Why the Tech World Is Going Crazy for Claude Code

24:57To 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 programming 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. It's a force multiplier. Yeah, and it sort of makes up for the fact that obviously, you know, you're doubling the staff on it.

25:35But because of how many fewer bugs, because you've both sets of eyes, 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 program. 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, 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.

26:07And I actually have a whole system now that I've designed where I use a task management system called Linear. So I have CloudCode write tasks off to Linear. And then I've worked with CloudCode 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 CloudCode. 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 saves it for me, us, to do together and we'll work on it together.

26:44And so if 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 fairly basic set of criteria that I use. But that to me is the fundamental distinction. And, you know, I find cloud code in that way to be just it sort of fits what I want to do and how I want to work much better. 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? 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.

27:31Maybe it'll take them a week instead of like two days with Claude Code. But how much does this actually change the workflow for an engineer? 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 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 a 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 a large scale software development project?

28:14It's coordination, right? It has become a coordination problem. And I spend a lot of time sort of now designing my Claude code system to ensure that code goes through all the proper checks and that it has all these things. 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 package. I want to make sure that it's actually going to build and that there's going to be no failures. That's a very easy check.

28:51It's either true or it's not true. There's also coders use linting. And so linting is a way to kind of look at its 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 essentially does static pattern analysis. And so, you know, you have it run all these things, but the more kind of opinionated you can be about that and the more steps you can have it go through.

29:30So 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. I've been – I have less and less been a person who writes code and more and more been a person who designs a system, in that case a company, with a bunch of people who write code. One of the funny things it seems to me is that setting aside Claude code, Claude itself has a reputation for – it's a nicer chatbot to talk to. People find it – you know, ChatGPT seems to really be psychophantic. think. I still think it's, I know it's improved, but I actually don't think it's improved or not.

30:09People like the pros style of Claude. And I'm curious that in the pair trading, pair trading, I'm thinking about finance, the pair 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 yeah i i don't know i it still can be very annoying i will tell you and it'll still sometimes um be overly um uh overly effusive with me about a design choice i made or sort of noticing something um which i i could live without i so i'm working on this project that's uh 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 assume for a moment that you are a quantitative linguistics with a PhD.

31:06Give 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, OK, that's nice to hear. I actually like, you know, I appreciate that. And it was like a very blunt. Not, you know, it's still like polite, but it was like this doesn't you haven't really shown anything. You haven't really established at all that your software does what it claims to. Yeah, I think so. I think stylistically, I kind of personally agree. 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.

31:45And 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. This is purely speculation, but that's always been my theory on. So it flatters you in a different way. I think it's flattering you in a much more subtle way. Interesting. But for a long time, just Anthropic has been producing the best coding models. I mean, there can be some debate there now, but 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.

32:24But it wasn't until this other model came out and they made that the default model. And, you know, for what it's worth, I think the other takeaway from that, which is a kind of big theme we see in the market as a 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 percent. 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, you know, with the amount of capex being spent on these models, like there's a next model that's going to come out that's going to be awesome.

33:03And 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 7%. Yeah, this is the only certainty with AI is like there's always going to be a new model. The worst model I'll ever use is the one that we're using today. That's right. That's right.

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35:11Are we all going to become coding illiterate? Are we just going to forget how to code if everyone's using, you know, general language to do it? Forget. I never learned. 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 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... You leaped ahead. Yeah, now I'm too smart to learn Python or HTML or whatever. I have a couple of takes on this one personally.

35:47So 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 tradeoff there was pretty good. We got the scientific revolution and a couple other things. So, you know, I think that's the sort of natural knee jerk. With that said, it is. It's very strange when you have people, you know, the cloud code team is talking about how little code they write.

36:30Now, I draw a distinction 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 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 nine-year-old vibe coded a website. Oh, wow. And for Secret Santa, she's now 10. She would get bad at me if I called her nine, but I think she vibe-coded when she was nine. But that's awesome, right?

37:06I 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 linguistics project. That's right. That's fun and interesting. But, yeah, I also think the other – 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. And it's just, but what's amazing is I'm running five of these sessions at a time, right?

37:40And 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 a 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 a 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.

38:11True elegance is restraint. That's what I say. What are you seeing in the companies you're working for? Like, I find it hard to believe, and I was maybe skeptical of But it feels like right now we're here with technology where like if our companies like, like I said, you can build charts of data in a way that used to be like someone would have had to get their hands dirty or et cetera. And 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? I think that it's hard to answer right now.

38:43Really? 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 at 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 thousands of dollars a year. It's a sort of fairly simple trade-off. So I do think there's a lot of downstream effects.

39:21I think the other thing that's happening 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.

39:53And I think a lot of those kind of, a lot of those pieces that used to just be kind of transferring knowledge. 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 then tell the senior editors is 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, Juan Yan, Kerry is important. And that a really important role within any organization is essentially the team that can translate between the generalist team and the specialist team.

40:37And so that's an interesting observation in the sort of engineering world of like, okay, these are tools that are in some sense translation tools. So we talked, 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. No, I'm just trying to make it for the good of the world. Right. Okay. When did that become a crime? I'm not monetizing it. But this opens up massive questions for software as a service, right, for SaaS. Because if everyone can write their own software, you can replicate anything that's out there that is currently charging money.

41:13What's going to happen to software? I think software is pretty screwed. A lot of it, at least. Not all of it. You know, you still, it 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 certain kinds of software that, you know, you just don't really want to be in the business of writing. You know, 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 sort of build versus buy pendulum has just swung.

41:49And, you know, I mean, I used to run a SaaS company and we sold to enterprises. And, you know, for a long time, 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. And so, but the price of that was, you know, won the price, right? 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 SaaS is you need to generalize problems. And so 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.

42:26And I think, and what I see just 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. 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.

43:05Right. 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 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? Salesforce.com.

43:37Salesforce.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 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 structure it 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 a JSON interface. I could describe exactly what I wanted the data structure to be, and it would give me back that information and that data structure.

44:20And 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. Well, so we have seen like a lot of software stocks. They look like melting ice cubes right now. Maybe they – so what is it – I want to talk – I mean, this is like our last year. 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.

44:50Maybe this will be a time to learn what sales... 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 onto 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 AI live in the same world as all your files.

45:21Yeah. So I think if we take CRM as the general category, the biggest players there are - That's customer relationship management. Customer relationship management. That's like where 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 really it, which is like inside companies, there is a huge group of people who exist to answer the question from management of what is the status of something.

46:03Right. 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. 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?

46:32And 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.

47:14Again, 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, you know, I think those folks are still sort of genuinely only sitting in a pretty good place where, you know, all software has to sit 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, sales is just one small piece.

47:50They 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 seats. 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, SaaS, you're always buying for a subset of the functionality. Nobody is using 100 % of the functionality of SaaS. 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.

48:24And so 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 the trick with AI is that the more specific you are with it, the better the output is. So it's like if outside of coding, if you just ask ChatGPZ 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 – then you're going to get it – the more of your own expertise you imbue in it, the further up above the median it's going to be.

49:01And it's going to be – 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. Joe, how much does Claude Code actually cost? Do you know? Well, I paid for the$200 a month version. But like... High roller. Yeah, I know. But, you know, I think you can get it with the pro version of like, or whatever, the version of that below$20. But I hit a limit fairly quickly. And I was like, I didn't have my website up. And then I paid$5 for the extra compute. And I was like, this is dumb. I think I'll just, yeah. Yeah. Okay. So we're going out to two nice dinners in a month.

49:43That's not, you know, when I think about that way, it doesn't seem that big of a deal. It's worth it to you. Okay. So I think we can all agree this is like a valuable service that CloudCode 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 CloudCode 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?

50:23Yeah. Well, so again, here, I think we have to distinguish between Claude Code and the Claude model. So in Claude 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, I don't know, something in the$1.50 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 3 Pro is the same price. Open AI, ChatGPT 5.2, they're all the same price. So the first thing 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.

51:02In fact, there was just some controversy amongst some nerds where OpenCode, which is a competitor to Claude Code, used to let you use your Claude Max$200. So the trick with the Claude Max plan is if you're just buying that number of tokens, it would cost you significantly more than$200. It is a super, super discounted plan. So like you are probably – you have the access. I have the access to use, I would guess, in the$1 ,000 or$2 ,000 of tokens for my$200 a month. So it's a very, very heavily subsidized plan. And OpenCode, which is an open source version of CloudCode, a sort of competitor, they had found a way that they would let you use your CloudMax plan with OpenCode.

51:51And Anthropic last week shut that down. And some open code people got very upset because they said, like, this is not what you're supposed to do. Or 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. 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.

52:34And that's where something like CloudCode is really interesting because it's a product that is very – it's just theirs. 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 OpenCode the other day.

53:08And 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. They want to own like the ecosystem, I guess. The environment. The environment. What a world. Noah, thank you so much for coming on AdLots. I was like dying to do an episode about this topic. Thanks for having me. Yeah. By the way, I don't have AI psychosis. I have a Claude Complex. 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. Oh. Oh, that is very good.

53:40It's like I do one pun finally for Tracy and she's just like, why is it for making that joke? Well, I was thinking about the joke. I was handing you a shirt. I finally make a pun, and you just jump right over it. Well, everyone keeps saying that Claude Code is AI psychosis for smart people, right? Like, how did that become a thing? It's just very addictive. Yeah. All right. But that was a good pun, right? It's also very bro-coded, I find. You think so? All of AI is bro-coded. This is true. We should talk more about this. You know who we should have? David Shore on. He's been doing a lot of polling about various demographics and how they feel about AI.

54:10We should, and he has some interesting stuff. Oh, I'd be into that. Yeah, we should do that. But anyway, Noah, thank you so much for coming on Outlaw. Thanks for having me.

54:28Well, that was fun, Tracy. I really, like, 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 know going down the rabbit hole and stuff. But like I, for the first time, unironically, I'm like, OK, this is transformative technology beyond being very impressive technology. Right. So I've been coming to a conclusion, which is that, you know, AI can be both underhyped and overvalued simultaneously. Like, and I feel like that's kind of where we are at the moment. Is that where you're making your stop call? Yeah.

55:05No, but seriously, like 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. Well, 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. And then the prices keep dropping.

55:43And I know Claude Code is, okay, they're attempting to create something that resembles a traditional software ecosystem that you feel is a user that you're locked into. But so far in my various, 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 movable. And I suspect, even though I have this file now on my desktop that has a file called ClaudeMD that gives instructions, et cetera, I'm certain that if I open this file with Codex or Google's, I could probably just pick it up the same. Yeah. I also think there's a fundamental issue with the lock-in strategy, because when you're talking about technology and the internet, it just feels very against the grain to try to lock people into anything.

56:32And we've seen various projects over the years. And it's a lot harder than it looks. 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? Yeah. People are, oh, I hate people. How many times have you, oh, 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 the point about how the user never uses all of the features that they actually, that the software got built for.

57:18And therefore, maybe the build versus buy calculation really starts to shift when they can just design that one feature very quickly. 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 you 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. We're going to see. And I don't know. I kind of think we're going to see quickly. Yeah. Again, that's the only certainty is like stuff is happening fast. Stuff is happening now. Yeah. OK, shall we leave it there?

57:50Let's leave it there. This has been another episode of the All Thoughts podcast. I'm Tracy Alloway. You can follow me at Tracy Allaway. And I'm Joe Weisenthal. You can follow me at The Stalwart. Follow our guest, Noah Breyer. He's at Hey, It's Noah. Follow our producers, Carmen Rodriguez at Carmen Armand, Dashiell Bennett at Dashbot, and Kale Brooks at Kale Brooks. And for more OddLots content, go to Bloomberg.com slash OddLots. We have a daily newsletter and all of our episodes. And you can chat about all of these topics 24-7 in our Discord, discord.gg slash OddLots. And if you enjoy OddLots, if you like it when we talk about advances in AI, then please leave us a positive review on your favorite podcast platform.

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