The AI paradox: More automation, more humans, more work | Dan Shipper

The last time you were on this podcast, you had this hot take that people were sleeping on Claude code. You were so unbelievably right. The premise of this episode is we're going to go through what else you predict will happen.

The AI jobpocalypse is not really a thing. I am super, super bullish on PMs and full-stack designers.

You guys are hiring, doubled in people in the past year, which is not what people would have expected from a company that is so AI forward.

I'm simultaneously extremely AI-pilled and very bullish on humans. Automation is a lie. Every agent needs a human. We have so much automation, so much AI, and I also work way more.

Creativity. It just feels like it's going to be more and more valuable to stand out from all the slop that people are shipping and launching constantly.

What models do in general is they make yesterday's human competence cheap, and so it becomes commoditized. It's not valuable anymore. What humans do is we go in there and we're like, yeah, we have all this frozen human competence from yesterday. How do I use this to like make something new and interesting?

What are some predictions for how the way we work is going to change?

It's going to bifurcate in 2 main ways. One is everyone's going to have at least one agent that they talk to that they can offload work to. Second is that most of the work that you do is actually going to happen on your computer in an environment like Codex or Claude Cowork.

What you're predicting here is the SaaS tools will run within Codex or Claude Code.

I think the SaaSpocalypse is dumb. I would buy SaaS stocks right now. What agents do is increase the number of users of SaaS, not get rid of it.

A lot of people are moving to CLI and trying to work from the terminal.

We speed ran the CLI era. It was nice while it lasted, but I think CLIs are over.

Today, my guest is Dan Schipper, CEO and founder of Every.

Dan and his team are building maybe the most AI-forward startup out there, and as a result, are very much living in the future of how work is going to look as AI becomes a bigger and bigger part of our day-to-day. Everybody at their company, including every non-technical person, uses Codex and Cowork and Claude Code to get much of their work done.

And this is why, way before anybody else, Dan saw the rise of cloud code and what is now cowork, which he predicted almost a year ago when he was on the podcast last time. So I asked Dan to come back on the podcast to share his current biggest predictions for how work is going to change over the coming year for most people. We chat about what work will look like at most companies at the end of this year, how the shape of the work we do will change, and who will do best in this coming future, slash what you need to be working on right now. Hint, hint, product managers and designers are going to do very well. Dan makes a lot of bold predictions and many quite contrarian takes that I was not expecting him to say.

And we are going to revisit this conversation exactly a year from today to see how much he got right. Before we get into it, do not forget to check out lennysproductpass.com for a free year of the hottest and most well-crafted AI products in the world, available exclusively to Lenny's newsletter subscribers.

With that, I bring you Dan Schipper. Dan, thank you so much for being here and welcome back to the podcast.

Thanks for having me. Always a pleasure to be with you.

The last time you were on this podcast, you had this kind of, it was almost like an offhand hot take that people were sleeping on Claude Code and in particular Claude Code for non-engineering work, for just like fixing files, sorting your hard drive, just all these things that people hadn't thought about. Nobody was talking about this. This was a year ago. You were so unbelievably right about this. It's just like unreal what has happened since then. They built Cowork, which was this whole, they built on this very specific idea using Claude Code for non-technical work. Codex is getting into this now. I imagine you've been seeing this. They're like leaning into this non-technical use of basically coding agents. I feel like this has also been a big part of Anthropic's success over the past year, just like how do non-technical people use this stuff. So you were just so ahead on this stuff. I even wrote a newsletter post building on this idea. I'm like, hey, this is interesting. I should dig into this. I asked people how to use Cloud Code for non-engineering work, and I just had like so many examples, and it's like my second most popular posting. So clearly you have a unique glimpse into where things are heading. So the premise of this episode is we're going to go through what else you predict will happen. in the future, how things will change for people building products. And I think it would be helpful to start with giving people a brief glimpse into just how you operate and how your team operates. That gives you this unique lens into where things are going. So just give us a sense of how you, how you work.

Thank you. Um, I, I really appreciate the introduction. Um, and yeah, I think one of the things about predicting the future or, or the way that we think about predicting the future at Every is that You, what you don't wanna do is prognosticate. What do you, what you wanna do instead is, is just live in it together. So everybody at Every is an AI early adopter. We're almost 30 people now. I think when, when we did our interview, we were 15. So we've doubled in size in the, in the last year. We're all early adopters and we have engineers, we have designers, we have writers, we have editors, we have salespeople, we have customer service people. And everybody has a little bit of that, um, whatever that thing is where you're just like, oh, I like to explore, I like to experiment, I'm very curious. And I'm like super all in on AI. And what I, what that does, I think, is it creates this like little pocket of the future where we're all living in it together and we get to be a little bit further ahead. 'Cause at any other company, there's like a mix of people. There's early adopters, there's like, there's sort of like the middle of the pack people, and there's people who are like very anti. And another thing that happens, which is really cool, is we get to, because of our role, um, you know, reviewing models and, and being a little bit of a tastemaker in AI, we get access to stuff before it comes out. So we get to beta test and alpha test and kind of help, help steer the direction of where things are going a little bit, which is very, very cool. And so when, when I think about predicting the future, um, it's actually when you create an environment like that, it's actually just about noticing what's going on. And I think what a core part of it too is writing about it. I think articulating what you're noticing, articulating the future kind of brings it about in this way that makes it real for you and your team and then anybody else who's like on the internet who's reading it. And so the Cloud Code thing, it was this, it's this very organic thing where for us, Um, we tried Claude Code when it came out. That's sort of our job. We try all the new stuff from all the new model companies. We try all the new stuff from the model companies. And at the time it was like a little bit early, but right around, I think like Sonnet 3.5 or Sonnet 3.7, we were testing that to do our vibe check on it. And we were like, holy shit, this is crazy. This is like really, you can, they got rid of the code editor. And so from that point on, we just basically, we run at this point now, we run like 6 products, software products internally. At that time we ran like maybe 2 or 3. And from that point on, we just started shifting to a world where everybody was, no one was looking at the code. Everybody was, you know, talking to their computer in English using Claude code in a terminal. And so I was able to see like, ooh, this is starting to happen. And then be— because my job is a little bit to just like push and play with stuff, I was like, I wonder if I could use this for like my writing. Like, how could I do that? And then it just like starts to unfold and you're like, okay, this is not ready yet, but it's obviously useful for me. You know, my, like, one of the things that we talk about internally is what I call the reach test, which is like, do you just like, when you wake up in the morning, do you like reach for it organically?

I love this combination of You are using the latest stuff. And I think this is, as you said, maybe an underrated skill is you're good at being self-aware of here's what's weird and new and different and interesting. So that's a really cool combination, partly because you have to write about it and you write about it. So I think that's like the perfect recipe for someone having a sense of where things are going.

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So the way that I'm gonna structure this conversation, there's gonna be basically 3 buckets of predictions. One is how the way we work is gonna change in the coming years. 2 is how, what the shape of the work we're gonna be doing is gonna look like and change. And then 3 is who is gonna be most successful in this future. Slash what should you be doing and working on now to be successful in this future?

Lenny, my only ask is we come on a year from now and then you score it. I wanna score it.

Okay. So this is a year from now.

Okay.

Okay. So is this, that's actually, uh, is this like your predictions for in a year, this is what it's gonna look like, or this is like the emerging future?

I think, like, I don't, I will probably say I don't have like an exact timeline. I think most of the stuff that I'm, I'm gonna talk about will be pretty apparent within a year, but it, it probably, it may, it may take longer. than that.

Okay.

But I think it will, it should within at least a year be like, not obviously wrong. Like it, it seem, it could, it should seem like it's moving in that direction to count.

Okay. May of 2027, we will review your predictions. Man.

Amazing.

Okay. I love this.

Okay.

So let's dive in. Uh, what are some predictions for how the way we work is going to change in the coming year?

One of my favorite questions, because I think if you look at the benchmarks, you're just, looking at, okay, like, yeah, AI is going to just take all of our jobs basically, you know. METER has this really cool benchmark where it's like, it measures how long it can, like, the newest models can do tasks autonomously. And it's like, oh, it's like, it can, what's it called? Oh, like Mythos Preview, the like big Anthropic model that everyone's like so worried about. It can do tasks of 17 hours at 50% accuracy. It's like, holy shit, that's crazy. And I think it is real. It's true. And the progress, like model progress, is going up exponentially. And my experience and my feeling is that we will look back in a year and say, we actually have a lot more work to do. Humans have a lot more work to do, even as models get better at doing work. And there's like a really interesting paradox there. And my, prediction for the, uh, like how, how work will, my, my big prediction of how work will change or, or how you will be doing work in a year is it's going to bifurcate in this in 2 main ways, how you, how you use agents. One is you're going to be doing, I think like what we figured you would be doing like 5 years ago when we thought about how work with AI works. Which is everyone's gonna have at least in their company, at least one agent that they talk to that, uh, can do work that they can offload work to. And we'll talk about like what that looks like, but it's essentially like OpenClaw. Second is that most of the work that you do is actually going to happen on your computer in an environment like Codex or Claude Cowork. That becomes the sort of operating system for, it becomes the sort of operating system for how you do all of your work, whether that's your email, the documents you create, like all that kind of stuff. It's gonna be on that kind of a surface. It's, that's becoming the, the clear competitive landscape. So there's, I wanna go in order of those 2. So the first one is you're gonna have agents you delegate to probably in Slack, but you know, anywhere. First thing that's interesting about that one is it's not clear what the architecture is gonna be like for that. Um, is everyone gonna have an agent? Uh, is every team gonna have an agent? Is it gonna be like just one agent? Is it like, do agents specialize? Is there this pa— like parallel shadow org chart? And when OpenCLaw first came out, everyone internally at Every adopted it, and I was very convinced that it would be a, everyone has their own agent. And there's like some real, really interesting things about that world of, you know, a parallel org chart. Agents in that world sort of become little reflections of you, which is like really cool and really interesting. It's like, if you ever, did you ever read The Golden Compass? It's like having a little daemon on your shoulder.

Yeah.

You know, it's like a little part of your soul.

Yeah.

Um, I, I really think like that's sort of what it looked like was happening. Um, and so I was very into personal agents and I have completely flipped. And I, I really think that, uh, the, the model for now is going to be a super agent, like one agent for the entire company. And I, you, you're starting to see this in some companies. So like, um, Shopify very famously has one, uh, Ramp has one now. Um, and, and I think there's some like really interesting reasons for that. I actually still think that the personal agent thing is coming, but what we found is there's all this hype with OpenCLaw. Everyone's like, I'm gonna set it up. I'm, it's so cool or whatever. And then everyone realizes it's like way too much work. This thing breaks all the time. I gotta like fumble around with it. I gotta be able to SSH into my server and like blah, blah, blah. And most people to do work at least just don't wanna spend that time or can't. Um, and the, the like fundamental underlying thing that drives that is whether it's OpenClaw or any other harness, in order for an AI agent to be useful right now, it really needs a human who cares about it. It really needs like a human personal connection with someone who's like watching what it does and make sure that it's doing the right thing. And that it's useful for people. And the minute you sever that connection, so the minute someone's like, ah, I don't want to maintain this dumb OpenClaw, is the minute the agent is not really that useful anymore. And that's why I think it has started to shift to a more one agent per company model, because for now, the ideal is you basically set up a forward deployed engineer or someone with that sort of profile who's responsible for making sure that that agent is working for the whole company. And then maybe you have some, like, some little team agents. Um, and I think as the models get better at being more independent, that will, like, shift down and you'll, it'll be more likely that we'll have more personal agents because we won't have to fuck around with all the internals. But, um, the model that I see working for us and for a lot of other companies, including the model companies, the model companies themselves are starting to see this, is when it comes to the sort of like async agents. It's really a, you know, you have one agent at the top that's like doing, sometimes it's everything. A lot of times it's, um, a particular kind of job that you've decided that everyone in the company needs an agent for, like data requests. And, uh, and then I think it will start to, it starts top at the top and then it sort of starts to trickle down where you may get more specialized agents and teams and all that kind of stuff. And the mechanism is agents need people who care about them.

That is so interesting, that point about you need to like garden your agent because there's context you have to keep adding to it. There's like, it breaks as you said, and it's just like once it's just too much work, you're like, okay, forget this thing. I'm gonna go back to Codex or Claude or something like that.

Exactly.

Okay, cool.

So this is a cool opportunity. So the idea, so what you're predicting here is, uh, companies will have this super agent that everyone can talk to. As you said, Shopify's got River, I think it's called. What's the Ramp one called?

I can't remember.

Okay. It's probably got a funny— okay. So, uh, so that's the prediction. Okay.

That's, that's the first prediction.

That's the first prediction.

Um, we will start with agents at the top that, uh, that are more general and are used by more people in the company. And then it will start to kind of grow down as the, as people get more used to these use cases, they get more specialized and, um, agents become Uh, less, uh, less fiddly. Like they just work better.

And is this mostly gonna be in Slack, do you predict?

For work?

Yeah, it seems to make sense. I think people, I, people love having the green bubbles on OpenClaw. Um, like, sorry, the, the blue bubbles on OpenClaw, like if you can use it with your iPhone. But I think there's this little thing in people's heads where they really like to keep their personal and work agents separate.

Mm-hmm.

And Um, I think there's a whole, there's a whole territory. Our, our COO, Brandon Gell, calls this, um, computer errands. There's like a, this whole territory of using personal agents for your computer errands. It's like, order my groceries or whatever. And it's like, there's so much of that that I think this is gonna, it's gonna be huge for. But, um, I focus, we focus mostly on the work stuff. Um, and I think that's gonna happen mostly in Slack.

Sweet.

Go Slack.

Should we, do you want to talk about the, uh, the other work surface?

Absolutely.

So like Codex, Cowork. Okay.

This is the— Let's do it.

I'm so excited about this one. I think it's the coolest thing. So basically what happened was Anthropic realized at some point that with Claude Code, if you put an agent on your computer and it runs on your computer, it has everything. It has access to everything that you have access to. It uses the terminal, so it has like basically superpowered access to it. And not only that, it really, these agents really understand how to use the terminal because there's so much content online about, about that. And it, it created this like super powerful coding paradigm, which is, um, you know, Anthropic was really doing it first. OpenAI for a while was, I, I, I, in my opinion, like very, very behind on this. And then in my opinion has surpassed them recently. It's really interesting. But they were very early on this when people were still thinking about coding agents or coding models as being really pair programmers. They were among the first to be like, no, and do it successfully. Like, there were people before them, like Devin, who I think had the big cloud environment, and OpenAI tried this too. But the the real adoption seems to have happened when you put it on your computer. So they figured that out. And then I think they figured out, along with their community, that once you have a coding agent on your computer that can build anything, it's actually really good for any kind of work you want to do. And people started just hacking Claude Code essentially to do all their work. So Anthropic then built Cowork, which is, you know, a little bit of a nicer wrapping around Claude Code, but it's fundamentally the same thing. And then I think, you know, I think OpenAI made a couple of different bets, but their main bet on a programming agent was the, the, the, the earlier versions of Codex were like very technical and they were like super smart, but they were like a little bit autistic. Like it was a little hard to, they, they didn't quite get what you meant. They get exact— they got exactly what you said. And I think maybe like 3 or 4 months ago, around the time that they launched 5.3, they started to move in this direction of, oh no, we get it. Like it's, this model is fast. It's like really good for general purpose knowledge work type tasks. And then they launched the Codex desktop app. And I think the Codex desktop app takes, if you look at all the lessons that like Anthropic learned, they went from Claude code to Cowork, and you can kind of see that in the tabs on the, on the Anthropic desktop app UI. I think OpenAI was just like, we, we see where this is going. Like, let's just skip to that. And so I think Codex right now, this is a horse race. Like, they're gonna have different positions. Um, but I, I think Codex right now is my daily driver. I like spend all, all my time in it basically. I flip to Claude every once in a while. But I think they're getting the paradigm right. And it's clear to me that whoever's in the lead, 'cause I, again, I think it'll change whoever's in the lead. It feels very obvious to me that all of the work that you do is going to be in one of those surfaces where, uh, for example, when I'm writing a document, Codex has a browser in, uh, in the app. It has an in-app browser. And when I'm writing a document, I just go into one of my one of my Codex threads, which I have one thread for every project. And I just open the in-app browser. I go to the document. I usually do it in Proof, which is this online Markdown editor I built. And then I just have Codex running and watching me in Proof. And Codex can see what I'm doing. I can see what Codex is doing. It's all kind of in one place, which is the— an extension of the same thing that made Cloud Code work really well originally. And I basically feel like I have this parallel work buddy that not only can it like respond and write in the document, but then it can go do research. It can go, it can use my computer to basically do anything that I can do on my computer. And that's like incredibly powerful. Um, and I do this with everything. Like I've been in, I've been at inbox zero for like 10 days straight now, which if you know me is crazy. I'm never like this. And that's because I literally just have Codex gather all my emails with Cora, which is our email agent. And then, um, it, it renders a little page. Uh, and I, I think I showed you this at the Anthropic event.

Mm-hmm.

It renders a little page and I just like monologue into it and just talk at each email. I'm like, okay, go, go research this. Oh, here's a question from our lawyers. Can you go like collect all of the, you know, documents from the last like 4 years and then put them into a report and send them? And it just does it. And so all the stuff that I would procrastinate on, I don't really procrastinate on anymore. And so I feel like there's this, for a long time we thought, I thought too, that the optimal experience of AI was gonna be take AI and put it in a browser. And I think the reverse is actually starting to happen and be like really, really valuable in a way that I did not expect, which is. Take the AI agent that you use all the time on your computer and put a browser in it so it can see everything you're doing. And that is just like a magical combination that I think will be, is very uncommon now. You can't even do this in Cloud Code, um, because they, they don't let you browse external websites inside of Cloud Code. So it's very uncommon now, but I think it will be super common in a year.

This is more profound than it may even sound. What I'm hearing is Instead of AI being baked into SaaS tools, what you're predicting here is, uh, you will, the SaaS tools will run within Codex or Claude Code.

That, that is, that is one, uh, really important, uh, second order effect of this is, um, okay. So yeah, like I'm, I'm using Prue for, or really any website, maybe PostHog or whatever. and I'm doing it inside of my agent, and the agent has access to the website, so it has access to everything that I have access to, and it has access to my whole computer. When I run the agent on that website, I'm using my tokens. I'm not using the vendor's tokens. I'm not using the app's tokens. And so it puts SaaS back in this place where, yeah, you want to make it friendly for an agent, and everyone's got a CLI now. You want to make the HTML really usable. You want to make sure that anything that happens in the CLI shows up for the user immediately, all that kind of stuff. There are a lot of issues to deal with. But once you do that, you actually don't really need to think about having an AI surface that's primarily going to be the thing that users use in the sense that you don't need to build an agent necessarily into your product. I think you can, and there's another really interesting bifurcation of this that we should talk about, which is that having 2 agents is better than one. I think for now there's this really cool thing where, uh, with Proof, for example, uh, anyone who uses it, I don't pay for tokens because they're just bringing, they bring their AI to, to Proof. And so it changes what you build as a SaaS company, uh, and you build it now for both humans and agents to use at the same time. And it changes your margins back to, well, I don't really have to pay for tokens anymore because the user's gonna bring the AI.

So I think this is a huge deal. So what you're describing here is, uh, more and more work that we do, more and more professional work. Is it just gonna happen within Codex or Claude Code? How, where does Cursor fit into this? Is that one of the, is, is there potential there?

That's a good question. I think that Cursor sees a lot of the same stuff and they're, and in some ways they have some of the same stuff, but it's better. Like I think that Cursor's cloud implementation is better than either OpenAI's or Anthropic's and is more advanced. And I think that Cursor has, at least so far, more distinctly chosen a lane. Like they're more distinctly choosing to be for programmers. And that may limit how far they get in here. Like, I think the definition programmer is expanding enough that they'll have a big market, but I don't know that they're gonna jump into like, okay, use this to make a slide deck or whatever. But it is really clear. That every model company is starting to realize how important it is to have a harness to get the most out of the, um, the model. And so where the, where all the platforms are moving is to a world where you, you're not just doing prompt and response. When you call the, the model at, at, on, on the OpenAI platform, the Anthropic platform, you are, they're literally like running the model on a computer that, that is in the cloud that they run and then giving you the result out of it. And they know that they, in order to get the best results out of the model, they need to offer that. And so you see, you know, Anthropic's got cloud-managed agents. OpenAI does not have a response yet, but I assume that that's going to happen. And now Cursor was just essentially acquired by SpaceX. It's not like a full acquisition, but it's close. So I think people are starting to realize, like, I can't just do the, like, model part of it. I have to have this, like, harness above it. And I think the ultimate form of that harness is like, I can do any kind of knowledge work. Cursor itself is, feels like one of the things that it's going to be a hard decision for them whether to stay just for coders or not.

So people building products that aren't OpenAI or Anthropic, if this proves to be true, the prediction here is they're going to be using your product over time inside of one of these agents. Uh, is there something you would do if you were one of those companies to prepare for that future?

I would, I would just prepare for that. So like, you know, for, for example, um, every more classic piece of productivity software, whether it's Slack or, uh, Word docs or PowerPoints or whatever, it's really mostly meant for a human to use. Um, and now people are doing CLI, so it's like meant for uh, an agent to use independently of a human. And we're moving into this new paradigm, I think, where the human and the agent are on the same piece of work together, and they're both doing things. And you need to have, I need to have visibility into what the agent is doing. The agent has to have visibility into what I'm doing. We have to go back and forth in this sort of like seamless way. And the kind of software that you make for that is gonna be very different. So for example, Like there's a lot of stuff that Proof doesn't have. I don't have to have a lot of the like Word document kind of like formatting or page breaks or like, you know, making tables or whatever, 'cause the agent just does it. I don't need to worry about that. It can do all the formatting for me. So you can make the products a lot simpler and faster to start than the legacy products are. And then there's all these other affordances that you need to start to have because the way agents interact with software is very different. So for example, agents can do a lot at once. They can just do like a billion different things to your document or your slide deck or your codebase or whatever. And how you display that to the user is going to be very different than the way you might display a human being concurrent in your document and doing stuff. You need, um, you need like approval, you need a sort of inbox that sort of summarizes here's all the stuff that's going to happen or has happened. You need, um, you need logs and the ability to roll it back real quick. So there's all those kinds of considerations that, um, that change the actual product. And then the underlying UX of it, or the underlying infrastructure you need, is different too, because, you know, agents can make a billion requests in like 3 seconds. So how are you going to deal with that, right? Um, this is exactly why, you know, GitHub is having problems right now, because, because the, the number of people using GitHub has skyrocketed exponentially, and it's really just people's agencies in GitHub. So I think it's this whole new world that is just starting. You're just starting to see like a little peek of it, but there's so many cool things about it. So for example, in Proof and some of our other products too, uh, when someone has a problem, they don't email support. Their agent sends a bug report. And an agent bug report is way better than a human bug report. report. It has like, here's exactly what I did, here's the exact repro steps, here's like, proof is open source, so here's what I think is going on in the codebase. And then we just get that, it becomes a GitHub issue, and then we can just like send off an agent to fix it. And you can't do that with everything, but it's so much better. And you can see the like the glimmers of this very fast like closed loop between I ran into something, a paper cut, a little feature I want, a little bug, and my agent just goes off and talks to the company agent, and then the company agent just goes and fixes it. That I think is incredibly cool.

So is a part of this that you— a lot of people are moving to CLI and trying to work from the terminal. Is part of this prediction that people will shift away from that and back to actual UX with agents kind of running alongside them?

CLIs are over. Um, we, we speed ran the CLI. era. It was nice while it lasted. But I think it's pretty clear— it's not that CLI— sorry. It's not that CLIs are going to completely go away. Obviously, they've been around for the last, like, 30 years or 40 years or 50 years or whatever. They will continue to be around. And I think there was this moment when cloud code was, like, so popular, or when cloud code was really starting to gain in popularity, that people were like, the the thing that's working is the fact that it's the CLI. And I don't think that's what it is. And when you move into an actual UI for this, you start to realize, um, we made GUIs for a reason. And it's just nicer to be in a GUI. And you can get all the same benefits inside, inside of a GUI, especially for non-programmer work. But I would, I would estimate that. Definitely the majority of the technical people inside of Every are not using CLIs anymore as their main work surface. I think a lot of programmers are still flipping into it every once in a while, but it's more or less they're using Codex, Cloud Code, Cursor, um, that kind of thing.

Awesome. Okay. I, I, I would, I definitely wanted to make that part clear. So coming back to kind of the, the big picture of the prediction here, there's kind of these 2 modes of work that you're anticipating. One is this kind of super agent within a company that you chat with through Slack, most likely, that can go off and do work and answer questions. And then there's on your computer running Codex or Claude Code. And within that, all the work that you normally do kind of on your computer is now going to be living within Codex or Claude Code, or maybe some third party that emerges that we're not even aware of yet.

Yes. And you're going to use apps inside of the internal browser of those Uh, of those tools. Wow.

Okay. Like listening to you talk about it, it's like, it may not feel as profound as it is, 'cause this is a big change to how we work. We don't currently have an AI that we talk to regularly in Slack, and we also don't work currently mostly in Codex or Clark Code. So this is actually a pretty massive shift.

I think so.

Is there anything else along these lines before we get into our next Prediction?

Well, a few things. I'm definitely not an agent maximalist. Like, I really think we're gonna have a lot of different agents that we use. Seems pretty clear to me. And I really do think that 2 agents are better than one. So what's a good example? When I have Codex interact with another agent, it can give so much more context about me and what I want than I would be able to type. And it can go back and forth talking about things that would take a long time for me to express directly to an agent, that you get this like speed-up effect when you assume that your users are, are using Codex or Claude Code or Cowork as their, as their basic way they access your app. And, uh, a really simple example, we have this, um, hosted OpenCLAW product, which we, we had it, we had on waitlist. We actually had to pause it because We started taking people off the waitlist and OpenCLaw is just a very hard agent harness to make work. It's like, it's moving so incredibly fast. And if you're like a platform for it, it just, it's like when things break, you can't fix it. It's very hard. But one of the things that we learned in that process is if you're, let's say you're building an agent product or a new, any new software experience. What you would assume, let's say, to set up an agent is you need to build like a little like web interface or a little Slack workflow that asks people about, okay, like, who are you and what are you gonna use this for? And like, what's your ideal, you know, dream outcome or whatever the things are that you would put on an onboarding checklist. If instead you just you just make a hard line of we are only going to service users who use Codex or Cowork. Um, what happens is you just paste something into— you just paste a prompt into Codex or Cowork, it goes and talks to the app, and the app can be either just a regular server or, or it can be its own agent. And Codex has so much information about you that it can just give it, here's all the stuff I've been working on with Dan. Um, here's all the ways that, you know, he might, he might want to use this app and then bring it back to me. And it's this very custom experience. And also for a technical product, like an agent, when something goes wrong, I can just tell Codex, go fix it. And Codex will go talk to the app and figure out what's going on for me. And so I think the whole paradigm starts to change when you assume that everyone's got an agent and those agents are talking to other agents in this like Really magical and important way.

There's a couple more things I want to touch on before we get started because there's like so much to talk about. Uh, one is you made this point about SaaS tools not using, like you can use tokens from the, uh, model companies basically when using a SaaS tool. Talk a bit more about that because that may change the business model for SaaS companies in the future. That feels like a big deal.

Well, I think it actually may, uh, save their margins. Because right now all the, all these companies are rushing to like add an agent to their offering and thinking, oh, the agent is going to be the main way that I, that people interact with me. And I think that, uh, and that costs tokens, obviously. And I actually think once I have, once I have Codex or Cowork as my main work surface, I still want to use SaaS. So this is another good prediction. I would buy SaaS stocks right now. I would, I think the SaaS apocalypse is dumb and SaaS stocks will be up majorly in the next couple years. Not investment advice, but, you know, I would buy SaaS stocks. So, so, so I think it saves your margin because now what you're, what the way that you're thinking then is not, I have to build AI into this, is it's more like, I have to make a piece of software that humans and AI want to collaborate on together. And that's hard, but it's, once you build it, it's a lot cheaper than assuming everyone's spending tokens. And it's, I think it's a good business. And part of the reason I'm so bullish on SaaS is A, everybody internally here is Uh, like I said, we've all got agents and we're all using Codex and whatever, and we still pay for a ton of SaaS, and our SaaS spend is up year over year. And we're not like vibe coding every single like little thing, you know? And I think that what agents do is increase the number of users of SaaS, not get rid of it. And so I think SaaS companies are going to see like an insane spike in the amount of demand that they have, because there's gonna be tons of agents using these products at like a very high volume. And like I said, that's a huge infrastructure challenge. There's a, there's a lot of like interesting pricing challenges, but, uh, it, it, it makes me very bullish on SaaS.

I love that. If anything else comes outta this conversation, Dan Shipper, SaaS is the future of AI. This B2B SaaS.

Hashtag send tweet.

I, I love just, yeah, this is, uh, quite contrarian. And the other interesting piece is that the fact that you guys are hiring, that you doubled in people in the past year, which is not what people would've expected from a company that is so AI forward. Talk about what your experience there of just, okay, we still actually need humans.

Automation is a lie. Um, in the sense that every time you automate something, in order to make sure the automation is working well, you need a human on top of it. like making sure that it's working well. And so, um, you know, I wrote this piece a couple years ago called the allocation, about the allocation economy, like the idea that the way that humans are going to work with AI is going to, is going to be like, like being a manager. And the thing that you have to remember about managers is like managers actually spend a lot of time working. Most managers are not like on the beach. They're like checking in with their employees all the time and, and, and, and trying to figure out, okay, how do we make this work good? How do we make it better? How's it doing?

How's this person doing?

All that kind of stuff. And I think there's, there's some differences between being a human manager and being a model manager, but fundamentally it still requires a lot of time and attention. And I think that we kind of miss that in the model discourse. And one of the reasons is benchmarks make it look like AI is more autonomous than it is. And by autonomy, I mean something specific by autonomy, and I'm going to try to express it. It's like a little hard to express, but I learned this for myself because I've been feeling this paradox a little bit. I've been feeling the, like, we have so much automation, so much AI, and I also work way more. And I think part of the paradox, part of the paradox started to like resolve for me a little bit when I made my own benchmark. So I made this senior, it's called the Senior Engineer Benchmark, and it's like, how good is AI versus a human engineer. And the way that I built it is, again, have this app Proof. I just vibe coded it on the side and like while running the rest of Every. And when we launched it, because it was completely vibe coded, it just started going down and I couldn't fix it. And it was very embarrassing. I had a lot of egg on my face. And like the product worked, we tested it internally. We had A lot of beta testers, but like the day after launch, it was like just every like 10 minutes the servers would go down and people were looking at me and I'd be like, I don't know what's going on. Like Codex, fix it. And Codex was like, I don't know what's going on. Um, or really Codex was like, uh, I do know what's going on. I fixed it. And then it, it would cause 4 other errors. And then you're just going around in a circle and I wasn't sleeping and I, I, I vibe coded so hard I got bursitis on my elbow. So, uh, that's a, there's a life lesson in there.

Vibe coder elbow.

Yeah. Um, so anyway, I got it. I got actually 2 different senior engineers to fix it independently. So I have 2 different rewrites of the codebase that, um, tells me how they did it, right? And so what I get to do is when we get new models, I just give the new model a prompt. I say like, this is vibe-coded slap. If you wanted to rewrite it, from first principles, how would you write it? Go do it. And all the models until GPT-5.5 got like a 30 out of 100. And senior, like a human senior engineer gets like high 80s, low 90s out of 100. So there's a lot to go. And then I tried GPT-5.5 and it got like a 62. And mind you, the 60 score was GPT-5.5 using an Opus 4.7 plan. Opus 4.7 plans are very good. GPT-5.5 is the only model though that has the sense of agency and confidence to just like rip out old code and just like actually rewrite from first principles. Other coding models, they kind of like try, they like end up papering over the edges or around the edges and they're like, oh, this is a big job. Like I'll just do a little patch. And you're like, no, I like specifically told you not to. So GPT-5.5, there's like a 30-point bump in the score, 60 out of 100. It's like very, it's very clear that in a year or less, it's going to be senior engineer level, right? And that gives you a certain picture in your mind, especially based on how I named the benchmark, which I think a lot of benchmarks do. And I can tell you that when we get to that point, I will be very, it will be very easy for me to change the benchmark to zero out the current model. So that gets a 0 out of 100. And so, for example, it seems like there's no skill or no thought into the prompt, which is, this is vibe, could it slap, like fix it from first principles. But actually it took me a while to get to a prompt that didn't give away the answer, but got the model to reveal what it's capable of. And the original prompt I gave it was the original prompt that I gave it when I was trying to fix the issue and production was going down, which is like I had woken up in the morning and I was like, okay, we had 4 or 5 reported issues yesterday. I want you to go through all the issues and then come to like a— make a plan for how to resolve all of them and go do it, right? And every coding model on the market, and I'm pretty sure This, here's a prediction. I'm pretty sure every coding model on the market will still do this in a year. Every coding model on the market will take that instruction seriously. And if I tell it, here's a bunch of issues, go fix it, they will just go try to fix the issues. What an actual human senior engineer does is they go look at the codebase and they're like, this is a piece of shit. This guy doesn't know what he's doing. And then they say, we're going to have to like actually rewrite a lot of this. And it's going to be hard and risky. I know you don't want to hear that, but like, we're going to have to do that. And if you asked the model, hey, like, should we do that? It'll, it'll probably, it'll probably get there, but it's not going to do it on its own. Um, and it, and there's a lot of incentives pushing against it doing that. And even if it does that, there's, there's always a higher frame for us to go. And so I think it's, it's really important, um, when, when we think about benchmark progress to think about it from that perspective, which is benchmarks rise on problems that we've framed, that we can articulate, that we can score. And there's a lot of work that's human work that, uh, it, it can't be scored until you write it down. But the act of thinking to prompt it or write it down, um, is, uh, is something that you can't measure, but like kind of means that even if the benchmarks get saturated, it doesn't mean the same thing as we, you totally replace all senior engineers. And it's, And I think it's why, even though the models are getting better at automation, I still hire engineers.

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One thing I mentioned recently on the podcast, I heard that Speaking of the code that you have of like humans writing code, uh, data labeling companies are buying code that was written before 2021, 2022, before AI became a thing as like very valuable data.

Artisanal human code.

Yeah, exactly. That's exactly right. And it's so interesting that that's exactly the kind of code used to build this model.

Well, what's interesting, so I want to, I want to clarify there. So I did not have a human write the code all by hand because I actually think that that's sort of, it feels silly to me. Like, I don't really care because I know if, if an engineer's not using AI, like, I'm not gonna work with them. I don't really care. It's like, it's sort of like, am I gonna race a human against a car? Like, I probably wouldn't do that, but, um, I would race a human in a car versus another human in a car and say which one's better. And in this case, uh, what the, the way the benchmark is structured is, yeah, like these human engineers used AI, but They used it in a way that I could not because I didn't understand it and I didn't have time and I didn't really want to like go in and try to understand the code base, to be honest. And I think that's a really important thing when we think about benchmarks is AI is a broadly distributed technology that any human can use. And when we are benchmarking it against humans, AI against humans, we're actually really always talking about one human using AI versus another human using AI because AI doesn't use itself. It, it may be able to in this like slightly somewhat recursive way, but there's, in any real use case, there's always a human like pretty close to it making sure that it's working. Okay.

I want to try to wrap up our first bucket. There's so much to talk about. I made a little list of things that I think people, uh, should do based on your predictions to be successful. We'll talk about this at the end too, but just a few things. Uh, one is start using Codex or Claude Code more and more for the work you're doing. And especially the browser, use tools inside of it.

2 is allow your agents to be, to use your products.

If you're building a SaaS tool, make it easy for agents to be a user, essentially. 3 is start thinking about some Slack bot that you can work with, like try out tools. Like I know Slack has their own Slack bot that I think is really good too, and I haven't played with it, but people really like it. So. look for, I guess, a tool that could become the AI agent within your company. Uh, buy SaaS stock ASAP. Not investment advice.

That, I think that's totally right. I will, like, my slight tweak is when you're thinking about building your software for agents, the current model is I'm building a CLI that an agent uses, but they're using it in a sort of like, I, they're being I delegated a task to the agent and the agent's using the CLI. And what we, what, where I think it's going is you and the agent are using the app together. The agent's probably using the CLI, but you're using the web interface and they're, they both need to be in sync. And that is, I think, a new challenge that's really interesting.

Awesome. Anything else before we get to our next category?

Buy SaaS.

That's the title. Oh man. Okay. So the second category of predictions is around just the shape of the work that we're going to be doing is going to change. What do you predict?

There's all this interesting stuff in terms of, in terms of the shape of work. Like once you're in this land where you've got, you know, these, you've got async, async agents off that you delegate work to, then you've got your like Codex, Claude code, like work surface that, that starts to happen. So, One thing that we see a lot internally, and you also see this in the big model companies, is the number of pull requests that you get skyrockets. We have people in consulting or in ops roles or whatever, or editors just making pull requests. And A, that's really cool, and it's a very different shape of work where you can expect that a higher percentage of your company or your users are gonna be doing things that previously only technical users can do. And what that does is it creates all this pressure on the other end for the people who have to deal with all of the new code for how to deal with that. And so I think there's a lot of, there's a lot of interesting things that happen with that. Like, so for example, Like OpenClaw, I mentioned that earlier. Pete gets like thousands of pull requests a day on OpenClaw, and then he has like, and then he just spins up like 50,000 Codex instances and then sorts through them and then merges like 1,000 of them. It's really crazy. I actually think that that's going to be more and more common. There's like, it brings up a lot of really interesting questions around which pull requests should you merge? And, you know, whenever you add capacity in one part of your process, like, it breaks things. It used to be really hard to build things and now it's very easy. So the point is not, can we build it? It's like, would it make sense with the rest of what we've built? And how do we keep a, like, sense of a coherent whole? And also, what do we delete? I think Anthropic does this really well. Like, they They delete a lot of stuff from Claude code to make sure that's not bloated. So I think there's a lot of that gonna happen on one side. There's a lot of, um, non-technical people can do technical work and then technical people are in charge of making sure that that work gets into a product or into a process in a cohesive, coherent way. And also their product people are gonna be doing that too. And I think that's quite cool.

Something I'm hearing from people is that now that everyone can do everything, like engineers can design, PMs can code, marketing people can ship stuff. There's just this like confusion about what the hell is my job anymore? What am I responsible for exactly? Like, am I supposed to be shipping stuff? Am I still a marketing person? And it's just creating a lot of confusion and uncertainty in the world.

I think that's for real. And one of the things that I think is special about Every is everyone is sort of a generalist and really loves like having their fingers in a lot of different pots or whatever the metaphor is. I think that'll probably settle down at some point and it'll feel more normal. Like marketing people are still gonna do marketing even if they're touching the website. Like that's just part of marketing now. But I also think that you can get a lot further being a generalist now, and that's like really cool, especially for, for smaller companies. The other thing that I think is interesting is there are definitely some new job roles that are a thing. And the thing that is becoming really clear is the whole forward-deployed engineer concept, I think, is for real. And it comes out of every agent needs a human. Like, you go to the big model companies, they have these agents that run internally. They have teams of people that run these agents. You know, and I, I don't think those teams are going away. The models are going to get more powerful, the agents are going to get more powerful, and the number of agents is going to grow, but people are still going to manage them. And so that looks like a very specific kind of person. And, you know, we have a couple of those people internally here, and it's like the, the people who are in charge of making sure your agents are working and doing the right thing. We also do consulting, so we, we we lend that out to people. And I think that's a big thing that people want. And it's another one of those places where you're like, hmm, automation was supposed to take away jobs, but it looks like it just created one or many, you know? And there's a specific type of engineer that really loves, you know, Nitesh, who's one of our, who fits this. He's an AI engineer and he fits the sort of forward deployed category, and he's on our team. He spends most of his time actually talking to one of our agents in Slack. We have an agent internally called Claudey, which runs our whole consulting practice. And he spends a lot of time in Slack. Like, there is code, and he is using Claude code and other things like that, but a lot of it is just talking to it and being like, why did you do this dumb thing? Like, let's fix that, you know? And so there are certain kinds of engineers that I think love that and love having their hands on the latest thing and also love making this like being that's like in, uh, in the works, in a workspace. And it looks a bit different than more traditional, building more traditional software.

And your sense there is we're not gonna, we're not near a place where these agents don't need a human. You've said that so many times now that agents need a human and there's kind of like the setup part and then there's the maintaining it forever part. And it feels like both are important, uh, is what I'm hearing. Like this is gonna be a job for a long time. AI is not going to get smart enough to just automate its— be fully automated for a while?

Yes. I'm simultaneously extremely AI-pilled, extremely, and very bullish on humans and the role of humans in making sure that AI is working well.

Interesting. Okay. So the 2 kind of buckets here that you're talking about, one is, um, like the way I think I hear what you described earlier is just the pace of shipping software and everything is just increasing, which also means Uh, there's so much more work reviewing all this sloppy output. I, I was just talking to a data science friend and he was saying how his team is just, his data science team is just, their job used to be do analysis, answer questions, see if this experiment was a good, was a, was positive. Now it's just everyone's doing that and they're sharing their results and they're, and they're like, no, this is not correct. And most of their job is now reviewing bad data science work.

Which is a problem. And it means that, and the same thing is happening with engineers. And it means that you need more, like you actually need engineers for this and you need data scientists. And it means that you haven't set up the appropriate systems or agents to help you with this. So like the way that it works inside of the big model companies, for example, like at least one of them has literally a data science bot that every single person in the org can query. That is hooked up to their data warehouse that knows who's who, so that it knows at the warehouse level like who has permission to access what. And so all of the basic questions, because there there's a team that sets up this bot, all of the basic questions that people might want to ask that it sometimes gets that might get wrong, that they're constantly making sure it's getting it right. And so the data science team doesn't have to answer all the like bullshit questions because. There's another team building an agent that, that, that is set up to do that really well. But if the team didn't exist, the data scientists would hate their lives.

Yeah, it does though make the job maybe less fun because you're just sitting there, you know, gardening people's sloppy work.

Well, that's what I think is like, it, it can actually make the job better because for the data scientists, you are now not dealing with all the silly requests you're dealing with. Um, the deep, the deeper questions that are harder for the, the team who's dealing with all the basic requests and building the agent to do that. Uh, it's, it's like filtering all that stuff out so you can focus.

Here's a question I've been thinking about. I was not planning to talk about this, but it's something that, uh, I've been thinking about. So the question is, which product tech role is the least changed now? So like engineers, 100% of code, AI now, it's It's like a completely different job. Product management, a lot of the, you know, PRDs are, you don't have to write as much. You can ship code. You don't have to wait for people. Design, the whole design process, dead according to a recent guest, just like there's no time to do the whole design process. Very different role. Data science, very different work now. There's marketing, there's sales. So here's the question. What do you think is the least fundamentally changed role so far?

Well, one interesting thing is, you know, I don't know if this counts, but like CEOs and investors, it seems still very, very optional whether or not they use this stuff.

Mm-hmm.

It seems that way. I, I, I think the opposite is actually true. Like my experience, and we do a lot of this with senior executives and senior leadership teams, my experience is that your company's only gonna go as far as your CEO goes in AI, and it's not something you can delegate. you have to have your hands in it, uh, because you don't— otherwise you don't have an intuition for it. But for a long time it has seemed like, yeah, that's something that the people who are doing the work have to do, but like, I don't have to do that. Like, I'll just tell them what to do. And, um, and so I think if you're a CEO, you kind of can get away with your day looking very similar. Uh, I, I think that will change rapidly at some point where it'll be like, oh no, I'm like way behind. But for now, Because, or maybe even middle managers, like those kinds of people I think are, are, it's fairly similar. I think like maybe sales because it's so, it's exactly what I mean. It's so in person.

That's, yeah, that's my vote.

You know, there it's sort of creeping up in the kind of BDR, like we can deal with a lot of, you know, BDR type or type queries. You're only talking to like people who actually want it. Um, and you can do for sales, it's like re— it's so useful to to like do research. Like my favorite Codex, like one of my favorite Codex experiences is we were hiring a head of L&D and I, you know, we always put out a job post, whatever, but I was like, I feel like there's this company called General Assembly in New York and they do like, they've done really good technology education for a long time. And so I was like, I feel like someone who is into who, who, who worked at General Assembly and is now into AI would be really good. And I just like literally typed it into Codex and then like went off and was doing something else. And I came back and it found like this, the perfect guy. It was like, worked at General Assembly, was an instructor, um, uh, like is super AI pilled and follows me on Twitter. So I just DM'd him and then I had dinner with him and it's like, that's crazy. You know, that would've taken so long before and, uh, super valuable for sales, for recruiting, all that kind of stuff.

Yeah. Sales is where my mind went. Like the top of funnel AI is helping a lot with sourcing and qualifying, things like that. It feels like the, the work of a salesperson is not fundamentally different. Yeah. And, and customer support's fundamentally changed. So that's interesting. Sales so far, so good for, for those folks. Yeah.

Okay.

So maybe just summarizing some of the predictions in this bucket of just like the shape of the work, how it's going to change. What I'm hearing so far is there's going to be a lot more reviewing of other people's output as a part of the work. And then 2, there's going to be a lot of like almost babysitting of AI agents to make them do the thing you want them to do for deploying and then just gardening them along the way, make sure they continue to do their work. Um, anything else before we get into our 3rd bucket?

I would sort of split it into less babysitting agents and more your forward deployed team is trying to build a whole system that makes it so that people who have less knowledge can use that system without like doing something dumb. And that's like a really interesting engineering challenge. I think babysitting kind of makes it feel like it's, yeah, you're just kind of like, you know, waiting for it to fuck up and then fixing it or whatever. And you can, you can, that can be the case, but I think a lot of it is this extremely interesting engineering challenge of building a system for, to enable everybody else in your organization to do what used to be a technical job. And then if you're not one of those people, like you're the data scientist or whatever, you can go a lot deeper with AI into like really important questions that eventually probably filter into the work that the, you know, the forward deployed engineering team is doing, but is like more generative and more new and, and, and you're, you're dealing with harder questions.

Yeah.

One other, one last thing that I think is really interesting is I think that we'll be reading way more AI-generated writing in documents and emails, and we will like it. And I think we're, we will already, we are already doing this in coding where we read plan documents. Like, I don't want an engineer to handwrite a plan document. That would be very silly. It would be, it would be obviously silly. And I think the same is true. You know, when we did our, our, uh, quarterly planning for Every at the end of 2025, we did it all with Notion agents and we just had a bunch of Notion agents and, or we had really one Notion agent and then we had a top-level company strategy and then we had everybody in the company just, um, talked to an agent and it asked them about what happened last year. How did it go? What were your goals? What do you want to do this year? What are your metrics? It pushed back and then it was like, how does it, how does this relate to the overall company idea? Like all that kind of stuff. And then I got, I got these like incredibly good AI-generated like strategy reports or plan, like quarterly plans for each part of the, each team. And then I could go in and be like, okay, who needs to, who's like, who needs to talk to each other? Like which teams need to talk to each other that like don't know they need to talk to each other? Um, and, uh, you know, who's, which one of these is like, like actually low quality or which one of these is high quality? Like all that kind of stuff makes it, it makes it a lot easier to process. Um, and I see that all the time now. Like I, I, I consistently get AI-generated stuff and there is a difference between an AI-generated document that's slop and not. And the slop one is it took them less time to make it than it takes me to read it. And they don't stand behind every line. So my expectation is if you send me an AI-generated document, I think that's great. And if we talk about it and it's clear you have no idea what's in it, like big no-no, not allowed to do that. And I, I think we have this, this aversion to AI-generated stuff that will go away because the kind of strategy document that GPT-5.5 can write when it's directed well by someone on my team is way better than like them just like dinking and dunking like, like their fingers on the keyboard.

Right. Like most people are really bad at writing documents, so the bar is low. Yeah.

And, and same thing with email. Like I, most of my email is written by GPT-5.5 and Codex right now. And I would, I honestly would prefer it to say that it's coming from GPT-5.5 and I may change it to do that, but I had this, I had this experience the other day where I had this, I had to send an email to, um, to one of our investors and I asked Codex, like, go do it. And usually, like, Codex knows to ask me and it usually does, but this time it didn't and it just sent the email and I didn't look at it at all. And I was like, fuck. And so I went to my sent and looked at it and I was like, oh, this is exactly what I would've sent. And so it's like, it's pretty close to, to that a lot of the time. Um, it can be like a little over-formal and there's a couple things that, that it's just when you really think about it, most of your email is kind of, it's not, it's kind of rote. It's kind of prosaic. It's kind of, I, I definitely wanna be the one to think about what it should say, like what, what it should say, but the actual sentences don't matter that much to me. Usually. Sometimes they do a lot. And this is coming from a writer. Like, I care a ton about writing. I think that human writing is incredibly important. And I expect we only publish human writing. Well, actually, we publish a mix of human and AI writing, but we always label it. Sometimes it's nice to have an AI co-author on certain things. I absolutely think that human writing is important. And I think that the reaction or the aversion to AI writing is silly.

It's such an interesting lens on that because when people think about AI writing, I think about social media and videos. And your point is internally, if you're just like working on planning and documents and email and things like that, like that is much less scary that it's AI written. And you're, to your point, people are already doing this. You almost prefer it a lot of times cuz people are really bad at doing this anyway. Totally.

We have this too for external stuff. Like we publish all these guides and the guides are often agent They're agent-assisted and the agent is a co-author and they're intended to be read both by humans and by agents. And that's because if you're writing a huge informational thing, I mean, you do this all the time, in order to really apply it, the best way to do that is just have your agent ingest it and remember the next time I'm doing pricing to remind me of this guide and we'll go through it together or whatever. It allows you to operationalize, uh, the ideas much better, and it allows you to go much deeper because agents can read like 10,000 pages in like a second. And so you, you can, you talk to the human about the story and the stuff that matters and the core ideas, and then the agent has all the details that it can then apply for you when you need it.

Awesome. Anything else in this category before we get into our final category?

No.

Okay, let's do it. So the final bucket is just who will be successful in this AI future that we are approaching slash what should people be working on to be successful in this next year or 2?

I am super, super bullish on PMs. And I know that your audience will probably love that. Um, but my, my anecdotal case that has convinced me of this is we have this guy internally, his name is Marcus. And he runs Spiral, which is our writing app. Marcus is a PM by training. He previously ran Axios's writing product and was a PM and had a big team and it got to tens of millions of revenue and ARR. And he took a year off that job and just got super AI pilled. And just learned how to use Cursor basically really well. Now I think he uses Cloud Code, but he was extremely Cursor-pilled for a long time. And he's— I would call him like lightly technical. Like knows what a database migration is. Like if he has to look at the code, I think he can understand it. But he's like— we never could have hired him to do this job even a year ago. But the coding models have gotten good enough That he can pair the kind of the technical knowledge that he does have with his really spiky product sense and sense for writing and sense for users. And it's like, it's so dangerous. Like he ships faster than almost anyone on the team. And he has such an eye for every single user, every single conversation. Like, what does it mean and how do we collect it into a story about like where we want to go next and what are the issues we need to fix and like all that kind of stuff. And I think that he feels liberated because he doesn't have to organize a whole team of people to do that. He can just do it. And it's super impressive and it makes me very, very bullish on any PM who gets like really AI built.

Music to my ears, Dan. Uh, you're making a lot of very happy listeners here. Uh, I've been saying this for a long time too. It's just like the skills you need to build are the things like the building now is done for you. What do you need to be good at? Figuring out what to build, figuring out if it's great. Figuring out what problems to solve. So I love that you're actually seeing this come to fruition.

I really believe it.

This could be the highest rated podcast episode of my whole podcast. I hope. Hell yeah.

It's going to be okay. SaaS is back. PMs are back.

You know, this is the most contrarian episode I've ever done. Oh my God.

So, okay. So the other people that I think are going to be like super superpower people, and again, this is because we see this internally, is full-stack designers. If you're a designer and you're in these tools all the time, you're so used to, okay, I make this beautiful interaction and the engineer just doesn't want to do it, or it doesn't happen the way I think it should happen, or there's all this stuff. And I see so many designers for us internally or externally where they now feel so empowered to go build stuff because they're like, I have all these ideas to make things look amazing and these interesting interactions. And that's the exact thing that it's really hard to do with vibe coding because it just all looks the same. So it all looks like slop and they can make stuff that looks so different and now they can actually build it. And what you see when we work with them internally is now they're just like, they're just making pull requests. Like they don't, they don't need to hand it off as much. Sometimes they do, but like a lot of times they just make pull requests and it's like the thing is built and that's it. And I think that's incredible for the way that companies work, but it's also, there's a huge opportunity for those people to become entrepreneurs and like start their own thing because they can, they can make stuff now. And I think designers are such creative people. And I think AI is like a super tool for anyone like that.

I so agree.

Even though there's Claude Design, there's all these AI designing tools. Like once you see it, you're like, that's definitely Claude Design. They're like, the creativity to your point is going to, it just feels like it's going to be more and more valuable. To, to stand out from all the slop that people are shipping and launching constantly. So I completely agree. It's, it's interesting that designer roles, I do, I do research on the job market and interestingly, designer roles have not grown in a while. So I'm waiting to see if that becomes a big trend, just like we need more designers.

Hmm. That is really interesting.

We'll see. Yeah, we'll see.

We'll see.

That, that might be a way to predict this is, are people hiring more designers? I don't know.

That is interesting.

Yeah. All right. Uh, so that's, so PM designer thriving, killing the world.

PM designer thriving. Um, I also just think generally the AI jobpocalypse is not really a thing. Absolutely, we see companies starting to reorganize, and I think that makes a lot of sense. I, I think, to be honest, a lot, a lot of the reorganization, you can say it's AI, but it's like we overhired and like the company's not doing as well and all that kind of, it was like coming and this is a good excuse. But the mass unemployment thing that some AI CEOs are talking about, I think that's not going to happen. The pattern that I see so far, and again, I don't have a total crystal ball, but I do feel like we've seen enough of the new model drops to have some sense of how this is going, is that what a new model drop does, or what models do in general, is they make yesterday's human competence cheap. So what I mean by that, is they ingest all this data of what has happened already and they make it really cheap to deploy that in whatever situation you want as your own, right? And what happens then is every— this is a new power that everyone has. So it gets adopted super rapidly and suddenly that stuff is everywhere. It's like suddenly anyone can make a landing page. There's new landing pages everywhere. Suddenly everyone can write. There's like slap tweets everywhere. But what's interesting is because it's all from, because it's all coming from these models and everyone's using basically the same models, uh, it all looks the same if you use it in the, in the most default basic way. And so that's, it becomes commoditized, like it's not valuable anymore. And what humans do is we sort of go in there and we're like, yeah, we have all this like frozen human competence from yesterday. How do I use this to make something new and interesting? And I really think that structurally, because of the way the models work, because of the financial incentives of model companies to make them compliant and aligned, structurally, they're always going to be trailing behind those people who are taking the models and using them to make new expertise or make new things that haven't been done that way before. For their very, very particular situation. And that stuff is going to get incorporated into the models. But again, it will create room for people to push further ahead. And I think that you see this in a small way in pretty much all the jobs is engineers. Suddenly everyone's an engineer, but that doesn't mean we fire the engineers. There's way more demand for engineers because you need the engineers to figure out, okay, this is all slap. How should this actually go in our codebase? And I think that's something that the benchmarks rising don't, doesn't really capture. And, uh, it feels like a thing that will take a long time to change.

People may be hearing in this, uh, prediction here of just, okay, the jobpocalypse, not gonna, people are not gonna be all fired. There's gonna be human jobs remaining for quite a while. It may be almost too comforting because you may, you probably have to change the way you operate. to still have a job in the future. Do you have any sense of just like, here's what you need to do to not be one of these layoffs?

Yes.

Yeah.

And I think that is actually super important. Um, the only thing you need to do is ride the models, and that means use them for whatever it is that you do. You know, we've talked about how Codex and Cowork are becoming the sort of standard operating system for work. If you're just doing that, and when new models come out, you're trying them and figuring out, okay, how can I— now there are new powers, how can I use them? Instead of just being like, I'm gonna like try to ignore it cuz it like makes me afraid, which I think is honestly, it's rational. It's a reasonable response. And also, uh, if you ride on top of them, they extend your powers in a way that doesn't leave you behind. Like you, you're, you're, you're part of the future and part of the way work happens. And I think that, uh, we're going to need people doing that. For a very, very long time.

I like this term, ride the model. So the, what's like, say a new model comes out, what do you think someone, say, working at, I don't know, Salesforce, say a PM at Salesforce, what should they do to ride the model?

Well, one of the things that's really interesting is a lot of companies like handicap their employees from even doing this because like, I don't know what model, I don't know if you can use the latest models at Salesforce, you know, like a lot of times you have to wait or it's, you know, whatever. So Maybe you have to do it on your, in your off time. But the thing that I really like to do with new models is play. And there, there are, there are certain things where I know it can't quite do it yet, but when a new model comes out, I like always turn the rock over again to be like, can it do it now? You know? Um, so it, you know, it could not do the senior engineer benchmark last time and I turned it over, turned the rock over again, and now it's at a 60 out of 100, which is like really good. So the way to ride the models is like not one specific thing, 'cause they're always changing, but it is to be curious and playful to apply the model, the new model to whatever it is that you care about, whether that's your job or something outside of your job, and to keep turning over rocks because it may not work now, but it may work eventually. It probably will work eventually. And The way that you use it matters. So what's really cool is that I think people think of the edge of AI as being in San Francisco, and I actually don't think that that's where it is. I think the edge of AI is wherever AI meets like a real human doing something, because the people in San Francisco, they're making it, but they don't actually know a lot about how to use it. They don't know, or at least they don't know everything about how to use it. They need to see how other people use it. And so you, whenever a new model comes out, you get to be one of the first person, one of the first people in the world to discover what it might be useful for. And that's, it's like a new discovery. And I think that's why, for example, we're in, we're in Brooklyn, but I, I really think of us and I think we are like quite far ahead of people in San Francisco because we just use them for everything. And, um, if people, uh, if people do that consistently, I think It's going to be very hard to lose.

That is one of the most amazing things about AI right now is no matter how much money you have or little money you have, you have access to the most advanced AI model. Like, it's not free, so you need some money. But like, you can get it immediately when it comes out. Maybe the only people that have an advantage are the people working at OpenAI or Anthropic. But otherwise, it's just like available.

I know I was at, I was at their event with you, their Code with Claude event with you last week and, or a couple weeks ago. And they're, they're like all using Mythos and I'm like, goddamn it. It's so annoying. But I think that's totally true. Like that is, if IBM had invented AI, you can bet it would not be like this. And it would be like a bajillion dollars and only like the Top companies could use it and they would be using it in the weirdest, most uninteresting ways. And I think there's, it's really important that AI was built in America and in the Silicon Valley culture that's like, we want to make intelligence too cheap to meter. Like, that's not the default stance. And it means that everyone has this broadly accessible tool that they can use. And I think that's amazing.

That's such a good point. And interestingly, it's also created the most fastest growing companies in history, the biggest companies in history.

That's true. Those Silicon Valley guys, they're smart.

If I zoom out on the conversation, it's really interesting. There's kind of these 2 sides to the coin. One is not a lot is actually like so much is not changing. SaaS continues, jobs not disappearing. We're still emailing each other. We're still working in Slack. Like a lot of the work not changed. On the other hand, every role transformed. Engineers don't write code. PMs don't write PRDs. Uh, design and design, you know, it's like, it's so interesting how much has changed, how much has not changed. I don't know. It's interesting that people think it's gonna be this whole new world, but in many ways it's okay. It'll continue the way it is with a lot of stuff around the edges.

That's, that's how I feel. Like I'm simultaneously so excited and it feels like everything has changed. And I'm so bullish on it and the progress that we're going to make and all that kind of stuff. And yeah, I just, I feel like there are, there are these things where they're going to be pretty similar to how they are. And that's probably good. And I think generally our intuitions about the future, the model that I have of what our intuitions are about the future is the intuitions that people had in the Middle Ages about like what happened at the end of the horizon. You know, it's like, are there dragons? Like, does it drop off into nothingness or whatever? You know, like a lot of people have a lot of deep intuition that there's something terrible going to happen over the horizon. And also that, uh, some people are like, there's something incredible. It's going to change everything. We're all going to be happy. It's a utopia. And what happens is you get there and you're like, there's some really cool things. There's some not cool things. And it's just another horizon. And I think that's, that's the way to think about the future. And until you get to that place where you're starting to see it, and I think we get to see it cuz we get to see it internally all the time, it's important not to let your, your mind get away from you and being like, this is gonna happen and this is gonna happen and whatever. Cuz you're, you're gonna tell a story that sounds, sounds so real in the moment. But, um, later on you're like, actually it's much more complex than that. And somewhere it's sort of a both everything's changed and nothing has.

Yeah.

Um, and once you get there, I think you're, you're sort of starting to see like, oh yeah, this is a real thing.

Part of it is that the AI companies are very good at scaring us about what might, might happen in the future. And I think that's actually shifting. I think they've realized maybe we should not freak everybody out about the dangers.

I, that PR strategy just does not make any sense to me. I, I do think that it's like genuine, but it's so ineffective. And, um, and I think it's also wrong.

How about we, um, end with maybe just like a few things listeners should do to be successful over the next year with the, the way the world is moving?

Buy the models. I would, uh, try all of your workflows in Codex or Cowork and see how that works. And if your company doesn't let you do it on your own time, I would try out some of these um, agent products like OpenClaw or Hermes, or, um, for less technical people, there's, there's like Victor. We have 1+1s. I, I would get comfortable with both of those ways of working and try to like, try to have fun. I think there's too much of, I'm doing this because I have FOMO, like it might, I might lose my job or like I might miss out on this big thing or whatever. And the best way to actually figure out interesting, useful things to do with AI is to like do something enjoyable.

We had a, um, Nikhil Singhal was on the podcast and the way he described it is you gotta find your moment of joy with AI. Once you find like, wow, I can't believe AI did this for me. This is awesome. I'm gonna keep building stuff.

Yeah, I agree.

So if you haven't seen that yet, then it's just like, try finding, try solving it. The thing I hear a lot is just find a problem in your life or work and see if AI can do it. Go to Lovable, go to Call it code, go to Replit, just try to build the thing. And often it's like, holy shit, this is so cool. Dan, is there anything else that we haven't covered? We've gone deep on so much. Is there anything else you wanted to share? Anything else you want to predict or just say before we get to a very exciting lightning round?

Yeah. Uh, I think we covered it. We, we did a lot. This is, this is awesome. And I'm very excited to see how well or poorly I do, uh, in a year. And I hope that you hold me to it.

We're gonna, we're gonna have AI score us. How about that?

Well, great.

Look, look at the world. Look at Dan's predictions. Here we go. Well, with that, Dan Schiffer, we've reached our very exciting lightning round. I've got 5 questions for you.

Are you ready?

I'm ready. What are 2 or 3 books that you find yourself recommending most to other people?

Um, obviously Annie Dillard. Um, I, everyone at Every has to read The Writing Life. Uh, like when you join, you get a copy and you have to read it. Uh, you only have to read the last chapter though. I think the last chapter is incredible and it is at the intersection of writing and technology and the future and it's like its relationship to the future and to time. And I think that's like, it's, it's everything about Every, like wrapped up into like a very tight chapter. It's so good. And I think Andy Dillard just generally is fantastic. What else do I recommend? I'll just, I'll just tell you a couple of things that I've read that I like. really liked recently. And whenever I like something, I always just like tell everyone about it. So I have recommended these a lot. I've been reading, one of the things I learned, which I didn't know, is Churchill's a really good writer. And he has a whole history of World War II that he wrote, and it's like a combination history and memoir. And I think that's so cool because he was there, you know, he did it. And there's something about what we do at Everywhere. I feel some like sort of kinship with that of like, we're building stuff, we're writing stuff, and it's very rare to find people that also do that. And, and so Churchill's History of World War II is fantastic. I just finished the first volume. I'm on the second volume. The Nazis just invaded France. Very, it's very captivating stuff. So that's one. I also just, I've been on like a little bit of like a quantum physics like kick recently. AI is very, actually very good for quantum physics if you get into it. And there's this book called The Rigor of Angels that I just finished, which is, um, it's like a, it's a history of ideas that relates, uh, Heisenberg, who has the, his uncertainty principle, um, Borges, who's, uh, uh, uh, like a, uh, uh, Argentinian, uh, fiction writer is wrote a bunch of great short stories that are actually starting to get like a lot of play now because they're very AI related and, um, and Kant and very cool, like super mind-blowing, lots of like interesting overlaps with AI stuff. And, uh, yeah, highly recommend.

I feel like we could have a whole podcast episode about your reading and, uh, books you recommend. I know this is a passion of yours. My current obsession is The Power Broker. I don't, I think we talked about it when I was visiting you. It's just so good. Never ends, but it's, uh, Surprisingly compelling to read through the history of New York. Okay. Second question. Do you, what is a recent movie or TV show you really see recently enjoyed if you have time for TV?

So I've been watching a lot of basketball, so that's one. Um, I'm, I became a Knicks fan like this, this year. So, uh, that's really fun. But, uh, I recently watched this, I guess it's like a, it's like a miniseries documentary called The Dark Wizard about this guy, Dean Potter, who he was like Alex Honnold before Alex Honnold was Alex Honnold. And, uh, he just has this like very extreme personality where he's like free soloing everything and then he's like, you know, base jumping in like a wingsuit and stuff like that. And it's sort of exploring his psychology and what happened to him. And, um, I, I don't know. I, I kind of like stuff like that. Like there's another one called Hundred Foot Wave where it's like about people who are trying to like big wave surfers. There's something about that that sort of, I guess it just reminds me of founders or whatever, but The Dark Wizard, highly recommend.

Is there a product you recently discovered that you really love?

Codex.

Okay.

It's like, it's the best. It's really good. It's really good.

Do you have a favorite life motto that you often come back to in work or in life?

Yes, I have several. The, the, like the core one that I wrote for myself in college was do things worth writing about and write things worth reading. And, uh, and then there's, there's this guy, Rob Berbeya, who's like very, um, very popular in like, you know, the AI meditation, like overlap discourse, which is also a big thing. Um, and who I also, I really like him. He's dead, but I think he's amazing. And I listened to like so many of his talks and there's like this one talk that he gives. where it's just like one sentence, but he just talks about like when you're dealing with stuff that's hard, what you wanna do is be able to relate to it from a position of spaciousness and strength. And there is something I think really interesting and important in that, like a lot of the meditation discourse or just generally like how do you deal with hard things? It's like a little bit more of like the David Goggins, like you just gotta like, just gotta like go for it kind of. And like just, um, and sometimes that, sometimes that can work. And also I think sometimes when you're dealing with things, so for example, when you're dealing with, I'm super afraid of like how AI is going to, um, uh, you know, change my job. It is, it has been very helpful for me to be like, am I coming at this from a vantage point of spaciousness and strength? And if not, can I like get there? Because it will be much more productive for me to deal with it from that place. And that has been very, very helpful for me. Wow.

I love that. Well, our final question, uh, just on the, on the theme of this conversation, curious if there's just like an AI tool that you think is still kind of underrated that you're just like recently, uh, I mean, like, I, I, I don't say Codex.

I hate to say this, but I have to because like any, anyone who knows me, like we were at this, this conference recently, an Anthropic conference, and I'm like telling like Boris and Kat from Cloud Code, like you have to try Codex. And, um, it's, it's just really good. And the things that you can do with it are so different. Um, uh, especially if you're using it with the in-app browser to do things like your emails or check, check analytics or like anything like that. It has completely transformed the way I work and I would be doing you a disservice if I like was searching for something else because it is that good.

Damn, that's wild. Do you feel like Anthropic can catch up and, or is this just like, well, they get—

No, yes, I think they can. I like, like I said, I think it's going to be a horse race and different people will be ahead at different times, but I think right now OpenAI has like, has, has gotten back the mandate of heaven a little bit. It's been, it was a rough couple, a couple months, like 6 months or so, but I think they're back.

Interesting. And, and you'd switch if one became—

I would, I would. People, people, it's funny. People are like, oh, are you like sponsored by OpenAI? And I'm like, no, I just like talk about what I like. I was super loud about Claude Code when that was the thing I really liked. And I'll just say what I like when When it happens, you know?

And to your point, people like, there's a lot of value in using both for different things.

So people— There is. I, I switch back and forth. Like, I, I truly do still use Claude a lot.

Yeah. Such a big market. Well, Dan, we did it. We, we went through so much. I can't wait to revisit this in a year slash, uh, get this out so people can start planning for this next year. Um, 2 final questions. Where can folks find you and Every? What should people know? And then how can listeners be useful to you?

You can find me on X at Dan Shipper, S-H-I-P-P-E-R, and you can subscribe to Every. Please subscribe to Every, every.to, every.to/subscribe. How can listeners be useful? You know, have fun with AI. Like seriously, it's, it's super fun. There's like a lot of, it's not necessarily useful to me, but like it's, it makes it, I think it makes everything better when people put their hands in it and just like start figuring it out together rather than like arguing about it. And, um, so the most useful thing you can do is like find ways to use it well in your life and share it.

Dan, thank you so much for being here.

Thank you. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review as that really helps other listeners find the podcast. You can find all past episodes or Learn more about the show at lennyspodcast.com. See you in the next episode.