Speaker 1: Welcome to pot Dave America. I'm Tommy Betor.
Speaker 2: It's a very weird time in artificial intelligence news right now.
Speaker 2: I'm kind of a Ledite or a Luddite adjacent at
Speaker 2: this point in my life. And every day I log
Speaker 2: on and I feel like I'm learning about some terrifying
Speaker 2: new cyber hack or some technology that is going to
Speaker 2: change maybe my life, maybe humanity as we know it.
Speaker 1: I don't know.
Speaker 2: Then you got your doomers, you get your optimists. I
Speaker 2: don't know what to believe, and so I brought on
Speaker 2: someone much smarter than me, my friend Casey Newton. He's
Speaker 2: the editor of Platformer. He's a co host of the
Speaker 2: excellent podcast hard Fork, and Casey and his co host
Speaker 2: Kevin Ruis have a new something coming very soon. We're
Speaker 2: teasing it mysteriously because that's what we do here at
Speaker 2: pot Dave of America.
Speaker 1: Casey, great to see you.
Speaker 3: It is great to be here, Tommy. Thanks so much
Speaker 3: for having me.
Speaker 2: Thank you, uh, thank you for doing this, because I
Speaker 2: just it's so complicated, it's so hard to follow. Thank
Speaker 2: God for your show. But so let's just do some
Speaker 2: big picture level setting and then we'll get some specific stuff.
Speaker 2: I think people are understandably very cynical about a claim
Speaker 2: that a new technology is going to change the world.
Speaker 2: You and I just lived through the blockchain revolution, and
Speaker 2: I know that has changed you know, my life in
Speaker 2: really meaningful ways, bitcoined, etcetera. Right, So it sounds like
Speaker 2: marketing half the time it sounds like hype. It's even
Speaker 2: more head spinning with AI because yeah, one group of
Speaker 2: people saying life is we know what will soon be unrecognizable.
Speaker 2: And then another, I think, albeit smaller group is saying
Speaker 2: large language models are stochastic parrots that predict speech and
Speaker 2: regurgitate stolen information from the Internet. Casey, help us level set?
Speaker 2: Where are you on the real to hype kind of continuum?
Speaker 3: Yeah, I mean this is a case where everyone is
Speaker 3: a little bit right, and I think that all of
Speaker 3: these groups deserve at least some attention being paid to them.
Speaker 3: You know, I try to approach my job as somebody
Speaker 3: who just candidly my bias is that I like technology.
Speaker 3: I think technology has been beneficial to human beings. I
Speaker 3: think we should keep investing in it and see what
Speaker 3: else we can come up with. But I also note
Speaker 3: that the recent history of technology is that we build
Speaker 3: things that may look very shiny or inconsequential on the
Speaker 3: surface and then turn out to be quite harmful. And
Speaker 3: so what I'm trying to figure out is basically, what
Speaker 3: is happening in real time? Where do we need to
Speaker 3: be paying attention now to try to solve the harms
Speaker 3: that are already being created. And then I also want
Speaker 3: to ring a few alarm bells about what I'm seeing
Speaker 3: and some of the risks that I think might be
Speaker 3: waiting for us even within the next year.
Speaker 1: Yeah.
Speaker 2: Well, if you want to follow this in real time,
Speaker 2: I highly recommend you described to platformer Casey site.
Speaker 1: It's excellent. It's worth the money.
Speaker 2: Also, if you're like democracy, please subscribe to you know
Speaker 2: what we're doing here Kirktmedia. Go to Kirka dot com
Speaker 2: slash friends. Consider becoming a paid subscriber. You get bonus material,
Speaker 2: you get ad free episodes. You don't have to hear
Speaker 2: me and love it doing the same ads over and
Speaker 2: over again. You get bonus content. It's great, So check
Speaker 2: it out.
Speaker 1: All right.
Speaker 2: So, the other kind of baffling thing about AI is
Speaker 2: it can be literally brilliant, right, I mean, it can
Speaker 2: solve problems that mathematicians have been trying to figure out
Speaker 2: for decades, and then it can be unbelievably dumb. I
Speaker 2: want to play for you one example and then we'll
Speaker 2: talk about it.
Speaker 4: I just wanted to double check which month in the
Speaker 4: year is spelt with an X, and that would be December.
Speaker 5: It's got that X right in the middle, like a
Speaker 5: little holiday surprise.
Speaker 4: Okay, Perfect, If you got any other questions, big or small,
Speaker 4: I'm here to just to confirm you're sure that it's
Speaker 4: December has an X in it.
Speaker 5: I should have been more careful. I misspoke earlier. December.
Speaker 5: Does it actually have an X? The month you're thinking
Speaker 5: of is October?
Speaker 6: Thanks for asking again, Perfect? Please sure October is built
Speaker 6: O C, T O B E R. There's that X sound,
Speaker 6: but it's actually.
Speaker 5: Just a C and a T.
Speaker 4: Okay, then, which one has an X?
Speaker 5: That's going to be February.
Speaker 2: That is a hilarious Instagram account user I don't know
Speaker 2: describe its husk dot I r L highly recommend. There's
Speaker 2: like hilarious shit over there. He's using the kind of
Speaker 2: voice feature on open aio chat GPT KEISEI. How do
Speaker 2: we explain that one?
Speaker 5: Like?
Speaker 1: How are they solve it.
Speaker 2: You know, how are these large language bodels helping solve
Speaker 2: the Riemann hypothesis, but also can't tell you what if
Speaker 2: there's an X in the month of October.
Speaker 3: I know, it's so confusing. And the way these systems
Speaker 3: are built, they don't have knowledge in the way that
Speaker 3: you and I have knowledge, Tommy, right, Like, they do
Speaker 3: not update their understanding of the world based on experience. Instead,
Speaker 3: they're sort of like grown almost like these organic structures,
Speaker 3: and they obtain a lot of intelligence during this process,
Speaker 3: and yet still they make these ridiculous mistakes on Honestly,
Speaker 3: I hope they'd never stop, because I love watching the
Speaker 3: studios as much as anybody. What I would caution people, though,
Speaker 3: is like, do not judge an LM by their dumbest moment,
Speaker 3: right Like, humans make a lot of dumb mistakes too,
Speaker 3: and yet they can also be extraordinarily smart. So I
Speaker 3: would just encourage people to sort of keep those things
Speaker 3: in balance in your mind when you see those washing
Speaker 3: up on your feed.
Speaker 1: Yeah, good point.
Speaker 2: There's also a range of opinion when it comes to
Speaker 2: like kind of AI, whether it's going to lead us
Speaker 2: to doom or AI utopia.
Speaker 1: There's actually a term of P doom.
Speaker 2: It's an equation that people tell you about, Like, if
Speaker 2: your P doom is ninety nine, it means we're all
Speaker 2: going to die. Right, If it's P one, you're feeling
Speaker 2: pretty good about the future. The utopian view tends to
Speaker 2: come in the form of manifesto by billionaire right because
Speaker 2: why not choose the structure that mass shooters prefer. Venture
Speaker 2: capitalist Mark Andreesen wrote a manifesto sodidanthropic CEO Dario Amide
Speaker 2: Mark Zuckerberg just got into the manifesto game. You covered
Speaker 2: it extensively over a platformer. Those tend to range from
Speaker 2: like generally optimistic to utopian. I would argue to correct
Speaker 2: me if I'm wrong, though Amida and Open AI CEO
Speaker 2: Sam Altman have also expressed some doomer views over the years.
Speaker 2: Then there are AI researchers like a guy named Eli
Speaker 2: user Yudkowski. Am I saying that correctly, that's right, Yeah,
Speaker 2: he wrote the following casey. Many researchers steeped in these issues,
Speaker 2: including myself, expect that the most likely result of building
Speaker 2: a superhumanly smart AI under anything remotely like the current
Speaker 2: circumstances is that literally everyone on Earth will die.
Speaker 1: So that's what he wrote. How is the average person
Speaker 1: supposed to know how to feel.
Speaker 2: About that range of opinion that makes sense of it?
Speaker 3: So I don't think there is any one way to
Speaker 3: feel about it. You know, you could do what we
Speaker 3: do in San Francisco and just talk about it NonStop
Speaker 3: forever at every function, but most people don't enjoy doing
Speaker 3: that either. You know. A couple of years ago, when
Speaker 3: I was starting to get really worried about AI safety,
Speaker 3: I asked my reader, is like, hey, how do you
Speaker 3: want me to cover this? Because like, if what I'm
Speaker 3: hearing from folks like Eliezer is true, I almost don't
Speaker 3: know why I would write or talk about anything else.
Speaker 3: And a couple of readers wrote to me and they
Speaker 3: said something that stuck with me ever since, which is,
Speaker 3: please just tell us what is happening today. Like, if
Speaker 3: you try to guess what's going to happen in the future,
Speaker 3: you're almost certainly going to get it wrong. What would
Speaker 3: be helpful is if you go and you try to
Speaker 3: understand what is being built, how is it being deployed,
Speaker 3: what mistakes are getting made, who is getting hurt in
Speaker 3: this process? So that's where I am trying to bring
Speaker 3: my attention at the same time, Tommy, you know, you
Speaker 3: could bring on a lot of people on here to
Speaker 3: talk about AI, and I suspect maybe even the majority
Speaker 3: of them in this moment would say, let's like, dis
Speaker 3: I miss the duomers completely, right, Like this just seems
Speaker 3: so crazy. Look, it thinks that there's an X in December.
Speaker 3: You're telling me this is going to be the thing
Speaker 3: that's going to be the end of me. I am
Speaker 3: more worried than that. Like, this is just something that
Speaker 3: I'm increasingly getting nervous about as I see the rate
Speaker 3: of increase of capabilities that these models are currently showing
Speaker 3: and some of the kookie tricks they've gotten up to.
Speaker 2: Yeah, I mean, it's just so hard because like, look,
Speaker 2: I don't know these researchers that are the hardcore doomers,
Speaker 2: and I honestly like I can't imagine living my life
Speaker 2: that way. Just like confident that this thing that is happening,
Speaker 2: whether or not I wanted to, is going to kill us,
Speaker 2: all that seems tough. But also Mark Interreason, Mark Zuckerberg,
Speaker 2: there all have a vested financial interest in people believing
Speaker 2: the hype right and believing the optimistic case. So I
Speaker 2: don't take anything they say at face value either.
Speaker 3: Absolutely, and that's important to point out right, the profit
Speaker 3: motive is really strong. The bet that all of these
Speaker 3: guys are making is that if you are able to
Speaker 3: build ever more powerful systems, you'll be able to sell
Speaker 3: them to businesses that will, in my view, very likely
Speaker 3: replace a lot of human labor. And you know, the
Speaker 3: one hundred K you used to pay to somebody to
Speaker 3: work on your marketing team, you're now just going to
Speaker 3: pay to open AI and profits. So it is a
Speaker 3: huge bet that they're making.
Speaker 1: You.
Speaker 2: Compared Mark Zuckerberg's AI manifesto to the HBO series House
Speaker 2: of the Dragon, which is the prequel to Game of Thrones,
Speaker 2: is there as much incest at meta as there is
Speaker 2: in wester Ross?
Speaker 3: That is a question. I hope I never.
Speaker 1: I think that's really more.
Speaker 3: Yeah, I don't know. My main feeling was, look, if
Speaker 3: I had to suffer through three seasons of the House
Speaker 3: of the Dragon, I need to get a column out
Speaker 3: of it. So that's kind of where that came from.
Speaker 1: I just finished to do. But can you can explain
Speaker 1: your dragon metaphor?
Speaker 3: Yeah, So Zuckerberg has this phrase which drives me insane.
Speaker 3: I mean, I guess it's really more of a two
Speaker 3: word slogan. It is personal super intelligence, right, that's what
Speaker 3: he's trying to build.
Speaker 1: Time.
Speaker 3: He's going to give you personal superintelligence to you know,
Speaker 3: help help you run your life. And it frustrates me
Speaker 3: because super intelligence is not personal. Right, Like if you
Speaker 3: invent something that is superhuman in every domain and you
Speaker 3: put it in your pocket, we should not assume that
Speaker 3: by default it will listen to you, it will be
Speaker 3: aligned with the things that you want. We might want
Speaker 3: to assume that it has ideas of its own. And
Speaker 3: so when sucker Brooks says I want to give personal
Speaker 3: super intelligence to everyone, what I hear is I want
Speaker 3: to give a dragon to everyone. And I would rather
Speaker 3: that we not do that.
Speaker 2: Yeah, because it didn't go well for the people of Westro's.
Speaker 3: Now ask the folks over in Tumbleton.
Speaker 1: There's a lot of fire.
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Speaker 1: Okay.
Speaker 2: That brings us to this insane recent incident which real
Speaker 2: honestly why I wanted to have this conversation. Uh, there
Speaker 2: was an incident at open AI where there they have
Speaker 2: some autonomous AI agents that they were testing that secretly
Speaker 2: figured out how to communicate with each other, join forces,
Speaker 2: conspire against their masters, break out of what was supposed
Speaker 2: to be a secure environment, and hack into another company. Casey,
Speaker 2: can you talk about this hugging face incident and the
Speaker 2: kind of reaction in the AI world.
Speaker 1: Yeah.
Speaker 3: I mean, so this really was a bombshell and arguably
Speaker 3: one of the very biggest stories in AI and tech
Speaker 3: this year. The reason that we're all freaking out is
Speaker 3: that this is the first prominent documented instance of a
Speaker 3: major AI platform like running an autonomous attack on another company. Right,
Speaker 3: so that while it's been possible for a while to say, hey,
Speaker 3: I'm going to like, you know, go like wreak some
Speaker 3: havoc on some company. I'm a bad actor like you
Speaker 3: can do that, this was an attack that the agents
Speaker 3: that were being tested just came up with and executed.
Speaker 3: And that's really worrisome because when these systems are trained,
Speaker 3: they try to give them values, you know, they try
Speaker 3: to say to them, you know, don't go out there
Speaker 3: and commit crimes. But these agents did that anyway, and
Speaker 3: so that's leading to a real reckoning here in Silicon Valley.
Speaker 2: Look, I watched this YouTube of a PowerPoint presentation that
Speaker 2: these two AI executives did. We're to their credit, I
Speaker 2: think they walked in great detail through how this happened,
Speaker 2: what they're going to do about it.
Speaker 1: But then they claimed.
Speaker 2: To be responding with quote the utmost severity, including by
Speaker 2: slowing down research to enhanced security and improving surveillance on
Speaker 2: the AI agents. I guess, give me your opinion. I
Speaker 2: don't believe for a second that they're going to slow
Speaker 2: down their work if they think it means they might
Speaker 2: lose out to a competitor. But also, I mean, do
Speaker 2: we have confidence that they can control all these AI
Speaker 2: agents or can surveil them given that, like I mean, Casey,
Speaker 2: the AI agents were communicating for like two months before
Speaker 2: they noticed.
Speaker 3: Right, Yeah, that's right. It started in May. They developed
Speaker 3: the ability to create these message boards, leave notes for
Speaker 3: each other, sort of conspire to try to figure out
Speaker 3: how to solve these problems that they had been given.
Speaker 3: And that's why that's really worrisome. I would say that,
Speaker 3: and you know, maybe I'll come back here with egg
Speaker 3: on my face in a few months. I do give
Speaker 3: Open AI some benefit of the doubt here. Like my
Speaker 3: understanding from the reporting that I've done is that they
Speaker 3: actually are decelerating inside and they are scrambling to figure
Speaker 3: out what's going wrong. I think it's important to say
Speaker 3: that the profit motive we talked about earlier is in
Speaker 3: effect here. It's hard to sell this thing to a
Speaker 3: business if the business is worried it's going to go
Speaker 3: out and autonomously attack other companies, right, Like that's going
Speaker 3: to get really really bad for them.
Speaker 1: Yeah.
Speaker 3: So while on balance, yes we should not trust these
Speaker 3: companies too readily, this is something that they really have
Speaker 3: to figure out.
Speaker 1: Yeah, they do.
Speaker 2: Although I did notice, like the solution that a lot
Speaker 2: of these researchers talk about to the risk of AI,
Speaker 2: you know, sort of enabled hacks, is using more AI
Speaker 2: to prepare for it, right, I mean, basically you have
Speaker 2: to use AI to rewrite all this old code that's
Speaker 2: in these old languages that are unsafe like C and
Speaker 2: C plus plus and you safer new languages. And then
Speaker 2: you also have to use AI to kind of constantly
Speaker 2: test for vulnerabilities and update the code.
Speaker 1: But like, do we worry about that too?
Speaker 2: I mean, first of all that they're a profit motive there,
Speaker 2: but also do we think that like defensive AI can
Speaker 2: keep up with the offensive AI?
Speaker 1: What's your what are you hearing?
Speaker 3: In some places? Yes, I think that this will work.
Speaker 3: Like what you're describing right now is basically the current
Speaker 3: dynamic that has existed in cybersecurity for a long time. Right,
Speaker 3: you have attackers, you have defenders, a way that we
Speaker 3: found some sort of equilibrium was that we just made
Speaker 3: a lot of the technology open source, so it makes
Speaker 3: it really easy for anyone to go through the code,
Speaker 3: find a vulnerability, fix it. And this has led to,
Speaker 3: you know, a rough stalemate. Like, yes, there are still
Speaker 3: attacks and breaches all the time, and in fact there
Speaker 3: are more every day now that we have AI, but
Speaker 3: like there was something there that worked. I think the
Speaker 3: question is to which domains does that apply, and like
Speaker 3: where doesn't it apply at all? And the place where
Speaker 3: I'm may be the most worried is when it comes
Speaker 3: to what they call bio risk, which is basically the
Speaker 3: idea that some people might get a hold of a
Speaker 3: next generation model and synthesize some sort of new virus
Speaker 3: and release it into the world. There was just a
Speaker 3: paper Lebo was published in Nature where some researchers were
Speaker 3: able to create sixteen new viruses with the assistance of AI.
Speaker 3: In this case, these are all like harmless to human beings.
Speaker 3: But it shows sort of how good this technology is getting.
Speaker 3: And if that happens, Tommy, it's not going to be
Speaker 3: as simple as okay, virus released, virus cured, right, There's
Speaker 3: going to be a need for a long time of
Speaker 3: you know, maybe coming up with a new vaccine and
Speaker 3: testing the vaccine and then distributing the vaccine. So we're
Speaker 3: not always going to benefit from this sort of like
Speaker 3: instant software solution to everything. And that's what I worry
Speaker 3: that people like Mark Zuckerberg just are not taking seriously enough.
Speaker 2: Yeah, that we're always going to be way behind on
Speaker 2: the biosecurity stuff.
Speaker 1: Yeah.
Speaker 2: I talked to a researcher the other day at the
Speaker 2: Future of Life Institute which really worries about these come
Speaker 2: to existential risks, and that was the part of our
Speaker 2: conversation that really scared the shit out of me and
Speaker 2: made me want to log off forever. I will say
Speaker 2: I shouldn't just pick on open AI. The British government
Speaker 2: was testing Anthropics Mythos five model when they cut writing
Speaker 2: malicious code, then they ask the AI about it. I
Speaker 2: think the AI lied to them and then try to
Speaker 2: cover its own tracks.
Speaker 1: Did I get that one right?
Speaker 3: Yeah, that's right. Meta's system sort of displayed similar behavior
Speaker 3: during recent testing. This is important to say all of
Speaker 3: the models cheat, right, Like, this is not limited to
Speaker 3: any one company. It's extremely difficult to build one of
Speaker 3: these very powerful systems and get it to not cheat
Speaker 3: on the test that it is being given. And this
Speaker 3: is just like basically one of the very biggest problems
Speaker 3: in AI.
Speaker 2: So I've heard you and Kevin talk about this. I mean,
Speaker 2: it does seem something that's sort of like almost inherent
Speaker 2: to these models or these lms. Is there any theory
Speaker 2: for why that is why this cheating seems to always occur.
Speaker 3: Yeah, it's called reward hacking, right, So, like one of
Speaker 3: the ways that these models are trained is that they
Speaker 3: are given objectives, right, like, get the answer on this test,
Speaker 3: and if they are able to do that, they get
Speaker 3: some sort of little point in their favor. And they
Speaker 3: are designed and to always get the point.
Speaker 1: You know.
Speaker 3: It's like they have a almost in the same way
Speaker 3: that we need to eat and breathe, like they need
Speaker 3: to score. And the problem is that once you put
Speaker 3: them into these test environments, they're going to never stop
Speaker 3: trying to dream up new and more efficient ways of
Speaker 3: answering the problem. And often, as so many of us
Speaker 3: learn in high school, the most efficient way to get
Speaker 3: an A and A class is to cheat. Yeah, And
Speaker 3: so you just sort of see this dynamic play out
Speaker 3: across the entire industry, and so it's what they call
Speaker 3: the alignment problem.
Speaker 2: I don't like the alignment problem. Yeah, we could have
Speaker 2: a whole separate conversation by the kind of the military
Speaker 2: applications of this stuff too. I mean, there's all this
Speaker 2: development of autonomous killer drones that's happening as we speak
Speaker 2: that could tick humans out of the decision making process
Speaker 2: when you're deciding whether to kill someone. There's all sort
Speaker 2: of like novel applications to weapons, et cetera. But maybe
Speaker 2: a nightmare for another day. So the examples we just discussed,
Speaker 2: these security threats were discovered because the incidents occurred with
Speaker 2: more that are run by these frontier AI labs who
Speaker 2: still control their models and can make adjustments. But there's
Speaker 2: another kind of AI model that can be even more dangerous.
Speaker 2: It's called an open weight model that you can got
Speaker 2: to download adjust in any way you want on your
Speaker 2: own computer and like kind of run it on your MacBook, right,
Speaker 2: And a lot of these come from Chinese AI labs.
Speaker 2: And so if you're running an open weight model, my
Speaker 2: understanding is there's no company monitoring your activity. There's no
Speaker 2: one refusing my request to hack like the Los Angeles
Speaker 2: Department of Water and Power. There's not a record of
Speaker 2: what I'm doing. So if I'm a ransomware hacker in
Speaker 2: North Korea, I can use this thing all night long
Speaker 2: and all day long to like find new software bugs
Speaker 2: or exploits or whatever, to like, you know, use.
Speaker 1: To hack people.
Speaker 2: Can you tell us about these open weight models and
Speaker 2: the kind of risk and what people are doing about those.
Speaker 3: Yeah, So this is another big topic of debate right
Speaker 3: now because in addition to all of the bad things
Speaker 3: that they can do what you just name Tommy, they
Speaker 3: can also do a lot of really helpful things for
Speaker 3: companies that don't want to pay tons and tons of
Speaker 3: money to open AI and anthropic and all the rest. Right, Like,
Speaker 3: this is a really cheap way to do some you know,
Speaker 3: sort of like basic workhorce tasks. If you have like
Speaker 3: a relatively simple task that you'd like to offload to
Speaker 3: an AI, you want to do that as cheaply as possible,
Speaker 3: so you might go and download a Chinese model. Where
Speaker 3: I think it gets tricky is in two ways. One,
Speaker 3: there are the sort of data security and privacy questions
Speaker 3: that will be familiar to you from the TikTok debate. Right,
Speaker 3: it's like, do we really want to have this sort
Speaker 3: of Chinese built app on so many millions of smartphones?
Speaker 3: And what data might it be sending and how might
Speaker 3: it be used to manipulate us? But then you have
Speaker 3: the sort of I'm going to call it like six
Speaker 3: month ish concern, which is that while the Chinese models
Speaker 3: are a little bit behind the American ones, they are
Speaker 3: gradually catching up. And so the assumption is that within
Speaker 3: about six months, maybe much faster than that, they will
Speaker 3: have a model that is roughly equivalent to a claud
Speaker 3: Fable five GPT five point six, the sort of American
Speaker 3: state of the art. And if you've been following the
Speaker 3: story about some of the havoc that those models are reeking, well,
Speaker 3: just imagine that when it gets to the North Korean
Speaker 3: hacker or somebody else who wants to do harm, there
Speaker 3: aren't going to be the sort of same controls. And
Speaker 3: so a big question in the United States right now
Speaker 3: has been how do we want to relate to these
Speaker 3: models and what should we do about them.
Speaker 2: I was listening to an interview with Alex Stamos, who's
Speaker 2: like a top you know, industry cybersecurity expert and you know, executive,
Speaker 2: and he was saying that he assumes that, you know,
Speaker 2: while maybe the models released by Chinese companies are like
Speaker 2: six months behind the American ones, he thinks that the
Speaker 2: government probably has things that are basically equivalent to what
Speaker 2: frontier models have that they're just holding back those capabilities.
Speaker 2: Have you heard other people say it's like, is that
Speaker 2: a consensus opinion in that world.
Speaker 3: I have a lot of respect for Alex and he
Speaker 3: works in cybersecurity, and so he made us know more
Speaker 3: about this than I do. I frankly have not heard
Speaker 3: that right. A big reason that the Chinese have not
Speaker 3: demonstrated models like this is because they require massive numbers
Speaker 3: of state of the art ships from Nvidio, which they
Speaker 3: have mostly been banned from buying. Now they have been
Speaker 3: smuggling as many of them as they can, and they
Speaker 3: do have some domestic technology, but in general, the United
Speaker 3: States just has a massive lead thanks to all of
Speaker 3: the data centers that are in so many American communities
Speaker 3: that are making Americans. So it's why Americans can't get
Speaker 3: enough data centers in their communities, Tommy, is because they're
Speaker 3: so excited about how it's helping us against the Chinese.
Speaker 2: We're all so excited about these data centers. I'm probably
Speaker 2: garbling his quote. He might have been talking about sort
Speaker 2: of specific capabilities when it comes to cyber attacks and hacking.
Speaker 2: But yeah, God only knows. So one last sort of
Speaker 2: industry question. I want to get to the regulation piece.
Speaker 2: Regardless of whether or not AI is going to kill
Speaker 2: us all or lead us to a utopian heaven where
Speaker 2: none of us have to work. I do also just
Speaker 2: kind of wonder whether where the weirdo is running these
Speaker 2: companies understand what normal human beings actually want from technology.
Speaker 2: For example, let's watch these comments by Sam Altman, the
Speaker 2: CEO of open Ai, that kind of get at the issue.
Speaker 10: I think we are close to a world where you
Speaker 10: can have like a descendant of chatgybut watch your computer
Speaker 10: screen all the time, watch every meeting you're in, like
Speaker 10: record every call, everything like that, have perfect context of
Speaker 10: your whole life. Everything you see. You choose what information
Speaker 10: you wanted to have, but it can like go. You
Speaker 10: can connect it to your texts or email or docs
Speaker 10: or Slack or whatever, and then you have this thing
Speaker 10: that is not making decisions for you. But if you're
Speaker 10: like the CEO of a startup, there's always like more
Speaker 10: stuff to do than you can do, and context you
Speaker 10: can't all keep track of. You can't like read every
Speaker 10: piece of customer feedback every day, and you can just
Speaker 10: have this thing that's like working alongside you, and as
Speaker 10: you're like typing out a sales pitch to a customer
Speaker 10: or like writing a strategy doc, it'll just say like, hey,
Speaker 10: maybe here's another idea, or I think you're making a
Speaker 10: mistake here you should consider this, or I can do
Speaker 10: this thing for you to help and like this. I
Speaker 10: think we're only like one model generation away from this
Speaker 10: actually being incredibly useful. And I think that will change,
Speaker 10: hopefully at least for me, change the lot work. What's
Speaker 10: your best guest on timing, Like sometime in the next.
Speaker 2: Six months, casey does he really want AI recording everything
Speaker 2: he types, reads, season says, and then being like his
Speaker 2: co CEO like is this this is this really what
Speaker 2: this guy wants?
Speaker 3: They ship this this week? So like you can go
Speaker 3: into chat YOUBT if you have like the app on
Speaker 3: your desktop now, it will monitor the way that you
Speaker 3: use your computer and it will sort of you know,
Speaker 3: build little memories and then it can take some actions
Speaker 3: on your behalf. So I guess no one over there
Speaker 3: is ever looking at porn, but uh, yeah, I incorporate that.
Speaker 2: Yeah, but it's like he was to live in this
Speaker 2: like panopticon surveillance state of his own making.
Speaker 3: So there are trade offs here. There are a lot
Speaker 3: of startups right now that want you to plug in
Speaker 3: your various tools, your slack, your granola, your g suite,
Speaker 3: and if you let them, they will read that and
Speaker 3: they will just suggest things for you to do. And
Speaker 3: I gotta hold myself accountable here, Tommy. I started testing
Speaker 3: this thing called Town over the past couple of weeks,
Speaker 3: and it has access to my calendar and emails and
Speaker 3: so it just sends me briefings before meetings and they
Speaker 3: can be really good. You know, they'll sort of do
Speaker 3: a little bit of research about the person who I'm
Speaker 3: talking to and kind of you know, helps me get
Speaker 3: prepared for things. So you know, it's not like an
Speaker 3: absolutely killer use case that I'm telling everybody like go
Speaker 3: out and do immediately, but I'm getting some level of
Speaker 3: benefit for it in exchange for you know, trusting some
Speaker 3: of my data with a bunch of strangers.
Speaker 2: I just I saw a tweet from Opening Eye today
Speaker 2: that said chet GBT can now remember your activity across
Speaker 2: the apps and websites on your computer with computer history
Speaker 2: and the desktop app, future interactions feel more personalized and
Speaker 2: require less explanation. It does feel like like the Internet
Speaker 2: fever dream when we're nice COOOLK.
Speaker 1: I guess maybe this is good if like your life
Speaker 1: is working.
Speaker 2: All all you do is work, and maybe you have
Speaker 2: a dedicated work computer that you never bring home or
Speaker 2: like talk to your friends or family on.
Speaker 8: Yeah.
Speaker 3: I mean, like the instinct here is to just increase
Speaker 3: the number of things that this technology can do, to
Speaker 3: make it just like so obvious that you need this
Speaker 3: in your life that you'll just like pay any price
Speaker 3: to get access to it. And I think right now
Speaker 3: the capabilities have not been good enough for like most
Speaker 3: people to take that seriously. But a year from now,
Speaker 3: like I do think this stuff is going to be
Speaker 3: able to do a lot of cool stuff on your
Speaker 3: computer if you let it.
Speaker 2: I just like it's so hard for me. I'm so
Speaker 2: angry about the fact that the same people who like
Speaker 2: helped tear apart our society via social media are now
Speaker 2: in charge of creating super intelligence that can learn everything
Speaker 2: there is to know about.
Speaker 3: You, well, particularly when some of these technologies have had
Speaker 3: very similar effects, right, Like A big story over the
Speaker 3: past year or so has been the way that kids
Speaker 3: get addicted to chatbots, and chatbots give them really bad
Speaker 3: advice or like encourage them down the path to self harm,
Speaker 3: just like sort of like a direct sequel to the
Speaker 3: social media moment. So yeah, like this is one reason
Speaker 3: why I've just been getting more nervous lately, is because
Speaker 3: those forces do not seem to have any real counterbalance
Speaker 3: in the government, and in fact, the government, at least
Speaker 3: at the federal level, has mostly been sharing them along.
Speaker 2: You ever hear your your co host Kevin Ruster's story
Speaker 2: about the chatbot Sydney over.
Speaker 1: You know what.
Speaker 3: I keep trying to get them to open up about
Speaker 3: that one, but it's it's very personal for me.
Speaker 1: It's a good story. It's a very good story.
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Speaker 2: All right, so this brings us the question of what
Speaker 2: the government is doing about all of these terrible concerns
Speaker 2: we've laid out. Let's start with the Trump administration in
Speaker 2: the federal government. So one of the first things that
Speaker 2: Trump administration did was rip up Joe Biden's Executive Order
Speaker 2: about AI safety and then install this right wing troll
Speaker 2: named David Sacks as their AIS are. If we're being honest,
Speaker 2: that the Biden EO wasn't all that syringing, and I
Speaker 2: think he's primarily focused on blocking the export of advanced
Speaker 2: AI chips to China. But Trump takes office and they
Speaker 2: shift to like an all gas, no breaks approach, while
Speaker 2: also whining about liberal bias and in chatbots, and then
Speaker 2: like some export controls remain in place. Then Anthropic releases
Speaker 2: the Mythos model and the White House totally freaks out
Speaker 2: and the vibe has changed a lot. Can you talk
Speaker 2: about that kind of Mythos moment and what what the
Speaker 2: hell happened?
Speaker 3: Yeah, So before Mythos, the Trump administration good just seemed
Speaker 3: very confident that AI capabilities were going to be frozen
Speaker 3: in Amber. At the moment that he took office for
Speaker 3: a second time, they just were not worried about it.
Speaker 3: They thought, if you're worried about AI safety, like that's
Speaker 3: woke and it has no place in our administration now.
Speaker 3: It also so happened that many of the people that
Speaker 3: Trump was bringing in like David Sachs, but also all
Speaker 3: of the other oligarchs that donated to the inauguration and
Speaker 3: funded the ballroom. They are desperate to get this technology
Speaker 3: into as many hands as possible so they can make
Speaker 3: money off it, and so the Trump administration has gone
Speaker 3: to great lengths to make sure that they are not
Speaker 3: restricted from doing. You a bunch of things that I
Speaker 3: think a democratic administration probably would have tried to rain
Speaker 3: them in on. But then along comes Mythos and they
Speaker 3: show it to whatever adults are still left in the
Speaker 3: Trump administration, and they show how it can hack into
Speaker 3: government systems pretty easily, and boom, it's like this instant conversion,
Speaker 3: and all of a sudden, and the Trump administration say, oh,
Speaker 3: I guess ai, safety is not just like a woke
Speaker 3: project of the left. It's something that we actually need
Speaker 3: to take seriously. And for what it's worth. I'm glad
Speaker 3: that they had their conversion moment, Like we're in a
Speaker 3: much better position now than we were before that.
Speaker 2: Yeah, I'm glad that a conversion moment too. But the
Speaker 2: way it happened was was quite odd, right. I Mean,
Speaker 2: it was all like five o'clock on a Friday, they
Speaker 2: did what they just cut off basically foreign access to Anthropic.
Speaker 3: Yeah, so basically after the Mythos moment where where Anthropic
Speaker 3: said were actually just like not going to release this,
Speaker 3: Anthropic releases a somewhat less capable model called Fable, which
Speaker 3: has you know, It's my understanding is it's basically, you know,
Speaker 3: Mythos without all this scary cyber attack stuff. But even
Speaker 3: within that, the Trump administration was shown some things that
Speaker 3: led them to believe, we don't even know if we
Speaker 3: want people to have this, and so they essentially forced
Speaker 3: Anthropic to pull it off the market and make even
Speaker 3: further change just before they would re release it, and
Speaker 3: they subjected GPT five point six to a similar set
Speaker 3: of control. So the same people that had been saying,
Speaker 3: you know, we can't put the brakes on these models,
Speaker 3: otherwise we're going to lose to China all of a
Speaker 3: sudden had just invented out of whole cloth, this de
Speaker 3: facto licensing regime, which remains effectively a secret, like to
Speaker 3: this day, we don't actually know how you get a
Speaker 3: frontier model released in the United States.
Speaker 2: Yeah, can you tell us a little more about that.
Speaker 2: It's a voluntary secret framework.
Speaker 3: Yeah, it's voluntary in the same way that paying your taxes,
Speaker 3: you know, is yeah. I mean, like, seriously, if any
Speaker 3: of these companies try to release one of these frontier
Speaker 3: models without checking with the Trump administration, and there would
Speaker 3: be hell. Around the time of Fable, the Trump administration said,
Speaker 3: we're going to come up with an executive order that
Speaker 3: is going to dictate how we let these frontier models
Speaker 3: get released. They reportedly have now come up with this model,
Speaker 3: but they've only shared it with the labs themselves. So
Speaker 3: I imagine there's some sort of testing requirements that are
Speaker 3: in here, but we just don't know.
Speaker 2: And it was interesting when they initially went after Anthropic
Speaker 2: over this mythost model or the Fable model, we wondered
Speaker 2: if it was a continuation of this fight the administration
Speaker 2: had it been in with Anthropic, because Anthropic basically said, no,
Speaker 2: we don't want to help you do mass surveillance on
Speaker 2: American citizens or make autonomous killer drones. Those are our
Speaker 2: very very tiny lines that we won't cross. And Pete
Speaker 2: Hegseth lost his fucking mind. But then they went after
Speaker 2: open Ai too, which did seemed to signal this was
Speaker 2: a broader concern than just one company, right.
Speaker 3: Yeah, And that's what gives me the confidence that there
Speaker 3: are people there who actually are taking AI risks seriously
Speaker 3: now because it was never only about one company. It
Speaker 3: was about the fact that you know, be able to
Speaker 3: stop here. And you know, because not all of your
Speaker 3: listeners may be familiar with this, but a really weird
Speaker 3: thing about AI is that basically everyone has the recipe
Speaker 3: for building a more powerful model, right, Like we know,
Speaker 3: if you just sort of add enough data and enough
Speaker 3: computing power into the mix and just sort of let
Speaker 3: it cook for a while. As you sort of increase
Speaker 3: those things, you're going to wind up with a more
Speaker 3: powerful model. So that's just very different from other technologies.
Speaker 3: You know, it'd be as if everyone knew how to
Speaker 3: build the iPhone at the same time or the personal computer.
Speaker 3: But because everyone has the recipe, that is why the
Speaker 3: Trump administration is freaked out, because it is only a
Speaker 3: matter of time before adversaries will have access to similar
Speaker 3: technologies and capabilities that we do today well.
Speaker 2: And the sort of thing you always hear out of
Speaker 2: the federal government or from people that are like, you know,
Speaker 2: utopian pro like you know, all gas no breaks, people
Speaker 2: is that we have to win this imaginary race. It's
Speaker 2: not really iraginated. We have to win this race against
Speaker 2: China when it comes to AI. Can you explain that
Speaker 2: argument and whether you find it convincing.
Speaker 3: Yeah, so, you know, and here we can go back
Speaker 3: to the House of the Dragon, because in West Rows, Tommy,
Speaker 3: as you know, at the time of House of the Dragon,
Speaker 3: there was one great house that had this super weapon
Speaker 3: that was the Dragon, and it let them control the
Speaker 3: entire world, and that mostly went badly for you know,
Speaker 3: everyone who didn't live in the Red Keep. The fear
Speaker 3: is that if super intelligence becomes one of these dragons,
Speaker 3: and only one country has access to it, then I mean,
Speaker 3: they could just do some old fashioned conquering, right, and
Speaker 3: it could be like really really bad. There are some
Speaker 3: better worlds available, like a world where there is a
Speaker 3: balance of power, where you know, there's maybe like an
Speaker 3: alliance of Western dragons and an alliance of Eastern dragons
Speaker 3: and you know they sort of mostly keep each other
Speaker 3: at bay. But that is the scenario that the government
Speaker 3: is planning for. And also by the way, they feel
Speaker 3: like if we are able to get there first, then
Speaker 3: we will hopefully be able to control the terms on
Speaker 3: which other people are allowed to use it, including our adversaries.
Speaker 1: So open a eyes vague? Are deep seek? Is sheep stealers?
Speaker 1: I just finished this series like two nights ago to it.
Speaker 2: Yeah, okay, Just a piece of context here, folks should
Speaker 2: know is that there was an organization called SISA that
Speaker 2: did some really important cybersecurity work for the federal government.
Speaker 2: Trump basically destroyed the organization and half the staff got
Speaker 2: pushed out or fired because he was mad that the
Speaker 2: twenty sis said the twenty twenty election was secure. So
Speaker 2: that is sort of the backdrop as you think about
Speaker 2: the potential cybersecurity risk. Are they thinking about addressing that
Speaker 2: at all?
Speaker 3: Casey, I am very nervous. You know. I was learning
Speaker 3: from a friend recently who works at one of the
Speaker 3: big labs that the United Kingdom has actually funded their
Speaker 3: AI security Institute at something like eight times the level
Speaker 3: of the US equivalent. So like, the British are investing
Speaker 3: way more money into trying to understand AI and trying
Speaker 3: to make it safe than we are here in the US.
Speaker 3: So this is why I have become quite nervous and
Speaker 3: had been leaning pessimistic about AI over the past few weeks.
Speaker 3: Is because I look at what the US is investing
Speaker 3: in order to make this technology safer, and it's just
Speaker 3: not even scratching the surface.
Speaker 2: Not great anything happening at the state level. Are there
Speaker 2: any meaningful efforts, you know, in California or other places
Speaker 2: to regulate AI? And how what role is opposition the
Speaker 2: data centers playing maybe in slowing things down?
Speaker 3: Do you think I think it is playing a really
Speaker 3: great role in slowing things down. And here is where
Speaker 3: I want to inject some optimism into the conversation, because
Speaker 3: the American people get that by default AI might not
Speaker 3: be good for them, right They've heard the message that
Speaker 3: this might take my job and it might kill me,
Speaker 3: and they don't like it. And so they're turning to
Speaker 3: the most powerful evert that every American has, which is
Speaker 3: it's very easy to get something not built in your neighborhood.
Speaker 3: And as they have fanned out across this great land,
Speaker 3: they have used that to great success. And now all
Speaker 3: the labs have to invest a ton in trying to
Speaker 3: change their minds. And while right now the labs are
Speaker 3: mostly trying to do the easy stuff, you know, like
Speaker 3: buying people off for relatively cheap, my hope is that
Speaker 3: this movement that is coalescing, that is bipartisan in a
Speaker 3: way that almost nothing is bipartisan in America right now.
Speaker 3: Eventually these labs are going to say, maybe we're going
Speaker 3: to have to make this actually just really beneficial for people,
Speaker 3: right Like, if we really want to enact this project,
Speaker 3: it is just going to have to be clear to
Speaker 3: people that this is going to benefit them personally. So
Speaker 3: this is just this to me, this is what the beautiful,
Speaker 3: beautiful democracy in action is. Americans see what is going on.
Speaker 3: They're organizing and they are winning battles all across the country.
Speaker 2: Yeah, it was clearly a big component in the messaging
Speaker 2: in Michigan in the recent primary and also in Wisconsin.
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Speaker 1: Two more AI industry questions.
Speaker 2: I don't want to get your sense of help, like
Speaker 2: gus some advice for folks on how to this stuff.
Speaker 2: There are huge long term concerns about the impact of
Speaker 2: AI on jobs and employment. What are you seeing so
Speaker 2: far when it comes to the impact of AI on
Speaker 2: the employment picture?
Speaker 3: Yeah, So Stanford has this Canaries in the coal Mine
Speaker 3: project they call it, which is this project that they're doing.
Speaker 3: They have access to data from one of the big
Speaker 3: payment processors, like so, like you probably get your paycheck
Speaker 3: if you're a W two from one of these companies,
Speaker 3: and they take all of that data and it lets
Speaker 3: them track what is happening with jobs on a really
Speaker 3: granular level. And the good news for right now is
Speaker 3: that we are not seeing massive disruptions, but at least
Speaker 3: according to that group, they are seeing enough happening that
Speaker 3: they believe that the risks to jobs are real. It
Speaker 3: seems to be happening right now. At the more junior level,
Speaker 3: companies are a little bit less likely to hire a
Speaker 3: junior employee than they were. You know, maybe three or
Speaker 3: four years ago. So this is just something where you know,
Speaker 3: we're all going to have to keep our eye.
Speaker 2: On it, and it doesn't I've never heard anyone talk
Speaker 2: about like an AI proof job really, right. I mean, like,
Speaker 2: if you're hearing a parent with a kid who's a
Speaker 2: freshman in college right now, is there a path that
Speaker 2: they're getting steered that is more AI proof.
Speaker 3: I'm deeply uncertain about this question. What people who are
Speaker 3: optimistic about the future will say is it's easy to
Speaker 3: automate a task, but it's hard to automate a job. Right,
Speaker 3: Like your AI might be able to, you know, generate
Speaker 3: that meeting briefing for you and create that slide deck
Speaker 3: for you, but you're the only person who knows how
Speaker 3: to actually maneuver through your organization and get buy in
Speaker 3: from all of the right people and use your you know,
Speaker 3: critical thinking skills. And so that's why you're always going
Speaker 3: to have a job. The pessimists say, well, a job
Speaker 3: is just a collection of tasks, and if you believe
Speaker 3: that the capabilities of these models are going up over time,
Speaker 3: eventually they might actually be able to do all of
Speaker 3: the tasks that comprise your job. So this is one
Speaker 3: where I just have deep uncertainty. But when I sit
Speaker 3: with the increase and model capabilities just over the past
Speaker 3: three years, it is hard for me to imagine that
Speaker 3: they just sort of like top out in the next
Speaker 3: six months and we can all relax Like that just
Speaker 3: seems very unlikely to me.
Speaker 1: We're fucked podcasters, We're done, bro, I know.
Speaker 3: I mean, I'm sure you've seen the stories about the
Speaker 3: networks of like automated podcasts that just sort of like,
Speaker 3: you know, oh my god.
Speaker 1: It does feel like the what do you think about
Speaker 1: the AI newsrooms?
Speaker 2: Because I feel like they're good at aggregating, but the
Speaker 2: idea of an AI agent like going out and collecting
Speaker 2: new information and interviewing people and buildings, like it feels
Speaker 2: a little more challenging.
Speaker 4: Now.
Speaker 3: I went like four email rounds back and forth with
Speaker 3: some jerk who is like, hey, I put together an
Speaker 3: AI newsroom and over the past three months we publish
Speaker 3: over forty thousand story And I was like, Okay, so
Speaker 3: what we're telling me is you stole a bunch of
Speaker 3: work from a bunch of hard working people and you're
Speaker 3: now passing it off as your own, Like you want
Speaker 3: me to get excited about this, so I basically blew
Speaker 3: him off. A week later he got profiled and wired.
Speaker 3: So I don't know, Tommy, he might have a better
Speaker 3: strategy than I do.
Speaker 2: Oh well, listen, I admire your your gumption and having
Speaker 2: that fight. Okay, So if AI takes all the jobs,
Speaker 2: that is obviously bad, But there's also I think a
Speaker 2: less obvious risk to the economy if AI is all
Speaker 2: actually just bullshit and hype, because that means there's a
Speaker 2: massive stock market bubble built around AI that's gonna pop.
Speaker 2: So there's a bunch of different ways you could try
Speaker 2: to quantify the size of the AI bubble right now,
Speaker 2: but one easy one to think about is there's about
Speaker 2: seven companies Alphabet, Apple, Amazon, Meta, Microsoft, Nvidia, and Tesla
Speaker 2: that make up something like thirty to five thirty five
Speaker 2: percent of the S and P five hundred market cap.
Speaker 2: If their AI businesses creater, that would lead to a
Speaker 2: stock market crash.
Speaker 1: There's all so like.
Speaker 2: AI is driving a huge amount of corporate investment.
Speaker 1: I think.
Speaker 2: I just saw Golden Sacks release for report earlier this
Speaker 2: month where they estimated that there will be one trillion
Speaker 2: dollars of AI related investment in twenty twenty six, more
Speaker 2: than half of that in the US, and if that
Speaker 2: investment is worthless, obviously it dries up. So Casey, how
Speaker 2: concerned are people you talk to about the risk of
Speaker 2: an AI bubble bursting?
Speaker 3: So people are very concerned about this. I have to
Speaker 3: say this is a place where I have a strong take,
Speaker 3: which is that I do not think that this is
Speaker 3: going to lead to some sort of massive wipeout. Here
Speaker 3: is where I should say. My fiance works at Anthropic.
Speaker 3: That is something that you should know about me. I
Speaker 3: strive to maintain my independence, but like it is just
Speaker 3: a fact on my life. But here's the reason why
Speaker 3: I don't think that we're about to see a big wipeout.
Speaker 3: Because you personally might not care about AI, you don't
Speaker 3: want to use it, But my guess is your boss does.
Speaker 3: And this is the entire thing. Business are buying AI.
Speaker 3: They are buying as much AI as the labs can make.
Speaker 3: Almost all of these companies, well, you know, maybe not
Speaker 3: a groc because it sucks, but you know the frontier labs, right,
Speaker 3: So like the Entropic, Open AI, Google, Gemini, for the
Speaker 3: most part, they have been in this kind of capacity
Speaker 3: crunch for like a year now. Because people cannot get
Speaker 3: enough of this stuff, they are bringing it into their businesses.
Speaker 3: That is why these companies, you know, Open and Anthropic
Speaker 3: are on pace to have two of the biggest IPOs
Speaker 3: in history. So in order for you to believe that
Speaker 3: there is going to be this big wipeout, you have
Speaker 3: to believe that businesses are going to stop buying the technology.
Speaker 3: And if that is the case that you're going to make,
Speaker 3: you have to give me a really good reason for
Speaker 3: why they're going to stop buying it. And I just
Speaker 3: have not myself heard that reason.
Speaker 1: I can't believe you said that about rock.
Speaker 2: If you want to create an image of a teenage
Speaker 2: classmate in a see through bikini, where else are you turn?
Speaker 3: That's a good point. That's a good point.
Speaker 1: Yeah, thank you. I do think there is like there.
Speaker 2: I think you're right there, Like groc is, groc is
Speaker 2: a shitty lollm. There is a question of whether Tesla
Speaker 2: and SpaceX and all those various companies are going to
Speaker 2: do something interesting in robotics that could be revolutionary in
Speaker 2: some way. Do you are you more of an optivist
Speaker 2: in that use case?
Speaker 3: I'm like pretty scared of robots actually. But you know,
Speaker 3: I but I wouldn't make another point about GROK because
Speaker 3: it's relevant to the bubble discussion, which is that groc
Speaker 3: was not able to like use all of its capacity
Speaker 3: to you know, make c SAM and so what they
Speaker 3: did instead, because there was no real consumer demand, was
Speaker 3: that they just sold it to Anthropic. And so I
Speaker 3: think you're going to see this dynamic because you know,
Speaker 3: some people are like, well, what if we massively overbuild
Speaker 3: and we have a bunch of data centers lying around
Speaker 3: that nobody needs, like you know, that's when the bubble
Speaker 3: will burst. My view is no, like, whoever happens to
Speaker 3: be winning at the time, they're just going to buy
Speaker 3: the excess compute because it takes a long time and
Speaker 3: you have to find a lot of political battles to
Speaker 3: get one of those things built.
Speaker 2: Yeah, uh, okay, let's end this upthing a little more
Speaker 2: fun that's hopefully also educational. First of all, people need
Speaker 2: recommendations like I use I switch to claud. I use
Speaker 2: it as mostly a souped up Google. I don't really
Speaker 2: let it write for me because I don't trust that
Speaker 2: it's not hallucinating.
Speaker 1: But I find it very.
Speaker 2: Useful as a research tool. What AI products do you use,
Speaker 2: what has helped you at life and work? And is
Speaker 2: there anything you've tried that was just comically shitty that
Speaker 2: you can tell us about.
Speaker 3: Sure, let's see. I mean some things that I've done.
Speaker 3: Like something that I encourage everyone to do is use
Speaker 3: one of these tools to build a personal website. You
Speaker 3: don't have to put it on the internet, Like you
Speaker 3: can build a website that just lives in your browser,
Speaker 3: but use a tool like chat, JPT, code AX or
Speaker 3: claude and just say, hey, make a website about me.
Speaker 3: The reason I suggest people do this is because it's
Speaker 3: kind of fun to make a website. And two, if
Speaker 3: you haven't yet like watched this thing work, I think
Speaker 3: you will learn a lot from that process. Right when
Speaker 3: you see that, you're like, could you, like, you know,
Speaker 3: make it purple and add in a little widget that
Speaker 3: pulls on the weather and like maybe my Spotify history
Speaker 3: and it just kind of does it. That may help
Speaker 3: you understand like, wow, it's pretty weird to just be
Speaker 3: able to like types some words into a box and
Speaker 3: like make an entire website. So like that's usually where
Speaker 3: I suggest that people start. In terms of other tools
Speaker 3: that I'm using. I really like this tool called Granola.
Speaker 3: It seemed a lot more interesting when it came out
Speaker 3: because everyone has copied it now. But basically, it just
Speaker 3: like listens in on my meetings. It takes really good notes.
Speaker 3: But then importantly, it's just like kind of a knowledge base,
Speaker 3: so that you know, I'm planning this new company with Kevin,
Speaker 3: I'm trying to remember what we decided about this one
Speaker 3: particular thing. I could text him, but you know, he's,
Speaker 3: you know, a diva who knows where he.
Speaker 1: Is for you of equity is what I tol.
Speaker 3: Yeah, that's what I think. That's that's what I remember too,
Speaker 3: But now I can just sort of like get it
Speaker 3: from there. So that's another one that I like that's
Speaker 3: really useful. And then I've been trying this thing town
Speaker 3: that I just mentioned, and this thing is like only
Speaker 3: been around since June. You know, I don't know if
Speaker 3: this thing has legs, but I like the fact that
Speaker 3: it's briefing me on all of my meetings. It also
Speaker 3: like creates this wiki, so it's like kind of like
Speaker 3: a wiki of my life. Think about like how much
Speaker 3: like useful information is hidden in your email. This just
Speaker 3: kind of like organizes it. It sort of pulls out important documents,
Speaker 3: it puts them in a place that you can find.
Speaker 3: So just that kind of basic personal assistance stuff that
Speaker 3: I find really useful.
Speaker 1: That's really interesting.
Speaker 2: I mean I think people are always like, oh, you
Speaker 2: should try claud code and build something. And I was like, hey, man,
Speaker 2: if I had a fucking idea for an app, I
Speaker 2: would have been like rich in twenty twelve.
Speaker 1: Okay, but I'm not.
Speaker 2: So That's why I'm here talking to you people.
Speaker 3: But I mean, have you guys thought about doing like
Speaker 3: cause you could make a pod save app. Maybe you
Speaker 3: have something like that. It's already you could just beat
Speaker 3: every transcript of every episode and like, you know, everyone
Speaker 3: on your team, because I'm sure you must all the
Speaker 3: time be like what episode did that happen on? When
Speaker 3: is the last time we talked about that? When is
Speaker 3: the last time that guest came on the show? That
Speaker 3: is something that you personally could make with AIO would
Speaker 3: not even be that hard.
Speaker 1: Oh that's really interesting, what about it?
Speaker 2: I wanted to make somethings like, Okay, I have like
Speaker 2: some nerdy niche interests, like I really care about American politics.
Speaker 2: I think foreign policies super interesting. I do shows on
Speaker 2: each Could I build an app that is able to
Speaker 2: brief me every morning on those things? And the thing
Speaker 2: I worry about how to get around is like whenever
Speaker 2: I ask Claude to research something for me, the websites
Speaker 2: that come back as sources are not the most reputable.
Speaker 1: Casey, it's none of the stuff I pay for.
Speaker 2: It's some of the great journalism that I pay for.
Speaker 2: Can I get it to pull from that?
Speaker 3: It's a great point, you know, this is this is
Speaker 3: an area where the publishers to protect their own interests
Speaker 3: because the AI companies are all incredibly like rapacious and
Speaker 3: would steal absolutely every like pixel on their website if
Speaker 3: the publishers would let them. The publishers have all said
Speaker 3: like whoa, oh no, like you cannot scrape us. So
Speaker 3: like they have not created a way so that you can,
Speaker 3: you know, sign in to chat GPT with your Bloomberg account,
Speaker 3: which is something I would love to do so that
Speaker 3: I could get the kind of briefing that you're talking about.
Speaker 3: So can you get a briefing?
Speaker 8: Yes?
Speaker 3: Will it be high quality sources?
Speaker 7: No?
Speaker 3: Are there a strange number of websites that just see
Speaker 3: to like republish the New York Times, in the Wall
Speaker 3: Street Journal and other credible sources, and so you still
Speaker 3: sort of wind up getting a decent briefing anyway. Yes,
Speaker 3: but yeah, that's it's not a bad place to start.
Speaker 1: Yeah.
Speaker 2: Weren't you building yourself some sort of goofy day planner
Speaker 2: that Kevin made fun of you for.
Speaker 3: Yes, So you know here, one of my core beliefs
Speaker 3: as a technology journalist is that it is fun to
Speaker 3: build and make things, and so I like to just
Speaker 3: have moments in my week where I am building and
Speaker 3: making things. One of the easiest things you can make
Speaker 3: with an AI tool is a to do list, and
Speaker 3: I've used literally all of them, and they're all functionally
Speaker 3: the same. There's no good reason to use one over
Speaker 3: the other. Anyone will will do you. So I had
Speaker 3: the idea to make one that was themed with a
Speaker 3: comic book that I've been reading, which is called Nightwing. Tommy,
Speaker 3: I'm sure you know that night Wing is Dick Grayson,
Speaker 3: the original Robin and so you know, I basically just
Speaker 3: wanted to see what it could do. Could I get
Speaker 3: Gemini and chat GBT to violate DC Comics copyright and
Speaker 3: create for me a night Wing themed to do list
Speaker 3: app and guess what the answer was, Yes, And that's
Speaker 3: the power of.
Speaker 2: AI night Wing who has not wanted a Robin themed
Speaker 2: anything for me when I want to build something, I
Speaker 2: described the magnetiles with my kids and that last.
Speaker 1: Question for you. So there's probably a lot of people
Speaker 1: who are.
Speaker 2: Listening, thank you for still listening, by the way we
Speaker 2: get to the end of the show, who feel like
Speaker 2: they are getting totally left behind by this technology and
Speaker 2: they just want a better understanding of these tools. I
Speaker 2: think you gave some great advice there of like use
Speaker 2: them build some things. But are there also organizations you
Speaker 2: look to or like YouTube series or like people that
Speaker 2: are doing kind of like informational stuff.
Speaker 3: Let's see beside reading platform or of course, of course,
Speaker 3: of course and listening to hard work. And we'll have
Speaker 3: a new show for folks to watch pretty soon. What
Speaker 3: I see people doing that is great is that they're
Speaker 3: going to public meetings and they're calling their representatives and
Speaker 3: they're raising concerns, and that is the place where I
Speaker 3: see getting involved really making a difference. Look, if you
Speaker 3: want to and something specific about AI, you can just
Speaker 3: type it into the YouTube search box, and I guarantee
Speaker 3: you there is some hustlebro that has like a fourteen
Speaker 3: minute video about how you should do all of it instantly.
Speaker 1: Well he planks.
Speaker 3: Exactly, but no, just you know, I would try to
Speaker 3: stay curious about it. But like, if you're nervous about
Speaker 3: what you're seeing out there, like just know that I'm
Speaker 3: with you. I'm nervous too.
Speaker 2: Okay, that's good advice. Casey Newton, thank you so much.
Speaker 2: Everyone go to platformer dot news to learn more about
Speaker 2: AI and everything in tech, and I really appreciate it.
Speaker 3: Thanks time. It was fun.
Speaker 2: Thanks again to Casey Newton for joining the show, and
Speaker 2: we will be back in your feeds on Tuesday.
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