Because the moment you slow down investing is the moment you realize you wasted all your previous investment in the technology. It's just sunk cost fallacy.
As the theory goes, there is a network effect forming. Early AI products with strong adoption get the users to implicitly and explicitly label data. This leads to improvements that are a moat due to “data flywheel”
So there’s a “first mover” type advantage because you can’t get that usage data anywhere else - not even paying participants - because there exists a domain gap between offline and online measurement.
There is a reasonable counter argument of “the bitterest lesson” which says all that matters is the improvements in foundational models.
Because the moment you slow down investing is the moment you realize you wasted all your previous investment in the technology. It's just sunk cost fallacy.
Anybody want to summarize for those of us who don't feel like shelling out over $500/yr for a WSJ subscription?
As the theory goes, there is a network effect forming. Early AI products with strong adoption get the users to implicitly and explicitly label data. This leads to improvements that are a moat due to “data flywheel”
So there’s a “first mover” type advantage because you can’t get that usage data anywhere else - not even paying participants - because there exists a domain gap between offline and online measurement.
There is a reasonable counter argument of “the bitterest lesson” which says all that matters is the improvements in foundational models.
https://archive.is/ptVBA
the tl;dr is "reasoning models".
And to be more specific, the fact that reasoning models have outputs orders of magnitude longer than other models.
It writes more text so it spends more compute.