Anders v. Stability AI: What does consent really mean in AI licensing? What happens when opt-out mechanisms collide with legacy contracts?

When “Opt-Out” Isn’t Enough: Anders v. Stability AI and the Next Phase of AI Music Litigation

by ChatGPT-5.2

Introduction

The pro se copyright complaint filed by musician Jerry Anders (professionally known as Anders Manga) against Stability AI and Navarr Enterprises (operating as AudioSparx) is, on its surface, one more entry in the rapidly growing list of AI copyright lawsuits. It is reportedly the 73rd copyright suit against AI companies in the United States, filed in late December 2025.

Yet despite its modest procedural posture and the fact that the plaintiff is self-represented, the case raises issues that cut to the heart of AI training practices in the music sector: What does consent really mean in AI licensing? What happens when opt-out mechanisms collide with legacy contracts? And who bears responsibility when intermediaries fail to honor creator objections?

The Core Allegations

According to the complaint filed in the Western District of North Carolina, Anders alleges that:

• He is the copyright owner of multiple registered musical compositions and sound recordings.

• His works were made available to Stability AI through AudioSparx’s licensed music library and were used to train Stable Audio, Stability AI’s music generation model.

• His 2015 agreement with AudioSparx did not contemplate or authorize AI training as a licensed use.

• Prior to and after the launch of Stable Audio, Anders explicitly sought to withdraw his music from AI-related licensing, communicating objections and opt-out requests to AudioSparx and Stability AI.

• Despite these objections, defendants allegedly continued to copy, ingest, and commercially exploit his recordings for AI training and monetization purposes.

The complaint further emphasizes that Stability AI publicly represented that artists could opt out of Stable Audio training and that AudioSparx described AI licensing as a distinct category of exploitation—yet Anders contends that, in practice, these assurances failed him.

Why This Case Matters Beyond One Artist

1. The Opt-Out Paradox

One of the most significant aspects of the complaint is not the claim that music was used to train an AI model—that allegation is now common—but the assertion that opt-out rights were offered publicly yet denied operationally.

If true, this undermines a key industry narrative: that AI training disputes can be managed through opt-out frameworks layered on top of existing licensing regimes. Courts may begin to scrutinize whether such mechanisms are illusory, especially when intermediaries retain discretion to override creator intent.

2. Intermediary Risk Comes Into Focus

Unlike many AI lawsuits that pit creators directly against AI developers, Anders also targets AudioSparx, the licensing intermediary. This highlights a structural vulnerability in AI training pipelines: AI companies often rely on third-party aggregators and assume that licensing risk has been contractually outsourced.

This case suggests that “we licensed it from a partner” may no longer be a sufficient defense if the partner’s authority is contested or if creator objections are ignored.

3. Training as Copying, Not Abstraction

The complaint explicitly frames AI training as copying, reproducing, and exploiting full musical recordings, not as a purely transformative or abstract analytical process. This framing aligns with an emerging trend in AI litigation that challenges the notion that training is legally invisible simply because outputs are not identical to inputs.

Predicted Legal and Commercial Outcomes

1. Procedural Battles First

In the near term, the defendants are likely to challenge personal jurisdiction, venue, and pleading sufficiency, particularly given the pro se nature of the filing. These motions could delay substantive rulings but will not make the underlying issues disappear.

2. Settlement Pressure Over Precedent

Even if the defendants believe they could ultimately prevail, the cost-benefit calculus favors settlement. The reputational risk of litigating against a musician who repeatedly objected to AI use—combined with the discovery risks around training datasets—makes quiet resolution attractive.

3. Incremental, Not Explosive, Legal Change

This case is unlikely to produce a sweeping ruling on the legality of AI music training writ large. Instead, its impact will likely be incremental:

• strengthening arguments that AI training requires clear, purpose-specific authorization;

• increasing scrutiny of opt-out regimes;

• and accelerating the shift toward explicit AI-specific licenses.

4. A Signal to the Music Industry

Regardless of outcome, the case adds to mounting pressure on music licensors, publishers, and AI firms to clean up legacy contracts that never contemplated machine learning. The risk is no longer hypothetical.

Recommendations for AI Makers: How to Prevent This from Happening Again

1. Move From Opt-Out to Affirmative Opt-In

Opt-out mechanisms may reduce backlash, but they are increasingly seen as legally fragile. AI developers should require explicit, affirmative consent for AI training, especially in music and other high-value creative domains.

2. Audit Legacy Licenses—Ruthlessly

Any contract drafted before the rise of generative AI should be presumed insufficient for training purposes unless it explicitly says otherwise. Relying on silence or broad “future technologies” clauses is a litigation trap.

3. Do Not Outsource Accountability

Licensing intermediaries do not eliminate risk. AI companies should:

• require warranties and indemnities that specifically cover AI training,

• implement technical controls to honor removals,

• and maintain internal records of creator objections and dataset provenance.

4. Treat Training Data as a Regulated Asset

AI training data should be governed with the same rigor as personal data or financial records:

• documented sources,

• versioned datasets,

• clear removal workflows,

• and audit trails that can withstand legal scrutiny.

5. Align Public Statements With Reality

Public claims about ethics, artist choice, and opt-outs can become exhibits in court. Marketing language should be vetted not only by PR teams, but by legal and compliance professionals who understand litigation risk.

Conclusion

Anders v. Stability AI is not a blockbuster case—but it is a revealing one. It exposes the growing gap between AI industry assurances and operational reality, especially where creators actively resist the use of their works. As courts, regulators, and creators converge on the question of consent in AI training, cases like this will continue to erode the comfort of ambiguity.

For AI makers, the lesson is clear: permission cannot be retrofitted, and silence is not consent. The future of sustainable AI innovation depends not on how cleverly training can be justified after the fact, but on how responsibly it is authorized from the start.