What We Lose When AI-Generated Music Sounds Better

Yesterday, I made a demo track. I quickly laid down piano and drums, then added a bass line. To be honest, it was awful. The clarinet melody was not bad, but it was nowhere near something I could play for someone else.

I tried Suno, an AI music generation service. In 30 seconds, it gave me a full-band sound with vocals on top. It was clean. It sounded good. And I deleted my demo. That was the moment I realized I had fallen into a trap.

The Structure of the Trap

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I call this phenomenon the “AI Polish Trap.” It is when the instant polish AI provides erodes a creator’s patience to hold onto unfinished ideas, eventually pushing them to choose outputs that converge toward a statistical average, and lowering the quality of music in the process.

B sounds “better” than A. That is true. The production quality is overwhelmingly better. The drums hit hard, the mix is smooth, and there are even vocals, so of course our ears prefer it. The problem is that “sounds better” is not the same thing as “is a better melody.” Unfortunately, our ears are not good at separating those two.

Melodies made by AI are statistical averages learned from massive datasets of hit songs. They offend no one, but surprise no one either. They converge toward safe and ordinary outcomes. Then perfect production is layered on top, making even ordinary melodies feel impressive.

Why We Fall Into It

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There are four reasons.

• First, immediate gratification. The human brain is not designed to resist what sounds good right now. Showing patience in front of a clean result made in 30 seconds is an evolutionarily uphill battle.
• Second, the comparison effect. Comparing my rough piano demo to AI’s polished production is an unfair game from the start. Repeated experiences like this bring thoughts like, “Why am I even doing this?” and eventually wear down creative self-esteem.
• Third, practical pressure. Under deadlines, delivery requirements, and intense competition, “fast and decent output” feels like an attractive survival strategy.
• Fourth, paradoxically, the more skilled you are, the more vulnerable you can be. Beginners may look at their own result and think, “This is not bad.” But professionals who have written hundreds of songs know every flaw in their own demo. That makes them even more susceptible to AI’s polished output.

A Fatal Outcome

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This does not end with simple skill decline. It forms a self-consuming downward loop. AI learns from existing songs. Creators adopt AI outputs as final products. Those products are released into the world as new content. Then AI learns from those results again. Eventually, the creative range narrows. Individuals degrade, genres flatten, and the entire ecosystem starts to look like personality-free mass production.

The Temptation of Steroids

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Here is an easy analogy. If you train in the gym every day in pain, you build real muscle. My rough demo is that process. Ugly, but real. Steroids, on the other hand, build muscle quickly. Fast and visually impressive. But there is no foundation. The moment you stop, it fades. Using AI outputs as final versions is not much different from taking steroids every day while telling yourself, “I am training hard.”

This Is Not Just a Music Problem

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Have you felt that writing long-form text has become harder lately? We hand even emails, proposals, and sometimes KakaoTalk replies to AI for drafts. The sentences are smooth, so we send them as is. Then one day, when we try to write without AI, we get stuck on the very first sentence.

To be honest, I also used AI while organizing this piece. Then I caught myself: “Even I am giving away control of my writing.” This trap is that natural. You fall in even when you know it is there.

At the same time, I found an interesting point. There is a saying: “Put garbage into AI, and you get pretty garbage out.” In reality, to get good results from AI, you need to organize your thoughts clearly, structure context, and communicate the core precisely. You cannot throw prompts carelessly like casual chat messages. This process itself can be a new form of thinking training. The essence is the same: hand everything over with “just write it for me,” and you decline; use it as a tool to refine your thinking, and you expand. Music is no different.

Standing on the Boundary

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There is one objection we should address: “Then is this not the same for virtual instruments?”

Virtual instruments are also degraded versions of real performance nuance. But did virtual instruments ruin music? If anything, they opened the door for people who could not afford to hire an orchestra to try writing symphonic music. What we gained was far greater than what we lost.

But there is a decisive difference. Virtual instruments replaced performance, not composition. Conceiving melodies, building harmonies, and designing form remained human decisions. If virtual instruments “changed the kind of brush,” generative AI “paints the picture itself.” When a tool replaces means of expression, creativity can expand. When it replaces creative decision-making itself, decline begins. Each time we use AI, we should ask: “Am I using this as a tool of expression, or am I outsourcing my decisions?”

Then How Should We Use It?

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This is not an argument for rejecting AI entirely. It is about method. I can feed my demo into Suno’s cover feature and try multiple prompts: “What if this were interpreted as jazz?” “What if this became electronic?” Many versions based on my melody appear. The key is to treat those results as tools for direction-finding, not final products. “So this melody feels like this in jazz. Let me borrow only this chord movement and rebuild it my way.” I keep decision-making, while AI expands the horizon of possibilities.

Also, I must make my own version before listening to AI output. Order matters. The moment AI comes first, my creative judgment starts to get contaminated. Finally, AI’s statistical average can be used as a compass for where not to go. Not as a reference, but as an anti-reference.

On One Counterargument

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While organizing this theory, I came up with another objection: “Even if AI converges to statistical averages, if that average is much broader than an individual’s experience, does it not actually expand diversity?”

That is a fair point. Most people live inside fences of personal taste. Someone raised in Korea is familiar with K-pop, while someone raised in an American suburb may be familiar with country music. The total music a person hears in a lifetime is tiny compared with all music humanity has created. AI, by contrast, has learned from Gamelan, Gregorian chant, and West African rhythms. For an individual, AI can indeed become a window to a wider world.

So a more precise diagnosis might be this: AI expands diversity at the individual level, while shrinking it at the collective level. Everyone moves beyond a narrow personal zone, but the expanded outcomes converge in similar places. A strange state where “everyone becomes more diverse, but in the same way.”

Moreover, we are still in an early stage where AI mainly learns from human output, so the expansion effect stands out. But as AI-generated content cycles back into training data again and again, homogenization pressure will intensify. What looks like expansion now may be the prelude to large-scale convergence.

Closing

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The reason an artist’s music is deeply loved is not simply sound quality. In one line of lyrics, in the resonance of a melody, listeners feel a world and a way of life unique to that artist.

People do not consume perfectly tuned sound alone. They consume the time, struggle, and story that shaped that sound. The “smooth fake” produced by AI through statistical probability cannot reach that domain, where even imperfections become individuality.

Sometimes an unpolished, rough sound can still comfort someone, because they know it came not from calculation but from a human confession. In the end, the moment music truly grips us is when the most human voice knocks on our hearts.

Even The Beatles’ “Yesterday” was not born as a masterpiece from the start. One day, Paul McCartney heard the melody in a dream and, trying not to forget it, filled in temporary lyrics using the breakfast words in front of him: “Scrambled eggs, oh my baby how I love your legs.” That was the beginning of one of the most-covered songs in history.

Good ideas usually sound insignificant and rough at first. But enduring that awkward stage and shaping unfinished fragments into usable gems is the essence of creation.

We should not hand even that noble, painful time over to AI. In one melody and one lyric line you create yourself lives the full trace of your life: landscapes you have seen, unnamed emotions you have felt, and the path of relentless thought you have walked.

Music that touches the soul matures only in human time. No one-click result can replace your music.