$1.2 billion fund, Windsurf, AI, and more with Kleiner Perkins’ Leigh Braswell

Our 21st venture fund, KP21, is $800 million on the venture side. And then on the growth side, we have a $1.2 billion growth fund that we call Select, Select 3. We believe that venture doesn't just scale with money. Even in the most trying times historically, there have been really history-making companies that are built if you take a very long view. I don't think things are doomed. And I think if you talk to somebody and they're not making any investments right now, they're going to miss a lot of really big companies.

Leigh Marie Braswell, welcome to Sorcery.

Thanks for having me, Molly. I'm so excited to be here.

Pumped to have you and pumped to get into all of the topics that we're gonna discuss, from windsurf to Kleiner Perkins, the succession planning, and more. This is gonna be so much fun.

I agree. Uh, where do you wanna get started first?

So let's start with Kleiner Perkins' legendary history. The firm has invested into Sun Microsystems, AOL, Intuit, Amazon, Google, Zynga, Looker, Slack, X, Spotify, Square, Brex, Plaid, Robinhood, Rippling, Glean, Figma, and many, many more. Wow, I'm like winded from saying all of that, but you guys have certainly made a big splash over the course of the 50 years that you've been around. So I I'd love to understand how big is the fund now and what is the thesis?

Absolutely. So we're currently deploying out of— this is our 21st venture fund, KP21. It's $800 million on the venture side. And then on the growth side, we have a $1.2 billion growth fund that we call Select, Select 3. And I guess what's maybe a bit unique is they're relatively similar fund sizes to our last set of funds. So I think what's very important to understand about KP's approach is that we believe that venture doesn't just scale with money. And there's elements of the business, especially at the earliest stages, that are just so unscalable, that are so relationship-focused, that that's why we've kind of kept the funds a relatively similar, similar size.

So fast forward 50 years, Kleiner Perkins has maybe over $10 billion AUM and has undergone a legendary generational talent transformation that many others are having a significant problem trying to solve for. This includes massive turnover, toxic cultures, lost portfolios, zombie investors, et cetera, alongside these very, very turbulent markets. Whereas it feels like KP has gathered the Avengers of investors to rebuild a legacy institution. I'd love to go a little bit deeper into that and the actual team leading this and the vision. Absolutely.

So yeah, we're 52 years old. We are an old, old venture capital fund. But what I do think, you know, we have done really well is sort of rally this next generation of KP. And I can kind of walk through that, how that happens. This is a story that hasn't been told that much. But probably around 8 or 9 years back, this is when we were finishing KP17, our, you know, '17 venture fund. And essentially what happened was, you know, Ted Schlein, John Doerr brought in Moomin to kind of champion the next generation of KP. And Mamoun very quickly tapped his partner, Ilya, to lead with him. And they brought on all of us over the past few funds. And so I think it's just really great that we've got this now core team of 9 investors. We each have majors and minors. We're very collaborative with each other. And we're generalists sort of across early and growth. But then also, we've got this super wide network of advisors. So All of these sort of past previous generations of Kleiner, many of them are still advisors. I see John around the office all the time. I see Brooke Byers around the office all the time. Brooke was helping me diligence something in bio the other day. So it's great that we have this network of advisors, but then the core investment team, the latest generation. And in many ways, the approach is very similar across decades with KP, which is this Very small team, really concentrated, really working with founders to build companies, to do things that don't scale, whether it's hiring your first engineer or hiring your head of sales, talking about strategy from what we've seen over 50 years of all these different types of portfolio companies that we've had that have been successful, what that looks like. So, you know, I think it— I'm just really lucky to kind of be a part of this, of this current gen at KB.

You mentioned a couple things in there I want to dig a little deeper into. So, you mentioned it's a small team. So, how many team members are on the investment side?

Wow. And that's across early and growth. Like, that's everything. That's for the $2 billion current set of funds.

How is it split between early and growth?

So, and this is an approach that I really like. I mean, I think ultimately, you know, our job is to find the very best founders. And I think that's regardless of what stage they're at, regardless of what type of company they're building, is to find the best founders and really be their partner. And so therefore, we don't— like, I'm a partner in the early funds, I'm a partner in the growth funds. I bring in growth investments, even though I'm mostly an early-stage investor. We are all in both. And there's definitely people who have preferences depending on your background, depending on just, yeah, what you tend to focus on. But we're all in both. We're all in there together.

Sorcery is brought to you by Brex, the financial stack trusted by more than 30,000 companies in including 1 in 3 venture-backed startups in the US. Nearly 40% of startups fail because they run out of cash. Brex is literally built to help founders avoid that. Unlike traditional banks that let your money sit idle, chipping away at it with fees, Brex is designed to help you spend smarter and move faster. Their all-in-one solution combines checking, treasury, and FDIC protection into one powerful account. You can send and receive money globally at lightning speeds Get 20 times the standard FDIC coverage through their partner banks and even high yield from day one. With same-day and even same-hour liquidity, access your funds anytime. Companies like Scale AI, DoorDash, ServiceTitan, Hims, Anthropic, Flexport, Robinhood, and Plaid trust and use Brex. Learn more at brex.com/banking-solutions. That's brex.com/banking-solutions. And you mentioned that you have majors and minors. So what does that mean?

It's interesting. I like kind of joke that my major is AI, and then like everything these days is AI. So whether it's consumer or fintech or what, there's a little bit of AI in there. But basically, this means that like, and I really, really love this, it essentially means, okay, you've got your major, AI for me, my minor, like dev tools, And you've got other investors at KP with overlapping majors and minors. And it's basically areas that we have more expertise in. And we can help each other out, share notes, work together on companies, especially when they're in these areas, because you don't really have to get us up to speed as much in those particular segments. Right. So, I mean, I was at— before I was investing, I was at Scale AI for 4 years. And, you know, long story short, I developed a really good bullshit detector for things in AI. And so, you know, if you're talking to an AI application company, an AI infra company, I've got a lot of context, and so that's why I call that my my major.

Well, that's important. I would understand having that bullshit detector would be a very good skill set now when every company is AI. So that's great. In terms of being split on early and growth, what's the typical check size and categories that you guys cover for both of them?

I mean, we're pretty we're pretty flexible. You know, like I'd say because we're such a tiny team, we do have a relatively concentrated portfolio. So Outside of a few exceptions, like sometimes we'll do special things for accelerators, but outside of those, you know, we're tending to write checks that are at least $1 million or up. And then it tends to be on the growth side, you know, our growth fund's a little over $1 billion. And so we don't want to be like way too concentrated. So probably the largest check that we would think about writing is like something in the like, you know, $100 to $150 range. And so that gives— that's a pretty wide range of check sizes. So. Really, you know, very, very flexible when it comes to partnering with companies.

So I want to go into the exit environment. Figma just filed to go public and OpenAI just announced a potential $3 billion acquisition of Windsurf, one of your portfolio companies, which I believe you led the investment into. That would be OpenAI's largest acquisition to date. Is Kleiner untouched by the macro turbulence? Where are we at with this and how does the team view the current exit environment?

So I cannot comment on either of those things in particular. I mean, I'm happy to talk about Windsurf generally. I am on their board. That is an investment that I'm very involved with. So I'm happy to chat more about that. In terms of the exit environment, I mean, what's interesting is primarily, you know, we're looking at quite early-stage companies. Especially me, who, you know, I mostly am looking at seed to Series B. And so, it's definitely like a bit, you know, far away from the eventual exit. And we tend to take a very long-term view. And so, I wouldn't say I have like, you know, any particular super hot takes on the exit environments, but it's certainly something that like we keep our eyes on as we're, you know, working with companies and pricing companies and things like that.

So, you don't think venture capital as an industry is doomed because the exit environment is limited now?

I don't think we're doomed. I think ultimately, like, even in the most trying times historically, there have been really history-making companies that are built if you take a very long view. And I'm really fortunate that that's kind of the default position of KP. And so, no, I don't think things are doomed. And I think if you talk to somebody and they're not making any investments right now, they're going to miss a lot of really big companies.

So I'd love to go into Windsurf specifically. I know you can't comment on Any rumored acquisitions, maybe something happens, maybe something doesn't. I'm not sure, but I'd love to know from the start, how did you find them and what was your thesis into the company?

For sure. So this goes back a long, long time ago. So I actually grew up doing competitive math. That's kind of the thing that got me outside of Alabama in the first place. And, you know, I think growing up doing competitive math, there's this tight network of people And so that's kind of where I, you know, I don't think Varun and I, we ever met. Varun's the CEO of Windsurf. But it's certainly where we kind of like first maybe heard of each other, probably talked on the forum somewhere at Art of Problem Solving, which is like this competitive math website. But ultimately, we met when we all went to MIT. So Douglas, who's Varun's co-founder, Varun, and me, we met at MIT and definitely had a lot of mutual friends and certainly had a lot of respect for them because they were just known as being really, really sharp guys. But then, fast forward over a decade later, I'm starting at Founders Fund as an investor. And I think it's literally like some of my first months of investing, I hear this rumor that Varun and Douglas are starting this company. It's called Exit Function, and Green Oaks is leading their seed. And it's already done. Like, it was a very fast process, and now it's done. And so, I get on— this shows you how long I've known Varun. I get on Facebook Messenger. And I, and I'm typing to Varun in our last chats, like, you know, eight years ago or ten years ago or something. And I'm typing to Varun like, hey man, like I'm you know I'm a VC. Like you know why did you want to talk to Founders Fund for your raise? And he's like you know and I think he had a really great relationship with the Green Oaks folks. And yeah, I have a ton of respect for them over there. And I think they you know obviously made a really great investment doing that seed. But basically what ended up happening was. as a way of kind of like atoning for my sin and not keeping in touch and not seeing the seed round, I angel invested. And then that became the start of a really great relationship with Varun and Douglas and the team. And I think, you know, I think being able to like show how, you know, involved that you can be, that really kind of sets you up in a better position as an institutional investor for future rounds. So when I was, you know, involved with the company, I was making a lot of customer intros. ExaFunction was a GPU virtualization company, totally different idea. a totally different name. They'd go through this once more before finally getting on OneServe. But basically what ended up happening was a lot of their customers were like self-driving car companies and I'd been at Scale. And so I could introduce them to a lot of customers. I helped hire their head of business. I sourced from a poker night, sourced this guy, and then he's now their head of business, this awesome man named Jeff. And I was just helping them with hiring, sort of strategy stuff. Rune and I would talk all the time, just sort of like being a good sounding board for him. Or I like to think. And then basically, you know, we were kind of in a better spot when they raised their A. Founders Fund came in, participated in that round. And then sort of the story continues to have lots of twists and turns. As soon as he raised, as soon as they raised the A, which was a big A, like at a, you know, high price competitive round.

What was the price? What was the round?

I think it was like a $20 to $25 million round. And the valuation was, you know, somewhere in the hundreds. I mean, they were like a best-in-class company, like in terms of the revenue, like somewhat customer concentrated, but really great growth, amazing team. Like the problem was clear, like these GPUs were way underutilized and X-Function helped you like sort of get the most out of them for these really intensive workflows. And so it was like, from an outside perspective, like this is going really well. And if you were like a normal non- crazy person, you'd just be like, oh, I'm like, I am the greatest CEO ever, you know, and I'm just gonna like keep doing what I'm doing. But Varun is just not that person. He is like, he's obsessed. He's obsessed with building the best, biggest company that he can build. And so he's just— and even if that requires like some very gutsy, uncomfortable moves. And essentially what happened was he and Douglas and the team saw what was happening with GitHub Copilot. And how, like, even in the beta, like, how useful it was, but then there were all these problems, but like, it's already so useful. Like, how are LLMs this good at completing what programmers can do? And they just saw this opportunity, like, there are all these enterprises that want to use this and they can't because it's not self-hosted. It's not going to work well in these, like, legacy codebases and weird languages. It's not going to work well outside the Microsoft ecosystem for obvious reasons. So, they just saw these pain points. They saw this revolutionary technology, and they were like, you know what? We could use a lot of our infrastructure expertise and some of the stuff we've built, and we could make, you know, a better coding assistant, especially for the enterprise. And so, they pivoted the company for the first time to this company, Codium. And they, you know, set up IDE extensions, mainly focusing on the enterprise, but then they also had a free version for just individual users, and those across many different IDEs. And then, you know, this was when I had, at some point, you know, moved on over to Kleiner Perkins. And when I got to KP, and, you know, I continued to catch up with Varun, I just, like, saw how well this was going. There were a few other things that gave us a lot of conviction. I mean, one, these models just kept getting better. It became more and more clear this is going to be a no-brainer for every programmer in the world. to have one of these assistants. 2, and this is one thing I absolutely love about KP, we have these super great CIO networks. So, these are people that are the CIOs of either high-growth tech companies or Fortune 50 companies who come to our office on a semi-regular cadence. And we just like pick their brain and they pick our brains for like, you know, what are the sort of emerging technologies that we should use at our companies today? And they were all saying like, these are all these pain points with Copilot. Like, we were really looking for something that looks like this. And so, it was just clear that Windsurf had really found like a need, a mission-critical need. And then, I just saw Varun and Douglas continue to just hire amazing engineers, really be obsessed with the customer, really figure out all these like non-obvious things about the market. And just also, like, I think it could be tempting when you're an engineer to like only want to hire technical people. But I think they were very clear-eyed about like, no, if we're going to go out to these enterprises, we need like a real— sales team. We can figure out a lot of stuff ourselves first, and we can put a lot of scaffolding and process for ourselves first. But you do need people who have been there, done that before to get to the next level. And so ultimately, built a lot of conviction, their approach, and then just like the founders themselves. I just think it is very hard to find people more truth-seeking, more courageous than Varun and Douglas and the team that they've built. And I think that's reflected. So then we did the B at KP. That was like, I think, $40 million round. And they continued to do really well. We helped them hire a head of sales. We've helped them a lot with their sales team build out. And then, yeah, I mean, they've just continued to absolutely crush it from a product perspective. Things are going really well. Here's the pattern again.

Here's—

you can maybe guess what's going to happen. I have this really funny anecdote where I literally am at this JP Morgan tech event with Varun. And they are literally every year, JP Morgan, they use tons of software. They have like a ridiculous number of developers. They have tens of thousands of developers at JP Morgan, like way more. You know, it's just mind-boggling how many developers they have, which makes sense. But they have a lot of external software that they use as a result. And every year they induct, I think, 1 or 2 companies into their Hall of Innovation. And so, these are like companies that have made the sort of biggest impact on JPMorgan. And Codium was one of those companies. And so, we were there just for the conference. And Varun goes up and gets this award. I'm just so proud of him. It's just incredible seeing such a young company enter into this group of really amazing companies. I think the other one that they inducted was like Snyk or something like that. And Varun, he comes up to me and he goes, yeah, that's great. But hey, I got to tell you something. And we go to the back, and he's like, well, you know, things are going really well, but I'm seeing all this AI stuff continue to get better, and we're just so limited by being an extension. I want to be the IDE. I think we'll be able to create a much better product for our users, even the enterprise ones. We can figure it out if we own the IDE, we fork VS Code. And I'm just like, Oh my goodness, it's like a totally different product. And he's like, yeah, we're going to call it Windsurf. It's going to be a totally different thing. And at that point, I'm just like, I totally, totally trust Varun. I trust even when he says something that I think at first might be crazy, that it's probably the right move. And it was. Ultimately, when we launched Windsurf, it really did not take very long for Windsurf to become very ubiquitous just because, yes, it's so much more powerful to own the IDE. You can do so much more. We also launched a version sort of for JetBrains, which is the other major IDE in the ecosystem. And so, yeah, it was just once again, like, a story of breakout success and usage. And, yeah, I think the team, just like, there's no limit to their ambition. They don't just want to make, you know, 10x developers even better, but they also want to make everyone a developer. Because, like, you know, it sounds like this sort of bogus thing to say, but It's happening today. Like, I have so many examples of non-technical friends who, you know, are literally like wanting to create like side project gifts. I have this friend, she's amazing, she's very artistic. She's like, I want to— my husband's a programmer and I want to make this like gift for him, this like puzzle hunt website thing. And she was able to do it in Windsor. It was like very complicated in like you know, a few hours and she can't program. And then I'm like talking to somebody and they're like a salesperson. They're like, yeah, we don't even need like the sales SaaS tooling anymore. We literally can cancel hundreds of thousands of dollars worth of subscriptions because I can use Windsor to build these like workflows that I need. And so it's like, it's happening today.

Isn't this called vibe coding?

It is vibe coding. It is vibe coding. But it is like so power— like vibe coding has become kind of this meme, but it is like so powerful today, not just for developers, but like for everybody else too. And so I think that's been what's been most shocking to me in the last year of just AI in general, is just like how well this technology already works for like non-technical users.

And the adoption is taking off tremendously, like it's going vertical. I think I read somewhere, and you could correct me if I'm wrong, that they've Acquired a million users in four months.

Yeah, that's right.

Yeah, that's tremendous. Like that's pretty, that's pretty wild. I also saw a stat that they're doing a hundred million in revenue.

Ah, no comment. No comments. My new favorite phrase. No comment on the revenue, but they are certainly doing well. And it's funny. I was I was on this podcast with Barun Grit, which is a kind of a Jewin who who who's Who's an amazing operating partner at KP. But he basically is like, you know, the triple, triple, double, double, double SaaS thing is like, you throw it out the window, you know? It's like there's a new, it's like 10x, 10x.

It's 10x, 10x, 10x.

Yeah, exactly. Exactly.

Yeah, we're seeing that. And the $100 million ARR mark is now a $1 million ARR mark. So the goalposts have moved. It's a new standard.

Totally. And it's crazy too because it's like not only are you seeing these increased growth rates, but yeah, there's been stuff written about this, but like with tiny teams, right? So like teams are a lot smaller than they used to be. Though I do think, you know, maybe my hot take is like if you're an enterprise business, we have not automated sales yet. We've not automated enterprise sales yet. And so I do still think if you're working with enterprises where there's just like so many Relationships, so much like data, so much integrations, like all of this like enterprise, like readiness stuff you have to do. It's just, it's not automated yet. You need people for that.

How big is their team?

How big is, oh my goodness, Windsurf. I think they're like 150 now-ish. Wow. Yeah. Yeah. Yeah.

Wow. That's impressive.

But I haven't checked in a while, so I, it could be, I could be, I could be too, I could be too low.

So. I'm curious from your perspective. You are a major investor in this category. You research and you watch every single company that's coming up. You're seeing these 10x multipliers across the board, whether it's revenue or valuations. How do you measure success for these companies? Or maybe it's for windsurf in particular, if we want to use that as an example. What are the metrics that you're using to evaluate the company and determine, okay, it's on a successful path, It can be underrated against peers, maybe it can't. I'm just really curious what the metrics you're watching are.

Yeah, I mean, I think like, you know, a lot of the, you know, as game-changing as AI is and like as much of a paradigm shift as it all is, I mean, ultimately as an investor, you're still looking for a lot of the same metrics. Yes, now you have all these examples of companies just like really breaking out Especially the more like prosumer-y ones, right? Because they can go viral and then there's just a lot of like PLG effectively. But yeah, I mean, you're still looking at like your standard like SaaS growth efficiency metrics. And but, you know, obviously depending on the space, maybe you're like, okay, you got to— they got to be a lot better if you like have comps in the market that are performing at some really, really high rate. I do think you have to be careful though, because I mean, we can talk more about this, but, you know, one, obviously, like, the model cost is going down a lot. But then at the same time, some of these companies are calling these models again and again and again to, like, provide the response to the user. And so, like, margins can be quite sketchy. And so that's like, you know, that's something that you certainly have to pay attention to, especially as you're underwriting more like things that are, that are growth investments.

Could you explain that a little bit further? Like, how do they get sketchy?

You can't just I'd say take revenue at the face value when, you know, let's say for example, you're— this is an AI application company. They're calling OpenAI models, they're calling Anthropic models, and they're like, you know, maybe charging the user a certain amount, but then they're calling the models, you know, and the cost there is more than that amount, right? Um, and so yes, they're making revenue, but they're losing money, you know, the more that the, that users use them. So I mean, you just have to be, you know, and maybe that's fine because the models get cheaper and you can charge the users the same, and then eventually you can get to like something that resembles more of a healthy margin. But yeah, at least today, there's certainly AI businesses that, yes, they're growing really fast, but the margins are quite negative. And so therefore, you have to bet on them raising more venture money until they can figure that out, whether that's from a— they develop their own models, models just get cheaper naturally. And this isn't a bad thing to bet on, but just as an investor, I think It's important to go eyes wide open into these sorts of things because it affects how much money they're going to have to raise in the future. You know, it just kind of affects, yeah, just their growth plan.

Yeah, this goes right into your BS detector and quite a narrative violation, might I add as well.

Yeah, I mean, yeah, it's been interesting. I mean, it's something that you and I have talked about a little bit, but I feel like now when people talk about ARR, which ARR is always what they say, It can mean just wildly, wildly different things. And so now I definitely, like the last 6 months, I've asked so many follow-up questions about, okay, like, you know, you've got— I don't take ARR at face value anymore. I got to like get to the nitty-gritty of what actually are you talking about? Because many, many times I have heard something that is not predictable, that is not recurring, be described as ARR. And that, you know, historically, you know, traditionally ARR is predictable, it's recurring, it's like signed, you know, contracts, usually they're live. And so like, it's fine to use these other metrics, but it's, I think, important to be intellectually honest as a founder and not call it ARR.

No, that's a valid concern because we have been seeing, and we will probably continue to see stories like this, companies like 11x. There are also previous companies like Frank that fudged their numbers or inaccurately accounted for revenue in, or who knows what ARR actually means these days. So no, it's a valid concern and it's something that's really prevalent. And the more you get into bubble territory, the more you are subject to potential fraud and scams and all of that kind of thing. I was really curious to bring this up because when we are in increased competition, when we are in an increased competition environment, that increases pressure on companies. And so some of this happens. From your perspective, how are you detecting this within the companies? You mentioned you're asking more questions, but what in particular are you looking out for?

Yeah, it's a great question. And it is like such an important part of the VC job to figure this out. You know, you've got a few different ways that, you know, like I probably tend to try to make sure I dot my i's and cross my t's when it comes to diligence. But I mean, one, there's obviously just asking questions, spending a lot of time with founders. I think in general, you know, it is underrated to spend a lot of time with people because ultimately you learn like From meeting to meeting, like, how does their talk track change? Do they accomplish what they say they're going to accomplish? Like, are they, you know, many of my, you know, best investments and relationships have been over many years. And so you can kind of get a sense for, like, do they actually know where the puck is going or not? Because you've been able to, to talk, to chat with them so long. And then also just kind of generally, do they, are they trustworthy? Are they intellectually honest? And I think that's, you can kind of judge intellectual honesty from talking to somebody, just the way. Are they proactively explaining discrepancies or are they just trying to kind of smoke and mirrors, hope you don't ask the follow-up question? I think the best founders absolutely love to answer follow-up questions. They're like, yeah, if this competitor does this, we're going to do that. Or if the OpenAI model is this much better, this is what's going to happen. Like the idea maze of situations, they're always very competent answering, I found, among the very best founders. I mean, I think there's other things you can do too. Like I mentioned, we have those CIO networks at KP. That's extremely helpful because, hey, I mean, we have relationships with these people. They're using these startups' products. You know, there's no better praise than when one of our CIOs that we trust or that we think like has a very high taste for software is like, hey, this startup, like, you know, is legit. And then that's just like great asymmetric information for us. I'm not saying that they're not in any other venture fund CIO network, but like, It's not a public thing, right? So that's always very helpful. And then, yeah, just like playing around with products yourself, not trusting demo videos in general, just, yeah, doing work on the space so that you can ask better questions to like elicit more detailed responses. But yeah, it's a really hard thing making sure that everything somebody says is true. But, you know, I think Because of that, I just try to spend more time on fewer companies.

Yeah, it's an interesting paradox because rounds are getting done faster, but you have to go deeper in diligence and you have to do more. I'm not sure if you do this, but more upfront work and more references. And maybe we're utilizing some of the AI tools out there to get as much information on the company as possible and do significant diligence. And make sure that it's not going to be some sort of trap.

Absolutely. Yeah. And I maybe have a few things that I do that sometimes people make a little bit of fun of me. But, you know, like, I am a predominantly outbound investor. Like, the vast majority of my investments have come from me reaching out to an entrepreneur and trying to basically get in front of what they're doing. And, you know, because as you said, these rounds come together so fast. So, if you don't already have a prepared mind, and a read on a founder and a good relationship with them, you're not going to be in the mix. You're not going to get the call. These sorts of the hottest things, in my experience, they don't get sent out to people. It's like founders that are aggressively approached with a term sheet. And then it's like, okay, well, who else maybe did I get to know before this fundraise? And so I'm just like, I think this is one of the biggest surprises, but I'm just constantly doing outbound. And constantly like proactively reaching out to people and just trying to develop my own thesis on people and things way before, you know, somebody gives them a term sheet. Or better yet, maybe I can develop my opinion and conviction so strongly that I can give them a term sheet ahead of when they thought they would get a term sheet. So it's certainly like, I agree with you, like these rounds happen so fast. And I think as a result, you have to like just change how, you know, I don't think there's the traditional like, You've got 4 weeks and they're gonna give you all this data upfront. And no, I think you have to like figure out scrappy ways to, to get a lot of that yourself.

Going back to our topic on competition and windsurf in particular, so they're in an increasingly competitive market. It is so competitive. We've Cursor, Copilot, Devin. There's so many in this environment. So how have they been able to rise above the pack with this impending news? Maybe it happens by now, maybe it doesn't, but How have they been able to gain that attention and that market share?

Yeah, it is like, so it's interesting. I mean, there are endless like possibilities with this technology that we have right now with LLMs and multimodal models. It's just like endless, the what's going to end up happening. But I mean, there's like these near-term, what I call like battleground spaces. And, you know, cogent is probably like the like most, most, most, most war zone right now. You've got customer support, and then you've got like, you know, the copywriting, maybe some areas of like sales AI that are starting to get really competitive. But yeah, cogent is just like an extremely, extremely competitive space. And so, I mean, I think like there's a few parts of Windsor strategy that have been really great. I mean, one, their team, their infrastructure experts. So they are just really, really good at being able to optimize these models and the product for what users care about, latency, accurate results, things like that. And then I think the strategy of like, let's become an enterprise-ready tool, but then also have this sort of like best-in-class PLG product to get leads for the enterprise motion. I just think that's a winning approach. And I think the team just proves time and time again with every wave that they ship rapidly that they really care about usability and their users. And I was just kind of talking about this the other day. This isn't like a new take, but I think it's very unclear right now what out of all these companies who has a moat. But I do think like speed right now is kind of the only moat that you can have. And hopefully, hopefully that will translate into, okay, like you get into these enterprises, maybe you learn and you become more personalized to certain users' workflows. There's switching costs. Maybe that becomes a moat. Or maybe like there's some amount of network effect when you start to collaborate with other people on the, on the Windsurf platform or something like that. But at least today, you know, the, the moat for all these companies is just like how fast they can move and You know, I think I think what's what's what's good about this space is it's huge, and you know, kind of as I was saying earlier, it's um a lot bigger than I thought.

The pendulum swings back before it was okay. Let's get to profitability as fast as possible, as fast as possible. Now it's growth at all costs. Save nothing. Go for it. Go big or go home.

Yeah, yeah. Well, I mean. In some areas, you know, in some areas there's always, there's always a nuance.

But if this is true and this, this acquisition goes through, a $3 billion acquisition by OpenAI, Lee Marie, you will be a legendary investor. You are a legendary investor and probably better than 99% of VCs out there. And everyone will be very jealous.

Holly, I can't comment, but you're gassing me up. You really are.

Yeah, just doing my job. Yeah.

No, no. Commenting for now.

All right, fair. Well, speaking of rising above the pack, the rise of AI was the theme of your latest fund announcement. I would love to go into this further. Like, what are the key trends in AI infrastructure and ML applications that you're watching?

For sure. Yes, we already talked about kind of the battleground spaces, and I think what's been—I mean, I'm very lucky that my partners, even before I. Got to KP two years ago, have been making investments in these spaces that have been doing really well. So, for example, we invested in Glean, or I should say, I mean, it was more of a we kind of incubated Glean. So we we had a we had a big role in kind of like getting Glean off the ground.

Another zero to a hundred million ARR track.

Yeah, I mean, yeah, Mamoon, he's definitely definitely a goat when it comes to just like amazing software companies, and this is no exception. I mean, Glean, we're we're super bullish on, and. Yeah. So that's like, I'd say that's in a battleground space. Like at this point now, you know, like enterprise knowledge discovery, I think that's becoming all of these, you know, Perplexity, all these other, all these other, all these other tools are now trying to kind of like get into that area too. So that's certainly like a battleground space. I mean, you have the medical scribe space. We're in Ambience, who's doing very well. So this is basically a tool that doctors use during doctor appointments. To like listen in. And then instead of them having to spend countless hours like writing down notes into their EHR, like as a follow-up email summary, like it's just done automatically. And this is kind of like a no-brainer. And then let's see, I think the only area that we don't have anything in right now that's like very obvious is customer support. And so, you know, we have a growth fund, we're looking, but, you know, I'd say those are like the areas where it's like the tech. Clearly there, the customer need clearly there. Like, people are just duking it out, competing for the big prize. And then you've got like, you know, and this is what I'm excited about and what I spend the vast majority of my day on is, okay, well, what are the other really big companies that can use these models to create, you know, end customer value to help customers cut costs, basically, you know, to do work? Versus just kind of like be sort of a software tool. And I mean, I think there's like a few areas that I'm that I'm really excited about. I mean, I think we are just at the the tip of the you know iceberg when it comes to enterprises actually using AI effectively. I think a lot of people use use ChatGPT like want to use AI, but in terms of actually like automating tedious back office tasks. Or, you know, being able to like come up with like new ideas really quickly or analyze like data. There's just all this stuff that they could be doing that they aren't because it's just really hard to integrate AI into like these, you know, giant enterprise data silos and things like that. So, you know, certainly looking for like companies that have a strong point of view on like, okay, this enterprise doesn't need to use BPOs for this sort of task. They can use, you know, maybe a BPO plus a combination with a software tool, or maybe just like fully automating certain types of processes end-to-end. I think I'm excited about— there's probably a lot of opportunities in finance to automate like financial analyst workflows. We don't yet have a bet in that space. But we have a lot of people that are on the KP investment team that used to work in investment banking or private equity or hedge funds. And the amount of like PowerPoints they had to put together or like Excel workflows or all that stuff, that's all been relatively untouched by AI. And, you know, you could imagine, especially as the models develop better quantitative understandings, like just being able to deal with numbers better, you could imagine those workflows being automated away. I mean, I think there's just a lot of like very domain-specific stuff, whether it's in supply chain, whether it's in like biotech, biopharma, Whether it's in like other parts of legal. I mean, we're all— we're in Harvey and they're growing really well, but there's just so many workflows there. Yeah, I just think like even the models today, you know, and hopefully the cost will continue to go down by 10x every year and we'll be able to unlock a lot of this. But there's just a lot today that is very rote and doesn't require a high degree of like, you know, creativity or whatever that these models can Can automate.

Hey, it's Molly. If you enjoy our interviews, check out our newsletter sorcery.b.c., where we deliver a once-a-week top deals and tech headlines email, and also go deeper on our podcast interviews. Subscribe to Sorcery today, and don't forget to subscribe to the podcast on YouTube, Spotify, Apple, or wherever you listen. Link in description to sign up. It's great because you have such severe expertise in all of it, and you've been watching it for a while that. I've learned a lot even in this short amount of time that we've been talking. But I know we didn't get a chance to go deep into your background yet, so I wanna do that. I will hype you up again, Leigh Marie. So part of your portfolio is Windsurf, Nooks, Chronosphere, Neon, and Persona. Could we just go back to Leigh Marie, Scale AI, and go forward from there? Let's talk about your background.

The way that I got to Scale AI was again through competition math, which is, which is hilarious. And also poker. Alex and I got very close on the MIT Poker Club. Before he dropped out, I think his sophomore year, to found Scale. So I was at Scale, let's see, pretty early on. I was like an intern when we were like 3. And then—

Wow.

Yeah, yeah, yeah. When it was like tiny, tiny.

Crazy. Oh my gosh.

Yeah. I mean, it was— Scale was the API for human labor back then. And I called it the Alex API because we didn't yet have like a task force. So if you put in— it was still an API and you could like, Basically, there's an endpoint where it's like, we'll make you a phone call or order you a pizza. And Alex would be on the back end doing that, which I'm like, this is one of his greatest skills, but he's always very in the details and just works extremely, extremely hard. But basically, I was there when we were like 3. I was probably full-time when we were around 7. Started as an engineer. This is back when, you know, I think Scale very quickly realized, like, you can't be the best API for everything. You have to pick. And, you know, they were a YC company. And so a lot of these self-driving car companies, especially through YC, started using the API. And that was just this incredible customer pull. Like, okay, this is like, if you want to build the ML platform of the future, where's the biggest pain point right now? It's providing labeled image data for self-driving cars. And so kind of rethought the whole product to just be around that, providing it, you know, basically end to end. You know, you don't have to manage the labelers, you don't have to manage the payments, you don't have to manage the quality. We do all of that for you. And I think that really, that value prop really resonated. And then very quickly, Alex realized there's an even bigger opportunity in LiDAR or 3D labeling for the same companies. So the vast majority of companies outside of Tesla believe that You have to use this 3D sensor, LiDAR, to have enough redundancy to make a really safe self-driving car. And so, as you know, the product surface area got bigger. I had always kind of wanted to be a PM at scale. I started as an engineer. Alex is very product-minded. But as the product surface area got bigger, you know, there was an opportunity for me to come in and like product manage. And at first, I was just like, I get to code and I get to do product management tasks, you know, and I get to organize the Jira. But eventually that became, you know, like, okay, where are we taking this product and how do we integrate ML to reduce our cost and things like that, which was extremely interesting. And so I was there for around 4 years. Like, in that time, Scale started working with basically all the software and car companies, also made inroads in defense, which was back when it was like, yeah, you know, that's like not cool. You know, there's like been this recent thing where it's cool to wanna work with government.

American dynamism, the gun down.

Yeah, there's like a term now, American dynamism, but like, Back then it was like, it was like not cool. And Alex—

Hard to sell into. Really long sales cycles.

Morally wrong. You know, like there was just like all of this evil. Evil. Yeah. Evil to help the US government achieve the US government's goals. But totally. Yeah. So it was like back then too. So that's even more admirable. But that's now become like a big part of the business. And then, you know, this was all pre— or maybe I was there maybe at the tail end of this. But we actually helped OpenAI with GPT-2. And I can't even remember thinking, like, what is this? I don't even know if this will ever— what are LLMs and transformers? And then I left. And then a few years later, now it's like, this is Scale's main business line. And I think that one thing I'll always say about Scale, Alex and his ability To put himself in the right rooms, know the right people, and be able to see, like, okay, where's all the revenue going to come from, from the next wave of what's going on in machine learning? Like, human intelligence, and I'm biased, but I think human intelligence and data will always be needed. And I have full confidence because he's done it time and time again that Alex is always going to be able to figure out where is that place where scale needs to build a product. to sort of like be able to bring the humans to the models. And so, like, you can't ask for a better first startup experience. Like, every 6 months, the company changed dramatically. It grew. I got to learn things about sales, marketing. I get to learn just in general, like, how chaotic a startup is. And I get all these people that will ask me, like, hey, Leigh Marie, like, my startup, oh, just like, it feels like we're changing things all the time and everything's on fire all the time. And I'm like, Great, because if it's not, there's like no customer need. Like if you are not like feeling like you are running around all the time and everything's on fire, then like there's probably not product market fit. Like there's probably it's it's not there's not that the only like hypergrowth startups even the ones that seem like they have their shit together they don't. It's just like it's always a constant fire and I think it's it's nice that I like lived through that because now when I like go partner with companies I'm like. If I see that craziness, if I see that like they're all over the place, they're, you know, being pulled in all these different directions, I'm like, okay, well, they've like stumbled onto something gold, you know.

I was talking with Bucky ahead of this and he nudged me over some questions for you. One of them in particular, actually most of them do come from your time at Scale AI. So, while you were there, you developed an eye for talent vortexes. Could you Explain this? What does that mean?

This is also the name of the game in venture, I think. But yeah, I mean, what really matters when you're building a startup is hiring extremely talented people. And, you know, it's this flywheel where you hire talented people and they'll hire more talented people. And if you stop hiring really talented people, then it's much, much harder to hire talented people because the bar is lower and it kind of reflects in all these downstream effects. It makes it harder to make good products, to sell bad products, et cetera. So I think at SCaLE, we were just obsessed with hiring extremely high-quality people.

Yeah.

Even so much so as I remember I got so frustrated my first year at SCaLE because it seemed like everything was— I was just overwhelmed. I was coding all day. I was labeling tasks all night. Alex was labeling tasks all night. We were all just like totally overworked. And then we would interview these candidates and, you know, like, these are smart people. And then, you know, Alex ultimately say, well, they're not, you know, no, I don't think they meet this bar. And I'm like, oh my God, if we could just have like 2 or 3 more people right now, it'd be like, it would make my life so much better. But ultimately what that did was a few things. Like, it just kept the engineering bar so high that, you know, then we went and were able to develop these sorts of great future products. And then also kind of created this like aura around the company, which I think can be, it's like this weird amorphous thing, but I think can be very useful down the road just with everything. Like, Scale just became like an MIT, like this is the selective place to work. And that sort of like helped us hire way better talent in the future and stuff like that. So, you know, it's like, it's always tough when you spend so much of your time on hiring and then you just keep rejecting all these people. But then at the same time, You know, I've just seen it play out. And now multiple companies where if you do that and you're very intentional about hiring, you know, you just create like a world-class team and things become a lot easier for you down the line. And so then how this, you know, translates to investing.

Yeah, that was going to be my second question.

Yeah, there's like a few, I'd say like a few different ways I do it. I mean, the funniest way is, you know, I remember one of my first investments at FF was Persona. that I worked on with a few other investors there, including Napoleon. And he's like, okay, well, you know, Leigh Marie, like, how strong is this engineering team? You were an engineer. And I remember I looked through, not saying you should always do this, but I remember I looked through every LinkedIn of every engineer just to like, 'cause we wanna understand like what types of like backgrounds are they hiring? What types of people are they hiring? And then also you just like looking through, like talking to people there, like looking through company reviews. And I think Persona in particular has just created this insane culture where not only do they have all these extremely experienced yet fast-moving engineers, but also their retention, like employee retention is just off the charts crazy. Like no one ever leaves because they have such an amazing engineering culture. And so I was like, this is an unfair advantage, even though it's a very crowded space, identity verification. Like they have a truly special team and it reflected in— we obviously did a lot of product feedback calls too. But like, you know, time and time again, we're hearing that, you know, this is the best product on the market. So there's that, there's that way. And then I'd say like, that's a relatively scaled out company. It was like a Series C, I think. But, you know, when it comes to like very early stage companies, one of my favorite things to do as an angel, and now one of my favorite things to do as a VC, is I have a lot of technical friends. And they very, you know, occasionally they're looking for jobs and I love to make matches. And so I'm always trying to If you just want to talk some ideas, I'll tell you companies inside the portfolio. I'll tell you companies outside the portfolio, whatever location, stage, whatever. It's my job to have a big map of all these companies in my head. And what I'll do is, I love to make matches because it makes me feel good. Because I mean, I love to see my friends crushing it. But then separately, I get a lot of data on what companies are able to recruit and retain really strong people. And so I've very frequently been, I've updated my model of a company like, oh my God, this talent vortex because I take somebody I used to work with at Scale that I know is like a really strong engineer. And then he's asking like, oh, well, maybe you should gimme an OpenAI referral. But then I'm like, oh, well, yeah, I will. And I did. But then I also was like, hey, you should also meet this super early stage startup. You'd be their first engineer. And I was just kind of lobbing it out. And then the startup managed to close him. And I was just like, holy moly, like, this is like, I update on the founder. I'm just like, wow, this person's like clearly, one, good at identifying talent, and then two, good at like convincing talent that has many options to go work there. So yeah, it's kind of like, how do I, you know, and I try to do these poker nights, and that's another way of like getting people together and getting insights and things like that. But how do I like find all these signals of like these brilliant people in my network? Where are they going? And like what companies are managing to snag them.

It's a much more thorough process than what is memed about quite frequently. And it's like LinkedIn turnover from OpenAI. So you're deep in the weeds.

Yeah, I know. I try to, I mean, ultimately I think venture is like, you know, you want to figure out like people that you trust and really smart people and You know, it's not like a super scalable thing, but I think if you like work hard enough, try hard enough, and you start to, you'll start to hear like the same names come over and over again. And you'll be like, okay, these are the places where like they're just really sneakily good at recruiting all the best people. And you should probably just like invest in those companies.

So I wanna pivot like a little bit, but go back to our conversation on like the AI-ification of companies. From your perspective, have you been seeing more bootstrap startups in this era? Like at what point? Let's just start there.

Yeah, I don't see like— I mean, and to be fair, I'm, you know, usually I'm looking like seed and after. So like, am I just because I look a little bit later than like maybe a pre-seed investor? But, um, I don't see a ton of bootstrapped. I do see there's this new term called Seed-strapped that I've been seeing more of.

Okay.

This is a real thing, Molly. You gotta look it up. This is new. This is seed-strapped, seed-strapped company. Basically what this is, is a company that like they raise a bit of venture funding, like a few million dollars to like get out the gate. Then they are so wildly successful with their tiny team and they're like crazy 10x, 10x revenue thing that like They are just hand over fist in money. Like, they don't need, they don't need more money. They don't need to raise a Series A. Like, maybe they don't even, like, maybe they don't even need to raise a Series B, you know? Like, maybe, maybe they live their, like, first round of funding after, after the seed is like a Series C.

I've been hearing this time and again. It's like companies, founders are like, yeah, I wanna raise this funding, but then no more after that. You know, this is the only round we're gonna raise. And I'm like, are you okay?

Usually that's totally like, you know, That's like never what happens. But then like for these companies, at least a few companies that like I know pretty well, that is kind of what's happening. Like, do I think they'll ever raise? Yes. Like, you know, I think ultimately they wanna build like a huge business and they gotta hire a sales team like we were talking about before. Like there's probably, and it's a competitive market and then you've got all these people that are like, you know, potentially like trying to compete with you and you need more resources. Like, yes, you'll probably have to raise again. But it'll just be interesting. I think we'll see multiple examples of like a company raised a seed and then a company raised like a Series C, and it's just going to be weird. But that's just, yeah, kind of, kind of how this, this world is.

Because you are so close to the ground, to the efficiencies on the early stage side of how to rebuild and build a company from the ground up with the most efficient team possible, with all these tools and all these cool things that are going on. How does this inform the legacy portfolio of growth companies? I know you don't do much on the growth side, but I'm really interested to know, like, you have companies like Brex, Plaid, Robinhood, Rippling, Square. These are all large companies. How, how are you or the team helping inform them restructure if they are to meet this new environment?

I mean, a lot of these companies are just being like, I mean, very proactive about all this. I mean, I think they all like are, you know, the vast majority of them realize that like, hey, like we need to make sure that like we're using the most cutting-edge AI tools when it comes to like cutting costs or making the user experience better or things like that. And you've been seeing a lot of companies like reinvigorated, like because they're able to really like create a great product with AI that they have today. And so, yeah, I mean, in general, I think a lot of these growth stage companies are doing a good job. And whether it's like, we do a lot of dinners with executives at these companies and talk through, okay, best practices, sharing best practices of what tools they're using, what they're doing, whether it's things like that, or just like, I mean, I mean, a lot of these companies are just incredible, incredible companies that figure stuff out.

Cut overhead, insert the robots, make the product better again.

Yeah. I mean, like, it's just incredible, like how much, you know, your day-to-day changes with these tools, right? Like now we've got deep research to write memos internally. And as you said, there's like all these tools that we can use to like glean insights like of, you know, who's going where and like what's, what's around.

Was that a little, was that a little plug for Glean?

Oh yeah. I, this, this is how I plug, you know, I like, I describe it, then you say it and then— This is not an advertisement.

This is not an advertisement, but Glean, if, if you wanna advertise for sourcing.

I use Glean a lot. Yeah. Yeah, they're incredible. Incredible tool. Definitely. We do this thing, portfolio review, and it's like every, I think every quarter we go through all of our, or most of our investments and like Glean does this thing where it will like summarize a lot of our board decks into like, okay, here's like the key metrics and commentary and it is a lifesaver. Yeah. Especially for my partners that have a lot more boards than me. I couldn't imagine doing that non-automated.

We got to check that out. Check it out, by the way. How many boards are you on?

Let's see. I mean, I am on— I have one board director seat, and that is Windsurf. And then I have a few observer seats. I still observe Neon's board, which is an investment I did at Founders Fund. We did part of their Series A. I'm an observer on Nooks' board. So they're an AI tool for salespeople. They do a lot of different things. They have a power dialer, which helps you cold call. really efficiently, a training product where you can train before the call. What are you going to say? And then also a new prospecting product, which is really great. But I'm an observer on that board. I met Dan, the founder, when I was at Scale, actually. So another Scale Talent Vortex outcome. And then also observer on a stealth investment. So TBD.

Ooh, a stealth investment. I'm going to guess no comment, but I'll take it.

Hopefully something to announce soon. But that, and then, I mean, I don't really— seed companies don't really have boards, but yeah, I work with like 3 seed companies.

It's a lot of work. And yeah, it's commendable. I've— yeah, I'll refrain from word salad, but we have to talk about poker. Okay. You've mentioned it a couple of times. Leigh Marie, can you please explain your poker situation? I heard the buy-in is like a million dollars per hand. Is that right?

It's actually $10 million. Uh, we are, we are playing for stacks. Yeah, no, no, it is not. Um, it is, it is, uh, very, very modest. I like tend to keep it— my, my, like, the whole point of the poker nights, and they're very like, they're like, like small poker night, like it's It's in my apartment in the marina. Like, we have a round dinner table and I put felt on top of it to make it look more like a poker table.

Wow.

Yeah, I got on Amazon.

There's like things like Molly's Game.

That's the goal without the fraud. But it's like usually like a few hundred dollar buy-in or maybe less. I want it to be like if there's startup founders, even that are very early stage that like to play poker, I want them to be able to play poker. And I want, you know, like, also personally, you know, just, I'd much rather keep it friendly than people really focused on, you know, just betting giant amounts of money. I think it's still like, if you're playing with $100, you care, you care about what the outcome is. That's the point of the money at all. Like, I think if you play for no money, people don't care. So you gotta like put a little bit of skin in the game to just encourage good gameplay. But ultimately, It's so that I, you know, I get to get to know people, like strengthen my relationship with them. A lot of times it's like founders, engineers. I've had people get hired from the poker nights. I've had people sell their software to other people at the poker nights.

Classic.

I've literally built my relationship with founders who I later invested in, effectively starting with a poker night, or maybe I'd met them once before and then like we got to know each other through poker. So it's just like, And then at Scale, we hired so many people that was our strategy. We would bring people in the office to play poker and then they would get to know us and things like that. So a lot of the most important relationships in my life have been formed through poker and it's something that I love to continue as a VC. And yeah, there's so many positive things I can say about it, but it's definitely, it's not the meme where it's like I'm I'm not, you know, like making investment decisions based on how people play poker, but I am like, you know, it's just a great opportunity to like get to know, get to know somebody.

So what kind of tips would you have for someone who's just starting a poker, like a YouTube channel? Do you have your own YouTube channel? There's a few things that I recommend.

There's this app called Poker Power, I think Poker Power app. Um, that's like for beginners and like they do like courses and things like that. that I've tried before that I think is really great. And then there's like, I mean, was on the poker club at MIT and the book that we would always reference is called The Grinder's Manual. It's like a PDF online, but in terms of like the theory of poker and like really learning how, yeah, to do like the math behind a lot of it is great. But yeah, also just like Playing and finding people that play at, you know, the stakes that, that you feel kind of feel comfortable with. I got very lucky. I didn't know how to play poker before college, but I was at Jane Street interning a few times, and instead of like going out on the town in New York, we would just stay in the office and we would play poker like after work every single day. And we played only $20 buy-ins, thank goodness. And so I learned how to play by just like, I'd play and I'd make terrible decisions, and then like my buddies would be like, hey, Leigh Marie, like do this instead of that. Or think about this and that. So, you know, just like figuring out, uh, places where you can like go and learn.

It's an amazing way to have your own community building while doing something that you love.

Totally. Totally.

We've covered a lot. Um, we went through windsurf as much as I could possibly get out of you, and I appreciate that. Um, we talked about the succession planning of Kleiner and how you guys have retained your legendary track record. And a little bit more on yourself and your background, especially with Scale AI. I mean, it's really tremendous. To end it out on a positive note, I'd love to know what are you most looking forward to this year?

I just think there's so much opportunity, like, to— in AI in particular, um, but also, also in a few other things too. But I'm just, like, very excited to continue to, to meet incredible founders and, and really partner with them. closely and help build their businesses. But yeah, I mean, ultimately, that's what we're looking for. And I've just never experienced, even back in 2021, when there was a very high deal volume, I've never experienced so many smart people wanting to become founders and the sort of why now and the tailwinds being so real. And I just think that there's so much opportunity right now to build to build a massive company. And so, yeah, I'm just, I'm really excited to continue to meet founders and, yeah, continue to make investments.

Fantastic. Well, Leigh Marie, it was a pleasure to have you on, and thank you so much for the time.

All right, Molly, thanks so much for having me. Really appreciate it. Great questions.