We Automated Everything With AI and Tripled Our Headcount

Summary

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Podcast Summary: AI & I – “We Automated Everything With AI and Tripled Our Headcount”

Host: Dan Shipper
Guest: Brandon (COO of EVERY)
Air Date: May 27, 2026

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Episode Overview

In this episode, Dan Shipper and EVERY’s COO, Brandon, dive into the paradox at the heart of “After Automation,” a provocative essay Dan published about the real-world impact of AI and agent-based automation. Despite implementing advanced AI tools throughout their company, EVERY hasn’t downsized—instead, they’ve rapidly expanded their human team. The discussion unpacks why automation hasn’t killed jobs at the cutting edge, explores the limits of AI autonomy, and probes the evolving relationship between humans and increasingly capable AI agents, all while fielding timely, devil’s-advocate questions about mass layoffs and the future of meaningful work.

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Key Discussion Points & Insights

1. The Paradox of Automation: Why More AI Means More Human Work

• Dan’s central claim: At EVERY, despite “AI-native, agent-native” workflow and heavy use of tools like Claude and Codex, human workload has increased, not decreased.
  • “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.” ([03:10])
• Headcount explosion: The team grew from 4 to 30 since GPT-3, and hiring is ongoing.
• Industry panic is overblown: While prominent voices warn AI will wipe out white-collar jobs, Dan argues that first impressions are misleading.
  • “Your intuitions when you first see a technology like this are usually very off.” ([03:54])

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2. The Sandwich Model & The Limits of AI Automation

• AI’s strength: Automates “yesterday’s expert competence”—making high-level outputs (writing/code) cheap and accessible.
• The consequence: Now everyone can generate near-expert work, leading to a flood of “reasonably good” but not truly expert outputs.
• Expert bottleneck: Flooded with AI-generated outputs, there’s more demand—not less—for expert humans to check, refine, and systematize this work:
  • “You get this abundance of stuff that used to be very expensive and looks like expensive work... but it's all kind of similar and not quite right.” ([08:42])
  • “Now there's a lot of demand for an expert to come in and help get that idea across the finish line.” ([09:08])

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3. Automation in Large, Mature Organizations

• Brandon’s challenge: Does this model hold at companies scaling into the thousands, or is this only true for dynamic startups?
• Dan’s response: Even in massive organizations, the real challenge is effective implementation. Poorly executed automation often backfires:
  • Companies jump on AI, lay off staff, but later rehire after realizing AI-based support is not yet sufficient ([24:00])
  • Human preference for human support (e.g., in call centers) acts as a brake on automation’s pace.

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4. The Nature of AI Agency, Autonomy & the Human-Machine Divide

• Agency vs. Autonomy: Today’s agents act for us; they don't self-direct or reject our instructions.
  • “Agent is very confusing because it implies agency. But agent means something that acts on behalf of someone else.” ([18:54])
• Human advantage: True autonomy—creative rejection, self-motivation—is an essential human trait not found in AI.
• Fundamental gap: No matter how advanced, AI looks “back at us and asks what to do next.” ([13:00])
  • “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.” ([11:22])

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5. AGI, Benchmarks, and Will AI Run Forever?

• Dan’s AGI definition: “Any agent that you never turn off. It makes economic sense to keep it running all the time...” ([20:40])
• Is perpetual-value AGI coming? Dan predicts yes, but stresses system-wide adoption is slower and messier than people assume.
• Economic implications: Even if AGI is always-on, it still lacks the self-generative drive of humans.

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6. The Reality of Corporate Layoffs & the ClickUp Example

• ClickUp’s mass layoff used as a case study: Is AI to blame?
• Dan & Brandon’s view: Layoffs are often due to business struggles or bloat, with AI as a convenient scapegoat.
  • “I don't think the company's doing that well...When companies don't do well, they lay people off.” ([28:10])

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7. Changing Value of Data and New Economic Models

• Knowledge as an asset: Once data (or expertise) is “out there,” its value quickly depreciates.
• Potential new models: Dan references revenue streams based on the uniqueness of a creator’s data contribution ([31:22]) and speculates on pension-style payments for unique knowledge work.
• Human value: The continual need for people who can do new work, not easily codified or replicated by models.

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8. Advice for Listeners: “Ride the Models”

• Dan’s actionable message: Adapt to use new models for your work—you’ll be more fulfilled, not replaced.
  • “If you just ride the models...you’re going to be fine.” ([34:35])
  • “You're going to have a job, you're going to do great work, and you don't have to worry.” ([40:02])

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Notable Quotes & Moments

• On Automation Anxiety:
  “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?” — Dan Sick ([00:00], repeated at [12:30])
• On experts’ enduring role:
  “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...And that increases the demand for experts.” — Dan Sick ([10:15])
• On the “tide rising” and Zeno’s Paradox:
  “Our eyes, whether you're an expert or not an expert in something, are always a little bit above where that waterline is…” — Brandon ([11:43])
• On the future division of labor:
  “Anything that you decide, anything that you can frame and be like, this is a repetitive thing that is working. You can just have your AI do it. But the minute the situation changes... you're going to need humans to decide that.” — Dan Sick ([26:19])
• Summative wisdom:
  “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.” — Dan Sick ([40:02])

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Timestamps for Important Segments

• Opening thoughts on AI’s limits and first impressions: [00:00]-[03:54]
• Dan describes the paradox of more automation, more work: [03:10]-[09:00]
• Flood of “okay” work, need for experts: [08:42]-[10:15]
• Human agency vs. AI “agency”: [18:54]-[20:27]
• Why layoffs blamed on AI aren’t what they seem (ClickUp): [24:00]-[29:00]
• Future economic/pay models for creators: [31:22]-[33:21]
• Actionable call to “ride the models”: [34:35]-[40:02]
• Personal reflections on writing with AI: [35:13]-[39:44]
• Dan’s one-sentence summary: [40:02]

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Conclusion

This episode challenges the dominant narrative of automation-driven job loss, drawing on rich experience and real data from an AI-native organization. It argues that, while tasks and workflows are evolving, human judgment, creativity—and even headcount—are more essential than ever. Embrace the new tools, adapt, and “ride the models”: the future of ambitious, meaningful work is wide open for those prepared to lead AI, not just react to it.

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Transcript

[Brandon]

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?

[Dan Sick]

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.

[Brandon]

So we are here because we're going to flip the script a little bit. I am going to be interviewing Dan Sick about the piece that he published yesterday, May 21, 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. 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, in 5 years?

[Dan Sick]

And who are you again?

[Brandon]

I'm Brandon. I'm our coo. And that's it.

[Dan Sick]

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. 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, since the GPT three days, we've grown from four people to 30 people and we're hiring more now. 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 half of entry level white collar jobs may be wiped out. Even people like Ken Griffin from Citadel, you can tell he just had this moment where someone showed him an AI doing an advanced data or finance question and he was like, holy shit, 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, 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. It's a group of early adopters. We have people in doing all sorts of work internally at every. And if something works here, 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 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?

[Brandon]

Yeah, when I read the piece, there wasn't 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 this is why. But I am going to come out of the gate and ask you 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.

[Dan Sick]

I don't think it was 8,000, it was 20,000. No, I think it was like 3,000. Fired entire economy.

[Brandon]

It was like 22% of his workforce.

[Dan Sick]

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

[Brandon]

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. Need to have 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 SOPs for managing their business. Does this manifesto and this thesis still hold true?

[Dan Sick]

It's a really good question. There are a couple different questions here. The first thing I want to do is lay out the argument, why does automation make more work?

[Brandon]

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

[Dan Sick]

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 the decision making and everything that's ever been written. And it makes that available to everyone for very cheap. 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 make a YouTube thumbnail. And the interesting thing is that when you do that, 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 internally, everyone's making pull requests, there's

[Brandon]

a lot of holy shit, this is crazy.

[Dan Sick]

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 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 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.

[Brandon]

Well, 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.

[Dan Sick]

Yeah, yeah, yeah, exactly. Me, for example, I know I've never witnessed that all of us, it's all of us. How many PRs have I, from personal

[Brandon]

experience, I have pushed so many PRs where I'm like, willy, I literally have no idea if this works, but here you go.

[Dan Sick]

And then he's like, shut the fuck up.

[Brandon]

Well, he's like. He's like, this is a good idea. But I just completely redid it.

[Dan Sick]

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 actually not quite right. And you need someone, you need an expert to actually figure it out. But what's interesting is when you flood the zone with all that kind of stuff, what used to be expensive because it's expert competence, is now cheap and now it looks the same. So everything sort of gets devalued. You get this abundance of stuff that 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 same situation. So its value gets a lot lower. It's immediately lower. 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 now there's a lot of demand for an expert to come in and help get that idea across the finish line. So that looks usually like experts are in demand for building systems to get the kind of, you could say, slop work that can now be produced by everyone and shepherd that into something that's actually useful. So an example would be we have repo rules and review guidelines and stuff like that, so that before you see a pr, before Willy sees a pr, hopefully 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 an entire inbox end to end in like a month or two. And that's like complete. That's totally, completely impossible. And so there. 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. It's all like, reasonably good. That work is all very, very similar and not quite a fit for the actual situation. And that that increases the demand for Experts who can, like, make it, like, make it actually good, make it actually different, make it actually appropriate for the. For the. 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 and they. Or an agent that can do something and they see it and they're like, holy shit. It's just. It just does everything in the end. And the reality is it's incredibly good. It's amazing. It totally changes how we do work and our experience so far, whatever 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,

[Brandon]

experts are more important than ever because they lay the groundwork for an agent to do amazing work, and only then can you have the other humans actually take that agent and do work that levels them up.

[Dan Sick]

Yeah.

[Brandon]

So 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 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, oh, fuck Achilles. Is it Achilles? Achilles, Achilles sprinting ahead of the tortoise, which, according to Zeno's paradox, that shouldn't happen, but in this world, it actually does. You prompt AI to do something, it blows your mind. It does that. 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? And I think that is until we have figured out AGI, and maybe even after that, probably after that, for a very, very long time, it will always be looking back at us and asking us for direction.

[Dan Sick]

That's basically the core of the argument, because I think you can say, oh, yeah, oh, yeah, Dan. It's maybe true now that it increases demand for experts, but this stuff's going to get good enough that it won't. Let's just look at the benchmarks. And there's a whole 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 a model to do a particular type of problem that is slightly larger, 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 capability. It's actually a very hard problem. And one of the reasons it's so hard is 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. We make this 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 do. And then models just do it better. And then you're like, oh my God, 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 so every time you try you get panicked and confused. And if you sort of step back. The fundamental thing that. Keeps the separation between humans and agents is we are building agents to do things that we want them to do, no matter how powerful they get. All of the economic and psychological and otherwise and technological forces are pushing the progress of AI toward a place where no matter what it does, it's looking back at you to decide what you want to do, what is valuable. And even after we get to AGI, theoretically AGI is going to do that too. If we thought it wasn't going to do that, we wouldn't build it. And that keeps this gap between humans and AI. And I think a good, a good example of this is 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, 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, they're not built to work that way. Yeah, they can push back a little bit, but they don't have this. They're, it's very far from the kind of like playful experimenting Like, I just want to do shit because I'm into it that, that humans have. And again, we're getting into territory of I'm saying things that humans, 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 BILT can make it feel a bit better. Let me explain. BILT 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, here's what I think is the most underrated part. BILT members also get access to a neighborhood concierge. It can make restaurant reservations, book fitness classes, and find new local spots. And it comes with rewards at over 45,000 retail merchant partners. It's sort of like having a personal assistant baked into where you live. It's simple. Being a renter and now owning a home is better. With BILT. Join the membership for where you live at joinbuilt.com dan that's J-O-I-N-B-I-L-T.com dan, make sure to use that URL so they know that we sent you. And now back to the episode.

[Brandon]

It is also inside of that play and that rejection where you have autonomy. 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 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 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.

[Dan Sick]

And 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 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. But that is very different from having agency, which is what even the smallest child has. And I don't think that 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?

[Brandon]

Yeah.

[Dan Sick]

Like it needs to be able to

[Brandon]

do that in order to do things that are scary to us.

[Dan Sick]

Yeah, yeah, yeah. That's. That's what I, that's what I think. And there's this. 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 I see no reason to doubt that that's going to continue to be the case.

[Brandon]

Yeah, I mean, we'd have to develop something that's like this. It's your definition of AGI, which is a good question of whether that's actually possible, which maybe you should explain to everyone. What AGI.

[Dan Sick]

I think a good definition of AGI is any agent that you never turn off. It makes economic sense to keep it running all the time and keep it running all the time in the sense of not open clause or Victor or whatever. 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 the idea is it's valuable enough that it can just keep running all the time.

[Brandon]

Okay, I want one word answers for the next two questions I'm going to ask. Do you think that will happen?

[Dan Sick]

Yes.

[Brandon]

Do you think that is a good thing?

[Dan Sick]

Yes.

[Brandon]

Explain your reasoning for the second answer.

[Dan Sick]

Here's.

[Brandon]

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.

[Dan Sick]

Yeah. Okay, we should definitely go back to the ClickUp Guy.

[Brandon]

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

[Dan Sick]

I don't know. I think ClickUp Guy is good.

[Brandon]

ClickUp Guy.

[Dan Sick]

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 I was already 8,000 words and I was not sleeping anyway, so I was like, I'm going to cut this. But as part of this, I wanted to see 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 because those are like the most like 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 like all of Reddit and like, 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 two months later they're like, oops, sorry, 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 are paying lip service to the new hype and they think the CEO thinks that they can cut a bunch of expenses and then it just doesn't really work very well.

[Brandon]

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

[Dan Sick]

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. The world is very complicated. There's billions and billions of examples for any kind of job. And so I think it's really important, even if we're hypothesizing 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.

[Brandon]

Definitely. There's also a good argument that working at a call center is not a job that anybody wants.

[Dan Sick]

It's not great.

[Brandon]

It's a job that you have to, to do because you need a job. And in a world where this technology exists, yes, we'll have to figure out a way that everybody can live a fulfilling life and eat, but it might actually be nice to not have that job, assuming you're taken care of in other ways.

[Dan Sick]

I think obviously the transition is a big deal, and these are real people with real lives, and some actually do love it. But also, yes, in general, being yelled at in a call center is 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 update and decide what matters. Because AI is going to wait for us to be like, what matters?

[Brandon]

Totally.

[Dan Sick]

And that is going to be part of every job. Because anything that you decide, anything that you can frame and be like, this is a repetitive thing that is 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, you're going to need humans to decide that. And I think that, that's, I think that's something that's very missing from what we talk about when we hypothesize these things. Back to the ClickUp guy, click up guy. So I don't know. He fired 30,000 people.

[Brandon]

I think it was more.

[Dan Sick]

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

[Brandon]

Yeah, it's pretty. Yeah. It's like, it's. Whoa. You could be more profitable.

[Dan Sick]

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

[Brandon]

The other thing that I don't like is and we're gonna pay people a million do. If they do great work. It's sort of like, okay, but you still have all these people that no longer have jobs. I just really don't think it's very tastefully done. And I think Jensen, 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.

[Dan Sick]

It's fairly. I think it's true.

[Brandon]

You should be doing more interesting things. Not firing, you know, people want to be profitable. I guess.

[Dan Sick]

Yeah.

[Brandon]

But idiots.

[Dan Sick]

So overrated.

[Brandon]

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

[Dan Sick]

Anyway, 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's doing that well.

[Brandon]

I mean It's a generic SaaS company. There's like they're.

[Dan Sick]

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

[Brandon]

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

[Dan Sick]

Exactly. Which is, you know, I mean, correlated with not doing well.

[Brandon]

That was square.

[Dan Sick]

Yeah.

[Brandon]

Like that just Jack Dorsey just can.

[Dan Sick]

Exactly.

[Brandon]

He just does that.

[Dan Sick]

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.

[Brandon]

And no surprise, Metaverse didn't work. So now they have a lot of people fire.

[Dan Sick]

Yes. So I think, yes, AI is involved in all of this stuff, but it's not like this clear thing of everyone's doing all the same jobs as before, but they're all just agents. It's actually. No, the company actually has to totally change strategies. And the people it needs and the structure it needs is just totally different. And that's not the clean narrative that I think people like to tell. And it's much easier to talk about. Just AI takes jobs. And 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. 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 tweeting about how well you're doing while you're firing people is not that. I think there's a lot of really interesting, creative ways to Meta, for example, is now key logging everyone's. Everyone's. All the stuff they enter into their computer because they're like, well, our people are the smartest people. So 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 when you sign an employment contract, the way that we thought about employment for a very long time was, I'm going to do this job and you're going to need me to keep doing it in order for it to keep getting done. But once you reach a point where 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.

[Brandon]

You should get a pension, you know, pension, maybe, maybe.

[Dan Sick]

Pensions are back.

[Brandon]

Pensions are back, baby.

[Dan Sick]

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

[Brandon]

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

[Dan Sick]

know, how much just like generic default prompting did you do to make this versus just like actually did a human

[Brandon]

actually think about this?

[Dan Sick]

Exactly, yeah. Generate a new idea. So but I think there could be something similar for I had this idea, I read this post a couple 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.

[Brandon]

Web3 is back.

[Dan Sick]

Now Web3 is back.

[Brandon]

We're going to track it all on the ledger blockchain.

[Dan Sick]

Yeah. Anyway, who knows the problem with that 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 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

[Brandon]

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

[Dan Sick]

Yeah, I think so. Anyway. 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 knowledge work. And, and I think it will certainly change, change them. And I think it is certainly it's like a big, it's a big thing that people have to take seriously. But my, 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. 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 want to, if you don't want to use the models at all, I think that that's still going to 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 whatever it is, I, 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.

[Brandon]

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.

[Dan Sick]

A lot of Celsius.

[Brandon]

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.

[Dan Sick]

Yeah,

[Brandon]

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 what does it take to think through these arguments?

[Dan Sick]

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 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 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. 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. 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.

[Brandon]

Or just constantly. It's a moving target.

[Dan Sick]

Yeah. And so that's. That's just like. That's very hard. I love that kind of shit. But it's also very. 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 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. 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? Help me figure out what I'm trying to say. And it would say things back. And I would be like, no, no. Oh, 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. 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 something needs to change there. There's something 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 like, 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? It's really nice to be able to be on a walk and be listening to it and thinking about it, which would be completely impossible otherwise. Yeah.

[Brandon]

All right, one more challenge for you, and we're going to have beers and put together the backyard. Can you articulate to everybody in one sentence that starts with, if you ride the models, than what this piece is trying to say?

[Dan Sick]

If you're at 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.

[Brandon]

Cheers.

[Dan Sick]

Cheers.

[Brandon]

Okay.

[Dan Sick]

All right.

[Brandon]

Good stuff, man.

[Dan Sick]

Good stuff.

[Brandon]

That was fun.

[Podcast Host]

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