AI Progress: A Bear Case ➹

Thane Ruthenis takes a long, detailed look at the latest round of AI hype, carefully dissecting every incremental advance and railing against the notion that bigger models automatically mean smarter or more generally capable systems.

A particular point I loved is that, for all the fanfare, many of these advancements merely shuffle around templates (and juggle user perception) rather than deliver genuine leaps in problem-solving. There’s a dry observation about how benchmarks and “vibe checks” often mask a deeper inability to push past known limits–a point well-made, even if it reads like a list of gripes on overblown promises.

I think this is well worth reading if you appreciate a sober technical critique, since it cuts against the hype that faster scaling and more compute will lead directly to transformative AI. Instead, it hints that what we’re really seeing is a series of elaborate rebrandings designed to maintain investor interest rather than deliver practical, autonomous intelligence.

I very much agree with nearly all of it.