Gustav Söderström: Catapulting Spotify to the Front of the AI Revolution

25:32And I think there are a couple of ways to think about it. One is, what do I think will happen in the world? and then also what is Spotify's view, regardless of what happens in the world. And I think there is going to be legislation at the end of the day and people are going to have to adapt to legislation. That will be the lower bar. And the legislation could be that, turns out, it's legal to train on anything. And then I think it's a market economy. Some companies will do that and then to compete, other companies will. And then that becomes the status quo. It could be that legislation says that that's not the case, that it emerges that, you know, you need to somehow reimburse or respect people who don't want their data to participate, right?

26:17So that will be the sort of the lower bar is what legislation sort of dictates. And I think it's very possible that legislation will dictate a reimbursement model. You know, you can compare to the Wild West of piracy that existed for a while. It doesn't really exist anymore. So I don't think the world has to be in that Wild West stage. I think some order will emerge. From Spotify's point of view, you know, as I said, our view is to have as many artists as possible on Spotify. And we actually want the artists to create more music, not to replace them. So from our point of view, we certainly respect their data.

26:56And, you know, regardless of if there are like loopholes that we could and so forth. and I think that's what you know I think that's why Spotify started was because the music model worked for consumers but not for creators so I think it'd be a step back to create a model that again works for consumers but not for creators so certainly for us the goal is to find a model where you can leverage the new technology and it works for the creator ecosystem them. But obviously, when disruptions happen, as with piracy, there is often a period of time first where it only works for one side of the marketplace.

27:37But that's not long term sustainable. Yeah, it seems like there could be legislation, but it almost seems impossible to build a system that could actually pull this off. So I don't know. I just wonder how that would play out. Maybe that's where crypto comes back. I was just going to say, yeah, some people like Fred Wilson have recently said that, you know, this could be the application for the blockchain, right? So everything, every piece of content that gets created basically gets minted to the blockchain and you can basically trace the provenance of a piece of content all the way back. I actually think it's an interesting use case.

28:14Yeah, I don't think he's wrong. I think it's potentially, I mean, for AI in general. So one of the problems you say, like, it's so complicated, right? Already, because there's the artists, there's the songwriters, there's mechanical rights, performing rights. You have so many different people involved, but it's only complex because you have humans doing it. If that was on a blockchain, you can make a billion transactions per day. It's actually not, it's just computation. So I think if you instrumented it programmatically, you could solve it. It just looks very complicated to humans. So I think he's right in that.

28:56And in general, I think the problem of authenticity, traceability, and who is who, you know, what is the fake content versus not, it is potentially one of the big applications for the blockchain. I agree with him on that. And it also seems like it would be made so much harder because there's so much content that exists today that was probably inspired by some other work that we don't have record of anywhere, right? I mean, this almost maps to the existing royalty and infrastructure we have in music today, which is so, so messy, right? And it's across so many different systems. I don't know. This almost sounds like an unsolvable problem, but I'm sure some smart people will figure it out.

29:38Let's talk about ChatGPT and sort of AI's impact on kind of product and business. ChatGPT was obviously the biggest story of the year last year, you know, reported to have in the hundreds of millions of users. is generating north of a billion dollars annually in revenue. It feels like - Pretty impressive first year. Yeah, crazy. You know, and so everyone talks about it like ChatGBT is sort of the iPhone of AI. It's like, it was the iPhone moment. But, you know, something that strikes me about this is it's a text conversation, right? You're chatting with an agent. And text as a UI seems like an outdated primitive, right?

30:20It feels like sort of a weird primitive to build a whole new platform and interface on top of. What do you think of that? So I agree. I think the premise that people use is you see it with the demos of, for example, the new Rabbit hardware. The paradigm that everyone is looking for is intelligence replaces apps or replaces separate UIs. And where does that come from? Well, if you have an executive assistant, for example, then you can say that you want something achieved and that person can work across several services and UIs to achieve that task, right? So I think it's a very rational thought that intelligence would replace the need for several different apps.

31:10And I think the best analogy is to anthropomorphize it and say like, if you had a really smart person helping you or working for you, what could you achieve? and then you could assume that AI could probably achieve that if it just keeps scaling. So that seems pretty reasonable to me. But I also think that people take it too far. And, you know, anthropomorphizing has its benefits because you can predict the future. Like, okay, so if I can do this with a very smart friend now and you think AI is going to get that smart in a year, then, okay, I can build that product in a year. So it's helpful. But the problem with anthropomorphizing is that you can only predict what a human could have done, not what an AI that can also read images or is much faster than a human could have done or, you know, that can also render images or, you know, generate it.

32:02So it limits you. I think it's both useful to anthropomorphize in the near term. I think it's dangerous to anthropomorphize and think it can only do what a human, what a single human could have done in the long term because it can probably do much more and things that a human could never do, right? So I think an interesting example is, and the framework that I use is, if you take that path of like, you know, I need to use five apps to complete this task. Now I have this UI, this AI that I can talk to through text or voice, and it completes them, whether it uses API or it uses sort of these, you know, it interacts directly with the UI as for example, the rabbit action model.

32:42It doesn't really matter. I think that seems very likely to work for productivity tasks, where there is something you didn't want to do. That gets a lot better, right? And it works with the anthropomorphization. Like you ask someone else to do it because you didn't want to do it. But I don't think it's that helpful when you think about entertainment. It makes no sense for me to ask my friend, like, could you just go and watch Netflix for me for a while? Because I really don't have time. I'm like, can you go and listen to Spotify? Like the whole point with entertainment, I think it works where you want to save time.

33:18Productivity is about saving time. Entertainment is about wasting time. So I don't think it is a very good framework for entertainment services. And so an example would be, if you look at something like Spotify, of course, you could say like, hey, Spotify, play this song. But that's a very narrow use case. And you can already do that with like a Google Home or something. but if you imagine that you had to say like hey ai can you go and scroll through the front page and tell me what you see so that i can choose something then a visual ui is going to kick ass it's going to be so much more effective because it's two-dimensional you have images right so i do not think that text will replace visual uis that doesn't make any sense to me i think that's taking it too far uh i still think like a user interface that can present images moving pictures and actually play sound is going to be vastly better than an AI in between that tries to relate what you would have seen if you looked at the UI, right?

34:18So personally, I don't think a Netflix or Spotify or Instagram is gonna sort of disappear into the background and be disintermediated by a text box. It doesn't really make sense from a productivity point of view because you're not trying to save time. You're actually trying to waste time. what I think is exciting though so if you don't anthropomorphize and you think instead what could a maximum product be not what could a human have done if you talked to them over the phone but if you said like which is a helpful model for some productivity tasks but not for entertainment I think you know the promise is an AI that can understand your intent when you speak and other inputs if you have them but I certainly think voice is a text is a very strong input, but can also render user interfaces that are much more dynamic.

35:09You know, today user interfaces have to be very specific and repetitive because they're not generated on demand, right? They're pre-programmed. So you have to think through, you know, which views you want in an app and so forth. You could imagine far into the future because these things can generate code. If you squint a little bit, you could almost imagine that you're simulating an app like Spotify and it's literally like rendering the app or the code in real time. And I don't think - Totally dynamic UI. Yeah, I don't think it will go that far, but you could go some ways. You could have like the search view could start becoming more dynamic.

35:46And sometimes it renders images if that's helpful for the search result. Sometimes it renders text fields. So you could imagine that the whole thing gets more dynamic and intelligent. Do you think the starting point for applications though become either text to voice? Like, is that now the new home screen, right? You go to your computer, your phone, and it's just a prompt, right? And that sort of starts everything or whatever you're going to be doing, be it wasting time or spending time or saving time. No, I don't think so. Because again, a prompt is only helpful if you know exactly what you want to do.

36:21If you have a task in mind that you want to do faster, an input box is great. But, you know, then if you take that, if you think that's enough, then Spotify should just be the search box on the front page. Right. And we know, we've tried. If you put the search box there, people struggle what to listen to. Right. People don't know what they want to do all the time. So I don't think it will be only that. But I do think that will be just a search is a big, big part of any service. It will be a big part of your life for many tasks. And back to like anthropomorphizing, think through like if If you had a super highly intelligent, very effective friend, you know, that worked for you for free, what would you use them for versus what wouldn't you use them for?

37:04You know, people are talking about AI as a platform shift and trying to compare it to previous platform shifts. Oh, it's the new iPhone. Oh, it's the new cloud computing. I tend to think of it as a resource like capital or labor or work, right? do you see it the same way and sort of how do you think it'll impact the business model of software moving forward so i do see it sort of the same way i think it's good to try to think about something different i think one way to think about ai is that you know we have a mechanical labor and that that has been automated and what's happening now is that sort of cognitive labor is being automated and it's a good framework to think through you know what happened when mechanical labor got automated and then try to figure out what might happen when cognitive labor gets automated so it's like cognitive machines in addition to sort of mechanical machines i think thinking of it as as literally intelligence like units of intelligence is interesting so you know it sounds weird.

38:15Like, what do you mean with units of intelligence? And like, you know, you're buying units of intelligence on tap. That makes no sense. And then you think about your HR department and what they're doing. And they're hiring like units of intelligence every day. That's what you're doing. You're trying to get as much intelligence into your company as possible, right? And you're paying a lot for the best intelligence. You have all these tests, you know, to figure out, you know, and at the core, the way you try to compete as a company, is to have the most intelligence in-house. People talk about talent, density, and so forth, right?

38:49And so if you think about it like that, it is intelligence, but now you can buy it sort of on tap or per unit. Then I think that can be helpful. Because the tricky thing to your point is it is so general. Intelligence is by definition completely general. They almost need to think of it as some sort of resource. And so I literally think of it as intelligence. But you get intelligence and you get execution, right, in a sense, for certain tasks. So it really is, the HR department hiring people is a really helpful analogy. It's scary to think just how massive it could be in this new framework, in this new model.

39:35I think what's going to happen sort of to sort of preempt the question maybe of labor and so forth, because you could say like, oh, now you can buy intelligence. The second that's one cent cheaper than hiring intelligence, you're only going to use computers. I don't think that's going to happen. It's the same as with musicians. That's not what we see today. What we do see very clearly is like a developer with Copilot is more productive than a developer without. So if you can buy more intelligence for that developer, if you do a Ray Kurzweil and say like, hey, the nanobots are here, you cannot buy NeoCortex in the cloud.

40:10Would you as a company buy more Neocortex for your developers? Yes, you would. And I think buying Copilot is like a weak analogy of buying more Neocortex for your developer. And so I think companies are going to start spending more and more on their existing staff to make them more and more productive. But I don't think in this competitive economy, you're actually going to reduce your labor force because then someone else is going to take their OPEX and outcompete you. So I think the pressure is to make your existing workforce more and more productive. Speaking of intelligence, GBT5 is on the horizon.

40:48What do you think the impact of this is going to be? How much more intelligent do you think it's going to be or feel than what we experience today? I mean, I don't know. We've lived through a couple of these now with GBT2, GBT3, 3.5 with RLHF and then GBT4. So to my previous point, in a way, I think it's pretty predictable that you will be blown away. So you won't be as blown away because you sat there and expected to be blown away. It's this weird thing when someone tells you a move is amazing. You're not as impressed as if you had no expectations. So personally, I'm expecting to be blown away and I probably will be.

41:32But that actually means it's more predictable. What I think is, I mean, I don't know that much. or actually I know almost nothing at all about GPT-5. I only know what most people know. My expectation is that it is more predictable because it's now going to get predictably better at existing dimensions. What happened at previous times was that you had these emergent capabilities where it did completely new things, right? And that is what surprised us. You know, when you have like chain of thought, you're like, Jesus, this thing is reasoning, right? And it couldn't do math at all, like not at all.

42:06Couldn't do one plus two. And then like, oh, I can do math now. You can reason around math and theory. So I don't think we'll get as surprised. I'm hoping I'm wrong here. Be interesting. But I don't think we'll be as surprised about completely new capabilities. We'll be blown away by how good the existing capabilities got. You know, they will be way past most humans. And I think you see this. Why am I saying this? Well, because if you look at the tests that are out there, These models now are pretty good at almost all human capabilities. So I don't actually know what it would be that emerged that it can't do at all anymore.

42:42It can sort of do everything. But if you compare it to image recognition, there was this forever image recognition didn't get any better. Like it barely couldn't recognize images at all. It was close to random before sort of deep learning came along. And then this race started and it got better than like random. and then you've got 60, 70, 80, 90, 95%, 96, 97%. And actually humans are only like something like 96, 97 % and it beat humans. And I think we're already on that ramp up trajectory of the dimensions itself, whether it's reasoning around physics and math or taking SATs or talking about legislation or something like that.

43:29So I would be, what would surprise me is some completely new ability that we hadn't seen at all. What I'm expecting is to be blown away by how good the existing capabilities now are. And, you know, hallucinations will be very few and far between. The reasoning capabilities will be much stronger. You can probably, you know, spend many tokens on getting to deeper reasoning. What would be really cool, which is very speculative, I think, is I think a lot of people in AI in general, for obvious reasons, are very interested in math. And I think like theoretical math is like the ultimate frontier. Can you create new math proofs that didn't exist before?

44:14So maybe one of the abilities, if I'm going to correct myself, that isn't really proven yet is can you actually make something new that didn't exist? And some people would say, of course you can. you just raise the temperature a bit and then you get some text that never existed. This is new. Other people would say like, it's not really new. You know, I want some sort of deductive reasoning, like a law of physics, not just something that was very close to the law of physics or something that existed. And I think what is interesting now is, you know, these models are starting to get this ability to reason at a higher level.

44:51Some people claim it's not reasoning. It's just sort of faking reasoning other people claim it's reasoning but if you accept that it is reasoning for a while if you if you um or if you uh pretend that it is reasoning for a while then you have this emergent space on top of of just the pattern recognition that is happening and you could imagine sort of doing something like reinforcement learning on that space where you ask it to reason through hundreds of thousands or millions of possible math theorems based on everything you learn about math But the beauty of math is you can then test if the theorem was true.

45:28It's one of these one-way functions that is hard to come up with, what's easy to prove if it's true. It's like crypto, right? It's very hard to break it, but you can very quickly verify it if it's true. So then you could imagine this agent that using an LLM tries to reason through, just brute force reason through, you know, all possible math theorems, sort of randomly like a reinforcement learning process. But it can verify. And it's going to reason based on pattern recognition, like it's seen this type of reasoning before. And maybe it could come up with math proofs that just never existed before.

46:09And then you could verify offline that they were true theorems. That might happen. I think an interesting analogy here is this mathematician named Ramanuyan, Indian mathematician, who is a great movie capturing this. But basically, he grew up very poorly in India. So self-taught mathematician just on the streets of, I don't remember which city in India. But he somehow developed this incredibly strong instinct for math. and it was discovered by, his math was discovered by some British mathematicians and he was sort of brought to England. But he had never learned proper math. So his theorems came to him in dreams from a God.

46:58And so when he was asked, like, how do you know this theorem is true? He answered, well, of course it's true. Like a God told me, right? And it turned out that actually many of his theorems were true, but not all of them. And then he learned formal math and he could figure out how to prove which of his dreams were true. So you could imagine AI, and what is cool about Ramanujan is, I think most mathematicians would say that math is not pattern recognition. It is true logical reasoning. But then you can't explain Ramanujan. He didn't have the logical reasoning, the form of math. He just had dreams.

47:29So maybe even math is sort of exploring exploration of pattern recognition. But then you need this mechanism of form of math to verify which is true. So that's a long-winded answer. Maybe math will be the thing that surprises us with GPT-5. OpenAI keeps talking about how AGI is coming. You know, we're getting closer and closer. Maybe that'll be GPT-5. But at this point, like, what is AGI? And does the definition even matter? I mean, you mentioned, you know, the models are reasoning. Are they reasoning or does it just seem like reasoning? And like I said, does it even matter? If we imagine that it's reasoning, isn't it kind of the same thing as it is reasoning?

48:13So I actually think you're right in that it doesn't matter. I think if you ask about the definition of AGI or even just I, intelligence, I think the best definition I've heard is probably Marcus Hutters of Hutter Prize fame. And he says that intelligence is the ability to achieve complex goals in a wide variety of environments. which sounds like a mouthful, but it's actually pretty straightforward. It's just the ability to achieve goals, complex goals in different settings. And humans are at some level on that spectrum. And actually, we're not at the same level. Einstein was quite a bit sharper than the rest of us, right?

48:56So it's not even a level to your point of like, does it really matter? So I don't think it matters. I think we've had super intelligence among us, you know, the Einsteins of the world. And that is also pretty promising because we seem to be doing fine. You know, he came up with relativity and you can argue that new innovations, you know, like nuclear and so forth came out of that. And maybe that will ruin us someday. But certainly everyone doesn't have the same intelligence. We're on the spectrum there. And that seems to work. It's unclear what happens if you go really, really, really, really far on that spectrum.

49:33I can't say that I know. But for that reason, I agree with you. I don't think just passing like the human level really matters because some humans already passed the average human level. Achieving goals in a wide variety of environments is interesting. It makes me think of these products like, well, products like the rabbit and more broadly, this notion of an action model, right? Everything right now, we're talking about language models, the rabbit and, you know, people that are thinking about that type of innovation are talking about action. models. When do you think we're going to make the leap from generation of content and writing and images and all this stuff to action taking?

50:15And what will be sort of the nearest term implications of this? Well, I think, you know, to your point of the rabbit and so forth, like it's already happening just very early. So, you know, one answer will be next week, maybe. Who knows? But But my point is, I think it's quite imminent. I think someone more skeptical would say like, well, we've had the idea of agents for a good while now, and they're really cool, but they're not that useful yet. I think that's also fair. But it seems pretty straightforward that at least, you know, semi-specialized agents that help you complete like something that would have required two tasks or three tasks or four tasks for specific use cases like traveling.

51:00Like, I don't see how it couldn't happen. So I think these agents are sort of going to sneak up on us. And I'm not so sure you're going to realize that you're talking to an agent. It might just look like when you used to talk to three services, you're now just talking to one and it's three behind the scenes. Right. And then, you know, do we get very general agents that does everything for you? Or is it going to be that, you know, each sort of service has its own agent? I don't know. But I think like action transformers and taking actions is going to happen very soon. I know that inside companies, I think Amazon published a paper this fall where they are talking about sort of these orchestrator LLMs.

51:46You know, from having worked at Spotify that a backend in machine learning is usually like, it's not the algorithm. It's actually like hundreds of different mini algorithms, right? They're all like individually tuned and so forth. and then together they sort of produce something that is almost like alchemy. You don't know how all these different algorithms are going to interact with each other. So you try to tune and understand. But the result looks like an algorithm. It's actually many algorithms. I think that is changing. I think people are starting to put these sort of orchestrator LLMs in front of their system that actually is more akin to like a single brain or algorithm that does take all the signals from you.

52:27and understands that, looks at the embeddings of your user history, for example, and then tries to talk to your APIs. So there's a layer in between the user and your APIs, which is an LLM that says like, oh, Mike is now saying this to his Alexa or typing this into Spotify or something. And then the LLM, instead of hard-coded rules for what will happen now, call this API and that API, the LLM says like, well, based on what Mike said and reasoning through chain of thought, I should probably call this API and do this for Mike and then this and then send this back to the screen. And that is an agent.

53:00You don't see it. You're not going to understand it's an agent, but it is an agent inside something like Spotify that reasons through what you did in the UI and actually talks to the backend. So I can see a future where a company builds like lots of capabilities, lots of APIs internally, and then just has a really big LLM fronting the user. And the LLM actually reasons through which APIs to call and what to do for the user, which is pretty cool because it could then do things that the programmer didn't necessarily predict, if you understand what I mean. And I think certainly if you want to build a voice assistant, that seems much more scalable than building these one-off custom flows for every possible use case that you could imagine.

53:41So I think agents are coming in various forms, and they're sort of already here in some forms, even though you don't see them. Speaking of agents and taking actions and AGI, I think about this analogy that Ilya from OpenAI gave about the risk of AI, comparing it to the threat of humans to animals, right? When humans have a goal that indirectly impacts animals, humans often don't stop to think about that impact. How do you think about AI and agents taking actions when the threat or the risk of the actions they take impact humans? yeah so i would just start off by saying i'm not particularly concerned about the existential risk that that many people are for various reasons and you know you probably shouldn't listen to me because i'm not really an ai expert but i am more concerned about the practical risks in the near term okay um you know it's like when you introduce anything new whether it's like uh you know, cigarettes, but that's a bad analogy because it's only bad, like cars, was going to have some negative effect on people's health because they stopped walking, right?

54:56I think there will be consequences. I think it's dumb to be naive and say that this is the one technology which has no risks and will have no consequences. Of course there will have consequences in their risks. So first of all, I want to say, I think it's very reasonable to be careful and invest in safety. And back to prediction, don't just try to predict the good things that could come, which are many, like AlphaFold and solving cancer and so forth. I think that will happen. But also try to predict the bad things and try to prevent them. That seems very reasonable to me. But on the timescale, I'm more worried about the near-term dumb AIs than the potential risk of the very smart future AIs.

55:33And so one question to ask yourself is, do you think the problem with the world today is that we have too much intelligence or too little? and if you ask me that question you know are you most worried about the most intelligent people around you the least intelligent people around you which caused the most havoc and uh if you think about you know ilia's point about who cares about other species is it that you know it seems like it's the smartest among us are the ones who seem to sort of care the most for other species because they understand that actually if if that bee over there goes extinct eventually that's my environment and my climate if you're smart and really understand causality or at least correlation very deeply you're going to get more careful it's actually when you're not that smart that you're dangerous right so from a very high level i would think that more intelligence is a good thing in the world the problem isn't that we have too much intelligence i think so one maybe the problem is to get these ais smart enough to start caring about and understanding its full ecosystem including the humans that actually are building and powering it.

56:43This isn't even smart from an AI to make humans go extinct. Certainly not too soon, right? So if you took in the very far future where somehow the AIs are fully self-sufficient, they run and build somehow all the factories with robots, then maybe. But I think that's extrapolating too far. In the foreseeable future, it'll be very dumb for the AI. It's only if it doesn't understand enough that it's going to make humans extinct. So maybe the big risk are the stupid AIs. and i don't know about this is very speculative but you know humans created empathy as sort of a feeling and you can argue that that's sort of some sort of divine good but if you're if you're not sort of creationist it has to have emerged from evolution somehow there must be some benefit to empathy and i think that since these you know since these things are trained on our thoughts and the task is to predict what we you know what we think what comes next and empathy seems to be a very important part of how we reason if you read all the text on the internet and you try to predict the next token if you want to predict how we think and how we come to our conclusions in these sentences it doesn't seem unlikely to me that these models would would develop or at least emulate something like empathy in order to achieve its goals right And I think this is an important point I want to make, that while people like Max Tegmark and others, they often call this an alien intelligence, right?

58:19And it scares people. It's like the aliens already landed on Earth. They're here now. It's called AI. I don't think that's the right way to think about it. I actually think it's the opposite. This intelligence is literally physically modeled on our brains and neurons. We really looked at the brain and, you know, first and foremost, the eyes. And we modeled the artificial neuron after the biological. So physically, it's modeled after us. And then we trained it on all our thoughts, exactly how we reason. So this is probably by far the most human intelligence we'll ever encounter because it is physically built.

58:59It works physically like human intelligence, and it is also trained on human intelligence. So, you know, I think calling this alien intelligence is the wrong way to think about it. And alien intelligence would be something vastly different. Everything we see is that these things get more and more like us. So if it's smart enough and it tries to model us, hopefully we'll understand its ecosystem, just as we are beginning to do now that we're getting smarter as a society, and we're trying to correct our ways instead of eradicating ourselves. So that's how I think about it. Gustav, this is a fascinating way to end the conversation and frankly, an optimistic one.

59:41Thank you so much for the time today. You are a very busy person, so we really appreciate you coming on. Thanks for having me. This was a great conversation. Thanks for listening to my conversation with Gustav for Generative Now. If you liked what you heard, please do us a favor and rate and review the episode. And most importantly, please subscribe to the show so you get notifications every time we come out with a new episode. All that stuff really does help. And if you'd like to learn more, you can follow Lightspeed at LightspeedVP on YouTube, Twitter X, LinkedIn, Instagram, anywhere else. Generative Now is produced by Lightspeed in partnership with Pod People.

1:00:18I am Michael Magnano, and we will be back soon with another fascinating conversation. See you later.