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The Future of Music is in AI; Thanks To Spotify

Spotify's journey with artificial intelligence (AI) and generative AI (Gen AI) has been a long and evolving process, marked by strategic acquisitions, innovative features, and a commitment to enhancing user experience.

“Listening is everything” is Spotify’s tagline, which represents not just their service but also their never-ending search for the ideal playlist. But what if Spotify could do more than simply play songs you enjoy? What if it could predict what you’ll like next, or even something you didn’t realise you wanted to hear? In a world with 100 million tunes and 600 million users, Spotify is banking big on AI to not just curate, but also predict your musical preferences. From AI DJs to mood-matching algorithms, the streaming giant is on an attempt to transform its massive collection into your personalised soundtrack. 

Spotify’s journey with artificial intelligence (AI) and generative AI (Gen AI) has been a long and evolving process, marked by strategic acquisitions, innovative features, and a commitment to enhancing user experience. Let’s explore the company’s AI journey in detail, highlighting key milestones, features, and the leaders who have been instrumental in shaping Spotify’s AI strategy.

Early AI Investments and Acquisitions

Spotify’s foray into AI began nearly a decade ago with a series of strategic acquisitions aimed at improving its music recommendation algorithms and user experience:

2013: Tunigo Acquisition

Spotify acquired Tunigo, a music discovery app, to enhance its music recommendation capabilities. Tunigo former CEO and co-founder Nick Holmstén explained that the acquisition was a natural fit, given their close collaboration with Spotify. “Tunigo has been dedicated to helping users discover the perfect music for any moment, and our partnership with Spotify over the years has made this a seamless transition,” he shared. “This merger is ideal for advancing music discovery. We’re passionate about both music and technology and are excited to continue innovating in this space.” A Spotify spokesperson emphasised Tunigo’s lengthy history as a partner, stating that Tunigo has continuously been among the top 10 applications since Spotify debuted its app platform in late November 2011.

2014: Echo Nest Acquisition

The company acquired Echo Nest, a music intelligence company, to further improve its recommendation algorithms. The Echo Nest specialised in producing music recommendations for streaming radio networks. This relationship boosted Spotify’s access to critical technology, which was already underpinning most of its offering. Despite the arrangement, The Echo Nest’s API remained free and open, benefiting competitors like Rdio, who used it for music recommendations. However, this acquisition gave Spotify control over technology that its competitors relied on, resulting in a precarious balance in the competitive music streaming market.

2015: Seed Scientific Acquisition

Spotify purchased Seed Scientific, bringing its whole staff of about 20 people into its New York headquarters. Seed Scientific specialised in algorithms for the commercial, public, and social sectors, offering services in data discovery, collecting, science, and visualisation. Their experience served as the cornerstone for Spotify’s new Advanced Analytics team, which combines math, science, design, and engineering to provide insights and tools.

The Advanced Analytics section, managed by Seed Scientific former founder and CEO Adam Bly and later Spotify’s VP of Data, sought to improve Spotify’s decision-making across product and business domains. It helps Spotify enhance song recommendations, follow suggestions, artist tour planning based on fan geography, and targeted advertising. 

2017: Sonalytic and Niland Acquisitions

Spotify added Niland, an AI firm, as its fourth purchase. Niland, situated in Paris, developed an API-based application that improved the accuracy of music search and suggestion. The Niland team collaborated with Spotify’s research and development team in New York to boost user personalisation and recommendation tools.

According to Spotify: “Niland has changed the game for how AI technology can optimize music search and recommendation capabilities and shares Spotify’s passion for surfacing the right content to the right user at the right time.”

AI-Powered Features and Innovations

As Spotify continued to invest in AI technology, it developed several features that leverage machine learning and AI to enhance user experience:

1. Discover Weekly

Launched in 2015, Discover Weekly is a personalized playlist that uses AI algorithms to recommend new music based on a user’s listening history. This feature has become one of Spotify’s most popular offerings, introducing users to new artists and songs tailored to their tastes. This personalized playlist was built by a powerful AI system that used many machine learning approaches. It begins by gathering massive quantities of information about your listening behavior, such as music played, skipped, and saved. The AI then uses collaborative filtering to compare your choices to those of similar users, as well as natural language processing to contextualize music-related text information. Advanced audio analysis and deep learning models, such as convolutional neural networks, investigate musical qualities of songs. The algorithm employs reinforcement learning to enhance its suggestions based on how you interact with the playlist. Balancing familiarity and exploration, the AI seeks to introduce listeners to new music while remaining within the taste profile. “It exceeded our expectations,” said Matt Ogle, Spotify’s Former Product Owner for Discover Weekly.

2. Spotify Wrapped

Introduced in 2016, Spotify Wrapped is an annual feature that uses AI to analyze users’ listening habits throughout the year, providing personalized insights and statistics. Spotify Wrapped, the platform’s year-end personalised user experience, uses AI and data analytics to create an engaging, shareable campaign. The programme analyses massive quantities of user data, such as listening habits, favourite artists, and genres, using machine learning algorithms to detect trends and patterns. AI-powered natural language processing generates personalised tales and insights, and computer vision techniques provide visually attractive images and animations. The campaign uses predictive analytics to forecast music trends and consumer preferences. Spotify’s AI also divides users into distinct “listening tribes” based on their common music preferences. AI not only personalises the experience, but it also ensures that millions of users’ data is processed quickly at the same time. 

There are half a billion people that listen to music online and the vast majority are doing so illegally. But if we bring those people over to the legal side and Spotify, what is going to happen is we are going to double the music industry and that will lead to more artists creating great new music. – said Daniel Ek, Founder and CEO at Spotify 

Recent Advancements in AI and Gen AI

In recent years, Spotify has made significant strides in incorporating more advanced AI and generative AI technologies into its platform:

1. AI DJ (2023)

Spotify’s AI DJ function, which debuted in February 2023, marks a huge advancement in personalised music streaming. This revolutionary tool combines Spotify’s personalisation technology, generative AI, and a dynamic AI voice. The AI DJ creates a playlist of music based on each user’s specific likes and listening patterns, interspersed with comments and background about the tunes and artists. It uses OpenAI technology to provide culturally relevant natural-language comments. 

The DJ’s voice is based on Spotify’s Voice of Spotify x Head of Cultural Partnerships, Xavier “X” Jernigan, and was generated using Sonantic‘s AI speech technology, which Spotify acquired. This feature enhances Spotify’s current personalisation capabilities by introducing a more human-like, interactive aspect to the listening experience. The AI DJ responds in real time to user comments, updating the queue based on listener interactions. This idea attempts to improve music discovery and personalisation by combining technology and human-like interaction in the music streaming sector. Emily Galloway, Head of Product Design for Personalization spoke about the AI DJ feature, she said:

“DJ is an entirely new way to listen, and a brand-new format, so there wasn’t a formula to follow when we were making decisions. We had to answer some core experiential questions like: ‘How do we take you on a journey with both familiar and unfamiliar music?'”

Ziad Sultan, VP of Personalization said, “ Putting generative AI technology in the hands of our music experts allows them to scale their expertise like never before.”

2. AI Playlist (2024)

Spotify’s AI Playlist function, which debuted in beta for Premium members in April 2024, uses powerful artificial intelligence to generate personalised playlists based on user input. This revolutionary technology allows users to create playlists using written descriptions, emoticons, or even abstract notions. The AI engine, which is driven by enormous language models and Spotify’s massive music collection, understands these instructions and curates a personalised playlist. It examines the user’s listening history, worldwide patterns, and the prompt’s semantic meaning. The AI can understand and respond to complicated, multifaceted demands, generating playlists tailored to various moods, activities, or topics. Users may modify the playlist by offering input, which helps the AI learn and improve its suggestions.

Gustav Söderström – Co-President, CPO & CTO mentioned in an interview that “Spotify has to change like everyone else.”

Spotify continues to spend extensively in AI and machine learning to improve its music recommendation and discovery tools. In 2024 and beyond, Spotify will prioritise personalisation and relevant suggestions to assist users in navigating its massive 100 million track catalogue. Key AI projects include the AI DJ, which employs generative AI to simulate radio-style announcements and assist consumers in discovering new music outside of their comfort zones. 

Spotify is also aiming to improve its array of recommendation features, including Discover Weekly, Blend, Daylist, and Made for You Mixes. The firm is leveraging AI to better understand user preferences across multiple circumstances and moods throughout the day, as seen by the Daylist function. Spotify intends to employ AI to balance familiarity and originality in suggestions, tackling the difficulty of determining when consumers want to explore new musical landscapes. The firm is merging AI technology with human skills from its music editors and experts to increase personalisation efforts and improve the overall listening experience for its 600 million customers.

Picture of Anshika Mathews
Anshika Mathews
Anshika is an Associate Research Analyst working for the AIM Leaders Council. She holds a keen interest in technology and related policy-making and its impact on society. She can be reached at anshika.mathews@aimresearch.co
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