I'm interning there and I conducted a poll.

The median AGI timeline of more than half of METR employees is before the end of 2030.

(AGI is defined as 95% of fully remote jobs from 2023 being automatable.)

I think if the question is "what do I do with my altruistic budget," then investing some of it to cash out later (with large returns) and donate much more is a valid option (as long as you have systems in place that actually make sure that happens). At small amounts (<$10M), I think the marginal negative effects on AGI timelines and similar factors are basically negligible compared to other factors.

Thanks for your comment. It prompted me to add a section on adaptability and resilience to the post.

I sadly don't have well-developed takes here, but others have pointed out in the past that there are some funding opportunities that are systematically avoided by big funders, where small funders could make a large difference (e.g. the funding of LessWrong!). I expect more of these to pop up as time goes on. 

Somewhat obviously, the burn rate of your altruistic budget should account for altruistic donation opportunities (possibly) disappearing post-ASI, but also account for the fact that investing and cashing it out later could also increase the size of the pot. (not financial advice)

(also, I have now edited the part of the post you quote to specify that I don't just mean financial capital, I mean other forms of capital as well)

Time in bed

I'd now change the numbers to around 15% automation and 25% faster software progress once we reach 90% on Verified. I expect that to happen by end of May median (but I'm still uncertain about the data quality and upper performance limit). 

(edited to change Aug to May on 12/20/2024)

I recently stopped using a sleep mask and blackout curtains and went from needing 9 hours of sleep to needing 7.5 hours of sleep without a noticeable drop in productivity. Consider experimenting with stuff like this.

Note that this is a very simplified version of a self-exfiltration process. It basically boils down to taking an already-working implementation of an LLM inference setup and copying it to another folder on the same computer with a bit of tinkering. This is easier than threat-model-relevant exfiltration scenarios which might involve a lot of guesswork, setting up efficient inference across many GPUs, and not tripping detection systems.

One weird detail I noticed is that in DeepSeek's results, they claim GPT-4o's pass@1 accuracy on MATH is 76.6%, but OpenAI claims it's 60.3% in their o1 blog post.  This is quite confusing as it's a large difference that seems hard to explain with different training checkpoints of 4o.