Relevant abstract just published in Neurology: DMT use in Cluster Headache: Interim Analysis of an International Survey (S23.003)

From a total 248 completed surveys, 100 participants met criteria for cluster headache. Of these, 84% were male and average age was 49 years. The most common place to have learned about DMT treatment was social media/​online. More than one mode of ingestion was often tried, but 75% had used a vape pen. Among those who used DMT to abort attacks (n = 85), the majority reported it completely eliminates pain (67.5%) and that it works every (45.7%) or almost every (35.8%) single time. Over half reported that DMT worked in under 30 seconds, and all participants reported either no change or an improvement in their health with DMT use.

I think impact-focussed funders underrate research on animal sentience, and, more broadly, on comparing welfare across species. I believe there is huge uncertainty, and ways of decreasing it. Here is some context about my uncertainty. In Bob Fischer’s book about comparing welfare across species, the tentative sentience-adjusted welfare range of shrimps is 8.0 % of that of humans. Welfare range is defined there as the difference between the maximum and minimum welfare per unit time among “realistic biological possibilities”. For sentience-adjusted welfare ranges proportional to “individual number of neurons”^“exponent”, and “exponent” from 0 to 2, which covers the best guesses that I consider reasonable, the sentience-adjusted welfare range of shrimps is 10^-12 to 1 times that of humans.

If someone had a pattern of fabrication and very poor understanding (and apparent confidence) like LLMs often do if used uncritically, I would be annoyed with them and possibly do any of the following:

1. Tell them to read and review more carefully, look for opposing arguments, etc..

2. Downvote such comments (and I very very rarely downvote).

3. Stop engaging with this person, because it wastes my time and may encourage them to waste others’ time.

Makes sense. I just think what is “poor understanding” is often sufficiently contentious for one to have a high bar for preferring not sharing over sharing with little verification. I also tend to default to let people decide the extent to which they want to engage with something.

My impression is that many people, including academics, use “consciousness” and “phenomenal consciousness” interchangeably, but they do so implicitly rejecting strong illusionism, would reject strong illusionism if asked directly, and typically don’t understand strong illusionism. Maybe many are open, though, I’m not sure.

Makes sense. I was not clear. However, by “without wanting to take a stance on illusionism or realism”, I meant many do not have a good picture of what strong illusionism means, and may be conflating it with eliminativism. I was doing this to some extent.

The amount of energy needed to refute bullshit is an order of magnitude bigger than that needed to produce it.

Verification is often easier than generation.

I was more referring to the diversification as implied by “don’t focus the vast majority of efforts on one cause”, which to me meant more “if you’re a decision maker over some amount of resources, you should diversify the allocation across cause areas”. Which I agree with, but it’s quite hard to really justify.

Yes, via nonlinearity you can get to diversification, but this means making additional assumptions beyond sampling error/​publication bias/​optimiser curse type effects.
The nonlinearity you’re describing matters on a movement level, but not on a individual decision makers level.
”Impact risk aversion” is another mechanism to get diversification which I think can be reasonable in cases where eg low impact reduces the probability of future donations or similar.

One channel I think is under explored and might work well as a justification for diversification in practice is something like this (I haven’t thought about this rigorously though): if I predictably optimise and my objective function is known to others, they will (in the worst case, possibly thru misaligned incentives) feed me biased information to influence my decision, and optimisation is very sensitive to noise, therefore I subject myself to adverse selection. So basically by not optimising but diversifying across good options you reduce the negative impact of this type of adverse selection. In this case, the “errors” are not iid. Hard to say how much diversification that yields.

Mox Movie Nights watched Slumdog Millionaire today and it reminded me of the why behind the work. We’re not here to EV-max, to gain impact points, to count utils. We’re here because the world is unfair, suffering is prolific, and need to find a way through it.

very exciting!!!

We enticed a real life comedian onto our podcast:

Err on the side of applying. Don’t think too much into the details of whether you will be getting selected—it’s a conference—meant to be for folks interested in the cause and don’t know much about it to gain more exposure.

After reading your post I don’t see the cause of doubt—to me it just seems hesitance in applying, which won’t help you in long run. Take bets and apply

Note you can also donate to Screwworm Free Future for the chance at helping to avoid extreme suffering for 30+million animals per year.

It’s a more speculative & risky donation but if one thinks their marginal dollars have a chance to speed up elimination by even 2-3 years then it is like 100 animals avoiding extreme suffering for every $1 donated.

https://​​www.every.org/​​screwworm-free-future

The potential effectiveness of such donations may put you over the threshold of making the job worth it.

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

Why would they need to shut down completely? It seems like it would work just as well to say “We won’t build any new models, but we’ll keep serving the ones we already have.” Then they wouldn’t be accelerating towards riskier models, it wouldn’t be AS bad for them financially (so it might have a better chance of happening) and they can still do safety research on the models they already have.

Oh, wow, that’s really interesting that you found that correlation.

One explanation of this correlation could be that the additive model focuses on the presence of different cognitive capacities, and animals may need higher neuron counts to have more cognitive capacities.

This would then center your worldview on how significant you think cognitive capacities are in determining welfare ranges.

Wonderful read, The framing point is something that was specifically compeling.

Also was very interested in the “Collaborate with Institutions” section. The action of incorporating animal welfare education into university curricula for further interdisciplinary involvement in animal welfare comes off as incredibly promising. I was wondering from you or anyone reading, thoughts on how this curricula could be expanded into spaces that are not, on first thought, related to animals or agriculture such as policy, journalism, business and more. In my thinking having some expectation of taking an animal welfare course throughout a students academic career could spark interest in spaces that are neglected in animal welfare. I’d love to hear thoughts on this in terms of feasibility and effectiveness.

Definitely agree that shooting down GPS satellites (or satellites of any kind, really) is a really agressive move that would make all countries super mad at you, thus probably only happens in a pretty serious “all or nothing” great-power conflict!

I’ll try to talk more about the military dynamics around GPS in my later post, although I’m not an expert. My impression is that although militaries do indeed have lots of backup systems, none of them is perfect and some systems (like many existing drones and guided bombs) do rely exclusively on GPS, so losing GPS would still be a big problem even though the military has done a lot of work to try and ensure that it wouldn’t be a totally overwhelming catastrophe.

I fear this is missing the main objective though. Maximising taste is solely for the purpose of maximising uptake of alternative meats. There isn’t an intrinsic good in having tasty meat alternatives.

The logic is: tastier meat alternatives —> more people eat meat alternatives —> less animal suffering.

It’s foundational argument is better tasting meat alternatives increase the number of people eating meat alternatives.

There are companies that exist to sell people meat alternatives. So their incentives are aligned. Profit maximising is just an incentive to do that most efficiently, so they’d do it if taste was the most cost effective way—I.e. if for every $1 spent on making meat alternatives taste better it led to $2 of sales, but $1 spent on marketing led to $1.5 of sales, then it’d be better value to spend on making it taste better.

My issue with this RFP is it presumes the market is failing. My question is where is the proof? It being underfunded by public agencies and private R&D suggests to me either massive vested interest (which may be true for public agencies but less clear why Beyond Meat wouldn’t want more customers if tastier meat alternatives was the best way to do that); or it suggests there is a more efficient method being employed by those with the incentives to find it. Unless you think the meat alternative providers don’t want more customers, or are incompetent (in which case why hasn’t a tastier competitor already appeared).

This whole RFP looks to fix a market failure, which I’m not clear exists. It also tries to do that with $10m, which by their on linked numbers is such a tiny fraction of money spent on tasting research. Unfortunately, this RFP has the hallmarks of a fund that would have to be so amazingly well spent to make a difference that the whole thing seems destined to have little to no impact.

A few comments, some of which you may be intending to cover in updates

• First of all we actually have a pretty decent idea of what happens in GNSS-denied environments because localised GNSS jamming is a thing,^[1] It’s especially a thing in combat zones, which means that people and infrastructure affected typically have other problems^[2]

• Because GNSS denial is a thing, militaries have alternative PNT systems to aid them in combat. So actively disabling GNSS satellites is a pretty extreme measure that mostly hurts civilians, including in about 250 countries not currently at war with you. And if it involves use of anti-satellite weapons or EMPs, probably takes out a whole bunch of other space infrastructure too^[3]

• As it’s a pretty extreme measure that annoys everyone worldwide without even offering you a decisive advantage in a local conflict, it’s most likely to happen during escalation of a great power conflict. Great power conflicts mean that sectors like maritime would be experiencing COVID level downturns already. The “solar storm” is more interesting because it might be largely unexpected (and would also likely impair a lot of non-GNSS comms stuff)

• But costs of nuisance level GNSS jamming in borderlands between states not actually at war (say, the Baltic...) has a scaled down version of this impact which I guess might be underestimated...
  
  
  Interested to see the followup

1. ^

   Similarly, people working with navigation systems prone to spoofed location results (maritime navigation) have to find workarounds

2. ^

   though electronic warfare can affect neutral neighbours and overflying aircraft too...

3. ^

   current generation GNSS satellites are in fairly empty medium earth orbits and can be disabled without kinetic weapons, but this doesn’t rule out collateral damage and likely won’t be the case for future GNSS (in part because they want more redundancy even though the system(s) just work.

One simple model is:

• each person can choose only one cause area

• errors are iid across people

• the x-risk coming from each cause decreases with each additional person working on this cause

Whether diversification is better (in expectation) depends on how a cause’s x-risk decreases as additional people work on this cause. If x-risk decreases linearly (the 1000th person makes the same marginal contribution as the 1st), then diversification is not better in expectation. But if the contribution to x-risk prevention is marginally decreasing in people, diversification is better.

(By diversification I mean each person choosing their top estimated x-risk cause individually. But it can also mean that some people deliberately do not work on the cause with the highest aggregated risk estimate.)

While some people might notice the fact the companies are making losses, others will notice the fact that they are valued in the billions! I am not sure if a shutdown would actually be the right move but it would definitely send a message.

I like diversification as a reaction to this type of uncertainty, but it does not trivially follow? I might be missing something—do you have a favourite minimal set of assumptions that rigorously yield diversification as a function of this?

Above all it implies don’t focus the vast majority of efforts on one cause.

That might not be practical for career choices,^[1] but it’s certainly possible for a funder or movement

1. ^

   though a corollary of it is “don’t assume that just because you’ve picked direct work that your career choice is maximally good and stuff like donations and helping others is just a distraction”. This is arguably true for speculative career choices even if the optimal cause is the correct one (i.e. even if AI x-risk really does dominate everything, lots of the promising approaches to resolving it that people might choose will have no impact)