I hope you don't mind me leaving a second comment, because it's kind of orthogonal to the first.
It’s no secret that an academic can easily find fertile fields by working with someone in a different department.
(Emphasis mine.) Speaking as an academic, I think this is far from true. Interdisciplinarity is a very popular buzzword, and we're all told to strive for it, but the vast majority of us don't find fertile grounds for cross-curricular collaboration when we try. It's possible on the overlap of mathematics and physics, or computer science and statistics, or literature and history, or geography and sociology, granted. But that doesn't mean that any mathematician can work with any physicist; most of the time, it means simply that applied mathematicians who work on physical questions can work with theoretical physicists with a strong mathematical background. I'm inclined to say that, just because work like this transcends the boundaries of your department (or your funding body, or...), doesn't necessarily mean it's interdisciplinary in any true sense. Your collaborator's skillset is probably very close to yours in skill-space.
The flipside of being very highly specialised - like Alice and Carol's skillsets in your diagram, but longer and thinner - is that, the more truly 'interdisciplinary' we're aiming to be, the smaller the intersection of our skillsets is, and so the less our ability to communicate our research to each other becomes. There might be very fertile ground for collaboration between a researcher in chemistry and a researcher in sociology, but before they can even begin to find out whether that ground exists, the chemist is going to need an intensive crash course in sociology and vice-versa, so that each knows what the other is even on about, what the basic methods and approaches are, etc.
(This meshes with my limited personal experience too. I am an academic who specialised in field X, but also happens to be reasonably highly trained in very different field Y. I am probably Pareto best, or at least one of a very small number of Pareto best, in a large area of skill-space in that intersection. But, at least from my vantage point, there aren't really any problems in the intersection of the two. Maybe my role should be to mediate between X researchers and Y researchers to search the problem-space more. But also maybe I'd be wasting their time. It's hard to know.)
I agree with many of the premises here, and I like this as a way of conceptualising skillsets, but I'm not sure I find it all that useful.
The main omission in this essay, to my mind, is any mention of skill interdependence. If you're one of the first people to discover the fertile gerontology/statistics niche, then you might get your 15 minutes of fame, and your early adopter status might give you a comparative advantage. But as soon as it becomes commonly recognised how fertile the ground is in this niche, there'll be tons of people right behind you chasing the low-hanging fruit.
Because of this, training programmes in gerontology start making statistics courses more robust and mandatory; research journals start publishing more statistics-heavy papers; labs start doing more statistics-heavy work; it becomes harder to get promoted, or get a foot in the door, without some familiarity with statistics. And so on. Everyone wants to be modern and interdisciplinary. But the inevitable result of this is that statistics eventually becomes a basic, fundamental prerequisite for calling yourself a gerontologist. The skills "gerontology" and "statistics" become strongly correlated. And now, suddenly, your two-dimensional picture has collapsed to a one-dimensional picture.
This may be a gross oversimplification, but I think the point approximately stands. As soon as a niche with high problem density is discovered, the density of skilled problem-solvers in that area quickly rises to meet it. You can only stand out if (a) you're an early adopter, (b) you have skills that others in your area would find it prohibitively difficult to replicate, or (c) your niche isn't too fruitful - i.e. nobody is interested in trying to rake in a share of the profit.