Group 1 - The core idea of DeepSeek-OCR is that it utilizes visual tokens to achieve a compression efficiency that is ten times greater than text tokens while maintaining 97% response accuracy [7][8] - DeepSeek-OCR introduces the concept of "Contextual Optical Compression," which processes text as a two-dimensional image rather than a one-dimensional symbol sequence, allowing for more efficient compression [7][8] - The AI community is focusing on the implications of using visual tokens over traditional text tokens, particularly in addressing the economic challenges of long-context processing in models that use the Next Token Prediction (NTP) mechanism [8][9] Group 2 - Huang Renxun argues that the current AI wave will not repeat the internet bubble, highlighting a shift in underlying logic and the emergence of full-stack AI factory competition that is changing the computing power landscape [2][3] - The next generation of intelligent systems may prioritize "energy efficiency advantages" over "computing power advantages," indicating a potential shift in how AI systems are developed and deployed [2][3] - The concept of an "intelligent economy" is discussed, questioning how far we are from realizing this vision, particularly in the context of digital labor and physical AI [2][3]