Did Klarna Really Automate 700 Jobs With AI? — With Sebastian Siemiatkowski

Sebastian Siemiatkowski is the CEO and co-founder of Klarna. Siemiatkowski joins Big Technology Podcast to discuss the company's aggressive efforts to implement AI across its operation, including in customer service where it says the technology is now doing the work of 700 people. We also discuss how Klarna is using generative AI in marketing, and whether it can do the work of strategists or simply discreet tasks. Stay tuned for the second half where we discuss how Klarna is using ChatGPT enterprise, the company's roller coaster ride amid big market swings, and whether the company plans to continue to grow its workforce in the age of AI. --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. For weekly updates on the show, sign up for the pod newsletter on LinkedIn: https://www.linkedin.com/newsletters/6901970121829801984/ Want a discount for Big Technology on Substack? Here’s 40% off for the first year: https://tinyurl.com/bigtechnology Questions? Feedback? Write to: [email protected] Learn more about your ad choices. Visit megaphone.fm/adchoices

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Alex Kantrowitz?

  Let's talk with the CEO of Klarna, one of the most enthusiastic adopters of generative AI, about how the technology is being applied in practice and whether it can really do the work of 700 customer service reps. That's coming up right after this. This episode is brought to you by Genesis. What does it actually take to put a Gentic AI to work across an entire enterprise? I recently spoke with Genesis chairman and CEO Tony Bates about why orchestration is emerging as a competitive advantage in customer experience and what companies need to get right in the AI era. Then I sat down with Adam Mitchell, head of enterprise business solutions at Voya Financial to go inside a large scale agentic CX transformation,

• 0:42Alex Kantrowitz?

  including the business decisions, trade offs and lessons along the way. You can watch both conversations now on my YouTube channel today. It's never been more challenging to get frontier compute. How do you know where to look, who to trust or how much to pay? Akash is an open cloud that provides easy access, flexibility and peace of mind. That's how companies like Razor were able to cut their inference cost by 15 times. Visit akash.network slash Alex for free consultation. That's akash.network slash Alex for free consultation. Welcome to Big Technology Podcast, a show for cool edited nuanced conversation of the tech world and beyond. We're joined today by Sebastian Shimiankovsky,

• 1:26Alex Kantrowitz?

  the CEO and co-founder of Klarna, which is a fintech company that specializes in buy now, pay later, but also has its own shopping app. And it's also a company that's been leading the charge in implementing generative AI, one of OpenAI's very early partners. Sebastian, welcome to the show. Great to see you. Thanks for having me. It's great to be here. So let's just start off with this stat that you guys put out a couple of months ago about how you have effectively built AI that's capable of doing the work of 700 customer service reps. And listeners of this show know I have a stance on this, which is that when I see these numbers from company, I generally think that they haven't actually replaced that many people with AI. Here's like one example, right?

• 2:08Alex Kantrowitz?

  Like IBM, they had an announcement in August 2023 saying that they're going to replace nearly 8,000 jobs with AI, but also as they're going through a layoff. And maybe this is a case where you've actually been able to, and I think your wording is pretty interesting and we'll get into it. Maybe this is a case where you've actually been able to hand off the work of 700 people to AI, but also like You guys did do a layoff of 700 people right beforehand. And so I always wonder, like, is this, is this like replacing people with AI? Or is it something a little bit different once you get back into the details? So you tell me, like, did you hand off the work of the 700 people directly to AI? Or is it something different that we should know about?

• 2:50Speaker 01

  Right, so the layoff of like the comparison to the 700 layoff is actually a misquote by a news magazine online. It's not accurate. It was two years ago when we had to, you know, change the amount of investments we were doing. We had to make layoffs and it just happened to be that the numbers are fairly similar, but it's just coincidence. So that's a separate thing. But taking that aside to answer your question, one, once you think about it this way, right? Like, I think it's almost better to give the like a little bit of the story and the context. When AI came along, we started a lot of initiatives internally to explore the capabilities of it. And we were quite free internally in the sense that we said, look, you know, it doesn't need to be like core business that you go after. So we built a lot of different things, some where there are more related to our business and some that we would expect other companies to build.

• 3:34Speaker 01

  That aren't necessarily core to what Klarna does. And one of the teams that happened to be very successful internally was a team that started creating basically a kind of co-pilot for customer service to resolve disputes. And disputes is one of the like more tricky thing in our customer service world because you collect data from a merchant, from a consumer. You have to decide based on that data, you know, one claims that they didn't receive the package. The other one claims that they sent the package and you have to decide whether the customer should, you know, keep the money or not, or whether you should persist in trying to have them pay for something, right? And so it's a kind of a difficult, it's almost like a mini court decision that you have to decide on. And so this team managed to do this in a very, very good way. So they managed to create a co-pilot.

• 4:19Speaker 01

  The co-pilot started helping these customer service agents collect this information from both merchants and consumers, started applying a more methodical approach to assessing these errands and then also providing decision support. And like, what should we do on this specific occasion? So that was kind of the beginning and. The disputes at us had always been like, there was a backlog of like thirty days. It's a quite complex matter, it's always frustrating to us because consumers wants to get answers very quickly and we need to collect a lot of information, so it takes a little bit too long time, et cetera, et cetera. Once they took the copilot live first, that in itself basically meant that in a few weeks, our backlog was down to zero, which was unheard of. And we even had this fun internal Slack message where an engineer is like, "We're out of errands, send us more errands, we want to run it on the copilot."

• 5:03Speaker 01

  Right, like so, it was at least an indication. And then we tasked that team to say, "Hey, would you be, would you want to try to actually build a full service, customer service kind of agent, right, based on what they have done?" And then they worked on that for another six months. Now, to your point, when you then think, "Did we really replace seven hundred or not?" What has happened is that like, I think if you look at Klarna, you know, if you look at American companies, a lot of them will have fairly advanced IVRs, right? So you would call in, press one for this, press two for that, and then they make like... What's an IVR? So like, you know, when you call a customer service support number, and it will be like, press one, press two, whatever, they will have these like fairly,

• 5:47Speaker 01

  you know, and we all hate them, we're not too happy about them, they're always like a little bit annoying because you just want to call to, you want to talk to a human. But the truth is, these systems, they do resolve quite a lot of customer service errands in that they start presenting you with facts and you're going to hang up and not talk to the agent because you kind of got what you needed, but it's a little bit annoying because you want to talk to a human. So Klarna was not very advanced in that. There were other, there are other companies that are more advanced in building out such kind of services, you know, some semi-automated services, which requires you to collect a little bit of information and present some information. And all companies have been doing this in a way to kind of reduce the number of errands that agents actually deal with on a day-to-day basis, right? Like, it's a pretty standard procedure.

• 6:27Speaker 01

  So we'll have to take that into account when you think about the potential savings that we had because we didn't have as much of advanced such systems. It was a little bit of a lower threshold for us to achieve this accomplishment, right? In addition to that, however, what was clear is that like, when we started exposed this chat AI agent to customers and they had the opportunity to interact with it as an alternative to a human agent, people, the customer satisfaction on that was equal to a human agent in many cases. And so that allowed us to say, well, we should then scale and propose to more customers to use this as an alternative.

• 7:09Speaker 01

  And to be fair, like all product development, all things that you've been doing to improve your app or whatever, I mean, partially you want customers to be able to self-serve and serve themselves. So any product improvement partially has an implication on, you know, reducing number of errands to customer service, right? If you have a really bad app, people will call you more often. If you have a good app, people will call you less often, right? There's always going to be that. Now, the difference was when we got this customer service AI agent to reach a level where it actually served a lot of errands and on a satisfaction level equal to what human agents many times did, and we took it live, the number of errands that our human

• 7:50Speaker 01

  agents needed to deal with that was removed in a single day was the equivalent of what 700 people used to do manually before, right? So that is actually true, and that has led to a saving. Now, in our case, we don't hire these people ourselves, we use customer service companies. And so these agents would go on and do other jobs for other companies in the shorter term because these companies employ hundreds of thousands of people. So when Klarna has less errands, somebody else will have more errands and they will go and work on them instead, right? But we still wanted to share this metric because we felt, look, directionally speaking, if this continues, it will obviously have implications on the number of customer service jobs that exist in the wider economy, right?

• 8:32Alex Kantrowitz?

  Yes.

• 8:33Speaker 01

  So we still thought it was a very worthwhile statistic to share.

• 8:36Alex Kantrowitz?

  So did you then reduce, so you outsource your customer service, did you then reduce sort of the headcount that you get from these outsourced vendors by 700?

• 8:45Speaker 01

  Yes.

• 8:46Alex Kantrowitz?

  Like what, so 700 less people from these companies?

• 8:50Speaker 01

  Yes.

• 8:51Alex Kantrowitz?

  And like full-time equivalent?

• 8:52Speaker 01

  We on average would have like about 3,000, it depends because you have to remember also like our, just like Amazon, we have much more transactions around Christmas because we're very online and also there will always be variations in these numbers, but like on the average, you would look at like 2 to 3,000 agents. And that was removed by about 700 when this, when this AI chat agent went live, which meant that it's actually a reduction in cost for us as well for paying these customer service companies that we, you know, we basically are suppliers. We reduced the spending with about $40 million on an annual basis.

• 9:24Alex Kantrowitz?

  What did these vendors say to you when you were able to make this happen?

• 9:28Speaker 01

  Well, they were not very happy because we tweeted about it and a few of them had very severe implications on their market cap because like one of them lost like over a billion dollars in market cap on the on the stock exchange. Now, that was definitely not our intent and we felt a bit embarrassed about it. That was not what we were trying to to accomplish by sharing this statistic. But no, but they I think that, you know, they are, you know, some obviously were, you know, they were first, they were asking like, where are all the errands? That was the first thing because they were surprised, obviously, to see that dramatic shift in number of errands that we were shipping to them. But then there was also like, I mean, since we shared the statistic, you know, some of them are more like, well, what what else could we do for you? And how could we grow the relationship? Some of them are more keen to learn and understand

• 10:12Speaker 01

  how did we do this? Because they're trying to offer similar services to their customers and so forth. So there's been like a mix of reactions.

• 10:17Alex Kantrowitz?

  Right. So give me an example of like what an AI customer service bot would be doing that a human customer service bot would have done previously?

• 10:26Speaker 01

  Well, it could be very specifically, it could be like more simple errands, like, you know, hey, I want to find out, can I delay the payment on this transaction? Because I don't want to pay now, I want to pay next week, right? Like, and previously, customer service may have instructed that individual on like, where in the app do you go and do that? Or I may even help you prolong it and give you a new due date on this specific payment, right? In this case, the AI would basically show like basically serve you directly in the app, the button to click and delay that, right? So that would be a good example of something that, you know, nowadays would be handled by AI as opposed to the human agent. One thing that we also observed in this, which I think is worth adding in this, is that, and I think most of us have

• 11:09Speaker 01

  had these experiences, when you interact with human agents over chat, right? Many times, and, you know, companies will always say that we try to avoid this, but it still happens and it's a General applicable practice is these agents will have five, six chat conversations going on at the same point of time. And we, we always, as customers, we experience that because we write something and then we're like, why are you not answering immediately? Like, give me an answer, right? Like, and, and, but at, since I've also sat on the other side as a customer service agent, I also know that like, that's also normal because maybe you, Alex, are pinging me something and then you're like, you get a phone call or something, so you're not writing anything. So I'm not going to sit and wait for Alex, you know, I'm going to go and have a few other conversations simultaneously. It's like, it makes sense because otherwise it would be very, very inefficient. However, we experienced that.

• 11:52Speaker 01

  Now, the difference with AI is that we don't need to do it that way. So what you see is the, I think the biggest difference is that in general, when a human started a conversation with another human, to resolve a single task took on average fourteen minutes. Right? Uh, just because of those delays that happened because people aren't really actively talking to each other all the time. Somebody say, oh, let me go and check or something, you know, there's a delay, et cetera. And here, uh, the resolution time went from fourteen minutes to two minutes. And that is because you get instantaneously response from the AI instead, right? And like the AI is kind of focused on your conversation, so to speak. Um, and so that is, I think, one of the biggest differences and also something that the customer, that drives up the customer satisfaction because you feel it's more immediate.

• 12:37Speaker 01

  Right? Uh, but it doesn't mean obviously that all errands are, you know, can be answered by the AI today. There's obviously still a lot of things that humans deal with because they're more complex and more difficult. And there's also a huge amount of customers, the first thing they write when we expose the AI agent to them is agent. Right? That's the first thing because a lot of people have had so much bad experiences with these AI bots that they just want to talk to a human, right? So like, that's also another thing that we see a lot of.

• 13:03Alex Kantrowitz?

  Yeah, because I, I gave it a shot. I was in your app yesterday and I wrote just, I want to refund. I want a refund for my order. And the bot writes back, I understand you're looking to get a refund to assist you better. This is what you have to do. One, exit this chat. Two, go to the customer service section under settings. Three, uh, select the purchase you need help with. And once you've done that, you can proceed with the steps you need for your refund. So effectively, it's not like the AI is necessarily going out and accomplishing it for me, but it's directing me to the place that I need to go to finish this action.

• 13:35Speaker 01

  And that's a very common thing. So what you see is that what, you know, one of the things where we were actually a little bit lucky when we compare, because we've talked to a lot of companies that are trying to do similar things, right? And so Claude was a little bit lucky in the sense that like, Before, eh, already like a few years ago, we had this vision for our customer service. It had nothing to do with AI at that point in time. It was just that like, if you, for example, ask like you did in that chat for a specific action, rather than just inform you of where to do it in the app, we would actually serve like a small widget that allows you to do it directly in the chat thread, right? So as I said, like, you know, we don't have that, we don't have those widgets for all actions. So the actions that you happen to ask for, we don't have that widget for. But there are other actions that we had such widgets for.

• 14:16Speaker 01

  And so what we see now with a lot of other companies trying to catch up with this idea is that they don't have widgets for anything. Like, it's always been, you know, customer service interacting with Alex and then going and doing that in a separate GUI or, you know, in separate software, right? In our case, we did had already a few, eh, quite a, quite a few of such widgets that we could serve into that thread that would allow you to do it. And that is partially what has allowed us to, you know, to get this going at a bigger. But to a point, you will always be able to find things that like, you know, eh, are yet not working or you know, yet at that level and so forth, right? So eh, and that's why I said I also made a comparison with IVR. If you had a very advanced such, you know, press one, press two system,

• 14:59Speaker 01

  then, you know, the difference between what we did and eh, the outcomes would have been not as great as seven hundred. It would still have been something, but it would not have been at the same level.

• 15:08Alex Kantrowitz?

  Yeah, I definitely hate those systems, so.

• 15:11Speaker 01

  Yeah.

• 15:12Alex Kantrowitz?

  Anyway, that's a subject for another conversation. Let me ask you this. There have been some problems with these customer service chatbots that they've hallucinated sometimes. I mean, I think like one of the examples that has been given was that um, an auto sales bot was like someone basically, you know, eh, conversed with it and then convinced it to give them a car for like half price. And then the dealer kind of had to owner, eh, had to honor that decision. Have, have, 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?

• 15:46Speaker 01

  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. Eh, so there you can, you can control that. But it has definitely hallucinated and it has answered incorrectly. Eh, but to us, the way we think about that is that like, you have to make a compare, you have to also recognize the fact that. Humans don't necessarily hallucinate, hopefully, 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.

• 16:30Speaker 01

  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, 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.

• 16:53Alex Kantrowitz?

  It's kind of like the logical self-driving car threshold, which we'll never see, but if like you can kill less people than human drivers, you should probably roll out the self-driving cars.

• 17:00Speaker 01

  Yeah, that's a difficult one. But but the the difficult thing for self-driving cars obviously is that human lives are at stake. So the acceptance within society for those mistakes will probably be at a very different level. In our case, it's a little bit more fine if it makes a mistake, right? It's still financial services. It's not like, you know, some music app or whatever. So like you still have a different level of compliance and you were a bank, we're fully regulated, so we have a lot of things that we need to live up to. But obviously, at least it's not lives at stake, right? Like in that sense.

• 17:28Alex Kantrowitz?

  So I was speaking with somebody about this move and they basically said, listen, like if Klarna was able to figure out a way to automate customer service this way, I think you've had the last public numbers, 2.3 million conversations and two thirds of your customer service chats happen with the bot. Basically, they're like, if this was working as well as Klarna claims, that they wouldn't be talking about it because they have this like edge over the competition and why give that up in public? So I put that question to you. Why talk about it?

• 17:55Speaker 01

  For two reasons. One is that like, you know, I've been. I've been part of a controversial industry for quite a while called buy now pay later. And people have criticized buy now pay later and I also see, I see why, but I also see strengths to what buy now pay later offers to the market compared to credit cards and the traditional banks. And so my learning from going through that of being first like a hailed amazing tech company doing awesome things and then starting being criticized for some of the also risks and you know, challenges associated with, I mean, if you provide credit, it's still credit, right? Like you can provide better credit or worse credit, but it's still credit and people will have opinions about credit. And my learning from that is that it's helpful to be proactive,

• 18:40Speaker 01

  that it actually is better to kind of lean forward and, and, and share things on a proactive basis rather than kind of have them, you know, appear later, right? So, so that's one reason my learning from that helped, made me believe that I think it's better of us to kind of be proactive and share these statistics and so forth. I also feel partially like, you know, that I sometimes feel that the, you know, when, when I see so much noise and discussions about AI, this and that or whatever, I actually feel that we have a more kind of a moral responsibility to share that we are actually seeing real results and that that's actually having implications on society today and hope

• 19:21Speaker 01

  to encourage people, specifically politicians in society, to actually treating this as a serious change that's coming and start thinking proactively about what to do that.

• 19:32Alex Kantrowitz?

  Yeah.

• 19:32Speaker 01

  So that's more like on a human level. And then finally, the third thing obviously is also self-promotion for sure. Like, you know, that's definitely also a play, right? Like, so obviously what we've seen as a consequence of sharing this is that more of the AI startups wants to work with Klarna because we're regarded as, you know, a thought leader or somebody doing something exciting in the space. We see more people want to work here, you know, et cetera. So there's like, obviously also that aspect, right? But it, but it's a combination of those three.

• 19:58Alex Kantrowitz?

  So throughout this conversation, you've hedged a couple of times about how powerful this stuff is because saying that if you had like a better phone tree, for instance, it might not have helped you save 700, you know, employees or not employees, 700 workers' time. So.

• 20:16Alex Kantrowitz?

  And you're not, you said there'd be some impact, but you're not 100% sure what the impact would be. That being said, is this stuff actually all that powerful? Or did it just kind of help you paper over like a different problem? Like, how should we, because I'm thinking like, all right, so we should think about the implications for society, but like, is this the moment? Or is this like a little bit less powerful than it seems, given the problems that existed beforehand?

• 20:39Speaker 01

  So I think, I want to make sure I understand your question correctly, but I, look, I think it's a very good question in the sense that, you know, it's always like, how, how, how much is hype and how much is real? Is that the question basically, right?

• 20:56Alex Kantrowitz?

  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?

• 21:10Speaker 01

  Oh, okay, sorry. 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 entrepreneurs and I, I don't want to mention their 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 lower because they had better prices than we had from a scale perspective.

• 21:51Speaker 01

  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 Seventy percent is AI and thirty percent is automation.

• 22:10Alex Kantrowitz?

  That's still a lot.

• 22:11Speaker 01

  Or fifty fifty.

• 22:11Alex Kantrowitz?

  Yeah.

• 22:12Speaker 01

  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, that 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 this is just the first version of this, right? Like, we've learned so much from the implementation and it's not like we just launched it and now we're letting it be, right? We're continuously improving on it, making it better and better. So I think that like, even though maybe the first iteration would only have been a substantial improvement compared to some other companies, if you give it one or two more years, it will definitely be an improvement versus what any other company is doing with general just computers, automation and so forth, right?

• 22:56Speaker 01

  So I think it's a little like, that's a more, I mean, maybe it's boring, it's not the headline catching answer, but I think it's the more real answer.

• 23:03Alex Kantrowitz?

  We want to unpack the nuance of this stuff, so.

• 23:05Speaker 01

  Yeah, yeah, yeah, yeah. But I think it, that's, I think that's the reality of things. That's, that's why I just want to lean in. I just want to learn using the technology as much as possible, but, um, you know, it's still going to take some time for it to fully mature, right?

• 23:18Alex Kantrowitz?

  And this extends beyond your customer service department. It's also in your marketing department. Let's see, you, you've tweeted, "Our in-house marketing team is half the size it was last year, but it is producing more." So, I mean, you said, "I think AI, AI is used for eighty percent of all copywriting within the company." I struggle to believe that could be that good, but I'm curious if you could tell us a little bit more about how AI is working within your marketing division? And is it taking on responsibilities of full workers like it is within customer service? Or is it more enhancing the impact that the marketers are having?

• 23:56Speaker 01

  So, CLA is active in over twenty countries, which means that we cover over twenty languages. And that in itself, like, you can imagine, like, I don't think everyone on the call as well would like agree, like, translations have basically been nailed, right? Like, if you look at like, Not only ChatGPT and OpenAI, but also if you look at companies like DeepL, the translation quality is extremely high. So if you think about our copy, you have to remember that a lot of those copy people were actually also performing copy in different languages and so forth. And so that in itself is just a massive reduction in number of work labor hours just to translate from one to another and so forth. I think in addition to that, a lot of copy-

• 24:37Alex Kantrowitz?

  I want to ask you as a CEO, are you comfortable allowing an AI translation in marketing copy to go in front of an audience without having a human take a look at it?

• 24:48Speaker 01

  It depends on what copy it is and where it sits, right? So if you think about like, it's actually quite interesting because we're a bank. For example, if we market our credit card, there are very strict requirements on the copy and how that is expressed, right? But there's also other things in our app, like we have, for example, descriptions of products that you can buy from our merchants. So you have things that are of lower level of sensitivity, right? So the thing is that you have to basically structure your information internally in a way where you start separating and understanding that some things will have higher levels of requirements and some things will have lower. And the point with eighty percent is obviously that's focusing on the areas where it's

• 25:30Speaker 01

  less critical if the translation isn't perfect. But there's a lot of human review still of the translations themselves. But it's a little bit like you can have AI write the code, but you're still going to review the code many times from a human. Previously, you just had to have both a human to write the code and to review it, right? So I think that, but so that again is a reduction in work effort, but it is still not like a full removal. Then what we've seen is also that AI becomes very powerful when you split it in multiple assistants doing different jobs. So if you, for example, have one AI bot write the copy and then another one review it according to rules,

• 26:13Speaker 01

  then you actually make that also even better higher quality and so forth. So you see a lot of the latest AI that we see, and at least apply within Klarna, is that you actually ask the AI to pretend to be different roles. One is the reviewer and another one is the writer. And then they kind of interact with each other as like a multi-agent team. And that actually gives even better quality outcomes. So, but to your point, obviously, if it's like- Credit card promotions, you're still gonna have humans review that and look at that.

• 26:40Alex Kantrowitz?

  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.

• 26:54Speaker 01

  Ah.

• 26:55Alex Kantrowitz?

  You think so?

• 26:57Speaker 01

  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'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 surveys, 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 to five and like, how happy are you with the office? How happy are you with your salary? How, 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.

• 27:39Speaker 01

  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 a very typical company doing these things. So we said to ourselves like, wow, you know what? Wouldn't 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 employee engagement service, what I really care the most about is the comments where people have written free text, because it's so much more interesting, like, you know, what does it mean to say one to five, this and that? It could mean so many different things for different people and stuff like that. When I read the comments, at least a little bit more concrete, and I get some like input on like, what do people think or whatever, right?

• 28:20Speaker 01

  So what we decided to do is we created, we built our own internal interviewer. So we let the AI interview our employees in a session, like ask these questions and synthesize and have a conversation with employees. It's not to replace human managers, because, you know, in the end, your manager is the one that's responsible for you being happy at work and all the stuff, right? But it's an, it's an additional, um, additional tool on top of that, to provide some additional insights and understanding of what's going on in the organization. And the funny thing is that. It worked extremely well. Our employees liked it. It was very thoughtful and we felt compared to a standard survey. It gave us much more interesting outcomes and understanding of what was going on in the organization.

• 29:04Speaker 01

  That again, doesn't replace the management. That is still the the primary objective, but like it still is much of much higher quality. So to your point, can you use the same for customers? Can you survey your customers and collect and synthesize information? Yes, we've seen that work as well. It actually becomes much more interesting. So again, like it doesn't replace all of these things. But what I feel with AI, what people forget is if you go to AI and you ask a general question, right? Like whatever, give me a great way to work with marketing. You get a very general answer. It's like reading bullshit corporate management literature. Like it's a, you know, it's a bit like good to great, hire the best. Thank you, but how the hell do I find them? What interview questions to ask like, I get it. It's important to hire the best people. What does that mean in practice, right?

• 29:49Speaker 01

  The interesting thing is though, if you take AI and you ask something much more concrete, which is like, imagine the front of a house. What would I expect to see at the front of the house? It will answer very correctly. It will say probably a few windows and a door, right? And so a lot of what people I think miss is that if you, if you take any work task, whatever work task we as humans do, and you derive it down in very, very small, concrete steps that are very specific. Then actually AI is quite effective at performing those. And and most of our more complex work tasks are really consisting of very small, specific work tasks that are combined together.

• 30:30Speaker 01

  And when you start thinking about it that way, you can actually make it do a lot of things like it. But you have to think about it in that kind of step.

• 30:37Alex Kantrowitz?

  Right.

• 30:38Speaker 01

  And so, yeah, so I actually think it could do more than you than one may think, right?

• 30:43Alex Kantrowitz?

  Right, but these are all tasks and it's yet been able to effectively orchestrate. And maybe that's coming, but it's not been able to orchestrate. Like you can't tell me that you would sort of trust like the core of what your marketing department does to a machine yet. Because it's involved, I mean, even if they have great, you know, great surveys that they can send to like different divisions about like what they need to do like the the that ultimate end where you're positioning and figuring out what the message is and what the benefit is to your target like that is something that humans still need to do.

• 31:15Speaker 01

  The synthesizing of information, I agree with you. At some level, you know, humans still obviously outperform AI a lot. The other thing that I believe humans really outperform AI on is creativity. Um, in my opinion, and it's a little bit, in a way, a little bit different with, it depends if you look at copy or text, or if you look at images. Because images are more fun in a way, like you have seen, you know, AI dream up some pretty cool images, right? That like you would like, who? I fun to see an artist that would have dreamt up that crazy image, right? It was pretty cool. So I think images are a bit different, but if you look at copy, one thing that I really see the challenge in how the L L Ms work is they work towards the average.

• 31:59Speaker 01

  Because that's how they're constructed. So that means that they're constantly, all the answers are pushing through the general answer. And like, I think everyone will know that like, if you invest in the stock market, on the average, you're going to see average returns.

• 32:12Alex Kantrowitz?

  Right.

• 32:12Speaker 01

  But if you in invest with a very strict contrarian own idea, you may lose a lot, but you may also win a lot. And I think that like the same applies to your point to copy is that like, if I want some amazing copy that, you know, articulates what Klarna does that's different, I would not rely on Chat G P to do that. I would rely on a human because it a human has a bigger is much better at kind of being far fetched and do something crazy and out there and different and and so forth. Why the L L M is just pushing it to the average all the time and the average doesn't sell.

• 32:44Alex Kantrowitz?

  Yeah, and you you've also been out front talking about how like you're using generative imagery versus um, images from, let's say, uh, you know, I don't know, stock image companies. And it but it's also like, I'm looking at your your app right now. I'm holding this up for listeners. It says like shop at Amazon with like a bunch of boxes. And now it's about to scroll to top pet deals with like actual specific information about the deals you have with Chewy. And then talking about the right season with Yeti. This isn't stuff that machines are able to handle yet. Maybe there's an image or.

• 33:20Speaker 01

  Ah.

• 33:21Alex Kantrowitz?

  So tell me the truth, tell me the truth.

• 33:22Speaker 01

  Yeah, yeah, yeah. I don't know if the images that you showed were AI generated. I can't tell you that. Sorry, but I can't.

• 33:28Alex Kantrowitz?

  They look like general stock, stock images that you would get versus like the AI gen. Okay, but sorry, go ahead.

• 33:34Speaker 01

  But I can't, so I can't tell you for those specific ones that you showed on the show. But what I've seen internally us do, right, is that you currently, the problem is if you just go to like one of those tools, you know, any of those image generating tools and you just write like, give me a, to your point, like, give me a picture of a guy with box images or whatever. They're not going to be on brand tonality. They're not going to look the way Klarna wants them to look. They will be a little bit odd. There will be tons of issues and to your point, you won't be able to apply them. But what we have seen that we can do is we can, eh, basically, again, put this through a mechanism of a number of things. So we can first text prompt an image. We can then take the same image as an input

• 34:20Speaker 01

  and move the image so it looks more on brand. We can then make assessment and we basically then move them like through a factory, basically of that. And the outcome picture you have at the end of that are actually usable directly in the app and are being applied. Now, again, would I do that for my, you know, Super Bowl campaign? Nope. Would I do that for category pictures in the app that are just there to say, hey, we have shoes, we have this? Yes, that we're already doing and applying. So you can see that you're, eh, that is definitely feasible, but you need to set up this production, you know, eh, environment where you basically are like, almost like a workflow. You take it through multiple models and multiple things to get to the quality levels that you're looking for.

• 35:00Alex Kantrowitz?

  And then finally, on the product experience, so people are going to be conversing with Klarna, I guess, through your customer service agent. But you have a vision to make that a much deeper experience where you're like looking through different sites and trying to shop. And there's effectively a shopping assistant right there with you.

• 35:19Speaker 01

  Yeah, I think, look, it's actually not only shopping in the sense like actually this started when, you know, as you may know, I've been, you know, running Clon for twenty years next year. It's been a long period of time. And we have pivoted kind of direction of the company, but in fifteen, so about ten years ago, we were pivoting away from being a B two B to more of a B two C company. And at that point in time, we asked ourselves like, okay, where is financial industries going? And partially this was because already in eleven, we acquired a company in Germany called Support. And they had been, they had built not AI based, but they had built basically an application that did a very simple thing in Germany to transfer money between bank accounts

• 36:02Speaker 01

  was a horrible experience. The banking apps were horrible, right? They were just like horrible. A lot of clicks, a lot of, it was just like a bad experience. They're still pretty bad, but sorry, go ahead.

• 36:09Speaker 01

  They're still pretty bad. So what they had done, they had said, look, they had basically built a plaid like experience where they said, Alex, give me your banking credentials, your password and your login. And there was basically a macro. It was basically like the old Apple macros that we used to have on the Apple computers back in the days. It was basically a macro. They logged in on your banking account on your behalf. And they scripted to send that payment on your behalf. So you didn't have to go through the ugly GUI of the bank. And when I saw that, to me, it was very inspirational because I felt almost like, wow, you know what, like, amazing. Can you imagine if you had like this digital assistant? They kind of did this thing on your behalf. So you didn't have to interact with these horrible GUIs and experiences of other companies and it would just do this on my behalf.

• 36:53Speaker 01

  Like, wouldn't that be great? And you have to remember, like, that's not a small business. We process, we do about a hundred billion dollars worth of volume. We do thirty billion volume on debit through that solution, through basically scripting. This doesn't work that way today. It's more API based, but the point is like for many years, we basically did millions of payments transactions through scripting on, you know, on banks.

• 37:18Speaker 01

  GUIs, right? And so, so it worked. You can put it to production. Now, at that point in time, it's not AI. So if like, if one of the banks changed the GUI, there would be a phone call down in Geeson in Frankfurt where the team sits and say, hey, it's broken. You need to fix the script. And somebody would, you know, wake up in the night and fix it because the bank had changed the GUI, right? Like, so. It's like robotic process automation. Yeah, exactly. Right. And you will see UI path and some other companies have come, you know, to some degree on that, on that. So. Oh, someone that has already been done, but it inspired us to believe that like, okay, what's the future of financial services? Well, the future of financial services, you wake up in the morning, your computer tells you, hey, I analyzed your mortgage. I realized I could save you $10 by switching from bank A to bank B. The only thing you need to do is to say yes to execute on that change.

• 38:04Speaker 01

  And so like that to us became the direction of where financial industry is going. It also means a reduction in excess profits because a lot of the banking profits are generated due to the fact that the switching costs are so high and we're not willing to switch. And so the competitive pressure is actually lower than it's perceived to be. And so that became a conviction of ours already in 15. And ever since, we've been trying to build services in that direction. Now, you know, we've come some part of it. We haven't, you know, nailed it, but that's the direction that we're at. Now, when we then saw ChatGPT, we felt like, oh my God, this is probably going to happen a little bit faster than we had previously envisioned. But it's a bit like self-driving cars. I personally believe that self-driving cars at some point in time will happen, but I don't know when.

• 38:49Speaker 01

  And my bet is my daughter is now 10 and I have always said, I don't think she's going to get a driver's license, but that's eight years out, right? So there's still like, there's still some time and maybe I'll be wrong, but that's been my bet on it. And I think it's like a little bit of similar here, right? Like so, and that's, that's what we're trying to do. Whether it's a shopping assistant or a digital financial assistant, but it's somebody who's helping you save time, save money and make you in more control of your finances.

• 39:15Alex Kantrowitz?

  Okay. So your company is also one of the most enthusiastic adopters of ChatGPT enterprise. I want to talk about that on the other side of this break, about how so many of your employees are in these tools every day and whether that's helping and whether that's sustainable. All right, we'll be back right after this. This episode is brought to you by App Point. Everyone's racing to roll out AI right now. Co-pilots, chatbots, agents doing real work. But here's the part nobody loves talking about. All that AI runs on your data and most teams have no single way to see it, secure it and prove it's under control. That's exactly what App Point does. For 25 years, they've

• 39:56Alex Kantrowitz?

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• 40:42Alex Kantrowitz?

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• 41:25Alex Kantrowitz?

  Learn how to stop online threats before they become real world attacks. The link's in the show notes.

• 41:35Alex Kantrowitz?

  And we're back here on Big Technology Podcast with Sebastian Shimian Kowski. He's the CEO of Klarna and the co-founder of the company. We've been talking about AI in the workplace. And Sebastian, your company is also using ChatGPT Enterprise with maybe the most enthusiasm of any company in the world. So these are just some stats you put out. 90% of your employees are using generative AI tools powered by OpenAI daily. Communications, marketing, and legal are using adoption with adoption rates of 93, 88, and 86% respectively. You're also seeing a wide variety of additional use cases from building software to streamlining customer services, service, which we've talked about.

• 42:17Alex Kantrowitz?

  Okay, first question for you, you're 90% daily active use on ChatGPT Enterprise. Has that gone down since you released those numbers? Or does it remain consistent?

• 42:26Speaker 01

  I don't know, I haven't checked, but I would believe that it's consistent.

• 42:30Alex Kantrowitz?

  What are the people using it for?

• 42:32Speaker 01

  You know, it's tons of things on day to day stuff like, you know, help me draft this, help me review this, help me check that text. We've also built a lot of what we internally refer to as CCTs. So this could basically be like different assessment tools. Like if you want to check, you know, is this text good enough for this purpose? As an example, actually, if you want to text, you want to test some copy to see if it's, you know, is it correct from a legal point of perspective? Is it following the policies and routines that we have? Stuff like that. So there's tons of different things that people are using it for. With that said, though, you know, you know, when, when like, when Bitcoin came along and these technologies,

• 43:17Speaker 01

  like I tried that as well. I personally didn't find the technology to solve a real problem, in my opinion. I didn't see how it was gonna help my mom prefer using Klarna over something else. Like, I may be wrong, you know, maybe it turns out that, you know, blockchain is the amazing technology that will disrupt the whole world, but I wasn't convinced. When, when I tried this, ChatGPT for the first time, I got very personally convinced that like, this is a technology that will have real time applications. And since then, we've been encouraging everyone in company to learn and apply it and learn how to use it in a productive way. With that said, though, like, you know, I have days when I don't use it at all, right? Like, I have days when I use it much more. And sometimes my, I have more successful use case and sometimes I have less.

• 44:03Speaker 01

  And but I, but what I want to do is encourage the company to learn, because a lot of things have actually, are day to day use cases that are actually very helpful. But it's also like by applying and learning that we explore and get to know what are the limitations? Why is it not working? What do we need to improve? And to us, we realized that

• 44:25Speaker 01

  It can either be not working because the technology is flawed and not good enough, or it can be not working because we are not set up to use it in a way that makes it productive. And many times we realize it's the latter. So we're changing a lot of our fundamental processes of how to work and so forth.

• 44:44Alex Kantrowitz?

  Right.

• 44:44Speaker 01

  To make sure that they also helps us and can make us more productive.

• 44:47Alex Kantrowitz?

  And some of these use cases are quite interesting. So this is from another article about your, about what you're doing. You said you use generative AI, namely OpenAI's ChatGPT. The communications team uses it to evaluate whether press articles written about the company are positive or negative. I see it. Do you really need an AI to tell you that? Can't you just kind of tell it by reading it?

• 45:10Speaker 01

  Well, you could, but I'm actually really proud of our communications team. They're so efficient. And we, you have to remember, like Klarna, as a company today, you would probably read, you know, you would have about forty articles written every day. And it's not only about the sentiment analysis of those. It's actually even a little bit more detail than that. It's very common. I would say about, I think we've analyzed it, about twenty percent of the articles that are written about us contain factual errors that are incorrect. And this has meant that our communication teams calls and tries to correct those errors, right? It could be anything from where the headquarters of the company is to statistics that are wrong or whatever.

• 45:49Speaker 01

  And so, and we call or email the newspapers to try to correct this to make sure that it's correct, right? That's a lot of manual work, right? That team would much rather be out there and like, you know, pitching a new story or building relationship with journalists or doing something else than doing that manual work. And so, like, having it assess those articles and identify those errors, and then even sometimes draft an email to the journalist to ask them to correct it. It's like a nice, it's a nice thing to avoid that manual work and spend time on something else.

• 46:19Alex Kantrowitz?

  Wait, the AI can actually pick out the errors in stories or is it just the sentiment? Dude, just check the negative stories for the errors.

• 46:25Speaker 01

  Very relevant point, Alex.

• 46:26Alex Kantrowitz?

  Yeah.

• 46:26Speaker 01

  That's actually exactly a very interesting thing. Because one of the key learnings we had the last twelve months is that

• 46:36Speaker 01

  There is an old rule in data scientist, which is shit in, shit out. And that still applies in AI world as well. If you feed the AI with a lot of noise and incorrect information, you cannot expect it to be able to answer such things. So in order for it to assess whether the information in the articles is accurate, you also need to have a very strict data set that says what is accurate information about the company? And you need to have that in a, in a, in a good, solid place. And one of the things that we've seen is that Klarna has been historically using a ton of enterprise systems, different ERP systems, which actually silos and puts more noise to the internal information. So we have our org charts in Workday.

• 47:19Speaker 01

  We have our clients in Salesforce. We have, you know, our suppliers in M files. We have tons of different pieces of information spread out on proprietary data systems with different structures and so forth. And that's hurting the ability for us to use that information in a standardized way to make AI work better. And so part of what we've been doing in the parallel with this is we realized that we need to standardize and centralize our information about the company, what we're doing. Because then both humans actually can make more productive work as well as, as AI on top of that, both AI. Like if, if there's too many silos of information in the company, if things are not transparent, not open, it makes it harder. And that's true both for AI as well as that, right?

• 48:04Speaker 01

  I think that like, that's why I wasn't too convinced when people were like, oh, look, I have a PDF reader. And AI can read a PDF and answer questions about like, yeah, but you know, the problem is in many companies, you have too much information and too much inconsistent information and too much duplicative information. So you have to think about both, how do you improve the information that you have? As well as like, how do you then use that to do things like this, which is, you know, check whether their data is correct or not in the article?

• 48:31Alex Kantrowitz?

  Yeah, there was a Wall Street Journal article talking about how companies were like really struggling to get this to work. When they tried to get AI to pull data, it would pull data from like the wrong year. And I think the key takeaway from that is that really that it just has to be, the data has to be clean for these things to work.

• 48:47Speaker 01

  Exactly.

• 48:47Alex Kantrowitz?

  And structure to us to some extent. You're also, your lawyers are also using chat GPT to write first drafts of contracts. And that's cutting the hours it's taking to draft a contract. I mean, that to me seems like a low hanging fruit type of area where like. You you have your lawyers like effectively draft the first draft and then they can do like some of the stuff that you're like actually paying for them for them for versus having them write up boilerplate contracts.

• 49:14Speaker 01

  Well, I think that's true. Again, coming back to like low hanging fruit and, you know, more difficult things, there will definitely be tasks that people could have like, you know, instead of drafting this, maybe you should have just shared a draft internally. So you didn't rewrite it every time, right? Did you really need to, you know, start from scratch every time? And now just because AI is there, people are applying it and like, oh, I'm saving that time, but you might as well just share the drafts. Like, obviously, there are use cases like that. Obviously, there are things like that as well happening where it's just like, this could also have been done by just a little bit more standardization and like simplification or sharing information internally. So it's a combination, obviously, but it it helps provoke the idea, right? It helps accelerate and then as long as I see the business implications

• 49:57Speaker 01

  and the business results, doesn't matter me too much, you know, how those are accomplished in that sense.

• 50:02Alex Kantrowitz?

  Right. Okay, I want to end with a question about this data buy now, pay later. Like, we've talked a lot about AI, but I would be remiss if I had the CEO of Klarna on the show and didn't ask a little bit about buy now, pay later. So buy now, pay later was obviously like a darling of the fintech industry and of tech. Apple tried it. Your valuation was in the 45.6 billion in 2021, but then went down 85% to 6.7 billion a year later. And this was the CNBC headline, Klarna valuation plunges 85% to 6.7 billion as buy now, pay later hype fades. So I'm just curious to hear two things. First of all, from you, how has

• 50:45Alex Kantrowitz?

  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?

• 51:06Speaker 01

  Yeah, so I mean, first on navigating up and downs, like I, you know, I think to some degree I benefit from the fact that I've been doing this for twenty 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 PayPal or, you know, a Square Block or whatever, they had the same eighty-five percent drop in the stock market during the same time, right?

• 51:49Speaker 01

  So we weren't singled out in that sense, but that was a general fintech and tech kind of 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, uh, you, you don't like making, which was a, a reduction in staff and stuff like that, which is very 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 company and for the, the employees that are still here and our shareholders and our customers and so forth.

• 52:30Speaker 01

  So I think we, we did the right thing and now we are a profitable company again, which we actually, you know, people don't know this, but Klarna was come, was profitable from two thousand five to two thousand eighteen. So we had a history of kind of running this a little bit differently than most tech companies, just like burn money. We have been profitable, but then when we came to the US, we invested heavily and that meant that we were loss making for some years. Um, now on the, um, on the other topic, on Bynapelator, it, to answer that, you have to like first define what is Bynapelator? Because at, at the core, what it is to me is We have had a credit card industry, which basically works in a way where you swipe your card, you get your monthly statement with all of your transactions, and then you're encouraged to revolve.

• 53:12Speaker 01

  And if you do so, you start building depth, and that depth earns the money, earns the bank a lot of money, right? Buy now, pay later. The difference with it, the way I define it, is that it's interest-free and it's installment-based. So you take a single transaction, you don't pay installment on, interest on it, and you pay it down in installments. You can offer that on a card as well, right? Like, you can rethink your credit card to make it work that way. You don't have to offer revolving. You can do installments on a credit card, right? But the concept of interest-free installments is, to me, a healthier credit concept than revolving. In addition to that, the difference with buy now, pay later, also in Klarna's case, we're our own network, right? So we are the equivalent of American Express, in the sense that we're a third-party network.

• 53:55Speaker 01

  We have Stripe and Adyen and other PSPs and acquirers that offer Klarna as a payment method, side-by-side with Visa, Mastercard. But we have a direct consumer relationship, just like Amex. So the network means that the fee that the merchant pays is not through Visa, as they do on Visa, but directly to us. And this means that there are less middlemen in between the merchant fee and the actual income to us as an issuer of giving you, as a customer, this offer. Allows us to offer an interest-free product, which merchants still are paying for on equal parts with a credit card, which actually also charges on the consumer side.

• 54:36Speaker 01

  So if you look at the total cost of payments in the US, and you look at both what the bank is earning by interest on your revolving, plus the merchant fees, it's actually a crazy $5.5 on a $100 spend. And we earn less. We do about $2.5 per $100 spend. So we do much less, but we also have much less cost, right? We're coming from a different cost profile. We're a small, we're lean fintech, we're not a big bank, et cetera. So you're accepting a lower revenue per customer, with the benefit of providing a slightly more, a better product for consumers from a credit perspective. Now, it's still credit. I always say that. Like, I'm fighting fire with fire. So I'm not saying, like, credit still has its issues and so forth, but in a future state,

• 55:21Speaker 01

  if people want to use an installment product with zero interest over a credit card, I think that's a better outcome. You should use debit. And then you occasionally use a buy now pay later as opposed to using a credit card for all of your spending. I think that's a better thing. And I always make the comparison back in the days, your card used to have press one for debit and press two for credit. But the banks removed that because you wouldn't build as big of a balance on a monthly basis if some of your transactions went on debit. So by having all of your transactions on credit, you were more likely to revolve and then the banks make more money. The average outstanding credit card balance is $5,000. The average outstanding balance on Klarna is 150. So it's a huge difference, right? So I think it's like, it's a, but, but there was a McKinsey

• 56:01Speaker 01

  study that said that in the US, there are about 20% of the consumers are what they call self-aware avoiders. These are customers who are like really looking for this kind of product as opposed to a credit card with massive bonus points or, you know, other services that are more interesting for them or heavy revolvers and stuff like that. So this is not the full population necessarily, but I think there's a, a good amount of people that prefer this kind of product and see value to it versus other options.

• 56:30Alex Kantrowitz?

  Yeah. Okay. This is really the last one and it's quick. As you start to put more AI into your business, do you expect to increase headcount or decrease headcount from here?

• 56:39Speaker 01

  Well, I, I, you know, you didn't ask about that, but actually one, another announcement that we've made is that we haven't been hiring since September. And since we, as many tech companies have a natural attrition rate, where about, you know, 20% leave on an annual basis. So people stay about five years, which is kind of typical for tech companies. This means that we are shrinking. So we used to be about 4,500, we're now 3,700 and we are basically shrinking on average about 70 people per month. And, and we've doing that because we don't want to do layoffs. So we are actively managing down because we see we can do more with less people. At the same point of time, in order to create, you know, some benefit for, for

• 57:23Speaker 01

  employees in this, we have said that like, And be very clear about it, which is that our total employee cost will go down, but our cost per employee will go up, right? So that is basically a commitment to our employees that like they will benefit from this in seeing higher salaries and more equity shared with them, which is what we're done and we just did that a few weeks ago. Now we distributed a lot of equity to our employees. So we're saying that like for the people that want to stay and participate in this, there's an upside to them as well in the fact that we're doing this, but we are shrinking and we will continue to shrink as a company will be much less, but doing much more.

• 58:02Alex Kantrowitz?

  Fascinating stuff. Sebastian, thanks so much for coming on. Great speaking with you.

• 58:06Speaker 01

  Thank you, Alex. Thanks for having me.

• 58:08Alex Kantrowitz?

  All right, everybody. Thanks so much for listening and we'll see you next time on Big Technology Podcast.

• 58:13Speaker 02

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