【导读】2025年底，当人类都在憧憬和等待一个全知全能的AI之神时，谷歌DeepMind却泼了一盆冷水！ 12月19日，谷歌DeepMind抛出了一个让人细思极恐又脑洞大开的新观点： 如果所谓的AGI（通用人工智能）并不是一个超级实体，而是「凑出来」的呢？ 论文地址：https://arxiv.org/abs/2512.16856 在人工智能发展的宏大叙事中，我们长期被一种单一的、近乎宗教般的想象所占据：通用人工智能（AGI）将以一个全知全能的「超级大脑」形式降临。 这种叙事深深植根于科幻文学与早期AI研究的土壤中，导致当下的AI安全与对齐研究主要聚焦于如何控制这个假设中的单体化存在。 而且包括人工智能教父Hinton等人都试图将人类价值观植入这个大脑，仿佛只要解决了这个超级单体的「心智」问题，人类的安全便有了保障。 然而，DeepMind这篇在2025年末发布的重磅论文《分布式AGI安全》犹如一道惊雷，彻底颠覆了这一根深蒂固的假设。 这种「单体AGI」假设存在巨大的盲区，甚至可能是一个危险的误导！ 它忽视了复杂系统演化的另一种极高可能性的路径，也是生物界和人类社会智慧产生的真实路径：分布式涌现。 这不仅仅是 ...

Group 1 - The conference "2025 AI Partner Conference" was held in Beijing, focusing on the theme of "Chinese Solutions" and discussing the latest breakthroughs and ecosystem of AI in China [1] - Key topics included the potential of superintelligent agents as the core form of the next generation of AI and the integration of AI across various industries [1] - Professor Tan Yinliang from CEIBS presented on how AI drives business value and productivity enhancement, emphasizing the need to understand AI's impact on the economy and society over the next decade [3][5] Group 2 - The historical context of the electricity revolution was used to illustrate how AI might similarly transform productivity, highlighting that initial technological adoption does not guarantee immediate productivity gains [4][5] - The concept of "management" was identified as crucial for realizing productivity improvements, requiring changes in organizational structure and business processes rather than mere technology substitution [5][6] - The evolution of AI is compared to the electricity era, with current stages including initial technological breakthroughs and early applications, indicating that many companies have yet to see significant impacts from AI [7][8] Group 3 - The upcoming "structural transformation period" is seen as critical for Chinese enterprises, where businesses will need to rethink processes and systems to fully leverage AI [7][8] - The final phase of AI development is expected to be a "mature expansion period," where AI will create new business models and competitive advantages through deep integration into core operations [8]

Group 1: Employment Trends - Despite concerns about automation leading to job losses, the number of professionals in interpreting and translation has increased by 7% over the past year in the U.S., indicating that AI may enhance efficiency and create new demand in certain sectors [1] - The unemployment rate for recent graduates is approximately 4%, which is historically low, suggesting that attributing job market challenges solely to AI lacks sufficient evidence [5] - Employment in white-collar jobs has slightly increased over the past year, even in roles considered most susceptible to AI impact [5] Group 2: Corporate Attitudes Towards AI - A notable shift in attitude is observed in companies like Klarna, where the CEO emphasized the continued necessity of human intervention in customer service despite AI automation [3] - Less than 10% of U.S. companies have scaled AI applications in core business processes, indicating that while enthusiasm for AI is high, practical implementation remains limited [7] - AI is primarily enhancing existing employee productivity rather than directly replacing jobs, allowing workers to focus on more creative and strategic tasks [7] Group 3: Investment and Market Sentiment - The capital market has shifted from initial enthusiasm for AI to a more cautious stance, with many companies feeling pressure after failing to achieve expected returns on AI investments [9] - The percentage of companies abandoning AI pilot projects has risen from 17% to 42% over the past year, reflecting challenges in effectively integrating AI into existing business models [9][12] - Major tech companies face significant challenges during this "trough of disillusionment," including data integration issues, talent shortages, high implementation costs, and compliance risks [12] Group 4: Long-term Economic Perspectives - The "Productivity J-Curve" theory suggests that the positive impacts of AI on productivity may not be immediately visible and could initially lead to stagnation as companies invest in necessary adjustments [14] - The "Modern Productivity Paradox" indicates that despite rapid advancements in AI, macroeconomic productivity growth remains sluggish, highlighting a potential disconnect between technological progress and productivity statistics [15] - Historical patterns show that transformative technologies often undergo phases of initial disappointment before leading to significant economic and social changes [16] Group 5: Societal Implications of AI - The focus on whether AI will replace human jobs may distract from more critical discussions about how AI can enhance productivity and overall wealth creation [17] - The historical context of the Industrial Revolution illustrates that while machines replaced many jobs, they also significantly increased overall productivity and wealth [18] - The core question surrounding AI's future is whether it will contribute to overall economic growth or exacerbate wealth distribution issues, impacting societal equity [19][20] Group 6: Future Considerations - Current discussions about AI often center on immediate concerns like job displacement and ethical considerations, potentially overlooking broader strategic issues [21] - The future of AI requires collaborative efforts from businesses, researchers, policymakers, and the public to create supportive frameworks for its development [22] - The ongoing evolution of AI presents both challenges and opportunities, necessitating a collective approach to ensure it serves the greater good of society [23]