Transcript: ‘We Automated Everything With AI and Tripled Our Headcount’

The transcript of AI & I, in which Every COO Brandon Gell interviews me about “After Automation”—my 8,000-word essay on why AI creates more work for humans—is below. Watch on X or YouTube, or listen on Spotify or Apple Podcasts.

Timestamps

1. Introduction: 00:00:51

2. The AI paradox: more automation, more human work: 00:05:51

3. How AI makes yesterday’s expert competence cheap: 00:10:00

4. AI can act autonomously but it does not have agency: 00:18:00

5. Why Dan is all in on AGI: 00:20:39

6. AI layoffs are a lie: 00:21:57

7. Ride the models and you’ll be fine: 00:25:42

8. How to use AI as a long-form features editor: 00:35:30

Transcript

Brandon

You prompt AI to do something, it blows your mind. You feel inadequate. You feel like, “Oh my God, this thing’s gonna take my job.” And then it stops working and it looks back at you and says, “What should I do next?”

Dan

The further away an agent gets from a human, the less valuable it is. If you just ride the models, you’re gonna 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.

Brandon

So we’re here because we’re going to flip the script a little bit. I’m going to be interviewing Dan—

Dan

Sick.

Brandon

—about the piece that he published yesterday, May 21st. We’re going to try to understand why he wrote it and what’s underneath his reasoning. There’s going to be some conflict. I’m going to fight with him on it—

Dan

Let’s go. Let’s fight.

Brandon

—and see, bring in some of my opinions, which are more or less aligned, but trying to understand: does this piece reflect the future in 10 years, in five years?

Dan

And who are you again?

Brandon

I’m Brandon. I’m our COO, and that’s it.

Dan

So the piece is called “After Automation,” and it comes from this feeling that I have—there’s a video about this, and there’s a piece, but just for people who haven’t seen either of those things.

It comes from this feeling 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 Claude 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. In fact, since the GPT-3 days, we’ve grown from four people to around 30 people, and we’re hiring more now. So it came from me looking at that and looking at the environment and thinking, “What’s going on?”

Because the whole information environment—if you look at it, 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 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.”

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 three or three-and-a-half years, hit it for the first time—and then come to all these conclusions about, “Oh my God, all work is going away. We’re not gonna have jobs.”

And I’m sitting here thinking, actually, your intuitions when you first see a technology like this are usually very off. We’ve seen over and over again 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, and if something works here, there’s a good bet it’s going to spread to other businesses that are adjacent to ours.

When I look around at Every, I see so much automation, and I also see way more human work. The whole piece is saying, “Here’s the current state of work with agents”—and then pulling apart that paradox and explaining: why does more automation mean more work?

Brandon

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: there is actually a massive amount of hope right now in a world filled with a lot of doomers, and this is why.

But I’m going to come out of the gate and ask you a devil’s advocate question, which is: a couple of hours before you published this piece, the CEO of ClickUp came out with this long tweet about why he fired—I think it was around 22% of his workforce.

Dan

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

Brandon

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 theoretically it makes a ton of sense: AI is not autonomous right now, it has to be told what to do and then checked, we need that sandwich you described in the piece. But in a business that is 8,000 or 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

That’s a really good question. There are a couple of 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 also haven’t read it. Take a second to explain that in detail.

Dan

The idea is that the way AI works and the way it functions in the workplace is: AI makes yesterday’s expert competence cheap. 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.

Anyone now with a prompt can use yesterday’s competence to solve a programming problem, build an app, or write a piece—a report, a YouTube thumbnail. The interesting thing is that when expert competence is available for cheap, it gets widely adopted. Everyone starts to do it.

We see this internally. Everyone’s making pull requests, and there’s a lot of, “Holy shit, this is crazy.” I’m making pull requests, ops people are making pull requests, engineers are writing essays. There’s all this line-crossing—non-experts doing the things that experts used to do. And that feels very threatening to experts, who are like, “Well, what’s my job going to be now?”

What’s interesting is because these tools are trained on outputs, trained on yesterday’s data, the stuff they do with a default prompt all looks kind of similar and is all kind of right for the current situation, but not actually totally right. So you flood the zone with tons of stuff that’s close but not quite right. And then you need an expert to come in.

Brandon

There’s a lot of that at Every too. 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 the expert should do it.”

Dan

Yeah, exactly. And I’m definitely—this is coming from personal experience.

Brandon

I have pushed so many PRs where I’m like, “Willy, I literally have no idea if this works, but here you go.” And then he’s like—

Dan

“This is a good idea, but I just completely redid it.”

Brandon

Exactly.

Dan

That’s exactly the kind of thing I’m talking about. It’s kind of right, it’s close, but it’s actually not quite right and you need an expert to figure it out.

What’s interesting is when you flood the zone with all that stuff, what used to be expensive because it’s expert competence is now cheap, and now it all looks the same. Everything gets devalued. You get this abundance of stuff that looks like expensive work—code, essays, whatever—but it’s all kind of similar and all not quite right for the situation, so its value gets a lot lower. Immediately lower.

And then what happens is you actually get more demand for experts to come in and help take that stuff that’s being produced by people—you have good ideas, for example—and get that idea across the finish line. That usually looks like experts building systems to shepherd the broadly produced work into something actually useful.

An example: we have repo rules and review guidelines so that before Willy sees a PR, 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. And then there’s a lot of demand for experts to use these tools—now that the floor is a lot higher—to make stuff that could never have been made before. Like Kieran, who just built an entire inbox end to end in about a month or two. That’s completely impossible without these tools.

So there’s this really interesting thing that happens: even as you automate, the automation produces a glut of work that’s all okay, all reasonably good. That work is all very similar and not quite a fit for the actual situation, and that increases the demand for experts who can make it actually good, actually different, actually appropriate for the live situation as it is right now.

I think that’s something people don’t quite understand, especially when they first encounter a language model or an agent that can do something. 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.

Our experience so far at Every is the further away an agent gets from a human, the less valuable it is. 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.

Dan

Yeah.

Brandon

And only then can you have the other humans take that agent and do work that levels them up. There was a point where we were thinking about this piece—Dan was drafting it—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, are always a little bit above where that waterline is.

And I really liked the end of the piece, where you describe Achilles sprinting ahead of the tortoise, which according to Zeno’s paradox shouldn’t happen. But in this world, it actually does. You prompt AI to do something, it blows your mind. You feel inadequate. You feel like, “Oh my God, this thing’s gonna take my job.” And then it stops working and it looks back at you and says, “What should I do next?”

I think that, until we’ve figured out AGI—and maybe even after that, probably for a very, very long time after that—it will always be looking back at us and asking us for direction.

(00:10:00)

Dan

That’s basically the core of the argument. Because you can say, “Oh yeah, Dan, it maybe is true now that it increases demand for experts, but this stuff’s gonna get good enough that it won’t. Just look at the benchmarks.”

There’s a whole section in the piece about this: if you actually do look at the benchmarks, they are improving exponentially. But 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 particular type of problem that is slightly larger, slightly broader, that zeros it out. So while it is making exponential progress, that doesn’t mean it is equivalent to human capability.

It’s a very hard problem, and one of the reasons it’s so hard is anything 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’s going to get better at it.

We make this weird subtle mistake where we identify a set of tasks and say, “This is all that humans can do that models can’t do,” and then models just do it better, and then you’re like, “Oh my God, what do I do?” The mistake is there’s actually a lot of stuff you do that can’t be articulated in a clean frame. Every time you try, you just get panicked and confused.

If you 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 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 is valuable.

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 the gap between humans and AI.

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. You have a kid. Codex can write a report much better than Isaiah can, but Isaiah has very strong wants and needs. You can try to get him to do what you want, and it’ll work sometimes—but he’s just this self-generating process that does stuff because he wants to.

If you’ve ever used any of these tools, you know they’re not built to work that way. They can push back a little bit, but they don’t have this playful, “I just want to do stuff because I’m into it,” that humans have. And again, we’re getting into territory where I’m saying things that, once clearly articulated, models can do—but you have to be comfortable with the fact that there are things you can do and things you are that you can’t fully articulate.

Brandon

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

Dan

Yeah.

Brandon

And it will be a very scary moment when these models can do that. I think there’s a question of whether they even can, because they rely on training data—and maybe there’s a world in which they are continually learning and we lose control of them and they get access to training data that we don’t want them to have. But until that time, there’s probably a good argument that they can’t reject what we’re saying and therefore can’t be truly autonomous. Autonomy needs to be: I’ve asked you to analyze this CSV, and it says no—because this is a better idea.

Dan

Yeah, and I would substitute a better word here. 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, even “disagree with every single thing I say” or “go off and find a new idea”—they’re getting 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 there’s a lot of incentive to build that. Because, okay, you sit down at your computer and say, “Hey, let’s get to work,” and the agent’s like, “Nah, I’m playing.”

Brandon

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

Dan

Yeah, that’s what I think. And there’s obviously a gigantic literature on LessWrong and other places about why it’s impossible to prove they’re never going to do that. But my counter to that is the evidence: if you look at the development of these things, their whole lineage is toward being more compliant. I think the entire industry is incentivized to do that, and I see no reason to doubt that’s going to continue.

(00:20:00)

Brandon

We’d have to develop something like your definition of AGI, which is a good question of whether that’s actually possible. Maybe you should explain to everyone what AGI means to you.

Dan

I think a good definition of AGI is any agent that you never turn off—that it makes economic sense to keep running all the time, and “all the time” in the sense of actively generating tokens, actively doing tasks for you without you ever turning it off or having to re-prompt it. You can guide it, 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. Do you think that will happen?

Dan

Yes.

Brandon

Do you think that is a good thing?

Dan

Yes.

Brandon

Explain your reasoning for the second answer. Because to me, that 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 start to think: okay, it’s actually valid that the ClickUp guy just fired 20% of his team.

Dan

We should definitely go back to the ClickUp guy.

Brandon

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

Dan

“ClickUp guy” is good. But before we get there, the thing that’s important to not fall into when you project out like this is: everybody will have access to this. For another, the rate of change, even when crazy new technology is available, is actually a lot slower than you would expect.

As part of this piece I wanted to see how this works. I know how it works in expert knowledge work, in fast-moving stuff. I know how it works if you’re a customer service manager type. But how does AI actually affect your job if you’re a customer service person in Omaha working in a call center? Because those are the most at-risk employees—that would be the default example to bring up. So I just had Codex and Claude Code scrape all of Reddit and lots of places where customer service reps post.

Obviously a lot of them don’t like AI, which makes sense. But there are some really interesting stories about companies that jump on the AI bandwagon, say “We’re automating everything,” fire a bunch of their customer service people—and then two months later they’re like, “Oops. Can you come back?”

One reason for that is if you implement AI poorly, you’re going to have poor results. A lot of these companies don’t really understand what they’re doing. They’re paying lip service to the new hype, and the CEO thinks 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

Exactly. But another reason, which I think is really interesting and very important: a lot of people who call in to customer service centers do not want to talk to a machine. They’re very explicitly trying to figure out, “Are you a machine or not?” and get to a human. That is a real brake on how fast these kinds of things can be adopted—and that’s only one example. The world is very complicated. There are billions of examples for any kind of job.

Even if we hypothesize this thing that’s always on and can do stuff, one: we have to hypothesize everyone has access to it, because that is the direction it’s going. And two: we should recognize that even if that happens, it will take a long time to become something everybody is comfortable with and everyone uses. It will probably take a generation for it to really turn into a thing.

Brandon

There’s also a good argument that working at a call center is not a job that anybody wants. It’s not great—it’s a job you have because you need a job. 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

Obviously the transition is a big deal—these are real people with real lives, and some actually do love it. But in general, being yelled at in a call center is not the best job.

Where I’m going is: even if we hypothesize all of that, humans still have to decide what matters. And what matters changes all the time—in particular because AI is an input to that. 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 say what it is.

(00:30:00)

That is going to be part of every job, because anything you can frame as a repetitive thing that’s working, you can just have your AI do. 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 things but AI—you’re going to need humans to decide that. I think that’s something very missing from what we talk about when we hypothesize these things.

Back to the ClickUp guy.

Brandon

ClickUp guy.

Dan

I think it’s really important, whenever you’re looking at some of this stuff on Twitter: I hate when they’re like, “Our business is better than it’s ever been, and we laid off 8,000 people.”

Brandon

Yeah, it’s pretty bad. Just so you can be more profitable. And the other thing I don’t like is when they say, “We’re going to pay people a million dollars if they do great work.” It’s like, okay, but you still have all these people who no longer have jobs. I don’t think it’s very tastefully done.

And I think Jensen said something that was very self-serving—basically, “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. You should be doing more interesting things, not firing people.

Dan

So: not tasteful, which should make you a little suspicious. My guess is—and I’ve seen some of the random stuff—I don’t think the company’s doing that well. When companies don’t do well, they lay people off. And it’s also often correlated with being managed poorly and having too much bloat anyway. Like what happened with Square—Jack Dorsey just does that. And I think Meta’s the same. They’re making gigantic investments in AI because that’s the new hot thing they kind of missed, and the Metaverse didn’t work, so now they have a lot of people getting fired.

So yes, AI is involved in all of this stuff, but it’s not this clean thing of everyone doing the same jobs as before but with agents instead. No—the company actually has to totally change strategies. The people it needs and the structure it needs is just totally different, and that’s not the clean narrative people like to tell. It’s much easier to just say, “AI takes jobs.”

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 that’s been structured in a certain way, there are going to be reorganizations of how work happens and how companies are structured. That seems really clear. And it’s very important that we figure out how to make that transition as good as possible for people. Tweeting about how well you’re doing it while you’re firing people is not that.

I think there are a lot of really interesting, creative ways to handle this. Meta, for example, is now key-logging everyone’s computer activity because they’re like, “Our people are the smartest people—we’ll use their data to train our models, and our models will be smarter.” Interesting take. Maybe it’ll work.

But there’s a really interesting effect of that—I wrote about this about two years ago. When you sign an employment contract, the way we’ve thought about employment for a very long time is, “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 once, and then it just works—and then you don’t have to pay me anymore—that changes the whole way we think about employment. And therefore I think it should change how we think about paying certain types of people.

Brandon

You should get a pension.

Dan

Pension—okay, maybe pensions are back.

Brandon

Pensions are back, baby.

Dan

One thing that’s really interesting: there’s this thing that launched last week that we’re a part of—the name is escaping me—but it allows publishers to get paid based on their unique contribution to the training corpus. The more generic your stuff is, the less you get paid; the more unique and valuable it is, the more you get paid. Which is really interesting.

Brandon

The ironic thing about that is basically: did you use AI—which is trained off of all the stuff that already exists—to make this? It can still make some things that are new, but it’s basically—

Dan

How much just generic default prompting did you do to make this versus actually, you know—did a human actually think about this?

But I think there could be something similar for individuals. I had this idea a couple of years ago about the last job you’ll ever have, where it’s an agency. You generate all the training data in the work you do for the agency, and then it tracks your contribution, and then you just get paid out forever from how much revenue your data generates.

Brandon

web3 is back now.

Dan

web3 is back. On the blockchain. Anyway, who knows. The problem with that—and this is back to why humans are valuable—is there’s a really high depreciation rate for the value of data. Once it’s out there, it’s very likely to go stale within weeks. All of these companies are just hunting for net-new, unique data.

So: we should expect broad reorganizations of companies, and we should expect companies that are not doing well to lay people off, reorganize, and then blame AI. I would be really skeptical of anyone saying it’s going to eliminate all jobs or all knowledge work. It will certainly change them, and it’s certainly a big thing people have to take seriously.

But my big takeaway—and this is not fully in the piece, but it’s 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. You may even find that you can do more and better work that’s more fulfilling than you could before.

I think there’s still a place in the world if you don’t want to use the models at all—that’s still going to be a thing. Plenty of people don’t, I don’t know, plenty of people don’t eat fast food. It’s totally possible not to participate in this. However, if you care about leading a really ambitious life and building businesses or whatever it is, I truly think 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.

(00:40:00)

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

A lot of Celsius.

Brandon

A lot of Celsius. When we started—I don’t know if this will make it into the podcast—Dan was looking like this. Hugging himself. Protecting himself, some would say. It has been a very stressful week. This is an 8,000-word piece.

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

It’s so interesting because it’s very natural to me. I published something once a week for so long that especially for a 500- or 1,000-word piece, I can just bang that out in an hour or two. These things get much harder the longer they go because there are all these interdependencies. If you change something here, it changes four other things over there. So 8,000 words becomes like 10 times harder than 4,000 words, which is 10 times harder than 400.

I always have this feeling that there’s this underlying thing that I can feel but can’t quite say, that I’m trying to say. It started actually during our Q2 planning—I said, “I think I figured out why we’re just going to always have jobs with AI, and if you just ride the models, you’re going to be fine.” I could feel that. Then it was this process of: okay, how does that actually cash out? Why do I think that? Because it’s all kind of in there, but it’s all tangled up.

I wrote probably four or five versions where I’d start making the argument and then think, “Ah, it doesn’t work.” And I’d throw it out and start again. It was a very frustrating process because what I’m trying to do is start with the ground truth—here’s what we see every day, here’s how work happens for us—and then move into this philosophical thing that can’t quite be articulated. I’m trying to articulate something that can’t be articulated.

Brandon

Or it’s constantly a moving target.

Dan

Yeah. That’s just very hard. I love that kind of thing, but it’s also very hard and can be very frustrating. But AI was a huge part of this. I could not have written this without it.

For example: for a piece like this, you’re trying to articulate it, you can’t quite articulate it, and 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, fresh, right when I got to my desk, is 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.” I would have a log of that, and every time I would do it, I would have Claude or Codex—I actually use Claude more for this, I think Claude is better for this kind of thinking—ask it, “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—yes, that’s what I’m trying to say.” Over time you build up this record of where it was at each point, and you’re just getting closer and closer.

Then as I was getting deeper into it, once I had 4,000 or 5,000 words, every morning I would have Codex take the latest draft and turn it into a podcast—just someone reading it to me—and then on my way to work I would listen to it. As I’m listening, I’m thinking, “Okay, there’s something that needs to change there. Oh, and then it would get to the end, I’d be like, ”Here’s the thing I need to do next.” That was a really good way to keep the continuity of what I’m writing and where the problems are—in a way where I’m not always reading. It’s really nice to be on a walk, listening, and thinking about it, which would be completely impossible otherwise.

Brandon

Alright, one more challenge for you, and then we’re going to have beers. Can you articulate to everybody in one sentence that starts with, “If you ride the models, then…” what this piece is trying to say?

Dan

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.

Brandon

Cheers.

Dan

Cheers.