In general I have about zero enthusiasm for trying to find defensible interpretations of things that Ed Zitron said, and I generally agree that the name of Zitr

In general I have about zero enthusiasm for trying to find defensible interpretations of things that Ed Zitron said, and I generally agree that the name of Zitron just largely needs to stop coming up in anti- and anti-anti-AI arguments since, it seems, he's just not a particularly insightful or reliable voice on the subject. That said, one or two of the specific assessments in Luu's article seem dubious as well, especially this one: > August 2025 https://www.wheresyoured.at/how-to-argue-with-an-ai-booster/ https://www.wheresyoured.at/how-to-argue-with-an-ai-booster/ : "These models have clearly hit a wall where training is hitting diminishing returns" > Wrong It was my understanding—and I'm no expert, so if someone does know better please correct me!—that indeed by the second half of 2025 training, and also post-training reinforcement-learning stuff, both hit seriously diminishing returns, and the thing that is continuing to scale well or pretty well is inference. See eg. https://www.tobyord.com/writing/mostly-inference-scaling https://www.tobyord.com/writing/mostly-inference-scaling . And in fact in the quoted and linked article https://www.wheresyoured.at/how-to-argue-with-an-ai-booster/ https://www.wheresyoured.at/how-to-argue-with-an-ai-booster/ Zitron comes up with something which looks like a recognisable explanation of this: > Because model developers hit a wall of diminishing returns, and the only way to make their models do more was to make them burn more tokens to generate a more accurate response (this is a very simple way of describing reasoning, a thing that OpenAI launched in September 2024 and others followed). > As a result, all the "gains" from "powerful new models" come from burning more and more tokens. AFAICT the other drivers of recent progress in LLMs have been: ploughing in lots and lots of specialised training data custom-made at piecework websites https://www.youtube.com/watch?v=4pG3SJQPAwk https://www.youtube.com/watch?v=4pG3SJQPAwk ; and work on harnesses and the like. AFAICT neither of those makes false the claim that "[t]hese models have clearly hit a wall where training is hitting diminishing returns" either. Similarly, even if some big new advance does cause training or post-training to start scaling like gangbusters again in 2027 or 2028 that wouldn't make the quoted statement clearly wrong: Zitron would clearly like you to infer that there won't be any further big advances soon in LLM training, but the quoted statement doesn't clearly make that claim. (Even if he had made that claim, and it did turn out to be wrong, it would be a relatively forgivable error, more on the "cloudy crystal ball" than "misstates currently known facts" end of the spectrum.) So: it seems that Luu took a fairly specific, objectively judgeable claim from Ed Zitron; and that claim was ... correct?; and Luu instead rated it "Wrong" without further elaboration. It seems that Luu interpreted the quoted claim as saying something like "model progress has ceased"; but it seems that's not what that specific claim (as opposed to whatever other things Zitron has said at other times and places) said.