According to Bankless, the challenge of private AI may be addressed through smaller models. The report suggests that while fully homomorphic encryption for large-scale applications remains distant, the requirements for achieving "good enough" privacy have been reduced. The development indicates a potential shift in focus toward more manageable AI models to overcome encryption hurdles. This could have implications for data privacy and security in AI applications, as researchers explore alternative approaches to protect sensitive information.

According to Bankless, the challenge of private AI may be addressed through smaller models. The report suggests that while fully homomorphic encryption for large-scale applications remains distant, the requirements for achieving "good enough" privacy have been reduced. The development indicates a potential shift in focus toward more manageable AI models to overcome encryption hurdles. This could have implications for data privacy and security in AI applications, as researchers explore alternative approaches to protect sensitive information.