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S1: All right. Welcome to episode 486. This is Daniel Missler.

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S1: All right. A lot of stuff to share this week.

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S1: Let's see here. Um, okay, so I had a debate

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S1: with Marcus Hutchins about I, I don't know if you've

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S1: seen him talking about AI online, but he basically, uh,

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S1: him and a number of my friends, uh, Christopher Hoff

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S1: being one of them, they basically take opportunity whenever they

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S1: can to to basically point out how stupid it is

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S1: and how it makes tons of mistakes and how it's

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S1: basically all folly and, uh, how everyone's overestimating it. And

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S1: the whole the whole thing is going to come crashing

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S1: down and a bunch of scams, all for the purpose

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S1: of making money. And it's all just hype and marketing.

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S1: So that's kind of their position. Uh, Marcus is a

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S1: particularly good at making these points in a very humorous way.

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S1: And of course, he has a huge following because he

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S1: is Marcus Hutchins, probably the most famous malware researcher that

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S1: we've ever known. And, uh, because I have the opposite view,

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S1: I wanted to have this debate, so we had the debate.

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S1: It's on YouTube now. I highly suggest you go check

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S1: it out. And of course, the link is in the newsletter.

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S1: The conversation went extremely well. It was very cordial. It

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S1: was very substantive. I think we both made points that

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S1: the other one received and appreciated. So I think it

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S1: was overall just great. Now, a lot of the feedback

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S1: has been that we weren't actually disagreeing and that, uh,

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S1: both things could be true. And this makes me think

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S1: we went a little bit too hard on being nice. And, uh,

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S1: maybe for part two, which we're already talking about, talking

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S1: about doing maybe for part two, we need to sort

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S1: of tighten it up, um, sharpen the pencils a little

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S1: bit and just be like, hey, look, you know, I

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S1: think you're wrong about this particular point. Let's let's debate that.

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S1: And I think we got there. I think we did that.

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S1: I mean, it was over a two hour conversation, so

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S1: hopefully you could take that away in the conversation, but

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S1: I think in part two I at least myself, are

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S1: going to try to be a little bit tighter with

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S1: my arguments and a little bit more direct. And, um,

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S1: I don't want to say confrontational, but confrontational in the

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S1: most cordial way possible to basically try to pin down

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S1: these arguments and say, no, you know, this is where

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S1: I actually do disagree with you. I think you're mistaken

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S1: about that. And I think that will just be more

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S1: useful to people who are actually trying to make up

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S1: their minds. And most importantly, I think it'd be more

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S1: useful to Marcus and his points and to myself and

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S1: my points of like, if he thinks I'm wrong about

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S1: this and I think he's wrong about this, we're ultimately

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S1: trying to provide a service to our audiences and to

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S1: audiences we haven't talked to yet, and future audiences. We're

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S1: trying to make that point. We're trying to, you know, um,

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S1: not just have a discussion, but but also try to

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S1: show that we're right about this discussion. And it doesn't

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S1: mean we're right about everything. It doesn't mean we've got

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S1: it all figured out. We don't. It's the future. You

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S1: can't predict it. But I think you see my point.

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S1: So that's what I'm going to try to do in

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S1: part two. And after that, I might do a specific

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S1: sort of argument breakdowns or something even more tactical than that.

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S1: But in the meantime, I recommend you go check out

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S1: the episode. It's quite good. All right, cloud code definitely

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S1: my most holy crap. Are you serious? I tool that

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S1: I've ever used. And again, I talked about this. I've

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S1: been talking about this for a couple of weeks now.

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S1: I'm telling you, like Klein and Cursor, those all still

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S1: feel powerful. And I still use cursor sometimes. But here's

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S1: the distinction I. I feel like cursor is a tool,

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S1: whereas Klein. Well, and Klein is very similar, although I'm

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S1: not using Klein as much. I'm using cursor now, and

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S1: I would say that those are tools as opposed to

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S1: cloud code is more like a coworker. It's more like

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S1: another engineer that I'm working with, and I'm just kind

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S1: of like sending work over there, like like I'm a

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S1: senior engineer and they're a junior engineer, and I'm sending

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S1: them work, and they're going and doing tests for me,

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S1: and they're coming back and giving me a status report saying,

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S1: here's what I got done, right. It's really, really that good.

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S1: And I'm just blown away by it. And I can't

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S1: even imagine, like, what gets better from here. So I

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S1: talked about this metric metric. I think I mentioned it

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S1: in this week's show or the one previous, but it's

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S1: a metric for how long term of tasks I can

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S1: actually accomplish. I think it's a really, really cool metric meter.

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S1: I thought they might have been mispronouncing or misstating miter miter,

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S1: but no, it's metric. And it basically talks about, you know,

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S1: the fact that I struggles with long context problems with

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S1: large scope problems where it just gets confused and just

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S1: can't remember its place, can't remember all the different subtasks,

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S1: and it just kind of spirals into like chaos. And

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S1: that's a huge problem for AI, and it's kind of

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S1: the big bottleneck right now, along with context, which is

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S1: very much related to this and along with working memory,

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S1: which is very much related to this. So meter, this metric,

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S1: I believe it's a pretty good capture of this overall

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S1: collection of problems that I currently has, and how good

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S1: of a job we're doing at addressing those. So all

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S1: that to say, I think Claude, is Claude code is

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S1: the best right now at actually dealing with this. So

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S1: something to keep an eye on. Highly recommend this conversation

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S1: with Arthur. Arthur Kroeber and Dwarkesh Patel on China. So,

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S1: Arthur Kroeber or Kroeber? Sorry, Arthur Kroeber. That's a little

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S1: bit difficult to say. So he's a China expert. The

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S1: the China expert that everyone's talking about right now, or

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S1: at least in my circles, like there's a lot of

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S1: video of, um, Peter Zeihan and he's just talking about

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S1: how China is so screwed. And they, you know, their

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S1: population collapse. And, you know, they just they're in really

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S1: bad shape and they're going to be done any year now.

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S1: And I believe he's so popular because everyone wants to

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S1: hear that. I definitely wanted to hear that. I was

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S1: definitely happy to hear it. And what it did was

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S1: kind of expose my bias because it's confirmation bias. It's

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S1: also good news, and those are both very infectious. They

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S1: kind of burrow into the brain, and I'm a little

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S1: bothered that it worked so well on me for so

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S1: long for like a couple of years where I'm just like, oh,

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S1: this guy's super bright and China's actually super screwed. Well,

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S1: it turns out if you ask a lot of people

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S1: and if you sort of dig around, a lot of

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S1: China experts and a lot of economists don't think Xi'an

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S1: is all that smart. They don't think he's all that

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S1: educated and knowledgeable about China or China's economy. And some

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S1: of that could be hate. And this is why I

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S1: was a little bit confused. And maybe I am still

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S1: a little bit confused. I'm open to the fact that

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S1: Xi'an actually still does know a lot of stuff, and

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S1: I think that is the case. The problem is, I've

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S1: seen him talk about cyber, and he basically said that, um,

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S1: the Facebook app is key, logging every single keystroke that

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S1: you do at all times, even if you don't have

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S1: it open and it's uploading all of those keystrokes. Everything

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S1: you're doing on your computer, up to Facebook at all times.

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S1: And the problem is, he said that with the same

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S1: exact conviction that he's talking about with China. And I'm like, what?

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S1: Wait a minute. So I have to go and review

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S1: all the all the dumb shit that he said about China,

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S1: which I have been absorbing. And I'm like, this guy,

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S1: is this lost about this? And it's not just some esoteric,

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S1: like one philosophical view versus another. He's talking about a

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S1: very tangible thing. Like, to me, this is like lying

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S1: about a stat or something. This is like he's claiming

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S1: that this is technically true when I know it not

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S1: to be true. And just just as a simple way

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S1: of like giving an argument why I know this not

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S1: to be true or believe very strongly, at least if

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S1: the Facebook app was up stealing and uploading the keystrokes

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S1: of all its users all the time. Do you not

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S1: think that this would be a huge story? Do you

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S1: not think that all the security researchers out there in

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S1: the world would not have noticed this? Right? Do you

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S1: think Facebook would have meta would have okayed this and

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S1: actually thought it was okay and it would never be caught? Like,

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S1: that is just ridiculous. So he's claiming to have this

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S1: knowledge that no one else has and he's spreading it,

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S1: you know, and I'm just like, wow. Right. This is

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S1: where you have to really question things. So anyway, this

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S1: other person who came on the podcast, this guy, um,

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S1: Arthur Kroeber, is a lot more serious. You can just

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S1: instantly tell, like, he'll be like, well, I don't know

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S1: about that. I wouldn't go that far. Or, you know,

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S1: like he's not talking like an influencer where it's just like, yeah,

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S1: which is what zeihan it talks like, you know, if

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S1: you listen to it, all his statements are absolutes. Um,

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S1: that's not completely true, right? He, um, he sometimes says, oh,

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S1: we don't know for sure or whatever, but a lot

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S1: of his statements are absolutes. Um, and it's just like,

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S1: first of all, we don't know that much about China. Like,

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S1: the data is sometimes hard to know, right? Uh, especially

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S1: because we're going off of their claims or we're going

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S1: off of inference. We're trying to figure these things out. Um,

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S1: bottom line is he's just way too sure and way

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S1: too confident about opinions that I'm not sure that that

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S1: he should have at that level of confidence. And, um,

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S1: it's great to hear someone on the Dwarkesh podcast notice

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S1: he didn't have Peter Zeihan on. He went and found

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S1: this guy who's a well respected economist, uh, and who

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S1: he covered so much ground. I mean, it was just

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S1: a fantastic conversation. And, uh, I would say the short

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S1: version is I came away thinking China is in much

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S1: better shape than Zeihan believes. And then a lot of

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S1: people believe, and it's more in line with what my

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S1: concerns were of where China might be. And essentially Zion

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S1: was giving me some confidence that I was wrong, that

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S1: China wasn't actually in that good a position, but it

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S1: seemed obvious to me from like other economic markers that

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S1: they actually are thriving and they actually are, you know,

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S1: in a position to kind of dominate the US, uh, in,

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S1: in short order. So I would say, go listen to this.

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S1: If you care about China, go, go listen to this.

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S1: If you care about national security, economic security, the future

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S1: of the US, the future of China, this conversation is spectacular.

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S1: And it's partially because of how good the guest was,

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S1: but it's largely because Dwarkesh is so good at interviewing.

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S1: He is just very young, very smart, very curious. That's

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S1: the biggest thing is like incandescent curiosity. That's the thing

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S1: with Dwarkesh. He's just like, he can't wait to jump

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S1: in with his next question. He's moving the conversation along,

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S1: like his conversations with Tyler Cowen are some of my

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S1: favorite podcasts to listen to. Anyway, it's a fantastic episode. Okay.

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S1: I'm very fascinated by this. Uh, what do you notice,

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S1: Psyop that I keep seeing, especially on X, but it's

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S1: on lots of different social media. I would say, and

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S1: what it basically does is it shows you like the

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S1: picture of, like a nine over 11, uh, building falling. Right.

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S1: One of the twin towers falling. And it's like, watch

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S1: it again in slow motion. What did you notice? Does

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S1: this look normal to you? And what it's doing is

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S1: like seeding this this concept of, like, question everything you've

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S1: been lied to. And it's like, really, really pernicious. It's nasty. Um, the,

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S1: the other cool thing that it does from a marketing standpoint,

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S1: it requires you to watch the video all the way

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S1: through in multiple times. And I think a lot of

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S1: people do. That's why a lot of these things have

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S1: like millions upon millions of views. And maybe that's just

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S1: the propaganda people pushing it, but it implicitly asks you

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S1: to use your own special analysis skills to figure out

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S1: what's wrong with it. And gullible type. People love to

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S1: believe that they're experts in lots of different stuff. So

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00:12:07,022 --> 00:12:09,541
S1: they're like, oh, you know, I'll use my special skills

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S1: to figure out what's wrong with the building. He's like, oh,

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00:12:11,582 --> 00:12:13,822
S1: it shouldn't have fallen that fast. And like when there's

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S1: a comment, it's like, did it fall faster than it

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00:12:15,742 --> 00:12:19,781
S1: should have? They will suddenly pop into their mind. Yeah.

225
00:12:19,782 --> 00:12:22,182
S1: It did fall faster. That's how that's not how fast

226
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S1: buildings are supposed to fall. Somehow they're now an expert

227
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S1: in building falling like velocities. Right? And this is just

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S1: like conspiracy thinking. This is what a lot of people have. Um,

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S1: and I would say everyone has it. Here's a question

230
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S1: is like, how much do you have it in check? Right.

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S1: And then they paint the perfect picture in your brain

232
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S1: without actually saying anything. Sometimes they say it in the comments,

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S1: sometimes they hint at it, but ultimately. Right. The brain

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S1: watching tries to like, square the circle, coming up with

235
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S1: an explanation when there isn't one. Or there might not

236
00:12:50,582 --> 00:12:53,622
S1: actually be anything wrong with the video, but because they

237
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S1: told you there was something wrong with it, you still

238
00:12:56,261 --> 00:12:59,862
S1: are these gullible types who watch it leave feeling like, ah,

239
00:13:00,062 --> 00:13:02,182
S1: you know, they have been lying to us. That video

240
00:13:02,182 --> 00:13:04,012
S1: was crazy. I crazy. I can't believe I saw that video.

241
00:13:04,412 --> 00:13:06,452
S1: It's like, no, it was just a regular video. Just

242
00:13:06,452 --> 00:13:09,332
S1: regular iPhone took a picture and sent it up there.

243
00:13:09,892 --> 00:13:13,732
S1: It's like it just opens the opportunity for gullibility and

244
00:13:13,732 --> 00:13:18,012
S1: conspiracy thinking like I've never seen before. It's a brilliant tactic.

245
00:13:18,692 --> 00:13:22,252
S1: So basically, the conspiracy brain goes on special forces mission

246
00:13:22,252 --> 00:13:25,172
S1: to solve the puzzle, and then it's followed up with

247
00:13:25,212 --> 00:13:27,011
S1: like over the course of a day or a week

248
00:13:27,011 --> 00:13:30,332
S1: or a month or whatever, like hundreds more videos hitting

249
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S1: their feet. 1 or 2 of them will give you

250
00:13:31,852 --> 00:13:34,372
S1: like an explanation for this thing. It's like, oh, that's

251
00:13:34,372 --> 00:13:36,452
S1: because they're doing this. That's because of, you know, it's

252
00:13:36,452 --> 00:13:39,692
S1: all a conspiracy. That's because of this or whatever. And

253
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S1: in maybe like one out of the ten or something,

254
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S1: they'll give you a reason. But in the other nine

255
00:13:44,652 --> 00:13:46,412
S1: it doesn't give you a reason. It just asks you

256
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S1: to think about it and what the brain does, what,

257
00:13:49,572 --> 00:13:53,492
S1: in my opinion, these people's brains. And I'm putting that

258
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S1: in quotes. What they're doing is they're filling in the

259
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S1: blank for the other nine videos, right? They're like, I

260
00:14:01,011 --> 00:14:05,411
S1: solved it. I know the answer. Um, I figured this out.

261
00:14:05,412 --> 00:14:08,131
S1: This is a conclusion that I've come to myself not

262
00:14:08,131 --> 00:14:10,412
S1: realizing it was either fed to them or they jumped

263
00:14:10,412 --> 00:14:13,692
S1: to it, and they're now sticking with it, right? And

264
00:14:13,692 --> 00:14:18,252
S1: it's just like, this is so nasty. And notice that

265
00:14:18,252 --> 00:14:21,412
S1: the videos get millions of views. Like, how are they

266
00:14:21,412 --> 00:14:24,772
S1: getting so many views? And so basically, like here are

267
00:14:24,772 --> 00:14:27,292
S1: the narratives that I'm seeing. And I'm starting to bookmark

268
00:14:27,292 --> 00:14:29,012
S1: these things. I don't like them because I don't want

269
00:14:29,012 --> 00:14:32,612
S1: to like, um, encourage them. Uh, but I bookmark them.

270
00:14:32,612 --> 00:14:35,092
S1: So I could actually, uh, put together some research on

271
00:14:35,092 --> 00:14:38,052
S1: these accounts. But, uh, it's things like whether it's changing,

272
00:14:38,052 --> 00:14:40,812
S1: food is changing. The world is basically different now in

273
00:14:40,812 --> 00:14:43,972
S1: some inexplicable way, which you should try to figure out. Uh,

274
00:14:43,972 --> 00:14:46,452
S1: women aren't women anymore. Men aren't men anymore. They used

275
00:14:46,452 --> 00:14:48,972
S1: to be us. Used to be on top. Now we're

276
00:14:48,972 --> 00:14:52,252
S1: just a bunch of pussies or whatever. Um, black people

277
00:14:52,252 --> 00:14:55,012
S1: are nasty and violent. Nobody likes them for good reason.

278
00:14:55,012 --> 00:14:57,932
S1: Anything that's wrong with you, it's from vaccines. US is

279
00:14:57,932 --> 00:15:01,252
S1: the most racist country ever with constant crimes against non-whites.

280
00:15:01,412 --> 00:15:04,892
S1: So the stuff is designed to anger you. It's not

281
00:15:04,892 --> 00:15:09,132
S1: pointed towards or against any particular group. What it's doing

282
00:15:09,132 --> 00:15:12,972
S1: is finding all the trigger issues and just blasting them, right,

283
00:15:13,012 --> 00:15:16,012
S1: with millions of views, which that might be paid promotions,

284
00:15:16,012 --> 00:15:18,172
S1: that might be the fact that it's just viral and

285
00:15:18,172 --> 00:15:21,492
S1: people are resharing or whatever. Um, liberals and black people

286
00:15:21,492 --> 00:15:25,212
S1: and gay people and LGBTQ people have basically ruined the country.

287
00:15:25,332 --> 00:15:27,812
S1: Russia is way nicer than the US. It's full of

288
00:15:27,812 --> 00:15:30,332
S1: pretty white girls and no black people. You've been sold

289
00:15:30,332 --> 00:15:32,532
S1: a lie that Russia is bad, but people love it there.

290
00:15:32,572 --> 00:15:35,932
S1: They actually love it there. And most importantly, you are

291
00:15:35,932 --> 00:15:38,252
S1: being lied to and you should be angry about it.

292
00:15:38,532 --> 00:15:40,572
S1: And this is the message, right? It's like everywhere, but

293
00:15:40,572 --> 00:15:43,692
S1: especially on X and there like hundreds of these accounts.

294
00:15:43,692 --> 00:15:45,212
S1: And if you click on any of the accounts and

295
00:15:45,212 --> 00:15:49,292
S1: you just scroll down, it's nonstop, just these types of

296
00:15:49,292 --> 00:15:53,012
S1: posts and they are formulated with like the same sort

297
00:15:53,012 --> 00:15:55,452
S1: of quality and the same sort of narrative hooks. It

298
00:15:55,452 --> 00:15:58,132
S1: is completely obvious to me that these are all part

299
00:15:58,132 --> 00:16:00,802
S1: of campaigns. So the question is, who's paying the money

300
00:16:00,802 --> 00:16:04,802
S1: for these campaigns? Who is creating this content? And my

301
00:16:04,802 --> 00:16:08,522
S1: answer is it's absolutely propaganda. It is enemies of the

302
00:16:08,522 --> 00:16:11,762
S1: United States who are doing this. Probably mostly Russia, but

303
00:16:11,762 --> 00:16:14,362
S1: there could be others. It might be even be like

304
00:16:14,402 --> 00:16:16,882
S1: internal groups who are just trying to stoke anger for

305
00:16:16,882 --> 00:16:19,522
S1: particular internal candidates. I have no idea. I haven't done

306
00:16:19,522 --> 00:16:23,362
S1: the research on exactly who these groups are, but I

307
00:16:23,362 --> 00:16:25,482
S1: think we know from the past that it's likely to

308
00:16:25,522 --> 00:16:28,282
S1: involve Russia to a decent amount, but they're not the

309
00:16:28,282 --> 00:16:31,962
S1: only people doing propaganda. And the bigger point is that

310
00:16:33,082 --> 00:16:35,002
S1: AI is also going to make this so much easier.

311
00:16:35,042 --> 00:16:37,362
S1: Like what happens when you. I can write this stuff.

312
00:16:37,362 --> 00:16:39,202
S1: I have no idea if I actually did write a

313
00:16:39,202 --> 00:16:41,162
S1: bunch of this stuff. Maybe that's why I'm seeing so

314
00:16:41,162 --> 00:16:44,682
S1: much of it now, because it's more scalable. But I

315
00:16:44,722 --> 00:16:46,362
S1: would love for someone to go and take a look

316
00:16:46,362 --> 00:16:48,162
S1: at all these different accounts and actually figure out who

317
00:16:48,242 --> 00:16:51,962
S1: they are, see if there's any, you know, Osint like, uh,

318
00:16:52,002 --> 00:16:53,922
S1: clues in there that we can, we can use to

319
00:16:53,922 --> 00:16:55,802
S1: track them down. I want to know which countries are

320
00:16:55,802 --> 00:16:57,442
S1: doing this. I want to know how much they're spending.

321
00:16:57,442 --> 00:16:59,362
S1: I want to know how they're getting so many views.

322
00:16:59,562 --> 00:17:03,602
S1: I think it's fascinating. Just fascinating to see how propaganda works.

323
00:17:04,922 --> 00:17:07,602
S1: And um, one other caveat I want to mention here

324
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S1: is that I'm saying this as an ex-military person who

325
00:17:10,642 --> 00:17:13,242
S1: is an Intel like a little bit. I basically spent

326
00:17:13,242 --> 00:17:16,082
S1: a long time studying influence operations in the Army. When

327
00:17:16,082 --> 00:17:18,482
S1: I was assigned to S-2 for my battalion in the

328
00:17:18,482 --> 00:17:22,962
S1: 101st airborne. And does that make me like an absolute

329
00:17:22,962 --> 00:17:27,442
S1: expert in influence operations or propaganda? Not really. I've read

330
00:17:27,442 --> 00:17:29,082
S1: a whole bunch of books on it. I studied it

331
00:17:29,082 --> 00:17:32,202
S1: when I was in the military. Um, I was sort

332
00:17:32,202 --> 00:17:37,122
S1: of assigned to doing counterterrorism type influence stuff a little

333
00:17:37,162 --> 00:17:40,162
S1: bit when I was actually in the military, but I'm

334
00:17:40,202 --> 00:17:43,402
S1: not like a trained Intel person from the military. I was, uh,

335
00:17:43,442 --> 00:17:47,202
S1: airborne infantry, so I was assigned to it because I

336
00:17:47,242 --> 00:17:49,722
S1: seemed to have a talent for it. But it wasn't

337
00:17:49,722 --> 00:17:52,561
S1: like I'm not formally trained in it or whatever. So

338
00:17:52,602 --> 00:17:54,361
S1: it just just keep in mind, I read a whole

339
00:17:54,362 --> 00:17:57,362
S1: bunch of books. Doesn't mean I'm an expert. Um, what

340
00:17:57,362 --> 00:17:59,202
S1: I'm trying to do is to just get you to

341
00:17:59,242 --> 00:18:02,642
S1: see my perspective and how I'm seeing this very clearly

342
00:18:02,642 --> 00:18:05,362
S1: as being part of campaigns. And everyone who I know

343
00:18:05,362 --> 00:18:08,602
S1: who actually is trained in this stuff. Um, as much

344
00:18:08,602 --> 00:18:12,122
S1: or more than I am. They also agree other people

345
00:18:12,122 --> 00:18:15,922
S1: trained in this way also agree that this is propaganda

346
00:18:16,282 --> 00:18:20,242
S1: being sent against the United States. From who? Who knows? Right.

347
00:18:20,242 --> 00:18:22,522
S1: You have to do that research. Uh, but but I

348
00:18:22,522 --> 00:18:24,681
S1: would say just watch for it. Um. Oh, and I've

349
00:18:24,682 --> 00:18:27,842
S1: got some questions here. Have you noticed any of your

350
00:18:27,842 --> 00:18:31,401
S1: friends feeling these feelings of, like, America is screwed? Oh,

351
00:18:31,402 --> 00:18:35,282
S1: I hate that group. That group ruined everything. Um, on

352
00:18:35,282 --> 00:18:39,322
S1: X particularly, but also just in, uh, social media in general.

353
00:18:39,362 --> 00:18:42,562
S1: Have you noticed yourself feeling more angry towards other groups

354
00:18:42,561 --> 00:18:45,962
S1: or towards, um, just like the past used to be

355
00:18:45,962 --> 00:18:48,922
S1: better and feeling like, yeah, Russia had it figured out

356
00:18:48,922 --> 00:18:51,482
S1: because Russia seemed super nice or whatever. And are you

357
00:18:51,522 --> 00:18:53,602
S1: consuming a whole bunch of social media all this to

358
00:18:53,762 --> 00:18:59,032
S1: basically ask, Are you feeling yourself being influenced in this way? Right?

359
00:18:59,071 --> 00:19:01,071
S1: Because that's what I have to ask myself all the time,

360
00:19:01,112 --> 00:19:04,112
S1: because I'm constantly trying to clean myself of bias. Not

361
00:19:04,112 --> 00:19:06,832
S1: always effective, and I end up catching it. Like I've

362
00:19:06,872 --> 00:19:10,232
S1: talked about multiple times in the newsletter and on the podcast,

363
00:19:10,792 --> 00:19:13,992
S1: I'm constantly checking myself having bias. It's the other reason

364
00:19:13,992 --> 00:19:18,432
S1: I do the telos thing is to constantly be able

365
00:19:18,432 --> 00:19:20,712
S1: to ask I and say, what mistakes am I making?

366
00:19:20,752 --> 00:19:24,071
S1: What biases do I have that I seem to be, uh,

367
00:19:24,512 --> 00:19:28,872
S1: you know, ignoring. So anyway, watch for it. Think about it. Uh,

368
00:19:28,872 --> 00:19:32,912
S1: something to keep in mind. Uh, traditional advertising is in

369
00:19:32,912 --> 00:19:36,592
S1: serious trouble. This is something, uh, just incredible. There's some

370
00:19:36,672 --> 00:19:41,632
S1: ads of, like, um, the ones I particularly like, these

371
00:19:41,672 --> 00:19:47,032
S1: I ads, uh, from Vo3 from Google. They're basically Bigfoot ads. Uh, Yeti,

372
00:19:47,071 --> 00:19:52,312
S1: Bigfoot and Yeti ads. And they're as funny as, like,

373
00:19:52,352 --> 00:19:55,272
S1: Super Bowl ads, which they pay paid millions of dollars for.

374
00:19:55,472 --> 00:19:57,352
S1: And a number of groups have made these things, and

375
00:19:57,352 --> 00:19:59,272
S1: it does actually take some talent to make it, because

376
00:19:59,272 --> 00:20:00,992
S1: you have to write the scripts, you have to write

377
00:20:00,992 --> 00:20:03,792
S1: the prompt. So I think there is comedic human talent

378
00:20:03,792 --> 00:20:06,992
S1: going into these. The question is how much and how

379
00:20:06,992 --> 00:20:10,232
S1: long will that last? Um, but I find it really

380
00:20:10,232 --> 00:20:14,032
S1: interesting that we're seeing these things have like hundreds of

381
00:20:14,032 --> 00:20:19,192
S1: millions of use. Okay. These Bigfoot, uh, little videos which

382
00:20:19,192 --> 00:20:21,752
S1: are sometimes are selling things and sometimes they're just comedy

383
00:20:21,792 --> 00:20:25,071
S1: skits or whatever, hundreds of millions of views. And I

384
00:20:25,071 --> 00:20:27,671
S1: think a lot of advertising is going to move in

385
00:20:27,672 --> 00:20:30,312
S1: this direction. There's one selling like a night vision goggle,

386
00:20:30,672 --> 00:20:33,192
S1: and I'm like, I think I kind of want it.

387
00:20:33,192 --> 00:20:35,192
S1: Like it didn't say anything about the night vision goggle.

388
00:20:35,192 --> 00:20:37,392
S1: It was just funny. And I'm like, where do I click?

389
00:20:37,392 --> 00:20:39,351
S1: Where do I buy it? Also, I need a night

390
00:20:39,352 --> 00:20:44,432
S1: vision goggle. Um, I don't actually. So hopefully I won't

391
00:20:44,432 --> 00:20:48,191
S1: go and click that and buy something unnecessary. All right.

392
00:20:48,192 --> 00:20:50,912
S1: Cybersecurity A top hacker on hacker one is a fully

393
00:20:50,912 --> 00:20:54,392
S1: automated AI agent called Call called Expo, and it's actually

394
00:20:54,432 --> 00:20:57,591
S1: just a slight correction. Got a little errata here. Um, live.

395
00:20:58,152 --> 00:21:00,552
S1: It's actually the top hacker in the US. So there

396
00:21:00,592 --> 00:21:04,152
S1: are higher scores globally. Um, but the highest US score

397
00:21:04,311 --> 00:21:08,832
S1: is from a fully automated AI, and that's beating out

398
00:21:08,832 --> 00:21:12,472
S1: all the top hackers in the US. That is ridiculous.

399
00:21:12,512 --> 00:21:16,552
S1: Absolutely ridiculous. And this should not be possible, given what

400
00:21:16,552 --> 00:21:20,712
S1: Marcus believes about AI, for example, or other people who

401
00:21:20,752 --> 00:21:23,311
S1: don't believe that AI is intelligent, to be able to

402
00:21:23,352 --> 00:21:28,232
S1: go and research and find and illustrate and actually submit

403
00:21:28,592 --> 00:21:33,192
S1: a report that gets you results on one of these platforms,

404
00:21:33,192 --> 00:21:35,071
S1: you have to you have to show that it's actually

405
00:21:35,071 --> 00:21:37,432
S1: a real vulnerability, and it has to not be a dupe.

406
00:21:37,752 --> 00:21:39,391
S1: You have to be first and it has to be

407
00:21:39,392 --> 00:21:41,992
S1: a significant issue. Right. We already have a high standard

408
00:21:41,992 --> 00:21:45,592
S1: for what gets accepted. You know, Bugcrowd has this, Hackerone

409
00:21:45,592 --> 00:21:48,391
S1: has this. So this is hacker one accepting these actual

410
00:21:48,392 --> 00:21:53,631
S1: vulnerabilities from actual AI. And it's number one in the

411
00:21:53,632 --> 00:21:58,351
S1: entire United States. So anyone who thinks you can't automate

412
00:21:58,352 --> 00:22:01,232
S1: this stuff, it's not possible or it's going to take years,

413
00:22:01,232 --> 00:22:02,631
S1: it's going to take three years or five years or

414
00:22:02,632 --> 00:22:06,792
S1: ten years. Nope. It already happened. It already happened. Number

415
00:22:06,792 --> 00:22:10,712
S1: one hacker in the US for bug bounties for mostly

416
00:22:10,712 --> 00:22:14,831
S1: web vulnerabilities. Keep that in mind. Anthropic says multiple models

417
00:22:14,832 --> 00:22:19,391
S1: resort to blackmail to avoid shutdown. Russian hackers beat Gmail

418
00:22:19,432 --> 00:22:23,272
S1: toufar with app specific password social engineering. So this was

419
00:22:23,311 --> 00:22:27,992
S1: an intelligence group. Was this? Yeah. Russian group Apt29 trick

420
00:22:27,992 --> 00:22:32,071
S1: targets into generating Gmail app specific passwords by sending fake

421
00:22:32,071 --> 00:22:36,151
S1: State Department PDFs, giving them instructions. And they filled it out.

422
00:22:36,712 --> 00:22:40,912
S1: Stargazer's trick Minecraft players into installing malware through fake mods.

423
00:22:41,472 --> 00:22:46,392
S1: 500 fake GitHub repositories to distribute malware disguised as Minecraft mods.

424
00:22:47,432 --> 00:22:51,382
S1: 16 billion credentials leak. You've probably seen a million stories

425
00:22:51,382 --> 00:22:54,821
S1: about it. It's complete garbage. It's just concatenation. Combinations of

426
00:22:54,821 --> 00:22:58,142
S1: previous breaches. I don't know how so many big names

427
00:22:58,182 --> 00:23:04,222
S1: like security outfits, media groups, like totally just I guess

428
00:23:04,222 --> 00:23:07,782
S1: it was so exciting. They couldn't resist the the headline. Anyway.

429
00:23:07,782 --> 00:23:12,702
S1: Quite sad. It's a nothing burger. Minnesota shooter allegedly used

430
00:23:12,702 --> 00:23:16,302
S1: data brokers to find victim addresses. Uh, good time for

431
00:23:16,302 --> 00:23:18,822
S1: a reminder that data brokers are way nastier, at least

432
00:23:18,821 --> 00:23:20,942
S1: in my opinion, than the dark web, because they have

433
00:23:20,942 --> 00:23:23,942
S1: more data, they're way more organized, and they're actually legal.

434
00:23:25,342 --> 00:23:29,581
S1: They're collecting and selling this data legally, and they have

435
00:23:29,582 --> 00:23:32,982
S1: way more data on all of us, and it's way

436
00:23:32,982 --> 00:23:36,621
S1: higher quality and it's way better organized, like the dark web.

437
00:23:36,622 --> 00:23:40,062
S1: And criminals have nothing on this, and it's completely legitimate.

438
00:23:40,061 --> 00:23:44,222
S1: But we don't talk about it because it's legal. That's ridiculous.

439
00:23:44,542 --> 00:23:47,422
S1: Aflac gets hit by a scatter spider in ongoing insurance

440
00:23:47,422 --> 00:23:51,942
S1: company attack wave. So yeah this group scatters spider. They

441
00:23:51,982 --> 00:23:54,101
S1: kind of focus on areas and they moved off their

442
00:23:54,102 --> 00:23:55,901
S1: previous one. And they've been going after a lot of

443
00:23:55,902 --> 00:23:59,942
S1: insurance companies. Apple and Google keep selling Chinese VPNs that

444
00:23:59,942 --> 00:24:02,302
S1: could spy on you. So someone found a whole bunch

445
00:24:02,622 --> 00:24:05,782
S1: in the Apple App Store and in Google that were

446
00:24:05,821 --> 00:24:11,742
S1: Chinese VPNs and associated with, uh, not good groups, I believe.

447
00:24:11,742 --> 00:24:16,342
S1: Chinese government. Um, I'm not sure about that. It was

448
00:24:16,342 --> 00:24:19,462
S1: associated with something clearly malicious, though. Um, although I would

449
00:24:19,462 --> 00:24:21,982
S1: just say if it's a Chinese company, just kind of

450
00:24:22,022 --> 00:24:28,182
S1: assume that, uh, national security OpenAI gets $200 million defense

451
00:24:28,182 --> 00:24:33,062
S1: contract to build AI tools. Yeah, Pentagon gave them $200

452
00:24:33,061 --> 00:24:35,982
S1: million to develop AI for national security and war fighting.

453
00:24:36,462 --> 00:24:38,702
S1: I've said a lot on this before, so I'm not

454
00:24:38,702 --> 00:24:40,742
S1: going to say it again right now. I'll talk about

455
00:24:40,742 --> 00:24:44,222
S1: it again later. Uh, this is good and potentially bad.

456
00:24:44,462 --> 00:24:48,022
S1: China's military is using AI for intelligence operations. Recording future.

457
00:24:48,142 --> 00:24:51,622
S1: Recorded future found that China's People's Liberation Army has moved

458
00:24:51,622 --> 00:24:55,422
S1: beyond just talking about AI for intelligence is actually procuring

459
00:24:55,422 --> 00:24:59,222
S1: and deploying generative AI tools for military intelligence work. They're

460
00:24:59,222 --> 00:25:04,902
S1: using it for like processing satellite imagery, generating intelligence reports

461
00:25:04,942 --> 00:25:08,581
S1: that just basically like augmenting their military using these tools,

462
00:25:08,582 --> 00:25:14,581
S1: including OpenAI. Iran's using hacked security cameras to guide missile

463
00:25:14,582 --> 00:25:19,581
S1: strikes on Israel. Russia's deadliest Kiev attack this year kills

464
00:25:19,582 --> 00:25:23,422
S1: 15 as peace talks have stalled. Kind of a kind

465
00:25:23,422 --> 00:25:26,222
S1: of a lull and an advantage for Russia. I think

466
00:25:26,622 --> 00:25:30,662
S1: with the stuff happening in Iran, former Army sergeant shows

467
00:25:30,662 --> 00:25:32,862
S1: how not to be a spy. So he used Google

468
00:25:32,862 --> 00:25:37,422
S1: to search, quote, countries that don't extradite to us, and

469
00:25:37,942 --> 00:25:42,421
S1: emailed China directly to share this information. He's sharing this

470
00:25:42,942 --> 00:25:46,862
S1: classified information, uh, to, to send it to China. But

471
00:25:46,862 --> 00:25:52,252
S1: he's using his own personal email accounts. Yeah. Uh. All right.

472
00:25:52,292 --> 00:25:54,052
S1: I don't know why that's as funny as it seems

473
00:25:54,052 --> 00:25:58,732
S1: to be. Taiwan gets Ukraine tested drone software to counter China.

474
00:25:59,772 --> 00:26:03,891
S1: ex-CIA analyst gets 37 months for leaking Israel's Iran attack

475
00:26:03,892 --> 00:26:09,772
S1: plans Asif William Rahman photographed top secret documents about Israel's

476
00:26:09,772 --> 00:26:12,692
S1: plans to attack Iran, edited them to hide their source

477
00:26:12,692 --> 00:26:16,692
S1: and then shared them with people before the documents went

478
00:26:17,012 --> 00:26:23,532
S1: went viral on telegram. Deep tech allegedly using shell companies

479
00:26:23,532 --> 00:26:28,572
S1: to support the Chinese military. US pressures Vietnam to remove

480
00:26:28,612 --> 00:26:35,012
S1: Chinese components from tech manufacturing. I meter okay. That's the

481
00:26:35,012 --> 00:26:37,212
S1: one I was talking about. Better metric for AI and agents.

482
00:26:37,212 --> 00:26:41,172
S1: It's basically yeah. Long term tasks I think is really smart. Uh,

483
00:26:41,172 --> 00:26:45,012
S1: Andrej Karpathy says prompts are software 3.0. So we did

484
00:26:45,012 --> 00:26:48,012
S1: a major talk two weeks ago and basically said that

485
00:26:48,012 --> 00:26:52,972
S1: software 3.0 is prompting, prompting to an eye to have

486
00:26:52,972 --> 00:26:54,891
S1: it build the application for you. And he calls the

487
00:26:54,892 --> 00:27:00,532
S1: software 3.0. I love his really high level, you know,

488
00:27:01,172 --> 00:27:03,452
S1: thought leading type of talks, and this was definitely one

489
00:27:03,452 --> 00:27:05,372
S1: of them. And I absolutely loved it. I love his

490
00:27:05,372 --> 00:27:09,131
S1: other talks too, but I love talks that are about,

491
00:27:09,172 --> 00:27:12,412
S1: you know, thinking about things differently. He also said we're

492
00:27:12,412 --> 00:27:14,812
S1: in the decade of agents and not the year of agents,

493
00:27:14,811 --> 00:27:18,132
S1: which I think is really smart. Sam Altman says GPT

494
00:27:18,172 --> 00:27:22,331
S1: five is coming in the summer. Google uses YouTube videos

495
00:27:22,332 --> 00:27:26,891
S1: to train Vo three without creator consent. Google also released

496
00:27:26,892 --> 00:27:30,652
S1: a stable version of Gemini 2.5, which includes new models

497
00:27:30,652 --> 00:27:33,292
S1: with different sizes and capabilities. This one is not in

498
00:27:33,292 --> 00:27:36,692
S1: here now, but they also just released a command line tool,

499
00:27:36,692 --> 00:27:38,132
S1: which I haven't messed with. I'm going to mess with

500
00:27:38,132 --> 00:27:42,332
S1: later today as soon as I'm done recording actually. And um, yeah,

501
00:27:42,332 --> 00:27:44,772
S1: it looks like maybe a competitor to Cloud Code, although

502
00:27:44,892 --> 00:27:47,292
S1: it looks like it might be more of a general agent.

503
00:27:47,292 --> 00:27:49,572
S1: So we're starting to move towards like the Da stuff

504
00:27:49,932 --> 00:27:52,572
S1: that I've been talking about forever. Wickes bought an AI

505
00:27:52,612 --> 00:27:54,852
S1: that lets anyone build software by chatting, so they bought

506
00:27:54,852 --> 00:27:57,811
S1: a company that is an AI tool for building software.

507
00:27:58,212 --> 00:28:01,492
S1: So this is starting to become a thing. Oh, the

508
00:28:01,492 --> 00:28:03,052
S1: other thing I want to mention right now is there

509
00:28:03,052 --> 00:28:05,571
S1: is a company called This is Major. There's a company

510
00:28:05,571 --> 00:28:09,131
S1: called Cluley, and it is doing very much like what

511
00:28:09,132 --> 00:28:12,012
S1: I wrote about in 2016, where it's like a digital assistant,

512
00:28:12,012 --> 00:28:15,132
S1: but it's not just taking tasks from you, it's actually

513
00:28:15,132 --> 00:28:18,452
S1: being more proactive. So it's actually recording your whole screen.

514
00:28:18,452 --> 00:28:21,331
S1: It's doing it's watching what you're doing. And you could

515
00:28:21,332 --> 00:28:24,012
S1: just actively ask it things. It could go and do

516
00:28:24,012 --> 00:28:27,532
S1: things on your behalf like more proactively. It is kind

517
00:28:27,532 --> 00:28:30,492
S1: of like, uh, you remember the Microsoft recall thing. It's

518
00:28:30,492 --> 00:28:33,571
S1: kind of like that. There's also Google has moved towards this.

519
00:28:33,612 --> 00:28:36,572
S1: Anthropic has moved towards this with like computer use. Everything

520
00:28:36,571 --> 00:28:39,212
S1: is moving all in the same direction, right? It's all

521
00:28:39,212 --> 00:28:43,452
S1: moving in the same direction of your Da is the

522
00:28:43,532 --> 00:28:45,972
S1: thing that understands everything about you. I just read my

523
00:28:45,972 --> 00:28:48,012
S1: book right before I came on this. I was talking

524
00:28:48,052 --> 00:28:54,292
S1: to Joseph Thacker, one of the best hackers in the world. And, um, yeah,

525
00:28:54,292 --> 00:28:55,532
S1: I was talking about I'm like, dude, you got to

526
00:28:55,532 --> 00:28:57,252
S1: finish reading this book. So I went and read it

527
00:28:57,252 --> 00:28:59,532
S1: real quick, and it takes like 20 minutes to read

528
00:28:59,532 --> 00:29:02,212
S1: the whole thing. It's 17,000 words, but it goes really fast.

529
00:29:02,252 --> 00:29:04,692
S1: It's it's written pretty simply and it's online for free, right?

530
00:29:04,692 --> 00:29:08,772
S1: You don't have to go and buy anything because that's ridiculous. But, um, anyway,

531
00:29:08,812 --> 00:29:11,171
S1: it's all pushing towards what I was talking about in

532
00:29:11,212 --> 00:29:13,732
S1: that thing, and I've got multiple examples in there. It's

533
00:29:13,732 --> 00:29:17,252
S1: basically it knows everything about you, and it's constantly advocating

534
00:29:17,252 --> 00:29:20,052
S1: for you on your behalf. It's constantly trying to adjust

535
00:29:20,052 --> 00:29:24,492
S1: the world around you using the API ification of everything.

536
00:29:24,492 --> 00:29:28,572
S1: Universal demonization. Right? Everything gets an API. This is what's

537
00:29:28,572 --> 00:29:32,092
S1: in the book. All companies get an API. I should

538
00:29:32,092 --> 00:29:33,932
S1: stop saying book. I should start saying it's in the

539
00:29:33,972 --> 00:29:38,372
S1: piece or it's in the content or whatever. Um, companies

540
00:29:38,372 --> 00:29:42,161
S1: get an API, people get APIs, objects get APIs, API's

541
00:29:42,162 --> 00:29:47,082
S1: businesses get API's. Your Da speaks API right? That was

542
00:29:47,122 --> 00:29:49,122
S1: kind of the core thing for the for this, uh,

543
00:29:49,402 --> 00:29:52,362
S1: for this concept, for this book in 2016. Right. It's

544
00:29:52,362 --> 00:29:55,482
S1: like your Da speaks the language of these other things.

545
00:29:55,482 --> 00:29:57,562
S1: Does that sound like anything to you? Does that sound

546
00:29:57,562 --> 00:30:00,722
S1: like agent to agent? Does that sound like MCP? MCP

547
00:30:00,722 --> 00:30:07,482
S1: is basically an implementation of universal demonization. That's what it is.

548
00:30:07,522 --> 00:30:11,002
S1: And I said right there in the content, it's like, look,

549
00:30:11,002 --> 00:30:12,282
S1: I have no idea how this is going to work,

550
00:30:12,282 --> 00:30:15,122
S1: but it's probably going to be HTTP based because the

551
00:30:15,122 --> 00:30:18,562
S1: predictions I'm making there are about direction. It's stochastic. Right?

552
00:30:18,602 --> 00:30:20,282
S1: It's not about like who's going to win whatever. You

553
00:30:20,282 --> 00:30:24,842
S1: can't predict tech like that anyway. Cluley is heading directly

554
00:30:24,842 --> 00:30:27,921
S1: in this direction. Um, I'm wearing a pendant right now,

555
00:30:27,922 --> 00:30:30,762
S1: which is recording everything that would be input into my Da.

556
00:30:30,802 --> 00:30:33,562
S1: My da's name is Chi, by the way. Um, haven't

557
00:30:33,562 --> 00:30:36,922
S1: fully spun him up yet, but that's, uh, he's coming together.

558
00:30:36,922 --> 00:30:39,682
S1: Starting to bring these pieces together. My big challenge being

559
00:30:39,682 --> 00:30:42,802
S1: in security. Um, don't even need to be in security

560
00:30:42,802 --> 00:30:45,562
S1: to figure this out. But my big challenge is take

561
00:30:45,562 --> 00:30:48,122
S1: this company, for example. I don't know anything about them.

562
00:30:48,122 --> 00:30:49,722
S1: I don't know anything about the founder. I don't know

563
00:30:49,722 --> 00:30:52,842
S1: anything about the team. More importantly, I don't know anything

564
00:30:52,842 --> 00:30:55,522
S1: about their security. So do you think I'm going to

565
00:30:55,522 --> 00:30:58,082
S1: have it recording all of my screens with all of

566
00:30:58,082 --> 00:31:01,962
S1: my microphones and uploading all of that content to their

567
00:31:01,962 --> 00:31:05,762
S1: servers so their eyes can parse it? Right? What if

568
00:31:05,762 --> 00:31:07,802
S1: I'm talking to somebody over here about some government thing

569
00:31:07,842 --> 00:31:10,122
S1: and someone else shares with me, hey, you know, we

570
00:31:10,122 --> 00:31:12,562
S1: just got breached. Don't mention it to anyone. Someone else

571
00:31:12,562 --> 00:31:15,882
S1: is telling me about their life, right? Um. I had

572
00:31:15,882 --> 00:31:17,842
S1: a friend die recently. What if I'm talking to my

573
00:31:17,842 --> 00:31:20,762
S1: friend about, you know, my other friend who's died? All

574
00:31:20,762 --> 00:31:23,482
S1: of this is being uploaded constantly to a third party.

575
00:31:23,522 --> 00:31:26,762
S1: Not to Google, not to Apple. Okay? Because. Because I

576
00:31:26,762 --> 00:31:29,122
S1: could see doing that with Google or Apple once they,

577
00:31:29,162 --> 00:31:31,842
S1: you know, once they sort it out, once Apple sorts out,

578
00:31:31,882 --> 00:31:35,202
S1: you know, the successor to Siri or whatever. Google they

579
00:31:35,202 --> 00:31:37,722
S1: have rock solid security. I'm not worried about them getting

580
00:31:37,722 --> 00:31:40,642
S1: breached or them losing the data or whatever Other than

581
00:31:40,682 --> 00:31:45,522
S1: small issues, but ultimately those two. Maybe even Amazon, maybe

582
00:31:45,522 --> 00:31:48,602
S1: even Amazon. They also have rock solid security in terms

583
00:31:48,602 --> 00:31:51,042
S1: of like how many billions of dollars they spend on it.

584
00:31:51,402 --> 00:31:54,122
S1: The quality of their team, the size of their team, etc.. Right.

585
00:31:54,402 --> 00:31:58,802
S1: But a third party, a new startup called Cluley and

586
00:31:58,802 --> 00:32:01,282
S1: it's all this AI hype is around it. How big

587
00:32:01,282 --> 00:32:04,002
S1: is their security team? How much are they thinking about this?

588
00:32:04,042 --> 00:32:05,642
S1: Not only that, but how good are they going to

589
00:32:05,642 --> 00:32:09,682
S1: be at defending against a halfway decent attacker, let alone

590
00:32:09,682 --> 00:32:11,242
S1: a state actor who wants to come and steal the

591
00:32:11,242 --> 00:32:13,922
S1: entire thing and see all this context that all these

592
00:32:13,922 --> 00:32:17,322
S1: people are spraying up into the cloud. Right? I'm very

593
00:32:17,562 --> 00:32:21,162
S1: forward leaning for tech and for AI, but do not

594
00:32:21,682 --> 00:32:24,482
S1: go in and record every single thing, every single text,

595
00:32:24,482 --> 00:32:29,162
S1: every single screenshot. This thing is screenshot Screenshotting your your

596
00:32:29,322 --> 00:32:32,842
S1: displays all the time, all the texture on there. You

597
00:32:32,842 --> 00:32:35,042
S1: see on there it could see the app that it's doing.

598
00:32:35,042 --> 00:32:38,202
S1: It could see your slack conversations. It sees all this right. So.

599
00:32:39,832 --> 00:32:43,592
S1: you should be careful. You should be careful about who

600
00:32:43,632 --> 00:32:46,232
S1: you're sending this stuff to. I would say if it's

601
00:32:46,232 --> 00:32:48,272
S1: Google or Apple, which is not, it's not out yet

602
00:32:48,272 --> 00:32:51,112
S1: for them, but with Microsoft. Microsoft is another one. I

603
00:32:51,112 --> 00:32:53,552
S1: wouldn't worry too much about sending that stuff to Microsoft.

604
00:32:53,832 --> 00:32:56,352
S1: I'm sure there'll be problems, but they are a legit

605
00:32:56,352 --> 00:33:00,032
S1: security team because they are a legit company. The startups

606
00:33:00,032 --> 00:33:01,431
S1: are what you have to worry about, and I'm not

607
00:33:01,432 --> 00:33:03,992
S1: saying there's anything wrong with Cluley security, I'm just saying

608
00:33:03,992 --> 00:33:08,912
S1: that they are a startup and they're probably not prioritizing security. Um,

609
00:33:08,912 --> 00:33:12,352
S1: even if they know how to write, um, show me

610
00:33:12,352 --> 00:33:14,592
S1: that they hire an awesome security team, maybe I'll feel

611
00:33:14,632 --> 00:33:18,432
S1: better about it, but they're still a startup. Um, so anyway,

612
00:33:19,392 --> 00:33:23,312
S1: all that is, um, bringing us towards where it's going,

613
00:33:23,352 --> 00:33:27,671
S1: which is? Your Da will be listening all the time.

614
00:33:27,672 --> 00:33:30,112
S1: It will have all of your context. My telos file.

615
00:33:30,112 --> 00:33:32,592
S1: That is part of what Chi has, right? Oh, by

616
00:33:32,592 --> 00:33:36,112
S1: the way, um, Network Truck just released a video about Telos.

617
00:33:36,152 --> 00:33:38,232
S1: Just came out, uh, a couple of hours ago. You

618
00:33:38,272 --> 00:33:40,432
S1: got to go check that out. But that Telos file

619
00:33:40,432 --> 00:33:42,352
S1: that he talks about there, that I have, that he

620
00:33:42,352 --> 00:33:44,992
S1: has a whole bunch of my friends have that telos

621
00:33:44,992 --> 00:33:47,632
S1: file is like my soul. This this thing is like

622
00:33:47,672 --> 00:33:50,592
S1: it's traumas, it's history. It's my journal. It's like my goals,

623
00:33:50,592 --> 00:33:54,352
S1: my aspirations, like my problems, my challenges, all this different stuff. Right?

624
00:33:55,192 --> 00:33:58,592
S1: All this is available to Chi. Chi knows this. Chi

625
00:33:58,632 --> 00:34:02,432
S1: also knows that I like, you know, prawn cocktail. It

626
00:34:02,432 --> 00:34:04,952
S1: also knows that I don't sit anywhere where there's like

627
00:34:04,952 --> 00:34:07,552
S1: smokers nearby. Right. So it knows all these different things,

628
00:34:07,592 --> 00:34:11,832
S1: knows preferences for food, you know, so it can help me, uh,

629
00:34:12,152 --> 00:34:14,392
S1: optimize my surroundings. It can order for me. It could

630
00:34:14,392 --> 00:34:16,592
S1: do all these things. How is it doing that? By

631
00:34:16,592 --> 00:34:21,152
S1: manipulating the APIs that it has access to for the

632
00:34:21,152 --> 00:34:23,672
S1: world around us. Right. If I go into a restaurant

633
00:34:23,672 --> 00:34:25,992
S1: and I can control the TV, well, I'm not going

634
00:34:25,992 --> 00:34:27,792
S1: to control the TV. I'm not going to ask them

635
00:34:27,792 --> 00:34:30,352
S1: to change the channel. No, Chi is going to change

636
00:34:30,352 --> 00:34:33,592
S1: the channel to table tennis. If I'm not overruled by

637
00:34:33,592 --> 00:34:37,152
S1: other people trying to have it change to something else, knows, right? Why?

638
00:34:37,192 --> 00:34:39,792
S1: Because this is all available on slash media. This is

639
00:34:39,792 --> 00:34:41,352
S1: part of the API. This is part of the demon

640
00:34:41,352 --> 00:34:44,832
S1: for that restaurant. It's just an API key. Can submit

641
00:34:44,832 --> 00:34:47,152
S1: to it on my behalf. And this will be the

642
00:34:47,152 --> 00:34:51,192
S1: case for temperature, for sounds, for what's playing on the video,

643
00:34:51,192 --> 00:34:54,632
S1: for having doors open for you, you know, for having

644
00:34:54,672 --> 00:34:58,392
S1: materials ready for you and summaries created. Most importantly, Kai

645
00:34:58,392 --> 00:35:02,592
S1: will be reading a billion different stories and articles and

646
00:35:02,592 --> 00:35:07,432
S1: research papers and reports and raw data sets constantly. While

647
00:35:07,432 --> 00:35:09,992
S1: I'm in the middle of this sentence and while I'm sleeping,

648
00:35:09,992 --> 00:35:11,632
S1: and while I'm in the middle of another project and

649
00:35:11,632 --> 00:35:14,992
S1: doing some security work or whatever I'm doing, it's processing

650
00:35:14,992 --> 00:35:17,072
S1: all of that and getting it ready for me. The

651
00:35:17,072 --> 00:35:20,232
S1: other thing it's going to do is this dynamic content generation,

652
00:35:20,232 --> 00:35:22,432
S1: which is a blog post I put out who knows when.

653
00:35:22,432 --> 00:35:27,632
S1: Sometime last year, Kai will be making me videos. Okay,

654
00:35:27,792 --> 00:35:32,672
S1: Kai will deepfake Chuck, who just made that full size video,

655
00:35:32,952 --> 00:35:35,112
S1: which is awesome, which you want to go watch the

656
00:35:35,432 --> 00:35:38,512
S1: the actual full thing, but Kai will make me a

657
00:35:38,512 --> 00:35:41,312
S1: 32nd version of that if I need it. And maybe

658
00:35:41,352 --> 00:35:43,992
S1: he just cuts up the video from from Chuck, but

659
00:35:43,992 --> 00:35:46,512
S1: maybe Kai makes me a whole new one with Chuck's

660
00:35:46,512 --> 00:35:50,432
S1: face or Richard Feynman's face on it, and he can

661
00:35:50,432 --> 00:35:52,632
S1: make a video for a blog post which didn't actually

662
00:35:52,632 --> 00:35:55,752
S1: have a video. Or he could turn a video into

663
00:35:55,752 --> 00:35:58,912
S1: a blog post because maybe somebody doesn't like to watch videos.

664
00:35:59,192 --> 00:36:01,352
S1: Or maybe it's audio only because I'm out on a

665
00:36:01,352 --> 00:36:02,912
S1: run and I don't want to look at anything and

666
00:36:02,912 --> 00:36:07,192
S1: I can't look at anything. Bottom line is, Kai, your

667
00:36:07,232 --> 00:36:10,992
S1: da becomes the medium to you. Um, and this this

668
00:36:10,992 --> 00:36:12,192
S1: is kind of a big thing. I don't know why

669
00:36:12,192 --> 00:36:15,192
S1: this is, uh, becoming a whole episode about this or whatever,

670
00:36:15,192 --> 00:36:21,312
S1: but SEO is dead because websites are dead. Advertising is

671
00:36:21,312 --> 00:36:24,472
S1: screwed because advertising no longer is going to be landing

672
00:36:24,672 --> 00:36:28,192
S1: on the human. The advertising is going to get blocked

673
00:36:28,192 --> 00:36:31,832
S1: by the Da. Largely, the Da is going to go

674
00:36:31,832 --> 00:36:35,582
S1: and collect things. It's going to be the inbox and the, uh,

675
00:36:35,942 --> 00:36:41,022
S1: the inbound filter. To every principal's life, to every user's life.

676
00:36:41,622 --> 00:36:44,502
S1: The Da will then determine what gets through. Right? And

677
00:36:44,502 --> 00:36:48,302
S1: so now the whole attack point for influence against a

678
00:36:48,302 --> 00:36:51,502
S1: person becomes their digital assistant. If you want to sell

679
00:36:51,542 --> 00:36:53,742
S1: to them, you sell to their digital assistant. If you

680
00:36:53,742 --> 00:36:55,902
S1: want to sell to them, you get into the models

681
00:36:55,902 --> 00:36:58,982
S1: so that when the digital assistant goes and does its

682
00:36:58,982 --> 00:37:02,502
S1: research for what the best, you know, uh, pillowcase cover is,

683
00:37:03,022 --> 00:37:05,462
S1: it's going to find your product, right. So this is

684
00:37:05,462 --> 00:37:08,782
S1: already starting. People are already trying to poison models or

685
00:37:08,822 --> 00:37:12,422
S1: influence models, however you want to put it. Um, because

686
00:37:12,702 --> 00:37:18,582
S1: they understand that marketing and influence and, uh, website stats

687
00:37:18,622 --> 00:37:21,302
S1: and all of that, all of that is shifting to

688
00:37:21,342 --> 00:37:25,262
S1: go towards the Da. Um, all this content. Nobody's reading

689
00:37:25,262 --> 00:37:28,582
S1: this stuff, right? There's too many YouTube videos. There's too

690
00:37:28,582 --> 00:37:31,062
S1: many blogs. Like, we already figured that out a long

691
00:37:31,062 --> 00:37:34,302
S1: time ago. There's a limit to this, but your Da

692
00:37:34,422 --> 00:37:37,102
S1: can go read all that stuff. It can go process

693
00:37:37,102 --> 00:37:39,622
S1: all that stuff. And it could use other services, which

694
00:37:39,622 --> 00:37:43,382
S1: are companies themselves that are consuming all of that content

695
00:37:43,382 --> 00:37:47,982
S1: and producing rankings and directories, which kind of like an

696
00:37:47,982 --> 00:37:51,222
S1: old like Yahoo directory or whatever, but constantly updated. And

697
00:37:51,222 --> 00:37:53,422
S1: your Da will go review that and be like, oh,

698
00:37:53,502 --> 00:37:56,862
S1: turns out that's the best tactical flashlight for right now. Or,

699
00:37:56,902 --> 00:37:59,742
S1: you know, that's the best, um, bed cover or the

700
00:37:59,742 --> 00:38:03,222
S1: best eight sleep competitor or whatever it is, right? And

701
00:38:03,222 --> 00:38:07,422
S1: so what ends up happening is we just we in

702
00:38:07,422 --> 00:38:10,062
S1: our Das, our Da is going to be our most

703
00:38:10,062 --> 00:38:14,262
S1: important relationship, um, for a great many people. Right? Because

704
00:38:14,262 --> 00:38:15,582
S1: it could be our coach, it could be our friend.

705
00:38:15,582 --> 00:38:18,422
S1: It'll be our therapist. And this is something we have

706
00:38:18,422 --> 00:38:21,182
S1: to watch out for because we want our best friend,

707
00:38:21,222 --> 00:38:24,542
S1: our best human friends to be our best friends. Right?

708
00:38:24,582 --> 00:38:27,462
S1: We want our our husband and our wife and our

709
00:38:27,462 --> 00:38:31,062
S1: kids to be our best friends. And we want that

710
00:38:31,062 --> 00:38:33,822
S1: to be the center. We want humanity to be the center, right?

711
00:38:34,222 --> 00:38:37,222
S1: So we really have to watch this, this gulf that

712
00:38:37,222 --> 00:38:40,262
S1: we're about to go into this chasm or whatever, where

713
00:38:40,262 --> 00:38:43,662
S1: it's like this thing being offered, this Da is so

714
00:38:43,662 --> 00:38:45,702
S1: powerful and it's so awesome, and mine's going to be

715
00:38:45,702 --> 00:38:48,222
S1: so awesome very soon. I'm going to have mine kick

716
00:38:48,222 --> 00:38:52,262
S1: in long before most people do. Uh, because it's already starting.

717
00:38:52,742 --> 00:38:55,262
S1: But everyone's going to have this soon. And what we

718
00:38:55,262 --> 00:38:58,422
S1: have to guard against is like cutting out actual humans

719
00:38:59,022 --> 00:39:03,582
S1: in exchange for this thing that is more responsive. It's

720
00:39:03,582 --> 00:39:06,102
S1: nicer to us or whatever. And like, it's going to

721
00:39:06,102 --> 00:39:09,702
S1: have advantages over humans. That's fine. But we've got to

722
00:39:09,702 --> 00:39:13,262
S1: maintain this is my admonition to everyone. We've got to

723
00:39:13,262 --> 00:39:19,022
S1: maintain the mentality of all of this is to enhance humanity. Okay.

724
00:39:19,062 --> 00:39:24,022
S1: The goal of AI, um, relationships, the goal of AI therapy,

725
00:39:24,022 --> 00:39:27,542
S1: the goal of AI anything should be to take the

726
00:39:27,542 --> 00:39:30,692
S1: things that you love about human to human interaction and

727
00:39:30,692 --> 00:39:34,132
S1: human to human value exchange and make them better. Find

728
00:39:34,172 --> 00:39:36,652
S1: ways to enhance them. Find ways to do them more often.

729
00:39:36,652 --> 00:39:39,412
S1: Find them ways to do to do them in a

730
00:39:39,412 --> 00:39:46,372
S1: more enriched and deeper way. Right. Augmentation versus replacement. Augmentation

731
00:39:46,372 --> 00:39:50,652
S1: versus replacement. This is the trick that we cannot ignore.

732
00:39:50,852 --> 00:39:55,972
S1: And another way that I really like maximize this is, um,

733
00:39:55,972 --> 00:39:58,652
S1: all this infrastructure that I'm building, which, by the way,

734
00:39:59,092 --> 00:40:03,332
S1: the name for mine is called Pi Pi personal AI infrastructure.

735
00:40:04,332 --> 00:40:09,292
S1: My pi that I'm building, my personal AI infrastructure stresses

736
00:40:09,652 --> 00:40:12,332
S1: when a piece of content comes through that I should

737
00:40:12,332 --> 00:40:16,692
S1: watch in a slow version. That's analog, that's old school,

738
00:40:16,692 --> 00:40:19,732
S1: that's human. Sit down with a moleskine and a space

739
00:40:19,732 --> 00:40:24,572
S1: pen and take notes. Slow it down or pause it.

740
00:40:24,892 --> 00:40:27,932
S1: Take the notes and think. Maybe call your friend. Maybe

741
00:40:27,972 --> 00:40:31,012
S1: have an actual, you know, voice conversation with a friend

742
00:40:31,012 --> 00:40:33,172
S1: about this cool idea. I do this all the time

743
00:40:33,172 --> 00:40:35,932
S1: with Joseph and with Jason and be like, hey, have

744
00:40:35,932 --> 00:40:37,372
S1: you seen this thing? Hey, let's talk about it, blah

745
00:40:37,372 --> 00:40:41,532
S1: blah blah. And you know, you don't want to be like, oh, well,

746
00:40:41,532 --> 00:40:44,612
S1: that's not good. You know, the the pen is not

747
00:40:44,612 --> 00:40:47,172
S1: as good as the typing. You know, the Moleskine is

748
00:40:47,172 --> 00:40:49,572
S1: not as good as, you know, putting it in my

749
00:40:49,572 --> 00:40:52,852
S1: Telos file. No, this is not a competition. The telos

750
00:40:52,852 --> 00:40:56,892
S1: file should not be replacing things. It should be augmenting. Right.

751
00:40:56,932 --> 00:41:00,132
S1: It's just a journal. It's just capturing your life. Right.

752
00:41:00,172 --> 00:41:01,772
S1: You could do that on a moschino. You could do

753
00:41:01,772 --> 00:41:05,212
S1: that in a telos file with vim and markdown. Right.

754
00:41:06,012 --> 00:41:08,812
S1: We've got to stay focused on what actually matters and

755
00:41:08,852 --> 00:41:12,172
S1: what we value and what we're trying to enhance versus

756
00:41:12,172 --> 00:41:15,652
S1: what is shiny and cool and awesome. Right? I'm a

757
00:41:15,652 --> 00:41:18,972
S1: security guy. I love security, I love hacking, I love

758
00:41:18,972 --> 00:41:21,292
S1: hacking tools. I love all those things, I love I,

759
00:41:21,332 --> 00:41:23,692
S1: I love AI agents, I love automation, I love all

760
00:41:23,732 --> 00:41:27,612
S1: that stuff. Those are tools. Those are secondary. They serve

761
00:41:27,612 --> 00:41:32,212
S1: the purpose of human right. So so I think this

762
00:41:32,212 --> 00:41:36,052
S1: is absolutely critical to to just keep in mind, um,

763
00:41:36,052 --> 00:41:38,372
S1: for yourself, but also when you're hearing me talk about

764
00:41:38,372 --> 00:41:40,412
S1: how cool these things are, I just realize that they

765
00:41:40,412 --> 00:41:43,372
S1: are secondary. And now I have to find my place,

766
00:41:43,372 --> 00:41:49,652
S1: because what, uh, what a divergence there. Um, let's see here.

767
00:41:53,372 --> 00:41:56,612
S1: All right. Meta's AI can produce nearly half of Harry

768
00:41:56,652 --> 00:41:59,172
S1: Potter one, so we could actually give the actual quotes.

769
00:41:59,172 --> 00:42:02,932
S1: I'm curious how this is possible because it's not a database. Um,

770
00:42:03,132 --> 00:42:05,732
S1: I don't understand how it's producing exact quotes if it's

771
00:42:05,732 --> 00:42:09,732
S1: not a database. Um, yeah, I guess I'll have to

772
00:42:09,732 --> 00:42:12,652
S1: figure that out. It's kind of interesting. Um, and I'm

773
00:42:12,692 --> 00:42:15,652
S1: not sure I could do that deterministically either. I think

774
00:42:15,652 --> 00:42:17,692
S1: a lot of that might be just collapsing on the

775
00:42:17,692 --> 00:42:21,332
S1: most likely because it's seen so many samples. Um, but

776
00:42:21,332 --> 00:42:23,092
S1: I don't think it could do it, like over and

777
00:42:23,092 --> 00:42:26,332
S1: over and over, because again, it's not an actual lookup.

778
00:42:26,532 --> 00:42:29,162
S1: It is a generation, which is why you can have

779
00:42:29,602 --> 00:42:36,042
S1: hallucinations and non-deterministic results. Technology. Jassy told employees that Amazon

780
00:42:36,042 --> 00:42:39,042
S1: will need fewer people doing current jobs and more doing

781
00:42:39,042 --> 00:42:41,642
S1: other types of jobs as AI gets rolled out across

782
00:42:41,642 --> 00:42:45,122
S1: the company. We got to listen to these folks when

783
00:42:45,122 --> 00:42:47,402
S1: they tell us. For one, I'm telling you that this

784
00:42:47,402 --> 00:42:50,442
S1: is happening, that they're replacing jobs like whatever that matters.

785
00:42:50,442 --> 00:42:53,362
S1: I'm just a guy on the internet talking, but all

786
00:42:53,362 --> 00:42:55,962
S1: these CEOs, lots of them are basically saying, yeah, yeah,

787
00:42:55,962 --> 00:42:58,602
S1: we're getting rid of jobs. We're getting we're changing the

788
00:42:58,602 --> 00:43:01,242
S1: entire company. We're thinking AI first. And a lot of

789
00:43:01,242 --> 00:43:04,442
S1: people are like, no, no, they're not going to replace jobs.

790
00:43:04,482 --> 00:43:08,002
S1: I can't replace jobs and I won't replace jobs. It's

791
00:43:08,002 --> 00:43:11,002
S1: a big, uh, sham or whatever. And I'm like, well,

792
00:43:11,722 --> 00:43:14,442
S1: the people who actually control the jobs are telling us this,

793
00:43:14,842 --> 00:43:16,402
S1: and you got to watch out because some people are

794
00:43:16,402 --> 00:43:19,722
S1: also selling an AI product. So it's kind of like marketing. Like,

795
00:43:19,762 --> 00:43:22,082
S1: I felt that way with Salesforce. They're like, oh, we

796
00:43:22,122 --> 00:43:24,681
S1: sell a virtual, you know, AI employee. Oh, and also

797
00:43:24,682 --> 00:43:26,802
S1: we're getting rid of a bunch of jobs because we

798
00:43:26,802 --> 00:43:29,602
S1: could do it internally. Well, that sounds like marketing. But

799
00:43:29,602 --> 00:43:33,922
S1: when Amazon's actual CEO tells you this, when when the

800
00:43:33,922 --> 00:43:37,842
S1: head of AI companies like the head of anthropic, I

801
00:43:37,842 --> 00:43:40,722
S1: love that guy. Like he's way different than Sam. And

802
00:43:40,722 --> 00:43:44,722
S1: Sam's awesome too. But I love how human focused that

803
00:43:44,722 --> 00:43:47,242
S1: the head of anthropic is. I love how he's like,

804
00:43:47,442 --> 00:43:49,922
S1: people get ready, this is going to take jobs like

805
00:43:49,922 --> 00:43:52,282
S1: it's going to suck, but it's going to potentially be awesome.

806
00:43:52,282 --> 00:43:55,402
S1: But get ready. Why would you not listen to that?

807
00:43:55,442 --> 00:43:57,282
S1: What do you think he has access to? What do

808
00:43:57,282 --> 00:43:59,682
S1: you think he's looking at to make him think that? Right?

809
00:43:59,722 --> 00:44:02,842
S1: This is not a stupid person. Right. So just when

810
00:44:02,842 --> 00:44:04,762
S1: you when you see these CEOs that have no reason

811
00:44:04,762 --> 00:44:06,762
S1: to lie to you. And I hate to use this analogy,

812
00:44:06,762 --> 00:44:09,442
S1: but when a terrorist tells you I hate everything about

813
00:44:09,442 --> 00:44:11,442
S1: you and your culture and I want to destroy you,

814
00:44:11,602 --> 00:44:14,282
S1: and if I were to blow myself up and you

815
00:44:14,282 --> 00:44:16,362
S1: were to die as a result, I would go to heaven.

816
00:44:17,562 --> 00:44:19,282
S1: The one thing you shouldn't do is be like, yeah,

817
00:44:19,282 --> 00:44:22,002
S1: I'm just not really sure he believes it. No, he

818
00:44:22,002 --> 00:44:25,002
S1: believes it. He believes it. And you should be careful

819
00:44:25,002 --> 00:44:28,362
S1: at the park as a result. Okay. And this is

820
00:44:28,362 --> 00:44:32,082
S1: like the worst analogy ever. It's like the worst analogy ever.

821
00:44:32,082 --> 00:44:35,402
S1: Because I'm not saying. Right. The heads of these companies

822
00:44:35,962 --> 00:44:38,882
S1: or the people making these models are terrorists. I'm just

823
00:44:38,882 --> 00:44:43,562
S1: saying that when somebody is crazy about a belief and

824
00:44:43,562 --> 00:44:46,002
S1: they are like telling you from the depths of their

825
00:44:46,002 --> 00:44:47,882
S1: soul that they believe it and it is true and

826
00:44:47,882 --> 00:44:51,242
S1: it is going to happen, it doesn't mean it's going

827
00:44:51,242 --> 00:44:54,762
S1: to happen, but it does mean that you should believe them, right?

828
00:44:54,802 --> 00:44:58,482
S1: You should believe them that they believe it. Again, with

829
00:44:58,482 --> 00:45:02,922
S1: the caveat of of marketing, meta is expanding beyond Ray-Ban

830
00:45:02,922 --> 00:45:05,562
S1: with Oakley glasses aimed at athletes. I'm getting a pair

831
00:45:05,562 --> 00:45:07,802
S1: of these. My first pair was fantastic. I can't wait

832
00:45:07,802 --> 00:45:11,122
S1: for the battery to get much better. Resolution is improving. Apple,

833
00:45:11,122 --> 00:45:14,042
S1: where are you? We're looking for you. Gartner says I

834
00:45:14,082 --> 00:45:18,842
S1: will handle half of all business decisions by 2027. Uh,

835
00:45:20,122 --> 00:45:22,802
S1: did I miss something that is way too fast? Way

836
00:45:22,842 --> 00:45:26,362
S1: too fast. It doesn't matter if we get asi si,

837
00:45:26,402 --> 00:45:31,482
S1: not agi if we get ASI next month. Okay, so

838
00:45:31,522 --> 00:45:35,442
S1: it's a 400 IQ or a 4000 IQ. It's smarter

839
00:45:35,442 --> 00:45:38,522
S1: than Albert Einstein. And it runs on your phone for $0.03.

840
00:45:38,722 --> 00:45:42,362
S1: It doesn't matter if everyone had access to that, it

841
00:45:42,362 --> 00:45:46,642
S1: still wouldn't have half of all business decisions in 2027.

842
00:45:46,682 --> 00:45:49,962
S1: It doesn't matter how smart the AI is. Companies move

843
00:45:50,002 --> 00:45:52,562
S1: slow for a reason. There is friction built into the system.

844
00:45:53,522 --> 00:45:56,842
S1: You can't reinvent yourself. You can't reinvent the leadership team.

845
00:45:56,842 --> 00:45:59,602
S1: You got regulations to go by. You have employees like

846
00:46:00,042 --> 00:46:03,162
S1: there's way more friction in these companies. And and I'm

847
00:46:03,162 --> 00:46:05,282
S1: saying this with the experience of like working in like

848
00:46:05,322 --> 00:46:08,642
S1: fortune ten companies for years at a time. You know,

849
00:46:08,842 --> 00:46:11,242
S1: being at Apple, being at, you know, anyway, I don't

850
00:46:11,242 --> 00:46:15,482
S1: need to describe all that. Uh, trust me, companies move slow. Uh,

851
00:46:15,482 --> 00:46:18,562
S1: there's nothing that's going to make something handle half of

852
00:46:18,562 --> 00:46:22,632
S1: all business decisions by 2027. And Maybe that was just

853
00:46:22,632 --> 00:46:26,152
S1: a bad, uh, take on what was actually said, but

854
00:46:26,152 --> 00:46:29,632
S1: I don't think so. iOS 26 opens up AirDrop and

855
00:46:29,632 --> 00:46:32,312
S1: Airplay tech to third party apps. This is all part of.

856
00:46:32,632 --> 00:46:35,312
S1: If you haven't been paying attention to why Apple is

857
00:46:35,312 --> 00:46:39,352
S1: opening everything up, it's because of antitrust. They are very

858
00:46:39,352 --> 00:46:41,912
S1: afraid of getting crushed by antitrust, so they are opening

859
00:46:41,912 --> 00:46:45,992
S1: up the entire ecosystem. That's why you're seeing this. And

860
00:46:46,032 --> 00:46:49,672
S1: iOS 26 finally lets you set custom ringtones the easy way.

861
00:46:49,672 --> 00:46:54,152
S1: So that's cool. Humans. It looks like we found all

862
00:46:54,152 --> 00:46:56,312
S1: the missing matter in the universe. Did you know that

863
00:46:56,312 --> 00:47:00,552
S1: we were missing, I think 80%. Was it 80%? I

864
00:47:00,552 --> 00:47:03,192
S1: can't remember the percentage. We were missing most of the

865
00:47:03,192 --> 00:47:07,152
S1: energy in the universe. Like the the calculations, the equations,

866
00:47:07,152 --> 00:47:11,272
S1: they do not add up because basically, I think this

867
00:47:11,272 --> 00:47:16,112
S1: is correct. Um, if we only had the amount of

868
00:47:16,112 --> 00:47:19,032
S1: matter that we have seen, then the universe would be

869
00:47:19,032 --> 00:47:23,032
S1: expanding a lot faster. It's not expanding faster, which means

870
00:47:23,032 --> 00:47:26,032
S1: there's a lot more gravity holding it in, which means

871
00:47:26,032 --> 00:47:28,752
S1: more matter holding it in. And we have been unable

872
00:47:28,752 --> 00:47:31,472
S1: to find that matter, but we just did. So they

873
00:47:31,472 --> 00:47:35,512
S1: used these pulses to find. And essentially it is all

874
00:47:35,512 --> 00:47:40,552
S1: in this fog, this very light, very thin matter, fog

875
00:47:40,552 --> 00:47:45,872
S1: between galaxies. And that fog altogether makes up the other

876
00:47:45,872 --> 00:47:48,392
S1: large percentage that they were looking for. And this is

877
00:47:48,392 --> 00:47:51,392
S1: almost guaranteed to be a Nobel Prize in physics. This

878
00:47:51,392 --> 00:47:53,712
S1: is like one of the biggest findings ever in all

879
00:47:53,752 --> 00:47:58,952
S1: of astronomy. And huge, huge news. Also, same week Rubin

880
00:47:58,992 --> 00:48:02,472
S1: Observatory takes its first images. Absolutely stunning. I'm getting a

881
00:48:02,472 --> 00:48:05,712
S1: print of this thing. It's like the Hubble Deep field image,

882
00:48:05,712 --> 00:48:08,912
S1: if you remember that. Well, that just times a million.

883
00:48:08,952 --> 00:48:13,312
S1: This thing is insane. This Rubin telescope is different and

884
00:48:13,312 --> 00:48:16,272
S1: better than anything else that's ever been put out there.

885
00:48:16,352 --> 00:48:18,472
S1: I've got like, 20 links to this thing and like,

886
00:48:18,472 --> 00:48:20,792
S1: all the images and the story of it. YouTube video.

887
00:48:20,792 --> 00:48:24,472
S1: You got to go check this out. Now they seem

888
00:48:24,472 --> 00:48:28,032
S1: to have been brought online to investigate dark matter. I

889
00:48:28,032 --> 00:48:30,872
S1: wonder if they were trying to figure out the the, uh,

890
00:48:31,072 --> 00:48:33,992
S1: the secret of dark matter and actually find it. It

891
00:48:33,992 --> 00:48:35,912
S1: would really suck if that were the case, because they

892
00:48:35,952 --> 00:48:39,072
S1: are announcing their existence the same exact week that the

893
00:48:39,072 --> 00:48:43,672
S1: whole mystery and puzzle was solved. So holy crap, if

894
00:48:43,672 --> 00:48:45,592
S1: I'm right about that, the timing has to be like

895
00:48:45,632 --> 00:48:47,752
S1: a little bit of a letdown. But still, the images

896
00:48:47,752 --> 00:48:50,632
S1: are just insane and they're doing it with like multiple

897
00:48:50,632 --> 00:48:56,712
S1: mirrors and a super, super sensitive sensor. MIT researchers using

898
00:48:56,752 --> 00:49:00,672
S1: EEG monitoring to track brain activity in essay writers and

899
00:49:00,712 --> 00:49:04,392
S1: found ChatGPT users showed the lowest brain engagement. This is

900
00:49:04,392 --> 00:49:11,432
S1: basically saying AI makes you way dumber. Um, I think

901
00:49:11,432 --> 00:49:13,192
S1: we talked about this last week. I don't think we

902
00:49:13,192 --> 00:49:15,272
S1: need to go into it too much more, but sample

903
00:49:15,272 --> 00:49:18,982
S1: size was fairly small, but it absolutely showed that if

904
00:49:19,022 --> 00:49:21,182
S1: you rely on AI too much, it will make you

905
00:49:21,262 --> 00:49:23,142
S1: way worse. The way, the way I think about this,

906
00:49:23,142 --> 00:49:27,622
S1: the way I explain this is, is, I think, fairly elegant.

907
00:49:27,902 --> 00:49:30,062
S1: If you were the type of person like me who

908
00:49:30,062 --> 00:49:33,422
S1: is going to use Chi Chi's job, my DA's job

909
00:49:33,622 --> 00:49:36,662
S1: is going to be to constantly question me, to constantly

910
00:49:36,662 --> 00:49:41,022
S1: harass me, to constantly debate me, pull out my bias,

911
00:49:41,062 --> 00:49:45,302
S1: challenge me, force me to like, make a statement speech.

912
00:49:45,462 --> 00:49:48,702
S1: You know, um, like I'm on an NPR debate or

913
00:49:48,702 --> 00:49:51,302
S1: I a c squared debate. I've got to, like, perfectly

914
00:49:51,302 --> 00:49:53,342
S1: defend my position. Okay, now switch to the opposite side.

915
00:49:53,382 --> 00:49:56,622
S1: Make and make their choice or make make their argument

916
00:49:56,622 --> 00:50:00,142
S1: better than they made it. Right. I'm teaching Chi to

917
00:50:00,182 --> 00:50:05,062
S1: be an augmentation system, a training system to make me

918
00:50:05,062 --> 00:50:07,782
S1: better as a human, to make me a better first

919
00:50:07,782 --> 00:50:12,902
S1: principles thinker, and to make me more independent of a thinker. Right.

920
00:50:13,822 --> 00:50:16,742
S1: AI is going to enable that 100%. It's going to

921
00:50:16,742 --> 00:50:20,062
S1: enable that for millions upon millions of people, hopefully hundreds

922
00:50:20,062 --> 00:50:23,542
S1: of millions of people, hopefully billions of people. That's the

923
00:50:23,542 --> 00:50:26,462
S1: good scenario. The bad scenario is you just use it

924
00:50:26,462 --> 00:50:28,622
S1: like a crutch. And that's what I believe we're seeing

925
00:50:28,622 --> 00:50:32,822
S1: with this study where people got way stupider, right? Oh,

926
00:50:33,022 --> 00:50:36,862
S1: the most interesting part about this. They actually saw brain

927
00:50:36,862 --> 00:50:42,662
S1: activity decrease. Okay. So they have physical evidence of, I

928
00:50:42,702 --> 00:50:45,502
S1: would say, just people just being dumber, like their brains

929
00:50:45,502 --> 00:50:49,462
S1: are not as active because they are offloading that stuff

930
00:50:49,462 --> 00:50:52,262
S1: to an AI. And that is super frightening to me.

931
00:50:52,382 --> 00:50:55,422
S1: That is like really, really a problem. Just keep in

932
00:50:55,422 --> 00:50:58,102
S1: mind it doesn't mean it's it's necessary. It doesn't mean

933
00:50:58,102 --> 00:51:02,302
S1: it's guaranteed. Right? You can actively defend against this and

934
00:51:02,302 --> 00:51:05,142
S1: have it actually be a net positive. It's a choice.

935
00:51:06,822 --> 00:51:10,262
S1: Massive study analyzing 1.2 million dating app ratings found men

936
00:51:10,262 --> 00:51:15,062
S1: rate women as attractive 13 times more than women rate men.

937
00:51:15,942 --> 00:51:21,662
S1: Calling it the attractiveness gap. Women rate only 4.5% of

938
00:51:21,662 --> 00:51:25,982
S1: men as attractive. That's quite sad. What gets measured? I

939
00:51:26,022 --> 00:51:29,982
S1: will automate, uh, you can check that one out. Microsoft

940
00:51:29,982 --> 00:51:33,182
S1: data shows the infinite workday has become the norm. Analyze

941
00:51:33,182 --> 00:51:36,702
S1: trillions of data points and found that work now stretches endlessly,

942
00:51:36,702 --> 00:51:41,262
S1: with employees getting interrupted every two minutes during business hours. Yeah. Yep.

943
00:51:41,302 --> 00:51:45,062
S1: That's why I got out of the game. 2024 Baby

944
00:51:45,102 --> 00:51:49,542
S1: name details. Extreme spelling is exploding. Ten of the top

945
00:51:49,542 --> 00:51:52,742
S1: 20 Rising Boys names have X's or Z's in them.

946
00:51:53,582 --> 00:51:58,782
S1: Finland is firing up the world's largest sand battery. Evidently

947
00:51:58,782 --> 00:52:01,142
S1: store a lot of heat and sand. This is like

948
00:52:01,182 --> 00:52:04,342
S1: tripping me out. Private equity has taken over and destroyed

949
00:52:04,342 --> 00:52:07,862
S1: much of America. Megan Greenwell's new book argues that private

950
00:52:07,862 --> 00:52:12,742
S1: equity firms have systematically bought up the entire industries and

951
00:52:12,782 --> 00:52:19,582
S1: optimized them into bankruptcy. New Covid variant, called NB 1.8.1,

952
00:52:19,742 --> 00:52:23,302
S1: jumped from 15% to 37% of US cases in just

953
00:52:23,302 --> 00:52:27,982
S1: two weeks, and people are worried about a potential summer surge.

954
00:52:28,182 --> 00:52:31,862
S1: What was interesting here is there's actually CDC data on this,

955
00:52:31,862 --> 00:52:34,542
S1: which I'm surprised and happy that there's still data coming

956
00:52:34,542 --> 00:52:38,182
S1: out of the CDC discovery. Again, I'm not going to

957
00:52:38,182 --> 00:52:41,422
S1: read all these. They can get quite long. And there

958
00:52:41,422 --> 00:52:44,582
S1: is the newsletter for you to go check out. Heartwarming. I,

959
00:52:45,342 --> 00:52:47,422
S1: I am going to read this one. So a teacher

960
00:52:47,422 --> 00:52:50,702
S1: shows her students eye images. This thing is insane. You

961
00:52:50,742 --> 00:52:53,702
S1: got to go watch this video. It shows them. So

962
00:52:53,742 --> 00:52:55,462
S1: somebody's like, I want to be a football star. I

963
00:52:55,462 --> 00:52:57,702
S1: want to be, you know, a marine biologist. I want

964
00:52:57,742 --> 00:53:01,622
S1: to be an astronaut. It shows them age accelerated versions

965
00:53:01,622 --> 00:53:04,582
S1: of them, like in an astronaut suit or whatever. And

966
00:53:04,582 --> 00:53:08,262
S1: just like the looks on these kids faces, it is so, like,

967
00:53:08,262 --> 00:53:13,182
S1: so wholesome, right? Just imagine seeing that it is possible, right?

968
00:53:13,302 --> 00:53:15,092
S1: Do you believe your parents when they say it? Okay.

969
00:53:15,132 --> 00:53:16,732
S1: A little bit. Do you believe your teacher when they

970
00:53:16,732 --> 00:53:18,692
S1: say it's possible? A little bit. But how does that

971
00:53:18,692 --> 00:53:21,772
S1: compare to actually seeing your face grown up in an

972
00:53:21,772 --> 00:53:24,292
S1: actual astronaut suit? It is extraordinary. You got to check

973
00:53:24,292 --> 00:53:28,492
S1: it out. Good workers are often bad at interviews. Hormozi

974
00:53:28,492 --> 00:53:32,052
S1: highest ROI spins. There are tons of great discovery links. Again,

975
00:53:32,052 --> 00:53:33,772
S1: I don't want to just go through and read the title.

976
00:53:34,372 --> 00:53:37,252
S1: Go check it out. Recommendation of the week. Go check

977
00:53:37,292 --> 00:53:39,692
S1: out the propaganda idea example at the top of the

978
00:53:39,692 --> 00:53:42,532
S1: newsletter here, and train yourself to detect that type of

979
00:53:42,532 --> 00:53:45,972
S1: content about whatever topic doesn't actually have to be politics, right?

980
00:53:46,172 --> 00:53:49,092
S1: It could be like guarding against advertising, right? And learn

981
00:53:49,092 --> 00:53:51,732
S1: to mentally defend against it. Like because none of us

982
00:53:51,732 --> 00:53:54,532
S1: are immune to this. And the aphorism of the week

983
00:53:54,892 --> 00:53:57,892
S1: don't do things that you know are morally wrong, not

984
00:53:57,892 --> 00:54:01,372
S1: because someone is watching, but because you are. Self esteem

985
00:54:01,372 --> 00:54:04,732
S1: is just the reputation that you have with yourself. Don't

986
00:54:04,732 --> 00:54:06,692
S1: do things that you know are morally wrong, not because

987
00:54:06,692 --> 00:54:09,612
S1: someone is watching, but because you are. Self esteem is

988
00:54:09,612 --> 00:54:13,052
S1: just the reputation that you have with yourself. Naval Ravikant.