"Brute force" is only held back by economics and hardware limitations.

There are still massive gains to be had from scaling up - but frontier training runs have converged on "about the largest model that we can fit into our existing hardware for training and inference". Going bigger than that comes with non-linear cost increases. The next generations of AI hardware are expected to push that envelope.

The reason why major AI companies prioritize things like reasoning modes and RLVR over scaling the base models up is that reasoning and RLVR give real world performance gains cheaper and faster. Once scaling up becomes cheaper, or once the gains you can squeeze out of RLVR deplete, they'll get back to scaling up once again.