Tech • AI • Robotics • Game
Music and AI: What Will Be Left for Artists? With Céline Magnano
5/10TL;DR
Artificial intelligence is reshaping music creation and distribution, offering new creative tools while intensifying long-standing problems in artist pay, market access and professional identity.
KEY POINTS
A fragile music economy predates AI
Céline Maniano, a French singer-songwriter, producer and adviser to independent artists, argues that the core crisis is not only technological but economic. After 25 years in the field and the launch of a recording studio in 2016, she says artists’ mental health has been weakened by unstable incomes, weak streaming payouts and a live sector increasingly dominated by expensive headliners. Festival programming, she notes, often sidelines emerging acts because major names have become the main revenue draw.
Rights distribution is still based on outdated measurement
One of the clearest failures, she says, concerns neighboring rights paid to performers. In one case, a duo received very little despite heavy airplay on major regional stations because collection and distribution systems rely on a listening panel that mainly tracks large national broadcasters. Regional stations still contribute money to the system, but plays there can go largely uncounted, highlighting a gap that newer technology could help close.
AI is expanding access to music-making
Maniano rejects the idea that AI users are not “real” creators. She says many newcomers discover music through tools such as generative platforms, then move on to instruments, production and technical learning. In that sense, AI can act as an entry point rather than a replacement, widening access to creation for people who previously lacked training or confidence.
Fear centers on jobs, legality and oversupply
Resistance from musicians is tied to real concerns: copyright disputes, unclear regulation, lower production costs and a flood of content. Maniano describes today’s environment as one of “abundance,” where AI can generate large volumes of music cheaply. That creates pressure on professionals already struggling to stand out and raises the risk that some tasks once done by paid specialists will be automated or reduced.
Two music economies may emerge
Maniano sees a likely split between low-cost, high-volume music made for passive listening and more labor-intensive “premium” music built around performers, instruments and live fandom. She compares the distinction to Louis Vuitton and Shein coexisting in fashion. She acknowledges the boundary is hard to define, especially as AI tools are increasingly embedded in mainstream production software rather than used only for full song generation.
Labels and scoring systems are starting to appear
The industry is moving toward disclosure rules for AI use in music, including labels attached to tracks. Maniano points to early discussions around a media “Nutri-Score” model that would estimate the share of human and AI contribution rather than using a simple yes-or-no label. She says fully AI-focused labels, AI-free labels and even an experimental AI radio format already exist, with classical music proving especially successful on one such station.
AI is already being used in practical, limited ways
Artists are not only using AI to generate entire songs. Maniano cites an electronic artist who used ElevenLabs voices in an otherwise self-produced project, creating a need for proper disclosure rather than outright rejection. She also describes a sound engineer using Claude to build custom plugins, showing how AI can support technical workflows and speed up repetitive tasks without replacing craft altogether.
Live performance could be a major testing ground
A new Théâtre de l’IA in Paris is exploring how artists can use automated visuals, clips and lighting tied to their music, whether or not the music itself is AI-made. That points to a broader role for AI in stage production, where automation could improve live shows for smaller artists who lack the budget for large technical teams.
Adaptation may matter more than purity
Maniano says some workers are already losing jobs, including people in creative fields adjacent to music. Her response is pragmatic: AI is not likely to disappear, so the choice is often between remaining a victim of the shift or becoming an active participant in it. She calls for “desanctifying” AI through education and guidance, especially for artists and professionals who have not yet adapted.
Success still depends on singularity
For young artists, Maniano says the key is not doing everything alone but building a distinctive identity. She argues that technical skill, self-questioning, strategic thinking and collaboration matter more than clinging to the myth of the all-in-one singer-songwriter. In an era of abundant content, originality of persona and direction may be more valuable than claiming complete artistic purity.
CONCLUSION
AI is not creating the music industry’s crisis from scratch, but it is accelerating every unresolved tension inside it. The next phase will likely depend less on whether AI is accepted and more on how rights, labeling, pay and artistic value are redefined around it.
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