Note that while Nuño says what is driving the labor results is the concave returns to recruitment, I think another stylized feature of the model is the depreciation of labor.
(I’m not fully sure this is correct and I didn’t go through the model) but maybe to see this, notice that L and the model relies on d >0 (depreciation being strictly positive).
If d ⇐ 0, the model seems to break, that is equation 8, has L negative or zero:
I didn’t do graduate macro theory (but I did dynamic structural IO), but I think it’s the canonically the opposite, typically capital (money) depreciates and labor deprecation isn’t standard (I’m happy to be corrected if wrong).
L represents the stock of EA labor, which in the model only rises through active recruitment and depreciates exogenously.
I’m not sure this EA labor depreciation is motivated empirically. It’s plausible many EAs were not recruited actively but found the movement from online content or observing direct work. From other movements, it seems movements can grow geometrically for some time, without active, expensive recruitment.
Additionally, remember that like most models, the narrative works through long run effects in “equilibrium”. It’s less likely this applies to the ~12 year old EA movement, as opposed to national economies these models are typically used for.
So what?
If my writing above is somewhat correct, the point of this comment is that while models have value for rigor, there’s just a lot of assumptions and stylistic details baked in. It’s important we are literate about this when communicating to non-experts and do not canonize or codify ideas inappropriately with models.
Ben Todd’s points in his recent talks e.g. EAG speech and the resources post, are important and correct. Movement money can grow faster than labor and EAs need to utilize greater resources effectively and alertly.
However, it’s more plausible the reason the funding overhang exists is that we are in a long bull market and these capital markets have worked well for donors. Related to this, the crypto boom, combined with the focus, work and ability of Bankman-Fried and others have contributed precious resources.
This is probably more responsible for the funding overhang, as opposed to a concave return function to recruitment or depreciation of labor. It’s important to be aware of the effect of canonizing things with math.
Hey, thanks for the comments. Your point about a bull market is welcome, and I think similar to the point that Phil made in the 80kh podcast. Some nitpicks:
Nino → Nuño
When people say that “capital depreciates”, they generally mean ” capital investments”, i.e., machinery, computers, etc.
Note that labor depreciates at a rate d, in the sense that people move out of the movement because of value drift, but it also increases in value because of productivity improvements (see the exponentials in the model)
But in models in which labor replicated itself (i.e., there was some “naturally arising movement-building”), we still didn’t see that earning to give (in the sense of earning a salary) was favored in the limit either.
Note that while Nuño says what is driving the labor results is the concave returns to recruitment, I think another stylized feature of the model is the depreciation of labor.
(I’m not fully sure this is correct and I didn’t go through the model) but maybe to see this, notice that L and the model relies on d >0 (depreciation being strictly positive).
If d ⇐ 0, the model seems to break, that is equation 8, has L negative or zero:
I didn’t do graduate macro theory (but I did dynamic structural IO), but I think it’s the canonically the opposite, typically capital (money) depreciates and labor deprecation isn’t standard (I’m happy to be corrected if wrong).
L represents the stock of EA labor, which in the model only rises through active recruitment and depreciates exogenously.
I’m not sure this EA labor depreciation is motivated empirically. It’s plausible many EAs were not recruited actively but found the movement from online content or observing direct work. From other movements, it seems movements can grow geometrically for some time, without active, expensive recruitment.
Additionally, remember that like most models, the narrative works through long run effects in “equilibrium”. It’s less likely this applies to the ~12 year old EA movement, as opposed to national economies these models are typically used for.
So what?
If my writing above is somewhat correct, the point of this comment is that while models have value for rigor, there’s just a lot of assumptions and stylistic details baked in. It’s important we are literate about this when communicating to non-experts and do not canonize or codify ideas inappropriately with models.
Ben Todd’s points in his recent talks e.g. EAG speech and the resources post, are important and correct. Movement money can grow faster than labor and EAs need to utilize greater resources effectively and alertly.
However, it’s more plausible the reason the funding overhang exists is that we are in a long bull market and these capital markets have worked well for donors. Related to this, the crypto boom, combined with the focus, work and ability of Bankman-Fried and others have contributed precious resources.
This is probably more responsible for the funding overhang, as opposed to a concave return function to recruitment or depreciation of labor. It’s important to be aware of the effect of canonizing things with math.
We should keep reminding ourselves that FTX’s value could easily fall by 90% in a big bear market.
Hey, thanks for the comments. Your point about a bull market is welcome, and I think similar to the point that Phil made in the 80kh podcast. Some nitpicks:
Nino → Nuño
When people say that “capital depreciates”, they generally mean ” capital investments”, i.e., machinery, computers, etc.
Note that labor depreciates at a rate d, in the sense that people move out of the movement because of value drift, but it also increases in value because of productivity improvements (see the exponentials in the model)
I think that depreciation of labor is actually empirically motivated, e.g., by https://forum.effectivealtruism.org/posts/eRQe4kkkH2pPzqvam/more-empirical-data-on-value-drift
But in models in which labor replicated itself (i.e., there was some “naturally arising movement-building”), we still didn’t see that earning to give (in the sense of earning a salary) was favored in the limit either.
I am sorry for the misspelling of your name. This is fixed.
This fault is mine, but it was not intentional, I think this was caused by autocorrect acting silently during writing.