Data processing, filtering and quality, not architecture, is the number one determinant of how good a language model turns out.

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Our reading — they may agree, disagree or merely touch the same thing. Closest first: a shared subject counts for most, then how near the wording is.

• What sits in a model's context is far clearer to it than its training data, which is trillions of tokens stirred into a soup of parameters. JDJeff Dean30 Jul 2026similar wording
• Large language models start in exactly the wrong place, because they try to get by without a goal and therefore without any sense of better or worse. RSRichard Sutton26 Sept 2025similar wording
• The language best suited to AI is simply the one the models have seen most of in training, which is why they do so well on JavaScript, TypeScript and Python. AHAnders Hejlsberg13 May 2026similar wording
• Language choice is a temporary artefact of training data; in the fullness of time every language will work equally well with AI. SYSteve Yegge11 Mar 2026similar wording
• Every machine-learning deployment that has paid off so far left a person making the decision, which is the only reason imperfect models were useful. CFChelsea Finn12 Aug 2026similar wording

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