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Panel: The VC Landscape for AI Startups & Advice for Founders
Posted Oct 06 | Views 95
# TransformX 2021
# Expert Panel
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SPEAKERS
Daniel Levine
Daniel Levine
Daniel Levine
Investor @ Accel

Daniel Levine first joined Accel in 2010. He focuses on product-first startups aimed at consumers, developers, and bottoms-up business users. Dan led Accel's investments in, and serves on the boards of, Gem, Mux, ReadMe, Scale, Sentry, Sprig, and Vercel. He led Accel’s investments in Atrato, Beek, Numeracy (acquired by Snowflake), and Searchlight. He also works with the teams at Bird, Checkr, Heptio (acquired by VMware), MessageBird, Rylo (acquired by VSCO), and others. Dan re-joined Accel after spending time at Dropbox, where he worked on the platform team. He helped open the platform to third-party developers and launched and managed many of the company’s developer-facing initiatives. Earlier, Dan co-founded Chartio, a Y Combinator-backed (S10) startup in the data visualization space, and prior to Chartio, worked on CrunchBase at TechCrunch. Dan is from Washington, DC and graduated from Yale.

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Daniel Levine first joined Accel in 2010. He focuses on product-first startups aimed at consumers, developers, and bottoms-up business users. Dan led Accel's investments in, and serves on the boards of, Gem, Mux, ReadMe, Scale, Sentry, Sprig, and Vercel. He led Accel’s investments in Atrato, Beek, Numeracy (acquired by Snowflake), and Searchlight. He also works with the teams at Bird, Checkr, Heptio (acquired by VMware), MessageBird, Rylo (acquired by VSCO), and others. Dan re-joined Accel after spending time at Dropbox, where he worked on the platform team. He helped open the platform to third-party developers and launched and managed many of the company’s developer-facing initiatives. Earlier, Dan co-founded Chartio, a Y Combinator-backed (S10) startup in the data visualization space, and prior to Chartio, worked on CrunchBase at TechCrunch. Dan is from Washington, DC and graduated from Yale.

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Caryn Marooney
Caryn Marooney
Caryn Marooney
General Partner @ Coatue

Caryn Marooney is General Partner at Coatue. Prior to Coatue, Caryn spent over 8 years running communications at Facebook. She sat on the board of Zendesk for over 6 years and currently sits on the board of Elastic Search. Prior to Facebook, Caryn co-founded OutCast, a GTM and branding agency, where she worked with companies including Salesforce.com, Amazon, Netflix and VMware. Caryn focuses on enterprise and AI/ML investing at Coatue. Caryn is originally from New York City and holds a bachelor’s degree from Cornell University.

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Caryn Marooney is General Partner at Coatue. Prior to Coatue, Caryn spent over 8 years running communications at Facebook. She sat on the board of Zendesk for over 6 years and currently sits on the board of Elastic Search. Prior to Facebook, Caryn co-founded OutCast, a GTM and branding agency, where she worked with companies including Salesforce.com, Amazon, Netflix and VMware. Caryn focuses on enterprise and AI/ML investing at Coatue. Caryn is originally from New York City and holds a bachelor’s degree from Cornell University.

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Anu Hariharan
Anu Hariharan
Anu Hariharan
Partner @ Y Combinator

Anu is a Partner at Y Combinator's Continuity Fund focused on growth stage investments. At YC Continuity, she led investments in Brex, Faire, Gusto, Convoy, Rappi, Instacart, among many others. She is personally passionate about global technology investing and the convergence of great entrepreneurs between US, China, and India, and has invested in a personal capacity in a few companies including Jinri Toutiao. Previously, Anu was a partner at Andreessen Horowitz, where she focused on consumer internet growth investments, and worked actively with the management teams of a number of portfolio companies including Airbnb, Instacart, Medium, OfferUp, and Udacity. Prior to Andreessen Horowitz, Anu was a Principal at The Boston Consulting Group's Private Equity practice in NYC where she led multiple growth equity due diligences in the consumer and fintech sector. She started her career as a senior engineer at Qualcomm and holds a MS in Electrical Engineering from Virginia Tech and MBA from The Wharton School.

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Anu is a Partner at Y Combinator's Continuity Fund focused on growth stage investments. At YC Continuity, she led investments in Brex, Faire, Gusto, Convoy, Rappi, Instacart, among many others. She is personally passionate about global technology investing and the convergence of great entrepreneurs between US, China, and India, and has invested in a personal capacity in a few companies including Jinri Toutiao. Previously, Anu was a partner at Andreessen Horowitz, where she focused on consumer internet growth investments, and worked actively with the management teams of a number of portfolio companies including Airbnb, Instacart, Medium, OfferUp, and Udacity. Prior to Andreessen Horowitz, Anu was a Principal at The Boston Consulting Group's Private Equity practice in NYC where she led multiple growth equity due diligences in the consumer and fintech sector. She started her career as a senior engineer at Qualcomm and holds a MS in Electrical Engineering from Virginia Tech and MBA from The Wharton School.

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SUMMARY

Venture capital (VC) investors have dramatically increased the pace of dealmaking in the field of AI and ML. In 2020, over 1600 rounds worth over $27b were closed by US-based startups in this space, and 2021 is on track to set an even higher record. Navigating this landscape can be difficult for founders. In addition to the operational aspects—recruiting a team, building technology, working with prospects and customers, finding product-market fit—founders also face fundraising-related decisions with implications for the years to come. What are investors looking for? Which ones have experience with AI startups? What should be expected post-investment? In this panel, top VC investors in the AI and ML space will share how they evaluate startups, give advice to founders raising capital, and explain best ways to leverage your existing (and future) investors.

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TRANSCRIPT

Speaker 2 (00:33): So welcome everyone. We have a mix of folks in the audience today. We'd love to better understand the investing landscape for startups that incorporate AI and ML technology. So in particular, we have a number of startup founding teams who are balancing both the operational aspects, like recruiting a team, building technology, working with prospects and customers while simultaneously having to navigate the fundraising process as well. So today we're joined by a set of really impressive investors who have thought about this space and they're here to share their wisdom, insights, and thoughts about how to approach and navigate this process. So without further ado, I'd love to go around and have folks quickly introduce themselves. So let's start with Karen.

Speaker 3 (01:16): Great. Hi, thanks for having me. I'm Karen Marooney. I'm a general partner at Cotu and my experience has been on the operating side. So for the last eight plus years, I've been at Facebook, my latest role, there was running communications and I had a variety of roles there during my tenure. And before Facebook, I co-founded a go to market and branding agency where I got to work with companies from the very beginning that were Salesforce and VM-ware and Netflix. So from early days, and my focus at Koto is in, is in ML, AI, and enterprise. So thank you.

Speaker 2 (01:57): Got it. Welcome. And then let's have a donuts.

Speaker 4 (02:02): Hi, I'm Dan. I'm a partner at Excel and on the, on the board of scale, notably, I've been at Excel for a little over seven years in this stint. And prior my background was actually a mathematics and then I was really mediocre at that. And then software engineering. Then I started a company and I was bad at that. So then I started working at companies and I was bad at that. And we just haven't figured out that I'm bad at investing yet, but I'm sure I'll prove that out soon. So I've stuck here for, for so far.

Speaker 2 (02:31): Awesome. And then a new,

Speaker 5 (02:34): Hi everyone. Thank you for having me. I'm a partner at YC continuity, which is why this growth fund that I helped lead along with my partner Ali Regani. I started my career as a engineer by training software engineer at Qualcomm helping design the first 3g handset. And then I like to say that I went to the dark side. So post business school, I did a five-year stint at BCG helped lead their private equity practice, and then spent two years at Andreessen Horowitz before joining wise.

Speaker 2 (03:04): Got it. And then I'll close things out with a, with a brief introduction. Remind my end as well. My name's Dean I and the chief of staff to our CEO here at scale, similar to you. I do, I was also on the dark side, started with consulting as well, spent some time in investing before joining scale. So let's dive right in the first area that I'd love to get your guys' thoughts on is around the investment landscape. So in particular, I'm curious, where do you see AI and ML being the most transformative for society and where are the areas that are the most promising and exciting for, for all of you? So feel free to, for anyone to jump in.

Speaker 3 (03:46): I think one of the, one of the truly interesting things about the landscape is how far off the sort of consumer Zeit gate Geist is. You know, when you think about AI and I can say this because my son is like, he watches determinator all the time and you, so you have this like bizarre, longstanding, like background noise about AI. That is, that has been around for so long and is so far removed about where we actually are and what we're actually doing and where things can go. And I think it's just a really interesting juxtaposition about sort of the, for some people in sort of normal culture, it feels like it's been around for so long and I bring this up and it has like a stair. It has a role in society that is far removed from its actual role. So I bring this up because we are so early.

Speaker 3 (04:38): And I think that is one of my major themes is how early we are and how exciting it is. So yes, we have the larger companies, the Fang companies who have great resources that are doing interesting things and a lot of research projects, but we're really, truly only at the beginning of this. And when we think about whether or not it's on the horizontal aspects of creating foundational technologies or verticalizing technologies, like what we're seeing in the auto industry, all of this is going to transform. And it's just so early days, that's how, how I think about it.

Speaker 2 (05:19): Got it. Dan thoughts. Sure.

Speaker 4 (05:25): Yeah. I mean, I think one of the tricky parts about our job is you kind of have an inclination where you want to be exciting and you want to be right and think about the future. And certainly I presents kind of a great opportunity for that, but, but I often, if I have to rely on the fact that I'm smarter than other people, including the folks in this panel, then I'm probably not gonna be very good at this job. So, so I, I tend to focus on the areas where it feels kind of obvious and, and almost iterative, which I think maybe is less fun, but, but, but matters in short terms, when I think about areas where AI very transformative, like one good example is I think there's a number of, of jobs out there that are, are, they're no fun, they're dangerous, they're tricky, they're complicated.

Speaker 4 (06:06): And not because they're a great creative process or because they should empower people, but because they just happen to be rote jobs and they need to be done and they're important, but not necessarily highly valued or things where people want to work. And, and so I think AI has a huge role to play in freeing people up to pursue great jobs. They want jobs that are more creatively focused or more focused on high value things and less on a more kind of rote boring tasks that people don't want to be doing. Aren't compensated particularly well for, and maybe result in kind of dangerous things. I mean, car driving is a great example of this, where, you know, in the old days, when maybe you had kind of Teamsters units and things like that, driving in some ways was a higher paid job, but it's now like a very, very common job in the United States.

Speaker 4 (06:52): It's not that highly paid. It's very dangerous and has a lot of other issues. And so it's a great area where a new technology can come in, can kind of make driving faster, easier, cheaper for folks, and then enable folks to go on and do more interesting things where right now we need those jobs done. So I, I, there's a whole area of interest for me where it's transformative around freeing up labor to focus on the most important things. And I'm very excited about that. I think another area that I'm quite excited by is I think there's a whole class of problems in AI where you don't think about them as AI problems, but under the hood. And I really improves things. And so we see this in kind of the large technology companies today. I don't think people necessarily think about, or most people think about a Google or a Facebook as an AI company, but huge aspects of their product suite are really improved upon with AI.

Speaker 4 (07:42): Maybe my favorite example is Google photos, which is a product I love. I'm not an investor in Google. I just love the product. And so many of the features of that product are, are powered by AI and made possible by that. And that's a delightful way where it's not an AI first product to the general consumer. You know, it's just an easier way for me to share photos of my kids with my parents when they really want to see them, where I got proactively suggested, you know, it looks like these are your kids. And you seem to like sharing those, your kid photos of your children with your parents, which is a great user experience. So I think that's really exciting. And then the last area that, that I think is really interesting, it's kind of the infrastructure layer, which has a lot of where something like scale exists, where not everyone is going to have the infrastructure capabilities of a company like a Google or Facebook, but they want to weave AI into their products. And they're not going to be just an AI company. They're going to be a photo company. They're going to be a media company. They're going to be an e-commerce company, but they want the value of AI and they need help doing it. And so for those companies, it's really AI infrastructure companies, giving them the abilities of a Google and Facebook and apple, Microsoft, Amazon, et cetera, to weave AI into their products and create great things for everyone.

Speaker 5 (08:57): Well, Karen and Dan have already covered quite a lot. And so I'll just go into maybe my two to three observations of having invested in this space and bringing the investor lens. So at YC, we don't do thematic investing, right? So we invest based on what the founders are applying for. So, and also AI has been talked about from the time I was an engineer, which is more than a decade ago, but I agree with Karen that we're still in the early innings. So as investors, what are we looking for? So in the consumer use case with both Dan and Karen touched on, you see AI in many forms, how does it improve your decision-making? You know, some would argue the Netflix recommendation engine is a form of that input, which helps you, you know, keeps improving the recommendations because it takes feedback. But for me, the first real example on the consumer use case was by dance.

Speaker 5 (09:48): I met inaudible and his team in 2016, and I was very skeptical of AI because everyone talked about AI and I automated a lot of scripts at Qualcomm. And I was like, you know, let's really differentiate what's AI was, is automation, right? Or what helps, you know, removing labor from certain tasks can be just pure automation, not necessarily smart decision making. And what by Dan's had done with AI on consumer application was eye opening. Most of the world hadn't even seen it. I mean, today you see it through the lens of tick-tock. They literally launched a newsfeed. That was their first product, which you could say was Reddit plus Twitter, think of that. So it had communities, but it also had daily news, but you can consume the 24 hour headline news, or you can consume news that interests you. Like if you wanted to read about gardening and the way they did it was not based on likes or commenced there, you actually spent time reading.

Speaker 5 (10:44): Did you take five because you ended off like the article, but if you keep reading five minutes of every gardening article, the Johns is already like gardening. And they're going to put that in the feed and that made the algorithm so much better. So everyone had this thesis that the app would look more like Twitter with maybe a 30% retention and it was dead wrong, right? Because it, you know, when the two in 2016, when I met the team, like the average user was spending 65 minutes on the app daily and all video consumption, small videos. And the advertisement conversion was the best I had seen because they knew you like gardening. So the ads were gardening related. It's like, great. Even today, Twitter doesn't show me the ads that I need to see. It's like, it's that everyone is seeing. So I think in consumer application, if you take, what are the things to focus on?

Speaker 5 (11:35): It's like iterative improving the, what is the value to the end user of that iteration? Are they taking feedback from us? And how is that improving? So in the case of tick-tock, you know, very quickly, you can see the videos that you really like, you get the ads you're like, and it's more tailored to you, right? I wish every other app caught on to it. I actually think startups will do this faster than large Fang companies because in China, by dance launched, when there was 10 sentence V chat and there was WEBO and yet by dance became, you know, a really big company just because they focused on the algorithm. So that's one consumer second on B2B. I layered it as how Dan said, like a big citizen infrastructure, because we are in the early innings. We need the infrastructure in place for data, labeling data tagging.

Speaker 5 (12:24): I mean, scale is obviously done extremely well, but we have a whole host of other companies really attacking that because you need good data to iterate on so that the cost of the decision or the output that you're getting is more iterative and it's more valuable. And then the third AI example, I'd use some of the YC companies that are working on are like really difficult problems. So take insurance, for example, there are startups that are working on, Hey, if we can take images of all the crashes in car, or, you know, you can take any industry or home insurance or ha you know, and how can we predict and process the claim faster. So you're seeing that, you know, we're still in the early innings, I'd say in YC, 10% of our batches batch companies now our infrastructure. And if you saw that four years ago, it was nothing close to 10, and majority of them are in AI.

Speaker 5 (13:17): And then the second piece of learning I've had is it actually takes a very long time for AI companies to show product market fit. And this was true for every single company take focal systems within YC, at least more than three years old, what they do is very simple. They have a PA, they, they have a pat on the shopping cart, in the grocery store, it takes pictures of everything on the inventory aisle, and it also takes consumer feedback. So it, it did rates whether really the item is in stock or not for the grocery store. Yeah. Makes a very long time for that to get to product market fit. Right. And so I think like the investors as investors, we really need to adjust the timescale for what we are looking for from an AI company. That's trying to solve more difficult problems than a B2B SAS company.

Speaker 2 (14:04): Yeah, no, that makes a ton of sense. And it's a really good segue into one area that I'd love to pick your guys' brains on, which is as you're evaluating a potential investment in the space, how do you sort of think about it as a, primarily the team, the idea, the traction, the union, the uniqueness of the, of the technology, like the total addressable market potential, like to your point, it does take some time for us to be able to see that product market fit. And so how does that play into your decision-making? So maybe we start with you again and do,

Speaker 5 (14:37): Yeah. So I, so there are two forms of decision making within VC, right? So at the early stage, it's absolutely based on the founder and the team, because most of our companies haven't launched. So when Ambien DEI, which does, you know, it's, it's focused more on the enterprise security, they didn't have a product, but they had a thesis that they had worked on at Stanford. And they had a plan on how, what proprietary data sets that they would iterate on and what the output would be. It took them, I'd say, well, over two years, but we bet on the team. So the early stage YC, which is pre-seed, that's on the team, on the growth stage for us, you know, you're usually two or three years around. So when we are evaluating, let's say a company like scale AI, we're looking for what, you know, what was the use case, the core use case that you launched with how it did, what is the type of data that you're collecting, if it's data labeling or any, you know, inaudible or tagging, what is the type of data that you're collecting?

Speaker 5 (15:35): How has that informed your models or decisions and what is the value you're willing to provide? Because a lot of you, even for scale, we are in the early innings, right? Or fully seeing the maximum output of what can this data actually help inform, but we just need to see the core use case working and what is the proprietary data set and how we treat evidence and then the vision for how the use case has transformed and what is that value to the end customer? So we would call at the growth stage, the end customers that are already using saying if the company truly automates this end to end, and this is the value of the decision to make it real, like people that are AI, if they say, Hey, I can help you get to the right sales leader. Well, how valuable is that to you, the end user? And is there noise? Can they actually get there or is it suboptimal? Right. So that's what we are evaluating at the growth stage.

Speaker 2 (16:29): Got it. Okay. And if we go a little bit stage by stage Dan, like how do you sort of think about it?

Speaker 4 (16:36): Sure. You know, for, I work across kind of the earliest stage team in Excel and also some of the growth side of things. And, and honestly, they're the core criteria when I'm evaluating any company are the same. And I think they would be the same independent of, of kind of industry and to a certain extent. So it wouldn't actually matter too much if they already I company or not. And, and actually a lot of this is learned from YC, kind of my formative learnings as a, as an entrepreneur and probably model a lot of my best things after YC. And it had a big influence on me. So w when I look at a company or any kind of opportunity for us, I look at when I say two and a half things. So facetiously, I look at team market and then the half is like, you know, if you had a hundred million dollars in 10 years and you've done nothing with it, something must be wrong about my evaluation of the team in the market.

Speaker 4 (17:21): So really the, the metrics and traction, I think those things tend to be trailing indicators, but if they're trailing, they still should happen eventually. And so, so maybe I missed something on a team and market, and that's how I think about metrics. And when I think about team and market, just to break those down a little bit more precisely with team, you know, the first thing for me is do I like this person? You know, I have to work with them. I have to want to root for them. I have to want to sell people on working with them. And it's all just a lot easier if you, if you like the person that really matters to me, you know, are they capable of being smart and capable of hard work? You know, my dream is to have a bunch of dumb, lazy, successful founders.

Speaker 4 (17:57): I certainly strongly for that, but it's nice to have the option just in case. And, and so often throughout the life cycle of a company it's, it's they're needed, unfortunately, but I prefer if you can be an idiot and lazy, and then the last one on team is can you hire other people, right? Can you multiply? Like you have kind of an ability to scale and bill and attract other great people on market. I look at two things, and this is probably the most perhaps YC like thing that I probably think about, which is, you know, on the one hand there's can this market be big, but I take a pretty optimistic view of that. And if even I can articulate how it can be very large, then I'm quite open-minded. I think it's too easy to look back at so many examples in history, and it's so easy to come up in our market.

Speaker 4 (18:37): Isn't big enough. I mean, I always remind people now and I feel like it's gotten long enough ago that people really forget this, but Airbnb really stands for air, bed and breakfast. And the actual meaning was actual air mattresses on living reforms. I mean, that's insane. I mean, kudos for it, community, a huge company, but I think it's hard for most smart people to say they would have predicted what Airbnb looks like today. If you were trying to be critical. I think if you're, open-minded optimistic, you'll have a better chance. So, so can it be really big, but I spent very little time on that beyond kind of optimism. And then the YC like pieces, you know, is there an insertion? You know, I think over time as companies survive, like one of the great ways to grow a company is going to be, you know, venture funding, you know, obviously I'm biased, but I think, I think venture funding is incredible.

Speaker 4 (19:25): I love it. But so we've got this to raise money, but the other way is they're going to have delight and love from their customers. They can build a great product and they can work that way in both cases benefit from having a great initial starting place. And it's, again, it's a proof point on the team's ability to get something done. But, you know, one thing I always say is, I think, I don't know if this is true anymore. Probably not in the current fundraising environment, but a few years ago, people would talk about how you have to get to a million dollars of ARR or something to raise a series. A and you know, this is kind of an insane thing to say out loud Excel and other ones here at a bunch of other funds. So we charged a lot of money, but the people who give us money, if it was as easy as you get to a million dollars in ARR and you raise, raise capital, I wouldn't be able to charge quite as much, you know, people wouldn't be hit, you know, how much this Vanguard charge, whatever.

Speaker 4 (20:08): It seems like a pretty simple thing to index on a million dollars and RR. And in fact, none of my companies have ever been at a million dollars of ARR at a series day scale had virtually no revenue, no real revenue, at least when we invest in that, that's the case for most of them. So I don't overreach on the metrics. They're more a trailing indicator. And in fact, candidly, when somebody pitches me and they have a million dollars brrrr, I almost immediately want to not like it because I have some pride of this. Isn't the milestone that matters. But that's me being again, an idiot, which happens a lot.

Speaker 3 (20:42): So, so at Cotu we have all stages. So we do investing from the earliest from seed stage to growth. So we really will work with a company at any stage, you know, from two people to, to a large growth round. So then what we look for is different depending on what it is and what stage. But when I think about it, it's around two factors, it's team, and then it's market and under team, there's a couple of things. There are sort of three main things. There's can they be the best? Have they shown some ability to execute and are they incredibly hungry? So those are, those are huge generalizations, but a lot of what we're looking for, what I'm personally looking for, and in founding teams, you almost get flavor of the day or of the decade. You know, there were the Google founders and they're super brilliant. And then there's mark Benioff and he's a great sales person, or you've got Diane Green and she's an amazing technologist. So you've got Slootman and he's a great like executer of sales. And you can, the market almost picks a flavor of what a founder is supposed to look like. And I don't believe that that at all, like you be the best you and what is best for your market. And you're going to be that next thing that everybody else follows.

Speaker 2 (22:00): Like, it's not about the archetypes, right?

Speaker 3 (22:02): It's not about the artist. I think that is a real trap that people fall into. And you, you really have to figure out the best of yourself to serve your company and what you, you uniquely bring to your vision and your company. And please forget the archetypes.

Speaker 2 (22:17): Yeah. That makes a ton of sense. So do you want to keep an eye on time? One area that I'd love to, for us to spend a little bit of time on is, you know, there's a number of founders in the audience today would love to understand, you know, what kind of advice that you guys can give to them. So one in one particular area would be what around fundraising. So what sort of advice do you give to founders of startups in this space that are looking to raise in the near future? And Dan, I go back to one thing that you mentioned earlier, right? Where just having that relationship and feeling that, that you can click with the entrepreneurs is so important. And so I'm curious how you guys, how you guys think about this and, you know, the tables are turned, what should the entrepreneurs be optimizing for as they have these conversations?

Speaker 4 (23:07): Yeah, it really depends what the entrepreneur wants to accomplish. You know, I, I, I think there's arguments to be made if you're a founder and you know, what you need to do that you're optimizing to an extent for either cheap cost of capital. I think that's certainly plausible. You could be optimizing for, for the partner that you work with, either at the firm and or the individual and because of things they can bring, whether that's just they're calming, influence, and they're relaxed when they work with you, which I think is a big value add whether it's particular customers, that they might have relationships with their partners. It could be something as simple as like the brand of the firm as the fits, like your ability to hire folks. You know, I think venture firms often, often they kind of act as brand lenders to early stage startups when folks are trying to learn a signal when people are trying to figure out where should I work?

Speaker 4 (23:56): So those are all the things you can optimize for. I mean, I think I'm, I'm, this is a good example of a situation where my, my age and I'm not that old despite lacking hair, but, but my age probably makes me, I, I probably know too much to be good for what an actual entrepreneur might want in this case, which is my age. I would like to work with people that I want to work with, that I would get to know, and I would spend the time. And I think that's more important to me, you know, I, I'm reasonably confident that I, you know, a higher percentage of people who are younger would think that's less important. Perhaps they, maybe haven't learned some of the lessons of, of having to work with folks. And, and, you know, if you think they're great, but it makes you miserable.

Speaker 4 (24:32): So it really does depend, my advice would be, and this is from my own opinion, which is biased, but, you know, get to know people, build a deep relationship and, and think about what it's like to work with them for a decade or something. And a lot of that advice boils down to simple things like, you know, will they be there? Will they be even keel? Will they have a long-term view as close to yours as possible? I think sometimes this is a, maybe a more interesting point that I think people would disagree with me on. But I think oftentimes entrepreneurs look for people who are more complimentary to them. And I think that has a lot of potential upside, but also a lot of risk. So I think people are lightly too biased towards complimentary skillsets when they look for their investors. And so I I'd probably be marginally wary of that as compared to the market norms.

Speaker 4 (25:15): But no, I, I think it's fine, great people. And then you should still care about the business. Like you're running a company, you know, there shouldn't be cheap cost of capital and you want to, your main focus is running a company, building a team like building a product, not raising capital, raising capital is a thing you do every once in a while. It, hopefully it doesn't take too long. It doesn't get in your way to accomplish the goals you have. And that doesn't mean letting investors spend forever doing diligence. When you have other opportunities of folks who are more excited, one quicker. So I do in your first parties to the business and then yourself, you know, as a, as an individual, and then you can kind of take a broader view the other things, but, you know, I, I would say, get to know the person. And then the second thing I would say that's maybe interesting is don't, over-rotate on like finding somebody complimentary to you necessarily that has a lot of forbidden fruit aspects to it, but also can lead to like more friction along the tenure journey. And some of that friction will probably push you in a positive way, but some of that friction might just be, you know, inherent to just being very different people. And life is too short and startups are too hard to add in too much stress as an option.

Speaker 2 (26:20): Yeah, no, that, that makes a ton of sense. I remember reading this statistic and I think it's true, but this idea that the average relationship between investors and the entrepreneurs is actually longer than the average length of a marriage in the U S so it's definitely, that's definitely something that's a real food for thought as, as people have developed these and decide who to take money from.

Speaker 4 (26:45): Yeah. But it's less important that I make coffee for my entrepreneurs than I do for my wife in the morning. But nonetheless, we get a lot of similar.

Speaker 2 (26:52): Yes, for sure. And then Karen, what, what advice do you have for the entrepreneurs that we have in our audience?

Speaker 3 (26:59): Yeah. I mean, I do think it depends what you're optimizing for. I would say that you do need to think about this as a long-term relationship. So when you think about that, whatever your problems are today are not going to be your problems of tomorrow. So you can get very short-sighted in terms of this complimentary thing of this person will solve my problem of today. And that's a short-sighted way of thinking about it. It's a Tom petty song and his lyric. It goes most things I worry about never happen anyway. So there's going to be so many things that go wrong, but you are totally incapable of knowing what they are today. So if you think that you're optimizing your partner or your thing for the specific thing, you're probably wrong. And it's probably short-sighted. So when you're thinking about this long-term partner, it needs to be somebody who can imagine solving problems with not the problem itself, because you don't know what they are yet.

Speaker 3 (27:59): And that you want to say like, can I imagine that, is this the person I want to text or call or zoom or get into a room with and figure it out and have healthy disagreements and discussions, but know that adding these people or this person helps you figure something out versus something specific because your problems are going to evolve and change as, as you do. So I do think it's, it is an important relationship. And I do think that the it's somewhat easy. You know, what is it? Success has a million fathers. So it's also like, it's very easy when things are good and it's really important. I'm also a big fan of even keel. Maybe that comes from like where I worked for a decade, like got his take on because there's going to be highs and lows and you need to have patients to generate enough good things, because there will be tons and tons of mistakes.

Speaker 3 (28:53): It was talking to a founder once that said, like, I just can't get this wrong. And I was like, you can, and you will get it wrong. So the question is, you know, then what, you know, not, you can't get it wrong cause that's paralyzing. And I think the third thing, you know, besides like ability to solve problems, ability to overcome when things go wrong and who that partner is, the third thing is also this sense of, are you aligned on the bigger vision? Because I do want to say it is the founder's vision, not the VC's vision. And you don't want to have the sense that I've seen certain people get into it. And it's like, every company is like a bunch of ingredients. Like you have eggs and chocolate and the VCs, like I've always wanted to make brownies. I've done the market research and the market really needs brownies. And the founders, like I got a cake I'm building the world's best cake and the VCs like, oh, brownies, brownies. And so you really, really want to make sure that this person's enabling your vision and isn't somehow just trying to infuse their desire or former life into yours.

Speaker 2 (30:05): Yeah, no, that makes a ton of sense. And then I knew what are your thoughts?

Speaker 5 (30:11): I'd say in this market, it's actually really, really easy to raise. So my advice is actually raise only the amount you want and don't get into the game of fundraising because honestly, like I see that through and through with the batch I see with the companies they've done three rounds within six months. That is, that sounds exciting. But remember like company building takes, I mean, Levon years or 11 plus to get to IPO and company building doesn't stop, right? The median time to IPO is 11 years. And first day of just product market fit. Second stage is you have to hire and scale. You're going to do mistakes. And then you get to the third layer. Like many of the companies that have gone public have had like the third version of their exacting, not the first and then that has trickle effects down. And what's happening in this market that I see is access to capital is easy, which is fantastic.

Speaker 5 (31:05): You should definitely make use of it, but raising too much money hurts you. I am seeing it time and time again because people think it's like the valuation signifies competitive traction. No it doesn't. You could have the highest valuation you would still be behind your competitor. Number two is somehow they think because I'm at a high valuation, I should hide all these people, but maybe you actually don't even have product market fit. Maybe your retention actually sucks and we need to fix that. And I actually, so you asked the question of how should I pick my partner? A really good partner will be honest about these things. And the diff you, you have money is green, right? You can get money from everybody, but ask yourself, be honest about what stage you're at. And w I would argue that at every stage, it matters who your partners, whether they're on your board or not, because out of the top 30 YC companies, except one, everybody went through at least two years of pain at some point, their respective points. So that's the data point it's going to happen. So you want to align yourself with people who are truly rooting you for success, which means they are pushing you. And I would actually test that even before you take money for them, are they actually giving you honest feedback? Are they poking you in the areas without any fear? And you kind of know, like that part of the business is not good, then you probably, you know, even though you are afraid to bring that partner, that partner is probably better for you, then someone

Speaker 3 (32:39): Who's just going to say

Speaker 2 (32:40): Yes, and yeah, great. They'll help take you to the next level. Yeah.

Speaker 3 (32:45): And just, I want to echo something else. A new said, because I think it's really important sometimes, especially early stage, you've just, can't tell how you're doing, because you don't have product market fit yet. You don't like, what are the milestones? And raising money makes you feel like success. It makes you feel like, you know, popular and I've seen it happen where again, it becomes more of the job because it's such positive reinforcement, especially in this environment. And then the teams believe it too. Oh, we're really successful because we can raise money. And I think one of the problems is, is that it has its place. Like what purpose is it serving? Has it served its purpose and then make sure that the employee base knows exactly where you are on the timeline and not to drink too much. Kool-Aid in terms of what this actually means. Like it enables us to do what for who not. Let me hold up a mirror and be super psyched.

Speaker 2 (33:41): Yeah, no, I love it. It's, it's, it's really about making sure the whole team knows that we're all rowing in the same direction and we're all, we're all pushing towards that same common goal. Awesome. And then the last question area that I had for you guys was around post-investment. So I'm curious how each of you engage with your portfolio company, founders and executives after investment. So maybe one way to think about this is what's something that you would love to help with portfolio companies or founders, but they're just, they haven't taken you up on it as much as you would have liked to. Maybe we start with Karen again.

Speaker 3 (34:22): Oh, I, I feel very, I think my companies are really very open to taking us up on, on any and all help. So I feel there's recruiting product pricing is a big one. We try to help them figure out pricing product market fit, depending on the stage. Again, recruiting, I have to say that five times, because it's so critical. We have a very large data science team and we try to make sure that our data, we have, we try to make sure that that team is also working on behalf of our portfolio companies. So we work with them to understand how that would help their business and what that means to them. And, and it's sort of just takes depending on what they need and when they need it. I feel very much like they're willing to call and ask. So I guess that's good.

Speaker 2 (35:11): Yeah. That's awesome. And then Dan, what about your experience?

Speaker 4 (35:16): It's similar to Karen's. I mean, in my seven years, working with companies like I've given, I've helped out with relationship advice. I've given feedback on Halloween costumes, I've given feedback on like dating app profiles. What would other people want? I think, I think where's maze, but where, where do I like spending more time? I think team build is really quite fun. And I think founder, like evolution is really fun and not so much founders, the founder of a business, but as people. And it's not to say that I have the right answers to be helpful, by the way, I'm just like one component of trying to be helpful, but it's a really cool experience. Our job getting to watch people over 10 years, you mentioned like the relationship is often longer than a marriage. You know, my wife and I have known each other for 12 years and we've changed a lot as people in that time.

Speaker 4 (35:58): And it's really fun to be, to just see each other, hopefully. And that's true of the founders I work with and watching them grow both as founders and CEOs of businesses, but also, you know, as, as friends, you know, and as employers, to an extent, and as partners in the business that the other executive they work with and teams that they work with. So those are all really exciting things, but, but team is probably the thing. Like I spent a lot of time on that. I wish people spent more time with me on, you know, like when I think about like scale just as a, a tangible example and some of those more joking things might've applied to scale, but I won't tell you which ones, but when I think about scale, it's kind of the areas that have helped. The thing I'm most proud of is the team stuff.

Speaker 4 (36:37): You know, I, I was lucky enough to work at Dropbox and I have to know a bunch of great people and those people now occupy senior old scale. I think, I think employ the first engineer hired at scale, a guy named Herman. Who's still at the company with somebody to work with the Dropbox, you know, Matt park on the operation side, somebody from Dropbox, you have council's there in Yogi. So just wonderful people. And that's just really, it's really fun. It's kind of a win-win for everyone. I think it's super critical to the company. And I wish more companies like went deep on that. And I think it's something that Alex and the team scale did really well. So I love helping people on, on the team stuff. It's just, it's a lot of fun.

Speaker 5 (37:13): Yeah. So at a continuity, I would say we focus on only one thing. That's our value proposition to our portfolio companies, which is company building. So if the accelerator is zero to one, we focused on company building. So we do that in two farms, we actually run the growth program, which is essentially a program run on how to scale as the CEO, Alex has gone through the program. Now Alex actually comes as a guest for the program. So it's taught by our scale founders, but it's really around how to scale as a CEO, how to hire exactly how to onboard them, how to do performance management culture. It's essentially we are leveraging the, or you could say open sourcing the company, building knowledge of the YC community because we have like 350 growth state startups, and they can all help the younger startups for the continuity portfolio companies.

Speaker 5 (37:56): Again, on the company building side, we actually do two things. One, we have an internal goal of like at least trying to source recruit place 50% of their exec team, because I think the leadership team is so important for the rest of the company building. We also have work at a startup that helps with a lot of engineering, hirings and continuity side. We actually do a lot more of that. And then the other thing we do constantly, which people, our portfolio companies are often surprised by is YC touches a lot of people, right? So through work at a startup, through founders, through their employees, we touch a lot of people. So we can do recruiting audits. We can do how, you know, candidates who rejected you, why they rejected you and surprise, surprise. They actually tell us more than they tell the company. So we actually take it on us on behalf of the portfolio companies to really push them on that.

Speaker 5 (38:45): Because we think that you have to have an excellent team and culture like the company operating system. That's what allows you to stand the test of time. That's all it is. Look at door dash was the Zuber door. That's just executed to the T and like kudos to that entire team, right. Without the team they wouldn't have gotten there. And so I think like we want to bring those best practices and foundation everywhere. And personally on my side, I think maybe I come from a more engineering context and the way I work with the CEOs, I just literally say, what's the top two issues for you right now. And let's see if I can take one off your plate or let me find a way to help you. So it's been on many, as Dan said, it could be on anything, right. And not like every company has stopped to every time.

Speaker 5 (39:33): So it comes in beams, but it could be, you know, one of them recently wanted help converting one of the customers. And so I spent a lot of time with that and if I don't focus on their top two, like it, it's not a natural instinct for them to come. It's kind of my working, working rhythm. So I always say what's the top two areas that's really keeping you up at night. And I don't. And I just pick one out of that or two, if I can do both and I just help help them a ton on those two.

Speaker 2 (40:01): Awesome. Well, we're, we're unfortunately at time, but Karen Dan, I knew thank you guys so much for taking the time to join us today.

Speaker 4 (40:13): Thank you. Thanks. Thank you so much.

Speaker 5 (40:16): Thank you so much. Thank you. Thank you.

Speaker 1 (40:30): inaudible.

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