Lenny's Podcast: Product | Career | Growth
00:30:03 · Lenny Rachitsky
[00:30:03] S1: So I tweeted about how much I love Grok Bot when it launched and a lot of people replied, they're like, "Wait, can't you just do all this with Codex and Cowork?" And you can. Technically everything, as far as I know, you can do with Grok Bot you can do with the other foundational models, the coding assistants. So let me just ask you this big question. What is it that you think you did that is so different, that made Grok Bot so successful?
00:30:28 · Roman Ugarte
[00:30:28] S0: I think it was two early decisions that at the time definitely did not feel obvious, but in hindsight, I think are critical to what makes Grok Bot work for people. And the first is you should never have to think about local and cloud and where are these workflows running? Does my computer have to be awake? If I kick it off from my phone, does it need to be tethered to my computer back at home? There's so much jank happening right now when people are trying to conceptualize where this runtime lives. And we made a really early decision that this should just all be in the cloud. And if it's in the cloud and it's this persistent colleague that has its own computer, it can do its own work, it has the same state everywhere you interact with it opens up a lot of really amazing opportunities to text your bot, kick it off from your phone. In the future, you should be able to call your bot from anywhere and it should be able to do real work.This is its own entity and it lives separately from your device. And I think that was a very important decision that current products I think haven't made that same decision. And I think it has a bunch of paper cuts as a result of it that users feel every day. I think the second decision was to take that one step further of not only should this be an agent loop that kind of runs in the cloud and you can interact with in various ways, it's actually really important that these bots have their own computer. And part of it is what I described earlier of there are a bunch of tasks that don't have well-supported MCPs and APIs. We as humans don't do our jobs via MCPs and APIs. We use a computer and we click on pixels and we kind of type things and input boxes and it's very important that your bot has those baseline capabilities as well. But even to go one step further, I think we're in a really weird moment right now that I think we're going to look back on and be like, I'm surprised that this is the way that a lot of people worked with AI, where you're onboarding these super intelligent new colleagues, these AI bots, and you're asking them to share the same computer that you have. It's crazy. If you were onboarding someone to your team and you said, "It's your first day, I'm going to onboard you.
00:32:41 · Roman Ugarte, Lenny Rachitsky
You don't have your own laptop, you're going to sit next to me, we're going to share this laptop forever and constantly trip over each other. You're going to have access to my credentials, I'm going to have access to your credentials." There's a good reason why that's not the way people operate. And I think bots and these kind of AI colleagues of the future will also need a way to onboard them that's somewhat similar and we've tried to push the product in that direction. [00:33:10] S1: That is so funny. Why do you think the other companies didn't do this? My guess is they were building off of their existing coding assistant platform and approach, and this was a pretty big shift.
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.