Core Viewpoint - The event highlighted the importance of integrating AI into daily workflows, emphasizing that the future workplace will differentiate between those who can leverage AI to enhance their capabilities and those who cannot [7][11]. Group 1: Event Overview - Dr. Du Yu, director of the Unknown AI Research Institute, conducted a training session at Shanghai Electric Group, focusing on AI [1]. - The session revolved around two new books: "DeepSeek User Guide: Practical Applications in All Professional Scenarios" and "DeepSeek: Understanding the Underlying Logic of the AI Era," which were well-received by participants [2][15]. Group 2: AI Integration and Methodology - DeepSeek is described as a set of pluggable workplace capability modules rather than just a chat tool, capable of generating industry reports in 3 minutes, summarizing 50-page meeting notes into a single PPT in 10 seconds, and automatically creating travel documents in one sentence [5]. - Participants experienced hands-on demonstrations, leading to comments about the ease of writing weekly reports [6]. Group 3: AI Value Model - Dr. Du presented the "AI Three-Layer Value Model," addressing concerns about AI replacing jobs by stating that future professionals will fall into two categories: those who use AI to amplify their work and those who do not [7]. - The three layers of value include: 1. Efficiency Layer: Automating repetitive tasks to free up time for high-value decisions [8]. 2. Insight Layer: Quickly identifying market opportunities or risks based on proprietary company data [8]. 3. Co-Creation Layer: Using AI as a "external brain" for brainstorming and iterating solutions [8]. Group 4: AI Thinking and Mindset - The core message of the books is that competitive advantage in the AI era lies not in the proficiency with tools but in internalizing "AI thinking" as a new operational system [11]. - The transition involves: 1. Moving from "doing it oneself" to "question-based management," where precise questions lead to valuable AI-generated results [12]. 2. Shifting from "experience-driven" to "data empathy," where AI enhances traditional methods by expanding sample sizes and revealing blind spots [13]. 3. Evolving from "closed-loop processes" to "iterative flywheels," where AI-generated drafts can be rapidly improved through human feedback [14].

Core Insights - The article discusses the need for a new way of thinking to effectively harness AI, distinguishing it from traditional internet thinking [1][3] - AI is not merely a tool but can become a value-creating entity through multi-agent systems [1][6] - The concept of "intelligent first" is emphasized as a guiding principle for organizations adopting AI [4][5] AI Thinking - AI thinking is defined as a new problem-solving methodology that applies the "AI First" principle in organizational processes [11] - It involves three core principles: Virtual-First Simulation, Rapid Scalable Trial and Error, and Computational Hedging [11][12][17] Virtual-First Simulation - This principle advocates for creating a digital model of the real world to simulate actions before actual resource investment [12][14] - It allows for low-cost exploration of possibilities, enhancing decision-making [14] Rapid Scalable Trial and Error - AI enables parallel testing of numerous scenarios at minimal costs, significantly speeding up the innovation process [15][16] - This capability transforms the traditional trial-and-error approach into a more efficient and scalable model [16] Computational Hedging - This principle suggests using inexpensive computational resources to mitigate the costs associated with physical resources [17] - AI can simulate complex interactions, reducing the need for extensive physical trials [17] Unmanned Companies - The culmination of AI thinking in organizations leads to the concept of "unmanned companies," where AI agents drive value creation [19][20] - In these companies, human roles shift from execution to design and governance [20] Technical Framework - The operational framework of unmanned companies is based on a universal world model architecture that simulates real-world dynamics [21] - This includes multi-agent behavior and nested models for strategic and operational planning [21][22] Current Applications - AI thinking is already influencing various sectors, such as manufacturing with digital twins and marketing through automated content generation [24][25] - In scientific research, AI accelerates hypothesis testing and validation processes [26] Future Outlook - The transition from an experience-driven to a simulation-driven business landscape is underway, with companies needing to develop high-fidelity world models [27] - Mastery of AI thinking will provide organizations with a competitive edge in agility, efficiency, and scalability [27]

Core Viewpoint - Artificial Intelligence (AI) is not the endpoint but the starting point for redefining the essence of business [1][34] Group 1: AI's Impact on Business - The rapid rise of AI is reshaping the global business landscape, becoming a strategic issue that no company can ignore [2] - AI brings not only technological updates but also systemic challenges to traditional business paradigms, organizational structures, and management philosophies [2][4] - Companies must actively embrace AI to gain a competitive edge and transition from passive adaptation to leading transformation [3] Group 2: China's Advantage and Challenges - Chinese companies have shown early advantages in AI practices across various fields such as smart manufacturing and digital marketing [4] - The leading position is rooted in China's complex market environment and high digitalization of user behavior, but it also brings deep structural challenges [4] - As AI moves to the core of business, companies face non-technical structural issues that require strategic, systemic responses rather than tactical fixes [4][6] Group 3: Building Cognitive Foundations - The evolution of AI technology is faster than any previous general technology, necessitating a systematic approach to information perception and technological insight [6] - Companies need to establish an open external knowledge network that includes academia, industry, and venture capital to better capture trends and understand emerging technologies [7] - Successful companies, like a major international group, are already building such networks to enhance their strategic insights [7] Group 4: Investment and ROI in AI - Understanding the return on investment (ROI) for AI projects is crucial for companies [8] - Many entrepreneurs report that their AI investments have not met expectations, highlighting the need for logical adjustments in AI investment strategies [9][10] - AI should be viewed as a strategic asset that evolves and adds value over time, requiring a shift from traditional ROI frameworks to a multi-dimensional evaluation system [11][13] Group 5: Governance and Human Factors in AI Transformation - The complexity of decision-making increases as AI becomes embedded in core business processes, necessitating dynamic thinking and organizational adjustments [15] - AI-related strategic decisions should involve cross-departmental collaboration rather than relying on a few individuals [16][18] - Companies need to establish dedicated AI strategy departments or appoint Chief AI Officers (CAIO) to integrate AI into their organizational framework [20] Group 6: Talent and Organizational Structure - The future organization will be characterized by high dynamism, adaptability, and ecological coexistence, moving away from traditional fixed roles [23] - Companies require a talent pool that integrates data understanding, AI application, and business innovation, as traditional roles will be significantly replaced [24] - Successful companies will be those that understand AI as a strategic imperative and leverage it for organizational transformation [30] Group 7: Strategic Cognition and Competitive Edge - A new wave of AI-native companies will emerge, fundamentally challenging traditional businesses by redefining industry logic [26][27] - Companies must have the courage and capability for self-disruption to transform effectively [28] - The competition will shift from merely using tools to building systematic learning and adaptability capabilities [31][32]

Group 1 - The rapid rise of artificial intelligence (AI) is reshaping the global business landscape, becoming a strategic issue that no company can ignore [2][3] - Companies must actively embrace AI to gain a competitive edge and transition from passive adaptation to leading transformation [2] - Chinese companies have shown early advantages in AI practices across various sectors, including smart manufacturing and digital marketing [3] Group 2 - The leading position of Chinese companies is rooted in a complex market environment and a highly digitalized user behavior [3] - As AI moves to the core of business operations, companies face structural challenges that require strategic responses rather than tactical fixes [3][4] - A systematic and structured information perception mechanism is essential for companies to respond to AI transformations [4] Group 3 - Companies should establish an open external knowledge network that includes academia, industry, and venture capital to better capture trends and understand emerging technologies [5] - Successful companies are leveraging corporate venture capital (CVC) to observe innovation mechanisms and adapt to the AI ecosystem [5][6] - The return on investment (ROI) for AI initiatives is crucial, and companies must evaluate the reasonableness of their AI investments [6][7] Group 4 - AI is not a one-time project but a strategic asset that evolves and adds value over time [7][8] - Companies need to adopt a multi-dimensional ROI assessment framework that allows for continuous validation and dynamic adjustments [9] - AI-related strategic decisions should involve cross-departmental collaboration rather than relying on a few individuals [10][11] Group 5 - High-level management must actively participate in AI strategy and foster a culture of shared understanding and capability [11][12] - Companies should create a culture that allows for "smart trial and error," accepting small, manageable failures as part of the learning process [12][13] - The future organization will require a talent pool that integrates data understanding, AI application, and business innovation [13][14] Group 6 - A new breed of AI-native companies will emerge, redefining industry logic and challenging traditional enterprises [14][15] - Companies need the courage and capability for self-disruption to adapt to the changing landscape [15][16] - The core of AI transformation lies in the synchronization of strategic recognition and organizational capability [16][17] Group 7 - The competition in the AI era will focus on systematic learning and adaptability rather than just tool usage [18] - Companies must embrace "AI thinking," which involves a comprehensive restructuring of strategy, organization, processes, and culture [18]

Core Insights - The emergence of DeepSeek, a domestic open-source AI model, has sparked discussions due to its free advantages, yet many still prefer to pay for ChatGPT, raising questions about user preferences and the quality of AI outputs [1][60]. - The output quality of AI tools is significantly influenced by user interaction, with 70% of the output quality depending on how users design their prompts [4][25]. Technical Differences - DeepSeek utilizes a mixed expert model with a training cost of $5.5 million, making it a cost-effective alternative compared to ChatGPT, which has training costs in the hundreds of millions [2]. - In the Chatbot Arena test, DeepSeek ranked third, demonstrating competitive performance, particularly excelling in mathematical reasoning with a 97.3% accuracy rate in the MATH-500 test [2]. Performance in Specific Scenarios - DeepSeek has shown superior performance in detailed analyses and creative writing tasks, providing comprehensive insights and deep thinking capabilities [3][17]. - The model's reasoning process is more transparent but requires structured prompts for optimal use, indicating that user guidance is crucial for maximizing its potential [7][12]. Cost and Efficiency - DeepSeek's pricing is 30% lower than ChatGPT, with a processing efficiency that is 20% higher and energy consumption reduced by 25% [8][9]. - For enterprises, private deployment of DeepSeek can be cost-effective in the long run, with a one-time server investment of around $200,000, avoiding ongoing API fees [9][10]. Deployment Flexibility - DeepSeek offers flexibility in deployment, allowing individual developers to run the 7B model on standard hardware, while enterprise setups can support high concurrency [11][10]. - The model's ability to run on lightweight devices significantly lowers the barrier for AI application [11]. Advanced Prompting Techniques - Mastery of advanced prompting techniques, such as "prompt chaining" and "reverse thinking," can significantly enhance the effectiveness of DeepSeek [13][14]. - The model's performance can be optimized by using multi-role prompts, allowing it to balance professionalism and readability [15][16]. Language Processing Capabilities - DeepSeek demonstrates a 92.7% accuracy rate in Chinese semantic understanding, surpassing ChatGPT's 89.3%, and supports classical literature analysis and dialect recognition [17]. Industry Applications - In finance, DeepSeek has improved investment decision efficiency by 40% for a securities company [18]. - In the medical field, it has achieved an 85% accuracy rate in disease diagnosis, nearing the level of professional doctors [19]. - For programming assistance, DeepSeek's error rate is 23% lower than GPT-4.5, with a 40% faster response time [20]. Complementary Nature of AI Tools - DeepSeek and ChatGPT are not mutually exclusive but serve as complementary tools, each suited for different tasks based on user needs [21][22]. - DeepSeek is preferable for deep reasoning, specialized knowledge, and data privacy, while ChatGPT excels in multi-modal interaction and creative content generation [24][22]. Importance of Prompting Skills - The ability to design effective prompts is becoming a core competency in the AI era, influencing the quality of AI outputs [54][55]. - The book "DeepSeek Application Advanced Tutorial" aims to enhance users' prompting skills and unlock the model's full potential [61].