以华为、百度、阿里、腾讯、深度求索等为代表的科技企业，已在大模型、自动驾驶、工业视觉、智能 终端等领域形成技术积累。更重要的是，它们贴近市场需求，能迅速将技术转化为产品和服务。这些企 业在AI方面的产品都不是实验室里的"纸上谈兵"，而是真实创造经济价值的创新实践。 新一轮重点产业链高质量发展行动，应以"链主"企业为核心，构建"大中小企业融通创新"的生态。政府 应通过财税激励、首台套采购、数据开放等方式，支持龙头企业牵头组建创新联合体，带动上下游专精 特新"小巨人"企业共同攻关。同时，完善知识产权保护制度，特别是针对AI生成内容、算法模型等新兴 客体的确权与维权机制，让企业敢于投入、乐于创新。 周城雄(中国科学院科技战略咨询研究院研究员、数智创新与治理研究中心副主任) 近日召开的中央经济工作会议将"坚持创新驱动，加紧培育壮大新动能"列为2026年经济工作的重点任 务，并明确提出"实施新一轮重点产业链高质量发展行动""深化拓展'人工智能+'，完善人工智能治 理"。这是国家对人工智能作为新质生产力关键引擎地位的再次确认。 在当前全球科技竞争日趋激烈、产业链加速重构的背景下，"人工智能+"已不再仅仅是技术层面的突 破 ...

Core Insights - Nvidia's stock price surpassed $207.86, marking a market capitalization of $5.05 trillion, making it the first company to reach this milestone [1] - Nvidia's rapid growth is attributed to its strategic decisions and the surge in demand for AI computing power, particularly following the emergence of generative AI technologies like ChatGPT [1] - The historical context of technological revolutions suggests that the success of AI as a general-purpose technology depends on the timely development of supporting infrastructure [2] Group 1: Nvidia's Market Position - Nvidia has transformed from a small graphics card company to a tech giant with a market cap exceeding the GDP of Germany and Japan [1] - The demand for AI computing power has led major AI companies to invest heavily in GPUs, benefiting Nvidia significantly [1] Group 2: Historical Context of Technological Revolutions - Major technological revolutions have historically been driven by general-purpose technologies, which require corresponding infrastructure for effective implementation [2] - The lessons from past revolutions, such as the steam engine and electricity, highlight the importance of infrastructure in realizing the full potential of new technologies [2][8] Group 3: Infrastructure and Standardization - The early stages of infrastructure development often face challenges such as lack of standardization, which can hinder efficiency and interoperability [22] - The AI infrastructure currently mirrors past scenarios where various entities operate independently, leading to fragmentation [6][22] - Establishing common standards is crucial for maximizing the potential of AI technologies and ensuring cohesive development [22] Group 4: Lessons from Past Crises - Historical technological bubbles have often resulted in over-investment in infrastructure, which later becomes foundational for future advancements [26][27] - The concept of "constructive destruction" suggests that while financial bubbles are risky, they can also provide essential infrastructure for future growth [26][27] - The key for the AI industry will be to effectively utilize the infrastructure developed during the current investment phase, regardless of potential market corrections [27][28]

Core Insights - AI is recognized as a new general-purpose technology (GPT) that has the potential to drive economic growth, but it requires significant time to impact productivity meaningfully [1][4][5] - Despite the ongoing AI revolution, productivity growth has not accelerated significantly, with the EU's hourly labor productivity declining by 0.6% in 2023 and only expected to grow by 0.4% in 2024 [4][5] - The adoption rate of AI in enterprises remains low, with the EU's average at 13.5% and the US at 9.2%, indicating that AI has not yet permeated traditional industries that need productivity improvements [8][10] - Investment in AI is increasing, with major tech companies allocating a significant portion of their revenue to capital expenditures, which is contributing to GDP growth despite low profitability in AI model firms [10][14] - The belief in AI's potential is driving economic growth more than the actual productivity gains from AI at this stage [18] Group 1: AI as a General-Purpose Technology - AI is characterized by continuous improvement, broad applicability, and complementary innovations, similar to historical GPTs like the steam engine and computer [1][4] - Historical data shows that it took decades for previous GPTs to significantly enhance productivity after their invention and commercialization [1][3] Group 2: Current Productivity Trends - The average labor productivity growth in the US since 2020 is 1.8%, below the long-term average of 2.2%, with future projections for AI's contribution to productivity growth being modest [5][4] - The EU's productivity growth from 1995 to 2019 averaged 1%, contrasting sharply with current projections [4] Group 3: AI Adoption Rates - AI adoption rates vary widely, with the EU ranging from 3.1% to 27.6% and the US at 9.2%, indicating that enterprise applications of AI are still in early stages [8][10] - The shift in value from chips and data to model providers is noted, but traditional industries have yet to see significant improvements in productivity [10] Group 4: Investment Trends - Major internet companies in the US and China are significantly increasing their capital expenditures, with the US companies averaging 27.4% of revenue and Chinese BAT averaging 12.5% [10][12] - AI data center spending is projected to contribute more to GDP growth than consumer spending for the first time, highlighting the shift in investment focus [14][17] Group 5: Future Perspectives on AI and Fusion Energy - The belief in AI's transformative potential is compared to historical expectations surrounding nuclear energy, with significant investments being made in both fields [18][19] - Nuclear fusion companies have raised substantial funding, indicating a growing interest in alternative energy solutions alongside AI advancements [25][26]