Artificial intelligence has made producing music easier. It has also made producing music almost infinitely more abundant.

Those two developments are happening at the same time. AI-assisted tools can give musicians access to production, mixing, mastering, analysis, and composition capabilities that previously required more specialized knowledge or resources. At the other extreme, fully generative systems can create enormous quantities of music with little human involvement.

The scale of the second trend is already striking. In June 2026, Deezer detected an average of 90,000 fully AI-generated tracks arriving on its platform every day. At its peak, AI music represented more than half of all new tracks being delivered to the service.

For Darren Herft, a global music executive whose commentary spans music, entertainment, technology, and business, the more important opportunity lies with the people using AI rather than the volume machines can produce.

Darren Herft has argued that the AI debate should extend beyond the small group of performers who dominate charts and streaming numbers. Millions of musicians create outside that spotlight, and AI could give more of them access to capabilities once concentrated among major studios, established artists, and large record labels.

That creates a more useful test for the technology.

The question is not simply how much music AI can generate. It is whether AI can expand the number of human artists who have a realistic opportunity to create, release, and build something around their work.

Music Has Always Had a High Cost of Entry

The barriers facing a new musician are not difficult to identify.

Professional recording historically required equipment, studio time, production knowledge, engineering, mixing, mastering, and eventually distribution and promotion. An artist could have talent and ideas without possessing the resources required to turn either into a competitive recording.

Digital technology reduced many of those barriers long before the current AI boom.

Affordable recording software moved production onto personal computers. Digital distributors made global releases possible without physical manufacturing. Streaming gave independent musicians access to the same broad distribution environment as established performers.

AI is pushing that process into another stage.

Berklee College of Music’s current AI in Music: Composition, Production, and Analysis course illustrates how broad the technology has already become. Students work with AI across music recognition, analysis, production, generation, mixing, mastering, style transfer, and voice conversion.

Its shorter AI for Music and Audio course covers source separation, melody extraction, chord recognition, production, composition, recommendation systems, and other applications.

These are not all substitutes for professional musicians or engineers. Many are tools that allow creators to perform particular tasks more efficiently or experiment with possibilities they might otherwise lack the resources to pursue.

That distinction is central to Darren Herft’s position.

He believes the long-term significance of AI may be less about replacing musicians and more about expanding what musicians can create, produce, and distribute themselves.

Independent Artists Are Already Using the Technology

This broader participation is not entirely theoretical.

A global survey conducted by Believe and TuneCore found that 27% of surveyed independent and self-releasing artists had already used AI music tools.

The applications were also broader than simply asking software to generate a song.

Artists expressed interest in AI for the creative process, marketing, and developing their fan bases. Half of respondents said they would consider making their music available for machine learning, although the survey also found strong interest in responsible AI practices.

That matters because independent musicians operate under different constraints from the industry’s biggest performers.

An established artist may have access to producers, engineers, marketing teams, management, label resources, and outside capital. An independent musician may be responsible for several of those functions personally.

AI does not need to replace any one profession completely to alter that equation.

If a creator can use technology to take an idea further before paying for studio time, the economics change. If a musician can experiment with arrangements or production approaches more cheaply, more ideas become affordable to test. If small teams can accomplish work that previously required larger teams, fewer resources are required simply to participate.

Darren Herft points to that increased accessibility as one of AI’s most important potential benefits.

The result is not guaranteed success. It is a lower threshold for getting started.

Participation Is Growing Inside an Already Large Market

The opportunity is significant because the music market itself continues to expand.

According to IFPI, global recorded music revenues reached $31.7 billion in 2025, up 6.4% from the previous year. It was the industry’s eleventh consecutive year of growth.

Paid streaming subscription revenue increased 8.8%, and the number of paid streaming subscription accounts worldwide reached 837 million.

That means emerging musicians are not entering a shrinking market.

They are entering a growing global market in which distribution has become extraordinarily accessible.

But access to the market and access to its revenue are two different things.

This is where Darren Herft’s argument becomes more demanding than simply saying AI democratizes music.

More people being able to create professional-quality material also means more people competing for the same listeners.

The technology can broaden participation without broadening attention at the same rate.

That tension is becoming impossible to ignore.

AI Is Creating an Abundance Problem

Deezer’s numbers show just how quickly the supply side of music can change.

In January 2025, the company was receiving approximately 10,000 fully AI-generated tracks per day. By January 2026, that number had risen to roughly 60,000. In April, it approached 75,000.

By June 2026, Deezer was detecting an average of approximately 90,000 AI-generated tracks per day.

At peak levels, more than half of the new music delivered to the platform was fully AI-generated.

Yet consumption tells a very different story.

Deezer reported earlier in 2026 that fully AI-generated music represented only around 1% to 3% of streams on its platform. The company also found that a large majority of streams on fully AI-generated tracks were fraudulent and removed those streams from its royalty pool.

That gap between supply and demand matters.

AI can make music abundant without making human attention abundant.

For working musicians, that means lowering the barrier to creation solves only part of the problem. Artists still need listeners, identity, trust, promotion, relationships, and a reason for an audience to care about their work.

Darren Herft’s existing commentary recognizes this tension. He sees AI as capable of helping more creators participate, while also emphasizing that the industry needs systems that continue to reward genuine human creativity.

Broader participation only matters if artists have a realistic opportunity to build sustainable careers from it.

The Opportunity Goes Beyond Generating Songs

Much of the public conversation about AI music focuses on systems that can produce complete tracks from prompts.

That is only one part of the technology.

For musicians, some of the most practical applications may be less dramatic.

AI can assist with separating vocals and instruments, analyzing audio, identifying chords, improving production workflows, mixing, mastering, generating ideas, and testing creative variations.

Berklee’s AI programs increasingly treat these capabilities as part of the working musician’s toolkit. Its Machine Learning for Musicians course has students develop machine-learning applications for music, while its broader emerging-technology programs incorporate AI into songwriting, production, analysis, and creative experimentation.

This matters for participation because assistive technology changes who can attempt sophisticated work.

A musician does not need to become a machine-learning engineer to benefit from a tool that simplifies a technical process.

A songwriter does not need to surrender authorship to use software to experiment with an arrangement.

A small independent producer does not need the resources of a major studio to access every new production capability.

Darren Herft’s position is that artists can use AI in precisely this way: as another tool for bringing ideas to life.

That creates a fundamentally different vision of AI music from one dominated by autonomous song generation.

The beneficiary is not the machine producing more content. It is the human creator gaining more capability.

More Creators Does Not Mean Equal Outcomes

There is also a risk in overstating what lower barriers accomplish.

Technology can democratize access without democratizing success.

The same streaming platforms that allow independent artists to reach audiences worldwide also force those artists to compete in an enormous global marketplace.

AI intensifies both sides.

It can reduce production costs and make professional capabilities more accessible. At the same time, it can increase the total amount of content competing for listeners.

Darren Herft’s commentary is particularly useful here because he does not treat broader participation as a guarantee that every creator will succeed.

His argument is that AI can create more opportunities for artists to release music, build audiences, and experiment with different approaches to their careers.

That is different from saying the economic rewards will suddenly become evenly distributed.

The most likely result is a larger field.

More creators will be capable of producing credible work. More artists will be able to test ideas without substantial upfront investment. More musicians will be able to reach global distribution without traditional industry backing.

Competition for attention may become even harder as a consequence.

For artists, the advantage will therefore come from combining greater technological capability with the things that remain difficult to automate: judgment, identity, audience relationships, performance, originality, and creative direction.

Darren Herft Says the Industry Must Look Beyond Its Biggest Stars

This is where Darren Herft’s broader argument about artists becomes especially important.

Music-industry debates naturally gravitate toward famous performers because those artists command the largest audiences, catalogs, and commercial stakes.

But they represent only one part of the creative economy.

Darren Herft has specifically argued that the conversation around AI should include the millions of musicians who continue creating and releasing work outside the industry’s highest levels of commercial success.

“We need to make sure that we are looking after and protecting these wonderfully creative people that make the amazing music we’ve all become accustomed to,” Darren Herft has said.

For Herft, this broader creative community is not peripheral to the AI discussion.

It may be where some of the technology’s most meaningful benefits appear.

An established artist already has access to sophisticated production resources. Giving that artist another tool can still improve a workflow, but the underlying capability was already available.

For a creator operating with a limited budget, access to the same type of capability can have a much larger effect.

This is why Darren Herft points to accessibility rather than automation alone.

If AI reduces the resources required to create competitive music, the technology potentially allows people who would otherwise remain outside professional music production to participate.

That is a much broader economic and creative consequence than simply producing songs faster.

Broader Participation Requires Artist Protection

There is one condition attached to that opportunity.

More creators entering an AI-enabled music economy will mean little if those creators cannot retain value from what they make.

Darren Herft has consistently connected the positive potential of AI with artist protection.

“As AI becomes more integrated into music creation, protecting genuine artists and ensuring they continue to be recognised for their work remains critically important,” Darren Herft has said.

His position is that innovation and artist protection should not be treated as opposing objectives.

AI can lower production barriers while creators retain ownership.

It can improve workflows while human contribution continues to receive recognition.

It can create new commercial possibilities while the industry develops systems for fair compensation, transparency, and responsible use.

Those principles become even more important in a market where AI can produce content at enormous scale.

The fact that Deezer can receive tens of thousands of fully generated tracks in a single day demonstrates how quickly supply can become detached from human creative effort.

The goal, from Darren Herft’s perspective, is not simply to maximize the amount of music that technology can produce.

It is to use technology in ways that strengthen the people who make music.

A Larger Creative Economy Is the Real Opportunity

AI is unlikely to make the music business easy.

It will not eliminate the competition for listeners. It will not guarantee that an independent artist can earn a living. It will not replace the importance of taste, originality, audience connection, or commercial judgment.

What it can do is reduce the number of resources someone needs before they can seriously participate.

That distinction matters.

A musician with a modest budget can experiment further.

An independent artist can access more sophisticated production capabilities.

A small creative team can accomplish more without immediately expanding its costs.

A songwriter can test ideas that previously might have required additional technical support.

Darren Herft believes these changes could produce a broader music ecosystem in which meaningful creative participation is not limited to a relatively small group of established performers.

That is a more consequential measure of AI’s impact than the number of songs a model can generate.

The technology becomes valuable when it increases human capability.

Conclusion

The scale of AI music is growing extraordinarily quickly.

Deezer’s experience shows what happens when generative technology makes content effectively unlimited: tens of thousands of tracks can enter a platform every day, while actual listener demand remains concentrated elsewhere.

But that is only one version of the AI music story.

The other is about individual musicians gaining access to tools that can help them create, produce, experiment, and release work with fewer resources.

Darren Herft points to that second development as one of AI’s most significant opportunities.

His argument is not that technology will make every artist successful. It is that the cost and complexity of participating in music can fall, allowing a wider range of creators to attempt things that previously required greater capital, infrastructure, or technical support.

The distinction between participation and success remains important.

AI can open the door. Artists still have to build something audiences value once they walk through it.

For Darren Herft, the strongest outcome is therefore not a music economy dominated by machine-generated abundance. It is a larger human creative economy in which technology gives more musicians the ability to create while ownership, recognition, and fair opportunities to earn remain firmly attached to the people behind the music.

FAQs

Why does Darren Herft see broader artist participation as a benefit of AI?

Darren Herft believes AI can make creative and production capabilities available to musicians who previously lacked access to expensive equipment, professional studios, or specialized technical resources. That can allow more independent creators to produce and release music.

Are independent musicians already using AI?

Yes. A global Believe and TuneCore survey found that 27% of surveyed independent and self-releasing artists had already used AI music tools. Artists showed particular interest in using AI during the creative process and for marketing and fan development.

Does broader access mean independent artists will earn more money?

Not automatically. Lower production barriers allow more artists to participate, but they also increase competition. Artists still need to attract listeners and build sustainable audiences around their work.

How much AI-generated music is reaching streaming platforms?

The volume is increasing rapidly. Deezer reported that fully AI-generated music averaged approximately 90,000 daily uploads in June 2026 and exceeded 50% of all new music delivered to the platform at peak levels.

Does Darren Herft support fully replacing musicians with AI?

No. Darren Herft’s commentary presents AI as a technology that can support human creativity. He has emphasized that genuine artists should remain protected, recognized, and able to earn from their creative contributions.

What would a broader AI-enabled music economy look like?

It would allow more musicians to access professional capabilities, experiment with ideas, produce music, and reach audiences without requiring the same level of capital or institutional backing. Darren Herft’s position is that this increased access should develop alongside protections for ownership, recognition, and fair compensation.