Generative artificial intelligence (AI) is creating two distinct music businesses rather than one transformed industry, according to Nicholas Gunn, founder of Blue Dot Music. One model is built on synthetic recordings generated and consumed quickly; the other remains centered on artist identity, ownership, history and direct audience relationships.
Gunn, a British-born multi-instrumentalist, producer and songwriter, said the split is already visible across digital service providers (DSPs) and direct-to-consumer (DTC) channels.
“Personally, I’m quite clear that we are entering into a time of two types of business in music. One that is generative AI driven that is highly transient represented on the DSPs, under AI labeling, and on generative AI sites such as Udio. The other being an artist centric model being represented on the DSPs without labeling and via DTC.”
Synthetic music and collapsing creation costs
Generative AI has made it possible to produce finished-sounding recordings, alternate versions, vocals, instrumentals and entire albums at a scale that was not possible a few years ago. The marginal cost of creating another recorded track is falling sharply.
That does not mean the output is necessarily poor. It means the economics of creation have changed, and large volumes of music are entering the streaming ecosystem.
The problem is that AI solved a scarcity problem the music industry did not actually have. There was never a shortage of music available to listeners. Before platforms such as Suno and Udio existed, there was already more recorded music available than any person could hear in a lifetime.
What remained scarce was attention, interest, cultural relevance, trust and the ability to create a relationship between an artist and an audience. AI increased inventory, but it did not automatically increase demand.
Two parallel economies
That new inventory can still become a substantial business, but it will not look like the artist business already understood. The generative side can serve a range of functional uses:
- Playlists
- Moods
- Games
- Fitness applications
- Meditation
- Retail environments
- Social media
- Personalized entertainment
A listener may request ninety minutes of instrumental music generated for a rainy morning in Boston, listen once and never encounter those recordings again. That is still music, but it is not necessarily an artist career; the value comes from satisfying a moment rather than building a catalog meant to be revisited.
A generative platform may create music because somebody needs it now, and the track may fulfill its purpose before disappearing into an effectively infinite pool of alternatives. There is no requirement that anyone remember the title, identify with the artist, buy a ticket or care what the creator produces next.
That model is not inherently problematic. Media is already consumed in similarly functional ways, but functional synthetic music and artist development are not simply two versions of the same business.
Artist-centered model and direct-to-consumer
The artist-centered model operates differently. It revolves around artists whose identity matters to listeners, where audiences are interested in what a particular person does next. An artist accumulates meaning over time through history, choices, changes in direction, successes, failures, surprises and a catalog that exists independently of any single recording.
Direct-to-consumer channels are becoming more important on this side of the business. They include:
- The website
- The mailing list
- Concerts
- Physical products
- Memberships
- Limited editions
- Community
- Direct communication
Streaming will still matter because it remains the dominant way most people encounter recorded music, but the strongest relationship will increasingly be the one that exists directly between artist and listener.
A generative system can make another song. It cannot automatically manufacture that history.
