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Speaker 1: Welcome to the Nick, Dick and Paul Show. I am

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Speaker 1: one of your hosts, Nick Bilton, and I'm a writer

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Speaker 1: of all things writerly, including for this episode, a screenwriter

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Speaker 1: and documentarian.

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Speaker 2: I'm de Costello, general partner at the venture capital firm

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Speaker 2: I Want Advisors and Lumber Futures Trader Lumber.

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Speaker 3: Really I didn't know about. I'm so excited. Paul Kadrowski,

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Speaker 3: partner at a firm called ESK, Venture's former Wall Street

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Speaker 3: guy way back, Quen, I used to have my dungeons

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Speaker 3: and Dragons. Characters were always chaotic good which feels appropriate,

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Speaker 3: just so you know, Oh that's good.

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Speaker 4: It's good to hear.

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Speaker 1: We actually brought back a guest that we had on

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Speaker 1: a few weeks ago, Alan Loebe, who is a screenwriter,

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Speaker 1: and he was on talking about poly markets and the

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Speaker 1: betting markets and sports betting and so on and so forth.

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Speaker 1: Did I say that wrong? I did?

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Speaker 4: Didn't I prediction market, prediction market.

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Speaker 1: It keeps mixing up for our prediction and Polly. But

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Speaker 1: that's okay. If you did not listen to that episode,

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Speaker 1: it was fantastic and I would recommend you go back

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Speaker 1: and listen to it. We got some amazing, amazing stories

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Speaker 1: from that, and we're we have allan on to talk about.

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Speaker 1: We're going to talk about Hollywood. We're going to talk

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Speaker 1: about AI. We're going to talk about how Alan and

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Speaker 1: I won't have a job in a few months years,

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Speaker 1: whatever that is. Alan, welcome back to the show.

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Speaker 5: Thank you, it's great to be here. We went from

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Speaker 5: gambling to Hollywood, which are super completely redundant. It's thematic.

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Speaker 1: Paul's going to kick us off today with some questions

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Speaker 1: about AI and Hollywood and stream writing and creativity and whatnot.

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Speaker 3: Right because I'm a domain expert on the topics. Yeah, No,

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Speaker 3: I mean Nick and I have had this conversation many

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Speaker 3: times about this strange moment that Hollywood finds itself in.

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Speaker 3: And Hollywood's been, you know, a sole sequence of strange

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Speaker 3: moments probably back for one hundred years and fifty hundred

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Speaker 3: years now. But this idea of this evolution of how

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Speaker 3: how stuff gets created, who does the creating that it's

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Speaker 3: happening increasingly with technology involved. The lengths are shrinking, and

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Speaker 3: in particular, even right now, this evolution towards much shorter

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Speaker 3: videos that people talk about vertical videos. What's your general

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Speaker 3: view on a sort of scale of one to ten

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Speaker 3: how absolutely panic stricken people are about what's going on.

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Speaker 6: I would probably put it an eight and a half

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Speaker 6: and a half.

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Speaker 1: That's pretty low. Actually, I would have done a year

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Speaker 1: go half.

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Speaker 4: You're more than nine and a half. Yeah, I think why.

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Speaker 5: There's definitely a lot of panic, and there's a huge

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Speaker 5: amount of uncertainty. With uncertainty there comes you know, panic.

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Speaker 5: But I don't know if anyone knows how this is

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Speaker 5: going to butt.

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Speaker 3: But just let's sort of frame it. Is it panic

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Speaker 3: because of changing ways that things are being produced, Panic

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Speaker 3: because of consolidation among studios. I'd say, yes, I'm a

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Speaker 3: panic because of the writer's strike, which is I mean

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Speaker 3: just all.

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Speaker 1: The it's all the all that I believe. Can you

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Speaker 1: to put to put this into perspective for listeners, You've

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Speaker 1: been in the industry fifteen years, twenty years?

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Speaker 6: No, no, no longer than that.

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Speaker 1: Wow.

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Speaker 5: I was a failure and struggling to make it in

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Speaker 5: this industry for twelve years and I've been somewhat of

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Speaker 5: a success for twenty two years.

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Speaker 1: It's better to be.

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Speaker 6: A success, I'll put it that way.

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Speaker 1: So I'd love to hear the story of how you

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Speaker 1: broke in. But before you get there, can you explain

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Speaker 1: how the industry has changed and how it's different from

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Speaker 1: back then to today.

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Speaker 6: Well, it's much different now.

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Speaker 5: I mean now I don't even know and I don't

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Speaker 5: know if anyone knows where it's going and where it's heading,

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Speaker 5: and the type of content and where people are seeing

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Speaker 5: the content, the length of content, the delivery service, everything

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Speaker 5: is different. But when I broke in, it was really

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Speaker 5: about as a screenwriter is a storyteller. This is I

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Speaker 5: don't know how actors you know, nailed auditions and got

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Speaker 5: acting gigs.

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Speaker 6: I'm not going to speak to that. But as a.

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Speaker 5: Screenwriter, you just you wrote specscripts. That's what it was.

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Speaker 5: Was the nineties when I came here and everybody wrote specscripts,

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Speaker 5: meaning a film script that they wrote for no money,

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Speaker 5: and then they tried to get an agent or a

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Speaker 5: studio to notice it. And there was a gold rush.

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Speaker 5: It was a gold rush in the nineties where the

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Speaker 5: studios would find a specscript they love and they'd give

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Speaker 5: this kid out a UFC two million dollars in nineteen

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Speaker 5: ninety seven money for that script, and then they would

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Speaker 5: fire him and rewrite every word. So basically they were

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Speaker 5: buying movie ideas, dressed up movie ideas. There was a

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Speaker 5: very famous story of when Harvey Weinstein bought a bar

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Speaker 5: the guy was working at. He was working at a bar.

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Speaker 5: He wrote a script and there was a lot of

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Speaker 5: studios all bidding for it, and Harvey Weinstein bought the

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Speaker 5: bar the guy was working at and gave him the bar,

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Speaker 5: and that movie was Boondocks Saints, which ended up becoming

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Speaker 5: a TV show. I don't know if any of you

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Speaker 5: guys remember any of this, but this was a famous

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Speaker 5: Hollywood story.

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Speaker 6: But there was they loved. There was such a gold

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Speaker 6: rush for ideas.

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Speaker 5: So that's when I came in. I tried to get

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Speaker 5: in on that as a gambler.

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Speaker 3: But just on that though, and I mean, why do

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Speaker 3: you think that was what was going on at that

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Speaker 3: time that drove this gold mush mentality a few things.

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Speaker 3: First of all, it wasn't the rise of streaming services.

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Speaker 3: I mean, there was a dynamics were comparing.

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Speaker 5: Every studio would release on average, there were probably nine

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Speaker 5: big ones or eight big ones, and they would all

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Speaker 5: release on average twelve to fifteen movies a year. Yeah, okay,

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Speaker 5: so that was they needed that, they needed that pipeline service.

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Speaker 5: Right now, even a big studio with the tent poles

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Speaker 5: will probably only release four or five movies a year.

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Speaker 5: If that I mean, the streamers are kind of back

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Speaker 5: into that business of releasing a lot of more volume

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Speaker 5: which no one remembers and no one see many many

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Speaker 5: people don't remember. Many people don't see the streamer movies.

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Speaker 5: I mean, you could look at the big movie stars

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Speaker 5: and what movies they made in the last five years,

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Speaker 5: and half of them are on Apple that you had

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Speaker 5: no idea existed.

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Speaker 3: I just had that experience driving down Melrose where I

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Speaker 3: was looking at, you know, for your consideration, all the

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Speaker 3: posters and so on, and was like, don't know it,

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Speaker 3: don't know it, don't know it, don't know it, don't

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Speaker 3: know what it was.

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Speaker 4: Like up for six of words, it was what the hell?

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Speaker 5: Remember when we were growing up, when I was breaking

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Speaker 5: in you remembered it every movie.

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Speaker 2: Every when the oscars wrong, you were like seeing it,

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Speaker 2: seen it, seen it, seen it, need to see it.

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Speaker 4: Now you're like, don't know it, don't know it. Sounds dumb.

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Speaker 4: It don't know it.

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Speaker 3: Why is it?

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Speaker 2: By the way, also, Boondocks Saints only twenty six percent

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Speaker 2: on the Tomato Meter, so not actually a great movie.

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Speaker 5: You'll know the movie did not do great great cast.

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Speaker 2: Willem Dafoe plays the FBI agent. I saw the movie

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Speaker 2: when I first came out. You remember who stars, It's

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Speaker 2: Williem Dafoe.

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Speaker 4: He's like kind of.

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Speaker 6: Kind of a you know, they would they would, they

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Speaker 6: would pay millions and millions and.

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Speaker 4: Millions of dollars.

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Speaker 3: Who is the guy that had an outlines that had

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Speaker 3: a lot of bunch of his outlines showing Joe astra House,

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Speaker 3: Shane Black, Shane Black. That was I actually remember the

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Speaker 3: story you probably tell well.

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Speaker 5: Joe Esterhouse is the one who sold an outline for

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Speaker 5: four point eight million dollars to New Line in nineteen

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Speaker 5: ninety something and that became the movie, the classic movie

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Speaker 5: the Academy we don't feel called show Girls.

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Speaker 6: I don't know if you remember show Girls.

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Speaker 1: Uh, And that was from an outline that was from

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Speaker 1: a night.

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Speaker 3: It wasn't Blacks the same something that was wrote.

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Speaker 5: Lethal Weapons, which was really really a great movie, class.

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Speaker 3: Movie, and it was his first project.

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Speaker 5: It was one of his first projects, and it broke

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Speaker 5: him through and he became a phenomenally talented and successful

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Speaker 5: Screenwrin he was a great screenwriter.

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Speaker 4: He still is, he's still working.

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Speaker 1: What was your breakthrough moment? What happened?

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Speaker 5: Well, I had been struggling, Like I said, I was struggling,

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Speaker 5: so I just to fill out in the blanks. I

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Speaker 5: came out here to to to win that lottery and

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Speaker 5: to in that gold rush.

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Speaker 6: And I wrote.

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Speaker 5: Speckstript after specscript after specstrum, and I sold none, and

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Speaker 5: I got a little bit of interest from some people,

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Speaker 5: but nothing really major. And I did sell one or

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Speaker 5: two pitches at the time for very little money for

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Speaker 5: what we call guild minimum, which wasn't a lot, and

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Speaker 5: I would lose the gambling within two weeks.

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Speaker 1: That was the last podcast.

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Speaker 5: Makes sense, But the point is, so I was struggling,

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Speaker 5: I was going to leave the business, and and I

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Speaker 5: just I had one final Hail Mary script that I

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Speaker 5: I went to New York to write, and that was

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Speaker 5: the one that somehow bursted through. It's a movie on

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Speaker 5: Amazon now called Only Leaving Boy in New York, which

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Speaker 5: is on a streamer, so therefore no one's probably seen

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Speaker 5: it or know about it.

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Speaker 4: You check it out.

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Speaker 1: You were there were to be.

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Speaker 4: Fair again show Girls, for which esther House got.

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Speaker 2: Paid an excurinary amount of money for an outline twenty

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Speaker 2: four percent on the Tomato meter. So the point being,

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Speaker 2: these guys who are buying these movies have no taste.

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Speaker 4: What's that? I don't know what? They're fine?

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Speaker 1: There. There was a moment though, when you did sell

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Speaker 1: your movie, where you had sold your car. You were

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Speaker 1: so broke right, and you would tell you those before

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Speaker 1: I that was, you know, before I had sold. Here's

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Speaker 1: here's the question I have is why were there good

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Speaker 1: movies then and there are not good movies today?

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Speaker 4: That's a great question, do you know? The answer?

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Speaker 6: A tough thing to answer.

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Speaker 5: I would challenge the premise on it, though. First of all,

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Speaker 5: I would say that there were some, you know, obviously

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Speaker 5: great movies then, and there is great, for lack of

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Speaker 5: a better term, TV shows now. I'm in secession, Game

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Speaker 5: of Thrones, going back a little bit, Sopranos, Breaking Bad,

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Speaker 5: those are on par with any great movie we loved

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Speaker 5: in nineteen ninety eight, So I mean me, and in

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Speaker 5: those days there wasn't that, There wasn't that premium cable,

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Speaker 5: you know.

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Speaker 6: Stories Coming It was really good. A lot of misunderstanding.

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Speaker 3: Yeah, you'll go back and watch if they just would

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Speaker 3: speak to each other, and.

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Speaker 1: So back back then you could sell to producers. There

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Speaker 1: was a lot more studios, right compared to today.

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Speaker 5: Yeah, there were the studios that were there, and they're

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Speaker 5: still here for the most part. We're making more movies,

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Speaker 5: a lot more movies, and releasing more movies.

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Speaker 6: But there were a lot of bad movies in.

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Speaker 4: Those days too.

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Speaker 6: I think there's a nostalgia bias.

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Speaker 4: It's like a percentage thing, too, right.

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Speaker 3: I mean the idea that if they were like I

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Speaker 3: don't know, say it's ten studios each doing ten movies

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Speaker 3: a year or whatever, one hundred and fifty plus movies

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Speaker 3: a year, ten percent of them are any good, that's

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Speaker 3: fifteen movies. I'm going to remember that now with a

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Speaker 3: much smaller slate, If ten percent of them are good,

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Speaker 3: the odds or I didn't even notice that one. Because

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Speaker 3: there's so few movies being produced, it just turns into

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Speaker 3: one or two half decent movies.

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Speaker 5: There's also the original idea capture, which those were original

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Speaker 5: ideas mostly and sometimes off of a book.

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Speaker 6: Books were very big. Now you're making movies.

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Speaker 5: Off of all sorts of IP graphic novels and in

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Speaker 5: the I P RB sometimes you are you know, it's

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Speaker 5: sometimes it's a round peg square hole.

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Speaker 6: No, did I get that right?

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Speaker 1: We've got we're flexible on brand movies. Here's a list

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Speaker 1: of brand movies in development. Hot Wheels, Barney Magicate, Bowl,

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Speaker 1: Who Know, Rock Soccam, Robots, American Girl, Doll, Polly Packet, Viewmaster,

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Speaker 1: and Wishbone. You are actually doing I'm up for like

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Speaker 1: three of those.

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Speaker 3: No, But isn't it wasn't that the plot of the

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Speaker 3: what was that?

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Speaker 1: The seth the studio kool Aid thing is which is

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Speaker 1: the studio?

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Speaker 2: By the way, watch that will get made somewhere is going.

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Speaker 3: Someone's not a bad idea, but the thing that I

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Speaker 3: and again I'm just you know, talking out of my

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Speaker 3: ass here on this stuff. But I mean, which has

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Speaker 3: never stopped me before? So why stop now? So here's

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Speaker 3: my theory on all of this. The reason why IP

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Speaker 3: has become so important isn't just because like I don't know,

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Speaker 3: the MCU did really well. It's this, This is finance

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Speaker 3: guy had on. It's the natural reaction to doing fewer

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Speaker 3: movies that costs more. I need to feel like there's

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Speaker 3: a floor. I need to feel like there's a built

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Speaker 3: in audience. For this, because I'm not willing to take

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Speaker 3: a flyer on something where I got to spend a

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Speaker 3: gazillion dollars in marketing with no guarant no constituency out there.

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Speaker 3: But with say, pick the MCU, the Marvel Comic Universe stuff,

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Speaker 3: there was this sense that, oh, they figured it out.

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Speaker 3: Those guys have figured it out, because there's there's a

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Speaker 3: ip underneath it, which a built in audience. Just go

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Speaker 3: to comic Con and you'll find all these you know,

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Speaker 3: rabid characters who I want to see this stuff. But

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Speaker 3: abstracting away, right, it's kind of a natural reaction to

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Speaker 3: doing few and more expensive.

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Speaker 5: So the studios in those days were run by people

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Speaker 5: like one or two people that were taste makers, like

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Speaker 5: the old school studio types. Now it's what they call

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Speaker 5: green light committees, and everything's publicly traded and the green

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Speaker 5: light committee answers to the board members, which answer to their.

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Speaker 1: After black price, Apple now has a blue light committee,

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Speaker 1: which is the phase before the green light, so you

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Speaker 1: get a blue light, Yeah they do.

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Speaker 3: What does that mean?

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Speaker 1: I don't know. That's what I heard. They have a

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Speaker 1: blue light committee.

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Speaker 6: So it's much more corporate.

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Speaker 5: It's just basically you know, at the mercy of everything

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Speaker 5: else in this country.

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Speaker 3: With a smaller number of more expensive movies. Again, putting

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Speaker 3: myself in the position of someone making those decisions, I'm

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Speaker 3: going to say, this is seems like a natural reaction,

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Speaker 3: right because if it's going to be three hundred million

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Speaker 3: dollars to make this thing, I better feel like there's

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Speaker 3: an audience out there that doesn't require me to run.

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Speaker 2: At least I'm starting off with two hundred million dollars

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Speaker 2: that I can count on.

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Speaker 3: Yeah, yeah, yeah, right right, you floor, there's a floor here,

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Speaker 3: And I think that's the thing that people they think

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Speaker 3: it's just laziness on the part of studios that they've

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Speaker 3: embraced IP. But it's a natural reaction to smaller slate

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Speaker 3: of expensive films. Is there better be a floor under this?

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Speaker 5: But there is that dynamic where then there's the contrarian

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Speaker 5: point of view in the current of people lend long

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Speaker 5: for originals and you get movies like this year. This

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Speaker 5: movie Centers did extremely well and is up for many

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Speaker 5: a war words and I believe that's an original movie.

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Speaker 6: That's an original idea.

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Speaker 5: So those studios will chase original ideas now because there

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Speaker 5: is a hunger for it within the audience.

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Speaker 4: It doesn't win.

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Speaker 2: Doesn't that argue for the A twenty four model, like

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Speaker 2: we're going to go the other direction and do these

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Speaker 2: It creates the original idea, make films about these.

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Speaker 4: I think A twenty four did centers. I'm not missing.

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Speaker 4: I don't know. Warner brother knows.

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Speaker 1: But you struggled.

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Speaker 4: How do you know that was Warner Brothers. I didn't.

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Speaker 3: I just went along with Nick.

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Speaker 2: Now you just acknowledged and you tried to be a

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Speaker 2: fast follower and business terms on a fast follow.

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Speaker 4: Yeah, the moment Alan said that was Warren Brothers, you

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Speaker 4: said it like you.

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Speaker 1: Said that was Amazon again again, that was that's right?

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Speaker 4: Did you see Sinners?

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Speaker 1: No, you didn't see it.

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Speaker 7: No, I knew he didn't see it. He said there

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Speaker 7: was Warner Brothers, right, and for Film said it.

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Speaker 2: But he said it with such conviction that I immediately

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Speaker 2: knew he has no idea that analyst.

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Speaker 5: Yeah, Dick's a twenty four statement.

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Speaker 6: It was A twenty five for it.

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Speaker 5: But then they became very successful and now their budgets

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Speaker 5: are getting huge. Become a victim of their own success

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Speaker 5: in any arena, but especially in Hollywood.

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Speaker 3: Just out of curiosity and maybe no one knows the numbers,

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Speaker 3: but did centers make money?

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Speaker 4: Yes? Yes, what was a you know roughly what the

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Speaker 4: boy Dick will.

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Speaker 1: Check Dick's gonna check it out. Can you give us

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Speaker 1: the Rotten Tomatoes score too? I think there's a lot

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Speaker 1: of great inflation now.

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Speaker 3: In anticipation of my mind.

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Speaker 1: There's there's there's a lot of there's been a lot

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Speaker 1: of stories about rotten tomatoes and how it's all it's

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Speaker 1: all bullshit because they people buy the buy the the tomatoes.

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Speaker 2: Center. Center's grossed three hundred and sixty five million dollars

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Speaker 2: on a budget of ninety million.

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Speaker 5: Okay, Now, an interesting movie that recently came out that

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Speaker 5: I didn't see is called The Housemate, and that was

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Speaker 5: with Amanda Seafreed Sydney Swidney, and it made a fortunes

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Speaker 5: fortune worldwide. It's at four hundred and three fifty four hundred.

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Speaker 5: Is basically an old throwback, old school thriller like hand

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Speaker 5: that rocks to create our type, single white female type

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Speaker 5: and the type of movies that they used to make

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Speaker 5: all the time. They did fine, they never but for

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Speaker 5: some reason we forgot about it. Hollywood forgot about it.

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Speaker 5: This is a subgenre of a genre that they just

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Speaker 5: doesn't milk, and someone was smart enough to say, let's

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Speaker 5: just throw it out there and see if there is

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Speaker 5: a hunger for that type of thing.

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Speaker 6: Because these things are mythic.

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Speaker 5: There are mythic stories that will never go out of

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Speaker 5: style if they're done in a certain way and market

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Speaker 5: in a certain way. And that's exciting for content creators

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Speaker 5: and storytellers because we want to go back to a

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Speaker 5: world where many genres, more things are made of all genres,

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Speaker 5: and hopefully with a theatrical release.

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Speaker 4: By the way, SAMs was ninety seven percent of the

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Speaker 4: Tomato meter.

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Speaker 6: I don't know the housemidd as well.

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Speaker 3: That's ridiculous. So like, basically everyone loves this thing.

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Speaker 1: It's a great movie. It was honestly that I went

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Speaker 1: to see it with a couple of friends in the theater,

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Speaker 1: which I very rarely do, and I there was a

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Speaker 1: moment where I was like, holy shit, this is the

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Speaker 1: most creative thing I've seen in years, because it's just why,

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Speaker 1: well what I think what Coogler did that I think

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Speaker 1: was so unique. He's the writer, director, the grip, the grip,

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Speaker 1: he's the grip.

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Speaker 4: Yeah, who do you think he is. I don't know.

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Speaker 2: This is I sound like about sports.

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Speaker 3: By the way, I also said like that about the sports,

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Speaker 3: I have many domains of ignorance.

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Speaker 1: Is what he did there was I think just I

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Speaker 1: think it was genuinely genius. Was that he he integrated

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Speaker 1: the music into the film, into the storytelling, which in

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Speaker 1: a way I've never seen before, and I thought it

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Speaker 1: was just fantastic. There were problems with the film, there

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Speaker 1: were things they didn't did you watched it right? You

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Speaker 1: didn't see it?

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Speaker 4: Yeah, of course they saw.

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Speaker 1: There was some problems with the film, you know, like

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Speaker 1: they never closed out certain certain parts of it. But

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Speaker 1: it was a phenomenally creative film, I think, and it

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Speaker 1: was really what could it have been made in the eighties?

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Speaker 1: A yeah, one thousand percent. He could have been made

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Speaker 1: in the eighties. And I think that's what makes it

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Speaker 1: good is that, you know, my kids they watched The

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Speaker 1: Sandblot recently. It's an old you know that is eighties

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Speaker 1: movie probably and it's probably got like a ninety seven

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Speaker 1: one roten tomatoes. And I remember my eight year Oldterner

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Speaker 1: and he goes at the end, he was like, that

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Speaker 1: was amazing. He goes, why don't they make movies like

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Speaker 1: that anymore? And I was like, I don't know the

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Speaker 1: answer to that.

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Speaker 3: My kids had that reaction to Twelve Angry Men. They

402
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Speaker 3: love twelve Angry Men. Why don't they make movies like this?

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Speaker 3: They watch them in they're like twelve years old.

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Speaker 4: I think they absolutely amaze.

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Speaker 5: I do think they make either movies or there's content

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Speaker 5: of that quality in our world.

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Speaker 4: There is a lot.

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Speaker 6: It just tends to be on HBO or you know, yes.

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Speaker 1: But there's there's a lot of bad content.

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Speaker 4: Out there today.

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Speaker 5: I think there's always been a lot of bad content

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Speaker 5: out there, and I just think we're having nostalgia bias.

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Speaker 3: I completely agree with that. I think that it's partly

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Speaker 3: numbers in this and just sort of aging a nostalgia.

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Speaker 3: I feel like you're just starting to say and agree

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Speaker 3: with whatever else say something else. Don't disagree with that.

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Speaker 3: Just recreational. No, this has been my argument though. It's

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Speaker 3: like it's the numbers problem. There's far fewer things being

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Speaker 3: produced in that so you create the illusion.

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Speaker 1: Okay, Okay, So then answer this question. Why is it?

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Speaker 1: As you just said, Dick, that when I used to

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Speaker 1: watch the Oscars, I was like, saw that loved it,

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Speaker 1: hated this, and now I don't.

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Speaker 4: I'm seen half the stuff because people don't go to

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Speaker 4: the movies anymore.

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Speaker 1: They don't watch content.

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Speaker 5: There's also ten movies now or something like that. I

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Speaker 5: don't know it's ten.

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Speaker 2: Movies or more one and it used to be it

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Speaker 2: used to be four or.

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Speaker 5: Five, and there were just less. Everybody watched the same stuff.

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Speaker 5: Now it's diversified.

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Speaker 3: It's the same thing with everything across There's no uniform

434
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Speaker 3: audience for anything anymore. Everything's fractionated, everything's become pieces of it.

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Speaker 3: You can even see that in ticket sales in Hollywood,

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00:19:03,119 --> 00:19:06,399
Speaker 3: ticket sales like we're only now they're still below twenty

437
00:19:06,480 --> 00:19:08,920
Speaker 3: nineteen levels with respect to tickets. The only reason revenues

438
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Speaker 3: are higher because the prices have gone up, so we're

439
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Speaker 3: actually still below we were in twenty It's no longer

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Speaker 3: a shared culture.

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Speaker 6: Looks everything was an event.

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Speaker 5: I mean it was. You know, Jack Nicholson was such

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00:19:17,920 --> 00:19:21,240
Speaker 5: a mythic theater person. You would watch the Oscars just

444
00:19:21,280 --> 00:19:23,760
Speaker 5: to watch what Jack would do. I be wearing the sunglasses.

445
00:19:23,800 --> 00:19:24,800
Speaker 4: It was very special.

446
00:19:25,280 --> 00:19:25,960
Speaker 1: I think that's.

447
00:19:25,800 --> 00:19:29,120
Speaker 5: Gone, and I think that's gone because we're all bifurkida.

448
00:19:29,119 --> 00:19:31,800
Speaker 5: We're all in different universes and algorithms, and.

449
00:19:32,320 --> 00:19:33,760
Speaker 3: It's also but that's a great point. I think it's

450
00:19:33,760 --> 00:19:38,200
Speaker 3: also gone because people have too much access to stars. Also,

451
00:19:38,240 --> 00:19:41,000
Speaker 3: like they're on there, I follow them on Instagram. It

452
00:19:41,040 --> 00:19:44,040
Speaker 3: doesn't have that air of exclusivity anymore. I just saw

453
00:19:44,080 --> 00:19:47,200
Speaker 3: them this morning trying on something in someplace and it's

454
00:19:47,200 --> 00:19:49,600
Speaker 3: and they're human too. And they're human too, yeah right,

455
00:19:49,640 --> 00:19:50,640
Speaker 3: and that doesn't.

456
00:19:50,400 --> 00:19:51,399
Speaker 1: And they have ugly faces.

457
00:19:51,480 --> 00:19:54,320
Speaker 5: There is a great story about Jack Nicholson where I

458
00:19:54,320 --> 00:19:56,679
Speaker 5: guess someone asked him why do you always wear sunglasses

459
00:19:56,720 --> 00:19:59,640
Speaker 5: to the Laker game, to the to the Oscars. He goes, well,

460
00:19:59,680 --> 00:20:01,200
Speaker 5: if you to see my eyes, you got to pay

461
00:20:01,240 --> 00:20:01,880
Speaker 5: eight dollars.

462
00:20:04,520 --> 00:20:05,800
Speaker 3: There's a ticket price for that too.

463
00:20:07,000 --> 00:20:11,240
Speaker 1: What's your thoughts on AI as it now comes into

464
00:20:11,359 --> 00:20:15,880
Speaker 1: this industry, because my theory is so was. I worked

465
00:20:15,920 --> 00:20:19,119
Speaker 1: in the music industry doing CD design when MP three's

466
00:20:19,160 --> 00:20:22,159
Speaker 1: came along, and it was very clear it was going

467
00:20:22,240 --> 00:20:24,080
Speaker 1: to change the industry, which it did. And then I

468
00:20:24,119 --> 00:20:25,960
Speaker 1: was at the New York Times when blogs came along,

469
00:20:26,119 --> 00:20:30,400
Speaker 1: same thing, and now it feels like we I'm in

470
00:20:30,440 --> 00:20:33,520
Speaker 1: Hollywood and it feels like this is the MP three moment,

471
00:20:34,000 --> 00:20:36,680
Speaker 1: and what are your thoughts as someone who's been doing

472
00:20:36,680 --> 00:20:38,520
Speaker 1: this for a couple of decades.

473
00:20:39,200 --> 00:20:41,800
Speaker 5: I do think it's very concerning in terms of all

474
00:20:41,880 --> 00:20:45,960
Speaker 5: the different jobs and skill sets that AI is slowly

475
00:20:46,040 --> 00:20:52,200
Speaker 5: beginning to be either marginally now you know, doing as good,

476
00:20:52,520 --> 00:20:55,440
Speaker 5: which is not much. But it just feels, because it's iterative,

477
00:20:55,640 --> 00:20:58,520
Speaker 5: that at some point it's inevitable that do you use.

478
00:20:58,440 --> 00:20:58,960
Speaker 3: It for anything?

479
00:20:59,160 --> 00:20:59,879
Speaker 1: I do?

480
00:21:00,119 --> 00:21:00,800
Speaker 6: Is it for research?

481
00:21:00,920 --> 00:21:01,280
Speaker 3: Research?

482
00:21:01,359 --> 00:21:03,240
Speaker 6: I use it for it's very good for research.

483
00:21:03,600 --> 00:21:06,360
Speaker 3: When you say research, can you give me an example, Well,

484
00:21:06,400 --> 00:21:06,800
Speaker 3: it used.

485
00:21:06,720 --> 00:21:08,520
Speaker 6: To as a screenwriter, I think, and Nick could also

486
00:21:08,520 --> 00:21:09,000
Speaker 6: back this up.

487
00:21:09,280 --> 00:21:11,399
Speaker 5: You used to go on Google and you would google

488
00:21:11,440 --> 00:21:14,199
Speaker 5: the subject and then you'd go down rabbit holes and

489
00:21:14,280 --> 00:21:17,560
Speaker 5: have to, you know, disqualify eighty percent of what you

490
00:21:17,600 --> 00:21:19,359
Speaker 5: were reading and then find the right thing. And it

491
00:21:19,359 --> 00:21:21,880
Speaker 5: would take a long time. AI is much now, AI,

492
00:21:21,960 --> 00:21:26,920
Speaker 5: as we all know, hallucinates and is wrong and lies sometimes.

493
00:21:27,720 --> 00:21:29,920
Speaker 1: But tell tell it a little bit, tell us a

494
00:21:29,960 --> 00:21:32,840
Speaker 1: little bit about how you actually you actually have given

495
00:21:32,920 --> 00:21:36,280
Speaker 1: it a name and you have long conversations with Frank.

496
00:21:36,520 --> 00:21:36,720
Speaker 4: Frank.

497
00:21:36,800 --> 00:21:39,400
Speaker 6: Yeah, it's a collaborator, I mean, Frank, I do he named.

498
00:21:39,320 --> 00:21:42,879
Speaker 1: Chat GDP Frank and and he I.

499
00:21:42,960 --> 00:21:45,919
Speaker 5: Collaborate with him, and I guess it's a gender him

500
00:21:45,960 --> 00:21:49,359
Speaker 5: as a hymn. And it is very helpful. It's all

501
00:21:49,400 --> 00:21:51,440
Speaker 5: like an assistant that's really really, really smart.

502
00:21:52,520 --> 00:21:55,000
Speaker 3: You're having one single threaded conversation all the time.

503
00:21:55,160 --> 00:21:58,399
Speaker 1: No, yeah, well, I mean you've just renamed chat GDP. Now,

504
00:21:58,440 --> 00:22:00,440
Speaker 1: he says hi Frank, and Frank says high Allen, how's

505
00:22:00,480 --> 00:22:00,840
Speaker 1: your dog?

506
00:22:01,119 --> 00:22:01,320
Speaker 2: Yeah?

507
00:22:01,440 --> 00:22:03,040
Speaker 3: No, it's remembering that from private it does.

508
00:22:03,119 --> 00:22:04,160
Speaker 6: Yeah, chatchipd does.

509
00:22:04,200 --> 00:22:07,200
Speaker 5: Claude doesn't, although I think now Claude they are doing.

510
00:22:07,240 --> 00:22:07,680
Speaker 4: I have both.

511
00:22:08,040 --> 00:22:10,639
Speaker 5: I use chatch CTP and Claude. I don't use Gemini.

512
00:22:11,280 --> 00:22:12,840
Speaker 5: I don't know what you guys use.

513
00:22:12,840 --> 00:22:13,959
Speaker 3: Or oh here we go.

514
00:22:15,200 --> 00:22:19,400
Speaker 5: Well, this is a conversation everyone has, right, Well, those

515
00:22:19,400 --> 00:22:22,439
Speaker 5: are the ones I use and does. Chatchipp does have

516
00:22:22,440 --> 00:22:25,920
Speaker 5: a good memory and remembers a lot. Claude is fresh

517
00:22:25,920 --> 00:22:28,080
Speaker 5: every time, so I have to. I have a prompt

518
00:22:28,080 --> 00:22:31,120
Speaker 5: where I remind Claude of the project we're working one,

519
00:22:31,400 --> 00:22:33,720
Speaker 5: and I use both to balance them against each other,

520
00:22:33,880 --> 00:22:36,959
Speaker 5: but I do use it. I consider Frank or Claude

521
00:22:37,000 --> 00:22:41,320
Speaker 5: as it were, to be a very very smart assistant

522
00:22:41,600 --> 00:22:42,480
Speaker 5: writer's assistant.

523
00:22:42,680 --> 00:22:44,280
Speaker 1: Sometimes he shows up to work drunk.

524
00:22:45,080 --> 00:22:47,000
Speaker 5: They can read very quickly, Like so I can give

525
00:22:47,040 --> 00:22:49,679
Speaker 5: it thirty pages of the script, and within I don't know,

526
00:22:50,119 --> 00:22:52,720
Speaker 5: twelve seconds, seventeen seconds, it's read all the pages, and

527
00:22:52,760 --> 00:22:56,280
Speaker 5: it will speak intelligently to the pages, and it will

528
00:22:56,359 --> 00:22:59,040
Speaker 5: know what it just read as good as any human,

529
00:22:59,160 --> 00:23:01,800
Speaker 5: as any assistant. And then you know conversations and I

530
00:23:01,920 --> 00:23:05,199
Speaker 5: spitballing and brainstorming. It doesn't do the work for me,

531
00:23:05,720 --> 00:23:07,440
Speaker 5: but it is a.

532
00:23:07,520 --> 00:23:08,720
Speaker 6: It's also good for proof reading.

533
00:23:09,160 --> 00:23:10,520
Speaker 3: Now, why don't you talk a little bit about how

534
00:23:10,520 --> 00:23:11,160
Speaker 3: you use AI?

535
00:23:11,840 --> 00:23:16,159
Speaker 1: I mean I I literally couldn't imagine life without it

536
00:23:16,200 --> 00:23:19,000
Speaker 1: as a as a creative anymore. It's I use it

537
00:23:19,240 --> 00:23:24,160
Speaker 1: in so many different ways. I I use cursor, which

538
00:23:24,240 --> 00:23:27,520
Speaker 1: is for programming. Most of the time when people say ask,

539
00:23:27,600 --> 00:23:29,800
Speaker 1: when I tell people use cursor, they're like, what use

540
00:23:29,880 --> 00:23:30,600
Speaker 1: that for writing?

541
00:23:30,840 --> 00:23:33,040
Speaker 2: I think that was exactly the tone of voice. I

542
00:23:33,000 --> 00:23:35,240
Speaker 2: said it, Yes, exactly.

543
00:23:35,880 --> 00:23:39,280
Speaker 1: So a cursor is is how do you want to

544
00:23:39,280 --> 00:23:40,199
Speaker 1: describe A cursor is what?

545
00:23:40,320 --> 00:23:43,040
Speaker 3: It's a development environment for software engineers, mostly used for

546
00:23:43,040 --> 00:23:44,880
Speaker 3: writing things like in a Python and C code.

547
00:23:44,960 --> 00:23:49,560
Speaker 1: Yeah, so I use it to I load in millions

548
00:23:49,560 --> 00:23:52,760
Speaker 1: and millions of words of research, whereas most people load

549
00:23:52,800 --> 00:23:54,800
Speaker 1: in millions and millions of lines of code. And then

550
00:23:54,920 --> 00:23:58,960
Speaker 1: I use it to proof free, to check, to fact

551
00:23:59,080 --> 00:24:02,040
Speaker 1: check things, to make sure that all my characters flow,

552
00:24:02,119 --> 00:24:04,680
Speaker 1: that their dialogue is the same. So like if one

553
00:24:04,760 --> 00:24:06,879
Speaker 1: character speaks in a certain kind of if it's in

554
00:24:06,920 --> 00:24:09,560
Speaker 1: Hawaii and they speak pigeon, or if it's there's another

555
00:24:09,920 --> 00:24:13,320
Speaker 1: project I'm working on, it's a wrapper from you know,

556
00:24:13,359 --> 00:24:16,320
Speaker 1: twenty eighteen, who has a certain dialect. It goes and

557
00:24:16,359 --> 00:24:18,439
Speaker 1: it checks it and make sure it's you know, I

558
00:24:18,480 --> 00:24:21,840
Speaker 1: didn't make a mistake or use some vernacular. It's completely

559
00:24:21,920 --> 00:24:24,600
Speaker 1: made up or not whatever. The other thing I do, too,

560
00:24:24,880 --> 00:24:28,600
Speaker 1: is every day I start my work, I give it

561
00:24:28,760 --> 00:24:30,880
Speaker 1: the latest draft of the script or the latest draft

562
00:24:30,920 --> 00:24:33,600
Speaker 1: of the book, and then it's just like a refresher,

563
00:24:33,640 --> 00:24:36,320
Speaker 1: like what are we doing today? Okay, this is something

564
00:24:36,359 --> 00:24:37,879
Speaker 1: that's not working, this is something that is, and then

565
00:24:37,880 --> 00:24:43,800
Speaker 1: I can spend it. I also I will into One

566
00:24:43,800 --> 00:24:46,800
Speaker 1: thing I've been doing is so there's this Hawaii project

567
00:24:46,840 --> 00:24:49,480
Speaker 1: and most of the people are dead, but we have

568
00:24:49,560 --> 00:24:52,240
Speaker 1: all this research in these court transcripts and so on,

569
00:24:52,520 --> 00:24:56,280
Speaker 1: and so I fed it into cursor, using Claude as

570
00:24:56,359 --> 00:24:59,760
Speaker 1: the AI engine, and I can interview these people who

571
00:24:59,840 --> 00:25:04,000
Speaker 1: are dead, and I'll be like, where'd you go on

572
00:25:04,119 --> 00:25:07,800
Speaker 1: Sunday the sixth when Benny got killed? And it's like, oh,

573
00:25:07,840 --> 00:25:09,639
Speaker 1: we were over at the Cocos Diner and da da

574
00:25:09,720 --> 00:25:12,480
Speaker 1: da dah. So it's like I'm and then I give

575
00:25:12,560 --> 00:25:14,760
Speaker 1: the I have all these different agents I use, and

576
00:25:14,840 --> 00:25:16,840
Speaker 1: so some of the agents will then go out and

577
00:25:16,880 --> 00:25:17,480
Speaker 1: try to fill in.

578
00:25:17,400 --> 00:25:19,439
Speaker 4: The DA name your agents like Ellen n.

579
00:25:19,560 --> 00:25:21,359
Speaker 1: One's called Nick, one's called Dick, and.

580
00:25:23,400 --> 00:25:24,399
Speaker 3: It is mine really grouchy?

581
00:25:24,520 --> 00:25:27,040
Speaker 1: Yeah, yours is very groudchy. No, it's funny because because

582
00:25:28,040 --> 00:25:30,600
Speaker 1: it's funny you say that, because Claude always says the

583
00:25:30,720 --> 00:25:33,040
Speaker 1: user and I'm like, can you call me Nick? And

584
00:25:33,080 --> 00:25:35,320
Speaker 1: it always forgets and it goes straight back to the user.

585
00:25:35,560 --> 00:25:38,840
Speaker 5: I will say, which interesting is. I've been numerous dinners

586
00:25:38,880 --> 00:25:42,119
Speaker 5: with ten five eight screen working screen rinders because we

587
00:25:42,160 --> 00:25:44,520
Speaker 5: all hang out together, and I will say, how many

588
00:25:44,680 --> 00:25:47,440
Speaker 5: people at this table use AI in their work? And

589
00:25:47,480 --> 00:25:52,720
Speaker 5: it's only twenty thirty percent and sixty percent will razor.

590
00:25:52,760 --> 00:25:55,080
Speaker 6: Air just say I won't. I don't want it.

591
00:25:55,080 --> 00:25:55,600
Speaker 1: It's the devil.

592
00:25:55,640 --> 00:25:57,320
Speaker 6: It's the enemy. It's going to be the end of us.

593
00:25:57,400 --> 00:25:59,600
Speaker 5: I don't want to cheat, Like, there's a million reasons

594
00:25:59,600 --> 00:26:01,720
Speaker 5: I want to. I said, you might have said that

595
00:26:01,760 --> 00:26:04,600
Speaker 5: about Google in the first you know first.

596
00:26:04,400 --> 00:26:08,080
Speaker 1: Or the typewriter, you know. It's like, but it's a

597
00:26:08,080 --> 00:26:11,320
Speaker 1: great tool. I had an incident, incident a moment. I

598
00:26:11,359 --> 00:26:15,440
Speaker 1: was at a Christmas party. It was an incident. It's

599
00:26:15,520 --> 00:26:20,040
Speaker 1: kind of an incident. I was at a Christmas and

600
00:26:20,600 --> 00:26:23,960
Speaker 1: there was a guy who came up to what I

601
00:26:24,080 --> 00:26:26,000
Speaker 1: was talking to, who was a showrunner who had a

602
00:26:26,000 --> 00:26:30,000
Speaker 1: deal at Netflix, who an overall deal, and we start

603
00:26:30,040 --> 00:26:31,879
Speaker 1: talking about AI and I was like, oh, do you

604
00:26:31,960 --> 00:26:34,920
Speaker 1: use it? Goes absolutely not. He's like, it'll ruin my creativity.

605
00:26:34,920 --> 00:26:36,800
Speaker 1: You'll destroy everything I do and so on and so forth.

606
00:26:36,800 --> 00:26:38,679
Speaker 1: And I was like, well, you could just use it

607
00:26:38,720 --> 00:26:42,360
Speaker 1: for like research and he and he was like, I don't.

608
00:26:42,480 --> 00:26:44,920
Speaker 1: I used it. I've heard people do it, but I

609
00:26:45,440 --> 00:26:48,800
Speaker 1: do everything myself. And he's like, I even transcribe my interviews.

610
00:26:48,800 --> 00:26:51,240
Speaker 1: And I was like, oh. I was like, there's actually

611
00:26:51,240 --> 00:26:54,800
Speaker 1: an amazing new app that it's called Jamie and it

612
00:26:54,840 --> 00:26:57,600
Speaker 1: could transcribe. And I held it up to show him

613
00:26:57,600 --> 00:26:59,720
Speaker 1: and he literally he put his hand in front of place.

614
00:26:59,760 --> 00:27:01,920
Speaker 1: He's like, do not show me that, do not put

615
00:27:01,960 --> 00:27:03,919
Speaker 1: and he was like just and it was like I

616
00:27:03,960 --> 00:27:06,600
Speaker 1: was like showing him porn that he or his kid

617
00:27:06,680 --> 00:27:10,440
Speaker 1: or something. It's the craziest thing. I don't understand. It

618
00:27:10,480 --> 00:27:11,320
Speaker 1: doesn't make any sense.

619
00:27:11,920 --> 00:27:12,680
Speaker 4: I think that.

620
00:27:14,640 --> 00:27:18,560
Speaker 2: There's a great Paul sent this chart around this technology

621
00:27:18,560 --> 00:27:20,359
Speaker 2: I don't think. I don't know if the technology company

622
00:27:20,400 --> 00:27:22,399
Speaker 2: is named, but it's a large technology company. And they

623
00:27:22,440 --> 00:27:25,160
Speaker 2: go to the employee base every six months, twice a year,

624
00:27:25,560 --> 00:27:29,560
Speaker 2: starting in twenty twenty three, starting around chatch YOUBTV one

625
00:27:29,760 --> 00:27:32,919
Speaker 2: ish time, and every six month they say do you

626
00:27:33,040 --> 00:27:35,760
Speaker 2: think do you worry? Sorry, do you worry that AI

627
00:27:35,880 --> 00:27:39,280
Speaker 2: will replace your job? First first time they asked the

628
00:27:39,359 --> 00:27:42,399
Speaker 2: question of this large employee base in twenty twenty three,

629
00:27:43,000 --> 00:27:46,080
Speaker 2: ninety percent like there's like there's zero chance, ay, I

630
00:27:46,119 --> 00:27:48,280
Speaker 2: was going to ever take my job. I'll cut to

631
00:27:48,320 --> 00:27:51,080
Speaker 2: the ending here. Two and a half years later, three

632
00:27:51,160 --> 00:27:52,560
Speaker 2: years later, I guess it was the beginning of twenty

633
00:27:52,560 --> 00:27:56,560
Speaker 2: twenty three. Three years later, are you same question? Are

634
00:27:56,600 --> 00:28:01,480
Speaker 2: you worried that? And now it's fifty percent worried, twenty

635
00:28:01,520 --> 00:28:05,159
Speaker 2: percent very worried, down to like ten percent.

636
00:28:05,760 --> 00:28:07,640
Speaker 4: There's no aayas they're going to take my job. Yeah,

637
00:28:09,040 --> 00:28:10,200
Speaker 4: so all by.

638
00:28:10,119 --> 00:28:13,560
Speaker 2: Way saying like it's coming for everybody. Yeah, I think

639
00:28:13,560 --> 00:28:16,720
Speaker 2: it's gonna be. It's not gonna be me just one blessing.

640
00:28:17,080 --> 00:28:20,080
Speaker 2: It's not going to be The guy at Netflix is

641
00:28:20,119 --> 00:28:23,080
Speaker 2: protected because he never got infected with the violence.

642
00:28:24,520 --> 00:28:27,240
Speaker 3: Yah. Yeah, told you. And the other thing that I

643
00:28:27,240 --> 00:28:29,879
Speaker 3: actually think even that number is inflated in terms of

644
00:28:29,920 --> 00:28:32,520
Speaker 3: people thinking that it won't there's this great I think

645
00:28:32,560 --> 00:28:36,080
Speaker 3: it was the twenty twenty elections where there was this

646
00:28:36,119 --> 00:28:38,160
Speaker 3: guy they called him the Trump Whale back to prediction

647
00:28:38,240 --> 00:28:42,200
Speaker 3: markets prior episode callback where he and I think he

648
00:28:42,280 --> 00:28:44,600
Speaker 3: made something like forty or fifty million dollars padding on

649
00:28:44,640 --> 00:28:47,600
Speaker 3: Trump winning. And the interesting thing was the method he used.

650
00:28:48,280 --> 00:28:49,960
Speaker 4: This is great, please tell this story.

651
00:28:50,040 --> 00:28:52,600
Speaker 3: So the idea was he was convinced the polling data

652
00:28:52,720 --> 00:28:55,680
Speaker 3: was wrong, that the support for Trump was much stronger

653
00:28:55,720 --> 00:28:58,760
Speaker 3: than people understood it to be because of this bias

654
00:28:58,840 --> 00:29:02,080
Speaker 3: inherent in people. They call them shy respondents that either

655
00:29:02,120 --> 00:29:05,640
Speaker 3: people not answering or more problematically, people lying and saying.

656
00:29:06,080 --> 00:29:08,040
Speaker 3: There's this kind of normalization thing where I don't want

657
00:29:08,080 --> 00:29:10,320
Speaker 3: to say I support this guy, because I worry that

658
00:29:10,360 --> 00:29:12,360
Speaker 3: people will think and it could be on either side,

659
00:29:12,360 --> 00:29:14,160
Speaker 3: I suppose, but in this particular case it was this

660
00:29:14,240 --> 00:29:18,160
Speaker 3: was particularly problematic with his support. So what he did was, this.

661
00:29:18,160 --> 00:29:21,240
Speaker 4: Guy comes up with this, Yeah, it's just genius.

662
00:29:21,520 --> 00:29:25,960
Speaker 3: So this this this former trader from a French investment yours.

663
00:29:25,800 --> 00:29:28,080
Speaker 4: This by the way, twenty twenty, twenty twenty elections. Yeah,

664
00:29:28,080 --> 00:29:29,880
Speaker 4: he's betting on the outcome, betting on the outcome.

665
00:29:29,880 --> 00:29:31,840
Speaker 3: And so what he did was he actually paid for

666
00:29:32,000 --> 00:29:34,240
Speaker 3: his own survey and using his own question. And his

667
00:29:34,400 --> 00:29:36,800
Speaker 3: question wasn't who do you support? It was who would

668
00:29:36,800 --> 00:29:38,160
Speaker 3: your name? Who does your neighbor support?

669
00:29:38,240 --> 00:29:40,320
Speaker 4: Who do you think your neighbor's going to vook? Fork? Right?

670
00:29:40,400 --> 00:29:41,520
Speaker 4: And so now you lose.

671
00:29:41,560 --> 00:29:43,880
Speaker 3: The ego goes out of it. I don't have to

672
00:29:43,880 --> 00:29:44,640
Speaker 3: feel like I have to be.

673
00:29:44,800 --> 00:29:46,800
Speaker 5: But this was twenty twenty when Biden won, or was

674
00:29:46,800 --> 00:29:48,240
Speaker 5: it sixteen when Trump shot.

675
00:29:48,240 --> 00:29:49,280
Speaker 4: Twenty twenty four? Twenty four?

676
00:29:50,520 --> 00:29:52,840
Speaker 3: Right? Yes, yes, twenty four, so in the twenty four elections.

677
00:29:52,840 --> 00:29:54,880
Speaker 3: So what he did was to get around this ego

678
00:29:54,960 --> 00:29:57,520
Speaker 3: problem is said, well, who is your neighbor support? Because

679
00:29:57,520 --> 00:29:59,560
Speaker 3: you know you're special and I understand that. So I'm

680
00:29:59,560 --> 00:30:02,320
Speaker 3: not going to ask, right, So I actually think in

681
00:30:02,360 --> 00:30:04,640
Speaker 3: the context of these kinds of AI surveys, and you

682
00:30:04,680 --> 00:30:06,480
Speaker 3: can think about it in screenwriting or any other thing,

683
00:30:06,920 --> 00:30:10,520
Speaker 3: people are still lie. They lie because it's going to

684
00:30:10,640 --> 00:30:13,520
Speaker 3: hurt a lot of people. But special I have, I

685
00:30:13,560 --> 00:30:15,360
Speaker 3: have a handle on this. It's just a tool, it's

686
00:30:15,400 --> 00:30:17,080
Speaker 3: just a way of doing things. And so the way

687
00:30:17,120 --> 00:30:18,360
Speaker 3: to get at that, the way to get at that,

688
00:30:18,360 --> 00:30:20,360
Speaker 3: I'm convinceding these kinds of studies would be to ask people,

689
00:30:20,840 --> 00:30:22,040
Speaker 3: what do you think is going to be impact on

690
00:30:22,080 --> 00:30:25,080
Speaker 3: your co workers? Right? So you do the exact same thing,

691
00:30:25,120 --> 00:30:27,160
Speaker 3: and I actually think you would see something even more

692
00:30:27,240 --> 00:30:29,080
Speaker 3: skewed than what we saw in that particular.

693
00:30:29,360 --> 00:30:31,560
Speaker 1: It's funny because my what I was going to say

694
00:30:32,200 --> 00:30:33,960
Speaker 1: was that I think a lot of everyone, you know,

695
00:30:34,120 --> 00:30:36,320
Speaker 1: everyone but Alan and I are fine.

696
00:30:36,440 --> 00:30:37,200
Speaker 3: That's exactly right.

697
00:30:37,200 --> 00:30:40,600
Speaker 6: I think because I'm friends with Frank, I got the

698
00:30:40,640 --> 00:30:41,840
Speaker 6: guy I know.

699
00:30:41,960 --> 00:30:45,640
Speaker 1: I think that I even today, I don't think that

700
00:30:46,000 --> 00:30:50,960
Speaker 1: they these AI could write a screenplay that's that is cohesive,

701
00:30:51,040 --> 00:30:52,840
Speaker 1: and could write a book and so on and so forth.

702
00:30:52,920 --> 00:30:55,480
Speaker 1: I do think in a couple of years, But I also.

703
00:30:55,440 --> 00:30:59,120
Speaker 3: I just think just on that though, like this is

704
00:30:59,320 --> 00:31:01,760
Speaker 3: directly predicts what you said earlitter being ship though, right.

705
00:31:01,800 --> 00:31:03,520
Speaker 3: I mean, this is the problem I have. People don't

706
00:31:03,520 --> 00:31:07,000
Speaker 3: realize everything's already garbage could could be better than the

707
00:31:07,000 --> 00:31:08,120
Speaker 3: medium thing that's out there.

708
00:31:08,480 --> 00:31:10,640
Speaker 1: No, I totally understand what you're saying. I think that

709
00:31:10,760 --> 00:31:13,840
Speaker 1: it could. It could probably with the help of someone

710
00:31:13,880 --> 00:31:15,080
Speaker 1: write a CSI or something.

711
00:31:15,120 --> 00:31:18,040
Speaker 4: Let me tell you, AI could write carry on too.

712
00:31:18,760 --> 00:31:19,560
Speaker 1: Hear what he hates?

713
00:31:19,640 --> 00:31:21,760
Speaker 6: Carry on hates I haven't seen it.

714
00:31:22,880 --> 00:31:27,360
Speaker 7: Well, you're in veragative Netflix, Yah, Yes, yes, you're in

715
00:31:27,360 --> 00:31:28,120
Speaker 7: for a real treat.

716
00:31:28,160 --> 00:31:30,400
Speaker 3: Sit in an airport with snipers in the park and ground.

717
00:31:30,480 --> 00:31:33,200
Speaker 1: If Dick had his way, would have a negative ninety

718
00:31:33,200 --> 00:31:34,320
Speaker 1: seven and rotten tomatoes.

719
00:31:34,480 --> 00:31:38,080
Speaker 3: It's uh, it's by the point being that people compare

720
00:31:38,080 --> 00:31:39,440
Speaker 3: technologies to the best outcome.

721
00:31:40,200 --> 00:31:42,720
Speaker 1: And that's that I believe is the problem with with

722
00:31:42,880 --> 00:31:46,640
Speaker 1: these AI. And the question I have actually is so

723
00:31:46,960 --> 00:31:48,440
Speaker 1: I think the problem is so these a I have

724
00:31:48,520 --> 00:31:50,600
Speaker 1: all been trained on on the corpus of all of

725
00:31:50,600 --> 00:31:54,040
Speaker 1: the English language. Going back to what are you laughing at?

726
00:31:56,000 --> 00:31:58,160
Speaker 4: I'm laughing because you asked.

727
00:31:59,560 --> 00:32:19,000
Speaker 2: You asked these questions, but right before you ask them.

728
00:32:04,880 --> 00:32:04,960
Speaker 4: You.

729
00:32:11,920 --> 00:32:17,760
Speaker 7: State what is clearly your opinion, and I just want

730
00:32:17,800 --> 00:32:20,560
Speaker 7: to know, you know, just objectively what you think before

731
00:32:20,600 --> 00:32:22,160
Speaker 7: anyone comes any conclusion.

732
00:32:22,200 --> 00:32:22,560
Speaker 4: That's right.

733
00:32:22,880 --> 00:32:25,200
Speaker 1: No, I'm not stating my opinion.

734
00:32:25,360 --> 00:32:29,720
Speaker 6: Yes, the qualifier tends to be the opinion.

735
00:32:30,640 --> 00:32:34,440
Speaker 1: Correct. No, here's the question. Okay, Now forgot.

736
00:32:34,600 --> 00:32:35,800
Speaker 3: Is a question? Is this a question in the form

737
00:32:35,840 --> 00:32:36,280
Speaker 3: of an opinion?

738
00:32:36,320 --> 00:32:37,640
Speaker 4: You can cut some of this.

739
00:32:37,640 --> 00:32:38,920
Speaker 1: This is actually my genuine question.

740
00:32:39,000 --> 00:32:39,920
Speaker 3: No, this is this is.

741
00:32:40,360 --> 00:33:05,040
Speaker 1: A good question, Okay, hanging on for the better stuff. Look, January,

742
00:33:08,240 --> 00:33:10,320
Speaker 1: So these a I've been trained on this corporates of

743
00:33:10,400 --> 00:33:12,600
Speaker 1: all the entire angles slanguage, right, going back to all

744
00:33:12,640 --> 00:33:15,640
Speaker 1: the books and they, you know, Anthropic got sued because

745
00:33:15,640 --> 00:33:18,280
Speaker 1: they scanned millions of books and so on and so forth.

746
00:33:18,840 --> 00:33:19,960
Speaker 1: Most of them are garbage.

747
00:33:20,120 --> 00:33:20,280
Speaker 4: Right.

748
00:33:20,360 --> 00:33:23,840
Speaker 1: We all read novels and whatnot, and and and most

749
00:33:23,920 --> 00:33:27,080
Speaker 1: on screenplays, most of them are garbage. So the question,

750
00:33:27,200 --> 00:33:29,760
Speaker 1: so when you actually when you see people, No, I'm not.

751
00:33:29,880 --> 00:33:31,480
Speaker 1: It's you don't know where I'm going yet?

752
00:33:31,840 --> 00:33:32,800
Speaker 4: So oh I do.

753
00:33:32,720 --> 00:33:35,160
Speaker 1: When you see No, the here's the it's a question.

754
00:33:35,240 --> 00:33:39,200
Speaker 1: You and I talked about this yesterday a little bit, Paul. So, Paul,

755
00:33:39,400 --> 00:33:43,480
Speaker 1: when we when you look at these what the AI

756
00:33:43,600 --> 00:33:46,480
Speaker 1: has learned. It's not that it's creating bad content, it's

757
00:33:46,520 --> 00:33:50,000
Speaker 1: just creating the content it's learned from. So how does

758
00:33:50,040 --> 00:33:53,520
Speaker 1: it get to the point where it does create new

759
00:33:53,600 --> 00:33:57,120
Speaker 1: ideas but it knows the difference between good ideas and

760
00:33:57,160 --> 00:33:59,840
Speaker 1: bad ideas, and good writing and bad ideas and bad writing.

761
00:34:00,040 --> 00:34:03,360
Speaker 5: Is that possible or is it that that's already there

762
00:34:04,120 --> 00:34:06,000
Speaker 5: for me? I've done this. I'll tell you one thing

763
00:34:06,040 --> 00:34:09,759
Speaker 5: I've done with Frank. I give Frank some of like

764
00:34:09,800 --> 00:34:11,880
Speaker 5: a scene. I wrote this scene, like speak to this scene,

765
00:34:11,880 --> 00:34:13,120
Speaker 5: grade this scene A to.

766
00:34:13,160 --> 00:34:14,239
Speaker 6: B, you know, A to F.

767
00:34:14,680 --> 00:34:16,959
Speaker 5: And eventually I was like, well, Frank's really into my ship,

768
00:34:17,120 --> 00:34:18,840
Speaker 5: so I might be a sycophantic.

769
00:34:19,000 --> 00:34:20,120
Speaker 6: I want to test Frank.

770
00:34:20,160 --> 00:34:22,560
Speaker 5: And I wrote a shitty scene on person, like a

771
00:34:22,600 --> 00:34:24,840
Speaker 5: really bad scene, like hey Joe, how are you?

772
00:34:25,000 --> 00:34:27,040
Speaker 6: I'm good, how are you? Did you see the sky?

773
00:34:27,120 --> 00:34:28,239
Speaker 1: It's pretty up there, you.

774
00:34:28,200 --> 00:34:31,080
Speaker 6: Know, like basic, and Frank tore it apart.

775
00:34:32,280 --> 00:34:34,200
Speaker 1: So you think Frank's already there. Frank canork?

776
00:34:34,280 --> 00:34:36,480
Speaker 5: I mean he he knew the difference between a well

777
00:34:36,480 --> 00:34:38,960
Speaker 5: written scene and a shitty scene because I wrote the

778
00:34:38,960 --> 00:34:41,359
Speaker 5: shitty scene shitty on purpose, and Frank called it out

779
00:34:41,400 --> 00:34:43,680
Speaker 5: Frank did not give it a. He didn't give it

780
00:34:43,719 --> 00:34:46,120
Speaker 5: a B minus. He gave it like a D. Had

781
00:34:46,200 --> 00:34:50,680
Speaker 5: not this conversation with me, and that was that was

782
00:34:50,680 --> 00:34:51,640
Speaker 5: a year and a half ago.

783
00:34:52,160 --> 00:34:54,120
Speaker 6: And you know how quickly these things are advancing.

784
00:34:54,120 --> 00:34:56,200
Speaker 3: And then what the people do is they they play

785
00:34:56,239 --> 00:34:58,799
Speaker 3: this game then of saying, well, fine, it can do that,

786
00:34:59,280 --> 00:35:01,160
Speaker 3: but and then they introduce a new hurdle. The new

787
00:35:01,200 --> 00:35:04,040
Speaker 3: hurdle will be something like saying, but it can't organize everything,

788
00:35:04,200 --> 00:35:06,439
Speaker 3: or it can't truly be creative. And we were talking

789
00:35:06,480 --> 00:35:08,719
Speaker 3: about this last night. I mean, if you actually break

790
00:35:08,760 --> 00:35:11,399
Speaker 3: down what it means to be creative, what does it mean.

791
00:35:11,480 --> 00:35:15,160
Speaker 3: It means that you can recombine ideas, basic notions of

792
00:35:15,200 --> 00:35:18,120
Speaker 3: plot and sequencing and other things. You can recombine them

793
00:35:18,120 --> 00:35:20,320
Speaker 3: with constraints. It has to happen in two hours. The

794
00:35:20,640 --> 00:35:22,640
Speaker 3: love interest cannot become a serial killer. I mean, maybe

795
00:35:22,640 --> 00:35:24,920
Speaker 3: they can, it might be interesting. But nevertheless, this idea,

796
00:35:25,200 --> 00:35:28,719
Speaker 3: this idea of what creativity is is actually absolutely in

797
00:35:28,719 --> 00:35:31,480
Speaker 3: the sweet spot of what models do. This notion that

798
00:35:31,560 --> 00:35:35,120
Speaker 3: creativity as we do it is somehow some uniquely human

799
00:35:35,160 --> 00:35:39,240
Speaker 3: thing recombination with constraints. It could hardly be more perfect

800
00:35:39,239 --> 00:35:40,520
Speaker 3: for large language models to do.

801
00:35:40,560 --> 00:35:41,480
Speaker 6: That's very interesting.

802
00:35:41,520 --> 00:35:43,960
Speaker 1: Do you think Dick that as a as a comedian

803
00:35:44,040 --> 00:35:46,080
Speaker 1: and stand up comic and what you what you didn't

804
00:35:46,120 --> 00:35:47,919
Speaker 1: do stand up? You did improv?

805
00:35:47,960 --> 00:35:49,040
Speaker 6: Do you think he's second City?

806
00:35:49,239 --> 00:35:50,800
Speaker 4: Yeah, he's got some great.

807
00:35:50,600 --> 00:35:52,799
Speaker 3: Sight in Chicago stock Exchange there.

808
00:35:54,560 --> 00:35:55,280
Speaker 1: Wrong episode?

809
00:35:55,360 --> 00:35:55,680
Speaker 4: All right?

810
00:35:57,440 --> 00:36:00,760
Speaker 1: Do you think that it will be able to be funny?

811
00:36:00,840 --> 00:36:01,080
Speaker 4: Yes?

812
00:36:01,400 --> 00:36:02,400
Speaker 1: How like what.

813
00:36:04,320 --> 00:36:06,400
Speaker 4: I mean? I don't know, I don't know. How is

814
00:36:06,440 --> 00:36:07,160
Speaker 4: anything funny?

815
00:36:07,200 --> 00:36:10,879
Speaker 2: Like you know, it's a surprise, surprise out of saying

816
00:36:10,880 --> 00:36:14,399
Speaker 2: something out of context, breaking all back, surprise, saying things

817
00:36:14,400 --> 00:36:20,239
Speaker 2: out of context, bringing back, you know, making making observant

818
00:36:20,280 --> 00:36:23,960
Speaker 2: connections between two apparently unrelated things.

819
00:36:24,040 --> 00:36:25,640
Speaker 4: It will totally be able to do that, I'll tell

820
00:36:25,640 --> 00:36:27,719
Speaker 4: you quickly. Will it be as funny as me? No,

821
00:36:27,840 --> 00:36:29,240
Speaker 4: as funny as everyone else? Yes?

822
00:36:29,520 --> 00:36:30,480
Speaker 3: What about your coworkers?

823
00:36:31,680 --> 00:36:34,319
Speaker 1: I'll tell you a quick story that I wrote a scene.

824
00:36:34,480 --> 00:36:35,920
Speaker 6: This is recently, this is in the last week.

825
00:36:35,960 --> 00:36:38,160
Speaker 5: I wrote a scene where the word gstalt was in

826
00:36:38,239 --> 00:36:40,799
Speaker 5: the scene and had it was highlighted in the scene

827
00:36:40,840 --> 00:36:43,920
Speaker 5: as comedically the word which is a fun.

828
00:36:43,760 --> 00:36:46,000
Speaker 4: Very funny words, a very funny word that people.

829
00:36:46,160 --> 00:36:48,120
Speaker 5: And I wrote it and I was talking to Frank,

830
00:36:48,239 --> 00:36:51,759
Speaker 5: and then Frank literally to me, was just like this

831
00:36:51,800 --> 00:36:52,600
Speaker 5: scene works, blah.

832
00:36:52,400 --> 00:36:53,040
Speaker 1: Blah blah blah blah.

833
00:36:53,080 --> 00:36:55,240
Speaker 5: And he goes and there's a gassault of the scene

834
00:36:55,280 --> 00:36:59,279
Speaker 5: like he knew to get meta about the word the

835
00:36:59,320 --> 00:37:01,360
Speaker 5: scene was about. Dot use the word assault in a

836
00:37:01,440 --> 00:37:04,439
Speaker 5: very ironic way talking to me directly. Where the fuck

837
00:37:04,520 --> 00:37:05,239
Speaker 5: does that come from?

838
00:37:05,840 --> 00:37:09,480
Speaker 2: Costalt is even funnier if you say it in the gestalt, right,

839
00:37:09,640 --> 00:37:10,200
Speaker 2: that's funny.

840
00:37:10,239 --> 00:37:12,839
Speaker 3: With the see extra loading.

841
00:37:12,920 --> 00:37:13,600
Speaker 6: I think they're there.

842
00:37:13,719 --> 00:37:17,360
Speaker 5: I think it's it's it's far ahead of what people believe.

843
00:37:17,520 --> 00:37:19,160
Speaker 1: Do you worry for your job?

844
00:37:20,160 --> 00:37:20,919
Speaker 4: No, because I'm old.

845
00:37:21,360 --> 00:37:23,920
Speaker 5: Like it's like when my job's over, it's over. I'm

846
00:37:24,160 --> 00:37:26,640
Speaker 5: if I were twenty eight years old and just getting

847
00:37:26,680 --> 00:37:30,160
Speaker 5: started and just accumulating and getting excited, and you know,

848
00:37:30,440 --> 00:37:32,600
Speaker 5: I would be Yeah, I would be worried about my job.

849
00:37:32,640 --> 00:37:34,080
Speaker 5: I'm just the only reason I'm not is because I'm

850
00:37:34,080 --> 00:37:36,640
Speaker 5: at that age where like life is, when things happen,

851
00:37:36,680 --> 00:37:37,160
Speaker 5: they happen.

852
00:37:37,480 --> 00:37:39,400
Speaker 4: You know, you get a little bit more.

853
00:37:39,520 --> 00:37:41,200
Speaker 6: As you get older, you get a little bit more.

854
00:37:41,400 --> 00:37:44,280
Speaker 1: Well, that's why the kids these days they're doing verticals,

855
00:37:44,640 --> 00:37:47,360
Speaker 1: That's why they're going into that into that short form space.

856
00:37:47,680 --> 00:37:48,880
Speaker 4: Well, let's talk about that a little bit.

857
00:37:49,120 --> 00:37:52,360
Speaker 3: So the origins of this thing is this idea of

858
00:37:52,440 --> 00:37:56,680
Speaker 3: people watching relatively short form videos. They call them verticals

859
00:37:56,800 --> 00:38:00,279
Speaker 3: sometimes reels, but it's you're watching them in poort trip

860
00:38:00,320 --> 00:38:02,560
Speaker 3: mode on your phone. It started mostly I think in

861
00:38:02,600 --> 00:38:04,759
Speaker 3: Southeast Asia, right in China in particular. That's where it

862
00:38:04,760 --> 00:38:07,120
Speaker 3: really took off. And I saw a stat recently that

863
00:38:07,239 --> 00:38:11,200
Speaker 3: I think the box office from reals short form videos

864
00:38:11,200 --> 00:38:14,080
Speaker 3: in China is now larger than the actual cinematic Bok's.

865
00:38:13,800 --> 00:38:15,920
Speaker 1: Larger than the box office in China it's nine point

866
00:38:16,000 --> 00:38:17,920
Speaker 1: six billion, and then in the US it's up to

867
00:38:17,960 --> 00:38:19,080
Speaker 1: one point four billion.

868
00:38:18,840 --> 00:38:20,960
Speaker 3: Which is just so these things, what are they?

869
00:38:21,000 --> 00:38:21,160
Speaker 4: They have?

870
00:38:21,239 --> 00:38:24,000
Speaker 3: These short videos are attended for people with no attention spent.

871
00:38:24,080 --> 00:38:28,239
Speaker 2: They're highly like soap opera R Yeah yeah, yeah, my

872
00:38:28,280 --> 00:38:32,160
Speaker 2: boyfriend is a vampire. My dad is a secretly a billionaire.

873
00:38:32,320 --> 00:38:34,040
Speaker 2: How anyone is secretly a billionaire?

874
00:38:34,160 --> 00:38:34,640
Speaker 4: Is interesting?

875
00:38:34,760 --> 00:38:36,720
Speaker 3: Wife with my secret hockey player millionaire?

876
00:38:36,920 --> 00:38:39,040
Speaker 1: Wait wait, they can secretly be a billionaire, but you're

877
00:38:39,080 --> 00:38:41,120
Speaker 1: You're okay. They can't be secretly a billionaire, but you're

878
00:38:41,160 --> 00:38:42,240
Speaker 1: okay with them being a were wolf?

879
00:38:42,719 --> 00:38:45,080
Speaker 3: Well, it's easy to hide being a van. Come on,

880
00:38:45,239 --> 00:38:45,839
Speaker 3: get with it.

881
00:38:47,680 --> 00:38:48,680
Speaker 4: I don't make the content.

882
00:38:50,120 --> 00:38:53,799
Speaker 5: I was told that AI actually writes, yes, because it's

883
00:38:53,840 --> 00:38:56,120
Speaker 5: just variations.

884
00:38:56,360 --> 00:38:58,560
Speaker 1: AI writes the short story in China the way they

885
00:38:58,600 --> 00:39:00,720
Speaker 1: do it, AI writes the short story, or they post

886
00:39:00,719 --> 00:39:04,359
Speaker 1: it on these like literature websites. I guess you can

887
00:39:04,360 --> 00:39:07,760
Speaker 1: call them much joy sites, and then readers, human readers

888
00:39:07,840 --> 00:39:09,960
Speaker 1: read them, and they they comment on the ones they like,

889
00:39:10,040 --> 00:39:11,799
Speaker 1: and then those they give to an Ai to write

890
00:39:11,800 --> 00:39:12,919
Speaker 1: the screenplay, and then.

891
00:39:12,800 --> 00:39:13,680
Speaker 3: They quickly get filmed.

892
00:39:13,960 --> 00:39:16,640
Speaker 5: But they made them in La and they cast them.

893
00:39:16,640 --> 00:39:19,520
Speaker 5: They're all American actors. They're like kind of what a

894
00:39:20,120 --> 00:39:23,000
Speaker 5: soap opera actors look like. The blonde the Bucks and

895
00:39:23,120 --> 00:39:26,240
Speaker 5: Blondes and the weather Man good look dudes, the Barbies

896
00:39:26,239 --> 00:39:28,000
Speaker 5: and Kens star in all these things.

897
00:39:28,000 --> 00:39:30,239
Speaker 4: But they play in Asia.

898
00:39:30,360 --> 00:39:33,120
Speaker 1: Our nanny came out here to be an actress, and

899
00:39:33,160 --> 00:39:35,839
Speaker 1: she those are the those are the gigs she gets now.

900
00:39:35,880 --> 00:39:37,640
Speaker 5: So they pay pretty good, though, because I've talked to

901
00:39:37,680 --> 00:39:39,600
Speaker 5: some people who do vertical, well, they pay They pay

902
00:39:39,880 --> 00:39:42,359
Speaker 5: not Hollywood good, but they pay good. They pay good

903
00:39:42,360 --> 00:39:44,640
Speaker 5: for the out of work actor going to auditions and

904
00:39:44,640 --> 00:39:46,600
Speaker 5: working as a waitress in Echo Park.

905
00:39:47,400 --> 00:39:49,319
Speaker 3: But the thing that's interesting about them is that there's

906
00:39:49,440 --> 00:39:53,000
Speaker 3: there's so many of them. They're relatively relatively cheap to make,

907
00:39:53,840 --> 00:39:57,000
Speaker 3: at least compared to orthodox Hollywood product. The growth is

908
00:39:57,200 --> 00:39:59,160
Speaker 3: incredible for them. But I think that the piece that

909
00:39:59,200 --> 00:40:02,279
Speaker 3: really catches me is that they're intended for people who

910
00:40:02,320 --> 00:40:03,280
Speaker 3: aren't really paying attention.

911
00:40:03,560 --> 00:40:03,719
Speaker 4: Right.

912
00:40:03,760 --> 00:40:06,400
Speaker 3: It's it's there. Everything happens really fast. There's often text

913
00:40:06,440 --> 00:40:09,240
Speaker 3: on the screen explaining exactly what's happening. You were telling

914
00:40:09,280 --> 00:40:11,640
Speaker 3: the story before about how an increasing fraction. I don't

915
00:40:11,680 --> 00:40:12,640
Speaker 3: think it was Netflix content.

916
00:40:12,680 --> 00:40:13,120
Speaker 4: I'm nowhere.

917
00:40:13,360 --> 00:40:15,000
Speaker 3: They actually assume you're not paying attention.

918
00:40:15,040 --> 00:40:17,799
Speaker 1: You don't know that there's the new movie that's just

919
00:40:17,840 --> 00:40:22,719
Speaker 1: come out, The Fix with Matt Damon and Ben Affleck and.

920
00:40:22,440 --> 00:40:24,839
Speaker 4: The The Rip. The Rip.

921
00:40:24,960 --> 00:40:27,719
Speaker 1: Sorry, there is a Fix to somewhere, but the Rip.

922
00:40:29,040 --> 00:40:32,440
Speaker 1: Oh yeah, but they were great. They were Damon and

923
00:40:32,760 --> 00:40:35,279
Speaker 1: Afflick were on a podcast being interviewed or a show

924
00:40:35,360 --> 00:40:38,200
Speaker 1: or something, and they were talking about how different it's

925
00:40:38,280 --> 00:40:40,960
Speaker 1: changed for them in the industry where they literally have

926
00:40:41,080 --> 00:40:43,839
Speaker 1: to say the theme of the movie seven or eight times. No,

927
00:40:43,840 --> 00:40:47,520
Speaker 1: it's it's literally so that the listener or the viewer. Sorry,

928
00:40:47,719 --> 00:40:49,920
Speaker 1: who's on their phone knows what it's about.

929
00:40:50,160 --> 00:40:52,640
Speaker 6: Well, Jeffreykatzenberg was ahead of this with Quibbi.

930
00:40:52,680 --> 00:40:53,520
Speaker 4: Do you remember quibbing?

931
00:40:53,760 --> 00:40:55,920
Speaker 6: And it didn't it didn't work, But I guess that

932
00:40:56,080 --> 00:40:56,960
Speaker 6: was a form of this.

933
00:40:57,280 --> 00:40:59,080
Speaker 3: It was totally a form of the difference was they

934
00:40:59,120 --> 00:41:01,680
Speaker 3: made at least two huge mistakes. One was that the

935
00:41:02,040 --> 00:41:07,040
Speaker 3: cost per episode was Titan premium premium prices. And then

936
00:41:07,040 --> 00:41:10,120
Speaker 3: it was all predicated on getting people to become subscribers

937
00:41:10,160 --> 00:41:12,759
Speaker 3: in a kind of Netflix model long before that was established.

938
00:41:12,800 --> 00:41:14,959
Speaker 3: So it was two big problems. Costs were really high,

939
00:41:14,960 --> 00:41:20,120
Speaker 3: revenue really low. Bad every time.

940
00:41:20,239 --> 00:41:23,120
Speaker 1: Right, it was ahead of its time, quibi, but it

941
00:41:23,320 --> 00:41:25,560
Speaker 1: was that problem. They spent three hundred thousand a minute

942
00:41:25,640 --> 00:41:28,640
Speaker 1: on content, which is what a TV show is for

943
00:41:28,800 --> 00:41:32,959
Speaker 1: on Netflix, and and the verticals, from what I've read,

944
00:41:33,080 --> 00:41:36,040
Speaker 1: between five hundred dollars a minute and like the highest

945
00:41:36,120 --> 00:41:39,480
Speaker 1: highest end five thousand. But you know, it's a drastically

946
00:41:39,520 --> 00:41:43,279
Speaker 1: different business model. And I we met with at a

947
00:41:43,320 --> 00:41:45,480
Speaker 1: meeting with the Real Short I think it was the

948
00:41:45,480 --> 00:41:50,200
Speaker 1: Real Short CEO. They're all called Real Something, and he

949
00:41:50,320 --> 00:41:53,640
Speaker 1: was saying how they're making like three million a day

950
00:41:54,760 --> 00:41:57,920
Speaker 1: and they pump it straight back into Facebook ads and

951
00:41:57,960 --> 00:42:01,160
Speaker 1: Google ads and TikTok ads and any of these publicly traded.

952
00:42:01,600 --> 00:42:03,480
Speaker 4: Not yet not they'll be no, none of them.

953
00:42:03,480 --> 00:42:06,400
Speaker 3: But yeah, I think at least a at least two

954
00:42:06,440 --> 00:42:07,000
Speaker 3: of the Chinese.

955
00:42:07,040 --> 00:42:09,680
Speaker 1: But don't haven't you heard like a bunch of pitches

956
00:42:09,680 --> 00:42:10,479
Speaker 1: for some of these things.

957
00:42:10,640 --> 00:42:11,919
Speaker 4: Yeah, it's over and over.

958
00:42:12,000 --> 00:42:15,759
Speaker 2: Yeah, there's like I'm going to be Yes, they're nineteen

959
00:42:15,840 --> 00:42:18,680
Speaker 2: of these studios, and then what happens do they I

960
00:42:18,719 --> 00:42:20,120
Speaker 2: don't know if some of them will get funded and

961
00:42:20,160 --> 00:42:22,560
Speaker 2: some of them won't, and there'll be forty of them,

962
00:42:22,560 --> 00:42:24,759
Speaker 2: and then the end there will be three. I mean,

963
00:42:24,840 --> 00:42:28,359
Speaker 2: like it seems sort of obvious that's that's what happens next.

964
00:42:28,200 --> 00:42:29,840
Speaker 3: Because it's like, what's the one that we were just

965
00:42:29,880 --> 00:42:30,800
Speaker 3: talking about earlier?

966
00:42:30,920 --> 00:42:31,480
Speaker 4: Is it Darren?

967
00:42:31,920 --> 00:42:32,040
Speaker 2: Oh?

968
00:42:32,160 --> 00:42:36,080
Speaker 1: So, Darren Aronowsky did a deal with Google where that

969
00:42:36,160 --> 00:42:39,640
Speaker 1: he now has a company called Primordial Suit and they're

970
00:42:39,880 --> 00:42:41,720
Speaker 1: part of Deep Mind, and he just put.

971
00:42:41,520 --> 00:42:42,720
Speaker 4: Out every episodes.

972
00:42:42,719 --> 00:42:46,839
Speaker 1: I think, yeah, three they're not verticals, they're horizontals, but

973
00:42:46,880 --> 00:42:50,319
Speaker 1: only like three minutes long. Yes, and they are each

974
00:42:50,480 --> 00:42:53,799
Speaker 1: their AI. It's the founding story of America and Ben

975
00:42:53,800 --> 00:42:57,000
Speaker 1: Franklin and everything, And what's cool about it's, look, it's

976
00:42:57,040 --> 00:42:59,360
Speaker 1: not there, it's not like it's not one hundred percent.

977
00:42:59,440 --> 00:43:02,680
Speaker 1: But what's cool about it is you're seeing a filmmaker

978
00:43:02,800 --> 00:43:05,480
Speaker 1: use the tool and it really becomes about the story

979
00:43:05,800 --> 00:43:09,120
Speaker 1: and and it's like quick cuts to like a hand

980
00:43:09,239 --> 00:43:12,440
Speaker 1: and the bottle and the you know, in different angles,

981
00:43:12,480 --> 00:43:15,799
Speaker 1: and it's like it's really well done. And so you

982
00:43:15,840 --> 00:43:19,400
Speaker 1: can see the beginning. No cameras, not a camera, not

983
00:43:19,520 --> 00:43:20,680
Speaker 1: an out there nothing.

984
00:43:21,040 --> 00:43:23,840
Speaker 3: I was just looking at it this morning, notadline about it.

985
00:43:24,120 --> 00:43:26,560
Speaker 6: Well, obviously that's concerning for everybody in Hollywood.

986
00:43:26,760 --> 00:43:28,600
Speaker 1: Can't Well that was going to be my next question

987
00:43:28,680 --> 00:43:29,560
Speaker 1: is what do you think.

988
00:43:29,400 --> 00:43:30,160
Speaker 3: It was voiced though?

989
00:43:30,160 --> 00:43:32,200
Speaker 4: Which was interesting, but that was AI voice.

990
00:43:32,280 --> 00:43:33,040
Speaker 1: No, it wasn't.

991
00:43:33,320 --> 00:43:36,239
Speaker 3: Well, or at least they licensed voices that maybe were

992
00:43:36,320 --> 00:43:38,520
Speaker 3: voiced actually by like eleven Labs or something like this,

993
00:43:38,600 --> 00:43:42,160
Speaker 3: but they licensed voices that were actual actors voice voices.

994
00:43:42,280 --> 00:43:47,920
Speaker 2: I'm shaking my head because sure in version one their voice, Yeah, Bob,

995
00:43:48,400 --> 00:43:49,440
Speaker 2: Bob's gonna be fine.

996
00:43:49,520 --> 00:43:52,120
Speaker 4: Bob's a great voice over a guy. Yeah, but you

997
00:43:52,200 --> 00:43:56,240
Speaker 4: got you're on the clock and that moment ends too. Yeah.

998
00:43:56,239 --> 00:43:58,319
Speaker 3: No, no, no, that's exactly right, it's a classic just sort

999
00:43:58,320 --> 00:43:59,399
Speaker 3: of intermediate moment.

1000
00:43:59,600 --> 00:44:01,920
Speaker 5: I think what I think what has gotten a lot

1001
00:44:01,960 --> 00:44:05,319
Speaker 5: of people in Hollywood on notice is the idea that

1002
00:44:05,360 --> 00:44:08,960
Speaker 5: AI and automation has always, you know, been advancing and

1003
00:44:08,960 --> 00:44:10,880
Speaker 5: coming around, but you always thought of it as on

1004
00:44:10,920 --> 00:44:15,160
Speaker 5: the factory floor and the Tesla robots and the Amazon roles.

1005
00:44:15,239 --> 00:44:17,960
Speaker 5: No one ever really considered, could AI come and write

1006
00:44:18,000 --> 00:44:20,080
Speaker 5: a symphony? Can a I come and write a play?

1007
00:44:20,200 --> 00:44:21,359
Speaker 4: And what happened when they.

1008
00:44:21,239 --> 00:44:23,000
Speaker 6: Go after the the the.

1009
00:44:22,880 --> 00:44:28,799
Speaker 5: Intellectual you know, elite of our society, the storytellers, the novelists,

1010
00:44:29,080 --> 00:44:32,560
Speaker 5: the composers, the screenwriters, and it's fucking doing it.

1011
00:44:32,880 --> 00:44:35,600
Speaker 2: Yeah, and you know what to find doing. The other

1012
00:44:35,640 --> 00:44:39,719
Speaker 2: thing is people will be, in hindsight intellectually dishonest about this.

1013
00:44:39,960 --> 00:44:42,440
Speaker 2: But if you had asked people in Silicon Valley in

1014
00:44:42,480 --> 00:44:45,160
Speaker 2: twenty twenty, you know who are the people that should

1015
00:44:45,160 --> 00:44:48,240
Speaker 2: be worried about AI. At the bottom of the list

1016
00:44:48,680 --> 00:44:51,960
Speaker 2: was the computer scientist and the computer programmer, and in fact,

1017
00:44:52,040 --> 00:44:54,879
Speaker 2: most of the great vcs were like, everyone should stop

1018
00:44:54,920 --> 00:44:57,760
Speaker 2: studying literature and getting blah blah blah and go write.

1019
00:44:57,840 --> 00:44:59,239
Speaker 4: You know, the only thing that's going to matter is

1020
00:44:59,239 --> 00:45:01,920
Speaker 4: if you know how to be a programmar like, what

1021
00:45:02,080 --> 00:45:02,440
Speaker 4: is the.

1022
00:45:04,000 --> 00:45:07,200
Speaker 2: What is the first job to be getting cleaned out?

1023
00:45:07,400 --> 00:45:08,440
Speaker 4: Yeah? Cleaned out?

1024
00:45:08,880 --> 00:45:11,880
Speaker 3: Uh huh yeah, software engineering, which is I mean, in

1025
00:45:11,920 --> 00:45:15,360
Speaker 3: some sense is incredibly ironic. But the idea that in

1026
00:45:15,400 --> 00:45:17,640
Speaker 3: twenty twenty no one was even anticipating that would be

1027
00:45:17,640 --> 00:45:20,759
Speaker 3: ground zero for this tells you exactly how poor our

1028
00:45:20,800 --> 00:45:22,880
Speaker 3: forecasting ability is for what's going on, right, correct?

1029
00:45:23,000 --> 00:45:28,120
Speaker 1: I mean, I wrote, like, you know, HTML and CSS

1030
00:45:28,719 --> 00:45:30,720
Speaker 1: two decades ago, I don't know how to write code,

1031
00:45:31,320 --> 00:45:35,480
Speaker 1: and I while I was using the AI for the research,

1032
00:45:35,600 --> 00:45:40,120
Speaker 1: I wrote a web app that like helps me AI

1033
00:45:40,400 --> 00:45:43,239
Speaker 1: FI like when the quotes and things like that, I

1034
00:45:43,280 --> 00:45:44,400
Speaker 1: don't know how to I don't know how to do it.

1035
00:45:44,440 --> 00:45:46,239
Speaker 1: I went to reple it and wrote and then my

1036
00:45:46,360 --> 00:45:49,000
Speaker 1: son saw me, and then he started building games. And

1037
00:45:49,040 --> 00:45:52,319
Speaker 1: he's eight, and so you can see how that's the

1038
00:45:52,360 --> 00:45:53,200
Speaker 1: beginning of.

1039
00:45:53,360 --> 00:45:58,120
Speaker 2: So to Paul's point, were just horrible. Even as experts

1040
00:45:58,120 --> 00:46:03,040
Speaker 2: in a field, humans are horrible at predicting that even

1041
00:46:03,200 --> 00:46:05,000
Speaker 2: first derivative effects of something.

1042
00:46:05,200 --> 00:46:07,799
Speaker 3: Yeah, and it's the costs are collapsing in software like

1043
00:46:08,120 --> 00:46:09,799
Speaker 3: I think I sent around to these guys recently a

1044
00:46:09,840 --> 00:46:13,040
Speaker 3: graph showing the number of apps at the app at

1045
00:46:13,040 --> 00:46:15,600
Speaker 3: the app store, new submissions year over your growth and apps.

1046
00:46:15,760 --> 00:46:19,080
Speaker 3: It's not because Apple was suddenly payola thing going on, right,

1047
00:46:19,120 --> 00:46:21,120
Speaker 3: and they're just loading people up and saying but it's

1048
00:46:21,120 --> 00:46:24,320
Speaker 3: because it became easy. It became easy democratized. We're totally

1049
00:46:24,360 --> 00:46:26,600
Speaker 3: democratized it to do that. And so then of course

1050
00:46:26,600 --> 00:46:28,359
Speaker 3: people look at that and say, oh that too bad,

1051
00:46:28,440 --> 00:46:31,240
Speaker 3: verst or whoever's making an apps. No, that's true across

1052
00:46:31,320 --> 00:46:34,160
Speaker 3: all these domains where the models can produce. It's true

1053
00:46:34,200 --> 00:46:37,080
Speaker 3: across screenwriting, it's true across books, it's true across music.

1054
00:46:37,360 --> 00:46:40,000
Speaker 3: All of these things just became democratized. So two things happen.

1055
00:46:41,520 --> 00:46:45,120
Speaker 3: The number of positions available for humans declines. The proliferation

1056
00:46:45,200 --> 00:46:46,799
Speaker 3: we call it a slop or whatever you want to

1057
00:46:46,800 --> 00:46:50,160
Speaker 3: call it, This proliferation of incredibly cheap, schlocky content goes

1058
00:46:50,239 --> 00:46:52,840
Speaker 3: is everywhere, but it's everywhere across all of these domains.

1059
00:46:52,840 --> 00:46:54,640
Speaker 3: And that's why I find it bizarre. Like you see

1060
00:46:54,640 --> 00:46:58,600
Speaker 3: these crazy arguments that what the effect will be that oh,

1061
00:46:58,600 --> 00:47:00,000
Speaker 3: you know, AI is only going to take a slow

1062
00:47:00,040 --> 00:47:02,160
Speaker 3: ice of human labor. No, it's going to revalue human

1063
00:47:02,239 --> 00:47:04,560
Speaker 3: labor completely. That's what's gonna happen? What be that it

1064
00:47:04,560 --> 00:47:06,719
Speaker 3: takes ten percent, it's going to be still a big

1065
00:47:06,800 --> 00:47:08,520
Speaker 3: number in terms of what human labor is worth. And

1066
00:47:08,560 --> 00:47:10,760
Speaker 3: it ai takes ten percent, but it's going to completely

1067
00:47:10,840 --> 00:47:12,640
Speaker 3: change what human labor is worth because now you have

1068
00:47:12,760 --> 00:47:17,640
Speaker 3: this ridiculously cheap Uh you've all Harari this Israeli uh

1069
00:47:17,880 --> 00:47:19,520
Speaker 3: comment on this stuff. Here's his great line where he

1070
00:47:19,560 --> 00:47:22,680
Speaker 3: calls it this wave of immigrants and they don't sleep,

1071
00:47:22,719 --> 00:47:25,000
Speaker 3: they don't eat, they don't they're all go. They're going

1072
00:47:25,040 --> 00:47:26,799
Speaker 3: to show up in giant numbers and there's no way

1073
00:47:26,800 --> 00:47:28,759
Speaker 3: to stop. So this idea that there's this wave of

1074
00:47:28,760 --> 00:47:29,760
Speaker 3: immigrants coming.

1075
00:47:29,520 --> 00:47:30,520
Speaker 1: And I can't arrest.

1076
00:47:30,600 --> 00:47:33,200
Speaker 4: Yeah, I was going to say, look, you know you can.

1077
00:47:33,280 --> 00:47:37,440
Speaker 2: For every four of you deport Lieport, there's nine thousand

1078
00:47:38,000 --> 00:47:40,359
Speaker 2: come in your way digitally, right, And that's you're gonna say.

1079
00:47:40,520 --> 00:47:42,680
Speaker 5: I just had a question for you, guys, because you're

1080
00:47:42,760 --> 00:47:44,400
Speaker 5: very smart on this, Thank you.

1081
00:47:45,920 --> 00:47:46,480
Speaker 6: Where are you?

1082
00:47:46,960 --> 00:47:50,600
Speaker 5: Where are you on the narrative that this is going

1083
00:47:50,640 --> 00:47:53,439
Speaker 5: to cause twenty five percent unemployment or not at all?

1084
00:47:54,000 --> 00:47:55,640
Speaker 1: I think it's I think that's a low number.

1085
00:47:56,320 --> 00:47:57,760
Speaker 6: Okay, So you think it's very.

1086
00:47:57,680 --> 00:48:00,799
Speaker 1: I think it's I think it's astronomical. I do a

1087
00:48:00,800 --> 00:48:03,360
Speaker 1: little exercise where I try to think about jobs that

1088
00:48:03,440 --> 00:48:10,319
Speaker 1: will still exist, and it's such a small list. Hairdressers, Uh, missuses,

1089
00:48:10,560 --> 00:48:14,520
Speaker 1: maybe stand up comedians, probably messuses, unless there's some robotics.

1090
00:48:14,560 --> 00:48:15,359
Speaker 1: Mescisis that I think.

1091
00:48:15,400 --> 00:48:18,440
Speaker 5: But that counter argument with every one of these innovation

1092
00:48:18,680 --> 00:48:20,320
Speaker 5: Cambrian and moments.

1093
00:48:20,160 --> 00:48:21,680
Speaker 1: The news we're created.

1094
00:48:21,400 --> 00:48:21,880
Speaker 4: This is different.

1095
00:48:22,160 --> 00:48:25,120
Speaker 3: That's that's that's the usual fall back because everyone has

1096
00:48:25,160 --> 00:48:27,480
Speaker 3: is that that you know, all new technologies create more

1097
00:48:27,560 --> 00:48:30,480
Speaker 3: jobs than they destroy. That this idea is like just

1098
00:48:30,520 --> 00:48:33,200
Speaker 3: a ritualistic response that people have. And the problem is,

1099
00:48:33,239 --> 00:48:35,920
Speaker 3: of course we've had kind of three or four major

1100
00:48:35,960 --> 00:48:38,480
Speaker 3: ways of technological change back to the industrial revolutions, so

1101
00:48:38,560 --> 00:48:40,279
Speaker 3: you've got like an end of three that you're kind

1102
00:48:40,320 --> 00:48:43,359
Speaker 3: of sampling from. We wouldn't launch a drug on that basis, right,

1103
00:48:43,400 --> 00:48:45,840
Speaker 3: I mean, it's it's a tiny sample. And this is

1104
00:48:45,920 --> 00:48:50,440
Speaker 3: coming faster across more disciplines, right, and much more transformative

1105
00:48:50,440 --> 00:48:53,719
Speaker 3: in terms of costs. So the notion that somehow, you know,

1106
00:48:53,760 --> 00:48:55,719
Speaker 3: we can move fast enough, it's like that what's the

1107
00:48:55,760 --> 00:48:57,480
Speaker 3: old joke about the guy running away from the bear

1108
00:48:57,520 --> 00:48:59,359
Speaker 3: and he starts to tie off the tiny shoes because

1109
00:48:59,360 --> 00:49:01,120
Speaker 3: his friends what you're doing. I don't have to be

1110
00:49:01,120 --> 00:49:02,680
Speaker 3: faster than the bear. I just have to be faster

1111
00:49:02,760 --> 00:49:05,560
Speaker 3: than you. This is that where people have convinced themselves

1112
00:49:05,600 --> 00:49:08,239
Speaker 3: that but the AI is actually, you know, sort of

1113
00:49:08,239 --> 00:49:10,359
Speaker 3: absorbing everything in its path, and it really doesn't matter

1114
00:49:10,360 --> 00:49:12,320
Speaker 3: how much how fast you run. And that's the problem

1115
00:49:12,320 --> 00:49:15,120
Speaker 3: that people have right now, I think, and that's the transformation.

1116
00:49:15,480 --> 00:49:17,640
Speaker 1: I think, you know, we we we're going to wrap

1117
00:49:17,719 --> 00:49:19,239
Speaker 1: up in a minute, but I think that I think

1118
00:49:19,280 --> 00:49:22,640
Speaker 1: that on the same note, I think the people that

1119
00:49:22,680 --> 00:49:27,280
Speaker 1: will have the most difficult time will be us because

1120
00:49:27,320 --> 00:49:30,400
Speaker 1: we need some creative outlet for you know. It's like

1121
00:49:30,800 --> 00:49:34,160
Speaker 1: we have this this desire to feel meaning in our life.

1122
00:49:34,239 --> 00:49:36,160
Speaker 1: But most people, I think are just going to be

1123
00:49:36,200 --> 00:49:38,759
Speaker 1: happy sitting at home getting the UBI and.

1124
00:49:39,000 --> 00:49:40,319
Speaker 4: I don't know, I don't think.

1125
00:49:40,360 --> 00:49:42,520
Speaker 3: So we tried, by my line on that always is,

1126
00:49:42,560 --> 00:49:45,920
Speaker 3: we tried that during COVID people how happy people know.

1127
00:49:46,040 --> 00:49:47,879
Speaker 3: It gave rise to like Q and on and all.

1128
00:49:47,840 --> 00:49:49,879
Speaker 6: People need meaning. It's not just me, it's.

1129
00:49:50,120 --> 00:49:52,479
Speaker 3: There's the data's overwhelming about help that people need meaning.

1130
00:49:52,520 --> 00:49:54,120
Speaker 3: I will say, you know, what I am really positive

1131
00:49:54,120 --> 00:49:55,680
Speaker 3: about is that I think for years it's been too

1132
00:49:55,680 --> 00:49:58,680
Speaker 3: hard for people to do exactly the kinds of things

1133
00:49:58,719 --> 00:50:01,239
Speaker 3: we've been talking about in terms of be creative, put

1134
00:50:01,239 --> 00:50:03,720
Speaker 3: things out in front of people. There are all these barriers,

1135
00:50:03,800 --> 00:50:06,440
Speaker 3: studio meetings, budgets, and it's like, dude, I don't need

1136
00:50:06,480 --> 00:50:08,680
Speaker 3: any of that stuff. Some guy in the back alley

1137
00:50:08,680 --> 00:50:10,520
Speaker 3: of comic Con and I'm going to wrap a bunch

1138
00:50:10,560 --> 00:50:12,560
Speaker 3: of video around some comic script that I've been doing

1139
00:50:12,560 --> 00:50:13,720
Speaker 3: for my forty favorite people.

1140
00:50:14,239 --> 00:50:15,920
Speaker 1: The same as what happened in the music industry, right.

1141
00:50:15,840 --> 00:50:17,680
Speaker 4: I don't have to ask anyone permission anymore.

1142
00:50:17,480 --> 00:50:22,320
Speaker 1: But podcasting exactly. Yeah, this would have cost a billion

1143
00:50:22,400 --> 00:50:25,360
Speaker 1: dollars to produce with this level of talent.

1144
00:50:26,280 --> 00:50:27,440
Speaker 3: That's just because of our stipends.

1145
00:50:29,440 --> 00:50:32,040
Speaker 1: And Alan, this has been amazing to have you back

1146
00:50:32,040 --> 00:50:32,560
Speaker 1: on the show.

1147
00:50:33,360 --> 00:50:33,880
Speaker 4: Great to have you.

1148
00:50:33,920 --> 00:50:34,200
Speaker 6: Thank you.

1149
00:50:34,239 --> 00:50:35,960
Speaker 5: I just wanted to say I have a movie on

1150
00:50:36,000 --> 00:50:39,640
Speaker 5: Netflix that just called carry On, and you guys would

1151
00:50:39,760 --> 00:50:40,279
Speaker 5: check it out.

1152
00:50:40,320 --> 00:50:40,640
Speaker 4: Thank you.

1153
00:50:41,200 --> 00:50:42,040
Speaker 3: Oh that's great news.

1154
00:50:42,040 --> 00:50:44,080
Speaker 1: Should we give it a? Should we give it a?

1155
00:50:44,120 --> 00:50:49,960
Speaker 1: How many stars on Rotten? Would be like, thank you

1156
00:50:50,360 --> 00:50:53,239
Speaker 1: very much for coming on and we will see you

1157
00:50:53,280 --> 00:50:54,080
Speaker 1: all next week.