This is an excellent breakdown, Peter. What really stood out to me is how much voice cloning still relies on the same fundamentals we’ve always been taught in professional VO work: consistency, clean recording environments, and disciplined mic technique. It’s interesting to see how those classic studio habits directly translate into better AI training data. The reminder about keeping everything unprocessed and maintaining a low noise floor is especially important. A lot of people coming into voice cloning probably don’t realize that the AI is essentially learning from every detail in the waveform, so things like mouth noise, room reflections, or inconsistent mic distance can actually become “baked into” the model. I also appreciate the point about reading naturally. It’s tempting to over-perform when recording datasets, but conversational delivery is really what gives the cloned voice flexibility later on. For those of us in VO, this feels less like replacing the craft and more like extending it into a new toolset. If done responsibly—with proper licensing and voice ownership—it can open up some interesting opportunities for scaling work that would otherwise be incredibly time-consuming. Great insights here. Thanks for putting this together.