The problem with it is that it's very easy to interpret that y-axis, "something good", as static. It's pretty hard to make sense of the model at all if you don't interpret as static, because your slope will bend all over the place, out of the plane, into multiple dimensions, etc. But once you've set your goal point, your "something good" axis, the natural temptation is to optimize your slope until you're steadily progressing against it. And that's dangerous, because you might forget that the "something good" axis was arbitrary to begin with.
Instead, I've become much more of a fan of John Boyd's "OODA loop" [1] model. Here, you're continually reacting to your environment, which is also continually changing around you. And the person or organization that can react faster usually has an advantage, because they can set the terms of the engagement. We can call that adaptation "learning", but the key point is that it's learning an environment that is dynamic, not static. Sometimes the environment will change in a way that invalidates all of your accumulated learning, and that's okay (and you don't really get a choice about it anyway).
This also drives home the point that choosing the environment you're adapting against is a pretty critical skill, and often dominates how well you adapt to it (i.e. your learning rate). I've seen some relatively mediocre people become billionaires because they picked the right industry and the right opportunity within it to join. Similarly, there are people who are brilliant problem solvers but end up in jail because the environment they are in rewards problem-solving that will get you sent there (think Omar from The Wire, or SBF from FTX).