –> 00:32:05.730
Ziccoia. I can’t even say it. I don’t know. But

00:32:05.730 –> 00:32:09.410
they essentially are trying to represent Tilly

00:32:09.410 –> 00:32:13.410
Norwood as like an actress, but obviously not

00:32:13.410 –> 00:32:16.910
a real actress, but essentially be agents to

00:32:16.910 –> 00:32:20.289
this AI actress and get her cast in AI films

00:32:20.289 –> 00:32:24.690
and things like that. And they announced this

00:32:24.690 –> 00:32:27.650
back the summer and it just exploded and people

00:32:27.650 –> 00:32:30.829
were so angry, including myself. And then a few

00:32:30.829 –> 00:32:34.829
weeks later, they released the first It was like

00:32:34.829 –> 00:32:37.210
a promotional video for their company and it

00:32:37.210 –> 00:32:40.069
featured her in it and that spot was called AI

00:32:40.069 –> 00:32:42.109
commissioner which will link to in the show notes

00:32:42.109 –> 00:32:45.309
as well and it’s really the first project to

00:32:45.309 –> 00:32:49.470
feature her and What do you think about that?

00:32:49.490 –> 00:32:51.730
Do you think that there’s a future for AI actress

00:32:51.730 –> 00:32:53.109
and actresses? What are they trying to do with

00:32:53.109 –> 00:32:57.569
that? I think it’s stupid and Companies corporations

00:32:57.569 –> 00:32:59.529
people at the end of the day want to make money

00:32:59.529 –> 00:33:01.650
and this is just a lane They can make money in

00:33:01.650 –> 00:33:03.869
they can do fast content you know, you don’t

00:33:03.869 –> 00:33:07.309
need to hire a person to do it. But as a consumer

00:33:07.309 –> 00:33:11.890
of films and, you know, if occasionally I’ll

00:33:11.890 –> 00:33:14.910
play a video game or watch a TV show, like I

00:33:14.910 –> 00:33:16.809
want to see something real. And I think a lot

00:33:16.809 –> 00:33:21.809
of people that care about this stuff do. So if

00:33:21.809 –> 00:33:25.269
the Tilly Norwoods of the world become this like

00:33:25.269 –> 00:33:28.250
phenomenon where now we have AI generated movies,

00:33:28.269 –> 00:33:30.509
I think there’ll be a lot of boycotting. There’ll

00:33:30.509 –> 00:33:32.769
probably be some people that will watch it. You

00:33:32.769 –> 00:33:34.730
know, I think people are also curious. But what

00:33:34.730 –> 00:33:36.710
about with actors in particular? Well, I think

00:33:36.710 –> 00:33:39.930
it’s fucked up to do to actors because that’s

00:33:39.930 –> 00:33:41.589
something, you know, as a little kid, I wanted

00:33:41.589 –> 00:33:43.730
to be an actor. I’m not good at it. I’m not great

00:33:43.730 –> 00:33:47.349
at lying. Um, but I, you know, that’s like a

00:33:47.349 –> 00:33:49.210
passion people have and it’s like, all right,

00:33:49.269 –> 00:33:50.930
we’ll just get a robot to do it. So it’s fucked

00:33:50.930 –> 00:33:53.190
up because they’re taking the place of it. Yeah.

00:33:53.190 –> 00:33:56.809
And it’s such like a human quality. You know,

00:33:56.930 –> 00:33:59.410
people, what they bring to acting is like their

00:33:59.410 –> 00:34:02.240
trauma. you know, their life experience, all

00:34:02.240 –> 00:34:06.680
this stuff that’s lived and AI is just patterns

00:34:06.680 –> 00:34:10.699
and BS. Yeah, it’s ones and zeros. And that’s

00:34:10.699 –> 00:34:12.559
actually a really good point, you know, because

00:34:12.559 –> 00:34:14.219
they always say, you know, eyes are the window

00:34:14.219 –> 00:34:17.699
of the soul and hands too, and things like that.

00:34:18.139 –> 00:34:20.960
And, you know, a lot of that expression are like

00:34:20.960 –> 00:34:23.840
these micro muscles and things under your eye

00:34:23.840 –> 00:34:27.039
that just a slight, subtle shift that us as social

00:34:27.039 –> 00:34:29.489
creatures. read and we read these body language

00:34:29.489 –> 00:34:31.210
that are almost imperceptible that we don’t even

00:34:31.210 –> 00:34:35.250
know we’re doing it. And A, can I, A, I even

00:34:35.250 –> 00:34:38.650
recreate that and B, you’re right. There’s no

00:34:38.650 –> 00:34:43.130
emotion driving it. And you can, I feel like

00:34:43.130 –> 00:34:46.409
you can tell as a human, at least still when

00:34:46.409 –> 00:34:49.750
that emotion is faked, you know, we’re so keyed

00:34:49.750 –> 00:34:51.949
into that as, as human beings. Yeah. I mean,

00:34:51.949 –> 00:34:57.300
I even Have umbrage with the uncanny valley thing

00:34:57.300 –> 00:35:01.460
that CGI like if I watch something I mean I watched

00:35:01.460 –> 00:35:04.599
What is it stranger things all the way through

00:35:04.599 –> 00:35:08.940
final season there? I believe you know their

00:35:08.940 –> 00:35:12.940
backdrop was a lot of CGI for these huge set

00:35:12.940 –> 00:35:15.619
pieces And I just looked at it it took me out

00:35:15.619 –> 00:35:18.820
of it like I hate that 100 % you know it’s terrible.

00:35:18.820 –> 00:35:22.880
That’s not even AI That’s CGI. Can you just explain

00:35:22.880 –> 00:35:24.920
for the audience if they don’t know what Uncanny

00:35:24.920 –> 00:35:28.900
Valley is? I’ll do my best. To me, it’s kind

00:35:28.900 –> 00:35:33.059
of a visual eyesore of where things almost look

00:35:33.059 –> 00:35:37.000
real but aren’t quite, and you can’t quite articulate

00:35:37.000 –> 00:35:40.679
that. And it’s particularly with humans. Well,

00:35:40.800 –> 00:35:46.019
I see it more and more on these shows that don’t

00:35:46.019 –> 00:35:48.280
film on location but need to create some vast

00:35:48.280 –> 00:35:53.960
backdrop. And it’s like, if I look at Hellraiser

00:35:53.960 –> 00:35:56.880
2, or I look at Labyrinth or something, that

00:35:56.880 –> 00:35:59.679
predates all this. That looks so much more interesting

00:35:59.679 –> 00:36:01.800
to me, even if I know it’s not a real place,

00:36:02.420 –> 00:36:05.039
than the new season of Stranger Things, Upside

00:36:05.039 –> 00:36:08.539
Down, whatever. You’re just seeing this synthetic

00:36:08.539 –> 00:36:11.760
-looking world behind. Exactly. You just feel

00:36:11.760 –> 00:36:14.699
the green screen effects of it. Like, yeah, I

00:36:14.699 –> 00:36:17.599
know exactly what you mean. It almost, like the

00:36:17.599 –> 00:36:19.280
A .I. McDonald’s thing, it has like a little

00:36:20.090 –> 00:36:22.269
cartoonish quality about it. It’s hard to explain.

00:36:22.409 –> 00:36:25.170
It’s not it’s not when you analyze it, but there’s

00:36:25.170 –> 00:36:29.289
just something about. It looks like almost too

00:36:29.289 –> 00:36:31.809
slick or something as well. Like, I guess it

00:36:31.809 –> 00:36:35.349
depends case by case, but I hate it. Yeah, no,

00:36:35.349 –> 00:36:38.429
that’s true. Yeah. So and you know what Tilly

00:36:38.429 –> 00:36:41.510
Norwood brings up for me, too, is just this couple

00:36:41.510 –> 00:36:45.530
of things. One is if you can have an AI generated

00:36:45.530 –> 00:36:47.170
movie star like these they’ve created with Tilly

00:36:47.170 –> 00:36:50.030
Norwood. What’s to stop anyone from making one

00:36:50.030 –> 00:36:55.389
and so why even have? One that reoccurs in films

00:36:55.389 –> 00:36:57.510
why why if you’re making AI films, why aren’t

00:36:57.510 –> 00:37:00.570
you creating new? Actors quote -unquote AI actors

00:37:00.570 –> 00:37:04.090
for each one why even have one? You know, and

00:37:04.090 –> 00:37:06.309
it’s what’s funny to me is they’re recreating

00:37:06.309 –> 00:37:09.269
like a fundamentally human phenomenon, which

00:37:09.269 –> 00:37:13.889
is We identify and grow to like individuals and

00:37:13.889 –> 00:37:16.000
we like seeing them in places, which is why I

00:37:16.000 –> 00:37:18.000
like George Clooney can command so much for a

00:37:18.000 –> 00:37:19.960
film, right? It’s because people like George

00:37:19.960 –> 00:37:24.679
Clooney. And when you go to AI, you know, they’re

00:37:24.679 –> 00:37:27.940
going to try to recreate, like build a personal

00:37:27.940 –> 00:37:32.039
brand, quote unquote, around an AI made up thing,

00:37:32.579 –> 00:37:35.559
just so they can make it feel more human in a

00:37:35.559 –> 00:37:39.760
way. Because I don’t know. To me, it just it’s

00:37:39.760 –> 00:37:44.380
kind of an interesting. What’s the word I’m looking

00:37:44.380 –> 00:37:48.039
for? like a paradox or something. I don’t know.

00:37:48.840 –> 00:37:50.539
But that’s just something to think about. And

00:37:50.539 –> 00:37:53.079
then the other thing is that it’s also interesting

00:37:53.079 –> 00:37:56.980
to me that the first A .I. quote unquote actress

00:37:56.980 –> 00:38:01.739
that was created was a young white pretty female.

00:38:02.679 –> 00:38:05.539
You know, of course, it wasn’t going to be like.

00:38:06.119 –> 00:38:10.360
You know, a person of color or, you know, old

00:38:10.360 –> 00:38:14.400
white. Male or or even an old white female and

00:38:14.400 –> 00:38:18.059
they’re gonna go for the most like generic like

00:38:18.059 –> 00:38:22.760
idealized social construct of like what You know

00:38:22.760 –> 00:38:25.039
people want to see or at least what they think

00:38:25.039 –> 00:38:27.420
people want to see so just a little interesting

00:38:27.420 –> 00:38:30.260
social commentary I think Which brings me to

00:38:30.260 –> 00:38:32.800
the next topic of conversation, which is Darren

00:38:32.800 –> 00:38:37.159
Aronofsky So it came out not too long ago early

00:38:37.159 –> 00:38:41.289
this spring that he launched New venture that’s

00:38:41.289 –> 00:38:45.269
an AI studio called Primordial Soup and It’s

00:38:45.269 –> 00:38:48.469
in partnership with Microsoft or sorry with Google’s

00:38:48.469 –> 00:38:50.929
DeepMind not Microsoft with Google’s DeepMind

00:38:50.929 –> 00:38:55.690
AI research team the idea being that they produce

00:38:55.690 –> 00:39:00.809
three short AI films and They get access to some

00:39:00.809 –> 00:39:04.070
of the new tools at Google and in exchange They

00:39:04.070 –> 00:39:06.510
give feedback and try to help them and with the

00:39:06.510 –> 00:39:09.880
direction of developing the tool And their first

00:39:09.880 –> 00:39:12.920
project from that was directed by Eliza McNitt

00:39:12.920 –> 00:39:15.119
and it’s called Ancestra, which premiered at

00:39:15.119 –> 00:39:18.159
Tribeca. How long ago? This spring. This spring.

00:39:18.340 –> 00:39:20.920
In May, or no, sorry, Tribeca was in June, so.

00:39:21.420 –> 00:39:24.159
So this is 2025. Yeah. So it has premiered. Yeah.

00:39:24.940 –> 00:39:27.519
And interesting enough, there has been reviews

00:39:27.519 –> 00:39:30.739
and they’re not great. In fact, one headline

00:39:30.739 –> 00:39:36.239
that I got a kick out of was. Ancestra makes

00:39:36.239 –> 00:39:40.099
the AI film hype feel pretty premature, which

00:39:40.099 –> 00:39:43.519
I thought was quite interesting. So that’s just

00:39:43.519 –> 00:39:45.539
something to note. And then along with that,

00:39:45.679 –> 00:39:48.960
Natasha Lyonne has done something similar. And

00:39:48.960 –> 00:39:52.079
she is also starting a company with Bryn Mooser,

00:39:52.900 –> 00:39:57.059
and it’s called Asterea Film. And their first

00:39:57.059 –> 00:39:59.920
film is called Uncanny Valley, which it’s a feature

00:39:59.920 –> 00:40:04.219
film. And both this and the Deronofsky… Darren

00:40:04.219 –> 00:40:06.900
Aronofsky produced film that premiered at Tribeca

00:40:06.900 –> 00:40:11.940
are both a mix of AI and live action and the

00:40:11.940 –> 00:40:16.320
Natasha Lyonne film called Uncanny Valley is

00:40:16.320 –> 00:40:21.579
Not out yet. It’s still in process but she in

00:40:21.579 –> 00:40:23.659
Both of them when they announced this was gonna

00:40:23.659 –> 00:40:26.199
happen. They got massive feedback Lots of people

00:40:26.199 –> 00:40:29.079
are upset backlash backlash. Sorry. Yeah, they

00:40:29.079 –> 00:40:31.860
got massive backlash. People are upset commenting

00:40:31.860 –> 00:40:34.130
on their posts all sorts of things, and they’ve

00:40:34.130 –> 00:40:37.449
both since defended it. And, you know, Natasha

00:40:37.449 –> 00:40:40.190
Lyon’s point is that she’s trying to do an ethical

00:40:40.190 –> 00:40:43.829
AI company and do this ethically, which obviously

00:40:43.829 –> 00:40:47.210
brings up the question of whether you can do

00:40:47.210 –> 00:40:50.289
it ethically. For all sorts of reasons, we could

00:40:50.289 –> 00:40:54.590
touch on a little bit. And, you know, she had

00:40:54.590 –> 00:40:57.309
a variety interview recently, and she said it’s

00:40:57.309 –> 00:40:58.909
all about protecting artists and confronting

00:40:58.909 –> 00:41:02.480
this oncoming wave. which is ridiculous, I think,

00:41:02.519 –> 00:41:04.280
when you’re talking about how you’re the one

00:41:04.280 –> 00:41:07.460
doing it. So she said, it’s comedic that people

00:41:07.460 –> 00:41:09.500
misunderstand headlines so readily because of

00:41:09.500 –> 00:41:11.800
our bizarre culture of not having reading comprehension.

00:41:12.159 –> 00:41:14.559
Suddenly I became some weird Darth Vader character

00:41:14.559 –> 00:41:17.440
or something. That’s crazy talk, but God bless

00:41:17.440 –> 00:41:21.119
addressing the backlash. I don’t know. I think

00:41:21.119 –> 00:41:23.519
her comments on the backlash are a little silly.

00:41:23.559 –> 00:41:26.559
Like she doesn’t understand as an actress why

00:41:26.559 –> 00:41:30.260
people might be upset on doing that. And she’s

00:41:30.260 –> 00:41:33.000
saying, I should clarify that the AI that they’re

00:41:33.000 –> 00:41:36.260
using is for like scene extension, like you were

00:41:36.260 –> 00:41:39.800
talking about in Stranger Things and more supportive

00:41:39.800 –> 00:41:43.539
types of uses of AI, but it ignores the whole

00:41:43.539 –> 00:41:45.579
point that there are people who do those jobs.

00:41:46.139 –> 00:41:50.360
Right. That’s, yeah, that’s a solid point. Yeah.

00:41:50.760 –> 00:41:53.320
I mean, what’s interesting is who knows, maybe

00:41:53.320 –> 00:41:57.000
she makes this and it’s mind blowing. Like maybe

00:41:57.000 –> 00:42:00.079
it’s a really cool… project, we can’t judge

00:42:00.079 –> 00:42:03.079
until we see it. Right. But at the same time,

00:42:03.099 –> 00:42:05.539
at what cost? Because that’s ultimately what

00:42:05.539 –> 00:42:07.840
is this really going to be opening Pandora’s

00:42:07.840 –> 00:42:11.559
box for copycats? And yeah, that’s the thing.

00:42:11.579 –> 00:42:13.679
It’s like she might do it ethically, like or

00:42:13.679 –> 00:42:16.920
some version of that. But people are going to

00:42:16.920 –> 00:42:19.219
learn from her experience. And are they going

00:42:19.219 –> 00:42:21.340
to do it ethically? Well, will the collective

00:42:21.340 –> 00:42:24.260
actions of a lot of people, even if they’re doing

00:42:24.260 –> 00:42:30.179
it ethically, amount to? a situation in which

00:42:30.179 –> 00:42:33.119
people are, you know, suffering to some extent

00:42:33.119 –> 00:42:36.860
by not having work or by, you know, being forced

00:42:36.860 –> 00:42:39.800
to consume really crappy films, you know? Like,

00:42:39.820 –> 00:42:42.019
I guess my point is, like, it’s easy to say it’s

00:42:42.019 –> 00:42:44.340
unethic without considering every way in which

00:42:44.340 –> 00:42:46.280
it could go wrong, and it’s impossible to do

00:42:46.280 –> 00:42:48.079
that, you know? So, like, she can’t say it’s

00:42:48.079 –> 00:42:50.280
ethical, I don’t think. But what’s interesting

00:42:50.280 –> 00:42:53.639
to me is in every gold rush, which AI is definitely

00:42:53.639 –> 00:42:57.920
a gold rush, there’s always some… people who

00:42:57.920 –> 00:43:00.400
decide to jump in early and be on the forefront

00:43:00.400 –> 00:43:02.659
of it because potentially because they see the

00:43:02.659 –> 00:43:04.380
writing on the wall and they know like, oh, if

00:43:04.380 –> 00:43:06.059
this comes to be, I want to be on the forefront

00:43:06.059 –> 00:43:09.139
of it. Because then at least I will capitalize

00:43:09.139 –> 00:43:12.079
off it rather than being kind of the one that

00:43:12.079 –> 00:43:14.539
that gets tossed aside because of it. Right.

00:43:14.860 –> 00:43:16.840
There’s this this quote you said Gold Rush that

00:43:16.840 –> 00:43:19.179
made me think of it. When there’s a gold rush,

00:43:19.480 –> 00:43:22.539
sell shovels. Right. And that’s exactly what

00:43:22.539 –> 00:43:25.329
I kind of think. Well, that’s what a lot of these

00:43:25.329 –> 00:43:27.409
AI companies are doing. But in some ways, that’s

00:43:27.409 –> 00:43:28.949
kind of what they’re doing. Because I think,

00:43:29.309 –> 00:43:31.809
for instance, that Darren Aronofsky probably

00:43:31.809 –> 00:43:33.570
got a lot of money from Google to announce that

00:43:33.570 –> 00:43:37.369
partnership. Oh, no doubt. Yep. And so he’s thinking

00:43:37.369 –> 00:43:38.710
like, hey, they’re going to do a partnership

00:43:38.710 –> 00:43:41.949
with someone. It should be me. And Darren Aronofsky,

00:43:42.670 –> 00:43:45.250
it’s a little unbranded for him because he’s

00:43:45.250 –> 00:43:47.230
kind of known for embracing new techniques in

00:43:47.230 –> 00:43:49.610
filmmaking and pushing the boundary of technology

00:43:49.610 –> 00:43:52.929
in that regard. And that is an argument that

00:43:52.929 –> 00:43:54.449
a lot of people make, which we’re going to get

00:43:54.449 –> 00:43:58.389
into the arguments for AI in a little bit. But

00:43:58.389 –> 00:44:02.150
I’m not sure I buy it 100%. I think it was really

00:44:02.150 –> 00:44:03.989
about the money, if I’m being honest. And I think

00:44:03.989 –> 00:44:07.730
that as filmmakers, we’re always trying to get

00:44:07.730 –> 00:44:11.710
funding. And if you’re doing something, both

00:44:11.710 –> 00:44:13.630
their announcements got a ton of press. And so

00:44:13.630 –> 00:44:16.530
if you can kind of be the project, the first

00:44:16.530 –> 00:44:19.619
one, like, that might be a way to generate money

00:44:19.619 –> 00:44:21.679
for whatever project you’re trying to make, especially

00:44:21.679 –> 00:44:24.659
if it’s controversial. Exactly. Controversy does

00:44:24.659 –> 00:44:28.860
breed attention, which breeds funding. So what

00:44:28.860 –> 00:44:31.619
this sort of brings us to the next thing that

00:44:31.619 –> 00:44:33.239
I’d like to talk about and get your input about,

00:44:33.300 –> 00:44:36.119
because there’s been a lot of talk about using

00:44:36.119 –> 00:44:39.760
AI in the part of the process in pre -production,

00:44:40.059 –> 00:44:44.340
particularly in. Screenwriting and as a screenwriter,

00:44:44.360 –> 00:44:46.860
I’m curious if you’ve had any action with AI

00:44:46.860 –> 00:44:51.760
or What’s your sense about it being? part of

00:44:51.760 –> 00:44:55.380
the the genesis of the story making process So

00:44:55.380 –> 00:44:58.019
I get why it’s tempting a friend of mine showed

00:44:58.019 –> 00:45:01.059
me before I even knew what chat GPT was he’s

00:45:01.059 –> 00:45:03.980
like give me an idea for a movie so I just Came

00:45:03.980 –> 00:45:06.719
up with something on the spot and ten seconds

00:45:06.719 –> 00:45:10.519
later There’s a whole treatment for this potential

00:45:10.519 –> 00:45:13.679
movie. And so then I looked at it and I’m like,

00:45:13.739 –> 00:45:17.579
that is crazy, right? But I was like, this feels

00:45:17.579 –> 00:45:21.079
formulaic. And then he’s like, add twist. And

00:45:21.079 –> 00:45:26.280
yeah, you could just go from zero to 60 with

00:45:26.280 –> 00:45:31.119
doing that. But as a writer, the interesting

00:45:31.119 –> 00:45:34.719
thing about writing to me is the process of discovery.

00:45:34.840 –> 00:45:36.460
You can have an outline. You can know where you’re

00:45:36.460 –> 00:45:39.530
going. And then you get a better idea. And you’re

00:45:39.530 –> 00:45:43.489
like, oh, shit. And discovering it that way.

00:45:43.610 –> 00:45:45.849
Whereas if you’re just copying something out

00:45:45.849 –> 00:45:50.630
of the ether that is patterns of thousands of

00:45:50.630 –> 00:45:53.010
different movies to give you some perfect formula

00:45:53.010 –> 00:45:56.769
for it, what the fuck is the point? Yeah. No,

00:45:56.769 –> 00:46:00.070
you’re right. I think the Coen brothers, I think

00:46:00.070 –> 00:46:02.070
it’s Ethan Coen who’s the primary writer out

00:46:02.070 –> 00:46:08.070
of the two, I’m pretty sure. I read a book. about

00:46:08.070 –> 00:46:10.630
their work in the past. And I remember something

00:46:10.630 –> 00:46:12.050
that’s always stood out to me, and I read this

00:46:12.050 –> 00:46:14.909
book 25 years ago, but something that stood out

00:46:14.909 –> 00:46:19.969
to me about it was he said, what makes his work

00:46:19.969 –> 00:46:21.710
different or what he tries to do as a writer

00:46:21.710 –> 00:46:25.510
is that whenever he writes something, the first

00:46:25.510 –> 00:46:28.510
version is usually the expected way. And then

00:46:28.510 –> 00:46:31.630
he usually looks at that and be like, what if

00:46:31.630 –> 00:46:35.039
we do the opposite? Or like, what if we take

00:46:35.039 –> 00:46:36.980
this in a totally different direction? And that’s

00:46:36.980 –> 00:46:40.000
what keeps their films feeling fresh and interesting

00:46:40.000 –> 00:46:42.739
a lot of times. That’s cool. I’d never heard

00:46:42.739 –> 00:46:46.139
that. Yeah. And so I’m kind of curious if maybe

00:46:46.139 –> 00:46:49.579
there’s a way to use AI just speculating here

00:46:49.579 –> 00:46:52.320
where you get the sort of expected formulaic

00:46:52.320 –> 00:46:53.460
draft, and then you’re like, OK, I’m going to

00:46:53.460 –> 00:46:57.500
do everything differently. You take it as a starting

00:46:57.500 –> 00:46:59.519
point and be like, OK, this is what not to do.

00:47:01.429 –> 00:47:05.110
If people start using AI to write scripts in

00:47:05.110 –> 00:47:08.789
such a way and then change them, it’s just like

00:47:08.789 –> 00:47:12.690
a shortcut. You know, I feel like you earn your

00:47:12.690 –> 00:47:15.789
projects by the blood, sweat, and tears if it’s

00:47:15.789 –> 00:47:18.010
something creative. Yeah, well said. You know,

00:47:18.150 –> 00:47:20.389
if you’re like a painter, you’re not just gonna

00:47:20.389 –> 00:47:21.650
be like, you know what, I’m just gonna press

00:47:21.650 –> 00:47:24.329
a button on a computer and tell it what to paint

00:47:24.329 –> 00:47:27.420
and be like, I’m a fucking artist. Right. No,

00:47:27.420 –> 00:47:29.719
it’s true. I mean, and also like there’s no soul

00:47:29.719 –> 00:47:33.480
in that. And, you know, I think people can see

00:47:33.480 –> 00:47:35.800
much like we were talking about the acting, like

00:47:35.800 –> 00:47:38.199
whether there’s like soul, like there’s a hand

00:47:38.199 –> 00:47:41.920
that made the thing. And, you know, with writing

00:47:41.920 –> 00:47:44.619
in particular, because we’re talking about formulating

00:47:44.619 –> 00:47:48.159
the story at like the base level, you know, AI

00:47:48.159 –> 00:47:51.760
really only generates based on what’s come before.

00:47:52.260 –> 00:47:54.420
So you’re never going to get a script out of

00:47:54.420 –> 00:47:57.840
that. That’s just totally New and innovative

00:47:57.840 –> 00:48:02.219
like idea that really pushes the the form of

00:48:02.219 –> 00:48:05.119
cinema forward It’s only ever gonna be like a

00:48:05.119 –> 00:48:07.539
rehashing of stories that we’ve told before right

00:48:07.539 –> 00:48:11.760
and to get away from cliches That’s where you

00:48:11.760 –> 00:48:15.260
that you hear right what you know, right? You

00:48:15.260 –> 00:48:17.900
know your life, you know those moments of things

00:48:17.900 –> 00:48:21.369
that happen that you know, you never would have

00:48:21.369 –> 00:48:23.750
thought of. It’s like, the truth is stranger

00:48:23.750 –> 00:48:25.710
than fiction. So I’ll give you an example. So

00:48:25.710 –> 00:48:28.989
years ago, got into an accident. My windshield

00:48:28.989 –> 00:48:33.130
was cracked. I had to take it to a mechanic,

00:48:33.130 –> 00:48:34.690
you know, wherever they’re gonna replace the

00:48:34.690 –> 00:48:37.710
windshield, but it was hot out. So I had the

00:48:37.710 –> 00:48:41.909
AC on and the guy told me he’s like… Those

00:48:41.909 –> 00:48:43.969
glass shards could get sucked in there and spit

00:48:43.969 –> 00:48:46.929
in your face And I was like, holy fuck but I

00:48:46.929 –> 00:48:48.710
was like that’s so I wrote that into a script

00:48:48.710 –> 00:48:51.289
because I’d never seen that right I never would

00:48:51.289 –> 00:48:53.829
have thought of that at that, you know, I was

00:48:53.829 –> 00:48:56.150
in an accident and that happened Yeah, so like

00:48:56.150 –> 00:48:59.230
a point, you know you have these Moments in life

00:48:59.230 –> 00:49:02.989
or something goes south or whatever and you as

00:49:02.989 –> 00:49:08.820
a writer you use you use it. Yeah Yeah Yeah And

00:49:08.820 –> 00:49:10.559
AI obviously doesn’t have that experience, like

00:49:10.559 –> 00:49:12.260
you were saying earlier, like the lived experience

00:49:12.260 –> 00:49:14.219
to incorporate things like that. I mean, it sort

00:49:14.219 –> 00:49:15.780
of has access to all of our lived experience,

00:49:16.039 –> 00:49:19.440
but I do think like inspiration comes from, you

00:49:19.440 –> 00:49:22.179
know, all sorts of strange places. And so you’re

00:49:22.179 –> 00:49:23.739
far more likely to come up with something unique

00:49:23.739 –> 00:49:27.000
that way. One last thing about like screenwriting

00:49:27.000 –> 00:49:31.099
AI and all that. is it’s not as new as we think

00:49:31.099 –> 00:49:34.980
it is. Back in 2016, there was a project called

00:49:34.980 –> 00:49:37.780
Impossible Things, a Canadian production company

00:49:37.780 –> 00:49:40.239
was trying to put together, and it achieved its

00:49:40.239 –> 00:49:42.340
Kickstarter goal. But it basically was going

00:49:42.340 –> 00:49:47.320
to integrate AI patterns to find the perfect

00:49:47.320 –> 00:49:50.119
formula for a movie and then have a human right

00:49:50.119 –> 00:49:53.139
with that in mind. And what’s interesting is

00:49:53.139 –> 00:49:56.719
it achieved its Kickstarter goal. But if you

00:49:56.719 –> 00:50:00.699
look at the IMDB page, it’s it’s in pre production.

00:50:00.739 –> 00:50:03.559
So I think it’s kind of a dead, dead end. But

00:50:03.559 –> 00:50:06.139
it’s just people are thinking about it back then.

00:50:06.139 –> 00:50:08.300
Yeah. You know, trying to get ahead of it. But

00:50:08.300 –> 00:50:10.099
yeah, I mean, yeah, you bring up a good point.

00:50:10.239 –> 00:50:14.960
I certainly as a new it’s like. I think certain

00:50:14.960 –> 00:50:17.800
people have been wanting this for a long time,

00:50:17.800 –> 00:50:19.739
you know, and been kind of working towards it.

00:50:19.739 –> 00:50:21.400
And there’s something sci fi about it, you know,

00:50:21.400 –> 00:50:23.300
it is sort of interesting. But, you know, when

00:50:23.300 –> 00:50:26.019
you’re speculating about future technologies,

00:50:26.179 –> 00:50:28.550
you’re always. often thinking about all of the

00:50:28.550 –> 00:50:30.670
positives and, you know, rarely thinking about

00:50:30.670 –> 00:50:33.289
the negatives. Right. And now here we are. The

00:50:33.289 –> 00:50:36.289
other thing, actually, one is that in my algorithms

00:50:36.289 –> 00:50:39.809
as a screenwriter, there’s companies like Greenlight

00:50:39.809 –> 00:50:44.489
Coverage that use AI to assess your your log

00:50:44.489 –> 00:50:48.250
lines. Yeah. Or probably even your scripts. because

00:50:48.250 –> 00:50:50.190
I can turn it around super quick. Have you done

00:50:50.190 –> 00:50:52.349
that before? No, but I was curious about it because

00:50:52.349 –> 00:50:54.889
they do a really good job of presenting itself.

00:50:55.329 –> 00:50:58.110
I think it was even before I realized AI was

00:50:58.110 –> 00:51:01.130
involved. We should run our script for Last Call

00:51:01.130 –> 00:51:02.710
Through just for the fun of it and see what happens.

00:51:03.010 –> 00:51:05.090
Jesus Christ, that would cost money to use it.

00:51:05.409 –> 00:51:08.670
Oh, yeah. Well, never mind then. So moving on

00:51:08.670 –> 00:51:12.570
to video generation, you did show me somebody

00:51:12.570 –> 00:51:15.360
shared with you Higgs field which you shared

00:51:15.360 –> 00:51:18.579
with me and Higgs field is essentially a software

00:51:18.579 –> 00:51:23.739
for generating really convincing and lifelike

00:51:23.739 –> 00:51:28.480
AI video and you can With that you can change

00:51:28.480 –> 00:51:31.760
what kind of camera what kind of lens? You can

00:51:31.760 –> 00:51:34.880
make the character persist throughout the scene

00:51:34.880 –> 00:51:37.400
or potentially maybe the film You know, I wanted

00:51:37.400 –> 00:51:39.500
to play around with it more, but I you have to

00:51:39.500 –> 00:51:41.650
pay for it to even really start playing with

00:51:41.650 –> 00:51:43.849
it and at least on the things that I wanted to

00:51:43.849 –> 00:51:47.710
try. And so I don’t have hands on with it. But

00:51:47.710 –> 00:51:50.969
what I saw was like it was really impressive.

00:51:51.030 –> 00:51:54.050
I will say is very, very, very impressive and

00:51:54.050 –> 00:51:56.909
somewhat scary because, you know, I don’t think

00:51:56.909 –> 00:51:59.030
it’s 100 percent there, but it shows promise

00:51:59.030 –> 00:52:01.769
in terms of like who knows what could happen.

00:52:02.309 –> 00:52:05.969
Right. I for all that type of stuff. You know,

00:52:06.030 –> 00:52:09.650
the friend he shared it with me. I agreed. I

00:52:09.650 –> 00:52:11.679
was like, all right, for previews, You could

00:52:11.679 –> 00:52:13.579
totally. Previs for sure. You could use that.

00:52:13.699 –> 00:52:14.980
And if you don’t guys don’t know what previs

00:52:14.980 –> 00:52:17.119
is, it’s like if you’re shooting a movie, you

00:52:17.119 –> 00:52:20.980
do your storyboards. You want like, you know

00:52:20.980 –> 00:52:23.000
what the movie looks like before you shoot it.

00:52:23.019 –> 00:52:24.599
So you kind of have a template. Yeah. And you

00:52:24.599 –> 00:52:28.000
could save time on set and. Yeah. So I could

00:52:28.000 –> 00:52:31.239
see the use case for that. But for sure, you

00:52:31.239 –> 00:52:33.480
just don’t want people then making movies that

00:52:33.480 –> 00:52:37.880
way. Yeah. Yeah. What I’ve kind of. still think

00:52:37.880 –> 00:52:40.239
our barriers to really video generation taking

00:52:40.239 –> 00:52:41.699
off, and I don’t even think that Higgs fields

00:52:41.699 –> 00:52:45.340
entirely solve this, is that limps sinking can

00:52:45.340 –> 00:52:50.059
still be an issue because we are automatically

00:52:50.059 –> 00:52:51.840
keyed into the shape of people’s mouths when

00:52:51.840 –> 00:52:55.440
they’re talking and even very subtle differences

00:52:55.440 –> 00:52:58.219
are highly noticeable and they’re pretty good

00:52:58.219 –> 00:53:01.949
with it these days. but there’s still some uncanny

00:53:01.949 –> 00:53:04.349
valley stuff in the mouth area when people are

00:53:04.349 –> 00:53:07.170
talking. Right. It’s, no, a good example is like

00:53:07.170 –> 00:53:10.130
sometimes if some streaming platforms, I’ve seen

00:53:10.130 –> 00:53:13.550
that where it’s like just off, maybe it’s like

00:53:13.550 –> 00:53:16.090
a European film and they’re not speaking English,

00:53:16.130 –> 00:53:18.170
but they have an English dub over it. Yeah, yeah,

00:53:18.170 –> 00:53:20.269
exactly. And you’re like, wait, this is, or even

00:53:20.269 –> 00:53:23.409
if there’s like a lag, it just drives me crazy.

00:53:23.590 –> 00:53:28.760
Yeah. No, it’s true. Sometimes what I think about

00:53:28.760 –> 00:53:33.179
with that is that with TikTok videos and YouTube,

00:53:34.039 –> 00:53:36.380
the way that kids approach social media these

00:53:36.380 –> 00:53:40.619
days is very postmodern, where anything can happen

00:53:40.619 –> 00:53:44.219
at any time and you’re less worried about things

00:53:44.219 –> 00:53:47.420
like continuity and even sometimes the quality

00:53:47.420 –> 00:53:50.079
of the shot or the effect that they’re doing.

00:53:50.480 –> 00:53:53.360
All of a sudden, cartoon sunglasses fall on someone’s

00:53:53.360 –> 00:53:57.239
face in a YouTube video to like… uh, signify

00:53:57.239 –> 00:53:58.579
that they just did something thug, you know,

00:53:58.579 –> 00:54:01.719
or whatever, or like, you know, just pauses and

00:54:01.719 –> 00:54:03.719
zooms in real quick. So it’s like, but it becomes

00:54:03.719 –> 00:54:05.840
hyper pixelated because you zoomed in so close,

00:54:05.840 –> 00:54:08.579
you know, things like that. And I just wonder

00:54:08.579 –> 00:54:10.880
if all of these things are actually just training

00:54:10.880 –> 00:54:15.860
us to accept like. You know, unintentionally

00:54:15.860 –> 00:54:18.019
so, but except like the uncanny Valley mouth

00:54:18.019 –> 00:54:20.280
thing, you know, like you’re talking about the

00:54:20.280 –> 00:54:22.639
dubbing of voiceover you want that’s more and

00:54:22.639 –> 00:54:24.960
more popular on. Netflix to watch foreign films

00:54:24.960 –> 00:54:29.280
with voice with dubbing. And, you know, do people

00:54:29.280 –> 00:54:31.820
care anymore if there’s uncanny valley stuff

00:54:31.820 –> 00:54:35.360
around the mouth? Like, who knows? I tend to

00:54:35.360 –> 00:54:38.820
read the subtitles like I it does bite you in

00:54:38.820 –> 00:54:40.719
the ass a little bit if there’s action and all

00:54:40.719 –> 00:54:42.900
this stuff and you’re trying to read those. But

00:54:42.900 –> 00:54:45.460
I’m good with reading the subtitles. Same. But

00:54:45.460 –> 00:54:48.000
like, we’re not the next generation. No, true.

00:54:48.219 –> 00:54:51.199
But at the same time, I think there’s some younger

00:54:51.199 –> 00:54:53.550
kids that probably we’ll see the older stuff

00:54:53.550 –> 00:54:56.530
and be like, no, this is cool. Like it for the

00:54:56.530 –> 00:54:58.809
reason we do. Just they’re born in the wrong

00:54:58.809 –> 00:55:01.769
generation. No, totally, totally. Yeah, the other

00:55:01.769 –> 00:55:05.010
problem that is still a problem with video generation,

00:55:05.309 –> 00:55:07.550
and this segment I think applies a lot to filmmakers

00:55:07.550 –> 00:55:09.670
because it’s the video generation that a lot

00:55:09.670 –> 00:55:12.469
of us are worried about. But another problem

00:55:12.469 –> 00:55:15.150
is eye lines. I’ve still yet to find, I looked

00:55:15.150 –> 00:55:18.210
for examples before we recorded this of scenes

00:55:18.210 –> 00:55:20.750
of more than one person’s talking to each other.

00:55:22.159 –> 00:55:25.460
but realistic videos, and you have to find one.

00:55:26.699 –> 00:55:28.480
And sometimes when you have more than one people

00:55:28.480 –> 00:55:30.960
talking in a scene, they’re kind of looking at

00:55:30.960 –> 00:55:33.159
other things or at the screen, but never at each

00:55:33.159 –> 00:55:36.320
other. And that’s something we intuitively know

00:55:36.320 –> 00:55:39.420
in the language of cinema, that when you cut

00:55:39.420 –> 00:55:41.820
from this person looking camera left, this person

00:55:41.820 –> 00:55:44.780
needs to look camera right. And like, you know,

00:55:44.960 –> 00:55:47.829
in geography… in the geography of the scene

00:55:47.829 –> 00:55:50.030
in the room where they should be looking. 180

00:55:50.030 –> 00:55:53.190
rule, right? Well, the 180 rule, yeah, it comes

00:55:53.190 –> 00:55:56.650
into play. I mean, for all I know, AI obeys the

00:55:56.650 –> 00:56:00.250
180 rule, but regardless, the continuity from

00:56:00.250 –> 00:56:02.710
shot to shot of where the person’s looking and

00:56:02.710 –> 00:56:04.730
where their hand is and all of that is off, and

00:56:04.730 –> 00:56:08.409
I’ve noticed that in some of the AI -generated

00:56:08.409 –> 00:56:11.309
videos I’ve seen, where it’s like, oh, he’s pointing

00:56:11.309 –> 00:56:12.670
to that part of the room, but where is that part

00:56:12.670 –> 00:56:14.130
of the room? We haven’t seen it yet. Like, it

00:56:14.130 –> 00:56:17.320
just… It doesn’t entirely make sense, the geography

00:56:17.320 –> 00:56:19.559
of the scene. Well, so that gets me wondering

00:56:19.559 –> 00:56:22.340
if AI is just going to start course correcting

00:56:22.340 –> 00:56:26.360
itself in terms of film or how we. But the problem

00:56:26.360 –> 00:56:29.619
is that AI doesn’t understand. This is why I’m

00:56:29.619 –> 00:56:31.719
not particularly scared about AI is AI doesn’t

00:56:31.719 –> 00:56:34.079
understand what it’s doing. It’s just looking

00:56:34.079 –> 00:56:36.440
at patterns and how things have been done before.

00:56:36.500 –> 00:56:39.739
But it doesn’t realize in those patterns, cutting

00:56:39.739 –> 00:56:44.059
between shots, because the pattern it like. it

00:56:44.059 –> 00:56:49.039
doesn’t quite understand that, you know, it’s

00:56:49.039 –> 00:56:51.739
looking at the other shot, the other person.

00:56:52.039 –> 00:56:54.179
So it’s like dumb, dumb to this stuff. And it

00:56:54.179 –> 00:56:56.440
really, so I think that’s going to be, those

00:56:56.440 –> 00:56:58.039
kinds of things are actually the last barrier

00:56:58.039 –> 00:57:00.679
for AI that, and there are hard problems to solve.

00:57:00.860 –> 00:57:03.019
And I’m not saying we’re never going to get there,

00:57:03.019 –> 00:57:06.860
but from what limited things I know about how

00:57:06.860 –> 00:57:09.440
it works, I do think it’s going to take another

00:57:09.440 –> 00:57:13.730
leap forward in, I don’t know if it’s the algorithms

00:57:13.730 –> 00:57:15.909
or a whole different approach to AI before I

00:57:15.909 –> 00:57:18.150
think that’s even really a thing that they’re

00:57:18.150 –> 00:57:20.070
going to be able to do. So I think that limits

00:57:20.070 –> 00:57:22.670
AI and from the video generation standpoint to

00:57:22.670 –> 00:57:28.210
being more used in montages or music videos or

00:57:28.210 –> 00:57:32.340
desperate kinds of elements that. our montage

00:57:32.340 –> 00:57:34.440
together to make a cohesive narrative. But I

00:57:34.440 –> 00:57:36.619
don’t know if we’re going to get like a full

00:57:36.619 –> 00:57:39.920
Stranger Things movie or whatever, because making

00:57:39.920 –> 00:57:42.360
those scenes and making them feel authentic is

00:57:42.360 –> 00:57:45.820
going to be really challenging. Yeah. And what’s

00:57:45.820 –> 00:57:49.619
also kind of fun, I mean, I’m being sardonic

00:57:49.619 –> 00:57:53.460
when I say that, is that it’s like these things

00:57:53.460 –> 00:57:55.800
with AI are nothing compared to like the real

00:57:55.800 –> 00:58:01.199
dangers. You know, it’s like the biggest threat

00:58:01.199 –> 00:58:04.039
of humanity, as the experts will tell you. Yeah.

00:58:04.480 –> 00:58:06.800
Well, let’s get into the arguments for AI, then

00:58:06.800 –> 00:58:08.380
we’ve kind of touched on them a little bit here

00:58:08.380 –> 00:58:10.980
and there or alluded to them. But, you know,

00:58:11.039 –> 00:58:16.380
a lot of people. Really suggests that that AI

00:58:16.380 –> 00:58:20.159
is like silly to resist it and that much like

00:58:20.159 –> 00:58:22.039
other technologies in the past, yeah, it’s going

00:58:22.039 –> 00:58:25.280
to disrupt. The industry and some people are

00:58:25.280 –> 00:58:27.460
gonna lose their job, but it’s gonna create new

00:58:27.460 –> 00:58:31.940
jobs somehow and it’s gonna If it’s a it’s a

00:58:31.940 –> 00:58:33.800
tool creative people are gonna learn how to use

00:58:33.800 –> 00:58:36.179
it and those people are gonna come out ahead

00:58:36.179 –> 00:58:38.780
and That’s what you hear from everyone from Mark

00:58:38.780 –> 00:58:42.000
Cuban down to you know, I mean particularly the

00:58:42.000 –> 00:58:46.800
CEOs of these AI companies Right. Well My thoughts

00:58:46.800 –> 00:58:50.900
on that are who, who are the, from what I understand,

00:58:51.059 –> 00:58:54.500
right? There’s no real guardrails for AI and

00:58:54.500 –> 00:58:57.380
you have every AI company racing to be the next

00:58:57.380 –> 00:59:01.460
Google. So that’s dangerous. You know, and it’s

00:59:01.460 –> 00:59:03.880
all, it all goes back to fucking money. You know,

00:59:03.920 –> 00:59:05.519
it’s all rich people. It’s all billionaires.

00:59:05.860 –> 00:59:10.199
It’s all the corporations who are, who are But

00:59:10.199 –> 00:59:11.860
dragging us. But do you buy that argument? Do

00:59:11.860 –> 00:59:13.559
you think that it’s really going to be? I mean,

00:59:13.599 –> 00:59:16.360
I think they believe that and they want you to

00:59:16.360 –> 00:59:18.840
believe it. Well, yeah. Yeah. But do you think,

00:59:18.920 –> 00:59:22.019
do you think, though, that there’s truth to it,

00:59:22.039 –> 00:59:24.000
though? Do you think that? I think there could

00:59:24.000 –> 00:59:26.860
be truth to that. But at the same time, like

00:59:26.860 –> 00:59:30.380
in the last year, you know, AI has not performed

00:59:30.380 –> 00:59:34.559
the way that it’s been projected to. Yeah. Yeah.

00:59:34.679 –> 00:59:37.630
You know, a lot of a lot of people. Call it a

00:59:37.630 –> 00:59:39.409
bubble like you were saying that like like this

00:59:39.409 –> 00:59:41.829
is all marketing hype, which I kind of fall in

00:59:41.829 –> 00:59:46.230
that argument but also The ex I actually heard

00:59:46.230 –> 00:59:49.929
a really interesting argument from a programmer

00:59:49.929 –> 00:59:57.010
He’s a youtuber named the primogen but he found

00:59:57.010 –> 01:00:01.889
this post by the CEO of of Microsoft who said

01:00:01.889 –> 01:00:07.179
we got to stop calling AI AI slop he’s like,

01:00:07.460 –> 01:00:10.559
you know, like as if we’re doing them a disservice

01:00:10.559 –> 01:00:13.340
and It’s of course it’s benefits him for people

01:00:13.340 –> 01:00:15.960
to stop calling AI slop a slot But the primogens

01:00:15.960 –> 01:00:18.900
argument was that nobody would be calling it

01:00:18.900 –> 01:00:22.800
AI slop if it were good You know, like you don’t

01:00:22.800 –> 01:00:26.070
see the need for other products that are good

01:00:26.070 –> 01:00:28.289
for them to go have to go out and chide people

01:00:28.289 –> 01:00:31.809
for calling them names or whatever and you know

01:00:31.809 –> 01:00:34.010
his larger point though is that there’s such

01:00:34.010 –> 01:00:37.590
a mismatch between what these CEOs are promising

01:00:37.590 –> 01:00:39.829
that AI can do and then when you get in front

01:00:39.829 –> 01:00:41.570
of it and actually try to use it like what it

01:00:41.570 –> 01:00:44.710
actually can do right and you feel that mismatch

01:00:44.840 –> 01:00:47.559
every day interacting with AI, at least I do.

01:00:47.960 –> 01:00:49.780
And I, you know, I’m probably not as much of

01:00:49.780 –> 01:00:51.800
an AI head as other people, but I, you know,

01:00:51.800 –> 01:00:53.940
I experiment with it. I try to use it sometimes

01:00:53.940 –> 01:00:57.320
if I, I don’t know, I’m doing like a refresh

01:00:57.320 –> 01:00:59.139
of a resume or I don’t know, whatever it could

01:00:59.139 –> 01:01:01.400
be, but like, ultimately I ended up redoing it

01:01:01.400 –> 01:01:03.199
all by hand anyways, because I’m so disappointed

01:01:03.199 –> 01:01:05.159
with what comes out of it. And I’m not, you know,

01:01:05.199 –> 01:01:07.179
I’m not using low -end models. I’m trying to

01:01:07.179 –> 01:01:08.840
do it with the best ones I have access to. For

01:01:08.840 –> 01:01:11.380
those people who are kind of on the fence and…

01:01:10.960 –> 01:01:14.440
experimenting with AI, I will say the one use

01:01:14.440 –> 01:01:17.219
that I get out of it being like such a lover

01:01:17.219 –> 01:01:20.400
of music and punk music is I will go to chat

01:01:20.400 –> 01:01:22.739
GPT and be like, you know, give me more songs

01:01:22.739 –> 01:01:25.400
like hybrid moments by the misfits that are of

01:01:25.400 –> 01:01:28.579
that error that are deep cuts and AI will present

01:01:28.579 –> 01:01:32.420
very interesting song choices and that I have

01:01:32.420 –> 01:01:35.530
never come across before. Yeah, and yeah, there’s

01:01:35.530 –> 01:01:37.550
there’s legitimate uses for AI and I wouldn’t

01:01:37.550 –> 01:01:39.630
I’m not trying to like argue personally that

01:01:39.630 –> 01:01:42.929
that there aren’t like by legitimate I mean like

01:01:42.929 –> 01:01:46.349
Look all technology technology is value agnostic

01:01:46.349 –> 01:01:50.190
in many ways. It’s how we use the The technology

01:01:50.190 –> 01:01:53.110
on what makes it sort of ethical or not ethical

01:01:53.110 –> 01:01:57.030
so there I think there are fairly like use cases

01:01:57.030 –> 01:02:00.769
for AI that are both incredibly helpful, but

01:02:00.769 –> 01:02:03.590
also not too impactful negatively on people.

01:02:03.829 –> 01:02:06.090
And I think what you said is a really good example.

01:02:06.269 –> 01:02:08.369
I mean, I think at the same time, someone gave

01:02:08.369 –> 01:02:10.269
me shit for it because it’s like the environmental

01:02:10.269 –> 01:02:13.469
cost of every time you, and I’m like, that’s,

01:02:13.469 –> 01:02:16.409
that’s fair. You know, this isn’t a meant to

01:02:16.409 –> 01:02:19.570
be an entire analysis of AI in general and like

01:02:19.570 –> 01:02:21.349
all the pros and cons of it, but that is a really

01:02:21.349 –> 01:02:23.170
good point. The environmental impact for all

01:02:23.170 –> 01:02:25.650
of this stuff is massive. You know, I mean, Yeah,

01:02:26.030 –> 01:02:32.329
I think we would be fine as a society or a race

01:02:32.329 –> 01:02:35.170
of species, I guess is what I’m, without AI.

01:02:35.409 –> 01:02:38.750
Like we don’t need this, but it’s here and Pandora’s

01:02:38.750 –> 01:02:40.570
box has been opened. Well, you know, and the

01:02:40.570 –> 01:02:43.170
thing is like, they’re selling it as like these

01:02:43.170 –> 01:02:45.170
like shortcuts, right? You can do everything

01:02:45.170 –> 01:02:48.010
quicker, faster, whatever. But like, you know,

01:02:48.170 –> 01:02:51.429
sometimes, You don’t want it to be quicker and

01:02:51.429 –> 01:02:53.610
faster because it’s about the process, particularly

01:02:53.610 –> 01:02:55.829
when it comes to creative things like filmmaking

01:02:55.829 –> 01:03:01.130
or painting. It’s not the end result that makes

01:03:01.130 –> 01:03:09.570
it art. It’s the process. Yeah. You create something

01:03:09.570 –> 01:03:13.670
and you go through hell, but you have these moments

01:03:13.670 –> 01:03:17.010
of joy doing it where you have a breakthrough

01:03:17.010 –> 01:03:18.750
and you’re like, holy fuck, this is really cool.

01:03:19.489 –> 01:03:22.710
And that’s why people are driven to create art.

01:03:23.230 –> 01:03:25.750
You know, there’s a struggle there and you’re

01:03:25.750 –> 01:03:27.650
compelled to do it if you’re like a creative

01:03:27.650 –> 01:03:31.050
type. Yep. I couldn’t agree more. That’s probably

01:03:31.050 –> 01:03:32.809
a good place to leave it. Do you have any closing

01:03:32.809 –> 01:03:35.409
thoughts? Anything you want to share? Yeah, fuck

01:03:35.409 –> 01:03:37.530
AI. I don’t know. Yeah, no, that’s a good closing

01:03:37.530 –> 01:03:41.650
thought, actually. I endorsed that comment. Party

01:03:41.650 –> 01:03:46.110
on. That’ll bring us to the end of this week’s

01:03:46.110 –> 01:03:49.659
episode of Nightmare Logic. Next week, we’re

01:03:49.659 –> 01:03:51.900
going to sit down with our friend and film historian

01:03:51.900 –> 01:03:54.719
David Delvalle to talk about what horror is and

01:03:54.719 –> 01:03:57.199
how it’s evolved from the 1960s when he was a

01:03:57.199 –> 01:04:00.199
kid until today. For this episode’s show notes,

01:04:00.420 –> 01:04:03.219
go to NightmareLogic .net and feel free to follow

01:04:03.219 –> 01:04:06.400
us on Instagram at NightmareLogicPod. We’d also

01:04:06.400 –> 01:04:08.539
like to give a shout out to our amazing composer

01:04:08.539 –> 01:04:12.059
Lars Lang -Peterson for our awesome intro. We

01:04:12.059 –> 01:04:14.460
are your hosts, Christopher Smith and Peter Sawyer.

01:04:14.820 –> 01:04:15.679
Until next time!