Meet Pete Soderling, general partner at Data Community Fund

Steven Loeb · November 18, 2021 · Short URL:

DCF, which focuses on companies using AI and machine learning, launched earlier this month

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. 

Pete Soderling is General Partner at Data Community Fund.

Soderling is the founder of Data Council and the Data Community Fund. As a former software engineer, repeat founder and investor in more than 40 data-oriented startups, his lifetime goal is to help 1,000 engineers start successful companies. Most importantly, Soderling is a community builder — from his earliest days of working with the data engineering community starting in 2013, he has witnessed the unique power of specialized networks to bring inspiration, knowledge and support to technical professionals. 

Previously, Soderling founded Hakka Labs (a social network for software engineers), Stratus Security (an early cloud-based API platform) and mechanikal (a software development agency in NYC). He is a graduate of New York University, has spoken at events such as TEDx, O’Reilly Strata and QCon, and has worked actively in supporting technical founders around the globe.

VatorNews: What is your investment philosophy or methodology?

Pete Soderling: I'm the founder of the Data Community Fund and we're a fund focused on B2B, data oriented companies. What we mean by that are companies who are using machine learning, data science, AI, in order to make the world a better place and/or building the database infrastructure that supports those kinds of companies. So, it's very data focused. Not so much consumer data, but on the B2B, data side is where we focus.

VN: AI, big data, machine learning, that's definitely an emerging space. It doesn't feel like there would be a firm focused on this space even a few years ago. So, where are we now with those technologies that allowed a firm like this to come along that focuses specifically on AI and machine learning?

PS: It started with the data infrastructure side and people realize now, because of Snowflake, and other companies in the categories, that the infrastructure to manage all of this data that actually drives the AI is absolutely critical. We saw this trend emerge even back in 2013; everybody was talking about the sexy quantity, data science things, but we realized that there was a subterranean layer of data engineering that really had to support all of those analysts and those data scientists in the first place, and no one was talking about data engineering at the time. We started the data engineering meetup, which not only attracted the data engineers but then it attracted the data scientists and analysts and all the other people who wanted to learn how to work better with each other across these nascent layers of the data space. So, we've seen a lot of this emerging in slow motion over the last six or eight years, and the fund is actually the latest manifestation of this: I'd started a conference around these topics all the way back in 2015.

VN: Why was now the right time to launch the fund, which you did earlier this month? 

PS: Like I said, there's much more secular interest and awareness now of these data platforms, because we're starting to move out of this area where it was just the tech companies that needed to be data driven, and now all companies are believing that they need to be data driven. The average company wants to believe that they can weaponize AI in some meaningful way, but the way to do that is just to get their data infrastructure house in order, and so there's been an explosion of interest in these types of data tooling and data infrastructure companies. That’s the precursor to actually realizing the AI benefits.

VN: Like you said, every company wants to be an AI company. I cover a lot of startups, and AI has become a bit of a buzzword. How do you differentiate between the real AI companies and the companies that just say they are? 

PS: It's a fair question and that's one reason that we've been successful in getting the LPs to trust some of our experience. Part of it is because I'm an engineer myself from the first internet bubble; I went to NYU, I studied information systems, I was an engineering founder, I started several companies that were dedicated to making the world a better place for other engineers by building engineering tooling. And so, I’ve had this deeply technical background myself and part of that allows us to be able to tell when a company has really material data science insights or algorithms behind the scenes, or if they've just slapped “.ai” on the end of a domain name, and are claiming that they're God's gift of “something plus AI” to the world, which doesn't always have as much depth as you'd like. So, part of that comes from just having a deeply technical background and part of it also comes from having this community of all these data and AI professionals that I've put together over the years and I can reach out to them and they help with diligence. I’ve basically built an expert there, so that's super helpful as well. 

VN: Are there specific verticals you like to invest in?

PS: The way of it is that we have two axes of our thesis. The horizontal axis is really horizontal, generalizable data infrastructure or machine learning tooling, and it's basically a subset of broader cloud and other kinds of infrastructure. It's this data infrastructure horizontal piece, and that cuts across all industries. So, we've made many investments in that category like Hex or Superconductive, which is a data testing company, and many, many across that horizontal axis, And when it comes to the vertical axis, I think of it actually as multiple vertical axes. So, we have applied to healthcare, AI applied to finance, AI applied to manufacturing and those areas of focus for us are a little bit more nascent. We'll be developing those as we go along, but that's really the specifics of the applied AI thesis that’s built on that generalizable, horizontal infrastructure. So we invest in both of those areas.

VN: So would you be investing in that healthcare company or in that FinTech company directly, are you investing in the company that would supply the data? I want to make it entirely clear.

PS: We invest in the companies. We invested in a company called Theseus, for instance, which is a healthcare company that is using trained machine learning models to figure out if a patient needs a spinal surgery or not. They have a partnership with UCLA Med Center where they have access 40,000 radiology scans as training data. And so, they're a startup who's bringing this product to market in the healthtech space.

VN: What's the big macro trend you're betting on?

PS: Every company, ultimately, is going to want to be a data company and then you have the intersection of everyone else moving to the cloud at the same time. If you really believe in cloud, it forces one to acknowledge that all of the combined market caps of Oracle and SAP and HP and Microsoft, all these companies are essentially going to end up in the cloud. And so, that's a massive, massive trend. And this whole trend of the data following these companies, and this infrastructure, into the cloud, is a very real one as well. And so, those are the two trends that are intersecting that drive the thesis for the fund.

VN: What is the size of your current fund and how many investments do you typically make in a year?

PS: The fund is $15 million. It's focused on very early stage, pre-seed and seed. The earlier the better. And part of that is based on some of the success that I had with SPVs on the AngelList platform even earlier than the fund, so there was a pre-fund to this fund that gave me some experience in the very earliest stage investing. 

The fund we'll do about 35 or so investments, and between the fund investments and the SPVs, we actually probably did 20 investments last year, so we've been quite prolific and quietly operating already in the background.

VN: What does that come out to be in dollar amount, both in the initial check and then over the life of the company?

PS: It's a little bit all over the map, partly because we didn't know how much money we're going to close initially, but let's just say that right now our model is we're investing $250,000 to $500,000 in the first checks into companies and then we have follow-ons for reserve.

VN: It almost sounds like you're investing at the idea on a napkin stage, so I would imagine at that point there's not going to be any traction companies because it’s too early for that. Or are there actual numbers that you want to see at that point?

PS: It's mostly too early, although there's two things, one of which proxies for traction, and the other is just the way you identify the team. In many cases, we've invested in open source companies that may actually have a meaningful open source project and/or community congealed around that project, or other companies using that open source project, even if it's a small number of companies. So, in some ways, if one is embracing open source, there's other challenges to open source business models and things like that, but you can get a proxy for the popularity of a project, and even sometimes the quality of the engineer or founder in ideating and creating that project by looking at some of the open source work. But, of course, not every company we invest in is open source either. 

VN: So, do you actually want to have them have a product at that point? It sounds like you do if you’re investing in open source products at that point.

PS: That's actually led into the bigger seed rounds now; we're seeing these mango seed rounds of an engineer that leaves Netflix who was the author and the shepherd of an open source project. That company is actually mango seed fundable because of Netflix, because of the traction in the open source, those are rounds that now they look like the Series As from five or 10 years ago. That's an extremely fundable project. I guess our goal would be to go even earlier, and support those founders just when they're thinking of that initial idea, because that's really the way to get the pole position at the valuation and the check size that we need. We’ve participated in those mango seed rounds a lot but we're also trying to go earlier where there's even less signal, so it's a little bit tricky

VN: Tell me about the team. What are you looking for in that founder or entrepreneur? What are the qualities that you want to see to make you want to invest?

PS: There's some internal heuristic that I guess I have, and I hope it's accurate, but I think a serial founder knows a fundable founder. That’s just one one way that I like to think of it and I hope that's true. But, more specifically, because we're investing so much in engineer founders, and in deeply, deeply technical people who have critical technical insight, the counterpoint to that is oftentimes they might be the most genius product people and engineers, but they may not have great go-to-market or sales or marketing skills. They might even like to talk to people to pitch things and that's challenging. So, if I ever find a founder who's also very outward, obviously they have to be a hustler, they have to be able to communicate, not be afraid of selling their product, not be afraid of learning the sales and the marketing skills, which sometimes are the antithesis of them, that's a real amazing match and those are the folks that I often jump on. 

VN: If they're not that person, it sounds like you want to see that they developed a team around them with people who would be those qualities, who could be the pitch man. 

PS: For sure. That could be the pitch man that has the go-to-market chops and/or this is how Data Council, the conference business, interacts with the Data Community Fund because Data Council can actually be a go-to-market backstop for them. They can give a technical talk at Data Council or we can put their content in our newsletters and we can help promote them or I can interview them on a podcast that I do. So, we have all these other community oriented channels set up for them that allow them to talk technically, but we're able to amplify that voice, and so that's a little bit of a stopgap for some of the shyness or the lack of go-to-market experience that some of these technical people would have. So, that's been a successful pairing and that's one of the reasons that Data Community Fund exists next to the Data Council conference series .

VN: The third thing that VCs often look for after product and team is the market. Obviously, you're investing early so it's almost like you're betting that that market will exist down the road, even if it doesn't exist yet. So, how do you look at that? How do you determine if that market is going to exist? 

PS: Obviously, we want to make bets in big markets, every VC does. In some ways, because we're going so early, that might be a little bit less critical than figuring out if the founder has a really key insight. I believe that there's always some key insight, that there's a “why” as to why this product needs to exist now in the face of many other competitors. And so, obviously, we try to match up to a large market, but we also try to pair that with a founder that has a really specific, critical insight that will allow them to get traction immediately in that market in a fast enough that way that they can attract follow-on funding. So, it’s that key insight that's absolutely critical, especially for pre-seed or seed stage companies; otherwise the funding just will not follow, even if they're in a big market.

VN: You talked a little bit earlier about the big seed rounds, and I've talked to a lot of VCs lately who have spoken about what's happening in the market and how later seed funds are now coming into the early stage, even seed rounds, causing the round sizes and also the valuations to get out of control. Is that what you've seen happening and what are your views on what that means?

PS: Yeah, absolutely. I mean, there's no question that's happening and a lot of Series A, even Series B funds, are having existential crises right now, because all the multistage funds and the p-style investors are coming down, not just into the B's but into the A's and even the seed rounds. So, everyone is feeling that pressure. It feels, honestly, like it's going to be a bit of a bloodbath in that whole middle section. There's lots of massive, massive funds that are putting downward pressure on that whole ecosystem. 

This is another reason that I'm so excited to be playing literally at the inception of these companies. We're very much at the top of the funnel because of Data Council and the conference series, and we get to see these founders before they've even left Facebook or left Netflix and we'll be developing programs to actually help them incubate their best ideas into companies. So, from a general ecosystem perspective, it puts us in just about the safest place because we're absolutely at the frontlines. Of course, there's tons of pre-seed risk and all kinds of other craziness that goes on with that but I think the whole venture world is really going to shake out in a different way. There's gonna be a different three to five years, and it's quite scary for a lot of the existing players. So we're, we're trying to find our P's and Q's and stick to our strategy which is very much at the inception of the company. That will be successful for us.

VN: What does that mean for those companies, the ones that do take those giant rounds early on? Because they might be taking more money than they need at a valuation they can't justify and then when it comes to raising a Series A or B, they may not have been able to justify getting all that money and then they start taking it down round. Do you see that already starting to happen, or do you think that's going to happen down the road?

PS: There are certain companies in that category in the big data and ML world that I've seen that have taken, one might argue, prematurely large amounts of money from big name VCs and they're listing in their strategy. As a founder myself, I believe that being capital , and even capital constrained, sometimes generates the best ideas and the best opportunities for a company. So, yeah, it is going to start happening and these companies won't have that pressure because the market is so flush with cash and I don't think it will necessarily end well for all of them. That's reality.

VN: If the big firms are coming in, it seems like they're going to edge out some of those smaller firms, but you're avoiding that by going even earlier. Do you see other firms who were in the seed stage, who maybe are getting priced out of rounds, now going earlier also? 

PS: They'll try. They definitely will try. It's hard to do, because, obviously all VC is a network-oriented business, and so the longer you've been in the game, the fatter your Rolodex is, and the more contacts you have, but that's the old way the game was played. It doesn't really respect the power of much larger network effects, or a brand. I mean, this is why Andreessen Horowitz wants to start a content business, a media business; it's because they know they can build a brand and reach that many more eyeballs, basically that many more founders, at scale. Very few VCs have ever thought about the media aspects of their business, and this is also why I pointed to Data Council as being our differentiator because it was a standalone conference community, content, basically a media company, that monetized through events for years. And so, we;ve already been nurturing that brand and that type of funnel, and that's what's going to protect us and give us the ability to actually succeed at going so early, because we can get enough leads in the top of the funnel in order to find the best companies and the great engineers that we want to back. I don't think it's a game that everybody will necessarily be able to play so easily.

VN: Talk about your sourcing. Somebody at Facebook or Netflix is developing something, but they may not be very far along. How do you find those people so that you can step in and get there that early?

PS: Well, a lot of it to date has been our very public secret weapon, which is the Data Council conference. We have an upcoming conference in Austin, which is our first IRL conference out of the pandemic and we did a submissions process and we have 140 talks from all of the best data startups, pre-seed startups, open source authors, companies that I didn't know exist yet, or are just starting to exist. So, that's one secret way; we just run this conference in public and we get all these amazing speakers who some extra percentage of are going to go out and start their own companies. And that's how I've made a lot of my best investments quietly for the last couple years.

VN: There are many venture funds out there today, how do you differentiate yourself to limited partners?

PS: It really is really centered around this combination of conference community, content, and funds. There's very few examples of this that have been around; I mean, there's a couple other ones, like TechCrunch used to do a conference that may have launched Michael Arrington into his investing career. There's a couple of examples that are out there, but nothing that's happened in data or AI or infrastructure. So, we're quite unique in that perspective. And when I went out to raise a first fund, which is typically very grueling for a fund manager, and was for me as well, I learned to really have confidence that our story was differentiated because most LPs responded so quickly to it and gave us the feedback that it was a truly unique model and there's not conference community content, venture thing that exists in the way that we were pitching it. So, that gave me a lot of confidence in developing that model, and really believing that that could be a future for venture.

VN: Venture is a two-way street, where investors also have to pitch themselves. How do you differentiate your fund to entrepreneurs?

PS: I’m very much an un-VC; I'm a four time engineer, founder, who started one of the world's first data engineering conferences that took off and now I just happen to have this pile of money that I get to invest in amazing companies and ecosystems. So, I don't maybe look like a finance guy and I speak to the engineers in a way that they can relate to and I have a lot of personal experience having founded companies myself. So, that resonates with founders and that just makes them feel lik I'm just a scrappy, hustler, ex-founder, and I just happen to have some money that I can give along with the advice and help and this big community that I can support them with. So, I think it feels quite different for founders and I'm really happy about that.

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? 

PS: There's a bunch of great ones. One is Hex, which I mentioned before, which does collaboration for the analytics teams. Data teams were not able to share insights and reports and assets with each other, so they created this awesome collaboration platform. They're a bunch of ex-Palantir, so they had experience in that space. Another one is Hightouch, which is building a reverse ETL system, this new notion of not just the ETL that comes into data warehouses on the one side the ingest, but now that we have all this clean data in our Snowflake, we get to exfiltrate it out to the business apps and import it back into Salesforce or Marketo or other places to actually make our automation better. And there's many others: EraDB, Elementl, there's too many to list.

VN: Tell me about your career and what led you to VC.

PS: The origin story, as I mentioned, is I'm an engineer from the late 90s and, after starting a bunch of companies, I was trying to figure out what to do next and and how to really stitch together the unique value that I could bring to other founders because I’d just been so passionate about certain things myself. And so, as I was working building Data Council, which was just designed to be basically a lifestyle business, I realized that I was getting pulled into conversations more and more often with founders from Data Council who wanted advice. And when I sat down at the end of the day and realized that those were the favorite conversations I had out of everything else I was doing the whole day, I thought, “Well, maybe this is something I should continue to do.” So, I kind of got pulled into it and really just by advising and supporting and them angel investing a bunch of the companies in the community. At one point I realized I’d actually built a better venture mousetrap because Snowflake was at the conference and Palantir was there and Datadog was there and DBT was there and all these amazing startups in the early days that would go on to be great companies. And when I put two and two together, I realized that I had to just continue on that journey because I was so satisfied helping these companies and a venture fund seems the best way to manifest that value. 

VN: What are some lessons you learned?

PS: I guess you can't just follow the pack. Obviously, you have to have your own conviction and a process for making decisions and really be committed to the values and the differentiation that you can add to the founder as well. So, really thinking about the product positioning of your own fund and how you differentiate is super critical, especially in the crowded world of venture. So, that's been something that I've learned a lot about over the last year or so.

VN: What excites you the most about your position as VC?

PS: It’s definitely being able to work with the brightest minds in data and have a material impact in helping them see their company ideas come to fruition. I've always wanted to surround myself with geeks and engineers and that's why I launched Data Council in the first place, and so one of my personal goals in life is to help 1,000 engineers start companies and Data Council, and the Data Community Fund, is the platform that really lets me realize that and I couldn't be happier than to be supporting these awesome entrepreneurs who have such amazing technical insights and helping them bring those products to market. It's been super gratifying. I wouldn't have it any other way.

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