I think what many people in this thread are trying to stumble over is that the way a computer prunes moves and the way humans prune moves is qualitatively different.
A computer prunes its moves from either an explicit or implicit (implicit when it's say a trained neural net) database of known positions, with some quantitative sense of strength (usually a probability to win or something like that).
A human needs to assign a narrative to particular branching pathways. These are qualitative instead of quantitative assessments.
A human isn't saying, if I make a certain move there is an 85% chance of winning, and so that makes it my best bet. They're assigning arbitrary structures and narratives to positions, hence why many positions, tactics, and strategies in chess and other games are given colourful names.
The two approaches are very different and have different strengths and weaknesses. Which is why the best play outcome is to combine the computer generated moves with the human generated moves.
The human approach is very good at generalising new information very quickly. Assigning unusual or unfamiliar information in a broader qualitative framework about what good play looks like, think about players who are trying to create certain structures, shapes and patterns on the board.
The computer is very good at applying knowledge about individual moves at great depths. But cannot combine it with any external information. All information about the success rates of moves are determined from the database of all past moves. The computer can't condition those probabilities on things like, does my opponent need to win, or only draw. Do they have a tendency to be aggressive or defensive. Probabilities of success only make sense when taking a population view of the computers input data (a literally impossible task if your talking about the kinds of neural nets used in chess).
So a hybrid approach lets good players condition computer generated moves based on external information. Maybe the computer generates a line of play with 80% confidence of winning, but the human can see that because of certain qualitative structures on the board, the opposing player is more likely to see the solution than the computers population, and so can recondition the lines of play on this new information, even if the human has no idea why the line of play should work 80% of the time. Lines of play that would otherwise have very similar success rates (differing by only a few percentage points say) can be re-ordered based on human judgement.
Both the computer and the human can tell obviously bad from obviously good moves. But their approach is very different when nuance is required.