I wrote something on a related idea a while back:

Disincentivizing deception in mesa optimizers with Model Tampering [LW · GW]

not sure if that would be legit for gift aid purposes

Based on https://www.gov.uk/government/publications/charities-detailed-guidance-notes/chapter-3-gift-aid#chapter-344-digital-giving-and-social-giving-accounts, doing this the naive way (one person collects money and gives it to the charity, everyone shares the tax rebate) is explicitly forbidden. Which makes sense, or everyone with a high income friend would have access to the 40% savings.

Not clear to me whether it would be allowed if the charity is in on it and everyone fills in their own gift aid declaration. But that's extra steps for all parties.

The main difference in my mind is that a human can never be as powerful as potential ASI and cannot dominate humanity without the support of sufficiently many cooperative humans. For a given power level, I agree that humans are likely scarier than an AI of that power level. The scary part about AI is that their power level isn't bounded by human biological constraints and the capacity to do harm or good is correlated with power level. Thus AI is more likely to produce extinction-level dangers as tail risk relative to humans even if it's more likely to be aligned on average.

Thanks for writing this. I think this topic is generally a blind spot for LessWrong users, and it's kind of embarrassing how little thought this community (myself included) has given to the question of whether a typical future with human control over AI is good.

(This actually slightly broadens the question, compared to yours. Because you talk about "a human" taking over the world with AGI, and make guesses about the personality of such a human after conditioning on them deciding to do that. But I'm not even confident that AGI-enabled control of the world by e.g. the US government would be good.)

Concretely, I think that a common perspective people take is: "What would it take for the future to go really really well, by my lights", and the answer to that question probably involves human control of AGI. But that's not really the action-relevant question. The action-relevant question, for deciding whether you want to try to solve alignment, is how the average world with human-controlled AGI compares to the average AGI-controlled world. And... I don't know, in part for the reasons you suggest.

Yeah, on further thought I think you're right. This is pretty pessimistic then, AI companies will find it easy to align AIs to money interests, and the rest of us will be in a "natives vs the East India Company" situation. More time to spend on alignment then matters only if some companies actually try to align AIs to something good instead, and I'm not sure any companies will do that.

How do you know that humans and LLMs/current RL agents do optimize the reward? Are there any known theorems or papers on this, because this claim is at least a little bit important.

You may answer here:

https://www.lesswrong.com/posts/GDnRrSTvFkcpShm78/when-is-reward-ever-the-optimization-target [LW · GW]

This is consistent with a model where AI alignment is heavily dependent on the data, and way less dependent on inductive biases/priors, so this is good news for alignment.

In one specific respect I'd like to challenge your point. I think fine-tuning models currently aligns them 'well-enough' to any target point of view. I think that the ethics shown by current LLMs are due to researchers actively putting them there. I've been doing red teaming exercises on LLMs for over a year now, and I find it quite easy to fine-tune them to be evil and murderous. Human texts help them understand morality, but don't make them care enough about it for it to be sticky in the face of fine-tuning.

It is an interesting paper (excerpts, related excerpts), but to temper expectations: like Peter Leeson's many papers 'explaining' this or that weird thing in history like trial-by-ordeal, this is more of a just-so story in which Allen comes up with a plausible-sounding model which he post hoc fits to some interesting historical details or anecdotes, and he doesn't present anything we would consider 'hard' evidence.

Stargate is not 2025, and going from $50bn to $80bn is in line with building a $25-40bn training system this year (an unusual expense distinct from other projects), a clue separate from SemiAnalysis claims.