The music industry's AI reckoning is already here

The music industry is facing its own Turing test. 

While practically every field is reckoning with how to navigate AI's impending transformation, the music industry sits at a unique crossroads: When a creation that we once believed to be distinctly human can be automated, what happens to the artist? 

The question about where AI fits into the music industry has evolved from debate into a cacophony. While the loudest voices are aggressively for it or against it, the reality of how musicians are using AI and the tech's risks are far more nuanced, according to a variety of experts we interviewed. The lines they’re drawing around how it should and shouldn't be used reveal a constantly moving target. 

"The dominant discourse is one of disruption—that AI is going to radically disrupt the industry," Robert Prey, professor of digital culture at Oxford University, told the Deep View. "You'll hear everything from, 'AI-generated artists are going to replace all human-generated music' to 'everybody can be a musician,' and 'it democratizes creativity.' There's both optimistic and very pessimistic versions of this disruption narrative." 

How AI runs the gamut

AI can appear in music in many ways. While some camps use AI like a smarter version of Clippy, asking it to "find these drums" or "clean up these vocals," others turn to platforms like Suno and Udio to generate music from the ground up, whether it’s specific segments, reimaginations, or creating an entire song from a prompt.

Liam Weiland, a freelance recording engineer in Los Angeles, has worked on songs entirely made with AI, as well as others that used it more selectively, such as ones "where the beat stayed AI, but then we redid all the vocals and wrote a different song on top." 

Even though platforms can fully generate songs, they aren’t necessarily primed to produce the final product. David Das, vice president of the Society of Composers and Lyricists, said Suno is intended to be a "stopping point along the way" to creation, as a "means to an end."

"Sometimes people take one song, and then they'll use AI to hear it in different genres or different potential sounds to see if they want to try to do that," Weiland told The Deep View. 

AI is also inextricably tied to stem separation, or stripping songs into pieces so that musicians can isolate instruments, remove vocals, or create backing tracks. Perhaps first brought to general knowledge by Kanye West’s Stem Player, which was sold with the release of 2021’s "Donda," stem separation is a vital part of production. It can play a role in remixing, sampling, or inspiring new creations. 

"There are a lot of really interesting creative applications for (stem separation), whether it's in remixing or fixing things or re-making legacy recordings," Jonathan Wyner, the head of artistic technology initiatives for Berklee Emerging Artistic Technology Lab at Berklee College of Music, told the Deep View. "There are certain things I can do in mastering that I could never have done before."

Wyner added that AI can be a helpful tool when musicians start with a vision and exercise "strong editorial control of the final version." Then, musicians can pull AI out of the equation and flesh out the piece themselves. Armin van Buuren, a Dutch Grammy-nominated DJ and producer, takes a similar approach. He told The Deep View that he uses AI to ideate and build out demos, then has artists sing or play parts that AI helped him ideate.  

"If you just accept its output as the whole finished thing," Wyner said, "then it's unlikely to be something that really reflects the artist."  

Carmine-Emanuele Cella, a professor and composer who serves as co-director of UC Berkeley’s Center for New Music and Audio Technologies, taps into AI when he’s composing to smooth over "boring, time-consuming, frustrating, and menial" tasks. Cella himself has developed software specifically for this task.

What leads to stronger outputs is interacting with the creative process, not just pushing a button or typing a prompt, Seth Forsgren, co-founder and head of product for Flow Music, told The Deep View. Without a vision, "It's not going to be a great piece of art, and you're probably not going to love it that much."

Even musicians who think the tool is going to speed up their processes find it can slow them down, Forsgren said: "Now they have a million options and they can generate an unbelievable array of ideas and samples."

AI in the music industry may also go the same route as AI in enterprise contexts: Niche, small models for specific genres or styles, Robert Rowe, a New York University professor who teaches about music innovation, told The Deep View. "If you can train a small model to capture part of your own musical style… then it's really going to take off."

The controversy 

Still, AI is tearing the music industry in two directions. More often than not, you’ll hear musicians railing against it, not championing it. 

Producer and musician Jack Antonoff called AI music creators "godless whores" in a viral post on Instagram. Paul McCartney joined more than 1,000 musicians in creating a silent protest album, titled "Is This What We Want?," to discourage the UK government from letting AI models train on copyrighted work without a license. 

King Gizzard & the Lizard Wizard, a band with more than 15 years of history, had to combat an AI-generated clone band called King Lizard Wizard, which appeared on Spotify after the group pulled its own music from the platform (specifically because of CEO Daniel Ek’s investment in AI military drone technology). 

When AI replaces musicians, even just for one instrument track on a song, the effects extend beyond reputational harm for the act itself: It cuts into a swath of jobs. Session players and players-for-hire are particularly at risk, Weiland said, especially if they’re unionized. If you want to add a bass track, for example, AI is faster and cheaper than bringing someone into the studio. 

All kinds of "middle-class musicians" can feel the impact, Prey said. That includes sync licensing staff and jingle writers, too. Entry-level jobs in music are primed to disappear. If AI does take those jobs away, the industry needs to create programs that allow emerging artists to break into the industry, Prey said. 

"There are probably some creators who are embarrassed about using (AI)," said Das. "And there are probably many creators who are experimenting with using it and don't quite understand the ramifications of (it) yet." 

The democratization pitch

The companies that are building AI music generation models are all generally driven by the same ideal: That the creation of music should be democratized, and that technological innovation can make that happen. 

The democratization argument is the pitch that has turned music generation platform Suno into a $5.4 billion company after a $400 million funding round in June, with CEO Mikey Shulman noting in the company's blog post announcing the round that "music creation is no longer the domain of a niche few." Shulman has gone so far as to say in an interview with the 20VC podcast last January that, even for those niche few, "It's not really enjoyable to make music now." 

AI isn't the first technology to democratize music. For instance, streaming broke down barriers that allowed artists to release music independently of major record labels, leading to the rise of "bedroom-produced records," Cecil Coleman, a drummer in Australian band Body Type, a former sync licensing worker, and a recent law school graduate, told The Deep View. "This is just the next step in that evolution," she said. 

Historically, the technology behind recording music democratized it for those who couldn't afford to attend concerts, Forsgren said.

"The precedent of technology democratizing music is just a trend for all time," Forsgren said. "Having a piano was something that only the rich could have back in the Middle Ages. That's very different from today, where everybody can pick up that instrument and make music."

Flow Music, which is built on top of Google's Lyria models and allows users to build songs using a "conversational producer" for an iterative song development process, was founded by Forsgren as Producer.ai in 2023 and acquired by Google in April. Flow offers generative music capabilities, allowing users to generate, extend, remix or edit songs from text, image or audio files, as well as generate lyrics and instrumentals individually. 

However, because full-blown generative use cases tend to stoke the strongest backlash, some AI companies are avoiding them entirely. Moises, an AI-powered music platform that pegs itself as the creative suite for musicians, "only works in building blocks," CEO Geraldo Ramos told The Deep View. Though Moises’ primary offering is stem separation, last year it launched its AI Studio, a generative tool that can create "context-aware" instrument stems around a musician's existing tracks. "We never do full song generation." 

"When you mix AI and music, people think about the 'Suno type' of AI, which is not always the case," Ramos said. "Many of the tools that help musicians and producers are not generative. But I think AI got this bad rep because of the generative side, and some people get sensitive about that." 

But even with fully-generative tooling, Forsgren said, it's a misconception that plugging in a prompt will automatically create a hit. AI is simply an extension of software like Ableton and Pro Tools—even if it makes production easier, you still have to know how to use it.

"We have these generative models: the Gemini model, the Lyria model, the Nano Banana model, the Omni model. Those are all what I think of as almost instruments, and we put those instruments into our studio," said Forsgren.

The data labyrinth

The problem with the democratization narrative, both legally and ethically, comes up when you remember what, exactly, is being democratized. One of the biggest criticisms of generative models for music is the data that these tools are built on. 

It's why Moises decided from the jump to never rely on user data to train its models, opting instead for licensing agreements and using in-house musicians to record music for training. "If you're a musician and you put your creations inside Moises, it's yours, we don't touch it," said Ramos. 

Because it's built on Google's Lyria models, Flow Music, meanwhile, follows Google's data policies as it relates to AI, Jeff Chang, director of product management at Google DeepMind, told The Deep View. It trains on music that it has legal rights to through terms of service and partner agreements. Additionally, Google Flow Music has a toggle settling that allows users to opt out of their data being used to train its models. 

Chang said the company has done "a lot of work behind the scenes" to make sure Lyria models don't simply replicate or regurgitate existing works. This avoids the other side of the copyright problem: When AI generates something that sounds a little too close to existing work. 

"We're working really hard to maximize the probability of that being a unique sound, a unique song, a unique musical thing that has never been heard before by anybody in the world," Chang said. 

But not every company has been as careful. Suno is embroiled in legal battles with major record labels Universal Music Group and Sony Music Entertainment, who allege that Suno trained its music generation model on copyrighted material without permission. The labels filed a second lawsuit against Suno in September, challenging its most recent model v6 and alleging that the company stole more than 62,000 recordings from the labels.  Additionally, a July report from 404 Media claims that Suno scraped millions of songs and lyrics from dozens of platforms, including YouTube Music, Deezer, and Genius. 

Udio, Suno's biggest rival, has faced similar allegations, and is still in active litigation with Sony Music, which alleges that the company copied more than 30,000 recordings from artists on its roster. Udio, however, has settled with both Warner Music Group and Universal, each landing on agreements to build licensed AI music creation platforms. These settlements landed the labels in even more hot water, with the American Federation of Musicians launching its own lawsuit against the labels for breach of contract.

Suno and Udio could not be made available for interviews with The Deep View.  

The brass tacks 

Wrapped up in legal battles, record labels are walking a tightrope. They want to embrace technology that could help their artists improve and experiment, while they navigate deciding which organizations to settle with and which to continue litigating against so they can best protect their artist rosters. It's why several of the settlements between labels and AI companies have led to partnerships. Both entities see a money making opportunity.

For instance:

• In September, Sony became the first music company to join the Alliance for Responsible Innovation in the Arts and Media, or ARIAM, an initiative focused on responsible AI deployment across creative and media industries, specifically focused on licensing. 

• Major labels and industry firms, including Warner, BMG and Believe, partnered with Suno in its launch of v6, its most recent model. Warner CEO Robert Kyncl told staff in a note that a "new creation-based revenue stream" will only grow as a result of AI, according to Axios.

• Additionally, BMG signed its own partnership with Suno in August to "develop entirely new music experiences" and ensure that artists under the BMG label that participate will be compensated. 

• Suno aside, August, Universal, Sony and Warner backed the $76 million Series B funding round for Stability AI, a generative model company that offers image and audio creation. 

• And Universal announced a multi-year partnership with AI voice firm ElevenLabs to create an "AI-powered music platform for fans" and develop tools for producers, artists and songwriters. 

"They seem conflicted," said Das. "They seem like they can't decide if they want to be on this train, because they can be in agreement with the new technology, or if they want to protest this agreement, because it's stealing from them. And in reality, they're trying to do both at the same time." 

Though Universal couldn't comment on ongoing litigation, Michael Nash, executive vice president and chief digital officer of the label, told The Deep View that deciding which AI companies are fit to partner with "comes down to alignment," determined by whether or not the artists rights and interests are prioritized and respected. "Is there an appropriate licensing framework?" said Nash. "Does the artist have control? And is there an economic model that rewards artists and songwriters for the value they bring?"

Though there are clear opportunities to create value with this technology, the industry is still navigating how to do so in a way that translates that value into revenue for artists and songwriters, said Nash. 

"We went through that process with streaming and user-generated content. AI requires that same kind of business-model innovation," he said. 

Starting from a point of copyright and associated rights could offer the foundation to work with these AI firms in a way that benefits all parties involved, he said. And copyright itself protects more than just the artist's revenue. It also protects the integrity of the original music, preventing others from misusing the work and distorting the artist's original intention. 

"Our starting point has always been the artists and songwriters," said Nash. "Defend their rights, advance their interests, and build creative and commercial opportunities from there." 

But copyright questions aren’t the only thing haunting the industry. 

Along with the challenges posed by these AI companies themselves, streaming has created an opportunity for bad actors to hack the system and abuse it. Because streaming royalties generally work as a collective pot that’s distributed based on listens, spam tracks can dilute the revenue pool of actual musicians. 

And AI is already starting to show itself as a force multiplier in creating those tracks: 

• Last September, Spotify announced that it had deleted 75 million spam tracks from its platform in the past year. And because Spotify doesn't explicitly label AI music on its platform, some have created trackers to fill in the gaps, including websites like SlopTracker and SoullessMusic.com. 

• Tidal, meanwhile, updated its policy so that 100% AI-generated tracks that are tagged with its AI detection badge, while allowed on the platform, are blocked from earning royalties. Any content that impersonates human artists, meanwhile, is stripped from the platform.

• YouTube is similarly cracking down on AI tracks, announcing in mid-October that it would label AI-generated songs across both its flagship streaming platform and YouTube Music. 

• In April, French streaming service Deezer said that almost 75,000 AI-generated songs are uploaded to the platform every day, representing around 44% of its total daily uploads. As a result, Deezer launched a tool to identify AI-generated tracks both on its platform and others.

• And while Apple Music hasn't revealed how many AI-generated tracks it's had to contend with, the company has acknowledged the issue by launching AI transparency tags, though only if the label a track comes from explicitly declares that a song is AI. 

"It's always hard to speak in absolutes when it comes to AI because it's all about how AI is being used," Sam Duboff, senior director and global head of marketing and policy at Spotify, told The Deep View. "An artist using AI as one part of their production process, intentionally … that's really different than using AI to flood a streaming service and try to steal royalties from the royalty pool or impersonate another artist."

Duboff said that the company has had to balance reaping the benefits of AI with fighting fraud, while also not "policing the creative tools that people use." Additionally, navigating the shifting perceptions that listeners and users have around AI has presented another dimension to this challenge. 

"The single biggest problem is trying to build long-term solutions that make sense when the technology and opinions about it are changing faster than you can keep up," said Duboff. 

Van Buuren said that things like bot tracks and dupes of real artists represent the worst-case scenario for AI. "There's a lot of people who are trying to cash in, and they're just being evil. They're copying other people. They're doing the wrong things," said van Buuren. 

And while the streamers are trying to bring the hammer down on bad actors, they, too, face a double-edged sword, attempting to embrace AI with one hand while fighting it with the other. Apple Music, YouTube Music and Spotify all brandish their own AI features, including AI-generated, prompt-based playlists and generative DJs.  

"The worst-case scenario is that the streaming services that we use get absolutely flooded by this type of music, and … there's no ability to distinguish between an AI-generated track and an emerging artist that you maybe haven't heard of before," Prey said. "What happens is the only artists that are able to build a career are the ones that already have name recognition, or the ones that have institutional backing behind them." 

The line in the sand 

Despite the narrative that AI companies want to empower musicians and democratize the act of creating music, rather than breaking the foundations of music, the fact of the matter is that AI is undoubtedly disrupting the industry. Along with completely shifting the avenues that musicians have available to make a living, AI is fundamentally transforming creation itself. 

AI has given people the ability to create music out of nothing other than an idea. And while the quality of the output of fully prompt-based AI music generation is generally questionable, musicians are still figuring out where along the spectrum AI use is ethically acceptable. And because the technology is changing everyday—as well as people's comfort levels with it—towing the line is a difficult balancing act, said Prey.

Some will only use it for brainstorming, only for ideation, or only to add one instrument track. And over time, the ethical line is going to change. Just as AutoTune was an industry concern until it became commonplace, Prey said, "a lot of artists are waiting for (AI use) to be normalized."

"It's definitely a moving target, and it always is with technologies like this," said Prey. "AI is not just using technology to assist you with certain things, it is literally coming up with the entire idea. Because we associate creativity so much with ideas, I think people are, rightly so, very suspicious of it." 

For Das, the ethical line is where the machine starts doing the creating for you. If a tool offers to think, brainstorm, and create for the musician, rather than acting as a partner, then the question shifts: Who wrote this song if someone prompted an engine to come up with ideas, build out instrumentals and vocals, or generate a composition entirely? 

"The line is … where it starts to infringe on the creation of the intellectual property, which I would define mostly as the composition," Das said. 

When you let AI overtake creation, Cella said, "You are not responsible for the process anymore. You are essentially enslaved to the outcomes." 

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