Karpathy's comments on the Sutton/Dwarkesh podcast (twitter.com)

I wasn’t able to retrieve Karpathy’s actual comments because the source (x.com) blocked access in this browser — the page requires JavaScript and wouldn’t render, so there’s no transcript or quote available to summarize. If you can paste the podcast transcript or a link that loads directly, I’ll produce a concise, accurate summary. For transparency: I’m not fabricating or inferring specific statements from the Sutton/Dwarkesh episode. That said, when Andrej Karpathy speaks on high-profile AI podcasts, the remarks typically matter to researchers and practitioners because they often touch on model scaling, training dynamics, interpretability, and deployment trade-offs. Expect commentary that could influence thinking about architecture choices (transformer variants, sparsity), training regimes (scaling laws, data curation, compute efficiency), and safety/robustness practices (evaluation benchmarks, alignment strategies). Such observations can alter research priorities, inform engineering decisions, and shape public discussion around open weights vs proprietary models. If you provide the episode text, I’ll convert it into a 2–3 paragraph technical summary highlighting the concrete claims, evidence offered, and practical implications for the AI/ML community.