The people who are concerned about existential risk from near-term AGI don’t think it’s only a justified worry if you account for lives in the distant future. They think it’s a justified worry if you only account for people who already alive right now.

The argument AI safety work is more cost-effective than AMF when considering only the next few generations is pretty weak.

Rethink Priorities did a more comprehensive survey on models of future x-risk and population sizes.

Governance, financing, and supply chain interventions can be randomised at state or district level

While I agree this is true in theory, is it practical? I imagine the size needed to power such a study is prohibitive except for the largest organisations, the answer still wouldn't be definitive (eg due to generalisation concerns), and there would be lots of measurement issues (eg residents in one district crossing to another district with better funded healthcare).

If you tell me I'm wrong, I'd definitely bow to your experience and knowledge in this field but this isn't obviously true.

Do you have any advice for people like myself considering whether to go to you, 80k, or both for 1-1 career advice?

Thanks for this post @alex lawsen, I continually revisit this as inspiration and to remember the usefulness of this process when I am making hard decisions, especially for my career.

From your blog, I know you are a big user of LLMs. I was wondering if you had successfully used them to replace, or complement, this process? When I feed one my Google Doc, I find the output is too scattergun or vague to be useful, compared to sharing the same Doc with friends.

If you've succeeded using LLMs, would you please share what prompts and models that have worked well for you?

Thanks, that's very useful for me trying to follow but not that deep in the models!

I feel like the overall takeaway is very different though. I've not fully understood the details in either argument so this is a little vibes based. You seemed to be arguing that below subsistence wages were fairly likely while here it seems to be that even falling wages require a weird combination of conditions.

What have I misunderstood?

This seems like good work but the headline and opening paragraph aren't supported when you've shown it might be log-normal. Log-normal and power distributions often have quite different consequences for how important it is to move to very extreme percentiles, and hence this difference can matter for lots of decisions relevant to EA.

While Sam Bowman is interesting and seems ideologically similar to many EAs, it's not obvious to me why he's on the 80k podcast. Does 80k think UK housebuilding is one of the world's most pressing problems? Is there something else I'm missing?

Naively, this seems like a great fit for EAIF, which is looking to fund more projects. Is there something I'm missing?