We Automated Everything With AI and Tripled Our Headcount

Dan Shipper runs one of the most AI-native companies today. Every has agents embedded in nearly every workflow—“if you swing a stick in our Slack, you're as likely to hit a human as an agent,” he says. And yet the company has grown from four people to 30 since GPT-3 came out, and is still hiring. Why does Dan believe there's more human work to do than ever? In a format flip for AI & I, Every's COO Brandon Gell turns the tables and interviews Dan about his latest essay, “After Automation”—an 8,000-word argument for why rising automation doesn't eliminate demand for human work, it increases it. The thesis: AI makes yesterday's expert competence cheap and widely available, which floods every field with output that's close but not quite right—and that creates more demand for the humans who can take it the rest of the way. Dan talked with Brandon about the paradox at the heart of agent-native work: The more AI can do, the more humans are needed to direct it, refine its output, and decide what matters next. If you found this episode interesting, please like, subscribe, comment, and share! To hear more from Dan Shipper: Subscribe to Every: https://every.to/subscribe Follow him on X: https://twitter.com/danshipper Links to resources mentioned in the episode: “After Automation” by Dan Shipper: https://every.to/chain-of-thought/after-automation Brandon Gell on Every: https://every.to/@brandon_5263 Join the membership for where you live at ⁠joinbilt.com/dan⁠ Timestamps: 00:00:51 Introduction 00:05:51 The AI paradox: more automation, more human work 00:10:00 How AI makes yesterday's expert competence cheap 00:18:00 AI can act autonomously but it does not have agency 00:20:39 Why Dan is all in on AGI 00:21:57 AI layoffs are a lie 00:25:42 Ride the models and you'll be fine 00:35:30 How to use AI as a long-form features editor

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

• 0:00Speaker 00

  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?

• 0:11Dan Shipper?

  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.

• 0:37Dan Shipper?

  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.

• 0:49Speaker 00

  So we are here because we're going to flip the script a little bit. I am going to be interviewing Dan.

• 0:55Dan Shipper?

  Sick.

• 0:56Speaker 00

  About the piece that he published yesterday, May 21st.

• 1:01Speaker 00

  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, I'm going to fight with him on it.

• 1:08Dan Shipper?

  Let's go. Let's fight.

• 1:09Speaker 00

  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?

• 1:21Dan Shipper?

  And who are you again?

• 1:23Speaker 00

  I'm Brandon. I'm our COO.

• 1:27Speaker 00

  And that's it.

• 1:29Dan Shipper?

  So the piece is called "After Automation."

• 1:33Dan Shipper?

  And it comes from this feeling that I have. And there's a video about this and there's, 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, as it gets. You know, if you swing a stick around in our Slack, you're, you're as likely to hit a human as you are an agent. Everyone's using Claude Code and Codex and all these tools to do their job every day.

• 2:05Dan Shipper?

  And yet, it feels like there's more human work to do than ever. And in fact, like since the GPT three days, like we've grown from four people to like thirty people and we're hiring more now. And so it 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, uh, Dario is out there saying like half of uh entry level white collar jobs may be wiped out. Even even people like um like Ken Griffin from Citadel is like, you can tell he just had this

• 2:41Dan Shipper?

  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

• 2:55Dan Shipper?

  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, 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 w- 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 it's a group of early adopters. We have people in doing all sorts of work internally at every. And if something works here,

• 3:39Dan Shipper?

  it there's a good bet that it's going to spread to other other places, other other businesses that are that are adjacent to ours. And so when I look around at every, I I see so much automation and I also see way more human work. So I was really um the 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 why does more automation mean more work?

• 4:13Speaker 00

  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,

• 4:23Speaker 00

  there is actually a massive amount of hope right now in a world that is filled with a lot of doomers.

• 4:32Speaker 00

  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 of 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.

• 4:54Dan Shipper?

  What, I don't, I don't think it was 8,000.

• 4:56Speaker 00

  It was 20,000.

• 4:59Speaker 00

  I think it was like 3,000. It was like 22% of, of his workforce.

• 5:04Dan Shipper?

  I don't think it was in thousands, but yes, it was, it was a, it was a lot of his workforce.

• 5:07Speaker 00

  Yeah. So my question to you is,

• 5:13Speaker 00

  in a business like every, we're growing super fast.

• 5:18Speaker 00

  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 it has to be checked. We need to have that, that sandwich that you described in, 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?

• 5:50Dan Shipper?

  It's a, it's a really good question. Are there 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?

• 6:00Speaker 00

  I'm sure many people listening to this also haven't read it. So, take a second to explain that in detail.

• 6:06Dan Shipper?

  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, write a piece like I did, write a report or,

• 6:51Dan Shipper?

  or, you know, make a YouTube thumbnail. And the interesting thing is that when you do that,

• 6:59Dan Shipper?

  when, 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 internal, everyone's making poor requests.

• 7:08Speaker 00

  There's a lot of holy shit. This is crazy.

• 7:10Dan Shipper?

  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, with a default prompt with, is it, the stuff that they do with a default prompt,

• 7:50Dan Shipper?

  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.

• 8:06Dan Shipper?

  And then you need to basically, and, and well,

• 8:09Speaker 00

  There's, there's a lot of that in every, there's a lot, there's a lot of people doing what seems like great work. And then you go under the hut, under the hood and you're like, this isn't quite right. Maybe like the expert should do it.

• 8:19Dan Shipper?

  Yeah. Yeah. Yeah. Exactly. Me, for example.

• 8:24Speaker 00

  No.

• 8:26Speaker 00

  I've never witnessed that. All of us, it's all of us. How many PRs have I pushed?

• 8:31Dan Shipper?

  Coming from personal experience.

• 8:33Speaker 00

  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,

• 8:37Dan Shipper?

  Shut the fuck up.

• 8:38Speaker 00

  Well, he's like, he's like, this is a good idea.

• 8:40Dan Shipper?

  Yeah.

• 8:40Speaker 00

  But I just completely redid it.

• 8:42Dan Shipper?

  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. 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 can sort of gets devalued. You get this like abundance of stuff that's like, uh, 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.

• 9:17Dan Shipper?

  And, 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 is, can now be produced by everyone and, uh, shepherd that into something that's actually useful. So an example would be, you know, we have repo rules and, and review guidelines and stuff like that.

• 9:59Dan Shipper?

  So that before you see a PR, before Willie sees a PR, hopefully it's, it's gone through a bunch of processes to make sure it's actually reasonably good. We have the same thing on the editorial side. So building systems for that. And then there's also a lot of demand for experts to use these tools now that the floor is a lot higher. Use these tools to make stuff that could never have been made before. And we do that all the time. Like we have Kieran, who just built, uh, an entire inbox end to end in like a month or two. And that's like completely, that's totally, completely impossible. And So there's this really interesting thing that happens that even as you automate, the automation produces a glut of work that's all okay, that's all reasonably good. That work is all very, very similar and not quite a fit for the actual situation.

• 10:43Dan Shipper?

  And that increases the demand for experts who can make it actually good, make it actually different, make it actually appropriate for the live situation as it is right now. And I think that's something that people don't quite understand, especially when they first encounter a language model or an agent that can do something and they see it and they're like, holy shit, it just does everything. And the reality is it's incredibly good. It's amazing. It totally changes how we do work. And our experience so far, whatever it is, the further away an agent gets from a human, the less valuable it is. And the human connection with an agent to actually do the work is the most important thing for making it work well.

• 11:28Speaker 00

  Experts are more important than ever because they lay the groundwork for an agent to do amazing work.

• 11:35Dan Shipper?

  Yeah.

• 11:35Speaker 00

  And only then can you have the other humans actually take that agent and do work that levels them up.

• 11:43Dan Shipper?

  Yeah.

• 11:43Speaker 00

  So, you know, there was a, there was a point where we were thinking about this piece. Dan was drafting this piece where the title was "The Tide Is Rising." And that was trying to emote this idea that like, the tide is rising. We are all able to do more work, better work, but our eyes, whether you're an expert or not an expert in something, are always a little bit above where that waterline is. And I really liked the end of the piece where you describe.

• 12:22Speaker 00

  Oh, fuck Achilles. Is it Achilles? Achilles, Achilles sprinting ahead of the tortoise. Which, you know, according to Zeno's paradox, like, that shouldn't happen. But in this world, it actually does. You know, you, you, you prompt AI to do something. It blows your mind. It does that. You feel inadequate. You feel like, oh, my God. This thing is going to take my job. And then it stops working and it looks back at you and says, what should I do next? And I think that is, until we have figured out AGI.

• 12:55Speaker 00

  And maybe even after that, probably after that, for a very, very long time.

• 13:01Speaker 00

  It will always be looking back at us and asking us for direction.

• 13:04Dan Shipper?

  That's, that's basically the core of the argument, because I think you can, you can say, oh, yeah, oh, yeah, Dan, like,

• 13:11Dan Shipper?

  it is, it's maybe true now that it increases demand for experts, but like, this stuff's going to get good enough that it won't. Let's just look at the benchmarks. And there's a whole, like, long section in the piece about, okay, if you actually do look at the benchmarks, they are improving exponentially. But also, when you look at them closely, once you saturate a benchmark, it's very easy to unsaturate it. It's very easy to find a new frame for, for a model to do a particular type of problem that

• 13:39Dan Shipper?

  is slightly larger, slightly, slightly broader, that zeros it out. So while it is making exponential progress, there's, it doesn't mean that it is equivalent to human capabilities. It's, it's actually a very hard problem. And one of the reasons it's so hard is

• 13:58Dan Shipper?

  anything that you say about what you can do differently than the model is going to be wrong. Because once it's articulated, once it's specified, a model can hill climb on it. A model is going to get better at it. And we make this, like, weird subtle mistake that we identify a set of tasks and we're like, this is all that humans can do. This is what humans can do that models can't do. And then models just do it better. And then you're like, oh, my God, like, what do I do? And the mistake is there's actually a lot of stuff that you do that can't be articulated, that can't be articulated in a clean frame. And, and so every time you try, you just sort of, you get, you get like panicked and confused. And if you sort of step back,

• 14:43Dan Shipper?

  The the the fundamental thing that you know, keeps keeps the keeps the separation between humans and agents is we are building agents to do things that we want them to do. There no matter how powerful they get, the all of the economic and psychological and

• 15:06Dan Shipper?

  and otherwise and technological forces are pushing the the progress of AI toward a place where no matter what it does, it's looking back at you to decide what is what you want to do, what is valuable. And even even after we get to AGI, like theoretically, AGI is going to do that, too. If if we thought it wasn't going to do that, we wouldn't build it. And that that keeps the sort of like gap between humans and AI. And I think a good a good example of this is

• 15:41Dan Shipper?

  the difference between something that can do a task really well and something that just has its own self motivated stuff that it wants to do. Like, you have a you have a kid.

• 15:54Dan Shipper?

  Like, you can, like, Codex can, I don't know, Codex can write a report much better than Isaiah can, but like Isaiah has very strong wants and needs. And you can try to get him to do what you want. And it's going to work sometimes, but also like he's just this self generating process that like does stuff that he wants to do. And if you've ever used any of these tools, like you know that there's a very

• 16:23Dan Shipper?

  they're not built to work that way. Yeah. 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 I'm into it that that humans have. And again, we're getting into territory of I'm saying things that humans are humans are different than models. Like, again, it's it's 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.

• 17:09Dan Shipper?

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• 17:54Dan Shipper?

  Make sure to use that URL so they know that we sent you. And now, back to the episode.

• 17:59Speaker 00

  It is also inside of that play and that rejection.

• 18:04Dan Shipper?

  Yeah.

• 18:05Speaker 00

  Where you have autonomy.

• 18:09Dan Shipper?

  Yeah.

• 18:09Speaker 00

  And it will be a very scary moment when these models can do that. And I think there's a question of can they even do that? Because they rely on training data and like that needs to be in the training data. 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

• 18:43Speaker 00

  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.

• 18:54Dan Shipper?

  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 I go off and find a new idea, whatever that task is, they're getting or will be very good at that.

• 19:26Dan Shipper?

  And but that is very different from having agency, which is what even the smallest child has.

• 19:35Speaker 00

  Yeah.

• 19:36Dan Shipper?

  And yeah, 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, like.

• 19:53Speaker 00

  It needs to be able to do that in order to do things that are scary to us.

• 19:58Dan Shipper?

  Yeah, yeah, yeah, that's, that's what I, that's what I think. And there is, obviously, there's a gigantic literature on less wrong in other places about like, why it's impossible to prove that they're never going to 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, and I see no reason to doubt that that's going to continue to be the case.

• 20:27Speaker 00

  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, what AGI.

• 20:39Dan Shipper?

  I think a good, I think a good definition of AGI is a, 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.

• 20:52Dan Shipper?

  Not like, you know, open clause or Victor or whatever, like you can ping it and it will respond to you all the time. It's the servers 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.

• 21:12Dan Shipper?

  The, the idea is it's, it's, it's valuable enough that it can just keep running all the time.

• 21:19Speaker 00

  Okay, I want one word answers for the next two questions.

• 21:24Speaker 00

  Do you think that will happen?

• 21:26Dan Shipper?

  Yes.

• 21:27Speaker 00

  Do you think that is a good thing?

• 21:28Dan Shipper?

  Yes.

• 21:29Speaker 00

  Explain your reasoning for the second.

• 21:33Dan Shipper?

  Um.

• 21:33Speaker 00

  Answer.

• 21:37Speaker 00

  And here's the reason 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.

• 21:58Dan Shipper?

  Yeah, okay, we should definitely go back to the ClickUp guy.

• 22:01Speaker 00

  Let's go back to ClickUp guy. What's his name?

• 22:03Dan Shipper?

  I don't know. I think ClickUp guy is.

• 22:05Speaker 00

  ClickUp guy.

• 22:08Dan Shipper?

  But before we get there, like, the thing that is important to not fall into when you project out like this is

• 22:19Dan Shipper?

  everybody will have access to this.

• 22:24Dan Shipper?

  For one. For another, the rate of change, even when like crazy new technology is available, is actually a lot slower than you would expect. So as part of this, 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 gonna cut this. But as part of this, I wanted to say, I wanted to see like, how, like, how does this work? I know how it works in like, you know, 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 like a customer service manager type person. But like, 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?

• 23:06Dan Shipper?

  Because those are like the most like at-risk employees. That would be the default example to bring up. So I was like, I want to just see what that's like. And so I just had Codex and Claude Code scrape like 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 they jump on the AI bandwagon. They're like, we're automating everything. They fire a bunch of their customer service people. And then like, two months later, they're like, oops, sorry. Like, can you come back? And one reason for that is.

• 23:47Dan Shipper?

  If you implement AI poorly, you're going to have poor results. And I think a lot of these companies like don't really understand what they're doing. And they just like are paying lip service to like the new hype. And they think the CEO thinks that they can like cut a, cut a bunch of expenses. And then it just doesn't really work very well.

• 24:01Speaker 00

  A lot of those people haven't actually played with it.

• 24:03Dan Shipper?

  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, I think it's really important, even if we're hypothesizing this, like this thing that can, that's always on, that can do stuff.

• 24:41Dan Shipper?

  One, we have to hypothesize everyone has access to it because that is the direction that it's going. And two, we should, we should recognize that even if that happens, the, the, 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 like really turn into a thing.

• 25:00Speaker 00

  Definitely.

• 25:02Speaker 00

  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, and, and eat.

• 25:21Speaker 00

  But it might actually be nice to not have that job, assuming you're taking care of another way.

• 25:25Dan Shipper?

  I think, I think that the, obviously the transition is, is a big deal. And these are like real people with real lives and, and some, some actually do love it. But also, yes, like in general, being in a, being yelled at in a call center is like not, not the best job. But I think that where I'm going is even if we hypothesize that.

• 25:45Dan Shipper?

  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.

• 26:01Dan Shipper?

  Uh, how do I even say this? It's very, it's very recursive. AI is changing the world really, really fast, which changes what's what matters, which, 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, what matters, you know?

• 26:17Speaker 00

  Totally.

• 26:18Dan Shipper?

  Um, and, and that is going to be part of every job. Uh, because anything that you decide, anything that you can frame and be like, this is a repetitive thing that is like working. You can just have your, have your, have your AI do it. But the minute the situation changes and situations change all the time. And they especially change all the time when it's not just humans changing, it's AI. Um, you, you're going to need humans to, to decide that. And, um,

• 26:44Dan Shipper?

  and I think that that's,

• 26:47Dan Shipper?

  I think that's something that's very missing from, from,

• 26:51Dan Shipper?

  you know, what we talk about when we hypothesize these things.

• 26:55Dan Shipper?

  Back to the click up guy.

• 26:56Speaker 00

  Click up guy.

• 26:57Dan Shipper?

  So,

• 26:59Dan Shipper?

  I don't know. He fired 30,000 people.

• 27:03Speaker 00

  I think it was more.

• 27:05Dan Shipper?

  And I think it's really important whenever you're looking at some of this shit on Twitter. First of all, I fuck, I hate. I hate when they're like, our business is better than it's ever been and we laid off 8,000 people.

• 27:16Speaker 00

  Yeah, it's pretty fucked. It's like, it's, well, it's so bad. Like, yeah.

• 27:20Dan Shipper?

  And why would you say, why would you brag about that?

• 27:22Speaker 00

  The other thing that I don't like is we're, and we're going to pay people a million dollars if they do great work. It's sort of like, okay, but you still have all these people that no longer have jobs. I don't, I just really don't think it's very tastefully done. And I think Jensen, he, he said something that was like very self-serving, which was basically like, if your answer to progress is firing people, you're not a very creative CEO. Very self-serving because obviously he wants people to use more AI. But I think it's true.

• 27:49Dan Shipper?

  It's fairly, yeah.

• 27:50Speaker 00

  Like, you should be doing more interesting things, not firing. You know, people want to be profitable, I guess, but idiots.

• 28:01Speaker 00

  Even though he's like, even though he's so overrated.

• 28:06Speaker 00

  But yeah, just, it's, it's, anyway, that's an aside. Just is not very tastefully done. Anyway.

• 28:11Dan Shipper?

  I, I, I, so, so a not tasteful, which should make you a little bit suspicious. And my, my guess is, and just seeing some like, some of the random stuff is, I don't think the company is doing that well.

• 28:25Speaker 00

  I mean, it's a generic SaaS company. There's like, yeah.

• 28:28Dan Shipper?

  Yes. And when companies don't do well, they lay people off. Um, and Meta.

• 28:35Speaker 00

  Or when companies are managed poorly and have too much bloat anyway.

• 28:37Dan Shipper?

  Exactly, which is, you know, correlated with not doing well.

• 28:41Speaker 00

  That was where, yeah, like that, just Jack Dorsey just can, he just does that.

• 28:45Dan Shipper?

  Like, and I think Meta is the same, like they're making all these, they're making gigantic investments in AI. Because that's like the new hot shit that they like, kind of missed, they kind of missed the boat on and.

• 28:56Speaker 00

  And no surprise Metaverse didn't work. So now I have a lot of people getting fired.

• 28:59Dan Shipper?

  Yes. So I think, yes, AI is involved in, in, in all of this stuff, but it is. It's not like this clear thing of. Everyone's doing all the same jobs as before, but they're all just like, they're all just agents. It's actually, no, no, the company actually has to like. Totally change strategies and the, the people it needs and the structure it needs is just totally different. And that's not the like clean narrative that I think people like to tell, and it's much easier to talk about just AI takes jobs. And. It seems, it seems definitely true that. Using these tools changes your workflow a lot. And because it changes your workflow, it changes what's hard and what's easy.

• 29:39Dan Shipper?

  Um, there, uh, especially if you're a big company and you've been structured in a certain way to work in a certain way, there's. There's going to be like reorganizations of how work happens and how companies are structured. That seems like. Really clear and it's very important that we figure out a way to. Make that transition as good as possible for people and. And tweeting about how well you're doing it while you're firing people is not that. Um, I think there's a lot of really interesting, like creative ways to, you know, Meta, for example, is now. Key logging everyone's, um. Everyone's, uh, all the stuff they enter into their computer because they're like, well, our people are the smartest people. So.

• 30:21Dan Shipper?

  We'll just use their data to train our models and our models will be smarter, which is an interesting take and maybe it'll work, but. There's this, I think there's this really interesting, I wrote about, I wrote about this like two years ago or something like that. There's this really interesting effect of that, which is.

• 30:37Dan Shipper?

  When you sign an employment contract, the way that we thought about employment for a very long time was I'm gonna do this job. And you're gonna need me to keep doing it in order for it to keep getting done.

• 30:54Dan Shipper?

  But once you reach a point where

• 30:57Dan Shipper?

  I do the job for you and then it just works. That sort of and then you can just you don't have to pay me anymore. That sort of changes the whole way that we think about employment. And therefore, I think it should, for example, change how we think about paying certain types of people.

• 31:16Speaker 00

  You should get a pension, you know?

• 31:17Dan Shipper?

  Pension, maybe, maybe pensions are back.

• 31:20Speaker 00

  Pensions are back, baby.

• 31:22Dan Shipper?

  Well, one thing that's really interesting is there's this thing that launched last week that we're part of. The the name is escaping me, but it allows publishers to get paid based on.

• 31:33Dan Shipper?

  Basically, it measures a publisher's unique contribution to the training corpus. And you get paid based on that. So the more generic your shit is, the less the less it's you get paid. And the then the more unique and and valuable it is, the more you get paid, which is really interesting.

• 31:48Speaker 00

  The the ironic thing about that is basically is like, did you this will be the case. Did you use AI, which is trained off of all the shit that already exists. And so it still can make some things that are new. But like, it's basically, you know, it's it's.

• 32:01Dan Shipper?

  How much how much just like generic default prompting did you do to make this versus just like actually?

• 32:06Speaker 00

  Did you even actually think about this?

• 32:07Dan Shipper?

  Exactly.

• 32:08Speaker 00

  Yeah.

• 32:08Dan Shipper?

  So but I think there's there could be something similar for I had this idea. I read this post a couple of years ago about the last job you'll ever had, you'll ever have where it's an agency. You generate all the training data in the work that you do for the agency. And then it tracks basically, what is your contribution? And then you just get paid out forever from how much revenue your data generates.

• 32:33Speaker 00

  Web three is back now.

• 32:36Speaker 00

  We're gonna track it all on the ledger.

• 32:38Dan Shipper?

  Blockchain. Yeah, anyway, who knows? The the problem with that, again, and this is this is back to why humans are valuable. But they're not. That's not the only reason why humans are valuable. We're valuable intrinsically, but. Um. One of the reasons why they're valuable for work is I I would guess, looking back at that article and looking and thinking about a lot of the stuff is there's a really high uh drop off rate. There's a really high, what's the, what's that word? Uh, there's really high depreciation of the value of data. Once it's, once it's out there, it's like very likely to go stale within like weeks. Um, there's some things that maybe not, but uh

• 33:21Speaker 00

  It's safe to say that all of these companies are at a place where they are just hunting for net new, unique data.

• 33:27Dan Shipper?

  Uh, yeah, I, I think, I think so. And um, so, so anyway, broad, we can, we, 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. Um, and I would, I would, I would really be skeptical of anyone who's saying that it's gonna eliminate all jobs or all knowledge work and um, and I think it will certainly change, change them. And I think it is uh, certainly, uh, it's like a big, it's a big thing that people have to take seriously, but my, my big takeaway, and, and this is not like fully in the piece, but it is what I really believe is if you just ride the models,

• 34:10Dan Shipper?

  if you just, when new models come out, learn to use them for the stuff that you do, whatever that is, you're gonna be fine. Um, and, and you may even hopefully find that you can do more and better work that's more fulfilling for you than you could before. Um, I think that there's still a place in the world, if you're, if you don't wanna, if you don't wanna use the models at all, I think that that's still gonna be a thing. Um, plenty of people don't, you know, I don't know, plenty of people don't eat fast food or whatever. I don't, I don't know what to compare it to. It's, it's totally possible not to participate in this. However, if you care about like leading a really ambitious life and, you know, building businesses or, or whatever it is, I, I truly think that this uh is going to

• 34:56Dan Shipper?

  make that more possible for more people. And as long as you ride the models, you're gonna be good.

• 35:04Speaker 00

  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.

• 35:13Dan Shipper?

  A lot of Celsius.

• 35:14Speaker 00

  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.

• 35:29Dan Shipper?

  Yeah.

• 35:31Speaker 00

  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

• 35:42Speaker 00

  like, what does it take to think through these arguments?

• 35:45Dan Shipper?

  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 1,000 word piece, like I can just bang that in like an hour or two. These things are get, 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, 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.

• 36:20Dan Shipper?

  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.

• 36:31Dan Shipper?

  And

• 36:33Dan Shipper?

  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? Cause it's all kind of in there, but it's, 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.

• 37:16Dan Shipper?

  And it was, it was actually very, it was a very frustrating process because you're try, I what, 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 to move and. 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 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, um,

• 37:57Dan Shipper?

  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,

• 38:15Dan Shipper?

  like fresh, right? When I wake up or, you know, right? When I get my, 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 from 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.

• 38:37Dan Shipper?

  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 would 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 good. 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,

• 39:13Dan Shipper?

  on my way to work, I would listen to the podcast. And as I'm listening, I'm like, okay, that there's something needs to change there. There's something that needs to change there. Oh, and then it would get to the end. 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 completely would be completely impossible otherwise. Yeah.

• 39:44Speaker 00

  All right, one more challenge for you, and we're gonna have beers and, and put together in the backyard.

• 39:51Speaker 00

  Can you articulate to everybody in one sentence that starts with, if you ride the models, then, what this piece is trying to say?

• 40:02Dan Shipper?

  If you're at the models, you're gonna be okay. You're gonna have a job. You're gonna do great work.

• 40:08Dan Shipper?

  And you don't have to worry.

• 40:12Speaker 00

  Cheers.

• 40:13Dan Shipper?

  Cheers.

• 40:18Speaker 00

  Okay.

• 40:19Dan Shipper?

  All right.

• 40:20Speaker 00

  Good stuff, man.

• 40:20Dan Shipper?

  Good stuff.

• 40:21Speaker 00

  That was fun.

• 40:29Speaker 02

  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.