Max Levchin: Affirm targets $100B GMV doubling at 25% annual growth with AI writing 75% of code

Speaker 1: Potentially. Potentially. So Well, our next guest, Max Levchin from Affirm, he's the co founder and CEO. He's returning to the show. We've been keeping him waiting far too long. Max, how are you doing?

Speaker 9: I'm great. Thanks for having me again. Third time's a charm.

Speaker 2: Third time. What have you learned

Speaker 7: about that?

Speaker 1: Third time can't be the charm. I want twenty fifth time to be charm. Let's keep it going. I love these.

Speaker 13: I enjoy this course.

Speaker 2: What have you learned about making coffee since the last Oh, yes. The important update first. Because I'm assuming you're never like, oh, I'm I'm pretty good at this.

Speaker 1: Laurels? Are you are you the

Speaker 9: final boss? I'm absolutely not. Okay. I took apart e sixty one group, which is the classic 1961, sort of that that's the granddaddy of all the espresso espresso group heads, and I wanted to learn how the mushroom valve works because I thought mine was clogged. It turned out not to be clogged, but I went down an extremely deep rabbit hole of taking apart an e 61, which I've actually never done before. So I'm now really boned up on how espresso machines worked in 1961 and and and since.

Speaker 1: But Yeah. Yeah. People always talk about like when you get into coffee, you go you effectively vertically integrate or go deeper in the supply chain. You start roasting the beans. You got to grow your own coffee. At a certain point, you have to be smelting the metals that go into the machines, understanding the alloys coming up with new chemical processes. You have to set up your own mind. You're you're you're putting all

Speaker 9: kinds of wrong ideas into

Speaker 1: my mind. Jack. Oh, not yet. Yeah. Yeah. Yeah. Yeah. Yeah. Did did did you smelt the metal that went into the espresso machine?

Speaker 9: I've not smelt anything lately. I'm

Speaker 1: Wow. Gonna get a real novice. Last time. Anyway, we're not here about my smelting experiences. Fantastic. I imagine that the smelting process is as intricate and as as rewarding as the coffee making process in some ways for

Speaker 8: Yeah.

Speaker 2: I I expect you to be able to try our coffee and understand the

Speaker 1: The hour. Exact hour. The machine. Yes.

Speaker 9: Possibly. But I I I'm I'm very very long depth. Okay. Anything that you can go very deep into, like, this is the time in general, but, like, depth as a competitive advantage is, like, a profound strength. So Yeah. The reason I'm so into whatever things I'm into is I found over the years that if you out depth your competitors, they just can't beat you. So I'm very pro smelting. I'm very pro going over the deep.

Speaker 1: Yeah. Yeah. That's interesting. Is there a translation or if you transpose that to sort of like advice for young people who might have anxiety about traditional career paths. I was sort of, you know, we look at like job numbers all the time and layoffs and at the same time I think about when I was a kid if I said, Oh, when you grow up you will be a live streamer who at an AI lab. It's like none of those words existed back in the nineties and here

Speaker 2: I Yeah. Also a lot of a lot of there's so much advice out there. It's like you want to be a generalist. This is the age of being, you

Speaker 1: know Yeah.

Speaker 2: A strong, you know. Maybe it's the opposite. And and when I when I look at people that are that are maybe sort of midway through their career, the highest earning, you know, the most respected Yeah. Are almost always the ones with extreme depth

Speaker 1: Expert where

Speaker 2: they can simply out compete anyone else in that role because they know more about it. Yeah. They put in more hours.

Speaker 1: Yeah. But yeah. But what are you thinking for young people?

Speaker 9: I I'm I'm definitely big on depth. I think I'll be these are like entirely non contrarian opinions but maybe they're contrarian right now. But I think it's a great time to get a computer science degree. I think if you're ultra deep into really understanding how software is made

Speaker 1: Yeah. You

Speaker 9: are like, everyone's going be a 10x engineer. If you're 1x engineer yesterday, you better be a 10x engineer tomorrow. That's the new baseline. But if you really, really get it, if you're smelting your own code I'm just going to go with the smelting analogy for a break. But if if if you're that good, you're going to be 10,000 x engineer. And like, you will be worth your weight in smelted gold. Who knows? But it it it's very, very powerful to be a deep expert. Like, you are the one AIs want to learn from. And like, that that is that is the unattainable Mount Olympus of value. And so I I would strongly suggest that being deep is great. Majoring in computer science is great. If you love computer science, like, this is the time to major in whatever it is you see yourself being as deep as possible because then you'll become absolutely valuable worlds.

Speaker 2: What about industry specific depth? Like, a founder comes to you and they're like, oh, I've been building I've been building an e commerce but really and and I've done quite well but really I'm interested in space. I I wanna work on that. You've done

Speaker 7: You're interested

Speaker 9: in space and you're not working on it and you're like, what are you doing? This is the best time in history to do that.

Speaker 2: Yeah. Yeah. But generally, like you've done quite well by just focusing on something in a category that you know better than maybe there's maybe like three, four other people in the world that understand the intersection of like money and technology the way that you do. And I feel like a lot of founders oftentimes will do one thing, feel like they kind of like climb the mountain and then some some will just go back and climb a very similar mountain and do quite well again. Others want to jump ship and do something completely different.

Speaker 9: So I tried both. My last startup in between the other payments startup was not in payments and it turns out that I'm very good at climbing this one mountain. I'm just going to keep climbing it because I also the reason I'm good at it is because I love depth and I love getting deeper and deeper deeper into payments, into how to make payments more expedient, transparent in the case of a firm. Payments are a lot of things, and access to credit is a totally new idea. Like, I've never touched credit until Affirm, and now, you know, maybe without false humility, I think we are definitely one of the very best credit underwriters in the world and are just getting better and better. We just announced yesterday we built an entire new model family loosely inspired by the attention mechanism that is powering all of your LMs out there, including your your parent company now. Some some ideas we borrowed from the from from from all all these really amazing discoveries and sort of pure, you know, language AI. And yet, it feels like we're just getting started. Like, every time I look at this sort of corpus of things that we can try to do, things we can build, a product we can launch, it's like, my god, we have much to do. Like, there's absolutely no chance I'll get to rest. I'm not sure I'll get to sleep after all. It's a great time to build stuff because everything is faster and more exciting and easier to start. Yeah. But the depth reveals itself deeper you dig.

Speaker 1: Okay. So, yeah, we sort of mentioned or we referred to it a little bit, the twenty twenty six investor forum. Who attended? What was the goal of that? Take us through that event. And then I have a whole bunch of follow-up questions about how you're communicating at the current time.

Speaker 9: Yeah. It was amazing. I'm still kind of slightly high on the whole thing. But the the thing that was really fascinating, so we did this event Yeah. Three years ago or similar event three years ago, and it's like a blink of an eye. Like, I remember it was in the same buildings. A of lot the same people attended, so these are mostly buy and sell side analysts, a bunch of our shareholders, a couple of our board members actually flew in for it, which was like a nice surprise to

Speaker 8: me. Great.

Speaker 9: And the entire management team gets on stage and they're like, here's what I work on, here's what the company is, and But like, the reason analysts are there, they're like, okay, tell us about your vision, Max, yada yada yada. Really, what will the next three years worth of gross targets gonna look like? And so, the the the main act I was the warm up act. I try to be funny, but the the main act was our CFO, Rob, who got on stage and said, alright, so we're going to grow to a $100,000,000,000 of GMV. We're we're just in the range of touching 50. We're going

Speaker 3: to double.

Speaker 9: And we're going to do this by compounding at 25% every year. Mhmm. And we're going to move our profitability target from three to 4%, which is our current range to 3.75 up. And so, we're we're going to up the floor. We're going to be more profitable and grow faster and it's like faster. We're a bigger company now. Like, well, what are we doing? Wait a second. Last time we did this three years ago, we said we're to compound at 20%. But instead we did it like 30 plus. Mhmm. And so just like this out of body experience of like we're getting bigger, the room was significantly larger. Same building, different floor, different room. Many more people, many more shareholders, and yet we're telling people like, hey, we're gonna compound that much faster than the last time. It's like, I guess it's like a flywheel, this network we've built. It keeps on spinning and spinning faster. So, it was amazing in a sense that, like, I I knew all these numbers, like, I obviously approved and worked with the team to make sure we feel great about them. When you sort of say it out loud, like, compare that to the last time we told you the same thing, we're gonna accelerate.

Speaker 7: Yeah.

Speaker 9: It's like, wow, that is like so cool. So anyway, so I'm I'm I'm still very high from like being able to say that out loud is very fun.

Speaker 1: So there's a flywheel. I imagine with a lot of financial companies, there's economies of scale. And so that seems doable. Are you also talking about TAM at this point because the company is so large? Or is it still early enough that that's not something that investors are coming to you asking to sort of justify, well, the global economy is only this big. And if you take 50% of it, well, there's nothing else.

Speaker 9: The good news is that a $100,000,000,000 Yeah. Payments in this country is but a drop in the bucket.

Speaker 1: Okay. That's good. Even

Speaker 9: a larger bucket. We're we're in no The US revolving credit, which is obviously a fraction of total total economic rotation, is like a trillion 3 right now.

Speaker 1: And

Speaker 9: a 100,000,000,000 is like then that's worth taking shares. So I I like to compare that number. People are not using revolving credit.

Speaker 1: They're using

Speaker 9: a firm. It's good for them. Not revolving. No fees, all that. And so we're not yet at the sort of time. By the way, these are all U. S. Numbers, we're already live in The U. K. And Canada, and we've announced a bunch of other countries.

Speaker 1: Okay. So speaking of the investors, obviously, it's very important to communicate with investors right now, both from your growth plans and also what's going on in the market. As you think back to the March time period, the SaaS pocalypse, every company got sort of beaten up. You came back really quickly. How much of that was show versus tell? How did you think about communicating to investors through that? How important was the investor communication versus just continuing to deliver on the actual metrics? Like what as you're confronted with one of these, there's a new narrative, how do you what's your playbook for actually working with investors to get them comfortable?

Speaker 13: You know,

Speaker 9: to be completely honest, I don't know. I think the well, and I'm I'm I'm hugely honest, I might as well tell the truth every time. So this particular time, we said nothing. We basically said, okay. I I guess the the sky's falling. Doesn't feel like it's falling on us. Yeah. We're printing a really, really good quarter.

Speaker 1: Yeah.

Speaker 9: We're about to report it. Yeah. We could, like, wave our hand in it, and then, like, you got it all wrong. We're fine. Yeah. Or just wait a few more weeks and, like, hey, here's what we printed. How about that? And, we're going to guide, we're going to reveal that we're having a pretty good quarter too. Yeah. And so, that's what we did this time and we just didn't spend too much time communicating Mhmm. How the story isn't true. Yeah. I don't know if that's like the best response. Like, we've definitely in the past, we would read, you know, the the most recent time when I thought we need to speak was when the rates were rising very quickly. We were screaming into the void of like, no, no, no. This business is fantastic at Zurp. It is fantastic at 5% Mhmm. Fed funds rate. It is just fine at a number that's higher than that. We love our value creation opportunities more or less at any rate. Like, obviously, you know, it breaks at some point, but US economy breaks at some point too Yeah. For all possible values of the federal funds rate, we're gonna be just fine. And a long explanation why, and I don't think anybody heard it. People like, yeah. Yeah. But but the rates are up, so you're obviously. Yeah. We're we're and so the the the dip in return of our stock is like like, you a great turnaround, but it basically amounts to people saying, wait a second. This thing is in just as much demand, does just as well, grow just as fast, just as profitably

Speaker 1: Yeah.

Speaker 9: Through whatever rate cycle.

Speaker 1: Yeah. I think we've talked about the history of effective interest rates on a firm. But we're now in a new regime, and the fear is not higher rates, it's higher inflation. And so are you do you do you already have an intuitive sense of how a different inflation regime or different inflation environment will affect a firm? Are you confident? How do you think about the

Speaker 2: relationship It's like a between I can imagine it's somewhat of a double edged sword and that, like, you know, higher prices means people are more likely to want to use credit, but maybe there's a drop in some purchasing activity of some items. Yeah. You know?

Speaker 1: Also just like if if you're getting $10 in two years and it's worth less, that that that should have an effect. But how how is it actually playing out?

Speaker 9: So a little too early to tell

Speaker 2: Yeah.

Speaker 9: But you were exactly right in a sense that we we saw this game play out in the last inflation spike just a few years ago. We absolutely saw an increase in demand Mhmm. Because as things became a little bit more expensive, people felt that it would be better if they weren't paying for them upfront. People who initially would say, yeah, doesn't really make sense for me to finance this thing. I have the cash. Like, I kind of want my cash to go a bit longer

Speaker 1: because Mhmm.

Speaker 9: I don't know exactly which way the price is going to go. So, we expect more demand. We have to be we're always very careful. I the number one line I give at at at every investor event is credit is job number zero. As a computer science major, I count from zero. Mhmm. And it it is the most important job. We we grow as fast as credit performance will allow us to grow, full stop. We have to print consistent credit returns. Otherwise, we lose credibility with our debt investors, and that that's the most important thing. So we will grow fast no faster than credit results will allow us to. We expect more demand. We don't yet see literally anything related to changing credit performance. We just Mhmm. Printed our results, and they're just as we expected them.

Speaker 1: Yeah.

Speaker 9: And so TBD, whether we see some deterioration of consumer credit due to prices, we really didn't see much of it at all through the last inflation cycle, which was quite dramatic, if you remember.

Speaker 8: Yeah.

Speaker 9: And so, we feel pretty great about our ability to underwrite kind of all eventualities. We are obviously not blind to the macro reality.

Speaker 3: Yeah.

Speaker 9: The minute before I got on stage and promised people 25% growth with an increased profit margin. So, you know, we must think we're gonna be okay, and and that is the case, in fact, the case.

Speaker 2: Yeah. How is is AI changing how you think about international expansion? Does it does it make teams more efficient in terms of adapting the product for different markets? Everything from language to just simply being able to ship more code? Or is international is your, like, international GTM just, like, much more, like, we know what it takes to launch a new market. If AI makes it slightly more efficient, great, but it's not gonna change the strategy?

Speaker 9: I think the one a of every list of things that have really changed, thanks to AI, is shipping code. Like, I think it's this is the best time to be a CEO with a computer science degree because, you feel what this means. You know what it's like to make code. You've done it, in my case, for the last forty years. I know how much easier it is, how much more effective Teams can be, how you can have three people in a room build a product over the weekend, which is an amazing thing that didn't exist six months ago. And so shipping code is absolutely we're seeing that in my latest shareholder letter actually showed an illustration of percentage of code written by AI at a firm and it's like and it's not like a slow rise. You're like, okay, we're trying, we're trying. We're convinced. 60% to start, 75% last month. So, it's just rocketing. It's not like people are writing less code or reviewing less code. It's just that much more productive. So, that helps us internationally. It helps us domestically. Helps us with everything. I think other parts of the business are much better with AI, so translation, obviously, services are fantastically effective with AI. You can do a lot of interesting things with legal. Anything that's text heavy, you have just a great helping hand. But the sort of night and day moment is writing code. Like, that that's one a through maybe, like, one p on on the list of things that are just stunningly effective.

Speaker 1: Are you expecting token cost to be, like, an important line item in the p and l going forward? Like, we've seen because there's so many stories of a lot of code written by AI. Sometimes that moves the needle. Sometimes you're just sort of doing what you needed to do and doing it more efficiently or faster. But at the same time, if you get hit with like $1,000,000,000 token bill in a year, that might be material. And so you you start having to do and it feels like we're at the earliest innings of ROI calculations. We're more in like the feeling of like, oh, everyone loves this. We're seeing good results.

Speaker 2: Max doesn't strike me as someone who's just gonna tell your team to

Speaker 12: Slop

Speaker 2: it use a lot of AI because obviously there's stories coming out of Amazon and Meta where teams are basically just like, I I wanna I want opt I want the optics Yeah. To be that I'm using more AI than anyone else on my team. So I'm just gonna run things overnight Yep. For no reason.

Speaker 9: Yeah. We are extremely extremely metrics driven company. Like, some might take issue with just how completely obsessed we are. We're like you know, you improve what you can measure. Like, if you're going on the feels, you're going to, you know, feel great until you don't. So we measure everything obsessively. We have, at the moment at least, fantastic ROI on our token spend.

Speaker 8: That's great.

Speaker 9: We are I would say, know, it's real numbers, so it's not like, oh, you know, don't care, don't look. We have a weekly report that's generated thanks to AI and and some very smart humans

Speaker 1: Yep.

Speaker 9: Who are monitoring our token use and tell us how each team is producing value and what it's costing us in tokens and Yeah. All the sort of various conclusions downstream. So we are already very mindful of what it means in terms of return on investment on to tokens, return to shareholders at the limit. Right now, it's, like, undoubtedly a very, very accretive thing.

Speaker 1: Mhmm.

Speaker 9: But we're also not assuming that, you know, like infinite budgets are just great. We also expect, by the way, prices of tokens will probably increase over time. Like, I think there's plenty of subsidies taking place. Yeah. You're now inside of one of those places, so you probably know the the true the true price.

Speaker 1: If we can circle back to specialization from AI creating effectively more generalists, interested in sort of computer science history, your career history. How have you processed like the there was a point where front end and back end engineering were two very separate disciplines. At the point where you could do JavaScript on the front end and the back end, you see more full stack engineers. Now you have engineers that are designers, designers that are engineers, designers that might be pushing server code and stuff. So I'm wondering how you're thinking about maintaining that idea of you want the specialized genius, the artisan, the true expert in your organization versus you also want to empower someone to move really fast with a small team. And so you might not need to staff 100 experts to do one thing. There's got to be attention there, so I'm wondering how you're grappling with that.

Speaker 9: It's a great question. You're totally right. There are definitely pockets of today's software engineering that accrete profoundly to specialists. If you are an extraordinary infrastructure engineer, you're far from being replaced by some mindless drone because it's as much art as it is science, intuition, experience. Like, I've seen this kind of a failure before. AI is good at it, but AI is a great tool. Like, you're in no danger of being supplanted just because so much depth goes into that. And, by the way, AI making a slight mistake could cost you a massive outage in the Yeah. Case of And so, the cost of error is very high. Like, an even better example, actually, in my world would be an underwriting model. Somebody who designs underwriting models it's like, hey, AI, go build me a great underwriting model. Come back when you're done. Like, here it is. I hallucinated one for you. Like, that's like real money lost that we will never see again if it, in fact, didn't hallucinate the right thing. And so many of these sort of ultra specialist roles, they are absolutely benefiting from AI, but that human is not just in control. They're not just steering. They're like reviewing every line of code. They have another human making sure that there's absolutely nothing that could go wrong. So that's sort of one thing. The on the completely other spectrum of this, is, I think, what you're alluding to to, you know, fun examples, we just had a product hackathon, so only product managers can attend. A lot of our product managers have CS degrees, so we're cheating here a little bit. But these people are like, hey, I love writing code, but I want to make it into things as opposed to code. And like, I I I want to ship a full product. So most of the folks haven't written code professionally in quite some time, maybe since college. They're like really good with Figma. They know how to design things. They have a sense for taste, but they like they they don't remember anymore what it's like to sort of go deep into some, you know, SQL query debugging. We had a 100 people, 37 projects in twenty seven hours went from a whiteboard plan to a shippable feature that was presented to the entire company. Like, hey, we have this thing. It's and we we graded it not just on so the the winners and the the whole grading, like, won first through whatever tenth, were all people that had to report not just like, here's my idea, here's what it looks like, here's my prototype. How close are we to shipping it?

Speaker 1: Like,

Speaker 9: are you ready? And the winner was absolutely probably the best idea, but also the ones that said, we're ready. Like, if if we're allowed to ship it tomorrow, we will. So that is like an extreme generalist who like has a vision

Speaker 8: Yeah.

Speaker 9: And a tokens and a feature in in twenty seven hours with like two other co conspirators and like a pizza box. So that is new and different and it's totally a new phenomenon.

Speaker 1: Well, this AI thing is going be terrible for the pizza industry because famously two pizza team, you only need one pizza teams now.

Speaker 9: 50% reduction

Speaker 1: 50% reduction. Short Domino's. This is disaster. I hadn't thought

Speaker 4: about this.

Speaker 9: We we sprung for a somewhat higher quality

Speaker 1: Oh, yes. Okay. So revenue is flat, Margin's potentially going up in

Speaker 2: the pizza industry. Okay. We got the bold piece. Question for you. How are you how are you What's your framework for evaluating the the all the different AI products being pitched to you across the rest the organization. Yeah. You have the benefit of being a technical, you know, CEO. A lot of other CEOs in your role are just like, just wanna buy AI everywhere. Just give me every part of my company. Just give me give me give me three pilots. I'll I'll pilot anything. Whereas, I think you're probably looking at some of this more non, you know, nondeterministic work saying like, hey, you know Yeah. You you can play around with it, but maybe you don't have budget.

Speaker 9: So I have a I have a great heuristic. This will be potentially worth the rest of my drill. If you know how to describe the evaluation criteria being used by the maker of the tool you're buying, it's probably worth piloting. If you can't, it is slop and you're being sold a story. So if so we know code gen works and is amazing, and it's only getting more amazing because we know how to validate code. And you can use another AI to validate code, but you can also just like, look, does it work? Does it pass the unit test? So the feedback loop of I gave you a thing, like codex was okay and then it was better and then it was great and now it's really pretty damn good. It's not because someone was coding faster. It's because it has really objective eval criteria and it just loops on making itself better with some degree of human influence. The same can be said for a bunch of industries. It cannot be said for others. Mhmm. Like, I'm I'm I'm not going to dig into examples too quickly, give them a short on time. But if you can say, oh, I know exactly what these guys are doing. Customer service centers. Is the consumer happy after they hung up the phone with an Can we ask them?

Speaker 7: Yes. What is We their

Speaker 2: satisfaction rate?

Speaker 9: Is there is there a way to say, that was good, that was bad, AI, do more of the good kind?

Speaker 1: Yeah.

Speaker 9: Fast feedback loop, high quality eval criteria

Speaker 8: Yeah.

Speaker 9: The great So we use a ton of agentic customer service because we know it'll just get better and better and better. Yeah. That's a great thing to buy. We're not a specialist. We are excited to buy it from a specialist. Somebody tells you, it's AI. It's gonna be a tool. It's gonna make you a better writer of fiction.

Speaker 2: Yeah. Yeah. Or even pitch decks are pitch decks are an example because like worst formatted pitch deck and having tremendous success with it, but it didn't have anything to do with the deck. It had to do with your delivery and who you are

Speaker 9: and So I I would not would not buy pitch deck AI assistant even if somebody paid me to do it because it's 95% the talk track

Speaker 2: And the company.

Speaker 9: So far, and the company, and the idea, and the market, and the TAM, and all those things. And so but most importantly, a 100 companies pitch their decks good old and made by AI. Seven out of a 100 get funded, 93% failure rate sucks. Should never raise money. Like, is that the conclusion? So I I I would I would stay away from the non objective or very slow feedback loop systems before I I consider buying tools there.

Speaker 1: I love it. Well, thank you so much for taking the time to come chat with us.

Speaker 2: Great to catch up.

Speaker 1: Have a great rest

Speaker 2: of your the progress. I'll come Thank the guide.

Speaker 9: Thank you. And I'll come back with smelting news.

Speaker 1: We'll talk to Have you a good one. Thanks. Goodbye. We have Delian Asparouhov from Founders Fund and BARDA joining in just a few minutes. While we wait for him to get here, he's coming in person, we will talk about Joe Lowe who I thought was a household name, but apparently is not. He is the fugitive behind the 1MDB scandal. It reads like IMDB, it's not 1MDB. It was a Malaysian financial fraud that led to the disappearance of 4 and a half billion dollars. And he's in the news today because he asked Donald Trump for a pardon while on the run, believe. So Joe Lo, the alleged mastermind of one of the greatest financial frauds in history. You gotta read this book. The I like the the camera's following you around.

Speaker 2: That's the new meta.

Speaker 1: New meta. On until you can get the You're

Speaker 2: on the run and you're just just popping up to say, I'd like a pardon, please.

Speaker 1: Yeah. Yeah. Yeah. Yeah. So, yeah. You gotta read this book, Billion Dollar Whale. Fantastic book. I thought it was gonna be turned into a movie at some point. It should be. It's it's a fascinating story. But we'll just give you the high level and we'll do a whole deep dive later. Joe Lo, the alleging I think mastermind

Speaker 2: we should we should take matters into our own hands and turn it into a play.

Speaker 1: A play. Okay. A stage play. I like a stage play.

Speaker 2: We haven't we haven't done a play yet

Speaker 6: on No.

Speaker 2: Not yet. Although a

Speaker 1: lot of the a lot of the red string poster board sessions, they feel like plays. They they're very well active. But I'm telling

Speaker 2: reading off.

Speaker 1: Yeah. Yeah. We don't have lines for

Speaker 2: this. Joe Joe lie.

Speaker 1: One in the

Speaker 2: room billionairely.

Speaker 1: Yeah. So Joe Lowe, the alleged mastermind alleged mastermind of one of the greatest financial frauds in history is asking president Trump for a pardon. The request was filed in recent weeks according to people familiar with the matter and if granted would remove US criminal charges against the fugitive Malaysian financier. A Justice Department website lists a pending request for pardon for completion of sentence under Joe Lo. The move represents the latest gambit in the extraordinary saga of Lo, a once little known businessman who prosecutors prove prosecutors say used subterfuge fake documents and payoffs to engineer the heist of billions of dollars from one MDB, which was a Malaysian government owned investment vehicle set up to promote economic growth. He's known for his lavish lifestyle, partying partying with Hollywood stars and political rulers. He was involved in there were a bunch of other people that went to jail for this. But he financed the Wolf of Wall Street in potentially the most ironic thing you could do as a fraud, finance a movie about a fraud to

Speaker 2: make some awesome movies now?

Speaker 1: I guess. It is a great movie and you can tell the budget was really flying with that one because they they crashed a Lamborghini Countach, is maybe $500,000 car. And they just I think they just destroyed it. And they destroyed maybe the actual I don't know. That might be not real.