Preparing Effective Altruism for an AI-Transformed World

Tobias Häberli

[This post was written quickly and presents the idea in broad strokes. I hope it prompts more nuanced and detailed discussions in the future.]

In recent years, many in the Effective Altruism community have shifted to working on AI risks, reflecting the growing consensus that AI will profoundly shape our future.

In response to this significant shift, there have been efforts to preserve a "principles-first EA" approach, or to give special thought into how to support non-AI causes. This has often led to discussions being framed around "AI Safety vs. everything else". And it feels like the community is somewhat divided along the following lines:

Those working on AI Safety, because they believe that transformative AI is coming.
Those focusing on other causes, implicitly acting as if transformative AI is not coming.^[1]

Instead of framing priorities this way, I believe it would be valuable for more people to adopt a mindset that assumes transformative AI is likely coming and asks: What should we work on in light of that?

If we accept that AI is likely to reshape the world over the next 10–15 years, this realisation will have major implications for all cause areas. But just to start, we should strongly ask ourselves: "Are current GHW & animal welfare projects robust to a future in which AI transforms economies, governance, and global systems?" If they aren't, they are unlikely to be the best use of resources.

 Importantly, this isn't an argument that everyone should work on AI Safety. It's an argument that all cause areas need to integrate the implications of transformative AI into their theory of change and strategic frameworks. To ignore these changes is to risk misallocating resources and pursuing projects that won't stand the test of time.

^{^}
Important to note: Many people believe that AI will be transformative, but choose not to work on it due to factors such as (perceived) lack of personal fit or opportunity, personal circumstances, or other practical considerations.

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Tobias HäberliJan 2213

One GHW example: The impact of AI tutoring on educational interventions (via Arjun Panickssery on LessWrong).

There have been at least 2 studies/impact evaluations of AI tutoring in African countries finding extraordinarily large effects:

Summer 2024 — 15–16-year olds in Nigeria
They had 800 students total. The treatment group studied with GPT-based Microsoft Copilot twice weekly for six weeks, studying English. They were just provided an initial prompt to start chatting—teachers had a minimal “orchestra conductor” role—but they achieved “the equivalent of two years of typical learning in just six weeks.”

February–August 2023 — 8–14-year-olds in Ghana
An educational network called Rising Academies tested their WhatsApp-based AI math tutor called Rori with 637 students in Ghana. Students in the treatment group received AI tutors during study hall. After eight months, 25% of the subjects attrited from inconsistent school attendance. Of the remainder, the treatment group increased their scores on a 35-question assessment by 5.13 points versus 2.12 points for the control group. This difference was “approximately equivalent to an extra year of learning” for the treatment group.

Should this significantly change how excited EAs are about educational interventions? I don't know, but I've also not seen a discussion of this on the forum (this post about MOOC & AI tutors that received ~zero engagement).

Kevin Xia 🔸Jan 2210

I think you make a really important point! You/anyone else interested in this may be interested in talking to @Constance Li and her work with @AI for Animals (Website)

ForumiteJan 228

Point 1: Broad agreement with a version of the original post's argument

Thanks for this. I think I agree with you that people in the global health and animal spaces should, at the margin, think more about the possibility of Transformative AI (TAI), and short-timeline TAI.

For animal-focussed people, maybe there’s an argument that because the default path of a non-TAI future is likely so bad for animals (eg persuading people to stop eating animals is really hard, persuading people to intervene to help wild animals is really hard, etc), that we might, actually, want to heavily “bet” on futures *with* TAI, because it’s only those futures which hold out the prospect of a big reduction in animal suffering. So we should optimise our actions for worlds where TAI happens, and try to maximise the chances that these futures go very well for non-human animals.

I think this is likely less true for global health and wellbeing, where plausibly the global trends look a lot better.

Point 2: Some reasons to be sceptical about claims of short-timeline Transformative AI

Having said that, there’s something about the apparent certainty that “TAI is nigh” in the original post, which prompted me to want to scribble down some push-back-y thoughts. Below are some plausible-sounding-to-me reasons to be sceptical about high-certainty claims that TAI is close. I don’t pretend that these lines of thought in-and-of-themselves demolish the case for short-timeline TAI, but I do think that they are worthy of consideration and discussion, and I’d be curious to hear what others make of them:

The prediction of short-timeline TAI is based on speculation about the future. Humans very often get this type of speculation wrong.
Global capital markets aren’t predicting short-timeline TAI. A lot of very bright people, who are highly incentivised to make accurate predictions about the future shape of the economy, are not betting that TAI is imminent.
Indeed, most people in the world don’t seem to think that TAI is imminent. This includes tonnes of *really* smart people, who have access to a lot of information.
There’s a rich history of very clever people making bold predictions about the future, based on reasonable-sounding assumptions and plausible chains of reasoning, which then don’t come true - e.g. Paul Ehrlich’s Population Bomb.
Sometimes, even the transhumanist community - where notions of AGI, AI catastrophe risk, etc, started out - get excited about a certain technological risk/trend, but then it turns out not to be such a big deal - e.g. nanotech, “grey goo”, etc in the ‘80s and ‘90s.
In the past, many radical predictions about the future, based on speculation and abstract chains of reasoning, have turned out to be wrong.
Perhaps there’s a community effect whereby we all hype ourselves up about TAI and short timelines. It’s exciting, scary and adrenaline-inducing to think that we might be about to live through ‘the end of times’.
Perhaps the meme of “TAI is just around the corner/it might kill us all” has a quality which is psychologically captivating, particularly for a certain type of mind (eg people who are into computer science, etc); perhaps this biases us. The human mind seems to be really drawn to “the end is nigh” type thinking.
Perhaps questioning the assumption of short-timeline TAI has become low-status within EA, and potentially risky in terms of reputation, funding, etc, so people are disincentivised to push back on it.

To restate: I don’t think any of these points torpedo the case for thinking that TAI is either inevitable, and/or imminent. I just think they are valid considerations when thinking about this topic, and are worthy of consideration/discussion, as we try to decide how to act in the world.

Tobias HäberliJan 223

Thanks for the thoughtful comment!

Re point 1: I agree that the likelihood and expected impact of transformative AI exist on a spectrum. I didn’t mean to imply certainty about timelines, but I chose not to focus on arguing for specific timelines in this post.

Regarding the specific points: they seem plausible but are mostly based on base rates and social dynamics. I think many people’s views, especially those working on AI, have shifted from being shaped primarily by abstract arguments to being informed by observable trends in AI capabilities and investments.

ForumiteJan 221

Cheers, and thanks for the thoughtful post! :)

I'm not sure that the observable trends in current AI capabilities definitely point to an almost-certainty of TAI. I love using the latest LLMs, I find them amazing, and I do find it plausible that next-gen models, plus making them more agent-like, might be amazing (and scary). And I find it very, very plausible to imagine big productivity boosts in knowledge work. But the claim that this will almost-certainly lead to a rapid and complete economic/scientific transformation still feels at least a bit speculative, to me, I think...

Will AldredJan 227

+1. I appreciated @RobertM ’s articulation of this problem for animal welfare in particular:

I think the interventions for ensuring that animal welfare is good after we hit transformative AI probably look very different from interventions in the pretty small slice of worlds where the world looks very boring in a few decades.
…
If we achieve transformative AI and then don’t all die (because we solved alignment), then I don’t think the world will continue to have an “agricultural industry” in any meaningful sense (or, really, any other traditional industry; strong nanotech seems like it ought to let you solve for nearly everything else). Even if the economics and sociology work out such that some people will want to continue farming real animals instead of enjoying the much cheaper cultured meat of vastly superior quality, there will be approximately nobody interested in ensuring those animals are suffering, and the cost for ensuring that they don’t suffer will be trivial.
[...] if you think it’s at all plausible that we achieve TAI in a way that locks in reflectively-unendorsed values which lead to huge quantities of animal suffering, that seems like it ought to dominate effectively all other considerations in terms of interventions w.r.t. future animal welfare.

I’ve actually tried asking/questioning a few animal welfare folks for their takes here, but I’ve yet to hear back anything that sounded compelling (to me). (If anyone reading this has an argument for why ‘standard’ animal welfare interventions are robust to the above, then I’d love to hear it!)

David_MossJan 226

Many people believe that AI will be transformative, but choose not to work on it due to factors such a (perceived) lack of personal fit or opportunity, personal circumstances, or other practical considerations.

There may be various other reasons why people choose to work on other areas, despite believing transformative AI is very likely, e.g. decision-theoretic or normative/meta-normative uncertainty.

Tobias HäberliJan 224

Thanks for adding this! I definitely didn’t want to suggest the list of reasons was exhaustive or that the division between the two 'camps' is clear-cut.

BerkeJan 225

Strong upvote! I want to say some stuff particularly within the context of global development:

The intersection of AI and global development seems surprisingly unsaturated within EA, or to be more specific, I think a surprisingly few number of EAs think about the following questions:

i) How to leverage AI for development (e.g. AI tools for education, healthcare)
ii) What interventions and strategies should be prioritized within global health and development in the light of AI developments? (basically the question you ask)

There seems to be a lot of people thinking about the first question outside of EA, so maybe that explains this dynamic, but I have the "hunch" that the primary reason why people don't focus on the first question too much is people deferring too much and selection effects, rather lack of any high-impact interventions. If you care about TAI, you are very likely to work on AI alignment & governance, if you don't want to work on TAI-related things (due to risk-aversion or any other argument/value), you just don't update that much based on AI developments and forecasts. This may also have to do with EA's ambiguity-averse/risk-averse attitude towards GHD characterized by exploiting evidence-based, interventions rather than exploring new highly promising interventions. I think if a student/professional were to come to an EA community-builder and asked "How can I pursue a high-impact career in/upskill in global health R&D or AI-for-development", number of community-builders that can give a sufficiently helpful answer is likely very few to none, I also likely wouldn't be able to give a good answer and point to communities/resources outside of the EA community.

(Maybe EAs in London or SF people discuss these, but I don't see any discussion of it online, neither do I see any spaces where people who could be discussing these can network/discuss together. If there is anyone who'd like to help create or run an online or in-person AI-for-development or global health R&D fellowship, feel free to shoot a message)

Chris LeongJan 224

I gave this a strong upvote because regardless of whether or not you agree with these timelines or Tobias' conclusion, this is a discussion that the community needs to be having.

I would love to see someone running a course focusing on this (something broader than the AI Safety Fundamentals course). Obviously this is speculative, but I wouldn't be surprised if the EA Infrastructure Fund were interested in funding a high-quality proposal to create such a course.