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Speaker 1: Welcome to Tech Stuff, a production from I Heart Radio.

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Speaker 1: He there, and welcome to tech Stuff. I'm your host

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Speaker 1: Jonathan Strickland. I'm an executive producer with iHeart Radio and

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Speaker 1: how the tech are You? Listener Gustavo Ricarte asked if

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Speaker 1: I could do an episode about the fintech company, a

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Speaker 1: firm which was founded by Max Levchin, one of the

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Speaker 1: co founders from PayPal. In fact, I would say this

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Speaker 1: episode is going to be at least as much about Max,

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Speaker 1: possibly more, but a firm will play a huge part

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Speaker 1: in it too. And you know what, this story has

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Speaker 1: some really amazing things in it. There's a lot of drama.

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Speaker 1: There's a cameo by Elon Musk, there's an evolutionary step

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Speaker 1: in artificial intelligence, and we're going to start it all

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Speaker 1: off with a little nuclear disaster. That's actually not a joke.

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Speaker 1: We really are, Okay. So Max Levchin, who is formally no,

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Speaker 1: not not former but for mole lead known as Maximilian Rafaelovitch.

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Speaker 1: Levchin grew up in Kiev in Ukraine. Now back then,

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Speaker 1: when he was a child, Ukraine was part of the

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Speaker 1: United Socialist Soviet Republic or USSR. They gave the Soviet Union,

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Speaker 1: and in nine on the outskirts of a city called

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Speaker 1: trip yat ninety miles north of Kiev, the Chernobyl nuclear

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Speaker 1: power plant experienced a meltdown. And perhaps one day I

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Speaker 1: will do a full episode about the Chernobyl disaster, though

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Speaker 1: it's it's kind of hard for me to justify doing

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Speaker 1: that because there's already an incredible mini series that aired

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Speaker 1: on HBO that gives amazing insight into what happened and

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Speaker 1: why the disaster was as catastrophic as it was. The

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Speaker 1: too long, didn't read version is there was a combination

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Speaker 1: of bad engineering decisions and worst bureaucratic red tape that

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Speaker 1: made a disastrous situation way worse than it could have

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Speaker 1: been otherwise. But suffice it to say that the disaster

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Speaker 1: at Chernobyl irradiated the surrounding area, which is putting it lightly.

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Speaker 1: Maybe one day I will go into more of the

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Speaker 1: technical side of what happened at Chernobyl um and just

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Speaker 1: leave all the bureaucratic stuff for that incredible mini series.

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Speaker 1: But let's get back to Kiev. So Max's mother, Elvina Seltzmann,

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Speaker 1: was a physicist and her job was at the Institute

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Speaker 1: of Food Science and Kiev, and specifically she worked in

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Speaker 1: the radiology lab, So her lab would receive samples of

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Speaker 1: food and then the lab would test those samples to

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Speaker 1: check on contamination and pollution, specifically the radioactive kind. I mean,

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Speaker 1: some of this was coming from the areas that were

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Speaker 1: around nuclear power plants, so this was a way of

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Speaker 1: keeping tabs on how things were going in those environments. Now,

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Speaker 1: the story goes that in April of nineteen six, not

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Speaker 1: long after the Chernobyl accident, which still had not been

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Speaker 1: fully revealed by the Russian government, Like, people knew that

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Speaker 1: there had been an incident in Chernobyl, but they didn't

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Speaker 1: know much more than that, because the government was trying

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Speaker 1: to keep a very tight lid on what had happened,

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Speaker 1: not just to prevent panic, but also to not let

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Speaker 1: the United States know about the severity of the problem,

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Speaker 1: because that would be admitting weakness. There were other elements

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Speaker 1: as well. Anyway, around that time, Elvina had received a

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Speaker 1: loaf of bread from the north of Kiev, so closer

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Speaker 1: to where Chernobyl was, and was meant to do tests

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Speaker 1: on it, and she saw that it was actually luminescing

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Speaker 1: it was lowing in her lab and immediately she knew

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Speaker 1: that this was an incredibly severe problem and that the

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Speaker 1: incident at Chernobyl was way more serious than what the

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Speaker 1: government was living on too, and so she wanted to

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Speaker 1: take the opportunity to try and rescue her family. So,

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Speaker 1: fearing for her family's safety, she rushed herself and her

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Speaker 1: family onto a train bound for Crimea, which is hundreds

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Speaker 1: of miles away and presumably in a zone that would

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Speaker 1: be safe from the radiation of Chernobyl. By the time

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Speaker 1: their train arrived in Crimea, the Soviet authorities were already

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Speaker 1: in action. They were using Geiger counters to scan each

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Speaker 1: and every person who had fled over to Crimea and

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Speaker 1: checking for radioactive contamination. When they scanned Max, the Geiger

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Speaker 1: counter went off, and in fact, according to the counter,

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Speaker 1: it looked like Max's leg was giving off radiation, and

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Speaker 1: so there was a very real possibility that doctors would

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Speaker 1: amputate Max's leg. There was uh, not a full understanding

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Speaker 1: of what radioactive or radiation poisoning entailed, though they were

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Speaker 1: getting a pretty drastic crash course in it in the

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Speaker 1: so Union at this time, so this was something that

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Speaker 1: could have happened. Max could have ended up having a

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Speaker 1: leg amputated when he was just a kid. But Elvina

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Speaker 1: had Max taken off his shoes and when they scanned

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Speaker 1: him again, the Geiger counter didn't go off, but it

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Speaker 1: did go off over the shoes, and it turned out

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Speaker 1: Max had stepped on a radiated thorn from a rose

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Speaker 1: bush and the thorn had stuck into issue and that's

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Speaker 1: what was tripping the Geiger counter. That is a heck

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Speaker 1: of a childhood memory. Now, just so y'all know, I

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Speaker 1: am only lightly older than Max, like literally by a

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Speaker 1: couple of weeks. I am older than Max. I cannot

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Speaker 1: imagine having had that experience as a child. Now. Max

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Speaker 1: was also really interested in computers. He had been programming

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Speaker 1: on things like calculators. He had designed programs on paper

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Speaker 1: because he didn't always have access to an actual calculator

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Speaker 1: or computer. He did have the opportunity to work on

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Speaker 1: some computers when he would occasionally help his mom in

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Speaker 1: the food science lab, but the family didn't own a

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Speaker 1: computer of their own. In as the Soviet Union was crumbling,

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Speaker 1: his family immigrated to the United States Because in the

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Speaker 1: Soviet Union, which was rapidly deunionizing, inflation was out of control.

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Speaker 1: The money was starting to become like completely valueless, so

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Speaker 1: this was a move for survival. They moved to the

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Speaker 1: United States, and he had held out hope that his

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Speaker 1: family would be able to buy a computer once they

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Speaker 1: got to the US, but they only had a few

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Speaker 1: hundred dollars to their name when they arrived in the States,

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Speaker 1: and of course they had no established credit in the States,

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Speaker 1: so there was really no chance for him to get

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Speaker 1: a computer of his own. So the reason I bring

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Speaker 1: that up is that, as it would turn out, the company,

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Speaker 1: a firm that we'll talk about later in this podcast,

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Speaker 1: is designed to help consumers buy things they need or

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Speaker 1: in some cases many cases, things they want when they

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Speaker 1: may not have the full amount of cash on hand

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Speaker 1: to actually buy it. So this story is really kind

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Speaker 1: of a fundamental one when it comes to why a

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Speaker 1: firm exists. But let's get back to Max for a bit.

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Speaker 1: His family settled in Chicago. Max was sixteen, and boy howdy,

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Speaker 1: this kid was way more forward thinking than I was.

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Speaker 1: So while I was running a D and D games

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Speaker 1: and then later on a D and D second Edition,

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Speaker 1: he was studying computer science. He attended the University of

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Speaker 1: Illinois at Urbana Champagne, and while there he founded several

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Speaker 1: companies as he studied computer science. This was in the

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Speaker 1: early days of the web. I think of this time

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Speaker 1: this is about the time I was finally coming around

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Speaker 1: to the Web. I had previously turned my nose up

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Speaker 1: at it when I first was exposed to the World

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Speaker 1: Wide Web, because I just thought it was slow and

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Speaker 1: clunky and that it didn't have the usefulness of something

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Speaker 1: like a tel net and FTP servers did. So I

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Speaker 1: was very The web is another one of those examples

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Speaker 1: of something where I was like, oh, this is never

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Speaker 1: going to take off. Clearly, I have a terrible ability

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Speaker 1: to see into the future. But le Gin saw the

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Speaker 1: potential to monetize a business on the web, and so

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Speaker 1: most of his early companies were ways of trying to

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Speaker 1: sell advertising space, like banner ads on websites. So he

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Speaker 1: was essentially acting as a broker, you know, pairing advertisers

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Speaker 1: with websites that are willing to host an ad. So

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Speaker 1: he did not invent the banner ad. It's not like

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Speaker 1: he's the guy who created that, but he got in

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Speaker 1: on the ground floor. Now, most of his companies didn't

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Speaker 1: go anywhere, but one of these advertising companies got the

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Speaker 1: attention of a larger entity in the advertising business. This

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Speaker 1: was a company that was called link Exchange, and link

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Speaker 1: Exchange was in turn, only in business for a couple

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Speaker 1: of years before it got acquired by Microsoft. It did

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Speaker 1: continue to operate under the brand name link Exchange for

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Speaker 1: several years even after that acquisition. But anyway, that's not

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Speaker 1: really important to our story. What is important is that

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Speaker 1: Legend being able to sell his business to link Exchange

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Speaker 1: meant that he actually had some decent money when he

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Speaker 1: graduated college, and he used that money to fund a

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Speaker 1: move from Chicago to California, you know, the tech mecca

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Speaker 1: of the United States, or Techa for short. I guess. Anyway,

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Speaker 1: he got there, didn't have quite enough money to really

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Speaker 1: establish himself, so he crashed at a friend's house, a

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Speaker 1: friend who was attending Stanford, and so he essentially grabbed

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Speaker 1: some floor space in his friend's apartment outside the University

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Speaker 1: of Stanford. And at least the story I read was

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Speaker 1: that he would spend his days kind of crashing lectures

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Speaker 1: at the university, just kind of coming in and listening

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Speaker 1: to lectures, which you know, you could get away with

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Speaker 1: with big universities. I mean, a lot of those lecture

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Speaker 1: halls seat hundreds of students, so it's not like they

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Speaker 1: check you when you come in to make sure that

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Speaker 1: you're taking the class. So he was sitting in on lectures.

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Speaker 1: And the story I heard was not that he was

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Speaker 1: so hungry for more information and wanted to get the

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Speaker 1: benefits of a stand Stanford University education without actually, you know,

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Speaker 1: enrolling in the school, but rather it was a more

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Speaker 1: practical consideration of just getting out of the apartment and

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Speaker 1: also getting out of the heat of the day staying

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Speaker 1: in a nice air conditioned location while watching a lecture. Now,

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Speaker 1: one of the lectures he attended, which really ended up

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Speaker 1: being more of a tiny seminar, was one that was

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Speaker 1: attended by like half a dozen people, and it was

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Speaker 1: led by an entrepreneur named Peter Thiel. And there's someone

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Speaker 1: else I could do a full episode about. Levechin and

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Speaker 1: Feel met after the seminar, and they chatted and found

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Speaker 1: that they had a lot in common. They both were

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Speaker 1: incredibly enthusiastic about tech and entrepreneurship, and ultimately they decided

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Speaker 1: to go into business together. So they first created a

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Speaker 1: company called field Link, which was a digital security company

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Speaker 1: and made software that allowed personal Digital assistance or p

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Speaker 1: d A s create an encrypted IT folder into which

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Speaker 1: the user could safely store data. Quick quick word on

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Speaker 1: PDAs or personal Digital assistants. These were devices that were

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Speaker 1: smartphones without the phone part. So before smartphones, we had

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Speaker 1: these devices called p d a s where you would

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Speaker 1: have things like you would store a contact list, and

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Speaker 1: you would have maybe a note taking app in in

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Speaker 1: that particular PDA, you would have a calendar, you might

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Speaker 1: have access to email, like you could connect to a

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Speaker 1: network and actually um send and receive emails. A lot

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Speaker 1: of these. The way that worked as you would synchronize

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Speaker 1: your p d A with a computer. So when you

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Speaker 1: would do that, it would load new emails into the

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Speaker 1: p d A which you could then read and respond to,

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Speaker 1: but it wouldn't actually send anything out. It didn't have

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Speaker 1: native connectivity. For a lot of these PDAs. It was

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Speaker 1: only when you synchronize with the computer again that it

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Speaker 1: would go through and say, hey, here's some emails this

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Speaker 1: guy I created or this woman created or this person

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Speaker 1: created when they were using this medi a, Uh, send

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Speaker 1: those out. And so you might compose an email at

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Speaker 1: two PM, but it doesn't go out until six pm

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Speaker 1: when you synchronized it with your computer. But PDAs were

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Speaker 1: an early hint of the revolution that was to come.

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Speaker 1: I mean, obviously when partnered with connectivity features, whether it

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Speaker 1: was wireless connectivity to like WiFi networks, or wireless connectivity

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Speaker 1: in the form of cellular connectivity, it would completely change

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Speaker 1: the world. That's where you know, the smartphone would really

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Speaker 1: pick up and and carry that forward. Well, they created

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Speaker 1: an offshoot service for this encryption solution that they had

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Speaker 1: created for PDAs, and that offshoot was a way to

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Speaker 1: transfer money digitally between p d a s where you

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Speaker 1: could have someone essentially digitally wire money to someone else

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Speaker 1: by connecting the two PDAs together. And they called this

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Speaker 1: service PayPal. That service turned out to be the most

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Speaker 1: popular aspect of field Link. Theel and Lefin would change

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Speaker 1: the company's name, but not to PayPal. They changed it

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Speaker 1: to a company called Confinity. We'll talk more about Confinity

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Speaker 1: and its evolution toward becoming PayPal after we take this

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Speaker 1: quick break. We're back and enter Elon Musk, who was

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Speaker 1: more than two decades removed from being the guy who

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Speaker 1: agreement while waving due diligence and then said just kidding

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Speaker 1: and tried to back out of the deal. At this point,

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Speaker 1: Musk was not anywhere close to that. He was a

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Speaker 1: student at Stanford and he had founded a company that

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Speaker 1: he called X dot Com. Now, like PayPal, X dot

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Speaker 1: Com was in the digital money transfer business, though this

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Speaker 1: was by linking accounts to email rather than linking accounts

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Speaker 1: to a personal digital assistant. Musk feel and left End

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Speaker 1: saw two potential pathways that were before them. So in

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Speaker 1: one path, X dot Com and Confinity slash PayPal would

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Speaker 1: squabble over market share. Both ventures were still very young

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Speaker 1: at this point and they could get nasty and potentially

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Speaker 1: put both companies at risk because there was another digital

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Speaker 1: money transfer service that was on the rise at this point.

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Speaker 1: So the worry was that if they tried to compete

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Speaker 1: against each other, they both would get defeated by this

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Speaker 1: third competitor. So the other pathway was to combine forces

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Speaker 1: and to corner the market early on, giving a much

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Speaker 1: better chance for success in the long run. So they

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Speaker 1: chose option to X dot Com and Confinity would combine.

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Speaker 1: They ultimately called the new company PayPal, and the service

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Speaker 1: would follow X dot COM's lead, with user accounts tied

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Speaker 1: to an email address rather than to a personal digital assistant.

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Speaker 1: This would put Levchin, feel and Musk, as well as

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Speaker 1: a bunch of others further down the line, into a

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Speaker 1: group that would later get the name the PayPal Mafia.

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Speaker 1: So this tongue in cheek nickname implies that this small

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Speaker 1: influential group would see their power and wealth grow substantially,

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Speaker 1: which is true. All three of the folks have described

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Speaker 1: so far are billionaires in the Musk, of course, is

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Speaker 1: the richest person on the planet, so it is very

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Speaker 1: much true that they saw their fortunes rise. Legend served

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Speaker 1: as Chief Technology Officer or c t O of PayPal.

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Speaker 1: THEIL initially served as chairman and Musk became the CEO

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Speaker 1: for a few months, but then THEEL would take on

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Speaker 1: the role of CEO as well. Musk would step down

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Speaker 1: later in that first year. One of Levchin's responsibilities was

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Speaker 1: coming up with a way to prevent fraud a PayPal

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Speaker 1: and Levechin and his team came up with a method

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Speaker 1: to prevent automated scripts from accessing PayPal services. In other words,

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Speaker 1: to prevent bots from being able to log into PayPal

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Speaker 1: and to go through transactions automatically, it had to be

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Speaker 1: a human being behind it. So the method that they

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Speaker 1: they relied on used distorted text that humans would still

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Speaker 1: be able to read but a program wouldn't, and it

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Speaker 1: was an example of what would later be referred to

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Speaker 1: as a capture. Capture stands for completely automated public touring

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Speaker 1: test to tell computers and humans apart. And to be clear,

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Speaker 1: Legend and his team didn't invent this. They were not

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Speaker 1: the first to use this particular approach to kind of

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Speaker 1: separate out the bots from the humans, but they would

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Speaker 1: be the first to really commercialize the process, and it

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Speaker 1: became known as the gal Speck Levechin test. Galsbeck in

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Speaker 1: this case refers to David Gausbeck, who served as a

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Speaker 1: technical architect at PayPal for about a decade. Now this

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Speaker 1: is where that evolutionary step in ai I mentioned at

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Speaker 1: the top of the show comes in. Let's break this

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Speaker 1: down first. The phrase Turing test is in the acronym capture, right,

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Speaker 1: And for those who are not familiar with what a

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Speaker 1: Turing test is, the phrase as we use it today

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Speaker 1: typically means some form of test to see if a

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Speaker 1: human can tell if a respondent is another human or

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Speaker 1: is in fact a machine and AI. So the classic

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Speaker 1: example is you have a human who sits down at

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Speaker 1: a computer terminal and they can type into a chat

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Speaker 1: program and they're trying to figure out if the entity

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Speaker 1: on the other side of this digital conversation is another

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Speaker 1: person or if it is actually AI. And if human

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Speaker 1: testers cannot reliably tell the difference, the AI is said

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Speaker 1: to have passed the Turing test. Now with a capture,

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Speaker 1: it's kind of a reverse of this, and the capture

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Speaker 1: itself is all tomate it. So instead of having a

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Speaker 1: human administering the test, you have the computer doing it.

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Speaker 1: And the idea is to create a scenario where a

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Speaker 1: human can easily, at least in theory, pass a test,

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Speaker 1: but a machine can't. So back in the primitive days

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Speaker 1: of the early two thousands, using a block of text

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Speaker 1: and image of text that has some distortion effects would

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Speaker 1: be enough. Humans would be able to recognize the swirling

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Speaker 1: characters sometimes presented in different colors, but computers, which we're

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Speaker 1: looking for more uniform representations of those characters wouldn't be

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Speaker 1: able to do it. So to the computer, the the

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Speaker 1: lower case are, for example, wouldn't look like an R,

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Speaker 1: so the computer would be stumped. It wouldn't know that

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Speaker 1: that was in fact an R Now that would set

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Speaker 1: off a see saw like relationship in the AI community.

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Speaker 1: So computer programmers would create software that would get better

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Speaker 1: at recognizing characters, even ones that had been distorted. That

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Speaker 1: would mean that earlier versions of capture would become obsolete,

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Speaker 1: because the whole purpose of capture is to prevent automated

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Speaker 1: scripts from accessing something. And so capture designers would go

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Speaker 1: back to the drawing board and launch a new version

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Speaker 1: of capture, one that would exploit the limitations of text

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Speaker 1: recognition software, for example. Then the computer scientists on the

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Speaker 1: other side would train up the next batch of recognition software,

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Speaker 1: which ultimately would defeat the newer capture strategy, and so

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Speaker 1: on and so forth. From one perspective, you could say

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Speaker 1: this was the evolution of computer security, and on the

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Speaker 1: other you could say it was the evolution of AI.

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Speaker 1: And it's kind of both um and in fact, we

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Speaker 1: can see even more uh overt examples of this when

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Speaker 1: you're looking at things like you know, you're presented with

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Speaker 1: a panel of nine different pictures and you're told to

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Speaker 1: pick all the pictures that have, say a fire hydrant

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Speaker 1: in them. Well, in that case, not only are you

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Speaker 1: UH acknowledging that you're a human by picking out all

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Speaker 1: the fire hydrants, you're also empowering an image recognition software

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Speaker 1: in the background because you are you're teaching the computer

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Speaker 1: systems this is what a fire hydrant is, and this

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Speaker 1: isn't So legend has played a part in that evolutionary process.

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Speaker 1: In two thousand two, PayPal would hold an i p O,

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Speaker 1: which is an initial public offering. Now, this is when

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Speaker 1: a privately held company goes into a cocoon and emerges

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Speaker 1: into a publicly traded company like a butterfly. UH. Investors

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Speaker 1: then can purchase shares of that company on a stock market.

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Speaker 1: PayPal made the transformation in February two two, with an

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Speaker 1: initial valuation of the company at around eight hundred million dollars. Meanwhile, eBay,

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Speaker 1: the web based auction site, had launched its own digital

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Speaker 1: payment processing service called bill Point with Wells Fargo back

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Speaker 1: in two thousands. So bill Point is the competitor I

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Speaker 1: was alluding to earlier, the one that x dot com

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Speaker 1: and Confinity needed to be concerned about. So bill Points

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Speaker 1: stood as the major competitor against PayPal, but by two

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Speaker 1: thousand two. Mid two thousand two, PayPal was on such

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Speaker 1: a steep climb that eBay sought out an acquisition, which

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Speaker 1: was announced in the summer of two thousand two and

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Speaker 1: completed toward the you know, in October two thousand two.

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Speaker 1: And the deal would be valued at one point five

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Speaker 1: billion dollars, which is um a fair chunk of change.

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Speaker 1: I think it was an all stock transaction. However, it

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Speaker 1: wasn't like a cash purchase, so PayPal went public and

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Speaker 1: it was acquired all in less than one year. Selections

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Speaker 1: share of PayPal meant that he was more than thirty

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Speaker 1: million dollars richer. As a result of this buyout, things

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Speaker 1: moved pretty quickly. eBay's culture was different from Paypals, and

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Speaker 1: that was a big issue personally. I've seen that happen

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Speaker 1: a lot, so tangential to this story. Once upon a time,

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Speaker 1: I worked at a company that did human resources management consulting.

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Speaker 1: I won't name them, but I worked for them for

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Speaker 1: seven years, and I like to say that I was

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Speaker 1: kind of working for the bobs from the film office space.

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Speaker 1: But one thing that our company really did was observed

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Speaker 1: company culture, particularly in the wake of an acquisition or merger.

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Speaker 1: That's because more often than not, there were disconnects between

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Speaker 1: the two or more cultures, and these often would become

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Speaker 1: the source of friction, frequently in the form of lower

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Speaker 1: employee morale and productivity. So addressing culture is important. It's

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Speaker 1: also really hard to do now. Whether or not eBay's

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Speaker 1: culture rub folks at PayPal the wrong way, I can't

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Speaker 1: really say, But I can't say that. Over the following

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Speaker 1: few years, more than half of the founding members of

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Speaker 1: PayPal chose to leave the company, and ultimately that included Levchin.

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Speaker 1: Although he hung out longer than most because he really

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Speaker 1: wanted to see a smooth transition over to eBay. Max

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Speaker 1: looked around his next opportunity, which apparently was a very

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Speaker 1: um traumatic experience for him because he found that if

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Speaker 1: he didn't have something really occupying his time, he didn't

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Speaker 1: know what to do with himself. His girlfriend at the

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Speaker 1: time ended up dumping him because she said that he

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Speaker 1: was like a junkie who was going without a fix

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Speaker 1: because he didn't have a project to work on. She

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Speaker 1: would eventually come back and the two would reconcile and

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Speaker 1: and ultimately mary and have kids. So it all worked out.

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Speaker 1: But at the time, it was looking pretty uncertain. So

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Speaker 1: one thing he did was he had kind of an

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Speaker 1: incubator that he designed, a company that's meant to help

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Speaker 1: other companies, you know, launch, and within it he developed

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Speaker 1: a sort of social media service that other platforms could use,

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Speaker 1: called slide Now. Originally, the plan was to create a

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Speaker 1: image oriented shopping service, so that when you went shopping

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Speaker 1: for stuff online, you would see photos of the things

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Speaker 1: you were browsing for and that would make the experience better.

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Speaker 1: But it then evolved into more of a photo sharing service,

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Speaker 1: and it wasn't a standalone competitor to like Facebook or

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Speaker 1: MySpace or that kind of thing. Instead, it was a

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Speaker 1: behind the scenes media sharing service that those sorts of

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Speaker 1: platforms you know, Facebook and MySpace could actually utilize. They

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Speaker 1: were essentially apps that could work within those frameworks. In

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Speaker 1: Google purchased slide for somewhere between a hundred two million

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Speaker 1: and two dwenty eight million bucks. Uh. The amount actually

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Speaker 1: depends upon which source you read, and according to a

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Speaker 1: two thousand seven Business Week article, Levchin had previously thought

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Speaker 1: that any buyout of less than one point five billion

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Speaker 1: dollars would be the mark of failure. So I guess

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Speaker 1: Sad Trombone, except Sad Trombone feels weird for a deal

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Speaker 1: that was still more than a d fifty million dollars

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Speaker 1: and maybe more than two hundred million, depending upon which

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Speaker 1: sources correct. Google would later shut down Slide, because of

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Speaker 1: course it did. I've done episodes about tools and services

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Speaker 1: that Google has introduced or acquired and then subsequently shut down.

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Speaker 1: It happens a lot some so that if you do

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Speaker 1: create something really cool and Google buys it, you should

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Speaker 1: probably resign yourself to the fact that the cool thing

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Speaker 1: you made is not long for this world anyway. Legend

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Speaker 1: briefly worked for Google, and according to some reports, that

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Speaker 1: was the whole purpose in the first place. The Google

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Speaker 1: bought Slide not to buy Slide, but to get access

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Speaker 1: to Lefjin, and he wasn't really happy there. He didn't

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Speaker 1: find that it was challenging him enough, so he left

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Speaker 1: the company in pretty short order. He had also invested

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Speaker 1: in various other startups along the way, including Yelp. In fact,

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Speaker 1: he was the largest initial shareholder of Yelp, and he

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Speaker 1: got a new idea for a company, the one that

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Speaker 1: would be called a firm. Now before a firm, there

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Speaker 1: was HVF. This initialism stands for hard, valuable and Fun

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Speaker 1: and it's another incubator. It's the second incubator that lef

433
00:26:53,080 --> 00:26:56,200
Speaker 1: Gin made. And again this is a company that helps

434
00:26:56,240 --> 00:26:59,840
Speaker 1: folks bring other companies to life. So it's a company

435
00:27:00,000 --> 00:27:04,000
Speaker 1: air you can kind of work on a concept and

436
00:27:04,080 --> 00:27:06,280
Speaker 1: work on it on every stage, all the way up

437
00:27:06,320 --> 00:27:09,600
Speaker 1: through the company launching. So essentially, HVF is kind of

438
00:27:09,600 --> 00:27:12,520
Speaker 1: a safe space for companies to take shape so that

439
00:27:12,600 --> 00:27:14,639
Speaker 1: they have the best chance of success when they actually

440
00:27:14,640 --> 00:27:17,320
Speaker 1: go live. So sometimes that might mean sitting on an

441
00:27:17,359 --> 00:27:20,560
Speaker 1: idea for a while because you're waiting or you're actively

442
00:27:20,560 --> 00:27:23,639
Speaker 1: trying to develop the technology that will actually support the

443
00:27:23,640 --> 00:27:26,399
Speaker 1: businesses mission. And maybe that I've got this great idea,

444
00:27:26,920 --> 00:27:29,199
Speaker 1: the tech to make it happen isn't there yet, but

445
00:27:29,240 --> 00:27:33,240
Speaker 1: it's on the horizon, and so I'm using the incubator

446
00:27:33,280 --> 00:27:35,360
Speaker 1: to kind of flesh out my business idea while I'm

447
00:27:35,400 --> 00:27:38,520
Speaker 1: waiting for the tech to mature. In other cases, it

448
00:27:38,560 --> 00:27:40,600
Speaker 1: could mean holding off until you've really nailed down the

449
00:27:40,640 --> 00:27:42,920
Speaker 1: business model for the company, because we've seen that over

450
00:27:42,920 --> 00:27:46,119
Speaker 1: and over where the tech is there, but the actual

451
00:27:46,160 --> 00:27:49,840
Speaker 1: plan on generating revenue isn't and ultimately the company fails.

452
00:27:49,880 --> 00:27:53,000
Speaker 1: So that's the purpose of the incubator. Well, a firm

453
00:27:53,080 --> 00:27:56,359
Speaker 1: would be a company that came out of HVF. The

454
00:27:56,400 --> 00:28:01,600
Speaker 1: concept behind a firm is relatively simple. For generations. Wealth

455
00:28:01,640 --> 00:28:05,320
Speaker 1: in America has been tied tightly to the concept of credit,

456
00:28:05,840 --> 00:28:08,480
Speaker 1: and to build up credit you need to make use

457
00:28:08,480 --> 00:28:12,360
Speaker 1: of tools like credit cards and loans, right, but these

458
00:28:12,359 --> 00:28:15,800
Speaker 1: can be tricky to understand, and for younger generations, stuff

459
00:28:15,840 --> 00:28:19,320
Speaker 1: like credit cards and loans represent a huge scary risk.

460
00:28:20,080 --> 00:28:23,159
Speaker 1: We've been through enough economic recessions we're heading towards another,

461
00:28:23,680 --> 00:28:25,840
Speaker 1: and the idea of having to purchase stuff on credit

462
00:28:26,119 --> 00:28:29,600
Speaker 1: is kind of scary. The threat of insurmountable debt is

463
00:28:29,720 --> 00:28:34,119
Speaker 1: very real, particularly for folks who already have substantial student debt.

464
00:28:34,200 --> 00:28:36,520
Speaker 1: That's why there's this big move to have more and

465
00:28:36,560 --> 00:28:40,760
Speaker 1: more student debt canceled, because it's holding people back from

466
00:28:40,760 --> 00:28:43,520
Speaker 1: being able to engage more fully with the economy. There

467
00:28:44,160 --> 00:28:47,600
Speaker 1: already saddled with such huge debt that they can't really

468
00:28:47,600 --> 00:28:51,280
Speaker 1: think ahead to anything else. It's also a big challenge

469
00:28:51,320 --> 00:28:53,960
Speaker 1: for people who have immigrated to the United States who

470
00:28:54,000 --> 00:28:57,360
Speaker 1: have no pre existing credit history in the US. It

471
00:28:57,440 --> 00:29:01,360
Speaker 1: can be hard to integrate into the system, and so

472
00:29:01,480 --> 00:29:04,520
Speaker 1: there are millions of folks who are left behind because

473
00:29:04,560 --> 00:29:08,920
Speaker 1: they aren't part of the traditional banking and credit system.

474
00:29:08,960 --> 00:29:14,840
Speaker 1: A firm is a fintech company that partners with merchants primarily,

475
00:29:14,880 --> 00:29:17,680
Speaker 1: although now they also have a standalone program, but we'll

476
00:29:17,680 --> 00:29:20,760
Speaker 1: get to that. So what affirm does it is it

477
00:29:20,800 --> 00:29:25,680
Speaker 1: offers a buy now, pay later solution to customers. So

478
00:29:26,440 --> 00:29:28,880
Speaker 1: let's say that you wanted to purchase a new computer.

479
00:29:29,000 --> 00:29:31,880
Speaker 1: Let's say that you're newly immigrated to the United States

480
00:29:31,920 --> 00:29:33,880
Speaker 1: and you want to purchase a new computer, but you

481
00:29:33,960 --> 00:29:36,040
Speaker 1: don't have the scratch to do it. You don't have

482
00:29:36,080 --> 00:29:39,719
Speaker 1: all the money that you would need to buy it outright. Well,

483
00:29:39,760 --> 00:29:42,240
Speaker 1: you might find that the seller you're looking at is

484
00:29:42,280 --> 00:29:45,240
Speaker 1: partnering with a firm, and in that case you can

485
00:29:45,280 --> 00:29:49,600
Speaker 1: take what is essentially a small loan to purchase the computer.

486
00:29:49,720 --> 00:29:53,480
Speaker 1: So it's a loan specifically for this purchase. Then you

487
00:29:53,560 --> 00:29:57,320
Speaker 1: pay back a firm in regular installments. A firm does

488
00:29:57,360 --> 00:29:59,440
Speaker 1: have a bank partner to make this work in the background.

489
00:29:59,520 --> 00:30:03,040
Speaker 1: It's c us River Bank, and there are a lot

490
00:30:03,080 --> 00:30:05,239
Speaker 1: of things that have to happen in the background. For

491
00:30:05,280 --> 00:30:09,040
Speaker 1: this to even be possible. We'll talk about that more

492
00:30:09,560 --> 00:30:12,680
Speaker 1: later in this episode two. Now, when we come back,

493
00:30:12,760 --> 00:30:15,520
Speaker 1: we're gonna talk more about what Affirm's model is like

494
00:30:15,680 --> 00:30:18,720
Speaker 1: and how it does what it does, and also why

495
00:30:18,840 --> 00:30:23,760
Speaker 1: some people have criticisms for this particular model of purchasing.

496
00:30:24,720 --> 00:30:36,600
Speaker 1: But before we do that, let's take a quick break. Okay.

497
00:30:36,680 --> 00:30:41,440
Speaker 1: So Affirm offers small loans to customers, loans that are

498
00:30:41,560 --> 00:30:44,920
Speaker 1: enough to purchase something, whatever it might be, and you

499
00:30:44,960 --> 00:30:47,720
Speaker 1: do that at the point of purchase, where you essentially

500
00:30:47,760 --> 00:30:51,680
Speaker 1: apply for this loan. Affirm promises no hidden fees, no

501
00:30:51,920 --> 00:30:55,880
Speaker 1: late charges. Uh you will if you get take out

502
00:30:56,320 --> 00:30:58,360
Speaker 1: a loan with a firm and you refuse to pay

503
00:30:58,360 --> 00:31:01,280
Speaker 1: it back, a firm can report to you to credit agencies,

504
00:31:01,320 --> 00:31:05,880
Speaker 1: so that does have consequences, but they don't ding you

505
00:31:06,000 --> 00:31:08,800
Speaker 1: for being a little late on your payments. You do

506
00:31:08,920 --> 00:31:12,280
Speaker 1: have to pay interest on the loan, otherwise a firm

507
00:31:12,360 --> 00:31:16,360
Speaker 1: would be writing off a way to make revenue. That

508
00:31:16,440 --> 00:31:20,000
Speaker 1: interest rate, however, varies by retailer, and at least according

509
00:31:20,040 --> 00:31:23,440
Speaker 1: to Medium, it can be anything from zero percent, meaning

510
00:31:23,840 --> 00:31:26,440
Speaker 1: that there's no interest at all. You're literally just paying

511
00:31:26,480 --> 00:31:30,240
Speaker 1: back whatever the cost of the item. Was, or it

512
00:31:30,280 --> 00:31:34,160
Speaker 1: could go all the way up to nearly thirty interest. Now,

513
00:31:34,200 --> 00:31:37,000
Speaker 1: whatever the interest rate is, it means that you'll pay

514
00:31:37,080 --> 00:31:39,880
Speaker 1: that much percentage on top of the final price tag

515
00:31:39,920 --> 00:31:43,120
Speaker 1: of the item you're buying. Right, So you know, if

516
00:31:43,120 --> 00:31:46,320
Speaker 1: it's a hundred dollars and it's a thirty interest, it

517
00:31:46,360 --> 00:31:49,120
Speaker 1: means ultimately you're paying back a hundred thirty dollars for

518
00:31:49,280 --> 00:31:53,520
Speaker 1: the item. Um. That's a very simple example, but you

519
00:31:53,560 --> 00:31:56,320
Speaker 1: get the idea. So the flip side of that is

520
00:31:56,320 --> 00:31:59,760
Speaker 1: you're playing paying in installments, right, so you're paying smaller

521
00:32:00,000 --> 00:32:04,960
Speaker 1: amounts over time, uh, that being three months, six months,

522
00:32:05,080 --> 00:32:08,720
Speaker 1: or twelve months. Also, Affirm states that customers can pay

523
00:32:08,720 --> 00:32:12,080
Speaker 1: off their loan early with no penalties or fees. So

524
00:32:12,320 --> 00:32:14,320
Speaker 1: if you have the money to do it, you can

525
00:32:14,360 --> 00:32:18,520
Speaker 1: pay off the balance and save yourself the interest that

526
00:32:18,560 --> 00:32:23,640
Speaker 1: would otherwise accrue over time. So how does Affirm determine

527
00:32:24,040 --> 00:32:26,080
Speaker 1: if a customer is a good fit for a loan?

528
00:32:26,960 --> 00:32:29,800
Speaker 1: Because you wouldn't just go out loaning out cash to everyone.

529
00:32:30,400 --> 00:32:34,360
Speaker 1: You need to at least be reasonably sure that whomever

530
00:32:34,400 --> 00:32:36,640
Speaker 1: you're learning the money out to is going to be

531
00:32:36,680 --> 00:32:38,840
Speaker 1: able to pay you back. Otherwise you're going to find

532
00:32:38,840 --> 00:32:42,080
Speaker 1: yourself out of business, essentially robbed by folks who took

533
00:32:42,080 --> 00:32:44,760
Speaker 1: out loans and then skipped on them, or people who

534
00:32:44,760 --> 00:32:47,280
Speaker 1: took out loans and then, through whatever reason, we're unable

535
00:32:47,320 --> 00:32:50,120
Speaker 1: to pay those loans back. In either case, the end

536
00:32:50,120 --> 00:32:53,000
Speaker 1: effect is that you are left holding the bag if

537
00:32:53,000 --> 00:32:55,320
Speaker 1: you've loaned that money out and they aren't paying you

538
00:32:55,360 --> 00:32:59,200
Speaker 1: back for it. So AFFIRM judges a person's ability to

539
00:32:59,200 --> 00:33:01,920
Speaker 1: pay back a loan own by doing a deep data

540
00:33:01,960 --> 00:33:06,760
Speaker 1: dive on that person. AFFIRM searches through tons of data,

541
00:33:07,160 --> 00:33:11,360
Speaker 1: including social media posts like this gets a bit creepy, y'all.

542
00:33:12,160 --> 00:33:14,960
Speaker 1: A firm is essentially crawling through your payment history on

543
00:33:15,000 --> 00:33:19,880
Speaker 1: any publicly available transactions, your social media history, getting an

544
00:33:19,920 --> 00:33:22,400
Speaker 1: idea of how financially responsible you are. So you know,

545
00:33:22,640 --> 00:33:24,640
Speaker 1: if you're posting lots of stuff about how you bought

546
00:33:24,680 --> 00:33:27,280
Speaker 1: that sweet scooter but you have nowhere to live now

547
00:33:27,320 --> 00:33:29,680
Speaker 1: that you can't make the rent, that might not help

548
00:33:29,720 --> 00:33:32,440
Speaker 1: you out. When you apply to use a firm on

549
00:33:32,480 --> 00:33:35,680
Speaker 1: a purchase, the evaluation only takes a few seconds, which

550
00:33:35,720 --> 00:33:38,240
Speaker 1: is incredible, and that is something I find both impressive

551
00:33:38,280 --> 00:33:41,520
Speaker 1: and scary. So there's this massive amount of information we're

552
00:33:41,520 --> 00:33:44,360
Speaker 1: talking about the software has to comb through all this data,

553
00:33:44,440 --> 00:33:47,960
Speaker 1: identify the points that relate to us, the potential customer,

554
00:33:48,400 --> 00:33:51,320
Speaker 1: and then come to a conclusion. Now, I assume that

555
00:33:51,360 --> 00:33:53,960
Speaker 1: behind the scenes, there's probably some sort of threshold or

556
00:33:54,080 --> 00:33:56,920
Speaker 1: score that a customer has to meet. Uh. It could

557
00:33:56,960 --> 00:33:59,520
Speaker 1: be codified as a score or a rating or something,

558
00:33:59,520 --> 00:34:01,440
Speaker 1: but I don't know because the inner workings of a

559
00:34:01,520 --> 00:34:05,080
Speaker 1: firm are mostly hidden. What I can say is that

560
00:34:05,160 --> 00:34:08,480
Speaker 1: it doesn't just look at a person's FICO score. Uh.

561
00:34:08,520 --> 00:34:11,399
Speaker 1: That's a score that judges a person's credit worthiness here

562
00:34:11,440 --> 00:34:14,920
Speaker 1: in the United States, though reportedly, at least think in

563
00:34:15,200 --> 00:34:18,879
Speaker 1: some journals I've read, a person does need a FIC

564
00:34:18,960 --> 00:34:21,920
Speaker 1: a score of around five fifty to qualify for an

565
00:34:21,920 --> 00:34:25,560
Speaker 1: affirm loan. Again, according to some sources, others dispute this.

566
00:34:25,680 --> 00:34:27,880
Speaker 1: They say that you don't need to have any particular

567
00:34:27,920 --> 00:34:30,920
Speaker 1: FICA score at all, so your guess is as good

568
00:34:30,920 --> 00:34:33,880
Speaker 1: as mine. Now, this is what it's called a soft

569
00:34:33,880 --> 00:34:37,279
Speaker 1: credit check. And that's important because and y'all, I know

570
00:34:37,600 --> 00:34:41,839
Speaker 1: that this is bonkers, but a credit check alone from

571
00:34:41,920 --> 00:34:45,719
Speaker 1: a lender, for example, can affect a person's credit score. So,

572
00:34:45,719 --> 00:34:48,520
Speaker 1: in other words, your credit score could be one thing.

573
00:34:49,000 --> 00:34:51,680
Speaker 1: Then a lender checks your credit score, then your credit

574
00:34:51,680 --> 00:34:54,120
Speaker 1: score could be lower as a result of the lender

575
00:34:54,239 --> 00:34:57,320
Speaker 1: checking it. This is kind of like the observer effect

576
00:34:57,320 --> 00:35:00,759
Speaker 1: in quantum mechanics, this idea that by observe something, you

577
00:35:00,760 --> 00:35:04,399
Speaker 1: actually affect the thing that is being observed. But yeah,

578
00:35:04,520 --> 00:35:07,279
Speaker 1: hard credit checks can ding a person's credit score, even

579
00:35:07,280 --> 00:35:10,600
Speaker 1: if that person is otherwise being a financially responsible person.

580
00:35:11,239 --> 00:35:14,440
Speaker 1: No wonder. Younger generations don't trust credit cards, And to

581
00:35:14,480 --> 00:35:16,879
Speaker 1: be clear, I'm not talking about when you check your

582
00:35:16,880 --> 00:35:19,400
Speaker 1: own credit that should never have an impact on your

583
00:35:19,400 --> 00:35:22,359
Speaker 1: credit score, but if a lender orders the credit check,

584
00:35:22,880 --> 00:35:26,480
Speaker 1: that potentially could do it. Now, the standard repayment plans,

585
00:35:26,520 --> 00:35:29,400
Speaker 1: like I said, come in three, six or twelve months installments,

586
00:35:29,400 --> 00:35:32,760
Speaker 1: And obviously the further out you spread payments over time,

587
00:35:32,840 --> 00:35:36,399
Speaker 1: the less the individual payments will be the monthly payments, right,

588
00:35:36,960 --> 00:35:39,880
Speaker 1: But then the longer you spread out, the more interest

589
00:35:39,960 --> 00:35:44,080
Speaker 1: you will pay accumulatively. So while a twelve month repayment

590
00:35:44,120 --> 00:35:46,720
Speaker 1: plan will mean you're paying out less money per month,

591
00:35:47,200 --> 00:35:49,080
Speaker 1: once you toll it up, you'll have paid more in

592
00:35:49,160 --> 00:35:51,640
Speaker 1: interest than if you had gone with three or six

593
00:35:51,680 --> 00:35:55,840
Speaker 1: months of payment plan. Anyway, a firm is essentially sizing

594
00:35:55,920 --> 00:35:58,359
Speaker 1: up each potential customer to get a judgment on if

595
00:35:58,400 --> 00:36:00,239
Speaker 1: that person will be able to pay back the loan,

596
00:36:00,880 --> 00:36:03,239
Speaker 1: and I can definitely see the business need for that,

597
00:36:03,840 --> 00:36:07,600
Speaker 1: but it also feels invasive because frankly, it is I mean,

598
00:36:07,600 --> 00:36:10,759
Speaker 1: it's going through your social media for goodness sakes, and

599
00:36:10,880 --> 00:36:13,920
Speaker 1: it just makes me lean further away from credit and

600
00:36:14,000 --> 00:36:16,480
Speaker 1: loans and stuff like that. And it really makes me

601
00:36:16,560 --> 00:36:21,160
Speaker 1: understand the appeal of debit card culture, where you're only

602
00:36:21,200 --> 00:36:23,839
Speaker 1: spending what you have. You know, you don't spend more

603
00:36:23,880 --> 00:36:25,799
Speaker 1: than what you have and then put yourself at risk.

604
00:36:26,160 --> 00:36:29,239
Speaker 1: But Leftin's whole perspective is fundamentally different from that. He's

605
00:36:29,280 --> 00:36:32,760
Speaker 1: looking at how people who for whatever reason, have little

606
00:36:32,800 --> 00:36:36,200
Speaker 1: to no access to traditional credit solutions and how they

607
00:36:36,239 --> 00:36:38,800
Speaker 1: can take advantage of purchasing power that otherwise would be

608
00:36:38,800 --> 00:36:41,680
Speaker 1: outside their grasp. So there are two sides to this coin,

609
00:36:41,719 --> 00:36:43,560
Speaker 1: and I think it really comes down to how each

610
00:36:43,600 --> 00:36:49,080
Speaker 1: individual out there judges how they feel about this situation. Like,

611
00:36:49,200 --> 00:36:52,040
Speaker 1: I have credit cards, so it's not like I have

612
00:36:52,120 --> 00:36:55,520
Speaker 1: completely abstained from this. I have a mortgage I pay,

613
00:36:55,640 --> 00:36:59,040
Speaker 1: so there are definitely cases where I've got this thing

614
00:36:59,080 --> 00:37:02,760
Speaker 1: going on right now, But I also can understand the

615
00:37:02,800 --> 00:37:05,480
Speaker 1: reluctance to get into that world is scary to have

616
00:37:05,560 --> 00:37:09,000
Speaker 1: that debt sitting over you. Now, for a firm to

617
00:37:09,040 --> 00:37:11,600
Speaker 1: work it originally had to partner with the merchants like

618
00:37:11,800 --> 00:37:14,120
Speaker 1: that was the only way was by getting a partnership,

619
00:37:14,680 --> 00:37:17,200
Speaker 1: and because a firm would assume the risk of the loans,

620
00:37:17,280 --> 00:37:20,440
Speaker 1: merchants tended to be pretty cool with using it. The

621
00:37:20,480 --> 00:37:24,280
Speaker 1: merchants get paid either way, right, So if the person

622
00:37:24,320 --> 00:37:27,640
Speaker 1: pays back a firm or doesn't, the merchant still gets

623
00:37:27,680 --> 00:37:30,759
Speaker 1: paid the actual price of the item, So that's great

624
00:37:30,800 --> 00:37:33,879
Speaker 1: for them. Now, a firm collects a small fee from

625
00:37:33,880 --> 00:37:37,640
Speaker 1: each purchase. Uh, they also get the interest that's generated

626
00:37:37,640 --> 00:37:40,799
Speaker 1: by the loan, and merchants can pay a firm for

627
00:37:40,920 --> 00:37:44,600
Speaker 1: different levels of interest rates. So if a merchant wants

628
00:37:44,640 --> 00:37:48,239
Speaker 1: to offer lower interest rates and therefore attract more buyers,

629
00:37:48,280 --> 00:37:51,160
Speaker 1: like if a merchant says, you pay zero percent interest

630
00:37:51,320 --> 00:37:54,120
Speaker 1: on this big ticket item, then it can pay a

631
00:37:54,239 --> 00:37:58,399
Speaker 1: firm a feed to do that and affirm well, this

632
00:37:58,480 --> 00:38:00,640
Speaker 1: is kind of in return for the money that affirm

633
00:38:00,719 --> 00:38:03,680
Speaker 1: quote unquote loses by not having interest on the loan.

634
00:38:04,320 --> 00:38:07,160
Speaker 1: Affirm will says sure, we'll offer them alone with no interest.

635
00:38:07,520 --> 00:38:10,000
Speaker 1: You have to pay us to compensate us for that,

636
00:38:10,520 --> 00:38:13,080
Speaker 1: and the merchant, if they want to move this big

637
00:38:13,080 --> 00:38:16,640
Speaker 1: ticket item, might do that, and then the consumer at

638
00:38:16,680 --> 00:38:19,799
Speaker 1: the other end gets a zero percent interest loan. Now

639
00:38:19,840 --> 00:38:23,600
Speaker 1: that's incredible, right. If it's saying you're gonna have up

640
00:38:23,600 --> 00:38:27,080
Speaker 1: to twelve months to pay this off and you will

641
00:38:27,080 --> 00:38:29,480
Speaker 1: be paying only the amount that you need in order

642
00:38:29,520 --> 00:38:32,000
Speaker 1: to buy the item, no interest on top of it,

643
00:38:32,520 --> 00:38:35,120
Speaker 1: that's that's an incredible deal. Doesn't happen all the time

644
00:38:35,160 --> 00:38:38,160
Speaker 1: because merchants again have to pay to offer those kind

645
00:38:38,160 --> 00:38:42,640
Speaker 1: of zero percent uh interest rates. Now. One of the

646
00:38:42,640 --> 00:38:46,200
Speaker 1: early merchants Affirm partnered with was one E. D. Flowers,

647
00:38:46,239 --> 00:38:49,080
Speaker 1: and slowly Affirmed began to grow. It began to add

648
00:38:49,120 --> 00:38:52,360
Speaker 1: more in network merchants over time. There's even a story

649
00:38:52,400 --> 00:38:54,880
Speaker 1: that at one point Legend wanted to buy a gong

650
00:38:55,520 --> 00:38:58,080
Speaker 1: so that the team could bang the gong whenever they

651
00:38:58,160 --> 00:39:02,000
Speaker 1: landed another major merchant part or. In two thousand seventeen,

652
00:39:02,320 --> 00:39:05,560
Speaker 1: Affirm launched its pay with a Firm service, which allows

653
00:39:05,600 --> 00:39:09,360
Speaker 1: loans for purchases from any retailer. Essentially, So I'm guessing

654
00:39:09,400 --> 00:39:12,560
Speaker 1: the gonging at that time must have been excruciating and

655
00:39:12,640 --> 00:39:16,600
Speaker 1: never ending. Now one partner that at least initially really

656
00:39:16,640 --> 00:39:19,879
Speaker 1: gave a firm a boost was Peloton, and this all

657
00:39:19,960 --> 00:39:23,560
Speaker 1: gets concentrated into that very short, intense span of time

658
00:39:23,640 --> 00:39:26,160
Speaker 1: during the early days of the pandemic, when much of

659
00:39:26,160 --> 00:39:29,839
Speaker 1: the world was under quarantine rules. That's when Peloton saw

660
00:39:29,840 --> 00:39:33,719
Speaker 1: an absolute explosion in orders. Now. Peloton, in case you

661
00:39:33,760 --> 00:39:37,440
Speaker 1: don't know, is a company that sells high end stationary

662
00:39:37,520 --> 00:39:43,120
Speaker 1: bikes and treadmills and offers web based subscription training services

663
00:39:43,440 --> 00:39:46,480
Speaker 1: so that you can take classes from the comfort of

664
00:39:46,560 --> 00:39:50,160
Speaker 1: your home. And they have different instructors who lead these classes,

665
00:39:50,200 --> 00:39:52,640
Speaker 1: so you can find an instructor that you vibe with,

666
00:39:53,160 --> 00:39:57,480
Speaker 1: take their classes on the equipment, and then Peloton gets

667
00:39:57,560 --> 00:40:00,319
Speaker 1: data from your workout. You get feedback, all that kind

668
00:40:00,320 --> 00:40:03,080
Speaker 1: of stuff. It's like working with a personal trainer, it's

669
00:40:03,120 --> 00:40:06,200
Speaker 1: just you're doing it remotely. Now, early in the pandemic,

670
00:40:06,640 --> 00:40:10,200
Speaker 1: Peloton saw this huge jump in orders and a firm

671
00:40:10,239 --> 00:40:13,080
Speaker 1: as a buy now, pay later company, got a ton

672
00:40:13,280 --> 00:40:16,280
Speaker 1: of business from folks who wanted a Peloton but didn't

673
00:40:16,280 --> 00:40:18,839
Speaker 1: wanted to shout. The two to three thousand dollars needed

674
00:40:18,840 --> 00:40:21,800
Speaker 1: to get one. There's a whole range of Peloton products,

675
00:40:21,840 --> 00:40:25,440
Speaker 1: and it goes from somewhere like around the dollar mark

676
00:40:25,480 --> 00:40:27,720
Speaker 1: all the way up to nearly four thousand dollars depending

677
00:40:27,760 --> 00:40:31,080
Speaker 1: upon what it is you're buying. Now. According to the Generalist,

678
00:40:31,160 --> 00:40:34,920
Speaker 1: Peloton sales at one time accounted for twenty eight percent

679
00:40:35,120 --> 00:40:41,400
Speaker 1: of affirms overall revenue. Now, when nearly a third of

680
00:40:41,520 --> 00:40:45,000
Speaker 1: all your revenue is coming from a single source, that

681
00:40:45,320 --> 00:40:48,560
Speaker 1: can spell danger if something happens to that single source,

682
00:40:49,080 --> 00:40:52,839
Speaker 1: and Peloton would have a dramatic fall from grace. In

683
00:40:52,960 --> 00:40:58,720
Speaker 1: late one early two, the company reportedly had warehouses full

684
00:40:58,760 --> 00:41:02,279
Speaker 1: of product that was selling like the after that burst

685
00:41:02,360 --> 00:41:05,440
Speaker 1: of interest from twenty came out. It turned out it

686
00:41:05,440 --> 00:41:09,040
Speaker 1: could not sustain itself, and as a result, Peloton found

687
00:41:09,040 --> 00:41:12,200
Speaker 1: itself flush with stock. So it had an enormous amount

688
00:41:12,200 --> 00:41:18,200
Speaker 1: of supply very low demand. Uh, it became really in

689
00:41:18,200 --> 00:41:21,040
Speaker 1: in a precarious position. The company actually had to put

690
00:41:21,080 --> 00:41:23,800
Speaker 1: a halt on production because it had nowhere to store

691
00:41:24,040 --> 00:41:26,759
Speaker 1: the ding dang durn things because they were already chock

692
00:41:26,800 --> 00:41:30,560
Speaker 1: full with inventory. And so this naturally raised questions about

693
00:41:30,560 --> 00:41:33,480
Speaker 1: how that was going to affect a firm, the company

694
00:41:33,520 --> 00:41:37,560
Speaker 1: that had depended heavily on those transactions. Now a firm

695
00:41:37,920 --> 00:41:41,840
Speaker 1: had actually gone public in twenty one. And you know

696
00:41:41,920 --> 00:41:44,200
Speaker 1: I mentioned already when I p O is the initial

697
00:41:44,200 --> 00:41:49,040
Speaker 1: public offering. A firm's initial public offering was a roaring success.

698
00:41:49,080 --> 00:41:52,239
Speaker 1: So the initial price for shares before it went on

699
00:41:52,320 --> 00:41:55,200
Speaker 1: sale on the market was estimated to be somewhere around

700
00:41:55,200 --> 00:41:58,120
Speaker 1: forty nine per share. That's what they thought it would

701
00:41:58,440 --> 00:42:01,960
Speaker 1: open at. In stead had opened at nearly around ninety

702
00:42:02,000 --> 00:42:04,920
Speaker 1: dollars per share. It closed trading for the day at

703
00:42:05,000 --> 00:42:10,960
Speaker 1: ninety cents per share, so nearly twice as much. Ultimately,

704
00:42:11,280 --> 00:42:16,040
Speaker 1: the stock price for Peloton in November of would hit

705
00:42:16,080 --> 00:42:20,440
Speaker 1: around a hundred sixty eight dollars per share. Now, by

706
00:42:20,440 --> 00:42:22,400
Speaker 1: the time it went public, a firm was working with

707
00:42:22,400 --> 00:42:24,960
Speaker 1: more than six thousand retailers, so it's not like all

708
00:42:25,000 --> 00:42:29,319
Speaker 1: of its financial eggs were in Peloton's shaky basket. But

709
00:42:29,400 --> 00:42:31,920
Speaker 1: this still raised concerns. I mean, twenty eight percent of

710
00:42:31,920 --> 00:42:35,520
Speaker 1: your revenue in twenty that's so much so when in

711
00:42:35,560 --> 00:42:40,800
Speaker 1: the late earnings call, when it became clear that Peloton

712
00:42:40,960 --> 00:42:45,800
Speaker 1: was in serious trouble, affirm reps quietly side scept questions

713
00:42:45,840 --> 00:42:49,160
Speaker 1: that related to Peloton because that was obviously a question

714
00:42:49,200 --> 00:42:51,560
Speaker 1: that was going to be very difficult to answer. And

715
00:42:51,640 --> 00:42:55,040
Speaker 1: chances are the folks in a firm we're working very

716
00:42:55,080 --> 00:42:57,080
Speaker 1: hard to find a solution themselves. At that time, it

717
00:42:57,080 --> 00:43:00,680
Speaker 1: didn't have much that they could say. But you know,

718
00:43:00,719 --> 00:43:03,279
Speaker 1: then we have the economic recession that's coming on, and

719
00:43:03,719 --> 00:43:07,400
Speaker 1: that paired with the pandemic has really created a unique situation.

720
00:43:07,880 --> 00:43:10,200
Speaker 1: It's had a big impact with a firm. Now, as

721
00:43:10,239 --> 00:43:13,720
Speaker 1: I record this, a firm's share price is at thirty

722
00:43:13,800 --> 00:43:18,120
Speaker 1: nine dollars and some change per share. That's ten dollars

723
00:43:18,160 --> 00:43:21,520
Speaker 1: lower than what a firm estimated it's opening price would

724
00:43:21,560 --> 00:43:24,239
Speaker 1: be during its I p O. It is a far

725
00:43:24,440 --> 00:43:27,600
Speaker 1: cry below the hundred sixty eight dollar height of the

726
00:43:27,640 --> 00:43:32,880
Speaker 1: stock from November. Now, that being said, it is an

727
00:43:32,880 --> 00:43:35,880
Speaker 1: improvement over where the stock was at a certain point

728
00:43:35,880 --> 00:43:39,120
Speaker 1: back in May and May it dropped down to below

729
00:43:39,200 --> 00:43:46,320
Speaker 1: fifteen dollars per share. So talk about dramatic changes in value.

730
00:43:46,440 --> 00:43:48,120
Speaker 1: Right to go from a high of a hundred and

731
00:43:48,120 --> 00:43:50,640
Speaker 1: sixty eight dollars to a low of fifteen dollars in

732
00:43:50,880 --> 00:43:56,280
Speaker 1: less than a year. Now, again, a firm has recovered

733
00:43:56,360 --> 00:43:58,839
Speaker 1: quite a bit since that fifteen dollar low. I think

734
00:43:58,840 --> 00:44:00,960
Speaker 1: it was like fourteen dollars six three cents if I'm

735
00:44:00,960 --> 00:44:06,320
Speaker 1: being more specific. But still thirty nine is not great,

736
00:44:06,400 --> 00:44:09,480
Speaker 1: right when you when you once were at a sixty eight. Now,

737
00:44:09,520 --> 00:44:12,160
Speaker 1: part of the issue is that so far a firm

738
00:44:12,200 --> 00:44:15,160
Speaker 1: has not been a profitable company. It has operated at

739
00:44:15,200 --> 00:44:17,480
Speaker 1: a loss. But then a lot of companies do that,

740
00:44:17,600 --> 00:44:20,680
Speaker 1: especially early on. Right and a firm expects to reach

741
00:44:20,760 --> 00:44:24,520
Speaker 1: income profitability by the end of fiscal year twenty twenty three,

742
00:44:24,640 --> 00:44:28,000
Speaker 1: so the end of its fiscal year next year, it

743
00:44:28,080 --> 00:44:30,759
Speaker 1: should be a profitable company, at least according to its

744
00:44:30,800 --> 00:44:34,560
Speaker 1: own projections. Now, at least some analysts think that affirm

745
00:44:34,680 --> 00:44:37,920
Speaker 1: is actually in pretty good position right now, and economic

746
00:44:37,960 --> 00:44:40,560
Speaker 1: recession could mean that more people will start to rely

747
00:44:40,800 --> 00:44:43,879
Speaker 1: on buy now, pay later solutions in order to get

748
00:44:43,920 --> 00:44:46,920
Speaker 1: the stuff they need. That being said, there are other concerns.

749
00:44:46,960 --> 00:44:49,879
Speaker 1: There are people who say, well, that could mean that

750
00:44:50,320 --> 00:44:52,839
Speaker 1: fewer people will be willing to do buy now, pay

751
00:44:52,920 --> 00:44:56,440
Speaker 1: later because they're worried about the debt. Then you have Apple,

752
00:44:56,560 --> 00:44:59,719
Speaker 1: which launched its own service called Apple Pay Later that

753
00:45:00,440 --> 00:45:03,879
Speaker 1: stands as a potential threat to affirms business. It also

754
00:45:03,960 --> 00:45:07,800
Speaker 1: reaffirms that a firm was onto something with this business model,

755
00:45:08,280 --> 00:45:11,760
Speaker 1: and a firm had to tighten its underwriting standards early

756
00:45:11,800 --> 00:45:13,920
Speaker 1: in the pandemic, meaning that the company had to become

757
00:45:13,960 --> 00:45:17,320
Speaker 1: more selective when it came to choosing who who would

758
00:45:17,320 --> 00:45:20,759
Speaker 1: get loans and who would not. Uh so much was

759
00:45:20,800 --> 00:45:23,600
Speaker 1: uncertain that you can understand where the company was coming from.

760
00:45:23,640 --> 00:45:25,680
Speaker 1: You know, we weren't sure which businesses we're going to

761
00:45:25,719 --> 00:45:28,920
Speaker 1: survive the conditions of the pandemic and which ones might

762
00:45:28,960 --> 00:45:32,000
Speaker 1: fade away. And yeah, talking about this is ikey because

763
00:45:32,040 --> 00:45:34,080
Speaker 1: at the end of the day, what you're really talking

764
00:45:34,120 --> 00:45:36,879
Speaker 1: about are the human beings who need money in order

765
00:45:36,880 --> 00:45:40,200
Speaker 1: to purchase stuff. I definitely feel way more sympathy for

766
00:45:40,239 --> 00:45:43,400
Speaker 1: the people who rely on buy now, pay later in

767
00:45:43,480 --> 00:45:47,560
Speaker 1: order to get necessities, rather than say, affluent folks who

768
00:45:47,600 --> 00:45:50,840
Speaker 1: just want to spread out their payments for an expensive

769
00:45:50,880 --> 00:45:53,200
Speaker 1: exercise bike in their homes. I don't feel a whole

770
00:45:53,200 --> 00:45:56,439
Speaker 1: lot of sympathy for them. Now, you might wonder how

771
00:45:56,480 --> 00:45:59,080
Speaker 1: the Heck Affirm can actually loan out money in the

772
00:45:59,080 --> 00:46:01,200
Speaker 1: first place. I mean, it's how how do you have

773
00:46:01,280 --> 00:46:05,240
Speaker 1: a company just magically grant money to people? Well affirm

774
00:46:05,360 --> 00:46:08,560
Speaker 1: borrows from around twenty different banks and pension funds and

775
00:46:08,600 --> 00:46:12,680
Speaker 1: other pools of cash, which, yeah, I mean pension funds. Man,

776
00:46:12,719 --> 00:46:14,239
Speaker 1: what a world we live in to think of a

777
00:46:14,280 --> 00:46:18,759
Speaker 1: pension fund is a viable place to borrow money from

778
00:46:18,800 --> 00:46:21,120
Speaker 1: that that a pension fund deps when someone wants to

779
00:46:21,120 --> 00:46:24,560
Speaker 1: buy an expensive toy. Now, maybe that's just me being

780
00:46:24,560 --> 00:46:29,359
Speaker 1: particularly anti capitalist today. Sorry about that anyway. As long

781
00:46:29,440 --> 00:46:32,720
Speaker 1: as folks are paying back their loans, everything works fine.

782
00:46:32,800 --> 00:46:35,839
Speaker 1: But if there is a surge in delinquencies, it puts

783
00:46:35,840 --> 00:46:37,879
Speaker 1: a firm in a really tough position because it will

784
00:46:37,880 --> 00:46:42,080
Speaker 1: still owe the borrowed money to those various funds and banks. Now,

785
00:46:42,480 --> 00:46:44,800
Speaker 1: I know I've talked a lot about finance in this episode,

786
00:46:45,040 --> 00:46:48,160
Speaker 1: but we have to remember this is all built upon technology, affirms.

787
00:46:48,200 --> 00:46:51,840
Speaker 1: Tech includes this process of evaluing a potential customer before

788
00:46:51,840 --> 00:46:55,080
Speaker 1: approving a loan or denying it. We're talking about digital

789
00:46:55,160 --> 00:46:58,880
Speaker 1: money transfers. We're talking about transactions that are mostly happening online.

790
00:46:59,320 --> 00:47:03,320
Speaker 1: So there is a big tech aspect to this um.

791
00:47:03,440 --> 00:47:05,560
Speaker 1: As for what's in the future for a firm, I

792
00:47:05,600 --> 00:47:08,480
Speaker 1: don't know. And as for my own personal thoughts on it,

793
00:47:08,520 --> 00:47:11,960
Speaker 1: I've got mixed feelings. On the one hand, I think

794
00:47:12,400 --> 00:47:15,160
Speaker 1: a better solution for most people is to put money

795
00:47:15,200 --> 00:47:19,400
Speaker 1: aside for purchases and just to to wait until you

796
00:47:19,520 --> 00:47:21,799
Speaker 1: have the money to make the purchase. So, in other words,

797
00:47:22,080 --> 00:47:25,239
Speaker 1: maybe using an app that lets you set aside the

798
00:47:25,280 --> 00:47:28,560
Speaker 1: same amount of money you would be spending on a

799
00:47:28,640 --> 00:47:33,080
Speaker 1: buy now, pay later scheme, but then just using that

800
00:47:33,160 --> 00:47:35,040
Speaker 1: to outright purchase the stuff, and then you don't have

801
00:47:35,080 --> 00:47:38,200
Speaker 1: to pay interest, right, You're just paying the asking price

802
00:47:38,239 --> 00:47:41,160
Speaker 1: for whatever the product is. But that means you have

803
00:47:41,239 --> 00:47:43,239
Speaker 1: to wait, and in some cases you may not be

804
00:47:43,360 --> 00:47:47,080
Speaker 1: able to write for necessities, you might not have that luxury.

805
00:47:47,200 --> 00:47:50,520
Speaker 1: And in other forms, I'm thinking, you know, there there

806
00:47:50,680 --> 00:47:52,680
Speaker 1: is so much of our world that is based upon

807
00:47:52,719 --> 00:47:56,040
Speaker 1: our ability to access credit, and for tons of folks

808
00:47:56,360 --> 00:48:00,439
Speaker 1: that's an avenue that just isn't a viable option. So yeah,

809
00:48:00,560 --> 00:48:03,560
Speaker 1: I have I have complicated feelings about this. I think

810
00:48:03,560 --> 00:48:07,480
Speaker 1: it's a valuable service. It's just a valuable service that

811
00:48:07,640 --> 00:48:11,399
Speaker 1: is built on top of or next to a an

812
00:48:11,440 --> 00:48:18,320
Speaker 1: infrastructure that I personally find somewhat scary and nikki, but hey,

813
00:48:18,719 --> 00:48:20,600
Speaker 1: you know, we got to navigate the world one way

814
00:48:20,680 --> 00:48:24,560
Speaker 1: or another, right anyway, That's the story. On a Firm

815
00:48:24,640 --> 00:48:27,680
Speaker 1: and Max Levchin hope you enjoyed. If you have suggestions

816
00:48:27,680 --> 00:48:30,680
Speaker 1: for future topics, make sure you reach out and get

817
00:48:30,719 --> 00:48:32,080
Speaker 1: in touch with me. One way to do that is

818
00:48:32,120 --> 00:48:34,920
Speaker 1: to download the I Heart Radio app. It's free to download.

819
00:48:35,400 --> 00:48:38,040
Speaker 1: Navigate over to tech Stuff, use a little microphone icon,

820
00:48:38,239 --> 00:48:42,280
Speaker 1: leave me a voice message, or send me a Twitter

821
00:48:42,440 --> 00:48:45,520
Speaker 1: request like this one was. And the Twitter handle we

822
00:48:45,640 --> 00:48:49,360
Speaker 1: use is tech stuff HSW and I'll talk to you

823
00:48:49,400 --> 00:48:59,560
Speaker 1: again really soon. Tex Stuff is an I Heart Radio production.

824
00:48:59,760 --> 00:49:02,600
Speaker 1: For more podcasts from I Heart Radio, visit the i

825
00:49:02,719 --> 00:49:05,960
Speaker 1: Heart Radio app, Apple Podcasts, or wherever you listen to

826
00:49:06,000 --> 00:49:06,920
Speaker 1: your favorite shows.