Updates from METR report: short timelines vs IE

It's important to distinguish between two different takeaways you could have from the recent METR report: 1. Shorter timelines: AI will soon fully automate AI R&D 2. Intelligence explosion more likely: Once AI fully automates AI R&D, AI progress will accelerate until we get superintelligence I will walk through my tentative views on each.  Strong update towards shorter timelines I think this is pretty clear. AI is already much better at AI research than I had realized and it is improving at a faster rate than I had realized.  So we're less far to go to full automation than I thought and we're moving towards it more quickly.  Moderate update towards an intelligence explosion. This one is more complicated. The key questions is about whether the feedback loop of AI improving AI is strong enough to accelerate over time, even holding compute fixed. We can break this feedback loop down as follows: more ECI -> more R&D labour -> more total R&D effort -> more ECI -> ... Now, let's assess what we can learn about each stage of this feedback loop. Stage 1: more ECI -> more R&D labour When you increase ECI, how much more quality adjusted labour do you get at AI R&D?  Well, recently the amount of quality-adjusted labour increased pretty fast! A moderate increase in ECI led to a big increase in labour.  So stage 1 is stronger than I used to think. Stage 2: more R&D labour -> more total R&D effort Can  improved labour have the same effect as increasing both compute and labour? Or does more labour not make much difference because compute experiments become a big bottleneck?  Well, we've already had a ~2x speed from more labour without getting bottlenecked by compute. That rules out strong "bottleneck" views that I've seen economists endorse.[1] But it doesn't rule out compute bottlenecks biting a bit later on. In my opinion, these were always the most plausible bottleneck views.  It also doesn't speak to the possibility that much algorithmic progress depends on sca