fair points. i was probably overstating the subsidy argument as if its some trap that snaps shut. compute costs do trend down and if moores law dynamics still roughly apply to accelerators then even a 10x gap closes in a few years. the analog/ASIC weight-baking point is interesting too, hadnt considered that as a near term efficiency path but makes sense given how low the precision floor is

where id still push back is that "inference becomes cheap" and "ai replaces good engineers" are two different claims. inference getting cheap means the floor tier of ai-assisted work becomes very accessible. but the gap between what claude can do as a fresh graduate (generous but i see what you mean) and what a senior with deep system context can do isnt a cost problem its a capability problem. 100x cheaper inference doesnt close that gap, it just means more cheap output faster

on interest rates yeah thats basically my argument stated more precisely. rates went up, cheap capital disappeared, companies that hired assuming perpetual growth had to correct. "ai efficiency" just plays better on an earnings call than "we overexpanded during ZIRP"

your last point about individuals vs big teams is actually a stronger version of the articles argument than the article itself makes. if most valuable work is done by individuals or small groups then the real loss from layoffs isnt headcount, its losing the specific 5-10 people who actually understood the system. the bloat was arguably already dead weight. but the cut doesnt discriminate, it takes both