Tracy Alloway (2:06)

Debatable.

Joe Weisenthal (2:07)

Debatable. But. But you're cool with that? You're cool with like me, like, oh yeah, I'm just going to like check in part time on Odd Lots when we have a recording.

Tracy Alloway (2:14)

Well, no, of course not.

Joe Weisenthal (2:15)

Okay, good.

Tracy Alloway (2:15)

Of course not.

Joe Weisenthal (2:16)

Good. That's the right answer. I want you to, I want you to be really sad. But like a few other people, you know, I have like caught the sort of like bug of like AI coding and I'm totally blown away. I'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 feeds, like Claude Code. Claude Code. Claude code. I'd use 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.

Tracy Alloway (2:56)

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 your 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?

Joe Weisenthal (3:19)

Oh, yeah, yeah, yeah.

Tracy Alloway (3:20)

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, and. And all of that or 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.

Joe Weisenthal (3:48)

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 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.

Tracy Alloway (4:30)

It actually does the stuff, it doesn't.

Joe Weisenthal (4:32)

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 cowork, it's 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 now they're taking care of that. And so you get this like, user interface that's just like, it's just getting easier and friendlier. There's almost no technical frictions at all anymore.

Tracy Alloway (5:09)

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.

Joe Weisenthal (5:16)

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 prem. One of the ideas, I guess, behind AGI is like, well, what happens when you have software that can train itself and so forth? And I don't really know if I buy that, but you do just see, like, how fast the iteration cycles are. And I think we want to get into this. In part, they're fast because a bunch of people are suddenly getting excited. 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. It creates code that there's no bugs. It works.

Tracy Alloway (6:02)

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 his entire job and he hasn't told anyone.

Joe Weisenthal (6:12)

I'm not exactly surprised because the other thing that I experimented with is, And I haven't 100% verified this, but on jobs day last week, I downloaded the full PDF and I just typed into the cloud code, like, find the most interesting details and make some charts based on. And it did it in like a couple minutes. I have no, like, ability to, 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.

Tracy Alloway (6:41)

Just that crucial detail.

Joe Weisenthal (6:43)

Yeah, I know. I. That's why I didn't want to like, oh, like, here's what. Here's the today's jobs report in charts. But I did most.

Tracy Alloway (6:48)

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

Joe Weisenthal (6:52)

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

Tracy Alloway (6:56)

What kind of file?

Joe Weisenthal (6:58)

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 and.

Tracy Alloway (7:13)

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 basically make them money for doing nothing and that was the prompt.

Joe Weisenthal (7:29)

And did they do that?

Tracy Alloway (7:31)

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 to do the same thing?

Joe Weisenthal (7:51)

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 cloud 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 guys because it's 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 chat GPT 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 Breyer. He is the co founder of Elefic, which is a consultancy that helps big companies deal with AI stuff. So Noah, thank you so much for coming on odd lots.

Noah Brier (8:42)

Thank you for having me.

Joe Weisenthal (8:43)

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

Noah Brier (8:51)

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

Joe Weisenthal (8:56)

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

Noah Brier (9:04)

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 GPT3 API, which had come out, I think that came out in November of 2021. And that was the first it was publicly available to everybody. And they had a large language model, as we know it today, available to them. So I was just testing and building things and I pretty immediately realized the very first thing I did where it just blew my mind was I built a web scraper. So I was 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.

Joe Weisenthal (10:25)

Yeah.

Tracy Alloway (10:26)

That HTML mention just brought up memories of me in the mid-90s on HTML goodies. Do you remember that site? I wonder if it's still. Is it still up? That would be wild. Does Claude code? Does that count as AGI? This seems to be the debate, right? Is it AGI?

Noah Brier (10:45)

I try not to wade into what's AGI and what's not. I think my guess 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.

Joe Weisenthal (11:09)

Oh.

Noah Brier (11:10)

And so I am kind of guessing that that's going to be what the conversation is like. It's just going to be a sort of forever moving goalpost. Because it turns out that the idea we had for what general intelligence looks like is not quite that. But I also think, you know, the computer scientists and the sort of Serious AI researchers would say that much of what's going on inside quadcode is not the model itself, it's the model paired with a human. And I think that is a pretty important distinction. But I don't know about AGI.

Tracy Alloway (11:56)

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Joe Weisenthal (13:31)

Complete disclosures available@public.com disclosures 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?

Noah Brier (13:59)

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 Microsoft product. It's a Microsoft product. So Microsoft owns GitHub, GitHub develop copilot. It was Microsoft had the partnership with OpenAI and so they 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 could 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. 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 there's famous mistakes on Stack Overflow of incorrect regex that now exists in every code base in the world. And so 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. And then ChatGPT came out and everybody was switching between IDE. And then I think really the next big piece 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. And they really just gave it some very basic functionality to operate within your machine. Right. And so, you know, if you really look at kind of what exists within cloud code, you're calling out to a model, and they gave it capability around sort of two big things. One is you can read and write files on your computer, and then two is that you can operate unix, the base commands, the BASH commands that exist in your environment. And again, because these models were trained on the Internet, and there's no greater source of information on the Internet than how to make the Internet. They know how to use UNIX commands incredibly well. Right? Because UNIX has existed for whatever it is, 60 years. And the way these commands were designed, they're all designed to be very, very simple. There's a find command and you know, there's a thing called grep, and it can search through a code base. And UNIX has this sort of beautiful way of tying one command to another, so you can take the output of one command and send it to another. And they kind of just gave the model access to these 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 realized. Like, one example that I think about a lot is just the challenge you have with all of these AI models is that they're stateless. So every time you talk to ChatGPT, it's sending your entire conversation history back to ChatGPT, because it has no saved history of that chat, right? And that's fine. It's the way it works. It's just fact. But it means that 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. Probably the single biggest challenge that exists inside these large language models, which is that they're fundamentally stateless.

Tracy Alloway (17:49)

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

Noah Brier (17:57)

No. So 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, 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 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 large. But I mean, it's like 100,000 tokens.

Tracy Alloway (18:44)

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

Noah Brier (18:50)

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

Tracy Alloway (19:21)

Yeah.

Joe Weisenthal (19:23)

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?

Noah Brier (19:38)

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

Joe Weisenthal (19:44)

Okay.

Noah Brier (19:44)

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.

Joe Weisenthal (20:13)

And, and then what does that mean, write off memory?

Noah Brier (20:15)

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 code base of mine, I have a set of documentation that explains how I like to do things 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, 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 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.

Tracy Alloway (21:52)

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.

Noah Brier (22:11)

I don't have a good answer to that. I think that it was kind of just a good idea. 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 Czerny, who's the lead developer on Cloud Coded Anthropic, he talks about latent demand a lot. Right. And latent demand is basically just, hey, look at the ways people are using these systems. And then figure out ways to make that a part of the product itself. I think what they've done brilliantly, and this is kind of easy when you have a community of developers who are nerds, who want to go talk about all the ways that they're using these things, is they have. I am amazed at the speed in which, you know, I have a small community of 15 ctos who all use this stuff religiously. And you know, when we first started that community, it took them a month to, I would see it in the chat and then a month later it would get built into cloud code. And then increasingly it's like a day later it feels like they're just, they're just listening to it. But I think they're just not only tapped in, but they're really fundamentally, you know, they're, they're dogfooding 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.

Joe Weisenthal (23:40)

So. Vs code and cursor. These are IDEs. Cloud code is not an IDE. What is. It's called a CLI.

Noah Brier (23:48)

Is that a CLI? A command line interface.

Joe Weisenthal (23:50)

Got it. And the other labs now they also have clis. So why are we all talking about Claude code? And I chatgpt is called Codex. I don't know what Geminize is called.

Noah Brier (24:00)

I think it's just called the Gemini Cli.

Joe Weisenthal (24:02)

Why are we all talking about Claude code rather than the other CLIs that kind of have the same thing? What is the difference?

Noah Brier (24:09)

I think first and foremost, they were first.

Joe Weisenthal (24:11)

Okay.

Noah Brier (24:12)

And I think they've had a lot more. And from my very personal opinion, I think they've done some things smarter and better as far as the permissioning model. So 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. And they have a very fine grained permissioning model where you can say, hey, I want to allow this. Just this one time. I want to always allow it.

Joe Weisenthal (24:35)

I always click always allow. I'm living on the edge.

Noah Brier (24:40)

Next time you run it, you can just do a flag that says dangerously skip permissions. And they call it YOLO mode. I think more fundamentally though, if I look at Codex vs Claude Code, I think it's a difference in philosophy around what you want AI to do. To me, Codex, which is excellent, is very focused on building an agent that you can just give something to and it'll just go do it. So I want to give it that task. I don't want to intercede, I don't want to give it any more feedback. And Claude 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 and force multiplier. Yeah. And it sort of makes up for the fact that obviously, you know, you're doubling the staff on it. But because of how many fewer bugs, because you have both sets of eyes, it it has seemed to work out for many folks. Most companies don't practice it, but I think cloud code fundamentally is much more designed in that way. It's a pair programmer. It's they, you know, whenever I start a project, I start in plan mode. So you start in plan mode, you put together a plan. I really, I mean I spend a lot of time in plan mode. You go through you, it gives you a plan back. It asks you how you feel. You can give it a whole bunch of direct. 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 I use a task management system called linear. So I have CLAUDE code write tasks off to Linear, and then 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 codecs 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 fairly basic set of criteria that I use. But that to me is the fundamental distinction. And 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.

Tracy Alloway (27:11)

Talk a little bit more about how it actually impacts the workflow of an engineer, because my impression was people can code, right? 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 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?

Noah Brier (27:44)

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 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? 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, it has all these things. The other thing that makes code a particularly good place to do this is that code is verifiable in a way that most other work is not. So with code you can verify that the build works. 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. 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 coding 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. 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. I've been not. I've 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.

Joe Weisenthal (29:51)

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. And, you know, ChatGPT seems to really be sycophantic. I still think it's. I know it's improved, but I actually don't think it's improved enough. I 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 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 codecs or whatever.

Noah Brier (30:37)

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

Joe Weisenthal (30:54)

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, okay, that's nice to hear. I actually, like, I appreciate that. And he was like, you're very blunt. Not, you know, it's still like, 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.

Noah Brier (31:32)

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

Joe Weisenthal (31:50)

It.

Noah Brier (31:50)

It's better at pretending it's us.

Joe Weisenthal (31:52)

Yeah.

Noah Brier (31:53)

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

Joe Weisenthal (31:58)

So it flatters you in a different way.

Noah Brier (32:00)

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

Joe Weisenthal (32:03)

Subtle way, yeah.

Noah Brier (32:03)

Interesting. But for a long time, Just Anthropic has been producing the best coding models. You know, I mean, there's. There can be some debate there now, but, you know, there's 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, I. For what it's worth, I think the other takeaway from that, which is a kind of big theme we see in the market, is 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 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.

Tracy Alloway (33:14)

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

Joe Weisenthal (33:19)

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

Tracy Alloway (33:22)

That's right. That's right.

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Noah Brier (34:28)

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Tracy Alloway (35:17)

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?

Joe Weisenthal (35:25)

Forget I never learned.

Tracy Alloway (35:26)

Yeah. Okay.

Joe Weisenthal (35:27)

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 drive, 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.

Noah Brier (35:50)

I have a couple takes on this one personally. So first one is I just think like, like this is the worry of all technology ever. There was A paper that came out that showed that people were, you know, they were forgetting more things or something because they were using ChatGPT. But you know, in Phaedrus, Plato was worried that people were going to forget things because they started writing things down. And you know, I think the trade off there was pretty good. We got the scientific revolution, 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 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 because they're, they, they claim it's for safety reasons or whatever, but I think fundamentally it's just they've got people on their turf. But I, I think that's incredible. I mean my, I, my, my nine year old Vibe coded a website.

Joe Weisenthal (37:03)

Oh wow.

Noah Brier (37:04)

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 when she was nine. But that, that's awesome, right? I don't know. That's amazing. That's a way for people to express themselves in a way that they, they couldn't before you did your, your linguistics project. That's, that's fun and interesting. But yeah, I, I also think the other, the, the thing that's happening with professional software developers when you hear from Anthropic or, or, you know, when I'm talking about, it's, you know, the, 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, 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 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.

Tracy Alloway (38:16)

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

Joe Weisenthal (38:19)

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 I were 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 or 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?

Noah Brier (38:45)

I think that it's hard to answer right now. I think that certainly I do think, I personally think if I look at the sort of layoffs in the technology industry over the last couple of years, I think some part of that is just looking at the output of these models and saying, hey, these models are able to produce it 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 A$50 per million tokens, or I can produce 65th percentile for however many hundreds of thousands of dollars a year. It's. It's a sort of fairly simple trade off, I think. So I, I do think there's a lot of downstream effects. I think the other thing that's happening is, is kind of like middle management is under threat because it's the realization that, hey, like, part of what these models are amazing at is, is I think of them as like a fuzzy interface. They can sort of turn any data into any other data, right? You can sort of transform data from one format to another. You can take a PDF and you can turn it into charts, right? And there's whole, you know, if you think about what product managers do, a lot of what product managers do is they take how people are using a product and they try to transform it into a format that engineers can then use to figure out what to do. And I think a lot of those kind of, A lot of those pieces that used to just be kind of transferring knowledge.

Joe Weisenthal (40:08)

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, Wanya and Carrie is important and that a really important role within any organization is essentially the, the team that can translate between the generalist team and the specialist team. Absolutely. And so I, that's an interesting observation in the sort of engineering world of like, okay, these are tools that are in some sense translation tools.

Tracy Alloway (40:50)

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.

Joe Weisenthal (40:59)

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

Tracy Alloway (41:01)

Right, okay.

Joe Weisenthal (41:02)

When did that become a crime?

Tracy Alloway (41:04)

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. What's going to happen to software?

Noah Brier (41:21)

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 the cloud provider software or not. You know, you still need to run this stuff somewhere. And I think 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, that business. But I, 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 SAS company and we sold to enterprises and you know, for a long time that, that, I think that made a lot of sense, right? Because like, hey, it just didn't make sense to try to build this thing on your own. And so, but the price of that was, you know, one, the price, right. Like, and, and it got to be more and more expensive. The other price was that for a lot of stuff you didn't need.

Public/Grainger Advertiser (42:17)

Right.

Noah Brier (42:17)

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. 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 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 lot of pieces where the software existed essentially as a kind of wrapper around a database. And now you're just going to, you know, with just the database you can do that. And then, you know, the other piece I'd say here is it's. This is not. There's a kind of confluence of circumstances where it's not just the coding, it's also the fact that you have AI to do a whole bunch of work. So, you know, if we pick on CRM for a second, right, like, you know, salesforce.com salesforce.com we can, you know, you look at what the interface of that is. And essentially it has existed to get salespeople to take unstructured data, which is sales meetings, and turn it into structured data that, so it can be stored in a database. And now you have AI and AI is very capable of taking unstructured data directly from the source. So you have people recording meetings and then it can 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. And we've just basically been having a bunch of humans do that work for a very long time, whether it's in CRM or project management or any of these other places. And the ability to just kind of get rid of that whole thing. I think it really does bring into question the value of a lot of these software companies.

Joe Weisenthal (44:39)

Well, so we have seen like a lot of software socks. They look like melting ice cubes right now. Maybe they. So what is it? 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 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 what 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 like live in the same world as all your files.

Noah Brier (45:27)

Yeah. So I think if you, if we take CRM as the general category so you know, the biggest players there are, that's customer relationship, customer relationship management. That's like where, you know, Salesforce does it, SAP does it, HubSpot does it for the mid market. You know, when I think about that product and I think about the way we've used it inside enterprise sales organizations, essentially, you know, it's a database of companies, it's a database of contacts, it's a database of deals you have in the pipeline. And it's a way to track all those deals. You guys hit on something before that I think is, is really it, which is like inside companies there is a huge group of people and who 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 room, 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? And essentially you were asking the salespeople to make the updates in the system to do that. And just very tactically, I mean, you know, I run a company now, we talk to a lot of, we have a lot of sales calls. We record those calls and they get transcribed. And the AI then looks through them and makes decisions about where this deal should be in the process. And it's much better than having somebody try to go update it because those people never updated anyway. The secret of all of this enterprise software is that nobody was using it the way that anybody wanted to anyway. And so, you know, 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 and you know, I think those folks are still sort of genuinely sitting in a pretty good place where, you know, all software has to sit on, 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, Salesforce has a whole AI thing and they're saying, hey, you shouldn't have humans inputting in Salesforce Sales is just one small piece. They have a whole customer support thing, which obviously also has an interesting implication where you're doing support with AI agents. And so some of it comes back to seats. I mean, it gets to be fairly complicated. But I think the fundamental underlying thing is, is anybody who buys software that is 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. And so, you know, if you can more narrowly focus that, that is where you could say, hey, we could solve this kind of more narrow problem. And not only can we solve it more narrowly, we can solve, 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 Chat GPZ 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. Guess what? Where the line is between what's AI and what's not AI is going to continue to get blurrier.

Tracy Alloway (49:16)

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

Joe Weisenthal (49:20)

Well, I paid for the 200amonth. $200 a month version, but like high roller. Yeah, I know, but you know, I think it's, you can get it with the pro version of like or whatever, the sub, 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 and I was like, this is dumb. I think I'll just. Yeah.

Tracy Alloway (49:45)

Oh, okay.

Joe Weisenthal (49:45)

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

Tracy Alloway (49:51)

It's worth it to you.

Joe Weisenthal (49:52)

Yeah.

Tracy Alloway (49:52)

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 and 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, substantial portion of time?

Noah Brier (50:28)

Yeah, 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. OpenAI ChatGPT 5.2 is, 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 Claud 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, you are probably, you have the access, I, I, I have the access to use, I would guess in the thousand or $2,000 of tokens for my $200 a month. So it's, it's a very, very heavily subsidized plan. And Open Code, which is an open Source version of Claude Code.

Joe Weisenthal (51:49)

A.

Noah Brier (51:49)

A sort of competitor. They had found a way that they would let you use your Claude Max plan with open code. And Anthropic shut that down.

Tracy Alloway (52:00)

Yeah.

Noah Brier (52:01)

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 I do think part of what they're trying to get at because is that at the very top models, these are all amazing. 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. Like, and that's where something like cloud code is really interesting because it's a product that is very. It's just there. So it's a piece of software. It's not an AI model. And so it's sort of.

Joe Weisenthal (52:50)

Of.

Noah Brier (52:51)

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 I think what they're trying to do is get developers like me to feel very comfortable inside that. So that when we go open, I still open Codex or try Gemini or. I was playing with open code the other day and it just. 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.

Tracy Alloway (53:21)

Right. They want to own, like, the ecosystem, the environment. The environment working.

Joe Weisenthal (53:25)

What a world. Noah, thank you so much for coming on Odd lots. I was, like, dying to do an episode about this topic.

Noah Brier (53:31)

Thanks for having me.

Joe Weisenthal (53:32)

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

Tracy Alloway (53:36)

Why is everyone making that joke?

Joe Weisenthal (53:38)

Wait, which joke?

Tracy Alloway (53:39)

The psychosis joke.

Joe Weisenthal (53:41)

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

Tracy Alloway (53:44)

Oh. Oh, that is very good.

Joe Weisenthal (53:46)

It's like, I do one pun finally for Tracy and she's just like, why was ever making that joke? Well, I was thinking about the joke. I was handing you a sir. I finally make a pun and you just jump right over it.

Tracy Alloway (53:54)

Well, everyone keeps saying that Claude code is AI psychosis for smart people.

Joe Weisenthal (53:59)

Right.

Tracy Alloway (53:59)

Like, how did that become a thing?

Joe Weisenthal (54:00)

It's very addictive.

Tracy Alloway (54:01)

Yeah. All right.

Joe Weisenthal (54:02)

But there's a Good pun.

Tracy Alloway (54:03)

It's also very bro coded, I find.

Joe Weisenthal (54:05)

You think so.

Tracy Alloway (54:05)

All of AI is bro coded.

Joe Weisenthal (54:08)

This is true. We should talk more about this. You know, we should have David Shore on. He's been doing a lot of poll polling about various demographics and how they feel about AI. We should. And you have some interesting stuff.

Tracy Alloway (54:17)

I'd be into that.

Joe Weisenthal (54:18)

Yeah, we should do that. Anyway, Noah, thank you so much for coming on off of it.

Noah Brier (54:21)

Thanks for having me.

Joe Weisenthal (54:34)

Well, that was fun. Tracy. I really like. I. It's obvious to anyone who's been within five minutes, five feet of me for the last two weeks, I'm like totally addicted to and gone down. I know. Gone down the rabbit hole and stuff. But like, I, for the first time, unironically am like, okay, this is transformative technology beyond being very impressive technology.

Tracy Alloway (54:55)

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.

Joe Weisenthal (55:09)

Is that where you're making your stock?

Noah Brier (55:10)

Yeah.

Tracy Alloway (55:11)

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

Joe Weisenthal (55:31)

Well, so that's very interesting. His point, which is that it's. The tokens are heavily subsidized still. And so that if you're paying and actually using that $200 Mac program and you actually use it to the limit, CLAUDE is going to lose money on this. And then the prices keep dropping. And I know, like, CLAUDE code is okay. They're attempting to create something that resembles a traditional software ecosystem that you feel is 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, very. It's very movable. And I suspect even though I have this file now on my desktop that has a file called Claude MD that gives instructions, etc. I'm certain that if I open this file with codecs or Google's, I could probably just pick it up the same.

Tracy Alloway (56:25)

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, to lock people into anything. And we've seen various projects over the years and it's, it's a lot harder than it looks.

Joe Weisenthal (56:44)

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, 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 ways. I do think he offered the best explanation I've heard of why the AI coding models are a threat to a lot of pretty big software businesses. Especially, especially the point about how the user never uses all of the features that they actually that the software got built for and therefore maybe the build versus buy calculation really starts to shift when they can just design that one feature very quickly.

Tracy Alloway (57:32)

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

Joe Weisenthal (57:47)

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

Tracy Alloway (57:50)

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

Joe Weisenthal (57:54)

Stuff is happening now. Yeah.

Tracy Alloway (57:55)

Okay. Shall we leave it there?

Joe Weisenthal (57:56)

Let's leave it there.

Tracy Alloway (57:57)

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

Joe Weisenthal (58:02)

And I'm Joe Weisen. 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-Obenit-Bot and Kale Brooks at Kalebrooks. 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.

Tracy Alloway (58:25)

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 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 podcast and follow the instructions there. Thanks for listening.

Joe Weisenthal (58:58)

Sam.