Notion 创始人关于 AI 的思考 —— Steam, Steel, and Infinite Minds

https://www.notion.com/blog/steam-steel-and-infinite-minds-ai

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.

每个时代都由其奇迹材料所塑造。钢铁锻造了镀金时代，半导体开启了数字时代。如今人工智能以无限智慧的姿态降临。如果说历史教会了我们什么，那就是掌握核心材料者定义时代。

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?

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报童。彼时十之六的美国人是农民。不出两代人光景，卡内基与其同辈便重塑了现代世界——铁轨取代马匹，电灯取代烛火，钢铁取代生铁。

此后，劳作场所从工厂转向写字楼。如今我在旧金山经营软件公司，为千万知识工作者打造工具。在这座产业之城里，人人都在谈论通用人工智能，但全球二十亿办公族大多尚未感知其存在。知识工作的未来图景将如何演变？当企业架构融入永不休眠的智能，又会引发怎样的变革？

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.")

这个未来往往难以预测，因为它总是伪装成过去的样子。早期的电话像电报一样简洁，早期的电影看起来像拍摄的舞台剧。（正如马歇尔·麦克卢汉所言："人们总是通过后视镜来驶向未来。"）

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.

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.

如今，我们将这种现象视为模仿谷歌搜索框的AI聊天机器人。我们正处于每次技术变革都会出现的那种令人不适的转型阶段。

对于未来将发生什么，我并非全知全能。但我喜欢用几个历史隐喻来思考AI如何在个人、组织乃至整个经济等不同层面发挥作用。

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.

个体：从自行车到汽车
最初的迹象可以从知识工作的祭司——程序员身上窥见一斑。

我的联合创始人西蒙是我们所说的10倍效率程序员，但他如今很少亲自写代码。走过他的办公桌时，你会看到他在同时指挥三四个AI编码助手，这些助手不仅打字更快，还能独立思考，这使他整体成为30-40倍效率的工程师。他会在午餐前或睡前排队任务，让AI在他离开时继续工作。他已化身为无限智能的管理者。

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.

20世纪80年代，史蒂夫·乔布斯将个人电脑称为"思想的自行车"。十年后，我们铺就了互联网这条"信息高速公路"。但如今，大多数知识工作仍由人力完成。这就像我们一直在高速公路上蹬自行车。

有了AI智能体，像西蒙这样的人已经从骑自行车升级为开汽车了。

其他类型的知识工作者何时才能开上汽车？必须解决两个问题。

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.

首先，上下文碎片化问题。编程时，工具和上下文通常集中于同一处：集成开发环境、代码仓库或终端。但通用知识工作却分散在数十种工具中。试想一个AI代理尝试起草产品简报：它需要从Slack讨论串、战略文件、仪表盘中的上季度数据以及仅存在于某人大脑中的组织记忆里提取信息。目前人类充当着粘合剂，通过复制粘贴和浏览器标签切换来整合这些碎片。在实现上下文整合之前，AI代理仍将局限于狭窄的使用场景。

第二个缺失要素是可验证性。代码具有神奇特性：可通过测试和错误进行验证。模型开发者借此训练AI提升编程能力（例如强化学习）。但如何验证项目管理是否得当？战略备忘录是否优秀？我们尚未找到提升通用知识工作模型的方法。因此人类仍需参与监督、指导并示范优秀标准。

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，"人工介入循环"并不总是可取的。这就像让专人检查流水线上的每个螺栓，或走在汽车前面清扫道路（参见：1865年红旗法案）。我们需要人类在制高点监督循环，而非置身其中。当工作情境完成整合且成果可验证时，数十亿劳动者将从踩踏板进阶到驾驶座，再从驾驶员升级为自动驾驶系统。

Organizations: steel and steam

Companies are a recent invention. They degrade as they scale and reach their limit.

组织：钢铁与蒸汽
公司是近代的发明。它们随着规模扩大而退化，最终达到极限。

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.

几百年前，大多数公司都是由十几个人组成的小作坊。如今我们拥有数十万员工的跨国公司。沟通基础设施（通过会议和信息连接的人脑）在指数级负荷下不堪重负。我们试图通过层级结构、流程和文档来解决这个问题。但我们一直在用人类规模的工具解决工业规模的问题，就像用木头建造摩天大楼。

两个历史隐喻展现了新型神奇材料将如何使未来组织呈现全然不同的面貌。

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.

首先是钢材。在钢材出现之前，19世纪的建筑物高度被限制在六七层以内。铁虽然坚固但易碎且沉重；增加楼层，结构就会因自身重量而倒塌。钢材改变了一切。它既坚固又具有延展性。框架可以更轻，墙壁可以更薄，突然间建筑物可以拔地而起数十层。新型建筑成为可能。

AI就是组织的钢材。它有望在工作流程中保持上下文，在需要时呈现决策而不带来干扰。人类交流不再需要成为承重墙。每周两小时的对齐会议变成了五分钟的异步回顾。需要三级审批的高管决策可能很快在几分钟内完成。公司可以实现真正的规模化扩展，而无需忍受我们曾认为不可避免的效率衰减。

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.

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.

第二个故事与蒸汽机有关。工业革命初期，早期的纺织厂依河而建，依靠水车提供动力。当蒸汽机问世后，工厂主最初只是简单地将水车替换为蒸汽机，其他一切照旧，生产效率提升有限。

真正的突破发生在工厂主意识到可以彻底摆脱水源束缚之时。他们建造了更大型的工厂，选址更靠近工人聚居地、港口和原材料产地，并围绕蒸汽机重新设计工厂布局（后来电力普及后，业主们进一步摒弃中央动力轴系统，为不同机器配置分散的小型发动机）。生产效率由此呈爆发式增长，第二次工业革命才真正拉开帷幕。

我们目前仍处于"替换水车"的阶段——将AI聊天机器人简单嫁接在现有工具上。当传统限制消失，企业能在你入睡时依靠无数智能体持续运转时，我们尚未重新构想出组织形态的新范式。

在我的公司Notion，我们已开始探索实践。除1000名员工外，700多个智能体正处理重复性工作：记录会议笔记、解答问题以整合团队知识、处理IT请求、记录客户反馈、协助新员工熟悉福利政策、撰写周度报告免去人工复制粘贴。这些只是蹒跚学步，真正的突破仅受限于我们的想象力和行动惰性。

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.

经济形态：从佛罗伦萨到超级都市
钢铁与蒸汽不仅改变了建筑与工厂，更重塑了城市形态。

数百年前的城市仍以人类尺度构建。四十分钟便可横穿佛罗伦萨，生活节奏由步行距离与人声所及范围决定。

钢铁框架催生摩天大楼，蒸汽机车连通都市与腹地。电梯、地铁、高速公路接踵而至。城市规模与密度呈爆发式增长——东京、重庆、达拉斯相继崛起。

这些绝非佛罗伦萨的简单放大版，而是全新的生存范式。超级都市令人迷失方向、淡化人际关联、增加探索难度，这种"不可读性"是规模扩张的代价。但它们也提供更多机遇与自由，容纳远超文艺复兴时期人类尺度城市所能承载的多元人群与复合活动。

知识经济即将经历同样变革。

如今知识工作贡献美国近半GDP，其运作仍囿于人类尺度：数十人团队、会议邮件主导的工作流、超数百人即效率递减的组织。我们始终在用石材与木材建造"佛罗伦萨"。

当AI智能体大规模上线，我们将构筑"东京式"生态：横跨数千人类与智能体的组织、无视时差持续运转的工作流、精准调配人类参与度的决策机制。

体验将截然不同——更高效、更高杠杆，初期也更具迷失感。周例会、季度规划、年度评估等传统节奏可能失效，新秩序随之诞生。我们牺牲部分可读性，换取规模与速度的飞跃。

Beyond the waterwheels

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.

超越水车时代 每个奇迹材料的诞生都需要人们停止用后视镜观察世界，转而开始想象新天地。卡内基凝视钢铁，看见了城市天际线。兰开夏郡的工厂主们注视蒸汽机，看见了不必依河而建的厂房。

我们仍处在人工智能的水车时代，生硬地将聊天机器人嵌入为人类设计的工作流程。必须停止要求AI仅充当副驾驶，而应开始构想：当人类组织被钢铁强化，当繁琐工作交由永不休眠的智能体处理，知识工作将呈现何种新形态。

钢铁。蒸汽。无限智能。下一幅天际线就在前方，静候我们筑就。