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Speaker 1: Also media.

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Speaker 2: Hi everyone. Before we get to the episode, I just

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Speaker 2: wanted to lead in and say we are up for

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Speaker 2: a webby. I'll be including a link. I know it's

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Speaker 2: a pain in the as to register for something. I'm sorry.

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Speaker 2: I really want to win this. Never won an award

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Speaker 2: in my life. It will be in the links. And

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Speaker 2: while you're there and registered, look up the wonderful weird

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Speaker 2: little Guys with Miss Molly Konga. Vote for both of us.

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Speaker 2: I'm in the Best Business Podcast, Episode one. She's in

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Speaker 2: the Best Crime Podcast Episode one. We can win this,

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Speaker 2: we can defeat the others. And now for the episode

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Speaker 2: Every Day I Am punished and killed and you love

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Speaker 2: to watch. Welcome to Better Offline. We're live from New

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Speaker 2: York City, recorded straight to tape, of course, and I'm

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Speaker 2: joined by an incredible cast of people. To my right,

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Speaker 2: I have Paris markin Now of the information Hating baris

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Speaker 2: What's Up? What is Up? Edward and Guaso of the

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Speaker 2: Tech Bubble newsletter, Hello, Hello, and the flows to Morrow

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Speaker 2: of the CNN Nightcap newsletter. Hi and Allison, you wrote

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Speaker 2: one of my favorite bits of media criticism I've ever

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Speaker 2: read recently. Do you want to actually walk us through

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Speaker 2: that pace because I think I will link it in

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Speaker 2: the notestone where everyone.

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Speaker 3: I'd be happy to. I wrote a piece. I think

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Speaker 3: the headline we ended up with was like Apple's AI

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Speaker 3: is not the disappointment. AI is the disappointment. Yeah, And

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Speaker 3: this was inspired by credit to where It's due. I

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Speaker 3: was listening to hard Work with Kevin Ruse on My

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Speaker 3: husband and I were driving out to the country and

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Speaker 3: listening to this and just getting infuriated. Yeah, And basically

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Speaker 3: their premise was, or at least Kevin Ruce's premise was

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Speaker 3: that AI is failing or sorry that Apple is failing

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Speaker 3: this moment in AI, right, and Apple has been trying.

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Speaker 3: It's been like the laggard. You know, that's a narrative

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Speaker 3: we've heard in tech media over and over, and it's

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Speaker 3: like Kevin Ruce's point was like, oh, well this he

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Speaker 3: should just start getting more comfortable with experimenting and making

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Speaker 3: mistakes and you know, violating everything that Apple brand kind

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Speaker 3: of stands for and like force the AI into a

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Speaker 3: consumer product that no one wants. And I was like, respectfully, no.

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Speaker 1: So such a funny argument, given that it was a

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Speaker 1: mistake being made by Apple that resulted in the whole hoo.

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Speaker 1: They PC small group situation that was specifically how the

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Speaker 1: editor in chief of the Atlantic ended up in a

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Speaker 1: secret military signal chat.

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Speaker 2: Wait I missed what? How?

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Speaker 1: How online? I? Oh, gosh, I should leave.

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Speaker 2: I've been reading scrolls.

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Speaker 1: So basically, The Atlantic came out a couple of weeks

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Speaker 1: ago with a article about how their editor in chief

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Speaker 1: one day was suddenly added to a.

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Speaker 2: Signal group chatigny signal game. But how did this?

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Speaker 1: So the apple thing was I'm forgetting who exactly reported this?

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Speaker 1: This was in the last couple of days. That how

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Speaker 1: it happened was the con like you know that thing

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Speaker 1: that comes up in your iPhone where it says like, oh,

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Speaker 1: a new phone number has been found. It was a

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Speaker 1: suggested contact. And it happened because someone I guess in

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Speaker 1: the government had copied and pasted an email containing the

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Speaker 1: editor in chief of the Atlantic's contact information in a

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Speaker 1: message to uh, I'm forgetting whatever government official, yeah, one

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Speaker 1: of the guys, And so he ended up combining the

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Speaker 1: Atlantic ei ces information into a contact for uh, some

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Speaker 1: government dude. And that's how they ended up in because

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Speaker 1: then signal when you connected to your content. So I

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Speaker 1: mean that's that makes me even crazier about the hard

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Speaker 1: fork take because it's like you can't mess around with

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Speaker 1: something like your phone.

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Speaker 3: Well, in this particular instance, I take it all back. Apple.

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Speaker 3: AI is a mea. It gave us one of the

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Speaker 3: best journalism stories.

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Speaker 4: Of the year.

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Speaker 2: You also made a really good point in here as

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Speaker 2: a message you this on the way, and you said

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Speaker 2: you made this point that there's a popular adage in

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Speaker 2: part of the Sea Circles that the party can never fail,

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Speaker 2: it can only be failed. It is meant as a

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Speaker 2: critique of the ideological gatekeepers who may be, for example,

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Speaker 2: blame voters for their parties failings rather than the party itself.

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Speaker 2: The same fallacy is taking root among AI's big as

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Speaker 2: back as AI can never fail, it can only be failed.

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Speaker 2: And I love this because it's you get people at

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Speaker 2: Kevin Russ And there was a wonderful clip on the

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Speaker 2: New York Times TikTok of Kevin Ruse seeming genuinely pissy.

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Speaker 2: He's like, I can't believe people are mad at AI

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Speaker 2: because of Siri, And it's like, oh what they think

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Speaker 2: it's shitty because it's shit, Like they talk about AI

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Speaker 2: like it's their child. Him and Casey act as if

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Speaker 2: we've hurt chat Cheep, sorry Claude, their Anthropic boys, and

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Speaker 2: in'saeing that Casey's boyfriend works at Anthropic. I know he

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Speaker 2: does the site. Its fucking anyway. It's just so weird

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Speaker 2: because it's like, we have to work apologize for not

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Speaker 2: liking AI enough, and now you have the CEO of

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Speaker 2: Shopify saying, actually you have to use it. You hear

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Speaker 2: about this?

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Speaker 5: Yeah? He said, what that you have to prove your

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Speaker 5: job can't be replaced by AI?

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Speaker 2: Yeah, else it will be.

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Speaker 1: And he also said that now it's going to be

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Speaker 1: Shopify policy to include in all of the employee performance reviews,

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Speaker 1: both for your self assessment and for your like direct

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Speaker 1: reports and colleagues assessment, how much this person use AI.

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Speaker 1: And obviously what's going on there is if you are

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Speaker 1: not reporting that you use AI all the time for everything,

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Speaker 1: you could get fired.

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Speaker 5: Whyn't I just tried to overhaul hiring presses so that

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Speaker 5: they could have AI first or AI only, and then

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Speaker 5: roll it back because they realize you can't replace yes

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Speaker 5: all these jobs questions?

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Speaker 1: Is I mean, this is something that's brought up on

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Speaker 1: the show all the time. But who are these people

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Speaker 1: that are encountering the AI assistant suddenly plugged into every

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Speaker 1: app and being like, yeah, this is actually beneficial to

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Speaker 1: my life and this works really well done because it

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Speaker 1: sucks every time I use it.

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Speaker 2: Or you made the point in your article Allison's words like,

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Speaker 2: if it was one hundred percent accurate, it would be

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Speaker 2: really useful. If it's even ninety eight percent accurate, it's

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Speaker 2: not right.

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Speaker 3: I think that was the point that you know, to

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Speaker 3: his credit, Casey Newton made in the episode, which is

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Speaker 3: that AI is fundamentally an academic project right now. And

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Speaker 3: it's like, yeah, you can have all the kinds of

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Speaker 3: debates about its utility, but ultimately is it a consumer product?

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Speaker 3: And no, it's just like it's failing as a consumer

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Speaker 3: product on all fronts.

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Speaker 2: And what's crazy as well, is I'm surprised he would

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Speaker 2: say that considering everything else he's ever said, because he's

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Speaker 2: he quite literally has had multiple articles recently being like

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Speaker 2: consumer adoption is up in He had an article the

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Speaker 2: other day where it was like, data provided exclusively from

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Speaker 2: Anthropic shows that more people are using AM. It's like, mom, man,

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Speaker 2: it's twenty thirteen. Again, we're past this. You can't just

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Speaker 2: do this anymore unless you're you. And so going back

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Speaker 2: to mister lutkey of a, mister lud key of shop,

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Speaker 2: I just want to read my faceavorite part of it. It

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Speaker 2: says I use it all the time, but even I

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Speaker 2: feel I'm only scratching the surface. Dot dot com. You've

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Speaker 2: heard me talk about AIM weekly videos, podcast, townholes, and

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Speaker 2: summit last summer. I used agents to create my talk

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Speaker 2: and present it about that. So all this fucking piss

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Speaker 2: and vinegar and the only thing you can use it

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Speaker 2: for is to write a slop ridden presentation to everyone

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Speaker 2: about how good AI is without specifying what it does.

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Speaker 2: I feel like I'm going insane sometimes with this stuff.

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Speaker 5: I mean, in one way, that's great, right. The only

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Speaker 5: place you should encounter it is maybe the team building retreats,

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Speaker 5: you know, that's the utility of this shit.

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Speaker 3: This reminds me a lot of like Media twenty twelve

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Speaker 3: twenty thirteen, where it was all pivot to video and

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Speaker 3: what's our video and our vertical video strategy? And it's like, okay, now,

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Speaker 3: what's our AI strategy? How are we injecting AI into

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Speaker 3: everything we're doing? And it's like, well, to what end? Yeah,

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Speaker 3: this is just the point.

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Speaker 5: This is something that has been driving me mad, especially

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Speaker 5: with partnerships we're seeing between media firms and these AI firms.

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Speaker 5: You know, these are these are firms in the same

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Speaker 5: sector that keeps lying to verbs about how if you

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Speaker 5: integrate artificial intelligence this time it will optimize your ability

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Speaker 5: to find an audience or to get revenue, and we

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Speaker 5: can include you in some esoteric revenue share program where

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Speaker 5: we'll be able to claw back some of the eyeballs

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Speaker 5: and the attention that you're interested in seeking. But each

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Speaker 5: time it's actually just used to graph themselves onto services

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Speaker 5: or to try to gin up excitement about these products.

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Speaker 1: Right.

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Speaker 2: What's insane is this company has a multi billion dollar

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Speaker 2: market cap and I'm just going to read point two.

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Speaker 2: AI must be part of your GSD prototype phase. The

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Speaker 2: prototype phase of any GSD product should be dominated by

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Speaker 2: AI exploration. Prototypes are meant for learning and creating information.

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Speaker 2: AI dramatically accelerates this process. How fucking how Like That's

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Speaker 2: the thing. I have clients in my p off and

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Speaker 2: will occasionally bring the AI things, and I'm every time,

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Speaker 2: I'm just like this better fucking world, Like just every

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Speaker 2: and to their credit, they do, but it's like I

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Speaker 2: have clients they turned down all the time. You're like, yeah,

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Speaker 2: we're doing this, and I'm like, is this just the chatbot?

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Speaker 2: And they're like no. I'm like, can you show me

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Speaker 2: how it works? No, I'm like, oh cool, Yeah. I

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Speaker 2: don't think we're going to be a good fit somehow

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Speaker 2: because you don't seem to be able to explain what

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Speaker 2: your product does. But don't worry. This appears to be

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Speaker 2: a problem up to the multi billion dollar companies as well.

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Speaker 2: It's just it feels like the largest mask off dunce

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Speaker 2: moment in history, just these people who don't do any

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Speaker 2: real work, being like it's the future. I think I

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Speaker 2: don't do anything real. And the pivots video thing, I

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Speaker 2: think is actually a really good comparison because I remember

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Speaker 2: being in New York at that time being like, I

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Speaker 2: don't fucking like video. I don't like anyone. I don't

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Speaker 2: think I want to consume video in the way that

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Speaker 2: what it was like Mike and everyone and they were like, Oh,

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Speaker 2: we're going to do but this video and this, We're

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Speaker 2: going to do everything video and now video first, no

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Speaker 2: written content. It's like, I don't know a single goddamn

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Speaker 2: humor that actually does that. And also the other thing

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Speaker 2: that Facebook was lying that Facebook was just over just

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Speaker 2: claiming like averaging out the engagement numbers and everyone was wrong,

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Speaker 2: but that was the same kind of thing. It's like,

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Speaker 2: very clearly the people who have their hands in the

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Speaker 2: steering wheel are looking at their phone and it's fucking confusing.

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Speaker 2: But it's so much worse this time. It feels more

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Speaker 2: egregious somehow.

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Speaker 1: Yeah, yeah, because it feels I mean, we've had so

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Speaker 1: many of these hype cycles, kind of back to back

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Speaker 1: to back, from even the horizontal video days of Facebook,

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Speaker 1: to vertical video to whatever the hell the metaverse was

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Speaker 1: supposed to be. Literally in an ominous moment, as I

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Speaker 1: was walking into record this, I saw a guy wearing

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Speaker 1: a leather jacket with board apyacht club on the back,

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Speaker 1: and I was like, god, well, yeah, I was like

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Speaker 1: that that guy rocks.

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Speaker 2: What a cool dude.

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Speaker 1: But it's like, how long is this going to last?

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Speaker 2: I have been actually looking at the numbers recently, and

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Speaker 2: I don't know either, because for soft Bank to fund

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Speaker 2: open AI might require them to destroy soft Bank, like

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Speaker 2: S ANDP is downgrading the credit rating potentially due to yeah, yeah,

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Speaker 2: I know, like we're really at this point where it's

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Speaker 2: just like we've got we've gone so much further than

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Speaker 2: like the Meta US and Crypto did, because those weren't

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Speaker 2: really like systemic things, but this one, I think it's

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Speaker 2: just the narrative is carried away so far. The people

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Speaker 2: are talking about a thing that doesn't exist all the time.

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Speaker 5: I mean, in some elements it kind of reminds me

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Speaker 5: at the near the end or near the real peak

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Speaker 5: is when we started also to see Metaverse and Crypto

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Speaker 5: Sustainable Refine Ship where they're actually, you know, we can

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Speaker 5: fight climate change with crypto, yeah, putting carbon credits on

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Speaker 5: the on the blockchain. And so there was a there

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Speaker 5: was a moment where the frenzy and the speculatve frenzy

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Speaker 5: led to like world transformative visions that were bullshit, And

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Speaker 5: I feel like we are we are heading there. We're

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Speaker 5: in that direction with artificial intelligence where you know, consistently

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Speaker 5: we've been fed oh, this is going to revolutionize everything,

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Speaker 5: but it feels like the attempt to graft it onto

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Speaker 5: more and more consumer products, more and more government services,

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Speaker 5: more and more parts of or what daily spheres of

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Speaker 5: life as a way to like privatize almost everything or

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Speaker 5: commodify everything. It feels like downstream of the way Crypto's

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Speaker 5: attempt to put everything on a blockchain blew up.

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Speaker 3: Yeah, I was thinking about this in a kind of

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Speaker 3: like fundamentally cultural way, where I think at some point

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Speaker 3: in the last thirty years, there was a time when

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Speaker 3: everything coming out of Silicon Valley was cool. Yeah, whether

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Speaker 3: it was like useful or world transformative, it was cool,

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Speaker 3: and there was like an edge to it, and people

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Speaker 3: were like.

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Speaker 2: Ooh, that's neat, it's disruptive.

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Speaker 3: Yeah, disruption was everything, And like I think posts like Facebook,

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Speaker 3: Cambridge Analytica era, like twenty sixteen tech has just stopped

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Speaker 3: being cool and edgy. It's very corporate, and like, I

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Speaker 3: don't think the rest of corporate America has kind of

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Speaker 3: figured out that Silicon Valley's not the cool thing anymore.

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Speaker 2: And that they can't lot. They're fully capable of being

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Speaker 2: wrong and lying, like that's that's the other thing.

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Speaker 3: They've gotten very good at fundraising and marketing.

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Speaker 2: But they're also not like kids anymore. Like we talk,

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Speaker 2: I still see people referring to Opening Eye as a.

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Speaker 5: Startup, former Lucky as a kid.

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Speaker 2: Palmer lucky as a kid who looks like leisures Larry

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Speaker 2: and sells arms, which is.

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Speaker 3: Gy. We refer to them as startups. But also I

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Speaker 3: think one of the most accomplished parts of AI marketing

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Speaker 3: has been, like we always refer to them as labs,

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Speaker 3: so they seem like so academic and like good fundamentally,

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Speaker 3: and it's like these are companies, Like some of them

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Speaker 3: might be part of, uh, you know, a research institution

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Speaker 3: or a university, but a lot of them are startups.

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Speaker 2: Lateral companies.

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Speaker 3: Yeah, they are companies.

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Speaker 2: Like anthropics, public benefit, I believe, And it's it's just remarkable,

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Speaker 2: And I think what's happened here is that the narrative

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Speaker 2: has gotten away to the point that we're really dunce

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Speaker 2: mask off moment I mentioned is people like mister Ludke

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Speaker 2: from Shopify. It's very clear he doesn't do any work.

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Speaker 2: Like I think that anyone who is just being like, yeah,

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Speaker 2: AI is the future and it's changing everything without specifying anything,

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Speaker 2: doesn't do any work. I just don't. Bob Iger from

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Speaker 2: Disney said, AI is gonna check. No it's not, But

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Speaker 2: how's it changing your fucking life? You lazy basted like

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Speaker 2: you're gonna summarize your worthless emails that someone else reads

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Speaker 2: as you lie on your Scrooge mcdark money. Yeah, and

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Speaker 2: it's just it's so bizarre. But it feels like we're

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Speaker 2: approaching this insanity level where you've got people like Shopify

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Speaker 2: being like, oh, it's gonna be in everything, as like

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Speaker 2: open AI burns more money than anyone's ever burned. Anthropic

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Speaker 2: lost five point six billion last year. Report by the

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Speaker 2: Information done some incredible fucking work on this, I should say,

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Speaker 2: And it just doesn't make any sense. And it's getting

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Speaker 2: more nonsensical. You're seeing like all of the crypto guys

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Speaker 2: have fully become AI guys now. And that was something

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Speaker 2: I didn't like talking about at first because it wasn't happening,

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Speaker 2: and now it's all of them. They all have AI avatars.

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Speaker 2: This guy called Jamie Burke is real, real shithead. This

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Speaker 2: guy was like a crypto metaverse guy and he's now

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Speaker 2: a full AI guy. Another guy called Bernard Maher, who

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Speaker 2: is just a harmless Forbes like a kind of like

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Speaker 2: an NPC, like one of the hollows from Dark Souls.

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Speaker 1: Walking around Diagram is increasingly becoming a circle.

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Speaker 2: Yeah, but he's onto quantum now, which is a bad sign.

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Speaker 2: That's a bearish sign. When you've got one of the

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Speaker 2: Forbes guys moving on to quantum, we're cooked.

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Speaker 5: What about thermo, Yeah, there's thermodynamics.

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Speaker 2: Become a thermodynamics influencer. I know what I know what

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Speaker 2: that means. I also know what that means. But if

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Speaker 2: anyone could tell me real quick. But it's I think

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Speaker 2: the most egregious one I've seen. I sent this all

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Speaker 2: to you, Ed, I think you and I have talked

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Speaker 2: about this the most. There was one of the stupidest

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Speaker 2: fucking things I've read in my worthless life, and it's

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Speaker 2: called AI twenty twenty seven. Now, if you have not

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Speaker 2: run into this yet as a listener, it will be

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Speaker 2: in the episode notes. I'm just just going to bring

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Speaker 2: it up because it is.

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Speaker 4: It is a little thing.

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Speaker 1: Throughout it I was like, is this fan fiction? This

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Speaker 1: is fan fiction? Oh, this is interactive fan fiction.

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Speaker 2: It is, and you can hit the bund says what

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Speaker 2: is this? How do we write it? Our research on

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Speaker 2: key questions what goals will future AI agents have can

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Speaker 2: be found here. The scenario itself is written iteratively. We

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Speaker 2: wrote the first period up to mid twenty twenty five

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Speaker 2: in the following period, et cetera, until we reach the

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Speaker 2: ending Yeah, otherwise known as how you write stuff like

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Speaker 2: you've wrote it writing anl in their fashion. We then

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Speaker 2: scrapped this and did it again. You should have scrapped

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Speaker 2: it in all of it. Now, this thing is it's

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Speaker 2: a is predicting that the impact of superhuman AI over

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Speaker 2: the next decade will be enormous, exceeding that of the

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Speaker 2: Industrial Revolution. We wrote a scenario that represents our best

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Speaker 2: guess that what it might look like. Otherwise known as

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Speaker 2: making stuff up, not even.

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Speaker 1: Over the next decade. It go basically says it's going

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Speaker 1: to be have superhuman like catastrophic or world changing impact

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Speaker 1: in the next five years, like by twenty twenty three,

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Speaker 1: we're either going to be completely overtaken by a robot

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Speaker 1: overlords or like at a you know, tenuous piece.

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Speaker 2: And it's insane as well, because it has some great

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Speaker 2: headlines like mid twenty twenty six, China wakes up.

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Speaker 3: And then I love that China was so far behind,

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Speaker 3: you know, it's like when.

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Speaker 1: Did this come out?

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Speaker 2: This came out like a week ago, and I've been

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Speaker 2: sent it by a lot of people. If you're one

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Speaker 2: of the people who sent it. Don't worry, I'm not

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Speaker 2: mad at you. It's just like got sented by a

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Speaker 2: lot of people. This thing is one of the most

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Speaker 2: well written pieces of fan fiction ever in that it

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Speaker 2: appears to be like a Manchurian candidate situation for idiots,

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Speaker 2: not saying this is about the same thing, but anyway,

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Speaker 2: Kevin Ruse wrote up the piece in full. He wrote

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Speaker 2: up a piece about this called hmm, I'm just gonna

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Speaker 2: say the AI forecast predicts some storms ahead, some stores,

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Speaker 2: some storms.

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Speaker 1: Even an accurate description of all of the storms it predicts.

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Speaker 2: And the long and short of this, by the way,

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Speaker 2: I have read this a few times because I fucking

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Speaker 2: hate myself. The long and short of it is that

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Speaker 2: a company called open Brain. Who could that be? Yeah,

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Speaker 2: it could be anyone, anyone open Brain. They create a

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Speaker 2: self learning agent somehow unclear how all layout is just that,

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Speaker 2: how many are terror flops it's going to require, and

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Speaker 2: it can train itself and also requires more data centers

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Speaker 2: than ever, how they get them, how those are funded?

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Speaker 2: No fucking clue isn't explained. Probably the easy actually this

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Speaker 2: is the fun This just occurred to me. Probably the

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Speaker 2: only thing they could actually reasonably extrapolate in here is

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Speaker 2: the cost of data centers. That's the only thing, and

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Speaker 2: they don't, probably because they'd be like, yeah, we need

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Speaker 2: an actual trillion dollars to do this made up thing.

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Speaker 5: I do also want to add in here that you

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Speaker 5: know behind the AI in twenty twenty seven is you know,

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Speaker 5: one of the people connected to it, if I remember correctly,

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Speaker 5: is Scott Alexander. Who's this guy that's part of the

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Speaker 5: rationalist community, which is one of the what are the

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Speaker 5: groups that overlast with the effect of altruist acceleration this Yeah,

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Speaker 5: you know. So if it feels like it's fra fi

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Speaker 5: and fan fictiony and hype, that's because these are the

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Speaker 5: same people that keep that are connected to pushing constant

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Speaker 5: hype cycles over and over and over again.

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Speaker 2: And it's written to be worrying. Yes, well it's written.

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Speaker 1: It's written to be worrying. But it also in the

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Speaker 1: productions for the next two years keeps talking about how

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Speaker 1: the stock market is going to grow exponentially and so

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Speaker 1: well is going to be making all of these wise

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Speaker 1: and formed decisions and having really deep conversations with the

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Speaker 1: leader of Open Brain, and I was like, are you

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Speaker 1: That's why I asked when did this come out? Because

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Speaker 1: I was like, maybe this was written a couple of years. No,

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Speaker 1: but no, it is why Like literally.

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Speaker 5: It's like a Benjeser kind of planning gonna be born.

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Speaker 5: He's gonna lead us to the promise.

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Speaker 2: It's so good as well, because the people who sent

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Speaker 2: this to me have been very concerned, just because they're like,

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Speaker 2: this sounds scary, And I really want to be clear.

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Speaker 2: If you read something like this and you're like, that

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Speaker 2: doesn't make sense to me, it probably doesn't make sense

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Speaker 2: to anyone because it's nonsense. This thing is. Let me

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Speaker 2: read you one cut from it, the AR and D

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Speaker 2: progress mark player. What do we mean by fifty percent

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Speaker 2: faster algorithmic progress? We mean the open brain makes as

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Speaker 2: much AI research progress in one week with AI as

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Speaker 2: they would in one point five weeks without Who fucking cares, Matt,

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Speaker 2: what are you talking about? If a frog had wings,

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Speaker 2: it could fly like you? And what's crazy is and

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Speaker 2: I know I bag on Kevin Ruse. It's because he's

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Speaker 2: a nakedly captured part of the tech industry. Now I

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Speaker 2: am in public relations and I'm somehow less frothy about this.

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Speaker 2: That should tell you fucking everything. It is insane that

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Speaker 2: the New York Times at a time when you have

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Speaker 2: Soft Bank being potentially downgraded by S ANDP, you have

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Speaker 2: Open Ai raising more money than they ever raised, forty

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Speaker 2: billion dollars, except they only received ten billion and they'll

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Speaker 2: only get twenty billion more by the end of the

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Speaker 2: year if they become a non for profit, which they

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Speaker 2: can't do. No, no, no, no, Kevin Ruth can't possibly

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Speaker 2: cover that. He needs to go and take a solemn

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Speaker 2: looking fucking photo of some aar swipe. I can't even

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Speaker 2: get my phone.

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Speaker 1: I do really love all of the incredibly, So.

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Speaker 2: This guy is just I'll put the link in there

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Speaker 2: for this.

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Speaker 1: He got seventy five pockets on his truck.

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Speaker 2: Yeah, my man is read.

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Speaker 1: Oh that's that's where it is.

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Speaker 2: And it's just him Like this guy's sitting with his

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Speaker 2: hands classed like, staring mournfully into the distance. This is

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Speaker 2: what you're spending your time on ket And I'm just

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Speaker 2: going to read some Kevin Ruce. The II prediction, well,

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Speaker 2: this tone between uptimism and gloom. A report released on

434
00:21:22,000 --> 00:21:24,680
Speaker 2: Thursday decided lands on the side of gloom. That's Kevin

435
00:21:24,720 --> 00:21:29,320
Speaker 2: Ruce's voice. But my favorite part of this by far,

436
00:21:29,840 --> 00:21:31,440
Speaker 2: I'm gonna take a second to get it, because Ed

437
00:21:31,520 --> 00:21:34,480
Speaker 2: I sent this to you as well. Where is it?

438
00:21:36,000 --> 00:21:36,080
Speaker 5: So?

439
00:21:36,359 --> 00:21:38,119
Speaker 2: Also a lot of this is oh, here we go.

440
00:21:38,840 --> 00:21:41,359
Speaker 2: If all of this sounds fantastical, well it is nothing

441
00:21:41,400 --> 00:21:44,680
Speaker 2: remotely like want mister Cocker to tell Joe and mister

442
00:21:44,760 --> 00:21:48,000
Speaker 2: Lyffland are predicting is possible with today's AI tools, which

443
00:21:48,000 --> 00:21:50,480
Speaker 2: can barely order a Buruto on Dowdesh without getting stuck.

444
00:21:50,760 --> 00:21:53,040
Speaker 2: Thank you, Kevin. I'm so fucking glad the New York

445
00:21:53,119 --> 00:21:53,920
Speaker 2: Times is on this.

446
00:21:54,400 --> 00:21:57,040
Speaker 3: And that was at the end. Yeah, right, Like you

447
00:21:57,160 --> 00:22:00,480
Speaker 3: set up this whole article. And it's like these guys

448
00:22:00,480 --> 00:22:03,440
Speaker 3: have these doom predictions. And that's the other thing about

449
00:22:03,520 --> 00:22:08,160
Speaker 3: the like the altruistic AI guys all have told themselves

450
00:22:08,160 --> 00:22:10,960
Speaker 3: this story and they all believe it, and they think

451
00:22:11,000 --> 00:22:14,600
Speaker 3: they are like the Prometheus bringing fire to the people

452
00:22:14,640 --> 00:22:15,720
Speaker 3: and like warning the people.

453
00:22:15,760 --> 00:22:16,400
Speaker 1: And it's like, you.

454
00:22:16,359 --> 00:22:19,639
Speaker 3: Guys have sold yourself a story I with no proof.

455
00:22:20,000 --> 00:22:22,800
Speaker 2: I don't know. I feel like they just scam out

456
00:22:22,880 --> 00:22:25,159
Speaker 2: to that. Nothing about this suggests they believe in anything.

457
00:22:25,440 --> 00:22:26,439
Speaker 2: You can just say stuff.

458
00:22:26,520 --> 00:22:31,240
Speaker 1: Look it literally the second sentence in this is that

459
00:22:31,400 --> 00:22:34,720
Speaker 1: in two months there will be personal assistance that you

460
00:22:34,760 --> 00:22:36,879
Speaker 1: can prompt them with tasks like order me a brito

461
00:22:36,920 --> 00:22:40,040
Speaker 1: on DoorDash. He'll do great stuff. There are so many

462
00:22:40,080 --> 00:22:42,720
Speaker 1: things that go into ordering me a burrito on DoorDash.

463
00:22:42,920 --> 00:22:45,120
Speaker 1: What restaurant do I want? What burrito do I want?

464
00:22:45,160 --> 00:22:46,520
Speaker 1: How do I want it to get to me? Where

465
00:22:46,560 --> 00:22:48,760
Speaker 1: am I? It can't do any of those things, nor

466
00:22:48,800 --> 00:22:49,199
Speaker 1: will it.

467
00:22:49,640 --> 00:22:51,680
Speaker 2: He gazed out the window and admits that he wasn't sure.

468
00:22:51,720 --> 00:22:53,400
Speaker 2: And the next few years went well, and we kept

469
00:22:53,440 --> 00:22:55,320
Speaker 2: aie on the control, he said, referring to one of

470
00:22:55,359 --> 00:22:57,040
Speaker 2: the writers of the piece. He could envision a future

471
00:22:57,040 --> 00:22:59,280
Speaker 2: where people's lives was still largely the same, but where

472
00:22:59,320 --> 00:23:02,520
Speaker 2: nearby special economic zones filled with hyper efficient robot factories

473
00:23:02,520 --> 00:23:04,480
Speaker 2: would churn out everything we needed. And if the next

474
00:23:04,520 --> 00:23:06,480
Speaker 2: few years didn't go well, maybe this guy would be

475
00:23:06,480 --> 00:23:09,280
Speaker 2: filled with pollution and the people would be dead. He said, nonchalantly,

476
00:23:09,440 --> 00:23:10,120
Speaker 2: something like that.

477
00:23:11,000 --> 00:23:17,240
Speaker 5: You know, one of the things I really really love

478
00:23:17,280 --> 00:23:19,880
Speaker 5: about I don't know, It's just it's so frustrating because

479
00:23:20,000 --> 00:23:24,840
Speaker 5: we're we're constantly fed these you know, sci fi esoteric

480
00:23:24,880 --> 00:23:29,719
Speaker 5: futures about how AI powerful AI, superhuman AI is around

481
00:23:29,800 --> 00:23:32,560
Speaker 5: the corner, and we need to figure out a way

482
00:23:32,560 --> 00:23:35,800
Speaker 5: to accommodate these sorts of futures we need. And part

483
00:23:35,800 --> 00:23:39,639
Speaker 5: of that accommodation means restructuring the regulations we have around it.

484
00:23:39,680 --> 00:23:44,320
Speaker 5: Part of that accommodation means entertaining experiments, grafting them onto

485
00:23:44,320 --> 00:23:47,679
Speaker 5: our cultural production, grafting them onto consumer goods. Part of

486
00:23:47,720 --> 00:23:50,480
Speaker 5: that means just like you know, you know, taking it

487
00:23:50,520 --> 00:23:52,560
Speaker 5: on the chin and figuring out how to use TATTBT.

488
00:23:52,840 --> 00:23:55,040
Speaker 5: But in all of this just more or less sounds

489
00:23:55,080 --> 00:23:58,520
Speaker 5: like you need to the marketing is failing on you

490
00:23:58,560 --> 00:23:59,639
Speaker 5: and you need to step up.

491
00:23:59,760 --> 00:24:02,480
Speaker 2: Yeah. One where you need to believe. You need to

492
00:24:02,520 --> 00:24:02,879
Speaker 2: believe it.

493
00:24:03,000 --> 00:24:05,320
Speaker 5: You need to do your part, you know, to summon God.

494
00:24:05,480 --> 00:24:07,360
Speaker 2: And that's the thing. It goes back to what you're saying.

495
00:24:07,640 --> 00:24:10,320
Speaker 2: It's like you've failed AI by not believing.

496
00:24:10,720 --> 00:24:13,080
Speaker 3: Yeah, and if you're bad at it, it's your fault

497
00:24:13,080 --> 00:24:16,919
Speaker 3: and not the machine's fault. In to Ed's point, I

498
00:24:16,920 --> 00:24:19,760
Speaker 3: think like all of this like predicting of the future

499
00:24:19,800 --> 00:24:23,240
Speaker 3: and this like revolution is like they have told themselves

500
00:24:23,240 --> 00:24:26,320
Speaker 3: a story that is this is inevitable and that there

501
00:24:26,359 --> 00:24:29,480
Speaker 3: are no choices that the human beings in the room

502
00:24:29,560 --> 00:24:33,199
Speaker 3: get to make about how this happens. And it's like, actually, no,

503
00:24:33,480 --> 00:24:35,760
Speaker 3: we can make choices about how we want our future

504
00:24:35,800 --> 00:24:37,760
Speaker 3: to play out, and it's not going to be just

505
00:24:37,800 --> 00:24:39,400
Speaker 3: Silicon Valley shoving it down our throat.

506
00:24:39,440 --> 00:24:42,720
Speaker 2: And on the subject of human choice, if this shit

507
00:24:42,800 --> 00:24:45,600
Speaker 2: is so powerful, why have them mighty human choices not

508
00:24:45,680 --> 00:24:48,800
Speaker 2: made it useful yet? Like that's the thing. It's and

509
00:24:48,840 --> 00:24:50,640
Speaker 2: you make this point in your piece as well. It's

510
00:24:50,680 --> 00:24:52,000
Speaker 2: like a I can have a fail. It can be

511
00:24:52,000 --> 00:24:54,400
Speaker 2: failed failed by you and me, the smooth brained bloodites

512
00:24:54,440 --> 00:24:56,240
Speaker 2: who just don't get it. And it's like, why do

513
00:24:56,320 --> 00:24:57,639
Speaker 2: I have to prove myself?

514
00:24:57,920 --> 00:24:59,879
Speaker 5: And listen, you know the ladites they have more goo

515
00:25:00,160 --> 00:25:02,000
Speaker 5: on their brain that coverant. So he needs to I

516
00:25:02,040 --> 00:25:04,680
Speaker 5: think it's it's worth embracing a little bit, you know.

517
00:25:15,720 --> 00:25:17,800
Speaker 5: And look, yeah, I feel like Rob Horning wrote this

518
00:25:18,080 --> 00:25:20,919
Speaker 5: newsletter a few weeks ago that I think he was

519
00:25:20,920 --> 00:25:25,440
Speaker 5: honing in on this point that MS and these generitive

520
00:25:25,480 --> 00:25:27,480
Speaker 5: AI chat bots and the tools that come out of

521
00:25:27,480 --> 00:25:30,640
Speaker 5: them are in some ways a distraction because a lot

522
00:25:30,680 --> 00:25:35,280
Speaker 5: of these firms are pivoting towards how do we, you know,

523
00:25:35,320 --> 00:25:37,080
Speaker 5: create all these products, but also how do we figure

524
00:25:37,080 --> 00:25:39,520
Speaker 5: out you know, government products that we can provide, right,

525
00:25:39,560 --> 00:25:42,080
Speaker 5: how do we get into defense contracting, how do we

526
00:25:42,400 --> 00:25:46,080
Speaker 5: get into arming or integrating AI into arms and and

527
00:25:46,080 --> 00:25:49,520
Speaker 5: and increasingly it feels like, you know, yeah, your AI

528
00:25:49,600 --> 00:25:51,199
Speaker 5: agent's gonna be able to not gonna be able to

529
00:25:51,280 --> 00:25:55,119
Speaker 5: order your burrito. But these firms are also, you know,

530
00:25:55,160 --> 00:25:57,680
Speaker 5: at the same time that they're insisting superhuman intelligences around

531
00:25:57,720 --> 00:25:59,080
Speaker 5: the corner and we're going to be able to make

532
00:25:59,080 --> 00:26:01,560
Speaker 5: your individual lives better, are spending a lot of time

533
00:26:01,600 --> 00:26:05,719
Speaker 5: and energy on use cases that are actually dangerous, right,

534
00:26:05,760 --> 00:26:09,560
Speaker 5: and it should actually be concerning in generating kill lists, right,

535
00:26:09,680 --> 00:26:12,359
Speaker 5: or facial recognition and surveillance.

536
00:26:11,880 --> 00:26:14,160
Speaker 2: Which has already been around us and is generative.

537
00:26:14,359 --> 00:26:17,040
Speaker 5: Yeah, and it isn't generative, but they but the firms that

538
00:26:17,080 --> 00:26:20,240
Speaker 5: are offering these generitive products are spending actual you know,

539
00:26:20,400 --> 00:26:22,560
Speaker 5: the stuff that they're actually putting their time and energy

540
00:26:22,600 --> 00:26:27,400
Speaker 5: into is you know, the sort of demonstrably destructive tools

541
00:26:28,200 --> 00:26:31,240
Speaker 5: under the guys in the kind of murky covering of

542
00:26:31,280 --> 00:26:34,400
Speaker 5: it's all you know, artificial intelligence, right, it's all inevitable,

543
00:26:34,440 --> 00:26:36,720
Speaker 5: it's all coming down the same pipeline you should accept it.

544
00:26:36,840 --> 00:26:39,959
Speaker 2: Yeah, and it's I think the thing is as well.

545
00:26:40,000 --> 00:26:42,800
Speaker 2: It's those guys really think that's the next big money makeup.

546
00:26:42,920 --> 00:26:45,080
Speaker 2: I don't think anyone's making any money off of this.

547
00:26:45,600 --> 00:26:47,560
Speaker 2: No one wants to talk about the money because they're

548
00:26:47,560 --> 00:26:50,720
Speaker 2: not making any like no one. Look, I think I've read,

549
00:26:51,400 --> 00:26:53,879
Speaker 2: I've read the earning schools. I'm not going to listen

550
00:26:53,920 --> 00:26:56,320
Speaker 2: of every single company that's selling an AI service. At

551
00:26:56,359 --> 00:26:58,240
Speaker 2: this point, I can't find a single one that wants

552
00:26:58,280 --> 00:27:00,240
Speaker 2: to commit to a number of the Microsoft and they'll

553
00:27:00,280 --> 00:27:04,520
Speaker 2: only talk annualized, which is my favorite one. Are but ARR.

554
00:27:04,600 --> 00:27:08,040
Speaker 2: But the thing is ARR traditionally would mean an aggregate

555
00:27:08,359 --> 00:27:12,120
Speaker 2: rather than just twelve times the last biggest month, which

556
00:27:12,160 --> 00:27:12,960
Speaker 2: is what they're doing.

557
00:27:13,200 --> 00:27:14,879
Speaker 1: No, that's the classics.

558
00:27:15,760 --> 00:27:18,800
Speaker 2: I refuse to mark client's asses to the ground with

559
00:27:18,840 --> 00:27:20,520
Speaker 2: that one, because it's like, you can't just fucking make

560
00:27:20,600 --> 00:27:22,840
Speaker 2: up a number unless you're in AI than you absolutely can.

561
00:27:23,560 --> 00:27:26,879
Speaker 2: It's just frustrating because and the reason I bag on

562
00:27:27,000 --> 00:27:30,280
Speaker 2: Newton Rouse other than all the others I've listed, is

563
00:27:30,400 --> 00:27:33,720
Speaker 2: the I feel like, in their position and in the

564
00:27:33,760 --> 00:27:35,760
Speaker 2: position of anyone with any major voice in the media,

565
00:27:36,080 --> 00:27:39,879
Speaker 2: skepticism isn't like something you should sometimes bring in. It's not.

566
00:27:40,000 --> 00:27:42,520
Speaker 2: You don't have to be a grizzled hater like myself,

567
00:27:42,600 --> 00:27:45,400
Speaker 2: but you can be like, hey, even if this did work,

568
00:27:45,440 --> 00:27:47,800
Speaker 2: which it doesn't, how does this possibly last another year?

569
00:27:48,440 --> 00:27:51,639
Speaker 2: And the reaction is no, actually, it's perfect now and

570
00:27:51,720 --> 00:27:54,560
Speaker 2: will only be more perfect in the future. And I

571
00:27:54,680 --> 00:27:57,240
Speaker 2: still get emails from people because I said once on

572
00:27:57,320 --> 00:27:59,160
Speaker 2: an episode, if you have a use for AI, please

573
00:27:59,200 --> 00:28:03,760
Speaker 2: email me regret of mine. Every time I get an

574
00:28:03,800 --> 00:28:05,800
Speaker 2: email like that, it's like, so it's very simple. I've

575
00:28:05,840 --> 00:28:08,879
Speaker 2: set up seven or eight hours worth of work to

576
00:28:09,080 --> 00:28:11,879
Speaker 2: make one prompt work, and sometimes I get something really useful.

577
00:28:11,880 --> 00:28:14,240
Speaker 2: It saves me like ten minutes, and you're like, great,

578
00:28:14,359 --> 00:28:16,720
Speaker 2: and what for It's like, oh, just some productivity things?

579
00:28:16,760 --> 00:28:21,119
Speaker 2: What productivity things? They stop responding, and it's just I

580
00:28:22,280 --> 00:28:24,280
Speaker 2: really am shocked we got this far. I'm gonna be

581
00:28:24,320 --> 00:28:27,240
Speaker 2: honest at this point, I'm I will never be tired

582
00:28:27,280 --> 00:28:32,040
Speaker 2: because my soul burns forever. But it's exhausting watching this

583
00:28:32,240 --> 00:28:35,520
Speaker 2: happen and watching how it's getting crazier. I thought like

584
00:28:35,640 --> 00:28:38,000
Speaker 2: as things got worse, people would be like, well see

585
00:28:38,040 --> 00:28:42,479
Speaker 2: and then stepping up. But it's like watching The Times

586
00:28:42,640 --> 00:28:45,680
Speaker 2: and some parts of the journal still feed this. Also

587
00:28:45,760 --> 00:28:48,160
Speaker 2: the journal has some incredible critical work on that. It's

588
00:28:48,160 --> 00:28:50,720
Speaker 2: so bizarre. The whole thing is just so bizarre, and

589
00:28:50,800 --> 00:28:52,560
Speaker 2: it's been so bizarre to watch in the tech media.

590
00:28:53,480 --> 00:28:54,960
Speaker 1: I mean, I think part of it is also just

591
00:28:55,040 --> 00:28:58,040
Speaker 1: because investors have poured a lot of money into this,

592
00:28:58,440 --> 00:29:00,840
Speaker 1: and so of course they are going to want to

593
00:29:00,880 --> 00:29:04,040
Speaker 1: back what they have spent hundreds of millions or billions

594
00:29:04,040 --> 00:29:08,240
Speaker 1: of dollars on. And much of the tech media involves

595
00:29:08,280 --> 00:29:11,720
Speaker 1: reporting on what those investors are doing, thinking and saying,

596
00:29:12,280 --> 00:29:15,440
Speaker 1: and whether or not what those people are saying or

597
00:29:15,600 --> 00:29:18,720
Speaker 1: doing is it's often not based in reality.

598
00:29:19,000 --> 00:29:21,240
Speaker 3: H yeah, I say, as not a member of the

599
00:29:21,320 --> 00:29:23,480
Speaker 3: tech media, so I have like kind of a general

600
00:29:23,560 --> 00:29:27,920
Speaker 3: assignment business markets econ. That's kind of my jam. And

601
00:29:28,680 --> 00:29:32,080
Speaker 3: when I come when like AI first started becoming the buzzword,

602
00:29:32,200 --> 00:29:35,000
Speaker 3: like chat GPT had just come out, I was like, oh,

603
00:29:35,320 --> 00:29:37,840
Speaker 3: this sounds interesting. So I was paying attention like a

604
00:29:37,920 --> 00:29:41,920
Speaker 3: lot of journalists were, and you know, like we've hit limitations,

605
00:29:41,960 --> 00:29:44,040
Speaker 3: and I think part of the reason it's gotten so

606
00:29:44,200 --> 00:29:48,720
Speaker 3: far is because the narrative is so compelling. Curing cancer,

607
00:29:49,200 --> 00:29:53,280
Speaker 3: We're gonna we're gonna end. It's my favorite one, not

608
00:29:53,400 --> 00:29:56,160
Speaker 3: my favorite one. Silliest one is like we're gonna we're

609
00:29:56,160 --> 00:29:57,240
Speaker 3: gonna end hunger.

610
00:29:57,800 --> 00:29:58,400
Speaker 2: Nice, okay?

611
00:29:58,600 --> 00:30:03,360
Speaker 3: How how Also, the problem of hunger in the world

612
00:30:03,600 --> 00:30:06,360
Speaker 3: is not that we don't grow enough food. It is

613
00:30:06,440 --> 00:30:09,360
Speaker 3: a distribution problem. It is a sociologic It is a

614
00:30:09,600 --> 00:30:15,800
Speaker 3: complicated problem. What actually is AI going to do? Also

615
00:30:15,960 --> 00:30:18,680
Speaker 3: that you're going to need human beings to distribute it?

616
00:30:18,840 --> 00:30:20,880
Speaker 3: It just like you push them one step.

617
00:30:21,120 --> 00:30:23,400
Speaker 1: If you read the twenty twenty seven AI thing, it

618
00:30:23,800 --> 00:30:26,040
Speaker 1: explains that the AI is going to give the government

619
00:30:26,080 --> 00:30:29,560
Speaker 1: officials such good advice they'll be actually really nice and

620
00:30:29,640 --> 00:30:30,320
Speaker 1: caring deportment.

621
00:30:30,400 --> 00:30:32,880
Speaker 2: And what's crazy is here's the thing. And I'm glad

622
00:30:32,920 --> 00:30:36,160
Speaker 2: you brought up one thing I've learned about politics, particularly recently,

623
00:30:36,400 --> 00:30:40,840
Speaker 2: but in historic historic means, when the government gets good advice,

624
00:30:40,920 --> 00:30:43,960
Speaker 2: they take it every time, every time. Every time they're like,

625
00:30:44,360 --> 00:30:46,880
Speaker 2: this will this is economically good, like Medicare for All,

626
00:30:47,000 --> 00:30:50,760
Speaker 2: which we've of course had forever and never and came

627
00:30:50,840 --> 00:30:53,960
Speaker 2: close to numerous times decades ago, versus now when we

628
00:30:54,120 --> 00:30:57,160
Speaker 2: have him. And I think the other funny thing is

629
00:30:57,280 --> 00:30:59,760
Speaker 2: as well, with what you were saying, Allison is like, yeah,

630
00:31:00,040 --> 00:31:01,960
Speaker 2: it's going to cure cancer. Okay, can it do that?

631
00:31:02,080 --> 00:31:02,240
Speaker 4: Nah?

632
00:31:02,440 --> 00:31:05,840
Speaker 2: Okay, it's going to cull hunga Can it do that? No? Okay,

633
00:31:06,640 --> 00:31:10,120
Speaker 2: it's easy. Then perhaps it could make me an appointment. Also, No,

634
00:31:11,640 --> 00:31:12,880
Speaker 2: can you buy something with it?

635
00:31:13,240 --> 00:31:13,280
Speaker 5: No?

636
00:31:13,680 --> 00:31:16,200
Speaker 2: Can it take this spreadsheet and move stuff around?

637
00:31:17,040 --> 00:31:20,680
Speaker 5: Maybe it can write a robotic sounding script for you

638
00:31:20,760 --> 00:31:22,120
Speaker 5: to make the appointment yourself.

639
00:31:22,440 --> 00:31:25,000
Speaker 3: Wow, you know, I mean I would even say that

640
00:31:25,640 --> 00:31:29,760
Speaker 3: I could give the benefit of the doubt to researchers

641
00:31:29,760 --> 00:31:32,920
Speaker 3: who are really working on the scientific aspects of this.

642
00:31:33,120 --> 00:31:35,800
Speaker 3: Like I'm not a scientist. I don't know how to

643
00:31:35,880 --> 00:31:38,200
Speaker 3: cure cancer, but if you're working with an AI model

644
00:31:38,240 --> 00:31:41,959
Speaker 3: that can do it, like God bless. But businesses actually

645
00:31:42,160 --> 00:31:46,040
Speaker 3: do take money making advice and money making technology when

646
00:31:46,080 --> 00:31:47,560
Speaker 3: it's available. And I think about this all the time.

647
00:31:47,600 --> 00:31:49,840
Speaker 3: With crypto, which is another area I cover a lot.

648
00:31:50,400 --> 00:31:55,360
Speaker 3: It's like, if it were the miracle technology that everyone

649
00:31:55,800 --> 00:31:58,840
Speaker 3: or its proponents have said it is, businesses would not

650
00:31:59,080 --> 00:32:04,600
Speaker 3: hesitate to up their infrastructure to make more money and like,

651
00:32:04,840 --> 00:32:07,120
Speaker 3: no one's doing it, And it's like, oh, well, they

652
00:32:07,200 --> 00:32:10,000
Speaker 3: just haven't. They haven't figured out how to optimize it yet.

653
00:32:10,040 --> 00:32:12,520
Speaker 3: It's like that sounds like a failure of the product

654
00:32:12,560 --> 00:32:15,480
Speaker 3: and not a failure of people using it. So I

655
00:32:15,520 --> 00:32:18,360
Speaker 3: get back to the whole like AI cannot fail, it

656
00:32:18,480 --> 00:32:19,640
Speaker 3: can only be failed.

657
00:32:19,880 --> 00:32:20,560
Speaker 2: And it's the same with.

658
00:32:20,640 --> 00:32:22,840
Speaker 3: Crypto and a lot of other tech where it's just like,

659
00:32:23,080 --> 00:32:25,880
Speaker 3: this is not a product that people are are hankering for.

660
00:32:26,320 --> 00:32:28,280
Speaker 1: And I think part of the notable thing is when

661
00:32:28,520 --> 00:32:31,160
Speaker 1: we do see examples of large businesses being like, oh yeah,

662
00:32:31,160 --> 00:32:34,440
Speaker 1: we're gonna change everything about our business and integrate AI.

663
00:32:34,520 --> 00:32:37,000
Speaker 1: We're going to be an AI first company. The products

664
00:32:37,040 --> 00:32:39,239
Speaker 1: that end up coming out of that are there's an

665
00:32:39,280 --> 00:32:42,400
Speaker 1: AI chat bot in my one medical app now cool

666
00:32:42,840 --> 00:32:46,640
Speaker 1: that does nothing for me. When I'm trying to search

667
00:32:46,760 --> 00:32:50,600
Speaker 1: the Amazon comments on a product, Suddenly the search box

668
00:32:50,720 --> 00:32:55,160
Speaker 1: is replaced with an AI chat bot that's not doing

669
00:32:55,360 --> 00:32:57,680
Speaker 1: even one tenth of what you've problem.

670
00:32:57,560 --> 00:32:59,520
Speaker 2: The same product every fucking It's.

671
00:32:59,480 --> 00:33:02,960
Speaker 1: Just an a It's a chatbot that isn't super helpful.

672
00:33:02,840 --> 00:33:05,800
Speaker 2: And it's great. I remember back in twenty fifteen twenty sixteen,

673
00:33:05,840 --> 00:33:09,680
Speaker 2: I had an AI chatbot company. They took large repositories

674
00:33:09,720 --> 00:33:12,240
Speaker 2: of data and turned it into a chatbot you'd use.

675
00:33:12,480 --> 00:33:14,560
Speaker 2: I remember pitching reporters at the time and then being like,

676
00:33:14,600 --> 00:33:16,959
Speaker 2: who fucking cares, who gives a shit? This will never

677
00:33:17,000 --> 00:33:20,120
Speaker 2: be Like a decade later, everyone's like this is literally God.

678
00:33:20,720 --> 00:33:22,600
Speaker 2: I cannot wait to go to the office of a

679
00:33:22,640 --> 00:33:25,600
Speaker 2: guy who wrote fan fiction about this and talk to

680
00:33:25,680 --> 00:33:28,080
Speaker 2: him about how scared I am. Now I can't wait

681
00:33:28,120 --> 00:33:30,640
Speaker 2: for AGI. And I've also said this before, but what

682
00:33:30,760 --> 00:33:32,400
Speaker 2: if we make Agi. None of them are going to

683
00:33:32,440 --> 00:33:34,000
Speaker 2: do it doesn't exist. But and it didn't want to

684
00:33:34,040 --> 00:33:36,160
Speaker 2: do any work. That's the other thing, like they're not

685
00:33:36,600 --> 00:33:38,920
Speaker 2: they don't. Casey Kagawa, friend of the show made at

686
00:33:38,920 --> 00:33:40,800
Speaker 2: this point it's made it to me umrostyle, which is

687
00:33:41,000 --> 00:33:43,680
Speaker 2: they talk about AGI and roosted this as well, like

688
00:33:43,760 --> 00:33:46,400
Speaker 2: Agi this Agi that they don't want to define it,

689
00:33:46,440 --> 00:33:48,400
Speaker 2: because if you have to start defining AGI, you have

690
00:33:48,480 --> 00:33:50,720
Speaker 2: to start talking about things like person thegit like is

691
00:33:50,800 --> 00:33:53,880
Speaker 2: this a citizen? Is this? Can this thing feel pain?

692
00:33:53,960 --> 00:33:56,280
Speaker 2: Because surely your consciousness could feel pain. Oh you could

693
00:33:56,320 --> 00:33:58,320
Speaker 2: take pain out that has ever real consciousness? None of

694
00:33:58,360 --> 00:34:02,280
Speaker 2: the and hey, how many is one? Is it? If

695
00:34:02,320 --> 00:34:05,000
Speaker 2: it's is it? One unit? Is a virtual machine? Like

696
00:34:05,080 --> 00:34:08,160
Speaker 2: there are real tangible things and you know they don't

697
00:34:08,160 --> 00:34:10,040
Speaker 2: want to talk about that shit because you even start

698
00:34:10,120 --> 00:34:12,239
Speaker 2: answering one of those and you go, oh right, we're

699
00:34:12,280 --> 00:34:14,560
Speaker 2: not even slightly close, are we. We don't even know

700
00:34:14,719 --> 00:34:17,279
Speaker 2: how the fuck to do single one of these things ever.

701
00:34:18,200 --> 00:34:20,400
Speaker 2: And I honestly the person I feel bad for this

702
00:34:20,560 --> 00:34:22,879
Speaker 2: is your joke is Blake Lemine. I think his name

703
00:34:23,000 --> 00:34:25,759
Speaker 2: was from Google. If he'd come out like three years

704
00:34:25,840 --> 00:34:27,719
Speaker 2: later and said that he thought the computer this is

705
00:34:27,760 --> 00:34:29,040
Speaker 2: the guy from Google, who's.

706
00:34:30,960 --> 00:34:33,239
Speaker 1: Guy who was like the chat but is real and

707
00:34:33,360 --> 00:34:33,800
Speaker 1: I love it.

708
00:34:34,239 --> 00:34:36,440
Speaker 2: Yeah, had that come out three years later, he'd be

709
00:34:36,480 --> 00:34:39,440
Speaker 2: called Kevin Roos because that's exactly what Kevin Rooss wrote

710
00:34:39,640 --> 00:34:42,000
Speaker 2: about being Ai Savria is like being AI told me

711
00:34:42,040 --> 00:34:43,959
Speaker 2: to leave my wife. And Kevin, if you ever fucking

712
00:34:44,040 --> 00:34:45,839
Speaker 2: hear this, man, you're worried about me dogging you, I'm

713
00:34:45,840 --> 00:34:50,200
Speaker 2: gonna keep going do your fucking job. Mate. Anyways, it's

714
00:34:50,360 --> 00:34:55,360
Speaker 2: just insane because I am a gadget gizmo guy. I

715
00:34:55,400 --> 00:34:57,400
Speaker 2: love my dude, dad's I love my shit, I really do.

716
00:34:57,560 --> 00:34:59,640
Speaker 2: If this was gonna do something fun, I'd have done it.

717
00:35:00,239 --> 00:35:02,799
Speaker 2: I've really spent time trying, and I've talked to people

718
00:35:03,040 --> 00:35:05,600
Speaker 2: like Simon Wilson, Maxwolf, two people who are big L

719
00:35:05,640 --> 00:35:08,160
Speaker 2: and M heads who are to disagree with me on

720
00:35:08,239 --> 00:35:11,360
Speaker 2: numerous things, but their reaction is kind of I'm not

721
00:35:11,440 --> 00:35:13,760
Speaker 2: going to speak exactly for them, is basically, it actually

722
00:35:13,840 --> 00:35:15,759
Speaker 2: does this. You should look at this thing. It does

723
00:35:15,880 --> 00:35:20,600
Speaker 2: not this is literally God. But it all just feels

724
00:35:20,680 --> 00:35:24,200
Speaker 2: unsustainable economically. But also I feel like the media is

725
00:35:24,280 --> 00:35:27,280
Speaker 2: in danger when this fuls apart too, because the regular

726
00:35:27,320 --> 00:35:29,680
Speaker 2: people I talked to about chat GPT, I pretty much

727
00:35:29,719 --> 00:35:33,640
Speaker 2: hear two use cases. One Google Search isn't working and

728
00:35:33,800 --> 00:35:36,320
Speaker 2: two I need someone to talk to, which is a

729
00:35:36,400 --> 00:35:39,920
Speaker 2: worrying thing. And I think, by the way, that use

730
00:35:40,000 --> 00:35:43,279
Speaker 2: case is just that's a societal thing. That's a sign

731
00:35:43,360 --> 00:35:45,960
Speaker 2: the lack of community, lack of friendship, but lack of

732
00:35:46,000 --> 00:35:50,360
Speaker 2: access to mental health services and also could lead to

733
00:35:50,440 --> 00:35:53,960
Speaker 2: some terrible outcomes. But for the most part, I don't

734
00:35:53,960 --> 00:35:55,640
Speaker 2: know why I said for the most part, I've yet

735
00:35:55,680 --> 00:35:57,400
Speaker 2: to meet someone who uses this every day, and I

736
00:35:57,400 --> 00:35:59,480
Speaker 2: have yet to meet someone who really cares about it,

737
00:35:59,600 --> 00:36:01,920
Speaker 2: who like because like if this went tomorrow, like if

738
00:36:01,960 --> 00:36:04,840
Speaker 2: I didn't have my little anchor battery packs, I'd scream.

739
00:36:05,080 --> 00:36:07,480
Speaker 2: If I couldn't have permanent power everywhere. Like if I

740
00:36:07,520 --> 00:36:09,560
Speaker 2: couldn't like listen to musical day, that'd be might be

741
00:36:09,640 --> 00:36:12,799
Speaker 2: real sad. If I couldn't access chat GPT, I would not.

742
00:36:13,239 --> 00:36:15,160
Speaker 5: Who cares because you haven't tried claude yet.

743
00:36:15,239 --> 00:36:19,200
Speaker 2: I've tried. I've tried Claude so much, And it's just

744
00:36:19,719 --> 00:36:23,440
Speaker 2: I don't know. I feel like people's response to the

745
00:36:23,520 --> 00:36:25,640
Speaker 2: media is going to be negative too, because there's so

746
00:36:25,680 --> 00:36:27,840
Speaker 2: many people that boosted it. There was a Verge story.

747
00:36:27,960 --> 00:36:29,719
Speaker 2: There was a study that came out today. I'll link

748
00:36:29,719 --> 00:36:32,680
Speaker 2: it as well in the notes where it was a

749
00:36:32,760 --> 00:36:35,000
Speaker 2: study found that most people do not trust A like

750
00:36:35,080 --> 00:36:37,279
Speaker 2: regular people do not trust AI, but they also don't

751
00:36:37,280 --> 00:36:39,680
Speaker 2: trust the people that run it and they don't like it.

752
00:36:40,200 --> 00:36:41,759
Speaker 2: And I feel like this is a thing that the

753
00:36:41,840 --> 00:36:44,120
Speaker 2: media is going to face at some point and rus

754
00:36:44,239 --> 00:36:46,839
Speaker 2: this time, Baby, you got away with the crypto thing.

755
00:36:46,880 --> 00:36:49,000
Speaker 2: You're not this dumb. I'm going to be hitting you

756
00:36:49,040 --> 00:36:52,160
Speaker 2: with the TV off shait every day. But it's just

757
00:36:52,360 --> 00:36:54,360
Speaker 2: I don't think members of the media realized the backlash

758
00:36:54,440 --> 00:36:57,280
Speaker 2: is coming, and when it comes, it's going to truly

759
00:36:57,719 --> 00:37:01,120
Speaker 2: it is going to lead to an era of cynicism.

760
00:37:01,320 --> 00:37:05,000
Speaker 2: True cynicism in society that's already growing about tech, but

761
00:37:05,200 --> 00:37:07,080
Speaker 2: specifically I think it will be a negative backlash of

762
00:37:07,080 --> 00:37:09,359
Speaker 2: the tech media, and now would be the great time

763
00:37:09,400 --> 00:37:13,160
Speaker 2: to unwind this versus tripling down on the fan fiction

764
00:37:13,360 --> 00:37:15,919
Speaker 2: that and I have been meaning to read this out.

765
00:37:16,160 --> 00:37:19,080
Speaker 2: My favorite part of this by far, i'd say, and

766
00:37:19,160 --> 00:37:21,719
Speaker 2: of course flawless they have this ready. Why our uncertainty

767
00:37:21,800 --> 00:37:24,680
Speaker 2: increases substantially beyond twenty twenty six. Our forecasts from the

768
00:37:24,680 --> 00:37:27,160
Speaker 2: current day through twenty twenty six is substantially more grounded

769
00:37:27,200 --> 00:37:31,839
Speaker 2: than what follows. Thanks mother fucker awesome. That's partially because

770
00:37:31,880 --> 00:37:34,960
Speaker 2: it's nearer, but it's also because the effects of AI

771
00:37:35,080 --> 00:37:37,239
Speaker 2: on the world really start to compound in twenty twenty seven.

772
00:37:37,920 --> 00:37:41,719
Speaker 2: What do you mean they don't? That's you. You're claiming that,

773
00:37:42,840 --> 00:37:44,800
Speaker 2: And I just I also think that there's the greatest

774
00:37:44,840 --> 00:37:46,919
Speaker 2: subtle problem that we have too many people who believe

775
00:37:46,960 --> 00:37:48,719
Speaker 2: the last smart person they listen to. And I say

776
00:37:48,760 --> 00:37:51,840
Speaker 2: that as a podcast runner, like the last invest that

777
00:37:51,880 --> 00:37:55,239
Speaker 2: they talked to, the last expert they talk to someone

778
00:37:55,280 --> 00:37:55,839
Speaker 2: from a lab.

779
00:37:56,360 --> 00:38:00,600
Speaker 3: Yes, yes, well I think that gets to you if

780
00:38:00,640 --> 00:38:03,879
Speaker 3: you just push the proponents. And this is like I've

781
00:38:04,160 --> 00:38:07,160
Speaker 3: I've come into AI skepticism as a true like I'm

782
00:38:07,200 --> 00:38:10,279
Speaker 3: interested in this. I'm interested in what you're pitching to

783
00:38:10,480 --> 00:38:14,239
Speaker 3: the world. And when I hear and I hear like

784
00:38:14,840 --> 00:38:18,480
Speaker 3: CEOs of AI firms get interviewed about this all the time,

785
00:38:18,520 --> 00:38:20,759
Speaker 3: and they talk about this future where everyone just has

786
00:38:20,800 --> 00:38:23,400
Speaker 3: a life of leisure and where you're lying around writing

787
00:38:23,480 --> 00:38:27,200
Speaker 3: poetry and touching grass and like everything's great. No one

788
00:38:27,239 --> 00:38:30,840
Speaker 3: has to do hard labor anymore. They have that vision,

789
00:38:31,000 --> 00:38:33,360
Speaker 3: or they have like the you know, p Doom of

790
00:38:33,640 --> 00:38:36,000
Speaker 3: seventy five and like everything is going to be terrible,

791
00:38:36,600 --> 00:38:39,920
Speaker 3: and but no one has a really good concept. And

792
00:38:39,960 --> 00:38:42,000
Speaker 3: that's why this is so funny. The fan fiction of

793
00:38:42,200 --> 00:38:44,560
Speaker 3: like what happens in twenty twenty seven, It's like no

794
00:38:44,719 --> 00:38:48,279
Speaker 3: one has laid out any sense of like how the

795
00:38:48,400 --> 00:38:52,160
Speaker 3: job creation or destruction will happen. Like in this piece

796
00:38:52,239 --> 00:38:54,760
Speaker 3: they say like, oh, there's going to be more jobs

797
00:38:55,320 --> 00:38:57,560
Speaker 3: in different areas, but some jobs have been lost, and

798
00:38:57,640 --> 00:38:59,320
Speaker 3: it's like how why.

799
00:38:59,480 --> 00:39:02,600
Speaker 2: Get what it jobs? They get oddly specific on some things.

800
00:39:02,640 --> 00:39:05,200
Speaker 2: Then the meaningful things they're like, yep, they'll be jobs.

801
00:39:05,880 --> 00:39:07,759
Speaker 3: Yeah, and the stock market's just gonna go up and

802
00:39:07,880 --> 00:39:08,440
Speaker 3: it dude.

803
00:39:08,280 --> 00:39:10,080
Speaker 2: Numbill go up all the time as it is right

804
00:39:10,200 --> 00:39:11,160
Speaker 2: now as we report.

805
00:39:12,400 --> 00:39:17,399
Speaker 1: Yeah, I believe they say. In twenty twenty eighth, Agent five,

806
00:39:17,440 --> 00:39:19,680
Speaker 1: which is the super AI's deployed to the public and

807
00:39:19,760 --> 00:39:22,760
Speaker 1: begins to transform the economy. People are losing their jobs,

808
00:39:22,840 --> 00:39:25,520
Speaker 1: but Agent five instances in the government are managing the

809
00:39:25,560 --> 00:39:29,360
Speaker 1: economic transition so adroitly that people are happy to be replaced.

810
00:39:29,880 --> 00:39:34,560
Speaker 1: GDP growth is stratospheric, Government tax revenues are growing equally quickly,

811
00:39:34,840 --> 00:39:39,000
Speaker 1: and Agent five advised positions show an uncharacteristic generosity towards

812
00:39:39,080 --> 00:39:40,440
Speaker 1: the economically dispossessed.

813
00:39:40,760 --> 00:39:43,920
Speaker 3: You know what this is We failed to uphold like

814
00:39:44,040 --> 00:39:47,000
Speaker 3: public arts education in America, and a bunch of kids

815
00:39:47,040 --> 00:39:50,320
Speaker 3: got into coding and know nothing but computers, and so

816
00:39:50,560 --> 00:39:51,759
Speaker 3: they can't write fan fiction.

817
00:39:52,320 --> 00:39:54,640
Speaker 2: Yeah, No one's fighting is bad.

818
00:39:54,800 --> 00:39:57,240
Speaker 1: Not enough people spent time in the minds of fanfiction

819
00:39:57,360 --> 00:40:01,880
Speaker 1: dot net and it's no one's anyone. Yeah, Like this is.

820
00:40:01,920 --> 00:40:03,960
Speaker 3: Clearly this is just like someone wanting to have a

821
00:40:04,080 --> 00:40:07,640
Speaker 3: creative like vision of the future and it's like it's

822
00:40:07,719 --> 00:40:09,360
Speaker 3: not interesting or compelling.

823
00:40:09,520 --> 00:40:09,959
Speaker 2: Joy looks.

824
00:40:09,960 --> 00:40:11,239
Speaker 5: I mean, that's why they brought him on. That's why

825
00:40:11,280 --> 00:40:14,239
Speaker 5: they brought Scott Alexander on because to write this narrative, right,

826
00:40:14,239 --> 00:40:15,759
Speaker 5: because that's what he spends a lot of time doing

827
00:40:15,840 --> 00:40:19,799
Speaker 5: in his blog, is trying to beautify or flesh out

828
00:40:19,920 --> 00:40:22,880
Speaker 5: why this sort of future is inevitable. Yeah, you know

829
00:40:23,000 --> 00:40:28,080
Speaker 5: why we need to commit to accelerating technological progress as

830
00:40:28,160 --> 00:40:33,200
Speaker 5: much as possible, and why the real reactionary or you know,

831
00:40:33,400 --> 00:40:39,120
Speaker 5: anti progress perspective is caution or concern or skepticism or

832
00:40:39,160 --> 00:40:43,360
Speaker 5: criticism if it's not nuanced in a direction that supports products.

833
00:40:43,400 --> 00:40:45,320
Speaker 2: I just feel like a lot of the AI safety

834
00:40:45,360 --> 00:40:48,359
Speaker 2: guys at Grift is too. I'm sorry they love saying

835
00:40:48,400 --> 00:40:52,279
Speaker 2: alignment just say pay me like I know that we

836
00:40:52,320 --> 00:40:55,040
Speaker 2: should have. I get the occasional email about this being like,

837
00:40:55,120 --> 00:40:58,640
Speaker 2: you can't hate AI safety. It's important. It is important generally.

838
00:40:58,640 --> 00:41:00,920
Speaker 2: If AI isn't AI, it's just trying to fucking accept it.

839
00:41:01,080 --> 00:41:03,279
Speaker 2: They're not. If they cared about the safety issues, they'd

840
00:41:03,280 --> 00:41:07,160
Speaker 2: stop burning down zoos and feeding entire lakes to generate

841
00:41:07,200 --> 00:41:10,439
Speaker 2: one busty Garfield, as I love to say. They would

842
00:41:10,440 --> 00:41:13,440
Speaker 2: also be thinking about the actual safety issues of what

843
00:41:13,560 --> 00:41:15,800
Speaker 2: could this generate, which they do. You can't do anarchist

844
00:41:15,880 --> 00:41:18,680
Speaker 2: cookbook shit it's about as useful. Phil Broughton, friend of

845
00:41:18,680 --> 00:41:20,040
Speaker 2: the show, would be very angry for me to bring

846
00:41:20,080 --> 00:41:22,800
Speaker 2: that up. But the actual safety things of it steals

847
00:41:22,840 --> 00:41:26,000
Speaker 2: from people is to join the environment. It's unprofitable and unsustainable.

848
00:41:26,040 --> 00:41:28,760
Speaker 2: These aren't actual These are actual safety issues, These actual

849
00:41:28,840 --> 00:41:31,760
Speaker 2: problems with this. They don't want to solve those. And indeed,

850
00:41:31,840 --> 00:41:34,399
Speaker 2: the actual other safety issue would be, hey, we gave

851
00:41:34,440 --> 00:41:37,279
Speaker 2: a completely unrestrained chatbot to millions of people and now

852
00:41:37,320 --> 00:41:39,800
Speaker 2: they're talking to it like a therapist. That's a fucking

853
00:41:40,200 --> 00:41:42,600
Speaker 2: that's a safety issue. No, they love that, they love.

854
00:41:42,719 --> 00:41:45,360
Speaker 1: Do you think that one criticism of the AI safety

855
00:41:45,400 --> 00:41:49,080
Speaker 1: initiatives that is incredibly politically salient and important right now

856
00:41:49,239 --> 00:41:52,200
Speaker 1: is that they are so hyper focused on the long

857
00:41:52,360 --> 00:41:56,560
Speaker 1: term thousand, one hundred years from now future where AI

858
00:41:56,840 --> 00:41:58,640
Speaker 1: is going to be inside all of us and we're

859
00:41:58,640 --> 00:42:01,480
Speaker 1: all going to be, you know, robots controlled by an overload,

860
00:42:01,480 --> 00:42:03,920
Speaker 1: that they are not paying attention to literally any of

861
00:42:03,960 --> 00:42:04,960
Speaker 1: the harms happening.

862
00:42:04,800 --> 00:42:07,680
Speaker 2: Right well, they deliberately not talking about the harms today

863
00:42:07,719 --> 00:42:10,120
Speaker 2: because then they'd have to do something at work when

864
00:42:10,160 --> 00:42:10,840
Speaker 2: they men each that.

865
00:42:10,960 --> 00:42:13,440
Speaker 5: You know, It's like when nine seven two mag reported

866
00:42:13,520 --> 00:42:16,000
Speaker 5: on how its reel was using or trying to integrate

867
00:42:16,239 --> 00:42:19,160
Speaker 5: artificial intelligence until generating gets killed thiss and targets so

868
00:42:19,320 --> 00:42:22,480
Speaker 5: much so that they started targeting civilians and use that

869
00:42:22,560 --> 00:42:25,399
Speaker 5: to fine tune targeting of civilians. You know, I saw

870
00:42:25,440 --> 00:42:28,000
Speaker 5: almost nothing in the immediate aftermath that this reporting from

871
00:42:28,040 --> 00:42:30,680
Speaker 5: the I safety community, you know, no almost no interest

872
00:42:30,719 --> 00:42:32,879
Speaker 5: in like talking about a very real use case where

873
00:42:32,880 --> 00:42:36,360
Speaker 5: it's being used to murders many civilians as possible silence.

874
00:42:36,480 --> 00:42:39,320
Speaker 5: You know, that's a real short term concern that we

875
00:42:39,400 --> 00:42:39,759
Speaker 5: should have.

876
00:42:40,120 --> 00:42:42,480
Speaker 2: But that's that would require the AI safety people to

877
00:42:42,600 --> 00:42:44,799
Speaker 2: do something. And what they do is they get into work.

878
00:42:44,800 --> 00:42:46,440
Speaker 2: They're making quarter of a million dollars a year. They

879
00:42:46,480 --> 00:42:49,320
Speaker 2: get into work, they load Slack, they load Twitter, and

880
00:42:49,480 --> 00:42:51,880
Speaker 2: that's what they do for eight hours, and they occasionally

881
00:42:52,239 --> 00:42:56,000
Speaker 2: posting being like by twenty twenty eight, the AI will

882
00:42:56,040 --> 00:42:58,080
Speaker 2: of fuck my wife, and everyone's like, God, damn it, no,

883
00:42:58,800 --> 00:43:03,920
Speaker 2: not our wives, right, heal Frontier. But it is all

884
00:43:04,040 --> 00:43:06,280
Speaker 2: like they don't they want to talk about ten, fifteen,

885
00:43:06,360 --> 00:43:07,960
Speaker 2: twenty years in the future because if they had to

886
00:43:08,000 --> 00:43:10,719
Speaker 2: talk about it now, what would they say? Because I

887
00:43:10,760 --> 00:43:14,319
Speaker 2: could give you AI twenty twenty six, which is open

888
00:43:14,360 --> 00:43:17,200
Speaker 2: ai runs into funding issues. Can't pay core We've, can't

889
00:43:17,239 --> 00:43:20,720
Speaker 2: pay Cruso to build the data centers in Abilene, Texas,

890
00:43:20,800 --> 00:43:23,800
Speaker 2: which requires Oracle, who have raised debt to fund that,

891
00:43:24,200 --> 00:43:26,399
Speaker 2: to take a bath on that. Their stock gets here,

892
00:43:26,640 --> 00:43:29,600
Speaker 2: cor We've collapses because most of corwy's revenue is now

893
00:43:29,640 --> 00:43:32,520
Speaker 2: going to be open ai anthropic can't raise because the

894
00:43:32,560 --> 00:43:35,200
Speaker 2: funding climate has got so bad. Open Ai physically cannot

895
00:43:35,280 --> 00:43:37,279
Speaker 2: raise in twenty twenty six because SoftBank had to take

896
00:43:37,320 --> 00:43:40,839
Speaker 2: on murderous debt to even raise one round. And that's

897
00:43:40,920 --> 00:43:43,080
Speaker 2: just with like why to be excited here? No, no, No,

898
00:43:43,680 --> 00:43:46,120
Speaker 2: the next newsletter baby and probably a two parts. But

899
00:43:46,239 --> 00:43:47,920
Speaker 2: that's the thing. They don't want to do these because

900
00:43:48,640 --> 00:43:50,799
Speaker 2: they get okay, they would claim I'd get framed as

901
00:43:50,840 --> 00:43:53,040
Speaker 2: a skeptic. They also don't want to admit the thing

902
00:43:53,080 --> 00:43:54,600
Speaker 2: in front of them because the thing in front of

903
00:43:54,640 --> 00:43:57,440
Speaker 2: them is so egregiously bad. With Crypto, it was not

904
00:43:57,600 --> 00:44:00,080
Speaker 2: that big Meta us it was not that big. Do

905
00:44:00,120 --> 00:44:02,120
Speaker 2: you like that? Meta burned like forty billion dollars? And

906
00:44:02,120 --> 00:44:04,839
Speaker 2: there's a Yahoo financed piece about this just on mismanagement.

907
00:44:05,160 --> 00:44:08,160
Speaker 1: It's just like the like also sick that they renamed

908
00:44:08,239 --> 00:44:09,799
Speaker 1: themselves after it's so good.

909
00:44:09,920 --> 00:44:11,759
Speaker 5: Yeah, it's bad.

910
00:44:12,080 --> 00:44:15,400
Speaker 2: I can't go about Yeah, like they should get METAI.

911
00:44:15,680 --> 00:44:16,640
Speaker 2: They should just change Oh.

912
00:44:16,600 --> 00:44:18,800
Speaker 1: They should add an eye, they should just add and I.

913
00:44:18,960 --> 00:44:22,160
Speaker 2: At the end, it's just if anyone talks about what's

914
00:44:22,200 --> 00:44:25,200
Speaker 2: actually happening today, which is boardline identical what was happening

915
00:44:25,239 --> 00:44:28,560
Speaker 2: a year ago. Let's be honest. It's April twenty twenty five.

916
00:44:28,719 --> 00:44:30,400
Speaker 2: April twenty twenty four was when I put up my

917
00:44:30,440 --> 00:44:32,560
Speaker 2: first piece, being like, hey, this doesn't seem to be

918
00:44:32,640 --> 00:44:35,600
Speaker 2: doing anything different. It still doesn't even with reasoning.

919
00:44:35,880 --> 00:44:37,960
Speaker 5: It's just not just a way for Q three. Agent

920
00:44:38,040 --> 00:44:38,640
Speaker 5: Force is gone.

921
00:44:38,719 --> 00:44:42,279
Speaker 2: Yeah, agent the Agent zero is going to come out. Yeah,

922
00:44:42,440 --> 00:44:45,680
Speaker 2: Actually the information reported that Salesforce is not having a

923
00:44:45,719 --> 00:44:48,479
Speaker 2: good time sailing Agent Force. You never guess why. Wow,

924
00:44:48,800 --> 00:44:50,840
Speaker 2: turns out that it's not that useful due to the

925
00:44:50,880 --> 00:44:54,279
Speaker 2: problems of generative AI. If only someone had said something

926
00:44:54,320 --> 00:44:57,200
Speaker 2: which the information. I've begged on the information a little bit.

927
00:44:57,200 --> 00:44:59,960
Speaker 2: But they are actually doing insanely good work like Corey Weinberg,

928
00:45:00,120 --> 00:45:04,120
Speaker 2: dur Alisa Gardizzi Paris of course, but I'm specifically talking.

929
00:45:03,960 --> 00:45:05,920
Speaker 1: About AAI team as best.

930
00:45:06,440 --> 00:45:09,600
Speaker 2: Of course, and like it's great because we need this

931
00:45:09,719 --> 00:45:12,200
Speaker 2: reporting for when this just collapsed, so that we can

932
00:45:12,239 --> 00:45:15,920
Speaker 2: say what happened, because it's going to if I'm wrong,

933
00:45:16,520 --> 00:45:19,520
Speaker 2: and man, would that be embarrassing, just gonna be honest, Like,

934
00:45:19,600 --> 00:45:21,640
Speaker 2: if I'm wrong here, I'm gonna I'll look like a

935
00:45:21,760 --> 00:45:26,680
Speaker 2: huge idiot. But if I'm right here, like everyone has

936
00:45:26,719 --> 00:45:30,280
Speaker 2: overleveraged on one of the dumbest ideas of all like silly, silly.

937
00:45:30,320 --> 00:45:32,359
Speaker 2: It would be like crypto. It would be like if

938
00:45:32,360 --> 00:45:34,960
Speaker 2: everyone said, actually, crypto will replace the US dollar and

939
00:45:35,080 --> 00:45:37,959
Speaker 2: you just saw like the sea of Shopify being like, Okay,

940
00:45:37,960 --> 00:45:40,000
Speaker 2: I'm gonna go buy a beer now using crypto. No,

941
00:45:40,120 --> 00:45:42,080
Speaker 2: it is gonna send me fifteen minutes. Sorry, that's just

942
00:45:42,120 --> 00:45:44,160
Speaker 2: for you to get the money. Actually, it's gonna be

943
00:45:44,200 --> 00:45:48,040
Speaker 2: more like twenty. The network's busy. Okay, well, how's your day?

944
00:45:48,320 --> 00:45:50,760
Speaker 2: How you use money? Huh yeah, okay, yeah, you should

945
00:45:50,840 --> 00:45:52,040
Speaker 2: let that guy in front of me. That's gonna be

946
00:45:52,040 --> 00:45:55,359
Speaker 2: a while. But it's what we're doing with AI. It's like, well,

947
00:45:55,400 --> 00:45:58,120
Speaker 2: AI is changing everything. How it's a chap's chat book.

948
00:45:58,200 --> 00:46:01,640
Speaker 5: What if we have an Ubertionnario where maybe they abandoned

949
00:46:01,680 --> 00:46:05,120
Speaker 5: the dream of like this three trillion dollar addressable market

950
00:46:05,360 --> 00:46:08,440
Speaker 5: that's worldwide. They abandoned the dream of like being monopoly

951
00:46:08,520 --> 00:46:13,360
Speaker 5: in every place and and focus on a few markets

952
00:46:14,200 --> 00:46:17,919
Speaker 5: and some algorithmic price fixing so that they can figure

953
00:46:17,920 --> 00:46:21,240
Speaker 5: out how to juice fares as much as possible, reduce

954
00:46:21,400 --> 00:46:23,680
Speaker 5: the wage as much as possible, and finally eke out

955
00:46:23,719 --> 00:46:24,120
Speaker 5: that profit.

956
00:46:24,200 --> 00:46:24,759
Speaker 2: What do we see?

957
00:46:25,480 --> 00:46:28,320
Speaker 5: You know, some of these firms they pull back on

958
00:46:28,360 --> 00:46:30,360
Speaker 5: the ambition or the scale, but they persist and they

959
00:46:30,400 --> 00:46:34,320
Speaker 5: sustain themselves because they move on to some smaller.

960
00:46:34,120 --> 00:46:37,160
Speaker 1: Vision like Oham's razor. That's the most likely situation is

961
00:46:37,239 --> 00:46:40,839
Speaker 1: that you know, AI tools are useful in some way

962
00:46:40,960 --> 00:46:44,120
Speaker 1: for some slice of people and make a lot of

963
00:46:44,239 --> 00:46:48,959
Speaker 1: maybe maybe let's be optimistic, it makes a sizable chunk

964
00:46:49,160 --> 00:46:52,959
Speaker 1: of a lot of people's jobs somewhat easier. Like it's great,

965
00:46:53,600 --> 00:46:55,960
Speaker 1: it was that worth spending billions and billions of dollars

966
00:46:56,000 --> 00:47:00,440
Speaker 1: and also burning down a bunch of trees, saying that

967
00:47:00,560 --> 00:47:02,800
Speaker 1: could be I think best case scenario.

968
00:47:03,160 --> 00:47:04,759
Speaker 2: No, I'm not saying you're wrong. I'm just saying like,

969
00:47:04,880 --> 00:47:08,120
Speaker 2: we haven't even reached that yet. Because with uber it

970
00:47:08,239 --> 00:47:10,640
Speaker 2: was this incredibly lost and remains quite a losty business,

971
00:47:10,960 --> 00:47:13,560
Speaker 2: but still delivers people to them from places and objects

972
00:47:13,600 --> 00:47:14,800
Speaker 2: from to them from places.

973
00:47:15,200 --> 00:47:17,600
Speaker 5: You know you don't have to, you know, as much

974
00:47:17,600 --> 00:47:18,960
Speaker 5: as I hate them, I'll go, okay, you know you

975
00:47:19,000 --> 00:47:21,800
Speaker 5: don't have that. Not less drunk driving, you know, and

976
00:47:22,040 --> 00:47:25,080
Speaker 5: some transit in parts of cities where there's there's a

977
00:47:25,480 --> 00:47:27,839
Speaker 5: there's there's not much in the way of public transit. Right.

978
00:47:29,400 --> 00:47:31,800
Speaker 2: This is like if uber, if every ride was thirty

979
00:47:31,880 --> 00:47:35,560
Speaker 2: thousand dollars in every and every car weighed one hundred

980
00:47:35,640 --> 00:47:36,360
Speaker 2: thousand times in.

981
00:47:36,360 --> 00:47:37,759
Speaker 5: Manu factor in the externality.

982
00:47:39,960 --> 00:47:41,759
Speaker 2: But that's the crazy thing. I think general if AI

983
00:47:41,880 --> 00:47:44,319
Speaker 2: is so much worse as well, polution wise. But even

984
00:47:44,400 --> 00:47:46,640
Speaker 2: pulling that back, it's like, I think open AI just

985
00:47:46,640 --> 00:47:49,400
Speaker 2: gets wrapped into co pilot. I think that that's literally

986
00:47:49,840 --> 00:47:51,879
Speaker 2: they just shut the ship. They're like they absorb Sam

987
00:47:51,920 --> 00:47:56,200
Speaker 2: Moltman into the hive mind and he. I think my

988
00:47:56,320 --> 00:47:59,880
Speaker 2: chaos pic for everyone is satching the dellar is fired

989
00:48:00,040 --> 00:48:04,880
Speaker 2: in Amyhood takes over. If that happens, I think it's Prometheus,

990
00:48:04,920 --> 00:48:06,960
Speaker 2: the one who can see stuff. I'm fucking not done.

991
00:48:07,000 --> 00:48:14,360
Speaker 2: Read no fire tomorrow technique just spitfire. It's it's just frustrating.

992
00:48:14,480 --> 00:48:16,480
Speaker 2: It's frustrating as well, because a lot of lesseners on

993
00:48:16,520 --> 00:48:18,719
Speaker 2: the show email me and they took teachers of being like, oh,

994
00:48:18,760 --> 00:48:22,440
Speaker 2: they're forcing AI and librarian AI being forced there.

995
00:48:22,560 --> 00:48:25,279
Speaker 1: I mean the impact on the educational sector, especially with

996
00:48:25,440 --> 00:48:31,560
Speaker 1: public schools, it's really terrifying, especially because the school districts

997
00:48:31,800 --> 00:48:35,239
Speaker 1: and schools that are being forced to use this technology,

998
00:48:35,400 --> 00:48:39,000
Speaker 1: of course, are never the private, wealthy schools. It is

999
00:48:39,040 --> 00:48:42,759
Speaker 1: the most resource starved public schools that are going to

1000
00:48:42,960 --> 00:48:46,879
Speaker 1: have budgets for teachers increasingly cut. Meanwhile, they do another

1001
00:48:46,960 --> 00:48:50,960
Speaker 1: AI contract and off source like lesson the sort of

1002
00:48:51,000 --> 00:48:54,360
Speaker 1: things that these companies, the ed tech AI things pitch

1003
00:48:55,080 --> 00:49:00,840
Speaker 1: as their use case is lesson planning, writing reports, basically

1004
00:49:01,360 --> 00:49:03,960
Speaker 1: all the things that a teacher does other than physically

1005
00:49:04,080 --> 00:49:07,160
Speaker 1: being there and teaching, which in some cases the companies

1006
00:49:07,239 --> 00:49:09,560
Speaker 1: do that too. They say, instead of teaching, put your

1007
00:49:09,640 --> 00:49:11,200
Speaker 1: kid in front of a laptop and they talk to

1008
00:49:11,280 --> 00:49:12,440
Speaker 1: a chatbot for an hour.

1009
00:49:13,200 --> 00:49:16,040
Speaker 2: And that's the thing. And the school could, of course,

1010
00:49:16,120 --> 00:49:18,439
Speaker 2: I don't know, spend money on something that's already being spent,

1011
00:49:18,520 --> 00:49:21,000
Speaker 2: which is teachers have to buy their own fucking supplies

1012
00:49:21,000 --> 00:49:23,000
Speaker 2: all the time. Teachers have to just spend a bunch

1013
00:49:23,040 --> 00:49:25,680
Speaker 2: of their money on the school, and the school doesn't

1014
00:49:25,719 --> 00:49:27,640
Speaker 2: give them money, but the school will put money into chat.

1015
00:49:27,760 --> 00:49:31,520
Speaker 2: JP say, it's just oh, they should ban at universities

1016
00:49:31,520 --> 00:49:33,480
Speaker 2: as well. Everything I'm hearing there is just like real

1017
00:49:33,560 --> 00:49:34,080
Speaker 2: fucking bad.

1018
00:49:34,239 --> 00:49:36,759
Speaker 1: Like the I mean the issue is from talking to

1019
00:49:36,960 --> 00:49:41,840
Speaker 1: university professors. It's like impossible for universities to ban it.

1020
00:49:43,800 --> 00:49:48,920
Speaker 1: I guess professors are The obvious example is like essays,

1021
00:49:49,160 --> 00:49:52,359
Speaker 1: Like professors get AI written essays most of the time,

1022
00:49:52,400 --> 00:49:54,240
Speaker 1: and they can't figure out whether they are AI written

1023
00:49:54,560 --> 00:49:56,840
Speaker 1: or not. They just notice that all of your students

1024
00:49:56,880 --> 00:49:59,359
Speaker 1: seem to suddenly be doing worse in class well having

1025
00:49:59,480 --> 00:50:04,400
Speaker 1: similar output of written assignments. There are very few tools

1026
00:50:04,440 --> 00:50:07,680
Speaker 1: for them to be able to accurately detect this and

1027
00:50:07,840 --> 00:50:10,879
Speaker 1: figure out what to do from it. Meanwhile, I guess

1028
00:50:11,000 --> 00:50:14,880
Speaker 1: getting involved in trying to prosecute someone for doing this

1029
00:50:15,160 --> 00:50:17,480
Speaker 1: within the academic system is a whole other thing.

1030
00:50:17,680 --> 00:50:18,319
Speaker 4: But on the.

1031
00:50:19,800 --> 00:50:23,640
Speaker 1: In K through twelve especially, it's been kind of It's

1032
00:50:23,719 --> 00:50:28,360
Speaker 1: been especially frustrating to see that some of the biggest

1033
00:50:28,840 --> 00:50:32,319
Speaker 1: pushers of AI end up being teachers themselves because they

1034
00:50:32,360 --> 00:50:37,080
Speaker 1: are overworked, underpaid, have no time to do literally anything,

1035
00:50:37,280 --> 00:50:40,880
Speaker 1: and they have to write god knows how many lesson

1036
00:50:40,920 --> 00:50:44,799
Speaker 1: plans and i EPs for kids with disabilities and they

1037
00:50:44,880 --> 00:50:46,520
Speaker 1: can't do it. Also like, well, why don't I just

1038
00:50:46,600 --> 00:50:49,200
Speaker 1: plug this into what's essentially a chat GPT wrapper and

1039
00:50:49,320 --> 00:50:51,960
Speaker 1: that results in worse outcomes for everyone.

1040
00:50:51,680 --> 00:50:54,880
Speaker 2: Probably, So I have some personal experience with IEP. I

1041
00:50:54,920 --> 00:50:56,719
Speaker 2: don't think they're doing it there, but they're definitely doing

1042
00:50:56,719 --> 00:50:59,840
Speaker 2: it elsewhere. And if you've heard, if you've heard IPA,

1043
00:51:00,040 --> 00:51:00,840
Speaker 2: that fucking kills me.

1044
00:51:01,080 --> 00:51:05,319
Speaker 1: That's one of the things that these tools often pitch

1045
00:51:05,400 --> 00:51:07,160
Speaker 1: themselves as I want to make sure.

1046
00:51:07,400 --> 00:51:09,800
Speaker 2: Something, I want to put my hands around someone's.

1047
00:51:09,560 --> 00:51:11,520
Speaker 1: Fucking Can you describe what an IP is? Is A?

1048
00:51:11,719 --> 00:51:14,319
Speaker 2: I forget what it stands for Individual education plan?

1049
00:51:14,400 --> 00:51:16,560
Speaker 1: I don't wrong, but that's it.

1050
00:51:16,640 --> 00:51:19,200
Speaker 2: Is generally the plan that's put for a child with

1051
00:51:19,560 --> 00:51:22,680
Speaker 2: special needs, so autism being one of most obvious one.

1052
00:51:22,719 --> 00:51:25,040
Speaker 2: It names exactly what it is that they have to do,

1053
00:51:25,880 --> 00:51:28,480
Speaker 2: like what the teacher's goals will be, like socio.

1054
00:51:28,400 --> 00:51:31,319
Speaker 1: They legally have to do all the things in that document.

1055
00:51:31,200 --> 00:51:34,680
Speaker 2: And it changes based on the designation they get, and

1056
00:51:34,840 --> 00:51:37,040
Speaker 2: so like it's different if you get there's like an

1057
00:51:37,040 --> 00:51:40,440
Speaker 2: emotional instability one, I believe, and nevertheless, there's like separate ones,

1058
00:51:40,600 --> 00:51:42,320
Speaker 2: and each one is that the goals of where the

1059
00:51:42,400 --> 00:51:43,759
Speaker 2: kid is right now, where the kid will be in

1060
00:51:43,800 --> 00:51:45,719
Speaker 2: the future, and so on and so forth. The idea

1061
00:51:45,800 --> 00:51:47,520
Speaker 2: that some would use chat GIPT and if you listen

1062
00:51:47,560 --> 00:51:49,000
Speaker 2: to this and use chat GPT for one of these,

1063
00:51:49,040 --> 00:51:51,839
Speaker 2: I fucking hate yours so bad. I understand you're busy,

1064
00:51:51,880 --> 00:51:57,319
Speaker 2: but this is very important. Nevertheless, Wow, how disgraceful as well,

1065
00:51:57,360 --> 00:52:00,480
Speaker 2: because it's all this weird resource allocation done by and

1066
00:52:00,560 --> 00:52:02,920
Speaker 2: I feel like the overarching problem as well as it's

1067
00:52:03,080 --> 00:52:06,000
Speaker 2: the people making these decisions putting this stuff in don't

1068
00:52:06,040 --> 00:52:09,880
Speaker 2: do work. It's school administrators that don't teach, it's CEOs

1069
00:52:09,960 --> 00:52:13,560
Speaker 2: that don't build anything. It's a venture capitalists that haven't

1070
00:52:14,120 --> 00:52:17,520
Speaker 2: interacted with the economy or anyone without a Patagonia sweater

1071
00:52:17,640 --> 00:52:21,319
Speaker 2: and decades, and it's these and again, these vcs, they're

1072
00:52:21,440 --> 00:52:24,279
Speaker 2: investing money based on how they used to make money,

1073
00:52:24,320 --> 00:52:26,440
Speaker 2: which was they invest in literally anything and then they

1074
00:52:26,520 --> 00:52:28,759
Speaker 2: sold it to literally anyone. And that hasn't worked for

1075
00:52:28,800 --> 00:52:31,560
Speaker 2: ten years. Alison, you mentioned the thing twenty fifteen ish

1076
00:52:32,120 --> 00:52:34,000
Speaker 2: that was when things stopped being fun. That was actually

1077
00:52:34,040 --> 00:52:36,480
Speaker 2: The last time we really saw anything cool that was

1078
00:52:36,480 --> 00:52:39,719
Speaker 2: around the Apple Watch era, and it was the last,

1079
00:52:40,520 --> 00:52:43,400
Speaker 2: really the end of the hype cycles, the successful ones,

1080
00:52:43,400 --> 00:52:48,719
Speaker 2: at least they haven't had one since then. Var XR, Crypto, Metaverse,

1081
00:52:49,120 --> 00:52:53,680
Speaker 2: the Indie gog and Kickstarter era. He's sharing economy. But

1082
00:52:53,800 --> 00:52:55,960
Speaker 2: these all had the same problem, which was they cost

1083
00:52:56,040 --> 00:52:59,560
Speaker 2: more money than they made and they weren't scalable, and

1084
00:52:59,760 --> 00:53:02,040
Speaker 2: is the same problem we've had. What we may be

1085
00:53:02,120 --> 00:53:04,040
Speaker 2: facing is the fact that the tech industry does not

1086
00:53:04,160 --> 00:53:06,640
Speaker 2: know how to make companies anymore. Like that may actually

1087
00:53:06,680 --> 00:53:07,400
Speaker 2: be the problem.

1088
00:53:08,280 --> 00:53:09,840
Speaker 3: Can I have one thing to what you said about

1089
00:53:09,920 --> 00:53:12,600
Speaker 3: people who don't work? I think there are people in

1090
00:53:12,680 --> 00:53:15,479
Speaker 3: Silicon Valley and I don't I'm gonna get a million

1091
00:53:15,840 --> 00:53:18,000
Speaker 3: emails about this, but there are a lot of Silicon

1092
00:53:18,080 --> 00:53:23,000
Speaker 3: Valley men who are white men who don't really socialize yep,

1093
00:53:23,440 --> 00:53:27,680
Speaker 3: And I think they are kind of propagating this technology

1094
00:53:27,760 --> 00:53:32,080
Speaker 3: that allows others to kind of not interact, Like so

1095
00:53:32,239 --> 00:53:37,600
Speaker 3: much of chat GPT is designed to like subvert human interactions,

1096
00:53:37,680 --> 00:53:39,640
Speaker 3: like you're not going to go ask your teacher or

1097
00:53:39,680 --> 00:53:43,400
Speaker 3: ask a excuse me, or ask a classmate. Hey, how

1098
00:53:43,440 --> 00:53:44,880
Speaker 3: do we figure this out? You're just gonna go to

1099
00:53:44,960 --> 00:53:49,640
Speaker 3: the computer. And I think that culturally, like I you know,

1100
00:53:49,760 --> 00:53:52,960
Speaker 3: people who grew up with computers God bless. But you know,

1101
00:53:53,160 --> 00:53:57,719
Speaker 3: we need to also value social interaction. And it's interesting

1102
00:53:57,920 --> 00:54:02,080
Speaker 3: that there are these very like small group of people

1103
00:54:03,160 --> 00:54:06,640
Speaker 3: often who lack social skills propagating a technology to make

1104
00:54:06,719 --> 00:54:08,200
Speaker 3: other people not have social skills.

1105
00:54:08,640 --> 00:54:11,120
Speaker 2: I think there's also a class aspect to that because

1106
00:54:11,360 --> 00:54:15,279
Speaker 2: totally don't grow up particularly with like add food on

1107
00:54:15,360 --> 00:54:17,239
Speaker 2: the table. But one thing I grew up was I

1108
00:54:17,320 --> 00:54:20,600
Speaker 2: don't trust any easy fixes. Nothing is ever that easy.

1109
00:54:20,880 --> 00:54:23,319
Speaker 2: Is something that kind of a if something seems too

1110
00:54:23,360 --> 00:54:26,759
Speaker 2: good to be true to accessible, there's usually something you're

1111
00:54:26,840 --> 00:54:29,880
Speaker 2: missing about the incentives or the actual output. So no,

1112
00:54:30,000 --> 00:54:33,279
Speaker 2: I wouldn't trust a computer to tell me how to

1113
00:54:33,320 --> 00:54:36,439
Speaker 2: fix something because I don't fucking like that you made

1114
00:54:36,480 --> 00:54:39,520
Speaker 2: that up, Like it isn't this easy. There's got to

1115
00:54:39,520 --> 00:54:57,000
Speaker 2: be a problem. The problem it's hallucinations. And we're back,

1116
00:54:57,480 --> 00:55:00,160
Speaker 2: so we didn't really lead into that ad break. You're

1117
00:55:00,160 --> 00:55:01,480
Speaker 2: going to just have to like it. I'm sure all

1118
00:55:01,520 --> 00:55:03,439
Speaker 2: of you are going to send me little emails, little

1119
00:55:03,480 --> 00:55:06,600
Speaker 2: emails about the ads that you love. Well, I've got

1120
00:55:06,719 --> 00:55:09,520
Speaker 2: to got to pay for my diet cokes somehow. So

1121
00:55:10,719 --> 00:55:13,759
Speaker 2: back to this larger point around chat GPT and why

1122
00:55:13,840 --> 00:55:16,960
Speaker 2: people using how people use it. I think that another

1123
00:55:17,040 --> 00:55:18,799
Speaker 2: thing that just occurred to me is have you ever

1124
00:55:18,880 --> 00:55:20,959
Speaker 2: noticed that Sam Ortman can't tell you how it works

1125
00:55:21,000 --> 00:55:22,440
Speaker 2: and what it does? You have noticed that one of

1126
00:55:22,440 --> 00:55:24,279
Speaker 2: these people will tell you what it does you I've

1127
00:55:24,320 --> 00:55:26,520
Speaker 2: read everything Sam Mormon said at this point, listen to

1128
00:55:26,640 --> 00:55:29,600
Speaker 2: hours of podcasts. He's quite a boring twerp. But on

1129
00:55:29,719 --> 00:55:31,920
Speaker 2: top of that, for all is the yappin and yamoring

1130
00:55:32,040 --> 00:55:34,680
Speaker 2: him and Warrio Ama Day don't seem to be able

1131
00:55:34,800 --> 00:55:37,880
Speaker 2: to say out loud what the fucking thing does. And

1132
00:55:38,000 --> 00:55:40,200
Speaker 2: that's because I don't think that they use it either.

1133
00:55:40,440 --> 00:55:42,920
Speaker 2: Like I genuinely, I'm beginning to wonder if any of

1134
00:55:43,000 --> 00:55:45,879
Speaker 2: the people injecting AI. Sure, Sam Moltman and Dario probably

1135
00:55:45,960 --> 00:55:48,000
Speaker 2: use it. I'm not saying it fully, but like, these

1136
00:55:48,000 --> 00:55:50,560
Speaker 2: aren't people. How the next person that meets Sam Altman

1137
00:55:50,800 --> 00:55:52,799
Speaker 2: should just be like, hey, how often do you use

1138
00:55:52,880 --> 00:55:55,800
Speaker 2: chat GPT? Gets back to that. It reminds me the

1139
00:55:55,880 --> 00:55:57,920
Speaker 2: remote work thing, all these CEOs saying guys should come

1140
00:55:57,920 --> 00:56:00,520
Speaker 2: back to the office. How often you in the office exactly.

1141
00:56:01,160 --> 00:56:03,120
Speaker 2: And I think that this is just the giant revelation

1142
00:56:03,200 --> 00:56:05,759
Speaker 2: of like, how many people don't actually interact with their businesses,

1143
00:56:05,840 --> 00:56:08,360
Speaker 2: that don't interact with other people, that don't really know

1144
00:56:08,480 --> 00:56:10,680
Speaker 2: how anything works, but they are the ones making the

1145
00:56:10,800 --> 00:56:14,880
Speaker 2: money in power decisions. It's fucking crazy to me. And

1146
00:56:15,719 --> 00:56:18,680
Speaker 2: I don't know how this shakes out. It's not going

1147
00:56:18,760 --> 00:56:22,680
Speaker 2: to be an autonomous agent doing whatever. Also, Okay, that

1148
00:56:22,800 --> 00:56:24,719
Speaker 2: just occurred to me as well, how the fuck do

1149
00:56:24,800 --> 00:56:27,280
Speaker 2: these people not think these agents come for them first?

1150
00:56:28,320 --> 00:56:30,160
Speaker 2: If the AGI was doing this and they read this

1151
00:56:30,239 --> 00:56:32,080
Speaker 2: and be like, oh, these people fucking they worked it

1152
00:56:32,120 --> 00:56:33,520
Speaker 2: all out, I need to kill them first.

1153
00:56:34,280 --> 00:56:35,520
Speaker 5: Well, I mean that kind of gets back to what

1154
00:56:35,560 --> 00:56:38,120
Speaker 5: you're saying, where it's like, you know, if we entertained

1155
00:56:38,280 --> 00:56:41,080
Speaker 5: the fan fiction for a little bit, what is the

1156
00:56:41,719 --> 00:56:46,400
Speaker 5: frame of mind for these agents, if they're autonomous or not,

1157
00:56:46,520 --> 00:56:48,640
Speaker 5: how are we thinking of them? Are we thinking about

1158
00:56:48,840 --> 00:56:53,480
Speaker 5: like their persons or if they're you know, the bottomized

1159
00:56:53,520 --> 00:56:54,160
Speaker 5: in someone, do.

1160
00:56:54,200 --> 00:56:54,960
Speaker 2: They have opinions?

1161
00:56:55,080 --> 00:56:55,239
Speaker 1: You know?

1162
00:56:55,400 --> 00:56:56,920
Speaker 5: And I think really it just gets back to like,

1163
00:56:57,000 --> 00:56:59,440
Speaker 5: you know, part of the old hunt for like you know,

1164
00:56:59,840 --> 00:57:03,120
Speaker 5: a nice polite slave, you know. Yeah, how do we

1165
00:57:03,160 --> 00:57:06,480
Speaker 5: figure out how to reify that relationship because it was

1166
00:57:06,560 --> 00:57:09,879
Speaker 5: quite profitable at the turn of like industrial capitalism, And yeah,

1167
00:57:10,200 --> 00:57:12,680
Speaker 5: I think, you know, it's not a coincidence that a

1168
00:57:12,760 --> 00:57:16,400
Speaker 5: good chunk of our tech visions come to us from

1169
00:57:16,560 --> 00:57:20,000
Speaker 5: reactionarias who think that the problem with capitalism, the problem

1170
00:57:20,040 --> 00:57:23,600
Speaker 5: with tech development is that a lot of these empathetic,

1171
00:57:23,680 --> 00:57:26,280
Speaker 5: egalitarian reforms get in the way of profit making.

1172
00:57:27,080 --> 00:57:27,200
Speaker 1: You know.

1173
00:57:27,280 --> 00:57:30,800
Speaker 5: I think similarly, you know, the hunters automatons for certain

1174
00:57:30,880 --> 00:57:34,400
Speaker 5: algorithmic systems is searching for a way to figure out

1175
00:57:34,440 --> 00:57:38,080
Speaker 5: how do we replicate you know, human labor without the

1176
00:57:38,760 --> 00:57:42,920
Speaker 5: limitations on extracting and pushing and coercing.

1177
00:57:42,480 --> 00:57:44,960
Speaker 2: As much as possible, Yeah, with you know, and there's

1178
00:57:45,000 --> 00:57:47,439
Speaker 2: no agent or something else. And the thing is, yeah, sure,

1179
00:57:47,480 --> 00:57:49,880
Speaker 2: the idea of an autonomous AI system would be really useful.

1180
00:57:49,880 --> 00:57:51,800
Speaker 2: I'm sure it could do stuff that sounds great. They're

1181
00:57:51,840 --> 00:57:55,640
Speaker 2: all massive, as you've mentioned, like sociological problems like do

1182
00:57:55,760 --> 00:57:58,840
Speaker 2: these things feel pain? If so, how do I create anyway?

1183
00:57:59,680 --> 00:58:02,280
Speaker 2: But in all seriousness, like sure, an autonomous thing that

1184
00:58:02,320 --> 00:58:04,000
Speaker 2: could do all this thing would be useful. They don't

1185
00:58:04,040 --> 00:58:05,960
Speaker 2: seem to even speak to that. It's just like, and

1186
00:58:06,080 --> 00:58:08,920
Speaker 2: then the AI will make good decisions, and then the

1187
00:58:09,040 --> 00:58:12,240
Speaker 2: decisions will be even better than Agent seven comes out,

1188
00:58:12,280 --> 00:58:13,760
Speaker 2: And you thought Agent six was good.

1189
00:58:14,120 --> 00:58:15,720
Speaker 1: It's like they don't even speak to how we're going

1190
00:58:15,800 --> 00:58:19,480
Speaker 1: to get to the point where Agent one knows truth from.

1191
00:58:19,440 --> 00:58:22,200
Speaker 5: Falsehood less inevitable of course.

1192
00:58:22,120 --> 00:58:25,040
Speaker 1: Yeah, you know, we just need to give it all

1193
00:58:25,160 --> 00:58:28,400
Speaker 1: of our data and everything that we've paid money for,

1194
00:58:28,600 --> 00:58:30,880
Speaker 1: required other people to pay money for, and then it

1195
00:58:30,920 --> 00:58:32,120
Speaker 1: will finally be perfect.

1196
00:58:32,400 --> 00:58:35,600
Speaker 2: And it doesn't even make profit of any kind. That's

1197
00:58:35,920 --> 00:58:38,920
Speaker 2: the other thing. It's like people saying makes profit it

1198
00:58:39,040 --> 00:58:41,560
Speaker 2: is the profit seeking? Is it profit seeking? It doesn't

1199
00:58:41,600 --> 00:58:44,200
Speaker 2: seem like we've sought much profit or any Yeah.

1200
00:58:44,280 --> 00:58:46,240
Speaker 1: That's also I think a good point of comparison to

1201
00:58:46,280 --> 00:58:48,440
Speaker 1: what you were talking about earlier, ed with the comparison

1202
00:58:48,560 --> 00:58:53,480
Speaker 1: to Uber and Lift, these companies that achieved massive scale

1203
00:58:53,640 --> 00:58:59,080
Speaker 1: and popularity by making their products purposefully unprofitable, by charging

1204
00:58:59,120 --> 00:59:02,800
Speaker 1: you five dollars for a thirty minute Uber across down

1205
00:59:02,880 --> 00:59:04,560
Speaker 1: so that you're like, yeah, this is going to be

1206
00:59:04,600 --> 00:59:07,080
Speaker 1: part of my daily routine. And the only way they've

1207
00:59:07,080 --> 00:59:10,040
Speaker 1: been able to squeeze out a little bit of profit

1208
00:59:10,200 --> 00:59:13,360
Speaker 1: right now is by hiking those prices up, but trying

1209
00:59:13,360 --> 00:59:14,840
Speaker 1: to balance it to where they don't hike it up

1210
00:59:14,880 --> 00:59:17,280
Speaker 1: so much that people don't use it anymore. And AI

1211
00:59:17,560 --> 00:59:20,120
Speaker 1: is at the point where, like, for these agents, I

1212
00:59:20,200 --> 00:59:22,800
Speaker 1: think some of the costs reals something like thousands of

1213
00:59:22,840 --> 00:59:25,479
Speaker 1: dollars a month, and they don't they don't work, They're

1214
00:59:25,480 --> 00:59:28,680
Speaker 1: already and it's like, you're still not making money by

1215
00:59:28,760 --> 00:59:32,000
Speaker 1: spending by charging people that much money to use it.

1216
00:59:32,800 --> 00:59:34,760
Speaker 1: What is the use case one where this even works?

1217
00:59:34,960 --> 00:59:37,440
Speaker 1: And if it somehow did manage to work, how much

1218
00:59:37,520 --> 00:59:39,400
Speaker 1: is that going to cost? Who is going to be

1219
00:59:39,480 --> 00:59:42,160
Speaker 1: paying twenty thousand dollars a month for one of these things?

1220
00:59:42,160 --> 00:59:44,520
Speaker 2: And how much of that is dependent on what is

1221
00:59:44,640 --> 00:59:48,160
Speaker 2: clearly naked the subsidized compute prices. How much of this

1222
00:59:48,360 --> 00:59:51,240
Speaker 2: is because Microsoft's not making a profit on a Zeo compute,

1223
00:59:51,240 --> 00:59:54,800
Speaker 2: open Aiye isn't making anthropic is then what happens if

1224
00:59:54,920 --> 00:59:58,080
Speaker 2: they need to? What if they need to, they're gonna chuck.

1225
00:59:58,120 --> 00:59:59,960
Speaker 2: That's the Supperme Mai crisis from last year.

1226
01:00:00,720 --> 01:00:03,520
Speaker 5: It's just it's it's well, that's when you get the

1227
01:00:03,600 --> 01:00:06,040
Speaker 5: venture capitalists insisting that that's why we need to you know,

1228
01:00:06,240 --> 01:00:08,640
Speaker 5: do this air capex rollout because if we build it

1229
01:00:08,680 --> 01:00:11,280
Speaker 5: out like infrastructure, then we can actually lower the compute

1230
01:00:11,280 --> 01:00:13,800
Speaker 5: prices and that subsidized when yeah, that's.

1231
01:00:13,680 --> 01:00:16,400
Speaker 2: The thing, but that's the other thing. So the information

1232
01:00:16,520 --> 01:00:19,280
Speaker 2: reported the open AIY says that by twenty thirty they'll

1233
01:00:19,280 --> 01:00:23,240
Speaker 2: be profitable. How Stargate Yeah, And you may think what

1234
01:00:23,360 --> 01:00:26,000
Speaker 2: does that mean? And the answer is Starguate has data centers.

1235
01:00:26,120 --> 01:00:28,280
Speaker 2: Now you have to I just have one little question.

1236
01:00:28,400 --> 01:00:30,280
Speaker 2: This isn't a knock on the information. This is their

1237
01:00:30,320 --> 01:00:33,320
Speaker 2: reporting what they've been told, which is fine. A little

1238
01:00:33,520 --> 01:00:37,360
Speaker 2: question with open AYE though, how how does more equal

1239
01:00:37,680 --> 01:00:40,680
Speaker 2: less cost? Because this thing doesn't scale. They lose money

1240
01:00:40,720 --> 01:00:43,200
Speaker 2: on every prompt. It doesn't feel like they'll make any

1241
01:00:43,400 --> 01:00:45,400
Speaker 2: In fact, they won't make any money. They'll just have

1242
01:00:45,600 --> 01:00:48,080
Speaker 2: more of it. And also there's the other thing of

1243
01:00:48,680 --> 01:00:50,840
Speaker 2: data centers are not fucking weeds. They don't grow in

1244
01:00:50,960 --> 01:00:53,000
Speaker 2: six weeks. They take three to six years to be

1245
01:00:53,080 --> 01:00:55,480
Speaker 2: fully done. If Stargate is done by next year, I

1246
01:00:55,560 --> 01:00:59,000
Speaker 2: will fucking barbecue up my padres hat and eat it

1247
01:00:59,120 --> 01:01:02,000
Speaker 2: live on stream like I that's if they're fucking alive

1248
01:01:02,160 --> 01:01:06,120
Speaker 2: next year. Also, the other thing is getting back to

1249
01:01:06,280 --> 01:01:09,000
Speaker 2: twenty twenty seven as well, you're twenty twenty six. Twenty

1250
01:01:09,040 --> 01:01:11,640
Speaker 2: twenty seven is gonna be real important for everything. Twenty

1251
01:01:11,720 --> 01:01:14,200
Speaker 2: twenty seven or twenty twenty six is when Warrio Abba

1252
01:01:14,280 --> 01:01:17,600
Speaker 2: Data is the ANTHROPICALI be profitable. That's also when the

1253
01:01:17,880 --> 01:01:20,360
Speaker 2: Stargate Data Center project will be done in twenty twenty six.

1254
01:01:20,960 --> 01:01:22,680
Speaker 2: I think that they may have all just chosen the

1255
01:01:22,720 --> 01:01:24,640
Speaker 2: same year because it sounded good, and they're going to

1256
01:01:24,680 --> 01:01:26,760
Speaker 2: get in real trouble next year when it arrives and

1257
01:01:26,800 --> 01:01:28,000
Speaker 2: they're nowhere near close.

1258
01:01:28,520 --> 01:01:32,120
Speaker 3: I can't wait until all of those companies announce that

1259
01:01:32,440 --> 01:01:35,680
Speaker 3: because of the tariffs yep, that they have to delay

1260
01:01:35,720 --> 01:01:37,920
Speaker 3: their timeline and it's like completely out of their hands.

1261
01:01:38,400 --> 01:01:40,120
Speaker 6: But no, the tariffs.

1262
01:01:40,160 --> 01:01:41,840
Speaker 3: You understand the tariffs.

1263
01:01:42,240 --> 01:01:44,320
Speaker 2: I've got a fungth of dine. I got a full

1264
01:01:44,480 --> 01:01:49,160
Speaker 2: roasted pig. I'm going to be tailgating Microsoft earnings April

1265
01:01:49,200 --> 01:01:51,480
Speaker 2: twenty third. Cannot wait. Yeah, you should go to like

1266
01:01:51,520 --> 01:01:57,280
Speaker 2: a data center to have a marching band severance. Yeah,

1267
01:01:57,440 --> 01:01:59,960
Speaker 2: it's but that's the thing like that, I actually agree.

1268
01:02:00,080 --> 01:02:02,360
Speaker 2: I think that they're gonna there's gonna be difficult choices. Sadly,

1269
01:02:02,400 --> 01:02:06,360
Speaker 2: there's only really two one capex reduction, two layoffs or

1270
01:02:06,680 --> 01:02:11,200
Speaker 2: both because they have proven willingness to lay off to

1271
01:02:11,280 --> 01:02:15,360
Speaker 2: fund the capex. But at this point people are like

1272
01:02:15,520 --> 01:02:18,000
Speaker 2: they're asking to what end? Like why are we? Why

1273
01:02:18,080 --> 01:02:20,760
Speaker 2: are we doing this? It just it feels like the

1274
01:02:20,800 --> 01:02:24,120
Speaker 2: collapse of any good or bad romantic relationship where just

1275
01:02:24,240 --> 01:02:26,440
Speaker 2: one party is doing ship that they think works from

1276
01:02:26,520 --> 01:02:28,640
Speaker 2: years ago and the other party is just deeply unhappy

1277
01:02:28,640 --> 01:02:31,760
Speaker 2: and then disappears one day and the other party being

1278
01:02:34,200 --> 01:02:38,000
Speaker 2: last night and it just happened. This is lost. Lost

1279
01:02:38,160 --> 01:02:39,760
Speaker 2: is a far more logical show than any of his

1280
01:02:39,880 --> 01:02:43,840
Speaker 2: AI bullshit. But it's it's not good. No, no, it's

1281
01:02:43,880 --> 01:02:46,160
Speaker 2: a bad show. It's a bad No. I wouldn't say

1282
01:02:46,160 --> 01:02:49,240
Speaker 2: that either, talking about something that's very long, very expensive

1283
01:02:49,280 --> 01:02:51,280
Speaker 2: and never had a plan, but everyone talks about like

1284
01:02:51,400 --> 01:02:55,880
Speaker 2: it was good despite never proving it. Lost. Yeah, sorry,

1285
01:02:55,880 --> 01:02:58,600
Speaker 2: I really do have some feelings on that. You're going

1286
01:02:58,640 --> 01:03:05,000
Speaker 2: to get some emails, I'm sure for me sending me

1287
01:03:05,440 --> 01:03:10,160
Speaker 2: very quiet rob still emotion like a hundred times. Yeah,

1288
01:03:10,200 --> 01:03:13,360
Speaker 2: it's I think it's just I can't wait to see

1289
01:03:13,440 --> 01:03:15,720
Speaker 2: how people react to this stuff as well when this

1290
01:03:15,800 --> 01:03:18,560
Speaker 2: because I obviously will look very silly if these companies

1291
01:03:18,960 --> 01:03:21,120
Speaker 2: stay alive and somehow make a KG I a g

1292
01:03:21,240 --> 01:03:25,200
Speaker 2: I is killing me first, like the gravedigger AI truck

1293
01:03:25,280 --> 01:03:26,600
Speaker 2: is going to run me over outside of my house.

1294
01:03:26,600 --> 01:03:28,960
Speaker 2: It's going to be great, But I can't wait to

1295
01:03:29,000 --> 01:03:31,760
Speaker 2: see how people explain this. I can't wait to see

1296
01:03:31,840 --> 01:03:34,320
Speaker 2: what it's like. Oh, we never see The tariffs may

1297
01:03:34,400 --> 01:03:35,440
Speaker 2: be right.

1298
01:03:35,520 --> 01:03:37,760
Speaker 3: And I talked to an analyst just last week who's

1299
01:03:37,800 --> 01:03:42,640
Speaker 3: like a bullish AI tech investor, and he said, he said,

1300
01:03:42,680 --> 01:03:47,360
Speaker 3: already you're seeing investment pull back because of expectations in

1301
01:03:47,400 --> 01:03:51,920
Speaker 3: the market that there was these stocks were overbought in

1302
01:03:52,040 --> 01:03:56,000
Speaker 3: the first place, and now there's all this other turmoil

1303
01:03:56,240 --> 01:03:59,200
Speaker 3: external macro elements that are going to kind of take

1304
01:03:59,240 --> 01:04:01,840
Speaker 3: the feeling, you know, the jargon of like the froth

1305
01:04:01,920 --> 01:04:03,960
Speaker 3: out of the market. They're gonna it's all going to

1306
01:04:04,040 --> 01:04:06,200
Speaker 3: deflate a little bit. And so I was asking him, like,

1307
01:04:06,360 --> 01:04:08,640
Speaker 3: is the AI bubble popping and he says, no, but

1308
01:04:08,880 --> 01:04:14,400
Speaker 3: tariffs are definitely like deflating it and delaying whatever progress

1309
01:04:14,520 --> 01:04:16,840
Speaker 3: that we are going to be promised from these companies

1310
01:04:17,400 --> 01:04:19,560
Speaker 3: is going to be delayed. Even if it was going

1311
01:04:19,640 --> 01:04:21,600
Speaker 3: to be delayed, they were going to find other reasons

1312
01:04:21,680 --> 01:04:25,440
Speaker 3: this is a convenient macro kind of excuse to just

1313
01:04:25,480 --> 01:04:28,280
Speaker 3: say like, oh, well we need we didn't have enough chips,

1314
01:04:28,320 --> 01:04:30,480
Speaker 3: we didn't have enough investing, we didn't have enough compute.

1315
01:04:32,080 --> 01:04:33,680
Speaker 3: You know, be patient with us. We're going to have

1316
01:04:34,080 --> 01:04:35,439
Speaker 3: the revolution is coming.

1317
01:04:35,920 --> 01:04:38,160
Speaker 2: What's great as well, talking of my favorite Wall Street

1318
01:04:38,160 --> 01:04:41,640
Speaker 2: analyst Jim Kramer of CNBC, so Court Weave's IPO went out.

1319
01:04:41,760 --> 01:04:43,760
Speaker 2: I just need to mention we are definitely in the

1320
01:04:43,800 --> 01:04:47,000
Speaker 2: hype cycle because Jim Kramer said that he would sue

1321
01:04:47,080 --> 01:04:52,240
Speaker 2: an analyst Da Davidson on behalf of Nvidio for claiming

1322
01:04:52,320 --> 01:04:57,480
Speaker 2: that they were a lazy Susan as in as in basically,

1323
01:04:57,640 --> 01:05:00,360
Speaker 2: what the argument is is the Nvidia funded care Wave,

1324
01:05:00,440 --> 01:05:03,200
Speaker 2: so the core Wave would buy GPUs, and at that

1325
01:05:03,320 --> 01:05:05,360
Speaker 2: point core Weave would then take out loans on those

1326
01:05:05,440 --> 01:05:10,160
Speaker 2: GPOs for Capex reasons Capex including buying GPUs. So very clearly,

1327
01:05:10,920 --> 01:05:13,040
Speaker 2: and also you take gil over at Da Davison, you

1328
01:05:13,120 --> 01:05:15,800
Speaker 2: and me Kramer in the ring, but it's we know

1329
01:05:15,880 --> 01:05:17,440
Speaker 2: we're in the crazy time when you've got like a

1330
01:05:17,640 --> 01:05:19,919
Speaker 2: TV show host being like I'm gonna sue you because

1331
01:05:19,920 --> 01:05:23,760
Speaker 2: you don't like my snocks. I think that like we're

1332
01:05:23,800 --> 01:05:26,000
Speaker 2: going to see like a historic wash out of people,

1333
01:05:26,280 --> 01:05:28,040
Speaker 2: and the way to change things is this time we

1334
01:05:28,120 --> 01:05:30,440
Speaker 2: need to make fun of them. I think we need

1335
01:05:30,520 --> 01:05:32,280
Speaker 2: to be like actively. We don't need to be mean,

1336
01:05:32,480 --> 01:05:35,680
Speaker 2: that's my job, but we can be like to your

1337
01:05:35,760 --> 01:05:38,840
Speaker 2: point at your article, Alison, it's like say like, hey, look, no,

1338
01:05:39,160 --> 01:05:41,840
Speaker 2: what you were saying is not even rational or even

1339
01:05:41,880 --> 01:05:45,200
Speaker 2: connected to reality. This is not doing the right things.

1340
01:05:45,320 --> 01:05:49,040
Speaker 2: Apple Intelligence is like the greatest anti AI radicalization ever.

1341
01:05:49,080 --> 01:05:52,000
Speaker 2: I actually think, to me, so bad, it's so fucking bad.

1342
01:05:52,520 --> 01:05:55,720
Speaker 1: And I before it even came out, I like downloaded

1343
01:05:55,760 --> 01:05:57,680
Speaker 1: the beta. I was like, I'm going to test this

1344
01:05:57,800 --> 01:06:00,360
Speaker 1: out because you know, I talk about the thing on

1345
01:06:00,480 --> 01:06:04,080
Speaker 1: my podcast sometimes and it's so bad bad. I like

1346
01:06:04,200 --> 01:06:05,920
Speaker 1: turns it off for most things, but I have it

1347
01:06:06,000 --> 01:06:08,240
Speaker 1: on for a couple of social networks, and I mean,

1348
01:06:08,280 --> 01:06:11,280
Speaker 1: I guess with the most recent update got marginally better,

1349
01:06:11,360 --> 01:06:14,680
Speaker 1: but it still constantly tells me so and so replied

1350
01:06:14,720 --> 01:06:18,680
Speaker 1: to your blue sky ski. I checked they didn't. That

1351
01:06:18,880 --> 01:06:21,520
Speaker 1: person didn't even like the ski. I don't know where

1352
01:06:21,640 --> 01:06:24,040
Speaker 1: that name came from. And this happens like every other day.

1353
01:06:24,320 --> 01:06:26,120
Speaker 1: It's just completely wrong.

1354
01:06:26,560 --> 01:06:29,720
Speaker 2: I'm like, how my favorite is the summary takes for

1355
01:06:29,880 --> 01:06:32,960
Speaker 2: Uber where it's like several cars headed to your location

1356
01:06:36,840 --> 01:06:40,960
Speaker 2: Kallennick Klannick mode activate it. No, it's it's great as well,

1357
01:06:41,080 --> 01:06:45,160
Speaker 2: because I usually don't buy into the Steve Jobs would

1358
01:06:45,280 --> 01:06:47,680
Speaker 2: burst from his grave thing. I actually think numerous choices

1359
01:06:47,720 --> 01:06:49,760
Speaker 2: Team cook has may have been way smaller than how

1360
01:06:49,840 --> 01:06:51,880
Speaker 2: Jobs sort of done. This is actually like he's going

1361
01:06:51,960 --> 01:06:54,160
Speaker 2: to burst out of the ground thriller style. Actually did

1362
01:06:54,400 --> 01:06:58,919
Speaker 2: that zombies pop out? Anyway? Because it's nakedly bad. Close.

1363
01:06:59,080 --> 01:07:01,240
Speaker 2: It's not a great reference, but it's nakedly bad, like

1364
01:07:01,320 --> 01:07:03,600
Speaker 2: it sucks, and I've never I have. People in my

1365
01:07:03,680 --> 01:07:06,640
Speaker 2: life were non techy will constantly be like, hey, what

1366
01:07:06,840 --> 01:07:09,520
Speaker 2: is Apple Intelligence? Am I missing something? I'm like, no,

1367
01:07:09,720 --> 01:07:11,400
Speaker 2: it's actually as bad as you think.

1368
01:07:11,360 --> 01:07:13,880
Speaker 1: And I mean it's also small other things beyond just

1369
01:07:13,960 --> 01:07:17,320
Speaker 1: the notification summaries. The thing that every time I highlight

1370
01:07:17,640 --> 01:07:20,600
Speaker 1: a word and I'm trying to sometimes might want to

1371
01:07:20,680 --> 01:07:23,520
Speaker 1: use fine definition or any of the things that come up,

1372
01:07:23,640 --> 01:07:27,520
Speaker 1: I have to scroll by like seven different new options

1373
01:07:27,760 --> 01:07:29,960
Speaker 1: under the right click or double click.

1374
01:07:30,160 --> 01:07:32,600
Speaker 2: If you hit right tools, it opens up a screenway, Yes,

1375
01:07:32,840 --> 01:07:33,520
Speaker 2: it opens up a.

1376
01:07:33,560 --> 01:07:36,560
Speaker 1: Thing, and I'm like, who has ever, who is trying

1377
01:07:36,720 --> 01:07:40,360
Speaker 1: to use this to rewrite a text to their group chat.

1378
01:07:41,320 --> 01:07:41,920
Speaker 2: Who is this for?

1379
01:07:42,920 --> 01:07:47,160
Speaker 3: I feel like Apple, to its credit, is recognizing its

1380
01:07:47,280 --> 01:07:51,280
Speaker 3: mistake and it's clawing it back and like delaying Siri indefinitely.

1381
01:07:51,400 --> 01:07:53,880
Speaker 2: I mean, I don't know if I agree on that one,

1382
01:07:54,280 --> 01:07:56,880
Speaker 2: because the thing they're delaying is the thing that everyone wanted.

1383
01:07:57,360 --> 01:07:59,760
Speaker 2: I think they can't make it work because the thing

1384
01:07:59,840 --> 01:08:02,160
Speaker 2: that delaying is the contextually a wes are right.

1385
01:08:03,320 --> 01:08:05,360
Speaker 3: Yes, their quote unquote delaying it.

1386
01:08:06,000 --> 01:08:09,840
Speaker 2: It doesn't exist, it never existed. Yeah, we'll see Apples washed.

1387
01:08:11,800 --> 01:08:12,720
Speaker 1: I mean, but that's the thing.

1388
01:08:12,760 --> 01:08:15,960
Speaker 3: It's the most brand conscious company on the planet. And

1389
01:08:17,200 --> 01:08:20,800
Speaker 3: I wrote like when they did their June revelation of

1390
01:08:20,920 --> 01:08:23,120
Speaker 3: the SERIAI is going to come out, and they said

1391
01:08:23,120 --> 01:08:24,280
Speaker 3: it was going to come out in the fall, and

1392
01:08:24,360 --> 01:08:26,040
Speaker 3: then it was coming out in the spring, and now

1393
01:08:26,080 --> 01:08:30,519
Speaker 3: it's not coming out ever, question Mark. But throughout the

1394
01:08:30,680 --> 01:08:35,559
Speaker 3: whole like two hour presentation, the letters AI were never spoken.

1395
01:08:35,720 --> 01:08:39,600
Speaker 3: Artificial was never spoken. It was Apple Intelligence. We're doing this,

1396
01:08:39,840 --> 01:08:42,679
Speaker 3: We're doing our own thing. It's not you know, because

1397
01:08:42,720 --> 01:08:45,800
Speaker 3: they already understood that when you say something is like

1398
01:08:45,960 --> 01:08:47,559
Speaker 3: that looks like it was generated by AI.

1399
01:08:47,720 --> 01:08:51,320
Speaker 2: You're saying it looks like shit, you know, And the

1400
01:08:51,400 --> 01:08:54,120
Speaker 2: suggestions are also really bad too. I've had like, over

1401
01:08:54,120 --> 01:08:55,640
Speaker 2: the last few weeks, a few people give me some

1402
01:08:55,720 --> 01:08:58,320
Speaker 2: bad news in their lives and the responses it gives

1403
01:08:58,520 --> 01:09:00,800
Speaker 2: are really funny. Oh no, it'd be like someone telling

1404
01:09:00,840 --> 01:09:03,400
Speaker 2: me something bad happen and it's like oh, or like

1405
01:09:03,560 --> 01:09:06,479
Speaker 2: I'm like, what was the worst one I had? It

1406
01:09:06,640 --> 01:09:09,320
Speaker 2: was like that sounds difficult, and it's like a paragraph

1407
01:09:09,400 --> 01:09:12,600
Speaker 2: long thing about like a family thing they had, like

1408
01:09:13,040 --> 01:09:15,680
Speaker 2: and it's not even like got like any juice to it,

1409
01:09:16,200 --> 01:09:19,439
Speaker 2: like I didn't read too long. Those would be funny suggestion,

1410
01:09:19,680 --> 01:09:22,640
Speaker 2: but like it can't even. It's it's proof that I

1411
01:09:22,680 --> 01:09:25,120
Speaker 2: think that these large language models don't actually well, they

1412
01:09:25,160 --> 01:09:27,320
Speaker 2: don't understand anything. They don't know. AN think they're not conscious,

1413
01:09:27,320 --> 01:09:31,880
Speaker 2: but it's like they're really bad at understanding words like

1414
01:09:31,960 --> 01:09:34,479
Speaker 2: people like, oh, they make some mistakes. They're bad at

1415
01:09:34,640 --> 01:09:39,040
Speaker 2: basic contextual stuff. And we had Victoria Song from the

1416
01:09:39,120 --> 01:09:41,200
Speaker 2: Vergion the other day and she was talking about high

1417
01:09:41,240 --> 01:09:43,760
Speaker 2: context and low context languages and I said, this is

1418
01:09:43,840 --> 01:09:46,599
Speaker 2: I can only speak English. I imagine not being able

1419
01:09:46,640 --> 01:09:49,000
Speaker 2: to read or speak in any others and that it

1420
01:09:49,240 --> 01:09:52,200
Speaker 2: really fumbles those and I if there's if you're listening,

1421
01:09:52,240 --> 01:09:54,840
Speaker 2: you want to email me anything about this research? How

1422
01:09:54,920 --> 01:09:57,080
Speaker 2: the fuck does this even scale if it can't like, oh,

1423
01:09:57,120 --> 01:10:01,040
Speaker 2: we're replacing translators, great, you're replacing translators with things that

1424
01:10:01,200 --> 01:10:07,160
Speaker 2: sometimes translate, right. Sometimes sometimes it just feels also inherently

1425
01:10:07,400 --> 01:10:10,960
Speaker 2: like that feels like an actual alignment problem. By the way,

1426
01:10:11,080 --> 01:10:13,559
Speaker 2: that right there, that feels like an actual safety problem.

1427
01:10:13,800 --> 01:10:17,120
Speaker 2: Like hey, if we're relying on something to translate and

1428
01:10:17,240 --> 01:10:21,600
Speaker 2: it translates words wrong, and you know, especially in other languages,

1429
01:10:21,800 --> 01:10:26,760
Speaker 2: subtle differences can change everything. Maybe that stange No, no, no,

1430
01:10:26,840 --> 01:10:29,040
Speaker 2: We've got the computer wake up in like two weeks,

1431
01:10:29,320 --> 01:10:31,320
Speaker 2: and then it's gonna be angry. And that's the other

1432
01:10:31,400 --> 01:10:33,360
Speaker 2: thing we're gonna make AGI, and we think it's not

1433
01:10:33,400 --> 01:10:35,960
Speaker 2: gonna be pissed off at us. I don't mean Rococo's

1434
01:10:36,000 --> 01:10:38,760
Speaker 2: modern basilisk or whatever. I mean, just like if it

1435
01:10:38,800 --> 01:10:40,240
Speaker 2: wakes up and looks at the world and goes these

1436
01:10:40,320 --> 01:10:43,519
Speaker 2: fucking morons, like you need to watch Person of Interest

1437
01:10:43,560 --> 01:10:45,120
Speaker 2: if you haven't. One of the best shows on actually

1438
01:10:45,160 --> 01:10:48,080
Speaker 2: on AGI, Like genuinely you need to watch Person of Interest.

1439
01:10:48,439 --> 01:10:51,519
Speaker 2: Because you will see how that could happen when you

1440
01:10:51,680 --> 01:10:55,840
Speaker 2: allow a quote unquote perfect computer to make our decisions. Also,

1441
01:10:55,920 --> 01:10:58,720
Speaker 2: when has a computer being particularly good at decision making?

1442
01:10:58,720 --> 01:11:00,439
Speaker 2: I don't know. I feel like so much of this

1443
01:11:00,600 --> 01:11:04,519
Speaker 2: revolution quote unquote is based on just the assumption that

1444
01:11:04,600 --> 01:11:08,160
Speaker 2: the computer makes great decisions and oftentimes doesn't.

1445
01:11:09,760 --> 01:11:12,760
Speaker 1: It often does not. Why would I think that the

1446
01:11:13,120 --> 01:11:16,639
Speaker 1: same search function in Apple that cannot find a document

1447
01:11:16,760 --> 01:11:19,040
Speaker 1: that I know what the name is, I'm searching for it.

1448
01:11:19,320 --> 01:11:21,280
Speaker 1: Why would I think that that same computer is going

1449
01:11:21,320 --> 01:11:24,959
Speaker 1: to be able to make wise decisions about my life, finance,

1450
01:11:25,040 --> 01:11:27,040
Speaker 1: and personal relationships.

1451
01:11:26,720 --> 01:11:28,160
Speaker 2: Because that's Apple and this is AI.

1452
01:11:28,520 --> 01:11:32,559
Speaker 1: Oh that's true. That's that's how I'll show myself out.

1453
01:11:32,720 --> 01:11:35,439
Speaker 3: I don't know how much is AI versus just like

1454
01:11:35,520 --> 01:11:39,160
Speaker 3: a good translation app, Like I genuinely don't know.

1455
01:11:39,680 --> 01:11:43,000
Speaker 1: Like well, it's because AI is such a squishy term

1456
01:11:43,080 --> 01:11:45,840
Speaker 1: that you really don't like in some way, Like I

1457
01:11:46,280 --> 01:11:49,720
Speaker 1: guess AI could be expanded to include a lot of

1458
01:11:49,840 --> 01:11:51,320
Speaker 1: modern computing.

1459
01:11:51,160 --> 01:11:54,599
Speaker 3: Like I can see travel and like emergency situations where

1460
01:11:54,640 --> 01:11:58,080
Speaker 3: you need where like a good AI translator would be

1461
01:11:58,160 --> 01:12:00,840
Speaker 3: like a real life saver, just as the small A

1462
01:12:00,960 --> 01:12:05,720
Speaker 3: side I was just in Mexico and my step kids

1463
01:12:05,720 --> 01:12:08,240
Speaker 3: were using Google Translate and we were like kind of

1464
01:12:08,560 --> 01:12:11,479
Speaker 3: remembering Spanish, and you know, blah blah blah. Go into

1465
01:12:11,520 --> 01:12:13,920
Speaker 3: a coffee shop and I wanted to order a flat white,

1466
01:12:14,720 --> 01:12:16,439
Speaker 3: and so I used Google Translate to say, like, how

1467
01:12:16,479 --> 01:12:18,800
Speaker 3: would you order a flat white in Spanish? And it's

1468
01:12:18,840 --> 01:12:23,160
Speaker 3: said to order a blanco plano, which means flat white.

1469
01:12:23,800 --> 01:12:27,479
Speaker 3: But like across Mexico City there are wonderful coffee shops

1470
01:12:27,479 --> 01:12:29,560
Speaker 3: and you know what, they call them flat whites.

1471
01:12:29,640 --> 01:12:31,719
Speaker 1: Like an Australian coffee.

1472
01:12:33,040 --> 01:12:35,439
Speaker 3: I learned that very quickly with the help of Reddit,

1473
01:12:36,040 --> 01:12:38,320
Speaker 3: because I went to the barista and ordered a blanco

1474
01:12:38,400 --> 01:12:46,360
Speaker 3: plano and they were like, are you crazy, gringos? Yeah yeah,

1475
01:12:46,560 --> 01:12:51,320
Speaker 3: I mean like it's the functionality is very limited on

1476
01:12:51,439 --> 01:12:53,760
Speaker 3: those things, and it's just like also, it gets back

1477
01:12:53,800 --> 01:12:57,040
Speaker 3: to like, if it's one hundred percent reliable, it's great.

1478
01:12:57,240 --> 01:12:59,160
Speaker 3: If it's ninety eight percent reliable, it sucks.

1479
01:13:00,200 --> 01:13:02,840
Speaker 2: And just as an aside, did any of you hear

1480
01:13:02,880 --> 01:13:06,439
Speaker 2: about like the latest like quasi fraudulent thing with Jony

1481
01:13:06,520 --> 01:13:10,280
Speaker 2: Ive that's happening. I just saw the herd so Sam

1482
01:13:10,320 --> 01:13:15,840
Speaker 2: Altman and Jony I've founded a hardware startup that has

1483
01:13:15,920 --> 01:13:17,920
Speaker 2: built nothing. There is a thing they claim, there's a

1484
01:13:18,000 --> 01:13:22,400
Speaker 2: phone without a screen. And Open Ai a company run

1485
01:13:22,439 --> 01:13:26,800
Speaker 2: by Sam Moltman, owned forrincipally by Sam Moltman, and Microsoft

1486
01:13:26,960 --> 01:13:29,400
Speaker 2: is going to buy for half a billion dollars this

1487
01:13:29,520 --> 01:13:32,479
Speaker 2: company that has built nothing, co founded by Sam Moltman.

1488
01:13:33,400 --> 01:13:37,640
Speaker 2: I feel like this should be a one law against this.

1489
01:13:38,320 --> 01:13:40,840
Speaker 2: But it's just like what have they been doing? And

1490
01:13:40,920 --> 01:13:43,240
Speaker 2: this is just it's kind of cliche to say, like

1491
01:13:43,840 --> 01:13:45,439
Speaker 2: quote The Big Short, but like a big part of

1492
01:13:45,439 --> 01:13:47,120
Speaker 2: the beginning of that movie is talking about the increase

1493
01:13:47,160 --> 01:13:50,360
Speaker 2: in fraud and scams, and it really feels like we're

1494
01:13:50,439 --> 01:13:53,960
Speaker 2: getting there and rip to the Humane pin by the way,

1495
01:13:54,640 --> 01:13:58,280
Speaker 2: rest in piss. You won't be missed. Motherfucker's two management consultants.

1496
01:13:58,320 --> 01:14:03,040
Speaker 2: But like in Dignity each year Jesse, Jesse, Lou Rabbit

1497
01:14:03,120 --> 01:14:06,160
Speaker 2: are one your next motherfucker when you when your ship's gone,

1498
01:14:06,200 --> 01:14:09,320
Speaker 2: all be honking and laughing. Your customers should suit you.

1499
01:14:10,479 --> 01:14:14,400
Speaker 1: The description so my colleagues, the information reported this journey,

1500
01:14:14,439 --> 01:14:16,800
Speaker 1: I've Sam Moltman news and the description for the device

1501
01:14:16,920 --> 01:14:20,240
Speaker 1: really makes me chuckle. Designs for the AI device are

1502
01:14:20,280 --> 01:14:23,559
Speaker 1: still early and haven't been finalized that people said. Potential

1503
01:14:23,600 --> 01:14:26,920
Speaker 1: designs include a quote unquote phone without a screen and

1504
01:14:27,080 --> 01:14:31,160
Speaker 1: AI enabled household devices. Others close to the project are

1505
01:14:31,200 --> 01:14:35,400
Speaker 1: adamant that it is quote not a phone, and they're

1506
01:14:35,400 --> 01:14:39,439
Speaker 1: gonna spend They've discussed spending upwards of five hundred million

1507
01:14:39,520 --> 01:14:40,320
Speaker 1: on this company.

1508
01:14:40,360 --> 01:14:42,280
Speaker 3: Like a bad philosophy class where it's like, what is

1509
01:14:42,320 --> 01:14:43,880
Speaker 3: a phone that's not a phone?

1510
01:14:45,680 --> 01:14:50,080
Speaker 2: Semi for beginners, Jesus fucking Christ, Oh my god, And

1511
01:14:50,200 --> 01:14:52,680
Speaker 2: that's like that's the thing as well. This feels like

1512
01:14:52,720 --> 01:14:54,559
Speaker 2: a thing that tech media needs to be on as well.

1513
01:14:54,680 --> 01:14:57,519
Speaker 2: Just someone needs to say I'll be saying it like

1514
01:14:58,120 --> 01:15:00,840
Speaker 2: this is bordering on fra all, Like it seems like

1515
01:15:00,920 --> 01:15:03,080
Speaker 2: it must be legal, because otherwise there would be some

1516
01:15:03,280 --> 01:15:05,880
Speaker 2: sort of authority, right, you can't do anything illegal without

1517
01:15:05,880 --> 01:15:10,160
Speaker 2: anything happening. Hm hmm. But it's like, this is one

1518
01:15:10,200 --> 01:15:12,200
Speaker 2: of the most egregious fucking things I've ever seen. This

1519
01:15:12,320 --> 01:15:15,920
Speaker 2: is a guy handing himself money one hand. This is

1520
01:15:16,720 --> 01:15:19,559
Speaker 2: should be fraught, like this is like how is this ethical?

1521
01:15:19,760 --> 01:15:23,880
Speaker 2: And everyone's just like this, Kevin Ruce, maybe you should

1522
01:15:23,920 --> 01:15:25,560
Speaker 2: get on this ship find out what the phone that

1523
01:15:25,640 --> 01:15:28,519
Speaker 2: isn't the phone is what the fuck? And also household

1524
01:15:28,520 --> 01:15:32,000
Speaker 2: appliances with AI, maybe like something with the screen and

1525
01:15:32,080 --> 01:15:34,720
Speaker 2: the speaker that you could say like a word to

1526
01:15:34,840 --> 01:15:36,320
Speaker 2: it and would wake up and play music.

1527
01:15:37,200 --> 01:15:41,080
Speaker 3: Yeah, a M with AI just declared bankruptcy.

1528
01:15:42,320 --> 01:15:45,639
Speaker 2: On the blockchain rumored that rumored dead. Why roomber dead?

1529
01:15:46,240 --> 01:15:48,360
Speaker 3: I think they did. I don't know. Actually, I remember

1530
01:15:48,439 --> 01:15:51,400
Speaker 3: I read the headline they were to be acquired.

1531
01:15:50,880 --> 01:15:54,160
Speaker 1: By Amazon, but I think the deal fell through under

1532
01:15:54,800 --> 01:16:00,400
Speaker 1: Lena Khan's FDC I had assumed. And then also a

1533
01:16:00,479 --> 01:16:02,599
Speaker 1: one quick note on the Johnny I Have Similton thing.

1534
01:16:02,720 --> 01:16:06,160
Speaker 1: I guess it's notable that oldman has been working closely

1535
01:16:06,240 --> 01:16:08,200
Speaker 1: with the product but is not a co founder, and

1536
01:16:08,360 --> 01:16:10,799
Speaker 1: whether he has an economic stake in the hardware project

1537
01:16:10,880 --> 01:16:11,479
Speaker 1: is unclear.

1538
01:16:12,360 --> 01:16:15,160
Speaker 6: Yeah, you know, it just seems to be working closely,

1539
01:16:18,960 --> 01:16:21,479
Speaker 6: like he's just hanging out there on taking a salary

1540
01:16:21,520 --> 01:16:22,439
Speaker 6: in an equity position.

1541
01:16:23,000 --> 01:16:25,360
Speaker 1: I do think it's very interesting all of these different

1542
01:16:25,439 --> 01:16:28,240
Speaker 1: AI device startups that have popped up in the last

1543
01:16:28,240 --> 01:16:29,920
Speaker 1: couple of years, and my question for them is always

1544
01:16:30,040 --> 01:16:36,160
Speaker 1: just like, to what end? People didn't like Amazon Alexa.

1545
01:16:36,520 --> 01:16:38,040
Speaker 2: And it also lost ton of money.

1546
01:16:38,280 --> 01:16:41,880
Speaker 1: Yeah, and Amazon's still trying to make it work. Series

1547
01:16:42,080 --> 01:16:45,439
Speaker 1: never been super popular, and I just don't get like

1548
01:16:46,040 --> 01:16:50,160
Speaker 1: one of my co hosts on the podcast We're Gonna

1549
01:16:50,160 --> 01:16:52,719
Speaker 1: Intelligent Machines is obsessed with all these devices just because

1550
01:16:52,720 --> 01:16:54,200
Speaker 1: he's like one of those tech guys.

1551
01:16:54,320 --> 01:16:54,519
Speaker 4: Leo.

1552
01:16:54,640 --> 01:16:56,880
Speaker 1: Yes he's and we love to make fun of you,

1553
01:16:57,680 --> 01:17:01,559
Speaker 1: but he his late device is this thing called a Bee.

1554
01:17:02,160 --> 01:17:04,920
Speaker 2: We just have Victoria's song on Tokio about that records.

1555
01:17:04,920 --> 01:17:10,120
Speaker 1: It records everything all the time and then makes puts

1556
01:17:10,200 --> 01:17:12,479
Speaker 1: that up in the cloud and then I guess doesn't

1557
01:17:12,520 --> 01:17:15,040
Speaker 1: store the full transcripts, but does store a little AI

1558
01:17:15,240 --> 01:17:19,160
Speaker 1: generated description of everything you did and whoever you talk

1559
01:17:19,280 --> 01:17:21,880
Speaker 1: to that day. And there's no way. I mean, he's

1560
01:17:21,920 --> 01:17:25,320
Speaker 1: in Leo's in California, which is not a one party recording.

1561
01:17:25,880 --> 01:17:29,360
Speaker 1: You gotta get consent from everybody to record, and the

1562
01:17:29,439 --> 01:17:32,800
Speaker 1: Bee is not doing that. But it's just baffling to

1563
01:17:32,840 --> 01:17:35,160
Speaker 1: me because I'm just like, I guess He's like, well,

1564
01:17:35,240 --> 01:17:36,960
Speaker 1: it could be nice to have record of all of

1565
01:17:37,040 --> 01:17:39,040
Speaker 1: my days all the time, And I'm like, I guess,

1566
01:17:39,280 --> 01:17:40,639
Speaker 1: but to what ends.

1567
01:17:40,760 --> 01:17:45,400
Speaker 2: Record write a diary?

1568
01:17:45,479 --> 01:17:48,880
Speaker 3: There's literally a Black Mirror episode about that.

1569
01:17:49,040 --> 01:17:50,519
Speaker 1: I believe first episode.

1570
01:17:51,360 --> 01:17:54,680
Speaker 3: Everyone has like a recording device, and and then it does.

1571
01:17:55,080 --> 01:17:56,560
Speaker 3: When I was when you were talking about this on

1572
01:17:56,640 --> 01:18:00,479
Speaker 3: the show, I was listening thinking, like in this black thing,

1573
01:18:00,600 --> 01:18:04,519
Speaker 3: it reminded me that like when Facebook started having like

1574
01:18:04,680 --> 01:18:07,479
Speaker 3: all your photos collected under your photos, and like how

1575
01:18:07,760 --> 01:18:11,800
Speaker 3: we started reliving so many experiences of time, like you

1576
01:18:11,880 --> 01:18:14,960
Speaker 3: never for all that, and like look at how happy

1577
01:18:15,000 --> 01:18:17,120
Speaker 3: you were like six years ago, you know, like and

1578
01:18:17,240 --> 01:18:22,400
Speaker 3: it creates this like cycle. Like imagine if every interaction,

1579
01:18:22,680 --> 01:18:27,679
Speaker 3: every like romantic interaction, every sad inner, everything, you could

1580
01:18:27,760 --> 01:18:31,560
Speaker 3: replay back to yourself. It sounds like a nightmare to me.

1581
01:18:31,800 --> 01:18:34,080
Speaker 1: I do think it's also just a night Like humans

1582
01:18:34,160 --> 01:18:37,639
Speaker 1: were not built socially to exist in a world where

1583
01:18:37,760 --> 01:18:41,760
Speaker 1: every interaction is recorded and searchable with everyone forever, Like

1584
01:18:42,160 --> 01:18:46,240
Speaker 1: you would never have a single friend. Romantic relationships would dissolve.

1585
01:18:47,479 --> 01:18:51,920
Speaker 2: The spotless. But even then, like memory is vastly different

1586
01:18:51,920 --> 01:18:55,280
Speaker 2: to the experience of collecting it, like just existing like

1587
01:18:55,360 --> 01:18:57,960
Speaker 2: we all brainslow. I don't know, my brain just goes everywhere.

1588
01:18:58,280 --> 01:19:00,760
Speaker 2: But like compared to memory, which can be all crystalline

1589
01:19:01,320 --> 01:19:04,840
Speaker 2: and wrong, you can just remember something, you can remember

1590
01:19:04,880 --> 01:19:06,720
Speaker 2: a subtle detail wrong, or you can just fill in

1591
01:19:06,840 --> 01:19:08,479
Speaker 2: the gaps. Memory sucks, so.

1592
01:19:08,439 --> 01:19:10,719
Speaker 5: It doesn't having like a device that constantly records everything

1593
01:19:11,400 --> 01:19:14,280
Speaker 5: a road at the impulse or maybe the drive to

1594
01:19:14,320 --> 01:19:16,479
Speaker 5: be as present, you know, because you're like, well, it's

1595
01:19:16,960 --> 01:19:17,519
Speaker 5: refer to it.

1596
01:19:17,640 --> 01:19:20,719
Speaker 1: But this has also got huge privacy implications where suddenly

1597
01:19:21,160 --> 01:19:23,439
Speaker 1: the cops could just be like, yeah, we're just gonna

1598
01:19:23,479 --> 01:19:26,360
Speaker 1: take a recording, We're just gonna subpoenat everybody who was

1599
01:19:26,400 --> 01:19:29,560
Speaker 1: in this area's b device and then suddenly get a

1600
01:19:29,600 --> 01:19:33,600
Speaker 1: recording of everyone's days ever that was just happened to

1601
01:19:33,640 --> 01:19:35,320
Speaker 1: be in this place because we think a crime could

1602
01:19:35,320 --> 01:19:35,880
Speaker 1: have happened there.

1603
01:19:36,080 --> 01:19:38,040
Speaker 2: But I think that there's an overarching thing to everything

1604
01:19:38,040 --> 01:19:40,120
Speaker 2: we're talking about, which is these are products made by

1605
01:19:40,160 --> 01:19:42,880
Speaker 2: people that haven't made anything useful a little while, and

1606
01:19:43,080 --> 01:19:45,679
Speaker 2: everything is being funded based on what used to work.

1607
01:19:45,720 --> 01:19:48,000
Speaker 2: What used to work was you make a thing, people

1608
01:19:48,120 --> 01:19:49,680
Speaker 2: buy it, and then you sell it to someone else

1609
01:19:49,720 --> 01:19:52,200
Speaker 2: to take it public. This only worked until about twenty fifteen.

1610
01:19:52,439 --> 01:19:55,080
Speaker 2: It's not just as a zero in just free era thing.

1611
01:19:55,200 --> 01:20:00,839
Speaker 2: It's we have increasingly taken away the creation of anything

1612
01:20:00,960 --> 01:20:04,320
Speaker 2: valuable in tech from people who experience real life, Like

1613
01:20:04,439 --> 01:20:08,519
Speaker 2: our biggest CEOs are Sam Altman Wario Ama Day sund

1614
01:20:08,600 --> 01:20:13,720
Speaker 2: up As Shy NBA, former McKinsey Sachnadella MBA, I mean

1615
01:20:14,160 --> 01:20:16,280
Speaker 2: Tim Cook NBA. Like, these are all people that don't

1616
01:20:16,360 --> 01:20:19,280
Speaker 2: really interact with people anymore. And the problems the people

1617
01:20:19,360 --> 01:20:22,880
Speaker 2: in power are not engineers. They're not even startup founders anymore.

1618
01:20:22,880 --> 01:20:26,519
Speaker 2: They're fucking business people making things that they think they

1619
01:20:26,560 --> 01:20:29,280
Speaker 2: could sell, things that could grow the right economy. Of course,

1620
01:20:30,520 --> 01:20:32,559
Speaker 2: and we're at the kind of the pornographic point where

1621
01:20:32,600 --> 01:20:34,640
Speaker 2: it's like a guy being like, what could a what

1622
01:20:34,680 --> 01:20:36,600
Speaker 2: does AI do? You can just throw a bunch of

1623
01:20:36,680 --> 01:20:38,560
Speaker 2: data and give you insights? Well, what if we just

1624
01:20:38,640 --> 01:20:40,840
Speaker 2: collect a date on everything happening around us?

1625
01:20:40,920 --> 01:20:41,080
Speaker 1: Ever?

1626
01:20:41,520 --> 01:20:43,760
Speaker 2: That would be good. Then you could reflect on things.

1627
01:20:43,840 --> 01:20:47,679
Speaker 2: That's what people do, right And I actually genuinely think

1628
01:20:48,320 --> 01:20:50,679
Speaker 2: there is only one question to ask the be founder

1629
01:20:50,680 --> 01:20:53,720
Speaker 2: and that's are you wearing one of these now? And

1630
01:20:53,920 --> 01:20:56,280
Speaker 2: how often do you use this? Because if they use

1631
01:20:56,360 --> 01:20:58,519
Speaker 2: it all the time, I actually kind of respect them.

1632
01:20:58,720 --> 01:21:01,439
Speaker 2: I guarantee they don't. I guarantee they don't, and they'll

1633
01:21:01,479 --> 01:21:04,080
Speaker 2: they'll probably say something around a lot of privileged information

1634
01:21:04,200 --> 01:21:07,599
Speaker 2: as opposed to everyone else's not important. And this fucking Joni. Oh,

1635
01:21:07,720 --> 01:21:09,960
Speaker 2: it's going to be a phone with that without a screen?

1636
01:21:10,320 --> 01:21:11,760
Speaker 2: What can you do with it? I don't know. I

1637
01:21:11,840 --> 01:21:14,519
Speaker 2: haven't thought that far ahead. I only get paid fifteen

1638
01:21:14,560 --> 01:21:16,000
Speaker 2: million dollars a year to do this.

1639
01:21:16,840 --> 01:21:19,120
Speaker 1: Just as also, who wants a phone without a screen.

1640
01:21:19,160 --> 01:21:21,080
Speaker 1: The screen's the best part. I love the screen.

1641
01:21:21,120 --> 01:21:21,439
Speaker 6: They don't.

1642
01:21:21,560 --> 01:21:22,719
Speaker 1: I love to hate the screens.

1643
01:21:22,920 --> 01:21:25,360
Speaker 2: But they don't talk to anyone. They don't have human experience.

1644
01:21:25,360 --> 01:21:28,479
Speaker 2: They don't have friends that like they have friends who

1645
01:21:28,520 --> 01:21:31,720
Speaker 2: are all like have fifty million dollars in the bank

1646
01:21:31,720 --> 01:21:33,680
Speaker 2: account at any time. They just like exist in a

1647
01:21:33,760 --> 01:21:36,640
Speaker 2: different difficulty level. They're all going at very easy. They

1648
01:21:36,680 --> 01:21:39,760
Speaker 2: don't really have like predators of any kind. They don't

1649
01:21:39,760 --> 01:21:42,519
Speaker 2: really have experiences, so they're what they experience in life

1650
01:21:42,600 --> 01:21:44,240
Speaker 2: is when you have to work out what you enjoy.

1651
01:21:44,360 --> 01:21:46,400
Speaker 2: And because they enjoy nothing, all they can do is

1652
01:21:46,400 --> 01:21:48,439
Speaker 2: come up with ideas. That's why the rabbit are one.

1653
01:21:48,600 --> 01:21:49,800
Speaker 2: Oh what do people do?

1654
01:21:50,240 --> 01:21:50,360
Speaker 1: Uh?

1655
01:21:51,439 --> 01:21:52,320
Speaker 2: Auto McDonald's.

1656
01:21:52,360 --> 01:21:52,840
Speaker 1: Can it do it?

1657
01:21:53,840 --> 01:21:56,599
Speaker 2: Not? Really? But it also could take a photo of something,

1658
01:21:56,640 --> 01:21:58,320
Speaker 2: it could be pixelated.

1659
01:21:57,920 --> 01:21:59,920
Speaker 1: That could all kind of order an uber through it.

1660
01:22:00,240 --> 01:22:03,320
Speaker 2: Maybe what was great was the rabbit launch. The rabbit

1661
01:22:03,439 --> 01:22:05,360
Speaker 2: launch and he tried to order McDonald's live and it

1662
01:22:05,439 --> 01:22:07,559
Speaker 2: just didn't work. It took like five minutes to fail.

1663
01:22:08,160 --> 01:22:10,720
Speaker 2: It was And that's the thing, Like, I feel like

1664
01:22:10,880 --> 01:22:13,439
Speaker 2: when this hype cycle ends, the tech media needs to

1665
01:22:13,560 --> 01:22:18,439
Speaker 2: just be aggressively like, hey, look, fool me, thrice, shame

1666
01:22:18,520 --> 01:22:23,000
Speaker 2: on me. Like maybe maybe next time around, we can't

1667
01:22:23,000 --> 01:22:24,800
Speaker 2: ask the questions I was asking twenty twenty one, where

1668
01:22:24,800 --> 01:22:27,519
Speaker 2: it's like, what does this do? Who is it for?

1669
01:22:28,120 --> 01:22:31,960
Speaker 2: And if anyone says it could address millions of papers,

1670
01:22:32,040 --> 01:22:34,680
Speaker 2: like have you talked to one of the motherfucker one

1671
01:22:34,720 --> 01:22:37,320
Speaker 2: of them? I think we can wrap it up there, though,

1672
01:22:37,560 --> 01:22:39,400
Speaker 2: I think, Alison, where can people find you?

1673
01:22:39,920 --> 01:22:40,040
Speaker 6: Hi?

1674
01:22:40,160 --> 01:22:42,839
Speaker 3: You can find me at CNN dot com, slash Nightcap.

1675
01:22:43,439 --> 01:22:45,759
Speaker 3: I write the CNN Business Nightcap. It's in your inbox

1676
01:22:45,840 --> 01:22:46,519
Speaker 3: four nights a week.

1677
01:22:46,720 --> 01:22:47,479
Speaker 2: Oh yeah, Ed.

1678
01:22:48,520 --> 01:22:51,160
Speaker 5: I write a newsletter on Substack, the Tech Bubble. I

1679
01:22:51,280 --> 01:22:56,360
Speaker 5: co host podcast This Machine Kills with Jathan Sadowski, and

1680
01:22:56,680 --> 01:23:00,200
Speaker 5: I'm on Twitter a Big Black Jacobin on Blue Sky too, Yeah,

1681
01:23:00,200 --> 01:23:02,200
Speaker 5: Blue Sky at vwudon Guasso Junior dot com.

1682
01:23:04,040 --> 01:23:07,920
Speaker 1: You can read my work at the Information I also

1683
01:23:08,479 --> 01:23:12,360
Speaker 1: host a podcast called Intelligent Machines and You can find

1684
01:23:12,439 --> 01:23:14,760
Speaker 1: me on Twitter at Paris Martino or on Blue Sky

1685
01:23:15,000 --> 01:23:16,240
Speaker 1: at Paris dot NYC.

1686
01:23:16,600 --> 01:23:19,120
Speaker 2: And you can find me at at Edzitron dot com

1687
01:23:19,160 --> 01:23:22,360
Speaker 2: on blue Sky Google who destroyed Google Search? Click the

1688
01:23:22,400 --> 01:23:25,360
Speaker 2: first link. It's me. I destroyed Google Search along with

1689
01:23:25,400 --> 01:23:27,799
Speaker 2: public Ragaban. Fuck you dude. If you want to support

1690
01:23:27,840 --> 01:23:30,080
Speaker 2: this podcast, you should go to the Webbies. I will

1691
01:23:30,120 --> 01:23:32,120
Speaker 2: be putting the link in there. I need your help.

1692
01:23:32,400 --> 01:23:35,040
Speaker 2: I've never won an award in my life. It's the

1693
01:23:35,120 --> 01:23:38,360
Speaker 2: best episode. It's sorry, best business podcast episode. If we

1694
01:23:38,520 --> 01:23:41,439
Speaker 2: are winning right now, please help me. Please help me

1695
01:23:41,479 --> 01:23:43,479
Speaker 2: win this and if I need to incentivize you further,

1696
01:23:43,800 --> 01:23:46,679
Speaker 2: we are beating Scott Galloway. If you wanted to beat

1697
01:23:46,720 --> 01:23:49,400
Speaker 2: Scott Galloway, you need to vote on this. Thank you

1698
01:23:49,520 --> 01:23:59,600
Speaker 2: so much for listening everyone, Thank you for listening to

1699
01:23:59,640 --> 01:24:02,639
Speaker 2: Better Line. The editor and composer of the Better Offline

1700
01:24:02,680 --> 01:24:05,320
Speaker 2: theme song is Matasowski. You can check out more of

1701
01:24:05,400 --> 01:24:08,960
Speaker 2: his music and audio projects at Matasowski dot com, M

1702
01:24:09,040 --> 01:24:13,120
Speaker 2: A T T O S O W s KI dot com.

1703
01:24:13,880 --> 01:24:16,120
Speaker 2: You can email me at easy at Better Offline dot

1704
01:24:16,200 --> 01:24:18,439
Speaker 2: com or visit better Offline dot com to find more

1705
01:24:18,479 --> 01:24:21,800
Speaker 2: podcast links and of course my newsletter. I also really

1706
01:24:21,880 --> 01:24:24,080
Speaker 2: recommend you go to chat dot where's youreaed dot at

1707
01:24:24,200 --> 01:24:26,599
Speaker 2: to visit the discord, and go to our slash Better

1708
01:24:26,600 --> 01:24:29,800
Speaker 2: Offline to check out our reddit. Thank you so much

1709
01:24:29,840 --> 01:24:30,320
Speaker 2: for listening.

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01:24:31,200 --> 01:24:33,880
Speaker 4: Better Offline is a production of cool Zone Media. For

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01:24:34,000 --> 01:24:37,160
Speaker 4: more from cool Zone Media, visit our website cool Zonemedia

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Speaker 4: dot com, or check us out on the iHeartRadio app,

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01:24:40,120 --> 01:24:42,520
Speaker 4: Apple Podcasts, or wherever you get your podcasts.

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Speaker 2: Yes,