Episode #1: Welcome To The Learning Corner

Hold on one second. Hey, buddy, I'm recording a podcast right now. I'll come see you before I go to school though. Okay, I'm recording though. We'll have to edit this out.

So it's the week of September 23rd. Welcome to the Learning Corner, a Precursor Ventures podcast.

Welcome to the Learning Corner by Precursor, where we talk about the articles and Goodreads that caught our teens attention over the past week. I'm Mia Farnam. I'm a principal here at Precursor Ventures, and I'm joined by our GP and managing partner, Charles Hudson. Charles, how you doing?

I'm great. I'm excited for Learning Corner.

I'm excited too. It's wildly early Charles's time. I've known you for four years, which is a hundred years in the venture time warp. So I've known that you have 47 hours in the day and everyone else has 24 hours. We're excited. Some of our initial listeners will be friends of Precursor, but do you mind briefly sharing more about the firm and the idea behind it?

So I started precursor in 2015 with this idea that pre seed founders needed more capital options. And I consistently was meeting all these founders who I thought had great ideas and just didn't have great access to capital and didn't have really much in the way of product or prototype to show people. And as you know, Mia, most VCs can engage with someone if they know the person or if there's a product to evaluate. But if you don't have either of those things, I think it's hard for a lot of people. And I said, what if we had a firm that just didn't require either of those things? And we would, as I joke with our LPs, we would engage with strangers with data and we wouldn't be afraid to do so. And so we've been doing that for the last 10 years and we've backed.

Over 400 companies 10 years in. How you feeling?

Never been more excited.

Awesome. Love to hear it. I know the idea for this podcast came about in a Starbucks in Midtown east, but the title Learning Corner already has some pretty deep roots across the fund and our team. So for the rest of the listeners, where does the name come from?

So I've always been maybe a chronic article overshare in our team Discord and I will drop things that I find interesting into our News and Goodreads channel. And one day I think it was actually Marina was like, you know what would be more interesting than randomly dropping articles into a Discord would be the ability to talk about them on a regular basis. And so we started doing Learning Corner every Tuesday morning. And it's just a chance for us to talk about a variety of things. Sometimes it's an interesting term sheet, sometimes it's something that happened in the ecosystem that I think we should all chat about. Sometimes somebody found an interesting article, went to an interesting event. It's just a chance for us to share some of the interesting things that we're finding on the Internet and in real life that impact the world of venture and technology. That's become one of my favorite meetings we have as a group every week.

The purpose of the podcast is to offer that to anyone in the venture landscape, to just have a couple looks into some articles and goodreads that our team found and some insights if they find it helpful. But without further ado, I think we should get it started. We can kick it off with the topics and go from there. So so topic number one 24 venture capitalists tell you what one thing do they wish people better understood about B.C. a series by Hunter Walk with the co founder of both Homebrew and Screen Door. We've been following this series by Hunter Walk for a bit on our team. So essentially he prompts 24 different VCs who are typically GPS on what they wish people better understood about the job. And and people in quotation marks can include anyone from politicians to founders to LPs, essentially anyone outside of the traditional VC group. And the most recent piece he put out had some really wonderful quotes, and there were two that I specifically wanted to bring here to discuss in a little bit more detail. The first was from Andre at Maple vc, who discussed how wonderful it is that VC reward difference in its foundational form. Quote this difference comes from the outlier returns driven by backing founders who are different. And then he goes on to call on to the fact that LPs are really not incentivized to take that risk, unfortunately. And Andre hopes that LPs were a bit more open to backing GPs who were in fact different. So similar to Hunter, I know you've been very involved in Screen Door, which is a fund of funds run by VCs focused on investing in new VCs. How does Andre's conundrum have downstream effects into the broader venture landscape?

Yeah, I mean, working on Screen door in my 25th hour every day is honestly, it's a lot of fun. And you know, when a handful of the GP advisors and the LPs that backscreen door got together, I think there was this belief that GPS had some ability to identify people who were maybe doing things that were different. And when I think about what can make a VC fund different, it can be the GP's background. So obviously, you know, people spinning out of really established venture platforms are pretty well understood. But if you are an independent angel, just an operator, if you're coming from some really different background, I think that that can make you stand out. Sometimes you're different because you're going after a market that's not really well understood. I know a bunch of people who were doing AI centric funds five years ago and there were a lot of questions like, can you build a whole fun just on an AI thesis? I think the answer to that is probably, yeah. Sometimes it's a geography that you're focused on. It's a region that people aren't as familiar with. I'd say the other one. And the thing I think actually is the hardest for LPs, which is there's something structural about your fund that's different. It could be like, in the case of Precursor, like our portfolio construction of 80 to 100 companies per fund is challenging for a lot of LPs. They're not used to seeing successful funds with portfolio constructions like that. And so I've, over time, become more sympathetic to the plight of LPs. As you know, Mia, it takes seven to 10 years to know if a strategy is going to work. And I think it takes a lot of courage to back somebody who doesn't fit the model. And, you know, for the listeners, I think if someone were to come to me and say, what's your advice for me? I would say you should run a concentrated strategy where you buy a lot of ownership, because most LPs that you will pitch have very finely tuned decision criteria for evaluating funds that fit that criteria. Ideally, you have a multi GP partnership of people who trust and like each other and have worked together. And when you deviate from that platonic ideal, everything about fundraising becomes much more difficult. So you should only do that if you really have deep conviction that the differences from that model are essential to what you want to do. And I've known Andre since he worked at Uber in Toronto. Andre is always somebody who I thought has had amazing access to early stage teams and has had this really unique ability to build relationships with founders at the earliest stage. And I've always felt like that's the thing you should be looking for as an lp, because ultimately the business isn't so much about you as a gp, choosing which Founders to back. It's about convincing the very best people to take your money instead of someone else's. The Screen Drawer Investment committee is an interesting collection. We've got three super talented people on the staff. We've got, you know, Jamie, Lisa and Lane. So everybody on the IC tends to approach these conversations with a different perspective based on what they do for a living. And I, I almost always ask gps, like, given your strategy, why are the very best people that are inspect for you going to choose you over anybody else? I find the answer to that question tends to be super revealing.

I almost feel like you have to walk this fine line of staying true to your thesis and building conviction into an investment area that you feel strongly of, while also being realistic of how LPs pick GPs to invest in. So at what point do you have to change direction or do you ever have to?

It's so funny. Me, I'll never forget this. When I was raising our first fund, I went and asked for some advice from one of the most respected. I won't name this person because I didn't ask for their permission. I asked one of the people who I respected the most, who's one of the OG seed investors who's been very successful. I went and showed him my deck and I had all of the like, really innovative, aggressive new ideas for precursor at the front of the deck and I pitched him and he sat there very politely and he goes, you've got this all backwards. What do you mean? He goes, this is going to scare people because all of these things that you think are new and innovative are risky and, and some of the people you're pitching really want vanilla ice cream with sprinkles. So I would suggest you focus on the parts of your fun that are easy to understand and normal and treat these innovations like sprinkles. These are things you're going to do to differentiate yourself or make your fun better. And he's like, I just worry that if you go out with this pitch, it's going to overwhelm people because it's too different. I didn't know what he meant at the time and now I totally get it. And he was absolutely right. And I made those changes and it made a pretty significant difference in our fundraise. And part of what I like about Screen Door is I think we've taken some smart bets on founders who, who I think are going to turn out to be excellent gps but don't exactly fit. Either the fund size is too small or they're solo GP or They're investing in a theme or thesis that like, isn't well understood or the person doesn't have a tremendously long track record. But we think through getting to know them in the portfolio that like, they're a star in the making. So I've really enjoyed it. And truthfully, like, as much as advice is useful when you're starting a fund, the thing that's way more valuable is money. And that's one thing that we can bring to the table screen door that like, you can't bring on your own.

Awesome. Thanks, Andre, for that. That was a great ad. The second quote in the piece was by Ryan Hoover from the Weekend Fund, who talked about the quote unquote drafts of escalating fund sizes and calling out the reality that deploying a $10 million fund is wildly different from operating a 100 million fund. Charles, I feel like I've had multiple conversations with you about this, but as someone who has essentially lived through this, curious to get your perspective here and you know, what's the decision process you went through each subsequent fund that was raised and what are the conversations you actually have to have with LPs along the way?

I think it's really interesting.

And this like tags back to screen door too. I always ask people like, what do you think Fund 3 looks like? Because we're usually meeting people for Fund 1 and it's kind of an unfair question because nobody knows what the future looks like. I think if you'd ask most managers in 2020, what does your third fund look like? It'd be 20 times the size of the fund they were that day. And if you ask most people today, they'd be like, flat would be good, flat would be fine.

And so when I started Freecursor, our first fund was 15 and I told people our strategy doesn't really work above $50 million. So you should think of us as a fund that will always be sub 50. There are a couple reasons for that. One, I was single GP and didn't really know how big the team would get. Second, most of our companies didn't need that much money from us to get from the place where we met them to the place where they tended to engage with us less. So I was like, you know, $50 million is enough to give people one pre seed check and one seed check and the good companies will graduate and like the other ones, like they won't. And so like I, I've always approached every fund saying like, well, what do we need? So I always go back and say like, well, what are we trying to do here? We're trying to get people from 0 to 1, like no product to a product with some data. Ideally it was seed round and then from a seed round to a series A. And then I find most people don't need us that much or they don't need us in the same way. The other thing when I started was like, I have no idea if people are going to let us invest in their A's and B's. Like, I don't know, like we're a brand new fund. For all I know, they're going to look at the pro rat aside letter and if we don't have one, they're not going to give us an allocation that didn't turn out to be true. So over time, a couple things changed. And I think this is true of a lot of managers. I think the good reasons to change your fund size and get larger are there's something about your model you've realized requires more capital. You believe you have the ability to deploy more money against the same strategy without changing your ability to access the most important deals. And for us, what I realized is like, well, very few of our companies were going seed to series A in one check. Most of them needed two checks. And I was like, well, if we're going to be a good partner to these companies, we need a bigger fund because I want to be able to write both of those seed checks when it's right. The other thing was people were offering us allocations in the A's and B's, which I hadn't put into the model. I'm like, I want to do these selectively, not in every case. And that requires more money. I do think though, the problem with AUM is that some people's ego is very tied to the amount of money they have under management.

Yep.

And early on my venture career, someone who I really respect once told me, nobody willingly raises a smaller fund. And like, that has stuck with me for a long time. Like, but what if it's the right thing? Like, what if you overshot? What if you realize, hey, we don't need. And he's like, nobody does it. Because everybody assumes if you raise a smaller fund it was cause you had to, not cause you chose to. And so I've always, I always ask myself honestly on a weekly basis, like, what's the maximum dollars we could deploy given the model and constraints we have? And we've talked about this internally. I think for us it's like between a hundred and one hundred twenty million bucks?

Yeah.

You get beyond that and like a bunch of the core assumptions in our fund break. But I think it's really tempting to grow your fund size. And I think the biggest mistake I see people make besides simply pursuing bigger fund size, cause it is more fees and more fees. Make sure day to day life more enjoyable.

Right.

The other mistake I see people make is, well, I wrote a 25k check in fund 1. I'm sure I can size my check up to 250. And then like you can size it up, you just might not get into the same companies. You might go from being the person who was super value add at 25 and produced a hundred k of value to a person who puts in 250k and still produces a hundred k of value. And the, and the founder's perception of their relationship with you and your value add will change pretty dramatically. So I always caution people on like doubling is a big deal.

Yep.

Like doubling your fund size is a big deal. It changes a lot. And so it's very hard to convince managers not to raise the maximum amount of money on offer. I found it's very hard.

It feels like sometimes fund managers forget that while they're raising their subsequent funds from fund one to fund four, there have been other people who've entered the space from a fund manager perspective. And other multi stage funds have continued to raise and build incubators underneath their platform. And so all of that affects you, even though you probably don't want it to, or tell LPs that it won't. I assume feeds into the fundraiser.

I always ask people, like, how elastic is your strategy? I would say series A strategies are actually fairly elastic. If you can get into an A and you can maintain like if you can get into a good A and you do a good job on Pareto rights, you should be able to like deploy a lot of money in the B and C if you want to. I just don't think precede and seed strategies are that elastic. I just think if you stretch them too hard, you snap the rubber band. So I, I always try to get people to think about like how much could you really stretch the strategy and still do what you say you're gonna do?

Love it. All right, well, we gotta move on. But thank you Hunter for continuing to share these awesome insights. We encourage everyone to follow that series. We'll include the link in this episode, of course. Topic number two, an article by the Economist titled OpenAI's new fundraising is Shaking Up Silicon Valley. This article surrounds rippling effects across venture from the news that OpenAI, the maker of ChatGPT, is on the brinks of reaching a roughly 150,000 billion valuation as it works towards raising $6.5 billion from investors and that valuation is second to only SpaceX in the US. And so the article highlights three specific challenges that this emergence of generative AI funding poses across the traditional venture landscape. The first is the amount of funding and therefore check size required to support these LLM companies is huge. And as a result it makes it pretty clear who has the ability to be a check writer here. Charles, what are the things that you probably need to believe to be true in order to write a check into this space? As a fund manager?

I mean I it's funny, a good friend of mine who's a very successful early stage deep tech VC sent me this article on a text and it was like hey, it's like they read your blog post. I wrote a post a while ago. I think it's very hard right now to be, I don't even say contrarian. I don't think we're contrarian on AI. I'd say to be cautious and conservative on AI. I think it's a very hard place to sit as an investor. It's very hard to also conversation to have with LPs because they're like look, all of the biggest multi stage funds that they're in are all in on this theme. And my view has been there's a lot of awesome AI companies that I think are super promising that don't make sense for a sub $100 million pre seed fund.

Sure.

Because the entry points are like hundreds of millions of dollars if not billions of dollars from a starting position. So you have to believe that these companies can become worth fifty to a hundred billion dollars in the future for those, let alone can you get access to these deals to believe that they're good investments. And I I'm just not there. And so some combination of reservations about what the technology can do today versus in the future plus entry plus our fund size. I'm like these just don't make sense for us. And I think they really only make sense for multi billion dollar if you think about it. I mean I think for those of you who've listened to Nick Charles podcast about the launch of Asylum Ventures, I think I maybe don't have quite as extreme of you as he does. But I will say this, the only people who I think can play that game are people who can say we could go multiple billions of dollars of capital invested into this company and it doesn't break our fund. Maybe it, maybe we end up 20% concentrated or maybe we end up 10% concentrated and we have a big chunk outside the fund. But you know, being several billion dollars of committed capital, not like net asset value, but like actual dollars in is fine. That is not, in my opinion, a game that most seed investors can afford to play. And my question is like, what happens if you're the third, the number three or number four, number five LLM and the price to develop the next model is in excess of what you can raise? What's the residual value of an LLM company that can't continue to innovate? Is it zero? Is it greater than zero? Is it one of these weird acqui hire license deals where you take the talent. It's sort of like being at a poker table and everyone's like, we're just going to continue to like pile in, raise pile in and, and you know, the pot's going to get a lot bigger. But at some point if you fold, you're, you might walk away with nothing.

Sure. So beyond the sovereign wealth funds and these massive billion dollar funds, I think most people forget that most of the funding for these massive LLM companies actually was raised by large tech companies. So Microsoft and Amazon have collectively invested $17 billion into either OpenAI or Anthropic. That pales in comparison to the check sizes that most pre seed and seed funds raise. In fact, another good point is PitchBook said that last year the average fund that was raised in America was around 150 million. That's it. The economist pointed out that OpenAI is seeking to raise more than 40x that from investors. So like, how does the math math?

And I think it's really interesting because I remember when Microsoft announced that original OpenAI deal, people were like, that's crazy. Doesn't seem crazy, but the benefit of hindsight. And if you're in the business of selling compute, there's probably no better customer for you than these big LLMs. So I think if you're those companies, you're like, look, we're investing like all strategics for a variety of reasons. Making money is one of them. Getting these companies to take a serious look at our infrastructure and use it as another one and third. Being close to these companies and ideally getting some insights into like what they're learning is probably the third one. So I think for those companies it maybe does make sense to do this because like, if you're Microsoft, you're like, hey, if this investment is part of a bigger BD deal. There's a lot of ways for us to win above and beyond the upside of being in the stock.

Next one that the article highlighted as another challenge is around the concept that investing in these new companies is very different than investing in Internet startups where you throw cash at a company to see it grow as quickly as possible. With these types of companies, the new game is to invest immeasurable amounts of money into the company upfront to invest in computing power and data to make the models as smart as possible. It's not a revolutionary thought, but it feels like every VC recently wants to have a stake in LLMs and generative AI. And I would go out on a limb to say perhaps LPs are also asking this of their fund managers as well. Wanted to know your thoughts.

I mentioned to an LP I'm like, the amount of dollar exposure you have to AI as an LP in big funds, it doesn't matter what you think about AI. Like someone told me goes doesn't matter. The managers that we've chosen to back have already voted for us. You gave them the capital. You trust their judgment. They made the decision that it was wise to put a significant amount of that capital to work in AI. So you are now very long AI and I hope that that decision is consistent with your beliefs about where the opportunities lie. And I do think there was like a bit of an LLM craze and I think people said like, I've got to have one of these in my portfolio. And I, I kind of get it in some ways. Like the business of venture is somewhat bubble driven. Like we get these like innovations and waves and the questions like can you find the companies that matter? And it always seems to turn out there are far fewer companies that really matter. In the end. I got a chance to spend some time with some of the management at OpenAI. It's the first company I've seen that reminds me of Google, not in terms of ambition but in terms of like all of the problems that like I remember when I met Google in the early days they were trying to solve this algorithm problem around search, but also this compute problem around how do we solve all these search queries and this data center problem because like they needed, they were growing faster than anyone. They were solving all of these problems mostly because they were encountering them in a way that others weren't and at a velocity that others weren't. And that's a lot of the, a lot of the internal tooling that's come out of Google too. Like that was in response to really hard compute. And OpenAI is the first company I've seen in a long time where when I talk to the people there, I'm like, oh, you guys are doing the same thing. You're trying to iterate on this reasoning model or whatever the next generation is of the core model. You're trying to solve a compute and data center problem. You're trying to solve a semiconductor silicon problem. Like you're trying to solve a lot of problems because you're experiencing this new market which is demanding things that tech doesn't provide. And I think the history has shown those companies end up being special. So as much as I think these AI LLM companies don't make sense for most venture funds, I do think if you're like, I have a multibillion dollar fund, what's the one bet I could make with asymmetric upside where I could generate a real return on billions of dollars of capital invested? Yeah, this is like a rational thing to do. If your starting position is I have a 5 to 10 billion venture fund that I'm trying to turn into a 2 to 3x net fund, it's kind of the one place you could say, I could see how it happens there. If you've invested in OpenAI at 8 or $10 billion on paper, you're up sure, 15x.

Yeah.

Which is iconic. Like that's not supposed to happen.

It ties into the last challenge, which is that the venture ecosystem is now tasked with working out how startups that rely on these LLMs can be profitable. So to our earlier conversation, since some VCs are being priced out of the LLM funding market, some are actually funding startups that can build on top of them. So you have your coding tools, your cluster support, your digital healthcare, what have you. But to what end does that make sense?

The other thing that I didn't appreciate that I like, I still haven't worked out what it means. There are two things I see happening that to me like make it very difficult to figure out, like when to start a company in this space. One is the cost of compute is coming way, way down. I believe both OpenAI and Anthropic believe that like if compute is cheaper, people will compute more stuff and you'll be able to like answer harder questions. And second, with each successive generation of these models, things that were harder and possible become possible. So for me, if you and I started an AI company today, we would have to solve a bunch of problems that exist today. And we would have to undertake a use case. That makes sense with the cost of AI today, six months from now, there could be something we spent six months building and fixing that the system just does for you. So you gotta throw that, you gotta throw that like hack of stuff away. And oh, by the way, the cost of comm. Of compute is now a quarter of what it was when we started.

Yeah.

So whatever architecture we built, assuming that compute cost is maybe less relevant and a brand new company could start now and be cool. I get to skip solving all the stuff that Charles and Mia solved because like, it's like you look at like what, like the reasoning that you can get in a new model.

Yeah.

People were like trying to figure out how to do that and now you're like, oh, like the model does it. So all the time we spent and the money scratching our head and all the money. And so a friend of mine, David Crenshaw, sent me a good article about this. He sent it to me on Twitter. And it's just sort of like in the end, all of this specialty hacking doesn't matter. It's just like you throw more compute at it and the models get better and the models do more of the work over time. That's a bad summarization of the article, which to me means it seems maddening to me to start a company because you know that in the future everything is going to be easier, which has always been true of software. But in this case, we're like two quarters from now. I remember I met a bunch of people who were trying to voice enable OpenAI a year and a half ago. I'm like, isn't OpenAI going to do this? No, they'll never do it. Here's what. And I was like, I kind of think they're going to do this. And they did it. So now if you're like primary value prop was like voice input, like it's offered at the platform level, you're welcome. And that's probably better for people that like the platform does it. So I don't know. I continue to think these applications are interesting, but I do think like riding the wave of these successive model improvements and the things that these companies are going to throw out of left field just makes it really hard. Makes it really hard.

Well, great. We got to move on to the last one. The third topic. It was Lead Edge Capital's letter that segmented the type of reporting information from companies. And this was circling on LinkedIn following a post by Better Tomorrow Ventures and Lead Edge Capital. Is a New York City based growth equity firm focus on Internet software and services and tech enabled services investments. The letter placed pretty concise messaging on company letterhead outlining the reality that there is in fact a hierarchy of information included in Dex about traction metrics from an investor's pov. At the top we have if companies have good cash profits they will report those. And at the very bottom we have quote, if companies don't have good page views, they report on that they were voted the best place to work in X, Y and Z city. Charles, I saw your comment. Nobody hides good news. Tell me more here.

Early in my venture career, someone I worked with told me nobody hides good news. This was someone who'd been in the finance department at Disney. Know anything about Disney Corp. Strategy and finance? It's a pretty intense, it was a pretty intense group back in the 90s. And he was just like, nobody has good news. He's like, if you're making a bunch of money, you're profitable. Like that's the lead. And you can kind of tell where somebody is by what they decide to lead with. And I love, one of the things I love about like growth equity people is they're kind of no BS and they're really direct and I have found what they said to be true. Whenever someone tells me about downloads, my immediate question is like, well, let's talk about acting like I, I realize without even ordering them this hierarchy, whatever somebody tells me as a metric, I always want to go, well, what's one? What's the one level metric that would be better if we knew it? So if someone's like these are, these are revenue. I'm like, okay, what's profits? What are gross margins? Like what are all these other things that I want to know? And I think it's like it's one of these things where it's like an obvious tell. But it's also like, I don't know what else you're supposed to do as a startup. If you're like a brand new company and you're like, all we have is downloads, that's all you can talk about, right?

All we have is the best place to work in an X, Y and city for pre season seed companies, what would be the hierarchy of information? Or maybe even more in simpler terms, the what's the one thing that you care about? Because to no fault of these super early founders, some of the more palpable news could quite literally be more intangible things like awards and media announcements.

It's funny, a lot of what I work with our portfolio companies on is like figuring out, well, what KPIs do we care about in this business and how can we at an early stage start thinking about tracking and measuring them. The results might not be great, but I find if you pick the right things to measure and track, you will get clues as to whether you're on the right track and then you can make trade offs. Like I was talking to somebody this weekend who's like, well you know, our monetization and retention is really strong but our growth is slow. I'm like, well we have to fix growth. And like I'm actually okay trading some of the success. Like we're probably over delivering in these other areas at the expense of growth. And so I think most startups it's this like you have to be good at multiple things at once. Like you could be profitable and like have super awesome retention and not growing and no one's going to care. So it's like how do we balance growth, profitability, good unit economics, all these things. And I think it's maddening for founders because sometimes the equation for what people want changes. In 2021 it was like if it costs you $10 to generate an incremental dollar of revenue, spend the $10 like people wanted growth, right? And then it switched and people are like this is life, this is venture. People change the balance between those things. And I think as a founder you have to adapt to that. Whether it's fair to ask you to do so or not, that is the job.

And I will also say I think there are a lot of investors who also don't know why they're asking specific questions.

I also think sometimes we have portfolio company founders who are like this person asks me for 5 year financials for a pre seed company. Like that tells you more about them than about you, tells you how they like to engage. This, this person might not be right investor for you.

Right? I'm going to deem this as a success for the first episode. Thank you Charles for taking time in the morning and also thank you to everyone who popped over to the Learning Corner. We'll be running this weekly so we hope you come back in. And in the meantime please share, subscribe and tune in next week for another episode. Thanks.

Thanks.

It.