Yes this is the hard part. AI can make it much cheaper for a donor to assess an unfamiliar organisation once there is enough evidence to work with, but it cannot manufacture information that is not visible in the first place. And there is probably a second-order problem here: once AI starts doing more of the assessment, organisations that are easier for machines to identify, document and compare may get an advantage simply because they are more legible to the system.

I have been thinking about this as a kind of “machine-legibility privilege.” It is one of the things I am trying to test with zooidfund: whether AI assessment can actually broaden the set of needs donors can consider outside their existing networks, without simply shifting the advantage toward whoever leaves the best digital trail.

The section on scale also made me think about what happens if more of the funding process becomes automated. AI could make it much cheaper to discover and assess large numbers of small organisations, which seems potentially very useful for exactly the kind of distributed funding being discussed here. But it also creates another version of the legibility problem: an automated system will need signals it can actually process, and those signals may favour organisations that can produce standardized evidence, reporting and data over organisations that are harder to describe but locally very effective.

So there may be a tension between making the funding infrastructure scalable and keeping the substantive judgment genuinely local. It seems possible to automate a lot of the boring infrastructure without requiring the organisations themselves to become more standardized, but I don't think that follows automatically.

Coming from a fairly traditional international organization, I think I can understand the asymmetry you describe. A lot of professional competence is difficult to teach because it is learned through repeated exposure to how institutions actually behave. You gradually get a feel for which proposals can survive internal processes, where formal authority differs from practical influence, and which ideas become much harder once they have to work across different departments of an organization, let alone different organizations.

Case discussions or “war stories” can work to surface this. Take for example a concrete AI policy proposal and ask people from different professional backgrounds to work through how they think it would fare inside a real institution. I suspect that would make some of this tacit knowledge much easier to see than trying to write it down as a set of principles.

Hi Roland, what are some of the big-if-true ideas you considered?

This was interesting to read, especially the point about the marginal cost of automating additional tasks falling as the underlying infrastructure gets better. One thing I was wondering about is how the maintenance side is developing as you add more automations.

At 11% you can probably still understand each automation fairly well, but if the aim is to automate a much larger share of program operations, some of the work presumably shifts from doing the task itself to keeping a growing set of automations working as forms, Drive structures, naming conventions, staff roles etc. change. I don't know if you are already measuring this, but something like human minutes per run or per program round, including review, exceptions and repairs, might be useful alongside the percentage of tasks automated. It could help distinguish automations that really reduce operational load from ones that mostly move that load somewhere less visible.

One difficulty with this kind of public-good infrastructure is that it is often unclear who the customer is. Lots of people may benefit if it exists, without any one of them having a strong incentive to pay for it, and that also makes it difficult for someone considering building it to tell whether they have found a real ecosystem need or just something that sounds useful in the abstract. I may have run into this while building zooidfund, where there are several plausible beneficiaries of the infrastructure but that does not necessarily translate into a clear demand signal.

So I wonder whether part of the missing infrastructure is actually on the funder side, with more explicit problem statements, RFPs, bounties or advance commitments around gaps they think are worth solving. That would still leave plenty of room for people to notice problems and take initiative, but it would make it easier to know when a particular gap is something others genuinely want solved before somebody spends a year building around it.

I like the vulture idea. A bit more sceptical about infotainment as you predicted. Awaremess interventions is a well trodden field. I wonder if the Ethiopia result is evidence for infotainment generally, or for something quite a bit narrower. The intervention was not really mass media in the usual sense, it was people watching stories about individuals from backgrounds deliberately made similar to their own, and if I understand the study correctly there were also peer effects depending on how many people in the village saw it.
That makes me a little unsure about the jump to cheap radio broadcasting. It may work, but perhaps the important part is actually identification with a very locally credible role model, plus people around you having seen the same thing, rather than the medium itself. If so the scalable version might need much more localisation than just producing a good programme and buying airtime.

I am not sure the comparison stays entirely symmetric once endogenous growth is added on the climate side. If climate damage can permanently change the growth path, then at least some GHD interventions should also have effects on growth through health, schooling, productivity, fertility and so on.

I assume some of that is already captured in the GiveWell/Coefficient models, but probably not the same kind of macro second-order effects. Since climate seems to become competitive mainly when these more uncertain effects are included, I would be interested in how much this matters.

Interesting idea to bring the two together. If we agree that our ability to predict the consequences of our actions is limited and we live in a fundamentally unpredictable world, would it follow that all else equal we should prioritize flexibility, ability to adapt and error correct in response to changing circumstance in our political organisation, which usually means decentralization, pluralism, rule of law and democracy?

I like this. I would maybe add these couple of points:

- "Cluelessness horizon" has a solid theoretical basis to it: human society is complex enough nonlinear system in which small differences can propagate to cause large changes. It may or may not be "chaotic system" in a strict mathematical sense, but it is non linear enough to say with some confidence that there is a finite horizon for predicting consequences of a particular event.

- There is no baseline. "Inaction" has the exact long tail of possible consequances any action does. One is affecting the world whether they donate 100 dollars to bed nets, spend it in a bar or light it on fire.

So everything disappears into uncertainty past a certain horizon. Only consequences that are before that event horizon matter for our decisions, and here bed nets are a very good candidate.