We asked for your questions, and you sent so many that one episode couldn’t hold them. This is part one of two — and part one is brought to you by two letters: A and I. That wasn’t the plan; it’s just where the mailbag pointed.

Philip Rothman and David MacDonald take on the growing expectation that artificial intelligence should be able to turn a scan, recording, or generated track into finished, usable sheet music. Why is that still so difficult? And where can AI genuinely help musicians and music preparation professionals today?

The discussion ranges from optical music recognition and audio transcription to proofreading, house styles, sample libraries, score following, and the potential for language models to communicate directly with notation software. Along the way, Philip proposes the “Gould test” as the music preparation equivalent of the Turing test.

Underneath it all are larger questions about the value of expertise, the role of human judgment, and where responsibility lies when the technology gets it wrong.

The rest of the mailbag — the questions that have nothing to do with AI — is coming next month.

Products mentioned

Notation software

Scanning and optical music recognition

Audio transcription

Playback and vocal synthesis

AI arranging and orchestration

Score following and page turning

MCP bridges (community projects)

Music generation

NYC Music Services / Notation Central

Other tools mentioned

Previous Scoring Notes posts and podcast episodes

Directly mentioned or closely related:

Other references

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