Thanks for doing this. This question is marked as required but I think should either be optional or have a "none" option:
 

Thanks, that's helpful. Do you have a sense of where we are on the current S-curve? E.g., if capabilities continue to progress in a straight line through the end of this year, is that evidence that we have found a new S-curve to stack on the current one?

the strength of this tail-wind that has driven much of AI progress since 2020 will halve

I feel confused about this point because I thought the argument you were making implies a non-constant "tailwind." E.g. for the next generation these factors will be 1/2 as important as before, then the one after that 1/4, and so on. Am I wrong?

Interesting ideas! For Guardian Angels, you say "it would probably be at least a major software project" - maybe we are imagining different things, but I feel like I have this already. 

e.g. I don't need a "heated-email guard plugin" which catches me in the middle of writing a heated email and redirects me because I don't write my own emails anyway. I would just ask an LLM to write the email and 1) it's unlikely that the LLM would say something heated and 2) for the kinds of mistakes that LLMs might make, it's easy enough to put something in the agents.md to ask it to check for these things before finalizing the draft.

(I think software engineering might be ahead of the curve here, where a bunch of tools have explicit guardian angels. E.g. when you tell the LLM "build feature X", what actually happens is that agent 1 writes the code, then agent 2 reviews it for bugs, agent 3 reviews it for security vulns, etc.)

Related, from an OAI researcher.

The AI Eval Singularity is Near

• AI capabilities seem to be doubling every 4-7 months
• Humanity's ability to measure capabilities is growing much more slowly
• This implies an "eval singularity": a point at which capabilities grow faster than our ability to measure them
• It seems like the singularity is ~here in cybersecurity, CBRN, and AI R&D (supporting quotes below)
• It's possible that this is temporary, but the people involved seem pretty worried

Appendix - quotes on eval saturation

Opus 4.6

• "For AI R&D capabilities, we found that Claude Opus 4.6 has saturated most of our
  automated evaluations, meaning they no longer provide useful evidence for ruling out ASL-4 level autonomy. We report them for completeness, and we will likely discontinue them going forward. Our determination rests primarily on an internal survey of Anthropic staff, in which 0 of 16 participants believed the model could be made into a drop-in replacement for an entry-level researcher with scaffolding and tooling improvements within three months."
• "For ASL-4 evaluations [of CBRN], our automated benchmarks are now largely saturated and no longer provide meaningful signal for rule-out (though as stated above, this is not indicative of harm; it simply means we can no longer rule out certain capabilities that may be pre-requisities to a model having ASL-4 capabilities)."
• It also saturated ~100% of the cyber evaluations

Codex-5.3

• "We are treating this model as High [for cybersecurity], even though we cannot be certain that it actually has these capabilities, because it meets the requirements of each of our canary thresholds and we therefore cannot rule out the possibility that it is in fact Cyber High."

Thanks for the article! I think if your definition of "long term" is "10 years," then EAAs actually do often think on this time horizon or longer, but maybe don't do so in the way you think is best. I think approximately all of the conversations about corporate commitments or government policy change that I have been involved in have operated on at least that timeline (sadly, this is how slowly these areas move). 

For example, you can see this CEA where @saulius projects out the impacts of broiler campaigns a cool 200 years, and links to estimates from ACE and AIM which use a constant discount rate.

Ah yeah good point, I updated the text.