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.
There's also an argument that impact diminishes by <20%: the hours you'll cut out first will be your least important hours (assuming you're prioritizing well).
I think the main argument for >20% is that you might get increasing returns from deep immersion and mastery of a field (this is a version of the point you made about "making it in the heavy tail").
I think it depends on the type of work you're doing. If you work at an EA org and do very generalist tasks with a lot of prioritizing on the go (for example, some of all of the following: hiring, headhunting/recruiting, developing strategy docs, mentoring, etc.), I could imagine that you lose <20%.
By contrast, if you're a researcher doing cutting-edge work, you may benefit from deep immersion, so I'd expect you to lose >20%.
Also, if you're on a career path where getting promoted is important (for instance because you want to make it to an influential position in government or academia), you almost certainly lose >20% because of the inherent competitiveness of the career track.
Another case where you lose >20% with 20% less hours: earning to give as normal employee (not as entrepreneur).
Salary is ~ linear with the hours worked. You can only donate the part of the salary above a certain baseline because you need the rest for your living costs*. Let's say you can donate 40% of your salary if you work 40h/week. If you work 32h/week, can only donate 20% of a full-time salary. That's 50% less impact for 20% less hours.
Caveat 1: You can also donate a fixed percentage, then it doesn't work like this.
Caveat 2: I'm neglecting non-donation impact here.