The music sector has consistently been at the forefront of innovation, from the advent of the iPod paving the way for modern smartphones, to streaming services with vast catalogues. Artificial Intelligence(AI) represents the latest frontier, with the industry among one of the earliest adopters of such technological advances. AI’s application ranges from tailoring music recommendations, to creating new innovative music and revenue streams for many creators. However, AI creations lack a human touch, which prompts the questions: is there a boundary we ought to respect?
One big strength seen in recent years with the use of AI is Spotify’s ‘Discover Weekly’ and other personalised playlists which many attribute to Spotify’s success. This recommendation system uses AI and machine learning to keep users engaged. Such advancements have allowed Spotify to remain the biggest streaming service against competitors such as Apple Music and YouTube Music.
AI algorithms delve into the metadata of tracks such as titles, genre tags and lyrics, establishing a robust content-based filtering system. This in-depth analysis enables Spotify to consistently deliver personalised experiences to users, setting it apart from competitors and showcasing the impact of such technological advances.
The demand for data to train AI, especially in today’s world can often intersects with privacy worries. Companies analyse user data to refine user experiences and recommendations to identify emerging music trends. However, users may hesitate to share details if they suspect their data — such as location, listening behaviour, and daily habits — could be misused. Many companies such as Spotify have been investigated for potential privacy violations for selling user data. Such practices highlight the critical importance of robust data protection laws and ethical practices, ensuring that technological advancements do not erode user privacy and confidence.
AI has the ability to create innovative unique music and melodies. AI allows artists to experiment and work with new sounds at a much faster pace. With nearly 60% of musicians already using AI to make music, it is no surprise that utilising it has had a large affect on current music already. This percentage is only set to increase as AI improves over the next few years. Using AI such as ChatGPT to write lyrics has already been on a steep incline. One Reddit user claims it has increased the speed he writes lyrics exponentially. However, fans report a lack of emotional depth with lyrics and songs written by such tools. This raises the question, what balance does a billion dollar industry strike with such advancements?
AI tools are also used to increase the quality of the final product, with its use in the realm of mixing and mastering. AI streamlines these processing, ensuring high quality music with little effort. Tools such as Orb Producer Suite suggest chord sequences, allowing producers to improve their own creations. With new methods such as Recurrent Neural Networks(RNN) being developed, such tools are only expected to improve and allow artists to create music of higher quality.
AI innovations like OpenAI’s Jukebox present a double-edged sword. While these tools can generate tracks mimicking legends like Katy Perry and Frank Sinatra, they also raise a significant concern. As Rebecca Withers states, “copyright law surrounding AI-generated art is a relatively new and complex area”. Artists and labels own the rights to their vocals and music, making unauthorized distribution a critical problem. The Guardian also reported last year that streaming sites have been urged not to let AI use music to clone pop stars. Beyond financial losses, such misuse risks diluting an artist’s distinct brand. Moreover, generating music in the style of deceased artists raises ethical concerns and borders on exploitation.
A major issue with AI is its inherent bias in many fields including music. AI used in recommendation and for marketing rely on a large amount of data. While this gives users a personalised experience with unique recommendations, it can risk a lack of diversity. AI algorithms tend to favour mainstream, well-known artists due to the abundance of data and popularity which reduces the reach of smaller more diverse artists. This data is also labelled by humans which can also reflect human bias. Overall this intersection needs great care to not reduce diversity.
Another major consequence with the trend of using AI in music production is the potential displacement of professionals such as sound engineers and producers. Such roles are now being automated with over 30% of artists using AI for mastering and 20% using it for music production. While this poses a job security risk, it also allows independent artists, who previously might have been constrained by budget limitations, to now harness AI to polish their work affordably. Such artists are now able to deliver music that rivals the quality of much larger artists.
While AI accelerates production and offers new creative tools, it raises the concern about emotional depth and cultural resonance. Such music can lack emotion and not fit culturally. This is due to the ever increasing integration of AI which leads to the loss of a human element that many listeners find authentic. Music provides listeners a unique experience and brings out emotions which may be lost with the use of AI generated music. As AI works on pattern matching, which may lead to lack of innovation and further lead to lack of diversity in music as artists focus on convenience and speed.
With the integration of AI in the music industry providing a remarkable leap in innovation, there is no doubt it poses both challenges and opportunities. AI has transformed the music consumption, creation, and production. From enhancing user experience by recommending new music to allowing artists to experiment at a much faster pace. Balancing this innovation with concerns such as copyright is crucial to ensure AI complements rather than undermines the human essence of music. AI should coexist and allow music to innovate rather than stagnate.
