The Exchanges

Q So what you're saying is basically that a human customer service agent is the quality of, is the equivalent of, uh, artisanally produced good?

A Um, I think partially, maybe not so much that, because what I wanted to get to is that once that happened, we started suddenly appreciate crafted items, right? So today, if you buy a piece of furniture that is done by artisan or, uh, an artist or somebody, you know, is done like, uh, humanly crafted, we actually pay a higher price for that than we pay for something that comes out of a standard factory in some, you know, uh, and it's just like manufactured by a machine. And so, our conclusion reflecting on these things was that, like, the human connection matters. People Appreciate talking to a human. They feel a human connection. There's an emotional connection. And we believe that like this means that there will be a higher appreciation. And if a company wants to be competitive, it, it will actually be a competitive edge to offer a human connection. And so, but obviously that's kind of a different type of human connection than maybe some of the Customer service we offered historically, because this will have to be, you know, high quality, ah, ah, Skilled people that are very familiar with Clona and understand Clona, and that was not always the case, right? We relied a lot on, like, outsourced agents. They maybe would come in. They barely knew our product. They were just, like, asked according to some very strict template to answer, like, have you paid or not paid? And that was…

AI assessment note: “I think partially, maybe not so much that, because what I wanted to get to”

Q auto sales bot was like someone basically, you know, uh, converse with it and then convinced it to give them a car for like half price. And then the dealer kind of had to owner. I had to honor that decision. Have you, have you, have you had any of these issues with your bots hallucinating or like pulling the wrong data? And if so, how have you navigated that?

A Not, not the wrong data, right? So you have to be very, I mean, they obviously have to put very strict standards into what, like what is accessible, not accessible to the AI application itself. Uh, so there you can, you can control that, but it has definitely hallucinated and it has answered incorrectly. Uh, but to us, the way we think about that is that like, You have to make a comparison. You have to also recognize the fact that humans don't necessarily hallucinate, uh, hopefully, uh, but they, they also do errors, and they will also answer incorrectly. So what we simply do is we read a lot of these transcripts on a continuous basis, and we do continuous quality checks to ensure that the error rate is not higher for the AI chatbot than it is for our human agents, and if we see that they are at least on par, Then we think that's an acceptable outcome, but it would be, you know, in, you know, we could never like promise that it never makes errors. Just like you can't promise that your human agents won't make errors because our human agents unfortunately also make errors, right? Like, so it's just about making sure that there are not, you know, a substantial bigger amount of errors that the AI is doing than the human agents are doing.

AI assessment note: “it has definitely hallucinated and it has answered incorrectly.”

Q So what you're saying basically is that, yes, this is a real issue that what a general AI is doing is making probabilistic sort of guesses as opposed to deterministic pulling from, you know, a logic tree. And that can be an issue with customer service. However, you're saying that this is a solvable problem. Am I capturing that right?

A It is a solvable problem. Now the question again, we can come back to, uh, is it an Elon Musk prediction to say we will launch something in a few months or is it a, and no, like that, that's a different topic, but yes, it's definitely a solvable problem. It's just that it, I think it, that what we see clearly as we've been hypothesizing and we believe found a solution to that problem, The, the challenge with it is it requires a new way of thinking for the people building the systems. Even if more and more is coded by cursor and other parts, you still need the humans that are working on this to adopt new ways of thinking and how they build systems, how they build data models, how they build all these things. And that in itself is actually one of the biggest challenges because.

AI assessment note: “It is a solvable problem. Now the question again, we can come back to”

Q Well, yeah, I mean, to be more direct, you've said that you'd see less of an impact if you had your phone tree built out a little bit more before you turn this over to AI. And I'm like, okay, so like how much of this is actually just like papering over like a pre-existing problem?

A That is a good question. Look, I, I've, I'm, I'm sorry. I wish I could give you like, A great answer to that question, but I haven't worked in another company and I can't really make that comparison. I've tried to talk to other entrepreneurs and I, I don't want to mention them by name because I'm not sure whether they want to share the details, but I have talked to others who have, for example, come much further in automation using non AI, so to speak before, like IVRs and systems of type and where, and also who has been tougher Negotiators with their customer service suppliers, and hence their cost per errand was low lower because they had better prices than we had from a scale perspective. And their savings by moving to AI was more limited than ours, right? So there is definitely an element to that, but it's very hard. It's very hard to answer that, right? Because it's so company by company specific. My belief though, if you ask me, is that like, I don't know, like maybe 70% is AI and 30% is automation or fifty-fifty. Yeah, but I still think it is that much, actually. That's still my belief, right? And then the other thing is, like, even when I talk to some companies that were really good at, like, had really low customer service costs, really good quality, and really high level of automation even before AI, I still feel that, like, I am super happy that we did this because t…

AI assessment note: “there is definitely an element to that... maybe 70% is AI and 30% is automation”

Q you, uh, how has it been navigating this sort of like up and down of the industry? And then what do you, and then secondly, what is the future of buy now, pay later, given that yes, Apple's out and it seems like This thing that used to be in the spotlight is now moving out of it. So what should we think about when we think about this service?

A Yeah. So, I mean, first on navigating up and downs, like I, uh, you know, I think to some degree I benefit from the fact that I've been doing this for 20 years. And as much as like this up and down was maybe the most media publicized and I've never been in that like spot eye, you know, or like as visible as this, I've gone through a lot of up and downs with the company, both valuation wise as well as anything else. Right. So I think that like, it was obviously very tough and I was sad and I was, you know, Very, you know, stressed by, by that. The public traded companies that we are often compared to like a PayPal or, you know, a square block or whatever, they had the same 85% drop in the stock market during the same time, right? So we weren't singled out in that sense, but that was a general fintech and tech kind of, uh, reduction in, in stock price. But still, because we're a private company, you know, it became such a bigger, you know, news and, and then obviously also at the same point of time, As investor sentiment changed, and we were at that point of time unprofitable, we had to make, you know, very tough decisions that are very, you know, ah, you, you don't like making, which was a reduction in staff and stuff like that, which is very, um, challenging to go through. Um, but at the same point of time, I feel like, you know, we have to do what we think is right for the com…

AI assessment note: “first on navigating up and downs, like I, uh, you know, I think”

Q Right. And it doesn't seem like AI can do some of like the core functions of marketing, right? Speak with a group that has something to market, understand their objectives, bring it to the creative agency with a brief, go back and forth, find a good midpoint. And then, ah, you think so?

A Well, look, I just, there's been some really cool things I've done. So like, I'll give you one example, right? We, um, one of the first AI applications we actually built internally, Was, and this was again, just like an idea that we just did. It, it's not Klarna's core business, but one of the, it's just a concept that we wanted to test. It was that when you do these kind of classical employee engagement service, right? Like which all companies do, like how happy are you working at Klarna or how happy you're working at Meta or, you know, whatever. And you say like one on a five and like, how happy are you with the office? How happy are you with your salary? How happy, you know, whatever. How happy are you with team? Do you trust your colleagues, et cetera. So a lot of people, a lot of companies do these surveys that You, you collect data, you know, people are supposed to say by on a scale, one to five, this and that, you know, whatever. And then you kind of synthesize that information. You try to, you know, analyze it, interpret it, put some kind of report, you know, et cetera, and spread that and so forth, right? It's very typical company doing these things. So we said to ourselves like, wow, you know what, wouldn't it be happy to like, wouldn't it be fun to like, it's still like so much of, at least to me, when I look at such empty engagement service, what I really care the m…

AI assessment note: “Well, look, I just, there's been some really cool things I've done.”