Core Insights - The article discusses the competitive landscape of AI, highlighting the significant advancements made by companies like Google with their Gemini model, which has surpassed the combined usage of all major models in China within 24 hours, reaching over 7 billion tokens [8][27]. - It emphasizes the importance of multi-modal capabilities and underlying architecture in determining the future success of AI technologies, rather than just focusing on language processing capabilities [14][16]. - The article warns that by mid-2026, companies relying on government subsidies and low-price bidding will face severe challenges, as investors will demand proof of profitability and positive cash flow [30][31]. Group 1 - Google's Gemini model achieved over 7 billion tokens in usage within 24 hours, indicating a significant leap in AI capabilities [8]. - The article points out that the current AI landscape is marked by a gap in multi-modal capabilities and underlying architecture, which are crucial for future competitiveness [14][16]. - Companies like Google are investing heavily in AI infrastructure, with Google spending $42 billion on AI in a single quarter, which accounted for 42% of their revenue [27]. Group 2 - The article highlights the disparity in funding density, with companies like Baidu investing ¥21 billion in AI, showcasing the financial gap in the industry [32][33]. - It suggests that companies must either become rule-makers or scenario owners to survive in the future AI landscape, emphasizing the need for substantial computational power and integration into core workflows [37]. - The focus should be on companies that have established a strong presence in specific fields, as they are more likely to endure economic cycles and provide real value [39].