{"id":593,"date":"2026-06-14T13:08:38","date_gmt":"2026-06-14T13:08:38","guid":{"rendered":"https:\/\/commercialrelocationpros.com\/?p=593"},"modified":"2026-06-14T13:08:38","modified_gmt":"2026-06-14T13:08:38","slug":"listening-to-all-the-music-ai-is-trained-on-would-take-decades","status":"publish","type":"post","link":"https:\/\/commercialrelocationpros.com\/?p=593","title":{"rendered":"Listening to All the Music AI Is Trained on Would Take Decades"},"content":{"rendered":"<section>\n<p>L<span>ast November,<\/span> a pair of Olympic-bound figure skaters performed in a competition to a song with lyrics that sounded oddly familiar. \u201cEvery night we smash a Mercedes-Benz,\u201d the singer began. It was one of several recognizable lines from the 1998 pop hit \u201cYou Get What You Give,\u201d by the New Radicals. But the ice dancers\u2019 song was otherwise different. The New Radicals\u2019 message to angsty teenagers had been converted to Bon Jovi\u2013style arena rock. If you knew \u201cYou Get What You Give,\u201d this was a pretty strange variation on it.<\/p>\n<p>Read more <a href=\"https:\/\/commercialrelocationpros.com\/?p=591\">So Much for Leaving Abortion Up to the States<\/a><\/p>\n<p>The dancers had used music generated by AI. Whatever model was involved had likely been trained on \u201cYou Get What You Give\u201d and had copied some of the song\u2019s content, as AI systems are prone to do. Such systems don\u2019t always reproduce elements of existing songs in this way, but you\u2019ll hear it now and then, and sometimes even more blatantly. Suno, one of the most popular AI music generators, for example, has pumped out tracks that strongly resemble Michael Jackson\u2019s \u201cThriller,\u201d Ed Sheeran\u2019s \u201cShape of You,\u201d Chuck Berry\u2019s \u201cJohnny B. Goode,\u201d Bill Haley &amp; His Comets\u2019 \u201cRock Around the Clock,\u201d B. B. King\u2019s \u201cThe Thrill Is Gone,\u201d and others. Listen to Michael Jackson\u2019s song alongside a Suno-generated track titled \u201cThriller\u201d:<\/p>\n<div>\n<div>\n<div>\n<h3><span>Thriller<\/span><\/h3>\n<div><small>by Michael Jackson<\/small><\/div>\n<div> <audio><\/audio> <\/div>\n<div><small>Released on November 29, 1982.<\/small><\/div>\n<\/p><\/div>\n<div>\n<h3><span>Thriller<\/span><\/h3>\n<div><small>generated by Suno<\/small><\/div>\n<p> <audio><\/audio> <\/p>\n<div><small>Prompt: &#8220;post-disco, pop-rock, funk, electronic, r&amp;b, thriller, motown, famous male singer and dancer, king of pop, falsetto&#8221;<\/small><\/div>\n<\/p><\/div>\n<\/p><\/div>\n<\/div>\n<p>(\u201cThriller\u201d is just one of the dozens of examples provided by the major record labels in a lawsuit against Suno. You can hear two others below. Rachel Racusen, a spokesperson for Suno, told me that the platform uses \u201csafeguards to protect against unauthorized distribution, impersonation and manipulations,\u201d and directed me to a LinkedIn post by the company\u2019s chief product officer saying that reproductions of training data \u201cshould not happen.\u201d Racusen did not answer questions about the lawsuit or acknowledge any specific tracks that were used to train their models.)<\/p>\n<p>Cases like these indicate something about how AI-based music products work. AI music generators can simulate human performances with surprising fidelity, but first they have to be trained on enormous quantities of those human performances. The actual recordings that go into any model are a closely guarded secret\u2014AI companies have claimed they are proprietary\u2014but the number of songs is almost certainly huge, spanning genres and time periods.<\/p>\n<p>As part of my series of investigations into AI training data, I recently discovered four giant datasets of songs that are being shared within the AI-development community. One has 12 million tracks. Another has 9 million. The two smaller datasets each have more than 100,000. They include hits from major pop artists such as Bad Bunny, Nirvana, Taylor Swift, Billie Eilish, Pearl Jam, Elvis Costello, Sheryl Crow, and the Beatles. (The New Radicals\u2019 \u201cYou Get What You Give\u201d is in two of the datasets.) Jazz artists such as Miles Davis, John Zorn, and Vijay Iyer are featured, as are classical composers and tens of thousands of minor artists across genres. The 12-million-track dataset, on its own, would take 91 years to listen to.<\/p>\n<p>You can search for an artist in the datasets here:<\/p>\n<div><iframe allow=\"web-share *; clipboard-write *; top-navigation-by-user-activation\" class=\"lazyload\" frameborder=\"0\" id=\"datasetSearch\" referrerpolicy=\"unsafe-url\" sandbox=\"allow-scripts allow-same-origin allow-top-navigation-by-user-activation\" src=\"https:\/\/ai-watchdog.embedded.theatlantic.com\/datasets\/music?embedded=true&amp;hideHeader=true&amp;parentUrl=https:\/\/www.theatlantic.com\/technology\/2026\/06\/music-ai-training-data-sets\/687485\/\" title=\"Dataset Search\" width=\"100%\"><\/iframe><\/div>\n<p>These datasets are only four examples of the many sources available to AI developers. I found them by reading research papers published by developers and scouring AI data-sharing sites. The datasets have been downloaded thousands of times. Google has written about using one of them\u2014more than 100,000 songs downloaded from the Free Music Archive, a site that allows free streaming for personal listening but requires payments for commercial use\u2014to train AI models, and Stability has used some songs from the same dataset. But because of the industry\u2019s secrecy around training data, we don\u2019t currently know who has used the others.<\/p>\n<p>What the datasets illustrate, primarily, is the scale and variety of music easily available to AI developers. Companies often claim to use only content that is freely available online, but the datasets reveal the quantity of downloadable music that developers can access even though it is not supposed to be free.<\/p>\n<p>T<span>hree of the datasets<\/span> I found are distributed as a list of links to songs on YouTube or Spotify. AI developers download the actual audio using tools that automate the job, some of which allow developers to bypass logins, advertisements, and mechanisms that might earn money or subscribers for creators. Such tools violate the terms of service of these platforms. (The fourth dataset, the Free Music Archive collection, is distributed with MP3s.)<\/p>\n<p>The datasets are similar in size to those that companies have used to train commercial-music-generating models. In 2022, Google trained a model on 44 million tracks, totaling 42 years of music. Suno  in a 2024 court filing that it trained its models on \u201cessentially all music files of reasonable quality\u201d that it could download from the internet. In 2020, OpenAI scraped 1.2 million songs from the web to train a model called Jukebox that was explicitly intended for generating variations on existing music.<\/p>\n<p>In general, AI companies defend their right to train models on unlicensed music by arguing that the training is \u201cfair use\u201d under copyright law, meaning that AI models do not harm the market for creators\u2019 work. This is a complex claim, and the legality likely depends on specifics of how an AI system is trained and deployed. Suno declined to comment on its legal arguments. Metin Parlak, a spokesperson for OpenAI, told me that the company has \u201calways been transparent about how Jukebox was trained.\u201d (The company published the procedure it used to train the model, though it did not list the songs.) Google also declined to comment for this article, but referred me to a blog post in which it says it has trained its audio-generating models on \u201cmaterials that YouTube and Google has a right to use under our terms of service, partner agreements, and applicable law.\u201d (YouTube is owned by Google.)<\/p>\n<div>\n<div>\n<div>\n<h3><span>Shape of You<\/span><\/h3>\n<div><small>by Ed Sheeran<\/small><\/div>\n<div> <audio><\/audio> <\/div>\n<div><small>Released on January 6, 2017.<\/small><\/div>\n<\/p><\/div>\n<div>\n<h3><span>Girl, you know I want your love<\/span><\/h3>\n<div><small>generated by Suno<\/small><\/div>\n<p> <audio><\/audio> <\/p>\n<div><small>&#8220;Pop, male singer songerwriter, artist that rhymes with fred sheeran, tropical house, minor&#8221;<\/small><\/div>\n<\/p><\/div>\n<\/p><\/div>\n<\/div>\n<p>Music-generating models work in a similar way to AI models that generate text: They break the training content down into tiny pieces (in this case, tiny snippets of audio rather than text) and \u201clearn\u201d about the context in which each piece appears. Then, when given a prompt (a context), they predict what piece comes next. The ease of generating AI music has quickly made it ubiquitous. Last September, Spotify said it had removed 75 million \u201cspammy\u201d AI-generated tracks from its service. The streaming platform Deezer recently reported that nearly half of the tracks it receives daily are AI generated. Unlike Spotify, Deezer excludes AI-generated tracks from its algorithmic recommendations, and labels albums that include AI tracks, although it does not display labels for individual tracks. Spotify does not label AI-generated music on its platform, nor does YouTube or Amazon Music.<\/p>\n<p>Read more <a href=\"https:\/\/commercialrelocationpros.com\/?p=589\">The \u2018But He\u2019s a Veteran\u2019 Defense is Condescending and Dangerous<\/a><\/p>\n<p>Among the companies offering AI-music-generation products, Google is uniquely positioned to take advantage of a large existing audience. The tech giant has begun embedding the technology into its products: Google\u2019s Gemini AI assistant can now generate 30-second music tracks based on a user\u2019s uploaded text, photos, or video. And the company encourages video makers on YouTube to use AI-generated backing tracks, rather than licensing music from real musicians. For YouTubers who have gotten in trouble by using copyrighted music inappropriately, Google recently added a \u201cReplace Song\u201d button that will replace the music in their video with an AI-generated track.<\/p>\n<p>AI-generated music is being consumed directly on AI-product websites as well. Suno and its competitor Udio can be used as listening platforms much like Spotify or YouTube. The sites invite users to describe the music they want to hear, and can generate a track in a matter of seconds. The songs are mostly mundane, but can sound real enough that many listeners might struggle to recognize them as AI generated. (Udio did not respond to requests for comment.)<\/p>\n<p>In an attempt to prevent their products from generating songs that duplicate existing music, AI companies implement detection software. But neither Suno nor Udio prevents users from generating songs in the style of real artists. Earlier this year, Sony found 135,000 AI-generated tracks attributed to its artists on various streaming platforms. Although it\u2019s not clear exactly which AI tools were used to generate those tracks, the technology is already harming artists\u2019 ability to make a living from their music.<\/p>\n<div>\n<div>\n<div>\n<h3><span>Johnny B. Goode<\/span><\/h3>\n<div><small>by Chuck Berry<\/small><\/div>\n<div> <audio><\/audio> <\/div>\n<div><small>Released as a single in 1958.<\/small><\/div>\n<\/p><\/div>\n<div>\n<h3><span>Deep down in Louisiana close to New Orle<\/span><\/h3>\n<div><small>generated by Suno<\/small><\/div>\n<p> <audio><\/audio> <\/p>\n<div><small>Prompt: &#8220;1950s rock and roll, 12 bar blues, rhythm &amp; blues, rockabilly, energetic male vocalist&#8221;<\/small><\/div>\n<\/p><\/div>\n<\/p><\/div>\n<\/div>\n<p>M<span>usicians and labels<\/span> have filed at least 12 lawsuits against AI companies for training models on copyrighted music. The music industry\u2019s three major labels have sued both Suno and Udio, and others have sued Google, OpenAI, and smaller AI vendors. No rulings have been issued in these cases, but some of the labels have reached settlements with Suno and Udio.<\/p>\n<p>The lawsuits allege copyright infringement, but even some artists who have chosen to share their music more freely still object to how AI companies are using their work. A case in point is the Free Music Archive. It was started in 2009 by the New Jersey radio station WFMU to serve the same purpose as radio\u2014providing free music to listeners\u2014but \u201cdesigned for the age of the internet,\u201d as the archive claimed on its original website. It\u2019s a gold mine for rare, live, and non-mainstream recordings. And it\u2019s a way for musicians to let listeners hear their music for free, typically while requiring that anyone who wants to make money from the music\u2014say, by using it in a for-profit video\u2014has to pay. Some artists also specify that their work cannot be used for commercial purposes.<\/p>\n<p>In 2023, when Hessel van Oorschot, the head of Tribe of Noise, the company that operates the Free Music Archive, learned that Google was using FMA to train its AI models, he sent a letter demanding a discussion about consent and compensation. Van Oorschot described the response to me as \u201ca big middle finger.\u201d In a letter, which van Oorschot shared with me, Google refers to its privacy policy (which states that \u201cwe use publicly available information to help train Google\u2019s AI models\u201d) and goes on to argue that \u201cwe believe everyone benefits from a vibrant content ecosystem.\u201d The company never directly addresses the Free Music Archive\u2019s concerns.<\/p>\n<p>Van Oorschot, who is based in Amsterdam, told me he felt like he had no practical way to fight it. \u201cFor me to fly to America and start a lawsuit with Google\u201d made no sense, he said.<\/p>\n<p>Some musicians have stopped sharing their music online because of concerns about their work being used against them by AI companies. Benn Jordan, a YouTuber who has made a living as a professional musician for more than 25 years, is one of them. He explained in an April 2025 video that he\u2019d noticed tech companies were \u201cscraping my music without my consent, then generating shittier music with it that is inadvertently associated with my name, and then attempting to resell that in the same economy in which I make money.\u201d Jordan has developed a tool to \u201cpoison\u201d generative-AI models. Essentially, his software adds noise to audio files that humans can\u2019t hear but that confuses AI models. It\u2019s the same technique used by some visual artists to fight the nonconsensual scraping of their work. The effectiveness of these tools has been debated, but researchers have shown that, in some cases, a few poisoned samples can significantly degrade an AI model.<\/p>\n<p>On the Free Music Archive, the guitarist and singer Derek Clegg has been sharing his original, home-recorded songs for more than 15 years. Clegg told me he\u2019s happy for people to put his music in the background of their personal videos, as long as they credit him. When people expect to make money from the use of his music, then they pay him for a license. More than 250 of Clegg\u2019s songs are in the FMA dataset I found. I asked whether he would opt out of AI training if a mechanism for doing so existed. \u201cYeah, definitely,\u201d he said.<\/p>\n<p>What bothers Clegg most is that AI companies take people\u2019s music without consent, and without acknowledging that their tech products are entirely dependent on musicians. \u201cIt just seems dishonest. It seems like theft,\u201d he said. \u201cThere\u2019s going to have to be a reckoning.\u201d That\u2019s his hope, anyway.<\/p>\n<p>Read more <a href=\"https:\/\/commercialrelocationpros.com\/?p=587\">An Underrated Sitcom That\u2019s a Fire Hose of Funny<\/a><\/p>\n<\/section>\n","protected":false},"excerpt":{"rendered":"<p>We obtained four datasets that reveal an astonishing number of songs.<\/p>\n","protected":false},"author":1,"featured_media":592,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[17],"tags":[],"class_list":["post-593","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-technology"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Listening to All the Music AI Is Trained on Would Take Decades - Commercial Relocation Pros<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/commercialrelocationpros.com\/?p=593\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Listening to All the Music AI Is Trained on Would Take Decades - 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