Victoria Monét and Jimmy Jam & Terry Lewis hard truth about AI and music

As part of its celebration of Black Music Month, Pandora launched an “Artists on AI” takeover on its Black Music Forever station, bringing together some of the most respected voices in R&B to weigh in on one of the most pressing conversations in the music industry today. The featured lineup includes Grammy winners Victoria Monét and Ella Mai, alongside Mario, Deborah Cox, Raheem DeVaughn, Alex Isley, Rock and Roll Hall of Fame inductees Earth, Wind and Fire, and legendary production duo Jimmy Jam and Terry Lewis. The result is a candid, wide-ranging dialogue about creativity, ownership, and where artificial intelligence fits into the future of music.

Jimmy Jam: regulation, compensation, and the road analogy

Jimmy Jam approached the conversation with a clear-eyed perspective rooted in what he sees as the irreplaceable quality of human-made music: its imperfections. For him, the spontaneity and unpredictability that come with human creativity are precisely what give music its emotional power. The idea of that element disappearing from the process is something he finds genuinely troubling.

On the question of regulation, he was equally direct. He argued that any use of an artist’s voice, likeness, or body of work without explicit permission is fundamentally disrespectful, and that compensation must be built into any framework that allows AI to train on existing music. His position is that creators whose work feeds AI systems deserve to be paid for that contribution, full stop.

To illustrate where he believes the industry needs to go, he drew a parallel to the evolution of the automobile. Cars arrived, roads got paved, speed limits were established, lanes were painted, and safety features were added over time. He sees AI as following a similar trajectory, one where guardrails, rules, and protections need to be layered in deliberately. He also made the case that AI-generated music should exist in its own separate category, with its own charts and its own award recognition, rather than competing alongside human-created work at institutions like the Grammys.

Terry Lewis: get on the train or get off the tracks

Terry Lewis brought a more pragmatic edge to the conversation, centering his position on a single concept: accountability. His argument is that because AI systems are trained primarily on successful music rather than failures, the artists behind those hits are directly fueling the technology. That contribution, in his view, demands compensation and acknowledgment.

But where Lewis departed from a purely critical stance was in his insistence that resistance alone is not a strategy. He laid out three positions a person can take in relation to AI: stand on the tracks and get run over, ride as a passenger, or get in the driver’s seat and learn to steer it. He placed himself firmly in the third category, drawing a comparison to the introduction of the synthesizer and Auto-Tune, technologies that disrupted music in their time but ultimately became tools in the hands of skilled creators.

His most pointed observation came through a football analogy. What makes elite performers special is that not everyone can do what they do. When AI lowers the barrier to making music so dramatically that everyone is producing songs from home, the market gets flooded and the perceived value of artistry collapses. His answer to that is not despair but elevation: be a better performer, be a more compelling interpreter of your art, because that is what AI will never be able to replicate.

Victoria Monét: stolen identities and missing protections

Victoria Monét brought an intensely personal dimension to the discussion that grounded the larger debate in lived experience. She began by drawing a comparison between AI and fast fashion, noting that people often embrace products without understanding the hidden costs behind them. If audiences truly understood what AI development costs in terms of human labor, environmental impact, and creative exploitation, she believes the conversation would shift dramatically.

Her central concern, however, is legal. She argued that the laws designed to protect human creators have not kept pace with how fast AI has moved. She pointed to the music sampling process as a model worth following: when an artist samples an existing song, there is a formal clearance process and the original creator is compensated. AI, by contrast, draws on existing voices, aesthetics, stories, and identities with no such accountability in place.

She made that argument concrete by describing a situation involving an AI-generated avatar that bore striking resemblances to herself and other real artists, carrying a similar name, a comparable look, a recognizable sound, and even backstory elements drawn from other artists’ real lives. Her framing of it was stark: it is still stealing, even if the face doing the stealing belongs to a machine rather than a person.

Monét was careful to acknowledge that she does not want to be dismissive of technology’s potential. But her bottom line was clear. Until the systems protecting human creators are properly built and enforced, AI in its current form is operating in a way that turns real people into casualties of someone else’s algorithm.