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Was the recent Velvet Sundown phenomenon a great music and media hoax, a sign of things to come, or just another example of what’s already happening?

In case you missed it, the breakout act was streamed hundreds of thousands of times before claims emerged the band and their music were (GenAI).

Despite the “band” , an “associate” later admitted it was indeed an “art hoax” . Much of the subsequent commentary was concerned with fairness – particularly that a “fake” band was “real” artists.

But Velvet Sundown is only the most recent example in a long history of computer generated and assisted music creation – going back to the 1950s when a chemistry professor named Lejaren Hiller debuted a .

By the 1980s, David Cope’s created music so close to the style of Chopin and Bach it fooled classically trained musicians.

Artist and composer Holly Herndon was highlighting a need for the of voice models and deepfakes several years before Grimes invited others to to make new music, and “Deepfake Drake” .

At the same time, music companies, including , and rapper-producer , have since inked record contracts for AI-generated work.

GenAI-powered tools, such as those offered by , and , have become commonplace in mixing and mastering since the late 2000s. Machine learning technology also .

Creativity and copyright

Despite this relatively long history of technology’s impact on music, it still tends to be framed as a future challenge. The New Zealand government’s , released this month, suggests we’re at a “pivotal moment” as the AI-powered future approaches.

In June, a from Manata Taonga/Ministry for Culture & Heritage explored “how digital technologies may transform the ways New Zealanders create, share and protect stories in 2040 and beyond”.

It joins other recent publications by the and New Zealand’s , which grapple with the future impacts of AI technologies.

One of the main issues is the use of copyright material to train AI systems. Last year, two AI startups, including the one used by Velvet Sundown, were sued by Sony, Universal and Warner for as part of their training data.

It’s possible the models have been trained on recordings by local musicians without their permission, too. But without any requirement for tech firms to disclose their training data it can’t be confirmed.

Even if we did know, the copyright implications for works created by AI in Aotearoa New Zealand aren’t clear. And it’s not possible for musicians to in any meaningful way.

This goes against the data governance model designed by . Māori writer members of music rights administrator APRA AMCOS have also about potential cultural appropriation and misuse due to GenAI.

Recent research suggesting GenAI work in creative industries is particularly worrying for local musicians who . But it’s not an isolated phenomenon.

In Australia, GenAI has to impersonate successful, emerging and dead artists. And French streaming service Deezer claims up to 20,000 tracks created by GenAI were being daily.

Regulation in the real world

There has been increased , including a brought last year against a musician who used bots to generate millions of streams for tracks created with GenAI.

But on social media, musicians now compete for attention with a flood of “”, with no real prospect of .

More troublingly, New Zealand law has been described as “” at combating deepfakes and that can .

The government’s prioritises adoption, innovation and a over these creative and cultural implications. But there is that regulatory intervention is warranted.

The European Union has requiring AI services to be transparent about what they have trained their models on, an important first step towards an .

An Australian senate committee has recommended , including transparency requirements in line with the EU. Denmark has , with plans to give every citizen copyright of their own facial features, voice and body, including specific protections for performing artists.

It’s nearly 10 years since the music business was described as the “” for other industries and a bellwether of broader cultural and economic shifts. How we address the current challenges presented by AI in music will have far-reaching implications.

Dr Dave Carter is an Associate Professor within the School of Music and Screen Arts, Dr Jesse Austin-Stewart is a Lecturer in the School of Music and Screen Arts and Professor Oli Wilson is the Associate Director Research at Toi Rauwhārangi College of Creative Arts.

This article was originally published on .