Agreed, thanks! I've moved that discussion down to timelines and probabilities.

I don't think I make the claim that a DSA is likely to be achieved by a human faction before AI takeover happens. My modal prediction (~58% as written in the post) for this whole process is that the AI takes over while the nations are trying to beat each other (or failing to coordinate). 

In the world where the leading project has a large secret lead and has solved superalignment (an unlikely intersection) then yes, I think a DSA is achievable.

Maybe a thing you're claiming is that my opening paragraphs don't emphasize AI takeover enough to properly convey my expectations of AI takeover. I'm pretty sympathetic to this point.

One analogy for AGI adoption I prefer over "tech diffusion a la the computer" is "employee turnover."

Assume you have an AI system which can do everything any worker could do, including walking around in an office, reading social cues, and doing everything else needed for an excellent human coworker.

Then, barring regulation or strong taste based preferences, any future hiring round will hire such a robot over a human. Then, the question of when most of the company are robots is just the question of when most of the workforce naturally turns over through hiring and firing, because all new incoming employees will be robots.

Of course, in this world, there wouldn't just be typical hiring rounds, and there would probably be massive layoffs of humans to replace humans with robots. But typical hiring rounds provide an upper bound on how long the process would take. If the only way the company to "adopt" AGI is to hire human-shaped things, then the AGI will be human-shaped.

This is not what automation will actually look like, it's just an upper bound on how long it'd take. In practice the time between ASI and 90% US unemployment ignoring regulation and x-risk would be more like 0-2 years because a superintelligence could come up with very quick plans to automate the economy, and the incentives will be much stronger than in typical hiring/firing decisions.

Another consideration is takeoff speeds: TAI happening earlier would mean further progress is more bottlenecked by compute and thus takeoff is slowed down. A slower takeoff enables more time for humans to inform their decisions (but might also make things harder in other ways).

The base models seem to have topped out their task length around 2023 at a few minutes (see on the plot that GPT-4o is little better than GPT-4).  Reasoning models use search to do better.

Note that Claude 3.5 Sonnet (Old) and Claude 3.5 Sonnet (New) have a longer time horizon than 4o: 18 minutes and 28 minutes compared to 9 minutes (Figure 5 in Measuring AI Ability to Complete Long Tasks). GPT-4.5 also has a longer time horizon.

Thanks for writing this.

Aside from maybe Nikola Jurkovic, nobody associated with AI 2027, as far as I can tell, is actually expecting things to go as fast as depicted.

I don't expect things to go this fast either - my median for AGI is in the second half of 2028, but the capabilities progression in AI 2027 is close to my modal timeline.

Note that the goal of "work on long-term research bets now so that a workforce of AI agents can automate it in a couple of years" implies somewhat different priorities than "work on long-term research bets to eventually have them pay off through human labor", notably:

1. The research direction needs to be actually pursued by the agents, either through the decision of the human leadership, or through the decision of AI agents that the human leadership defers to. This means that if some long-term research bet isn't respected by lab leadership, it's unlikely to be pursued by their workforce of AI agents.
   1. This implies that a major focus of current researchers should be on credibility and having a widely agreed-on theory of change. If this is lacking, then the research will likely never be pursued by the AI agent workforce and all the work will likely have been for nothing.
      1. Maybe there is some hope that despite a research direction being unpopular among lab leadership, the AI agents will realize its usefulness on their own, and possibly persuade the lab leadership to let them expend compute on the research direction in question. Or maybe the agents will have so much free reign over research that they don't even need to ask for permission to pursue new research directions.
2. Setting oneself up for providing oversight to AI agents. There might be a period during which agents are very capable at research engineering / execution but not research management, and leading AGI companies are eager to hire human experts to supervise large numbers of AI agents. If one doesn't have legible credentials or good relations with AGI companies, they are less likely to be hired during this period.
3. Delaying engineering-heavy projects until engineering is cheap relative to other types of work.

(some of these push in opposite directions, e.g., engineering-heavy research outputs might be especially good for legibility)

I expect the trend to speed up before 2029 for a few reasons:

1. AI accelerating AI progress once we reach 10s of hours of time horizon.
2. The trend might be "inherently" superexponential. It might be that unlocking some planning capability generalizes very well from 1-week to 1-year tasks and we just go through those doublings very quickly.

1. All of the above but it seems pretty hard to have an impact as a high schooler, and many impact avenues aren't technically "positions" (e.g. influencer)
2. I think that everyone expect "Extremely resilient individuals who expect to get an impactful position (including independent research) very quickly" is probably better off following the strategy.