Joe Weisenthal: By the way, I don't have a psychosis. I have a Claude complex.

Tracy Alloway: Why is everyone making that joke? Wait. Which joke? The psychosis joke. I thought you were going to be proud of me for saying Claude Complex.

Joe Weisenthal: Oh, that is very good. I think I do one pun finally for Tracy, and I was over making that joke.

Tracy Alloway: Well, I was thinking that was a joke. I had to go. I finally make a pun, and you just jump right over it.

Joe Weisenthal: Well, everyone keeps saying that Claude code is I psychosis for smart people, right? Like, how did that become addictive?

Tracy Alloway: Yeah. All right. But there's a good. Fun also very bro coded. I find.

Joe Weisenthal: Hello and welcome to another episode of the Odd Lots podcast. I'm Joe Weisenthal

Tracy Alloway: And I'm Tracy Alloway. So, Tracy you're cool. Like, if I like, you know, just start doing this part time as I like, build out my software business. Right. Like you're cool about that.

Joe Weisenthal: I was going to say I've been thinking about AI and productivity. And so far your productivity has gone down, Joe. So instead of doing all thoughts things, you're coding your own software.

Tracy Alloway: Except that I'm creating content for the Odd Lots newsletter about coding and that is productivity. A creative.

Joe Weisenthal: Debatable. Debatable. But but you're cool with that. You're cool with like me. Like, oh, I'm just going to, like, check in part time on Odd Lots when we have a record.

Tracy Alloway: Well, oh. Of course not.

Joe Weisenthal: Okay, good, good, good. That's the right answer. I want you to. I want you to be really sad. But like a few other people, you know, I have, like, caught the sort of, like, bug of, like, AI coding, and I'm totally blown away. I like, played with it from the beginning. I started playing around with it last year, but then over the holidays and I've been writing about this in the newsletter suddenly, like, my Twitter feed is like club code, plug code, plug code and you just cursor before which I was very impressed by at the time. And so when I got home from vacation, one of the first things I did is like, figure out how to install plug code on my computer. And I was like, oh, I am like hooked. And this is actually like, I see why I have my Twitter feed is just like people posting about this.

Tracy Alloway: All right. So I have to say, I have not tried it because I only have a work computer and I can't install new software. And I probably definitely cannot install new software that then makes changes to existing software. I don't think Bloomberg would like that. But I have seen the hype. Lots of people talking about it. Have you seen, Claude Co-work? Have you heard of. Oh, yeah. Yeah, yeah, yeah.

Joe Weisenthal: So one of the criticisms of Claude Code was that, you know, like, okay, you code, but you still need some background knowledge and coding because, like, you know, the interface is kind of like. Is. And, and all of that or 1990s, co-work apparently, like, goes a step further for, for normal people, including it makes it super, super easy. And the funniest thing is that apparently Claude code actually coded.

Tracy Alloway: Yeah.

Joe Weisenthal: So the so this is like really relates to my experience last year and then this year, which is that even last year, like trying to use the AI coding tools, it was an annoying process because there were various things that you had to do in the actual command line of the computer that were like, I didn't, I don't know, command line vernacular. And you have to like install these libraries and stuff.

Tracy Alloway: Yeah.

Joe Weisenthal: So there was this sort of like barrier that existed and but what's the what's really changed in the last year or with the, with Claude code, which has actually been around for a while and I should have like played with it before is the like because it sits on your computer. It's sort of takes away in deeper access and so when you talk about. It, it actually does the stuff. It does. Is it just like, oh, it's like, oh, we're going to need to install this open source natural language processing library. It just does it automatically. Instead of me trying to like figure out like what are the right keystrokes to pull that in or why is this not going into the right file folder or whatever. And so like, like Co-work, it's like all, like all of these sort of like little friction, like these technical things like command line use are very rapidly are like dissipating.

Tracy Alloway: Yeah.

Joe Weisenthal: And so that like then you have something like Co-work where it's just like they know they're taking care of that. And so you get this like user interface that's just like it's just getting easier and friendlier. There's almost no technical frictions at all anymore.

Tracy Alloway: Also, it feels very iterative, like the code is improving upon itself.

Joe Weisenthal: Yeah.

Tracy Alloway: At this point I think that was one of Claude's main selling points.

Joe Weisenthal: Well, this is like you've seen like people talk about like, oh, is AGI here? And this is like part of the debate because one of the ideas, I guess, behind AGI is like, well, what happens when you have software that can train itself and so forth? And I don't really know if I buy that, but you do just see, like how fast the iteration cycles are. And I think we want to get into this in part, they're fast because a bunch of people are suddenly getting excited, and then the human provides this sort of like we're sowing the seeds of our own demise because we're so enthusiastically participating in the evolution. But I just like it's suddenly clear, like, oh, this is going to change. I think computing. And the other thing is the code works like it creates code that like this is like there's no bugs. You know, it works.

Tracy Alloway: Did you see speaking of automating yourself?

Joe Weisenthal: Yeah.

Tracy Alloway: See there was a post on Reddit from a lawyer who said he's basically used cloud code to automate, like, his entire job, and he hasn't told anyone.

Joe Weisenthal: I'm not exactly surprised, because the other thing that I experimented with is, and I haven't 100% verified this, but on jobs day last week, I downloaded the full PDF and I just typed into the cloud code like find the most interesting details and make some charts based on. And it did it in like a couple of minutes. I have no like, ability. Like I've never like built charts myself and or whatever, or like designer or whatever. And I didn't totally confirm yet that the data was all correct, but I'm pretty sure it was because everything I spot, so I didn't.

Tracy Alloway: Just that crucial. Detail, I know I didn't.

Joe Weisenthal: That's why I didn't want to like, oh, like, here's what, here's the here's jobs report and charts. But it might.

Tracy Alloway: But what application did it actually build it in the charts?

Joe Weisenthal: I don't know. I just had a like that's the thing. I had a file on my computer at that point.

Tracy Alloway: What kind of file?

Joe Weisenthal: Like a PNG file. Like an image file?

Tracy Alloway: Yeah, that's the crazy thing, I don't know.

Joe Weisenthal: And so there was just this image that had a bunch of charts, and my spark chunks did suggest, like, I didn't see anything off. And people get paid money to, like, build that kind of stuff for, like, analysts and stuff like that. Right. So this is the other big question. If everyone can build their own software, what actually happens to software? And I was reading something. I forget who it was by, but someone used cloud code to create. They wanted a website that would basically make them money for doing nothing. And that was the prompt. And did they do?

Tracy Alloway: Yeah.

Joe Weisenthal: So the idea that, the model came up with was you can sell prompts, packages of good prompts and sell them for like 40 bucks and you'll make tons of money. And I was thinking about that, like, okay, it's possible to make money that way. But also, why wouldn't I just use cloud code. To. Do the same thing? There are many big questions that we as an economy are going to have to think about. And I think my main takeaway is we're going to have to think about this sooner rather than later. But what is cloud code? Why is everyone so hyped about it? And what is it about this particular piece of software that versus what exists from OpenAI and Gemini and all this stuff? Like, why is this captured everyone's imagination?

Tracy Alloway: We really do have the perfect guys because it's someone who, unlike me, has been getting their hands dirty and this stuff for longer. One of the few people that I know who is into LMS before ChatGPT existed and was actually using them via the API, and was actually talking about their technical capacity to do things like coding even before November of 2022. So truly the perfect guys we're going to be speaking with Noah Brier. He's the co-founder of L'échec, a consultancy that helps big companies deal with AI stuff. So, Noah, thank you so much for coming on, and let's thank.

Noah Brier: You for having. Me.

Joe Weisenthal: What's the deal? How are you like using lemons before chat, GPT existed? I don't know, I know very few people who were doing that.

Noah Brier: I had the good fortune of shutting down a startup in 2022, and so I had a lot of free time on my hands.

Joe Weisenthal: And then how are you using it, though? Like how do you look at your like, how did you aware that there was this thing that could be of potential use to.

Noah Brier: So my very first thing I was doing was using GitHub copilot, which at the time was built into VS code and it was autocomplete inside VS code. So it was a nice and pretty immediately realized that there were certain coding tasks that it could just handle completely. Anything that was very pattern based. So if you write code, you write a lot of tests. If you write tests, every test kind of follows the same pattern and you want it to follow the same pattern, you're looking for that structure in and over time, because it was looking at your code base, it was able to basically autocomplete it. I also started playing with the GPT three API, which had come out, I think that came out in November of 2021, and that was the first time it was publicly available to everybody. And they had a large language model as we know it today, available to them. So I was just testing and building things, and I pretty immediately realized the very first thing I did where it just blew my mind, was I built a web scraper. So I was I was just trying to pull pricing data from a website. And I've done a lot of this in my career. It's maybe the most annoying task you have to do in all of coding, because HTML is the most miserable language to have to parse. And I just had this thing where I took the page, I took the content, I took the text, and I gave it to the AI, and I asked it to give me back the pricing table, and it gave me back the pricing table. And I just thought, I'll never do it the other way again.

Tracy Alloway: That's is that HTML mentioned? Just brought up like memories of me in like the mid-nineties on HTML.

Joe Weisenthal: Good. Easy to remember that site.

Tracy Alloway: Yeah.

Joe Weisenthal: I wonder if it's still is it still up? That would be wild.

Tracy Alloway: Does clawed code? Does that count as AGI? This seems to be the debate right? Is it AGI?

Noah Brier: I try not to wade into what's AGI and what's not. I think my guess on on AGI, for what it's worth, is that it's probably going to be a conversation like the Turing test, where everybody thought it was really, really important. For a really long time, we thought the Turing test was the biggest thing for 70 years or whatever. And then. ChatGPT very clearly passed the Turing test, and now everybody pretends like it's just that they forgot. They pretend that it never mattered. Oh, and so I am kind of guessing that that's going to be what the conversation is like. It's just going to be a sort of forever moving goalpost. Because it turns out that the idea we had for what general intelligence looks like is not quite that. But I also think, you know, the computer scientists and the sort of serious AI researchers would say that much of what's going on inside cloud code is not the model itself, it's the model paired with a human. And I think that is a pretty important distinction. But I don't know about AGI.

Tracy Alloway: Well, okay, so you were using, GPT to code prior to the release of ChatGPT. So therefore coding models have been around a long time. So what is for those who haven't played or played around with it. What is Claude code? Because again, coding models have been around for a long time. If people maybe have heard of cursor Copilot or some of these other harnesses, etc., what is cloud code?

Noah Brier: So if we back up first and we go to copilot because so copilot was the first sort of commercial application of a large language model. By most accounts. And what copilot did in its initial instantiation was just. Auto some Microsoft. Product.

Tracy Alloway: It's a Microsoft product.

Noah Brier: It's a Microsoft product. So Microsoft owns GitHub, GitHub developer Copilot. It was Microsoft had the partnership with OpenAI. And so they built it in and what it was doing was doing autocomplete. So if you're writing code, a lot of writing code is boilerplate or trying to remember the name of a function. And, you know, the reason StackOverflow existed was because you can never remember the exact name of that function or, or the exact regex that you need to use in order to find and replace something. And so you would go search for it. And they realized that you could just build that into the IDE, your code editor and, and have it autocomplete for you. And it was pretty amazing. Yeah. Then, ChatGPT came out, and even before that, I had built a simple chat bot for myself because I realized that, hey, I could just ask this and instead of going and searching StackOverflow, it was totally capable of answering code questions, and it was capable of writing regex or doing these things. And did it make mistakes? Yes. But like there's famous mistakes on StackOverflow of incorrect regex that now exists in every codebase in the world. And so, you know, there were a lot of us just kind of playing with these things and realizing they were a huge boon.

Noah Brier: And so I think really the next step is Curser comes out. And the thing is to realize that copilot didn't was that it wasn't good enough to have autocomplete. You also needed the Q&A because you have these things that you can't just autocomplete. You want to be able to ask the question and answer it. And then ChatGPT came out and everybody was switching between idea. And then I think really the next big piece is that cloud code came out, and what Cloud Code did that was so remarkable was they took the same set of models, really. And they took them out of the chat bot and they really just gave it some very basic functionality to operate within your machine. Right. And so, you know, if you really look at kind of what exists within cloud code, you're calling out to a model and they gave it capability around sort of two big things. One is you can read and write files on your computer. And then two is that you can operate Unix. The, the base commands, the bash commands that exist in your environment.

Noah Brier: And again, because these models were trained on the internet and there's no greater source of information on the internet than how to make the internet, they know how to use Unix commands incredibly well. Right? Because Unix has existed for whatever it is 60 years. And the way these commands were designed, they're all designed to be very, very simple. There's a find command and, you know, there's a thing called grep and it can search to a code base. And Unix has this sort of beautiful way of tying one command to another so you can take the output of one command and send it to another. And they kind of just gave the model access to these 2 or 3 very simple things. And it kind of turned out that it unlocked a whole bunch of functionality that I don't think even the people who built it fully realized.

Noah Brier: Like one example that I think about a lot is just the challenge you have with all of these AI models is that they're stateless. So every time you talk to ChatGPT, it's sending your entire conversation history back to ChatGPT because it has no saved history of that chat. Right? And that's fine. It's the way it works. It just fact, but it means that, you know, it forgets things. It doesn't know conversation, a conversation and one very easy way to save your state is just write it to a file. And so you give it right access and it can create files. And now all of a sudden you've overcome this like probably the single biggest challenge that exists inside these large language models, which is that they're fundamentally stateless.

Tracy Alloway: So Claude writes itself little like memory notes, right, to to remember the entire context of the conversation. And that's how it solved that problem.

Noah Brier: No. So there's sort of two things going on in cloud code beneath the hood. There's one thing that works exactly like ChatGPT or any of these other ones, which is it's maintaining a conversation history. So every message you send it and every, action, it takes, it's recording to a log. Right. Which is just one big file that's really no different than what ChatGPT can do. Where it gets really interesting, though, is it can also write files that it can then read. So whereas that conversation history is all saved off and eventually that conversation gets too long and it needs to do a thing called compaction. And when it compacts it, it tries to sort of just remember the bits because there the total window is, is, is large.

Tracy Alloway: But I mean, it's like 100,000 tokens. So that's what I mean by memory notes. Right? It compacts the information into the important stuff. So it doesn't retrieve. It does that.

Noah Brier: It only does that at the end. Like once it runs out of space, okay. Once it runs out of context window. So it has 200,000 tokens, I think, and 200,000 tokens in rough terms is probably 150,000 words. It says, okay, it's time for me to compact all of this stuff. And so it still saves your whole history on your computer. You still have the entire message. But for that session, it just compacts it down to this, you know, maybe 25,000 token memory.

Tracy Alloway: 25. What it was. Yeah.

Tracy Alloway: And is this, like something that was not obvious before as a solution like this compaction. How important is it for this being like, okay, as a human, I can work on this on a project for a long time. Like how much of an unlock was there?

Noah Brier: I'm not sure compaction was the okay. I think the compaction functionality is helpful. Okay. The way ChatGPT does it for what it's worth is they don't do compaction, they just forget your messages eventually. So if you're in one chat, eventually your oldest message is going to fall off the back. For coding, that's probably less helpful, but there are trade offs. I have both techniques work. I think fundamentally the thing that is special about cloud code is not the compaction, it's the it's the ability to write and read files on your computer.

Tracy Alloway: Yeah. Which means you can always write off memories.

Joe Weisenthal: And then what does that mean? Right off memory.

Noah Brier: So you could say, hey, I it's really important that I remember this thing for future sessions. I want to always work this way. So in a code base of mine, I have a set of documentation that explains how I like to do things, and cloud code makes a mistake. And so the next time I can write a memory, essentially it's written as a thing they call a skill. And you can write it off and you say, hey, whenever you run into this, I want you to operate in this kind of way. And that existing across every session is really a thing you can only do when you can store it as a file.

Noah Brier: Yeah, it's a thing you can't do in quite the same way when you're operating in this environment where it's just going back and forth to the API. So this access to the file system is one really big piece. And then the second is, is just the Unix commands, I mean computers, every computer program lives on top of the sort of baseline functions. And the way that the designers of Unix built them is really elegant and they're very small. They all do one thing and they're all composable. And in coding terms, composable means they can be they can be chained together. Right. And so you can say, hey, look for files that mention this word and then from those files, I want you to take this second action. And then from the output of that action, I want you to take a third action. And that's just built into Unix. You literally just put a little pipe in between and you just pipe them from one to another and, and that's it. And so you give it access to write these commands, and all of a sudden it gets these sort of second and third order effects that are just incredibly powerful and built over a really long time.

Tracy Alloway: So how much of Claude code, the way it's different to other models, how much of that was overcoming technological challenges versus like just having a good idea because hearing you describe it, I mean, giving access to a computer seems like kind of obvious. Like, let's, let's just do that.

Noah Brier: I don't have a good answer to that. I think that it was kind of just a good idea. Yeah. I think they did some patterns really well. They're clearly incredibly talented, not just engineers, but kind of thinkers about how to structure it. Like, the, the primitives inside called code are just smart. And then the thing that they've done and Boris Cherny, who's the, lead developer on cloud coded anthropic, he talks about, latent demand a lot. Right. And latent demand is basically just, hey, look at the ways people are using these systems and then figure out ways to make that a part of the product itself.

Noah Brier: I think what they've done brilliantly, and this is kind of easy when you have a community of developers who are nerds, who want to go talk about all the ways that they're using these things, is they have. I am amazed at the speed in which, you know, I have a small community of 15 CTOs who all use this stuff religiously. And, you know, when we first started that community, it took them a month to I would see it in the chat, and then a month later, it would get built into cloud code. And then increasingly like it's like a day later, it feels like they're just they're just listening to it. But I think they're just not only tapped in, but they're really fundamentally, you know, they're they're dog fooding. They use their own products when you, you know, they talk about the productivity engineering, productivity at anthropic. You know, despite growing at a crazy clip, it continues to go up. And, you know, anybody who's built had to manage large scale pieces of software, large scale codebases knows that's not the norm.

Tracy Alloway: So vs code and cursor, these are ideas. Cloud code is not an ID, it's a CLI. A clear command line interface. Got it.

Joe Weisenthal: And the other labs now they also have close.

Tracy Alloway: So why are we all talk about cloud code and AI charging. Because users are called Codex. I don't know what Gemini's is called. I think it's just called the gem.

Joe Weisenthal: Okay, so.

Tracy Alloway: Why are we all talk about cloud code rather than the other classes that kind of have the same thing? Like what is the difference?

Noah Brier: I think first and foremost they were first. Okay. So and I think they've they've had a lot more. And you know, from my very personal opinion, I think they've done some things smarter and better as far as the Permissioning models. So, you know, one of the really dangerous things is you've got this thing running on your computer. You don't want it to go and delete everything. Right? And, they have a very fine grained permissioning model where you can say, hey, I want to allow this just this one time. I want to always allow it. You know. I always click, always allow. I living on the edge. You can, you can, next time you run it, you can just do a flag that says dangerously skip permissions. And and it'll just, they call it yolo mode.

Noah Brier: I think I think more fundamentally, though, if I look at at Codex versus Cloud Code, I think it's, a difference in philosophy around what you want. AI to do. To me, Codex, which is excellent, is very focused on building an agent that you can just give something to and it'll just go do it. So I want to give it that task. I don't want to intercede. I don't want to give it any more feedback. And cloud code is much more designed to be, kind of a pair programmer. And so, you know, in engineering, pair programing has existed for a while. It's a really weird sort of productivity thing where you put two engineers on the same problem and it turns out that you can get better code and worse, multiply.

Tracy Alloway: Yeah.

Noah Brier: And it sort of makes up for the fact that obviously, you know, you're doubling the staff on it, but because of how many fewer bugs because you have both sets of eyes, it it has seemed to work out for many folks. Most companies don't practice it. But I think cloud code fundamentally is much more designed in that way. It's a pair programmer. It's, they, you know, whenever I start a project, I start in plan mode. So you start in plan mode, you put together a plan. I really I mean, I spend a lot of time in plan mode. You go through you, it gives you a plan back. It asks you how you feel. You can give it a whole bunch of direction, and then it's only then that it goes off and it goes into it. So, you know, we're working together and I actually have a whole system now that I've designed where, I use a task management system called linear. So I have quad code write tasks off to linear. And then I've worked, with cloud code to write a document that helps sort of decide a set of heuristics to decide when you should assign it to Codex versus when you should give it to cloud code. And so if it's tightly defined enough and simple enough, I just send it off to Codex. And it does it totally independently. And then if it's complicated enough that I think it requires my time and attention, then it it saves it for me, us to do together. And we'll work on it together. And so if it's, it's sort of touching, kind of important enough if it's changing some part of the data model, there's these other kind of, you know, fairly basic set of criteria that I use. And, but that to me is the fundamental distinction. And, you know, I find cloud code in that way to be just in it sort of fits what I want to do and how I want to work. Much better.

Joe Weisenthal: Talk a little bit more about how it actually impacts the workflow of an engineer, because, you know, my impression was people can code, right? Like the coding problem is kind of solved at this point. And even if you can't code, even if you're not a professional engineer, you can hire someone from like India or Indonesia or wherever to just write you a code. Maybe it'll take them a week instead of like two days with cloud code. But how much does this actually change the workflow for an engineer?

Noah Brier: As completely as it could be changed. I mean, I would say that over the last three months I've written personally, I don't know, a few hundred lines of code, like, I, I am mostly a manager of a set of agents who are writing code on my behalf. And, you know, increasingly, what I think is interesting, I've been thinking about this bunch lately is like, in some ways, it's just bringing me back to the core challenge that has always existed in software development, which is how do you manage, large scale software development projects? Coordination. Right. It has become a coordination problem. And, and I spent a lot of time sort of now designing my cloud code system to ensure that code goes through all the proper checks and that it it has all these things.

Noah Brier: The other thing that, you know, makes code a particularly good place to do this is that code is verifiable in a way that, you know, most other work is not. So, you know, with code, you can verify that the build works, right? So you can say, hey, I want to build this, I want to build this package. I want to make sure that it's actually going to build and that there's not going to be no failures. That's a very easy check. It's either true or it's not true. There's also coders use linting. And so linting is a way to kind of look it's static code analysis. So it basically tries to sort of find, things in your code base that are not going to work ahead of time, where you can predict that obviously, you can't predict, Alan Turing, proved that you can't predict, with certainty whether code is going to run. But there are certain patterns and things that it can find. It's essentially does static pattern analysis. And, so, you know, you haven't run all these things, but, the more kind of opinionated you can be about that and the more steps you can have a go through. So I find, you know, now I'm kind of the designer, which honestly, as an entrepreneur and as a CEO of companies like that's kind of always been my job. Like I've been up not I have less and less been a person who writes code. And more and more I've been a person who designs a system, in that case, a company with a bunch of people who write code.

Joe Weisenthal: One of the funny things it seems to me is that setting aside clawed code code itself has a reputation for. It's a nicer chat bot to talk to people, find it, you know, ChatGPT seems to really be sycophantic. I still think it's. I know it's improved, but I actually don't think it's improved enough. It's still. People like the prose style of, claw clawed. And I'm curious that in the pair trading, pair trading, I'm thinking about finance, the peer engineering model, whether there is also an edge there, which is like here is a chat bot that is not annoying to talk to while you're iterating, and whether that is like a meaningful distinction between, you know, coding with Codex or whatever.

Noah Brier: Yeah, I, I don't know, I it still can be very annoying, I can tell you. And it'll still sometimes, be overly, overly effusive with me about a design choice I made or sort of noticed something, which I could live without. I so I'm working on this project that's, doing this linguistic things, and I eventually had to say, like, give it to me straight. How bad is this? And then so I said, I said, actually, what I said was and assume for a moment that you are a quantitative linguistics for the PhD. Give me your honest assessment of where we are with this. And it said, like you've developed a nice toy and there's no evidence that it actually does. And I was like, okay, that's nice to hear. I actually like I, you know, I appreciate that. And it was like a very blunt, no, you know, it's still like polite, but it was like, this does it you do have a really showing anything you haven't really established at all that your software does what it claims to.

Noah Brier: Yeah, I think so. I think stylistically I kind of personally agree. I my theory, by the way on on Claude versus OpenAI ChatGPT models is I think Claude is actually better at sort of reflecting what you give it. And so I think part of why we think it's better is it it's better at pretending it's us. Yeah. And so we tend to like that is this is purely, speculation. But that's always been my theory on. On to flatters you in a different. Way. I think it's flattering you in a much. More subtle way. Yeah. Interesting. But, for a long time, just, anthropic has been producing the best coding models, you know? I mean, there's there can be some debate there now, but, you know, there's a great story from cursor, actually, where cursor basically wasn't that good. And then sonnet 3.5 came out and all of a sudden cursor was amazing and cursor became a tool that everybody started using. But it wasn't until this other model came out and they made that the default model.

Noah Brier: And, you know, I for what it's worth, I think the other takeaway from that, which is a kind of big theme we see in the market, is the thing that, the Cloud Code team has talked about is, you just constantly have to be building ahead with AI in a way that is very unique in the world of software, where you kind of always want to build things that are working at like 70 or 80%, because if you really spend the time to get it up to 90 or 100, you're going to lose all the gains you get when the next model comes out. And the, you know, with the amount of CapEx being spent at these models, like there's a next model that's going to come out, that's going to be awesome, and you just kind of want to be downstream from that, and you don't want to waste six months getting an extra 3% when that new model is going to give you an extra seven.

Tracy Alloway: Yeah.

Joe Weisenthal: This is the only certainty with AI is like, there's always going to be a new model.

Tracy Alloway: It's the worst model ever used is the one that we're using.

Joe Weisenthal: That's right, that's right.

Joe Weisenthal: Are we all going to become coding illiterate? Are we just going to forget how to code if everyone's using, you know, general language.

Tracy Alloway: To forget I never learned.

Joe Weisenthal: Yeah, okay. You know what I've been thinking about? You know that, Scott Karp, the, CEO of Palantir. He has that line. He's like, when I was young, I was too poor to have a car or so I didn't get a. So I never learned to drive. And now I'm too rich. So I never learned to drive. I feel like when I was young, I was too dumb to learn to code. And now.

Tracy Alloway: You leaped. Ahead.

Joe Weisenthal: Yeah. No, I'm too smart to learn Python or HTML or whatever.

Noah Brier: I have a couple of takes on this one personally. So, the first one is I just think like this is the worry of all technology ever. There was a paper that came out that showed that people were, you know, they were forgetting more things or something because they were using ChatGPT. But, you know, in Phaedrus, Plato was worried that people were going to forget things because they started writing things down. And, you know, I think the trade off there was pretty good. We got this scientific revolution, a couple other things. So, you know, I think that's the sort of natural kneejerk. With that said, it is I it's very strange when you have people, you know, the Cloud code team is talking about how little code they write.

Noah Brier: Now, I draw a distinction in between the sort of vibe coding and the kind of amateur people who have never written code. And I think that is amazing, by the way. And I think, there's a lot of software developers who are really mad about that, because they're, they, they claim it's for safety reasons or whatever. But I think fundamentally it's just they've got people on their turf. But I think that's incredible. I mean, my, I, my, my nine year old, vibe coded a website.

Tracy Alloway: Oh, wow.

Noah Brier: And, for secrets. Santa. She's now ten. She would get mad at me if I called her nine, but I think she got it when she was nine. But, that that's awesome, right? I don't know, that's amazing. That's a way for people to express themselves in a way that they couldn't before. You did your your linguistics project. That's that's fun and interesting. But yeah, I, I also think the other the, the thing that's happening with professional software developers, when you hear from anthropic or you know what I'm talking about, it's, you know, the code is going through this process and, you know, all the code still gets reviewed by people. We're not letting it get out the door if it's not at the same level as human.

Noah Brier: And it's just but what's amazing is I'm, I'm running five of these sessions at a time. Right. And so I've got like, software being developed in parallel in a way that is unimaginable. And, you know, the other thing is just, now the best software engineers wrote the least code. Anyway, you know, the sort of classic story of, like, the difference between a junior developer and or senior developer is that a junior developer gets a problem and they sit down and they put their fingers on the keyboard and they start writing code. And, senior developer gets a problem and sits there for three hours and tries to figure out what the best way to solve it is, and then spends five minutes writing code to get it done.

Tracy Alloway: True elegance is restraint. That's what I say.

Joe Weisenthal: What are you seeing in the companies you're working for? Like, I find it hard to believe, and I was maybe skeptical of this, but it feels like right now we're here with technology. We're like, if I were like, companies like, like I said, you can build a charts of data in a way that used to be like someone would have had to get their hands dirty or etc. in the companies that you talk to is right now this having an effect on how they think about what positions they're hiring for and the skills they're looking for and so forth.

Noah Brier: I think that it's hard to answer right there. I think that certainly, I do think, I personally think if I look at the sort of layoffs in the technology industry over the last couple of years, I think some part of that is just looking at the output of these models and saying, hey, these models are able to produce it, you know, the median. And I have a whole bunch of sort of middle managers who are producing at the 65th percentile. And it's like, I can produce median for $1.50 per million tokens, or I can produce 65th percentile for however many hundreds of thousand dollars a year. It's a sort of fairly simple trade off. I think.

Noah Brier: So I do think there's a lot of downstream effects. I think the other thing that's happening is, is kind of like middle management is under threat because it's the realization that, hey, like part of what these models are amazing at is, is, I think of them as like a fuzzy interface. They can sort of turn any data into any other data, right? You can sort of transform data from one format to another. You can take a PDF and you can turn it into charts. Right. And there's whole people who exist. Or, you know, if you think about what product managers do, a lot of what product managers do is they take how people are using a product and they try to transform it into a, format that engineers can then use to figure out what to do. And I think a lot of those kind of, a lot of those pieces that used to just be kind of transferring knowledge.

Joe Weisenthal: I've always said, Tracy, I think one of the most important roles in any organization is essentially translation work. And you see it in the newsroom where it's like, here is a team specialized in emerging market currencies, and then they have to like they have to then tell the senior editors what they're working on. But the senior editors who are maybe more generalist don't really know, like, why like some sort of like, you know, one.

Tracy Alloway: Yeah.

Joe Weisenthal: And Carrie is important and that a really important role within any organization is essentially the, the team that can translate between the generalist team and the specialist.

Noah Brier: Absolutely.

Joe Weisenthal: And so I that's an interesting observation in the sort of engineering world is like, okay, these are tools that are in some sense translation tool.

Tracy Alloway: So we talked to I agree completely by the way, but we talked about vibe coding. And Joe has this application, that I don't think you're looking to monetize.

Joe Weisenthal: No, I'm just trying to make it for the good of the world.

Tracy Alloway: Right. Okay.

Tracy Alloway: When did that become a common, What did. But like this opens up massive questions for software as a service, right. For SAS. Because if everyone can write their own software, you can replicate anything that's out there that is currently charging money. What's going to happen to software?

Noah Brier: I think software is pretty screwed. A lot of it at least. Not all of it. You know, you still, depends on whether you call that cloud provider software or not. You know, you still need to run this stuff somewhere. And I think there's, there's certain kinds of software that, you know, you just don't really want to be in the business of writing. You know, I, as someone who's tried to build a project management system, I'd really rather. I don't think anybody should be in that business. But I do think fundamentally, I mean, we see this every day inside enterprises. The the sort of build versus buy pendulum has just swung. And, you know, I mean, I used to run a SAS company and we sold to enterprises and, you know, for a long time that, that I think that made a lot of sense, right? Because like, hey, it just didn't make sense to try to build this thing on your own.

Noah Brier: And so but the price of that was, you know, won the price, right? Like, and it got to be more and more expensive. The other price was that you were paying for a lot of stuff you didn't need. Right? Because the whole job of building SAS is you need to generalize problems and say you build things that are going to work for everybody, and that means either you have to sort of adapt or you have to build this sort of very configurable, software. And I think and what I see just, you know, firsthand is that, inside these organizations, you can now solve very specific problems that are highly valuable. And not only can you solve them better than generic software, but you can actually, in a lot of ways, do it for less money because you're trying to tackle less stuff. You didn't need the 16 other features you bought it for, the one that you really, really cared about.

Noah Brier: And so, I think that part of it, you know, I don't like there's, I definitely think there are pieces of the software industry that are going to, you know, come out the other side. You're going to nobody wants to deal with payroll, right? Like, you know, somebody, you're still going to buy some payroll software and you're still going to have that. But, you know, I do think there are a lot of pieces where the software existed essentially as a kind of wrapper around a database. And now you're just going to, you know, with just the database, you can do that. And then, you know, the other piece I'd say here is it's this is not this is a kind of confluence of circumstances where it's not just the coding, it's also the fact that you have AI to do a whole bunch of work. So, you know, if we pick on CRM for a second, right, like, you know. Salesforce.com.

Joe Weisenthal: Salesforce.com.

Noah Brier: Salesforce.com, we can, you know, you look at what the interface of that is. And essentially it has existed to get salespeople to take unstructured data, which is sales meetings, and turn it into structured data that so it can be stored in a database. And now you have AI. And AI is very capable of taking unstructured data directly from the source. So you have people recording meetings and then it can structured into any data that you want. This is one of the very first sort of mind blowing moments I had was that I could give it, Json interface. I could describe exactly what I wanted the data structure to be, and it would give me back that information in that data structure. And we've just basically been having a bunch of humans do that work for a very long time, whether it's in CRM or project management or any of these other places, and the ability to just kind of get rid of that whole thing. I think it really does bring into question the value of a lot of these software companies.

Tracy Alloway: Well, so we have seen like a lot of software stocks, they look like melting ice cubes right now. Maybe the so what I want to talk I mean this is like you know, our listeners who are investors, there's a pretty high stakes question of like what residual value there is. But talk a little bit more about Salesforce. Maybe this should be a time to learn about what it actually does as it's massively being disrupted. Now we get around to learning what Salesforce is. But I know it's like many things, there are apps that people built on to Salesforce, but this sounds like we're hitting on one. I think probably one of the crucial questions for like the future of the software industry. So talk a little bit more about like the current approach and what people are buying when they buy a package or subscribe to a service from Salesforce. And then what the unlock opportunity is from having, I like, live in the same world as all your files.

Noah Brier: Yeah. So I think if you if we take CRM as a general category, say, you know, the biggest players there are.

Joe Weisenthal: That's customer relationship.

Noah Brier: Customer relationship management, that's like where you know Salesforce does it, SAP does it, HubSpot does it for the mid-market. You know, when I think about that product and I think about the way we've used it inside enterprise sales organizations, essentially, you know, it's a database of companies. It's a database of contacts. It's a database of deals you have in the pipeline, and it's a way to track all those deals you guys hit on something before that I think is is really it, which is like inside companies. There is a huge group of people and who exists to answer the question from management of what is the status of something. Right. And you know, that can be sales management, it can be product management. It doesn't matter. Right? It could be within a newsroom. Somebody wants to know what the status is and somebody else exists to go figure out what the answer to that question is.

Noah Brier: And so fundamentally, I think those CRM tools are bought first and foremost to answer what is the status, right? What's my pipeline look like? And to answer what your pipeline looks like, you need a bunch of salespeople putting deals in. And those deals are associated with contacts and companies, and they say, when is that deal going to close? And and essentially you were asking the salespeople to make the updates in the system to do that. And just very tactically, I mean, you know, I run a company now, we talk to a lot of we have a lot of sales calls. We record those calls and they get transcribed. And the AI then looks through them and makes decisions about where this deal should be in the process. And it's much better than having somebody try to go update it, because those people never updated. Anyway, the secret of all of this enterprise software is that nobody was using it the way that anybody wanted to anyway. And so, you know, I think that that is sort of, you know, a lot of what's happening there.

Noah Brier: Again, it's sort of some of it's the coding, some of it's just the core capabilities. And then, you know, you still need databases, right? So it's like, you know, you look at what Databricks and Snowflake and, you know, I think those folks are still sort of genuinely sitting in a pretty good place where, you know, all software has to sit on, sort of on top of some database that you can sort of read and write to. But, you know, I think some of those categories that were specifically focused on kind of like human input. Now, of course, you know, Salesforce has a whole AI thing and they're saying, hey, you shouldn't have humans inputting in Salesforce, you know, to at sales is just one small piece. I have a whole customer support thing, which obviously also has an interesting implication where you know, you're doing support with AI agents. And so some of it comes back to seeds. I mean, you know, it gets to be fairly complicated, but I do think I think the fundamental underlying thing is anybody who buys software that is, you know, SAS, you're always buying for a subset of the functionality that there's nobody is using 100% of the functionality of SAS. And so there's always a trade off that's happening there where, you know, you're spending more money than you need to because you're not using all of these pieces. And so, you know, if you can more narrowly focus that, that is where you could say, hey, we could solve this kind of more narrow problem. And not only can we solve it more narrowly, we can solve it way more effectively. Because, you know, the trick with AI is that the more specific you are with it, the better the output is, right? So it's like if you know, if outside of coding, if you just asked ChatGPT to write you a story, it's going to write you a very, very median story. Right? Sort of exactly the median. But if you work with it and you, you know, then you're going to get it. The more of your own expertise you imbuing it, the further up above the median it's going to be. And it's going to be, you know, of course, that also means it's less where the line is between what's AI and what's not. AI is going to continue to get blurrier.

Tracy Alloway: Joe, how much does cloud code actually cost? Do you know?

Joe Weisenthal: Well, I paid for the, 200 or $200 a month version, but,

Tracy Alloway: Like high roller.

Joe Weisenthal: Yeah. No, but, you know, I think it's. You could get it with the pro version of like, or whatever the, the version of that below $20. But I hit a limit fairly quickly and I was like, I didn't have my website up. So like and then I bought the fact. Then I paid $5 for the extra compute and I was like, this is dumb. I think I'll just.

Tracy Alloway: Yeah. Oh, okay. So we going out to two nice dinners, right? That's not you know, when I think about that way, it doesn't seem that big of a deal.

Joe Weisenthal: It's worth it to you.

Tracy Alloway: Yeah. Okay. So I think we can all agree this is like a valuable service that cloud code is providing. But we touched on this in the intro. It seems like the models just keep replicating themselves really, really quickly. So anything that cloud code can do, I would expect another model will come in in like a month, maybe less, and do the exact same thing. What does that mean for the actual like valuations of these companies and the models like how are they going to monetize it when it seems so difficult to actually differentiate yourself, especially for like a substantial portion of time?

Noah Brier: Yeah. Well, so again here, I think we have to distinguish between cloud code and the cloud model. So in Cloud Code's case, if you're using, you know the latest version, you're using opus 4.5 which is the model. Opus 4.5 has a price of, you know, something in the dollar, 50 to $2 for a million input tokens and whatever it is on the output, which is like roughly the going rate for cutting edge models. Gemini three Pro is the same price. OpenAI, ChatGPT 5.2 is there. They're all the same price. So the first thing is, is you have to differentiate between those. And so I think a big part of what anthropic is trying to do is they're trying to lock people into cloud code.

Noah Brier: In fact, there's just some, controversy amongst some nerds, where, open code, which is a competitor to cloud Code, used to let you use your cloud Max $200. So the trick with the cloud Max plan is if you're just buying those, that number of tokens, it would cost you significantly more than $200. It is a super, super discounted plan. You are probably you have the access I have the access to use, I would guess in the thousand or $2,000 of tokens, for my $200 a month. So it's it's a very, very heavily subsidized plan and open code, which is an open source version of cloud code, a sort of competitor. They had found a way that they would let you use your cloud Max plan with open code and anthropic last week. Shut that down. Yeah. And some open code, people got very upset, because they said, like, this is not what you're supposed to do, or. I mean, I'm not sure exactly what they said. I never felt like I got a particularly good argument out of it. But, you know, I do think part of what they're trying to get at, because is that, you know, at the very top models like these are all amazing, like the Google, OpenAI and anthropic, their best models are all on par with each other.

Noah Brier: I mean, I would move them around a little bit. I still think opus 4.5 is the best model out there. But you know, I mean that might change tomorrow. Like, and that's where something like Cloud Code is really interesting because it's a, a product that is very it's just theirs. It's not it's a piece of software. It's not an AI model. And so it's sort of it's less able to be disrupted. Now, again, I think if somebody else wanted to copy that. Exactly, they could. Codex has one, Gemini has one. I just think they take a very different tact with it where it's much less. And so, you know, I think what they're trying to do is get developers like me to feel very comfortable inside that, so that when we go open, I still open Codex or try Gemini or I was playing with Open Code the other day and, it just doesn't feel familiar in the same way that, you know, if you're trying to move somebody from a PC to a mac, it doesn't feel familiar, right?

Tracy Alloway: They want to own like the ecosystem, the. Environment.

Noah Brier: Environment. Work on One world.

Joe Weisenthal: Noah, thank you so much for coming on Atlas. I was like dying to do an episode about this topic.

Tracy Alloway: By the way, I don't have I psychosis, I have a cloud complex. Why is everyone making that joke? Wait. Which joke? The psychosis joke. I thought you're going to be proud of me for saying cloud complex. Oh.

Joe Weisenthal: Oh, that is very good. I it's like I do one pun finally for Tracy.

Tracy Alloway: No, it's like, well, I was over making that joke.

Joe Weisenthal: Well, I was thinking that was a joke. I was handed a shirt.

Tracy Alloway: I finally make a pun and you just jump right over it.

Joe Weisenthal: Well, everyone keeps saying that cloud code is AI psychosis for smart people, right? Like, how did that become a thing?

Tracy Alloway: Yeah. All right. But there's a good. Fun also very bro coded I find.

Joe Weisenthal: You think so all of. AI is bro coded.

Tracy Alloway: This is true. We should talk more about this. You know, we should have David Shaw on. He's been doing a lot of polling about various demographics and how they feel about AI. We should, And, yeah, some interesting I. See into that.

Joe Weisenthal: Yeah, we should, do that. Anyway, Noah, thank you so much for coming on out, love.

Noah Brier: Thanks for having me.

Joe Weisenthal: Well, that was fun. Tracy. I really like I. It's obvious to anyone who's been within five minutes, five feet of me for the last two weeks. I'm like, totally addicted and gone down. I never gone down the rabbit hole and stuff. But like, I for the first time unironically, I'm like, okay, this is transformative technology beyond being very impressive. Technology, right?

Tracy Alloway: So I've been coming to a conclusion, which is that, you know, I can be both under hyped, and overvalued simultaneously. Like, I feel like that's kind of where we are at the moment, where.

Joe Weisenthal: You're making your stock call.

Tracy Alloway: Yeah. No, but seriously like it. It's a big deal. It's going to change the way we work. But is it monetizable? Can you differentiate the actual models? The better the technology gets, like, the easier it is to just do what everyone else is doing. And also like the compute gets cheaper and cheaper. So I just don't know how you monetize this.

Joe Weisenthal: Well.

Tracy Alloway: So that's very interesting. His point, which is that it's the tokens are heavily subsidized still. Yeah. And so that if you're paying and actually using that $200 max program and you actually use it to the limit, Claude is going to lose money on this. Right? And then the prices keep dropping. And I know, like Claude code is okay. They're attempting to create something that resembles a traditional software ecosystem that you feel as a user that you're locked into. But so far in my various like since November 2022, when I started playing with AI, it hasn't felt like anyone has established lock in with anything. And it's very, it's very movable. And I suspect even though I have this file now on my desktop that has a file called Claude mode that gives instructions, etc., I'm certain that if I open this file with Codex or Googles, I could probably just pick it up the same.

Joe Weisenthal: Yeah.

Tracy Alloway: I also think there's a fundamental issue with the lock in strategy, because when you're talking about technology in the internet, like it just feels very against the grain to try to lock people into anything. And we've seen various projects over the years and it's it's a lot harder than it looks.

Joe Weisenthal: Yeah. I mean, I guess I would say it's a lot harder than it looks. But then we also know the flip side, which is that tons of people are locked into software that they hate. Right?

Tracy Alloway: Yeah. People are. Oh, I hate people.

Joe Weisenthal: How many times have you or I hate outlook, right? Or I hate Microsoft Teams and I hate this and I spend money on it every month and my organization can't move off of it or we can't migrate off of it. So I do think that cuts both ways. I do think he offered the best explanation I've heard of why the AI coding models are a threat to a lot of pretty big software businesses, especially, especially the point about how the user never uses all of the features that they actually that the software got built for. And therefore maybe the build versus buy calculation really starts to shift when they can just design that one feature very quickly.

Tracy Alloway: I totally agree. On the software side, it seems like an existential threat, but just like the locked in ecosystem of a particular model. I know he said it's not actually a model, but that seems like a bigger issue to me. I don't know, I guess we'll see.

Joe Weisenthal: We're going to see and I don't know, I kind of think we're gonna see quickly.

Tracy Alloway: Yeah.

Joe Weisenthal: That's again, that's the only certainty is like, stuff is happening. Stuff is. Happening now. Yeah.

Tracy Alloway: All right. Shall we leave it there?

Joe Weisenthal: Let's leave it there.

Tracy Alloway: Okay. This has been another episode of the Odd Lots podcast. I'm Tracy Alloway. You can follow me @tracyalloway

Joe Weisenthal: And I'm Joe Weisenthal. You can follow me @thestalwart Follow our guest Noah Brier. He's @Heyitsnoah Follow our producers Carmen Rodriguez @carmenarmen, Dashiel Bennett @dashbot and Cale Brooks @calebrooks

Joe Weisenthal: And for more Odd Lots content, and to see what Joe has actually been working on in Cloud Code, check out our daily newsletter. You can find that at bloomberg.com. Forward slash Odd Lots. And you can join fellow listeners in conversation 24 seven in our discord, discord.GG slash Odd Lots. And if you enjoyed this conversation, if you like it when we talk about AI, then please leave a comment or like the video. Or better yet, subscribe. Thanks for watching.