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 then at the end of that process, you may move some people around globally to make sure that you don't have something really important and there are two people missing. That's not optimal for the companies. You may move some people around, but largely we try to let people keep the resourcing. So lots of those problems that you run into.
But I would say the biggest risk with this model, it sounds nice if you're perfectly synchronized, The drawback of that model is that the planning is very expensive. So you have to be really good at planning And we've had to build our own tooling. We tried some external tooling for planning. That wasn't good enough.
And if the planning doesn't work, the overhead just grows very quickly versus execution. We execute for six months in order for the overhead to not get too big. But we can't go to a year. Then you can't react. A quarter is too short. It's too much planning overhead versus execution. So the planning is the thing that you have to get really good at. And I'm not going to say we're really good.
But we're getting better all the time. It's the thing that I care the most about, making sure that the planning is reasonably big. If you can do it, for us, it's critical because the whole of Spotify's product strategy is that we have large distribution closing in on 700 million MAUs for a single application.
And our entire strategy is basically, we decided this many years ago before it was popular, but you saw the Chinese starting to build super apps, whereas the Western world built one app per use case. We've adopted the Chinese super app idea and said, The hardest thing is going to be to get installs. You can see the average number of installs from the App Store dropping below one on average.
So distribution became the most important thing. And then we chose when we did podcasts and later books and videos, we're going to build it in the same application because then we can leverage our own distribution. But that has drawbacks. You have to have an organization because then everything is dependent on each other. You're going to ship one app to the app store and everyone is a stakeholder.
So you cannot divide and conquer. You cannot say, well, the book team, you can run ahead or the music team, you do this. No, everyone has to wait for everyone. So because of our consumer strategy, the company needed to be synchronized. And because it needed to be synchronized, we needed a really strong planning process. So it's an outcome of our consumer strategy.
And what I would say is it's not the right one. It's the right one for us. We're good at doing global changes, like changing the entire UI because we're synchronized. But we're probably much slower than other companies at trying something because it needs to go through a lot of planning. I don't think you can win in planning.
The best you can hope for is to be quite good at the important things and not so good at the less important things.
I want to come back to something very interesting you said around the adoption of some of the tooling that's at the most cutting edge. So let's take Cursor as an example of a company that now everyone's familiar with. $10 billion valuation. It seems like every software engineer is using Cursor to make themselves better.
But the way you framed it is so cool that, yeah, sure, but that's new code primarily. That's a fraction of one-eighth of their time. In the pie chart, it's a very small sliver that's being addressed by Cursor at big companies. Can you describe how you think this will play out? Because
It feels like the public markets especially, I guess private markets too, are very curious about how AI companies and products and tools will address this much bigger part of the pie that sounds like really hasn't been hit too directly yet.
There are a couple of things that are interesting that I don't think are super obvious. One is, it used to be that every developer started using Cursor. But now, I'm starting to see a lot more non-developers using Cursor.
And that's partially because the industry is starting to agree on this protocol called MCP, Model Context Protocol, which means that if you take your internal services and you wrap them in an MCP, you can speak English to your infrastructure. So if you're a developer now, or if you're a designer, for example, or a product person, let's say, you want to prototype a feature in Spotify.
One workflow is you take the existing Spotify, you double click and screenshot it, you upload that into cursor and say, wire this up, clickable in HTML. And then if your services are wrapped in MCP, you could theoretically say, now wire this up to my like songs feed or something.
And you can prototype even though you're not a developer because the infrastructure is wrapped in English language now through MCP. I think that's an important thing. So I think you're going to see many more people using Cursor than just developers. I'm starting to see, I had one of my PMs who is in Sweden. She's from New Zealand. Doesn't speak Swedish. She did her taxes in Cursor.
Managed to wrap the Swedish tax authority in an MCP. Not a developer. So I think it's going to grow outside of developers. But I think this points to what is actually happening in many of these big companies, which is why the startups can move faster. So if you think of a company like Spotify has tons of infrastructure. You have the database with play history going 15 years back.
You have who was in the family plan. That's one server. This is one data set somewhere. Your taste graph is a data set and so forth. Now here comes the big AI companies and they give you this reasoning engine. Some of them are open source. So basically for free, you get what is getting close to AI.
So now you have this thing that you thought would be incredibly expensive and you'll get it almost for free. It's a gift. You start using it. What is the first problem you run into? You say, how has my music listening changed over the last year? That's not exposed as an API. Because in the previous machine learning world, that data, the listening data 15 years back, it's on cold storage somewhere.
And an engineer would have had to do an SQL job that may have taken a week to pull it up. Then you would have trained a model. Then you put it back in cold storage. Now you want to be able to reason over that in real time. You need to expose all your data as APIs in real time. So actually, my biggest job to enable AI is not AI engineering.
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