Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News.
Speaker 2: Hello and welcome to another episode of the Ad Thoughts Podcast.
Speaker 2: I'm Tracy Alloway.
Speaker 3: And I'm Joe Wisenthal.
Speaker 2: So, Joe, we continue to publish some of the conversations
Speaker 2: from our recent live show in New York, and I
Speaker 2: hope people didn't mind. They didn't seem to, they seem
Speaker 2: to enjoy it. But we did do a little bit
Speaker 2: of media industry naval gazing.
Speaker 3: Totally, so as we've been talking about it. If you
Speaker 3: listened to past episodes from the show, you know, the
Speaker 3: theme obviously had this big like sort of future of markets,
Speaker 3: future of trading theme. But you know, I would say,
Speaker 3: and maybe this is like I'm very biased here, but
Speaker 3: I would say that information dissemination collection And I guess
Speaker 3: you say, journalism is an important part of markets, right,
Speaker 3: how people get informed?
Speaker 2: Say journalism? Like that journalism?
Speaker 3: No, But you know, like the thing like in the
Speaker 3: way if you think about like the floor and the
Speaker 3: faun of the market ecology, then the people who like
Speaker 3: report the news, digest the news, explain the news, highlight
Speaker 3: what news is relevant, what is not relevant are important
Speaker 3: actors in that system and in a period of so
Speaker 3: much change and uncertainty. The question of like A, how
Speaker 3: you decide what's important and b how you convey that
Speaker 3: to an audience. These are really tough questions.
Speaker 2: Yeah, so, I think there's two major challenges here. One
Speaker 2: of them is what you just said, which is like,
Speaker 2: how do you explain these huge technological shifts to people
Speaker 2: who may not you know, they might be outside of
Speaker 2: the tech industry. How do you explain like an incremental
Speaker 2: improvement in a model to the average lay person. And
Speaker 2: then secondly the other big challenges. The media itself is
Speaker 2: caught in the crosshairs of the debate over whether AI
Speaker 2: is going to take all the jobs.
Speaker 4: Right.
Speaker 3: That's it, right, So any one of us who's in
Speaker 3: the business of trying to consume, figure out what's important
Speaker 3: and relay it is also thinking about well AI do
Speaker 3: a better job than we do, and precisely that included
Speaker 3: writing newsletters, are producing podcasts.
Speaker 5: That's right.
Speaker 2: So we had a trio of perfect guests for this
Speaker 2: particular discussion. All of them have been on the show before.
Speaker 2: We had James van Gelan. He is, of course the
Speaker 2: founder of Satrini Research and the author of the viral
Speaker 2: AI Jobs Doom scenario as well as Jasmine's son. She
Speaker 2: writes a substack that's all about AI and the culture
Speaker 2: of Silicon Valley and tech as well as samro He
Speaker 2: is the author of one of my favorite newsletters with
Speaker 2: the best name, the t kre So take a listen.
Speaker 3: Where to even begin?
Speaker 5: I'll start with.
Speaker 2: James because he's next to me and I just want
Speaker 2: to know what his life is like. Right now, do
Speaker 2: people like recognize you on the street and scream at
Speaker 2: you about AI job losses?
Speaker 6: I've done two credible death threats, but not on the street. Okay,
Speaker 6: I guess yeah, now yeah, I mean you know better
Speaker 6: when it's no person right, But now I've I luckily,
Speaker 6: I have been very much an internet personality and not someone
Speaker 6: that does a lot of taped interviews.
Speaker 7: Like this.
Speaker 3: Percent of you. No, But in case if you randomly don't,
Speaker 3: I assume you do. When was it that February or
Speaker 3: something you wrote a post about a theoretical possibility of
Speaker 3: you know, we're all going to lose our jobs but anyway,
Speaker 3: so I could see why people were upset about that. Jasmine,
Speaker 3: you write about AI and you sort of like try
Speaker 3: to when I read you're writing it's like bridging a
Speaker 3: gap right to some extent, because there are people, most
Speaker 3: of them in San Francisco, often on a lot of
Speaker 3: drugs or maybe they're on an occult or something like that,
Speaker 3: and there's like these are like the people who are
Speaker 3: like going to influence the rest of our lives. How
Speaker 3: do you think about the question of, like what is
Speaker 3: actually important to communicate to a broader public.
Speaker 7: Yeah, I mean it's really interesting because I think the
Speaker 7: vast majority of aimedia coming out of Silicon Valley is
Speaker 7: buy AI people for other AI people, and it serves
Speaker 7: that purpose really well, right, Like you got really in
Speaker 7: depth podcasts, you got the Door Hush podcast, you got
Speaker 7: this whole ecosystem of sub sex. One thing that I
Speaker 7: really noticed in that I think most people who listen
Speaker 7: to the comms coming out of the industry leaders will
Speaker 7: notice is they're saying all these crazy things without like
Speaker 7: four second thinking that like any normal person might hear
Speaker 7: them say it, and they're like, oh, man, why don't
Speaker 7: these people like AI very much? Just like I don't know,
Speaker 7: You've been telling them you're going to take all their
Speaker 7: jobs and kill everybody for the last several years, and
Speaker 7: like you're on the record saying this stuff, and so
Speaker 7: I think it's really interesting where it's not like crypto
Speaker 7: where only a small fraction of sort of the broad
Speaker 7: public ever deeply engaged with it.
Speaker 5: AI is something that, whether people want to or not,
Speaker 5: is impacting their lives.
Speaker 7: It's on their social feeds, their kids are using it,
Speaker 7: it's in their workplace, and so people have all these
Speaker 7: questions about the intersection of AI and politics, AI and affordability,
Speaker 7: AI and parenting and education, and the majority of I
Speaker 7: think AI media historically has not really focused on that.
Speaker 7: It's been more of the business and technology community. And
Speaker 7: so I'm sort of trying to say, like, okay, but
Speaker 7: what about the.
Speaker 1: Rest of us?
Speaker 2: Sam, you write a newsletter and the tag line is basically,
Speaker 2: you know, stock markets usually go up over time. You've
Speaker 2: you've been very right for the past couple of years
Speaker 2: because they've certainly gone up, But like, are things starting
Speaker 2: to get uncomfortable for you when people are talking about
Speaker 2: AI bubbles and overvaluations?
Speaker 5: Oh?
Speaker 4: Yeah, things are always uncomfortable for me. I think that's
Speaker 4: sort of like one of the most important things about
Speaker 4: understanding this idea of stocks UJ go up is the
Speaker 4: key word there is usually right, Like every way you
Speaker 4: cut the data historically, even good times and bad times
Speaker 4: and bull markets and bear markets, you always have like
Speaker 4: these periods of volatility where you know, stocks do go down,
Speaker 4: like that's the that's the catch share with stocks uj
Speaker 4: go up is that they go down a lot often. So, yeah,
Speaker 4: of course I'm nervous, especially when you're at at peaks.
Speaker 4: I mean, of course, the data will also tell you
Speaker 4: that the you know, twelve month returns after all time
Speaker 4: highsac tend to be higher than when you're at low's.
Speaker 4: But yeah, of course I'm nervous. I mean, I think
Speaker 4: that's like very healthy for anybody in the investor class.
Speaker 4: Is even no matter how optimistic you might be over
Speaker 4: the near term or long term, you have to be
Speaker 4: prepared for those big drawdowns because they do happen, and
Speaker 4: they happen.
Speaker 3: Frequently, Jasmine. You know, so James wrote his piece about
Speaker 3: you know, the potential at least and it wasn't a forecast,
Speaker 3: it was a prediction, but a scenario.
Speaker 6: We've also written others right, right, but no one.
Speaker 3: Likes the idea of like mass job laws. But I've
Speaker 3: been reading some other stuff and there's a lot of
Speaker 3: people who don't talk about AI job laws. They talk
Speaker 3: about AI will literally kill every single person in the
Speaker 3: world if the labs don't do a good job of
Speaker 3: aligning the models. How seriously do you think the public
Speaker 3: should take that specific element that if the models are misdesigned,
Speaker 3: they will destroy humanity as we know it.
Speaker 7: Ah Man contents, Oh god, they call this a pe
Speaker 7: doom out in San Francisco. I won't give you a probability,
Speaker 7: I suppose, but without getting to the question of like,
Speaker 7: will literally every single person die? I do think there
Speaker 7: is a very good reason to be concerned about misalignment
Speaker 7: and ROGUAEI, because when you think about it, there's sometimes
Speaker 7: people say that safety and the safety of a model
Speaker 7: is maybe intention with these goals like acceleration, and is
Speaker 7: the model really useful?
Speaker 5: And I think the thing that's.
Speaker 7: Really interesting is in the history of AI research is
Speaker 7: often the people who make the biggest technical advances and
Speaker 7: these foundational models who end up the misconcerned about misalignment.
Speaker 5: And that's because actually the product functionality.
Speaker 7: Of the models, their utility is only as good as
Speaker 7: how controllable they are and how much.
Speaker 5: They do what you expect.
Speaker 7: So when you say, hey, like AI agent like go
Speaker 7: off and like make me a million dollars or whatever,
Speaker 7: like whether that model goes and does so in a
Speaker 7: law abiding and safe and non violent way. The safety
Speaker 7: of the model, the alignment of the model is very
Speaker 7: correlated with whether it is economically useful as well.
Speaker 5: So I think that it's.
Speaker 7: Important that we not hold them in total tension. And
Speaker 7: I think there are really good reasons to be concerned
Speaker 7: with whether these agents are doing the things that we
Speaker 7: expect them to do.
Speaker 2: Just going back to AI job losses, and I guess
Speaker 2: any of you can answer this question, and please like
Speaker 2: feel free to chime in on every question that we ask.
Speaker 2: But you know, you also hear a lot of the
Speaker 2: big tech CEOs talk about being genuinely concerned about the
Speaker 2: future of society once AI is developed and starts like
Speaker 2: taking people's jobs. Some people interpret that as AI CEO
Speaker 2: is basically talking their own book and being you know,
Speaker 2: like hyping up the capabilities of AI. And then you
Speaker 2: have some people who argue that like maybe they're genuinely concerned,
Speaker 2: not least because they're going to get like molotov cocktails
Speaker 2: thrown at their houses and things like that.
Speaker 5: Where do you fall in that spectrum.
Speaker 2: Is this like a real concern when you talk to
Speaker 2: senior people in Silicon Valley?
Speaker 6: I do think that most of the people that I've
Speaker 6: spoken to who are worried about it are genuinely worried
Speaker 6: about it. Something that I'd add to it is pretty
Speaker 6: much unanimously throughout history, when we've had any sort of
Speaker 6: technological leap forward, it's been a positive thing. And I
Speaker 6: think that AI will mirror that. It's just the question is,
Speaker 6: we've never had a technological kind of advancement this quickly.
Speaker 6: We you know, we go from having the Industrial Revolution
Speaker 6: and the mechanization of agriculture and ninety five percent of
Speaker 6: people used to work in agriculture, and then now I
Speaker 6: think it's five percent too. And I'm very happy to
Speaker 6: work in an air conditioned office rather than in a field.
Speaker 6: A little bit. Yeah, so maybe not. But the thing
Speaker 6: is that will happen. I think believe very strongly it
Speaker 6: will happen with AI. It's just what happens in the interim.
Speaker 6: What happens if the models get so good and and
Speaker 6: can and people kind of adopt them with the same
Speaker 6: approach and use them correct over five years instead of
Speaker 6: one hundred. So I think speaking to some of the
Speaker 6: senior people, they would echo the fact that over the
Speaker 6: next thirty years, AI is going to be an immense
Speaker 6: force for good and going to make people's lives more comfortable.
Speaker 6: They're just worried about the pace of the trend.
Speaker 4: Yeah, I think the transition is like super important because
Speaker 4: as much as everyone likes this idea, like even on
Speaker 4: the corporate side or the shareholder side, like the promise
Speaker 4: of all this productivity that's unlocked by AI, and you know,
Speaker 4: maybe some people are thinking quietly in terms of like, oh,
Speaker 4: this is going to replace all of our workforce. But
Speaker 4: you know, the economy sort of stops working when no
Speaker 4: one has jobs and no has income. Right, It's like,
Speaker 4: it's great that you have this huge profit margin, but
Speaker 4: you have no revenue because everyone's unemployed and they have
Speaker 4: any money. And you know, those people aren't shopping, those
Speaker 4: people aren't buying you know, washing machines and the washing
Speaker 4: machine manufacturer kit, you know, by you know, mental parts.
Speaker 4: And suddenly we're digging up less copper, and the whole
Speaker 4: economy shrinks because no one has jobs. Then the whole
Speaker 4: point of this exercise becomes sort of meaningless, So which
Speaker 4: is why I think you're increasingly hearing from folks on
Speaker 4: this side, you know, talking about things like you know,
Speaker 4: basic income and guarantee jobs and all these things. So
Speaker 4: it's I think that's where you know, the conversation sort
Speaker 4: of goes, is, all right, let's make this assumption that
Speaker 4: we have all this productivity because of job loss. Well,
Speaker 4: that doesn't work.
Speaker 5: You know.
Speaker 4: We people need to have money to go out and
Speaker 4: spend and buy these products that are being produced so efficiently.
Speaker 7: And I mean it's worth noting that, like for example,
Speaker 7: like people politicians will point to say, retraining is the default.
Speaker 7: We have never had a successful large scale reskilling program
Speaker 7: in the history of the United States. During say, you know,
Speaker 7: the de industrialization. You lost all these factory jobs. It
Speaker 7: wasn't that many jobs, and we created way more net jobs,
Speaker 7: say in Silicon Valley, but the steel workers did not
Speaker 7: learn to code, right, And so people are right to
Speaker 7: be concerned, both morally and about the political backlash that
Speaker 7: could occur even with relatively small numbers of net job loss.
Speaker 3: Sam like, I love your newsletter, I read it and
Speaker 3: paid subscribe. Where all that I think I could train
Speaker 3: a model to write a newsletter that say stocks go up? Yeah,
Speaker 3: I think I could, like I believe or not?
Speaker 4: My uh my, my sister. My sister recently got into
Speaker 4: claude coding and she did this project with herself and
Speaker 4: and thought that, you know, I find this really interesting.
Speaker 4: And basically she created this app that was like a
Speaker 4: sam robot that was informed by you know, stuff that
Speaker 4: I've published and like by by bilin all this free
Speaker 4: stuff that's out there, and it's like, I was kind
Speaker 4: of offended first of all, but yeah, it's a problem.
Speaker 3: Do you worry about it? Is it absolute proprietor of
Speaker 3: a yeah, the company, you know, it's one.
Speaker 4: It's like there was this time where you could go
Speaker 4: out and say, well, you know, something that you can't
Speaker 4: really replace is you know, the individual's voice, the personality
Speaker 4: or whatever. And it's like I, you know, I run
Speaker 4: some of this stuff too, and run these queries and
Speaker 4: say like, well, how would Samua write this? And it's
Speaker 4: like it sounds like me? And sometimes it actually you know,
Speaker 4: uses the language and comes up with the words faster
Speaker 4: than how I would. So it's like, you know, what
Speaker 4: I do has to be about something more than just
Speaker 4: having the samual voice right, so to answer the question
Speaker 4: like the problem and the challenge and I you know,
Speaker 4: James and Jasmine and we talked about this a little
Speaker 4: bit in the back is like, you know, where do
Speaker 4: you add that value? And it's like it's it can't
Speaker 4: be something that's like just sort of replicable or or
Speaker 4: something that you know, someone who might be interested in
Speaker 4: what you're reading or writing about can put into a
Speaker 4: query and get back like, you know, the toughest part
Speaker 4: about my job, and I think a lot of our
Speaker 4: jobs is being able to come up with those ideas
Speaker 4: and those angles that no one's asking for. Yeah, and
Speaker 4: you know, again like I write about the stock market
Speaker 4: usually going up, which, by the way, even before AI,
Speaker 4: it's kind of a ridiculous thing to be writing about
Speaker 4: because everyone who has money in their fore one ks
Speaker 4: and iras, I've already been told this. They already know this.
Speaker 4: So it's like why do I exist? And you know,
Speaker 4: it's a relatively small market of people who are reading
Speaker 4: what I'm writing about, But yeah, I guess there's something
Speaker 4: about And not to toot my own hornets, this is
Speaker 4: reader feedback that I'm getting, but I've been told that
Speaker 4: there's something about whether it's the cadence or the timing
Speaker 4: of things I do write about, or the way I
Speaker 4: frame it that feels a little bit more human than
Speaker 4: the research or the analysis that you know, the chief
Speaker 4: investment strategists or whatever is sending out to their financial
Speaker 4: advisor who's regurgitating it back to them.
Speaker 2: James, one of the things you've been doing that I
Speaker 2: don't think a bot can replicate just yet is sending
Speaker 2: actual human beings or a human being to the strait
Speaker 2: of hormone. Is that like the edge in media? Is
Speaker 2: it like first person experiments and data gathering?
Speaker 6: Yeah, I mean you just have to look at this
Speaker 6: from I'm pretty confident that investment research won't exist in
Speaker 6: the same way that it does now. The Internet kind
Speaker 6: of made it democratized, so to speak. The distribution. It
Speaker 6: was like the only the banks had distribution. Now we
Speaker 6: have distribution and we can utilize that. But if we're
Speaker 6: just utilizing it to kind of do the same thing
Speaker 6: that the banks are doing, well, there is so much
Speaker 6: investment research that these models have been trained on, and
Speaker 6: as they get better, they will pretty much be able
Speaker 6: to do the same thing. So it becomes very much
Speaker 6: about doing something different and I think that there's like
Speaker 6: two ways you can do that. One you can be
Speaker 6: right all the time, so hopefully you know fingers crossed well,
Speaker 6: you know, I think that that one aspect is like
Speaker 6: like when I came on Ale's was the only media
Speaker 6: that I did after that piece, which I was very
Speaker 6: happy to thank you. I said, one of your questions
Speaker 6: show was like, why do you think this so viral?
Speaker 6: And I think it's like, this is not like tooting
Speaker 6: my own horn. It's just sometimes you go on a
Speaker 6: hot streak. And when we started that was very much
Speaker 6: in like a year of just like one hundred percent
Speaker 6: correct calls, and you know, obviously I've had a bunch
Speaker 6: of wrong ones since then. But it was like a
Speaker 6: bunch of people were reading something and they would read
Speaker 6: it and they would say, well, if I hadn't read
Speaker 6: this piece, I wouldn't have bought in video in twenty
Speaker 6: twenty three, or wouldn't have bought Eskehinich something. So I
Speaker 6: generated value from it. And then if you can add
Speaker 6: into that something that the model can't do with a
Speaker 6: query they can't be out in the real world, then
Speaker 6: it's going on in the real world. If there's if
Speaker 6: you know in five years, the robots come and then
Speaker 6: the models can go out in the real world. We'll
Speaker 6: have to find something else that they can't do.
Speaker 3: Judgment, How do you think about this question of like
Speaker 3: what the human writer can still bring to the table.
Speaker 3: The one time you were on Autolaus before was nothing
Speaker 3: about you were talking about Chinese peptize.
Speaker 2: But you did go to a peptid rate now, yeah.
Speaker 3: So like that is sort of your equivalent of sending
Speaker 3: an analyst to the Strait of Homers.
Speaker 7: We were talking about this, I would be the San
Speaker 7: Francisco spy that Jay sets up. I guess I'm I've
Speaker 7: doxed myself already, but I can get away.
Speaker 3: But is that like, do you feel like that's a
Speaker 3: big like yes, margat that like being in it in
Speaker 3: some way physically in it is that it is like
Speaker 3: what you can do at least currently that the AI can.
Speaker 7: Basically, I mean, I'm very bullish on secrets and I'm
Speaker 7: very bullish on gossip, right and and.
Speaker 5: I mean that's kind of what journalism is in the end.
Speaker 7: It's you have what is not in the trading data,
Speaker 7: Like data is like one of the most valuable things.
Speaker 7: It's like chips, data algorithms, right, and the data's out
Speaker 7: there in the world, and they've scraped the entire Internet.
Speaker 5: They've torn the covers off all these books.
Speaker 7: Their markre is like paying hundreds of dollars for these
Speaker 7: experts to like feed all their knowledge into the models.
Speaker 5: But the thing is, the world's constantly changing. It's very dynamic.
Speaker 7: They're parts of the world, parties u straits that are
Speaker 7: unexplored by humans. And if you go and you are
Speaker 7: the first person to see that, or you're the first
Speaker 7: person to be there at a critical moment in time
Speaker 7: as news is happening, as news is breaking, that kind
Speaker 7: of actual reporting is like more valuable than ever. One
Speaker 7: way that I think about reporting is that's you have
Speaker 7: this private knowledge that's maybe known in whisper networks, in
Speaker 7: the you know, in some party scene or whatever, or
Speaker 7: you have tacit knowledge that nobody's really written down yet.
Speaker 7: And as a reporter, my job is to take the taseit,
Speaker 7: or to take the private and to turn it into
Speaker 7: public knowledge. Like that particular task I think is extremely
Speaker 7: robust in.
Speaker 6: The era of AI.
Speaker 2: Okay, so speaking of secrets and gossip and going out
Speaker 2: into the real world, you recently came back from a
Speaker 2: trip to China, right where you were sort of comparing
Speaker 2: I guess how China is thinking and undertaking AI versus
Speaker 2: the US. What's your big takeaway?
Speaker 7: Oh yeah, I mean China is one of those places
Speaker 7: where it's hard to get a sense of without going
Speaker 7: in person, because it's so restructed.
Speaker 5: What's on the public Internet, and we have these off internets.
Speaker 7: One interesting thing is the way that I think about
Speaker 7: USAI research is it sort of had three eras of
Speaker 7: American AI. You had the academic era, maybe you'd call
Speaker 7: the boom kicking off with image net or alphag. You
Speaker 7: had the commercial era, let's call it kicked off with CHATGBT,
Speaker 7: you know, the maybe geopolitical era that's sort of Pentagon
Speaker 7: in Mythos drama. And China is weirdly still in the
Speaker 7: academic era. More so, it's very collaborative, very pro open source.
Speaker 7: The labs and the researchers themselves seem unconcerned pretty much
Speaker 7: with the sort of.
Speaker 5: Big philosophical and geopolitical questions, and that's there's sort of.
Speaker 7: A division of labor where because the party and the
Speaker 7: government is so active in shaping exactly what AI's role
Speaker 7: in society ought to be. Then the companies themselves have
Speaker 7: sort of abdicated that responsibility, and they're also much more
Speaker 7: focused on collaborating because they'd see collaboration as the only
Speaker 7: way to sort of maybe have a chance against the
Speaker 7: US front here, just.
Speaker 3: To follow up on this a little bit, because so
Speaker 3: much of the American AI conversation is suffused with both
Speaker 3: the job loss question and then the even more sinister
Speaker 3: question of like will the models turn against us? Is
Speaker 3: that to mention there as well, that's sort of the
Speaker 3: safety alignment part is that a big part of the
Speaker 3: Chinese AI culture.
Speaker 7: Not a lot, And I mean we ask researchers that
Speaker 7: a lot of the top Chinese labs how much they
Speaker 7: thought about safety, and it was clear that it was
Speaker 7: less of a propriety. One is that they're compute constrained, right, Like,
Speaker 7: so one open AYE researcher is allocated more compute than
Speaker 7: like an entire Chinese lab, oftentimes.
Speaker 5: Because of the chip controls.
Speaker 7: And so when you're that constrained, you're probably not going
Speaker 7: to devote as many as much compute to safety and alignment.
Speaker 5: So that's one thing.
Speaker 7: The other thing I noticed is just that China has
Speaker 7: a little bit of this more I almost like I
Speaker 7: call it like it's not techno optimism, which sometimes people confuse.
Speaker 7: It's more like technodeterminism or this pragmatic approach, which is
Speaker 7: that technology has always progressed, automation like mechanization, like the
Speaker 7: nutural revolution, like technology has always progressed.
Speaker 5: It's overall made life better. There's no way to stop it.
Speaker 7: There's not really a culture of protests and resistance in
Speaker 7: China because of the political environment, and so as an
Speaker 7: individual or as a company, there's no point in being like, oh,
Speaker 7: we don't want this, We're going to regulate it, We're going.
Speaker 5: To refuse it.
Speaker 7: Like it's like you adopt or else you are going
Speaker 7: to fall off, You're going to get left behind.
Speaker 5: And so every individual is much more concerned with how can.
Speaker 7: I adopt like open claw or whatever it is, as
Speaker 7: as I can so that I get this job, because
Speaker 7: if I don't do it, there's a million people behind
Speaker 7: me who are going to take it instead.
Speaker 2: James, I know you're approaching the US versus China AI question.
Speaker 2: I guess both from an investment perspective and also from
Speaker 2: a social economic perspective. Per your doom scenario, not Jesus
Speaker 2: doom scenario, but like, where do you stand on this
Speaker 2: particular question.
Speaker 6: I think that it's very important that we continue to
Speaker 6: sell chips to China. I think that if you were
Speaker 6: to cut China completely off from chips or crack down
Speaker 6: on some of the smuggling, it would It's there's like
Speaker 6: a sun Zoo thing about you, like you build your
Speaker 6: enemy at Golden Bridge upon which to retreat. I think
Speaker 6: it's very similar here where we don't really want to
Speaker 6: encourage even more capacity for China to take the reins
Speaker 6: in Ai. And if we can control certain aspects of
Speaker 6: the infrastructure stack, then the US absolutely should. I think
Speaker 6: that the next thing that we'll see, and this is
Speaker 6: like a really I think, kind of spicy take if
Speaker 6: you were to look at the price action of the
Speaker 6: memory complex, I think that we will see within the
Speaker 6: next like Chinese deep seek moment will not be about
Speaker 6: a model, It will be about hardware. That's so, is it?
Speaker 5: Oh?
Speaker 1: Really?
Speaker 6: I thought Wellex that I think you know, everyone's kind
Speaker 6: of piling into uh, the bottleneck trade so to speak,
Speaker 6: and and uh, I think there's kind of a new
Speaker 6: vintage of investors that are doing this bottleneck investing without
Speaker 6: the awareness that bottlenecks are made to be widened. And
Speaker 6: I think that what's going to happen if we continue
Speaker 6: to see this kind of meteoric rise in de ram prices, uh,
Speaker 6: is that just like every other technological bottleneck, there will
Speaker 6: be a bunch of nerds in their basement that are
Speaker 6: very very incentivized to fix this. And there's there are
Speaker 6: many ways that you can do that. One is, you know,
Speaker 6: you match flash bandwidth d RAM. It's one hundred times cheaper,
Speaker 6: like like it's so And I think that with China
Speaker 6: CXMT is there like a Micron skha, you know, And
Speaker 6: with the IPO happening this year, there will be a
Speaker 6: lot of capital that will I.
Speaker 3: Wondering about this because it's like the models are so
Speaker 3: like why even store any photos or images or whatever
Speaker 3: in a big bank of memory data? The models could
Speaker 3: just recreate it on command. And so I've been wondering
Speaker 3: if like in the end, like storage is not going
Speaker 3: to be that big of a deal because the model
Speaker 3: just like, yeah, let's see that photo of like, you know,
Speaker 3: me and my son's birthday, and the model will just
Speaker 3: create it and why did I need to save it
Speaker 3: to my iPhone?
Speaker 6: I think that the reason why it's it's interesting because
Speaker 6: if you look at like memories like a key component
Speaker 6: of the GPU. Rightah, so but then why didn't you
Speaker 6: know in video was rallying for a year and a
Speaker 6: half before memory started rallying, right, So why did that happen? Well,
Speaker 6: it's because of the event of agentic AI and having
Speaker 6: to remember Sam.
Speaker 3: I have a question for you, Like, for the theme
Speaker 3: of your substack, generally speaking, stocks go up? Is the
Speaker 3: sub theme? Just ignore the everyone else on the stage
Speaker 3: because you're just gonna get distracted and you're gonna get
Speaker 3: freaked out and whatever. And then allimately ignore all the
Speaker 3: odd lacked episodes, ignore all the news, ignore the doomers,
Speaker 3: because in the end, the only thing that can happen
Speaker 3: if you paint to the news is you do something stupid.
Speaker 3: Then you missed the long run.
Speaker 4: No, I'm actually the complete opposite of that, okay. And
Speaker 4: you know, like I said before, like you know, I'm
Speaker 4: always worried, okay, And this is like start of the
Speaker 4: message I try to, you know, communicate out to the world.
Speaker 4: Like you know, I do a lot of things and
Speaker 4: communicate a lot of things that are kind of counterintuitive, Like,
Speaker 4: you know, I do check my prow in K plan
Speaker 4: every single day, you know, I do. You know, Instead,
Speaker 4: you know a lot of advisors and professionals and stuff
Speaker 4: will go on TV and say, you know, forget your
Speaker 4: you know, for and K password or ignore politics or
Speaker 4: ignore what's go on, and I run. You know, we
Speaker 4: have a long history of getting past all this stuff.
Speaker 4: I think that's all incredibly silly. I think you really
Speaker 4: do have to think about how bad things are at
Speaker 4: a given time. That way, you sort of build up
Speaker 4: those memories when you know, ten years from now, when
Speaker 4: there is another war, you do remember how bad things
Speaker 4: were in the past and how you know, markets evolved
Speaker 4: out of all that stuff. So yeah, like I think
Speaker 4: it's you know, to ignore things makes you sort of
Speaker 4: more vulnerable to making mistakes. So it's like, yeah, be
Speaker 4: really conscious of like where there are job losses, where
Speaker 4: there will be industries that fall apart, because all the
Speaker 4: lessons you learn today from all the bad things that happen,
Speaker 4: and when you lose your job and when your neighbors
Speaker 4: lose their job, and then you know, a couple of
Speaker 4: years from now, you know, maybe the market's higher. You know,
Speaker 4: ten years from that, it's going to happen all over again,
Speaker 4: and you're more robust when that stuff happens. So yeah,
Speaker 4: I think it's a complete mistake to ignore terrible things
Speaker 4: going on as someone who's optimistic in the long run.
Speaker 6: Good.
Speaker 2: I have one last question for all three of you,
Speaker 2: and it ties into something that Joe and I have
Speaker 2: experienced on the podcast, which is a lot of our
Speaker 2: episodes are starting to feel very surreal. You know, we're
Speaker 2: talking about like space elevators and data centers in space,
Speaker 2: and companies with like trillions of dollars of market capitalization
Speaker 2: and lines that always seem to go up seemingly forever.
Speaker 2: Everything feels very sci fi and surreal at the moment.
Speaker 2: What's the most surreal thing or like data point that
Speaker 2: you have seen or witnessed in person in recent months?
Speaker 4: The first thing that comes to mind, like I said before,
Speaker 4: like prompting, I think this is actually Gemini asked this
Speaker 4: to like, you know, how would I write this? Oh yeah,
Speaker 4: and came back and I remember thinking, gosh, not only
Speaker 4: would I have sounded like that, but it's actually using
Speaker 4: you know, slightly you know, words I would have run
Speaker 4: through at the sarus and actively chosen to fold into
Speaker 4: my writing. That was really, you know, kind of scary.
Speaker 4: It really made me think about separating out, like exactly
Speaker 4: what is it that I'm offering to subscribers. Yeah, but
Speaker 4: being able to sort of see increasingly see yourself in
Speaker 4: these machines that are getting better at, you know, replicating
Speaker 4: human behavior.
Speaker 6: I think that probably for me, there's like a quiet
Speaker 6: kind of robotics boom that's occurring outside of like you
Speaker 6: like people are kind of waiting until the humanoid robot
Speaker 6: comes and folds their laundry and everything. But the dream Yeah,
Speaker 6: for me for sure. But the thing is like Amazon
Speaker 6: this year will employ more twice as many robots as
Speaker 6: they do humans. You're starting to see in a lot
Speaker 6: of the earnings reports, like from a Panic for example,
Speaker 6: that like factory automation is really experience a huge inflection
Speaker 6: that's not commensurate with other segments of that are selling
Speaker 6: into factories. And I think if you just imagine, like
Speaker 6: what level is robotics going to be at when it's
Speaker 6: fully like like automated the factory, automated, the warehouses, by
Speaker 6: the time it actually comes into your home that's kind
Speaker 6: of that's like, that's going to make things so much more.
Speaker 2: Just to be clear, Amazon is not employing the robot.
Speaker 6: Right, you just know that, like when this happens, you
Speaker 6: will be on a podcast talking about robot rights.
Speaker 4: Yeah.
Speaker 6: I can already hear it.
Speaker 5: I mean already.
Speaker 7: It's perfect that James said that because I was going
Speaker 7: to talk about my trip to the Unitary office in
Speaker 7: Hungjo and being both face to face with humanoids, which
Speaker 7: is an extremely surreal, Uncanny Valley experience. They are good dancers,
Speaker 7: they are good fighters, They're incredibly mobile.
Speaker 5: The quadrupeds are they look like bug dogs.
Speaker 7: They're like climbing all over and those are actively being
Speaker 7: deployed in factories for inspection and surveillance. We saw a
Speaker 7: twenty four to seven pharmacy that was in operation where
Speaker 7: a humanoid robot would take stuff off and drop it
Speaker 7: in a box and delivery drivers, Chinese delivery drivers were
Speaker 7: coming in and out.
Speaker 5: It was fully in operation.
Speaker 7: And so China's pushing as fast as they can on
Speaker 7: the factory automation and the physical AI stuff. And when
Speaker 7: you see it, you just look at it and you're like,
Speaker 7: that's clearly where the feature is headed by.
Speaker 3: The way I always do the test in a new model,
Speaker 3: LLLLM comes out, I say, like, write ten tweets in
Speaker 3: my voice and there's still I don't think I can do.
Speaker 3: But there was one from Claude and it said the
Speaker 3: tenure Yield doesn't care about your feelings and frankly, it
Speaker 3: doesn't care about mine either, and I was like, you
Speaker 3: know what, It's not really a thing I would say,
Speaker 3: but that's kind of a good tweet anyway. Thank you
Speaker 3: all so much, James, Jasmine and Sam for coming on
Speaker 3: a lad flag. That's a lot of fun.
Speaker 2: So that was our conversation with James van Gelan of
Speaker 2: Satrini Research, Jasmine's son, the Substack author, and Samro, the
Speaker 2: editor of the newsletter t K. I'm Tracy Alloway. You
Speaker 2: can follow me at Tracy Alloway.
Speaker 3: And I'm Joe Wasnental. You can follow me at the Stalwart.
Speaker 3: Follow our guest James van Gielan he's at Satrini, Samro
Speaker 3: at Sam Row and Jasmine's son at Jazz New Sun.
Speaker 3: Follow our producers Carmen Rodriguez at Carmen armand dash Ol
Speaker 3: Bennett at Dashbot, Keil Brooks at Kelbrooks and Kevin Lozano
Speaker 3: at Kevin Lloyd Lozano and from our Odd Lots content.
Speaker 3: Go to Bloomberg dot com slash odd Lots, we're the
Speaker 3: daily newsletter and all of our episodes, and you can
Speaker 3: chat about all of these topics twenty four to seven
Speaker 3: in our discord discord dot gg slash od logs.
Speaker 2: And if you enjoy odd Lots, if you like it
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Speaker 2: All you need to do is find the Bloomberg channel
Speaker 2: on Apple Podcasts and follow the instructions there. Thanks for listening.
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