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I sat down with Max Levchin, Co-Founder and CEO of Affirm and one of the living legends of tech and fintech. He co-founded PayPal with Peter Thiel and Elon Musk, invented anti-fraud technology that’s still foundational today, and has spent the last 14 years building Affirm into one of the largest publicly traded fintechs in the world with a mission to replace credit cards with transparent lending.
He is one of the most thoughtful and brilliant founders I’ve spoken with.
We discussed the backstory of Affirm and their deliberate removal of late fees and compounding interest, why he believes the credit card is a broken product and how they’re fixing it, Affirm’s powerful network effects, what the Citrini Research bear case got wrong, and what he learned working directly with Elon Musk and Peter Thiel.
No Late Fees Was Never Just a Marketing Decision
When describing Affirm, their no-late-fee policy is probably the most discussed feature of the company, and most people understand it as a consumer-friendly branding move. Max Levchin sees it a bit differently. For him, removing late fees was a triple-purpose strategic decision that simultaneously solved three problems: brand, talent, and underwriting quality.
When a customer misses a payment, Affirm sends a reminder. It does not slap them with a late fee. The customer, who has been conditioned by every other financial institution to expect punishment, is typically surprised. That surprise creates incredible loyalty. Levchin says people stop him at airports to tell him they love his company, and he challenges any lender or bank to match that degree of consumer love.
The talent angle is a bit less obvious: Affirm’s pitch to quantitative talent on Wall Street is, come work on bond math and complex distributions, and you’ll never have to explain at a cocktail party that you make money by charging late fees. Many of those original hires are still at the company 14 years later.
But the underwriting story is probably the one that matters most. Banks have a structural perverse incentive: when delinquencies rise, late fee income rises with them, creating a cushion that absorbs bad underwriting. Affirm removes that cushion on purpose. If their underwriting model is wrong, the company simply loses money. Which means the customer’s incentives and the company’s incentives are both aligned. That constraint has forced Affirm to build underwriting capabilities that Levchin believes are now the company’s primary competitive advantage.
He frames the deeper problem through mathematics: revolving debt is an exponential function, and human brains are not wired to intuit exponential curves. We are, as he puts it, neural networks that are just not very good at computing exponential functions. Affirm’s product is designed around the opposite: simple interest, pre-priced, with a fixed payoff date.
He is obviously not anti-credit. He borrowed to go to college and considers it one of the best decisions he ever made. His argument is that access to credit is essential, but having permanent debt is terrible. With national credit card debt now sitting just under $1.5 trillion (and roughly half of it revolving), Max points to it as validation that the problem Affirm is solving has only gotten larger in the US.
The Network Is the Moat
Max is one of the most successful serial entrepreneurs alive and he says he only starts network businesses. In a network, once you achieve product-market fit, the flywheel becomes self-reinforcing and the moat of this business only strengthens through time. In Affirm’s case, merchant penetration drives consumer usage with no incremental marketing spend. A consumer who has a good experience financing a TV with Affirm spots the Affirm logo at another merchant and decides to use it there too. As more consumers use the product, more merchants want to offer it. The cycle compounds.
A close comparison is American Express. Fifteen years ago, Amex had the reputation of being a premium, exclusive network: Visa and MasterCard required, Amex optional. Amex broke through that barrier by reaching a critical mass of consumers who actively asked merchants to accept their card. Max argues the same dynamic is playing out in real-time with Affirm. The company is now embedded in Shop Pay, Apple Pay, Google Pay, Amazon Pay, and its own Affirm card… each of these channels reinforces the others.
Why AI Agents Won’t Replace Networks Built Over 14 Years (What Citrini Got Wrong)
Earlier this year, Citrini Research published an essay arguing that AI agents would disintermediate companies like Affirm, and the stock dropped a meaningfully that week. Levchin read the piece and called it “well-written but fundamentally flawed”, because it made the same mistake most technologists make: overvaluing software and undervaluing the non-technical work that actually builds durable businesses.
In Citrini’s imagined future, thousands of successful DoorDash competitors emerge because the app is easy to replicate. Max Levchin’s point is that the app was never the hard part. DoorDash’s advantage is the years of painstaking work it took to integrate enough restaurants, grocery stores, and delivery locations into a network that a consumer never has to think about whether a given place is available. When you open DoorDash, you don’t wonder if it’s accepted. That seamlessness is not a software problem. It is a network problem, and network problems take years to solve regardless of how good the software is.
The same logic applies to Affirm. The company is now embedded in Shop Pay, Apple Pay, Google Pay, Amazon Pay, and its own card. Millions of merchants accept it. Millions of consumers use it. That network took 14 years to build (and counting), merchant by merchant, integration by integration. An AI agent can replicate an interface in hours. It cannot replicate the trust of millions of consumers, the contractual relationships with hundreds of thousands of merchants, or the underwriting data built on billions of transactions. The notion that software alone can shortcut that process misunderstands what makes payments companies durable.
The Levchin Prize and a Lifetime in Cryptography
Before Affirm and before PayPal, Max Levchin was a cryptographer. His career in computer science has been, in his words, a constant attempt to lean back into math. Number theory led to cryptography, which led to security, which led to payments. That arc eventually led him to create the Levchin Prize for Real-World Cryptography, an annual award recognizing practical, real-world contributions to the field of cryptography.
The inspiration for the prize grew out of a dinner conversation about ten years ago. Max had built friendships across the cryptography community during the early days of PayPal and realized there was no equivalent to the Nobel Prize or the Fields Medal for people doing applied work in the field. He asked around the table why no one had created something, and when no one stepped forward, he decided to do it himself. He set up the funds and handed it off to an independent body to judge and award each year.
The recipients have spanned half a century of contributions to the field. One of the earliest winners was Moxie Marlinspike and the team behind Signal, which Max Levchin considers a great example of what the prize is meant to highlight: a beautifully engineered, open-source system that is verifiably secure. Other recipients include Diffie and Hellman, the progenitors of much of the public key cryptography in use today, people who had never been formally recognized with a dedicated cryptography award. For Levchin, the prize is a way to stay connected to the field he loves even as he spends most of his time running Affirm.
What Elon Musk and Peter Thiel Taught Him About Leadership
Levchin worked closely with both Musk and Thiel during the PayPal years, what did he learn from them?
Elon Musk’s superpower, according to Levchin, is an extreme version of the leadership and entrepreneurship truism that a decision now is better than the right decision later. Musk takes this to the absolute limit: often wrong, never in doubt. He will train his focus on a problem, declare a solution, march the troops forward, and if it turns out to be wrong, retry without embarrassment. For someone with a scientific background, this shoot-first approach is counterintuitive, but Max acknowledges he has picked up a lot of that attitude.
Peter Thiel’s superpower is different. When Thiel sees great work, he becomes genuinely, visibly excited. Levchin uses the word “giddy.” Most importantly, it is not performative. Thiel cannot fake it. And when the CEO of your company is that invested in your work, it creates a motivational force that no incentive structure can replicate. Everyone around him feels that their work is the most important thing happening right now and that they cannot disappoint their boss with their outcome. Levchin says this always pushed him to do his best work at PayPal.
Movie Recommendation
Seven Samurai. Max Levchin has watched Akira Kurosawa’s 1953 film 113 times (!) and recommends it to every founder he mentors, because the real story is not about elite warriors defeating bandits. It is about a leader named Kambei who takes a group of unorganized, demoralized villagers and turns them into an army capable of winning a fight they were sure they would lose. When you think about it, that is the ultimate startup story. For founders, the job is not to fight the battle alone, but to build a team capable of winning a seemingly impossible market.
The Unfiltered Q&A with Max Levchin
Miguel Armaza: Your grandmother was an astrophysicist, your mother a physicist. That’s probably what fueled your love for math?
Max Levchin: Yes, although the same grandmother spent a lot of time dissuading me from majoring in math, because there is no Nobel Prize in mathematics. She told me over and over again. The legend has it that Nobel’s daughter married a mathematician and he didn’t approve the marriage. I don’t actually know that’s true, but it was lore my grandmother told me. I majored in computer science in part because I really wanted to do math, and math was definitely frowned upon. My grandmother was very pro physics, tolerant of computer science, not super excited about math as a major. But my career in computer science is a sort of constant attempt to lean back into math, which every time touches this incredible, vaguely accidental luck. When I was in college, all I wanted to do was math, and I ended up just kind of leaning into number theory, which is discrete mathematics, the part that is really useful for three things in practice: cryptography, which was my first love, computer graphics, and machine learning. I just sort of fell in love with this one part of math in college, and it keeps on having these amazing commercial applications.
Miguel Armaza: On the accent, I’ve always wondered. You came to the US at age 16. I know you learned English by watching TV shows like Diff’rent Strokes and the news. How did you master a perfect American accent?
Max Levchin: It’s definitely a very conscious effort. I was a reasonably well trained musician by the time I came to the US. I played clarinet since the age of six or so. And if you are a woodwind player in particular, you really have a good sense of control of your embouchure, which is a fancy word for musculature of the lips. And you presumably have enough of a musical ear to be able to say, wait a second, that’s not quite right, I need to work on that vowel or that phoneme. I definitely worked on the accent very hard, and I’m still, amusingly enough, slightly self-conscious of the accent. My high school buddies that I still keep in touch with, that heard me fresh off the boat, sometimes mock me or troll me by telling me that accent is coming back, and I immediately tense up and try to figure out where I screwed up. It’s definitely no longer conscious modification of the speech, but I’m definitely conscious of the fact that I used to have one and I have to maintain the perfect accent. The reason for it was actually pretty simple. I realized as soon as I got to my high school in Chicago that there were kids who spoke like the locals and the ones who didn’t just didn’t get the same treatment. Like it or not, I have to blend in.
Miguel Armaza: Google recently announced they believe their quantum computer could break RSA-2048 with much less compute power than initially anticipated. Your thoughts?
Max Levchin: It’s not really a problem. It’s something we’ve been living with. We being the practitioners and people who are interested in the field for 20 years now, with a full understanding that quantum computing is coming. It’s a matter of when, not if. It is a fundamental promise of quantum computing that you will have the ability to brute force, or quasi brute force, these schemes that power the likes of RSA and several other cryptographic primitives. We have plenty of post-quantum, quantum-resistant crypto algorithms. I don’t think there’s plenty of teams that are scrambling to say, wait a second, we thought this is coming 10 years from now, we were caught asleep with a switch. Most sort of well-managed systems are long, at least planned, for a thoughtful transition to a post-quantum future. The world that was rocked is more of a newspaper headline, less serious system design.
Miguel Armaza: I heard you attended quite a few cypher-punk events in the 90s, and you suspect that whoever was Satoshi definitely attended some of those events. Any thoughts on who this could be?
Max Levchin: For a long time I thought Adam Back was, in fact, Satoshi. I’ve met Dr. Back. He swears it’s not him. I have no reason not to believe him. I therefore revise my views to one level of indirection. That is to say, I think he knows who it is, but it’s probably not him. It is less likely Jack Dorsey in my mind than Adam Back, even after Adam says that it’s not him. But it’s also perhaps because when I was hanging around cypherpunks, I did not meet Jack Dorsey. I met him later. So in my mind, that person is already, I’ve already intersected with them, whoever they are. It’s probably more likely a group than one person. Just feels more like a work of a couple of really thoughtful people, because hash cash and stuff that Adam Back put together quite publicly, without being pseudonymous, is a foundation of what’s gone into the original Bitcoin paper. It feels like a collaboration, not just one man’s genius.
Miguel Armaza: What did you learn working with Elon Musk?
Max Levchin: He has an incredible sense of conviction about just about everything he says and does, which he is probably the first person to tell you he’s not 100% right about. There’s sort of a typical truism in leadership and founder leadership in particular, where decision now is better than wrong decision now is better than right decision later. I think he takes it to the absolute extreme, where often wrong, never in doubt. And that is a superpower of his, where he will train his aim on a problem, see a solution, declare it to be so, tell the troops to march forward. It’s probably better than 50/50, right? But it doesn’t matter. He doesn’t mind being wrong, and he will just retry and retry and retry again. I think that’s certainly something, especially for someone with a kind of proto-scientific background, a very difficult thing to get behind. 25 years ago, 30 years ago, in the 90s, I would have said that’s a crazy thing to do, let me lean back, think it through, plan out the game. But I’d certainly picked up quite a lot of the gotta-decide sort of attitude.
Miguel Armaza: How about Peter Thiel? What did you learn working with Peter Thiel?
Max Levchin: Elon and Peter actually share that sort of decisiveness as a superpower, they’re quite similar. But Peter has another thing, which I mean, I didn’t work together that long, so I haven’t seen him in the sort of full gamut of leadership skills. Peter has one that is truly unique. He becomes so genuinely excited for the work you’re doing, like you can’t fake it. He will become not just a willing participant or a supporter, he can be giddy with excitement when he sees you do great work. And that motivates people like you will not believe. You sort of feel like you have to do your best work. Like this guy is so invested in what I’m doing, he may be more invested than I am, I can’t let him down. And so if he is the CEO of a company you’re running, it’s an incredible leadership skill, because everyone around him knows that he is so invested in what we’re doing together, in your work specifically. Your work is really important to him right now. So that’s a great skill, and a great sort of, I’m not sure it’s a skill versus an innate behavior of his, but it’s always motivated me to do my best work at PayPal.
Miguel Armaza: The concept of no late fees. You’re essentially saying, if I make a poor underwriting decision, I have to live with it, and I’ll take the hit. There’s no other way to monetize this. Do you think this is one of the most important decisions you made for Affirm?
Max Levchin: I think so. It obviously goes beyond no late fees. No late fees is kind of the easiest one to explain, because everybody has such a visceral, oh, I hate late fees, it’s so annoying. And we didn’t stop there. We don’t compound interest, so it’s always simple interest loan. We pre-price interest. You know upfront how many dollars it’ll cost, and it can go up from there. We don’t do deferred interest. And so we sort of eliminated all of these things, and they were all invented by the industry for two reasons. The carrot is, I’ll give you a nice rate, maybe I’ll give you even a very low interest rate. But if you are late, or if you’re not following my exact schedule, you need a way to slap your wrist. And so that’s when the late fees come in. It’s not actually the real reason. The truth is a little bit darker, and I can very quickly prove that it is, in fact, the case that none of these are effective mechanisms. The darker, truer reason is inside the fine print that explains all these fee schedules, typically on the back of your credit card statement. The business model of the majority of issuers, where they derive something like half of their profits from late fees, deferred interest, all that junk. When we started the company, we had multiple reasons why we didn’t want to do this. One, we wanted to build a brand, and we wanted our story to be, hey, you can rely on us not to take advantage of you when you were sloppy or when you actually have had something bad happen. Just as importantly, we wanted to attract the brightest minds in machine learning or data science. So what made you go join a hedge fund and say, well, I really love financial mathematics, like bond math, like sort of complicated compounding curves. I love that stuff. And I didn’t want to work in banking because I don’t want to explain myself at cocktail parties. The third one, kind of the most important reason, is we thought from the very beginning that our advantage would be underwriting. And nothing makes you better at a thing than constraints.
Miguel Armaza: How have you adjusted your underwriting models with all the LLM technology? Is there unstructured data that wasn’t being taken into account that now you can?
Max Levchin: For majority of the world, it seems that the ChatGPT moments or the introduction of LLMs into the CS vernacular was like this binary thing where neural networks were written off, scrap heap of history, and suddenly they’re back. And if you’re in the industry, it’s always been a continuum. Like you look for new modeling technique, you ask yourself what academic papers have been shown to pick up any incremental alpha, where can you find another technique, another idea? We’re now deploying models that were built with the full appreciation for just how powerful the attention mechanism is, and like that, kind of the attention is what you need. We have sort of a whole new family of models that we’ve been deploying very successfully in fraud fighting in particular, but also the applications in credit of course. But long before that, we sort of asked the question, can you find incremental information value simply by outsourcing feature finding to LLM models? And that’s been the case well understood by the industry at this point, but I think we were there either first or close to first. We’ve been sort of on this journey of whatever the latest hotness in machine learning research is, we want it. We want to be the first ones to try it, run it against what we have today, and make sure that we’re staying ahead of the train and have been very successful at it.
Miguel Armaza: Credit card debt at a national level in the US is about $1.5 trillion. I think half of it revolves. One of your goals is to fight the credit card industry. Do you feel like you’re succeeding? Do you feel like the tide is turning?
Max Levchin: We’re not actually fighting the industry, as sort of David and Goliath as it sounds. Not exactly the goal. The goal is, in fact, to fight revolving. I think revolving debt for consumers is just a profoundly bad idea. Back to math, we are not neural networks. We are actually, we are neural networks. But we’re just not very good at computing exponential functions and revolving on a progressively larger amount. If you’re in debt for $5,000 now, and you paid off a little, and you spend a little, and it’s back to $5,000, and then the interest compounds again, and you look at your balance, and next thing you know it’s $10,000, and you really can’t quite figure out what happened. People are very bad estimating exponential curves. And so our goal at Affirm has always been to provide an alternative that gives them access to credit, that offers them a product that doesn’t confuse them. I firmly believe that access to credit is really, really important. Permanent debt is terrible. And so what we’re trying to navigate is this product that gives anyone who needs it and can afford it, in fact, excellent access to credit without the burden of getting into debt that they can’t explain, can’t work out of.
Miguel Armaza: About a month ago, Citrini Research made big headlines. What did they get wrong?
Max Levchin: I think the temptation to shrug off the difficulty of some of the non-technical accomplishments in these companies is a lot. The example they used was in their imaginary future, there are now thousands of successful DoorDash competitors because it’s so easy to replicate the app. DoorDash app is not the important reason behind DoorDash’s success. It’s the fact that they painstakingly built up that same flywheel where they got enough restaurants and enough grocery stores and enough places where they’re accepted to actually integrate their connection to their network. And so when I open up the DoorDash app, I’m not, generally speaking, looking, is it accepted? You know, do I need to get some other app? Like the notion of a fragmented ecosystem of restaurant-specific apps or grocery store-specific apps is, like, on its face, obviously silly. Like you want to go to a single, concentrated source. And like the DoorDash primarily worries about UberEats. They are not worried about someone saying, well, I have the best app that I cloned from DoorDash, because it’ll take years, just like DoorDash, to get into enough restaurants to matter. Some of their ideas were certainly easy to believe and relatively powerful. But I think the glossing over like, well, you know, they just distribute it somehow, don’t worry about it, software is all that matters. After all that mattered, the world would look a little bit different.
Miguel Armaza: You’ve talked about watching and re-watching Seven Samurai. And you recommend that to entrepreneurs often. Why is that?
Max Levchin: I think it’s one of the most entertaining and sort of artistically beautiful masterclasses on leadership. The short version of the plot is a bunch of unorganized villagers get attacked by these terrible bandits. They hire a very small team of professional soldiers, led by a veteran, to defend them. But what actually happens is the soldier, the character’s name is Kambei, builds a team, an army, but it’s really a team of not just the samurai but the villagers, organizes them into an army to beat the bandits. And it’s a beautifully shot, it’s a 1953 movie, all black and white, sort of very stark, but it’s an amazing story, and it’s a great story of how to lead people who do not believe they can win.
Miguel Armaza: Lastly, what did you strongly believe when you started Affirm that you no longer believe today?
Max Levchin: When we started, I thought the primary reason for choosing to try Affirm would be affordability, where people would say, I don’t have access to credit, or I don’t like the cost of credit that I have, I’m going to rationally choose this new product that, at the time, would never have heard of. It seems like it’s fair and interesting and it’ll be affordable. The reality turned out to be all that, but the flywheel of the brand, where people actually remember the treatment they receive from us, they’ll know late fees, the total transparency, the no compounding, like all the stuff that we talked about, turned out to be much more important. I always thought of it as an internal advantage, where I would recruit the best team, I would be able to hold my head up high and say, we are the good guys in lending. But I never really expected it to be a thing that our audiences or our user base would say, these guys are good guys. Like, no, it’s credit. Like, you need access to credit, and then you forget who gave it to you. Not. So it turns out that people actually remember, and they really take to our brand with a great degree of love. I think the best thing so I’m always wearing my logo, which part of it is just sort of, you run the company you got, you got to wear the colors. But I also do it because whenever I’m outside the office, there’s always someone who stops you on the street or especially at the airports, like, do you work for Affirm? Like, yes. Like, I love your company. I challenge any lender, any bank, any financial services provider, to have that degree of consumer love. It’s super gratifying, and I don’t think I expected it.
This interview has been edited and condensed for clarity.
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