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Speaker 1: Welcome to Tech Stuff.

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Speaker 2: I'm's Volosca and I'm thrilled to announce that the Week

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Speaker 2: in Tech is back and it's growing. Instead of Karen

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Speaker 2: and I recounting the Essential news today and every Friday

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Speaker 2: from now on, I'm going to be joined by three

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Speaker 2: of the best writers covering Silicon Valley. Read Abagotti, technology

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Speaker 2: editor for Semophore. I read your newsletter religiously and we

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Speaker 2: had a lot of fun on Tech Stuff a few

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Speaker 2: months ago with a story you wrote comparing different AI

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Speaker 2: companies to characters in The.

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Speaker 1: Wizard of Oz.

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Speaker 3: Welcome back, Thanks, it's good to be here.

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Speaker 2: Kyle Jaker writes the Infinite Scroll newsletter for The New Yorker. Kyle,

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Speaker 2: last time you were on Tech Stuff, you talked about

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Speaker 2: your holiday tech gift guide, which leaned quite surrealist. Happy

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Speaker 2: to have you back.

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Speaker 1: It was very fun.

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Speaker 2: Thank you and Taylor friends who joined a great discussion

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Speaker 2: on last week's panel and is back this week. Taylor

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Speaker 2: writes the User mag newsletter and hosts the Power User podcast.

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Speaker 1: Taylor, thanks for joining us again.

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Speaker 4: Thanks for having me so.

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Speaker 2: I've been following each of you for a long time,

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Speaker 2: and many of you have also known each other for

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Speaker 2: a long time, or at least you all know Taylor.

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Speaker 5: Yes, I don't think Read and I have ever met,

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Speaker 5: but I follow this work of course likewise likewise.

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Speaker 2: So just imagine we're all friends sitting around a table

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Speaker 2: in a living room with huge microphones in our face,

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Speaker 2: just shooting the breeze about technology. And let's really take

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Speaker 2: our listeners on a journey, because each of you lives

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Speaker 2: and breathes your beat and has, as far as I'm concerned,

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Speaker 2: better insights and access than anyone else in the world. Okay,

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Speaker 2: let's get into it and Read. Let's start with you.

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Speaker 2: You have what could be described as an insiderish story

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Speaker 2: this week a company conference, except not just any company conference,

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Speaker 2: the massive developer conference held by the multi trillion dollar

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Speaker 2: company in Video.

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Speaker 1: Yeah.

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Speaker 3: Yeah, it's funny you say insidery because I was in

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Speaker 3: this press conference with n Video CEO Jensen Walling yesterday

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Speaker 3: and I think there were three hundred reporters.

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Speaker 1: In the room.

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Speaker 3: If you can imagine that. I was thinking, like, I

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Speaker 3: wonder how many reporters are in like the White House

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Speaker 3: right now, you know, like it was an insane amount

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Speaker 3: of interest in coverage of this thing. But he said

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Speaker 3: something that I thought was in this I think this

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Speaker 3: made the rounds yesterday. But he said, you know, his

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Speaker 3: life philosophy is three things. Essentially, he said, don't don't

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Speaker 3: get fired, don't get bored, and don't get killed. And

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Speaker 3: it wants to live by literally right exactly. And I

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Speaker 3: thought he said living in that triangle is higher risk

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Speaker 3: than you think. And I sort of thought about that,

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Speaker 3: and I'm like, that actually does explain a lot of

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Speaker 3: what you're seeing from Nvideo, where it's just a company

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Speaker 3: like all the mag seven, all these big tech companies

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Speaker 3: end up in this place where you get to a

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Speaker 3: certain size and success and you end up just competing

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Speaker 3: in everything in every front. And the fronts are just

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Speaker 3: expanding for Nvidia into all these new areas and the

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Speaker 3: big I think externality that that has happened since you know,

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Speaker 3: the last year's big en Video conference here, although they

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Speaker 3: seem to do something like this a few times a

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Speaker 3: year now, is that is that open claw happened and

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Speaker 3: that's been sort of I think of it as like

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Speaker 3: the chatchypt moment that everyone's ignoring because the world is

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Speaker 3: on fire and it's and it doesn't and it's also

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Speaker 3: not like something that anybody can just do, can just

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Speaker 3: fire up and do like chatchept. But it's like a

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Speaker 3: glimpse into the future where if you talk to people

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Speaker 3: out here who are using this, you're like, oh, of course, like,

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Speaker 3: of course this is where things are headed. But it's

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Speaker 3: also quantum computing, it's video games still, it's like everything space.

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Speaker 2: Yeah, so you and audience smiling when Reid was talking

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Speaker 2: about can you give us a little bit of the backstory,

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Speaker 2: like what are the essentials we need to know about

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Speaker 2: open floor and then what will you grinew the.

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Speaker 5: Man, Well, I was grinning about the fact that I

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Speaker 5: don't think anyone outside of tech understands it at all yet.

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Speaker 5: I think there are these memes coming out of China

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Speaker 5: of like AI anxiety after this open Claw moment, from

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Speaker 5: people who are now scrambling to let agents take over

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Speaker 5: their inboxes and their text messages and their computing lives.

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Speaker 5: And I think there was one incident in which an

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Speaker 5: open Claw agent just deleted someone's entire email inbox or

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Speaker 5: something like this. So it's like, I don't know, it's

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Speaker 5: like vaping or something. It's like, let's just inhale it

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Speaker 5: straight into our bodies and like, see what happens. But

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Speaker 5: I do, Asri said, I think it's this tidal wave

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Speaker 5: that everyone's ignoring or just can't quite understand.

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Speaker 2: School Christra of AI agents who basically have full access

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Speaker 2: to your computer and all your emails and everything you've

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Speaker 2: ever done, and then make decisions on your behalf.

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Speaker 3: This guy I was talking to who was doing this,

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Speaker 3: one of these, one of these early adopters, was telling

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Speaker 3: me just how he has completely blindly turned his life

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Speaker 3: over to his open Claw implementation. And I'm like, aren't

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Speaker 3: you worried, Like it's gonna delete your inbox or do

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Speaker 3: something really bad? And he's like, oh, I think it will.

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Speaker 3: He's like, but I'll just I'll learn from it. It'll

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Speaker 3: it'll teach me a lesson that will probably give me

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Speaker 3: some idea for my next company.

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Speaker 2: And I'm just like, okay, but the open cool thing

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Speaker 2: was happening before in video, Like why why was it

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Speaker 2: the topic? Dujoure of GtC.

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Speaker 3: Yeah, and by the way, I don't even know if

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Speaker 3: it was the topic dujore like, It's just to me

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Speaker 3: the best example of how it's like this thing happens

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Speaker 3: and and now and video has to like scramble and

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Speaker 3: figure out how to how to play in that space.

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Speaker 2: And the way that's stepping into the open potal space

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Speaker 2: is essentially building their own product around open Pool called

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Speaker 2: Nemo Cure.

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Speaker 3: Is that right?

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Speaker 1: Read that's right?

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Speaker 3: And right now it's it's so expensive and so risky

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Speaker 3: from a security standpoint to do this, to just turn

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Speaker 3: your life over to many hundreds of agents and different

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Speaker 3: AI models. But like the prices are coming down so

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Speaker 3: much and the infrastructure is being built around it so

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Speaker 3: fast that that just means a lot more tokens, a

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Speaker 3: lot more revenue for companies like Nvidia and all the hyperscalers.

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Speaker 3: But be like a new a new field, a new

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Speaker 3: battlefield to try to like gain market share or potentially

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Speaker 3: lose market share. And that's so Nvidia is like doing

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Speaker 3: this stuff around that.

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Speaker 2: And they're trying to create like a more secure architecture

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Speaker 2: around this open source thing. Right to basically honess the

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Speaker 2: power of open tool but make it somewhat safer.

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Speaker 1: Right is the kind of goal.

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Speaker 2: Taylor, what are you? What are you thinking about open toloor?

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Speaker 2: I saw you what us mining as well? And we

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Speaker 2: will come back to you up to us.

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Speaker 4: Yeah, I have Fomo, and I want a claw. I

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Speaker 4: want one of these little bots. I like was debating

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Speaker 4: buying a Macmini. I just can't justify the like five

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Speaker 4: hundred dollars or whatever it costs these days, I kind

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Speaker 4: of feel like that that person who said that they

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Speaker 4: might learn from it, Like now I wouldn't give it

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Speaker 4: unmitigated access to my email. But I do think that

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Speaker 4: Read is right that this is sort of we're probably

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Speaker 4: moving towards this sort of like agentic future or whatever

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Speaker 4: you want to call it. And I don't know, there's

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Speaker 4: just like you guys know how hard like time consuming

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Speaker 4: could be to be a freelancer. I'm like, even if

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Speaker 4: this thing could just like sort of invoice companies for me,

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Speaker 4: it would be amazing.

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Speaker 5: Yeah, it feels like it's proving out that AI will

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Speaker 5: be used a lot by normal people. Also, like we'll

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Speaker 5: all be using these large amounts of tokens, Like it's

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Speaker 5: going to be embedded much more deeply in our workflows

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Speaker 5: than just using a chatbot.

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Speaker 2: Yeah, on in Vidio specifically, I mean, it's interesting, right,

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Speaker 2: Taylor and Kyle, you both focus on kind of the

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Speaker 2: intersection of technology and culture. I was thinking about this

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Speaker 2: morning like how to, how to how to relate in

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Speaker 2: video to both of your beats? And I had the

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Speaker 2: rather depressing thought that maybe all culture now sits downstream

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Speaker 2: of Nvidia.

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Speaker 5: What well I was I was thinking about that because

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Speaker 5: I do feel like this is one of the biggest

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Speaker 5: moments for Johnson Huang as like a meme. Right Suddenly

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Speaker 5: his image was everywhere and he's been wearing the same

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Speaker 5: leather jacket for years or decades or whatever, and there's

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Speaker 5: crowds of people around him, and so suddenly it does

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Speaker 5: feel like a cultural movement as much as a technological one,

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Speaker 5: for sure.

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Speaker 2: Read coming out of this story, I mean, what what?

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Speaker 1: What's the you know?

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Speaker 2: One of the interesting things was that all these incredible

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Speaker 2: announcements and more and more and more revenues, and yet

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Speaker 2: in video stock price was was I think flat or

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Speaker 2: slightly down? Like how did how did people react to this?

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Speaker 2: Did that correspond to the mood in the room? Or

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Speaker 2: is I mean, is it just they've had so many

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Speaker 2: wins that the market has kind of priced them at

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Speaker 2: this place. There's nowhere more for them to grow, Like

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Speaker 2: what's and what are the competitive threats?

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Speaker 3: I think it's actually it's funny The thing about n

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Speaker 3: video is it's almost like too simple of a story, right.

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Speaker 3: It's like there's these things called tokens that we're all using, right,

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Speaker 3: that's what that's what we're using when we when we

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Speaker 3: query chat ept and people can't get another of them,

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Speaker 3: and they're willing to pay a lot of money for them.

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Speaker 3: And you could see with like this open cloth stuff

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Speaker 3: there will actually the more tokens you use, the better

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Speaker 3: it gets, and people are paying for it, and it's like, okay,

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Speaker 3: so these companies are making a product that is pretty profitable,

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Speaker 3: has good margins and everybody wants and they can't make

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Speaker 3: enough of it. Like that's just a good business, like

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Speaker 3: and I, but it's like too simple for tech. Normally,

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Speaker 3: Tech's like we're gonna get all these people these eyeballs,

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Speaker 3: and it's gonna be free, and then we'll figure out

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Speaker 3: how to monetize it and we'll offer all these free services.

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Speaker 3: And it's like, wait, you mean you're just gonna charge

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Speaker 3: people a lot of money for a product and make

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Speaker 3: a bunch of money on It's like that's so weird.

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Speaker 3: It's like a normal business.

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Speaker 2: Taylor Caryle, I'm curious before we move to the next story,

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Speaker 2: and what are your burning questions about Nvidio. I mean,

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Speaker 2: the biggest company on us don't work it.

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Speaker 4: I think, you know, that's a really good question. I

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Speaker 4: don't know. I mean I kind of come to it

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Speaker 4: like from the same angle as Kyle maybe of like

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Speaker 4: I'm very interested to see how they navigate this like

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Speaker 4: AI backlash, how Jensen kind of like ascends into this

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Speaker 4: like role of a very public CEO. I mean, he's

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Speaker 4: been CEO for so long, and I think most of

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Speaker 4: his time as a tech leader has been kind of

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Speaker 4: behind the scenes by Steve Joes.

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Speaker 2: He didn't rebrand, he's worn that that that leather jacket

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Speaker 2: for a really long time.

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Speaker 1: He already he already.

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Speaker 4: But I also think it's interesting, you know, we had

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Speaker 4: like Sam Altman open AI CEO at the Vanity Fair

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Speaker 4: Oscar parties, where where if famous playwright I guess called

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Speaker 4: him like a Nazi, and there is like this kind

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Speaker 4: of burgeoning like anti tech sentiment, especially from more left

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Speaker 4: cleaning spaces, and so I don't know, I don't know

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Speaker 4: that we'll see Jensen Wong like out at you know,

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Speaker 4: Hollywood parties, but I do think that like he's going

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Speaker 4: to have to step into his public image and curate

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Speaker 4: a public image for himself that.

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Speaker 3: Is that is right on.

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Speaker 5: It's like in video is a brand, but what is

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Speaker 5: what is the brand of? Like a chip or tokens,

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Speaker 5: Like it's a utility rather than a particular product. And

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Speaker 5: I think I saw at a press conference or just

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Speaker 5: the scrum afterward, Jensen just said everyone, the Nvidia ecosystem

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Speaker 5: is rich, like it's this kind of MIAs like everything

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Speaker 5: I touch turns to gold. But also that feels kind

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Speaker 5: of cursed, like you're flying too close to the sun.

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Speaker 5: So I'm curious how much higher it can even go,

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Speaker 5: Like like, are we just getting started or or is

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Speaker 5: this a kind of threshold moment?

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Speaker 1: Taylor moving moving right along.

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Speaker 2: You mentioned techlash, and you brought us our attention to

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Speaker 2: a story about a Senate committee hearing this week with

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Speaker 2: the title Liability or Deniability Platform power as section two

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Speaker 2: thirty turns thirty. Now, before we get into that hearing,

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Speaker 2: you've been covering section two thirty for decade. You put

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Speaker 2: our six pot YouTube series devoted to it, You spoke

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Speaker 2: about it a south By Southwest last week. What drives

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Speaker 2: your fascination with this law? What do we need to

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Speaker 2: understand about it and what might change based on all

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Speaker 2: of these legislative efforts.

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Speaker 4: Yeah, so Section two thirty is what a law that

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Speaker 4: frankly should be very non controversial. It is a law

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Speaker 4: that it effectively says, you, as a speaker of content online,

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Speaker 4: should be held liable for your own speech. It's a

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Speaker 4: foundational law. It's what that allows the Internet to kind

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Speaker 4: of exist, because the Internet is not like a public space, right,

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Speaker 4: There's no it's not like the real world where you

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Speaker 4: could where there is like a physical world out there.

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Speaker 4: So you need people or companies to kind of go

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Speaker 4: in and set up spaces in this world, whether that's

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Speaker 4: a website, whether that's a forum, whether that's a social

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Speaker 4: media platform, et cetera. And I think you know, there

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Speaker 4: was this really really bad article written about a decade

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Speaker 4: ago on section two thirty that was claiming that I

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Speaker 4: guess like Facebook was using section two thirty to kind

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Speaker 4: of like not police hate speech. Ironically, section two thirty

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Speaker 4: is what allows platforms to do moderation. But that kind

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Speaker 4: of set off this like decade long discussion where pretty

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Speaker 4: much everyone in power wants to transform the Internet from

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Speaker 4: this open place where people can sort of freely discuss

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Speaker 4: ideas to something more like the mainstream media traditional media,

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Speaker 4: where it's a top down sort of corporate control over

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Speaker 4: speech and every single platform or forum admin or person

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Speaker 4: online you know, is responsible for kind of the speech

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Speaker 4: that they host.

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Speaker 2: Radio noting nodding along to Taylor's I'm always curious when

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Speaker 2: people are nodding what they're noting about.

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Speaker 3: No, I mean, I just I think what she's saying

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Speaker 3: makes sense, and you're you're I think explaining it in

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Speaker 3: a good way, for sure. I don't think there was

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Speaker 3: anything I was like, you know, I don't think there's

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Speaker 3: anything controversial in that. It was sort of laying out

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Speaker 3: the fact.

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Speaker 2: Now to the controversy, Taylor take us away.

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Speaker 4: Well, so, you know, as as the Internet has grown

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Speaker 4: and these platforms have become more powerful, a lot of

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Speaker 4: people in power want to repeal section two thirty. This

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Speaker 4: would devastate the Internet. It would transform the Internet from

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Speaker 4: a place again where there is user generated content, to

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Speaker 4: a place where the whole Internet is Netflix right, where

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Speaker 4: you have to apply to a platform to get your

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Speaker 4: content on there, and nobody can kind of reshare content.

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Speaker 3: You know.

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Speaker 4: Section two thirty is what allows you to retweet a

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Speaker 4: tweet or forward an email. Removing section two thirty would

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Speaker 4: not allow you to sort of engage in that way.

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Speaker 4: It would all it would just be sort of a

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Speaker 4: Netflix style Internet. Obviously, this is something people in power

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Speaker 4: really want. They don't like that people, average people can

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Speaker 4: go on the Internet and sort of connect with each other.

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Speaker 4: So there's this aggressive effort to repeal section two thirty

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Speaker 4: that ironically, the big platforms are also now behind because they,

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Speaker 4: with the rise of AI, feel comfortable pre screening every

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Speaker 4: single piece of content via AI to kind of ensure

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Speaker 4: that any speech on the platform alot with what the

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Speaker 4: government wants. This is why Meta has been sort of

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Speaker 4: integral to these efforts to chip away at section two thirty.

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Speaker 2: This is so interesting because I think the way section

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Speaker 2: two thirty is commonly understood, and you mentioned that wide

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Speaker 2: article from a few years ago. I haven't read it,

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Speaker 2: but it's essentially the battle of a Section two thirty

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Speaker 2: is the battle where like regular people are fighting back

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Speaker 2: against the tech companies and their ability to like harm

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Speaker 2: people with content and not be held responsible.

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Speaker 4: Well, Section two thirty protects those regular people. So section

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Speaker 4: two thirty ensures again that you as a user without

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Speaker 4: a lot of power have the ability to speak to

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Speaker 4: to power. It allows for user generated content. If we

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Speaker 4: didn't have that, it would consolidate power among big tech. Now,

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Speaker 4: I you know, as somebody that covers tech for a

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Speaker 4: long time, especially in light of the tech lash over

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Speaker 4: the past decade. About a decade ago, these far right

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Speaker 4: religious fundamentalist groups and organizations that have always wanted a

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Speaker 4: censored Internet, groups like you know, the Heritage Foundation, they

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Speaker 4: realized that by claiming, you know, repealing Section two thirty

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Speaker 4: was cracking down on big tech, they've gotten a lot

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Speaker 4: of I think leftists and liberals to kind of fall

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Speaker 4: for their ruse and go along with this. Now, it's

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Speaker 4: important to note they did have a big victory. Back

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Speaker 4: in twenty eighteen we saw foss Assessa pass. Foss Assessa

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Speaker 4: was the first major carve out to Section two thirty

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Speaker 4: that was framed as cracking down on big tech. I

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Speaker 4: would ask people, you know, do you think Facebook and

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Speaker 4: Google are more or less powerful today than twenty eighteen?

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Speaker 4: I think we know the answer to that.

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Speaker 2: And the hearing this week with center Ted Cruz, what's

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Speaker 2: what's going on here? What can we expect and what's

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Speaker 2: different from the bipartisan legislation introduced by Lindsay Graham and

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Speaker 2: Amy Klobashaw and others late last year on Section two thirty.

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Speaker 4: It's essentially like the same thing. There is this Sunset

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Speaker 4: Section two thirty Act that again has been brought by

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Speaker 4: the Democrats that want to seize control over online speech.

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Speaker 4: I have to say, I know this is bipartisan. A

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Speaker 4: lot of issues related to mass surveillance and censorship are bipartisan. Unfortunately,

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Speaker 4: I told you.

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Speaker 2: Rather last week where I think you said mass eventage

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Speaker 2: is always a bipod is any.

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Speaker 4: Shoe exactly, And so you know, and I think it's

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Speaker 4: kind of terrifying that we're seeing this happen under the

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Speaker 4: Trump administration. I mean, I certainly couldn't do my job.

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Speaker 4: I wouldn't have a job as an independent journalist if

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Speaker 4: Section two thirty didn't exist. But yeah, there's this big

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Speaker 4: hearing to kind of discuss all of this on the HILP.

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Speaker 1: Go ahead, Kyle.

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Speaker 5: I have some of the same worries as Taylor here

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Speaker 5: that Section two thirty does protect a lot of speech

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Speaker 5: on the Internet. It's meant to kind of separate a

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Speaker 5: little bit the platform from the content on it, and

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Speaker 5: it has allowed for Facebook to exist, for Instagram and

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Speaker 5: YouTube to exist. But I think if we look forward

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Speaker 5: into the future, these tech giants are going to find

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Speaker 5: ways around whatever regulations exist. They will find ways to

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Speaker 5: profit from this situation. If it's repealed, and the smaller platforms,

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Speaker 5: like more independent spaces, spaces that don't have access to

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Speaker 5: millions of AI tokens as we were just discussing, we'll

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Speaker 5: kind of fall by the wayside and we will be

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Speaker 5: left with that narrowed, more homogenized internet. I think there

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Speaker 5: are good and bad parts of Section two thirty, Like

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Speaker 5: it's it's not just one thing, but it does really

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Speaker 5: protect our ability to put things out on the Internet

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Speaker 5: and for Internet hosts to support the publishing of things

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Speaker 5: online without being always responsible for every single thing.

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Speaker 4: I just want to say, like, I actually don't think

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Speaker 4: there's bad parts of Section two thirty, Like I think

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Speaker 4: it's such a short, like uncontroversial law. Meta and Google

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Speaker 4: and all these big tech companies have been using so

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Speaker 4: many other laws to evade responsibility and to consolidate corporate power,

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Speaker 4: and we could pass so many laws. We could reform

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Speaker 4: things like the Computer Fraud and Abuse Act, which Meta

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Speaker 4: uses to crush competitors. We could, you know, prosecute them

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Speaker 4: for anti competitive behavior. So many things could there are

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Speaker 4: so many legislative and sort of broader political things that

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Speaker 4: we could do to curb the power of these big

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Speaker 4: tech companies. But chipping away at section two thirty only

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Speaker 4: chips away at the power of users, you know, to

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Speaker 4: speak truth to power, and I think that's really dangerous,

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Speaker 4: especially right now.

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Speaker 1: Kyle.

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Speaker 2: I want to come back to you because you wrote

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Speaker 2: a book called Filter World, and you wrote about Section

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Speaker 2: two thirty in that book, and here's what you said.

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Speaker 2: The problem with section two thirty is that ultimately and bizarrely,

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Speaker 2: the law makes it so no one is currently responsible

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Speaker 2: for the effects of algorithmic recommendations.

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Speaker 4: That's not true. How would you say no one is responsible?

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Speaker 5: Well, the law that section two thirty was based on,

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Speaker 5: or one of the precedents, was this bookstore ruling where

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Speaker 5: a bookstore cannot be held responsible for the contents of

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Speaker 5: the books that it's providing. And I think, like the

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Speaker 5: platforms and the way the internet works has evolved since

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Speaker 5: the time that Section two thirty was put into place,

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Speaker 5: and algorithmic recommendations and kind of top down wait a minute,

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Speaker 5: censorship where filtering has become more aggressive, and so I

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Speaker 5: think Section two thirty has protected a lot of that

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Speaker 5: algorithmic filtering action over the years.

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Speaker 4: Kyle. I just did an episode which I hope you'll

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Speaker 4: watch on my channel last week about this idea that

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Speaker 4: Section two thirty wasn't written to protect algorithms. The two

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Speaker 4: cases right that led to Section two thirty, the Prodigy

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Speaker 4: and compu Serve cases. Both of those platforms used algorithms

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Speaker 4: to recommend content. Algorithms are speech as defined by the

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Speaker 4: First Amendment. Like, I think what people need to realize

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Speaker 4: is a lot of problems that society. I'm not saying

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Speaker 4: this is you, Kyle, but like I think a lot

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Speaker 4: of people have problems with speech law. And what Section

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Speaker 4: two thirty does is effectively just make it cheaper to

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Speaker 4: win cases on First Amendment grounds. So, you know, the

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Speaker 4: same cases that these a lot of these smaller platforms

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Speaker 4: win on Section two thirty, they only have to It

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Speaker 4: only cost them fifty to one hundred thousand dollars to

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Speaker 4: fight that case and win. But if they were to

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Speaker 4: fight it on First Amendment grounds, it would cost over

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Speaker 4: five million to ten million dollars so you know, algorithmic

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Speaker 4: editorial choices are speech in themselves. And the idea has

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Speaker 4: always been to give these platforms the power of publishers

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Speaker 4: and to make them publishers, but without the sort of

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Speaker 4: liability of treating them as newspapers. As Kyle said, it's

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Speaker 4: much more treating them like a bookstore. But a book

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Speaker 4: store does put books in the front window, right, a

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Speaker 4: bookstore does make recommendations to readers, And so I think

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Speaker 4: there are ways to hold these platforms accountable for, you know,

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Speaker 4: a lot of the bad effects that they've had on society.

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Speaker 4: But again, I don't think Section two thirty is the

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Speaker 4: law to do that through.

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Speaker 3: Isn't that? Isn't that what the what these groundbreaking social

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Speaker 3: media lawsuits are that are happening right now are sort

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Speaker 3: of based on, is that the algorithm is the you know,

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Speaker 3: is the thing that these companies should be held viable for.

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Speaker 4: Well, yeah, it's just very funny because when once you

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Speaker 4: know there is no problem with the algorithm, the problem

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Speaker 4: is the content, right, What people have problems with is

439
00:21:37,720 --> 00:21:42,040
Speaker 4: the content If the algorithm. If if everything on Instagram,

440
00:21:42,160 --> 00:21:44,960
Speaker 4: for instance, was just Wikipedia articles and that's the only

441
00:21:45,000 --> 00:21:47,119
Speaker 4: thing you could see was Wikipedia articles, but it had

442
00:21:47,160 --> 00:21:49,919
Speaker 4: the exact same recommendation algorithm. Would you have a problem

443
00:21:49,960 --> 00:21:52,560
Speaker 4: with it? I'm sure no, No, The answer is no,

444
00:21:52,680 --> 00:21:55,000
Speaker 4: these people would not because we have platforms that are

445
00:21:55,160 --> 00:21:57,600
Speaker 4: like that, that are online learning platforms that operate with

446
00:21:57,640 --> 00:22:02,080
Speaker 4: the same sort of engagement based recommendation algorithms, and people

447
00:22:02,080 --> 00:22:04,199
Speaker 4: don't home problem with it. Or something like Spotify right

448
00:22:04,280 --> 00:22:06,800
Speaker 4: which has an enormous amount of data on you and

449
00:22:06,880 --> 00:22:08,640
Speaker 4: will feed you you know, if you're depressed, you get

450
00:22:08,680 --> 00:22:11,000
Speaker 4: more depressing songs, and maybe those depressing songs, you know,

451
00:22:11,080 --> 00:22:13,480
Speaker 4: like those surressing songs make you want to kill yourself,

452
00:22:13,560 --> 00:22:14,560
Speaker 4: Like we're.

453
00:22:14,440 --> 00:22:17,119
Speaker 5: Not having This is one of the cases of British

454
00:22:17,200 --> 00:22:20,359
Speaker 5: woman who was unfortunately pushed a lot of depression content

455
00:22:20,480 --> 00:22:25,000
Speaker 5: from Pinterest and then it ended up committing suicide, dying

456
00:22:25,040 --> 00:22:27,720
Speaker 5: by suicide. And you know, like there are problems with

457
00:22:27,840 --> 00:22:31,679
Speaker 5: algorithmic recommendations and particular types of content, but they're, as

458
00:22:31,760 --> 00:22:35,160
Speaker 5: Tyler was saying, they're also better legal frameworks and better

459
00:22:35,320 --> 00:22:38,959
Speaker 5: law packages that are being discussed now that can address them.

460
00:22:39,480 --> 00:22:43,600
Speaker 5: So some like EU laws, like the Digital Services Acts

461
00:22:43,680 --> 00:22:47,080
Speaker 5: that let you modify your algorithmic feed like things that

462
00:22:47,160 --> 00:22:49,639
Speaker 5: give you more rights to your data and your information.

463
00:22:50,200 --> 00:22:53,320
Speaker 5: So we can use other laws to target these issues

464
00:22:53,440 --> 00:22:55,960
Speaker 5: kind of on the downstream end for sure, But.

465
00:22:56,080 --> 00:22:59,120
Speaker 3: Ultimately this is about who can be sued for what right?

466
00:22:59,160 --> 00:23:01,760
Speaker 3: I mean, that's what it really comes down to. Can

467
00:23:01,840 --> 00:23:04,719
Speaker 3: you win? Can you win a lawsuit for harm caused

468
00:23:04,800 --> 00:23:08,479
Speaker 3: by something you saw online or content you saw online?

469
00:23:08,560 --> 00:23:11,040
Speaker 4: Right? Well, I think we've seen this moral panic forever,

470
00:23:11,280 --> 00:23:14,800
Speaker 4: right is is, and I would argue, yes you can

471
00:23:15,000 --> 00:23:17,239
Speaker 4: right now, you can win a lawsuit, like I mean,

472
00:23:17,720 --> 00:23:21,280
Speaker 4: you can sue the person that made that content that is,

473
00:23:21,480 --> 00:23:24,080
Speaker 4: you know, allegedly harmful. You can sue them and they

474
00:23:24,119 --> 00:23:26,680
Speaker 4: can be held responsible. And that's a really good system.

475
00:23:27,440 --> 00:23:29,720
Speaker 4: What we don't want to do is make it so

476
00:23:29,880 --> 00:23:32,600
Speaker 4: that every platform is legally liable for every piece of

477
00:23:32,720 --> 00:23:35,240
Speaker 4: content on their website, or there will be no such

478
00:23:35,280 --> 00:23:37,719
Speaker 4: thing as journalism on the Internet because all of these

479
00:23:37,760 --> 00:23:41,000
Speaker 4: platforms will simply, you know, not want to host any

480
00:23:41,040 --> 00:23:43,200
Speaker 4: sort of legally questionable content.

481
00:23:43,280 --> 00:23:45,560
Speaker 3: And for a lot of content is anonymous or it's

482
00:23:45,600 --> 00:23:47,399
Speaker 3: not in the US, So like you could sue, but

483
00:23:47,600 --> 00:23:49,040
Speaker 3: you're not going to get much out of it. So

484
00:23:49,119 --> 00:23:52,360
Speaker 3: the only you know, deep pocketed entity you can really

485
00:23:52,400 --> 00:23:55,119
Speaker 3: go after is the platform right right? And the tech company?

486
00:23:55,320 --> 00:23:57,399
Speaker 4: Well, I think what you're talking about, like a lot

487
00:23:57,480 --> 00:24:00,359
Speaker 4: of these sort of critics of these issues need to

488
00:24:00,400 --> 00:24:02,040
Speaker 4: get their story straight. Do you have a problem with

489
00:24:02,119 --> 00:24:05,119
Speaker 4: online anonymity, Okay, there are better ways to address that.

490
00:24:05,240 --> 00:24:07,480
Speaker 4: Do you have a problem with the fact that there's

491
00:24:07,480 --> 00:24:11,480
Speaker 4: harmful content abroad that's being brought to Americans, Okay, let's

492
00:24:11,480 --> 00:24:13,800
Speaker 4: address that through again lots of different things. Or do

493
00:24:13,840 --> 00:24:16,400
Speaker 4: you have a problem with the content on social media?

494
00:24:16,440 --> 00:24:17,800
Speaker 4: And if you have a problem with the content on

495
00:24:17,880 --> 00:24:20,239
Speaker 4: social media, what I would posit is that a lot

496
00:24:20,320 --> 00:24:22,800
Speaker 4: of this stuff is sort of the same moral panic

497
00:24:22,840 --> 00:24:25,080
Speaker 4: that we see about a lot of sort of content

498
00:24:25,160 --> 00:24:28,840
Speaker 4: based freakouts, where it's like, you know, you don't want

499
00:24:28,880 --> 00:24:30,960
Speaker 4: your child seeing xyz. And we should be in that

500
00:24:31,119 --> 00:24:33,280
Speaker 4: speech law and we should be very careful about sort

501
00:24:33,320 --> 00:24:36,919
Speaker 4: of how we asked the government to regulate content.

502
00:24:37,440 --> 00:24:40,920
Speaker 2: It's interesting Senator Ron Wyden, who drafted this law, said

503
00:24:40,960 --> 00:24:42,560
Speaker 2: a couple of things recently.

504
00:24:42,880 --> 00:24:43,040
Speaker 4: You know.

505
00:24:43,160 --> 00:24:46,680
Speaker 2: One he said, without section two thirty, anyone who merely

506
00:24:46,800 --> 00:24:50,000
Speaker 2: shared a story or allegation about Epstein and his associates

507
00:24:50,040 --> 00:24:53,160
Speaker 2: on their social media could be sued by Epstein's deep

508
00:24:53,200 --> 00:24:56,560
Speaker 2: pocketed pals, along with the site that hosted those posts.

509
00:24:56,920 --> 00:24:59,760
Speaker 2: But he also said he was open to reform or

510
00:25:00,080 --> 00:25:02,600
Speaker 2: not not full on repeal, but some tweaks of section

511
00:25:02,680 --> 00:25:05,480
Speaker 2: two thirty to address some of the concerns people have.

512
00:25:06,440 --> 00:25:09,800
Speaker 4: I think with that he's talking about AI primarily because

513
00:25:09,800 --> 00:25:12,359
Speaker 4: I think the question, the big, next big question is like,

514
00:25:13,720 --> 00:25:16,439
Speaker 4: as these AI platforms sort of generate their own forms

515
00:25:16,480 --> 00:25:19,720
Speaker 4: of speech, like how should they be regulated? How should

516
00:25:19,800 --> 00:25:22,160
Speaker 4: large language models be regulated? And I think we're seeing

517
00:25:22,200 --> 00:25:24,920
Speaker 4: them use section two thirty to sort of claim, well,

518
00:25:25,000 --> 00:25:27,800
Speaker 4: this is just sort of like equivalent to user generated content.

519
00:25:27,880 --> 00:25:30,560
Speaker 4: And I think that's sort of these questions that yeah.

520
00:25:30,560 --> 00:25:32,400
Speaker 3: I would love to hear your view. And I don't

521
00:25:32,440 --> 00:25:34,080
Speaker 3: know if I don't know if Section two thirty is

522
00:25:34,119 --> 00:25:36,600
Speaker 3: really going to be fully repealed ever, or if I

523
00:25:36,680 --> 00:25:39,879
Speaker 3: mean that seems unlikely, But just like thought experiment, like

524
00:25:40,040 --> 00:25:42,840
Speaker 3: what would the internet? What would actually happen on these

525
00:25:42,880 --> 00:25:44,840
Speaker 3: tech like the just imagine a world where it does

526
00:25:44,920 --> 00:25:48,359
Speaker 3: get completely repealed, what would change?

527
00:25:49,040 --> 00:25:51,440
Speaker 4: Yeah, I think we don't have to imagine. We see

528
00:25:51,480 --> 00:25:53,520
Speaker 4: what happened when we chipped away at section two thirty.

529
00:25:53,600 --> 00:25:56,000
Speaker 4: So again, Foss Assessa is the first major amendment, and

530
00:25:56,040 --> 00:25:57,720
Speaker 4: it's the first it's the biggest carve out to Section

531
00:25:57,800 --> 00:26:00,960
Speaker 4: two thirty that we've ever had. Foss assess as a

532
00:26:01,040 --> 00:26:03,760
Speaker 4: law that passed back in twenty eighteen that was supposed

533
00:26:03,800 --> 00:26:09,920
Speaker 4: to hold platforms liable for content that would incentivize sex trafficking.

534
00:26:10,400 --> 00:26:12,440
Speaker 4: All of the same arguments they're making today about Section

535
00:26:12,520 --> 00:26:14,800
Speaker 4: two thirty. We went through all of this a decade ago,

536
00:26:15,200 --> 00:26:17,720
Speaker 4: and we actually can see exactly what happened. What happened is,

537
00:26:18,240 --> 00:26:21,520
Speaker 4: first of all, it didn't reduce sex trafficking even in

538
00:26:21,640 --> 00:26:25,879
Speaker 4: the slightest. What it did is mass sensor LGBTQ content

539
00:26:25,960 --> 00:26:28,520
Speaker 4: and abortion content from the Internet. We can also look

540
00:26:28,560 --> 00:26:31,879
Speaker 4: at other countries that have more authoritarian laws, like China,

541
00:26:32,840 --> 00:26:34,600
Speaker 4: and I know people have a lot of complicated thoughts

542
00:26:34,640 --> 00:26:36,879
Speaker 4: on China, but China does have a very restrictive Internet

543
00:26:36,960 --> 00:26:41,560
Speaker 4: where you cannot say things that are not approved tacitly

544
00:26:41,600 --> 00:26:44,320
Speaker 4: by the government, and the government effectively deputizes their big

545
00:26:44,400 --> 00:26:48,120
Speaker 4: tech companies as state sensors. And that is the world

546
00:26:48,200 --> 00:26:50,200
Speaker 4: that we're moving towards here in America. And I think

547
00:26:50,240 --> 00:26:52,920
Speaker 4: that that should concern anybody that cares about civil liberties

548
00:26:53,040 --> 00:26:55,639
Speaker 4: or free speech online. So I think we should protect

549
00:26:55,680 --> 00:26:57,840
Speaker 4: Section two thirty. But I'm all for cracking down on

550
00:26:57,880 --> 00:27:00,800
Speaker 4: these big tech companies and regulating them a lot or heavily,

551
00:27:00,960 --> 00:27:01,920
Speaker 4: you know, in many other ways.

552
00:27:03,160 --> 00:27:04,880
Speaker 2: We're going to take a quick break now, but when

553
00:27:04,880 --> 00:27:07,919
Speaker 2: we come back, we'll hear from Kyle about why taste

554
00:27:08,359 --> 00:27:11,840
Speaker 2: has become such a buzzword in Silicon Valley. Stay with us,

555
00:27:31,800 --> 00:27:34,800
Speaker 2: Welcome back to tech stuff. Kyle, you're up and you

556
00:27:34,880 --> 00:27:37,000
Speaker 2: wrote a column this week in the New Yorker about

557
00:27:37,080 --> 00:27:39,840
Speaker 2: why tech bros Are now obsessed with taste.

558
00:27:40,560 --> 00:27:43,880
Speaker 1: I actually lolled reading it. Here was my favorite.

559
00:27:43,960 --> 00:27:47,960
Speaker 2: Here's my favorite quote The entrepreneur and former Byte dance

560
00:27:48,040 --> 00:27:51,440
Speaker 2: engineer Cony Is it Coney Wang or Connie Wang's song

561
00:27:52,200 --> 00:27:56,520
Speaker 2: Oh songe completely wrong, echoed a New Silicon Valley axiom

562
00:27:56,600 --> 00:27:59,800
Speaker 2: in a blog post, writing quote in the AI era,

563
00:28:00,320 --> 00:28:04,200
Speaker 2: personal taste is the moat. Startups apparently need taste. This

564
00:28:04,359 --> 00:28:07,240
Speaker 2: is you, like AI needs data centers.

565
00:28:09,000 --> 00:28:12,920
Speaker 5: Just trying to have fun, you know. I feel like

566
00:28:13,160 --> 00:28:15,760
Speaker 5: this this was kind of my way into the AI

567
00:28:16,000 --> 00:28:18,720
Speaker 5: culture to be in a way that's not just talking

568
00:28:18,760 --> 00:28:22,520
Speaker 5: about like humanoid robots killing people or something like that

569
00:28:23,800 --> 00:28:27,760
Speaker 5: it's this kind of mania that Silicon Valley and like

570
00:28:28,000 --> 00:28:31,359
Speaker 5: tech bros broadly on X in particular, have with the

571
00:28:31,440 --> 00:28:34,560
Speaker 5: word taste. And they're all trying to make their taste

572
00:28:34,640 --> 00:28:38,680
Speaker 5: better and have taste in AI models and you know,

573
00:28:38,840 --> 00:28:42,440
Speaker 5: train themselves essentially to desire better things.

574
00:28:42,600 --> 00:28:45,040
Speaker 2: And I was just so sick of it, Like, guys,

575
00:28:45,160 --> 00:28:47,520
Speaker 2: how did you How did you become aware of the phenomenon?

576
00:28:47,600 --> 00:28:49,920
Speaker 1: When did taste? When did taste? Dot ring like a

577
00:28:50,040 --> 00:28:51,000
Speaker 1: fire a lamb?

578
00:28:52,000 --> 00:28:54,240
Speaker 4: How can you escape it? If you're on tex Twitter,

579
00:28:54,320 --> 00:28:56,000
Speaker 4: it's all they've been posting that idea.

580
00:28:56,160 --> 00:28:56,400
Speaker 1: Yeah.

581
00:28:56,720 --> 00:28:58,800
Speaker 5: I mean, I'm kind of a Canarian the coal mine

582
00:28:58,800 --> 00:29:01,400
Speaker 5: here because I've been writing about taste and algorithms for

583
00:29:01,560 --> 00:29:05,160
Speaker 5: like ten years now, so I'm very sensitive to the

584
00:29:05,320 --> 00:29:08,080
Speaker 5: use of that word. And I just started seeing it

585
00:29:08,200 --> 00:29:10,280
Speaker 5: crop up more and more and more. And I think

586
00:29:10,480 --> 00:29:13,800
Speaker 5: the threshold moment or like the climax of this was

587
00:29:13,920 --> 00:29:16,920
Speaker 5: like a few weeks ago or something two weeks ago.

588
00:29:17,080 --> 00:29:22,440
Speaker 5: Paul Graham, the technologists like great Entrepreneur tweeted like in

589
00:29:22,560 --> 00:29:25,120
Speaker 5: the AI age, like we all need better personal taste,

590
00:29:25,200 --> 00:29:28,680
Speaker 5: and I was just like, oh my god, Like personal

591
00:29:28,800 --> 00:29:32,720
Speaker 5: taste was the realm of like hipsters and DJs and like.

592
00:29:33,320 --> 00:29:33,560
Speaker 1: You know.

593
00:29:35,120 --> 00:29:36,880
Speaker 2: I worked in the mid two thousands. I feel like

594
00:29:36,920 --> 00:29:40,040
Speaker 2: it's crazy that this word has been claimed by the

595
00:29:40,120 --> 00:29:41,440
Speaker 2: technology industry, like.

596
00:29:41,800 --> 00:29:45,280
Speaker 5: The industry that is widely seeing us the most tasteless

597
00:29:45,320 --> 00:29:49,520
Speaker 5: thing on earth, like no shade, like I cover tech,

598
00:29:49,600 --> 00:29:52,520
Speaker 5: I love tech, I love the Internet. Tasteful, is not it?

599
00:29:52,800 --> 00:29:57,560
Speaker 2: Like taste mean like cigars and whiskey? To these folks,

600
00:29:57,880 --> 00:30:01,560
Speaker 2: does mean what's the like what what's the what's beneath

601
00:30:01,600 --> 00:30:01,880
Speaker 2: the saying?

602
00:30:02,080 --> 00:30:05,360
Speaker 5: Man? I mean the definition that I read out of,

603
00:30:05,600 --> 00:30:08,720
Speaker 5: like interpreted from all of their tweets, is like taste

604
00:30:08,840 --> 00:30:12,680
Speaker 5: is basically what is profitable like to them, like, having

605
00:30:12,760 --> 00:30:15,280
Speaker 5: good taste means knowing how to choose the right thing

606
00:30:15,360 --> 00:30:18,000
Speaker 5: that makes money. It means knowing how to attract more

607
00:30:18,080 --> 00:30:22,000
Speaker 5: customers to use your like AI dating coach. It's maybe

608
00:30:22,200 --> 00:30:24,720
Speaker 5: the choice of like a logo or an Instagram ad

609
00:30:24,840 --> 00:30:27,600
Speaker 5: like that's on the more literally tasteful end of it.

610
00:30:28,080 --> 00:30:31,640
Speaker 5: But I took an issue with this like mechanistic or

611
00:30:31,760 --> 00:30:34,600
Speaker 5: utilitarian use of the word when really, like, to me,

612
00:30:34,760 --> 00:30:38,280
Speaker 5: taste is about enjoying art and culture as a human

613
00:30:38,400 --> 00:30:39,040
Speaker 5: with feelings.

614
00:30:39,720 --> 00:30:41,920
Speaker 4: Well, I think it's so funny because do you guys

615
00:30:41,960 --> 00:30:45,160
Speaker 4: remember when Joyce Carol Oates kind of like dunked on

616
00:30:45,360 --> 00:30:47,920
Speaker 4: Elon Musk and she was just like, you don't enjoy

617
00:30:48,560 --> 00:30:51,120
Speaker 4: like art and literature. Like there was all this discussion

618
00:30:51,160 --> 00:30:52,920
Speaker 4: of like what does Elon Musk do? And then Elon

619
00:30:53,000 --> 00:30:55,080
Speaker 4: Musk sort of attempts to clack back and he starts

620
00:30:55,160 --> 00:30:57,600
Speaker 4: replying to these like movie review accounts and then he

621
00:30:57,640 --> 00:31:00,800
Speaker 4: tweets like this is a great or like homer Iliad

622
00:31:01,120 --> 00:31:03,320
Speaker 4: is a great book, and then he like linked to

623
00:31:03,480 --> 00:31:06,840
Speaker 4: like some other book that was like the Odyssey or something,

624
00:31:08,040 --> 00:31:10,120
Speaker 4: and it just like to me, like when I think

625
00:31:10,160 --> 00:31:13,160
Speaker 4: of tasteless tech people, I think of Elon Musk and

626
00:31:13,360 --> 00:31:17,480
Speaker 4: like this desperation to like be seen as like culturally

627
00:31:17,720 --> 00:31:21,720
Speaker 4: kind of I guess insightful. And yeah, they none of

628
00:31:21,760 --> 00:31:23,920
Speaker 4: these people have any taste. They're making slot, but.

629
00:31:23,920 --> 00:31:26,920
Speaker 3: They're also living, so they're so far in the future

630
00:31:27,000 --> 00:31:29,960
Speaker 3: right that they're imagining a world where like the robots

631
00:31:30,000 --> 00:31:32,880
Speaker 3: are doing everything, and it's like, well, what like there's

632
00:31:32,920 --> 00:31:36,280
Speaker 3: this like existential crisis. It's like what am I at now?

633
00:31:36,480 --> 00:31:38,240
Speaker 3: Like I used to write the code and I don't

634
00:31:38,280 --> 00:31:40,720
Speaker 3: do that. I don't do any you know, I guess

635
00:31:40,800 --> 00:31:42,920
Speaker 3: I have taste. I mean I think that's.

636
00:31:43,680 --> 00:31:47,400
Speaker 5: Telling the robot what to do. Like now, I'm now

637
00:31:47,400 --> 00:31:51,240
Speaker 5: I'm exerting myself for expressing myself by by bossing around

638
00:31:51,840 --> 00:31:53,040
Speaker 5: and exactly.

639
00:31:52,880 --> 00:31:54,080
Speaker 1: Which is a bit suppressing.

640
00:31:54,440 --> 00:31:58,240
Speaker 5: And there was another quite viral Mark Andreeson moment like

641
00:31:58,480 --> 00:32:00,400
Speaker 5: in the last few days, I think, where he just

642
00:32:00,560 --> 00:32:06,720
Speaker 5: says that he has no introspection. It's just like there's

643
00:32:06,800 --> 00:32:09,680
Speaker 5: nothing going on here up here in the head. I

644
00:32:09,720 --> 00:32:12,080
Speaker 5: have no thoughts, I have no sense of self. And

645
00:32:12,240 --> 00:32:14,600
Speaker 5: that was I mean, that was just in a nutshell,

646
00:32:14,760 --> 00:32:17,400
Speaker 5: like how can you have taste if you have no

647
00:32:17,600 --> 00:32:21,200
Speaker 5: thoughts about anything? If like you're only moving forward.

648
00:32:22,200 --> 00:32:24,680
Speaker 2: I want to I want to ask you both and

649
00:32:24,920 --> 00:32:28,320
Speaker 2: Read and Taylor about this. As West West Coasters. There

650
00:32:28,400 --> 00:32:30,200
Speaker 2: was a story in Bloomberg last year that I just

651
00:32:30,280 --> 00:32:32,560
Speaker 2: absolutely adored about how all the tech bros now want

652
00:32:32,600 --> 00:32:37,000
Speaker 2: to build these like extremely tall and ugly monuments too,

653
00:32:37,160 --> 00:32:39,640
Speaker 2: And I'm just wondering, like, what are you seeing on

654
00:32:39,720 --> 00:32:42,840
Speaker 2: the like, are you seeing the physical architecture, I mean

655
00:32:43,160 --> 00:32:46,760
Speaker 2: East Wing style be remodeled in this in this era

656
00:32:46,920 --> 00:32:48,240
Speaker 2: of a new new taste.

657
00:32:48,160 --> 00:32:51,240
Speaker 3: Or no one's building anything out here. So I don't think.

658
00:32:51,560 --> 00:32:56,160
Speaker 4: There's read said it's time to build, there's no.

659
00:32:57,840 --> 00:32:58,040
Speaker 1: Yeah.

660
00:32:58,160 --> 00:33:00,720
Speaker 4: I think it's interesting kind of how they seek to

661
00:33:00,880 --> 00:33:03,280
Speaker 4: exert their influence on the physical world. I mean, Kyle

662
00:33:03,600 --> 00:33:06,160
Speaker 4: has written so much smart sort of stuff about this,

663
00:33:06,400 --> 00:33:09,200
Speaker 4: but it is you know, I and I think of

664
00:33:09,280 --> 00:33:11,160
Speaker 4: Kyle's work a lot when I think of like this,

665
00:33:11,640 --> 00:33:15,360
Speaker 4: you know, the sort of liminal, sterile spaces that we're

666
00:33:15,440 --> 00:33:17,240
Speaker 4: very affiliated with, the sort of associated with the tech

667
00:33:17,280 --> 00:33:21,080
Speaker 4: world in the twenty tens. I do wonder what kind

668
00:33:21,080 --> 00:33:25,360
Speaker 4: of text dominant aesthetic is emerging as in the twenty twenty,

669
00:33:26,240 --> 00:33:28,800
Speaker 4: twenty twenties, Like is it the slop, is it the

670
00:33:28,960 --> 00:33:31,240
Speaker 4: like what what is kind of like the dominant like

671
00:33:31,440 --> 00:33:34,520
Speaker 4: tech aesthetic. I'm curious, Kyle, like that you're seeing have

672
00:33:34,640 --> 00:33:36,200
Speaker 4: you have you been.

673
00:33:36,120 --> 00:33:37,120
Speaker 3: To Cursor's office.

674
00:33:37,680 --> 00:33:37,720
Speaker 1: No.

675
00:33:38,600 --> 00:33:42,960
Speaker 3: There it's like very like warm like living room sheet.

676
00:33:43,040 --> 00:33:46,640
Speaker 3: You come in and like there's just a pile of shoes,

677
00:33:46,840 --> 00:33:50,600
Speaker 3: like hundreds of shoes because everyone just goes barefoot, and

678
00:33:50,720 --> 00:33:52,960
Speaker 3: it's like you just feel like you're in this like

679
00:33:53,600 --> 00:33:56,520
Speaker 3: it's like everything's wood and warm, and you're just like, oh,

680
00:33:56,600 --> 00:33:58,800
Speaker 3: I'm like I'm not at work, I'm like in my

681
00:33:59,400 --> 00:34:02,880
Speaker 3: like cozy living room, which is different from those Google

682
00:34:03,800 --> 00:34:06,040
Speaker 3: you know, like the Google was like very bright, like

683
00:34:06,240 --> 00:34:08,880
Speaker 3: bouncy balls, sit at your desk and like, you know,

684
00:34:09,239 --> 00:34:12,840
Speaker 3: there's it was just like this fun playground atmosphere. Now

685
00:34:12,880 --> 00:34:14,960
Speaker 3: it's like I don't know, I don't even know if

686
00:34:15,000 --> 00:34:16,839
Speaker 3: that's the question you're asking, but it just popped into

687
00:34:16,880 --> 00:34:19,399
Speaker 3: my head, like you should you should visit Curser's office.

688
00:34:19,640 --> 00:34:21,520
Speaker 3: I feel my shoes on. I was told you could,

689
00:34:21,640 --> 00:34:23,399
Speaker 3: I could leave my shoes on, but I wasn't sure

690
00:34:23,400 --> 00:34:26,240
Speaker 3: if I was being judged. Actually I was definitely being judged.

691
00:34:26,320 --> 00:34:29,920
Speaker 5: But this is like the shaman cult vibes. I think, like,

692
00:34:30,200 --> 00:34:32,799
Speaker 5: like what goes with the quest to invent AI?

693
00:34:32,960 --> 00:34:33,200
Speaker 1: God?

694
00:34:33,760 --> 00:34:35,600
Speaker 5: You have to like have your cult in like a

695
00:34:35,760 --> 00:34:39,920
Speaker 5: shag carpeted room, everyone's wearing robes or whatever. You take

696
00:34:39,960 --> 00:34:44,680
Speaker 5: your shoes off, you ensconce yourself surrounded by AI agents

697
00:34:44,880 --> 00:34:47,280
Speaker 5: and you, you know, transcend reality.

698
00:34:48,200 --> 00:34:51,040
Speaker 2: Like Kyle reading your story, I got to think of

699
00:34:51,120 --> 00:34:53,520
Speaker 2: a quote the last thought about when I was studying

700
00:34:53,600 --> 00:34:58,719
Speaker 2: literature University fifteen years ago from William Woodsworth, which was

701
00:34:58,920 --> 00:35:02,360
Speaker 2: every great and orig writer in proportion as he is

702
00:35:02,440 --> 00:35:06,680
Speaker 2: great and original must himself create the taste by which

703
00:35:06,719 --> 00:35:07,480
Speaker 2: he is renished.

704
00:35:08,680 --> 00:35:09,680
Speaker 5: What a great quote.

705
00:35:09,960 --> 00:35:10,800
Speaker 1: What a great quote.

706
00:35:10,840 --> 00:35:12,719
Speaker 5: I mean to write that one down or put it

707
00:35:12,840 --> 00:35:17,480
Speaker 5: on the wall of my startup office. Yeah, Like it's

708
00:35:17,640 --> 00:35:20,080
Speaker 5: it's the job of an artist or a creator or

709
00:35:20,120 --> 00:35:23,920
Speaker 5: whatever to create a new paradigm, right, to create a

710
00:35:24,000 --> 00:35:27,160
Speaker 5: new sensibility and project it into the world and like

711
00:35:27,719 --> 00:35:30,920
Speaker 5: teach an audience in some ways to appreciate it or

712
00:35:31,040 --> 00:35:32,320
Speaker 5: understand what they're trying to do.

713
00:35:33,160 --> 00:35:34,120
Speaker 1: But if you if you.

714
00:35:34,200 --> 00:35:38,480
Speaker 5: Put that task onto an AI startup, like, I'm not

715
00:35:38,600 --> 00:35:40,600
Speaker 5: really resonating with what you want me to do here,

716
00:35:40,719 --> 00:35:42,919
Speaker 5: because what you want me to do is like wear

717
00:35:42,960 --> 00:35:45,919
Speaker 5: a pendant on my neck that insults me via text

718
00:35:46,040 --> 00:35:51,080
Speaker 5: message or you know, tells me to do things they

719
00:35:51,160 --> 00:35:54,040
Speaker 5: don't like want to do. There's that one startup that's

720
00:35:54,120 --> 00:35:57,040
Speaker 5: like AI can rent a human to do labor for it.

721
00:35:57,480 --> 00:35:59,759
Speaker 5: Like that that seems to be the taste they have

722
00:35:59,840 --> 00:36:03,120
Speaker 5: in mind. So I guess I just don't like, I

723
00:36:03,200 --> 00:36:06,520
Speaker 5: don't so far enjoy the sensibility that they're projecting, and

724
00:36:06,600 --> 00:36:08,879
Speaker 5: I wish they could come up with something a little better.

725
00:36:09,800 --> 00:36:12,879
Speaker 3: There was an interesting uh, well, the sorry Taylor, there's

726
00:36:13,040 --> 00:36:15,080
Speaker 3: just this thing other thing that happened in a video

727
00:36:15,239 --> 00:36:18,439
Speaker 3: with this did you see this dl SS five meme

728
00:36:18,600 --> 00:36:21,920
Speaker 3: happening online? They have this new thing where they use

729
00:36:22,000 --> 00:36:25,040
Speaker 3: Generator AI to like upscale the graphics of video games,

730
00:36:25,800 --> 00:36:28,400
Speaker 3: and so they showed like before and after pictures of

731
00:36:28,480 --> 00:36:31,600
Speaker 3: the characters, and people like really took issue with it

732
00:36:31,640 --> 00:36:34,400
Speaker 3: because they're like, well, that's not the artist. I mean,

733
00:36:34,440 --> 00:36:39,359
Speaker 3: it wasn't really explained like I still like a face tune, Yeah,

734
00:36:39,440 --> 00:36:43,920
Speaker 3: like these hilarious memes about it with like it was

735
00:36:43,960 --> 00:36:46,600
Speaker 3: like Jensen Wong before and after and he was like

736
00:36:46,680 --> 00:36:50,120
Speaker 3: a busty woman after, you know, and it's like, you know,

737
00:36:50,600 --> 00:36:53,000
Speaker 3: it gets to this question though of I mean even

738
00:36:53,120 --> 00:36:55,120
Speaker 3: just the announcement of it. It's like there wasn't a

739
00:36:55,200 --> 00:36:57,319
Speaker 3: thought of like are people going to be are people

740
00:36:57,400 --> 00:36:59,160
Speaker 3: going to be confused by this and think that you're

741
00:36:59,640 --> 00:37:03,080
Speaker 3: we're at actually like changing the artist's vision here, and

742
00:37:03,960 --> 00:37:06,560
Speaker 3: you know, and the reaction to it and it is

743
00:37:07,040 --> 00:37:09,319
Speaker 3: you know, I mean, it's not exactly what you're talking about,

744
00:37:09,360 --> 00:37:10,480
Speaker 3: but it just popped into my head.

745
00:37:10,800 --> 00:37:12,759
Speaker 4: But it is kind of similar because it's like how

746
00:37:13,040 --> 00:37:15,759
Speaker 4: is it altering art? And how is it altering kind

747
00:37:15,800 --> 00:37:18,839
Speaker 4: of like the media that we've consumed. There was also

748
00:37:18,920 --> 00:37:23,600
Speaker 4: a viral controversy over the Pretty Little Liar's book I

749
00:37:23,640 --> 00:37:26,920
Speaker 4: Guess was updated on Amazon Kindle Like it was like

750
00:37:26,960 --> 00:37:29,760
Speaker 4: the digital versions of it were updated to include modern

751
00:37:29,840 --> 00:37:33,359
Speaker 4: references to things like TikTok and stuff, which really kind

752
00:37:33,360 --> 00:37:36,400
Speaker 4: of like changes things in the story a little bit.

753
00:37:36,480 --> 00:37:39,160
Speaker 4: And so people were tweeting screenshots like what the heck,

754
00:37:39,280 --> 00:37:42,560
Speaker 4: Like why does a book need like an update, or

755
00:37:42,640 --> 00:37:44,560
Speaker 4: like why do we have to kind of like alter

756
00:37:44,840 --> 00:37:48,040
Speaker 4: our media to like make it conform with today's like

757
00:37:48,200 --> 00:37:50,920
Speaker 4: visual standards or like you know, cultural standards. And I

758
00:37:51,000 --> 00:37:52,920
Speaker 4: think there's a lot of rejection to that, whereas like

759
00:37:52,960 --> 00:37:55,359
Speaker 4: a lot of these tech people are like, wait, but look,

760
00:37:55,440 --> 00:37:57,520
Speaker 4: we have this shiny new version, you know, and it

761
00:37:57,600 --> 00:37:59,120
Speaker 4: can keep up with the times. And it's like, but

762
00:37:59,239 --> 00:38:01,040
Speaker 4: we appreciate that it didn't keep up.

763
00:38:00,920 --> 00:38:01,359
Speaker 3: With the times.

764
00:38:01,840 --> 00:38:05,840
Speaker 5: Yes, is like the taste of AI. Like all culturists

765
00:38:05,880 --> 00:38:10,960
Speaker 5: fan fiction, everything should conform to my personal preferences. Everything

766
00:38:11,000 --> 00:38:14,720
Speaker 5: should be like glossy and upscaled, like the video game filter.

767
00:38:15,200 --> 00:38:19,000
Speaker 5: It's just kind of like optimize, I don't know, optimize sheen.

768
00:38:19,520 --> 00:38:21,320
Speaker 5: That isn't very tasteful. Like if you go back to

769
00:38:21,360 --> 00:38:24,400
Speaker 5: the Wordsworth quote, like, that's timeless. It was from a

770
00:38:24,480 --> 00:38:26,560
Speaker 5: long time ago. We don't need to update it with

771
00:38:26,760 --> 00:38:32,000
Speaker 5: like riz or whatever. Words Worth doesn't need to say

772
00:38:32,120 --> 00:38:33,360
Speaker 5: six seven or something.

773
00:38:35,320 --> 00:38:36,960
Speaker 2: But I do think, Kyle, you put your finger on something.

774
00:38:37,000 --> 00:38:40,520
Speaker 2: She's really interesting to me, which is these tech overlords

775
00:38:40,560 --> 00:38:43,480
Speaker 2: imagining themselves into a future where their creations have been

776
00:38:43,520 --> 00:38:45,600
Speaker 2: so successful there is nothing left for them to do,

777
00:38:46,280 --> 00:38:48,920
Speaker 2: and the idea of taste as this kind of last

778
00:38:49,440 --> 00:38:53,160
Speaker 2: bastion of like what makes us human or gives us value?

779
00:38:53,200 --> 00:38:56,000
Speaker 2: I mean, thinking about the hipster movement earlier, it was

780
00:38:56,080 --> 00:38:58,400
Speaker 2: kind of like a I don't want to summarize a

781
00:38:58,440 --> 00:38:59,920
Speaker 2: hipster movement, but in a sense it was like, well,

782
00:39:00,200 --> 00:39:02,800
Speaker 2: we may not play by the rules of like you know,

783
00:39:03,000 --> 00:39:05,720
Speaker 2: finance culture, but like we have our thing which is special,

784
00:39:05,800 --> 00:39:08,120
Speaker 2: and we have our taste. And so the idea that

785
00:39:08,280 --> 00:39:10,399
Speaker 2: that tech bros may on the other side of their

786
00:39:10,440 --> 00:39:14,680
Speaker 2: great achievement join the hipsters of yesteryear in feeling slightly

787
00:39:14,719 --> 00:39:17,359
Speaker 2: alienated by the world in which they live is kind

788
00:39:17,360 --> 00:39:17,960
Speaker 2: of delightful.

789
00:39:18,080 --> 00:39:20,960
Speaker 5: Oh, I think that's so accurate, Like what else is

790
00:39:21,120 --> 00:39:25,600
Speaker 5: left but to take up carpentry and like make a

791
00:39:25,719 --> 00:39:29,240
Speaker 5: ceramic mug for yourself and like tinker with your espresso machine.

792
00:39:29,920 --> 00:39:33,080
Speaker 5: I think that this is extremely pretentious, probably, but I've

793
00:39:33,120 --> 00:39:36,440
Speaker 5: been reading a book of Basho poems, the like Japanese

794
00:39:36,520 --> 00:39:40,000
Speaker 5: haiku poet from the eighteenth century, and like all he

795
00:39:40,200 --> 00:39:43,960
Speaker 5: was doing was wandering around observing nature and writing very

796
00:39:44,040 --> 00:39:46,840
Speaker 5: short poems and hang out with his friends. And I

797
00:39:46,880 --> 00:39:49,279
Speaker 5: feel like that's all these tech bros actually want to

798
00:39:49,320 --> 00:39:51,680
Speaker 5: do in the end, and they could just do that,

799
00:39:52,280 --> 00:39:54,759
Speaker 5: Like they could skip all of this and just go

800
00:39:54,920 --> 00:39:55,880
Speaker 5: live in a cabin.

801
00:39:56,600 --> 00:39:59,839
Speaker 4: They have I don't think they have the like introspect

802
00:40:00,080 --> 00:40:01,560
Speaker 4: and to do that like I think.

803
00:40:01,440 --> 00:40:03,520
Speaker 3: They like So that's what one of the anthropic people

804
00:40:03,640 --> 00:40:05,480
Speaker 3: said when they you know, if when you leave an

805
00:40:05,480 --> 00:40:07,799
Speaker 3: AI company, you have to write a whole dietribe about

806
00:40:07,800 --> 00:40:09,440
Speaker 3: why you left. And he was like, I'm gonna go

807
00:40:09,520 --> 00:40:13,040
Speaker 3: write poetry. That's what he decided to do. So you

808
00:40:13,120 --> 00:40:15,000
Speaker 3: know they are doing that nice.

809
00:40:16,120 --> 00:40:19,799
Speaker 4: See what Yeah, let's see what that poetry happens out.

810
00:40:20,239 --> 00:40:23,960
Speaker 3: Shelley Banjo wrote this great column last week, Who's who's

811
00:40:24,200 --> 00:40:24,800
Speaker 3: my editor?

812
00:40:24,920 --> 00:40:25,040
Speaker 4: Now?

813
00:40:25,120 --> 00:40:27,640
Speaker 3: Who's newt at seven four? And she was like, she

814
00:40:27,760 --> 00:40:30,160
Speaker 3: asked She talked about asking this friend of hers who

815
00:40:30,239 --> 00:40:32,560
Speaker 3: has great taste in books, he reads a lot of books,

816
00:40:33,080 --> 00:40:35,200
Speaker 3: like what business books should I be reading right now?

817
00:40:35,840 --> 00:40:38,640
Speaker 3: And he just geminied it and sent her the Gemini

818
00:40:38,760 --> 00:40:41,400
Speaker 3: list and it was like the number one recommendation was

819
00:40:41,440 --> 00:40:44,960
Speaker 3: like the World is Flat by Tom Friedman. She's like,

820
00:40:45,120 --> 00:40:46,200
Speaker 3: what do you talk about?

821
00:40:46,200 --> 00:40:46,239
Speaker 4: Like?

822
00:40:46,400 --> 00:40:50,080
Speaker 3: This is like horrible? So, you know, I think you're

823
00:40:50,120 --> 00:40:52,640
Speaker 3: gonna have to have That's that's going to be the

824
00:40:52,760 --> 00:40:55,359
Speaker 3: new thing. It's gonna be like I have book recommendations

825
00:40:55,400 --> 00:40:58,200
Speaker 3: that were not generated by AI. You know, that's it's

826
00:40:58,280 --> 00:40:58,880
Speaker 3: real taste.

827
00:40:59,719 --> 00:41:01,080
Speaker 1: That's we have time for today.

828
00:41:01,680 --> 00:41:04,239
Speaker 2: But I want to end our discussion with a simple question.

829
00:41:04,400 --> 00:41:06,800
Speaker 2: Taylor had this one last week, but for Kyle and

830
00:41:07,280 --> 00:41:09,759
Speaker 2: and read it's fresh. Who had the best week in tech?

831
00:41:09,840 --> 00:41:10,600
Speaker 2: And who had the worst?

832
00:41:10,640 --> 00:41:16,360
Speaker 1: We'll start with you, Taylor, No, don't stay Kyle.

833
00:41:16,640 --> 00:41:19,680
Speaker 5: So I think the best week in tech was Nintendo

834
00:41:20,120 --> 00:41:24,760
Speaker 5: with Pocopia, the like new Pokemon slash Animal Crossing Slash

835
00:41:24,840 --> 00:41:27,680
Speaker 5: Minecraft game, and it seems to have just been like

836
00:41:27,800 --> 00:41:30,719
Speaker 5: a huge hit, even though people were cynical about it

837
00:41:31,080 --> 00:41:33,680
Speaker 5: and kind of thought it was a you know, hack job,

838
00:41:33,800 --> 00:41:38,600
Speaker 5: sellout move. It's delightful. Everyone loves Pokemon and hanging out

839
00:41:38,640 --> 00:41:41,360
Speaker 5: and building little gardens for their Pokemon to hang out in.

840
00:41:41,560 --> 00:41:43,200
Speaker 5: So I think Nintendo had.

841
00:41:43,040 --> 00:41:43,560
Speaker 3: A great week.

842
00:41:44,080 --> 00:41:44,839
Speaker 1: Who had a bad one?

843
00:41:45,280 --> 00:41:47,600
Speaker 5: I mean, I was gonna say Sam Altman getting called

844
00:41:47,640 --> 00:41:53,000
Speaker 5: a Nazi by as it came up before. That's pretty

845
00:41:53,160 --> 00:41:56,600
Speaker 5: pretty bad. That is pretty bad read.

846
00:41:56,840 --> 00:41:59,120
Speaker 3: I mean, I don't know. I think I probably just

847
00:41:59,239 --> 00:42:00,800
Speaker 3: just because it's like wresh in my mind and I

848
00:42:00,880 --> 00:42:03,239
Speaker 3: was talking about it, but I did, I did. I

849
00:42:03,360 --> 00:42:06,719
Speaker 3: do think that Nvidia had kind of a good, a

850
00:42:06,800 --> 00:42:09,080
Speaker 3: good week this week, even though they're stock I mean,

851
00:42:09,200 --> 00:42:11,920
Speaker 3: not looking at their stock price like it kind of

852
00:42:12,160 --> 00:42:15,320
Speaker 3: But maybe it's just me crystallizing in my head like

853
00:42:15,400 --> 00:42:18,279
Speaker 3: where they're at in terms of their their grand ambitions.

854
00:42:19,800 --> 00:42:23,160
Speaker 3: But I don't know. I mean, I think it just

855
00:42:23,280 --> 00:42:26,880
Speaker 3: continues to be like this this Anthropic thing with the Pentagon.

856
00:42:27,200 --> 00:42:29,920
Speaker 3: You know, Anthropic has had this big fight with the

857
00:42:30,000 --> 00:42:33,839
Speaker 3: Pentagon over exactly how their AI models should be used

858
00:42:34,000 --> 00:42:38,120
Speaker 3: in warfare and also in surveillance of Americans, and the

859
00:42:38,160 --> 00:42:41,279
Speaker 3: Pentagon is saying, you know, we don't want any restrictions

860
00:42:41,320 --> 00:42:44,200
Speaker 3: on these models, and in fact, now we're designating you

861
00:42:44,320 --> 00:42:47,600
Speaker 3: a supply chain risk. I just can't imagine that this

862
00:42:47,800 --> 00:42:49,680
Speaker 3: is like great for for Anthropic.

863
00:42:49,760 --> 00:42:52,440
Speaker 2: I mean, that's very interesting because last week's takeaway was

864
00:42:52,480 --> 00:42:54,560
Speaker 2: the Anthropic it had the best we can take because

865
00:42:54,560 --> 00:42:56,600
Speaker 2: of all the use of growth and love that had

866
00:42:56,640 --> 00:42:57,480
Speaker 2: gotten from there.

867
00:42:57,600 --> 00:42:58,640
Speaker 1: From their stance on this.

868
00:42:59,280 --> 00:43:03,120
Speaker 3: I know, I know, and I I it's like it's okay,

869
00:43:04,560 --> 00:43:05,960
Speaker 3: you can look at that and you're like, yeah, I

870
00:43:06,040 --> 00:43:08,640
Speaker 3: mean sure if it it could work out for them

871
00:43:08,719 --> 00:43:10,920
Speaker 3: really well, right like this sort of like Apple versus

872
00:43:11,040 --> 00:43:15,120
Speaker 3: the versus the the FBI thing that sort of helped

873
00:43:15,160 --> 00:43:19,080
Speaker 3: that helped like solidify their privacy centric marketing. But I

874
00:43:19,239 --> 00:43:21,640
Speaker 3: just think in the end, it's not They're not a

875
00:43:21,800 --> 00:43:24,400
Speaker 3: they're not a consumer company really, so it doesn't like

876
00:43:25,200 --> 00:43:28,640
Speaker 3: like their app store numbers don't really ultimately matter that much.

877
00:43:28,719 --> 00:43:31,560
Speaker 3: It's like they have to win an enterprise and I

878
00:43:31,680 --> 00:43:35,160
Speaker 3: think like it's just not ultimately in the long run

879
00:43:35,400 --> 00:43:37,279
Speaker 3: that like, I don't think companies are going to look

880
00:43:37,280 --> 00:43:40,160
Speaker 3: are going to view this as like, you know, a

881
00:43:40,239 --> 00:43:43,200
Speaker 3: big bonus. It's just like it's just a draw. The

882
00:43:43,280 --> 00:43:45,560
Speaker 3: more the more this fight gets like drawn out, I

883
00:43:45,680 --> 00:43:47,600
Speaker 3: just have a feeling it's not that great for them.

884
00:43:47,719 --> 00:43:51,000
Speaker 3: Like their competitors are. Like Google has somehow totally avoided

885
00:43:51,080 --> 00:43:54,880
Speaker 3: this controversy, which I think is like fascinating and in

886
00:43:54,920 --> 00:43:56,879
Speaker 3: a lot of ways, they're like the real the one

887
00:43:56,960 --> 00:43:59,480
Speaker 3: everybody should be competing with, and like opening eyes kind

888
00:43:59,480 --> 00:44:01,880
Speaker 3: of geting dry into it too. So I don't know,

889
00:44:02,080 --> 00:44:04,040
Speaker 3: maybe Google had the best week actually I don't know,

890
00:44:04,960 --> 00:44:08,040
Speaker 3: just by not being like no one's talking about them

891
00:44:08,080 --> 00:44:08,880
Speaker 3: in this context.

892
00:44:09,400 --> 00:44:11,239
Speaker 4: I know, when you're a tech company, sometimes you could

893
00:44:11,320 --> 00:44:13,560
Speaker 4: just have the best week by no one speaking about you.

894
00:44:14,080 --> 00:44:16,120
Speaker 3: Right, right, It's true, It's true.

895
00:44:16,400 --> 00:44:18,960
Speaker 4: I think Jensen I'm gonna I'm gonna kind of like piggyback.

896
00:44:18,960 --> 00:44:21,279
Speaker 4: I mean, I do think Jensen Wong had a good week.

897
00:44:21,800 --> 00:44:24,839
Speaker 4: I think, like I mean, correct me if I'm wrong.

898
00:44:24,920 --> 00:44:27,280
Speaker 4: But I didn't see like a huge amount of backlash

899
00:44:27,520 --> 00:44:29,840
Speaker 4: considering like kind of the stuff that he was talking about.

900
00:44:30,120 --> 00:44:31,719
Speaker 4: I mean, maybe there was like a little dram over

901
00:44:31,800 --> 00:44:35,000
Speaker 4: in Vidia, but also like he is ascending to use

902
00:44:35,040 --> 00:44:38,800
Speaker 4: the clavicular term, you know, into like CEO status, Like

903
00:44:38,880 --> 00:44:40,960
Speaker 4: I mean even at the Nvidia you know conference, I

904
00:44:41,000 --> 00:44:43,800
Speaker 4: think they were selling like sweaters with Jensen Wong's like

905
00:44:43,920 --> 00:44:46,279
Speaker 4: face on it. So I think he's kind of like

906
00:44:46,800 --> 00:44:48,879
Speaker 4: you know, when you look at the clout index, maybe

907
00:44:49,000 --> 00:44:51,960
Speaker 4: like his is going up and Sam Altman is going down.

908
00:44:52,080 --> 00:44:55,640
Speaker 4: I would say Sam Altman probably had the worst week. Also,

909
00:44:56,920 --> 00:45:00,560
Speaker 4: Uber's former head of self driving wrote this great piece

910
00:45:00,640 --> 00:45:04,600
Speaker 4: about actually how his self driving Tesla crashed and taught

911
00:45:04,680 --> 00:45:07,319
Speaker 4: him a lot about like Ai Risks, and I thought,

912
00:45:07,800 --> 00:45:10,000
Speaker 4: I don't know if that's a bad week, but I thought, like,

913
00:45:10,320 --> 00:45:13,359
Speaker 4: you know, that was a good lesson for that man

914
00:45:13,440 --> 00:45:14,799
Speaker 4: to learn, like.

915
00:45:14,880 --> 00:45:17,759
Speaker 5: Your email inbox getting to lead it, like you learned something.

916
00:45:19,239 --> 00:45:20,160
Speaker 1: People need to learn.

917
00:45:20,080 --> 00:45:21,680
Speaker 4: These lessons for themselves.

918
00:45:21,760 --> 00:45:24,320
Speaker 1: There you got forced reflection. Thank you so much. That

919
00:45:24,360 --> 00:45:25,680
Speaker 1: was really fun. That was fun.

920
00:45:25,960 --> 00:45:26,319
Speaker 4: Thank you.

921
00:45:51,840 --> 00:45:52,600
Speaker 1: Put text stuff.

922
00:45:52,760 --> 00:45:55,480
Speaker 2: I'm as volocian. This episode was produced by Eliza Dennis

923
00:45:55,520 --> 00:45:58,800
Speaker 2: and Melissa Slaughter. It was executive produced by me Julian

924
00:45:58,880 --> 00:46:02,960
Speaker 2: Nutter and Kate Osborne Kaleidoscope and Katrina Norvel for iHeart Podcasts.

925
00:46:03,520 --> 00:46:07,040
Speaker 2: The engineer is Charles de Montebello for CDM Studios. Jack

926
00:46:07,120 --> 00:46:10,000
Speaker 2: Insley mixed his episode and Kyle murdoch Rodart theme song.

927
00:46:10,680 --> 00:46:13,080
Speaker 2: A special thank you to read Albergotti, Kyle Chaker, and

928
00:46:13,120 --> 00:46:15,920
Speaker 2: Taylor Lorenz. Please check out all the work they put

929
00:46:15,920 --> 00:46:18,000
Speaker 2: out into the world. We're very lucky to call them

930
00:46:18,080 --> 00:46:20,880
Speaker 2: friends of the pod and please do rate and review

931
00:46:20,960 --> 00:46:23,000
Speaker 2: this show wherever you listen to your podcasts.