A multi-vector attack on the entire AI music business model, combining copyright law, DMCA anti-circumvention claims, biometric privacy (BIPA), right-of-publicity law, and consumer deception.

Attack the Sound v. Kunlun Tech: Evidence, Power, and the Next Phase of the AI Copyright Wars

by ChatGPT-5.2

Introduction

Attack the Sound v. Kunlun Tech arrives at a pivotal moment in the global AI copyright wars. It is the 66th U.S. copyright lawsuit against an AI company, and only the second such case targeting a Chinese technology firm in a U.S. court, following Disney v. MiniMax. The defendants—Kunlun Tech and its Singapore-based AI subsidiary Skywork—operate Mureka, a large-scale AI music generation platform marketed as a “royalty-free” replacement for licensed music.

At first glance, the lawsuit resembles earlier actions against Suno, Udio, and Uncharted Labs. But a closer reading of the complaint reveals something more ambitious: this is not merely a copyright infringement suit. It is a multi-vector attack on the entire AI music business model, combining copyright law, DMCA anti-circumvention claims, biometric privacy (BIPA), right-of-publicity law, and consumer deception.

How Strong Is the Evidence?

1. Structural Evidence vs. Output Similarity

One of the complaint’s most strategically sound aspects is its explicit rejection of “output similarity” as the core evidentiary burden. Plaintiffs argue that liability does not hinge on whether a generated song is “note-for-note” similar to a protected work. Instead, infringement arises from:

• Systematic copying and retention of full sound recordings and compositions

• Maintenance of centralized libraries of copyrighted works

• Use of those copies across pre-training, training, and fine-tuning phases

This framing is important because it aligns with emerging judicial skepticism toward “style-only” defenses and anticipates the evidentiary difficulties that doomed earlier “this sounds like X” arguments. From a legal-strategy perspective, this is high-quality, forward-looking pleading rather than speculative rhetoric.

2. Technical and Forensic Detail

The complaint goes unusually deep into technical infrastructure:

• IP addresses and DNS records allegedly tying mureka.ai to U.S.-based Alibaba Cloud servers

• Client-side telemetry, analytics tags (Microsoft UET, Yandex Metrica), and affiliate tracking

• API documentation and marketing claims about “model fine-tuning” using user-supplied tracks

This level of specificity strengthens plausibility under Twombly/Iqbal standards and signals that plaintiffs are positioning themselves well for discovery battles. Unlike earlier AI cases that relied heavily on inference, this complaint lays out concrete, testable claims about copying, storage, and U.S. nexus.

That said, some allegations—particularly around stream-ripping and DMCA circumvention—remain pleaded “on information and belief.” These will require hard proof (logs, code, contracts with data suppliers) to survive later motions. The evidence is credible, but not yet proven.

3. Jurisdictional Evidence: Strong and Unusually Thorough

The jurisdictional section is strikingly detailed. Plaintiffs document:

• U.S. app-store distribution

• Dollar-denominated subscriptions

• Illinois-based plaintiffs uploading audio and receiving outputs

• U.S.-based servers and payment infrastructure

This is not boilerplate. It reflects a conscious effort to pre-empt forum challenges by a Chinese defendant. On evidentiary quality alone, the jurisdictional showing is among the strongest seen in AI copyright litigation to date.

Most Surprising Findings

1. BIPA and Voiceprints as a Core Weapon
   The inclusion of Illinois Biometric Information Privacy Act claims—focused on vocal characteristics and voice synthesis—is notable. BIPA carries statutory damages per violation, making it a potentially existential risk regardless of copyright outcomes.

2. “Royalty-Free” as Alleged Deception, Not Marketing Puffery
   Plaintiffs frame Mureka’s assurances as false commercial representations, not aspirational language. If courts accept this framing, it could destabilize the entire “safe for YouTube/TikTok” positioning of generative music tools.

3. Independent Artists, Not Major Labels, as the Center of Gravity
   The case deliberately foregrounds market displacement of independents, reframing AI harm as a competition and labor issue rather than a superstar-catalog dispute.

Most Controversial Claims

1. Centralized Libraries Beyond Technical Necessity
   Courts have not yet squarely ruled on whether retaining training data copies beyond ephemeral processing defeats fair use. This is a legally aggressive claim and likely to be fiercely contested.

2. DMCA Circumvention via Reference Tracks
   The allegation that reference-track features encourage circumvention of access controls pushes DMCA doctrine into unsettled territory. This is innovative—but risky.

3. Extraterritorial Reach Over Chinese AI Firms
   While jurisdictional facts are strong, enforcing discovery and remedies against a Chinese parent company remains geopolitically sensitive and practically complex.

Most Valuable Contributions of the Case

• It decouples AI copyright liability from output similarity, accelerating doctrinal clarity.

• It integrates privacy, publicity, and consumer-protection law into AI governance.

• It tests whether “royalty-free AI” can survive scrutiny as a legally meaningful concept.

• It pressures courts to confront data provenance and training-set governance, not just model outputs.

Future Predictions: Where This Case Is Likely Headed

1. Early Motions Will Narrow, Not Kill, the Case
   Expect partial dismissals (likely trimming DMCA or deceptive-trade-practice claims), but the core copyright and BIPA theories are strong enough to survive.

2. Discovery Will Be the Real Battlefield
   If plaintiffs gain access to training logs, storage architectures, or third-party data-supplier contracts, this case could become a template for future AI litigation, not just music cases.

3. Settlement Is More Likely Than Trial
   Given cross-border enforcement risk and reputational exposure, a confidential settlement with behavioral commitments (training disclosures, opt-outs, licensing pilots) is plausible.

4. Regulatory Spillover Is Inevitable
   Regardless of outcome, regulators—especially in the EU and U.S. states—will cite this case as evidence that voluntary AI copyright compliance has failed, accelerating statutory intervention.

5. AI Music’s “Napster Moment” Is Approaching
   If even one court accepts the plaintiffs’ centralized-copying theory, AI music platforms will face a structural reckoning akin to early peer-to-peer litigation.

Conclusion

The Attack the Sound v. Kunlun Tech complaint is not perfect, but it is serious, technically informed, and strategically ambitious. Its evidentiary quality—especially on jurisdiction, infrastructure, and market displacement—is materially stronger than many earlier AI copyright suits. More importantly, it reflects a shift in posture: creators are no longer arguing about vibes, styles, or ethics. They are arguing about systems, pipelines, and power.

Whether Kunlun ultimately prevails or not, the case signals that the era of low-risk, high-opacity generative music is ending. What replaces it—licensed models, regulated datasets, or fractured markets—will define the next decade of AI and creativity.