AI models trained on enormously large datasets, specifically large language or vision-language 
models, represent  a phenomenon that has by far outgrown the notion of “being just a tool”. 
Regardless  of  how  and  for  what  purpose  such  models  are  practically  employed,  they  in 
themselves represent a valuable object of study. “In themselves” in this context includes not 
only all the culturally dependent patterns learned from the training data, but also the culturally 
dependent  attempts  to  control,  modify  or  erase  culturally  dependent  patterns  in  the  training 
data.  Focusing  on  the  comparative  analysis  of  outputs  from  two  very  popular  text-to-image 
synthesis models, DALL·E 2 [1] and Stable Diffusion [2], this paper tries to tackle the pros 
and cons of striving towards culturally agnostic vs. culturally specific AI models.  
 
Implemented  in  one  way  or  another,  most  commonly  the  “guiding  principle”  behind  many
The Myth of Culturally Agnostic AI Models