 Awesome, thank you. So I get to follow up a lot of great presentations today on, I would say, different facets of what might be the first and most critical milestone when you're starting a company, which is finding product market fit. I think today I want to take a bit of a step back and share with you this mental model or theory for how to think about product market fit that wasn't at all obvious to me in the beginning, but has become increasingly lucid and clear to me over the last nine years building Affinity. And so I'll literally take you through the story of Affinity, how we found PMF, how that story really translated into this theory and kind of back propagated into it, and then five lessons that are going to be practically useful to you on your own sort of adventure. So first of all, a bit about me. I'm Ray, co-founder and co-CEO at Affinity. Some quick backstory. So my family actually moved to Silicon Valley from Singapore when I was just five years old. And I pretty much started myself as an SF Bay Area life at this point, like I spent my entire life within a small perimeter around San Francisco. And we originally moved because my dad had kind of decided that he had enough of this sort of academia thing and wanted to give the startup thing a try. He had been a physics professor up until then. And so that among many other things I think shaped a lot of the environments that I grew up in. Put another way, I pretty much grew up in a family of scientists. And I think despite ultimately going into just hardcore software and starting a software company many years down the line, there was this kernel of that upbringing that never really quite left me. Because science is really more of this framework for thinking and truth-seeking more than it is any specific discipline or sub-field within science. And so I promise that's going to come back into the story a little bit later as to how it affects the theory of finding a product market fit. First of all, just a bit about what is Affinity? I think it'd be remiss without sharing a bit about this. So we build next-gen relationship management software for deal makers, in particular venture private equity, private capital investors. So to think CRM, contact management, professional networks. These basically this reimagined approach to all those really critical pieces of software. Our most fundamental belief as a company is that relationships power the most important industries in the world, and they're the most valuable asset ultimately for every firm. But the tools that those industries are using are really broken for two reasons. So the first problem that we identified was they're all predicated on this human data entry, right? People often say your CRM data is only as good as the humans that are actually entering that data, garbage in, garbage out. And people hate entering data, it turns out. And especially that's the case if you're an investor who's taking 15 back-to-back meetings, getting over 100 emails every single day, it's just simply impossible. And the second problem that we observed in the beginning, which is a by-product of the first problem, is that because there's no elegant or like truthful data set about relationships, basically guesswork and memory are the gold standards for how people make decisions about their relationships. So answering questions like, who should you spend time with? Who are you dropping the ball on? Who knows who? Who can broker the best intro? Usually people answer those questions by either guessing or using their gut. And it turned out they're going to be probably either lucky or wrong. And so when we started Affinity, we had this very simple, in the beginning almost naive insight about how you solve those problems in a 10x better way. Which is if you tap into this wealth of data, the billions of data points that are living inside people's inboxes and calendars and networks, we could both put their data entry that had to be done by humans on autopilot while generating these relationship insights that would change the game for their deal-making. And so we call those insights relationship intelligence, and that's what Affinity is, where the relationship intelligence platform for deal-makers. So hopefully that gives you a bit of context about our journey into PMF or the problem that we were trying to solve. In late 2014, early 2015, I dropped out of the CS program at Stanford and my good friend Shobham to go and found Affinity. And I think in a bit of a funny testament to what would later become the vision of our company, there was a lot of luck that was involved in the beginning of the company. And a lot of that had to do with happening to meet the right people and build the right relationships at the right time. So this is actually our first professional photo together. We both looked a lot younger back then. It was taken from the office of a venture firm whose founding partners basically went on to change our lives. That firm was Pear Ventures, and it was founded by Marr Hershenson and Pageman Azad. I actually think Marr is a speaker at this conference. She's speaking tomorrow, and that's our sitting over there in the back, actually. So Pageman and Marr were just two key figures among this legendary cohort of investors and founders and builders who made the introductions that opened our eyes up to what we now call the relationship-driven economy. And those were really the conversations that gave the spark that created Affinity down the line. And today, Affinity serves north of 3,000 investment firms in over 80 countries. We built a product that a lot of customers love, including a number of the folks who are attending this conference. Here's just a few of them and some kind words from one of our customers, Kevin, from Bank Capital Ventures on the left. Okay, so my talk today is about finding product market fit. Actually, first, just a really quick show of hands in the audience. How many of us in the audience are currently founders? Okay. No, keep your hand up, or no, hands still up. Keep your hand up if you haven't found product market fit yet, or you don't know if you haven't, or you're not quite sure. Okay, all right, that's very helpful. Okay, so let's make sure the question, what is product market fit? Right? Here's a definition that I really like. You know, it's when you've delivered a product that your market wants with overwhelmingly clear proof or overwhelmingly clear evidence. And the emphasis here really is on the second part of that equation or definition, overwhelmingly clear proof. Because a part of YPMF fundamentally is so elusive, is that it's extremely tempting to want to believe that you found it and achieved it when you haven't, because you have such a strong desire as a founder to want for it to be true. But wanting something to be true is not the same as it being true, right? And that starts to become a really, really real problem when you start taking actions that are commensurate with the prior that you've actually achieved YPMF. You start hiring, increasing burn, scaling and optimizing for scale when you haven't actually proven without a doubt first that you've made something that people really want. I remember one of my personal first reactions to the idea of product market fit actually being one of confusion. Like, how is this not just incredibly self-evident or obvious? Right? Like, what's so profound about the fact that you should be making something people want? Here's what's profound about it. It's not that it matters, because it obviously does. It's how little almost everything else matters in comparison in the early days. It's because when you start a company, especially if you've come off of having a job previously, you suddenly have what feels like this infinite freedom, right? You have these, like, infinite degrees of flexibility. You can literally direct your time and energy and attention to working on anything. And so it's precisely because of that freedom that it's supremely easy to get distracted about what really matters. Like, you can literally ask yourself a question, like, what should you spend the next hour of your time on? Should you write code, or should you be talking to users, doing research, networking like you are here, hiring, doing something else? PMF's power is serving as a North Star that guides how you prioritize your time. Because you can quite literally ask yourself the question, what are the highest leverage actions in prioritized order empirically that it can't be taking right now that drives the most meaningful progress toward proper market fit? Especially if those are things that you either hate doing as a founder, or that might lead to a truth that you're afraid about confronting. And if one of the things that you're doing within the next hour is not one of those actions, you probably shouldn't be doing it. Because in the beginning, almost everything that you're doing is in service of this North Star goal, right? Talking to customers, building product, hiring, raising capital, none of those things are the goal into themselves. They're simply levers or wedges that you can leverage in order to drive toward greater proper market fit. So I think rigorously, and kind of work backwards from this question to figure out and guide what you spend your time on. Earlier, I mentioned that I grew up in a family of scientists. Over here on the left-hand side is NTU, where my dad used to teach physics and where I spent some of my early childhood. And I think in a lot of ways that upbringing shaped not just a lot of what we talked about around the dinner table, but also my worldview. I mean, of course, I did literally shape what I thought it wanted to be for most of my life, which was to do hardcore science and research, specifically hardcore biology research. So this is the UC Davis Genome Center in California, which holds the place in my heart. I used to do a lot of wet lab genome sequencing work out there. But I think science is really more than just a specific sub-discipline, like genomics or biology, right? It's a framework for truth-seeking. It teaches this experimental probabilistic, like Bayesian, open-minded humility for how you look at and perceive the world. When I started affinity, I had this preconception in my head that somehow going into the world of business was this entirely different universe I was stepping into. You take off the lab coat and put on the suit or the hoodie or whatever it is. But I came to realize the two actually are surprisingly more in common that meets the eye. Because like science, doing a startup is ultimately an exercise in seeking the truth. You have a market. You have a hypothesis about a problem that market faces that you think you can deliver a 10x better solution for. That hypothesis and its expression is your startup. And the onus is on you as a founder to go prove your hypothesis either correct or incorrect. Either of those is equally important, right? So building a company, ultimately, it is a scientific journey. It's this journey of proving hypothesis after hypothesis, product market fit, does your business model actually work into the economics work? In the future, future product lines, future markets, your long-term strategy and defensibility, your long-term vision is all that rigorous and doesn't actually hold. It's really this journey that kind of never ends. So this is my second lesson. Visualize product market fit as the first hypothesis that you have to prove. And like any other hypothesis, it has a couple of common characteristics. First of all, by definition, hypothesis is fundamentally probabilistic. It might be right and it might be wrong. You can literally actually visualize it as this probability that exists on a spectrum and that changes over time as you learn more about the nature of the problem that you're solving and who you're building for and whose answer you can never know with certainty until you actually test it. That mentality is hard, right? It takes a certain degree of humility because it's in human nature to want to get attached to our ideas, to want to believe that we're right. We're so passionate about what we're building about. Second, you can increase your conviction about whether your hypothesis is right or wrong by taking actions like talking to customers, like doing market research, understanding what's painful and what technology enables today and where it's going in the future. Those are things that increase your probability of being less wrong over time, bringing you closer to the truth. Now, because any hypothesis is probabilistic, I think it's an important implication there. Anything is technically possible, right? This is, in my opinion, why definitionally there is luck in building startups. Like hypothetically, all of us here could just close our eyes right now, try to dream up the next great startup idea, ignore all the common wisdom about talking to customers, doing research, no validation, anything. And that might be the next multi-billion dollar idea. It's possible, right? It's just highly improbable. And last but not least, any hypothesis is testable. So ultimately what that means in our world is making something, distributing it and seeing if people actually want it or not. There's a market reality. Your mission in the beginning is to go understand that reality. And by the way, one thing about that, that market reality couldn't care less about how opinionated or passionate you are about your idea. Doesn't care about how many long hours you've worked on it or how articulate you might be about talking about it or writing about it. It's very easy to talk about whether something is a good idea in your mind or not. Opinions are cheap. But the ultimate judgment, the final arbiter, of whether you're right or wrong is achieved only through the act of building a product or service, shipping it, iterating, testing itself. And in the end, in some senses, the truth always reveals itself no matter how opinionated you are. Now, I think one thing I do want to call out is that there is a noticeable difference between scientific and startup hypotheses. Because in natural science, when hypothetically, you've designed your experiment correctly. You've got your control variables. You have your experiment variable. Metaphorically, you're kind of at that point casting this question out into the universe and leaving it up to the universe to tell you whether you're right or wrong, right? It's kind of out of your hands at that point. In startups, it's a bit different because success depends as much on the universe that is your market as it does on your own execution because you control what you build, how you build it, how you distribute it. And so obviously when things are working, if you have users that love what you're doing, your growth is taking off, that's obvious. You've proven your hypothesis is correct, right? But where things get really agonizing is when the things aren't working yet. Because then you're facing this question, why isn't it working? Is it because of my own execution? If someone else hypothetically in the world were to go try to solve the exact same problem with a different approach, would they be able to do it? Or is this something about the market or universe that's signaling something to me? It's telling me something and rejecting my hypothesis, right? And unfortunately, there's no surefire way to answer that question. You have to ultimately develop your own conviction as to what the answer is. So there's this prayer that I used to read or I had read in grade school. I don't know, it seemed really pedantic but I came to revisit it on my startup journey and ended up realizing it describes this quest for a product market fit really well. It's called a serenity prayer and it goes something like this. So it says, God grant me the serenity to accept the things that cannot change, the courage to change the things I can and the wisdom to know the difference between the two. It's really that last part, the wisdom to know the difference that is the ultimate impossible, very difficult to answer question. And so maybe my advice overall is like say your serenity prayer every night before you go to bed and hopefully that will keep you lucid about the nature of the battle that you're fighting. And with that, let me tell you the quick story of how affinity fan product market fit. So I think every company has a unique origin story, right? There's no right or wrong story and in the case of affinity, interestingly enough actually, my co-founder and I were in a lot of ways outsiders to the industries that we ended up building for. We were both studying computer science in college at the time and it was luck that had his cross paths as a number of just incredible legendary investors, builders in Silicon Valley who basically opened up our eyes to the world of relationship driven industries. They made the intros and led to more intros that ended up kind of opening up this summer of literally hundreds of conversations with every financial professional services industry in the world. So we had a basic mission. We wanted to figure out what the heck these people do for a living and how did software and data actually play a role in it? And we were pretty mind-blown by what we learned. I think one thing just to learn that there were people in the world that built relationships literally for a living, like for a hardcore engineer that just seemed really crazy. But also we kept hearing the same pattern over and over again, which was that universally everyone seemed to hate their relationship software. Like if you were to literally like map out the Venn diagram, industries that build relationships for a living and people who love the relationship management software, the intersection was practically non-existent. It was super, super slim. And so what was broken? We literally asked people that question. We also spent this inordinate amount of time with them, kind of shadowing their literal day-to-day jobs firsthand, seeing every pixel of software that they touched in their day-to-day work lives. And we noticed those two categories of problems that I talked about earlier. So we became obsessed with these industries and we wanted to really understand why do these problems exist? Is there a 10x better way to solve those problems? And it was only then that we started investigating or learning more about the technology landscape and the technical unlock that might lead us to build a 10x better solution. Because at the time, this is the early 2010s, the technical protocols that allows you to programmatically access data from streams like email and calendar, communications, networks, we're just starting to gain adoption. And there's all these interesting products that are kind of being built on top of it. It was everything from can you build a faster email client to a new marketing automation tools. And so we had a very simple insight. We said if we could tap into this enormous ocean of data that every firm in the world owned but didn't realize it owned and was literally wasting, we could simultaneously put their data entry on autopilot while developing these insights that could change the game for deal making and intros. And so we built that core technology, used that to build our flagship product, Affinity CRM. And we started by focusing on this sector that was really pounding the table the loudest for the better solution, which was private capital investors. And that became the foundation of Affinity. So my takeaway you from this is you should really obsess with your market and the problem that you're solving first, not the product or solution. And that's hard, right? I think founders too often hang on too tightly under the solutions and rather than truly genuinely obsessing with the problem and why it exists because the problem in the market is the real opportunity. Your V1 idea of how you solve that problem is just the hypothesis and it's usually wrong. And I find that to be a particularly hard thing, especially for founders who are designers or engineers or creative people to go do, because when you have all that creative firepower at your fingertips, it's really easy to get attached to your creations, right? But that's the wrong mindset. I think importantly, as a founder, you're not a creative. You're not an artist. In art, the artists themselves is at the center of the universe. It's all about their identity and their self-expression and their latest breakup or how they made a lot of money and why that makes them super important or whatever. As a founder, you're a problem solver. Your user, not you, is at the center of the universe. And so your prerogative is to understand them deeply and to make their lives better. Okay, so back to affinity. So after we had these hundreds of initial conversations, we heard the same pains over and over again. We started developing this conviction, this hunch that maybe a 10x better solution was actually possible. And so these are two pictures of areas we spent a lot of time in. It's a Sandhill Road in Menlo Park and South Park in San Francisco. It's where a lot of venture investors, our early customers, were based. In the beginning, we made it our literal mission to sit down with every single firm on Sandhill Road in South Park and understand how do they operate? What if they be open to a technical solution like ours? And if they did, to literally be building and designing the product with them. Literally, like we used to alternate day-by-day between taking an Uber up to Menlo Park and taking the couch train to San Francisco to sit down with every firm that would talk to us and walk them step-by-step through affinity. We often worked out of their literal offices to get real-time feedback on our product, observe every single point of friction that they encountered and to design and build the next iterations with them. And by the way, unsurprisingly, our first iteration or STAB, second STAB, third STAB, what we thought would be the killer feature for affinity, ended up being completely off. It was only through the effort of launching and iterating quickly, spending this inhuman amount of time with our early customers, that we converged on the solution that validated our core hypothesis. And that paid off. Like sooner than not, people started using affinity a lot. This was like all the time, not just on weekdays, consistently on weekends. It started achieving usage that looked something more like that of a viral consumer product than it did of a traditional enterprise SaaS product. So this is my fourth lesson. Spend an inordinate amount of time and energy with your early customers and make them successful and intentionally overkill in doing this. If you have any reason to even doubt whether you're doing it enough, you're probably not. So like go pressure yourself, try to figure out what that next model looks like and go do it. It's only until you've developed true empathy with your users, you can literally walk a day in their shoes and understand the contours and the minutia of their psychology, their mental state as they interact with every person and product, including your own, that you can start seeing the matrix of the real problems, the real feedback, the real opportunities that they themselves might not even be able to express to you. Now, obviously that's not gonna be scaled a longer term, right? But that's okay, you know, I'm definitely not the first one to coin this, but don't hesitate to do things that don't scale in the beginning to help make your first customers ultra successful. As long as it doesn't bust the economics down the line, but anyway, that's a separate thing. So finally, in spending all this time with our early customers, we also invested systematically in developing a way to measure product market fit. So on the left here was the offices of one of our first customers, Formation 8 in San Francisco. And in our regular visits, it's not just to them, but to every single pilot customer that we had, we explicitly sat down with them and defined personalized, like one-on-one success criteria and metrics based on their reasons for wanting to try out affinity. Sometimes that was a hard metric, right? So you could say like, hey, are you using the product, so product usage? How much data entry have we automated? So the volume of data entry automated. Other times, even if it was a qualitative, subjective feeling, we found a way to convert that into a soft metric. So you could do everything on a scale from one to five. Okay, you told us you had this problem. How does it feel like it's being solved now that you're, I don't know, two weeks or a month into using the product, right? Or how disappointed would you be if you can no longer use affinity? Would you be super disappointed, somewhat disappointed, not disappointed? There are all ways to kind of capture that and turn it into a bit of a soft metric. So we did a flavor of that personalized assessment with every single customer every single week and iterated their feedback until they were either crushing their success criteria or we realized mutually that it wasn't going to be the right solution for the minutia of problems that they wanted to have solved. And that was like painful. It was like so much effort went into that, right? But it was worth it. Internally, we also aligned on this slate of product usage metrics that signaled to us a customer was likely to get enough value from the product that they would end up sticking on the platform. And so we set a North Circle in particular of having daily usage. We thought that was really necessary to get to really become the firm's true new CRM and to really drive toward the problem of lack of CRM or adoption. And then we instrumented those metrics and tracked them maniacally. So this is one tool that we used a lot in the early days and still use today, it's Amplitude. It's a great product analytics tool. I wholly recommend looking into it. There's a whole bunch of others, Mixpanel and others. I personally lived out of our amplitude dashboards in the early days of affinity. Like I was literally checking our dashboards every other hour. I'd also recommend a session viewing tool if you want to understand the contours of our usage more than just a number, especially if you're not actually able to easily visit your early customers in person. So this is my final takeaway here, right? Which is you should really measure product market fit explicitly and intentionally. It's crucial because both how you choose to measure it and then where you set this subjective goal post yourself can make all the difference between whether you've actually found product market fit or you're just gonna be deluding yourself into thinking you found it when you haven't actually. There's a ton of frameworks you can go about trying to use to do this. But I'm gonna be truthful, the ultimate best approach really depends case by case on the kind of business that you're building. There's not like a single one and done kind of a thing. For example, if you're building a core piece of workflow software like Affinity, might make sense to strive for daily usage, right? Whereas if you're building a marketplace, maybe that doesn't make as much sense and that's okay. So I'd start from the first principles of what are you building? Imagine what does an overwhelmingly successful user or customer base actually look like and then work backwards from there to determine what you want to measure. And finally, you have to set some goals for what product market fit means based on those metrics. My truthful overall advice here is err on the goal of setting, the side of setting an overly ambitious goal. Like where most startups go to die is when they think they fit PMF because they low balled their goal, they want it so badly to be true. Rarely if ever do I see a company setting too high of a bar for customer love. It's very hard to over index on that. So I think just to conclude with a story of how we ultimately found product market fit at Affinity, crazily enough in a bit of a counter story to most near it is, we probably over indexed on it. So this is kind of the opposite of what a lot of companies struggle with. At Affinity, our early team just felt I guess so much more supremely comfortable like giving our product out for free in the beginning. We called them pilots and just relentlessly building and iterating on their feedback that we weren't really sure when to start charging our first customers for it. And we started charging literally when one of our early pilots forced the price out of us. Like I remember one day like they sat us down and they told us, look, we become so dependent and like addicted to this thing that you built for us. So if you were to tell us tomorrow that literally it costs tens of millions of dollars or something like that, something ridiculous, like we'd be screwed. We'd be putting this between like a rock and a hard place. And so that was when we realized, huh, okay, we think we've found product market fit and it's time to start scaling. And it was a very rapid ascent from there to our first million of ARR and our first hundred paying customers. So remember, I think product market fit for all like the lore that exists around it, it's just the first checkpoint on a long journey that's building a startup. It's button one of many hypotheses that you have to either initially prove or prove in parallel in the beginning. So think of it really as I liken it to like the tutorial level of video game. If you can't be the tutorial, there is no rest of the game to play, right? And so don't take actions that are commensurate with like what you would do, assuming that you've actually unlocked the rest of the game. State leaders are focused in the beginning of what really matters and cut out the rest of the noise. Okay, so recapping my fellow lessons for you. One, remember how little everything else matters in comparison. That's the counterintuitive thing about product market fit in the beginning. Two, visualize it as a first hypothesis to be proven. And like any other hypothesis, it's probabilistic. It can be right, but it also can't be wrong. Your objective as a founder is to seek the truth and to become less wrong over time. Three, obsess with your market and problem first, not your product or solution. Four, spend an inordinate amount of time and energy with early customers. Do inhuman things, even if they don't scale initially to make things successful. And finally, measure product market fit explicitly and intentionally and set a high goal that signals incontrovertible proof that you've made something people want. Don't sandbag it at the risk of lying to yourself. Starting a company is a scientific journey. That framework has worked well for me and I hope it'll work well for you. So good luck to everyone in the stage, in the audience for getting to your first checkpoint and thank you.