Can AI Make You a Better Music Producer?

Can AI make you a better music producer? I got asked exactly that on the AI Experience podcast with Julien Redelsperger, and it turned into a long conversation about how electronic music actually gets made, what AI is good and bad at, and who owns a track when a machine touched it. The full episode is below, and here's where I landed.

Production Is Curation

The best way to think of music production is like film production. If you're producing a piece of music, you are responsible end to end for the result. When you put it out in the world, you're saying this is what I have produced, my name is on it, I take responsibility for it regardless of how it was made. There's music where somebody just recorded the sound of nature, but they were the one who chose that recording and curated it into the world.

That's the frame I keep coming back to with AI. You're not really creating sounds so much as curating sounds and forming them into patterns. You might have played them, you might have found them in a sample library, you might have made them with AI. The important part is you decided those were the ones you were using, and then you assembled them into a piece.

The Sound In Your Head Is a Myth

We always talk about making the sound in your head. I don't think that's really right. I don't think that's how most people work.

The way most electronic producers actually work is two kinds of sessions. In one, you're just messing around with a synthesizer with no particular goal in mind, making sounds and saving the best ones. The next day you come back in a completely different mental state, thinking logically: we need an intro, we need a verse, the things have to fit together. The whole process has always been curation more than generate-from-a-blank-page. So AI just gives you another source to generate things from.

Honestly, I don't use it much for that right now. It's slow. It takes me out of the flow, sitting there waiting for things to generate. I'd rather just twist the knobs. It's faster.

Where AI Actually Helps

Where AI genuinely helps is the technical layer. We built a Mix Assistant at Futureproof that analyzes a mix, figures out what genre you're working in, and compares your frequencies against where that genre usually sits. I was just testing it and it told me my kick drum was weak, that turning it up wouldn't fix it, and that I should layer a bassier kick underneath. That's real, useful feedback. But it can't tell me I'm on the wrong track entirely, or that I should try a different kind of music, or that I should go for a walk in the park. The AI doesn't know what the right thing to do is. It knows most people do it like this, and that will probably work if you try it.

One technical aside for the curious: very few models can analyze music at all. Gemini 2.5 Pro is the current king. GPT-4o could do it, later GPT models lost it, Claude can't really do it. Music analysis just isn't a focus for the labs.

On Suno and Prompted Music

What about prompting whole tracks? Clearly millions of people think Suno is good enough to have fun with, and they're paying for it. But that music is getting very few listens. And when I play with Suno for half an hour, I get really tired of it, because all the tracks sound like they were made by the same machine somehow. There's an irreducible fuzziness you can never quite get rid of. It will get better. The roadblock is more about data access and licensing than technology.

Here's the thing though: the people most interested in learning modern production aren't that interested in prompting finished tracks. They want to go through the creative process. That's why they make music in the first place. It's an intricate hobby, like hot rodding a car or building your own computer. The studies back this up. The top genres people generate with AI are country, pop, and gospel. Electronic music barely registers. Nobody is prompting Dubstep.

On Authorship

This has been a live conversation in western art history for a hundred years. Marcel Duchamp put a urinal in a museum and said this is my piece. He was the one who decided to bring it there and hang it on the wall. So philosophically, if you say this is my album, I believe in it, it expresses what I'm trying to say, it doesn't matter how it was made. If what you stand behind is cheesy pop tracks you made in an hour in Suno, maybe that says more about you. Legally it's a different story. The lawsuits are unresolved, and I know composers in LA who won't touch these tools because their contracts make them personally liable for warranting 100% original music.

The 80/20 Frame

The practical takeaway is the 80/20 frame. Often AI can get you to 80% of a project, but the last 20% it just can't do. I spend all my time now on that 20% and do a better job because I'm focusing on it. And that 20% is almost always taste: making decisions about what's better than another thing.

Find Your Own Sound

Which means the worst strategy in music right now is aiming to be copycat number 684 of some famous artist. By the time you get to that sound, it won't be that anymore. You need to be on the uptick of something. Spend more time listening than producing, intake a broad variety of things, learn the history of your genre. Miles Davis never rested on his past accomplishments. Don't just follow in the footsteps of what somebody else did. Strike out and do something new.

Try It Yourself

If the hybrid approach holds up the way I think it does, AI for the fundamentals and human mentors for taste and identity, that's exactly how we built Futureproof Music School. You can try it free for 14 days at futureproofmusicschool.com.