The issue is not just scaling compute, but scaling it in a rate that meets the increase in complexity of the problems that are not currently solved. If that is O(n) then what you say probably stands. If that is eg O(n^8) or exponential etc, then there is no hope to actually get good enough scaling by just increasing compute in a normal rate. Then AI technology will still be improving, but improving to a halt, practically stagnating.
o3 will be interesting if it offers indeed a novel technology to handle problem solving, something that is able to learn from few novel examples efficiently and adapt. That's what intelligence actually is. Maybe this is the case. If, on the other hand, it is a smart way to pair CoT within an evaluation loop (as the author hints as possibility) then it is probable that, while this _can_ handle a class of problems that current LLMs cannot, it is not really this kind of learning, meaning that it will not be able to scale to more complex, real world tasks with a problem space that is too large and thus less amenable to such a technique. It is still interesting, because having a good enough evaluator may be very important step, but it would mean that we are not yet there.
o3 will be interesting if it offers indeed a novel technology to handle problem solving, something that is able to learn from few novel examples efficiently and adapt. That's what intelligence actually is. Maybe this is the case. If, on the other hand, it is a smart way to pair CoT within an evaluation loop (as the author hints as possibility) then it is probable that, while this _can_ handle a class of problems that current LLMs cannot, it is not really this kind of learning, meaning that it will not be able to scale to more complex, real world tasks with a problem space that is too large and thus less amenable to such a technique. It is still interesting, because having a good enough evaluator may be very important step, but it would mean that we are not yet there.
We will learn soon enough I suppose.