I think soon we'll be pricing any kind of tasks by their compute costs. So basically, human = $50/task, AI = $6,000/task, use human. If AI beats human, use AI? Ofc that's considering both get 100% scores on the task
The thing is given what we've seen from distillation and tech, even if its 6,000/task... that will come down drastically over time through optimization and just... faster more efficient processing hardware and software.
That makes something like this competitive in ~3 years
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.
And o1 cost $15/$60 for 1M in/out, so the estimated costs on the graph would match for a single task, not the whole benchmark.