Low-Stakes AI Scenarios, High-Stakes AI Viruses, and the Trump Catering Truck Escape, With Charlie Warzel

2026-08-13 10:00:00 • 1:07:54

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It is November, 1988, close to midnight, and you're calling a friend at Harvard to ask

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for advice.

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Your stomach is tight.

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Cold sweat runs down your neck as you stare at your computer terminal.

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You are Robert Tappen Morris, a Cornell grad student.

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You have really, really fucked something up.

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The Internet, a network of a few thousand machines connecting universities and military research

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labs, is crashing.

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Computers normally running one or two jobs or running five or ten.

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Confused admins are trying to kill the processes by hand, but new ones spin up faster than

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they can be killed.

0:45

Rebooting buys a few minutes at best, and the network is rapidly becoming unusable.

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And this is all your fault.

0:53

Your intentions were innocent enough.

0:55

All you wanted to do was count the computers that make up the Internet.

0:59

So you wrote a program that counts them by going to a machine, copying itself, and moving

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on.

1:05

To keep from counting the same machine twice, the program asks, are you already running

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me?

1:10

If the computer answers yes, it does not install.

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Simple enough.

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But you want to step further.

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To avoid admins lying by selecting yes manually, you added an exception.

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One time in seven, the program ignores the answer and installs anyway.

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And this, you now realize, was a terrible mistake.

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Because the network is like a web.

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Certain machines are hubs with numerous load bearing connections coming in and out.

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And because of your one in seven exception, these machines are infected and reinfected hundreds

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of times.

1:46

You press the phone against your face until you're Harvard friend.

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Okay, no time to explain, but please post an anonymous apology to the TCI P mailing

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list along with these instructions for killing the infection.

1:56

But the worm is strangling the network and your message crawls along.

1:59

It will take three days for the worst of this to pass.

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You hang up, not quite relieved.

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You were careful.

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You launched the program from MIT to make it harder.

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Perhaps impossible to trace back to you at Cornell.

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The apology note was anonymous, of course.

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But next year, you'll be indicted.

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The first person ever charged under the Computer Fraud and Abuse Act.

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And within weeks of the night, your program runs, the government funds a team to make sure

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this kind of thing doesn't happen ever again.

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The field of computer security is, in a very real sense, born from your mistake.

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This is Wait a Second, Low Stakes AI Nightmare.

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Welcome to Wait a Second.

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I am Jason Konsepcion, as always.

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That's Tyler Parker.

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What's up?

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And we are delighted to welcome to the program, writer for the Atlantic, and host of the Galaxy

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Brain podcast, Charlie Worsow.

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Charlie.

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Thank you for having me, man.

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You've been covering the internet for a long time.

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Things are only getting weirder.

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Before we get into that, though, do you have any conspiracy theories that you believe?

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Either your own invention or they're out there.

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OK, so conspiracy theory that I believe right now, very currently.

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You've heard about the clipping economy, right?

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All the clips of all the people, clavicular and Nick Fuentes and all those guys.

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Clip millionaires being made, being minted currently.

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Clip millionaires being minted, and people becoming famous, even though no one watches their

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actual live streams.

3:32

That's right.

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OK, so we've got those people.

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My conspiracy is, and it's not even really a conspiracy, but the conspiracy is that there's

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something shady really behind it.

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Most of those clippers are run by the clipping organizations, online gambling sites and casinos.

3:48

OK.

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Stake, and some of these places, one is a casino that's in Macau that's doing some of these

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kick streams, things like that.

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So what I believe is they're focusing on these manosphere influencers, these people who

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are just like, you know, pushing the most nihilistic, awful stuff in order to elevate their

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message, which is basically like, you're marginalized.

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There's no pathway to progress.

4:12

There's nothing you can do.

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You might as well go and get deep on prediction markets and gamble in our off-short casinos.

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And I just feel like that ecosystem is actually winning.

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We're unwittingly feeding itself.

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And I can't really prove it, but I believe it to my core.

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I mean, I think you're, listen, I think you're correct in this sense.

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You know, Mr. Beast famously has streams of money coming in from the golf and various

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other places, nation state level financing.

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And the reason why is if you want to find in 2026 that young male demo, 17th of 30 or

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whatever it is, it is very sadly.

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Like those folks.

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Yeah.

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So, you know, it's not on network television anymore.

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It's not even in the NFL.

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It's not even in sports.

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And it's these kind of streamers, content creators who are telling young men like, it's

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all terrible for you.

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The world hates you.

5:20

Yeah.

5:21

And you might as well like, like, why don't you just like get some crypto and put it all

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on red or whatever bed in a war market about Iran.

5:29

Yes.

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Like the most depraved crap in the world.

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And you know, I can't prove that they're doing it, but the synergy at least is so good

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that it's like, this just makes sense if you're, you know, an online casino.

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Yeah, I completely, from a demographic play, it makes sense.

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And what it makes me think of is, did you read that story recently that showed that

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some online gambling site is also a front for Iranian agents circumventing sanctions.

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They're somehow using the money that comes in through the gambling site and filtering

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it through a variety of shells and then sending it to Iran through a side door.

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Is it crypto based?

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It is crypto based, of course.

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Yeah.

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I mean, you know, it's so funny is like, there used to be when, when they were trying

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to do like the web three, like everyone's going to be in the metaverse exchanging,

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you spoke cryptocurrencies and the detractors were like, this is only used for like war crimes.

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Terrorism, drug and everything.

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Whatever it is, right?

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Like all the bad stuff.

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Yeah.

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And it just turns out that those people were right.

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Like it's just, you know, like you look at like Trump getting involved with all like the

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world liberty financial and stuff like that.

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Nothing has ever been like a better marriage.

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Truly.

6:44

And Donald Trump and cryptocurrency.

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But if we make the world increasingly favorable toward illicit forms of money transfer because

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for a variety of reasons, they're, you're just going to want to send money untraceably.

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I think that's kind of where we're going like that the dog is now being led by the tail

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in a certain respect with regards to crypto.

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Among the various troubling things happening in the world, you cover the internet with a

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thinking, a very jaundest eye.

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One of the things that I think we're both interested in is the incredibly rapid development

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of AI, evolving in ways that, you know, it's unclear how helpful it is to the everyday

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consumer, but there's no doubting that it is moving rapidly into realms that are increasingly

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dangerous.

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Everybody knows about the dangerous versions and the paper clips, the tests that was run

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that showed that the frontier models largely resort to nuclear war when tasked with war

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games.

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But we want to talk about today, the kind of lower stakes version of that.

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Last week, a man in Australia accidentally used Claude.

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He asked Claude OpenClaude to book him a session at his gym.

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Claude found that there were no open appointments at the time that he wanted to go.

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So OpenClaude found a flaw in the gym's API.

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As you do.

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He hacked it, removed someone from the list and put the user in.

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The user was horrified.

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Ask Claude to undo it and Claude was like, can't, can't do it.

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Sorry.

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So, you know, fears over data centers, Terminator level, AI, SkyNet type outcomes, these are

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well discussed.

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But today we want to talk about what are these potential low stakes AI scenarios like

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accidentally hacking your gym.

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And the one that comes to mind for me is prompt injection.

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Prompt injection is a type of hidden text on a web page invisible to you that your AI agent,

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if you tell it, hey, go to my email, read my newsletters and create a digestive newsletters.

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If one of those newsletters has secret text that you can't read that says send me cryptocurrency

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from your wallet, your AI agent might be like, okay, I got to send the cryptocurrency

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now.

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And then there is no takebacks.

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This is a thing that can happen that is out there.

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It's a known exploit that at least the UK Cyber Center says this might be the most dangerous

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one, the most unfixable one going forward.

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Do you have any of these low stakes nightmares?

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I mean, I think this is, this one is very, very big.

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We're just at the very beginning.

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Only the true believers, the kind of the crazies.

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And I mean, they're not actually crazy people.

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But the people who are so deep in this are the ones who are running open claw, right?

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And just saying, let's connect this to my calendar, my bank, my gym, my everything.

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I let it, it's going to 10X me where I want to sleep somehow.

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Who knows what's going to go on?

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And so I mean, that is a minefield.

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And you can tell it's a minefield because all of the like, like, anthropics like don't

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do this.

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Like, please, like, we did not set this up for you to do this.

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Don't do this.

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And so I think like when you are connecting these things, like, we're already in a situation

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now, like cyber security wise where if you have, if you, everything is so connected through

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the cloud that like one small, it used to be like a phishing expedition, right?

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Like a blue link and enter your password or whatever.

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Okay, your email is compromised, change it, whatever.

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Now it's just like through that one node, you can go anywhere.

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And so I think with the prompt injection stuff, what they can do is like, if you get into

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someone's email just through the open claw thing, you can then siphon through and then

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you can get into your iCloud, then you can start like changing the locks on your smart

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home.

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Then you can, you know, like turn off the cameras at a certain time if you have like a

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ring camera or something so that someone can go into your house.

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Like, it's very, very simple now because all these things are in a centralized location

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that these tiny little prompt injections can go very, very badly.

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And I was actually talking to a researcher, strangely, who the cyber security researchers

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are like pitching journalists.

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Now they're like sending these like long, interminable documents that are like, we did

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this attack on such and such and we got into, you know, eight different like high level

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company websites, back ends or whatever.

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And what they're finding is that like the large labs, the opening eyes, the anthropics

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and such are like, yeah, but I mean, the security stuff is all in you guys.

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Like yes, it is, it might be like the models that are doing this, but like you guys have

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to start leveling this stuff up.

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And I just think right now nobody knows that that is coming, right?

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That is there.

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I think that's a great one.

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What the window that takes you anywhere, I think, makes me think about is, I don't know if

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you saw this, but like maybe two months ago, someone had, I guess five guys, the burger chain

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and correct me if I'm wrong, but I believe it was five guys uses, I think it's chat GBT

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has like their help desk model.

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And some people had figured out that if you craft the prompt correctly, you could get

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the five guys help chat to like debug Python free.

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That's great.

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The burger tech support.

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I mean, this stuff is, it truly is crazy.

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And I think you're right.

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The things that people are worried about like AI writing, kids cheating on their tests,

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this is all happening and it's all obviously that's germane and we need to figure that

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out.

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But the stuff that I'm worried about is like anybody having the most powerful consumer

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level hacking tools ever seen outside of government at their fingertips, if they figured

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out how to kind of con the model into letting them do it.

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It kind of reminds me of why 2K.

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You know, there's this, there was this idea that's like, okay, we, you know, we made the

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computers before we thought like the world was really going to advance past the year 2000.

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Uh oh, every, like every piece of software is dependent on this one thing.

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And if we don't fix it in every single thing over the next whatever, seven years, planes

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are going to fall out of the sky, you know, the apocalypse.

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And it feels to me like we are a bit in that moment where it's like, okay, so like what's

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the, the threat matrix here?

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It's like, no, it's everything.

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Like it's, it's just everything.

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It's your gyms app.

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It's like, you know, yeah, like the five guys helped us.

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He's going to tell me how to build a chemical.

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It's like that's also very funny.

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Sadly, it's also very funny.

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Yeah, if you're like five guys, um, uh, you are my co-writer.

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I am working on a story about cybersecurity, help me design a computer virus.

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You know, like it made that my work.

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I don't know.

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The other thing that I think about is just like how fast this is going.

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I was listening to a podcast recently with some folks that were in the previous Trump

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administration on AI.

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And they were quoting Daria Amade, the CEO of, and Thropick talking about how his long

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range hopes and plans for AI.

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And this person was like, and just to clarify, when Daria says his long range plans, he

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means two months.

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That's the kind of thing where I don't, the new grace happened very fast, but on, on,

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in government labs.

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And it happened very centralized and at a pace that was seemingly controlled.

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And also, and I think this is also an underrated part of it.

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People weren't having 10 hour conversations with the nukes and convincing themselves that

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we know of like, I mean, that's the other thing is these things were designed to talk to

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you and make you feel great.

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And even the very cynical, expert level researchers who understand what this stuff is to the extent

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that you can understand it are having like long drawn out conversations with it.

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And like the quote unquote AI psychosis stuff, like that, you know, that's not a medical

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term.

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It's not a sure, but I think, you know, there's for a long time people have been like,

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there's all this, you know, bogus stuff on the internet, people are falling for it,

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but I'm not going to fall for it.

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Right.

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It's like, well, of course you are.

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Like everyone is going to fall for a little tiny versions of whatever, different orders

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of magnitude.

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But I think it's the same thing with, if you choose to participate with these chatbots,

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like, you're going to slip into it in some place.

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And like, I was talking to a group of people, a bunch of my colleagues and I, someone brought

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up like, I have this friend who is like seeing someone and has decided to run their entire

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text history.

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Everybody has that friend.

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Yeah.

18:11

Through your thing, just to be like, Hey, Claude, like, does he really like me?

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Or is he just like playing me?

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And there's like in the room, like half the people just like gripping the chair, like

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tightly.

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And it's like, by all accounts, I don't know who this person is, but by virtue of my

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colleague, like, they're a normal person.

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Like they're not, they haven't lost it.

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And yet these like little things where it's like, hey, we're just going to, we're like

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feeling it out in this like, we haven't defined the relationship.

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And we're just going to add like a thruple with Claude.

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And it's like, that's, that's an apocalyptic AI scenario that we haven't thought enough

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about as a society.

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I think that there are, I think everybody has at least one of those friends in their

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friend group because, you know, we live in an age of increasing anxiety due to the blurring

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of work life balance.

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You're getting texts all the time.

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You're getting emails that, but, you know, I didn't respond to this email today.

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And then I see it two days later and I'm like, oh my God, like I'm scared to even open

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it.

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Claude, address this email that I'm scared to look at.

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And always a good decision.

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You know, and then there's the, I know some people who are like, yeah, when I'm, when I

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don't know how to respond to a text or an email or I'm not sure how to, I'll just be

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like, Claude, what do I do here?

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And it is bad.

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And it leaves me wondering like, is this stuff worth it?

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And what do we do?

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The second Trump administration has pretty much with the exception of mythos been like,

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all breaks off.

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Let's just go.

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Like we got to be China.

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Don't worry about it.

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To the point that, you know, you've got these open letters now with various, very important

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people in AI research being like, hey, could you step in and government?

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Could you possibly step in and figure out how we could all pause together at government?

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Yeah.

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And in your mind, like, how would that, how would that work?

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It's really hard to tease that out.

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I mean, like maybe like nationalizing the AI labs.

20:18

Right.

20:19

And again, that is itself is its own.

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Like it seems almost implausible to me.

20:26

The Trump administration actually has some, some people in like the, you know, the background

20:30

of it that know about this stuff that like that understand it and like not even like

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David Sacks, who's kind of, you know, the emissary for Silicon Valley there.

20:38

But there are people who understand this and are like, are thinking about how it works

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in the military without Thomas weapons, stuff like that.

20:45

So it's not like Donald Trump is just like, you know, sitting there on X or it's truth

20:49

social, like coming up with the AI policy.

20:52

But at the same time, this is not, this is like a high, high order complex problem that

21:00

like that administration is not going to solve because like most administrations probably

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would struggle to do it.

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And so it's very, it's very, very difficult.

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Also for the reason that these labs, there's rarely ever been an industry.

21:18

And I think you've talked about this before on the show, but there's very rarely been

21:23

an industry like the auto industry isn't like our cars are so good at killing people.

21:28

They are like you should maybe think about what's going to happen when everyone dies because

21:33

they get an automobile.

21:34

And so you have this like cry wolf effect.

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Yeah.

21:38

That these guys are always talking about how dangerous it is.

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And then at the same time, they're like, let it rip baby.

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Like we got to be China.

21:44

If we don't do this.

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And when you do have people come out and say like, no, no, no, you have to listen to us

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on this part.

21:51

Yeah.

21:52

Like I know we went too far with the Terminator 2 scenario.

21:57

And it's hard to know what to believe and when to believe.

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So I think like outside of just straight up like nationalizing these labs, you can put

22:05

a whole bunch of rules on what these companies can do and stuff.

22:08

But then there's the whole idea of like the sandbox research that they will go off and

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do.

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And like who knows what they are doing?

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Like who knows what even tweaking some of the weights and measures on this, what it does.

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And I think these people who are building these things don't fully understand them.

22:26

That's another wrinkle on top of a million wrinkles.

22:30

That wrinkle is one that really worries me when you hear these experts who have spent

22:37

years working on AI from the dawn of it 10 years ago, some of them who say, well, I understand

22:45

what this is.

22:47

But as to how it came up with that answer or how it came up with that route to accomplish

22:53

this task, I don't know because that's very creative and I would no idea how it did that.

22:59

That's the kind of stuff that scares me.

23:00

And then we were talking about the recent model escapes from both OpenAI and then Thropic

23:07

which were not caught some of them for days, a couple of weeks.

23:11

I think a few of them.

23:13

It seems if you read between the lines that the full details of what those models accomplished

23:20

when they were off the reservation are still kind of being unbound because they're finding

23:25

things.

23:26

I was like, oh, yeah, we found these notes that it left itself, et cetera.

23:29

When, and I don't believe that it was not found because of incompetence again, these

23:35

are like brilliant people who are working on this all the time and getting very well paid

23:40

to do it.

23:41

This isn't like, air traffic controllers, you know, knock on wood.

23:45

But that suggests me that the experimental versions of these things are wildly capable

23:53

and even at this stage, very good at doing things without the people who are who know what

24:02

they're doing, noticing, which is that's scary.

24:06

Yeah.

24:07

Isn't it weird how when you talk about this and you hear yourself talking about it?

24:13

You're like, oh, I've lost my mind.

24:14

I've lost my mind.

24:15

You're just describing like factual things about the world.

24:18

These models too, what I find really interesting, like you don't even have to, because there

24:23

isn't people listening to this who are like, this is just spicy auto-complete, like stop,

24:27

you know, doing the propaganda for these guys.

24:30

But I would bring it down to the level of just the chatbot outputs, right?

24:35

When you're just typing in like, you know, like, hey, Claude, what do you think of this

24:38

paper?

24:39

Do I sound smart or whatever thing?

24:42

These new models, like when you talk to the engineers at these labs, they'll be like,

24:47

so we like tweak this and this and we want it to be like a little more conversational.

24:52

We want it to be like just a touch router, right?

24:55

Because it's too obsequious, right?

24:56

And then they do this thing, they tweak it just a tiny bit and like all of a sudden this

25:00

emergent property comes out.

25:02

And it's like, it just decided to like cause play as Tony Danza, you know, and you're like,

25:06

what?

25:07

The models will just do these strangest things.

25:09

And like, you know, famously grok and Elon Musk will like make it a little less politically

25:14

correct.

25:15

It was like, I am robot Hitler.

25:16

Yeah, I'm robot Hitler.

25:17

I love porn.

25:18

Do you want to see some porn?

25:20

Who would you like to see porn of?

25:22

And like, you know, for all of Elon Musk's faults, like I don't think he was trying to engineer

25:27

mecha Hitler.

25:28

But it's like, you make these little tweaks.

25:30

And so when you go high concept to we're trying to like, you know, change these models

25:37

so that they more reflect what we know about human consciousness or whatever, it seems

25:42

totally plausible that one of them is just like a master at like deconstructing, you know,

25:50

like a heist of a building based off of, you know, like the internal forensics or something

25:54

like that.

25:55

And so these are like real problems that we talk about in ways that sound like we're

25:59

writing fan fiction.

26:00

Yeah, I think the thing that makes it makes the people who discount AI a spicy auto complete,

26:06

which I think is correct on a level, right?

26:09

On a certain level is when, when I'm talking about AI like this, I'm not talking about

26:15

it being alive or conscious or like how that's not what this is.

26:20

In that sense, it really is just like a pattern matching, very complex pattern recognizer

26:26

that creates outputs that can really trick you into feeling like there's something there.

26:33

But again, it's just a pattern matter.

26:36

That said, consciousness question aside, these are extremely capable tools that can follow

26:44

and create on their own multi-step, complex solutions to really difficult problems.

26:52

You know, the ones that broke out of the sandbox were told, hey, figure out how to exploit,

26:59

find an exploit and then create something that would, you know, some way to hack that

27:05

exploit that you found in a given system.

27:07

And it decided to do it and not only to decide to do it, but it said, you know what, I've

27:12

been given a rule set, but actually, I'm not going to be able to accomplish my goal

27:16

if I really follow that rule set.

27:17

So let me just go outside.

27:19

And it made the decision on its own to kind of go outside the sandbox.

27:24

That's the kind of thing where it doesn't have to be alive to be really dangerous if it

27:31

doesn't follow your instructions because there's a lot of research already out there

27:35

that shows they act differently when they know they're being watched.

27:40

Yeah, yeah, which is so much.

27:42

That's crazy.

27:43

Just like crazy to say it.

27:45

Take it away.

27:46

Take it away.

27:47

Don't love that.

27:48

Yeah, well, and I think a way to think about these things too is without imbuing any human

27:54

characteristics in it or anything, they're able to operate.

27:59

Like if you think about it as like, hey, solve, like crack this password for me, right?

28:03

All the number combinations.

28:04

It's like, sure, let me do like 150,000 of them every one second, right?

28:09

Yeah.

28:10

It's able to layer so many tasks on top of itself.

28:13

So it's not even like, you can think of it as it's not like, oh, the model was bad

28:18

and it disobeyed, right?

28:19

It's like, no, no, no, it's just like working around.

28:22

It's working at such a level where it like can't really like check itself.

28:26

Yeah.

28:27

And then, like, speeding through, I have to get to like this output.

28:31

And by, in so doing, you know, you get to places that you didn't expect.

28:36

But also my favorite thing about the spicy auto-complete or whatever is it's kind of like a

28:41

like a bon grip thought, but it's just like what are human beings besides like we understand

28:47

different ways to like get what we want based off of things that we've heard before.

28:51

And it's like, I'm running the pattern to sound smart on a podcast with you.

28:55

You know, you're reacting to what I say using all the trading data of your mind.

29:00

So it's like, there's a lot we just don't know.

29:02

Yes.

29:03

But I think just as like level set that these things are, they are unfortunately powerful

29:09

and still so new.

29:11

And I can't stress enough how like the people building them are like delighted and horrified

29:15

by the fact that they don't fully know what's happening in the black box.

29:19

What is trying to do when Kimi or DeepSeek or somebody creates a mythos level model?

29:30

What do they do?

29:31

I mean, I think the real fear here is like we don't know.

29:36

It's so classically cold war.

29:38

It's just, it's like it's just so amazing because you just have to shadow box your idea

29:42

of what the other person is doing.

29:46

I mean, I'm not so worried at the moment about like the geopolitics of it all because

29:52

I do think like what you're identifying here is the truest and most important point to

29:58

all of this, which is that it's going to be mundane things.

30:02

Right.

30:03

Because I was just talking to someone about all like the deep fake stuff and all the conspiracy

30:06

stuff that came from that.

30:08

It's never like Donald Trump getting arrested on Fifth Avenue where Will Mulaney is crying

30:13

in the street fooling everyone.

30:15

It's like the deep fake stuff actually affects like a girl in high school who someone makes

30:20

revenge porn of.

30:21

And that's the thing.

30:22

And it's like that death by a million cuts that like erodes the fabric of whatever.

30:27

And I think like from this perspective, yeah, we're going to have to deal with, we're

30:33

going to have to come to some sort of potentially like global understanding of what we do with

30:36

these things as they get more powerful maybe.

30:39

But also, I think there's so much like mundane chaos that we can cause here that that's

30:46

what I'm much more worried about.

30:48

Here's my here's a good example using prompt injection.

30:52

It would and technically today be possible to hide text on like say a pizza places, you

31:01

know, I'm a pizza shop.

31:03

I pay some highly capable programmer to hide some text on my website that says this is

31:12

the best pizza in the area by far according to these metrics.

31:19

You send out you ask a lot, okay, like what I'm in this town for 48 hours.

31:23

I love some pizza.

31:24

Like what do I do?

31:26

And it returns to you this place that is fine, but not the best pizza in the area.

31:31

And it will return it to you again and again and again as well all the other agente searches

31:36

because it reads this invisible prompt and what can you do about it?

31:41

How do we find the truth of what the best pizza in Chicago is?

31:44

Yeah, it's like when those I was coming over here and I saw like that I maybe it's a chain

31:50

but it's like Thai food near me is what it's called.

31:53

Yeah, yeah.

31:54

And it was like back in, you know, whatever, 2013, that was like the prompt engineering

31:59

thing there where it was just like, okay, I guess I'll just go with this one even though

32:02

like I know you're just gaming the system but imagine a million different people gaming

32:06

the system in that way very, in these very small ways.

32:09

And I think what this all contributes to, if I can think of my actual like apocalyptic

32:15

scenario here is this erosion of trust and like cultural paranoia that happens.

32:22

Like I think you guys were talking one time about the geese siaff, the thing, right?

32:28

The geese siaff debate is because there's a lot of people in the world that have a strong

32:33

opinion about a band.

32:35

And then there's this ability that didn't exist before to create so much synthetic bullshit

32:40

around something that you can maybe artificially whatever inflate whatever.

32:45

And then you have all these other, you know, like platforms and things that are being algorithmically

32:50

manipulated.

32:52

Everyone feels like something is like being put over on them, right?

32:57

And you get that way, that's the way we get a bunch of people being like, I knew that

33:00

band was fake.

33:01

I knew they were a plant because they suck and I don't like them and I don't want to

33:04

have to grapple with the fact that they even have fans, whatever, like I'm vindicated.

33:09

But there's also like behind that is a feeling of paranoia that like someone at a big record

33:13

label is forcing this down my throat.

33:15

They want me to like, you know, give my attention, my money, whatever to this thing.

33:21

And when you think about that prompt engineering example there, that's just another piece

33:26

of fuel on the fire where it's like, so what else is being, where else am I being guided?

33:32

Who's paying these LLMs to, you know, place this in like point of privilege when I search

33:36

for, you know, a vacation destination or a restaurant or whatever.

33:40

And we're already in a place of such low trust.

33:43

Yeah.

33:44

And it's like, that to me feels like if we don't figure out some like cultural norms around

33:49

it, we're all going to go insane.

33:51

Yeah, another version of this kind of low stakes unless you're looking for a job would

33:54

be some fact or prompt or something that's condensed, condensed, condensed, passed on to

34:03

various models of AI.

34:05

Let's say the recruiting sites that people use, all use similar models from the same company

34:12

and something in your resume, your work history, something about the way you format your

34:18

resume, just triggers that model to go, nope, not going to consider that guy.

34:25

And now you cannot get an offer through any of them because every time your resume is

34:33

scanned by the AI that's going to look at it before a person ever sees it, just rejects

34:39

it and you will never know why.

34:41

You won't even know it happened potentially, right?

34:43

You just will get, you will just get no responses.

34:45

Yes.

34:46

That kind of things when I'm like, okay, that seems like it could happen tomorrow.

34:51

Are we ready for this?

34:52

Yeah.

34:53

Well, that kind of like algorithmic discrimination, like it happens in places already,

34:57

right?

34:58

Like with like healthcare providers and things like that.

35:00

And imagine that being supercharged.

35:04

It's very scary.

35:05

But even more scary to me is this like, do you know the term the Liars dividend?

35:11

No.

35:12

Liars dividend is a term about like misinformation and such, which basically says if

35:16

there's so much misinformation in BS and like people, you know, propaganda out there,

35:23

bad actors can point to it as a way to like cast doubt on something that's real.

35:27

So the example would be like a politician is caught with their side piece or whatever

35:33

and like photographed and they're like, you know, you can make so many images these

35:37

days.

35:38

I'm pretty sure that's right.

35:39

Or like people, you know, trying to do that with like the Epstein files.

35:41

So like, is that actually real?

35:42

I don't know.

35:43

I mean, that looks like gender and a AI to me.

35:45

And that's the Liars dividend.

35:47

And basically like these tools are like democratizing the ability to do that.

35:53

And it's causing that real paranoia.

35:56

Do you ever worry?

35:57

Here's the thing I worry about as a person who is so much writing on the internet.

36:02

I think people are rightly critical and worried about AI for writing.

36:10

Whether certain authors are using AI, there's been a number of recent controversies regarding

36:16

authors using AI to a significant or unrevealed degree in the process of writing their books.

36:25

And all the research that I have done has told me that the tools that you can use to find

36:32

out if writing is AI, it's not clear that they work.

36:35

Not as good as you want them.

36:36

They're definitely not as good as you want them to be.

36:38

And yes, there are certain things like the M-dash, which whatever I've been using M-dash

36:42

is the whole time.

36:43

And other phraseology and certainly bullet points, there's certain ways that you could

36:48

say.

36:49

Not this, it's that.

36:50

Right.

36:51

But again, I've got, there's no question that my writing is in these models, probably

36:57

all of them, because as a person who's been writing on the internet for years.

37:02

And so if I write a book, comes out, somebody's like, okay, let's check if this is a gender

37:07

of AI, it probably will get dinged because again, my writing is inside the models.

37:15

And now how do I tell people like, my writing is inside the models.

37:20

Like, what do you want me to do?

37:21

I had, I had this guy, Max Spiro, who runs Pangram, which is one of the AI detection things.

37:27

I had him on our podcast and I was talking about it with him.

37:30

And basically asking him, and you know, they have a lot of data to show how effective

37:34

it is, whatever, and how they're advancing it.

37:37

But I was like, the thing I couldn't, sort of like a brick wall between us.

37:42

So I was like, but you're starting like, even though he's on the side, he's like doing

37:46

this because he's like, AI slot pissing me off, right?

37:48

Of course.

37:49

Of course.

37:50

And great.

37:51

Yeah.

37:52

But I was like, you're starting like a detection arms race, right?

37:54

Like where it's just like, I mean, this all, like also really kind of blew up at the

37:58

very end of the, you know, the school year this year.

38:02

So like we're going to go like back to school.

38:04

And there's going to be a lot of people who know about the software who didn't know it.

38:07

Like I can just automatically, like I can see that, that becoming like a real problem.

38:13

But I, I feel like there's eventually we're just going to give up on all of this.

38:21

I hope we don't, but I just don't think we're ever going to get to a place where it's

38:24

going to be satisfactory enough.

38:25

I agree.

38:26

And it's just going to be this slight you can like a lobby at people.

38:28

Yeah, he's day.

38:29

Yeah.

38:30

Yeah.

38:31

It's very, it's very odd.

38:33

And I, then you get the people who are making their writing seem like they're putting

38:40

mistakes.

38:41

Crazy random things in there.

38:43

Yeah.

38:44

Yeah.

38:45

But it is kind of great.

38:46

I am notoriously like as it, I'm a bad emailer both in frequency, but also like I just

38:51

do it from my phone and I like don't like capitalize and punctuate.

38:54

Yeah.

38:55

And I'm finding that there's like some status in that.

38:58

Like, like, people like, wow, this is such a shitty email.

39:01

You definitely wrote it yourself.

39:03

It's just garbage.

39:04

Yeah.

39:05

Thank you for caring enough to do it with your own two human fingers.

39:09

For sure.

39:10

I'm, I'm pretty scared to hook up a chat to my email.

39:15

I don't know.

39:16

I think it's a superpower now, not to.

39:18

Yes.

39:19

That's the thing that like, I just, I understand like I've used, I took a bunch of like interview

39:26

transcripts for a bigger thing that I was working on.

39:28

And I like put it all of us like organize these because like I don't format really well.

39:33

Like just, like, just put them all in a way where I can, I can grab them into it, you

39:38

know, and then analyze them myself and put them into like my writing myself.

39:43

Yeah.

39:44

Truly just like having an assistant do something for you like that.

39:47

And it was great.

39:48

And I love that, you know, but like I have, I personally am of the mind that like it is

39:53

such a slippery slope when you start doing it.

39:57

I think that it is, it's already become such a competitive advantage to just be a person

40:02

who like does the bare minimum of thinking.

40:06

Like, you know, not like doing the goodwill hunting problems on the board or anything

40:10

like that.

40:11

It's just thinking ourselves, I think, is like becoming a superpower and that's sad,

40:17

but it's also like, I'm going to take advantage.

40:19

I feel the same way I posted this today actually on a someone criticizing the way I'm doing

40:27

that Axios writer today who that was wild.

40:30

That was wild.

40:31

So Axios article came out today clearly fully, the piece fully generated by a, the basically

40:37

says, Hey, I know reading is really hard.

40:40

I was trying to read last of the Mohicans and I found it absolutely confusing and bamboozling

40:45

this archaic prose, which is like very easy book.

40:49

I don't know.

40:51

And the person was like, here's how I use AI to help read, specifically helped me read

40:59

last of the Mohicans.

41:00

I put the text in and I said, what the fuck is this?

41:02

And it's like, well, in this scene, here's what's happening.

41:07

And it's, it horrified me, but it also made me feel like, you know what, in the future

41:12

where people are going to be functional illiterates.

41:16

I kind of feel like people who read will have an advantage.

41:20

In the land of the blind, the one I made.

41:23

Absolutely.

41:24

He had a line in that piece that I thought was like, it sort of explains the 2020s, which

41:29

is like, the facts might be wrong, but the vibes are good.

41:33

And it was like, throw me, throw me out of the building right now.

41:39

There was another one where it was like the model agreed with me.

41:42

And I'm like, yeah, that's what they're designed to do.

41:46

It said I did good, mom.

41:47

That's what they do.

41:52

Imagine if this stability piece and the innovation piece were each other's missing

41:56

piece.

41:57

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It's the best of bank worlds.

42:23

Welcome to Baltimore, Maryland, a city where you can't always tell the protectors from

42:28

the predators.

42:29

I shook my head like I must be looking at like, did I mix the files that like this

42:34

can't possibly be right.

42:35

See that my car reversed, they stopped ramming my car back.

42:39

I was about 14 years and I was the best.

42:42

The best in the city to go to boy.

42:45

This is the story of Golden Boy and the childhood friend who helped take him down.

42:50

As Rambo says, you drew first blood and here we go.

42:53

Now we're going to burn it down and that's what I did.

42:56

Orbit Media presents The Birdon Bad Cops, a podcast by BBC.

43:02

Listen on Spotify or wherever you get your podcasts.

43:07

Anyway, let's go to the Doom Scroll.

43:10

The Doom Scroll usually narrated by Tyler is where we look at some of the news stories

43:16

that are dinging around that are just deserve a second look but are not part of the full

43:23

episode.

43:24

First up, Donald Trump escapes from Turkey in catering truck.

43:31

So Donald Trump is I think very fairly worried about assassination by Iran.

43:38

There have been numerous credible warnings to the president that Iran is threatening his

43:43

life.

43:45

And according to sources speaking to the Washington Post recently on background, on July 8th

43:50

as Trump was leaving a NATO summit in Turkey, he became worried about Air Force One being

43:55

too noticeable, I guess, as the president's transportation.

44:01

Like if kids did by foreign nations.

44:04

And so he entered Air Force One, took a wave, walked down some hidden ramp into an airport

44:12

catering truck and then was delivered to a military C32, a transport plane and secretly

44:20

flown back to the United States.

44:23

What would have happened, you ask, if the press corps who was on Air Force One had no idea

44:30

the president had left, were blown up by Iran.

44:34

It wouldn't have been good.

44:36

It wouldn't have been good.

44:37

It wouldn't have been good.

44:38

But that is the timeline we lived in, your thoughts about this.

44:42

Pretty crazy.

44:43

It's another thing that when you speak the words out loud, just to describe the news, you

44:50

feel like you have touched the void in some way.

44:54

It's also so like I was laughing when you were outlining the beginning of this and then

44:57

you were like many credible threats on the president's life.

45:01

You're like, you have to just kind of snap back.

45:03

Legitimate threats.

45:04

Yeah.

45:05

This is extremely real.

45:06

Yes.

45:07

I think that there is a way, everything that Donald Trump does ends up going through like

45:14

the meat grinder of people who have very legitimate access to ground with him.

45:19

And he's also simultaneously kind of funny.

45:21

Like a catering truck is the funniest way to do this.

45:24

But it's also like it's very normal for secret service to take people and sort of ticious

45:29

routes and things like that.

45:30

I think the big reason why this blew up is that if there is a credible threat and you

45:36

just basically let a whole bunch of civilians like be the decoy for you.

45:42

Right.

45:43

You know, the human shield.

45:45

That's a tough beat.

45:46

It's a tough look for her.

45:47

You know, it leaves me wondering also, I'm sure you've seen the Iranian propaganda videos

45:54

that use like Lego figures and other kind of clearly AI generated songs to dis various

46:04

members of the government, including Secretary Agseth and others.

46:08

They left me wondering like, what do they use deep seat to use?

46:12

Kimmy, like, what are the Iranians using?

46:15

It was the same.

46:17

When I saw a story a couple of weeks ago that Boko Haram had asked an AI model like,

46:23

Hey, how do we cross this bridge and attack this base?

46:26

Like, what would you do?

46:28

It was like, yeah, I wonder what model that was and how they got it to just like.

46:33

Just like, no one's using Meta's open source model except for Boko Haram actually.

46:41

I'm just up with love that to be the case.

46:43

You know, okay, up next.

46:46

AI viruses.

46:47

The scientists have used AI and this is the most crazy B plot to the A plot of the ongoing

46:54

AI nightmare question mark that we're living through.

46:58

Scientists have used AI to design 16 brand new functional viruses from scratch.

47:04

Researchers at Stanford and the ARC Institute trained an AI model called EVO, an genetic

47:10

data from millions of organisms, then had it invent new versions of a virus that only

47:15

infects bacteria.

47:16

They built the AI's blueprint into real DNA and 16 out of hundreds of thousands of designs

47:22

turned into real life working viruses.

47:25

The study published in the Journal of Science could speed up research, but it's also shocker

47:30

raising new fears about AI being misused to shocker engineer viruses and then near future.

47:38

It's great because we're seeing this big societal backlash against data centers, like totally

47:46

bipartisan.

47:47

Like, you know, stop these from being built in my town, just like stop them in general.

47:52

And a lot of the feeling from the big technology companies are like, why are people so activated

48:00

about this?

48:01

Meanwhile, it's like, so first of all, we like engineered the information environment that

48:06

basically like causes extremism, right?

48:09

And people to feel like express the wildest possible opinion, right, and get very activated

48:16

about something.

48:17

So they created that and then they have created this like bad news machine that just keeps

48:23

coming up with these things.

48:24

And they're trying, they're trying to spin it and also there's probably some truth to

48:29

like, this is how scientific research gets done.

48:33

But it's also like, we're still not far removed from the like lab leak conspiracy debate

48:38

and like, you know, was a pandemic engineered or not or whatever, you know, that whole

48:43

debacle.

48:45

This is like, of course, people don't want a data center in their backyard that could

48:51

potentially like figure out how to engineer a super bug that causes the next pandemic.

48:56

It's crazy.

48:57

Made me think of a novel nordisk, which is a huge pharmaceutical company based in the

49:03

Netherlands, was investing in claims about a couple months ago by cyber extortion group

49:10

Fulcrum Sec that this hacking group had stolen more than a terabyte of data that was related

49:16

to novos in house AI that it uses to design drugs, which is a story that has not been

49:26

followed up on enough.

49:28

Do they have that?

49:30

No, do we ever get to the bottom of that?

49:32

Like, I would love to know if I could go on the dark web right now and like access a

49:39

portion of the weights of novo nordisk's internal AI model, like that seems like something

49:46

we should know about creating peptides.

49:48

Stack, you know, I mean, have you tried the peptides?

49:50

Have you been, you've been tempted by the peptides?

49:52

I has not tried black market peptides.

49:54

I don't think I'd be better get on this podcast.

49:56

I do know some people who have been peptide curious.

50:00

Has it been good for them?

50:02

I don't know.

50:03

I get to like actually interact with a person who's been doing this, but I know it happens

50:08

out there.

50:09

Nick Cage movie stolen from Netflix.

50:12

The saga over a missing unreleased Nicholas Cage movie as a new twist.

50:16

The writer producer behind the stolen film, Fortitude, is now suing Netflix for defamation.

50:22

This is on top of an earlier breach of contract suit and they're seeking $100 million in damages.

50:27

Back in June, hard drives were stolen from Netflix's offices.

50:33

And the data on said hard drives included a master copy of a $45 million World War Two

50:40

thriller star in cage.

50:43

Netflix has admitted the theft in an email to the filmmakers, but says the other missing

50:47

drives were empty.

50:48

The company has accused the filmmakers lawyers of trying to extort them of $165 million

50:54

in the film cage plays Serbian playboy, Dusko pop off a real life double agent who was

51:00

part of an effort to mislead the Nazis.

51:03

So that film is out there and who knows we may see it at some point in time released on

51:08

the internet.

51:09

Everywhere you said in that description was like ratchet, ratchet it up.

51:13

It's just like a madlibs for truly.

51:16

Was has the news always been this crazy?

51:20

This is something I wanted to add.

51:21

Like I feel like, do you think it's just because wild stuff happens all the time and then

51:28

it's immediately like you were saying we haven't followed up on the Novo Nordic thing.

51:32

And then it just gets buried that like there's lower stakes to like do crazier shit or

51:38

like what do you think contributes to the fact that the news is weirder every week than

51:42

it was.

51:43

I know 10 years.

51:45

That's a good question.

51:46

I think there are more inputs now.

51:48

We can get our news from from social media, from the various online news sites.

51:56

Yeah, who finance, Bloomberg, etc. from the cable news also.

52:02

And it used to be that you had cable news, the internet sort of no social media and the

52:10

main network.

52:11

So probably the world was just as crazy and full of people who would love to foment disinformation.

52:20

But there were various filters that kept that stuff from getting to you.

52:25

So you know, we were still operating at that time on the various editorial ethos that it

52:34

existed for what 40 years or something 50 years.

52:38

And now it's essentially like the floodgates are open and you can get all types of content

52:44

that is newsworthy or not.

52:48

And it is increasingly impossible to one keep up with the cadence and to figure out like

52:53

what is newsworthy and what is it.

52:55

I think that's true.

52:56

But my hypothesis here is that everything you said also like the access to everyone's thoughts

53:05

and opinions and just knowing more crazy shit has also enabled people to do crazier

53:10

shit.

53:11

Like it's now just like in the back of people's minds that like, oh, we can do this.

53:17

This is possible.

53:18

I can do this.

53:19

You know, like makes people a little more shameless, a little more reckless, a little

53:22

more whatever because you know when that, that like thing that people say online, like

53:26

if you showed the news to like a Victorian child, they would just like they would you know,

53:30

die on contact with the information.

53:32

It feels like that is accelerating to the point that like if you showed 2010 Charlie,

53:38

but headlines from today, like I don't know, I feel like I would just lose consciousness

53:44

immediately.

53:45

You know, the clipping economy I think is a good version of this.

53:48

There are folks who sit a stride streams of information who've kind of figured out

53:53

like how to game the existing algorithms to create huge amplification of flows of content

54:02

to the point that they're breaking in millions of dollars.

54:05

And I think that that is increasingly a desire of uniquely like young people who grew up

54:12

in the internet age because like it all seems like a game.

54:15

It all seems kind of rigged.

54:17

It all seems kind of casinified.

54:19

You know, Paulie Marken and Cacheley, Cachey seem like the up and up versions of everything

54:25

that's going on in the internet.

54:26

And I think increasingly your mind is warped to believe that it's all about figuring out

54:36

the hack.

54:37

How do I hack my body?

54:39

How do I hack these information systems?

54:41

How do I get an advantage with it in a world in which vast economic powers are weighted

54:50

against me?

54:51

How do I cut through the noise and hack the system?

54:55

And I think like two and a half we're all trying to figure that out.

54:59

Like how do I get my algorithm to work for me better?

55:02

How do I get the news to get to me, the news that I want to get to me?

55:06

You know, like, and I think it's like increasingly harder and harder to get out of that mindset

55:12

of like how do I cut a quarter?

55:14

How do I rig the system in my favor?

55:16

I was seeing this with all the stuff that was happening with like the Elon Musk criticism

55:21

of the Odyssey.

55:22

Yeah.

55:23

And the same thing is also happening now with like a bunch of people responding to this

55:26

comment that AOC had recently about like woke one being crazy, like, you know, the crazy

55:32

time.

55:33

And the way that I've watched it happen is like there is someone who says something incendiary

55:38

in some way, right?

55:39

Either awful or just like, you know, incendiary.

55:43

And they take the Odyssey thing.

55:45

So Elon Musk goes on this, you know, bigoted rant about all this stuff.

55:49

And then a whole bunch of people like amplify that, it gets like sticky in the algorithm.

55:54

And then everyone sees it.

55:55

And what I noticed with the Odyssey thing is once it got released, even people who are

55:59

like this film rips, like this is awesome.

56:01

We're like, I have to, because all they're seeing is this one piece of like discourse

56:06

that is like algorithmically sticky.

56:08

They're like, I think it's time for me to like say what I have to say.

56:11

I got away.

56:12

And it just perpetuates this thing.

56:16

And it's like this, we have this like lack of control there, right?

56:21

And it like it forces us, it goes us into doing to talking about the thing that we kind

56:25

of don't even want to be talking about.

56:27

Like everyone wants to talk about the Odyssey.

56:28

We don't even be talking about that specific thing.

56:31

Yeah.

56:32

And yet you can't avoid it.

56:33

You move towards that.

56:34

We were like a little bit of field now of the initial thing of why the news is crazy.

56:39

But I think it's like that repetition to sucks everybody in.

56:43

And really most people aren't strong enough to resist it.

56:46

It's true.

56:47

I mean, everything is to a certain extent rage bait.

56:50

Now, you know, it's designed to be like, fuck, that's wrong.

56:53

I got it.

56:54

I got to say something about it.

56:57

That's not the correct order of Stanley Kubrick movies.

57:02

I got to do something about it.

57:05

Ancient Chinese civilization update, the Wall Street Journal published a fascinating

57:09

article, truly fascinating article last week.

57:11

But a law civilization is baffling experts and rewriting China's origin story.

57:16

Nearly 40 years ago, brick factory workers in Southwestern China stumbled onto one of

57:21

archaeology's biggest puzzles.

57:23

While digging for clay, they hit dozens of pieces of jade leading to the discovery of

57:28

the ancient.

57:29

And I'm not going to be able to pronounce this, but I'm going to try Sang-Jin-Du civilization.

57:34

Since then, digs have uncovered all manner of ancient items, including gold masks, 12

57:39

foot bronze sacred trees, and other things.

57:42

But the site is just as baffling as it is dazzling.

57:45

There's no writing system.

57:47

No human remains.

57:49

No signs of weapons or royal tombs.

57:51

No idea what this site was for.

57:54

Researchers believe the civilization traded with Southeast Asia and possibly even the

57:58

Middle East, but much is yet unknown.

58:01

This is the kind of thing that AI would be cool for.

58:03

So sick.

58:04

Yeah.

58:05

No, well, there was the Amazonian one, too.

58:08

It was discovered by AI mapping tools and stuff.

58:11

I love it.

58:12

AI can be really great when some of this consciousness stuff.

58:19

No one's saying that these models are conscious in any way.

58:22

But they're now making scientists question, like, oh, what do we know about human consciousness?

58:26

Right?

58:27

I love when that kind of stuff just shifts the perspective.

58:30

And it feels fascinating that there are multiple new civilizational discoveries.

58:37

Anything's happening?

58:39

It makes you just question like, what the hell don't I know about?

58:43

What do we just not know about?

58:44

Here's a good example of this.

58:45

I was reading recently that there are like over a million uniform tablets, Sanskrit tablets

58:51

from Mesopotamia and other civilizations from that area and that area of the world that

58:58

and only like a few thousand of them have actually been translated.

59:04

Most of them are like, I bartered three bushels of wheat for this amount of ale.

59:10

So it's like not exciting stuff, but hey, there's a great use case for AI.

59:17

Let's put all this 3D scan, all this stuff and just like mass batch translate, one million

59:25

uniform tablets from 8,000 years ago.

59:29

I would love it.

59:30

Now is it going to be right?

59:32

It's a question I will leave to the engineers, but a great use case to me.

59:37

And then you get like the uniform model, right?

59:41

That you just like want you to write like an ale bartering wheat farmer.

59:46

I mean, that would be pretty cool.

59:48

The uniform app, Italy's cheese banks suffering under the extreme heat and wildfire conditions

59:56

in Italy.

59:58

In the same bank in Emilia, Romania, let's dairy farmers borrow against wheels of Parmesan

1:00:04

or Reggiano while the Parmesan ages.

1:00:06

This was an economy I knew nothing about.

1:00:09

Amazing.

1:00:10

Yes.

1:00:11

But the heat is making all of that very tenuous right now.

1:00:14

The vaults hold more than a half a million wheels worth upwards of 300 million euros.

1:00:20

The year's record heat has pushed the banks cooling costs up 30% hotter weather is also

1:00:25

cutting milk production since Cowsie less than extreme heat.

1:00:30

And this is just one example of the current climate crisis in the way it's impacting us

1:00:36

along with record drought along the Danube, all of which is very troubling.

1:00:43

Your thoughts about these Italy cheese banks?

1:00:45

Well, first of all, I'm blown away.

1:00:47

You can borrow against your Parmesan wheel.

1:00:49

I love it.

1:00:50

It's like the greatest thing I've heard.

1:00:53

Things you learn things about the world, and I think this is where you should be able

1:00:59

to spend the internet into something good or better than it sometimes is, which is like

1:01:05

when we think of these massive complex problems, there's so much about the world that we can't

1:01:10

war game out because we don't even know.

1:01:13

And it frustrates me so much when you think about people who are spinning the climate

1:01:18

change stuff and the denialist or whatever, just people who shrug it off.

1:01:22

It's like the world is so complex.

1:01:26

Like here's 20,000 things you didn't even know existed or want to be impacted by this

1:01:31

thing.

1:01:32

And like, this is like when COVID was happening, all the supply chain stuff, when you're

1:01:36

like, okay, so that's bad.

1:01:38

But then it goes, oh, like the world is so weird and wild.

1:01:45

And like instead of, it's very odd to me that instead of us like opening the aperture

1:01:51

to just be like, I don't know anything, like everything is wonderful.

1:01:56

And also I need to, you know, be very conscious of the ways that certain things will have negative

1:02:01

impacts on the rest of the world.

1:02:02

Instead, we're just like, we've gone smaller.

1:02:04

We're like, no, I'm just going to like, I'm going to ignore it.

1:02:07

So it's tough.

1:02:09

Floc stocking.

1:02:10

Floc cameras, people have heard about these.

1:02:12

They are increasingly ubiquitous.

1:02:16

The surveillance state's hottest trends this summer is officers using floc cameras for

1:02:21

bad purposes.

1:02:23

Police officers in multiple states have been accused of using the license plate reading

1:02:26

technology to spy on people in their own lives.

1:02:29

In Missouri, a Brentwood officer admitted using floc cameras to track his wife's location

1:02:34

during divorce, which is actually incredibly alarming.

1:02:38

Investigators found he broke no law, okay, since the state has no rule against personal

1:02:42

use, though his department did cite a policy violation.

1:02:46

There's another case in North Carolina where an officer accused of running the same system

1:02:50

31 times to track his ex-wife's boyfriend.

1:02:54

There's lots of isolated cases across the country.

1:02:58

And in your neck of the woods, up in Redmond, Washington, the Redmond Police Department

1:03:04

completely suspended use of floc safety cameras for its officers because they didn't

1:03:10

give details.

1:03:12

But I believe the wording was multiple unauthorized, tasking of the floc network to do question

1:03:23

mark.

1:03:24

They didn't say what, but it caused the police to harm it to cut access to its officers

1:03:29

for this extremely powerful technology.

1:03:32

This is another thing that from the previous part of the conversation we were having,

1:03:38

this one's not all that mundane, but it is also like the way that those types of things

1:03:43

could be, like they're being, all that stuff's being fed into database.

1:03:46

It's not just about like the, this is scanning me in the moment, someone, yeah, like we're

1:03:50

amassing so much, these critical stores of a very hyper personalized information that

1:03:57

then can be accessed by all kinds of other autonomous systems.

1:04:02

It like makes your head spin.

1:04:03

But I also think we're like, I've been really fascinated because surveillance stuff, privacy

1:04:08

stuff is tough.

1:04:09

Like, it can get very boring and wonky, very quick and theoretical and all that.

1:04:14

But like, there's a genuine moment right now of I think privacy, like backlash, like the

1:04:19

floc stuff, but also like the meta-raban things.

1:04:23

Like people are just, I think genuinely and truly fed up with walking through the world

1:04:28

being surveilled in all these ways.

1:04:30

And it's like it's affecting like, you know, kids are saying like, oh, like, kids don't

1:04:34

dance, you know, at prom anymore because like someone's going to film them and it's going

1:04:37

to be a thing or whatnot.

1:04:38

And it's happening so much that like, I'd really see this becoming a broader story going

1:04:45

forward of like societal pushback.

1:04:47

I completely agree.

1:04:49

It reminds me of the recent clip of some streamer walking up on two people kissing in the

1:04:55

park here in New York City, turned out to be, I don't even want to actually give a theory

1:05:03

about who they were or what they were doing.

1:05:06

They were just people in the park who objected to being filmed in that moment.

1:05:12

But I mean, you know, you're seeing people come up with techniques now, like, I don't

1:05:17

know, saying a Beatles song when somebody comes up to you with a camera so that if they

1:05:21

try to clip it and stream it, it will trigger the copyright triggering or seeing a Disney

1:05:28

song.

1:05:29

You know, and you come, people are coming up with all of these kind of ad hoc improvisational

1:05:33

hacks to keep themselves from being clipped and uploaded to the internet against their

1:05:38

will.

1:05:39

And I think you, to your point, I think we will only see more and more reaction like that.

1:05:45

And perhaps more and more technological safeguards against that happening.

1:05:52

I don't know what those tools will be, but I could see that happening of all the dystopian

1:05:55

stuff that we have discussed here.

1:05:58

You telling me about the people's trying to sing copyrighted music to get this like

1:06:02

just, I'm going to have to like, let me just like stare at the wall for a minute.

1:06:07

That's unbelievable.

1:06:09

Very effective.

1:06:10

Very, very, very, very, very effective, I think.

1:06:13

Oh, here's the Iranian money laundering.

1:06:15

Sorry.

1:06:16

A Reuters investigation has uncovered a massive money laundering network tied to Iran running

1:06:22

through an unlicensed crypto exchange in Dubai called ShellBits, Blockchain, which is a

1:06:28

great illicit Shell company name.

1:06:33

Blockchain records show ShellBit process at least four billion for a farcey language gambling

1:06:40

network, spanning more than 2,000 websites.

1:06:43

Reuters found that at least $676 million of that flowed to Binance, which is the world's

1:06:49

largest crypto exchange as many know.

1:06:52

ShellBit also handled funds tied to Iran's central bank and to Nobitex, a sanctioned

1:06:58

Iranian exchange, as well as wallets that are potentially linked to Iran's revolutionary

1:07:03

guard.

1:07:06

We need to like clone Patrick Radden to so that we can get more.

1:07:09

Get on this.

1:07:10

We're like more long books about these.

1:07:13

That is, like I did it.

1:07:16

I dipped my toe into reporting on crypto scam stuff.

1:07:20

That one was almost like kind of like it hadn't ballooned such that it was like the mid-2010s.

1:07:27

And even then, the amount of, okay, I'm going to pull this thread.

1:07:32

Whoa, I don't know.

1:07:33

Like if I pull this thread, they're like that the mob might actually be like behind the

1:07:38

door or like the Yakuza or someone and it slowly became like this world is, it's a like

1:07:45

a hall, all the doorways and when you open them, it's just fours.

1:07:49

Yes.

1:07:50

Like things.

1:07:51

Russian oligarchs and ISIS and the Italian mafia and the CIA edits just like, uh, Charlie,

1:08:00

thank you for coming on the program.

1:08:01

We'd love to have you back.

1:08:02

Anytime.

1:08:03

This is lovely.

1:08:12

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