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Speaker 1: Also media.

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Speaker 2: Hello and welcome to Better Offline. I'm, of course your

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Speaker 2: host ed zitron and today I am joined for a

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Speaker 2: dot com special by Matt Rosoff, the editor in chief

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Speaker 2: of the Register, who has been in Silicon Valley.

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Speaker 1: I believe three hundred years. You said it was three

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Speaker 1: hundred and twenty five. Yeah, so I'm actually a vampire.

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Speaker 2: That's nice. Oh, what like Kevin O'Leary and Martin Supreme. Sorry,

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Speaker 2: Martin Supreme. That's a thing he says in it. Have

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Speaker 2: you heard about that?

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Speaker 1: No, I don't know, but I haven't seen that yet.

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Speaker 1: Ping Pie doesn't really do it for me.

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Speaker 2: I didn't really like it, but there is a bit

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Speaker 2: in it where Kevin O'Leary goes, I'm a fucking vampire.

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Speaker 2: I've lived for this many years and I read about this.

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Speaker 2: I did not like the movie, but I read about it.

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Speaker 2: I was like, oh, I would actually love it if

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Speaker 2: this was true. And apparently they did film it with fangs,

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Speaker 2: and they should have done that. They should have just

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Speaker 2: had him be a vampire at the end. However, he's

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Speaker 2: regularly in the sun, so I'm not sure how they

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Speaker 2: would have squared that circle. AnyWho, my time only computers.

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Speaker 2: So Matt, you were what were you We're just going

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Speaker 2: to go straight into it. What were you doing in

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Speaker 2: the dot com bubble era?

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Speaker 1: Like?

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Speaker 2: Where did you begin and where did you find yourself

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Speaker 2: as it progressed?

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Speaker 1: Yeah, it's an interesting blast from my very distant past.

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Speaker 1: But I moved to San Francisco in nineteen ninety two,

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Speaker 1: sort of the tail end of the Gulf War recession,

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Speaker 1: so there wasn't much.

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Speaker 3: Going on then.

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Speaker 1: I was interning doing different kinds of od jobs, copy editing,

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Speaker 1: doing some of those kinds of things, and then in

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Speaker 1: nineteen ninety five I joined a little startup that was

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Speaker 1: going to publish content on the World Wide Web. They

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Speaker 1: didn't call it content then, I guess it was. They

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Speaker 1: called it.

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Speaker 3: An online magazine.

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Speaker 1: They already had a TV network and that was Senet,

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Speaker 1: So cnet is still out today in a very different format.

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Speaker 1: But I was employee number forty two there. Started off

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Speaker 1: as an editorial assistant, and my very first task there

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Speaker 1: was to come up with an email newsletter, which we

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Speaker 1: called Digital Dispatch, and it was mostly summaries of stuff

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Speaker 1: that we were publishing on the site, and then occasional

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Speaker 1: snarky commentary. I remember a couple of years later, we

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Speaker 1: did ten reasons why Wired's IPO failed, and one of

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Speaker 1: them was the prospectus was printed on neon green and

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Speaker 1: red paper, which we thought was OK, funny and cute

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Speaker 1: at the time.

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Speaker 3: So anyway, that was a lot of fun.

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Speaker 1: We were, you know, growing at a very rapid pace.

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Speaker 1: I think I was there from ninety five to ninety nine.

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Speaker 1: Like I said, I was employee number forty two on

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Speaker 1: the way in, and I think when I left there

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Speaker 1: were over five hundred people working there with that. One

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Speaker 1: was owned by Zif Davis at the time, it was

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Speaker 1: not Cnet, was an independent company. It had a number

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Speaker 1: of early venture investors, including Paul Allen, the Microsoft co founder.

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Speaker 3: I don't remember the other ones.

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Speaker 1: The CEO was guy named Halsey Minor, and no, it

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Speaker 1: was it was completely you know, it was privately held

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Speaker 1: and weipo'd in ninety seven or ninety eight, I'm not, oh.

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Speaker 2: Just before that was just before things started to fall apart, right.

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Speaker 1: Well, ninety eight was still go go go. I think

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Speaker 1: it really kind of went on until two thousand and

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Speaker 1: two thousand and one was when things started to turn. So,

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Speaker 1: you know, it was fun being a company that was

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Speaker 1: growing fast.

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Speaker 3: I was able to do a.

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Speaker 1: Whole bunch of different kinds of things. I was writing

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Speaker 1: TV scripts and doing CD ROM reviews back when multimedia

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Speaker 1: was a thing, and writing features and I interviewed a

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Speaker 1: two way pager from this little company called Research in

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Speaker 1: Motion before the BlackBerry was out, and I interviewed one

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Speaker 1: of the first MP three players. I think it was

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Speaker 1: the Rio, and I was like, Wow, interesting idea, but

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Speaker 1: it can only hold like twelve songs, so this is

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Speaker 1: a complete waste time. It'll never go was before the

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Speaker 1: Creative Nomad. Then I think around the same time. I

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Speaker 1: don't remember which one was first, but yeah.

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Speaker 2: I remember the Creative Nomad could have like three hundred

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Speaker 2: songs on it. I felt like, oh, yes, Sultan of

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Speaker 2: BRUNEI I was the fanciest man ever, three hundred songs.

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Speaker 2: How would I ever choose?

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Speaker 3: Yeah, that's it, that's right. Yeah. No, this was a

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Speaker 3: lot less than that.

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Speaker 1: I mean, we had to sort of do math and

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Speaker 1: go backwards and we're like, wait, that's only twelve songs.

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Speaker 1: That's worthless. So anyway, and then you know, took some

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Speaker 1: filthy dot com Luker went backpacking for a little bit

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Speaker 1: with my girlfriend at the time, who later became my wife.

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Speaker 1: And then when I moved back, moved back to Seattle,

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Speaker 1: and I think we came back on the day that

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Speaker 1: Microsoft lost a verdict during its big antitrust case.

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Speaker 2: Yeah, the MS one.

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Speaker 3: Right, Yeah, this was well, this was anti trust.

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Speaker 1: Basically they had a whole bunch of different charges against them.

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Speaker 1: Tying the browser to Windows was kind of the big one,

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Speaker 1: you know, and making deals with PC makers to bundle

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Speaker 1: certain software and not allowing them to bundle other software

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Speaker 1: and all kinds.

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Speaker 2: And incentivizing bundling MS dos. Yeah, I vaguely know this story.

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Speaker 3: Yeah, it was.

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Speaker 1: It was really about like Netscape Navigator was the thing,

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Speaker 1: and you know, that's what we were all using in

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Speaker 1: nineteen ninety five. It started with Mosaic and it was Navigator,

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Speaker 1: and then Microsoft built Internet Explorer in the Windows ninety

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Speaker 1: five and four years later Navigator didn't really exist for

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Speaker 1: all intents and purposes.

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Speaker 3: So it was But anyway, that was the day.

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Speaker 1: It was Judge Thomas Penfield Jackson, and he ordered them

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Speaker 1: to be broken into two companies, an APPS company, which

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Speaker 1: is office and all those products, and an OPS company,

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Speaker 1: which was Windows. I believe now maybe remembering this slightly

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Speaker 1: wrong and I don't have a resource in front.

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Speaker 2: Of YEA very recently covered this and you will back on.

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Speaker 3: Yeah.

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Speaker 1: So but I think that was kind of the peak,

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Speaker 1: and I think that was actually perhaps the beginning of

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Speaker 1: the end. So it's even though Microsoft had had very

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Speaker 1: little to do directly with the dot com bubble, there

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Speaker 1: was sort of this sense that, oh, the tightest turning anyway,

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Speaker 1: long story short, I moved back to Seattle and I

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Speaker 1: found work with this startup. At the time, they were

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Speaker 1: called a Cadio and the idea was that they were

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Speaker 1: going to do online education, so they wanted to have

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Speaker 1: articles written and I was the computers tech guy. We

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Speaker 1: were going to you know, training people on computer technology.

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Speaker 1: And it was the most incompetently run company I have

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Speaker 1: ever seen been a part of. I walked in immediately

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Speaker 1: realized that I needed to get out.

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Speaker 3: So by way of example, they didn't have so you

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Speaker 3: know what. Yeah, it was this was.

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Speaker 1: Early two thousands, so this is still this was when

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Speaker 1: companies were getting funding on a promise and going public

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Speaker 1: on powerpoints. Still it was still still at the peak,

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Speaker 1: but we could see it starting to turn. So you know,

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Speaker 1: these guys had enough money to hire a bunch of people,

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Speaker 1: and they had a cool loft in Pineer Square, which

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Speaker 1: is you know, brick building and all this kind of stuff.

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Speaker 3: But they didn't.

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Speaker 1: So they you know, they hired me, and they're like,

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Speaker 1: go get some freely, you know, fine, start hiring freelancers,

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Speaker 1: and we need to get some content up. They had

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Speaker 1: no way to put the content on the internet. They

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Speaker 1: didn't have a CMS, they didn't know what a CMS was.

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Speaker 1: And we were using Visual Source Safe, which is a

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Speaker 1: Microsoft product used for you know, creating code and sharing code.

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Speaker 1: It was that level. And I remember later here over,

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Speaker 1: you know, hearing secondhand that somebody had overheard a very

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Speaker 1: tense meeting between the CEO and the investors where one

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Speaker 1: of the investors yelled through a closed door, we might

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Speaker 1: as well just sell the servers back to the investment bank.

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Speaker 1: So I quickly got out of there, found a job

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Speaker 1: at Directions on. Microsoft did that for ten years, and

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Speaker 1: they were out of business by I believe, early two

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Speaker 1: thousand and one.

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Speaker 2: So about when did it become obvious something was wrong.

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Speaker 1: So it was never obvious that something was wrong large scale,

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Speaker 1: but it was obvious that a lot of the specific

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Speaker 1: individual companies that were part of this were completely hot air.

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Speaker 1: There was nothing there. There weren't revenues, there weren't customers,

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Speaker 1: or they were you know, selling dollars for seventy five

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Speaker 1: cents basically. So you know, pets dot Com was a

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Speaker 1: big one, Cosmo dot Com was another one, And all

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Speaker 1: these ideas came around later and were more nearly profitable

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Speaker 1: in the second go round. So Cosmo dot Com was

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Speaker 1: just home delivery. It was like Instacart.

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Speaker 2: It's basically Cosmo was Instacom, and pets dot Com was Chewy.

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Speaker 2: And I looked at Chewy's revenues recently, three point one

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Speaker 2: billion dollars in a quarter fifty million dollars of profit,

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Speaker 2: which is good. But also one of those business is

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Speaker 2: what I hear metrics like that, I get scared.

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Speaker 1: Yeah, it's fine, it's a you know, it's a low

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Speaker 1: margin retail business.

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Speaker 3: That's fine.

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Speaker 1: Retail retail's good, you know, it's it's okay, it's real. Yeah,

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Speaker 1: it's it's you sell things for more than you make

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Speaker 1: them for. That's that's how it's supposed to work. So

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Speaker 1: I think there were just so many of those. The

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Speaker 1: other one was there was a business and I think

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Speaker 1: they were only based in Seattle, called Lackeye dot com.

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Speaker 1: And I remember my uh my boss at directions at

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Speaker 1: the time said that, you know, this is when I

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Speaker 1: knew that the revolution was impending. Is when you have

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Speaker 1: a website called Lackeye dot com that is basically a

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Speaker 1: personal assistant. I guess it was kind of like task Rabbit,

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Speaker 1: that kind of thing.

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Speaker 2: Yeah, but even toss grab it has never really is

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Speaker 2: tosk grab it even profitable.

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Speaker 1: They got bought by Ikea, so it's buried now. I

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Speaker 1: don't I don't think they ever were.

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Speaker 2: My lackey dot com. Okay, I I hate to interrupt

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Speaker 2: to read a Wikipedia page, but it was founded in

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Speaker 2: April not you know, you know, by a guy called

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Speaker 2: Brian McGarvey and another guy called Brendan Bonnacle.

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Speaker 1: I remember that name. Yeah, I remember that name with

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Speaker 1: an I right correct, yes.

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Speaker 2: Yes, yeah. In the memo he had a memo that

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Speaker 2: was on fuck Company. It said this is still a startup.

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Speaker 2: And he complains that it is now six forty five

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Speaker 2: PM and they're only twelve people in our office. We

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Speaker 2: have sixty four people who work here in Seattle. It's

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Speaker 2: totally unacceptable. So the more things change, the more things

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Speaker 2: stay the same.

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Speaker 1: I think that's true. Yeah, there are there are absolutely

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Speaker 1: some similarities and some differences between today and them.

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Speaker 2: But what about the the telecom side though, was it

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Speaker 2: did everybody just think that was going to work out

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Speaker 2: until it didn't? What was their a point when the

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Speaker 2: Loosens and the wind stars of the world got scared

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Speaker 2: or scary?

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Speaker 3: Yeah.

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Speaker 1: I was less close to that at the time. I

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Speaker 1: was pretty focused on end user consumer stuff. But I

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Speaker 1: remember having friends who were sort of retail trading and

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Speaker 1: talking to me about Siena, Siena Networks, It's going to

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Speaker 1: be huge. And I think those busts came a little

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Speaker 1: bit later, right with WORLDCLM. I think that was maybe

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Speaker 1: a bit after the doc Bom bust. And I'm a

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Speaker 1: little bit less familiar with how all of that worked.

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Speaker 1: But as I understand it, there was a lot of

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Speaker 1: circular There were a lot of circular deals and deals

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Speaker 1: that weren't really deals, and deals that were agreements but

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Speaker 1: actual no money changing hands.

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Speaker 2: Yeah, it's interesting because as I dig into this, and

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Speaker 2: I've spent the last week or two digging into kind

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Speaker 2: of how we got to this conversation, I realized it

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Speaker 2: seems like there were two very specific The dot com

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Speaker 2: bubble was actually like two bits. It was the dot

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Speaker 2: com website stuff, and then the follow up of the

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Speaker 2: the the what was it the telecommunications fiber.

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Speaker 1: Boom fiber boom, Yeah, it was, you know, it was

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Speaker 1: the it was the the websites. There was definitely some

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Speaker 1: circularity there too. Everybody knew that everybody was buying advertisements

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Speaker 1: from each other. But you know, you remember AOL bought

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Speaker 1: Time Warner and then that valuation collapsed. I mean, there

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Speaker 1: were some really huge deals that turned out to be

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Speaker 1: in the end worth almost nothing. And then I think

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Speaker 1: once you're once, you know, once all of those things

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Speaker 1: fell apart and the end user picture wasn't there A

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Speaker 1: lot of the infrastructure valuations came crashing down as well.

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Speaker 2: What was the moment you knew? Like, when was the moment?

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Speaker 2: What was was there an event where you went, oh shit,

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Speaker 2: and yeah?

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Speaker 1: When I joined that startup and I wasn't I couldn't

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Speaker 1: believe they had any money, So that was that was

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Speaker 1: a spring of two thousand. I was like, it felt

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Speaker 1: like they were just sort of handing money out to anybody,

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Speaker 1: and I mean, this guy had a record. I think

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Speaker 1: he had had a CRM company that competed with Salesforce

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Speaker 1: that either sold or went public, So he wasn't a

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Speaker 1: total nobody. It's just they couldn't hire enough people to

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Speaker 1: actually get anything smart done on time. It just wasn't

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Speaker 1: It just wasn't working, and everybody was kind of showing

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Speaker 1: up to the office and play acting. There were some

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Speaker 1: moments during the nineties that were, you know, kind of crazy,

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Speaker 1: like see it they funded this party. I played bass

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Speaker 1: and I was playing base and sort of this pickup

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Speaker 1: band at work, and they basically rented out the.

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Speaker 3: Film more for our holiday party.

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Speaker 1: And you know, we played on a stage in front

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Speaker 1: of twelve hundred or however, maybe the film is not

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Speaker 1: that big, five hundred people. And then they had a

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Speaker 1: I can't remember the name of the of the you know,

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Speaker 1: they had an actual working band come on after us.

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Speaker 1: But it was a lot of fun. The singer we

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Speaker 1: were playing with nailed, Janis Joblin. She did a really

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Speaker 1: spectacular job. We had a couple of good songs. So

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Speaker 1: but that was those kinds of things were regular, like

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Speaker 1: that was just expected these big parties, and nobody really

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Speaker 1: asked where the money was coming from. I do remember

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Speaker 1: after Senet went public. I don't remember what year this was,

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Speaker 1: ninety eight or ninety nine. I actually looked at an

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Speaker 1: earnings report and I was like, oh, we're losing money.

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Speaker 1: I didn't know you could stay in business and lose money.

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Speaker 1: How naive I was.

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Speaker 2: That's so see, that's the thing I admit as I've

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Speaker 2: got further into finance in general and learned stuff the

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Speaker 2: amount of companies that just burn cash and then die,

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Speaker 2: but also the ones that are burning cash right now,

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Speaker 2: and the amount of people who just say, yeah, it's

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Speaker 2: actually fine, it's good that happens. It's like a completely

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Speaker 2: like a completely different world that has only worked a

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Speaker 2: few times in the past. It's actually not worked many

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Speaker 2: many more times.

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Speaker 3: Right, most of the time it doesn't work.

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Speaker 1: But I think the bet is when it does work,

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Speaker 1: it pays off spectacularly.

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Speaker 2: Right.

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Speaker 1: And the question, I don't think Google was ever losing

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Speaker 1: huge amounts of money, but Amazon did for some years.

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Speaker 2: Yeah, I've I've looked into it, and the companies that

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Speaker 2: have actually grown and been successful have never burned anywhere

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Speaker 2: near as much money as these AI companies. AWS was

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Speaker 2: like sixty nine billion over nine years, and that was

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Speaker 2: all of Amazon's Capex, not just AWS focused.

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Speaker 1: Yeah, yeah, no, they yeah, And again there was to me,

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Speaker 1: the Internet at large and the World Wide Web as

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Speaker 1: we used to call it, was a lot more immediately

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Speaker 1: obviously huge, and it was going to change a lot

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Speaker 1: of things and how business was done and how media

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Speaker 1: was distributed.

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Speaker 3: It was it was clear.

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Speaker 1: It was just a question of when and where the

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Speaker 1: technology had to go in which parts had to evolve.

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Speaker 1: You know, you needed consumers to have much more bandwidth

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Speaker 1: before video became a realistic thing and so forth, and

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Speaker 1: you know, they were gonna be fits and starts, but

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Speaker 1: I think everybody pretty much knew that it was not

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Speaker 1: fake like these things, some of these companies were going

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Speaker 1: to succeed. Something was gonna happen. When I look at AI,

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Speaker 1: to me, it does not seem nearly as revolutionary. I

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Speaker 1: don't even really like to call it AI because it's

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Speaker 1: not intelligent, but that's the that's the term that we use.

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Speaker 1: But it's you know, it's right, uh, you know, matrix math,

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Speaker 1: determinating determined, you know, basically auto complete. So it's interesting.

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Speaker 1: It can do some stuff that can be valuable in

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Speaker 1: certain situations. But it doesn't to me feel immediately massively

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Speaker 1: obvious that this is a huge change the internet did. Yeah,

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Speaker 1: web browsers did the iPhone. The first time I played

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Speaker 1: with one of those, I was kind of a skeptic

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Speaker 1: because I mean, I had a palm pilot and I'd

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Speaker 1: been yeah, yeah, I had around, Yeah, I had a

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Speaker 1: compact them, so keep going no, no, you know, and

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Speaker 1: you're you're you're playing with that stylist. And then someone

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Speaker 1: brought an iPhone into work and I was like, oh, Apple, whatever,

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Speaker 1: they're not going to do this, and I was like, oh, responsive,

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Speaker 1: touch screen, email, autocomplete maps, I'm like, this thing's cool.

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Speaker 3: And then it got better from there.

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Speaker 1: So yeah, I haven't felt that, and I've used all

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Speaker 1: the chatbots.

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Speaker 3: I've played around with them.

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Speaker 1: I don't code, but you know, from what I understand

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Speaker 1: from people who do, is if you're a non coder,

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Speaker 1: it can create some serviceable apps just based on your ideas,

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Speaker 1: which seems pretty magical. But if you do code and

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Speaker 1: you actually look under the hood, they're not maintainable. You know,

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Speaker 1: you can't deploy them like they you know, they they

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Speaker 1: tend to have a lot of problems unless you sort

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Speaker 1: of know what you're doing. And I certainly know as

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Speaker 1: a writer, you know, I can immediately detect AI generated

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Speaker 1: writing and it's not good.

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Speaker 2: That's actually a really good thing to focus on. So

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Speaker 2: when you got into the internet industry, was there that

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Speaker 2: sense of wonderment. I'm not even being like a shield.

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Speaker 2: I'm just like because I remember when I first used

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Speaker 2: the internet in like nineteen ninety seven, I want to say, yeah,

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Speaker 2: like a child and being amazed by it, but even

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Speaker 2: like the iPhone moment as well. But when you were

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Speaker 2: getting into it in like the mid to late nineties,

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Speaker 2: was there that sense of wonderment and excitement?

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Speaker 1: Oh?

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Speaker 3: Absolutely.

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Speaker 2: So.

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Speaker 3: There had been online services for a while.

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Speaker 1: People used AOL compu serve, and you know, you hang

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Speaker 1: out in chat rooms and you do these keywords and

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Speaker 1: it would call up sort of a specific I don't

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Speaker 1: even remember what they were called, but i'd call them

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Speaker 1: apps now. But the web was interesting because it was

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Speaker 1: immediately accessible to everyone. Anyone could theoretically set up a website,

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Speaker 1: and it was yeah, immediately huge, and it was a

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Speaker 1: little hard to separate some of the hype from legitimate excitement.

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Speaker 1: But I remember in San Francisco even in nineteen ninety five,

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Speaker 1: and you know, you had had wired magazines sort of

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Speaker 1: sounding sounding the bells for it for several years by

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Speaker 1: that point. But in nineteen ninety five, everybody was online,

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Speaker 1: everybody was getting online, and you know, you had tons

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Speaker 1: of TV commercials, and everybody was talking about the World

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Speaker 1: Wide Web on the evening news, and it just it

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Speaker 1: was immediately clear that this was a new thing and

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Speaker 1: it was a new way that we were going to

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Speaker 1: do a lot of stuff. Right, So, yeah, there was

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Speaker 1: there was massive excitement over it.

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Speaker 2: Was there any kind of AI sorry, I same leading

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Speaker 2: question then? Were there dissenters? Were there people other than

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Speaker 2: the obvious news we call call that everyone sends me

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Speaker 2: every time, you know, the one I'm talking about the dynalltics,

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Speaker 2: weird gloss things now.

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Speaker 1: But it was that descent there was, and I think

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Speaker 1: a lot of the descent, interestingly, was centered around some

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Speaker 1: of the same kinds of criticisms that we've heard over

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Speaker 1: the last five, six, seven years. So it wasn't that

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Speaker 1: this is stupid or this isn't going to work. It

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Speaker 1: was you know, there are no guard rails, there are

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Speaker 1: no there's no regulation. This thing is going to run

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Speaker 1: a muck. It's gonna you know, how how are we

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Speaker 1: going to protect against fraud? How are we going to

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Speaker 1: protect against people, you know, using it for crime? How

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Speaker 1: are we going to protect against you know, this kind

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Speaker 1: of information that maybe shouldn't be available to everybody. So

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Speaker 1: there were those kinds of questions, and people were, you know,

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Speaker 1: maybe we should slow down a little bit and put

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Speaker 1: some guardrails and pass some laws around this thing. Most

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Speaker 1: of that's that, Yeah, most of it didn't get done.

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Speaker 1: You know, the Telecoms Act, you know whatever, Section two

401
00:20:11,240 --> 00:20:14,439
Speaker 1: thirty whatever, the big Omnimus Bill that that was a

402
00:20:14,440 --> 00:20:16,360
Speaker 1: part of that was an attempt to deal with some

403
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Speaker 1: of that stuff.

404
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Speaker 3: But it was pretty it was pretty lenient.

405
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Speaker 1: It was more about sort of protecting companies from from liability.

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Speaker 1: But there were some yeah, there was some at least

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Speaker 1: some thought around it, but.

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Speaker 2: But there wasn't just people saying this won't work in

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Speaker 2: the same way.

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Speaker 1: Very few, very few, I mean there, you know, I

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Speaker 1: suppose you could go into the boardroom of a newspaper

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Speaker 1: at the time and probably would have heard some arguments

413
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Speaker 1: that now in retrospect sound insane. But you know, who

414
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Speaker 1: wants to read on a screen that was one that

415
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Speaker 1: you sometimes heard people want to have a piece of

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Speaker 1: paper in their hand and you know, print is print

417
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Speaker 1: is the way nobody wants to read on a screen,

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Speaker 1: or you know, this is this is this is sort

419
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Speaker 1: of a nerd toy. This is never going to take

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Speaker 1: off among you know, mainstream people. There were issues with

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Speaker 1: internet access. It wasn't available everywhere, especially high speed. I

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00:21:11,800 --> 00:21:13,919
Speaker 1: mean we were using pretty low speed modems for a

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Speaker 1: very very long time, particularly in rural parts of the country.

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Speaker 1: So there was some skepticism around that, you know, And

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Speaker 1: part of it, I have to be honest, is I

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Speaker 1: was in my twenties at the time, so I think

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Speaker 1: people at that age tend to be much more go

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Speaker 1: go about the future than perhaps old cynics as as

429
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Speaker 1: we are now.

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Speaker 2: But there wasn't any kind of well maybe there was,

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Speaker 2: you can tell me. There wasn't a kind of bifurcation

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Speaker 2: between people who were like pro internet anti internet in

433
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Speaker 2: the same way.

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Speaker 3: Nah.

435
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Speaker 1: No, I don't remember that anyway, No, I really don't

436
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Speaker 1: think so. Yeah, it was all it was all hands

437
00:21:51,040 --> 00:21:52,480
Speaker 1: on deck. I mean, I mean again, like this was

438
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Speaker 1: on TV. This is like you know, advertisements, there were

439
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Speaker 1: there were commercials. There were people on that evening news,

440
00:21:58,880 --> 00:22:02,080
Speaker 1: you know, reaching, reaching mom. You know my parents who

441
00:22:02,080 --> 00:22:08,240
Speaker 1: are boomers, They were fully aware and gung ho about

442
00:22:08,320 --> 00:22:11,400
Speaker 1: the web by ninety seven or ninety eight, and that's

443
00:22:11,440 --> 00:22:15,360
Speaker 1: I think that's who was investing. So, you know, one

444
00:22:15,400 --> 00:22:18,119
Speaker 1: of the big differences between then and today it was

445
00:22:18,440 --> 00:22:21,560
Speaker 1: these companies were going public and it was really fueled

446
00:22:22,720 --> 00:22:25,920
Speaker 1: in large part by retail investors. You know, you wanted

447
00:22:26,000 --> 00:22:28,840
Speaker 1: to buy stocks on the stock market in these new

448
00:22:28,880 --> 00:22:32,200
Speaker 1: companies that represented the future. So you know, you had

449
00:22:33,280 --> 00:22:38,040
Speaker 1: doctors and dentists and you know, professors with a little

450
00:22:38,080 --> 00:22:40,960
Speaker 1: money and people putting their retirement savings into these things,

451
00:22:41,040 --> 00:22:43,800
Speaker 1: and they were the ones who probably got wiped out

452
00:22:43,840 --> 00:22:46,280
Speaker 1: the most in you know, two thousand and one through

453
00:22:46,320 --> 00:22:47,240
Speaker 1: two thousand and four.

454
00:22:48,560 --> 00:22:52,600
Speaker 2: Yeah, And I mean, was was that before e trade?

455
00:22:52,760 --> 00:22:53,000
Speaker 3: Was it?

456
00:22:53,160 --> 00:22:55,880
Speaker 2: Do you start to call a stockbroker? I literally don't

457
00:22:55,920 --> 00:22:57,880
Speaker 2: know how they bought stock back then.

458
00:22:57,920 --> 00:23:02,639
Speaker 1: I realized, Yeah, I think E trade started in the

459
00:23:02,680 --> 00:23:04,879
Speaker 1: dot com boom, if I recall late in.

460
00:23:04,920 --> 00:23:08,439
Speaker 2: Nineties, because I remember in England at the time, the

461
00:23:08,520 --> 00:23:10,920
Speaker 2: story is about how E trade led to people losing

462
00:23:10,960 --> 00:23:13,560
Speaker 2: tons of money, and there was one thing I'll credit

463
00:23:13,600 --> 00:23:15,560
Speaker 2: the press with at the time as being like, yeah,

464
00:23:15,600 --> 00:23:17,840
Speaker 2: you shouldn't bet on the stock market unless you know

465
00:23:17,840 --> 00:23:19,240
Speaker 2: what you're doing, right.

466
00:23:20,520 --> 00:23:22,399
Speaker 3: Yes, index funds hold forever.

467
00:23:22,560 --> 00:23:25,520
Speaker 1: That's the that's the random walk down Wall Street, the

468
00:23:25,520 --> 00:23:29,240
Speaker 1: only investment strategy that works. But yeah, people were playing,

469
00:23:29,359 --> 00:23:31,879
Speaker 1: you know, they wanted they saw these ridiculous returns and

470
00:23:31,920 --> 00:23:35,960
Speaker 1: they wanted a piece of it. So yeah, I think

471
00:23:36,440 --> 00:23:39,280
Speaker 1: online trading was definitely a thing. You know, you had

472
00:23:39,280 --> 00:23:42,639
Speaker 1: the Yahoo Chat forums that's where people would pump and

473
00:23:42,720 --> 00:23:43,959
Speaker 1: dump these penny stocks.

474
00:23:44,000 --> 00:23:44,800
Speaker 3: They were all clad and.

475
00:23:44,920 --> 00:23:47,600
Speaker 2: Really they were like there were actual pump groups back then.

476
00:23:47,960 --> 00:23:51,520
Speaker 1: Oh yeah, I mean yah. Yahoo Chat was sort of

477
00:23:51,560 --> 00:23:53,720
Speaker 1: infamous for it. Like you would hear about something, you'd

478
00:23:53,720 --> 00:23:55,720
Speaker 1: hear it was a lot like what happened with crypto

479
00:23:55,840 --> 00:23:59,680
Speaker 1: in twenty one twenty two. You know, someone issues a

480
00:23:59,720 --> 00:24:01,720
Speaker 1: new time and everybody rushes in to buy it and

481
00:24:01,720 --> 00:24:03,159
Speaker 1: then it crashed. You know, there's a rug pull and

482
00:24:03,200 --> 00:24:06,400
Speaker 1: it crashes, like it was. Those kinds of things were happening.

483
00:24:06,440 --> 00:24:09,439
Speaker 1: But Yahoo, I remember being that was the forum that

484
00:24:09,520 --> 00:24:12,000
Speaker 1: I was the most familiar with back then. It was

485
00:24:12,000 --> 00:24:13,760
Speaker 1: probably going on on AOL too.

486
00:24:14,640 --> 00:24:18,359
Speaker 2: That's magical. Oh, the innocent days. It was the I

487
00:24:18,400 --> 00:24:22,520
Speaker 2: remember when I I'm Gonna do the It was a

488
00:24:22,600 --> 00:24:24,359
Speaker 2: Rockahaua speech and the end of Blade round of the

489
00:24:24,359 --> 00:24:28,240
Speaker 2: things I've seen in telegram groups about icos. But it's

490
00:24:29,040 --> 00:24:31,720
Speaker 2: it's kind of it's adorable to hear that stuff happening,

491
00:24:31,800 --> 00:24:35,280
Speaker 2: until you remember people lost lots of money. But it

492
00:24:35,320 --> 00:24:39,439
Speaker 2: doesn't feel like it doesn't sound like maybe it was

493
00:24:39,480 --> 00:24:42,159
Speaker 2: a different media climate, like there was that kind of

494
00:24:42,920 --> 00:24:46,320
Speaker 2: almost have There wasn't the noxious part of it where

495
00:24:46,320 --> 00:24:47,879
Speaker 2: it's like, oh, if you don't get on the internet,

496
00:24:47,920 --> 00:24:50,080
Speaker 2: you're gonna be left behind, You're an idiot, or maybe

497
00:24:50,080 --> 00:24:50,520
Speaker 2: I'm wrong.

498
00:24:52,520 --> 00:24:55,800
Speaker 1: Not so much of that, but it was it was

499
00:24:55,920 --> 00:24:59,119
Speaker 1: just presumed that this is the future and if you

500
00:24:59,160 --> 00:25:01,119
Speaker 1: don't want part of the if you don't want to

501
00:25:01,160 --> 00:25:03,959
Speaker 1: be part of the future, that's fine, But you know,

502
00:25:04,040 --> 00:25:06,960
Speaker 1: why don't you go be a survivalist and live off

503
00:25:06,960 --> 00:25:09,240
Speaker 1: the grid in the woods? Kind of right, It was

504
00:25:09,359 --> 00:25:12,719
Speaker 1: just assumed. I mean, it's like, you know, do you

505
00:25:12,800 --> 00:25:15,560
Speaker 1: watch TV. There was a time when if you said

506
00:25:15,600 --> 00:25:18,560
Speaker 1: I don't have a TV you were very pretentious and

507
00:25:18,720 --> 00:25:21,359
Speaker 1: very special. Now it seems like everybody's cutting cable. But

508
00:25:21,880 --> 00:25:23,399
Speaker 1: so it was sort of like that. It was just

509
00:25:23,480 --> 00:25:26,320
Speaker 1: presumed that everybody was eventually going to get on board.

510
00:25:26,400 --> 00:25:29,000
Speaker 1: I mean, who needs a personal computer in their home?

511
00:25:29,080 --> 00:25:31,200
Speaker 1: That was a real question in the late eighties, right,

512
00:25:31,480 --> 00:25:34,600
Speaker 1: Oh PC on every desktop? Yeah, sure, bill, whatever you say.

513
00:25:35,440 --> 00:25:37,000
Speaker 1: Who wants one of these things? And then you know,

514
00:25:37,080 --> 00:25:39,959
Speaker 1: you find justifications for it. Oh, well you can you know,

515
00:25:40,000 --> 00:25:42,600
Speaker 1: you can pay your bills automatically, and you can do

516
00:25:42,640 --> 00:25:45,920
Speaker 1: this and that. And suddenly when Internet Explorer was built

517
00:25:45,960 --> 00:25:48,520
Speaker 1: into Windows and everybody could get online, that was the thing,

518
00:25:48,800 --> 00:25:50,760
Speaker 1: like that was, oh, of course, everybody has to have

519
00:25:50,800 --> 00:25:52,560
Speaker 1: a PC and we're all going to be on the web,

520
00:25:52,960 --> 00:25:53,800
Speaker 1: surfing the web.

521
00:25:54,440 --> 00:25:58,679
Speaker 2: It also feels like it was a genuine too, like

522
00:25:58,720 --> 00:26:00,560
Speaker 2: a lot of these things would just too early and

523
00:26:00,640 --> 00:26:04,119
Speaker 2: run too crazy because the actual fundament of it, it

524
00:26:04,160 --> 00:26:06,600
Speaker 2: sounds like, was people were excited to get on the internet,

525
00:26:06,680 --> 00:26:10,800
Speaker 2: which was which was obviously a beneficial thing and had

526
00:26:10,840 --> 00:26:13,840
Speaker 2: immediate returns, like people could use it and say this

527
00:26:13,920 --> 00:26:16,560
Speaker 2: is useful in my life in this manner, right.

528
00:26:16,720 --> 00:26:19,600
Speaker 1: I think the initial thing was access to information. You

529
00:26:19,600 --> 00:26:21,880
Speaker 1: could you could find out a lot of things very

530
00:26:21,880 --> 00:26:24,680
Speaker 1: fast and actually and there were some communication aspects, right,

531
00:26:24,720 --> 00:26:27,879
Speaker 1: You could talk to anybody anywhere in the world pretty easily,

532
00:26:27,920 --> 00:26:28,639
Speaker 1: pretty quickly.

533
00:26:29,600 --> 00:26:30,400
Speaker 3: And then other things.

534
00:26:30,440 --> 00:26:33,800
Speaker 1: You know, e commerce was very early, but you know,

535
00:26:34,040 --> 00:26:36,320
Speaker 1: suddenly you don't have to go to the bookstore and

536
00:26:36,359 --> 00:26:38,320
Speaker 1: ask them to order. I worked at a bookstore for

537
00:26:38,359 --> 00:26:40,680
Speaker 1: a year in nineteen ninety, and you know, it was

538
00:26:40,720 --> 00:26:42,359
Speaker 1: a thing if you don't have the book in stock,

539
00:26:42,400 --> 00:26:43,960
Speaker 1: they come in and you order the book, and then

540
00:26:44,000 --> 00:26:45,639
Speaker 1: they come back a week later, and suddenly you just

541
00:26:45,680 --> 00:26:48,280
Speaker 1: press a button and the you know, the book will come.

542
00:26:48,359 --> 00:26:51,600
Speaker 1: And then they started adding music. There were a lot

543
00:26:51,640 --> 00:26:55,720
Speaker 1: of you know, again, the lack of bandwidth among a

544
00:26:55,720 --> 00:26:59,159
Speaker 1: lot of consumers did sort of stall the idea of

545
00:26:59,240 --> 00:27:02,280
Speaker 1: digital video, but it eventually came around in two thousand

546
00:27:02,320 --> 00:27:05,080
Speaker 1: and one or two. That's when YouTube really took off.

547
00:27:05,600 --> 00:27:08,360
Speaker 1: Real networks had streaming video and Microsoft.

548
00:27:08,000 --> 00:27:09,679
Speaker 3: Oh real real player.

549
00:27:10,400 --> 00:27:16,240
Speaker 1: Yeah, buffer, Remember we're all buffering. I interviewed Rob Glazer

550
00:27:16,240 --> 00:27:17,600
Speaker 1: a couple times. You know, he had the right idea.

551
00:27:17,600 --> 00:27:20,440
Speaker 1: But again, Microsoft sort of, you know, took that market

552
00:27:20,640 --> 00:27:23,760
Speaker 1: pretty quickly with Windows Media Player, and then Apple came

553
00:27:23,760 --> 00:27:27,800
Speaker 1: along and I they kind of blew everybody else away. So, yeah,

554
00:27:27,840 --> 00:27:31,080
Speaker 1: all of the ideas were there, and I think most

555
00:27:31,119 --> 00:27:35,159
Speaker 1: of what we were sort of experimenting with later became

556
00:27:35,240 --> 00:27:41,359
Speaker 1: real businesses. I mean, you know LimeWire and all the

557
00:27:41,400 --> 00:27:45,800
Speaker 1: sort of music MP three trading sites that was later

558
00:27:46,160 --> 00:27:49,720
Speaker 1: late nineties, I guess, early two thousands. That all eventually

559
00:27:49,720 --> 00:27:52,920
Speaker 1: became legitimized through Spotify and Apple Music and so on.

560
00:27:53,000 --> 00:27:56,320
Speaker 1: So every idea was already there. It was just maybe

561
00:27:56,359 --> 00:27:59,120
Speaker 1: it's a little bit underground. Maybe it doesn't work quite right,

562
00:27:59,160 --> 00:28:01,359
Speaker 1: it's a little clunky, it's a little cluegy, or maybe

563
00:28:01,359 --> 00:28:03,480
Speaker 1: the companies were just you know, they borrowed too much

564
00:28:03,520 --> 00:28:05,919
Speaker 1: money too fast and couldn't turn it around.

565
00:28:05,960 --> 00:28:09,600
Speaker 2: Which feels almost entirely different to the AI bubble, like

566
00:28:09,680 --> 00:28:12,720
Speaker 2: it's they such in a Dela on the morning of

567
00:28:12,760 --> 00:28:16,800
Speaker 2: recording this, uh At Davos was saying, Yeah, the thing

568
00:28:16,880 --> 00:28:19,560
Speaker 2: that might make the suspeculative bubble if nobody but if

569
00:28:19,560 --> 00:28:24,680
Speaker 2: nobody buys it. But if nobody buys AI. Not really,

570
00:28:24,760 --> 00:28:27,480
Speaker 2: he's so cool. I love that we live in this area.

571
00:28:27,520 --> 00:28:30,040
Speaker 2: I'm going to read you this sentence actually because this

572
00:28:30,160 --> 00:28:32,840
Speaker 2: really got me. Microsoft chief executive Search and a Delas

573
00:28:32,880 --> 00:28:35,639
Speaker 2: warn that AI risk becoming a speculative bubble unless its

574
00:28:35,760 --> 00:28:38,960
Speaker 2: use is spread beyond big tech companies and wealthy economies.

575
00:28:39,160 --> 00:28:40,960
Speaker 2: For this not to be a bubble, by definition, it

576
00:28:41,000 --> 00:28:43,520
Speaker 2: requires that the benefits of this are much more evenly spread.

577
00:28:44,680 --> 00:28:46,760
Speaker 2: He knowed that a telltale sign of it's abobble would

578
00:28:46,760 --> 00:28:48,760
Speaker 2: be if only tech groups are benefiting from the rise

579
00:28:48,800 --> 00:28:51,080
Speaker 2: of AI rather than the companies and other sectors.

580
00:28:51,480 --> 00:28:56,240
Speaker 1: Yeah, that's good, good shit self evident. Yes, correct, he's

581
00:28:56,240 --> 00:28:56,680
Speaker 1: not wrong.

582
00:28:57,800 --> 00:29:02,080
Speaker 2: Creative perhaps if no one uses also, isn't his job.

583
00:29:02,360 --> 00:29:06,840
Speaker 2: But nevertheless, there was a question I had. So something

584
00:29:06,880 --> 00:29:10,800
Speaker 2: that frustrates me with this whole conversation is how flippantly

585
00:29:10,960 --> 00:29:15,560
Speaker 2: people say, oh, it's a bubble, but sometimes bubbles are good.

586
00:29:16,040 --> 00:29:17,520
Speaker 2: What was the aftermath like?

587
00:29:20,360 --> 00:29:23,360
Speaker 1: So this is where things get a little bit complicated,

588
00:29:23,440 --> 00:29:28,920
Speaker 1: because I don't think that you can really fairly separate

589
00:29:31,000 --> 00:29:36,239
Speaker 1: events from other events that happen at the same time. So,

590
00:29:36,320 --> 00:29:40,400
Speaker 1: the the dot com bubble did burst, but then you

591
00:29:40,480 --> 00:29:43,040
Speaker 1: had nine to eleven happen in September of two thousand

592
00:29:43,080 --> 00:29:45,000
Speaker 1: and one, sort of in the late middle of it,

593
00:29:45,520 --> 00:29:49,480
Speaker 1: and that literally changed everything. It changed confidence, it changed

594
00:29:49,680 --> 00:29:53,880
Speaker 1: where everybody's attention was going. You know, you had money

595
00:29:53,880 --> 00:29:58,160
Speaker 1: flowing into completely different places. Everybody got much more conservative

596
00:29:58,240 --> 00:29:59,000
Speaker 1: and so forth.

597
00:29:59,120 --> 00:30:01,600
Speaker 3: So I think.

598
00:30:02,880 --> 00:30:05,560
Speaker 1: The real effect of the dot com bust, I mean,

599
00:30:05,600 --> 00:30:08,240
Speaker 1: you had a lot of people my age, people who

600
00:30:08,280 --> 00:30:11,520
Speaker 1: were in their twenties and the go go nineties, suddenly

601
00:30:11,560 --> 00:30:15,400
Speaker 1: lose their jobs or suddenly not have the great paychecks

602
00:30:15,440 --> 00:30:16,320
Speaker 1: that they.

603
00:30:16,200 --> 00:30:17,600
Speaker 3: Had become accustomed to.

604
00:30:19,120 --> 00:30:21,760
Speaker 1: And you had a lot of retail traders get wiped out.

605
00:30:22,000 --> 00:30:25,560
Speaker 1: You know, some people lost their lost their life savings

606
00:30:25,640 --> 00:30:28,040
Speaker 1: or a big portion of their life savings. And you know,

607
00:30:28,160 --> 00:30:31,560
Speaker 1: my attitude toward investing has always been caveat empt or

608
00:30:31,640 --> 00:30:34,160
Speaker 1: you shouldn't be betting if you can't afford it. You

609
00:30:34,200 --> 00:30:36,240
Speaker 1: should put your money in an index fund, and you

610
00:30:36,240 --> 00:30:38,560
Speaker 1: should hold on to that until you retire. That's the

611
00:30:38,600 --> 00:30:41,880
Speaker 1: safest thing to do. But you know, some people say, hey,

612
00:30:41,920 --> 00:30:45,040
Speaker 1: I've got ten percent of this of my holdings are

613
00:30:45,080 --> 00:30:46,760
Speaker 1: play money, and I'm just going to make some bets

614
00:30:46,800 --> 00:30:49,120
Speaker 1: and see what happens. And then you find other people

615
00:30:49,160 --> 00:30:50,840
Speaker 1: who who go too far with it.

616
00:30:51,680 --> 00:30:52,880
Speaker 2: But what about people in tech?

617
00:30:55,120 --> 00:31:00,320
Speaker 1: You know, in tech, most those skills didn't become useless, right,

618
00:31:00,400 --> 00:31:03,600
Speaker 1: so the technology was always changing. If you were you know,

619
00:31:03,680 --> 00:31:07,880
Speaker 1: learning HTML and JavaScript and Java programming, and then you

620
00:31:07,920 --> 00:31:12,560
Speaker 1: could transition into new different kinds of things. The technology

621
00:31:12,600 --> 00:31:15,320
Speaker 1: continued to evolve, so people who had those technical skills

622
00:31:15,360 --> 00:31:19,200
Speaker 1: could continue forward and find other interesting things to do.

623
00:31:19,280 --> 00:31:22,360
Speaker 1: And then, you know, honestly, after the sort of telecoms

624
00:31:22,360 --> 00:31:24,800
Speaker 1: bus two thousand and four, two thousand and five, you

625
00:31:24,920 --> 00:31:27,520
Speaker 1: had a pretty long boom. I mean, there was the

626
00:31:27,720 --> 00:31:31,600
Speaker 1: financial collapse of eight around the housing market, but the

627
00:31:31,720 --> 00:31:36,120
Speaker 1: tech industry independently was building a lot of stuff. Tons

628
00:31:36,120 --> 00:31:38,400
Speaker 1: of companies got started in the wake of the dot

629
00:31:38,480 --> 00:31:41,440
Speaker 1: com bust, and many of those companies are still around today.

630
00:31:41,480 --> 00:31:44,680
Speaker 1: So I don't feel like people in the tech industry

631
00:31:44,760 --> 00:31:48,320
Speaker 1: were permanently hurt. You know, there was a couple of

632
00:31:48,360 --> 00:31:51,120
Speaker 1: years set back, and then they generally were able to

633
00:31:51,160 --> 00:31:53,880
Speaker 1: learn new skills, apply what they'd already learned, and go

634
00:31:53,920 --> 00:31:56,360
Speaker 1: find other things that some of which turned out to

635
00:31:56,400 --> 00:31:57,160
Speaker 1: be really lucrative.

636
00:31:57,960 --> 00:31:58,240
Speaker 3: Right.

637
00:31:58,720 --> 00:32:01,880
Speaker 2: But I think, I think, how can I phrase this?

638
00:32:02,840 --> 00:32:05,880
Speaker 2: I think that people are very flippant about the consequences

639
00:32:05,880 --> 00:32:08,920
Speaker 2: of a potential bubble collapse. I think that that's really

640
00:32:08,920 --> 00:32:13,160
Speaker 2: what I'm that's where my concern you're talking about today today, Yes,

641
00:32:13,360 --> 00:32:15,440
Speaker 2: because people compare it to the dult com bubble, and

642
00:32:15,480 --> 00:32:18,440
Speaker 2: they say, well, after the dult kombubble, there was that value,

643
00:32:18,520 --> 00:32:19,800
Speaker 2: which is completely correct.

644
00:32:20,440 --> 00:32:29,040
Speaker 1: But yeah, again, I think the other thing is about today.

645
00:32:29,200 --> 00:32:34,600
Speaker 1: So the circumstances around the world are very different. You know,

646
00:32:34,760 --> 00:32:39,360
Speaker 1: ninety two ninety three, the Cold War was over. There

647
00:32:39,440 --> 00:32:43,000
Speaker 1: was this peace dividend. They were turning army bases into

648
00:32:43,120 --> 00:32:51,560
Speaker 1: national parks. Everybody was sort of enjoying, you know, good times.

649
00:32:51,640 --> 00:32:54,000
Speaker 1: There was a book called The End of History by

650
00:32:54,080 --> 00:32:56,160
Speaker 1: Francis Fukuyama that's sort of posited.

651
00:32:56,200 --> 00:32:57,040
Speaker 3: Hey, this is it.

652
00:32:57,720 --> 00:33:00,360
Speaker 1: You know, we're here in this permanent utopia world. Of course,

653
00:33:00,360 --> 00:33:02,240
Speaker 1: events intervened and I think nine to eleven was the

654
00:33:02,520 --> 00:33:05,080
Speaker 1: big turning point there, and there's some other stuff around

655
00:33:05,080 --> 00:33:06,760
Speaker 1: that time, you know.

656
00:33:06,720 --> 00:33:08,520
Speaker 3: The contested election of two thousand.

657
00:33:08,600 --> 00:33:10,560
Speaker 1: Now, contested elections are such a common thing we don't

658
00:33:10,560 --> 00:33:12,600
Speaker 1: really think about them anymore, but that was the first

659
00:33:12,600 --> 00:33:17,680
Speaker 1: big one in my lifetime. The situation today, I think

660
00:33:17,760 --> 00:33:21,800
Speaker 1: you would agree, is very different. You know, we have

661
00:33:22,560 --> 00:33:27,840
Speaker 1: an unprecedentedly chaotic leader in the United States who seems

662
00:33:27,880 --> 00:33:33,840
Speaker 1: to be bent on ripping down old world orders that

663
00:33:33,960 --> 00:33:38,320
Speaker 1: have enabled stability since World War Two. I mean, if

664
00:33:38,400 --> 00:33:42,520
Speaker 1: you're talking about seizing land from a NATO ally, and

665
00:33:42,560 --> 00:33:45,120
Speaker 1: you're seeing Europe strike all these free trade deals with

666
00:33:45,240 --> 00:33:47,640
Speaker 1: the rest of the world, and so forth, and so on.

667
00:33:47,960 --> 00:33:49,880
Speaker 1: We could go on and on about this, but I don't.

668
00:33:50,120 --> 00:33:54,720
Speaker 1: I'm not an analytical expert. But the background scenario is

669
00:33:54,880 --> 00:33:57,200
Speaker 1: very different, and I think you could almost make a

670
00:33:57,240 --> 00:34:03,600
Speaker 1: case for AI being sort of the logical outgrowth of

671
00:34:04,040 --> 00:34:09,320
Speaker 1: a certain number of years of rot in the business

672
00:34:09,360 --> 00:34:12,160
Speaker 1: and in the economy. And I think you've made that argument.

673
00:34:12,800 --> 00:34:15,200
Speaker 1: You know, you can decide where that started. Did it

674
00:34:15,280 --> 00:34:18,080
Speaker 1: start in the eighties and nineties when the US started

675
00:34:18,120 --> 00:34:21,600
Speaker 1: off shoring and outsourcing everything to China and other countries.

676
00:34:21,640 --> 00:34:26,560
Speaker 1: Did it start after that? But it's been somewhat clear

677
00:34:26,640 --> 00:34:29,879
Speaker 1: for some time that a lot of the easy sort

678
00:34:29,880 --> 00:34:32,600
Speaker 1: of technological gains that we got from the mobile revolution

679
00:34:32,719 --> 00:34:39,320
Speaker 1: and from the cloud, those have mostly been gathered like those. Yeah,

680
00:34:39,360 --> 00:34:41,400
Speaker 1: and there's not that much more upside. You can get

681
00:34:41,400 --> 00:34:44,120
Speaker 1: a little efficiency here and a little efficiency there. Everybody

682
00:34:44,160 --> 00:34:46,040
Speaker 1: kind of knows how the internet works, you know, what

683
00:34:46,160 --> 00:34:50,080
Speaker 1: advertising looks like, you know, so this sort of to

684
00:34:50,200 --> 00:34:54,680
Speaker 1: me seems like it's it's sort of the last big push.

685
00:34:54,719 --> 00:34:57,560
Speaker 1: And you know, you have a lot of people in

686
00:34:58,000 --> 00:35:02,040
Speaker 1: big bureaucratic jobs who you've written about this, they don't

687
00:35:02,280 --> 00:35:05,440
Speaker 1: really produce much day to day. They produce a lot

688
00:35:05,440 --> 00:35:07,680
Speaker 1: of emails and a lot of presentations, and they sit

689
00:35:07,719 --> 00:35:10,560
Speaker 1: in meeting occasionally they got to make a yes no

690
00:35:11,280 --> 00:35:13,520
Speaker 1: decision and they got to you know, mune some data

691
00:35:13,560 --> 00:35:15,360
Speaker 1: together and say, Okay, it looks wiser if we do

692
00:35:15,400 --> 00:35:17,920
Speaker 1: this instead of doing that. But you know, a lot

693
00:35:17,920 --> 00:35:20,920
Speaker 1: of it is performative, and I think, you know, AI

694
00:35:21,040 --> 00:35:23,400
Speaker 1: is great for that. I don't have to create the

695
00:35:23,400 --> 00:35:26,400
Speaker 1: spreadsheet anymore. I don't have to create the presentation anymore.

696
00:35:26,840 --> 00:35:28,520
Speaker 1: I don't have to write the email that no one's

697
00:35:28,560 --> 00:35:30,120
Speaker 1: going to read, and then they don't have to read

698
00:35:30,160 --> 00:35:32,560
Speaker 1: it because they can have AI summarized for them. I

699
00:35:32,560 --> 00:35:35,640
Speaker 1: think it's good for that kind of good enough stuff.

700
00:35:35,680 --> 00:35:38,760
Speaker 1: And there are a lot of jobs that are good enough.

701
00:35:39,520 --> 00:35:41,760
Speaker 1: But I want to it's a bigger problem with the economy.

702
00:35:41,800 --> 00:35:43,520
Speaker 1: I don't think it's all about AI. I think AI

703
00:35:43,680 --> 00:35:46,720
Speaker 1: is a symptom rather than perhaps a cause.

704
00:35:47,360 --> 00:35:51,120
Speaker 2: But even then, it's like, can it do presentations? Because

705
00:35:51,520 --> 00:35:53,920
Speaker 2: I've never been a like I've genuinely tried just to

706
00:35:53,920 --> 00:35:55,480
Speaker 2: see if it could do it. It's never been able

707
00:35:55,520 --> 00:35:57,320
Speaker 2: to do it. I think it's an interest.

708
00:35:57,840 --> 00:36:00,520
Speaker 1: Yeah, I mean I've seen presentations created with AI that

709
00:36:00,600 --> 00:36:01,279
Speaker 1: are pretty good.

710
00:36:01,280 --> 00:36:02,680
Speaker 3: I think you get it, gets you.

711
00:36:02,600 --> 00:36:04,360
Speaker 1: A certain percentage of the way there, and then you

712
00:36:04,400 --> 00:36:05,200
Speaker 1: tweak it a little bit.

713
00:36:05,719 --> 00:36:08,719
Speaker 2: Okay, yeah, it's just that. But but then you kind

714
00:36:08,760 --> 00:36:10,800
Speaker 2: of look at it and say, great, did we really

715
00:36:10,880 --> 00:36:14,440
Speaker 2: need to spend however, many hundreds of billions of dollars

716
00:36:15,120 --> 00:36:32,320
Speaker 2: to get a spreadsheet maker. I also think that people

717
00:36:32,320 --> 00:36:35,080
Speaker 2: are just being flippant about the idea of a financial

718
00:36:35,080 --> 00:36:40,760
Speaker 2: collapse as well. That's what that's what's worrying me as well.

719
00:36:40,880 --> 00:36:43,040
Speaker 1: Right, But again, I think that there are a lot

720
00:36:43,080 --> 00:36:48,040
Speaker 1: of potential reasons for a potential financial collapse, and AI

721
00:36:48,360 --> 00:36:51,040
Speaker 1: is one of some number.

722
00:36:51,239 --> 00:36:52,480
Speaker 3: I don't think it.

723
00:36:52,560 --> 00:36:57,640
Speaker 1: Again, the dot com bust was not entirely attributable to

724
00:36:58,680 --> 00:37:02,240
Speaker 1: ridiculous valuations. There were things going on in other parts

725
00:37:02,280 --> 00:37:05,239
Speaker 1: of the world. There was a change of in confidence,

726
00:37:05,360 --> 00:37:07,920
Speaker 1: and I think here again, you've got you've got a

727
00:37:07,920 --> 00:37:10,120
Speaker 1: lot of disruption and a lot of disorder and a

728
00:37:10,120 --> 00:37:12,239
Speaker 1: lot of chaos. So there are many many things that

729
00:37:12,880 --> 00:37:16,600
Speaker 1: could happen. But I agree, I think people maybe underestimate

730
00:37:16,680 --> 00:37:20,960
Speaker 1: how how deep this is. Then again then again, you know,

731
00:37:21,120 --> 00:37:23,359
Speaker 1: who are the big investors in this? I mean it's

732
00:37:23,440 --> 00:37:28,360
Speaker 1: mostly really large tech companies with massive, massive revenues and massive.

733
00:37:27,960 --> 00:37:32,640
Speaker 2: Cash maybe highly anymore though one hundred and seventy eight

734
00:37:32,719 --> 00:37:36,239
Speaker 2: point five billion dollars of data center credit deals, you've

735
00:37:36,239 --> 00:37:40,000
Speaker 2: got venture capital is actually they yeah, venture capital is

736
00:37:40,040 --> 00:37:42,600
Speaker 2: also half a venture capital is going into it. But

737
00:37:43,120 --> 00:37:45,240
Speaker 2: the other thing that shocked me as well was back

738
00:37:45,280 --> 00:37:48,000
Speaker 2: then in the dot com boom, venture capital was so

739
00:37:48,120 --> 00:37:48,880
Speaker 2: much smaller.

740
00:37:48,960 --> 00:37:51,600
Speaker 1: Oh it was tiny, but yeah, you need it was

741
00:37:51,640 --> 00:37:54,160
Speaker 1: investment banking is where is where you found the money.

742
00:37:54,239 --> 00:37:58,840
Speaker 1: So venture capital though, you know, that's not their money.

743
00:37:58,920 --> 00:38:01,319
Speaker 1: They're they're playing with somebody else's money. So whose money

744
00:38:01,360 --> 00:38:02,920
Speaker 1: are they playing with? Who are the LPs, Well, we

745
00:38:02,920 --> 00:38:07,040
Speaker 1: don't really know, but it's it's personal wealth funds, it's

746
00:38:07,080 --> 00:38:09,880
Speaker 1: sovereign wealth funds, you know, it's it's Middle Eastern and

747
00:38:10,239 --> 00:38:14,400
Speaker 1: other country sovereign wealth funds. Sure, there's teachers pensions, and

748
00:38:14,400 --> 00:38:17,399
Speaker 1: there's university endowments, and there's some other things that that

749
00:38:17,440 --> 00:38:22,880
Speaker 1: that those LPs are holding that will actually harm you know,

750
00:38:23,320 --> 00:38:24,560
Speaker 1: normal working folk.

751
00:38:25,000 --> 00:38:26,399
Speaker 3: But I think a lot of it is.

752
00:38:28,200 --> 00:38:32,359
Speaker 1: Massive excess wealth at the top of society where people

753
00:38:32,400 --> 00:38:35,399
Speaker 1: are looking for new, new kinds of returns, they're looking

754
00:38:35,400 --> 00:38:38,080
Speaker 1: for outsized returns because all the easy all the easy

755
00:38:38,080 --> 00:38:41,279
Speaker 1: money is sort of already taken. So I don't know

756
00:38:42,200 --> 00:38:44,719
Speaker 1: if that. I think where it hurts is when you

757
00:38:44,719 --> 00:38:47,280
Speaker 1: think about, Okay, the you know, the real estate carry

758
00:38:47,280 --> 00:38:49,880
Speaker 1: on effects, all the people who are employed now and

759
00:38:49,920 --> 00:38:53,640
Speaker 1: will be suddenly unemployed. Those those kinds of things could

760
00:38:53,640 --> 00:38:56,440
Speaker 1: really hurt. But I just it doesn't seem to me

761
00:38:56,560 --> 00:39:01,320
Speaker 1: like you have the retail trader exposure as you did

762
00:39:01,719 --> 00:39:03,680
Speaker 1: during the dot com boom. Yeah, okay, if there's a

763
00:39:03,760 --> 00:39:07,319
Speaker 1: huge AI bust and Nvidia loses a massive portion of

764
00:39:07,320 --> 00:39:10,440
Speaker 1: its value, sure people are going to hurt who have

765
00:39:10,520 --> 00:39:12,879
Speaker 1: invested in in video, but they're not going to get

766
00:39:12,880 --> 00:39:17,440
Speaker 1: wiped out. I don't see Nvidia disappearing. I don't see Amazon, Microsoft, Oracle.

767
00:39:17,640 --> 00:39:20,439
Speaker 1: I don't see any of those companies disappearing as a result, and.

768
00:39:20,360 --> 00:39:23,120
Speaker 2: Neither do I. I think the thing that people need

769
00:39:23,160 --> 00:39:26,920
Speaker 2: to understand is that a wipeout isn't going to be

770
00:39:27,000 --> 00:39:29,799
Speaker 2: what hurts. It's going to be a draw down. It's

771
00:39:29,840 --> 00:39:32,800
Speaker 2: going to be because in Vidia eighty eight percent of

772
00:39:32,840 --> 00:39:36,560
Speaker 2: the revenue's data center like and I even looked back

773
00:39:36,680 --> 00:39:38,879
Speaker 2: for a premium I date a few weeks ago where

774
00:39:38,920 --> 00:39:42,239
Speaker 2: it was the amount of revenue that like Loosened or

775
00:39:43,200 --> 00:39:47,080
Speaker 2: Corning or whomever had in those specific segments, or how

776
00:39:47,160 --> 00:39:50,960
Speaker 2: weighted they were on their customers, Like I think Loosen's

777
00:39:50,960 --> 00:39:53,960
Speaker 2: top customers were eight and T and Verizin, but those

778
00:39:54,040 --> 00:39:57,400
Speaker 2: only represented thirteen to fourteen percent. In Nvidia has customers

779
00:39:57,400 --> 00:40:00,000
Speaker 2: that make up fifteen to twenty percent of their revue

780
00:40:00,040 --> 00:40:03,920
Speaker 2: new each quarter. They have massive centralization around four to

781
00:40:03,960 --> 00:40:08,160
Speaker 2: five customers that diversified revenues dropping. It's just it isn't

782
00:40:08,200 --> 00:40:10,680
Speaker 2: that in video is going to die. It's the half

783
00:40:11,239 --> 00:40:14,360
Speaker 2: to eighty percent of their revenue is going to disappear.

784
00:40:14,480 --> 00:40:18,640
Speaker 2: It happened with crypto with them, yeah, I'm really worried.

785
00:40:18,680 --> 00:40:20,640
Speaker 1: They were never as Yeah, they were never as dependent

786
00:40:20,680 --> 00:40:23,760
Speaker 1: on Crypto. They've tried a lot of things over the years.

787
00:40:23,760 --> 00:40:25,560
Speaker 1: I mean right, they were doing so chips for self

788
00:40:25,600 --> 00:40:27,520
Speaker 1: driving cars and all kinds of things.

789
00:40:27,520 --> 00:40:28,480
Speaker 3: I do.

790
00:40:29,000 --> 00:40:31,719
Speaker 1: I am not as technical as the writers on the

791
00:40:31,760 --> 00:40:34,719
Speaker 1: registers staff to bias Man, for example, does a great

792
00:40:34,760 --> 00:40:39,400
Speaker 1: job covering GPUs and data centers. It is positive that

793
00:40:39,480 --> 00:40:42,279
Speaker 1: there are other potential uses for GPUs that could be

794
00:40:42,400 --> 00:40:44,800
Speaker 1: very interesting with in terms of high performance computing and

795
00:40:45,200 --> 00:40:48,640
Speaker 1: some other things that are AI adjacent, you know, different

796
00:40:48,719 --> 00:40:50,560
Speaker 1: kinds of data processing and so forth.

797
00:40:50,600 --> 00:40:53,120
Speaker 3: So it may not what we call AI.

798
00:40:53,320 --> 00:40:56,120
Speaker 1: Even if that you know that those end use cases

799
00:40:56,200 --> 00:40:59,240
Speaker 1: and turn out to be eh, you know, vibe coding, eh,

800
00:40:59,320 --> 00:41:02,880
Speaker 1: chatbots eh, even though those aren't it. Some of these,

801
00:41:03,640 --> 00:41:05,600
Speaker 1: some of the some of the hardware may be repurposable

802
00:41:05,640 --> 00:41:08,279
Speaker 1: into other functions. I don't think it. It's just not

803
00:41:08,360 --> 00:41:08,600
Speaker 1: at this.

804
00:41:09,040 --> 00:41:11,000
Speaker 2: But not at the scale that they're building.

805
00:41:11,000 --> 00:41:16,319
Speaker 1: Though. No, no, again, I would be stunned if all

806
00:41:16,400 --> 00:41:18,800
Speaker 1: of the data centers that are being promised by twenty

807
00:41:18,880 --> 00:41:20,040
Speaker 1: thirty are actually built.

808
00:41:20,080 --> 00:41:21,400
Speaker 3: But maybe I don't know.

809
00:41:21,520 --> 00:41:24,640
Speaker 2: I also just don't think that that will be happening.

810
00:41:25,000 --> 00:41:28,399
Speaker 2: I just don't see it happening. I am looking through

811
00:41:28,400 --> 00:41:31,360
Speaker 2: the old dot com companies, though I really can't find

812
00:41:31,400 --> 00:41:34,920
Speaker 2: anything like open AI. They are truly special.

813
00:41:35,400 --> 00:41:35,839
Speaker 1: They are.

814
00:41:36,440 --> 00:41:39,000
Speaker 2: It's like they took a bit of Global Crossing, a

815
00:41:39,040 --> 00:41:42,120
Speaker 2: bit of Enron, a bit of loosen but also a

816
00:41:42,160 --> 00:41:46,279
Speaker 2: bit of pets dot Com just kind of amazed what

817
00:41:46,760 --> 00:41:50,000
Speaker 2: Silicon Valley can still spoot out. No like innovations but

818
00:41:50,360 --> 00:41:54,640
Speaker 2: financial collapses like these, We've never seen one this big, folks.

819
00:41:55,200 --> 00:41:57,200
Speaker 3: It's massive. Yeah, it's it's there.

820
00:41:57,560 --> 00:41:59,560
Speaker 1: There are there are a lot of commitments there in

821
00:41:59,600 --> 00:42:04,200
Speaker 1: The value is massive, massive, massive. I do think their

822
00:42:04,280 --> 00:42:08,760
Speaker 1: announcement last week or their acknowledgment that they're finally getting

823
00:42:08,800 --> 00:42:11,680
Speaker 1: into advertising is interesting. And I know we've talked about

824
00:42:11,680 --> 00:42:16,759
Speaker 1: this offline before, but when I think about all of

825
00:42:16,800 --> 00:42:20,800
Speaker 1: the information that a chatbot has about a heavy user,

826
00:42:21,960 --> 00:42:26,759
Speaker 1: they actually should theoretically be able to target ads at

827
00:42:26,840 --> 00:42:29,520
Speaker 1: least as well as Facebook and at least as well

828
00:42:29,560 --> 00:42:30,120
Speaker 1: as Google.

829
00:42:30,280 --> 00:42:31,279
Speaker 3: Right, so good to know.

830
00:42:31,480 --> 00:42:36,200
Speaker 2: About that, because Google and Facebook have very hard demographic

831
00:42:36,360 --> 00:42:40,520
Speaker 2: data and very like a nuanced layer of cookies that

832
00:42:40,560 --> 00:42:43,799
Speaker 2: can track I don't know if, especially as these are

833
00:42:43,840 --> 00:42:49,000
Speaker 2: free users, we don't know how much actual personal data.

834
00:42:49,080 --> 00:42:53,040
Speaker 2: Like Facebook's big advantage was that when you registered, you

835
00:42:53,080 --> 00:42:57,799
Speaker 2: gave them the demographic data they needed to target right right.

836
00:42:57,960 --> 00:43:01,200
Speaker 1: I don't know how much they're collecting, but if they

837
00:43:01,239 --> 00:43:04,279
Speaker 1: can still say, well, I know everything that you're interested in,

838
00:43:04,320 --> 00:43:07,799
Speaker 1: everything that you've been talking about for the last year,

839
00:43:08,520 --> 00:43:10,640
Speaker 1: they should be able to track that back to an

840
00:43:10,680 --> 00:43:11,719
Speaker 1: individual user. I don't know.

841
00:43:11,760 --> 00:43:12,239
Speaker 3: I don't know the.

842
00:43:12,200 --> 00:43:16,000
Speaker 2: Details maybe, but they've said low billions of revenue. But

843
00:43:16,040 --> 00:43:19,040
Speaker 2: it is. It is just interesting because in any I

844
00:43:19,400 --> 00:43:22,759
Speaker 2: maybe you know this, I looking through the history of

845
00:43:22,800 --> 00:43:25,960
Speaker 2: the computer, have never seen a point when everybody was

846
00:43:26,040 --> 00:43:28,680
Speaker 2: just screaming that something that didn't work would work.

847
00:43:31,120 --> 00:43:35,120
Speaker 1: Yeah, it's uh wow, it's hard to it's hard to

848
00:43:35,160 --> 00:43:38,640
Speaker 1: remember something that has been you know, this hyped.

849
00:43:38,680 --> 00:43:42,480
Speaker 3: And again I always leave. I always try to approach

850
00:43:42,480 --> 00:43:42,719
Speaker 3: this with.

851
00:43:42,719 --> 00:43:46,920
Speaker 1: An open mind because I am, you know, older, so

852
00:43:47,080 --> 00:43:50,719
Speaker 1: I tend to be more cynical with experience. But yeah,

853
00:43:50,840 --> 00:43:53,680
Speaker 1: I sort of shrug and I'm like, I don't quite

854
00:43:53,920 --> 00:43:57,680
Speaker 1: understand the big deal here. Doesn't seem to really do

855
00:43:57,719 --> 00:44:00,480
Speaker 1: what it's supposed to do. I mean, I'm still you know,

856
00:44:00,800 --> 00:44:02,880
Speaker 1: Gemini has foist it on me every day when I

857
00:44:02,920 --> 00:44:08,160
Speaker 1: try to do Google searches, and it's nearly almost always

858
00:44:08,760 --> 00:44:12,560
Speaker 1: slightly wrong, which is kind of worse than being completely wrong,

859
00:44:12,600 --> 00:44:14,840
Speaker 1: because it lulls you into this false sense of security

860
00:44:14,880 --> 00:44:16,440
Speaker 1: and you're like, wait a minute, that doesn't seem right,

861
00:44:16,480 --> 00:44:18,040
Speaker 1: and then you go to the source link and it

862
00:44:18,200 --> 00:44:18,960
Speaker 1: just got it wrong.

863
00:44:19,160 --> 00:44:22,759
Speaker 2: Like yeah, the beginning of the Google chapter of my

864
00:44:22,840 --> 00:44:26,160
Speaker 2: book that's coming out next year is I went to

865
00:44:26,239 --> 00:44:29,239
Speaker 2: look for how many people, how many users Google has,

866
00:44:29,960 --> 00:44:32,719
Speaker 2: and it's just me going insane as I get an

867
00:44:32,719 --> 00:44:37,000
Speaker 2: AI generated answer that's generated based on two SEO articles

868
00:44:37,040 --> 00:44:38,600
Speaker 2: that do not cite their sources.

869
00:44:39,280 --> 00:44:42,040
Speaker 1: Right, there's a lot of that, so it's taking what

870
00:44:42,320 --> 00:44:44,400
Speaker 1: all this kind of garbage data. And I mean, as

871
00:44:45,120 --> 00:44:48,520
Speaker 1: an editor, I'm I have for the last fifteen years

872
00:44:48,560 --> 00:44:53,120
Speaker 1: been hounding reporters who try to source stuff off the internet.

873
00:44:53,200 --> 00:44:54,880
Speaker 1: I'm like, where'd you get that from? How do you

874
00:44:54,960 --> 00:44:57,920
Speaker 1: know that's a reliable source? And AI just sort of

875
00:44:58,160 --> 00:45:01,799
Speaker 1: absorbs it all and presents and Lodi da, here you go.

876
00:45:02,000 --> 00:45:03,560
Speaker 1: They don't have any you know, they don't care, they

877
00:45:03,560 --> 00:45:07,919
Speaker 1: don't have any checks. It just comes from wherever. So yeah,

878
00:45:08,320 --> 00:45:10,759
Speaker 1: the one, the one thing I would what you were

879
00:45:10,800 --> 00:45:13,480
Speaker 1: saying about never seen sort of this much happen. I

880
00:45:13,480 --> 00:45:17,160
Speaker 1: do remember before the Segue came out and people were

881
00:45:17,160 --> 00:45:19,160
Speaker 1: saying I can't remember who, if it was Steve Jobs

882
00:45:19,239 --> 00:45:21,040
Speaker 1: or somebody, but somebody was saying this is going to

883
00:45:21,280 --> 00:45:24,600
Speaker 1: revolutionize transportation and change cities.

884
00:45:24,640 --> 00:45:27,360
Speaker 3: And we were like, WHOA, what could this thing be?

885
00:45:27,480 --> 00:45:31,040
Speaker 1: And then it was the Segue. Maybe not, but that

886
00:45:31,160 --> 00:45:34,799
Speaker 1: was one very small, isolated thing I don't. Yeah, I

887
00:45:34,800 --> 00:45:37,360
Speaker 1: don't recall sort of large scale where everybody in the

888
00:45:37,360 --> 00:45:40,840
Speaker 1: industry was saying this is going to revolutionize everything, and

889
00:45:40,960 --> 00:45:45,080
Speaker 1: end users are kind of going, I don't know how

890
00:45:45,120 --> 00:45:47,040
Speaker 1: many times a day, co pilot comes up on my

891
00:45:47,880 --> 00:45:50,759
Speaker 1: Windows PC and I dismissed it immediately because I didn't

892
00:45:50,800 --> 00:45:52,279
Speaker 1: really want it to show up. It just sort of

893
00:45:52,320 --> 00:45:55,160
Speaker 1: pops up. I hit a key combo too fast. I'm like, thanks, Nah,

894
00:45:55,280 --> 00:45:55,719
Speaker 1: I'm good.

895
00:45:56,880 --> 00:45:58,719
Speaker 2: I keep thinking about the thing you said, though, It's

896
00:45:58,800 --> 00:46:02,280
Speaker 2: like the conditions that different. But at the same time,

897
00:46:04,680 --> 00:46:07,040
Speaker 2: we've read like it's not like we're in a new

898
00:46:07,080 --> 00:46:10,160
Speaker 2: frontier of new ideas where everybody has something new that

899
00:46:10,280 --> 00:46:13,440
Speaker 2: might work. It's where it almost feels like the end

900
00:46:13,480 --> 00:46:16,520
Speaker 2: of something. It feels like the dot com bubble was

901
00:46:16,560 --> 00:46:19,239
Speaker 2: created by the excitement about a beginning rather than this.

902
00:46:19,960 --> 00:46:22,600
Speaker 2: It is people trying to pretend that something's happening and

903
00:46:22,640 --> 00:46:26,160
Speaker 2: that we're not at the end of an ela. I

904
00:46:26,160 --> 00:46:28,120
Speaker 2: should say, I don't think we're at the end of society.

905
00:46:28,640 --> 00:46:33,200
Speaker 1: Yeah, I mean, I think we are probably seeing probably

906
00:46:33,200 --> 00:46:37,040
Speaker 1: the beginning of new pretty major global shifts and power,

907
00:46:37,080 --> 00:46:42,440
Speaker 1: and you know, some of the guiding societal philosophies that

908
00:46:42,520 --> 00:46:46,000
Speaker 1: guide that will change. And yeah, there's there's there's a

909
00:46:46,000 --> 00:46:49,520
Speaker 1: lot of different things going on right now. You know,

910
00:46:49,560 --> 00:46:51,719
Speaker 1: I started a climate section at the NBC and was

911
00:46:51,840 --> 00:46:55,279
Speaker 1: very deeply entrenched in climate tech and some of the

912
00:46:55,320 --> 00:46:56,880
Speaker 1: climate politics.

913
00:46:56,320 --> 00:46:57,440
Speaker 3: And things around that.

914
00:46:57,600 --> 00:47:00,839
Speaker 1: And you talk to the green piled folks and they think,

915
00:47:00,880 --> 00:47:02,960
Speaker 1: you know, eighty years from now, we're all doomed.

916
00:47:03,160 --> 00:47:04,560
Speaker 3: So do I believe that?

917
00:47:04,600 --> 00:47:06,960
Speaker 1: No, not necessarily, but I do feel like things are

918
00:47:07,080 --> 00:47:09,440
Speaker 1: changing pretty rapidly and pretty dramatically on a lot of

919
00:47:09,440 --> 00:47:12,040
Speaker 1: different fronts, and AIS just kind of this, Hey, let's

920
00:47:12,120 --> 00:47:14,919
Speaker 1: let's keep this, let's keep this playbook running this place.

921
00:47:15,000 --> 00:47:18,120
Speaker 2: Yeah, let's keep doing That's really it. It's like keeping

922
00:47:18,280 --> 00:47:20,680
Speaker 2: let's keep doing what we've done the whole time during

923
00:47:20,680 --> 00:47:23,160
Speaker 2: the dult kombubble, people would trying new shit.

924
00:47:23,640 --> 00:47:23,960
Speaker 3: It was.

925
00:47:25,320 --> 00:47:27,920
Speaker 1: It goes all the way back to semiconductors. I mean, this,

926
00:47:27,920 --> 00:47:30,600
Speaker 1: this tech boom has been going on since the since

927
00:47:30,640 --> 00:47:33,960
Speaker 1: the nineteen sixties, really in different waves, but it's been

928
00:47:33,960 --> 00:47:36,480
Speaker 1: going on and on and on. I mean, I do

929
00:47:36,520 --> 00:47:39,080
Speaker 1: think like when Bill Gates said a computer on every

930
00:47:39,120 --> 00:47:42,800
Speaker 1: desktop whenever, that was in the eighties, that was nuts.

931
00:47:42,960 --> 00:47:44,880
Speaker 1: And then it was a computer on every desk and

932
00:47:44,960 --> 00:47:47,239
Speaker 1: in every home, and that was even more nuts, but

933
00:47:47,400 --> 00:47:50,919
Speaker 1: it happened. So, you know, I think that it's been

934
00:47:51,160 --> 00:47:53,080
Speaker 1: I don't want to say easy, but it's been running

935
00:47:53,160 --> 00:47:56,440
Speaker 1: so well for so long that I think everybody just

936
00:47:56,719 --> 00:47:58,680
Speaker 1: kind of wants it to keep running.

937
00:47:58,719 --> 00:48:01,040
Speaker 3: And I'm not. I don't know, I'm not. I'm with you.

938
00:48:01,080 --> 00:48:04,120
Speaker 1: I'm not seeing it yet. I'm not seeing a ton

939
00:48:04,280 --> 00:48:06,920
Speaker 1: of day to days. I mean, I mean, you know,

940
00:48:06,760 --> 00:48:09,319
Speaker 1: you look at the teenage demographic.

941
00:48:09,360 --> 00:48:10,480
Speaker 3: That's kind of who I always go to.

942
00:48:10,520 --> 00:48:13,239
Speaker 1: And my son is fifteen, and he's very cynical about

943
00:48:13,360 --> 00:48:16,560
Speaker 1: like he jokes about obvious AI writing and like they

944
00:48:16,600 --> 00:48:20,600
Speaker 1: make fun of phrases and terms that are so AI.

945
00:48:20,680 --> 00:48:22,920
Speaker 1: And he'll see a you know, a poster and advertisement

946
00:48:22,960 --> 00:48:25,600
Speaker 1: he's like, that's so AI and it's it's something that

947
00:48:25,680 --> 00:48:27,320
Speaker 1: you scoff at and make it.

948
00:48:27,440 --> 00:48:31,120
Speaker 2: Oh they're fuck, No, okay, AI is fuck. Then I'm sorry.

949
00:48:31,120 --> 00:48:35,080
Speaker 2: If you've got teenagers who are making fun of calling

950
00:48:35,160 --> 00:48:39,080
Speaker 2: bad stuff AI, right, I don't. I'm actually serious. That

951
00:48:39,320 --> 00:48:45,600
Speaker 2: is legitimately lethal. Teenagers dumb people. I'm I'm thirty nine

952
00:48:45,680 --> 00:48:48,040
Speaker 2: years old now, like I was, I've never been cooler

953
00:48:48,320 --> 00:48:51,200
Speaker 2: a year of my life. But I know that when if,

954
00:48:51,440 --> 00:48:53,200
Speaker 2: because that's in because you read a lot of things

955
00:48:53,239 --> 00:48:56,000
Speaker 2: that people try and say, well, young people love AI

956
00:48:56,560 --> 00:48:57,919
Speaker 2: not be my experience either.

957
00:48:59,160 --> 00:49:05,280
Speaker 1: I don't young people love TikTok. Young people love Instagram reels.

958
00:49:06,560 --> 00:49:08,760
Speaker 1: You know, they're on they're on vertical they're scrolling vertical

959
00:49:08,880 --> 00:49:09,439
Speaker 1: video all.

960
00:49:09,400 --> 00:49:10,880
Speaker 3: Day and and commony.

961
00:49:12,239 --> 00:49:13,880
Speaker 1: I don't even want to say they love you know,

962
00:49:13,960 --> 00:49:16,200
Speaker 1: Snap or any social media because it's all you know,

963
00:49:16,239 --> 00:49:18,799
Speaker 1: it's all text change, right, It's all you just get

964
00:49:18,840 --> 00:49:21,360
Speaker 1: your different text strings. Like. There's a lot of stuff

965
00:49:21,400 --> 00:49:26,440
Speaker 1: that they love. Games, huge online games, you know, participatory games.

966
00:49:27,160 --> 00:49:30,279
Speaker 1: But yeah, I don't see them hanging out all.

967
00:49:30,239 --> 00:49:31,200
Speaker 3: Day with their chatbot.

968
00:49:31,440 --> 00:49:32,799
Speaker 1: You know, once in a while, if you have something

969
00:49:32,800 --> 00:49:34,480
Speaker 1: you really want to you're curious and you want to

970
00:49:34,480 --> 00:49:38,319
Speaker 1: experiment with something, it'll prototype something quite quickly for you.

971
00:49:38,360 --> 00:49:40,879
Speaker 1: And I think people do that sometimes and find it useful.

972
00:49:40,880 --> 00:49:42,480
Speaker 1: It's like, Okay, this is a prototype, this is cool.

973
00:49:42,520 --> 00:49:44,040
Speaker 1: This would have taken me six hours of grunt work

974
00:49:44,080 --> 00:49:46,080
Speaker 1: to generate. Thanks for doing that for me. So there

975
00:49:46,080 --> 00:49:47,719
Speaker 1: are some you know, I don't want to I don't

976
00:49:47,719 --> 00:49:49,560
Speaker 1: want to slam and say it's totally useless in all

977
00:49:49,560 --> 00:49:53,120
Speaker 1: scenarios because it's not it's a it's just it's sort

978
00:49:53,120 --> 00:49:55,560
Speaker 1: of an extension of a compute. It's just a better computer.

979
00:49:55,680 --> 00:49:59,200
Speaker 1: In some ways it helps you do certain things, some fast.

980
00:49:59,600 --> 00:50:01,879
Speaker 2: But some times it's a better that's the other thing.

981
00:50:01,920 --> 00:50:06,399
Speaker 2: It's like, I have this growing theory about AI psychosis

982
00:50:06,440 --> 00:50:09,239
Speaker 2: being far more common than people think, because I think

983
00:50:09,280 --> 00:50:11,719
Speaker 2: that there is something that happens when people use a

984
00:50:11,760 --> 00:50:15,400
Speaker 2: large language model and what they extrapolate from there that

985
00:50:15,480 --> 00:50:18,359
Speaker 2: makes some people do insane things like I don't know

986
00:50:19,080 --> 00:50:22,680
Speaker 2: by two hundred billion dollars worth of data centers and GPUs.

987
00:50:23,360 --> 00:50:26,719
Speaker 2: And on one hand, you might argue, oh, that's just

988
00:50:26,800 --> 00:50:30,520
Speaker 2: business idiots being business idiots. No, I think such An

989
00:50:30,520 --> 00:50:34,120
Speaker 2: Adela and sun Dar Pashai used an LM and went,

990
00:50:34,200 --> 00:50:37,000
Speaker 2: holy shit, I need to do this. I mean I

991
00:50:37,040 --> 00:50:40,239
Speaker 2: got told once that such An Adella's whole reason that

992
00:50:40,360 --> 00:50:44,680
Speaker 2: he started this facade was because he saw chat GPT

993
00:50:44,760 --> 00:50:47,000
Speaker 2: and wanted it in Bing. I think that there is

994
00:50:47,040 --> 00:50:51,560
Speaker 2: that everyone is experiencing micro versions of Kevin Bruce's Chat

995
00:50:51,600 --> 00:50:53,719
Speaker 2: GPT told me to leave my wife thing. It's just

996
00:50:54,080 --> 00:50:58,480
Speaker 2: what do you believe AI will do next, Like what

997
00:50:58,560 --> 00:51:00,600
Speaker 2: do you think it will do? And what do you

998
00:51:00,680 --> 00:51:03,160
Speaker 2: believe will happen if you don't use it today? And

999
00:51:03,200 --> 00:51:05,319
Speaker 2: it's just very bizarre. Sorry that was that was more

1000
00:51:05,360 --> 00:51:06,799
Speaker 2: of a point than a question, I should be clear.

1001
00:51:07,000 --> 00:51:10,279
Speaker 1: But how many people are subject to that? I don't

1002
00:51:10,280 --> 00:51:13,440
Speaker 1: think it's a huge number. Yeah, to some degree. The

1003
00:51:13,480 --> 00:51:16,280
Speaker 1: folks who are, you know, the sachun Adelas of the world,

1004
00:51:16,360 --> 00:51:21,120
Speaker 1: are I should say, someone insulated from the trials and

1005
00:51:21,120 --> 00:51:24,160
Speaker 1: tribulations that most of us experience. They're insulated from their money.

1006
00:51:24,160 --> 00:51:26,080
Speaker 1: And if you are someone like Sam Altman or Elon

1007
00:51:26,160 --> 00:51:29,200
Speaker 1: Musk who is surrounded by a cadre of people who

1008
00:51:29,360 --> 00:51:33,040
Speaker 1: always say yes to everything you suggest and and you know,

1009
00:51:33,080 --> 00:51:36,000
Speaker 1: are constantly saying you are a genius. You're a genius, man,

1010
00:51:36,080 --> 00:51:39,080
Speaker 1: You're a genius. I think, you know, some of these

1011
00:51:39,160 --> 00:51:43,120
Speaker 1: chatbots almost duplicate that mentality, like it's like, yeah, I

1012
00:51:43,160 --> 00:51:45,560
Speaker 1: want someone to tell me that whatever I'm doing, I

1013
00:51:45,640 --> 00:51:49,480
Speaker 1: am great and I am special and I have inside

1014
00:51:49,520 --> 00:51:51,240
Speaker 1: knowledge that nobody else has.

1015
00:51:51,719 --> 00:51:54,919
Speaker 2: That's a nice but that's kind of what I'm getting at.

1016
00:51:55,680 --> 00:51:58,960
Speaker 2: Using a large language but convinces some people that they

1017
00:51:59,000 --> 00:52:01,799
Speaker 2: are in the field future and creates this kind of

1018
00:52:01,840 --> 00:52:03,160
Speaker 2: religious feeling around it.

1019
00:52:03,160 --> 00:52:03,520
Speaker 1: I don't know.

1020
00:52:03,560 --> 00:52:06,120
Speaker 2: I hate to explore this idea more. I've just never

1021
00:52:06,440 --> 00:52:09,479
Speaker 2: I've seen it in like game FAQs, when people would

1022
00:52:09,560 --> 00:52:14,279
Speaker 2: argue between PlayStation and Nintendo and Xbox. I've seen, but

1023
00:52:14,400 --> 00:52:17,120
Speaker 2: not like that. You don't have people just saying the

1024
00:52:17,280 --> 00:52:19,680
Speaker 2: Xbox can do things things it can't.

1025
00:52:20,000 --> 00:52:21,680
Speaker 3: But it's part of what we're seeing.

1026
00:52:21,719 --> 00:52:27,320
Speaker 1: I think that feeling it's pervasive in our society right now. Regardless,

1027
00:52:27,440 --> 00:52:31,239
Speaker 1: we have this sort of strong current of anti expert

1028
00:52:31,560 --> 00:52:34,560
Speaker 1: right the people who've studied this for years are no

1029
00:52:34,760 --> 00:52:38,120
Speaker 1: smarter than me. I'm really smart because I have my

1030
00:52:38,239 --> 00:52:40,600
Speaker 1: own special knowledge and.

1031
00:52:40,280 --> 00:52:41,239
Speaker 3: Nobody else has it.

1032
00:52:41,360 --> 00:52:43,200
Speaker 1: And I'm going to tell you the secret that I've

1033
00:52:43,280 --> 00:52:45,600
Speaker 1: learned that's, you know, really important.

1034
00:52:45,760 --> 00:52:46,480
Speaker 3: I think that.

1035
00:52:47,280 --> 00:52:49,279
Speaker 1: Whatever if you want to call it psychosis or that

1036
00:52:49,400 --> 00:52:53,400
Speaker 1: narcissism that comes out of fifteen years of social media,

1037
00:52:53,640 --> 00:52:57,799
Speaker 1: like that's old enough to remember. In fact, Facebook didn't

1038
00:52:57,800 --> 00:53:00,239
Speaker 1: come out until I was already an adult and and

1039
00:53:00,320 --> 00:53:02,640
Speaker 1: had a child. I mean, Facebook didn't really become mainstream.

1040
00:53:02,760 --> 00:53:04,400
Speaker 1: I had a two year old kid. So I'm old

1041
00:53:04,760 --> 00:53:09,520
Speaker 1: and I remember how sort of society changed and how

1042
00:53:09,640 --> 00:53:13,640
Speaker 1: people interacting with one another changed dramatically as social media

1043
00:53:13,680 --> 00:53:16,520
Speaker 1: became popular, and it was much more about you're on

1044
00:53:16,600 --> 00:53:19,239
Speaker 1: stage all the time and look at my life, and

1045
00:53:19,280 --> 00:53:21,239
Speaker 1: your life is better than my life. We I mean,

1046
00:53:21,280 --> 00:53:23,560
Speaker 1: there's some of that that's always been around, but it

1047
00:53:23,640 --> 00:53:26,759
Speaker 1: just wasn't so easy to indulge.

1048
00:53:26,239 --> 00:53:29,400
Speaker 3: That side of oneself pre social media.

1049
00:53:29,440 --> 00:53:33,000
Speaker 1: So I think, you know, again, everything that was set

1050
00:53:33,200 --> 00:53:37,640
Speaker 1: in the past just continues along its way. Like I

1051
00:53:37,680 --> 00:53:41,480
Speaker 1: think again, I don't think AI is necessarily, you know,

1052
00:53:41,600 --> 00:53:44,720
Speaker 1: the one thing that is different. It's a natural outgrowth

1053
00:53:44,760 --> 00:53:46,960
Speaker 1: of a lot of different trends that have been happening

1054
00:53:46,960 --> 00:53:47,880
Speaker 1: for a long time.

1055
00:53:49,400 --> 00:53:52,680
Speaker 2: Yeah, I mean, I keep thinking a long term jok

1056
00:53:52,719 --> 00:53:55,680
Speaker 2: I make is posting it's the beginning of history with

1057
00:53:55,719 --> 00:54:00,040
Speaker 2: a big smiley face, because I actually worry that that

1058
00:54:00,040 --> 00:54:02,440
Speaker 2: that's what like, It's not that everything's coming to an end.

1059
00:54:02,440 --> 00:54:05,000
Speaker 2: It's that one era is ending and another is beginning

1060
00:54:05,920 --> 00:54:07,440
Speaker 2: aggressively and loudly.

1061
00:54:07,520 --> 00:54:08,120
Speaker 3: This is the.

1062
00:54:08,080 --> 00:54:11,040
Speaker 1: Dawning of the Aquarius.

1063
00:54:12,480 --> 00:54:15,120
Speaker 2: All right, I think that's a great place to end it.

1064
00:54:15,160 --> 00:54:18,200
Speaker 2: Matt rose Off, editor in chief of The Register, Thank

1065
00:54:18,200 --> 00:54:19,600
Speaker 2: you so much for joining me.

1066
00:54:19,840 --> 00:54:21,439
Speaker 3: All right, Thank you ed a lot of fun.

1067
00:54:22,080 --> 00:54:25,000
Speaker 2: Thank you everyone to listen. Thank you everyone to listening.

1068
00:54:25,080 --> 00:54:27,160
Speaker 2: I'm not going to edit that out. You'll have him

1069
00:54:27,160 --> 00:54:28,680
Speaker 2: on log this week as well. Maybe I'll chuck you

1070
00:54:28,760 --> 00:54:31,279
Speaker 2: an extra one if I'm feeling generous. Thanks everyone for

1071
00:54:31,960 --> 00:54:34,720
Speaker 2: stomaching the four part of last week, and yeah, catch

1072
00:54:34,719 --> 00:54:45,239
Speaker 2: you soon. Thank you for listening to Better Offline.

1073
00:54:45,360 --> 00:54:47,799
Speaker 4: The editor and composer of the Better Offline theme song

1074
00:54:47,880 --> 00:54:50,520
Speaker 4: is Mattasowski. You can check out more of his music

1075
00:54:50,520 --> 00:54:54,200
Speaker 4: and audio projects at Mattasowski dot com, m A T

1076
00:54:54,200 --> 00:54:58,640
Speaker 4: T O s O W s ki dot com. You

1077
00:54:58,680 --> 00:55:01,320
Speaker 4: can email me an easy Better Offline dot com or

1078
00:55:01,400 --> 00:55:03,920
Speaker 4: visit Better Offline dot com to find more podcast links

1079
00:55:03,920 --> 00:55:07,080
Speaker 4: and of course my newsletter. I also really recommend you

1080
00:55:07,080 --> 00:55:09,239
Speaker 4: go to chat dot Where's Youread dot at to visit

1081
00:55:09,280 --> 00:55:11,359
Speaker 4: the discord, and go to our slash.

1082
00:55:11,080 --> 00:55:14,200
Speaker 2: Better Offline to check out I'll Reddit. Thank you so

1083
00:55:14,320 --> 00:55:15,080
Speaker 2: much for listening.

1084
00:55:15,880 --> 00:55:18,600
Speaker 1: Better Offline is a production of cool Zone Media. For

1085
00:55:18,719 --> 00:55:21,880
Speaker 1: more from cool Zone Media, visit our website cool Zonemedia

1086
00:55:21,920 --> 00:55:24,799
Speaker 1: dot com, or check us out on the iHeartRadio app,

1087
00:55:24,840 --> 00:55:27,560
Speaker 1: Apple Podcasts, or wherever you get your podcasts.