What makes sense is to do both.
Race down the transformer highway as fast as possible and explore other AI methods.
One need not preclude the other.
My understanding is that the resources are limited and even AI labs with all the funding in the world don't have enough hardware.
Except doing both..is..so..hard when the future payoff of one is more unclear and you are faced with quarterly earnings reports.
Look at what happened to Meta where they squandered a lead, had some mixed results, and reorged to more product certainty and away from fundamental research.
as fast as possible implies spending the resources that way.
Going close to as fast as possible while saving a stream for the other approaches makes sense as an approach, but let's not ignore that there's a compromise/tradeoff here. Might not matter in orgs with near infinite resources