How to Identify High-Potential Founders: Insights from a Global Accelerator

Hey everybody, welcome. My name is Jonathan Gretchen, co-founder and CEO of the Founder Institute. Thank you everybody for joining today. I'm already seeing that there is a very active chat, which is amazing. Sorry that we're getting started a couple, couple minutes late here, just some technical difficulties, but I see Sean in the chat from Miami. Hey Sean. David just graduated from the FI Japan cohort. David, congratulations, number one. Number 2, it's a little late for you, man. You maybe— I know startups are all about the hustle, and I'm glad to see you awake, but try to get some sleep too as you're building out that business. All right, it's a marathon, not a sprint. And I see a lot of other people from India, Chicago, Georgia— the country Georgia, not the state— Maryland, Toronto. Amazing. Thank you everybody for joining. So I am going to go through a topic today around how to identify high-potential founders. All right. And to be honest, the kind of the target audience for this presentation is more other funds, other accelerators, or really any organization that seeks to find, work, help empower early-stage companies, right? And I basically broken down this presentation to a couple of different components. First, I'll just go through some of the basic startup evaluation methods, then start to dive into what the Founder Institute has done in this realm, go through some of the results that we found, and then give some tips, you know, for an accelerator or a fund or any organization that's trying to cultivate and help startups, giving them some tips on how to build what I would call an unfair advantage. As they go kind of into their journey to identify and cultivate the startups that they work with. The other thing that I should mention is that at the end of my presentation, I will do Q&A. All right. So if you do have any questions, please feel free to throw them into the chat. Diane from the FI team is in there right now, and we're super advanced here. I have a Google Doc that we all have access to that she's going to be throwing the questions into. Just so that I'm not kind of distracted by the chat while I'm going through the presentation. But, but please don't be shy. Throw any questions into there. And then after the Q&A, we're also gonna do a networking session. Alright. Some of you may be familiar with Airmeet. It's a pretty cool platform. Once I close this presentation, we will go back into this virtual networking lounge where you can jump into different tables. And hopefully, you can make some connections in there. There should be a good number of accelerator managers fund managers, you know, maybe NGO managers, people in government trying to cultivate great startups, as well as some founders as well. So hopefully you can stick around and take advantage of some of that networking. All right, so let me quickly get into Startup Evaluation 101, all right? And this is going to be the fastest part of my presentation because it's not where kind of the Founder Institute has built up our expertise, and it's kind of at a stage later than where the Founder Institute usually works with people. And usually any fund accelerator, you know, they'll have some kind of methodology. They will have— they will supplement things with interviews and things like that. But at the end of the day, in the realm of startups, it's always going to just come down to data and metrics, right? It always trumps everything. And on fi.co/benchmarks, these are just some data points that we've been publishing now. I think we first published this late last year, I believe. And we're continuing to update it now. I think the last time we updated it was in March. So to be honest, we'll probably update it again this fall when we start getting a better idea. But, you know, we have over 6,000, almost 7,000 graduate companies now in the Founder Institute. So we see a lot of term sheets, we see a lot of deals coming through from all over the world. So as a result of that, it was like, all right, well, let's start sharing what we are seeing are the prerequisites or reaching a certain funding round, right? So anybody who is funding startups or helping startups, they're always going to have some variation of this in their head, right? And here again, these are general numbers, kind of normalized a little bit across the globe, but these are the numbers that, that we see that kind of merit, okay, being at this kind of funding round. And, you know, a lot of the time it's traction is a word that's used a lot, right? And traction can mean a lot of different things. For example, I'll work with the founder and they're building a biotech company, right? And they're like, well, obviously I'm not gonna have like a live product, you know, anytime in the next like 5 to 10 years even, right? So it's traction just means a lot of different things. In that scenario, I would put traction under like deep tech, right? Like if, yeah, if you can't get your product out there, maybe it's not some simple widget or some simple app that you could easily launch to get out in the marketplace and start to drive usage numbers and things like that. You could still show momentum to investors or any accelerator, but that momentum can come in a different form, right? Let's say if you were a biotech company that you were able to convince some amazing people with experience in the biotech industry to become part of your company, that is a form of traction, right, that an accelerator or an investor will see because they're like, all right, well, if this person is devoting their time to this and they know what they're doing, and there's probably something here, right? So we try to, um, it's another chart that we have on fi.co/benchmarks. We try to really distill down into, okay, what does traction actually mean for a lot of the deals that we're seeing come through the pipeline. So yeah, I'd encourage you to take a look at fi.co/benchmarks, right? But what I'm really going to go through today in a lot more detail is what do you do when you have to evaluate a business with no data, right? That's where I think things become a lot more interesting. It's where things become a lot more challenging. Sorry. And that's what's going to be the focus today. So let me talk a little bit about what we've done in the Founder Institute. So some background. All right. The Founder Institute, our founding story, just quickly, myself and Adeo Ressi, my co-founder, he had started a gaming company called Game Trust in New York City. And I joined on, you know, very green, right out of college. Out of college, didn't really know what the hell I was doing. It was a fast-growing company, and we were able to, to build that company into, into a decent exit, right? During that time at that company, the treatment that we received from investors was like abysmal, all right? Like clipboards were thrown in investor meetings. Uh, one of the investors— Elon Musk was one of our investors, right? It's a pretty decent investor, and pretty decent person to have on your board of directors. One of our investors was able to kind of formulate a coup and remove Elon Musk from our board of directors and replace him with who? His son, right? So we just, at that time, the economy was really still reeling from the dot-com crash of the early 2000s, right? I'm talking about 2005, 2006, 2007 here. So the treatment of founders by investors was just horrid. Okay. The investors were just, had all of the power. There were things like exploding term sheets, right? Investors were just openly sharing data about companies. It was just, it was a bad thing. You know, I think some of this maybe was centered a little bit more in New York too, but I know in the Valley some of this same stuff was happening. So after that company, we created something called defunded.com. Defunded.com was an anonymous place, completely anonymous. Once you signed up onto the site, once you were validated, we basically assigned you a random key and we didn't know who you were anymore, right? We did that to make sure that we couldn't get sued, or at least that no lawsuits would go through because it was total anonymity. It was an anonymous place for founders who were raising funding or who had raised funding to share ratings and reviews on investors. Okay, um, now here's the thing, right? For that anonymity part to work, we had to literally review personally every single applicant that came onto this site, because at the end of the day, we didn't want this to be a useless investor-bashing forum, right? Investors by nature have to say no to the vast majority, right? 90% or something. Of opportunities that they see. So if we just had all those founders that got rejected talking crap about them on a forum, right, it wouldn't be particularly useful for anybody. So we were validating every founder that applied to this forum to make sure that they either had raised funding or were really in the process of raising funding. And what we saw was that the vast majority of people that were applying to the site, we had to reject. Because they thought that their next stage in the journey was to raise funding. But in reality, they were 100,000, 200,000 steps away from raising funding, right? They were basically at like this back of the napkin kind of stage and they thought, okay, if I have this idea, this thing in my head, now my next step is to raise funding, right? And we saw that massive gap where we needed to provide that education, that structure to actually go through the steps, to get to a point where they actually were ready for funding. And that's what spawned the Funded Founder Institute, is what it was first called. Pretty, pretty much a handful of a name there, right? Eventually, once it started to take off, we shortened it down to just Founder Institute. But that really was the nexus of what we did, right? And in the beginning, our mission was to activate and empower these talented, motivated entrepreneurs essentially at this idea stage, right? So as a result, that question of how do you measure someone's potential when there's no data, right, in a lot of these cases, or if there is data, it's any data that you get is going to be unreliable, crazy subjective, or just very incomplete, right? The earlier you work with companies and startups and founders, the less data that you're going to have. And for us especially, a good portion of the companies we work with still to this day, I should even stop calling them companies. They're not companies, right? No company is formed yet. We work with people pre-incorporation. We work with people when they still have full-time jobs, right? When they were still just looking at companies. So all of that data, all of those metrics, those benchmarks that I showed you before, It's just stuff that we don't have, we don't have access to. And when we first went through the design of how we were going to build this accelerator, we very quickly came to the conclusion that, look, we're at such a stage with founders that have ideas that if we try to judge their ideas at the idea stage, like, it's just going to lead to complete and utter failure, right? And we thought that for a couple of different reasons. Number one, If you look, when most people try to judge ideas, they always have a natural slant and a natural bias towards trying to find ideas that are super original. This goes for both founders, investors, accelerators alike. Originality gets significantly more credit than it really should. It should be— it's— and, and there was a study that basically went through this where it was just saying that, look, it's— we studied, uh, all of these different companies. Some of them had it sort of the original idea that made a market, so to speak, and then there were the few others that, that basically took the lessons and the groundbreaking things that that first company did and then acted super, super quickly, were entrepreneurial, iterated really fast, delivered something that the customer really wanted, and those were the ones that were significantly more successful, right? So the, just the whole concept of originality is very overrated, number one. And then number two, it's even just thinking that you can judge an idea at the idea stage. It, it just is completely backwards, right? It's using a thinking that the value in, in the idea is basically this lightning bolt moment, right, that maybe you saw in a cartoon when you were a kid, where you get this lightning bolt and all of a sudden you have like this lottery ticket in your hand of an idea and all you have to do is cash it in, right? And that's just really not the way that startups work. Great ideas are just the seed. Great ideas are raised, not born. And successful businesses are, are almost never the exact same idea that they started with, right? I would actually challenge somebody to find me some that are. It's really, really hard to find them when you look into the founding parts of a company, right? There were several video games where there were certain features in the video game they were building were the only things kind of that the customers liked. So they ditched the video game and just focused on the messaging tools or the video or the photo sharing tools. Right? There were, I mean, there's countless examples of just the pivots that are made within companies where that first idea, it's just the seed. Does it have some importance? Yes. But all of the value honestly is made during this nonlinear journey, right? It's through the testing, it's through the evaluation. And what we always tell startups that come into our program, is that all the value in a startup idea is not in the idea itself, it's in the evolution and execution of that idea over time in response to market customer feedback, right? So who makes those decisions? Who kind of shepherds that journey along? Who guides that vision? Who takes the feedback? Who takes feedback and realizes this is bad feedback and kind of brushes it aside, right? A lot of the time it's saying no more than yes. The people that do that are the founders, right? And I like to bring up the Winklevii here because I think it's just a good example. At the time, you know, if you watch that movie, The Social Network, they're painted as victims. But at the time, what was the idea? It was a social network for universities. Social networks at that time were seen as a terrible, terrible business, okay, as a whole. Taking that social network business and then putting it to a university where people are broke is— it was even a more— would have been a more terrible business idea if you had tried to judge that idea at that time, at the idea stage, right? All of the value in Facebook was created by the founder, right? And taking some idea that would have looked stupid but shepherding it along over time in response to that. To that customer feedback. And, you know, since day one for us, and, and this is something that we have always stressed at Founder Institute, is we believe that the founder is the greatest variable in the success or failure of any startup, right? And there's a lot of literature out there, trust me, from people much smarter than me. People like Marc Andreessen, uh, have always planted the flag on saying, look, market's most important, market's most important, right? Because the market A great founder in a bad market can still fail, right? But a bad founder in a good market can still succeed because the market will sort of pull them out of mediocrity, right? And all those things are true, you know, but for us at our stage that we work with companies at the idea stage before there's a lot of data, they have the flexibility to move between markets, right? And that's why we've always put so much stress on finding the right founders, because those founders will find the right markets, they will find the right opportunities, and they will pivot as needed, um, to see the business grow. So I bring all this up because, again, going back to the original question, it was like, all right, if we don't have data, what if we could create an objective test that would predict success, right? And again, looking at the founder, if the founder is the greatest variable in success or failure, then let's look at the founder as I, you know, I hate to say it, but like an asset, right? And let's see if we can quantify that founder to see the likelihood that they will be able to take the millions and millions of data points and inputs that are going to come in actually building a company, make the right decisions over time, and build something that's valuable. Um, and, you know, obviously the implications of developing a test from the beginning that we saw were pretty huge, right? You can quantify this variable, you can evaluate founders at scale, it could allow you to operate across borders and cultures, which was something that we always wanted to do, right? Because again, you can't— how do you evaluate a startup idea, you know, in some city in Africa, for example, if you're from Silicon Valley, right? It's just totally irrelevant. An idea that may sound stupid in Silicon Valley is an amazing idea in Africa. right? So again, trying to remove that bias was the only way that we could realistically become a global accelerator. Um, and, you know, just removing that bias gives us sort of this advantage as well of kind of uncovering these diamonds in the rough that other people are overlooking, right? These, uh, market inefficiencies. I think Moneyball, uh, just that whole concept, you know, really popularized this. But it was that idea, it was to say, okay, look, you know, maybe we're not trying to compete with some of these other accelerators. Let's try to find the people with the raw materials that we think we can build out to create successful businesses. So the first test that we created was called the Predictive Admissions Assessment. I'd say that the naming on this was about just as good as the Funded Founder Institute, right? But this first assessment really focused on a couple of different things, all right. We worked with a pretty renowned social scientist that we had already been working with for several years at that company, Game Trust, and we tried to figure out, all right, well, how would we go about actually identifying entrepreneurial personality traits? So the first version of our test, it took some people almost 3 hours, okay. I'm not even sure how we got people to take it, but they did, uh, and this just threw the whole gamut of them, at them, of just batteries and online tests that we could deliver in an online form that would bring us back some social science-based data, right? And this was a lot, a lot of it was focused around the Big Five personality traits. You can Google that, it's a pretty common concept in social science. We tested things like IQ, we tested things like fluid intelligence, and we tested things like personality disorders as well. And if anybody has taken the test on here, and you can learn more about it at dna.fi.co, but really the bones of it come in two places. All right, and there's two puzzles up here if anybody wants to guess at what the answer is. I can give you the answers, but these puzzles at the top literally are designed to measure somebody's ability to learn a new rule set quickly and then apply that learning to solve a problem, right? And honestly, that— just think about that sentence that I just said. That is critical for any entrepreneur, right? You know, being the smartest person in the world really doesn't matter as an entrepreneur. It's your ability to just learn a lot of things really, really quickly because you're going to be tackling new problems every day. The landscape around you is constantly shifting. It's just an environment of complete and utter chaos, to be honest with you, right? So your ability to learn a new rule set very, very quickly and apply it to new problems, this relates to a trait called fluid intelligence. And that is something that we have always tested for, and it definitely was part of the first test. And if you see in these questions here, really the idea is that, you know, this over here is showing you a sequence, right? This is the rule set. And then this over here is showing you an incomplete sequence. So based on learning this rule set, what would be the next shape in the sequence here, right? So that these are some of the puzzles that we give here to really determine somebody's fluid intelligence. So then the second part of the test since day one has been these personality traits, right? So we force people to select kind of imperfect traits, right? It's a forced selection process, which has always been proven to kind of provide a little bit more validity to results, right? Because you always kind of have to design these questions in a way that, uh, that tries to get the truth out of people. Um, but the idea with these questions is to say, hey, look, you know, some of these questions, and in a lot of cases for people, both of them are true. In some cases, maybe none of them are true. But we are trying to point them to saying, look, which is the one that most fits you, even in an imperfect scenario? Right? So that is, that is the idea there. And that allows us to really hone in on personality traits that people have. And then we talk about like, how do you define success? Right? So we took that test, that test gave us all of this data about everybody that was applying to our programs. And then, you know, the only way for us to really then track back and identify personality traits that correlated with success was then to define success. What is success, right? And the way that we always define success, at least in the beginning, was it was the ability for an entrepreneur in our program, right, and after the program to take an idea, quickly iterate on that idea, and then achieve actual milestones in the business. Things like driving revenue, raising funding, you know, building a following, getting a great team, and things like that. And the way that we did that, right, it didn't just stop at the test. The test got us the raw data, and then we needed the performance data to correlate it against. So in the FI program itself, all right, if anybody— I saw there was a grad here in the chat before from Japan, so if they're here, they'll know that every single week the founders in our program are continuously reviewed, right, 1 to 5 scaled, and they're reviewed on their pitch, their ideas, and their progress. The whole design of the program was so that these ratings would come from a diverse panel Right? It's the local leaders in their local market, is mentors in their local program, most of which who are from their local market and their peers. Right? And the— we also have two detailed reviews where our two detailed review sessions where we go more in depth. But at the end of the day, we're collecting over 100 data points for any founder across time, right, within the program. And then after the program, our portfolio team is also rating them based their long-term performance, right? Are they raising funding? Are they generating revenue, etc.? All of that data then gets continually fed back into the system. All right, so I know that was probably the boring part of the presentation, but I just wanted to explain to everybody, you know, look, this is the system that we set up. It was a combination of a test where we tried to grab in the beginning as much data as we could about the founders, not make a lot of assumptions about what may correlate and what may not, And this was a 3-hour test almost for some people. And then over time, and I think we took an 18-month period before we really started to go back and do the regression analysis where we started to say, all right, over time, how can we then see the founders that are successful, right? And how we define success, what are the traits that correlate? So this breakthrough did come at about the 18-month scale, and it's when we developed something called an RES, the Relative Entrepreneurial Score. And this relative entrepreneurial score essentially was an algorithm, a combination of a lot of the different traits that we saw, and it proved to be pretty damn effective, right? We immediately could have an 85%, you know, kind of predictive score. And it didn't matter where you were based or what stage you were at, at least in our program, right? It's a little biased there because we were very early stage, right? But it didn't matter what stage within the early stage you were at. Didn't matter what your skill set was or anything like that, right? And we were able to kind of predict this at an 85% clip. So let me quickly go through what were some of the biggest findings that we found out in this first iteration of the test, right? So fluid intelligence, as I mentioned before, that's in those puzzles, definitely correlates very strongly with entrepreneurial success, right? It's not your IQ. IQ is sort of raw brain power. Fluid intelligence is your ability to to just learn a lot of things really quickly, be crazy adaptable to the circumstances around you, has a ton of correlation. High openness, and I think the image is wrong here, but high openness is something that we also found to correlate really, really well. And this is something that, you know, typically people will call it around creativity. It's not just being creative though, that's probably the wrong way to put it. It's your openness to new experience. It's your openness to, new ideas, right? It's not being closed-minded. Definitely had a major correlation as well. Something called agreeableness we didn't see as being high or low, but it really was much more to have moderate agreeableness. So let me explain agreeableness. If somebody on a scale of 0 to 100, and that's what we measure on, somebody with 100% agreeableness is a total pushover will probably, you know, never stand up for themselves, never kind of put their foot in the ground kind of person. Whereas the person who's a zero is probably the most annoying, you know, probably has no friends kind of person, right? An insufferable, you know, whatever you want to call them, right? So obviously finding somebody towards the middle of that, we found to correlate very, very well with entrepreneurial success. And it makes sense. Right? Somebody who can put their foot in the ground when needed, right? But then take a step back and, you know, be much more sort of passive and agreeable when needed as well, you know, was super important. And one other thing, and this really wasn't too related to the personality testing that we did, but we found it super interesting too, was that we found age and professional experience to correlate really well. Also, right? And this has always sort of informed what we've done at Founder Institute. A lot of the people that come through Founder Institute are people, you know, not necessarily that have come right out of school. We do get a good number of those, but the vast majority of people that do come through Founder Institute programs have been in the workforce, right? Have been in a professional environment and, you know, have some professional expertise that they've built up. And some of the bad founder DNA that we have identified too were things like predatory aggressiveness, you know, excuse-making, deceit, emotional instability. I mean, it— startups are rollercoaster, right? So emotional instability was always something that we saw right off the bat that correlated very, very clearly. And things like narcissism as well. Some of the things in this first version of the test that we saw that just didn't correlate, so that we removed and thankfully made the test much shorter for people that took it, right? One of the things was IQ, right? We saw that fluid intelligence, it's also called crystallized intelligence, definitely correlated much more than IQ. And IQ, we almost saw no correlation. Whatsoever. And one that surprised us was conscientiousness. All right. So conscientiousness was— it kind of measures somebody's responsibility and industriousness. Right. And we saw that, wow, if this isn't correlating correctly, then maybe we're doing something wrong in our calculations and maybe we need to sort of redefine and break out the traits within conscientiousness, which is sort of a large definition, that combination of a lot of different subtraits. And that's what we did with the next version of the update. This update that we did a couple of years ago, I'll speed through this, right, to spare you any of the technical details, but it was almost a 2-year process. It involved a lot of scientists. It involved a lot of peer review, literature review. It involved a lot of analysis by people that are much smarter than me, but it allowed us to take the findings from that first test the 10 years of data at that point that we had accumulated from that test, right? Hundreds, over 100,000 results across 6 continents, countless countries, cities, etc. And then really start to dive in and break out into the traits that we found that either correlated with success, right? Rather positively, negatively, or moderately. And These are the traits that we basically have started to hone in on. Now, I should say that all the traits in our algorithms are weighted differently, right? So again, something like agreeableness now has been broken out into something— or yeah, it's agreeableness, right? It's not the higher agreeable, it's not better, right? It's moderate. I'd say that a lot of the naming on these, they're kind of colloquial for us, right? They're the combination of a lot of different traits that we have just named so that people can understand them. Right? So something like social skills, for example, you know, takes into account agreeableness. It takes into account a couple of different things. And again, you know, not all of them are positive. But these are the traits that we've honed in on. And let me kind of dive into a couple of them and look at some interesting ratings and rankings that we've seen across the world too. So curiosity, and the last bullet here tells you that curiosity, we actually had what's known in research as a a ceiling effect with curiosity. And I guess in hindsight it's probably not surprising, right? A ceiling effect— this meant that we got like the results of everybody that we tested were so much higher than the norm, than your average person, um, that, that it almost started to make our results like hard to distinguish, uh, and, and sort of invalidated them, right? And I bring that up here because it basically tells you that, look, if somebody— if a founder doesn't have high curiosity, if we're seeing seeing it in such a large scale that it's literally throwing our results off for this trait to the point where we have to figure out a way to measure it differently. Because everybody who takes our test, right, and obviously there's selection bias there. So who's taking our tests? It's people that want to, that are interested in entrepreneurship, right? They're going to have this massive level of curiosity. So, and some of the places where we've seen the highest of it is Santa Cruz in the U.S., which interesting. Santa Cruz is kind of a funky city here in the US, so I kind of found that a little bit funny. Uh, Lubumbashi, Perth, Seoul, and Gothenburg around the world. And, and we saw as a region, um, Latin America had, uh, the most, uh, cities within the, the top 25 of, of any of the other cities around the world for this trait. Um, high adaptability— this definitely pulled in some of the fluid intelligence components that I mentioned before. Some of the places where this was highest was in Europe. 6 out of the 10 highest cities that we saw on the adaptability rating were based in Europe. And people that are adaptable, you know, not only can they adapt to new situations and have that fluid intelligence to solve new problems and just to kind of shift, shift, you know, left side brain, right side brain kind of thing really, really quickly. But they actually were energized by the challenge of getting a lot of these different tasks to do. Right. And we saw that highest across our cities in Europe. Perseverance was another one that definitely correlated. And we saw this highest really across Latin America. Right. And not very many cities in North America were in here. And, you know, at least to me, kind of armchair psychologist, it's like, all right, well, Detroit is a city where people have had a lot of perseverance, right? Detroit has been through a lot economically here in the US. So it sort of didn't surprise me that if I were to name one city that we've operated in, in the United States, which one would come up with the highest perseverance on the testing, I probably would have named Detroit. Right. And it also doesn't surprise me that the vast majority of these cities weren't in more developed markets like North America and in Europe. Reflection was another one that we really started to hone in on. So reflective people, you know, there's definitely empathy plays into this, but really at the end of the day, somebody who's reflective is the kind of person that whenever a project is done, whenever any experiment is done, succeed or fail, whatever, they're going to put a lot of the time into that postmortem, right? They're going to value the experience of learning, and they're going to value, you know, just, just that process of, of reflecting not only on themselves but on any project or any work that they do, right? So I guess again, it's probably not surprising that in terms of doing A/B testing and doing all the iteration that you need to do as a startup founder, that being highly reflective was going to be a trait that we saw to correlate with success. And self-reliance was a big one as well, right? These were people that we actually saw this kind of correlate a little bit more than even some of the collaborative traits, right? Where the people that had that self that, you know, confidence in their own ability, the— that got the satisfaction of, you know, accomplishing things themselves. We saw that really, really correlate too. And this was a place where we saw, you know, the United States, North America really show up a lot more in some of the ratings and the rankings that we did. We also saw a lot of groups of traits. So this is where, to be honest, we're just scratching the surface on a lot of our research, right? But when you start to combine traits, so in this case, when we combined people that were highly proactive, highly competitive, that had a high risk tolerance, and were very decisive people, we saw amazing correlation with companies that were able to, from the start, you know, sort of this ignition, just move super, super quickly, learn a lot really, really quickly. And create a lot of progress, right? In some cases, that progress led to failure, but it created a lot of progress, a lot of learning where they were able to learn super, super quickly. And that's very important for any early-stage entrepreneur, right? Even if you're learning that what you're doing doesn't work, it's infinitely better to learn that quickly rather than to stretch that out over the course of several years, right? So especially for a program like ours that tries to do things and try to get get founders to learning very, very quickly so they can get to a fundable state, um, you know, looking for founders with these specific traits have been, you know, very effective for us. And, um, you know, that— so that's sort of the beginning of the spectrum. And then on the long end of the spectrum, when you're looking at founders that have really shown long-term, you know, the ability to endure and to build, you know, companies with long-term success, we've seen traits like high autonomy, fluid intelligence, uh, and emotional control factor a lot more in. And it also factored in with, you know, the ability to raise institutional funding, right? And again, I would, I would say you need emotional control to be able to raise funding as a founder, all right? Like, I, I probably could have told you that before we did the research, but, uh, you definitely need that. And, and being able to do things on yourself and to be able to adjust to all the needs and the wants that investors have, we've seen that all correlated in the data. So taking our data now, applying it to teams, right? And again, this is somewhere where we're just scratching the surface in some of this research. But when we looked at what are good team members, what are members of teams that not only correlated with successful startups, but teams that didn't have a lot of drama, didn't have breakups and things like that. These were the traits that, you know, basically were present in enduring teams. It doesn't mean that every single team member needs to have these traits. If they do, then that makes them an amazing team member, right? But it was very important for some team members or for these traits to be present across the team in order for that team to essentially be functional. And, you know, when you looked at co-founding teams, it was sort of a combination of that too, where we said, all right, well, we have all of these 26 traits, right? And we found that if you started to look at the different categories of these traits, so if somebody, for example, was very low in several of the communication traits on a team, than somebody that was somewhat high or at least average in several communication traits. It didn't have to be the exact same ones, but they needed some of that counterbalance elsewhere on the team in order to make it all work. All right, and we saw this across a lot of these different categories here. If there were certain team members that had very low self-reliance or very low risk tolerance or very low decisiveness, Right? We needed to see other members on that team that had higher traits within that category to counterbalance it. And it was kind of amazing to us how much this correlated with the success of some of the teams that we worked with. But, you know, to be honest with you, we're still just sort of scratching the surface on that research, and we're going to dive in a lot more. But I think it's just really interesting, and it kind of validated some of the thoughts that we had too, where it was like, look, teams are about balance. Right. It's about counteracting each other's strengths and weaknesses. Nobody's going to bring every single raw material to the table. It's about finding a team that has those raw materials to complement each other, not just on the skills front. Right. Which is where I think a lot of people kind of limit their thinking where it's just, okay, I need a hacker, I need a hipster, and I need a hustler kind of mentality. Right. It was much more just on the soft skills front. Was super important as well. And then the last thing that I'll say here on Teams was something called psychological safety. So it was a term that was coined by a Harvard professor, and basically psychological safety was presenting in a team dynamics kind of environment where it's, look, you need to have an environment where some of these skills are present within the team in order for the team members to have this psychological safety to be creative, to go take risks, right? To be able to just freely share ideas and stuff without, you know, fear of repercussion and kind of, you know, castigation and things like that, like being castigated. It's just you have to have these traits present within a team on somebody in order for it to create this psychological safety environment. And again, the teams that had these traits present, right? Again, it's usually present across a couple of different team members, but the teams that had these things present were the ones that outperformed. Okay. So I'm gonna wrap things up here, then we'll get to Q&A. So if anybody has questions, please do throw them into the chat and we'll get to them shortly. And if you do wanna network with other accelerator managers, with other founders, other investors, please do stick around as well. But the last part I'm going to go through here is, you know, as an accelerator, as a funder, as a fund or economic development organization, or really any organization that's trying to work and help with startups, how can you kind of start to design your methodology for identifying high-potential founders, right? And we've shown you here what the Founder Institute has done. We have taken a very social science you know, attitude towards it from day one. We've been doing this now for almost 15 years, and that is basically how we have designed the methodology. But, you know, really, when I talk to funds and any new accelerators that are getting going, I always say to them, it's that, you know, you always have to just be looking for what is the unfair advantage that you will have. Right, most funds that are going and pitching LPs, right, the people that are investing in their funds that they then invest in startups, most of these funds, most of these accelerators, right, their pitch to the, the LPs, the people with the real money, is always talking about what the unfair advantages they have, um, that can create outsized returns on any of the investments that they're making, right? Um, and if you look at the market, and these are very generalized, right, but I, I kind of started to break these up into different categories. So, you know, right now, access to mission-critical services, it's always been a big play, especially for corporate VCs, right? Google Ventures, Microsoft Ventures, etc. They've always supplemented the money that they provide with also, you know, extremely discounted cloud services and things like that, things that the startups needed to be successful in addition to the money. These days with AI, NVIDIA's investments, and even look at Microsoft's investment into OpenAI, their value add there or their unfair advantage was their ability to say, okay, with Microsoft, they were giving them access to crazy amounts of cloud computing power that they needed, which was a mission-critical service for their business. NVIDIA is doing this right now where they're not just investing in AI startups with money, They're giving those startups access to the chips that they create that are pretty damn critical towards making the large language models work that are pretty much the basis of the AI of all these companies. So, you know, there's always this kind of value-add component that a lot of startups or that a lot of accelerators and funds have. And Andreessen Horowitz sort of, they were the first ones to think about their value-add as this agency model, right? Where they weren't just providing startups, but they brought in marketers on their team that would then work with the startups to help them market, right? They brought in PR people, they brought in tech people, right? And they just kind of provided this whole suite of services. The second part, I'd say, normally has been access, right? Sequoia really was the one that pioneered— I mean, every investor is like, oh, I have access to deals, I have access access to deals, right? Sequoia really kind of operationalized this through their Sequoia Scouts program, where they were enabling, you know, a few people, people that they invested in that they knew were kind of insiders in different networks, to become scouts on their behalf and to share in the upside of any investments that they made using Sequoia's money, right? That got them into deals. Like, the most famous one was with Jason Calacanis, uh, got them into the Uber deal. right? And that's gotten them into a lot of these other insider deals. And companies like SignalFire, as well as a fund right now where, you know, they don't just provide access to the insider networks, they provide access to the insider kind of data and insights, right? Helping you find companies that maybe other investors aren't looking at. Uh, and they do that. SignalFire is a company I've admired for a while. Since day one, it's been a fund that's really been modeled on building algorithms that are taking in all these data points from the internet, right? And basically creating these internal algorithms that will score to say, look, these are the companies we should be paying attention to right now that other people aren't, right? Based on all of the available data on the internet. So these are different things that they've done. You know, I'd say Y Combinator in the beginning, right? They were very much focused on getting high-quality programmers and talent. Hacker News was created originally for kind of tinkerers, people that just loved playing with technology. And when they married that with their stage, right, working with people at a very early stage and with their model, which provided these tinkerers and these technicians with a very small amount of money to essentially be quote unquote ramen profitable for the first couple of months to get something off off the ground, right? That sort of was their unfair advantage in the beginning. And, you know, obviously at this point that advantage has morphed, but that's how they got started. And then look, if you have perhaps a ton of money, um, then, then that's an unfair advantage too, right? And we've seen that, and, and I don't think to, to some of the best results, especially in this last, uh, boom that we've seen in startups and venture capital, where a lot of institutional or kind of higher-level investors have been coming down the stack and just throwing a ton of money at companies. But I think what a lot of people get wrong, right, like if I was starting an accelerator today, if I was starting a fund today, just from everything that I've seen, um, I think what a lot of people get wrong is, is they always think, look, I, I just, we just have to focus on finding the best founders and the best companies and that's how we're going to be successful, right? And look, that's true, right? Again, the founder is the greatest variable in success or failure of any early start, right? I will pound my fist for that point, you know, to the day I die. But I think, you know, for— there's so many investors, there's so many funds out there right now. I think the opportunity is for the people that, you know, look just beyond the sourcing. It's they combined the sourcing and the process, and that's how they create their unfair advantage, right? Um, and here's just some examples, right? So if you look at— it's not just the sourcing and the process. Another way to look to call it would be, you know, first you need to identify the companies that you're going to work with, then you need to cultivate the companies that they're going to work with, and hopefully that equals something that's valuable, right? For the Founder Institute, I mean, we've used this test basically, uh, to create our unfair advantage, so to speak, right? This has allowed us to target in on aspiring and early-stage founders at an earlier stage in, in markets with ideas and all this kind of stuff that a lot of other investors are ignoring because, to be honest, they're, they're way too early to even be investible in, right? So we work with them at this stage, and the test allows us to do that. But it's only then, by combining those raw materials with our process, which is to give them an incredible amount of structure, direction, and feedback to get them, uh, to their first funding— it's only the combination of those two things that makes the model work, right? If we just found the founders with the raw materials and then just, just threw some kind of generic, okay, you know, do customer development, watch some videos, blah blah blah kind of curriculum at them, they wouldn't be successful, right? We're combining the raw materials with extreme structure and focus to help them become successful. And I think if you look at Entrepreneur First, I think they're doing some super interesting things too, right, where they've created this unfair advantage by combining these two elements. They identify people by really targeting solo scientists, PhDs, domain experts, but solo people that typically have never started a company and that maybe are interested in doing so, right? And then their process is to team them up in sort of this petri dish, right? And kind of just have them very quickly iterate and go through different teams and co-founders and things like that to see what fits. right? And, and that's kind of the advantage that they've created, and they've, they've had some success there. And I bring up Loyal VC here too. Loyal VC is a great partner of ours, and they've invested in almost 150 FI companies at this point, right? And I think they've created a pretty interesting model too to form their unfair advantage, where they essentially will take, uh, very early stage companies from validated networks all across the globe, right? FI is one of these validated Networks. They have a few others, um, and then they invest in these companies, uh, you know, for a small amount just to work with them. I think it's for a 6-month period, if I'm not mistaken, uh, where they work closely with them, they advise them, right? They see the metrics, they see the, the kind of progress that they're making, and then they invest like 10x in, um, a 10x or 5x amount or something like that into the companies that they like to work with and that they see that are fast growers. right? So again, it's, it's that there's so much advantage in marrying the sourcing and the process, um, to create this unfair advantage, right? And if you think about it, how many ways can you divvy up how you're going to source founders and how you're going to target them, right? Just listing out a few here, you can go by stage, right? Early stage, whatever. You can break the stages into all these little microcomponent sectors. Skills, geography, team, technologies, the way they, you know, the customers that they have, the way that they distribute the products that they have, right? And then on the cultivation side, on the process, again, it's infinite possibilities here, right? It's on the way that you cultivate them, on the expertise you bring to the table, on the data that you have, on the distribution or the networks that you can provide them access to, right? All of these different things, no matter who you are as an accelerator, as a fund, right, you should have some of these advantages. And when you can put them together, right, and think about all the permutations of advantages that you could create here just by mixing— and this is just a super quick list, right, for each of these, there's a lot of other ones— um, you can create really interesting unfair advantages, right? If I was starting a funder accelerator today, this is where I would focus, because I think way too many people just kind of play this arms race. Of saying, I'm gonna raise the biggest fund because then I'll have the ability to get the best founders because I can give them the most money at the best terms. And that is a game that some people can play, right? For sure. But you can also, again, if you can strategically marry, all right, maybe I'm not gonna find the founders that are looking for the most amount of money at the best terms, whatever, right? I'm gonna look for the founders within this narrow scope, right? And then I'm going to marry that with these other skills that I have, and that's what's going to create my unfair advantage. That's what's going to create amazing companies. Because it's more than just trying to find great founders, right? It's the sourcing that you have, the founders that you target, you know, needs to inform the process that you have to make them great. And similarly, right, the way that you make them great has to inform the way that you find them and the way that you identify them. It's the combination of those two things is essentially how you create greatness and how you can create super valuable companies. Um, all right, so that's all I got, everybody. If you want to learn more about what we're doing, um, you can learn more about the, the test at dna.fi.co. For any accelerator managers or fund managers, uh, that are in attendance, we are running a boot camp to share more insights like this into the best practices and things that we've now learned running accelerator programs all around the world for early-stage founders for over 15 years now. Gray hairs in my hair prove it. And you can learn more about Founder Institute at fi.co. All right, so let's go to some questions here. I'm getting some hearts. And some thumbs up. That's all good to see. You know, admittedly, I did go through some of the boring stuff on the test there, and I've always felt like it's super important to outline the methodology that we've used to show people that, look, we've designed this test from the standpoint of ignorance, to be honest with you, right? Where we don't know what we don't know. We just wanted to collect as much data as possible, not only from the founders on the test, but then their performance over time. And then see which data correlated. And that's always been our approach. And I've always found that kind of going through that process, the why of the test and just some of the innards of how it works and our research is pretty important. Otherwise I get 1,000 questions as to what the validity is around that. Okay, so let me get some questions. So just looking at the chat here. So, all right. And if there are more questions, please do add them into the chat. So several people want to know if the DNA test is available outside of applying the FI or if a third party can use it to assess their founders. So yes, right now this is something that we just released about a week or two ago. I mean, literally, we just removed the, the hide the no robots.txt. If anybody who's familiar with SEO probably knows what that is, but we unhid this website from Google and search engines basically about 10 days ago, but it is dna.fi.co. All right. So you— anybody can take the test there now outside of the FI application and you can get access to limited results. So then you can upgrade to get bigger results. And we do work with a lot of third parties to use our test as well. And there are options there where you can get in touch with us to learn how you can do that. We've worked with corporations, for example, that use the test. To engage some of the people in their company around their entrepreneurship activities to find the most innovative people within their workforce. We've worked with governments to find high-potential founders within the programs that they were running so they can kind of double down on them. And we've worked with a number of different funds as well and accelerators. And generally with funds or accelerators, we see two use cases. It's either accelerators that have that have a large volume of applicants, and this is a way that will help them kind of evaluate them at scale. Um, or it's on the other side of the equation, sort of at the end of the funnel, where it is in helping them to just get another data point around, uh, founders when maybe they're on the fence about an investment, or just to go deep dive into it, right? Um, and I guess that probably brings up another point too, and I think it will answer some of the questions that I'm seeing in here. When we set out to create this test, it was never to say, okay, this is going to be the end-all be-all, right? If someone does well on this test, they're going to be a great entrepreneur. If they don't, they're going to be a terrible entrepreneur, right? We've been surprised frequently. I think when you work with startups, it's all about outliers, right? Finding the outliers that any successful entrepreneur is sort of an outlier in and of itself. So for us, we've always looked at it, look, this is an extra data point, okay? We think it's an important data point because it's a data point that quantifies what we believe is the greatest variable in success or failure of a company, especially at that idea stage when you're not going to have other performance metrics, right? But again, for us, it's always just another data point, and we always warn and tell people that we work with that license the test, like, look, this is you know, it's, it's pretty accurate. But again, it's, you know, depending on the kind of founder that you want to work with and what you're looking for, just use it as an extra data point, you know, to supplement everything that it is else that you're doing. Um, Gregory asked, do all founders need to be great founders based on the FI test, and is it more important that the rest of the founders are compatible with the main founder? So Gregory, it's, you know, kind of a continuation of that answer. You know, no, trust me, a lot— we've had people that have performed really poorly on the test that have done well, and, and we've made adjustments over time, but it's never going to be perfect. I'd say right now our admissions team, it is the primary criteria for admissions, right, accepting people to the FI program, but it's not the only, right? We look at other things as well. So, you know, it definitely is not geared to be an end-all be-all. And there's so many factors when it comes into entrepreneurship. You know, I think humble successful entrepreneurs will tell you that luck played a very large part in their success. Okay, and I would concur with that. It does. So it's a combination of a lot of different things, but especially for these early stages, we find that, you know, a lot of these traits give founders the highest predictive ability, right? It gives them the highest potential to deal with all that uncertainty, to maybe take advantage of some luck, right? And to sidestep some bad luck and those things to be able to get through, to be able to get through that process. Halal asked, do the qualifications change over time? Do you need to measure at various points in time to see progress? So Halal, yes, it does. And This is where this revamp of the test that we did just a few years ago, where in the beginning the test was really, it was all about the start, right? It was very heavily weighted on a lot of the ratings in the program and within the first 18 months that a founder left our program. Number one, because that's the only data we had for a long time, right? Because we were just getting started. But number two, it's because that really was, you know, and is the core of FI is to help people go from the zero to one state to take an idea, and get it to this first funding, right? So that's what we focused on. Now, when we did that revamp of the test, we did start to then take into play, you know, all right, what are some more of the long-term success metrics? And I think I went through some of those. I mean, when we saw, when we started to look at long-term, it was correlating a lot more. Sorry, I'm trying to I can look at it on my presentation here. I can't find it at the moment. I'm sorry. Having some technical difficulties. But, but, but yes, it is the traits change over time. Right? Definitely things like planning and organization and stuff like that became much more into play as the, as the company evolved. And You know, really, after a company leaves Founder Institute, the ratings that they're getting are almost mostly from our portfolio team. And at that point, it's very much kind of black and white, cut and dry, right? It's like, okay, did you try to raise funding? Were you successful, right? What is your revenue this year? What is the growth, right? It becomes much more binary after that. So, you know, we definitely see different traits play in, you know, as the companies evolve. Chad asks, is learning more important than profit in the long term, in the short term, with the expectation that profit will come later? It's an interesting question, Chad. It varies by company is the first thing that I'll say, right? If you are a company that realistically can start to generate revenue in the short term and profit, then by all means you should do that. Number one, it will give you optionality and sustainability, uh, rather than just being fully dependent on raising money from investors to survive. Number two, there is no better way to validate customer demand than to ask for somebody's credit card. Um, and, uh, you know, so, so yes, like, you should always value those things in the short term, but I do know that a lot of businesses is just not as viable to do that, right? Hardware, whatever, things need be built. You know, but to the second part of your question is, is learning more important? You know, I think anecdotally to me, I am always looking for people that can learn super, super quickly and love to learn super, super quickly. All right. You know, there's a popular saying that great entrepreneurs know what they don't know. right? They're okay saying, wow, I have no idea how to answer that question, but you know what, I'll get back to you and I'll figure it out, right? Those are the kinds of people that we typically look for in FI, and we've seen those kinds of people really validated through the testing, through the things like resiliency and fluid intelligence, um, and adaptability and openness and, and, you know, empathy to, to, you know, different, uh, to feedback that they get and things like that. So I think, you know, anecdotally in Founder Institute, in our programs, it's usually you ask a lot of our local leaders, we can pretty quickly spot if they make it through the testing. A lot of founders that come into the program and you know what, you could pretty much quickly see that they're not there to learn, they're there to argue, right? Where they think that, all right, if I'm pitching, and I get feedback on my pitch. I'm here at sort of a debate club where I need to stand up for my idea and prove to you that you're wrong and I'm right, at which point you will think that I'm so smart that you're going to buy into my idea. Right. And those are the types of people, you know, can some of those people be successful in startups? Of course. Right. But this is where, you know, I bring up back to the point again where it's all about marrying how you identify your start, your founders that you want to work with, and then you build a process that cultivates them, right? We need founders that are coachable to come through our programs. And so that's what we focused on. So, you know, the ability to learn super, super quickly is something that we love. It's something that we have seen correlate, right? I wouldn't put that face-to-face against profit as you did in the question here, right? Because I think there's a lot of other variables involved. But yes, learning, the ability to learn super, super quickly, and not just the ability but the desire and the deriving of energy from learning new things very, very quickly, I think is incredibly important for entrepreneurs, especially at the idea and early stages. You know, generating profit and those things, I think that's a different conversation. So, all right, let's see some of the other questions here. So, Chad is asking, it is known that conservatives are less open to new experiences. Does that mean that there are less startups led by conservative personality types? If so, is this phenomenon only occurring in tech startups? Chad, to be honest with you, and I'll kind of cite the last question, I don't know the answer to that question. Right. We're following the data here. Definitely with the Founder Institute, we take applicants, you know, not only of any, you know, political leaning. I mean, our applicants are thoroughly from, I think it was 136 countries around the world. Right. So we're talking about variations across geography, politics, religion, everything. Right. And we have a And we have basically correlated against that. So it's an interesting question that you bring up for sure. I don't know the answer to it. I do know that according to our research, it's the founders that are open to new experiences, the founders that are a little bit more risk-taking, the founders that are open to new ideas and things like that tend to correlate more. With, uh, with startup success. So Phil was asking, given our research, why do most investment decisions still rely on intuition, gut reaction, and biases? Phil, if I could answer that question, I think we could solve a lot of the world's problems, right? Um, I'm not sure. I, I think that We are moving towards a business environment across everything that's a lot more data-driven, right? I brought up the Moneyball example before. You know, people are realizing more and more that if there is a sufficient data set, right? And sometimes there isn't, and that's fine. And then you do have to rely a lot more on gut reaction and kind of intuition. But if there is a sufficient data set, that the truth is usually in the data, right? I think it takes time for that mentality to permeate different markets and the solving of different problems, right? We have seen it completely take over sports, for example, right? The way that athletes now are judged, especially baseball, if anybody follows baseball, I mean, it's crazy. The amount that it's guided by analytics now and just quantitative data, right? So it's taking over different things, you know, at a different rate. I think it's inevitable for it to take over, you know, not only just evaluation of a startup or a job candidate or anything like that, right? But I do think that there's still always going to be the human element. And this is why I don't you know, I bring up that where for us the test was never supposed to be the end-all. It's another data point, right? If you're interviewing somebody and you don't like them, right, for whatever reason, you just think, look, I'm not going to work well with this person, um, and our test says that, that they're going to be a great entrepreneur, right? Still up to you to make that decision. You have to balance those, those different data points. But I think it's only a matter of time, Phil, before it really permeates this market more and more. We're seeing it more and more, right? When we first brought this up in 2009, people thought we were crazy. I think, you know, Paul Graham in a— it was, I think it was an entrepreneur magazine— was cited as saying impossible, right? There are so many variables that there's no way you could predict success, right? And that's fine. Um, you know, and I think if you predict— if you ask people that same question today, With the datasets that we all have and the new methodologies, I think they'll be much more amenable to thinking, you know what, this could work. And at the very least, it could be a very valuable data point that you can combine with all these other data points to make a more informed decision. Let me see, and I'll take— if anybody has last questions, please do throw them into the chat right now. I do have to jump in a couple of minutes, so just last call for questions. So let's see, we have a question here from Ivan. It's, do we see the founders' performance scores change over time, perhaps by going through the FI program or on-the-job training? So we've done a lot of testing with other non-founders and things like that on our test, right, to see the, the change over time. When we did this massive revamp, uh, over the last couple of years, we also had a large number of graduates, uh, from FI, the early days, that we went back to and said, hey, you know, can you take this test again, right, to see if there were changes. Now, we did see small changes over time, right? But I think there wasn't enough change in there over time that we kind of put a lot of thought into kind of validating that, right? So to be honest, I think a lot of these things don't change over time, or the vast majority of them don't, at least those personality traits, right? Because these are typically things that are pretty ingrained into the raw materials of who you are as person. But I do know some of the traits are learnable, to be honest with you. It's just, it's escaping me at the moment. But of those 26 traits, there are a few, right? I think a few, there's like 2 or 3 that are traits that have been proven through peer-reviewed research to be able to be improved, learned, and/or unlearned on the other side over time. But to be honest, we haven't really seen too much of that. So we've been focused much more on, you know, trying to get better over time in determining success, right, of the companies and saying, okay, what is success? You raise funding, you create a profit, you hire this many people, you know, you got this much market share, those kinds of things. And then trying to correlate that back to the test results to try to find more accuracy there. Alright, and I think there is a— Cynthia is asking here, so the test views towards, skews towards visual learners, and with those puzzles, that is a bias, right? Any test will have a bias. Those visuals that I showed you were visuals from the old test. In the new version of the test, we did trust me, go to great lengths to create visuals that were much easier for people to comprehend, especially people that had, you know, that were colorblind and things like that. All of the language has been— the old test was translated into 9 different languages, but we found that that even introduced a lot more bias in terms of the translation services, right? No matter how much we tried to eliminate that bias. So this new version of the test, actually all of the questions are written, I believe it's at a second grade level, second or third grade level, right? So that to ensure that any translation that was taking place on it wouldn't introduce confusion and any kind of cultural bias to the test. But for sure, Cynthia, you know, there is some bias in terms of those, the visual, you know, problem-solving components of it. You know, when we designed the test, we basically looked, we worked with social scientists, we worked PhDs, and we tried to determine, okay, what are the methodologies that we can use that will have the least amount of bias amongst all the different methodologies? And, and that was the one that, that we settled on. Okay, um, thank you so much, everybody. I appreciate all of the questions. Hopefully this was, this was informative for you. If you do want to learn more about the assessment, go to dna fi.co. Any accelerator managers or fund managers in here, if you want to learn more about some of the methodologies that we use to work with companies and to help them, help make them successful, please do check out our bootcamp, fi.co/bootcamp/acceleratorlab. And I'm sure we'll throw them into the chat. And I do hope to host another one of these sessions soon. To be honest, we have So much data on this test that we don't know what to do with it. We are continuing to go through it and trying to find more and more insights. We are expecting, for example, in the next couple of weeks just to start releasing some more of those kind of top 5 charts across a lot of the different traits, because in a lot of cases it's kind of funny to see, look, yeah, it's like everybody thinks, oh, Silicon Valley must be where the most innovative people are. And things like that, right? But when we start to dive into the data, it really is not the case. Um, what we have found is that there are talented entrepreneurs everywhere, right? They maybe just don't have access to the knowledge, to the skills, to the networks, to the structures, um, that they need to be successful. And from day one, that's always what FI has been about, is to activate those people and to empower them. All right. Thanks everybody. Hope you have a good day and I hope to talk to you soon. Bye.