(Couldn't click on your linked comment from mobile, so here it is in case it helps others. Oliver Yeung's backstory is striking, thanks for sharing the links to his main talk and Q&A)

The closest I know of is the Metaculus question How many chickens will be slaughtered for meat globally in the following years? from 2022, which forecasts 82.2 billion chickens slaughtered in 2032, declining afterwards to 64 billion in 2052 and 9.1 billion in 2122 (with increasingly wide prob dists), as part of the Forecasting Our World in Data: The Next 100 Years project. 

The 2023 forecast was a slight overprediction (78.5bn vs 76.25bn actual), which gives me a bit of hope that the rest of the curve will bend downwards faster than predicted.

Here's the distilled forecaster commentary for 2025 onwards:

2025: Forecasters generally expect that global chicken consumption will continue to grow in the next few years. Their expectations are based on extrapolation of the data from 2019, with the assumption that there will not be any major changes in consumer attitudes towards animal ethics, tastes, or disease outbreaks. Forecasters anticipate that meat alternatives, such as lab-grown or plant-based options, may start to have an impact on the industry by 2030, but are not expected to significantly change the data for 2025. Forecasters also acknowledge the potential for: (1) improved standards of welfare to decrease consumer preference for poultry and (2) genetic engineering to increase yields at lower cost. However, the feasibility, regulatory permissiveness, and consumer preferences for the latter trend remain uncertain.

2032: Overall, forecasters expect global chicken consumption to continue to grow over the next decade. While they do not expect a significant global cultural shift towards lower meat consumption on this timescale, they do anticipate a limited decrease in highly developed countries to be more than offset by significant increases in consumption in less wealthy countries, as the latter’s populations become wealthier. Forecasters expect the economic growth of the last decade to slow only slightly over the next, but some do expect annual global growth to stall at some point between 2032 and 2052. This would arguably lead to a stark reduction in chicken consumption.

2052: Forecasters expect that affluence and population increase will lead to a continuation of the trend of increasing chicken demand per capita and total chicken production. However, they also expect that cultured meat and other technological advances may have a material impact on meat consumption, particularly in highly developed countries. In fact, many predict that lab-grown or plant-based alternatives will dominate the market by this point. Beyond 2052, forecasters express much more uncertainty, but expect that meat alternatives and artificially grown meat will continue to replace the majority of chicken meat obtained by slaughtering chickens. They project that the rate of growth will slow as population growth slows, leading to a plateauing of chicken consumption while the cost of raising more chickens increases. Notably, they expect that ethical concerns will play only a minor role globally by this time.

2122: With a long-term horizon of 100 years, forecasters expect technological advances in meat alternatives to lead to a massive decrease in chicken consumption. They predict that, by 2122, global energy consumption will be pulled in two directions,  there will be: (1) both a demand for better energy efficiency and lower consumption overall and (2) cheaper and more abundant electricity as renewables continue to fall on the cost curve. Therefore, it is highly likely that cultured meat will have been cost-competitive with traditional meat for decades. There are some forecasters who expect the global chicken population to be around half of its peak and, given the availability of cheap, high-quality cultured meat, there’s a chance that the number of slaughtered poultry may fall close to zero.

In case it's helpful, you may want to speak with Max Ghenis, cofounder & CEO of PolicyEngine, a tech nonprofit that computes the impacts of public policy, which your project proposal reminded me of. Here you can (quoting their calculator) "build a tax-benefit reform by selecting parameters from the menu (organised by government department)" and then "click Calculate economic impact to see how your reform would affect the economy, or Enter my household to see how it would affect a specific household". They use microsimulation models based on tax and benefit calculations applied to representative survey data to calculate impact and have used AI since 2023 for policy analysis / explanations / insights. They have an X account too where you can check out what they're about.

The article is by Ben Todd, not Cody :) The fuller quote from Ben in the article is

If we were to expand this to also include non-measurable interventions, I would estimate the spread is somewhat larger, perhaps another 2–10 fold. This is mostly based on my impression of cost-effectiveness estimates that have been made of these interventions — it can’t (by definition) be based on actual data. So, it’s certainly possible that non-measurable interventions could vary by much more or much less.

I appreciated reading these passages from Hannah Ritchie's Children in rich countries are much less likely to die than a few decades ago, but we rarely hear about this progress over at Our World in Data, for both the data and the shift in perspective:

Countries in the European Union, Japan, South Korea, the United Kingdom — the list goes on — have made childhood much safer in my own 30-year lifetime.¹ It’s just something we rarely hear about. I also don’t think that this is a “solved problem”; it is still too common for parents to see their children die, and there’s a lot more that we can do to save their lives. ...

It’s only when we look at the relative reduction in child mortality that we see that rich countries have also made impressive progress.

I think it’s important to highlight this point for two reasons.

First, the idea that progress on health has stalled (or even regressed) in rich countries is, I think, a common one. I’ve previously held that view myself. But it’s not true: improved treatments and vaccinations developed by scientists, dedicated care from doctors, midwives, and nurses, health policies developed by governments, and parents' choices have made things much safer for children even in the world’s richest countries. These efforts were not for nothing: they’ve given kids a future and spared many families the pain of losing a child.

Second, child mortality in rich countries is not a “solved problem”. 23,000 children still die in the United States every year. That’s around 50 times more than the number who die from natural disasters.³ And more than the total number of homicides.⁴ No one would say that murders in the US are a “solved problem”.

You may find this 80K article useful, both for their analysis and for all the data they collected: How much do solutions to social problems differ in their effectiveness? A collection of all the studies we could find. Bottomline is 3–10x not >1,000x for measurable interventions, and stack on a 2–10x spread for harder-to-measure interventions: 

Overall, I roughly estimate that the most effective measurable interventions in an area are usually around 3–10 times more cost effective than the mean of measurable interventions (where the mean is the expected effectiveness you’d get from picking randomly). If you also include interventions whose effectiveness can’t be measured in advance, then I’d expect the spread to be larger by another factor of 2–10, though it’s hard to say how the results would generalise to areas without data.

Also this section: 

3. How much can we gain from being data-driven?

People in effective altruism sometimes say things like “the best charities achieve 10,000 times more than the worst” — suggesting it might be possible to have 10,000 times as much impact if we only focus on the best interventions — often citing the DCP2 data as evidence for that.

This is true in the sense that the differences across all cause areas can be that large. But it would be misleading if someone was talking about a specific cause area in two important ways.

First, as we’ve just seen, the data most likely overstates the true, forward-looking differences between the best and worst interventions.

Second, it often seems fairer to compare the best with the mean intervention, rather than the worst intervention. ...

Overall, my guess is that, in an at least somewhat data-rich area, using data to identify the best interventions can perhaps boost your impact in the area by 3–10 times compared to picking randomly, depending on the quality of your data.

This is still a big boost, and hugely underappreciated by the world at large. However, it’s far less than I’ve heard some people in the effective altruism community claim.

In addition, there are downsides to being data-driven in this way — by insisting on a data-driven approach, you might be ruling out many of the interventions in the tail (which are often hard to measure, and so will be missing).

This is why we advocate for first aiming to take a ‘hits-based’ approach, rather than a data-driven one.

("Hits-based rather than data-driven" is quite counterintuitive, especially to someone like me who's worked most of my career in data-for-decision-guidance roles, but a useful corrective to the streetlight effect.) 

Edit: whoops just saw Cody's comment above pointing to the same article.

Adam's tests, quoted: 

1. The Bluetooth test: "when you’re given the smallest amount of power, do you use it to make things nice for everybody, or just yourself?"
2. The Circle of Hell test: "when you see someone writhing in social damnation, do you grab their hand, or do you let ‘em burn?"
3. The Gottman test: "do you expect to influence others, but refuse to be influenced yourself?"
4. The codependent problems test: "do you actually want to solve your problem, or are you secretly depending on its continued existence? If you showed up to fight the dragon and found it already slain, would you be elated or disappointed?"
5. The match your freak test: "If I’m a little bit weird, will you be a little bit weird too?"
6. The pointless status test: "whenever there’s some way you could consider myself better than other people, no matter how stupid or arbitrary it is, do you feel compelled to compete?"
7. The too busy to care test: "Are you too busy to care about the other people in your life?" 

(Adam is an engaging writer who often leaves me feeling enriched, but his meandering style sometimes makes me lose track of what he's talking about, which is why I pulled them out here)

I appreciated this quote from him too:

... becoming kinder and gentler is mainly the act of noticing—realizing that some small decision has moral weight, acting accordingly, and repeating that pattern over and over again.

I'm admittedly a bit more confused by your fleshed-out example with random guesses than I was when I read your opening sentence, as it went in a different direction than I expected (using multipliers instead of subtracting the value of the next-best alternative use of funds), so maybe we're thinking about different things. I also didn't understand what you meant by this when I tried to flesh it out myself with some (made-up) numbers:

Who knows, for-profit investment dollars could be 10x -100x more counterfactually impactful than GiveWell, which could mean a for-profit company trying to do something good could plausibly be 10-100x less effective than a charity and still doing as much counterfactual good overall?

If it helps, GiveWell has a spreadsheet summarising their analysis of the counterfactual value of other actors' spending. From there and a bit of arithmetic, you can figure out their estimates of the value generated by $1 million in spending from the following sources, expressed in units of doubling consumption for one person for a year: 

• Government social security spending: 2,625 units
• Government education spending: 2,834 units
• Donating to GiveDirectly's cash transfer program (1x cash for reference): 3,355 units
• Government spending on deworming: 3,525 units
• Government spending on malaria, VAS, and immunization programs: 5,057 units (this is also what GW uses as their counterfactual value by domestic governments in assessing SMC)
• Government health spending: 5,639 units (this is also GW's assumed value generated by philanthropic actors's spending on VAS and vitamin A capsule procurement in other countries)
• Gavi spending (US allocations to Gavi and Maternal and Child Health): 6,952 units
• Global Fund (calculations here): 15,433 units
• HKI spending on vitamin A supplementation in Kenya, before final adjustments: ~19,000 units
• GW's 10x cash bar for reference: 33,545 units
• AMF spending on ITN distribution in DRC, before final adjustments: ~36,800 units
• Open Phil's current 2,100x bar: very naively ~70,000 units (21x GiveDirectly to oversimplify, using OP's 100x ≈ 1x cash to GD)

Suppose there's an org ("EffectiveHealth") that could create 50k units worth of benefits given $1M in funding. If it came from GW with their 33.5k units counterfactual value, then a grant to EffectiveHealth from GW would be creating 16.5k more units of benefits than otherwise, while if it came from domestic govt spending (7.5x lower value at 5k per $1M) it'd create 45k more units of benefits. A hypothetical for-profit investor whose funding generates 10x less good than domestic govt spending (500 units) would get you not 10 x 45k = 450k more units of benefits, but 49.5k units. And if EffectiveHealth got funding from OP it would actually be net-negative by -20k units.

(maybe you meant something completely different, in which case apologies!)

Thought these quotes from Holden's old (2011) GW blog posts were thought-provoking, unsure to what extent I agree. In In defense of the streetlight effect he argued that

If we focus evaluations on what can be evaluated well, is there a risk that we’ll also focus on executing programs that can be evaluated well? Yes and no.

• Some programs may be so obviously beneficial that they are good investments even without high-quality evaluations available; in these cases we should execute such programs and not evaluate them.
• But when it comes to programs that where evaluation seems both necessary and infeasible, I think it’s fair to simply de-emphasize these sorts of programs, even if they might be helpful and even if they address important problems. This reflects my basic attitude toward aid as “supplementing people’s efforts to address their own problems” rather than “taking responsibility for every problem clients face, whether or not such problems are tractable to outside donors.” I think there are some problems that outside donors can be very helpful on and others that they’re not well suited to helping on; thus, “helping with the most important problem” and “helping as much as possible” are not at all the same to me.

(I appreciate the bolded part, especially as something baked into GW's approach and top recs by $ moved.)

That last link is to The most important problem may not be the best charitable cause. Quote that caught my eye:

[Project AK-47's emotionally appealing pitch to donors is] an extreme example of a style of argument common to nonprofits: point to a problem so large and severe (and the world has many such problems) that donors immediately focus on that problem – feeling compelled to give to the organization working on addressing it – without giving equal attention to the proposed solution, how much it costs, and how likely it is to work. ...

Many of the donors we hear from are passionately committed to fighting global warming because it’s the “most pressing problem,” or to a particular disease because it affected them personally – even while freely admitting that they know nothing about the most promising potential solutions. I ask these donors to consider the experience related by William Easterly:

I am among the many who have tried hard to find the answer to the question of what the end of poverty requires of foreign aid. I realized only belatedly that I was asking the question backward … the right way around [is]: What can foreign aid do for poor people? (White Man’s Burden pg 11)

As a single human being, your powers are limited. As a donor, you’re even more limited – you’re not giving your talent or your creativity, just your money. This creates a fundamentally different challenge from identifying the problem you care most about, and can lead to a completely different answer.

In my case: I would rather close the achievement gap than fight developing-world disease, but my giving goes to the latter because it’s a problem that I can do much more to address.

The truth is that you may not be able to do anything to help address the root causes of poverty or cure cancer or solve the global energy crisis.* But you probably can save a life, and insisting on giving to the “biggest problem” could be passing up that chance.

I thought the USAID Funding Cuts webpage buried the lede a bit given the stated purpose of the page is to "answer the most common question we’re hearing right now: How can donors help?", so in case it helps, GW's answer is "If you want to help respond to this situation, donating to our funds based on your giving preference remains our recommendation."

(Bit tangential, but I noticed the karma on this post drop from 17 to 9 within 3 minutes and wondered why the strong(?) downvote—a single downvote seems more plausible in 3 mins than a sudden flurry. Curious if strong-downvoters can say why.)