Streaming Broke Musicians First

출처: Dev.to

In September 2025, a 31-year-old poet from Olive Branch, Mississippi named Telisha “Nikki” Jones watched her AI-generated R&B project, Xania Monet, debut at number one on Billboard’s R&B Digital Song Sales chart. Jones had never considered herself a singer. She had spent years writing deeply personal poetry, running a printing company, and singing quietly in church. Then she discovered Suno, a generative AI music platform, and began feeding her poems into it. Within four months, record labels were locked in a bidding war that reached three million dollars. Hallwood Media, led by former Geffen Records president Neil Jacobson, won.

The reaction from the music industry was swift and visceral. R&B singer Kehlani took to TikTok, declaring: “There is an AI R&B artist who just signed a multi-million-dollar deal, and the person is doing none of the work. I don’ t respect it.” Victoria Monet told Vanity Fair that it was “hard to comprehend that, within a prompt, my name was not used for this artist to capitalise on,” pointing to the uncanny resemblance between herself and the AI avatar. SZA posted a screenshot questioning why anyone would “devalue our music.” Producer Jermaine Dupri compared the acceptance of AI artists to the Milli Vanilli scandal. The public narrative crystallised quickly: AI music was inauthentic, parasitic, and threatening to real artistry.

These responses are understandable. They are also, in a fundamental sense, aimed at the wrong target. The anxieties surfacing around AI-generated music are real, but the debate as currently framed obscures something far more consequential than questions of authenticity or artistic merit. What is actually at stake is a systems-level crisis about how musicians sustain themselves economically, how listeners discover music, and how the infrastructure of a multibillion-dollar industry distributes value. The moral framing of this argument, with its emphasis on “real” versus “fake” artistry, has become a convenient distraction from structural failures that predate generative AI by at least a decade.

Consider what it actually took for Breaking Rust, an AI-generated country music project created by Aubierre Rivaldo Taylor, to top Billboard’s Country Digital Song Sales chart in late 2025. According to Luminate data, roughly 2,500 digital downloads were sufficient for its track “Walk My Walk” to claim the number one position. As Andrew Chow noted in TIME magazine, the digital music sales charts have long been vulnerable to manipulation, and the significance of the achievement was questionable. Country radio stations flatly refused to add Breaking Rust to their rotations. Radio consultant Joel Raab told Billboard that listeners “react negatively to the idea of AI voices on their stations.” Leslie Fram, founder of FEMco, called it “a notable wake-up call but not yet an existential threat,” adding that “in country, where authenticity and storytelling are core, this could erode trust if fans feel manipulated.”

Yet the headlines read as though something seismic had occurred. By mid-November, one third of the top ten on Billboard’s Country Digital Song Sales chart was composed of AI-assisted artists. The framing invited a binary debate: should AI music be permitted on the charts or not? What went unexamined was why the charts themselves had become so easy to game, and why a few thousand downloads could generate the appearance of mainstream success on platforms that were never designed to handle the current volume of content.

That volume is staggering. According to Luminate data published in January 2026, an average of 106,000 new tracks were delivered to streaming services each day throughout 2025, a seven per cent increase from 99,000 daily in 2024. There were 253 million music tracks sitting on audio streaming platforms by the close of 2025. Nearly half of those tracks, some 120.5 million, received fewer than ten streams. Three quarters received fewer than 100 annual streams. A full 88 per cent received fewer than 1,000 streams.

These are not primarily AI numbers. The content flood was already well underway before tools like Suno and Udio made it trivially easy for anyone with a text prompt to generate a passable song. Spotify was already receiving roughly 60,000 uploads per day before the AI surge. The oversaturation problem, in other words, is structural. AI has accelerated it enormously, but it did not create it.

The streaming economy operates on a pro-rata model. All subscription revenue is pooled together, then distributed based on total platform streams. If a track accounts for one per cent of all streams on Spotify in a given month, it receives one per cent of the royalty pool. Per-stream payouts on Spotify hover between $0.003 and $0.005. Only 1.4 per cent of Spotify’s artists earn more than $1,000 per year from the platform.

When Spotify announced in January 2026 that it had paid out more than $11 billion to the music industry in 2025, the figure sounded extraordinary. But as industry analysts have consistently pointed out, the distribution of that money is radically unequal. According to Luminate, just 541,000 tracks, representing barely 0.2 per cent of all available music, accounted for 49.4 per cent of total global audio streaming consumption. The vast majority of working musicians compete for scraps from the remaining half.

The platform’s own policies have compounded the problem for smaller artists. In April 2024, Spotify introduced a minimum threshold requiring tracks to accumulate at least 1,000 streams in the previous twelve months before they could generate any royalties at all. The company framed this as fraud prevention, arguing that processing micropayments for low-stream tracks cost more than the payouts themselves. But the effects have been severe. According to Digital Music News, roughly 87 per cent of songs on the platform fall below this threshold. An estimated $47 million in annual royalties that previously trickled to independent artists was effectively redirected to the platform’s top performers and the three major labels that represent them. A survey reported by Digital Music News found that 85 per cent of independent respondents experienced revenue reductions, with 65 per cent reporting “significant negative impact.” The European independent music body Impala criticised the policy for “stripping revenue from independent labels and niche genres, disproportionately impacting classical, jazz, regional and non-English repertoire.”

Mark Mulligan, the analyst behind MIDiA Research’s annual reports, has characterised the broader situation as an approaching pivot point. “Industries arrive at pivot points when an accumulation of fissures coalesce into one big crack,” he wrote. “Streaming is approaching such a point.” The challenges, Mulligan argued, come from multiple directions: major rightsholders feeling investor pressure, artists struggling to cut through clutter, royalties failing to add up for professional artists, and music becoming commodified.

AI did not cause this royalty crisis. But it has weaponised the existing vulnerabilities. According to the IMS Business Report 2025, compiled by Mulligan and MIDiA Research, 60 million people used AI software to create music in 2024. Suno alone attracted 46.9 million monthly visits, according to Semrush, a remarkable surge for a platform that only launched in March 2024. Each of these users can generate finished tracks in seconds. Many of those tracks end up on streaming platforms, where they enter the same royalty pool as music made by human professionals who spent years honing their craft.

The result is a dilution problem. More tracks in the pool means each individual track receives a smaller share of financial return.