Time is not a very interesting dimension here, because humans don't use the same CPU as huge GPU clusters. The binary "is it able to reach a solution given enough resources?" is more interesting (for GPT/Claude the answer is a clear negative).
If these problems were important to solve, redundantly by thousands or millions of people (like the real work that most people do), far more people would put in the effort to learn how to solve these problems.
It's just a weird comparison. Contests are very artificially structured in ways that don't make sense for comparing to computers.
Now, you may say how time is not a useful dimension here, but really, this is where we are seeing a lot of these advances come from: general researchers today do get access to huge compute capability, allowing them to quickly iterate on different ideas. In a sense, they can be less smart about their use of resources and simply try things out: this does drive the innovation (compared to waiting for their turn on a supercomputer nearby).
And finally, time is essential even for humans: given a couple hundred years, they will find the proof for Fermat's last theorem, but they might not do it in 4.5h. Since we are comparing AI capabilities to humans in the article, it's very possible that increased compute will never allow AI to find novel proofs we have not come up with either. That's where the AI bit comes in: we know that brute searching through the entire possible space of proofs is still too expensive for our compute capabilities, so we need AI to emulate our "intuition" when looking for the direction to narrow down the search.
So there are really two reasons time matters: 1. getting enough of compute might still be far away (heck, prime factorization and elliptic curves are still the basis of the most of world cryptography for that reason) and 2. maybe it's not even enough to increase compute capability to make huge jumps in problem solving capabilities (iow, maybe we are reaching maximum of where the approach can take us).
In other words model which can solve any/most school / college exam problem isn't necessarily smart. It can be just a database, or, in fact, a lookup table. Smarter version can be a lookup + 1 step test. Not saying it's bad, but it doesn't scale to less formalized domains. BTW, in this case formalization was done by humans.