(I can’t really tell if this post is trying to argue the overhang is increasing or just that there is some moderately sized overhang ongoingly.)
It has increased on some axes (companies are racing as fast as they can and the capital and research is by far LONG scaling), and reduced on some others (low-hanging fruits get plucked first).
The main point is that it is there and consistently under-estimated.
For instance, there are still massive returns to spending an hour on learning and experimenting with prompt engineering techniques. Let alone more advanced approaches.
This thus leads to a bias of over-estimating the safety of our systems, except if you expect that our evaluators are better elicitators than not only existing AI research engineers, but like, the ones over the next two, five or ten years.
It has increased on some axes (companies are racing as fast as they can and the capital and research is by far LONG scaling), and reduced on some others (low-hanging fruits get plucked first).
The main point is that it is there and consistently under-estimated.
For instance, there are still massive returns to spending an hour on learning and experimenting with prompt engineering techniques. Let alone more advanced approaches.
This thus leads to a bias of over-estimating the safety of our systems, except if you expect that our evaluators are better elicitators than not only existing AI research engineers, but like, the ones over the next two, five or ten years.