What most LPs miss, and why venture is quietly changing shape
Here’s a strange truth about venture capital: it’s an industry obsessed with pattern recognition, yet remarkably bad at measuring its own patterns.
We have spreadsheets for startups. Metrics for growth. Frameworks for product-market fit. But when it comes to evaluating the people allocating the capital, especially emerging managers, we mostly rely on vibes, pedigree, and coffee chats.
That worked when the industry was small. It breaks the moment it scales.
Eric Woo has spent most of his career living inside that contradiction. He’s evaluated hundreds of emerging managers as an LP, helped institutionalize syndicates at AngelList, and now, as the co-founder and CEO of Revere, he’s building infrastructure to make venture underwriting a little less mystical.
This post distills the core ideas from our conversation for anyone who didn’t listen to the episode, or wants the signal without the audio.
Venture underwriting is still mostly vibes
Let’s start with the uncomfortable part.
Despite all the talk of rigor, most LP decisions about emerging managers still come down to:
Do I trust this person?
Do other smart people seem to trust this person?
Does this story feel coherent?
None of that is irrational. Venture is a human business. But it’s dangerously incomplete.
Eric’s experience across fund-of-funds platforms revealed a recurring problem: LPs are forced to compare managers using narratives that aren’t standardized, aren’t comparable, and often aren’t falsifiable.
Everyone claims to be “value-add.”
Everyone claims to have “differentiation.”
Very few can prove either.
As more capital flows into early-stage venture, that ambiguity becomes a bottleneck. Not just for LPs, but for the managers themselves.
The signal that actually matters: founders know who their first call is
Across hundreds of GP evaluations, one signal kept resurfacing.
Not pedigree.
Not fund size.
Not even early markups.
The signal was simple: who founders call first when things break.
The best managers aren’t fungible. Founders know exactly which investor actually helps, who picks up the phone, who can close a customer intro, hire, or next round.
That relationship shows up long before outcomes do.
Eric frames it this way: product-market fit de-risks a startup. Founder-GP fit de-risks a fund.
If a GP consistently helps companies reach their first real customers, not hypotheticals, not decks, but paying users, that’s an early indicator of future fund performance.
Most LPs underweight this because it’s hard to measure. But difficulty doesn’t make it less real.
“Value-add” is meaningless unless you can show your work
Here’s where things get awkward.
Ask ten GPs how they add value, and you’ll hear ten confident answers. Ask them to show evidence, and the room gets quieter.
Eric’s work at Revere started with a basic question: what if GPs had to operationalize their claims?
Not marketing slides. Actual tracking.
How many customer intros led to revenue?
How many hires came from the GP’s network?
How often did founders actually use the GP as a resource?
The insight wasn’t that some managers are better than others. Everyone already knows that.
The insight was that the act of measuring value-add changes behavior. Managers who track it tend to improve it. Managers who don’t often overestimate it.
This isn’t about turning venture into a spreadsheet. It’s about accountability.
AI changes venture ops first, not venture judgment
There’s a lot of hype about AI replacing investors. That’s not what’s happening.
What is happening is quieter and more consequential: AI is eating the operational layer of venture.
Screening.
Summarization.
Diligence workflows.
Portfolio monitoring.
These were once labor-intensive, expensive functions. Now they’re table stakes.
But judgment, deciding who to back, when to sell, how to support, still resists full automation. Especially in early-stage venture, where the data is sparse and the variables are human.
Eric’s view is pragmatic: AI doesn’t replace the GP. It raises the baseline.
The next generation of standout managers won’t be those who “use AI.” They’ll be the ones who redeploy the time AI saves into deeper founder relationships, better networks, and more thoughtful capital allocation.
1x DPI is a psychological unlock
Here’s a counterintuitive LP insight that doesn’t get enough airtime.
Returning 1x DPI early matters more than people admit.
Not because it’s the goal, it’s not, but because it changes the emotional math. Once LPs get their capital back, everything else feels like upside. Trust increases. Patience expands. The GP gets breathing room.
Secondaries make this more possible than before. Selling a portion of a breakout position to return capital doesn’t mean giving up on upside. It means de-risking the relationship.
This is less about financial engineering and more about human behavior. LPs are people, not IRRs on legs.
Venture is becoming a financial product, whether we like it or not
Zoom out, and a bigger pattern emerges.
Venture capital is being pulled toward new distribution channels, private wealth, RIAs, global capital, that demand consistency and clarity, not just hero narratives.
That doesn’t kill the art of venture. It creates parallel strategies.
On one side, concentrated, conviction-driven funds hunting for generational companies.
On the other, diversified, index-like approaches aiming for repeatable top-quartile outcomes through volume and process.
Neither is “right.” They serve different end customers.
The mistake is pretending one can be both without tradeoffs.
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Chapters:
00:01 – Eric Woo intro, Revere VC
01:00 – Engineering to finance pivot, CFA escape hatch
02:30 – Pricing CDOs before the financial crisis
04:20 – Front-row seat to the GFC
05:50 – First startup experience, search engine marketing
08:30 – Entering venture via fund of funds
10:40 – Early micro-VC and seed investing era
13:00 – Institutionalizing emerging managers
15:00 – Heuristics for backing new GPs
17:10 – Specialization and GP differentiation
19:30 – AngelList and the rise of syndicates
22:00 – Family offices and venture as participation sport
24:30 – Scaling challenges in GP–LP matchmaking
26:10 – What never changes in evaluating GPs
27:50 – Founder–VC relationship as core signal
29:10 – The origin of Revere
30:40 – Rating emerging managers with data
33:00 – Quantifying value-add
35:30 – Gaming the system vs real substance
38:00 – AI in GP self-assessment
41:00 – Structured vs unstructured data in venture
44:00 – AI, fees, and fund economics
47:30 – Transparency as LP differentiation
50:00 – Revere roadmap and Velvet merger
52:30 – Network effects and venture platforms
55:10 – Venture distribution and new capital channels
57:20 – Efficient frontier of early-stage venture
59:40 – Concentration vs volume strategies
Transcript
Brian Bell (00:00:57):
Hey, everyone, welcome back to the Ignite podcast. Today, we’re thrilled to have Eric Wu on the mic. He’s the co-founder and CEO of Revere VC, a platform reinventing how allocators evaluate and manage emerging funds. Very interesting for us. Eric blends deep investing chops with product sensibility. He’s a CFA. All three levels?
Eric Woo (00:01:14):
All three levels. Yeah, absolutely.
Brian Bell (00:01:16):
All three levels, Eric? Yeah. That’s amazing. Engineer, former head of institutional capital at AngelList, and longtime advocate for data transparency and infrastructure in private markets. Let’s get going. Thanks for coming on.
Eric Woo (00:01:26):
Appreciate it, Brian. Glad to be here and excited to share some thoughts and wisdom, especially where we are in the venture markets today, definitely going through its own evolution.
Brian Bell (00:01:35):
Yeah. Well, I’d like to start at the beginning, kind of get your backstory. What’s your origin story?
Eric Woo (00:01:40):
Well, it starts with trying to try my hand as an engineer so went through four grueling years of engineering school at berkeley figured out very quickly i didn’t have the disposition to be an engineer yeah absolutely and that was really for me a formative moment because i wanted to transition into the world of finance. And speaking of CFA, that was my escape hatch from engineering was first job out of school. People were going out and partying on weekends and weekdays and I was studying for the CFA exam, but it was definitely something very worthwhile because it got my foot in the door on the finance route. So that was really kind of chapter one for me was figuring out I had some technical sensibility, but really wanted to pursue a career in finance if it was a natural way to do that without um you know essentially going through business school um and that was it’s kind of shocking but that was like 2004 when i completed the cfa exam so i’ve been kind of 20 years through that experience. And then obviously a couple of additional chapters, you know, moving into venture, even experience the global financial crisis in a very unique and interesting way. But we’d love to unpack all this stuff as we get through this conversation.
Brian Bell (00:02:59):
Yeah, it’s fascinating. I just did the level one and I kind of used it as a way to get my first analyst job on Wall Street. I was finding I wasn’t getting callbacks. This is 2005 or 2006, roughly the same timeframe. I was like, okay, I’ll just do the CFA. And that got me the job. And then I quickly realized I didn’t want to do the job that I got. Anybody who’s listened to this podcast knows this story, but I won’t repeat it. So what did you do next? What was your first job post CFA?
Eric Woo (00:03:26):
First job, working for an insurance company in risk management. And for those of the audience who are old enough to remember this, I was actually pricing CDOs. And this was during 2005 to 2007. For the insurance company, they had a financial guarantee division that was pricing CDOs. A lot of these CDOs that were going to market, I was running the models, pricing the risk. And then at the same time, we were doing some interesting things with hedging in the synthetic indexes on basically asset-backed securities. And you saw some interesting names like Goldman Sachs was short the market. And you had Lehman Brothers, Bear Stearns, long the market from a hedging basis. And so if you fast forward through that period, obviously 2007, 2008, we saw a lot of carnage in structured finance CDO markets that led to the global financial crisis. So that was my first front row seat experience in terms of what financial engineering can do to the markets. And then that was where I said, you know, again, what’s my next escape hatch? You know, this world of structured finance that led me to venture capital and startup life.
Brian Bell (00:04:39):
What did that transition look like? You’re working, doing this, and then kind of what happened next?
Eric Woo (00:04:43):
So I ended up working as search engine marketing. So this is circa 2008, 2009. So if you remember search engines like Google and Yahoo and MSN, they all had, you know, basically you know keywords paid keywords and people were doing ads on on keywords and this was the new innovative way to do online marketing and then someone behind the scenes at all these companies or uh you would hire essentially a big agency that would run complex Excel models to figure out where there’s optimization of pricing. So it’s like, you know what, if I bid $0.05 more for this keyword, now my placement is number three versus number five. We know conversion ratios on the top three are 20% higher. So it was an interesting... direct correlation or kind of translation of what I had been doing on the modeling side in terms of pricing models and pricing risk, but applying it into a new category and working within. At that stage, this company was called AccountNow. They were a series B pack company. So it was kind of coming in at a mid-stage startup company. Fascinating experience, but that was really the first entrepreneurial experience for me that really started to uncover you know sort of this ethos of you know what it means to like be building something for a fast-growing company so did that for about a year and then i got called by a good friend that was running a venture capital fund of funds and this was northgate capital and they were hiring their first analyst and so as is typical especially being based here in silica valley the network of venture capitals Just, you know, word of mouth, somebody calls you up and says, hey, come join and help us out and we’ll see where this goes. That was my first foray into venture capital. That was 2009. So I went through a few different iterations, different chapters from that five-year period, but it was all kind of identity crisis for me. And here we are kind of 15 years later from that first venture experience, and I’m still in it.
Brian Bell (00:06:45):
Yeah, I mean, once you get sucked in, it’s hard to imagine doing anything else. That’s personally how I feel. I’m not sure how you feel, but once you kind of get in, it’s kind of intoxicating, right? Investing in startups and just being in the industry and right at the tip of the sphere of innovation and capital. Looks like you had three main arcs here before Revere, Northgate, Top Tier, and AngelList. Maybe you can kind of walk us through each of those experiences and what you kind of took away and how it led to the other.
Eric Woo (00:07:10):
Yeah, so the Northgate experience was actually really interesting because I, you know, the first job function was actually in the back office. So I was working with auditors. They were, you know, Northgate at the time had quite a sizable co-investment portfolio in addition to their venture capital funded funds. And at the time, a lot of the regulations were saying, well, look, you’ve got to mark to market your direct holdings. You’ve got to run some option pricing models and come up with something that kind of felt scientific, which was almost impossible at that time. So my first experience was actually understanding valuations, running models again to price the direct holdings. and that and then you know a lot of this was also things like you know fund formation they were going to market helping them with a lot of the lpa type of stuff so first first year and a half at northgate was back office works learning sort of the mechanism the mechanics of how to run a venture fund of funds and then slowly i start to get familiar with the portfolios started enjoying some of the partner calls and meetings especially as managers would be coming through and doing their pitches and started taking notes and sort of seeing and understanding, appreciating. And again, everything at the time was much more art and relationships than running, you know, option pricing models and cashflow models to actually evaluate the risk. So very, very new, a new muscle for me. But what I, what I found especially intriguing was at that time, you know, the cottage industry of venture was now having this interesting offshoot of seed and micro VCs. You had people like mike maples at floodgate and steve anderson at baseline and of course kind of at that time emergence was coming onto the scene and sort of touting this concept of you know sas software investing which sounded so foreign at the time and that that preview of what the job function of a solo gp someone who had an angel track record or came from the operating background coming into the industry of venture capital saying you know what i can uniquely help founders beyond just the capital and not only that i wanted to help founders in that zero to one period of of their of their company formation and that really was the birth of seed investing so it really became something that was like you know ron conway and his band of of angel investors to now people now starting funds and getting institutional lp capital So that Northgate experience for me was the first taste of this. And really, this is very early days.
Eric Woo (00:09:45):
And then the transition from Northgate to top tier was partly because I had left Northgate and had started to conceptualize my own fund of funds concept within MicroVC. And again, it speaks to how excited and how passionate I was about that space. So it paints a picture I was quite naive about sort of raising a fund of funds for something that was such a new concept at the time. But I was very fortunate to connect with people at top tier capital who were just getting their, what they call their alpha program off the ground, investing in funds that were kind of that sub $150 million fund target. And so during that period, kind of 2015 to 2018, really had the privilege of, you know, helping and managing a pretty sizable pool of capital. I think we had done about 23 years. investments in the space of the emerging manager category. And then of course, there’s several hundred managers along the way that you meet and you start to sift through, start to build your own pattern of recognition. But most critically for me, I think it was just so many personal relationships. And that’s the one thing that I think continues today is if you look at the entire trajectory of venture dating back to the 60s and 70s is these are not only business partners, right? In terms of you know, LPs investing in GPs, but these are our personal relationships and the trust and credibility that comes with that is such a critical and important part of that underwriting process. So I’ll pause there because that kind of spans the institutional part of my career. And then I had sort of my own kind of midlife crisis or mid-career crisis when I started to move over to Angelus. But yeah, happy to answer any questions on the institutional side.
Brian Bell (00:11:29):
Yeah, that’s fascinating. For an emerging manager like myself and probably plenty of people listening, the question always is, how do you break through and get institutional capital as an emerging manager? And you’ve looked at hundreds, if not thousands, of these emerging managers. What kind of heuristics or muscle memory did you build up on evaluating these emerging GPs?
Eric Woo (00:11:53):
I like to compartmentalize this into different waves. The early wave, like I said, the baseline, the floodgates, that was very much just their scarcity to the number of funds and managers that were operating in the space. You truly did have the ability to build very long-lasting relationships and really get deep understanding of the fabric of how people were thinking. The Northgate and top tier experience was now, I call it sort of the institutionalization, right? The legitimacy of a fund, you know, someone like Ulos from Streamline Ventures, right, who came from Globespan, had an incredible angel track record raising his first fund. That track record translating into running and building his own franchise was institutional in nature, not only because of the pedigree and track record of these individuals, but also the validation that LPs, institutional LPs, were saying, you know what, this is an important part of that venture allocation in our portfolio. And that kind of was the spark. that led to just more and more people coming into the market. So you asked about heuristics. It used to be just good old perseverance, let’s sweat and tears, seeing people show up. Honestly, I remember some managers would just, every time they came through San Francisco, they’d call me up and grab coffee, give me the update. And over the course of a couple of years, you start to get to know people and that starts to actually help with the advocacy of me taking managers to the investment committee and i think today or sort of as you move from you know 2018 2019 vintages into 2020 2021 22 obviously covet was kind of an important ingredient in terms of changing how we’ve interacted in that process of diligence but that concept of you know what’s the special sauce what’s your differentiation what’s your specialization i mean you’ve heard these these terms thrown around so so frequently over the last five years it really was an era of specialization right not just in terms of the category but looking at where are you adding value right both economic value to the portfolio companies that you’re investing but sort of the value add as a manager to your portfolio companies. And that honestly was something not completely conceptualized, you know, 10 years ago, right? It was just like, hey, be good stewards of capital, be a good board member, help companies when they had problems and asked. But now all of a sudden, every single emerging manager, yourself included, has a playbook. or multiple playbooks, right? How do we get that first customer for our portfolio companies? How do we help them through the process of raising their next round and positioning that and actually going out on their behalf and talking to follow on investors? I mean, everything has become around sort of the specialized toolkit that you provide. And in that vein, it just becomes increasingly harder to say, okay, well, what’s unique and different about your toolkit? versus someone else’s, right? So absent of the benefit of five, six years of your companies growing, raising full-on rounds and exiting, it’s a sea of people who are all helpful. Definitely, it’s adding a lot of value, but for LPs, it’s tremendous confusion.
Brian Bell (00:15:10):
Yeah, it’s kind of hard to sift through. And we’ll circle back on these questions as we continue the conversation, but I’d love to hear about this mid-career crisis and what happened there.
Eric Woo (00:15:18):
Part of it was thinking through what I felt was sort of diminishing returns when you work in large organizations that, to their credit, right, all funded funds and asset managers, you know, they grow assets and, you know, they become more of a financial access product than the artisanal nature of like you know delivering alpha on returns and and just being you know thought leaders so as top tier was becoming more successful growing expanding sort of the lp footprint just felt like sort of the this category of emerging managers within the portfolio was you know i wouldn’t say it’s an afterthought but just become sort of a smaller part of the the ford the ford kind of strategic vision so i had some some friends at angelus at the time and so for for your audience those that are familiar with my angels obviously brian you are you’ve been active on the angel’s platform it had moved from something where syndicate leads were bringing their best deals to now syndicate leads saying you know what this track record that i built and the lps that i’ve met on the platform this was a natural avenue for me to start my own fund you know nano funds small funds five million ten million dollars to get up and running and so angelist was was faced with an increasing supply of syndicate leads they’re saying you know what i want to try this and do this full time so i was brought in i was recruited really yeah you can lead on angelist you know first
Brian Bell (00:16:44):
couple years just syndicating deals what year was this for you and
Eric Woo (00:16:49):
Yeah, this was 2018. So that was the time where a lot of the syndicate leads were now starting funds. And increasingly, Angelus had a stronger footprint in the family office LP world. So if you look at both sides of that, you had increasingly sophisticated LPs, family offices, and some RIAs. And then you had syndicate leads now starting funds. And that just naturally meant that there was a little bit more of a an institutional platform, right? So my job, my goal, and this is what I found so fascinating is like, Eric, can you build, can you come and understand, obviously, you know, my background, I knew the dynamics of GPs and LPs, but can you build a product experience for matching syndicate leads who are now doing deals and doing funds with family offices that by nature of them, call me on Angelus, I call it sort of like participation sport. You know, they looked at venture as part of their portfolio, not for, you know, people have used this term, it’s not like they were gambling, but they wanted to be involved. They wanted to hear, they wanted to meet you and understand which interesting companies that you’re investing in so they can kind of tell their friends that they’re doing this thing called venture capital.
Brian Bell (00:17:56):
It’s interesting you mentioned that. With family offices, it seems to almost sometimes be a signaling mechanism to their other family offices they know. It’s like, oh yeah, I was in a fund that was in Stripe, stuff like that.
Eric Woo (00:18:08):
Well, venture, and I think this is still true today, honestly, it’s the one asset class that gives you a preview of this future that you can’t buy that exposure anywhere else. I mean, yeah, you can say someone like Google or Meta or X has AI exposure, but it’s that versus like, imagine you being an early investor indirectly in open AI or Anthropic. or you know palantir you know venture is the only asset class where you can get that exposure way earlier in the life cycle so that is that’s something especially with family offices like i said they look at venture as just a way that is that’s uncorrelated to the rest of their portfolio so but yeah just to finish the thought on angelus i think it was a wildly successful experiment of them trying to formalize this and obviously you know spvs being able to create spvs on demand and all the infrastructure that they’ve built to productize the entire stack of how you run a venture fund has been amazing so that was and then that kind of took us into to covet and and obviously covet changed a lot of things um notwithstanding a lot of capital just simply dried up into the in this in the seed category
Brian Bell (00:19:18):
And so how long were you there? What are you kind of looking back? What are you proud of? What you built at AngelList? What were some of the challenges that you faced, you know, kind of connecting these family offices to merging managers and how’d that whole experience go?
Eric Woo (00:19:30):
So I was there for a couple of years. And again, this was like kind of right up into COVID, but that was kind of a natural journey. jumping off point, you know, some of the interesting things I learned is like you actually can, I call product ties, you know, sort of this matching algorithm. And a lot of it was taking all the things that I remember when I was doing like reference calls with other LPs and saying, oh, like, you know, Eric, what do you like about this fund? And I started actually developing my own internal scorecard for assessing fund managers. So one of the fascinating things that happened through these series of conversations is like, I just came up with Something that looked like an underwriting model and started to develop ways to quantify that right through numbers and not just anecdotes. Some of the interesting challenges is it was a super hard thing to scale. And I’ll give you a very specific example. So if I, let’s say, Brian, I matched you with a family office, but, you know, put you guys together and say, hey, you got to meet Brian. He’s exactly the type of fund you’re looking for. That family office or maybe that ultra high net worth is like, okay, awesome. I made the investment, but it’s like, I do one a year, right? So the scalability of, you know, you have this amazing experience, you build a network, a community, there’s network effects happening, but the scale was so hard, right? When you’re looking at sort of, I call retail plus, because just people didn’t have, you know, unlimited budgets. And once they did one, they’re like, okay, two or three years later, they’ll kind of be back in market and then do they continue to re-up in venture? So scalability at the time was always, there was always kind of a ceiling to that, right? And that was what was, I think, challenging for a high growth company like Angelus that wanted to think in the terms of like, how do we get a thousand funds? How do we get capital for a thousand funds, right? So, but I think, you know, today some of these friction points are different, right? You have like marketplace platforms like iCapital opto that have sort of created the fund of funds venture fund of funds as a financial product so distribution wise it’s become a lot easier to to pull in capital into smaller funds won’t get too far ahead in in the story but i think if anything to take away from from this kind of outline is each of these phases and the cycles in the venture evolution as it relates to emerging fund managers, is both a construct of what’s the macro in terms of capital flows, and then also what is the true kind of value proposition of a GP presenting themselves to the LP market. I think every three to five years, it changes. And the best managers, the ones that have continued to raise more and more funds, they adapt to that. and the story changes. And it’s absolutely okay that story changes. And I would argue if your story’s not changing and adapting, you are falling behind.
Brian Bell (00:22:16):
That’s fascinating. I like that you have to adapt to the times, right? Because times do change and AI is changing everything right now, obviously. But what are some truisms or things that never change about evaluating GPs and their funds?
Eric Woo (00:22:28):
Yeah, this is an easy one. And I say this is easy now because now having been a founder for five years, the universal truth or sort of the first principles of this is really the founder VC dynamic. It’s not fungible, right? You can’t, I mean, this concept, who’s the first call? I think about my cap table, you know, I can clearly say these are the two or three VCs that I always call when I run into problems. So that’s the universal truth is the best fund managers, the best VCs have a very unique improvised relationship with their portfolio founders.
Brian Bell (00:23:03):
Yeah, I love that. So as a LP, if you’re listening, the lesson there is try to back channel with some of the founders and talk to them directly and say, hey, how is this GP on your cap table? Was that worth it taking that check from that GP or not? And are they your first, second or third call to get help? And how helpful have they been per dollar invested? Exactly. Is another way of measuring that too, right? Yeah. I got a million dollar check sitting on the cap table and I don’t feel like I can call that GP or don’t want to. That’s versus, you know, maybe I accepted a smaller check from an emerging manager that works really hard and I can call that person and that’s a differentiator.
Eric Woo (00:23:40):
Well, and Brian, the spectrum of how you can measure that referenceability is expanded today, right? And, you know, I mentioned the underwriting model. that i started to think through at angelist and you know we’ve applied a lot of this to what revere built it’s the first customer right so being able to pull the portfolio and say like how did you help influence or close that you know pilot customer for your portfolio companies how did you help make that you know vet that first sales hire for that portfolio company that led to then the subsequent revenue growth So that the data set from which we can measure this concept of like that founder VC relationship and how they’re adding value is so much more varied and complex today. I just think what is challenging, especially for LPs, is there’s no kind of standardized contract of this. It’s not like you throw this in a model. And most of the times you’re just asking this in like a reference call. And so how do you codify, how do you structure that type of information? It’s tremendously challenging.
Brian Bell (00:24:40):
So what was the aha moment for Revere? Did you say at some point like, hey, this is missing in the industry and somebody’s got to build it? Tell us kind of the backstory.
Eric Woo (00:24:47):
Yeah, very similar to along those lines. I think it was partly I had enough confidence and conviction to really start something on my own. You know, again, I had kind of 10 years earlier from today, I’d try to start my own fund of funds, but doing a fund of funds as a startup versus doing a true startup where you have an idea. And at the time it was myself and my co-founder, Chris, were in the kind of the onset of COVID or, you know, staring at each other in a room where I say like, you know, What can we truly build that’s disruptive in the next five to 10 years? And do we want to do that, right? It’s a lot of life and career risk. But we just felt so strongly about this idea that we were coming into a new cycle in the venture market. And then secondly, we were so well-equipped as individuals and our collective GP and LP network to say, you know what, we can do this, even if it’s in the depths of COVID. So that was less an aha moment, but just, I would say, a little bit of navel gazing and saying, you know what, no one else is doing this. And as we all know, like kind of in the depths of the cycle, that’s usually where the next great kind of iconic companies are started.
Brian Bell (00:25:56):
And so what is the idea? What does Revere do? What problem are you solving?
Eric Woo (00:25:59):
so when we originally started it was just purely you know let’s just bring data and transparency to the problem because it still is today hard to get accurate data hard to get enough data hard to trust the data source so when we first started the the first product was was a lot of that early blueprint of a rating system for emerging managers and it was essentially what the way i would have written an investment memo to evaluate you know your fund and i had 20 different subcategories, everything ranging from, you know, operating experience, track record, you know, what’s your, how do you manage the portfolio, like the investment operation. So we had 20 different categories and we went off and essentially took the first 50 fund managers. These were basically our friends who, God bless them, offered to be guinea pigs to get scored and rated. And we asked them a series of questions and we started to put together the distribution of answers. And then from the distribution of answers, we’d say, you know what, like, these answers here really evoke something that i would consider like you know above average or best in class and we took that rubric and we started to put a score behind that and then we went out to the market formally and said you know what we’re gonna we’re gonna be so bold as to say like you know at the time we call it kind of the morning star report for venture capital it’s like we would go out there and solicit GPs to come and, again, share their data room, go through an interview. We would completely open up our draft of the investment memo because we understand the nature of this dynamic. It’s like we needed to give something back to the GP, help them understand, hey, strengths and weaknesses. And GPs were not paying, right? We wanted to avoid that conflict. LPs were paying us for the diligence reports, paying us to actually go through and buy the reports. And so it was very well aligned.
Brian Bell (00:27:47):
Did the GP have an opportunity to like go, oh, I wasn’t clear. You rated me a three out of five on that thing. But like, let me like try to improve that by giving you another response here. It wasn’t very, let me give you more detail here so I can get a four out of five at least on that. Like how does that kind of, how does that tension work?
Eric Woo (00:28:03):
Yeah, I think when we were, you know, if you look at kind of the second phase of the underwriting model post, you know, guinea pig phase, we started having enough data to either validate or refute that. So if you came back and say, hey, like, you know, why was I rated a three out of five? I would be able to say, well, look, I’ve got, you know, all these answers and like, you clearly fit sort of in this slice of like a three out of five. So one way to respond is like, you know, and I’ll give you the example of like, you know, the value add, like we actually started to measure or ask for people to give us their tracking spreadsheets of how much revenue they were influencing or generating on behalf of their portfolio companies. And so what was fascinating is like, so the first time someone did that and then shared that with us, it says, all right, absolutely, we’re bumping you up. Now, again, we’re not an audit firm. It’s not like we’re calling founders and validating it, but just a mere tracking and thought process of like, hey, this is important for us to do internally as a measurement of how we are adding value. That started to influence the scores. So what you have happened over time.
Brian Bell (00:29:08):
I’m thinking about this at Team Ignite, like how I would game the system here, as it were, to try to get higher scores. Because for us, it’s all about the network. And it’s not about me, the GP, helping the founder. It’s about the network helping the founder. And trying to suss out the details of that is really difficult. Because it’s thousands of little interactions that happen in the network that have an emergent quality of value. And it would be really hard, I think in Revere’s system to kind of paint that picture in a proper way of like, oh yeah, here’s how exactly how much revenue we influenced or how many people we help hire, how many dollars we help fundraise. We have anecdotes, like founders are like, hey, you single-handedly filled our round, but those are just kind of anecdotes.
Eric Woo (00:29:50):
Yeah, and it’s a starting point. But I think that was where we felt that just simply coming up with a scoring system and then having that scoring system make its way out into the GP community of saying, oh, like, and then, you know, again, we shared the rubric so people can understand where they’re coming from. But what you just said was absolutely critical because even with the anecdotes, we can start to kind of create the buckets of like, hey, just the fact that you are measuring and doing something, whereas there’s a lot of people who aren’t doing anything in that category.
Brian Bell (00:30:23):
Yeah. Now they have no evidence, right? It’s like, oh, we add a ton of value. Great. Show us how you add value. Like I add value. Trust me.
Eric Woo (00:30:30):
Exactly. Right. And this is like, again, from the LP side speaking, you know, we hear this all the time, but then as you drill a couple layers deeper, you just want to see that there’s substance there. So that by itself at least helps with the distribution. So all this is, you know, if you, if you look at kind of just math and statistics, like you absolutely, to be intellectually honest, you can’t have every manager be like way on the upper end of the scoring system. Right. That’s what we had to refine over time. And we did this for two years.
Brian Bell (00:30:57):
Yeah, you almost have to enforce a natural distribution here around the mean, right? Correct, correct. But there is a power law at play too, right? I would imagine there’s a power law in GPs and funds as there is in the underlying asset class. Some are just way better, right?
Eric Woo (00:31:14):
absolutely silly but you you need enough data points right so we we had gone through about 200 fully vetted rated fund managers and we had another you know 250 that had you know shared a lot of information but we haven’t like kind of did the full report and so we were just about to kind of unlock you know and on the back end we were we were looking at all the distributions and we were kind of asking ourselves that question, right? Like, are we seeing enough of a distribution? And then are we starting to see where the outliers start to happen? And we were starting to see this, you know, obviously SVB, Silicon Valley bank crisis kind of changed the dynamics of just like how much capital is flowing into the ecosystem from the LP side that essentially made it very difficult from just a pure monetization standpoint. But we continue and up to today, like we still continue to capture the data. We are kind of actively commercializing the reports. Now, Not to jump too far ahead, but we do want to pick that up again because I think it’s such an important forcing function in terms of easing the friction in the market. And that problem has only gotten worse today over the last couple of years.
Brian Bell (00:32:20):
Yeah, it’s interesting. I don’t know if you have me rated on your system, but maybe after the call at some point we can rate Team Ignite. One thing I recently did for my founders or all the 50, 60 pitches I get a day, is I kind of open sourced my rubric. You know, I’ve written an article on it and I’ve kind of refined it over time many times. Like, here’s what good, better, and best looks like and kind of what I care about. But I went one step further recently, which is I kind of put it behind a GPT wrapper. So you could upload all your files, your resume, your LinkedIn profiles, all your all your data, your pitch deck, your financials. And I’ll literally give you the report back that I would look at with AI to evaluate whether or not I should meet you. And so now founders can go out there and literally see what I would see before I see it so that they can iterate on it and maybe put their best foot forward. Have you thought about how you do that in this industry, like with GPs?
Eric Woo (00:33:19):
Well, I think this is where, you know, AI is going to be game changing for us, right? So when we were doing these reports, it was all humans, myself and a team of analysts kind of doing the writing, the evaluation. Now, like you literally can throw a data room into GPT and you can have it kind of produce reports and then even in a chat interface kind of drill down. the velocity in which you can take a large number of data points and data files and be able to kind of start to summarize it. And then even from a prompting and training perspective, say, hey, look, here’s my rubric. And by the way, I’m going to feed all these historical scores and reports to fine tune it. I mean, this is the experiment that we’re working on internally. It’s going to start to look very much like what you said, like where somebody almost can kind of self-assess and there will be a report to say like, here’s like your, you know, imagine a heat map and like, here’s your strengths and weaknesses. And then if that could kind of be, and you know, really where we’re going with this is we want this to kind of look a little bit like kind of the Carfax report. Right. We want every fund manager in the market to have the review report as just purely we’ve shared our data. The data has been processed and there’s a level of seriousness in terms of how we are approaching raising this fund. And then that becomes an engagement point because, you know, if Revere’s well-respected and trusted LPs can look at that report as a screening tool. And then what you do is you eliminate all those kind of cold calls, you know, LinkedIn intros, and you give the LP is a tool from a screening perspective, say, you know what, if I put in, these are the categories I’m interested in, here are the attributes of fund managers I like and I don’t like. Here’s the five reports that spit out, say, here are your matches. And then go talk to those five funds and spend time with those GPs. That’s how you actually eliminate a lot of the friction, right? Not only just from the time perspective, but if the match is better, the likelihood of capital formation is going to be a lot higher.
Brian Bell (00:35:21):
Yeah, and I think what you’re describing is the tension between AI data automation versus human judgment. How do you guys balance that in your platform?
Eric Woo (00:35:30):
Yeah, it’s a really interesting question because we do have our own kind of AI portfolio management solutions that kind of post Revere rating reports, we ended up kind of pivoting more toward the SaaS side where we build portfolio management software. And portfolio management monitoring and reporting, absolutely, there’s AI tools now that could you know, scrape information, very accurate, produce summaries, and then, you know, give you dynamic dashboards. We ourselves are using AI and then we’re delivering that to our clients through kind of a SaaS platform for monitoring their portfolio. But the slippery slope here from a reporting standpoint or an operations back office standpoint, AI is like amazing. It’s going to, you know, revolutionize private markets in terms of that operating function the investment function on the other hand now again put aside things like private credit or private equity where you you have enough real data points to actually feed this into an ai model venture for the most part is still sizing up the individual right what are their motivations what is their capacity like is if i invest in in fund one are they going to be around for fund two and fund three there’s a lot of humanistic things that are very hard to structure and capture. Now, I do have a thesis on this, and this is something that we’re going to be working on next year. And the AI, I believe, is actually the future of AI as it relates to capital formation in venture merging fund managers is capturing unstructured data. So imagine how many networking events you go to, GPLP conferences, coffee meetings. Imagine if you could take that quantum of conversations with those LPs. and take that unstructured data and somehow structure it as sort of a score, like a GP score and LP score, and then use that as a matching algorithm. So that’s the intellectual exercise that we’re gonna be working on is like, how can we measure unstructured interactions in combination with the structured interactions to come up with a better matching algorithm.
Brian Bell (00:37:33):
Yeah, it’s a fascinating thing. And AI is impacting startups. I’m seeing startups grow faster than ever for sure. But it’s also, I think it’s going to impact venture at some point. I mean, it’s definitely impacted us at Team Ignite. I mean, I’m a solo GP with the team Ignite behind us. That’s the large network. But Basically, the way Team Ignite runs is a bunch of AI contractors, automation software, and myself. And so if you think about it, if you follow that thread to its logical through line in the future, do you even need like a 2% annual management fee for 10 years to run capital anymore? Because I can put an some contractor with an MBA in Argentina for, I don’t know, 15, 20 bucks an hour, processing all my due diligence with AI. And this is literally how we’re doing it right now. I don’t have to hire some MBA in the Bay Area or some two, $300,000 a year person. I can hire a $20,000 a year person, equip them with 150 IQ AI to evaluate, deal screen, dump the due diligence docs. And I’m thinking about this, would it be compelling to LPs to kind of tell that story and say, hey, here’s how we’re different at Team Ignite. We leverage the network, but we also leverage AI software and automation and contractors to go faster and deeper. We don’t need to charge 2%, by the way. Maybe we charge 1.5% or 1%. And maybe we make it up on carry and we charge a 25% carry instead of a 20%. Or we just keep it like one in 20. Do you think that story would resonate with LPC? Like, wow, that’s interesting and differentiated. Maybe you’re really not adding value there at all because you don’t have lots of really senior people for the startups to talk to.
Eric Woo (00:39:16):
It’s an interesting question because I think just the use of AI tools within fund managers that’s already happening today and i would argue it’s kind of table stakes to have some tooling internally that brings some sort of efficiency to just you know how you manage the pipeline and then how you’re processing it so i think lps already have that expectation that that all gps in some shape or form right whether it’s buyer build are are using and leveraging these tools to the extent that becomes differentiation or if you’re now saying like that helps me potentially lower the management fee that makes me attractive to an LP because, you know, as we know, a lot of LPs are just sensitive to paying management fee. I think that’s, I don’t think it’s a, It’s a primary decision point because I also do recognize that the vast majority of LPs who are investing in this category of smaller funds, there’s already enough confluence of materials that say like they outperform and, you know, there’s small in nature and you need the management fees, right? Just to even pay a salary for a solo GP living in, you know, New York or LA or San Francisco.
Brian Bell (00:40:23):
Yeah, if you’re running a $10 million fund, you need the two... Yeah, you need the 2% if you have a 10 or $15 million fund. I’m talking about you get up to a $100 million fund. How do larger funds do it? I heard they’ll taper it after the investment period. They’ll go two for three or four years and then they’ll go one for the rest.
Eric Woo (00:40:42):
yeah i think there’s ways to get creative but again i always just come back to this like if if an lp is you know outside of you like being in a university endowment investing in a you know a big mega fund like negotiating on fees is missing the point right you’re you’re looking for alpha generation and you’re expecting an underwriting five to ten x at a fund level for this category and so the fees should be diminished right i mean if anything you would be arguing about the carry but again that’s why you’re in this business as a gp but to to bring it back home i think ai tools for gps table stakes today lps are expecting it where it can drive kind of lp interest i think is actually going back to that i use that word transparency so imagine if you were able to now share your diligence with lps right So as I mentioned before, a lot of LPs who are investing in this space are strategically curious about the companies you’re investing in. So if the AI playbook or the AI tooling allows you to surface more interesting information, right? Again, not sensitive information to your LPs, then that’s fascinating. So again, going back to your example, Brian, people can upload the data room and a little report spits out. So imagine your lps having access to those reports right both the reports of the of the companies that you invested in maybe even the ones that you didn’t invest in because there’s a lot of lps that still want to see that stuff it’s not a fit for you as a fund but maybe a category where that’s where they’re interested in. So even having that kind of deal flow database and be able to interact with it through a chat interface or look at reports, I would argue that’s tremendously differentiated for LPs from a transparency level.
Brian Bell (00:42:25):
Yeah, I got to be careful. Some founders don’t like that, right? Of course. Of course.
Eric Woo (00:42:31):
Of course. Yeah. It’s not like you would want to publish financials, but just even be able to just like have these summaries. I mean, again, we all know and use PitchBook, right? So when LPs have access to PitchBook, so I’m not saying you have to go and like recreate PitchBook, but it’s just going back to the ethos of like, why would an LP invest in you? It’s like, you know, just the concept of transparency. Here’s one way that that additional transparency is manifesting itself. Again, a lot of fund managers are also now doing follow-on co-investment opportunities. whether it’s spvs follow-on vehicles but this is another important component of what you can offer with lps right they don’t have the time they don’t have the access to deal flow and so if you now have as part of your platform right the ability for lps to invest directly in some of these high growth companies that’s another lever to build an engagement model with the lps
Brian Bell (00:43:19):
So what are you excited about right now for Revere over the next year or two?
Eric Woo (00:43:24):
So we are, I think it’s probably by the time you guys get through the editing of this, it’ll be public information, but we’ve merged with another company, a company’s called Velvet. They are AI native tools for GPs to help manage the pipeline, all the due diligence, essentially the analysts in the box. In addition, combining our forces on the GP and LP networking side, we’re going to be launching sort of our event series, we call it the underground, right? So it’s just bring back the ethos of getting GPs and LPs together in person, which I think is really, I call it, you know, old is new again, right? And old in this space where people are struggling to raise money from LPs is you just got to find the right curation opportunities and bring people together under the banner of Revere and Velvet. so i’m really excited about that combination and building out sort of the network effects there and of course all this is underpinning you know how do we start to use data to monetize use ai to monetize all the data that we’ve been tracking on both sides.
Brian Bell (00:44:22):
both sides yeah that’s exciting well i’d love to get a demo of velvet because i build a lot of my own tooling because i built a lot of ai and automation over the years and repli makes it pretty easy but it’s something that’s always at the like the last thing i do you know it’s like okay now i need to build some ai and automation around this process but yeah i hesitate i don’t know how you think about that if i’m going out fundraising i can say hey look at all my homegrown tooling that i’ve built myself look at this great demo versus like yeah i use this platform velvet that everybody else uses how do you think about that what would be more impressive as an lp like oh brian built all his own stuff for team ignite that’s really cool yeah
Eric Woo (00:44:58):
Well, I do think it is starting to get commoditized a little bit, right? And some of this is the stuff that, you know, the internal cycles that we’re having on the product side within Revere and Velvet is staying ahead of that innovation curve. So I think the buy versus build, I think, is personal preference. I think the expectation is LPs want to see that there is efficiency in terms of how you’re processing data. I think the next iteration is really in, you know, again, you have a very large network, right? If you said Team Ignite, is how do you start to represent and quantify the value of the network? So I think the next phase of who I would classify as the heuristic of who’s a credible, not only credible, but who are the outliers in the emerging fund category, it’s really like quantifying the network, the extended network that they bring to bear, again, not only on behalf of their portfolio companies, but how you potentially, again, you know this, right? A lot of your LPs can be value-add to your portfolio companies so i think that’s really the next frontier because that’s always been a little bit of the walled garden right but if you can start to get connectivity between your gp you know your fellow co-investors that you gps that you co-invest alongside you got their portfolio company founders you got your portfolio company founders and then you’ve got your lps and lps now all of a sudden you have an extended network that anyone who’s a member of the team ignite platform or community has access to this extended network that that becomes tremendously valuable
Brian Bell (00:46:28):
so what are you excited uh you know in the long term next five or ten years and maybe we could start with like you know how has venture capital changed you know you’ve been doing this for a long time been in the game you know 15 plus years what are you seeing in the market today and then we’ll talk about the future after that
Eric Woo (00:46:44):
Yeah, it’s all distribution today. I would say today in the next five years, you’re now the opportunity. It’s like, how does venture capital as an asset class find distribution channels into new pockets of capital? Increasingly, that’s the RIA market, private wealth. There’s a lot of overseas capital. There’s tremendous renewed interest from corporate VCs to invest in venture funds at the early stages. So how can we build pipes and infrastructure to get better distribution? It’s really, I would say two things. Number one, it’s just venture starts to look like a financial product, right? So the same way you might buy an index fund, like there’s a venture fund of funds that kind of looks like an index, right? Or a thematic fund of funds.
Brian Bell (00:47:28):
That’s exactly the vision for Team Ignite. And I might have to do an evergreen fund publicly traded structure at some point, but we basically want to become that index for early stage. And so institutionals kind of go, because there’s 30,000 companies a year worldwide that get pre-Series A funding. Now, we’re not going to buy 30,000 companies. That’s ridiculous. Even though there was an Angelus study where you bought, remember the study? You probably were involved with the study.
Eric Woo (00:47:52):
Yes, of course.
Brian Bell (00:47:53):
If you invested in every single AngelList deal, you’d be in the top quartile of VCs. Of returns. Yeah. Remember the study? Yeah, of course. So that’s kind of our vision. I think I’m happy at Team Ignite. If I can be in the top, you know, everybody wants to be in the top decile, but if I can be in the top quartile, I’ll be able to continuously raise funds forever. And then it’s mathematically putting our CFA brain into venture capital. It’s like, okay, mathematically, how many do you need to invest in to hit the efficient frontier of the asset class, the risk-adjusted return, maximize your chances of... I’m sure you’ve done some pretty deep thinking on that. How do you think about that in the early stage? Because I’ve written probably a dozen articles on this at this point. And a lot of the studies I cited were angelist studies and others. And there’s different ways of approaching the problem. You can run Monte Carlo’s, you can use probabilistic reasoning. How do you think about that? What’s the efficient frontier of early stage venture?
Eric Woo (00:48:44):
Well, you know, as you go earlier, you know, more shots on goal. And I think it’s depending on the study, you know, you’re looking at every kind of vintage year, you need at least a few hundred to be able to kind of find enough shots on goal that get you the ability or the probability to hit, you know, the requisite number of outliers to generate.
Brian Bell (00:49:04):
And that’s a few hundred per year?
Eric Woo (00:49:06):
Yeah, yeah, yeah, yeah.
Brian Bell (00:49:08):
Yeah, that’s kind of the vision. That’s where we’ve kind of arrived. We don’t have the capital to go do 300 a year, but we will probably fund after next. And so we want to basically scale up to do about a thousand per fund, which would be about 300 a year over a three year deployment period, which is crazy. It’s a crazy number of startups.
Eric Woo (00:49:26):
it’s a different construct so if you think about you know the original thread was like new distribution channels are opening those distribution channels are requiring or demanding something that looks like a financial product and as a financial product you’re trying to deliver consistent returns right consistent sort of above median returns so if you if you take that sequence then that means the fundamental strategy that you have to develop you know again we have we didn’t talk about evergreen structures which is kind of a conversation not to itself yeah but then the fundamental construct is you do need to find again that kind of efficient frontier of enough shots on goal to deliver let’s just call it two to three x returns in a diversified fashion right then you’re you’re absolutely in that that top quartile conversation and that’s that’s very antithetical to the roots of venture which is like you know hey i’m going to be a little bit more concentrated i’m ownership driven i’m going to you know find the next stripe and you know that’s going to make my career now all of a sudden like you know i’m sequoia or a benchmark and it’s not to say that those two things can’t coexist but they are fundamentally two different types of strategies and it all comes back to you know who are the end you know who’s the end customer right so something that i would say kind of the artistic way of doing venture in the old school fashion i mean you’re trying to get into endowments and foundations the new way right is distribution to rias that want a financial product where you know a private wealth manager could put 2% of a client into this venture capital index or something that looks like that that gives them uncorrelated or non-correlated returns to the rest of the portfolio but there’s some confidence that this is kind of better than just simply spraying and praying so these are all fascinating things that are going to happen in the next five years and it’s going to just increase the TAM of the venture industry overall
Brian Bell (00:51:17):
Right. Yeah, if you have the consistent, predictable returns within normal bounds, and it is uncorrelated, right? Because there’s, venture doesn’t have the beta that other asset classes do. What is the best structure? Is it the 10-year fund? Or do you have to do like some sort of evergreen kind of REIT kind of tradable structure?
Eric Woo (00:51:36):
yeah in the long term it is evergreen now what what is required to unlock the full potential of evergreen structures as a very active secondary market because today you can’t even for these late-stage unicorn companies i mean there’s still a pretty wide variation of you know bid-ask spread right and then evaluations are kind of astronomical now for AI companies. So the key unlock for that evergreen structure just kind of demands enough liquidity or enough of a secondary market to be able to predictably get capital in and out in a fashion that the evergreen structure starts to become more efficient. It’s never going to be perfect, but I think that’s really... Because if you have a basket of
Brian Bell (00:52:20):
a thousand startups and somebody like sells a big LP sells the entire position and say your net asset value is a billion dollars I’m just making up numbers now you got to liquidate a hundred million worth of secondary to go fulfill that order right to redeem that request that’s It’s a sticky situation because the market is uneven, as we were just talking about before recording how frothy secondaries are right now. The market is uneven for secondaries depending on basically the brand mystique of that secondary, right? And so you could end up in these weird situations where, oh, I’m sitting on a bunch of Anthropic and I can sell that and get that redemption fulfilled. But that is not the best thing for all the other LPs remaining in that venture fund, right? Because venture fund is a power law. And so you almost have to do the closed structure, I think, and say, nope, you’re kind of in it until the end and everybody’s in the same boat. And maybe that’s why it’s always been this limited partner tenure
Eric Woo (00:53:15):
Yeah, nothing’s going to flip overnight. But I think where there’s a lot of positive signs that now that we do have a pretty active secondary market is even in the 10-year structure for an early stage company, when you do have those breakout winners, generating DPI doesn’t have to be completely reliant on an IPO or an IPO.
Brian Bell (00:53:35):
because I got you and now I’m getting free advice here on the podcast. If you have an opportunity to 1x DPI your fund, you could sell, let’s say you’re sitting on a unicorn and you could 2x the entire fund by selling the entire thing. Just selling in the next round, just selling the entire holding. Or you could sell like 50% and you 1x DPI, or you just sell 25% and you 0.5x DPI, or you just kind of let it ride. What do you think is the best thing to do in that situation?
Eric Woo (00:54:02):
next dpi is a pretty pretty magical number because i think psychologically for lps it’s all upside after 1x and now it’s not entirely true because again underwriting venture is like you know at least two three four x but psychologically given how protracted the liquidity cycles are x is pretty awesome so i would say start with the optimization of 1x versus 2x 2x is amazing it’s great definitely kind of put you upper upper decile from uh just a dpi perspective but 1x is a psychological one So at that point, it’s like, hey, look, we’ve returned capital. And now, you know, the rest of the portfolio can continue to ride. And then I can give you some scenario analysis of like, hey, it’s going to be no more than, no less than another 1x. But, you know, if we ride this out, it’s another 5x. You know, I think that logic definitely computes with LPs.
Brian Bell (00:54:52):
Good advice. Well, let’s wrap up with some rapid fire. What’s a false assumption about emerging managers or GP investing that you want more LPs to unlearn?
Eric Woo (00:55:01):
Yeah, this is a good one. I think it’s perfectly okay for a GP to have one or two funds and not continue to like go on and be forced to like, oh my gosh, I got to find a second partner or I need a succession plan. It’s like I’m an older GP. Strategies change, circumstances change, the markets change. I think it’s absolutely something from an LP mindset. Like you should be thinking about this. It’s like I have to invest in the next big franchise. So I think that’s something that I would encourage LPs in this category.
Brian Bell (00:55:31):
What’s a signal or metric you’ve seen in a fund early on that eventually correlated strongly with outperformance?
Eric Woo (00:55:37):
Helping portfolio companies get that first customer or anything in that product market fit category. is such a critical milestone, you know, speaking as a founder, right? And I’m coming at this lens, not as an LP as a founder, going like, where did I de-risk my business? It’s really, you know, finding that true product market fit, which generally coincides with, you know, your first set of, you know, pilots converting to paying customers. So if there’s one singular kind of metric that I would encourage both GPs and LPs to look at as a key indicator, it would be.
Brian Bell (00:56:11):
What’s a fund you admire, small, underrated, doing something interesting and why? Besides Team Ignite, of course.
Eric Woo (00:56:17):
Of course. Yeah, I go way back with CC at Boom Capital. Boom Capital is a deep tech, very scientific oriented fund. It’s always been kind of very modest size, you know, less than $50 million. And so I’ve had the benefit of following cc’s journey since you know even before she started the fun but you know what what i admire about about it is it’s it’s it’s non-consensus right you know they were talking about like brain tech before people were even like fathoming like what does brain tech mean they were talking about ai and they were they were working with researchers and putting together peer groups of like how do researchers with these amazing you know white papers how do they commercialize it so definitely admire and respect CC for, you know, staying true to what the ethos was of the original boom capital. And then also doubling down on sort of like the network and the community. She’s been amazing in terms of, and even engaging her LPs, right? She invites her LPs to all these events and all these like thought leadership seminars and stuff with like key researchers. So the level of access has been amazing. So that’s just one example, of course, and I’ve got a whole bunch more, but I know this is rapid fire.
Brian Bell (00:57:27):
Let’s take the inverse. What’s an investment or fund decision you regret or wish you did differently and what lesson came from it? And you don’t have to name names, obviously, but you can speak into your ally.
Eric Woo (00:57:36):
I think I would put in two buckets. One bucket is where you um where they’re kind of partner like if there’s two or three partners where there is some partner dynamic you maybe couldn’t pinpoint at the time where it’s like you know what it just doesn’t feel harmonious right or maybe it was a shotgun wedding of two partners who spun out different firms and they came together and on paper it looked amazing but then when they actually got into the the job function of working together like things didn’t work out so that’s always one and i’ve had a couple examples But that’s one. The other one is I think companies that tried to, I call kind of, you know, if we think about Andreessen, right, the platform of Andreessen of sort of hiring a bunch of people to extend the capabilities of how they can add value to their portfolio companies, I think you can take that to a fault. Because if you say this on a pitch deck and you actually don’t deliver, which this is the example that I’m thinking of, then it looks really bad. And at that point, it’s just like you’re on the blacklist. Like you said, you’re going to do all this stuff. You had all the facade of the platform and you never delivered, right? That’s basically a kiss of death.
Brian Bell (00:58:41):
Yeah, I love that. how do you personally at revere guard against rating capture or in-group bias inside your team we always run an investment committee so the entire team of analysts and people who are kind of looking at fund managers and again it’s not just like i’d say senior people like myself and my co-founder chris we have people who are like out of school and they just started doing the rating process and trained up on it and they bring kind of all sorts of curiosity and kind of innocent questions but those questions are informative.
Eric Woo (00:59:11):
So I think the committee process is still one that’s both authentic, genuine, and important in terms of the decision making, again, for ratings, but even beyond that.
Brian Bell (00:59:20):
Well, I could talk to you for another hour. I was trying to cut it short just to stay within time. Where can folks find you online and find out more about Revere?
Eric Woo (00:59:27):
Yeah, LinkedIn is the best place to hit me up. The preview here is, you know, Revere and Velvet coming together. Check us out.
Brian Bell (00:59:33):
Yeah, congratulations.
Eric Woo (00:59:35):
Yeah, appreciate it. Velvet.vc.
Brian Bell (00:59:37):
And we’ll definitely have this episode out after you announce that and I’ll make sure of it. But, you know, any kind of takeaways on that kind of five or six year journey of being a founder, now being in a larger company, be kind of like, yeah, any final takeaways on that?
Eric Woo (00:59:49):
yeah i think now that i’ve kind of worn all the hats you know gplp and founder it’s i even have even more conviction on on building stuff right so i think the ethos here as you as a gp investing in very early stage companies you’re investing in builders know i’m building this like there’s no other there’s no better place to be and then the second part of that is capital is increasingly becoming global so i’ve spent a fair amount of time in asia doing fundraising when i was at top tier um i spent a lot of time getting customers right so half of revere’s customers are actually based in asia but that’s the other thing i would say in terms of next frontier is you know global access to global capital is going to become a really important component to the venture capital industry.
Brian Bell (01:00:36):
Yeah, so cool. Well, I can’t wait to watch your progress over the coming years.
Eric Woo (01:00:40):
Appreciate it. Thanks for having me on, Brian. This was an amazing conversation.
Brian Bell (01:00:44):
Yeah, likewise. Thanks for coming
