Lenny's Podcast: Product | Career | Growth
00:01:26 · Dan Shipper, Lenny Rachitsky
[00:01:26] S1: We speed ran the CLI era. It was nice while it lasted, but I think CLIs are over. [00:01:33] S0: 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. [00:03:01] S1: Thanks for having me. Always a pleasure to be with you.
00:03:03 · Lenny Rachitsky
[00:03:03] S0: 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. 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.
00:04:29 · Dan Shipper
[00:04:29] S1: 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. 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. 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.
00:07:01 · Dan Shipper, Lenny Rachitsky
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. 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? [00:07:48] S0: 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. [00:09:39] S1: Lenny, my only ask is we come on a year from now and then you score it. I want to score it. [00:09:45] S0: 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?
00:09:45 · Lenny Rachitsky, Dan Shipper
[00:09:45] S0: 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? [00:09:58] S1: 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. [00:10:07] S0: Okay. [inaudible 00:10:08]. [00:10:08] S1: So it should within at least a year be not obviously wrong. It should seem like it's moving in that direction to count. [00:10:16] S0: Okay. May of 2027, we will review your predictions. [00:10:22] S1: Amazing. [00:10:24] S0: 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?
00:10:29 · Dan Shipper
[00:10:29] S1: 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. METR 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. 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. 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?