The Truth About Software Development with Carl Brown

24:28And then at some point, we have a thing called a pull request, which is basically a thing that says, okay, I would like this to go into production now or more or less. I would like this to get promoted into the next phase now. And then someone theoretically will look at it and go, okay, that's fine. And then click the yes button or say, hey, you forgot about this, go look at this, or that kind of thing, right? And the pull request is kind of the unit of work, kind of. So with GitHub, you almost use it like an organizational code dump, where you centralize all the code. Sorry, just for me, non-coding as well.

25:01And I think that the LLM industry has done a really good job of dancing around these terms and selling them to people like me. Well, they weren't selling. They didn't work on me. I'm too stupid. but it's where they've just like been like okay yeah well lots of people use copilot that's good and this is good because software's coding but it kind of feels like i don't know all of this is taking the one thing like one major part out of software development and ruining it and i don't even mean coding i mean it's the intentionality behind software design and infrastructure and It seems like they're removing intention in multiple parts.

25:47So the way I would say it is when they talk about the AI being able to do the work of a programmer, what they're doing is they're devaluing all of the stuff that's not just hacking code. Right. And so what they're saying is that basically the job of a developer is basically just, you know, typing, basically. Right. And that all of the work that we do to understand what the problem actually is and how it needs to work and what other problems are likely to show up when we try to do that and how to avoid those things as we go and that kind of thing. All that work is basically not important.

26:25And I mean, I'm going to say two words which will probably annoy you. This is, I feel like vibe coding is the other part of this. So if I'm correct, correct me if I'm wrong, Vibe Coding is just typing stuff into an LLM and software comes out and hopefully it works? Yeah, Vibe Coding is basically when you intentionally try, well, I don't know about intentionally, but basically you make a point of not digging into the code and looking at what the LLM is doing. And you basically say, okay, I would like something that does X, right? I would like a game where I fly airplanes around a city or something, right?

27:00And then you get what it spits out and then you say, you know, okay, let me try it. Okay, well, can we have more airplanes? And okay, can we have some balloons with, you know, signs on them now? And can we do this kind of thing? And then you don't think about what the side effects are. You don't think about what things could go wrong. You don't think about air conditions, that kind of stuff. And you just hope that whatever you look at has the right vibe and that, you know, if it looks like kind of what you wanted, that probably it's going to be fine. Or hopefully it's going to be fine. How do you feel about Vibe Coding?

27:33So I do it sometimes. Vibe Coding is great for a thing that you're going to do once and then throw away. Yeah. Right. So if it's like, you know, okay, I want to do a thing. I want to translate this thing to, you know, I want to make this table go into this format over here or that kind of thing. You do it. You get the output you want. You throw the code away. No big deal, right? Like a prototype almost. Yeah, basically. And so, you know, we call them spikes or tracer bullets sometimes. It's like, you know, let me get a thing that works at all. Right. And then let me see what I can learn from that to move into my big maintainable project.

28:08But for anything that's like, you know, this thing needs to run for a while. This thing needs to not get hacked. This thing needs to, you know, not crash. It's a really bad idea. Yeah. And at some point, I feel like someone building a product that they don't really understand the workings of. It's kind of almost identical to generating a story with ChatGPT, except kind of more complex and more prone to errors. Yeah. And the other thing is that there's an adversarial component, right? How so? People will intentionally try to go hack that thing that's sitting on the internet in a way that they don't intentionally try to go mess with the story that you wrote.

28:48Right. Right. And so even if it works all by itself, that doesn't mean it's going to work when somebody starts pounding on it intentionally trying to break it. And if they can break it, then that's a whole other set of problems that you now have. It feels like quality assurance is just never part. Oh, no. Are they claiming they're going to do quality assurance with large language models? Yeah, they must. Some people are, yeah. I mean, to be honest, a lot of companies have just been getting rid of quality assurance over the years, right? Oh, really? When I worked at IBM, we didn't have quality assurance at all.

29:22No, seriously, they would do this. I was in IBM's cloud group, and they would do these, what do they call them, hackathon kind of things? They didn't call them that. I don't remember what they called it. But basically, everybody in all the other development groups would get together and basically bang on the code that was about to get released from some other group to try to see if they could break it. Right. But they didn't have dedicated testers anymore because they decided, I guess, that they didn't think they were worth the money. I don't know. But we had some issues because of that. When did that movement happen?

29:53I was in, I don't know. So I was at IBM in like 2017, 2018. Right. So it would have been sometime prior to that. When I got there, they didn't have any QA folks. Really just feels like it's the management problem as well. It's the management kind of people. I would think so. It's a real shame as well. And forgive me if I'm forgetting exactly where. Yeah. You've mentioned as well that there is like compound scar tissue from AI generated code, a larger problem of lots of this code being generated with AI. Well, that's my expectation, right? Yeah, just a potential worry. Right, right. So the more of this we get and the more issues that we have, the more stuff we're going to have to dig out of, right?

30:38And what I'm honestly envisioning at some point in the, I don't know how long it'll take, the crypto bubble took way longer to pop than I expected. So I don't know how long it's going to be before this one does. But I'm expecting that there's going to be this big push to try to clean up a bunch of this crap here in a few years once people realize that a lot of the code that's being written and generated right now has all of these vulnerabilities that nobody's bothering to check for at the moment. Right. And those vulnerabilities, again, non-technical way, I read that it was like they call upon things on GitHub that don't exist.

31:11So bad actors create something that resembles what it's pulling from. So that's a more specific kind of one. I mean, there are a lot of things. I mean, so there have been computer viruses since the 80s, right? Right. You know, the morris worm and that kind of stuff. And basically, there are known ways that code, you have to write it in a particular way in order for it to be secure, right? Right. And even then, sometimes people come up with novel ways of making something not secure. And how do you have to write it to make it secure, if it's possible to explain? Well, I mean, there's a big, long list of rules, right?

31:45I mean, one thing you can do is you can use languages that are what they call safer. But still, you have to make sure that any input that you get from the network, you're really, really careful to make sure that it doesn't get to overwrite parts of your program that actually execute things. you have to make sure that it doesn't have the opportunity to be able to write to places on your disk that it shouldn't be able to write to. You have to be able to make sure that it doesn't have access to read data that it shouldn't be able to read, you know, all that kind of stuff. And when those things don't happen, you end up with, you know, so-and-so got hacked, you know, turns out that somebody we think maybe China is reading the email of the, you know, people in Congress.

32:29you get another letter in the mail that says your social security number has been leaked by some credit checking firm or something like that. Even like, I think it was what the big target data breach from a while back was through the HVAC system. It's just, except now we've got, and that was with humans writing the code. Right. Imagine if we didn't know, oh God, it really does feel like the young people are going, like, actually, no, I take it back. You were talking about Agile the other day. I'm going to ask you to explain that in a second. But it's like, it sounds like for almost decades, they've been gnawing away at, management's been gnawing away at the sides of building good software and building good software culture.

33:16Yes. I mean, there's an argument that says that we never got it right in the first place. But I mean, if you think about it, software has been a thing for what, 50 years, 60 years, 70 years, right? I mean, compare that to like construction engineering or bridge building or that kind of stuff, right? We're still, relatively speaking, in our infancy as an industry. It's been a constant evolution, and a lot of times the things that we did to solve a problem that we had ended up causing other problems. So going back to Agile, long ago, we used to manage software projects the same way we managed bridge building and building construction projects.

34:01And it turns out that when you're going to build a bridge, you know beforehand what you need to build a bridge to do. When you're building software, a lot of times people are changing their minds as you go. Right. And you build a thing and you show it to them. They're like, oh, why don't we put this over here and why don't we change this and that kind of thing. Right. Right. Because you don't have the same kind of constraints, physical constraints that you do when you're trying to build a bridge. And so we got in this problem where you would create these project plans about how you were going to build this thing.

34:28and you would never be anywhere close to on time because things would change the whole time. Right. And so they created this thing called the Agile methodology. I'm drastically simplifying. There were steps in the middle, but basically, so this Agile thing is where we, instead of saying, okay, so this is what the whole project's going to look like. We're going to be doing, we're going to be done in six months and then things changing along the way. We basically block off a thing called a sprint. It's a week or two or a month, maybe, it depends. And then, you know, everybody picks their own sprint length.

34:55And then you go, okay, I'm only going to talk about what's going to happen in the next sprint or two, right? And then you get to the end of that two weeks and you go, okay, cool. This is what we got done. What do we want to do next? And then, okay, that's what we got done. What do we want to do next? And that kind of thing. And that way, as you go, you have the opportunity to change things. You have an opportunity to roll changes into the process, that kind of thing, right? The problem with that is kind of the same way that dates always ran out in waterfall land. Projects can go way, way longer than they were expected to at the beginning because everybody's focused on just two weeks at a time and you never kind of take a big step back like you ought to and go, okay, wait, we were supposed to be done two months ago.

35:37When are we going to wrap this up? Right. And how has that led to things getting worse? Is it that just software culture, software development culture has been focused on short-term perpetually? The short-term is part of it. Part of it is there are unscrupulous developers out there that basically want to extend the length of the project so they can get more money out of it. Right. Right? That's always the case. But the other thing is that you end up with a real... A lot of times you end up with a real lack of long-term planning and long-term understanding. Right. Right? Because everybody's... It's the same kind of thing.

36:17Companies are only worried about what happens next quarter, right? If you're only worried about what's going to happen the next week or the next four weeks, the things that you look at tend not to have the longer term implications that sometimes you need, right? And there are times you get close to the end and you're like, oh, we didn't think about this. What are we building? This is going to be a problem. And also if you're in a two or three week thing, you're probably not thinking even what you did last sprint. Maybe last one, but not like two or three ones ago. Is this a problem throughout organizations of all sizes?

36:54Is this a consultancy problem? Is it everywhere? It's most places. There are some places that are usually in startups. We're a lot more ad hoc and we're a lot more focused on trying to get things done. basically the idea is the larger you get as an organization and the more money you're throwing at it and the more management control you want, the more of this overhead you put in place and the more complicated things get just as a management structure kind of thing. So this is something you'd seen like at Google and at Amazon as well? Oh, absolutely. So do you think it has the same organizational effects?

37:44Or?

37:47Largely, yes. So those organizations tend to be, well, those organizations historically have tended to be, before the recent, like, in-shittification wave. Those, I'm assuming I can swear on this. Yeah, yeah, yeah. Fuck it, it's all fine. So those organizations have historically been fairly more engineering driven, which means that you typically have people higher in the organization that are technical and have been programmers and who understand some of the implications. And so they tend to try, at least we try, to run interference with management and to try to make sure everybody's on the same page and that kind of stuff.

38:33a lot of, not all, but a lot of problems can get lessened if you have people in the organization that are at higher level, whose job is not to manage people, but whose job is basically to keep track and coordinate between different groups that are doing different technical things. Right, to make sure people aren't building the same thing, I'm guessing, or are building the right thing in the right way. Yeah, and that what this group is building is going to impact what this group is building at some point in the future and making sure that when you get to the point where those two things need to talk to each other, they're both aware enough of what the other one is doing that the two things hook together correctly.

39:10Yeah. So based on my analyses of these companies, that's definitely gone out the window. I mean, even with LLM integration, so there was a Johnson and Johnson story that went out in Wall Street Journal a couple of weeks ago where it was like they had 890 LLM or generative AI projects, of which the Pareto principle wins again. 10-15 % of them were actually useful. And the thing that stunned me about that, other than the fact it confirmed my biases, which I love, was the fact that there were 890 at the fucking things, and no one was like, should we have this many? That there was no software engineering culture that was like, hey, are we all chasing our tails?

39:51Is this useless? But it sounds like they were all focused on their little boxes. Yeah, I mean, so the other thing, so understand that, again, greatly oversimplifying, a lot of the new stuff that's happened with the large language machines, large language models and generative AI, people didn't expect, right? It was kind of a surprise when you throw a whole bunch more data at a large language model and it started spitting out text in a way that nobody really, there was no mathematical reason to expect it to be able to be as good at generating autocomplete stuff as it was. And so there's this belief that if we did the thing and we unexpectedly got more than we asked for, if we do more of the thing, maybe we'll unexpectedly get more of what we wanted.

40:39That hasn't seemed to really pan out the last couple of years from what I can see. But that we don't really understand enough about this to know whether it's going to work. So we might as well throw spaghetti at the wall and see if it sticks because it might. kind of mentality is kind of pervasive at the moment. And everybody's, there's a lot of FOMO. There's a lot of like, you know, well, our competitors are probably doing this. And so we don't want to get left behind. It's kind of reminds me of the rumors that they talked about back in the eighties when the CIA was doing all this psychic research because supposedly the Russians were doing psychic research and it was all complete crap, but both sides were convinced that the other side was making some progress.

41:20And so everybody was dumping a ton of money into it. LLMMK Ultra exactly yes title of the episode

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44:29So, okay, LMK Ultra aside, is this something you're seeing in software development, though? Because I know I've seen it in management where it's just going to, like, shut the shit in there. This seems like it's an important thing, right? or is this, are you seeing it within software development? So I am seeing it within software planning, right? So when managers are sitting down and saying, okay, we need to build this new thing. We need to create a new group. We need to split this group apart. We need to decide what our headcount's going to be for next year. There's a lot of, okay, and what do we think the AI is going to do next year?

45:02And how many headcount do we think that's going to save us? And that kind of thing, right? There are some companies, Duolingo is one, Klarna is one. BP, the former British Petroleum, what, last year had a thing where they said they were cutting 70 % of their contract software developers. And in most of these, they've kind of rolled them back as well. I don't think Duolingo has yet. No, this is just being unfair to you. They like a day ago. Really? We're just like, ah, we're kind of... It's so funny. It's so funny. It's so funny that this just keeps happening in exactly the same way. It's like, oh, what a surprise.

45:36Human beings can do stuff. Yeah. But it kind of gets back to, I think, what you've said about everything with LLMs. It's like, you can teach something to say, yeah, I think the right thing you're looking for is this, but you can't teach it context. And that's been a point you've made again and again. It seems the job of a software engineer is highly contextual, unless you're in the earlier days. Yeah. And I liken it sometimes to the Memento guy from the Memento movie, right? Where they can't form long-term memories. and do you really want the Memento guy to be the person that's building the software that makes the 737 Max be able to compensate for its control input?

46:14Yeah. The thing is though with that argument they would argue and I know that there is a better argument here they would argue well what if we just give it everything that's ever happened? What if we just show every single thing we've ever done on GitHub? Surely then it would understand. So, what I have seen from the papers that I have read is that LLMs have a basically squishy middle context problem, kind of the way that you do, right? So, if somebody gives you a big document to read or a big long documentary to watch or something, and then they ask you questions, what they're going to find is that you remember a lot more from the beginning of that and the end of that than you do from the middle of that, right?

46:56And LLMs have the same kind of problem, right? And the other problem that the LLMs seem to have is that when you give them a whole bunch of instructions, just instructions, pile on instructions, pile on instructions, they can either get confused and forget some of the instructions, or they deadlock, or they just start going, okay, I can't satisfy all of these, so I'm not even going to bother to satisfy any of them, or they'll pick one or two. the fact that you can take a million tokens and you can stick that in the memory block that the GPU is going to process doesn't necessarily mean that all of the tokens in that memory block are actually going to be treated equally and going to be understood, right?

47:43In theory, maybe if you could train your, if you could like custom train an LLM and modify all of its weights based on exactly what your stuff was and do that like day after day after day after day as things changed, you would theoretically get better. I still don't think it would be, you know, I still don't think it would understand the context as well, but that would be ridiculously expensive. Yeah, and at that point you could train a person. Yes, I mean, the person would probably be more annoying. I mean, the point, I mean, a lot of this seems to be really, you know, we don't like dealing with the prima donna programmer kind of thing, right?

48:34There's this, you know, I mean, not just programmers, right? We also don't want to deal with the prima donna reporters or the prima donna illustrators or the prima donna you know we just want to get rid of these people right these annoying they ask for stuff they want money yeah and days off and sick leave and you know health care and this is disgusting how dare it's it's so it's frustrating as well because it across software development and everything but especially with software developers feels just very insulting because it doesn't seem like this stuff actually here's a better question have you seen much of an improvement with like 01, 03, like these reasoning?

49:15Do you think the reasoning models change things for the better? And if so, how?

49:22So a little. They don't make as many stupid mistakes. It's basically what it boils down to. Going back to your first thing though, right? I mean, so there was a piece, actually a couple of pieces recently. One of them was about, you know, tech workers are just like the rest of us. They're miserable. I'll give you links to these. The other one was a Cory Doctorow piece that was like, the future of Amazon coders is the present of Amazon warehouse workers or vice versa. So there's a lot of, there has been a lot of deference given to software developers over time because, you know, we have been kind of the engine that's made a lot of the last 20, 30 years work.

50:11And there's a desire to make that not so anymore, right? And to make us just as interchangeable as everybody else. I guess from an economic standpoint I kind of don't blame them. I understand why they're trying to do what they're doing I don't think that the warehouse workers should be treated the way the warehouse workers are treated much less everybody else gets treated that way and it's been a lot worse since the giant layoffs at Twitter now X when that happened and the thing didn't crash and completely burn like everybody was, or not everybody, but a lot of people were expecting it to, the sentiment became, well, maybe all these software developers aren't as important as they, you know, we've always thought they were.

51:01And, you know, we will see over time what the end result of that is. My guess is it's going to end up being a mess. But, you know, I'm a software developer, right? I'm going to, it behooves me for it to be a mess, right? So it might just be my bias that's getting in the way. I actually, I think that you're right though, because I remember back in 2021 and onward, the kind of post remote work, the remote work, there was the whole anti-remote work push, but there was this, the whole quiet quitting and things like that in 2022, where it was like software engineering, they're just there, they expect to be treated so well because 2021 saw the insane hiring.

51:41You saw tech companies like parking software workers. I think that played into it as well, where all of these companies who chose to pay these software engineers, they were the ones that made the offers, got pissed off that they'd done so and thought we should cut all labor down to size. And then along comes coding. Almost makes me wonder if most of these venture capitalists talking about this don't know how to code themselves. You gotta wonder. I don't know many that do. Yeah. I know some that have at some point, but... The best thing is at some point, it's like they're not part of modern software development culture, which I know sounds kind of wanky, but I mean, just how an organization builds software feels like something they should know.

52:27But then again, they don't know how to build a real organization either. So who the fuck? Yeah. Well, I mean, honestly, a lot of it, I've been in organizations that VCs basically killed. Right. Because, you know, we built a thing, that thing was, you know, a reasonable business, but VCs don't want a reasonable business. They want either 100x return or they want a tax write-off, and they don't want anything in between, right? Yeah. So, I mean, what they're looking for is really, I mean, they're not trying to run a regular business, right? They're not trying to do the normal process. They're trying to either, you know, hit one out of the park or throw it away and move on.

53:08and so the rules for them are different because what they're trying to accomplish is not what the rest of us are trying to accomplish as a general rule it's a constant theme of the fucking show it's just like you have these people that don't code saying how coders should code like Dario Amadei the other day saying that this year we're going to have the first one person software company with a billion dollars revenue or something like that I feel like there are some people who should not be allowed to speak as much sometimes. But it's just frustrating and insulting. But now that you've got me thinking about it, it does feel like this is an attempt to reset the labor market finally coming for software developers.

53:46And I don't mean finally in a good way. Right. It feels like being in that industry at the moment, it really feels like that. Is it scary right now? Is it scary right now? Not for me because I'm old enough to be semi-retired, right? Right. But I mean, I've been talking to a lot of folks. I've been having a interviewing a bunch of folks that are that are listeners to my channel and kind of trying to get a feel for what's going on. And I've talked to folks that are, you know, like I said, I talked to some folks that were like, you know, I work for a big bank. They're cramming Copilot down our throat, whether they want it or not.

54:26I've talked to some folks that are like, every time I sit down with my boss, I'm thinking that, you know, this is going to be the day that I'm going to find out that my group is getting cut. the way the other three groups in the company is getting cut, there's a lot of artificial productivity requirement increases kind of thing. Like what? Just, you know, we expect more tickets closed per, you know, two-week period than, you know, we've had before because we're giving you this AI now, so you ought to be more productive, that kind of thing. Would a ticket necessarily be something that you just right-toed for?

55:02Or is it more than just that? So generally, it's more than just that. But generally, the ticket, that's kind of the way that we track the work that we do in a lot of organizations, right? And some tickets are like, I'm building a new thing. And those are kind of easier to predict. And some tickets are, this thing isn't behaving right. Go figure out where the bug is. And those are a lot harder to predict. but they have these things. Agile has this thing called a velocity graph where basically you see how many tickets per person get closed over time. And people want to see the slope of that line change because they're giving you AI.

55:39And I'm guessing the people telling you to change that don't know what they're talking about. That seems to be the case. Great. So, I mean, the good news in theory, right? I don't know to what extent this is going to happen, But in theory, if they keep telling people, you know, the slope of that line should be changing because you have AI now. Over time, if we see the slope of that line not changing, right, then theoretically it will be proof that the AI is not providing the return that people expected. Or you're not using it right. Well, yes, there's always that you're not prompting it right.

56:15That is basically what I am. People, one of the many reasons I want you on is like, I want to have people that actually code on to talk about this stuff. Because it's really easy as a layman myself and for others to just be like, oh, but this does replace coding. And it sounds like it really doesn't. Like it can help. It can be like a force multiplier to an extent. But even past the initial steps, it just isn't there. Well, I mean, so the best analogy I've always found to writing code is actually just writing, right? I mean, you can get ChatGBT to spit out a few paragraphs for you, right? But, you know, you end up with, you know, the legal briefs that have the story that's made up or the, you know, just things that aren't connected to reality or stuff that, you know, when people read them, they're like, I mean, you can tell the difference between the AI slop generated, you know, like the stupid, the insert from the Chicago Sun-Times and the Philadelphia Enquirer, you know, about all the things you can do this summer, right, that like made up books and all that kind of, I mean, like, but even the articles that weren't the ones that were making up stuff, you read the, you know, this is what's going to be happening this summer, this is what the weather is going to be like, and you're reading and you're like, there's no like insight here, there's no thought here, There's no, you know, there's nothing in here that I get to the end of this.

57:41I've read the whole thing. I understand the whole thing, but I don't have anything I can walk away with. Right. And AI agents aren't coming along to replace software. You're not scared of Devin? I am not scared of Devin. So, well, actually, I kind of am. I am scared that Devin is going to make a mess of things and then more things are going to get hacked and that's going to end up being worse for everybody on the internet. How would it do that? Yeah. By like we were talking about before, right? So when you write code that isn't secure, right? And you write code that, you know, uses a library that's got an old version of a thing that there's a known bug in it, but you don't bother to check to see if there's a fix for that bug, or you don't use best practices when it comes to writing code and that kind of thing.

58:26Or you don't think about the kinds of maintainability issues that you're going to have. and you do things like you ship out code in an Internet of Things thing, a light bulb, right, or an Internet Wi-Fi router that cannot be patched over the Internet that has a bug in it, right? And now it's like that thing is going to have a bug in it forever and you're going to have to find all the ones on the Earth and turn them off before someone's not going to be able to take them and be able to hack them and use them to attack somebody else from there. I mean, IoT has a huge problem Oh yeah The cheaper ones have the spyware stuff And crypto mining But yeah, the ones that have Really nasty vulnerabilities And they have no way of being updated Once they leave the factory Right, and it's just As long as they're out there, they're going to be a problem Literally for everybody on the internet Jesus Well, what can To wrap us up What can a new engineer Yeah, someone new to software development, what can they learn right now?

59:31You've kind of done a video on this, but I think it's a good place to wrap us up. What can they start learning to actually get ahead, to actually prepare for all of this? That's a really good question. So you can't, these days, you can't really be able to be an engineer. You can't get hired as an engineer without some ability to talk about being able to do prompts and use some kind of AI code editor or that kind of thing. It's just an expectation of the job now. Whether it should be or not, it's a different thing. I mean, like I said before, there are situations where you tell it what you want, and it will type faster than you possibly can.

1:00:10So, you know, that's not necessarily bad. You need to understand that. You need to figure out, well, okay, I'll get back to something else. You need to figure out basically how to test the thing, right? So, how do you make sure that the code that it spits out does what you meant it to do? And what I'm expecting is that we're going to spend more time thinking about testing and thinking more about, you know, trying to find exceptions and that kind of thing than we have in the past because the code that's actually being generated is going to be less likely to be quality than it was in the past. Right.

1:00:42The problem is it has become the case in the programming industry that the things you need to do to get through the interview to get hired have very little resemblance to the things that you actually do on the job that you need to actually do a good job. Right. And so that's a whole different, we could probably have a whole other podcast episode just about the interviewing problem. But the main thing right now, it's, so right now, the whole hiring thing, and this isn't, I don't think, true for just programmers, but it's especially true for programmers, is all bots that customize your resume and write a custom cover letter and then send them over to the, submit the thing to the bot that's screening the resume and screening the cover letter, right?

1:01:28And that, getting it to the point where you can actually talk to a human is a nightmare right now. So the whole hiring system is kind of broken. So the actually getting to the point where you can get hired is a nightmare at the moment. But the thing that you can do is figure out what kinds of things that AI are good at, is good at. And one of the things that AI is pretty good at is things that don't matter as much, right? So like, you know, AI can pick the layout of a site potentially, right? And you could have it pick two or three of them and you can basically do what's called an A-B test and you can randomly assign people to it.

1:02:03You can figure out which one of them performs better and you can throw the rest of them away. And even then at some point, you will probably want the design customized. Yeah, I mean, but... I think there will be a lot of things where people can kind of get something that's kind of good enough to get started. Right. Right. And I think that to some extent, this is going to be kind of a boon for the industry in the longer term, where somebody who can't program right now, but who has some idea of kind of what they want, can do like a vibe coding thing. they can validate that the market that they want to try to attack exists, right?

1:02:42And that people want to use the kind of thing that they built. And then they can bring in somebody to actually build it right. You know what I mean? And those kinds of things wouldn't necessarily have been able to happen in the complete absence of AI. So it's not, I don't think, completely useless. And there are times when, as a developer, there are things that we're not good at, like writing marketing copy and that kind of stuff, that if we're trying to do a project for ourselves, you know, a lot of that stuff, we can just outsource to the AI because it's not the thing that keeps the project from actually breaking and getting hacked and that kind of thing, right?

1:03:15So it's kind of like, there's this concept where you need to keep the things that are part of your competitive advantage in-house and everything else you can kind of outsource to somebody else. The kinds of things you can outsource to somebody else are the kinds of things that you potentially you could throw an AI at because they're not core to you. But even then it's like, Like, it doesn't seem like that's a ton of things right now or will be. Again, it's basically two things. It's things that where the quality of the thing doesn't matter really, right? Which every business has those kinds of things, right?

1:03:47And they're the kinds of things where you can define a metric that you can test the AI against and let it try over and over and over and over and over again until it gets to the point where it's good enough. Yeah. So if your metric is more people click on this button than the button before, then you can have the AI create a whole bunch of different ways to skin that button. And then you can say, okay, so the one that tested best is the one we're going to keep. That's the thing you can throw an AI at, right? Because you've got a well-defined way of checking in. No telling how long it's going to take, but you have a well-defined way of checking to see if it's working right or not.

1:04:25Yeah. Yeah. I mean, for years, I've had the theory that this industry was a$20 to$25 billion total addressable market, pretending to be a trillion dollar one. And everything you're saying really suggests that it's like you're describing things like platform as a service. Things that you use in tandem with very real people and intentional ideas. Yeah, this is, I don't see a world in which this is a, we replace all the humans, you know, the whole like, you know, this is going to displace 80 % of the white collar workers in the world. I just, you know, the only people that are really going to be replaced anytime soon are people that either weren't doing a great job to start with or people whose bosses don't understand what they were doing to the point that the boss thought that what they were doing mattered.

1:05:18And my guess is that there's going to be regret at that point and that at some point they're going to have to bring those people back. Well, Carl, this has been such a wonderful conversation. where can people find you um i am internet of bugs at youtube is probably the easiest place to find me um and then there are links on that that channel to point at other things and you've been listening to me ed zitron you've been listening to better offline thank you everyone for listening and yeah we'll catch you next week thank you for listening to better offline the editor and composer of the Better Offline theme song is Matosowski.

1:05:56You can check out more of his music and audio projects at matosowski.com. M-A-T-T-O-S-O-W-S-K-I.com. You can email me at ez at betteroffline.com or visit betteroffline.com to find more podcast links and of course my newsletter. I also really recommend you go to chat.wheresyoured.at to visit the discord and go to r 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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