It's the exact opposite.
The bitter lesson is that simply scaling training on more games—including self-play—trumps any hand-crafted human input, whether that's fine-tuning on human commentary or clever engineering tricks.
Current models are just high-dimensional interpolation engines. The denser the data sampling, the more accurate the interpolation gets. Given a choice between denser sampling and anything else, denser sampling always wins. That is the bitter lesson.
Computer chess is the canonical example of this.
But the harness still matters.
In the case of stockfish, the harness is a tree search around the neural network evaluations.
Denser sampling only seems useful if the problem domain is in some way smooth - interpolatable. If you run it on a fractal problem domain you just learn more special cases. Chess is fractal.