not sure how many will get this reference but "AI" for science and math is like super-shoes for runners
at first we are blown away by the impossible improvements including sub-2-hour realworld marathon and every other PR/CR/WR is dialed down
but then the improvements slow and reach a stall point because of the limit of technology and the source of the achievement
ie. sub-2-hour marathon yes, sub-1-hour never happening (rollerblade inline-skate record is 1-hour marathon)
The fact we see a lift is not the same as evidence that the lift is unbounded.
The lift being finite is supported by the fact improvements have come at the edges: improvements from human feedback, improvements in harnesses, improvements on model compatibility with harnesses, improvements in inference efficiency with new architectures, etc. If we were just training better models from scratch that would be one thing, but we are just making better use of a tool we've developed.
As a programmer, I am mostly interested in whether my role is sustainable long-term and whether the models will get better. I don't feel in jeopardy yet, but two more years like this and the calculus of hiring software engineers could shift even further. QAs are already overwhelmed with work
but with super-shoes more and more runners are qualifying for boston marathon and even olympic trials marathon where it would have been impossible for them previously
and that's what "AI" currently does, it allows average people to immediately "pick the brain" of every expert in every field, in every scientific paper, without previously reading a single other google result, something that would have been impossible for them previously (super-shoes for the brain? too far?)
but "AI" isn't creating new knowledge, it's just stitching together existing knowledge from patterns that would have taken years by human hand if even possible at all, it's going to "hit the wall" eventually (in its current form)
basically everything Benjamin Franklin did was trial and error because no-one understood what electricity was in the slightest
almost everything Edison did was trial and error too, he had his lab try thousands of materials for his long lasting lightbulb filament
even the most advanced "AI" today is just machine-learning going through everything already known trying to piece together previously discovered facts, admittedly at levels and detail impossible by human hands
but that means there are limits and it's not really "AI"