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lexicap

	WEBVTT

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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

	17:23.520 --> 17:29.680
	because it's, I mean, it's bad metadata, long download times, all kinds of stuff.

	17:29.680 --> 17:37.520
	And what Spotify does is basically first rewards artists and second makes the experience of

	17:37.520 --> 17:38.720
	exploring music much better.

	17:38.720 --> 17:42.560
	I mean, the same is true, I think for movies and so on.

	17:42.560 --> 17:48.080
	That piracy reveals in the software space, for example, I'm a huge user and fan of Adobe

	17:48.080 --> 17:54.720
	products and there was much more incentive to pirate Adobe products before they went

	17:54.720 --> 17:56.400
	to a monthly subscription plan.

	17:57.120 --> 18:04.640
	And now all of the said friends that used to pirate Adobe products that I know now actually

	18:04.640 --> 18:06.880
	pay gladly for the monthly subscription.

	18:06.880 --> 18:08.000
	Yeah, I think you're right.

	18:08.000 --> 18:11.360
	I think it's a sign of an opportunity for product development.

	18:11.360 --> 18:19.120
	And that sometimes there's a product market fit before there's a business model fit in

	18:19.120 --> 18:19.840
	product development.

	18:19.840 --> 18:21.760
	I think that's a sign of it.

	18:21.760 --> 18:24.320
	In Sweden, I think it was a bit of both.

	18:24.320 --> 18:30.480
	There was a culture where we even had a political party called the Pirate Party.

	18:30.480 --> 18:35.120
	And this was during the time when people said that information should be free.

	18:35.120 --> 18:38.080
	It was somehow wrong to charge for ones and zeros.

	18:38.080 --> 18:43.600
	So I think people felt that artists should probably make some money somehow else and

	18:43.600 --> 18:44.880
	concerts or something.

	18:44.880 --> 18:49.920
	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.