PhD to $100M Revenue: Rebuilding SMB Lending with AI | Sahill Poddar, Co-founder and CEO of Parafin

21:05Simply because you started to plant an idea that, hey, here's$50 ,000. It's a few clicks away. And then, you know, in two weeks later, that subconscious mind has spun up ideas how to use that$50 ,000 and the business comes back and accepts the offer. So that dynamic is, you know, impossible to crack when you're going direct to small businesses, which is why many have, you know, any provider who's tried this in the past has not been successful and why Parafin is different.

21:32Turner Novak:It's like the same, the same analogy with like VC and startups, like the best, the best startups probably don't need the VC's money. Yeah. So like if you're if you're if you're constantly only relying on like the founders that are coming inbound pitching you, it's like, well, maybe there's a little bit of adverse selection there versus like the guy who's just building, crushing it, doesn't need your money. And then you have like the venture side. It's like, you know, everyone trying to talk to that guy or that girl to give them money. It's like maybe similar dynamic, different profile of business, but some parallels.

22:02Yeah, no, absolutely. And BC, I fundamentally believe, is a cottage industry, and you can actually be very successful in the cottage industry if you're very charming. So charm kind of, the best venture capitalists are very charming people, and that's how they get the right to win the deals, and they can be successful in the cottage industry. But, you know, me being charming is not going to help, you know, a paraffin acquired a hundred thousand small business. You know, that has to come through scale, through code, through mathematics and engineering more than charm of anyone individual on the team.

22:34Turner Novak:All right. So I do want to talk to you about charming people and getting big customers and then maybe some fundraising stuff, too. But I do want to not skip this. And I kind of have to ask this question first to get into it. So I'm just going back to the very early days. I think you might have mentioned you did a PhD in particle physics. That's right. How do you go from that to where we are today? How did that even come about? Yeah. So in some ways, I was born in an entrepreneurial family in India. I was almost like the black sheep trying to do academics. It took a lot of explaining to my family members as to what a PhD in particle physics really meant.

23:10What is it actually? What does that mean? Yeah. So in short, you know, there are four fundamental forces in nature that physicists like to believe exists. There's electromagnetism, which is responsible for light and electricity. There's a strong force, which is what keeps the stable together. There's the weak force, which is responsible for, you know, PdK. And you can kind of get to the age of fossils using that. And the fourth one is gravity. And so particle physics and research in particle physics is an attempt to experimentally verify theories that try to come up with one explanation for all of these forces existing.

23:50And the Large Hadron Collider at CERN, which is the largest particle collider where I did my PhD, is a pursue in that effort. You know, there's a few thousand people from all around the world, including the best U.S. universities, who are working there trying to discover the forces in nature and explain why they exist.

24:12Turner Novak:And so what kind of stuff were you working on? Yeah. So I worked on a few different things. In 2012, there was a particle called the Higgs boson, which was the last missing piece in the standard model. And the standard model basically takes three of the four forces and is able to combine them into one. That was the missing experimental evidence that ties all of the standard model together. To make it a fourth piece? No, it's like sort of one force that has three different ways in which it exists in the world, but it's really one force or one model that can hold it all. And by one model, I mean it's essentially a group theory, a Lie algebra model, which physicists like to consolidate models and have one giant model that is able to explain many different things.

24:57And so this is an attempt at that. So it takes three out of the four and is able to consolidate into one. There was one experimental evidence that was missing. that experimental evidence basically proves why objects have mass. And so why can you and I not move at the speed of light? And it's because we have mass. And why do we have mass? And why do some objects have more mass than others is explained by the Higgs boson. What's more important, though, is a PhD in particle physics is fundamentally a PhD in data science. because at the end of the day what you're doing is you're taking a very large number of data sets, crunching them through algorithms and through code, doing deep statistical analysis, machine learning, in order to separate signal from noise.

25:48So to see these type of experimental evidence for something like the Higgs boson, physicists collide billions of particles at once and try to collect billions of those collisions. happening through several years and try to look for signal versus noise. And in doing so, you end up equipping yourself with the latest and greatest in the machine learning tools.

26:13Turner Novak:And so the Higgs boson collider? Higgs boson is the name of the particle. A large hadron collider is, yeah. And this was, it's in, is it in Switzerland? That's right. Is it underground, like super deep underground or something? It's roughly, if I remember correctly, 100 meters underground. on the border of Switzerland and France, just on the outskirts of Geneva. Is it this like super long tunnel or something too? How does it work? It's circular, more or less circular tunnel. And so you kind of have to take an elevator and go down in order to view it. But most of the times when the particles are colliding, in fact, all the time when the particle is colliding, you're not allowed to go underground because it's highly radioactive and you probably get torched.

26:59So everyone's sitting up above. Everyone sitting up above in control rooms, monitoring screens, and essentially the whole system through monitoring systems that we've built that feed us back data that can be looked at using code. So does it get super hot in there, like thousands or tens of thousands of degrees? Yeah, I would say more than, there's certainly heat, but more than heat, there's radiation that would be invisible to the eye. It would be like a particle that's probably like ripping your skin or whatever, or like tearing organs because it's going so fast kind of thing.

27:36Turner Novak:Okay. Interesting. And so how does that actually work? Like when you're running the test, do you like put some material in there and like you put something on it or like put an x-ray or like some kind of beam? Like how does it all happen? Yeah, it actually starts off, you know, given the scale of the experiment, it's a 27 kilometer circumference tunnel. Oh, wow. That's huge. It's massive. It's a circle? It's a circle. And, you know, there's particle detectors on either sides of the circle that are sort of concentric to the circle that are measuring collisions coming out of the circle. It all starts off with just a bottle of hydrogen.

28:15So you have a bottle that has hydrogen in it, and you connect that. you let the hydrogen flow into the pipes and you start stripping out neutrons from the hydrogen to get a single beam of protons, which then go around the beam, around that circumference and start colliding at those concentric circles that have been established over the circumferences. Does that speed it up? Is that what that does? No, you want to get like a steady beam of protons because the collision is happening between proton-proton particles. In colliding protons, you can essentially learn about the fundamentals of it, of what, you know, a proton comprises of these particles called quarks.

28:57So a proton is like up, up, down. And so you're basically getting those quarks to collide. And proton just happens to be a really good particle to circulate in the beam because you can get it up to very high speed. and you can use what are superconducting magnets in order to get it there.

29:16Turner Novak:So let's say you get this all up and running and it's going like, what do you, what's the thing that's being studied? Like, what do you get out of this from like watching them collide? Yeah. So it's, it's a fundamental, you know, it's, it's fundamental physics. So you're trying to, we as humans are trying to improve our understanding of the very nuts and bolts of physics and physics theories. There's, you know, fundamental physics is not the person of it is not necessarily you have any immediate applications, but there are things that emerge out of it that are hard to predict. You know, one of the things that emerged out of the experiments at CERN was the World Wide Web.

29:52So the World Wide Web was invented at CERN because two researchers went back to their hometowns after having worked there. I believe it was England and France. And this person called Sir Tim Berners-Lee, who was English, he basically wanted to send data over to his French colleague and therefore invented the World Wide Web to do that. So that's kind of a spinoff effect. The second kind of spinoff that we've seen at the Large Hadron Collider is the ability to build large superconducting magnets and systems, in this case, a 27-kilometer circumference system. And what that essentially does is it teaches us how to build these large-scale engineering systems and maintaining them, right?

30:34And what is a superconductor? A superconductor is an object through which particles can flow through without any resistance, right? Which is kind of very weird, like that exists.

30:48Turner Novak:Is that what space is like? Like when you're in like deep space with nothing around, is that what the same environment you're in? Not quite. There is very little resistance there. But in this case, we're talking about like electrical resistance. So, you know, electricity, there's always resistance in the way we think power. If you try to move an electron or a proton through a circuit or any sort of space, you will start to feel some resistance. But if there's a superconductor around it, it can move without any resistance. And the reason for that is through this new sort of phenomena that was discovered by actually an American physicist, Douglas Bardini.

31:28He's the only physicist to have won two Nobel Prizes. One for discovering the BCS theory of superconductivity, and the second for inventing the transistor, which are both monumental.

31:40Turner Novak:Transistor, this is in the 50s, the reason that computers exist. That's right. Okay. Wow, that's crazy. So then what was the point of the whole CERN project? What were you guys trying to discover again? Yeah. So part of it was discovering the Higgs so we can say, hey, we understand the standard model really well. Yeah. And, you know, let's kind of put a stamp of approval and let's look for newer things. The part of the newer things was also discovering things like the existence of extra dimensions. And so the simplest way to explain that is in physics, we see that the three forces, electromagnetism, strong force and weak force, are all much stronger than gravity.

32:24One simple explanation is that the reason gravity is perceived weak to us is because it is escaping into extra dimensions. And as a result, when we try to measure it, we are only measuring its strength in the 3 plus 1 dimensions that we are aware of, which is 3 space and 1 time dimension. The mathematics of it works really well. However, not everything where the mathematics works is nature's secret. You kind of still have to experimentally test that. The test we do at CERN is to essentially measure whether graviton or gravity particle is escaping into extra dimensions. And what we really discovered there and continues to be true is that because where the Higgs was found, the mass at which the Higgs was found meant that a lot of these new types of theories are not going to be discovered at CERN.

33:17And wait, why is that? Yeah, it kind of, you know, the mass of the Higgs constrains a lot of new theories and says, you know, in order to kind of mathematically have a lot of these new theories exist, you have to let Higgs be a little bit heavier than it was experimentally observed to be. So by achieving the Higgs discovery, which was a phenomenal achievement and actually led to Peter Higgs winning the Nobel Prize in 2012, a lot of the future dreams of CERN were in some ways shattered, which led to folks like myself branching out of physics after my PhD and moving to industry.

33:58Turner Novak:So you mentioned before, though, you were thinking about leaving, like going out of academia, more in the industry. How did that come about? Because I know you were at Robin Hood. You didn't actually go right to Robin Hood. What was kind of the journey there? Yeah. So once I finished my PhD, it was pretty clear to me that the opportunities that were presented at CERN would be fairly limiting in terms of how exciting it would be for what it meant for our understanding of the universe. So I decided to look around and see where could my skills be applied. And data science was picking up as a pretty hot topic in tech.

34:33This is like early 2010s. And so, you know, I was living in Germany back then doing my PhD in Europe. And so I decided to move to industry and use my skills in the tech sector. And it just happened so that within technology, one of the largest data sets that existed, I think till today, is on advertising and how to monetize ads data for businesses. And so I worked for a few months at a startup based out of Berlin and before moving to Meta, then Facebook, and being part of their ads team in Europe and grow that business, which at the time was the fastest growing geo for Meta and Facebook.

35:17Turner Novak:So then what kind of stuff are you doing there when you're running, working with these large data sets? Like for somebody who is not aware, just like the scope or the scale of that, what does that entail inside Meta? Yeah. So what that entails is essentially using large data sets from customers interacting with ads, clicking on ads, buying behavior, and across many different advertisers. And you can imagine like a company like Facebook has essentially billions. It's kind of the floor on any kind of data set they have with this. Using that to predict what future performance for advertisers could look like and coming up with better targeting mechanisms.

36:01And so, again, not only is Facebook collecting data on, you know, what ads you're clicking on, but also what pages are interacting with. Facebook has this thing called the Facebook Pixel, which is on pretty much most websites across the world. to see how you're interacting with other websites. That's right. And so even if you have not told Facebook that you like Nike shoes as an example, but you're on Nike shoe website, Facebook knows. And Facebook knows who you're dating by virtue of the two people living together in the same house or who your partner is, et cetera. And so they can leverage that type of information to create better and better targeting.

36:40Like if you went to a Nike shoe website, they'll probably show your wife the ad as well because like, hey, as a household, they're probably interested in Nike shoes. And you have like partners in a household.

36:50Turner Novak:Like that's interesting, yeah. Oh yeah, partners in a household. I mean, you know, you have to kind of disambiguate between roommates and partners, but they're able to do that pretty, pretty easily. Their models, they can even predict if two people are going to start dating, but haven't started dating yet. Interesting. Just based on, you know, interactions and models that they have. And what helps them kind of, get a true data set is very often people will post their relationship status on Facebook back then and say, hey, I'm dating X, Y, Z, or I started dating X, Y, Z. I got married to this person.

37:24And so they can really, they have a full picture of when that happens and how their interaction started and how that evolved over time. And so you can use those type of signals to target people better, to make sure you're showing them more contextual ads, which is better than showing, you know, an ad for a random thing. It's better for the user because they're seeing something more relevant to them. It's better for the advertiser because they're reaching these users at a much more cost-efficient manner. And it's just overall better for the ecosystem. And it allows a company like Facebook to scale and spend huge amounts of money on CapEx in building things like Lama.

38:01So Facebook in particular has been exceptional in giving back to the community, you know, having built some of the best open source projects, but also So now with AI being open source, so data science is an excellent tool to help companies like Facebook, Google, et cetera, monetize their ads business and ads network by getting advertisers more efficient and by getting the ads that we put in front of customers a lot more contextual.

38:29Turner Novak:Are there any lessons from the Facebook days that have been helpful today with Parafin, like specifically on this, a lot of data science across all these different platforms? You probably have more data than just a Facebook platform at this point. Yeah, so, you know, it's obviously an exceptionally well-run company, both Facebook and Parafin. But the thing that Facebook does really well is emphasize that done is better than perfect. And, you know, the speed of getting things done. I mean, today there's other companies as well that are kind of, you know, have maybe have even taken the baton from them.

39:10But just like early 2010s, Facebook was moving extremely quickly, much faster than others in the space. And even for its own size and had a pretty low bureaucracy, fast moving culture. Done is better than perfect, was plastered across pretty much every Facebook office on their walls. And so that's one cultural trait that we value a lot. But we call it the ability to compress time and get things done since I think it's largely true that speed is the only real advantage that a venture-backed company has against an incumbent. And if you lose sight of that, it can be pretty dangerous. So keeping that as part of our culture at DNA is extremely important.

39:52Turner Novak:And so then talking about Robinhood, I know it took you a while to convince them to give you that. How did that kind of all come out? Because it wasn't like an immediate, like, bang, interview, you're hired. How did that process go? Yeah. So, you know, I happened to be in the U.S. for, you know, working for Facebook. And I was on a U.S. trip and I really wanted to move to the Bay Area and be closer to the metal in terms of, you know, being in Silicon Valley, working for a tech company. And through some stroke of luck, I met a venture investor who had invested in Robinhood, a seed fund, which now is defunct.

40:32Oh, wow. And that person introduced me to Robinhood. And even that was like extremely serendipitous because I went to this person's office. The fund was called Rothenberg Ventures. And I went to their office, and as I was talking to one of their partners, he was showing me around his virtual reality lab and talking about all these other companies. And none of them are really interesting or really relevant to someone who could do data science. And then as I was about to walk out of his office, he just said, hey, I have one more thing for you. There's this early-stage company called Robinhood that I've invested in.

41:10Maybe you should talk to them. The founders are also mathematicians and physicists. And so I was like, I'd love to. I'd just read about them having raised around. Had they launched yet? I think then probably on the cusp of launching, I think it was maybe a little bit pre-launch. And so I reached out and I got connected to who then became my manager there. He was the head of data science and data engineering and was one of the first five employees of the firm. And as I got to know him over time, I was borderline harassing him with each trip that I was making out of the Bay Area to work for Facebook and saying, hey, can you meet me again?

41:54Can you meet me again? And at some point he just turned around and said, hey, dude, do you want to just come into an interview? And I was like, yes, like that's exactly what I was hoping for. So I met them and roughly it was a few dozen people interviewed, did not hear back from them for several months. and then one fine day, I get a response from the HR saying that, hey, they've accepted me and they have an offer, et cetera. So just out of the blue. And they'd like to work on a work permit and a work visa, which by the way is also not trivial to get for the US, as I'm sure you're aware. And so in the end, it all worked out.

42:34They helped me through the process, but I think the end-to-end process took about a year of me first contacting them versus me starting working there. And so, yeah, you know, at the end of the day, persistence and perseverance paid off. They probably saw something in me that they liked. And I was very fortunate to be part of the early team there.

42:56Turner Novak:And what was the, well, probably like the single greatest lesson you learned at Robinhood? Yeah. Well, Robinhood taught me many lessons. And so before I started working for Robinhood, I knew nothing about financial services. In fact, the first time I bought a stock was after I met the Robinhood team. I went back to Germany, opened a German brokerage account and started buying stocks to even see what the whole experience was like. By the way, I was roughly 30 years old then. Today, when you look at 21, 22 years old - We got high school, middle schoolers in Robinhood and Glass. Exactly. And so I was pretty much much older than one would expect as someone dabbling into stock trading in the US.

43:43But in Europe, most people do not trade stocks, right? Like it's much more different mentality. Like it's more of cash preserving capital versus investing and trying to grow capital. And so culturally, it's a very different place. in. So yeah, many lessons learned along the way. I think everything about the U.S. equity markets that I knew until I left the company was due to Robinhood. Everything I knew about option trading was due to Robinhood. Everything I knew about building a fast-growing venture-backed company was due to Robinhood. I learned how Robinhood thinks about building products, achieving product market fit, measuring product market fit.

44:27But how do they?

44:28Turner Novak:How does Robinhood think about building products? Yeah, I think first and foremost, you have to kind of ask yourself the question, you know, what is the status quo? Where do you have a right to play? And most importantly, where do you have a right to win? And so extending equity trading into option trading, you know, in hindsight seems pretty obvious. But option trading, as it turns out, is a much, much more complex thing to enable, which is why we haven't really seen many other Robinhood competitors enable that. And, you know, you can easily lose your shirt if you get it wrong. And so how do you kind of go from a working system, which is already complex, to building something that is 10x more complex, has, you know, many different risk considerations, CapEx considerations, responsiveness considerations, entering new product categories.

45:19Like Robinhood, until very late, until very recently, was treated like the stock trading app with fun money in it. But more recently, if you've been tracking the company, they've been growing AUM very steadily and launching all these cool new products. There's a Robinhood credit card, there's a retirement account, there's passive investing, there's crypto, et cetera. And so how do you think about entering adjacent markets? By the time I joined that company, they had pretty strong product market fit on the core product, even though it was on tens of thousands of users. You could see that there was some magic happening there where each user was coming back pretty much every day to track their portfolio.

45:55Turner Novak:Interesting. And I think the thing that Robinhood got really right in hindsight with their first product category compared to competitors. So if you remember the time there was Wealthfront and taking off at the same time, there's Robinhood in this few other sort of wealth management, passive investing. Better Mint was maybe one. Better Mint is another one, yes. And both those products got to a pretty, I have pretty decent AUM over time. Yeah, I think Wealthfront just found to go public, didn't they? That's right. I think$80 billion, roughly$350 billion of revenue. So yeah, I think pretty profitable, like 40 % margins of AUM ring rise.

46:29Turner Novak:Like good business, like good profitable company, probably worth a decent amount in the public markets. Yeah, but not growing as fast as, you know, Robinhood, for example, not entering as many adjacent product categories. And if you were to kind of say who replaces, who is the Charles Schwab of the new era, I think very quickly you would deduce that the answer is Robinhood and not something like WellFront. I think what Robinhood got really right is the team there early understood fundamentally that this dream and this hope of unlimited wealth generation. And if you can kind of capture that, which is kind of ingrained in American culture, right?

47:10Like if you do the right things, there is unlimited upside potential that exists in the United States and kind of productizing that and tying that to US equity markets led to like great things. In Wealthfront, by definition, there's only sort of 10 % return you can get max on an annual basis because you're tracking the SPY or whatever your favorite index is. But in Robinhood, you are kind of in charge of your own destiny by investing in the right categories or the right stocks and doing so at the right time, you can beat the market like 10 or 100x over. And capturing that zeitgeist in the American part, zeitgeist, and tying that to US equity markets and productizing that is what they got absolutely right from the get-go.

47:54Turner Novak:Yeah, that's a very American thing of individualist ability to go and pick a la carte, like the freedom of choice, but then also like, you know, unlimited upside, optimism, you know, et cetera. I feel like Wealthfront's maybe more like the European approach or something like that where it's like, you know, neither is wrong, but. Yeah, I would say Wealthfront is pretty good on the capital preservation side and Robinhood is better on the capital, you know, it's, Robinhood is good to make money and Wealthfront's good to kind of, Robinhood's good to get rich, Wealthfront's good to stay rich or the Wealthfront approach is good to stay rich, but, you know, So fundamentally, if you're going after the millennial user base that are more likely to adopt a mobile product, you want to attract how to get rich cohort and not the how to stay rich cohort.

48:43Turner Novak:Yeah, it reminds me when I think of when I'm talking to, I don't know, like Gen Z, people like 19 leaving college or whatever. Like I feel like the mindset right now that kind of young people have is you kind of have to – it seems like the philosophy is you kind of need to shoot for lottery tickets. Like that's really the only way to get rich is like winning the jackpot. Like it's hard to kind of do the traditional American dream and like the wealth front approach of like slowly grind it. Like you just don't get anywhere. So you have to just keep looking for those spikes where like you like you win the lottery or like, you know, you buy NVIDIA call options and you get a thousand X and that's how you get rich.

49:21Turner Novak:It was just very different from the way growing up. Mine was like buying my first stock through Scottrade, right? And I do all this research and like you got to contribute to your retirement account every month and like, you know, make really smart decisions and like go slow. So, yeah, I would say yes. Yes, but there's sort of even on the lottery ticket side of things, like there's measured ways of doing things that are more repeatable that to someone on the outside might look like a lottery ticket. Yeah, venture capital right there. I mean, yeah, that's right. You know, there's like funds that have vastly exceeded market returns and done so for several decades and remain as the best in business.

50:07And that is a systematic sort of formulaic way of doing things that's repeatable versus like buying an NVIDIA call option before earnings and hoping the stock pops. That is more in the extreme lottery ticket category versus a measured way of doing things. So since the latter is not repeatable, you never know if you're going to be able to trade that same Envide option call and make those same returns again.

50:30Turner Novak:Did you learn any interesting sort of growth related? Because you were kind of on the growth team at Robinhood. Did you learn any sort of growth frameworks or things that you've kind of been able to carry through throughout your career from the time at Robinhood? Yeah, absolutely. So, you know, my time at Facebook actually taught me, and there was some great material which Facebook published internally then, but I think it was external since then, around measuring product market fit, focusing on the aha moment of why is a customer coming and what makes them stay in an app. And so we kind of ran those similar analysis at Robinhood.

51:08We were seeing extremely retentive user base, which were a clear sign of a product market fit in a large market. but we kind of take one step further and say, okay, why are these users being retained? Let's try and understand what is the action that is leading to retention. And if you do the right set of data science analyses, you can back out into the fact or the insight that when a user comes and when they buy their first stock, and in fact, when they sell their first stock is what leads to the maximum retention. Really? Because I would think sell like you're withdrawing your money. Yeah, well, the sell matters because that's when you realize gains, right?

51:48And you can kind of track actions and be like, okay, buy leads to good retention, but actually sell leads to even better retention. And there's not that much slippage between the buy and sell, right? Because most people buying a stock would eventually want to sell it. So let's try to make sure that we can make people do that buy-sell action as quickly as possible. So how do we back this into something that can help us grow the user base? And so we took that insight and, you know, one other sort of experiment that we ran at Robin, which was vastly successful, was our referral program. And so initially, the Robin referral was some flavor of if you refer a friend, you can get$10, which you can do whatever you like, where most likely you'll buy a stock.

52:31Yeah, that seems like pretty straightforward.

52:32Turner Novak:That's a pretty common growth strategy. A lot of people give out free money. Yeah. So it is a common growth strategy, but I want to say in 2015, 2016, it was not as widely adopted by the market as you would expect it to be. But that alone doesn't work as well, like giving$10, because you could be doing whatever you want with$10. We haven't really given you a quantum of the experience that we want you to have from the product. And so how can we change that into a quantum experience? So then you can kind of go one step further and say, okay, you can only buy a stock with this$10. It turns out that doesn't work as well either.

53:09What the team did then is we essentially took ideas from behavioral economics that humans love unknown variable rewards. And people love scratching a card from a lottery ticket and seeing what they got and things

53:23Turner Novak:like that. That's a great white elephant gift, by the way, is you just buy like$25 of lottery tickets and put it out. And people go crazy over that. If you ever do a white elephant party, bring lottery tickets. Always a hit. That's a great tip. That's a great tip. Or maybe an unknown stock, right? Or unknown stock. Yeah. So that's what you did at Robinhood. So with Robinhood, we went with the unknown variable reward and the reward was, it could be a stock of one of N companies, the expected value of which is call it$10. But there was a small probability that you got a stock of Facebook or an Apple, which I think around the time was like 100 plus or 200 plus dollars.

53:59But most likely you would get a stock that was like, you know,$10 or slightly under$10.

54:03Turner Novak:So you would get one share of stock and the price was like around$10. Like it might be like GE worth$9.50 or like AT &T trading at like$17 or something. You just get one share? You just get one share. And that way, what we've done is we've given you a quantum of the smallest possible experience of the product. Because now you have a stock, you're tracking it. You'll likely sell it to cash out. Yeah, because you're like, what is this AT &T? Like, I don't want this. And by the way, we know that once you buy and sell a stock, you're highly retentive. So these users will turn out to be users that eventually generate revenue and high LTV.

54:40So we took that insight and that particular experiment, just blew every other experiment out of the water in terms of performance, CAC and LTV performance. And we just started pouring the fuel on the fire. And, you know, I think it's obviously difficult to attribute how many users Robinhood has today from this. But I would say a big amount of the user base was captured using this technique. technique. That was a mix of some learnings I had from Facebook, behavioral economics, the Robinhood team's referral program, and tying that all together. And a few different iterations of that that led to the winning formula and the winning experiment.

55:22A second one was gaming the Google ads network. And maybe gaming is not the right word because it sounds like we did something - Like she did or something. Something fishy, but it was mostly like being highly scientific around how do we leverage Google's ad network to send us the best users that Google has for our product. And so using same similar techniques, we basically built a LTV prediction model very early on. And based on the user's one or two day action, you can tell Google that, you know, you really like this user. Send me more of this. There's a trade-off that if you wait for too long to send Google the signal, Google's ability weakens to send you similar users.

56:05But if you send it too early, your ability to identify good users is not good enough.

56:10Turner Novak:Isn't tested enough or refined enough. So you have to kind of build a model which relies on a little amount of data and sends what we call a post back fast enough to the Google network. So this is within like two days is what you found? Yeah, we found that actually within one to two days, there's enough traces that a high LTV user leaves in the product. And we can basically use that to inform Google's ad network to send us more similar users. Is this like lookalikes? Is that what you'd call it? These are lookalikes, but these are custom built lookalikes. These are post-back lookalikes. So we are posting back ourselves what kind of lookalike we want versus Google telling us what kind of lookalike we should get.

56:49So highly, highly customized version of a lookalike.

56:51Turner Novak:So what was the leading indicator of a really good Robinhood user, a profitable user? Yeah. So it was around the number of pages they were looking at, what kind of pages they were looking at within the product. Were they interacting with the bank linking flow? Were they initiating a transfer? What instruments, even what instruments they were buying initially led to kind of intel on their long-term LTV? So you could tell how active they would be, how much money they might have, and what kind of dollar amount might be flowing through. And then go back to Google and say, find people that have maybe this income threshold or searching for these specific types of finance or investing related topics.

57:33Turner Novak:That's right. Okay. Interesting. And then it's interesting too when you talk about the reward-based referral program. So it sounds like you basically, you figure out what the, like, what are the profitable, what are the most valuable users that you want and figure out how to incentivize getting more of those. I mean, it sounds super simple. I'm like, you know, really kind of reflecting back on it, but do people not do that usually or? No. So it is, it does sound simple. And I think that is, it's a good sign that it sounds simple. Like any sort of insight that, you know. Like it's too complicated.

58:13Turner Novak:You can't replicate it. Like, is it real? Post-hoc should sound like simple, but what actually it entails in building and experimenting and testing it is a highly scientific endeavor. But we should be able to explain it in the simplest way as possible. And, you know, as simple as it sounds, it does take quite a lot of effort from multiple different folks on the team to get there. Can you predict churn? So that's another problem we worked at at Robinhood and obviously is a big part of our modeling at Parafin is to predict the churn of a small business. Either they completely go out of business or they leave the platform that we require them through.

58:51At Robinhood, we were also predicting churn and basically measuring user activity, what pages are they looking at, how much time they're spending on the app. Did they just do a terrible trade and they lost a bunch of money, so they're unhappy, et cetera. in order to predict churn, and we were pretty good at it.

59:07Turner Novak:So then you tie that back in also to the acquisition of figuring out, can you predict what types of behaviors they might do in the future that those behaviors lead to churn? Yeah, I think the actionable on churn is there's a few different things. You can kind of fix things in the app that are broken, which are leading to customer churn. But that typically you will also get signal from your customer support teams that will hear that, hey, users X, Y, Z are complaining about this. The second category is, you know, if users are losing a lot of money and churning, you can be better around educating them around what they're about to buy and sell.

59:43You know, hey, this is a volatile stock. These are the metrics of the company. So beware what you're getting into. And doing so in a way that, you know, is in the American spirit that you are doing your best to educate but not making the decision for them. And then the third category is a little bit harder when folks are just saying, hey, I'm kind of done investing now because I have some other personal life financial goal that I want to meet. Maybe it's buying a house.

1:00:15Turner Novak:So you'll sell and withdraw. Yeah. And that's a little bit harder based on the data you have at Robinhood to predict. And you can still predict it, but get an insight into why they're doing that is a bit harder. And I think the best way to address that is to build financial tools that can help capture those transactions as well. So, you know, we've seen Robinhood get into mortgages recently. Oh, do they? I was just going to ask. Do they have that yet? Yeah, they do. I mean, I was playing around with my app a few days ago and I saw that, you know, you can apply for a home loan through Robinhood.

1:00:45They have a partnership with Sage Loans, I believe. And so capturing more surface area through products such that you can meet the user as they're doing more financial transactions is the way to stop that churn.

1:00:57Turner Novak:Okay. So then how did Paraffin kind of come about? Like, I know you're at Robin for a while. How did this whole, like, it seems like from what we described, you had an entrepreneurial family. So maybe it was like the indications were always there, but it doesn't seem like you were like growing up. I was like, I want to start a company. How did this all kind of come about? Yeah. So entrepreneurial families are certainly, you know, I think there's always been a push from the family. Hey, when are you going to start your own business? I know this academia thing, like you're messing around with stock trading.

1:01:27Like, come on. Exactly. And they were kind of disappointed in me when I was leaving Meta to join Robinhood. Or not disappointed, but rather questioning the decision. Hey, why are you leaving a well-established company like Facebook to join, like, you know. Like a startup. Like a startup. And, you know, taking a pay card, et cetera, et cetera. So there was some educating that has to be done. It's no fault of theirs. It's such a fast evolving world that I'd probably have similar concerns if my son was making these decisions at some point. So there's obviously that push from the family, hey, when are you going to start your own business?

1:02:03And then when we were at Robinhood, what we saw was we were undergoing through the mobile era a consumer finance renaissance of sorts. There was Coinbase, Chime, Robinhood, Venmo, and maybe like 50 other apps trying to compete for the U.S. consumer for financial services. However, small businesses that have been close to both our hearts, my heart and mind, was not being addressed the same way with financial technology. Yes, there was Square and Stripe to some extent doing things where they were making it easier for businesses to process payments and kind of solving for funding through earnings problem.

1:02:44But no one was really solving funding through debt problem for small businesses. with the exception of Square Capital and to some extent, Shopify Capital. Square Capital was doing a decent amount of embedded lending and Shopify had just kind of started on that journey doing embedded lending as well. Those like certain types of business,

1:03:03Turner Novak:like Shopify, it's just e-commerce sellers. That's right. It's pretty, you know, a couple of percentages, percentage of like all business in the US and like Square, it's like a physical store location. And like, again, it's not like, it's a pretty large swath that's not being served by those products. That's right. And they do what is called a first-party product in that they only serve the merchants that are transacting using Square or transacting using Shopify. But there is a vast sea of opportunity outside of those platforms, with platforms like Walmart, DoorDash, Amazon, and all the different patent processors that are out there that are not Stripe and Square.

1:03:39And so we saw this opportunity of essentially bringing a similar product to life, what Square had done in the brick-and-mortar space and Shopify had done in the e-commerce space. but doing it in a way that is more robust than what they do, and that is being vertically agnostic. When you're vertically agnostic, you are inherently less risky to your capital supplier because you're not indexed on any one particular vertical and therefore macro movements in that vertical. For example, 2021, we saw a big boost in e-commerce driven by COVID, but 20 through 23, we saw mean reversion in e-commerce. And so if you were doing e-commerce landing in 21, you could be fooled to think that this party will continue and you should be giving bigger and bigger loans

1:04:20Turner Novak:to these e-commerce businesses. I mean, if you're looking at the spreadsheet, AOV going up, CAC going down, velocity, growth rates increasing. And if you're just looking at data and numbers, they can fool you. Exactly. And if you gave out loans to e-commerce businesses in 2021 with those biases, come 2022, you were having difficulty getting that money back. I mean, probably like on average, the average e-commerce seller probably shrunk in 2022. That's right. And that's not good if you're lending them money. That's right. That's right. And by being vertically agnostic, we can essentially absorb macro movements in any one particular vertical.

1:05:00So today we work with pretty much every major US vertical that is out there. There's about, in the small business category, About 60-ish million Americans work for small and medium businesses. The single largest category in that is restaurants, which employs somewhere around 12-ish million Americans. And so naturally, the distribution of businesses we work with is indexed on their contribution to the economic activity. But we cover pretty much every major US portfolio.

1:05:37Turner Novak:So what about like McDonald's? Is that like a certain person's like excluding the big chains? McDonald's locations have taken loans on paraffin through DoorDash. Oh, because they're a franchise owned, obviously. That's right. We've had McDonald's owners take cash advances via DoorDash. We've had Papa John's. We've had, you know, both large sort of national franchises, but also regional chains that have 10, 20 locations use our product. And so I know that you, I mean, you did quite a bit of research before you kind of got into this. Like, did you know I'm going to do small business lending or was it like a process of like doing some research?

1:06:16Turner Novak:I know user research is like a really, there's a really strong culture at Robinhood. Did you learn anything there that related to how you approach this? Yeah, for sure. So one of the things, you know, which was at the time we really had to fight the urge to was write code at the beginning of starting of Paraffin. We were three technical founders when we started the business. The natural hunch was, let's get started. Let's start writing code. Let's build something that we can sell. But instead, we were very intentional about just going out there and spending the first few months talking to the market and talking to platforms.

1:06:52And I still distinctly remember we would show up in the office and the three of us look at one another and be like, hey, what are we doing? This is why we have these 10 calls lined up for the day? Customer conversations? Potential customer conversations. Many of them were not the right ICP, but we were just going one after the other, talking to these platforms and even D2C businesses trying to understand where the pain points, where they lie.

1:07:14Turner Novak:So you knew SMB lending or business lending in some capacity? Yeah. Actually, even one step before that, we knew SMB financial services. One hypothesis that we had, which was falsified, was that we could build a zero cost payment network on the back of ACH Rails. And because we were doing a lot of that at Robinhood, moving, you know, a lot of money from consumers to the brokerage and be like, hey, if we could bring this experience backed by something like Plaid to the SMB space, we can make it cheaper for small businesses to earn money. Yeah. And if you look at like Target has an ACH, do you know Target's red card?

1:07:51Yes.

1:07:52Turner Novak:Yeah. So if people are not familiar, Target has this thing called the Target red card. It's probably like 10 % of all Target's revenue is just run on basically this internal card program. It's like the Target credit card or the Target debit card. And when people pay with the Target red card, it's all on ACH. So they, on about 10 % of their, I don't know,$40 billion business, I actually don't know how big Target is today. But if you think about 10%, you're not paying any payment process fees. So that 2 % to 3%. So, I mean, I don't know how big they are today, but that's probably like$100 million in costs that you cut by just building this thing.

1:08:27Turner Novak:And it's probably mostly free cash flow. And if you capitalize that, that's like a billion dollars of market cap or maybe more than that that Target has saved by building this internal program. It's actually pretty powerful. Yeah, and it's incredible. And, you know, many countries out there like India, Brazil have naturally real-time payments. There's some version of that here in the U.S. as well. But the system that we all rely on more often is ACH. And so we were asking ourselves the question as what can you do in this space? It turns out small businesses actually don't care about the 3%. Or rather, it's not the top priority.

1:09:05They would rather not compromise the conversion in order to save that 3%. And anytime you kind of put a non-card-related product in front of the American consumer, you know, they may shy away from that or might start looking for how do I use a card to make this transaction happen. So I think over time, like someone like Apple or, you know, some version of some company like that is well positioned to break the mold there. But I don't think this is something that we wanted to pursue doing because we very quickly falsified that this was wrong. And what, in fact, mattered more to small businesses is not to make that funding through earning bucket more efficient, but being able to fund their business through debt, which was a completely untapped opportunity.

1:09:51That was a lot, solving for a much bigger need, was much more of a green field. And the emergence and the scaling of marketplaces, vertical SaaS, point of sale solution, meant that we could now tap into data sources that would allow us to build a product and sell it, you know, which is responsible, which is sustainable, and which can, you know, help essentially lead to a venture backed company. in the sense that we can raise capital and generate good returns on it for our investors because we've identified a large market where we can scale into using code and data science.

1:10:29Turner Novak:Yeah, I mean, it seems like that's been a big thing you've harped on a couple of times is like the differentiation is like helping them fund with debt versus funding with equity. Yeah, funding with earnings. With earnings, yeah. Yeah, funding through sales. Yeah. Which is what payment solves for. Payment solves for the funding through earnings and then, you know, paraffin source for funding through debt. Okay. And you, I know a lot of times people, you'll, people ask you for advice on fundraising. Did you, did you raise like a little bit of money around this time to, to start building stuff? Or did you, did you wait until you already had some customers?

1:10:59Turner Novak:How did that go? Yeah. So I want to say our seed financing was probably one that I would never advise a founder to follow. It was very unique. My co-founders and I did not have work permits to start our own company. We had work permits to continue working at Robinhood, but not to start our own company. And so we went up to Robert Investor, Rivet Capital, who's our largest shareholder. And we said we wanted to start our own company. And we had sort of half-big ideas around small business financial services. And they mentioned that we should go and have the conversation with Vlad and come out in the open and let him know.

1:11:38And once we did that, they'd be more than happy to fund the business. And so we went ahead and did that. And in doing so, you know, they got what was the best reference check from Vlad, which is when they asked him what he thought about us, he said he was disappointed that we were leaving and he didn't want us to leave. And, you know, when a venture investor hears that, they obviously want to fund you even more than they did like a few minutes ago.

1:12:03Turner Novak:That's always the best signal. It's like prior boss, like doesn't want you to leave, trying to keep you, but he's investing because like they know that you're going to be good. Yeah, that's right. Right. So Rivet ended up leading our seed round. They've been great partners ever since. We did not run a conventional seed funding process. We did not talk to too many investors. It was just a handful and literally like a couple and Rivet was one of them and the one that we liked the most. And this was because you needed some money to pay for visas? That as well. So there was a chicken egg problem in that we had to fund the company in order to get visas and in order to start working.

1:12:36And obviously, that is, you know, to a venture capitalist who you don't know well, they'll be like, well, I'm not going to really put money in your bank account if there's no guarantee.

1:12:43Turner Novak:Like, show me that you've, like, built some product, talked customers, et cetera. Exactly. At this point, we had no product. We barely had a bank account to put the money in. And so we got that set up. Rivet funded it. We ended up getting visas. That led to us starting Parafen. So it was a very unconventional fundraising round, the seed round. But it started to get more conventional from the Series A onwards, where we started to go to more investors. But in the early days, even for Series A, most of our Rolodex was existing Robinhood investors. Since we deeply trusted them, we knew Robinhood had had excellent experience in working with them.

1:13:18And some of us had also interacted with these investors as part of Robinhood's fundraising round. For example, I would pitch for investors when Robinhood would be fundraising.

1:13:25Turner Novak:And then you mentioned before that DoorDash is a customer that you work with. I know like they were, there's a pretty early customer that you had. How, how did that go? I don't know if they were the first, but like getting the first people to agree to work with you, because the approach that you took, it's like, hey, obviously going direct to the business might be easier as a startup of like, hey, random business. Like we're trying this thing, try our product. But when you go to like DoorDash, like, hey, we're a startup, doesn't exist. Like, how did you land that? Yeah. Honestly, it, there was a lot of uncertainty.

1:13:58In hindsight, there was a lot of uncertainty. And I would say, you know, huge kudos to the DoorDash team for taking a bet on an unproven company. I distinctly remember there was one conversation where we had with them and they were like, what's the size of the company? And I turned around and said, we are all on the call right now. And it was just like five of us on a call with three of them. Okay. That's pretty big, big-ish. You outnumbered them at least. We outnumbered them. But we had pitched to them early. They were very intrigued by the product. What helped us was the DoorDash team had some folks who had worked at Square, so they understood what the impact of having such a product is for their customer base.

1:14:37They were extremely scrappy, so they didn't care that we didn't have a built-out product. They were willing to do things on a very manual basis to get it up and running and testing product market fit, which typically large companies don't like to do. and after we made the pitch and presented them, they kind of disappeared for a few months and they said, hey, we are busy, we'll be back and we thought we had lost them forever and they disappeared into the ether. But what turned out to be true is that they went public at that time. And so they were not doing any net new sort of vendor deals, specifically one around like financial services.

1:15:10And then as soon as they went public, one month later, I saw them on one of my documents, I got a notification, which I shared with them that someone at DoorDash is on this.

1:15:19Turner Novak:And then a few minutes later, we got an email saying, hey, we would love to jump on a call and push this. Oh, so like a Google Docs? Exactly, exactly. I think it was Docsend or one of these other corops. Oh, nice, okay. But I saw it in the evening and I distinctly remember messaging my co-founders and we were all like, you know, giving ourselves like the rocket emoji in Slack. And yeah, that's, and they moved pretty quickly. So, you know, we got an agreement signed. They, I believe they also ran an RFP with Stripe, which was the other, which is a competitor. Stripe Capital has a competing product in the market.

1:15:52And DoorDash is one of their largest customers. And what we understood in our conversations with these large platforms is that they actually do not want to partner with Stripe for lending because lending is a way for Stripe to entrench platforms deeper into their payment ecosystem. And as you're growing, you actually want to negotiate your payments rate to be cheaper and cheaper. And you lose that leverage if you are signaling that they're willing to be entrenched in their payment ecosystem. And so by counter-positioning and saying, hey, we can actually build this in a payment process agnostic manner, we have enough ACH payments experience from Robinhood that we believe we can manage the risk here.

1:16:28We were able to offer a product that allowed platforms like DoorDash to break away from Stripe. And since then, we've actually got many other Stripe customers. MindBody, which is Stripe Connect customers, does payments with Stripe, lending with Paraffin. Jobber, which is Stripe payments customer, does payments with Stripe, lending with Paraffin. So we've been able to amass both non-Stripe customers where the larger opportunity lies, but also Stripe.

1:16:51Turner Novak:And you'd never really done sales before? I have never done sales before. So how did you get good at sales? It seems like you like it now. It seems like you kind of like this, like chasing the deal, closing it, getting the rush. What did you learn to appreciate about it? Yeah, I think I've certainly become a deal junkie for lack of a better term. Um, my co-founder jokes that, you know, I can sell a bald guy, a comb, even prior to starting Paraffin, because I would keep pitching him on, on, on various different things. Um, so I think it was just some part of the DNA. It just, it just kind of got to flourish once I started Paraffin.

1:17:30And what I've seen work is, you know, in, in our product category, buyers are typically extremely smart. They are very well informed. and with the age of AI, you have to assume that knowledge is free-flowing and all the time. So I really try to get as analytical as possible in a sales process and down to the details and getting all the details right. We've seen that really helps. Aligning incentives really helps. And showing hunger for their business really helps. Our pitch to DoorDash in the early days was, the way I would end one of the calls was, or tell our DoorDash POC that you have in front of you on the call is the DoorDash capital team.

1:18:12Turner Novak:Like we will work for like your dedicated support team. We will work as hard for this product as an internal team would. And so perhaps even more. And so feel free to push us as much as possible at any time of the day, at any day of the week. And we'll make this happen. And just going with that commitment to every single customer is important. You know, B2B infrastructure or SaaS for that matter, the way I like to frame it is it's the art of selling mass-made suits, but making the buyer feel that they're buying a custom-made suit, right? So as a B2B buyer, you want to know that this vendor is dropping everything else and just paying attention.

1:18:53Turner Novak:Like you're the only customer. But as a B2B vendor, you want to be able to build a product that you can sell to many people. So in some ways, behind the scenes, you're building a mass market product, but to each customer, you have to make them feel like it's personalized for them. So you're building a mass manufacturing suits, but you're selling it to each customer like it's a custom made suit. Is there anything else that you kind of found like secret sauce in this like partnerships go to market? So there's a few tricks that we've used in the past that have helped us in some ways. For example, when platforms, one of the questions that platforms would really come up with is like, we don't think our customers need this product.

1:19:32Interesting. I was like, all right, we can actually debunk that pretty quickly. How about I run a survey with my own money and go and reach out to your customers because I can do that in the open web and ask them questions about, do you need capital? How likely are you if platform X were to offer it? And so on and so forth. And it turns out, you know, And you will discover the same thing over and over again, which is businesses do value getting easy access to capital. They are more likely to take it from people who they know versus people who they do not know. And so we would take these surveys to them and say, hey, look, your own customers are saying that they need this.

1:20:10a second sort of aha moment for us was one day I was with my then girlfriend now wife walking out of lunch from a friend's place on a weekend and walked in we walked into an auto shop

1:20:23Turner Novak:and I told I told my wife that hey I need to speak to the auto shop like about your car well no it wasn't about the car okay so I went in there and and and you know she came along with me and I asked him hey like you know how long you've been running a business I see you only have one lift to lift cars up so you can fix them. Have you ever thought about getting a second lift? And he says, yeah, I would love to have a second lift. I say, have you ever considered taking a loan? And he's saying, no, but I keep getting flyers from all these different people. I don't trust them. I said, okay, which payment processor do you use?

1:20:50What's the software you use to run your business? And he said, well, I use this payment processor called 360 payments. And I'm like, what if they were to give you a loan? Would you then take that? And he was like, yeah, I think if they were to give me a loan, I'm much more likely to take it because I trust them. I've been running my business on them for years. And the next thing was I went back to my desk and I found a friend who knew someone from 360 Payments, harassed him till he made the intro, and they became one of the customers as well. And so there was an aha moment that went off. We had a hypothesis around it, but we've seen it time and again, and this is yet another anecdote, which proved the hypothesis without even having to build the product that small businesses are much, much more likely to engage in a product, especially one which is, you know, related to financing their business when it comes from a trusted source.

1:21:35And what source is more trustworthy than a tool they already use every day to run their business?

1:21:41Turner Novak:And it sounds like for both of those examples you just gave, it's the thing you mentioned earlier is both of those was like collecting data points to bring back to the customer to convince them that, like, you know, this is something they should consider. That's right. That's right. And so we would get I would get on a call when we got on a call with 360. we basically told them, hey, we've spoken to your customer and this is what they told us. And they, you know, they're much more likely to buy the story, not push back and want to the next part of the conversation. And that remains true till today.

1:22:09You know, a friend of mine was at a med spa recently and he mentioned, hey, I'm at this med spa and I think you should talk to the owner here. And so I got on a call and very quickly, you know, got a hang of like which vertical SaaS she was using and then would she take a loan? And then you contact the vertical SaaS and be like, hey, I've spoken to this business of yours and they want our product. You know, like what's stopping you from launching this? So I think you have to keep, you know, I wish there was like a formulaic way. The reality is you have to kind of push on a hundred different things and keep doing that on a regular basis.

1:22:49And only then can you do these materialized and meaningful relationships and meaningful partnerships and meaningful revenue for us.

1:22:57Turner Novak:Do you have a favorite founder or CEO or company that you've learned from throughout history? Yeah, I would say it's probably Vlad at Robinhood. Really? Okay. What's the biggest takeaway from that? I think the biggest takeaway is that Robinhood has gone through a very difficult time. And, you know, having IPO at 38 bucks, stock popped at 80 and then fell to like six or seven dollars. I forget what. There was GME. There was a lot of negative media, negative press about it, lack of trust. And they've been able to turn that around into, you know, massive amounts of revenue, a huge product surface area, re-earning trust, and doing so in a very short amount of time in financial services, I think is practically unprecedented.

1:23:46And what I've learned from him is just perseverance. He's not someone who gives up easily. He just keeps pushing and pushing. And I think doing that with strong first principles and having hope and enthusiasm about the future is the way to do it. And I think realizing that, hey, while trust, once you lose it, it's pretty bad. Like if you put one foot in front of the other and you do that on a daily basis, you can actually win it back if you're doing it right.

1:24:16Turner Novak:Yeah, it's probably an incredible way to end it. That was a good last talking point. There's a lot of fun. Likewise. Yeah, I really enjoyed it. And thank you for listening. Thanks to Ramp for supporting this episode. upgrade your corporate card and get$250 at ramp.com slash the peel. If you missed it, check out last week's episode with Garrett Lord on how Handshake beat LinkedIn with Gen Z on his fast-growing AI data labeling business. Tune in next week for Ryan Hoover, founder of Product Hunt and Weekend Fund, with advice on building a community around your products and everything he's learned since he started investing full-time.

1:24:52Turner Novak:If you like this conversation, please like, comment, and subscribe, and name your next AI model after me. If you don't want to miss a future episode, subscribe to my newsletter, The Split, linked in the description to get each episode plus a transcript emailed directly to your inbox every week. Thanks again for listening. See you next time.