A few minutes into Bill Simmons’ Yacht Rock: A Documentary, it hits you—how did an entire genre emerge decades after its prime, and how did music fans collectively agree on what belonged in it? The smooth, polished sound of late ’70s and early ’80s artists like Michael McDonald, Steely Dan, and Christopher Cross had always existed, but it wasn’t until much later that it found a definitive label: Yacht Rock.

While music historians and passionate fans did the heavy lifting to define the genre, one can’t help but wonder—could AI and knowledge graphs have made this reclassification easier?

The Challenge: Identifying a Genre That Wasn’t Originally There

Music streaming services rely on traditional genre classifications—classic rock, soft rock, adult contemporary—but listeners often crave something more specific. When the Yacht Rock label gained traction, songs that fit the vibe were scattered across different categories, making them harder to find in one place.

Reclassifying decades-old music into a new genre presents a challenge: how do you group tracks together in a way that feels authentic and accurate without manually sifting through millions of songs? Traditional tagging systems, which rely on static metadata, fall short of capturing the nuances of style, instrumentation, and cultural context.

This is where AI-driven content tagging, large language models (LLMs), and knowledge graphs could have played a role in defining Yacht Rock with greater precision and scale.

Step 1: Using LLMs to Identify the Characteristics of Yacht Rock

Imagine training an LLM on music reviews, artist interviews, and fan discussions to determine what makes Yacht Rock unique. The model could identify key descriptors, such as:

• Smooth production with jazz and R&B influences
• Frequent use of electric piano and saxophone
• Lyrics centered around escapism, love, and sailing themes
• Artists like Michael McDonald, Hall & Oates, and Kenny Loggins

By comparing these attributes to existing song metadata, AI could have surfaced tracks that fit the Yacht Rock profile—even if they were originally categorized under “soft rock” or “pop.”

Step 2: Structuring Knowledge with a Music Genre Graph

To ensure accuracy and consistency, a knowledge graph could map relationships between artists, albums, and songs based on shared instrumentation, production styles, and collaborations. Instead of rigid genre labels, the graph could establish dynamic connections. For example:

• Michael McDonald’s collaborations with Steely Dan strengthen the link between their sounds.
• Toto’s session work on various smooth rock productions solidifies their place in the genre.
• “Sailing” by Christopher Cross embodies Yacht Rock, even if it was originally tagged as “soft rock.”

With a knowledge graph, genre classification could be continuously refined as new insights emerged—rather than relying on a static decision made by curators.

Step 3: Automating Content Tagging for Playlists

Once a more precise definition of Yacht Rock was established, an AI-driven content tagging system could automatically update streaming libraries. Instead of manually curating playlists, an automated system could scan and tag songs in real time, ensuring that a “Yacht Rock Essentials” playlist actually reflected the genre’s defining elements.

The Possibilities of AI and Knowledge Graphs in Music Classification

While Yacht Rock: A Documentary tells the story of how a community of fans and historians brought the genre to life, it also sparks the question—how many other musical styles remain hidden in the gaps of traditional classification? AI and knowledge graphs could be powerful tools in refining and redefining genres, ensuring that listeners can discover the music they love with ease.

Had these technologies been available during Yacht Rock’s rise to prominence, they could have made the genre’s recognition a much smoother journey—just like the music itself.