alt Hacker NewsGarbage. You can't include training by the companies that develop an llm in the comparison against companies that merely use the same llm. Apples and potatoes.
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Excatly, further more it really depends on who is using it and how value is compared not $. Senior engineers growing without ai will probably deliver 10x in value then ppl who just found out about ai yesterday. Doesn’t matter how much you spent dolar wise
Apples and potatoes are both something people will need to eat if we want to see it from the human utility perspective, and they both require some land space to be allocated for their culture (though one can of course conjugate both culture).
If you want to take the DDG LLM summary at fate value, apples are lower in calories and sugar but higher in fiber compared to potatoes, which are richer in vitamins and minerals like potassium and vitamin B6. Overall, apples provide more dietary fiber, while potatoes offer more protein and essential nutrients.
Comparison rarely lead to one obvious all superior option that discard every other considerations.
OpenAI and Anthropic aren't charities, so whatever cost they inccur for training will be passed down to the companies using the models. So you absolute should include it.
Apples and oranges, or chalk and cheese. Why would you say apples and potatoes?
I don't know, compute is compute. Arguably making complex software with LLMs isn't all that different from training a model to do a thing. You're throwing a lot of compute at the problem and hoping for a stochastic solution. The distinction will become even blurrier with time.
Though I agree it might be informative to split it by industry sector.
Exactly, it's like saying Shell is spending a fortune on fuel compared to what they spend on employees, if you count oil extraction costs as 'fuel'.