Why the Tech World Is Going Crazy for Claude Code

In the AI industry, there's always a hot new thing. First it was ChatGPT. Then it was the image generators. There was the DeepSeek moment. In the latter half of last year, everyone was excited about how good Google's Gemini was. In January 2026, the new hot thing everyone is talking about is Claude Code. But of course, the AI models have been able to generate lines of code for a long time now. So what is it about Claude Code that has people so excited? Why is it that people are asking: "Is this AGI?" On this episode, we speak with Noah Brier, the co-founder of Alpehic, a consultancy firm that helps large organizations implement AI technology. Noah has been using the Large Language Models for longer than just about anyone, since even before ChatGPT existed. He explains to us the evolution of AI-assisted coding, what Claude Code actually is, and why it is that traditional software firms have been getting destroyed in the stock market lately. Read more:Meta Begins Job Cuts as It Shifts From Metaverse to AI DevicesAI Coding Startup Replit Nears Funding at $9 Billion Valuation Only http://Bloomberg.com subscribers can get the Odd Lots newsletter in their inbox each week, plus unlimited access to the site and app. Subscribe at bloomberg.com/subscriptions/oddlots Subscribe to the Odd Lots NewsletterJoin the conversation: discord.gg/oddlotsSee omnystudio.com/listener for privacy information.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Tracy Alloway?

  There are some market stories where you want every detail. Joe and I have made quite a few podcasts on that basis. But sometimes you've only got 10 minutes and just want to know what's moving markets fast. That's the Barclays Brief Podcast. Every week, experts from Barclays Markets and Research get you up to speed on what's happening and what to watch next. Concise, focused, and brief. Search Barclays Brief wherever you get your podcasts.

• 0:26Speaker 01

  This is the Bloomberg Tech Minute brought to you by ChatGPT. I'm Carol Muster. Getting hired at SpaceX requires exceptional ability and may include having Elon Musk review a candidate's resume. So writes Bloomberg Business Week's Lauren Grush. She reports that the company culture is defined by Musk and job candidates must understand that a commitment to SpaceX means adhering to this culture and exhibiting loyalty to Elon Musk. To get hired, candidates must demonstrate their achievements from past jobs and hands-on experience matters more to recruiters than advanced degrees, with the company looking for people with raw, innate intelligence. A spokesperson for SpaceX didn't respond to a request for comment on its recruiting

• 1:08Speaker 01

  and hiring practices, but according to recruiters, advisors, and those who've interviewed for jobs there, it's the space exploration dream that draws people to the company, and Musk invokes that dream regularly to remind workers why they're there. Prospective hires are told that the job will require stamina, including in job ads that candidates, quote, "must be willing to work extended hours and weekends as needed." One major perk for all that hard work? Employees' access to equity, either the right to buy shares or payment in the form of restricted stock units that vest over time. That's the Bloomberg Tech Minute brought to you by ChatGPT. Get started at chatgpt.com.

• 1:52Speaker 02

  Bloomberg Audio Studios. Podcasts, radio, news.

• 2:07Joe Weisenthal?

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

• 2:12Tracy Alloway?

  And I'm Tracy Alloway.

• 2:14Joe Weisenthal?

  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, right?

• 2:22Tracy Alloway?

  I was gonna say, I've been thinking about AI and productivity and so far, your productivity has gone down, Joe.

• 2:29Joe Weisenthal?

  No.

• 2:30Tracy Alloway?

  Because instead of doing Odd Lots things, you're coding your own software.

• 2:33Joe Weisenthal?

  Except that I'm creating content for the Odd Lots newsletter about coding and that is productivity accretive.

• 2:40Tracy Alloway?

  Debatable.

• 2:41Joe Weisenthal?

  Debatable, but, but you're cool with that. You're cool with like me like, oh, I'm just gonna like check in part-time on Odd Lots when we have a recording.

• 2:48Tracy Alloway?

  No, of course not.

• 2:50Joe Weisenthal?

  Okay, good, good. Of course not. Good, that's the right answer. I want you to be, 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've 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 Claude Code, Claude Code, Claude Code. I had used 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 Claude Code on my computer. And I was like, oh, I am like hooked. This is actually like, I see why half my Twitter feed is just like people posting about this.

• 3:31Tracy 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 Cowork? Have you heard of that?

• 3:54Joe Weisenthal?

  Oh, yeah, yeah, yeah.

• 3:55Tracy Alloway?

  Yeah, so one of the criticisms of Claude Code was that, you know, like, okay, you code, but you still need some background knowledge in coding because like, you know, the interface is kind of like 1980s and, and all of that are 1990s. Cowork apparently like goes a step further for, for normal people in coding and makes it super, super easy. And the funniest thing is that apparently Claude Code actually coded cowork.

• 4:23Joe 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 are 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. So there was this sort of like barrier that existed and, but what's, 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 that like, because it sits on your computer, it sort of takes away, it de-abstracts it. And so when you talk about like

• 5:05Tracy Alloway?

  No, it actually does the stuff.

• 5:06Joe Weisenthal?

  It does it. 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 cowork, it's like, all, like, all of these sort of like little frictions, like these technical things, like command line user, very rapidly are like dissipating. And so that like, then you have something like cowork 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.

• 5:44Tracy Alloway?

  Also, it feels very iterative, like the code is improving upon itself at this point, and I think that was one of Claude's main selling points.

• 5:51Joe 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 the 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 because in part they're fast because a bunch of people are suddenly getting excited. So 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.

• 6:36Tracy Alloway?

  Did you see, speaking of automating yourself, did you see there was a post on Reddit from a lawyer who said he's basically used Claude code to automate like his entire job and he hasn't told anyone?

• 6:46Joe 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 Claude 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 by hand or whatever, or like designing 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 checked. So I didn't.

• 7:16Tracy Alloway?

  Just that crucial detail.

• 7:17Joe Weisenthal?

  I know, I didn't, that's why I didn't want to like, oh, like here's what, here's the today's jobs report and charts. But I did most my.

• 7:23Tracy Alloway?

  But what application did it actually build it in, the charts?

• 7:26Joe Weisenthal?

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

• 7:31Tracy Alloway?

  What kind of file?

• 7:32Joe Weisenthal?

  Like a PNG file, like an image file. That's the crazy thing. I don't know. And so there was just this image that had a bunch of charts and my spot checks 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.

• 7:47Tracy Alloway?

  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 Claude code to create, they wanted a website that would be. Basically make them money for doing nothing. And that was the prompt. And

• 8:04Joe Weisenthal?

  Did they do it?

• 8:05Tracy Alloway?

  Yeah. 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 Claude Code?

• 8:23Joe Weisenthal?

  To generate prompts.

• 8:23Tracy Alloway?

  To do the same thing.

• 8:25Joe Weisenthal?

  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 these sooner rather than later. But what is Claude Code? Why is everyone so hyped about it? Like, what is it about this particular piece of software that versus what exists from OpenAI and Gemini and all this stuff? Like, why has this captured everyone's imagination? We really do have the perfect guest because as someone who, unlike me, has been getting their hands dirty in this stuff for longer, one of the few people that I know who is into LLMs 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 guest, we're going to be speaking with Noah Brier.

• 9:08Joe Weisenthal?

  He is the co-founder of Alephic, which is a consultancy that helps big companies deal with AI stuff. So Noah, thank you so much for coming on Odd Lots.

• 9:16Speaker 04

  Thank you for having me.

• 9:18Joe Weisenthal?

  What is 11? What's the deal? How were you like using LLMs before ChatGPT existed? I don't know. I know very few people who are doing that.

• 9:26Speaker 04

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

• 9:31Joe Weisenthal?

  And then how are you using it, though? Like, how do you, like, how did you aware that there was this thing that could be of potential use to you? What were

• 9:38Speaker 04

  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. 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.

• 10:23Speaker 04

  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. That's it. Yeah.

• 11:00Tracy Alloway?

  Um, that HTML mention just brought up like memories of me in like the mid nineties on HTML goodies. Do you remember that site? Yeah. I wonder if it's still, is it still up? That would be wild. Um, does Claude code, does that count as AGI? This seems to be the debate, right? Is it AGI?

• 11:20Speaker 04

  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 we. It's not just that they forgot, they pretend that it never mattered. Oh. And so I, 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.

• 12:05Speaker 04

  Much of what's going on inside Claude 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.

• 12:30Joe Weisenthal?

  There are some market stories where you want every detail. Tracy and I have made quite a few podcasts on that basis, but sometimes you've only got 10 minutes and need to know what's moving markets and why. That's the Barclays Brief podcast. Every week, experts from Barclays Markets and research get you up to speed on what matters and what to watch next. About the time it takes to grab a coffee. So search Barclays Brief wherever you get your podcasts.

• 12:56Speaker 01

  This is the Bloomberg Tech Minute brought to you by ChatGPT. I'm Carol Muster. Getting hired at SpaceX requires exceptional ability and may include having Elon Musk review a candidate's resume. So writes Bloomberg Business Week's Lauren Grush. She reports that the company culture is defined by Musk and job candidates must understand that a commitment to SpaceX means adhering to this culture and exhibiting loyalty to Elon Musk. To get hired, candidates must demonstrate their achievements from past jobs and hands-on experience matters more to recruiters than advanced degrees, with the company looking for people with raw innate intelligence. A spokesperson for SpaceX didn't respond to a request for comment on its recruiting and hiring practices.

• 13:39Speaker 01

  But according to recruiters, advisors and those who've interviewed for jobs there, it's the space exploration dream that draws people to the company. And Musk invokes that dream regularly to remind workers why they're there. Prospective hires are told that the job will require stamina, including in job ads that candidates, quote, "must be willing to work extended hours and weekends as needed." One major perk for all that hard work? Employees' access to equity, either the right to buy shares or payment in the form of restricted stock units that vest over time. That's the Bloomberg Tech Minute brought to you by ChatGPT. Get started at ChatGPT.com.

• 14:19Joe Weisenthal?

  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 around with it, what is Claude code? Because again, coding models have been around for a long time. People maybe have heard of Cursor or Copilot or some of these other harnesses, et cetera. What is Claude code?

• 14:43Speaker 04

  So if we back up first and we go to Copilot. 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-

• 14:57Joe Weisenthal?

  Is this a Microsoft product?

• 14:58Speaker 04

  It's a Microsoft product. So Microsoft owns GitHub, GitHub developed 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 Stack Overflow existed was because you can never remember the exact name of that function 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 have it autocomplete for you. And it was pretty amazing.

• 15:37Joe Weisenthal?

  Yeah.

• 15:38Speaker 04

  Then ChatGPT came out. And even before that, I had built a simple chatbot for myself because I realized that, hey, I could just ask this. And instead of going and searching Stack Overflow, 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 Stack Overflow of incorrect regex that now exists in every codebase in the world. And so, you know, I think there were a lot of us just kind of playing with these things and realizing they were a huge boon. And so I think really the next step is Cursor comes out. And the thing Cursor realized that Copilot didn't was that it wasn't good enough to have autocomplete. You also needed the Q and A because you have these things that you can't just autocomplete. You want to be able to ask the question and answer it.

• 16:22Speaker 04

  And then ChatGPT came out and everybody was switching between IDE. And then I think really the next big- Is that Claude Code came out and what Claude Code did that was so remarkable was they took the same set of models really. And they took them out of the chatbot 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 Claude Code, you're calling out to a model and you 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.

• 17:04Speaker 04

  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, sixty 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 the called grep and it can search through a code base. And Unix has this sort of beautiful way of tying one command to another. So you can see, take the output of one command and send it to another. And they kind of just gave the model access to these two or three 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 realize.

• 17:46Speaker 04

  Like one example that I 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 chat GPT, it's sending your entire conversation history back to chat GPT. Because it has no saved history of that chat, right? And that's fine. It's the way it works. It's just fact, but it means that, you know, it forgets things. It doesn't know conversation to conversation. And one very easy way to save your state is just write it to a file. And so you give it write 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,

• 18:30Speaker 04

  which is that they're fundamentally stateless.

• 18:32Tracy 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?

• 18:41Speaker 04

  No, so there there's sort of two things going on in Claude 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.

• 18:58Tracy Alloway?

  Right.

• 18:58Speaker 04

  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 they're the total window is is is

• 19:25Speaker 04

  large, but I mean, it's like 100,000 tokens.

• 19:28Tracy Alloway?

  Yeah, that's what I mean by memory notes, right? It compacts the information into the important stuff that it can then retrieve.

• 19:34Speaker 04

  It does that. It only does that at the end, like, once it runs out of space.

• 19:38Tracy Alloway?

  Okay.

• 19:39Speaker 04

  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.

• 19:47Speaker 04

  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.

• 19:59Tracy Alloway?

  Right.

• 19:59Speaker 04

  You know, maybe 25,000 token memory of what it was.

• 20:06Tracy Alloway?

  Yeah.

• 20:07Joe Weisenthal?

  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 that?

• 20:23Speaker 04

  I'm not sure compaction was the unlock. I think the compaction functionality is helpful.

• 20:28Joe Weisenthal?

  Okay.

• 20:29Speaker 04

  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. Both techniques work. I think fundamentally, the thing that is special about Claude Code is not the compaction. It's the ability to write and read files on your computer. Which means you can always write off memories. What does that mean, write off memories? So you could say, hey, it's really important that I remember this thing for future sessions. I want to always work this way. So in a codebase of mine, I have a set of documentation that explains how I like to do things.

• 21:13Speaker 04

  And Claude 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. 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 just the Unix commands. I mean, computers, every computer program lives on top of these sort of baseline functions. And the way that the designers of Unix built them is really elegant.

• 21:58Speaker 04

  And they're very small. They all do one thing and they're all composable. And in coding terms, composable means 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 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.

• 22:36Tracy 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 just do that.

• 22:55Speaker 04

  I don't have a good answer to that. I think that it was kind of just a good idea.

• 22:59Tracy Alloway?

  Yeah.

• 23:00Speaker 04

  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 primitives inside Claude Code are just smart. And then the thing that they've done, and Boris Cherny, who's the lead developer on Claude Code at 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. 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,

• 23:39Speaker 04

  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 Claude Code. And then increasingly, 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 dog fooding it. They, 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, and, you know, anybody who's built, had to manage large scale pieces of software, large scale code bases, knows that's not the norm.

• 24:24Joe Weisenthal?

  So VS Code and Cursor, these are IDEs. Claude Code is not an IDE. What is it? It's called CLI, is that?

• 24:32Speaker 04

  CLI, a command line interface.

• 24:34Joe Weisenthal?

  Got it. And the other labs now, they also have CLI's. So why are we all talking about Claude Code? And I, ChatGPT uses it called Codex. I don't know what Gemini's is called.

• 24:45Speaker 04

  I think it's just called the Gemini CLI.

• 24:46Joe Weisenthal?

  Why are we all talking about Claude Code rather than the other CLI's that kind of have the same thing? Like, what is the difference?

• 24:53Speaker 04

  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, want to always allow it. I always click always allow. I'm living on the edge. You can, you can, uh, next time you run it, you can just do a flag that says dangerously skip permissions. And, and it'll just, uh, they call it YOLO mode. I think, I think more fundamentally though, if I look at, at Codex versus Claude Code,

• 25:38Speaker 04

  I think it's a, 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 Claude Code is much more designed to be a 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

• 26:21Speaker 04

  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 Claude Code fundamentally is much more designed in that way. It's a pair programmer. It's, uh, they, you know, whenever I start a project, uh, 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 its, so, you know, we're working together and I actually have a whole system now that I've designed where, um, uh, I use a, a task management system called Linear. So I have Claude Code write tasks off to Linear and then.

• 27:03Speaker 04

  I've worked with Claude 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 Claude 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 saves it for me, us, to do together. And we'll work on it together. And 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, you know, fairly basic set of criteria that I use.

• 27:42Speaker 04

  But that to me is the fundamental distinction. And, you know, I find Claude Code in that way to be just, it sort of fits what I want to do and how I want to work much better.

• 27:55Tracy Alloway?

  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 Claude Code. But how much does this actually change the workflow for an engineer?

• 28:28Speaker 04

  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?

• 29:01Tracy Alloway?

  Right, it's coordination, right?

• 29:03Speaker 04

  It has become a coordination problem. And I spend a lot of time sort of now designing.

• 29:10Speaker 04

  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. 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. 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 at its static code analysis. So it basically tries to sort of find things in your code base that are not going to work

• 29:55Speaker 04

  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 haven't 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. 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, 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.

• 30:34Joe Weisenthal?

  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. I still think it's, I know it's improved, but I actually don't think it's improved or not. Still, people like the prose 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 chatbot that is not annoying to talk to while you're iterating, and whether that

• 31:15Joe Weisenthal?

  is like a meaningful distinction between, you know, coding with Codex or whatever.

• 31:21Speaker 04

  Yeah, I I don't know. It still can be very annoying, I will tell you, and it'll still sometimes be overly overly effusive with me about a design choice, I mean, or sort of noticing something, which I could live without.

• 31:38Joe Weisenthal?

  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, assume for a moment that you are a quantitative linguistics with a 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, OK, that's nice to hear. I actually, like, you know, I appreciate that. And it was like, you're 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.

• 32:16Speaker 04

  Yeah, I think. So I think stylistically, I kind of personally agree. My theory, by the way, 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.

• 32:37Joe Weisenthal?

  Yeah.

• 32:37Speaker 04

  And so we tend to like that. This is purely speculation, but that's always been my theory on.

• 32:43Joe Weisenthal?

  So it flatters you in a different way.

• 32:44Speaker 04

  I think it's flattering you in a much more subtle way.

• 32:47Joe Weisenthal?

  Yeah, interesting.

• 32:48Speaker 04

  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. 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 is a thing that the Claude 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

• 33:30Speaker 04

  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, 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 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.

• 33:58Tracy Alloway?

  Yeah, this is the only certainty with AI is like there's always going to be a new model, right?

• 34:03Joe Weisenthal?

  The worst model we'll ever use is the one that we're using today.

• 34:06Tracy Alloway?

  That's right, that's right.

• 34:23Speaker 01

  This is the Bloomberg Tech Minute brought to you by ChatGPT. I'm Carol Muster. Getting hired at SpaceX requires exceptional ability and may include having Elon Musk review a candidate's resume, so writes Bloomberg Businessweek's Lauren Grush. She reports that the company culture is defined by Musk and job candidates must understand that a commitment to SpaceX means adhering to this culture and exhibiting loyalty to Elon Musk. To get hired, candidates must demonstrate their achievements from past jobs and hands-on experience matters more to recruiters than advanced degrees, with the company looking for people with raw innate intelligence. A spokesperson for SpaceX didn't respond to a request for comment on its recruiting

• 35:05Speaker 01

  and hiring practices, but according to recruiters, advisors, and those who've interviewed for jobs there, it's the space exploration dream that draws people to the company and Musk invokes that dream regularly to remind workers why they're there. Prospective hires are told that the job will require stamina, including in job ads that candidates quote "must be willing to work extended hours and weekends as needed." One major perk for all that hard work? Employees' access to equity, either the right to buy shares or payment in the form of restricted stock units that vest over time. That's the Bloomberg Tech Minute brought to you by ChatGPT. Get started at ChatGPT.com.

• 35:46Speaker 05

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• 36:16Tracy Alloway?

  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 to do it?

• 36:24Joe Weisenthal?

  Forget, I never learned.

• 36:25Tracy Alloway?

  Yeah, okay.

• 36:26Joe Weisenthal?

  You know what I've been thinking about? You know that Scott Karp, the CEO of Palantir? And 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.

• 36:44Tracy Alloway?

  You leaped ahead.

• 36:45Joe Weisenthal?

  Yeah, now I'm too smart to learn Python or HTML or whatever.

• 36:49Speaker 04

  I have a couple of takes on this personally, one personally. So 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,

• 37:03Speaker 04

  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 the scientific revolution, a couple of 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 Claude code team is talking about how little code they write. Now, 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.

• 37:48Speaker 04

  Because they're, 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.

• 38:03Tracy Alloway?

  Oh, wow.

• 38:03Speaker 04

  And for Secret Santa, she's now 10. She would get mad at me if I called her nine. But I think she vibe-coded it when she was nine. But 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 linguistics project.

• 38:18Joe Weisenthal?

  That's right.

• 38:19Speaker 04

  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 when 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? 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 a senior developer is that

• 39:02Speaker 04

  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.

• 39:15Tracy Alloway?

  True elegance is restraint. That's what I say.

• 39:18Joe 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 where like if our like 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, et cetera. 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?

• 39:44Speaker 04

  I think that... It's hard to answer right now.

• 39:48Joe Weisenthal?

  Really?

• 39:49Speaker 04

  I think that certainly, I do think, I personally think if I look at the sort of layoffs in the technology industry of 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, 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 thousands of dollars a year. It's a sort of fairly simple trade-off. I think, so I do think there's a lot of downstream effects. I 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

• 40:33Speaker 04

  amazing at 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.

• 41:08Joe 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 generalists, don't really know like, why like, some sort of like, you know, Wan-Yen Carey 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.

• 41:41Tracy Alloway?

  Absolutely.

• 41:42Joe Weisenthal?

  And 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.

• 41:49Tracy Alloway?

  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.

• 41:58Joe Weisenthal?

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

• 42:00Tracy Alloway?

  Right, okay.

• 42:01Joe Weisenthal?

  When did that become a crime?

• 42:03Tracy Alloway?

  But like 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. What's going to happen to software?

• 42:19Speaker 04

  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, there are 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 the sort of build versus buy pendulum has just swung. And, and, you know, I mean, I used to run a SaaS company and we sold to enterprises. And, you know, for a long time, that, that, I think that made a lot of sense, right?

• 43:03Speaker 04

  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, one, 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 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. 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.

• 43:44Speaker 04

  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 gonna, you know, come out the other side. You're gonna, nobody wants to deal with payroll, right? Like, you know, somebody, you're still gonna buy some payroll software and you're still Gonna 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 gonna, you know, with just the database, you can do that.

• 44:25Speaker 04

  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,

• 44:41Joe Weisenthal?

  Salesforce.com.

• 44:42Speaker 04

  Salesforce.com.

• 44:45Speaker 04

  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 can be stored in the 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.

• 45:25Speaker 04

  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, it, I think it really does bring into question the value of a lot of these software companies.

• 45:38Joe Weisenthal?

  Well, so we have seen like a lot of software stocks, they look like melting ice cubes right now. Maybe they, so what is, so 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 will be a time to learn what 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 when 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.

• 46:19Joe Weisenthal?

  And then what the unlock opportunity is from having AI like live in the same world as all your files.

• 46:26Speaker 04

  Yeah, so I think if we take CRM as a general category, so you know, the biggest players there are That's customer relationship management. Customer relationship management, that's like what Salesforce does it, SAP does it, HubSpot does it for the mid-market.

• 46:42Speaker 04

  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. 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. And so fundamentally, I think those CRM tools are bought first and foremost to answer what is the status, right?

• 47:26Speaker 04

  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 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 update it anyway.

• 48:07Speaker 04

  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. 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, 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 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,

• 48:50Speaker 04

  you shouldn't have humans inputting in Salesforce, you know, at sales is just one small piece. They 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 that that's, 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. 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

• 49:35Speaker 04

  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 ask 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 imbue in 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.

• 50:15Tracy Alloway?

  Joe, how much does Claude code actually cost? Do you know?

• 50:19Speaker 04

  Well, I paid for the $200 a month version, but like.

• 50:25Tracy Alloway?

  High roller.

• 50:25Speaker 04

  Yeah, I know.

• 50:26Joe Weisenthal?

  But you know, I think it's 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. So like, and then I bought the five, then I paid $5 for the extra compute. I was like, this is dumb. I think, yeah.

• 50:44Tracy Alloway?

  Okay, so.

• 50:45Joe Weisenthal?

  Going out to two nice dinners in a month. It's not, you know, when I think about that way, it doesn't seem that big of a deal.

• 50:50Tracy Alloway?

  It's worth it to you. Okay, so I think we can all agree, this is like a valuable service that Claude 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 Claude 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? Yeah.

• 51:28Speaker 04

  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.

• 51:55Speaker 04

  OpenAI ChatGPT 5.2 is, 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 Claude Code. In fact, there was just some controversy amongst some nerds where Open Code, 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 those, that number of tokens, it would cost you significantly more than $200. It is a super, super discounted plan. So like you, you are probably, you have the access, I have the access to use.

• 52:36Speaker 04

  I would guess in the thousand 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 Claude Code, a sort of competitor, they had found a way that they would let you use your Claude Max plan with OpenCode. And Anthropic, last week, shut that down.

• 52:59Tracy Alloway?

  Yeah.

• 52:59Speaker 04

  And some OpenCode 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 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.

• 53:39Speaker 04

  And that's where something like Claude Code 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. 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.

• 54:20Tracy Alloway?

  Right, they want to own like the ecosystem.

• 54:22Joe Weisenthal?

  The environment that they work on. What a world. Noah, thank you so much for coming on Odd Lots. I was like dying to do an episode about this topic.

• 54:30Speaker 04

  Thanks for having me.

• 54:31Joe Weisenthal?

  By the way, I don't have AI psychosis. I have a Claude complex.

• 54:35Tracy Alloway?

  Why is everyone making that joke?

• 54:37Joe Weisenthal?

  Wait, which joke?

• 54:38Tracy Alloway?

  The psychosis joke.

• 54:40Joe Weisenthal?

  I thought you were gonna be proud of me for saying Claude complex.

• 54:43Tracy Alloway?

  Oh, oh, that is very good.

• 54:45Joe Weisenthal?

  I just like, I do one pun finally for Tracy and she's just like, well, I was over making that joke.

• 54:49Tracy Alloway?

  Well, I was thinking about the joke.

• 54:50Joe Weisenthal?

  I was handing you a, sir, I finally make a pun and you just jump right over it.

• 54:53Tracy Alloway?

  Well, everyone keeps saying that Claude code is AI psychosis for smart people, right? Like, how did that become a thing? Yeah, all right.

• 55:01Joe Weisenthal?

  But that was a good pun, right?

• 55:02Tracy Alloway?

  It's also very bro coded, I find.

• 55:04Joe Weisenthal?

  You think so?

• 55:04Tracy Alloway?

  All of AI is bro coded.

• 55:06Joe Weisenthal?

  Uh, 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, um, and he had some interesting stuff.

• 55:16Tracy Alloway?

  I'd be into that.

• 55:16Joe Weisenthal?

  Yeah, we should, uh, do that. Anyway, Noah, thank you so much for coming on our podcast. Thanks for having me.

• 55:33Joe Weisenthal?

  Well, that was fun, Tracy. I really, like, I was, 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, 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.

• 55:54Tracy Alloway?

  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.

• 56:08Joe Weisenthal?

  Is that where you're making your stock call?

• 56:09Tracy Alloway?

  Yeah, no, but seriously, like, it, it's a big deal. It's gonna 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.

• 56:30Joe Weisenthal?

  Well, so that's very interesting, his point, which is that it's, the tokens are heavily subsidized still.

• 56:36Tracy Alloway?

  Yeah.

• 56:37Joe Weisenthal?

  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.

• 56:46Tracy Alloway?

  Right.

• 56:46Joe Weisenthal?

  And then the prices keep dropping. And I know. Oh, 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 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.

• 57:24Tracy Alloway?

  Yeah, I also think there's a fundamental issue with the lock-in strategy, because when you're talking about technology and 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 a lot harder than it looks.

• 57:43Joe 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 in the software that they hate, right?

• 57:51Tracy Alloway?

  Yeah.

• 57:51Joe Weisenthal?

  People are like, oh, I hate, 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, 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.

• 58:31Tracy 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 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.

• 58:46Joe Weisenthal?

  We're gonna see. And I don't know. I kind of think we're gonna see quickly.

• 58:49Tracy Alloway?

  Yeah, that's again, that's the only certainty is like, stuff is happening.

• 58:53Joe Weisenthal?

  Stuff is happening now. Yeah.

• 58:54Tracy Alloway?

  Okay, shall we leave it there?

• 58:55Joe Weisenthal?

  Let's leave it there.

• 58:56Tracy Alloway?

  This has been another episode of the Odd Lots Podcast. I'm Tracy Alockway. You can follow me at Tracy Alockway.

• 59:01Joe Weisenthal?

  And I'm Joe Weisenthal. You can follow me at the stalwart. Follow our guest Noah Brier. He's at Hey, It's Noah. Follow our producers, Carmen Rodriguez at Carmen Armand, Dashel Bennett at Dashbot, and Kale Brooks at Kale Brooks. And for more Odd Lots content, go to Bloomberg.com/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/oddlots.

• 59:24Tracy Alloway?

  And if you enjoy Odd Lots, if you like it when we talk about advances in AI, then please leave us a positive review on your favorite podcast platform. And remember, if you are a Bloomberg subscriber, you can listen to all of our episodes absolutely ad-free. All you need to do is find the Bloomberg channel on Apple Podcasts and follow the instructions there. Thanks for listening.

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