This is absolutely correct.

But I would subtly add it's also about the training data, which is optimized towards English. Not intentionally, just incidentally. (And this is a really hot area of improvement with a long way to go.)

Which is largely why the tokenization part that you mentioned is what it is.

My understanding (which may be wrong) is that English is best but most of the Romance languages do okay because English has borrowed from them so much.

I think you only start getting seriously bad impacts in non-Romance languages where they have their own alphabet, or use the English alphabet phonetically causing letters to appear next to each other that never would in English. E.g. in English "qu" is a pretty obvious token, you rarely see a Q without a U after it. Happens all the time in anglicized Middle Eastern words, though. Quran is like a whole other thing, you'd almost always expect another vowel after qu, not a consonant.

I've heard English is one of the most expressive languages, but is complex to learn due to absorbing words and ideas from so many other languages. Lots of contradictions, situational rules, and unwritten rules (like the order of adjectives).

Given the complexities involved, maybe we should be impressed by someone who masters even one!

Can't you train a model to translate tokens from human languages to a separate NN token language with a compressed representation like a VAE? It seems like a leaky abstraction to have these trillion parameter models be dependant on the fact that English gets tokenized in a certain way, and I assume this has been researched a lot already.

Having studied Japanese at university and living there for a bit my interest naturally flowed in that direction, so I ran a quick check on OpenAI's tokeniser, nothing close to an actual study. I ran two tests of everyday English and its Japanese equivalent and another using more technical vocabulary across both languages. These were my results: Tokeniser Everyday JP/EN Technical JP/EN GPT-3 2.69x (183/68) 3.48x (233/67) GPT-4 2.16x (147/68) 2.53x (167/66) GPT-5.x 1.49x (101/68) 1.74x (115/66) It shows that the gap clearly has shrunk a fair bit with Japanese going from 2.7x English on GPT-3 to 1.5x on GPT-5.x for everyday text. The same passage of text yielded flat results for English across each tokeniser, so all the improvement went into Japanese. I also found that the Japanese polite form used to cost extra on GPT-3 but is now effectively free. The polite version of the Japanese passage was 9 characters longer (133 vs 124), and contained endings such as ました, ません and でした which have all become single tokens in newer tokenisers. Perhaps most interestingly, katakana loanwords seemed to add a lot of extra cost. For example, a word like パスワード(password) is split into four tokens (パ / ス / ワ / ード), while "password" is one token in English. In this example, the exact katakana string likely did not appear frequently enough in the training data, unlike the common hiragana clusters such as the polite endings, and the English word “password”, which earned their own tokens. The newer tokenisers continue to split less common kanji (like 靴 and 濡) into byte pieces, but katakana stands out as even everyday words are broken apart. It'd be interesting to run these checks on the same Qwen, Gemma, Llama tokenisers the study uses.