AI Industrial Revolution: Where Do We Stand Now?

Original Author: Will Awang

Over the past year, I attended several AI-themed industry conferences. Guests on stage took turns demonstrating AI's latest tricks, while people in the audience held up their phones to take pictures of the screen, posted them on social media, and then went back to scrolling. But back in the office, it was the same weekly meetings, the same approvals, the same weekly reports. Big companies have already written token consumption into KPIs; some people become model workers by using scripts to inflate their usage metrics. Those people on social media—today it's a Claude revolution, tomorrow Codex is amazing, the day after Gemini is great—are they truly embracing a revolution, or just rushing from one trend to the next?

This is all noise, not the answer I'm looking for.

The real question isn't whether AI is powerful enough—the steam engine has already been built. The question is who will be the first to tear down the old workshop.

The true start of the Industrial Revolution wasn't the day James Watt improved the steam engine; it was the day a factory owner in Lancashire decided to move away from the river and rebuild his workshop around the steam engine. AI's most important moment will be the same—not the day the large model was invented, but the day the first organization decides to dismantle the old processes and rebuild its production methods around AI. That day hasn't arrived yet. But it's on its way.

Two people saw this coming quite early. Notion CEO Ivan Zhao wrote "Steam, Steel, and Infinite Minds" at the end of 2025, making a cold assessment: We are still in the "water wheel replacement" phase—adding AI chatbots to existing tools, but no one is redesigning the factory. Former OpenAI employee Leopold Aschenbrenner took a different path: he wrote a 165-page document, "Situational Awareness," and then started a fund, growing it from $225 million to $13.68 billion, all betting on AI infrastructure. One is looking inward, the other is betting outward.

This article isn't about them. It's about us—where we stand right now, and which part of history we are repeating.

( Power-loom weaving, engraving by J. Tingle after Thomas Allom, 1835 / Wikimedia Commons )

1. The Workshop is Still Old

For most people, the day goes something like this: They save ten minutes writing an email with AI in the morning; then spend two hours in a weekly meeting that probably didn't need to happen; in the afternoon, they copy and paste the same set of data between three different tools; and in the evening, they post on social media saying "AI is amazing." The ten minutes saved are silently consumed by the old processes.

Similarly, when the steam engine appeared, factory owners initially just replaced the water wheel with a steam engine, changing nothing else—the factory was still built by the river, still had multiple floors, and still used a central driveshaft to power the entire production line. We put ChatGPT into Slack, add Copilot to Office, and embed an AI chat window into our workflows—doing the same thing. The tool is upgraded, but the workshop hasn't changed.

But a new machine doesn't mean a new workshop. As Marshall McLuhan wisely said:

We drive into the future using only our rearview mirror. Using old processes to accommodate new tools is like early films merely being filmed stage plays. The real breakthrough comes when someone completely frees the steam engine from the river and redesigns the entire production method around the new power source.

Comparing the timeline of the Industrial Revolution with AI might help us locate where we are on the map:

Today's timeline is extremely compressed. The Industrial Revolution took 60 years from the steam engine to the railway mania; AI has taken only 7 years from the Transformer to the data center construction boom.

Speed isn't the issue; the issue is where we are stuck—the first four rows all represent the stage of putting new machines in an old workshop. The steam engine is installed, the railways are being laid, but the production method remains unchanged. Only the sixth row represents a true watershed. We are likely stuck between these two steps.

The steam engine is in hand, but the workshop is still old.

2. All the Money is Piled into the Layer Farthest from the Factory

Infrastructure is always overbuilt. In the end, it's the investors who go bankrupt, not the infrastructure.

In 1846, the British Parliament passed 263 railway acts, approving the construction of 9,500 miles of new railways. At its peak, railway investment accounted for 13% of Britain's GDP. Railway shares could be bought with just a 10% deposit, and the middle class jumped in en masse. The bubble burst in 1847. One-third of the approved lines were never built, and countless investors lost everything. Charles Darwin lost 60% on his railway stocks, and he was much luckier than most.

But the railways remained.

Today's AI infrastructure is following the same path. Goldman Sachs estimates that global AI infrastructure capital expenditure will reach $765 billion in 2026, and is projected to hit $1.6 trillion annually by 2031. The capital expenditure of hyper-scale cloud providers as a percentage of their operating cash flow has risen from about 40% in 2023 to nearly 70% in 2025. AI-related investments already account for about a quarter of all US investment. Aschenbrenner's $13.68 billion is betting on this layer—he's not betting on which application will win, but on the underlying computing power itself.

This capital cycle is isomorphic to real estate development. Building a data center is like building a building: the land is electricity, the building materials are GPUs and storage, the contractor is the data center construction company, the developer is the cloud provider, the tenants are AI application companies, and the rent is API revenue. The cloud provider's business model is akin to using rental income to cover mortgages—using API revenue to cover data center capital expenditure, waiting for the valuation jump that an AI application explosion would bring.

(Computing Power Real Estate: Each generation has its own infrastructure)

The core risk is the same: Is the speed at which API unit prices are falling offset by the growth rate of call volume? What if rent falls below the mortgage threshold—this is the nightmare every real estate developer knows best. The lesson of 2008 wasn't that too many houses were built, but that the type of houses built didn't match the real demand. AI's equivalent risk is: an oversupply of general-purpose computing power, but a persistent scarcity of specialized capabilities needed for high-value scenarios like financial compliance and medical diagnosis.

Railways, real estate, AI—infrastructure investments across three eras share the same rule: overbuilding is the norm, building materials suppliers always lose pricing power, and long-term returns always belong to the owners of "prime locations." Just look at the Q1 fund holdings of Wall Street—chances are 80% is concentrated in this infrastructure layer: NVIDIA, data centers, cloud infrastructure. But the railway mania taught us one thing: This is not the full picture of the AI revolution, nor even the layer with the highest returns.

What are the "prime locations" of AI? It's unique industry data and deeply embedded workflows. For an individual, the true "prime location" isn't the stocks you hold; it's your irreplaceable judgment and industry knowledge—provided you have already rebuilt the way you use them around AI.

The real returns are in the next layer. But the transition from infrastructure to value creation is not seamless. There's a gap in between—historically, this gap has swallowed decades.

3. Who is Dismantling the Workshop

The people tearing down the workshop and the people "improving efficiency with AI" are not doing the same thing.

Ivan Zhao's co-founder, Simon, used to be a "10x programmer." Now he rarely writes code himself—he simultaneously controls three or four AI coding agents, achieving 30 to 40 times efficiency. Notion now has 1,000 employees and over 700 AI agents. The difference isn't the tools; it's that Simon dismantled his own old workshop, while most people just replaced a water wheel.

600 million Chinese users have used generative AI tools, a year-over-year increase of 142%—making this the world's largest AI demand pool. Yet almost no Chinese company has rebuilt its core workflows around AI. The world's largest demand side, paired with an almost static supply-side organizational change. This contrast itself is a signal: the problem isn't that the tools aren't good enough; it's that the organization hasn't caught up. The context of knowledge work is scattered across dozens of tools and dozens of people's minds, output is unverifiable, and no one knows how to judge whether a strategic memo is effective.

(Labor market impacts of AI: A new measure and early evidence)

Anthropic has already started acting on a larger scale. They published an Economic Index, using real usage data to map out which tasks and industries AI will replace first, and then started building according to this map: forming a joint venture with Goldman Sachs, Blackstone, and Hellman & Friedman to create an AI-native enterprise service company; establishing a global alliance with KPMG, connecting 276,000 employees to Claude; and forming a business group with Accenture, training 30,000 people, focusing on finance, life sciences, and healthcare.

These consulting firms aren't playing the role of AI users; they are the railway engineers of AI—they don't build the steam engine or lay the tracks; they help companies tear down the old factories and rebuild production lines around the new power source. Without this role, most factory owners wouldn't know where to start.

Signals are already flashing. The sharpest one comes from the job market.

Young people aged 22-25 entering occupations highly exposed to AI are 14% less likely to find a job compared to their peers entering low-exposure occupations. Entry-level positions are already being squeezed.

If I were a new graduate, this number would directly affect my job search. If I were a manager, the next batch of entry-level positions I hire for might not be filled by people.

Organizations are transforming; what about individuals? My degree, my resume, the industry experience I've accumulated over the years—these are my water wheels. They once powered my entire production line, but the steam engine has arrived. Being from a prestigious university is no longer a moat; it only proves that I once built a decent factory by the river.

The question now is whether we have the ability to leave that river.

Anthropic's data shows that users who have used an AI tool for more than 6 months have a task success rate 10% higher than new users. Those who started six months early already have a 10% lead, and this gap will compound over time.

But so far, not a single company has gone bankrupt for not using AI; my own law firm is actually making great strides using AI. The winners haven't been selected by the market yet. The learning curve is real—those who started early are already building an advantage, but most people are still at the starting line.

4. My Next Job Title Doesn't Have a Name Yet

Will my current professional title still exist in ten years? How many of the tools I used daily five years ago are still around today? The answer to both is probably no. But I don't know what will replace them—because those things don't exist yet.

It's always been this way throughout history. New things aren't planned; they grow on their own once old constraints disappear.

Before the railways were built, Britain was a collection of isolated local economies. The price of cotton cloth in Manchester could be 30% different from the price in London. Every city had its own time standard, and no one thought it was a problem. In the twenty years after the railways were built, everything changed. A unified national market emerged for the first time, price differences were eliminated; standard time wasn't invented, it was forced into existence by the railways; stationmasters, telegraph operators, travel agents—these jobs didn't exist at all before the railways.

No one foresaw the department store when they were laying railway tracks. No one foresaw standard time when they were building the steam engine.

(Steam, Steel and Infinite Minds)

The history of cities tells the same story. A few hundred years ago, cities were human-scale—a forty-minute walk across Florence. Steel frames made skyscrapers possible, railways connected cities to their hinterlands, and elevators, subways, and highways followed. Tokyo, Chongqing, Dallas—these aren't larger versions of Florence; they represent entirely new ways of living.

Current knowledge work is also human-scale. Teams of a few dozen people, paced by meetings and emails, become overwhelmed beyond a few hundred. We are building Florence with stone and wood. AI is making "Tokyo" possible—organizations composed of thousands of AI agents and people, with workflows running continuously across time zones. The old weekly meetings, quarterly plans, and annual reviews may no longer be meaningful.

Simon no longer writes code—his job has become "managing AI agents." This position didn't exist two years ago. My next professional title probably doesn't have a name yet. But some people are already building that future whose name we can't yet call.

5. What the New Workshop Looks Like

After tearing down the old workshop, what do you build? YC's answer is: let the company improve itself.

Their internal system can now modify its own code overnight. An employee ran a query during the day, and it failed. A supervisor agent read about this failure, traced the cause, wrote code to fix it, submitted it for review, and deployed it. The same query worked fine the next day. The whole thing happened while everyone was asleep.

This isn't AI helping people produce 30% more. This is the system closing a complete loop on its own, figuring out how to get better.

YC partner Tom Blomfield, in an internal talk, called this form of company a "recursive self-improving AI loop." His assessment was direct: Most companies are still like Roman legions—commands cascade down, information flows up, with people acting as conduits for that information. AI doesn't just improve the efficiency of a single link; it breaks the very premise upon which this entire hierarchical structure exists.

His new logic is: Burn tokens, not heads. The bottleneck is shifting from human power to computing power. YC's data shows that companies in their Demo Day batch have about 5 times higher revenue per employee than 18 months ago. The role of middle management is being taken over by AI—"coordination" no longer needs people. Everyone should be an IC, a builder, an operator. Every task should have a named person responsible, not a committee.

There is