AI Industrial Revolution: Where Do We Stand Now

Original Author: Will Awang

Over the past year, I've attended several industry conferences focused on AI. Speakers on stage took turns demonstrating flashy AI tricks, while the audience held up their phones to capture the screen, posting to their social feeds before scrolling on. But back in the office, it was the same weekly meetings, the same approvals, the same weekly reports. Big tech companies have already incorporated Token consumption into KPIs, and some people use scripts to inflate their usage and become "model workers." The people in my feed hail a Claude revolution one day, praise Codex the next, and shout "Gemini forever" the day after — are they truly embracing a revolution, or just rushing to the next trendy thing?

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 Industrial Revolution truly began not when Watt improved the steam engine, but when a factory owner in Lancashire decided to move away from the river and rebuild the workshop around the steam engine. The most important moment for AI 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 very early on. Notion CEO Ivan Zhao wrote "Steam, Steel, and Infinite Minds" in late 2025, offering a cold assessment: we are still in the "replace the water wheel" phase — attaching AI chatbots to existing tools, but no one has redesigned the factory yet. Leopold Aschenbrenner, a former OpenAI employee, took a different path: he wrote the 165-page "Situational Awareness," then built a fund, growing it from $225 million to $13.68 billion, all betting on AI infrastructure. One looks inward; the other bets outward.

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

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

I. The Workshop is Still Old

A typical day for most people goes like this: they use AI to write an email in the morning, saving ten minutes; then spend two hours in a weekly meeting that could have been an email; spend the afternoon copying and pasting the same set of data between three different tools; and post on social media in the evening saying "AI is amazing." The ten minutes saved are effortlessly consumed by the old processes.

Similarly, when the steam engine appeared, factory owners initially just replaced the water wheel with a steam engine, keeping everything else the same — the factory was still built by the river, still a multi-story building, still using a central drive shaft to power the entire production line. We put ChatGPT into Slack, add Copilot to Office, embed an AI chat window into our workflow — we are doing the same thing. The tool has been upgraded, but the workshop hasn't changed.

But a new machine doesn't mean a new workshop. Marshall McLuhan put it well: We drive into the future using only our rearview mirror. Fitting new tools into old processes is like early films being nothing more than filmed stage plays. The real breakthrough only 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 pinpoint where we are on the map:

Today's timelines are 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 problem. The problem is where we are stuck — the first four rows are all in the stage of old workshops with new machines. The steam engine is installed, railways are being laid, but the production methods remain unchanged. The sixth row is the real turning point. We are likely stuck between these two steps.

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

II. All the Money is on the Layer Furthest 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 railway. At its peak, railway investment accounted for 13% of UK GDP. Railway stocks could be bought with just a 10% deposit, and the middle class flooded in. 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 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, potentially reaching $1.6 trillion annually by 2031. The ratio of capital expenditure to operating cash flow for hyperscale cloud providers has risen from about 40% in 2023 to nearly 70% in 2025. AI-related investments now account for roughly a quarter of all US investment. Aschenbrenner’s $13.68 billion bet is on this layer — he's not betting on which application will win, but on the underlying computing power itself.

This capital cycle is structurally identical to real estate development. Building a data center is like building a property: land is electricity, building materials are GPUs and storage, contractors are data center construction firms, developers are cloud providers, tenants are AI application companies, and rent is API revenue. The cloud providers' business model is paying off the mortgage with rent — using API revenue to cover data center CapEx, waiting for the valuation leap from the AI application explosion.

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

The core risk is the same: is the speed of API price decline offset by the growth rate of call volume? What if rental income falls below the mortgage payment — this is the nightmare every real estate developer knows well. The lesson from 2008 wasn't that too many houses were built, but that the houses built didn't match the structure of real demand. The equivalent risk for AI is: an oversupply of general-purpose computing power, but a persistent scarcity of specialized capabilities that can handle 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, material suppliers always lose pricing power, and long-term returns always belong to those who hold the "prime locations." Just look at the Q1 fund holdings on Wall Street — probably 80% is concentrated in this infrastructure layer: NVIDIA, data centers, cloud infrastructure. But the railway mania taught us: this is not the full picture of the AI revolution, nor is it even the layer with the highest returns.

What are the "prime locations" for AI? Unique industry data and deeply embedded workflows. For individuals, the real "prime location" isn't the stocks they hold, but their own irreplaceable judgment and industry knowledge — provided they have already rebuilt the way they use them around AI.

The real returns are in the next layer. But there's a seamless gap between infrastructure and value creation. Historically, this gap has swallowed decades.

III. Who is Tearing Down the Workshop

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

Ivan Zhao's co-founder, Simon, was once a "10x programmer." Now he rarely writes code himself — he simultaneously manages three or four AI coding agents, achieving 30x to 40x efficiency. Notion now has 1,000 employees and over 700 AI Agents. The difference isn't the tool; Simon tore down his own old workshop, while most people just replaced their water wheel.

600 million Chinese users have used generative AI tools, a 142% year-over-year increase — this is 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, matched with an almost static organizational transformation on the supply side. This contradiction itself is a signal: it's not that the tools are insufficient, 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 heads. 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 is already making moves on a larger scale. They released an Economic Index, using real usage data to map which tasks and industries AI will replace first, and then building according to this blueprint: they formed a joint venture with Goldman Sachs, Blackstone, and Hellman & Friedman to create an AI-native enterprise service company; established a global alliance with KPMG, connecting 276,000 employees to Claude; and partnered with Accenture to form a business group, 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 enterprises dismantle their old factories and rebuild their production lines around the new power source. Without this role, most factory owners wouldn't know where to start.

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

Young people aged 22-25 entering occupations highly exposed to AI have a 14% lower probability of finding a job compared to their peers entering low-exposure occupations. Entry-level positions are already being squeezed.

If I were a recent 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 no longer be filled by humans.

Organizations are being dismantled. 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 already arrived. Top university degrees are no longer a moat; they are just proof that I once built a decent factory by the river.

The question now is, do we have the ability to leave that river?

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

But no company has gone bankrupt yet for not using AI. My own advisory firm is still making great strides with AI. The winners haven't been selected by the market yet. The learning curve is real — those who started early are already accumulating advantages, but most people are still at the starting line.

IV. My Next Job Title Doesn't Exist Yet

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

This has been the case every time in history. New things aren't planned; they emerge 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 local time standard, and no one thought there was a problem. Within twenty years of the railways being built, everything changed. A unified national market appeared for the first time, and price differences were erased. Standard time was forced into existence by the railways, not invented by someone. Station masters, telegraph operators, travel agents — these jobs simply didn't exist 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 built on a human scale — a forty-minute walk across Florence. The steel frame 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 are entirely new ways of life.

Knowledge work today is also on a human scale. Teams of a few dozen people, paced by meetings and emails, buckle under the weight of more than a few hundred. We are building Florence with stone and wood. AI makes "Tokyo" possible — organizations composed of thousands of AI agents and humans, with workflows running continuously across time zones. The old weekly meetings, quarterly planning, and annual reviews might no longer make sense.

Simon doesn't write code anymore — his job has become "managing AI agents." This position didn't exist two years ago. My next job title might not have a name yet. But some people are already building the future we can't name yet.

V. 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 now modifies its own code at night. An employee made a query during the day that failed to run. A supervision agent read about this failure, traced the cause, wrote code to fix it, submitted it for review, and deployed it. The same query worked the next day. The whole thing happened while everyone was asleep.

This isn't AI helping people become 30% more productive. This is a system completing an entire closed loop by itself, figuring out how to get better on its own.

YC partner Tom Blomfield, in an internal talk, called this company form a "recursively self-improving AI loop." His assessment was direct: most companies are still Roman legions — orders passed down, information passed up, with humans acting as conduits for information. AI doesn't just break the efficiency of a single link; it breaks the very premise of this entire hierarchical structure's existence.

The new logic he proposes is: burn tokens, don't burn heads. The bottleneck is shifting from human labor to computing power. The data YC sees is that companies reaching Demo Day have about 5 times higher revenue per employee compared to 18 months ago. The role of middle management is being taken over by AI — "collaboration" is no longer a task for humans. Everyone should be an IC, a builder, an operator. Every task has a named person responsible, not a committee.

There is one prerequisite: the company must be "readable" by AI. Things that aren't recorded simply haven't happened for AI. YC now feeds all partner emails into a database, records all Slack messages and office hour recordings. One partner used 2