I don't have a good object-level answer, but maybe thinking through this model can be helpful.
Big picture description: We think that a person's impact is heavy tailed. Suppose that the distribution of a person's impact is determined by some concave function of hours worked. We want that working more hours increases the mean of the impact distribution, and probably also the variance, given that this distribution is heavy-tailed. But we plausibly want that additional hours affect the distribution less and less, if we're prioritising perfectly (as Lukas suggests) -- that's what concavity gives us. If talent and luck play important roles in determining impact, then this function will be (close to) flat, so that additional hours don't change the distribution much. If talent is important, then the distributions for different people might be quite different and signals about how talented a person is are informative about what their distribution looks like.
This defines a person's expected impact in terms of hours worked. We can then see whether this function is linear or concave or convex etc., which will answer your question.
More concretely: suppose that a person's impact is lognormally distributed with parameters μ and σ, that μ is an increasing, concave function of hours worked, h, and that σ is fixed. I chose this formulation because it's simple but still enlightening, and has some important features: expected impact, eμ(h)+σ22, is increasing in hours worked and the variance is also increasing in hours worked. I'm leaving σ fixed for simplicity. Suppose also that μ(h)=logh, which then implies that expected impact is heσ22, i.e. expected impact is linear in hours worked.
Obviously, this probably doesn't describe reality very well, but we can ask what changes if we change the underlying assumptions. For example, it seems pretty plausible that impact is heavier-tailed than lognormally distributed, which suggests, holding everything else equal, that expected impact is convex in hours worked, so you lose more than 20% impact by working 20% less.
Getting a good sense of what the function of hours worked (here μ(h)) should look like is super hard in the abstract, but seems more doable in concrete cases like the one described above. Here, the median impact is eμ(h)=h, if μ(h)=logh, so the median impact is linear in hours worked. This doesn't seem super plausible to me. I'd guess that the median impact is concave in hours worked, which would require μ to be more concave than log, which suggests, holding everything else equal, that expected impact is concave in hours worked. I'm not sure how this changes if you consider other distributions though -- it's a peculiarity of the lognormal distribution that the mean is linear in the median, if σ is held fixed, so things could look quite different with other distributions (or if we tried to determine μ and σ from h jointly).
Median impact being linear in hours worked seems unlikely globally -- like, if I halved my hours, I think I'd more than half my median impact; if I doubled them, I don't think I would double my median impact (setting burnout concerns aside). But it seems more plausible that median impact could be close to linear over the margins you're talking about. So maybe this suggests that the model isn't too bad for median impact, and that if impact is heavier-tailed than lognormal, then expected impact is indeed convex in hours worked.
This doesn't directly answer your question very well but I think you could get a pretty good intuition for things by playing around with a few models like this.
Thanks Hauke that's helpful. Yes, the above would be mainly because you run out of steam at 100h/week. I want to clarify that I assume this effect doesn't exist. I'm not talking about working 20% less and then relaxing. The 20% of time lost would also go into work, but that work has no benefit for career capital or impact.