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.
The stronger skeptical case is that IPOs, valuations, pledges, DAFs, and foundation stakes are several gates away from fast, flexible, AIS/EA-directed grants.
The interactive model lets readers vary assumptions about Anthropic valuation, founder ownership, pledge follow-through, employee giving, OpenAI Foundation allocation, lockups, deployment rates, and grantmaker capacity.
Some commentary. I mostly agree with the page, but I will focus on the bits where I see room for improvement:
All 8 gates look correct to me, but they don’t all deserve equal emphasis.
Gate 1 says IPOs have lock-ups. That’s true but I basically don’t think that matters because lock-ups are very predictable: they will announce how long it is, and that’s exactly how long it will be. There’s no uncertainty. The main reason it’s relevant is that a lockup gives more time for AI valuations to fluctuate or collapse, but the text doesn’t even mention this.
Gate 5 and Gate 7 seem like they’re saying the same thing.
Gate 8 (“Bay social incentives”) seems uninteresting since it’s not a claim in the same category as the others. It’s more like a meta-level reason why people might not think about the other 7 gates.
Would be cool for the BOTEC to use distributions rather than point estimates. (Squiggle is good for this, and Squigglehub even has a built-in way to have AI generate models.) IMO distributions are a lot more informative than point estimates.
It looks like the default estimates in the BOTEC are pulled from the sources, but it’s not clear which estimates came from which sources. There should be inline citations.
“Anthropic valuation” variable should specifically be the valuation at the end of the 6-month lockup. Doesn’t matter much for a point estimate but it would increase the variance if there variable were a probability distribution.
Unclear what the “Founder pledge” variable refers to. Is it the % of pledgers’ wealth that they’ve pledged to donate? If so, the default of 80% seems really high?
“Employee committed pool” is defined in terms of dollars rather than as a % of company valuation, which seems weird. Shouldn’t it depend on the value of the equity?
This model is supposed to illustrate how a lot of people are being too optimistic, but even then, I think most of the point estimates in the model are too optimistic. Consider that e.g. the median self-reported earner-to-give only donates (IIRC) 3% of their income.
IMO “Deployment by end-2026” should use a different date. IPO 3-6 months from now plus 6 months lockup means no money will be deployed in 2026, unless Anthropic does a fast IPO + early lockup release. Even by the end of 2027, you’re talking about a 3-9 month turnaround time on lockup ending → grants being disbursed. FTX Foundation donated $190 million (pre-clawbacks) in about 6 months, which was ridiculously fast compared to a typical foundation, and that was still a pretty small % of its long-term budget (or at least, what was believed to be its long-term budget before FTX collapsed).
I would delete the OpenAI Foundation bit because (1) the model has enough parameters already and (2) I doubt OpenAI Foundation will give much money to causes that look good by EA lights.
“Grantmaker capacity multiplier” seems nonsensical as written. Shouldn’t the capacity max out at 1x? If grantmakers are a complete non-bottleneck, then the other parameters will dictate the amount disbursed; if they’re a bottleneck, then the amount disbursed will be less. There’s no way for grantmaker capacity to have a multiplier >1x.
Also this would make more sense as a dollar amount, not a multiplier. Like there’s a fixed total amount that grantmakers can reasonably disburse. You could model it in a more complicated way but IMO a simple cap is the way to do it. Or maybe don’t use this parameter at all. I think it’s probably worth including, but keep it simple.
“Field absorption ceiling” is structured more sensibly than “Grantmaker capacity multiplier”, but these two seem redundant because they’re closely related. If orgs have more capacity to expand, grantmakers can deploy money faster by giving to those orgs. If there are more grantmakers, they can create more and bigger RFPs. etc. I would include one variable or the other, but not both.
“The steelman could be too pessimistic if founders or employees treat liquidity as an urgent moral obligation” – TBH the BOTEC as written seems to me like it’s already pricing in that founders/employees will treat donations as urgent, e.g. it’s implying that Anthropic money will be disbursed faster than FTX Foundation money, which itself was disbursed at historic speed. IMO most likely reason why the model will end up underestimating is that Anthropic market cap ends up being like 10x higher than predicted.
My downward adjustments to the model aren’t even the pessimistic case. The pessimistic case* is that the AI field collapses (investor funding dries up or something) and Anthropic stock is worth $0. Base rate says there’s like a 50% chance that that will happen. Even optimistically, you should expect at least a 10–20% chance that Anthropic stockholders get nothing.
The recommendations under “How to plan if the skeptical case is live” don’t really make sense. AFAIK ~zero orgs are planning as if they’re guaranteed to get a huge pile of donations 1–2 years from now. I believe nonprofits mainly plan based on the money they already have on their books + short-term (<1 year) fundraising expectations. “How to plan if the skeptical case is live” is just “business as usual”.
That section says “Funders and field builders should prioritize grantmaker capacity”, but that’s what to do if the skeptical case is wrong, not if the skeptical case is right.
“What would update this memo?” – as with the gates, no sense of prioritization is given. IMO by far the biggest uncertainty, about which we will get more information in the future, is: What will Anthropic’s valuation be when the lockup ends? “Concrete donor vehicles” is also important evidence, but we won’t get that until probably 6-24 months later.
*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
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.
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
More citations, tooltips, direct links, and quoted support for key factual claims, greater reasoning transparency, auditing.
“Updated the model/text in response to his critiques: especially lockup timing, over-optimistic deployment speed, founder pledge ambiguity, OpenAI Foundation treatment, grantmaker capacity, collapse risk, and the need for distributions rather than just point estimates.”
Clarified “the conversion terms are sequential gates”, Made “realization,” “follow-through,” “allocation,” and “deployment by end-2026” more distinct, reduced risk it seems like double-adjusting
Reframe original ‘calculator’
Added a new tab for correlated uncertainty, added shared latent factors such as liquidity plumbing, donor intent, grantmaking capacity, local optimism, and deployment pressure.
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”).
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.
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:
All 8 gates look correct to me, but they don’t all deserve equal emphasis.
Gate 1 says IPOs have lock-ups. That’s true but I basically don’t think that matters because lock-ups are very predictable: they will announce how long it is, and that’s exactly how long it will be. There’s no uncertainty. The main reason it’s relevant is that a lockup gives more time for AI valuations to fluctuate or collapse, but the text doesn’t even mention this.
Gate 5 and Gate 7 seem like they’re saying the same thing.
Gate 8 (“Bay social incentives”) seems uninteresting since it’s not a claim in the same category as the others. It’s more like a meta-level reason why people might not think about the other 7 gates.
Would be cool for the BOTEC to use distributions rather than point estimates. (Squiggle is good for this, and Squigglehub even has a built-in way to have AI generate models.) IMO distributions are a lot more informative than point estimates.
It looks like the default estimates in the BOTEC are pulled from the sources, but it’s not clear which estimates came from which sources. There should be inline citations.
“Anthropic valuation” variable should specifically be the valuation at the end of the 6-month lockup. Doesn’t matter much for a point estimate but it would increase the variance if there variable were a probability distribution.
Unclear what the “Founder pledge” variable refers to. Is it the % of pledgers’ wealth that they’ve pledged to donate? If so, the default of 80% seems really high?
“Employee committed pool” is defined in terms of dollars rather than as a % of company valuation, which seems weird. Shouldn’t it depend on the value of the equity?
This model is supposed to illustrate how a lot of people are being too optimistic, but even then, I think most of the point estimates in the model are too optimistic. Consider that e.g. the median self-reported earner-to-give only donates (IIRC) 3% of their income.
IMO “Deployment by end-2026” should use a different date. IPO 3-6 months from now plus 6 months lockup means no money will be deployed in 2026, unless Anthropic does a fast IPO + early lockup release. Even by the end of 2027, you’re talking about a 3-9 month turnaround time on lockup ending → grants being disbursed. FTX Foundation donated $190 million (pre-clawbacks) in about 6 months, which was ridiculously fast compared to a typical foundation, and that was still a pretty small % of its long-term budget (or at least, what was believed to be its long-term budget before FTX collapsed).
I would delete the OpenAI Foundation bit because (1) the model has enough parameters already and (2) I doubt OpenAI Foundation will give much money to causes that look good by EA lights.
“Grantmaker capacity multiplier” seems nonsensical as written. Shouldn’t the capacity max out at 1x? If grantmakers are a complete non-bottleneck, then the other parameters will dictate the amount disbursed; if they’re a bottleneck, then the amount disbursed will be less. There’s no way for grantmaker capacity to have a multiplier >1x.
Also this would make more sense as a dollar amount, not a multiplier. Like there’s a fixed total amount that grantmakers can reasonably disburse. You could model it in a more complicated way but IMO a simple cap is the way to do it. Or maybe don’t use this parameter at all. I think it’s probably worth including, but keep it simple.
“Field absorption ceiling” is structured more sensibly than “Grantmaker capacity multiplier”, but these two seem redundant because they’re closely related. If orgs have more capacity to expand, grantmakers can deploy money faster by giving to those orgs. If there are more grantmakers, they can create more and bigger RFPs. etc. I would include one variable or the other, but not both.
“The steelman could be too pessimistic if founders or employees treat liquidity as an urgent moral obligation” – TBH the BOTEC as written seems to me like it’s already pricing in that founders/employees will treat donations as urgent, e.g. it’s implying that Anthropic money will be disbursed faster than FTX Foundation money, which itself was disbursed at historic speed. IMO most likely reason why the model will end up underestimating is that Anthropic market cap ends up being like 10x higher than predicted.
My downward adjustments to the model aren’t even the pessimistic case. The pessimistic case* is that the AI field collapses (investor funding dries up or something) and Anthropic stock is worth $0. Base rate says there’s like a 50% chance that that will happen. Even optimistically, you should expect at least a 10–20% chance that Anthropic stockholders get nothing.
The recommendations under “How to plan if the skeptical case is live” don’t really make sense. AFAIK ~zero orgs are planning as if they’re guaranteed to get a huge pile of donations 1–2 years from now. I believe nonprofits mainly plan based on the money they already have on their books + short-term (<1 year) fundraising expectations. “How to plan if the skeptical case is live” is just “business as usual”.
That section says “Funders and field builders should prioritize grantmaker capacity”, but that’s what to do if the skeptical case is wrong, not if the skeptical case is right.
“What would update this memo?” – as with the gates, no sense of prioritization is given. IMO by far the biggest uncertainty, about which we will get more information in the future, is: What will Anthropic’s valuation be when the lockup ends? “Concrete donor vehicles” is also important evidence, but we won’t get that until probably 6-24 months later.
*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
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.
(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
More citations, tooltips, direct links, and quoted support for key factual claims, greater reasoning transparency, auditing.
“Updated the model/text in response to his critiques: especially lockup timing, over-optimistic deployment speed, founder pledge ambiguity, OpenAI Foundation treatment, grantmaker capacity, collapse risk, and the need for distributions rather than just point estimates.”
Clarified “the conversion terms are sequential gates”, Made “realization,” “follow-through,” “allocation,” and “deployment by end-2026” more distinct, reduced risk it seems like double-adjusting
Reframe original ‘calculator’
Added a new tab for correlated uncertainty, added shared latent factors such as liquidity plumbing, donor intent, grantmaking capacity, local optimism, and deployment pressure.
Clearer planning simulator
Equations/derivations page
Automated evidence monitor added
Added a “Submit your estimate” form.
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 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)