Music Isn’t a Problem to Be Solved

As someone who builds and teaches AI/ML systems and is a jazz musician, composer, improviser, and educator, I’ve noticed that Gen AI is becoming a buzzword for musicians on social media. Suno CEO Mikey Shulman said in an interview that “it's not really enjoyable to make music now.” Spotify rolled out an AI Playlist feature that lets Premium users generate entire playlists from a single text prompt. It’s obvious that these companies see music as something that can be produced on demand to cut out musicians. These trends are so disconcerting to me that I feel a strong responsibility to write this article, clear up common misunderstanding, and mitigate fear with fact.

Generative AI companies like Suno don’t just offer tools. They’re turning music into something akin to fast food. Suno’s own site says it can create “complete, original songs” from “a single text prompt in under a minute,” and that “no experience” is needed. The same page lists “Impatience is a virtue” as a company value.

For art, this statement is the poison pill.

The difficulty of making music is how a musician grows artistically. You don’t become a composer by skipping the struggle of composition. You don’t develop as a producer by avoiding difficult creative decisions. And you don’t learn to play guitar without practicing scales and understanding the fretboard. Instant outputs that require no effort might feel satisfying in the moment, but they bypass the artistic process.

The real danger is not artificial intelligence itself. The danger lies in using it as a replacement for the effort that shapes artistic identity. When creation becomes a prompt, it’s easy to outsource not just the work, but the thinking and judgment that come with it. A 2025 study by Michael Gerlich found a strong negative correlation between frequent AI tool use and critical thinking skills.

Human brains build unique neural connections through personal experience like practicing and composing music. The strength of these connections, called synaptic weights, grows the more we practice, create, and struggle. Creativity introduces variation that helps prevent ideas from becoming uniform and, worse, average.

Generative AI works differently. It compresses large amounts of existing music, much of it copyrighted, into a statistical average. When you prompt it, the model generates outputs by sampling from those averages rather than creating original music from actual experience or musicianship.

This process accelerates what biologist Richard Dawkins called memetic evolution, the spread of ideas, styles, and sounds through imitation. When new music is increasingly generated from the same statistical averages instead of diverse human experience, musical ideas start to lose their variety and begin sounding the same.

The more people rely on these tools, the more feedback loops start to form. AI-generated music creeps into the training data and influences future outputs. This process, known as model collapse, leads to a gradual loss of diversity and originality. A 2025 study of commercial AI music generators, including Suno, found that when prompted for regional or niche genres, the models tend to drift toward mainstream styles, flattening the diversity of the music they produce. It doesn’t require total adoption of generative AI to begin this process. It begins once a meaningful amount of new music is generated this way.

Human creativity is one of the strongest protections against this type of compression. The solution is not to reject AI entirely. These tools are here to stay. Instead, we should avoid using generative AI as a replacement for music creation. Humans should control the vision and make the meaningful decisions, while AI serves as a supporting tool for technical tasks, iteration, and exploration. Used this way, the result can be stronger than what either a human or AI could achieve alone. This is what AI has to offer musicians.

With Cosmoharmonics, I make music that I could only create with the help of AI. It’s the kind of music I dreamed of making even as a professional. I use AI to help bring ideas to life, but I still make the music and the creative decisions myself. That’s also why I started Barrios AI and wrote the book Artificial Intelligence for Musicians. I didn’t write it to give people shortcuts. I wrote it to give musicians tools that help them go further than they could on their own.

When teaching AI and Machine Learning for CBT Nuggets, I keep coming back to a quote from kache (@yacineMTB): “you can outsource your thinking, but you cannot outsource your understanding.” That same principle applies to creativity. If you only use prompts to make music, you’re handing over your creativity. Over time, that will flatten what you make. And at that point, the least interesting part of AI-generated music is the person doing the prompting.