引言
春晚机器人惊艳全场，硅谷 AI 代理暗战已起。丙午马年，新周期正式启幕，新思考沸腾。Ivan Zhao《蒸汽、钢铁与无限心智》如灯塔照路，让零散思考聚沙成塔。作为科创赋能者，我们坚信：AI 重构组织，效率定义未来，今岁以全新姿态，共启科创新纪元。
一、我们正处在一个时代的拐点
每个时代，都有一种定义它的 “奇迹材料”。钢铁，定义了镀金时代；半导体，开启了数字时代；而今天，AI，就是这个时代的无限心智。
历史从不重复，但总是押韵。当蒸汽机取代水车，当钢铁撑起摩天大楼，当 AI 开始接管知识工作 ——我们正在经历的，不是工具迭代，而是文明级别的生产力跃迁。
本文核心观点与金句，均来自 Notion 联合创始人兼 CEO Ivan Zhao（赵伊） 官方原文：Steam, Steel, and Infinite Minds原文链接：https://www.notion.com/blog/steam-steel-and-infinite-minds-ai，本文后附中英文全文。
二、AI 时代，三大能力正在重新定价
a. 执行力在贬值，判断力在升值
AI 让写代码、写文案、做方案成为低成本能力，决定做什么、不做什么，才是真正稀缺的资产。
b. 知识不再是优势，结构化理解才是
不是 “知道得多”，而是能把复杂世界压缩成可行动模型、可跨领域迁移的人，才能真正拉开差距。
c. 审美与品味成为硬能力
当内容与产出无限供给，选择、取舍、风格与价值观，才是 AI 难以替代的核心竞争力。
三、核心精华・中英对照
1. 时代的本质
中文：每个时代都由其奇迹材料塑造。钢铁铸就镀金时代，半导体开启数字时代。如今，AI 以无限心智降临。掌控材料者，定义时代。English：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. Those who master the material define the era.
2. 未来总伪装成过去
中文：未来总以过去的模样出现，我们正处在新技术变革最关键的过渡期。English：This future always disguises itself as the past. We’re deep in the transition phase of every technology shift.
3. 个人：从自行车到汽车
中文：乔布斯称电脑是 “心智的自行车”，而 AI 代理，让我们直接迈入驾驶汽车的时代。人，将成为无限心智的管理者。English：Steve Jobs called PCs bicycles for the mind. With AI agents, we graduate from riding to driving. Humans become managers of infinite minds.
4. 组织：AI 是新时代的钢铁
中文：AI 就是组织的钢铁，让企业真正规模化扩张，不再被人力、沟通、流程拖累。English：AI is steel for organizations. It lets companies scale without the decay we once thought inevitable.
5. 我们仍在 “替换水车” 的阶段
中文：今天的 AI 大多只是嫁接在旧流程里。真正的革命，是重构组织、工作与效率的底层逻辑。English：We’re still swapping waterwheels for steam. We haven’t reimagined work for infinite minds that never sleep.
6. 经济：从佛罗伦萨到超级都市
中文：知识经济将从人力规模的 “佛罗伦萨”，走向 AI 驱动的 “超级都市”—— 更大、更快、更无限。English：We built Florences with stone; now we build Tokyos with AI.
7. 最终宣言
中文：钢铁、蒸汽、无限心智。下一片天际线，正在等我们建造。English：Steel. Steam. Infinite minds. The next skyline is waiting for us to build it.
四、创始人结语
AI 不是工具，是新的生产资料；AI 代理不是辅助，是新的组织成员；AI 时代不是未来，是正在发生的现在。
从春晚机器人到硅谷 AI 暗战，丙午马年，所有行业都将被重新定义。
我们以科创赋能为使命，用 AI、新范式、无限心智，让效率提升，让创造回归，让判断力成为核心竞争力。
蒸汽已至，钢铁已备，心智无限。2026，与时代同行，定义下一个时代。
本文参考：Ivan Zhao, Steam, Steel, and Infinite Minds, Notion Official Blog, 2025原文链接：https://www.notion.com/blog/steam-steel-and-infinite-minds-ai
作者：赵伊（Ivan Zhao），Notion联合创始人兼CEO
每个时代都由其奇迹材料塑造。钢铁铸就了镀金时代，半导体开启了数字时代。如今，人工智能以“无限心智”的形态降临。历史若能昭示什么，那便是：掌控这一材料的人，将定义这个时代。
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波的报童。彼时，六成美国人都是农民。短短两代人时间里，卡内基与同代人共同铸就了现代世界——马匹被铁路取代，烛光让位于电力，铁器升级为钢铁。
从那以后，工作场景从工厂转向了办公室。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。在这座产业之城，人人都在谈论通用人工智能（AGI），但全球20亿办公室职员中的大多数，尚未真切感受到它的影响。不久之后，知识工作将呈现怎样的面貌？当组织结构图中纳入了“永不休眠的心智”，又会发生什么？
未来之所以难以预测，是因为它总以过去的模样伪装自己。早期的电话沟通如同电报般言简意赅，早期的电影宛如拍摄下来的舞台剧——这正是马歇尔·麦克卢汉所说的“通过后视镜驶向未来”。
如今最普及的人工智能形态，恰似过去的谷歌搜索。正如马歇尔·麦克卢汉所言：“我们总是通过后视镜驶向未来。”如今，我们看到的便是模仿谷歌搜索框的人工智能聊天机器人。我们正深陷于每一次新技术变革都会伴随的尴尬过渡期。
对于未来会发生什么，我并非知晓所有答案。但我喜欢借助几个历史隐喻，思考人工智能如何在不同层面——从个人、组织到整个经济体——发挥作用。
这一变革的最初迹象，出现在知识工作的“高阶从业者”——程序员身上。
我的联合创始人西蒙曾是我们口中的“10倍程序员”，但如今他几乎不再亲自写代码。走过他的办公桌，你会看到他同时调度着三四个人工智能编程代理——它们不只是打字更快，更能自主思考，这让西蒙俨然成为了“30-40倍工程师”。他会在午饭前或睡前下达任务队列，让这些代理在他离开时继续工作。他已然成为“无限心智的管理者”。
20世纪80年代，史蒂夫·乔布斯将个人电脑称为“心智的自行车”。十年后，我们铺就了“信息高速公路”——也就是互联网。但如今，大多数知识工作仍依赖人力驱动，就像我们一直在高速公路上蹬着自行车前行。
借助人工智能代理，像西蒙这样的人已经从“骑自行车”升级为“开汽车”。
其他类型的知识工作者何时才能拥有自己的“汽车”？有两个问题必须解决。
首先是上下文碎片化。对于编程而言，工具与上下文往往集中在一处：集成开发环境、代码仓库、终端。但通用知识工作的信息却散落在数十种工具中。试想一个人工智能代理试图起草一份产品简报：它需要从Slack对话、战略文档、上季度数据看板，以及仅存在于某个人脑海中的组织记忆里提取信息。如今，人类是粘合剂，通过复制粘贴与切换浏览器标签将一切串联起来。在上下文被整合之前，代理只能局限于狭窄的应用场景。
其次是可验证性缺失。代码有一个神奇的特性：你可以通过测试与报错来验证它。模型开发者利用这一点训练人工智能提升编程能力（例如强化学习）。但我们如何验证一个项目管理得好不好，或者一份战略备忘录是否出色？我们尚未找到优化通用知识工作模型的方法。因此，人类仍需参与其中，进行监督、引导，并展示何为优秀。
今年的编程代理让我们明白，“人类在回路中”并非总是可取的。这就像让专人检查工厂流水线上的每一颗螺栓，或是在汽车前方步行开路（参见1865年《红旗法案》）。我们希望人类从高杠杆视角监督回路，而非身处其中。一旦上下文得到整合、工作可被验证，数十亿劳动者将从“蹬车”升级为“驾车”，再从“驾车”迈向“自动驾驶”。
公司是近代的发明。它们在扩张中逐渐退化，直至触及极限。
几百年前，大多数公司只是十几人的作坊。如今我们拥有员工数十万的跨国企业。沟通基础设施（由会议与消息连接的人类大脑）在指数级负载下不堪重负。我们试图用层级、流程与文档来解决问题，但我们一直在用人力规模的工具解决工业级问题，就像用木材建造摩天大楼。
两个历史隐喻揭示了，在新奇迹材料的加持下，未来组织将呈现怎样的不同面貌。
第一个隐喻是钢铁。在钢铁出现之前，19世纪的建筑最高只能建六七层。铁坚固但脆且重，楼层越高，结构越容易因自重坍塌。钢铁改变了一切。它坚固且柔韧，框架可以更轻，墙体可以更薄，建筑突然能拔地数十层。全新的建筑形态成为可能。
人工智能就是组织的钢铁。它有潜力在工作流中维持上下文感知，并在需要时精准触发决策而无信息过载。人类沟通不再是承重墙。每周两小时的对齐会议可以变成五分钟的异步回顾。需要三层审批的高管决策，或许很快就能在几分钟内完成。公司可以真正实现规模化扩张，而无需承受我们习以为常的退化。
第二个故事是关于蒸汽机。工业革命初期，早期纺织厂依河而建，靠水车驱动。蒸汽机出现后，工厂主最初只是用水车换蒸汽机，其余一切照旧，生产力提升有限。
真正的突破，发生在工厂主们意识到可以彻底摆脱水力依赖之时。他们在靠近工人、港口与原材料的地方建造更大的工厂，并围绕蒸汽机重新设计厂房（后来电力普及后，业主进一步去中心化，不再依赖中央传动轴，而是在工厂各处为不同机器配备小型发动机）。生产力随之爆发，第二次工业革命才真正全面展开。
如今，我们仍处于“替换水车”的阶段——将人工智能聊天机器人简单嫁接到现有工具上。我们尚未重新构想：当旧有的限制不复存在，当公司可以依靠“永不休眠的无限心智”运转时，组织会呈现怎样的形态？
在Notion，我们已经开始尝试。除了1000名员工，目前已有700多个人工智能代理在处理重复性工作：记录会议纪要、解答问题以整合组织内部的隐性知识、处理IT需求、记录客户反馈、帮助新员工熟悉员工福利、撰写每周状态报告（省去人们复制粘贴的麻烦）。这仅仅是起步。真正的收益，只受限于我们的想象力与惰性。
几百年前，城市都是“人力规模”的。你只需四十分钟就能走完佛罗伦萨全城，生活节奏由“人能走多远、声音能传多远”决定。
后来，钢铁框架让摩天大楼成为可能，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。城市的规模和密度呈爆炸式增长——东京、重庆、达拉斯，皆是如此。
这些城市并非佛罗伦萨的“放大版”，而是全新的生活方式。特大城市令人迷茫、充满匿名性、难以导航——这种“难以理解性”是规模扩大的代价。但与此同时，它们也提供了更多机遇、更多自由：更多人以更多样的组合方式做更多事情，这是文艺复兴时期“人力规模”的城市无法承载的。
如今，知识工作贡献了美国近一半的国内生产总值（GDP）。但其中大部分仍以“人力规模”运转：几十人的团队、以会议和电子邮件为节奏的工作流程、人数超过数百就会不堪重负的组织。我们用“石头和木材”建造了一个个“佛罗伦萨”。
当人工智能代理大规模投入应用，我们将开始建造“东京”：由数千名人工智能代理和人类共同组成的组织、跨时区持续运转且无需等待任何人醒来的工作流程、仅需适量人类参与监督的决策过程。
这一切会让人感到陌生：起初会更快、更具杠杆效应，但也更令人迷茫。每周会议、季度规划、年度评审的节奏可能不再合理，新的节奏将应运而生。我们会失去一些“可理解性”，但会收获规模与速度。
每一种奇迹材料，都要求人们放下“后视镜”看世界，转而构想全新的可能。卡内基看到钢铁，便预见了城市天际线；兰开夏郡的工厂主看到蒸汽机，便想到了摆脱河流束缚的工厂车间。
如今，我们仍处于人工智能的“水车时代”——将聊天机器人简单嫁接到为人类设计的工作流程中。我们需要停止只把人工智能当作“副驾驶”，而应构想：当人类组织被“钢铁”加固，当繁杂事务被委托给“永不休眠的心智”，知识工作会呈现怎样的全新形态？
钢铁、蒸汽、无限心智。下一片城市天际线已然浮现，等待我们去建造。
英文全文《Steam, Steel, and Infinite Minds》
By Ivan Zhao, Co-founder & CEO
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.
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?
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 looks 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.
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.
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.
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.
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.
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.
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.
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.
Those who master the material define the era
夜雨聆风
