The human difference

But while AI is transforming the front end of music creation, its impact is not evenly distributed across the production chain.

In fact, one of the most striking effects of the AI boom is that not every role is disappearing. Some are proving remarkably resilient.

Mixing is one of them.

Mixing is the stage where vocals, instruments, and effects are balanced into a finished track. It is technical, but also deeply subjective. And that subjectivity is precisely what has made it resistant to automation.

The reason is partly economic. As AI reduces the cost of composing and arranging music, more production budgets are being redirected toward the final stages — where human judgment still carries the greatest value.

Cashmere Studios in Shanghai offers a glimpse into why.

For its founder, Kaka, mixing is less about engineering and more about taste.

"Clients don't come to a mixing engineer just for technical output," he says. "It's like asking a friend for fashion advice. You want aesthetic judgment, not a correct answer."

He argues that mixing resists automation because it depends on choices that are difficult to quantify: how warm a vocal should sound, how far an instrument should sit in the mix, or how emotionally a chorus should land.

There is also a structural reason AI struggles here. Much of professional mixing relies on proprietary tools, hardware-specific workflows, and tacit knowledge that is rarely documented. In industry terms, it is a "black box" process — difficult to capture, and even harder to train at scale.

While AI can now generate complete songs, it is still far less capable of finishing them.

This imbalance is shaping a broader reality across the industry: AI is not replacing music creation outright. It is removing the repetitive, standardized layers while increasing the value of judgment-heavy work.

That shift is already showing up in earnings.

Independent musicians report declining income from certain types of commercial work, especially functional music for advertisements, corporate events, and short-form video content. These are exactly the areas where AI performs best: fast, cheap, and "good enough".

One industry description captures this new aesthetic bluntly:AI-generated music often sounds like a "standard face" — technically correct, polished, but lacking identity.

At Cashmere Studios, Kaka has witnessed the shift firsthand. Corporate commissions have declined as companies increasingly generate music in-house using AI tools. Film and game studios, once reliable clients for licensed tracks and live recordings, are also experimenting with AI-generated demos before approaching human musicians.

"Before, they would buy music or hire musicians to test ideas," he says. "Now they often start with AI."

Still, the industry is not simply shrinking. It is splitting.

On one side, AI is rapidly absorbing functional, low-emotion content. On the other, demand is growing for work rooted in human interpretation, cultural understanding and emotional nuance.

The broader picture is not replacement, but rebalancing.

Even as production becomes more automated, the industry is becoming more dependent on human judgment.

That may be the AI era's greatest paradox: the easier it becomes to generate music, the harder it becomes to create something truly worth listening to.

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