Lenny's Podcast: Product | Career | Growth
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?
00:13:04 · Dan Shipper, Lenny Rachitsky
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? [00:13:33] S0: Yeah. [00:13:34] S1: It's like having a little daemon on your shoulder ... [00:13:36] S0: [inaudible 00:13:36].
00:13:36 · Dan Shipper
[00:13:36] S1: 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. 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. 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.
00:16:02 · Dan Shipper, Lenny Rachitsky
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. [00:16:24] S0: 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." [00:16:38] S1: Exactly. [00:16:39] S0: 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? [00:16:49] S1: I can't remember. [00:16:50] S0: Okay. It's probably got to fun name. Okay. So that's the prediction. Okay. [00:16:56] S1: That's the first prediction ... [00:16:57] S0: That's the first prediction. [00:16:59] S1: 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. [00:17:22] S0: And is this mostly going to be in Slack, do you predict?
00:17:22 · Lenny Rachitsky, Dan Shipper
[00:17:22] S0: And is this mostly going to be in Slack, do you predict? [00:17:25] S1: 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. [00:18:07] S0: Sweet. Go Slack. [00:18:09] S1: Do you want to talk about the other work surface? [00:18:09] S0: Absolutely. [00:18:12] S1: Like Codex, Cowork. Okay. This is the one that- [00:18:15] S0: Let's do it.
00:18:15 · Dan Shipper
[00:18:15] S1: 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. 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. 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.