Or you can invent better models or discover more efficient ways to train existing ones. You know - do something other than dumb scaling up - like what Hinton (backprop, 1987), Lecun (convnets, 1989), or Vaswani, et al. (transformers, 2017) did.
I mean, don't get me wrong, I'm all for improvements in AI efficiency, but maybe there isn't that much low-hanging fruit to pick? Tons of papers get published on transformers optimization techniques and barely any of them seem to stick.
This is exactly what people told OpenAI 8 years ago and look where we are now.