Matt Ober: How Dan Loeb's Fmr Data Chief Finds an Unfair Edge in Funds

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How do I build a data edge with no team or budget?

Matt Ober's first move: go all in on Claude and ensure every tool has an MCP connection. Fund admin, LP communications, compliance, capital calls, unified through MCP. Operations automate and you return to what you are paid to do.

In this episode of Making Billions, Ryan Miller sits down with Matt Ober, General Partner at Social Leverage. Their riveting conversation covers building fund data infrastructure from nothing using MCP-connected tools! Separating alpha data from beta data before spending a dollar & why most AI fundraising tools are gimmicks. 

They discuss how prediction markets are becoming the most important new institutional signal, and the single discipline separating managers who turn data into returns from those who burn through budgets with nothing to show.

What is the difference between alpha data and beta data?

Alpha is a fleeting trading advantage. Beta is sticky and pays the bills. The data that was once edge is now infrastructure. The new edge is using AI to synthesize more data faster.

[THE HOST]: Ryan Miller is a fund manager, capital strategist, and former CFO turned angel investor in technology and energy. He is the founder of Fund Raise Capital and Aequor Capital Partners, and has mentored over 1,000 fund managers across private equity, private credit, venture capital, real estate, and alternative assets globally.

[THE GUEST]: Matt Ober, General Partner at Social Leverage, the seed-stage firm with over 500 million dollars AUM and more than 150 portfolio companies. He holds the CAIA charter, one of the most rigorous credentials in alternative asset management.

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Transcript

Ryan Miller

Hey, welcome to another episode of Making Billions. I'm your host, Ryan Miller, and today I have my dear friend Matt Ober. Matt is the general partner at Social Leverage, the seed stage firm with over 500 million AUM and more than 150 companies behind. Before that, he ran data strategy at the Quant Powerhouse WorldQuant and helped launch WorldQuant Ventures after cutting his teeth at Bloomberg. He's a CAIA charter holder and a cross-sell side, buy side, and venture. There are very few people on the planet who have seen how data actually creates an edge from every seed in the market. So what does this mean? Well, it means that the manager who learns to turn data into an advantage doesn't just pick better, they build a moat the big institutions can't easily copy. And Matt is about to show you and I how to do it.

Ryan Miller

Before we dive in, just a word from our sponsor. When doing deals, we all know that raising capital is the one thing that unlocks everything. That's why I've partnered with Reef Pass Investors that are actively funding deals right now. So if you're a deal syndicator or founder thinking about launching an M&A focused buy and build platform, reach out to Reef Pass Investors at reefpassInvestors.com.  They are one of the best investors in the game that are helping you launch a new long-term holding company. So here's what I want you to do: click the description in the notes and contact them for a discovery call and potentially get an invite to pitch your next M&A deal. Now, let's get back to the show. 

Ryan Miller

Matt, welcome to the show, man. 

Matt Ober

Thanks for having me. Excited to show fund managers how to turn data into the you know the edge that big institutions can't match.

Ryan Miller

Awesome, man. It's good to have you. Let's dive in. I know you've done some amazing things with data and you've turned that into funds. So yeah, I'm also very excited to have you here. So you've built the data science analytics and risk platform at Third Point. That's I believe Dan Loeb's multi-billion dollar edge fund. And you did that after running data strategy at WorldCom. And today you're a general partner at Social Leverage Bank. It's a firm that's built over 500 million AUM on more of 150 portfolio companies. So here's where I want to start. If you were launching a fund today with no data team, no budget, what are the first three moves you'd make to build a data edge that big institutions just can't copy?

Matt Ober

Yeah, I think in a world where I didn't have a team and everything around me, I'd go all in on Claude. I think also any software provider or tool that I would use have to be available through an MCP. So you think about the basics of just running a fund, you need a fund admin like Carta, have to have the MCP connection, LP communications, that as well. You know, you think about something like Beehive, which is a company we're invested in, which is a newsletter platform, all of that can be done through MCP. And then that just makes all of your capital calls and even your compliance using something like Archive Intel, which we you know invested in, just able to capture everything, store it, make sure you're saying yes, you know, compliant if you're SEC registered. But I think if you tie everything through Claude, I mean then you can start to build skills and use cloud code and really just automate, you know, all of your processes. 

Ryan Miller

All of your oh, I love that. So then I think when you're talking about like fundament and your communications, all this operational stuff. I think what you're talking about is to say, yeah, obviously we got to do that, but do it in a way, use software or things that have that MCP connectivity that's available. Once you have that, now you can start creating. Well, you didn't say this, but I will, but now you can start creating operations and fund management of the future. You can really start to dial in and create that edge. Is that what we're hearing here?

Matt Ober

Yeah, and I mean take away all the operational headaches and LP engagement that you need to do to you know, obviously build a fund and run a fund. And now you can get back to spending your time on what you're actually getting paid to do, which is, you know, investing in you know companies, finding founders, public markets, if that's what you're focused on.

Ryan Miller

Brilliant. So then I'm wondering about alpha data versus beta data. So, how does a fund manager tell the difference between a data set that sells fleeting trading advantage to one that sells essential sticky infrastructure before they even spend a single dollar on it?

Matt Ober

I mean, listen, I think the secret is like everybody wants alpha, but a data vendor wants to sell you that alpha, but, they really want to be beta, right? Like beta pays the bills, beta's sticky, beta's around for a long time. You live and die by alpha. So I think, you know, if it's too good to be true, it probably is. You know, you think about like 10-15 years ago, using credit card transaction data was like the real alpha in the hedge fund space and the investing world, right? Understanding on a daily, weekly basis how much people were spending in Starbucks. I would argue now if you're a real institutional investor and you're not using consumer transaction, credit card, debit card data, you're kind of investing blind, right? Data's available, it's correlated, it can predict things, but it's not really alpha. I think it's I think essentially it's just no, it's a building block. You need so different than you need fundamental data.

Ryan Miller

Yeah, it's pretty much standard now and in your ability to run a fund is to do a lot of those things that used to be an edge. But we're saying, I think what you're saying is there does, there still is an edge. You need to have one in business, it can exist, but the way of gaining an edge in the past through certain data brokers, it it's so standard now. It's essentially beta. Now the new way is using AI to manage data that helps with speed, but it also helps with further insight. Is that am I hearing that right?

Matt Ober

I think that's right. I mean, listen, I still think the best firms in the world are finding new sources of data, and data is used differently for different strategies. And so I think we all want to consume 100x more data these days, but pay less for it. But over time we'll spend more because, you know, if it gives us an edge, we're happy to pay for it. 

Ryan Miller

Yeah, you got it. I mean, and one thing, and I'd love to get your opinion on it. I certainly have a strong opinion here, but it seems right now in this age of AI and using it for business, which is awesome, and I love it and I hope people do it. But right now, and you keep me honest, man, if you think I'm misplaced, please keep me honest. But it seems like right now there's a lot of  AI promises by Vibecode Bros, I call them, that will create these things. Like right now, you see a lot that are saying, hey, we've got this AI thing, it's just gonna raise you money. You need to raise 500 million, subscribe to my AI tool and it'll go get it for you. I'm not so convinced right now. Maybe I'm just being pessimistic about this. I don't think so. What I'm saying is it seems like there's a lot of gimmicks. And right now, on this question, we're talking about separating what's real versus what's helpful and what gives you an edge. Have you noticed it seems a little gimmicky? Maybe promises are a little too strong right now, or do you think this is we're living in the future and everything's gonna work out well as it's being promised? What do you see? Is there gimmicks or not?

Matt Ober

I still think it's early, and I think that there's a cash run where there's a lot of opportunities to make money. The fundraising side is probably no different than the I'm a company and like you can do sales and make create right, use this tool and you'll get 20 demos a month. And you know, listen, some of these agents work. I think just like sales than like fundraising, like if you're cold emailing and LinkedIn and blasting everybody in the world, sure, you're gonna get some meetings. Maybe you close something. But the reality is like it's not gonna work for the long run. Eventually, it's like you're probably gonna have more negative outcomes from that than positive. And you know, that agent hallucinates or does something wrong, you know, kind of there's not a it's not easy to come back from.

Ryan Miller

Especially in a highly regulated market that we operate in. That's depending on what you're deploying AI to do, that is completely honest unacceptable, especially solicitation laws, and they may break them for you, and there's a lot to do. And I just get a little concerned that it feels a little gimmicky, like the top layer of the AI, not the infrastructure stuff, but top layer. So I think what I'm hearing and is hey, yeah, of course, data is great, it helps us to gain an edge. But AI is best used within a firm that creates value than trying to define your firm by AI, if that makes sense. 

Matt Ober

I think so. I mean, listen, I think there are firms that are defined by AI, and that's in their ethos. You know, Social Leverage, we've you know gone all in on AI in the last year and like see my partner Gary and the rest of the team like really built some really cool things, but I wouldn't say like you look at Social Leverage and like it's an AI firm. It's not WorldQuant, Two Sigma, you know, on the you know, on the venture side, there's you know, Signal Fire, Ensemble and guys that like they have data science teams, like that's their ethos. So like it's doable. And yeah, I listen, I think it's probably too good to be true if there's an AI tool that's just gonna do your fundraising for you. If it was that easy, we'd all be raising you know billions of dollars. 

Ryan Miller

Agreed. No one's gonna cut a hundred million dollar check to a bot. So I I get it. There has to be a human element, but with this AI, and once you figure out how those two work together, my gosh, it's gonna be amazing in our field and many others as well. I love it. So walk me through the order that you would build a hedge funds data infrastructure in. So what gets set up first, second, third, maybe pull me through that.

Matt Ober

Yeah, and that's it. I think each strategy and size of each firm is gonna be different. And like, are you starting from nothing? Are you having to you know build while the airplane's still running, right? It's obviously easier to build from nothing, but that's not always possible. I think you know the basics of like, are you gonna use Databricks? Are you gonna use Snowflake, like leveraging Amazon, Google, or Microsoft for your cloud infrastructure probably depends on if you're a Microsoft shop and you're gonna get Office and you're just gonna bundle it. I think Databricks obviously has a great infrastructure, especially if you have a data science team and you want to really leverage you know all of the tools they're building, even from an AI native perspective, doing a lot of stuff around helping you control your costs. And then you need the basics, right? Price and volume data and just like you know, the security masters. And then I think you start to think about you know more modern data tools, right? There's Carbon Arc, which is like a consumption-based data marketplace that kind of gives you access to everything and an aggregation of MCP, you know, fiscal AI, which for investors in, which is you know, competitor to like the fact sets and SPs of the world for your fundamental data, which also can come through an MCP. And then you gotta think about what you're investing in. Are you a consumer, tech, you know, what area are you focused on? And then you got then it can kind of make it easier to figure out do I need that consumer transaction data and credit cards and you know, app analytics, or you know, if I'm focused on the energy space, obviously it's gonna be a very different ballgame.

Ryan Miller

Brilliant. Yeah. Okay. And you know, with thousands of data vendors pitching you, I see them all the time. There they come in various ones. Some they're like, here's a list of investors that I probably scraped for my company's pitchbook profile, and some analysts have selling it for a thousand bucks a pop. That's not what I'm talking about. But you with thousands of data vendors pitching you and everybody else in this industry, myself included, what is your exact filter for deciding which data sets are worth testing and which are just noise and that nonsense I was talking about before?

Matt Ober

Had a uniqueness and like, you know, is it something we've seen or something we're looking for? And then there's just a whole host of other things, like how much history does the data have? What does it cover? What securities? Is it point in time? Meaning, like, can we under if it's been around for a long time, like do we understand like what it would have been like to be that client five, 10 years ago, the day of you know, a financial crisis or some sort of event? And then also it's you know, thinking about how correlated that strategy or that data is to something we already have. Like, is it bringing us any additional alpha? But again, like you know, each data set, the same data set can be valuable to different firms for different reasons. 

Ryan Miller

Got it. Okay. And when you run through those data sets, so you mentioned you do correlation analysis, any other filters that you have in place even before you buy it, but how do you determine to say this is the data set that's useful? This is not.

Matt Ober

I mean, I think there's the trustworthy of the data vendor themselves, especially if they're not new and they've been around for a long time. But you know, it can be how many data points does it have, how often does it update, like the frequency, you know, what's the delivery? Is it available in Databricks and Snowflake or MCP, or is it just flat files still or just a front end? So, you know, all those basics that make it easier to figure out, you know, does it fit for the firm we're running?

Ryan Miller

Brilliant. My question for you then is how would you instrument a fund management business? We'll say like a trading strategy. What does that manager measure so they know within weeks whether decisions are actually working?

Matt Ober

Yeah, I mean, listen, I think going from a deck to decision in a few minutes rather than weeks is like how do you more implement like your AI strategy? Right. So all the meetings that you've had, all the conversations that you're doing, all your research, if that's flowing into your research management system or your CRM and AI is you know trained to take all of that information and create your investment memos for you, right? I even think about it from a hiring perspective. You know, there's you know you go through hundreds and hundreds of resumes and all these phone screens. Like there's companies like Ribbon AI, which are doing voice, voice AI, where like you could have all these people interview with your voice AI and run a scoring model and then filter down those hundred people down to 25, listen to all those interviews on a 2x speed while you're committing to the office, and then you're down to four people that you actually want to meet with. And maybe half of those, maybe most of those people would never have even gotten a phone screen. So because you can use voice AI, you can speed all of that up.

Ryan Miller

I remember you telling me that story. I thought that was the coolest thing where you can listen to people's interviews and the whole transcript on your way to work. So you can see the efficiency, just that one example that that kind of plants your flag to say, this stuff's gonna change the game that you can do more with less, which is kind of the capitalist ways. Yeah. So how does a fund manager turn public writing and the community? Because we it's the world we live in, you got to build community now, apparently. So, how do you go from turning the public writing, community building, all of that stuff where you get followers coming into deal sourcing and a deal sourcing engine? So just so the best opportunities come first instead of going to those bigger funds.

Matt Ober

You know, credit my partner Howard for doing this and who's been writing for you know back to the WordPress days. But you know, we invested  in AI because we believe in like owning your own content and newsletter and you know the emails of people that you want to stay in contact with and you know, not leaving it to the algorithm, whether that's Instagram, Twitter, LinkedIn, or whatever it may be. And so, like, you know, writing I think strengthens your own thought process, but then puts your ideas out there for you know the relationships you've built for such a long time or the new relationships you want to build for you know deal flow to come inbound and then helps you think of your thesis. And then you obviously have to have your own filter on like what deal flow is good versus just you know noise. I know for even myself, writing has been, you know, I'm not trying to grow my newsletter to hundreds of thousands of people. I think that you know there's value to more people, but also by have the right, right readers come to your own almost extra network.

Ryan Miller

Hey, if you're finding value out of this discussion, could you do me a huge favor? Could you just take a second and hit that like or subscribe button? It costs you nothing, but it tells the algorithm that this is valuable information and it helps us to get it to more people. Thank you. You're incredible. Now let's get back to the show.

Ryan Miller

I love that, man. You know, a few years ago on the show, we had someone, a friend of mine named Sarah Sullivan who had a wonderful strategy on what exactly what you talked about. And I think she raised about 500 million in her first three years. She's fine, me telling this, but she would have a newsletter and then a webinar, and that was one of the funnels is she would build the audience, do drip campaigns for real estate, and then at the end of that month's long or whatever it is, have a webinar that also provides value. And she said, Ryan, like every time I do a call, I raise maybe around 20 million per phone call for my living room. Like, I hope people can, and if you're listening and not watching, you can see Matt's nodding, I'm nodding. We're like, yeah, that's the world we live in. Imagine raising 20 million dollars for your fund or your deal or whatever in a month after you've already done that campaign. It's absolutely phenomenal. So using that data, building that audience, it's a wonderful way. And so she went from zero to 500 million just a few years of these newsletter to webinar campaigns, building that funnel. So um, have you ever seen anything like that?

Matt Ober

No, I've not seen it executed like that. So it's impressive. 

Ryan Miller

Now's it never too late to try. So that it's really cool. And you guys are an investor in Beehive, so you know that space all too well. So I love that. Now, there's real data and there's noise. And so, you know, when you look at a startup claiming a data moat, let's say, what are the specific tells that the moat is real versus the specific tells that it's just a meringue?

Matt Ober

I think it matters also how long the company's been around, right? A new company having a data moat is a little bit harder to wrap your head around versus something that's been around three, five, ten years. You know, I think I asked myself, like, what's proprietary about it and like how do they capture the information? And if it's just web scraping, you know, it's tough to have a moat unless maybe you've been doing it  for 5, 10 years, and you know, similar to there's a website called the Wayback Machine, which kind of shows you like the history of the internet, and maybe they've been capturing and storing things that's you know hard to access now. I think also like maybe you have an exclusivity to data set, you know, be oppressive if you, you know, you're the only one with access to some specific data set, based software relationship or an entity that you own, and then you have like more insights, you know. Give an example, maybe you have a partnership with the company that does all the software for yoga studios, and you understand better, you know, the demand for new yoga clothing gear, if you know where that's leading to how many people are in gyms and gyms in different cities, and you know what the opportunities is there. So like just access to unique information, I think always like the niche data sets are probably easier to have a moat in than the you know the more generic stuff.

Ryan Miller

So the riches are in the niches, as I've heard. So it sounds like that, that still holds true on on what you're talking about as well, man. So with AI eating the analyst job, I don't know if you've seen some of those convocations where new grads are booing their, it was like I think it was the one of the head founder or CEO of Google or somebody, it was really fast, but never booing. I've never seen it before. So they're not happy, some people are not happy with that. So AI, with the AI eating the analyst job, allegedly, what is the one data capability a fund should build in the next 12 months that AI makes more valuable, not less?

Matt Ober

I mean, I think you need some sort of research management system. You know, in the venture world, it's probably done like with your CRM with like an affinity or even Carta has a CRM. And you know, in the hedge fund public market space, these research management systems have been around a long time, Tamali, BipSync, Verity. But it's the place that you capture your own internal research, right? Your own models. Like imagine being a hedge fund, you know, like that I worked at, and you have third points there around 20 years, like being able to understand why did they make an invest why did they go activist on Yahoo or on Nestle? And what were the, you know, what were the metrics, what was the buildup, what was the positioning, and then training AI to constantly be scanning the markets and then say, hey, based off of this, you know, announcement by XYZ company, this is a very similar setup to what you did previously. There's a great opportunity for all these levers that you pulled to do it again, or maybe it's the same company, right? And it's back to having the issues that you found earlier. I think it's your own data moat that can't be replicated, right? And I think like the quicker you can organize that and store it and make it accessible to leverage new technologies like AI, like we have, like the more you know, more valuable your friends should be.

Ryan Miller

Perfect. And so you mentioned Tamali, there was some other ones.Can you just repeat what those RMS. 

Matt Ober

There's Tamali, BibSync, Verity, which was Mackey? You know, I think Facet and SP have their own versions now, but you know, obviously that comes with being part of a larger ecosystem of data. 

Ryan Miller

Awesome. Okay, perfect. So now prediction markets, that's a very substantial business. So, what is the first practical way a serious fund manager can start pulling real signal out of prediction markets starting today?

Matt Ober

I think prediction markets right now, from an investor perspective, is like the new media, right? Like what's happening with the war and where do we think interest rates are going? You know, are we that people can start to place their thoughts through prediction markets? You know, prediction markets, you know, if they've been around for 20 years. I remember like predicted and like going back to my hedge fund life where we used to look at them, but like more offshore and smaller, but at least give you some insights. I think that um if you think of the backbone of prediction markets being insider information, like it starts to move markets pretty quickly. I also think that the KPI markets on the investor side is the greatest opportunity that we have, which is like you know, you spend all this time as an investor trying to understand the core KPIs of business. Like for Uber, it's probably how many rides are they gonna do or how many deliveries they're gonna do for Uber Eats. If you've forecasted and predicted that accurate every quarter, how do you actually make money off of that? It doesn't mean that earnings is it doesn't mean even if that drives their earnings, Uber could come out and say we're investing all of our money in autonomous vehicles and then the stock like goes down because people don't like that, right? Or they could say, like, we're spending the China again and people don't like that either. So like you might get the KPIs right, but you don't actually know how that's gonna move the stock. The KPI markets and prediction markets, I think, is the biggest opportunity, right? Which is like, I know how many deliveries Uber is gonna do, and I can actually put money where, where that  you know forecast is going.

Ryan Miller

Great. That is an awesome, exciting, lofty claim that it's one of the biggest opportunities right now. How does somebody, in your opinion, if they're just starting out, they're emerging fund manager? How would somebody start to explore, if not take advantage of that big opportunity you're talking about? How do they bring that into their world to monetize it for themselves?

Matt Ober

I mean, there already are those markets, right?  Kalshi already has KPI markets. They're powered by fiscal AI, so they've got a partnership there. The liquidity probably isn't as deep as you want. That being said, like I'm sure that there's over-the-counter markets that can be done through, you know, the Citadel, Jane, Susquehanas of the world. I also think more volume's coming, right? It's as much as prediction markets have taken off, it's still early. So I think I would just be building out the muscle on how do you forecast and predict the KPIs for the investments I'm making and start to paper trade those prediction markets with the goal of being ready to execute once there's actually liquidity you need, depending on your front size.

Ryan Miller

Brilliant. Now, sometimes, and I recently did an episode of cognitive biases, and one of them ties into analysis paralysis where you're just like, I need more data and more data and more data. And sometimes you can nail your own feet to the ground and be too afraid to move with this paralysis. So, how does a manager know when they've crossed the line from useful data into expensive noise that's quietly dragging down a fund manager's returns?

Matt Ober

I think you've got to always be challenging yourself on the data that you have and testing it. Like you need to be tracking your performance per data set. Like how accurate has it been and how correlated is it to the other stuff? Like, if we took this out of our process, would it change our returns or change our decision-making process? You know, I believe more information is powerful and like there's no real like such thing as information overload. It's just like filter failure. And in an AI world, we all want a thousand X more data, or at least access to it, and we want to pay less per data set, but like overall, we're gonna spend more on data if it's giving us more insight. Like you'd obviously spend more and more money if you're making more and more. And so I think, you know, yeah, in the data world, like I think a lot of people are starting to believe that you know, the more data you can consume, the more money you can make, or the more money you can manage.

Ryan Miller

So, really, what this is, I think I'm translating a little bit of what you're saying, but keep me honest, please, is I think what you're saying is yep, more data is better, but it doesn't have to paralyze you. Like my wind up suggested is to say, well, actually, the great thing is now with AI. It lets you go back where we said, what's the first thing you should do? And you said, make sure you got MCP, be cloud native. And so I think what I'm connecting the dots that you're saying here is to say, no, yes, in the past, without AI, more data, it just clogs up the whole process. But with AI, you actually can consume it and not slow down the process. Is that what I'm hearing?

Matt Ober

I think that's correct. I think also with AI, you can really get a sense of like take this data out of the process and what does it change? Does it change our decision making? Would we have done something differently? 

Ryan Miller

Brilliant. Yes, you did say that. 

Matt Ober

Yeah, I think the last thing I'll say is there's a value for a lot of firms to buy data that they know is incorrect, but they know moves markets. I think the most sophisticated firms know that this data set is always incorrect on these KPI forecasts or this or that, but that others maybe still are using it and it moves the markets, that knowing it what it's gonna say and knowing that it's been incorrect this many times, you know, 60% of the time, we're gonna bet against it because we know that everybody's going one way and we should be going the other.

Ryan Miller

I love that. So then this makes me wonder. So we have we're talking data and MCP and all these things and analyzing more data without slowing down. In fact, it's probably speeding up, so you're doing more faster. But then there's coming, there comes in discipline of managers. And so, from your opinion, what's the single discipline that separates managers who turn data into returns from those who just burn money buying data sets and tokens and they're just burning money? What's the discipline that separates those two?

Matt Ober

Yeah, I think you need to obviously have a really strong process from not just backtesting and letting things run out of sample to see like how well it actually performs with what you're doing from an investment perspective, but you know, there's the power of the network, like know who you're dealing with on the business side, like who runs this company, who are the people you're gonna work with, you know, what's gonna happen when the data doesn't you know get delivered and stops your entire process. You know, I think like everything in life,  your network and relationships are as important, if not more important than everything else. And then like how accurate has this been over the long run? Like not just because it didn't work for a few months doesn't mean it didn't work over the last two years. And then it's really just how does it affect your own portfolio, right? The value of the data for me might be different than it is for you.

Ryan Miller

Okay, yeah, that absolutely makes sense, man. If a fund manager who's listening right now and they want to be irreplaceable in 10 years, what should they start building into their process starting this quarter?

Matt Ober

I think what they need to start building in this process, I think it's the deep domain expertise. You know, like I think obviously generals can be good, but, you know, if you're going to stay relevant versus AI, like you're gonna have the you're gonna have the know-how, the nuances, and the experiences that allow you to go deeper. And obviously having the network in that industry and it being able to pick up the phone and talk to others that have maybe unique information or insights, I think like that really is gonna set people apart.

Ryan Miller

I love that. You know, I always say I would say what I'm known for is raising capital and teaching people how to do that and launching funds. But, I always say the three most valuable assets in your possession are your reputation, your relationships, and your results. And I would say maybe you, your domain expertise, data, quant, all that stuff, you've you've absolutely nailed it in all those areas. Is that kind of am I picking up what you're laying down, brother?

Matt Ober

Yeah, for me, it's like, you know, it's the data, it's the financial and capital markets, like not just the venture side, but also just like, you know, from a money management perspective outside of venture.

Ryan Miller

I love that, man. So just to before we wrap things up, man, this has been awesome. I would be, I know we've spoken a few times before this, but it's always good to see you again, man. Before we wrap things up, anything else you'd like to say, maybe ways if people want to learn more about you and enter your worlds, how do they do that? How do they find you?

Matt Ober

Yeah, we. our website's great, socialleverage.com. We have a newsletter there. I read my own newsletter at mattober.co. I'm active on LinkedIn. You know, we're pretty open book as a firm. Share all the companies we invest in on our website. And happy to chat.

Ryan Miller

I love that, man. So just to wrap things up, be sure the data is relevant. So making sure that relevancy is important. I know Matt, you run correlation designs and tests on a lot of your data. The other thing we talked about is the tools that you use need to have MCP. If you're building a firm that is future-proof, this is a very easy thing just to check. I do it as well, just so you know. I do it as well as look to these things and say, what tool? Because they have competitors, but only these ones have that MCP at a minimum API, but MCP. And then the next one is look for edges in your data and in your data access. You do these things, and you too will be well on your way in your pursuit  of Making Billions.