Steam, Steel, and Infinite Minds蒸汽、钢铁与无限智慧
Every era is shaped by its miracle material. Steel forged the Gilded Age. Semiconductors switched on the Digital Age. Now AI has arrived as infinite minds. If history teaches us anything, those who master the material define the era.每一个时代都由其标志性的奇迹材料所塑造。钢铁锻造了镀金时代，半导体开启了数字时代。如今，AI 以“无限智慧”的姿态降临。如果历史能给我们带来什么启示，那就是：谁掌握了这种材料，谁就定义了那个时代。
Left: teenage Andrew Carnegie and his younger brother.左图：少年时期的安德鲁·卡内基（Andrew Carnegie）及其弟弟。
Right: Pittsburg steel factories during the Glided Age.右图：镀金时代的匹兹堡钢铁厂。
In the 1850s, Andrew Carnegie ran through muddy Pittsburgh streets as a telegraph boy. Six in ten Americans were farmers. Within two generations, Carnegie and his peers forged the modern world. Horses gave way to railroads, candlelight to electricity, iron to steel.19 世纪 50 年代，安德鲁·卡内基还是个在匹兹堡泥泞街道上奔跑的电报送信员。当时，十个美国人中有六个是农民。在两代人的时间里，卡内基和他的同侪锻造了现代世界。马匹让位于铁路，烛光让位于电力，铁让位于钢。
Since then, work shifted from factories to offices. Today I run a software company in San Francisco, building tools for millions of knowledge workers. In this industry town, everyone is talking about AGI, but most of the two billion desk workers have yet to feel it. What will knowledge work look like soon? What happens when the org chart absorbs minds that never sleep?从那时起，工作场所从工厂转移到了办公室。如今，我在旧金山经营着一家软件公司，为数百万知识工作者打造工具。在这个行业之城，每个人都在谈论 AGI（通用人工智能），但那二十亿办公族中的大多数人尚未真正感受到它的影响。不久后的知识工作会是什么样？当组织架构中融入了永不疲倦的智慧时，会发生什么？
Early movies often looked like stage plays, with one camera focused on the stage.早期的电影看起来往往像舞台剧，只有一台摄像机对着舞台拍摄。
This future is often difficult to predict because it always disguises itself as the past. Early phone calls were concise like telegrams. Early movies looked like filmed plays. (This is what Marshall McLuhan called "driving to the future via the rearview window.")这种未来往往难以预测，因为它总是伪装成过去的样子。早期的电话通话像电报一样简洁，早期的电影看起来像拍摄的舞台剧。（这就是马歇尔·麦克卢汉所说的“通过后视镜驾驶进入未来”。）
The most popular form of AI today look like Google search of the past. To quote Marshall McLuhan: "we are always driving into the future via the rearview window."当今最流行的 AI 形态看起来就像过去的谷歌搜索。借用马歇尔·麦克卢汉的话：“我们总是通过后视镜驾驶进入未来。”
Today, we see this as AI chatbots which mimic Google search boxes. We're now deep in that uncomfortable transition phase which happens with every new technology shift.今天，我们看到的 AI 聊天机器人大多模仿着谷歌搜索框。我们正处于每项新技术变革中都会出现的那个令人不适的过渡阶段。
I don't have all the answers on what comes next. But I like to play with a few historical metaphors to think about how AI can work at different scales, from individuals to organizations to whole economies.对于接下来会发生什么，我并没有所有答案。但我喜欢运用一些历史隐喻来思考 AI 如何在从个人、组织到整个经济体的不同规模上发挥作用。
Individuals: from bicycles to cars个人：从自行车到汽车
The first glimpses can be found with the high priests of knowledge work: programmers.我们可以在知识工作的“大祭司”——程序员身上看到最初的端倪。
My co-founder Simon was what we call a 10× programmer, but he rarely writes code these days. Walk by his desk and you'll see him orchestrating three or four AI coding agents at once, and they don't just type faster, they think, which together makes him a 30-40× engineer. He queues tasks before lunch or bed, letting them work while he's away. He's become a manager of infinite minds.我的联合创始人 Simon 曾是我们所说的“十倍速程序员”，但他现在很少写代码了。走过他的工位，你会看到他同时指挥着三四个 AI 编程代理。它们不仅打字更快，而且会思考，这使他成为了一个“三四十倍速”的工程师。他在午餐前或睡前排好任务，让 AI 在他离开时工作。他已经成为“无限智慧”的管理者。
A 1970s Scientific American study on locomotion efficiency inspired Steve Jobs's famous 'bicycle for the mind' metaphor. Except we've been pedaling on the Information Superhighway for decades since.20 世纪 70 年代《科学美国人》关于运动效率的一项研究启发了史蒂夫·乔布斯著名的“思维自行车”隐喻。只不过，此后的几十年里，我们一直是在“信息高速公路”上骑自行车。
In the 1980s, Steve Jobs called personal computers "bicycles for the mind." A decade later, we paved the "information superhighway" that is the internet. But today, most knowledge work is still human-powered. It's like we've been pedaling bicycles on the autobahn.20 世纪 80 年代，史蒂夫·乔布斯将个人电脑称为“思维的自行车”。十年后，我们铺设了所谓的互联网“信息高速公路”。但今天，大多数知识工作依然依靠人力驱动。这就好比我们一直在高速公路上骑自行车。
With AI agents, someone like Simon has graduated from riding a bicycle to driving a car.有了 AI 代理，像 Simon 这样的人已经从骑自行车升级到了开汽车。
When will other types of knowledge workers get cars? Two problems must be solved.其他类型的知识工作者何时才能开上汽车？必须先解决两个问题。
Comparing with coding agent, why is it more difficult for AI to help with knowledge work? Because knowledge work is more fragmented and less verifiable.与编程代理相比，为什么 AI 辅助其他知识工作更难？因为知识工作更加碎片化，且更难以验证。
First, context fragmentation. For coding, tools and context tend to live in one place: the IDE, the repo, the terminal. But general knowledge work is scattered across dozens of tools. Imagine an AI agent trying to draft a product brief: it needs to pull from Slack threads, a strategy doc, last quarter's metrics in a dashboard, and institutional memory that lives only in someone's head. Today, humans are the glue, stitching all that together with copy-paste and switching between browser tabs. Until that context is consolidated, agents will stay stuck in narrow use-cases.首先是语境碎片化。对于编程而言，工具和上下文往往集中在一个地方：集成开发环境（IDE）、代码仓库、终端。但一般的知识工作分散在几十种工具中。想象一下，一个 AI 代理试图起草一份产品简介：它需要从 Slack 聊天记录、战略文档、仪表盘上的上季度指标，以及仅存在于某人脑中的机构记忆中提取信息。今天，人类充当了粘合剂，通过复制粘贴和在浏览器标签页之间切换来拼凑这一切。在上下文整合之前，代理将局限于狭窄的用例中。
The second missing ingredient is verifiability. Code has a magical property: you can verify it with tests and errors. Model makers use this to train AI to get better at coding (e.g. reinforcement learning). But how do you verify if a project is managed well, or if a strategy memo is any good? We haven't yet found ways to improve models for general knowledge work. So humans still need to be in the loop to supervise, guide, and show what good looks like.第二个缺失的要素是可验证性。代码有一个神奇的特性：你可以通过测试和报错来验证它。模型开发者利用这一点来训练 AI 提高编程能力（例如强化学习）。但你如何验证一个项目管理得好不好，或者一份战略备忘录写得好不好？我们尚未找到改进通用知识工作模型的方法。因此，人类仍然需要参与其中，进行监督、引导，并展示什么是“好”的标准。
The Red Flag Act of 1865 required a flag bearer to walk ahead of the vehicle while it drove down the street (repealed in 1896). An example of undesirable "human in the loop."1865 年的《红旗法案》要求汽车在街上行驶时，必须有一名旗手走在车前（该法案于 1896 年废除）。这是一个“人在回路”模式下不理想的例子。
Programming agents this year taught us that having a "human-in-the-loop" isn't always desirable. It's like having someone personally inspect every bolt on a factory line, or walk in front of a car to clear the road (see: the Red Flag Act of 1865). We want humans to supervise the loops from a leveraged point, not be in them. Once context is consolidated and work is verifiable, billions of workers will go from pedaling to driving, and then from driving to self-driving.今年的编程代理教会了我们，拥有“人在回路”（human-in-the-loop）并不总是理想的。这就像让一个人亲自检查工厂流水线上的每一个螺栓，或者走在汽车前面清理道路（参见 1865 年的《红旗法案》）。我们希望人类从更高的杠杆点监督循环，而不是身处循环之中。一旦上下文被整合且工作可验证，数十亿工作者将从骑自行车转变为开车，进而从开车转变为自动驾驶。
Organizations: steel and steam组织：钢铁与蒸汽
Companies are a recent invention. They degrade as they scale and reach their limit.公司是一个近代的发明。它们在扩张时会退化，并最终达到极限。
Organizational chart for the New York and Erie Railroad, 1855. The modern corporation and org chart evolved with the railroad companies, which were the first enterprises that needed to coordinate thousands of people across great distances.1855 年纽约伊利铁路的组织架构图。现代企业和组织架构图随着铁路公司的发展而演变，它们是第一批需要在远距离协调成千上万人的企业。
A few hundred years ago, most companies were workshops of a dozen people. Now we have multinationals with hundreds of thousands. The communication infrastructure (human brains connected by meetings and messages) buckles under exponential load. We try to solve this with hierarchy, process, and documentation. But we've been solving an industrial-scale problem with human-scale tools, like building a skyscraper with wood.几百年前，大多数公司是十几人的作坊。现在我们有了拥有数十万员工的跨国公司。通信基础设施（通过会议和消息连接的人类大脑）在指数级负荷下不堪重负。我们试图通过层级、流程和文档来解决这个问题。但我们一直试图用人类尺度的工具去解决工业规模的问题，就像用木头建造摩天大楼一样。
Two historical metaphors show how future organizations can look differently with new miracle materials.两个历史隐喻展示了未来的组织如何利用新的奇迹材料呈现出不同的面貌。
A wonder of steel: the Woolworth building was the tallest building in the world upon completion in NYC, 1913.钢铁的奇迹：伍尔沃斯大厦（Woolworth Building）于 1913 年在纽约建成时，是当时世界上最高的建筑。
The first is steel. Before steel, buildings in the 19th century had a limit of six or seven floors. Iron was strong but brittle and heavy; add more floors, and the structure collapsed under its own weight. Steel changed everything. It's strong yet malleable. Frames could be lighter, walls thinner, and suddenly buildings could rise dozens of stories. New kinds of buildings became possible.第一个隐喻是钢铁。在钢铁出现之前，19 世纪的建筑高度限制在六七层。铁虽然坚硬，但脆且重；增加楼层，结构就会在自身重量下坍塌。钢铁改变了一切。它既坚固又具有延展性。框架可以更轻，墙壁可以更薄，建筑突然间可以升至几十层高。新型建筑成为可能。
AI is steel for organizations. It has the potential to maintain context across workflows and surface decisions when needed without the noise. Human communication no longer has to be the load-bearing wall. The weekly two-hour alignment meeting becomes a five-minute async review. The executive decision that required three levels of approval might soon happen in minutes. Companies can scale, truly scale, without the degradation we've accepted as inevitable.AI 就是组织的“钢铁”。它有潜力在工作流程中保持上下文，并在需要时浮现决策，而无需额外的噪音。人类的沟通不再必须成为承重墙。每周两小时的对齐会议可以变成五分钟的异步审查。曾经需要三级审批的高管决策，很快可能在几分钟内完成。企业可以实现真正的规模化，而不会出现我们原本认为不可避免的退化。
A mill with a water wheel to power its operations. Water was powerful but unreliable and restricted mills to a few locations and seasonality.一个利用水车驱动运作的磨坊。水力虽然强大但不可靠，且将磨坊限制在特定地点和季节性中。
The second story is about the steam engine. At the beginning of the Industrial Revolution, early textile factories sat next to rivers and streams and were powered by waterwheels. When the steam engine arrived, factory owners initially swapped waterwheels for steam engines and kept everything else the same. Productivity gains were modest.第二个故事关于蒸汽机。工业革命初期，早期的纺织厂坐落在河流溪流旁，由水车驱动。当蒸汽机出现时，工厂主最初只是用蒸汽机替换了水车，其他一切照旧。生产力的提升微乎其微。
The real breakthrough came when factory owners realized they could decouple from water entirely. They built larger mills closer to workers, ports, and raw materials. And they redesigned their factories around steam engines (Later, when electricity came online, owners further decentralized away from a central power shaft and placed smaller engines around the factory for different machines.) Productivity exploded, and the Second Industrial Revolution really took off.真正的突破在于工厂主意识到他们可以完全摆脱对水力的依赖。他们将更大的工厂建在靠近工人、港口和原材料的地方。他们围绕蒸汽机重新设计了工厂。（后来，当电力普及，工厂主进一步将动力从中心传动轴分散开来，在工厂各处为不同机器配备了小型发动机。）生产力爆发式增长，第二次工业革命真正起飞了。
This 1835 engraving by Thomas Allom depicts a textile factory in Lancashire, UK. It was powered by steam engines.这张托马斯·阿洛姆（Thomas Allom）于 1835 年创作的版画描绘了英国兰开夏郡的一家纺织厂，它由蒸汽机驱动。
We're still in the "swap out the waterwheel" phase. AI chatbots bolted onto existing tools. We haven't reimagined what organizations look like when the old constraints dissolve and your company can run on infinite minds that work while you sleep.我们目前仍处于“替换水车”的阶段。AI 聊天机器人被安装在现有的工具上。我们还没有重新构想当旧的约束消失，公司可以在永不疲倦的无限智慧驱动下运行时，组织会是什么样子。
At my company Notion, we have been experimenting. Alongside our 1,000 employees, more than 700 agents now handle repetitive work. They take meeting notes and answer questions to synthesize tribal knowledge. They field IT requests and log customer feedback. They help new hires onboard with employee benefits. They write weekly status reports so people don't have to copy-paste. And this is just baby steps. The real gains are limited only by our imagination and inertia.在我的公司 Notion，我们一直在进行实验。除了 1,000 名员工外，现在有超过 700 个代理在处理重复性工作。它们负责记录会议纪要、回答问题以整合内部知识。它们处理 IT 请求并记录客户反馈。它们帮助新员工办理入职和福利手续。它们撰写每周状态报告，这样人们就不必进行复制粘贴。而这仅仅是起步。真正的收益仅受限于我们的想象力和惰性。
Economies: from Florence to megacities经济：从佛罗伦萨到超级都市
Steel and steam didn't just change buildings and factories. They changed cities.钢铁和蒸汽不仅改变了建筑和工厂，它们还改变了城市。
Until a few hundred years ago, cities were human-scaled. You could walk across Florence in forty minutes. The rhythm of life was set by how far a person could walk, how loud a voice could carry.直到几百年前，城市还是人类尺度的。你可以在四十分钟内走过佛罗伦萨。生活的节奏由人的步行距离、嗓音的传播范围所决定。
Then steel frames made skyscrapers possible. Steam engines powered railways that connected city centers to hinterlands. Elevators, subways, highways followed. Cities exploded in scale and density. Tokyo. Chongqing. Dallas.随后，钢框架使摩天大楼成为可能。蒸汽机驱动的铁路将市中心与腹地连接起来。电梯、地铁、公路接踵而至。城市在规模和密度上爆发式增长。东京、重庆、达拉斯。
These aren't just bigger versions of Florence. They're different ways of living. Megacities are disorienting, anonymous, harder to navigate. That illegibility is the price of scale. But they also offer more opportunity, more freedom. More people doing more things in more combinations than a human-scaled Renaissance city could support.这些不仅仅是佛罗伦萨的放大版。它们是不同的生活方式。超级都市令人迷失、匿名、难以导航。这种“难以辨识”是规模化带来的代价。但它们也提供了更多的机会、更多的自由。它们支持了比人类尺度的文艺复兴城市所能容纳的更多的人、更多的事以及更多的组合。
I think the knowledge economy is about to undergo the same transformation.我认为知识经济即将经历同样的转型。
Today, knowledge work represents nearly half of America's GDP. Most of it still operates at human scale: teams of dozens, workflows paced by meetings and email, organizations that buckle past a few hundred people. We've built Florences with stone and wood.今天，知识工作占美国 GDP 近一半。其中大部分仍以人类尺度运作：几十人的团队，由会议和电子邮件驱动的工作流程，以及在超过几百人规模后就会陷入困境的组织。我们是用石头和木头建造了“佛罗伦萨”。
When AI agents come online at scale, we'll be building Tokyos. Organizations that span thousands of agents and humans. Workflows that run continuously, across time zones, without waiting for someone to wake up. Decisions synthesized with just the right amount of human in the loop.当 AI 代理大规模上线时，我们将建造“东京”。组织将跨越数千个代理和人类。工作流程将在跨时区的情况下持续运行，无需等待某人醒来。决策将在恰当的“人在回路”程度下进行综合。
It will feel different. Faster, more leveraged, but also more disorienting at first. The rhythms of the weekly meeting, the quarterly planning cycle, and the annual review may stop making sense. New rhythms emerge. We lose some legibility. We gain scale and speed.感觉会大不相同。更快、杠杆效应更强，但起初也会更令人困惑。每周会议、季度规划周期和年度评估的节奏可能不再有意义。新的节奏将会出现。我们失去了一些易读性，但获得了规模和速度。
Beyond the waterwheels超越水车
Every miracle material required people to stop seeing the world via the rearview mirror and start imagining the new one. Carnegie looked at steel and saw city skylines. Lancashire mill owners looked at steam engines and saw factory floors free from rivers.每一种奇迹材料都要求人们停止通过后视镜观察世界，开始构想新的世界。卡内基看着钢铁，看到了城市天际线。兰开夏郡的工厂主看着蒸汽机，看到了摆脱河流束缚的工厂车间。
We are still in the waterwheel phase of AI, bolting chatbots onto workflows designed for humans. We need to stop asking AI to be merely our copilots. We need to imagine what knowledge work could look like when human organizations are reinforced with steel, when busywork is delegated to minds that never sleep.我们仍处于 AI 的“水车”阶段，将聊天机器人安装在为人类设计的工作流程上。我们需要停止仅仅要求 AI 做我们的副驾驶。我们需要构想当人类组织用钢铁加固，当琐事被委托给永不疲倦的智慧时，知识工作会是什么样子。
Steel. Steam. Infinite minds. The next skyline is there, waiting for us to build it.钢铁、蒸汽、无限智慧。下一道天际线就在那里，等待我们去建造。
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