ever wish your music app could like actually explain why it thinks you'll love a song?
Yeah.
Instead of just kind of throwing a bunch of tracks at you.
All right.
Well, that's exactly what Spotify is trying to do.
And today we're going to be diving deep into their latest experiment with AI.
Okay.
All about personalized narratives for music recommendations.
So like making it feel more personal.
Yeah.
Recommendations from a friend who gets your music taste.
Yeah.
That's pretty cool.
I actually read their blog post about it.
Oh, cool.
Contextualized recommendations through personalized narratives using LLMs.
Okay.
Now you're just using big words to sound smart.
Well, maybe a little, but LLMs are actually pretty fascinating.
All right.
So break it down for me.
What are LLMs?
LLMs are large language models.
They're basically the brains behind a lot of the AI stuff we see these days.
Like think chat, GPT.
Okay.
Yeah.
I've heard of that.
They're trained on tons and tons of data.
Right.
And they can generate text that sounds really human like.
That's so cool.
And even like hold conversations.
Wow.
Yeah.
So basically Spotify wants to use this conversational AI to tell us why they think we'd like a certain song.
Exactly.
So instead of just seeing an album cover, you might get a little blurb that says something like,
this band's latest single is a total metal core adrenaline rush.
Interesting.
It's almost like they're trying to like actually capture the way a friend would describe a song.
That's exactly the vibe they're going for.
Like making it more personal and engaging.
And you know what their early tests show that,
Like.
that these explanations actually make listeners up to four times more likely to check out the recommendation.
Wow.
That's huge.
But are they using any specific type of LLM?
Like there are so many out there right now.
Yeah.
There are.
They actually tried out a bunch of different ones, but they found that Meta's LLM models worked best for what they wanted to do.
Okay.
So what makes LLM so special?
Why LLM overall the others?
Well, for one, LLM already knows a lot about music, podcasts, just the whole entertainment world in general.
And it's also really adaptable.
Okay.
So Spotify can like fine tune it to do very specific things for them.
So LLM is the engine behind all this.
Yeah.
You could say that.
Interesting.
So how are they actually using it?
What are they doing with it?
Well, they're focusing on two big use cases.
First contextualized recommendations, which we just talked about.
Right.
And then they're using it to make their AIDJ feature even better.
Oh yeah.
I've heard about that.
It's like having your own personal radio stage, right?
Pretty much.
But with LLM's, it goes way beyond just playing songs.
Now the AIDJ can actually give you personalized commentary in real time.
I'm listening.
Like it's actually talking to you.
Okay.
So paint me a picture.
What kind of commentary are we talking about?
Well, could be anything really.
Okay.
It could be like a fun fact about the artist or the story behind a song or even connecting it back to your own
listening history.
Oh wow.
Like maybe the AIDJ remembers that you used to listen to a certain band a lot a few years ago.
And then it points out how the new song you're hearing reminds it of their older stuff.
Wow.
Yeah.
It's pretty cool.
That's super cool.
It's like the AIDJ is becoming this like musical buddy who's sharing all its knowledge and insights with you.
Right.
But you know, with all this talk about AI, I got to ask, are human music experts going to get replaced by algorithms?
That's the question, isn't it?
Yeah.
But Spotify is making it very clear.
Human expertise is still super important.
Okay.
They've actually got music editors working alongside the LLMs making sure the AIDJ's commentary is relevant, insightful, and you know, culturally sensitive.
Makes sense.
All that good stuff.
So it's more like a collaboration like human and artificial intelligence working together.
Exactly.
It's all about finding that sweet spot where AI can enhance human creativity and knowledge, not replace it.
Not like that.
So it sounds like Spotify is really trying to be thoughtful about all of this.
Yeah.
But I imagine there are some pretty big technical challenges when you're dealing with these massive AI models, right?
Oh, absolutely.
Like one of the biggest ones is just the sheer computing power that you need to train these LLMs.
Yeah.
That makes sense.
It's not just about having smart algorithms.
It's about having the infrastructure to actually support them.
So Spotify had to invest in some serious hardware to make this all happen.
For sure.
I mean, they had to come up with whole new systems and techniques just to deal with the demands of LLM training.
Like, you know, they even created a system that saves their progress during training so they don't lose everything if like there's a system failure or something.
That's smart.
You'll be a nightmare to have to start from scratch every time.
Totally.
But training the model is only one part of it, right?
Yeah.
They also have to figure out how to make all of this work smoothly for millions of users.
You got it.
Getting these AI features to run smoothly for so many people.
Yeah.
That's a huge challenge.
I bet.
Spotify is using a combination of these like lightweight AI models and some really clever optimization tricks just to make sure that everything runs quickly and seamlessly.
So it sounds like they're having to get pretty creative on the engineering side to balance the power of these AI models with the need for a good user experience.
Exactly.
It's a delicate balance for sure.
And you know, while we're on the topic of Spotify's AI strategy, there's another thing I wanted to touch on.
Their commitment to open source.
Oh, yeah.
That's a big one.
So how does open source fit into all of this?
Well, Spotify is super active in the open source AI community.
So they're not only benefiting from all the amazing tools and innovations that other people are creating, but they're also giving back and sharing their own advancements.
So it's like a two way street where everybody benefits from the shared knowledge.
Exactly.
It's a win-win for Spotify and the AI community as a whole.
I like that.
This has been a super fascinating look at all the technical stuff going on behind the scenes.
But let's zoom out for a second.
What does all of this actually mean for the average listener?
Right.
How does it change our experience with music and podcasts?
Well, I think Spotify's vision here is to help us connect with audio content in a much deeper way.
Okay.
You know, they want to take the guesswork out of discovering new artists and help us understand why we might love a particular song or podcast.
It's like having a personal music expert who just knows you inside and out.
That's a great way to put it.
And it goes beyond just convenience too.
Okay.
Imagine an AI DJ that's introducing you to genres and artists that you never would have found on your own.
That's actually what excites me the most about all of this.
The possibility of like breaking out of my little music bubble and discovering something completely new.
Exactly.
And this kind of personalized discovery could have a huge impact on the entire music industry.
Really?
How so?
Think about it.
If AI can help us discover new artists more effectively,
it means more opportunities for those artists to connect with their audience.
It's almost like it levels the playing field a bit.
Giving those independent artists a better chance of being heard.
That's the idea.
And it could even help us rediscover artists that we might have forgotten about
or find some hidden gems in our own music libraries.
Right.
Like we always talk about how vast the music universe is.
But now we have AI to help us navigate it and find those hidden treasures.
Exactly.
And all of this isn't just limited to music either.
They want to do the same thing for podcasts too.
That makes sense.
There's so many podcasts out there now.
Right.
It's overwhelming.
It can be hard to know where to even start.
Exactly.
So AI could really help cut through all the noise and find the shows that we'd actually enjoy.
And it can also help us discover shows that align with our interests,
even if they're not the most popular ones out there.
It's like having a personal podcast curator who's sorting through all the options for you.
Pretty much.
It's all about creating a deeper connection between the creators and the listeners
and building a richer audio ecosystem.
It sounds like a win-win for everybody involved.
But, you know, let's be real for a second.
Are there any potential downsides to all of this?
Or any ethical concerns we should be thinking about?
Oh, for sure.
I think one of the biggest concerns is this idea of filter bubbles or echo chambers.
Oh, right.
If AI is only recommending content based on what we've already listened to,
it might just reinforce our existing tastes and prevent us from discovering anything truly new.
So instead of expanding our horizons, it could actually limit our exploration.
Exactly.
And that's definitely something to be aware of.
I think the key is to find that balance between personalization and discovery,
offering recommendations that both align with our tastes
and challenge us to try something different.
We need that element of surprise.
That unexpected discovery is what makes music so exciting.
Totally.
Yeah, it really seems like a tough balance to strike, right?
Yeah.
Giving people what they want,
but also pushing them a little bit outside their comfort zone.
Yeah, totally.
And that's something that Spotify and I think the whole AI community
is going to have to really wrestle with as this technology gets better and better.
It's not just a technical problem.
It's kind of a philosophical one too, you know?
It really is.
So we've talked a lot about the potential benefits,
but are there any risks we should be thinking about?
Like anything that worries you about all of this?
Yeah, definitely.
I mean, besides the whole echo chamber thing,
there's also this question of potential misuse, right?
Yeah, pretty mean.
Like imagine an AI that's been programmed to push certain artists or genres over others,
maybe for commercial reasons or even political agendas.
Okay, yeah, that's a little creepy.
Yeah, it's kind of unsettling.
It makes you think about who's really controlling what we listen to.
Right.
Like is it us or is it the algorithm?
Exactly.
Yeah.
As AI gets more powerful,
we need to be having these conversations about the ethics of it all.
We need to make sure it's used to help people, not control them.
It's a good reminder that technology is never really neutral.
Right.
It always reflects the values of whoever created it.
That's a great point.
Yeah.
Wow, this whole deep dive into Spotify's AI experiments has been super interesting.
Yeah, it has.
It feels like we're just scratching the surface of what's possible with AI and music.
It really does.
It's such a fast moving field and I think Spotify's work with these LLMs is a perfect example of how
AI is changing the way we experience music.
Yeah, it's exciting.
So to sum it all up, Spotify is experimenting with these AI powered narratives to make music
and podcast discovery better.
They're using this thing called the LLAMA model from Meta.
Right.
And focusing on giving us more context for recommendations and making that AI DJ feature even cooler.
And it sounds like there's a lot of potential, but also some things to watch out for like the echo chambers
and making sure AI is used responsibly.
Absolutely.
Well, this has been another great deep dive.
As always, we want to leave you with something to think about.
So Spotify wants to make these personalized narratives that really get us.
But could AI actually help us break out of our musical echo chambers?
Could it actually introduce us to music we'd never find on our own?
That is the question.
What do you think?
Let us know.
Head over to our social media and tell us what you think about the future of AI and music.
We want to hear from you.
This is the Deep Dive, signing off.