The Next Wave: Future AI Threats That Could Reshape Music Rights Forever

From deepfake vocals to AI-generated compositions, AI has already completely upended the music industry. For many, this new and evolving technology already poses significant roadblocks to the creative industries, but the reality is that AI's evolution has only just begun, and tomorrow's threats are soon to become a new baseline. 

In particular, as new advancements emerge across the broader tech landscape, the risks to music copyright grow more complex and harder to predict. This blog explores four future-facing AI threats—Quantum Computing, Model Context Protocol, Agentic AI, and Local AI—and what they could mean for the future of music copyright.

1. Quantum Computing: Increased Speed + Scale

Quantum computing is an emerging field of technology that leverages the principles of quantum mechanics to perform complex computations far faster than traditional computers. Unlike classical machines that process bits in binary, quantum computers use qubits, which can represent multiple states simultaneously—enabling massive parallel processing power.

This acceleration holds the potential to supercharge AI training by orders of magnitude. What used to take weeks or months to train could happen in hours—meaning the cloning of voices, the imitation of musical styles, and the generation of derivative works could happen on an industrial scale. For music rights holders, this means more replication, faster than ever before, and with even fewer signals to track. Detection tools will need to evolve at the same breakneck pace to remain effective.

2. Model Context Protocol: Transparent Access, Murky Implications

Model Context Protocol (MCP) is a new open standard designed to connect AI models directly to data sources—everything from cloud drives to enterprise systems. Its purpose is to make AI assistants more contextually aware and relevant in their responses by breaking down the silos that separate AI models from the datasets they interact with.

While this development has enormous potential for increasing AI utility, it also raises serious concerns for the music industry. By allowing seamless access between AI tools and vast databases, MCP could make it significantly easier for AI systems to access and utilize commercial music content—including the works of independent or emerging artists. This increases the likelihood of unauthorized training, stylistic imitation, and even voice replication.

3. Agentic AI: Independent, Unpredictable Creative Actors

Agentic AI refers to systems that can make autonomous decisions, set objectives, and carry out tasks without ongoing human input. These models go beyond simply responding to prompts—they can initiate actions, interact with multiple data sources, and iterate on their own outputs based on feedback or predefined goals.

In a musical context, this could mean an AI model that independently decides to generate an album designed to appeal to trending algorithms, combining vocal characteristics and genre stylings pulled from public datasets. Unlike today's generative models, which still rely on user direction, agentic AI could essentially become a self-publishing artist—one who is not bound by copyright law, licensing agreements, or ethical considerations.

This autonomy creates unprecedented legal grey areas, especially when the content produced resembles existing works or artists. For rights holders, this raises fundamental questions about accountability and enforcement: If there’s no clear user prompt or directive, who is legally responsible for infringement?

4. Local AI: Power to the (Untraceable) People

Local AI refers to models that run directly on personal devices, rather than relying on cloud computing or centralized servers. This is increasingly possible as models become smaller and more efficient, capable of functioning offline without a drop in performance. While this democratizes access to powerful tools, it also decentralizes risk.

For music, this means anyone with a modern smartphone or laptop can download an AI model capable of replicating a well-known artist’s voice, generating instrumentals in a popular style, or manipulating audio with near-professional precision—all without triggering any detection system or leaving a digital footprint.

Traditional enforcement mechanisms, which rely on monitoring public platforms or cloud-based activity, are rendered ineffective in this context. The implications are significant: piracy, unauthorized remixes, and vocal deepfakes could proliferate unchecked, especially in underground or peer-to-peer networks. Without the ability to trace these activities back to a central server, holding anyone accountable becomes nearly impossible.

Learn More: Managing AI Threats Within Music

If you're an artist, label, publisher, or music business stakeholder looking to better understand these complex challenges, we recommend checking out our recent webinar with the Music Business Association: The Future of AI and Music: Balancing Innovation, Licensing, Ethics, and Copyright

With insights from expert guest speakers, the workshop dives into many of the themes explored in this article—examining how AI is reshaping music creation and consumption, and what the industry must do to ensure innovation doesn’t come at the cost of creators' rights.

Whether you're just beginning to grapple with AI’s implications or already searching for solutions, the insights from this event are invaluable for preparing for what's next.

You can watch the webinar here.