This topic is discussed in the episode “Can AI Make You a Better Music Producer?” of AI Experience, where Julien Redelsperger speaks with John von Seggern, founder and CEO of Futureproof Music School, about AI music production, electronic music production, AI in music education, and the future of music producer skills.

The central question is no longer whether AI can help make music. It can. The more difficult question is what happens when AI-generated music becomes abundant and when music production with AI becomes normal. In that context, creative taste in music becomes the real differentiator.

AI is making music production easier than ever

For decades, music production required access to equipment, software, training, and time. Electronic music production still demands knowledge of sound design, arrangement, mixing, mastering, and genre conventions. AI music tools do not remove that complexity, but they make parts of it more accessible.

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The shift is visible at platform level. Deezer reported in April 2026 that it receives nearly 75,000 fully AI-generated tracks per day, representing about 44% of daily uploads on its platform. The same company said this equals more than 2 million AI-generated tracks per month, while AI-generated music still represents only 1% to 3% of total streams on Deezer. That gap matters. AI music production has lowered the cost of making and uploading music. It has not solved the harder problem: making music that people want to hear, remember, and return to.

John von Seggern frames music production as responsibility rather than mere output:

“You are responsible end to end for the result.”

That sentence is important because it shifts the debate away from tools and toward ownership. Music production with AI still requires someone to decide what the track is, what it is trying to do, and whether it deserves to be released.

Why more output does not mean better music

The rise of AI-generated music creates an abundance problem. More tracks do not automatically mean more meaning, more originality, or more cultural value. In fact, the easier production becomes, the more difficult selection becomes. This is where creative taste in music becomes essential. Taste is not a vague preference. It is the ability to hear difference, detect weakness, recognize cliché, understand context, and decide what should stay or disappear. In AI music production, taste becomes the filter between endless generation and a track that carries a point of view.

Deezer’s 2026 data also shows why quantity is not the whole story. The platform said 85% of streams generated by fully AI-generated tracks in 2025 were detected as fraudulent and demonetized. In other words, part of the AI-generated music explosion is not a creative boom. It is also an industrial-scale upload problem.

The producer’s role is changing

Music production has never been only about creating from nothing. A producer selects sounds, shapes patterns, adjusts references, removes weak material, and builds coherence. Electronic music production has always been especially close to curation: samples, presets, loops, textures, drum kits, effects chains, and references are constantly selected and transformed. John von Seggern makes this clear when he says:

“The whole process of making electronic music... has always been like kind of a curation process.”

That quote matters because it prevents a common misunderstanding. AI did not introduce selection into music production. It intensified it. AI music tools add another source of material. They can generate a loop, propose a beat, suggest a variation, or help with mastering. But music production with AI still depends on the producer’s ability to choose. The producer does not disappear. The producer becomes more accountable for selection.

When AI music tools become widely available, access stops being the main advantage. Everyone can experiment. Everyone can generate. Everyone can publish. The competitive edge shifts toward creative taste in music. Taste helps a producer avoid imitation. It helps them understand when a track sounds too close to the dataset, too close to a trend, or too close to the average output of a machine. It also helps them know when to ignore a technically correct suggestion because it weakens the emotional direction of the track.

This is consistent with broader research on generative AI. The OECD noted in 2025 that generative AI can support creative industries by accelerating idea generation and knowledge recombination, but also stressed the need for human expertise and oversight, especially in complex tasks where AI output must be interpreted through domain knowledge. For music producer skills, that means one thing: AI can help you move faster, but taste tells you where to go.

AI can help with technique, but struggles with intent

AI music production is already useful for technical feedback. In the episode, John von Seggern describes a system that can analyze a mix, infer the genre, and detect whether balance, frequencies, or bass range fit the conventions of that genre. That is a practical use case for AI in music education: the machine acts like a technical coach between human mentor sessions.

This matters for beginners. If a student is learning electronic music production, they may not hear why a low end feels muddy or why a kick drum lacks weight. AI music tools can translate those problems into actionable suggestions. They can identify patterns, flag issues, and explain technical concepts repeatedly. That is where AI in music education has clear value. It can support fundamentals. It can personalize explanations. It can reduce friction. It can help students build basic music producer skills before they are ready for deeper artistic critique. The limitation appears when production becomes aesthetic. AI can recognize patterns, but it does not truly know what the music is trying to become. It can suggest what usually works. It cannot decide what should exist. John von Seggern puts it clearly:

“As you become more advanced, it becomes more a matter of taste and aesthetic decisions.”

AI-generated music can follow conventions. It can imitate genre patterns. It can produce competent surfaces. But human creativity and AI do not operate in the same way. Human creativity is shaped by memory, intention, culture, taste, frustration, risk, and lived experience.

The last 20% is where authorship lives

One of the most useful frames from the episode is the “80/20” idea. AI may help complete a large part of a project: drafting, analyzing, generating, correcting, polishing. But the final part often demands judgment. John von Seggern says AI can get you “to like 80% of your project,” while the last part requires more personal involvement. This is not a rejection of AI music tools. It is a more precise way to understand them. In music production, that last 20% includes decisions that are difficult to outsource: whether the groove feels right, whether the arrangement has tension, whether the sound is too generic, whether the track belongs to the artist’s world.

This is where creative taste in music becomes visible. The final decisions shape the work. They turn output into authorship. AI-generated music also raises legal and philosophical questions about authorship. The legal side is still evolving. In 2025, the U.S. Copyright Office clarified that AI-assisted works can receive copyright protection when sufficient human creativity is present, while fully machine-generated work without meaningful human contribution remains more difficult to protect.

The OECD also highlighted in 2025 that generative AI has intensified questions around data scraping, AI training data, and intellectual property rights. That context is particularly relevant for AI music production because music models may rely on large bodies of recorded work, raising questions about authorization, transparency, and compensation.

But authorship is not only legal. It is also artistic. In the episode, John von Seggern argues that if an artist says, “this is my sound and I stand behind it,” that act of responsibility matters. The quote reframes authorship as accountability: not just how something was made, but whether the artist owns the decision to release it. For the future of music production, this is decisive. The more AI-generated music enters the market, the more listeners may look for a human point of view.

What future music producers will need to learn

Prompting can generate material. Listening gives that material meaning. That distinction may define the next generation of music producer skills. If AI music tools can produce endless ideas, the best producers will not be those who generate the most. They will be those who listen better. They will understand references, history, genre evolution, rhythm, sound systems, club culture, mixing conventions, and audience expectations. This is especially true in electronic music production, where sonic identity often comes from microscopic decisions: low-end pressure, transient shape, spatial movement, texture, saturation, repetition, release timing. AI in music education can help explain these concepts, but listening trains judgment. The future of music production may therefore reward depth over speed. The producer who has listened widely will recognize when AI-generated music sounds plausible but empty. The producer who has studied a genre’s history will hear when a track copies the surface without understanding the culture.

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AI is making music production faster, cheaper, and more accessible. That is a real change. AI music tools can help with fundamentals, mixing, mastering, ideation, and feedback. AI in music education can support beginners and reduce technical barriers. Music production with AI will likely become part of ordinary creative workflows. But abundance changes the hierarchy of skills. When AI-generated music becomes easy to produce, creative taste in music becomes harder to ignore. The valuable producer is not the one who can create the most files. It is the one who can make decisions worth hearing. That is why the future of music production will depend on more than software. It will depend on listening, judgment, authorship, and responsibility. In other words, on the music producer skills that are least reducible to automation.

This topic is discussed in the episode “Can AI Make You a Better Music Producer?” of AI Experience with John von Seggern, where the conversation goes deeper into AI music production, electronic music production, human creativity and AI, and the question every creator now faces: when the machine can generate almost anything, what are you willing to stand behind?

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