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Speaker 1: Bloomberg Audio Studios, Podcasts, radio news.

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Speaker 2: Hello, Hello, Hello, welcome to on Air Fast twenty twenty six.

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Speaker 2: Thank you so much. I'm Kristin Meinzer. I'm your MC

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Speaker 2: for the day. And in case you don't know who

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Speaker 2: I am, I'm a podcaster, I'm a royal watcher. I'm

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Speaker 2: a culture critic. I've been very busy over the last

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Speaker 2: week with some royal news you may have heard of,

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Speaker 2: so you may know me from those things. But today

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Speaker 2: is not about me. It is about the fantastic talent

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Speaker 2: that is going to be on the stage and at

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Speaker 2: this festival. Let's get to our first live taping of

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Speaker 2: the day, Bloomberg's Odd Lots podcast. The hosts of Odd

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Speaker 2: Lots are Joe Wisenthal and Tracy Alloway. Please welcome them

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Speaker 2: to the stage, along with their very special guest, Henry Blodgett.

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Speaker 2: Thank you so much. Welcome to the stage.

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Speaker 1: Thank you so much.

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Speaker 3: And hello and welcome to a live episode of the

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Speaker 3: Odd Lots podcast.

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Speaker 4: I'm Joe Wisenthal and I'm Tracy Alloway.

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Speaker 3: Tracy, We're definitely not going to do this, but it

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Speaker 3: definitely feels like almost.

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Speaker 1: Every episode could be about AI these days. Right, Like

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Speaker 1: it's just touching.

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Speaker 3: So all the things we talk about are solely getting

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Speaker 3: subsumed into this topic that we're not going to do it,

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Speaker 3: but we probably could.

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Speaker 4: It's not just that every episode could be on AI,

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Speaker 4: it's that every episode could probably be made by AI

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Speaker 4: at this point, right. And in fact, we've had a

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Speaker 4: few people last year. I remember there were a few

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Speaker 4: people who made AI generated episodes of the podcast and

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Speaker 4: they weren't that bad. And I have to think if

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Speaker 4: they did it today, they would be even better.

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Speaker 1: It would definitely be better anyway, not better than us,

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Speaker 1: but they would be better than they were. I'm not

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Speaker 1: sure they'd be better than they were a year ago anyway.

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Speaker 3: So we have to talk about AI and what it

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Speaker 3: is doing to the market, finance, economics, et cetera. And

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Speaker 3: of course we have to talk about what it means

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Speaker 3: for us in the media industry. And I'm really excited

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Speaker 3: to say we do have the perfect guest.

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Speaker 1: We're going to be.

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Speaker 3: Speaking here with Henry Blodgett, the CEO of Regenerator. He

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Speaker 3: used to be my boss when I was at a

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Speaker 3: Business Insider, someone who has a background in both media

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Speaker 3: and of course Wall Street and finance, so the perfect

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Speaker 3: person to talk about all the different dimensions.

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Speaker 4: Truly the perfect guest.

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Speaker 3: All right, Henry, thank you so much for coming back

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Speaker 3: on odlots.

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Speaker 5: Thank you for having me so.

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Speaker 3: The last time we talked to was in May of

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Speaker 3: last year, and the theme then was everyone was talking

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Speaker 3: about here are these huge evaluations for AI companies, how

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Speaker 3: could they possibly be justified? And that was an interesting

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Speaker 3: conversation And it feels like the narrative has now flipped

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Speaker 3: one hundred and eighty degrees where it's like these companies

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Speaker 3: are so powerful they're going to destroy every other company

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Speaker 3: they're wake and every software company, a payments provider, et cetera.

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Speaker 3: Has been like plunging really since the start of the year.

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Speaker 3: How have you like updated your views? Where are you

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Speaker 3: now versus where you were in same May.

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Speaker 5: We are early in the development of the industry. This

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Speaker 5: looks a lot like the Internet in the nineteen nineties

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Speaker 5: for the if you remember, basically, when you have a

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Speaker 5: huge opportunity like this early on, you can have a

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Speaker 5: huge range of reasonable predictions about what is going to happen.

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Speaker 5: And here we one day it's catastrophe. None of us

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Speaker 5: are ever going to have a job. Again, everything's going

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Speaker 5: to create her other days, Hey, it's just a fancy

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Speaker 5: word process or no worries. Open ai is way overvalued.

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Speaker 5: I will say that since we talked last I believe

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Speaker 5: that we were aghast at the open ai three hundred

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Speaker 5: billion dollar valuation. I believe the latest one is eight

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Speaker 5: hundred billion, So we are climbing. But there are other

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Speaker 5: companies out there that are worth many times that. So

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Speaker 5: if one does think that AI is going to take

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Speaker 5: over the world and open Ai is the Google of

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Speaker 5: the AI era, which is the thesis which I think

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Speaker 5: is very misguided, we can get into, then one can

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Speaker 5: imagine that a company might be worth many multiples of

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Speaker 5: some of the companies that are out there.

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Speaker 4: So one of the crazy things that happened this week

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Speaker 4: speaking of AI and media, is there's a guy. We've

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Speaker 4: had him on the show, James Galen aka Satriny. He

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Speaker 4: runs a hedge fund also publishes an investment newsletter. He

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Speaker 4: basically wrote a doom case scenario of the year twenty

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Speaker 4: twenty eight where everything had become AI and because everything

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Speaker 4: was AI, no one had any jobs, no one was

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Speaker 4: actually buying anything in the economy, and we basically had

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Speaker 4: an economic and financial crisis when I read that piece

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Speaker 4: and then saw the market reaction to it, so stocks

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Speaker 4: fell pretty dramatically, and people were attributing it to basically

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Speaker 4: a newsletter that was a thought piece. I thought that

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Speaker 4: was kind of insane, and it speaks to what you

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Speaker 4: just said, which is, when we're in this really uncertain time,

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Speaker 4: you can go from sort of euphoria to doomerism very

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Speaker 4: very quickly.

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Speaker 5: Exactly because it's all predictions. We're all looking into the

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Speaker 5: hazy future saying how big is it going to be?

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Speaker 5: These small changes in your assumptions make a huge difference

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Speaker 5: in the current value. I will say, because that research

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Speaker 5: report was both heralded and pillarid, this is exactly fully

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Speaker 5: what you want analysts to do. You want them to

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Speaker 5: look out and take a strong position and say here's

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Speaker 5: what it can look like. And I thought it was

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Speaker 5: a fascinating report. I will point out that even though

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Speaker 5: as you just characterized it, that this is economic armageddon,

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Speaker 5: one of the first sentences of the report says ten

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Speaker 5: percent unemployment, which means ninety percent employment. Most of us

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Speaker 5: still have a job, and yes there are changes in industries,

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Speaker 5: but most of us are still working. So I was

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Speaker 5: surprised after hearing about this predicts catastrophe that I picked

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Speaker 5: it up. It's like, well, no, it predicts a big

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Speaker 5: technology change that sometimes takes the economy awhile to digest,

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Speaker 5: but not armageddon.

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Speaker 3: What do you think though, it says like about the

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Speaker 3: market right now? I mean, and it's kind of an

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Speaker 3: interaction of a market story in a media story, because

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Speaker 3: here's a substack piece that really did seem to move

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Speaker 3: the market.

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Speaker 1: Quite a bit on Monday, right yeah.

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Speaker 3: What does it say about the market environment that, because

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Speaker 3: it's all your right, the software stocks have been getting

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Speaker 3: clovered all year, and our AI agents, I mean, we'll

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Speaker 3: get into that, gonna somehow make it so we don't

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Speaker 3: need to buy regular business software anymore. I don't totally

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Speaker 3: understand that, but this has been the fear all year.

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Speaker 3: What does it sort of say about the market slash

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Speaker 3: media environment that a substack piece like this could just

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Speaker 3: go absolutely megaviral like that and move the market.

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Speaker 5: Everybody is twitchy. Valuations were high to begin with. We're

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Speaker 5: all reevaluating our views every day saying, am I crazy

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Speaker 5: thinking it could be this? Maybe I'm crazy? I have

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Speaker 5: to panic a little bit so we've seen stocks roll over.

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Speaker 5: The multiples are compressing a little bit again. So far

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Speaker 5: from armageddon. It's people saying, okay, there may be some

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Speaker 5: change here. And to your point, the big Bayer case

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Speaker 5: now is that, oh my goodness, claud Code is so good,

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Speaker 5: no one will ever buy software anymore. You'll just say, hey,

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Speaker 5: make me the software.

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Speaker 1: Yeah, I've done that.

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Speaker 5: Yeah you have. You're a big experimenter. Yeah, but I

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Speaker 5: would say you were probably not spending billions of dollars

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Speaker 5: in enterprise software before I do.

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Speaker 3: I don't know a thousands of people out in my

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Speaker 3: payroll who they need to get it at Fried you

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Speaker 3: know exactly.

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Speaker 5: You depend on this. And one of the nice things

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Speaker 5: about having a company standing behind a product is you

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Speaker 5: actually can hold them accountable for it. And maybe claud

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Speaker 5: Code doesn't make your enterprise software the you why you want,

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Speaker 5: somebody doesn't get paid. So as I hear that, to me,

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Speaker 5: that is hysteria. I don't understand it. Software companies are

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Speaker 5: not going away. Companies that are not in the software

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Speaker 5: business are not going to suddenly say, oh, let's have

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Speaker 5: a junior person who understands AI build all our software

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Speaker 5: for us. It is not going to happen, but overall,

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Speaker 5: big changes coming, lots of disruption. Do we know for

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Speaker 5: sure what that's going to do to profits of these

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Speaker 5: companies and change in the economy. We do not, And

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Speaker 5: that is called the discount rate getting higher. It feels riskier.

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Speaker 5: And on that day, Monday, everybody was feeling suddenly risk averse.

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Speaker 5: Today I'll point out as we tape, everyone's saying, no problem.

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Speaker 5: Off to the races again.

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Speaker 4: You've obviously lived and been very active and influential in

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Speaker 4: a previous technological I guess cycle of disruption. What do

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Speaker 4: you think people are getting wrong here, especially on Wall

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Speaker 4: Street when it comes to their approach on this particular

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Speaker 4: one AI versus dot Com.

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Speaker 5: I think this goes way beyond Wall Street. I think

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Speaker 5: there really is a big camp still, and the case

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Speaker 5: gets made and then it gets tamped down, and then

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Speaker 5: it comes back even more strongly that there really will

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Speaker 5: be no jobs for humans anymore. And what I would

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Speaker 5: say to that is, I think the bigger risk with

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Speaker 5: AI is actually that somebody invents an agenttic system that

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Speaker 5: actually gets out in the world and does a lot

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Speaker 5: of damage. I think that is a bigger risk. If

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Speaker 5: you look at technology job transitions over time, like the

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Speaker 5: big one, which is agriculture to industrial. Two hundred years ago,

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Speaker 5: ninety three percent of us worked on farms. Now it's

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Speaker 5: two percent. That's a big transition, and yet all through

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Speaker 5: that the number of jobs grow grew. And same thing

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Speaker 5: with things like telephone operators with steam engines. You look

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Speaker 5: at any piece of it, there is disruption and pain

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Speaker 5: and I'm not minimizing that for the people whose jobs

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Speaker 5: are changed or disrupted by it. But the economy overall

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Speaker 5: so far in history has gone on to create a

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Speaker 5: lot more jobs. And so when you see the lay

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Speaker 5: waste to jobs predictions, you don't also often get the

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Speaker 5: other side, which is how many new jobs are going

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Speaker 5: to be created?

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Speaker 4: Well, this is exactly what I want to ask you,

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Speaker 4: because we hear that all the time during technological advancement cycles,

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Speaker 4: new jobs are created. But I think the difficulty with

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Speaker 4: AI is that it's hard to see what those new

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Speaker 4: jobs are we better are.

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Speaker 2: Going to be?

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Speaker 4: Yeah, like, what are we better at than a supercomputer

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Speaker 4: that can code a program or write an article in

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Speaker 4: less than a minute.

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Speaker 1: Or mimical voice and do a podcast.

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Speaker 5: Okay, so how where are we three years in at

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Speaker 5: this point. These job doom predictions started twenty minutes after

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Speaker 5: chat GPT came out. Where are we maybe a little

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Speaker 5: bit at the margin, we're seeing something maybe, but maybe

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Speaker 5: it's still that companies way over hired after the COVID

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Speaker 5: boom or now sort of right sizing themselves. As you say,

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Speaker 5: I can go into LA, I can go to Google.

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Speaker 5: I think it is drop in a topic and say

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Speaker 5: make this into a great Odd Lots podcast.

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Speaker 1: Some people have said that Google.

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Speaker 3: Multiple people think that Google's notebook LM, which does that

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Speaker 3: creates a podcast out of a document.

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Speaker 1: Multiple people have said they.

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Speaker 3: Trained this on Jo and Tracy like this sounds like

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Speaker 3: other people are claiming that too, but yes they could have.

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Speaker 5: I'm just saying, you guys continue to grow. All I

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Speaker 5: hear more and more about is oh, you gotta listen

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Speaker 5: to Odd Lots grow and growing, growing, growing, growing. And

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Speaker 5: I don't think we were talking earlier that you've radically changed,

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Speaker 5: you made the way you make the show. We're still

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Speaker 5: recording it here. You didn't just queue up lams. Who

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Speaker 5: is going to rush to do the big interview with Google.

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Speaker 5: They're not. They're gonna come do it with you guys,

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Speaker 5: because they love you, and your audience loves you. So

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Speaker 5: I'm just saying, even in something that seemingly looks so

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Speaker 5: ripe to be destroyed, you guys cost money. Why is

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Speaker 5: Bloomberg spending it away right to defend I'm just saying optimistic.

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Speaker 1: So I mean I hear both ways, and thank you

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Speaker 1: for saying that.

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Speaker 3: On the other hand, you know, people might not have

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Speaker 3: the same affinity for someone who does claims adjusted and

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Speaker 3: at insurance. They don't really care if they have the

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Speaker 3: human touch for that or tech support. I want to

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Speaker 3: ask a related question. It also relates to the piece

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Speaker 3: that came out on one day, and it relates to

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Speaker 3: this question of whether open ai is could be.

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Speaker 1: The Google of the next generation.

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Speaker 3: One of the arguments that the piece stipulates, and I

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Speaker 3: think it sort of fits with what I have observed,

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Speaker 3: is that ai doesn't seem to have network.

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Speaker 1: Effects in the same way.

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Speaker 3: So it's like you could switch from O chat, GPT

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Speaker 3: to claude with almost no issues and it's just not

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Speaker 3: an issue. Whereas after the first time I used Google

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Speaker 3: in two though I never used Yahoo again, that was

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Speaker 3: the last time I'd ever.

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Speaker 1: Used another search engine.

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Speaker 3: This seems this is one of the arguments in the

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Speaker 3: piece that there isn't gonna be the same stickiness and

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Speaker 3: network effects in the AI era in the way that

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Speaker 3: defined the last twenty five years of the Internet. And

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Speaker 3: so when you think about like open ai versus Google

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Speaker 3: this year versus the dot com era, does that resonate

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Speaker 3: to I.

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Speaker 5: Think that what folks who would say that open ai

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Speaker 5: is the Google of the era would say that more

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Speaker 5: and more, you're gonna build more of your life into it.

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Speaker 5: It's going to know everything about you, it's going to

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Speaker 5: custom so it's gonna be the switching costs are going

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Speaker 5: to grow. But to this point they have not. And

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Speaker 5: I'll tell you the thing that really made me think

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Speaker 5: they were in trouble, and I still think that given

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Speaker 5: the valuation and given the general consensus that they've already won,

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Speaker 5: was when Google Gemini came out and people said, oh

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Speaker 5: it's better. Yeah, they passed them, and all the bulls said, oh,

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Speaker 5: just wait till the next version of open ai comes out.

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Speaker 5: I will say, if you go back into the nineteen nineties,

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Speaker 5: Amazon was very controversial. A lot of people thought it

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Speaker 5: would go bankrupt. It's just a bookstore. Barnes and Noble

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Speaker 5: is gonna put them out of business as soon as

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Speaker 5: they have a website or Walmart or whatever. Walmart and

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Speaker 5: Barnes and Noble never got anywhere near Amazon. Amazon was

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Speaker 5: so far out ahead always and still almost went bankrupt

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Speaker 5: by the way, but nobody was ever close. And in

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Speaker 5: this case, less than two years after the product comes out,

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Speaker 5: the incumbent has caught up to me. That's when I said, like, whoa, Okay,

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Speaker 5: maybe this actually is going to be like the Internet,

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Speaker 5: which is the first five years all of us, including me,

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Speaker 5: were saying like, oh, just buy the leaders, no problem,

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Speaker 5: like they're the ones that are gonna survive. You know

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Speaker 5: how many survived. Of that first five hundred companies that

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Speaker 5: went public in the nineteen nineties, one went on to

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Speaker 5: actually make investors a lot of money after the crash.

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Speaker 5: That was Amazon. Two others that I know of, Cisco,

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Speaker 5: after twenty five years, finally got back to its stock prize.

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Speaker 5: eBay did okay for a while and then kind of

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Speaker 5: sputtered out a little bit. That's out of hundreds and

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Speaker 5: hundreds of companies. So I think it's very possible that yes,

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Speaker 5: AI is huge. It's a great idea. We all want it,

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Speaker 5: and somebody is going to come along soon and due

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Speaker 5: to open ai what Google did to Yahoo, which by

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Speaker 5: the way, Yahoo was the big search engine winner and

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Speaker 5: then Google destroyed it. So I just think it's way

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Speaker 5: too early to say that we open a eyes the Google.

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Speaker 4: We started by mentioning the open ai valuation, which everyone

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Speaker 4: was a gas stat last year. Now maybe not so much.

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Speaker 4: But even if you assume a future where open ai

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Speaker 4: or whoever comes in and disrupts every single industry, and

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Speaker 4: there are thousands of employees and companies that are paying

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Speaker 4: two hundred dollars a month or whatever to access the

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Speaker 4: new LM whatever the new model is, open ai, at

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Speaker 4: least for right now, is still losing money on every

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Speaker 4: customer that's like a power user. So how does that

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Speaker 4: actually translate into a working business model.

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Speaker 5: It's a good question. I think what the bulls would

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Speaker 5: say is the cost of what they do is going

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Speaker 5: to plummet, and so the lines are going to cross

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Speaker 5: here pretty soon. If they own the whole market, suddenly

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Speaker 5: they start to become incredibly profitable. We are not there yet.

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Speaker 5: And I think when we talked last time at a

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Speaker 5: three hundred billion dollar valuation, which everybody was horrified by

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Speaker 5: at that point. But if you looked at the projections,

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Speaker 5: the projections were three years from now, they'll do one

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Speaker 5: hundred billion of revenue. And if you at that point

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Speaker 5: were looking at it saying, okay, if I believe that,

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Speaker 5: then it's three times revenue, the economics will probably change.

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Speaker 5: It starts to look a little more reasonable. Now we're

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Speaker 5: at eight hundred billion, so it's a much bigger multiple,

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Speaker 5: and you've got to actually have a much bigger revenue

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Speaker 5: stream and profits. But that's the argument. I don't see

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Speaker 5: it yet. I gotta say the economics are incredibly difficult

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Speaker 5: for them.

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Speaker 1: They seem brutal. And there's another element too.

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Speaker 3: So obviously, unlike during the Web two point zero era

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Speaker 3: where there was like cheap, plentiful compute, obviously, the capital

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Speaker 3: expenditure budgets.

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Speaker 1: To these companies are just eyewatering. They're spending like crazy.

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Speaker 3: There's also this other element that I think is interesting,

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Speaker 3: which is at least of a few of these labs,

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Speaker 3: like the people found to them are true believers, Like

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Speaker 3: they think they're like they think they're on a mission

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Speaker 3: to build AGI is going to change everything. Build God,

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Speaker 3: Build God, right, build a digital God, and it's like

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Speaker 3: they're not going to stop at anything. They're not gonna

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Speaker 3: be like, you know what, it's pretty good, let's tap

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Speaker 3: the brakes. We're pretty happy with how this model works.

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Speaker 1: You know, I don't, or at least I don't get

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Speaker 1: that a privy.

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Speaker 4: Also, they've couched it as this like existential race, like

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Speaker 4: there's only gonna be one company that dominates in the

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Speaker 4: and so the upper limit on spending to become that

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Speaker 4: company is infinity.

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Speaker 5: Yes, However, there is a limit to money. And when

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Speaker 5: you look at when people are raising money, you watch

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Speaker 5: where they're going for it. Sure, the vcs, that's one

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Speaker 5: pool of money, and then the big strategics, and then

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Speaker 5: the vcs who missed the first time and are feeling

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Speaker 5: so embarrassed about it they'll pay up to get in.

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Speaker 5: Then it is the international funds that are watching all

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Speaker 5: this stuff. Yeah, exactly. So it's some point you run

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Speaker 5: out of money, and we are talking about a company

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Speaker 5: that is raising hundreds of billions of dollars just to

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Speaker 5: keep the engine running and just to keep pace. This

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Speaker 5: is the other problem. Google, Facebook, Microsoft are generating tens

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Speaker 5: of billions of dollars of free cash flow every year

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Speaker 5: that they can use to buy chips open AI has

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Speaker 5: to raise it in the open market. So if those

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Speaker 5: economics don't start to change soon, we're gonna run out

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Speaker 5: of money.

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Speaker 4: Joe and I were talking about this before we came

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Speaker 4: on stage. But another thing happened recently was there is

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Speaker 4: a karaoke machine making company that announced it was now

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Speaker 4: an AI company, which when I see headlines like that,

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Speaker 4: remind me a lot of the crypto boom, but also

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Speaker 4: remind me a lot of the dot com boom, and

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Speaker 4: we are starting to see more and more of those.

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Speaker 4: Wasn't there a toilet company recently?

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Speaker 3: The toilet owner is legit, so like apparently, no, no, no,

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Speaker 3: for real, So you said it was not like, oh,

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Speaker 3: we're an AI company. Apparently it's the Japanese toilet company

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Speaker 3: and they have some like porcelain or something like that.

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Speaker 3: It's very important for like semiconductors or something. So everyone's like, oh,

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Speaker 3: get out of the toilet business. You have this access

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Speaker 3: to this material.

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Speaker 1: But so that one was kind of legit. The karaoke one.

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Speaker 4: Okay, okay, so yeah, the toilet one. Maybe the karaoke one.

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Speaker 4: When you see headlines like that, what do you think

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Speaker 4: do you think like this is a rational pivot into

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Speaker 4: a booming market and everyone should be experimenting with AI

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Speaker 4: at the moment, or do you think like, nah, this

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Speaker 4: is getting crazy.

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Speaker 5: I think we are in a euphoric bubble period where

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Speaker 5: we just discovered this amazing new thing. Nobody knows what

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Speaker 5: the future is, nobody knows what's gonna work, and we

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Speaker 5: are effectively creating this enormous R and D lab, which is, hey,

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Speaker 5: what you can do something, here's some money, go out

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Speaker 5: and experiment. I hope you'll be one of the winners.

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Speaker 5: Fifty to one hundred years ago, all of that stuff

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Speaker 5: happened in labs like Edison's lab or Bell Labs inside

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Speaker 5: these big corporations. You never saw it. Now everything is

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Speaker 5: a startup. You see it. It's happening in front of you.

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Speaker 5: But there's no question that most of the companies are

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Speaker 5: gonna fail. Happens aget in again and again. And I

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Speaker 5: think what investors are saying when they see that is okay,

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Speaker 5: you know that is a quick flip. It's hey, I'll

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Speaker 5: get it. Other people. A lot of other people love AI.

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Speaker 5: They'll buy it after I do, and it'll go up

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Speaker 5: and then I'll sell it.

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Speaker 3: I want to ask you a question about capital markets

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Speaker 3: and how they're different to the late nineties of the

404
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Speaker 3: dot com bubble, because one really big thing that's happening

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Speaker 3: these days is, yes, these companies are in private but

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Speaker 3: there is a lot of stock floating around, and there

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Speaker 3: are these SPVs that will buy a choir, a chunk

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Speaker 3: of Anthropic and sell them on a secondary market, and

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Speaker 3: people are trying to get They're much more liquid than

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Speaker 3: a private company would have been in the late nineties.

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Speaker 1: I get someone someone messaged me.

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Speaker 3: They're like, do you want to buy a little I like, no,

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Speaker 3: I don't do that, but they're like, would you like

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Speaker 3: to buy a little bit of Anthropic at a three

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Speaker 3: hundred and fifty billion dollar valuation? But you must get

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Speaker 3: a bunch of those sort of messages from people. And

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Speaker 3: I'm curious about your perspective on this world, because the

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Speaker 3: other thing is like, there's no financials, You have no

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Speaker 3: idea like what their p and L is for these

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Speaker 3: private company what's your take on some of this quasi

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Speaker 3: liquid private market stock.

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Speaker 5: Yeah, and let us just say then when our phones

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Speaker 5: are ringing, yeah, I know that's the right thing.

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Speaker 1: Money out there It's like, if someone is reaching out

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Speaker 1: to me, I can't be that much money.

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Speaker 5: Big, big warning signs going on over there. So I

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Speaker 5: think one of the things that's changed versus the dot

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Speaker 5: com boom in the nineteen nineties and before that is

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Speaker 5: companies used to go public very early before, and you

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Speaker 5: can invest in Amazon, for example, at a four hundred

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Speaker 5: million dollar valuation, sounds like a lot. It's worth trillions

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Speaker 5: of dollars. Now there's been incredible appreciation. Individuals can't do

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Speaker 5: that anymore, and there's good reason for it. People lost

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Speaker 5: a lot of money after the dot com crash. We

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Speaker 5: didn't like that. We want to protect everybody. So now

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Speaker 5: companies go public a lot later, the threat of lawsuits

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Speaker 5: and all of the different regulation that you've got to do,

438
00:21:18,960 --> 00:21:22,000
Speaker 5: so it's later. But the bummer about it is a

439
00:21:22,040 --> 00:21:25,440
Speaker 5: lot of investors who actually do have the risk tolerance

440
00:21:25,920 --> 00:21:28,679
Speaker 5: can't access them. And so, yes, you've had a new

441
00:21:29,240 --> 00:21:32,320
Speaker 5: business that has been built around Okay, let's get that

442
00:21:32,400 --> 00:21:36,000
Speaker 5: private stock. Somehow get it, I think. And the problem is, again,

443
00:21:36,080 --> 00:21:39,560
Speaker 5: if you choose right great company, it works, but it's

444
00:21:39,640 --> 00:21:42,520
Speaker 5: more fees packed around it. It's much more you know

445
00:21:42,560 --> 00:21:45,760
Speaker 5: that intermediary that called you has to get paid, so

446
00:21:45,840 --> 00:21:48,520
Speaker 5: that's somebody else. It's more difficult to trade. So I

447
00:21:48,560 --> 00:21:51,119
Speaker 5: think it is the market trying to solve the problem

448
00:21:51,440 --> 00:21:55,160
Speaker 5: that it's much harder to make money in open AI

449
00:21:55,520 --> 00:21:58,200
Speaker 5: at eight hundred billion or Anthropic at three hundred and

450
00:21:58,240 --> 00:22:01,120
Speaker 5: fifty billion than it is in the seed round when

451
00:22:01,160 --> 00:22:03,880
Speaker 5: it was a few hundred million. And so you've got

452
00:22:03,920 --> 00:22:06,280
Speaker 5: a lot of people clamoring to get in. But it's

453
00:22:06,320 --> 00:22:10,080
Speaker 5: the same problem. You don't even infect less companies go public.

454
00:22:10,160 --> 00:22:12,320
Speaker 5: They do, or they are releasing a lot of information,

455
00:22:12,520 --> 00:22:13,520
Speaker 5: whereas these guys are not.

456
00:22:30,200 --> 00:22:32,639
Speaker 4: Should we do some media stuff? All right, let the

457
00:22:32,880 --> 00:22:36,200
Speaker 4: media naval gazing begin, Okay, So we started out by

458
00:22:36,400 --> 00:22:38,639
Speaker 4: saying that, you know, we were talking about the Satrini

459
00:22:38,680 --> 00:22:41,080
Speaker 4: piece that got it went viral, but it also got

460
00:22:41,080 --> 00:22:47,159
Speaker 4: a backlash. You did your own AI newsroom experiment newsletter

461
00:22:47,280 --> 00:22:50,919
Speaker 4: on substack that also got a bit of a backlash.

462
00:22:51,119 --> 00:22:52,400
Speaker 4: Were you surprised at that one?

463
00:22:53,200 --> 00:22:55,160
Speaker 5: Yes, I was surprised. Basically, what I did I left

464
00:22:55,200 --> 00:22:57,800
Speaker 5: business inside or I started this new thing Regenerator and

465
00:22:57,840 --> 00:22:59,960
Speaker 5: a podcast too. Trying your business.

466
00:22:59,760 --> 00:23:03,200
Speaker 4: It's very difficult talk about this is the most important

467
00:23:03,240 --> 00:23:06,680
Speaker 4: aspect of podcasting podcast.

468
00:23:05,880 --> 00:23:09,520
Speaker 5: Exactly, yes, podcast called Solutions with check it out? Yes please.

469
00:23:09,920 --> 00:23:13,720
Speaker 5: So I left and it was just me. So I said, okay, great,

470
00:23:13,880 --> 00:23:16,280
Speaker 5: let's see what it can do, because if you try

471
00:23:16,280 --> 00:23:19,320
Speaker 5: that as a CEO of a company, people freak out, justifiably.

472
00:23:19,480 --> 00:23:22,040
Speaker 5: Oh my goodness, they're going to discover something. My job's threatened.

473
00:23:22,240 --> 00:23:25,400
Speaker 5: We are all worried about our jobs being threatened. Including

474
00:23:25,440 --> 00:23:29,520
Speaker 5: by the way, do we need articles any more? Articles?

475
00:23:29,680 --> 00:23:31,359
Speaker 5: I mean, boy, there are a lot of great articles

476
00:23:31,359 --> 00:23:33,680
Speaker 5: written everybody. In fact, this is the problem in media.

477
00:23:33,760 --> 00:23:35,639
Speaker 5: There's way too much of it. Let's just get that

478
00:23:35,680 --> 00:23:38,720
Speaker 5: out there anyway. So yes, I experimented with everything, and

479
00:23:38,760 --> 00:23:40,960
Speaker 5: the first thing that I did was, oh, if I

480
00:23:40,960 --> 00:23:43,520
Speaker 5: were actually starting a publication and hiring a team, who

481
00:23:43,560 --> 00:23:45,679
Speaker 5: would I hire. Well, I'd hire five people. I have

482
00:23:45,680 --> 00:23:48,040
Speaker 5: a managing editor, and I'd have a few reporters and

483
00:23:48,119 --> 00:23:50,879
Speaker 5: so forth. So I asked chat GPT, can you help me?

484
00:23:51,000 --> 00:23:55,040
Speaker 5: CHATCHEPET said sure, let's make them. So in two hours

485
00:23:55,080 --> 00:23:57,680
Speaker 5: we made them. They all had different personalities, they had

486
00:23:57,720 --> 00:24:00,840
Speaker 5: head shots, et cetera. I wrote this, We had like

487
00:24:01,000 --> 00:24:03,720
Speaker 5: slack going where we're all talking about stories, and it

488
00:24:03,800 --> 00:24:07,840
Speaker 5: was pretty good, not spectacular but pretty good, and so

489
00:24:07,920 --> 00:24:10,280
Speaker 5: that was cool, so I wrote about it. Yes, I

490
00:24:10,359 --> 00:24:14,440
Speaker 5: also made the mistake of complimenting one of the headshots

491
00:24:14,840 --> 00:24:18,879
Speaker 5: and was immediately lambasted for that, which actually helped me

492
00:24:19,040 --> 00:24:21,160
Speaker 5: hone my own AI morality.

493
00:24:21,200 --> 00:24:23,000
Speaker 4: Well, I have a question on this. If you had

494
00:24:23,080 --> 00:24:25,840
Speaker 4: had a human editor for that piece, do you think

495
00:24:25,840 --> 00:24:29,600
Speaker 4: you would have complimented your AI generated managing editor on

496
00:24:29,640 --> 00:24:30,840
Speaker 4: her appearance.

497
00:24:30,600 --> 00:24:33,800
Speaker 5: The piece needed to be editing edited. This is the problem.

498
00:24:34,000 --> 00:24:35,560
Speaker 5: You were out on your own. You don't have great

499
00:24:35,640 --> 00:24:39,360
Speaker 5: editors protecting you anymore. You let your enthusiasm bubble over

500
00:24:39,400 --> 00:24:41,199
Speaker 5: a little bit too much. You know who I have

501
00:24:41,400 --> 00:24:44,440
Speaker 5: edited my stuff now sometimes if I feel like I'm

502
00:24:44,480 --> 00:24:48,280
Speaker 5: maybe a little bit too fasty as enthusiastic JATGBT or

503
00:24:48,359 --> 00:24:50,240
Speaker 5: Claude and very good spots it.

504
00:24:50,280 --> 00:24:53,119
Speaker 3: Anyway, you talk to us about that newsroom dynamic particularly,

505
00:24:53,200 --> 00:24:57,639
Speaker 3: you know, every editor at a newsroom, every every newsroom right.

506
00:24:57,520 --> 00:25:00,080
Speaker 1: Now is like trying to figure out something right. You

507
00:25:00,160 --> 00:25:01,840
Speaker 1: can't like alienate.

508
00:25:01,400 --> 00:25:03,720
Speaker 3: The entire newsroom and say like, oh you all have

509
00:25:03,800 --> 00:25:07,320
Speaker 3: to be doing whats Yeah.

510
00:25:07,080 --> 00:25:08,680
Speaker 1: Like that doesn't work.

511
00:25:08,760 --> 00:25:10,960
Speaker 3: The newsroom obviously hates it, and I would say probably

512
00:25:10,960 --> 00:25:13,400
Speaker 3: for good reason. But on the other hand, everyone has

513
00:25:13,440 --> 00:25:17,720
Speaker 3: to be like figuring stuff out. What should newsroom executives,

514
00:25:17,760 --> 00:25:20,520
Speaker 3: whether it's editors in chief or whatever, how should they

515
00:25:20,520 --> 00:25:22,920
Speaker 3: be thinking about solving this problem?

516
00:25:23,200 --> 00:25:24,879
Speaker 5: So, I mean, so going back the other thing, the

517
00:25:24,920 --> 00:25:27,480
Speaker 5: other thing that I learned from publishing, that was just

518
00:25:27,600 --> 00:25:31,240
Speaker 5: how much anxiety there is, especially in younger generation, about

519
00:25:31,520 --> 00:25:33,119
Speaker 5: jobs that have been there for a long time that

520
00:25:33,160 --> 00:25:36,560
Speaker 5: are just disappearing. And that was extremely clear to me,

521
00:25:36,600 --> 00:25:38,840
Speaker 5: and I would have been even more Yeah, I understand it.

522
00:25:38,880 --> 00:25:41,760
Speaker 5: I mean it is scary. And so what does a

523
00:25:41,800 --> 00:25:44,560
Speaker 5: newsroom do. I think, let's look at what's going on media.

524
00:25:44,680 --> 00:25:48,760
Speaker 5: The problem again is there's way too much media. There

525
00:25:48,800 --> 00:25:52,520
Speaker 5: are so many amazing articles, for example, produced every day.

526
00:25:52,600 --> 00:25:54,480
Speaker 5: When I open the New York Times or the Wall

527
00:25:54,480 --> 00:25:58,760
Speaker 5: Street Journal or Bloomberg, I want to read dozens of articles.

528
00:25:58,840 --> 00:26:02,359
Speaker 5: I might get to one or two. And distribution has

529
00:26:02,480 --> 00:26:07,520
Speaker 5: changed radically five years ago. Facebook, Google, we're driving enormous

530
00:26:07,560 --> 00:26:11,480
Speaker 5: distribution to lots of different publishers. That is all now changing.

531
00:26:11,600 --> 00:26:13,440
Speaker 5: We are going back to something that looks like the

532
00:26:13,520 --> 00:26:17,240
Speaker 5: nineteen nineties, where actually you have to have a direct

533
00:26:17,280 --> 00:26:21,320
Speaker 5: relationship with your subscriber. The industry is under a ton

534
00:26:21,400 --> 00:26:26,760
Speaker 5: of pressure, both from advertising but also we have maximized

535
00:26:26,760 --> 00:26:29,639
Speaker 5: the amount of time as humans that we can spend

536
00:26:29,960 --> 00:26:33,760
Speaker 5: consuming media. This is very different from twenty years ago

537
00:26:33,840 --> 00:26:36,399
Speaker 5: when we start a business socider. You remember this, Like

538
00:26:36,720 --> 00:26:39,200
Speaker 5: in the nineteen nineties, media was very mature.

539
00:26:39,680 --> 00:26:42,120
Speaker 1: It was, says twenty years ago. By the way, that's

540
00:26:42,160 --> 00:26:43,960
Speaker 1: like thirty years Oh, wit to figure.

541
00:26:43,960 --> 00:26:47,040
Speaker 5: Okay, but let's go back to the ninety nineties. Even farther.

542
00:26:47,320 --> 00:26:50,520
Speaker 5: Media was very mature. Magazines totally mature, very hard to

543
00:26:50,560 --> 00:26:53,000
Speaker 5: start one. You needed fifty million dollars and you need

544
00:26:53,000 --> 00:26:55,880
Speaker 5: to hire all these great people, and then maybe five

545
00:26:55,960 --> 00:26:59,200
Speaker 5: years down the road you might, if you're lucky, become profitable.

546
00:26:59,280 --> 00:27:02,560
Speaker 5: Very hard, very hard to get into newspapers, television, even worse.

547
00:27:02,640 --> 00:27:05,520
Speaker 5: Your slave for a long long time getting coffee, so

548
00:27:06,119 --> 00:27:10,680
Speaker 5: very very tough. Then desktops came along and got connected.

549
00:27:10,920 --> 00:27:13,400
Speaker 5: So that was suddenly twelve hours a day that people

550
00:27:13,440 --> 00:27:15,640
Speaker 5: are sitting in the office. They need to know what's

551
00:27:15,640 --> 00:27:19,080
Speaker 5: going on. It's a new opportunity. Then phones came and

552
00:27:19,119 --> 00:27:21,159
Speaker 5: that's the thing that really drove business Insider. In the

553
00:27:21,119 --> 00:27:23,720
Speaker 5: earlier we're standing around, we want to know what's happening.

554
00:27:24,040 --> 00:27:28,399
Speaker 5: Nobody else is serving that, so big, big opportunity. But

555
00:27:28,600 --> 00:27:32,440
Speaker 5: over time all the folks in the industry caught up.

556
00:27:32,960 --> 00:27:35,880
Speaker 5: New York Times is terrific, so is Wall Street Journal,

557
00:27:35,920 --> 00:27:39,160
Speaker 5: so it's business insiders, so it's Bloomberg. These companies are

558
00:27:39,280 --> 00:27:42,080
Speaker 5: really really good at this now. And if you just

559
00:27:42,160 --> 00:27:46,240
Speaker 5: look at, for example, political coverage, we don't need one

560
00:27:46,320 --> 00:27:50,440
Speaker 5: hundred White House reporters, we just don't. And yet that's

561
00:27:50,520 --> 00:27:52,640
Speaker 5: the thing that everybody's interested in, so of course you're

562
00:27:52,640 --> 00:27:55,159
Speaker 5: going to have a reporter focus on that. And yet

563
00:27:55,560 --> 00:27:57,719
Speaker 5: there's just too much capacity. And then the other thing

564
00:27:57,760 --> 00:28:00,199
Speaker 5: that happened, and I'll finished up very quickly, is the

565
00:28:00,280 --> 00:28:04,680
Speaker 5: Internet blew up the protective mote around all of the

566
00:28:04,760 --> 00:28:08,320
Speaker 5: nineteen nineties era media. Print and TV used to be

567
00:28:08,359 --> 00:28:12,080
Speaker 5: completely separate. Now they're the same. You used to not

568
00:28:12,160 --> 00:28:14,600
Speaker 5: have a television company be able to own a newspaper.

569
00:28:14,800 --> 00:28:17,159
Speaker 5: Now we've done away with that. Everybody can own everything,

570
00:28:17,359 --> 00:28:21,040
Speaker 5: everybody can do everything, and it's all a click away.

571
00:28:21,240 --> 00:28:24,800
Speaker 5: So it has become even more intensely competitive. And so

572
00:28:25,040 --> 00:28:27,760
Speaker 5: it's never been an easy industry, but we are back

573
00:28:27,800 --> 00:28:31,840
Speaker 5: to it being an incredibly intensely competitive industry, and I

574
00:28:31,880 --> 00:28:34,000
Speaker 5: do think there's just in general too much of it.

575
00:28:34,680 --> 00:28:38,160
Speaker 4: So when we think about the impact of AI on media,

576
00:28:38,640 --> 00:28:40,560
Speaker 4: it feels to me like people are talking about two

577
00:28:40,640 --> 00:28:43,120
Speaker 4: paths here, and one is where you know, the big

578
00:28:43,200 --> 00:28:46,000
Speaker 4: chat platforms are basically they become the front page of

579
00:28:46,000 --> 00:28:49,440
Speaker 4: the internet, right and you type in whatever query you

580
00:28:49,520 --> 00:28:52,240
Speaker 4: have today, how did the State of the Union address

581
00:28:52,280 --> 00:28:54,760
Speaker 4: go last night or something like that, and it spits

582
00:28:54,800 --> 00:28:56,840
Speaker 4: out a bunch of information that's based on a bunch

583
00:28:56,840 --> 00:29:00,560
Speaker 4: of articles that you, yourself, are not paying for. And

584
00:29:00,600 --> 00:29:02,600
Speaker 4: then the other thing that I hear is well, actually,

585
00:29:02,640 --> 00:29:06,920
Speaker 4: in the age of Internet slop AI slop, that distribution

586
00:29:07,280 --> 00:29:09,640
Speaker 4: and quality is going to become even more important. So

587
00:29:09,720 --> 00:29:12,000
Speaker 4: people are going to want to go to the New

588
00:29:12,080 --> 00:29:15,160
Speaker 4: York Times or hopefully to Bloomberg or wherever and get

589
00:29:15,320 --> 00:29:18,840
Speaker 4: that take on last night's events. Where do you stand

590
00:29:18,960 --> 00:29:21,800
Speaker 4: on that sort of You know, it's a very binary

591
00:29:21,880 --> 00:29:24,080
Speaker 4: outcome to me, which way do you think we're going?

592
00:29:24,640 --> 00:29:27,760
Speaker 5: I think this is great for the brands that make it,

593
00:29:27,960 --> 00:29:31,479
Speaker 5: and again, nobody has a right to exist. It's going

594
00:29:31,520 --> 00:29:33,640
Speaker 5: to be a fight, But for the brands that make it,

595
00:29:33,640 --> 00:29:38,600
Speaker 5: it is terrific. I trust Bloomberg. I read it Bloomberg article.

596
00:29:38,720 --> 00:29:40,680
Speaker 5: I know the reporter knows what they're doing and they

597
00:29:40,680 --> 00:29:43,760
Speaker 5: know the subject. I know there is editing there. Occasionally

598
00:29:43,760 --> 00:29:46,360
Speaker 5: there's a mistake, but it is an honest mistake that

599
00:29:46,400 --> 00:29:50,320
Speaker 5: will be fixed quickly. Compare that to what I see

600
00:29:50,360 --> 00:29:54,840
Speaker 5: on social media, especially now where AI can create photos

601
00:29:54,880 --> 00:29:57,760
Speaker 5: and videos and everything else. I don't know. I know

602
00:29:57,920 --> 00:30:01,320
Speaker 5: the New York Times will immediately dive in if something

603
00:30:01,360 --> 00:30:05,240
Speaker 5: is bubbling up, some video that's apparently scandalous that we've

604
00:30:05,240 --> 00:30:07,760
Speaker 5: all got to look at. I know they'll do work

605
00:30:07,800 --> 00:30:10,560
Speaker 5: on it. Maybe we'll sometime get to the point where

606
00:30:10,600 --> 00:30:14,480
Speaker 5: they can be fooled. Not yet, and so I trust them.

607
00:30:14,560 --> 00:30:18,160
Speaker 5: And you actually need a brand and a great organization

608
00:30:18,240 --> 00:30:21,120
Speaker 5: to do that. And people say, wow, yes, but everybody

609
00:30:21,160 --> 00:30:23,320
Speaker 5: out there, you know, who knows what's true and it's over.

610
00:30:23,760 --> 00:30:26,320
Speaker 5: That has always been the case. At its peak in

611
00:30:26,400 --> 00:30:30,840
Speaker 5: the print era, a million people read The New York Times.

612
00:30:31,640 --> 00:30:35,480
Speaker 5: That's it. Everybody else heard what was said from their friends,

613
00:30:35,880 --> 00:30:38,560
Speaker 5: or they heard something else, they never thought about it.

614
00:30:38,640 --> 00:30:41,320
Speaker 5: And so the idea that yeah, there's stuff out there

615
00:30:41,400 --> 00:30:44,720
Speaker 5: that some people believe, There's always been stuff out there

616
00:30:44,720 --> 00:30:47,400
Speaker 5: that some people believe. So I think it's great for brands,

617
00:30:47,400 --> 00:30:48,720
Speaker 5: but I do think it's gonna be a fight. It's

618
00:30:48,760 --> 00:30:50,680
Speaker 5: never going to be an easy industry talk.

619
00:30:50,840 --> 00:30:54,160
Speaker 3: Just more about this question though, of like, Okay, everyone,

620
00:30:54,200 --> 00:30:55,800
Speaker 3: you know New York Times, like anyone else, they have

621
00:30:55,840 --> 00:30:57,480
Speaker 3: to figure this out, right, They have to figure out,

622
00:30:57,480 --> 00:30:58,880
Speaker 3: like what can they leverage it.

623
00:30:59,040 --> 00:30:59,960
Speaker 1: I'm doing scare quotes.

624
00:31:00,560 --> 00:31:02,400
Speaker 3: I hate that word, you know, but it's like the

625
00:31:02,440 --> 00:31:04,440
Speaker 3: word that everyone used. Could it be leveraged in some

626
00:31:04,480 --> 00:31:07,640
Speaker 3: way in the context of the traditional newsroom AI?

627
00:31:07,960 --> 00:31:09,440
Speaker 1: Yeah, And so.

628
00:31:09,440 --> 00:31:11,280
Speaker 5: Going back to your question, which I realized I didn't

629
00:31:11,520 --> 00:31:14,080
Speaker 5: get to before, So what what should news.

630
00:31:14,000 --> 00:31:15,040
Speaker 1: How do they handle that?

631
00:31:15,080 --> 00:31:17,880
Speaker 3: Like, because everyone here's you have to like run your

632
00:31:18,000 --> 00:31:21,240
Speaker 3: runder text through this chatbot or train your train the

633
00:31:21,280 --> 00:31:23,320
Speaker 3: AI that's going to replace whatever or they feel, you know,

634
00:31:23,760 --> 00:31:26,720
Speaker 3: very reasonable fear, how do you how can anyone overcome that?

635
00:31:26,960 --> 00:31:30,280
Speaker 5: So I think there there is the opportunity for there

636
00:31:30,320 --> 00:31:33,040
Speaker 5: to be a lot of productivity enhancements in what we

637
00:31:33,120 --> 00:31:36,280
Speaker 5: have today. We were talking earlier. You both use research

638
00:31:36,600 --> 00:31:40,200
Speaker 5: chattyp for research. Me too. It's great. That is a

639
00:31:40,360 --> 00:31:44,520
Speaker 5: very different That is one use of media is me

640
00:31:45,320 --> 00:31:47,920
Speaker 5: looking for information going out It used to be that

641
00:31:47,960 --> 00:31:50,120
Speaker 5: I would search for articles. Now I do it with

642
00:31:50,200 --> 00:31:54,480
Speaker 5: Claude or chat GPT. That is a smaller piece than

643
00:31:54,920 --> 00:31:58,840
Speaker 5: what most media has lived on forever, which is I

644
00:31:58,840 --> 00:32:02,720
Speaker 5: don't know what I'm interested in. WHOA, that's an interesting story.

645
00:32:03,040 --> 00:32:06,840
Speaker 5: And that's the serendipitous consumption of media. That's why newspaper

646
00:32:06,840 --> 00:32:10,400
Speaker 5: headlines have been like this forever. It's why magazine covers matter.

647
00:32:10,800 --> 00:32:12,840
Speaker 5: We don't know what we want until we see it,

648
00:32:13,000 --> 00:32:16,600
Speaker 5: and then we get it. And it's why in our

649
00:32:16,680 --> 00:32:20,440
Speaker 5: business there are many different talents you need to be

650
00:32:20,680 --> 00:32:25,520
Speaker 5: extraordinarily talented in the business. One is sure reporting, accuracy, expertise,

651
00:32:25,600 --> 00:32:27,600
Speaker 5: so forth. You also have to know what people care

652
00:32:27,640 --> 00:32:30,520
Speaker 5: about and what a good story is and why it matters.

653
00:32:31,160 --> 00:32:34,120
Speaker 5: And I don't think that changes. And so where do

654
00:32:34,160 --> 00:32:38,920
Speaker 5: we look around the interestry research, drafting stories? Why not?

655
00:32:39,320 --> 00:32:41,600
Speaker 5: And when people are aghast at that, let me just

656
00:32:41,640 --> 00:32:45,600
Speaker 5: say back in the print era, for newspapers, especially a

657
00:32:45,680 --> 00:32:48,600
Speaker 5: daily newspaper, what would happen is the reporters would be

658
00:32:48,640 --> 00:32:51,480
Speaker 5: out in the field, they would get information, they would

659
00:32:51,760 --> 00:32:54,840
Speaker 5: call it into the rewrite desk. Verbally, they didn't have

660
00:32:54,840 --> 00:32:57,080
Speaker 5: anything to write it with. It's just say you're the facts.

661
00:32:57,280 --> 00:32:59,760
Speaker 5: The rewriters would write it. And in the last twenty

662
00:32:59,840 --> 00:33:03,040
Speaker 5: years or thirty years, we who have been trained in

663
00:33:03,120 --> 00:33:05,280
Speaker 5: writing and we value it and we go to Columbia

664
00:33:05,320 --> 00:33:11,600
Speaker 5: Journalism School, we have conflated reporting and writing as journalism.

665
00:33:11,800 --> 00:33:14,400
Speaker 5: And I do think there are opportunities now where Okay,

666
00:33:14,400 --> 00:33:17,440
Speaker 5: the writing lift may may be lightened by this. And

667
00:33:18,000 --> 00:33:20,880
Speaker 5: I'm not saying say here's the article and just publish it.

668
00:33:21,160 --> 00:33:23,840
Speaker 5: I'm just saying, you know what, perplexity is pretty good

669
00:33:24,280 --> 00:33:29,280
Speaker 5: at writing a well informed, well sourced story in six seconds,

670
00:33:29,880 --> 00:33:33,040
Speaker 5: and maybe that actually accelerates what you're doing and then

671
00:33:33,080 --> 00:33:35,360
Speaker 5: maybe you can add your piece to the top or

672
00:33:35,360 --> 00:33:37,360
Speaker 5: what have you. So I do think their uses.

673
00:33:37,120 --> 00:33:39,760
Speaker 4: For it too, So I actually I agree with that argument.

674
00:33:39,840 --> 00:33:42,959
Speaker 4: I think like social skills and investigative skills are going

675
00:33:43,000 --> 00:33:45,800
Speaker 4: to become more important in the age of AI. However,

676
00:33:46,320 --> 00:33:48,320
Speaker 4: what you always hear is, well, you're gonna need You're

677
00:33:48,360 --> 00:33:50,560
Speaker 4: going to need to produce scoops. Right, You're gonna have

678
00:33:50,600 --> 00:33:53,400
Speaker 4: to find the stuff that the models don't already know about,

679
00:33:53,440 --> 00:33:56,080
Speaker 4: and that's going to be the valuable aspect of journalism.

680
00:33:56,560 --> 00:33:58,680
Speaker 4: But the problem seems to be that those scoops get

681
00:33:58,680 --> 00:34:02,840
Speaker 4: commodified so freaking quickly that I'm not sure that actually

682
00:34:02,920 --> 00:34:05,760
Speaker 4: generates much value for the news organization.

683
00:34:06,320 --> 00:34:10,480
Speaker 5: It is very hard to protect news. You can't. But

684
00:34:11,600 --> 00:34:15,719
Speaker 5: the organizations that produce it all the time are gonna

685
00:34:15,760 --> 00:34:18,400
Speaker 5: have a huge advantage because it's going to be worth

686
00:34:18,560 --> 00:34:21,360
Speaker 5: subscribing to them. And that's where That's where the New

687
00:34:21,440 --> 00:34:23,839
Speaker 5: York Times, the Welster Journal, and Bloomberg that's where it's

688
00:34:23,880 --> 00:34:27,120
Speaker 5: coming from. And Bloomberg's an interesting one because you really

689
00:34:27,239 --> 00:34:33,280
Speaker 5: serve a somewhat small but highly committed base where Bloomberg

690
00:34:33,360 --> 00:34:35,439
Speaker 5: reporters are breaking stuff all the time that allow people

691
00:34:35,480 --> 00:34:37,759
Speaker 5: to make or lose money. They're going to care and

692
00:34:37,800 --> 00:34:41,160
Speaker 5: they're gonna have that terminal and chackb is not doing that.

693
00:34:41,400 --> 00:34:43,520
Speaker 5: So part of what I hear you say is wait

694
00:34:43,560 --> 00:34:48,800
Speaker 5: a minute, it has to be differentiated. Yeah, it does. Actually,

695
00:34:48,840 --> 00:34:50,920
Speaker 5: there's a lot less value than there used to be

696
00:34:51,200 --> 00:34:55,120
Speaker 5: in somebody said something interesting, let's write it in an

697
00:34:55,160 --> 00:34:57,040
Speaker 5: intelligent way, do a little more work and get it

698
00:34:57,080 --> 00:35:00,440
Speaker 5: to somebody else. That is not as useful anymore for

699
00:35:00,480 --> 00:35:04,920
Speaker 5: the reason that you describe. But those companies, if they

700
00:35:04,920 --> 00:35:06,919
Speaker 5: want to compete with each other and they do chet,

701
00:35:06,960 --> 00:35:09,279
Speaker 5: GPT and Claude right now, they are going to have

702
00:35:09,320 --> 00:35:12,160
Speaker 5: to pay for that information. And that's where a Bloomberg

703
00:35:12,280 --> 00:35:15,160
Speaker 5: is in great position. And say another thing, it's not

704
00:35:15,239 --> 00:35:19,600
Speaker 5: just the big generalists. It is the niche providers who

705
00:35:19,600 --> 00:35:23,279
Speaker 5: are experts like you guys and do an amazing job

706
00:35:23,400 --> 00:35:26,359
Speaker 5: in a particular area that people really care about. Those

707
00:35:26,360 --> 00:35:28,080
Speaker 5: are gonna Those are going to survive. And I'll say

708
00:35:28,080 --> 00:35:30,960
Speaker 5: one more thing, which is Jeff Bezos was an investor

709
00:35:31,640 --> 00:35:33,759
Speaker 5: and business Cider is incredibly helpful to talk to him,

710
00:35:33,840 --> 00:35:36,680
Speaker 5: very smart. I realize now he's become kind of not

711
00:35:37,200 --> 00:35:39,880
Speaker 5: applauded in the journalims of industry. But one of the

712
00:35:39,960 --> 00:35:42,560
Speaker 5: things that he liked to say is everybody wants to

713
00:35:42,600 --> 00:35:45,879
Speaker 5: know what's changing and what it means. It's actually more

714
00:35:45,960 --> 00:35:48,719
Speaker 5: useful to step back and say what is going to

715
00:35:48,800 --> 00:35:51,880
Speaker 5: stay the same. What is going to stay the same

716
00:35:52,239 --> 00:35:55,720
Speaker 5: in media is that we are always wanted. We're always

717
00:35:55,719 --> 00:35:58,440
Speaker 5: going to want to know what's happening and what it means,

718
00:35:58,680 --> 00:36:01,239
Speaker 5: and we're always going to want to be entertained, and

719
00:36:01,320 --> 00:36:06,719
Speaker 5: that's what media does. It's tough, but until actually there

720
00:36:06,760 --> 00:36:09,320
Speaker 5: are no more humans, we are going to want media

721
00:36:09,400 --> 00:36:10,040
Speaker 5: to deliver that.

722
00:36:10,120 --> 00:36:10,799
Speaker 1: I agree with that.

723
00:36:11,040 --> 00:36:12,759
Speaker 3: You know, there's some really good stories by the way,

724
00:36:12,800 --> 00:36:15,239
Speaker 3: about the like the New York Post and daily news

725
00:36:15,280 --> 00:36:18,160
Speaker 3: like report like the crime reporters out in the fields,

726
00:36:18,200 --> 00:36:19,799
Speaker 3: and then they would call the rewrite desk and then

727
00:36:19,840 --> 00:36:22,440
Speaker 3: they would like translate it into like tabloid to speak

728
00:36:22,760 --> 00:36:24,719
Speaker 3: is a that's a cool, that'd be cool cool.

729
00:36:24,800 --> 00:36:27,840
Speaker 4: I remember sending notes on a BlackBerry in like the

730
00:36:27,880 --> 00:36:30,120
Speaker 4: early two thousands, and then someone in the newsroom would

731
00:36:30,280 --> 00:36:32,160
Speaker 4: translate it into readable words.

732
00:36:32,600 --> 00:36:34,760
Speaker 3: With a few minutes left, I want to talk about

733
00:36:34,920 --> 00:36:37,200
Speaker 3: you mentioned, Jeff Bezos. I want to talk about the

734
00:36:37,239 --> 00:36:41,520
Speaker 3: business environment right now. When President Trump won, there was

735
00:36:41,560 --> 00:36:44,200
Speaker 3: a lot of headlines about you know, these CEOs. They'd

736
00:36:44,200 --> 00:36:46,279
Speaker 3: be like, well, now we free speech, and now we

737
00:36:46,320 --> 00:36:48,840
Speaker 3: can speak our minds and you know, none of this

738
00:36:48,960 --> 00:36:51,000
Speaker 3: woke stuff where we have to like be careful about

739
00:36:51,000 --> 00:36:53,799
Speaker 3: what we speak. And like I get the impression that

740
00:36:54,280 --> 00:36:56,960
Speaker 3: it is literally the exact opposite, and that CEOs have

741
00:36:57,040 --> 00:37:00,160
Speaker 3: never been more scared, there never been more timid, and

742
00:37:00,200 --> 00:37:02,440
Speaker 3: they like are obsequious to him, and they like do

743
00:37:02,520 --> 00:37:04,080
Speaker 3: these big pilgrimages and stuff.

744
00:37:04,360 --> 00:37:06,240
Speaker 1: And meanwhile, we know they hate the tariffs.

745
00:37:06,280 --> 00:37:08,680
Speaker 3: We know they hate all the stuff going on. What

746
00:37:08,719 --> 00:37:11,319
Speaker 3: do you like make of that? Because he's not even

747
00:37:11,360 --> 00:37:13,520
Speaker 3: that popular anymore. He's actually, like, arguably like one of

748
00:37:13,560 --> 00:37:16,040
Speaker 3: the least popular presidents ever. And yet I don't get

749
00:37:16,080 --> 00:37:18,000
Speaker 3: the impression that there's been much of a change in

750
00:37:18,000 --> 00:37:21,200
Speaker 3: the sort of like world of CEOs and rich guys

751
00:37:21,200 --> 00:37:23,440
Speaker 3: and billionaires about speaking their minds.

752
00:37:23,800 --> 00:37:27,160
Speaker 5: Yes, the whole free speech thing was always I want

753
00:37:27,239 --> 00:37:29,680
Speaker 5: more speech than I believe in, so I can say

754
00:37:29,719 --> 00:37:32,080
Speaker 5: whatever I want. You can't say what you want, so

755
00:37:32,280 --> 00:37:36,920
Speaker 5: it was gonna ludicrous. I think that in general, the

756
00:37:36,960 --> 00:37:40,759
Speaker 5: big companies and CEOs, especially because President Trump won the

757
00:37:40,840 --> 00:37:45,680
Speaker 5: last election, clearly they said, Okay, I believe in democracy,

758
00:37:46,160 --> 00:37:49,200
Speaker 5: I'm going to be pragmatic and take care of my

759
00:37:49,320 --> 00:37:54,400
Speaker 5: team and my company. And also for most public company CEOs,

760
00:37:55,400 --> 00:37:58,759
Speaker 5: they are easily removed and they know that. And your

761
00:37:58,840 --> 00:38:01,359
Speaker 5: job is to actually take care of your company and shareholders.

762
00:38:01,400 --> 00:38:04,600
Speaker 5: It is not to be a free speech crusader and

763
00:38:04,719 --> 00:38:07,640
Speaker 5: stand up for your personal thing that you believe in.

764
00:38:08,080 --> 00:38:10,200
Speaker 5: And so I think there was a lot of pragmatism.

765
00:38:10,719 --> 00:38:13,360
Speaker 5: I think that may start to change here. So I

766
00:38:13,840 --> 00:38:16,360
Speaker 5: feel like things have happened in the last few months.

767
00:38:16,400 --> 00:38:19,080
Speaker 5: We may be starting to see a change, but I

768
00:38:19,080 --> 00:38:21,640
Speaker 5: think it is mainly pragmatism. And you can look at

769
00:38:21,680 --> 00:38:24,200
Speaker 5: that from the outside and so that's outrageous, but I

770
00:38:24,239 --> 00:38:26,359
Speaker 5: think they were looking out for their companies and their

771
00:38:26,400 --> 00:38:27,680
Speaker 5: teams and their shareholders.

772
00:38:28,000 --> 00:38:30,440
Speaker 4: I want to go back to media and naval raising

773
00:38:30,480 --> 00:38:33,200
Speaker 4: because I've got more questions, and I actually hesitate to

774
00:38:33,320 --> 00:38:35,759
Speaker 4: ask this one because I fear that your response is

775
00:38:35,800 --> 00:38:37,359
Speaker 4: going to get back to asking Joe and I to

776
00:38:37,440 --> 00:38:38,640
Speaker 4: justify our own salaries.

777
00:38:38,840 --> 00:38:40,600
Speaker 5: I ad, I'm telling you why you're not going to

778
00:38:40,600 --> 00:38:42,640
Speaker 5: be replaced by notebook LM.

779
00:38:42,840 --> 00:38:46,040
Speaker 4: Okay, I'm going to ask you to justify a particular

780
00:38:46,040 --> 00:38:50,000
Speaker 4: paycheck kind of Okay, when you sold Business Insider to

781
00:38:50,080 --> 00:38:50,880
Speaker 4: Axel Springer, what.

782
00:38:50,920 --> 00:38:53,600
Speaker 5: Year was that, twenty fifteen? Twenty fifteen?

783
00:38:53,640 --> 00:38:57,280
Speaker 4: Okay, if you were selling Business Insider to Axel Springer, now,

784
00:38:58,080 --> 00:39:01,600
Speaker 4: how different would your which to them be in twenty

785
00:39:01,640 --> 00:39:04,120
Speaker 4: twenty six versus twenty fifteen.

786
00:39:05,160 --> 00:39:09,120
Speaker 5: I would say that I think one of Business Desiders advantages,

787
00:39:09,320 --> 00:39:11,799
Speaker 5: and Joe was a big part of this was we

788
00:39:11,800 --> 00:39:16,520
Speaker 5: were not trying to defend the old way that journalism

789
00:39:16,600 --> 00:39:19,359
Speaker 5: was done in print and television. We were trying to

790
00:39:19,440 --> 00:39:23,840
Speaker 5: bring rigorous journalism to a new medium where people wanted

791
00:39:23,880 --> 00:39:28,160
Speaker 5: different things, where distribution was very different. So we were

792
00:39:28,600 --> 00:39:33,799
Speaker 5: experimenters and innovators, and for every idea that worked, we

793
00:39:33,880 --> 00:39:36,959
Speaker 5: had twenty five ideas that were dumb and didn't work.

794
00:39:37,040 --> 00:39:40,120
Speaker 5: I take responsibility for all of them. That was the

795
00:39:40,160 --> 00:39:42,560
Speaker 5: only way to figure out what worked. And one of

796
00:39:42,600 --> 00:39:45,480
Speaker 5: the things we noticed relatively early is hey, we can

797
00:39:45,480 --> 00:39:48,399
Speaker 5: figure out what people like and we want to serve them.

798
00:39:48,560 --> 00:39:50,160
Speaker 5: And we did a good job at that, and I

799
00:39:50,200 --> 00:39:52,080
Speaker 5: think that was the that was the thing that made

800
00:39:52,080 --> 00:39:56,280
Speaker 5: businesses that are successful. I think now for all companies,

801
00:39:56,440 --> 00:39:59,399
Speaker 5: stuffing your head in the sand and wishing AI would

802
00:39:59,400 --> 00:40:02,879
Speaker 5: go away is not a great strategy. So what would

803
00:40:02,960 --> 00:40:05,279
Speaker 5: be What can we do? And again, one of the

804
00:40:05,320 --> 00:40:08,440
Speaker 5: things that's changed as Google and Facebook and social have

805
00:40:09,719 --> 00:40:12,480
Speaker 5: totally dried up as sources of distribution is we are

806
00:40:12,560 --> 00:40:17,680
Speaker 5: back in this direct subscriber world, just like magazines and

807
00:40:17,719 --> 00:40:20,680
Speaker 5: newspapers way back in the world. So that is serving

808
00:40:20,880 --> 00:40:23,360
Speaker 5: your subscribers. This is what you, guys do. You have

809
00:40:23,440 --> 00:40:26,360
Speaker 5: this incredibly passionate audience. They know you, they want to

810
00:40:26,360 --> 00:40:28,600
Speaker 5: hear from you every week, and so that's what media

811
00:40:28,640 --> 00:40:30,279
Speaker 5: companies need. To do. So that's what I would say

812
00:40:30,480 --> 00:40:32,919
Speaker 5: is we are as focused as we have ever been

813
00:40:33,160 --> 00:40:37,600
Speaker 5: on serving our readers, viewers, people who love us.

814
00:40:38,440 --> 00:40:42,960
Speaker 3: So you are now in subscribe very subscriber oriented media.

815
00:40:43,080 --> 00:40:45,880
Speaker 1: You have a newsletter, you have a podcast, et cetera.

816
00:40:46,280 --> 00:40:49,879
Speaker 3: Other than realizing like, actually you know editors are good

817
00:40:49,960 --> 00:40:52,279
Speaker 3: and so forth, what else have you learned or what's

818
00:40:52,320 --> 00:40:56,880
Speaker 3: been notable about your own personal journey into subscriber based media.

819
00:40:56,920 --> 00:40:58,200
Speaker 1: What does surprise do well?

820
00:40:58,239 --> 00:41:00,279
Speaker 5: So I give you a I'll think So A lot

821
00:41:00,320 --> 00:41:02,440
Speaker 5: of what I've been doing the last year or two

822
00:41:02,719 --> 00:41:06,640
Speaker 5: is writing novels. This is something I wanted to do forever.

823
00:41:06,760 --> 00:41:09,520
Speaker 5: It's like, Okay, now's the time. And I talked to

824
00:41:09,560 --> 00:41:11,400
Speaker 5: another friend of mine who used to be a screenwriter,

825
00:41:11,480 --> 00:41:14,279
Speaker 5: and I confess that to him. He's like, dude, that

826
00:41:14,480 --> 00:41:19,920
Speaker 5: is crazy. Just go to Claude. Tell Claude what you want,

827
00:41:20,520 --> 00:41:22,839
Speaker 5: and Claude will spit out three hundred and twenty five

828
00:41:22,880 --> 00:41:26,600
Speaker 5: pages and it'll be pretty good. And I don't I

829
00:41:26,680 --> 00:41:29,200
Speaker 5: believe it and it'll take twenty minutes. And so I

830
00:41:29,239 --> 00:41:31,960
Speaker 5: feel like the world's biggest idiot. From a business perspective.

831
00:41:32,360 --> 00:41:34,839
Speaker 5: On the other hand, I did want to do it.

832
00:41:35,280 --> 00:41:38,359
Speaker 5: I'm glad I did it. It's really funny. Tell us

833
00:41:38,400 --> 00:41:40,879
Speaker 5: what's coming out next on It's called The Upgrade. It's

834
00:41:40,920 --> 00:41:43,440
Speaker 5: about a tech billionaire who wants to use AI to

835
00:41:43,480 --> 00:41:44,320
Speaker 5: take over the world.

836
00:41:44,400 --> 00:41:45,560
Speaker 4: That doesn't sound like fiction.

837
00:41:45,680 --> 00:41:48,080
Speaker 5: It's nonfiction, but it'll be out. It's going to be

838
00:41:48,080 --> 00:41:51,400
Speaker 5: out in beta. It's a new form of books a

839
00:41:51,440 --> 00:41:53,640
Speaker 5: screen test. Yes, it's been in alpha for a while.

840
00:41:53,640 --> 00:41:56,279
Speaker 5: A lot of people have read it's different. No, it's

841
00:41:56,320 --> 00:41:58,120
Speaker 5: a book book, but there's gonna be a period. But

842
00:41:58,440 --> 00:42:00,560
Speaker 5: I invite you to read it. Thank you if you

843
00:42:00,600 --> 00:42:03,000
Speaker 5: would send me what you think. And then when I

844
00:42:03,080 --> 00:42:06,080
Speaker 5: publish it for real, it may will have benefited from

845
00:42:06,120 --> 00:42:09,280
Speaker 5: your reading anyway. So I feel like a complete idiot

846
00:42:09,360 --> 00:42:11,600
Speaker 5: for doing it. But what I will say is for

847
00:42:11,719 --> 00:42:16,440
Speaker 5: me as an analyst and writer and speaker, part of

848
00:42:16,440 --> 00:42:19,400
Speaker 5: the joy is the process. Yea, and I learned so

849
00:42:19,560 --> 00:42:22,480
Speaker 5: much and this is what I don't understand about how

850
00:42:22,520 --> 00:42:24,279
Speaker 5: research is going to be conducted. One of the things

851
00:42:24,320 --> 00:42:26,719
Speaker 5: that I was, Hey, write me a research report about

852
00:42:26,960 --> 00:42:29,520
Speaker 5: X six minutes later it comes back. Is better than

853
00:42:29,520 --> 00:42:31,360
Speaker 5: a research report that I would have written as a

854
00:42:31,880 --> 00:42:34,000
Speaker 5: young analyst when I didn't know anything, and it took

855
00:42:34,040 --> 00:42:36,920
Speaker 5: six minutes. So I said, okay, I'm not going to

856
00:42:36,960 --> 00:42:40,480
Speaker 5: write any research reports anymore. But when it took me

857
00:42:40,680 --> 00:42:43,279
Speaker 5: a month to do that as a twenty five year old,

858
00:42:43,800 --> 00:42:46,960
Speaker 5: I learned a lot through that process. And so now

859
00:42:47,000 --> 00:42:49,320
Speaker 5: I don't know how you learn whether you be reading

860
00:42:49,360 --> 00:42:51,160
Speaker 5: the report like I don't. That's the thing I'm really

861
00:42:51,160 --> 00:42:53,560
Speaker 5: struggling with. But I will say so for me, the

862
00:42:53,640 --> 00:42:56,359
Speaker 5: process has been interesting. I hope you like the book.

863
00:42:56,400 --> 00:42:57,960
Speaker 5: If you like it, maybe I'll do another one. That

864
00:42:57,960 --> 00:43:00,880
Speaker 5: would be great. But what I would say is on

865
00:43:00,960 --> 00:43:03,359
Speaker 5: this whole thing, for what are humans going to do?

866
00:43:04,200 --> 00:43:08,240
Speaker 5: You're a chess player. It has been forty years since

867
00:43:08,280 --> 00:43:12,880
Speaker 5: our phones could clabor us in chess. Chess is bigger

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00:43:12,920 --> 00:43:16,520
Speaker 5: than it is ever true in person. So my hope

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00:43:16,600 --> 00:43:20,440
Speaker 5: is we have ai research and write stuff that we

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00:43:20,480 --> 00:43:23,400
Speaker 5: don't really want to do, and we save our writing

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00:43:23,600 --> 00:43:26,840
Speaker 5: and orating and everything else for the stuff that we

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00:43:26,880 --> 00:43:27,880
Speaker 5: are really passionate about.

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00:43:28,400 --> 00:43:29,160
Speaker 1: Henry blogIn.

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00:43:29,360 --> 00:43:31,480
Speaker 3: Thank you so much and thanks for joining us here

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00:43:31,480 --> 00:43:32,680
Speaker 3: at all on air Fast.

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00:43:32,719 --> 00:43:33,439
Speaker 1: There's a lot of fun.

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00:43:33,760 --> 00:43:50,040
Speaker 4: Thank you so much, Henny. This has been another episode

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00:43:50,080 --> 00:43:52,600
Speaker 4: of the au Thoughts podcast. I'm Tracy Alloway. You can

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00:43:52,640 --> 00:43:54,880
Speaker 4: follow me at Tracy Alloway.

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00:43:54,560 --> 00:43:57,160
Speaker 3: And I'm Joe Wisenthal. You can follow me at The Stalwart.

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882
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Speaker 3: Bennett at Dashbot, and kill Brooks at Kalebrooks. And for

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Speaker 3: more odd Laws content, go to Bloomberg dot com slash Oddlogs.

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Speaker 4: Loots And if you enjoy odd Lots then please leave

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Speaker 4: us a positive review on your favorite podcast platform. And remember,

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