Invest Like the Best with Patrick O'Shaughnessy
Gustav Söderström - How Spotify Thinks - [Invest Like the Best, EP.424]
Tue, 20 May 2025
My guest today is Gustav Söderström. Gustav is the Co-President, Chief Product Officer & Chief Technology Officer at Spotify. Gustav lets us behind the scenes on how Spotify thinks about the future of audio and video, and what leadership lessons he's learned from making mistakes and taking risks in a rapidly changing technological landscape. He shares fascinating insights on their synchronized team structure and how they have positioned themselves as "the R&D department" for the entire music industry. We discuss their integration of AI, their unique "bets board" process for allocating resources, and how they've evolved from a music service into a multimedia platform with over 650 million users. Please enjoy my conversation with Gustav Söderström. For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- This episode is brought to you by Ramp. Ramp’s mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to Ramp.com/invest to sign up for free and get a $250 welcome bonus. – This episode is brought to you by AlphaSense. AlphaSense has completely transformed the research process with cutting-edge AI technology and a vast collection of top-tier, reliable business content. Invest Like the Best listeners can get a free trial now at Alpha-Sense.com/Invest and experience firsthand how AlphaSense and Tegus help you make smarter decisions faster. – This episode is brought to you by Ridgeline. Ridgeline has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Head to ridgelineapps.com to learn more about the platform. ----- Editing and post-production work for this episode was provided by The Podcast Consultant (https://thepodcastconsultant.com). Show Notes: (00:00:00) Welcome to Invest Like the Best (00:05:27) Spotify's Journey Through Technological Shifts (00:06:28) The Impact of Generative AI on Consumer Products (00:09:36) AI in Coding and Productivity (00:11:11) Consumer Engagement and AI Playlisting (00:14:43) Strategic Frameworks and Decision-Making at Spotify (00:19:39) The Bets Process: Structured Innovation (00:31:11) The Future of AI and Business Models (00:44:31) The Future of AI and Inference Costs (00:46:21) The Concept of Computronium and Infinite Computing (00:47:02) David Deutsch and the Beginning of Infinity (00:48:55) The Power of Explanations and Human Understanding (00:54:03) Spotify's Free Tier and Consumer Needs (00:56:45) Spotify's Relationship with the Music Industry (01:03:03) The Rise of Podcasting and Audiobooks (01:15:45) Personal Interests and Continuous Learning (01:20:32) The Kindest Thing Anyone Has Ever Done For Gustav
Chapter 1: What is the main topic of this episode?
Something I speak about frequently on Invest Like the Best is the idea of life's work. A more fun way to think about it is that I'm looking for maniacs on a mission. This is the basis for our investment firm, Positive Sum, and it's the reason why I'm so enthusiastic about our presenting sponsor, Ramp.
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The Ramp team is relentless, and the product continues to evolve to save you time that you would never have dreamed of getting back. To me, there is nothing more interesting than technologies that reduce friction for other entrepreneurs to be able to build the thing that they want to. So much attention has gone to cloud computing, APIs, and other ways of making life easy for founders.
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Chapter 2: How has Spotify adapted to technological shifts?
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Ridgeline gets me so excited because every investment professional knows the core challenge that they solve. You love the core work of investing, but operational complexities eat up valuable time and energy. That's where Ridgeline comes in. Ridgeline is an all-in-one operating system designed specifically for investment managers, and their momentum has been incredible.
With about $350 billion now committed to the platform and a 60% increase in customers since just October, firms are flocking to Ridgeline for good reason. They've been leading the investment management tech industry in AI for over a year with 100% of their users opting into their AI capabilities, putting them light years ahead of other vendors thanks to their single source of data.
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Ridgeline has created a comprehensive cloud platform that handles everything in real time, from trading and portfolio management to compliance and client reporting. It's worth reaching out to Ridgeline to see what the experience can be like with a single platform. Visit RidgelineApps.com to schedule a demo. Hello and welcome, everyone. I'm Patrick O'Shaughnessy, and this is Invest Like the Best.
This show is an open-ended exploration of markets, ideas, stories, and strategies that will help you better invest both your time and your money. If you enjoy these conversations and want to go deeper, check out Colossus Review, our quarterly publication with in-depth profiles of the people shaping business and investing.
You can find Colossus Review along with all of our podcasts at joincolossus.com.
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Chapter 3: What role does AI play in Spotify's product strategy?
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.
You're copy pasting prompts from Twitter or whatever. In an app like Spotify, how willing are people to get not lazy and really descriptive about what they actually want? What have you learned about the nature of people's laziness versus willing to put a lot of work in to get the thing that they want via that more rich uplink.
Yeah, so that's probably the most exciting thing for us of this generative AI age and the dual uplink paradigm. So previously, we mostly relied on some explicit input when you playlist. That's high value information. You are sitting there thinking, this song goes really well with this song and that song.
So if you think about it as labeling, even though you're playlisting for yourself, you're sort of labeling these tracks, at least in relation to each other. And you're putting a lot of effort in it. And that was and is our big advantage in music recommendations, even though generative recommendation systems are starting to take over from these more old school collaborative systems.
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Chapter 4: How does Spotify innovate with its 'bets board' process?
Yeah.
And that just more of our work is going to feel like we're working with a team, speaking to a team, using natural language to prototype things, to try things. And that will diffuse slowly through the entire, not only just the hours of the software developer, but the hours of each of the other functional areas.
Yeah, I think so. It's hard to see where it's going to land because you're somewhere right now, but we're pretty certain that that somewhere is on this curve. So you can be pretty certain that... The workflows you see right now are not going to be the same. And that's actually one of the problems. How much are we going to build for what we see right now?
When you know the models are going to be more capable, there are going to be different tooling very soon. So you don't want to overfit too much to the moment. A reasonable...
view of a modern company is that all of its data is exposed in real time and you have some tool on top like cursor or something else maybe different tools for different skills maybe a tool more the licensing team at spotify may have a different tool to reason over all the contracts and quickly say do we think we can do this in that market and what do we need to license to do this but also the product team could ask that
licensing engine. We have 15 years of contracts, both current and previous. So this AI has a lot of insight into what music licensing looks like, more than any single person in Spotify, if you train it that way. There will probably be slightly custom interfaces for different skills. I'm not sure which is going to win out. But I think it's going to look something like that.
Right now, what we see people doing is they're sharing examples of prompts they used for the workflows and then prototypes that they've used. And that feels like very much a point in time. It's kind of hacky and different things.
If you were to calibrate the world out there, so few people have the inside view that you do, where you're excited by this technology, you're trying to embrace it, you're only able to embrace it so fast in the ways that we've described. In a 1 to 10 point scale or something like this, what score would you give how much this is impacting you so far? And how crazy this might get.
People are very excited that this is going to literally change everything. And there's some people that are actually worried about how much, how powerful it might be. From a practical real world standpoint, could you calibrate us a little bit as one of the few people that actually is both excited about it and also faces reality on a daily basis?
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