First of all, it obviously changes everything. A shortsighted person requires prescription glasses, someone that is fundamentally unable to count is incurable from our perspective. LLMs could do all of these things if we either solve tokenization or simply adapt the tokenizer to relevant tasks. This is already being done for program code, it's just that aside from gotcha arguments, nobody really cares about letter counting that much.
Secondly, the analogy was meant to convey that the intelligence of a system is not at all related to the problems at its interface. No one would say that legally blind people are less insightful or intelligent, they just require you to transform input into representations accounting for their interface problems.
Thirdly, as I thought was obvious, the tokenizer is not a uniform blur. For example, a word like "count" could be tokenized as "c|ount" or " coun|t" (note the space) or ". count" depending on the surrounding context. Each of these versions will have tokens of different lengths, and associated different letter counts. If you've been told that the cube had 10, 11 or 12 trillion constituent parts by various people depending on the random circumstances you've talked to them in, then you would absolutely start guessing through the common answers you've been given.
ChatGPT can't count, the problem is the tokenizer.
I do find it funny we're trying to chat with an AI that is "equivalent to a legally blind person with no correction"
> You would just say one, because you don't even know that atoms are a thing.
My point also. I wouldnt start guessing "10" and then "11" and then "12" when asked to double check only to capitulate when told the correct answer.