The Platform Paradox: How Spotify Became The Sheriff And Chief Beneficiary Of AI Music Fraud
In Brief
How Spotify polices AI music fraud while aggressively building its own AI tools, exposing the deep power asymmetries that leave working musicians paying the price.
On July 16, 2026, Lorde opened Instagram and told a tech platform to keep its artificial intelligence away from her art. Spotify’s “About the Song” feature — an AI tool still in beta that scrapes third-party sources to generate contextual blurbs — had produced a hallucinated narrative about one of her tracks, attributing a stage performance to the wrong song entirely. “I’m going to go out on a limb and say we don’t want this,” she wrote. “Reducing a song to an AI-generated meaning right at the source feels like it limits free interpretation.”
Spotify removed the text. A spokesperson called it a beta feature that would improve with feedback. But Lorde’s complaint was not merely about a factual error. It was about the creeping colonization of music by machine-generated interpretation — the platform’s assumption that AI curation should be the default setting of artistic work.
For every superstar with a platform and a legal team, thousands of working musicians fight a quieter, more insidious battle. Owen Lyman-Schmidt, half of the Philadelphia gutter-folk duo Makeshift Hammer, discovered in 2024 that an entity called “Carey Dupont” had stolen his band’s recordings, slightly altered them, and uploaded them to Spotify under AI-generated album art and nonsense track titles. The fake artist had accumulated 650,000 streams. The real band had 40,000.
These two stories are not separate phenomena. They are the twin faces of the same crisis: Spotify’s increasingly fraught relationship with artificial intelligence, and the music industry’s scramble to respond to a technology that threatens to swallow the very notion of creative authorship.
The Fraud Machine
This is not a story about a few bad actors. It is a story about an industrial-scale operation that has turned AI music generation into what analysts estimate is a $4 billion fraud ecosystem.
Suno, a leading AI music generator, reached $300 million in annual recurring revenue by February 2026 and was valued at $2.45 billion. Its 2 million paid subscribers generate an estimated 7 million songs every day — equivalent to Spotify’s entire historical catalog every two weeks. Most will never be heard by a human ear. But a fraction are weaponized.
Fraudsters have evolved their tactics. Instead of using bot farms to replay a small number of tracks millions of times — which triggers detection — they now flood platforms with millions of AI-generated tracks and stream each just a few thousand times, collecting royalties without raising alarms. Apple Music confirmed it demonetized 2 billion fraudulent streams in 2025, diverting nearly $17 million from legitimate artists. Deezer reported that up to 85% of streams on AI-generated music were fraudulent, and by 2026, over 30,000 fully AI-generated tracks were uploaded to its platform daily.
The most brazen case is that of Michael Smith, a North Carolina musician who pleaded guilty in March 2026 in the first federal criminal streaming fraud case in U.S. history. Smith used AI to generate hundreds of thousands of songs and deployed bot networks across thousands of accounts, generating over 660,000 streams per day at peak and fraudulently collecting more than $10 million in royalties. He agreed to forfeit over $8 million and faces sentencing on July 29, 2026 — a date that will likely set the precedent for how seriously the U.S. treats AI-enabled music fraud.
But Smith was an outlier in scale, not method. Makeshift Hammer’s experience reveals how the scam works at the grassroots level. A bot farm scrapes recordings from platforms like SoundCloud, alters them minimally to evade detection, repackages them with AI-generated artwork and near-miss titles, then cranks out automated listens. “Carey Dupont” had no website, no social media, no concert history — just a Spotify profile and 650,000 streams. When Lyman-Schmidt filed takedown notices with Spotify and SoundCloud, he received automated responses and, from Spotify, silence. Three and a half months after his initial complaint — and only after his story was published — did Spotify inform him the tracks would be removed “pending an investigation.”
The cruelty is not merely the theft. It is the economics. Spotify pays artists through a “streamshare” model: the total royalty pool is divided proportionally among all streams. When a scammer adds 650,000 fake streams to a pool that previously held 40,000 legitimate ones, the same total payout is now split 94% to the fraudster and 6% to the original artist. The platform loses nothing. The fraudster profits. The musician pays.
The Sheriff and the Outlaw
Spotify is not ignoring the problem. In September 2025, the company refined its AI policy around transparency, strict prohibition of unauthorized voice cloning, and aggressive spam filtering. In the prior 12 months, Spotify removed over 75 million tracks for spam and low-quality content.
Then, on April 30, 2026, Spotify launched the “Verified by Spotify” badge — a green checkmark to help listeners distinguish authentic human artists from AI personas. But the badge authenticates the artist identity, not the content. A verified human can still upload fully AI-generated tracks. And AI-persona artists are explicitly ineligible. Spotify is drawing a line around who you are while taking no position on what you make.
This is telling. Spotify does not want to judge artistic merit. It wants engagement. And AI, for all its problems, drives engagement.
This tension crystallized on July 14, 2026 — two days before Lorde’s complaint — when Spotify launched a ChatGPT-like music assistant for Premium users. On the Q1 2026 earnings call, co-CEO Gustav Söderström stated that Spotify has “the capabilities and technologies we need” to build a derivatives business on top of existing artists’ music. He did not disclose what those capabilities were trained on. Creators have no disclosure framework, no audit trail, and no compensation mechanism regarding whether their work feeds Spotify’s generative systems.
Spotify is building AI tools that interpret, recommend, and potentially regenerate music, while deploying AI tools to detect AI-generated fraud. It is the sheriff and the outlaw, occupying both sides of a conflict it helped create.
An Industry Caught Flat-Footed
The music industry’s response to this crisis has been a patchwork of legislative lobbying, legal improvisation, and reluctant commercial accommodation — and at every turn, the power asymmetry between major players and working musicians becomes more stark.
At GRAMMYs On The Hill 2026 in April, the Recording Academy championed three bipartisan bills: the NO FAKES Act, establishing a federal right protecting voice and likeness from AI deepfakes; the TRAIN Act, giving creators visibility into AI training datasets; and the CLEAR Act, mandating transparency in AI systems. The urgency behind this legislative push is not abstract. Copyright law protects recordings, not voice itself — a gap that AI voice cloning has turned into a weapon.
In January 2026, folk musician Murphy Campbell discovered AI-cloned versions of her voice distributed on her own Spotify profile. The perpetrator then used the distributor’s Content ID access to file copyright claims against Campbell’s original YouTube videos, causing her to lose monetization on her own content. She described it as being “in a weird limbo where I’m telling robots to take down music robots made.” Sony Music has since requested removal of more than 135,000 songs created to impersonate artists on its roster — a figure the company admits is likely a fraction of the actual volume. In April 2026, Taylor Swift filed three trademark applications with the USPTO — two sound marks covering her voice — to fill what attorney Josh Gerben called “a gap that trademarks may help fill” now that AI can mimic an artist without copying an existing recording.
While artists scramble for legal protection, the major labels have taken a different tack. Universal Music Group and Warner Music Group reached licensing agreements with AI music company Udio, covering label-controlled rights with plans for an artist opt-in platform. The message is clear: if you cannot beat AI, license it. But the independent sector has pushed back. The Independent Music Publishers Forum has urged publishers to reject AI licensing agreements that fail to allocate at least 50% to songwriters — a warning that the economic benefits of AI deals may never reach the creators whose work trained the machines.
Yet for all the lobbying and deal-making, the real power to reshape this landscape lies not in Congress or boardrooms, but in courthouses. The entire AI music economy is provisional, awaiting two landmark rulings scheduled for this summer. In Sony Music Entertainment v. Suno, currently before U.S. District Judge F. Dennis Saylor IV in Massachusetts, a fair-use summary judgment hearing was held in July 2026 — the first federal case to address whether training a generative AI model on copyrighted recordings constitutes fair use. No ruling has been issued. In GEMA v. Suno, the Munich Regional Court is expected to deliver a verdict on July 31, 2026, turning on Germany’s text-and-data-mining exception — a different legal theory that could produce a divergent outcome. Together, these decisions will determine whether AI training data becomes a priced licensing line item or remains effectively free. If training is deemed fair use, the floodgates open wider. If not, the economics of AI music generation change overnight.
But even these legal battles, however consequential, risk obscuring the human reality beneath the policy debates. This is a story about power asymmetry, and nowhere is that clearer than in who gets heard and who gets ignored. Lorde posts an Instagram Story and gets a response from Spotify within hours. Owen Lyman-Schmidt spends three and a half months filing takedown notices, consulting lawyers, and publishing an investigative article before the platform acts on a clear case of theft. Major labels negotiate licensing deals with AI companies. Independent musicians get Discovery Mode, a Spotify program that lets them accept an even lower royalty rate for playlist exposure.
The platform’s architecture reflects this hierarchy. Spotify’s streamshare model favors volume over value, catalog over curation, automation over accountability. AI fits this model perfectly. It produces infinite content at near-zero marginal cost, feeds the algorithm’s hunger for novelty, and shifts the financial risk of fraud onto the very artists the platform claims to serve. What makes this moment different from previous disruptions — the synthesizer, the sampler, the MP3 — is the speed and opacity of the change. AI does not merely replicate or distribute music. It interprets it, impersonates it, and potentially replaces it, while legal and ethical frameworks lag years behind.
Lorde asked for an opt-out. Makeshift Hammer asked for justice. The platform, so far, has offered neither. It has offered only more features, more AI, and more silence.
Until that changes, the ghost in the stream is not the AI. It is the artist, watching their own work disappear into a machine they never asked to build.
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About The Author
Alisa, a dedicated journalist at the MPost, specializes in crypto, AI, investments, and the expansive realm of Web3. With a keen eye for emerging trends and technologies, she delivers comprehensive coverage to inform and engage readers in the ever-evolving landscape of digital finance.
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Alisa, a dedicated journalist at the MPost, specializes in crypto, AI, investments, and the expansive realm of Web3. With a keen eye for emerging trends and technologies, she delivers comprehensive coverage to inform and engage readers in the ever-evolving landscape of digital finance.
