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 The following is a conversation with Gustav Sorenstrom.

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 He's the chief research and development officer at Spotify,

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 leading their product design, data technology and engineering teams.

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 As I've said before, in my research and in life in general,

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 I love music, listening to it and creating it.

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 And using technology, especially personalization through machine learning,

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 to enrich the music discovery and listening experience.

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 That is what Spotify has been doing for years, continually innovating,

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 defining how we experience music as a society in the digital age.

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 That's what Gustav and I talk about, among many other topics,

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 including our shared appreciation of the movie True Romance,

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 in my view, one of the great movies of all time.

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 This is the Artificial Intelligence Podcast.

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 If you enjoy it, subscribe on YouTube, give it five stars on iTunes,

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 support on Patreon or simply connect with me on Twitter at Lex Friedman,

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 spelled F R I D M A N.

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 And now, here's my conversation with Gustav Sorenstrom.

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 Spotify has over 50 million songs in its catalog.

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 So let me ask the all important question.

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 I feel like you're the right person to ask.

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 What is the definitive greatest song of all time?

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 It varies for me, personally.

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 So you can't speak definitively for everyone?

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 I wouldn't believe very much in machine learning if I did, right?

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 Because everyone had the same taste.

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 So for you, what is... you have to pick. What is the song?

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 All right, so it's pretty easy for me.

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 There's this song called You're So Cool, Hans Zimmer, a soundtrack to True Romance.

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 It was a movie that made a big impression on me.

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 And it's kind of been following me through my life.

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 I actually had it play at my wedding.

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 I sat with the organist and helped him play it on an organ,

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 which was a pretty interesting experience.

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 That is probably my, I would say, top three movie of all time.

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 Yeah, this is an incredible movie.

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 Yeah, and it came out during my formative years.

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 And as I've discovered in music, you shape your music taste during those years.

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 So it definitely affected me quite a bit.

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 Did it affect you in any other kind of way?

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 Well, the movie itself affected me back then.

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 It was a big part of culture.

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 I didn't really adopt any characters from the movie,

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 but it was a great story of love, fantastic actors.

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 And really, I didn't even know who Hans Zimmer was at the time, but fantastic music.

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 And so that song has followed me.

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 And the movie actually has followed me throughout my life.

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 That was Quentin Tarantino, actually, I think, director or producer.

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 So it's not Stairway to Heaven or Bohemian Rhapsody.

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 Those are great.

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 They're not my personal favorites, but I've realized that people have different tastes.

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 And that's a big part of what we do.

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 Well, for me, I would have to stick with Stairway to Heaven.

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 So 35,000 years ago, I looked this up on Wikipedia,

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 flute like instruments started being used in caves as part of hunting rituals.

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 And primitive cultural gatherings, things like that.

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 This is the birth of music.

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 Since then, we had a few folks, Beethoven, Elvis, Beatles, Justin Bieber, of course, Drake.

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 So in your view, let's start like high level philosophical.

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 What is the purpose of music on this planet of ours?

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 I think music has many different purposes.

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 I think there's certainly a big purpose, which is the same as much of entertainment,

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 which is escapism and to be able to live in some sort of other mental state for a while.

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 But I also think you have the opposite of escaping,

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 which is to help you focus on something you are actually doing.

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 Because I think people use music as a tool to tune the brain

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 to the activities that they are actually doing.

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 And it's kind of like, in one sense, maybe it's the rawest signal.

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 If you think about the brain as neural networks,

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 it's maybe the most efficient hack we can do to actually actively tune it

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 into some state that you want to be.

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 You can do it in other ways.

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 You can tell stories to put people in a certain mood.

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 But music is probably very effective to get you to a certain mood very fast, I think.

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 You know, there's a social component historically to music,

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 where people listen to music together.

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 I was just thinking about this, that to me, and you mentioned machine learning,

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 but to me personally, music is a really private thing.

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 I'm speaking for myself, I listen to music,

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 like almost nobody knows the kind of things I have in my library,

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 except people who are really close to me and they really only know a certain percentage.

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 There's like some weird stuff that I'm almost probably embarrassed by, right?

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 It's called the guilty pleasures, right?

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 Everyone has the guilty pleasures, yeah.

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 Hopefully they're not too bad, but for me, it's personal.

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 Do you think of music as something that's social or as something that's personal?

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 Or does it vary?

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 So I think it's the same answer that you use it for both.

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 We've thought a lot about this during these 10 years at Spotify, obviously.

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 In one sense, as you said, music is incredibly

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 social, you go to concerts and so forth.

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 On the other hand, it is your escape and everyone has these things that are very personal to them.

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 So what we've found is that when it comes to, most people claim that they have a friend or two

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 that they are heavily inspired by and that they listen to.

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 So I actually think music is very social, but in a smaller group setting,

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 it's an intimate form of, it's an intimate relationship.

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 It's not something that you necessarily share broadly.

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 Now, at concerts, you can argue you do, but then you've gathered a lot of people

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 that you have something in common with.

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 I think this broadcast sharing of music is something we tried on social networks and so forth.

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 But it turns out that people aren't super interested in sharing their music.

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 They aren't super interested in what their friends listen to.

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 They're interested in understanding if they have something in common perhaps with a friend,

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 but not just as information.

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 Right, that's really interesting.

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 I was just thinking of it this morning, listening to Spotify.

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 I really have a pretty intimate relationship with Spotify, with my playlists, right?

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 I've had them for many years now and they've grown with me together.

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 There's an intimate relationship you have with a library of music that you've developed.

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 And we'll talk about different ways we can play with that.

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 Can you do the impossible task and try to give a history of music listening

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 from your perspective from before the internet and after the internet

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 and just kind of everything leading up to streaming with Spotify and so on?

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 I'll try.

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 It could be a 100 year podcast.

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 I'll try to do a brief version.

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 There are some things that I think are very interesting during the history of music,

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 which is that before recorded music, to be able to enjoy music,

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 you actually had to be where the music was produced

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 because you couldn't record it and time shift it, right?

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 Creation and consumption had to happen at the same time, basically concerts.

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 And so you either had to get to the nearest village to listen to music.

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 And while that was cumbersome and it severely limited the distribution of music,

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 it also had some different qualities,

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 which was that the creator could always interact with the audience.

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 It was always live.

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 And also there was no time cap on the music.

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 So I think it's not a coincidence that these early classical works,

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 they're much longer than the three minutes.

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 The three minutes came in as a restriction of the first wax disc that could only contain

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 a three minute song on one side, right?

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 So actually the recorded music severely limited or put constraints.

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 I won't say limit.

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 I mean, constraints are often good,

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 but it put very hard constraints on the music format.

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 So you kind of said, instead of doing this opus on many tens of minutes or something,

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 now you get three and a half minutes because then you're out of wax on this disc.

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 But in return, you get an amazing distribution.

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 Your reach will widen, right?

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 Just on that point real quick.

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 Without the mass scale distribution, there's a scarcity component

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 where you kind of look forward to it.

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 We had that, it's like the Netflix versus HBO Game of Thrones.

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 You like wait for the event because you can't really listen to it.

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 So you like look forward to it and then it's like,

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 you derive perhaps more pleasure because it's more rare for you to listen to a particular piece.

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 You think there's value to that scarcity?

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 Yeah, I think that that is definitely a thing.

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 And there's always this component of if you have something in infinite amounts,

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 will you value it as much?

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 Probably not.

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 Humanity is always seeking some, it's relative.

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 So you're always seeking something you didn't have.

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 And when you have it, you don't appreciate it as much.

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 So I think that's probably true.

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 But I think that that's probably true.

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 But I think that's why concerts exist.

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 So you can actually have both.

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 But I think net, if you couldn't listen to music in your car driving, that'd be worse.

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 That cost will be bigger than the benefit of the anticipation I think that you would have.

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 So, yeah, it started with live concerts.

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 Then it's being able to, you know, the phonograph invented, right?

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 That you start to be able to record music.

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 Exactly.

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 So then you got this massive distribution that made it possible to create two things.

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 I think, first of all, cultural phenomenons, they probably need distribution to be able to happen.

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 But it also opened access to, you know, for a new kind of artist.

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 So you started to have these phenomenons like Beatles and Elvis and so forth.

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 That would really, a function of distribution, I think, obviously of talent and innovation.

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 But there was also technical component.

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 And of course, the next big innovation to come along was radio.

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 Broadcast radio.

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 And I think radio is interesting because it started not as a music medium.

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 It started as an information medium for news.

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 And then radio needed to find something to fill the time with so that they could honestly

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 play more ads and make more money.

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 And music was free.

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 So then you had this massive distribution where you could program to people.

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 I think those things, that ecosystem, is what created the ability for hits.

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 But it was also a very broadcast medium.

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 So you would tend to get these massive, massive hits, but maybe not such a long tail.

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 In terms of choice of everybody listens to the same stuff.

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 Yeah.

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 And as you said, I think there are some social benefits to that.

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 I think, for example, there's a high statistical chance that if I talk about the latest episode

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 of Game of Thrones, we have something to talk about, just statistically.

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 In the age of individual choice, maybe some of that goes away.

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 So I do see the value of shared cultural components, but I also obviously love personalization.

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 And so let's catch this up to the internet.

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 So maybe Napster, well, first of all, there's MP3s, tapes, CDs.

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 There was a digitalization of music with a CD, really.

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 It was physical distribution, but the music became digital.

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 And so they were files, but basically boxed software, to use a software analogy.

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 And then you could start downloading these files.

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 And I think there are two interesting things that happened.

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 Back to music used to be longer before it was constrained by the distribution medium.

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 I don't think that was a coincidence.

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 And then really the only music genre to have developed mostly after music was a file again

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 on the internet is EDM.

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 And EDM is often much longer than the traditional music.

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 I think it's interesting to think about the fact that music is no longer constrained in

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 minutes per song or something.

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 It's a legacy of an old distribution technology.

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 And you see some of this new music that breaks the format.

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 Not so much as I would have expected actually by now, but it still happens.

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 So first of all, I don't really know what EDM is.

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 Electronic dance music.

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 Yeah.

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 You could say Avicii.

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 Avicii was one of the biggest in this genre.

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 So the main constraint is of time.

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 Something like a three, four, five minute song.

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 So you could have songs that were eight minutes, 10 minutes and so forth.

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 Because it started as a digital product that you downloaded.

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 So you didn't have this constraint anymore.

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 So I think it's something really interesting that I don't think has fully happened yet.

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 We're kind of jumping ahead a little bit to where we are, but I think there's tons of format

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 innovation in music that should happen now, that couldn't happen when you needed to really

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 adhere to the distribution constraints.

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 If you didn't adhere to that, you would get no distribution.

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 So Björk, for example, the Icelandic artist, she made a full iPad app as an album.

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 That was very expensive.

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 Even though the app store has great distribution, she gets nowhere near the distribution versus

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 staying within the three minute format.

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 So I think now that music is fully digital inside these streaming services, there is

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 the opportunity to change the format again and allow creators to be much more creative

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 without limiting their distribution ability.

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 That's interesting that you're right.

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 It's surprising that we don't see that taken advantage more often.

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 It's almost like the constraints of the distribution from the 50s and 60s have molded the culture

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 to where we want the five, three to five minute song than anything else, not just.

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 So we want the song as consumers and as artists, because I write a lot of music and I never

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 even thought about writing something longer than 10 minutes.

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 It's really interesting that those constraints.

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 Because all your training data has been three and a half minute songs, right?

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 It's right.

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 Okay, so yes, digitization of data led to then mp3s.

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 Yeah, so I think you had this file then that was distributed physically, but then you had

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 the components of digital distribution and then the internet happened and there was this

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 vacuum where you had a format that could be digitally shipped, but there was no business

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 model.

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 And then all these pirate networks happened, Napster and in Pirate Island.

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 Napster and in Sweden Pirate Bay, which was one of the biggest.

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 And I think from a consumer point of view, which kind of leads up to the inception of

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 Spotify, from a consumer point of view, consumers for the first time had this access model to

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 music where they could, without kind of any marginal cost, they could try different tracks.

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 You could use music in new ways.

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 There was no marginal cost.

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 And that was a fantastic consumer experience to have access to all the music ever made,

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 I think was fantastic.

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 But it was also horrible for artists because there was no business model around it.

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 So they didn't make any money.

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 So the user need almost drove the user interface before there was a business model.

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 And then there were these download stores that allowed you to download files, which

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 was a solution, but it didn't solve the access problem.

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 There was still a marginal cost of 99 cents to try one more track.

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 And I think that that heavily limits how you listen to music.

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 The example I always give is, you know, in Spotify, a huge amount of people listen to

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 music while they sleep, while they go to sleep and while they sleep.

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 If that costed you 99 cents per three minutes, you probably wouldn't do that.

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 And you would be much less adventurous if there was a real dollar cost to exploring

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 music.

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 So the access model is interesting in that it changes your music behavior.

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 You can be, you can take much more risk because there's no marginal cost to it.

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 Maybe let me linger on piracy for a second, because I find, especially coming from Russia,

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 piracy is something that's very interesting to me.

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 Not me, of course, ever, but I have friends who have partook in piracy of music, software,

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 TV shows, sporting events.

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 And usually to me, what that shows is not that they're, they can actually pay the money

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 and they're not trying to save money.

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 They're choosing the best experience.

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 So what to me, piracy shows is a business opportunity in all these domains.

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 And that's where I think you're right.

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 Spotify stepped in is basically piracy was an experience.

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 You can explore with fine music you like, and actually the interface of piracy is horrible

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 because it's, I mean, it's bad metadata, long download times, all kinds of stuff.

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 And what Spotify does is basically first rewards artists and second makes the experience of

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 exploring music much better.

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 I mean, the same is true, I think for movies and so on.

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 That piracy reveals in the software space, for example, I'm a huge user and fan of Adobe

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 products and there was much more incentive to pirate Adobe products before they went

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 to a monthly subscription plan.

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 And now all of the said friends that used to pirate Adobe products that I know now actually

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 pay gladly for the monthly subscription.

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 Yeah, I think you're right.

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 I think it's a sign of an opportunity for product development.

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 And that sometimes there's a product market fit before there's a business model fit in

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 product development.

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 I think that's a sign of it.

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 In Sweden, I think it was a bit of both.

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 There was a culture where we even had a political party called the Pirate Party.

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 And this was during the time when people said that information should be free.

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 It was somehow wrong to charge for ones and zeros.

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 So I think people felt that artists should probably make some money somehow else and

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 concerts or something.

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 So at least in Sweden, it was part really social acceptance, even at the political level.

18:49.920 --> 18:56.800
 But that also forced Spotify to compete with free, which I don't think would actually

18:56.800 --> 18:58.560
 could have happened anywhere else in the world.

18:58.560 --> 19:03.120
 The music industry needed to be doing bad enough to take that risk.

19:03.120 --> 19:04.800
 And Sweden was like the perfect testing ground.

19:04.800 --> 19:10.640
 It had government funded high bandwidth, low latency broadband, which meant that the product

19:10.640 --> 19:11.440
 would work.

19:11.440 --> 19:14.000
 And it was also there was no music revenue anyway.

19:14.000 --> 19:17.600
 So they were kind of like, I don't think this is going to work, but why not?

19:18.800 --> 19:21.920
 So this product is one that I don't think could have happened in America, the world's

19:21.920 --> 19:23.200
 largest music market, for example.

19:23.920 --> 19:25.600
 So how do you compete with free?

19:25.600 --> 19:30.640
 Because that's an interesting world of the internet where most people don't like to

19:30.640 --> 19:31.520
 pay for things.

19:31.520 --> 19:35.360
 So Spotify steps in and tries to, yes, compete with free.

19:36.080 --> 19:36.640
 How do you do it?

19:37.120 --> 19:38.240
 So I think two things.

19:38.240 --> 19:41.680
 One is people are starting to pay for things on the internet.

19:41.680 --> 19:47.440
 I think one way to think about it was that advertising was the first business model because

19:47.440 --> 19:49.200
 no one would put a credit card on the internet.

19:49.200 --> 19:51.040
 Transactional with Amazon was the second.

19:51.600 --> 19:52.960
 And maybe subscription is the third.

19:52.960 --> 19:55.680
 And if you look offline, subscription is the biggest of those.

19:56.480 --> 19:57.600
 So that may still happen.

19:57.600 --> 19:59.040
 I think people are starting to pay for things.

19:59.040 --> 20:01.680
 But definitely back then, we needed to compete with free.

20:02.480 --> 20:07.600
 And the first thing you need to do is obviously to lower the price to free and then you need

20:07.600 --> 20:09.440
 to be better somehow.

20:09.440 --> 20:15.040
 And the way that Spotify was better was on the user experience, on the actual performance,

20:15.040 --> 20:24.640
 the latency of, you know, even if you had high bandwidth broadband, it would still take

20:24.640 --> 20:30.800
 you 30 seconds to a minute to download one of these tracks.

20:30.800 --> 20:35.360
 So the Spotify experience of starting within the perceptual limit of immediacy, about 250

20:35.360 --> 20:41.520
 milliseconds, meant that the whole trick was it felt as if you had downloaded all of Pirate

20:41.520 --> 20:41.680
 Bay.

20:41.680 --> 20:42.800
 It was on your hard drive.

20:42.800 --> 20:44.400
 It was that fast, even though it wasn't.

20:45.360 --> 20:46.720
 And it was still free.

20:46.720 --> 20:50.400
 But somehow you were actually still being a legal citizen.

20:50.400 --> 20:54.160
 And that was the trick that Spotify managed to pull off.

20:54.880 --> 20:58.240
 So I've actually heard you say this or write this.

20:58.240 --> 21:02.400
 And I was surprised that I wasn't aware of it because I just took it for granted.

21:02.400 --> 21:05.920
 You know, whenever an awesome thing comes along, you're just like, of course, it has

21:05.920 --> 21:06.480
 to be this way.

21:07.360 --> 21:08.560
 That's exactly right.

21:08.560 --> 21:14.720
 That it felt like the entire world's libraries at my fingertips because of that latency being

21:14.720 --> 21:15.440
 reduced.

21:15.440 --> 21:18.640
 What was the technical challenge in reducing the latency?

21:18.640 --> 21:25.280
 So there was a group of really, really talented engineers, one of them called Ludwig Strigius.

21:25.280 --> 21:32.080
 He wrote the, actually from Gothenburg, he wrote the initial, the uTorrent client, which

21:32.080 --> 21:37.760
 is kind of an interesting backstory to Spotify, that we have one of the top developers from

21:38.480 --> 21:39.840
 uTorrent clients as well.

21:39.840 --> 21:42.320
 So he wrote uTorrent, the world's smallest uTorrent client.

21:42.320 --> 21:49.440
 And then he was acquired very early by Daniel and Martin, who founded Spotify, and they

21:49.440 --> 21:53.040
 actually sold the uTorrent client to BitTorrent, but kept Ludwig.

21:53.040 --> 21:58.240
 So Spotify had a lot of experience within peer to peer networking.

21:59.040 --> 22:04.560
 So the original innovation was a distribution innovation, where Spotify built an end to

22:04.560 --> 22:08.160
 end media distribution system up until only a few years ago, we actually hosted all the

22:08.160 --> 22:09.440
 music ourselves.

22:09.440 --> 22:13.360
 So we had both the service side and the client, and that meant that we could do things such

22:13.360 --> 22:19.200
 as having a peer to peer solution to use local caching on the client side, because back then

22:19.200 --> 22:20.800
 the world was mostly desktop.

22:20.800 --> 22:26.240
 But we could also do things like hack the TCP protocols, things like Nagel's algorithm

22:26.240 --> 22:31.200
 for kind of exponential back off, or ramp up and just go full throttle and optimize

22:31.200 --> 22:33.760
 for latency at the cost of bandwidth.

22:33.760 --> 22:39.200
 And all of this end to end control meant that we could do an experience that felt like a

22:39.200 --> 22:40.480
 step change.

22:40.480 --> 22:46.720
 These days, we actually are on GCP, we don't host our own stuff, and everyone is really

22:46.720 --> 22:47.360
 fast these days.

22:47.360 --> 22:49.440
 So that was the initial competitive advantage.

22:49.440 --> 22:51.440
 But then obviously, you have to move on over time.

22:51.440 --> 22:54.480
 And that was over 10 years ago, right?

22:54.480 --> 22:55.840
 That was in 2008.

22:55.840 --> 22:57.520
 The product was launched in Sweden.

22:57.520 --> 22:59.440
 It was in a beta, I think, 2007.

22:59.440 --> 23:00.800
 And it was on the desktop, right?

23:00.800 --> 23:01.840
 It was desktop only.

23:01.840 --> 23:03.840
 There's no phone.

23:03.840 --> 23:04.480
 There was no phone.

23:04.480 --> 23:07.920
 The iPhone came out in 2008.

23:07.920 --> 23:10.480
 But the App Store came out one year later, I think.

23:10.480 --> 23:13.120
 So the writing was on the wall, but there was no phone yet.

23:14.160 --> 23:19.680
 You've mentioned that people would use Spotify to discover the songs they like, and then

23:19.680 --> 23:24.880
 they would torrent those songs to so they can copy it to their phone.

23:24.880 --> 23:25.840
 Just hilarious.

23:25.840 --> 23:26.320
 Exactly.

23:26.320 --> 23:27.440
 Not torrent, pirate.

23:27.440 --> 23:32.800
 Seriously, piracy does seem to be like a good guide for business models.

23:33.520 --> 23:34.560
 Video content.

23:34.560 --> 23:37.600
 As far as I know, Spotify doesn't have video content.

23:37.600 --> 23:42.080
 Well, we do have music videos, and we do have videos on the service.

23:42.080 --> 23:48.320
 But the way we think about ourselves is that we're an audio service, and we think that

23:48.320 --> 23:52.800
 if you look at the amount of time that people spend on audio, it's actually very similar

23:52.800 --> 23:55.200
 to the amount of time that people spend on music.

23:55.200 --> 23:58.640
 It's very similar to the amount of time that people spend on video.

23:58.640 --> 24:02.000
 So the opportunity should be equally big.

24:02.000 --> 24:03.520
 But today, it's not at all valued.

24:03.520 --> 24:05.040
 Videos value much higher.

24:05.040 --> 24:08.320
 So we think it's basically completely undervalued.

24:08.320 --> 24:10.560
 So we think of ourselves as an audio service.

24:10.560 --> 24:14.000
 But within that audio service, I think video can make a lot of sense.

24:14.000 --> 24:19.040
 I think when you're discovering an artist, you probably do want to see them and understand

24:19.040 --> 24:21.200
 who they are, to understand their identity.

24:21.200 --> 24:22.400
 You won't see that video every time.

24:22.400 --> 24:25.120
 90% of the time, the phone is going to be in your pocket.

24:25.120 --> 24:27.280
 For podcasters, you use video.

24:27.280 --> 24:28.560
 I think that can make a ton of sense.

24:28.560 --> 24:33.600
 So we do have video, but we're an audio service where, think of it as we call it internally,

24:33.600 --> 24:35.120
 backgroundable video.

24:35.120 --> 24:38.720
 Video that is helpful, but isn't the driver of the narrative.

24:39.440 --> 24:48.560
 I think also, if we look at YouTube, there's quite a few folks who listen to music on YouTube.

24:48.560 --> 24:55.280
 So in some sense, YouTube is a bit of a competitor to Spotify, which is very strange to me that

24:55.280 --> 24:57.360
 people use YouTube to listen to music.

24:57.920 --> 25:00.640
 They play essentially the music videos, right?

25:00.640 --> 25:03.360
 But don't watch the videos and put it in their pocket.

25:03.360 --> 25:12.240
 Well, I think it's similar to what, strangely, maybe it's similar to what we were for the

25:12.240 --> 25:20.640
 piracy networks, where YouTube, for historical reasons, have a lot of music videos.

25:20.640 --> 25:25.040
 So people use YouTube for a lot of the discovery part of the process, I think.

25:25.040 --> 25:29.520
 But then it's not a really good sort of, quote unquote, MP3 player, because it doesn't even

25:29.520 --> 25:29.920
 background.

25:29.920 --> 25:31.600
 Then you have to keep the app in the foreground.

25:31.600 --> 25:36.160
 So it's not a good consumption tool, but it's a decently good discovery.

25:36.160 --> 25:37.840
 I mean, I think YouTube is a fantastic product.

25:38.400 --> 25:40.320
 And I use it for all kinds of purposes.

25:40.320 --> 25:41.040
 That's true.

25:41.040 --> 25:46.560
 If I were to admit something, I do use YouTube a little bit to assist in the discovery process

25:46.560 --> 25:47.280
 of songs.

25:47.280 --> 25:50.320
 And then if I like it, I'll add it to Spotify.

25:50.320 --> 25:51.760
 But that's OK.

25:51.760 --> 25:52.560
 That's OK with us.

25:53.600 --> 25:55.520
 OK, so sorry, we're jumping around a little bit.

25:55.520 --> 25:57.920
 So it's kind of incredible.

25:58.560 --> 26:01.440
 You look at Napster, you look at the early days of Spotify.

26:03.440 --> 26:06.080
 One fascinating point is how do you grow a user base?

26:06.640 --> 26:08.320
 So you're there in Sweden.

26:08.960 --> 26:10.320
 You have an idea.

26:10.320 --> 26:12.480
 I saw the initial sketches that look terrible.

26:14.160 --> 26:18.240
 How do you grow a user base from a few folks to millions?

26:19.280 --> 26:21.680
 I think there are a bunch of tactical answers.

26:22.240 --> 26:24.160
 So first of all, I think you need a great product.

26:24.160 --> 26:30.080
 I don't think you take a bad product and market it to be successful.

26:30.080 --> 26:31.120
 So you need a great product.

26:31.120 --> 26:34.720
 But sorry to interrupt, but it's a totally new way to listen to music, too.

26:34.720 --> 26:38.560
 So it's not just did people realize immediately that Spotify is a great product?

26:38.560 --> 26:40.240
 No, I think they did.

26:40.240 --> 26:45.280
 So back to the point of piracy, it was a totally new way to listen to music legally.

26:45.840 --> 26:48.960
 But people had been used to the access model in Sweden

26:48.960 --> 26:50.880
 and the rest of the world for a long time through piracy.

26:50.880 --> 26:54.160
 So one way to think about Spotify, it was just legal and fast piracy.

26:54.720 --> 26:56.240
 And so people have been using it for a long time.

26:56.960 --> 26:59.040
 So they weren't alien to it.

26:59.040 --> 27:01.360
 They didn't really understand how it could be illegal

27:01.360 --> 27:03.920
 because it seemed too fast and too good to be true,

27:03.920 --> 27:06.960
 which I think is a great product proposition if you can be too good to be true.

27:06.960 --> 27:09.760
 But what I saw again and again was people showing each other,

27:09.760 --> 27:13.200
 clicking the song, showing how fast it started and say, can you believe this?

27:13.200 --> 27:16.320
 So I really think it was about speed.

27:16.320 --> 27:22.000
 Then we also had an invite program that was really meant for scaling

27:22.000 --> 27:23.280
 because we hosted our own service.

27:23.280 --> 27:25.040
 We needed to control scaling.

27:25.040 --> 27:27.600
 But that built a lot of expectation.

27:27.600 --> 27:32.880
 And I don't want to say hype because hype implies that it wasn't true.

27:32.880 --> 27:38.560
 Excitement around the product. And we've replicated that when we launched in the US.

27:38.560 --> 27:41.200
 We also built up an invite only program first.

27:41.200 --> 27:46.160
 There are lots of tactics, but I think you need a great product to solve some problem.

27:46.160 --> 27:51.440
 And basically the key innovation, there was technology,

27:51.440 --> 27:55.600
 but on a meta level, the innovation was really the access model versus the ownership model.

27:55.600 --> 27:56.880
 And that was tricky.

27:56.880 --> 28:01.440
 A lot of people said that they wanted to be able to do it.

28:01.440 --> 28:03.680
 I mean, they wanted to own their music.

28:04.480 --> 28:07.520
 They would never kind of rent it or borrow it.

28:07.520 --> 28:09.120
 But I think the fact that we had a free tier,

28:09.120 --> 28:14.000
 which meant that you get to keep this music for life as well, helped quite a lot.

28:14.560 --> 28:18.560
 So this is an interesting psychological point that maybe you can speak to.

28:18.560 --> 28:20.080
 It was a big shift for me.

28:22.240 --> 28:24.800
 It's almost like I had to go to therapy for this.

28:26.240 --> 28:29.360
 I think I would describe my early listening experience,

28:29.360 --> 28:32.480
 and I think a lot of my friends do, as basically hoarding music.

28:33.280 --> 28:35.920
 As you're like slowly, one song by one song,

28:35.920 --> 28:39.920
 or maybe albums, gathering a collection of music that you love.

28:40.960 --> 28:42.080
 And you own it.

28:42.080 --> 28:46.160
 It's like often, especially with CDs or tape, you like physically had it.

28:46.960 --> 28:50.240
 And what Spotify, what I had to come to grips with,

28:50.240 --> 28:55.520
 it was kind of liberating actually, is to throw away all the music.

28:55.520 --> 28:58.480
 I've had this therapy session with lots of people.

28:58.480 --> 29:02.560
 And I think the mental trick is, so actually we've seen the user data.

29:02.560 --> 29:05.040
 When Spotify started, a lot of people did the exact same thing.

29:05.040 --> 29:08.240
 They started hoarding as if the music would disappear.

29:09.280 --> 29:10.880
 Almost the equivalent of downloading.

29:10.880 --> 29:16.080
 And so we had these playlists that had limits of like a few hundred thousand tracks.

29:16.080 --> 29:17.360
 We figured no one will ever.

29:17.360 --> 29:18.560
 Well, they do.

29:18.560 --> 29:20.960
 Nuts and hundreds and hundreds of thousands of tracks.

29:20.960 --> 29:25.760
 And to this day, some people want to actually save, quote unquote,

29:25.760 --> 29:26.960
 and then play the entire catalog.

29:26.960 --> 29:32.880
 But I think the therapy session goes something like instead of throwing away your music,

29:34.080 --> 29:37.760
 if you took your files and you stored them in the locker at Google,

29:38.720 --> 29:39.680
 it'd be a streaming service.

29:39.680 --> 29:42.720
 It's just that in that locker, you have all the world's music now for free.

29:42.720 --> 29:45.520
 So instead of giving away your music, you got all the music.

29:45.520 --> 29:46.720
 It's yours.

29:46.720 --> 29:50.240
 You could think of it as having a copy of the world's catalog there forever.

29:50.240 --> 29:52.720
 So you actually got more music instead of less.

29:52.720 --> 29:58.720
 It's just that you just took that hard disk and you sent it to someone who stored it for you.

29:58.720 --> 30:01.440
 And once you go through that mental journey, I'm like, it's still my files.

30:01.440 --> 30:02.560
 They're just over there.

30:02.560 --> 30:05.520
 And I just have 40 million or 50 million or something now.

30:05.520 --> 30:07.600
 Then people are like, OK, that's good.

30:07.600 --> 30:10.880
 The problem is, I think, because you paid us a subscription,

30:11.840 --> 30:14.000
 if we hadn't had the free tier where you would feel like,

30:14.000 --> 30:17.120
 even if I don't want to pay anymore, I still get to keep them.

30:17.120 --> 30:18.480
 You keep your playlist forever.

30:18.480 --> 30:20.240
 They don't disappear even though you stop paying.

30:20.240 --> 30:21.760
 I think that was really important.

30:21.760 --> 30:25.440
 If we would have started as, you know, you can put in all this time,

30:25.440 --> 30:27.280
 but if you stop paying, you lose all your work.

30:27.280 --> 30:31.760
 I think that would have been a big challenge and was the big challenge for a lot of our competitors.

30:31.760 --> 30:34.880
 That's another reason why I think the free tier is really important.

30:34.880 --> 30:37.600
 That people need to feel the security, that the work they put in,

30:37.600 --> 30:39.920
 it will never disappear, even if they decide not to pay.

30:40.800 --> 30:42.880
 I like how you put the work you put in.

30:42.880 --> 30:44.480
 I actually stopped even thinking of it that way.

30:44.480 --> 30:50.080
 I just actually Spotify taught me to just enjoy music as opposed to.

30:50.080 --> 30:57.200
 As opposed to what I was doing before, which is like in an unhealthy way, hoarding music.

30:58.560 --> 31:01.280
 Which I found that because I was doing that,

31:01.280 --> 31:06.880
 I was listening to a small selection of songs way too much to where I was getting sick of them.

31:07.520 --> 31:11.680
 Whereas Spotify, the more liberating kind of approach is I was just enjoying.

31:11.680 --> 31:13.920
 Of course, I listened to Stairway to Heaven over and over,

31:13.920 --> 31:18.240
 but because of the extra variety, I don't get as sick of them.

31:18.240 --> 31:20.640
 There's an interesting statistic I saw.

31:21.520 --> 31:26.640
 So Spotify has, maybe you can correct me, but over 50 million songs, tracks,

31:27.600 --> 31:30.000
 and over 3 billion playlists.

31:31.360 --> 31:35.520
 So 50 million songs and 3 billion playlists.

31:35.520 --> 31:37.600
 60 times more playlist songs.

31:38.480 --> 31:39.360
 What do you make of that?

31:39.920 --> 31:40.160
 Yeah.

31:40.160 --> 31:48.320
 So the way I think about it is that from a statistician or machine learning point of view,

31:48.320 --> 31:52.000
 you have all these, if you want to think about reinforcement learning,

31:52.000 --> 31:54.320
 you have this state space of all the tracks.

31:54.320 --> 31:57.280
 You can take different journeys through this world.

32:00.160 --> 32:05.200
 I think of these as people helping themselves and each other,

32:05.200 --> 32:08.720
 creating interesting vectors through this space of tracks.

32:08.720 --> 32:14.080
 And then it's not so surprising that across many tens of millions of atomic units,

32:14.080 --> 32:17.280
 there will be billions of paths that make sense.

32:17.280 --> 32:21.920
 And we're probably pretty quite far away from having found all of them.

32:21.920 --> 32:26.640
 So kind of our job now is users, when Spotify started,

32:26.640 --> 32:30.000
 it was really a search box that was for the time pretty powerful.

32:30.000 --> 32:34.400
 And then I'd like to refer to it as this programming language called playlisting,

32:34.400 --> 32:36.800
 where if you, as you probably were pretty good at music,

32:36.800 --> 32:39.120
 you knew your new releases, you knew your back catalog,

32:39.120 --> 32:40.480
 you knew your star with the heaven,

32:40.480 --> 32:43.200
 you could create a soundtrack for yourself using this playlisting tool,

32:43.200 --> 32:46.720
 this like meta programming language for music to soundtrack your life.

32:47.360 --> 32:50.160
 And people who were good at music, it's back to how do you scale the product.

32:50.960 --> 32:53.760
 For people who are good at music, that wasn't actually enough.

32:53.760 --> 32:55.840
 If you had the catalog and a good search tool,

32:55.840 --> 32:57.120
 and you can create your own sessions,

32:57.120 --> 33:01.120
 you could create really good a soundtrack for your entire life.

33:01.120 --> 33:04.000
 Probably perfectly personalized because you did it yourself.

33:04.000 --> 33:06.880
 But the problem was most people, many people aren't that good at music.

33:06.880 --> 33:08.480
 They just can't spend the time.

33:08.480 --> 33:10.800
 Even if you're very good at music, it's going to be hard to keep up.

33:10.800 --> 33:16.400
 So what we did to try to scale this was to essentially try to build,

33:16.400 --> 33:20.480
 you can think of them as agents that this friend that some people had

33:20.480 --> 33:22.800
 that helped them navigate this music catalog.

33:22.800 --> 33:24.240
 That's what we're trying to do for you.

33:24.800 --> 33:32.640
 But also there is something like 200 million active users.

33:32.640 --> 33:34.480
 1 million active users on Spotify.

33:35.040 --> 33:36.640
 So there it's okay.

33:36.640 --> 33:38.720
 So from the machine learning perspective,

33:39.760 --> 33:45.760
 you have these 200 million people plus they're creating.

33:45.760 --> 33:49.840
 It's really interesting to think of a playlist as,

33:51.760 --> 33:53.200
 I mean, I don't know if you meant it that way,

33:53.200 --> 33:54.880
 but it's almost like a programming language.

33:54.880 --> 34:01.120
 It's or at least a trace of exploration of those individual agents.

34:01.120 --> 34:06.000
 The listeners and you have all this new tracks coming in.

34:06.000 --> 34:11.680
 So it's a fascinating space that is ripe for machine learning.

34:11.680 --> 34:17.440
 So is there, is it possible, how can playlists be used as data

34:18.080 --> 34:23.360
 in terms of machine learning and to help Spotify organize the music?

34:24.160 --> 34:29.680
 So we found in our data, not surprising that people who play listed lots

34:29.680 --> 34:30.720
 they retain much better.

34:30.720 --> 34:32.240
 They had a great experience.

34:32.240 --> 34:35.360
 And so our first attempt was to playlist for users.

34:35.920 --> 34:41.360
 And so we acquired this company called Tunigo of editors and professional playlisters

34:41.360 --> 34:45.600
 and kind of leveraged the maximum of human intelligence

34:45.600 --> 34:51.440
 to help build kind of these vectors through the track space for people.

34:52.480 --> 34:54.320
 And that broadened the product.

34:54.320 --> 34:57.840
 But then the obvious next, and we use statistical means,

34:57.840 --> 35:02.080
 where they could see when they created a playlist, how did that playlist perform?

35:02.080 --> 35:04.800
 They could see skips of the songs, they could see how the songs perform,

35:04.800 --> 35:10.720
 and they manually iterated the playlist to maximize performance for a large group of people.

35:10.720 --> 35:14.480
 But there were never enough editors to playlists for you personally.

35:14.480 --> 35:17.680
 So the promise of machine learning was to go from kind of group personalization

35:18.240 --> 35:22.640
 using editors and tools and statistics to individualization.

35:22.640 --> 35:28.160
 And then what's so interesting about the 3 billion playlists we have is we ended,

35:28.160 --> 35:29.360
 the truth is we lucked out.

35:29.360 --> 35:32.880
 This was not a priority strategy, as is often the case.

35:32.880 --> 35:35.920
 It looks really smart in hindsight, but it was dumb luck.

35:37.440 --> 35:42.160
 We looked at these playlists and we had some people in the company,

35:42.160 --> 35:43.840
 a person named Eric Beranodson.

35:43.840 --> 35:48.560
 He was really good at machine learning already back then in like 2007, 2008.

35:48.560 --> 35:51.600
 Back then it was mostly collaborative filtering and so forth.

35:51.600 --> 35:57.920
 But we realized that what this is, is people are grouping tracks for themselves

35:57.920 --> 35:59.920
 that have some semantic meaning to them.

36:00.640 --> 36:04.160
 And then they actually label it with a playlist name as well.

36:04.160 --> 36:09.040
 So in a sense, people were grouping tracks along semantic dimensions and labeling them.

36:09.840 --> 36:15.840
 And so could you use that information to find that latent embedding?

36:15.840 --> 36:19.920
 And so we started playing around with collaborative filtering

36:20.960 --> 36:24.160
 and we saw tremendous success with it.

36:24.160 --> 36:28.320
 Basically trying to extract some of these dimensions.

36:28.320 --> 36:30.160
 And if you think about it, it's not surprising at all.

36:30.880 --> 36:34.880
 It'd be quite surprising if playlists were actually random,

36:34.880 --> 36:36.160
 if they had no semantic meaning.

36:36.880 --> 36:39.200
 For most people, they group these tracks for some reason.

36:39.840 --> 36:43.120
 So we just happened across this incredible data set.

36:43.120 --> 36:46.240
 Where people are taking these tens of millions of tracks

36:46.800 --> 36:49.280
 and group them along different semantic vectors.

36:49.280 --> 36:52.720
 And the semantics being outside the individual users.

36:52.720 --> 36:54.400
 So it's some kind of universal.

36:54.400 --> 36:59.760
 There's a universal embedding that holds across people on this earth.

36:59.760 --> 37:05.440
 Yes, I do think that the embeddings you find are going to be reflective of the people who play listed.

37:05.440 --> 37:09.040
 So if you have a lot of indie lovers who play list,

37:09.040 --> 37:13.440
 your embedding is going to perform better there.

37:14.800 --> 37:20.560
 But what we found was that yes, there were these latent similarities.

37:20.560 --> 37:22.000
 They were very powerful.

37:22.000 --> 37:28.720
 And it was interesting because I think that the people who play listed the most initially

37:28.720 --> 37:32.640
 were the so called music aficionados who were really into music.

37:32.640 --> 37:34.240
 And they often had a certain...

37:34.240 --> 37:38.240
 Their taste was often geared towards a certain type of music.

37:38.800 --> 37:42.160
 And so what surprised us, if you look at the problem from the outside,

37:42.160 --> 37:47.840
 you might expect that the algorithms would start performing best with mainstreamers first.

37:47.840 --> 37:51.360
 Because it somehow feels like an easier problem to solve mainstream taste

37:51.360 --> 37:52.640
 than really particular taste.

37:53.360 --> 37:55.120
 It was the complete opposite for us.

37:55.120 --> 37:58.640
 The recommendations performed fantastically for people who saw themselves as

37:59.280 --> 38:00.960
 having very unique taste.

38:00.960 --> 38:03.280
 That's probably because all of them play listed.

38:03.280 --> 38:05.120
 And they didn't perform so well for mainstreamers.

38:05.120 --> 38:09.440
 They actually thought they were a bit too particular and unorthodox.

38:09.440 --> 38:12.000
 So we had the complete opposite of what we expected.

38:12.000 --> 38:13.920
 Success within the hardest problem first,

38:13.920 --> 38:16.560
 and then had to try to scale to more mainstream recommendations.

38:17.600 --> 38:24.160
 So you've also acquired Echo Nest that analyzes song data.

38:24.160 --> 38:28.400
 So in your view, maybe you can talk about,

38:28.400 --> 38:31.680
 so what kind of data is there from a machine learning perspective?

38:31.680 --> 38:35.680
 From a machine learning perspective, there's a huge amount.

38:35.680 --> 38:40.640
 We're talking about playlisting and just user data of what people are listening to,

38:40.640 --> 38:43.920
 the playlist they're constructing, and so on.

38:44.640 --> 38:48.080
 And then there's the actual data within a song.

38:48.080 --> 38:51.920
 What makes a song, I don't know, the actual waveforms.

38:54.160 --> 38:55.120
 How do you mix the two?

38:55.680 --> 38:57.200
 How much value is there in each?

38:57.200 --> 39:03.120
 To me, it seems like user data is a romantic notion

39:03.120 --> 39:05.840
 that the song itself would contain useful information.

39:05.840 --> 39:09.840
 But if I were to guess, user data would be much more powerful,

39:09.840 --> 39:11.840
 like playlists would be much more powerful.

39:11.840 --> 39:13.680
 Yeah, so we use both.

39:14.800 --> 39:18.800
 Our biggest success initially was with playlist data

39:18.800 --> 39:21.920
 without understanding anything about the structure of the song.

39:22.480 --> 39:25.520
 But when we acquired Echo Nest, they had the inverse problem.

39:25.520 --> 39:27.440
 They actually didn't have any play data.

39:27.440 --> 39:29.680
 They were just, they were a provider of recommendations,

39:29.680 --> 39:31.280
 but they didn't actually have any play data.

39:31.840 --> 39:35.760
 So they looked at the structure of songs, sonically,

39:36.640 --> 39:40.400
 and they looked at Wikipedia for cultural references and so forth, right?

39:40.400 --> 39:41.920
 And did a lot of NLU and so forth.

39:41.920 --> 39:46.880
 So we got that skill into the company and combined kind of our user data

39:47.600 --> 39:51.600
 with their kind of content based.

39:51.600 --> 39:53.200
 So you can think of it as we were user based

39:53.200 --> 39:54.880
 and they were content based in their recommendations.

39:54.880 --> 39:56.960
 And we combined those two.

39:56.960 --> 40:00.240
 And for some cases where you have a new song that has no play data,

40:00.240 --> 40:04.960
 obviously you have to try to go by either who the artist is

40:04.960 --> 40:09.760
 or the sonic information in the song or what it's similar to.

40:09.760 --> 40:12.720
 So there's definitely a value in both and we do a lot in both,

40:12.720 --> 40:16.080
 but I would say, yes, the user data captures things

40:16.080 --> 40:19.680
 that have to do with culture in the greater society

40:19.680 --> 40:23.440
 that you would never see in the content itself.

40:23.440 --> 40:27.920
 But that said, we have seen, we have a research lab in Paris

40:28.880 --> 40:32.960
 when we can talk more about that on machine learning on the creator side,

40:32.960 --> 40:34.880
 what it can do for creators, not just for the consumers,

40:35.520 --> 40:38.640
 but where we looked at how does the structure of a song

40:38.640 --> 40:40.800
 actually affect the listening behavior?

40:40.800 --> 40:43.120
 And it turns out that there is a lot of,

40:43.120 --> 40:48.480
 we can predict things like skips based on the song itself.

40:48.480 --> 40:50.880
 We could say that maybe you should move that chorus a bit

40:50.880 --> 40:52.720
 because your skip is going to go up here.

40:52.720 --> 40:54.400
 There is a lot of latent structure in the music,

40:54.400 --> 40:57.520
 which is not surprising because it is some sort of mind hack.

40:58.640 --> 41:00.960
 So there should be structure. That's probably what we respond to.

41:00.960 --> 41:04.560
 You just blew my mind actually from the creator perspective.

41:05.520 --> 41:07.280
 So that's a really interesting topic

41:08.000 --> 41:11.920
 that probably most creators aren't taking advantage of, right?

41:11.920 --> 41:15.920
 So I've recently got to interact with a few folks,

41:15.920 --> 41:24.320
 YouTubers who are like obsessed with this idea of what do I do

41:24.320 --> 41:27.840
 to make sure people keep watching the video?

41:27.840 --> 41:32.080
 And they like look at the analytics of which point do people turn it off and so on.

41:32.720 --> 41:35.040
 First of all, I don't think that's healthy,

41:35.040 --> 41:37.600
 but it's because you can do it a little too much.

41:38.320 --> 41:42.240
 But it is a really powerful tool for helping the creative process.

41:42.240 --> 41:46.480
 You just made me realize you could do the same thing for creation of music.

41:47.280 --> 41:49.360
 And so is that something you've looked into?

41:51.360 --> 41:54.800
 And can you speak to how much opportunity there is for that kind of thing?

41:54.800 --> 41:59.200
 Yeah, so I listened to the podcast with Ziraj and I thought it was fantastic

41:59.200 --> 42:03.600
 and I reacted to the same thing where he said he posted something in the morning,

42:04.160 --> 42:06.560
 immediately watched the feedback where the drop off was

42:06.560 --> 42:08.400
 and then responded to that in the afternoon,

42:08.400 --> 42:12.080
 which is quite different from how people make podcasts, for example.

42:12.080 --> 42:12.880
 Yes, exactly.

42:12.880 --> 42:15.040
 I mean, the feedback loop is almost non existent.

42:15.040 --> 42:21.120
 So if we back out one level, I think actually both for music and podcasts,

42:21.120 --> 42:23.600
 which we also do at Spotify,

42:23.600 --> 42:27.440
 I think there's a tremendous opportunity just for the creation workflow.

42:27.440 --> 42:30.960
 And I think it's really interesting speaking to you who,

42:30.960 --> 42:34.160
 because you're a musician, a developer, and a podcaster.

42:34.720 --> 42:36.560
 If you think about those three different roles,

42:36.560 --> 42:38.880
 if you make the leap as a musician,

42:38.880 --> 42:42.080
 if you think about it as a software tool chain, really,

42:42.960 --> 42:46.320
 your DAW with the stems, that's the IDE, right?

42:46.320 --> 42:50.400
 That's where you work in source code format with what you're creating.

42:51.120 --> 42:52.320
 Then you sit around and you play with that.

42:52.320 --> 42:56.960
 And when you're happy, you compile that thing into some sort of AAC or MP3 or something.

42:57.520 --> 42:59.040
 You do that because you get distribution.

42:59.040 --> 43:02.240
 There are so many runtimes for that MP3 across the world in car stairs and stuff.

43:02.240 --> 43:03.920
 So if you kind of compile this execution,

43:03.920 --> 43:08.720
 you ship it out in kind of an old fashioned boxed software analogy.

43:09.280 --> 43:11.760
 And then you hope for the best, right?

43:11.760 --> 43:16.080
 But as a software developer, you would never do that.

43:16.080 --> 43:18.640
 First, you go on GitHub and you collaborate with other creators.

43:19.440 --> 43:22.800
 And then you think it'd be crazy to just ship one version of your software

43:22.800 --> 43:26.800
 without doing an A B test, without any feedback loop.

43:26.800 --> 43:28.320
 Issue tracking.

43:28.320 --> 43:28.880
 Exactly.

43:28.880 --> 43:31.760
 And then you would look at the feedback loop and say,

43:31.760 --> 43:34.160
 try to optimize that thing, right?

43:34.160 --> 43:37.840
 So I think if you think of it as a very specific software tool chain,

43:38.880 --> 43:42.880
 it looks quite arcane, the tools that a music creator has

43:42.880 --> 43:44.480
 versus what a software developer has.

43:45.360 --> 43:47.040
 So that's kind of how we think about it.

43:48.400 --> 43:52.640
 Why wouldn't a music creator have something like GitHub

43:52.640 --> 43:54.000
 where you could collaborate much more easily?

43:54.000 --> 43:56.560
 So we bought this company called Soundtrap,

43:56.560 --> 44:01.680
 which has a kind of Google Docs for music approach, where you can collaborate

44:01.680 --> 44:04.880
 with other people on the kind of source code format with Stems.

44:05.600 --> 44:09.600
 And I think introducing things like AI tools there to help you

44:09.600 --> 44:19.280
 as you're creating music, both in helping you put accompaniment to your music,

44:19.280 --> 44:24.400
 like drums or something, help you master and mix automatically,

44:24.400 --> 44:26.720
 help you understand how this track will perform.

44:26.720 --> 44:29.600
 Exactly what you would expect as a software developer.

44:29.600 --> 44:30.880
 I think it makes a lot of sense.

44:30.880 --> 44:33.520
 And I think the same goes for a podcaster.

44:33.520 --> 44:36.320
 I think podcasters will expect to have the same kind of feedback loop

44:36.320 --> 44:39.520
 that Siraj has, like, why wouldn't you?

44:39.520 --> 44:40.800
 Maybe it's not healthy, but...

44:41.520 --> 44:45.120
 Sorry, I wanted to criticize the fact because you can overdo it

44:45.120 --> 44:49.760
 because a lot of the, and we're in a new era of that.

44:49.760 --> 44:56.400
 So you can become addicted to it and therefore, what people say,

44:56.400 --> 44:59.680
 you become a slave to the YouTube algorithm or sort of,

45:00.640 --> 45:04.400
 it's always a danger of a new technology as opposed to say,

45:04.400 --> 45:11.600
 if you're creating a song, becoming too obsessed about the intro riff to the song

45:11.600 --> 45:15.440
 that keeps people listening versus actually the entirety of the creation process.

45:15.440 --> 45:16.160
 It's a balance.

45:16.160 --> 45:19.680
 But the fact that there's zero, I mean, you're blowing my mind right now,

45:19.680 --> 45:24.960
 because you're completely right that there is no signal whatsoever.

45:24.960 --> 45:28.960
 There's no feedback whatsoever on the creation process and music or podcasting,

45:30.000 --> 45:30.880
 almost at all.

45:31.680 --> 45:39.360
 And are you saying that Spotify is hoping to help create tools to, not tools, but...

45:39.360 --> 45:41.680
 No, tools actually.

45:41.680 --> 45:42.640
 Actually, tools.

45:42.640 --> 45:47.200
 Tools for creators.

45:47.200 --> 45:47.760
 Absolutely.

45:48.320 --> 45:53.520
 So we've made some acquisitions the last few years around music creation,

45:53.520 --> 45:57.280
 this company called Soundtrap, which is a digital audio workstation,

45:57.280 --> 45:59.040
 but that is browser based.

45:59.040 --> 46:01.200
 And their focus was really the Google Docs approach.

46:01.200 --> 46:06.080
 We can collaborate with people much more easily than you could in previous tools.

46:06.080 --> 46:09.280
 So we have some of these tools that we're working with that we want to make accessible

46:09.280 --> 46:12.960
 and then we can connect it with our consumption data.

46:12.960 --> 46:16.000
 We can create this feedback loop where we could help you understand,

46:16.800 --> 46:20.960
 we could help you create and help you understand how you will perform.

46:20.960 --> 46:24.560
 We also acquired this other company within podcasting called Anchor,

46:24.560 --> 46:28.400
 which is one of the biggest podcasting tools, mobile focused.

46:28.400 --> 46:32.800
 So really focused on simple creation or easy access to creation.

46:32.800 --> 46:34.960
 But that also gives us this feedback loop.

46:34.960 --> 46:40.640
 And even before that, we invested in something called Spotify for Artists

46:40.640 --> 46:43.600
 and Spotify for Podcasters, which is an app that you can download,

46:43.600 --> 46:45.360
 you can verify that you are that creator.

46:46.000 --> 46:51.680
 And then you get things that software developers have had for years.

46:51.680 --> 46:55.520
 You can see where, if you look at your podcast, for example, on Spotify

46:55.520 --> 46:58.720
 or a song that you released, you can see how it's performing,

46:58.720 --> 47:01.280
 which cities it's performing in, who's listening to it,

47:01.280 --> 47:02.800
 what's the demographic breakup.

47:02.800 --> 47:05.840
 So similar in the sense that you can understand

47:05.840 --> 47:07.920
 how you're actually doing on the platform.

47:08.880 --> 47:10.480
 So we definitely want to build tools.

47:10.480 --> 47:15.200
 I think you also interviewed the head of research for Adobe.

47:15.920 --> 47:19.680
 And I think that's an, back to Photoshop that you like,

47:19.680 --> 47:21.680
 I think that's an interesting analogy as well.

47:22.800 --> 47:28.000
 Photoshop, I think, has been very innovative in helping photographers and artists.

47:28.000 --> 47:32.320
 And I think there should be the same kind of tools for music creators,

47:32.320 --> 47:35.680
 where you could get AI assistance, for example, as you're creating music,

47:36.640 --> 47:38.880
 as you can do with Adobe, where you can,

47:38.880 --> 47:41.440
 I want a sky over here and you can get help creating that sky.

47:42.000 --> 47:46.800
 The really fascinating thing is what Adobe doesn't have

47:47.520 --> 47:49.760
 is a distribution for the content you create.

47:50.400 --> 47:55.840
 So you don't have the data of if I create, if I, you know,

47:55.840 --> 47:58.720
 whatever creation I make in Photoshop or Premiere,

47:59.360 --> 48:02.480
 I can't get like immediate feedback like I can on YouTube,

48:02.480 --> 48:05.360
 for example, about the way people are responding.

48:05.360 --> 48:11.120
 And if Spotify is creating those tools, that's a really exciting actually world.

48:11.680 --> 48:14.720
 But let's talk a little about podcasts.

48:16.720 --> 48:18.720
 So I have trouble talking to one person.

48:20.000 --> 48:23.120
 So it's a bit terrifying and kind of hard to fathom,

48:23.120 --> 48:29.440
 but on average, 60 to 100,000 people will listen to this episode.

48:30.320 --> 48:32.240
 Okay, so it's intimidating.

48:32.240 --> 48:33.120
 Yeah, it's intimidating.

48:34.320 --> 48:35.680
 So I hosted on Blueberry.

48:36.720 --> 48:38.560
 I don't know if I'm pronouncing that correctly, actually.

48:39.520 --> 48:42.400
 It looks like most people listen to it on Apple Podcasts,

48:42.400 --> 48:48.480
 Cast Box and Pocket Casts, and only about a thousand listen on Spotify.

48:48.480 --> 48:53.040
 It's just my podcast, right?

48:53.840 --> 49:00.960
 So where do you see a time when Spotify will dominate this?

49:00.960 --> 49:06.000
 So Spotify is relatively new into this podcasting site.

49:06.000 --> 49:06.960
 Yeah, in podcasting.

49:07.520 --> 49:09.920
 What's the deal with podcasting and Spotify?

49:10.800 --> 49:13.440
 How serious is Spotify about podcasting?

49:13.440 --> 49:16.800
 Do you see a time where everybody would listen to, you know,

49:16.800 --> 49:21.520
 probably a huge amount of people, majority perhaps listen to music on Spotify?

49:22.400 --> 49:26.880
 Do you see a time when the same is true for podcasting?

49:26.880 --> 49:28.560
 Well, I certainly hope so.

49:28.560 --> 49:29.360
 That is our mission.

49:29.360 --> 49:34.160
 Our mission as a company is actually to enable a million creators to live off of their art,

49:34.160 --> 49:35.840
 and a billion people be inspired by it.

49:35.840 --> 49:40.000
 And what I think is interesting about that mission is it actually puts the creators first,

49:40.640 --> 49:43.040
 even though it started as a consumer focused company,

49:43.040 --> 49:44.800
 and it's just to be able to live off of their art,

49:44.800 --> 49:47.280
 not just make some money off of their art as well.

49:47.840 --> 49:49.920
 So it's quite an ambitious project.

49:51.920 --> 49:53.920
 So we think about creators of all kinds,

49:53.920 --> 50:00.160
 and we kind of expanded our mission from being music to being audio a while back.

50:01.120 --> 50:07.360
 And that's not so much because we think we made that decision.

50:08.400 --> 50:10.800
 We think that decision was made for us.

50:10.800 --> 50:12.960
 We think the world made that decision.

50:12.960 --> 50:16.560
 Whether we like it or not, when you put in your headphones,

50:16.560 --> 50:24.400
 you're going to make a choice between music and a new episode of your podcast or something else.

50:25.440 --> 50:26.960
 We're in that world whether we like it or not.

50:26.960 --> 50:28.960
 And that's how radio works.

50:28.960 --> 50:32.320
 So we decided that we think it's about audio.

50:32.320 --> 50:34.480
 You can see the rise of audiobooks and so forth.

50:34.480 --> 50:36.480
 We think audio is a great opportunity.

50:36.480 --> 50:37.600
 So we decided to enter it.

50:37.600 --> 50:45.280
 And obviously, Apple and Apple Podcasts is absolutely dominating in podcasting,

50:45.280 --> 50:48.480
 and we didn't have a single podcast only like two years ago.

50:49.440 --> 50:54.560
 What we did though was we looked at this and said,

50:54.560 --> 50:55.920
 can we bring something to this?

50:56.480 --> 50:59.200
 We want to do this, but back to the original Spotify,

50:59.200 --> 51:03.840
 we have to do something that consumers actually value to be able to do this.

51:03.840 --> 51:09.840
 And the reason we've gone from not existing at all to being quite a wide margin,

51:09.840 --> 51:15.680
 the second largest podcast consumption, still wide gap to iTunes, but we're growing quite fast.

51:16.480 --> 51:19.440
 I think it's because when we looked at the consumer problem,

51:20.320 --> 51:26.960
 people said surprisingly that they wanted their podcasts and music in the same application.

51:26.960 --> 51:29.760
 So what we did was we took a little bit of a different approach where we said,

51:29.760 --> 51:31.440
 instead of building a separate podcast app,

51:31.440 --> 51:33.680
 we thought, is there a consumer problem to solve here?

51:33.680 --> 51:35.680
 Because the others are very successful already.

51:35.680 --> 51:38.960
 And we thought there was in making a more seamless experience

51:38.960 --> 51:43.120
 where you can have your podcast and your music in the same application,

51:43.680 --> 51:45.440
 because we think it's audio to you.

51:45.440 --> 51:46.800
 And that has been successful.

51:46.800 --> 51:51.840
 And that meant that we actually had 200 million people to offer this to instead of starting from zero.

51:52.400 --> 51:56.880
 So I think we have a good chance because we're taking a different approach than the competition.

51:56.880 --> 51:59.120
 And back to the other thing I mentioned about

51:59.120 --> 52:02.240
 creators, because we're looking at the end to end flow.

52:02.800 --> 52:06.400
 I think there's a tremendous amount of innovation to do around podcast as a format.

52:07.040 --> 52:12.640
 When we have creation tools and consumption, I think we could start improving what podcasting is.

52:12.640 --> 52:18.960
 I mean, podcast is this opaque, big, like one, two hour file that you're streaming,

52:19.520 --> 52:24.240
 which it really doesn't make that much sense in 2019 that it's not interactive.

52:24.240 --> 52:26.000
 There's no feedback loops, nothing like that.

52:26.000 --> 52:29.760
 So I think if we're going to win, it's going to have to be because we build a better product

52:29.760 --> 52:31.760
 for creators and for consumers.

52:32.480 --> 52:34.640
 So we'll see, but it's certainly our goal.

52:34.640 --> 52:35.600
 We have a long way to go.

52:36.240 --> 52:38.160
 Well, the creators part is really exciting.

52:38.160 --> 52:40.160
 You already, you got me hooked there.

52:40.160 --> 52:41.760
 Cause the only stats I have,

52:42.320 --> 52:47.760
 Blueberry just recently added the stats of whether it's listened to the end or not.

52:48.560 --> 52:52.320
 And that's like a huge improvement, but that's still

52:52.320 --> 52:54.960
 nowhere to where you could possibly go in terms of statistics.

52:54.960 --> 52:57.200
 You just download the Spotify podcasters up and verify.

52:57.200 --> 52:59.920
 And then, then you'll know where people dropped out in this episode.

52:59.920 --> 53:00.400
 Oh, wow.

53:00.400 --> 53:00.900
 Okay.

53:01.600 --> 53:02.800
 The moment I started talking.

53:02.800 --> 53:03.360
 Okay.

53:03.360 --> 53:06.800
 I might be depressed by this, but okay.

53:06.800 --> 53:13.040
 So one, um, one other question is the original Spotify for music.

53:14.400 --> 53:19.120
 And I have a question about podcasting in this line is the idea of podcasting

53:19.120 --> 53:22.880
 about podcasting in this line is the idea of albums.

53:23.440 --> 53:28.800
 I have, uh, what did you, uh, music aficionados, uh, friends who are really,

53:29.440 --> 53:33.280
 uh, big fans of music often, uh, really enjoy albums,

53:33.280 --> 53:35.840
 listening to entire albums of, of an artist.

53:36.400 --> 53:40.960
 Correct me if I'm wrong, but I feel like Spotify has helped

53:40.960 --> 53:44.240
 replace the idea of an album with playlists.

53:44.240 --> 53:46.000
 So you create your own albums.

53:46.000 --> 53:48.880
 It's, it's kind of the way, at least I've experienced music

53:48.880 --> 53:50.480
 and I've really enjoyed it that way.

53:51.040 --> 53:54.320
 One of the things that was missing in podcasting for me,

53:54.880 --> 53:55.920
 I don't know if it's missing.

53:56.320 --> 53:56.880
 I don't know.

53:56.880 --> 53:59.920
 It's an open question for me, but the way I listened to podcasts is

53:59.920 --> 54:01.600
 the way I would listen to albums.

54:02.080 --> 54:05.440
 So I take a Joe Rogan experience and that's an album.

54:05.600 --> 54:09.680
 And I listened, you know, I like, I, I put that on and I listened one

54:09.680 --> 54:12.640
 episode after the next, then there's a sequence and so on.

54:12.640 --> 54:17.520
 Is there a room for doing what you did for music or doing what

54:17.520 --> 54:22.880
 Spotify did for music, but, uh, creating playlists, sort of, uh,

54:22.880 --> 54:26.080
 this kind of playlisting idea of breaking apart from podcasting,

54:27.120 --> 54:31.680
 uh, from individual podcasts and creating kind of, uh, this interplay

54:31.680 --> 54:33.760
 or, or have you thought about that space?

54:33.760 --> 54:34.800
 Uh, it's a great question.

54:34.800 --> 54:38.640
 So I think in, um, in music, you're right.

54:38.720 --> 54:39.920
 Basically you bought an album.

54:39.920 --> 54:42.720
 So it was like, you bought a small catalog of like 10 tracks, right?

54:42.800 --> 54:46.160
 It was, it was, again, it was actually a lot of, a lot of consumption.

54:46.720 --> 54:49.360
 You think it's about what you like, but it's based on the business model.

54:49.680 --> 54:53.920
 So you paid for this 10 track service and then you listened to that for a while.

54:54.240 --> 54:57.760
 And then when, when everything was flat priced, you tended to listen differently.

54:58.480 --> 55:01.360
 Now, so, so I think the, I think the album is still tremendously important.

55:01.360 --> 55:03.360
 That's why we have it and you can save albums and so forth.

55:03.360 --> 55:06.480
 And you have a huge amount of people who really listen according to albums.

55:06.480 --> 55:09.840
 And I like that because it is a creator format, you can tell a longer story

55:10.240 --> 55:11.440
 over several tracks.

55:12.000 --> 55:13.840
 And so some people listen to just one track.

55:13.840 --> 55:15.840
 Some people actually want to hear that whole story.

55:17.520 --> 55:21.520
 Now in podcast, I think, I think it's different.

55:21.600 --> 55:24.960
 You can argue that podcasts might be more like shows on Netflix.

55:25.600 --> 55:29.200
 Have like a full season of Narcos and you're probably not going to do like

55:29.200 --> 55:32.800
 one episode of Narcos and then one of House of Cards, like, like, you know,

55:33.440 --> 55:34.480
 there's a narrative there.

55:34.480 --> 55:37.440
 And you, you, you love the cast and you love these characters.

55:37.440 --> 55:40.480
 So I think people will, people love shows.

55:42.000 --> 55:44.800
 And I think they will, they will listen to those shows.

55:44.880 --> 55:46.880
 I do think you follow a bunch of shows at the same time.

55:46.880 --> 55:50.480
 So there's certainly an opportunity to bring you the latest episode of, you

55:50.480 --> 55:53.040
 know, whatever the five, six, 10 things that, that you're into.

55:54.560 --> 56:00.000
 But, but I think, I think people are going to listen to specific hosts and love

56:00.000 --> 56:01.600
 those hosts for a long time.

56:01.600 --> 56:06.880
 Because I think there's something different with podcasts where, um, this

56:06.880 --> 56:11.280
 format of the, the, the, the, the, the experience of the, of the audience is

56:11.280 --> 56:12.800
 actually sitting here right between us.

56:13.360 --> 56:16.960
 Whereas if you look at something on TV, the audio actually would come from, you

56:16.960 --> 56:20.080
 would sit over there and the audio would come to you from both of us as if you

56:20.080 --> 56:22.000
 were watching, not as you were part of the conversation.

56:22.560 --> 56:27.280
 So my experience is having listened to podcasts like yours and Joe Rogan is, I

56:27.280 --> 56:28.720
 feel like I know all of these people.

56:28.720 --> 56:30.240
 They, they have a lot of experience.

56:30.240 --> 56:33.600
 I know all of these people, they have no idea who I am, but I feel like I've

56:33.600 --> 56:35.040
 listened to so many hours of that.

56:35.040 --> 56:38.800
 It's very different from me watching a, watching like a TV show or an interview.

56:39.440 --> 56:44.560
 So I think you, you kind of, um, fall in love with people and, um, experience

56:44.560 --> 56:45.760
 in a, in a different way.

56:45.760 --> 56:49.280
 So I think, I think shows and hosts are going to be very, uh, very important.

56:49.280 --> 56:52.160
 I don't think that's going to go away into some sort of thing where, where you

56:52.160 --> 56:53.360
 don't even know who you're listening to.

56:53.360 --> 56:54.320
 I don't think that's going to happen.

56:55.040 --> 56:59.760
 What I do think is I think there's a tremendous discovery opportunity in

56:59.760 --> 57:03.040
 podcast because the catalog is growing quite quickly.

57:03.920 --> 57:10.800
 And I think podcast is only a few, like five, 600,000 shows right now.

57:11.360 --> 57:16.080
 If you look back to YouTube as another analogy of creators, no one really knows

57:16.080 --> 57:20.400
 if you would lift the lid on YouTube, but it's probably billions of episodes.

57:21.120 --> 57:24.960
 And so I think the podcast catalog would probably grow tremendously because the

57:24.960 --> 57:27.040
 creation tools are getting easier.

57:27.040 --> 57:30.800
 And then you're going to have this discovery opportunity that I think is

57:30.800 --> 57:31.280
 really big.

57:31.280 --> 57:35.600
 So, so a lot of people tell me that they love their shows, but discovering

57:35.600 --> 57:36.880
 podcasts kind of suck.

57:36.880 --> 57:38.720
 It's really hard to get into new show.

57:38.720 --> 57:39.840
 They're usually quite long.

57:39.840 --> 57:40.960
 It's a big time investment.

57:40.960 --> 57:44.080
 So I think there's plenty of opportunity in the discovery part.

57:45.600 --> 57:46.560
 Yeah, for sure.

57:46.560 --> 57:51.200
 A hundred percent in, in even the dumbest, there's so many low hanging fruit too.

57:51.200 --> 57:59.680
 Uh, for example, just knowing what episode to listen to first to try out a podcast.

57:59.680 --> 58:00.400
 Exactly.

58:00.400 --> 58:03.360
 Uh, because most podcasts don't have an order to them.

58:03.920 --> 58:10.880
 Uh, they, they can be listened to out of order and sorry to say some are better

58:10.880 --> 58:12.560
 than others episodes.

58:12.560 --> 58:14.960
 So some episodes of Joe Rogan are better than others.

58:15.520 --> 58:20.400
 And it's nice to know, uh, which you should listen to, to try it out.

58:20.400 --> 58:26.320
 And there's, uh, as far as I know, almost no information, uh, in terms of like, uh,

58:26.320 --> 58:28.640
 upvotes on how good an episode is.

58:28.640 --> 58:29.280
 Exactly.

58:29.280 --> 58:33.520
 So I think part of the problem is, uh, you, it's kind of like music.

58:33.520 --> 58:34.480
 There isn't one answer.

58:34.480 --> 58:37.440
 People use music for different things and there's actually many different types of music.

58:37.440 --> 58:40.560
 There's workout music and there's classical piano music and focus music and,

58:41.200 --> 58:42.640
 and, and, uh, so forth.

58:42.640 --> 58:44.080
 I think the same with podcasts.

58:44.080 --> 58:45.360
 Some podcasts are sequential.

58:45.360 --> 58:48.400
 They're supposed to be listened to in, in order.

58:48.400 --> 58:51.040
 It's actually, it's actually telling a narrative.

58:51.040 --> 58:55.840
 Some podcasts are one topic, uh, kind of like yours, but different guests.

58:55.840 --> 58:57.280
 So you could jump in anywhere.

58:57.280 --> 58:59.440
 Some podcasts actually have completely different topics.

58:59.440 --> 59:04.560
 And for those podcasts, it might be that I want, you know, we should recommend one episode

59:04.560 --> 59:09.280
 because it's about AI from someone, but then they talk about something that you're not

59:09.280 --> 59:10.880
 interested in the rest of the episodes.

59:10.880 --> 59:15.040
 So I think our, what we're spending a lot of time on now is just first understanding

59:15.040 --> 59:21.520
 the domain and creating kind of the knowledge graph of how do these objects relate and how

59:21.520 --> 59:22.240
 do people consume.

59:22.240 --> 59:24.800
 And I think we'll find that it's going to be, it's going to be different.

59:26.000 --> 59:31.280
 I'm excited because you're the, uh, Spotify is the first people I'm aware of that are

59:32.240 --> 59:34.800
 trying to do this for podcasting.

59:34.800 --> 59:38.240
 Podcasting has been like a wild west up until now.

59:38.240 --> 59:43.120
 It's been a very, we want to be very careful though, because it's been a very good wild

59:43.120 --> 59:45.680
 west, I think it's this fragile ecosystem.

59:46.320 --> 59:52.080
 And I, we want to make sure that you don't barge in and say like, Oh, we're going to

59:52.080 --> 59:53.440
 internetize this thing.

59:53.440 --> 59:56.640
 And you have to think about the creators.

59:56.640 --> 1:00:01.040
 You have to understand how they get distribution today, who listens to how they make money

1:00:01.040 --> 1:00:05.520
 today, try to, you know, make sure that their business model works, that they understand.

1:00:06.080 --> 1:00:10.880
 I think it's back to doing something to improving their products, like feedback loops and

1:00:10.880 --> 1:00:11.440
 distribution.

1:00:11.440 --> 1:00:17.280
 So jumping back into terms of this fascinating world of a recommender system and listening

1:00:17.280 --> 1:00:24.320
 to music and using machine learning to analyze things, do you think it's better to what

1:00:24.320 --> 1:00:30.160
 currently, correct me if I'm wrong, but currently Spotify lets people pick what they listen

1:00:30.160 --> 1:00:31.680
 to the most part.

1:00:31.680 --> 1:00:35.040
 There's a discovery process, but you kind of organize playlists.

1:00:35.040 --> 1:00:39.840
 Is it better to let people pick what they listen to or recommend what they should listen

1:00:39.840 --> 1:00:44.960
 to something like stations by Spotify that I saw that you're playing around with?

1:00:44.960 --> 1:00:47.520
 Maybe you can tell me what's the status of that.

1:00:47.520 --> 1:00:52.880
 This is a Pandora style app that just kind of, as opposed to you select the music you

1:00:52.880 --> 1:00:57.760
 listen to, it kind of feeds you the music you listen to.

1:00:58.400 --> 1:01:00.800
 What's the status of stations by Spotify?

1:01:00.800 --> 1:01:01.920
 What's its future?

1:01:01.920 --> 1:01:07.040
 The story of Spotify, as we have grown, has been that we made it more accessible to different

1:01:07.040 --> 1:01:14.000
 audiences and stations is another one of those where the question is, some people want to

1:01:14.000 --> 1:01:14.720
 be very specific.

1:01:14.720 --> 1:01:18.560
 They actually want to hear Starway to Heaven right now, that needs to be very easy to do.

1:01:19.760 --> 1:01:26.080
 And some people, or even the same person, at some point might say, I want to feel upbeat

1:01:26.080 --> 1:01:32.800
 or I want to feel happy or I want songs to sing in the car.

1:01:32.800 --> 1:01:38.720
 So they put in the information at a very different level and then we need to translate that into

1:01:38.720 --> 1:01:40.560
 what that means musically.

1:01:40.560 --> 1:01:45.440
 So stations is a test to create like a consumption input vector that is much simpler where you

1:01:45.440 --> 1:01:49.520
 can just tune it a little bit and see if that increases the overall reach.

1:01:49.520 --> 1:01:56.000
 But we're trying to kind of serve the entire gamut of super advanced so called music aficionados

1:01:56.000 --> 1:02:02.560
 all the way to people who they love listening to music but it's not their number one priority

1:02:02.560 --> 1:02:03.200
 in life.

1:02:03.200 --> 1:02:06.160
 They're not going to sit and follow every new release from every new artist.

1:02:06.160 --> 1:02:11.120
 They need to be able to influence music at a different level.

1:02:11.120 --> 1:02:17.360
 So you can think of it as different products and I think one of the interesting things

1:02:17.360 --> 1:02:22.080
 to answer your question on if it's better to let the user choose or to play, I think

1:02:22.080 --> 1:02:28.720
 the answer is the challenge when machine learning kind of came along, there was a lot of thinking

1:02:28.720 --> 1:02:33.120
 about what does product development mean in a machine learning context.

1:02:33.920 --> 1:02:38.880
 People like Andrew Ng, for example, when he went to Baidu, he started doing a lot of practical

1:02:38.880 --> 1:02:43.280
 machine learning, went from academia and he thought a lot about this and he had this notion

1:02:43.280 --> 1:02:47.760
 that a product manager, designer and engineer, they used to work around this wireframe to

1:02:47.760 --> 1:02:49.440
 kind of describe what the product should look like.

1:02:49.440 --> 1:02:54.080
 It was something to talk about when you're doing a chatbot or a playlist, what are you

1:02:54.080 --> 1:02:54.640
 going to say?

1:02:54.640 --> 1:02:55.520
 It should be good.

1:02:55.520 --> 1:02:57.360
 That's not a good product description.

1:02:57.360 --> 1:02:58.400
 So how do you do that?

1:02:58.400 --> 1:03:03.120
 And he came up with this notion that the test set is the new wireframe.

1:03:03.120 --> 1:03:06.960
 The job of the product manager is to source a good test set that is representative of

1:03:06.960 --> 1:03:10.640
 what, like if you say I want to play this, that is songs to sing in the car.

1:03:11.520 --> 1:03:15.360
 The job of the product manager is to go and source a good test set of what that means.

1:03:15.360 --> 1:03:20.000
 So then you can work with engineering to have algorithms to try to produce that.

1:03:20.000 --> 1:03:25.600
 So we try to think a lot about how to structure product development for a machine learning

1:03:25.600 --> 1:03:26.320
 age.

1:03:26.320 --> 1:03:30.000
 And what we discovered was that a lot of it is actually in the expectation.

1:03:30.560 --> 1:03:33.120
 And you can go two ways.

1:03:33.120 --> 1:03:40.880
 So let's say that if you set the expectation with the user that this is a discovery product,

1:03:40.880 --> 1:03:45.280
 like Discover Weekly, you're actually setting the expectation that most of what we show

1:03:45.280 --> 1:03:46.800
 you will not be relevant.

1:03:46.800 --> 1:03:50.400
 When you're in the discovery process, you're going to accept that actually if you find

1:03:50.400 --> 1:03:55.200
 one gem every Monday that you totally love, you're probably going to be happy.

1:03:55.200 --> 1:04:00.240
 Even though the statistical meaning, one out of 10 is terrible or one out of 20 is terrible

1:04:00.240 --> 1:04:02.640
 from a user point of view because the setting was discovery is fine.

1:04:03.440 --> 1:04:04.640
 Sorry to interrupt real quick.

1:04:05.360 --> 1:04:11.600
 I just actually learned about Discover Weekly, which is a Spotify, I don't know, it's a

1:04:11.600 --> 1:04:15.360
 feature of Spotify that shows you cool songs to listen to.

1:04:16.640 --> 1:04:18.160
 Maybe I can do issue tracking.

1:04:18.160 --> 1:04:19.760
 I couldn't find it on my Spotify app.

1:04:20.640 --> 1:04:21.680
 It's in your library.

1:04:21.680 --> 1:04:22.640
 It's in the library.

1:04:22.640 --> 1:04:23.760
 It's in the list of library.

1:04:23.760 --> 1:04:25.040
 Because I was like, whoa, this is cool.

1:04:25.040 --> 1:04:26.320
 I didn't know this existed.

1:04:26.320 --> 1:04:27.440
 And I tried to find it.

1:04:27.440 --> 1:04:28.800
 But okay.

1:04:28.800 --> 1:04:31.040
 I will show it to you and feedback to our product team.

1:04:31.920 --> 1:04:32.720
 There you go.

1:04:32.720 --> 1:04:34.480
 But yeah, so yeah, sorry.

1:04:34.480 --> 1:04:42.160
 Just to mention the expectation there is basically that you're going to discover new songs.

1:04:42.160 --> 1:04:42.400
 Yeah.

1:04:42.400 --> 1:04:47.200
 So then you can be quite adventurous in the recommendations you do.

1:04:47.920 --> 1:04:53.120
 But we have another product called Daily Mix, which kind of implies that these are only

1:04:53.120 --> 1:04:54.000
 going to be your favorites.

1:04:54.560 --> 1:04:58.320
 So if you have one out of 10 that is good and nine out of 10 that doesn't work for you,

1:04:58.320 --> 1:04:59.600
 you're going to think it's a horrible product.

1:04:59.600 --> 1:05:03.040
 So actually a lot of the product development we learned over the years is about setting

1:05:03.040 --> 1:05:04.080
 the right expectations.

1:05:04.080 --> 1:05:09.680
 So for Daily Mix, you know, algorithmically, we would pick among things that feel very

1:05:09.680 --> 1:05:11.280
 safe in your taste space.

1:05:11.280 --> 1:05:15.520
 Whereas Discover Weekly, we go kind of wild because the expectation is most of this is

1:05:15.520 --> 1:05:16.400
 not going to.

1:05:16.400 --> 1:05:20.960
 So a lot of that, a lot of to answer your question there, a lot of should you let the

1:05:20.960 --> 1:05:21.600
 user pick or not?

1:05:21.600 --> 1:05:22.560
 It depends.

1:05:23.360 --> 1:05:26.720
 We have some products where the whole point is that the user can click play, put the phone

1:05:26.720 --> 1:05:30.000
 in the pocket, and it should be really good music for like an hour.

1:05:30.000 --> 1:05:35.120
 We have other products where you probably need to say like, no, no, save, no, no.

1:05:35.120 --> 1:05:36.160
 And it's very interactive.

1:05:37.040 --> 1:05:37.440
 I see.

1:05:37.440 --> 1:05:38.000
 That makes sense.

1:05:38.000 --> 1:05:41.920
 And then the radio product, the stations product is one of these like click play, put in your

1:05:41.920 --> 1:05:42.720
 pocket for hours.

1:05:43.360 --> 1:05:44.160
 That's really interesting.

1:05:44.160 --> 1:05:50.880
 So you're thinking of different test sets for different users and trying to create products

1:05:50.880 --> 1:05:57.840
 that sort of optimize for those test sets that represent a specific set of users.

1:05:57.840 --> 1:06:06.160
 Yes, I think one thing that I think is interesting is we invested quite heavily in editorial

1:06:06.160 --> 1:06:09.520
 in people creating playlists using statistical data.

1:06:09.520 --> 1:06:10.800
 And that was successful for us.

1:06:10.800 --> 1:06:12.960
 And then we also invested in machine learning.

1:06:13.600 --> 1:06:18.000
 And for the longest time within Spotify and within the rest of the industry, there was

1:06:18.000 --> 1:06:23.360
 always this narrative of humans versus the machine, algo versus editorial.

1:06:23.360 --> 1:06:27.600
 And editors would say like, well, if I had that data, if I could see your

1:06:27.600 --> 1:06:31.680
 playlisting history and I made a choice for you, I would have made a better choice.

1:06:31.680 --> 1:06:35.200
 And they would have because they're much smarter than these algorithms.

1:06:35.200 --> 1:06:38.880
 The human is incredibly smart compared to our algorithms.

1:06:38.880 --> 1:06:40.880
 They can take culture into account and so forth.

1:06:41.440 --> 1:06:47.600
 The problem is that they can't make 200 million decisions per hour for every user that logs

1:06:47.600 --> 1:06:47.680
 in.

1:06:47.680 --> 1:06:51.760
 So the algo may be not as sophisticated, but much more efficient.

1:06:51.760 --> 1:06:54.480
 So there was this contradiction.

1:06:54.480 --> 1:07:00.160
 But then a few years ago, we started focusing on this kind of human in the loop thinking

1:07:00.160 --> 1:07:01.280
 around machine learning.

1:07:01.280 --> 1:07:06.480
 And we actually coined an internal term for it called algotorial, a combination of algorithms

1:07:07.120 --> 1:07:15.040
 and editors, where if we take a concrete example, you think of the editor, this paid

1:07:15.040 --> 1:07:20.400
 expert that we have that's really good at something like soul, hip hop, EDM, something,

1:07:20.400 --> 1:07:20.720
 right?

1:07:20.720 --> 1:07:22.800
 They're a true expert, no one in the industry.

1:07:22.800 --> 1:07:24.480
 So they have all the cultural knowledge.

1:07:24.480 --> 1:07:26.560
 You think of them as the product manager.

1:07:26.560 --> 1:07:32.880
 And you say that, let's say that you want to create a, you think that there's a product

1:07:32.880 --> 1:07:36.160
 need in the world for something like songs to sing in the car or songs to sing in the

1:07:36.160 --> 1:07:36.560
 shower.

1:07:36.560 --> 1:07:38.400
 I'm taking that example because it exists.

1:07:38.400 --> 1:07:41.840
 People love to scream songs in the car when they drive, right?

1:07:42.560 --> 1:07:45.520
 So you want to create that product and you have this product manager who's a musical

1:07:45.520 --> 1:07:46.000
 expert.

1:07:46.640 --> 1:07:50.800
 They create, they come up with a concept, like I think this is a missing thing in humanity,

1:07:50.800 --> 1:07:52.800
 like a playlist called songs to sing in the car.

1:07:53.920 --> 1:07:59.840
 They create the framing, the image, the title, and they create a test set of, they create

1:07:59.840 --> 1:08:04.480
 a group of songs, like a few thousand songs out of the catalog that they manually curate

1:08:04.480 --> 1:08:06.960
 that are known songs that are great to sing in the car.

1:08:07.520 --> 1:08:09.840
 And they can take like true romance into account.

1:08:09.840 --> 1:08:12.400
 They understand things that our algorithms do not at all.

1:08:12.400 --> 1:08:14.480
 So they have this huge set of tracks.

1:08:14.480 --> 1:08:19.600
 Then when we deliver that to you, we look at your taste vectors and you get the 20 tracks

1:08:19.600 --> 1:08:21.760
 that are songs to sing in the car in your taste.

1:08:22.560 --> 1:08:29.520
 So you have personalization and editorial input in the same process, if that makes sense.

1:08:29.520 --> 1:08:30.880
 Yeah, it makes total sense.

1:08:30.880 --> 1:08:32.480
 And I have several questions around that.

1:08:32.480 --> 1:08:35.280
 This is like fascinating.

1:08:36.080 --> 1:08:36.560
 Okay.

1:08:36.560 --> 1:08:44.720
 So first, it is a little bit surprising to me that the world expert humans are outperforming

1:08:44.720 --> 1:08:49.920
 machines at specifying songs to sing in the car.

1:08:50.960 --> 1:08:53.680
 So maybe you could talk to that a little bit.

1:08:53.680 --> 1:08:57.040
 I don't know if you can put it into words, but what is it?

1:08:57.760 --> 1:08:59.520
 How difficult is this problem?

1:09:01.680 --> 1:09:06.720
 Do you really, I guess what I'm trying to ask is there, how difficult is it to encode

1:09:06.720 --> 1:09:14.640
 the cultural references, the context of the song, the artists, all those things together?

1:09:14.640 --> 1:09:16.720
 Can machine learning really not do that?

1:09:17.360 --> 1:09:23.040
 I mean, I think machine learning is great at replicating patterns if you have the patterns.

1:09:23.040 --> 1:09:27.680
 But if you try to write with me a spec of what song's greatest song to sing in the car

1:09:27.680 --> 1:09:30.320
 definition is, is it loud?

1:09:30.320 --> 1:09:31.520
 Does it have many choruses?

1:09:31.520 --> 1:09:32.800
 Should it have been in movies?

1:09:32.800 --> 1:09:35.680
 It quickly gets incredibly complicated, right?

1:09:35.680 --> 1:09:36.880
 Yeah.

1:09:36.880 --> 1:09:40.960
 And a lot of it may not be in the structure of the song or the title.

1:09:40.960 --> 1:09:44.880
 It could be cultural references because, you know, it was a history.

1:09:44.880 --> 1:09:51.360
 So the definition problems quickly get, and I think that was the insight of Andrew Ng

1:09:51.360 --> 1:09:55.440
 when he said the job of the product manager is to understand these things that algorithms

1:09:55.440 --> 1:09:58.640
 don't and then define what that looks like.

1:09:58.640 --> 1:10:00.880
 And then you have something to train towards, right?

1:10:00.880 --> 1:10:02.720
 Then you have kind of the test set.

1:10:02.720 --> 1:10:06.960
 And then so today the editors create this pool of tracks and then we personalize.

1:10:06.960 --> 1:10:11.120
 You could easily imagine that once you have this set, you could have some automatic exploration

1:10:11.120 --> 1:10:13.840
 on the rest of the catalog because then you understand what it is.

1:10:14.480 --> 1:10:19.600
 And then the other side of it, when machine learning does help is this taste vector.

1:10:20.560 --> 1:10:26.960
 How hard is it to construct a vector that represents the things an individual human

1:10:26.960 --> 1:10:30.080
 likes, this human preference?

1:10:30.080 --> 1:10:37.120
 So you can, you know, music isn't like, it's not like Amazon, like things you usually buy.

1:10:38.320 --> 1:10:39.920
 Music seems more amorphous.

1:10:39.920 --> 1:10:42.560
 Like it's this thing that's hard to specify.

1:10:42.560 --> 1:10:48.080
 Like what is, you know, if you look at my playlist, what is the music that I love?

1:10:48.080 --> 1:10:48.640
 It's harder.

1:10:49.360 --> 1:10:54.080
 It seems to be much more difficult to specify concretely.

1:10:54.080 --> 1:10:57.120
 So how hard is it to build a taste vector?

1:10:57.120 --> 1:11:00.000
 It is very hard in the sense that you need a lot of data.

1:11:00.720 --> 1:11:05.520
 And I think what we found was that, so it's not a stationary problem.

1:11:06.240 --> 1:11:07.200
 It changes over time.

1:11:08.720 --> 1:11:15.680
 And so we've gone through the journey of, if you've done a lot of computer vision,

1:11:15.680 --> 1:11:18.320
 obviously I've done a bunch of computer vision in my past.

1:11:18.320 --> 1:11:24.160
 And we started kind of with the handcrafted heuristics for, you know, this is kind of

1:11:24.160 --> 1:11:24.800
 indie music.

1:11:24.800 --> 1:11:25.360
 This is this.

1:11:25.360 --> 1:11:27.440
 And if you consume this, you'd probably like this.

1:11:27.440 --> 1:11:31.200
 So we have, we started there and we have some of that still.

1:11:31.200 --> 1:11:34.720
 Then what was interesting about the playlist data was that you could find these latent

1:11:34.720 --> 1:11:37.520
 things that wouldn't necessarily even make sense to you.

1:11:38.800 --> 1:11:42.880
 That could even capture maybe cultural references because they cooccurred.

1:11:42.880 --> 1:11:48.160
 Things that wouldn't have appeared kind of mechanistically either in the content or so

1:11:48.160 --> 1:11:48.400
 forth.

1:11:48.400 --> 1:12:01.280
 So I think that, I think the core assumption is that there are patterns in almost

1:12:01.280 --> 1:12:01.840
 everything.

1:12:02.640 --> 1:12:06.960
 And if there are patterns, these embedding techniques are getting better and better now.

1:12:06.960 --> 1:12:12.400
 Now, as everyone else, we're also using kind of deep embeddings where you can encode

1:12:12.400 --> 1:12:14.400
 binary values and so forth.

1:12:14.400 --> 1:12:21.280
 And what I think is interesting is this process to try to find things that do not

1:12:21.280 --> 1:12:23.920
 necessarily, you wouldn't actually have guessed.

1:12:23.920 --> 1:12:28.560
 So it is very hard in an engineering sense to find the right dimensions.

1:12:28.560 --> 1:12:33.920
 It's an incredible scalability problem to do for hundreds of millions of users and to

1:12:33.920 --> 1:12:34.880
 update it every day.

1:12:35.920 --> 1:12:42.160
 But in theory, in theory embeddings isn't that complicated.

1:12:42.160 --> 1:12:46.240
 The fact that you try to find some principal components or something like that, dimensionality

1:12:46.240 --> 1:12:47.040
 reduction and so forth.

1:12:47.040 --> 1:12:48.240
 So the theory, I guess, is easy.

1:12:48.240 --> 1:12:50.480
 The practice is very, very hard.

1:12:50.480 --> 1:12:53.120
 And it's a huge engineering challenge.

1:12:53.120 --> 1:12:58.400
 But fortunately, we have some amazing both research and engineering teams in this space.

1:12:58.400 --> 1:13:03.200
 Yeah, I guess the question is all, I mean, it's similar.

1:13:03.200 --> 1:13:05.360
 I deal with it with autonomous vehicle spaces.

1:13:05.360 --> 1:13:07.680
 The question is how hard is driving?

1:13:07.680 --> 1:13:12.560
 And here is basically the question is of edge cases.

1:13:14.560 --> 1:13:22.240
 So embedding probably works, not probably, but I would imagine works well in a lot of

1:13:22.240 --> 1:13:22.740
 cases.

1:13:24.000 --> 1:13:25.840
 So there's a bunch of questions that arise then.

1:13:25.840 --> 1:13:33.760
 So do song preferences, does your taste vector depend on context, like mood, right?

1:13:33.760 --> 1:13:39.840
 So there's different moods, and so how does that take in it?

1:13:41.840 --> 1:13:44.320
 Is it possible to take that as a consideration?

1:13:44.320 --> 1:13:49.840
 Or do you just leave that as a interface problem that allows the user to just control it?

1:13:49.840 --> 1:13:55.440
 So when I'm looking for workout music, I kind of specify it by choosing certain playlists,

1:13:55.440 --> 1:13:56.560
 doing certain search.

1:13:56.560 --> 1:13:58.560
 Yeah, so that's a great point.

1:13:58.560 --> 1:14:00.080
 Back to the product development.

1:14:00.080 --> 1:14:04.480
 You could try to spend a few years trying to predict which mood you're in automatically

1:14:04.480 --> 1:14:08.320
 when you open Spotify, or you create a tab which is happy and sad, right?

1:14:08.320 --> 1:14:10.880
 And you're going to be right 100% of the time with one click.

1:14:10.880 --> 1:14:14.880
 Now, it's probably much better to let the user tell you if they're happy or sad, or

1:14:14.880 --> 1:14:15.840
 if they want to work out.

1:14:15.840 --> 1:14:20.480
 On the other hand, if your user interface becomes 2,000 tabs, you're introducing so

1:14:20.480 --> 1:14:22.080
 much friction so no one will use the product.

1:14:22.080 --> 1:14:23.520
 So then you have to get better.

1:14:24.080 --> 1:14:26.800
 So it's this thing where you have to be able to get better.

1:14:26.800 --> 1:14:32.640
 So then you have to get better, so it's this thing where I think maybe it was, I don't

1:14:32.640 --> 1:14:35.040
 remember who coined it, but it's called fault tolerant UIs, right?

1:14:35.040 --> 1:14:40.640
 You build a UI that is tolerant of being wrong, and then you can be much less right in your

1:14:42.000 --> 1:14:43.120
 algorithms.

1:14:43.120 --> 1:14:45.440
 So we've had to learn a lot of that.

1:14:45.440 --> 1:14:52.160
 Building the right UI that fits where the machine learning is, and a great discovery

1:14:52.160 --> 1:14:58.720
 there, which was by the teams during one of our hack days, was this thing of taking discovery,

1:14:58.720 --> 1:15:04.880
 packaging it into a playlist, and saying that these are new tracks that we think you might

1:15:04.880 --> 1:15:05.920
 like based on this.

1:15:05.920 --> 1:15:09.440
 And setting the right expectation made it a great product.

1:15:09.440 --> 1:15:15.920
 So I think we have this benefit that, for example, Tesla doesn't have that we can change

1:15:15.920 --> 1:15:16.800
 the expectation.

1:15:16.800 --> 1:15:18.640
 We can build a fault tolerant setting.

1:15:18.640 --> 1:15:23.040
 It's very hard to be fault tolerant when you're driving at 100 miles per hour or something.

1:15:23.760 --> 1:15:30.000
 And we have the luxury of being able to say that of being wrong if we have the right UI,

1:15:30.000 --> 1:15:33.440
 which gives us different abilities to take more risk.

1:15:33.440 --> 1:15:36.960
 So I actually think the self driving problem is much harder.

1:15:37.680 --> 1:15:38.720
 Oh, yeah, for sure.

1:15:39.680 --> 1:15:44.240
 It's much less fun because people die.

1:15:44.240 --> 1:15:45.200
 Exactly.

1:15:45.200 --> 1:15:55.040
 And in Spotify, it's such a more fun problem because failure is beautiful in a way.

1:15:55.040 --> 1:15:56.320
 It leads to exploration.

1:15:56.320 --> 1:15:58.640
 So it's a really fun reinforcement learning problem.

1:15:58.640 --> 1:16:02.800
 The worst case scenario is you get these WTF tweets like, how did I get this?

1:16:02.800 --> 1:16:03.600
 This song, yeah.

1:16:03.600 --> 1:16:05.440
 Which is a lot better than the self driving.

1:16:05.440 --> 1:16:14.400
 Exactly, so what's the feedback that a user, what's the signal that a user provides into

1:16:14.400 --> 1:16:15.440
 the system?

1:16:15.440 --> 1:16:17.920
 So you mentioned skipping.

1:16:19.360 --> 1:16:20.880
 What is like the strongest signal?

1:16:22.000 --> 1:16:23.520
 You didn't mention clicking like.

1:16:24.800 --> 1:16:27.600
 So we have a few signals that are important.

1:16:27.600 --> 1:16:30.240
 Obviously playing, playing through.

1:16:30.240 --> 1:16:36.560
 So one of the benefits of music, actually, even compared to podcasts or movies is the

1:16:36.560 --> 1:16:38.720
 object itself is really only about three minutes.

1:16:39.280 --> 1:16:44.320
 So you get a lot of chances to recommend and the feedback loop is every three minutes instead

1:16:44.320 --> 1:16:45.760
 of every two hours or something.

1:16:45.760 --> 1:16:50.320
 So you actually get kind of noisy, but quite fast feedback.

1:16:50.880 --> 1:16:55.200
 And so you can see if people play through, which is the inverse of skip really.

1:16:55.200 --> 1:16:56.560
 That's an important signal.

1:16:56.560 --> 1:17:00.320
 On the other hand, much of the consumption happens when your phone is in your pocket.

1:17:00.320 --> 1:17:03.040
 Maybe you're running or driving or you're playing on a speaker.

1:17:03.040 --> 1:17:05.600
 And so you not skipping doesn't mean that you love that song.

1:17:05.600 --> 1:17:08.960
 It may be that it wasn't bad enough that you would walk up and skip.

1:17:08.960 --> 1:17:10.560
 So it's a noisy signal.

1:17:10.560 --> 1:17:14.000
 Then we have the equivalent of the like, which is you saved it to your library.

1:17:14.000 --> 1:17:15.920
 That's a pretty strong signal of affection.

1:17:16.720 --> 1:17:21.280
 And then we have the more explicit signal of playlisting.

1:17:21.280 --> 1:17:23.920
 Like you took the time to create a playlist, you put it in there.

1:17:23.920 --> 1:17:28.960
 There's a very little small chance that if you took all that trouble, this is not a really

1:17:28.960 --> 1:17:30.480
 important track to you.

1:17:30.480 --> 1:17:34.000
 And then we understand also what are the tracks it relates to.

1:17:34.000 --> 1:17:39.120
 So we have the playlisting, we have the like, and then we have the listening or skip.

1:17:39.120 --> 1:17:43.360
 And you have to have very different approaches to all of them because of different levels

1:17:43.360 --> 1:17:44.400
 of noise.

1:17:44.400 --> 1:17:49.760
 One is very voluminous, but noisy, and the other is rare, but you can probably trust it.

1:17:49.760 --> 1:17:55.680
 Yeah, it's interesting because I think between those signals captures all the information

1:17:55.680 --> 1:17:57.040
 you'd want to capture.

1:17:57.040 --> 1:18:01.520
 I mean, there's a feeling, a shallow feeling for me that there's sometimes that I'll hear

1:18:01.520 --> 1:18:05.920
 a song that's like, yes, this is, you know, this was the right song for the moment.

1:18:05.920 --> 1:18:10.720
 But there's really no way to express that fact except by listening through it all the

1:18:10.720 --> 1:18:14.240
 way and maybe playing it again at that time or something.

1:18:14.240 --> 1:18:19.680
 But there's no need for a button that says this was the best song I could have heard

1:18:19.680 --> 1:18:20.400
 at this moment.

1:18:20.400 --> 1:18:24.080
 Well, we're playing around with that, with kind of the thumbs up concept saying like,

1:18:24.080 --> 1:18:25.200
 I really like this.

1:18:25.200 --> 1:18:27.520
 Just kind of talking to the algorithm.

1:18:27.520 --> 1:18:30.640
 It's unclear if that's the best way for humans to interact.

1:18:30.640 --> 1:18:31.200
 Maybe it is.

1:18:31.200 --> 1:18:35.600
 Maybe they should think of Spotify as a person, an agent sitting there trying to serve you

1:18:35.600 --> 1:18:38.080
 and you can say like, bad Spotify, good Spotify.

1:18:38.720 --> 1:18:42.880
 Right now, the analogy we've had is more, you shouldn't think of us.

1:18:42.880 --> 1:18:44.400
 We should be invisible.

1:18:44.400 --> 1:18:48.320
 And the feedback is if you save it, it's kind of you work for yourself.

1:18:48.320 --> 1:18:50.960
 You do a playlist because you think it's great and we can learn from that.

1:18:50.960 --> 1:18:55.200
 It's kind of back to Tesla, how they kind of have this shadow mode.

1:18:55.200 --> 1:18:56.720
 They sit in what you drive.

1:18:56.720 --> 1:18:58.560
 We kind of took the same analogy.

1:18:58.560 --> 1:19:02.800
 We sit in what you playlist and then maybe we can offer you an autopilot where you can

1:19:02.800 --> 1:19:04.640
 take over for a while or something like that.

1:19:04.640 --> 1:19:08.240
 And then back off if you say like, that's not good enough.

1:19:08.240 --> 1:19:11.600
 But I think it's interesting to figure out what your mental model is.

1:19:11.600 --> 1:19:18.880
 If Spotify is an AI that you talk to, which I think might be a bit too abstract for many

1:19:18.880 --> 1:19:24.320
 consumers, or if you still think of it as it's my music app, but it's just more helpful.

1:19:24.320 --> 1:19:30.160
 And it depends on the device it's running on, which brings us to smart speakers.

1:19:31.040 --> 1:19:38.400
 So I have a lot of the Spotify listening I do is on devices I can talk to, whether it's

1:19:38.400 --> 1:19:39.920
 from Amazon, Google or Apple.

1:19:39.920 --> 1:19:42.320
 What's the role of Spotify on those devices?

1:19:42.320 --> 1:19:46.720
 How do you think of it differently than on the phone or on the desktop?

1:19:47.840 --> 1:19:52.080
 There are a few things to say about the first of all, it's incredibly exciting.

1:19:52.080 --> 1:19:55.760
 They're growing like crazy, especially here in the US.

1:19:58.320 --> 1:20:09.200
 And it's solving a consumer need that I think is, you can think of it as just remote interactivity.

1:20:09.200 --> 1:20:11.840
 You can control this thing from across the room.

1:20:11.840 --> 1:20:16.880
 And it may feel like a small thing, but it turns out that friction matters to consumers

1:20:16.880 --> 1:20:22.000
 being able to say play, pause and so forth from across the room is very powerful.

1:20:22.000 --> 1:20:25.200
 So basically, you made the living room interactive now.

1:20:26.000 --> 1:20:33.600
 And what we see in our data is that the number one use case for these speakers is music,

1:20:33.600 --> 1:20:34.960
 music and podcast.

1:20:34.960 --> 1:20:39.920
 So fortunately for us, it's been important to these companies to have those use case

1:20:39.920 --> 1:20:40.640
 covered.

1:20:40.640 --> 1:20:42.080
 So they want to Spotify on this.

1:20:42.080 --> 1:20:44.320
 We have very good relationships with them.

1:20:45.840 --> 1:20:49.200
 And we're seeing tremendous success with them.

1:20:51.200 --> 1:20:54.640
 What I think is interesting about them is it's already working.

1:20:57.360 --> 1:21:02.720
 We kind of had this epiphany many years ago, back when we started using Sonos.

1:21:02.720 --> 1:21:06.800
 If you went through all the trouble of setting up your Sonos system, you had this magical

1:21:06.800 --> 1:21:10.400
 experience where you had all the music ever made in your living room.

1:21:10.400 --> 1:21:16.320
 And we made this assumption that the home, everyone used to have a CD player at home,

1:21:16.320 --> 1:21:19.040
 but they never managed to get their files working in the home.

1:21:19.040 --> 1:21:22.240
 Having this network attached storage was too cumbersome for most consumers.

1:21:22.960 --> 1:21:26.480
 So we made the assumption that the home would skip from the CD all the way to streaming

1:21:26.480 --> 1:21:31.120
 books, where you would buy the steering and would have all the music built in.

1:21:31.120 --> 1:21:32.640
 That took longer than we thought.

1:21:32.640 --> 1:21:36.080
 But with the voice speakers, that was the unlocking that made kind of the connected

1:21:36.080 --> 1:21:38.240
 speaker happen in the home.

1:21:39.760 --> 1:21:41.520
 So it really exploded.

1:21:41.520 --> 1:21:45.760
 And we saw this engagement that we predicted would happen.

1:21:45.760 --> 1:21:48.560
 What I think is interesting, though, is where it's going from now.

1:21:49.120 --> 1:21:51.120
 Right now, you think of them as voice speakers.

1:21:51.920 --> 1:21:58.640
 But I think if you look at Google I.O., for example, they just added a camera to it, where

1:21:58.640 --> 1:22:04.240
 when the alarm goes off, instead of saying, hey, Google, stop, you can just wave your

1:22:04.240 --> 1:22:05.040
 hand.

1:22:05.040 --> 1:22:11.920
 So I think they're going to think more of it as an agent or as an assistant, truly an

1:22:11.920 --> 1:22:12.400
 assistant.

1:22:12.400 --> 1:22:17.040
 And an assistant that can see you is going to be much more effective than a blind assistant.

1:22:17.040 --> 1:22:18.480
 So I think these things will morph.

1:22:18.480 --> 1:22:22.560
 And we won't necessarily think of them as, quote unquote, voice speakers anymore.

1:22:22.560 --> 1:22:29.200
 Just as interactive access to the Internet in the home.

1:22:29.200 --> 1:22:34.080
 But I still think that the biggest use case for those will be audio.

1:22:34.080 --> 1:22:36.640
 So for that reason, we're investing heavily in it.

1:22:36.640 --> 1:22:43.520
 And we built our own NLU stack to be able to the challenge here is, how do you innovate

1:22:43.520 --> 1:22:44.240
 in that world?

1:22:44.240 --> 1:22:48.320
 It lowers friction for consumers, but it's also much more constrained.

1:22:48.320 --> 1:22:51.600
 You have no pixels to play with in an audio only world.

1:22:51.600 --> 1:22:54.880
 It's really the vocabulary that is the interface.

1:22:54.880 --> 1:22:58.560
 So we started investing and playing around quite a lot with that, trying to understand

1:22:58.560 --> 1:23:03.360
 what the future will be of you speaking and gesturing and waving at your music.

1:23:03.360 --> 1:23:08.480
 And actually, you're actually nudging closer to the autonomous vehicle space because from

1:23:08.480 --> 1:23:14.080
 everything I've seen, the level of frustration people experience upon failure of natural

1:23:14.080 --> 1:23:18.320
 language understanding is much higher than failure in other contexts.

1:23:18.320 --> 1:23:20.400
 People get frustrated really fast.

1:23:20.400 --> 1:23:25.600
 So if you screw that experience up even just a little bit, they give up really quickly.

1:23:25.600 --> 1:23:26.320
 Yeah.

1:23:26.320 --> 1:23:28.320
 And I think you see that in the data.

1:23:28.320 --> 1:23:36.160
 While it's tremendously successful, the most common interactions are play, pause and next.

1:23:36.160 --> 1:23:39.440
 The things where if you compare it to taking up your phone, unlocking it, bringing up the

1:23:39.440 --> 1:23:44.160
 app and skipping, clicking skip, it was much lower friction.

1:23:44.160 --> 1:23:49.280
 But then for longer, more complicated things like, can you find me that song about the

1:23:49.280 --> 1:23:51.920
 people still bring up the phone and search and then play it on their speaker?

1:23:51.920 --> 1:23:56.960
 So we tried again to build a fault tolerant UI where for the more complicated things,

1:23:56.960 --> 1:24:02.480
 you can still pick up your phone, have powerful full keyboard search and then try to optimize

1:24:02.480 --> 1:24:07.280
 for where there is actually lower friction and try to it's kind of like the test autopilot

1:24:07.280 --> 1:24:07.840
 thing.

1:24:07.840 --> 1:24:11.040
 You have to be at the level where you're helpful.

1:24:11.040 --> 1:24:15.040
 If you're too smart and just in the way, people are going to get frustrated.

1:24:15.040 --> 1:24:18.080
 And first of all, I'm not obsessed with stairway to heaven.

1:24:18.080 --> 1:24:19.440
 It's just a good song.

1:24:19.440 --> 1:24:22.880
 But let me mention that as a use case because it's an interesting one.

1:24:22.880 --> 1:24:28.160
 I've literally told one of I don't want to say the name of the speaker because when people

1:24:28.160 --> 1:24:30.320
 are listening to it, it'll make their speaker go off.

1:24:30.320 --> 1:24:34.720
 But I talked to the speaker and I say play stairway to heaven.

1:24:34.720 --> 1:24:40.320
 And every time it like not every time, but a large percentage of the time plays the wrong

1:24:40.320 --> 1:24:41.440
 stairway to heaven.

1:24:41.440 --> 1:24:48.240
 It plays like some cover of the and that part of the experience.

1:24:48.240 --> 1:24:55.120
 I actually wonder from a business perspective, does Spotify control that entire experience

1:24:55.120 --> 1:24:55.600
 or no?

1:24:56.160 --> 1:25:01.680
 It seems like the NLU, the natural language stuff is controlled by the speaker and then

1:25:01.680 --> 1:25:04.640
 Spotify stays at a layer below that.

1:25:04.640 --> 1:25:07.040
 It's a good and complicated question.

1:25:07.040 --> 1:25:11.200
 Some of which is dependent on the on the partners.

1:25:11.200 --> 1:25:13.280
 So it's hard to comment on the on the specifics.

1:25:13.280 --> 1:25:15.840
 But the question is the right one.

1:25:15.840 --> 1:25:21.280
 The challenge is if you can't use any of the personalization, I mean, we know which stairway

1:25:21.280 --> 1:25:21.840
 to heaven.

1:25:21.840 --> 1:25:26.400
 And the truth is maybe for for one person, it is exactly the cover that they want.

1:25:26.400 --> 1:25:31.440
 And they would be very frustrated if a place I think we I think we default to the right

1:25:31.440 --> 1:25:31.760
 version.

1:25:31.760 --> 1:25:35.280
 But but you actually want to be able to do the cover for the person that just played

1:25:35.280 --> 1:25:36.320
 the cover 50 times.

1:25:36.320 --> 1:25:38.400
 Or Spotify is just going to seem stupid.

1:25:38.400 --> 1:25:40.160
 So you want to be able to leverage the personalization.

1:25:40.160 --> 1:25:46.320
 But you have this stack where you have the the ASR and this thing called the end best

1:25:46.320 --> 1:25:48.480
 list of the best guesses here.

1:25:48.480 --> 1:25:50.480
 And then the position comes in at the end.

1:25:50.480 --> 1:25:53.280
 You actually want the person to be here when you're guessing about what they actually

1:25:53.280 --> 1:25:54.000
 meant.

1:25:54.000 --> 1:26:00.160
 So we're working with these partners and it's a complicated it's a complicated thing

1:26:00.160 --> 1:26:02.880
 where you want to you want to be able.

1:26:02.880 --> 1:26:06.800
 So first of all, you want to be very careful with your users data.

1:26:06.800 --> 1:26:09.200
 You don't want to share your users data without the permission.

1:26:09.200 --> 1:26:11.680
 But you want to share some data so that their experience gets better.

1:26:12.640 --> 1:26:15.760
 So that these partners can understand enough, but not too much and so forth.

1:26:16.400 --> 1:26:21.760
 So it's really the trick is that it's like a business driven relationship where you're

1:26:21.760 --> 1:26:26.960
 doing product development across companies together, which is which is really complicated.

1:26:26.960 --> 1:26:32.960
 But this is exactly why we built our own NLU so that we actually can make personalized

1:26:32.960 --> 1:26:36.320
 guesses, because this is the biggest frustration from a user point of view.

1:26:36.320 --> 1:26:40.160
 They don't understand about ASR and best list and and business deals.

1:26:40.160 --> 1:26:41.440
 They're like, how hard can it be?

1:26:41.440 --> 1:26:45.120
 I was told this thing 50 times this version and still the place the wrong thing.

1:26:45.120 --> 1:26:46.240
 It can't it can't be hard.

1:26:47.040 --> 1:26:48.640
 So we try to take the user approach.

1:26:48.640 --> 1:26:53.360
 If the user the user is not going to understand the complications of business, we have to

1:26:53.360 --> 1:26:53.760
 solve it.

1:26:53.760 --> 1:27:02.240
 So let's talk about sort of a complicated subject that I myself I'm quite torn about

1:27:02.960 --> 1:27:07.600
 the idea sort of of paying artists.

1:27:08.640 --> 1:27:08.880
 Right.

1:27:09.840 --> 1:27:17.200
 I saw as of August 31st, 2018, over 11 billion dollars were paid to rights holders.

1:27:17.200 --> 1:27:21.200
 So and further distributed to artists from Spotify.

1:27:21.200 --> 1:27:23.840
 So a lot of money is being paid to artists.

1:27:23.840 --> 1:27:30.800
 First of all, the whole time as a consumer for me, when I look at Spotify, I'm not sure

1:27:30.800 --> 1:27:34.880
 I'm remembering correctly, but I think you said exactly how I feel, which is this is

1:27:34.880 --> 1:27:36.240
 too good to be true.

1:27:36.240 --> 1:27:42.480
 Like when I start using Spotify, I assume you guys will go bankrupt in like a month.

1:27:43.040 --> 1:27:44.400
 It's like this is too good.

1:27:44.400 --> 1:27:45.200
 A lot of people did.

1:27:47.040 --> 1:27:48.960
 I was like, this is amazing.

1:27:48.960 --> 1:27:53.200
 So one question I have is sort of the bigger question.

1:27:53.200 --> 1:27:55.200
 How do you make money in this complicated world?

1:27:55.840 --> 1:28:03.840
 How do you deal with the relationship with record labels who are complicated?

1:28:04.800 --> 1:28:14.080
 These big you're essentially have the task of herding cats, but like rich and powerful

1:28:14.080 --> 1:28:21.520
 powerful cats, and also have the task of paying artists enough and paying those labels enough

1:28:21.520 --> 1:28:26.480
 and still making money in the Internet space where people are not willing to pay hundreds

1:28:26.480 --> 1:28:27.360
 of dollars a month.

1:28:27.920 --> 1:28:30.720
 So how do you navigate the space?

1:28:30.720 --> 1:28:31.600
 How do you navigate?

1:28:31.600 --> 1:28:32.560
 That's a beautiful description.

1:28:32.560 --> 1:28:33.520
 Herding rich cats.

1:28:34.720 --> 1:28:35.280
 That before.

1:28:37.200 --> 1:28:42.880
 It is very complicated, and I think certainly actually betting against Spotify has been

1:28:42.880 --> 1:28:45.040
 statistically a very smart thing to do.

1:28:45.040 --> 1:28:52.880
 Just looking at the at the line of roadkill in music streaming services, it's it's kind

1:28:52.880 --> 1:28:58.560
 of I think if I understood the complexity when I joined Spotify, unfortunately, fortunately,

1:28:58.560 --> 1:29:03.440
 I didn't know enough about the music industry to understand the complexities, because then

1:29:03.440 --> 1:29:05.600
 I would have made a more rational guess that it wouldn't work.

1:29:06.240 --> 1:29:08.480
 So, you know, ignorance is bliss.

1:29:08.480 --> 1:29:13.200
 But I think there have been a few distinct challenges.

1:29:13.200 --> 1:29:17.600
 I think, as I said, one of the things that made it work at all was that Sweden and the

1:29:17.600 --> 1:29:19.200
 Nordics was a lost market.

1:29:19.840 --> 1:29:24.160
 So there was no risk for labels to try this.

1:29:25.120 --> 1:29:29.040
 I don't think it would have worked if if the market was healthy.

1:29:29.760 --> 1:29:32.160
 So that was the initial condition.

1:29:33.120 --> 1:29:36.160
 Then we had this tremendous challenge with the model itself.

1:29:36.160 --> 1:29:39.520
 So now most people were pirating.

1:29:39.520 --> 1:29:45.120
 But for the people who bought a download or a CD, the artists would get all the revenue

1:29:45.120 --> 1:29:48.000
 for all the future plays then, right?

1:29:48.000 --> 1:29:51.840
 So you got it all up front, whereas the streaming model was like almost nothing day one, almost

1:29:51.840 --> 1:29:52.800
 nothing day two.

1:29:52.800 --> 1:29:58.720
 And then at some point, this curve of incremental revenue would intersect with your day one

1:29:58.720 --> 1:29:59.220
 payment.

1:29:59.840 --> 1:30:05.280
 And that took a long time to play out before before the music labels, they understood

1:30:05.280 --> 1:30:05.780
 that.

1:30:05.780 --> 1:30:09.600
 But on the artist side, it took a lot of time to understand that actually, if I have a big

1:30:09.600 --> 1:30:14.000
 hit that is going to be played for many years, this is a much better model because I get

1:30:14.000 --> 1:30:18.000
 paid based on how much people use the product, not how much they thought they would use it

1:30:18.000 --> 1:30:19.040
 day one or so forth.

1:30:20.080 --> 1:30:22.880
 So it was a complicated model to get across.

1:30:22.880 --> 1:30:24.000
 But time helped with that.

1:30:24.000 --> 1:30:30.640
 And now the revenues to the music industry actually are bigger again than it's gone through

1:30:30.640 --> 1:30:32.000
 this incredible dip and now they're back up.

1:30:32.000 --> 1:30:36.480
 And so we're very proud of having been a part of that.

1:30:37.920 --> 1:30:39.520
 So there have been distinct problems.

1:30:39.520 --> 1:30:45.920
 I think when it comes to the labels, we have taken the painful approach.

1:30:46.720 --> 1:30:52.400
 Some of our competition at the time, they kind of looked at other companies and said,

1:30:52.400 --> 1:30:56.160
 if we just ignore the rights, we get really big, really fast.

1:30:56.160 --> 1:31:00.480
 We're going to be too big for the labels to kind of, too big to fail.

1:31:00.480 --> 1:31:01.120
 They're not going to kill us.

1:31:01.120 --> 1:31:02.160
 We didn't take that approach.

1:31:02.160 --> 1:31:06.960
 We went legal from day one and we negotiated and negotiated and negotiated.

1:31:06.960 --> 1:31:07.600
 It was very slow.

1:31:07.600 --> 1:31:08.240
 It was very frustrating.

1:31:08.240 --> 1:31:12.240
 We were angry at seeing other companies taking shortcuts and seeming to get away with it.

1:31:12.800 --> 1:31:18.160
 It was this game theory thing where over many rounds of playing the game, this would be

1:31:18.160 --> 1:31:19.200
 the right strategy.

1:31:19.200 --> 1:31:25.680
 And even though clearly there's a lot of frustrations at times during renegotiations, there is this

1:31:25.680 --> 1:31:30.800
 there is this weird trust where we have been honest and fair.

1:31:31.760 --> 1:31:32.480
 We've never screwed them.

1:31:32.480 --> 1:31:33.680
 They've never screwed us.

1:31:33.680 --> 1:31:39.280
 It's 10 years, but there's this trust and like they know that if music doesn't get

1:31:39.280 --> 1:31:43.360
 really big, if lots of people do not want to listen to music and want to pay for it,

1:31:43.360 --> 1:31:44.960
 Spotify has no business model.

1:31:44.960 --> 1:31:47.040
 So we actually are incredibly aligned.

1:31:48.240 --> 1:31:51.840
 Other companies, not to be tense, but other companies have other business models where

1:31:51.840 --> 1:31:56.400
 even if they made no money from music, they'd still be profitable companies.

1:31:56.400 --> 1:31:57.200
 But Spotify won't.

1:31:57.200 --> 1:32:02.400
 So I think the industry sees that we are actually aligned business wise.

1:32:03.120 --> 1:32:09.040
 So there is this trust that allows us to do product development, even if it's scary,

1:32:11.040 --> 1:32:12.560
 taking risks.

1:32:12.560 --> 1:32:17.200
 The free model itself was an incredible risk for the music industry to take that they should

1:32:17.200 --> 1:32:17.920
 get credit for.

1:32:17.920 --> 1:32:20.400
 Now, some of it was that they had nothing to lose in the game.

1:32:20.400 --> 1:32:22.240
 Some of it was that they had nothing to lose in Sweden.

1:32:22.240 --> 1:32:24.560
 But frankly, a lot of the labels also took risk.

1:32:25.840 --> 1:32:31.360
 And so I think we built up that trust with I think herding of cats sounds a bit.

1:32:32.320 --> 1:32:33.120
 What's the word?

1:32:33.120 --> 1:32:35.280
 It sounds like dismissive of the cats.

1:32:35.280 --> 1:32:35.920
 Dismissive.

1:32:35.920 --> 1:32:37.200
 No, every cat matters.

1:32:37.200 --> 1:32:39.360
 They're all beautiful and very important.

1:32:39.360 --> 1:32:39.920
 Exactly.

1:32:39.920 --> 1:32:42.800
 They've taken a lot of risks and certainly it's been frustrating.

1:32:44.960 --> 1:32:47.600
 So it's really like playing it's game theory.

1:32:47.600 --> 1:32:53.920
 If you play the game many times, then you can have the statistical outcome that you

1:32:53.920 --> 1:32:54.560
 bet on.

1:32:54.560 --> 1:32:57.520
 And it feels very painful when you're in the middle of that thing.

1:32:57.520 --> 1:33:00.480
 I mean, there's risk, there's trust, there's relationships.

1:33:00.480 --> 1:33:07.200
 From just having read the biography of Steve Jobs, similar kind of relationships were discussed

1:33:07.200 --> 1:33:08.400
 in iTunes.

1:33:08.400 --> 1:33:12.640
 The idea of selling a song for a dollar was very uncomfortable for labels.

1:33:12.640 --> 1:33:13.760
 Exactly.

1:33:13.760 --> 1:33:16.400
 And there was no, it was the same kind of thing.

1:33:16.400 --> 1:33:21.840
 It was trust, it was game theory as a lot of relationships that had to be built.

1:33:21.840 --> 1:33:28.880
 And it's really a terrifyingly difficult process that Apple could go through a little

1:33:28.880 --> 1:33:31.920
 bit because they could afford for that process to fail.

1:33:32.720 --> 1:33:37.600
 For Spotify, it seems terrifying because you can't.

1:33:37.600 --> 1:33:44.240
 Initially, I think a lot of it comes down to honestly Daniel and his tenacity in negotiating,

1:33:44.240 --> 1:33:50.800
 which seems like an impossible task because he was completely unknown and so forth.

1:33:50.800 --> 1:33:54.160
 But maybe that was also the reason that it worked.

1:33:56.480 --> 1:34:03.120
 But I think game theory is probably the best way to think about it.

1:34:03.120 --> 1:34:08.800
 You could go straight for this Nash equilibrium that someone is going to defect or you play

1:34:08.800 --> 1:34:14.240
 it many times, you try to actually go for the top left, the corporations sell.

1:34:14.240 --> 1:34:19.680
 Is there any magical reason why Spotify seems to have won this?

1:34:20.400 --> 1:34:25.360
 So a lot of people have tried to do what Spotify tried to do and Spotify has come out.

1:34:25.360 --> 1:34:29.280
 Well, so the answer is that there's no magical reason because I don't believe in magic.

1:34:30.000 --> 1:34:32.240
 But I think there are there are reasons.

1:34:32.240 --> 1:34:39.600
 And I think some of them are that people have misunderstood a lot of what we actually do.

1:34:40.400 --> 1:34:43.520
 The actual Spotify model is very complicated.

1:34:43.520 --> 1:34:49.200
 They've looked at the premium model and said, it seems like you can charge $9.99 for music

1:34:49.200 --> 1:34:52.000
 and people are going to pay, but that's not what happened.

1:34:52.000 --> 1:34:55.680
 Actually, when we launched the original mobile product, everyone said they would never pay.

1:34:56.640 --> 1:35:01.200
 What happened was they started on the free product and then their engagement grew so

1:35:01.200 --> 1:35:05.680
 much that eventually they said, maybe it is worth $9.99, right?

1:35:05.680 --> 1:35:08.880
 It's your propensity to pay gross with your engagement.

1:35:08.880 --> 1:35:11.600
 So we have this super complicated business model.

1:35:11.600 --> 1:35:15.200
 We operate two different business models, advertising and premium at the same time.

1:35:15.760 --> 1:35:17.680
 And I think that is hard to replicate.

1:35:17.680 --> 1:35:22.320
 I struggle to think of other companies that run large scale advertising and subscription

1:35:22.320 --> 1:35:23.440
 products at the same time.

1:35:24.400 --> 1:35:28.480
 So I think the business model is actually much more complicated than people think it is.

1:35:28.480 --> 1:35:32.800
 And so some people went after just the premium part without the free part and ran into a

1:35:32.800 --> 1:35:35.120
 wall where no one wanted to pay.

1:35:35.120 --> 1:35:40.400
 Some people went after just music should be free, just ads, which doesn't give you enough

1:35:40.400 --> 1:35:42.080
 revenue and doesn't work for the music industry.

1:35:42.880 --> 1:35:46.560
 So I think that combination is kind of opaque from the outside.

1:35:46.560 --> 1:35:51.040
 So maybe I shouldn't say it here and reveal the secret, but that turns out to be hard

1:35:51.040 --> 1:35:54.400
 to replicate than you would think.

1:35:54.400 --> 1:35:57.040
 So there's a lot of brilliant business strategies out there.

1:35:57.040 --> 1:35:58.720
 Brilliant business strategy here.

1:36:00.240 --> 1:36:01.280
 Brilliance or luck?

1:36:01.280 --> 1:36:03.520
 Probably more luck, but it doesn't really matter.

1:36:03.520 --> 1:36:05.440
 It looks brilliant in retrospect.

1:36:05.440 --> 1:36:06.480
 Let's call it brilliant.

1:36:07.840 --> 1:36:09.760
 Yeah, when the books are written, they'll be brilliant.

1:36:10.480 --> 1:36:14.480
 You've mentioned that your philosophy is to embrace change.

1:36:16.720 --> 1:36:23.600
 So how will the music streaming and music listening world change over the next 10 years,

1:36:23.600 --> 1:36:24.640
 20 years?

1:36:24.640 --> 1:36:26.960
 You look out into the far future.

1:36:26.960 --> 1:36:27.520
 What do you think?

1:36:28.960 --> 1:36:35.200
 I think that music and for that matter, audio podcasts, audiobooks, I think it's one of

1:36:35.200 --> 1:36:36.720
 the few core human needs.

1:36:37.360 --> 1:36:41.680
 I think it there is no good reason to me why it shouldn't be at the scale of something

1:36:41.680 --> 1:36:44.160
 like messaging or social networking.

1:36:44.160 --> 1:36:48.160
 I don't think it's a niche thing to listen to music or news or something.

1:36:48.160 --> 1:36:50.880
 So I think scale is obviously one of the things that I really hope for.

1:36:50.880 --> 1:36:54.400
 I think I hope that it's going to be billions of users.

1:36:54.400 --> 1:36:58.160
 I hope eventually everyone in the world gets access to all the world's music ever made.

1:36:58.720 --> 1:37:01.120
 So obviously, I think it's going to be a much bigger business.

1:37:01.120 --> 1:37:03.040
 Otherwise, we wouldn't be betting this big.

1:37:05.040 --> 1:37:13.600
 Now, if you look more at how it is consumed, what I'm hoping is back to this analogy of

1:37:13.600 --> 1:37:22.800
 the software tool chain, where I think I sometimes internally I make this analogy to text messaging.

1:37:22.800 --> 1:37:28.480
 Text messaging was also based on standards in the area of mobile carriers.

1:37:28.480 --> 1:37:32.720
 You had the SMS, the 140 character, 120 character SMS.

1:37:33.600 --> 1:37:36.080
 And it was great because everyone agreed on the standards.

1:37:36.080 --> 1:37:40.480
 So as a consumer, you got a lot of distributions and interoperability, but it was a very constrained

1:37:40.480 --> 1:37:40.980
 format.

1:37:41.680 --> 1:37:45.840
 And when the industry wanted to add pictures to that format to do the MMS, I looked it

1:37:45.840 --> 1:37:48.720
 up and I think it took from the late 80s to early 2000s.

1:37:48.720 --> 1:37:53.040
 This is like a 15, 20 year product cycle to bring pictures into that.

1:37:53.920 --> 1:38:00.240
 Now, once that entire value chain of creation and consumption got wrapped in one software

1:38:00.240 --> 1:38:07.280
 stack within something like Snapchat or WhatsApp, the first week they added disappearing messages.

1:38:07.280 --> 1:38:09.600
 Then two weeks later, they added stories.

1:38:09.600 --> 1:38:14.560
 The pace of innovation when you're on one software stack and you can affect both creation

1:38:14.560 --> 1:38:17.120
 and consumption, I think it's going to be rapid.

1:38:17.120 --> 1:38:22.320
 So with these streaming services, we now, for the first time in history, have enough,

1:38:22.320 --> 1:38:25.040
 I hope, people on one of these services.

1:38:25.040 --> 1:38:29.600
 Actually, whether it's Spotify or Amazon or Apple or YouTube, and hopefully enough

1:38:29.600 --> 1:38:32.320
 creators that you can actually start working with the format again.

1:38:32.320 --> 1:38:33.760
 And that excites me.

1:38:33.760 --> 1:38:39.200
 I think being able to change these constraints from 100 years, that could really do something

1:38:39.200 --> 1:38:40.160
 interesting.

1:38:40.160 --> 1:38:45.680
 I really hope it's not just going to be the iteration on the same thing for the next 10

1:38:45.680 --> 1:38:47.360
 to 20 years as well.

1:38:47.360 --> 1:38:52.000
 Yeah, changing the creation of music, the creation of audio, the creation of podcasts

1:38:52.000 --> 1:38:54.400
 is a really fascinating possibility.

1:38:54.400 --> 1:38:59.040
 I myself don't understand what it is about podcasts that's so intimate.

1:38:59.680 --> 1:39:00.480
 It just is.

1:39:00.480 --> 1:39:01.840
 I listen to a lot of podcasts.

1:39:01.840 --> 1:39:09.680
 I think it touches on a deep human need for connection that people do feel like they're

1:39:09.680 --> 1:39:12.960
 connected to when they listen.

1:39:12.960 --> 1:39:17.600
 I don't understand what the psychology of that is, but in this world that's becoming

1:39:17.600 --> 1:39:24.160
 more and more disconnected, it feels like this is fulfilling a certain kind of need.

1:39:24.800 --> 1:39:30.080
 And empowering the creator as opposed to just the listener is really interesting.

1:39:32.480 --> 1:39:34.240
 I'm really excited that you're working on this.

1:39:34.240 --> 1:39:38.800
 Yeah, I think one of the things that is inspiring for our teams to work on podcasts is exactly

1:39:38.800 --> 1:39:44.720
 that, whether you think, like I probably do, that it's something biological about perceiving

1:39:44.720 --> 1:39:47.840
 to be in the middle of the conversation that makes you listen in a different way.

1:39:47.840 --> 1:39:48.640
 It doesn't really matter.

1:39:48.640 --> 1:39:50.240
 People seem to perceive it differently.

1:39:50.240 --> 1:39:55.600
 And there was this narrative for a long time that if you look at video, everything kind

1:39:55.600 --> 1:39:59.840
 of in the foreground, it got shorter and shorter and shorter because of financial pressures

1:39:59.840 --> 1:40:01.600
 and monetization and so forth.

1:40:01.600 --> 1:40:06.240
 And eventually, at the end, there's almost like 20 seconds clip, people just screaming

1:40:06.240 --> 1:40:14.640
 something and I feel really good about the fact that you could have interpreted that

1:40:14.640 --> 1:40:16.880
 as people have no attention span anymore.

1:40:16.880 --> 1:40:18.400
 They don't want to listen to things.

1:40:18.400 --> 1:40:20.000
 They're not interested in deeper stories.

1:40:22.000 --> 1:40:23.280
 People are getting dumber.

1:40:23.280 --> 1:40:26.720
 But then podcasts came along and it's almost like, no, no, the need still existed.

1:40:28.000 --> 1:40:32.240
 But maybe it was the fact that you're not prepared to look at your phone like this for

1:40:32.240 --> 1:40:32.740
 two hours.

1:40:32.740 --> 1:40:36.500
 But if you can drive at the same time, it seems like people really want to dig deeper

1:40:36.500 --> 1:40:38.820
 and they want to hear like the more complicated version.

1:40:38.820 --> 1:40:42.980
 So to me, that is very inspiring that that podcast is actually long form.

1:40:42.980 --> 1:40:48.340
 It gives me a lot of hope for humanity that people seem really interested in hearing deeper,

1:40:48.340 --> 1:40:49.940
 more complicated conversations.

1:40:49.940 --> 1:40:52.100
 This is I don't understand it.

1:40:52.100 --> 1:40:53.140
 It's fascinating.

1:40:53.140 --> 1:40:57.620
 So the majority for this podcast, listen to the whole thing.

1:40:57.620 --> 1:41:02.500
 This whole conversation we've been talking for an hour and 45 minutes.

1:41:02.500 --> 1:41:06.580
 And somebody will I mean, most people will be listening to these words I'm speaking right

1:41:06.580 --> 1:41:06.580
 now.

1:41:06.580 --> 1:41:07.080
 It's crazy.

1:41:07.080 --> 1:41:10.740
 You wouldn't have thought that 10 years ago with where the world seemed to go.

1:41:10.740 --> 1:41:12.100
 That's very positive, I think.

1:41:12.100 --> 1:41:13.300
 That's really exciting.

1:41:13.300 --> 1:41:17.060
 And empowering the creator there is really exciting.

1:41:17.700 --> 1:41:18.740
 Last question.

1:41:18.740 --> 1:41:22.660
 You also have a passion for just mobile in general.

1:41:22.660 --> 1:41:32.660
 How do you see the smartphone world, the digital space of smartphones and just everything that's

1:41:32.660 --> 1:41:39.780
 on the move, whether it's Internet of Things and so on, changing over the next 10 years

1:41:39.780 --> 1:41:40.500
 and so on?

1:41:41.460 --> 1:41:47.460
 I think that one way to think about it is that computing might be moving out of these

1:41:47.460 --> 1:41:55.140
 multipurpose devices, the computer we had and the phone, into specific purpose devices.

1:41:55.140 --> 1:42:01.060
 And it will be ambient that at least in my home, you just shout something at someone

1:42:01.060 --> 1:42:03.380
 and there's always one of these speakers close enough.

1:42:03.380 --> 1:42:06.980
 And so you start behaving differently.

1:42:06.980 --> 1:42:11.460
 It's as if you have the Internet ambient, ambiently around you and you can ask it things.

1:42:11.460 --> 1:42:15.780
 So I think computing will kind of get more integrated and we won't necessarily think

1:42:15.780 --> 1:42:21.060
 of it as connected to a device in the same way that we do today.

1:42:21.700 --> 1:42:22.900
 I don't know the path to that.

1:42:22.900 --> 1:42:29.860
 Maybe we used to have these desktop computers and then we partially replaced that with the

1:42:30.340 --> 1:42:32.740
 laptops and left the desktop at home when I work.

1:42:32.740 --> 1:42:37.380
 And then we got these phones and we started leaving the mobile phones.

1:42:37.380 --> 1:42:41.540
 We had the desktop at home when I work and then we got these phones and we started leaving

1:42:41.540 --> 1:42:42.820
 the laptop at home for a while.

1:42:42.820 --> 1:42:47.460
 And maybe for stretches of time you're going to start using the watch and you can leave

1:42:47.460 --> 1:42:50.020
 your phone at home for a run or something.

1:42:50.580 --> 1:42:58.420
 And we're on this progressive path where I think what is happening with voice is that

1:43:00.740 --> 1:43:06.820
 you have an interaction paradigm that doesn't require as large physical devices.

1:43:06.820 --> 1:43:12.820
 So I definitely think there's a future where you can have your AirPods and your watch and

1:43:12.820 --> 1:43:14.980
 you can do a lot of computing.

1:43:15.860 --> 1:43:20.020
 And I don't think it's going to be this binary thing.

1:43:20.020 --> 1:43:23.380
 I think it's going to be like many of us still have a laptop, we just use it less.

1:43:23.940 --> 1:43:25.940
 And so you shift your consumption over.

1:43:26.820 --> 1:43:31.940
 And I don't know about AR glasses and so forth.

1:43:31.940 --> 1:43:32.740
 I'm excited about it.

1:43:32.740 --> 1:43:35.700
 I spent a lot of time in that area, but I still think it's quite far away.

1:43:35.700 --> 1:43:37.540
 AR, VR, all of that.

1:43:37.540 --> 1:43:39.780
 Yeah, VR is happening and working.

1:43:39.780 --> 1:43:43.940
 I think the recent Oculus Quest is quite impressive.

1:43:43.940 --> 1:43:45.300
 I think AR is further away.

1:43:45.300 --> 1:43:46.580
 At least that type of AR.

1:43:48.100 --> 1:43:54.660
 But I do think your phone or watch or glasses understanding where you are and maybe what

1:43:54.660 --> 1:43:56.980
 you're looking at and being able to give you audio cues about that.

1:43:56.980 --> 1:43:58.580
 Or you can say like, what is this?

1:43:58.580 --> 1:43:59.700
 And it tells you what it is.

1:44:00.980 --> 1:44:02.340
 That I think might happen.

1:44:02.340 --> 1:44:08.020
 You use your watch or your glasses as a mouse pointer on reality.

1:44:08.020 --> 1:44:09.460
 I think it might be a while before...

1:44:09.460 --> 1:44:10.180
 I might be wrong.

1:44:10.180 --> 1:44:10.820
 I hope I'm wrong.

1:44:10.820 --> 1:44:14.820
 I think it might be a while before we walk around with these big lab glasses that project

1:44:14.820 --> 1:44:15.620
 things.

1:44:15.620 --> 1:44:16.260
 I agree with you.

1:44:16.820 --> 1:44:22.260
 It's actually really difficult when you have to understand the physical world enough to

1:44:23.060 --> 1:44:23.940
 project onto it.

1:44:25.300 --> 1:44:26.740
 I lied about the last question.

1:44:26.740 --> 1:44:32.660
 Go ahead, because I just thought of audio and my favorite topic, which is the movie

1:44:32.660 --> 1:44:41.140
 Her, do you think, whether it's part of Spotify or not, we'll have, I don't know if you've

1:44:41.140 --> 1:44:42.180
 seen the movie Her.

1:44:42.180 --> 1:44:42.660
 Absolutely.

1:44:45.060 --> 1:44:53.300
 And there, audio is the primary form of interaction and the connection with another entity that

1:44:53.300 --> 1:44:59.300
 you can actually have a relationship with, that you fall in love with based on voice

1:44:59.300 --> 1:45:00.740
 alone, audio alone.

1:45:00.740 --> 1:45:04.820
 Do you think that's possible, first of all, based on audio alone to fall in love with

1:45:04.820 --> 1:45:05.380
 somebody?

1:45:05.380 --> 1:45:06.580
 Somebody or...

1:45:06.580 --> 1:45:08.020
 Well, yeah, let's go with somebody.

1:45:08.020 --> 1:45:11.700
 Just have a relationship based on audio alone.

1:45:11.700 --> 1:45:18.500
 And second question to that, can we create an artificial intelligence system that allows

1:45:18.500 --> 1:45:21.940
 one to fall in love with it and her, him with you?

1:45:21.940 --> 1:45:29.940
 So this is my personal answer, speaking for me as a person, the answer is quite unequivocally

1:45:29.940 --> 1:45:32.020
 yes on both.

1:45:32.820 --> 1:45:36.580
 I think what we just said about podcasts and the feeling of being in the middle of a

1:45:36.580 --> 1:45:42.660
 conversation, if you could have an assistant where, and we just said that feels like a

1:45:42.660 --> 1:45:43.940
 very personal setting.

1:45:43.940 --> 1:45:47.380
 So if you walk around with these headphones and this thing, you're speaking with this

1:45:47.380 --> 1:45:49.940
 thing all of the time that feels like it's in your brain.

1:45:49.940 --> 1:45:53.700
 I think it's going to be much easier to fall in love with than something that would be

1:45:53.700 --> 1:45:54.740
 on your screen.

1:45:54.740 --> 1:45:56.340
 I think that's entirely possible.

1:45:56.340 --> 1:46:00.500
 And then from the, you can probably answer this better than me, but from the concept

1:46:00.500 --> 1:46:07.060
 of if it's going to be possible to build a machine that can achieve that, I think whether

1:46:07.060 --> 1:46:12.740
 you think of it as, if you can fake it, the philosophical zombie that assimilates it enough

1:46:12.740 --> 1:46:17.700
 or it somehow actually is, I think there's, it's only a question.

1:46:17.700 --> 1:46:20.500
 It's only a question if you ask me about time, I'd have a different answer.

1:46:20.500 --> 1:46:24.580
 But if you say I've given some half infinite time, absolutely.

1:46:24.580 --> 1:46:28.260
 I think it's just atoms and arrangement of information.

1:46:29.620 --> 1:46:33.220
 Well, I personally think that love is a lot simpler than people think.

1:46:33.780 --> 1:46:37.780
 So we started with true romance and ended in love.

1:46:37.780 --> 1:46:39.780
 I don't see a better place to end.

1:46:39.780 --> 1:46:40.340
 Beautiful.

1:46:40.340 --> 1:46:41.860
 Gustav, thanks so much for talking today.

1:46:41.860 --> 1:46:42.420
 Thank you so much.

1:46:42.420 --> 1:46:43.140
 It was a lot of fun.

1:46:43.140 --> 1:46:49.300
 It was fun.