I disagree. Sure let them play and see if they can improve. But this model has more compute and more training data than the predecessors it fails to surpass. That only means their training regime is inferior if their predecessors did so much more with so much less. That inferiority should not be encouraged.

It depends! If a startup is entering with a large model to face other larger models, it must be better at least in 1 meaningful dimension.

500B params performing worse than other OSS of the same size is pretty meaningless if no one will use it.