AI-Driven Investing: Alex Sen & Nnamdi Okike on Transforming Venture Capital and Private Equity

Prompting is the new skill at the entry level.

You won't need as many people to do the work.

I don't think people quite know how good AI can be.

You're moving into this world that a lot of people are determining, like a system of action.

Software itself as a concept was just totally changing.

AI will force the industry to continue to evolve.

Money is not the differentiator for VCs anymore. It's like the stuff you can do on top.

There's a new wave that could really displace those companies.

Welcome to the venture capital podcast today. Peter Harris and I are today joined by the CEO Alex Sen and Namdi Okiki. And Namdi is an investor in Alex's company. And we're going to talk about AI driven investing and kind of the future of it. But first, Alex, do you want to, you can explain it better than I can, but do you want to explain what your business is? And then, Nandy, like, you know, the follow up question would be, you know, what you saw and why you decided to invest.

Yeah, totally. So thanks for having me, John and, and Peter. I started Meridian about two years ago. I'd spent nearly 10 years in the private equity business as an investor. I was at firms like Blackstone and Thoma Bravo. So some of the world's biggest firms, and I would say one of the common themes of that career was just wrestling with horrible software pretty much across the stack. CRM in particular was the worst offender to me, partly because of how bad the legacy tools were, but partly because of how great I thought the promise of these tools could be. Theoretically, you know, the CRM and private equity is this piece of digital real estate that sits right at the center of information flow. So who are you meeting with, who you're emailing, what deals move through your pipeline and what deals are you passing on? And, you know, all of that associated data and put together, that's just this insane data asset that you could run all sorts of intelligence on top of. Where should we be spending our time? What is our mandate? Who are the kind of highest promised deals and companies that we need to get in front of? And the tools that I was using, they were actually just like a source of headache and friction. Like they weren't helping me, they were actually slowing me down. And so I really saw this great opportunity, particularly as we'd just gone through a deal cloud implementation, which is sort of one of the legacy tools that PE firms are using. And it was just clunky, it wasn't integrated, it was really expensive. And right at that time, ChatGPT was coming out and it became clear that software itself as a concept was just totally changing. You could build it faster, you could build it better, you could build it with AI vertically integrated and you could just integrate it again with all of this data that's out there in the world on private companies that as a private equity investor you need to start tapping in order to meet the best companies faster than your competitors. So we built a next gen CRM and deal sourcing tool that helps private equity firms map their landscape, track the companies they care about, and ultimately source proprietary deals. So deals that are not brought to you by investment banks in auctions that ultimately lead to you paying a higher price or just losing deals to your competitors. So we've been fortunate enough to have been adopted now by some of the world's largest firms and we're growing really fast and in many parts thanks to Namdi and 645 Ventures who led our seed round. They saw some of the potential and actually 645 ventures is one of the most forward looking firms I think when it comes to using software and technology to source deals. They were kind of doing what Meridian does many years before Meridian started doing it. So they'd actually built a lot of this stuff themselves. So. Really interesting. Yeah, Namdi, I'll hand over to you.

Yeah, that was a $7 million round, right?

That was, yeah.

Part of, part of my job is to help you guys humble brag, so.

Thank you, John. Yeah, appreciate that.

That's never easy to do. That shows traction.

Well, first of all, great to be here guys. Really appreciate Peter and John, you guys having me and Alex really covered a lot of it. I'll provide the perspective coming from an investor, an investor that as Alex described, has been, I think for us and myself in particular, really forward thinking around the use of software within venture capital. So we started our fund 10 years ago. I co founded it with my partner Aaron. And part of our mission statement in belief was that you could use software, analytics and data to do the job of the VC better and really supercharge your operation. So we like to say we built our firm on top of a software platform we call Voyager. And the whole idea behind that was to be able to use software and data to eliminate a lot of the traditional shortcomings of venture capital. For all the things that VCs do, well, there are a lot of things that are roadblocks, everything from the inability to track companies. And we do early stage investing. So companies morph and Change and pivot and evolve. And if you're passing on a company like most VC firms do, with every deal that most deals they look at, it might become something great. And you want to be able to track it and understand how it's changing. There are other elements. You want to be able to slice and dice the data and look at trends and patterns in markets. You want to be able to go back to the research and diligence you did and be able to understand what it's telling you and if a market's ready for prime time, as we say. So there was a lot of things that we wanted to be able to do that necessitated the use of software in data and analytics. And we started our firm over 10 years ago. So at that time is really the pre AI era. What we were looking to build was really, I would describe it as a smart data repository to be able to do some level of analytics and to be able to see patterns and trends. But we were pre, you know, GPT, pre A lot of the more advanced AI that's been built, you know, pregen AI. At the time, people were talking about machine learning. And so it's been amazing to see over the past decade the evolution of software and technology. And like Alex was describing, now you're truly able to apply AI throughout your operation. And so for us as investors, one, we've always been believers in this model. As we started to see new software companies evolving that were truly AI driven, that really piqued our interest because we felt that the technology was now ready for primetime, where more firms would adopt it. If you think about the history of software within venture private equity, you have had big players. But typically, like Alex described, the tools haven't really been great. They've been, you know, static repositories, you know, things that firms just kind of deal with and use but don't love. And, you know, there have been some big entrenched players and we felt that really for the first time in a long time, there was a new wave that could really displace those companies. And so that led us to say, okay, is this a category we want to invest in in terms of putting capital into a new company? And that really enabled us to start to get very active and eventually lead the seed round for Meridian.

Well, congrats.

Thanks. Yeah, we've been excited to partner with them and we feel like they're ahead of the curve. And I think a lot of it is Alex's experience. Having seen the shortcomings of existing products firsthand. We have A term we use called purity of motivation. It describes the why behind a founding team's desire to start a business. And we feel in the case of Meridian, that truly is there. Alex saw the problem, he wanted to solve his own problem. It catalyzed him starting a company. And then you know, it's, that's a great place to start and it's a great kind of founding motivation.

What did Alex see that other people didn't see?

You know I think the, the first reason people told me not to start this company is know when I said hey, I'm really interested in like solving this problem was the problem's not big enough. Like private equity, venture capital, like these are small industries, these are like, and typically, you know, VCs kind of look like SMBs, right? It's like six or seven people private equity, they, they've just been bad software bias historically. And some of that is because you look at the history of the industry, Even in the 2000s it was like, you know, early 2000s, it was like Henry Kravis in a room with a few other people doing like big buyouts. You, you didn't need these big software systems. But you fast forward. I mean it's just incredible the, the growth that the alternatives industry has been through. In general. Private equity firms look like enterprises now. They have thousands of people. You get VC firms like 645 ventures that are super institutionalized. You know, they have a bunch of people speaking to companies. They have really formal investment committee processes. They need workflow tools. So I think the first thing that I thought that was, you know, maybe a bit counter, counterintuitive two years ago was this is actually a big software market. What's fascinating when you think about M and A and we have customers that are not just private equity in vc. We have private credit investment banks, hedge funds, we even have corporates like Fortune 500 companies that are doing M and A. So what's interesting is buying and selling private companies is actually a huge problem now that, that you know, is so inefficient that needs to be solved. There are now many more private equity back companies than public companies in the US and so I kind of took a step back and looked at all the big multi billion dollar companies that you've heard of that serve public companies and public company investors, Bloomberg, Fact, Thompson Raiders, etc and I think a lot of companies serving the private markets are now going to kind of reach that size. So I think TAM was something people didn't see that I saw that where now it's really playing out and it's really broad. And I think the other thing that I saw in terms of the opportunity was combining the workflow software with data. And that was something that just hadn't happened before. Like you could build a great CRM like Salesforce did. You know, in the 90s, it's not so great anymore. It's like really bad now, in fact. But that was useful if you have a bunch of people manually entering the data all the time. So, like, we're looking at a new deal. There are 10 data points we want to capture revenue, gross margin, EBITDA, whatever. Like, let's make sure all of our associates and junior people are just constantly plugging numbers in. It works until it doesn't. Like, good luck getting, you know, a bunch of junior VC people to just like constantly be doing manual data entry. And the second that people stop, it becomes a stale, dead database. Like, it's not useful to you anymore. Now, on the flip side, there are all these companies like PitchBook, Source Scrub, Grata, Nven, Crunchbase, like just this long list, I'm sure you guys are familiar with them, that are phenomenally expensive, that give you all this data on companies. And as a junior guy in private equity, you kind of sit there sitting between the databases and the CRM and you're just like copying and pasting data from one to the other. We really saw the opportunity to unify that. Like, if you're working in a CRM, meeting, companies, logging notes, et cetera, you should feel confident that all the data that's out there on the businesses you're speaking to is getting piped in automatically. And so we think over time that we can actually start to own a lot more of that kind of unified workflow data stack.

Are you pumping in or bringing in data from like, PitchBook and CrunchBase, or do you have your own data set?

We do both. So we can act like Switzerland. We have APIs that kind of natively integrate with all of the major data players. But we also are really investing in our own data stack. And this whole private company data industry is kind of getting rocked right now. By AI, I mean, a few years ago it made sense for you to hire a bunch of people offshore to go through these transcripts, go through these filings, press releases, et cetera, and just pull out numbers and structure them so that you could sell them back to investors. It's one of the perfect AI use cases, right? Taking unstructured data that's publicly available and structuring it. And so There is so much that we've been able to achieve with AI agents going out there and researching the companies in your pipeline. And we also buy data from other places, so we aggregate our own data, we bundle it with our pricing and we think over time we're going to take a lot of wallet share from these data providers.

Okay, right now, does it, does your software, do you have a benchmark about how many people that it frees up at a firm or the equivalent work workload it handles?

I think a lot of deal sourcing and I'd love to hear name these perspectives here, but there is so much manual work that's involved in just creating these lists, figuring out who you want to speak to and running the logistics of actually setting up these meetings. What we do is we take a lot of that work off people's plates and we let them do, you know, the really human part of investing, which is sitting down with the CEO and deciding if that's a person you want to back or not. And so, you know, we free up hours and hours a day of emailing back and forth, figuring out when you last met someone, collating your notes, preparing a pre meeting brief for your boss who's about to go meet, you know, the CEO of a cool company. The tool does that, right? And I think that's the magic of AI Agentix software is that it can actually do stuff now. It can kind of proactively say, we know you're meeting with this person, here's a bunch of notes that can help you prepare for that meeting. Or you said you wanted to speak to this guy in six months time when he was raising. You haven't spoken to him yet. Hey, you should go email him right now. So we really try to make our software proactive versus like retroactive, which is just, you know, putting notes into a, into a system of record.

Okay, how, Maddie, how, how big do you think the TAM is for this market or how do you approach that?

That's a great question. You know, definitely in the several billion, I can look back at how we sized it, but definitely in the multiple billion dollar range in growing. We do think AI is going to grow the, the TAM meaningfully to the point that Alex brought up in the previous world where it was just a static database or you know, you mentioned like a pitchbook, right, which is a source of data. Many of the underlying processes that private equity and venture firms were doing weren't really automatable and that's really where the true value comes from. So if you just kind of step back and think about it. You might be willing to pay, you might be willing to pay X for a data source or Y for a place to store your data, but you, you pay multiple times that if it actually can one enable you to get to a deal faster, have a high probability of winning a deal, or even just see a deal you never would have seen. Not to mention you have to hire less folks. And I think that's something that I think is, is likely going to result in many, you know, professional services firms where at the entry level you won't need as many people do the work. Because to the point that Alex made, if the software is flagging companies, sourcing companies for you, identifying them like that's a lot of what the analyst job is or associate job might be at a fund. Now the good news is you can free people up to do other things, right? You can empower them a lot with a lot more knowledge and information and you know, enable them to cover more companies or cover the right companies, which I think every fund is aiming to do. It's like spend less time on the deals that aren't going to fit your, your strike zone. And the reality is in venture and private equity, like, you spend a lot of time on deals that aren't a fit for your firm because you don't have the information to know that, right? So you're doing a lot of manual work to figure out, okay, how much revenue does company have or is it like truly in my, you know, strike zone versus if you have that information, you can just better optimize your time for the deals you want to win, right. And the things that actually are important to differentiate yourself with a team. So long story short, we think the market's going to grow. But when we looked at the market, part of what we typically do is we say, okay, like given what we know about the market today, is it large enough, if everything, if things go right, for this to be what we call a fund returner? And what intrigued us, I think, to Alex's point, was even in the past decade where the market isn't a market that people perceive to be large, there were some pretty sizable exits. Companies like Efron, Dynamo Software, There are multiple companies that have been billion dollar plus exits. And so we said to ourselves, that's interesting. And these are companies that have gotten to 100 plus million revenues. So we said to ourselves, if the market's going to grow and in our view be a lot larger and AI is truly a game changer, then theoretically you should see the winners being even bigger than they've been in the past. And I think that got us very intrigued to say, look, if we can identify a company that we think has that potential, when it's all said and done, you could see a much bigger outcome than you'd seen historically. And again, I'm looking at our list of companies. We were looking at businesses like Efron, which got over 100 million revenues, or some of the newer players, Prequen. There's DealCloud, which is pretty sizable. There are companies like FactSet, which provide data to a bunch of different types of companies, more data providers. And, and so I think historically you saw companies either playing in the data world or more like static data repository. And now you're moving into this world that a lot of people are determining, like a system of action which actually can do things for you, which are just much more valuable in this kind of like VC private equity operations, you know, world. So those things got us excited about the market potential.

Got it. So your hypothesis that AI increases the TAM size for, for the, for software, for private equity.

Yeah, definitely. And we think it could increase it meaningfully. And we think again, it's because it's doing a lot more of the things that historically you would have paid people to do, but it's also enabling better performance. And we are like, these industries are me ventures, the epitome of this. It's like a pop. People talk about the term power law, but it truly is like if, if software helps you find a deal that is a fund returner, like you theoretically pay a lot for that. Now it's. You don't know that going in, but I think, really, I think where it proves its value is again, like enabling you to do certain things much better. And, you know, if you can identify a company you wouldn't have seen before, like Alex is saying, even identify when the founder is raising. I mean, that's like just one piece of the puzzle. But, you know, if you're tracking thousands of companies, you know, it's very hard manually to know like this company is going to be raising money. Right. Or it's hitting an inflection point or what have you. And like timing is everything in, in, in our worlds. So there's just so many areas of value. So, yeah, short story is we think, we think AI grows the TAM meaningfully.

Got it. When I was at the SASTER conference, Jason Lemkin's approach, which sounds like it's different from yours and it may just be different markets, is that his opinion is that AI like that a lot of the SaaS companies right now can't charge for AI. It's just something that customers expect to be included.

Now, I think, yeah, I mean, AI is increasingly table stakes. It is so hard for legacy software companies to effectively integrate AI. Like anyone can slap a chatbot on top of a database and say, okay, we've got AI. How useful is that? Not particularly. I think especially in private equity systems are record. There's so much nuance that you don't get by just, you know, plugging in a very basic database into AI in terms of combining unstructured and structured data, for example, like reading financials, accurately understanding, you know, the concepts between how a company might evolve over like even the diligence period. So that there's a lot of nuance. But I think in terms of the value you get from AI, Namdi kind of nailed it is like our AI is actually doing stuff. We know the workflows that a VC analyst is going through every day where he looks at his thousand companies that he's tracking and we can not only make his life easier, we can do a lot of those things for him. We can skim the news every morning on the companies that he covers. We can go check if they've recently hired or laid off a bunch of people. We can go see if there have been personnel changes, those companies. So we can just start to track these events over literally tens and tens of thousands of CRM objects and distill that into just a few actionable items that you can start to attack every single day to make yourself a better, higher powered investor. So I think as much as people want to have AI, I don't think people quite know how good AI can be right now because they've been playing with Salesforce's chatbot and that's just a better way to read and pivot data. It's not actually doing things. So software increasingly, and I think software and AI are going to be, you know, one thing in a couple of years. People won't think of them as being separate. But I think the bar for software now is that it does your work for you to some extent. And that's why to Namdi's point, we can actually start charging more for it. Like our, our tool is actually more expensive than our competitors, but we're still seeing success kind of displacing our competitors.

Got it. Peter, how do you look at the space?

So I'm curious, what do you guys think? So there's a lot of Discussion, debate about whether or not venture capital gets disrupted by AI. And I'm curious, kind of both of your perspectives on that, right? Like is the end state of Meridian that you raise a fund that layers on top the AI and you just cut out Namby altogether, right?

And what.

And me, right?

And what we're trying to do, you know, like when I was in private equity and you'd think about these big auction processes, right, where it's like Raymond James is going to send a SIM to 100 middle market private equity investors and then the same data room to everyone. And like the actual outputs are factual. So the cohort analysis, like there's a right and a wrong answer. And so everyone's going to do the same cohort analysis, everyone's going to build a model that's roughly the same. Like there are only so many inputs. But you would see firms underwriting valuations just totally differently. They had a different mental model. One guy just felt like the CEO was a killer and he read that guy, right, or he read him wrong and thought he was a killer and he wasn't the killer. So there's just a level of human intuition, I think, particularly on the people side, that is actually what makes you a really good private equity investor. The reason why, you know, you have to do all this mechanical work as a junior guy, I think is, is really just to prepare you to get to the senior level because it's the senior guys making the decisions who can read the room, read the people, or read the situation. So my sense is Namdi at some point might be able to run this firm entirely by himself without all of the people that he has doing a lot of that work. But you're still going to need him meeting people like me and deciding, is this someone I trust with 7 million bucks?

Yeah, I think he's going to acquire Meridian and be the next trillionaire.

Very good.

And actually that, that's an interesting point. I'm curious before, before we turn it over to namdi, because you're the founder, right?

That's right.

And so to a certain extent, like, why take money from Namdi, right? Like, is there a world in which you're like, hey, you know, if, if I just upload a bunch of stuff to the AI and the AI cuts me a check, is that, is that just as good right now? Now that. Now obviously, like a little hard to say, like, ah, yeah, the AI is just as good or better on this call. Right? But on this podcast. But I mean that, that is kind of the question I saw an a LinkedIn post the other day from a friend of mine and he was like, my wife sat down, she Vibe, she had worked in like private equity. She vibe coded an app that analyzes and outputs a decision. And he put it out there and he was like, apply, anyone can apply. And if the AI says yes, we will cut you a check. And the structure is an rvf, so revenue based financing and they had a limit and all this stuff. But like it was like, wow, okay, like that's interesting. And they were specifically going after, you know, startups and stuff.

So yeah, you know, listen, all of that stuff is interesting and I love seeing people vibe code and all the stuff that they're creating. Again, to not to humble brag, but I think John gave me permission. We for sure, we got a lot of term sheets. You know, we, we, we got a lot of term sheets at the same value. And Namdi and his firm kind of really differentiated themselves just by the things they could do for us as well. So it wasn't just really about the money. And you know, an AI VC firm could have written me a check, that's for sure. But they wouldn't have helped me get in front of customers, they wouldn't have helped me hiring. They wouldn't have helped me do all the things that, you know, you don't get necessarily as a solo founder. Like it can be lonely and you don't get as many resources as you do if you do Y Combinator or some of those accelerators that we didn't do.

Right.

So I think in many ways money is not the differentiator for VCs anymore. It's like the stuff you can do on top.

All right, Namby, what do you think?

Yeah, no, no, these are great, These are great discussion points and I'm just thinking through how to assemble my thoughts. So I think there's a few things you guys have touched on. I do think one, there are certain aspects of the venture capital and private equity process that AI can do, I would say, as well as humans today and likely better in the future. Those are really a lot of the analytical tasks. Right. So crunching huge number of, you know, look at a huge number of companies and figuring out which ones are most likely to fit your model or analyzing, you know, a lot of call transcripts to pull out the most valuable information from them. And we use those tools today, for example, in our operations. I think they're analytical steps in the process that AI even now is doing as well and in the future will do A lot better. There are a lot of things outside of that in a venture capital operation, private equity operation. I think Alex touched on a lot of those in terms of, you know, customer intros, connections, a lot of things that do require some level of human interaction at least today. I mean maybe in 10 years you might have, you know, some of those things being somewhat automated. But I think that will take, will take some time. So I think on a net basis, I think what AI ideally will do is one, enable firms to step up their game in some of the areas where they're still relying on very manual steps. And also there, there won't be an excuse, for example for not being able to like cover a company. I mean now you know, know, practically speaking, like a lot of VCs will talk to a company and like forget they even talk to them because like they don't have time to you know, look through the notes and remember to call them back. Right? Like that those things shouldn't happen. Right. Like a lot of the areas where VCS are like time constrained or like basically like people constraint, like those things should go away. So I do feel that overall like investors should be able to invest better and prioritize the more important steps that they really have, you know, that that inherently are like where they should shine in terms of like number of firms that will exist. I think theoretically, I mean again like you know, there shouldn't be firms like that are performing really poorly. And I think firms that don't adopt tech and AI, like, I think some of them like won't exist. I think folks that resist, you know, this, this wave, I think it's likely that they're going to be really left behind. And I think in venture especially you do have some folks that like adhere to the like really old school mentalities and I think they've worked for a long time but I think now I think it's likely that like some of those things really won't work.

Well.

You have like a, I think it's interesting to think about like you know, firms that have like very few people and what they will do. I do think it's more likely that that would exist in certain areas like say the hedge fund world or you know, areas where like you don't do a lot of like say you're like a non activist hedge fund, you're just really picking stocks, you know, things like that. I could see, you know, like very few people being needed for that, you know, and, and, and so, and yeah, we'll see. I mean I Think it'd be really interesting. I do think like in VC and pe, like folks will, will try that and I think it'll be interesting to see the results of it. I do think that there are still some elements of, and I've always been anti, like gut feel and subjectivity. I think we, we err on the side of like discrediting some of that stuff. But I do think at the end of the day, like, that, that does exist in investing. There are certain things that are really hard to quantify and at the end of the day you have to make a decision. And I think there is, there is some value in like some of those learned human behaviors that maybe it will take a while for AI to truly like, understand. So. But I think it's gonna be fascinating. I do think like firms will be transformed in some fundamental ways.

Yeah, I think like, there's some elements too of like. And I don't know if Alex does this to you, but I have founders that call me at midnight and they're like crap or let me vent or you know, whatever. And so to Alex's earlier point, it's, it's not so much the capital, right. It's, it's all the other things that allow a firm to differentiate itself. And one of those is feeling like you have someone in your corner. Right. Especially because it can be lonely. Like to Alex's earlier point, right.

So yeah, it can be a lonely road. And especially, you know, your lead investor. Like I just came from a, you know, multiple hour dinner with some of our founders and we were catching up and talking and chatting and you know, that human level of connection I think is valuable both for the founder and for the investor. You know, we, we really like working with founders. You know, we pride ourselves in helping them. And I think that stuff, you know, it's hard to replicate with technology.

I'm curious what your thoughts. So this is something that's been on my mind a lot lately and that is, I think AI is incredible for helping people with experience up their productivity. I think it is very challenging for junior staff from a perspective of like, I don't even need junior staff anymore because I can just like take the data room, dump it into AI and have it punch out an investment memo for me. That in many ways is better now. Now granted, like, we run a student fund where we teach students how to do venture investing. And that's a big part of what we do. So like, nice. Their, their stuff is never that great. Um, and that's part of the point that's fine, but, but even like junior analysts, you know, that have some experience, right. We're going to get to a point where the AI will produce superior research. And I think about how to me like, my ability to craft an amazing prompt that gives me the insights that I need comes from 16 years of venture experience. And I worry about, you know, the junior people getting that experience. I'm, I'm curious how you guys think about that and, and, and if there are solutions that you're seeing or, or you know, where we end up from.

That perspective, I can jump in on that and would love your thoughts. Alex too. I do worry about that. I worry about that a lot, especially when it comes to people learning how to be investors. Yeah, I came up through the traditional way. Like, I started as an analyst and I was a, I call myself a glorified cold caller, but my job was basically calling up founders, talking to them, and then running a bunch of analyses on their, on their businesses and then presenting the work to my senior, you know, team members and saying, hey, this issuing deal we should look at. And if I think about like the things I did as a junior person, many of those things I don't need, like as a now somebody running a fund, I don't need junior people to do, like I can myself, you know, like run a market sizing, you know, run, you know, competitor analysis pretty quickly now. Not to mention there's just a lot of that manual work that used to be done that I don't need somebody junior to do. And so to your point, like, I think this is the case across different, like kind of white collar jobs or professional services jobs where the entry level experiences that you get will be fundamentally different. And I do think a lot about one, like, will there be enough jobs to kind of like enable folks to join the industry, but also like, what are they going to be doing? Because to your point, like, it used to be the case and it's always been the case that like the more senior level stuff, like, takes experience and learning, but if you're not able to like have that experience in learning, like, how are you going to get to that, you know, senior level? What's that going to look like? You know, like, I don't think we'd hire somebody to come in and just start making decisions on investments. Right? Like, so, you know, like, so it is kind of interesting to think about, like, what are firms going to look like? And like, is there going to be that like, level of training, you know, for, for junior people? So a Little bit worrisome.

Yeah. But it's also one of those things, before we turn it over to Alex, is like you have, to your earlier point, you have to be using AI because if you're not, you get left behind. Right. And there will be a shakeout of like firms that are like, oh, this AI stuff. Right. And they're probably going to go away. To your earlier point.

Yeah.

So it's a little bit of this catch 22, but yeah, sorry, go ahead, Alex.

It's a great question. I think private investing is such an apprenticeship model like that. There's a reason why firms spend so much time recruiting and then training people and then forcing you to do all this like super repeatable manual work. It's because it does actually make you a better principal, which makes you a better partner. I went through the traditional route too. I used to kind of sleep under my desk, you know, mecha cap private equity firms. And I look back at some of the work I did and it's almost crazy that someone could, could have done what would have taken me five days in like five minutes. Like I would painstakingly build these models and write these, invest my memories. And I almost wish I could go back in time and like, you know, show myself how quickly someone could do it. Now it's kind of mind blowing, but I learned it a lot, right? Like I. You learn how to kind of craft these three statement models. It's, it's, it's kind of a joke in the industry, but like if you don't know how the cash flow statement impacts the balance sheet and vice versa, which is like the classic interview question, like, you know, good luck when you're a partner underwriting an insurance company or whatever it may be. So there's just, there is just some technical know how that's really valuable. Prompting is the new skill.

Right.

Like, I think you need to learn how to work alongside the AI tools and make yourself like a 100x analysts. So I think it's very similar to software engineering now or like the best software engineers are also the best using AI and so they're the ones that get that multiplier effect. So I think firms need to be intentional about how to train and bringing these AI tools like from the ground up and sort of, you know, I remember first learning how to use Excel macros and I was like, oh my God, like I'm twice as productive now and that was cool. And then I started forcing all of my analysts to use Excel macros and like, you know, the basic one is you take the mouse away from someone and it sucks for like three days and then turns out three weeks later, they're actually way better at Excel. So I think, like, it's kind of that analogy, like you got to take the mouse away from people, you got to give them the macros. And then, you know, this, this just happens to be a 100x macro, not just like a 1.5x macro. So, yeah, I think you have to be intentional about it, but I do think a lot of people aren't going to make it. And I think another perspective I have on this is during COVID you know, a lot of right after Covid, we were hiring, you know, associates. So people that had done their analyst years remotely and I couldn't find good people because they hadn't learned what it meant to sit in an office, sit and communicate with your boss in person, speak during a meeting, you know, and they would come and they'd kind of speak in their first meeting and they'd be like, sweating, like they didn't know how to do it because they hadn't spent those two years like in the bullpen doing the job. So, yeah, I mean, I think, I think people are going to be differentiated by their ability to adopt the technology and leverage themselves.

Yeah, I mean, it could also open up, you know. Well, I think one of the big things is I'll be curious to see how education leverages AI. And when I say education, I mean clearly, like universities, K12, all of that, but also like corporate education. What. How do you leverage AI to accelerate learning? Because it feels like that should be a possibility of, like, you don't need 16 years of venture experience because AI can, like, accelerate that learning and you can condense it into a year or something like that. Right. And so that, those are some of the things that I like, get think about and get excited about because, yeah, the flip side is if you can learn it all in a year and you get really good, we could have a whole bunch of venture funds, you know, of one man shops, one woman shops. Right. That are out there and providing, you know, really great, interesting value add to entrepreneurs and supporting them.

I'm also, every time just creates more.

Jobs for other things too. Right. So anyways, go ahead.

No, I was going to say, I mean, every time Namdi looks at a new deal, he's got to become an expert on that specific space or subsector. Right. And so I think he probably wouldn't have been comfortable a few years ago looking at a space you knew nothing about you can accelerate your education, you know, on each opportunity in a way that you couldn't before.

Yep, he nurtured his gut.

You know.

So that gut versus what we talk about in the space.

So the other thing that I wonder about too is that like, as AI clearly like makes us more productive as investors. And that's the same for a lot of, you know, white collar jobs in particular and eventually blue collar as. As well, of course. But does it create kind of like an inflationary effect where the people that don't want to do it get washed out, but then it just becomes the, the status quo. Like I think about when my car started driving itself and people are like, oh, it's gonna be great. You're gonna have all this like free time to like watch shows and like. And I'm like, no, I'm not. I'm gonna, I'm gonna be working. Like it's gonna give me an hour of work on my commute. That's all it's gonna do. Right. So I wonder about that a little bit too. And, and like, clearly like it is the future and so you have to do it. But curious how you think about some of that.

I think the bar will go up and it should go up in terms of how firms operate and how good they are. Like, I've been adventure a long time. Like, you know, don't age myself, but I started investing in the early 2000s. So, you know, I've seen this industry evolved pretty dramatically. We're back in that time, small number of firms, you know, like seed didn't exist. You know, you had a lot of generalist firms investing across areas, but like, you know, very little specialization. You know, as a cottage industry, super hard to get in front of a vc. There was like a lot of realities about, about venture that don't. That are very different today. Like now there's thousands of funds, there's folks starting funds with, with, with, you know, no experience in, in being able to raise money, in some cases doing well. There's a lot of specialists now. Like, regardless of what you're building, there's a fund that just does that, you know, in your geography, you know, the super hyper specialization. So the industry has evolved a lot and my guess is like AI will force the industry to continue to evolve. You know, I think how it evolves specifically will be super interesting. You know, like, so for example, if you can get up to speed on a deal super quickly, like will that enable more firms to be generalist again, where you can look at a Lot of different things. And like, so, so one thing I think a lot about is like, again, like, AI is great for the analytical steps. And so we have to think about, okay, like, how does that impact the analytical aspect of the firm? But then the other things like value add and partnership and some of those elements, like, can they be supercharged as well? Like, you know, how would that impact firm operations in that regard? But to your point, like, people will still be working definitely. I think, I think, I do think, like, the overall bar is going to be higher. I think the given. Any given firm will be able to cover more companies and probably cover them in with more detail. My guess is, like, it's going to be more competitive, is my guess. Like, one thing that I haven't loved about the industry is like, the speed of decision making now where, you know, you have deals that get done in like a period of week or two and, you know, like, almost like a shotgun marriage type of thing. Because, like, some firms move extremely quickly and they give a founder a term sheet, like, very fast. And like, a lot of founders want to take the best price. And I feel like in the world of AI, like, that might even be supercharged. Like, you have your algorithm that tells you as you invest and you write a check, like, you know, on the spot, things like that. So, like, I, I don't love that about the industry because I think, like, inevitably there are some, like, very human aspects that you can't really, like, speed up that much. You know, like, it's hard to, like, speed up the relationship between a founder and firm, like, you know, regardless of how much analysis you did. Like, so, so I, so I, I think it's going to, like, fundamentally change the industry for good and good, probably good and bad ways. I hope that, like, the bar goes up in terms of, like, quality of investing and also, like, quality of like, partnership and help. But I think cynically, I do think, like, it might exacerbate, like, some of the behaviors that I think haven't been great for the industry overall. Again, because, like, you know, you're democratizing some aspects. And I think some firms might be like, okay, well, the other things don't matter don't matter that much. Like, theoretically, you could have an algorithm that just enables you to pay a higher price and, you know, like, you're like, okay, I'm just going to win the best deals and pay much higher than somebody else. And I'm sure some firms will try that strategy. Maybe like the, the tiger strategy. A couple years ago on Steroids you know, using AI, like see that happening, right? So I do think like it might magnify and supercharge like some negative behaviors too. Be really interesting to see though.

Yeah, probably makes venture more efficient and private equity more efficient and probably frankly drives down some of the, the alpha right. In the industry as, as the economy gets more efficient. Right. You see, you know, economic profits compress.

I think that's right.

But I, but I think the advice I would have to founders is like, look, you're probably going to get more term sheets. You're going to get closer to what the true price of your company should be. Slow down, take your time, spend it like, figure out like, okay, if everyone's kind of going to come in around the same because they're all using, you know, roughly the same tools, figure out who it is you want to be married to. Right. Give it some time. Anyways, sounded like you had a comment, Alex.

No, I totally agree. I mean, I think it makes all industries more competitive. I think VC is no different. I mean, it's certainly making the SaaS industry more competitive and it raises the bar. And I think about from my business, people ask me all the time like, hey, you know, I vibe coded this app, you know, aren't you worried that some guy in a garage is going to go, you know, vi code a next gen private equity CRM? And my response is sort of, well, imagine we have 25 of the world's best engineers all utilizing those tools as well. And they're not just able to, you know, vibe code a CRM. They're able to do stuff that you would never even have been able to imagine software could do. So it makes your expectation as a buyer of this software just so much higher. Like we just need to do way better and build better. And I think it's going to be the same case in venture capital. Like, yes, you could underwrite a thousand deals a year using AI, but you just have to be a fundamentally better firm in every level now to compete against all these other different businesses. So I think it's actually going to result in really great value for founders and it's going to have to make VC firms be better, which I think.

Is, is a net positive for everybody. Yeah, as much as, as much as I don't want to have to work harder, it's good for me.

Peter's got to start working now.

That's what I heard.

No more of these trips to here.

No swear.

Well, I know we are, we're short on time, so thanks everyone for joining. Alex, I know you're. You're super busy as a founder. And Namdi, I know you're super busy in the. In the Venture space, so thank you for your time. We'll include your links down here. And we appreciate you joining the podcast.

Thanks so much.

Thank you, guys. Real thanks a lot.