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Grammy-winning producer Timbaland has taken on a new role as a strategic advisor to Suno, an AI music company that can generate full songs at the click of a button.
News of the deal comes four months after the three major music companies collectively sued Suno (and competitor Udio) for alleged infringement of their copyrighted sound recordings “at an almost unimaginable scale.”
According to a press release from Suno, Timbaland has been a “top user” of the platform for months, and this announcement formalizes his involvement with Suno. The partnership will be kicked off with Timbaland previewing his latest single “Love Again” exclusively on Suno’s platform.
Then, Suno users will be able to participate in a remix contest, which will include feedback and judging from Timbaland himself and over $100,000 in prizes for winning remixes. Timbaland will also release the top two remixes of “Love Again” on streaming services, including Spotify, Apple Music and more.
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Additionally, as part of being a strategic advisor to Suno, Timbaland will assume an “active” role in the “day-to-day product development and strategic creative direction” of new generative AI tools, says the company in a press release.
Suno is one of the most advanced generative AI music companies on the market today. Using simple text prompts, users can generate voice, lyrics and instrumentals in seconds. On May 21, Suno announced that it had raised $125 million in funding across multiple funding rounds, including investments from including Lightspeed Venture Partners, Nat Friedman and Daniel Gross, Matrix and Founder Collective. Suno also said it had been working closely with a team of advisors, including 3LAU, Aaron Levie, Alexandr Wang, Amjad Masad, Andrej Karpathy, Aravind Srinivas, Brendan Iribe, Flosstradamus, Fred Ehrsam, Guillermo Rauch and Shane Mac.
Though many have marveled at its uncanny music-making capabilities, the music business establishment also feared that Suno might have been trained on copyrighted material without consent. (At the time, Suno declined to state what materials were in its training data, and whether or not it included copyrighted music).
Then, Billboard broke the news on June 20 that the major labels were weighing the idea of a lawsuit against Suno and Udio, alleging widespread copyright infringement of their sound recordings for the purposes of AI training. After the lawsuit was officially filed four days later, Suno and Udio then hired top law firm Latham & Watkins, and filed lengthy responses to fire back at the labels. Suno noted it was “no secret” that the company had ingested “essentially all music files of reasonable quality that are accessible on the open Internet” and that it was “fair use” to use these files.
“When I heard what Suno was doing, I was immediately curious,” says Timbaland of the partnership. “After witnessing the potential, I knew I had to be a part of it. By combining forces, we have a unique opportunity to make A.I. work for the artist community and not the other way around. We’re seizing that opportunity, and we’re going to open up the floodgates for generations of artists to flourish on this new frontier. I’m excited and grateful to Suno for this opportunity.”
“It’s an honor to work with a legend like Timbaland,” says Mikey Shulman, CEO of Suno. “At Suno, we’re really excited about exploring new ways for fans to engage with their favorite artists. With Timbaland’s guidance, we’re helping musicians create music at the speed of their ideas—whether they’re just starting out or already selling out stadiums. We couldn’t be more excited for what’s ahead!”
The newest upsurge in artificial intelligence technology is streamlining the tedious tasks that run beneath the glamor of the industry, from simplifying marketing strategies to easing direct fan engagement to handling financial intricacies. And as this ecosystem matures, companies are discovering unprecedented methods to not only navigate but thrive within these new paradigms.
In our previous guest column, we explored how the wave of music tech startups is empowering musicians, artists and the creative process. Now, we shift our focus to the technologies revolutionizing the business side of the industry, including artist services, ticketing, fan engagement and more.
Music marketing has continued to evolve and become increasingly data-driven. A natural next step after creation and distribution, marketing involves creating assets for a campaign to effectively engage with the right audience. Traditionally, this has been a resource-intensive task, but now, AI-driven startups are providing efficiencies by automating much of this process.
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Startups like Symphony and un:hurd are now providing automated campaign management services, handling everything from social media ads to DSP and playlist pitching from a single automated hub. Some of these platforms even incorporate financial management tools into their offerings.
“Having financial management tools integrated into one platform allows for better revenue management and planning,” says Rameen Satar, founder/CEO of the financial management platform BANDS. “Overall, a unified platform simplifies the complexities of managing a music career, empowering musicians to focus more on their creative work and succeed in the industry.”
One hot topic as of late has been superfan monetization, with multiple startups creating platforms for artists to engage with and monetize their fan bases directly. From fan-designed merchandise on Softside to artist-to-fan streaming platform Vault.fm, which recently partnered with James Blake, these platforms provide personalized fan experiences including exclusive content, NFTs, merchandise, early access to tickets and bespoke offerings.
Drew Thurlow and Rufy Anam Ghazi
Courtesy Photo
“The future of fan engagement will be community-driven. No two fan communities are alike, so engagement will be bespoke to each artist,” says Andy Apple, co-founder/CEO of superfan platform Mellomanic. “Artists will each have their own unique culture, but share one commonality: Every community will align, organize and innovate to support the goals of the artist.”
Managing metadata and accounting royalties through the global web of streaming services is another area seeing innovation. With nearly 220 million tracks now registered at DSPs, according to content ID company Audible Magic, startups are stepping in to offer solutions across the music distribution and monetization chain. New tools are being developed to organize and search catalogs, manage track credits and splits, handle incomes, find unclaimed royalties, and clean up metadata errors.
”While we have well-publicized challenges still around artist remuneration, there are innovation opportunities across the value chain, driving growth through improved operations and new models,” says Gareth Deakin of Sonorous Global Consulting, a London-based consultancy that works with labels and music creators to best use emerging technologies.
Another issue that some AI companies have stepped in help solve is preventing fraud — a significant concern stemming from the ease of music distribution and the sheer volume of new music being released every day. Startups are helping labels and digital service providers address this problem with anti-piracy, content detection and audio fingerprinting technology. Beatdapp, for instance, which developed groundbreaking AI technology to detect fake streams, has partnered with Universal Music Group, SoundExchange and Napster. Elsewhere, MatchTune has patented an algorithm that detects AI-generated and manipulated audio, and a few others are developing tech to ensure the ethical use of copyrighted material by connecting rights holders and AI developers for fair compensation. Music recognition technology (MRT), which also utilizes audio fingerprinting technology, is becoming a prominent way to identify, track and monetize music plays across various platforms, including on-ground venues and other commercial spaces.
In the live music industry, there has been minimal innovation in ticketing, especially at the club level. That’s starting to turn around, however, as new technologies are emerging to automate the tracking of ticket sales and counts, thereby helping agents and promoters reduce manual workloads.
RealCount is one such startup that helps artists, agencies and promoters make sense of ticketing data. “We see RealCount as a second brain for promoters, agents and venues, automating the tracking of ticket counts and sales data from any point of sale,” says Diane Gremore, the company’s founder/CEO. Other exciting developments are taking place in how live events are experienced virtually, with platforms like Condense delivering immersive 3D content in real time.
Drew Thurlow is the founder of Opening Ceremony Media where he advises music and music tech companies. Previously he was senior vp of A&R at Sony Music, and director of artists partnerships & industry relations at Pandora. His first book, about music & AI, will be released by Routledge in early 2026.
Rufy Anam Ghazi is a seasoned music business professional with over eight years of experience in product development, data analysis, research, business strategy, and partnerships. Known for her data-driven decision-making and innovative approach, she has successfully led product development, market analysis, and strategic growth initiatives, fostering strong industry relationships.
The ASCAP Lab, ASCAP’s innovation program, has announced this year’s cohort for their AI and the Business of Music Challenge. Featuring CRESQA, Music Tomorrow, RoEx, SoundSafe.ai and Wavelets AI, these start-ups will take part in a 12-week course, in partnership with NYC Media Lab, led by the NYU Tandon School of Engineering, to receive mentorship and small grants to develop their ideas.
As part of the initiative, the start-ups will receive hands-on support from the ASCAP Lab, as well as ASCAP’s network of writer and publisher members, to help them optimize their products for the music creator community.
While last year’s cohort of companies focused on AI for music creation and experience, the 2024 AI and the Business of Music Challenge is much more focused on commercial solutions that can help the music industry better manage data and improve workflows.
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ASCAP Chief Strategy and Digital Officer Nick Lehman says of the 2024 cohort: “ASCAP’s creator-first, future-forward commitment makes it imperative for us to embrace technology while simultaneously protecting the rights of creators. The dialogue, understanding and relationships that the ASCAP Lab Challenge creates with the music startup community enable us to drive progress for the industry and deliver on this commitment.”
Meet the ASCAP Lab Challenge teams for 2024 below:
CRESQA: An AI social media content assistant designed for songwriters and musicians that automates the process of social media strategy development and helps generate fully personalized post ideas and schedules for TikTok, Instagram, YouTube Shorts, Facebook and more.
Music Tomorrow: Analytics tools that monitor and boost artists’ algorithmic performance on streaming platforms, using AI for advanced audience insights and automation that improve an artist’s content discoverability, listener engagement and team efficiency.
RoEx: AI-driven tools for multitrack mixing, mastering, audio cleanup and quality control, designed to streamline and enhance the final steps of the creative process by delivering a professional and balanced mix with ease.
SoundSafe.ai: Robust, state-of-the-art audio watermarking using AI to enhance security, reporting and the detection of real-time piracy and/or audio deepfakes.
Wavelets AI: Tools for artists, labels, copyright holders, content distributors and DSPs that help reduce IP infringement by detecting AI vocals in music.
The Warner Music Group (WMG) has struck a new multiyear licensing deal with Meta, Billboard has learned. The partnership, which covers both Warner’s recorded music and Warner Chappell publishing operations, will be across all of Meta’s platforms — Facebook, Instagram, Messenger, Horizon and Threads — and will also include WhatsApp for the first time, Billboard […]
On Sept. 4, the public learned of the first-ever U.S. criminal case addressing streaming fraud. In the indictment, federal prosecutors claim that a North Carolina-based musician named Michael “Mike” Smith stole $10 million dollars from streaming services by using bots to artificially inflate the streaming numbers for hundreds of thousands of mostly AI-generated songs. A day later, Billboard reported a link between Smith and the popular generative AI music company Boomy; Boomy’s CEO Alex Mitchell and Smith were listed on hundreds of tracks as co-writers.
(The AI company and its CEO that supplied songs to Smith were not charged with any crime and were left unnamed in the indictment. Mitchell replied to Billboard’s request for comment, saying, “We were shocked by the details in the recently filed indictment of Michael Smith, which we are reviewing. Michael Smith consistently represented himself as legitimate.”)
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This case marks the end of generative AI music’s honeymoon phase (or “hype” phase) with the music industry establishment. Though there have always been naysayers about AI in the music business, the industry’s top leaders have been largely optimistic about it, provided AI tools were used ethically and responsibly. “If we strike the right balance, I believe AI will amplify human imagination and enrich musical creativity in extraordinary new ways,” said Lucian Grainge, Universal Music Group’s chairman/CEO, in a statement about UMG’s partnership with YouTube for its AI Music Incubator. “You have to embrace technology [like AI], because it’s not like you can put technology in a bottle,” WMG CEO Robert Kyncl said during an onstage interview at the Code Conference last September.
Each major music label group has established its own partnerships to get in on the AI gold rush since late 2022. UMG coupled with YouTube for an AI incubator program and SoundLabs for “responsible” AI plug-ins. Sony Music started collaborating with Vermillio for an AI remix project around David Gilmour and The Orb’s latest album. Warner Music Group’s ADA struck a deal with Boomy, which was previously distributing its tracks with Downtown, and invested in dynamic music company Lifescore.
Artists and producers jumped in, too — from Lauv’s collaboration with Hooky to create an AI-assisted Korean-language translation of his newest single to 3LAU’s investment in Suno. Songwriters reportedly used AI voices on pitch records. Artists like Drake and Timbaland used unauthorized AI voices to resurrect stars like Tupac Shakur and Notorious B.I.G. in songs they posted to social media. Metro Boomin sampled an AI song from Udio to create his viral “BBL Drizzy” remix. (Drake later sampled “BBL Drizzy” himself in his feature on the song “U My Everything” by Sexyy Red.) The estate of “La Vie En Rose” singer Edith Piaf, in partnership with WMG, developed an animated documentary of her life, using AI voices and images. The list goes on.
While these industry leaders haven’t spoken publicly about the overall state of AI music in a few months, I can’t imagine their tone is now as sunny as it once was, given the events of the summer. It all started with Sony Music releasing a statement that warned over 700 AI companies to not scrape the label group’s copyrighted data in May. Then Billboard broke the news that the majors were filing a sweeping copyright infringement lawsuit against Suno and Udio in June. In July, WMG issued a similar warning to AI companies as Sony had. In August, Billboard reported that AI music adoption has been much slower than was anticipated, the NO FAKES Act was introduced to the Senate, and Donald Trump deepfaked a false Taylor Swift endorsement of his presidential run on Truth Social — an event that Swift herself referenced as a driving factor in her social media post endorsing Kamala Harris for president.
And finally, the AI music streaming fraud case dropped. It proved what many had feared: AI music flooding onto streaming services is diverting significant sums of royalties away from human artists, while also making streaming fraud harder to detect. I imagine Grainge is particularly interested in this case, given that its findings support his recent crusade to change the way streaming services pay out royalties to benefit “professional artists” over hobbyists, white noise makers and AI content generators.
When I posted my follow up reporting on LinkedIn, Declan McGlynn, director of communications for Voice-Swap, an “ethical” AI voice company, summed up people’s feelings well in his comment: “Can yall stop stealing shit for like, five seconds[?] Makes it so much harder for the rest of us.”
One AI music executive told me that the majors have said that they would use a “carrot and stick” approach to this growing field, providing opportunities to the good guys and meting out punishment for the bad guys. Some of those carrots were handed out early while the hype was still fresh around AI because music companies wanted to appear innovative — and because they were desperate to prove to shareholders and artists that they learned from the mistakes of Napster, iTunes, early YouTube and TikTok. Now that they’ve made their point and the initial shock of these models has worn off, the majors have started using those sticks.
This summer, then, has represented a serious vibe shift, to borrow New York magazine’s memeable term. All this recent bad press for generative AI music, including the reports about slow adoption, seems destined to result in far fewer new partnerships announced between generative AI music companies and the music business establishment, at least for the time being. Investment could be harder to come by, too. Some players who benefitted from early hype but never amassed an audience or formed a strong business will start to fall.
This doesn’t mean that generative AI music-related companies won’t find their place in the industry eventually — some certainly will. This is just a common phase in the life cycle of new tech. Investors will probably increasingly turn their attention to other AI music companies, those largely not of the generative variety, that promise to solve the problems created by generative AI music. Metadata management and attribution, fingerprinting, AI music detection, music discovery — it’s a lot less sexy than a consumer-facing product making songs at the click of a button, but it’s a lot safer, and is solving real problems in practical ways.
There’s still time to continue to set the guardrails for generative AI music before it is adopted en masse. The music business has already started working toward protecting artists’ names, images, likenesses and voices and has fought back against unauthorized AI training on their copyrights. Now it’s time for the streaming services to join in and finally set some rules for how AI generated music is treated on its platforms.
This story was published as part of Billboard’s new music technology newsletter ‘Machine Learnings.’ Sign up for ‘Machine Learnings,’ and Billboard’s other newsletters, here.
If you have any tips about the AI music streaming fraud case, Billboard is continuing to report on it. Please reach out to krobinson@billboard.com.
As more music industry entrepreneurs rush into the nascent AI sector, the number of new companies seems to grow by the day. To help artists, creators and others navigate the space, Billboard has compiled a directory of music-centric AI startups.
Given how quickly the sector is growing, this is not an exhaustive list, but it will continue to be updated. The directory also does not make judgment calls about the quality of the models’ outputs and whether their training process is “ethical.” It is an agnostic directory of what is available. Potential users should research any company they are considering.
Although a number of the following companies fit into more than one business sector, for the sake of brevity, no company is listed more than once.
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To learn more about what is considered to be an “ethical” AI model, please read our AI FAQs, where key questions are answered by top experts in the field, or visit Fairly Trained, a nonprofit dedicated to certifying “ethical” AI music models.
General Music Creation
AIVA: A music generator that also provides additional editing tools so that users can edit the generated songs and make them their own.
Beatoven: A text-to-music generator that provides royalty-free music for content creators.
Boomy: This music generator creates instrumentals using a number of controllable parameters such as genre and BPM. It also allows users to publish and monetize their generated works.
Create: A stem and sample arrangement tool created by Splice. This model uses AI to generate new arrangements of different Splice samples, which are intended to spark the songwriting process and help users find new samples.
Gennie: A text-to-music generator created by Soundation that produces 12-second-long samples.
Hydra II: A text-to-music generator created by Rightsify that aims to create royalty-free music for commercial spaces. It is trained on Rightsify’s owned catalog of songs.
Infinite Album: A music generator that provides “fully licensed” and “copyright safe” AI music for gamers.
Jen: A text-to-music generator created by Futureverse that was trained on 40 licensed music catalogs and uses blockchain technology to verify and timestamp its creations.
Lemonaide: A “melodic idea” generator. This model creates musical ideas in MIDI form to help songwriters get started on their next idea.
MusicGen: A text-to-music generator created by Meta.
Music LM: A text-to-music generator created by Google.
Ripple: A music generator created by ByteDance. This product can convert a hummed melody into an instrumental and can expand upon the result.
Song Starter: A music generator created by BandLab that is designed to help young artists start new song ideas.
Soundful: This company has collaborated with Kaskade, Starrah and other artists and producers to create their own AI beat generators, a new play on the “type-beat.”
SoundGen: A text-to-music generator that can also act as a “musical assistant” to help flesh out a creator’s music.
Soundraw: A generator that creates royalty-free beats, some of which have been used by Trippie Redd, Fivio Foreign and French Montana.
Stable Audio: A text-to-music generator created by Stability AI. This model also offers audio-to-audio generation, which enables users to manipulate any uploaded audio sample using text prompts.
Suno: A text-to-music generator. This model can create lyrics, vocals and instrumentals with the click of a button. Suno and another generator, Udio, are currently being sued by the three major music companies for alleged widespread copyright infringement during the training process. Suno and Udio claim the training qualifies as fair use under U.S. copyright law and contend the lawsuits are attempts to stifle independent competition.
Tuney: A music generator. This model is known for soundtracking brand advertisements and offering “adaptive music” to make a generated track better fit any given project.
Udio: A text-to-music generator that can create lyrics, vocals and instrumentals with a keyboard stroke. This model is best known for generating “BBL Drizzy,” a parody song by comic Willonius Hatcher that was then sampled by Metro Boomin and became a viral hit. Udio, like Suno, is defending itself against a copyright infringement lawsuit filed by the three major music companies. Udio and Suno claim their training counts as fair use and accuse the label groups of attempting to stifle independent competition.
Voice Conversion
Covers.AI: A voice filter platform created by Mayk.It. The platform offers the ability to build your own AI voice, as well as try on the voices of characters like SpongeBob, Mario or Ash Ketcham.
Elf.Tech: A Grimes voice filter created by CreateSafe and Grimes. This tool is the first major artist-voice converter, and Grimes debuted it in response to the virality of Ghostwriter977’s “Heart on My Sleeve,” which deepfaked the voices of Drake and The Weeknd.
Hooky: A voice filter platform best known for its official partnership with Lauv, who used Hooky technology to translate his song “Love U Like That” into Korean.
Kits.AI: A voice filter, stem separation and mastering platform. This company can provide DIY voice cloning as well as a suite of other generic types of voices. It is certified by Fairly Trained.
Supertone: A voice filter platform, acquired by HYBE, that allows users to change their voice in real time. It also offers a tool called Clear to remove noise and reverb from vocal stems.
Voice-Swap: A voice filter and stem separation platform. This company offers an exclusive roster of artist voices to choose from, including Imogen Heap, and it hopes to become an “agency” for artists’ voices.
Vocoflex: A voice filter plug-in created by Dreamtonics that offers the ability to change the tone of a singer’s voice in real time.
Stem Separation
Audioshake: A stem separation and lyric transcription tool. This company is best known for its recent participation in Disney’s accelerator program.
LALA.AI: A stem separation and voice conversion tool.
Moises AI: A stem separation, pitch-changer, chord detection and smart metronome tool created by Music AI.
Sounds.Studio: A stem separation tool created by Never Before Heard Sounds.
Stem-Swap: A stem separation tool created by Voice-Swap.
Dynamic Music
Endel: A personalized soundscape generator that enhances activities including sleep and focus. The company also releases collaborations with artists like Grimes, James Blake and 6LACK.
Lifescore: A personalized soundtrack generator that enhances activities like driving, working out and more.
Plus Music.AI: A personalized soundtrack generator for video-game play.
Reactional Music: A personalized soundtrack generator that adapts music with actions taken in video games in real time.
Management
Drop Track: An AI-powered music publicity tool.
Musical AI: An AI-powered rights management tool that enables rights holders to manage their catalog and license their works for generative AI training as desired.
Musiio: An AI music tagging and search tool owned by SoundCloud. This tool creates fingerprints to better track and search songs, and it automates tagging songs by mood, keywords, language, genre and lyrical content.
Triniti: A suite of AI tools for music creation, marketing, management and distribution created by CreateSafe. It is best known for the AI voice application programming interface behind Grimes’ Elf.Tech synthetic voice model.
Other
Hook: An AI music remix app that allows users to create mashups and edits with proper licensing in place.
LANDR: A suite of plug-ins and producer services, many of which are powered by AI, including an AI mastering tool.
Morpho: A timbre transfer tool created by Neutone.
From Ghostwriter’s “fake Drake” song to Metro Boomin‘s “BBL Drizzy,” a lot has happened in a very short time when it comes to the evolution of AI’s use in music. And it’s much more prevalent than the headlines suggest. Every day, songwriters are using AI voices to better target pitch records to artists, producers are trying out AI beats and samples, film/TV licensing experts are using AI stem separation to help them clean up old audio, estates and catalog owners are using AI to better market older songs, and superfans are using AI to create next-level fan fiction and UGC about their favorite artists.
For those just starting out in the brave new world of AI music, and understanding all the buzzwords that come with it, Billboard contacted some of the sector’s leading experts to get answers to top questions.
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What are some of the most common ways AI is already being used by songwriters and producers?
TRINITY, music producer: As a producer and songwriter, I use AI and feel inspired by AI tools every day. For example, I love using Splice Create Mode. It allows me to search through the Splice sample catalog while coming up with ideas quickly, and then I export it into my DAW Studio One. It keeps the flow of my sessions going as I create. I heard we’ll soon be able to record vocal ideas into Create Mode, which will be even more intuitive and fun. Also, the Izotope Ozone suite is great. The suite has mastering and mixing assistant AI tools built into its plug-ins. These tools help producers and songwriters mix and master tracks and song ideas.
I’ve also heard other songwriters and producers using AI to get started with song ideas. When you feel blocked, you have AI tools like Jen, Melody Studio and Lemonaide to help you come up with new chord progressions. Also, Akai MPC AI and LALA AI are both great for stem splitting, which allows you to separate [out] any part of the music. For example, if I just want to solo and sample the drums in a record, I can do that now in minutes.
AI is not meant to replace us as producers and songwriters. It’s meant to inspire and push our creativity. It’s all about your perspective and how you use it. The future is now; we should embrace it. Just think about how far we have come from the flip phones to the phones we have now that feel more limitless every day. I believe the foundation and heart of us as producers and songwriters will never get lost. We must master our craft to become the greatest producers and songwriters. AI in music creation is meant to assist and free [up] more mental space while I create. I think of AI as my J.A.R.V.I.S. and I’m Iron Man.
How can a user tell if a generative AI company is considered “ethical” or not?
Michael Pelczynski, chief strategy and impact officer, Voice-Swap: If you’re paying for services from a generative AI company, ask yourself, “Where is my money going?” If you’re an artist, producer or songwriter, this question becomes even more crucial: “Why?” Because as a customer, the impact of your usage directly affects you and your rights as a creator. Not many companies in this space truly lead by example when it comes to ethical practices. Doing so requires effort, time and money. It’s more than just marketing yourself as ethical. To make AI use safer and more accessible for musicians, make sure the platform or company you choose compensates everyone involved, both for the final product and for the training sources.
Two of the most popular [ways to determine whether a company is ethical] are the Fairly Trained certification that highlights companies committed to ethical AI training practices, and the BMAT x Voice-Swap technical certification that sets new standards for the ethical and legal utilization of AI-generated voices.
When a generative AI company says it has “ethically” sourced the data it trained on, what does that usually mean?
Alex Bestall, founder and CEO, Rightsify and Global Copyright Exchange (GCX): [Ethical datasets] require [an AI company to] license the works and get opt-ins from the rights holders and contributors… Beyond copyright, it is also important for vocalists whose likeness is used in a dataset to have a clear opt-in.
What are some examples of AI that can be useful to music-makers that are not generative?
Jessica Powell, CEO, AudioShake: There are loads of tools powered by AI that are not generative. Loop and sample suggestion are a great way to help producers and artists brainstorm the next steps in a track. Stem separation can open up a recording for synch licensing, immersive mixing or remixing. And metadata tagging can help prepare a song for synch-licensing opportunities, playlisting and other experiences that require an understanding of genre, BPM and other factors.
In the last year, several lawsuits have been filed between artists of various fields and generative AI companies, primarily concerning the training process. What is the controversy about?
Shara Senderoff, co-founder, Futureverse and Raised in Space: The heart of the controversy lies in generative AI companies using copyrighted work to train their models without artists’ permission. Creators argue that this practice infringes on their intellectual property rights, as these AI models can produce content closely resembling their original works. This raises significant legal and ethical questions about creative ownership and the value of human artistry in the digital age. The creator community is incensed [by] seeing AI companies profit from their efforts without proper recognition or compensation.
Are there any tools out there today that can be used to detect generative AI use in music? Why are these tools important to have?
Amadea Choplin, COO, Pex: The more reliable tools available today use automated content recognition (ACR) and music recognition technology (MRT) to identify uses of existing AI-generated music. Pex can recognize new uses of existing AI tracks, detect impersonations of artists via voice identification and help determine when music is likely to be AI-generated. Other companies that can detect AI-generated music include Believe and Deezer; however, we have not tested them ourselves. We are living in the most content-dense period in human history where any person with a smartphone can be a creator in an instant, and AI-powered technology is fueling this growth. Tools that operate at mass scale are critical to correctly identifying creators and ensuring they are properly compensated for their creations.
Romain Simiand, chief product officer, Ircam Amplify: Most AI detection tools provide only one side of the coin. As an example, tools such as aivoicedetector.com are primarily meant to detect deepfakes for speech. IRCAM Amplify focuses primarily on prompt-based tools used widely. Yet, because we know this approach is not bulletproof, we are currently supercharging our product to highlight voice clones and identify per-stem AI-generated content. Another interesting contender is resemble.ai, but while it seems their approach is similar, the methodology described diverges greatly.
Finally, we have pex.com, which focuses on voice identification. I haven’t tested the tool but this approach seems to require the original catalog to be made available, which is a potential problem.
AI recognition tools like the AI Generated Detector released by IRCAM Amplify and the others mentioned above help with the fair use and distribution of AI-generated content.
We think AI can be a creativity booster in the music sector, but it is as important to be able to recognize those tracks that have been generated with AI [automatically] as well as identifying deepfakes — videos and audio that are typically used maliciously or to spread false information.
In the United States, what laws are currently being proposed to protect artists from AI vocal deepfakes?
Morna Willens, chief policy officer, RIAA: Policymakers in the U.S. have been focused on guardrails for artificial intelligence that promote innovation while protecting all of us from unconsented use of our images and voices to create invasive deepfakes and voice clones. Across legislative efforts, First Amendment speech protections are expressly covered and provisions are in place to help remove damaging AI content that would violate these laws.
On the federal level, Reps. María Elvira Salazar (R-FL), Madeleine Dean (D-PA), Nathaniel Moran (R-TX), Joe Morelle (D-NY) and Rob Wittman (R-VA) introduced the No Artificial Intelligence Fake Replicas and Unauthorized Duplications Act to create a national framework that would safeguard Americans from their voice and likeness being used in nonconsensual AI-generated imitations.
Sens. Chris Coons (D-DE), Marsha Blackburn (R-TN), Amy Klobuchar (D-MN) and Thom Tillis (R-NC) released a discussion draft of a bill called Nurture Originals, Foster Art and Keep Entertainment Safe Act with similar aims of protecting individuals from AI deepfakes and voice clones. While not yet formally introduced, we’re hopeful that the final version will provide strong and comprehensive protections against exploitive AI content.
Most recently, Sens. Blackburn, Maria Cantwell (D-WA) and Martin Heinrich (D-NM) introduced the Content Origin Protection and Integrity From Edited and Deepfaked Media Act, offering federal transparency guidelines for authenticating and detecting AI-generated content while also holding violators accountable for harmful deepfakes.
In the states, existing “right of publicity” laws address some of the harms caused by unconsented deepfakes and voice clones, and policymakers are working to strengthen and update these. The landmark Ensuring Likeness Voice and Image Security Act made Tennessee the first state to update its laws to address the threats posed by unconsented AI deepfakes and voice clones. Many states are similarly considering updates to local laws for the AI era.
RIAA has worked on behalf of the artists, rights holders and the creative community to educate policymakers on the impact of AI — both challenges and opportunities. These efforts are a promising start, and we’ll continue to advocate for artists and the entire music ecosystem as technologies develop and new issues emerge.
What legal consequences could a user face for releasing a song that deepfakes another artist’s voice? Could that user be shielded from liability if the song is clearly meant to be parody?
Joseph Fishman, music law professor, Vanderbilt University: The most important area of law that the user would need to worry about is publicity rights, also known as name/image/likeness laws, or NIL. For now, the scope of publicity rights varies state by state, though Congress is working on enacting an additional federal version whose details are still up for grabs. Several states include voice as a protected aspect of the rights holder’s identity. Some companies in the past have gotten in legal trouble for mimicking a celebrity’s voice, but so far those cases have involved commercial advertisements. Whether one could get in similar trouble simply for using vocal mimicry in a new song, outside of the commercial context, is a different and largely untested question. This year, Tennessee became the first state to expand its publicity rights statute to cover that scenario expressly, and other jurisdictions may soon follow. We still don’t know whether that expansion would survive a First Amendment challenge.
If the song is an obvious parody, the user should be on safer ground. There’s pretty widespread agreement that using someone’s likeness for parody or other forms of criticism is protected speech under the First Amendment. Some state publicity rights statutes even include specific parody exemptions.
When Michael “Mike” Smith was indicted Wednesday (Sept. 4) over allegations that he used an AI music company to create “hundreds of thousands” of songs and then used bots to artificially earn $10 million in streaming income since 2017, prosecutors claimed that some of the money flowed back to that AI music company. The indictment also claimed that Smith was in consistent contact with its CEO — but it never revealed their names.
ASCAP/BMI Songview records and the MLC database indicate that Alex Mitchell, CEO/founder of popular AI music company Boomy, is listed as the co-writer on at least hundreds of the 200,000 plus songs that are registered to Smith. Boomy also released a song, “This Isn’t Real Life,” jointly with Smith, CVBZ and Stunna 4 Vegas.
In a statement to Billboard, Mitchell says: “We were shocked by the details in the recently filed indictment of Michael Smith, which we are reviewing. Michael Smith consistently represented himself as legitimate.”
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The indictment alleges that around 2018, “Smith began working with the Chief Executive Officer of an [unnamed] AI music company and a music promoter to create thousands of songs that Smith could then fraudulently stream.” Within months, the CEO was allegedly providing Smith with “thousands of songs each week.”
In June 2019, the indictment says that Smith reported to the AI music CEO and the promoter that “we are at 88 million TOTAL STREAMS so far!!!” Smith explained to the CEO and promoter that his streams were earning about $110,000 per month and that the two men were each receiving 10% of the proceeds. Smith later asked the AI CEO to provide him with another 10,000 AI songs so that he could “spread this out more” with his streams. The indictment states that this was “to evade detection from streaming platforms.”
Eventually, according to the indictment, Smith entered a “Master Services Agreement” with this AI music company that supplied Smith with 1,000-10,000 songs per month. The deal stated that Smith would have “full ownership of the intellectual property rights in the songs.” In turn, Smith would provide the AI company with metadata and the “greater of $2,000 or 15% of the streaming revenue” he generated from the AI songs.
“Keep in mind what we’re doing musically here… this is not ‘music,’ it’s ‘instant music’ ;)” the AI CEO wrote to Smith in an email that was included in the indictment.
Mitchell’s publisher is listed as Songtrust, a publishing administration company owned by Downtown, which typically earns a percentage of signees’ royalties in exchange for services. Smith’s publisher, Smithhouse Music Publishing, also lists Songtrust as its point of contact on Songview.
A representative for Songtrust declined Billboard’s request for comment. However, a source close to the matter tells Billboard that Smith and Mitchell’s Songtrust deals were terminated more than a year ago.
While it is not unheard of for an AI company to be approached by customers who are looking to buy a large number of songs, multiple AI music executives tell Billboard that it is common to know why the customer wants the tracks and to do “KYC,” or “know your client,” checks to ensure they are above board.
Typically, customers for large sums of songs tend to be companies that are seeking cheap music alternatives, often for social media content. Other requests tend to come from unknown individuals outside of the U.S., especially streaming fraud hotspots like Poland, Ukraine, Russia, Vietnam and Brazil. These parties are often denied. Two sources say it’s surprising to see a CEO’s name listed in the credits as a songwriter when these transactions occur.
Boomy has been at the forefront of AI music since its infancy. Records vary as to when Boomy launched in beta, with some online sources saying 2018 and others saying 2019. It officially debuted in 2021, according to an announcement from Axios. The company claims on its website to have made over 20 million AI-generated tracks to date.
Boomy has also won the respect of the music industry establishment. For years, Boomy was distributing many of its AI tracks through a partnership with New York-based music services giant Downtown. Though this partnership was in place during the same time frame as Smith’s alleged fraudulent activities, it is unclear if any of Smith’s allegedly fraudulent AI tracks were distributed through Downtown. The indictment does state, however, that Smith used two distributors to upload content from 2017-2024, one based in New York and one based in Florida.
In May 2023, Boomy told users via Discord that Spotify had shut down its ability to upload songs to the DSP and that some of their released tracks had been removed. “This decision was made by Spotify and Boomy’s distributor in order to enable a review of potentially anomalous activity,” Boomy said at the time. Spotify later confirmed that the “anomalous activity” was related to possible streaming fraud detected on certain tracks. A Spotify spokesperson said at the time, “Artificial streaming is a longstanding, industry-wide issue that Spotify is working to stamp out across our service.”
In fall 2023, Boomy announced that it had partnered with fraud detection company Beatdapp to combat streaming manipulation. A month later, Boomy also announced that it had reached a new distribution partnership with ADA Worldwide, a company under the Warner Music Group (WMG) umbrella.
WMG is one of Boomy’s top investors, making both a pre-seed round as well as a seed round investment. Other Boomy investors include Sound Media Ventures, First Check Ventures, Intonation Ventures, Future Labs, Boost VC and Scrum Venture, according to Crunchbase.
According to Songview and the MLC database, the same tracks that list Smith and Mitchell as co-writers also list a music industry veteran named Bram Bessoff, founder of promotional platform Indiehitmaker. Typically, these tracks allocate 10% of publishing ownership and royalties to Bessoff, which matches the amount the indictment indicates was paid to the unnamed promoter. Bessoff’s publisher is listed as Songtrust as well. (A source close to the matter says Bessoff’s deal with Songtrust was also terminated more than a year ago).
Bessoff declined Billboard’s request for comment, citing his cooperation in the ongoing investigation.
By the mid-2010s, the power of the playlist — the Spotify playlist to be exact — loomed large in the music business: Everyone knew a spot on Rap Caviar could mint a rap hit overnight; a placement on Fresh Finds could induce a label bidding war; and a lower-than-expected ranking on New Music Friday could ruin a label project manager’s Thursday night.
But in the 2020s, challengers — namely TikTok, with its potent and mysterious algorithm that serves social media users with addictive snippets of songs as they scroll — have threatened Spotify’s reign as music industry kingmaker. Still, Spotify’s editorial playlists remain one of the most important vehicles for music promotion, and its 100-plus member global team, led by its global head of editorial Sulinna Ong, has evolved to meet the changing times.
“Our editorial expertise is both an art and a science,” says Ong, who has led the company through its recent efforts to use technology to offer more personalized playlist options, like its AI DJ, Daylist and daily mixes. “We’re always thinking about how we can introduce you to your next favorite song to your next favorite artist. How do we provide context to get you to engage? Today, the challenge is cutting through the noise to get your attention.”
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In conversation with Billboard, Ong talks about training the AI DJ with the editors’ human expertise, using playlists to differentiate Spotify from its competition and looking ahead to Generation Alpha (ages 0-14).
I’ve seen such a shift in the editorial strategy at Spotify in the last couple years. Daylist, personalized editorial playlists (marked by the “made for you” tag), daily mixes, AI DJ and more. Did those inspire your team to push into these personalized editorial playlists?
To start off, it’s useful to zoom out and think about how people listen to music. The way people listen to music is fluid and curation and editorial has to be fluid as well. We have to understand the changes.
Curators have always been at the core of Spotify’s identity, right from the early days of the company. Back in 2012, Spotify’s music team started with three editors, and it quickly grew to more than 100 around the world today. These curators started by curating what became known as our flagship editorial playlists — Today’s Top Hits, Rap Caviar, Viva Latino. Over time that expanded to playlists like Altar, Lorem, Pollen, etc. Those are all still important.
But around 2018, editors made their first attempts to bridge human curation from our flagship editorial playlists with personalization engines. 2018 is the year when the technology arose with personalization and machine learning to open up these possibilities. At that time, we started making more personalized playlists where the tracks fit with an overall mood or moment curated by editors but varied for each listener — like My Life Is A Movie, Beastmode, Classic Roadtrip Songs. Editors will select a number of songs that they feel fit that playlist. Let’s say for example we have 200 songs selected, you might see the 100 of those that are most aligned with your taste.
Discover Weekly and Release Radar are tailored to listener activity and have been around much longer. Did those inspire your team to push into these personalized editorial playlists around 2018?
Yes, exactly. Algorithmic playlists, like Release Radar [and] Discover Weekly, we found that users liked them [and] that inspired us to then work with the product teams and ask, “What is the next step of this?” Spotify has more than 500 million users. We knew that it would keep growing and as a human curator, you can’t manually curate to that entire pool. Technology can fill in that gap and increase our possibilities. A lot of times, I see narratives where people call this a dichotomy — either playlists are human-made or machine-made. We don’t see it that way.
In 2024, personalization and machine learning are even more important technologies for streaming music and watching content. We’ve kept investing in cutting-edge personalization and it’s making a real impact — 81% of our listeners cite personalization as their favorite thing about Spotify. Our static editorial playlists are still very powerful, but we also have made these other listening experiences to round out the picture.
How someone listens is never one thing. Do you only want to watch movies? No, you want to watch a movie sometimes; other times you want to watch a 20-minute TV show. We have to understand the various ways that you might like to [listen].
Daylist, for example, is very ephemeral. It only exists for a certain amount of time. The appeal is in the title — it also really resonates for a younger audience.
Did your team always intend that Daylist, which often gives users crazy titles like “Whimsical Downtown Vibes Tuesday Evening,” could be shareable — even memeable — on social media?
Absolutely. It’s very shareable. It’s a bite-sized chunk of daily joy that you get that you can post about online.
It reminds me of the innately shareable nature of Spotify Wrapped.
There is a lineage there. It is similar because it’s a reminder of what you’re listening to. But it’s repackaged in a humorous way — light and fun and it updates so it keeps people coming back.
How do you think Spotify’s editorial team differentiates itself from competitors like Apple and Amazon?
Early on, we understood that editorial expertise around the world is really valuable, and it was needed to set us apart. So we have editors all around the world. They are really the music experts of the company. They are focused on understanding the music and the cultural scenes where they are.
We have what we call “editorial philosophy.” One of the tenets of that is our Global Curation Groups, or “GCGs” for short. Once a week, editors from around the world meet and identify tracks that are doing well and should flow from one market to another. We talk about music trends, artists we are excited about. We talk about new music mainly but also music that is resurfacing from social media trends.
This is how we got ahead on spreading genres like K-pop seven years ago. We were playlisting it and advocating for it spreading around the world. Musica Mexicana and Amapiano — we were early [with those] too. We predicted that streaming would reduce the barriers of entry in terms of language, so we see genres and artists coming from non-Western, non-English speaking countries really making an impact on the global music scene.
How was the AI DJ trained to give the commentary and context it gives?
We’ve essentially spun up a writers’ room. We have our editors work with our product team and script writers to add in some context about the artists and tracks that the DJ can share with listeners. The info they feed in can be musical facts, culturally-relevant insights. We want listeners to feel connected to the artists they hear on a human level. At the end of the day, this approach to programming also really helps us broaden out the pool of exposure, particularly for undiscovered artists and tracks. We’ve seen that people who hear the commentary from DJ are more likely to listen to a song they would have otherwise skipped.
When Spotify editorial playlists started, the cool, young, influential audience was millennials. Now it’s Gen Z. What challenges did that generational shift pose?
We think about this every day in our work. Now, we’re even thinking about the next generation after Gen Z, Gen Alpha [children age 14 and younger]. I think the key difference is our move away from genre lines. Where we once had a strictly rock playlist, we are now building playlists like POV or My Life Is A Movie. It’s a lifestyle or an experience playlist. We also see that younger listeners like to experiment with lots of different listening experiences. We try to be very playful about our curation and offer those more ephemeral daily playlists.
What are you seeing with Gen Alpha so far? I’m sure many of them are still on their parents’ accounts, but do you have any insight into how they might see music differently than other generations as they mature?
Gaming. Gaming is really an important space for them. Music is part of the fabric of how we play games now — actually, that’s how these kids often discover and experience music, especially on Discord and big MMOs — massive multiplayer games. We think about this culture a lot because it is mainstream culture for someone of that age.
Gaming is so interesting because it is such a dynamic, controllable medium. Recorded music, however, is totally static. There have been a few startups, though, that are experimenting with music that can morph as you play the game.
Yeah, we’re working on making things playful. There’s a gamification in using Daylist, right? It’s a habit. You come back because you want to see what’s new. We see the AI DJ as another way to make music listening more interactive, less static.
Spotify has been known as a destination for music discovery for a long time. Now, listeners are increasingly turning to TikTok and social media for this. How do you make sure music discovery still continues within Spotify for its users?
That comes down to, again, the editorial expertise and the GCGs I mentioned before. We have 100-plus people whose job it is to be the most tapped-in people in terms of what’s happening around the world in their genre. That’s our biggest strength in terms of discovery because we have a large team of people focused on it. Technology just adds on to that human expertise.
Back when Spotify playlists first got popular, a lot of people compared the editors to the new generation of radio DJs. How do you feel about that comparison?
It’s not a one-to-one comparison. I can understand the logic of how some people might get there. But, if I’m very frank, the editorial job that we do is not about us. Radio DJs, it’s all about them, their personality. It’s not about them as a DJ or a front face of a show. Not to be disparaging to radio DJs — their role is important — it’s just not the same thing. I don’t think we are gatekeepers. I say that because it is never about me or us as editors. It’s about the music, the artist and the audience’s experience. It’s very simple: I want to introduce you to your next favorite song. Yes, we have influence. I recognize that in the industry. It’s one I take very seriously. That’s a privilege and a responsibility, but it is not about us at the end of the day.
This story was published as part of Billboard’s new music technology newsletter ‘Machine Learnings.’ Sign up for ‘Machine Learnings,’ and Billboard’s other newsletters, here.
A North Carolina musician has been indicted by federal prosecutors over allegations that he used AI to help create “hundreds of thousands” of songs and then used the AI tracks to earn more than $10 million in fraudulent streaming royalty payments since 2017.
In a newly unsealed indictment, Manhattan federal prosecutors charged the musician, Michael Smith, 52, with three counts of wire fraud, wire fraud conspiracy and money laundering conspiracy. According to the indictment, Smith was aided by the CEO of an unnamed AI music company as well as other co-conspirators in the U.S. and around the world, and some of the millions he was paid were funneled back to the AI music company.
According to the indictment, the hundreds of thousands of AI songs Smith allegedly helped create were available on music streaming platforms like Spotify, Amazon Music, Apple Music and YouTube Music. It also claims Smith has made “false and misleading” statements to the streaming platforms, as well as collection societies including the Mechanical Licensing Collective (the MLC) and distributors, to “promote and conceal” his alleged fraud.
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Because of Smith’s alleged activities, he diverted over $1 million in streaming payments per year that “ultimately should have been paid to the songwriters and artists whose works were streamed legitimately by real consumers,” says the indictment.
The indictment also details exactly how Smith allegedly pulled off the scheme he’s accused of. First, it says he gathered thousands of email accounts, often in the names of fictitious identities, to create thousands of so-called “bot accounts” on the streaming platforms. At its peak, Smith’s operation allegedly had “as many as 10,000 active bot accounts” running; he also allegedly hired a number of co-conspirators in the U.S. and abroad to do the data entry work of signing up those accounts. “Make up names and addresses,” reads an email from Smith to an alleged co-conspirator dated May 11, 2017, that was included in the indictment.
To maximize income, the indictment states that Smith often paid for “family plans” on streaming platforms “typically using proceeds generated by his fraudulent scheme” because they are the “most economical way to purchase multiple accounts on streaming services.”
Smith then used cloud computing services and other means to cause the accounts to “continuously stream songs that he owned” and make it look legitimate. The indictment alleges that Smith knew he was in the wrong and used a number of methods to “conceal his fraudulent scheme,” ranging from fictitious email names and VPNs to instructing his co-conspirators to be “undetectable” in their efforts.
In emails sent in late 2018 and obtained by the government, Smith told co-conspirators to not be suspicious while running up tons of streams on the same song. “We need to get a TON of songs fast to make this work around the anti fraud policies these guys are all using now,” Smith wrote in the emails.
Indeed, there have been a number of measures taken up by the music business to try to curb this kind of fraudulent streaming activity in recent years. Anti-streaming fraud start-up Beatdapp, for example, has become an industry leader, hired by a number of top distributors, streaming services and labels to identify and prevent fraud. Additionally, severl independent DIY distributors including TuneCore, Distrokid and CD Baby have recently banded together to form “Music Fights Fraud,” a coalition that shares a database and other resources to prevent fraudsters from hopping from service to service to avoid detection.
Last year, Spotify and Deezer came out with revamped royalty systems that proposed new penalties for fraudulent activity. Still, it seems fraudsters study these new efforts and continue to evolve their efforts to evade detection.
The rise of quickly generated AI songs has been a major point of concern for streaming fraud experts because it allows bad actors to spread their false streaming activity over a larger number of songs and create more competition for streaming dollars. To date, AI songs are not paid out any differently from human-made songs on streaming platforms. A lawsuit filed by Sony Music, Warner Music Group and Universal Music Group against AI companies Suno and Udio in June summed up the industry’s fears well, warning that AI songs from these companies “saturate the market with machine-generated content that will directly compete with, cheapen and ultimately drown out the genuine sound recordings on which [the services were] built.”
Though Smith is said to be a musician himself with a small catalog of his own, the indictment states that he leaned on AI music to quickly amass a much larger catalog.
The indictment alleges that around 2018, “Smith began working with the Chief Executive Officer of an unnamed AI music company and a music promoter to create thousands of thousands of songs that Smith could then fraudulently stream.” Within months, the CEO of the AI company was allegedly providing Smith with “thousands of songs each week.” Eventually, Smith entered a “Master Services Agreement” with the AI company that supplied Smith with 1,000-10,000 songs per month, agreeing that Smith would have “full ownership of the intellectual property rights in the songs.” In turn, Smith would provide the AI company with metadata and the “greater of $2,000 or 15% of the streaming revenue” he generated from the AI songs.
“Keep in mind what we’re doing musically here… this is not ‘music,’ it’s ‘instant music’ ;)”, reads an email from the AI company’s CEO to Smith that was included in the indictment.
Over time, various players in the music business questioned Smith’s activities, including a streaming platform, a music distributor and the MLC. By March and April 2023, the MLC halted royalty payments to Smith and confronted him about his possible fraud. In response, Smith and his representatives “repeatedly lied” about the supposed fraud and AI-generated creations, says the indictment.
Christie M. Curtis, FBI acting assistant director, said of the indictment, “The defendant’s alleged scheme played upon the integrity of the music industry by a concerted attempt to circumvent the streaming platforms’ policies. The FBI remains dedicated to plucking out those who manipulate advanced technology to receive illicit profits and infringe on the genuine artistic talent of others.”
Kris Ahrend, CEO of the MLC, added, “Today’s DOJ indictment shines a light on the serious problem of streaming fraud for the music industry. As the DOJ recognized, The MLC identified and challenged the alleged misconduct, and withheld payment of the associated mechanical royalties, which further validates the importance of The MLC’s ongoing efforts to combat fraud and protect songwriters.”