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

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Speaker 2: Hello and welcome to another episode of the Odd Lots podcast.

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Speaker 2: I'm Joe Wassental and I'm Tracy Alloway. So, Tracy, you're

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Speaker 2: cool like if I, like, you know, just start doing

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Speaker 2: this part time as I like build out my software business, right, Like,

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Speaker 2: you're cool about that.

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Speaker 3: I was gonna say, I've been thinking about AI and productivity,

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Speaker 3: and so far your productivity has gone down, Joe, because

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Speaker 3: instead of doing Odd Lots things, you're coding your own software.

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Speaker 2: Except that I'm creating content for the Odd Lots newsletter

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Speaker 2: about coding, and that is productivity a creative debatable debatable,

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Speaker 2: But but you're cool with that. You're cool with like

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Speaker 2: me like, oh, I'm just gonna like check in part

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Speaker 2: time on Odd Lots when we have a recording.

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Speaker 3: Well, so of course not okay, good, of course not.

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Speaker 2: Yeah, that's the right answer. I want you to yes,

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Speaker 2: I want you to be really sad. But like a

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Speaker 2: few other people, you know, I have like caught the

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Speaker 2: sort of like bug of like AI coding and I'm

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Speaker 2: totally blown away. I've like played with it from the

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Speaker 2: big I started playing around with it last year, but

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Speaker 2: then over the holidays. I've been writing about this in

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Speaker 2: the newsletter. Suddenly like my Twitter feed is like clug code,

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Speaker 2: clug code, clug code, I do just cursor before, which

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Speaker 2: I was very impressed by at the time. And so

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Speaker 2: when I got home from vacation, one of the first

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Speaker 2: things I did is like figure out how to install

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Speaker 2: clug code on my computer. And I was like, oh,

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Speaker 2: I am like hooked. This is actually like I see

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Speaker 2: why I have my Twitter feed is just like people

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Speaker 2: posting about this.

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Speaker 4: All right.

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Speaker 3: So I have to say I have not tried it

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Speaker 3: because I only have a work computer and I can't

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Speaker 3: install new software, and I probably definitely cannot install new

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Speaker 3: software that then makes changes to your existing software. I

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Speaker 3: don't think Bloomberg would like that, but I have seen

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Speaker 3: the hype. Lots of people talk talking about it. Have

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Speaker 3: you seen claud Cowork.

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Speaker 2: Have you heard of Oh yeah, yeah, yeah yeah.

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Speaker 3: So one of the criticisms of claud code was that

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Speaker 3: you know, like, okay code, but you still need some

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Speaker 3: background knowledge in coding because like, you know, the interface

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Speaker 3: is kind of like sties and all of that, or

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Speaker 3: nineteen nineties Cowork apparently like goes a step further for

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Speaker 3: normal people in quoting. It makes it super super easy.

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Speaker 3: And the funniest thing is that apparently cloud code actually

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Speaker 3: coded Cowork.

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Speaker 2: So this is like really relates to my experience last

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Speaker 2: year and then this year, which is that even last year,

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Speaker 2: like trying to use the AI coding tools, it was

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Speaker 2: an annoying process because there are various things that you

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Speaker 2: had to do in the actual command line of the

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Speaker 2: computer that were like I didn't I don't know command

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Speaker 2: line vernacular, and you have to install these libraries and

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Speaker 2: stuff like that. So there was this sort of like

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Speaker 2: barrier that existed. But what's really changed in the last

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Speaker 2: year or with the with Claude code, which has actually

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Speaker 2: been around for a while and I should have like

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Speaker 2: played with it before, is that like, because it sits

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Speaker 2: on your computer, it sort of takes away it de

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Speaker 2: abstracts it and so when you talk.

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Speaker 3: About like that it actually does the stuff.

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Speaker 2: It does it it just like oh, it's like, oh,

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Speaker 2: we're gonna need to install this open source natural language

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Speaker 2: processing library. It just does it automatically. Instead of me

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Speaker 2: trying like figure out like what are the right keystrokes

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Speaker 2: to pull that in or why is this not going

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Speaker 2: into the right file folder or whatever, and so like

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Speaker 2: what like Cowork, it's like all like all of these

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Speaker 2: sort of like little frictions, like these technical things like

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Speaker 2: command line user very rapidly are like dissipating and so

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Speaker 2: that like then you have something like Cowork where it's

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Speaker 2: just like they know they're taking care of that, and

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Speaker 2: so you get this like user interface that's just like

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Speaker 2: it's just getting easier and friendlier. There's almost no technical

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Speaker 2: frictions at all anymore.

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Speaker 3: Also, it feels very iterative, like the code is improving

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Speaker 3: upon itself at this point I think was one of

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Speaker 3: Claude's main selling points.

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Speaker 2: Well, this is like you've seen like people talk about like,

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Speaker 2: oh is AGI here? And this is like part of

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Speaker 2: the debate because the premp one of the ideas I

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Speaker 2: guess behind AGI is like, well what happens when you

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Speaker 2: have software that can train itself and so forth? And

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Speaker 2: I don't really know if I buy that, but you

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Speaker 2: do just see like how fast the iteration cycles are,

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Speaker 2: and I think we want to get into this. In part,

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Speaker 2: they're fast because a bunch of people are suddenly getting excited.

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Speaker 2: So then the human provides this sort of like we're

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Speaker 2: sowing the seeds of our own demise because we're so

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Speaker 2: enthusiastically participating in the evolution. But I just like it's

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Speaker 2: suddenly clear, like, oh, this is going to change I

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Speaker 2: think computing. And the other thing is the code works,

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Speaker 2: like it creates code that like this is like there's

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Speaker 2: no bugs. You know it works.

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Speaker 3: Did you see speaking of automating yourself, you see there

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Speaker 3: was a post on Reddit from a lawyer who said

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Speaker 3: he's basically used claud code to automate like his entire job,

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Speaker 3: and he hasn't told anyone.

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Speaker 2: I'm not exactly surprised because the other thing that I

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Speaker 2: experimented with is and I haven't one hundred percent verified this,

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Speaker 2: but on jobs Day last week, I downloaded the full

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Speaker 2: pdf and I just typed into the cloud code like

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Speaker 2: find the most interesting details and make some charts based

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Speaker 2: on and it did it in like a couple of minutes.

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Speaker 2: I have no like ability to like I've never like

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Speaker 2: built charts myself or whatever, like designing or whatever. And

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Speaker 2: I didn't totally confirm yet that the data was all correct,

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Speaker 2: but I'm pretty sure it was because everything I spot checked,

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Speaker 2: so I didn't just that crucial I didn't tell you

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Speaker 2: I know I didn't. That's why I didn't want to like, oh,

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Speaker 2: like here's what, here's the today's jobs reporting charts.

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Speaker 5: But I did.

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Speaker 3: So what application did it actually build it in the charts?

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Speaker 2: I don't know. I just had a file like that's

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Speaker 2: the thing. I had a file on my computer at

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Speaker 2: that point. What kind of file, like a P and

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Speaker 2: G file, like an image file, that's the crazy thing.

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Speaker 5: I don't know.

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Speaker 2: And so there was just this image that had a

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Speaker 2: bunch of charts, and my spot checks did suggest like

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Speaker 2: I didn't see anything off. And people get paid money

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Speaker 2: to like build that kind of stuff for like analysts

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Speaker 2: and stuff like that.

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Speaker 3: And right, so this is the other big question. If

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Speaker 3: everyone can build their own software, what actually happens to software?

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Speaker 3: And I was reading something I forget who it was by,

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Speaker 3: but someone used claud code to create they wanted a

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Speaker 3: website that would basically make the money for doing nothing,

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Speaker 3: and that was the prompt And did.

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Speaker 2: They do it?

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Speaker 6: Yeah.

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Speaker 3: So the idea that the model came up with was

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Speaker 3: you can sell prompts, packages of good prompts and sell

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Speaker 3: them for like forty bucks and you'll make tons of money.

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Speaker 3: And I was thinking about that like, Okay, it's possible

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Speaker 3: to make money that way, but also why wouldn't I

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Speaker 3: just use claud code to do the same thing.

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Speaker 2: There are many big questions that we use an economy

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Speaker 2: are going to have to think about, and I think

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Speaker 2: my main takeaway is we're gonna have to think about

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Speaker 2: these sooner red than later. But what is clud code?

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Speaker 2: Why is everyone so hyped about it? Like what is

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Speaker 2: it about this particular piece of software versus what exists

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Speaker 2: from open Aye and Gemini and all this stuff, Like

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Speaker 2: why is this captured everyone's imagination? We really do have

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Speaker 2: the perfect guess because it's someone who, unlike me, has

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Speaker 2: been getting their hands and the stuff for longer. One

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Speaker 2: of the few people that I know who was into

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Speaker 2: lollms before chat GPT existed and was actually using them

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Speaker 2: via the API, and was actually talking about their technical

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Speaker 2: capacity to do things like coding even before November of

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Speaker 2: twenty twenty two. So truly the perfect guests We're going

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Speaker 2: to be speaking with Noah Bryer. He is the co

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Speaker 2: founder of Alefic, which is a consultancy that helps big

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Speaker 2: companies deal with AI stuff. So, uh, Noah, thank you

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Speaker 2: so much for coming on odd lots.

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Speaker 5: Thank you for having me.

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Speaker 2: What is eleven? What's the deal? How were you like

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Speaker 2: using lollms before chat GPT existed. I don't know. I

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Speaker 2: know very few people who were doing that.

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Speaker 6: I had the good fortune of shutting down a startup

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Speaker 6: in twenty twenty two, and so I had a lot

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Speaker 6: of free time on my hands.

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Speaker 2: And then how are you using it though?

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Speaker 3: Like?

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Speaker 2: How did you look at your like how did you

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Speaker 2: aware that there was this thing that could be of

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Speaker 2: potential used to.

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Speaker 6: What was So my very first thing I was doing

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Speaker 6: was using gethub Copilot, which at the time was built

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Speaker 6: into VS code, and it was.

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Speaker 5: Autocomplete inside VS code.

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Speaker 6: So it was a nice and pretty immediately realized that

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Speaker 6: there were certain coding tasks that it could just handle completely.

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Speaker 6: Anything that was very pattern based. So if you write code,

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Speaker 6: you write a lot of tests. If you write tests,

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Speaker 6: every test kind of follows the same pattern, and you

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Speaker 6: want it to follow the same pattern. You're looking for

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Speaker 6: that structure, and over time, because it was looking at

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Speaker 6: your code base, it was able to basically autocomplete it.

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Speaker 6: I also started playing with the GPT three API, which

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Speaker 6: had come out. I think that came out in November

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Speaker 6: of twenty twenty one, and that was the first time

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Speaker 6: it was publicly available to everybody, and they had a

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Speaker 6: large language, Boddel as we know it today, available to them.

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Speaker 6: So I was just testing and building things, and I

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Speaker 6: pretty immediately realized the very first thing I did where

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Speaker 6: it just blew my mind was I built a web scraper.

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Speaker 6: So I was just trying to pull pricing data from

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Speaker 6: a website. And I've done a lot of this in

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Speaker 6: my career. It's maybe the most annoying task ift to

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Speaker 6: do in all of coding, because HTML is the most

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Speaker 6: miserable language to have to parse. And I just had

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Speaker 6: this thing where I took the page, I took the content,

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Speaker 6: I took the text, and I gave it to the

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Speaker 6: AI and I asked it to give me back the

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Speaker 6: pricing table and give me back the pricing table. And

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Speaker 6: I just thought, I'll never do it the other way again.

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Speaker 5: That's it.

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Speaker 3: Yeah, that HTML mentioned just brought up like memories of

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Speaker 3: me in like the mid nineties on HTML goodies. Do

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Speaker 3: you remember that side? Yeah? I wonder if it's still

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Speaker 3: Is it still up? That would be wild? Does cloud code?

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Speaker 3: Does that count as AGI? This seems to be the debate,

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Speaker 3: right is it AGI?

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Speaker 6: I try not to wait into what's AGI and what's not.

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Speaker 6: I think my guess on AGI, for what it's worth

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Speaker 6: is that it's probably going to be a conversation like

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Speaker 6: the Turing Test, where everybody thought it was really really

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Speaker 6: important for a really long time. We thought the Turing

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Speaker 6: Test was the biggest thing for seventy years or whatever,

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Speaker 6: and then CHATCHYBT very clearly passed the Turning Test, and

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Speaker 6: now everybody pretends, like we it's not just that they forgot,

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Speaker 6: they pretend that it never mattered. Oh and so I

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Speaker 6: am kind of guessing that that's going to be what

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Speaker 6: the conversation. It's like, it's just going to be a

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Speaker 6: sort of forever moving goalpost, because it turns out that

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Speaker 6: the idea we had for what general intelligence looks like

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Speaker 6: is not quite that. But I also think, you know,

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Speaker 6: the computer scientists and the sort of serious AI researchers

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Speaker 6: would say that much of what's going on inside clad

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Speaker 6: code is not the model itself. It's the model paired

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Speaker 6: with a human and I think that is a pretty

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Speaker 6: important distinction.

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Speaker 5: But I don't know about AGI.

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Speaker 2: Well, okay, so you were using GPT to code prior

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Speaker 2: to the release of CHAGUPT. So therefore, coding models have

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Speaker 2: been around a long time. So what is for those

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Speaker 2: who haven't played around with it? What is claude code?

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Speaker 2: Because again, coding models have been around for a long time.

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Speaker 2: Well maybe have heard of cursor, copilot or some of

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Speaker 2: these other harnesses, et cetera. What is cloud code?

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Speaker 5: So if we back up first and we go to copilot.

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Speaker 6: So Copilot was the first sort of commercial application of

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Speaker 6: a largelanguage model by most accounts, And what Copilot did

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Speaker 6: in its initial instantiation was just.

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Speaker 2: Auto Microsoft product.

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Speaker 6: It's a Microsoft product, so Microsoft doones, get hub, getthub,

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Speaker 6: develop copilot. It was Microsoft had the partnership with open Ai,

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Speaker 6: and so they built it in and what it was

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Speaker 6: doing was doing autocomplete. So if you're writing code, a

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Speaker 6: lot of writing code is boiler plate or trying to

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Speaker 6: remember the name of a function.

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Speaker 5: And you know, the reason stack.

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Speaker 6: Overflow existed was because you can never remember the exact

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Speaker 6: name of that function or the exact rejects that you

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Speaker 6: need to use in order to find and replace something,

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Speaker 6: and so you would go search for it. And they

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Speaker 6: realized that you could just build that into the ID

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Speaker 6: your code editor and and have it autocomplete for you,

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Speaker 6: and it was pretty amazing. Then, uh, Chatchipt came out,

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Speaker 6: and even before that, I had built a simple chatbot

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Speaker 6: for myself because I realized that, hey, I could just

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Speaker 6: ask this and instead of going and searching stack overflow.

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Speaker 6: It was totally capable of answering code questions, and it

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Speaker 6: was capable of writing rejects or doing these things and

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Speaker 6: didn't make mistakes. Yes, but like there's famous mistakes on

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Speaker 6: stack overflow of incorrect rejects that now exists in every

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Speaker 6: code base in the world, and so you know, I

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Speaker 6: think there were a lot of us just kind of

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Speaker 6: playing with these things and realizing they were a huge boon.

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Speaker 6: And so I think, really the next step is Cursor

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Speaker 6: comes out. And the thing Curser realized that co Pilot

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Speaker 6: didn't was that it wasn't good enough to have autocomplete.

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Speaker 6: You also needed the Q and A because you have

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Speaker 6: these things that you can't just auto complete. You want

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Speaker 6: to be able to ask the question and answer it.

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Speaker 6: And then Chatchipt came out and everybody was switching between

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Speaker 6: ID and then I think, really the next big piece

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Speaker 6: is that claud code came out. And what claud Code

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Speaker 6: did that was so remarkable was they took the same

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Speaker 6: set of models really and they took them out of

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Speaker 6: the chatbot and they really just gave it some very

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Speaker 6: basic functionality to operate within your machine.

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Speaker 5: Right.

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Speaker 6: And so you know, if you really look at kind

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Speaker 6: of what exists within claud code, you're calling out to

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Speaker 6: a model, and they gave it capability around sort of

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Speaker 6: two big things. One is you can read and write

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Speaker 6: files on your computer, and then two is that you

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Speaker 6: can operate Unix the base commands, the bashed commands that

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Speaker 6: exist in your environment. And again, because these models were

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Speaker 6: trained on the Internet, and there's no greater source of

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Speaker 6: information on the Internet than how to make the Internet,

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Speaker 6: they know how to use Unix commands incredibly well, right,

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Speaker 6: because Unix has existed for whatever it is, sixty years,

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Speaker 6: and the way these commands were designed, they're all designed

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Speaker 6: to be very very simple. There's a find command, and

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Speaker 6: you know, there's some thing called re rep and it

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Speaker 6: can search to a code base. And Unix has this

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Speaker 6: sort of beautiful way of tying one command to another,

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Speaker 6: so you can take the output of one command and

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Speaker 6: send it to another. And they kind of just gave

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Speaker 6: the model access to these two or three very simple things,

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Speaker 6: and it kind of turned out that it unlocked a

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Speaker 6: whole bunch of functionality. I don't think even the people

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Speaker 6: who built it fully realized. Like one example that I

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Speaker 6: think about a lot is just the challenge you have

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Speaker 6: with all of these.

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Speaker 5: AI models is that they're stateless.

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Speaker 6: So every time you talk to chat GPT, it's sending

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Speaker 6: your entire conversation history back to chat GPT because it

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Speaker 6: has no saved history of that chat, right, And that's fine,

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Speaker 6: it's the way it works. It's just fact. But it

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Speaker 6: means that you know, it forgets things. It doesn't no

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Speaker 6: conversation to conversation. And one very easy way to save

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Speaker 6: your state is just write it to a file. And

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Speaker 6: so you give it right access and it can create files.

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Speaker 6: And now all of a sudden you've overcome this, like

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Speaker 6: probably the single biggest challenge that exists inside these large

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Speaker 6: language models, which is that they're fundamentally stateless.

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Speaker 3: So Claude writes itself little like memory notes right to

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Speaker 3: remember the entire ca context of the conversation, and that's

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Speaker 3: how it solved that problem.

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Speaker 6: No, so there's sort of two things going on in

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Speaker 6: claude code.

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Speaker 5: Beneath the hood.

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Speaker 6: There's one thing that works exactly like chetchipt or any

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Speaker 6: of these other ones, which is it's maintaining a conversation history.

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Speaker 6: So every message you send it and every action it takes,

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Speaker 6: it's recording to a log, which is just one big file.

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Speaker 6: That's really no different than what chet ChiPT can do.

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Speaker 6: Where it gets really interesting though, is it can also

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Speaker 6: write files that it can then read, so whereas that

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Speaker 6: conversation history is all saved off, and eventually that conversation

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Speaker 6: gets too long and it needs to do a thing

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Speaker 6: called compaction. And when it compacts it, it tries to

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Speaker 6: sort of just remember the bits because there the total.

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Speaker 5: Window is large. But I mean it's like one hundred

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Speaker 5: thousand tokens I mean.

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Speaker 3: By memory notes, right. It compacts the information into the

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Speaker 3: important stuff, then retreat.

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Speaker 5: It does that.

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Speaker 6: It only does that at the end, like once it

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Speaker 6: runs out of space, once it runs out of context window.

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Speaker 6: So it has two hundred thousand tokens I think, and

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Speaker 6: two hundred thousand tokens in rough terms is probably one

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Speaker 6: hundred and fifty thousand words. It says, okay, it's time

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Speaker 6: for me to compact all of this stuff, and so

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Speaker 6: it still saves your whole history.

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Speaker 5: On your computer.

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Speaker 6: You still have the entire message, but for that session,

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Speaker 6: it just compacts it down to this, you know, maybe

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Speaker 6: twenty five thousand token memory of what it was.

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Speaker 2: And is this like something that was not obvious before

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Speaker 2: as a solution like this compaction? How important is it

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Speaker 2: for this being like okay? As a human I can

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Speaker 2: work on this on a project for a long time,

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Speaker 2: Like how much of an an unlock was there?

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Speaker 6: I'm not sure compaction was the okaylock. I think the

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Speaker 6: compaction functionality is helpful. The way chat GPT does it,

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Speaker 6: for what it's worth is they don't do compaction. They

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Speaker 6: just forget your messages eventually. So if you're in one chat,

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Speaker 6: eventually your oldest message is going to fall off the back.

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Speaker 6: For coding, that's probably less helpful, but there are trade offs.

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Speaker 6: I both techniques work. I think fundamentally, the thing that

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Speaker 6: is special about cloud code is not the compaction. It's

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Speaker 6: the ability to write and read files on your computer,

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Speaker 6: which means you can always write off memories.

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Speaker 5: And then what does that mean?

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Speaker 2: Write off memory?

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Speaker 6: So you could say, hey, it's really important that I

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Speaker 6: remember this thing for future sessions. I want to always

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Speaker 6: work this way. So in a code base of mine,

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Speaker 6: I have a set of documentation that explains how I

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Speaker 6: like to do things. And cloud code makes a mistake,

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Speaker 6: and so the next time I can write a memory,

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Speaker 6: essentially it's written as a thing they call a skill,

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Speaker 6: and you can write it off and you say, hey,

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Speaker 6: whenever you run into this, I want you to operate

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Speaker 6: in this kind of way. And that existing across every

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Speaker 6: session is really a thing you can only do when

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Speaker 6: you can store it as a file. It's a thing

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Speaker 6: you can't do in quite the same way when you're

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Speaker 6: operating in this environment or it's just going back and

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Speaker 6: forth to the API. So this access to the file

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Speaker 6: system is one really big piece. And then the second

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Speaker 6: is is just the Unix commands, I mean computers. Every

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Speaker 6: computer program lives on top of these sort of baseline

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Speaker 6: functions and the way that the designers of Unix built

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Speaker 6: them is really elegant, and they're very small. They all

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Speaker 6: do one thing, and they're all composable, and in coding terms,

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Speaker 6: composable means they can be chained together, right, and so

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Speaker 6: you can say, hey, look for files that mentioned this word,

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Speaker 6: and then from those files, I want you to take

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Speaker 6: this second action, and then from the output of that action,

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Speaker 6: I want you to take a third action.

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Speaker 5: And that's just built into Unix.

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Speaker 6: You literally just put a little pipe in between and

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Speaker 6: you just pipe them from one to another and that's it.

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Speaker 6: And so you give it access to write these commands,

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Speaker 6: and all of a sudden, it gets these sort of

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Speaker 6: second and third order effects that are just incredibly powerful

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Speaker 6: and built over a really long time.

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Speaker 3: So how much of claud code, the way it's different

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Speaker 3: to other models, how much of that was overcoming technological

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Speaker 3: challenges versus just having a good idea, because hearing you

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Speaker 3: describe it, I mean, giving access to a computer seems

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Speaker 3: like kind of obvious, like let's just do that.

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Speaker 6: I don't have a good answer to that. I think

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Speaker 6: that it was kind of just a good idea. Yeah,

404
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Speaker 6: I think they did some patterns really well. They're clearly

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Speaker 6: incredibly talented, not just engineers, but kind of thinkers about

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Speaker 6: how to structure it. Like the primitives inside cloud code

407
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Speaker 6: are just smart. And then the thing that they've done

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Speaker 6: and Boris Cherney, who's the lead developer on cloud coded anthropic,

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Speaker 6: he talks about light and demand a lot right, and

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Speaker 6: Layton Demand is basically just, hey, look at the ways

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Speaker 6: people are using these systems and then figure out ways

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Speaker 6: to make that a part of the product itself. I

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Speaker 6: think what they've done brilliantly, and this is kind of

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Speaker 6: easy when you have a community of developers who are

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Speaker 6: nerds who want to go talk about all the ways

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Speaker 6: that they're using these things. Is they have I am

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Speaker 6: amazed at the speed in which you know, I have

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Speaker 6: a small community of fifteen CTOs who all use this

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Speaker 6: stuff religiously. And you know, when we first started that community,

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Speaker 6: it took them a month to I would see it

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Speaker 6: in the chat, and then a month later it would

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Speaker 6: get built into cloud code, and then increasingly.

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Speaker 5: It's like a day later. It feels like they're just

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Speaker 5: they're just listening to it.

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Speaker 6: But I think they're just not only tapped in, but

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Speaker 6: they're really fundamentally you know, they're dog fooding and they

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Speaker 6: use their own products when you you know, they talk

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Speaker 6: about the productivity engineering productivity at Anthropic. You know, despite

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Speaker 6: growing at a crazy clip, it continues to go up.

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Speaker 6: And you know, anybody who's built had to manage large

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Speaker 6: scale pieces of software, large scale code basis knows that's

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Speaker 6: not the norm.

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Speaker 2: So vs code and cursor, these are IDEs. Cloud code

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Speaker 2: is not an ID. What it's called a CLI is.

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Speaker 5: A CLI a command line interface? Got it?

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Speaker 2: And the other lab Now they also have CLIs. So

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Speaker 2: why are we all talking about claud code and I

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Speaker 2: CHATTYPTS is called codex. I don't know what Geminis is called.

439
00:21:07,080 --> 00:21:08,000
Speaker 5: I think it's just called the gem.

440
00:21:08,760 --> 00:21:11,720
Speaker 2: Why are we all talking about claud code rather than

441
00:21:11,840 --> 00:21:14,320
Speaker 2: the other cla is that kind of have the same thing?

442
00:21:14,400 --> 00:21:15,320
Speaker 2: Like what is the difference?

443
00:21:15,880 --> 00:21:18,040
Speaker 6: I think, first and foremost they were first, okay, so

444
00:21:18,560 --> 00:21:20,600
Speaker 6: and I think they've they've had a lot more and

445
00:21:20,920 --> 00:21:24,399
Speaker 6: you know, from my very personal opinion, I think they've

446
00:21:24,680 --> 00:21:28,879
Speaker 6: done something smarter and better as far as the permissioning model.

447
00:21:29,000 --> 00:21:30,720
Speaker 6: So you know, one of the really dangerous things is

448
00:21:30,720 --> 00:21:32,360
Speaker 6: you've got the same running on your computer. You don't

449
00:21:32,400 --> 00:21:36,080
Speaker 6: want it to go and delete everything. And they have

450
00:21:36,119 --> 00:21:38,679
Speaker 6: a very fine grain permissioning model where you can say, hey,

451
00:21:38,720 --> 00:21:40,520
Speaker 6: I want to allow this just this one time. I

452
00:21:40,520 --> 00:21:41,560
Speaker 6: want to always allow it.

453
00:21:41,680 --> 00:21:43,840
Speaker 2: You know, I always click always allow. I'm living on

454
00:21:43,840 --> 00:21:44,240
Speaker 2: the edge.

455
00:21:44,480 --> 00:21:47,040
Speaker 6: You can you can next time you run it. You

456
00:21:47,040 --> 00:21:50,080
Speaker 6: can just do a flag that says dangerously skip permissions

457
00:21:50,920 --> 00:21:54,840
Speaker 6: and and it'll just They call it yolo mode. I

458
00:21:54,880 --> 00:21:58,560
Speaker 6: think I think more fundamentally though, if I look at

459
00:21:58,760 --> 00:22:02,439
Speaker 6: at Codex versus Code, I think it's a difference in

460
00:22:02,480 --> 00:22:07,760
Speaker 6: philosophy around what you want AI to do to me. Codex,

461
00:22:08,400 --> 00:22:12,800
Speaker 6: which is excellent, is very focused on building an agent

462
00:22:12,880 --> 00:22:15,199
Speaker 6: that you could just give something to and it'll just

463
00:22:15,280 --> 00:22:17,600
Speaker 6: go do it. So I want to give it that task.

464
00:22:17,680 --> 00:22:19,560
Speaker 6: I don't want to intercede. I don't want to give

465
00:22:19,600 --> 00:22:24,680
Speaker 6: it any more feedback. And claud code is much more

466
00:22:24,720 --> 00:22:28,159
Speaker 6: designed to be kind of a pair programmer. And so

467
00:22:28,320 --> 00:22:31,040
Speaker 6: you know, in engineering, pair programming has existed for a while.

468
00:22:31,280 --> 00:22:34,320
Speaker 6: It's a really weird sort of productivity thing where you

469
00:22:34,400 --> 00:22:36,480
Speaker 6: put two engineers on the same problem and it turns

470
00:22:36,480 --> 00:22:40,120
Speaker 6: out that you can get better code and multiplier Yeah,

471
00:22:40,160 --> 00:22:41,800
Speaker 6: and it sort of makes up for the fact that

472
00:22:41,800 --> 00:22:44,240
Speaker 6: obviously you know you're doubling the staff on it. But

473
00:22:44,359 --> 00:22:47,240
Speaker 6: because of how many fewer bugs because you've both said suvies,

474
00:22:47,720 --> 00:22:50,200
Speaker 6: it has seemed to work out for many folks. Most

475
00:22:50,280 --> 00:22:53,840
Speaker 6: companies don't practice it, but I think cloud code fundamentally

476
00:22:53,880 --> 00:22:56,120
Speaker 6: is much more designed in that way.

477
00:22:56,200 --> 00:22:58,280
Speaker 5: It's a pair programmer. It's they. You know.

478
00:22:58,320 --> 00:23:01,560
Speaker 6: Whenever I start a project, I start in plan mode.

479
00:23:01,600 --> 00:23:03,440
Speaker 6: So you start in plan mode. You put together a plan.

480
00:23:03,480 --> 00:23:04,639
Speaker 6: I really, I mean it has spent a lot of

481
00:23:04,720 --> 00:23:07,159
Speaker 6: time in plan mode. You go through, it gives you

482
00:23:07,160 --> 00:23:09,000
Speaker 6: a plan, back, it asks you how you feel you

483
00:23:09,000 --> 00:23:11,320
Speaker 6: can give it a whole bunch of direction, and then

484
00:23:11,359 --> 00:23:13,760
Speaker 6: it's only then that it goes off and it goes

485
00:23:13,800 --> 00:23:16,359
Speaker 6: into it. So you know, we're working together. And I

486
00:23:16,359 --> 00:23:18,560
Speaker 6: actually have a whole system now that I've designed where

487
00:23:19,680 --> 00:23:22,280
Speaker 6: I use a task management system called Linear, So I

488
00:23:22,280 --> 00:23:25,359
Speaker 6: have claud Code write tasks off to Linear, and then

489
00:23:25,640 --> 00:23:29,480
Speaker 6: I've worked with claud Code to write a document that

490
00:23:29,600 --> 00:23:31,919
Speaker 6: helps sort of decide a set of heuristics to decide

491
00:23:31,960 --> 00:23:34,240
Speaker 6: when you should assign it to Codex versus when you

492
00:23:34,280 --> 00:23:36,120
Speaker 6: should give it to claud Code. And so if it's

493
00:23:36,119 --> 00:23:38,240
Speaker 6: tightly defined enough and simple enough, I just send it

494
00:23:38,240 --> 00:23:41,200
Speaker 6: off to Codex and it does it totally independently. And

495
00:23:41,240 --> 00:23:44,280
Speaker 6: then if it's complicated enough that I think it requires

496
00:23:44,320 --> 00:23:47,879
Speaker 6: my time and attention, then it saves it for me

497
00:23:48,920 --> 00:23:52,840
Speaker 6: us to do together, and we'll work on it together.

498
00:23:52,880 --> 00:23:55,520
Speaker 6: And so if it's sort of touching a kind of

499
00:23:55,600 --> 00:23:58,520
Speaker 6: important enough if it's changing some part of the data model.

500
00:23:58,880 --> 00:24:01,440
Speaker 6: There's these other kind of you know, fairly basic extet

501
00:24:01,480 --> 00:24:05,080
Speaker 6: of criteria that I use, But that to me is

502
00:24:05,080 --> 00:24:09,480
Speaker 6: the fundamental distinction. And you know, I find cloud code

503
00:24:09,480 --> 00:24:12,800
Speaker 6: in that way to be just it sort of fits

504
00:24:12,920 --> 00:24:15,359
Speaker 6: what I want to do and how I want to

505
00:24:15,400 --> 00:24:17,080
Speaker 6: work much better.

506
00:24:17,640 --> 00:24:21,320
Speaker 3: Talk a little bit more about how it actually impacts

507
00:24:21,440 --> 00:24:24,960
Speaker 3: the workflow of an engineer, because you know, my impression

508
00:24:25,160 --> 00:24:29,760
Speaker 3: was people can code, right, Like, the coding problem is

509
00:24:29,880 --> 00:24:31,919
Speaker 3: kind of solved at this point, and even if you

510
00:24:32,000 --> 00:24:34,760
Speaker 3: can't code, even if you're not a professional engineer, you

511
00:24:34,800 --> 00:24:38,880
Speaker 3: can hire someone from like India or Indonesia or wherever

512
00:24:39,040 --> 00:24:40,920
Speaker 3: to just write you a code. Maybe it'll take them

513
00:24:40,920 --> 00:24:44,120
Speaker 3: a week instead of like two days with claud code.

514
00:24:44,240 --> 00:24:49,040
Speaker 3: But how much does this actually change the workflow for

515
00:24:49,080 --> 00:24:50,399
Speaker 3: an engineer.

516
00:24:50,880 --> 00:24:53,399
Speaker 6: As completely as it could be changed. I mean, I

517
00:24:53,440 --> 00:24:58,879
Speaker 6: would say that over the last three months, I've written, personally,

518
00:24:59,640 --> 00:25:01,679
Speaker 6: I don't know a few hundred lines of code. Like

519
00:25:01,840 --> 00:25:05,920
Speaker 6: I am mostly a manager of a set of agents

520
00:25:05,960 --> 00:25:08,879
Speaker 6: who are writing code on my behalf. And you know,

521
00:25:08,960 --> 00:25:11,680
Speaker 6: increasingly what I think is interesting. I've been thinking about

522
00:25:11,680 --> 00:25:14,400
Speaker 6: this a bunch lately, is like, in some ways, it's

523
00:25:14,400 --> 00:25:16,479
Speaker 6: just bringing me back to the core challenge that has

524
00:25:16,480 --> 00:25:19,639
Speaker 6: always existed in software development, which is how do you

525
00:25:19,720 --> 00:25:25,480
Speaker 6: manage all large scale software development project acrossation. It has

526
00:25:25,520 --> 00:25:29,040
Speaker 6: become a coordination problem, and I spend a lot of

527
00:25:29,080 --> 00:25:33,560
Speaker 6: time sort of now designing my CLAUD code system to

528
00:25:33,680 --> 00:25:36,920
Speaker 6: ensure that code goes through all the proper spec checks

529
00:25:36,960 --> 00:25:39,800
Speaker 6: and that it has all these things. The other thing

530
00:25:39,880 --> 00:25:43,359
Speaker 6: that you know makes code a particularly good place to

531
00:25:43,359 --> 00:25:45,840
Speaker 6: do this is that code is verifiable in a way

532
00:25:45,840 --> 00:25:49,119
Speaker 6: that you know most other work is not. So you know,

533
00:25:49,440 --> 00:25:52,840
Speaker 6: with code, you can verify that the build works right.

534
00:25:52,880 --> 00:25:53,639
Speaker 5: So you can say, hey, I.

535
00:25:53,680 --> 00:25:55,919
Speaker 6: Want to build this, I want to build this package.

536
00:25:55,960 --> 00:25:57,320
Speaker 6: I want to make sure that it's actually going to

537
00:25:57,359 --> 00:25:59,200
Speaker 6: build and that there's going to be no failures. That's

538
00:25:59,200 --> 00:26:01,520
Speaker 6: a very easy check. It's either true or it's not true.

539
00:26:01,880 --> 00:26:06,639
Speaker 6: There's also coders use linting, and so linting is a

540
00:26:06,680 --> 00:26:10,280
Speaker 6: way to kind of look at it's static code analysis,

541
00:26:10,280 --> 00:26:15,560
Speaker 6: so it basically tries to sort of find things in

542
00:26:15,600 --> 00:26:17,720
Speaker 6: your code base that are not going to work ahead

543
00:26:17,760 --> 00:26:20,160
Speaker 6: of time where you can predict that. Obviously, you can't

544
00:26:20,160 --> 00:26:25,120
Speaker 6: predict Alan Turing prove that you can't predict with certainty

545
00:26:25,119 --> 00:26:26,960
Speaker 6: whether code is going to run, but there's certain patterns

546
00:26:26,960 --> 00:26:29,560
Speaker 6: and things that it can find. It's essentially does static

547
00:26:29,680 --> 00:26:32,520
Speaker 6: pattern analysis, and so you know, you have it run

548
00:26:32,560 --> 00:26:36,359
Speaker 6: all these things. But the more kind of opinionated you

549
00:26:36,359 --> 00:26:38,280
Speaker 6: can be about that, and the more steps you can

550
00:26:38,280 --> 00:26:40,080
Speaker 6: have it go through, so I find, you know, now

551
00:26:40,119 --> 00:26:42,880
Speaker 6: I'm kind of the designer, which, honestly, as an entrepreneur

552
00:26:42,920 --> 00:26:45,640
Speaker 6: and as a CEO of company is like that's kind

553
00:26:45,640 --> 00:26:48,520
Speaker 6: of always been my job. Like I've been not I

554
00:26:48,560 --> 00:26:50,360
Speaker 6: have less and less been a person who writes code

555
00:26:50,400 --> 00:26:52,720
Speaker 6: and more and more been a person who designs a

556
00:26:52,800 --> 00:26:54,480
Speaker 6: system in that case of company with a bunch of

557
00:26:54,520 --> 00:26:56,840
Speaker 6: people who write code.

558
00:26:57,440 --> 00:27:00,199
Speaker 2: One of the funny things it seems to me is

559
00:27:00,240 --> 00:27:05,280
Speaker 2: that setting aside Claud code, Claude itself has a reputation

560
00:27:05,560 --> 00:27:08,720
Speaker 2: for it's a nicer chat bot to talk to. People

561
00:27:08,840 --> 00:27:13,439
Speaker 2: find it, and you know, chatgbt seems to really be

562
00:27:13,520 --> 00:27:16,040
Speaker 2: psycho fantic. I still think it's I know it's improved,

563
00:27:16,040 --> 00:27:17,600
Speaker 2: but I actually don't think it's improved or not. I

564
00:27:17,680 --> 00:27:22,719
Speaker 2: still people like the prose style of Claud Claude and

565
00:27:22,760 --> 00:27:26,360
Speaker 2: I'm curious that in the pair trading pair trading, I'm

566
00:27:26,359 --> 00:27:30,199
Speaker 2: thinking about finance, the pair engineering model, whether there is

567
00:27:30,320 --> 00:27:33,159
Speaker 2: also an edge there which is like, here is a

568
00:27:33,280 --> 00:27:36,440
Speaker 2: chat bot that is not annoying to talk to while

569
00:27:36,440 --> 00:27:39,280
Speaker 2: you're iterating, and whether that is like a meaningful distinction

570
00:27:39,720 --> 00:27:43,000
Speaker 2: between you know, coding with codex or whatever.

571
00:27:43,840 --> 00:27:47,800
Speaker 6: Yeah, I I don't know. It still can be very annoying,

572
00:27:47,840 --> 00:27:52,560
Speaker 6: I can tell you. And it'll still sometimes be overly,

573
00:27:54,480 --> 00:27:57,239
Speaker 6: overly effusive with me about a design choice, I mean,

574
00:27:57,680 --> 00:28:00,640
Speaker 6: or sort of notice something which I could live without.

575
00:28:01,000 --> 00:28:04,160
Speaker 2: So I work on this project that's doing this linguistic

576
00:28:04,200 --> 00:28:07,040
Speaker 2: things and I eventually had to say, like, give it

577
00:28:07,040 --> 00:28:09,399
Speaker 2: to me straight, how bad is this? And then so

578
00:28:09,440 --> 00:28:12,000
Speaker 2: I said, I said, Actually, what I said was assume

579
00:28:12,080 --> 00:28:14,359
Speaker 2: for a moment that you are a quantitative linguistics with

580
00:28:14,440 --> 00:28:17,920
Speaker 2: a PhD. Give me your honest assessment of where we.

581
00:28:17,880 --> 00:28:18,280
Speaker 5: Are with this.

582
00:28:18,840 --> 00:28:22,280
Speaker 2: And it said like you've developed a nice toy and

583
00:28:22,359 --> 00:28:25,520
Speaker 2: there's no evidence that it actually does. And I was like, okay,

584
00:28:25,520 --> 00:28:27,480
Speaker 2: that's nice to hear. I actually like, you know, I

585
00:28:27,560 --> 00:28:30,280
Speaker 2: appreciate that, and I thank you very blunt. Now you know,

586
00:28:30,320 --> 00:28:33,760
Speaker 2: it's still like polite, but it was like, this, doesn't

587
00:28:33,800 --> 00:28:36,600
Speaker 2: you haven't really shown anything. You haven't really established at

588
00:28:36,600 --> 00:28:38,280
Speaker 2: all that your saltware does what it claims to.

589
00:28:38,600 --> 00:28:42,560
Speaker 6: Yeah, I think so. I think stylistically I kind of

590
00:28:42,600 --> 00:28:46,920
Speaker 6: personally agree. My theory by the way, on Claude versus

591
00:28:47,920 --> 00:28:50,800
Speaker 6: Opening Eye Chat GPT models is I think Claude is

592
00:28:50,840 --> 00:28:54,000
Speaker 6: actually better at sort of reflecting what you give it.

593
00:28:54,640 --> 00:28:56,240
Speaker 6: And so I think part of why we think it's

594
00:28:56,280 --> 00:28:57,920
Speaker 6: better is it it's better at.

595
00:28:57,800 --> 00:28:59,080
Speaker 5: Pretending it's us.

596
00:28:59,280 --> 00:29:01,520
Speaker 6: Yeah, And so we tend to like that is this

597
00:29:01,600 --> 00:29:05,160
Speaker 6: is purely speculation, But that's always been my theory on so.

598
00:29:05,200 --> 00:29:06,440
Speaker 2: It flatters you in a different way.

599
00:29:06,720 --> 00:29:09,520
Speaker 5: I think it's flattering you in a much more way.

600
00:29:09,600 --> 00:29:09,840
Speaker 2: Yeah.

601
00:29:09,960 --> 00:29:15,360
Speaker 6: Interest But for a long time, just Anthropic has been

602
00:29:15,400 --> 00:29:18,040
Speaker 6: producing the best coding models, you know. I mean there's

603
00:29:18,240 --> 00:29:21,160
Speaker 6: there can be some debate there now, but you know,

604
00:29:21,200 --> 00:29:24,280
Speaker 6: there's a great curse story from Cursor actually where Cursor

605
00:29:24,480 --> 00:29:27,920
Speaker 6: basically wasn't that good, and then Sonnet three point five

606
00:29:27,960 --> 00:29:30,760
Speaker 6: came out and all of a sudden, Cursor was amazing,

607
00:29:30,960 --> 00:29:33,760
Speaker 6: and Cursor became a tool that everybody started using. But

608
00:29:33,800 --> 00:29:35,840
Speaker 6: it wasn't until this other model came out and they

609
00:29:35,880 --> 00:29:38,800
Speaker 6: made that the default model, and you know, I for

610
00:29:38,840 --> 00:29:40,720
Speaker 6: what it's worth. I think the other takeaway from that,

611
00:29:40,840 --> 00:29:43,480
Speaker 6: which is a kind of big theme we see in

612
00:29:43,520 --> 00:29:46,160
Speaker 6: the market as a thing that the cloud Code team

613
00:29:46,200 --> 00:29:49,160
Speaker 6: has talked about, is you constantly have to be building

614
00:29:49,320 --> 00:29:52,600
Speaker 6: ahead with AI in a way that is very unique

615
00:29:52,640 --> 00:29:55,280
Speaker 6: in the world of software, where you kind of always

616
00:29:55,320 --> 00:29:58,160
Speaker 6: want to build things that are working at like seventy

617
00:29:58,240 --> 00:30:00,480
Speaker 6: or eighty percent, because if you really spend the time

618
00:30:00,520 --> 00:30:02,640
Speaker 6: to get it up to ninety or one hundred, you're

619
00:30:02,680 --> 00:30:04,240
Speaker 6: going to lose all the games you get when the

620
00:30:04,280 --> 00:30:07,160
Speaker 6: next model comes out, and the you know, with the

621
00:30:07,200 --> 00:30:09,560
Speaker 6: amount of capex being spent on these models, like there's

622
00:30:09,600 --> 00:30:11,480
Speaker 6: a next model that's going to come out that's going

623
00:30:11,520 --> 00:30:13,240
Speaker 6: to be awesome, and you just kind of want to

624
00:30:13,240 --> 00:30:15,480
Speaker 6: be downstream from that, and you don't want to waste

625
00:30:15,520 --> 00:30:18,400
Speaker 6: six months getting an extra three percent when that new

626
00:30:18,400 --> 00:30:19,960
Speaker 6: model is going to give you an extra seven.

627
00:30:20,840 --> 00:30:23,160
Speaker 3: Yeah, this is the only certainty with AI is like

628
00:30:23,280 --> 00:30:24,680
Speaker 3: there's always going to be a new model.

629
00:30:25,440 --> 00:30:27,920
Speaker 2: The worst model ever use is the one that we're using.

630
00:30:28,240 --> 00:30:40,120
Speaker 4: That's right, That's right.

631
00:30:44,560 --> 00:30:47,800
Speaker 3: Are we all going to become coding illiterate? Are we

632
00:30:47,880 --> 00:30:50,360
Speaker 3: just going to forget how to code. If everyone's using

633
00:30:50,640 --> 00:30:52,440
Speaker 3: you know, general language to do.

634
00:30:52,480 --> 00:30:54,600
Speaker 2: Forget, I've never learned. Yeah, okay, you know what I've

635
00:30:54,600 --> 00:30:55,240
Speaker 2: been thinking about.

636
00:30:55,560 --> 00:30:56,040
Speaker 1: You know that.

637
00:30:57,680 --> 00:31:01,160
Speaker 2: Scott Karp, the CEO Palanteer, here is that line. He's like,

638
00:31:01,400 --> 00:31:03,120
Speaker 2: when I was young, I was too poor to have

639
00:31:03,160 --> 00:31:04,760
Speaker 2: a car, or so I didn't get a so I

640
00:31:04,800 --> 00:31:06,960
Speaker 2: never learned to drive. And now I'm too rich, so

641
00:31:07,000 --> 00:31:09,240
Speaker 2: I never learned to drive. I feel like when I

642
00:31:09,280 --> 00:31:11,240
Speaker 2: was young, I was too dumb to learn to code,

643
00:31:11,400 --> 00:31:12,320
Speaker 2: and now.

644
00:31:12,480 --> 00:31:13,320
Speaker 3: You leaped ahead.

645
00:31:13,440 --> 00:31:16,080
Speaker 2: Yeah, now I'm too smart to learn Python or TML

646
00:31:16,160 --> 00:31:16,560
Speaker 2: or whatever.

647
00:31:17,440 --> 00:31:19,920
Speaker 6: I have a couple of takes on this person one personally.

648
00:31:19,960 --> 00:31:23,880
Speaker 6: So first one is I just think like this is

649
00:31:23,920 --> 00:31:25,160
Speaker 6: the worry of all technology.

650
00:31:25,240 --> 00:31:25,440
Speaker 5: Ever.

651
00:31:25,600 --> 00:31:28,440
Speaker 6: There was a paper that came out that showed that

652
00:31:28,720 --> 00:31:32,400
Speaker 6: people were, uh, you know, they were forgetting more things

653
00:31:32,480 --> 00:31:34,520
Speaker 6: or something because they were using chat GBD. But you know,

654
00:31:34,760 --> 00:31:38,800
Speaker 6: uh in Phadrious Plato was worried that people were gonna

655
00:31:39,040 --> 00:31:42,440
Speaker 6: forget things because they started writing things down. And you know,

656
00:31:42,480 --> 00:31:44,160
Speaker 6: I think the trade off there was pretty good. We

657
00:31:44,200 --> 00:31:47,840
Speaker 6: got the scientific revolution a couple of other things, So, uh,

658
00:31:48,400 --> 00:31:51,040
Speaker 6: you know, I think that's the sort of natural knee jerk.

659
00:31:52,720 --> 00:31:57,200
Speaker 6: With that said, it is. I it's very strange when

660
00:31:57,240 --> 00:32:00,160
Speaker 6: you have people, you know, the Cloud Code team is

661
00:32:00,200 --> 00:32:04,080
Speaker 6: talking about how little code they write. Now, I draw

662
00:32:04,080 --> 00:32:06,800
Speaker 6: a distinction between the sort of Vibe coding and the

663
00:32:07,080 --> 00:32:10,479
Speaker 6: kind of amateur people who have never written code. And

664
00:32:10,840 --> 00:32:12,800
Speaker 6: I think that is amazing, by the way, And I

665
00:32:12,800 --> 00:32:15,800
Speaker 6: think there's a lot of software developers who are really

666
00:32:15,800 --> 00:32:19,680
Speaker 6: mad about that because there they claim it's for safety

667
00:32:19,680 --> 00:32:21,800
Speaker 6: reasons or whatever, but I think fundamentally it's just they've

668
00:32:21,800 --> 00:32:22,680
Speaker 6: got people on their turf.

669
00:32:24,040 --> 00:32:25,480
Speaker 5: But I think that's incredible.

670
00:32:25,480 --> 00:32:29,240
Speaker 6: I mean, my hat, my my nine year old Vibe

671
00:32:29,240 --> 00:32:32,720
Speaker 6: coded a website and for Secret Santa.

672
00:32:33,240 --> 00:32:33,880
Speaker 5: She's now ten.

673
00:32:34,080 --> 00:32:35,520
Speaker 6: She would get bad at me if I called her nine,

674
00:32:35,560 --> 00:32:37,600
Speaker 6: but I think she vibe good at when she was nine.

675
00:32:38,040 --> 00:32:40,440
Speaker 6: But that that's awesome, right, I don't know, that's amazing.

676
00:32:40,480 --> 00:32:43,000
Speaker 6: That's a way for people to express themselves in a

677
00:32:43,040 --> 00:32:46,440
Speaker 6: way that they couldn't before you did your your linguistics process.

678
00:32:46,480 --> 00:32:51,480
Speaker 6: That's that's fun and interesting. But yeah, I also think

679
00:32:51,520 --> 00:32:55,120
Speaker 6: the other the thing that's happening with professional software developers,

680
00:32:55,120 --> 00:32:57,120
Speaker 6: when you hear from anthropic or you know, when I'm

681
00:32:57,120 --> 00:33:01,320
Speaker 6: talking about it's you know, the code going through this process,

682
00:33:01,360 --> 00:33:05,000
Speaker 6: and you know, all the code still gets reviewed by people.

683
00:33:05,040 --> 00:33:06,760
Speaker 6: We're not letting it get out the door if it's

684
00:33:06,800 --> 00:33:09,040
Speaker 6: not at the same level as human and it's just

685
00:33:09,200 --> 00:33:12,000
Speaker 6: But what's amazing is I'm I'm running five of these

686
00:33:12,000 --> 00:33:13,880
Speaker 6: sessions at a time, right, and so I've got like

687
00:33:14,000 --> 00:33:17,440
Speaker 6: software being developed in parallel in a way that is unimaginable.

688
00:33:17,880 --> 00:33:21,200
Speaker 6: And you know the other thing is just now the

689
00:33:21,240 --> 00:33:24,920
Speaker 6: best software engineers wrote the least code anyway. You know

690
00:33:25,000 --> 00:33:27,760
Speaker 6: the sort of classic story of like the difference between

691
00:33:27,760 --> 00:33:30,960
Speaker 6: a junior developer or senior developers that a junior developer

692
00:33:31,680 --> 00:33:33,360
Speaker 6: gets a problem and they sit down and they put

693
00:33:33,360 --> 00:33:35,200
Speaker 6: their fingers on the keyboard and they start writing code.

694
00:33:35,680 --> 00:33:38,400
Speaker 6: And a senior developer gets a problem and sits there

695
00:33:38,440 --> 00:33:40,520
Speaker 6: for three hours and tries to figure out what the

696
00:33:40,520 --> 00:33:42,200
Speaker 6: best way to solve it is and then spends five

697
00:33:42,240 --> 00:33:43,720
Speaker 6: minutes writing code to get it done.

698
00:33:43,960 --> 00:33:46,400
Speaker 3: True elegance is restraint. That's what I say.

699
00:33:46,840 --> 00:33:47,280
Speaker 4: What are you.

700
00:33:47,280 --> 00:33:50,440
Speaker 2: Seeing in the companies you're working for? Like, I find

701
00:33:50,480 --> 00:33:53,200
Speaker 2: it hard to believe, and I was maybe skeptical of this,

702
00:33:53,320 --> 00:33:55,800
Speaker 2: but it feels like right now we're here with technology

703
00:33:55,800 --> 00:33:58,640
Speaker 2: where like a viral like company is like, like I said,

704
00:33:58,640 --> 00:34:00,920
Speaker 2: you can build charts of data in a way that

705
00:34:01,040 --> 00:34:02,800
Speaker 2: used to be like someone would have had to get

706
00:34:02,800 --> 00:34:04,760
Speaker 2: their hands dirty, ear, et cetera. And the companies that

707
00:34:04,840 --> 00:34:07,720
Speaker 2: you talk to is right now that having an effect

708
00:34:07,800 --> 00:34:09,920
Speaker 2: on how they think about what positions they're hiring for

709
00:34:09,960 --> 00:34:11,680
Speaker 2: and the skills they're looking for and so forth.

710
00:34:12,200 --> 00:34:17,360
Speaker 6: I think that it's hard to answer, right, really, I

711
00:34:17,440 --> 00:34:22,239
Speaker 6: think that certainly. I do think I personally think if

712
00:34:22,280 --> 00:34:24,440
Speaker 6: I look at the sort of layoffs in the technology

713
00:34:24,480 --> 00:34:26,080
Speaker 6: industry of the last couple of years, I think some

714
00:34:26,200 --> 00:34:28,560
Speaker 6: part of that is just looking at the output of

715
00:34:28,640 --> 00:34:33,000
Speaker 6: these models and saying, hey, these models are able to

716
00:34:33,000 --> 00:34:35,880
Speaker 6: produce it, you know, the median, and I have a

717
00:34:35,880 --> 00:34:38,120
Speaker 6: whole bunch of sort of middle managers who are producing

718
00:34:38,160 --> 00:34:41,040
Speaker 6: it the sixty fifth percentile. And it's like I can

719
00:34:41,120 --> 00:34:44,440
Speaker 6: produce median for a dollar fifty per million tokens, or

720
00:34:44,480 --> 00:34:48,440
Speaker 6: I can produce sixty fifth percentile for haveever many hundreds

721
00:34:48,480 --> 00:34:50,440
Speaker 6: of thousands dollars a year. It's a sort of fairly

722
00:34:50,480 --> 00:34:53,560
Speaker 6: simple trade off, I think. So I do think there's

723
00:34:53,560 --> 00:34:55,120
Speaker 6: a lot of downstream effects. I think the other thing

724
00:34:55,160 --> 00:34:57,600
Speaker 6: that's happening. Is is kind of like middle management is

725
00:34:57,680 --> 00:35:00,680
Speaker 6: under threat because it's the realization that hey, like, part

726
00:35:00,719 --> 00:35:03,400
Speaker 6: of what these models are amazing at is is I

727
00:35:03,480 --> 00:35:05,279
Speaker 6: think of them as like a fuzzy interface. They can

728
00:35:05,280 --> 00:35:07,920
Speaker 6: sort of turn any data into any other data, right.

729
00:35:07,960 --> 00:35:11,200
Speaker 6: You can sort of transform data from one format to another.

730
00:35:11,360 --> 00:35:13,000
Speaker 6: You can take a PDF and you can turn it

731
00:35:13,040 --> 00:35:15,279
Speaker 6: into charts, right, And there's whole people who exist or

732
00:35:15,400 --> 00:35:17,120
Speaker 6: you know, if you think about what product managers do

733
00:35:17,160 --> 00:35:18,960
Speaker 6: a lot of what product managers do is they take

734
00:35:19,239 --> 00:35:21,040
Speaker 6: how people are using a product and they try to

735
00:35:21,080 --> 00:35:25,319
Speaker 6: transform it into a format that engineers can then use

736
00:35:25,400 --> 00:35:27,000
Speaker 6: to figure out what to do. And I think a

737
00:35:27,040 --> 00:35:31,080
Speaker 6: lot of those kind of a lot of those pieces

738
00:35:31,239 --> 00:35:35,880
Speaker 6: that used to just be kind of transferring knowledge.

739
00:35:36,080 --> 00:35:39,520
Speaker 2: I've always said, Tracy, I think one of the most

740
00:35:39,560 --> 00:35:42,920
Speaker 2: important roles in any organization is essentially translation work. And

741
00:35:42,960 --> 00:35:46,120
Speaker 2: you see it in the newsroom where it's like here's

742
00:35:46,160 --> 00:35:50,919
Speaker 2: a team specialized in emerging market currencies, and then they

743
00:35:50,960 --> 00:35:53,280
Speaker 2: have to like they have to then tell the senior

744
00:35:53,400 --> 00:35:56,040
Speaker 2: editors what they're working on. But the senior editors, who

745
00:35:56,040 --> 00:35:58,920
Speaker 2: are maybe more generalists, don't really know like why, like

746
00:35:59,000 --> 00:36:02,920
Speaker 2: some sort of like you know, one yen carry is important,

747
00:36:03,160 --> 00:36:05,799
Speaker 2: and that a really important role within any organization is

748
00:36:05,880 --> 00:36:08,879
Speaker 2: essentially the team that can translate between the generalist team

749
00:36:08,920 --> 00:36:11,760
Speaker 2: and the specialist team. Absolutely, and so that's an interesting

750
00:36:11,800 --> 00:36:14,120
Speaker 2: observation and the sort of engineering world of like, okay,

751
00:36:14,120 --> 00:36:17,000
Speaker 2: these are tools that are in sometimes translation tools.

752
00:36:17,480 --> 00:36:19,840
Speaker 3: So we talked I agree completely, by the way, but

753
00:36:20,160 --> 00:36:23,359
Speaker 3: we talked about Vibe coding and Joe has this application

754
00:36:24,160 --> 00:36:26,279
Speaker 3: that I don't think you're looking to monetize.

755
00:36:26,400 --> 00:36:27,920
Speaker 2: No, it's I'm just trying to make it for the

756
00:36:28,000 --> 00:36:30,160
Speaker 2: good of the world, right, Okay, so when did that

757
00:36:30,160 --> 00:36:30,880
Speaker 2: become a crown?

758
00:36:32,040 --> 00:36:34,680
Speaker 3: I'm not a lotizing it, But like this opens up

759
00:36:34,680 --> 00:36:38,840
Speaker 3: massive questions for software as a service, right versas because

760
00:36:39,000 --> 00:36:43,280
Speaker 3: if everyone can write their own software, you can replicate

761
00:36:43,320 --> 00:36:46,399
Speaker 3: anything that's out there that is currently charging money. What's

762
00:36:46,400 --> 00:36:47,400
Speaker 3: going to happen to software?

763
00:36:48,200 --> 00:36:50,839
Speaker 6: I think software is pretty screwed. A lot of it,

764
00:36:50,880 --> 00:36:53,120
Speaker 6: at least not all of it. You know, you still

765
00:36:53,480 --> 00:36:55,880
Speaker 6: it depends on whether you call that cloud provider software

766
00:36:55,960 --> 00:36:57,640
Speaker 6: or not. You know, you still need to run this

767
00:36:57,640 --> 00:37:00,360
Speaker 6: stuff somewhere. And I think there's there's certain kind of

768
00:37:00,400 --> 00:37:02,920
Speaker 6: software that you know, you just don't really want to

769
00:37:02,960 --> 00:37:06,160
Speaker 6: be in the business of writing. You know, I as

770
00:37:06,200 --> 00:37:09,040
Speaker 6: someone who's tried to build a project management system, I'd

771
00:37:09,080 --> 00:37:11,960
Speaker 6: really rather I don't think anybody should be in that business.

772
00:37:13,400 --> 00:37:14,600
Speaker 5: But I do think.

773
00:37:14,560 --> 00:37:17,560
Speaker 6: Fundamentally, I mean, we see this every day inside enterprises.

774
00:37:17,600 --> 00:37:22,520
Speaker 6: The sort of build versus buy pendulum has just swung.

775
00:37:22,600 --> 00:37:24,680
Speaker 6: And you know, I mean I used to run a

776
00:37:24,719 --> 00:37:28,239
Speaker 6: SaaS company and we sold to enterprises, and you know,

777
00:37:28,320 --> 00:37:30,680
Speaker 6: for a long time that I think that made a

778
00:37:30,680 --> 00:37:33,399
Speaker 6: lot of sense, right because like, hey, it just didn't

779
00:37:33,400 --> 00:37:35,479
Speaker 6: make sense to try to build this thing on your own.

780
00:37:36,080 --> 00:37:38,279
Speaker 6: And so but the price of that was you know,

781
00:37:38,360 --> 00:37:40,480
Speaker 6: won the price, right, like, and it got to be

782
00:37:40,520 --> 00:37:42,440
Speaker 6: more and more expensive. The other price was that you

783
00:37:42,440 --> 00:37:44,520
Speaker 6: were paying for a lot of stuff you didn't need, right,

784
00:37:44,520 --> 00:37:47,759
Speaker 6: because the whole job of building SaaS is you need

785
00:37:47,800 --> 00:37:51,359
Speaker 6: to generalize problems, and so you build things that are

786
00:37:51,400 --> 00:37:52,719
Speaker 6: going to work for everybody.

787
00:37:53,040 --> 00:37:54,680
Speaker 5: And that means either you.

788
00:37:54,600 --> 00:37:56,279
Speaker 6: Have to sort of adapt or you have to build

789
00:37:56,320 --> 00:38:00,880
Speaker 6: this sort of very configurable software. And I think and

790
00:38:01,400 --> 00:38:03,560
Speaker 6: what I see you just you know firsthand, is that

791
00:38:05,080 --> 00:38:10,440
Speaker 6: inside these organizations you can now solve very specific problems

792
00:38:10,440 --> 00:38:14,680
Speaker 6: that are highly valuable and not only can you solve

793
00:38:14,680 --> 00:38:18,279
Speaker 6: them better than generic software, But you can actually in

794
00:38:18,320 --> 00:38:20,360
Speaker 6: a lot of ways do it for less money because

795
00:38:20,360 --> 00:38:23,000
Speaker 6: you're trying to tackle less stuff. You didn't need the

796
00:38:23,080 --> 00:38:25,319
Speaker 6: sixteen other features. You bought it for the one that

797
00:38:25,400 --> 00:38:29,480
Speaker 6: you really really cared about. And so I think that

798
00:38:29,640 --> 00:38:31,400
Speaker 6: part of it, you know, I don't like there's I

799
00:38:31,680 --> 00:38:34,120
Speaker 6: definitely think there are pieces of the software industry that

800
00:38:34,239 --> 00:38:36,200
Speaker 6: are gonna, you know, come out the other side. You're

801
00:38:36,200 --> 00:38:38,799
Speaker 6: gonna nobody wants to deal with payroll, right like you know,

802
00:38:38,840 --> 00:38:41,480
Speaker 6: somebody you're still gonna buy some payroll software and you're

803
00:38:41,520 --> 00:38:44,000
Speaker 6: still gonna have that. But you know, I do think

804
00:38:44,000 --> 00:38:46,840
Speaker 6: there are a lot of pieces where the software existed

805
00:38:46,960 --> 00:38:50,000
Speaker 6: essentially as a kind of wrapper around a database, and

806
00:38:50,040 --> 00:38:53,120
Speaker 6: now you're just gonna, you know, with just the database,

807
00:38:53,160 --> 00:38:55,120
Speaker 6: you can do that. And then you know, the other

808
00:38:55,160 --> 00:38:57,040
Speaker 6: piece i'd say here is it's this is not this

809
00:38:57,160 --> 00:38:59,239
Speaker 6: is a kind of confluence of circumstances where it's not

810
00:38:59,360 --> 00:39:03,880
Speaker 6: just the coding, it's also the fact that you have

811
00:39:04,080 --> 00:39:06,200
Speaker 6: ai to do a whole bunch of work. So you know,

812
00:39:06,200 --> 00:39:08,719
Speaker 6: if we pick on CRM for a second, right, like

813
00:39:09,120 --> 00:39:13,960
Speaker 6: you know, Salesforce dot Com, Salesforce dot com. We can

814
00:39:14,239 --> 00:39:16,400
Speaker 6: you know, you look at what the interface of that is.

815
00:39:16,440 --> 00:39:20,120
Speaker 6: And essentially it has existed to get salespeople to take

816
00:39:20,239 --> 00:39:22,839
Speaker 6: unstructured data, which is sales meetings, and turn it into

817
00:39:22,880 --> 00:39:25,839
Speaker 6: structured data that so can be stored into database. And

818
00:39:25,920 --> 00:39:29,960
Speaker 6: now you have AI, and AI is very capable of

819
00:39:30,040 --> 00:39:33,680
Speaker 6: taking unstructured data directly from the source, so you have

820
00:39:33,719 --> 00:39:37,160
Speaker 6: people recording meetings, and then it can structure it into

821
00:39:37,200 --> 00:39:39,400
Speaker 6: any data that you want. This is one of the

822
00:39:39,480 --> 00:39:42,320
Speaker 6: very first sort of mind blowing moments I had was

823
00:39:42,360 --> 00:39:46,399
Speaker 6: that I could give it a Jason interface. I could

824
00:39:46,400 --> 00:39:49,480
Speaker 6: describe exactly what I wanted the data structure to be,

825
00:39:49,920 --> 00:39:52,480
Speaker 6: and it would give me back that information in that

826
00:39:52,560 --> 00:39:55,080
Speaker 6: data structure. And we've just basically been having a bunch

827
00:39:55,080 --> 00:39:56,799
Speaker 6: of humans do that work for a very long time,

828
00:39:56,840 --> 00:39:58,880
Speaker 6: whether it's in CRM or project management or any of

829
00:39:58,880 --> 00:40:00,800
Speaker 6: these other places. And the ability to just kind of

830
00:40:00,840 --> 00:40:02,640
Speaker 6: get rid of that whole thing, I think it really

831
00:40:02,640 --> 00:40:05,480
Speaker 6: does bring into question the value of a lot of

832
00:40:05,480 --> 00:40:06,400
Speaker 6: these software companies.

833
00:40:06,520 --> 00:40:08,840
Speaker 2: Well, so we have seen like a lot of software sucks.

834
00:40:08,880 --> 00:40:11,960
Speaker 2: They look like melting ice cubes right now. Maybe they

835
00:40:12,440 --> 00:40:14,799
Speaker 2: So what is it I want to talk? I mean

836
00:40:14,840 --> 00:40:17,000
Speaker 2: this is like you know, our listeners who are investors,

837
00:40:17,080 --> 00:40:19,800
Speaker 2: there's a pretty high stakes question of like what residual

838
00:40:19,960 --> 00:40:22,960
Speaker 2: value there is. But talk a little bit more about Salesforce.

839
00:40:23,000 --> 00:40:24,480
Speaker 2: Maybe there would be a time to learn what sale

840
00:40:24,840 --> 00:40:28,040
Speaker 2: what it actually does as it's massively being disrupted. Now

841
00:40:28,040 --> 00:40:30,560
Speaker 2: we get around to learning what Salesforce is. But I

842
00:40:30,600 --> 00:40:32,279
Speaker 2: know it's like many things that there are apps that

843
00:40:32,280 --> 00:40:35,160
Speaker 2: people built onto Salesforce. But this sounds like we're hitting

844
00:40:35,200 --> 00:40:37,080
Speaker 2: on when I think probably one of the crucial questions

845
00:40:37,080 --> 00:40:39,480
Speaker 2: for like the future of the software industry. So talk

846
00:40:39,520 --> 00:40:41,960
Speaker 2: a little bit more about like the current approach and

847
00:40:42,000 --> 00:40:44,879
Speaker 2: what people are buying when they buy a package or

848
00:40:45,160 --> 00:40:48,400
Speaker 2: subscribe to a service from Salesforce, and then what the

849
00:40:48,560 --> 00:40:53,040
Speaker 2: unlocked opportunity is from having AI like live in the

850
00:40:53,120 --> 00:40:54,520
Speaker 2: same world as all your files.

851
00:40:55,040 --> 00:40:58,759
Speaker 6: Yeah, so I think if we take CRM as the

852
00:40:58,800 --> 00:41:01,640
Speaker 6: general category, know the biggest players there are.

853
00:41:01,640 --> 00:41:02,719
Speaker 2: That's customer relationship.

854
00:41:02,719 --> 00:41:05,920
Speaker 6: Customer relationship management, that's like what you know, Salesforce does it,

855
00:41:05,960 --> 00:41:08,600
Speaker 6: SAP does it, HubSpot does it for the mid market.

856
00:41:10,160 --> 00:41:12,040
Speaker 6: You know, when I think about that product and I

857
00:41:12,040 --> 00:41:15,600
Speaker 6: think about the way we've used it inside enterprise sales organizations, essentially,

858
00:41:15,680 --> 00:41:18,280
Speaker 6: you know, it's a database of companies, it's a database

859
00:41:18,280 --> 00:41:20,360
Speaker 6: of contacts, it's a database of deals you have in

860
00:41:20,400 --> 00:41:22,799
Speaker 6: the pipeline, and it's a way to track all those

861
00:41:22,840 --> 00:41:25,200
Speaker 6: deals you guys hit on something before that I think

862
00:41:25,239 --> 00:41:30,680
Speaker 6: is really it, which is like inside companies, there is

863
00:41:30,719 --> 00:41:33,400
Speaker 6: a huge group of people and who exist to answer

864
00:41:33,440 --> 00:41:36,000
Speaker 6: the question from management of what is the status of

865
00:41:36,080 --> 00:41:38,720
Speaker 6: something right? And you know that can be sales management,

866
00:41:38,760 --> 00:41:40,360
Speaker 6: it can be product management, it doesn't matter, right. It

867
00:41:40,400 --> 00:41:43,520
Speaker 6: could be within a newsroom. Somebody wants to know what

868
00:41:43,560 --> 00:41:45,879
Speaker 6: the status is and somebody else exists to go figure

869
00:41:45,920 --> 00:41:49,200
Speaker 6: out what the answer to that question is. And so fundamentally,

870
00:41:49,719 --> 00:41:52,880
Speaker 6: I think those CRM tools are bought first and foremost

871
00:41:53,120 --> 00:41:55,760
Speaker 6: to answer what is the status right? What's my pipeline

872
00:41:55,760 --> 00:41:58,040
Speaker 6: look like? And to answer what your pipeline looks like,

873
00:41:58,040 --> 00:42:01,319
Speaker 6: you need a bunch of salespeople putting deals in and

874
00:42:01,360 --> 00:42:03,520
Speaker 6: those deals are associated with contacts and companies and they

875
00:42:03,520 --> 00:42:07,240
Speaker 6: say when is that deal going to close? And essentially

876
00:42:07,239 --> 00:42:09,880
Speaker 6: you were asking the sales people to make the updates

877
00:42:09,880 --> 00:42:14,560
Speaker 6: in the system to do that and just very tactically,

878
00:42:14,680 --> 00:42:17,319
Speaker 6: I mean, you know, I run a company now we

879
00:42:17,440 --> 00:42:18,880
Speaker 6: talk to a lot of we have a lot of

880
00:42:18,880 --> 00:42:23,400
Speaker 6: sales calls. We record those calls and they get transcribed,

881
00:42:23,440 --> 00:42:26,440
Speaker 6: and the AI then looks through them and makes decisions

882
00:42:26,480 --> 00:42:30,440
Speaker 6: about where this deal should be in the process. And

883
00:42:31,400 --> 00:42:34,080
Speaker 6: it's much better than having somebody try to go update it,

884
00:42:34,160 --> 00:42:36,600
Speaker 6: because those people never updated anywhere. The secret of all

885
00:42:36,640 --> 00:42:39,239
Speaker 6: of this enterprise software is that nobody was using it

886
00:42:39,239 --> 00:42:42,520
Speaker 6: the way that anybody wanted to anyway. And so, you know,

887
00:42:43,120 --> 00:42:46,680
Speaker 6: I think that that is sort of you know, a

888
00:42:46,680 --> 00:42:48,359
Speaker 6: lot of what's happening there. Again, it's sort of some

889
00:42:48,400 --> 00:42:50,640
Speaker 6: of it's the coding, some of it's just the core capabilities.

890
00:42:50,680 --> 00:42:52,759
Speaker 6: And then you know, you still need databases, right, so

891
00:42:52,800 --> 00:42:54,320
Speaker 6: it's like, you know, you look at what data bricks

892
00:42:54,320 --> 00:42:56,400
Speaker 6: and stuff like. You know, I think those folks are

893
00:42:56,440 --> 00:42:58,840
Speaker 6: still sort of genuinely sitting in a pretty good place

894
00:42:58,880 --> 00:43:01,919
Speaker 6: where you know, all software has to sit on side

895
00:43:02,320 --> 00:43:05,000
Speaker 6: on top of some database that you can sort of

896
00:43:05,040 --> 00:43:08,080
Speaker 6: read and write to. But you know, I think some

897
00:43:08,120 --> 00:43:13,239
Speaker 6: of those categories that were specifically focused on kind of

898
00:43:13,320 --> 00:43:16,799
Speaker 6: like human input. Now, of course, you know, Salesforce has

899
00:43:16,800 --> 00:43:18,880
Speaker 6: a whole AI thing and they're saying, hey, you shouldn't

900
00:43:18,880 --> 00:43:22,320
Speaker 6: have humans inputting in salesforce. You know, at sales is

901
00:43:22,400 --> 00:43:24,799
Speaker 6: just one small piece. They have a whole customer support thing,

902
00:43:25,080 --> 00:43:28,680
Speaker 6: which obviously also has an interesting implication where you know

903
00:43:28,680 --> 00:43:31,160
Speaker 6: you're doing support with AI agents and so some of

904
00:43:31,200 --> 00:43:32,880
Speaker 6: it comes back to seats. I mean, you know, it

905
00:43:32,920 --> 00:43:36,000
Speaker 6: gets to be fairly complicated, but I do think I

906
00:43:36,000 --> 00:43:40,280
Speaker 6: think the fundamental underlying thing is anybody who buys software

907
00:43:41,040 --> 00:43:44,920
Speaker 6: that is, you know, uh SaaS, you're always buying for

908
00:43:44,960 --> 00:43:48,480
Speaker 6: a subset of the functionality that's nobody is using one

909
00:43:48,520 --> 00:43:50,839
Speaker 6: hundred percent of the functionality of SaaS, and so there's

910
00:43:50,840 --> 00:43:52,880
Speaker 6: always a trade off that's happening there where you know

911
00:43:53,000 --> 00:43:55,399
Speaker 6: you're spending more money than you need to because you're

912
00:43:55,440 --> 00:43:58,080
Speaker 6: not using all of these pieces. And so you know,

913
00:43:58,160 --> 00:44:00,600
Speaker 6: if you can more narrowly focus that, that is where

914
00:44:00,680 --> 00:44:02,560
Speaker 6: you could say, hey, we could solve this kind of

915
00:44:02,560 --> 00:44:03,440
Speaker 6: more narrow problem.

916
00:44:03,480 --> 00:44:05,160
Speaker 5: And not only can we solve it more narrowly, we

917
00:44:05,160 --> 00:44:05,760
Speaker 5: can solve.

918
00:44:05,600 --> 00:44:08,520
Speaker 6: It way more effectively, because you know, the trick with

919
00:44:08,600 --> 00:44:12,040
Speaker 6: AI is that the more specific you are with it,

920
00:44:12,120 --> 00:44:14,080
Speaker 6: the better the output is. Right, So it's like, if

921
00:44:14,239 --> 00:44:16,399
Speaker 6: you know, if outside of coding, if you just ask

922
00:44:16,480 --> 00:44:19,000
Speaker 6: chet gpz to write you a story, it's going to

923
00:44:19,040 --> 00:44:22,520
Speaker 6: write you a very very median story, right, sort of

924
00:44:22,560 --> 00:44:26,120
Speaker 6: exactly the median. But if you work with it and

925
00:44:26,160 --> 00:44:28,640
Speaker 6: you you know, then you're going to get it. The

926
00:44:28,680 --> 00:44:32,600
Speaker 6: more of your own expertise, you imbuing it, the further

927
00:44:32,840 --> 00:44:34,440
Speaker 6: up above the media, and it's going to be and

928
00:44:34,480 --> 00:44:36,160
Speaker 6: it's going to be you know. Of course, that also

929
00:44:36,200 --> 00:44:39,640
Speaker 6: means it's less where the line is between what's AI

930
00:44:39,680 --> 00:44:41,839
Speaker 6: and what's not AI is going to continue to get

931
00:44:42,160 --> 00:44:43,759
Speaker 6: Loreer Joe, how.

932
00:44:43,719 --> 00:44:45,760
Speaker 3: Much does Claude Code actually cost?

933
00:44:46,280 --> 00:44:46,640
Speaker 4: Do you know?

934
00:44:47,280 --> 00:44:51,319
Speaker 2: Well? I paid for the two two hundred dollars a

935
00:44:51,320 --> 00:44:55,719
Speaker 2: month version, but like high roller, yeah, I know, but uh,

936
00:44:55,880 --> 00:44:57,360
Speaker 2: you know, I think it's you could get it with

937
00:44:57,400 --> 00:44:59,759
Speaker 2: the pro version of like or whatever. The sub version

938
00:44:59,760 --> 00:45:01,480
Speaker 2: of that blow twenty dollars. But I hit a limit

939
00:45:01,640 --> 00:45:03,120
Speaker 2: fairly quickly, and I was like, I didn't have my

940
00:45:03,160 --> 00:45:05,399
Speaker 2: website up so like, and then I bought the five.

941
00:45:05,560 --> 00:45:09,200
Speaker 2: Then I paid five dollars for the extra compute, and

942
00:45:09,239 --> 00:45:10,160
Speaker 2: I was like, this is dumb.

943
00:45:10,239 --> 00:45:14,120
Speaker 3: I think, yeah, okay, so we're going out to two.

944
00:45:14,080 --> 00:45:16,640
Speaker 2: Nice dinners right month. That's not you know, when I

945
00:45:16,640 --> 00:45:18,080
Speaker 2: think about that way, it doesn't see that big.

946
00:45:17,920 --> 00:45:19,680
Speaker 3: Of a deal it's worth it to you. Yeah, okay,

947
00:45:19,719 --> 00:45:21,600
Speaker 3: so I think we can all agree this is like

948
00:45:21,680 --> 00:45:26,319
Speaker 3: a valuable service that claud Code is providing. But we

949
00:45:26,400 --> 00:45:29,080
Speaker 3: touched on this in the intro. It seems like the

950
00:45:29,160 --> 00:45:33,320
Speaker 3: models just keep replicating themselves really really quickly. So anything

951
00:45:33,320 --> 00:45:36,520
Speaker 3: that claud Code can do, I would expect another model

952
00:45:36,560 --> 00:45:39,120
Speaker 3: will come in in like a month maybe less and

953
00:45:39,160 --> 00:45:41,880
Speaker 3: do the exact same thing. What does that mean for

954
00:45:41,960 --> 00:45:46,279
Speaker 3: the actual like valuations of these companies and the models, Like,

955
00:45:46,360 --> 00:45:48,360
Speaker 3: how are they going to monetize it when it seems

956
00:45:48,440 --> 00:45:53,239
Speaker 3: so difficult to actually differentiate yourself, especially for like a

957
00:45:53,280 --> 00:45:55,719
Speaker 3: substantial portion of time.

958
00:45:55,800 --> 00:45:58,279
Speaker 6: Yeah, well, so again here I think we have to

959
00:45:58,440 --> 00:46:02,480
Speaker 6: distinguish between claud Code and the Claude model. So in

960
00:46:02,760 --> 00:46:06,040
Speaker 6: claud Code's case, if you're using you know, the latest version,

961
00:46:06,040 --> 00:46:08,680
Speaker 6: you're using Opus four point five, which is the model.

962
00:46:08,760 --> 00:46:11,640
Speaker 6: Opus four point five has a price of something in

963
00:46:11,680 --> 00:46:14,480
Speaker 6: the dollar fifty to two dollars for a million input

964
00:46:14,560 --> 00:46:16,880
Speaker 6: tokens and whatever it is on the output, which is

965
00:46:16,920 --> 00:46:19,640
Speaker 6: like roughly the going rate for cutting edge modles. Gemini

966
00:46:19,800 --> 00:46:25,160
Speaker 6: three pro is the same price, open a Chachipt five

967
00:46:25,200 --> 00:46:27,560
Speaker 6: point two is. They're all the same price. So the

968
00:46:27,560 --> 00:46:29,759
Speaker 6: first thing is is you have to differentiate between those.

969
00:46:30,760 --> 00:46:32,960
Speaker 6: And so I think a big part of what Anthropic

970
00:46:33,040 --> 00:46:34,880
Speaker 6: is trying to do is they're trying to lock people

971
00:46:34,880 --> 00:46:39,200
Speaker 6: into claud Code. In fact, there's just some controversy amongst

972
00:46:39,480 --> 00:46:43,560
Speaker 6: some nerds where open Code, which is a competitor to

973
00:46:43,640 --> 00:46:47,879
Speaker 6: claud Code, used to let you use your Claude Max

974
00:46:47,880 --> 00:46:50,120
Speaker 6: two hundred dollars. So the trick with the Claude Max

975
00:46:50,160 --> 00:46:53,320
Speaker 6: plan is if you're just buying those that number of tokens,

976
00:46:53,320 --> 00:46:56,000
Speaker 6: it would cost you significantly more than two hundred dollars.

977
00:46:56,040 --> 00:47:00,400
Speaker 6: It is a super super discounted plan. So like you,

978
00:47:00,400 --> 00:47:03,560
Speaker 6: you are probably you have the access, I have the

979
00:47:03,600 --> 00:47:06,239
Speaker 6: access to use I would guess in the thousand or

980
00:47:06,239 --> 00:47:10,560
Speaker 6: two thousand dollars of tokens for my two hundred dollars

981
00:47:10,640 --> 00:47:13,359
Speaker 6: a month. So it's a very very heavily subsidized plan.

982
00:47:13,640 --> 00:47:15,840
Speaker 6: And open Code, which is an open source version of

983
00:47:16,000 --> 00:47:19,920
Speaker 6: Claude code, a sort of competitor. They had found a

984
00:47:19,920 --> 00:47:22,600
Speaker 6: way that they would let you use your Claude Max

985
00:47:22,640 --> 00:47:27,680
Speaker 6: plan with open code, and Anthropic last week shut that down,

986
00:47:28,360 --> 00:47:31,640
Speaker 6: and some open Code people got very upset because they said, like,

987
00:47:31,680 --> 00:47:34,680
Speaker 6: this is not what you're supposed to do or I'm

988
00:47:34,719 --> 00:47:37,520
Speaker 6: not sure exactly what they said. I never felt like

989
00:47:37,800 --> 00:47:40,360
Speaker 6: I got a particularly good argument out of it. But

990
00:47:41,360 --> 00:47:42,839
Speaker 6: you know, I do think part of what they're trying

991
00:47:42,880 --> 00:47:47,120
Speaker 6: to get at because is that, you know, at the

992
00:47:47,200 --> 00:47:50,239
Speaker 6: very top, models like these are all amazing, like the

993
00:47:50,520 --> 00:47:56,200
Speaker 6: Google Opening and Anthropic, their best models are all on

994
00:47:56,320 --> 00:47:58,480
Speaker 6: par with each other. I mean, I would move them

995
00:47:58,480 --> 00:48:01,239
Speaker 6: around a little bit. I still think Opus four point

996
00:48:01,239 --> 00:48:03,120
Speaker 6: five is the best model out there, but you know,

997
00:48:03,160 --> 00:48:08,439
Speaker 6: I mean that might change tomorrow. Like and that's where

998
00:48:08,480 --> 00:48:10,560
Speaker 6: something like cloud code is really interesting because it's a

999
00:48:11,200 --> 00:48:14,080
Speaker 6: product that is very it's just theirs. It's a piece

1000
00:48:14,080 --> 00:48:17,120
Speaker 6: of software. It's not an AI model, and so it's

1001
00:48:17,160 --> 00:48:21,000
Speaker 6: sort of it's less able to be disrupted. Now again,

1002
00:48:21,120 --> 00:48:24,080
Speaker 6: I think if if somebody else wanted to copy that exactly,

1003
00:48:24,800 --> 00:48:28,319
Speaker 6: they could. Codex has one, Gemini has one. I just

1004
00:48:28,320 --> 00:48:30,439
Speaker 6: think they take a very different tact with it where

1005
00:48:30,440 --> 00:48:32,360
Speaker 6: it's much less and so, you know, I think what

1006
00:48:32,360 --> 00:48:34,120
Speaker 6: they're trying to do is get developers like me to

1007
00:48:34,160 --> 00:48:36,840
Speaker 6: feel very comfortable inside that so that when we go open,

1008
00:48:37,320 --> 00:48:39,799
Speaker 6: I still open Codex or tried Gemini, or I was

1009
00:48:39,840 --> 00:48:43,040
Speaker 6: playing with open code the other day, and it just

1010
00:48:43,040 --> 00:48:44,960
Speaker 6: doesn't feel familiar in the same way that you know,

1011
00:48:44,960 --> 00:48:47,120
Speaker 6: if you're trying to move somebody from a PC to

1012
00:48:47,160 --> 00:48:48,280
Speaker 6: a MAC, it doesn't feel familiar.

1013
00:48:48,360 --> 00:48:48,480
Speaker 1: Right.

1014
00:48:48,480 --> 00:48:50,680
Speaker 3: They want to own like the ecosystem.

1015
00:48:50,080 --> 00:48:54,160
Speaker 2: The environment, that environment or what a world. Noah, thank

1016
00:48:54,160 --> 00:48:56,200
Speaker 2: you so much for coming on Odd Laws. I was

1017
00:48:56,239 --> 00:48:58,560
Speaker 2: like dying to do an episode about this topic.

1018
00:48:58,600 --> 00:48:59,359
Speaker 5: Thanks for having me.

1019
00:49:00,080 --> 00:49:03,160
Speaker 2: Way, I don't have AI psychosis. I have a claud complex.

1020
00:49:03,600 --> 00:49:05,239
Speaker 3: Why is everyone making that joke?

1021
00:49:05,360 --> 00:49:05,560
Speaker 4: Wait?

1022
00:49:05,880 --> 00:49:06,439
Speaker 2: Which joke?

1023
00:49:07,000 --> 00:49:08,080
Speaker 3: The psychosis joke?

1024
00:49:08,600 --> 00:49:10,279
Speaker 2: I think you're going to be proud of me for

1025
00:49:10,360 --> 00:49:11,480
Speaker 2: saying Claude complex.

1026
00:49:11,760 --> 00:49:13,040
Speaker 3: Oh oh, that is very good.

1027
00:49:13,160 --> 00:49:16,400
Speaker 2: I do one pun finally for Tracy And why was

1028
00:49:16,400 --> 00:49:17,239
Speaker 2: it for making that joke?

1029
00:49:17,320 --> 00:49:18,640
Speaker 5: Well, I was a joke.

1030
00:49:18,840 --> 00:49:20,759
Speaker 2: I was handing your sir. I finally make a pun,

1031
00:49:20,880 --> 00:49:21,879
Speaker 2: and you just jump right over.

1032
00:49:22,160 --> 00:49:25,520
Speaker 3: Everyone keeps saying that claud code is AI psychosis for

1033
00:49:25,600 --> 00:49:27,600
Speaker 3: smart people, right, Like, how did that beck.

1034
00:49:27,520 --> 00:49:27,920
Speaker 1: Come a thing?

1035
00:49:28,600 --> 00:49:28,960
Speaker 3: Yeah?

1036
00:49:29,080 --> 00:49:30,439
Speaker 2: All right, but there's a good pun.

1037
00:49:30,560 --> 00:49:33,160
Speaker 3: Also very pro coded. I find you think so all

1038
00:49:33,160 --> 00:49:34,360
Speaker 3: of AI is pro coded.

1039
00:49:34,880 --> 00:49:37,279
Speaker 2: Uh, this is true. We should talk more about this,

1040
00:49:37,320 --> 00:49:39,120
Speaker 2: you know, we should have David Shorn, he's been doing

1041
00:49:39,120 --> 00:49:41,319
Speaker 2: a lot of polling about various demographics and how they

1042
00:49:41,320 --> 00:49:45,319
Speaker 2: feel about AI. We should need some interesting that. Yeah,

1043
00:49:45,320 --> 00:49:47,719
Speaker 2: we should do that anyway. No, thank you so much

1044
00:49:47,719 --> 00:49:48,480
Speaker 2: for coming on offline.

1045
00:49:48,520 --> 00:49:49,319
Speaker 5: Thanks for having me.

1046
00:50:01,480 --> 00:50:03,960
Speaker 2: Well that was fun, Tracy, I really like I It's

1047
00:50:04,200 --> 00:50:07,399
Speaker 2: obvious to anyone who's been within five minutes five feet

1048
00:50:07,440 --> 00:50:09,320
Speaker 2: of me for the last two weeks. I'm like totally

1049
00:50:09,400 --> 00:50:12,720
Speaker 2: addicted and gone down, I know, gone down the rabbit

1050
00:50:12,760 --> 00:50:16,840
Speaker 2: hole and stuff. But like I for the first time unronically,

1051
00:50:17,000 --> 00:50:21,160
Speaker 2: I'm like, Okay, this is transformative technology beyond being very

1052
00:50:21,160 --> 00:50:22,120
Speaker 2: impressive technology.

1053
00:50:22,200 --> 00:50:26,000
Speaker 3: Right, So I've been coming to a conclusion, which is

1054
00:50:26,040 --> 00:50:31,040
Speaker 3: that you know, AI can be both under hyped and

1055
00:50:31,160 --> 00:50:34,920
Speaker 3: overvalued simultaneously. Like, and I feel like that's kind of

1056
00:50:34,920 --> 00:50:36,480
Speaker 3: where we are at the moment.

1057
00:50:36,239 --> 00:50:37,560
Speaker 2: That were you're making your stock call.

1058
00:50:37,840 --> 00:50:42,040
Speaker 3: Yeah no, but seriously, like it it's a big deal.

1059
00:50:42,360 --> 00:50:45,400
Speaker 3: It's going to change the way we work. But is

1060
00:50:45,400 --> 00:50:48,839
Speaker 3: it monetizable? Can you differentiate the actual models? The better

1061
00:50:48,920 --> 00:50:52,440
Speaker 3: the technology gets, like, the easier it is to just

1062
00:50:52,480 --> 00:50:54,960
Speaker 3: do what everyone else is doing. And also like the

1063
00:50:55,040 --> 00:50:57,520
Speaker 3: compute gets cheaper and cheaper. So I just don't know

1064
00:50:57,560 --> 00:50:59,200
Speaker 3: how you monetize.

1065
00:50:58,719 --> 00:51:01,719
Speaker 2: This well, so that's very interesting his point, which is

1066
00:51:01,719 --> 00:51:05,440
Speaker 2: that it's the tokens are heavily subsidized still, and so

1067
00:51:05,440 --> 00:51:07,800
Speaker 2: that if you're paying and actually using that two hundred

1068
00:51:07,800 --> 00:51:10,880
Speaker 2: dollars max program and you actually use it to the limit,

1069
00:51:11,400 --> 00:51:14,640
Speaker 2: Claude is going to lose money on this, and then

1070
00:51:14,680 --> 00:51:18,040
Speaker 2: the prices keep dropping. And I know, like claud code

1071
00:51:18,200 --> 00:51:21,400
Speaker 2: is okay, they're attempting to create something that resembles a

1072
00:51:21,440 --> 00:51:24,400
Speaker 2: traditional software ecosystem that you feel as a user that

1073
00:51:24,440 --> 00:51:28,719
Speaker 2: you're locked into. But so far, in my various like

1074
00:51:29,000 --> 00:51:32,200
Speaker 2: since November twenty twenty two when I started playing with AI,

1075
00:51:32,680 --> 00:51:35,920
Speaker 2: it hasn't felt like anyone has established lock in with anything.

1076
00:51:36,160 --> 00:51:41,120
Speaker 2: And it's very it's very movable, and I suspect even

1077
00:51:41,160 --> 00:51:43,319
Speaker 2: though I have this file now on my desktop that

1078
00:51:43,400 --> 00:51:47,400
Speaker 2: has a file called claud md that gives instructions, et cetera,

1079
00:51:47,800 --> 00:51:50,320
Speaker 2: I'm certain that if I open this file with Codex

1080
00:51:50,400 --> 00:51:52,800
Speaker 2: or Googles, I could probably just pick it up the same.

1081
00:51:52,920 --> 00:51:55,640
Speaker 3: Yeah, I also think there's a fundamental issue with the

1082
00:51:55,640 --> 00:51:58,840
Speaker 3: lock in strategy because when you're talking about technology on

1083
00:51:58,880 --> 00:52:02,719
Speaker 3: the Internet, it just feels very against the grain to

1084
00:52:02,920 --> 00:52:06,600
Speaker 3: try to lock people into anything and We've seen various

1085
00:52:06,600 --> 00:52:10,319
Speaker 3: projects over the years, and it's a lot harder than

1086
00:52:10,320 --> 00:52:10,840
Speaker 3: it looks.

1087
00:52:11,840 --> 00:52:13,840
Speaker 2: Yeah, I mean, I guess I would say it's a

1088
00:52:13,840 --> 00:52:15,600
Speaker 2: lot harder than it looks. But then we also know

1089
00:52:15,640 --> 00:52:17,680
Speaker 2: the flip side, which is that tons of people are

1090
00:52:17,719 --> 00:52:19,040
Speaker 2: locked into software that they hate.

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00:52:19,160 --> 00:52:19,279
Speaker 1: Right.

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00:52:19,360 --> 00:52:21,680
Speaker 2: Yeah, people are Oh I hate people. How many times

1093
00:52:21,680 --> 00:52:24,399
Speaker 2: have you? Oh, I hate Outlook right? Or I hate

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00:52:24,480 --> 00:52:27,520
Speaker 2: Microsoft teams and I hate this and I spend money

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00:52:27,520 --> 00:52:29,879
Speaker 2: on it every month and my organization can't move off

1096
00:52:29,880 --> 00:52:31,719
Speaker 2: of it or we can't migrate off of it. So

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00:52:31,760 --> 00:52:34,160
Speaker 2: I do think that cuts both ways. I do think

1098
00:52:34,520 --> 00:52:38,840
Speaker 2: he offered the best explanation I've heard of why the

1099
00:52:38,960 --> 00:52:42,279
Speaker 2: AI coding models are a threat to a lot of

1100
00:52:42,320 --> 00:52:46,440
Speaker 2: pretty big software businesses, especially especially the point about how

1101
00:52:46,480 --> 00:52:49,279
Speaker 2: the user never uses all of the features that they

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00:52:49,320 --> 00:52:52,680
Speaker 2: actually that the software got built for, and therefore maybe

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00:52:52,719 --> 00:52:56,759
Speaker 2: the build versus by calculation really starts to shift when

1104
00:52:56,760 --> 00:52:58,920
Speaker 2: they can just design that one feature very quickly.

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00:52:59,560 --> 00:53:02,200
Speaker 3: I totally agree on the software side, it seems like

1106
00:53:02,239 --> 00:53:05,720
Speaker 3: an existential threat, but just like the locked in ecosystem

1107
00:53:05,960 --> 00:53:09,480
Speaker 3: of a particular model. I know you said it's not

1108
00:53:09,520 --> 00:53:12,360
Speaker 3: actually a model, but that seems like a bigger issue

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00:53:12,360 --> 00:53:13,279
Speaker 3: to me. I don't know.

1110
00:53:13,320 --> 00:53:15,239
Speaker 2: I guess we'll see, we're going to see and I

1111
00:53:15,280 --> 00:53:17,600
Speaker 2: don't know. I kind of think we're going to see quickly. Yeah,

1112
00:53:17,840 --> 00:53:18,720
Speaker 2: that's again.

1113
00:53:18,800 --> 00:53:21,160
Speaker 3: That's the only certainty is like stuff is happening?

1114
00:53:21,360 --> 00:53:22,120
Speaker 2: What is happening now?

1115
00:53:22,239 --> 00:53:22,439
Speaker 4: Yeah?

1116
00:53:22,520 --> 00:53:23,520
Speaker 3: Okay, shall we leave it there?

1117
00:53:23,600 --> 00:53:24,359
Speaker 2: Let's leave it there.

1118
00:53:24,480 --> 00:53:26,760
Speaker 3: This has been another episode of the Odd Lots podcast.

1119
00:53:26,840 --> 00:53:29,360
Speaker 3: I'm Tracy Alloway. You can follow me at Tracy.

1120
00:53:29,160 --> 00:53:31,600
Speaker 2: Alloway and I'm Jolly Isn't All. You can follow me

1121
00:53:31,680 --> 00:53:34,719
Speaker 2: at the Stalwart. Follow our guest Noah Brier, He's at Hey,

1122
00:53:34,760 --> 00:53:38,160
Speaker 2: It's Noah. Follow our producers Kerman Rodriguez at Kerman Arman,

1123
00:53:38,280 --> 00:53:41,400
Speaker 2: Dashill Bennett at Dashbot, and Kilbrooks at Kilbrooks. And for

1124
00:53:41,440 --> 00:53:43,960
Speaker 2: more odd Laws content, go to Bloomberg dot com slash

1125
00:53:43,960 --> 00:53:46,640
Speaker 2: odd Lots with a daily newsletter and all of our episodes,

1126
00:53:46,800 --> 00:53:48,680
Speaker 2: and you can chat about all of these topics twenty

1127
00:53:48,719 --> 00:53:51,800
Speaker 2: four to seven in our discord Discord dot gg slash

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00:53:51,800 --> 00:53:52,280
Speaker 2: odd Lots.

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00:53:52,520 --> 00:53:54,560
Speaker 3: And if you enjoy odd Lots, if you like it

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00:53:54,600 --> 00:53:57,719
Speaker 3: when we talk about advances in AI, then please leave

1131
00:53:57,800 --> 00:54:01,400
Speaker 3: us a positive review on your favorite podcast platform. And remember,

1132
00:54:01,440 --> 00:54:03,960
Speaker 3: if you are a Bloomberg subscriber, you can listen to

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Speaker 3: and follow the instructions there. Thanks for listening.