“We shouldn’t make these machines because we want them to be good for us, or good instead of us… we should make them because they can help us be better ourselves.”

—Bruce Holsinger, Culpability

Spotting sessions reveal something most AI music tools still miss: the hard part is not simply generating a cue, but helping a creative team clarify what the scene needs before the first note is written.

Generative music may be most useful before consensus forms—giving each stakeholder room to test an instinct, hear where it succeeds, and bring clearer ideas into the collaborative process.

A faster answer can clarify a conversation. It can also harden a poorly framed request before anyone notices what is missing.

When generative AI can supply nearly everything a listener hears, what has the person working with it contributed? A transformed sample, a song for my daughter and this essay reveal why “AI-made” names the tool but cannot tell us where the creativity lived.