Maybe the right UI is a generative UI. Imagine the entire layout of the workspace is actually something made just in time based on what you're trying to do. Cove is now going to be building out the workspace.
to build out the workspace is trying to first understand what you're trying to do, how to break down that problem, and how to help you make progress. And that includes, for example, for you to be able to automatically get the transcript. As product builders, we're trying to kind of predict the future. And that's already hard, but the future feels like it's changing faster and faster now. Steven built Uber Eats from scratch.
of course with his team to a $25 billion business. The initial group that worked on Needs was a really interesting combination of some folks that had been at Uber for a very long period of time. Some of the original ops people, some of the original engineers. And they're mixed with a new set of folks on, for example, the product and design side. That mixture of new perspective, but also folks that really knew the Uber culture and really knew how to get things done at Uber.
was a really interesting kind of like a cocktail for an actual team. Welcome, everyone. My guest today is Steven, the co-founder of Cove. Cove is a Sequoia-backed startup that lets you work with AI through a groundbreaking visual interface instead of using chat thread. And before Cove, Stephen built Uber Eats from scratch, of course, with his team, to a $25 billion business. So I'm super excited to have him demo Cove and talk about how he's thinking about AI beyond the chatbot. Welcome, Stephen.
Hi, thanks for having me. Yeah. So why don't we get right into it? I would love for you to show us how Cove works. I don't know, maybe we can do like a travel use case. Like I'm heading to Japan soon. show how cove works in that context sounds good we'll try to plan your trip for you so let's do it all right so um so this is cove and what we'll do is we'll go ahead and tell cove
And so you said you're going to head to Japan soon, right? And what cities are you going to be going to? Yeah, I'm going to Tokyo and then Kanazawa and Osaka, my family. Say I'm thinking. Tokyo Kanazawa So Cove is now going to be building out the workspace to try to help you figure out how. As it builds out the workspace, it's trying to first understand what you're trying to do, how to break down that problem, and how to help you make progress. Let's see what it does.
so what you see here is a couple different cards we're going to close this for a second because we're going to talk about that And so we have a couple different cards here. And so the first is a card that is showing some of the cities that we mentioned. And so it is showing kind of a comparison across. much time you want to spend there or the cases. And then it's also starting to build out an itinerary.
And you'll see that there's a card here that actually is trying to almost elicit different preferences from you because Cove may want to know different pieces of information before it fully is able to... sort of plan. So we'll fill out some of these things in a bit, but maybe I'll So Cove is a visual workspace. And so you can think of it as a canvas. And we're going to be able to explore a lot. In addition to that, everything in this space is fully edited.
I feel like for folks that are used to working with a chatbot, you're used to kind of looking at a chatbot's answer and then need to kind of copy paste that or watch it kind of restream in as you kind of make edits. A code is different. I can come in here and I can just change anything. So why don't we add some more content to this space? So for example, I'm going to drag in a new card and then let's say, let's say a table of kid friends.
might be helpful. And so I could just start to create that content myself. Cove is also always available to help fill it in. So I clicked fill. And so now this table is being... And what you'll find is when you're working in Cove, what we're trying to do is almost kind of create the experience of how you tend to collaborate with other people, right? So if we're working on something together, we'd be iterating together, building.
And so similarly, you can do that with code as well. And so let's say, for example, we wanted to add some photos to this. I'll just say add some photos to the table. Yeah, this is awesome. Yeah, it's kind of like, I guess it's kind of like a Figma, but, you know, I guess AI is... and you're working together too right so as you noticed when i said add some photos it didn't restream in then
A new column was added right there and now it's filling in tables. And we just feel like that's a much more natural way of working with AI where you're co-creating, not needing to kind of see all this content restream in. this might be on your list ghibli museum my kids are so so for example that might be something that you're interested in going
And so and then a couple other things to kind of call out. So for Cove, as you add more content to the space, the AI is always trying to understand the context of what you're working on, the information in the space and how those things. And so one of the things that it does is it tries to anticipate other things that you might want to do. And so anytime you have a piece of content, you can see that there's actually suggestions here over other things that you might want.
museum websites, you might want to add more museums to the table. And so it's really easy to just continue to iterate on things like really quickly. should we talk about here? Is it kind of like, because with the chatbot, you only have a single thread, right? I guess you can go. previous prompts but but like this feels like it's like a multi-threader thing or like it's kind of that's right that's right yeah so you can do you can invoke to ai to do various things all in parallel and those things
And then the other thing to keep in mind is like right now I'm the only one in this space, but this can be a multiplayer space. You could come in here as well. You could be doing things at the same time. And when you're here with me working together, we can both invoke. All right, so maybe we'll start to kind of build out an itinerary, right? So as I mentioned, there's a card here that actually is able for you.
And one thing that we've found is as you work with AI, sometimes people don't know exactly what's the kind of additional context that you might want to provide. And so we actually have these cards where it actually kind of helps give you hints like, oh, actually, if you provide us information.
in terms of what you're doing in addition to that when you fill in this content sometimes this content can actually change other cards in the space So for example, let's say that we're going to go for seven days, spend most of the time in Tokyo. chill travel style main intro Nature. Nature. Yeah. Food. Definitely. Oh yeah. Food. And then we'll say kid-friendly activities maybe. Yeah. Somewhere they can baby.
All right, cool. And so anytime you start to do more things in Cove in this space, it's going to take this information. as it helps you but in addition to that there's actually this button that says do updates and so it actually has connected this preference information to this itinerary card and so if you click do updates and it's going to actually automatically
And so as you continue to explore, as you continue to learn, you add more context to the space and things are able to continue to update based on what you decide. So you're able to kind of... Yeah, it looks great. It looks like a first visit is at the Gabley Museum. That's right. So it's using the context of that car that we created. And, you know, context is something that we think is really interesting in terms of just how you... that in this product because there is, let's call it implicit.
So an example here is we learned about the Ghibli Museum and went ahead and started to use that when it created the itinerary. But you can also direct it as well as you start to add more cards. You can actually add mention cards to do specific things for cards connecting to each other. And so there's a lot of like really...
So you can basically, so instead of like one input box, now you have each of these cards, you can add more roles or add more inputs, right? That's right. To add cards. And also there's many different types of cards. So you can add URLs, you can add PDFs, you can drop in YouTube videos. All these things are additional ways you can kind of build content. And with chatbots, I normally have to copy and paste. But you're seeing that this one you can just share. Like I want to show my wife.
That's right. I just share it. Yeah, exactly. So just like, you know, a lot of the other kind of multiplayer collaboration products you're used to doing Google Docs, Figma over the cases, you can share this out to particular people or you can make it read only. the link. So you can have kind of like all that sort of collaboration as you work with AI. You can also download these cards. You can actually move them. all right let me give you a stress test so like uh can you ask it to
10 snacks in Japan. 10 premium snacks. I don't want... This episode is brought to you by Merge. Product leaders cringe when they hear the word integration. They're not fun for you to build, launch or maintain. And they probably aren't what led you to product work in the first place. Luckily, the folks at Merge are obsessed with integrations. They built a single API that helps SaaS companies launch over 200 integrations in weeks, not quarters. Think of Merge like Plaid or for B2B SaaS.
companies like Ramp, Drata, and Electric use Merge to access their customers' accounting data for bill reconciliation, file storage data for searchable databases, and HRIS data for auto-provisioning access. for their customers employees if you need ai ready data for your saas product then merge is also the fastest way to get it.
So if you want to solve your company's integration dilemma once and for all, book a meeting and receive a $50 Amazon gift card when you attend. That's merch.dev slash Peter Yang. Now back to the episode. So I'll do that. One thing I'd point out here is that there's also, you know, COVID is always trying to anticipate how you might, like something you might want to do next.
And so as I come in here, if I don't type in anything to the chat, I can also see recommendations of other activities or other pieces of information I might want to dive into. So I can click on any of these. but we're going to focus on your snacks. So let's say, create a card. Yeah, I want pictures too. So you got to add pictures. Okay. Okay. All right. Create a card.
Maybe by region. Sure. yeah this is my way of uh making my wife happy you know the fine dining restaurants and stuff that'll break my bank bring an extra suitcase so you can uh fill it up with this stuff Oh, nice. Yeah, I haven't heard of some of these. Oh, Tokyo Banana is really good. So I definitely suggest that. And where is it getting the images from? Like just from? Yeah, so there's various providers that we use in Cove for all the different things.
You can do web search. We have images and various other things as well. The images here are actually through Bing. It's actually like the Bing API from Edge standpoint. And I finally used for Bing. Yeah. Okay. Yeah, this looks good, man. I think I definitely tried some of these before. The white chocolate ones are great. Yeah, the Japanese have a very high craft for their snacks. Yeah, absolutely.
So I noticed you have on the left here an AI app feature. So what is that? Yes. So this is something that we just recently launched. And so I can show you a demo of something. So maybe I'll show you a different space. And so this is a space where imagine you are putting in your class materials because you're studying. And so this is actually a very common use case.
lecture notes um you know like lecture videos they can put all into the space and then all that becomes part of the context of the ai and they can do things like ask questions about the content or summary So now with AI, with this new feature that we launched, you can actually build AI apps right here within Cove. And so I'll show you an example. I'm just going to drag out this card.
video for example and so this video has has already been transcribed because I dropped into the space and then now I'm going coding but you just dropped a youtube video into the space That's correct. Correct. Yeah. So so so you can drop in videos, PDFs, you know, Word documents. And so all of that just automatically. added to the context and the space. And that includes, for example, for YouTube being able...
get as you add content into Cove. And then now you can actually create these AI applications. And because these AI applications are in the workspace, they can actually use here is that I can actually take that lecture transcript, and then now we're going to create a multiple choice quiz on it. And so this is Cove coding away. And so this is going to take maybe a minute.
This is another app that's in this space. So imagine this person studying physics. They're learning about the three-body problem. And so this is just a little visualization. change the different parameters for each planet and then you can click play. We have some crazy orbits going on right now based on it. Yeah, so this is an app that, so what was the prompt? Just like build a simulation? Yeah, create a visualization of three bodies.
And so, yep, so here's our multiple choice app. So that's been created. So we can test your cosmology. Let's see. What's the primary? Nice. And so if I'm the instructor... you can see me make the changes live, right? Ah, yeah. So, you know, because you are creating these... within a co-workspace, then there's a bunch of other properties as part of the workspace that you just kind of get for free. And so an example of...
just like how we talked about in the Japan workspace, you can invite other people and you can be collaborating together. You can do the same thing with these apps as well. So you can come in. space. And then you could be answering questions. I could be watching you answer questions. that you're able to use with other people. And so that's one thing that we're excited to see, just like the different types of things people create. Yeah, it could be really fun.
cursors around right something similar yeah yeah yeah no there definitely is when you think about using cove in a work context where it's teams collaborating it is really fascinating to think about a group of people working together Just in time, create any application.
We've seen teams create apps to do things like vote on what, where to go for their next offsite, right? There's kind of like, you know, like things like that they can do, or there actually can be like more workflow or other things. It's really fun to see kind of this combination of being able to use context within the space and also be able to bring other people together. And then you're able to have these. And you kind of mix all those things together.
Can you show us a, I noticed you have a bunch of other tabs open. Can you show us? Use cases? Yeah, for sure. I'll show you. This is a fun one because it's a little bit more of a personal one. This is actually my daughter's space. So my daughter is 11 years old and she always has some sort of project going on. The current project is she's setting up. And so for folks who have not done this before, and I count myself.
those people there's a lot of things that we didn't know you had to do to set up an aquarium and one of the things is you can't just put water in and just throw fish in you actually have to do this thing where you cycle the water so the water to support Phish. She's dropping this picture of shrimp here. And so this space is an interesting one because this is basically, you can almost see this journey of this project.
She first kind of was learning about how you cycle an aquarium. And as she kind of was going through that, then there's this car that got created, which shows, okay, this is actually the process of the water. ammonia goes up, then nitrite goes up, then nitrite goes up, then nitrate goes up. And so you basically have to measure the water on a regular basis to see this kind of process happening. ready and so as she was learning about it she was like oh maybe i should just create an app to help
And so she created an app to help basically track the progress of her aquarium. And so this is the app. She is able to put in each of the measurements of water. And then she hits add measurement. And then it goes ahead and creates the graph to show that sort of progress. And interestingly, it actually even tells her what part of the...
the cycle that the water is going through based on the data. And so this is fun because, you know, when the app got created, it used the context of this image, right, to kind of figure out, okay, this... And as you kind of continue down her journey, she bought some moss and put it in the aquarium. And then these snails started to.
And then because, you know, she's like already doing this thing in Cove and she had this particular problem of like, oh, now I want to figure out what type of snails these are. She just came to this workspace, created it. And this is her snail identification app. She's able to ask, answer different questions about the snail and it'll tell it.
And so it's just really fascinating to see kind of when you put the power of into the hands of different people, and you also put it in the context of a workspace where they're already working through a particular problem, then really interesting things happen. yeah this is awesome like uh stuff but uh but i love like in those projects you can only do like different chat threads but here you can make different apps and it saves the context throughout
Your daughter seems pretty advanced and I don't think I ever want to have an aquarium anymore. It seems kind of complicated. Yes, we'll see. I'm sure there'll be a much longer journey that happens in this space. Let's see. What else? How about the one about Palo Alto? What was that about? Oh, yeah. Well, I think this was just to show an example where as we start to empower people to create these... Sometimes people may not actually realize that an AI app might be something helpful.
And so in this case, a user has typed in, this helped me figure out whether I should move from Paul to Vancouver. with recently who was trying to think through this big decision. They're using code. to do it and so you type in this task and these cards start previously in terms of the demo, but now there's actually a new card that has appeared as well, which is like, oh, well, if you're thinking about doing this, maybe an app would be...
And so in this case, it's saying like, hey, if you're considering doing this move, maybe you actually want to create an app which will help you figure out. And so this is something that we find really interesting because we've built other features within Cove where it's all around the AI trying to anticipate what you might want to do. That could be adding another row to the table.
This is a kind of a new version of that, right? Which is like, okay, actually, maybe what you're trying to work on, an interactive experience would really be helpful. So why don't you consider doing it? Click a button. oh nice so the ai suggesting that i make an app that's correct Whether to make an app or just make a table or like a chart. Yeah, it's a really, really good question. I think as we have thought about how to design the product, this is one of the things we talk a lot.
you have particular capabilities that you're probably Are each of those almost like different buttons and actions that a user is supposed to use themselves? Or is it actually the judgment of the AI to be able to figure out when you might... use particular things. And what we found is that being able to provide both The AI in many times actually can have really good judgments in terms of when it might want to actually use a particular tool at its disposal. Users also should be able to...
An example is web search, right? So in Cove, if you do something where recent information on the web would be helpful, then Cove will actually just automatically do a web search to start to get that information. You can also just say like, please. In this case, there are other considerations when you think about these kind of AI applications. One is just the amount of time.
And so, you know, that's one of the reasons why we have this sort of suggestion here that we show to the user because it is a pretty intentional. to use it. And so in this case, being able to almost provide that speed bump where you actually are able to suggest to the user, but then actually have them say, yes, I want that, is something that I think makes a lot of The thing that gets really fun is the thing about how that evolves.
as we kind of approach a place where the models allow us to be able to code more and more things in a faster sort of rates, then what does that mean in terms of the product? And so as we have started to kind of build this sort of capability within Cove, right now, the definition of these... are these individual cards that appear within the workspace. But over time, we actually think that more and more of the entire experience...
And so we'll start to see that sort of transition as the fundamental. And so that's something we get really excited about. yeah exactly exactly you know like when you like at at its core for cove what we're trying to do is try to figure out what is the right you And if you think about like, you know, what's the right UI for gender? maybe the right UI is a generative UI, right? And so imagine the entire layout.
and all its contents is actually something made just in time based on what Now that might sound a little bit sci-fi right now, but I think as the capabilities of, as underlying capabilities improve, like those are the software. try to build towards. And so I think we're taking the first step here with these AI apps. um but like let's talk a little more about how you built cove so far so you've been building it for like a over a year now right or yeah yeah so so how did you go from like you know
at Uber to build this AI thing. And also like what kind of... Yeah, yeah, for sure. So I can give you a little bit of the context of the kind of journey of how we got here. So when we left Uber, there was a group of us that had gone through that journey. And we're really excited to do something else together. And it just so coincided with an incredible time to be able to explore all these interesting things.
And so at first we were really like first trying to get an understanding of the core capabilities of AI. gender of music. We built something that was more of an AI travel planner. And as we were doing that, we kept on kind of coming back to almost this kind of core realization, which is those experiences that we're building were very... And as we started to think about the capabilities of AI increasing over time, the question that we had for ourselves was, is this actually fundamental?
In a world where AI capabilities can be more analogous to what I expect with collaborating with another human, then how do I collaborate with other humans? And should that also be more similar to how? And so that was a real initial inspiration. Right. And if you think about, you know, how we might. And then when you compare that to a chat thread, then I think you start to realize this, like how fundamentally limiting a chat thread is, right? Yeah.
Exactly. Exactly. When you think about how people think. First of all, thinking is a pretty messy process, right? You're going to like, you branch in a lot of different directions, like it's nonlinear, like you explore in a lot of different things. The linear rigidity. Then to your point, when you work with other people, you need to have a shared context. If we're in the same room, we might be in front of a whiteboard.
And so context defined in a chat thread is very hard to be able to do. And so, you know, those were the kind of starting things that got. Cove, in many ways, we kind of think of it as kind of looking at what has happened in the past as we've seen fundamental technology change and also trying to apply that.
You know, when you look at personal computing, moving from command line interfaces to graphic user interfaces, when you look at phones before and after they had touchscreens, each kind of fundamental change required almost like a new... to get the most out of that underlying technology. And so we think it's very similar in terms of AI and chat threads. And so we're just on that journey.
Yeah, that's a really good point. I didn't think about it that way. I think we're pretty old, right? So we were around when there was like DOS and then you had to put it. like Mac, where you can have a visual interface. That's right. That's a really good point.
the stage right right yeah yeah yeah and so for each of these kind of like um shifts there tend to be new primitives that get created new interactions that get created that unlock more of those And so that's in many ways what we're really trying to tease out as we're building coves. to really allow us to match what the capabilities are.
so how do you uh do this in practice right because like this tech is like advancing pretty fast and then you're trying to do something pretty groundbreaking but then you have to use the existing whatever Is it kind of a pain to do this? Yeah, it is. I mean, well, I wouldn't use the word pain. I think we're having a lot of fun trying to figure out how to piece this stuff together. But I do think, well, maybe first I can give some context over how Cove is built.
So we feel very fortunate because we can sit on top of all the incredible things that all the other companies. And so as we kind of think about how to integrate... Cove is always trying to think through that sort of kind of trade-off for a particular task what's the performance you need what's the cost what's the speed right it's basically kind of looking at all those different trade-offs and then being able to figure out what the right
And so when you use Cove today, maybe you'll have suggestions powered by Llama, right? But then coding powered by Sonnet and then perplexity providing some... And so in some ways, we feel really fortunate.
tap into each of these things in a way that we think makes sense and put them together into one product experience. The problem that you call out is still a very important one, right? Because I think fundamentally... the fundamental challenges of all of us building an AI right now is that As product builders, we're trying to predict the future. And that's already hard.
but the future feels like it's changing faster and faster now right and so because of that like it requires kind of like you know like some additional sort of almost like skills and ways of operating And so, as an example, as a team, I feel like we have, as a startup, you pride yourself with being able to be really...
But even as a small startup, we're eight folks. The speed of how AI is advancing also stress tests how we operate as a group too. And so I think you really have to embrace that as you're kind of working through. I also think that it really causes you about how you focus your time in terms of what problems you're trying to And in addition to that, when do you think it's the right moment to release different features?
in terms of focusing on the right problem. With this kind of rapidly changing... foundation that we're trying to build on. you can sometimes fall into a trap where you might not be focused on the right thing because that thing will actually be solved by a model. When we were building one of our early prototypes, this was before we started building Cove, I remember we were having a chat with someone, a very smart person. We were trying to grapple with this context.
And he made this comment in a conversation where he's like, well, you could spend the next couple months trying to, you know, do some things to try to help with this context window, but I'm pretty sure the models are going to be better at this in a couple months anyways. So you should focus on something else. And I always remember that conversation.
to work through these sort of things because you have to really try to figure out where that focus is. And then in terms of the readiness of features, Sometimes you get the timing wrong, right? Like you might anticipate that a model. is going to be really ready for this particular capability but then it's not completely there yet and you're like but i want to get user feedback and like you know you kind of put out there hoping that
It's going to continue to get better. For example, with the generative app things that we're showing, do I wish that it could code those apps faster? Do I think that that capability is going to happen as the models advance? Yes, for sure. And so we're going to put it out there knowing that that experience is going to improve. We're not going to hold it because...
That's the way that the technology is going to be evolving regardless. And so we can embrace that as we build product. Got it. Yeah, yeah.
most part so but but i i noticed i mean it's like a very purposeful ui design right like you said you're you actually set the expectation that you have to wait because it's not it's just generate but but what's the example of something that uh doesn't depend on like the model advancing is it like some Well, yeah, I mean, I do think that you have these sort of capabilities of the AI, and then you also have the fundamental features.
And so in many ways, what we're trying to do is we're trying to advance across each of those things. Sometimes those things are coupled. So it's not like the roadmap is completely bound just to this sort of capability of the AI itself, right? there are some features I certainly feel like that's the case but like I think it's the more interesting questions ends up starting to become like okay well how how much do you lean towards those AI features versus there's other things as well
And, you know, I always ask product builders and founders, like, do you have, you probably have a vision, right? Like, of what you want the code to be, but how long is your roadmap? yeah well yeah well um yes interesting question and i do think that Use two different words there, vision versus roadmap, right? And I do think that it's important to separate out those two things. I think that you can have conviction on a longer-term vision, but have a lot of near-term uncertainty.
And so I think that that's maybe an interesting way to describe what it feels like to build an AI right now, right? Is that like, you know, we all feel. particular future that we're trying to fast forward and you know there's a reason why we all took the leap to try to like build that but exactly how we get there that that that ground is shifting and so we have to be like really advancements continue to come out. And do you think, is your vision kind of like that AI brain?
Do you have a Pythie one-eye statement for this thing? Well, I think the... Like, you know, there's more that we can kind of talk about there. But I do, for us, it's very much trying to think through what is that magical workspace? And so when we first started, it was very much around, okay, we need to have this workspace.
We also need to be able to create an AI that has more of a collaborative sort of features that allow it to progress from being more of like an intern helper to be more of a fundamental thought partner. I think more recently as we... of apps, the kind of new sort of like dimension to that is, well, the workspace itself can actually be more fundamentally generative. And so that's something.
okay well maybe the right way to collaborate with ai is also like like the workspace itself is actually transforming based on what you need and so that feels critical piece in terms of kind of rethinking what that And you talk about, you know, you don't want to build with them. agent thing or yeah yeah yeah i mean i think like um well if you if you look at um
that might be its own podcast to talk about that in more detail. But like, but if you, if you look at what has been happening recently, there certainly is like a lot of talk on. Asian is funny, right? Because I think it's become one of those words where you might get 10 people and they have 10 different definitions of exactly.
but like certainly certainly you have you know that as an area um you have more of like deep research as as And so when you look at those kind of capabilities, let's call Asian basically being able to do AI. Right. And then let's think of like reasoning as instead of trying to create an AI experience where I am going to try to get immediacy in terms of output, I'm actually giving AI more time to be more thorough.
name. Like those are things that certainly make sense. And I think for us, we kind of think of it as like, once again, if we're trying to think about AI as a great collaboration. Are those collaborative relationships that you're used to as you work with other people? Absolutely. I'm used to being able to like offload some tasks to someone. come back to me, right? But like, when I work with those people, I still need to have a place where I'm orchestrating those actions, right?
I still need to have a place where even when they come back to me, we're able to continue to iterate on that work in some sort of way. Those are places where we think it actually continues to fit very naturally in terms of having... I'm working through a particular problem as I make progress working through that problem agents to be able to do things. Maybe there's particular status.
gonna come back me to like and those based on what they've done then further things will change in my workspace you know or like hey i pause on my workspace but I get an email because actually Cove has actually done more work on my behalf and then has told me like, hey, you should come back now because now you can actually do. And so like, those are things I think in terms of your question.
Those expectations from a product standpoint, I think we'll start to become more integrated with AI products as the capabilities improve. And we very much see... in terms of fitting well, in terms of what we're building at Cove. Yeah, I think right now... yeah if you guys can i mean i'm not sure if you're talking about the work use case but if you can it must be a better Right. Yeah. Yeah. It's almost like, what is your, what's your inbox for the.
So when we talk about orchestration, like I think that's like, you know, similar to that, right? You need to play. So let's kind of wrap up by talking briefly about your Uber experience. So I guess, you know, you do this awesome business at Uber. I guess what I'm getting at is like, what were some key skills?
that the product folks on the team had to actually deliver this $25 billion business over five years or something. And then the next question I'm going to ask is like, what kind of skills did you have to learn in this AI era? But let's focus on Uber. first. Yeah, for sure. Yeah. So let's talk about Uber. Maybe to give a little bit of context. So I joined Uber in 2014 and it was to help create a new business.
time ubers just rides and so um the team name was the uber everything team because they're trying to figure out everything else to do besides moving people around and so you know the um one of the really interesting things And when you think about large companies who have been able to successfully. business at that magnitude in terms of size, there's not really a huge list, right? I mean, Amazon AWS definitely is like... but it tends to be less common.
know a lot of times like when i kind of reflect upon our time on eats it's trying to like almost think through like what are the kind of right conditions that you need to be able to kind of have the right internal And I think for us, there was a couple of things that was really helpful for that almost initial formula when we formed a team. The first one, and anytime you talk with people, I go...
And absolutely, that's the first one for us. But I think the specific thing I call out from a people standpoint is the initial group that worked on Eats was a really interesting combination of some folks that had been at Uber for a very long time. And then they're mixed with a new set of folks on, for example, the process.
That mixture of new perspective, but also folks that really knew the Uber culture and really knew how to get things done at Uber was a really interesting kind of like cocktail for an initial team. The second thing, interestingly, I guess I would call it almost ambition. Like when you're a startup.
you're almost like just always trying to fight for survival, right? Being able to survive the next day is like success, right? And you want to keep on going. Interestingly, when you're trying to do something within a larger company, you have this shadow of... always, you know, casting its shadow on you. And so for us, you know, when we set up the team, the way that we talked about it was our goal is to build a business. ambitious statement, but it was also a very clarified.
Because I think that whenever we iterate on a lot of different ideas, and there are a lot of moments where when we're working on something, I had some level of success. I was like, oh, you know, if we were a startup, we'd be really happy. However, do we actually believe that this idea is gonna be at the same size as rides? If so, we should keep on going. If not, it's time for us to continue to pivot to change.
And so being able to like really have that sort of anchor of expectation and be able to like have the paranoia of like, okay, actually, is this, is this going to be the right thing? Causes, I think, to get to a better, a better results and a better, a better. And then the third thing would be. When you're doing one of these things, a lot of times like when companies are trying to think about creating like another line.
Sometimes a structure is almost like a hedge bet, right? It's like, oh, we should have a group of folks experiment on this particular thing because, you know, maybe this could be like... The problem is if that is kind of your mindset, then whenever things get hard. then you start to deprioritize those things, right? The core business that's working needs more people do X, Y, Z. Okay, let's have less people.
And then those new ideas die. And so for us, it was very different. I think like for Uber, everyone collectively agreed from the leadership. We needed to figure out these new businesses because it was existential for Uber. And because of that, I think it caused, interestingly, more of almost like a patience to be able to like take the time necessary to actually figure out what we.
So we actually, I think, had the right kind of cover and executive like buy-in to be able to actually like go through all those different pivots before we actually figured out what was eats and be able to.
And did you keep the team relatively small too? The team in the beginning was quite small. Yeah, we had a small set of folks that were a bunch of really strong generalists being able to just do a bunch of really... um and so we kept it in that sort of mode for quite a while before before we found the thing we're like okay now it's time to scale and then we Yeah, you got to be like a VC kind of like, you know, with the founders. That's right. That's right. It's exactly right. And so.
You need to have leadership that truly are founder in their perspective to be able to also have an internal team be able to operate in that particular manner. so i think this uh whole like team of generalists thing is like really it's kind of like a hopefully a trend with this uh now that we're entering ai and and um you know i feel like a lot of like writing docs all of theirs doing something i was trying to align the ceo and uh
Any advice for them to have a better life? I do, I think, agree with the sentiment that... Organizations have grown. You've seen more of this sort of specialization with various functions. um and it was you know sometimes as your skill team that you need to build, right? But the world's also changing. And as you start to see more individual empowerment with all these AI tools, then I do think... tools to do a lot of different things. So we'll see.
on record of at least predicting that that might be the case. I think one of the questions you asked was how PMs can prepare in terms of There's probably two things, two kind of categories maybe I call out. The first is just like in terms of almost like how you operate as a PM. And, you know, there's a lot of things that we can talk about around like how you can use AI.
the PM. But maybe the thing that I would call we're we should be progressing to a place where people almost kind of rethink how what their fundamental relationship is with ai right if like it's really easy to humanize ai right so you can think of ai you know the intern that like helps you do research on the side right but that's very different from thinking of ai as a true collaboration On the Cove team, we have this picture of John Lennon and Paul McCartney.
doing like making music together and we almost like we love that picture because we hope that one day we can have that sort of How can you actually get to a point where you have truly a collaborative thought partner that can actually work with you in that sort of deep manner? And I think there's already the capabilities of the models and also the AI products that people are creating are allowing you to try to... when engineers are wanting to make progress on something.
When you're working through a problem, you want to be able to get a thought partner. And with AI, hopefully now everyone has a thought partner available to them anytime. And so as a PM, I think you want to start to be able to try to work with AI in that. And then the other category I call out is actually just like being able to just like jump in and get more fingertippy in terms of your intuition.
I feel like there's a lot of PMs I have conversations with where they may not be working on something on a day-to-day basis that's directly related to AI. And so as a result, they haven't really taken the time. in and really understand and learn and tinker. And I think a lot of times it's actually
I feel like I've heard almost two different things. One is like sometimes PMs over and... so they're like oh like i need to find a class to take so i can learn about transformers or like read the transformer paper as long as i feel like they kind of like you know go through that sort of almost like more academic route
And then sometimes you have PMs, I don't know, they'll listen to a podcast or the cases and they'll hear like, oh, PM skills is going to be really important to write like evals, right? And they're like, oh, like, okay, I'm going to need to learn about.
i actually think it's like a lot simpler than that right like i think people just need to start like you need to like be able to break out from your day-to-day working on whatever you do to just like get start to tinker and get more fingertip in terms because you like you start to build more of a fun On the team, we almost kind of call it like your AI spidey sense, right? You start to kind of almost like...
start to get that sort of understanding almost on a per model basis, right? As you start to kind of do more things. And if you're able to build that sort of like kind of context, then you can start to connect the dots over like how you're going to. And it's easy to jump into, but I just feel like there's a lot of people that just feel like for some reason they're not doing it yet. And so that's why I really encourage people to do. Yeah, it feels like a lot of people don't have the agent.
through the stuff like you know uh you gotta learn to take a course or like you know do a lot of stuff but like with ai just jumping and just jumping and make a bunch of It's fun. Exactly. Yeah. I mean, you can go to an AI and you can almost just like approximate a backend for some service by just creating some sort of prompt and then just see how the response.
Right. I mean, like that's even without all the prototyping and vibe coding, other things that like, you know, are starting to kind of emerge. So. So, yes, the barriers were already low and are continuing to drop.
So I think that's the first step. Awesome. So that's a pretty good message to end on. Yeah, like I personally probably talk to AI more than... hopefully less than my wife but uh yeah and also like uh make time to tinker and that's really important that that is like you don't need like these frameworks process or whatever the hell you just need to actually use the products and that's
Cool. Well, where can people find Cove? Yeah, so you can go to Cove.ai. It's free to try. So definitely encourage you to come check us out. We love getting your feedback. You read every single piece of feedback that comes in. We try to talk with as many users as possible. So we'd love to hear from you. All right. All right. Thanks so much, Stephen. It's been an awesome conversation. Yeah. Thanks. Appreciate it.