Lenny's Podcast: Product | Career | Growth - Lessons from scaling Spotify: The science of product, taking risky bets, and how AI is already impacting the future of music | Gustav Söderström (Co-President, CPO, and CTO at Spotify)

Starting point is 00:00:00 The internet sort of started with curation, often user curation. So you took something, some good, like people or books or music, and you digitize it and you put it online. And then you asked users to curate it. And that was your Facebook, Spotify, and so forth. And then after a while, the world switched from curation to recommendation, where instead of people doing that work, you had algorithms. And that was a big change. It required us and others to actually rethink the entire user experience. And sometimes the business model as well.

Starting point is 00:00:26 And I think we're entering now is we're going from your curation to, recommendation to generation. And I suspect it will be as big of the shift that you will eventually have to rethink your products. We have to rethink the user interface and the experience for recommendation first era. And so what does that mean in the generative area? No one really knows yet. Welcome to Lenny's podcast where I interview world-class product leaders and growth experts to learn from their hard-won experiences building and growing today's most successful products. Today, my guest is Gustav Soderstrom. Gustav is a product legend. And he's now the co-president chief product and chief technology officer at Spotify, where he's responsible for Spotify's

Starting point is 00:01:05 global product and technology strategy, and oversees the product, design data, and engineering teams at the company. I've had Gustav on my wish list of dream guests to have on this podcast since the day I launched the podcast, and I'm so happy we made it happen. In our conversation, we dig into what Gustav has learned about taking big bets and what to do when they don't work out, how Spotify moved away from squads, and how they structure their teams now, how AI is already impacting their product, and also the future of music generated by AI, also why all great products need to pull some kind of magic trick,

Starting point is 00:01:38 how accurately succession represents Swedish business culture, and his hilarious analogy of peeing in your pants. Enjoy this episode with Gustav Sutterstrom, after a short word from our sponsors. This episode is brought to you by Microsoft Clarity, a free, easy-to-use tool that captures how real people are actually using your site. You can watch live session replays to discover where users are breezing through your flow

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Starting point is 00:03:52 Epo supports tests on the front end, on the back end, email marketing, even machine learning claims. Check at Epo at getepPO.com. That's getepo.com and 10X your experiment velocity. Gustav, welcome to the podcast. Thanks for having me, Lenny. It's pleasure to be here. It's my pleasure to have you on. So at this point, you've been at Spotify for over 14 years, which is a rare feat. in the tech world, and you've held a lot of different roles while you've been at Spotify.

Starting point is 00:04:25 Can you just start off by giving us a sense of what these various roles and what you've done over the years at Spotify and then just what are you, what are you up to these days? What are you responsible for now? So I came into Spotify in early 2009, late 2008. And my job then, I had been an entrepreneur, started some of my own companies. in the back then very, very early sort of feature phone smartphone space. So I had a bunch of knowledge there. I had sold a company to Yahoo in the mobile space.

Starting point is 00:04:56 I worked there for a while. I came back to Sweden. And then I met through a mutual friend, Daniel Lech, the CEO and co-founder of Spotify. And they had built the desktop product already, the free streaming desktop product. And it was amazing. And I could try it. But they needed someone to figure out, to do with mobile. And because I had been an entrepreneur in that space, I got that job.

Starting point is 00:05:20 So my job was to head up mobile for Spotify and figure out what the mobile offering would be, which was a challenge because obviously Spotify desktop was a free on-demand streaming application. And back then, specifically with edge networks, you couldn't really stream at all in real time. The performance wasn't there. And also you couldn't fund that with an ads model. So it was a product and business model innovation that was a lot of fun. So that's how I started. Then after a few years, I took on all of product development for Spotify. Then a few years later, I actually took on the technology responsibility, sort of the CTO role for Spotify as well.

Starting point is 00:06:03 And recently, my official title is co-president of Spotify, together with Alex Nordstrom. So we kind of run half of the company each. I run the product and technology side, and he runs the business and content side. So that's the super fast version. Aside from getting more responsibilities, like taking on the technology department, it has been sort of the same job by title.

Starting point is 00:06:28 I've always reported to Daniel, but because Spotify has grown so much every six to 12 months, it's been like starting at a new company. First, it was sort of a Swedish-Nordic challenge, and then it was a European challenge and then it was getting into the US and then we became a public company. So it's sort of as if I had jumped around

Starting point is 00:06:50 between a lot of jobs actually, even though it was largely the same title and role. Your story makes me think of the classic Be careful what you're good at because you end up taking on more and more and clearly you've been given more and more responsibility over the years and so clearly things are going well

Starting point is 00:07:06 and you're doing well, shifting a little bit. So you're on my podcast currently. You actually have your own podcast, which was kind of this limited series on the product story of Spotify, which I've listened to and loved. And it's kind of surreal to listen to your voice in real time because I've been listening to that recently in preparation for this conversation. Two questions, just what made you decide to launch your own podcast knowing you had a full-time job and a lot going on? And the production value for your podcast was very high for what I could tell. And then, too, just what did you learn from that experience in terms of the product you ended up building and just like empathize? with the podcast creator side.

Starting point is 00:07:41 There were a bunch of different reasons why I did that. One is, and not a small one, I think like you, I love writing, and I have this secret creator dream in me. You know, I used to write blog posts a long time ago, and I write internally a lot. You can't write that much externally when you work at a company like this. Yeah. But I love writing and talking and presenting. So there was certainly that.

Starting point is 00:08:06 And then no small part was to, from a prior point of view, to empathize with one of our main constituents, the podcast creator. I'm unfortunately not a great musician. I try to play instruments and so forth, but I don't have any records. I don't sing very well. But it decided to make a podcast. And that taught me a huge amount about what it's like to be a creator, how creating different styles of podcast. For example, we wanted to do a higher production cost podcast with, music, and then right away you run into a bunch of problems. Spotify is actually pretty well positioned to solve, but still, like, it's really hard

Starting point is 00:08:47 to have music in a podcast from a rights perspective. So you get, you understand all these problems that podcasters have, and you can be better at solving them. But the biggest benefit and the real reason for, for doing the public podcast was that I had actually done an internal podcast through sort of a hack where we, could gate the podcast only employees. And I try to figure out internally how to build more culture around Spotify and sort of help define for new employees and existing employees,

Starting point is 00:09:22 who we are, the mistakes we did, the successes we had, and how we think about strategies specifically and product strategy, because we were quite well known externally for technology and the squads and all of these things, not so much for product strategy. And because I love storytelling more than Google, I decided to do an internal podcast. And I went around and I interviewed actually Daniels direct reports, so the CMO, the CHRO and CFO and so forth, and just ask them about the bunch of stuff.

Starting point is 00:09:54 And the idea was to make them more approachable for employees because I felt listening to podcasts, you know, even these people that have no idea who I am because I've never met them, I feel like I know them. I feel like I know how they think. And I just like them much more. So the secret idea was, what if you could get to know your leaders much better than you do through occasional meetings or some town hall? So I did that internally. And because I'm a product person, we ended up talking a lot about product and product strategy.

Starting point is 00:10:22 And people internally really like that. So next time, the question was, what if people that don't even work at Spotify yet could feel as if they knew people at Spotify? That would be great. most leaders in most companies are very opaque and appear as some sort of otherworldly creatures that aren't really real, I think, when you see them in like business papers or something. So what if you have heard them talk for an hour or so? So that was a general idea. So a combination of recruitment tool, sharing more about how we think about product strategy,

Starting point is 00:11:00 and just because I think it was a lot of fun, I got to interview a bunch of smart and interesting people both externally and internally. Did it have the effect that you were hoping after looking back? I think it did. The podcast did well. And no, we did not give it our own sort of promotion. I had to compete as everyone else, which also gives you a lot of empathy for the problem of like, okay, now you have a product. What about user acquisition? How do you actually get people to listen to it? So it did achieve what I wanted in the sense that we have this thing called intro days where especially in the past few years when we've hired a lot,

Starting point is 00:11:39 we actually fly people to Stockholm for sort of an onboarding session to learn about Spotify. And the leadership is on stage, talking about what they do and the departments and strategy and so forth. And it's very common that people come and tell me that, oh, you know, I listen to this podcast or this in the episode and it's at least one of the key reasons why I joined or sometimes the reason why I joined. So it's sort of anecdotal. but it may be in the many tens of people at least have said it.

Starting point is 00:12:09 So that seems to work. That's really interesting. Just again, and this comes up a few times on the podcast, it's just the power of content in all these different ways for hiring, for culture building. And it sounds like internally it was the original goal is just internally build this culture around strategy. That was the original goal.

Starting point is 00:12:25 Make senior leadership more approachable and reduce the distance and then also share more of the thinking and entertaining or, way rather than just through talks that people end up not reading. I love that. So I was listening to it, as I said. And what was really interesting is I think episode four was actually all about AI. And I think your first kind of attempts at leveraging machine learning and AI within Spotify. And I think that's what led to Discover Weekly and a few other tools.

Starting point is 00:12:53 And that was like years ago. And it's interesting listening to it now where AI is again like, you know, a huge deal. And so I'm curious very tactically on the product team, what you advise. product managers and product teams on how to think about AI in their product thinking and also just in their day-to-day work. I can give a few examples there. I don't know that we're more sophisticated than anyone else, but what we're doing at least the traditional machine learning for quite a long time. And I think in the podcast, I think I talk about the journey of the internet in sort of stages. And one way to think about it is that the internet sort of started with curate.

Starting point is 00:13:34 of the user curation. So you took something, some good, like people or books or music, and you digitize it and you put it online. And then you asked users to curate it. And that was your Facebook, Spotify, and so forth. And then after a while, the world switched from curation to recommendation, where instead of people doing that work, you had algorithms. And that was a big change.

Starting point is 00:13:55 It required us and others to actually rethink the entire user experience and sometimes the business model as well. And I think what we're entering now is we're going from your curation to recommendation to generation. And I suspect it will be as big of a shift that you will eventually have to rethink your products. So that's one lens. So I tend to talk to my teams about even though it's all machine learning, I asked them to think of this as something completely different. The recommendation era was one type of machine learning. The generation era is a different type.

Starting point is 00:14:33 So don't think of it as just more of the same. Think of it as something actually completely new instead. And what we learned in, well, a few things. So if you look at this new era of large language models and diffusion models and so forth, there are two types of applications. As I said for the recommendation era, we had to rethink the user interface and the experience for recommendation first era. And so what does that mean in the generative area?

Starting point is 00:14:59 No one really knows yet. There are a bunch, as usual, there are a a bunch of iterative improvements. So we use these large language models to improve our recommendations. You can have bigger vectors that can have more cultural knowledge. You can use it for safety classification on podcasts and no one has listened to yet and so forth. There's lots of obvious improvements and we're doing those. But so far, we've only really done one sort of real generative product in the hard definition, which is a product that couldn't have existed without generative AI,

Starting point is 00:15:33 and that is the AI DJ. So that's a concept that we've been thinking about for a very long time. And the AI DJ is you press a button, a digitized person. There's a real person named X, and we digitized X. So he's now an AI,

Starting point is 00:15:51 comes on and talks to you about music that you like and suggest music, and you can listen to it. And if you don't like it, you can kind of call him back, and he says, now let's listen to something maybe from a few summers ago, or here's some new stuff that, you know,

Starting point is 00:16:05 we're trending yesterday, last of us episode or something like that. So that product couldn't have existed without generative AI, both generating the voice and generating what the content of what the voice says. So you can have individualized, personalized voice at the scale of, you know, half a billion people. And so we had the use case we had seen for many, many years. Sometimes people call it the radio use case.

Starting point is 00:16:34 We call it the zero intent use case internally. When you actually don't know what you want to listen to at all. Spotify wasn't that good. Spotify was good when you knew, at least roughly, you knew the use case or what you wanted to do if it was a workout or dinner. We had lots of options for all of those. But if you really didn't know it all, it was hard to open Spotify and sort of stare at it.

Starting point is 00:16:56 And people used to say longingly, you know, that this was the one thing that radio was good. that. Radio was quite bad, to be honest. I mean, it's not personalized to you at all. It's not on demand. You come in in the middle of things. It's actually terrible in many ways. But people still often say that there was

Starting point is 00:17:12 something good about it. I think that something was the fact that you had a knob and you could just switch between contexts. It's like, no, boring, boring, boring, boring. Okay, this is good. And Spotify never had that mode of like, I don't know what I want, but I want to sort of cycle through things until I find something that I like.

Starting point is 00:17:30 And I think with the AI DJ, that's actually the use case we managed to solve. So X comes on and says, I'm going to suggest something to you that you can listen to. And if you like it, you can keep listening. But if you don't like it, you kind of bring him back again and you change genre. And for one reason or another, we tried to solve that for many times for a long time, but just starting to play a random song without any context as to why you would hear this, it just didn't, never worked. So that was our first sort of foray into a product.

Starting point is 00:18:00 that couldn't exist before. And I think to your question of principles around that, there are a few pretty distinct principles that we've learned. One that I really like that is not my principle at all. I think it is straight from Chris Dixon is the principle of fault-tolerant user interfaces. So I can't say how many times during the early machine learning era when we said, you know, we're moving from curation to recommendation. I saw a design sketch that was a single big play button.

Starting point is 00:18:31 Because clearly that is the simplest user interface you can do. But if you don't understand the performance of your machine learning, you can't design for it. The quality of your machine learning, if you're going to have a single play button, needs to be literally 100% or zero prediction error. And that's never the case, right? So let's say that you have a one in five hit,

Starting point is 00:18:54 four or five things are done. Then you need a UI that, probably at least shows five things at the same time on screen. So you have a one in five of something being relevant on screen. So you need to understand the performance of your machine learning to design for it. It needs to be fault-tolerant. And often you need an escape hatch for the user. So you make a prediction.

Starting point is 00:19:14 But if you were wrong, it needs to be super easy for the user to say, no, you're wrong. I want to go to my library or to this or to that. So we have that principle of having fault-tolerant user interface and a user interface that corresponds to the current performance. of your algorithms. And I think that is going to be true for generative machine learning as well.

Starting point is 00:19:35 I think a very clear example actually is mid-journey. If you think about the early mid-jury user interface inside the Discord channel, actually generating an image was very, very slow. It took a long time to generate high-quality image, and they could have built a silver-button thing where you put in a prompt, you wait for minutes.

Starting point is 00:19:56 You get an image. And I think one out of four times is going to be bad. So you would have been disappointed three out of four times. And it's a minute each. So like four minutes later, you'd be, this is a shitty product. What they did was they generated four simultaneous low-rest images very quickly. And you could say like, so apparently their performance was probably one in four. That's why they showed four and not six.

Starting point is 00:20:21 And so one in four was obviously was usually pretty good. You click that one and either continue to iterate or scale it up. So that's also an example of, I think, people understanding where the performance of generative AI was when they built the UI. So it's something that, you know, I would be inspired by. And for the AI DJ specifically, another principle is to try to avoid this urge of just wanting to show off the technology and have this voice sex talk and talk and talk. You have to remember that people came there for the music. So the principle for the AI DJ coming from the team, by the way, this was a bottoms up product actually. it required a lot of support.

Starting point is 00:20:58 We actually required big companies and so forth to be able to build it. But the idea has been built by teams, bottom up. So the principle there was literally to do as little as possible and get out of the way. And I think that was really helpful. You know, it's not telling you what the weather is and what happened in the news and going on and on and on about this band. It is trying to get you to the music. And I think that's why it's working because it is working very well for us. I love this distinction between recommendation and generation.

Starting point is 00:21:28 And this kind of begs the question of there's this trend that I imagine you're seeing if people auto-generating music using artists, you know, catalog. Like there's this Drake and the weekend thing that came out a week or two ago. Where do you think this ends up going? And how do you think artists adjust to this world where music can just be audited into this play button is like all of it is generated versus just like the DJ in between the songs? first big caveat is this is just super early. No one knows anything, you know, about how this is going

Starting point is 00:21:59 to play out or the legal landscape and so forth. But I think it's going to be to have a lot of impact. And I think if we talk about two things, one is what it could do for music. The other is the right situation. And if rights holders are getting compensated and so forth. So we talk about the first thing in isolation. I think an interesting example is right about when I grew up, up, Avichi came along. And it's interesting to think about because Avichi was not really considered by the existing music industry as a real artist, because he couldn't really play an instrument and he couldn't sing. And he was just sitting with this computer in this dog, the audio workstation. And so it wasn't really considered real music. And I think now all of us

Starting point is 00:22:45 consider it very real music and that he had tremendous real musical talent. So I think right now we're probably in the face where people say this isn't real music and it's somehow fake. I think the way to think about these diffusion models, if and when they get good enough at generating music, is probably the same, like an instrument. It's just a much more powerful instrument. And we'll probably see a new type of creator that wasn't proficient at an instrument, and they couldn't assemble a full orchestra and do the thing that they had in their head.

Starting point is 00:23:21 and they can now generate very, very new things. I also think, by the way, that there is this distinction between AI music and real music. That doesn't exist, for sure. Very talented real musicians are using AI to get better and to help create new ideas. So that distinction doesn't really exist. It's all going to be AI.

Starting point is 00:23:44 The question is what percentage, which makes the problem harder, because you can't talk about if it should exist or not. You have to talk about what percentage. should exist and who gets to use it or not. But I think the way to think about it is probably as an instrument that could help create a huge amount of art. And I think this is not news to you who probably use these things a lot,

Starting point is 00:24:08 but I think if you don't use these generative models, there is the perception that you tell it to create a hit and you will get that. That's not how it works. actually what these models do is because they've been listening to a lot of music, they are very good at doing something that sounds very similar to what already exists. Actually being original is very hard.

Starting point is 00:24:31 And from one point of view, as it now gets easier to create more generic music, it will actually be more difficult than ever to be truly unique. So I still think there would be tremendous skill in creating something truly unique. And my hope would be that what happened with the DAW and that technology jump, was you got a whole new genre like EDM. You couldn't really produce it with an orchestra or live.

Starting point is 00:24:56 And maybe we'll see completely new music styles with these technologies. I think that would be very exciting. So that's on the positive side. But then you have the rights issue, which I have a lot of empathy for. And Spotify specifically has seen this before. So we had a different technology shift like this, which was the technology shift to online downloads of music and piracy and peer-to-peer. So first it was a big technology shift in peer-to-peer,

Starting point is 00:25:22 and it was exciting for consumers. More consumers started listening to more music than ever. And I think that's where we are now with generative AI. There's a new technology, but it also required a new business model before creators and industry could actually participate and benefit from this. And that's obviously self-serving to say because we were a big part of innovating that business model, but I still think that's what's necessary. and I hope that that's what I and we could be part of.

Starting point is 00:25:50 So I think we've seen the first part, the technology shift, and there would probably be a lot of discussion and chaos here, which I have a lot of empathy for. But I think we haven't seen the second part yet. What is a model where this could be a benefit? What would actually happen after piracy is that the music industry got bigger than ever, not just as big, but bigger than ever. And I think that could happen with,

Starting point is 00:26:15 this technology as well. But we're right in the beginning. So along the same lines, something else you teach is this idea of all truly great products have to pull some kind of magic trick. This comes up in your podcast a lot, and I think you mentioned this other places. And thinking about all the stuff you're talking about here, it feels like, in a sense, everything's going to feel like magic, because AI is kind of big into it. I think when we did the AI DJ, we did a small version of that. When people first listen to it, we could see that reaction in user testing when they wondered like, so the magic trick there was that how could they record this person saying

Starting point is 00:26:53 so many different things because it's talking about my music. So the magic trick was obviously, didn't record a person saying it's generated. And that magic trick wears off. You hear it all the time now and so forth. But it was one of those magic tricks. So I still think that concept is important and it seems to correlate with products sort of going viral and taking off.

Starting point is 00:27:15 And I think it was the same using something like Dali or Stable Diffusion on Mid Journey the first time. It completely seemed like a magic trick. And obviously there is no magic. It's just data and statistics. But I think getting to that point and iterating a product to the point where it feels like magic the first time is very helpful. And it's often a question of just getting the performance to a certain level,

Starting point is 00:27:42 scoping down, removing things. There's a lot of fine-tuning, I think, that makes you cross that line from it's cool and impressive, but not magic to it feels like magic. I don't understand how this could be done. Yeah, it reminds me of the launch of GPT, which ended up being the biggest, most fastest growing product in history, and it's like the epitome of a magic trick. It feels like actual magic. Absolutely, absolutely.

Starting point is 00:28:10 And to most people, it is still very mad, actually to a lot of us and even to researchers. It's a little bit magical. No one really understands fully. So I guess there's maybe some magic left in the world. Absolutely. I think a lot of people are worried about not understanding what's going on there. Shifting to the way you all build product at Spotify. So Spotify is kind of famous for popularizing this idea of squads and tribes.

Starting point is 00:28:35 And correct me if I'm wrong, but you guys have kind of moved away from that approach. Yeah, that's right. Okay, so I love to understand just like why you shifted and what you kind of learned from that approach to building product. And then just like, how do you organize the teams now? What do you do now? This was something that we focused a lot on early. And it turned out to be smart of us to name these things into squads and chapters and so forth. It wasn't really, well, maybe it was sort of deliberately branding.

Starting point is 00:29:05 But it wasn't for purposes of branding that we made it up. We made it up because we thought it was a good structure to use. and we needed names for things. And this was the early internet era, so you were allowed to make things up. And so it was very good for where we were at the time, and it certainly helped us in recruiting. It'd become a little bit of a cost to us

Starting point is 00:29:24 because people still think that we organized that way, and it's not a very efficient way of being organized at this scale, or maybe even if you started over right now, because we've learned more. But I think the big differences is the idea with the squad specifically, was twofold. They were supposed to be small

Starting point is 00:29:44 and sort of full stacks. It should be about seven people, and it should have front and back end, mobile, QA, agile coaches and so forth. And it should be very autonomous, was the idea. And that's really,

Starting point is 00:29:58 will be shifted. So first of all, as you grow the company, scaling in increments of seven engineers just creates a ton of overhead. So obviously our teams now tend to be much bigger, maybe two, three times that at least. per like manager to maybe have like 14 or something instead of seven and just less

Starting point is 00:30:17 overhead roles. And so that's one. It looks more traditional as you learn more and it's reasonable as you scale. The second big thing I think we struggle with was back then when I joined, the average age at Spotify was, I mean, I was the oldest and this was 14 years ago. I think the average age was probably under 30 or something. And it was in most tech companies. And so we had coming from Sweden, which is a different culture than the U.S.

Starting point is 00:30:53 And I love a lot of things about Swedish culture. I think we managed to keep the best parts. But Sweden is a very sort of bottoms up autonomous culture. There's this famous drawing on how you make decisions in Sweden in the U.S. I think it's just a hierarchy in Sweden. It's kind of a circle. You said in a circle, no one is in the middle. There is no leader and so forth.

Starting point is 00:31:13 So I think by sort of culture, we're very inspired by this super autonomous thing. And I think the idea with autonomy is very reasonable and the right one, which is we work and we are hiring the smartest people we can find. And we pay high salaries for that. So if you're hiring smart people, one way to think about it is, you're renting brain power. So if you're renting all of this expensive brain power, and then you give them no room to think for themselves,

Starting point is 00:31:44 that doesn't sound smart. Then you should actually hire less smart people and keep your cost down or something. So I think you have to give a bunch of autonomy to actually maximize the value of the investment you're making. So that's very reasonable. You would give a lot of space for people to use as much of their talent and capacity as possible.

Starting point is 00:32:05 But the problem with that is if you put autonomy very far towards the leaves of the organization, and also if you combine that with having a very junior organization, which we did back then, there's a fair chance that you're just going to produce heat. You're going to have 100 squads with 100 strategies running in 100 directions. And Spotify has been there in that camp. I mean, we managed to get somewhere for sure in spite of this, but I'd struggle to say we were efficient in doing that. So we've done a few things.

Starting point is 00:32:39 The team structure is more traditional, larger teams, less overhead, and we've been specifically working with where in the org do we put the autonomy? Because the extremes are at the leaves, and we were there. The other extreme may be at the top, let's say maybe something like Twitter. There's one person. Both have problems. If you have it at the leaves, you're going to produce a lot of heat. If you have it at the top, you need someone with a lot of capacity,

Starting point is 00:33:07 and Elon has a lot of capacity, but you are, by definition, going to bottleneck. All decisions have to go through there. And Daniel, it's not his personality that he even wants to make all the decisions. He wants to maximize throughput rather than to bottleneck the throughput. So the question is, if it's not at the top and not at the very bottom, where do you put it? And what we've found, which I don't think is very contrary. at all. I think this is the case in most companies is around sort of the VP level. So if you have Daniel, then you have the C level, myself and others, then you have sort of the VP level.

Starting point is 00:33:42 That is a good mix of instead of having one person in the company think, so only Daniel then and the rest just do, you have on the VP level in a company like this many tens, to maybe hundreds of people that have a lot of autonomy to think. So you get a good amount of freedom of thought and people thinking in different directions. But it's not like 8,000 people. And these people on the VP level are both quite a lot of them, but they're also usually quite senior. They have a lot of pattern recognition.

Starting point is 00:34:16 So I think that solves for, it's like a good, if you think of it as an optimization problem, it's kind of a good optimization space. So the autonomy level in Spotify now tends to be quite high at the VP level and then lower around those levels. And when you say autonomy, what does that actually mean? Is it the VP of, say, the podcasting product has a lot of say over what happens and there's not a ton of, I don't know, like how involved are people above?

Starting point is 00:34:45 And I know Maya is the VP of product, I believe, for the podcast product, who I think is going to come on the podcast someday. What does that mean in terms of Tommy for her, what practically? So it means that I would ask Maya. to define a strategy for what we do in podcasting, how are we going to be different, why would a podcast want to be here, whereas in another company,

Starting point is 00:35:08 I would make that strategy or another company, Daniel would make that strategy. Same with the AI DJ, for example, came from our personalization team. And so that was a bet that they made. So they have autonomy to make those kinds of bets and define strategies. Same with the user interface. We have an experienced team.

Starting point is 00:35:28 I can talk about the org structure later, but I put a lot of autonomy on the VP of experience to define and suggest what it is that we want to do. And in other companies, I would define all of that myself, for example. Just going even a little bit further here. I know you have just like strong opinions on the way to organize teams and how the organization kind of helps you optimize for specific things. What are your kind of just thoughts along those lines

Starting point is 00:35:56 and what do you have you learned about the impact of organization and what you're optimizing for? Yeah, so I talk about sort of an idealized spectrum or maybe not idealized, but exaggerated spectrum. Nothing is really true, but you create extremes to make a point, right? So on one spectrum, you have something like Amazon, which is known for two pizza teams. no dependencies. You try to minimize dependency so you can run in parallel. Teams compete with each other,

Starting point is 00:36:32 even on the same project and so forth, but they have direct access to the user. And so the benefit here is if you have an idea, the time to get the user is very low. And it has worked for them. It's produced, you know, it's produced Kindle, it produced Alexa.

Starting point is 00:36:48 It's produced a lot of very novel things. There are a few interesting downsides here. One downside that I'm extremely impressed with Jeff Bezos for seeing is if you have teams that compete with each other, the incentives are to

Starting point is 00:37:05 hide your results, hide your code, and that should make for an organization that gets no platform leverage because no one is cooperating. And I think this, either he had that insight or because he saw this, he had to do this, but

Starting point is 00:37:20 he's well known for pushing extremely hard on hard APIs. If you don't create hard APIs to your technology, you're out. And if you think about it, it has to be that way, because otherwise no one would do it. And a hard API is essentially, like everyone knows how to use this API and connect to this team to interface. Exactly. You have to expose your technology to others. You have to maintain those APIs, and they have to be very structured because otherwise the whole thing would collapse as everyone's supposed to compete because there are no incentives. You have to centrally force that. And interestingly, even though theoretically then they're the worst position to have a structured platform.

Starting point is 00:37:55 I think because they forced it so hard, they were the ones who did Amazon Web Services because they had such hard-defined APIs because of this rule that it was easier for them to turn it inside out and expose it the rest of the world. Whereas if you look at something like Google, I think they struggled more with externalizing their APIs, maybe because it is so friendly and soft, so they didn't need as hard APIs on the inside because there was no competition. People could just go into each other's code. So it's interesting anecdote around it.

Starting point is 00:38:22 But the main point is you're faster there, but it's going to be hard to cooperate. And so you will see something like maybe exaggerating a bit. Sometimes you'll see multiple search boxes on the same page from different teams. And this has been true in Spotify, by the way, as well. You've seen like multiple toasters on the now playing view coming up from different teams because they're working when we were in the autonomous mode, everyone running. And then, so you get the benefit of speed,

Starting point is 00:38:51 but you get the drawback of kind of shipping your org chart and shipping complexity to the end user. But clearly that's been the right choice for Amazon because they're a trillion-dollar company. But then on the other spectrum, you have something like Apple, who's also a trillion-dollar company. So clearly both models work,

Starting point is 00:39:10 where you would never see two search boxes from the same team popping up on an iPhone. that shit is centrally organized by something that is close to single individual. So they are instead in probably the world's biggest, largest functional org, they're doing as much. If you think about what goes into Apple, it's, I mean, they certainly do everything we do. They have music service, podcast service, audio books, and they have a billion other services. So it's not like they have an easier problem.

Starting point is 00:39:40 And yet they build something that feels more like it was built by a, a single developer for single user. So they centralize and they have this bottlenecking function that everything has to go through and be decided how it fits with everything else. And so that has the benefit of the user experience being simpler and not shipping the org chart and increasing complexity, but it also has the drawback of speed. Without having facts on it, I've heard people working at Apple

Starting point is 00:40:09 have said like, yeah, it took seven years to get that thing to market because it just had to wait in the pipeline. So you have these extremes. And I think the most interesting example, I think, to think about is when you double-click the power button on an iPhone, the Apple Pay comes up, that decision, how did that happen? You can imagine that all the services team would like to pop up when you double-click that button. And so someone had to decide, should music come up, should payments come up, should something else come up.

Starting point is 00:40:46 And so they have a different structure there. And on that spectrum of centralized versus decentralized, because of our strategy, which is we're a single application, trying to add, or not trying to, we have added multiple types of content with actually very different business models on the back end, you know, rev shares and royalties and book deals and so forth, into a single user experience. That is our strategy.

Starting point is 00:41:09 We think the user experience in keeping that simple is the most important thing. So we've chosen more of the centralized model where these different sort of vertical businesses, if you think about it, the music business, podcast, audiobooks business, they have to go through a single recommendation organization because that's another problem. Which one do you recommend to which user should be a book or podcast or music

Starting point is 00:41:30 and how do you weigh them against each other? And also the user interface could easily get incredibly complicated if everyone built their own UI. The music team built their UI and then someone had added features on top. So that's how we chose to optimize. But it is based on our strategy, and I think both models work. This episode is brought to you by Eco.

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Starting point is 00:42:45 Sounded good to be true? Go to eco.com slash Lenny, sign up for an onboarding and find out why it isn't. Lenny's podcast listeners who attend an eco welcome session will get an exclusive 4% API on deposits over $1,000. Learn more at eco.com slash Lenny. That's ECO.com slash Lenny. It's interesting in these two examples you gave Apple and Amazon. They're two of the biggest companies in the world, and they're like, at the extremes of these two into the spectrum.

Starting point is 00:43:14 And it's interesting, most companies are somewhere in the middle. I wonder if there's just like a benefit to being in an extreme and that ends up being really important. I think so. And in almost all industries, you have this smiling curve concept, right? Where you want to be at the extremes of the smiling curve. That's where big business opportunities are, but not in the middle. So it's probably true in terms of organization moments as well.

Starting point is 00:43:34 Speaking of extremes, I want to talk a bit about taking big bets. So you guys have this big launch event. recently where you basically redesign the whole primary feed of Spotify to make it feel more like where kind of apps are going like TikTok reels feel of just, you know, stream and you start hearing videos and music starts playing. And some people loved it. Some people did not. And I'm curious as a product leader, how you think about thinking long term and dealing with people that are just like, what the hell's change? I hate change. Stop changing things. How do you think about that? Who do you listen to? Who do you ignore? How do you know it to stay the course? How do you approach that?

Starting point is 00:44:10 Yeah, you're being very kind. There was a lot of negative feedback on Twitter on some of that. So let me actually kind of dig into some detail, because I think this is really, for product people listening to this, this is an interesting lesson that I think few people, few companies talk about. Because you don't really want to talk about, you want to talk about everything that went exactly as you thought they would,

Starting point is 00:44:35 and you don't want to talk about the things that didn't go exactly as you thought they would. So I'll go through kind of what we are trying to achieve and what we learn. So Spotify is mainly a background application, and for a long time, we've been considered very good at background music and podcast recommendation. When the phone is in your pocket, you're listening to like an EDM playlist or, you know, pop playlist or something. We're really good at inserting another EDM track there or another pop track there or something like that in the background. What we hear from users again and again, though, is that, you know, they say that they get trapped in a taste bubble.

Starting point is 00:45:16 So, you know, I love my Spotify. I love this. But I am a little bit bored with EDM now. And Spotify is not suggesting something completely new. And if you think about that problem, it may sound similar to the recommendation problem. It's just another recommendation problem. But it's actually fundamentally different. because when you're recommending another EDM track inside the EDM playlist,

Starting point is 00:45:40 you have a lot of signal from that user that they like EDM. But if you're going to recommend a completely new genre, by definition, you have no idea. Because if you had an idea, it wasn't new to them, so you can't know anything. So back to hit rate, your hit rate is going to be incredibly low when you suggest something completely new to the user. So this problem of helping people get out of their taste bubble, isn't that easy as it sounds?

Starting point is 00:46:09 And we can't really take some genre that maybe isn't typical. So I'm a big fan of regatom, for example. It's not typically, it's not that common in Sweden. And if you would look at the rest of my profile, it's kind of EDM heaviness, you probably wouldn't have guessed it. And Spotify wouldn't have guessed it. So if I'm listening to my favorite EDM playlist in the background, or maybe my metal playlist, metal is very big in Sweden.

Starting point is 00:46:34 It's really hard for us to just insert a reggaeton track in the middle of that. You know, most people are going to think Spotify's broken. What the hell are they thinking, right? So that doesn't really work. So in order to help people break out of their taste bubbles, you need something different. You need something where your hit ratio can be very low, and you need people to expect it to be very low.

Starting point is 00:46:59 So when we recommend things in the background, our hit ratio needs to be at least nine out of 10. Maybe one dot is okay, but if you get five dodds, you're going to think we broke your playlist on your session. We need something where one out of 10 is a success. If you find one jam out of 10 tries, you're very happy. So you need a completely different paradigm.

Starting point is 00:47:23 And you also need to be able to go through many candidates quickly, because the hit rate is so low. You can't take three minutes per item. It's like, okay, I didn't like this, and it's still like two minutes left before the next one comes on. You need to quickly say, no, no, no. So the obvious candidates for this are these feed type experience, where you can go through lots of content.

Starting point is 00:47:45 You're expecting the hit ratio to be much lower, and if you don't like it, the cost is very low. You just swipe. This is the reason why people have been, when they want to break out of their taste bubbles, or when they come into Spotify and listen to something completely new, it is usually because they found it on one of these services, like a TikTok or YouTube or something,

Starting point is 00:48:05 where they get exposed to lots of new content. So people were asking us for these tools. And so that's what we wanted to solve for. And so we built a bunch of features, feed-like structures where you can go through either a new genre with many tracks, or a podcast channel with a genre with many episodes, or even fold playlists.

Starting point is 00:48:28 And we implemented those, and we put them in something called sub-feeds. So in the current experience, and this is rollout worldwide. If you click the podcast sub-feed, you get a feed of podcast episodes, click the music sub-feeds, you get a feed of playlists,

Starting point is 00:48:44 where you can quickly, you know, you can go through many playlists, and if you don't understand the name, you can quickly hear what they sound like and check out a few tracks and understand if this is for you. And if you go to the search and browse paste, page you can find completely new genres that you can quickly go through.

Starting point is 00:49:00 And so those are working as we intended. People go through them, go to them when they want to find your music, they browse through them, and they save new songs. So they're working as we intended. The thing that didn't work as we intended was when users asked us for this again and again, we took sort of the sum of these things and we put it on home because people ask so much about discovery and we can see clearly how correlated the discoveries with retention on Spotify and so forth.

Starting point is 00:49:27 But what we misjudged or failed to or rather learned about our own homepage is that the way it works right now, and this is what you can see in the Twitter comments. If you remove the angry voices and sort of try to see what they're saying, they're saying the following, which is actually quite clear in the quantitative data as well, that if you look what people do on Spotify's homepage, the current one, it is almost 90% what we call recall. So it is either getting to a session that you're already in or a specific playlist that you know you want to get to, or at least a specific use case. So you come in with a high intent.

Starting point is 00:50:09 They actually knew what you wanted. And maybe only 10% of the time is a true discovery. Like, I don't know what I want. So if you think about that, is 90% recall and 10% discovery. when we tested the design, so the sub feeds were working and not working, but when we tested the sum of them on home, we kind of switched it from 90 to 10 to 10.90.

Starting point is 00:50:30 So 10% recall, 90% discovery. And while people want discovery, they probably don't want 90% discovery instead of 90% recall. So if you then look at the comments on Twitter, what they're saying is like, hey, I can't find my playlist anymore. Like, where are these things? They're not really complaining about the discovery. They're complaining about the things they don't get it.

Starting point is 00:50:50 anymore. And we can see this in the quant data as well, and you can see traffic shifting from home into search and into library, which is a clear sign. People are trying to find the things they can't find anymore. And you can even see people then trying to use these discovery tools,

Starting point is 00:51:06 which are optimized for quickly understanding new things, to do the recall. Like, where is that workout playlist I know I want? And it's actually a very bad UI for recall. It's kind of like a slot machine. Very unpredictable if you ever get to that workout playlist. It was optimized for finding new things, not for recall of existing things.

Starting point is 00:51:24 When you do recall, you want the dense UI with many items on screen because you know what it is you're looking for us. You don't need a lot of real estate. When you do and discover of new things, you want a lot of users, a lot of pixels, and you probably want sound because you don't know what it is. So what kind of what we learn about our UI, and I think there's maybe a little bit of, you know, product jealousy here. You always look at other experiences. And if you look around, it could be forgiven for thinking that most other products, if you look at something like YouTube, for example, their homepage is exactly that. It's a huge single item discovery feed with only new items.

Starting point is 00:52:04 And people don't seem to tweet angrily about how angry they are at YouTube and say that you love YouTube and it's a big product. And I think what we discovered was that we actually did something really well on our homepage, which was supporting you being inside a multiple sessions. at the same time. So you could be in the middle of two podcasts and an audiobook, and also then, actually, I just want to get to that workout playlist. I don't remember the name of it, but I know it's workout. We actually did that part really well. I would venture to say much better than the other experiences where you literally have to go to some tab and into library and start browsing

Starting point is 00:52:40 to get back to where you were. And so maybe it's path dependent. Because we have done recall pretty well. People got, I think, reasonably upset when they couldn't do the recall anymore. And we really don't want, we didn't want to lose that because it was one of the things we did well and underestimated. And my takeaway is actually we do it better than other experiences. So we certainly want to keep that. So what we did was now we're just updating the hypothesis to achieve the same goal, which is these things are working. And when people want to discover, they use them and they seem to work, they can, they can also. So get better, you know, you're on this like hill climbing journey from machine learning point

Starting point is 00:53:24 of view. But the question is, how do you make sure that whenever people feel that they are in that I'm trapped in my taste bubble, they understand that these things are there and they're easy to use. So now we have a version of home that we're also testing, obviously, where these things are very available but voluntary and you can still do all of the recall. And so from my point of view, this is the reason we A-B test, because you know, you want to be scientific about it. And, you know, you want to learn as much as possible about your own product and your users.

Starting point is 00:54:00 And now I'm sharing a lot of the learnings. Maybe we should keep them to ourselves. But my hunch is that it's going to make it a much better product. But what I told my teams when we went into this, because I've done this a few times, like redesigning, I think there is a, there are two fundamentally different types of product development. One is designing a new feature. It is hard to make it, but it's voluntary for people to use. So you do the AI DJ.

Starting point is 00:54:31 Some people love it. That's fine. If you don't like it, it didn't make it worse for you. But when you redesign, it is much more tricky because it's not voluntary to participate in the redesign. So there's a cost even, you know, for people, people who don't like it. You have a very tricky problem here, which is there are going to be two types of feedback. One is you did something and it was right, but people are upset because you change stuff.

Starting point is 00:55:02 The other is you did something and it wasn't right and people are also upset. But for good reasons. And so how do you separate these two? because I think I explained this to when we talk through this with my teams, I think the analogy to think about is you have your desktop, your physical desktop. You have your computer in one place. You have a pencil over here. You have your notebook over there.

Starting point is 00:55:26 And I come in and I just rearrange all of it. And you have spent, in our case, maybe 12 years with that setup. It doesn't matter if I have a lot of quantitative data that my new setup is better. you're going to get upset because you are effective in this old setup. And it's hard to tell those apart. The most classic use case is the Facebook news feed, which people were very upset about when it became a single newsfeed. But it turned out to solve a lot of user problems that you didn't have to run around all

Starting point is 00:55:55 Facebook collecting events yourself. So there are some ways of understanding if you've made it better, but people's habits are broken or if it's not better. And one thing is, for example, to look at new user cohorts, that don't have that behavior versus all user cohorts and so forth. So we went through all of this with the teams before we did it. I said this is going to be painful. Probably going to be a lot of tweets because chances that we get it exactly right are very low.

Starting point is 00:56:21 So for that reason, it hasn't been very hard on the team. It is hard. You know, you want to respond to people, but the right way to do it is to listen, understand, try a new hypothesis to really figure out what's going on. So I think I've done it. maybe three or four times now, one, three, maybe one unsuccessful, two successfully. It's kind of knew what I was getting into. So it's almost like you punish yourself, very painful, but also the most exciting things.

Starting point is 00:56:52 And I think any product person knows that the easiest, the most straightforward thing to do is to iterate around where you are. There's no risk. You're not going to get fired. No user is going to get angry. But everyone also knows that, you have anything. Eventually, if you don't adopt new technologies, new paradigms, etc, you're going to get replaced. You have to find this balance of trying new things.

Starting point is 00:57:15 And that's, you know, when you work in software, you have this tool of AB testing and being scientific about it. When you build hardware, it's worse. If you're wrong, you're wrong. You can't update. I love this story. I so appreciate you sharing it. I imagine also with a big launch like this, you can't actually AB test it ahead of time because of the press season. They're like, oh, my God, look that Spotify's doing.

Starting point is 00:57:36 and so you're kind of limited there, I imagine, right? You couldn't really test this ahead of time. The hardest thing about this is if you're trying something completely new, the MVP needs to be very big, so you can build a new UI. But if you didn't do algorithms for a single item feed,

Starting point is 00:57:53 you can't tell if it was the right idea, but poor machine learning, right? UI poor machine learning or the other. You have to build a lot. I mean, that gets quite expensive. That's actually the biggest why it's painful is not really the feedback from the outside. it is the cost you have to take on the inside.

Starting point is 00:58:11 You incur a lot of cost, so you're really hoping you're right. And in our cases, the changes on the homepage aren't that hard for us to do. The important thing is that the underlying hypothesis of can we help you break out of your taste bubble actually works and then you update the acquisition funnels into that experience.

Starting point is 00:58:32 But I think the problem is, that you need to get so many things in place to be able to say, you might get a false negative, just because you didn't do it well enough. That's the biggest chance, I think, with these big rewrites, where everyone has to update everything before you can know if you're right or wrong. What was the process like of helping you understand what is not working and what is working and what you wanted to change?

Starting point is 00:58:58 Like, I imagine there's a bunch of data you're looking at, some tweets, things like that. What was kind of like the tactical, oh, shoot, something's not going the way we expected. Here's what we should do. Well, the feeds we tested, but the home feed, we roll out and tested afterwards. And we tested that on users, a few different variants of it. And then we got the data back. And we look more at the quantitative data.

Starting point is 00:59:21 And we do a lot of user research where people sit and use the feeds to understand and build like our own theory of mind of what is working and what is not working. And then obviously you look at user feedback, of course, and some users are very good at expressing what is that isn't working. Others are not as good as expressing what isn't working. So it can be hard to parse that. But certainly that's a factor as well. And so then once you do that, then you have quantitative data to look at.

Starting point is 00:59:55 And then you sit in reason through what do you think is right and wrong, what are different hypotheses, what is working? and was not working, and then just update and test again and again until you find, until you prove or sort of disprove your hypothesis. Trying to be as scientific as possible about it. And also, I think the biggest risk, also when you've invested so much time in something is getting precious about things, you have to just be brutal. You have to believe in things 100% until the data says no.

Starting point is 01:00:27 And then you believe in something else 100%. that sounds easy. It's very hard to do. To the extent that people get upset when you do it, because for some reason, people don't like when people change their mind. It is what we should want from everyone. I would love a politician who said,

Starting point is 01:00:45 I looked at the data, and I realized, actually, this is right, and now I believe this, but we hate politicians to do that. You know, they feel untrustworthy, and, like, we ridicule them. So I think that's the biggest risk with anyone, you just have to be like unemotional

Starting point is 01:01:01 and just look at the proof in the data. And then, you know, if you do that, just move on and then you get to where you want to be. You solve the same problem, but you adapt. I really like that philosophy. Essentially, it's the idea of strong opinions loosely held, right? Exactly. Exactly what it is. And it sounds so easy, but it's hard.

Starting point is 01:01:23 Right, because to your point, people don't like, don't respect someone changing their mind. They're like, oh, I see. wrong the whole time and they were so confident about being wrong. Yeah, exactly. And it's unclear why it is what we should want. But I think it has something to do with human psychology. We actually tend to love prophets and people who hold very strong opinions with very little data. Those are the people who look at a lot of data. Actually, that we don't like. Not sure why. We're flawed. A lot creatures.

Starting point is 01:01:59 For sure. Is there something that you've recently changed your mind about along these same lines that maybe comes to mind of like, oh, yeah. No, I think these learnings about the what our own design system and homepage does really well, maybe better than others, that we don't sort of want to wash out with the bathwater or whatever the uttering expression is. I think that's the biggest current learning. I'm actually very, very happy about.

Starting point is 01:02:25 Yeah. I love learning that we're doing some really well that we didn't really realize necessarily, and maybe we should lead into that more. Exactly. Going in a somewhat different direction, Shishir Marotra suggested to ask you something. He's on your board, I believe. And he suggested to ask you about your 10% planning time. What is that about?

Starting point is 01:02:46 This is a concept that I think Shishira has used for a long time, ever since he worked at YouTube. And the idea is that roughly you shouldn't be spending more than 10% of your 10% of your time planning versus executing or building, which means that if you're working quarterly sort of 10 weeks, you should spend one week planning. We work in sort of six-month increment. So we try to spend two weeks planning and are roughly successful.

Starting point is 01:03:15 And this is actually when we talk about org models, give a shout out to Brian Chesky at Airbnb, who is actually one of the first, to have these more contrarian org models. He's much more Apple-esque than most of Silicon Valley. He also works in six-month increments. So he has a lot of experience in that as well. So that's what the 10% planning time is.

Starting point is 01:03:39 And I think if you find yourself planning much more than that, you're either planning too much or your execution period is just too short for that amount of planning. It's a rule of thumb, but I find that it works. I asked a few PMs what I should ask you, PMs that work at Spotify, actually, that I haven't told you. And someone pointed out that you, you always bring a lot of energy and clarity to a room.

Starting point is 01:04:02 That's something they see you as really strong at. What have you learned about, just the importance of that or just how to do that well as a leader? Well, that's great to hear. I didn't know that. So I'm trying to figure out what to answer. I think that the energy, I don't know. I guess I'm just excited about what I do.

Starting point is 01:04:24 I've always been excited about technology. I love seeing new things. My core drive is still this notion of, you know, you see something, which I think you'll empathize with that doesn't exist yet. And you're like, wow, I wonder if that could exist. That would be so cool. And then in order to get people to do it, you try to share that excitement. So I don't think I can bring a lot of energy for something I'm not excited about.

Starting point is 01:04:52 So I kind of have to work on things. actually believe in and that I'm excited about. And so maybe then the energy comes more naturally. Unfortunately, for me, so far, Spotify has been in this phase where a lot of innovation is allowed and I'm even asked to try to do new cool things. Maybe I would have less energy for a pure optimization phase. On the clarity, I've always liked trying to explain things. It's a well-known fact that the best way to understand something is to try to explain

Starting point is 01:05:24 to someone else. So I go around explaining things to people who didn't ask for it and not to sound smart, but to see if I actually understood it. And so maybe it's that practice. And on that note, I actually do ask my leaders that work for me and I ask them to ask their leaders to always explain themselves. And I think when we talk a little bit about autonomy and so forth, I don't think we don't promise everyone that they have to agree. But I think the promise we should make to all employees is that even if they don't agree, they should be entitled to understand why you're making the decision. What I don't think is acceptable is to say, no, we're going to do it this way,

Starting point is 01:06:11 because I'm more senior. I've seen this a bunch of times. You are not smart enough, like all of those things. I think you have to explain yourself. So you owe an explanation. And I find that valuable back to like the only one. way to understand something is to explain it, because it usually turns out that if you can't explain it yourself, you probably don't really even understand it yourself. Sometimes, I think

Starting point is 01:06:36 it's possible that you can have product instincts that are good, but you can't express them. But most often, when people say there's something there, but they can't explain it, they actually don't understand themselves. And many times there actually isn't anything there. And also, if you can explain it as a product person, that knowledge is now shared. So it just becomes much more effective for the organization. So sometimes I try to provoke people a little bit and say, you know, when people ask, like, how much is art versus science? You know, I say it's 0% art, 0% magic, and 100% science.

Starting point is 01:07:18 And that's because I want to force people to try to explain it. I think we used the word art and magic. We have historically used the word art and magic for anything that we couldn't yet explain. You know, genetics was with magic and art until it was science. And, you know, quantum physics with magic until it was science. And most recently, actually, intelligence and creativity was art and magic until it was statistics in an LLM.

Starting point is 01:07:50 So I think I try to push people to say, Are you sure you can't explain this? Because that forces people to think through. So that's maybe I like it and I try to force it on people. So maybe that's why people think I sometimes bring clarity. I love that. Question along those lines. Is there a system or an approach to explaining that you recommend?

Starting point is 01:08:13 Is it just like write it out in a document? Is it explaining in a certain style? Or is it just like however is natural to the person? I used to write everything and then write and rewrite. write and make it more and more condensed. So that worked for me. I don't write as much anymore. Now I tend to walk and talk in my head, myself.

Starting point is 01:08:36 What I actually do is I found it's different for different people. A lot of people want to bounce something with someone else. That's how they think. You kind of repeat the same thing again and again and you get some feedback on it. And so I used to write a lot. I sometimes do when it's an idea I want to understand better. And at some point in my life, I would love to write something real like a book or something. But what I do increasingly now is I do my one-on-ones with peers or people who report to me or something.

Starting point is 01:09:07 And I just put on AirPods and do like a distributed walk-and-talk. Both people are walking, but in different locations. And you spend an hour discussing something. That has actually turned out to be very, very fruitful. So then you get the power of you're not alone, so you get more brain power than your own. And I think, you know, I don't think there is strong evolutionary proof for this, but there's certainly indications that you're thinking better when you're walking, whether it's because you're oxygenating your brain or because it's evolutionary for some other reason.

Starting point is 01:09:44 I'm not sure. But I found that walking, talking, and thinking, actually, even if you're not in person, just over AirPods. It's super effective. It was the pandemic that kind of forced this. I thought we would get less creative and that strategizing was suffered during the pandemic. And I found the opposite.

Starting point is 01:10:02 We had more of this than ever, and I started thinking about why. And I think it's all of these walking talks that we did. You kind of throw out there that you want to write a book someday. What do you think your book would be about? I have no idea. No idea. Statistically, it's probably going to be about something that I did a lot.

Starting point is 01:10:18 So it has to be about something with technology your product or something. But I would love to write something fictional. That would be a lot of fun. Oh, boy. I'll pre-order as soon as that's up. Another concept I wanted to touch on that another PM suggested, which is he called it the P in the Pants analogy.

Starting point is 01:10:37 Does that ring a bell? And is that interesting to talk about? I don't know exactly which occasion this person is referring to, but I know I've used that analogy a few times. Okay. promising? I don't know if it's like a Swedish analogy, because I thought it was more widely known.

Starting point is 01:10:57 But the idea is that you do something. So the saying is that's like peeing in your pants in cold weather. It feels really warm and nice to begin with. And then after a while, you start to regret it. It's about being short term, basically. So now I just say that, I just say like, that's like peeing in the pants inside, because people know what I mean.

Starting point is 01:11:19 It's short-term thing. That's a hilarious way of communicating that idea. Must be a Swedish thing. Yes, I think Swedish people do it for some reason. Apparently, others don't. Maybe because it's cold a lot of time of the year. Yes, that's probably, this is a saying in cold climate. In the warm, it doesn't out.

Starting point is 01:11:36 No one understands what you mean. Speaking of Sweden, do you have succession? Yes, I do. Okay, so Sweden has become a big part of the show, specifically the company trying to, I guess I don't want to spoil, but there's a character that's really important. Yes, exactly, that is Swedish. And so I'm curious just, what do you think of the way they portray the Swedish culture

Starting point is 01:11:56 and Swedish business dealings? It's super fun to see this as a Sweden. And I guess first and foremost, like anyone or any person or any country that gets represented by super tall, well-built, great-looking Alexander Scar Scarscourt, should probably be pretty happy. So that's good. Then I think this is episode where they are in Norway, not giving away too much. There are elements that are authentic. There's a lot of, I think, paid brand positioning from a Swedish brand name Jelreven,

Starting point is 01:12:36 which I think means Arctic folks, which is actually a very popular outdoor brand in Sweden. So that's kind of authentic. the sauna things and so forth are authentic. So it's like it's real, but it's exaggerated. Actually, the thing that isn't very authentic is his negotiation style. Swedish people tend to be serious, cautious, and this guy's more of a player. So he's not the typical Swedish businessman from a negotiation tactic point of view, I think. Yeah, it doesn't make me think of the way you described it.

Starting point is 01:13:14 We didn't people sit in a circle and no one's in the center. No, exactly. He's very much in the center. And then when people go saunas, they're just like a chant, sauna, sauna, sauna. Exactly. The last episode. It is a great show. I love it.

Starting point is 01:13:26 I love it. This season is insane. I am so curious where it all goes. Maybe just the last question before. Very exciting lightning round. Spotify is at this point the biggest podcasting platform for me, specifically, and I think globally. And I love using it. It works great.

Starting point is 01:13:43 I'm curious just what's next for Spotify and specifically Spotify podcasting. There are two sides to it. It's for Spotify creators and for Spotify listeners. For Spotify creators, there are two things. One is, and this is what we talked about at stream on. We talked about it most of for music, music discovery, but it's the same problem, it's the same problem and even harder for podcast.

Starting point is 01:14:07 So we're still focused very heavily on helping podcast, podcast, creators, find more audience. This is, like I said, it's even a bigger problem to break out of your habits and your bubbles in podcasting. It's such a big investment

Starting point is 01:14:23 to find a new podcast. So that is something I think we could and should do really well. So we keep investing a lot there. And as I said, you'll see more as we roll up more features now. The other big need for creators is monetizing,

Starting point is 01:14:40 And you can monetize today in many ways with DAI and Spotify SAI and so forth. But we're working hard to expand that and make it better because the industry is starting to mature. And I think this is one of the biggest needs and the biggest things we could do for creators to help them monetize better. Actually, both free and paid. We also have paid podcast. So that's on the creator side. On the consumer side, I don't want to share too much. We've shown that we're investing a lot in Discovery.

Starting point is 01:15:13 I want to keep some secrets for when they roll out, but we are investing a lot in the user experience itself. I think it's far from optimal yet, what it could be. One thing that I can show that we're investing a lot in is just the ubiquity and playback across different devices and in cars and all these things that we've done well for music. But I think the listening experience can get a lot more seamless. I think search can get better, the data about podcasts.

Starting point is 01:15:44 And I don't want to say too much. But looking at, you know, AI and generative technology, there's a lot that can be done. All right. Well, I'll take what I can get. With that, we've reached our very exciting lightning round. I've got six questions for you, Gustav. Are you ready? I think I am.

Starting point is 01:16:02 Let's do it. We'll find out. What are two or three books that you've recommended most to other people? Okay. this is why I try to squeeze in seven into two and three. So if we start with the own product, I think it's well known, but one that I would recommend probably people to read is Seven Powers by Hamilton Hammer, which Netflix has used a lot.

Starting point is 01:16:23 We use a lot. It's just if you're starting out, it's great to have a strategy framework. No strategy framework is right, but having one is better than none. Another in sort of the space of mental models and frameworks, I think is that the complete investor by Charlie Munger. So it's, yes, it's about investment, but really it's a bunch of mental models that he uses. And I think the key takeaway is you have a problem. You should always apply three different models to it because what models do is they simplify and reduce dimensionality.

Starting point is 01:16:56 You know, the world has probably infinite dimensions and they reduce this to maybe three or four. And the risk with that is you happen to get rid of a really important dimension, like, you know, maybe pandemic diseases or something. but if you use three models that have different dimensions and was reduced in different ways statistically and in it comes to the same conclusion even the second model you apply vastly increases your chances that you're right so that was a good book to read then i think if we go outside of product i'm very interested in just science and mathematics so a few quick ones the mystery of the aleph an amazing book something's deeply hidden by Sean Carroll on the Everettian interpretation of quantum mechanics.

Starting point is 01:17:43 Helgoland by Colin Rovella on the relational interpretation of quantum mechanics. The beginning of infinity and the fabric of reality by David Deutsch. The case against reality by Donald Hoffman on sort of evolution versus truth and that evolution doesn't optimize for seeing the truth just for fitness. Goetel's proof, I think is an amazing book on his incompleteness. theorem that in any axiomatic systems there will be true statements that can never be proven, which is a weird thing to think about. And then maybe one of my favorites is the demon in the machine by Paul Davis that I think is

Starting point is 01:18:24 lesser known on how information is really just entropy and this concept of information engines, that you can power something by just information and exhaust is also information. that was not a quick list. No, but I was just going to say you've set the record for the most number of books, but it also shows how you've become so insightful and wise is just, you know, reading books like these. And so I think if people are looking to, you know, get to a place that you're at now, I think there's the lesson. I'll keep the others much shorter, I promise.

Starting point is 01:18:57 It's all good. We got time. Okay, next question. What's our favorite recent movie or TV show? So we talked about succession, and it is a recent, favorite. So I'll just previously take something that isn't recent but is an absolute favorite,

Starting point is 01:19:10 which is Hold and Catch Fire, which I think is on FX. Amazing show. If you ever worked in technology, it kind of starts out in the Silicon Prairie in the 80s and follows up to present day. Amazing show. Hold and catch fire. I watched some of it. I actually fell off of it,

Starting point is 01:19:27 but it's a good reminder to go check it out. Gotta go back. I'm going to go back. What's a favorite recent interview question you like to ask? I don't ask it, but my favorite question is Lex Friedman's small ending question that is usually something like, so what's the meaning of it all? I like that. It's a tough question to get. I'm so tempted to ask you, but...

Starting point is 01:19:48 No, don't. Okay. Let's move on. That'll be another part. That'll be our second take at this. What are some favorite products you've recently discovered that you love? The obvious one is chat GPT, GPT4, and just, playing around with that, trying to create bots for yourself that do different things for you and so forth.

Starting point is 01:20:07 But I don't think that's probably true for everyone. The other really favorite is something you've written about and talked about, which is Duolingo, which I think is both very impressive from a product point of view, the execution and what they've done. It is also insanely used in my family. We have a family account and everyone is using it and competing every day. So I'm both impressed by the product. use the product quite a lot. What languages are folks learning within your family? In my family, it's Spanish right now. How's it going? Bien. You get a gold style. I only have like a few

Starting point is 01:20:48 thousand XP. I'm not that good yet. No, I don't know if that's good. That sounds pretty good. Next question, what's something relatively minor you've changed in your product development process? That's had a tremendous impact on your team's ability to execute. I'm not sure I've done anything minor that had a tremendous impact usually it takes something bigger to get a big impact.

Starting point is 01:21:12 I think maybe one thing that I've tried to do back to like clarity and so forth is this thing I mentioned about, I'm trying to push a lot for what I call theocratic debate where the idea is obviously that the best idea wins, not the most

Starting point is 01:21:28 senior idea and so forth. And and trying to push for this notion of having people explain themselves, not saying, like, I think there's something there, I have a feeling, or something like that. And apparently, as you said, that has had some impact because people apparently say that about me. So that's probably the biggest thing.

Starting point is 01:21:52 Final question, what is one fun ritual of the Spotify product team? And is it Saunas? So Spotify is so big now that we don't, It's quite local, actually, different parts of Spotify, different product rituals. I sort of accidentally created one ritual many years ago, maybe 12 years ago, when we talked about where a product, which phase the product is in. I know, we needed some definition. So I think sort of off the cuff, I said like, well, you know, it's four phases.

Starting point is 01:22:24 It's think it, build it, ship it, tweak it. And in the think it phase, you should, it should be cheap, you know, not a lot of of money spent. In the builded phase, you're going to start spending a lot of money. So then you must have reduced the risk in the think it phase that you're right. And then you have the ship it phase and then you go over and tweak it. And it was something that wasn't that thought through. But it's funny because I still hear it.

Starting point is 01:22:49 Sometimes even from other companies, like, oh, we're in the think it face or we're in the tweaked face. So it kind of stuck. I don't know that's very good, but it's stuck. It is catchy. I think that anything getting stuck in people's head is a success. Gustav, thank you so much for being here. We are two for two for Swedish people.

Starting point is 01:23:06 Gustav with an F. Ulstomer was on the podcast. Who is also an amazing person? Also an amazing person. I feel very jealous of people that get to work with you and for you. Thank you again for being here. Two final questions. Where can folks find you online if they want to learn more? Maybe reach out, ask some questions.

Starting point is 01:23:23 He says this at Gustav S. Okay. Say it again? At Gustav S. Awesome. And then final question is, Just how can listeners be useful to you? Just reach out.

Starting point is 01:23:34 I do read feedback and I try to remove the angry comments and understand what they're actually thinking and why they're upset or what's not working. And then the reaching out would you recommend an angry tweet at you or more of an email to that email address you shared? Well, the at Gustavs is the Twitter handle. So just tweet at me. And you can be nice as well. It's okay. Amazing.

Starting point is 01:23:58 Gustav, thank you so much for being here. Thank you for having you, Lenny. It's been a pleasure. Bye, everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review, as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at Lenny'spodcast.com. See you in the next episode.

Lenny's Podcast: Product | Career | Growth - Lessons from scaling Spotify: The science of product, taking risky bets, and how AI is already impacting the future of music | Gustav Söderström (Co-President, CPO, and CTO at Spotify)

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