Lenny's Podcast: Product | Career | Growth
00:33:24 · Roman Ugarte
[00:33:24] S0: I think a lot of it comes down to starting from scratch and how freeing that is. And we felt that ourselves where a lot of the primitives in Grok Bot we had attempted or we had built in other ways, cloud infrastructure we built for coding agents. The way that you could maybe name agents and talk to them as discrete entities, it's a pattern that we're also seeing for developers bringing specific named agents into Slack. But instead of trying to retrofit those concepts into some new surface or into an existing surface, which I think would've been the strong default I think for many companies, we decided to start from scratch and we decided to just try to get these things really right for general knowledge work, which is a new audience. And then second, for the point in time that we're at now where the models are very capable and if you give them the right tools and the right infrastructure, they can do a lot. But a lot of these ideas, these aren't strokes of genius on our part and I think for good reason. I think these are primitives that had already been getting attention and product market fit by other products, the open claws of the world. And I think we took a lot of inspiration from that and tried to productize it into a bit of a tighter surface, something that required a little bit less setup and I think it was more accessible to more people. And so I think our competitors and other tools that have been trying to solve these types of problems, I think we're all seeing the same opportunity. I think we're seeing a lot of the feedback from the market, but I think it's just been hard to act on if you're stuck in the existing paradigm. And if you have a lot of sunk cost in that existing paradigm, it's very painful to create a new thing from scratch. And I think a lot of that is what allowed the product to just work and click for so many people.
00:35:17 · Lenny Rachitsky
[00:35:17] S1: So what I'm hearing here is the keys to success of what made this breakout a cloud-based computer for every bot instead of locally, a name kind of specific bot. And by the way, there's this, we're all moving from agents to bots now. Nice job. It feels like you guys have pushed it over and now, okay, we're all bots now. So a bot per kind of task use case, very unique versus a thread conversation or something or a one-off job. And then it feels like it just works, was a core part of this. And you talked about how long it took to get to that place of like, okay, now it actually works really well. You mentioned OpenClaw. Obviously this is inspired by that, which to me, when I first used OpenClaw, like, holy, this is the future. How could we not have this? And then Hermes came out and everyone's been trying to build the OpenClaw that works very easily for everybody. Can you say more about just how OpenClaw and that story informed the way you guys thought about it?
00:36:20 · Roman Ugarte
[00:36:20] S0: Yeah, so I think OpenClaw got two major things right that when we were seeing the way the market was reacting to OpenClaw and ourselves using the product, we found quite exciting. I think the first thing was the models are really smart and they're going to continue to get smarter, but even at current capability levels, if you can just give your bot access to the tools that you use to do your job, it can get a lot of the way there. In a lot of the places where people think AI is dumb or maybe not as impactful as it's been promised, a lot of that I think is downstream of it just being harnessed in the wrong way. And so if you give access to a much larger set of things, if it has access to its own computer, how far can you go? And I think OpenClaw really forced that question for many people. And then I think the second way OpenClaw changed the mental model of AI was really viewing these things much more as colleagues and teammates and people and personifying it a bit more and it being this helper entity that has access to your life and can extend you even further. And so we took a lot of that and I think what Grok Bot maybe extended was, A, it needs to be really easy to set up and the hacky, you have a VPN at home and a Mac mini set up, clearly it was not going to scale to millions of users. Clearly it's not going to be the way importantly that businesses take advantage of this technology. And so we really wanted to build an amazing product with that in mind. And then second is I think there are a lot of places, a lot of rough edges to stand down and just make a delightful product experience and make these things just work and try to remove some of the abstractions that power users of AI are very familiar with, things like skills, for example. How can we make a Grok Bot user not even have to know what a skill is? They should never have to type a slash command. These things should be created in the background as a useful primitive that the bots have access to, but something that users, it's not incumbent on them to always be on the cutting edge of AI. And so that's really where we tried to innovate and I think there's still more room to go there.
00:38:40 · Lenny Rachitsky
[00:38:40] S1: As you say that, I have my Mac mini with my formerly live OpenClaw on there and that was an era and it's so awesome the work that it has inspired. I know it continues. I know there's still a lot of value to OpenClaw, but when I saw Claire Vo, who's been the biggest proponent of Open Claw and it's become a core part of the way she lives and works with her kids and does all her work, she just switched all over OpenClaws. She shut them all down and switched to Grok Bot. It sounds funny, but that's actually a huge milestone of just how much things have shifted. What's the vision for Grok Bot? Where does this go? What does this look like in the future? What's the ideal platonic version of Grok Bot?
00:39:31 · Roman Ugarte
[00:39:31] S0: The ultimate vision of Grok Bot is incredibly simple, which is you should have a team of AI bots that help you with your job and help you with your life. And it should really feel like a team. It should really feel like teammates that are autonomous are helping you. You can steer them in various ways. You don't have to micromanage them. They have access to the tools necessary to do great ambitious work. And one thing we really use as a north star on the product side in building this is as we kind of get closer to this teammate future, how can we, in every product decision we make, think about this less from the perspective of a SaaS product and more from the perspective of we're trying to build useful AI teammates. And so there have been a bunch of examples where we kind of have to push ourselves to be more like colleague pilled in a way. We sometimes use that term where we're having a product debate about something, there are good arguments on one side, there are good arguments on another side, both paths feel sensible. In product land, this maybe doesn't feel like there's a clear cut answer. And then you zoom out a little bit and you remove yourself from the tech company-ness of it all and you start thinking, how would a human do this? What would you want from your teammate in this exact situation? And oftentimes the answer is really clarifying and pretty unanimous. There's oftentimes not a lot of disagreement among the room of how a human teammate you would prefer to work with in a certain way. And then once that answer is there, well then we just need to build it and there are product implications, there are model implications, there's a lot of things that need to go right to actually deliver on that experience. But in some ways it's not a rocket science, doesn't require you being a genius. You just need to ask the question of what would you want from a human teammate and can we push AI to behave in a similar way? And so to give some examples of that, we've been thinking about what the right voice experience with these bots should be. And I think in the context of a human, for example, we have a really good analog of a lot of times I'm slacking back and forth with a teammate, we're sharing context, and a lot of times it's just much simpler to get on a five-minute huddle with them and just press huddle, talk back and forth.
00:41:51 · Roman Ugarte, Lenny Rachitsky
I share my screen, I show exactly what's on my mind, they share their screen, we hop off, and then we continue async from there. And that's not really an experience that any AI product has gotten right right now, and it is deeply integral to the way that I think humans collaborate. And so we want to build something like that. And there are a bunch of other examples of these very clear patterns that just work that I think you should also feel when working with AI. [00:42:19] S1: I love this term colleague build, and it's come up so many times over the course of this chat already how that is kind of a through line to making these decisions. For example, the computer example you gave is so good. Obviously people would have their own computer. The naming piece is also very important part of that. A big question on my mind in this space, and I'm so curious to get your take, is the separation between work and personal. Do you think people will have two different assistants, a work and a personal, or do you think it'll be one?