Relevant especially with the rise of AI. At first quantity matters here but “as training sets grow ever larger, it’s often more efficient to do this than to acquire the next token; beyond a certain point, data quality scales better than data quantity“.
“So there you have it: 5000+ words on data pricing. We’ve covered use cases and users; quality and quantity; internal and external value factors; pricing axes and maturity curves; table stakes and usage rights; and much more.“
