40% of VC Dollars Went to 10 Companies. What Happens Now?

I think we've gone through this. I don't know if it's 12 stages of grief or whatever you want to describe it. We definitely have gone from denial. Big funds aren't a problem to acceptance. Big funds are having an impact on seed to this new world where people are trying to figure out if this is true, what do I do?

And I do actually wonder, and I don't really know how they do this. Like for the authors who were able to kind of figure out that these models were training on pirated versions of their material, was it just them using the tools and realizing that I didn't give rights for this? Because if that's the case, it'll take you to represent yourself as an artist in order to kind of like build the pace and speed for a case like this. That seems like a lot of work to gain that sort of momentum.

And in many ways I feel like all of this failure and gnashing of teeth is a necessary part of transformation. Because if it was just as easy as giving everybody in your company chatgpt and stepping back, this wouldn't be that revolutionary.

Welcome to the Learning Corner by Precursor. I'm Mia Farnam and I'm joined by our GP and Managing Partner Charles Hudson, where we will be talking through the articles and goodreads that caught our team's attention over the past week. Our first topic this week is a new blog post from Euclid Ventures called We have Met the Enemy and he is Us. Euclid Ventures is also on the leaderboard for top people we feature here. I felt like the piece was a wonderful blend of so many podcast episodes we've done over the last few months, with a large takeaway being that venture capital has drifted away from its roots as a market for independent thinkers. As we've mentioned many times before, megafunds and small number of startups are now attracting a large share of the capital. Remember, nearly 40% of all US VC dollars last year went to just 10 companies. This level of concentration has created a consensus driven environment where many people pursue the same deals, reducing differentiation and limiting genuine discovery. The piece also highlights how the influence of mega funds extends beyond returns and into the broader early stage ecosystem. Large and small funds alike are chasing an extreme version of the power law model, searching for multi billion dollar outcomes. The data however, suggests that this approach rarely produces results and analyses show little correlation between deal price and success and the share of startups advancing from C to Series A has declined compared to previous years. As we've mentioned, the takeaway is that consensus investing and copycat behavior may be weakening the health of the market RA rather than fostering innovation. If you had to give someone one piece to summarize the general vibes and questions bubbling up across early stage venture capital in today's market, I'd honestly choose this one. Anything else Charles, that you felt the Euclid team highlighted here that hasn't been walked through in as much depth before?

I think you can draw a through line from Rob Go's piece that we talked about and what we talked about previously about consensus versus non consensus. The one thing I will say is I've noticed a shift in the we'll call them the seed specialist funds, funds like ours that don't have aspirations to become big multi stage players. I think we've gone through this. I don't know if it's 12 stages of grief or whatever you want to describe it. We've definitely have gone from denial like big funds aren't a problem to like acceptance. Big funds are having an impact on seed to this new world where people are trying to figure out if this is true, what do I do and what does this mean for me now? That the sort of the shock of capital concentration both at the company level and at the fund level is kind of an established reality. And a lot of fund managers I've talked to in their more honest moments have said, hey, I think I'm good at this job, but I'm not an OpenAI or anthropic. Those aren't companies that are in our portfolio and we're not some huge mega fund. What do I still have the right to exist? And I think there's just a lot of soul searching I'm seeing going on, particularly among seed fund managers who are trying to figure out what does it mean to be a seed fund manager in this world. What role are we filling in the ecosystem? And I'd argue if the role you're filling the ecosystem is slightly front running multi stage funds on the things that they will eventually get to, that feels like an arbitrage that will eventually get squeezed. And the irony is the further you try to get away from the shadow of the big funds and what they're interested in, what they fund, the less overlap you have and the harder it is to talk to limited partners and other people about why the stuff you're doing is interesting if it's not in the wheelhouse of those big funds.

Yeah, maybe an emerging fund manager might ask you because you've done this for 5 funds, how do you Thread the needle of something in the middle. It feels like between those two strata.

I think the things I would tell them is you have to be right. So the things you've picked, they ultimately have to be right. They can't just be different and interesting or just things you find fascinating but aren't in fact good businesses. So that's 1, 2. I would say you have to have an LP base that understands your strategy and understands the timeline for your strategy. So if you're doing something that's like currently out of favor and will remain out of favor for a while, make sure you have LPs who have the stomach for that. If you have somebody that's out of favor but you feel like it's on the cusp of an inflection point, that's a really different story. I think it's. It's emotionally difficult to sort of sit out the party or to do other things and have people tell you that you're either wasting your time or the things that you're working on are dumb. That's not easy to hear day in and day out. And not everybody can do that.

I think for some fund managers, they wonder if to your point of finding like minded LPs who understand the journey, if what you're looking for isn't multi stage venture fund world. If they meet LPs who are already in a multi stage fund, would they ever be interested in jumping into something that doesn't look like that?

I find There are some LPs I meet. They think of the big funds as the building blocks of their portfolio, their core anchor positions. And one of two things happens. They either get into the big funds they want to be in, then they're like great, we are done with venture capital. We are in the names we want to be in. We don't actually need anything else. Whatever we're going to get from venture, we'll get from this group of people. There's another set of folks who say now that I have the foundation done, I can roll up my sleeves and do the stuff that's maybe more interesting. I can go find a sector specialist fund in some area that I'm interested in. I can go find a generalist fund that's stage specific, that's doing something I think is interesting. And I find people tend to hard bifurcate once they've got the base of their portfolio built.

Well, speaking of big funds, our next topic this week is a post from Sequoia titled the AI Productivity Paradox. High adoption, low transformation. It digs into a question we've been discussing a lot here. If AI adoption is happening everywhere, why aren't companies seeing real lifts in productivity or bottom line results yet? The piece walked through both the MIT Nanda report we chatted through last week, in addition to Bryn Jolson's Canaries in the Coal Mine paper that highlights the progress and challenges posed by a specifically across the labor market and entry level roles for founders building in a space. The big takeaway is that tools need to go way deeper than surface level output generation. The winners will be the ones designing AI products that actually integrate into messy real world workflows, adapt to how teams work, and solve specific unsexy business problems that live behind the scenes. It's a really useful reminder that we're still in the very early innings and the gap between using AI and actually operationalizing it is where a lot of opportunity still lives. I have a few questions questions here, but wanted to pause for your high level opinions first.

I read another article that was I think it might have been an Axios. It was the practice leader from Accenture who does enterprise AI transformation and she said there's a difference between transforming enterprise and using AI. And I'm like, I don't know. Those who've listened to this podcast probably are used to my somewhat skeptical view of where we are in the adoption cycle. And in many ways I feel like all of this failure and gnashing of teeth is a necessary part of transformation, because if it was just as easy as giving everybody in your company ChatGPT and stepping back, this wouldn't be that revolutionary. The revolutionary work is going to come in enterprise transformation, which requires not just giving everybody AI tools, or not just doing AI show and tell every week, but actually digging in to workflows and processes that you have and reimagining them. And anyone who sold the enterprise knows that's not something you do overnight. So this idea that just because we had the ChatGPT moment, that in a year these big lumbering enterprises were going to magically transform to take advantage of AI, I think was always a pipe dream. But it's a pipe dream in some ways that has fueled all this enthusiasm. Because I think the CEOs that I've talked to of large organizations, they know that they have to get this right eventually. And just because these initial attempts have failed doesn't mean they're going to quit. They're going to try to learn from their mistakes. And I think it could be the back half of this decade, which I know is a lot longer than my peers think the back half of this decade before people can really, really point to this stuff working across their organization in ways that are easy to imagine but hard to see today.

It reminds me of back in consulting days when you would do workday SIP Oracle implementations. First of all, the implementation would take half a year and then the entire process after that. The post transformation was extremely painful. It was a whole two year, three year engagement. Consulting teams were there trying to manage comms handhold people and that was for something that was a little bit more tangible than most AI tools today. It is funny that people assume that there's going to be so much value extracted immediately when obviously to your point it's going to take so much longer.

And also there's people involved and different people have different levels of enthusiasm and adoption for AI. But I think within enterprises very few things happen without coordination. So someone's going to have to decide this is what we're going to do in finance with AI. These are the tools we're going to use, these are the processes going to automate and here's how we're going to measure success. And I think we haven't had that yet before. It's been like, let's just throw AI at finance and like, let's see what the finance team comes up with. That's not going to work in most cases.

Eric, who's the author of Canaries in the Coal Mine piece, he used a J curve graph to describe his productivity paradox back in the 90s and how AI will truly transform the labor market over time. The graph shows how an introduction of new tech initially causes a decline in productivity before organizations adapt to be able to capture the productivity benefit. This is very reminiscent of computers in the 80s and 90s and its prediction is that AI will likely take a similar case. So what does this mean for investors and founders building today? I know that you think the time horizon's a little bit longer for value add, but.

Well, the real question is, is this going to be a linear change or a step change? So if it's a linear change where like everyone's just going to get a little bit better until they're good, then if you're an AI startup today and you have a long Runway, you can probably just use that long Runway to kind of ride that curve. But if it's discontinuous, this is hypothetical. At the end of 2026, everybody says we're just scrapping everything we've done and started and we're starting over now that We've played with V1. We're going to push reset on all the projects we tried and do them better in the same way that the first time people built the website it kind of sucked.

Yeah.

And they're like, oh, now that we know what we want our website to do, we're going to actually build the website that we want, not the website we thought we wanted. And you start over. But it means throwing away a lot of the stuff you did before because the new capabilities are so much better. If that's the case, then having a huge balance sheet isn't actually as useful unless you can use that balance sheet to stay nimble. When that big reset happens, I suspect it won't be a huge reset, but I don't know. I spent two days teaching myself how to use cursor and CLAUDE code and building some things and I totally got lost in coding. And I will say this, it broaden my horizons for other things I could build. And I think what we're going to have are these iteration loops for people try things, imagine the future, try to build the future. They fail, they get closer. That allows them to imagine an even more distant future. And it's just going to take time. It's just going to take time, which I know no investor wants to hear. I think everybody wants to think this generation of companies is the set of companies that matters. We thought that about companies in the 90s too, and we were right in some cases and wrong in others.

Our last topic is an article by Reuters that shared the news that Anthropic agreed to pay $1.5 billion to settle an ongoing author class action suit filed last year where a group of authors sued Anthropic for using their work to train Claude models without their permission. Lawyers representing the authors said that this was a landmark event as it was the largest copyright recovery in history and the first of its kind in this AI era. While the judge ruled that Anthropic made fair use of these authors work to train Claude earlier in the summer the millions of pirated copies of book books stored in a central library were deemed a violation of rights. Anthropic and other foundational model companies response is that they make fair use of copyrighted material to generate new innovative content. A trial was scheduled to begin before 2026 to determine how much Anthropic owed with potential damages ranging into the hundreds of billions of dollars. The debate over quote unquote fair use is pretty confusing to me and largely up for legal interpretation with very little precedent to guide judges any Thoughts from your end on this one?

You know, I think it's, it is interesting, I think, about Fair use with YouTube and with music and all of these other domains. I think the difference is the Internet is just a lot bigger now and LLMs are a lot bigger than the products that we were arguing about this over before. So if you think that anthropic and chatgpt are going to be people's primary interface to information, $1.6 billion is not a lot of money for content that will be accessed and viewed by hundreds of millions or billions of people on a regular basis. And in many of these things, like once you've settled, unless there's some ongoing revenue share or some economic arrangement, it's a pay once license deal. And, and that, that to me doesn't feel like a lot of money if that's the end state. At the same time, I think the desire to stay on the cutting edge of training these models by getting your hands out as much information as you can creates certainly in my mind, creates a temptation to do things like this. I don't know enough about the specifics of the case to know what, what's being alleged. I've read through it, but I'm, I'm not a lawyer, nor will I ever be one. But I do think there are issues here. You know, early on we had this with actors, voices and name and likeness with things like 11 labs, like, hey, once you have my voice and you can clone it and use it forever. Like, what's that worth? I think we don't know.

And similarly, like, what are the ways that you can get around copyright that still thread this needle of being legal? Like, I rem. Maybe it was chatgpt, Scarlett Johansson's voice. And she came up, that's my voice. And they're like, no, it's not. And I think like there, these are only going to continue to happen at more volume. And I do actually wonder and I, and I don't really know how they do this. Like, for the authors who were able to kind of figure out that these models were training on pirated versions of their material, was it just them using the tools and realizing that I didn't give rights for this because if that's the case, it'll take you to represent yourself as an artist in order to kind of like build the pace and speed for a case like this, that seems like a lot of work to gain that sort of momentum.

And I mean, on the Internet, we figured this out eventually. You know, there were, do not crawl like there were ways to say, hey, leave my content alone. There were paywall. But I think the interesting thing is with agents, a lot of the things that we built in the Internet, like, oh, this is a site that's protected by a password. Well, an agent could navigate that fairly simply. It's like not that difficult. And I just think the stakes are so high right now. I'll be very curious to see what happens. My sense is that the appetite for large language models to get their hands on as much training, data and content as possible will be the driving force of strategy. And it means that at the margin it is a little bit of a weird prisoner's dilemma. You're like, well, what if I follow the rule and this other LLM cheats? This is like a total non sequitur. The one thing I've learned watching the Internet, it's an unfortunate thing, if you are a large company like Google or Microsoft, it is often better to infringe and get the benefit. Whatever penalties Google is ultimately assessed for whatever they're accused of doing. With Chrome, you think about that, you're like, well, if you're Google, I hate to say it, don't you think you're better off having Chrome as the dominant browser and paying a fine 10 or 15 years later? Same thing with Microsoft. So I think the history has been that in these competitive network effect driven or winner take all markets, it is probably a better business strategy to win now and pay later than it is to actually stick to the rules. And I think there's a set of regulators who understand that and I think there's a set of companies who understand that and we'll see how that tug of war plays out.

And even the folks who aren't winning now, paying later, I think they're probably experimenting with ways to whittle around what's legal, what's not. We're big fans of tennis in my household and when the number one tennis player, Janik Sinner, he got dinged for using illegal substances from a massage lotion, he didn't get penalized that hard. We have close friends who play in challenger tournaments. They're also on the ATP tour and they mentioned that all the top 10 tennis players are constantly, every year working with the best people in the business to try substances that aren't illegal today, but they are illegal next year. And so basically every year they rev, but they have an edge over everyone else because they have the money and the pace and the access to get there. So not illegal today, but it will be soon. Did that mean that it's allowed? If you're Yannick center, don't come for.

Me routine Alcaraz at my house.

One last question here. If you're a founder building at the application layer in the creative or artistic space, especially where copyright might come into play, what should you be prioritizing right now if you have control?

I think it's really interesting if you look at the history of even 11 labs, they kind of bumped their toe, I guess is the best way to describe it, with the sort of the Hollywood copyright universe. That's a set of folks who have pretty successfully battled almost every technology wave and managed to extract something of value for the content that they have. And there was a period of time where I think people thought, gee, maybe what 11 labs, maybe that bump stubbing their toe will be the end of that company. And it's very much not been the case. And so I think if you're going to be in the creative field, I think you have to have a plan for how are you going to engage with rights holders and license holders. And in some cases there's the Napster strategy, which is you try to get big enough and force them to deal with you on your terms. That can be tough. There can be the Spotify strategy, which is you engage with them sort of on their own terms, but try to make the deal work for you as well. But you kind of acknowledge where they're coming from. There's a bunch of different models here, but I think this is going to be highly fraught and some of these things go away. I remember three years ago everyone's like Gen AI and art. This is like the worst thing. And that the, the battlefield seems to have shifted to other domains. Not saying that it's been settled, it's just like the energy shifted to a different place.

Well, we are all out of time today. Thank you, Charles, for calling in. Thank you to our listeners for stopping by. Please like and subscribe and we will talk with you all next week. Thank you, Bye.