回想几年前，我们担心的还是大数据杀熟或信息茧房；而到了今天，当 AI 开始替我们要方案、做决策 时，我们最担心的是它的黑盒与失控。 2026 年 1 月 27 日，腾讯研究院主办的 腾 讯 科 技向善创新节 202 6 正式举办。 腾讯集团 副 总裁、 腾讯研究院院长司晓 先生在现场进行了上午场闭幕发言。 以下为司晓先生的发言全文： 各位朋友，大家上午好。 刚才大屏幕上那个只有一分半钟的视频，看得我心里很感慨。那个老人和孩子面对 AI 时的提问，其实 也是我们每个人心底的提问：在这个技术飞速狂奔的时代，我们到底是更从容了，还是更焦虑了？ 今天我们齐聚在这里，是 2026 年。回望过去，腾讯提出"科技向善"已经八年了。 九年前，当我们第一次提出"科技向善"时，更多是一种底线思维：技术要有边界， 要善用科技，避免滥 用，杜绝恶用，科技要努力解决自身发展带来的社会问题。 这是我们的根基，是我们在数字社会立足的 根本。 但这几年，世界变了。随着大模型、生成式 AI 的爆发，技术不再仅仅是像水和电那样的基础设施，它 开始有了"像人"的一面——它能对话、能创作，甚至能替我们做决策。 在这个过程中，我们也一直在修正和进阶我 ...

Core Viewpoint - The article discusses the devaluation of intelligence and education in the context of AI transformation, suggesting that traditional educational qualifications may lose their value as AI becomes more prevalent in the workforce [4]. Group 1: Intelligence as a Service - The concept of "Intelligence as a Service" (IaaS) indicates that intelligence is no longer a privilege of human brains but can be accessed on demand, similar to utilities like electricity [6]. - AI's ability to perform tasks traditionally done by highly educated individuals leads to a significant devaluation of traditional educational qualifications, as knowledge becomes a common commodity [6][7]. - The demand for traditional white-collar jobs is declining, with a reported 22% decrease in positions that typically require higher education, particularly in finance, human resources, and administrative roles [7]. Group 2: Educational Inflation and Devaluation - In 2024, China's higher education gross enrollment rate reached 60.8%, with nearly 47 million students enrolled, indicating a saturation of the education market [10]. - The average starting salary for new graduates in first-tier cities is around 6,000 to 7,000 yuan, which is lower than many vocational roles, highlighting the "high investment, low return" phenomenon of education [11]. - The competition for higher education has intensified, with graduate school acceptance rates around 3.5:1 to 4:1, leading to an oversupply of graduates [12]. Group 3: Shifts in Employment Landscape - The trend of graduates from prestigious universities applying for lower-tier jobs reflects a shift in the value of education, where job stability and benefits are prioritized over traditional career paths [15][16]. - The increasing appeal of stable government jobs and lower-tier positions among elite graduates indicates a desire for job security in an uncertain economic environment [17]. - The phenomenon of highly educated individuals taking on low-skilled jobs illustrates the disconnect between educational qualifications and available high-value positions in the market [18]. Group 4: The Future of Education and Skills - The article posits that viewing education as a one-time investment is misguided; instead, it should be seen as a temporary ticket that requires continuous skill development beyond formal education [19]. - The rapid evolution of knowledge due to AI means that the shelf life of a degree has significantly shortened, with many skills becoming obsolete within 18 months [21]. - The focus should shift from what one knows to how one adapts to unknown situations, emphasizing the importance of practical skills over formal qualifications in the AI era [21].

Core Viewpoint - The article discusses concerns about AI's role as a writing tool, suggesting it may lead to a "homogenization revolution" that affects writing styles and original thinking, potentially resulting in a degree of uniformity in language expression [1] Group 1: Historical Context and Perspectives - Historical concerns about new technologies impacting human cognition are echoed in the current discourse on AI, with past technologies like writing and the internet facing similar scrutiny [4] - These historical worries have often proven unfounded, as technology has generally enhanced human productivity and civilization rather than diminished it [4][5] - The article emphasizes that the influence of technology is not linear; human society adapts and interacts dynamically with technological advancements [5] Group 2: AI's Role in Society - AI is positioned as a tool that can elevate societal intelligence levels rather than merely contributing to mediocrity [9][10] - Generative AI bridges the gap between knowledge and tools, making creative capabilities more accessible to the general public at a low marginal cost [11] - AI's capabilities in multimodal creation significantly lower the barriers for individuals to produce high-quality creative works, transforming the creative landscape [12] Group 3: The Impact on Creativity and Standards - AI sets a higher baseline for societal intelligence, allowing even educated individuals to expand their cognitive boundaries and enhance their creative outputs [13] - The overall elevation of societal intelligence may lead to a more discerning public that demands higher quality content, thereby pushing creators to produce more innovative and emotionally resonant works [14] - The emergence of a vibrant grassroots creative ecosystem is noted, where ordinary users leverage AI tools to create works that sometimes surpass official versions [14][15] Group 4: Human-AI Collaboration - The relationship between humans and AI is evolving from a tool-based interaction to a partnership, where humans guide and collaborate with AI to achieve superior outcomes [18][19] - The ideal human-AI relationship emphasizes human agency in setting goals and providing unique insights, while AI serves as an efficient information processor [19] - Maintaining human subjectivity and critical thinking is crucial in the interaction with AI to avoid becoming overly reliant on its outputs [21]

Group 1 - The core idea is that the wave of large models is transforming industries, with "intelligent+" representing a cognitive revolution and ecological reconstruction, embedding new genes into various sectors [1] - The Chinese intelligent economy is on the brink of explosion, requiring clarity on what to add (new cognition, new data, new technology) and how to implement it (cloud intelligence, digital trust, π-type talent, full participation, and mechanism reconstruction) to achieve industrial upgrades [1][2] Group 2 - New cognition involves embracing paradigm shifts and clarifying boundaries, with management feeling both excitement and anxiety about the rapid advancements in AI technology [2][3] - Companies exhibit a dual mindset towards AI, with some eager to implement it quickly while others face stagnation due to limited application scenarios and unmet expectations [2][3] Group 3 - Intelligent+ signifies a shift from human experience-based decision-making to human-machine collaboration, where AI enhances human capabilities rather than replacing them [3][4] - The evolution of AI applications is categorized into waves, with each wave unlocking deeper capabilities and potential applications across various industries [4][5] Group 4 - High-quality industry data is crucial for the successful implementation of large models, necessitating the breaking down of departmental silos to enhance data flow and real-time access [6][7] - Companies like LexisNexis and Mayo Clinic have successfully addressed data silos through innovative technologies, enabling better data utilization and decision-making [7][8] Group 5 - The emergence of "dark data" presents new opportunities for decision-making, as unstructured data becomes a valuable asset for businesses [8][9] - Continuous user interaction and feedback are essential for optimizing intelligent systems, exemplified by GitHub Copilot's learning mechanism [9] Group 6 - The integration of new technologies, particularly generative AI, is pivotal for the intelligent+ movement, requiring a combination of various enabling technologies [10][11] - Knowledge engines are highlighted as effective solutions for enhancing service accuracy and efficiency in customer support scenarios [11][12] Group 7 - AI agents represent a promising area for intelligent+ applications, transforming from mere tools to proactive partners in task execution [13] - Companies like Microsoft and HomeToGo are leveraging AI agents to streamline processes and enhance operational efficiency [13] Group 8 - The transition to cloud-based models is essential for cost-effectiveness and continuous upgrades, with significant price reductions in cloud services facilitating broader access to AI capabilities [14][15] - The competition among large models will increasingly focus on cost-effectiveness, sustainability, and service ecosystems [15] Group 9 - Establishing digital trust through service-level agreements (SLAs) is crucial for fostering confidence in AI systems, moving from subjective trust to quantifiable metrics [16][17] - Mechanisms for algorithm transparency, vulnerability disclosure, and emergency response are necessary to build a robust digital trust framework [17] Group 10 - The development of π-type talent, who bridge the gap between technology and business, is vital for realizing the potential of intelligent+ [18][19] - Companies like Microsoft are implementing comprehensive training programs to cultivate AI literacy across all levels of the organization [19][20] Group 11 - Full participation from all employees is essential for the successful implementation of intelligent+, requiring mechanisms that encourage innovation and collaboration [22][23] - Organizations must establish systems that empower employees to contribute to AI initiatives, transforming them from users to co-creators [23] Group 12 - Organizational restructuring is necessary to facilitate the integration of AI, moving away from traditional hierarchical models to more agile, decentralized structures [24][25] - Companies like Walmart and Spotify exemplify successful organizational transformations that enable rapid AI adoption and innovation [25][26] Group 13 - The future of intelligent+ lies in the concept of "Intelligence as a Service," where cognitive capabilities are offered as on-demand services across various industries [29][32] - The evolution of AI will lead to the emergence of personalized software and intelligent agents that cater to specific business needs [32] Group 14 - The growth of intelligent+ is likened to the growth of bamboo, where foundational work is done before visible results emerge, emphasizing the importance of patience and preparation [35][37] - The convergence of cognitive revolution, cloud intelligence, and new trust mechanisms will mark a significant turning point in industrial upgrades and human-machine collaboration [37]

Group 1 - The core idea of the article emphasizes that the wave of large models is transforming industries, and "Intelligent+" is not just about technology integration but also involves cognitive revolution and ecological restructuring [1] - The article discusses the need to clarify what to add (new cognition, new data, new technology) and how to implement these changes (cloud intelligence, digital trust, π-type talent, full participation, and mechanism reconstruction) to achieve industrial upgrades [1][15] Group 2 - New cognition involves embracing paradigm shifts, clarifying boundaries, and balancing urgency with patience in adopting AI technologies [3] - The article highlights the dual mindset of corporate leaders towards AI, where there is both eagerness to implement AI and a tendency to stall due to unmet expectations [3][4] - Intelligent+ signifies a shift from human experience-based decision-making to human-machine collaboration, where AI enhances human capabilities rather than replacing them [4] Group 3 - New data is crucial for the success of large models, and organizations must overcome challenges such as breaking down departmental silos to allow data flow [7][8] - The article emphasizes the importance of leveraging "dark data" and transforming unstructured data into actionable insights for better decision-making [9][10] - Establishing a feedback loop through continuous user interaction is essential for optimizing intelligent systems [10] Group 4 - New technology encompasses not only generative AI but also traditional AI technologies, emphasizing a collaborative approach among various technological layers [11] - Knowledge engines are highlighted as effective solutions for enhancing customer service and operational efficiency in organizations [12] - AI agents are identified as a key area for future growth, enabling deeper human-machine collaboration and task execution [13] Group 5 - The article outlines five steps to successfully implement intelligent solutions, starting with cloud intelligence as a cost-effective and efficient solution for deploying large models [16] - Rebuilding digital trust through service-level agreements (SLAs) is essential for establishing a reliable framework in the digital age [18][19] - The need for π-type talent, who can bridge the gap between technology and business, is emphasized as a critical factor for successful AI integration [21][22] Group 6 - The article stresses the importance of full participation from all employees in the AI transformation process, moving from top-down initiatives to inclusive engagement [24][25] - Organizations must establish mechanisms that encourage innovation and allow employees to contribute actively to AI initiatives [25] - The restructuring of organizational DNA is necessary to facilitate the integration of AI into business processes, moving away from traditional hierarchical structures [26][27] Group 7 - The concept of "Intelligence as a Service" is introduced, suggesting a shift towards on-demand intelligent services that can be utilized across various industries [31][32] - The article concludes with a metaphor comparing the growth of AI to bamboo, highlighting the importance of foundational work before visible results emerge [38][41]