Cheap AI Tracks Are Crowding Human Arrangers Out of China’s Playlist Economy

（本文作者为 Chelsea_Sun，钛媒体经授权发布）

NextFin News — China’s streaming libraries are filling up faster than anyone can listen. Daily intake on major platforms has climbed past 100,000 tracks, up from roughly 70,000 not long ago. Industry estimates put artificial-intelligence titles above a fifth of that flood.

The commercial consequence is blunt. Work that used to support working arrangers is being repriced against software that never sleeps and never quotes five thousand yuan for a single cue.

Corporate anthems, short-drama beds, and retail background loops were never about timeless melody. They needed to sound finished, stay on brand, and arrive on time. Those buyers now compare a traditional studio quote with an AI package that can deliver a stack of options for a few hundred yuan. Many choose the stack.

Professionals who built careers on that mid-market volume report sharp drops in commissions. Some have left for teaching or other service work. Others have changed how they sell. They let the model generate structures and styles, throw most of the output away, and spend human time on the parts machines still handle poorly—dynamics, breathing room, the small irregularities that make a track feel performed rather than assembled. List prices fall. Throughput rises. Margin can hold if the remaining jobs are deeper and the pure commodity work is abandoned.

Volume demand that only needs average quality is moving to machines. That trend looks irreversible at current cost levels.

At the other end, live presence, name recognition, and projects that insist on real players for decisive passages still command a premium. Film and higher-end commissions increasingly allow AI sketches while requiring recorded instruments where it counts. Concert tickets are still sold to see a person on a stage. That lane is narrower, not closed.

The painful tier is the competent middle: skilled enough to have made a living, not famous enough to sell scarcity. Photography did not erase painting; it hollowed out the portrait trade that competed with a camera on price. Music is running a similar filter.

Lower barriers have pulled in people with no formal training. Prompt craft, account packaging, and steady uploading can produce modest platform payouts and occasional custom fees. A custom song for a local client can clear a few thousand yuan. Passive income exists in small amounts.

Scale works against the newcomer as much as the veteran. Charts fill with competent-sounding AI material. Buyout offers arrive low. Melodies reappear under other names. Standing out on a single track is harder when eighty-point songs are effectively unlimited. Execution and packaging matter more than the novelty of having “made music.”

Blind listening has already shown that strong machine output can score well when labels are hidden. Finished sound alone is no longer a reliable human monopoly.

What remains harder to automate is context: who made the track, why, and whether anyone will pay to be in the room when it is performed. As long as some listeners and some commissioners value that difference, human craft keeps a market. The industrial fact underneath is simpler. A finished-sounding song can now be approximated for the price of a modest meal and an evening of iteration.

Platforms will keep accepting the uploads. Pay will keep flowing to whoever matches the buyer’s real standard—average and cheap, or scarce and alive.