In recent years, the development of "world models" has been progressing rapidly within the AI field. This technology internalizes the laws of the physical world within AI, holding the potential to bring innovation to domains such as video generation, robotics, and autonomous driving. However, an increasing number of companies leading this development are intentionally concealing their technology stacks, training data, and evaluation methodologies.
To secure first-mover advantages, companies are withholding technical details as "commercial secrets." This trend is accelerating the black box nature of AI, raising concerns among the research community and regulatory authorities. The opacity of internal model structures makes it difficult to verify unintended behaviors and biases.
Unlike conventional language models, world models aim to replicate the real world and physical laws within a simulation environment. The societal impact of this technology is immeasurable. Consequently, amidst fierce development competition, discussions are growing louder regarding the need for transparency as an industry standard from the perspective of verifying model reliability and ensuring safety.
While AI companies' confidentiality policies are expected to persist for some time, balancing development competition with social accountability will become a critical issue in next-generation AI development. The entire AI ecosystem is required to respond sincerely on how to ensure objective verifiability without compromising the speed of technological innovation.