Lenny's Podcast: Product | Growth | Career: 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)

The internet sort of started with curation, often user curation, so you took something,

some good, like people or books or music, and you digitized 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 that required us and others

to actually rethink the entire user experience, and sometimes the business model as well. 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 a 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 era? 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

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, how accurately succession

represents Swedish business culture, and his hilarious analogy of peeing in your pants.

Enjoy this episode with Gustav Soderstrom after a short word from our sponsors.

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Gustav, welcome to the podcast.

Thanks for having me, Lenny. Pleased 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. 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 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 the company to

Yahoo in the mobile space. I worked there for a while. I came back to Sweden, and then I met

through a mutual friend, Danny Lek, 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 what to do with mobile. And because I had been an

entrepreneur in that space, I got that job. 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. 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 a super fast version. Aside from

getting more responsibilities, like taking on the technology department, it has been sort of the

same job by title. 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 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, 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 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 two, just what did you learn from that experience in terms of

the product you ended up building and just empathizing with the podcast creator side of it?

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. 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. But I love writing and talking and presenting. So, there was certainly

that. And then no small part was to, from a product 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 I decided to make a podcast. And that taught me a huge amount about what it's like to be a

creator, how, you know, creating different styles of podcasts. For example, we wanted to do a sort

of higher production cost podcast with music. And then right away, you run into a bunch of problems.

As Spotify is actually pretty well positioned to solve, but still, like, it's really hard 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 doing the public podcast was that I had actually done an internal podcast to sort of a

hack where we could gate the podcast only and please. And I try to figure out internally how to

how to build more culture around Spotify and sort of help define for new employees and existing

employees who we are, the mistakes we did, the successes we had, and how we think about

strategies specifically in 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 Docs, I decided to do an internal podcast. And

I went around and I interviewed, actually, Daniel's direct reports, so the CMO, the CHRO, and

CFO and so forth, and just ask them about a bunch of stuff. 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 product strategy. And people internally really liked 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'd be great, because 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 had 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. 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

inter-days, where especially in the past few years, when we've had a lot, 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 their departments and strategy and so forth.

And it's very common that people come and tell me that, oh, you know, I listened 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 who said it. 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 and all these different ways for hiring, for culture building.

And it sounds like internally, the original goal is just internally build this kind of

culture and strategy. That was the original goal, make senior leadership more approachable

and sort of reduce the distance and then also share more of the thinking in an entertaining way,

rather than just through docs that people end up 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 in AI within

Spotify. And I think that's what led to Discover Weekly and a few other tools.

And that was like years ago. And it's interesting listening to it now where AI is again,

like 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 and their product,

thinking and also just in their day-to-day work.

I can give a few examples there. And I don't know that we're more sophisticated than anyone else,

but what we're doing is 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 curation,

often user curation. So, you took something, some good, like people or books or music,

and you digitized 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. 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. 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 era? No one really knows

yet. Now there are a bunch, as usual, there are 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 models. You can use it for safety classification and podcasts that no one has

listened to yet and so forth. So 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. 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 person, a digitized person. There's a real person named X and we digitized X. So he's

now an AI, 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, okay, now let's listen

to something maybe from a few summers ago, or here's some new stuff that, you know, we're trending

yesterday. And last, you know, 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, 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, like we had lots of options for all of those. But if you really didn't know

at all, it was hard to open Spotify and sort of stare at it. And people used to say longingly,

you know, that this was the one thing that radio was good at. 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 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. And I think with the 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

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 the science sketch that

was a single big play button. 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, you know, a one in five hit 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. There needs to be fault tolerant. And often you need an escape

hatch for the user. If you were, so you make a prediction, 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, of your algorithms. And I think that is going to

be true for generator machine learning as well. I think a very clear example actually is mid-journey.

You think about the early mid-journey user interface inside the discord channel.

Actually generating an image was very, very slow. It took a long time to generate a high quality

image and they could have built a silver button thing where you put in a prompt, you wait for

minutes, 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'll be, this is a shitty product. What they did was they generated four

simultaneous low-res images very quickly. And you could say like, so apparently their performance

was probably one in four. That's why they four showed four and not six. 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 that'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, talk and talk and 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. We actually

required big companies and so forth to be able to build it. But the idea has been, had 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. Yeah, 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. And this kind of begs the question of

there's this trend that I imagine you're seeing of people auto generating music

using artists, you know, catalog, like there's this Drake in 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 auto generated? You know, 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, no one knows anything, you know, about how this is going 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, Avicii came along. And it's

interesting to think about because Avicii 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 door, the audio workstation. And so it wasn't really

considered real music. And I think now all of us 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 these diffusion

models, even 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 any instrument. And they couldn't assemble a full orchestra and do

the thing that they had in their head. 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. 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. 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. 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 door and

that technology jump was you got a whole new genre like EDM. You couldn't really produce it

with an orchestra or live. 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

or music and piracy and peer-to-peer. So first, it was a big technology shift in peer-to-peer.

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 in the 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 AI and we could be part of. So I think we've seen the first part, the technology shift.

And there will 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 actually happened 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 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 DJ, we did a small version of that.

When people first listened to it, we could see that reaction in user testing.

So the magic trick there was that, how could they record this person saying 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. And I think it was the same

using something like Dali or Stable Diffusion or 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 certain levels,

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 launcher 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. 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. And 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. 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. 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's become a little bit of a

cost to us 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 difference is the idea with the squads specifically was

twofold. They were supposed to be small and sort of full stacks of squads,

be about seven people. And it should have front and back and mobile, QA, agile coaches and so

forth. And it should be very autonomous was the idea. And that's really 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 overhead

roles. And so that's one, it looks more traditional, as you learn more and as 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 wasn't most tech companies. And so we had coming from Sweden,

which is a different culture than the US. 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 of how you make decisions in Sweden in the US. I think it's

just a hierarchy in Sweden. It's kind of a circle. You sit in a circle, no one is in the middle.

There is no leader and so forth. 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 brainpower.

So if you're renting all of this expensive brainpower, and then you give them no room to think

for themselves, that doesn't sound smart, then you should actually hire less smart people and

like keep your costs 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.

But the problem with that is, if you put autonomy very far towards the leaves of the

organization, and then 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. 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. 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 maybe 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, 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 the VP level. So if you have

Daniel, then you have the C level myself and others, then you have the VP level. That is a good mix

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

think 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. 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? And I

know Maya is the VP of product I believe for the podcast. Exactly. Who I think is going to come on

the podcast someday. What does that mean in terms of Tommy for her, 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, I would make that strategy or

another company, Daniel would make that strategy. Same with the DJ, for example, came from one of

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 experience

team 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 and what 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. It's not really, 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 dependencies so you can run in parallel.

Teams compete with each other, 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 to user is very low.

And it has worked for them. It's produced Kindle, it produced Lex, 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

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 he's well known for pushing extremely hard on hard APIs. Like,

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, 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

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 an interesting anecdote around it. 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 through and Spotify, by the way, as well. You've seen 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, 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

where you would never see two search boxes from the same team popping up on an iPhone.

That is centrally organized by something that is close to a 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, they certainly do everything we do.

They have a music service, podcast service, audiobooks, and they have a billion other services.

So it's not like they have an easier problem. And yet they build something that feels more like it

was built by a single developer for a 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 have said like, yeah, seven years to get that thing to market

because you 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 that payment come up? Should something else come up? 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, rev shares and royalties and book deals and so forth into a single user experience.

That is our strategy. We think the user experience and 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 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 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.

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That's ECO.com slash Lenny. It's interesting 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. And it's interesting, most companies are somewhere in the middle. I wonder

if there's just a benefit to being at an extreme and that ends up being really important.

I think so. In almost all industries, you have the smiling curve concept, right?

Or you want to be at the extremes of the smiling curve. And that's what big business

opportunities are, but not in the middle. So it's probably true in terms of organizational

models as well. Speaking of extremes, I want to talk a bit about taking big bets. So you guys

had this big launch event recently where you basically redesigned the whole primary feed

of Spotify to make it feel more like we're kind of apps are going to 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 to stay the course? How do you approach that?

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. 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 learned.

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 and you're listening to like an EDM playlist or 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

they say that they get trapped in a taste bubble. So 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,

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 the taste bubble isn't as easy as it sounds.

And we can't really take some, you know, some genre that maybe isn't typical. So

I'm a big fan of reggaeton, for example. It's not typically, it's not that common in Sweden.

And if you would look at the rest of my profiles, kind of EDM habits, 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, 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 is 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. 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,

you know, five dots, you're going to think we broke your playlist in 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. And you also need to be

able to go through many candidates quickly, right? 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 fee type experience where you can go through lots of content. 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.

And then this is the reason why people have been, when they want to break out of their,

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 Tik Tok or YouTube or

something, 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 shot, a new genre with many tracks,

or a podcast channel with a genre with many episodes, or even fold playlists.

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, 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 page, you can find completely new genres that you can

quickly go through. And so those are working as we intended. People go through them, go to them

when they want to find new 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 some of these things, and we put it on home because people

ask so much about discovery, and we can see clearly how correlated the discovery is with

retention on Spotify and so forth. 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 at 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. 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, it's 90% recall

and 10% discovery. When we tested the design, so the sub feeds were working and all working,

but when we tested the sum of them on home, we kind of switched it from 90 to 10 to 1090.

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

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,

find the things they can't find anymore. And you can even see people then trying to use these

discovery tools, 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 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.

When you do recall, you want to dense UI with many items on screen, because you know what it is

you're looking for. You don't need a lot of real estate. When you do discover new things, you want

a lot of user interface, a lot of pixels, and you probably won't sound because you don't know what

it is. So what kind of what we learned about our UI, and I think there's maybe, 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. And people don't seem to tweet angrily about about how angry they are at YouTube to say

they 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

of 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 your, to some tab and

into library and start browsing to get back to where you were. And so maybe it's path dependent.

If we had, you know, because we have done Recall pretty well, people got, I think,

reasonably upset when they couldn't find it, 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 get better, you know, you're on this like hill

climbing journey from machine learning point 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 AB 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. 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, I agree the signing,

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, but it's, but it's voluntary for people to use.

So you do the AIDJ. 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 there's a cost even, you know, for people who don't like it.

And 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.

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 your pencil

over here, you have your notebook over there. 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 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 news feed. But it

turned out to solve a lot of user problems that you didn't have to run around all of 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 old 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 exactly right are very low.

So for that reason, it hasn't been very hard on the team. It is hard, you want to respond to

people, but the right way to do it is to listen, understand, try new hypothesis to really figure

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 new what I was getting into. So it's almost like

you punish yourself, very painful, but also the most exciting things. And I think

any product person knows that the easiest and 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 eventually, if you don't adopt new technologies, new paradigms,

et cetera, you're going to get replaced. You have to find this balance of trying new things.

And that's when you work in software, you have this tool of A.B. 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 A.B. test it ahead of time

because of the press season, they're like, oh my God, look what Spotify is doing. And so you're

kind of limited there, 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, 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. And I guess 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. You incur a lot of cost as 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. 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, for 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? I imagine there's a bunch of data you're looking at, some tweets, things like

that. What was 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 rolled 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 looked more at the quantitative data. And we do a lot of user research where

people say they didn't use the feeds to understand and build like our own

theoretical 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, 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. And then you sit in recent through what do you think is right and wrong,

what are different hypotheses, what is working, what is not working, and then just update and

test again and again 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. 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, I looked at the data and I realized, actually, this is right and now

I believe this, but we hate politicians to do that. They feel untrustworthy and we ridicule them.

So I think that's the biggest risk with anyone. You just have to be unemotional

and just look at the proof in the data. And then, if you do that, just move on and then

you get to where you want to be and 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.

Right, because to your point, people don't respect someone changing their mind. They're like,

oh, I see. They were wrong the whole time and they were so confident about being wrong.

Yeah, exactly. And I'm clear why this 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 and actually adapt,

which we don't like. I'm not sure why. Or flawed, flawed creatures. 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 what our home,

the science 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 entire expression is. I think that's

the biggest current learning I'm actually very, very happy about. 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, Shashir Marotra suggested

ask you something he's on your board, I believe. And he suggested ask you about your 10% planning

time. What is that about? This is a concept that I think Shashir 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 time planning versus executing or building, which means that if you work in

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. 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, I think, 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. 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 always bring a lot of energy and clarity to a room. 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. I've always been excited about technology. I love seeing new things. My

core drive is still this notion of 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 be

bringing a lot of energy for something I'm not excited about. 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 it 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 because I'm more

senior. I've seen this a bunch of times, you're not smart enough, like all of those things.

I think you have to explain yourself. So your own explanation. And I find that valuable back to like

the only 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 it's

possible that you can have product instincts that are good, but you can't express them.

But most of them, 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't 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, some people ask like how much is art versus science? I say it's 0% art, 0% magic,

and 100% science. And that's because I want to force people to try to explain it. I think we

use the word art and magic. We have historically used the word art and magic for anything that we

couldn't yet explain. Genetics was what magic and art until it was science. And quantum physics

was magic until it was science. And most recently, actually, intelligence and creativity was art and

magic until it was statistics in an LLM. So I think I try to push people to say, are you sure you

can 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? 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 use to write everything and then

write and rewrite 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. What I actually do is I, and I found

this different for different people. A lot of people want to balance 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. 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's 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, I'm not sure. But I found that walking, talking, and thinking, actually, even

if you're not in person, just over AirPods, is 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. We had more ideas than ever. And I started thinking about why.

And I think it's all of these walking talks that we did.

You kind of threw 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 it

did a lot. So it has to be about something with technology or product or something. But I would

love to write something fictional. That'd 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 and

the Pants analogy. 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. 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 that's like peeing in the pants and stuff, because people know what I

mean. It's a 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 times a year. That's probably, this is a saying in cold

climate. In the warm, it doesn't help. 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 trend. 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 and Swedish business dealings? It's super fun to see this

as a Swede. And I guess first and foremost, anyone or any person or any country that gets

represented by a super tall, well-built, great-looking Alexander Skarskård 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 named Jelle Reven, which I think means Arctic Fox, which is actually 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. We're

in Sweden. 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, sauna, they're just like a chant, sauna, sauna. Exactly. The last

episode. It is a great show. I love it. I love it. This season is insane. I am so curious where it all

goes. Maybe just the last question before a 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. 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 StreamOn. We talked about

it mostly for music, music discovery, but the same problem. It's the same problem and even harder

for podcasts. So we're still focused very heavily on helping podcast creators find more audience.

This is, like I said, it's even a bigger problem to break up, break out of your habits and your

bubbles and podcasting. Such a big investment to find a new podcast. That is something I think we

could and should do really well. We keep investing a lot there. As I said, you'll see more as we

roll up more features now. The other big need for creators is monetization. You can monetize

today in many ways with DAI and Spotify SEI and so forth. We're working hard to expand that and make

it better because the industry is starting to mature. 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. 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. 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 share 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. I don't want to say too much, but

looking at AI and generative technology, there's a lot that can be done.

All right. 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. Let's do it.

Let's 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. If we start with the own product,

I think it's well known, but one that I would recommend for people to read is Seven Powers by

Hamilton Hammer, which Netflix has used a lot. We use a lot. 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 the Complete

Investor by Charlie Munger. Yes, it's about investment, but really it's a bunch of mental

models that he uses. 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.

The world has probably infinite dimensions, and it reduces to maybe three or four,

and the risk with that is you happen to get rid of a really important dimension,

like maybe pandemic diseases or something. But if you use three models that have different

dimensions and was reduced in different ways statistically, and 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 deeply hidden by Sean Carroll on the

Everettian interpretation of quantum mechanics. Helgo Land 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.

Gerdl'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.

It's 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 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 reading books like these.

And so I think if people are looking to get to a place that you're at now,

I think there's the lesson. I'll keep the others much shorter, I promise.

It's all good. We got time. Okay, next question. What's a favorite recent movie or TV show?

So we talked about succession and it is a recent favorite. So I'll just frivolously take

something that isn't recent but is an absolute favorite, 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, 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... 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,

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 and also used 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 star. I only have like a few thousand XP. I'm not that good yet, so...

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 big impact. I think maybe one thing that I've tried to do

back to Clarity and so forth is this thing I mentioned about, I'm trying to push a lot for what

I call Socratic Debate, where the idea is obviously that the best idea wins, not the most senior idea

and so forth. Trying to push for this notion of having people explain themselves, not saying

like, I think there's something there or I have a feeling or something like that. Apparently,

as you said, that has had some impact because people apparently say that about me. That's

probably the biggest thing. Final question. What is one fun ritual of the Spotify product team and

is it saunas? Spotify is so big now that it's quite local actually. Different parts of Spotify

have different product rituals. I accidentally created one ritual many years ago, maybe 12 years

when we talked about which phase a product is in. We needed some definition. I think

sort of off the cuff. I said, well, it's four phases. It's think it, build it, ship it, tweak it.

In the think it phase, it should be cheap. Not a lot of money spent. In the build it phase,

you're going to start spending a lot of money. Then you must have reduced the risk in the think

it phase that you're right. Then you have the ship it phase and then you go over and tweak it.

It was something that wasn't that thought through. But it's funny because I still hear it. Sometimes

even from other companies, like, oh, we're in the think it phase or we're in the tweak it phase.

So it kind of stuck. I don't know. It's very good, but it's stuck.

It is catchy. I think 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. Gustav with an F

Ulstermer 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. Working folks find you online. If they want to learn more, maybe reach

out, ask some questions. 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. 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 with, do you recommend

an angry tweet at you or more of an email to that email address you shared?

Well, the at Gustav S is the Twitter handle. So just tweet at me. You can be nice as well.

Amazing. Gustav, thank you so much for being here.

Thank you for having me, 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

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past episodes or learn more about the show at Lenny's podcast.com. See you in the next episode.

Machine-generated transcript that may contain inaccuracies.