Gustav Söderström
speaker
294 appearances
1 recordings
1 series
first heard May 2025
last heard May 2025
Gustav Söderström’s voice in public audio — every appearance, attributed to the second.
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And usually when these macro winds come, we have a saying internally that you can have the macro wind blow in your face. It's not going to change its direction. So you basically need to reposition yourself so you get the wind at your back and can surf this macro wind, or some people call it the macro wave that you surf. So we've been through a few of these.
The first one was really the smartphone when that came along. Spotify was really well positioned for the internet before the smartphone. where we had a free-to-own desktop, and that's where we acquired users, and they built a playlist, and they retained themselves. And then mobility, to listen on the go, was a paid feature on Spotify.
And that was fine when the majority was computers and the minority was smartphones. And then when smartphones took off, we faced an existential crisis where there started to be consumers who didn't have a desktop. They only had a phone, so they had no free experience, and our entire model died.
So that was one of those moments we had to reposition the entire business model, actually, and figure out how do we do a free tier on mobile that doesn't cannibalize the paid feature, which was mobility. And we can talk about how we figured that out later. But that was one of those examples. And I think this is a similar one.
The big question to me is, does this require a business model change, where is it, quote unquote, just a product change? The other thing that is different, I think, about AI is that it's not going to touch one thing. It touches the consumer product, but it also touches your productivity and competitiveness as a company. So there are lots of different angles to start. But
As you said, we were quite early with machine learning. The journey we had was we saw users coming on Spotify and then they started playlisting and that retained themselves. But it was only a certain amount of people who were good at playlisting because you have to know the catalog in your head, the new releases, the back catalog. So some people retain themselves really well.
And then we tried to scale that behavior by having editors who created playlists for people who couldn't play this that well. And we saw people using social to find inspiration. Eventually, machine learning started happening, and we saw this opportunity of building a music friend for everyone. So that's where we started. We started investing in that and got quite good at that.
I think some people say that AI is just machine learning. It's just a new word. And it's an interesting question. What is the difference? I think the difference between what people used to call machine learning and what we call generative AI is that the statistical machine learning is an output mechanism. And I think the epitome of that age is the full screen TikTok feed.
UIs shape themselves after technology that powers them to maximize metrics. And I think that is the UI that maximizes the statistical explore-exploit paradigm of old school machine learning. What happens with generative AI, I think the big shift is that you can take natural language input. And so even if technically they're both machine learning, I think of generative AI as the new age.
And the big shift is that it's two-way. If you think about Spotify, for example, as a consumer product, the way it looks, it's almost like old school broadband. The broadband where you have maybe one megabit downlink, but only 150 kilobit uplink. So a lot of bandwidth down, but not a lot of feedback. This is what most consumer services look like.
You have streaming video on the downlink, a lot of information per second. But the uplink is only a few clicks and swipes. It's a very, very narrow signal. And this is what the previous machine learning age focused on. I think what changes in the age of generative AI is that the uplink can now be English language. It can be almost as rich as the downlink.
And I think that requires all of us consumer companies to, in the limit, totally rethink the product. So if you just do the deduction of what I said, if the full-screen TikTok feed is the epitome of the ML paradigm, the asymmetric downlink-uplink paradigm, what are the chances that that is also the epitome of this generative AI age? I don't think so.
I think consumer products are going to change fundamentally. I can't predict exactly how. I think they're going to be much more symmetric in terms of information you receive versus information you give. And I think if you fast forward five to 10 years, almost all big consumer products are going to be a conversation to some extent, rather than this service that you use.
So really the job for us on the product side is to try to figure out what is the next paradigm. And I don't know exactly what it is yet. We're experimenting. And if I did know, I probably wouldn't tell you right now. I'd sit on it for a bit. But this is where we're on the product side.
And then we can talk a bit about the productivity side as well, where there are the obvious gains in terms of coding productivity, where we are using all the tools that everyone else is doing. But as a big company, there are a few differences from the startups.
Because so far, generative AI and coding has had the most impact when you write net new code, which is a lot of what you do as a startup and a tiny bit of what you do as a big company. Most of it is just refactoring, et cetera. And I think I saw some statistic that In a big company, you basically code one out of every eight hours in a day.
So not only is coding only one eighth of the time, of that one eighth of the time, that new code is very small. So I actually think the biggest impact is yet to come when it comes to coding. That's two things. These models are getting big enough to understand really large and complex code bases like Spotify's. And we're not quite there where these things can refactor effectively.
our code base, it doesn't have quite a deep understanding. But it will. And that will be a big shift. The other is doing automatic peer review. We're just on the verge of that working. It's not quite good enough that you can trust it. So a lot of developers didn't wait for their code to be in review and come back. So I think we're seeing that ramp.
I think we're going to see it ramp a lot in the next few years. But then the really interesting side is these other seven hours, what a developer does, which is a lot of communication, planning, working with designers, prototyping, meetings. Those things I think will actually have as big or even more impact than the coding itself.
I want to start with this downlink, uplink part. What have you learned about consumers' willingness to put a lot of effort into the uplink? It seems like the chat interfaces, the GPTs of the world, we know that people are willing to do a lot of back and forth because it's the native interface. You're going there expecting to write a lot of stuff.
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