SAMSIDDHA@AI

Tarak Dhurjati

Recently I gave prompts of my poem published in the anthology ” The Unedited juvenile verses , Part-1, ” available on Amazon. I added an interlude “dum dum dum tipu tipu tipu tipu” to make it funny. Hats off to the power of AI, it generated a great song which I have posted above. But it opened up many questions in my mind on the IP landsacpe. Below are some answers I have tried to put together. The original lyrics of the song above can be found on my flickr page,https://www.flickr.com/photos/amygdalane/7758729416/

The rise of generative AI platforms such as Suno and ElevenLabs has quietly but fundamentally altered the architecture of music creation. What once required years of training, access to studios, and collaboration across composers, vocalists, and producers can now be executed through a well-crafted prompt. A full song—lyrics, melody, instrumentation, and even a convincing human voice—can emerge in seconds. But beneath this convenience lies a structural disruption: the decoupling of creativity from authorship, and consequently, from ownership.

At the heart of this shift is a legal system that has not yet caught up. Copyright law, across jurisdictions, still rests on a foundational principle—human authorship. This creates an immediate paradox. If a song is generated entirely by AI, even if prompted by a human, it may not qualify for copyright protection at all. In effect, such works risk falling into a grey zone resembling the public domain, where exclusivity—the core economic engine of creative industries—becomes difficult to enforce. The implication is stark: the easier it becomes to create music, the harder it becomes to own it.

This is where the role of the modern musician is being redefined. The creator is no longer just a composer or performer, but a “prompt architect,” someone who shapes inputs rather than directly crafting outputs. However, from a legal standpoint, not all inputs are equal. A vague instruction—“create a sad indie song”—offers little ground for ownership. In contrast, a structured creative process involving original lyrics, melodic direction, arrangement decisions, and iterative refinement begins to establish a layer of human authorship. The distinction between AI-generated and AI-assisted work is no longer semantic; it is the line that determines whether a piece of music can be owned, licensed, and monetized.

Complicating matters further is the question of how these AI systems are trained. Many generative music models have been built on vast datasets that include copyrighted works, often without explicit permission from original creators. This has triggered a wave of legal challenges from record labels and artists, arguing that such training constitutes unauthorized use. At the same time, the industry is beginning to pivot from confrontation to collaboration, with licensing agreements emerging between AI platforms and music rights holders. This signals a broader transition—from an unregulated frontier to a negotiated ecosystem where data, models, and outputs are all part of a layered rights structure.

Yet even as platforms evolve, their terms of service introduce another layer of ambiguity. Tools like Suno and ElevenLabs may grant users commercial usage rights, but this is not equivalent to copyright ownership. It is a contractual permission, not a statutory right. You may be allowed to distribute and monetize a track, but you may not have the legal authority to prevent others from creating similar outputs using the same or similar prompts. In traditional music, ownership provides defensibility; in AI music, that defensibility is often diluted.

For serious musicians, this does not mean abandoning AI—it means approaching it with strategic discipline. The most effective approach is to treat AI as an instrument rather than an author. This involves embedding clear human creativity into the process: writing original lyrics, guiding composition, restructuring outputs, and, where possible, re-recording or reproducing elements outside the AI platform. Maintaining detailed records of prompts, iterations, and edits can also serve as a form of authorship evidence, particularly in a landscape where disputes are likely to increase.

There is also a growing need to think in terms of a new intellectual property stack. In AI music, ownership is no longer a single layer but a composite: the prompt, the generated output, the underlying model, the training data, and even the voice or stylistic likeness being emulated. Each of these layers may be governed by different rights and stakeholders. For instance, generating a song “in the style of” a well-known artist may not only raise copyright concerns but also issues related to personality and publicity rights. The boundaries of creative borrowing, homage, and infringement are being redrawn in real time.

Ultimately, AI is not eliminating creativity; it is redefining scarcity. When anyone can generate music instantly, the value shifts from the act of creation to the authenticity of expression and the clarity of ownership. The musicians who will thrive in this environment are not those who resist AI, but those who integrate it while preserving a distinct human signature and a defensible claim over their work. In this sense, the future of music may not belong to those who can create the fastest, but to those who can prove, with clarity and intent, that what they have created is truly their own.