Artificial intelligence has fundamentally changed how we create music. Today, tools like Suno can generate remarkably sophisticated recordings in minutes. They can produce realistic vocals, convincing instrumentation, complex arrangements, and production quality that would have required considerable studio time just a few years ago. As someone who has spent decades working with music and technology, I find these developments fascinating. But I've noticed something while producing AI-assisted music. Just because a song sounds good doesn't mean it's memorable. And that distinction matters more than ever.
The Hum Test
Here's a simple experiment I think every music producer should try. Play your latest song for someone who's never heard it before. Play it once. Then ask them to hum something they remember. Maybe it's the chorus. Maybe it's a guitar riff, a bassline, or a distinctive melodic phrase. Anything. If they can't remember a single musical idea, your song might be missing something important.
A hook.
Of course, not every great song reveals itself on the first listen. Some of my favorite recordings took time to appreciate. But the experiment raises an important question. Are we creating songs that sound impressive, or songs people actually want to hear again?
The Problem With AI-Generated Music
One of the things I've discovered while working with Suno is how easily different songs can begin to sound alike. Even when I change the lyrics, musical styles, or production instructions, I sometimes hear familiar chord progressions, similar melodic patterns, and predictable arrangements. This becomes particularly noticeable when working with an established AI artist persona.
The consistency that helps maintain an artist's recognizable sound can also make successive recordings feel too similar. I've experienced this while developing songs for K.C. Brooks, our contemporary Texas country artist.
Sometimes I generate a new song and immediately recognize musical patterns that remind me of something we've already produced. That's when I know it's time to make changes. Maybe the chord progression needs to be different. Maybe the tempo needs adjusting. Perhaps the instrumentation or song structure needs rethinking. And sometimes the best solution is to pick up an instrument and develop a musical idea myself.
The point is that I don't want to release a collection of songs that sound like variations of the same recording. I want each song to have its own identity.
Behind the Music: K.C. Brooks and “This Ain't My First Rodeo”
One example of how I approach AI-assisted music production is K.C. Brooks' song, “This Ain't My First Rodeo.” The concept centers on a familiar expression that immediately communicates confidence and experience. In the song, that expression becomes a playful but pointed declaration from a young woman who knows when someone isn't being honest with her.
It's a strong lyrical hook because listeners already understand the phrase. But having a good lyrical idea is only the beginning. I wanted the music to reflect the song's personality. The arrangement needed to feel upbeat, confident, and danceable. Something closer to a contemporary Texas country two-step than a traditional honky-tonk recording.
Working with Suno, I focused on shaping that sound. I wanted to avoid excessive steel guitar, exaggerated twang, and the kind of overpowering rock guitars that can sometimes dominate AI-generated country arrangements. I also wanted a short electric guitar solo that complemented the song rather than interrupting its momentum. These decisions might seem relatively small, but collectively they help establish a song's musical identity. The AI generated musical possibilities. My job was to decide which ideas supported the song and which ones didn't.
I also handle final mastering for recordings released through Learnetics Music. For me, an AI-generated recording is a starting point for the production process, not necessarily the finished product. I wasn't aiming for a polished AI recording right off the bat, but I want it to be the best song when it's done, so I'll keep working through the process until I am satisfied with the results.
The final result was a country song with a persona, personality, attitude, and a recognizable hook.
https://open.spotify.com/track/4rg4Ym1lLAAAc0sLEt3OxV?si=b422ad9e48be4e39
One Great Musical Idea Can Be Enough
Think about some of the songs you've loved over the years. Often, there's one element that makes them instantly recognizable. A guitar riff. A bassline. A distinctive rhythm. A vocal melody. You don't necessarily need ten brilliant musical ideas in one song. Sometimes one is enough. The trick is to make that idea central to the arrangement. Repeat it enough that listeners recognize it, but introduce subtle variations so it remains interesting. Change the instrumentation. Adjust the harmony. Introduce a rhythmic variation. Let the vocal delivery evolve.
Six Things I Consider When Producing AI Music
Over time, I've developed a few principles that guide how I approach AI-assisted production.
1. Start with a memorable idea. Before worrying about production quality, I ask whether the song has something worth remembering. A compelling chorus, an interesting melody, or a distinctive instrumental phrase.
2. Don't accept the first generation. Suno can produce impressive results, but that doesn't mean the first version is necessarily the best one. I experiment with arrangements, tempos, and instrumentation until the song begins to develop its own character.
3. Watch for repetitive musical patterns. When several songs start using similar chord progressions or melodic structures, I deliberately change direction. Maintaining an artist's identity shouldn't mean every recording sounds the same.
4. Don't overproduce. Sometimes removing an instrument or simplifying an arrangement makes the strongest musical idea stand out more. More layers don't automatically make better music.
5. Bring human musicianship into the process. I believe musicians who use AI have an advantage and an opportunity to combine their traditional skills with new technology. Playing an original bassline, developing a guitar riff, rewriting a melody, or restructuring an arrangement can meaningfully shape the song's final sound.
6. Listen like a fan, not just a producer. It's easy to become obsessed with technical details. The mix, the EQ, the mastering, the stereo image. Those things matter, but I also step away from the production process and listen to the song as if I were hearing it for the first time. Does it connect emotionally? Does something stand out? Would I want to hear it again?
AI Doesn't Replace Musical Judgment
I don't see AI as the end of human creativity in music. I see it as another tool. An extraordinarily powerful one. Throughout my career, I've watched technology transform how we record, produce, distribute, and consume music. AI is another major step in that evolution. But technology alone doesn't determine whether a song is good. That still requires creative decisions.
Which melody works best?
Does the arrangement support the lyrics?
Is the chorus memorable?
Does the performance communicate the right emotion?
AI can generate hundreds of possibilities. The real creative challenge is knowing which ones are worth developing. And sometimes, rather than generating another song, the better decision is to spend more time improving the one you already have.
The Real Measure of a Song
Ultimately, I don't think listeners care nearly as much about the technology used to create a song as they do about how that song makes them feel. They want music that connects with them. Music that makes them smile, dance, remember someone, or think about a moment in their lives. Music they want to hear again. That's what I'm trying to accomplish with the artists and recordings we're developing at Learnetics Music. Not simply demonstrating what AI can do. Creating music worth remembering. Because long after someone forgets how sophisticated the production sounded, they might still be humming the melody.
And to me, that's what makes a song successful.
Fito Kahn is the founder and producer of Learnetics Music, where he explores the intersection of artificial intelligence, traditional musicianship, and modern music production. His work includes developing AI-assisted artists, producing original recordings, and helping independent musicians navigate emerging creative technologies.
