Pod Insights How I Invest with David Weisburd E307: Why Size Is the Enemy of Venture Returns w/Glenn Solomon About this episode How do you compete with Sequoia and Andreessen while running a $650M fund and still expect to outperform?
In this episode, I sit down with Glenn Solomon, Managing Partner at Notable Capital, to break down how focused early-stage investing can outperform mega-platform venture funds. Glenn explains why 70%+ of venture dollars now go into mega-rounds over $100M, why Notable stays disciplined at seed and Series A, and how delivering “unscalable” founder support creates real edge. We also go deep on Anthropic, the so-called “software apocalypse,” AGI narratives, and where durable alpha exists in an AI-dominated world.
Transcript Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:00 David Weisburd? You compete against the Sequoias and Andreessen's of the world with a $650 million fund. How do you differentiate?
0:05 Speaker 01 For your listeners who may not be familiar with Notable Capital, we were the US-focused team that previously built GGV Capital. We split from our Asia colleagues in late 2023 due to geopolitics, and we formally rebranded as Notable Capital in March of 2024. There's a chart I saw recently, an SVB chart, that I thought helped crystallize for me how we're competing and differentiating with the giants. There's actually 350 billion that went into venture rounds in 2025. An incredible number, astounding number, but 250 billion of that 350 went into rounds that were over 100 million in size, with the majority of that 250 going into rounds that were over a half trillion in size. These are mega rounds, and they're really what we used to consider IPOs. Companies are staying private longer.
0:50 Speaker 01 They're accessing the private market pre-IPO capital in size. So if you're a large platform fund and you couldn't deploy enormous checks into late-stage, high-growth companies, that's very interesting. The other 100 billion of that 350 billion is invested in rounds sized under 100 million. And at Notable, we're really focused on investing in seed, Series A, Series B. So really, the bottom half, let's say, of the 100 million-type round size and below, these are early-stage companies. We're investing early, average valuations probably plus minus 80 to 150 in valuation. So much different complexion than these mega rounds. And it's the only thing we do. We certainly compete in these early-stage rounds with larger funds for early-stage investments, but our win rate in these types of rounds is very, very high. And I really think that's because of our focus.
1:35 David Weisburd? Right now is a very hot M&A market as well as an IPO market. Does that change how you go about investing?
1:42 Speaker 01 We really try to stay disciplined and ensure that we invest over a two-and-a-half to three-year period in each fund. That gives us some time diversification, which we found invaluable in our prior funds. I mentioned earlier, we're an early-stage-oriented investor. The average deal we're doing as Notable Capital is a Series A. The average ownership we're getting is double-digit. These companies have many years typically until they exit. So while 2026 may turn out to be a very active and attractive year for exits, and we'll take advantage of that with our existing portfolio, the companies that we're investing in now, if they're successful, are going to exit many years from now. We try to obviously use what we can and inform our investing from what we see on
2:27 Speaker 01 the field at every stage at all times. But we are early-stage venture investors and it takes, you know, five to 10 years, sometimes even longer than 10 years to help grow really, really successful businesses.
2:43 David Weisburd? Fund one was $650 million, so not quite a large multi-stage fund, not quite a small fund. In what ways is that an advantage to you and which, what ways is it a disadvantage?
2:57 Speaker 01 I hear from a lot of LPs that they want to invest either in the platforms, brand name firms where they can deploy a lot of capital,
3:10 Speaker 01 or, you know, very focused, smaller funds. And I think that makes sense. I would put us in the category of smaller focused funds. I think it's rational to assume, and this is our expectation, that investing in notable, you should expect us to deliver better returns than platform funds. We're investing earlier. That's not to say that the platform funds can't do well, but we're seeing, as I mentioned earlier, great performance from our 650 million that we're investing. You know, we're over 2X M O I C after two years. Our gross IRRs are nearing 200% right now. I don't expect us to be able to maintain that level of performance
3:53 Speaker 01 over time, but I do think that it's a harbinger of a signal of a great portfolio that we're building. And I expect us to be able to continue to do that fund after fund.
4:05 David Weisburd? So you don't have quite the coffers of the large funds, but you do have a sizable budget. Where do you choose to concentrate that budget and where do you get the most bang for your buck? One of the hardest things of investing is seeing what's shifting before everyone else does. For decades, only the largest hedge funds could afford extensive channel research programs to spot inflection points before earnings and to stay ahead of consensus. Meanwhile, smaller funds have been forced to cobble together ad hoc channel intelligence or rely on stale reports from sell-side shops. But channel checks are no longer a luxury, they're becoming table stakes for the industry. The challenges has always been scale, speed, and consistency. That's where AlphaSense comes in. AlphaSense is refining channel research. Instead of static point-in-time reports, AlphaSense channel checks delivers
4:49 David Weisburd? a continuously refreshed view of demand, pricing, and competitive dynamics powered by interviews with real operators, suppliers, distributors, and channel partners across the value chain. Thousands of consistent channel conversations every month deliver clean, comparable signals, helping investors spot inflection points weeks before they show up in earnings or consensus estimates. The best part, these proprietary channel checks integrate directly into AlphaSense's research platform, trusted by 75% of the world's top hedge funds, with access to over 500 million premium sources. From company filings and brokerage research, to news trade journals and more than 240,000 expert call transcripts. That context turns raw signal into conviction. The first to see wins, the
5:33 David Weisburd? rest follow. Check it out for yourself at alphascense.com/HowIInvest.
5:39 Speaker 01 We have to deliver for our founders. We have to make sure that they feel like, you know, while they may have three or four name-brand firms on their cap table, that notable is the first call, and that notable is the firm they're going to recommend their, you know, founder friends to first. The only way we can do that is if we have a great platform team. I mentioned, you know, we're a 42-person company today. Half of our team is represented in investment and platform. And we are delivering for each founder, we want each founder to feel like they're our only founder. And we can actually do that with eight to 10 new investments per year. The large firms who maybe are making 100 or 200 investments per year.
6:21 Speaker 01 I, again, I think they're, they're great at what they do, but I challenge. You know, if I was an LP, I'd ask if I was doing diligence on a large firm, I'd, I'd call some of the founders and say, have you ever met the name partners?
6:32 David Weisburd? Is that the pitch to the founders that they're getting? The person at the firm, they're getting the person with the 20 years of experience versus maybe a newer general partner?
6:40 Speaker 01 The pitch to the founder, and they hear this from the founders with whom we work today. Is that we play venture a little differently than most other firms that we truly operate as a team. And I asked founders who were pitching, I say, talk to any of our other founders. We, we're striving to be 100% referenceable. Maybe we're not perfect, but you should talk to any one of our existing founders. And one question you should ask is, how many people do you know by name? That you've spoken to in the last month at notable capital? And I believe the answer is typically five to eight people. It's not just me if I'm involved with a deal or my partner, Orin, or my partner, Hans, or my partner, Jeff.
7:22 Speaker 01 It's going to be one or two other folks on the investment team and probably four or five people on the platform team that they're regularly working with. And we try to deliver a very cohesive experience to the founder. So it's not as if they're talking to each of us and we're not talking to each other. We are all in the background syncing with each other so that we know what we're all providing in terms of value to a company. And the, the recipe changes over time. Founders need different things at different times. We delivered for one of our series A cybersecurity companies, a Fortune 500 introduction, and we helped them close that deal. That really moves the needle. More recently, for an AI company that we invested in, in our most recent vintage,
8:03 Speaker 01 that's doing extremely well, extremely well, has grown over 10x in the last two years in terms of revenue. We're on that, in that company with three other firms that, you know, your listeners would know well, who are, you know, all would be classified in the platform realm. If you talk to that set of founders, they'll tell you, yes, you know, the single most important hire we've made in our business in the last year, who's really helped us grow the business, was a hire that the notable folks suggested to us.
8:31 David Weisburd? If you think about where could the large multi-stage firms really not replicate what you're doing, you're doing the unscalable thing, you're providing a value add per dollar that the large funds simply cannot do.
8:41 Speaker 01 You said on one of your recent podcasts, David, like, size is the enemy of return.
8:47 Speaker 01 You know, we want to, we want to be super aligned with our LPs. We, we are big investors in our own fund. Much, our GP commit is much larger than is standard.
8:57 David Weisburd? What's, what's a GP commit?
8:58 Speaker 01 I, I won't get into detail, but it's much larger than standard. You know, and so we're highly aligned with our LPs. You know, we, we, we want to, we want a great return on our funds. We also want to be consistent. So you won't see us, we may not ever have the top 1% returning fund, but oftentimes I think the strategies that lead funds to, you know, to make firms to make top 1% on a fund, also lead them to be, you know, 99% on other funds. And we want to be, you know, top quartile to top decile each fund. That's what people should expect. That's how we're operating, keeping our size constrained so that the, the, every dollar we put to work has, has a lot of octane behind it.
9:40 David Weisburd? One of your biggest positions is in Anthropic. The AI market is changing almost on a daily basis. Tell me about where we sit today between the different LMS, Anthropic, Google, OpenAI, and Croc.
9:54 Speaker 01 History repeats. This is not our first rodeo. We have seen computing paradigms shift several times over the years. And computing paradigm shifts are good for business, for VCs. They create a lot of disruption and they create new opportunity for startups to take, take share from incumbents. We were particularly attracted to Anthropic because when we invested, which is now going on a year and a half ago, year to year and a half ago, it was clear they were emerging as the lab that was most focused on serving needs of the enterprise. And we felt like it was a strategic opportunity for us to invest in what we thought would be a really important frontier lab in the world of AI. We didn't have the vision to realize that they would grow as fast as they've grown.
10:37 Speaker 01 You know, recently they announced that they have eclipsed a $14 billion run rate. When we invested, the company was under a billion in run rate. You know, I don't think we saw 14x this quickly in the cards.
10:49 David Weisburd? There was this narrative, maybe six months ago, that the L Ls had their own lanes. OpenAI was consumer, Anthropic via Claude was coding or was enterprise. There seems to be convergence going on. Everyone's attacking everyone else's beachhead. What do you see as the future? Do you think that they converge into direct competition on different verticals? Or do you think they'll kind of keep staying in their lane?
11:13 Speaker 01 The ambition is high on, you know, certainly at Anthropic. But ambition, I expect to be very, very high at OpenAI, certainly at, at XAI and, and, um, you know, Google and Meta, et cetera. And it's in Nvidia's interest to see, you know, a thousand flowers bloom as well. So I think there are forces at work that would suggest that competition will continue to be intense. And we won't, we won't see that die down for some period of time. If you think about the, the markets that Anthropic and the others are going after, these are not traditional software markets. These are labor markets. That's why you've seen Anthropic grow so quickly over the last year plus.
11:54 Speaker 01 If they were just going after software and, and software markets, they would be, you know, too penetrated now to make any sense. They're replacing labor expense in many areas and will continue to do so. And labor is, you know, an orders of magnitude larger than software in terms of market size. So I think the first thing to realize, if you're looking at this market. Is there will certainly be bumps along the way, but we are headed to a world where the anthropics of the world and the others that are competing with them will take more and more labor budget and win that labor budget. That allows them, I think, to become very, very large businesses over time, probably larger than we've ever seen.
12:38 Speaker 01 And so while they're competing, they're also very underpenetrated in these markets. And so, yes, the competition is fierce, but it's not nearly the same kind of competition you'd see in a market that's close to fully penetrated.
12:51 David Weisburd? So you say that these markets are huge, which is intuitive. What markets are you talking about? And give me a sense for the penetration of these markets today.
12:59 Speaker 01 You don't have to let your imagination wander too far to say, okay, in with with coding, I don't think we're going to have fewer software developers in the world, but clearly we're going to have more software and we already are seeing a lot more software get created. You know, it's a meme now at at our startups. When we ask them about how many, you know, we visit their office and ask them about their headcount and we'll say, well, where's, where's Claude getting to sit and how many Claude have you hired? And we're going to see more software getting created over time. But if you think about the dollars spent on the tens of millions of software developers each year,
13:36 Speaker 01 that's a multi-trillion dollar market. Just software alone, right? Now you think about, let's say they're successful in getting into finance and and already that that looks quite promising. How many finance professionals are in the world whose jobs can be augmented or in some way upleveled because of, you know, Claude for finance or other AIs in finance? We're talking about multi-trillion dollars of market opportunity. So that's what when I say those markets are huge, that's what I mean. And you can just keep going down the list. And, you know, this is why I think it's such an exciting time to be an investor in this market.
14:16 David Weisburd? If you conservatively apply even a 10x multiple on revenue, some of these markets have 10, 20, 30 trillion in market cap available.
14:27 Speaker 01 I don't think that's hyperbole. Now, David, it may take quite some time to get there. I think software as an industry has been able to adopt more rapidly than other industries and sectors will adopt AI. But we may be surprised at how quickly some other industries tip as well. So, look, today, I've been doing this long enough to remember when, you know, we never thought there'd be a public company worth a trillion dollars today, and, you know, Nvidia and several others are worth three, four, five trillion.
15:01 David Weisburd? There's been what some are dubbing the software apocalypse in the market, where software companies and multiples were just crushed with some of these Claude updates and some of these tools that Anthropic has sent out. To what degree is that software apocalypse overstated and to what degree is it understated?
15:21 Speaker 01 The pendulum swings too far one way and the other. When everybody was all in on software in the 2021, 22 era, or maybe 20 and 20, you know, early 21 era, and the multiples got crazy high, that was the pendulum swing too far one way. I think, assuming that all software companies are going to zero, which is sort of where the fever in the market right now, is also the pendulum swing too far the other way. I do think that there's legitimate risk to some very strong historical software franchises. If you think about what makes a software company valuable, it's certainly the bits themselves, but there's more to it than that. If you look at companies that have been historically successful, like a ServiceNow or Salesforce, part of what makes them successful is their unique distribution.
16:04 Speaker 01 Part of what makes them successful is their unique data and know-how. You know, Palantir, same thing. And so while Claude and other AIs like it are making it much less expensive to recreate the bits, the bits are only part of the story.
16:22 David Weisburd? There's this theory that the value, the software layer is going to be commoditized and all the value add is where formerly software companies now services companies presumably are going to capture that value. What do you think? And have we seen the early stages of that?
16:36 Speaker 01 Like, software businesses are also rapidly adopting AI themselves. And so, you know, some of these traditional software businesses that, you know, we saw this in the cloud era, for example, in cloud, you know, in the 2010 to 2014 timeframe, when the hyperscalers, which at that time were called the public clouds, started spending real money on CapEx, which, you know, looks cute today, but they were spending, you know, 20 and 30 billion. So a lop off a zero from where they are today and investors were panicked about that spend. What we saw in that era was there were a lot of startups that did very well and disrupted incumbents and took share.
17:18 Speaker 01 But there were also incumbents that adopted cloud technology quickly and actually did quite well, created a lot of value for shareholders that bet on them. I think we'll see the same with AI. There are some incumbents in the software space that are going to bring AI, that are ready rapidly bring AI into their own companies, meaning they're building their products faster, more inexpensively, which means they can build more and solve more customer need.
17:46 David Weisburd? Now that we have Claude Bot now called Open Claw, some technical people believe that we're either already surpassed AGI or we're gonna go into, we're gonna be post AGI in the next couple of years. How does investing change in a post AGI world?
18:01 Speaker 01 I listened to Fei-Fei Li, who I respect dearly, who's one of the, you know, real frontier thinkers in the space, in my opinion, and has been in it for years. And I think her conclusion is like, yeah, AGI is more a state of mind. Maybe she didn't quite use those words, but I don't think that there's a moment in time when the world would, you know, it's not as binary as a zero or a one. It's a, it's a phase that we're going through. I think we're seeing just such rapid advancement. In model performance across so many domains and dimensions. And I think we're in a period where we're going to continue to see that kind of rapid improvement across many, many different disciplines for many years to come.
18:41 Speaker 01 These are, I think, decades-long opportunities that sit in front of us today.
18:47 David Weisburd? People were panicked in the industrial revolution where many people lost their farming jobs and then they're industrialized. And we have these cycles, internet replaced many things that were done offline. Part of the reason for that is that humans lack the cognitive ability to derive two, three, four steps ahead. And these opportunities become evident to human beings only one step before. So now we could finally see, oh, there's developer tools. Now if people have developer tools, there's value-add services on top of those. But we're not thinking about what if droids need to talk to humans or need to trade crypto. Like, there's so many layers of complexity that the human mind is just not set to be able to think that many steps into the future.
19:31 Speaker 01 I agree with that. I think, you know, there's a wonderful book I read a couple of years ago that rhymes with your assertion there. A book called Range, R-A-N-G-E. The author, I believe, is Eckstein, David Eckstein, or something like that. And it talks about like, where does innovation really come from? And the TLDR is that, and he traces lots of innovation that, you know, has come from mankind over time. And it tends to come not from deep, deep, deep-focused thinkers and trained people in a specific domain. It tends to come from folks who have range, who actually have knowledge and understanding of things that are occurring in many disciplines
20:11 Speaker 01 and are able to borrow, you know, a pattern or an innovation that occurred in one area and apply it to another and maybe adapt it for the specific needs of that vertical. But then, you know, it's that kind of application across domains that unlocks innovation. We can't understand. Everything that's coming, but if you look at, you know, just look at Anthropic, for instance, I think, um, you know, I don't know that they had planned for Claude Code to be such a huge success and for them to do so well in the coding model area first, but they've clearly taken a bunch of the innovations that have powered their success
20:54 Speaker 01 in coding and have been able to apply it now to finance and to cybersecurity and other areas. And I think, you know, that the benefits as they get deeper into each of these sectors will compound because they can now jump and take some of those insights and the models will do this themselves to the next set of business problems, the next set of uh, science problems, et cetera. And yeah, it's very difficult for us to comprehend where that goes over time.
21:23 David Weisburd? If and when AI reaches AGI phase, what are the best places that investors could capitalize?
21:29 Speaker 01 It's a great question. If you're a cybersecurity leader, if you're a data leader, if you're a developer, like your world's been rocked, but that's a great opportunity for, you know, founders, building tools for these folks, building platforms for these folks. What sits above the infrastructure layer are applications, consumer applications, business applications. And as the, the infrastructure becomes sturdier, the applications that sit on top can become more effective, more expansive. Um, you know, I think it's, it's very exciting. We've already seen advancements in healthcare, for example. We've got an investment that's doing quite well, applying AI against pharmaceuticals and that supply chain. I think we're going
22:13 Speaker 01 to see a lot more of that over time. Legal has obviously been a space, again, a very large space, mostly labor-based space that is clearly moving pretty quickly. Uh, we have an investment in finance and accounting. That's another space, very large, where we've seen the early signs of AI replacing and augmenting labor spend. We're an investor in a great application company that my partners, Hans and Chelsea, led us into called Whisper Flow. And for those of you who don't know, and a lot of you, probably a lot of listeners probably do know Whisper Flow because it's gotten quite popular. It's an incredibly powerful way to translate voice.
22:51 Speaker 01 Into effective digital communication and it allows you with the power of voice to power not just messaging, but increasingly managing your your your digital life, your applications, et cetera. And it's very, very accurate. And it's it's really become a quite a popular service as a result. It's a company we're very excited about. I'd argue it's net new. I can't remember a year that I've been in the business where people weren't talking about man machine interface and the importance of, you know, innovation there. And I feel like we never made any progress. And all of a sudden, this company Whisperflow, without even knowing it was man machine interface, taking advantage of AI models, is like the most powerful man machine interface technology I've seen to date. And I think it has dramatic potential
23:35 Speaker 01 as a result. These are the kinds of net new opportunities that I think exist as well.
23:39 David Weisburd? Said another way, there's some really exciting technology. In order to go from the technologists and the hobbyists to the early adopters, you need some killer app. The early adopters are that they're willing to to take risks on technology. They they're highly pragmatic. So they want to know, I could use this application to accomplish this goal. And until that killer app is made for the technology, it sometimes just stays stays extremely niche.
24:02 Speaker 01 That's a great observation. It's also abundantly clear that AI has jumped that the bit has flipped there for AI. You know, it was November of 2022. We all remember the day, right? And ChatGPT comes along and everyone tried it for the first time and it was so easy. And it created a wild user experience unlike anything we'd ever seen. One of the charts we showed in an offsite recently to talk about our strategy around AI internally was the route to 100 million users for technologies that had grown very fast in the past, like Instagram and TikTok. And ChatGPT blew them away in terms of how quickly it got to that number of users. It's just, you know, it's it's a great indicator that, yes, the world was ready.
24:46 Speaker 01 Obviously Claude Code has done the same thing. And I think, yeah, the world is now very leaned in and looking for the next set of great AI based innovations. So that's that's exciting.
24:58 David Weisburd? If you could go back to 1991, you just won three national championships in tennis, and you had to give yourself only one timeless piece of advice to carry with you through your career. What would that one piece of advice be?
25:11 Speaker 01 Well, first, David, I want to give you props for tracing my background back that far. I grew up in New York and I went to Stanford thinking I was going to be a pro tennis player. I think chasing shiny objects is very, it's human nature to do so. And particularly as an investor in almost any asset class, it's very easy to chase shiny objects. I think generally speaking, people get themselves into trouble by doing so. So staying focused and dusting in your strengths, you know, trying to figure out what your core advantages are and really honing those core advantages is key.
25:40 David Weisburd? I think the tricky thing about not chasing shiny objects, that's obviously a negative framing on it, is you don't know if you're in the first inning, you're in the third inning, or you're in the ninth inning and the market's about to go down. And I think you mentioned it's, it is human nature to chase. So you have to really take a step back and look at it from first principles. Is Anthropic at $380 billion, is it overvalued? Or like we discussed, are these, these decade billion dollar market cap opportunities still available? And you have to make that first principle assessment of what inning of that company or that trend that you're in.
26:13 Speaker 01 I love that framing. Most of the investments we make are early, seed, series A, sometimes series B. Despite that, our loss rates have been very low. A big reason for that is our focus, David. We're not investing in areas that we don't have deep expertise in when we make these early stage bets. And we're informed by the, the networks of professionals who operate in these spaces. You know, when we're making a security bet, we're talking to 75 chief security officers and asking them, "Hey, is this something you'd buy? What do, what do you need to see before you'd say yes? How high is this on your priority list?" Those are critical, critical questions to answer. So we're making very informed decisions, even though they're early. And when I say chasing the shiny object, I think it's sometimes going to spaces where you just don't have the ability to gain that conviction.
26:56 Speaker 01 But I see a lot of lending behavior. I see people saying, "Well, others are doing it, so maybe I should too." And I think you've got to resist that temptation and invest when you have conviction. That's what we try to do at Notable.
27:08 David Weisburd? Said another way, your expertise and your networks within a specific sector is the alpha. And when you go into another sector, you're just betting on beta.
27:17 Speaker 01 Higher financial acumen way of saying what I was trying to say, yes.
27:21 David Weisburd? Well, Glenn, this has been absolute masterclass. Thanks so much for jumping on podcasts and look forward to continuing this conversation live.
27:27 Speaker 01 Likewise, David, and congrats on, you know, you're surpassing the 300 episode milestone recently and love your podcast. Anytime listener, first time caller today.
27:36 David Weisburd? Thank you, Glenn. That's it for today's episode of How to Invest. If this conversation gave you new insights or ideas, do me a quick favor. Share with one person in your network who'd find it valuable or leave a short review wherever you listen. This helps more investors discover the show and keeps us bringing you these conversations week after week. Thank you for your continued support.
Previous E306: Can VCs Actually Pick Winners? w/Eric Bahn Next E308: The Future of LP Liquidity Page updated October 31, 2026 · transcript and summary are machine-generated