Better Offline - AI Isn't Making Engineers 10x As Productive with Colton Voege

Episode Date: September 19, 2025

In this episode, Ed Zitron is joined by software engineer Colt Voege to talk about how AI isn’t 10xing the productivity of software engineering, the truth about coding LLMs, and what we can actually expect AI to do for software in the future. YOU CAN NOW BUY BETTER OFFLINE MERCH! Go to https://cottonbureau.com/people/better-offline and use code FREE99 for free shipping on orders of $99 or more. BUY A LIMITED EDITION BETTER OFFLINE CHALLENGE COIN! https://cottonbureau.com/p/XSH74N/challenge-coin/better-offline-challenge-coin#/29269226/gold-metal-1.75in --- LINKS: https://www.tinyurl.com/betterofflinelinks Newsletter: https://www.wheresyoured.at/ Reddit: https://www.reddit.com/r/BetterOffline/  Discord: chat.wheresyoured.at Ed's Socials: https://twitter.com/edzitron https://www.instagram.com/edzitronSee omnystudio.com/listener for privacy information.

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or wherever you get your podcast. AllZone Media. Hello and welcome to Better Offline. I'm your host, Ed Zittron, as ever buy our merchandise, subscribe to the newsletter. It's all in the episode notes. And today I'm joined by programmer Colton Voguey, who wrote an excellent piece about a month ago called No, AI is not making engineers 10X as powerful. Colt, thanks for joining me. Thank you for having me. So tell me a little bit about your day-to-day work. What do you do for a living? Yeah, so I'm a software engineer and I work specifically in what's called web application development.

So that's kind of like building rich applications that work in the web browser. So think of something like Amazon or Google Drive or something where you can do a lot of interactive features within the application within a browser. Right. So kind of like the fundament of most cloud software. Yeah, I would say that if not the majority of engineers work in web application. development than probably the plurality do right now. And it's also a large amount of how people interact with software is now in this way, though. Yeah, exactly. Yeah. And that's why most developers perhaps work in this field now.

And you'd think something like that would be perfect for AI coding environment, surely. It is definitely the most applicable place for AI coding of all of them. for there's sort of a there's sort of a thing where people are like you know oh you know LLMs don't really work really well for my language and they'll be talking about something like Rust which is more of like a

high performance language rather than a web language What is a high performance language in this case? Just something that's designed to work on like weak hardware or at like extremely high speed so like for example like video games aren't built in Rust

they're usually built in a language called C++, but they're built in a high performance language because you're trying to max out as much as you can do. You're taking advantage of the high performance compute you have. Yeah. Whereas with a website like Amazon or something like that, you're really not pushing the limit in terms of interactivity, and so more modest languages are fine. Got it. So you laid it out really nicely in the piece so you can have to repeat a few things I imagine. but run me through why AI coding tools can't actually make you a 10x engineer or a 10 times better engineer.

Yeah, so basically, AI is really good at, AI is almost shockingly good at generating code, and it's almost shockingly good at generating code that runs. and it can answer a lot of questions that are that are like difficult or at least you know annoying is probably the better word it's really good at dealing with things that are annoying it's really good at you know so in coding we have this concept called boilerplate and it's it's just code that you have to kind of rewrite a lot and as an engineer ideally you don't have to write a lot of boilerplate ideally you automate things and abstract things so you you don't need to do that very often But it's sort of like a requirement of the job. And it's really good at writing that because, you know, it's just, it's kind of like low, intent, high volume code. Right. A quantity over quality type thing. So it's really good at stuff like that.

The problem is that generating code really isn't the hard part of being a software engineer. It's one of the things that really matters. And it's like, obviously, if you go to college to learn how to code, you're going to spend most of the time. doing is typing code. But it's really just like one thing that you're doing. Like in reality, you're doing a lot more thinking about like, okay, how does this work with the systems I already have? How do I avoid creating what's called tech debt? So tech debt is basically like an easy way of thinking about it is when you write code that makes writing future code harder. So an example would be, you know, you want to write like a piece of logic that let's say computes sales tax.

If you're making an online store, you'd want to compute sales tax based on where they are. You only want to write that once. You don't want to have two different places that handle sales tax because then if Illinois changes their sales tax rate, you have to change two places instead of one. Doesn't the very nature of code also mean that it naturally creates tech debt? You know, you can try to mitigate as much as you can, but there's even an idea like that all code is tech debt. And what LLMs, so kind of what I mentioned before, you know, LLMs are really good at producing boilerplate. But if you get to the point where boilerplate is really easy to produce

and you're not constantly thinking like, okay, how do I avoid doing this in the future? You start, yeah, exactly. You start generating more and more code. And just like generating code isn't, it's kind of like writing in terms of like, you know, writing a book, like a good writer writes fewer words rather than more. Yeah, brevity is the soul of wit. Now, it's because what I've been realizing is that there is this delta between people that actually write software and people that are excited about AI coding. Because I had Carl Brown from the internet of bugs on. And it was this thing of, yeah, being a software engineer is not just writing 100 lines of code and then giving the thumbs up to your boss.

You have to interact with various different parts of the organization. Early on in the pieces where you had this bit called The Math. and I'll link to this, obviously, in the episode notes, where it's any software engineers worked on actual code and natural company knows that you have these steps and where it isn't just like, I code, and then I give the thumbs up and I'm done for the day.

You have to go through a reviewer. You have to wait for them to get back to you. You refer to context switching as well, where you have to change different windows and do different things. There's a shit ton of just intermediary work that is nothing to do with actually writing code, right? Yeah, absolutely. I mean, it really is more of, you know, there's parts of coding that is more science than there's parts of coding that are more art and more, you know, in college it's really common if you're getting computer science degree to take communications classes because you just have to interact with a lot of people.

So the standard way that a thing gets done in a software project is you have somebody called a product manager. And that person's, that person's job is to think about what the product is. as it exists now and what should be in the future and what features we should build? Like basically, more than anything, it's, you know, what should we build next? And then you have designers who are going to decide how that thing should look.

And engineers have to be this meeting influence because they're the only ones in the process who actually know what it's like, how hard it is going to be to build something. So a frequent interaction is like, you know, a product manager is like, oh, we should add this and an engineer has to kind of step in and be like, no, that's borderline impossible.

Or, you know, that would take, six months. Right. And coding LLMs don't fix that problem. They don't. Do they actually make it easier to develop products? There are uses for LLMs in coding. And I quite like using.

I'm not even denying that. I'm just like, how, like, what do those uses end up actually creating? Basically, you know, what I found is that I don't really like using LLMs for most product work, even though they're good at, you know, say like, oh, add a button here that does this. They can be good at that, especially if you have a code base that works really well with LLMs. So some programming languages, some tools, some, we call them libraries and coding. You know, they're basically like shared software. Some of those things work really well with LOMs because the LLMs are just trained on the

internet. So sites like Stack Overflow and sites like leak code and stuff like that. And so if they have a large body of this code to work within their training data, like these tools and these languages, they tend to be better at writing them. And so, you know, I work primarily in JavaScript, as do most web application engineers. And LMs are quite good at JavaScript. But I still don't like to use it that much because it tends to just not understand context very well. And what does that mean in practicality? So it's kind of just like, you know, understanding the existing resources that you've already built in your codebase.

So this is like the avoiding writing the same like sales tax thing twice. They tend to default to rewriting things. And they tend to struggle to reuse the same like style. So, you know, an important thing in coding is maintaining consistent like styles of your code. So like there's a whole theory and practice of. actual, like, how your code should look, like, as, like, how the text should, like, how you should avoid, what things you should avoid because there's bad patterns, there's good patterns, and, you know, there's everything in between. I think it's very similar to, like, you know, when image generation

AI kind of came onto the scene, you had a lot of people being like, all right, well, graphic designers are done, you know, they're out of a job. They don't need to do anything. Because I can generate a logo now, and it looks pretty good. Like, I can generate a logo for my nail salon or whatever, and look at it, it looks great. But as soon as you try to generate a second logo that looks like the first one, and, you know, maybe has a slight modification or something like that,

it's really, it's really bad at it. It's really bad at using the context, and you're like, okay, now I need a graphic designer. And as soon as you need, you know, you hit up a priori one anyway. Yeah, exactly. And so, and, you know, that's, it's very similar to engineers. a lot of what you're doing is sort of working around context and avoiding redoing things and avoiding moving away from your existing styles.

And what is the critical nature of styles? Is that because is that so that people looking at your stuff in the future can say, okay, this is what they were going for? This is so that everything doesn't break? Yes, exactly. It's about consistency. It's about, so there are certain, you know, for example, JavaScript, which is the main language I work in, is almost infamous for basically allowing you as the developer

to do all sorts of buck wild stuff that you should never do coding-wise. Like a lot of old patterns, a lot of recycled stuff, a lot of, it just lets you, it's an extremely varied language in the things it supports. And so what you want to do when you're writing in JavaScript is only use the good parts. You want to strategically avoid doing

bunch of bad things. And some of those bad things, there's tools that will automatically detect if you're doing them. Another podcast from some SNL late night comedy guy, not quite. Unhumor me with Robert Smygel and friends, me and hilarious guests from Jim Gaffigan to Bob Odenkirk to David Letterman, help make you funnier. This week, my guest, SNL's Mikey Day and head writer Streeter Seidel, help an a cappella band with their between songs banter. The worst singer in the group. The worst? Yeah. Me. Is there anything to the idea that because you're from Harvard,

you only got in because your parents made a huge donation. The yard birds, right? That's the name. The Harvard Yardt. They're open. Do you have a name suggestion? We're open. Since you guys are middle-aged, one erection.

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but don't ever feel like you don't feel on. Don't let that be the reason you don't do it. An Olympic champs Gabby Thomas and Katie Ladeki. The ability to show a gold medal to someone and have their face light up and smile, that means the world to me. And that's what motivates me to win more gold medals. At our level, at this scale, like being able to fail in front of the entire world,

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We're two men who were completely clueless to reality TV, who now have covered dancing, with the stars, traitors, and we're gearing up for the season finale of Survivor. So yeah, now we're experts. I know we annoyed a lot of our listeners by our severe lack of survivor knowledge. That is the point of the show. I'm just going to remind you. I have watched some Survivor. I obviously haven't watched enough.

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So make sure to tune in to Pod Meets Twirled for all our Survivor 50 takes. Listen to PodMeets Twirled on the IHeart Radio app, Apple Podcasts, or wherever you get your podcasts. What are these bad things? Is it just the job? What's so special about Java that it allows you to do that? Yeah, so JavaScript, which is technically different from Java. Yeah, no problem.

Basically, the reason JavaScript has all these bad parts is because it was created in 10 days as a toy language at Mosaic, which was the precursor to Firefox, you know, the first browser that really gained traction and set the internet. I'm old enough to have used Mosaic somehow. Yeah, so JavaScript was created in like 10 days, almost like just is like a thing to test,

like just something to try. Like, okay, what if we put a coding language into the browser that people could just like ship with their websites? And in response to that, other companies said, okay, well, we need to support JavaScript as well. And so when Internet Explorer came out, they shipped their own version of JavaScript,

which was slightly different. And so you have this engineer that was kind of built on these very hacked-together principles that was then rebuilt with slightly different principles. And then what you have now is 30 years later, sort of the conglomeration of all those things into one language. and it's improved dramatically in that time. But the nature of coding languages is there's a lot of backwards compatibility, like a video game where you want to be able to build a computer that can run a game from 1991.

So you want the browser to be able to still run code largely that was written in 1995 or whatever. So a lot of that stuff still exists, and what they've done is introduce new patterns that can be better. And so there's a sort of an equal amount of avoiding that. And also just consistency. So there are times where there's two good ways to write ways to do something, but you always want to do it only in one way so that any time someone reads it, they see that pattern and they say, okay, I know exactly what this does. Right.

And this fundamentally feels like something large language models are bad at, because they don't know anything and they don't create anything unique either. They just repeat. Yeah, exactly. I mean, they are statistical inference models. And so they are very good at generating what they think should come next based on probabilities. And you can, when you're training a large language model, you can try to push it in one way, push it in another way to say, like, no, don't do that, don't do that. But like, trying to do that on the broad spectrum of all things code is basically impossible.

And so you're going to get things that default to the way that they were done on the internet. and so much, like I said, JavaScript is 30 years old. It's extremely, it's gone through a lot of dark times in terms of patterns. And so on the internet, there are just fast swaths of terrible JavaScript. And, you know... So it's just these models are trained on bad code as well as good code. Yeah, exactly. And, you know, I'm sure they're trying very hard when they're training the models to filter some of this stuff out,

but trying to do a broad filter on hundreds of thousands, millions even of web pages of code. I mean, JavaScript's been around for 30 years, as have most programming languages that are in use right now. Right, but this particular one seems particularly chaotic in how it's sprawled. Yes, JavaScript is a particularly thorny language, yeah. So what you're describing, I'm not trying to put words in your mouth, is that this stuff doesn't do the stuff that everyone saying it does. Like, it's not replacing engineers. It doesn't even seem like it could replace engineers. Like, it's just not, it doesn't do, in fact, maybe it's fair to say that soft, it doesn't do software engineering. That's almost the perfect way to put it. It does coding, but it doesn't do

software engineering. And software engineering is kind of this broader practice of like everything that comes together around coding. So, you know, some people really integrate LLMs deeply into their everyday work, and they do similar work to what I do. So, you know, there are people who are primarily having, like, LLMs write their code, and they can be good engineers. But they're intervening pretty constantly from what I understand, and, you know, they're having to sort of redirect it, make sure it stays on patterns and all that stuff. And there's just kind of, you know, this is the reason that I don't really use LLMs that much, is there's just a constant tension. between the

lack of context they have and what you want them to do and constantly reviewing the code and making sure it's up to standards when I know I could just write it and it'll take about as much time as it would take prompting and reprompting. It's just, I get better code that way

and that's what I care about most. So do you think it's kind of a mirage almost, the productivity benefits? I think it would vary a lot. I think yes broadly. I think that there are productivity benefits, but these huge outsides, like, oh my gosh, I'm galaxy-brained right now.

I'm doing so much. I think that that is largely a mirage based on like extrapolation of like small wins. I talk about this a little bit in my article that, you know, there are, one thing that I use LLMs for a lot, and not a lot sometimes, but that they are really good at is, you know, sometimes you're writing code and you're writing code

and you're like, I need to write a thing that I will only run once and I will throw in the trash, or I need to write a thing, and it uses this tool that, like, I don't have the time to learn. But, like, I'd really like to have this tool. Like, I'd really like to just, like, use this once. And so you can vibe code something and not really understand what the code does. You can run it once and not, you know, validate the output and make sure it works fine. And, you know, I've saved in that time, you know, it might have taken me five hours. to learn how to use this tool properly and learn how to use it with good standards.

It could even have taken me more and instead I spent 20 minutes. Wouldn't that be dangerous because you don't know how it works? Exactly why I like to only use it for these like one-time like low. You know, so for example, I wrote some code the other week and I was basically, I basically refactored some existing code so I adjusted how it worked a little bit. And I realized there was a way that I could break some existing code with it, but I didn't have an easy way of checking across the entire codebase. So we have tests in our codebase that would, you know, catch issues,

but there's always some code that isn't quite up to par with tests. And so I had this idea that, you know, a really simple language parser could go through and make sure that this code was right. You know, something that is only like 30 lines of code. but language parsing is really complicated, and the tools to do it are, you know, there are a lot of them, and they're very well-supported

because language parsing is a huge deal. But it would take me a lot of time to learn that. So I just vibe-coded a little tool. I said, hey, find me every case where I use this function and I call it like this in this entire code base, or actually I said write a script that will find me that. And I looked at the code. I was like, yeah, that looks right, that looks right.

looks right. I ran it. It said there was no issues in my code base, so I intentionally created an issue just to make sure it worked, and it worked. It showed it. And so then I got rid of the intentional issue and I was like, okay, this is probably good. And I pushed my code and it turned out to be good. But that's, you know, that also seems low stakes. Exactly. And it's what I would say wouldn't scale. So if I really needed to start doing like language parsing constantly, like I was doing it daily at my job, I simply would have to learn. Like you said, you know, how do you know that there aren't any issues? I would have to learn the tools.

Right. So the time that I saved was by avoiding learning for this one thing. But eventually, like, if you're going to make something your full-time job, you have to learn it because you can't fully trust the output. Because otherwise it isn't your job. Like, you were just kind of mimicking. Yeah. And, you know, you have to, you know, the LLMs make mistakes. in occasionally extremely catastrophic ways.

There's a thing called slop squatting. Have you heard of slop squatting? No, but please tell me. I love this term so much already. Yeah, so basically, you might have heard of like domain squatting. So this is where like, you know. I think I know what this is and I'm very excited to hear more. Yeah, so like, you know, this was a thing where like in 1991, you're like, okay, I think

the internet's going to be big, so I'm going to grab Nike.com. Right. I'm just going to hold it. So that's squatting. a domain. So slap squatting is basically where you the LLMs, you know, they're statistical

inference machines. They don't actually understand what they're doing. And so, you know, sometimes they will import software. They will Is this when it looks on GitHub for something that doesn't

exist? Yes. And it will just add code to your project that's like, import this thing and it will install it and it'll say like okay this is the library that you want to use and it's it's not right it's either misspelled or it's you know for example you would be looking for a library called left dash pad and it would import something called left pad with no dash and so what people realized is that they can you know when the lLMs there's statistical machines and so they frequently they do the same thing a lot they make the same mistakes

a lot, so you could grab that library left pad with no DAG and put code in there that works, that does the thing that the library is supposed to do, but also retrieves all of the secrets that are in your environment or like looks for crypto wallets or like production database passwords and stuff like that. And if you're someone that can't read code or can't read that kind of code, you would have no idea this is happening. You'd have no idea. And even if you're somebody who does read code really well, if you, you know, look at that,

you said it's imported left-bite-out, you're like, that sounds right, that looks right. Like, unless you are really familiar with this library, and even if you are familiar, you might just like gloss right over it. So it's really, really, really dangerous. And like, this is the type of thing that could, you know, like maybe if the LLM is making you even twice as productive, you know, that doesn't mean much. if there's a chance you could destroy your entire company with a catastrophic security breach, you know? Yeah.

Leak all your database to this hacker. That's not good. Yeah, exactly. And is that becoming more prevalent? I haven't heard much of it like happening in the wild, but it's just one of those things that is bound to happen because again, these are just statistical bottles. They don't have the ability to really reason about the actual nature. of the things that they're doing. They can try, you know, they can make a sub-LLM call and ask the

other element, like, does this look right? But then you're, you know, burning more and more tokens. And also at some point, you are trusting the statistical model to measure a statistical model's ability to do its job. Yes, exactly. And, you know, it kind of devolves. Anybody who's used an LLM for coding knows that the deeper you go into like a single prompt, like the more back and fourth, the larger the context window, the more garbage it gets. And so, like, as you have things like working off of other LLM input, which is effectively what you're doing in a large context window, you know, it's just what the LLM is sort of reprocessing the text that it generated and that you've added to it, it steadily gets worse the later in the context window. So basically all of these sort

of mitigations are, you know, they've made surprising progress with the way that, like, you know, like this don't happen as like just raw hallucinations of like I think this library exists. They happen a lot less now than they used to, but they're just always, they're always going to and they're also always going to be there. Yeah, pretty much. It's not really something you could, unless we invent new maths. Yeah, I mean, at least with the way we approach AI right now, which is based purely on language as tokens. And it can't really think. And it can't really fundamentally, like, understand things outside of, you know, word probabilities. Another podcast from some SNL late-night comedy guy, not quite.

Unhumor me with Robert Smygel and friends. Me and hilarious guests from Jim Gaffigan to Bob Odenkirk to David Letterman, help make you funnier. This week, my guest, SNL's Mikey Day and head writer Streeter Seidel, help an acapella band with their between songs banter. There's that worst singer in the group. The worst? Yeah.

Me. Is there anything to the idea that because you're from Harvard, you only got in because your parents made a huge donation. The yard birds, right? That's the name. The Harvard yard, but they're open. Do you have a name suggestion? We're open.

Since you guys are middle-aged, one erection. Listen to humor me with Robert Smigel and Friends on the I-Heart Radio app, Apple Podcasts, or wherever you get your podcast. You're the name. I need some jokes to make me seem funny. Run a business and not thinking about podcasting, think again. More Americans listen to podcasts than ads supported streaming music from Spotify and Pandora. And as the number one podcaster, IHearts twice as large as the next two combined.

So whatever your customers listen to, they'll hear your message. Plus, only IHeart can extend your message to audiences across broadcast radio. Think podcasting can help your business. Think IHeart. Streaming, radio, and podcasting. Call 8444. I-Hart to get started. That's 844-844-I-Hart. Life throws hurdles big and small. The question is, how do you conquer them? On Hurtle with Emily Abadi, we sit down with the most inspiring women in sports

and wellness, professional athletes, coaches, and Olympic champions to talk about the challenges that shaped them and the mindset that keeps them going. From the WNBA standout, Kate Martin, and rising hockey star, Layla Edwards. If a boy can do it, I don't see why a girl can't. Like, I've never understood that. Like, it didn't make sense in my brain. It's hard to be in spaces that no one looks like you, but don't ever feel like you don't belong. Don't let that be the reason you don't do it. An Olympic champs Gabby Thomas and Katie Ladeki. The ability to show a gold medal to someone and have their face light up and smile, that means the world to me.

And that's what motivates me to win more gold medals. At our level, at this scale, like being able to fail in front of the entire world. Like, I can do anything. I can do anything. Because resilience isn't just about winning. It's about showing up, even when it's hard. Listen to Hurtle with Emily Abadi on the IHeartRadio app, Apple Podcasts, or wherever you get your podcasts.

Presented by Capital One, founding partner of IHart Women's Sports. Hey, everyone, it's Ryder Strong and Will Ferdell from PodMeets World. And now the Pod Meets Twirled podcast. We're two men who were completely clueless to reality TV, who now have covered Dancing with the Stars, traitors, and we're gearing up for the season finale of Survivor. So yeah, now we're actually. I know we annoyed a lot of our listeners by our severe lack of survivor knowledge.

That is the point of the show. I'm just going to remind you. I have watched some survivor. I obviously haven't watched enough. Did people not like it? Yeah. Just because we? Yeah.

We'll be recapping the big conclusion in the 50th season from the final attempts at gameplay to the desperate pleas of finalists to a bunch of who. Again, we are experts. So make sure to tune into Pod Meets Twirled for all our Survivor 50 takes. Listen to PodMeets Twirled on the IHeart Radio app, Apple Podcasts, or wherever you get your podcasts. So where is this pressure coming from? Because it feels like it's everywhere. And you've got people like Paul Graham who are wanking on about, oh, I met a guy who writes 10,000 lines of code, I think he said.

What is it, are we just finding out how many people don't know how coding works? I think a lot of that, yes. Like I said, it's exactly the same way as like people, you know, when the first image generation models were like, oh, great, we don't need graphic designers anymore. And then they, or, you know, oh, great, we don't need customer support chat anymore. Because they fundamentally don't understand what those roles do. You know, they think graphic designer and they think image generator. But a graphic designer is a human being that's dealing with different stakeholders, dealing with people saying like, no, no, no, the logo can't have that. Or, you know, yes, the logo must have that.

Yeah, yeah, exactly. And they're dealing with, you know, different requirements. And then they're needing to make variations, which is something that LMs are not always very good at. Or I should just say, generative AI is not always very good at. Yeah. It's so strange. Part of it is people who, you know, like I said,

you get these like brief bursts where you're like, oh my God, I just save so much time. And you extrapolate that. So some of it I think is engineers who are just like, actual engineers who know how to code who see these things and they think like I did this today and like I must have been so much more productive

as a result. You know, I saw this one thing happen, but they don't really actually measure in depth what they produced and was it more than what they would have produced. There have been some studies to measure that and they haven't looked particularly good for LLMs. You know, if you actually compare people, you know, using AI versus not using AI.

the results don't always look particularly great for AI. And one thing that's very common out of that is that people overestimate their performance. And I think that might be a problem across all. Like a lot of people don't, I also think my grander theory with all of this is a lot of people don't know what work is. Like a lot of these investors and managers and such and even people in the media don't seem to actually know what jobs are and how the jobs work. And they think that things like coding is just, like I said earlier. Yeah, just walk into work, I'll write 10,000 lines of code, and I walk home. But now I can write 20 billion lines of code, because that's all my job is.

Yeah, absolutely. I mean, this is something I talk about in my article is that there's always this degree of separation. And the people who are talking the most about AI coding aren't really coders, and they're not really providing detailed reproduction steps. You know, I know engineers who love using AI like every single day and they use it for like all of their projects. And I know really good ones to do that. And if I asked them like, hey, how could I like be more likely, be more like you? How could I be a better coder?

One of the last things they'll say is probably, you know, start using AI more. It's really just a tool to accomplish part of their job. And so, so yeah, I think there's a lot of, you know, there's probably, you know, plenty of genuine people who just like, you know, they've never written a line of code in their life. They pull up lovable. They say, generate me an app that does this. And, like, they legitimately are like, oh, my gosh, it actually worked. Like, I can code. And they just, you know, naturally, you know, they don't realize it.

They don't realize that, like, there is so much more to this actual practice. And, you know, they're just not in. tune with the way that coding actually works. Yeah, it's a little bit sad as well because it really feels like a lot of this is just the point you've made about like the image generators. It's just this

immediate moment of wow, look what this could do. Imagine what it could do next and then you look at what it could do next and it can't. Like it looks like it can generate code but it can't actually generate software. It doesn't seem to do the steps that make software functional and scale

because there's these tendrils. from software into the infrastructure to make sure it can be shown in different places or to make sure that it actually functions on a day-to-day basic. Yeah. And it's like, yeah. It's a pretty simple like curve. Like, you know, you start out and it generates so much code that's like pretty correct really fast.

If you start a completely bare project from just like a lovable prompt or something like that. But as you go on, you know, it's like the curve kind of flattens and it goes down and it becomes less and less productive. And I think eventually usually like pure vibe coded projects hit a pretty big wall because you're just introducing so much code and it's not consistent. It's not using shared tools. And so eventually you just end up with this, you know, they call it spaghetti code. It's, you know, code that is so interwoven and difficult to understand understand that like you can't actually see what's going on with it. Yeah, it feels like context is the whole problem.

Just because even not say, like, LLM generated writing is dog shit. And I think it's worse than code because code, code is functional in a way that I don't think writing has to be. Like writing conveys meaning, but good writing is usually more than just, I am entering this writing into someone's brain so something happens in the way that code is.

But with writing, they share the same problem, which is great writing has contextual awareness. It builds, it connects. There is an argument or there is an evocation from it. In the case with software, it appears to be, if you don't know every reason that everything was done and fully understand the reasons that were previous and the reasons that are happening right now, you kind of will fuck something up naturally. Even if you do know how to code, if you just don't read any of the notes, if it's not clear why things were done, things will break anyway, right? Yeah, and great writing.

You know, it knows when to, you know, you know, a great writer knows, you know, when they need to explain something and when they don't need to explain something. You know, they know, you know, I'm writing a trade publication. Like, I don't need to explain how friction works or I'm writing, you know, a public press release. Like, I do need to explain how friction works or, you know, whatever. So it's very, you know, these are the things that the LMs are like, yeah, exactly, just like very not good at. They can generate stuff that looks good, but it's just the more you try to build on top of it, the more it'll end up restating. Like, you know, LOMs are really good at writing like the classic school level five-paragraphed essay. But everybody who actually writes anything at all knows that like the five-paragraphed essay that you wrote in high school are like terrible.

And like nobody wants to read something structured as like, you know, premise, three argumentative paragraphs conclusion. Like that's, it's really bad, like, unconvincing writing. And it's the same with, you know, LMs are really good at making toys and like, like, quick things that, like, are fun or, you know, maybe a little bit useful in certain situations. But really bad at writing things that, you know, like a book level type things. Like, you know, AIs are horrible at writing books because they restate things and they lose track of what they're talking about and all of that stuff. stuff it creates, right? The more it looks over, the more confused it gets. Exactly. It's really, really similar. I think that coding has really just followed the same trajectory as like all of these other things where we're like, oh, we don't need copyrighters anymore. Oh, we don't need

designers anymore. We don't need graphic designers. We don't need this, that, and the other. We don't need lawyers anymore. We can have an LLM be a lawyer. And you just very quickly realize that like these jobs aren't, you know, dumb factory like producing, you know, pulling a lever type jobs. They're about interaction.

And that's not an insult to factory labor. That is very hard work. But it's not like a repetitive action that is always the same thing. No, not at all. And like a factory worker, you know,

is doing a lot more than just pulling levers. Yeah. Of course. But it's not like just hitting a button. But I think people condense coding to this thing. Yeah, it's very similar to like robotics in factories too where you know the promise is like oh you know we'll just have a robot do this thing that a human does but the human is doing a lot more than just you know pulling the lever they're like observing the process they're making sure that things are not getting broken or getting gummed up and stuff like that. So there's just there's just limits to what machines can do when they're not actually intelligent and that's just what it comes down to. do you buy any of these companies like Google are writing 30% of their code with AI? The thing about those numbers is that they are super easy.

Maybe not to game, but to... First of all, I don't know anyone who's actually measuring this in a really, like, effective way. Because the thing about your coding editor is that it's... A lot of people, I don't like to use the AI auto-completion, but some people do, you know, there's pieces where you'll, you know, you'll start writing some boilerplate and it'll, like, pop up, you know, what it thinks you mean to write, and you'll just hit tab, the tab key on your keyboard, and it'll just, like, finish the line of code that you were writing. And maybe it was wrong. And that would be written in AI. Yeah, yeah, exactly. And it might even, very often what it produces is wrong, but it's like 75% right. So you're like, oh, I can just like use these keystrokes. I can save these keystrokes and I can just fix what it did wrong. Oh, God, it's like saying autocorrect wrote parts of your book. Yeah, it really is. And, you know, there are times where, like, an AI does fully write the code for a feature,

so people use, you know, things where they can prompt an LLM, and it will, with, like, what they want the feature to be or what they want the bug fix to be. And it will go and will write all the code, and it will make the sort of, we call it a merge request, but that's the request for new code that then gets reviewed. It will go all that way. But the thing is, you know, it might have written the code, but somebody took time away from, you know, their normal job,

which would just be writing code, to write a really good prompt to make sure it didn't screw it up and then re-interact with it. And so, you know, did it write the code? Yes. Did it do the task? Not really, because it needed somebody else to do some support work to make it even possible for it to do it. And that person could have, that person needed to be technical and needed to be able to say like, oh, you need to look in this part of the code base. And so you end up just getting the same type of like actual work from the actual specialist who knows how to code, the same amount of work.

They're just doing something slightly different. Cole, it's been such a pleasure having you here. Where can people find you? For sure. My blog is colton.dev, C-O-L-T-O-N. Dev. I don't post that often because I work full-time and I just post when something really gets to me, but I might say some things here and there.

And you have your excellent blog that I brought you on for. Yeah, yeah, that's my most recent one. You can feel free to check it out. I'm sure the link to that will be in the description. But yeah, it was great being here. Thanks so much. Thank you for listening to Better Offline. The editor and composer of the Better Offline theme song is Matt Rosowski.

You can check out more of his music and audio projects at Mattisowski.com. M-A-T-T-O-S-O-S-K-I.com. You can email me at E-Z at Better Offline.com or visit Better Offline.com to find more podcast links and, of course, my newsletter. I also really recommend you go to chat. Where's your ed. dot at to visit the discord and go to our slash better offline to check out

our Reddit. Thank you so much for listening. Better Offline is a production of Cool Zone Media. For more from Cool Zone Media, visit our website, coolzonemedia.com, or check us out on the IHeartRadio app, Apple Podcasts, or wherever you get your podcasts.

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