Speaker 1: As media your witness to a great becoming. It's better
Speaker 1: offline and I'm ed Zetron. Today we're joined by computer
Speaker 1: science professor and tech writer Caln Newport for Hater Season.
Speaker 1: Cal thank you for joining me.
Speaker 2: Always happy to do some heding.
Speaker 1: I suppose, well, you had an excellent YouTube that I'll
Speaker 1: be linking in the show notes about the mistakes in
Speaker 1: AI reporting. Though I would my hater in me says,
Speaker 1: I don't think these are mistakes, but you really you touched.
Speaker 1: One of the best videos I've seen on AI report
Speaker 1: or AI in general was what you did. But it's
Speaker 1: basically this thing of like the digital iic that these
Speaker 1: stories are meant to make you feel uncomfortable, and of
Speaker 1: course this full astonishment thing. You know what, Just run
Speaker 1: the tape. Tell me a little bit about what the
Speaker 1: bits that you found, because I watched it going yeah, yeah, yeah,
Speaker 1: like an angry person.
Speaker 2: Yeah I had that. I could imagine you. When I
Speaker 2: was recording that video, I was like, I bet ed
Speaker 2: is well now the great time exactly? Well, I mean,
Speaker 2: let me just give the context, right, So I'm in
Speaker 2: an interesting situation for observing this because you know, I
Speaker 2: am a computer scientist, so I'm not afraid of the technologies.
Speaker 2: I'm happy to talk about transformers and feed forward networks
Speaker 2: and diffusion models and like, that's not that scary to me.
Speaker 2: But I also write about technology. My main journalistic home
Speaker 2: is to New Yorker, where I write a lot about
Speaker 2: you know, I do AI journalism there, so I'm up
Speaker 2: on that as well. And so I'm often noticing things
Speaker 2: in journalism. When I see AI coverage that is faulty,
Speaker 2: it really catches my attention because I have a foot
Speaker 2: in both of these worlds. So there's a lot of
Speaker 2: good AI report out there. There's also a lot of trash,
Speaker 2: and I really wanted to help people figure out how
Speaker 2: do you sort it? Like how do you figure out
Speaker 2: if you're reading something? Should I pull the ripcord on
Speaker 2: this article? Like this is not helping me, Like how
Speaker 2: do you know if it's good or not? So I
Speaker 2: was like, Okay, here's what I'll do is I'll come
Speaker 2: up with the three most common traps I see in
Speaker 2: AI reporting that makes me want to, you know, throw
Speaker 2: my iPad at the wall. And I came up with
Speaker 2: three and I'll just give you the three names. I
Speaker 2: made up. All these names don't. I don't think they're great.
Speaker 2: My producer thinks he loves them. But here we go
Speaker 2: vibe reporting that's number one. So that is where you
Speaker 2: will omit certain facts and put loosely related quotes next
Speaker 2: to each other in a way that creates a general
Speaker 2: vibe that you want to be true but it's not
Speaker 2: quite true. So you don't actually make a concrete claim
Speaker 2: that's not true, but you imply that claim by what
Speaker 2: you omit or what you put in your story, or
Speaker 2: what quotes you put next to it. So you'll put
Speaker 2: a quote, for example, about layoffs at the gaming division
Speaker 2: at Microsoft, next to an unrelated quote about concerns about
Speaker 2: AI and it's impact on jobs. Now you have the vibe, oh, man,
Speaker 2: all these people just got laid off because of AI.
Speaker 2: AI is taking jobs, where in reality the layouts had
Speaker 2: nothing to do with AI. But you put these things
Speaker 2: next to each other, you give that sense. Then I
Speaker 2: had a mining digital ick. So to me, that's any
Speaker 2: AI story where you take an example from the edges
Speaker 2: of AI, like something that wireheads and San Francisco are
Speaker 2: up to, and you just tell a story that's unsettling
Speaker 2: without talking about any of the technical details like well,
Speaker 2: what's different here? Was there a technical innovation we need
Speaker 2: to know about, and not discussing any concrete implications. Oh,
Speaker 2: this means this is going to change in the future,
Speaker 2: or it's going to have an impact on this sector.
Speaker 2: You're just telling a story to unsettle, and I think
Speaker 2: a lot of the coverage of molt Book and open
Speaker 2: Claw fell into that. And then finally this astonishment, which
Speaker 2: is more of a YouTube phenomenon than a print journalism phenomenon.
Speaker 2: But that's where every single thing that happens in AI
Speaker 2: is insane, amazing, terrifying. Everything is going to change, and
Speaker 2: so you constantly create this atmosphere of something seismic just happened,
Speaker 2: so that the consumer of the infra ends up in
Speaker 2: a bit of a panic, like I can put my
Speaker 2: finger on exactly what's terrible, but like everything terrible is happening.
Speaker 2: Those three traps, to me, should be automatic rip chords
Speaker 2: from what you're reading or watching.
Speaker 1: The only disagreement I'll have is that you would say
Speaker 1: that this isn't the majority of AI journalism, because I
Speaker 1: actually argue it would be, especially that kind of the
Speaker 1: beginning one. The vibe reporting is very common because The
Speaker 1: big one right now I'm seeing is this AI software
Speaker 1: AI is replacing software thing. If you are a reporter
Speaker 1: and cal is not making this statement I am. If
Speaker 1: you're a reporter and you bring up anthropic cowork around anything,
Speaker 1: you are wrong. I was about to call you a name,
Speaker 1: but I'm being nice today for some reason. All Right,
Speaker 1: you're a dip shit, because you are a dip shit.
Speaker 1: If you look at claude cowork, which is a thing
Speaker 1: for fucking around on your desktop, and you say this
Speaker 1: is going to compete with Salesforce, you just don't know
Speaker 1: what you're talking about. You are wrong. But then one
Speaker 1: abstraction higher is this idea that claud code is going
Speaker 1: to destroy SaaS software as a service, and the idea
Speaker 1: being that software as a service is this thing that
Speaker 1: people are just going to stop building their own CRMs.
Speaker 1: They're going to stop building their own per seat software things,
Speaker 1: but they're going to build it internally. This just I
Speaker 1: don't know if you've seen this, cow It just reflects
Speaker 1: a complete lack of understanding of how software works. Yeah,
Speaker 1: because you don't pay Microsoft for Microsoft three sixty five
Speaker 1: teams because you can't build your own word or what
Speaker 1: have you. I don't think you could. But nevertheless, it's
Speaker 1: also because they maintain it, because they make sure it
Speaker 1: doesn't break, or if it breaks, they fix the bit.
Speaker 1: So they make sure that it stays up all the time.
Speaker 1: They make sure it's accessible that has secure log in.
Speaker 2: Well, look, there's a few things going on here. One
Speaker 2: of these things I reported on last month. I did
Speaker 2: a big New Yorker piece on agents. Right, so this
Speaker 2: is relevant to the cloud code and how people are
Speaker 2: thinking about the current future, and there was this basically,
Speaker 2: here's what seems to have happened. Cloud code and these
Speaker 2: other command line interface agents can do really cool things,
Speaker 2: and I'm using the work cool here in a very
Speaker 2: careful fine, yeah cool.
Speaker 1: I would like you to be like completely like give
Speaker 1: people the actual explanation here.
Speaker 2: Yeah, so cool, meaning like Oculus. Right, you put on
Speaker 2: the the Oculus visors for the first time. Everyone had
Speaker 2: the same reaction. This is really cool. Now that's separate
Speaker 2: from that's a trillion dollar business, Like, let's put a side,
Speaker 2: but this is really cool. I'm seeing three D in
Speaker 2: a world where it tracks my head. Cloud code and
Speaker 2: other command line interface coding tools became like that. For programmers,
Speaker 2: it was like really fun the watch it doing multi
Speaker 2: step execution of the construction of demos or this or that,
Speaker 2: and the reason why it could do those cool demos
Speaker 2: it was sort of well suited for that world because
Speaker 2: that's a world that exists only in text. So cloud
Speaker 2: code works on a command line interface. It's all text based,
Speaker 2: and it works with a filesystem. You can write files,
Speaker 2: edit files, sind files to compilers. It's all exists in
Speaker 2: a small number of commands on a command line interface.
Speaker 2: It's all in a world of text, because that's where
Speaker 2: computer programs are built. That's a perfect case for lms,
Speaker 2: which love dealing with text, and they love dealing with
Speaker 2: structure text like computer code. There was an extrapolation that
Speaker 2: then happened, and really this caught on January of twenty five,
Speaker 2: which is where you first began to get this sentiment
Speaker 2: of oh, it's doing such cool things over in the
Speaker 2: world of command line interfaces in code. Certainly these agents
Speaker 2: can now soon do similar cool things and like all
Speaker 2: different things we do on a computer, and that is
Speaker 2: what laid this foundation of people were so impressed, programmers
Speaker 2: were so impressed by the coolness of what was happening
Speaker 2: with cloud code and the other command line interface tools
Speaker 2: that they extrapolated that vibe over to other computer usage.
Speaker 2: Is the point of that article I reported was, Oh,
Speaker 2: it turns out like everything else we do on computer
Speaker 2: is much harder. It's not six text commands on a
Speaker 2: command line interface creating structure code that you can compile
Speaker 2: and test to see if it compiled or not. It's
Speaker 2: much messier. The interfaces are visual. I don't realize what
Speaker 2: complexity goes into the things we click and select doing
Speaker 2: something as simple as even trying to just book like
Speaker 2: a hotel in a new city or something like this.
Speaker 2: And if you use a language model as to underline
Speaker 2: logic and decision engine of making actual actions in the
Speaker 2: real world, well, the language models make things up and
Speaker 2: get things wrong or a little bit wrong twenty percent
Speaker 2: of the time.
Speaker 1: And a little bit wrong in computer science means breaking everything.
Speaker 2: Well, it means a lot like it's okay in code
Speaker 2: because they say, oh, that didn't compile, let's try again.
Speaker 2: But when you're booking a hotel room, as I sort
Speaker 2: of detailed that article, it means like you ended up
Speaker 2: in the wrong city two years from now, and the
Speaker 2: room costs six thousand dollars, right, and so it just
Speaker 2: didn't work. So twenty twenty five was supposed to be
Speaker 2: the year of the Agents and it just didn't work
Speaker 2: and they don't really know how to fix it. But
Speaker 2: we're still vibes. This is Vibe reporting. Yeah, but cloud
Speaker 2: code is like really exciting, and so why can't we
Speaker 2: do that with everything else in the computer. It's actually
Speaker 2: a much harder problem than they were letting on.
Speaker 1: Well. The thing is I've seen, especially like in twenty
Speaker 1: twenty six, I've seen a lot more cloud code stuff.
Speaker 1: Was there's been a very big consent manufacturing operation going
Speaker 1: on right now. Wall Street Journal, Atlantic, CNBC Didrew Bosa.
Speaker 1: This is a statement from me, not cow did you
Speaker 1: both from CNBC should be fucking ashamed of herself going
Speaker 1: on CNBC every day just going cord Code's got a
Speaker 1: destryal software. She's on Twitter because she was able to
Speaker 1: vibe code a some sort of Monday clone, which is
Speaker 1: just like a it's a project management tool. It's like
Speaker 1: I made some software that worked. This is everything now.
Speaker 1: But that's kind of what you've been talking about with
Speaker 1: this Vibe reporting, where it's like I did a small thing.
Speaker 1: Now all things will be done in this manner, whether
Speaker 1: it's possible, God no, God no. But she she, like
Speaker 1: many reporters, are able to find a lot of people
Speaker 1: who are invested in AI, who will absolutely go on
Speaker 1: TV and say, Yep, it's completely true, that's gonna happen
Speaker 1: one hundred percent. It's just so strange because it makes
Speaker 1: me feel paranoid and kind of conspiratle when you look
Speaker 1: at so the majority of news about AI, and it
Speaker 1: is this viper POI. It's these vast extrapolations from sixteen
Speaker 1: thousand job losses Amazon. They mentioned AI. This plus this
Speaker 1: equals the AI is replacing people. It's just so it
Speaker 1: makes me feel like uncomfortable with the world.
Speaker 2: Yeah, well, can we sit for a moment on that
Speaker 2: Amazon example, because I think it's a great please please,
Speaker 2: it frustrates me that one frustling. All right, So Amazon
Speaker 2: lays off sixteen thousand people, right, all right, it's covered
Speaker 2: in two different ways, so the vibe reporting way it's
Speaker 2: covered I in my newsletter, in my podcast, I looked
Speaker 2: an example from Quartz and it was covered as clearly
Speaker 2: intended to imply Amazon laid off sixteen thousand people because
Speaker 2: of AI. They're being replaced by AI. They put the
Speaker 2: subhead of the article was the CEO of Amazon talking
Speaker 2: about how AI is going to increasingly disrupt the job force,
Speaker 2: and then the article itself, no alternative explanations are given
Speaker 2: for these layoffs. They kind of just give the details
Speaker 2: of like here's how many people are laid off and
Speaker 2: here's where they figured it out, and then they put
Speaker 2: a couple of quotes in there about AI being very
Speaker 2: disruptive and being able to automate jobs. It turns out
Speaker 2: those layoffs had nothing to do with AI, and you
Speaker 2: can find other reporting that focused because it was reporting
Speaker 2: that was for the financial market, so it was trying
Speaker 2: to focus on what the hell is actually happening, and
Speaker 2: the deeper reporting was like, yeah, they laid off a
Speaker 2: bunch of managers because like a lot of tech companies,
Speaker 2: they over hired during the pandemic because cloud computing became
Speaker 2: much in demand during the pandemics, So a lot of
Speaker 2: tech companies over hired during the pandemic and they're all
Speaker 2: shedding those jobs again. And Amazon's pretty ruthless about this, right,
Speaker 2: They're always looking for excuses to fire people, and they said,
Speaker 2: we have too much bureaucratic bloat, there's too many managers.
Speaker 2: We're going to fire a bunch of these managers we
Speaker 2: hired so that we can be more lean. Again, that
Speaker 2: has nothing to do with AI. In fact, this is
Speaker 2: the second or third round of these firings that they've
Speaker 2: planned to do, the first round occurring before chat GPT
Speaker 2: was even released. Like, this has nothing to do with
Speaker 2: jobs being replaced by AI. But then you can vibe
Speaker 2: report it because like, well, technically speaking, Amazon also is
Speaker 2: investing money in AI products, So technically speaking, money saved
Speaker 2: by firing these managers could be respent on AI. So
Speaker 2: you can say with a like a semi straight face,
Speaker 2: they fired people because of AI. But clearly, you know,
Speaker 2: the impression that you're giving to the reader is that
Speaker 2: they were replaced by AI and it had nothing to
Speaker 2: do with it. And I heard, by the way, so
Speaker 2: I wrote about that. I put in that video. I've
Speaker 2: heard on background from multiple Amazon executives since they were like,
Speaker 2: we were completely baffled by this coverage that was implying
Speaker 2: that people were being fired here to do with AI.
Speaker 2: This is just what we do at Amazon. We're ruthless,
Speaker 2: Like if you're not earning your keep, we fire you.
Speaker 2: They were completely baffled by that coverage and like things
Speaker 2: for pointing it out, like what.
Speaker 1: I go to be honest, cal if I got an
Speaker 1: email like that from an Amazon executive, A tont go
Speaker 1: fuck themselves? And I mean this nicely because Andy Jesse
Speaker 1: last year June seventeen, twenty twenty five, the whole thing
Speaker 1: about today and virtually every corner of the company we
Speaker 1: use in generate if I had to make customers lives better.
Speaker 1: I believe Amazon benefits from that obfuscation, and I think
Speaker 1: they deliberately fuel it. Now that may be executives who
Speaker 1: disagree with.
Speaker 2: It, well, these were lower level managers, right, so not executs.
Speaker 2: Shouldn't say those like people who work there that were
Speaker 2: like oh yeah, no, no, no, they're not firing people
Speaker 2: because like AI, they're just being brutal.
Speaker 1: Right, right, they're the people who tell the truth.
Speaker 2: Yeah, exactly exactly, But no, you are right. I think
Speaker 2: for the there has been a lot of vibe reporting.
Speaker 2: Basically the end I've been covering this for the last
Speaker 2: two years, the entire sequence of job reductions that were
Speaker 2: post pandemic corrections, so the entire tech industry over hired
Speaker 2: during the pandemic. The entire tech industry cuts ups in
Speaker 2: the last few years because they hire too many people
Speaker 2: and now they have to correct back to where they
Speaker 2: were pre pandemic. Consistently, across the board, these cuts are
Speaker 2: vibe reported is due to AI consistently. You see exactly
Speaker 2: that story. We see it, and I'm a computer scientist.
Speaker 2: We see this in coverage of computer science majors as well.
Speaker 2: Same idea. Computer science majors historically directly tied to the
Speaker 2: tech industry. If they're cutting in a downside, majors go down.
Speaker 2: If they're hiring. I mean, it's just economic. It's not
Speaker 2: surprising when the technistry is booming, we get a lot
Speaker 2: more majors because they're good jobs, right, And so computer
Speaker 2: science majors went down in the last year or two
Speaker 2: as the tech company started cutting. That was reported in
Speaker 2: the Atlantic as kids are not majoring in AI because
Speaker 2: AI makes the the.
Speaker 1: Surprise majoring and kompsa.
Speaker 2: You mean yeah, because AI is going to do all
Speaker 2: these jobs. Because we've had these cycles every five years,
Speaker 2: we have this cycle there, this is nothing anyways.
Speaker 1: No, no, I'm and the Atlantic has just done a
Speaker 1: piss poor job with this stuff. I'm I'm heighten and
Speaker 1: I'm heighten on them because they had an awful claud
Speaker 1: code thing. They just you know, I'm gonna bring it
Speaker 1: up and read the title. I'm not gonna say the
Speaker 1: reporter's name because I think that that's that's main. But
Speaker 1: let's look at this move over chat GPT. You're about
Speaker 1: to hear a lot more about Claude coding. Know why
Speaker 1: you're gonna hear about that? The fucking Atlantic is writing
Speaker 1: about it, and it's just over the whole days. Alex
Speaker 1: lieberm And had an idea what if he could create
Speaker 1: Spotify rapped for his text messages without writing a single
Speaker 1: line of code. Liberman, co founder of the media outlet
Speaker 1: morning Brew, created I message wrapped. I just want to
Speaker 1: start here and say that guy doesn't do any fucking work,
Speaker 1: and I know people who work there like I just
Speaker 1: want to start by saying, if that's the best you've got,
Speaker 1: and that probably involves throwing a giraffe and the entire
Speaker 1: zoo into a vat of acid to make the GPUs move.
Speaker 1: You're meant to read this, and the deliberate effect you're
Speaker 1: meant to have is you're meant to be scared and
Speaker 1: excited like it is a kind of a mishmash between
Speaker 1: the vibe reporting and the I guess it's the folksting.
Speaker 1: In fact, this might be a triple score because this
Speaker 1: is meant to feel you, make you feel with discomfort,
Speaker 1: but also make you freak up but also be excited,
Speaker 1: a rare triple score. It's just stories like these pissed
Speaker 1: me off because I'm fine with people going this could
Speaker 1: do this. I don't mind it happens, But when it's
Speaker 1: just like, hmm, feed me the slop right into my mouth, asshole,
Speaker 1: make me feel, make me feel all the marketing things
Speaker 1: all at once. First of all, I'm not impressed by
Speaker 1: the idea of doing I message s grapped. That's not
Speaker 1: That's not a thing a human being with that with
Speaker 1: like friends and hobbies does. I've been I've been watching
Speaker 1: the first season The True Detective. I've got many more
Speaker 1: shows I could watch. Never once would I think what
Speaker 1: if I could get it wrapped of all my messages?
Speaker 1: What a fucking psychotic thing to do. But when you
Speaker 1: read this, it's like yours. You spent your holidays with
Speaker 1: your family, wrote one tech Polticy expert. That's nice. I
Speaker 1: spent my holidays with Claude code.
Speaker 2: Well, it's fun.
Speaker 1: That's cool.
Speaker 2: It's fun to create, uh these sort of demo apps
Speaker 2: if you're like engineering minded, So in that sense, they're
Speaker 2: like most Yeah, the most cynical analysis here is that
Speaker 2: cloud code is like model trains for engineering minded people.
Speaker 2: It's a fun hobby I can put. Look at this
Speaker 2: like I made a thing that can Like I was
Speaker 2: talking to a friend of mine yesterday, computer scientist, and
Speaker 2: you're like, oh, I built a thing where I can,
Speaker 2: you know whatever, email an appointment to a thing that
Speaker 2: gets parsed by an LM and then goes to my calendar.
Speaker 2: That's just fun for him, in the same way that
Speaker 2: someone else might be like, hey, I built a cabinet
Speaker 2: and like it's really nice, Like look, I got the
Speaker 2: wood to go together, and like I'm proud. I could
Speaker 2: just on a cabinet or whatever.
Speaker 1: Except you build, Except you built something with your hands,
Speaker 1: and a cabinet can have things put in it. It's
Speaker 1: I just it feels like Lego almost, Like it's more
Speaker 1: like it's toy software that has some functionality. Because the
Speaker 1: big thing I'm waiting for with the vibe coding stuff
Speaker 1: is an actual product, you know what I mean.
Speaker 2: But you know that doesn't go well. I mean, look,
Speaker 2: this is the story of This is the story of maltbook,
Speaker 2: right because.
Speaker 1: Oh yeah, tell me more about moltbook. We were just
Speaker 1: talked about this.
Speaker 2: The whole can of worms to open there, I'm just
Speaker 2: going to open like the top of the nearest can,
Speaker 2: which is before even getting the what molt book is
Speaker 2: and is not. You know, it was in the news
Speaker 2: all the time. It was vibe coded and immediately was
Speaker 2: just full of terrible security holes because it was vibe coded.
Speaker 2: And it turned out that you could get the API key,
Speaker 2: so the key you use when you access the paid
Speaker 2: service to get used in LLLM, you have an API key,
Speaker 2: so they know who to charge. You could just steal
Speaker 2: everyone's API key who was using it because the guy
Speaker 2: just vibe coded it and so no one was actually
Speaker 2: there looking at the code. But what but what I
Speaker 2: think is going on? Okay, So here's what I would
Speaker 2: like to see. I would like to see more reporting.
Speaker 2: That would be how are people using X Like to me,
Speaker 2: that's very interesting, yeah, right, how are.
Speaker 1: People using agree?
Speaker 2: Yeah? And the problem is those answers right now. And
Speaker 2: this is confounding. I think that people who are very
Speaker 2: excited by the potential and coolness of these things in isolation,
Speaker 2: the answer to how are people using X is often
Speaker 2: not nearly as exciting as you would guess. And I
Speaker 2: think the reality with I mean, I go quite have
Speaker 2: my arms around cloud code. I do know there's a
Speaker 2: lot of people who are building kind of like internal
Speaker 2: tools or personal tools with it, which I think is cool.
Speaker 2: Most people aren't interested in that, but for some people
Speaker 2: they are. And you know, I think it's fun. Computer
Speaker 2: programming is fun, right, so like the ability to make
Speaker 2: a program work is and some of those tools are useful.
Speaker 2: But this is that's not a major industry on its own.
Speaker 2: You know, I'm designing a tool for my small company
Speaker 2: that makes it easier for us to do whatever. That's cool,
Speaker 2: but that's not like a trillion dollar industry. I can't
Speaker 2: get my arms around yet exactly how professional computer programmers
Speaker 2: are using it. They're all talking about it, really yeah,
Speaker 2: but I can't get I can't get my arms around
Speaker 2: it yet. Yeah, what do you what's what's your sense of.
Speaker 1: I'm gonna be honest, I was going to ask you.
Speaker 1: I was literally going to ask have you heard people
Speaker 1: using it? Because if you go on X, the Everything
Speaker 1: app if you put on pulhasmat suit, you go on
Speaker 1: X and you go and look. And the way that
Speaker 1: people talk about this is like they have connected into
Speaker 1: the matrix that they are now a thousand X engineer.
Speaker 1: But when you go and look at what they do,
Speaker 1: no one actually says. There's always these these kind of
Speaker 1: vibe store, the vibe tales, the mythology where it's like
Speaker 1: I had a problem vaguely that would have taken me
Speaker 1: X number of hours, but when I used claud code,
Speaker 1: it solved it immediately and caught two bugs that I
Speaker 1: didn't know about. It's very Marine Todd's style. Then everybody
Speaker 1: clapped yeah, and it's you were a computer science teacher
Speaker 1: and you you don't know either, which makes me think
Speaker 1: it's not as big a deal as people are saying.
Speaker 1: My other bit of evidence is that at the end
Speaker 1: of last year, Anthropics claud Code revenue was one point
Speaker 1: one billion annualized, so about ninety to one hundred million
Speaker 1: a month for a revolution. That feels low somehow.
Speaker 2: Yeah, I mean what I know as a computer science
Speaker 2: you can't rate performance oriented code. You can't write safe code.
Speaker 2: You can't write code it has to sort of juggle
Speaker 2: sort of complex out of scenarios. I mean, you just
Speaker 2: need good programmers' eyes on it building this code.
Speaker 1: Why is that? Is there a way of explaining to
Speaker 1: a known code of why.
Speaker 2: That is code? There's like a poetic element to it,
Speaker 2: you know, it's Writing good computer code is difficult. You're
Speaker 2: often you're dealing with, you know, what is my problem here?
Speaker 2: And I want an elegant way of sort of satisfying
Speaker 2: this problem. You're often drawing from pretty nuanced algorithms and
Speaker 2: data structures to try to figure out how am I
Speaker 2: going to organize information and efficiently access it? When performance
Speaker 2: comes into play, there's a lot of really subtle decisions
Speaker 2: to make about, you know, how am I going to
Speaker 2: store use things in such a way that we don't
Speaker 2: get bogged down when we're trying to execute things. It
Speaker 2: just becomes I'm I don't code as much anymore. Exo'm
Speaker 2: a theoretician, but I did my whole life since I
Speaker 2: was you know, seven, And it's a there's a it's
Speaker 2: an art form. Right When you're using cloud code, you're
Speaker 2: not really supposed to look at the code, and so
Speaker 2: I think that takes a lot of uses probably off
Speaker 2: the table. It's like the use cases. You're supposed to
Speaker 2: have these different instances of cloud code running, and this
Speaker 2: one's going to write code, and then this one's going
Speaker 2: to write test for that code, and then the cloud
Speaker 2: code is going to run the test and then try
Speaker 2: to fix the code if it doesn't matches the test.
Speaker 2: But your eyes aren't on the code at all. And
Speaker 2: there's I mean, obviously for a lot of programming, that's
Speaker 2: an issue. But then the other thing I've heard about
Speaker 2: the computer programming industry is it's very stratified. There's a
Speaker 2: smaller number of like really good, serious programmers that produce
Speaker 2: like ninety percent of the really important, valuable code on
Speaker 2: which everything runs. I don't think they would touch cloud
Speaker 2: code with a ten foot pole, like they're good at
Speaker 2: what they do. And then you have these like huge
Speaker 2: strata of people writing like JavaScript and sort of hacking
Speaker 2: together Python, and you know it's like not very good code.
Speaker 2: And then it's I guess you could replace some of
Speaker 2: the functional it's functional enough, but I can Yeah, I
Speaker 2: can't get my arms around it yet. But I do
Speaker 2: get you know, I have a lot of sources, so
Speaker 2: I hear from you know, I do hear from people
Speaker 2: that are talking about how cool cloud code is. Is cool.
Speaker 2: I hear from a lot of professional programmers that are like, yeah,
Speaker 2: we don't use we can't use this. We're trying to
Speaker 2: write serious programs, like we have to sell this software,
Speaker 2: like this is this is not solving a problem we
Speaker 2: have or you know we so you know, I don't know,
Speaker 2: But the problem is that that's what the reporting should be. Hey,
Speaker 2: here we are at this company looking over the shoulder
Speaker 2: of people. What are they doing. Let's talk to the
Speaker 2: engineering teams on background of this tech company exactly what
Speaker 2: role is going on here? And I think what a
Speaker 2: lot of reporting has become on AI is your hype laundering.
Speaker 2: So you look at the discussion about the technology happening
Speaker 2: from more engineering minded people, you convince yourself as a reporter,
Speaker 2: I can't understand the engineering, but I will trust the
Speaker 2: people who do, and then you launder what you're sensing
Speaker 2: from that hype into your articles, not realizing that like
Speaker 2: nerds like me, we get hyped up about stuff, and
Speaker 2: we get super excited about stuff and we go create like, yeah,
Speaker 2: you can't just launder our hype into this is what's happening.
Speaker 2: And so it's like reporting on a war where you
Speaker 2: have no one embedded, there's no one actually on the
Speaker 2: ground where the battles are happening. You're just responding to
Speaker 2: the press conferences that the generals are holding, you know,
Speaker 2: back in the Pentagon. Like it's not a way to
Speaker 2: report on what's actually going on.
Speaker 1: And I think the other thing as well is there
Speaker 1: is a if you don't do this hype laundering. I
Speaker 1: don't know how these If you're a report listening to
Speaker 1: this and you have a thought about this, send it
Speaker 1: to me anonymously. Is it trod Thatt seventy six on signal.
Speaker 1: But my thought is as well, is there's probably a
Speaker 1: problem with rationalization as well, because if you look at
Speaker 1: this and you say, Okay, well it's kind of cool,
Speaker 1: it's fun in whatever indeterminate way, it doesn't seem like
Speaker 1: serious software, like actual real deal software is being made
Speaker 1: with it. But then the CEO of Google says thirty
Speaker 1: percent of code is written in AI, which is bullshit.
Speaker 1: And I've heard from so many software engineers, well, it
Speaker 1: couldn't be that everyone's just wrong, right, It couldn't be
Speaker 1: a case of that everyone is making the most egregious
Speaker 1: capital expenditure fuck up of all time. This will be historic,
Speaker 1: I believe, worse than railways, digital beanie babies, but done
Speaker 1: at the scale of laser tag arenas. Now, that can't
Speaker 1: possibly be that, because everyone else is saying this is
Speaker 1: exciting and good. And at that point they choose instead
Speaker 1: of being like worried, instead of being a bit anxious
Speaker 1: about this, they say, well, Amazon Web Services spent a
Speaker 1: lot of money, so this spends a lot of money
Speaker 1: money too, so this is actually good. It's actually good.
Speaker 1: And indeed these people seem emphatic and excited, and I,
Speaker 1: as a non coder, can build a fudged CRM that
Speaker 1: probably would not withstand even the laziest haacker. I can
Speaker 1: do this, and thus it will extrapolate further from there.
Speaker 1: And what sucks is what the people that I believe
Speaker 1: actually will be hurt by this are retail investors. I think,
Speaker 1: regular people buying stocks in these companies or selling software
Speaker 1: stocks because they believe that color code will replace them.
Speaker 1: And altomately, I think it's just gonna be a blood bath.
Speaker 1: For people's furrow. One case that could have been avoided,
Speaker 1: except it would require reports to do something uncomfortable, and
Speaker 1: I don't think they want to do that. Ema.
Speaker 2: I think there's two things going on. This is what
Speaker 2: I've decided with reporting. First of all, it's an asymmetric risk.
Speaker 2: So a lot of reporters are like, look, there's not
Speaker 2: a major risk if I'm excited about this and it
Speaker 2: doesn't pan out, because we could be like, yeah, surprisingly
Speaker 2: this didn't pan out and with some factors we couldn't see.
Speaker 2: But they do feel like there's a huge amount of
Speaker 2: risk of saying this is not a big deal and
Speaker 2: then it is, and so it's definitely an asymmetric risk.
Speaker 2: We saw a lot of this during like COVID as well.
Speaker 2: Right is like you wanted it was less There would
Speaker 2: be less harm if you were too alarmist about something,
Speaker 2: but there could be a lot of harm. They felt
Speaker 2: like reputationally, I feel like this is not a big deal,
Speaker 2: and it was, and so there's definitely an asymmetric risk profile.
Speaker 2: There's also like a meaning defining profile. It's just really
Speaker 2: exciting to think everything is going to be disrupted in
Speaker 2: chain unrecognizably. It sort of gives a focus and meaning
Speaker 2: to like an otherwise somewhat chaotic and disrupted world that
Speaker 2: we're in right now. And so there's that aspect too,
Speaker 2: is that people want to believe there is something massive
Speaker 2: about to happen because in some sense it wipes away
Speaker 2: all the bad stuff that's happening. Who cares, none of
Speaker 2: this is relevant because this much bigger thing is coming.
Speaker 2: So I think that gets wrapped into it as well.
Speaker 2: I think the economic reporters are more on this because
Speaker 2: like their whole job is to try to I mean,
Speaker 2: they're not on it. I think after your work they're
Speaker 2: on it more.
Speaker 1: But like, oh no, if I agree, I am reading
Speaker 1: Big Series. There's an article in Bloomberg I'm trying to
Speaker 1: shove through archive dot is because they were so main
Speaker 1: and unfit to my friend Steve Burke at Games Next
Speaker 1: as that won't pay them. But it's more shit about
Speaker 1: like the software narrative, the fact that software is being
Speaker 1: disrupted by Claude code. You get the same pallid reporting
Speaker 1: from Bloomberg and even the Financial Time about Anthropic and
Speaker 1: the fd is generally pretty good. Well, they're like, yeah,
Speaker 1: they're just gonna make thirty billion dollars next year. It's
Speaker 1: just fanciful. It's there's the skepticism, the cynicism doesn't exist.
Speaker 1: And I get I agree on that there is a
Speaker 1: bubble reporting.
Speaker 2: The last fall, there was a period where everyone did
Speaker 2: the bubble reporting. After GPG five, you had a two
Speaker 2: month period where every major publication did bubble reportings. But you're, yeah,
Speaker 2: I guess that did kind of die off.
Speaker 1: It died off because they hear one nice thing from
Speaker 1: Jensen Wong and they're like, well, I'm sold.
Speaker 2: Like that jacket that jackets looking pretty sharp. I mean,
Speaker 2: someone who wears that jacket. How could they be wrong?
Speaker 1: Would a man that has a shiny jacket like the
Speaker 1: Gleat singer of Corn wears at concerts? Would he lie?
Speaker 1: And it's just what really bothers me. As far as
Speaker 1: the economic reporting though, is the Oracle, because it's like
Speaker 1: Oracle needs to prepay three hundred billion dollars over five
Speaker 1: years by op AI, a company that, if we're to
Speaker 1: believe reporting, which I do not, they made thirteen billion
Speaker 1: dollars last year in revenue and lost. Sorry they've made
Speaker 1: thirteen billion and lost. They claim nine. I think it's
Speaker 1: probably higher. How are they meant to pay thirty to
Speaker 1: sixty billion dollars a year in a year, And everyone's like, well,
Speaker 1: they'll work it out. How's Oracle meant to build those
Speaker 1: data centers? Those data centers are gonna cost one hundred
Speaker 1: and eighty nine billion dollars. They've raised one hundred billion, Okay,
Speaker 1: how they meant to do that? And everyone's just like, oh,
Speaker 1: they'll work it out. If I wish I could do
Speaker 1: this with the fucking bank, I need a five hundred
Speaker 1: million dollar house, I'll pay for it at some point,
Speaker 1: all right, it's fine, and the news would run articles
Speaker 1: about my genius housing purchases. It's just it's one of
Speaker 1: those things where and you said, well the asymmetric risk
Speaker 1: doesn't exist, it does for some of these reporters because
Speaker 1: I've been saving their their bylines for years. Because I
Speaker 1: actually think that there needs to be some sort of
Speaker 1: reckoning with this, because if you look back, there are
Speaker 1: major financial outlets that did the same, that were literally
Speaker 1: propping up Sam Bankman freed two weeks before FTX collapsed,
Speaker 1: who then went on immediately to cover AI fucking and
Speaker 1: now they're peddling bullshit for anthropic and sorry, I'm kind
Speaker 1: of hating. Well, I guess it's hated season, so I can.
Speaker 1: It just frustrates me because regular people are being scared.
Speaker 1: They're being scared by the kind of astonishment which I
Speaker 1: actually loved the astonishment reporting of like, oh well, open
Speaker 1: clawd has proven open claw has proven that ai agi
Speaker 1: is here, or they've built their own social networks that
Speaker 1: we should be scared, scared insane did Yeah, And it's like,
Speaker 1: I assume you saw that for the listeners. By the way,
Speaker 1: open Claw had this this fucking claude bot whatever it is.
Speaker 1: They had their own social network where the quote unquote
Speaker 1: lms would post, but it turns out that those posts
Speaker 1: would just make by their owners.
Speaker 2: Yeah, and this is just I mean, this is a
Speaker 2: good case study.
Speaker 1: Right.
Speaker 2: This one bothered me because like writer friends I know
Speaker 2: who are not technology related, we're texting this to me.
Speaker 2: They were worried, right, They're texting me these articles like
Speaker 2: this seems bad, Like this seems like something really bad.
Speaker 2: They were really getting the digital ic really strongly off
Speaker 2: of these articles. And I would start reading these articles
Speaker 2: and I say, well, there's no discussion of what is
Speaker 2: the technical breakthrough here? And what are the concrete implications,
Speaker 2: Because it turns out there was zero technical breakthroughs. There
Speaker 2: is no new AI technology connected to open claw, which
Speaker 2: used to be called molt whatever mold or open clawed
Speaker 2: or whatever open claw is, I think where they ended
Speaker 2: up right, which is an open source library or framework
Speaker 2: for building AI agents powered by elims. There's no new
Speaker 2: AI technology involved in this at all. The agents you
Speaker 2: build are just accessing off the shelf elms that we're
Speaker 2: all using for chatbots anyways. You can aim it at
Speaker 2: whatever commercial chatpot you want. There's no new framework for
Speaker 2: how the agents work. It's the same sort of react
Speaker 2: loop that we've been trying for the last two or
Speaker 2: three years, where you just you have a program. It's
Speaker 2: like a bit of Python code that asks an LM, hey,
Speaker 2: here's what I want to do, here's the tools you
Speaker 2: have available. Come up with a plan, and then it
Speaker 2: sends it back a plan, and then the Python code
Speaker 2: takes the first step out of that text and says, okay,
Speaker 2: here's the tools available. What should I do to execute
Speaker 2: this step? And then like the LM will give you
Speaker 2: some steps and then the Python code runs those steps
Speaker 2: and then you just go back and forth right. That's
Speaker 2: like this basic react, which.
Speaker 1: Is exactly how Manus worked as well.
Speaker 2: Everything there's nothing new about this.
Speaker 1: Manus was this AI agent that Facebook might be Meta
Speaker 1: might be acquiring. They literally when you use it, it
Speaker 1: just writes prithon code for every step.
Speaker 2: Well, so it's yeah, so this, I mean, there's nothing
Speaker 2: new here technologically. The only thing that was new about
Speaker 2: openclaw is it was open source, so it made it
Speaker 2: easy for anyone to write bad agents. And the other
Speaker 2: thing that made it interesting to wireheads in San Francisco
Speaker 2: is that the commercial products where people are trying to
Speaker 2: build these companies, they have common sense security, like, well,
Speaker 2: probably if it's just a Python program blindly doing what
Speaker 2: an LLM tells it to do, like it probably shouldn't
Speaker 2: have access to like your credit cards or to your
Speaker 2: hard drive or whatever. At open cloud like you can
Speaker 2: just you can give it access to anything you want
Speaker 2: on your computer, and so you could build really cool
Speaker 2: demos that are also like incredibly insecure and unsafe. And
Speaker 2: so it was fun for hobbyists, but there was no
Speaker 2: new technology there. Nothing was new, So it was just
Speaker 2: a way for other people like hobbyists to build their
Speaker 2: own agents. That were like less safe than the more
Speaker 2: carefully built Like what people don't here's a story people
Speaker 2: don't know is like in the immediate aftermath of chat GPT.
Speaker 2: So we're in early twenty twenty three, right, so chatchepts
Speaker 2: come out. Open AI goes on a road tour of
Speaker 2: major publications, right, and they're to try to Hey, here's
Speaker 2: what's going on. You need to write about this. In
Speaker 2: early twenty twenty three, they're like, the next thing we're
Speaker 2: going to offer is something like these agents. They call
Speaker 2: them plugins, and you can install plugins that basically can
Speaker 2: do actions on behalf of your LLLM queries. You could
Speaker 2: have like a book an airline flight plug in and say, hey,
Speaker 2: chatchipt book me a flight, and a language model can't
Speaker 2: do anything but produce text, but then the plugin could
Speaker 2: take the text and go and book you a flight.
Speaker 2: And they're like, this is the future, obviously, and that
Speaker 2: project disappeared because oh, it's incredibly unsafe and unreliable to
Speaker 2: have code that could interact with the real world that's
Speaker 2: following commands from an unreliable who's stayed an LLM like
Speaker 2: that went away not because the technology was hard, but
Speaker 2: because it's not safe. So there's nothing new. My whole
Speaker 2: article on agents, they tried this all at twenty twenty five,
Speaker 2: and we're failing to get this type of agent to
Speaker 2: work for anything outside of basically like producing computer code.
Speaker 2: So open clause is nothing new. It was just a
Speaker 2: way for other people to build these things. The only
Speaker 2: interesting stories were security whole stories. But it was reported. Man,
Speaker 2: I was listening to the All In podcast and they
Speaker 2: were tasing why this is the future of AI, Like
Speaker 2: this is it. Everyone is going to have of an
Speaker 2: open claw agent as like a personal assistant, and this
Speaker 2: is like the biggest thing in open AI. And I
Speaker 2: don't know who it was. One of those guys was like, yeah,
Speaker 2: we replaced our podcast producer, you know, with this agent
Speaker 2: who can like email guests on our behalf and book
Speaker 2: it on our calendar. And well it's costing about a
Speaker 2: thousand dollars a day right now to run it. But like,
Speaker 2: I don't know why we're going to need employees in
Speaker 2: the future.
Speaker 1: Oh yeah brother, Yeah.
Speaker 2: So anyways, like it's a non if you said what's
Speaker 2: the technical implications, not if you say, what are the
Speaker 2: concrete implications? The best story they can have is like
Speaker 2: maybe when people I don't know what it is. All
Speaker 2: the companies will try to build these things for years,
Speaker 2: so I don't know, but it was reported like just
Speaker 2: something icky is happening, and that molt book was an
Speaker 2: application that was built for these agents to communicate on
Speaker 2: like a Reddit style social network. They vibe coded that
Speaker 2: framework and it was full of security holes as we
Speaker 2: mentioned before, and there was like a small number of
Speaker 2: users that created a huge amount of agents, and they
Speaker 2: were just kind of like prompting and prodding their agents
Speaker 2: to produce, like let's talk about creating around church or
Speaker 2: killing humanity. It's like, guys, this you have Python code
Speaker 2: asking lllms to like write text in the style of
Speaker 2: like the matrix, and you're posting it on a fake
Speaker 2: social network. The real story here should be where they
Speaker 2: don't these people have things to do? Don't they have jobs?
Speaker 1: Like what is y? Yeah?
Speaker 2: Yeah, yeah, really brilliant my model train, I added a
Speaker 2: tunnel so that one really got no.
Speaker 1: I actually will push back on that. Mudile train listeners
Speaker 1: I think make up twenty percent of the listenership of
Speaker 1: this podcast course. Also, modile trains are cool. Signaling to
Speaker 1: my fellow autists out there. But you know, mudtile trains
Speaker 1: are a physical thing which you build it, you build
Speaker 1: a little a little city. I think that delightful. Respect
Speaker 1: to those with this. It's like if they and you
Speaker 1: know what, if they were just saying I have been
Speaker 1: fucking around with some software and it did something cool,
Speaker 1: I'd respect the shit out. Then I'd be like, yeah,
Speaker 1: enjoy yourself. It's going away. This is a little subsidized rites,
Speaker 1: but have fun with that. I don't know why you
Speaker 1: need a max studio, but good luck. No, they are like,
Speaker 1: this is the future. But I'm gonna be honest, cal
Speaker 1: My real question is what does openclaw actually do? Because
Speaker 1: when I went and read what it actually I read
Speaker 1: so many posts you said it books callen does and
Speaker 1: does this. I could find no proof that anyone successfully
Speaker 1: did that.
Speaker 2: And it doesn't. It's it's just a series of library
Speaker 2: calls that you can use to build your own program
Speaker 2: in theory that would do that. So open claw itself
Speaker 2: is just like a series of like interfaces and hooks
Speaker 2: that makes it easy to write a program that talks
Speaker 2: to other services basically and makes calls to LLLM. So
Speaker 2: it's just like a wrapper in which you can write
Speaker 2: your own code, and some people are trying, Yeah, like
Speaker 2: you can you can write, you can have it talk
Speaker 2: to your calendar, you can have it talk to your
Speaker 2: email and theory and.
Speaker 1: Look, but does it doesn't work?
Speaker 2: Well, it's it's just asking an lell you can simulate.
Speaker 2: I mean, I did this for my New Yorker piece.
Speaker 2: I was like, look, I can just simulate being an agent.
Speaker 2: Just ask any LLM, here's what I'm trying to do.
Speaker 2: Give me like the steps for doing this or whatever,
Speaker 2: and like anything else you ask of an LOLLM, it
Speaker 2: will give you answers that sound very reasonable in general
Speaker 2: and then have like a lot of issues in the details.
Speaker 2: Which is why agents based on llms have struggled because
Speaker 2: if you it's fine if you as a human are
Speaker 2: talking to an M, because you don't realize how much
Speaker 2: filtering and tweaking. You're like, well, that's kind of ignore
Speaker 2: that and this is good, or let me ask you
Speaker 2: to redo it. It's really an issue if you write
Speaker 2: a program that just says I will do whatever. I'll
Speaker 2: ask the LLM for a plan and then just do
Speaker 2: whatever it says because it doesn't know that doesn't really
Speaker 2: make sen or like this sounds generally generally reasonable, but
Speaker 2: with like issues in the details. Does it work out
Speaker 2: when you execute it? I mean, in a practical sense,
Speaker 2: can it? They said only all in podcasts, three dumb
Speaker 2: bitches saying exactly, that's the main reference. Don't use that
Speaker 2: word usually those people sitting around going blah blah blah.
Speaker 1: Oh we've made it and replaced our podcast producer with
Speaker 1: an LM that can make appointments and send emails in practice.
Speaker 1: Is that true? I severely doubt that. I just I Also,
Speaker 1: you're spending on thousand dollars a day, so you're paying
Speaker 1: three hundred and sixty five thousand dollars a year for this, right?
Speaker 1: Are you really or are you just? Have you? If
Speaker 1: it is it completely it probably isn't. And that's the
Speaker 1: thing I don't I get why they all in guys
Speaker 1: do it because they probably have investments and they're boosters TVPN,
Speaker 1: same fucking deal. It really rules that the two largest
Speaker 1: like tech things in the valley are just state media
Speaker 1: book for Silicon Valley. What gets me is when you
Speaker 1: get like the Atlantic CNBC Business Insider in places like
Speaker 1: that doing stuff like this, and it bothers me because
Speaker 1: they don't even need the hate on it. They could
Speaker 1: just say, yeah, I could do this, it's pretty cool,
Speaker 1: right yeah, But I guess that that doesn't get the clear.
Speaker 2: It's not an interesting story. That's not the real story
Speaker 2: is not always that interesting. It's like like hobbyists are
Speaker 2: building these tools that kind of do cool things but
Speaker 2: make mistakes, and it's a little expensive. Like the most
Speaker 2: interesting story out of open claw is the only thing
Speaker 2: I think is going to be impactful out of it
Speaker 2: is because it was so expensive, right, like that thousand
Speaker 2: dollars a day. What it's forcing people, these hobbyists to
Speaker 2: do is to turn to like cheaper alternatives for models,
Speaker 2: and that I do think is significant, this idea that
Speaker 2: there's open source models out there, they're significantly cheaper than
Speaker 2: trying to use like open ai or claud. More and
Speaker 2: more people are running their own local models because it
Speaker 2: turns out, for you know, most specific uses you might
Speaker 2: use in LLM, you don't need like a super fine
Speaker 2: tune trillion parameter beast of a model running in some
Speaker 2: data center somewhere. It's like, you know what, I'm parsing
Speaker 2: my email that try to extract like suggested times. I'm
Speaker 2: fine with like a twenty billion parameter model that can
Speaker 2: easily fit in a single GPU on a thing I have,
Speaker 2: you know, we share in our office or something like that.
Speaker 2: So to me, that's the most interesting story out of
Speaker 2: open claw is the bad news it could be for
Speaker 2: the big companies as people get more comfortable with we
Speaker 2: don't needed these formula one car versions of language models
Speaker 2: for the stuff we're actually doing. We're fine with the
Speaker 2: forward focus, right, And I think that's a transition that's
Speaker 2: going to That's a transition that to me is more interest.
Speaker 2: That's what we should be writing about. Like, that's an
Speaker 2: interesting idea to me is that you have these huge,
Speaker 2: high valuation companies, but you also have all these open
Speaker 2: source models, like the weights are just out there in
Speaker 2: the public domain that can do ninety eight percent of
Speaker 2: what people care about, and you're beginning to get low
Speaker 2: cost competitive services where people can just spin those up
Speaker 2: in cheaper data centers. That's interesting to me. That's an
Speaker 2: economic story. AI creating its own church is not as
Speaker 2: not as relevant to me.
Speaker 1: I'm already working on a story full this Friday in
Speaker 1: fact oh my premium newsletter about the fact that actually
Speaker 1: the margins of serving GPU compute are dog shit like
Speaker 1: the best of the best of like a co location
Speaker 1: place like Applied Digital, it's like twenty seven percent gross
Speaker 1: margins and that's at one hundred percent utilization. Anything below
Speaker 1: point seven they're burning cash. I hear this date center
Speaker 1: out in North Dakota losing a million dollars a day.
Speaker 1: That's the thing. This is even with these low these
Speaker 1: lower cost models on device could be interesting.
Speaker 2: That's what's going to happen. I think, I think undevice
Speaker 2: is what's going to happen.
Speaker 1: I just remembered something. This is a classic bullshit story
Speaker 1: that I see every so often. So have you read
Speaker 1: any of the stories that are like, yeah, Claude can
Speaker 1: now work for hours uninterrupted? Have you read about these?
Speaker 1: You heard this? You've seen this.
Speaker 2: Work for our Oh yeah, yeah, we were talking about
Speaker 2: the Yeah, the multi step agenda execution.
Speaker 1: Well, no way, it keeps coming back.
Speaker 2: Yeah, yeah.
Speaker 1: AI on the CNBC AI on the verge of eight
Speaker 1: hour job shift without burnout or break is it is
Speaker 1: twenty four hour our AI workday. Next going to just
Speaker 1: censor myself what I was going to say there, because
Speaker 1: it's just like yeah, it can work for hours. Is
Speaker 1: the output good?
Speaker 2: Does it a problem? Yeah, just a lose?
Speaker 1: Does it? Will?
Speaker 2: Just keep reading? Call it? Recall it? Recall it like
Speaker 2: I can. I can write a Python program that calls
Speaker 2: an hour for twenty four hours.
Speaker 1: If you need me to burn something for hours on end,
Speaker 1: I just give me some gasoline. I got your baby.
Speaker 1: I've sort this right out. Be much cheaper, but it's
Speaker 1: like yeah. By September twenty twenty five, Claudes on It
Speaker 1: four point five is reported to run autonomously for up
Speaker 1: to thirty hours, reported with com I mean, but that's
Speaker 1: more vibe reporting. It's you're meant to read that and go, wow,
Speaker 1: this thing is performing tasks that are useful, that execute code,
Speaker 1: that do something, and in that thirty hours that is
Speaker 1: equivalent to thirty human working hours versus they sat there
Speaker 1: and pissed their pants for like twenty nine point five
Speaker 1: of those at least well.
Speaker 2: And also that those are like specialized TSK typically that
Speaker 2: have clear milestones and testing, so it's like it can
Speaker 2: do something. The loop can try and try until it
Speaker 2: passes the test, and then you know that's done, and
Speaker 2: then you can move on to the next step. And
Speaker 2: then you can keep calling to l M and executing
Speaker 2: until it passes the next test and you can move on.
Speaker 2: And in theory like it's yeah, they're avoiding error cascade
Speaker 2: because it's testable and they can keep retrying or something
Speaker 2: like that. I mean, this was like the meter had
Speaker 2: this problem with their graph of how long of tasks
Speaker 2: AI can now do, and it was like the sort
Speaker 2: of like super arbitrary decision of like, oh, here's a
Speaker 2: test that takes a human five hours, Now AI can
Speaker 2: do that. Here's a task, whereas really more about like
Speaker 2: how many things in a row can you do without
Speaker 2: the errors cascading out of control or something like that.
Speaker 2: It's very different.
Speaker 1: You're being way nicer than you should be, in my opinion,
Speaker 1: just because they don't even explain. They don't even say like, yeah,
Speaker 1: and we managed to make it through it and let's
Speaker 1: go bus right hours c NBC just fucking woo just
Speaker 1: for tearing their shirts off and screaming.
Speaker 2: But it goes back to the two things. We need
Speaker 2: this report and you need technical the details of the
Speaker 2: relevant technical innovation, and a discussion of concrete implications in
Speaker 2: your future implications of that breakthrough. That's what I'm always
Speaker 2: looking for, So, like, if you want to cover a
Speaker 2: story like that, like, well, what does that mean? What
Speaker 2: is the technical breakthrough? Right? What does this mean technically
Speaker 2: you can do thirty hours of work or eight hours?
Speaker 2: What is the work? What was changed? What did they figure?
Speaker 2: What was happening before? What technical breakthrough made this possible now?
Speaker 2: And then what are the concrete implications? What specific things now?
Speaker 2: Can we directly this will now allow us to do? What?
Speaker 2: Tell us what jobs are going away? Tell us what
Speaker 2: tool we're going to see, like you have to, but
Speaker 2: we avoid that, right because then you're putting your chips
Speaker 2: down and then those things don't come along, And so
Speaker 2: I'm always looking for that. What's the technical innovation and
Speaker 2: what are the concrete implications? If you don't have that,
Speaker 2: you're mining emotions.
Speaker 1: You're mining emotions or just helping boost stook. That's what
Speaker 1: really bothers me because I hate that they're scaring people.
Speaker 1: I really really hate that they're doing marketing like they're
Speaker 1: just doing like I run a PIAFA. I've dealt with
Speaker 1: ether these states startups for like fifteen years. There isn't
Speaker 1: a single early stage startup that would get a percentage
Speaker 1: point of this bullshit, Like you email the reporter about
Speaker 1: like a series a start. They're like, all right, all right, motherfucker,
Speaker 1: how much revenue are they profitable? Yet? Why not? Why
Speaker 1: not explain to me right now? Anthropics like we're gonna
Speaker 1: gonna burn a hundred billion on training. I guess what
Speaker 1: do you think? And they're like, yeah, I love it.
Speaker 1: I actually think that's huge, that's great. I'm not And
Speaker 1: to be clear, I think that this scrutiny should be
Speaker 1: from the beginning to the end. I think that everyone
Speaker 1: should face this scrutiny. Yeah, I'm not saying that I
Speaker 1: should get an easier I'm saying, actually everyone should. Anthropics
Speaker 1: should face the most brutal scrutiny, and I guess that
Speaker 1: they don't because they want access. It's just very dull
Speaker 1: and annoying, and it's just helping already rich guys like
Speaker 1: Wario Ama Day, who should not have more money. Listen
Speaker 1: to him speak, he needs he needs to face some stress.
Speaker 1: I think some stress would help him grow. But caw,
Speaker 1: as we wrap up, I did actually have like a
Speaker 1: technical thing that wants an idea of being perclaying. So
Speaker 1: I think that this term training with models is being
Speaker 1: misused and used in a way that is kind of
Speaker 1: vibe reporting style, which is they use training as a
Speaker 1: word that suggests that it will stop, that they will
Speaker 1: stop training these models. But from correct me if I'm wrong.
Speaker 1: Training is everything from building a new model to updating
Speaker 1: and models current parameters. Correct.
Speaker 2: Yeah, there's pre training and post training is the right
Speaker 2: way to think about it. So the pre training is unsupervised.
Speaker 2: That's where you take all the text that's ever been written,
Speaker 2: and you will take a real piece of text written
Speaker 2: by a human, and you'll cut it off at an
Speaker 2: arbitrary point, and you'll tell the language model your guess
Speaker 2: the word that came next. There's a real word that
Speaker 2: came next. This is a real writing Guess the word
Speaker 2: that came next, and then it guesses, and then you
Speaker 2: adjust the weight so it gets closer to the right answer.
Speaker 2: That's prehen you adjust the waits. What are you doing,
Speaker 2: You're just so you're a training algorithm called back propagation.
Speaker 2: This is a Jeff Hinton, you know innovation where you're
Speaker 2: you're going through and you're adjusting the weights all the
Speaker 2: way through the layers in such a way that the
Speaker 2: answer it gives for this particular test gets a little
Speaker 2: bit closer to the right answer, because you know the
Speaker 2: right answer that's pre training, that's pre trains unsupervised. So
Speaker 2: you take Hamlet or you take Dickens like the best
Speaker 2: of times it was the worst of and then you
Speaker 2: give that to the thing what were to come next?
Speaker 2: And you know it says Bacon like, we're going to
Speaker 2: adjust these weights now in a way that like your
Speaker 2: answer gets a little bit closer towards times. Right. Okay,
Speaker 2: that's pre training. Then you get post training, where you
Speaker 2: already have trained, so you have this network. All the
Speaker 2: weights have been set through this massive multi month you know,
Speaker 2: billion dollar pre training, and now you want to go
Speaker 2: through and you want to tweak this to avoid certain
Speaker 2: types of behaviors or to influence towards certain types of behaviors.
Speaker 2: So for post training, it's almost always based off of
Speaker 2: you have inputs like prompts and correct answers. This is
Speaker 2: the right way to respond to this question, right, So
Speaker 2: you've you've you have of questions and answers. You give
Speaker 2: the prompt to the LLM, it spits out some answer,
Speaker 2: and now what you're doing you're using is called reinforcement learning.
Speaker 2: It's a general technology, but you're using techniques from reinforcement
Speaker 2: learning the sort of like zap it like you would
Speaker 2: zap a dog when you're dog training it to if
Speaker 2: it's a bad answer, you zap it, so you get
Speaker 2: those weights away from that answer, and if it's a
Speaker 2: good answer, you give it a treat like Okay, this
Speaker 2: is post this is post training. And so that's where
Speaker 2: you've moved past the word guessing game, which is where
Speaker 2: all of the sort of general smarts comes from these models,
Speaker 2: And now you're you're you're doing the sort of zapping
Speaker 2: and treat training around very specific things, right, so this
Speaker 2: is where you keep going. Yeah, So like so you'll
Speaker 2: go through and like ask it questions where the answers
Speaker 2: might be like about building bombs or whatever, and every
Speaker 2: time it spits out an answer about a bomb, you
Speaker 2: give it like a really bad negative shock, and you
Speaker 2: like definitely turn off those circuits, like we don't want
Speaker 2: you to spit out answers by bombs, Like that's where
Speaker 2: all the guardrails come from. Or if you want it
Speaker 2: to get better at doing like a particular math exam,
Speaker 2: you can give it like lots of questions from that
Speaker 2: math exam and then you have the right answer and
Speaker 2: you can kind of zap it to move it towards
Speaker 2: what the answers look like on this math exam. So
Speaker 2: so post training is more focused. You have particular types
Speaker 2: of behavior you're trying to sort of instill in this
Speaker 2: already pre trained massive network. That's mainly where the focus
Speaker 2: turned after GPT four. So GPT four was like the
Speaker 2: extent of pre training making it smarter. After GPT four,
Speaker 2: trying to make those models even bigger and pre train
Speaker 2: them longer didn't lead to much performance increases. So everything
Speaker 2: we got between four in the lead up to five
Speaker 2: was post training, and that's when they began focusing on
Speaker 2: metrics because if I have a particular metric, I can
Speaker 2: post train a model to do well on that test.
Speaker 2: And so everything became about metrics and post now we
Speaker 2: can do this thing better or look at this thing
Speaker 2: we do better, And so that's kind of the game
Speaker 2: that's played now is we do lots of post training
Speaker 2: that requires like much more specific data because you need
Speaker 2: like right answers, pairs of props, and correct answers. So
Speaker 2: only certain things we can do this with. But that's
Speaker 2: the game we've been playing since like twenty that's one.
Speaker 1: Do they do that with models that exist now? So
Speaker 1: this is basically updates, right?
Speaker 2: Yeah? They do it on a semi regular basis. Yeah,
Speaker 2: but they usually there'll be a name change, like GPT
Speaker 2: five to two is different than five to one, different
Speaker 2: than five.
Speaker 1: But don't they update the current models?
Speaker 2: They don't repretrain them. That's too expensive.
Speaker 1: But they no, no, I'm not saying that. And say
Speaker 1: do they post train the current models to make them
Speaker 1: better at stuff?
Speaker 2: Yeah?
Speaker 1: To tweak things.
Speaker 2: Yeah.
Speaker 1: So that's This is a very long way to get
Speaker 1: to a point I'm kind of making. Which is one
Speaker 1: of the problems with VIBE reporting on this is training
Speaker 1: is framed as this thing like inference is framed as
Speaker 1: op X that is permanent that you cannot avoid influence
Speaker 1: being creating the outputs. Training is framed as this R
Speaker 1: and D mysticism, which is just out there and you're
Speaker 1: not like training. They never say this, but you hear training,
Speaker 1: you think, oh, you train and then you perform, and
Speaker 1: so training would end. But from what I understand, training
Speaker 1: is as common and necessary an expense as influence at
Speaker 1: this point.
Speaker 2: Yeah, it's the only way you improve or update things, right, Like, so,
Speaker 2: if you had a Microsoft through sixty five software, you're
Speaker 2: constantly sending updates and patches and whatever is you like
Speaker 2: to add new features or whatever. In the AI model world,
Speaker 2: it's it's yeah, it's posting more rounds of post training.
Speaker 2: It's the only way to make any sort of improvement
Speaker 2: or fixing bugs. Like you're like, oh, here's something that's
Speaker 2: saying that we're really upset about. Okay, let's go in
Speaker 2: and do some zapping. Let's get out to zapper, give
Speaker 2: it a bunch of examples of saying that bad thing,
Speaker 2: and let zap and say don't do that. And now
Speaker 2: it's like very unlikely to do that. So yeah, they're
Speaker 2: constantly they're constantly doing that. Otherwise, your a stasis, which
Speaker 2: is different than the way most people actually think of it. Differently,
Speaker 2: they think that the model is somehow like learning online
Speaker 2: that like, which as it's absolutely not true that as
Speaker 2: you talk to it, it's learning and it's getting uh,
Speaker 2: it's getting better. And they're like, but wait a second,
Speaker 2: something I talked about earlier, it must be getting smarter.
Speaker 2: The model doesn't care about you, just your local software.
Speaker 2: You don't realize this. It's including like huge bits of
Speaker 2: stuff you've talked to it before. In the prompt that's
Speaker 2: going to the model. It's like, here's a bunch of
Speaker 2: stuff that Ed has submitted to you in the past. Okay,
Speaker 2: now here's his current question. So it's the model hasn't changed.
Speaker 2: The model doesn't have memory a canunt of justice. It
Speaker 2: doesn't adjust its memory in real time. It's all static.
Speaker 2: There is no dynamic memory involved in these models. They're
Speaker 2: fixed until you go out and post train it and
Speaker 2: then you bring the new weights back in the data center.
Speaker 2: And that's the thing. Do an inference now.
Speaker 1: But that's the thing like this. The reason I bring
Speaker 1: it up as the kind of closing vibe story is
Speaker 1: because training is very clearly getting more expensive what they
Speaker 1: say profitable in inference, which again doesn't really make sense
Speaker 1: to me. But putting that aside, they're not. But even
Speaker 1: if they were, if training never stops, then who cares
Speaker 1: about profitable in inference? Like it just means that you
Speaker 1: will get more or expensive forever. Inference is just yeah, yeah,
Speaker 1: differences pooh, training is pooh. That's just that's I'm not
Speaker 1: putting that one in.
Speaker 2: A yeah, okay, it's an interesting question, right because it's
Speaker 2: also getting like the what's an altman who is making
Speaker 2: those comments about well, we just didn't have to pay
Speaker 2: for the training. This would be part I.
Speaker 1: Didn't have all these expenses, I would be so profitable.
Speaker 2: The one distinction that's maybe relevant there not to be
Speaker 2: an apologist, but the one one distinction that is relevant
Speaker 2: is pre training versus posts. So pre training is insanely expensive, right,
Speaker 2: because you're you're training something on all the all the
Speaker 2: words in the world, and it takes months. So it's
Speaker 2: it's just like you're running a data center that's going
Speaker 2: to have nowadays up to six digit GPUs uh running
Speaker 2: full time for months just to get that pre training done,
Speaker 2: and you have to pay for all of that, right,
Speaker 2: So that's all right time, you're not getting money, you're
Speaker 2: just paying for training. Post training is also expensive. It's
Speaker 2: not that expensive though, because as expensive as pre training,
Speaker 2: because each post training session it's a way, way, way
Speaker 2: way smaller data set that you're post training on. It's like,
Speaker 2: all right, we generated like ten thousand examples of people,
Speaker 2: you know, responding to questions in a racist way, and
Speaker 2: those ten thousand examples will use to reinforcement learn and
Speaker 2: try to move it away from answering those type of
Speaker 2: questions in a racist way. That's like not that big
Speaker 2: of a data set compared to we're going to train
Speaker 2: this on every word written that we have access to.
Speaker 2: So the post training is not as expensive as pre training.
Speaker 1: Unless you're doing it all the time.
Speaker 2: Yeah, that's true.
Speaker 1: That's the thing if you're doing it all the time
Speaker 1: and it takes months of pre training, but you're constantly
Speaker 1: doing something like post training for months. Yeah, I mean
Speaker 1: post training not functionally the same thing.
Speaker 2: It's the same thing. I think this is a fair point.
Speaker 2: When you when you have a particular example that you're
Speaker 2: post training on like one prompt with an answer, it's
Speaker 2: kind of like you're doing inference in reverse, right, So
Speaker 2: you're going from your back propagating from one side of
Speaker 2: the network to the beginning, as a post are going
Speaker 2: from the beginning to the end. Now, it's more expensive
Speaker 2: than that because when you're just doing inference, the fundamental
Speaker 2: operation is basic multiplication. You're just multiplying numbers in the
Speaker 2: big table backpropagation which you use to training. You're it
Speaker 2: is multiplication. When you do a bunch of it's a
Speaker 2: bunch of derivatives because you're constantly you need to calculate
Speaker 2: like the derivative of these. I mean like it doesn't
Speaker 2: really matter, but you kind of need that. You want
Speaker 2: the derivative because you want the gradient to scent to
Speaker 2: be towards like better and away from worse and derivatives.
Speaker 2: My understanding is this is like more expensive operations per
Speaker 2: weight that you're trying to change because you're not just
Speaker 2: multiplying a number, You're having to calculate derivatives and becomes
Speaker 2: a little bit more complicated. So yeah, it's like inference
Speaker 2: in reverse, but also like a little bit more expensive.
Speaker 2: So if you if you have ten thousand sample question responses,
Speaker 2: you're going to use the post train, it's kind of
Speaker 2: like ten thousand users sent prompts and there are particularly
Speaker 2: expensive prompts, and you had to pay for that instead
Speaker 2: of like them paying for it. So yeah, it's good
Speaker 2: to think of it as like inference and reverse.
Speaker 1: It's also an ongoing cost, Like it's everyone is leaning
Speaker 1: on this idea that this stuff will magically become profitable.
Speaker 1: I don't know if you've seen the cash flow diagrams
Speaker 1: of anthropic and open ai. But there is a mysterious
Speaker 1: math going on where year twenty twenty eight, twenty twenty
Speaker 1: nine they just become profitable.
Speaker 2: Well, I was going to ask you about this. Last month,
Speaker 2: there's this announcement that open ai had some massive increase
Speaker 2: in revenue.
Speaker 1: Yeah, well this is actually a great vibe reporting thing,
Speaker 1: and that's annualized revenue. They said they hit twenty billion
Speaker 1: in annualized revenue, which would mean one point sixty seven
Speaker 1: billion in a month. Now, important details. We don't know
Speaker 1: how they're defining a month. We don't know if they
Speaker 1: mean twenty eight days, twenty nine days, thirty days. We
Speaker 1: don't know if they mean a calendar month, so the
Speaker 1: month of November or December, or do they just bean
Speaker 1: any thirty days. We don't know if they're doing insane math,
Speaker 1: which happens very rarely, that this company feels like one
Speaker 1: that might do it. Just my gut instinct is they
Speaker 1: may be doing his seven day period and we're going
Speaker 1: to turn it into a month like they We don't
Speaker 1: know how they're doing this. And they also coupled this
Speaker 1: by saying that as compute grows, revenue grows. I don't
Speaker 1: know if you seen this.
Speaker 2: No, it's like, say, their formula, yeah, okay.
Speaker 1: Yeah, it's an insane formula that does not map to
Speaker 1: any economics. Like it's just it's the kind of thing
Speaker 1: that if we had a functioning business and tech press,
Speaker 1: that would just be scrutinized to the bone, that would
Speaker 1: just be ripped apart and say, what the fuck does
Speaker 1: that mean? Because if this were true, if you simply
Speaker 1: add more gigawattch at more revenue, then you would simply
Speaker 1: print more money, like it would be a money print
Speaker 1: of us.
Speaker 2: Like my movie theater did well. Therefore, yeah, if I
Speaker 2: build one hundred thousand movie theaters, we're gonna make one
Speaker 2: hundred thousand more you know, times the amount of money.
Speaker 2: And it's like, well, wait that theater was in Manhattan
Speaker 2: and it was like really well run, and they're yeah,
Speaker 2: well okay, I assume I assumed as much trying to
Speaker 2: understand that story. Okay, yeah, the annualized revenue stuff, I
Speaker 2: think I even use that term talking to someone I
Speaker 2: credit you as like Trum would say, look out for
Speaker 2: annualized revenue.
Speaker 1: It is. The funny thing is with that as well,
Speaker 1: it's more viabe reporting because arr standard. It's very standard
Speaker 1: in SaaS companies that sell on a per seat basis,
Speaker 1: So a sales force would do a well even they
Speaker 1: are doing annualized now with the AI revenue, but it
Speaker 1: would be a software company has one hundred seats they
Speaker 1: sell to a company and they charge fifteen bucks a
Speaker 1: bit a seat per month, and they charge that annually,
Speaker 1: and the actual cost of each user is fairly measurable
Speaker 1: because they're doing CPU based stuff. Like it gets expensive
Speaker 1: at scale because there's a lot of people, but it
Speaker 1: doesn't get multiply multi I can't say the word. It
Speaker 1: doesn't get much more expensive as you grow with AI.
Speaker 1: It's actually because of the way large language models work,
Speaker 1: your most excited customers are your most expensive.
Speaker 2: Yeah, because they blow whatever whatever their monthly revenue is,
Speaker 2: whatever their cost is, they blow past it, so.
Speaker 1: You can't even do a percy revenue. It's just every
Speaker 1: all of these things. When I say them out loud,
Speaker 1: I'm like, I feel like this should be more obvious.
Speaker 1: It's why I loved your video because it was like
Speaker 1: thank you someone else, Well, here's the.
Speaker 2: Question, here's my here's my question, here's like the dangerous
Speaker 2: question I actually put out a video last week. It
Speaker 2: was like dangerous question. We're now on year three or
Speaker 2: four of like the I'm counting new year starting like
Speaker 2: New Year twenty twenty three or whatever of people saying,
Speaker 2: oh my god, this is so cool. These massive disruptions
Speaker 2: they're going to change everything is imminent, and year after
Speaker 2: year we've said that I'm not yet seeing the massive disruptions,
Speaker 2: like not the not the stories of what might be
Speaker 2: disrupted or the stories of what's different, but like, how
Speaker 2: many years do we have to go without industries crumbling
Speaker 2: or major new economic players that didn't exist before, or
Speaker 2: complete restructuring of huge companies around this technology? How many
Speaker 2: years we have to go? So I did a video,
Speaker 2: right I went, I found the Reddit thread where someone
Speaker 2: just asked this question. This was from like earlier in
Speaker 2: the month. They were like, outside of the vibe coding stuff,
Speaker 2: what what are the like, what are people? What are
Speaker 2: the big tools? Like, what are the big things that
Speaker 2: have come out of this technology? They're changing things? And
Speaker 2: I read through this whole thread and it was interesting,
Speaker 2: there's not people don't have much like well, you know,
Speaker 2: like it could these are like really small case studies.
Speaker 2: I used it to help, you know, gather clean up
Speaker 2: data that I got from whatever. I was like, this
Speaker 2: is like such a nerdy specific use case, and so
Speaker 2: I went through that thread in a video and this
Speaker 2: has kind of been my question. It's like, it is
Speaker 2: very cool technology, but how do we know where this
Speaker 2: is going to fall? Like to me, the scale is
Speaker 2: it would go like this, like blockchain software, then Oculus VR,
Speaker 2: then maybe internet to electricity. Right, so we're gonna have
Speaker 2: a scale of disruption.
Speaker 1: Right.
Speaker 2: Blockchain software is something where the premise it's made no
Speaker 2: sense and it was never going to get off the
Speaker 2: ground and there was going to be no impact on
Speaker 2: the world. And you know, because I'm a my training
Speaker 2: in CS is in distributed system theory. I was there
Speaker 2: in twenty twenty saying guys, let me just tell you
Speaker 2: this is nonsense. No Web three is not about to
Speaker 2: take off. None of this makes sense. And that was true.
Speaker 2: That did nothing. Then you have like Oculus VR. It
Speaker 2: really is cool, right you put on these things like
Speaker 2: that is awesome, Like I love this technology, but it's
Speaker 2: having a hard time having any real major impact because
Speaker 2: people aren't sure what.
Speaker 1: Times use of it. Also, most people don't necessarily have
Speaker 1: a great experience initially because it's extremely dependent on where
Speaker 1: you are, who you are, the size of your skull
Speaker 1: not kind of yeah so.
Speaker 2: Big right for a limited group of people, it's really cool,
Speaker 2: but it fails to come out. Then the Internet is
Speaker 2: like really disruptive, changed a lot of things more. It's
Speaker 2: not so much as like whole industries disappeared or whatever,
Speaker 2: but it like changed the way a lot of industries
Speaker 2: actually function. And then like electricity, you could say, like
Speaker 2: it just completely changed what day to day existence of.
Speaker 1: Business was, like how existence of exactly?
Speaker 2: Yeah, And so that the question like that everyone should
Speaker 2: be asking is where is the ENERVEI going to fall
Speaker 2: on this? And you know, I would say, right now,
Speaker 2: this is what gives me yelled at. I'm not saying
Speaker 2: this is the prediction necessarily going forward, but right now
Speaker 2: I don't think it's got passed much farther past the
Speaker 2: oculus part of that scale where it's really cool. There's
Speaker 2: very cool things. Chat Gipt is very cool. It's very
Speaker 2: cool that it can like have that comprehension and no
Speaker 2: one thought it could do that, But we haven't yet
Speaker 2: figured out what.
Speaker 1: Comprehension. It's no, it's a text I mean.
Speaker 2: We take it for granted, but for CS people, the
Speaker 2: ability that like I can add, hey, give me text
Speaker 2: that like whatever in the style of a poem that
Speaker 2: does whatever, and that includes a character from Star Wars,
Speaker 2: and then it can give you text it does that.
Speaker 2: That comprehension, like for computer scientist was like, oh, we
Speaker 2: didn't really know how to consistently one yeah. Yeah, that's
Speaker 2: like very cool. Yeah, but we haven't got past this
Speaker 2: is like the the surprising thing of this field is
Speaker 2: we're not really past the oculus stage yet, like where
Speaker 2: they're like, for certain this is vibe coding is really cool.
Speaker 2: The comprehension is cool, so like sora is weird, but
Speaker 2: like it's cool, you can do that, But the markets
Speaker 2: are not None of these have markets yet, right, Like that,
Speaker 2: there's not big markets in any of these yet. And
Speaker 2: will how far will it go from oculus to the Internet.
Speaker 2: To me, that is like the number one question, the
Speaker 2: number two question, the number three question of all reporting
Speaker 2: on this, and almost no one's talking about that. It's
Speaker 2: just hype laundering. We'll take this hype, will extrapolate, it
Speaker 2: will react to that extrapolation. That's kind of what reporting is, right,
Speaker 2: now in AI, Whereas to me, this is the hugest question.
Speaker 2: If this ends up oculus, retail investors are going to
Speaker 2: get screwed. If it ends up Internet, all right, that's like,
Speaker 2: that's like a really interesting significant story. If it ends
Speaker 2: up electricity, obviously that really matters. But like, I don't
Speaker 2: know anyone who actually thinks it's going to be that disruptive.
Speaker 2: Not the current technology. That's the story to me. Not
Speaker 2: let's like extrapolate us. You know, hey, what are these
Speaker 2: things are creating a church? Or let's let's let's hype
Speaker 2: launder extrapolate that and react to our extrapol That's not
Speaker 2: really reporting so much as speculative fiction writing. I guess
Speaker 2: I don't know quite what to call it. But this
Speaker 2: is the real question, where exactly are we now and
Speaker 2: what are the possibilities of like where this is going
Speaker 2: to go positive and negative? I don't we have enough
Speaker 2: talk on that.
Speaker 1: I fully agree, Cal, it's been such a pleasure in
Speaker 1: having you. Thank you for joining me.
Speaker 2: Always happy to talk shop, always happy to hate with you.
Speaker 2: I guess in your season, I like.
Speaker 1: It, Hey, season is the best. We will be back
Speaker 1: this week with either a monologu or an interview. I
Speaker 1: have not decided, because I've got wonderful Corey Quinn interview
Speaker 1: I just did, so I'm considering putting that in a monologue.
Speaker 1: You'll find out on Friday. Anyway, this has been Better
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