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

**Lenny Rachitsky** (00:00:00):
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.

**Dan Shipper** (00:00:10):
The AI jobpocalypse is not really a thing. I am super, super bullish on PMs and full stack designers.

**Lenny Rachitsky** (00:00:18):
You guys are hiring, doubled in people in the past year, which is not what people would've expected from a company that is so AI-forward.

**Dan Shipper** (00:00:24):
I'm simultaneously extremely AI pilled and very bullish on humans. Automation is a lot. Every agent needs a human. We have so much automation, so much AI, and I also work way more.

**Lenny Rachitsky** (00:00:34):
Creativity, it just feels like it's going to be more and more valuable to stand out from all the slot that people are shipping and launching constantly.

**Dan Shipper** (00:00:40):
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 make something new and interesting?"

**Lenny Rachitsky** (00:00:54):
What are some predictions for how the way we work is going to change?

**Dan Shipper** (00:00:57):
It's going to bifurcate in two 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.

**Lenny Rachitsky** (00:01:13):
What you're predicting here is the SaaS tools will run within Codex or Claude Code.

**Dan Shipper** (00:01:17):
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.

**Lenny Rachitsky** (00:01:24):
A lot of people are moving to CLI and trying to work from the terminal.

**Dan Shipper** (00:01:26):
We speed ran the CLI era. It was nice while it lasted, but I think CLIs are over.

**Lenny Rachitsky** (00:01:33):
Today, my guest is Dan Shipper, 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 Claude 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/what you need to be working on right now.

**Dan Shipper** (00:03:01):
Thanks for having me. Always a pleasure to be with you.

**Lenny Rachitsky** (00:03:03):
The last time you were on this podcast, you had this, 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 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 unreal what has happened since then. They built Cowork, which 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.

**Lenny Rachitsky** (00:03:48):
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 do you use Claude Code for non-engineering work?" And I just had so many examples and it's like my second most popular post. 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 work.

**Dan Shipper** (00:04:29):
Thank you. I really appreciate the introduction. And yeah, I think one of the things about predicting the future or the way that we think about predicting the future at Every is that what you don't want to do is prognosticate. What you want to do instead is just live in it together. So everybody at Every is an AI early adopter. We're almost 30 people now. I think when we did our interview, we were 15, so we've doubled in size 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, 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 super all-in on AI." And what that does, I think, is it creates this little pocket of the future where we're all living in it together and we get to be a little bit further ahead because at any other company, there's a mix of people.

**Dan Shipper** (00:05:31):
There's early adopters. There's the middle of the pack people and there's people who are very anti. And another thing that happens, which is really cool is we get to, because of our role reviewing models and being a little bit of a pacemaker in AI, we get access to stuff before it comes out. So we get to beta test and alpha test and help steer the direction of where things are going a little bit, which is very, very cool. And so when I think about predicting the future, it's actually, when you create an environment like that, it's actually just about noticing what's going on. I think a core part of it too is writing about it. I think articulating what you're noticing, articulating the future 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.

**Dan Shipper** (00:06:24):
And so the Claude Code thing, it's this very organic thing where, for us, we tried Claude Code when it came out. That's our job. We tried 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." They got rid of the code editor. And so from that point on, we just basically, at this point now we run like six software products internally. At that time we ran maybe two or three. And from that point on, we just started shifting to a world where no one was looking at the code. Everybody was talking to their computer in English, using Claude Code in the terminal.

**Dan Shipper** (00:07:19):
And so I was able to see like, "Ooh, this is starting to happen." And then because my job is a little bit to just push and play with stuff, I was like, "I wonder if I could use this for my writing. How could I do that?" And then it just starts to unfold and you're like, "Okay, this is not ready yet, but it's obviously useful for me." One of the things that we talk about internally is what I call the reach test, which is like, when you wake up in the morning, do you reach for it organically?

**Lenny Rachitsky** (00:07:48):
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 the perfect recipe for someone having a sense of where things are going.

**Dan Shipper** (00:09:39):
Lenny, my only ask is we come on a year from now and then you score it. I want to score it.

**Lenny Rachitsky** (00:09:45):
Okay. So this is a year from now. Okay. Okay. Is this like your predictions for, in a year, this is what it's going to look like? Or this is like the emerging future?

**Dan Shipper** (00:09:58):
I will probably say, I don't have an exact timeline. I think most of the stuff that I'm going to talk about will be pretty apparent within a year, but it may take longer than that.

**Lenny Rachitsky** (00:10:07):
Okay. [inaudible 00:10:08].

**Dan Shipper** (00:10:08):
So it should within at least a year be not obviously wrong. It should seem like it's moving in that direction to count.

**Lenny Rachitsky** (00:10:16):
Okay. May of 2027, we will review your predictions.

**Dan Shipper** (00:10:22):
Amazing.

**Lenny Rachitsky** (00:10:24):
Okay. I love this. Okay. So let's dive in. What are some predictions for how the way we work is going to change in the coming year?

**Dan Shipper** (00:10:29):
One of my favorite questions, because I think if you look at the benchmarks, you're just looking at, okay, yeah, AI is going to just take all of our jobs, basically. Meter has this really cool benchmark where it's like it measures how long the newest models can do tasks autonomously and it's like, oh, it can, what's it called? Oh, Mythos preview, the big anthropic model that everyone's 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, 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 a really interesting paradox there. And my big prediction of how work will change or how you will be doing work in a year is it's going to bifurcate in this in two main ways, how you use agents.

**Dan Shipper** (00:11:41):
One is you're going to be doing, I think what we figured you would be doing five years ago when we thought about how work with AI works, which is everyone's going to have at least in their company, at least one agent that they talk to, that can do work, that they can offload work to. And we'll talk about 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 operating system for ... It becomes a operating system for how you do all of your work, whether that's your email, the documents you create, all that stuff. It's going to be on that kind of surface. That's becoming the clear competitive landscape.

**Dan Shipper** (00:12:37):
I want to go in order of those two. So the first one is you're going to have agents you delegate to probably in Slack, but anywhere. First thing that's interesting about that one is it's not clear what the architecture is going to be like for that. Is everyone going to have an agent? Is every team going to have an agent? Is it going to be just one agent? Do agents specialize? Is there this 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 everyone has their own agent and there's some really interesting things about that world of a parallel org chart. Agents in that world become little reflections of you, which is really cool and really interesting. It's like if you ever ... Did you ever read the Golden Compass?

**Lenny Rachitsky** (00:13:33):
Yeah.

**Dan Shipper** (00:13:34):
It's like having a little daemon on your shoulder ...

**Lenny Rachitsky** (00:13:36):
[inaudible 00:13:36].

**Dan Shipper** (00:13:36):
That's a little part of your soul. I really think that's what it looked like was happening. And so I was very into personal agents and I have completely flipped and I really think that the model, for now, is going to be a super agent, like one agent for the entire company and you're starting to see this in some companies. So Shopify very famously has one, Ramp has one now and I think there's some 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 going to set it up. It's so cool," or whatever. And then everyone realizes it's like way too much work. This thing breaks all the time. I got to fumble around with it. I got to be able to SSH into my server and like blah, blah, blah. And most people, to do work at least, just don't want to spend that time, or can't.

**Dan Shipper** (00:14:38):
And the 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 a human personal connection with someone who's watching what it does and makes 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 profile who's responsible for making sure that that agent is working for the whole company and then maybe you have some little team agents.

**Dan Shipper** (00:15:34):
And I think as the models get better at being more independent, that will shift down and 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 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 like async agents, it's really a, you have one agent at the top that's like doing, sometimes it's everything. A lot of times it's a particular job that you've decided that everyone in the company needs an agent for, like data requests. And then I think it starts at the top and then it 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.

**Lenny Rachitsky** (00:16:24):
That is so interesting, that point about you need to garden your agent because there's context you have to keep adding to it. It breaks, as you said, and once it's just too much work, you're like, "Okay, forget this thing. I'm going to go back to Codex or Claude or something like that."

**Dan Shipper** (00:16:38):
Exactly.

**Lenny Rachitsky** (00:16:39):
Okay, cool. So this is a cool opportunity. So the idea, so what you're predicting here is 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?

**Dan Shipper** (00:16:49):
I can't remember.

**Lenny Rachitsky** (00:16:50):
Okay. It's probably got to fun name. Okay. So that's the prediction. Okay.

**Dan Shipper** (00:16:56):
That's the first prediction ...

**Lenny Rachitsky** (00:16:57):
That's the first prediction.

**Dan Shipper** (00:16:59):
Is we will start with agents at the top that are more general and are used by more people in the company and then it will start to grow down, as people get more used to these use cases, they get more specialized and agents become less ... Fiddly. They just work better.

**Lenny Rachitsky** (00:17:22):
And is this mostly going to be in Slack, do you predict?

**Dan Shipper** (00:17:25):
For work? Yeah, it seems to make sense. I think people love having the green bubbles on OpenClaw. Sorry, the blue bubbles on OpenClaw, 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. And I think there's a whole territory. Our COO, Brandon Gell, calls this computer errands. There's 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 it's going to be huge for, but we focus mostly on the work stuff and I think that's going to happen mostly in Slack.

**Lenny Rachitsky** (00:18:07):
Sweet. Go Slack.

**Dan Shipper** (00:18:09):
Do you want to talk about the other work surface?

**Lenny Rachitsky** (00:18:09):
Absolutely.

**Dan Shipper** (00:18:12):
Like Codex, Cowork. Okay. This is the one that-

**Lenny Rachitsky** (00:18:15):
Let's do it.

**Dan Shipper** (00:18:15):
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 access to everything that you have access to. It uses the terminal, so it has basically superpowered access to it. And not only that, these agents really understand how to use the terminal because there's so much content online about that and it created this super powerful coding paradigm, which is Anthropic was really doing it first. OpenAI, for a while, was, in my opinion, 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.

**Dan Shipper** (00:19:17):
There were people before them, like Devin, who, I think, had the big cloud environment and OpenAI tried this too, but 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 of their work. Anthropic then built Cowork, which is a little bit of a nicer wrapping around Claude Code, but is fundamentally the same thing. And then I think OpenAI made a couple of different bets, but their main bet on a programming agent was the earlier versions of Codex were very technical and they were super smart, but they were a little bit autistic.

**Dan Shipper** (00:20:13):
It was a little hard to ... They didn't quite get what you meant. They got exactly what you said. And I think maybe like three or four months ago, around the time that they launched 5.3, they started to move in this direction of, "Oh no, we get it. This model is fast. It's 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 Anthropic learned, they went from Claude code to Cowork and you can see that in the tabs on the Anthropic desktop app UI. I think OpenAI was just like, "We see where this is going. Let's just skip to that." And so I think Codex right now, this is a horse race. They're going to have different positions, but I think Codex right now, it's my daily driver.

**Dan Shipper** (00:21:06):
I spend 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 is in the lead, because 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, for example, when I'm writing a document, Codex has a browser in the app. It has an in-app browser. And when I'm writing a document, I just go into 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.

**Dan Shipper** (00:21:49):
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 in one place, which is an extension of the same thing that made Claude Code work really well originally. And I basically feel like I have this parallel work buddy that not only can it respond and write in the document, but then it can go do research, it can use my computer to basically do anything that I can do on my computer and that's incredibly powerful. And I do this with everything. I've been at inbox zero for 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 Quora, which is our email agent. And then it renders a little page, and I think I showed you this at the Anthropic event. It renders a little page and I just monologue into it and just talk out each email.

**Dan Shipper** (00:22:49):
I'm like, "Okay, go research this. Oh, here's a question from our lawyers. Can you go collect all of the documents from the last four 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, I thought too that the optimal experience of AI was going to be take AI and put it in a browser. And I think the reverse is actually starting to happen and be 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 is very uncommon now. You can't even do this in Claude Code because they don't let you browse external websites inside of Claude Code. So it's very uncommon now, but I think it will be super common in a year.

**Lenny Rachitsky** (00:23:45):
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 the SaaS tools will run within Codex or Claude Code?

**Dan Shipper** (00:24:02):
That is one-

**Lenny Rachitsky** (00:24:00):
... code.

**Dan Shipper** (00:24:02):
That is one really important second order effect of this is ... Okay, so yeah, I'm using Proof 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.

**Dan Shipper** (00:25:03):
I think you can, and there's another really interesting bifurcation of this that we should talk about, which is that having two agents is better than one. But I think for now there's this really cool thing where with Proof, for example, anyone who uses it, I don't pay for tokens because they bring their AI to Proof. And so, it changes what you build as a SaaS company 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 going to bring AI.

**Lenny Rachitsky** (00:25:39):
So, I think this is a huge deal. So, what you're describing here is more and more work that we do, more and more professional work, is it just going to happen within Codex or Claude Code? Where does Cursor fit into this? Is there potential there?

**Dan Shipper** (00:25:52):
That's a good question. I think that Cursor sees a lot of the same stuff, and in some ways they have some of the same stuff but it's better. I think that Cursor's cloud implementation is better than either OpenAIs or Anthropics and is more advanced, and I think that Cursor has at least so far more distinctly chosen a lane. They're more distinctly choosing to be for programmers, and that may limit how far they get in here. I think the definition programmer is expanding enough that they'll have a big market, but I don't know that they're going to 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 model. And so, where all the platforms are moving is to a world where you're not just doing prompt and response.

**Dan Shipper** (00:26:51):
When you call the model on the OpenAI platform, the Anthropic platform, they're literally running the model on a computer that is in the cloud that they run and then giving you the result out of it. And they know that in order to get the best results of the model they need to offer that. And so, you see 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 I can't just do the model part of it, I have to have this harness above it. And I think the ultimate form of that harness is like I can do any kind of knowledge work. Cursor itself 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.

**Lenny Rachitsky** (00:27:42):
So, if 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. Is there something you would do if you were one of those companies to prepare for that future?

**Dan Shipper** (00:27:59):
I would just prepare for that. So for example, every more classic piece of productivity software, whether it's Slack, or Word docs, or PowerPoints or whatever, it's really mostly meant for a human to use. And now people are doing CLI so it's meant for 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 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 seamless way. And the kind of software that you make for that is going to be very different. So for example, there's a lot of stuff that Proof doesn't have. I don't have to have a lot of the Word document formatting, or page breaks, or making tables or whatever, because the agent just does it. I don't need to worry about that, it can do all the formatting for me.

**Dan Shipper** (00:29:03):
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 a billion different things to your document, or your slide deck, or your code base 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 approval, you need a inbox that summarizes, "Here's all the stuff that's going to happen," or has happened. You need logs and the ability to roll it back real quick.

**Dan Shipper** (00:29:46):
So, there's all those kinds of considerations that change the actual product, and then the underlying UX of it or the underlying infrastructure you need is different too, because agents can make a billion requests in three seconds. So, how are you going to deal with that? This is exactly why GitHub is having problems right now because the number of people using GitHub is skyrocketing 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 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, 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. It has like, "Here's exactly what I did, here's the exact repro steps, here's like ..."

**Dan Shipper** (00:30:40):
Proof is open source so, "Here's what I think is going on in the code base." And then we just get that, it becomes a GitHub issue, and then we can just 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 glimmers of this very fast 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.

**Lenny Rachitsky** (00:31:13):
So, as a part of this that a lot of people are moving to CLI and trying to work from the terminal, is part of this prediction that people shift away from that and back to actual UX with agents-

**Dan Shipper** (00:31:23):
Oh, yeah. 

**Lenny Rachitsky** (00:31:24):
... running alongside them?

**Dan Shipper** (00:31:25):
CLIs are over. We speed ran the CLI era. It was nice while it lasted, but I think it's pretty clear ... 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 is this moment when Claude Code was so popular, or when Claude Code was really starting to gain in popularity that people were like, "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 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 of a GUI, especially for non-programmer work. But I would estimate that definitely the majority of the technical people inside of Every are not using CLIs anymore as their main work surface.

**Dan Shipper** (00:32:27):
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, Claude Code, Cursor, that kind of thing.

**Lenny Rachitsky** (00:32:35):
Awesome, okay. I definitely wanted to make that part clear. So, coming back to the big picture of the prediction here, there's these two modes of work that you're anticipating. One is this 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 on your computers now are going to be living within Codex or Claude Code, or maybe some third party that emerges that we're not even aware of yet.

**Dan Shipper** (00:33:06):
Yes. And you're going to use apps inside of the internal browser of those tools.

**Lenny Rachitsky** (00:33:14):
Wow, okay. Listening to you talk about it, it may not feel as profound as it is, because 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 Claude Code. So, this is actually a pretty massive shift.

**Dan Shipper** (00:33:32):
I think so.

**Lenny Rachitsky** (00:33:35):
Is there anything else along these lines before we get into our next prediction?

**Dan Shipper** (00:33:38):
Well, a few things. I'm definitely not an agent maximalist, I really think we're going to have a lot of different agents that we use. It seems pretty clear to me. And I really do think that two 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 speed up effect when you assume that your users are using Codex, or Claude Code, or Cowork as their basic way they access your app. And a really simple example, we have this hosted OpenClaw product, which we had on waitlist. We actually had to pause it because we started taking people off the waitlist, and OpenClaw is just a very a very hard agent harness to make work.

**Dan Shipper** (00:34:41):
It's moving so incredibly fast, and if you're like a platform for it, 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 let's say you're building an agent product or any new software experience, what you would assume, let's say to set up an agent, is you need to build a little web interface or a little Slack workflow that asks people about, "Okay, who are you and what are you going to use this for? And what's your ideal dream outcome?" Or whatever the things you are that you would put on an onboarding checklist.

**Dan Shipper** (00:35:24):
If instead you just make a hard line of we are only going to service users who use Codex or Cowork, what happens is 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 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, here's all the ways that 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 really magical and important way.

**Lenny Rachitsky** (00:36:22):
There's a couple more things I want to touch on before we get started, because there's so much to talk about. One is you made this point about SaaS tools not using ... You can use tokens from the 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.

**Dan Shipper** (00:36:41):
Well, I think it actually may save their margins, because right now all these companies are rushing to add a agent to their offering and thinking, "Oh, the agent is going to be the main way that people interact with me." And that cost tokens, obviously. And I actually think 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 think the SaaS apocalypse is dumb, and SaaS stocks will be up majorly in the next couple years. Not investment advice, but I would buy SaaS stocks.

**Dan Shipper** (00:37:25):
So, I think it saves your margin because now the way that you're thinking then is not I have to build AI into this, 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 once you build it, it's a lot cheaper than assuming everyone's spending tokens, and I think it's a good business. And part of the reason I'm so bullish on SaaS is A, everybody internally here is, like I said, we have 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 vibe coding every single little thing. 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 an insane spike in the amount of demand that they have, because there's going to be tons of agents using these products at a very high volume.

**Dan Shipper** (00:38:36):
And like I said, that's a huge infrastructure challenge. There's a lot of interesting pricing challenges, but it makes me very bullish on SaaS.

**Lenny Rachitsky** (00:38:47):
I love that. If anything else comes out of this conversation, Dan Shipper, SaaS is the future of AI. B2B SaaS.

**Dan Shipper** (00:38:56):
Hashtag send tweet.

**Lenny Rachitsky** (00:38:59):
Yeah, this is 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 have expected from a company that is so AI forward. Talk about your experience there of just, okay, we still actually need humans.

**Dan Shipper** (00:39:15):
Automation is a lie, 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 making sure that it's working well. And so, I wrote this piece a couple years ago about the allocation economy, the idea that the way that humans are going to work with AI is going to be 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 on the beach, they're checking in with their employees all the time 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 some differences between being a human manager and being a model manager, but fundamentally it still requires a lot of time and attention.

**Dan Shipper** (00:40:07):
And I think that we 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 this. It's 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 that we have so much automation, so much AI, and I also work way more. And I think part of the paradox started to 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 while running the rest of Every.

**Dan Shipper** (00:41:01):
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 the product worked, we tested it internally. We had a lot of beta testers, but the day after launch it was like just every 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. Codex, fix it." And Codex is like, "I don't know what's going on." Or really Codex was like, "I do know what's going on, I fixed it." And then it would cause four other errors and then you're just going around in a circle, and I wasn't sleeping, and I vibe coded so hard I got bursitis on my elbow. So, there's a life lesson in there. 

**Lenny Rachitsky** (00:41:42):
Vibe coder elbow.

**Dan Shipper** (00:41:46):
So anyway, I got actually two different senior engineers to fix it independently. So, I have two different rewrites of the code base that tells me how they did it. 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 slop. If you wanted to rewrite it from first principles, how would you rewrite it? Go do it." And all the models until GPT 5.5 got like a 30 out of 100, and 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 actually rewrite from first principles.

**Dan Shipper** (00:42:48):
Other coding models, they end up papering over the edges or around the edges, and they're like, "Oh, this is a big job. I'll just do a little patch." And you're like, "No, I 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 very clear that in a year or less it's going to be senior engineer level, 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, it will be very easy for me to change the benchmark to zero out the current model, so that gets a zero 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 coder slop. Fix it from first principles.

**Dan Shipper** (00:43:41):
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 four or five reported issues yesterday. I want you to go through all the issues and then make a plan for how to resolve all of them, and go do it." 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 in the market will take that instruction seriously.

**Dan Shipper** (00:44:32):
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 code base 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 actually rewrite a lot of this and it's going to be hard and risky. I know you all want to hear that, but we're going to have to do that." And if you asked the model, "Hey, should we do that?" It'll probably get there, but it's not going to do it on its own. And there's a lot of incentives pushing against it doing that. And even if it does that, there's always a higher frame for us to go. And so, I think it's really important 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.

**Dan Shipper** (00:45:25):
And there's a lot of work that's human work that it can't be scored until you write it down, but the act of thinking to prompt it or write it down is something that you can't measure but means that even if the benchmarks get saturated, it doesn't mean the same thing as you totally replace all senior engineers. And I think it's why even though the models are getting better at automation, I still hire engineers.

**Lenny Rachitsky** (00:45:52):
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One thing I mentioned recently on the podcast, I heard that speaking of the code that you have of humans writing code, data labeling companies are buying code that was written before 2021, 2022, before AI became a thing is like very valuable data [inaudible 00:47:16]

**Dan Shipper** (00:47:15):
Artisanal human code. 

**Lenny Rachitsky** (00:47:17):
Yeah, exactly. That's exactly right. And it's so interesting that that's exactly the kind of code used to build this model.

**Dan Shipper** (00:47:23):
Well, what's interesting, so I want to clarify there. So, I did not have a human write the code all by hand, because I actually think that it feels silly to me. I don't really care, because I know if an engineer's not using AI I'm not going to work with them. I don't really care. It's like am I going to race a human against a car? I probably wouldn't do that. But I would race a human in a car versus another human in a car and say, "Which one's better?" And in this case, the way the benchmark is structured is, yeah, 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-

**Dan Shipper** (00:48:00):
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 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 against humans, AI against humans, we're actually really always talking about one human using AI versus another human using AI 'cause AI doesn't use itself. It may be able to in this slightly somewhat recursive way, but in any real use case, there's always a human pretty close to it making sure that it's working.

**Lenny Rachitsky** (00:48:37):
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 should do based on your predictions to be successful. We'll talk about this at the end too, but just a few things. 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. Two is allow agents to use your products. If you're building a SaaS tool, make it easy for agents to be a user essentially. Three is start thinking about some Slack bot that you can work with, like try out tools. 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.

**Lenny Rachitsky** (00:49:24):
Buy SaaS stock ASAP, not investment advice.

**Dan Shipper** (00:49:32):
I think that's totally right. 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 they're being... I delegated to task the agent and the agent's using the CLI. And 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 both need to be in sync. And that is, I think, a new challenge that's really interesting.

**Lenny Rachitsky** (00:50:06):
Awesome. Anything else before we get to our next category?

**Dan Shipper** (00:50:10):
Buy SaaS.

**Lenny Rachitsky** (00:50:12):
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?

**Dan Shipper** (00:50:25):
There's all this interesting stuff in terms of the shape of work. Once you're in this land where you've got async agents off that you delegate work to, then you've got your Codex Claude Code work surface 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 is like 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 that's a very different shape of work where you can expect that a higher percentage of your company or your users are going to 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.

**Dan Shipper** (00:51:26):
And so I think there's a lot of interesting things that happen with that. So for example, like OpenClaw, I mentioned that earlier. Pete gets thousands of poll requests a day on OpenClaw and then he just spins up 50,000 Codex instances and then sorts through them and then merges a thousand of them. It's really crazy. I actually think that that's going to be more and more common.

**Dan Shipper** (00:51:55):
It brings up a lot of really interesting questions around which poll requests should you merge. And whenever you add capacity in one part of your process, 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 sense of a coherent whole. And also, what do we delete? I think Anthropic does this really well. They delete a lot of stuff from Claude Code to make sure that it's not bloated. So I think there's a lot of that going to happen on one side. There's a lot of 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.

**Dan Shipper** (00:52:47):
And also their product people are going to be doing that too. And I think that's quite cool.

**Lenny Rachitsky** (00:52:52):
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 confusion about what the hell is my job anymore. What am I responsible for exactly? Am I supposed to be shipping stuff? Am I still a marketing person? And it's just creating a lot of confusion and certainty in the world, just a comment.

**Dan Shipper** (00:53:15):
I mean, it'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 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. Marketing people are still going to do marketing even if they're touching the website. 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 smaller companies. The other thing that I think is interesting is there are definitely some new job roles that are a thing.

**Dan Shipper** (00:53:52):
And the thing that is becoming really clear is the whole Ford deployed engineer concept I think is for real and it comes out of every agent needs a human. You go to the big model companies, they have these agents that run internally, they have like teams of people that run these agents and 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 we have a couple of those people internally here and it's like the people who are in charge of making sure your agents are working and doing the right thing. We also do consulting. So we lend that out to people and I think that's a big thing that people want.

**Dan Shipper** (00:54:44):
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.

**Dan Shipper** (00:54:53):
And there's a specific type of engineer that really loves... Nitesh, who fits this, he's an AI engineer and he fits this 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 Claudy, which runs our whole consulting practice and he spends a lot of time in Slack. 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? Let's fix that." And so there's certain kinds of engineers that I think love that and love having their hands on the latest thing and also love making this being that's in a workspace and it looks a bit different than more traditional, building more traditional software.

**Lenny Rachitsky** (00:55:45):
And your sense there is 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 feels like both are important. Is what I'm hearing like this is going to be a job for a long time. AI is not going to get smart enough to just be fully automated for a while.

**Dan Shipper** (00:56:06):
Yes. I'm simultaneously extremely AI-pilled, extremely, and very bullish on humans and the role of humans and making sure that AI is working well.

**Lenny Rachitsky** (00:56:16):
Interesting. Okay. So the two kind of buckets here that you're talking about, one is 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 there's so much more work reviewing all this sloppy output. 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 positive. Now it's just everyone's doing that and they're sharing their results and they're like, "No, this is not correct." And most of their job is now reviewing bad data science work.

**Dan Shipper** (00:56:53):
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 that 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 the way that it works inside of the big model companies, for example, 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 who has permission to access what. And so all of the basic questions, because there's a team that sets up this bot, all of the basic questions that people might want to ask that it sometimes that might get wrong, that they're constantly making sure it's getting it right.

**Dan Shipper** (00:57:42):
And so the data science team doesn't have to answer all the bullshit questions because there's another team building an agent that is set up to do that really well. But if the team didn't exist, the data scientists would hate their lives.

**Lenny Rachitsky** (00:57:56):
Yeah. It does though make the job maybe less fun because you're just sitting there gardening people's sloppy work versus-

**Dan Shipper** (00:58:04):
Well, that's what I think is like 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 the deeper questions that are harder for the team who's dealing with all the basic requests and building an agent to do that. It's like filtering all that stuff out so you can focus.

**Lenny Rachitsky** (00:58:24):
Here's a question I've been thinking about. I was not planning to talk about this, but it's something that I've been thinking about. So the question is which product tech role is the least changed now? So engineers, 100% of code AI now. It's like a completely different job. Product management, a lot of the 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 recent guests, 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?

**Dan Shipper** (00:59:06):
Well, on interesting thing is, I don't know if this counts, but CEOs and investors, it seems still very, very optional whether or not they use this stuff. It seems that way. I think the opposite is actually true. My experience, and we do a lot of this with senior executives and senior leadership teams, my experience is that your company's only going to 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 because 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 I don't have to do that. I'll just tell them what to do. And so I think if you're a CEO, you kind of can get away with your day looking very similar.

**Dan Shipper** (00:59:54):
I think that will change rapidly at some point where it'll be like, "Oh no, I'm way behind." But for now, or maybe even middle managers, those kinds of people, I think it's fairly similar. I think maybe sales because it's so in person.

**Lenny Rachitsky** (01:00:11):
Yeah, that's my vote.

**Dan Shipper** (01:00:14):
It's sort of creeping up in the kind of BDR. We can deal with a lot of BDR type queries. You're only talking to people who actually want it. For sales, it's so useful to do research. My favorite Codex, one of my favorite Codex experiences is we're hiring a head of L&D and 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've done really good technology education for a long time." And so I was like, "I feel like someone who worked at General Assembly and is now into AI would be really good." And I just literally typed it into Codex and then went off and was doing something else and I came back and it found this the perfect guy. It was like worked at General Assembly, was an instructor, is super AI-pilled and follows me on Twitter.

**Dan Shipper** (01:01:17):
So I just DM'd him and then I had dinner with him and it's like, that's crazy. That would've taken so long before and super valuable for sales, for recruiting, all that kind of stuff.

**Lenny Rachitsky** (01:01:27):
Yeah. Sales is where my mind went. The top of funnel AI is helping a lot with sourcing and qualifying things like that. It feels like the work of a salesperson is not fundamentally different and

**Lenny Rachitsky** (01:01:39):
Customer support's fundamentally changed. So that's interesting. Sales so far so good for those folks. Okay. So maybe just summarizing some of the predictions in this bucket of just the shape of the work, how it's going to change. What I'm hearing so far is just going to be a lot more reviewing of other people's output as a part of the work. And then two, 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. Anything else before we get into our third bucket?

**Dan Shipper** (01:02:11):
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 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 waiting for it to fuck up and then fixing it or whatever. And that can be the case, but I think a lot of it is this extremely interesting engineering challenge of building a system 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 really important questions that eventually probably filter into the work that the forward deployed engineering team is doing, but is like more generative and more new and you're dealing with harder questions. One last thing that I think is really interesting is I think that we will be reading way more AI generated writing in documents and emails and we will like it.

**Dan Shipper** (01:03:20):
And I think we are already doing this in coding where we read plan documents. I don't want an engineer to hand write a plan document. That would be very silly. It would be obviously silly. And I think the same is true when we did our 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 we had really one Notion agent and then we had a top level company strategy and then we had everybody in the company just 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 this relate to the overall company idea? All that kind of stuff.

**Dan Shipper** (01:04:10):
And then I got these incredibly good AI generated strategy reports or quarterly plans for each part of each team. And then I could go in and be like, "Okay, who needs to talk to each other? Which teams need to talk to each other that don't know they need to talk to each other? And which one of these is actually low quality or which one of these is high quality?" All that kind of stuff, it makes it a lot easier to process. And I see that all the time now. I consistently get AI generated stuff and there's a difference between an AI generated document that's slop and not. And the slap 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, big no-no, not allowed to do that. And I think we have this aversion to AI-generated stuff that will go away because the kind of strategy document that GBT 5.5 can write when it's directed well by someone on my team is way better than them just like dinking and dunking their fingers on the keyboard.

**Lenny Rachitsky** (01:05:29):
Right. Most people are really bad at writing strategy documents. The bar is low.

**Dan Shipper** (01:05:33):
Yeah. And same thing with email. Most of my email is written by GPT 5.5 in Codex right now. And 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 experience the other day where I had to send an email to one of our investors and I asked Codex to go do it. And 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 pretty close to that a lot of the time. It can be a little overformal and there's a couple of things that... It's just when you really think about it, most of your email is kind of... It's kind of wrote. It's kind of prosaic.

**Dan Shipper** (01:06:34):
I definitely want to be the one to think about what it should say, what it should say, but the actual sentences don't matter that much to me usually. Sometimes I do a lot and this is coming from a writer. 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.

**Lenny Rachitsky** (01:07:10):
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 working on planning and documents and email and things like that, that is much less scary that it's AI written. And to your point, people are already doing this, you almost prefer it a lot of times because people are really bad at doing anyway.

**Dan Shipper** (01:07:30):
We have this too for external stuff. 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.

**Dan Shipper** (01:07:47):
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 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 talk to the human about the story and the stuff that matters and the core ideas and the agent has all the details that it can then apply for you when you need it.

**Lenny Rachitsky** (01:08:21):
Awesome. Anything else in this category before we get into our final category?

**Dan Shipper** (01:08:26):
No.

**Lenny Rachitsky** (01:08:27):
Okay, let's do it. So the final bucket is just who will be successful in this AI future that we are approaching/what should people be working on to be successful in this next year or two?

**Dan Shipper** (01:08:40):
I am super, super bullish on PMs. And I know that your audience will probably love that, but 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' writing product and was a PM and had a big team and it got to tens of millions of revenue in 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 Claude Code, but he was extremely cursor pilled for a long time. And I would call him lightly technical, like knows what a database migration is. If he has to look at the code, I think he can understand it, but 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 technical knowledge that he does have with his really spiky product sense and sense for writing and sense for users and it's so dangerous.

**Dan Shipper** (01:10:01):
He ships faster than almost anyone on the team and he has such a eye for every single user, every single conversation, like what does it mean and how do we collect it into a story about where we want to go next, and what are the issues we need to fix? And 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 really AI-pilled.

**Lenny Rachitsky** (01:10:29):
Music to my ears, Dan, you're making a lot of very happy listeners here. 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.

**Dan Shipper** (01:10:48):
I really believe it.

**Lenny Rachitsky** (01:10:50):
This could be the highest rated podcast episode of my whole podcast. Everyone's going to be like-

**Dan Shipper** (01:10:50):
I love it.

**Lenny Rachitsky** (01:10:50):
... hell yeah.

**Dan Shipper** (01:10:55):
It's going to be okay. SaaS is back, PMs are back.

**Lenny Rachitsky** (01:10:58):
This is the most contrarian episode I've ever done. Oh my God.

**Dan Shipper** (01:11:06):
Okay. So the other people that I think are going to be super, super power 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 Slap and they can make stuff that looks so different and now they can actually build it.

**Dan Shipper** (01:11:51):
And what you see when we work with them internally is now they're just making pull requests. They don't need to hand it off as much. Sometimes they do, but a lot of times they just make pull requests and-

**Dan Shipper** (01:12:00):
... Need to hand it off as much. Sometimes they do, but a lot of times they just make pull requests and it's like the thing is built and that's it. 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 start their own thing because they can make stuff now. And I think designers are such creative people, and I think AI is a super tool for anyone like that.

**Lenny Rachitsky** (01:12:23):
I so agree. Even though there's cloud design, there's all these AI designing tools, once you see it, you're like, "That's definitely cloud design." The creativity to your point, 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. So I completely agree. It's interesting that designer roles, 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.

**Dan Shipper** (01:12:55):
That is really interesting. We'll see.

**Lenny Rachitsky** (01:12:58):
We'll see. We'll see. That might be a way to predict this is are people hiring more designers? I don't know.

**Dan Shipper** (01:13:02):
That is interesting.

**Lenny Rachitsky** (01:13:04):
Yeah. All right. So PM Designer thriving, killing-

**Dan Shipper** (01:13:09):
PM Designer thriving. I also just think generally the AI job apocalypse is not really a thing. Absolutely we see companies starting to reorganize and I think that makes a lot of sense. I think to be honest, a lot of the reorganization, you can say it's AI, but it's like we overhired and the company's not doing as well and all that kind of ... It was coming and this is a good excuse. But the mass unemployment thing I think 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.

**Dan Shipper** (01:13:56):
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.

**Dan Shipper** (01:14:10):
And what happens then is this is a new power that everyone has, so it gets adopted super rapidly and suddenly that stuff is everywhere. It's suddenly anyone can make a landing page. There's new landing pages everywhere. Suddenly everyone can write. There's slop tweets everywhere. But what's interesting is because it's all coming from these models and everyone's using basically the same models, it all looks the same if you use it in the most default basic way. And so it becomes commoditized. It's not valuable anymore.

**Dan Shipper** (01:14:50):
And what humans do is we sort of go in there, and we're like, "Yeah," we have all this 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.

**Dan Shipper** (01:15:34):
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 slop. How should this actually go in our code base? And I think that's something that the benchmarks rising doesn't really capture and it feels like a thing that will take a long time to change.

**Lenny Rachitsky** (01:16:02):
People may be hearing in this prediction here of just, okay, the job apocalypse not going to, people are not going to be all fired. There's going to be human jobs remaining for quite a while. It may be almost too comforting because 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?

**Dan Shipper** (01:16:25):
Yes. And I think that is actually super important. The only thing you need to do is ride the models and that means use them for whatever it is that you do. 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, now there are new powers. How can I use them? Instead of just being like, "I'm going to try to ignore it because it makes me afraid." Which I think is honestly, it's rational. It's a reasonable response.

**Dan Shipper** (01:16:56):
And also if you ride on top of them, they extend your powers in a way that doesn't leave you behind. You're part of the future and part of the way work happens. And I think that we're going to need people doing that for a very, very long time.

**Lenny Rachitsky** (01:17:16):
I like this term, ride the model. So 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?

**Dan Shipper** (01:17:27):
Well, one of the things that's really interesting is a lot of companies handicap their employees from even doing this because I don't know if you can use the latest models at Salesforce. A lot of times you have to wait or it's whatever. So maybe you have to do it in your off time. But the thing that I really like to do with new models is play, and there are certain things where I know it can't quite do it yet, but when a new model comes out, I always turn the rock over again to be like, "Can I do it now?" So it could not do the senior engineer benchmark last time, and I turned the rock over again and now it's at a 60 out of 100, which is really good. So the way to ride the models is not one specific thing because 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.

**Dan Shipper** (01:18:33):
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 a real human doing something because the people at 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.

**Dan Shipper** (01:19:02):
And so whenever a new model comes out, you get to be one of the first people in the world to discover what it might be useful for. And it's like a new discovery. And I think that's why, for example, we're in Brooklyn, but I really think of us, and I think we are quite far ahead of people in San Francisco because we just use them for everything. And if people do that consistently, I think it's going to be very hard to lose.

**Lenny Rachitsky** (01:19:35):
That is one of the most amazing things about AI right now is no matter how much money you have or a little money you have, you have access to the most advanced AI model. It's not free, so you need some money and 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 available.

**Dan Shipper** (01:19:59):
I know. I was at their event with you, their Code with Claude event with you last week or a couple weeks ago and they're all using mythos and I'm like, "God, damn it." It's so annoying, but I think that's totally true. If IBM had invented AI, you can bet it would not be like this, and it would be a bajillion dollars and only the top companies could use it and they would be using it in the weirdest, most uninteresting ways. 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." 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.

**Lenny Rachitsky** (01:20:51):
That's such a good point. And interestingly, it's also created the most fastest growing companies in history, the biggest companies in history.

**Dan Shipper** (01:20:57):
That's true. Those Silicon Valley guys, they're smart.

**Lenny Rachitsky** (01:21:02):
If I zoom out on the conversation, it's really interesting. There's kind of these two sides to the coin. One is not a lot is actually, so much is not changing. SaaS continues, jobs not disappearing. We're still emailing each other. We're still working on Slack. A lot of the work not changed. On the other hand, every role transformed, engineers don't write code, PMs don't write PRDs, design and design. 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 going to 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.

**Dan Shipper** (01:21:38):
That's how I feel. 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 making, all that kind of stuff. And, yeah, I feel like 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 what happened at the end of the horizon. It's like, are there dragons? Does it drop off into nothingness or whatever? A lot of people have a lot of deep intuition that there's something terrible going to happen over the horizon and also that 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."

**Dan Shipper** (01:22:28):
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 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 because we get to see it internally all the time. It's important not to let your mind get away from you and being like, "This is going to happen and this is going to happen and whatever." Because you're going to tell a story that sounds so real in the moment, but 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. And once you get there, I think you're sort of starting to see like, "Oh, yeah, this is a real thing."

**Lenny Rachitsky** (01:23:11):
Part of it is the AI companies are very good at scaring us about what 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.

**Dan Shipper** (01:23:21):
That PR strategy just does not make any sense to me. I do think that it's genuine, but it's so ineffective, and I think it's also wrong.

**Lenny Rachitsky** (01:23:32):
How about we end with maybe just a few things listeners should do to be successful over the next year with the way the world is moving.

**Dan Shipper** (01:23:42):
Ride the models. I would 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 agent products like OpenClaw or Hermes or for less technical people, there's like Victor, we have OnePlus ones. I would get comfortable with both of those ways of working and try to have fun. I think there's too much of I'm doing this because I have FOMO, I might lose my job or 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 do something enjoyable.

**Lenny Rachitsky** (01:24:32):
Nikhil Singal was on the podcast and the way he described it is you got to find your moment of joy with AI once you find like, "Wow, I can't believe AI did this for me. This is awesome. We're going to keep building stuff."

**Dan Shipper** (01:24:42):
Yeah, I agree.

**Lenny Rachitsky** (01:24:43):
So if you haven't seen that yet, then it's just like, try, fine, 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 apply code, go to Replit, just try to build the thing and often it's like, holy shit, this is so cool."

**Lenny Rachitsky** (01:24:58):
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?

**Dan Shipper** (01:25:09):
I think we covered it. We did a lot. This is awesome, and I'm very excited to see how well or poorly I do in a year, and I hope that you hold me to it.

**Lenny Rachitsky** (01:25:19):
We're going to have AI score us. How about that?

**Dan Shipper** (01:25:21):
Great.

**Lenny Rachitsky** (01:25:22):
Look at the world like a dance prediction. See where it goes. Well, with that, Dan Shipper, we've reached a very exciting lightning round. I've got five questions for you. Are you ready?

**Dan Shipper** (01:25:30):
I'm ready.

**Lenny Rachitsky** (01:25:31):
What are two or three books that you find yourself recommending most to other people?

**Dan Shipper** (01:25:36):
Obviously Annie Dillard. Everyone at every has to read The Writing Life. When you join, you get a copy and you have to read it. 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 its relationship to the future and to time. It's everything about every wrapped up into a very tight chapter. It's so good. And I think Annie Dillard just generally is fantastic.

**Dan Shipper** (01:26:11):
What else do I recommend? I'll just tell you a couple things that I've read that I've really liked recently, and whenever I like something, I always just 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 is 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, he did it. And there's something about what we do at everywhere. I feel some 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.

**Dan Shipper** (01:26:48):
And so Churchill History of World War II is fantastic. I just finished the first volume. I'm on the second volume. The Nazis just invaded France. It's very captivating stuff. So that's one. I've been on a little bit of a quantum physics kick recently. AI is 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, it's a history of ideas that relates Heisenberg, who has his uncertainty principle. Borges, who's an Argentinian fiction writer, wrote a bunch of great short stories. They're actually starting to get a lot of play now because they're very AI related. And Cont and very cool, super mind-blowing. Lots of interesting overlaps with AI stuff and, yeah, highly recommend.

**Lenny Rachitsky** (01:27:51):
I feel like we got a whole podcast episode about your reading and books you recommend. I know this is a passion of yours. My current obsession is The Power Broker. And I think we talked about it when I was visiting you. It's just-

**Dan Shipper** (01:28:03):
So good.

**Lenny Rachitsky** (01:28:04):
Never ends, but it's surprisingly compelling to read through the history of New York. Okay. Second question, what is a recent movie or TV show you recently enjoyed if you have time for TV?

**Dan Shipper** (01:28:15):
So I've been watching a lot of basketball, so that's one. I became a Knicks fan this year, so that's really fun. But I recently watched this, I guess it's 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 he just has this very extreme personality where he's free soloing everything and then he's base jumping in a wingsuit and stuff like that and it's sort of exploring his psychology and what happened to him. And I don't know, I kind of like stuff like that.

**Dan Shipper** (01:28:58):
There's another one called Hundred Foot Wave where it's about people who are trying to select big wave surfers. There's something about that that sort of, I guess just reminds me of Founders or whatever. But The Dark Wizard, highly recommend.

**Lenny Rachitsky** (01:29:09):
Is there a product you recently discovered that you really love?

**Dan Shipper** (01:29:12):
Codex.

**Lenny Rachitsky** (01:29:13):
Okay.

**Dan Shipper** (01:29:15):
It's really good. It's really good.

**Lenny Rachitsky** (01:29:18):
Do you have a favorite life motto that you often come back to in work or in life?

**Dan Shipper** (01:29:22):
Yes, I have several. The core one that I wrote for myself in college was do things worth writing about and write things worth reading. And then there's this guy, Rob Burbea, who's very popular in the AI meditation overlap discourse, which is also a big thing and who I also, I really like him. He's dead, but I think he's amazing. And I've listened to so many of his talks and there's this one talk that he gives where it's just one sentence, but he just talks about when you're dealing with stuff that's hard, what you want to do is be able to relate to it from a position of spaciousness and strength.

**Dan Shipper** (01:30:13):
And there is something I think really interesting and important in that. A lot of the meditation discourse are just generally like, how do you deal with hard things? It's a little bit more of the David Goggins.

**Dan Shipper** (01:30:24):
You just got to go for it kind of. And 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 how AI is going to change my job, 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 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.

**Lenny Rachitsky** (01:31:04):
Wow. I love that. Well, our final question, just on the theme of this conversation, curious if there's just an AI tool that you think is still kind of underrated that you're just recently people should know about. Don't say, "Codex."

**Dan Shipper** (01:31:21):
I hate to say this, but I have to because anyone who knows me... We were at this conference recently, an Anthropic conference, and I'm telling Boris and Kat from Claude Code, "You have to try Codex." And it's just really good and the things that you can do with it are so different, especially if you're using it with the in app browser to do things like do your emails or check analytics or anything like that. It has completely transformed the way I work and I would be doing you a disservice if I was searching for something else because is that good.

**Lenny Rachitsky** (01:31:59):
Wow, damn, that's wild. Do you feel like Anthropic can catch up or is this just like, "Well, they get [inaudible 01:32:07]"

**Dan Shipper** (01:32:06):
No, yes, I think they can. 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 gotten back the mandate of heaven a little bit. It was a rough couple months, like six months or so, but I think they're back.

**Lenny Rachitsky** (01:32:25):
Interesting. And you'd switch if on became-

**Dan Shipper** (01:32:28):
I would. I would. It's funny. People are like, "Oh, are you sponsored by OpenAI?" I'm like, "No, I just 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 it happens.

**Lenny Rachitsky** (01:32:42):
And to your point, there is a lot of value in using both for different things.

**Dan Shipper** (01:32:46):
There is. I switched back and forth. I truly do still use Claude a lot.

**Lenny Rachitsky** (01:32:50):
Yeah. Such a big market. Well, Dan, we did it. We went through so much. I can't wait to revisit this in a year/get this out so people can start planning for this next year. Two final questions. Where can folks find you and every, what should people know? And then how can listeners be useful to you?

**Dan Shipper** (01:33:07):
You can find me on X@danshipper, S-H-I-P-P-E-R, and you can subscribe to every, please subscribe to every.to, every.to/subscribe. How can listeners be useful? Have fun with AI. Seriously, it's super fun. It's not necessarily useful to me, but I think it makes everything better when people put their hands in it and just start figuring it out together rather than arguing about it. And so the most useful thing you can do is find ways to use it well in your life and share it.

**Lenny Rachitsky** (01:33:40):
Dan, thank you so much for being here.

**Dan Shipper** (01:33:42):
Thank you.

**Lenny Rachitsky** (01:33:43):
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.