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.
每个时代都有属于它的划时代介质。钢铁锻造了镀金时代，半导体点亮了数字时代。而今天，AI 正以"无限心智"的姿态登场。历史反复证明：谁掌握了核心材料，谁就定义了那个时代。
Left: teenage Andrew Carnegie and his younger brother.
Right: Pittsburg steel factories during the Glided Age.
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童。那时，六成美国人以务农为生。仅仅两代人的时间，卡内基和他的同辈便锻造出了现代世界：马车让位于铁路，烛火让位于电灯，生铁让位于钢铁。
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?
自那以后，工作从工厂转移到了办公室。如今，我在旧金山经营一家软件公司，为数百万知识工作者打造工具。在这座科技重镇，人人都在谈论 AGI（通用人工智能），但全球二十亿办公室工作者中的大多数，尚未真正感受到它的存在。知识工作很快会变成什么样？当组织架构开始吸纳那些永不休眠的"思维"时，会发生什么？
Early movies often looked like stage plays, with one camera focused on the stage.
早期的电影看起来常常就像舞台剧，因为只有一台摄像机聚焦在舞台上。
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.")
未来往往难以预测，因为它总是乔装改扮成过去的样子。早期的电话像电报一样简洁，早期的电影看起来就像拍摄下来的舞台剧。（这就是马歇尔·麦克卢汉所说的"通过后视镜驶向未来"。）
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."
如今最流行的人工智能形态，看起来就像是过去的"谷歌搜索"。借用马歇尔·麦克卢汉的话来说："我们总是通过后视镜驶入未来。"
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.
如今，我们看到的AI聊天机器人，正是在模仿谷歌的搜索框。我们正深陷每一次技术变革都会伴随的尴尬转型期。
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.
我没有关于未来的全部答案。但我喜欢借助几个历史隐喻来思考 AI 如何在不同尺度上发挥作用，从个人到组织，再到整个经济。
Individuals: from bicycles to cars
个人层面：从自行车到汽车
The first glimpses can be found with the high priests of knowledge work: programmers.
最早的变化迹象，出现在知识工作的"大祭司"们身上：程序员。
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"10 倍程序员"，但他现在几乎不亲自写代码了。走过他的工位，你会看到他同时协调三四个 AI 编程智能体，它们不只是打字更快，它们会思考。这让 Simon 成了一个 30 到 40 倍效率的工程师。他会在午餐前或睡前排好任务队列，让这些智能体在他不在时继续工作。他成了"无限心智"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.
1970年代《科学美国人》杂志上一项关于 locomotion 效率的研究，激发了乔布斯著名的"头脑的自行车"比喻。然而自那时起，我们在信息高速公路上已经骑行了几十年。
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.
1980 年代，乔布斯把个人电脑比作"思维的自行车"。十年后，我们铺设了名为互联网的"信息高速公路"。但时至今日，大多数知识工作仍然由人力驱动。就好像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 智能体，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什么时候也能开上"汽车"？有两个问题必须解决。
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.
与编程助手相比，为什么AI更难帮助处理知识型工作？因为知识型工作更为碎片化且难以验证。
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.
第一，上下文碎片化。对于编程来说，工具和上下文往往集中在一处：IDE、代码仓库、终端。但一般的知识工作分散在几十个工具里。想象一个 AI 智能体试图起草一份产品简报：它需要从 Slack 对话、战略文档、上季度的数据仪表盘，甚至只存在于某人脑子里的组织记忆中提取信息。如今，人类充当着"粘合剂"，通过复制粘贴和在浏览器标签页之间切换来拼凑一切。除非这些上下文被整合起来，否则智能体只能困在狭窄的应用场景里。
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。代码有一种神奇的特性：你可以通过测试和报错来验证它。模型开发者利用这一点训练 AI，让它在编程方面越来越强（比如强化学习）。但你怎么验证一个项目管理得好不好，或者一份战略备忘录写得如何？我们还没有找到改进模型以处理一般知识工作的方法。所以，人类仍然需要"在回路中"（in the loop）监督、引导，并示范什么是好的成果。
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》规定，车辆在街上行驶时需由一名旗手在车前步行引导（该法案于1896年废除）。这正是"人机交互"环节中一个典型的反面案例。
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.
今年编程智能体的发展告诉我们，"人类在回路中"并非总是理想状态。这就像让人亲自检查流水线上的每一颗螺栓，或者走在汽车前面给它开道（参见：1865 年的《红旗法案》）。我们希望人类从一个更有杠杆效应的位置来监督这些循环，而不是被困在里面。一旦上下文被整合、工作变得可验证，数十亿工作者就能从"骑自行车"升级到"开车"，再从"开车"升级到"自动驾驶"。
Organizations: steel and steam
组织层面：钢铁与蒸汽
Companies are a recent invention. They degrade as they scale and reach their limit.
公司是一种晚近的发明。它们在扩张过程中会逐渐衰退，并最终触及极限。
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年纽约和伊利铁路公司的组织结构图。现代企业及其组织架构正是随着铁路公司的发展而演变形成的，因为铁路公司是最早需要协调数千名员工进行远距离运作的企业，从而催生了这种管理结构。
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，大多数公司不过是十几个人的作坊。如今我们有了拥有几十万员工的跨国企业。人脑通过会议和消息连接而成的通信基础设施，在指数级增长的负载下不堪重负。我们试图用层级、流程和文档来解决这个问题。但我们一直在用人类尺度的工具解决工业级的问题，就像用木头建摩天大楼。
Two historical metaphors show how future organizations can look differently with new miracle materials.
两个历史隐喻可以展示，有了新的"奇迹材料"，未来的组织会是什么样子。
A wonder of steel: the Woolworth building was the tallest building in the world upon completion in NYC, 1913.
钢铁奇迹：1913年在纽约落成的伍尔沃斯大楼，曾是当时全球最高的建筑。
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.
第一个是钢铁。在钢铁出现之前，19 世纪的建筑最高只能建到六七层。生铁虽然坚固，但又脆又重；层数一多，建筑就会在自身重量下坍塌。钢铁改变了一切。它既坚固又具有韧性。框架可以更轻，墙壁可以更薄，建筑突然可以升至几十层。新型建筑由此成为可能。
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 是组织的钢铁。它有潜力在工作流程中维护上下文，在需要时呈现决策，而不产生噪音。人类的沟通不再必须是承重墙。每周两小时的对齐会议可能变成五分钟的异步审核。需要三层审批的高管决策可能很快就能在几分钟内完成。公司可以真正地扩张，而不必承受我们以为无法避免的那种组织退化。
A mill with a water wheel to power its operations. Water was powerful but unreliable and restricted mills to a few locations and seasonality.
使用水车驱动的磨坊。水力虽强大，却具有不可靠性，且只能局限于少数地点，并受季节变化制约。
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.
第二个故事是关于蒸汽机的。在工业革命初期，早期的纺织厂坐落在河流和溪流旁边，靠水车驱动。当蒸汽机问世时，工厂主最初只是把水车换成了蒸汽机，其他一切照旧。生产力的提升很有限。
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.真正的突破发生在工厂主意识到他们可以完全摆脱对水源的依赖之后。他们在更靠近工人、港口和原材料的地方建起了更大的工厂。他们围绕蒸汽机重新设计了工厂（后来，当电力出现时，工厂主进一步去中心化，不再依赖一根中央动力轴，而是在工厂各处为不同机器安装小型电机）。生产力由此爆发，第二次工业革命真正启动了。
This 1835 engraving by Thomas Allom depicts a textile factory in Lancashire, UK. It was powered by steam engines.
这幅由托马斯·阿洛姆创作的1835年版画，描绘了英国兰开夏郡的一家纺织厂，当时已由蒸汽机驱动。
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.
我们仍处于"把水车换成蒸汽机"的阶段。AI 聊天机器人像螺丝一样被拧到现有工具上，我们还没有重新想象：当旧的约束消失、公司可以靠在你睡觉时仍在工作的"无限心智"来运转时，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 Notion，我们一直在做实验。除了 1,000 名员工之外，现在还有超过 700 个智能体在处理重复性工作。它们做会议记录，回答问题以整合组织知识，处理 IT 请求，记录客户反馈，帮助新员工熟悉员工福利，撰写周报以免大家再复制粘贴。而这只是刚刚起步。真正的收益，只受限于我们的想象力和惯性。
Economies: from Florence to megacities
经济层面：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，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遍佛罗伦萨。生活的节奏由一个人能走多远、声音能传多远来决定。
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.
然后，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、高速公路相继出现。城市在规模和密度上爆炸式增长。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.
这些不只是更大号的佛罗伦萨，它们代表着不同的生活方式。超级都市令人眩晕、匿名、更难导航。这种"不可读性"是规模的代价。但反过来，它也释放了前所未有的机会和自由：更多的人、用更多的方式、在更多的组合里做更多的事；这类密度与复杂度，放在一座以步行尺度运转的文艺复兴城市里，根本无从承载。
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变。
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 的近一半。其中大部分仍以人类尺度运转：几十人的团队、由会议和邮件决定节奏的工作流、组织一旦超过几百人就开始"拧巴"。我们一直在用石头和木头建造佛罗伦萨。
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 智能体大规模上线，我们将开始建造"东京"：组织里同时存在成千上万的人与智能体；工作流跨时区持续运转，不必等谁醒来；决策也不必层层拉齐，而是由系统综合出结果，再让人类在最关键处介入------介入得"刚刚好"。
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.
这会带来不同的感受。更快、更有杠杆效应，但起初也会更令人眩晕。每周例会、季度规划、年度评审这些旧节奏，可能逐渐失效；新的节奏会自然长出来。我们会牺牲一些可读性，换来规模和速度。
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.
每一种划时代的介质都要求人们停止透过后视镜看世界，开始想象新的世界。卡内基看着钢铁，看到了城市的天际线。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"，把聊天机器人拧进为人类设计的流程里，让它充当副驾驶。真正需要发生的，是更激进的想象：当组织被"钢铁"加固，当琐碎工作交给永不休眠的思维，知识工作会变成怎样？
Steel. Steam. Infinite minds. The next skyline is there, waiting for us to build it.
钢铁。蒸汽。无限心智。下一道天际线已经在那里，等待我们去建造。