Steam, Steel, and Infinite Intelligence

Written by: Ivan Zhao, CEO of Notion

Compiled by: AididiaoJP, Foresight News

Each era is shaped by its unique technological raw materials. Steel forged the Gilded Age, and semiconductors ushered in the digital age. Today, artificial intelligence arrives in the form of infinite intelligence. History tells us: those who master the raw materials define the era.

每个时代都是由其独特的技术“原材料”所塑造的。钢铁推动了“镀金时代”的发展，而半导体则开启了数字时代；如今，人工智能正以“无限智能”的形式出现。历史告诉我们：那些掌握了这些技术“原材料”的人，才能真正定义一个时代。

Left: Young Andrew Carnegie and his brother. Right: A steel mill in Pittsburgh during the Gilded Age.

左图：年轻的安德鲁·卡内基和他的兄弟。右图：镀金时代（Gilded Age）时期的匹兹堡钢铁厂。

In the 1850s, Andrew Carnegie was a telegraph messenger running through the muddy streets of Pittsburgh, a time when six out of ten Americans were farmers. Just two generations later, Carnegie and his peers forged the modern world. Horses gave way to railroads, candlelight to electric light, and iron to steel.

19 世纪 50 年代，安德鲁·卡内基还是一名在匹兹堡泥泞街道上送电报的信使；当时，美国有六成的人口是农民。仅仅两代人之后，卡内基和他的同龄人就共同创造了现代社会：马匹被铁路取代，蜡烛光被电灯取代，铁也被钢铁所取代。

Since then, work has shifted from factories to offices. Today, I run a software company in San Francisco, building tools for thousands of knowledge workers. In this tech town, everyone is talking about Artificial General Intelligence (AGI), but the vast majority of the two billion office workers have yet to feel its presence. What will knowledge work look like soon? What will happen when organizations are infused with intelligence that never rests?

从那时起，工作的场所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，为成千上万的知识工作者开发相关工具。在这个科技城市里，每个人都在谈论“通用人工智能”（AGI），但全球约 20 亿的办公室工作者中，绝大多数人仍然没有感受到人工智能的影响。未来的知识工作会是什么样子呢？当组织被赋予了永不休止的智能时，会发生什么变化呢？

Early films often resembled stage plays, with a single camera pointed at the stage.

早期的电影往往类似于舞台剧：只使用一台摄像机来拍摄舞台上的场景。

The future is often hard to predict because it is always disguised as the past. Early phone calls were as brief as telegrams, and early films were like recorded stage plays. As Marshall McLuhan said: “We look at the present through a rear-view mirror. We march backwards into the future.”

未来往往难以预测，因为它总是以“过去”的形式伪装着自己。早期的电话通话内容非常简短（几乎与电报一样简短），早期的电影则类似于被录制下来的舞台剧。正如马歇尔·麦克卢汉所说：“我们是通过‘后视镜’来看待现在的；我们其实是在‘倒着前进’，走向未来的。”

Information

我们理解新事物时,总是用旧的经验和框架去解读。就像开车时通过后视镜看到的是已经过去的景象,我们对”现在”的理解实际上建立在”过去”的基础上。

具体体现 技术革新的误读: 早期的汽车被称为”无马马车”,电影被叫做”会动的照片”,电视最初被当作”可视化的广播”。我们总是用旧媒介的概念去理解新媒介,而看不到新事物本身的革命性。 媒介认知的滞后: 当印刷术出现时,人们还在用手抄本的思维模式去理解它;互联网早期,人们把网站当成”电子报纸”。我们习惯性地把新技术当作旧技术的延伸,而非全新的存在。 文化惯性: 我们用农业时代的时间观理解工业社会,用工业时代的组织方式理解信息时代。每个时代的人都带着上一个时代的”眼镜”。

为什么”倒着前进”? 因为我们的注意力集中在已知的东西上,而真正塑造未来的力量——新技术、新媒介带来的深层变革——往往在我们的”视野盲区”中悄然发生。等我们意识到时,变革已经完成了。

现实意义 这提醒我们:要理解当下正在发生什么,不能只依赖过去的经验。需要有意识地跳出”后视镜思维”,去感知那些还未被旧框架捕捉的新质变化。

Today’s most common form of AI still looks like the Google search of the past. To quote McLuhan again: “We look at the present through a rear-view mirror.” Today, we see AI chatbots imitating the Google search box. We are deep in that uncomfortable transition period that occurs with every technological shift.

如今，最常见的人工智能应用形式仍然类似于过去的谷歌搜索功能。用麦克卢汉的话来说：“我们是通过‘后视镜’来看待当下的。”如今，我们看到的人工智能聊天机器人其实就是在模仿谷歌搜索框的功能。我们正处于每一次技术变革都会带来的那种令人不适的过渡期之中。

I don’t have all the answers for what the future holds. But I like to use a few historical metaphors to think about how AI might operate at different levels: the individual, the organization, and the entire economy.

我并不掌握关于未来的一切答案，但我喜欢用一些历史上的例子来思考人工智能可能在不同层面（个人、组织以及整个经济体系）中发挥的作用。

Individual: From Bicycle to Car

The first signs can be seen in the “high-level practitioners” of knowledge work: programmers.

这些变化的最初迹象可以在那些从事知识性工作（如编程）的“高级从业者”身上看到。

My co-founder Simon was once a “10x programmer,” but lately he rarely writes code himself. Walking past his desk, you’d see him orchestrating three or four AI programming assistants simultaneously. These assistants not only type faster but also think, making him a 30 to 40 times more efficient engineer. He often queues up tasks before lunch or bed, letting the AI work while he’s away. He has become a manager of infinite intelligence.

我的联合创始人西蒙曾经是一位非常出色的程序员（他的编程能力被评价为“10 倍于普通程序员的水平”），但最近他很少亲自编写代码了。当你经过他的办公桌时，会发现他正在同时使用三到四个人工智能编程助手来协助工作。这些助手不仅打字速度更快，还具有“思考”能力，因此让西蒙的工作效率提高了 30 到 40 倍。他通常会在午餐前或睡前将需要完成的任务提前安排好，然后让人工智能助手来处理这些任务；这样在他离开办公室的时候，这些助手就可以继续自动工作了。可以说，西蒙已经成为了拥有“无限智能”的管理者——他的工作效率和决策能力都得到了极大的提升。

A 1970s Scientific American study on locomotion efficiency inspired Steve Jobs’ famous “bicycle for the mind” metaphor. It’s just that for decades since, we’ve been “pedaling bicycles” on the information superhighway.

20 世纪 70 年代，《科学美国人》杂志上刊登的一项关于运动效率的研究，启发了史蒂夫·乔布斯提出的那个著名的比喻——“大脑也需要‘自行车’来帮助其高效运转”。只不过从那以后，我们一直在“信息高速公路”上不断“骑行”（即不断获取、处理信息）。

In the 1980s, Steve Jobs called the personal computer a “bicycle for the mind.” A decade later, we paved the “information superhighway” called the internet. But today, most knowledge work still relies on human power. It’s like we’ve been riding bicycles on a highway.

20 世纪 80 年代，史蒂夫·乔布斯将个人电脑比作“帮助人们思考的工具”（或者说，个人电脑是“辅助人类思维的工具”）。十年后，我们建成了被称为“互联网”的信息高速公路。然而如今，大多数需要人类智慧来完成的工作仍然依赖于人类的劳动。这就好比我们明明已经拥有了高速公路，却仍然在骑着自行车去完成那些工作一样——虽然高速公路可以让我们更快地到达目的地，但自行车本身并不能替代人类的智慧和创造力。

With AI assistants, people like Simon have upgraded from riding bicycles to driving cars.

借助人工智能助手，像西蒙这样的人已经从骑自行车的方式升级到了驾驶汽车的方式。

When will other types of knowledge workers get to “drive cars”? Two problems must be solved.

其他类型的知识工作者什么时候才能“开车”（即能够独立完成需要技术或专业技能的工作）呢？首先，有两个问题必须解决。

Why is AI-assisted knowledge work harder than programming assistance? Because knowledge work is more fragmented and harder to verify.

为什么人工智能辅助的知识处理工作比编程辅助工作更困难呢？因为知识处理涉及的信息更加零散、分散，且这些信息的真实性更难以验证。

First is contextual fragmentation. In programming, tools and context are often centralized: the integrated development environment, code repositories, the terminal. But general knowledge work is scattered across dozens of tools. Imagine an AI assistant trying to draft a product brief: it would need to pull information from Slack threads, strategy documents, last quarter’s data in a dashboard, and organizational memory that only exists in someone’s head. For now, humans are the glue, copy-pasting and switching browser tabs to piece everything together. As long as the context isn’t integrated, AI assistants will be limited to narrow uses.

首先，是信息来源的碎片化问题。在编程领域，各种工具和所需的信息通常都集中在特定的系统中（例如集成开发环境、代码仓库、终端等）；然而，在一般的知识处理工作中，这些信息却分散在数十个不同的工具中。试想一下：如果让一个人工智能助手来起草产品说明书，它需要从 Slack 聊天记录中提取信息、查阅战略文档、查看上季度的数据（这些数据存储在仪表盘中），以及获取那些仅存在于某人脑海中的“组织记忆”（即某些非结构化信息）。目前，人类仍然扮演着“粘合剂”的角色——通过复制粘贴或切换浏览器标签页来整合这些分散的信息。只要这些信息来源没有被有效地整合在一起，人工智能助手的功能就会受到严重限制，其应用范围也会非常有限。

The second missing element is verifiability. Code has a magical property: you can verify it through tests and errors. Model developers leverage this, using methods like reinforcement learning to train AIs to code better. But how do you verify if a project is well-managed, or if a strategic memo is excellent? We haven’t yet found a way to improve models for general knowledge work. Therefore, humans still need to stay in the loop to supervise, guide, and demonstrate what is “good.”

第二个缺失的要素是“可验证性”。代码具有一个独特的特性：可以通过测试和错误来验证其正确性。模型开发者利用这一特性，通过强化学习等方法来训练人工智能（AI），使其能够编写出更高质量的代码。但是，如何验证一个项目的管理是否得当，或者一份战略文件是否真正优秀呢？到目前为止，我们还没有找到任何方法来改进那些用于处理“通用知识”（即非特定领域问题）的模型。因此，人类仍然需要参与其中，对这些项目进行监督、指导，并明确什么是“好的”（即什么是正确的、有效的解决方案）。

The 1865 Red Flag Act required cars on public roads to be preceded by a person on foot waving a red flag (repealed in 1896).

1865 年颁布的《红旗法》规定：在公共道路上行驶的车辆必须由一名行人走在车辆前方，该行人负责挥动红旗以警示其他车辆（该法律于 1896 年被废除）。

This year’s programming assistant practice shows us that “human-in-the-loop” is not always ideal. It’s like having a person check every bolt on an assembly line, or walk in front of a car to pave the way (see the 1865 Red Flag Act). We should have humans supervising the loop from a higher level, not being inside it. Once context is integrated and work becomes verifiable, billions of workers will shift from “pedaling bicycles” to “driving cars,” and from “driving” to “autopilot.”

今年的编程辅助系统实践告诉我们：“人在系统中参与控制”的方式并不总是最理想的解决方案。这就好比让一个人去检查装配线上的每一个螺栓，或者让一个人站在汽车前方为汽车行驶“开路”（想想 1865 年的《红旗法案》吧）。我们应该让人类从更高的层面来监督整个系统的运行过程，而不是让人类直接参与到系统的具体操作中。一旦系统能够自动整合相关信息并确保工作的准确性，那么数十亿的劳动者将从“手动操作”（比如骑自行车）转变为“自动化操作”（比如驾驶汽车），最终甚至完全依赖“自动驾驶系统”来完成工作。

Organization: Steel and Steam

Companies are a modern invention. They become less efficient as they scale, eventually hitting a limit.

公司是现代社会的产物；随着规模的扩大，它们的运营效率会逐渐下降，最终会达到某种“极限”（即无法再继续提高效率的状态）。

1855 organizational chart of the New York and Erie Railroad Company. The modern corporation and its organizational structure evolved with railroad companies, the first enterprises requiring coordination of thousands of people over long distances.

1855 年纽约与伊利铁路公司的组织结构图。这种现代企业的组织形式及其管理结构是随着铁路行业的发展而逐渐形成的；铁路企业是最早需要跨长距离协调成千上万员工工作的大型企业。

A few hundred years ago, most companies were workshops of a dozen people. Today we have multinational corporations with hundreds of thousands of employees. The communication infrastructure, relying on meetings and human brains connected by messages, buckles under exponentially increasing load. We try to solve this with hierarchies, processes, and documents, but this is like building skyscrapers out of wood; it’s using human-scale tools to solve industrial-scale problems.

几百年前，大多数公司都只是由十几个人组成的小作坊；如今，我们已经拥有了拥有数十万员工的跨国企业。现有的沟通机制（主要依靠会议以及人与人之间的书面交流）在日益增加的沟通需求面前显得不堪重负。我们试图通过建立严格的层级结构、制定繁琐的流程以及使用大量的文件来解决问题，但这其实就像用木头来建造摩天大楼一样——用“人力所能承受的工具”去解决“工业规模”的问题。

Two historical metaphors illustrate how the future might look different when organizations have new technological raw materials.

有两个历史性的比喻可以说明：当组织掌握了新的技术资源（即“新的技术原材料”）时，未来可能会发生怎样的变化。

The miracle of steel: The Woolworth Building in New York, completed in 1913, was once the world’s tallest building.

钢铁的奇迹：位于纽约的伍尔沃斯大厦（Woolworth Building）于 1913 年竣工，曾是世界上最高的建筑。

The first is steel. Before steel, 19th-century building heights were limited to six or seven stories. Iron was strong but brittle and heavy; add more floors, and the structure would collapse under its own weight. Steel changed everything. It was strong yet flexible. Frames could be lighter, walls thinner, and buildings soared to dozens of stories. New types of architecture became possible.

首先是钢铁。在钢铁出现之前，19 世纪的建筑高度被限制在六到七层之间。虽然铁具有很高的强度，但它非常脆弱且重量庞大；如果增加更多的楼层，建筑物就会因自身的重量而倒塌。钢铁的出现彻底改变了这一切：它既坚固又具有柔韧性。建筑结构可以变得更轻便，墙壁也可以变得更薄，从而使得建筑物能够达到数十层的高度。这样一来，各种新型的建筑风格也就成为可能了。

AI is the “steel” for organizations. It promises to maintain contextual coherence across workflows, presenting decisions when needed without noise. Human communication no longer has to be the load-bearing wall. Weekly two-hour alignment meetings might become five-minute asynchronous reviews; executive decisions requiring three layers of approval might be made in minutes. Companies can truly scale without the efficiency decay we’ve come to accept as inevitable.

人工智能（AI）堪称企业的“核心竞争力”。它能够确保各项工作流程之间的逻辑一致性，并在需要时及时、准确地提供决策支持（而不会产生任何干扰或混乱）。人类的沟通方式不再需要承担那些“支撑整个系统运转”的重任。过去每周需要花费两小时才能完成的协调会议，现在可能只需通过五分钟的异步沟通就能完成；那些原本需要经过多层审批才能做出的高管决策，现在也可能在几分钟内就得到解决。这样一来，企业就可以实现真正的规模扩张，而无需再忍受那些我们早已视为“不可避免”的效率下降现象。

A mill powered by a waterwheel. Water power was powerful but unreliable and limited by location and season.

这是一家由水轮机驱动的磨坊。水力虽然非常强大，但却不可靠，其使用效果会受到地理位置和季节的影响（即水量的变化会影响磨坊的运行效率）。

The second story is about the steam engine. In the early Industrial Revolution, early textile factories were built by rivers, powered by waterwheels. When the steam engine appeared, factory owners initially just replaced the waterwheel with a steam engine, leaving everything else the same. Productivity gains were limited.

第二个故事是关于蒸汽机的。在工业革命初期，许多纺织工厂都是建在河流附近，并依靠水轮机来驱动生产设备。当蒸汽机出现后，工厂主们最初只是将水轮机替换成了蒸汽机，其他生产流程则保持不变。因此，生产效率的提升非常有限。

The real breakthrough came when owners realized they could completely break free from the water source. They built larger factories near workers, ports, and raw materials, and redesigned the layout around the steam engine (Later, with electrification, owners further broke free from the central power shaft, distributing small motors throughout the factory to power individual machines). Productivity exploded, and the Second Industrial Revolution truly took off.

真正的突破发生在企业主们意识到自己可以完全摆脱对水资源的依赖时。他们将工厂建在工人居住区、港口以及原材料产地附近，并重新设计了工厂的布局（尤其是考虑到蒸汽机的使用）。后来，随着电力的普及，企业主们进一步摆脱了对中央动力系统的依赖，将小型电动机分散布置在整个工厂中，为每台机器提供动力。这极大地提高了生产效率，第二次工业革命也因此真正开始了。

An 1835 engraving by Thomas Allom depicting a steam-powered textile mill in Lancashire, England.

这幅版画由托马斯·阿洛姆（Thomas Allom）于 1835 年创作，描绘了位于英格兰兰开夏郡（Lancashire）的一家使用蒸汽动力运行的纺织厂。

We are still in the “replacing the waterwheel” stage. Stuffing AI chatbots into workflows designed for humans, we haven’t yet reimagined what organizations will look like when old constraints vanish and companies can run on infinite intelligence that works while you sleep.

我们仍然处于“用新技术取代旧技术”的阶段：只是将人工智能聊天机器人简单地整合到原本为人类设计的工作流程中罢了。我们还没有真正思考过：当那些旧有的限制被消除、企业能够利用那些在人类睡眠时仍在运行的智能系统来运作时，组织会变成什么样子。

At my company Notion, we have been experimenting. Besides 1,000 employees, there are now over 700 AI assistants handling repetitive work: taking meeting notes, answering questions to consolidate team knowledge, handling IT requests, logging customer feedback, helping new hires learn about benefits, writing weekly status reports to avoid manual copy-pasting… This is just toddling. The true potential is limited only by our imagination and inertia.

在我的公司 Notion 中，我们一直在不断进行创新与实验。目前，除了 1,000 名员工之外，还有超过 700 个人工智能助手负责处理各种重复性工作：记录会议内容、回答问题以帮助团队成员更好地理解相关信息、处理与信息技术相关的问题、记录客户的反馈意见、协助新员工了解公司的福利政策、撰写每周的工作进展报告（从而避免手动复制粘贴数据等）。这些只是人工智能应用的基础功能而已；其真正的潜力还远未被完全挖掘。人工智能的潜力其实仅受我们的想象力与现有习惯（或惰性）的限制罢了。

Economy: From Florence to Megacities

Steel and steam changed not just buildings and factories, but cities.

钢铁与蒸汽不仅改变了建筑物和工厂的外观，也彻底改变了整个城市的结构与面貌。

Until a few hundred years ago, cities were on a human scale. You could walk across Florence in forty minutes. The pace of life was determined by walking distance and the range of the human voice.

直到几百年前，城市的规模仍然符合人类的活动范围；你可以在四十分钟内走完佛罗伦萨的全部路程。人们的生活节奏受到步行距离以及人类声音传播范围的制约。

Then, steel frame structures made skyscrapers possible; steam engine-powered railroads connected city centers with their hinterlands; elevators, subways, and highways followed. The scale and density of cities exploded – Tokyo, Chongqing, Dallas.

随后，钢框架结构的出现使得摩天大楼的建设成为可能；由蒸汽机驱动的铁路将城市中心与周边地区连接起来；电梯、地铁和高速公路也应运而生。城市的规模与人口密度迅速增长——东京、重庆、达拉斯等城市的发展尤为显著。

These are not just enlarged Florences; they are entirely new ways of life. Megacities are disorienting, anonymous, and hard to navigate. This “illegibility” is the price of scale. But they also offer more opportunity, more freedom, supporting more people in more diverse combinations doing more activities than was possible in a human-scale Renaissance city.

这些城市不仅仅是“放大版的佛罗伦萨”而已；它们代表着全新的生活方式。大城市往往令人感到迷茫、缺乏归属感，且难以在其中自如地生活或导航。这种“混乱无序”的状态其实是城市规模扩张所带来的必然代价。不过，大城市也带来了更多的机会与自由：它们能够容纳更多的人，支持人们以更加多样化的形式参与各种活动——这些活动的内容与规模，都是传统意义上的“文艺复兴时期城市”所无法实现的。

I believe the knowledge economy is about to undergo the same transformation.

我认为，知识经济也即将经历同样的变革。

Today, knowledge work accounts for nearly half of US GDP, but its operation mostly remains on a human scale: teams of dozens, workflows dependent on the rhythm of meetings and emails, organizations that struggle beyond a hundred people… We have been building “Florences” out of stone and wood.

如今，知识型产业（即那些依赖知识、创新和技能来创造价值的产业）已经占美国国内生产总值（GDP）的近一半；然而，这些产业的运作方式仍然主要依赖于传统的人力资源管理方式：团队规模通常只有几十人，工作流程依赖于会议和电子邮件的协调，而且那些员工人数超过一百人的组织往往难以实现高效运作……我们一直在用石头和木材来建造那些象征着传统生产方式的“‘佛罗伦萨’式’建筑’（即那些代表传统生产方式的基础设施或组织结构）。”

When AI assistants are deployed at scale, we will build “Tokyos”: organizations composed of thousands of AIs 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 input.

当人工智能助手被大规模应用时，我们将构建出类似“东京”这样的组织：这些组织由成千上万的人工智能系统与人类员工共同组成；其工作流程可以跨越不同的时区持续运行（而无需等待任何人醒来）；决策过程则会在适当的人类参与下完成（即：人工智能系统会结合人类的意见或建议来做出最终决策）。

It will be a different experience: faster, with more leverage, but also more dizzying at first. The rhythms of weekly meetings, quarterly planning, and annual reviews may no longer apply; new rhythms will emerge. We will lose some legibility, but we will gain scale and speed.

这将是一种全新的体验：工作节奏会更快，可利用的资源也会更多，但一开始可能会让人感到有些“头晕目眩”（即难以适应新的工作方式）。以往每周召开会议、每季度进行规划以及每年进行年度评估的惯例可能不再适用；新的工作模式将会逐渐形成。虽然某些工作流程的透明度（或可预测性）会降低，但我们将会获得更大的规模和更快的工作效率。

Beyond the Waterwheel

Every technological material demands that people stop looking at the world through the rear-view mirror and start imagining a new world. Carnegie gazed at steel and saw city skylines; the Lancashire factory owner looked at the steam engine and saw factory floors away from the river.

每种技术材料都要求人们不再用过去的眼光来看待世界（即不再局限于过去的经验或传统方式），而是开始想象一个全新的世界。卡内基看到钢铁后，便想到了城市的天际线；而那位兰开夏郡的工厂主看到蒸汽机后，便想到了那些远离河流的工厂车间。

We are still in the “waterwheel stage” of AI, bolting chatbots onto workflows designed for humans. We shouldn’t just settle for AI as a co-pilot; we need to imagine: what will knowledge work look like when human organizations are reinforced with steel, when trivial tasks are delegated to intelligence that never rests.

我们仍处于人工智能发展的“初级阶段”——只是将聊天机器人（chatbots）简单地整合到为人类设计的工作流程中罢了。我们不应仅仅满足于让人工智能作为人类的“辅助工具”；我们需要思考：当人类组织得到人工智能的强大支持、当那些繁琐、重复性的任务被交给那些永不疲倦的智能系统来处理时，未来的工作方式将会是怎样的。

Steel, steam, and infinite intelligence. The next skyline is ahead, waiting for us to build it.

钢铁、蒸汽，以及无穷的智慧……下一座现代化的城市天际线正在前方等待着我们去创造它。