Gustav Söderström - How Spotify Thinks - [Invest Like the Best, EP.424]

Transcript from https://podmenti.com/t/03306a246a23f930

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Mm.
My guest today is Gustav Soderstrom.
Gustav is the co president, chief product officer, and chief technology officer at Spotify.
Gustav lets us behind the scenes on how Spotify thinks about the future of audio and video, and what leadership lessons he's learned from making mistakes and taking risks in a rapidly changing technology landscape.
He shares fascinating insights on their synchronized team structure.
And how they position themselves as the R and D department for the entire music industry.
We discussed their integration of AI, their unique Bets Board process for allocating resources.
And how they've evolved from a music service into a multimedia platform with over six hundred and fifty million users.
Please enjoy my great conversation with Gustav Soderstrum.
Maybe a
Fun place to begin is the obvious place.
Everyone is facing this giant shift in technology.
My friend Ravi Gupta calls this imperative AI or die. That companies even the big
sexy established technology companies.
need to find ways to embrace and use this new technology.
as it unfolds or face elimination.
I would love to hear how you and Spotify are thinking about this challenge.
I know you've embraced it very quickly and you were very early to using machine learning and data science.
All over the product.
But this is a big
shift and
You and Daniel and the team are some of the most thought people about
addressing shifts like this, and you've done it before.
Walk us through in some detail.
how you first felt it, what you did about it, what it's like to be at a big company.
and process something like this.
Yeah, it's a great question. I think it's the right description, at least
In the longer term, I think it is AI or die.
Just like it was
Smartphone or die, and before that internet or die, computer or die.
This is one of those shifts that
It's not your choice.
Whether you adopt it or not, it's gonna happen to you. It's the epitome of a macro window.
And usually when these macro wins come, we have a thing internally
That you can have the macaron blow in your face.
it's not gonna change its direction. So you basically need to reposition yourself so you get the wind at your back and can surf this macro wind or or some people call the macrow wave that you surf.
So we've been through a few of these. The first one was really the smartphone when that came along.
Spotify was really well positioned for the internet before the smartphone.
Where
We had a free turn desktop.
And that's where we've acquired users and they built a playlist
And they retain themselves.
And then mobility.
To listen on the go was a paid feature on Spotify.
And that was fine.
When the majority was computers and the minority was smartphones.
And then when smartphones took off.
We faced an existential crisis where there started to be consumers who didn't have a desktop.
They only had a phone, so they had no free experience and our entire model died.
So that was one of those moments we had to reposition the entire business model actually and figure out how do we do a free tier on mobile.
That doesn't cannibalize.
The paid feature was what's mobility in.
We can talk about how we figure that out later.
But that was one of those examples. And I think
This is a similar one.
The big question to me is
Does this require a business model change?
Where is it quote unquote just
A product change.
The other thing that is different, I think, about AI is that
It's not gonna touch one thing. It touches the consumer product, but it also touches your productivity.
And competitiveness as a company.
So there are lots of different angles to start, but
As you said, we were quite early with machine learning. The journey we had was we saw users coming on Spotify.
And then they
Started playlisting.
And that retain themselves.
But it was only a certain amount of
people who are good at playlisting because you have to know the catalog in your head, then you release it's the back catalogue. So some people retain themselves really well.
And then we try to scale that behavior by having editors who create a playlist for people who couldn't play this that well.
And we saw people using social to find inspiration.
Eventually.
Machine learning started happening and we saw this opportunity of building a
Music friend for everyone.
So that's where we started. We started investing in that and got quite good at that.
I think some people say that
AI is just machine learning, it's just a new word.
And it's an interesting question, what is the difference?
I think the difference between what people used to call machine learning
And what we call generative AI is that
The statistical machine learning
And I think the epitome of that age
It's the full screen TikTok feed.
UIs shape themselves after technology that powers them to maximize
metrics and I think that is the UI that maximizes the statistical explore exploits
Paradigm of old school.
Machine learning.
What happens with generative AI, I think the big shift is that you can take natural language input.
And so
even if technically they're both machine learning, I think of generative AI as the new age.
I think the big shift is that it's two way.
If you think about Spotify, for example, this consumer product
The way it looks. It's almost like a
Old school broadband.
The broadband where you have maybe one megabit downlink.
but only like hundred and fifty kilobit uplink.
So a lot of bandwidth down
But not a lot of feedback.
This is what most consumer services look like.
You have streaming video on the down link, a lot of information per second.
But the uplink is only a few clicks and swipes. It's very, very narrow.
Signal.
And this is what the previous machine learning age focused on.
I think what changes in the age of generative AI is that the uplink
Can now be English language. It can be almost as rich as the down lake.
And I think that requires all of us consumer companies to
in the limit, totally rethink the product. So if you just do the deduction on what I said,
If the full screen TikTok feed is the epitome of
The ML paradigm.
The asymmetric
Down the Goupling Paradigm.
What are the chances that that is also the epitome of this generative AIH? I don't think so. I think consumer products are gonna change
Fundamentally I can't predict exactly how.
I think they're gonna be
much more symmetric in terms of information you receive versus information you give.
And I think
if you fast forward five to ten years, almost all big consumer products are gonna be
A conversation
To some extent.
Rather than this.
service that you use.
So really the job for us on the product side is try to figure out what is the next
Paradigm. And I don't know exactly what it is yet. We're experimenting.
And if I did know, I probably wouldn't tell you right now. Sit on it for a bit.
But this is where on the product side.
Then we can talk a bit about
The productivity side as well.
Where
There are the obvious gains in terms of coding productivity.
where we are using all the tools that everyone else is doing.
But as a big company, there are a few differences from the startups.
Because so far
Generative AI encoding has had the most impact when you write net new code.
Which is a lot of what you do as a startup and a tiny bit of what you do as a big company.
Most of it is
Just refactoring.
Cetera.
And I think I saw some statistics that
In a big company you basically code one out of every eight hours in a day.
So not only this
coding only one eighth of the time.
Oh, that one I thought of the time. That new code is very small.
So I actually think the biggest impact is yet to come.
When it comes to coding.
That's two things. These models are getting
big enough to understand really large and complex codebes like Spotify's.
And we're not quite there.
Where these things can refactor.
Or code base.
It doesn't have quite that deep understanding. But it will.
And that would be a big shift. The other is
Doing automatic peer review.
We're just on the verge of that working, it's not quite good enough that you can trust it. So a lot of developers didn't wait.
For the code to be in review and come back.
So I think we're
Seeing that ramp, I think we're gonna see ramp a lot.
In the next few years.
But then
the really interesting side is these other seven hours what a developer does, which is a lot of
communication, planning, working with designers, prototyping.
Meetings, those things I think will actually have as big or even more impact than the coding itself. I'm gonna start with this down link, uplink part.
What have you learned about
consumers willingness to put a lot of effort into the uplink. It seems like the
Chat interfaces, the GPTs of the world.
we know that people are willing to do a lot of back and forth because it's the native interface.
you're going there expecting to write a lot of stuff, you're copy pasting prompts from Twitter or whatever.
In an app like Spotify, how willing are people to get
not lazy and really descriptive about what they actually want. What have you learned about the nature of
People's laziness versus willing to put a lot of work in to get the thing that they want via that more rich uplink.
Yeah, so that's
Probably the most exciting thing for us of this
Generative.
AIH.
And the dual uplink paradigm. So
Previously.
We mostly relied on
Some explicit input when you playlist. That's high value information.
You are sitting there thinking this song goes really well with this song and that song.
So you think about it as labeling.
Even though you're playlisting for yourself, you're sort of labeling these tracks, at least in relation to each other.
And you're putting a lot of effort in it. And that was an is our big advantage in music recommendations, even though generative recommendation systems are starting to take over from these more old school collaborative systems.
So we had some really strong signal like that where you
quite seldomly invested a lot of time.
In producing a data set that
Described you about playlisting.
But most of the time.

We just had skips and the challenge for us is the phone is in the pocket.
So even if we had the thumbs up down.
You're not gonna take out the phone every time and say, I didn't like this because of that, or even do a thumbs up, thumbs down requires you to
Take up your phone, unlock it, open Spotify.
What you can do from your airphone is to skip.
So we have the skip signal.
But that is a very blunt signal.
So we play you a song and you skip it. That could be
Because you absolutely hated it.
Could be because you love it.
But it's a hundred times. You're tired of it. It could be that
You love it.
You're not tired of it.
But you're at the gym. So jazz is not the right thing.
All of those just look like a skip to us.
We have a lot of that signal, but it's blunt.
And you will never get to perfect personalization through that.
Now what we find
With generative AI, one of the first services that we've launched.
That is live now for the countries that's
Something called AI playlisting.
We literally use an L L M.
Let us train on your listening data and world knowledge and so forth.
And you can literally tell us in English.

You could play list songs and maybe put a title on it and we could guess. This is probably a running playlist
So we could do something.
Now you can say I want a running playlist that is EDM, I want big drops, I want it to be 160 BPM.
And then you get a suggestion from the LM and then you can keep a few tracks and say these were good, these were not good, now refine it. I don't like these artists, but I want more of that.
So for us it's the first time that we get that kind of fidelity of what is actually
In the user's mind.
When we think about Spotify is
We always tried to
reproduce a small part of your neural cortex on our service.
It's just very hard with a click stream of skips.
Now when you tell us what is in your mind.
It gets easier to approximate
you as a person. So this is really the first time that we have that signal.
One way I like to think about it is
When we do user research, we do two things. We do quantitative testing, A B test, but before that we do qualitative testing.
We interview a few people deeply.
To understand the need.
Then we build a product and then we A B test to see if we're right.
The promise of generative AI is really
A deep ongoing
Qualitative user research with almost seven hundred million users all the time.
It sounds big, but if you squint at it, that's kinda what it is.
It's interesting how many different ways you could take the product
with this new technology.
I would be really curious to know the apparatus inside of Spotify the leadership team, the product team.
And literally how you run the process.
of deciding what to do with your
You have a big team, obviously, but no matter what, you have limited effort, limited units of you can apply. So there's a huge space of stuff you could do with this technology and the exciting advantage that you have of the seven hundred million users.
What is the literal meeting by meetings process, the setup process look like? And the reason I'm asking this question is so many companies face
this same challenge. It's exciting but also scary.
that they need to get the innovation before somebody else does and disrupts them. So what is the background process for how you arrive at the things you might try?
There are really two things. We have a very structured process
That I want to talk through how it works.
But there are also some concepts that we use. And over the years I've introduced some strategic frameworks to the company.
Which I know you were passionate about like seven powers from Helmut.
Bundling framework to shimmer off.
He's on the board, right? He's on the board. Secret weapon. Exactly. Very good secret weapon.
And also I found Better Simpler Strategy by Felix Oldhausen to be very good. What's that one? It's a concept of the value stick where you have willingness to pay.
It's very important for us the way we measure values or willingness to pay.
But what it introduces is also the willingness to sell.
If you think about your stuff.
Like what is their willingness to sell their services to you? And everyone focuses on increasing the willingness to pay.
But you can also s increase or depending on how you think about decrease the willingness to sell. And it turns out the best companies in the world are not necessarily the ones who actually pay the most, it's the ones with the most interesting mission, the best culture, et cetera.
So because we're a bundle service where
We try to just
Give users.
More and more value.
All the time. We put in lots of music, that's value. Then we put in more podcasts, that's value. Now we put in books, it's more value.
This framework of
Willingness to pay and willingness to sell.
fits our business really well. And the job of us is to keep
The willingness to pay
quite far from the actual price.
That gap is how much consumer surplus you're giving. And our goal as a service is to make sure that this Spotify is just an amazing deal. You're always going to feel.
The willingness to pay, the actual value you perceive.
is way over the price.
That we have.
So we use that framework quite a lot. So
Introducing these frameworks, not just in the business work, but also in the product and technology work.
Makes people.
Think in more structured ways. It makes people have a vocabulary.
We can talk about network effects, amortization.
Brand power, all of these things.
So I spent a lot of time getting the teams to use these frameworks.
So that we have structured strategic thinking.
And the other thing I've tried to push as a thesis is that
I'm a big fan of Socratic debate. I'm amazed like many other people how far
The Greeks and the Romans came with just discussion, even though they didn't have science, just reasoning. Strong reasoning is very useful.
So I try to push this
Provocative line of
Talk is cheap.
So we should do a lot of it.
That's the counter to like moving fast and breaking things.
It's so cheap to talk, so we should actually do a bit more of it.
It took the Greeks to the concept of the Atom.
So we do a lot of talking and ideation that is quite structured.
And I do that with my leadership team and often the leadership team sort of plus one.
Which means the the VP layer and sort of the director plus.
And have a lot of time.
Just for discussing
Concept.
Back to one of my heroes in life, David Deutsch.
His book The Beginning of Infinity and The Fabric of Reality shaped me quite a lot.
And he talks about something called good explanations.
And he has a list of what a good explanation is.
It obviously needs to be falsifiable and so forth, but
It also needs
To reach.
It needs to scale.
An OK explanation explains this phenomenon, but it doesn't scale to other phenomenon. It doesn't scale up and down.
Really good explanation of scales.
From explaining how the earth works to the solar system to the planets.
But it's also very hard to vary.
Which I think is often underestimated.
If you have an explanation and you can switch it out for another explanation that
Explains the same thing. If you explain the weather using gods, you can switch out this god for that god. It's probably not a good explanation.
It needs to be very hard to vary. If you vary it
It doesn't explain it anymore.
The last thing he says is that explanations should not just be predictive.
That's not an explanation, that's a model. An explanation needs to explain why.
So I try to push my teams
Even if something works in an A B test, I tend to say like I don't want to launch it until you have a good theory of why it works.
Because if you figure out the why
It's the difference between
Pattern recognition?
And actually understanding something.
Pattern recognition is useful. That's called seniority.
I love people with good pattern recognition, but if they can explain why it works, it scales to the entire world. Other people can use that knowledge. It's it's much, much more valuable.
So those are some of the concepts that I've tried to put into the org over time.
So I think that's important because that shapes the culture.
Then we have the structured process, which is we execute for six months at a time.
We have something called a bet' process.
Where
All the V Psych.
Which is about fourteen. So one of the benefits of Spotify is this so small that
All the VPs, the entire company can fit in one room.
And we meet three hours every Tuesday.
So the company's completely synchronized, for good and bad, and we can talk about that.
Later.
But so every six months.
These VP's date pitch.
Literally pitch.
As if we were a V C and they were startup.
The bets that they think the company should do.
And why?
It's very much like a startup process.
You don't get to use the fact that
Gustave or Alex or Daniel may like you. This is like a V C meeting, you have to convince us.
So they pitch.
Then me and the other co president Alex Nordstrom.
We decide based on these pitches a global stack rank.
This time we have forty four bets.
That's usually between thirty and maybe fifty.
We stack rank them from one to forty four.
And we go out to the organs say, Now try to resource this.
And they start from the top and then maybe they get uh
thirty and say this is what we can do in the next six months and then they commit
To those things.
And we start executing. And it's a good mix of
Bottoms up innovation where you leverage not just Daniel, not just me and Alex, but
All the V Psych.
and the layers below to come up with good ideas because they're closest to the user.
But then there's global synchronization. We stack rank them, make sure that they fit a single strategy.
And it's back to the organ they commit.
As I think you know, you're gonna be much better at delivering something if you were the one who said I can do this than if your boss said you can do this.
So that's the process, but leading up to that.
We have something called a prototyping phase. So the previous six months
We prototype
In the combination of Figma increasingly Jen AI tools.
What Spotify should look like after the next six months or could look like
And this prototype also helps.
Synchronized the entire company.
What I found previously was that
When people submitted these bets, everyone had in their mind
What their great feature would be.
You start building and then down the line you realize that you are not actually aligned.
And then you get a lot of fighting towards the end of the cycle.
where this thing doesn't work with that thing and things don't work out so well.
What I've tried to do now
Together with Alex Nordstrom.
We synchronise the entire company.
Alex and I don't have our direct reports team. We meet as a single team three hours every Tuesday.
And
We try to use the fact that we're small as an advantage instead of as a disadvantage versus our competitors who are very, very large companies. And so
We
prototype everything up front. So all the so called quote unquote fighting.
happens before you actually commit to doing something and you have something you can hold in your hand.
Say this is what Spotify would look like if we pull this off. So that's the combination of cultural input.
And then a very structured process for actually making it work.
I have so many questions about process.
The first is how that three hour meeting on Tuesday works. What is the structure of that meeting?
It's called the E Team.
Execution team.
So it's very focused on execution of the company. And the idea is that if you have five day working weeks
There's never on average more than two and a half days.
Before you, if you're blocked on something, can escalate.
To me.
Alex and all the other VPs. So the idea is you should never be blocked more than max two and a half days.
Because we run this synchronized ship.
If you're blocked.
gets very expensive because everyone else is downstream of you.
So if you're running a synchronized operation the way we're doing
Escalation processes are very important and resolution is very important.
So big part of that meeting is
People say we're off track here.
Um
Dependent on
This man and woman over there who hasn't done what they said.
And the beautiful thing about
being able to have all the VPs in the same room is
met so many meetings that I'm sure you've been in, people say, Okay, we'll take that offline. I'll talk later. And what we said is you're not allowed to say the word offline or later. Because that person is in the room. Yeah. I'm dependent on
Maybe Anna over there for this.
But then Anna is actually there.
And then I can say.
Okay, I didn't know that or I'm gonna solve that.
So it's real time resolution.
Very simple in theory, but incredibly powerful in practice. Most companies don't do it.
So this notion of no
Taking it offline, taking it later, real time resolution. That's why it's three hours.
So that's one thing of this meeting.
Another principle we have in that meeting except
Nothing goes offline.
Is you actually can't bring
Your direct reports.
For good and bad.
The idea is that if you bring in a lot of direct reports
Two things are gonna happen.
One is
The V P
Is not gonna get forced to know the details as much. So I'm trying to literally force the VPs to solve it themselves, because I want them to be in the details. So you're not allowed to bring anyone else.
And to explain your thing.
You have to be on top of it enough to explain it to yourself.
The other benefit of that is over time these groups gets very tight.
Because you don't switch people out all the time.
So you build strong rapport, people can be honest, no one is afraid.
It's a very strong and high functioning team.
So that's a lot of what we do.
The other part is
Strategy I'm looking for.
So let's say that something we've been working on.
For some time now that it's public, we wanted to introduce music videos, and the team goes off and says, What does that take in terms of licensing product?
What is the cost implications for the company for the PL? They come and present to the ET team, like this is what we want to do, this is how long we think it should take. So it's a combination of
Keeping the engine running.
And never stopping.

And also planning for the future. But we don't really plan in that. It's too big to have detailed planning that happens in focus rooms.
With smaller groups with experts.
And then they come and present
To that team.
We're not the only company that does this.
I know
Airbnb does something similar. I spoke a lot to Bronchewski about it.
I think Netflix may have had something similar at the time, but they're now divided into
content of business and product. What I think is important about this is
It's both the business and the product side.
And we talk a lot of product there. So the business people in Spotify, they know an awful lot about A high
about what a mono repo is.
They're there for the protestation on technology, but on the flip
All my product people and engineers, they know exactly what the P and L looks like. They know our goals
They know what cross margin is.
They know everything. So that's quite unique. And that gives them a CO perspective.
That I think disappears in many companies because we put on these roles of you're a product person, so you're not supposed to understand finance. That's not true. If you're the CO you have to understand all of it.
If people are listening and are curious about this Bettsboard process where you can submit projects and
Seems like a really elegant way to allocate
Capital.
What advice would you give them about the pros and cons
Of this process.
And I know you've been doing it a long time, how it's changed over time to reflect the learnings of what makes it work or fail.
So the concept itself is actually really straightforward. It comes from the Kanban board. It comes from the developer.
community, which actually comes from car manufacturing eventually. It's really the concept of stack ranking.
Which is very easy.
in theory and very hard in practice. Very few people manage to say
This is actually more important than that.
They're just saying these things are very important, both of them. And when you press them they say no, they're equally important.
But then they're not ranked.
So the real secret is to stack rank.
And say you have your two darlings.
But if you have to kill one of them, which do you kill first, in reverse order?
So very easy but hard to do.
Across the entire company to agree on that.
But once you have it
It gives so much clarity to the orc.
'Cause what happens when you say these three things are equally important, but they're not really
They never are. You're gonna have to choose.
Is you just push the decision down the org. And this VPU is on the hook for that thing. It's gonna start fighting this VPU is on the hook for the other thing.
And if you as a leader don't bring clarity
You're gonna set your org up for fighting. And people are very nice. They're gonna
Think that they don't like each other.
So just the stack ranking.
I'm being
completely transparent across the entire company means that if I come to you
And I say I needed to do this. And you say, Yeah, but I'm doing this. We look at the board and say, Oh, right, we should do this.
It's very simple, but very effective.
And when you do that, there are a bunch of things you run into.
Theoretical questions of
Okay, once you have this
That's for done.
You resource it.
Globally.
Do you go through every developer and says, Oh, let's just try to get as far as we can.
That planning process is hell.
If everyone is up for grabs. None of my VPs have any
estimate of h what resources they will have, you basically disempower your entire V P
It's effective in a sense,'cause you do perfect resourcing.
But it's incredibly efficient to do the planning.
So then the question is
How do you divide it?
Into blocks.
The structure we have is we have a platform organization.
They work with G C P and the cloud and the developer tools and so forth and security and all of that.
Then we have an experienced organization that's responsible for the entire consumer product across mobile, car
desktop, etcetera, then um
Personalization organization, because that's so important to us.
That does all the AI and the recommendations and balances between books, music, podcasts, video, et cetera.
And then we have
Three business verticals, music, podcast, and books.
So they have their own resources.
And what we do is we start by
Asking them to resource as far as they can.
With the resources they have.
without stealing from each other.
And then we get as far as we can because you need to give them predictability.
For them to be able to plan their own work.
And then at the end of that process
You may move some people around globally.
to make sure that you don't have something really important and there are two people missing.
That's not optimal for the companies. You may move some people around, but largely
We try to let people keep the resourcing. So lots of those problems that you run into.
But I would say the biggest
risk with this model. It sounds nice if you're perfectly synchronized.
The drawback of that not less that the planning is very expensive.
So you have to be really
Good at planning.
And we've had to build our own tooling. We tried some external tooling for planning.
That wasn't good enough.
And if the planning doesn't work, the overhead just grows very quickly versus execution.
We execute for six months.
In order for the overhead to not get too big.
But we can't go to a year, then you can't react. A quarter is too short, it's too much planning over headverse execution.
So the planning is to
thing that you have to get really good at. And I'm not gonna say we're really good.
But we're getting better all the time. It's the thing that I care the most about.
Making sure that the planning is reasonably big.
If you can do it, for us it's critical.
Because the whole of Spotify's product strategy is that
We have large distribution closing in on seven hundred million M use.
For a single application.
And our entire strategy.
Yes.
Basically we decided this many years ago before it was popular, but
You saw the Chinese starting to build super apps.
Whereas the Western world built one app per use case, we've adopted the Chinese super app idea and said
The hardest thing
is gonna be to get installs. You could see the average number of installs from the app store dropping below one on average.
So disabusion became the most important thing.
And then we chose when we did podcasts and later books and videos, we're gonna build it in the same application.
Because then we can leverage our own distribution.
But that has drawbacks.
You have to have an organization because then everything is dependent on each other. You're gonna ship one app to the app store.
And everyone is a stakeholder. So you cannot divide and conquer.
You cannot say, Well, the book team, you can run ahead or
The music team, you do this. No, everyone has to wait for everyone.
So because of our consumer strategy
the company needed to be synchronized and because it needed to be synchronized, we needed a really strong planning process. It's an outcome of our consumer strategy.
And what I would say is it's not the right one. It's the right one for us. We're good at doing global changes.
Like changing the entire UI because we're synchronized.
But we're probably much slower than other companies at something because it needs to go through a lot of planning.
I don't think you can win in planning.
The best you can hope for is to be
Quite good at the important things and not so good at the less important things.
I wanna come back to
something very interesting you said around
The adoption of some of the tooling that's at the most cutting edge. So let's take Cursor as an example of a company that now everyone's familiar with,$10 million valuation.
Seems like every software engineer is using cursor to make themselves better.
But the way you framed it was so cool that yeah, sure, but that's new code primarily. That's a fraction of one eighth of their time. In the pie chart, that's a very small sliver that's being addressed by a cursor at big companies.
Can you describe how you think this will play out because
It feels like the public markets especially
I guess private markets too.
are very curious about
how AI companies and products and tools
will address this much bigger part of the pie that sounds like really hasn't been hit.

There are a couple of things that are interesting that I don't think are super obvious.
One is it used to be that every developer started using Cursor.
But now
I'm starting to see a lot more non-developers using cursor. And that's partially because the industry is starting to agree on this protocol called MCP model context protocol.
Which means that if you take your internal services and you wrap them in an MCP
You can speak English to your infrastructure. So if you're a developer now, or if you're a designer, for example, or a product person.
Let's say
You want to prototype a feature.
And Spotify.
One workflow is
You take the assistant Spotify, you double click and screenshot it.
You upload that into cursor and say, Why this up?
Clickable in HTML.
And then if your services are wrapped in MCP, you could theoretically say
Now wire this up to my like songs feed or something.
And you can prototype even though you're not a developer because the infrastructure is wrapped in English language now through MCP. I think that's an important

I think you're gonna see many more
people using curses and just developers. I'm starting to see
I had one of my PMs.
Who is in Sweden. She's from New Zealand.
Doesn't speak Swedish. She did her taxes in cursor. Managed to wrap the Swedish tax authority in an MCP. Not a developer.
I think it's gonna grow outside of developers.
But I think this points to what is actually happening in many of these big companies.
Which is why the start ups can move faster. So
If you think of a company like Spotify has tons of infrastructure.
Yeah.
The database with play history going fifteen years back, you have who was in the family plan, that's one
Server this is one data set somewhere.
Your taste graph is a data set and so forth.
Now here comes the big AI companies and they give you this reasoning engine.
Some of them are open source so basically for free you get
What is
Getting close to A GI.
So now you have this
thing that you thought would be incredibly expensive and you'll get it almost for free. It's a gift.
You start using it.
What is the first problem you run into?
You say how has my music uh listening changed over the last year?
That's not exposed as an API because in the previous machine learning world
That data
Listening data fifteen years back, it's on cold storage somewhere.
And an engineer would have had to do an SQL job that may have taken a week to pull it up.
Then you would have trained the models, then you put it back in cold storage.
Now you want to be able to reason over that in real time.
You need to expose all your data.
as APIs in real time.
So actually
My biggest job
To enable AI.
is not AI engineering. It's old school engineering exposing all this data that we have so that you can have
A reasoning engine.
reason for you as a product person, or actually for me as a consumer potential over my own data in real time.
So I think that's what's happening. So the combination of now there's a standard MCPs that you can wrap these APIs in.
And many of these come at least us trying to expose all of this data.
Means that
A business person, a lawyer.
A product personal designer will be able to use cursor.
Without having to code.
And they can actually
At least prototype or talk to real services.
That I think is the journey.
They were on it started with developers.
But I think as you expose the infrastructure and wrap it in APIs.
I think it's gonna have to go outside.
Is a way to say that
And lose a lot, but summarise it that
What we've seen happen with developers is gonna happen
I'm surprised that it's cursor that they're using. That's quite interesting, but with other similar tools.
And that just more of our work is gonna feel like we're working with a team, speaking to a team, using natural language to prototype things, to try things, and that will diffuse slowly through

Not only just the hours of the software developer, but the hours of each of the other functional areas. Yeah.
I think so.

It's hard to see where it's gonna land because you're somewhere right now.
But we're pretty certain that that somewhere is on this curve.
So you can be pretty certain that
The workflows you see right now are not gonna be the same, and that's actually one of the problems. How much are we gonna
build for what we see right now.
when you know the models are gonna be more capable, there are gonna be different tooling.
Very soon.
So you you don't want to overfit too much.
To the moment.
A reasonable
view of a modern company.
Is that all of its data is exposed
in real time and you have some tool on top, like Cursure or something else, maybe different tools for different skills.
Maybe two more.
The licensing team at Spotify may have a different tool.
To reason over all the contracts and quickly say, Do we think we can do this in that market and what do we need to license to do this? But also the product team could
Ask that.
Licensing engine.
We have fifteen years of contracts.
Both current and previous. So this AI has a lot of insight into what music licensing looks like more than any single person in Spotify if you train it that way.
There will probably be slightly custom interfaces for different skills.
I'm not sure which is gonna win out.
But I think it's gonna look.
Something like that. Right now what we see people doing is they're sharing examples of prompts they used for the workflows.
And then
Prototypes.
That they've used.
And that feels like very much a point in time. It's kind of hacky and different things.
If you were to calibrate
The world out there
So few people have the inside view that you do.
where you're excited by this technology, you're trying to embrace it, you're only able to embrace it so fast in the ways that we've described.
on a one to ten point scale or something like this.
What score would you give
how much this is impacting
You so far.
And
how crazy this might get. People are very excited that this is gonna literally change everything.
And there's some people that are actually worried about how much how powerful it might be.
From a practical real world standpoint, could you calibrate us a little bit as one of the few people that actually is both excited about it and also faces reality on a daily basis?
If you want to be as realistic as possible about it, you take the developer use case.
I've seen studies from other big companies that if you actually measure out of a developer's time
the speed up is seven percent or something, which sounds very disappointing because of all these things.
Coding is a small part, net new is a small part of that.
And so forth.
So I think right now
It's a bit overhyped in terms of actual impact, at least for these big companies.
But I think it's gonna turn it into the opposite. And I think right now people are overexcited versus the actual impact.
But I think the opposite is gonna happen.
I think is going to have
Tremendous impact.
Over the longer term.
What I see people doing right now.
It depends. I mean, I use it a lot personally. I see a lot of my
developers and product people and designers.
use it all the time for just productivity purposes.
putting things into an engine, asking it for the summary and so forth. Those things happen all the time.
It's hard for me to estimate how much that speeds them up already.
It certainly does.
I think the really big impact
comes as you
reshape these companies from this technology. Right now we're just tacking it on top.
But as I said.
You have to reshape it and rebuild it for this work.
where a reasoning engine can reason in real time.
over the entire company's data, but that requires actually a lot of retooling. That's why startups are ahead.
They don't have to rebuild. They don't have fifteen years of data.
So they're probably
In the future, which is why they feel like no no.
Gustave is wrong. The impact is really big already.
I think it is.
For a startup.
think it takes a bit longer for big companies and big companies like us we have to
Shape up and accelerate in order to not
Be behind.
I'm sure they would all like to have seven hundred million monthly active users to experiment with, though.
On that topic, you mentioned
going through mobile and the experience of
Not only was everything was changing as a result of mobile, but actually the business model also needed to change. We've really talked about product so far.
And there's more to ask about product, but
Talk about business model.
What would be the world in which
As a result of this technology, Spotify's whole business model
needs to change and how do you go about evaluating something like that?
We've seen a few of those examples of business models.
And I tend to tell my product teams and everyone says that.
the world is disrupted and changed by technology. And I think that's true in the sense that the underlying force is technology itself.
And it's this gift that keeps on giving. It gives you computers, internet, smartphones, ML, AI, quantum computing.
And these gifts keep coming almost on a schedule and they actually come
Closer and closer. Previously technology companies were not called technology companies. As a side note, they were called car companies.
But it was a technology companies are
Pharmaceutical that was the state of the art technology right then.
But because these microwaves came so far apart.
They call themselves a car company. They never became
ubiquitous technology companies like overfitted to that.
I think somewhere in the nineties around
Google, Amazon, et cetera.
Throw it started coming so fast that
People try to pin them down.
Amazon is a books company and they were like, No, not really. We're doing books but
Here's other stuff we're selling and then okay, you're the everything store company. It's like, nah, not really. Now we're selling
So I think these companies
Are the first set of companies
To have technology as the strategy. The previous ones took.
one wave as the strategy and then IBM comes along and does computers as a strategy or first memory and so forth.
I think this is the first wave of general technology companies, which interestingly might mean that they could be
I mean companies almost always die after a while.
These could be the first companies that never die because they're ubiquitous technology companies. Whatever the technology gift is, just try to have a company that can quickly wrap around it, figure out the product and business model.
So I think that's interesting, and that's how I think about Spotify.
Yes, we're
music company and then a podcast company and then a book company and a video company.
But it's really about trying to anticipate technology.
Figure out what it can do.
And then adapt the product and often the business model.
So I said uh mobile was one of these things where we need to change the business model.
And I think
What happens when one of these technology gifts comes along is
There is a big change when the technology happens.
Piracy.
Big havoc.
But the real change happens when someone also figures out the business model. So I tell my product teams
Technology can do
Good things.
And then your business model can really change the world. But without a business model
They're seldom like large scale.
change. You can destroy a lot of things, but you never really create value.
So mobile was the first where we need to figure out a feature on mobile without cannibalizing our
Pay Tair.
And what we did there was we looked at our data and saw that fifty percent of premium users
We're listening in shuffle mode.
So we said what if we take
Shuffle as a feature.
Give that away for free.
It should be fifty percent of premium consumptions are very valuable, but it's not gonna be a hundred percent of anyone's premium consumption, so no cannibalization.
And we managed to create a tier where
You could playlist all your favorite songs in a playlist.
Press play, put the phone in your pocket and listen forever for free in the background.
So that was a business model innovation.
Along with technology.
The most previous one
was audiobooks where there were audiobooks in the US a la carte.
And sure, we did some nice innovation around being able to stream that book, but the real thing is not stream a book.
You've been able to stream audio for a long time.
Innovation that was the business model to be able to bundle
Audiobooks into Spotify Premium.
It's almost like music. Music was also a la carte and quite niche. And once we made it an access model
With no marginal cost.
It got way larger, and that's what we think about audiobooks as well.
So we've seen a few of those managed to adopt them. To your question.
Is AI going to do that? Do we need to
Change the business model.
I'm not sure.
I think there is one glaring thing that is different.
Which is the previous
V C model
Coming all the way back from chips and silicon was
You make a big upfront investment and then you amortize and you get to almost zero marginal cost.
That's how software worked.
It's not how AI works. The marginal cost is high.
And you need to cover it.
So you could say that that should change everyone's business model. You're gonna need to somehow either monetize very effectively through ads or charge users. And you see open AI.
Being a subscription product.
And I think you're gonna see more of those.
So the marginal cost is a net new thing.
For Spotify.
It's interesting because
We're like the one technology company.
That always had a marginal cost. One more stream.
was a marginal cost to labels. So we grew up in a world where if we were too successful on the free tier.
We could go bankrupt overnight.
Which was never true for Twitter or Facebook, which is why V C said just go crazy.
More about the monetization later.
Spotify could never do that because we could go bankrupt overnight. So we always had to worry about monetization and the balance between free tier
and pay ther conversion and free term monetization.
So the good thing for us is we're fairly used to marginal cost in our business model.
I think you're gonna see those things.
It's very likely that some consumers are gonna want tons and tons of entrance.
And because that's a marginal cost, you're probably gonna have to pay somehow for that as a consumer.
So I think you're gonna see more tearing of consumer products.
Based on how much inference you want.
But for us that's not that
new. We've had several tears already.
I'm curious because I'm an investor in a company called Etch that's gonna be one of these companies that pushes down that inference cost. Like the history of compute, you're gonna see this incredible
consumer surplus and consumer benefit that comes from cheaper and cheaper
unit by unit inference cost.
But the countervailing force is that we would just use more of it, more reasoning tokens, more whatever.
So it makes me wonder
How much more you can imagine.
better models being useful to you.
It seems like if we just froze
reasoning and model capabilities today.
We probably still have
decade plus of digestion to do of how we could use these models to make better products, better features, whatever.
Can you imagine?
Another couple orders of magnitude better models.
opening up lots of features that you can't currently do. Is that a thing? Or do you think we have what we need? And therefore inference we could expect to be really cheap.
So I both subscribe to the product overhang idea, but there's a huge product overhang and if we froze
I think we would see
Products ship that look amazing for several years.
Before we exhausted
Well we have so subscribe to that.
But I also subscribe to that there is no limit for computes.
Eventually you get to computrum.
But if you look at just the physics of computerium. It's the most computational universe could do.
Theoretically. Yeah.
We're very far from that limit.
So I think we're gonna go all the way there.
Before we stop.
And I think we're gonna be very inventive.
There is a nice analogy.
I don't know who came up with it, but I think Ben Evans talks about it.
quite often. You know when the spreadsheet came along?
The idea was the same.
Now all the counters are gonna
Go out of business.
What happened was we could just not imagine
If calculation.
Cost went to zero.
What's gonna happen?
Is you could imagine that
The value of doing that is going to go to zero because there were so many accountants in the world stuff that what happened was we just started doing massively more accounting.
When there's no cost to spread sheeting.
You're gonna start uh do models to predict the futures of this asset or good or something into the future forever.
We just came up with so much more spreadsheeting.
That you could do.
And it's bigger than ever. I think from a financial point of view
When the cost of something drops.
The demand usually increases more than the the drop. And I think that's
bound to happen with intelligence. It is the ultimate thing.
And to say like now I have enough intelligence is not interesting
I think we're gonna be ashamed of how mundane things we spend.
Inference on?
Could my coffee be one degree warmer tomorrow? If it's truly
No cost asking the questions. I think people will.
Maybe now's the time to ask you about sitting in the back garden with David Deutsch and talking to him about this concept of the beginning of infinity. Computronium made me think of your interest in this topic and your answer there that
There's no endpoint here.
It's just gonna keep going. We're gonna keep learning, keep deploying our new technology. Can you talk about that book, why it influenced you, your conversation with him?
Yeah, so David Deutsch has been a hero of mine since
I read The Beginning of Infinity and then he wrote another book called The Fabric of Reality.
He's considered a father of quantum computing.
Obviously, quantum computing is one of these gifts that
technology is gonna give us and it's about to get very real, I think, very soon.
So I've always been interested in because quantum mechanics is the most
Insane thing.
On this planet.
We live in what we consider this reality, but
If you go to the bottom layer, this is not reality.
It's just some three dimensional projection that we live in.
The fabric of reality had a big impact on me.
He's an Everettian, he believes in multiple words scenario.
I read that book and it blew my mind. Then Begin of Infinity
It's maybe his most famous book.
Whereas fabrical reality is really about quantum computing.
And how quantum computer works.
Beginning of Infinity is very philosophical and he has a bunch of big ideas there. He's a very positive person.
And now at seventy plus, I finally got to interview him in his garden in Os Oxford.
is not of great health, so it had to be outdoors, you know, distance.
And everyone is very negative on the future.
There's so many problems they could go wrong and all things could go wrong climate.
He's actually very positive about the future. He's clear that there are risks.
But he sees us going
out there into the stars.
I asked him where do you think we are in a million years? And he's like, Well, maybe we're this far outside of the solar system, but not quite there. He's very certain we're gonna get there. So very positive person, and when I asked him.
About his life.
He's very content.
with his life is very happy. So he's just an inspiring person still at this age.
I wish I
We'll be like him in that age.
But this book.
has a few concepts that I've tried to apply at Spotify and one of them is
He talks about
The power of explanations.
And he thinks the human mind is infinitely scalable. He does not think there's a limit to what we can understand.
because of explanations. And I think this is something that a lot of people
I agree with that, but a lot of people disagree. Certainly there are things we could never understand. His view is no
There is no limit to what we can understand.
We are the only species who broke that barrier because we have explanations.
Other species have patent recognition?
They can do things and learn the pattern that this works.
There's some cultural transfer maybe of looking at someone else doing that pattern, a bird can see another bird.
Some species can teach their kids
But they never produce explanations.
He has a definition of a good explanation.
He's very inspired by Karl Popper as a philosopher.
It's his house code.
So he takes a bit from Popper.
It takes a bit from science.
So he says obviously that a good explanation.
Has to be
Falsifiable.
But he says a few other things that I think are obvious in retrospect, but not before. Says that a good explanation has to scale.
Has to have a reach.
What does he mean with that?
Explain something quite locally. You can have an explanation about
For example, the sun revolving around the earth, which explains a bunch of stuff.
But it doesn't scale.
To other planets. It's a better explanation.
is to have the earth revolving around the sun. It just scales better.
to different scales. So a good explanation has to scale up and down.
A good explanation has to be compatible with all previous explanations.
But most interestingly he says that a good explanation has to be hard to vary.
This I find very obvious, but also very
Non obvious to people.
So what does it mean with a good explanation has to be
Hard to vary.
He means that for example if your explanation for the weather on the planet is that now
Thor is angry.
So there's thunder there.
It's an explanation, but it's too easy to vary it. You can say well now someone else is angry, they also had a hammer.
It's too easy to vary and get the same result. A good explanation.
If you move one of the parameters
The entire thing is not predictive anymore.
Then you're probably close to the truth.
I think this is so interesting because the problem with most conspiracy theorists that people love
is they're so easy to vary. You can just exchange that character for some other crazy person and did something crazy and still gonna produce the same thing.
So if it's too easy to change people in a conspiracy theory
It's probably not true.
So I think that's something very powerful. Like good explanations need to be very hard to vary.
So this is something I've tried to
Instill in my org.
And
I think there's an interesting meta point here, which is
People ask me as a product person how much
of product development is magic and how much is science.
And I try to be provocative in saying I think it's
Exactly. A hundred percent science is zero percent magic.
And people get provoked because it implies
That there's no skill?
I say it to provoke.
What I mean is that
Certainly people are gonna have pattern recognition.
in this neural network, they've seen a lot of examples. That's what we call seniority.
And people have seen a lot of things. They're gonna get instinctively
to the right conclusion faster than others. So that is valuable.
And I want lots of seniority.
I don't discard seniority.
And it brings you a lot of value. You can save a lot of time, a lot of mistakes.
But the reason you call it magic is because that person can't explain it.
It isn't actually magic. It's just science. It's just you are not smart enough to explain yourself.
If you could think even further and explain it.
and come up with an explanation for what you see the way David Dodge does.
It's so much more valuable for the company.
If you have a theory.
Instead of saying no, Patrick, my intuition is this.
You're not smart enough to understand it, so I'm not gonna tell you. Just do what I say. Maybe I'm right.
Maybe I'm wrong.
But it's not very helpful for you.
When I leave the company you're gonna take over, you're like
I have no idea why they did that. You have to develop your own intuition and your own pattern recognition.
But if I come up with an explanation
Which is I think the psychological behavior of people, you know, it's like coneman's lost adversity.
Or prospect theory. I think people value losing something one and a half times the the value of getting it. So therefore we should
Not just launch feature and test it because it's one point five X hard
More expensive to remove it.
Then you have a theory and it can spread across the company in a week. And now everyone has that.
So I really want to force people in my company, even if we see something working in an A B test.
I try to tell them that I don't want to launch it until they at least have a theory over why it works. Even if it's super clear.
There's a lot of pressure to launch it'cause there's engagement value and monetization.
But I want you to at least have a theory.
'Cause then over time the company builds up a consumer theory.
And if you have a strong consumer theory.
Then you can predict things that were very unlikely.
The what what David Deutsch also says is that pattern recognition
Will iteratively get you more on the same path?
But it's never gonna jump all the way from the geocentric to the heliocentric model.
Only an explanation can take you to
quantum physics entirely unintuitive. No pattern recognition gets you to
Maybe it's a wave and a part of that s.
What's an example internally of a great explanation that then led to the geo to heliocentric type of jump?
What's an example of how that actually played out?
I think a good example
That is public is the free tur.
That I told you about
We only had a paid mobile tier.
You actually paid to get mobility on Spotify.
Now smartphones are scaling, users don't have a computer, we need a free chair.
The competition that was YouTube, they were foreground
on demand with video.
The pattern recognition, the obvious thing would have been to
Say let's do that. It's proven.
But what we did instead and specifically attributed person named Charlie Hellman
Was to reason around it.
From first principle and say, Okay, let's look at our usage of Spotify.
If we limited our licence to the same thing, it only works in the foreground as soon as you
Look at the screen, the music stops.
How much of the listening is in the foreground?
Turns out back then it was nine percent or something. So you have ninety one percent of the use case being in the background. We probably want to get something else.
The user need there.
It's probably background listening. And you then you look at the app store.
Is there a way?
to listen to music for free in the background in the app store. The closest thing was Pandora.
But that was radio. You can not listen to your favorite songs.
So then we said
We would like a consumer product where you can listen to your favorite songs.
With your phone in the pocket.
Forever for free.
So the problem with that is
That's almost a premium use case.
We just launched that it's gonna cannibalize
A premium tier.
So what do we do?
Then we looked at the premium usage and we saw that premium users about fifty percent of the time
They were shuffling their playlist.
They were using on demand features, searching and clicking and playing specific songs fifty percent of the time.
But they were shuffling players fifty percent.
So then we thought what if we take this that seems to be something that
Even when you have on demand, you voluntarily shuffle. It's a big use case.
We give that away for free.
That should mean that none of the premium users convert back to free,'cause they still want their fifty percent on demand.
But you're giving a lot of value away.
For free.
So we try to model a consumer need.
Reason around it.
came up with this shuffle background here that was very, very, very unintuitive.
Even the people inside the company say that's a
Terrible idea.
But we trusted the data, and I was even skeptical of it myself. I was like
Look at this on the map. Shouldn't we try time caps or a lot of people just want us to try long free trials.
But the problem with the free trial is even if
Nokia, I think. Nokia comes with music. They tried
A year long free trial.
But even then the user knew that if I start investing in playlist now, a year from now, my playlist investment
is gonna disappear.
So they never started investing.
So we went with this shuffle tier and this is what made growth explode.
And to this day, that's our differentiation against the other services. It's the only way to listen to music for free forever with your phone in the business. So that's an example of theorizing and explaining.
Rather than
Pattern recognition.
I'd love to talk about the evolution of the relationship with the music industry. It's a company that
unquestionably has wholesale changed.
music, which is so interesting and so cool.
You've been here a long time. Thinking back to the early days, it's amazing the impact that it's had.
And from a investor's perspective, one of the things that many were always keyed in on is just the gross margin of the business, just how much
transfer pricing problem.
Are you always gonna have that?
matter how big you get, the music industry that owns the IP is just gonna always take their same cut of the meat.
Talk about how you've thought about that change over time. It seems like it's been
both a good relationship for them, but also a very patient
Path for Spotify.
Maybe just give us the insight into how it's worked and how you think about it.
Yeah, for sure.
I grew up and Spotify grew up in the era of piracy.
In Sweden, which was the worst market, and there's this famous quote from a UK
Label exec to a Swedish label exec.
Around early two thousand.
With the Swedish label exact show the P N L.
Of one of these Swedish companies, and they said, That's not a business, that's a hobby. That's how broken it was. And that's actually why Spotify could happen, because the music industry.
was prepared to take risk in Sweden.
And I wanna give a lot of credit to the music industry. They took a lot of risk with Spotify. Spotify took an enormous amount of risk.
enormous amount of capital risk.
We MD a lot. We ate a lot of the risk, but certainly they took a lot of risk.
I think the music industry certainly deserves the success.
As does Spotify.
I joined in two thousand eight.
Some around two thousand twelve or something.
I started saying that my team the
R and D team.
And all of Spotify, we are the R and D department of the music industry. And first people were like
What do you mean?
And I'm like, Well look at it. It's an entire industry that doesn't have an R and D department.
Mobile phones has an R and D departments called you know Apple or Google, everyone has a lot of R and D.
But there's no one D spend.
In the music industry.
And I think that's turned out to be true.
And if you look at the trajectory this year
Is the first year of profitability for Spotify since its founding. People say that there's a lot of talk about Spotify sharing enough of the revenue. We share about 70%.
But the truth is the other thirty percent we haven't kept
We've invested all of that in the music industry and then more.
So we were unprofitable.
for fifteen years. We just invested, invested, and invested. So a ton of patients. And at the same time actually
The music industry has been profitable. Spotify has been unprofitable. I think it's fair to say we are literally the R and D department of the music industry.
We invested and had had losses for fifteen years and the music industry has been gaining
profit. Now that is not sustainable forever.
we need it to get profitable. We can't be the R and D department of the music industry.
Unless we can
have the best machine learning engineers, the best product people, developers, et cetera, for that.
You need to be profitable. Turns out these people are expensive.
Because they're sought after.
We are a very, very patient and long term company and we invested for a long time.
But it was just time about two years ago.
We decided now it's time for us to become profitable.
To take control of our own fate.
In terms of being able to invest in ourselves.
So yes, we're profitable.
But we're actually investing almost all of that back into
More people, more product, more AI.
Now we just have our own
investment vehicle instead of having to ask
private investors initially or the street for more money.
So that's how I think about it. Really as the R and D department of the music industry and I think we've done a good job.
This year we paid out
Over ten billion.
And that's up from
One billion, I think.
Almost ten years ago, it's just steadily increased.
The music industry is bigger than it ever was. People still talk about the heyday.
The C D era. Yeah. The truth is the music business is bigger than it was
Back then. So this is the best it's ever been. It is better than ever.
More money than ever, the pie is both bigger and higher, but it's also getting sliced up. But that's because more people take a shot.
And it feels very wrong for us to say, No, the creator's up until twenty twenty.
They were good, but no one should be able to try after twenty twenty.
New creators should be able to try to do
Music.
So that's the dynamic. And I think a way to think about this is
People talk about the per stream payouts and so forth a lot.
And Spotify should
share more per stream. There are two things that are happening. When other companies say that they share more per stream.
The industry doesn't pay per stream, they pay per subscriber.
We have more than twice the engagement of our competitive services.
So if you take the same ten dollars.
You listen twice as much as Spotify, the per stream is half.
So these are the companies that have higher per stream because they have a
Worst product. We've learned from the labels that we have twice the engagement and half the churn of
Competing services.
So
That's a curse where the per stream model just the better we are as a product, the lower the per stream is gonna look.
But we're looking at the aggregate number and we're leading everyone else there, where the vast majority of these payouts. So I think if you look overall, the model is working. We took a lot of investment and now the industry is getting a huge return.
And Spotify also.
It's profitable now.
And the way to grow this pie.
It's now
We are closing in on three hundred million paid subscribers, closing in on seven hundred million MAUs.
It's a f about five and a million page subscribers, I think, in the world.
We're almost three hundred of those.
But that's half a billion.
out of the world's population.
If you look at markets like Sweden
On average, you can just look at the public numbers, we're convert about forty percent.
But if you look at the mature markets, I won't give you the exact number, but it is much higher. And if you look at the emerging, it's low. So the average is forty.
That's not the average across the world. That's a blend of
Low and high converting.
And so far throughout our history.
Everything starts to look more and more like Sweden the more time passes. So the solution to this is just to
Scale it faster.
Better free trader that gets more people on.
That converts to premium.
We think there should be billions of people paying for music.
And that's how you make the pie.
Truly bigger. The Revshare is actually a red herring, so
Let's say that we share seventy percent today ish.
Let's say two thirds to make it easier.
Even if we were a charity.
And we paid out a hundred percent.
That would only be
One point five X what you get today.
X pennies per stream.
It's too little.
Even if we were a charity, it would be one point five.
The solution is not the Rev show we're giving away the vast majority.
The solution is to quickly scale the amount of people paying for music. And if you just look at the numbers.
You just have to
Keep going and it's going to get to billions of users paying and then several billions. Then the music industry is absolutely massive.
I just think the music industry
It's undervalued.
Terminal is gonna be much bigger than it looks.
I'm curious how
Your
Thinking about the podcasting world. This is something that
We're sitting here doing right now.
I've been doing for a long time.
Now it seems we've entered this interesting new era where
When I started doing this I remember it was quite I would call it low status.
when I told people about it in twenty sixteen, they either didn't know what it was or thought it was silly.
And now, especially in the US with what happened around the election and the importance of podcasts in the election, it seems as though it's hit some tipping point where
Basically anybody that
Might
Make sense to have a podcast now has one.
Or is launching one. And it's the corporate marketing strategy is to get a podcast and it's the communication strategy is to go on them.
So it's really exploded in importance and visibility.
What role
has and will Spotify play in all this? And just what do you think about
Podcasting and its importance.
The reason we went into podcasting, one thing that I'm very precious about when it comes to Spotify
And so is Daniel and the other co president, Alex, who's my closest partner.
Is that
There are many ways we could go.
As a company.
And I think your business model to some extent steers you. If you're an advertising business model mostly, you're gonna be steered towards
Any additional engagement.
Fortunately for us we're a mostly a subscription based business model.
So we focus more on retention and you're gonna vote with your wallets every month if you want to keep paying for us.
We're not as steered towards
Engagement at any cost.
So
Having been at Spotify for a long time when this happened.
One of the things that made me feel very good about Spotify was that
When people used it, when they lost an hour on Spotify, they felt very good about it. You lost an hour on
Music.
You come out feeling it. That was a good hour. One of the reasons I really
pushed quite hard for podcasts in the company was that
I was using it myself and a lot of our developers were using it and I saw it being hacked into the product at Hack Week.
Every year.
People wanted them there. We just said our developers is like a small sample of the world.
What if they're a good sample of the work?
So that was one push. We saw people
Using it internally and hacking it.
But what made us decide on it was that
It was this format, everything in the world was getting more and more short form.
People are bite sized.
And attention spans were going down. And there was this counterforce, which was long form discussions
Deep
People spoke in full sentences.
about quantum physics or whatever.
And that just felt like something very important and good for the world.
And so we looked at it.
We saw that it seemed to be growing from a small base.
We saw the biggest competitors.
Being asleep at the wheel.
We did basically the Peter Thiel idea of it's better to go after small markets early.
and bet on organic growth and try to take a small share of a mature market. It looks less risk in the mature market.
To get one percent.
But the thing people miss is the costumer acquisition cost in a mature market is is massive. Whereas the costumer acquisition cost in a new market is usually small.
So we decided to go for it because we thought it was something that was in line with music.
If you lose an hour.
In a deep
Podcast.
You come out feeling like you learned something.
And this is the reason we also went into books.
Because it's in line with that we want to be this nutritious service.
There are two litmus tests for this. One is
If you lose an hour on Spotify
How do you come out feeling versus if you lose an hour doom scrolling in the bathroom?
How do you feel about that? In one case you feel like you ate a lot of candy.
Like you're a lot of energy in you, but it's bad calories.
In the case of Spotify, you feel like you learned something. The other litmus test that we have is
See a lot of parents.
Restricting screen time for their kids and saying go to go to Spotify instead. Which means
That expresses how
They feel about it?
And how we feel about it.
So that was one of the reasons to go into podcasts. It was part philosophical.
But we also saw the market opportunity of a small market that was poised to grow.
And we saw need in early adopters and trying to hack it in, and then we did this bet on leveraging our own distribution.
Combining it with music.
Saying that
The market is this big right now.
But what if we could expose podcasts?
To people who listen to music. Could we grow the market?
So that's the bet we did. And the truth is
Audio book is something similar.
In the US audi books was a very niche behavior.
Ten, eleven million or something.
People who paid for them a la carte?
And the idea was
That's a limitation because of the business model when you pay all a cart.
You're not gonna explore new books.
At fifteen dollar per book cost.
Just as in music.
When you pay ninety nine cents per song, you're not gonna soundtrack your sleep. It's too expensive at ninety nine cents per three minutes.
But what if we had no marginal cost model?
Well you can just explore.
Is Audi Book much bigger than it looks? Is the business model that is wrong?
So again it was seeing a market that
Look pretty small.
But you can see in the Nordics
where you have the access model.
That it's getting very mainstream.
So a ban on the market, but it was the same philosophical discussion.
Are these good calories? Is this nutritious? Is this in line with Spotify's mission of being the place where you go
When you want to feel good about yourself instead of when you wanna feel bad about yourself.
I remember the very first time I ever talked to Daniel walking along the West Side Highway here many years ago.
He talked about this notion of Spotify needing to be better than free. It was a cool idea.
if you think about podcasting it very different than
music. When someone listens to this show on Spotify, you don't owe me anything.
How do you think about the way that podcasting and then obviously books is a little bit maybe more like music and I'd like to hear
how you think about it.
How do you think about if that's the supply of the stuff that people are listening to on Spotify or watching on Spotify, the ways in which that
affects your business model and the bundle. We didn't know before we started
If podcasting and later audiobooks would be cannibalistic.
To the other media types or not?
But it turns out it's not. So the easiest model to think about it is
Spotify is a bundle now.
You pay some price, or you have the advertising based here.
And you get a bunch of value.
And our job is to try to
Increase the value you get.
Do you value it more?
And then over time, maybe we can capture some of that value by price racing.
Approached this a few times.
Which is part of why we're profitable now.
But that's because we had such user surplus in value. That's because we kept just stacking value.
And value are two things.
Value or features like
personalization and just a
Really good product.
But the other
Value is different types of media.
So what we see is that
I've a user that uses music, they have a certain amount of consumption.
When you add podcast.
It's just more. It's not more.
It's an infinite game, it looks like. At least for now. We haven't run out of time in the background yet.
Then when you add audiobooks, it's just more retention.
More time spent and more willingness to pay.
So that's how we think about it as a business model. Then on the back end
They have very different business model. I think we may be one of the most
complex companies in the world on the back end.
Because for music is a
Cool based royalty model.
Podcast, as you know, is advertising based largely
But now we also have this Spotify partner program where
You don't have Spotify ads in the premium tier.
If you're paying, so you get more uninterrupted.
So that's another business model?
Which is part of the premium bundle, and then you have audiobooks.
Which the publishing industry works in a third way, very different.
Where we also have
certain amount of time included in the premium tier and then a top up if you run over that.
One of the really complicated things about Spotify
I don't think it's appreciated is on the front end.
It's one app, one consumer, just go between them.
But there are very different implications of where you click in that UI.
In terms of triggering different business models and so forth.
So to model a company financially it's actually quite hard. We have to predict your user behavior, where you click matters and
We have the personalization.
That has different impacts in terms of cost and so forth.
So we've had to build a system, we call it the Spotify Machine, and that's why I said
I have one experience organization.
And the job of this experience organization is to make sure that all of this complexity, all of these teams who
theoretically could be set up to compete with each other to fix their P L.
That never ships to the user.
There's one person
who is the responsible person for the consumer experience.
And that person's job is to make sure that as you go between mobile and desktop then
Car and speakers.
The thing makes sense. It's like the gatekeeper.
against the org, holding them back from the user, behind them, protecting the user.
But it's also the same in personalization. I have a personalization organization.
because you have the same incentives of programming music
versus podcast versus books.
Everyone wants to take market share and so forth.
So it's the same problem. We have to optimize for the user and sort of protect the user from the internal
Incentives.
of teams and business models.
That makes Spotify pretty
unique company. We're like one thing on the front and and we're many different things on the back and with different products.
If you think about let's say five years from now.
And you dream as big as you can possibly dream for where Spotify might go from where it is today to where it will be in five years. Paint us that picture. Certainly I've hope we've cracked the billion use for.
Line but
Has a subscription, I hope we're
becoming one of the biggest media subscriptions in the world?
And we add more and more value to that.
So hopefully
Music is
bigger than it ever was. I'm hoping that audiobooks is a mainstream phenomenon.
as it is in Scandinavia where there's almost as many people that listen to music listen to audiobooks.
I think that would be enough good for the world.
But I also hope we've added a few more of these verticals.
You can't say what they are.
The subscription model, the bundling model that we didn't talk so much about.
We can differentiate on product or on content, but largely we tend to license commodity content.
We don't work with exclusivities, at least not anymore. We try them podcast for a while.
So you can differentiate on the product and consumption.
Of the commodity content.
But you can also differentiate it on the offering.
So for example
If you look at Spotify now versus other offerings.
Some other offerings have the same music.
Some other offerings I have some on the same podcast.
You can not really find
the combination of music podcasts and audiobooks. That's a unique thing. So to use bundling theory.
To create more and more of a differentiated, unique thing that is Spotify.
I think it's very exciting and I think you will see more innovation on
The bundling business model.

I'm the product guy.
I'm very interested in business models. I've been a CEO myself. So I think you will see a lot of innovation there.
What's the key to a good bundle? And I'm also curious, you said you experimented with exclusive content that was only available on platform and less of that now.
What drives a decision like that and how do you think about
Other people that might want to create a bundle else.
When we looked at podcasts.
You look at something like Netflix and it's this beautiful
Business model.
And insanely good execution as well, on top of that.

And it looked like
To us like
That could be interesting.
I think when you're a product company that wants to come out of the content, you always have this envy of what if we could differentiate through content? Then life is going to be super easy. You always think the other thing that someone else is doing is easy and your thing is hard. And it's usually very hard to do the other thing.
So
We tried exclusivity in podcasts as a way to differentiate this service.
But I think it was ultimately a bad bet.
Because the macro trend for
The whole
thing with podcasts was that the production cost was so low.

The production cost was low and then go in and do exclusivities on top of that.
It's kind of counter purpose in a way.
The whole point is more like YouTube in that
This is very cheap content, so you can get a lot of it. You don't have to be right. As soon as you go into Excity game.
You gotta be right. You gotta be a content picker.
And that's a very hard skill that Netflix does extremely well.
But we had this opportunity. We didn't have to pick content. We could just get all of it and use machine learning to serve you what you wanted and me what I wanted.
And there wasn't this capital intensive need there that there is in producing costume dramas. There's the bad
Strategic decision.
That we did.
We also betted a lot on celebrities.
And they are celebrities, but they're not always good podcast hosts.
And the podcast hosts that were really good. They grew up through this organic system.
So there are two ways to always be right.
One is to to always guess right, and the other is to just change your mind whenever you're wrong. So
We decided to change our mind and say this looks like the age of syndication.
Creators actually want to be everywhere. They create a video or they create music. They want to be everywhere, okay?
Let's embrace that.
I mean music we were always a platform.
We never played with exclusivity.
We said we want the maximum catalogue.
Books were doing maximum catalogue.
Let's just embrace that in podcast as well.
So we
Pivoted strategy.
Say there's a lot of cost.
Which is part of what we're doing well.
And it's also improved the catalogue greatly and now we're on a really good
trajectory with our podcast viewing.
So it was an example of that strategy and I think
The important thing is to admit in and change your mind.
The real cost.
Is when you try to defend your pathway.
What things do you do outside of Spotify in your life that most
Prepare you or make you capable to do the best job that you can in Spotify.
Very fast. So a lot of my time
Is just trying to keep up.
With what is happening.
I was on a vacation in Lisbon with my family recently.
And I spent a lot of time with them seeing Lisbon, which is a beautiful city.
But then I asked them for one day off from work and off from the family.
To just indulge myself. This time I was going back to trying to code a bit.
Use all these new tools, stay on top of
What's happening? Sometimes it's
Reading.
Physics or
math or something. It's a combination of keeping up with what is happening, which is hard.
Because it's moving so fast, but also stimulate myself mentally.
I have to
Have something that I'm
excited about at any point in time. And it can be new things.
Like AI and what it would mean.
But it can be age old things that I just didn't know, like learning more about physics or math or something.
I've read a lot of philosophy for a while.
Because it's just an interesting area.
You think through all the big questions of intelligence and consciousness and all of those things and you can spend ten years there.
Just reading all of that.
And now I feel capped out a little bit.
When you start reading you're like, Yeah, I'm gonna crack this and then
Turns out I didn't crack it. People have been trying to crack consciousness for a while.
But it's so deeply interesting. It kept me excited about life for a very long time. I was never like a big math person in school. I was okay, but not great, but I found myself getting very
Excited about Math the older I got.
As you start reading a bit of philosophy, you get into things like Girdles incompleteness theorem and
constructive mathematics and
These things.
That they're loosely related to work.
But they keep you energized. But they keep me energized. And actually it turns out that a lot of my product people and engineers are deeply interested in these things. So I just have something very interesting to talk to people around me about. And then I do sports. I do Brazilian Jiu Jitsu.
With my kids, which is very rewarding. What does that taught you?
Humbleness.
You come in and you think you do something and you get absolutely smashed by someone half your size and they're not even sweating.
And you're like, Okay.
Technique matters. It's technique. It's leverage. Yeah. The beautiful thing about Brazilian Jiu Jitsu is that the thing is real.
There's a long story behind it, but the net is that Japanese person bought jujitsu to a Brazilian family.
There were a bunch of brothers.
Who fought a lot.
There was one brother that was just underdeveloped versus the others. He was just not very strong.
So he could not beat his brothers.
So he started taking Japanese to jutsu.
And figuring out
He could just use physics, just leverage.
And slowly, slowly he started beating all his brothers and that became Brazilian Jiu Jitsu.
So it was literally he had to solve the problem, he could not use power. I don't know that.
And then this family put up
All these competitions I like it from an evolutionary
Product point of view is
They said, Okay, anyone come here. Karate kickboxing, just try it. Open
They fought in these basements, just evolving the sport, proving that it was real.
A lot of martial arts, it's magic and secret and
They never test their skills.
It works very well in practice.
The other thing I like about it is that
I've done a lot of other martial arts.
Boxing and tie boxing and stuff.
Those things are great as exercise.
But for self protection it's not very good. You cannot punch someone in the face, you're gonna get sued.
as protection. The beautiful thing about martial arts, which is called the gentle sport, is
You control people, you constrain them.
And you can adapt the level of violence. This is why police use jujitsu and not Thai boxing.
'Cause you can regulate the violence to the other person. You can control them without hurting them.
So that's why I think everyone should use it.
And practice it. It's
good both for self discipline because you get humble there's also actually
Useful and you can use it without harming other people.
People probably don't know this because how would they?
But Spotify, you and Daniel especially have been probably the most influential
people and certainly the company
on me and how I've thought about building
our businesses over time.
And a lot of that comes back to the stuff that you don't see.
I purposefully talked about a bunch of it today with you, the bet's board, the complexity that's hidden behind a beautiful consumer experience.
You were the first person years ago to describe the Bets Board concept to me, and we've used that very effectively, and so many lessons from Daniel on how to think about.
What matters to users and
I think Spotify is not only an incredible product.
But it's also one where the product is a reflection of the company behind it.
And
I think it's one
worth studying by listening to conversations like this one
Because for me, what it's done is raised the bar of ambition and the standard for excellence.
of how a company should be constructed to mirror the needs that it has, that its unique needs.
But also just the character and the discipline of the people running it. So it's been so fun to do this with you and thank you so much for all the lessons over the years. Well, you know the closing question that I have for everyone. What is the kindest thing that anyone's ever done for you?
The thing that made me really
Excel in my role.
was being allowed to take a lot of risk by Daniel.
So I've actually screwed up a bunch of things and Spotify that didn't work.
And I never felt
that I was gonna get fired for it.
And he actually encouraged that.
And I got the second chance.
And that's what made me have the higher ambition instead of holding back.
For risk of failure.
So I think it's a series of those things, being allowed to mess up things.
That it's probably had the biggest impact on my professional career.
Can you give an example of a bad mistake that you made and
How he and the org made you feel through that process so that you could be reemboldened to take more risk again?
I was interested in
New user interfaces.
Many years ago.
I took the company
Very hard on a journey
For an interface.
That at the time was like very provocative. The idea was
Spotify just starts playing things.
You swipe up
To get
to the next genre and you swipe left or right to get other things within the same genre.
And now you would say that sounds almost like TikTok. This is performed musically.
But two things happen.
It was very
Provocative, it started playing things without you asking.
So people were upset, but I pushed pretty hard because I was convinced that immediately
The idea was
You just sound your way to what you want to hear.
In a very low friction interface.
And it was maybe a decent idea.
But it was before machine learning.
It just did not work at all. You can just not get there.
And we built this, it was called moments.
The UI. We used editors on the back end, which is did not work at all.
So the idea was far, far ahead of
Where the technology was.
And it cost it a lot of money. We actually announced it. There's a video of us presenting this user interface and so forth. People
Luckily forgot it.
But it just didn't work.
We had A B test it and it looked okay.
Which is what we launched.
Then we discovered there was a bug in the A B test. Oh my god. When it was live and it actually underperformed
Drastically what we had.
So we'd had to roll it back, and I'd taken the entire organization.
On this excursion at lost a year or something in a very competitive business.
That was a good opportunity to get fired.
And I didn't.
Daniel was like I understand I agreed with the thoughts and the ideas.
What was the mistake? And the mistake was the machine learning.
Was not there.
We were not good enough to get you there in enough.
Swipes. And he was more like Jeff Bezos and matches the inputs, not the outputs.
If the inputs are bad, if the ideas are fuzzy and stupid.
That's a problem.
But you're not gonna be always right, even with good ideas.
And I heard him say this, Jeff Bezos quote, I judge you by the inputs you had.
Not the outputs. Because the problem with judging the outputs is
You could just get lucky.
And you get promoted even though you're not very good just by luck.
Whereas if you look at the inputs.
Just give'em more chances if if there's structured ideation and execution eventually gonna get right.
Yeah. So just got more chances.
And that may me actually take more risk.
Instead of scaling down on the risk.
But I felt very, very, very burnt.
For a long time. There are jokes internally about moments.
What a powerful story and mindset for us all to adopt. Such a great closing story, Gustave. Thanks so much for your time.
Thanks for having me.
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