I feel really weird about this article, because I at least spiritually agree with the conclusion and my lower bound on AGI extinction scenario is "automated industry grows 40%/y and at first everybody becomes filthy rich and in next decade necrosphere devours everything" and this scenario seems to be weirdly underdiscussed relatively to "AGI develops self-sustaining supertech and kills everyone within one month" by both skeptics and doomers. 

Thus said, I disagree with almost everything else. 

You underestimate effect of RnD automation because you seem to ignore that a lot of bottlenecks in RnD and adoption of results are actually human bottlenecks. Like, even if we automate everything in RnD which is not free scientific creativity, we are still bottlenecked by facts that humans can keep in working memory only 10 things and that multitasking basically doesn't exist and that you can perform several hours of hard intellectual work per day maximum and that you need 28 years to grow scientist and that there is only so many people actually capable to produce science and that science progresses one funeral at time, et cetera, et cetera. The same with adoption: even if innovation exists, there is only so many people actually capable to understand it and implement while integrating into existing workflow in maintainable way and they have only so many productive working hours per day.

Even if we dial every other productivity factor into infinity, there is a still hard cap on productivity growth from inherent human limitations and population. When we achieve AI automation, we substitute these limitations with what is achievable inside physical limitations. 

Your assumption "everybody thinks that RnD is going to be automated first because RnD is mostly abstract reasoning" is wrong, because actual reason why RnD is going to be automated relatively early is because it's very valuable, especially if you count opportunity costs. Every time you pay AI researcher money to automate narrow task, you waste money which could be spent on creating an army on 100k artificial researchers who could automate 100k tasks. I think this holds even if you assume pretty long timelines, because w.r.t. of mentioned human bottlenecks everything you can automate before RnD automation is going to be automated rather poorly (and, unlike science, you can't run self-improvement from here).

Thought it would be useful to pull out your plot and surrounding text, which seemed cruxy:

At first glance, the job of a scientist might seem like it leans very heavily on abstract reasoning... In such a world, AIs would greatly accelerate R&D before AIs are broadly deployed across the economy to take over more common jobs, such as retail workers, real estate agents, or IT professionals. In short, AIs would “first automate science, then automate everything else.”

But this picture is likely wrong. In reality, most R&D jobs require much more than abstract reasoning skills. ... To demonstrate this, we used GPT-4.5 to label tasks across 12 common R&D occupations into one of three categories, depending on whether it thinks the task can be performed using only abstract reasoning skills, whether it requires complex computer-use skills (but not physical presence), or whether it one needs to be physically present to complete the task. See this link to our conversation with GPT-4.5 to find our methodology and results.

This plot reveals a more nuanced picture of what scientific research actually entails. Contrary to the assumption that research is largely an abstract reasoning task, the reality is that much of it involves physical manipulation and advanced agency. To fully automate R&D, AI systems likely require the ability to autonomously operate computer GUIs, coordinate effectively with human teams, possess strong executive functioning skills to complete highly complex projects over long time horizons, and manipulate their physical environment to conduct experiments.

Yet, by the time AI reaches the level required to fully perform this diverse array of skills at a high level of capability, it is likely that a broad swath of more routine jobs will have already been automated. This contradicts the notion that AI will “first automate science, then automate everything else.” Instead, a more plausible prediction is that AI automation will first automate a large share of the general workforce, across a very wide range of industries, before it reaches the level needed to fully take over R&D.