The recent forecasting is overrated post got me thinking:
Solution Seeking a Problem
When talking about forecasting, people often ask questions like “How can we leverage forecasting into better decisions?” This is the wrong way to go about solving problems.
Intuitively, that seems correct, and I've relied on the expression "when you have a hammer, everything looks like a nail." This got me thinking: is it necessarily the wrong way, or is this a truism?
If I have a legitimately useful and powerful tool, isn't it indeed valuable to look around for problems that it can help solve? E.g., if we have discovered a way to harness electricity, shouldn't think about the ways it can be used to improve communication, build labor-saving devices, power factories, etc? If we have something that has demonstrated potential to generate reliable information (supposing that forecasting could do this) shouldn't we look for fruitful opportunities to apply it?
With a set of tools and a set of problems, why is it more useful for one side to do the searching than the other? (Sorry, maybe this is getting too meta and belongs in its own shortform?)


NB -- this is almost entirely AI generated, with some back and forth prompts and corrections
I'm sharing a steelman against a live assumption in Bay/EA/AIS circles: that large AI-lab-adjacent philanthropy is likely to arrive soon enough, and in a sufficiently usable form, that organizations should plan around it.
https://uj-ai-wealth-philanthropy-steelman.netlify.app/
Original motivating thread/comment: https://forum.effectivealtruism.org/posts/dtF6wBjH7yBD4kqLz/noah-birnbaum-s-quick-takes?commentId=sGRyGF5wjaaoMFmfK
@Noah Birnbaum
Some commentary. I mostly agree with the page, but I will focus on the bits where I see room for improvement:
*this is pessimistic for donations but I would actually prefer that this happen because it would lengthen timelines. so in a way it's the optimistic outcome
(Thanks. Considering each of these, will add them and discuss them in the hosted page, and then request updates.)
Added and responded to your comments on the page (the hypothesis comments), and then I asked Codex to update to these https://uj-ai-wealth-philanthropy-steelman.netlify.app/ ... I haven't inspected the latest version in detail yet, though.
Some highlights of particular interest to @MichaelDickens , Tobias, and readers/modelers
https://uj-ai-wealth-philanthropy-steelman.netlify.app/
NB it may be getting too complicated to oversee for now, we may want to simplify it
I totally agree on using distributions, that's something that can be incorporated in, I've done so in other models/interfaces like here for cultured meat. It's by no means straightforward though; the extent to which the uncertainty is dependent/correlated tends to make a big difference.
I guess I see the deterministic 'model' as more of an interface people could use as a starting point, playing around with each parameter interactively and getting a sense of how these disturbances would affect the aggregate forecast.
I found the 'founder deployment by end-2026' the hardest to set. It comes a bit as a surprise at the end, as I was already taking into account some considerations before, and the descriptions seem to do as well (e.g. "assets after lockups, taxes, sale timing", and "execution delays").
I submitted an estimate
Biggest (easily-fixable) outstanding issue is I still don't think it makes sense to model deployment by end-2026 because the IPO lockup probably won't have ended by then.
having a think about this.
OK I think the revised language makes it clerer (see updated version of site ... referring to 'timing gate' etc)