How I Invest with David Weisburd - E413: How AI Will Reinvent Venture Capital

Episode Date: August 7, 2026

Most venture firms are experimenting with AI. Footwork rebuilt the entire firm around it. David sits down with Nikhil Trivedi, Co-Founder & General Partner at Footwork, to explore what an AI-native venture capital firm actually looks like, how autonomous agents are changing sourcing, diligence, portfolio management, and investment decisions, and why judgment and relationships may be the last true competitive advantages in venture capital.

What does it mean to be an AI-native venture capital firm? What it means to be AI-native is that you're not just sticking AI into the firm's existing processes, hoping that something's going to change. It's actually thinking about the whole firm from the ground up with AI at the core. And so there's one example of this that's quite basic, right? A non-AI-native venture firm, in my opinion, is a firm that still has humans, who are actually looking at the CRM every day to figure out what's in the deal pipeline.

An AI native firm is where the humans in the firm don't even look at the CRM because that's updated agentically, it's happening in the background, and where things are being done for you proactively through the use of AI. And so I think that's the amazing opportunity that firms like us have right now,

which is to rethink our entire processes with AI from the ground up. We're recording right now. It's 1.30 on a Tuesday. How have you used AI today? Let's take the prep doc that you sent over before this podcast. I think I got it from you late last night. I took a quick look at it. When I woke up today to take a look at the dock again,

I had a number of bullet point suggestions based on to talk through your talking points based on our agents having gone and looked at all the context in our firm, the conversations we've had about being an AI native firm, the logs of what our agents have been doing over the past couple weeks. And so I had bullet points prep for me with very little input from myself based on our use of AI. And so that's just one way I used it today. Another way is I had a board meeting this morning that I just said, came from to this podcast. And we got a board deck last night for the company. Wasn't that much ahead of the meeting. But I got notes on what has changed in the business

between this board meeting and the last one. What are some of the conversations that we've had back and forth over email since the last board meeting and how did they relate to what we're about to go in and discuss in this board meeting? And so I feel like I showed up as a board member with just a lot more background understanding of what had transpired between sessions than I would have otherwise. And it wasn't like I actually stared at the prep during the meeting to figure out what questions to ask. But I did feel better prepared for the conversation that we were about to have based on that came from our AI agents. Let's go into the plumbing of footwork, an AI native VC firm. How have you constructed the firm to integrate AI?

Give me some specific examples. We've gone through different evolutions on this, right? So initially we were of course just using chat GPT and Claude, sticking data into it, figuring out sort of what can the models do. That was sort of V1. V2 was us starting to build agents. And initially, actually, agents on our local machines. So you've probably had this experience too, David,

where if you fire up cloud code and you start building an agent, it can do things for you on your desktop. If you have a Mac Mini in the office or at home, you can run these agents there as well. But we had some funny experiences where we wanted to use our agents, for example, to do a brief on a company or to go find out a piece of information about our portfolio companies. And the agents wouldn't be able to do that

because the laptop of the person running the agent was closed. And so V3 for us was migrating these agents to the cloud, was having a central database, Postgres database, where all of the context from the firm lives that enables us to build agents on top of it. And so that's the phase that we're in right now. And that's a phase where agents are always on. They're doing things constantly in the background for us.

We can run them at any time. And they're also picking out context based on the query from us versus leveraging APIs to tap into each individual system, if that makes sense, which is a lot more costly from a token standpoint. It takes a lot longer to get back results. I'm sure we'll have a V4 and a V5 to talk about over the next year, because things are just moving so quickly on the capabilities of the models, the stacks that make the most sense as well. How much does AI cost you on an organization wide basis? The cost for us is actually still pretty small.

It's in the thousands of dollars monthly. It's not yet in the tens of thousands of dollars monthly, and certainly not more than that at this point. Again, we are a very small firm. It's only five people at footwork who are full-time employees. We're running three or four agents really aggressively at the moment. So the cost is actually still pretty minimal for us. I want to get into the weeds in a bit,

but at a high level, What is AI specifically doing for framework? I wrote a blog post earlier this year talking about so the jobs to be done within a firm like ours and where AI can add value. And so when you think about the venture capital flowchart, it is find, decide, win, help exit. Those are the five functions that we have as investors. And then there's also a firm building function where those are internal processes, finance at the firm relationships that we have with our LPs, fundraising. AI is adding value across all of those dimensions.

So find, decide, win, help exit and firm building today. Let me try to give you a few specific examples. Our agents are named after right now football managers. Both my partner Mike and I are very into football or soccer, as we call it in America. And so we have an agent named PEP, an agent named Forgie, an agent named Mo off to three of football's best and most famous managers. PEP is the agent that briefs us on companies that we're looking at or companies that we perhaps should be looking at.

So it's the main agent on the find dimension right now. And so every morning I get briefs from PEP on the companies that I'm seeing that day, how they perhaps relate to companies I've. seen in the past, what I thought about those companies in the past, just to context load me as I go into those sessions with new companies. And it's becoming more and more proactive about what it can do. So imagine things like suggesting other companies you should chat with that are related to this particular company if you find it interesting in the meeting. Foggi is our agent that has full knowledge of everything at Footwork.

And so yesterday I asked Foggi, can you just take a look at the closing docks of this particular portfolio company and remind me who's on the board of the company officially? That was an answer that it was able to give back. And so it's kind of our internal company brain agent. Those two examples for you that are hopefully fun to think about that we're getting leverage from today with AI. And you said something interesting earlier. You said you're not using CRMs, like a typical VC would be inputting data.

You're using AI to populate those CRMs. Talk to me about the interaction between AI and human and your processes. When we started working on this, we were wondering, what will our stack look like in the future? Will we even need a CRM? and we actually have found that there's real benefit to still having a CRM. So we still have affinity as our CRM, but I don't look at affinity much at all anymore because the way I interact with affinity and the data within is through our agents.

And so as an example of what we do, when I have a meeting with a new company, I take notes on that company. If we're using Greno La in the meeting, granola has notes as well. But our agents automatically take those notes, put it into the CRM, and then they track how we're conversing about the company over time to update where that company is in our pipeline.

So our agents will know if I've passed on the company, because it will see that via an email to the company. It will see that via an internal conversation we have, such as a team meeting conversation where we talk about the pipeline and we make decisions on which ones we're excited about and diving into and which ones we're less excited about and are moving on from. Before all this, I would actually manually take my notes, stick them into affinity. I don't do that anymore. I would also manually go and take a look at the pipeline and update the status of each company. I don't do that anymore.

But we do actually still have the CRM. It is a single source of truth still for us, or one of the source of the truth, I should say, for us. That is a, it. important because over time, what it's captured is how we've thought about every single company, which decision we made, and that context is very important to have these agents be able to improve and learn from themselves. There's this management consulting term today called change management, which is just a fancy word for talking about how everybody now needs to integrate AI and what are the best practices for that. What have been the best practices for you for integrating it within footwork. The first step for us was just making sure that everyone was familiar with the tools.

Everyone felt empowered to take a little bit of time to set things up such that processes could be automated over time. And we started talking about every week in our team meeting how every single person is using AI. So you kind of had to show up to that meeting with something to talk about. And then, of course, there is, there's a distribution in people's usage of AI of a time, but our philosophy was like the floor for everyone can be raised through automating processes, through creating agents. And then some people are going to find kind of the really high ceilings of possibilities. And hopefully those can flow back into the average being a higher floor people's usage.

At our portfolio companies, we've seen a bunch of different types of efforts to get people to use AI. We've seen companies mandate AI usage or else you won't be at the company. We've seen companies closely track people's token usage and have leaderboards that sort of enable people to see how different people are using AI, companies that have systems for folks who are more AI native to mentor folks that are a little bit behind in that learning and to sort of to graduate in their learning as a result and for those mentors to be incentivized and rewarded for that work alongside their individual work at the company.

I think that all of us are in this phase of just trying to figure it out. The interesting thing is that no one is really an expert yet, even the people at the very bleeding edge or discovering every day, it can suddenly do this for me that I never would have thought possible. So the key is just to be in that learning mode and mindset. And so I think that's what everyone has been trying to figure out how to promote internally. Maybe you could double click on that. What are some ways to get the organization and this learning mindset? It's making sure that People who are more at the frontier are sharing those learnings with everybody else. In our firm has had an aha moment.

And they've been different for every individual. Like for when they realize, wow, AI can do this. And it's meaningfully making my day-to-day job better as a result. And so just trying to find ways to get people to the ahas to then realize, wow, I should go deeper on this. I should. I've become obsessed by it myself. That's the key. It's kind of like what teachers have to do in education, right?

Everyone learns a little bit differently. Everyone has different skills. And the keys to get someone to a magic moment where they feel so passionate and curious that they go figure it out for themselves. I found the same both in our firm as well as in portfolio companies is that the processes that seem to permeate across the organization starts with somebody getting an idea,

but most importantly executing it, essentially derisking it, and trying this out, figuring out all the kinks, locking it in, using it every day, and then teaching other people in the organization exactly what they did, kind of this end-to-end solution on one individual, it works, everybody could see that works for this person, and then he or she goes out and teaches everybody else on the team.

Is that how it's worked for footwork, or do you have, an AI person that figures out the best practices, works out the kinks, and then integrates it into the other partners? Initially, we were doing all this work ourselves. And then what we realized is we think we'll really benefit

from having somebody who's fully focused here. And so we went out to make that hire. And we found Andrea, who joined us a couple months ago, who is fully focused on everything AI internally. So he's our AI lead. I think of him as kind of the operations person of the future that every venture firm will have,

but perhaps even almost every company will have, because what he does is manage the agents internally. And so I think an operations person of the past would have only managed humans, but I think an operations person of the future manages AI agents as well as perhaps humans this is part of that day-to-day job. So Andrea owns all of our AI work now.

He's building agents. He's sort of updating our context layer every week. He's taking learnings back to our team on capabilities that he's seen unlock for himself or for others on the team. A side benefit of this is having someone who's just playing with the frontier capabilities, are the models on a daily basis, I think is very helpful in informing our overall thesis and worldview for investing. That is another thing that we are having more and more brainstorming sessions about and that is getting updated in the background based on all the conversations that we're

having as a firm. What does footwork think about the world in 2026? What do we think are the most investable areas and opportunities for us? What are less interesting? for us and why. And so having Andrea kind of be the DRI on this, I think, is already shown to be very beneficial. And we'll see what unfolds in the years ahead. Most investors don't lose because they lack information. They lose because they can't separate signal from noise. Every day, thousands of earnings calls, SAC filings, expert interviews, and market updates,

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going to alpha-sense.com slash how I invest. That's alpha-sense.com slash how I invest. I just recorded with Thomas Scriven, who's head of private equity at University of Pennsylvania Endowment, and he invested in the manager, three former Palantier engineers, and they will deploy themselves, their bio-managers now, and they'll deploy themselves into portfolio companies

and figure out their biggest AI bottlenecks for five to six weeks, unlock these bottlenecks, and that's kind of how they're using that to get into the top companies. That's super cool. When this moment in time where every organization is trying to figure out how to best leverage AI. Some have a mandate to do it. Others just know that they should be doing it. And we're all trying to figure this out. And so I think that is a really interesting wedge to get to

know companies really well and figure out what to invest in or to gain access to something that you're excited about. You mentioned that you're using one of your agents, I believe it's Mo, focuses internally within footwork on the firm itself. Do you see that evolving? venture capital into new business models or more scalable ways to deploy capital? That's a good question. There's that possibility. I think we still feel like our overall business hasn't changed and doesn't need to change. We love partnering with just a handful of companies every year, working really closely with those founders, leading rounds, getting our ownership in the initial check, yet felt that innovation is

required on the model itself. But again, I think we're in this phase where the world is changing rapidly and we'd be silly not to think about how will our overall industry change and what's our place in it that perhaps needs to be adjusted as a result. We'll talk about looking for AI firms that provide value via AI, but there's also a bunch of firms that are utilizing AI internally. What are you looking for that tells you that somebody's going to win? in this new world. The gene, the trait in founders that we look for more than ever is their slope of learning. How quickly are they able to learn and adjust themselves?

Because when the world is moving as quickly as it is, it feels like that is maybe the most important trait to have. And so how does that show up in actually evaluating companies? we ask questions such as what's changing you're thinking about the business this week, take different functional areas or topics that are thinking about and seeing even in a couple different days of meetings

has their thinking adjusted in a certain way, are they learning something on the field that has them thinking differently about an element to their business? And so that is a really important characteristic for us to look for in founders and one that we were always interested in, perhaps less fixated on than we are in this moment in time.

Footwork is one of the first venture capital firms that, in least I'm aware of, that's using AI as an investment partner to help make decisions on investments. What's the story of how that came to be? How it came to be is that we were seeing all these companies leverage AI and have completely new businesses as a result of the advances in the LLMs.

And we felt like the only way for us to invest in that world, to have a different perspective, is to try to become an AI-Native firm ourselves. And we felt we had the unique capability to do it because we're only a handful of people versus a firm that is much bigger and has many more legacy processes as a result. We started on this journey several years ago. we've gone more and more all in on it in 2026,

as the models themselves have become better, as we've realized, we can really get leverage out of this. And so how it shows up to us, thinking partner as an investment partner is because our agents have context and all the decisions that we've made historically, and also on companies that we wish we were a part of along the way,

but we didn't get to meet, or we didn't prioritize. What we're trying to do is leverage these agents to help us make better decisions. At the end of the day, that's what could drive a tremendous amount of value for us. Every time we now go in and try to make a call on a company, it feels like we're better prepared to make that decision than ever before. It feels like our judgment is captured and therefore should improve over time. We'll see if we feel like we really got leverage out of it only in many years from now

when we see the full result of these decisions. One of the really hard parts about venture capital is just how long these cycles take for us. But we're optimistic that we'll realize it was an amazing idea to have invested in this in 2026. Does the AI get its own vote? Is it helping improve your thinking? How are you actually leveraging it on the investment? It doesn't get its own vote today, but it has perspective on what are the key questions that we should ask. It tries to parrot back to us, hey, here's where you're seeing it spike, and here's how you've seen similar companies spike in the past and what the outcomes were of those decisions.

And so it's not yet an end-to-end decision-maker thinking partner. But it is providing us leverage to, I think, have better judgment ourselves. And I think the potential over time is for it to really become a decision maker and judge or companies. The models are just not quite there yet. The context layer we have internally, I think still needs to be bolstered for that to actually be the case. Look, we're only three and a half years into this LLM.

era. And so the models are only going to get better over time. I'm optimistic about what we could see three and a half years from now as a result. And you have growing up, I thought managing money meant paying bills and balancing a checkbook. But as you know, that is only a small piece of the financial puzzle. Managing your money takes more than just checking your bank account every once in a while. And great financial decisions come from having a complete picture and proactive management of your income, expenses, and investments. Take control of your finances with monoron. It brings together all of your accounts, investments, saving goals, and spending into one place, making it much easier to understand where your money is going and whether you're actually on track

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Unfortunately, the entry-level analyst role, it doesn't feel like one that we need to have at footwork. And I think other firms will come to the same conclusion. Now, will we still potentially hire analysts as an industry? Yes, but I think those analysts will be different in their jobs, will be wielders of the AI products. And so that's just one area that I think will really evolve. The back office of firms should also look much leaner over time. I think a lot of those processes can be automated. And then I think the key for us as humans building and running these firms and investing

is going to be back to the work of judging companies, where, again, I think that the last place that AI will really be able to make end-to-end actions. Lots of pieces of find, decide when help, exit, and firm building will get automated. There'll be fewer people needed to run a venture firm. And the people who are in the firm as humans will have more of their time freed up to just spend time with entrepreneurs and others in the ecosystem, to think and to make decisions versus to do tasks

and a bunch of the other things that come with company building for anybody. Those are the last two things that get automated, decision making and relationships? Is there anything else you put in that bucket? I think that's right. Relationships manifest themselves across sourcing, across winning, across helping companies, of course.

across exiting companies too. So I think it is those two things. I think it's relationships across all those dimensions and it is fundamentally about human judgment and decision making that will be the last things to be AI'd away. At some point there was about 3,000 firms, 3,000 venture firms, many of those are on their last fund.

How do you see AI accelerate the extinction of emerging managers, do you see it lowering the barriers to entry for new managers? How do you see that playing out? I think it does lower the barrier to entry. I wrote this piece six years ago about the rise of solo capitalists

and I think it is actually easier than ever to start a firm solo, just be a one-person venture firm and to get leverage from AI to do a lot of the other tasks that come with running a firm. I also think that there should be natural extinction and venture. Too few firms that actually deserve to exist for many, many years. So many films end up surviving as zombies,

and too few of the really great films end up sticking around. So I just think it's healthy to have more death than our ecosystem. And films that just don't adapt. or don't have the energy to do it shouldn't be around 10, 15, 20 years from now. So I think that's a natural process that will continue to unfold in our industry. You remind me of Plank's principle,

which is scientists don't change their mind, they just die. There needs to be a certain refresh in industry in order to really, especially when there's evolutions like AI. Yep. Yeah. You write one of the biggest newsletters

in venture capital, the next big thing. How does that help you? VC. Just fleshing out my own thinking through writing has been super helpful for me over the years. So I started it not because I thought I'd amassed that big of a following, but because I wanted to become a better thinker and writing was just a way to do that. So this is actually one place where I haven't leveraged AI. One of the posts that I've written over the years have come from being generated by AI,

because the whole purpose for me is to be a better thinker through writing. So that's been a core benefit. The other benefits have been, of course, having a brand that stands for something through my writing has been helpful in sourcing companies. Writing about a company when we announced an investment has led to several hires for that company because someone sees that post

and reaches out to me or reaches out directly to the company and says, hey, I'm really interested in this. It has led to people getting excited about a company and follow on financings. And then the final thing I'd say

is so much of the battle that all of us have in our industry given what you said earlier about the thousands of firms that are out there is just being top of mind. And so what's interesting is every time I publish a post,

I'll see a spike. in people coming out of the woodwork to say, oh, I thought of you for this company, or I thought of you as someone, a talented person you should meet. And most of the time, they barely have anything to do with what I just wrote about. But because I showed up in their inbox

through the blog post, through the newsletter, they're reminded of my existence. And so I think it's helpful just for that as well. So for them or first call alpha, me and my business partner call this random, random. Anytime somebody reaches out to us, when we ultimately try to attribute, it's usually to a LinkedIn post or to a podcast or to a YouTube. It's always in the context of something. People don't

just randomly think of people that they're not talking to every day. For sure, for sure. Writing actually also helped us in fundraising. A number of LPs started following my writing in advance of me starting footwork and so lots of very direct positive results from the writing and there is just a randomness

to our business that we all have to accept as reality and so I think maximizing the surface area for serendipity to happen is a huge part of all of our jobs in venture

you mentioned that the newsletter helps you develop your thinking more. Is that just because you have to put your words on paper? Or is that the interaction with the audience that helps you improve your thinking? That's a little bit of both, actually. I think the comments I get on posts,

the responses I get absolutely help my thinking. But also just the act of putting something down on paper helps me suss out the idea better. And so it is very much both of those things. Yeah, it's the Carl Popper theory of falsifiability, which is that all knowledge comes from the iterative search for truth by iterating your knowledge of something. For example, on my podcast, I go on and I talk about a framework that I have, which oftentimes is directly true and then each guest will tweak it a little bit. We'll add more nuance. We'll explain where it might not be true.

And you're just constantly iterating on your thesis and, getting closer and closer to ground trip. If you're starting from scratch yet, no LPs, no reputation, how would you go about building an AI native firm today? I would start building agents from the get-go to work on different elements of the role of venture capitalist, find, decide when to help exit and film building.

I would do that actually before hiring any human to the team. I would make sure that everything I do is legible to AI and the LLMs. And so tracking every conversation, writing down as much of thinking as possible, or speaking it out, and making sure that it's captured so that there's a learning loop potential for the agents across these different dimensions of the business. I would also go out and talk about the AI internal work publicly, because obviously part of the reason for this conversation is that this is of interest to a number of folks out there.

And so I'd perhaps think about building in public. And we're hoping to publish more and more of our work at Footwork to try to do this as well. But I think that that could also help you stand out as a venture firm on being AI native. It could resonate with a set of founders who are doing the same thing in their companies and see you as like-minded. And so I think there's a bunch of stuff in there to do that could lead to quite a bit of differentiation. The bar, unfortunately, is not that high in our industry.

Very curious on this concept of building in public. Eric Torenberg, who now runs media for Andreessen, co-founded this podcast with me. He's been one of these early adopters of stating what you're going to do publicly. To me, there's always some reservation on that in terms of sharing all your secret sauce with the market. What are some of those benefits?

And how do you think about that? We've been talking about this actively as well because we don't want everyone to know everything that we're doing. But what I've seen over the time is there are so many benefits, sort of being public from a learning standpoint, back to this idea of maximizing the potential for, serendipity and great lucky things to happen

is very true in our work. And so in many ways the benefits outweigh the costs, especially when you're an underdog. When you have nothing really to lose, you haven't yet built a winning

franchise or something to try to protect. And so by the way, this applies to for companies, not just venture firms. We have a set of companies that we've never announced the investment of because the founders really don't want to be on other venture people's radar. They want to build in private.

They don't want the distraction of that. They're able to hire talent without being public. They're able to get customers without being very public about what they're doing. And a set that's completely the opposite end of the spectrum where they're building everything public because that is the way they have built a brand and that is the way that they are becoming a category leader or have the potential to you have to figure out what game is authentic to you and what game is the winning game to play and that can be very different depending on your circumstances the market you're in the strategy that you have you almost have to evaluate that

on a concept by concept basis. So from one extreme, you might have an incredible source of alpha, your Renaissance technology, you don't want to give your secret formula. On the other hand, giving away the information could end up making you grow your network effect or your flywheel.

I had that sort of a podcast. Every LP that I knew I would put on the podcast, which to some people, it's kind of a crazy thing to bring these LPs to the entire network. But as I did that, I built my relationship with LPs, other people started introducing me to other LPs,

and it was this positive sum game that was playing, but I think it's a little bit overly simplistic to think of it as always build in public or never build in public. I think there's a lot of nuance here that you really have to constantly think about. Totally agree. And in general, I subscribe to the positive sum philosophy

and as you can tell through the writing and through sort of what's happened in my career as well. What do you think in venture capital really compounds the most if you had to choose one or two things over your career? Brand certainly is the first thing that comes to mind that compounds when you're associated with strong category leading companies, with success. Other founders want to be attached to that too.

So I think brand may actually be the lost remaining moat for every business, not just venture firm, especially as software, for example, becomes easier and easy to build. The other thing is related to brand, but distinct, which is the network. What we are already seeing five years into footwork is more and more of the companies we're getting excited about are referred to us by founders that we've backed. or people who we've invested with already, who have seen us in action and think highly of us. And so brand and network are the two things that I think absolutely compound in the venture business.

If you could go back to when you first started footwork and you could give yourself one timeless piece of advice that had nothing to do with AI. What would that be? Exceptional companies deserve exceptions. And what I mean by that is you have a model when you set out to raise a fund and, you know, portfolio construction is going to be this. Here's the likely average ownership we're going to have in companies.

And at the footwork, we've done a great job executing on that. But I think what's important for us to always remember is our entire industry is based on those small handful of exceptional companies. And so when we see those and when we feel that something is truly exceptional, we should be willing to make exceptions ourselves to just get into those companies, whether that means sacrificing ownership or doing it at the latest stage or doing it earlier than we thought we were going to invest. I think that's where the real money is made in our industry is where you bend the rules

and where you know you have to be flexible and to make a an exception. And so that is the timeless piece of advice that exceptional companies deserve exceptions. The number one piece of feedback that LPs oftentimes tell GPs is we don't like surprises. We like GPs that do what they say they were going to do. How do you deal with that friction from LPs of just sticking to your knitting? You absolutely benefit as a fun manager from doing what you said you were going to do, but at the end of the day, the most important thing is your returns. And I think if you look through venture history, there are many examples of those decisions that looked crazy at the time that looked like they were really off spec or exceptional.

I'm thinking about at the time, coastal adventure's investment in Open AI was the largest investment they'd ever made. It felt so exceptional that they actually sent a note to their LPs about just that investment to explain it. And yet that will go down as one of the great venture investments of all time. And there are so many examples of that in the history of our industry that ultimately LPs are going to be so grateful and will reward you for having made a couple of exceptions if one of those exceptions turns out to be an unbelievable success. And that's what they're hiring all of us to do at the end of the day.

Before we started recording, you're telling me that you had seven endowments as LPs. You've only raised two funds. How are you able to attract such elite limited partners so early on your franchise? We were really fortunate to have several universities and several foundations as partners from our first fund. And when I look back at why we were able to do that, I think number one, we got fortunate with the timing of our first fund. We raised it in early 2021, which in retrospect was a great time. But I also think beyond just the luck, we were thoughtful and very intentional about what we wanted to build with footwork. Mike and I spent many months simulating what it could look like to build a firm together.

We made angel investments together to do that. But we're making sure that we were really aligned on core values and principles as people and as firm builders before we decided to do this. And I think that alignment showed up once we said, yes, let's do it in the conversations we had with LPs. we also benefited from strong track records as in my case an investor, in Mike's case as an operator. And so we checked the box on that too. But I think where our story really spiked was on the intentionality of how we thought about what we wanted to build, the alignment that we had as co-founders and partners. And then we got a few awesome early believers,

which propelled our fundraise to be pretty swift. We started officially raising in Jan of 21. We were oversubscribed by March. We closed on April 13th of 21. And it was a foster process for our second fund, partially because we just had a really great base of LPs and fund one that made up almost all of our fund too as well. It's one of the most underrated aspects about raising from LPs. I call it double-gated

process. A lot of GPs, they talk to 100 LPs and they have five convert. And they say they think it's a 5% conversion, which it may technically be, but really it's double-gated. 90% decide not to do work, 10% do work, and 50% of those that do work. And then you start having to ask yourself, what's upstream of the 10% that do work? And oftentimes, they in least initially are borrowing conviction from some of these anchor LPs are some of these cornerstone LPs. Yep, yep. I've gotten a number of folks who've started films after us reach out for advice. And so I have tried to be helpful to folks like their conviction and Tamash at theory and

non and Zavain and adamant dimension and others that have come after us. I've reiterated this advice again and again to folks like that, which is what was surprising to me was how. we would not hear from an LP for a while. And then suddenly they would come back to us and say, we're super excited to dig in. The thing that I think we did a good job of was with each LP, we would update them pretty frequently on what's unfolding for us.

So I think roughly weekly, we would say, hey, here's the latest, which is we've actually signed the first term sheet for our post investment that we're going to have in the fund. And if you want more information about that, happy to tell you about it. And then, hey, we now have our first institutional investor who's committed to the fund. And then we actually now have two really large institutions that have committed to our first fund. And we think we're going to end up closing on the timeline that we set out. We sort of had this drip campaign that built momentum in our fundraise.

And BAPS, that's part of what you're alluding to. Some have used this analogy of this like orchestra where it just classical music orchestra where it just keeps on getting louder and louder until the end and then everybody's kind of banging their instruments. You kind of want to build up this momentum until at some point it becomes inevitable for different investors. What other pieces of advice did you give to these top spinouts like Sarah from Conviction or Thomas from theory?

A couple of the things that I've said over and over again, quality matters in that initial LP base. And so we ended up actually having, having quite a bit of choice for who to work with. And so we picked LPs based on just the quality of their roster of managers, the references that we did on them to understand how they show up in the market. We also wanted some diversity of capital too. And so I've tried to give advice on sort of the best construct,

especially for folks that are likely to have choice in their LP base. One thing we really benefited from was the first. high we made at footwork. Catherine on our team is our chief of staff and she's been with us since before we closed fun one. But she's just instrumental to setting up all the back office processes. If I would do it again, it would still be Catherine, but also a set of AI agents at the very beginning to build a firm with. But given this advice to other folks, which is if you can find someone who's just an amazing all-rounder, an athlete who can handle a bunch of the stuff that you don't want to deal with or shouldn't really be dealing with in the early days, it's going to

give you a tremendous amount of benefit to make sure you're focused on finding the next great company, making investment decisions, et cetera. And then I think the thing that I said earlier about exceptional companies as of exceptions is something I've reiterated to other managers as well, which is, yes, you said you were going to do something when you raised the fund, but don't shy away from breaking the rules. So those are some of the things that I think are important for all of us to be thinking about. What's some of the mistakes that you made in your first couple of funds that you're evolving as you go into your fund three presumably in the next couple of years?

Perhaps we're too rigid on portfolio construction, and hence why I talk about just the new, need for more exceptions and to not be afraid of those. We started pre the chat GPT moment. And so it was hard to anticipate it. If all of us had done, we would have obviously been in a better position than we are right now with the types of companies we invested in 2021 and early 22, let's say. We could have responded even more quickly post the chat GPT moment is something I reflect on and making sure the next time we feel there's a serious wave happening that we're really quick to respond, we're really quick to learn, we're really quick to adjust,

will be important. So those are some of the things that I think we could have done better. The other thing is, the one thing I underestimated when starting the firm is just how demanding all of the activity in starting a firm is and how much less time you have for writing and thinking on a consistent basis, such as I felt like I had before starting the firm. And so that's another thing I want to adjust if I could have gone back in time. Well, Nicale, this was an absolute masterclass. Thanks so much for stopping by. Thanks, David. This was a lot of fun. If you enjoyed this conversation, want to hear more conversations with many of the world's leading investors. Subscribe below.