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Venture capital used to be a cottage industry, with very few investing in tomorrow's products and services. Oh, how times have changed!
While there are more startups than ever, there's also more money chasing them. In this series, we look at the new (or relatively new) VCs in the early stages: seed and Series A.
But just who are these funds and venture capitalists that run them? What kinds of investments do they like making, and how do they see themselves in the VC landscape?
We're highlighting key members of the community to find out.
Sailesh Ramakrishnan is a Partner at rocketship.vc.
Prior to rocketship.vc, Ramakrishnan was CTO and co-founder of LocBox, a startup focused on marketing for local businesses. He worked with rocketship partners Anand Rajaraman Venky Harinarayan at their previous startup Kosmix, and continued on to Walmart as a Director of Engineering at at WalmartLabs.
Before jumping into the startup world, Ramakrishnan worked as a Computer Scientist at NASA Ames Research Center. He earned his Bachelors degree in Civil Engineering from IIT Madras, his Masters degree in Construction Management from Virginia Tech University and Intelligent Systems from University of Pittsburgh. He was a Ph.D. candidate in Artificial Intelligence at the University of Michigan.
VatorNews: What is your investment philosophy or methodology?
Sailesh Ramakrishnan: The fund was started by Anand Rajaraman, Venky Harinarayan and myself in 2014. Anand came up with the thought that, ‘There's a lot of activity on Wall Street about data-driven, algorithmic investing on funds, and Wall Street is making a killing. Our area of expertise is in startups, so has there been any way in which that particular approach of using data and investing has been applied to venture investing, especially at the early stage?’ That was the question they had asked me. In my experience with my previous startup, where Google Ventures was one of our investors, I had sensed that that Google was asking for a whole lot more data than typically you’d see from other VCS. So, I thought, ‘Ok, there’s something here, people are using the data in a way, so it will be interesting to explore this.’
We collected some data, built the model, and started identifying companies. As we honed these models, it started looking way more promising than one would typically find. And so, as it started getting more and more promising, we figured that the only real way to validate it was to start using it by making investments. We made a handful of investments from personal capital in late 2014, and those companies continued to do really, really well as we went on to early 2015. Then the focus shifted; since we are computer scientists, we were thinking about this like, ‘okay, so the prototype works, we now need to scale this out because this will only function at real scale.’ The solution to that was to start a fund, and that's how we started Rocketship Fund I.
Anand and Venky have PhDs in computer science from Stanford. They started their first company out of Stanford called Junglee, which was sold to Amazon and worked with Jeff Bezos directly for a few years. In 2000, they left Amazon to start a venture fund where Jeff was one of their anchor investors; that fund did really well. In 2005, they started their second company, called Kosmix, which is where I joined them. Prior to Kosmix, I was working as a computer scientist at NASA Ames here in Mountain View, where I worked on AI for the next generation of Mars rovers, while I was working on my PhD in computer science from the University of Michigan. So, we went on the Kosmix journey together, which was then acquired by Walmart to create Walmart Labs, which was the technical innovation center within Walmart. We started with our Kosmix team of 40 odd people, and Walmart Labs is now over 7,500 people within Walmart and has made significant contributions to helping them become a strong technical challenger to Amazon, in terms of both in-house technology, as well as e-commerce. I left Walmart about six months after the acquisition to found another startup with another colleague of mine, which we sold to Square back in 2014. So, 2014 is when all of us were able to ideate on what became the Rocketship.
Rocketship Fund I was launched in 2015 and what we tried to do was to learn how the data-driven approach could be applied to the process of venture investing. By way of context, the early fund that Anand and Venky invested in was an early stage, seed stage fund. Separately, another anecdote, and claim to fame, is that they were early Series A investors in Facebook. So, our natural focal point ended up being seed and Series A investments.
Being from the Valley, we presumed that we would be investing in the Valley or in the US, but the data was showing us many, many different things than what we originally expected. The first was we became a global investor, because the data was showing us enormous opportunities all over the world; we're talking about companies in India, in Southeast Asia, in Latin America, including Brazil and Argentina. Even places in Northern Africa, and many countries in Europe. So, we became a global investor pretty quickly. Just shy of half of our investments are outside the US. The second was that the data indicated to us what an appropriate stage for investing is for a given company. Sometimes, when you are thinking baout a seed investment, there's not enough data about the company at that early stage to help make that investment. So, our investments now span from the seed, Series A and Series B. Series B is sometimes an investment in a company, especially outside of the US, where it’s actually closer to a US Series A, but from a round name perspective it’s a Series B. So, seed, Series A and Series B is what we invest in.
The third aspect, again to situate ourselves in the venture ecosystem, is our check sizes. We had a $40 million fund, so we could not be a lead investor for a Series A, because Series As are arranged for $5 to $8 million. We couldn't put so much money concentrated in a single company. So, if we were to be a lead investor, it would be closer to the seed stage investments, and we would be a significant value-added investor in the Series A and Series B.
That's how our investment progressed with Fund I, which is currently fully invested and it’s on track to do quite well. Based on that projected execution, we just launched our second fund, Rocketship Fund II, which is $100 million. Again, carrying forward the same process but now with slightly larger checks, allowing us to perhaps lead seed, and be co-leads in Series As as well. And then also set aside more money for follow-on investing, after our initial checks in the company.
VN: How do you define being a data-driven VC?
SR: A traditional VC gets their deal flow through two different channels: one is inbound leads, where they already have a brand name and everybody knows them, like Sequoia, Andreessen, Lightspeed, Matrix and so on. So, a lot of entrepreneurs approach them asking for investment. The second is they have networks where they are present in a wide variety of startup ecosystems, from Stanford alumni to meetups to conferences to thought and media leadership in particular spaces, that allow them to identify potentially interesting companies. So, that's typically how their deal flow comes to them.
We not only do that but a significant portion of our deal flow comes from us identifying potential investment opportunities from data. So, we have one of the largest databases of startup activity in the world: names of companies, founders, what are they doing in terms of businesses, traction metrics, social media metrics, and so on. Many, many different data sources that are combined to give us a data-driven picture of startup activity. On top of this, we run machine learning algorithms that allow us to identify potentially interesting startups from that data, which allows us to say, ‘Hey, these are really going to be interesting companies.’ We proactively reach out to them, so ours is an outbound process, where traditional VCs have an inbound process. We reach out proactively to these companies and say, ‘Hey, our algorithms think something fantastic is going on within your company. We would like to understand what that is and potentially invest.’ And so, that's the difference between a more traditional VC and us, as we call it, as a data-driven VC.
VN: What are your categories of interest?
SR: A traditional VC typically has those silos, if you will, where there are B2B partners, B2C partners, there are people who are experts in particular verticals. We are not that way. Because our deal flow is data driven, we see deals all across the spectrum. So, one way to think about us is that we are both sector agnostic, as well as business model agnostic. On any given day, we are looking at both B2B and B2C deals, with the common thread being that there is something about these companies, from their data, that our algorithms find significantly interesting enough for us to want to talk to them.
Having said that, the data also tends to tack very closely with what trends are happening within the market. So, when you look at our portfolio, you'll find a bunch of companies in FinTech, not because we focused on it but because the data told us there was a significant amount of activity in FinTech that naturally guided our investments. Our portfolio is much, much more diverse than a traditional Silicon Valley investor, and much more guided by what the trends in the data are showing.
In general, there isn’t a single particular sector that we don't invest in. Having said that, there are some obvious places and where we’re a little bit more careful, mainly because of the inherent dynamics of the sector. So, for example, pharma would require typically large amounts of capital before you can actually see something materially in terms of adoption or revenue or metrics or so on. For a fund of our size, $140 million in total management, those are sometimes a little too large to take without the backing of the data in order to make an investment. There are different business models even in these sectors where we have considered investment, so never say never, but these are areas where significant capital requirements usually make us a little bit more careful.
VN: What's the big macro trend you're betting on?
SR: There are several macro trends that are happening and it's not so much an investment bet but more of what we are observing.
First off, perhaps this is obvious to journalists such as yourselves, but the common person doesn't understand this as much: that entrepreneurship has become significantly global. There are amazing companies being built anywhere from Bangalore, India to São Paulo in Brazil to Jakarta in Indonesia to somewhere in Norway. So, entrepreneurship is global and because of the fact that we do a lot of our work online, it's possible to build a world-changing company from someplace that is not necessarily on the current startup beaten path. Take, Skype, for example: it came out of a small country in Europe without a significant sort of startup ecosystem, and yet it changed the world significantly.
Sitting here in Silicon Valley, there's no real way you can find these kinds of companies, unless you look at this through the lens of data. Other VCs have seen this but they reacted to it in a much more traditional way where they started creating satellite offices. Sequoia, for example, has Sequoia China and Sequoia India, where they are basing the remote satellites on the locations in order to see what's happening in those ecosystems. But what is the next ecosystem is in Indonesia, or in São Paulo in Brazil? It’s not necessarily a scalable approach to be creating these satellite offices. And so, what we are doing is riding that wave and saying, ‘Entrepreneurship and the returns are going to be global. How do we efficiently and effectively capture that perspective?’ The way to do that is through the lens of data.
The second is the equal intent, where a lot of data is being generated about companies today, both by themselves, as well as almost like an exhaust of their successful execution. So, you can see things about the company that are external to the company: LinkedIn profiles, you can see their marketing, their social media presence, newspaper and other articles written by journalists such as yourself, as well as reviews, fundraising announcements and whatnot. You can see a whole host of information that is becoming available about a company, and that information can be used to accurately direct your efforts to what's really happening in the ecosystem, without necessarily being there. That allows, once again, a data-driven VC like ours to find opportunities that perhaps a more traditional investor is not going to see, unless and until the company reaches a sort of a global scale, if you will, like a Series B or C that make that makes them see it, but at that point the valuation of the company will be pretty high. And so, we're able to see companies at a much earlier stage, and be able to make investments in them, even though we're not geographically in the same ecosystem.
VN: How many investments do you typically make out of your fund and how much is that in dollar amount for you?
SR: The current fund is a $100 million dollar fund, and we expect to invest somewhere on the order of 25 companies. Check sizes range from $1 million to $5 million, with the median check size being $3 million, and that's the total amount in the company. The initial check size might be completely a function of when we catch the company; if we meet at the company at a seed stage, then the check size will be smaller and then the follow-on will be aggregated up to the between the $3 and $5 million mark. If we catch the company at the Series B level, we'll probably deploy all $3 million. So, the check size is more of a function of the round.
VN: What kind of traction does a startup need for you to invest? Do you have any specific numbers?
SR: Traction is one of the features in our data driven model, but it's not the only feature and it's not necessarily a requirement for us to make an investment. In fact, we have invested in a companies that have no traction. The major thing about this is that we, as investors, typically use to assess the company are features in a model, and traction is one of them, but it's not a necessary condition, it's not necessary to have traction for us to make an investment. Having said that, of course, as the models use the data and learn about these companies, there are some sort of measures or metrics that arise. Like, for a seed stage company, there's not much of an expectation of revenue, but if you have revenue that's great. That gives us more data to evaluate you. If you're a Series A company, then it’s almost a given that you have some amount of revenue, and that varies depending on the category you're in, and the growth rates that you're experiencing. I'm hesitating to give you exact numbers, not because we don't have them but because it's much more nuanced in terms of the category, and the growth rate of the business.
What we are looking for is strong, sustainable growth, which is defined as growth that is, in its fundamentals, either organic or unit economic positive, where you have enough of a history to show that that can be sustained. So, in terms of traction, we want to see at least six months of data to show that that strong, sustainable growth can be sustained over the long term and that the metrics underneath it are strong enough for us to validate our hypothesis.
VN: What do you look for in the team that makes you want to invest?
SR: There are two parts to this answer: the first is the data, and the second is the human value. One of the things that I perhaps did not clearly explain to you is our approach to investing is a hybrid approach, where the data plays its role, both in identifying opportunities and providing us guidance on the quality of the company as it goes forward, but the human partners and their judgment is just as significantly important. What rocketship has been very successful in is in marrying these two assets together in order to make sound investment decisions.
When it comes to the data part, we’re looking at many aspects of the founder and the founding team that we can look at from a data perspective: their backgrounds, their education history, their prior experience in companies and in founding startups and so on. Those are things that are more amenable to sort of numerical computation.
The humans are looking at that, but also looking for qualities that we believe are good indicators of a startup's ability to be successful. Some of those include the way in which they can clearly explain the vision of the company, the clarity of vision, the simplicity and clarity with which they're able to communicate. Their track record and ability to hire is the second indicator, typically. And then the third is their vision in terms of, not necessarily blind ambition but, ambition as to how large of a businesses they want to build. Can they see the bigger picture? So, these are some of the indicators that personally I have seen the good indicators. There is some amount of sort of magic in here. Founders, when they create companies, they are doing something magical.
So, all of these things are part of that process. We tried to be a little bit more structured in this process. At the end of the day, it also could be as simple as this person just wowed us, but in rocketships’ perspective, that's only one component in the decision. We value that part, alongside all these other parts about the startup, like the traction, the category, the market, the problem that it’s addressing, and come to a much more coherent decision.
VN: If you see a company that maybe the data doesn't necessarily point out that this is going to be successful, but you really believe in that person, will you still invest?
SR: We have considered investing. This is where, like I said, the marriage of the data, as well as the human evaluation, comes into play. The data is only telling us what has happened, and some of the problems that the business is currently facing. It does not tell us whether those problems have been solved or are solvable or if they're fundamental to the business itself. If we learn from the data that this is a fundamental issue with the business itself, but not even the best founder can solve it, then it makes us a little bit more cautious, it makes us want to dig in further to understand. But if it's something that is natural in the growth of a company, and if this is something that we believe the the founding team has the ability to resolve as they go forward, that is a risk we’re willing to take. Fundamentally, what any investor is looking for is that the problems underneath it are solvable by the team.
VN: How have you seen valuations be affected by what’s been happening over the last few months, including COVID and the economic downturn?
SR: We have definitely seen an effect. We’ve seen companies falling into three broad categories: the first category is where COVID is providing them engagement. It's not necessarily that they're taking advantage of COVID, as much as they were in the midst of a transition that COVID accelerated. For example, companies moving to the cloud, rather than any on-site installation, that transition to the cloud has now been accelerated. Some of these delivery services, where if you’re not going to be able to shop then we want more stuff to come to you, those have been activated as well. And so, in those sectors where the acceleration is sustainable, those companies are doing well, and valuations have either held, or have gone up. You can see something that should have taken perhaps a year to happen is now happening in three months. So, cloud, ecommerce, healthcare, education, all of these sectors are seeing this, and you have the pieces being laid out in front of you pretty clearly that valuations have held.
The second category are companies that are negatively impacted by COVID. These are companies that have relied on physical travel or event companies, for example. Many of them are facing challenges. For those kinds of companies, valuations are decreased but they’re decreasing more in line with the revenue numbers, if you will. If you are unfortunately forced to raise in this situation, it has to be reflected on some of the fundamentals. So, there is a little bit more of a correction towards fundamentals.
And then the third class are companies that are doing okay; they’re not necessarily doing great, like the first category, they’re not negatively impacted as the second category. So, for those, we are seeing valuations decrease, but they're not necessarily expected to do a full round at this point. They're more doing things like a bridge round, or a small round. When they were doing a $10 million Series A, now they’re doing a $3 million round. But it's not necessarily a reflection of the valuations as much as they're taking in smaller amounts of cash in order to weather the storm, if you will.
Of course, what I haven't mentioned is companies whose fundamentals are under significant stress. There’s natural startup mortality; startups are a hard business. They're fundamentally trying to do something new in the world and so there's always that risk of failure and COVID has unfortunately put certain types of companies under significant pressure. So, startups can go out of business in this situation as well. We are also seeing that but it's not necessarily out of sync with traditional startup mortality. If there's one thing startups know how to do it's to innovate, to find another path. And so all of these companies are finding ways to innovate, to stay afloat. In general, good companies are getting full deals done, companies that are doing okay are getting bridge or small rounds done, and companies under stress are either innovating to weather the stress out, or are raising at lower valuations, not necessarily reflective of their full potential, but more reflective of their current numbers.
VN: There are many venture funds out there today, how do you differentiate yourself to limited partners?
SR: VCs invest in amazing companies doing amazing technological transformation, but the VC business itself hasn’t technically transformed itself. We see ourselves as the next generation of VC, where we do everything VCs are currently doing, but we just do this with significantly more technologically enhanced. Where, before, traditional VCs were getting deal flow through the network, we have just made it more efficient by doing it with data.
What does that offer to limited partners? Well, it offers a better aperture towards the next generation of companies that have been created, where now our aperture is wider, we are able to do it more efficiently. So, for example, a traditional VC, the way they would capture more and more of their sector is to add more and more partners, because, in order to look at this other company, you need a partner in that space. We are able to do that in a much more efficient way. So, the way to understand it is simply that: we are the next generation, the 2.0 version of VC, where data is not what defines us, the data is what enables us to be more efficient. So, it's not just about data, it's about the fact that we are trying to fundamentally innovate in our own business, to make ourselves much more effective when deploying capital.
VN: Venture is a two-way street, where investors also have to pitch themselves. How do you differentiate your fund to entrepreneurs?
SR: Obviously this is something that we face every day. Our algorithm finds some of the best companies in the world, and oftentimes I’m trying to help them understand why they should take investment from us. Startups and founders are much more choosy in terms of the investors they add to their investing team. This is not just for only the hot startups; we make this pitch to every time that we talk to.
So, what do we have to offer? First of all, we are a very different kind of fund. The composition of the fund is not traditional investors: it’s entrepreneurs, we are multiple time founders ourselves, so we have been through this journey and experienced those experiences that you, as a startup, are going through. So, that provides most startups with something very different on their investment tables than they used to have. The second thing is we are practicing data scientists. All of us have had careers in computer science and data science for the last many odd years; Anand is an adjunct professo atr Stanford, where he teaches one of the most popular data mining courses. So, we are practicing data scientists on a daily basis. We write code in creating these models and data today is an integral part of pretty much every startup’s strategy. They’re all looking at how to use their own data to become efficient and that's something we are very strong practitioners of. That hands on expertise is something we have to offer in spades. So, the data that we're already using to identify these startups also allows us to guide them as they continue to execute, so we can see potential competitors, other models that are being successful in the same space, in other geographies, and provide that data as guidance.
The other thing I would point to is our network. Every VC says, ‘We have a great network, we can compete with everybody.’ Once again, because of who we are, and the fact that we are founders, has allowed us to build a network that is very different from a traditional VC’s network. Our connections are with other founders, with other folks who have been successful similarly, and so we're able to network and connect with people and connect our startups to them at a much more different level that is operational.
VN: What are some of the investments you’ve made that you're super excited about? Why did you want to invest in those companies?
SR: This is a controversial question to ask a VC. All of my investments are fantastic investments, and I would wish all of them to be tremendously successful. So, I’m equally proud of every one of those founders and the hard journey that they’re going through to make an amazing change in the world.
Now, to answer a slightly different question from the one you asked, as to which companies in our portfolio are doing exceeding well, here are the companies that are doing something amazing that anybody looking at them should be proud of.
I’ll start with one of my earliest investments, in a company called Edpuzzle, whose work in this particular pandemic has become amazingly critical. Edpuzzle provides a platform for teachers to convert any video into a lesson but, more importantly, convert it in such a way that you can intersperse the lesson with questions or tips or confirmation, so that, as the student goes through the lesson, the teacher can get feedback that the learning is happening. If you just set up an assignment, or tell them to go see this video, you don't know how much of the content of the video that students have actually cracked.
Before this pandemic they were in nearly all in middle and high schools in the US, where teachers were using them. And this was completely organic growth because teachers just love it. COVID has made it an imperative that schools go to remote turning, so a significant number of school districts are now adopting it. The reason why we are proud of them is not just because of what they have built, but the fact that, even in this pandemic, they're able to make such a big tremendous difference. The company has done phenomenally well, they're about to grow like 7X this year because of the pandemic making them almost an indispensable tool for teachers as they move to remote learning.
Another company that has done phenomenally well is a company in the B2B space in India, called Moglix. Businesses buy a lot of consumables from other businesses, like oils or fasteners, even small things like nails. They created an online marketplace for that to happen. They were the fastest growing business to business ecommerce company in Asia last year, so tremendous growth. Not just in India, but all over the world, businesses are trying to look for efficiencies in the way they operate, and this platform has become tremendously valuable. In addition to that, Moglix also included software that ties in with the platform that allows you to compute what’s called value-added tax, which is a tax that is actually paid every step of the manufacturing process where you’ve added value. Value-added tax requires you to keep track of what your inputs and costs are, and what your output in order are, to file these taxes. That software becomes important in every jurisdiction where value-added taxes are used, which is most of Europe. India moved to value- added taxation about two years ago, and this software became a tremendous value-add for allowing more Moglix to expand out from India to many other countries. So, Moglix’s products are shipped to over 95 countries, and so it's been growing tremendously fast.
A third company, again from India, that has been super exciting, is called NoBroker, which is a consumer company, just to give you a different flavor of the kind of company we invest in. It's one of the largest real estate platforms in India and it was started with the premise that, ‘We want to eliminate the broker and put landlords directly in front of potential lenders and home sellers to be able to get in contact with potential home buyers.’ That platform now has become such a significant way by which people, especially in COVID times, are able to communicate with each other. You can rent without necessarily meeting people face-to-face, you can pay rent on the platform, you can do things like take additional services, like furniture movers, cleaners, whatnot, including things like leasing furniture and appliances, all through this platform. That has been experiencing tremendous growth.
Back here in the US, there are a couple of companies that are doing amazingly well. Wasabi is a company we invested in that is providing business to business storage. It’s the competitor of a service that Amazon offers called S3, which is remote backup storage. The founders of this company are also the founder and CEO of a previous backup company for consumers called Carbonite. They created this business backup service, and it has been growing leaps and bounds, once again because, during COVID, more and more companies have become aware of needing to put more of their assets in the cloud. So, Wasabi is growing and has increased its pace of growth 4X week-over-week. They are doing phenomenally well.
Another that is in the B2B2C space is a company called Urgently, which is taking something as mundane as the roadside assistance and bringing it to the age of apps. With Urgently, you don't have to pick up the phone and call your tow truck, you press a button on your app and you are immediately notified that, ‘Here’s a tow truck, here’s when it’s leaving, and here’s when it’s expected to reach and it's going to take care of your emergency needs right away.’ The reason why I said it’s B2B2C is Urgently sells directly to insurance companies, as well as OEMs such as Mercedes Benz, Volvo, Porsche, and so on, where brands are taking stronger ownership of the problems that the owners of their cars are experiencing.
Finally, I want to talk about a couple of our recent investments. We recently invested in a company called Crosschq and that's another prototypical example of a company that is bringing some very new kind of innovation, which is very relevant in the COVID times. One of the things we all do when we're trying to hire people are reference checks and, until recently, the data from the those reference checks were just in the heads of the people doing them, and never used any further after the hiring decision is made. What Crosschq does is bring a lot of that onto the cloud, so that data can be used, through an AI lens, to actually predict the quality and the potential success of that hire. It's bringing an enormously new, and never before seen, data to a computable fashion, in order to guide you to make better hiring decisions. This is very relevant in COVID times, where you can't actually spend too much of your time in in-person interviews and reference checks and getting that gut sense of who this person is before you hire them. Crosschq is offering a way of doing that that is much more exciting.
VN: What are some lessons you learned?
SR: I was not a VC before we started this; I guided other startups and so on, but I was not a formal VC and hadn’t worked with other VC funds. So, I learned a lot of lessons about being a VC, including all the constraints and computations that go into making that VC decision from the other side of the table. I have significantly more understanding and respect for the decision making and thoughtfulness that a good VC goes through in order to make these investments.
From a fund perspective, we are amazed by the quality and experience that these founders bring to us from all over the world. Talking to somebody who is in Vietnam, or in Argentina, and have them discuss their business with the amount of authority and insight that you'll find with any Silicon Valley founder, is amazing. The entrepreneurship going global is actually anchored into these amazing founders who are all over the world, and have the vision of changing the world. That was one significant learning.
The other significant learning was timing is important. One of the most important things is to know when to talk to a company in order to make an investment. Our algorithms actually have worked a lot in getting that timing right, because if you talk to them too early, then they're too busy building their company to spend time talking to a VC. And if you talk late, then the deal has already happened and you cannot participate. So, getting the timing right is a significant skill that we needed to delve into and have our algorithms work on.
The third is something I've already answered before, which is to have a very clear answer to, ‘Why us?’ As I said, we answer that by using practical examples of how we have helped founders through their journey. That has resonated a whole lot more now than we have real examples of how we have actually helped. That has made a big difference to these founders. They already saw a kindred spirit thanks to our backgrounds, with us having been through it, but the fact that we have actually gone well beyond that has made a big difference.
VN: What excites you the most about your position as VC?
SR: Having been a founder myself, there’s some amount of nostalgia about, ‘I wish I could be building that company,’ when I'm talking to these people. When you talk to a good founder who is super excited by what they’re doing, it’s hard to not be carried along and that is most exciting to me. I'm living their life vicariously in that moment, and that's super exciting. I'm also very happy that if I can, in some minuscule way, help them realize that world changing vision. That I've been part of that potential success, that is also super exciting.
Mostly, like I said, I enjoy talking to founders. I enjoy, for that period of time, living their excitement and their life, because I personally cannot build all of these companies myself, but seeing them building it, talking to them, is amazing. To be clear, these are these founders who are of different geographies, genders, experience levels. They are young, old, male, female, different backgrounds. Learning about them, talking to them, understanding where they're from, that's been super, super exciting.
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