Quantitative Models and Construction Methods Model: DeepSeek-R1 - **Model Construction Idea**: The DeepSeek-R1 model aims to innovate in AI technology by reducing dependency on high-end imported GPUs and enhancing cost-effectiveness and performance in global markets[5][12][30] - **Model Construction Process**: - The model is based on the DeepSeek-V3 architecture and applies reinforcement learning techniques during the post-training phase to significantly improve inference capabilities with minimal labeled data[33] - The model's performance in tasks such as mathematics, coding, and natural language inference is on par with OpenAI's o1 official version[33] - The team also introduced six distilled small models using knowledge distillation techniques, with the 32B and 70B versions surpassing OpenAI o1-mini in several capabilities[34] - The model's training cost was $5.576 million, only 1/10th of GPT-4o's training cost, and its API call cost is 1/30th of OpenAI's similar services[38] - **Formula**: $$ \text{SUE} = \frac{\text{Single Quarter Net Profit} - \text{Expected Net Profit}}{\text{Standard Deviation of Net Profit YoY Change over the Past 8 Quarters}} $$ where Expected Net Profit = Last Year's Same Quarter Actual Net Profit + Average YoY Change in Net Profit over the Past 8 Quarters[55] - **Model Evaluation**: The model is highly cost-effective and adaptable to different application environments, breaking the traditional AI industry's reliance on "stacking computing power and capital"[38][43] Model Backtesting Results - **DeepSeek-R1 Model**: - **AIME pass@1**: 9.3 - **AIME cons@64**: 13.4 - **MATH-500 pass@1**: 74.6 - **GPQA Diamond pass@1**: 49.9 - **LiveCodeBench pass@1**: 32.9 - **CodeForces rating**: 759.0[36] Quantitative Factors and Construction Methods Factor: Standardized Unexpected Earnings (SUE) - **Factor Construction Idea**: SUE is used to measure the growth potential and latest marginal changes in the prosperity of the industry and individual stocks[57] - **Factor Construction Process**: - SUE is calculated as: $$ \text{SUE} = \frac{\text{Single Quarter Net Profit} - \text{Expected Net Profit}}{\text{Standard Deviation of Net Profit YoY Change over the Past 8 Quarters}} $$ where Expected Net Profit = Last Year's Same Quarter Actual Net Profit + Average YoY Change in Net Profit over the Past 8 Quarters[55] - **Factor Evaluation**: SUE effectively measures future earnings growth and the latest marginal changes in prosperity, representing the future trend changes in the industry[57] Factor Backtesting Results - **SUE Factor**: - **2022**: -29.8% - **2023**: 15.9% - **2024**: 20.1% - **2025 YTD**: 11.0%[65]

Group 1 - The BRICS cooperation is highlighted as an important platform for communication and collaboration among the Global South, with the digital economy emerging as a new link [1][4] - Kuaishou, as an internet short video company, has achieved significant milestones with a GMV of one trillion, revenue of hundreds of billions, and profits in the tens of billions, showcasing its global leadership in video understanding and recommendation algorithms [3][4] - Kuaishou's international expansion has connected more countries and practitioners, with over 22 million global users and more than 60 million monthly active users in Brazil, where it has launched e-commerce operations [3][4] Group 2 - Kuaishou has invested over 7 billion Brazilian Reais in Brazil, supporting nearly 1 million local creators on its platform, Kwai, and contributing to the local economy [4] - The company emphasizes its role in the "Digital Silk Road" between China and Brazil, aiming to bring value, technological capabilities, and community warmth to more people [4] - The forum was attended by representatives from 250 organizations, including media, think tanks, government agencies, and enterprises from 36 countries, indicating a strong interest in BRICS cooperation [4]

Core Viewpoint - The article discusses the transition of major companies from heavy investment in AI to the monetization phase, highlighting the varying degrees of success and the challenges faced in achieving profitability from AI initiatives [3][5][35]. Group 1: AI Investment and Monetization - Over the past two years, AI has become a significant focus for both domestic and international tech giants, with substantial financial investments made [4][5]. - A report indicates that several startups have achieved high revenue per employee, with AI unicorn Midjourney generating $500 million in annual revenue with a team of 40, translating to an average of $1.66 million per employee [4]. - Major companies like Baidu, Alibaba, and Tencent have emphasized the importance of AI in their financial reports, signaling a shift from investment to revenue generation [5][20][21]. Group 2: AI Business Models - The article categorizes the AI business models of major companies into four types: Model as Product, Model as Service, AI as Function, and "Selling Shovels" [7][8]. - "Model as Product" involves creating specific applications based on self-developed large models, primarily targeting consumer markets, with subscription-based revenue models [8][9]. - "Model as Service" targets B2B clients, offering trained AI models through cloud platforms, which has shown clear monetization potential [10][11]. - "AI as Function" integrates AI capabilities into existing products to enhance efficiency, contributing indirectly to profitability [11][13]. - "Selling Shovels" refers to providing foundational infrastructure and services to other companies, which requires significant investment and has a longer product cycle [15][16]. Group 3: Company Performance and Market Position - Companies are categorized into three tiers based on their AI monetization capabilities: - **First Tier**: Baidu, Alibaba, Tencent, and Huawei, where AI significantly contributes to overall revenue [18][19]. - **Second Tier**: Kuaishou, ByteDance, and Meitu, which are beginning to see the benefits of AI in their core operations [28][30]. - **Third Tier**: iFlytek and Kunlun Wanwei, which are still in the investment phase with less immediate revenue impact [31]. - Baidu's non-online marketing revenue, driven by AI, increased from 25.9 billion in 2022 to 31.7 billion in 2024, with a 40% year-on-year growth in Q1 2025 [20]. - Alibaba's cloud intelligence group reported a revenue of 30.1 billion in Q1 2025, reflecting an 18% year-on-year growth, indicating AI's role as a growth engine [21][22]. Group 4: Challenges in AI Monetization - Despite the promising revenue growth, companies face challenges in achieving profitability due to high R&D and marketing costs, with Tencent and Alibaba's annual R&D expenditures exceeding 100 billion [37][39]. - The article notes that while some companies have begun to see revenue from AI, the path to sustainable profitability remains complex, with many still not achieving positive cash flow from AI initiatives [43].

Core Viewpoint - The media plays a crucial role in fostering cooperation and mutual trust among Shanghai Cooperation Organization (SCO) member states, especially in the context of global challenges such as economic globalization setbacks and geopolitical tensions [2][3]. Group 1: Media Cooperation and Development - The media dialogue emphasized the importance of cooperation, mutual benefit, and respect for diverse civilizations to achieve common development [2][3]. - Participants discussed how SCO media can deepen collaboration to address global information dissemination challenges and create a fairer international communication order [2][3]. - The partnership between Uzbekistan's "People's Voice" and Chinese media focuses on combating fake news and enhancing information security [2][3]. Group 2: Technological Innovation in Media - New technologies like AIGC and VR are seen as tools to enrich reporting and enhance media integration [4][5]. - The introduction of AIGC has significantly reduced production time and costs for media content, exemplified by the creation of a fantasy micro-drama that took only two months instead of six [5]. - Multi-language broadcasting is recommended to amplify the SCO's voice internationally [5]. Group 3: Pursuit of Truth in Journalism - The conference highlighted the necessity for media to uphold truth and integrity, countering misinformation, especially regarding regions like Xinjiang [6][7]. - The establishment of a multi-language media matrix by Xinjiang Daily aims to effectively communicate the region's narrative and counter false reports [7]. - Collaboration among media from different countries is encouraged to enhance transparency and address authenticity disputes in reporting [7].

Core Insights - The AI video sector has seen a shift in focus from OpenAI's Sora to new players like Keke and Jiemeng, with industry players now prioritizing the reduction of the gap between AI video production and consumption [4][5][6] - The competition among AI video players is intensifying, with frequent updates and new model releases expected in 2025, indicating a rapid evolution in the industry [4][12][26] - There is a growing concern among mid-tier AIGC entrepreneurs regarding the commercial viability of AI video, as production costs remain high while client budgets are decreasing [4][16][18] Group 1: Industry Dynamics - The AI video landscape is becoming increasingly crowded, with numerous players emerging and competing for market share [23][26] - The focus of competition has shifted from model parameters to three key dimensions: consistency, usability, and playability [6][13][14] - Many AI video products are becoming homogenized in terms of functionality, leading to increased competition on quality, cost, and interaction forms [5][16] Group 2: Technological Advancements - AI video players are enhancing video generation consistency by improving frame transitions and scene realism, which are critical for quality [9][11] - Major players are iterating their foundational models regularly, with updates occurring at least every six months to maintain competitive advantage [11][12] - New features such as dynamic editing capabilities and end-to-end production tools are being developed to improve usability for creators [13][14] Group 3: Market Challenges - Despite the proliferation of tools and features, many creators express anxiety over rising production costs and decreasing project budgets [16][18][21] - The pricing strategies in the AI video market are not leading to significant reductions in costs, with many companies maintaining high prices for advanced models [20][21] - The complexity of video creation demands a multi-platform approach, as no single company currently meets all needs in the market [27]