Steam, Steel, and Infinite Intelligence

Written by: Ivan Zhao

Translated by: AididiaoJP, Foresight News

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

Left image: Young Andrew Carnegie and his brother. Right image: Steel mills in Pittsburgh during the Gilded Age.

In the 1850s, Andrew Carnegie was a telegram messenger running through muddy streets in Pittsburgh. At that time, 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 lamps, iron to steel.

Since then, work 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 talks about Artificial General Intelligence (AGI), but most of the two billion office workers have yet to feel its presence. Soon, what will knowledge work look like? When organizational structures incorporate never-resting intelligence, what will happen?

Early films often resembled stage plays, with a camera filming a stage.

The future is often unpredictable because it always disguises itself as the past. Early calls were as brief as telegrams; early films resembled recorded stage plays. As Marshall McLuhan said: “We always look at the future through rearview mirrors.”

Today’s most common AI still looks like the old Google search. Quoting McLuhan: “We always look at the future through rearview mirrors.” Today, what we see is AI chatbots mimicking Google search boxes. We are deeply immersed in the uncomfortable transition period that always accompanies technological revolutions.

I don’t have all the answers about what the future will be like. But I like to use a few historical metaphors to think about how AI might play roles at different levels—personal, organizational, and even economic.

Personal: From Bicycles to Automobiles

The earliest signs can be seen in high-level practitioners of knowledge work—programmers.

My co-founder Simon was once a “tenfold programmer,” but lately he rarely writes code himself. Walking past his desk, you see him coordinating three or four AI coding assistants simultaneously. These assistants not only type faster but also think, making him an engineer whose efficiency has increased by 30 to 40 times. He often pre-sets task queues before lunch or bedtime, letting AI continue working when he leaves. He has become a manager of infinite intelligence.

A 1970s Scientific American study on movement efficiency inspired Steve Jobs to propose the famous metaphor of the “bicycle of thought.” But in the decades since, we’ve been riding bicycles on the information superhighway.

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

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

When will other types of knowledge workers “get in the car”? Two questions need to be addressed.

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

First is contextual fragmentation. In programming, tools and contexts are often centralized: integrated development environments, code repositories, terminals. But general knowledge work is scattered across dozens of tools. Imagine an AI assistant trying to draft a product description: it needs to extract information from Slack discussions, strategic documents, last quarter’s data in dashboards, and organizational memories stored only in individual minds. Currently, humans are the glue, copying and pasting, switching between browser tabs. Without integrated contexts, AI assistants can only serve narrow purposes.

The second missing element is verifiability. Code has a magical property: you can verify it through tests and error messages. Model developers leverage this by training AI to code better through reinforcement learning. But how do you verify whether a project management is good or a strategic memo is excellent? We haven’t yet found ways to improve the general knowledge work model. Therefore, humans still need to supervise, guide, and demonstrate what “good” looks like.

The 1865 Red Flag Act required automobiles to be led by a person walking in front with a flag (this law was repealed in 1896).

This year’s programming assistant practices tell us that “human in the loop” isn’t always ideal. It’s like having a person check each bolt on an assembly line or walk in front of a car to clear the way (see the 1865 Red Flag Act). We should have humans oversee the loop from a higher vantage point, rather than being inside it. Once contexts are integrated and work becomes verifiable, hundreds of millions of workers will shift from “peddling bicycles” to “driving cars,” and then from “driving” to “autonomous driving.”

Organizational: Steel and Steam

Companies are a modern invention; as they scale, efficiency diminishes and eventually hits a limit.

A 1855 organizational chart of the New York and Erie Railroad. Modern companies and their organizational structures evolved from railroads, which were among the earliest enterprises requiring long-distance coordination of thousands of people.

Hundreds of years ago, most companies were just small workshops with a dozen or so people. Today, we have multinational corporations with hundreds of thousands of employees. Communication infrastructure relies on meetings and interconnected human brains, which become overwhelmed under exponential growth. We try to solve this with hierarchies, processes, and documents, but that’s like building skyscrapers out of wood—using human-scale tools to solve industrial-scale problems.

Two historical metaphors illustrate how organizations might look when equipped with new technological raw materials.

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

The first is steel. Before steel, 19th-century buildings were limited to six or seven stories. Iron was strong but brittle and heavy; adding floors would cause structural collapse under its own weight. Steel changed everything. It was strong and flexible, allowing lighter frameworks, thinner walls, and buildings soaring dozens of stories—making new architectural possibilities possible.

AI is the “steel” of organizations. It promises to maintain contextual coherence across workflows, presenting decisions when needed without noise. Human communication no longer needs to serve as load-bearing walls. Weekly alignment meetings could become five-minute asynchronous reviews; high-level decisions requiring three layers of approval might be completed in minutes. Companies can truly scale without the efficiency decay we once considered inevitable.

A mill powered by a water wheel. Water power is strong but unstable and limited by location and season.

The second story is about the steam engine. In the early Industrial Revolution, textile factories were built along rivers, driven by water wheels. When the steam engine appeared, factory owners initially replaced water wheels with steam engines, but everything else remained the same, and productivity increased only modestly.

The real breakthrough came when factory owners realized they could completely free themselves from water sources. They built larger factories near workers, ports, and raw materials, and redesigned layouts around steam engines (later, with electricity, factory owners further detached from central power shafts, dispersing small engines to power different machines). Productivity exploded, and the Second Industrial Revolution truly began.

A 1835 engraving by Thomas Allom depicts a steam-powered textile factory in Lancashire, UK.

We are still in the “replacing water wheels” stage. Forcing AI chatbots into human-designed workflows is only a partial fix. We have yet to reimagine what organizations will look like when old constraints disappear and they can rely on infinite intelligence working even while you sleep.

At my company Notion, we’ve been experimenting. Besides 1,000 employees, over 700 AI assistants handle repetitive tasks: recording meetings, answering questions to consolidate team knowledge, handling IT requests, recording customer feedback, onboarding new employees, writing weekly status reports to avoid manual copy-paste… We are only at the beginning. The true potential is limited only by our imagination and inertia.

Economy: From Florence to Megacities

Steel and steam changed not only buildings and factories but also cities.

Until a few hundred years ago, cities were on a human scale. You could walk across Florence in forty minutes; the rhythm of life was determined by human walking distances and sound propagation.

Later, steel frameworks made skyscrapers possible; steam-powered railways connected city centers with hinterlands; elevators, subways, and highways followed. The scale and density of cities expanded rapidly—Tokyo, Chongqing, Dallas.

These are not just enlarged versions of Florence; they are entirely new ways of living. Megacities can be overwhelming, anonymous, and difficult to manage. This “indistinguishability” is the cost of scale. But they also offer more opportunities, more freedom, and support more diverse activities—an artistic renaissance city on a human scale cannot match.

I believe the knowledge economy is about to undergo a similar transformation.

Today, knowledge work accounts for nearly half of US GDP, but its operation still largely remains on a human scale: teams of dozens, workflows relying on meetings and emails, organizations that struggle beyond a hundred people… We are still building “Florence” with stone and wood.

When AI assistants are widely adopted, we will build “Tokyo”—organizations composed of thousands of AI and human collaborators; workflows that run across time zones without waiting for someone to wake up; decisions synthesized with the right level of human participation.

It will be a different experience: faster, with greater leverage, but initially also more dizzying. Weekly meetings, quarterly planning, annual reviews may no longer be relevant; new rhythms will emerge. We may lose some clarity but gain scale and speed.

Beyond Water Wheels

Every technological raw material demands that people stop looking at the world through rearview mirrors and start imagining new worlds. Carnegie gazed at steel and saw the city skyline; Lancashire factory owners looked at the steam engine and envisioned factories away from rivers.

We are still in the “water wheel” stage of AI—forcing chatbots into workflows designed for humans. We shouldn’t be content with AI as a co-pilot; we must imagine: when human organizations are reinforced with steel, and trivial tasks are entrusted to never-resting intelligence, what will the landscape of knowledge work look like?

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