炒股就看金麒麟分析师研报，权威，专业，及时，全面，助您挖掘潜力主题机会！ 来源：雪涛宏观笔记 2028年AI末日预言是场完美的头脑风暴，但现实经济是个超级混沌系统。历史反复证明，当逻辑推导 出的远景过于极端时，预言者往往低估了人类看似低效、实则极具韧性的自适应能力。 文：国金宏观宋雪涛 在每个历史节点，都不乏名家对未来的豪迈期待：1930年凯恩斯的《我们孙辈经济的可能性》 （Economic Possibilities for our Grandchildren），1932年罗素的《闲暇颂》（In Praise of Idleness）， 1987年索罗的《生产力悖论》（We'd Better Watch Out），以及2013年格雷伯的《胡扯工作》（On the Phenomenon of Bullshit Jobs： A Work Theory）这些在人类智慧殿堂顶层的社科学家都证明了一点：在 重大技术变革面前，人类对于未来社会发展路径往往知之甚少。 2026年是理解AI如何影响宏观经济的关键年份，在"向前看"过程中，需要在逻辑底层上嵌套更多主观判 断，这也是"2028年AI废土世界文学"如此有吸引力的重 ...

1. Report Industry Investment Rating - Not provided in the document 2. Core View of the Report - Citrini Research's 2028 AI doomsday prophecy is a perfect brainstorm, but the real - world economy is a super - chaotic system. Prophets often underestimate human beings' self - adaptive ability. There is no reason to be overly pessimistic about the AI era [2][4][15] 3. Summary by Relevant Catalogs John Keynes - "Economic Possibilities for our Grandchildren": Endless Human Desires - Keynes in 1930 predicted that by 2030, living standards would increase 4 - 8 times and humans would work 15 hours a week, similar to Citrini's AI doomsday theory. However, this view underestimates the expansion of human desires. AI may eliminate old jobs, but human desires will create new ones [6] - The AI doomsday theory assumes that humans will stop striving and enjoy leisure passively, which does not conform to social development logic. "Freedom" and "fairness" are the core drivers and ultimate goals of economic development [7][8] Bertrand Russell - "In Praise of Idleness": Leisure as a "Civilization Asset" rather than a "Political Liability" - "In Praise of Idleness" is like the origin of the 2028 AI doomsday prophecy but with a milder tone. The real - world response to technological progress is different from Russell's assumption, and the AI doomsday theory over - worries about the loss of purchasing power due to job loss [9] - Leisure is a "civilization asset" that can be transformed into creativity. The political system will take measures to hedge against high unemployment, and the AI doomsday theory ignores this spontaneous adjustment [10] - Both "In Praise of Idleness" and the AI doomsday theory have flaws, and the resilience of the social structure is stronger than expected [11] Robert Solow - "The Solow Paradox": Inertia of Production Relations - The AI doomsday theory criticizes "The Solow Paradox" and warns that the "invisibility" of technology progress may bring huge impacts. However, historical experience shows that the transformation of technology into productivity is relatively slow, and the so - called "doomsday" will be offset by the "time - lag effect" [12] - The impact of AI on employment and production relations is less significant than macro - factors and the 2020 public health event. Enterprises are complex interest - game entities, and AI may face resistance. Even if production relations change, human participation will become a scarce asset [13] David Graeber - "On the Phenomenon of Bullshit Jobs: A Work Theory": Ending Meaningless Bullshit Jobs - "On the Phenomenon of Bullshit Jobs" directly refutes the AI doomsday theory. AI can be a trigger for efficiency regression by replacing meaningless jobs, rather than a catalyst for social collapse [14] - AI provides an opportunity for re - distribution, forcing society to shift from "job - centered" to "people - centered". The so - called "doomsday" is actually the end of "bullshit jobs" and a shattering of the collective perception of "everyone must be busy" [15]

Core Insights - The article discusses Joel Mokyr's groundbreaking work "Enlightened Economy," which challenges the "technological determinism" framework in explaining the Industrial Revolution, emphasizing the role of the Enlightenment and institutional environments in fostering economic growth [3][4]. Group 1: Industrial Revolution Insights - Mokyr argues that the Industrial Revolution was not solely a product of technological breakthroughs but rather a result of the Enlightenment's "intellectual revolution" and the resonance with institutional environments [3][4]. - He highlights the importance of "knowledge capitalization and sharing" mechanisms that transformed technological inventions from random attempts to systematic dissemination, serving as the true source of sustained economic growth [3][4]. - The article critiques the traditional view that focuses on technology alone, suggesting that the real question is why technology was created, improved, and widely applied at unprecedented speeds during this period [3][4]. Group 2: Knowledge Sharing and Economic Development - The article draws parallels between Mokyr's framework and China's rapid industrialization over 75 years, attributing it to a belief in knowledge sharing under the mission-driven leadership of the Communist Party [6]. - It emphasizes that the Chinese government's commitment to improving cultural levels and industrialization through science and technology, along with supportive industrial policies, laid the foundation for knowledge sharing and free flow [6]. - The significance of Mokyr's work is highlighted as a historical reference for understanding China's current strategy to develop "new quality productivity," which requires not just investment in R&D but also the creation of a vibrant innovation ecosystem [6].

Core Viewpoint - The article discusses the transformative impact of artificial intelligence (AI) across various industries, emphasizing that AI serves as a "filter" that prompts humanity to reassess its unique qualities, such as judgment, resilience, vitality, and self-awareness, which are becoming increasingly valuable in the AI era [2][4][15]. Group 1: AI as a Transformative Force - AI is penetrating traditionally knowledge-based industries like law, finance, healthcare, and media, processing information and generating solutions with high efficiency [3]. - The emergence of AI poses a risk to many stable knowledge-based jobs, leading to a critical reflection on how human value can be expressed in a world where machines can perform tasks faster and better [4][6]. Group 2: Shifting Definitions of Value - The definition of "elite" or "valuable talent" evolves alongside technological advancements, with AI raising the bar from "knowledge acquisition" to "intelligent application" [6][24]. - As knowledge becomes readily accessible, the focus shifts from what individuals know to how they can utilize that knowledge and navigate the unknown [6][24]. Group 3: Essential Human Qualities - Judgment and proactivity are highlighted as crucial in an age of information overload, where AI can generate numerous options, but human discernment is needed to identify the most relevant solutions [8][18]. - Resilience is emphasized as a vital human trait, allowing individuals to learn from failures and adapt, contrasting with AI's tendency to halt when faced with errors [18]. Group 4: The Role of Intuition and Self-Awareness - Intuition is identified as a key driver of innovation, representing the innate human ability to create and think outside conventional boundaries [9][10]. - Self-awareness is crucial for maintaining judgment, resilience, and creativity, especially in a rapidly changing environment where work and life boundaries blur [19]. Group 5: Moving Beyond Technological Determinism - The article argues against technological determinism, which suggests that technology is the sole driver of social change, advocating instead for active human engagement in shaping technological impacts [20]. - Individuals are encouraged to cultivate irreplaceable qualities through proactive learning and exploration, while society must adapt educational paradigms to focus on skill development rather than rote knowledge [21]. Group 6: The Future of Human and AI Collaboration - The ultimate significance of AI may lie in its ability to redirect focus towards essential human traits, allowing for a deeper engagement with emotions, creativity, and care [21][22]. - In the face of AI advancements, the future will be shaped by those who harness new technologies while leveraging their inherent human strengths, such as resilience and wisdom [22][14].

Core Viewpoint - The emergence of artificial intelligence (AI) is reshaping various industries, highlighting the importance of human qualities such as judgment, resilience, vitality, and self-awareness, which are becoming increasingly valuable in the face of algorithmic advancements [1][2][3][4][5][6][7][8] Group 1: AI as a Selection Tool - The definition of "elite" or "valuable talent" evolves alongside technological advancements, with AI serving as the latest and most powerful benchmark [2] - The shift in competitive standards from "knowledge acquisition" to "intelligent application" emphasizes the need for individuals to adeptly use AI tools [2][3] - As knowledge becomes easily accessible, the focus shifts from what individuals know to how they can utilize that knowledge and navigate the unknown [2][3] Group 2: Human Qualities in the AI Era - Judgment and proactivity are essential in an age of information overload, where AI can generate numerous solutions but lacks the ability to understand human emotions and societal nuances [3][4] - Resilience, the ability to learn from mistakes and adapt, is a critical human trait that AI cannot replicate [3][4] - Intuition and creativity are highlighted as key drivers of innovation, with human capabilities pushing beyond the limits of AI optimization [4][5] Group 3: The Role of Self-Awareness - Self-awareness is crucial for maintaining judgment, resilience, and creativity amidst external chaos, allowing individuals to navigate their emotional states and pressures effectively [4][5] - The ability to understand and respond to unspoken human needs is a valuable skill that AI struggles to achieve, emphasizing the importance of emotional intelligence [5][6] Group 4: Moving Beyond Technological Determinism - The discussion emphasizes the need to move away from technological determinism, which views technology as the sole driver of societal change, advocating for proactive human engagement with technology [6][7] - Individuals are encouraged to cultivate irreplaceable qualities through diverse experiences, emotional regulation, and genuine social interactions [6][7] Group 5: Educational and Societal Transformation - A systemic shift in education is necessary, focusing on skill development rather than rote knowledge, promoting project-based learning and emotional intelligence [7] - Organizations should foster a culture that rewards innovation and tolerates valuable failures, providing psychological safety for employees [7][8] - The ultimate significance of AI lies in its ability to prompt a return to essential human qualities, allowing individuals to focus on emotional, creative, and exploratory aspects of life [7][8]

Core Viewpoint - The article discusses the cognitive revolution in the news industry driven by generative AI, emphasizing the transformation of news production processes and the evolving relationship between journalists and technology [6][10][11]. Group 1: Historical Context of Technological Outsourcing - The history of human technological advancement can be viewed as a process of "outsourcing" human capabilities, both physical and cognitive [5][8]. - The evolution of media has consistently extended human cognitive abilities, from the invention of writing to the internet, which has facilitated global knowledge sharing [8][9]. Group 2: Impact of Generative AI on News Industry - Generative AI represents a deeper version of cognitive outsourcing, significantly altering the workflow in journalism by transforming traditional processes into a more collaborative model between AI and journalists [10][11]. - The traditional linear workflow of news production has been restructured, allowing for faster content generation and distribution, with AI assisting in various stages of the process [11][12]. Group 3: Changing Roles of Journalists - Journalists are transitioning from active information gatherers to information curators and content validators, raising questions about the implications of this shift [13][14]. - Different media organizations are responding to generative AI in varied ways, with some embracing the technology while others resist it, reflecting a spectrum of adaptation strategies [13][14]. Group 4: Resilience of the News Industry - The article argues against the deterministic view that technology will completely replace journalism, highlighting the unique human qualities that remain irreplaceable, such as empathy, critical thinking, and deep contextual understanding [15][16]. - Historical trends show that journalism has consistently adapted to technological changes, suggesting that the industry will continue to evolve rather than disappear [14][15]. Group 5: Future of Journalism in the Age of AI - The future of journalism will likely involve a focus on depth and quality of content, with human journalists concentrating on in-depth reporting and analysis, while AI handles more routine tasks [19][20]. - The article concludes that the integration of AI should enhance human qualities in journalism, positioning these traits as essential for the industry's survival and relevance [22][20].

Core Viewpoint - The article discusses the social processes surrounding technology, particularly focusing on the historical context of the mechanical reaper and pneumatic forming machines, highlighting how technological advancements can lead to economic changes while also exacerbating labor exploitation and wealth inequality [2][3][24]. Group 1: Historical Context and Technological Impact - The invention of the mechanical reaper by Cyrus McCormick significantly improved agricultural productivity and contributed to the industrial revolution in the United States [1]. - After McCormick's death, his successor, Cyrus McCormick Jr., reduced worker wages and replaced skilled labor with pneumatic forming machines, leading to lower quality production but higher profits [1][2]. - The use of pneumatic forming machines allowed for mass production of mechanical reapers, intensifying labor exploitation and capital accumulation [2][3]. Group 2: Economic Theories and Technology - The article critiques technological determinism, which posits that technology autonomously drives social and economic changes, arguing instead that technology is influenced by political, economic, and cultural factors [4][5]. - Public funding plays a crucial role in technological advancement, as seen in the U.S. where government support has historically driven innovation in various sectors [6][7]. - The relationship between technology and economic development is complex, with public finance often not translating technological gains into broader social benefits [8][9]. Group 3: International Trade and Development - Daron Acemoglu's analysis indicates that the same technology can have different impacts on developed and developing countries, with the latter often unable to benefit from imported technologies due to mismatched labor skills [10][11]. - Global value chains allow developing countries to access technology, but the technologies introduced are often not suited for their labor markets, leading to limited economic benefits [12][13]. - The article highlights that the introduction of labor-saving technologies in developing countries can exacerbate existing inequalities and fail to create sufficient employment opportunities [14][15][16]. Group 4: Conclusion and Implications - While technological advancements can lower costs and improve efficiency, they do not guarantee economic development, as the distribution of economic benefits remains a critical issue [22][23]. - The discussion on the social processes of technology emphasizes the need for policies that ensure technological advancements contribute to broader economic and social welfare [25].

Core Viewpoint - The article argues that the narrative of Shanghai's waste classification failure is misleading, as data and industry feedback indicate that the system has been successful in improving waste management and resource recovery [2][19]. Group 1: Misconceptions about Waste Classification - A popular argument claims that advanced incineration technology renders waste sorting unnecessary, suggesting that sorting is merely a formality [3][4]. - Critics assert that waste sorting efforts are futile because collected waste is often mixed during transportation and processing [3][4]. - The notion of overcapacity in incineration plants is presented as evidence of the failure of the waste classification system [3][4]. Group 2: Evidence of Success - Industry feedback indicates that the quality of recyclable materials from Shanghai has improved significantly since the implementation of waste classification, with stable supply and reduced costs for recyclers [4][5]. - Official data shows that since the implementation of the waste management regulations in July 2019, the daily collection of recyclable materials has increased from approximately 4,000 tons to over 7,973 tons by 2024 [9]. - The amount of dry waste collected daily has decreased from 21,500 tons in 2018 to 17,200 tons in 2024, indicating a reduction in waste needing incineration [9][10]. Group 3: Understanding Incineration Plant Capacity - The phenomenon of incineration plants being underutilized is attributed to a successful reduction in the total amount of waste requiring incineration, rather than a failure of the classification system [7][10]. - The increase in incineration capacity from 13,300 tons per day to 28,000 tons per day reflects proactive planning to achieve "zero landfill" goals [11][10]. - The reduction in "other waste" is a direct result of effective waste sorting, which has led to a significant decrease in the volume of waste sent for incineration [10][11]. Group 4: Importance of Waste Sorting - Waste sorting is essential for providing high-quality fuel for incineration, as mixed waste can lead to inefficiencies and increased pollution [14][15]. - Proper sorting enhances the calorific value of waste, making incineration more efficient and environmentally friendly [14][15]. - The establishment of a comprehensive waste sorting system in Shanghai has improved the overall waste management process, countering claims of widespread mixing of sorted waste [15][16]. Group 5: Future Considerations - The article suggests that while current waste sorting efforts are necessary, future technological advancements may provide more efficient solutions for waste management [21][24]. - Innovations such as AI-based sorting robots and biodegradable materials could eventually reduce the need for manual sorting [22][23]. - The transition to these advanced technologies may take time, highlighting the importance of maintaining current waste management practices in the interim [24][25].

Core Viewpoint - The article argues against the notion that Shanghai's waste classification system has failed, presenting evidence that the system has actually improved waste management and recycling rates. Group 1: Waste Classification Success - Since the implementation of the waste management regulations in July 2019, the recovery rate of recyclable materials has increased from approximately 4,000 tons per day to over 7,973 tons per day by 2024 [16] - The amount of dry waste collected has decreased from 21,500 tons per day in 2018 to 17,200 tons per day in 2024, indicating a reduction in waste generation [16] - The volume of wet waste has increased from about 9,000 tons per day to over 12,000 tons per day, showing effective separation and processing [16] Group 2: Misinterpretation of Data - The claim that incineration plants are "underutilized" is misleading; it reflects the success of waste classification, as less "other waste" is being sent for incineration [19][18] - The increase in incineration capacity from 13,300 tons per day to 28,000 tons per day, with the number of incineration facilities rising from 9 to 15, indicates proactive planning to handle future waste [20][21] - The reduction in "other waste" is a direct result of successful waste classification, not a failure of the system [19] Group 3: Importance of Waste Sorting - Effective waste sorting enhances the quality of fuel for incineration, improving energy recovery and reducing pollution [32][27] - The presence of contaminants in mixed waste can lead to increased emissions of harmful substances, making sorting essential for environmental protection [28][31] - The establishment of a comprehensive waste sorting system in Shanghai ensures that different types of waste are collected and processed appropriately [34] Group 4: Future Considerations - The article suggests that while current waste classification efforts are beneficial, future technological advancements may provide more efficient solutions for waste management [46][49] - The potential for AI and machine vision technologies to automate waste sorting could reduce the burden on residents and improve efficiency [43] - The development of biodegradable materials may eventually eliminate the need for extensive waste sorting, but such advancements are still in progress [44][49]

Core Insights - The article discusses how large language models (LLMs) enhance information collection and filtering capabilities, likening them to satellite navigation systems that help users navigate the information landscape [1][2] - It emphasizes the importance of active judgment and selection in utilizing LLMs, which is crucial for individuals in the technological age [4] Group 1: Relationship Between Technology and Society - There are two contrasting views on the relationship between new technology and human societal evolution: a pessimistic view that sees potential threats from breakthrough technologies, and an optimistic view that believes in the progressive benefits of technological advancement [5] - The authors argue against technological determinism, asserting that human wisdom allows for weighing choices and planning for various potential scenarios, thus emphasizing a balanced approach to technology [5] Group 2: Individual Empowerment Through Technology - The authors highlight that technology acts as an amplifier of human capabilities, enabling individuals to enhance their creativity, productivity, and influence through AI [6] - A framework is proposed in "AI Empowerment" that outlines ten principles for individuals to effectively collaborate with technology, ensuring they benefit from the new capabilities and the collective value created by all users [6]