The announcement arrives as streaming platforms grapple with an existential threat fundamentally reshaping the music economy. Schusser emphasized that Apple Music has adopted an increasingly aggressive posture toward streaming fraud, implementing a sliding penalty system since 2022 that deducts between five and twenty-five percent of potential royalties from accounts linked to fraudulent activity. However, the company announced this week that it has doubled down on enforcement, raising penalties to a range of ten to fifty percent of would-be royalties, citing the explosive surge in AI-generated content as the primary catalyst for the stricter measures. The escalation reflects growing industry recognition that existing deterrents have proven insufficient against bad actors who weaponize artificial intelligence to manufacture endless streams of synthetic music specifically designed to game royalty distribution systems.
According to recent data from competing platform Deezer, the French streaming service now receives approximately sixty thousand AI-generated tracks daily, representing thirty-nine percent of all uploads to the platform. Even more alarming, Deezer reported that up to eighty-five percent of streams generated by fully AI-produced music were flagged as fraudulent in 2025, depending on the month analyzed. This contrasts sharply with the overall fraud rate across Deezer’s entire catalog, which stood at just eight percent of all streams last year. The disparity reveals how AI music has become the vehicle of choice for organized fraud operations seeking to exploit the pro-rata royalty pool that streaming platforms use to compensate rights holders.
The mechanics of contemporary streaming fraud have evolved dramatically from early manipulation tactics. Historically, fraudsters would upload a limited number of tracks and deploy bot networks to stream those songs millions of times, generating obvious spikes in listening data that triggered platform detection systems. Modern operations have grown considerably more sophisticated. Bad actors now leverage generative AI tools like Suno and Udio to produce thousands or even hundreds of thousands of unique tracks at virtually zero cost, spreading fraudulent streams across this massive catalog to avoid detection thresholds. Each individual song might accumulate only a few thousand plays, generating modest royalties per track but collectively siphoning millions of dollars annually when scaled across enormous song libraries managed by automated bot farms.
Industry analysts estimate that streaming fraud represents a multi-billion dollar problem affecting the global recorded music market, which the International Federation of the Phonographic Industry valued at twenty point four billion dollars in 2024. Conservative estimates suggest fraud accounts for one to three percent of all streams, though some fraud detection specialists argue the actual figure approaches ten percent on certain platforms. At those levels, fraudulent activity could be diverting anywhere from two hundred million to over two billion dollars annually from legitimate artists, songwriters, producers, and rights holders who depend on streaming royalties for their livelihoods. The economic stakes extend beyond individual creators to the entire music ecosystem, threatening the financial viability of independent labels, distributors, and music publishers.
Apple Music’s two billion fraudulent streams in 2025 translate to approximately seventeen million dollars in royalties that would have been improperly distributed had the platform’s detection systems failed to identify and exclude the manipulated plays. Using industry-standard per-stream rates, this figure represents money reclaimed and redirected to honest artists rather than fraud networks. Schusser framed the enforcement actions explicitly as a zero-sum redistribution: money extracted from fraudsters directly benefits legitimate creators through increased per-stream payouts. The company positions its enhanced penalties as both punitive deterrent and economic correction, aiming to restore integrity to the royalty distribution mechanism that underpins the streaming business model.
The proliferation of AI-generated music has dramatically lowered barriers to entry for would-be fraudsters. Creating synthetic tracks requires no musical training, expensive equipment, or studio time. Sophisticated generative AI platforms can produce professional-sounding compositions in genres ranging from ambient instrumental to pop vocals based on simple text prompts. A single individual with access to AI music generation tools and basic automation scripts can manufacture thousands of tracks daily, upload them through digital distribution services, and coordinate bot networks to stream the content systematically. The entire operation can be executed remotely with minimal capital investment, making it attractive to organized crime networks and opportunistic individuals alike.
Detection has become an arms race between platform security teams and increasingly sophisticated fraud operations. Apple Music employs real-time monitoring systems that analyze streaming patterns, geographic distribution of plays, listening duration, skip rates, and dozens of other behavioral signals to distinguish genuine engagement from artificial manipulation. The company collaborates with distributors, chart providers, and industry partners to share intelligence about suspicious activity and coordinate enforcement actions. However, fraudsters continuously adapt their tactics, employing residential proxies to mask bot origins, mimicking human listening patterns with variable playback behavior, and distributing streams across multiple fake artist profiles to fragment detection signals.
The human cost of streaming fraud extends beyond abstract economic calculations. Independent artists operating on razor-thin margins find their already modest streaming income diluted by fraudulent plays that compete for shares of the finite royalty pool. Emerging musicians struggle to gain algorithmic visibility when bot-driven fake tracks saturate recommendation systems and playlist consideration. Legitimate promotional efforts get drowned out by artificial engagement that distorts platform metrics and misleads industry professionals about genuine audience interest. The cumulative effect threatens to undermine trust in streaming as a viable revenue source for working musicians.
Several high-profile cases have brought streaming fraud into the public spotlight. In September 2024, North Carolina resident Michael Smith was charged by federal authorities with wire fraud, conspiracy, and money laundering after allegedly orchestrating a seven-year scheme that generated over ten million dollars through AI music and bot-driven streaming manipulation. According to the indictment, Smith partnered with the CEO of an AI music company who provided him with hundreds of thousands of synthetic tracks released under fabricated artist names with absurd dictionary-inspired monikers like “Calm Knuckles” and “Zygophyceae.” Smith allegedly operated over one thousand bot accounts that continuously streamed his AI catalog, generating approximately thirty-three hundred dollars daily at the operation’s peak.
The Smith case exemplifies the industrial scale that modern streaming fraud can achieve. Unlike amateur manipulation attempts, Smith’s alleged operation demonstrated sophisticated understanding of platform detection systems and royalty mechanics. By spreading plays across an enormous catalog of AI-generated tracks, each accumulating modest stream counts, the operation remained below algorithmic red flags for years before investigative journalism and platform security teams identified suspicious patterns. The case represents only a fraction of overall streaming fraud, with industry insiders suggesting countless smaller operations continue undetected while larger fraud networks operate from jurisdictions with limited extradition agreements.
Platform responses have varied significantly. Spotify, the industry’s dominant player with over five hundred million users globally, maintains that less than one percent of streams on its service are fraudulent, though the company acknowledges dedicating substantial engineering resources to detection and removal efforts. The platform reportedly eliminated seventy-five million “spammy” tracks in September 2025 as part of ongoing cleanup initiatives targeting low-quality filler content, fake artists, and bot-driven manipulation. However, critics argue that percentage-based fraud claims obscure the absolute scale of the problem given Spotify’s massive user base and trillions of annual streams.
Deezer has positioned itself as the most aggressive combatant against AI fraud, recently announcing it successfully detected and removed up to eighty-five percent of fraudulent streams associated with fully AI-generated music in 2025. The Paris-based platform has licensed its proprietary AI detection technology to SACEM, France’s copyright society, enabling broader industry access to tools that can identify synthetic music from major generators like Suno and Udio with claimed one hundred percent accuracy. Deezer has also implemented policies excluding all fully AI-generated content from algorithmic recommendations and editorial playlists, effectively quarantining synthetic music from organic discovery mechanisms regardless of fraud status.
These divergent approaches reflect underlying strategic tensions about how streaming services should balance innovation against integrity. Some industry voices advocate for complete prohibition of AI-generated music uploads, arguing that the technology’s primary application has proven to be fraud facilitation rather than legitimate artistic expression. Others contend that blanket bans would unfairly penalize artists experimenting with AI as a creative tool while driving fraudulent operations toward more sophisticated evasion tactics. The debate parallels broader societal discussions about AI regulation, weighing potential benefits against demonstrated harms in contexts where malicious actors exploit emerging technologies faster than protective systems can adapt.
The economic incentive structure underlying streaming fraud remains stubbornly intact. As long as royalty pools function as zero-sum games where each fraudulent stream dilutes payouts to legitimate creators, bad actors will find ways to exploit system vulnerabilities for profit. Some experts argue that fundamental changes to royalty distribution models may prove necessary, potentially including minimum stream thresholds, user-centric payment systems that direct subscription fees exclusively to content each user actually consumes, or artist verification requirements that establish provenance before enabling monetization. Each approach carries trade-offs that could disadvantage certain creator categories while attempting to protect the overall ecosystem.
Looking ahead, industry observers anticipate continued escalation in both fraud sophistication and platform countermeasures. The next generation of AI-generated music will likely prove even more difficult to distinguish from human-created content as models improve in quality and stylistic range. Fraudsters will deploy more advanced bot networks capable of replicating nuanced listening behaviors including playlist interaction, social sharing, and cross-platform engagement patterns. Detection systems will need to evolve beyond technical fingerprinting toward comprehensive behavioral analysis while platforms grapple with privacy concerns and computational costs of surveillance-heavy enforcement regimes.
Apple Music’s penalty increases and Deezer’s detection tool licensing signal that major platforms recognize streaming fraud as an existential challenge requiring coordinated industry response rather than competitive secrecy. The International Federation of the Phonographic Industry has called for global cooperation among streaming services, distributors, technology companies, and law enforcement to combat what it characterizes as organized criminal activity systematically looting royalty pools. Whether such cooperation materializes amid commercial competition and conflicting regional regulatory frameworks remains uncertain.
For working musicians navigating this turbulent landscape, the fraud crisis adds another layer of precarity to already challenging economic conditions. Streaming royalties have never provided comfortable income for most artists, with per-stream rates typically ranging from fractions of a cent to at most a few cents depending on subscription type and listener geography. Fraud dilution makes these modest payouts even smaller while algorithmic contamination by bot-driven fake content reduces discovery opportunities for genuine artists seeking audiences. The cumulative effect pushes more creators toward alternative revenue models including live performance, merchandise, direct fan support platforms, and sync licensing for film and advertising.
The streaming fraud epidemic ultimately poses fundamental questions about the sustainability of current digital music economics. If platforms cannot effectively prevent billions of fraudulent streams from contaminating royalty pools, the legitimacy of streaming as the music industry’s primary revenue engine faces mounting scrutiny. Artists, songwriters, and rights holders may increasingly question whether existing systems adequately protect their economic interests or if technological disruption has created fundamentally broken distribution mechanisms that benefit bad actors at creators’ expense. Apple Music’s two billion fraudulent stream disclosure, while demonstrating detection capability, simultaneously confirms the massive scale of ongoing theft that continues despite platform efforts.
