Generative AI and Online Music Labor: Studio Professionals on Digital Labour Marketplaces

According to the research by Miranda, 2021, generating music with artificial intelligence is not new. As seen on the same research, by 1957, two engineers, Lejaren Hiller and Leonard Isaacson, had created an algorithm-based digital computer that composed the first song by a machine. By 1997, we already had a machine that beat a human composer in the race to compose a new piece in the style of Bach, as per Johnson, 1997. Then when we reached 2024, a company released its first text-to-music generation tool, Suno, which has reached 2.5 million active users, as stated on the research by Business of Apps, 2025.

As Pepple and Muthuthantrige (2026) argue, artificial intelligence not just changes industries and how we work, but it changes the very essence of what we think of work. While AI delivers the efficiency these tools promised, it also creates its own inequalities in the workplace. It threatens them by displacing human labour or changing the relationship structures into algorithmic systems. Also, as seen in CISAC (2024), 24% of music creators’ revenue is at risk of loss by 2028.

The Influence of AI in the Music Industry

AI is influencing the music industry in many ways including its functionality in composing music. Before in history, composing music used to require thorough technical skill and being creative. Also, composers, producers and musicians would have collaborations. Now in the AI world these tools let users generate compositions in any genre they wish, benefiting from the machine learning algorithms that have the capacity to analyze huge amounts of data of already existing music (created by humans).

These tools work by the knowledge acquired from learning patterns in melodies, harmonies, rhythms, and the characteristics of various genres.

The ethical concerns keep rising for the use of AI in music producing and in other art forms. Mimicking other artist’s styles and voices are considered as a critical topic as if it is the right of the AI tools to make a use of existing art forms, in many cases without the consent of the artists.

The Studio Musicians and Their Efforts

Even though there is little scientific research on the area, the exploitation of studio musicians is not new, according to Herbst & Albrechti, 2018. The study promotes that studio musicians are often not given enough credit in the public, despite their significant contributions to the records they play on. Producers often expect studio musicians to be perfect in the studio, so studio time is reduced, even though they do not have a score in hand most of the time.

Their own creative input is also expected in the industry. In today’s music industry, recording musicians are also likely to have their own project studios to work remotely and to develop basic engineering skills for their own instruments. Even though they are at the top of their field and very important to the recording industry, the same research indicates that they are often unable to finance studio musicians solely from recording gigs; therefore, they have to hold second jobs, such as playing live shows.

This article is based on the research prepared by mixakademisi.com.

The Rise of Generative AI Tools

Recent generative artificial intelligence tools such as Suno and Udio have been open to the public for only the last couple of years, as seen on the research by Business of Apps, 2025. Nevertheless, we already see people making music with generative AI tools, and going on tours based on AI-generated arrangements according to Yücelen, 2025. In this particular setting, session musicians who were once responsible for creating and playing their own parts, or for playing the parts a composer has written for them, are now responsible for figuring out the parts an AI tool generated and then playing those parts to an audience in concerts.

Generative AI tools are increasingly used to produce low-cost musical products at a massive scale.

“More than 112,000 new tracks are published”

According to Sonarwork, more than 112,000 new tracks are published on the music streaming platforms daily. Almost half of these tracks somehow use AI. Moreover, 1 in 4 music producers include AI tools in their music making process in their work-life.

Deezer recently reported that fully AI-generated music now accounts for around 34% of all tracks delivered to its service each day, totaling more than 50,000 new AI tracks. What these figures do not capture is the large number of songs that are first generated by AI and then recreated, arranged, or mixed by professional musicians, producers, and engineers on online music marketplaces.

In this emerging division of labour, human professionals are increasingly pushed into often low-paid polishing and recreational gigs on online labour marketplaces rather than occupying the core creative role.

The Experiences of Professional Musicians on Online Labor Marketplaces

As a result of what we’ve discovered from the previous research, we decided to look into and analyze how generative AI reshapes the labour process and experiences of musicians in professional music work on online labor marketplaces.

Focusing on independent musicians and producers who sell their services on platforms such as SoundBetter, Airgigs, and Fiverr we aim to ask: how does generative AI transform what independent musicians and producers are paid for, and how they understand their own creative role? 

Mix Akademisi asked 4 professional musicians what would be the outcomes of generative AI music for their daily works. And wondered;

• How is generative AI influencing the organization, content, and conditions of professional music work on digital labour marketplaces such as AirGigs, SoundBetter, and Fiverr?

• How does generative AI shape the tasks, workflow, and professional experience of musicians and music producers on online labour markets such as AirGigs, SoundBetter, and Fiverr?

• How do professional musicians and producers working on online labour markets reinterpret their creative role when working with AI-generated work?

After talking to the musicians, it leaded to founding out interesting real-life experiences.

“There's not much artistic freedom involved in it if it's replacing stuff in songs.”

Jon Sudbury

Swansea, UK

See the full report and discover experiences of studio musicians here.

Creative Labor of Studio Musicians

Menger’s (1999) work on how artistic work is organized around high uncertainty and instability by design. Earnings are, on average, lower than those of comparable professions, and income is highly unequal. Artists often work in several jobs, sometimes in their areas of expertise and sometimes not. Also, Gill and Pratt's (2008) work on creative labor shows the precarity in the field. They say that cultural workers are often seen as idealized, creative, and entrepreneurial.

Still, in reality, their jobs are usually temporary, with long and irregular hours, blurred lines between work and personal life, and constant worry about losing their jobs. Many workers also have a strong emotional connection to their jobs and want to "do what they love." This can lead to self-exploitation instead of working together to make things better. Studio musicians, on the other hand, are a specific group whose situation has only recently been studied in more detail by Herbst and Albercht (2018). Their study shows that even leading studio musicians often struggle to make a living solely from studio work.

Creative labour is already structurally precarious, highly competitive, and strongly unequal. This project uses this framework to examine how generative AI and online labour marketplaces affect the existing patterns.

The Cost of Hiring Professional Musicians

The cost of recording in a professionally designed studio can be nearly $1,000 just for one day. Even if the recording takes place in a home studio, it may mean the creators will need to spend so much time setting up equipment like mics.

When it comes to hiring professional session musicians, be it vocalists or instrument professionals, it can cost around $200–$500 per session. Beside, the mixing and mastering processes also require to pay for around $200–$1,000 per track to hire the mix engineers.

How Do Musicians Use AI in Their Processes?

When it comes to how musicians prefer using AI in their workflow, looking at LANDR’s published research in November 2025, it shows that the technical tasks take up the majority of the purpose of using AI.

LANDR’s published report on November 2025 says that 79% of the respondents out of 1,241 attendants use AI for technical tasks while 66% says the reason is creative tasks and 52% is for promotion.

For the AI song generators, the usage stays at %29.

According to 70% of the respondents, their view stays positive for AI when the goal is to use it for technical or promotion tasks. %46 of them chose it as a positive for creative tasks.