I’m a computational physicist, I generally donate to global health. I am skeptical of AI x-risk and of big R Rationalism, and I intend on explaining why in great detail.
titotal
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Point 1 was a typo, thanks for pointing it out!
Point 2 is something that confused me at first as well. The reason they are different is that we are looking at the performance of the top apparent threat. If we were perfectly good estimators, this would be the same as the top actual threat, but we aren’t: the threat of the top pick is generally going to be lower than the top actual threat due to uncertainty and the curse.
For the grounded estimator, the process of ranking threats gives useful information, and it means that the top threat picked is much higher than you would get from picking at random. Whereas in the speculative case, we are much closer to just picking at random, and thats reflected in the yellow curve which looks a lot like the power law sampling we are drawing from.
Here is an example run I did where I tuned down the alpha to 1.5 and tuned the lognormal standard deviation down to 1.5:
Here, the top actual threat really is 7 orders of magnitude more dangerous than the bottom evaluated threat. However, the top apparent threat is overestimated by a factor of 10,000 or so.
If I do a bunch of runs with these settings, the median overestimate is over 100x:
So even if I trust your vibes here (which do not seem to be based on anything), the curse can still hit quite badly. I personally believe that the spread of numbers that people make up is going to be higher than the spread of actual threats: when we look at actual surveys you get a highest-lowest estimate spread of 11 orders of magnitude for some questions.
One thing that might be confusing you is that the power law model assumes that only threats above a certain threshold of actual danger are considered (this is the xmin factor). Obviously nuclear risk is a much greater risk than stubbing your toe, but it’s not going to show up the model.
Are top existential risk estimates 50,000 times too high? An optimizer’s curse model and analysis
Academia: defers significantly more
This has not been my experience from 9 years of academia in physics and material science. Opinions published in scientific papers must be backed up with reference to actual evidence, not merely opinion. When deferral happens behind the scenes, it’s usually justified by the person in question being an actual expert that knows their shit.
EA is far worse: I sometimes see people defer to random blog posters who have zero expertise in the subject they are talking about.
Does disaster frequency follow power laws? It’s complicated
Interesting analysis! I’m somewhat skeptical that space based cells will perform as well as you find in this model. This seems like a classic case where something that looks good on paper will run into problems in real world applications for something as complicated as putting massive datacenters in space. I don’t think your model is accounting for murphy’s law here.
Also, on a technical note, do you have the source for the starlink solar cell specifications used for the phrase “Back-calculated from Starlink V3 specifications (50,400 W from 554 kg array, 257 m² area)”? This seems like one of the critical figures of your model but I couldn’t find where you linked the source for it.
It is extremely difficult to determine base rates for something like sexual harassment, because it’s an offence that allows for ambiguity and plausible deniability, because there’s room for retaliation, etc, and it will strongly depend on how much people trust the bodies they are reporting to.
What we can do is look at the responses to the incidents that do get raised, and the experiences of victims, and judge whether or not they live up to the standards we want to see in a group that takes sexual harrasment seriously. I do not think the grades are very good on this front.
Only 3 months ago we had a writeup detailing a shockingly terrible response to sexual harrassment by one of the most prominent EA orgs out there. The response is far worse than anything I’ve ever seen at any organisation I’ve ever been in. This indicates to me that the environment is nowhere the high standards that should be aimed for.
Regardless of the actual base rates, the question that matters the most is whether there is room for improvement, and I think it’s blindingly obvious that the answer is yes.
LLM disclosure in general is just a good idea to do. The internet is absolutely flooded with LLM-written spam at the moment, so if people detect LLM writing with no context it’s natural to assume your post is spam as well. This is a shame when someone who is a non-native speaker has just used it for translation or whatnot.
Personally I’d recommend against using LLM-written text if you can help it, as in the age of spam the value of cultivating your own stylistic voice is increasing.
Analysing the extreme spread in existential risk survey estimates
I think you would benefit from re-reading the article in question. For example, they directly adress your point 1 by pointing out that consumer diffusion figures are often misleading by expressing figures in terms of “percentage of people that use chatbots on occasion”, rather than on frequency of use.
Point 3 is not even an argument, just a restatement of what they believe: yes, they think AI domination will take decades. They state the reasons they believe this very clearly in the section “Diffusion is limited by the speed of human, organizational, and institutional change”: if you disagree with this, you have to present actual arguments. From what I know, most economists would agree with them.
Point 5 is not an argument either: they are not to blame for how you interpret their “vibes”. If people interpret “AI will be akin to the internet” as anything other than “AI will be akin to the internet” that’s their fault, not the authors.
As for point 6, I’m confused as to what your position is here. Do you think that AI systems are merely cheating on every single benchmark? In the section “benchmarks do not mention real-world utility”, I took them as referring to benchmarks that are actually meaningful: saying that while they genuinely are good at taking law tests, even non-contaminated ones, that this doesn’t translate into being a good lawyer because of the aspects that are not easily measurable. I don’t see how this is a contradiction to any of their previous work?
I think “speck of dust in the eye” was a bad choice for the central example of this debate, because in some situations a speck in your eye can be literally zero painful, and in others it can be actually quite painful and distressing. I think this leads to miscommunications and poor intuitions.
My preferred alternative would be something like “lightly scratching your palm with your fingernail”. And while this is technically pain, I find a single light scratch to be so minor that it has literally zero effect on my levels of happiness: in fact I will sometimes do this to myself on purpose when I get sufficiently bored.
I therefore think that that premise 1: “mild pain is bad”, is wrong for sufficiently small definitions of “mild pain”. I think you need a threshold of badness for the argument to work. Furthermore, I think most people who would side with the “dust specks” also have some threshold where they would pick the torture: for example if it was “punching a billion people in the face vs torture one person”.
To be clear, I wasn’t saying that complexity itself was the cause of consciousness, just that some level of algorithmic complexity may be a requirement for consciousness. This seems like a common position: the prospect of present or future LLM sentience is a subject of debate, but it’s rare to see a similar debate about the sentience of a pocket calculator.
A brain and a digital simulation have some similarities, but they also have a lot of differences. One of those differences is that the brains are running on “laws of physics” algorithms that are overwhelmingly faster and more complex than that of digital simulations. They didn’t need to evolve these “algorithms”: it’s inherent to any biological process. Seth identifies several other differences as well: continuous operation, embodiment, etc. His position seems to be that at least one of these differences may result in a lack of consciousness.
disclaimer: I am not too well-versed on the philosophy here so I could be saying dumb things, feel free to correct:
From my computational physics experience I know that it is physically impossible to simulate the exact electrical properties of a system of a couple hundred atoms on a classical digital computer, due to a blowup in computational complexity.
The laws of physics could be described as an algorithm, but the algorithm in question is on a level of complexity that is impossible for digital simulations to match. I think it’s generally agreed that some degree of complexity is required for consciousness: it doesn’t seem insane to say that that complexity might lie past what is digitally simulatable in practice.
The question of digital consciousness seems to depend on whether simulated abstracted approximations to the physical process of thinking are close enough to produce the same effect.
I am somewhat concerned about data contamination here: Are you sure that the original Givewell writeup has at no point leaked into your model’s analysis? Ie: was any of givewell’s analysis online before the august 2025 knowledge cutoff for GPT, or did your agents look at the Givewell report as part of their research?
Yeah, the future described in this post isn’t particuarly “weird”, per se, it’s just using the assumption that every technology that has been hypothetically proposed for the future will be created by ASI soon after AGI arrives.
I think the future will be a lot more unpredictable than this. Analolgously, I can imagine someone from 1965 being very confused about a future where immensely powerful computers can fit in your pocket, but human spaceflight had gone no further than the moon. It’s very hard to predict in advance the constraints and shortcomings of future technology, or the practical and logistical factors that affect what is achieved.
Have you considered that the reason these policies are not increasing AI usage is that AI usage is not particularly useful for many applications? Particularly when it comes to something like animal advocacy, I’m struggling to think of many things you’d actually need a full model subscription for (rather than just asking the occasional question to a free model).
I think the original policies are fine: they let people evaluate and decide for themselves how useful AI models are, and adjust strategies accordingly. Trying to pressure people to use AI beyond this level is going to make your team less effective.
You correctly point out that “AI safety leaders” is a group that selects for high concern about AI, which means that the average is skewed towards high concern, relative to experts more generally.
I would like to add that the same is probably true (to a lesser extent) for AGI timeline estimates: People that think that AGI is very far away are less likely to think that AI safety is a pressing concern and are thus less motivated to become AI safety leaders. Also, people who are concerned about present-day AI risks, but don’t think AGI is imminent often call themselves “AI ethicists”, rather than AI safety people. These “AI ethicists” are unlikely to show up to a “summit on existential security”.
To be clear, I think it’s good to write this article, but we should always be mindful of selection effects when interpreting surveys.
Unfortunately, most estimates of LLM energy use are somewhat out of date due to the rise of reasoning models. A small amount of personal usage is probably still not that energy intensive, but I don’t think it’s negligible anymore.
The most up-to-date estimates I’ve seen of AI energy use is this paper here. I recommend you look at table 4. For the o3 reasoning model, which is probably the closest analogue to todays reasoning models, a short query costs something like 7 Wh, a medium query is 20 Wh, and a long query is 30 Wh. Using a non-reasoning model like GPT-4o was much less intensive at like 0.4 Wh for a small query, however in my experience the results tend to be a lot worse.
So if you end up using like 10 medium queries to a reasoning model over the course of a project, that would add up to 0.2 kWh: if you use 100 queries, that would be 2 kWh. The typical household energy use is something like 30 kWh per day. So the impact is small, but non-neglible: probably there are other things you can do that will have a bigger impact on energy use.
Personally, I would be worried about cognitive offloading: I think that an overreliance on AI can hamper your ability to learn things, if you offload mentally difficult tasks to the AI.
This interpretation is not true. Thiel was talking specifically about money going to Gates in the event of Musk dying:
That’s how Thiel said he persuaded Musk. He said he looked up actuarial tables and found the probability of Musk’s death in the coming year equated to giving $1.4 billion to Gates, who has long sparred with the Tesla CEO.
“What am I supposed to do—give it to my children?” Musk responded, in Thiel’s telling. “You know, it would be much worse to give it to Bill Gates.”
I think this would only make sense if Musk had specifically willed his pledge money to the gates foundation?
I can add four more to the list of possible dangers:
Effective altruists tend to be more utilitarian, more “maximizing” with regards to ethics, and more willing to bite philosophical bullets, compared to the general population. EA’s involvement in AI safety could end up spreading these beliefs to future AI. It’s well established how this sort of utilitarian reasoning can go wrong (utility monsters, etc), so this could increase the chance of disaster.
Effective altruists also have an implicit epistemology that differs from typical subject matter experts. If there are flaws in parts of the epistemology of EA or other groups involved in AI safety (which I believe to be true), then these flaws could be absorbed by future AI systems, and give rise to negative outcomes due to poor decisonmaking, etc.
People that are informed by AI safety arguments could decide that extreme actions are necessary to avoid AI doom, resulting in violence or rash actions that cause extreme harm.
The focus on AI safety in particular could result in less attention being paid to other threats to humanity that turn out to be more pressing, but are neglected due to the overrepresentation of AI safety people in the existential threat field.