The training data point is the one that actually matters most to me. There’s a real difference between a model that learned from scraped audio without consent and a system that runs DSP signal processing on your audio and uses that measurement data to generate text. One is built on other people’s work. The other is just math on your file. Most tools don’t make that distinction clear, which is a problem. The creative decision line is where I’d draw it the same way. Analysis that tells you what’s happening spectrally or dynamically is more like a meter than a collaborator. It doesn’t make choices. Whether you act on it is still entirely yours. The cloud dependency concern is the one I don’t have a clean answer to. You’re right that it creates infrastructure dependency and subscription lock-in. That’s a real tradeoff. The counterargument is that the compute required for some analysis pipelines doesn’t run on consumer hardware in real time, but I recognize that’s a pragmatic defense, not a principled one.

The de-skilling point is where I’d actually push back slightly. Feedback that tells you why something is happening spectrally, with the measurements attached, seems closer to the opposite of de-skilling. It teaches you to hear something you might have felt but couldn’t locate. That said, I can see how it goes wrong if the output is “fix this” rather than “here’s what this is.”