The "world model" has become a highly hyped yet ambiguous term in the AI industry over the past two years, with Jensen Huang framing "Physical AI" and world models as the core of next-generation AI at GTC, while autonomous driving companies, robotics manufacturers, and video generation teams each apply the label to their own technologies. World models are rapidly becoming a new "universal label," much like the metaverse and large language models before, driven by industry anxiety over diminishing marginal returns from large language models as internet text data is fully mined. However, world models face significant gaps and dilemmas before practical usability, presenting a systematic puzzle spanning three dimensions: concepts, data, and architecture. The first major dilemma is the concept itself, as the industry has never reached consensus on the definition, technical routes, or evaluation standards, with different companies using the same term for completely different technologies. This conceptual chaos stems from players across different tracks leveraging the world model narrative to seize technical discourse power for the next generation of AI, such as content generation companies repackaging video generation to elevate valuation space, and robotics enterprises describing modeling capabilities for robotic arms to build technical barriers.