Speaker 1: Bloomberg Audio Studios, Podcasts, radio News.
Speaker 2: Hello and welcome to another episode of the Odd Lots podcast.
Speaker 2: I'm Joe Wassental and I'm Tracy Alloway. So, Tracy, you're
Speaker 2: cool like if I, like, you know, just start doing
Speaker 2: this part time as I like build out my software business, right, Like,
Speaker 2: you're cool about that.
Speaker 3: I was gonna say, I've been thinking about AI and productivity,
Speaker 3: and so far your productivity has gone down, Joe, because
Speaker 3: instead of doing Odd Lots things, you're coding your own software.
Speaker 2: Except that I'm creating content for the Odd Lots newsletter
Speaker 2: about coding, and that is productivity a creative debatable debatable,
Speaker 2: But but you're cool with that. You're cool with like
Speaker 2: me like, oh, I'm just gonna like check in part
Speaker 2: time on Odd Lots when we have a recording.
Speaker 3: Well, so of course not okay, good, of course not.
Speaker 2: Yeah, that's the right answer. I want you to yes,
Speaker 2: I want you to be really sad. But like a
Speaker 2: few other people, you know, I have like caught the
Speaker 2: sort of like bug of like AI coding and I'm
Speaker 2: totally blown away. I've like played with it from the
Speaker 2: big I started playing around with it last year, but
Speaker 2: then over the holidays. I've been writing about this in
Speaker 2: the newsletter. Suddenly, like my Twitter feed is like clud code,
Speaker 2: clug code, clud code. I do just cursor before, which
Speaker 2: I was very impressed by at the time. And so
Speaker 2: when I got home from vacation, one of the first
Speaker 2: things I did is like figure out how to install
Speaker 2: clug code on my computer. And I was like, oh,
Speaker 2: I am like hooked. This is actually like I see
Speaker 2: why I have my Twitter feed is just like people
Speaker 2: posting about this.
Speaker 4: All right.
Speaker 3: So I have to say I have not tried it
Speaker 3: because I only have a work computer and I can't
Speaker 3: install new software, and I probably definitely cannot install new
Speaker 3: software that then makes changes to.
Speaker 2: Your existing software.
Speaker 3: I don't think Bloomberg would like that, but I have
Speaker 3: seen the hype. Lots of people talk talking about it.
Speaker 3: Have you seen claud Cowork Have you heard.
Speaker 2: Of Oh yeah, yeah, yeah yeah.
Speaker 3: So one of the criticisms of claud code was that
Speaker 3: you know, like, okay code, but you still need some
Speaker 3: background knowledge in coding, because like, you know, the interface
Speaker 3: is kind of like it pies and all of that
Speaker 3: or nineteen nineties Cowork apparently like goes a step further
Speaker 3: for normal people in quoting. It makes it super super easy.
Speaker 3: And the funniest thing is that apparently cloud code actually
Speaker 3: coded Cowork.
Speaker 2: So this is like really relates to my experience last
Speaker 2: year and then this year, which is that even last year,
Speaker 2: like trying to use the AI coding tools, it was
Speaker 2: an annoying process because there are various things that you
Speaker 2: had to do in the actual command line of the
Speaker 2: computer that were like I didn't I don't know command
Speaker 2: line vernacular, and you have to install these libraries and
Speaker 2: stuff like that. So there was this sort of like
Speaker 2: barrier that existed. But what's really changed in the last
Speaker 2: year or with the with Claude code, which has actually
Speaker 2: been around for a while and I should have like
Speaker 2: played with it before, is that like, because it sits
Speaker 2: on your computer, it sort of takes away it de
Speaker 2: abstracts it and so when you talk.
Speaker 3: About like that it actually does the stuff.
Speaker 2: It does it it just like oh, it's like, oh,
Speaker 2: we're gonna need to install this open source natural language
Speaker 2: processing library. It just does it automatically. Instead of me
Speaker 2: trying like figure out like what are the right keystrokes
Speaker 2: to pull that in or why is this not going
Speaker 2: into the right file? Folder or whatever, and so like
Speaker 2: what like Cowork, it's like all like all of these
Speaker 2: sort of like little frictions, like these technical things like
Speaker 2: command line user very rapidly are like dissipating and so
Speaker 2: that like then you have something like Cowork where it's
Speaker 2: just like they know they're taking care of that, and
Speaker 2: so you get this like user interface that's just like
Speaker 2: it's just getting easier and friendlier. There's almost no technical
Speaker 2: frictions at all anymore.
Speaker 3: Also, it feels very iterative, like the code is improving
Speaker 3: upon itself at this point I think was one of
Speaker 3: Claude's main selling points.
Speaker 2: Well, this is like you've seen like people talk about like,
Speaker 2: oh is AGI here? And this is like part of
Speaker 2: the debate because the premp one of the ideas I
Speaker 2: guess behind AGI is like, well what happens when you
Speaker 2: have software that can train itself and so forth? And
Speaker 2: I don't really know if I buy that, but you
Speaker 2: do just see like how fast the iteration cycles are,
Speaker 2: and I think we want to get into this. In part,
Speaker 2: they're fast because a bunch of people are suddenly getting excited.
Speaker 2: So then the human provides this sort of like we're
Speaker 2: sowing the seeds of our own demise because we're so
Speaker 2: enthusiastically participating in the evolution. But I just like it's
Speaker 2: suddenly clear, like, oh, this is going to change I
Speaker 2: think computing. And the other thing is the code works,
Speaker 2: like it creates code that like this is like there's
Speaker 2: no bugs. You know it works.
Speaker 3: Did you see speaking of automating yourself, you see there
Speaker 3: was a post on Reddit from a lawyer who said
Speaker 3: he's basically used claud code to automate like his entire job,
Speaker 3: and he hasn't told anyone.
Speaker 2: I'm not exactly surprised because the other thing that I
Speaker 2: experimented with is and I haven't one hundred percent verified this,
Speaker 2: but on jobs Day last week, I downloaded the full
Speaker 2: pdf and I just typed into the cloud code like
Speaker 2: find the most interesting details and make some charts based
Speaker 2: on and it did it in like a couple of minutes.
Speaker 2: I have no like ability to like I've never like
Speaker 2: built charts myself or whatever, like designing or whatever. And
Speaker 2: I didn't totally confirm yet that the data was all correct,
Speaker 2: but I'm pretty sure it was because everything I spot checked,
Speaker 2: so I didn't just that crucial I didn't tell you
Speaker 2: I know I didn't. That's why I didn't want to like, oh,
Speaker 2: like here's what, here's the today's jobs reporting charts.
Speaker 5: But I did.
Speaker 3: So what application did it actually build it in the charts?
Speaker 2: I don't know. I just had a file like that's
Speaker 2: the thing. I had a file on my computer at
Speaker 2: that point. What kind of file, like a P and
Speaker 2: G file, like an image file, that's the crazy thing.
Speaker 2: I don't know. And so there was just this image
Speaker 2: that had a bunch of charts, and my spot checks
Speaker 2: did suggest like I didn't see anything off. And people
Speaker 2: get paid money to like build that kind of stuff
Speaker 2: for like analysts and stuff like that.
Speaker 3: And right, so this is the other big question. If
Speaker 3: everyone can build their own software, what actually happens to software?
Speaker 3: And I was reading something I forget who it was by,
Speaker 3: but someone used claud code to create they wanted a
Speaker 3: website that would basically make the money for doing nothing,
Speaker 3: and that was the prompt And did.
Speaker 2: They do it?
Speaker 6: Yeah.
Speaker 3: So the idea that the model came up with was
Speaker 3: you can sell prompts, packages of good prompts and sell
Speaker 3: them for like forty bucks and you'll make tons of money.
Speaker 3: And I was thinking about that like, Okay, it's possible
Speaker 3: to make money that way, but also why wouldn't I
Speaker 3: just use claud code to do the same thing.
Speaker 2: There are many big questions that we use an economy
Speaker 2: are going to have to think about, and I think
Speaker 2: my main takeaway is we're gonna have to think about
Speaker 2: these sooner red than later. But what is clud code?
Speaker 2: Why is everyone so hyped about it? Like what is
Speaker 2: it about this particular piece of software versus what exists
Speaker 2: from open Aye and Gemini and all this stuff, Like
Speaker 2: why is this captured everyone's imagination? We really do have
Speaker 2: the perfect guess because it's someone who, unlike me, has
Speaker 2: been getting their hands and the stuff for longer. One
Speaker 2: of the few people that I know who is into
Speaker 2: lollms before chat GPT existed and was actually using them
Speaker 2: via the API, and was actually talking about their technical
Speaker 2: capacity to do things like coding even before November of
Speaker 2: twenty twenty two. So truly the perfect guests We're going
Speaker 2: to be speaking with Noah Bryer. He is the co
Speaker 2: founder of Alefic, which is a consultancy that helps big
Speaker 2: companies deal with AI stuff. So, uh, Noah, thank you
Speaker 2: so much for coming on odd lots.
Speaker 5: Thank you for having me.
Speaker 2: What is eleven? What's the deal? How are you like
Speaker 2: using llms before chat GPT existed? I don't know. I
Speaker 2: know very few people who were doing that.
Speaker 6: I had the good fortune of shutting down a startup
Speaker 6: in twenty twenty two, and so I had a lot
Speaker 6: of free time on my hands.
Speaker 2: And then how are you using it though?
Speaker 3: Like?
Speaker 2: How did you look at your like how did you
Speaker 2: aware that there was this thing that could be of
Speaker 2: potential used to.
Speaker 6: What was So my very first thing I was doing
Speaker 6: was using gethub Copilot, which at the time was built
Speaker 6: into VS code, and it was autocomplete inside VS code.
Speaker 6: So it was a nice and pretty immediately realized that
Speaker 6: there were certain coding tasks that it could just handle completely.
Speaker 6: Anything that was very pattern based. So if you write code,
Speaker 6: you write a lot of tests. If you write tests,
Speaker 6: every test kind of follows the same pattern, and you
Speaker 6: want it to follow the same pattern. You're looking for
Speaker 6: that structure, and over time, because it was looking at
Speaker 6: your code base, it was able to basically autocomplete it.
Speaker 6: I also started playing with the GPT three API, which
Speaker 6: had come out. I think that came out in November
Speaker 6: of twenty twenty one, and that was the first time
Speaker 6: it was publicly available to everybody, and they had a
Speaker 6: large language, Boddel as we know it today, available to them.
Speaker 6: So I was just testing and building things, and I
Speaker 6: pretty immediately realized the very first thing I did where
Speaker 6: it just blew my mind was I built a web scraper.
Speaker 6: So I was just trying to pull pricing data from
Speaker 6: a website. And I've done a lot of this in
Speaker 6: my career. It's maybe the most annoying task you have
Speaker 6: to do in all of coding, because HTML is the
Speaker 6: most miserable language to have to parse. And I just
Speaker 6: had this thing where I took the page, I took
Speaker 6: the content, I took the text, and I gave it
Speaker 6: to the AI and I asked it to give me
Speaker 6: back the pricing table and give me back the pricing table.
Speaker 6: And I just thought, I'll never do it the other
Speaker 6: way again.
Speaker 5: That's it.
Speaker 3: Yeah, that HTML mentioned just brought up like memories of
Speaker 3: me in like the mid nineties on HTML goodies. Do
Speaker 3: you remember that side? Yeah? I wonder if it's still
Speaker 3: Is it still up? That would be wild? Does cloud code?
Speaker 3: Does that count as AGI? This seems to be the debate,
Speaker 3: right is it AGI?
Speaker 6: I try not to wait into what's AGI and what's not.
Speaker 6: I think my guess on AGI, for what it's worth
Speaker 6: is that it's probably going to be a conversation like
Speaker 6: the Turing Test, where everybody thought it was really really
Speaker 6: important for a really long time. We thought the Turing
Speaker 6: Test was the biggest thing for seventy years or whatever,
Speaker 6: and then CHATCHYBT very clearly passed the Turing test, and
Speaker 6: now everybody pretends, like we it's not just that they forgot,
Speaker 6: they pretend that it never mattered. Oh and so I
Speaker 6: am kind of guessing that that's going to be what
Speaker 6: the conversation. It's like, it's just going to be a
Speaker 6: sort of forever moving goalpost, because it turns out that
Speaker 6: the idea we had for what general intelligence looks like
Speaker 6: is not quite that. But I also think, you know,
Speaker 6: the computer scientists and the sort of serious AI researchers
Speaker 6: would say that much of what's going on inside claud
Speaker 6: code is not the model itself. It's the model paired
Speaker 6: with a human and I think that is a pretty
Speaker 6: important distinction.
Speaker 5: But I don't know about AGI.
Speaker 2: Well, okay, so you were using GPT to code prior
Speaker 2: to the release of jag GUPT. So therefore, coding models
Speaker 2: have been around a long time. So what is for
Speaker 2: those who haven't played around with it? What is claude code?
Speaker 2: Because again, coding models have been around for a long time.
Speaker 2: Well maybe have heard of cursor, copilot or some of
Speaker 2: these other harnesses, et cetera. What is cloud code?
Speaker 5: So if we back up first and we go to copilot.
Speaker 6: So Copilot was the first sort of commercial application of
Speaker 6: a largelanguage model by most accounts, And what Copilot did
Speaker 6: in its initial instantiation was just.
Speaker 2: Auto Microsoft product.
Speaker 6: It's a Microsoft product, so Microsoft doones, get hub, getthub,
Speaker 6: develop copilot. It was Microsoft had the partnership with open Ai,
Speaker 6: and so they built it in and what it was
Speaker 6: doing was doing autocomplete. So if you're writing code, a
Speaker 6: lot of writing code is boiler plate or trying to
Speaker 6: remember the name of a function. And you know, the
Speaker 6: reason stack overflow existed was because you can never remember
Speaker 6: the exact name of that function or the exact rejects
Speaker 6: that you need to use in order to find and
Speaker 6: replace something, and so you would go search for it.
Speaker 6: And they realized that you could just build that into
Speaker 6: the ID your code editor and and have it autocomplete
Speaker 6: for you, and it was pretty amazing. Then, uh, Chatchipt
Speaker 6: came out, and even before that, I had built a
Speaker 6: simple chatbot for myself because I realized that, hey, I
Speaker 6: could just ask this and instead of going and searching
Speaker 6: stack overflow. It was totally capable of answering code questions,
Speaker 6: and it was capable of writing rejects or doing these
Speaker 6: things and didn't make mistakes. Yes, but like there's famous
Speaker 6: mistakes on stack overflow of incorrect rejects that now exists
Speaker 6: in every code base in the world, and so you know,
Speaker 6: I think there were a lot of us just kind
Speaker 6: of playing with these things and realizing they were a
Speaker 6: huge boon. And so I think, really the next step
Speaker 6: is Cursor comes out. And the thing Curser realized that
Speaker 6: co Pilot didn't was that it wasn't good enough to
Speaker 6: have autocomplete. You also needed the Q and A because
Speaker 6: you have these things that you can't just auto complete.
Speaker 6: You want to be able to ask the question and
Speaker 6: answer it. And then Chatchipt came out and everybody was
Speaker 6: switching between ID and then I think, really the next
Speaker 6: big piece is that claud code came out. And what
Speaker 6: claud Code did that was so remarkable was they took
Speaker 6: the same set of models really and they took them
Speaker 6: out of the chatbot and they really just gave it
Speaker 6: some very basic functionality to operate within your machine.
Speaker 5: Right.
Speaker 6: And so you know, if you really look at kind
Speaker 6: of what exists within claud code, you're calling out to
Speaker 6: a model, and they gave it capability around sort of
Speaker 6: two big things. One is you can read and write
Speaker 6: files on your computer, and then two is that you
Speaker 6: can operate Unix the base commands, the bashed commands that
Speaker 6: exist in your environment. And again, because these models were
Speaker 6: trained on the Internet, and there's no greater source of
Speaker 6: information on the Internet than how to make the Internet,
Speaker 6: they know how to use Unix commands incredibly well, right,
Speaker 6: because Unix has existed for whatever it is, sixty years,
Speaker 6: and the way these commands were designed, they're all designed
Speaker 6: to be very very simple. There's a find command, and
Speaker 6: you know, there's some thing called re rep and it
Speaker 6: can search to a code base. And Unix has this
Speaker 6: sort of beautiful way of tying one command to another,
Speaker 6: so you can take the output of one command and
Speaker 6: send it to another. And they kind of just gave
Speaker 6: the model access to these two or three very simple things,
Speaker 6: and it kind of turned out that it unlocked a
Speaker 6: whole bunch of functionality. I don't think even the people
Speaker 6: who built it fully realized. Like one example that I
Speaker 6: think about a lot is just the challenge you have
Speaker 6: with all of these.
Speaker 5: AI models is that they're stateless.
Speaker 6: So every time you talk to chat GPT, it's sending
Speaker 6: your entire conversation history back to chat GPT because it
Speaker 6: has no saved history of that chat, right, And that's fine,
Speaker 6: it's the way it works. It's just fact. But it
Speaker 6: means that you know, it forgets things. It doesn't no
Speaker 6: conversation to conversation. And one very easy way to save
Speaker 6: your state is just write it to a file. And
Speaker 6: so you give it right access and it can create files.
Speaker 6: And now all of a sudden you've overcome this, like
Speaker 6: probably the single biggest challenge that exists inside these large
Speaker 6: language models, which is that they're fundamentally stateless.
Speaker 3: So Claude writes itself little like memory notes right to
Speaker 3: remember the entire ca context of the conversation, and that's
Speaker 3: how it solved that problem.
Speaker 6: No, so there's sort of two things going on in
Speaker 6: claude code.
Speaker 5: Beneath the hood.
Speaker 6: There's one thing that works exactly like chetchipt or any
Speaker 6: of these other ones, which is it's maintaining a conversation history.
Speaker 6: So every message you send it and every action it takes,
Speaker 6: it's recording to a log, which is just one big file.
Speaker 6: That's really no different than what chet ChiPT can do.
Speaker 6: Where it gets really interesting though, is it can also
Speaker 6: write files that it can then read, so whereas that
Speaker 6: conversation history is all saved off, and eventually that conversation
Speaker 6: gets too long and it needs to do a thing
Speaker 6: called compaction. And when it compacts it, it tries to
Speaker 6: sort of just remember the bits because there the total.
Speaker 5: Window is.
Speaker 6: Large. But I mean it's like one hundred thousand tokens
Speaker 6: I mean.
Speaker 3: By memory notes, right. It compacts the information into the
Speaker 3: important stuff, then retreat.
Speaker 5: It does that.
Speaker 6: It only does that at the end, like once it
Speaker 6: runs out of space, once it runs out of context window.
Speaker 6: So it has two hundred thousand tokens I think, and
Speaker 6: two hundred thousand tokens in rough terms is probably one
Speaker 6: hundred and fifty thousand words. It says, okay, it's time
Speaker 6: for me to compact all of this stuff, and so
Speaker 6: it still saves your whole history.
Speaker 5: On your computer.
Speaker 6: You still have the entire message, but for that session,
Speaker 6: it just compacts it down to this, you know, maybe
Speaker 6: twenty five thousand token memory of what it was.
Speaker 2: And is this like something that was not obvious before
Speaker 2: as a solution like this compaction? How important is it
Speaker 2: for this being? Like okay? As a human I can
Speaker 2: work on this on a project for a long time,
Speaker 2: like how much of an an unlock was there?
Speaker 6: I'm not sure compaction was the okay walk. I think
Speaker 6: the compaction functionality is helpful. The way chat GPT does it,
Speaker 6: for what it's worth is they don't do compaction. They
Speaker 6: just forget your messages eventually. So if you're in one chat,
Speaker 6: eventually your oldest message is going to fall off the back.
Speaker 6: For coding, that's probably less helpful, but there are trade offs.
Speaker 6: I both techniques work. I think fundamentally, the thing that
Speaker 6: is special about cloud code is not the compaction. It's
Speaker 6: the ability to write and read files on your computer,
Speaker 6: which means you can always write off memories.
Speaker 2: And then what does that mean? Write off memory?
Speaker 6: So you could say, hey, it's really important that I
Speaker 6: remember this thing for future sessions. I want to always
Speaker 6: work this way. So in a code base of mine,
Speaker 6: I have a set of documentation that explains how I
Speaker 6: like to do things. And cloud code makes a mistake,
Speaker 6: and so the next time I can write a memory,
Speaker 6: essentially it's written as a thing they call a skill,
Speaker 6: and you can write it off and you say, hey,
Speaker 6: whenever you run into this, I want you to operate
Speaker 6: in this kind of way. And that existing across every
Speaker 6: session is really a thing you can only do when
Speaker 6: you can store it as a file. It's a thing
Speaker 6: you can't do in quite the same way when you're
Speaker 6: operating in this environment or it's just going back and
Speaker 6: forth to the API. So this access to the file
Speaker 6: system is one really big piece. And then the second
Speaker 6: is is just the Unix commands, I mean computers. Every
Speaker 6: computer program lives on top of these sort of baseline
Speaker 6: functions and the way that the designers of Unix built
Speaker 6: them is really elegant, and they're very small. They all
Speaker 6: do one thing, and they're all composable, and in coding terms,
Speaker 6: composable means they can be chained together, right, and so
Speaker 6: you can say, hey, look for files that mentioned this word,
Speaker 6: and then from those files, I want you to take
Speaker 6: this second action, and then from the output of that action,
Speaker 6: I want you to take a third action.
Speaker 5: And that's just built into Unix.
Speaker 6: You literally just put a little pipe in between and
Speaker 6: you just pipe them from one to another and that's it.
Speaker 6: And so you give it access to write these commands,
Speaker 6: and all of a sudden, it gets these sort of
Speaker 6: second and third order effects that are just incredibly powerful
Speaker 6: and built over a really long time.
Speaker 3: So how much of claud code, the way it's different
Speaker 3: to other models, how much of that was overcoming technological
Speaker 3: challenges versus just having a good idea, because hearing you
Speaker 3: describe it, I mean, giving access to a computer seems
Speaker 3: like kind of obvious, like let's just do that.
Speaker 6: I don't have a good answer to that. I think
Speaker 6: that it was kind of just a good idea. Yeah,
Speaker 6: I think they did some patterns really well. They're clearly
Speaker 6: incredibly talented, not just engineers, but kind of thinkers about
Speaker 6: how to structure it. Like the primitives inside cloud code
Speaker 6: are just smart. And then the thing that they've done
Speaker 6: and Boris Cherney, who's the lead developer on cloud coded anthropic,
Speaker 6: he talks about light and demand a lot right, and
Speaker 6: Layton Demand is basically just, hey, look at the ways
Speaker 6: people are using these systems and then figure out ways
Speaker 6: to make that a part of the product itself. I
Speaker 6: think what they've done brilliantly, and this is kind of
Speaker 6: easy when you have a community of developers who are
Speaker 6: nerds who want to go talk about all the ways
Speaker 6: that they're using these things. Is they have I am
Speaker 6: amazed at the speed in which you know, I have
Speaker 6: a small community of fifteen CTOs who all use this
Speaker 6: stuff religiously. And you know, when we first started that community,
Speaker 6: it took them a month to I would see it
Speaker 6: in the chat, and then a month later it would
Speaker 6: get built into cloud code, and then increasingly.
Speaker 5: It's like a day later. It feels like they're just
Speaker 5: they're just listening to it.
Speaker 6: But I think they're just not only tapped in, but
Speaker 6: they're really fundamentally, you know, they're dog fooding it. They
Speaker 6: use their own products when you you know, they talk
Speaker 6: about the productivity engineering productivity at Anthropic. You know, despite
Speaker 6: growing at a crazy clip, it continues to go up.
Speaker 6: And you know, anybody who's built had to manage large
Speaker 6: scale pieces of software, large scale code basis knows that's
Speaker 6: not the norm.
Speaker 2: So VS code and cursor, these are IDEs. Cloud code
Speaker 2: is not an ID. What it's called a CLI is.
Speaker 6: A CLI a command line interface.
Speaker 2: Got it and the other labs now they also have CLIs.
Speaker 2: So why are we all talking about claud code and
Speaker 2: I chattypts is called codex. I don't know what Geminis
Speaker 2: is called.
Speaker 5: I think it's just called the gem.
Speaker 2: Why are we all talking about claud code rather than
Speaker 2: the other cla is that kind of have the same thing? Like,
Speaker 2: what is the difference?
Speaker 6: I think, first and foremost they were first, okay, so
Speaker 6: and I think they've they've had a lot more and
Speaker 6: you know, from my very personal opinion, I think they've
Speaker 6: done something smarter and better as far as the permissioning model.
Speaker 6: So you know, one of the really dangerous things is
Speaker 6: you've got the same running on your computer. You don't
Speaker 6: want it to go and delete everything. And they have
Speaker 6: a very fine grain permissioning model where you can say, hey,
Speaker 6: I want to allow this just this one time. I
Speaker 6: want to always allow it.
Speaker 2: You know, I always click always allow. I'm living on
Speaker 2: the edge.
Speaker 6: You can you can next time you run it. You
Speaker 6: can just do a flag that says dangerously skip permissions
Speaker 6: and and it'll just They call it yolo mode. I
Speaker 6: think I think more fundamentally though, if I look at
Speaker 6: at Codex versus Code, I think it's a difference in
Speaker 6: philosophy around what you want AI to do to me. Codex,
Speaker 6: which is excellent, is very focused on building an agent
Speaker 6: that you could just give something to and it'll just
Speaker 6: go do it. So I want to give it that task.
Speaker 6: I don't want to intercede. I don't want to give
Speaker 6: it any more feedback. And claud code is much more
Speaker 6: designed to be kind of a pair programmer. And so
Speaker 6: you know, in engineering, pair programming has existed for a while.
Speaker 6: It's a really weird sort of productivity thing where you
Speaker 6: put two engineers on the same problem and it turns
Speaker 6: out that you can get better code and multiplier Yeah,
Speaker 6: and it sort of makes up for the fact that
Speaker 6: obviously you know you're doubling the staff on it. But
Speaker 6: because of how many fewer bugs because you've both said svies,
Speaker 6: it has seemed to work out for many folks. Most
Speaker 6: companies don't practice it, but I think cloud code fundamentally
Speaker 6: is much more designed in that way.
Speaker 5: It's a pair programmer. It's they. You know.
Speaker 6: Whenever I start a project, I start in plan mode.
Speaker 6: So you start in plan mode. You put together a plan.
Speaker 6: I really, I mean it has spent a lot of
Speaker 6: time in plan mode. You go through, it gives you
Speaker 6: a plan, back, it asks you how you feel you
Speaker 6: can give it a whole bunch of direction, and then
Speaker 6: it's only then that it goes off and it goes
Speaker 6: into it. So you know, we're working together. And I
Speaker 6: actually have a whole system now that I've designed where
Speaker 6: I use a task management system called Linear, So I
Speaker 6: have claud Code write tasks off to Linear, and then
Speaker 6: I've worked with claud Code to write a document that
Speaker 6: helps sort of decide a set of heuristics to decide
Speaker 6: when you should assign it to Codex versus when you
Speaker 6: should give it to claud Code. And so if it's
Speaker 6: tightly defined enough and simple enough, I just send it
Speaker 6: off to Codex and it does it totally independently. And
Speaker 6: then if it's complicated enough that I think it requires
Speaker 6: my time and attention, then it saves it for me
Speaker 6: us to do together, and we'll work on it together.
Speaker 6: And so if it's sort of touching a kind of
Speaker 6: important enough if it's changing some part of the data model.
Speaker 6: There's these other kind of you know, fairly basic extet
Speaker 6: of criteria that I use, But that to me is
Speaker 6: the fundamental distinction. And you know, I find cloud code
Speaker 6: in that way to be just it sort of fits
Speaker 6: what I want to do and how I want to
Speaker 6: work much better.
Speaker 3: Talk a little bit more about how it actually impacts
Speaker 3: the workflow of an engineer, because you know, my impression
Speaker 3: was people can code, right, Like, the coding problem is
Speaker 3: kind of solved at this point, and even if you
Speaker 3: can't code, even if you're not a professional engineer, you
Speaker 3: can hire someone from like India or Indonesia or wherever
Speaker 3: to just write you a code. Maybe it'll take them
Speaker 3: a week instead of like two days with claud code.
Speaker 3: But how much does this actually change the workflow for
Speaker 3: an engineer.
Speaker 6: As completely as it could be changed. I mean, I
Speaker 6: would say that over the last three months, I've written, personally,
Speaker 6: I don't know a few hundred lines of code. Like
Speaker 6: I am mostly a manager of a set of agents
Speaker 6: who are writing code on my behalf. And you know,
Speaker 6: increasingly what I think is interesting. I've been thinking about
Speaker 6: this a bunch lately, is like, in some ways, it's
Speaker 6: just bringing me back to the core challenge that has
Speaker 6: always existed in software development, which is how do you
Speaker 6: manage all large scale software.
Speaker 5: Development project acrossation.
Speaker 6: It has become a coordination problem, and I spend a
Speaker 6: lot of time sort of now designing my CLAUD code
Speaker 6: system to ensure that code goes through all the proper
Speaker 6: spec checks and that it has all these things. The
Speaker 6: other thing that you know makes code a particularly good
Speaker 6: place to do this is that code is verifiable in
Speaker 6: a way that you know most other work is not.
Speaker 6: So you know, with code, you can verify that the
Speaker 6: build works right. So you can say, hey, I want
Speaker 6: to build this, I want to build this package. I
Speaker 6: want to make sure that it's actually going to build
Speaker 6: and that there's going to be no failures. That's a
Speaker 6: very easy check. It's either true or it's not true.
Speaker 6: There's also coders use linting, and so linting is a
Speaker 6: way to kind of look at it's static code analysis,
Speaker 6: so it basically tries to sort.
Speaker 5: Of find.
Speaker 6: Things in your code base that are not going to
Speaker 6: work ahead of time where you can predict that. Obviously,
Speaker 6: you can't predict Alan Turing prove that you can't predict
Speaker 6: with certainty whether code is going to run, but there's
Speaker 6: certain patterns and things that it can find. It's essentially
Speaker 6: does static pattern analysis, and so you know, you have
Speaker 6: it run all these things. But the more kind of
Speaker 6: opinionated you can be about that, and the more steps
Speaker 6: you can have it go through, so I find, you know,
Speaker 6: now I'm kind of the designer, which, honestly, as an
Speaker 6: entrepreneur and as a CEO of company is like that's
Speaker 6: kind of always been my job. Like I've been not
Speaker 6: I have less and less been a person who writes
Speaker 6: code and more and more been a person who designs
Speaker 6: a system in that case of company with a bunch
Speaker 6: of people who write code.
Speaker 2: One of the funny things it seems to me is
Speaker 2: that setting aside Claud code, Claude itself has a reputation
Speaker 2: for it's a nicer chat bot to talk to. People
Speaker 2: find it, and you know, chat gbt seems to really
Speaker 2: be psycho fantic. I still think it's I know it's improved,
Speaker 2: but I actually don't think it's improved or not. I
Speaker 2: still people like the prose style of Claud Claude and
Speaker 2: I'm curious that in the pair trading pair trading, I'm
Speaker 2: thinking about finance, the pair engineering model, whether there is
Speaker 2: also an edge there which is like, here is a
Speaker 2: chat bot that is not annoying to talk to while
Speaker 2: you're iterating, and whether that is like a meaningful distinction
Speaker 2: between you know, coding with codex or whatever.
Speaker 6: Yeah, I I don't know. It still can be very annoying,
Speaker 6: I can tell you. And it'll still sometimes be overly,
Speaker 6: overly effusive with me about a design choice, I mean,
Speaker 6: or sort of notice something which I could live without.
Speaker 2: So I work on this project that's doing this linguistic
Speaker 2: things and I eventually had to say, like, give it
Speaker 2: to me straight, how bad is this? And then so
Speaker 2: I said, I said, Actually, what I said was assume
Speaker 2: for a moment that you are a quantitative linguistics for
Speaker 2: the PhD. Give me your honest assessment of where we
Speaker 2: are with this. And it said like you've developed a
Speaker 2: nice toy and there's no evidence that it actually does.
Speaker 2: And I was like, okay, that's nice to hear. I
Speaker 2: actually like, you know, I appreciate that, and I thank
Speaker 2: you very blunt, not you know, it's still like polite,
Speaker 2: but it was like, this, doesn't you haven't really shown anything.
Speaker 2: You haven't really established at all that your saltware does
Speaker 2: what it claims to.
Speaker 6: Yeah, I think so. I think stylistically I kind of
Speaker 6: personally agree. My theory by the way, on Claude versus
Speaker 6: Opening Eye Chat GPT models is I think Claude is
Speaker 6: actually better at sort of reflecting what you give it.
Speaker 6: And so I think part of why we think it's
Speaker 6: better is it it's better at.
Speaker 5: Pretending it's us.
Speaker 6: Yeah, And so we tend to like that is this
Speaker 6: is purely speculation, but that's always been my theory on so.
Speaker 2: It flatters you in a different way.
Speaker 5: I think it's flattering you in a much more way.
Speaker 1: Yeah.
Speaker 6: Interest But for a long time, just Anthropic has been
Speaker 6: producing the best coding models, you know. I mean there's
Speaker 6: there can be some debate there now, but you know,
Speaker 6: there's a great curse story from Cursor actually where Cursor
Speaker 6: basically wasn't that good, and then SunNet three point five
Speaker 6: came out and all of a sudden, Cursor was amazing,
Speaker 6: and Cursor became a tool that everybody started using. But
Speaker 6: it wasn't until this other model came out and they
Speaker 6: made that the default model, and you know, I for
Speaker 6: what it's worth. I think the other takeaway from that,
Speaker 6: which is a kind of big theme we see in
Speaker 6: the market as a thing that the cloud Code team
Speaker 6: has talked about, is you constantly have to be building
Speaker 6: ahead with AI in a way that is very unique
Speaker 6: in the world of software, where you kind of always
Speaker 6: want to build things that are working at like seventy
Speaker 6: or eighty percent, because if you really spend the time
Speaker 6: to get it up to ninety or one hundred, you're
Speaker 6: going to lose all the games you get when the
Speaker 6: next model comes out, and the you know, with the
Speaker 6: amount of capex being spent on these models, like there's
Speaker 6: a next model that's going to come out that's going
Speaker 6: to be awesome, and you just kind of want to
Speaker 6: be downstream from that, and you don't want to waste
Speaker 6: six months getting an extra three percent when that new
Speaker 6: model is going to give you an extra seven.
Speaker 3: Yeah, this is the only certainty with AI is like
Speaker 3: there's always going to be a new model.
Speaker 2: The worst model ever use is the one that we're using.
Speaker 4: That's right, That's right.
Speaker 3: Are we all going to become coding illiterate? Are we
Speaker 3: just going to forget how to code. If everyone's using
Speaker 3: you know, general language to do.
Speaker 2: Forget, I've never learned. Yeah, okay, you know what I've
Speaker 2: been thinking about.
Speaker 1: You know that.
Speaker 2: Scott Karp, the CEO Palanteer, here's that line. He's like,
Speaker 2: when I was young, I was too poor to have
Speaker 2: a car, or so I didn't get a so I
Speaker 2: never learned to drive. And now I'm too rich, so
Speaker 2: I never learned to drive. I feel like when I
Speaker 2: was young, I was too dumb to learn to code,
Speaker 2: and now.
Speaker 3: You leaped ahead.
Speaker 2: Yeah, now I'm too smart to learn Python or HTML
Speaker 2: or whatever.
Speaker 6: I have a couple of takes on this person one personally.
Speaker 6: So first one is I just think like this is
Speaker 6: the worry of all technology.
Speaker 5: Ever.
Speaker 6: There was a paper that came out that showed that
Speaker 6: people were, uh, you know, they were forgetting more things
Speaker 6: or something because they were using chat GBD. But you know, uh,
Speaker 6: in Phadrious Plato was worried that people were gonna forget
Speaker 6: things because they started writing things down. And you know,
Speaker 6: I think the trade off there was pretty good. We
Speaker 6: got the scientific revolution a couple of other things, So, uh,
Speaker 6: you know, I think that's the sort of natural knee jerk.
Speaker 6: With that said, it is. I it's very strange when
Speaker 6: you have people, you know, the Cloud Code team is
Speaker 6: talking about how little code they write. Now, I draw
Speaker 6: a distinction between the sort of Vibe coding and the
Speaker 6: kind of amateur people who have never written code. And
Speaker 6: I think that is amazing, by the way, And I
Speaker 6: think there's a lot of software developers who are really
Speaker 6: mad about that because there they claim it's for safety
Speaker 6: reasons or whatever, but I think fundamentally it's just they've
Speaker 6: got people on their turf. But I think that's incredible.
Speaker 6: I mean, my hat, my my nine year old Vibe
Speaker 6: coded a website wo and for Secret Santa. She's now ten.
Speaker 6: She would get bad at me if I called her nine,
Speaker 6: but I think she vibe good at when she was nine.
Speaker 6: But that that's awesome, right, I don't know, that's amazing.
Speaker 6: That's a way for people to express themselves in a
Speaker 6: way that they couldn't before you did your your linguistics process.
Speaker 6: That's that's fun and interesting. But yeah, I also think
Speaker 6: the other the the thing that's happening with professional software
Speaker 6: developers when you hear from anthropic or you know, when
Speaker 6: I'm talking about it's you know, the code going through
Speaker 6: this process, and you know, all the code still gets
Speaker 6: reviewed by people. We're not letting it get out the
Speaker 6: door if it's not at the same level as human
Speaker 6: and it's just But what's amazing is I'm I'm running
Speaker 6: five of these sessions at a time, right, and so
Speaker 6: I've got like software being developed in parallel in a
Speaker 6: way that is unimaginable. And you know the other thing
Speaker 6: is just now, the best software engineers wrote the least
Speaker 6: code anyway. You know the sort of classic story of
Speaker 6: like the difference between a junior developer or senior developers
Speaker 6: that a junior developer gets a problem and they sit
Speaker 6: down and they put their fingers on the keyboard and
Speaker 6: they start writing code. And a senior developer gets a
Speaker 6: problem and sits there for three hours and tries to
Speaker 6: figure out what the best way to solve it is
Speaker 6: and then spends five minutes writing code to get it done.
Speaker 3: True elegance is restraint. That's what I say.
Speaker 4: What are you.
Speaker 2: Seeing in the companies you're working for? Like, I find
Speaker 2: it hard to believe and I was maybe skeptical of this,
Speaker 2: but it feels like right now we're here with technology,
Speaker 2: where like if viral like company is like, like I said,
Speaker 2: you can build charts of data in a way that
Speaker 2: used to be like someone would have had to get
Speaker 2: their hands dirty, ear, et cetera. And the companies that
Speaker 2: you talk to is right now that having an effect
Speaker 2: on how they think about what positions they're hiring for
Speaker 2: and the skills they're looking for and so forth.
Speaker 6: I think that it's hard to answer, right, really, I
Speaker 6: think that certainly. I do think I personally think if
Speaker 6: I look at the sort of layoffs in the technology
Speaker 6: industry of the last couple of years, I think some
Speaker 6: part of that is just looking at the output of
Speaker 6: these models and saying, hey, these models are able to
Speaker 6: produce it, you know, the median, and I have a
Speaker 6: whole bunch of sort of middle managers who are producing
Speaker 6: it the sixty fifth percentile. And it's like I can
Speaker 6: produce median for a dollar fifty per million tokens, or
Speaker 6: I can produce sixty fifth percentile for hover many hundreds
Speaker 6: of thousands dollars a year. It's it's a sort of
Speaker 6: fairly simple trade off, I think. So I do think
Speaker 6: there's a lot of downstream effects. I think the other
Speaker 6: thing that's happening, is is kind of like middle management
Speaker 6: is under threat because it's the realization that hey, like,
Speaker 6: part of what these models are amazing at is is
Speaker 6: I think of them as like a fuzzy interface. They
Speaker 6: can sort of turn any data into any other data, right.
Speaker 6: You can sort of transform data from one format to another.
Speaker 6: You can take a PDF and you can turn it
Speaker 6: into charts, right, And there's whole people who exist or
Speaker 6: you know, if you think about what product managers do
Speaker 6: a lot of what product managers do is they take
Speaker 6: how people are using a product and they try to
Speaker 6: transform it into a format that engineers can then use
Speaker 6: to figure out what to do. And I think a
Speaker 6: lot of those kind of a lot of those pieces
Speaker 6: that used to just be kind of transferring knowledge.
Speaker 2: I've always said, Tracy, I think one of the most
Speaker 2: important roles in any organization is essentially translation work. And
Speaker 2: you see it in the newsroom where it's like here's
Speaker 2: a team specialized in emerging market currencies, and then they
Speaker 2: have to like they have to then tell the senior
Speaker 2: editors what they're working on. But the senior editors, who
Speaker 2: are maybe more generalists, don't really know like why like
Speaker 2: some sort of like you know, one yen carry is
Speaker 2: important and that a really important role within any organization
Speaker 2: is essentially the team that can translate between the generalist
Speaker 2: team and the specialist team. Absolutely, and so that's an
Speaker 2: interesting observation and the sort of engineering world of like, okay,
Speaker 2: these are tools that are in sometimes translation tools.
Speaker 3: So we talked I agree completely, by the way, but
Speaker 3: we talked about vibe coding and Joe has this application
Speaker 3: that I don't think you're looking to monetize.
Speaker 2: No, it's I'm just trying to make it for the
Speaker 2: good of the world, right, Okay, so when did that
Speaker 2: become a crown? I'm not a lotizing it, But.
Speaker 3: Like this opens up massive questions for software as a service,
Speaker 3: right versas because if everyone can write their own software,
Speaker 3: you can replicate anything that's out there that is currently
Speaker 3: charging money. What's going to happen to software?
Speaker 6: I think software is pretty screwed. A lot of it,
Speaker 6: at least not all of it. You know, you still
Speaker 6: it depends on whether you call that cloud provider software
Speaker 6: or not. You know, you still need to run this
Speaker 6: stuff somewhere. And I think there's there's certain kind of
Speaker 6: software that you know, you just don't really want to
Speaker 6: be in the business of writing, you know, I as
Speaker 6: someone who's tried to build a project management system, I'd
Speaker 6: really rather I don't think anybody should be in that business.
Speaker 6: But I do think fundamentally, I mean, we see this
Speaker 6: every day inside enterprises. The sort of build versus buy
Speaker 6: pendulum has just swung. And you know, I mean I
Speaker 6: used to run a SaaS company and we sold to enterprises,
Speaker 6: and you know, for a long time that I think
Speaker 6: that made a lot of sense, right because like, hey,
Speaker 6: it just didn't make sense to try to build this
Speaker 6: thing on your own. And so but the price of
Speaker 6: that was, you know, won the price, right, like, and
Speaker 6: it got to be more and more expensive. The other
Speaker 6: price was that you were paying for a lot of
Speaker 6: stuff you didn't need, right, because the whole job of
Speaker 6: building SaaS is you need to generalize problems, and so
Speaker 6: you build things that are going to work for everybody.
Speaker 5: And that means either you.
Speaker 6: Have to sort of adapt or you have to build
Speaker 6: this sort of very configurable software. And I think, and
Speaker 6: what I see you just you know firsthand, is that
Speaker 6: inside these organizations you can now solve very specific problems
Speaker 6: that are highly valuable and not only can you solve
Speaker 6: them better than generic software, but you can actually, in
Speaker 6: a lot of ways do it for less money because
Speaker 6: you're trying to tackle less stuff. You didn't need the
Speaker 6: sixteen other features. You bought it for the one that
Speaker 6: you really really cared about. And so I think that
Speaker 6: part of it, you know, I don't like there's I
Speaker 6: definitely think there are pieces of the software industry that
Speaker 6: are gonna, you know, come out the other side. You're
Speaker 6: gonna nobody wants to deal with payroll, right like you know,
Speaker 6: somebody you're still gonna buy some payroll software and you're
Speaker 6: still gonna have that. But you know, I do think
Speaker 6: there are a lot of pieces where the software existed
Speaker 6: essentially as a kind of wrapper around a database, and
Speaker 6: now you're just gonna, you know, with just the database,
Speaker 6: you can do that. And then you know, the other
Speaker 6: piece I'd say here is it's this is not this
Speaker 6: is a kind of confluence of circumstances where it's not
Speaker 6: just the coding, it's also the fact that you have
Speaker 6: ai to do a whole bunch of work. So you know,
Speaker 6: if we pick on CRM for a second, right, like
Speaker 6: you know, Salesforce dot Com Salesforce dot com. We can
Speaker 6: you know, you look at what the interface of that is.
Speaker 6: And essentially it has existed to get salespeople to take
Speaker 6: unstructured data, which is sales meetings, and turn it into
Speaker 6: structured data that so can be stored into database. And
Speaker 6: now you have AI, and AI is very capable of
Speaker 6: taking unstructured data directly from the source, so you have
Speaker 6: people recording meetings, and then it can structure it into
Speaker 6: any data that you want. This is one of the
Speaker 6: very first sort of mind blowing moments I had was
Speaker 6: that I could give it a Jason interface. I could
Speaker 6: describe exactly what I wanted the data structure to be,
Speaker 6: and it would give me back that information in that
Speaker 6: data structure. And we've just basically been having a bunch
Speaker 6: of humans do that work for a very long time,
Speaker 6: whether it's in CRM or project management or any of
Speaker 6: these other places. And the ability to just kind of
Speaker 6: get rid of that whole thing, I think it really
Speaker 6: does bring into question the value of a lot of
Speaker 6: these software companies.
Speaker 2: Well, so we have seen like a lot of software sucks.
Speaker 2: They look like melting ice cubes right now. Maybe they
Speaker 2: So what is it I want to talk. I mean,
Speaker 2: this is like you know, our listeners who are investors,
Speaker 2: there's a pretty high stakes question of like what residual
Speaker 2: value there is. But talk a little bit more about Salesforce.
Speaker 2: Maybe there would be a time to learn what sale
Speaker 2: what it actually does as it's massively being disrupted. Now
Speaker 2: we get around to learning what Salesforce is. But I
Speaker 2: know it's like many things there are apps that people
Speaker 2: built onto Salesforce. But this sounds like we're hitting on
Speaker 2: when I think probably one of the crucial questions for
Speaker 2: like the future of the software industry. So talk a
Speaker 2: little bit more about like the current approach and what
Speaker 2: people are buying when they buy a package or subscribe
Speaker 2: to a service from Salesforce, and then what the unlocked
Speaker 2: opportunity is from having AI like live in the same
Speaker 2: world as all your files.
Speaker 6: Yeah, so I think if we take CRM as the
Speaker 6: general category, know the biggest players there are.
Speaker 2: That's customer relationship.
Speaker 6: Customer relationship management, that's like what you know, Salesforce does it,
Speaker 6: SVP does it, HubSpot does it for the mid market.
Speaker 6: You know, when I think about that product and I
Speaker 6: think about the way we've used it inside enterprise sales organizations. Essentially,
Speaker 6: you know, it's a database of companies, it's a database
Speaker 6: of contacts, it's a database of deals you have in
Speaker 6: the pipeline, and it's a way to track all those
Speaker 6: deals you guys hit on something before that I think
Speaker 6: is really it, which is like inside companies, there is
Speaker 6: a huge group of people and who exist to answer
Speaker 6: the question from management of what is the status of
Speaker 6: something right? And you know that can be sales management,
Speaker 6: it can be product management, it doesn't matter, right, it
Speaker 6: could be within a newsroom. Somebody wants to know what
Speaker 6: the status is and somebody else exists to go figure
Speaker 6: out what the answer to that question is. And so fundamentally,
Speaker 6: I think those CRM tools are bought first and foremost
Speaker 6: to answer what is the status right? What's my pipeline
Speaker 6: look like? And to answer what your pipeline looks like,
Speaker 6: you need a bunch of salespeople putting deals in and
Speaker 6: those deals are associated with contacts and companies and they
Speaker 6: say when is that deal going to close? And essentially
Speaker 6: you were asking the sales people to make the updates
Speaker 6: in the system to do that and just very tactically,
Speaker 6: I mean, you know, I run a company. Now we
Speaker 6: talk to a lot of we have a lot of
Speaker 6: sales calls. We record those calls and they get transcribed,
Speaker 6: and the AI then looks through them and makes decisions
Speaker 6: about where this deal should be in the process. And
Speaker 6: it's much better than having somebody try to go update it,
Speaker 6: because those people never updated anywhere. The secret of all
Speaker 6: of this enterprise software is that nobody was using it
Speaker 6: the way that anybody wanted to anyway. And so you know,
Speaker 6: I think that that is sort of you know, a
Speaker 6: lot of what's happening there. Again, it's sort of some
Speaker 6: of it's the coding, some of it's just the core capabilities.
Speaker 6: And then you know, you still need databases, right, so
Speaker 6: it's like, you know, you look at what data bricks
Speaker 6: and stuff like. You know, I think those folks are
Speaker 6: still sort of genuinely sitting in a pretty good place
Speaker 6: where you know, all software has to sit on side
Speaker 6: on top of some database that you can sort of
Speaker 6: read and write to. But you know, I think some
Speaker 6: of those categories that were specifically focused on kind of
Speaker 6: like human input. Now, of course, you know, Salesforce has
Speaker 6: a whole AI thing and they're saying, hey, you shouldn't
Speaker 6: have humans inputting in salesforce. You know, at sales is
Speaker 6: just one small piece. They have a whole customer support thing,
Speaker 6: which obviously also has an interesting implication where you know
Speaker 6: you're doing support with AI agents and so some of
Speaker 6: it comes back to seats. I mean, you know, it
Speaker 6: gets to be fairly complicated, but I do think I
Speaker 6: think the fundamental underlying thing is anybody who buys software
Speaker 6: that is, you know, uh SaaS, you're always buying for
Speaker 6: a subset of the functionality that's nobody is using one
Speaker 6: hundred percent of the functionality of SaaS, and so there's
Speaker 6: always a trade off that's happening there where you know
Speaker 6: you're spending more money than you need to because you're
Speaker 6: not using all of these pieces. And so you know,
Speaker 6: if you can more narrowly focus that, that is where
Speaker 6: you could say, hey, we could solve this kind of
Speaker 6: more narrow problem. And not only can we solve it
Speaker 6: more narrowly, we can solve it way more effectively because
Speaker 6: you know, the trick with AI is that the more
Speaker 6: specific you are with it, the better the output is. Right,
Speaker 6: So it's like, if you know, if outside of coding,
Speaker 6: if you just ask chet gpz to write you a story.
Speaker 6: It's going to write you a very very median story, right,
Speaker 6: sort of exactly the median. But if you work with
Speaker 6: it and you you know, then you're going to get it.
Speaker 6: The more of your own expertise, you imbuing it, the
Speaker 6: further up above the media, and it's going to be
Speaker 6: and it's going to be you know. Of course that
Speaker 6: also means it's less where the line is between what's
Speaker 6: AI and what's not AI is going to continue to
Speaker 6: get lorier, Joe, how.
Speaker 3: Much does Claude Code actually cost?
Speaker 4: Do you know?
Speaker 5: Well?
Speaker 2: I paid for the two two hundred dollars a month version,
Speaker 2: but like high roller, yeah, I know, but uh, you know,
Speaker 2: I think it's you can get it with the pro
Speaker 2: version of like or whatever. The sub version of that
Speaker 2: blow twenty dollars. But I hit a limit fairly quickly,
Speaker 2: and I was like, I didn't have my website up
Speaker 2: so like, and then I bought the five. Then I
Speaker 2: paid five dollars for the extra compute, and I was like,
Speaker 2: this is dumb.
Speaker 3: I think, yeah, okay, so we're going out to.
Speaker 2: Two nice dinners right month, that's not you know, when
Speaker 2: I think about that way, it doesn't see that big.
Speaker 3: Of a deal, it's worth it to you. Yeah, okay,
Speaker 3: so I think we can all agree this is like
Speaker 3: a valuable service that claud Code is providing. But we
Speaker 3: touched on this in the intro. It seems like the
Speaker 3: models just keep replicating themselves really really quickly. So anything
Speaker 3: that claud Code can do, I would expect another model
Speaker 3: will come in in like a month maybe less and
Speaker 3: do the exact same thing. What does that mean for
Speaker 3: the actual like valuations of these companies and the models, Like,
Speaker 3: how are they going to monetize it when it seems
Speaker 3: so difficult to actually differentiate yourself, especially for like a
Speaker 3: substantial portion of time.
Speaker 6: Yeah, well, so again here I think we have to
Speaker 6: distinguish between claud Code and the Claude model. So in
Speaker 6: claud Code's case, if you're using you know, the latest version,
Speaker 6: you're using Opus four point five, which is the model.
Speaker 6: Opus four point five has a price of something in
Speaker 6: the dollar fifty to two dollars for a million input
Speaker 6: tokens and whatever it is on the output, which is
Speaker 6: like roughly the going rate for cutting edge modles. Gemini
Speaker 6: three pro is the same price open a Chachipt five
Speaker 6: point two is. They're all the same price. So the
Speaker 6: first thing is is you have to differentiate between those.
Speaker 6: And so I think a big part of what Anthropic
Speaker 6: is trying to do is they're trying to lock people
Speaker 6: into claud Code. In fact, there's just some controversy amongst
Speaker 6: some nerds where open Code, which is a competitor to
Speaker 6: claud Code, used to let you use your Claude Max
Speaker 6: two hundred dollars. So the trick with the Claude Max
Speaker 6: plan is if you're just buying those that number of tokens,
Speaker 6: it would cost you significantly more than two hundred dollars.
Speaker 6: It is a super super discounted plan. So like you,
Speaker 6: you are probably you have the access, I have the
Speaker 6: access to use I would guess in the thousand or
Speaker 6: two thousand dollars of tokens for my two hundred dollars
Speaker 6: a month. So it's a very very heavily subsidized plan.
Speaker 6: And open Code, which is an open source version of
Speaker 6: Claude code, a sort of competitor. They had found a
Speaker 6: way that they would let you use your Claude Max
Speaker 6: plan with open code, and Anthropic last week shut that down,
Speaker 6: and some open Code people got very upset because they said,
Speaker 6: like this is not what you're supposed to do or
Speaker 6: I'm not sure exactly what they said. I never felt
Speaker 6: like I got a particularly good argument out of it.
Speaker 6: But you know, I do think part of what they're
Speaker 6: trying to get at because is that, you know, at
Speaker 6: the very top, models like these are all amazing, like
Speaker 6: the Google Opening and Anthropic, their best models are all
Speaker 6: on par with each other. I mean, I would move
Speaker 6: them around a little bit. I still think Opus four
Speaker 6: point five is the best model out there, but you know,
Speaker 6: I mean that might change tomorrow. Like and that's where
Speaker 6: something like cloud code is really interesting because it's a
Speaker 6: product that is very it's just theirs. It's a piece
Speaker 6: of software. It's not an AI model, and so it's
Speaker 6: sort of it's less able to be disrupted. Now again,
Speaker 6: I think if if somebody else wanted to copy that exactly,
Speaker 6: they could. Codex has one, Gemini has one. I just
Speaker 6: think they take a very different tact with it where
Speaker 6: it's much less and so, you know, I think what
Speaker 6: they're trying to do is get developers like me to
Speaker 6: feel very comfortable inside that so that when we go
Speaker 6: open I still open Codex or tried Gemini, or I
Speaker 6: was playing with open code the other day, and it
Speaker 6: just doesn't feel familiar in the same way that you know,
Speaker 6: if you're trying to move somebody from a PC to
Speaker 6: a MAC, it doesn't feel familiar. Right.
Speaker 3: They want to own like the ecosystem.
Speaker 2: The environment, that environment or what a world. Noah, thank
Speaker 2: you so much for coming on odd Laws. I was
Speaker 2: like dying to do an episode about this topic.
Speaker 5: Thanks for having me.
Speaker 2: Way, I don't have AI psychosis. I have a claud complex.
Speaker 3: Why is everyone making that joke?
Speaker 2: Wait, which joke?
Speaker 3: The psychosis joke?
Speaker 2: I think you were going to be proud of me
Speaker 2: for saying Claude complex.
Speaker 3: Oh oh that is very good.
Speaker 2: I do one pun finally for Tracy And why was
Speaker 2: it for making that joke?
Speaker 5: Well, I was a joke.
Speaker 2: I was handing your sirt. I finally make a pun,
Speaker 2: and you just jump right over.
Speaker 3: Everyone keeps saying that claud code is AI psychosis for
Speaker 3: smart people, right, Like, how did that beck come a thing? Yeah?
Speaker 2: All right, but there's a good pun.
Speaker 3: Also very pro coded. I find you think so all
Speaker 3: of AI is pro coded.
Speaker 2: Uh, this is true. We should talk more about this,
Speaker 2: you know, we should have David Shorn he's been doing
Speaker 2: a lot of polling about various demographics and how they
Speaker 2: feel about AI. We should need some interesting that. Yeah,
Speaker 2: we should do that anyway. No, thank you so much
Speaker 2: for coming on offline.
Speaker 5: Thanks for having me.
Speaker 2: Well that was fun, Tracy. I really like I It's
Speaker 2: obvious to anyone who's been within five minutes five feet
Speaker 2: of me for the last two weeks. I'm like totally
Speaker 2: addicted and gone down, I know, gone down the rabbit
Speaker 2: hole and stuff. But like I for the first time, unironically,
Speaker 2: I'm like, Okay, this is transformative technology beyond being very
Speaker 2: impressive technology.
Speaker 3: Right, So I've been coming to a conclusion, which is
Speaker 3: that you know, AI can be both under hyped and
Speaker 3: overvalued simultaneously. Like and I feel like that's kind of
Speaker 3: where we are at the moment.
Speaker 2: That were you're making your stock call.
Speaker 3: Yeah no, but seriously, like it it's a big deal.
Speaker 3: It's going to change the way we work, But is
Speaker 3: it monetizable? Can you differentiate the actual models? The better
Speaker 3: the technology gets, like, the easier it is to just
Speaker 3: do what everyone else is doing. And also like the
Speaker 3: compute gets cheaper and cheaper. So I just don't know
Speaker 3: how you monetize this.
Speaker 2: Well, so that's very interesting his point, which is that
Speaker 2: it's the tokens are heavily subsidized still, and so that
Speaker 2: if you're paying and actually using that two hundred dollars
Speaker 2: max program and you actually use it to the limit,
Speaker 2: Claude is going to lose money on this and then
Speaker 2: the prices keep dropping. And I know, like claud code
Speaker 2: is okay, they're attempting to create something that resembles a
Speaker 2: traditional software ecosystem that you feel as a user that
Speaker 2: you're locked into. But so far, in my various like
Speaker 2: since November twenty twenty two when I started playing with AI,
Speaker 2: it hasn't felt like anyone has established lock in with anything.
Speaker 2: And it's very it's very movable, and I suspect even
Speaker 2: though I have this file now on my desktop that
Speaker 2: has a file called claud md that gives instructions, et cetera,
Speaker 2: I'm certain that if I open this file with Codex
Speaker 2: or Googles, I could probably just pick it up the same.
Speaker 3: Yeah. I also think there's a fundamental issue with the
Speaker 3: lock in strategy because when you're talking about technology on
Speaker 3: the Internet, it just feels very against the grain to
Speaker 3: try to lock people into anything, and we've seen various
Speaker 3: projects over the years, and it's a lot harder than
Speaker 3: it looks.
Speaker 2: Yeah, I mean, I guess I would say it's a
Speaker 2: lot harder than it looks. But then we also know
Speaker 2: the flip side, which is that tons of people are
Speaker 2: locked into software that they hate, right, Yeah, people are
Speaker 2: Oh I hate people. How many times have you Oh,
Speaker 2: I hate Outlook right? Or I hate Microsoft teams and
Speaker 2: I hate this and I spend money on it every
Speaker 2: month and my organization can't move off of it, or
Speaker 2: we can't migrate off of it. So I do think
Speaker 2: that cuts both ways. I do think he offered the
Speaker 2: best explanation I've heard of why the AI coding models
Speaker 2: are a threat to a lot of pretty big software businesses,
Speaker 2: especially especially the point about how the user never uses
Speaker 2: all of the features that they actually that the software
Speaker 2: got built for, and therefore maybe the build versus by
Speaker 2: calculation really starts to shift when they can just design
Speaker 2: that one feature very quickly.
Speaker 3: I totally agree on the software side, it seems like
Speaker 3: an existential threat, but just like the locked in ecosystem
Speaker 3: of a particular model. I know he said it's not
Speaker 3: actually a model, but that seems like a bigger issue
Speaker 3: to me. I don't know. I guess we'll see.
Speaker 2: We're going to see and I don't know. I kind
Speaker 2: of think we're going to see quickly.
Speaker 3: Yeah, that's again, that's the only certainty is like stuff
Speaker 3: is happening.
Speaker 2: What is happening now?
Speaker 4: Yeah?
Speaker 3: Okay, shall we leave it there?
Speaker 2: Let's leave it there.
Speaker 3: This has been another episode of the Odd Lots podcast.
Speaker 3: I'm Tracy Alloway. You can follow me at Tracy Alloway.
Speaker 2: And I'm Jolly Isn't all. You can follow me at
Speaker 2: the Stalwart. Follow our guest Noah Brier, He's at Hey,
Speaker 2: It's Noah. Follow our producers Kerman, Rodriguez at Carman Arman, Dashill,
Speaker 2: ben Att at Dashbot, and Kilbrooks at Kilbrooks. And for
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