We Automated Everything With AI and Tripled Our Headcount

You prompt AI to do something, it blows your mind. You feel inadequate. You feel like, oh my God, this thing's going to take my job. And then it stops working and it looks back at you and says, what should I do next? The further away an agent gets from a human, the less valuable it is. If you just ride the models, you're going to be fine. If you care about leading a really ambitious life, I truly think that this is going to make that more possible for more people. Every is the only subscription you need to stay at the edge of AI. If you care about being on top of the latest models and using the latest tools, you have to subscribe to Every to separate out the signal from the noise. Go to every.to/subscribe today. So, we are here because we're going to flip the script a little bit. I am going to be interviewing Dan about the piece that he published yesterday, May 21st. And we're going to try to understand why he wrote it, what's underneath his reasoning for it. There's going to be some conflict. I'm going to fight with him on it. Let's go. Let's fight. And see, you know, bring in some of my opinions, which are more or less aligned, but trying to understand, does this, is this piece going to reflect the future in 10 years and five years? And who are you again? I'm Brandon. I'm our COO. And that's it. So, the piece is called After Automation. And it comes from this feeling that I have. And there's a video about this and there's a piece, but just for people who have not seen either of those things, it comes from this feeling that I have that at Every, we are, as AI native, as agent native as it gets, you know, if you swing a stick around in our Slack, you're as likely to hit a human as you are an agent. Everyone's using cloud code and codex and all these tools to do their job every day. And yet, it feels like there's more human work to do than ever. And in fact, like since the GPT-3 days, like we've grown from four people to like 30 people and we're hiring more now. And so it came from me looking at that and then looking at the environment and being like, what's going on? Because the whole information environment, if you look at Dario, is out there saying like half of entry level white collar jobs may be wiped out. Even people like Ken Griffin from Citadel is like, you can tell he just had this moment where someone showed him an AI doing like an advanced data or finance question. And he was like, holy shit. Like that's what I would pay PhDs to do for me. And it just did it. And I feel like I'm watching a lot of people who maybe don't have a ton of experience with agents and don't have a ton of experience with the curve of improvement that we've been riding for the last like three and a half years hit it for the first time and then come to all these conclusions about, oh my God, like all work is going away. We're not going to have jobs. And I'm just like sitting here being like, actually, your intuitions when you first see a technology like this are usually very off. And we've seen a lot over and over again over the years that Every is a very good bellwether for where things are going because it's a group of early adopters. We have people doing all sorts of work internally at Every. And if something works here, there's a good bet that it's going to spread to other places, other businesses that are adjacent to ours. And so when I look around at Every, I see so much automation and I also see way more human work. So I was really, the whole piece is saying, here's the current state of work with agents. And then pulling apart that paradox and sort of explaining, why does more automation mean more work? Yeah, when I read the piece, it was, there wasn't like an explicit call to action in it, but I sort of felt this call to action of like, there is actually a massive amount of hope right now in a world that is filled with a lot of doomers. And, and this is why. But I am going to come out of the gate and ask you a devil's advocate question, which is a couple hours before you publish this piece, the CEO of ClickUp came out with this long tweet about why he fired 8,000 people and 3,000 people. I don't, I don't think it was 8,000. It was 20,000. No, I'm just kidding. It was like 3,000. Fired the entire economy. It was like 22% of his workforce. I don't think it was in thousands, but yes, it was a, it was a lot of his workforce. Yeah. So my question to you is, in a business like Every, we're growing super fast. What you wrote makes a lot of sense to me. And what you wrote theoretically makes a ton of sense in that AI is not autonomous right now. It has to be told what to do and then has to be checked. We need to have that, that sandwich that you described in the piece. But in a business that is 8,000 people, 10,000 people, that is mature and has built ways of managing like SOPs for managing their business, does this manifesto and this thesis still hold true? It's a, it's a really good question. There are a couple, there are a couple of different questions here. The first thing I want to do is like lay out the argument. Why does, why does automation make more work? I'm sure many people listening to this also haven't read it. So take a second to explain that in detail. I will do that. So basically the idea is the way that AI works and the way it functions in the workplace is AI makes yesterday's expert competence cheap. And by that, I mean, AI is trained on all of our outputs, all of the code and the writing and the design and decision making and everything that's ever been written. And it makes that available to everyone for very cheap. So you can, so anyone now with a prompt can use yesterday's competence to solve a programming problem, build an app, or write a piece like I did, write a report, or, you know, make a YouTube thumbnail. And the interesting thing is that when you do that, when expert competence is available for cheap, it gets really widely adopted. So everyone starts to do it. Everyone starts to like, you know, we see this internally. Everyone's making pull requests. There's a lot of Holy shit. This is crazy. Yeah. And, and, and like I'm making pull requests and ops people are making pull requests. And, you know, engineers are like writing essays and, you know, there's all this line crossing basically for non-experts to do the thing that experts used to do. And that feels very threatening to experts. And they're like, well, what's my job going to be now? And what's interesting about that is because these tools are trained on outputs, are trained on yesterday's data, the stuff that they do is with a default prompt, with, is it, the stuff that they do with a default prompt all looks kind of similar and is all kind of right for the current situation, but it's like not actually totally right. And so what happens is you sort of like flood the zone with tons of stuff. That's like close, but not quite right. And then you need to basically like, and well, there's a, there's a lot of that at Every too. There's a lot of people doing what seems like great work. And then you go under the hood and you're like, this isn't quite right. Maybe like the expert should do it. Yeah. Yeah, yeah, exactly. Um, me, for example, I. No. I've never witnessed that. All of us. It's all of us. How many PRs have I pushed? Definitely coming from personal experience. I have pushed so many PRs where I'm like, Willie, I literally have no idea if this works, but here you go. And then he's like, shut the fuck up. Well, he's like, he's like, this is a good idea, but I just completely redid it. Yeah, yeah, yeah, yeah. Exactly. So, so that's the kind of thing. That's the kind of thing I'm talking about. It's like, it's kind of right. It's close, but it's like, it's actually not quite right. And you need someone, you need an expert to actually figure it out. But what's, what's interesting is when you flood the zone with all that kind of stuff, it, what used to be expensive because it's it's expert competence is now cheap and now it looks the same. So everything sort of gets devalued. You get this like abundance of stuff. That's like used to be very expensive and looks like expensive work like code and essays and whatever, but it's all kind of similar and all not quite right for the situation. So its value gets a lot lower. It's immediately lower. Um, and then what happens is you actually get more demand for experts to come in and help take that stuff that is being produced by people. And like you have good ideas, for example, but uh, but now there's a lot of demand for an expert to come in and help get that idea across the finish line. So that can, that, that looks usually like experts are in demand for building systems to get the kind of, you know, you could say slop work that isn't, can now be produced by everyone and uh shepherd that into something that's actually useful. Um, so an example would be, you know, we have repo rules and review guidelines and stuff like that. So that before you see a PR before Willie sees a PR, They're not built to work that way. They can push back a little bit, but they don't have this, it's very far from the kind of like playful experimenting, like I just want to do shit because I'm into it, that humans have. And again, we're getting into territory if I'm saying things that humans are different than models. Again, these are things that once you clearly articulate them, models can do, but you have to be comfortable with the fact that there are things that you can do and things that you are that you can't fully articulate. Hey, Dan here. We can all agree that housing is expensive. Whether you're renting or paying a mortgage, it doesn't matter which one you're paying, it stings every month. But Built can make it feel a bit better. Let me explain. Built lets you earn rewards on your rent. And now, you can earn rewards on your mortgage too. Every housing payment earns you points you can use towards flights, towards Lyft rides. The flights you can redeem are with top airline and hotel partners like United and Hyatt. Personally, I'd be redeeming my points for business class travel, but you know, pick your poison. But here's what I think is the most underrated part. 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And maybe there's a world in which they are continually learning and we lose control of them and they start to get access to training data that we don't want them to have access to. But until that time, I think there's probably a good argument that they can't reject what we're saying and therefore can't be autonomous. Autonomy needs to be, I've asked you to analyze this CSV and it says no because this is a better idea than doing that. Yeah. And I would actually, I would substitute, I think a better word, I think agent is very confusing because it implies agency. But agent means something that acts on behalf of someone else. I think they have, I think these are agents that are getting very good at being autonomous in the sense that if I send you out on a task, whatever that task is, that task could be disagree with every single thing I say. It could be, go off and find a new idea. Whatever that task is, they're getting or will be very good at that. But that is very different from having agency, which is what even the smallest child has. And I don't think that there's a lot of, there's not a lot of incentive to build that because, okay, you're sitting down at your computer, you're like, hey, like, let's get to work. And like the agent's like, nah, I'm like, I'm playing, you know? It needs to be able to do that in order to do things that are scary to us. Yeah, yeah, yeah, that's what I think. And there's this, obviously there's a gigantic literature on less wrong and other places about like why it's impossible to prove that they're never gonna do that or whatever. But my counter to that is the evidence, if you look at the development of these things, the evidence is that, and the whole lineage is toward being more compliant. And I think the entire industry is incentivized to do that. And I see no reason to doubt that that's gonna continue to be the case. Yeah, I mean, we'd have to develop something that's like this. It's your definition of AGI, which like is a good question of whether that's actually possible, which maybe you should explain to everyone what AGI means. I think a good definition of AGI is any agent that you never turn off, that it makes economic sense to keep it running all the time. And keep it running all the time in the sense of not like, you know, OpenClause or Victor or whatever, like you can ping it and it will respond to you all the time. It's the server's on. But I mean generating tokens, actively doing tasks for you without you ever turning it off or having to re-prompt it. You can probably, like you can guide it or whatever, but the idea is that it's valuable enough that it can just keep running all the time. Okay, I want one-word answers for the next two questions I'm going to ask. Do you think that will happen? Yes. Do you think that is a good thing? Yes. Explain your reasoning for the second answer. And here's the reasoning for my question. That, to me, seems to be where things start to get a little off the rails, where it makes economic sense for these things to run all the time. Because then I sort of start to think, okay, it's actually valid that the ClickUp guy just fired 20% of his team. Well, yeah. Okay, we should definitely go back to the ClickUp guy. Let's go back to ClickUp guy. What's his name? I don't know. I've been ClickUp guy. ClickUp guy. But before we get there, the thing that is important to not fall into when you project out like this is everybody will have access to this, for one. For another, the rate of change, even when crazy new technology is available, is actually a lot slower than you would expect. So as part of this piece, I didn't end up covering it because I think it requires a lot more space and it was already 8,000 words and I was like not sleeping anyway. So I was like, I'm going to cut this. But as part of this, I wanted to say, I wanted to see, how does this work? I know how it works in expert knowledge work, like fast-moving stuff. I know how it works. We have customer service, so I know how it works if you're a customer service manager type person. But how does AI actually affect your job if you're a customer service person in Omaha or whatever and you work in a call center? Because those are the most at-risk employees. That would be the default example to bring up. So I was like, I'm going to just see what that's like. And so I just had codex and cloud code scrape all of Reddit and like lots of places where customer service reps post. And obviously, a lot of them don't like AI, which makes sense. But there's some really interesting stories there about companies that jumped on the AI bandwagon. They're like, we're automating everything. They fire a bunch of their customer service people. And then two months later, they're like, oops, sorry. Like, can you come back? And one reason for that is if you implement AI poorly, you're going to have poor results. And I think a lot of these companies don't really understand what they're doing and they just like are paying lip service to the new hype and they think the CEO thinks that they can like cut a bunch of expenses and then it just doesn't really work very well. Some of those people haven't actually played with it. Yeah, exactly. Yeah. But another reason, which I think is really interesting and is very important, is a lot of people who call in to customer service centers do not want to talk to a machine. Do not. And are very explicitly trying to figure out, are you a machine or not, and get to a fucking human. And that is a real break on how fast these kinds of things can be adopted. And that's only one example. There's like, the world is very complicated. There's like billions and billions of examples for any kind of job. And so I think it's really important, even if we're hypothesizing this like this thing that's always on that can do stuff, one, we have to hypothesize everyone has access to it because that is the direction that it's going. And two, we should recognize that even if that happens, it will take a long time for it to become something that everybody is comfortable with and everyone uses. And it will take probably a generation for it to really turn into a thing. Definitely. There's also a good argument that like, working at a call center is not a job that anybody wants. It's a job that you have to do because you need a job. And in a world where this technology exists, like, yes, we'll have to figure out a way that like everybody can live a fulfilling life and eat. But it might actually be nice to not have that job, assuming you're taking care of another way. I think that obviously the transition is a big deal and these are like real people with real lives. And some actually do love it, but also, yes, like in general, being yelled at in a call center is like not the best job. But I think that where I'm going is even if we hypothesize that, humans still have to decide what matters and what matters changes all the time. And it changes all the time in particular because AI is an input to that. So it is both, how do I even say this? It's very recursive. AI is changing the world really fast, which changes what matters, which puts more onus on us to like update and decide what matters because AI is going to wait for us to be like, what matters? You know? Totally. And that is going to be part of every job because anything that you decide, anything that you can frame and be like Again, and this is back to why humans are valuable, but that's not the only reason why humans are valuable. We're valuable intrinsically. But one of the reasons why they're valuable for work is, I would guess, looking back at that article and looking and thinking about a lot of this stuff is, there's a really high drop-off rate. There's a really high, what's that word? There's a really high depreciation of the value of data. Once it's out there, it's very likely to go stale within weeks. There's some things that maybe not, but, yeah. It's safe to say that all of these companies are at a place where they are just hunting for net new unique data. Yeah, I think so. And so anyway, broad, we should expect broad reorganizations of companies, and we should expect companies that are not doing well to lay people off or reorganize and then blame AI. And I would really be skeptical of anyone who's saying that it's going to eliminate all jobs or all in the knowledge work. And I think it will certainly change them, and I think it is certainly, it's like a big thing that people have to take seriously, but my big takeaway, and this is not like fully in the piece, but it is what I really believe, is if you just ride the models, if you just, when new models come out, learn to use them for the stuff that you do, whatever that is, you're going to be fine. And you may even, hopefully, find that you can do more and better work that's more fulfilling for you than you could before. I think that there's still a place in the world. If you don't want to use the models at all, I think that that's still going to be a thing. Plenty of people don't, you know, I don't know, plenty of people don't eat fast food or whatever. I don't know what to compare it to. It's totally possible not to participate in this. However, if you care about like leading a really ambitious life and building businesses or whatever it is, I truly think that this is going to make that more possible for more people. And as long as you ride the models, you're going to be good. I think that's a very good call to action. I want to end by asking you something about what it takes to write a piece like this. So a lot of Celsius. A lot of Celsius. So when we started, I don't know if it'll make this, if this will make it into the podcast, but when we started, Dan was sort of like looking like this. He was hugging himself, protecting himself, some would say. It has been a very stressful week. This is an 8,000 word piece. Yeah. Most people are not writers. Can you share what it's like to not just write an 8,000 word piece, which is a very big piece, but like, what does it take to think through these arguments? It's so interesting because it's very natural to me because I wrote once a week. I published something once a week for so long that especially like a, you know, 500 word or a thousand word piece. Like I can just bang that in like an hour or two. These things get much harder the longer they go because there's all these interdependencies. So if you change something here, it changes four other things over here and whatever. So 8,000 words becomes like, it's like 10 times harder than 4,000 words, which is 10 times harder than 400. I found that, and I always have this feeling that there's this underlying thing that I can feel, but I can't quite say that I'm trying to say. And it started actually, if you remember, we did our, I guess it was Q2 planning. And I was like, I think that we can, I think I figured out why. This is after I did proof. I think I figured out why we're just going to always have jobs with AI. And like, if you just ride the models, you're going to be fine. Like, I think I, I think I can feel that. And then it was just this process to be like, okay, how does that actually cash out? Like, why do I think that? Because it's all kind of in there, but it's all tangled up. And I wrote like, probably four or five versions where I would start it and I was like making the argument and I was like, that doesn't work. And then I would be like, oh, but how about this? And I would like, throw it out and like start again. And it was, it was actually very, it was a very frustrating process because You're trying, what I'm trying to do is start with the ground truth of, here's what we see every day. Here's what, here's how work happens for us. And then move into this, well, like philosophical thing that, like, it can't actually be articulated. I'm trying to articulate something that can't be articulated. Yeah. Or is constantly moving target. Yeah. And so that's, that's just like, that's very hard. I love that kind of shit, but it's also very, very hard and, and can be very frustrating, but AI was like a huge part of this. Like I could not have written this without it. For example, one of the things I loved that I started to do is, you know, for a piece like this, you're trying to articulate it. You can't quite articulate it. And the only way to do that, the only way to do it is to articulate it over and over and over again until it works. And you've really got to keep it in your head, especially if you're doing lots of other stuff. So what I would do in the morning is I would like fresh right when I wake up or, you know, right when I get to my desk, I would be like, I would just monologue into my computer into a proof document. Here's what the piece is about front to back. Here's the argument front to back. And then I would have a log of that. And every time I would do it, I'd be like, okay, Claude or codex, like, and I actually use Claude more for this. I think Claude is better for this kind of thinking. What am I really trying to say? Like, help me figure out what I'm trying to say. And it would say things back and I'd be like, no, no, that's like, that's what I'm trying to say. And then over time you kind of build up this record of here's what it was here. Here's what it was here. And you're just, I'm just getting closer and closer and closer. And then what I would do is as I was getting deeper into it and I was like, you know, I have 4,000 words and 5,000 words every morning. I would have codex take the latest draft and turn it into a podcast of just like someone reading, reading it to me. And then on my way to work, I would on my way to work, I would listen to the podcast. And as I'm listening, I'm like, okay, that there's a thing that needs to change there. There's a thing that needs to change there. Oh, and then it would get to the end. I'd be like, okay, here's the thing that I need to do next. And that was a really good way to kind of keep the continuity of what am I, what am I doing? What am I writing? Where are the problems in a way where I'm not always reading. Like it's really nice to be able to like be on a walk and be listening to it and thinking about it, which was a couple of would be completely impossible otherwise. Yeah. All right. One more challenge for you. And we're going to have beers and put together in the backyard. Can you articulate to everybody in one sentence that starts with, if you ride the models, then what this piece is trying to say. If you ride the models, you're going to be okay. You're going to have a job. You're going to do great work and you don't have to worry. Cheers. Cheers. Okay. All right. Good stuff, man. Good stuff. That was fun. Oh my gosh, folks, you absolutely, positively have to smash that like button and subscribe to AI and I. Why? Because this show is the epitome of awesomeness. It's like finding a treasure chest in your backyard, but instead of gold, it's filled with pure, unadulterated knowledge bombs about ChatGPT. Every episode is a roller coaster of emotions, insights, and laughter that will leave you on the edge of your seat, craving for more. It's not just a show. It's a journey into the future with Dan shipper as the captain of the spaceship. So do yourself a favor, hit like, smash subscribe, and strap in for the ride of your life. And now without any further ado, let me just say, Dan, I'm absolutely hopelessly in love with you.