作者：吴晨 封图：图虫创意 1882年3月19日，巴塞罗那的春日还带着一丝寒意，圣家堂的第一块基石在瓜尔迪亚夫河附近的空地上埋下。当时没人能想到，这座教堂会成为跨越三个 世纪的工程奇迹。 1883年，年仅31岁的建筑师安东尼·高迪接手设计圣家堂，从此将自己的余生与这座建筑牢牢绑定。 高迪对圣家堂的愿景，从来不仅仅是一座教堂。他要让石头说话，让建筑成为自然与信仰的对话：外墙每一块石材的弧度模仿树干的生长、海浪的起伏， 内部立柱如森林中的树干般向上分叉；顶部的彩色玻璃透过阳光，会在地面投下如彩虹般的光影…… 跨越三个世纪的圣家堂，本身就是乐观主义精神的具象化——发起一项无法在个人生命周期内完成的事业，让梦想在代代相传中持续生长。 凯文·凯利就非常推崇这种长期主义的项目。今年10月初，我与他在中国共同推广我们合著的新书《2049》，在密集的交流中，我们持续深化对未来二十 五年的思考。 讨论逐步形成了一个重要的思考框架——我们需要不断校准对技术发展、中美博弈、人与AI关系等一系列重大问题的认知，并有两个观点清晰地浮现出 来。 第一，我们反复琢磨：为什么在充满不确定性的当下，更要坚定地保持乐观？圣家堂就是最好的答案。人 ...

接下来，我们将从咨询实践出发，系统讲清：为什么文件与现场会产生巨大落差？它会带来哪些隐性风 险？企业又该如何真正做到"文件一致于现场"？ 当"纸上的流程"遇上"现场的真实" 在制造业现场，有一种情况几乎人人都遇到过：一条成熟稳定的产线要搬迁或在其他工厂复刻。所有文 件都已经准备得整整齐齐——几十页的作业指导书、流程文件、治具清单、SOP 一样不差。团队信心 满满，觉得新工厂一定能顺利投产。 但现实常常给人当头一棒。刚开线第一周，节拍掉了、品质飘红了、员工抱怨了，现场一片手忙脚乱。 问题的根源往往既不在设备，也不在人员，而是——那些写得很漂亮的搬迁指导书，与现场真实的操作 方式根本对不上。 这绝不是简单的"搬线"问题，而是许多企业长期忽视的一件事：现场在不断改进，但文件没有同步更 新。 在制造业，我们常见一个让人头疼的场景：一条运行顺畅的老产线被复制到新工厂，所有文件、工艺指 导、标准流程都准备得井井有条，团队信心满满地启动项目——结果投产第一周就乱了套：节拍下降、 质量飘红、返工不断、员工抱怨连连。 更糟的是，没有人能说清楚问题出在哪。人们这时才后知后觉：真实世界的操作方式，和文件上写的流 程，并不是同一件事 ...

Core Insights - McKinsey has received an award from OpenAI for being a major client in token consumption, raising questions about the traditional consulting model as it relies on AI-generated content [1][3][4] - The consulting industry is undergoing a significant transformation as firms like McKinsey and BCG embrace AI technologies to enhance operational efficiency and redefine their service offerings [5][19] AI Integration in Consulting Firms - McKinsey has been proactive in AI adoption, having acquired QuantumBlack in 2015, which has since evolved into its AI-native consulting division [7][10][13] - The launch of McKinsey's internal AI, Lilli, has allowed consultants to automate PPT generation and streamline research processes, with over 70% of employees using it [14][18] - BCG has developed multiple internal AI tools, with nearly 90% of its employees utilizing AI in their daily work, indicating a competitive push in AI integration [21][25] Workforce Changes and Challenges - McKinsey has laid off over 5,000 employees, approximately 10% of its workforce, attributed to overexpansion during the pandemic and the impact of AI on job roles [27][28][30] - The rise of AI has led to increased productivity, with AI handling about 30% of information gathering tasks, raising concerns about the future of entry-level positions [32][33][56] - The consulting industry is witnessing a decline in entry-level hiring, with a 54% drop in recruitment for junior consultants, as firms prioritize experienced hires [60][63] Emergence of AI-Driven Startups - New AI-driven companies are emerging, offering alternatives to traditional consulting services, targeting small to medium-sized enterprises that cannot afford established firms like McKinsey [49][52] - These startups are leveraging AI to automate consulting processes, posing a competitive threat to traditional firms by providing cost-effective and immediate solutions [41][53] The Future of Consulting - The consulting industry is undergoing a fundamental transformation, with AI replacing traditional roles and altering the career trajectory for new consultants [55][72] - Despite the challenges posed by AI, there remains a belief that human consultants will still be needed for complex problem-solving and insights that AI cannot replicate [69][70]

Group 1: Core Insights - Omar Shams emphasizes that while chips are important, energy supply is the key constraint for the long-term development of AI. The slow expansion of the US power grid contrasts with China's annual addition of power capacity exceeding that of the UK and France combined [3][5][6] - Shams proposes the idea of deploying solar power stations on the Moon or in space to support AI computing power, highlighting the need for innovative energy solutions [3][6][7] - The competition in AI infrastructure between the US and China is increasingly defined by energy supply differences, which could impact the future of AI development [3][5][6] Group 2: Talent and Knowledge in AI - The scarcity of theoretical physicists is highlighted as a valuable asset in AI research, with Shams noting that physical intuition plays a crucial role in optimizing loss functions and understanding complex AI models [3][20][24] - There is a distinction between "secrets" and "tacit knowledge" in AI, where the latter, derived from experience and intuition, is seen as the core competitive advantage for top AI talent [3][10][14] - The demand for software development talent is undergoing a transformation, with predictions that AI tools could lead to a 30% reduction in programmer jobs within two years, particularly affecting junior positions [3][15][19] Group 3: AI Agent Technology and Its Impact - AI agent technology is moving from concept validation to practical application, with tools like Cursor and GitHub Copilot significantly changing the software development landscape [3][16][17] - In the legal sector, AI companies like Harvey are generating substantial revenue, indicating a trend where AI assistants are becoming essential in white-collar jobs [3][17] - The introduction of AI assistants is expected to reshape workflows, either by assisting human workers or directly replacing certain roles, leading to a higher standard in the software industry [3][17][19] Group 4: The Role of Physics in AI - Shams discusses his transition from theoretical physics to AI, emphasizing how the intuition and visualization skills developed in physics contribute to understanding AI processes [3][21][24] - The ability to handle continuous mathematics and emergent phenomena, learned through physics training, aligns well with the mathematical nature of large-scale neural networks [3][24][25] - While physicists may lack sensitivity to discrete algorithms and engineering details, their continuous thinking often proves more effective at larger scales [3][25][26]