Lenny's Podcast: Product | Career | Growth
00:51:13 · Roman Ugarte
[00:51:13] S0: One pattern that I've seen from many users that is simple, but I think there's a lot of depth if you keep investing in making it better, and this is where I can get kind of nerdy about optimizing my setup, is Grok Bot as an infovore in some ways of just consuming huge quantities of information, removing that from your own cognitive load, giving you peace, and then coming to you with the stuff that's important. And I think the V1 implementation of that, which many people do, is Grok Bot sits on top of Slack and it sits on top of email, and I tell it high level, "Here's my role at the company, here's kind of what I care about. I want you to notify me in these cases. In these cases, you don't need to ping me directly, but you should include this in your daily roundup that I read every day." I think that's the V1 implementation. I'm not sure what the V10 implementation is, but maybe I'm at V3 or four, which is you can give these bots a complete fire hose of information. So I have mine hooked up to every mention of Grok Bot ever on X, and it's interacting with our internal context. It's interacting with the QA tester to see if it can repro any bugs or feedback that we're getting. I'm hooked up to my own messaging services to quickly act on feedback and reach out to people. And I think there's this just always on chief of staff entity that can preserve your focus on the things that actually matter, but is always kind of surveilling to see if there's anything that should get your attention. And we've seen some funny cases of people actually giving their Grok Bots, which I have not done this yet, but maybe soon, giving their Grok Bots access, the ability to page them. And so if something super urgent happens and they're at a coffee or whatever, they get paged by Grok Bot, which is the type of thing that you only want to do if it's urgent and you really want to trust that Grok Bot does not have false positives. So far, those people have reported that it's been very helpful and successful. But I think we're going to see more of that type of stuff where the agent or the bot should actually be more proactive to you than you reaching out to it. And I think that will be the next shift in AI.
00:53:36 · Lenny Rachitsky
[00:53:36] S1: There's a number of things that I've been very impressed with watching your team operate. One is speed, which I want to talk about, but the other is how you are. There's awareness that this is a moment in time to capture a lot of market share and really take as much of the market as you can before somebody comes around and they're like, okay, now we got something awesome, especially one of the foundation labs. So watching just how many free accounts you guys are giving out. Also, the focus on use case is so smart because it's such a novel thing and you open it up and it's like, what do I do with this? And there's such a focus on, okay, here's a bunch of things people do with it. And then there's all this talk on Twitter and just all the ways people are using a template. It makes so much sense. Just these two kind of focuses, from what I can tell, get as many people on as possible, as fast as possible until somebody's like, okay, because someone's going to come around and be like, all right, here's the next thing. Super smart and also the use case focus. I know you were a go-to-market person at Cursor before this. Anything you want to share there about just the approach to go-to-market right now for getting this out there?
00:54:43 · Roman Ugarte
[00:54:43] S0: I think the pattern we saw for coding will be somewhat similar to what we see for general knowledge work. And I think we've learned a lot from that on the go-to-market side and more generally just building practical AI that people use. And I think we as a company have culturally really cared about not building demoware, building actually useful stuff in the world and kind of obsessing over that. And there are so many shiny objects and fun prototypes to build, but ultimately that's a very different problem than getting this in the hands of millions of people and having it transform companies. So that's really, I think, culturally where we've always been focused. And so I think on the go-to-market side, what we saw for coding was a very simple pattern, which was there was an early adopter crowd. The early adopter crowd would use these coding tools and really push them to the limits, and they would mostly push them to the limits on individual projects. On nights and weekends, I'm thinking 2023 earlier, people would go home from work. At work, they were using a basic IDE, this is pre-AI, and then at home they'd work on a side project and they'd be using Cursor or they'd be using the latest and greatest AI coding tool. And that would give them an extreme amount of acceleration. It would feel like they were experiencing the future, and then they would come back to work and they would demand it. And they would say, "I cannot picture working any other way than this. I feel like I'm completely walking through molasses right now. This needs to change." And I think for knowledge work, we're going to see a similar pattern of people really feeling the aha moment, sometimes in a personal capacity. And I think we're certainly seeing a lot of this on X right now. You see all these examples of Grok Bot controlling their home robot computer, home robot vacuum cleaner or Grok Bot helping them save money on their Tesla charger negotiation, all of these fun use cases. But I think the next step is going to be, this is not a consumer product. We think this is going to transform businesses, we think this is going to transform teams, and it will be bots coming into teams and contributing really economically valuable work, especially as they get much smarter.
00:57:10 · Roman Ugarte, Lenny Rachitsky
And so on the go-to-market side, we're certainly making a big push on prioritizing businesses and thinking about not just the single player use case of working with a single bot, but how does a bot work inside of a broader team? How does a bot work inside of real company systems that are complicated and there's a lot of context and a lot of history to understand? What does memory look like in a broader organization versus a single individual you're catering to? And I think there are a lot of unanswered questions there, but I do think Grok Bot is the right primitive to create this switch to agents for the rest of the company outside of coding, and that's a place where we're quite focused right now. [00:57:56] S1: And along those lines, it's very clear you all understand the power of distribution and how you need to find both an amazing product and get distribution right because Grok Bot's amazing, but the combination of how smart you guys have been with getting it out there in all these different ways is really impressive. And I think that shows you what it takes these days to build something that's really successful. I want to ask about the brand of the different brands around this product and the company, just so people can try to understand, because I know you're going through a transition, acquisition, SpaceX, all these things. So there's Grok Bot, there's Cursors. So talk about the products and the way to think about these different brands today, and I know it'll probably continue to evolve just so we could communicate about it correctly.
00:58:42 · Roman Ugarte
[00:58:42] S0: Definitely. Yeah. I think there are three big pillars right now of SpaceXAI. So the first pillar is the coding product instead of products, and right now that's Cursor and Grok Build. And I think we're big believers that having a professional work surface for developers and for the engineering part of the organization is going to be really critical. And right now people use Grok Bot sometimes to kick off cloud agents or to merge PRs or to do QA, a bunch of engineering adjacent tasks. But ultimately when you're shipping production software, we're big believers that is going to require a product where every pixel is optimized for that end user. So we're making big investments there. The second category is general knowledge work, and we think bot is a really exciting step in that direction. I think there's a lot more work to do of making it more useful, extending it to new surfaces, it really feeling like an AI teammate that you can delegate work to, especially inside of companies and businesses. So that's kind of the second pillar. And then third is the general model effort. We want to train the smartest models in the world that are really capable. And I think one thing that somewhat distinguishes SpaceXAI from other AI labs is I think our goal is less to build, chase super intelligence or some kind of vague aspirational ideal. And the goal is actually very practical, which is to build useful AI. And we do that on the product side, we do that on the model side. And I think part of that is also just cultural of the group of people contributing to these models are engineers and people who came into the model training effort from a very applied mindset. And I think that's what gets this company going and I think is actually a slightly different direction from some of the other competitors out there.
01:00:43 · Lenny Rachitsky
[01:00:43] S1: Super interesting. Okay, there's a couple directions I want to go. One is you have this tweet that is, I think you pinned it or maybe it's your last tweet. It's up there in your timeline if people check you out. So the tweet is, "An AI that does 100% of the job feels categorically different from one that gets you 90% there. I've significantly updated what I think AI is capable of." Say more about that.