AI作为＂无限心智＂:重塑知识工作、组织与经济的新时代奇迹材料
作者：微信文章Notion的创始人赵伊万（Ivan Zhao）写了一篇长文，标题是《蒸汽、钢铁与无限心智》。
🏭 核心论点：AI作为新时代的奇迹材料
每个时代都由其“奇迹材料”定义：钢铁铸就了镀金时代，半导体开启了数字时代，而AI作为“无限心智”，正成为塑造当前时代的关键力量。历史证明，掌握这种材料的人将定义整个时代。
🔄 技术转型的历史规律：后视镜现象
未来难以预测，因为新技术总是“伪装成过去”出现：
早期电话像电报一样简洁。
早期电影如同拍摄的舞台剧。
当前AI表现为模仿谷歌搜索框的聊天机器人。
正如马歇尔·麦克卢汉所言：“我们总是通过后视镜驶向未来。”当前正处于每种新技术转变时都会出现的“不适过渡阶段”。
👤 个体层面：从自行车到汽车
(一) 程序员的先行案例
10倍程序员已进化为同时管理3-4个AI编码代理的角色。
效率提升：从10倍工程师跃升至30-40倍工程师。
工作模式：可在午餐或睡眠时让AI代理继续工作，成为“无限心智的管理者”。
(二) 乔布斯“思维自行车”隐喻的升级
1980年代：乔布斯将个人电脑称为“思维自行车”。
互联网时代：铺设了“信息高速公路”。
当前现状：大多数知识工作仍依赖人力驱动，如同“在高速公路上骑自行车”。
AI带来的变革：从骑自行车升级为驾驶汽车。
(三) AI普及的两大障碍
| 障碍| 具体表现 | 编码领域对比 |
| --| --| --|
| 1\. 上下文碎片化 | 知识工作分散在数十种工具中（如Slack对话、策略文档、仪表板指标等） | 编码工具和上下文集中在一处（如IDE、代码库、终端） |
| 2\. 可验证性缺失 | 难以验证项目管理质量或战略备忘录的优劣 | 代码可通过测试和错误来验证，支持强化学习训练AI |
(四) “人类在环”的进化方向
不理想模式：如同“检查工厂生产线的每个螺栓”，或像1865年英国《红旗法案》要求“有人在汽车前步行引路”。
理想模式：人类从“在环中”转变为“在环上监督”。
最终目标：从驾驶过渡到自动驾驶。
🏢 组织层面：钢铁与蒸汽
(一) 组织的当前困境
现代公司是较新的发明，规模扩大时会退化并达到极限。
沟通基础设施（通过会议和消息连接的人类大脑）在指数级负载下崩溃。
解决方案局限：依赖层级、流程和文档，如同“用木材建造摩天大楼”。
(二) 钢铁隐喻：组织的结构革命
前钢铁时代：19世纪的建筑高度被限制在6-7层，因为铁材料虽然坚固，但质地脆且沉重，增加楼层会导致结构自我摧毁。
钢铁带来的变革：钢铁强度高且有延展性，使得建筑框架更轻、墙体更薄，建筑高度得以升至数十层。
AI作为组织的钢铁：
能够跨工作流维护上下文。
可以按需呈现决策信息，且没有噪音干扰。
人类沟通不再是组织的“承重墙”。
成果体现为：两小时的对齐会议缩短为五分钟的异步审查；三级审批决策可在分钟级内完成。
(三) 蒸汽机隐喻：组织的流程重构
早期工业革命：纺织厂依赖水车提供动力，这使其受制于地理位置和季节变化。
初期蒸汽机应用：仅仅是用蒸汽机替换水车，而保留了工厂的其他结构，因此生产力提升有限。
真正突破：工厂完全脱离水源限制，得以重新设计布局，靠近工人、港口和原材料产地。
电力时代的进一步演进：从中央动力轴驱动，转变为为不同机器配备独立的小型发动机。
当前AI应用阶段：仍处于“替换水车”的阶段，即只是将聊天机器人嫁接到现有工具上。
未来潜力：当组织能摆脱旧有约束，将可以运行在“永不睡眠的无限心智”之上。
(四) Notion公司的实践案例
规模：拥有1000名员工和700多个AI代理。
AI代理应用场景：
进行会议记录和问题解答，综合“部落知识”。
处理IT请求和记录客户反馈。
帮助新员工了解公司福利政策。
自动生成每周状态报告，避免了复制粘贴的繁琐工作。
🌐 经济层面：从佛罗伦萨到特大城市
(一) 城市规模的历史变革
前工业时代：人类尺度的城市，例如佛罗伦萨，可以在40分钟内步行穿越。
钢铁与蒸汽的影响：
钢铁框架使建造摩天大楼成为可能。
蒸汽机驱动铁路，连接了市中心与腹地。
电梯、地铁、高速公路等交通方式相继出现。
结果：城市的规模和密度急剧增长，形成了东京、重庆、达拉斯等特大城市。
(二) 特大城市的双刃剑效应
| 挑战| 机遇|
| --| --|
| 迷失方向、匿名性、导航困难 | 更多机会、更多自由 |
| 规模带来的“不可读性” | 更多人能以更多样的组合方式做更多事情 |
(三) 知识经济的类似转型
当前状态：知识工作已占美国GDP近一半，但其运行仍停留在人类规模：
团队规模通常为数十人。
工作流程以会议和电子邮件为节奏。
组织规模超过数百人时，效率就会崩溃。
这种状态如同“用石头和木材建造的佛罗伦萨”。
未来展望：AI代理的规模化应用将催生“东京式”的知识经济：
组织将跨越数千名AI代理和人类。
工作流可以连续运行、跨时区，无需等待人类醒来。
决策由适量的人类参与并综合完成。
🚀 未来展望：超越“后视镜”思维
每一种奇迹材料都要求人们停止通过后视镜看世界，开始想象新世界：
卡内基从钢铁中看到了城市天际线。
兰开夏郡的工厂主从蒸汽机中看到了摆脱河流限制的工厂。
当前挑战：我们需要停止仅仅将AI视为“副驾驶”，开始想象：
用钢铁般坚固的AI来强化人类组织。
将琐事委托给永不睡眠的“无限心智”。
钢铁、蒸汽与无限心智——下一个天际线正等待我们去建造。
📝 补充细节
《红旗法案》背景：1865年，英国通过了《机动车法案》(Locomotive Act)，要求每辆汽车必须由三人操作，其中一人需在车前50码处步行并高举红旗，且车速不得超过每小时4英里。这项法案严重阻碍了汽车的发展。
“五篇大文章”未提及：本文未涉及中国的“五篇大文章”相关内容，主要聚焦于AI对知识工作的影响。
Notion公司定位：作者运营着一家软件公司，为数百万知识工作者构建工具，目前正积极探索AI在组织中的应用。
原文
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.
Left: teenage Andrew Carnegie and his younger brother. Right: Pittsburgh 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.
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?
Early movies often looked like stage plays, with one camera focused on the stage.
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.")
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."
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.
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.
Individuals: from bicycles to cars
The first glimpses can be found with the high priests of knowledge work: programmers.
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.
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.
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.
With AI agents, someone like Simon has graduated from riding a bicycle to driving a car.
When will other types of knowledge workers get cars? Two problems must be solved.
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.
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.
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.
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."
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.
Organizations: steel and steam
Companies are a recent invention. They degrade as they scale and reach their limit.
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.
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.
Two historical metaphors show how future organizations can look differently with new miracle materials.
A wonder of steel: the Woolworth building was the tallest building in the world upon completion in NYC, 1913.
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.
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.
A mill with a water wheel to power its operations. Water was powerful but unreliable and restricted mills to a few locations and seasonality.
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.
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.
This 1835 engraving by Thomas Allom depicts a textile factory in Lancashire, UK. It was powered by steam engines.
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.
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.
Economies: from Florence to megacities
Steel and steam didn't just change buildings and factories. They changed cities.
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.
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.
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.
I think the knowledge economy is about to undergo the same transformation.
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.
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.
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.
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.
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.
Steel. Steam. Infinite minds. The next skyline is there, waiting for us to build it.
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