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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,
-
-15:32.480 --> 15:33.920
- I think was fantastic.
-
-15:34.560 --> 15:38.000
- But it was also horrible for artists because there was no business model around it.
-
-15:38.000 --> 15:39.600
- So they didn't make any money.
-
-15:39.600 --> 15:46.400
- So the user need almost drove the user interface before there was a business model.
-
-15:46.400 --> 15:52.160
- And then there were these download stores that allowed you to download files, which
-
-15:52.160 --> 15:55.040
- was a solution, but it didn't solve the access problem.
-
-15:55.040 --> 15:58.560
- There was still a marginal cost of 99 cents to try one more track.
-
-15:58.560 --> 16:01.920
- And I think that that heavily limits how you listen to music.
-
-16:01.920 --> 16:07.600
- The example I always give is, you know, in Spotify, a huge amount of people listen to
-
-16:07.600 --> 16:10.320
- music while they sleep, while they go to sleep and while they sleep.
-
-16:11.280 --> 16:14.960
- If that costed you 99 cents per three minutes, you probably wouldn't do that.
-
-16:15.520 --> 16:18.640
- And you would be much less adventurous if there was a real dollar cost to exploring
-
-16:18.640 --> 16:19.200
- music.
-
-16:19.200 --> 16:22.320
- So the access model is interesting in that it changes your music behavior.
-
-16:22.320 --> 16:26.560
- You can be, you can take much more risk because there's no marginal cost to it.
-
-16:27.680 --> 16:32.320
- Maybe let me linger on piracy for a second, because I find, especially coming from Russia,
-
-16:33.200 --> 16:36.560
- piracy is something that's very interesting to me.
-
-16:39.440 --> 16:49.040
- Not me, of course, ever, but I have friends who have partook in piracy of music, software,
-
-16:49.040 --> 16:51.600
- TV shows, sporting events.
-
-16:52.400 --> 16:57.920
- And usually to me, what that shows is not that they're, they can actually pay the money
-
-16:58.400 --> 16:59.600
- and they're not trying to save money.
-
-17:00.480 --> 17:02.800
- They're choosing the best experience.
-
-17:03.760 --> 17:08.560
- So what to me, piracy shows is a business opportunity in all these domains.
-
-17:08.560 --> 17:11.120
- And that's where I think you're right.
-
-17:11.120 --> 17:15.840
- Spotify stepped in is basically piracy was an experience.
-
-17:15.840 --> 17:23.520
- 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
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-1:40:36.500 --> 1:40:38.820
- and they want to hear like the more complicated version.
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-1:40:38.820 --> 1:40:42.980
- So to me, that is very inspiring that that podcast is actually long form.
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-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,
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-1:40:48.340 --> 1:40:49.940
- more complicated conversations.
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-1:40:49.940 --> 1:40:52.100
- This is I don't understand it.
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-1:40:52.100 --> 1:40:53.140
- It's fascinating.
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-1:40:53.140 --> 1:40:57.620
- So the majority for this podcast, listen to the whole thing.
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-1:40:57.620 --> 1:41:02.500
- This whole conversation we've been talking for an hour and 45 minutes.
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-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
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-1:41:06.580 --> 1:41:06.580
- now.
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-1:41:06.580 --> 1:41:07.080
- It's crazy.
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-1:41:07.080 --> 1:41:10.740
- You wouldn't have thought that 10 years ago with where the world seemed to go.
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-1:41:10.740 --> 1:41:12.100
- That's very positive, I think.
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-1:41:12.100 --> 1:41:13.300
- That's really exciting.
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-1:41:13.300 --> 1:41:17.060
- And empowering the creator there is really exciting.
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-1:41:17.700 --> 1:41:18.740
- Last question.
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-1:41:18.740 --> 1:41:22.660
- You also have a passion for just mobile in general.
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-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
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-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
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-1:41:39.780 --> 1:41:40.500
- and so on?
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-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
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-1:41:47.460 --> 1:41:55.140
- multipurpose devices, the computer we had and the phone, into specific purpose devices.
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-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
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-1:42:01.060 --> 1:42:03.380
- and there's always one of these speakers close enough.
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-1:42:03.380 --> 1:42:06.980
- And so you start behaving differently.
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-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.
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-1:42:11.460 --> 1:42:15.780
- So I think computing will kind of get more integrated and we won't necessarily think
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-1:42:15.780 --> 1:42:21.060
- of it as connected to a device in the same way that we do today.
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-1:42:21.700 --> 1:42:22.900
- I don't know the path to that.
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-1:42:22.900 --> 1:42:29.860
- Maybe we used to have these desktop computers and then we partially replaced that with the
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-1:42:30.340 --> 1:42:32.740
- laptops and left the desktop at home when I work.
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-1:42:32.740 --> 1:42:37.380
- And then we got these phones and we started leaving the mobile phones.
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-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
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-1:42:41.540 --> 1:42:42.820
- the laptop at home for a while.
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-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
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-1:42:47.460 --> 1:42:50.020
- your phone at home for a run or something.
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-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
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-1:43:00.740 --> 1:43:06.820
- you have an interaction paradigm that doesn't require as large physical devices.
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-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
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-1:43:12.820 --> 1:43:14.980
- you can do a lot of computing.
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-1:43:15.860 --> 1:43:20.020
- And I don't think it's going to be this binary thing.
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-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.
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-1:43:23.940 --> 1:43:25.940
- And so you shift your consumption over.
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-1:43:26.820 --> 1:43:31.940
- And I don't know about AR glasses and so forth.
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-1:43:31.940 --> 1:43:32.740
- I'm excited about it.
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-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.
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-1:43:35.700 --> 1:43:37.540
- AR, VR, all of that.
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-1:43:37.540 --> 1:43:39.780
- Yeah, VR is happening and working.
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-1:43:39.780 --> 1:43:43.940
- I think the recent Oculus Quest is quite impressive.
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-1:43:43.940 --> 1:43:45.300
- I think AR is further away.
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-1:43:45.300 --> 1:43:46.580
- At least that type of AR.
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-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
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-1:43:54.660 --> 1:43:56.980
- you're looking at and being able to give you audio cues about that.
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-1:43:56.980 --> 1:43:58.580
- Or you can say like, what is this?
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-1:43:58.580 --> 1:43:59.700
- And it tells you what it is.
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-1:44:00.980 --> 1:44:02.340
- That I think might happen.
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-1:44:02.340 --> 1:44:08.020
- You use your watch or your glasses as a mouse pointer on reality.
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-1:44:08.020 --> 1:44:09.460
- I think it might be a while before...
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-1:44:09.460 --> 1:44:10.180
- I might be wrong.
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-1:44:10.180 --> 1:44:10.820
- I hope I'm wrong.
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-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
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-1:44:14.820 --> 1:44:15.620
- things.
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-1:44:15.620 --> 1:44:16.260
- I agree with you.
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-1:44:16.820 --> 1:44:22.260
- It's actually really difficult when you have to understand the physical world enough to
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-1:44:23.060 --> 1:44:23.940
- project onto it.
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-1:44:25.300 --> 1:44:26.740
- I lied about the last question.
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-1:44:26.740 --> 1:44:32.660
- Go ahead, because I just thought of audio and my favorite topic, which is the movie
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-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.
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-1:44:42.180 --> 1:44:42.660
- Absolutely.
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-1:44:45.060 --> 1:44:53.300
- And there, audio is the primary form of interaction and the connection with another entity that
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-1:44:53.300 --> 1:44:59.300
- you can actually have a relationship with, that you fall in love with based on voice
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-1:44:59.300 --> 1:45:00.740
- alone, audio alone.
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-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
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-1:45:04.820 --> 1:45:05.380
- somebody?
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-1:45:05.380 --> 1:45:06.580
- Somebody or...
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-1:45:06.580 --> 1:45:08.020
- Well, yeah, let's go with somebody.
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-1:45:08.020 --> 1:45:11.700
- Just have a relationship based on audio alone.
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-1:45:11.700 --> 1:45:18.500
- And second question to that, can we create an artificial intelligence system that allows
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-1:45:18.500 --> 1:45:21.940
- one to fall in love with it and her, him with you?
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-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
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-1:45:29.940 --> 1:45:32.020
- yes on both.
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-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
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-1:45:36.580 --> 1:45:42.660
- conversation, if you could have an assistant where, and we just said that feels like a
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-1:45:42.660 --> 1:45:43.940
- very personal setting.
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-1:45:43.940 --> 1:45:47.380
- So if you walk around with these headphones and this thing, you're speaking with this
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-1:45:47.380 --> 1:45:49.940
- thing all of the time that feels like it's in your brain.
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-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
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-1:45:53.700 --> 1:45:54.740
- on your screen.
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-1:45:54.740 --> 1:45:56.340
- I think that's entirely possible.
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-1:45:56.340 --> 1:46:00.500
- And then from the, you can probably answer this better than me, but from the concept
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-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
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-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
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-1:46:12.740 --> 1:46:17.700
- or it somehow actually is, I think there's, it's only a question.
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-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.
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-1:46:20.500 --> 1:46:24.580
- But if you say I've given some half infinite time, absolutely.
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-1:46:24.580 --> 1:46:28.260
- I think it's just atoms and arrangement of information.
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-1:46:29.620 --> 1:46:33.220
- Well, I personally think that love is a lot simpler than people think.
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-1:46:33.780 --> 1:46:37.780
- So we started with true romance and ended in love.
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-1:46:37.780 --> 1:46:39.780
- I don't see a better place to end.
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-1:46:39.780 --> 1:46:40.340
- Beautiful.
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-1:46:40.340 --> 1:46:41.860
- Gustav, thanks so much for talking today.
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-1:46:41.860 --> 1:46:42.420
- Thank you so much.
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-1:46:42.420 --> 1:46:43.140
- It was a lot of fun.
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-1:46:43.140 --> 1:46:49.300
- It was fun.
-