Can AEO Trigger Music Discovery for New Artists?

When an artist has no audience yet, the metadata has to do the introducing. That’s a machine problem, and it looks a lot like the one I already solve.

Sorilbran Stone

Visibility Engineer · Founder, Five-Talent Strategy House

Published June 26, 2026 Last updated October 12, 2026

Understanding how to trigger discovery is as valuable to musicians as building a fanbase. And… I think my black belt in AEO would actually translate pretty cleanly to recommendation engines on music streaming platforms.

What Discovery Looked Like, Historically

Back in the day, albums dropped on Tuesdays, so Tuesdays were a big deal. I loved reading Billboard and R&R to see what was coming out, and it wasn’t unusual for me to head to my local record store that same week to pick up a new release.

As a rock fan in the ’80s, I also got wind of new music through the lyric sheets printed in rock magazines.

And then there was the Top 8 at 8 on FM 98 in Detroit. That list was mostly proven hitters, with the occasional new song landing on the list with a bullet. Or whatever OG deejays said.

My point is, music discovery usually came through known channels: conversations, magazines, radio deejays. And those channels were typically architected by industry insiders crafting trends, not by your personal tastes and preferences.

Today, Recommendation Engines Drive Music Discovery

Music discovery doesn’t work that way. Not anymore.

While I do find out about new music through links from friends and the praise team at church, most of the time my next favorite song (the song that makes me lose my breath and stop being able to focus) is gonna find me through the recommendation engine of whatever platform I’m listening on. I’ve come to expect it. I curate my engagement on streaming platforms to make sure of it.

Back in my Spotify days, the platform was so good at matching music to my taste that I’d spend Friday mornings just listening to the new music playlist and thumbs-upping anything that hit. Anything I wanted more of. Now, YouTube Music is my go-to.

Preamble’s over. Let’s get to the point.

River Crombie and the Sync License Rabbit Hole

Over the weekend, I was thinking about music: my music, the music of my musician friends, and how critical streaming platforms are for discovering music. Which got me thinking about a song that’s been stuck in my head for months. River Crombie’s version of “Simple Man,” the theme song for the Amazon series The Terminal List: Dark Wolf. Love that show. Like, LOVE it.

The day before yesterday, River Crombie showed up in my YouTube feed while I was watching neuroscience videos. So I clicked. Then I clicked another one. Then another one. Nirvana’s “Heart-Shaped Box.” The Cranberries’ “Zombie.” Radiohead’s “Creep.”

A bunch of emotionally heavy songs stripped down to an acoustic guitar and one guy sitting in a corner somewhere, singing in a way I can feel. Now, I am a sucker for boys with guitars. Always have been. Roots music. Americana. Folk. Soul. That whole world. My grandfather was a blues songwriter. Never saw him without his guitar. So, it’s in my bones.

But as I’m looking at River Crombie (a new boy with a guitar), I notice almost everything I’m seeing by him was uploaded in the last one to two years.

And my first thought is: Who is this guy? Did he just come out of nowhere?

Then my brain jumps straight to licensing. How did his version of “Simple Man” end up as the theme song? Why not just use Lynyrd Skynyrd? What happened there?

And now I’m down a completely different rabbit hole, trying to understand sync licensing, publishing, and music placement.

The Metadata Problem

The funny thing is, I wasn’t actually interested in licensing. Not really. It’s a hot topic with indie musicians, and River Crombie’s a good example of why. I didn’t know the guy before I heard his voice on my favorite show. Now I’m ready to buy him coffees on Patreon.

But an idea stood up in my mind as I was thinking through his Americana roots. How did the music director (or whatever that person is called) find River’s song? If it wasn’t a personal relationship, could it have been an algorithm? And if so, what data had to be tied to that song for machines to surface it?

Because when I look at a lot of musicians I know, they don’t really think about metadata. They think about songs. The song is done. The album cover is nice. Let’s make a video. Let’s upload it. Move on.

But then I compare the Song Credits tab for a local Detroit artist to what I typically see in the Song Credits tab for bigger indie artists, or even major artists. And there’s a huge friggin gap.

That’s when I saw the gap that needed to be filled. And in that gap, just enough space to build a bridge connecting my current world of SEO and AEO to the world of music distribution.

Discovery Versus Visibility

I took the conversation to ChatGPT. Tell me what information is needed, Maverick. What do distributors usually ask for that you need to have on hand? Maverick gave me a thorough breakdown of the info needed. Information audiences would need access to. Information that would help songs get played.

But something in the framing was bugging me.

The entire conversation was built on the assumption that the band had an audience to play for. That they’d already built a fanbase and were releasing music to it.

But what happens when they haven’t built a fanbase? We’re not judging whether they should build one. Every brand needs an audience. But the reality is that many musicians don’t build a big following before they release music. They release the music and hope folks will stream it when they find it later.

So my mind shifted. Assuming the musician has no audience, how do we get the machine to surface their music to the right audience so they can start building one?

If we treat YouTube Music’s never-ending feed like the social media algorithm it is, how does one need to tag the music (or content, as I think of it) so that fanless musicians still get surfaced in data-driven recommendations? Because if I can figure that out, I can solve machine problems for musicians, too.

That’s when my mind moved from visibility to triggering discovery. Making music recommendable when no one is looking for it. That’s not a people problem. That’s a math problem. And Mama loves math problems. These kinds, at least.

Rethinking Distribution as a Discovery Strategy

At that point, I shifted my thinking about distribution from access and visibility to discovery.

Visibility is getting seen. Discovery is getting matched.

A song can be sitting on every major streaming platform and still be functionally invisible, because the system doesn’t know what to do with it. That’s the part that has my attention. I’m trying to understand how the recommendation systems inside music streaming platforms decide who should hear a song first.

Musicians know to tell people how to get the song on Spotify. Get the song on Apple Music. Get the song on YouTube Music. Get the song on TikTok. Get the song everywhere people listen to music.

And yes. Fine. Great. Necessary. But being available is not the same as being discoverable.

What if nobody knows who you are? What if you don’t have an audience waiting for the release? Then the only path to getting your music heard and appreciated is making it findable. In a situation like that, the metadata has to do a lot of the heavy lifting, helping the machine answer three questions:

And if that’s true, the quality of the metadata matters a lot more than most musicians probably realize.

A Million Genres I Don’t Know

That led me into another problem. There are entire genres that didn’t exist when I was active in music. Or maybe they existed and I just wasn’t paying attention. Amapiano. Bedroom pop. Alternative R&B. Neo-folk variations. Indie subgenres. All kinds of things. Things I know exist, but couldn’t pick out of a lineup.

The first time I even paid attention to Amapiano was when I was trying to figure out how to categorize Willow’s Empathogen. One question led to another. One genre led to ten more. One artist led to twenty more.

And now I have this growing realization: if I’m going to help musicians with publishing administration and distribution (i.e., teaching machines about musicians), I need to get much better at understanding genres I don’t personally listen to.

Genre is metadata. It’s classification and discovery infrastructure. It’s taxonomy. And if I don’t nail the primary and secondary genre (if I go too broad, or too old, or too lazy), the song may get sent to the wrong shelf.

That matters because listeners don’t only discover music by genre anymore. They discover it by vibe, by mood, by use case, by memory, by identity, by emotional state, by proximity to something they already love.

I have to know those signals and classifications like the back of my hand, so I can teach the machines where a song belongs before enough people have listened to it to generate behavioral data.

And for a new artist with no audience, that early classification may be the difference between getting tested with the right listeners and disappearing into the catalog.

The Secondary Genre Question

What I’m particularly interested in is the secondary genre. Most of the musicians I know are older musicians. Their work tends to come out of traditional genres: R&B, soul, gospel, blues, country, rock.

But modern music rarely stays in one lane.

The first time my daughter played Kane Brown for me, I immediately said, “That’s not country. That’s a Timbaland beat.”

She thought that was hilarious. “You’re such a Boomer, Mom.” I’m not. But I was serious. I could hear the old-school syncopated rhythms and unexpected pauses in the percussion track (a style Timbaland made famous in the early 2000s) laced all up and through Kane Brown’s “country” song.

Same thing happens when I listen to Chris Stapleton. Tell me “White Horse” isn’t giving Richie Sambora vibes and I’ll smack your idiot face. Country music getting an ’80s arena-rock makeover is making me like country music.

But then there’s the subgenre my friend contemptuously calls “face-tat trap country,” which…

[sigh]

Okay. Whatever.

But I’d need to know that language (trap country) to help machines identify a song as a trap country song. So it doesn’t end up in the feed of a sixty-three-year-old George Strait fan.

So now I’m asking a different question: How do recommendation engines think about those overlaps?

A country song may pull from gospel. A soul song may carry folk structure. A worship song may have pop production. A blues record may feel like classic rock. An Americana artist may belong beside an acoustic R&B artist more than beside someone who wears the same genre label.

So when a musician picks one genre from the drop-down menu and hurries through the rest of the fields when pushing a song out through a distribution service, are they accidentally flattening the map? Are they telling the machine less than it needs to know?

That’s where the secondary genre starts to feel important to me. It refines the routing.

The primary genre may tell the system what world you come from. The secondary genre may tell it who else might love you.

Looking for Metadata in Spotify

At some point, I decided to stop theorizing and just go look. I opened Spotify. Stephen Day was already on my screen, so I clicked through. I was hoping to find a place where artists could describe individual songs. Some kind of field where you could say, “This song sits somewhere between southern soul, acoustic pop, and ’70s singer-songwriter sadness.”

I didn’t find that.

What I found instead was his bio. And Stephen Day’s bio was fascinating.

Different sections of it talked about different things. Growing up in church, his father being a pastor. Southern roots. Then a whole section dedicated to influences. Then another listing the artists he’d toured with.

And I remember staring at it thinking, “Holy crap! This is adjacency architecture.”

The bio wasn’t just a biography. It was metadata written in paragraph form.

Everything I hoped would exist at the song level was being established at the artist level. The bio was helping the system understand where Stephen Day belonged. Who he sounded like. Whose audience might overlap with his. Who he should stand next to on the shelf.

Important friggin find. Glad I looked.

If you know anything about me, you know I’m a stickler for a founder having a good canonical bio that helps machines understand who they are.

But here’s the twist. Companies are trying to find their blue puddle and stand out. Musicians are trying to help people and machines understand who they’re most like, not how they’re most different. Because music is a vibe, and the goal of a streaming algorithm isn’t to find the unicorn. It’s to match listeners with songs that are least likely to kill their vibe.

In a machine-mediated discovery environment, a musician’s bio is where context and adjacency get built. It’s where they name the lineage and surface the sonic neighborhood. It’s where they create recognizable adjacencies for humans and machines at the same time.

That is architecture.

How did I not see that before?

The Problem Is Still Recognition, Though

The cool thing about music is that each song gets its own set of data points. The song itself becomes an entity. (I talked about this a bit in the AC/DC licensing blurb in this article on the conditions needed to become recommendable to machines.) Which means a song can be surfaced to listeners who may never get recommendations for anything else in the artist’s catalog.

The entity architecture of the song is as important as the architecture of the artist. But at the end of the day, the goal is the same: recognition, machine legibility, and helping systems understand what they’re looking at.

I started the day thinking about music distribution. I thought I was learning about publishing administration. I thought I was figuring out how to help local musicians get more organized so they could find more ways to make money.

And I am.

But underneath all of that is the same problem I’ve been obsessed with for years.

Machines don’t recommend what they don’t recognize.

Whether we’re talking about a person, a company, a city, or a song, the challenge is remarkably similar. You have to teach the system what the thing is. You have to teach it what it’s adjacent to. You have to teach it where it belongs.

And that means someone like me, who’s spent the last 15 years in SEO and the last two in AEO, and who reads music as content to optimize, can be valuable to musicians.

As unsexy and unmusical as that sounds, being machine-readable is becoming more and more important for independent creatives.

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ABOUT THE AUTHOR

Sorilbran Stone

Visibility Engineer, founder of Five-Talent Strategy House, and lead of the Detroit Media & Innovation Lab. She helps growth-stage businesses, expert-led brands, and creative companies build the machine-readable infrastructure that gets their ideas, IP, products, and innovations recognized, accurately attributed, and surfaced across search, AI systems, and recommendation engines.