Steam, steel, and AI: Infinite Intelligence

By the end of 2025, we all need to stop looking at the world through the rearview mirror and instead look into the future, starting to imagine a completely new world. Carnegie saw steel and city skylines; the factory owners of Lancashire saw steam engines and factory workshops freed from the constraints of rivers.

Author: Will Awang , Investment and Financing Lawyer specializing in Web3 & Digital Assets; Independent Researcher specializing in Tokenization, RWA, Payments, and DeSci

Only by stepping back can we detach ourselves from the sweeping tide of the times and contemplate the relationship between crypto, blockchain, and AI, and only then can we consider their future as "global citizens." Perhaps this is what Marshall McLuhan meant when he said, " driving into the future through the rearview mirror ."

This article, "Steam, Steel, and Infinite Minds," by Notion CEO Zhao Yiwan (@ivanhzhao), explores how AI is reshaping knowledge work and organizational structures. From individuals to organizations to the entire economy, AI is evolving from a simple tool into a deeply integrated productive force. The author uses historical metaphors such as steel, windmills, steam engines, and Florence to illustrate how new technologies are changing work methods and organizational forms.

By the end of 2025, we all need to stop looking at the world through the rearview mirror and instead look into the future , starting to imagine a completely new world. Carnegie saw steel and city skylines; the factory owners of Lancashire saw steam engines and factory workshops freed from the constraints of rivers.

Here we see: Xiao Feng : Blockchain is the next-generation financial infrastructure and a fundamental prerequisite for the Fourth Industrial Revolution ; DigiFT Henry : From the handshake under the sycamore tree to consensus on the blockchain, to the future of on-chain finance ; AIsa Jordan : The on-chain payment system of AI Agent is the key to unlocking the AI economic system . And our current exploration of AI has only just begun.

True progress is limited only by our imagination and inertia. Let us explore and move forward together.

Enjoy the following:

https://twitter.com/ivanhzhao/status/2003192654545539400?s=12&t=yZHnQ_MFJ1lzu_4wUkm5ag

Every era is shaped by its marvelous materials. Steel forged the Gilded Age, semiconductors ushered in the Digital Age, and now artificial intelligence has emerged with its boundless intelligence. If history can teach us anything, it is that those who wield these materials define the era.

Left: Andrew Carnegie and his brother as teenagers. Right: Pittsburgh steel mill during the Gilded Age.

In the 1850s, Andrew Carnegie ran through the muddy streets of Pittsburgh as a telegraph boy. At that time, six out of ten Americans were farmers. Yet, in just two generations, Carnegie and his contemporaries shaped the modern world. Horse-drawn carriages were replaced by railroads, candlelight gave way to electricity, and ironware was replaced by steel.

Since then, my focus has shifted from the factory to the office. Today, I run a software company in San Francisco, building tools for millions of knowledge workers. In this tech-driven town, everyone's talking about Artificial General Intelligence (AGI), but most of the world's two billion office workers haven't yet felt its impact. What will knowledge work look like soon? What will happen when organizational structures absorb those tireless "minds"?

This future is often difficult to predict because it always masquerades as the past. Early telephone calls were as concise as telegrams, and early films resembled stage plays. (This is what Marshall McLuhan meant by " driving into the future through the rearview mirror .")

The most popular form of AI today looks like Google Search of the past.

Today, we're seeing AI chatbots mimicking Google's search box. We're in that uncomfortable transition phase that comes with every technological revolution.

I don't have all the answers about what will happen next. But I like to use historical metaphors to think about how AI can work at different scales, from individuals to organizations to entire economies.

Personal: From Bicycles to Cars

We can first see this phenomenon in the leading figures of knowledge work—programmers.

My co-founder Simon used to be what we called a "ten-time programmer," but he rarely writes code now. If you walk up to his desk, you'll see him simultaneously controlling three or four AI coding agents. These agents not only type fast but also think, making him 30 to 40 times more efficient. He schedules tasks before lunch or bedtime so they continue working while he's away. He's now like a manager overseeing countless intelligent agents.

A 1970s study on transportation efficiency published in Scientific American inspired Steve Jobs' famous "bicycle of ideas" metaphor. However, since then, we've been riding the information superhighway for decades.

In the 1980s, Steve Jobs called the personal computer a "bicycle of thought." A decade later, we paved the "information superhighway" known as the internet. But today, most knowledge work still relies on human labor. It's like riding a bicycle on a highway.

With the help of AI agents, people like Simon have already upgraded from riding bicycles to driving cars.

When will other types of knowledge workers be able to own cars? Two questions need to be addressed.

Why is it harder for AI to help knowledge work compared to programming agents? Because knowledge work is more fragmented and harder to verify.

First, there's the fragmentation of context. For programming, tools and context are often centralized in one place: an Integrated Development Environment (IDE), a code repository, or a terminal. But general knowledge work is scattered across dozens of tools. Imagine an AI agent trying to draft a product brief: it needs to extract information from Slack chat logs, strategy documents, last quarter's data from dashboards, and institutional memories that only exist in someone's head. Today, humans act as the glue, stitching all this together by copying and pasting and switching between browser tabs. Unless these contexts are integrated, the agent will remain confined to narrow use cases.

The second missing element is verifiability. Code has a magical property: you can verify it through testing and errors. Model makers use this to train AI to be better at programming (e.g., reinforcement learning). But how do you verify if a project is managed well, or if a strategic memorandum is effective? We haven't yet found a way to improve models to fit general knowledge work. Therefore, humans still need to be involved, supervising, guiding, and demonstrating what constitutes a good model.

The Red Flag Act of 1865 required that a flag bearer walk in front of vehicles traveling along the street (this act was repealed in 1896). This is an example of the less-than-ideal state of "humanity in a cycle."

This year's AI programming agents have shown us that "human-machine collaboration" is not always desirable. It's like having someone physically inspect every bolt on a production line, or walking in front of a car to clear the way (refer to the Red Flag Act of 1865). We want humans to oversee the process from a leveraged point, rather than being involved in it. Once the context is integrated and the work becomes verifiable, billions of workers will upgrade from cycling to driving cars, and from driving cars to autonomous driving.

Organization: Steel and Steam

Companies are a relatively recent phenomenon. As they grow, they gradually decline and eventually reach their limits.

Organizational chart of the New York and Erie Railroad Company in 1855. Modern corporations and organizational structures evolved alongside railroad companies, which were among the first businesses to need to coordinate thousands of people over vast distances.

Centuries ago, most companies were workshops with just a dozen or so people. Today, multinational corporations employ hundreds of thousands. Communication infrastructure (the human brain connected through meetings and information) is overwhelmed by this exponentially growing load. We try to solve this problem with hierarchical structures, processes, and documentation. But we've been using human-scale tools to solve industrial-scale problems, like trying to build a skyscraper with wood.

Two historical metaphors suggest that, with the help of new materials, future organizations can take on a different appearance.

A marvel of steel: When the Woolworth Building was completed in New York in 1913, it was the tallest building in the world.

The first was steel. Before steel, 19th-century buildings were limited to six or seven stories. Iron, though strong, was brittle and heavy; adding more floors would cause the structure to collapse under its own weight. Steel changed all that. It was both strong and malleable. Frames could be lighter, walls could be thinner, and suddenly, buildings could rise dozens of stories. New building types became possible.

AI is the steel of an organization. It has the potential to maintain context in workflows and elevate decisions when needed, without noise. Human communication no longer needs to be a load-bearing wall. Two-hour weekly alignment meetings become five-minute asynchronous reviews. Executive decisions that require three levels of approval can be completed in minutes. Companies can truly scale without the degradation we've always considered inevitable.

A mill powered by waterwheels. Water, though powerful, is unreliable, limiting the mill to operating only in a few locations and during specific seasons.

The second story is about the steam engine. In the early days of the Industrial Revolution, early textile factories were located beside rivers and streams and were powered by waterwheels. When the steam engine appeared, factory owners initially simply replaced their waterwheels with steam engines, leaving everything else unchanged. The increase in production efficiency was limited.

The real breakthrough came when factory owners realized they could completely break free from their dependence on water. They built larger mills, closer to workers, ports, and raw materials. And they redesigned their factories around the steam engine (later, with the widespread adoption of electricity, factory owners further decentralized the power source from a central axis, placing smaller engines around the factory to power different machines). As a result, productivity skyrocketed, and the Second Industrial Revolution truly began.

This 1835 engraving by Thomas Allom depicts a textile mill in Lancashire, England, powered by a steam engine.

We're still in the "replacing the waterwheel" phase. We've simply added AI chatbots to existing tools. We haven't yet reimagined what your organization will look like when the old limitations disappear, and your company can operate on an unlimited number of AI systems that are working even while you sleep.

At my company, Notion, we've been experimenting. In addition to our 1,000 employees, we now have over 700 AI agents handling repetitive tasks. They take meeting notes and answer questions to consolidate tribal knowledge. They process IT requests and record customer feedback. They help new employees with repetitive tasks. They handle meeting minutes and answer questions to consolidate internal knowledge. They process IT requests and record customer feedback. They help new employees understand employee benefits. They write weekly status reports so everyone doesn't have to copy and paste. And this is just a small step. Real progress is only limited by our imagination and inertia.

Economy: From Florence to Mega-Cities

Steel and steam not only transformed buildings and factories, but they also transformed cities.

Until a few centuries ago, cities were measured in human terms. You could walk through Florence in forty minutes. The pace of life was determined by how far a person could walk and how far a voice could travel.

Then, steel frames made skyscrapers possible. Steam-powered railways connected city centers to the hinterland. Elevators, subways, and highways followed. Cities expanded dramatically in size and density. Tokyo, Chongqing, Dallas.

These are not merely enlarged versions of Florence. They represent different lifestyles. Mega-cities can be disorienting, filled with a sense of alienation, making it difficult to discern directions. This disorientation is the price of scale. But they also offer greater opportunities and freedom. More people are engaged in more jobs, and the ways of combining are far more diverse—something that moderately sized cities of the Renaissance could not match.

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

Today, knowledge work accounts for nearly half of the US GDP. Much of it still operates on a human scale: teams of dozens, workflows dictated by meetings and emails, and organizations overwhelmed when they exceed a few hundred people. We are building Florence with stone and wood.

When AI agents are deployed at scale, we will begin building organizations like those in Tokyo, comprised of tens of thousands of AI agents and personnel. Workflows will operate continuously across time zones, without waiting for anyone to wake up. Decision-making will incorporate well-timed human intervention.

This will feel different. Faster, more leveraged, but also more disorienting at first. The rhythm of weekly meetings, quarterly planning cycles, and annual reviews may no longer make sense. A new rhythm will emerge. We'll lose some clarity, but we'll gain scale and speed.

Beyond the waterwheel

The emergence of every amazing material demands that people stop looking at the world through the rearview mirror and begin to imagine a new world. Carnegie saw steel and a city skyline. The factory owners of Lancashire saw the steam engine and factory workshops without rivers.

We are still in the early stages of AI development, awkwardly applying chatbots to workflows designed for humans. We need to stop demanding that AI merely serve as our co-pilot. We need to imagine what knowledge work will look like when human organizations are as solid as steel, and when tedious tasks are delegated to tireless minds.

Steel, steam, and boundless intelligence. The next skyline is there, waiting for us to shape it.

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