What I Approve and What I Decline: A Curator's Take on AI Music
You made something you love. A generation came together, the genre was right, the vibe was right, and you sent it out to a playlist or a curator. It got declined, or worse, it got ignored. And you cannot figure out why, because the song is genuinely good.
I can tell you why, because I am the person who declined it.
I curate a playlist on SubmitHub. Hard-rocking modern country with a party vibe, a specific lane. Every week, thirty to forty songs get submitted to that one playlist alone, from people I have never met, hoping I will add their track. So on this one, I am not the artist. I am the gate. And after a couple of years of this, a pattern showed up in my approvals and declines that I cannot unsee.
The submissions sort into three stacks
Every week, without me trying, they fall into three groups.
The top stack, my highest approval rate, is AI-generated songs that somebody took the time to fully master. I want to be honest about how those sound, because it surprised me. They sound finished. They nail the genre, they hit the exact vibe the playlist is for, and when one comes on I think, that is exactly what I am looking for. Those get on at a high rate.
The middle stack is human artists. Real people, real instruments, real recordings. And my approval rate for them is actually lower than for the mastered AI tracks, for two reasons that are worth keeping separate. The polish is often hobbyist, a home studio doing its best, which you can hear even when the mix is fine. And the songs tend to wander. They do not commit to a lane or a mood, so they drift instead of selling me on anything.
The bottom stack is AI songs that were not mastered. The raw generation, downloaded and sent straight to me. I decline nearly all of them.
The comparison that should stop you
Here is the part that reframed the whole thing for me.
The song in my top stack and the song in my bottom stack are frequently the same quality of song. Same genre accuracy. Same vibe. Same theme nailed. The unmastered ones often get everything right that the generation is supposed to get right. The song underneath is good. It is right there.
And I decline it anyway.
The only variable that changed between the track I approve and the track I reject is the master. Same core song. One got finished, one did not, and that single difference moved it from a near-automatic yes to a near-automatic no.
I did not set out to run an experiment, but that is effectively what my approval log is. The song is the constant. The master is the variable. And the variable decides the outcome.
The tracks are not winning because they are AI
It would be easy to hear all this as "AI beats humans now." That is not the lesson.
The mastered AI tracks are not winning because they are AI. They are winning because somebody finished them. The generation nailed the genre, sure, the tools are genuinely good at that now. But nailing the genre is not what got the song placed. Half my bottom stack nailed the genre too. What got the top stack placed is that after the generation, a human took the track into a DAW and finished it. Balanced it, glued it, mastered it to the standard my ear expects when I decide what goes on a real playlist.
The genre was won in the generation. The placement was won in the finish.
And that should encourage you, whichever side of the AI question you sit on, because the finish is a skill. Not talent you are born with, not a budget, not a record deal. A process you can run the same way every time. The people beating you in my inbox mostly did not out-talent you. They finished the job you stopped one step short of.
What the finish actually is
Once the song is right, finishing it comes down to a handful of jobs. Balance, so the vocal is clear and nothing fights. Glue, so the parts feel like one record instead of stacked stems. Loudness, brought up to what streaming expects without crushing it flat. And the final checks, making sure it holds up on your phone, in the car, on earbuds, everywhere it will actually get heard. None of it is magic. All of it is learnable. And all of it is the difference, in my inbox, between the top stack and the bottom.
If you want to learn that finish, taking a raw AI export and turning it into a master that sounds like the top stack, that is the entire back half of what I teach, and it lives in the Red Lab Library. Every Unlock book, the mastering methodology, the Red Lab Protocol research behind it, and Fader to walk you through it in real time. One system, one price, ninety-seven dollars.
Whether you learn it from me or anywhere else, learn it. Because the curators you are submitting to are hearing exactly what I am hearing.
Get the Red Lab Library at jgbeatslab.com/red-lab-library.
