Core Viewpoint - The rapid development of generative AI technology is transforming various industries, including education, where companies like Fenbi are increasingly investing in AI to enhance their offerings and respond to market demands [1][4][10]. Group 1: AI Development in Education - OpenAI launched a version of ChatGPT tailored for higher education, named GPT-edu, to provide personalized services for students and teachers [1]. - Domestic companies such as NetEase Youdao, TAL Education, and Fenbi have begun developing their own educational AI models, with Fenbi introducing AI teachers and question-answering systems [2][4]. - Fenbi's president announced a commitment to increase AI R&D investment by 30% annually and collaborate with top institutions to build an educational AI model laboratory [7]. Group 2: Market Dynamics and Competition - The public examination training market is facing intense competition, with Fenbi needing to enhance its product competitiveness through continuous investment in AI [4][29]. - Despite a high number of applicants for civil service exams, Fenbi's revenue and net profit have declined, indicating a challenging market environment [14][11]. - Fenbi's average monthly active users reached 9.3 million by June 30, but many users prefer more cost-effective options, impacting conversion rates to paid users [13]. Group 3: Financial Performance - Fenbi reported a revenue of 1.49 billion RMB for the first half of 2025, a decrease of 8.5% year-on-year, with net profit down 18.34% to 227 million RMB [14][15]. - Other major players in the public examination training sector, such as Zhonggong Education, also reported revenue declines, highlighting the competitive landscape [16][17]. Group 4: AI Product Performance - Fenbi's AI question-answering system has shown initial commercial success, with approximately 50,000 sales and revenue of around 20 million RMB [20]. - The AI question-answering system reportedly improved user learning efficiency by 29% to 40%, with average mock exam scores increasing by 15 to 20 points [24]. - The AI interview evaluation tool has seen significant engagement, with 470 million evaluations conducted and 350,000 users participating [26]. Group 5: Challenges and Future Outlook - Despite the promising start, AI has not yet significantly reduced costs or improved Fenbi's financial performance, as the company attributes cost reductions to overall revenue declines [27]. - Fenbi faces competition not only from other training institutions but also from AI model companies that offer free or lower-cost alternatives [35]. - The company must continue to innovate and provide effective, user-friendly AI products to capture market share and meet user expectations [36].

Core Viewpoint - The core viewpoint of the article emphasizes the potential of generative AI technology to transform the service industry into a manufacturing-like model, addressing the challenges of providing high-quality, low-cost services at scale, which is currently seen as a paradox in many service sectors [4][7][8]. Summary by Sections AI Application Opportunities - The article discusses the entrepreneurial opportunities in AI applications, particularly in converting service industries into manufacturing-like operations, thereby overcoming the "impossible triangle" of low cost, high quality, and large-scale service delivery [4][7]. - Generative AI is seen as a solution to provide personalized services at scale, which has not yet been fully realized in the service sector [7][8]. Challenges in AI Implementation - The current lack of explosive commercialization of AI applications is attributed to issues such as model hallucinations, inaccurate reasoning, and uncertain outcomes [4][10]. - The need for teams to balance model uncertainty with business tolerance is highlighted, emphasizing the importance of understanding both business and AI technology [4][10]. Historical Context and Comparisons - A comparison is made to the mobile application explosion over a decade ago, which was facilitated by the maturity of foundational technologies like 5G and smartphones, suggesting that similar foundational advancements are needed for AI applications to thrive [9][10]. Business Transformation Pathway - The article outlines a pragmatic approach for AI application development, starting with establishing a business loop to validate application scenarios, followed by gradually integrating AI models into the business processes [12][13]. - The importance of cloud-based data collection and high-quality feature sets for training AI models is emphasized [12]. Organizational Structure for AI Applications - The article stresses the necessity of having a high density of talent that combines industry expertise with AI knowledge, as well as fostering a culture of practical innovation [15][16]. - Human-machine collaboration is identified as a foundational operational paradigm for companies in the intelligent era [15][16]. Conclusion - The article concludes with a summary of guiding principles for AI application development: "business-driven, intelligent-driven, human-machine collaboration, and practical innovation" [16].