Diving Deep with Max Levchin

Thank you for having me.

I think that's actually a fairly common thing in the world of startups and innovations and I think it's maybe more than anything a difference between being a futurist and a product designer. And a lot of us product designers think that seeing the future is hard and that's our job and that's certainly what I was doing in the PayPal days. And yeah, it's actually the opposite. You can pretty readily see the future just by reading enough of science fiction books. Like if you crack open anything from the turn of the 20th century like Jules Verne, you promptly see, oh well, he thought of submarines and all these amazing technologies that we now take totally for granted. And I think that trains us as we grow up to be product designers to think all I need to do is look into my mind's eye and the time domain doesn't matter. Like, if Jules could see the submarines because they were just being talked about then and poof, we now have nuclear power submarines. What I need to do is see what the future looks like. And we saw Palm Pilots. I saw the first Palm Pilot was the first real handheld device that would fit into your pocket versus all the things that came before. Newton was supposed to be a handheld device, but it was more of a brick, not a thing that would fit into your pocket. And so, as I saw that, I was like, 'Oh goodness, that's a computer that fits into the palm of your hand.' So, what's the future going to look like? It's going to need to have real security because it's going to have my most sacred and secret documents and my money and my contracts and all these things. So what we need is to go build encryption libraries that work on really low power devices. That was the impetus behind the original Confinity and that's totally true. It all came to be. But if you are a thoughtful product designer, your answer is actually there are zero contracts and zero anything of value that sits on this. Fewer than 1 million devices sold, mostly to nerds like me who just want to play with how to write code on this thing. And so the comeuppance that I got back then was it was very clear in the moment that we were too early. And the conversations internally were a little bit more like, all right, so some of these ideas are great but they're clearly 5 years out of time. How do we make sense with what we have today? And the honest answer then and now is it has nothing to do with handheld devices and nothing to do with Palm Pilots. It has everything to do with the fact that there's absolutely no way to move money online. And we invented an extremely elaborate way to move money using two Palm Pilots. And that's cool, but you just needed to move money. And the product market fit there came by hitting us in the face and saying your web demo is more useful than your product, because the web doesn't have this functionality at all. So, it's a long way of answering it, but I'm often on the record with this notion of hard, valuable, and fun. And it stems from the right around the same time actually, Peter and I had this conversation that I still remember vividly where he said something along the lines of the thing you built is really hard, but I'm just not so sure it's valuable. And most things that are really valuable are hard, but not everything that's hard is really valuable. And so you should always understand which way the arrow of causality is pointing because then you can decide whether it's something that you really want to spend your next 10, 15 years or in my case back then four years working on. And so as we saw the market pull, my real lesson back then was, hey, from now on when I'm going to invent things, I'm going to try to make sure I have a problem in mind versus a puzzle. Solving puzzles is great and I love solving puzzles, but real problems are more important.

Yeah, it's funny. I have exactly the same outlook on this problem. I don't call it the 15% problem, but I think it is uniquely afflicting the fintech universe. So, I call it the Mint problem. Every 3 years someone says, you know what I need is to connect all my accounts and get a perfect dashboard of my finances and really know what my cash flow looks like and I don't want to color code it, and with AI, and in 3D. And the people who are like, 'Oh my god, where have you been my entire life? I got to have this thing. Of course I'll subscribe for $30 a month or a year or whatever the cost.' Those are the same exact people who had that exact reaction to the last one of these and the one before and the one before and the one before, going all the way back to Mint which was the first one of these. Connect your bank account, difficult as it is, Yodlee's half-broken, doesn't matter, it's so much easier than doing this in Excel. And of course that very same group, probably their parents or the genetic precedence of these people, were like, 'All I want is to balance my checkbook. I got this piece of paper and I'm never going to miss a dollar or a penny.' And that group of people is maybe, I think 15 is very generous. I think it's like 3% of the population. And so that group is your total target market. And for some of them, $30 a month is too much and they're not going to subscribe. But most of them are very good with money, so they actually have the money to burn on having a better tool. But you're sharpening an already very, very sharp pencil. You're not expanding your bag of pencils and pens. You're just saying for this particular consumer, the power user of personal finance, I got you your next Mint. Let's go. And there are a handful of these apps right now and they're absolutely stunning and yes, they're just that much better and yet I know what their growth curve looks like. It tops out. So that's where we agree on that front. The thing that I try to do, and I'm again back to over the years I spent a fair amount of my time rewiring my brain from being that of a software engineer to being that of a software designer. And software engineers, which I still am one as much as you know, try as I might, love a puzzle and build for themselves. And great software designers or product managers, product designers, the first thing they do is they say, 'Hey, let's embody the mind of a normal someone who lives in middle America who is not in fact a well-educated, computer science aware, design conscious, someone who's deeply obsessed with balancing their checkbook.' In fact, the canonical person I loved to imagine when we were designing Affirm was a mother with a baby in her arm pushing a cart in a multi-lane checkout environment trying to pay for something. You have one hand. You got a line behind you. You got a screaming baby and a cashier who's overworked and underpaid. And the cashier is asking you, 'Hey, are these organic or not organic bananas? I can't tell from the label.' That's what that experience feels like. And you have to deeply feel that to design a product that doesn't suck. And so the idea of, 'Oh cool, I'm just going to go into my app and dial me up a digital payment option' is like, sure, or I'll go for the thing I know how to use, which is what's top of my wallet. And so I think just having that mindset and reminding yourself you're not building for yourself. If you are, you're going to run into that 1% problem again and again and again. And over time you become better and better at it. I remember giving a talk at Startup School or something years ago where I said something along the lines of you have to embody the mind of a 17-year-old girl when you are trying to build a social media product. And the room looked at me like, 'What exactly are you talking about? And why does this sound vaguely sus?' I don't think you think what I think it means. You need to be so one with your customer that you understand what motivates them. And the baby on your arm is a real problem. I used to go to Whole Foods and watch people check out specifically to understand how this actually works. And it works pretty simply. You put down the wallet, you pull it out with one hand, and that's the card that you're going to use. And so plastic is not going away anytime soon because of that specific modality.

Part of why I do my own customer support on Twitter. That's how we met, through me trying to offer customer service. It's not just because I care about the product. I certainly do. I want to have that first touch moment when someone is upset or confused or unhappy. You can read your own copy a thousand times. The number of times I'd written what I thought was a great piece of product copy, 'Use this in the following way,' and then show it to someone, 'What exactly do you mean by this word?' Does the word 'sync' or 'phasotatron' mean nothing to you, you idiot? No, it turns out to be not a commonly used word when it comes to day-to-day consumer physics. And so that I absolutely do. One of my favorite things to do when I travel, and in fact every city I've ever been to more than once I can name my favorite grocery store to go to to people watch, primarily to figure out how they shop, what they buy. You learn a lot. There's a Japanese themed grocery store underground right next to my favorite hotel in Shanghai. And I would go there right after getting off the plane because I wanted to see if they changed the way they place things on shelves. What's eye level? What's below eye level? They added self-checkout way before anybody else did. I was like, 'Oh, I'm dying to understand how the self-checkout thing works.' They had a person help you self-checkout because it turns out to actually be harder to self-checkout than not.

Good idea. I'll take a note to do it.

This is a brief historical detour. But Affirm was birthed in a very slow 9-month process or so where I was hanging around with a couple of friends who eventually became my co-founders but who I mostly knew from PayPal years and we talked a lot about underwriting and how we build these super elaborate models. When we were at PayPal, random forests were already a thing as a modeling technique. But we absolutely pioneered it. No one in the industry that at least I was aware of was doing random forest to do fraud fighting and we built these ensemble models that attacked each other and voted on outcomes and did all the sort of under/oversampling techniques. A lot of these things were just coming out of our own research. We were reading all the academic papers obviously, Berkeley is where a lot of this stuff came out, and yet we were definitely being told by a lot of people, 'What are you doing? There's some very basic ways of doing this.' And at the time my answer was, 'But if you imagine this thing as a curve of events, you are literally living in these lumpy sums. You're barely approximating the curve. You don't understand integration. These things are infinitely differentiable. You should only be using integration. You should not be trying to build up a bunch of rectangular blocks to sort of use.' Your mom would appreciate this analogy maybe more than most. And so we're like, people who compete with us, they are stuck in middle school math and we can't wait to get to grad school. And that was always, and it's a little bit of hubris, obviously at the time we probably thought we were the smartest tools in the shed and we certainly weren't, but we definitely were a lot smarter than some of the players that we had to compete with. And so the same cast of characters but 15 years older and a little bit more grounded and slightly less self-obsessed. So on the one hand, we learned a bunch of things, there are a lot of other smart people doing a lot of other smart things maybe we didn't really know about them, but this notion of a lot of competitors, a lot of people who have to solve these problems just choose the easy way out. They take a score that someone built not for the purpose. FICO is a brilliant company and an amazing collection of people and research, but the tools that FICO creates were all published for the world that was largely offline. And so the decision to say yes or no was around the person, their ability and willingness to repay at the time of underwriting a line of credit or a mortgage or an insurance policy or something that is a moment in time, some big thing. So even though the event chain or the event curve is infinitely differentiable, they just didn't have the luxury of saying, 'And by the way, we reserve the right to change our mind next time you swipe your card because that just wasn't a thing.' You could do it to fight fraud a little bit, but you can't change the risk assessment and certainly couldn't reprice it. And so you have to reprice it on the spot once for every 5 years on average or whatever. And so we had all these conversations and basically, if you're willing to do calculus or slightly more complicated math, surely we're going to find some real savings. The area under the curve we're going to fit with our approximations. It's just going to be much smoother. We're not going to have these big lumpy boxes that are adding up to something roughly resembling the area under the curve. And so that was the foundational logic behind Affirm, that we can do better through math. And the 'Better Living Through Mathematics' is obviously a reference to 'Better Living Through Chemistry' or whatever the exact line of that album. But it's in our DNA, this idea that you can and should not be afraid of applying progressively more sophisticated math to these problems if you have the opportunity to benefit from it. And we thought that point of sale lending in particular, which by the way was a later idea, we really settled on point of sale lending because it's a self-distributing loan type. We weren't particularly married to the idea of this or that loan type. We just thought we would do better underwriting because we could do it in real time. We then said, well, we have four computer scientists starting a lending company. What do we know about distributing loans? Well, the one place where you could have a loan is when somebody's already saying, 'Yes, I'll buy this thing and I would like to use a credit product.' Sure. And well, literally we're competing with a thing that was underwritten on average three and a half years ago. And by the way, a merchant price that was set by some independent Salesforce salesperson who sold you a box that is literally from the 1980s with an integrated chip from eight generations ago. That thing is not computing anything. It's just collecting an MDR. So if we could float both the MDR and the EPR, we could really find the perfect approximation of the risk curve. So that's the origin story, that's where it came from. The thing that actually doesn't get a lot of airplay and it's worth highlighting in terms of what turned out to be important. So there's a hypothesis, right? We were having lunch in our favorite Middle Eastern place and sort of bantering around. 'Wouldn't it be cool if we...' By the time we were having this conversation, random forest was not just a thing, it was everyone's thing. And the modeling for that was like, okay, everybody's doing it now. So what's the latest hotness? What should we do? And so you looked at gradient boosting and all the next things that are now of course well understood and well accepted. So we could find some interesting area under the curve from this as we were poking around all this stuff. We basically asked ourselves, given that we know that at PayPal we were the tallest, most intelligent species because we were willing to read these papers and apply all these interesting bits of math, and now everyone caught up. So it's not a permanent advantage. So how do you build a more permanent advantage? And so the idea we had which has turned out to be completely true is you build a factory to produce model after model after model so you're not beholden to a single technique. You're not beholden to a single new paper. You don't just read about random forests first and then suddenly everybody caught up. You have to have a machinery to fully industrialize the production of the next generation of model. Both in terms of we have new data and so we need to process it differently and learn from it. But when and if there is a new paper or a new technique that sort of kicks you out of your chair and you're like, 'Oh my god, this is a completely different way to model things,' you're ready to just pick that up and go. Which applies today. So we had that moment in 2017, 2018 when 'Attention is All You Need' came out and so every one of us who's in the industry said, 'Wait a second.' First of all, when we were doing this at PayPal, I remember trying to build a neural network driven way to detect fraud and fight it and it totally flopped. It just was not functional. And it comported well with my experience in college doing this stuff. Neural networks were supposed to be a thing in the 70s and I went to college in the '90s and they still weren't a thing.

I remember reading 'The Age of Thinking Machines' and going like, 'Oh my god, it's here now. I just need to build a bigger computer.' And we built a lot of big computers in the '90s and nothing happened. And then in 2017, it did. And it was this amazing moment where suddenly, wait a second, you can actually build a simpler thing, a lot simpler than some of the earlier networks that people were playing with. And it really seems to work. And so the thing that we were able to do at Affirm is say, all right, obviously we're not in the business of chatbots or LLMs, but we are in the business of building new models from the latest scientific ideas and observations. And so we have been building models based on a lot of the ideas from 'Attention is All You Need' and surrounding papers before and after, and we have some really stunning results to show for it. And so I don't want to front on anything too exciting here, but we've been consistent in our ability to stay ahead of the research with the most cutting edge research because we invested in building out this model building factory, which has been a really, really fun thing to be a part of, to be honest.

It's definitely one of the reasons why AI is not taking any of our jobs, or not many of our jobs in my foreseeable future, is because ultimately the question you're asking and many in that class of questions are very human. You can program an AI to do X or Y and you can tell it to try to be creative and it'll sort of span the between X and Y, but to get out of the box you need creativity, which is primarily curiosity, intuition, and taste. And we're pretty far from encoding taste. We're quite far from encoding intuition. And curiosity can be programmed, but curiosity in and of itself is not enough. You kind of have to know which lines you're going to paint outside of. So, the way we try to encode those three behaviors inside our very human team is a combination of just challenging them to ask, are we plateauing or not? And you need metrics to tell you yes you are or no you're not. And so there are some great, well agreed upon metrics in statistical learning and AI and all the, I mean this is a well-studied area of math. And so you have that very same area under the curve where you can look at how the model's ability to rank is progressing and ask the question, so how much better has it gotten from version N to version N+1? And if your area under the curve improvements are gently asymptoting towards nothing, you're not winning. You're doing fine. You're not losing. But everyone else who is taking these leaps forward is catching up, definition, unless they're asleep at the switch. And you're kind of thinking in the back of your head, if we're kind of flattening out, there's someone behind me who's going up just earlier in the curve. You got to be very aware of the fact that you're not the only one competing. And so every year, every 6 months, we ask ourselves, what have we not tried that at the downside would actually bring the area under the curve down? Which is okay because we already have this performance and it's great and we love it. But what's the high beta thing to do? What crazy thing can we pull out of a bag now? For example, can we try something that's transformer based? Yeah, there's all sorts of risks, not just like what if it doesn't work, but also what if it runs, you know, the classic thing to worry about with transformers is hallucinations. So what if it hallucinates a result that isn't real? What if it hallucinates a result that isn't compliant? So there's all the very serious things that you want to worry about as an underwriter and certainly somebody who is subject to all the relevant underwriting related regulations. But if you don't try it, you're not going to find out that it's working. And that's how we keep ourselves honest. We ask the question, given what we know about the industry, if for this particular family of models the curve is bending towards flat and we know everybody else is chasing us, what new curve are we doing? And by the way, this logic has to apply not just to underwriting but also to product. We have a very similar approach to product building where every time we look at our growth curve, we ask ourselves, all right, so if we are just the first in a pack, who's behind us and how far below the elbow of this particular curve are they? Because if they're not that far, we better be inventing a new curve.

Lots of good threads to pull on there. So I too use the word explainability all the time. Over the last couple of years as I've been looking at all the latest greatness in AI and ML, I've become more and more convinced that we'll probably solve explainability really, really well. Specifically with LLMs and things that are sort of hang off the LLM tree, if you will. The most interesting problem that I think needs to be solved for this new class of models to really come into its own is replayability, which is a code word for getting a different result while running a model with the same exact inputs. You know why FICO is so valuable? It's stable. It's actually too stable in my opinion for a variety of reasons, but it has to work a few years at a time without costing lenders too much money. Affirm's internal transaction score, internal user level scoring system that we built sort of half a decade after we built the transactional one, they're significantly more sensitive but they rely on real-time data and they're 100% replayable. The same exact real-time data run through that model will give you exactly the same decision, same score, same everything. And so I think that's the bar you have to meet with these transformer-like, again, I'm throwing around terminology and I'm deliberately being vague just because I don't want to give anything away to my esteemed...

And so I think this explainability...

The thing is actually almost like a downstream problem from replayability, and replayability is a navigable part of these models. You can go back to some really great papers out there, very recent like last 30 days, that I just looked at, that were sort of dismantling this notion that in the older versions of GPTs, which are now just one of several contenders here, if you set temperature to zero, you would get the same exact response every time. Right? The answer is wrong. If you actually set the randomness factor from the possible candidates for the next token, you just say, 'Oh, always pick the very top one. Don't mess around.' You think you'd get exactly the same response. And the answer is no. There's some really fun kind of, if you remember your college days, numerical methods is probably the name of the class you would have to take to know what I'm talking about. You will immediately recall things like fixed length precision. So you have a 64-bit float, and you decide that maybe 48 bits is going to be representing your floating point, and that's 48 bits of precision. So if you need a 49th bit of precision to define a float, you don't have it. It's going to get rounded down to 48, and that's it. And by the way, a lot of the modern models actually use like 8-bit precision, so they're much, much more narrow. So because of that, you have compounding of errors and all that good stuff. So you actually do end up with some fairly easy to understand ways for why hallucinations and lack of predictability, lack of replayability were a thing. But then there's more. I'll stop there because I was like, 'Whoa, this is blowing my mind.' I thought I understood it, and I forgot which lab put it out, but it was sort of a thing that I thought I understood better than the average bear. I came in here thinking that I got this, and I'm walking out knowing that I know I don't. I know more now that I read the paper.

I thought I sort of left my arrogance, checked my arrogance at the door at the founding of a firm, but it turns out I still got arrogance to give up. Anyway, so back to the more tangible, maybe easier to reason about regulation side of things. So I actually think a lot of this will sound like totally upside down from what maybe as a Silicon Valley entrepreneur I'm expected to say, but first of all, I think regulation is more good than bad in a lot of cases. Some regulation is dumb. Some regulation is outdated, profoundly outdated, and it's mostly when the regulator or the writer of the regulation, which is frequently enough not actually the same person who's charged with enforcing it, writes things down almost too literally. Like 'Thou shalt not use an 8-bit integer. Only 16-bit integer will do' is total lunacy. If you ever see that in the regulation, that is a dumb regulation, because before you know it, by the time it's written and published and enforceable, 16-bit integers will be a thing that no one ever cares about because you had 128-bit integers now. So anytime it gets super specific as to the implementation of the regulation, it always ends up falling short in my opinion. On the flip side, things like you can't discriminate against people based on who they are. You should be allowed to do so financially based on what their financial state is, is really sound. It made sense when they wrote down the very first words of fair lending, and it's still true today. And so holding yourself to the standard of don't discriminate based on who the person is, where they're from, what they believe, who they marry, all the obvious things that we take for granted now but did not take for granted in the 1950s, I think they're still true today. And that serves us really well. And the thing that is not in the law, which is great, it doesn't say things like, 'And by the way, if you're doing this with a computer, different measuring stick.' It's like I don't care if you're doing this with an abacus or in your head or with a scorecard or a FICO score. If you end up discriminating based on race, that is not okay. Full stop. So I think that regulation is fantastically great and doesn't need any words changed. I think some of the regulatory attempts to sort of corral technology into the technology understanding of the moment frequently backfires because technology moves faster than the regulators can, and that is how it should be. You don't want to change regulations too often because it's too hard to enforce. So that's sort of the basics, and the reason, by the way, for my philosophy on regulation writ large, not just in financial services. Yeah, so I grew up in the Soviet Union, and that means I saw Eastern European flavor of socialism up close and personal, and it sucked. Anytime someone tells you it worked in the Nordics, it's okay. The Soviet Union almost got it right, but then they didn't. It doesn't work. It's inherently corrupt. The idea that everyone's equal, but those distributing the money get to keep a little bit more than others, is abhorrent, and it is exactly what happens. The classic joke about the Soviet Union was that people who ran grocery stores were always fat. You know, maybe they stole from the shelves, maybe it was genetic. One of those two. And so socialism does not work. And yet free market economies, though they self-correct, sometimes take too long. The classic example of the pizza shop that doesn't sell to gays is like, 'Wow, that's really offensive. Fortunately, there'll be another pizza shop next door that will sell to gays and have a big rainbow flag. It'll be great.' But in practice, how long before the second pizza shop opens and kills the one that is prejudiced and bad? I think in practice, a regulation that says thou shalt not discriminate based on creed or sexual orientation is actually a pretty good regulatory thing to do, because it just runs the fact that most of us understand it's actually not cool in the 21st century to discriminate based on something that's completely unrelated to what you do as a business. So I think that's the sort of speeding up towards a normal, sane, not stupid way of the world, perhaps pushing the market along versus waiting for the market to get there. That is, in my mind, the primary purpose of regulation, any kind of regulation. So as a consequence, generally speaking, if it's thoughtful, if it protects consumers, if it's there to speed towards the natural equilibrium that most reasonable people can agree should be there, big fan. Let's have more of it.

Yeah, and there's definitely no one-size-fits-all. If you want to be truly risk-free, stay home and don't start companies, because that's one way to do it. So we did a couple of things, and they're not exhaustive. We did even more than I'm about to describe, and as I go, I'm sure I'll think of more, and I'll go until you stop me. So one of the very first things we did, we basically said we should not do late fees and compounding interest. We just won't do that. And you can actually mount a reasonably good argument why that's a dumb idea. Late fees are there to slap your wrist and tell you, 'Hey, stop being sloppy. You should pay on time.' Because when you're not, everyone in the system is paying a tiny amount for you to have the freedom to pay whenever you want instead of actually sticking to a schedule. And I think that's actually true. Everything I just said, I happen to believe it. As a libertarian, or at least I was probably more ardent libertarian 25 years ago when I was also a lot more arrogant. Hopefully I've become slightly more humble and aware of other points of view, but that argument stands the test of time reasonably well. The reason not to charge late fees, more than anything else, is twofold. One, it profoundly aligns you with your customers as far as financial well-being. If you charge late fees and the borrower is late, you'll make more money. So there's kind of a little bit of an agreement and a little bit of a bet. I bet you'll be good for the money. I'm going to give you a loan, and you bet that I won't screw you when you are late or something bad happens and you can't make your payment on time. In the normal world of late fees and compounding interest, the bet I'm making is you think I'm on your side. I hope you don't notice that I'm not, because I want you to over time pay me everything back, but I kind of want you to be late because then I'll make more money. And the marginal cost of that gain is nothing. Literally $38 for you to skip a day of payment is an amazing business opportunity for a lender. The margin on that is basically infinite. And so the argument we made was if we get to make money only when consumers behave in the way that we ask them to behave, versus ask them but wink wink don't do this too frequently, we would build a better underwriting engine. So as an incentive not to screw up, to build the best approximation of the risk curve, we should keep ourselves as pure as possible relative to the revenue-making opportunity of a lender. That is, if we say we're going to loan you $1,000 and you'll pay us back $50 in interest, that is the most we should ever have. No fees, no late fees, no compounding, no deferred interest, none of the sort of cool bets where I bet you'll be on time, but I bet you're going to be less on time than you think you will be. And so that product innovation came entirely from this 'let's just be purist because it'll force us to be good.' That's part one. Part two, because we knew we were building a completely new class of products, you're totally right. As we were doing this, I would sit down with a partner bank or a law firm to hire and explain what we're trying to do. And the reaction was inevitably the same. 'Okay, so part one, you're not charging late fees. You're going to lose your shirt. That's like half the profits in the industry. Don't be an idiot.' And I'd say, 'Yeah, but we made a decision that that's what we're going to do.' But more importantly, they would say things like, 'Okay, I don't even understand if this is a loan. Is this a retail installment contract? Is that what it is? No. Okay. It's not a line. So you have to show Truth in Lending disclosure every time, but you don't want to do that, do you? A Truth in Lending disclosure for every purchase in a store is just going to slow everything down.' And in that moment, I had a choice to make. I said, 'No, actually, we are going to show a TIL every single time. You're totally right. It is going to be something like a bump in the road. But I think if we're claiming that we are completely on your side, there are no late fees, no compounding, etc., an extra screen showing you that you will owe nothing extra will maybe slow you down a tiny bit, but it will make you feel like you're making a more responsible decision, give you more sense of control, but more importantly give you clarity that we're not just marketing ourselves as a no late fee company. We really will commit to you in a formal way that there are no late fees.' So I convinced myself in the moment that this is going to work, that conversion is not going to suffer so badly. And as we did this, I met with a bunch of regulators quite some time ago now, and they all asked me, 'Hey, so how are you cheating? Are you doing any of these things?' And I said, 'No, I really am not. We're building a product that's just going to not be a cheat code.' And the more I talked to them, the more I understood that most of these people became regulators because they genuinely care. They're not like, 'I'm a lawyer and I need to wield a stick and find bad guys in the industry.' They all came to it from the place of there are people who are just too busy, too tired, too overworked to read the fine print, and my job as a lawyer is to protect them, to make sure the fine print doesn't say anything too horribly nasty. And that actually influenced one of the core values we have, which is no fine print. I literally had this conversation enough times, 'You know what, we're just not going to have fine print so that people who regulate us don't have to read it, and the consumers more importantly don't have to read it.' Anyway, so my regulatory experience or the relationship was really shaped by these early conversations. I am not trying to find shortcuts. I really want a product where people feel great about using it. So as I was doing this and we're trying to build a team, part three, and I really will stop because it's all memories, this is great. I remember starting to talk to early engineers. Of course, as we were building our AI team, I really wanted the very best people. At PayPal, because ML and AI were so underutilized, we had this total pick of the very best people in academia and the industry, and it was great. By the time we were starting Affirm, Palantir was a thing. Everyone wanted to hire really great ML engineers. So I was trying to ask, 'Where will I find a gusher of people who are really good in machine learning and artificial intelligence and are interested in what we're doing?' And I realized that probably a lot of people who would normally work at a quant fund, who would go to Wall Street and build these major scale models and train them on their proprietary data, because we knew we would have a ton of data because we're embedded at the point of sale, it would be really interesting to train these things. So where do you get the best people? And the thought that came to me, which turned out to be exactly right, is if you build a product that they're not embarrassed to talk about to their parents and friends, they wouldn't go for the money. Because if I sit on Wall Street and squeeze pennies out of the market, my job is efficiency of capital. That's a cool story, but it's shorthand for 'I make a lot of money pushing bits around trying to find the next transaction that the other guys haven't caught on to yet.' What we're building is actually helping real people buy real things at states that are not party to this Wall Street bonanza. So if you have that connection, if you didn't come from New York or LA and you're not just naturally drawn to the Wall Street money, you love the idea of helping people like your parents or your cousins come into whatever it is they need to buy, and you feel great about building machine learning models, you just don't love the lending world. Well, this particular lending world is unlike anything you've ever seen. It's no fees, no screwing the customer. We have this extremely high mission that we really believe in and care about, and we're not afraid of the regulators. We love talking to them and explaining who we are. We like responding to information requests because when they say, 'Please disclose all your fees,' it's cool, a sheet of zeros. And so that early team foundation, especially on the engineering and ML side, was really this: 'Hey, you will be proud of what you do. You will not be embarrassed by who you work for or how we do our business. You will never have to say, 'Oh yeah, I'm sorry, I'm the one who gave you the 0% with an asterisk clone.' We don't do those. And it's exciting to be on that team.' So those were the three foundational trends that fed our choices on the product construction.

So that used to be a major worry of mine, because when we were tiny, I was in everything. I would stick my fingers in every pie, in every meeting, signing off. And back in the day, we would hire product managers, and I would tell them, 'You got one question to ask me about late fees.' I know you came from financial services. People who didn't come from financial services just didn't have the affliction. But if you spend a lot of time in financial services, classic FIS, you'll be like, 'So you guys don't charge late fees. You know, there's a lot of money in there, right?' And I'd say, 'Yes, let me explain. Here's why. We want to align ourselves with our customers.' 'Okay, cool, cool, cool. That was your first one. You can ask me again. And if it's the same question, you'll be fired. If it's the same question but in an interesting new thoughtful way, and you had an insight, and you want to make the argument for stretching the cost of time value of money across the entire user base, I'll humor that. But if it's 'I thought about it and we should charge late fees because it's more money,' you should not work here. You didn't get it.' So that was year one or two. And now it's almost year 15, and it is true that I'm not really involved in hiring every single product manager. I still interview quite a number of people, but mostly not. And so it turns out that if you are completely obsessive about the cultural DNA of the company in the early days and you just never ever compromise early, it actually becomes a self-sustaining thing. This is the experiment that you can only run once. I don't know this is true for everybody, but if you look at the average tenure of senior people at Affirm, Libor, our president, responsible for both product and engineering these days, he started as CTO and kind of just operations and risk, by the way. He's picked up a lot of things over the last 10 years that he's been here. He was also my college friend, so we've known each other for a very long time. And Michael, our chief operating officer, is now in his seven and a half year. And Pat, our head of sales, is like 10 and a half years here. So you have a lot of these figures of importance and authority and key sort of cultural significance. They all bought into this, and they weren't just interviewed by me. I was there with them, incubating the product ideas and making mistakes and realizing that we built something that nobody cared about or built something that really took off, and handling internal objections to why don't we do X or Y. 10 or 12 years ago, somebody asking me about late fees was kind of obvious from the very beginning, but there were plenty of other much more subtle things like, 'Why don't we do this or that?' And at some point, I'd think, 'I don't know, but I'm going to have to go think about it pretty hard because this is an important culture-setting moment.' Sometimes I thought yes, this makes sense. Other times I didn't. But in the early days, you embed all these decisions in the popular conscious of the company. This is the shared DNA. And as you get to 2,500 people, which is where we are now, you have a lot of people who have been there through the years of this crucible of 'What is Affirm? How does it do when this kind of moral problem or moral hazard floats up in front of us?' So it's more than just me. In fact, I used to make this joke, and now that I'm in my 50s, I actually become maybe slightly too sensitive to it, but if a lightning strikes me, not to make it too morbid, I don't think a single thing would change. I think the company would just self-enforce the DNA with or without its founder. I'm probably one of the more passionate people about the way we do business, but I think I'm far from the only one. I don't think it's this one Max holding up the dam of late fees. Quite the opposite. I think if a lightning strikes me or I become senile, which was the 50-year-old joke, and started asking my own questions like 'Why don't we charge late fees? Seems like a really profitable thing to do,' they would rebel against you and lock you in your office. They would literally toss me off the cliff like, 'Oh, the old man's lost it. We got to get rid of him. We are the company that does not charge late fees under any circumstances.' So I think it's so enshrined in who we are and how we do what we do that it's entirely a life of its own. I used to take great pride in the idea that we never changed a word of our mission. It's actually fairly awkwardly written down. I wrote it kind of off the cuff, 'Honest financial products that improve lives. Cool. I like it. Let's go with that.' And two years later, I was like, 'We should probably change it. Could be a little bit more clever, you know, punchier.' And so I was like, 'No, no, no, you can't. It was written down when you were starting the company, and 15 years in, I can't change a word. If I did, somebody would be like, 'Oh my god, he is modifying the mission. A comma has been introduced. She's compromising.' Can't do that. That's like the Declaration of Independence. I'm okay with this part being calcified. You cannot allow calcification of model building techniques or willingness to chase the latest hotness in machine learning, but calcifying a great mission is totally cool.

So it's actually refreshing to hear your analysis because it's wrong. But that's actually like it's wrong and it's right and it's wrong. From the very beginning, the north star, when you're trying to build a thing that doesn't exist, you're always leaning into analogies like 'We're like Uber but for songs' or something. So I was trying to explain to our earliest backers what we were going to be when we grow up. And the shorthand was always we're going to be like American Express but in 21st century technology. You would see us both as your source of credit but much more importantly as a network where more and more consumers would find us and more and more merchants, and assuming this idea of honest financial products, no fees, no screwing around, no gimmicks, you would prefer us. And so as your preference for Affirm would grow, so would the number of merchants saying, 'Well, I can't ignore it. It's just too many people who like this thing so much.' It would create a flywheel of more consumers that like us because we are honest would naturally grow into more merchants. More merchants would then essentially act as an advertising engine for us with more consumers saying, 'I've seen this logo before. It's on a bunch of places now. Gosh, I've seen it on Amazon.' So it would just be a flywheel. So we've always had this point of view that we are building a network, and one side has to reinforce the other. In fact, we are a much larger company now than we've been through the years, but we're still inevitably engineering-constrained. So as we prioritize the next thing to build, the first question I ask is, 'Does this help one side of the network or both?' And if it's both, that is the highest value thing we can be doing. Building something like we could offer our consumer something really neat like everybody gets a free hat. How is the merchant going to care? They're not. 'Okay, it's nice that everybody gets a hat.' Or access to airport lounges, that's great, but that's not really helping the merchant side of the equation. It probably helps consumer retention, makes them feel like we care for them even more, which we certainly do. But a better version would be something like create a platform where all the merchants can promote their discounted APR deals. So we had this really cool thing called Big Nothing Day. The entire management team, but also the company's really big into silly puns and strange funny movies and things like that. There's a fair amount of goofiness going on. We're a very serious company, so if you're as serious as we are about math, you have to take it out elsewhere. So a lot of my shareholder letters make references to The Big Lebowski and things like that. My pronouns are El Duderino. Anyway, so as we built our app, we thought about what it was used for. Initially, the use of the app was of course to pay your bills, set up your auto pay, all that stuff. And then it's like, 'Well, we might as well make a catalog of all the merchants that allow Affirm because that'd be great. Consumers would be able to find if I'm going to go to store X online or offline, is Affirm available?' Then I'm like, 'Well, some of these merchants offer 0% APRs, and that's really great, and we should let them highlight that in our app.' So about a couple of months ago, we had this idea like why don't we have a big festival where we have a day where any merchant that wants to offer zero will do so. And we will make it our business to tell all of our consumers that these merchants are offering 0% loans. Zero interest is the best price. And of course, this being Affirm, it's not like zero asterisk secret message. Zero means zero. You're paying for a $1,000 thing over 12 months, and it's going to cost you $1,000. Pretty flipping good. It's better than any credit card can possibly offer you. So that's what we did, and it's a great example of a thing that we built whole cloth from nothing over the course of a few weeks. And over many months, we actually signed up a bunch of merchants who wanted to participate in this extravaganza of zeros, and it was huge. People loved it, and it was a very good explanation of who we are to our consumers and to our merchants at the same time. So this singular moment, singular set of features, singular set of productization is a good example of how we think about building products and why the card and the app and the point of sale and all these things, they all play together to just further the spin of this flywheel of more merchants care about accepting Affirm and using our 0% and other promotional capabilities that we offer to inspire more consumers that will come back and shop at more of these merchants and feel good about how they're buying it and make sure it's affordable and there are no late fees and all that stuff. So that is the crux of our product strategy, our network building strategy, all of that. And that's why I was sort of chuckling at 'Yes, we are very much a merchant company because we wanted merchants to believe that we will bring them more sales, and then we did, and we have a lot of consumers, like 24 million of them last quarter, 12 months trailing. And that's enough now for merchants to be like, 'I should offer Affirm.' Having just Visa Mastercard logo, that's not enough. I got to have the Affirm logo too now on my door. So as that flies on, it just becomes like, 'Well, we're now much more of a consumer company, aren't we?' Because we have this app and the card, and of course the more of that we have, the more merchants are like, 'Whoa, I definitely need to make sure Affirm is available.' So it's like the next question you get a year from now will be, 'Boy, you really seem to be leaning into merchants.' And the story is like this pushing on one side of the push. We have two hands, and we're pushing both in the same direction.

So that particular one, and a class of these problems, is kind of very familiar territory. Long before we had this, the eternal name for this product is AC/DC, which I did not name, but it's one of the better names I think we have. It's Affirm Card on other debit cards. So it's AC/DC. I'm sure it's being marketed. I thought it was great. Another t-shirt. Yeah, I'll come to you for old t-shirt ideas. I have merch. Fintech merch is a bit of a passion of mine. I usually sport at least two, more often than three, Affirm logos. You will not have a photo that I just took in front of 10 Downing Street in London where I was fortunate enough to be invited to speak with some very senior people there. I had to wear a suit and tie, and I was like, 'Oh my god, I feel naked. There's not an Affirm logo on my body.' So I snuck in Affirm socks. I have a photo of me in front of 10 Downing Street pulling up my leg cheekily showing Affirm socks. I would not be seen without it. So the point on navigating this class of conflict. Long before this became AC/DC, we had a kind of natural channel conflict. We are embedded in Shop Pay and in Apple Pay and in Google Pay, and we are directly integrated sometimes or a lot of times now. So one question you have to ask is inevitably some of these are better than others. Maybe some of our partners are more successful at negotiating a better deal with us. Maybe we're more successful at negotiating a deal with them. The economics are similar, it's a payments business, so there are natural constraints as to what can and cannot happen. But you're going to have channel conflict. And the honest answer that you have to give, or the question you have to ask and the answer that falls out of it, is 'What's better for the end consumer?' Clearly the merchant decides, 'I'm going to put up N logos on my site because I know my consumers are excited to pay with Shop. Shop Pay is the best converting wallet on the web.'

That's what they sometimes say out loud. It's almost certainly true. And that's what those sites prefer. But they're not crazy. They're not going to exclude Apple Pay or other types of pays because people have preferences. And so our goal was always like, look, we need to be available behind every door. When you push on a door that says payments here, if you find a firm there, it has to be about the same experience. You have to know that you're getting the no late fee experience and everything else that's sort of behind that.

And so the notion of channel conflict between wallets and other financial services where we are embedded has long been with us and we've long navigated the good problem of how do you cope with people sometimes look for this door or sometimes for that door when they choose their wallets and a firm is in both. You just make sure that the merchant gets the same value and the consumer gets the expected user experience and the value. And so as we did this, we sort of asked ourselves, all right, so in the world where people have a preferred debit card and a checking account and a bank relationship that they're just never going to walk away from, do we want them to have a firm card? Of course, that'd be lovely. A firm card goes to the people who are really committed to a firm. They use the firm a bunch of times. They love it. They're excited to go use our card for everything. There are plenty of people that say a firm is great. I use it in 12 different places per year. That's it. And for other things, I have a debit card from my bank X and boy, it's great. I just want to do that. And so as we were reasoning about this, we thought, all right, so if that's another door and they're always going to open that door when it comes to paying for things that aren't considered deeply thoughtful purchase, a firm is ultimately where we came from is still kind of where we are a lot. And that is when it matters to you, when you're thinking about it, you should use a firm. It's pretty clear that if you're looking at a $1,000 thing or a $500 thing, whatever is a meaningful amount of money for you, when it matters to you to think about it, a firm is always going to be the best option. No fees.

That is, I would argue we've won that, but we're winning it. Nothing's permanent, but we're doing well there. Sure. And so that's the strongest kind of brand and intellectual stronghold of what a firm means for people. If you have other parts of your financial life, we don't want to tell you, oh yeah, you've got to drop your banking relationship. Banking is local. You have a deep relationship with whatever bank you go to wherever you are in the world. And so if we could bring us and what makes us special into that instead of trying to uproot it, I think that's the better path at least from how we've built our business. We tend to play well with the ecosystem versus trying to antagonize the ecosystem. And so that's where that project came from. And we've seen a pretty significant amount of interest from folks that are looking from the outside into this BNPL world and saying, my consumers are using these products and I have no idea how I can offer something like this even though I know it matters to them. So I think that was the right choice for us to make. It also speaks to this idea of a network. Ultimately, what we're doing is connecting people who have completely different preferences, who sometimes love Google Pay and sometimes only use Shop Pay because that's what they're completely conditioned to. And so, if we're inside both, it certainly makes sense we would be inside a debit card or inside another payment instrument that we haven't said yes to out loud. Anyway, so that's the philosophy that led to AC/DC and that's how we're going about it and it has been so far at least the reception has been quite positive.

It's already happened. People have tried, it didn't work.

That is indeed the pitch. That is your huge

Actually I would argue it's a great way to borrow. It's a crappy way to repay, to put a finer point on it.

Too easy. And the structure is designed to reward the lender and kind of screw the borrower.

I think the practical reality on the ground kind of speaks for itself maybe better than the philosophy. I agree with you, the worry about

is very real and you could probably even start from the there's not just one BNPL so you suddenly have access to all these BNPLs and your credit cards still there. So I think there are a couple of things that you can do that we do as a firm all of them and I certainly am very vocal about the industry what should be doing this as well. So, first of all, practically speaking, typical American household if they're revolving, it's about $10,000 of revolving debt. So, that's kind of the average and it has been the average for quite a while. Typical outstanding balance to a firm in that cohort is $700. So just to give you a sense for scale, but it's also if you look at the annual spend we are encroaching on like a typical borrower in that world also spends on the order of 10 to 12,000 they are spending on the order of $300 to $4,000 with us and so I'm a little bit apples to oranging it because I'm quoting data from our card which is a little bit more frequent. So, you can apply discounts a little bit,

You borrow $3,000 from a firm, you don't revolve and at any given moment you owe us 700 bucks. That's a pretty good trade. You are paying it down faster than you're spending it. And again, there's a little bit of hand waving here in terms of the cohorts I'm comparing are slightly different, but they're not that different. And so, that's a really good sign. And so, first of all, it's practically true that whether they're using credit cards or not, and of course, I'd rather they didn't revolve,

sure moving some of that spend into a device that's better, a 0% version of it is most certainly better because then you can potentially go in perpetuity and you're just getting paid to borrow money, which unfortunately not our business model. But it's good when retailers or manufacturers want to make it that much easier for you to say yes to a thing by just essentially subsidizing your interest. So that's just factual what's going on in our world. So how do we do it? One going back to the very beginning no late fees makes you really care about underwriting. If you were writing checks left and right to see what happens because you have the crutch of just paving over it carefully with some late fees and compounding interest. Sure. Then you're not that far from being another credit card provider. If you're holding yourself to a standard of if you're not going to pay me on time, I'm going to lose money. You have two things. One, you want to be good at underwriting, but two, you also want to be mindful of other borrowing. You can't just say, 'Hey, you're borrowing $1,000 for me. You've always been good. Here it is again. No late fees.' You have to be mindful of, well, but what else have you done lately? If you take out a giant loan from Prosper and it looks great, but your credit card's back to fully utilized

No, you're not actually good for another thousand. You may be lying to yourself and you think it's cool, but it is not. And the answer is going to be you're going to have to save up for a down payment or possibly the answer is no because you are overextended. And as often as we say no,

we're pretty firm on it because we have no way of benefiting if you borrow and can't pay us back. So that's part one. Part two, and this is the part where I sort of yell about this with the industry quite a lot. It is high time that BNPL is reported to the credit bureaus.