If you think 100,000 songs a day going into the market is a big number, “you have no idea what’s coming next,” says Alex Mitchell, founder/CEO of Boomy, a music creation platform that can compose an instrumental at the click of an icon.
Boomy is one of many so-called “generative artificial intelligence” music companies — others include Soundful, BandLab’s SongStarter and Authentic Artists — founded to democratize songwriting and production even more than the synthesizer did in the 1970s, the drum machine in the ’80s and ’90s, digital audio workstations in the 2000s and sample and beat libraries in the 2010s.
In each of those cases, however, trained musicians were required to operate this technology in order to produce songs. The selling point of generative AI is that no musical knowledge or training is necessary. Anyone can potentially create a hit song with the help of computers that evolve with each artificially produced guitar lick or drumbeat.
Not surprisingly, the technology breakthrough has also generated anxiety among professional musicians, producers, engineers and others in the recorded-music industry who worry that their livelihoods could potentially be threatened.
“In our pursuit of the next best technology, we don’t think enough about the impact [generative AI] could have on real people,” says Abe Batshon, CEO of BeatStars, a subscription-based platform that licenses beats. “Are we really helping musicians create, or are we just cutting out jobs for producers?”
Not so, say the entrepreneurs who work in the emerging business. From their perspective, generative AI tools are simply the next step in technology’s long legacy of shaping the way music is created and recorded.
“When the drum machine came out, drummers were scared it would take their jobs away,” says Diaa El All, founder/CEO of Soundful, another AI music-generation application that was tested by hit-makers such as Caroline Pennell, Madison Love and Matthew Koma at a recent songwriting camp in Los Angeles. “But then they saw what Prince and others were able to create with it.”
El All says the music that Soundful can instantly generate, based on user-set parameters, like beats per minute or genre, is simply meant to be a “jumping-off point” for writers to build songs. “The human element,” he says, “will never be replaced.”
BandLab CEO Meng Ru Kuok says that having tools to spark song creation makes a huge difference for young music-makers, who, so far, seem to be the biggest adopters of this technology. Meng claims his AI-powered SongStarter tool, which generates a simple musical loop over which creators can fashion a song, makes new BandLab users “80% more likely to actually share their music as opposed to writing from zero.” (Billboard and BandLab collaborated on Bringing BandLab to Billboard, a portal that highlights emerging artists.)
Other applications for generative AI include creating “entirely new formats for listening,” as Endel co-founder/CEO Oleg Stavitsky says. This includes personalized music for gaming, wellness and soundtracks. Lifescore modulates human-made scores in real time, which can reflect how a player is faring in a video game, for example; Endel generates soundscapes, based on user biometrics, to promote sleep, focus or other states (Lifescore also has a similar wellness application); and Tuney targets creators who need dynamic, personalized background music for videos or podcasts but do not have a budget for licensing.
These entrepreneurs contend that generative AI will empower the growth of the “creator economy,” which is already worth over $100 billion and counting, according to Influencer Marketing Hub. “We’re seeing the blur of the line between creator and consumer, audience and performer,” says Mitchell. “It’s a new creative class.”
In the future Mitchell and El All both seem to imagine, every person can have the ability to create songs, much like the average iPhone user already has the ability to capture high-quality photos or videos on the fly. It doesn’t mean everyone will be a professional, but it could become a similarly common pastime.
The public’s fascination with — and fear of — generative AI reached a new milestone this year with the introduction of DALL-E 2, a generator that instantaneously creates images based on text inputs and with a surprising level of precision.
Musician Holly Herndon, who has used AI tools in her songwriting and creative direction for years, says that in the next decade, it will be as easy to generate a great song as it is to generate an image. “The entertainment industries we are familiar with will change radically when media is so easy and abundant,” she says. “The impact is going to be dramatic and very alien to what we are used to.”
Mac Boucher, creative technologist and co-creator of non-fungible token project WarNymph along with his sister Grimes, agrees. “We will all become creators and be capable of creating anything.”
If these predictions are fulfilled, the music business, which is already grappling with oversaturation, will need to recalibrate. Instead of focusing on consumption and owning intellectual property, more companies may shift to artist services and the development of tools that aid song creation — similar to Downtown Music Holdings’ decision to sell off its 145,000-song catalog over the last two years and focus on serving the needs of independent talent.
Major music companies are also investing in and establishing relationships with AI startups. Hipgnosis, Reservoir, Concord and Primary Wave are among those that have worked with AI stem separation company Audioshake, while Warner Music Group has invested in Boomy, Authentic Artists and Lifescore.
The advancement of AI-generated music has understandably sparked a debate over its ethical and legal use. Currently, the U.S. Copyright Office will not register a work created solely by AI, but it will register works created with human input. However, what constitutes that input has yet to be clearly defined.
Answers to these questions are being worked out in court. In 2019, industry leader Open AI issued a comment to the U.S. Patent and Trademark Office, arguing that using copyrighted material for training an AI program should be considered fair use, although many copyright owners and some other AI companies disagree.
Now one of Open AI’s projects, which was made in collaboration with Microsoft and Github, is battling a class-action suit over a similar issue. Copilot, which is AI designed to generate computer code, was accused of often replicating copyrighted code because it was trained on billions of lines of protected material made by human developers.
The executives interviewed for this story say they hire musicians to create training material for their programs and do not touch copyright-protected songs.
“I don’t think songwriters and producers are talking about [AI] enough,” says music attorney Karl Fowlkes. “This kind of feels like a dark, impending thing coming our way, and we need to sort out the legal questions.”
Fowlkes says the most important challenge to AI-generated music will come when these tools begin creating songs that emulate specific musicians, much like DALL-E 2 can generate images clearly inspired by copyrighted works from talents like Andy Warhol or Jean-Michel Basquiat.
Mitchell says that Boomy may cross that threshold in the next year. “I don’t think it would be crazy to say that if we can line up the right framework to pay for the rights [to copyrighted music], to see something from us sooner than people might think on that front,” he says. “we’re looking at what it’s going to take to produce at the level of DALL-E 2 for music.”