Peter Wagner of Wing VC joins Nick to discuss Lessons from Seed Investing in Snowflake and Gong, Why the Next Super-Cycle is Here and How to Invest in It, and Insights on Firm Building, Decision Making, and Talent Spotting. In this episode we cover:
- Early-Stage Investing and Company Building in B2B Technology
- Investment Decisions and Market Fit
- AI’s Impact on Business and Investment Strategies
- Data Platforms, ETL, and Vector Data in AI
- Venture Capital Team Building and Talent Selection
Guest Links:
The hosts of The Full Ratchet are Nick Moran and Nate Pierotti of New Stack Ventures, a venture capital firm committed to investing in founders outside of the Bay Area.
Want to keep up to date with The Full Ratchet? Follow us on social.
You can learn more about New Stack Ventures by visiting our LinkedIn and Twitter.
Are you a founder looking for your next investor? Visit our free tool VC-Rank and we’ll send a list of potential investors right to your inbox!
Peter Wagner joins us today from Palo Alto. He’s a Founding Partner at Wing VC, an early stage venture fund investing in tech. There he has invested in companies including Snowflake, Gong, Pinecone, and Jellyfish amongst others. Prior to founding Wing VC, he helped lead Accel Partners as managing partner for 15 years. Peter, welcome to the show!
0:22
Nick, thanks for having me.
0:23
Yeah, it’s a big pleasure to connect, we would love to hear kind of your quick backstory in your path to becoming an investor.
0:29
Yeah, I was educated as a physicist and didn’t really know what Silicon Valley was. I grew up in New York, and eventually found my way out here taking a job as a product manager at a company called Silicon Graphics in the early 90s. It was a sort of high flyer in that era 3d graphics workstations, and had a great time there worked with a spectacular group colleagues, many of whom have gone on to do awesome startup things and or become venture investors or both. And I was myself looking at joining a startup in 1996. started meeting several, none of those were quite right. But I met some of the venture backers, and some of them asked me if I wanted to try venture capital. So I ended up joining excel in 96, as sort of a very green associate, didn’t know anything about anything, and then ended up ended up staying there for 15 years and, you know, eventually founding wings thereafter.
1:28
Do you remember any of the early advice that was formative for you? And who gave it?
1:34
Yeah, sure. You know, when I was in business school, I was talking to one of the professors there that was sort of very well established and well known, and startup and venture circles, a guy named Bill Solomon. And I had written a business plan for a software company, and I wanted his feedback on it, it was something I was gonna found or CO found with a college roommate. And I went and sat in Bill’s office, and he says, You know, I like this plan, this plan is pretty good. The worst thing about this plan is you. It’s like, Thanks, Bill, like, What do you mean by that? And he says, Well, you don’t really don’t really have any experience, you know. So what my advice for you would be maybe, like, go go get a job working in an operating role in sort of the most relevant best managed company, you can find and do that for like, you know, at least four years, and then maybe come back to this idea or some other idea. So, so I actually took that advice. And that’s how I ended up going to go to work at Silicon Graphics. And it was, it was great, it’s really good advice actually give that advice to people myself a lot. Not that they want to hear it. But I think it’s pretty relevant. Another one, I was getting into venture another good piece of advice. And this was from a guy named John Johnson, who was a TVI. And then later, August capital and, and I was sitting with John and, and at the end of our discussion, I asked him, like, Hey, John, like, you know, like, my first year in the business, you know, how many investments do you think? Do you think I should, you know, aim to make any thought about it for a while, you know, like, big pregnant pause, and he goes, zero, actually give that advice to people too. And again, no one, no one takes it and I didn’t take it either. But it’s actually pretty good.
3:12
Well, you got to feel the burn to learn sometimes. But yeah, I get it. I didn’t make an investment for my first year. And then Peter, can you give us
3:19
the ended up making for, you know, so totally, totally deviated from, you know, what John said, but you know, it was it was 1996 and early 97. It was, I got, you know, it’s very forgiving time. And I’ve been pretty lucky, like three of those four investments that I made, the first year went public. And, and one of them was founded by Gaurav Garg, who is my co founder here at wing and so, I’m really glad I had the opportunity to get to know him and work with him.
3:44
Amazing. And, Peter, can you give us an overview of kind of the firm, you know, stage sector investment approach?
3:51
Yeah, well, when is a pure play around early stage investing in long term company building in b2b technology. So by early stage will lead seed and series A financings. And it’s truly early, I think of all the investments we’ve made over, you know, the 10 years that Wayne has formerly been in business, you know, only, like five have had more than a million in ARR, or revenue. And those have all been like less than two. So the vast majority have no revenue, you know, no product in the market, or maybe maybe, you know, just a couple $100,000 worth of ARR. So, so we’re really focused on on those early stages. And we’re also very focused on long term company building. So everything we do, we’re, you know, holding to the standard of the potential to be an independent public company of enduring value. And that’s a long journey. So a lot of lot of the way we go about the business and the type of resources that we have on the team here, and the way we work with the companies is, is aimed at, you know, that long journey and you know, we’ve done it a few times. Now, you know, between all of us here, we’ve, you know, been very early investors or perhaps even found jurors have 23 companies now that have made it to that multibillion dollar independent public offering. And so there’s a little little bit of pattern recognition, little bit of muscle memory in terms of what, you know what that takes that we try, try and apply with early stage investing that we do. We’re very focused thematically, you know, we only work in in b2b technology. And then specifically within that, you know, that’s a broad universe. But specifically within that we always have a crisp point of view about what matters. And I’d say, you know, really, for almost all of our history, we’ve been working on what we now call the AI first transformation in business as to how, you know, AI and all the technologies that support it, and all the applications that put it to work in a how to businesses use that to drive better results, better outcomes, if we didn’t necessarily use those words, when we first started. Know, 10 years ago, we were talking about data mobility in the cloud, as collectively constituting the next paradigm for business technology. And, you know, over the course of that, you know, really the data vector became ascendant and paved the way for practical AI in the business context, which is where we are now.
6:06
Got it. Yeah, I want to talk more on the thesis before we do. So you mentioned that many of your investments have less than a million of ARR. Many of them also are even pre commercialization. You know, on the show, we’ve discussed a variety of metrics and milestones over the years, there have been quite a few early stage investors that look pretty smart laying out their formulas and algorithms for marketplace or SAS success, I’d be curious to kind of get your quick take, and in your stance on investment decisions that are primarily on the basis of go to market metrics.
6:41
Yeah, well, you know, metrics have their place, and they’re certainly, you know, when we’re, when we’re building the companies, we pay close attention to them. And it’s part of investment decisions, too. But when you’re doing to early stage stuff, you know, metrics are hard to come by. Now, if you don’t have a product in the market, or maybe it’s just barely in the market, there is not, you know, as much quantitative evidence, you know, most of those metrics are measures of adoption, you know, on pace of adoption, and, you know, efficiency of going to market and, you know, that, that takes a little while to emerge. So, we have to do, you know, very different types of analysis, strategic analysis around markets around technology around, you know, potentials for dislocation and disruption in those markets, you know, obviously, people, and many other really important factors. And one of the reasons that we focus on b2b is because, you know, we think that there’s more predictability in the buyer behavior in, you know, where the buyer is, is, is in their work life or as a business than in consumer spaces, where you’re, it’s very hard, almost a fool’s errand to try and predict consumer behavior. So, so that, you know, you can be very analytical in sort of trying to anticipate things like product market fit, if you’ve done it for a while, if you focused in in domains and have the right the right networks and resources around you. And that does make it you know, sort of safe for pre adoption, or pre metrics investing, which, you know, if you’re developing a product that is significant and has a, you know, meaningful development cycle associated with it, you know, that it’s just not, it’s not going to get off first base unless someone is willing to take that kind of real estate risk. And that’s where like, that’s we that was one of the things that we’re built to do.
8:26
You know, Peter last night, I was at a dinner hosted by a friend who runs a firm called DESE ends. And there were some VCs there, as well as some institutional allocators. And this kind of age old question came up of market versus founder. Clearly, we all want both, you know, how do you think about the two of those in the balance?
8:46
Yeah, yeah. Well, you know, they need to go together, too. Right. So that whole founder Market Fit phrase, you know, is one that has a lot of currency. And there’s certainly a lot of, you know, I think a lot of truth to that, right, is the founder authentic to the opportunity, by which, by which I mean, you know, are they in, were they immersed in it previously, before founding the company, so that they, you know, they have that unique vantage point from which they can perceive the opportunity, right, the ability to see something that others others did not. That’s like, a really important factor that that we look for when, you know, when we’re making investment decisions. So, so you really have to think about founders and the opportunity, as instead of 111 combined thing,
9:30
perfect, but the earned insights. So Peter, earlier you mentioned kind of the DMC shift data mobility and cloud, what do you think is the next big supercycle in b2b technology?
9:41
Yeah, the AI first transformation of business and, you know, was was made possible by the data revolution as well as the rise of widely available scalable cost effective cloud computing resources that you know those two things, but you know, really provided both the fuel you know, and the engine for for AI, and, you know, now, you know, you can look back and you can see, you know, the precursors. I mean, there’s companies that we invested in, you know, whatever, 2020 13, you know, that you can look at now and say, Yeah, you know, these are these are AI first businesses, that at the time, they were sort of interesting anecdotes, you know, announced 2023, you know, the anecdotes, you know, have coalesced, you know, to where there were a wave, right. It’s, it’s no longer kind of the seminal, seminal sort of leading indicator, but it’s actually everything interesting and VW technology is, is, you know, leveraging AI is helping build AI is sort of contributing to this new AI first tech stack. And that’s really what, what our thesis is now, and, you know, we think, you know, these super cycles run. And I don’t want to say like, why, because I don’t know why, but they tend to run for 15 years, approximately. And so I feel like we’re in we’re in the very early innings of this AI first supercycle. And if you look back, I mean, what were the prior super cycles, you know, that was kind of a client server PC era supercycle then there was the commercialization of the internet supercycle right then there was, you know, DMC, that supercycle and now, you know, I think we’re, you know, we’re training, they tend to, you know, look like S curves, if you are, if you will, in terms of value created and adoption, and pretty much every every other metric. And I think we’ve stepped onto the, you know, the new S curve here, and we’ve got a really, really exciting and steep curve ahead, as it relates to as it relates to AI, and how how that will just become ubiquitous, you know, it’s, it’s not really anything, I think that, you know, five years from now, we’re going to call out as some sort of some sort of distinctive and separate thing. It’ll be a pervasive property of internet software, of all viable technology products.
11:54
Love it, where do you think we’re at in the cycle? Like, what inning? Would you say we’re on?
11:59
Yeah, one is it which is not to say, there hasn’t been AI, you know, put to good use previously, but again, that those were, you know, there’s always you can always look back and see, like, the anecdote, you know, the the preceding thing, and, you know, a lot of this was developed in the consumer space. Right. So AI has been, you know, generating a lot of value at places like Google and metal and elsewhere for for a long time, you know, but what’s happened now is it’s sort of broken out and become mainstream. And obviously, I’m, I’m a b2b, focused person. So it’s really entered the b2b sphere in a big way. I think, I think just recently, how do you advise
12:36
your existing portfolio companies, you know, on how to leverage AI? And how to incorporate that both internally and externally? You know, for the F for their efforts?
12:49
Yeah. Well, you know, so there’s a whole, there’s a whole set of application companies that are sort of going through a replatforming, you know, so maybe they were using simpler forms of, you know, of data and analytics and machine learning, you know, and they’re now sort of moving in a more aggressively on to new AI platforms, generative AI and taking advantage of a lot of things that are relatively newly available. So that replatforming effort in these application companies, I think, is really important, and some are doing a great job. And, you know, you know, like, my friends have gone, you know, which is a company that was founded in 2016. So, you know, well before GPT, or LLM, but have been incredibly agile, and their whole architecture and product concept is incredibly well suited to taking advantage of generative AI, you know, both stuff that’s available from from vendors, as well as things that they’re developing internally. And, and so I think that, you know, the genius of these application companies is not necessarily Oh, I’m going to develop net new artificial intelligence technology, but how do I apply that to the business processes to deliver the business outcomes that my users care about? Right, that’s, that’s a big fat piece of value. The guy that’s got a good application company is built to deliver so so that’s, that, you know, we’re, we work closely with, with the companies on where appropriate at the infrastructure level. So it’s interesting because, you know, we’re infrastructure investors too. We’re not just application companies. And so things like snowflake or or, you know, more recently, pine cone, you know, data infrastructure, data platforms that support all this fabulous AI processing, and all these great AI first applications, you know, a lot of the work we do is down with infrastructure and tooling. And something, you know, sometimes the replatforming can be harder for those companies, you know, they were really built to support a different technical paradigm. And now you know, it’s like out with that, and then with in with the new, some of them can be stranded others, others, like snowflake, you know, do a great job of expanding, expanding their platforms to really, you know, continue to take advantage of their strengths, but also support a lot of the net new work that the customers are trying to do. So, you know, that that can be a fascinating set of discussions, you know, down the stack as well. I had One founder told me the other day, he’s like anything you did before November 2222 is worthless, throw it away. And he’s gonna He’s not totally wrong. But, you know, certainly there is there is a lot of whitespace and a lot of a lot of opportunity to clean, pretty valuable real estate in the, you know, the new AI first tech stack.
15:20
Peter, do you think in videos the 10 years from now, if you’re a betting man, would you bet that they’re the leading processing company in 10 years? Or do you think someone else will emerge that kind of looks at processing differently, and maybe better builds a better processor that’s more capable in sort of this evolving AI landscape?
15:42
Yeah, I would not bet against Nvidia. I think Nvidia is a beast. And they are very, very savvy. And as you can see, you know, die, but the performance of the company and just you know, Jensen is an amazing and amazing leader. And the rest of the team is really strong, too. I think there’ll be other businesses built elsewhere. And it’s not like they’re going to capture all the value, you know, so I think there’ll be the leader. But, you know, there are other other opportunities for for processor type companies may be closer to the edge, for example, where, you know, there’ll be there’ll be other good companies bill,
16:18
Peter, many companies claim that they’re AI companies, many investors claim that they’re, they invest in AI companies, how do you define an AI company?
16:28
Yeah, great question. I think I think that pretty soon we won’t even think of that term will mean nothing, you know, so back in the 90s, when I got into venture, there were companies that call themselves internet companies. Yeah. Right. Like, you know, the show be like, Oh, I’m an internet company, oh, or VCs? Or, like, I’m doing internet deals, you know, like, what does that term mean? Now, like, oh, well, it’s like, it’s pointless. Every company is an internet company, like, could you imagine being the guy that? Well, I’m doing the non internet deals? And I think we’ll, we’ll see, we’ll see that with AI, like, you know, all software will be, you know, either enabling some forms of AI or leveraging, you know, AI in really fundamental ways. And it’ll be it’ll be like oxygen, you know, be super important, but not something you talk about every day. And, you know, today, right, you know, this whole idea that sort of like AI is distinct from, like, everything we were doing before. I think there’s confusion around that, like, I think I think there’s a lot of the world that maybe hasn’t been working in this, you know, quite so long, that conflates AI with LLM. So they think they’re like synonymous and an LLM is really important piece of technology, and makes a lot of things possible. And it’s sort of transformational as an enabling technology. But that it is not like the sort of the whole fullness of AI not not even close, you know, it is it’s part of the puzzle, it might not even be the best place from a venture investment point of view. It’s capital intensive, it’s highly competitive, you know, these are sort of really large scale programs that, you know, at least as currently defined, may not necessarily lend themselves to the classic idea of venture, achieving, you know, high degrees of leverage, creating a lot of value on small amounts of capital and a lot of insight, right, you know, that sort of venture model, you know, some of these sort of feel more like Manhattan Project model, which, you know, is important, but not, may not drive the best investor returns.
18:31
I’m curious to get your take on qualifying and disqualifying factors in AI companies, you know, what, what characteristics must be present for a potential winning AI first technology stack?
18:45
Yeah, well, so that, you know, the stack is evolving really rapidly. And it’s one of the things that makes it a fun place to invest in a challenging place to invest. So like what what’s, you know, what’s actually an important layer, that, you know, that a startup can own and defend, you know, versus what’s just like, a property of some adjacent layer that, you know, will be subsumed and won’t actually be independent, at least not for long. I mean, that’s, that’s a hard piece of 3d Chess to figure out. A lot of it depends on developer interests, you know, frankly, and developers, sometimes there’s, it’s hard to tell it’s hard to predict developer behavior sometimes as it is to predict consumer behavior. So so it’s, it’s it moves fast. And as a result, opportunities are, you know, sort of born and destroyed, you know, you know, pretty darn quickly. So, you know, I sometimes try and think about analogies to you know, past paradigms, you know, so okay, you know, that there, we’ve got a new, a new stack being born, there’s a new set of workloads or things that people are trying to accomplish. There’s a new set of technologies that are available, but a lot of the functions a lot of the things that we had to do earlier, we still have to do, you know, but we have to do it around, you know, this new this new opportunity set, this new technology set and so kind of mapping functions from sort of last year to this year is a useful exercise. I mean, it’s not not foolproof, but it’s a decent way to think about it, usually, usually the functional requirements don’t go away. And you know, but they do often have to be replac formed for, you know, the new, the new super cycles,
20:17
where we’ll start to be able to create invent real value.
20:22
Yeah, well, you know, I’ve worked a lot at the data layer. So, you know, data platforms are something that we, you know, are keenly interested in it, when we’ve done a lot of historical work there, you know, with snowflake, you know, obviously, very well known data platform company, and still continuing to expand its importance. And, you know, more recent ones, like pine cone, which is very much an add first data platform, you know, born out of the rise of vector data, you know, which is kind of the native, the native data type for for AI, and all the different things that developers in particular want to do want to do with vector data as they build out AI systems. And so I just, I am a big fan of the opportunities in and around in and around the data layer, I think that, you know, no one gets below you. So you, you have some real difficulty, assuming you can establish some viability there, you know, there’s, you know, just a lot of power there now that, you know, they don’t come along, those opportunities don’t come along all that frequently, you know, but when they do, whether it’s a snowflake in a Databricks, or hopefully, a pine cone or whatever else, you know, they can, they can really be of outsized value. So that’s, that’s one super exciting area. But you know, we’re also thinking of the rest of, you know, the rest of the stack the rest of the chain, that goes, you know, the builds on that data platform. And, you know, this is where the analogy thing comes in again, so there’s a whole world of ETL, or, you know, what, what came to be called the ELT in the in the sort of Cloud Data Platform world, which also led to some really interesting and valuable companies being created that sort of rode the momentum of people like snowflake. So some examples would be five Tran or DVT. In a very interesting private companies that you know, sort of adapted old school ETL, ie Informatica stuff to the cloud data platform era. And that was, we look ahead to the vector data era, you know, what, where’s you know, is, are there companies that are, you know, going to kind of play that role, you know, for vector data and working with, with vector data platforms, and are sort of ideally suited to, you know, the processing requirements, that that type of data is likely to need, or the data sources, you know, the people are going to want to vectorize and, and use in, in these AI applications, you know, so there’s a lot of a lot of nuance, we’re spending a lot of time, you know, looking at opportunities like that to, you know, there’s a whole, it’s an interesting change with the data. Like, it’s not just what’s happening in, you know, with my, with LLM, but you know, even upstream from that, I have to create something called an embedding. That’s a vector representation of, you know, whatever my, whatever the data of interest might be, how is that happening? You know, you know, the syllables taken for granted these days, it’s like, you know, we start with the embeddings, but it’s like, Well, okay, how did I get to the embeddings? You know, I mean, so that feels like a big opportunity to, are they the right embeddings? You know, should they be different, depending on what I’m trying to do with them? You know, so these are a lot of a lot of questions that I think haven’t really been asked outside of academia, you know, very much and so there’s company opportunity there, too.
23:24
Yeah. When it comes to data structuring, normalizing munging organizing, do you think incumbent players in? I mean, you’ve invested in some right, do you think incumbent players take on those issues? Or do you think upstarts?
23:40
Yeah, no, they’ll definitely take it on. And so kind of, you know, how much, and if I think whether you’re going to sort of stick with an incumbent, or go with the, you know, the new sort of native vendor is going to depend, I think, on how important it is to. So like, if, if AI is the appetizer and not the meal, you know, for a particular customer, then you know, that I think there’ll be likely to be, you know, happy enough with whatever additional bolt on capabilities, you know, their existing vendors might bring to market. But if you are like an AI first business, and this really is the heart of what you’re doing, or it’s a major workload, you know, then you’re probably going to be looking at best of breed, you know, certainly, that’s, that’s been the historical pattern. And so, you know, it’d be a lot of market to go around as a result, you know, like, and it will be, you know, again, you know, not not not a type of processing or type of data that you know, only lives in one place, you know, I think, see this be, you know, become an attribute of a lot of different systems. Perfect.
24:42
Peter, what is your most contrarian take on AI? Right? What give us a take that you think most of your peers in the venture industry might not agree with?
24:53
I think my most contrarian take might be that large scale LLM model developers may not be the, you know, the best source of returns. From a venture investment point of view, you know, we’ve seen a lot of money flowing into, you know, any sort of trio of PhDs that used to work at open AI, and now or want to come raise hundreds of millions of dollars at very high, very high valuations with maybe maybe a major strategy, maybe not, you know, we’ve been, you know, sort of standing down on most of those types of opportunities where, you know, we’re very, so that that might be viewed as somewhat constrained.
25:32
So if not LLM, then you know, what are some of the other candidates that are useful? You’re the most, you’re spending the most time on?
25:41
Well, like, you know, you know, we think we think of MLMs as an important enabling technology where, and, you know, so how do you help businesses make use of it? You know, how do you? How do you use your own data, you know, for fine tuning and provision of context, for example, so the notion of a private LLM that’s highly adapted to the customer’s own problems and data. That’s pretty interesting. We think that’s going to be an important trend. And there’s a lot of infrastructure and tooling required to support that. So, you know, I’m not, we’re not negative on LLM, but it’s more, okay, of what sort, you know, are we going to do an open AI clone? Or is, are we going to sort of try and enable the open source ecosystem with with some of the missing pieces that allow customers to get value from that, that might be more venture approachable than, you know, taking on the, you know, the moonshot type project?
26:35
Peter growth is, of course, a very important goal for venture backed companies, why should growth not necessarily be the goal for the venture firm itself?
26:44
Yeah, great question. You know, growth can be the enemy, for a venture firm. And again, that growth in the portfolio, but growth in the firm itself, you know, you might measure it a couple of ways, like so growth in assets under management, you know, can deflect strategy in a in a pretty in a pretty profound way. For example, our early stage strategy would could not really be pursued, if we raised billion dollar plus funds, every fun cycle, we that that would cause us to have to, you know, look for larger deals, which probably means later stages in order to deploy capital of that, at that scale. And so you know, capital has gravity, and it pulls strategy, you really want to go strategy first, and then capitalize the strategy. And if it’s something that’s focused on company building, and early stage work, which historically has been where the best returns are, then you really have to have to think carefully about matching, you know, the amount of capital with with that, that type of opportunity. Another measure of scale might be number of people. And as you scale the number of people around the table, you change the decision process to and that can have a profound effect. So if you get above, you know, sort of single digit, you know, high single digits, you start needing things like sub teams, and hierarchy, and you are forced away from a flat structure with sort of high bandwidth, you know, very direct mode, a discussion that, you know, we believe, leads to the best investment decisions. So, you know, that’s, that’s another another way that scale can, you know, start to be at odds with returns? And so, you know, I think venture firms need to decide, are they in the scale business? So the returns business, and you can usually neutralize? Well, that’s certainly been the historical experience, but you can be too small also, right. So there’s sort of a sizing, you know, if you’re, if like, for us, you know, we’re, you know, we play the role of lead investors over the long term, you know, with companies that are hard to build. And, you know, if we’re below critical mass, we’re not, we’re not credible to that effort, right. So so we need to be big enough to play that role, but not so big, that we get deflected off strategy.
29:02
So how does wing make decisions?
29:05
Yeah, wing is, is an example of that flat, you know, sort of non hierarchical one team model. So, you know, one of the one, one of the reasons that our kind of strategic focus or investment thesis is so, so tight, is because it allows every member of the investment team to be a full participant in every decision we’re making. So you’re not allowed to say, Oh, that’s not really my area, you know, let’s let those guys you know, make that decision. And I’ll just sort of hang out because I’m not an expert there. It’s like no, you have to have an assertive informed opinion and so by, you know, sort of bounding our areas of activity, you know, we you know, we can actually do that, you know, it is it is a it is a decision process where anyone can say no, that it’s not often that someone would lie on the tracks and just a block, block the train necessarily, but it does mean that the you know, the Have, the advocates have to engage the skeptics, and, you know, meanwhile the skeptics have to, you know, be very articulate in what their concerns are. And it really results in strong back and forth, and, you know, teases additional insight or, or, or wisdom, you know, out of the process that might otherwise have just been, you know, glossed over, if you didn’t have that kind of, you know, sometimes call it a struggle session, which is, you know, maybe a cultural revolution, you know, it feels that way a little bit,
30:30
you know, there’s always, or there can always be a number of good reasons not to do a deal. Sometimes you need, you know, one or two really good reasons to do a deal. And if you ask enough people, and you dig deep enough, you’re gonna find real objections on anything. So how do you with it with a team, you know, of partners? How do you not let those objections prevent you from, you know, leaning into a really great reason to do the
31:00
deal? Yeah, no, I mean, I like the way you put that, you know, it’s like the, the sort of, there’s always reasons to not do something, I mean, especially, you know, where we operate in the early stages, I mean, the things are malformed and incomplete. You know, there, you know, as my as my, my partner, Gaurav would say, you know, they’re missing entire limbs, right? You know, so there’s ample reasons to not do something, but it’s not even worth talking about those, if there aren’t, like, you know, one or two, you know, really, you know, compelling reasons to do this. Right. And, and if those are compelling enough, then, you know, then our job becomes like, Okay, how do we how do we deal with these negatives? Like, are these negatives, just absolute fatal flaw? Is nothing we can do about? Or is this just work? You know, and things that we can, you know, partner with the founders on dealing with? And yeah, won’t be easy, but, you know, it’s, that’s, you know, you know, tractable, you know, and as long as, as long as, you know, you got a couple things going right for you, you know, then you can get to work on those things. And it’s worth it. Obviously, you know, like product market fit is one of those things, which, you know, if you have it, you can make a lot of other mistakes and do really well. And if you don’t have it, it doesn’t matter how well you do on these other things, you’re doomed, you know, so, so, understanding, you know, the reality and the potential for product market fit is one of the most important things that we have to spend time on. And I can’t fall back on metrics to measure it. Because we’re ahead of the metrics. So you know, you’re, you’re having to do a lot of other things to try and understand, you know, the potential for for high product market fit.
32:37
So what does that process look like post investment, like with a founder? are you advocating a lot of experimentation and whatnot to find product market fit? Are you trying to define what product market fit might look like for a company after investment? And then driving on a path to get there? You know, talk us through your process?
32:55
Yeah. Well, certainly, I mean, experimentation is great. Some businesses really lend itself to it, or there’s, you know, maybe are kind of more fundamental, deep tech, so it’s a little harder to run run experiments in some of those. But, you know, another thing that we do like it was sort of kind of a key enabler of our businesses, we built this very large customer network. So this is a similar, highly calibrated place relationships that we have with leading decision makers, buyers, implementers thought leaders in that are in the customer segments that we care about the people that are the most relevant in adopting the products that the wing portfolio producers, and it’s a lot of them and they span a lot of segments. They’re not it’s not all to CIOs, you know, I mean, CIOs are very important, but they’re not usually making specific buying decisions themselves, you know, so you go down the organization and find, you know, the people that are really the key for the different categories that our companies are competing in. And so by leaning on that customer network, both before we make an investment decision, and then also giving the companies access to that network, after you know, we’re invested, we can, you know, both identify, and, you know, through feedback cycles, improve product market fit, you know, in a higher fidelity accelerated way. So, that was like a day one principle for weighing was like, Okay, we’re gonna do this. You know, I think the, in our initial slides, we called it the, the market validation machine. And, you know, since then it’s, it’s, it’s obviously come a long way. It’s not just for that single purpose, but that that is a big a big part of what we try and do. You’re,
34:36
you built a remarkable team of folks that when they have their strengths, without mentioning specific names, I’m curious about how you thought about assembling a group that has the right balance of skills, and gives your team the edge to sort of optimize overall performance and outperform your peers.
34:59
You know, Well, you know, Garth and I were really fortunate because we had some experience with this in our prior firms, you know, so I’ve been at Excel for 15 years, God had been at Sequoia for a decade. And, you know, both of us were examples of people that were brought in, you know, from the outside, and then kind of learned the business in an apprenticeship mode, alongside some of the really great venture capitalists of the time, you know, both our partners inside our firms, but are also our CO investors. So we got to work with, you know, on boards, where we were like, the young whippersnappers, and they were the, the deans of the industry, and, and so having gone through that process ourselves, and then, you know, it certainly in my case, helped others go through that process to where I would be identifying people to bring into the firm, and then helping helping them develop in their careers. You know, we we learned a bit about what about what works and what doesn’t, I think Edexcel in particular, you know, obviously, I’m more familiar with that, you know, we have a great track record of identifying, you know, future venture talent, and getting those people up to speed and effective. And if you look at sort of the people that came through that system, Excel, but you know, some that are still there, but others that have gone on to other, you know, great firms and great careers elsewhere. It’s really a tremendous diaspora of talent. And, you know, maybe we weren’t as good at retaining that talent, which is another story. But certainly, the identification and development was something that our firm, you know, really learned how to do well, so we tried to bring those those learnings to win, and then adapt them for the times to obviously, it’s, you know, it’s 2023, it’s, it’s many different rules apply, and in different things matters. So, you know, so we’ve, you’ve changed the game a bit, too, but, but that whole, you know, that whole process of identifying great raw material, you know, to sort of put into the system, you know, that’s a whole art, and then, you know, the way you develop a new investor, that’s a whole art as well, you know, part of part of our, you know, we were talking about how to swing make decisions, and how does our team operate, you know, one of the we think it makes you can make better decisions through this flat, sort of high bandwidth, somewhat contentious mode of interaction, it you also help develop people very quickly, because, you know, folks are thrown into it very quickly. Now, there are guardrails, you know, so you want to put people in a position to succeed, but, you know, our new investors are not carrying someone’s bag for three years, you know, before, you know, before stepping onto the stage, they’re sort of in the middle of those debates, having high degrees of accountability and responsibility, having the sleepless night, you know, when you’re advocating for something, that that you, you know, are trying to build conviction around. And, and so that that is, you know, tremendous accelerant, you know, to someone’s development process,
37:50
those are really good insights on the development side, are there any key things you can still distill down on the raw material side and selecting talent?
37:58
Yeah, well, you know, so that, I mean, there’s resume factors that get you in the discussion, which are sort of boring to talk about, you know, and then the thing that, you know, allows you to develop conviction about a candidate to bring them onto the team, these these are not on the resume, right. These are intangibles, and they have a lot of that has to do with judgment. So in judgment, you know, having good judgment is different than being smart. You know, like, smart people are like a dime a dozen in Silicon Valley. Like that, you know, so that, that, again, that sort of gets you in the room. But you know, the intangibles, of making good judgments amidst tons of uncertainty, you know, that, that that’s hard to assess, you know, I mean, that like, might be the most important thing. But it’s extraordinarily hard to assess. There’s other intangibles just around salesmanship. You know, like, I mean, venture is, you know, has has the attributes of the sales business, it has other attributes do, obviously, we’re buying and selling at the same time. But, you know, there’s, you know, we’ve worked with tons of people that, like, don’t work with us and don’t have to, you know, it reminds me a little bit of my, you know, early career as a product manager, where you had, you know, responsibility for everything and authority over nothing. You had to work with, like, a combination of humility and Guile, you know, to get anything. And it’s kind of like that, as you know, in venture as well, you know, you’re it is not the easiest thing.
39:24
Peter, venture firms all claimed to add value, what are the right ways to do that?
39:28
Yeah, great. I mean, great question. You know, the way we think about it at weighing is we try and understand what what are things that are really important to portfolio company development, that they would have an almost impossible time doing that themselves. If it’s something that that they can do, you know, with a higher or a consultant, that probably isn’t something that we should be investing in at the venture firm, right, because that means, you know, that those needs can be met in the market elsewhere. You know, we’re not trying to insource You know, the, you know, every last every last capability, but if it’s something that a new company would struggle to do for a combination of reasons, you know, then maybe that’s where we can really move the needle. And it turns out that a lot of those things have to do with networks, you know, because it takes a long time to develop a network, and it also to have a meaningful, you know, sort of set relationship, you know, you have to be relevant to the other side, right. So, as a venture firm, you know, we have a whole bunch of things that we work in, you know, so we have that diversity of the areas that we invest in, we also have a long time frame, you know, so we can, you know, long before we’ve invested in a particular company, we may have been building relationships for 1020 years, with particular key people. And then, so, you know, that was a brand new startups like, okay, t equals zero, now I’m getting going, and, you know, maybe some of the individuals might have relationships, but, you know, we have sort of that the scale that comes with time, and also the fact that we are operating in a whole range of opportunities, whereas, you know, a particular company is only really operating on its own. And so, so we can, you know, we have kind of relevance, we have resources, we have time to develop really, really deep networks that can move the needle for companies. So what are the networks? Right? Well, I mentioned earlier, the customer network. So you know, we have the, you know, the ability and the relevance, to build interesting and trusted relationships across a lot of key decision makers in the customer base, and then provide access to the insights those people have, you know, for the portfolios by, you know, making the right connections that are valuable to both sides, Talent Network, that’s another network, right? You know, there’s a whole universe of candidates that could be really important leaders in, in portfolio companies, we can go and develop relationships with people over over a timeframe a years, you know, without any particular opportunity in mind, but just because we think they’re talented, and, you know, they’re going to be relevant to something that we do, and, you know, will, we will invest years and getting to know that person and, you know, sort of creating opportunities for them being valuable to them, you know, and it’s, so it’s not a transactional thing, like, okay, you know, I need a higher get a candidate, you know, it may end up resulting in a higher, but, you know, we’re building a network as a resource that, you know, can, you know, that the portfolio and us can benefit from, I mean, there’s other networks, too, that are important to us, you know, we also have an extensive media network. So and these are, you know, journalists and influencers and bloggers and, you know, other other voices out there, that, you know, we have built relationships with and credibility with, because they know, what we’re interested in, and that we kind of know what we’re talking about, and that that can be very valuable to companies who might otherwise not have access to that. So, you know, that sort of layer, you know, where you take advantage, the fact that it’s a multiple play game, across a diversity of topics and interest areas, you know, that’s, that’s something where you know, where we will, will focus what we call our founders success team, which are, you know, dedicated people on our team that, you know, build and manage those networks. And then, you know, try and activate the networks for, you know, the value of everybody that’s participating.
43:11
Perfect. Peter, if we can feature anyone here on the show, who do you think we should interview and what topic? Would you like to hear them speak about?
43:18
Jesus on eternal life?
43:21
Final answer.
43:22
That’s my answer.
43:24
Okay, I will work on it. Peter. Peter, what book article or video, would you recommend the listeners something in recent memory that you found informative or inspiring?
43:34
I would recommend the making of the atomic bomb by Richard Rhodes.
43:38
Very good. Peter, do you have any habits, tactics or techniques that are a secret weapon?
43:43
Well, I read sailboats as a hobby. And there’s a lot of things about competitive sailing that are instructive for venture capital. Well, you’re dealing with so many factors, you don’t control. When you’re racing, a sailboat, you’re the, you know, like nature, like wind waves, stuff like that. Also, your competitors are out there, you know, on the same course, intentionally trying to get in each other’s way, and block each other strategically. And, you know, so you don’t control what those people do. And so, just kind of trying to make progress amidst all these uncontrollables and some, some of which are like unforeseeable, and some of which are semi foreseeable. That that’s really good training for venture.
44:29
And then finally hear Peter, what’s the best way for listeners to connect with you and follow along with wing?
44:35
Yeah, the wing website is a great resource, a lot of lot of what we think and believe we publish there, and a lot of that also finds its way onto my LinkedIn. So that’s another another good place to connect with me.
44:48
Okay, very good. And he is Peter Wagner. The firm is wing VC. Peter, thanks so much for joining us today. It was a true pleasure.
44:55
Thank you. Thanks for having me. Thank you, sir.
Transcribed by https://otter.ai
