For independent musicians, the economics of making music have always been unforgiving. Recording, production, engineering, mastering, promotion, and distribution all consume money before an artist knows whether a song will find an audience.
Artificial intelligence is beginning to change parts of that equation.
AI-assisted systems can separate stems, analyze recordings, generate musical ideas, assist with mixing and mastering, and accelerate parts of the production process. Berklee College of Music now teaches students to build machine-learning applications including generative audio systems, neural audio plugins, and tools for mixing and mastering.
For Darren Herft, a global music executive whose commentary covers music, technology, business, and the creative economy, this is one of the more promising sides of AI in music. His position is that AI should be considered not only in terms of what it might disrupt, but also in terms of what it makes possible for working artists.
Darren Herft sees particular potential for independent creators. Artists without major-label resources can increasingly access capabilities that once required larger budgets, specialized expertise, or professional infrastructure. That does not remove the difficulty of building a music career. It does change what an artist can potentially accomplish before needing that infrastructure.
The Cost of Creating Music Is Starting to Shift
The first barrier AI can lower is straightforward: production.
Professional music has traditionally required access to a collection of specialized resources. Studio time costs money. Producers and engineers cost money. Mixing and mastering require either technical expertise or outside professionals. Even artists producing from home need software, equipment, and the knowledge required to use them.
Digital technology had already reduced those costs dramatically before generative AI arrived. What AI adds is the ability to automate or assist with tasks that previously depended more heavily on technical knowledge or specialist labor.
Berklee’s current AI curriculum provides a useful picture of what is already possible. Its AI in Music: Composition, Production, and Analysis course covers music recognition, analysis, intelligent mixing and mastering, algorithmic composition, lyric and music generation, style transfer, and voice conversion. Its Machine Learning for Musicians course goes further into building customized music applications.
For Darren Herft, the important point is not that every artist should perform every task alone.
It is that the minimum resources required to experiment are falling.
Herft’s existing commentary identifies studio time, production expertise, and technical equipment as traditional barriers for musicians. He sees AI-assisted idea generation, production improvements, arrangement experimentation, and streamlined workflows as ways to reduce some of those obstacles.
An independent musician who can develop an idea further before paying for outside production has more control over where limited resources are spent. An artist who can test several arrangements before entering a studio can arrive with a clearer creative direction. A producer who automates routine technical work can spend more time making decisions that actually require judgment.
The economic benefit is not that professional expertise becomes worthless. It is that artists can potentially buy less of it before discovering whether an idea is worth pursuing.
Independent Artists Have More Tools, but Also More Competition
Lower production barriers create an obvious benefit, but they also create a harder problem.
If it becomes easier for one independent artist to make and release music, it becomes easier for everyone else too.
The recorded music market is already enormous. According to IFPI, global recorded music revenue reached $31.7 billion in 2025, while paid streaming subscription accounts reached 837 million users. Streaming represented 52.4% of global recorded music revenue.
Those numbers demonstrate the size of the opportunity. They do not mean that opportunity is evenly distributed.
Independent musicians are competing for attention in a global market where the supply of music is enormous. AI can make production cheaper, but cheaper production also increases the amount of material capable of entering the same market.
Darren Herft’s view accounts for both sides of that equation. His existing commentary recognizes that streaming has given artists access to global audiences while simultaneously intensifying competition for listeners. His argument is that lower production costs can still matter because they give more artists the ability to release music, experiment with different approaches, and attempt to build audiences without major-label support.
That is a more realistic way to understand the AI advantage.
AI does not level the entire playing field. It lowers some of the costs required to get onto it.
Artists Are Already Experimenting With AI
This is not purely a prediction about what musicians might eventually do.
Independent artists have already begun experimenting with the technology.
A TuneCore survey reported by Music Business Worldwide found that 27% of surveyed independent artists had already used AI music tools. Among AI users, applications extended beyond music creation into artwork, promotional assets, and fan engagement.
A separate producer survey found that 25% of surveyed music producers were already using AI, although attitudes differed sharply between assistive AI and systems that generate music directly. Nearly three-quarters of the AI users surveyed were relying exclusively on free tools.
That last figure is particularly relevant to Darren Herft’s argument.
If useful AI capabilities are available at little or no upfront cost, they are accessible to musicians who could never justify spending heavily on experimental technology. The economic threshold for trying something new becomes much lower.
But the distinction between assistive and generative AI also matters.
Darren Herft’s supplied commentary consistently presents AI as a technology that can support human creators. He argues that artists should use new tools to improve workflows and expand creative possibilities without treating AI as a substitute for the creativity behind the work.
That distinction increasingly matters both creatively and commercially.
The Real Advantage May Be Creative Iteration
Some of AI’s value is difficult to measure purely in dollars.
Consider what happens before a song is finished.
Artists discard ideas. Arrangements are changed. Recordings are revised. Producers test sounds that do not work. A songwriter can spend hours pursuing a direction that ultimately goes nowhere.
AI can compress some of that experimentation.
An artist might generate variations on an idea, separate elements from an existing recording, test different approaches to an arrangement, or use an AI-assisted production tool to identify possibilities worth developing further.
Berklee’s AI for Music and Audio course teaches musicians to work with AI across analysis, production, composition, source separation, mixing, mastering, and generative applications.
This is where Darren Herft’s argument becomes more interesting than the claim that AI simply makes music cheaper.
It can make experimentation cheaper.
For an established artist with substantial resources, that may improve efficiency. For an independent musician financing projects personally, it can determine whether an idea is affordable enough to pursue at all.
Herft’s commentary points specifically to the ability of AI tools to help musicians generate ideas, improve production quality, experiment with arrangements, and bring projects to completion more efficiently.
The artist remains responsible for deciding what deserves to survive that process.
Lower Barriers Do Not Eliminate the Need for Human Contribution
There is a limit to the democratization argument.
Making production easier does not make audiences easier to win.
An artist still needs something people want to hear. More importantly, the legal framework developing around generative AI continues to place substantial importance on human creative contribution.
In 2025, the U.S. Copyright Office concluded that generative AI outputs can receive copyright protection where a human author has determined sufficient expressive elements. AI can assist a creative process without preventing copyright protection, but simply supplying prompts is not enough by itself.
The distinction aligns closely with Darren Herft’s position.
His commentary argues that artists who contribute creativity, judgment, and direction should continue to be recognized for their work. He does not frame artist protection as opposition to AI. He frames it as a requirement for an AI-enabled music economy that remains worth participating in.
“The number one aspect that we all want to see protected is that genuine creative music artists are not compromised,” Darren Herft has said.
That gives the lower-barriers argument an important boundary.
AI is most economically useful to independent artists when it increases what one person or a small team can accomplish. It becomes more complicated when technology begins replacing the human contribution on which ownership, attribution, and artist identity depend.
The Independent Artist Is Becoming a More Capable Small Business
There is another reason AI could disproportionately matter to independent musicians.
Many independent artists effectively operate small businesses.
The same person may be responsible for writing and recording music, commissioning artwork, organizing releases, producing promotional material, communicating with audiences, managing distribution, and making commercial decisions.
Major artists have teams for many of those functions. Independent artists frequently do not.
This means the significance of AI is broader than whether it can generate music.
The TuneCore survey provides an early indication of that broader use. Independent artists reported using AI for artwork and promotional assets as well as music-related applications.
The economic logic is straightforward. Every task an artist can perform efficiently without adding another recurring cost preserves limited capital for the areas where outside expertise matters most.
Darren Herft’s commentary points toward precisely this broader opportunity. His view is that AI’s long-term impact may be less about replacing musicians than about expanding what artists can create, produce, and distribute independently.
For an independent artist, that distinction is substantial.
The competitive advantage may not come from creating an entire song with AI. It may come from using technology selectively across dozens of smaller tasks that previously consumed money, time, or both.
Lower Costs Make Fair Compensation More Important, Not Less
There is an obvious danger in celebrating lower costs without asking whose costs are being lowered.
If one company’s efficiency comes from using creative work without permission, the economics look very different to the artist whose work supplied the value.
Darren Herft has been consistent on this point. His positive view of AI for creators sits alongside a strong emphasis on compensation, recognition, ownership, and artist protection.
His stated goal is not to slow technological innovation, but to ensure that innovation continues to reward creativity and support the people contributing to the music ecosystem.
The policy environment is moving in the same direction.
The U.S. Copyright Office’s AI initiative has examined questions involving digital replicas, copyrightability, and the use of copyrighted material in AI training. Its work reflects how quickly issues that once appeared theoretical have become practical questions for creators and technology companies.
For independent musicians, the stakes may be particularly high.
Large rights holders have legal teams and negotiating leverage. Individual creators often do not.
If AI is going to lower barriers for independent artists in a meaningful way, those artists need to remain participants in the value being created rather than simply becoming inexpensive inputs for somebody else’s technology.
For Darren Herft, those goals are compatible. AI can make sophisticated capabilities more accessible while the industry develops stronger standards around ownership, attribution, and compensation.
The Bigger Opportunity Is Participation
The strongest argument for AI in independent music is ultimately not that it will manufacture successful artists.
It cannot guarantee an audience. It cannot make every song commercially viable. It cannot eliminate competition, and cheaper production may actually increase it.
What it can do is reduce the resources required to participate.
That is the opportunity Darren Herft keeps returning to in his commentary. AI tools can lower production costs, improve workflows, increase access to professional capabilities, and allow creators to experiment without requiring the same infrastructure that established artists can access.
The result could be a wider creative economy.
More musicians can attempt projects. More artists can develop ideas further before seeking outside financing or professional support. Small teams can potentially accomplish work that previously required larger ones.
Some of those artists will still fail commercially. Others may use the savings created by technology to invest more heavily in the things AI cannot easily provide, including live performance, audience relationships, distinctive creative identities, and professional collaboration.
Lowering the barrier is not the same as guaranteeing the outcome.
It simply means more artists get the opportunity to try.
Conclusion
AI’s most consequential contribution to independent music may prove to be less dramatic than replacing musicians or generating hit songs.
It may be reducing the cost of turning an idea into something that can compete.
The technology is already being used for production, analysis, experimentation, artwork, promotional work, and other tasks surrounding a music career. Professional training programs are incorporating AI into music production and engineering, while copyright authorities are drawing increasingly important distinctions between AI assistance and human creative authorship.
Darren Herft sees the opportunity primarily through the artist.
His position is that AI can expand access to capabilities once concentrated among creators with greater resources. For independent musicians, that can mean lower costs, faster experimentation, more control over production, and a greater ability to bring work to market independently.
But Darren Herft’s support for that opportunity comes with a clear condition. Artists must remain recognized, protected, and able to earn from the creativity they contribute.
That is ultimately the more useful standard for judging AI in music. Not whether machines can make more music, but whether the technology allows more human artists to build, create, and compete.
FAQs
Who is Darren Herft?
Darren Herft is a global music executive whose work spans music, entertainment, technology, and business. His industry commentary focuses on developments affecting artists, streaming platforms, artificial intelligence, and the wider creative economy.
How can AI lower barriers for independent musicians?
AI-assisted tools can reduce the time or specialist resources required for tasks such as idea generation, audio analysis, source separation, mixing, mastering, and experimentation. This can allow independent creators to take projects further with smaller budgets before bringing in additional professional support.
Does Darren Herft believe AI should replace musicians?
No. Darren Herft’s commentary consistently presents AI as a tool that can support artists. He argues that human creativity, judgment, direction, and recognition should remain central as AI becomes more widely used.
Are independent artists already using AI?
Yes. A TuneCore survey reported that 27% of surveyed independent artists had used AI music tools, with applications ranging from creative work to artwork and promotional assets.
Can music created with AI receive copyright protection?
In the United States, the Copyright Office says AI-assisted work can qualify for copyright protection when there is sufficient human authorship. Purely AI-determined expressive material is treated differently, and prompts alone generally do not provide sufficient human control.
Why does Darren Herft connect AI opportunity with artist protection?
Darren Herft’s position is that lowering barriers only produces a healthier music economy if creators continue to receive recognition, ownership, and fair opportunities to earn. He sees innovation and artist protection as compatible rather than competing objectives.
