In the year 1700, the most powerful man in Europe—perhaps Louis XIV of France—could still die from a simple infected scratch or a bout of smallpox. Despite his gold-leafed halls and thousands of servants, he lived in a world without antibiotics, refrigeration, or even basic indoor plumbing.

Today, a college student with a modest part-time job lives a life that would seem like sorcery to that King. We have the sum of human knowledge in our pockets, we can cross oceans in hours, and we survive illnesses that once toppled empires.

We often take this progress for granted, but it wasn’t a smooth climb. It was propelled by a massive, often chaotic upheaval known as the Industrial Revolution. Today, we find ourselves standing at the edge of a similar precipice: the AI Revolution. While the tools have changed from iron and steam to code and “compute,” the story remains remarkably the same. To understand where we are going, we have to look at where we’ve been—and the best way to do that is through the eyes of two people born centuries apart.

Two Lives, One Pattern

Meet Thomas. It is 1800, and Thomas is a master handloom weaver in a small English village. He is highly skilled; his hands move with a rhythmic precision developed over decades. He is the backbone of his community, earning a stable living that allows him to provide for his family.

Now, meet Alex. It is 2025, and Alex is a junior data analyst at a medium-sized marketing firm. Alex spends the day cleaning spreadsheets, building dashboards, and writing reports that summarize consumer trends. Like Thomas, Alex is skilled, educated, and feels relatively secure in a career path that seemed “future-proof” only five years ago.

Both Thomas and Alex are about to experience a shift that will redefine what it means to “work.”

Life Before the Great Gears Turned

To appreciate the scale of change, we have to remember how narrow life was before the Industrial Revolution. For most of human history, 70% to 90% of the population worked in agriculture. Work wasn’t a “career”; it was a grueling, sun-up to sun-down battle for survival.

There was no electricity to light the night, no refrigeration to keep food from rotting, and no sanitation systems to whisk away disease. The statistics from this era are sobering. In the late 1700s, the average life expectancy at birth hovered between 24 and 35 years. This wasn’t because everyone died at 30, but because child mortality was staggeringly high—roughly one in three children did not live to see their fifth birthday.

Life was harsh, local, and limited. If you were born a farmer, you died a farmer. If you were a weaver like Thomas, your world was defined by the physical limits of your own muscles.

The King and the College Student

We often imagine history as a slow crawl, but the Industrial Revolution was a leap. If we compare a pre-industrial King to a modern average person, the “baseline” of quality of life has been raised so much it’s almost unrecognizable.

The King had power, yes. He had musicians to play for him, but he couldn’t hit “play” on a Spotify playlist featuring any song ever recorded. He had the best doctors of his time, but they likely used leeches or bloodletting because they didn’t know germs existed. He had fresh ice brought from mountains at great expense, while we have freezers that keep our ice cream solid for pennies a day.

The point isn’t that wealth doesn’t matter today—it does. The point is that technology raised the “floor” for everyone. The most basic modern life includes luxuries that the wealthiest elites of 1700 couldn’t buy with all the gold in their treasuries.

When the Steam Met the Loom

The disruption for Thomas began with the steam engine and the power loom. Suddenly, a machine could do in an hour what took Thomas a week. Steam didn’t just assist him; it threatened to replace him.

Work shifted. People left the fresh air of the countryside for the soot-stained air of new industrial cities. Thomas saw his specialized skills lose their market value almost overnight. In some regions of England, the wages for handloom weavers plummeted by more than 50% as factories flooded the market with cheaper, machine-made cloth.

This led to a period of intense fear and resistance. A group known as the Luddites began breaking into factories and smashing the machines that were “stealing” their bread. They weren’t anti-technology because they hated progress; they were terrified because their livelihood was disappearing before they had a chance to adapt.

The Messy Middle of Progress

History books often skip the “messy middle.” We see the Industrial Revolution as a success because we live in the “long term,” but for those living through the transition, it was painful and unequal.

However, as the old roles faded, new ones emerged that Thomas couldn’t have imagined. The world needed factory managers, mechanical engineers, steamship captains, and railway clerks. Urbanization exploded. In England, the urban population grew from roughly 20% to over 70% in just a few generations.

The long-term outcomes were undeniable: life expectancy began its climb from 30 toward 70 and beyond. Goods became “deflationary”—meaning they got much cheaper. Take the humble iron nail. Before automation, a blacksmith had to hammer every single one by hand. As machines took over, the cost of nails fell by 99%.

This was devastating for the blacksmith who only knew how to make nails. But for everyone else? It meant they could suddenly afford to build better houses, buy furniture, and grow the economy in ways that created even more jobs.

Enter Alex: The AI Revolution

Now, the pattern repeats with Alex. If the Industrial Revolution replaced human muscle, the AI Revolution is augmenting—and in some cases, replacing—human cognition.

Alex is watching as AI tools begin to automate parts of the data analysis process. An AI can now clean a dataset, spot a trend, and write a summary in seconds. This isn’t just happening in data; it’s hitting writing, coding, legal research, and medical diagnostics.

Like the blacksmith with his nails, Alex is seeing the “cost” of basic cognitive tasks fall toward zero. If Alex’s only value is “moving data from point A to point B,” the future looks as bleak as it did for the handloom weaver.

What Exactly Is This “New Machine”?

To navigate this, Alex (and all of us) needs to understand what AI actually is. It’s not a single “brain” in a box. It’s a hierarchy of technologies:

  • Artificial Intelligence: The broad goal of making machines mimic human intelligence.
  • Machine Learning (ML): A subset where computers learn from data rather than following strict programmed rules.
  • Neural Networks: A specific type of ML inspired by the human brain, involving layers of mathematical “neurons.”
  • Large Language Models (LLMs): A further subset (like ChatGPT) that predicts the “next word” in a sequence based on massive amounts of data.

Then there is the concept of “Compute.” In the Industrial Revolution, the currency of power was coal and steam. Today, it is “compute”—the raw processing power required to run these massive mathematical models. Training a Large Language Model involves billions of matrix calculations and massive amounts of memory. Just as a larger steam engine could power a bigger factory, more “compute” allows for smarter, more capable AI.

The Predictability of Failed Predictions

Because this change feels so fast, people often make extreme predictions. In 2016, some experts, including AI pioneer Geoffrey Hinton, suggested that we should stop training radiologists because AI would soon be better at reading X-rays.

Fast forward to today: we have more radiologists than ever. Why? Because while AI is great at spotting a pattern on a scan, the job of a radiologist involves much more—consulting with other doctors, making complex ethical decisions, and overseeing the whole diagnostic process.

The biggest risk isn’t that there will be “no jobs.” The risk is the speed of change. The Industrial Revolution took six generations to play out; the AI Revolution might take one.

Adapting: Being “Water”

Some roles will become obsolete, just like the carriage driver or the village blacksmith. Other roles will be transformed—the cab driver becomes a logistics manager; the writer becomes an editor of AI-generated drafts. And some roles are currently unimaginable, much like the “computer programmer” or “broadcast anchor” would have been to Thomas in 1800.

The key to survival in this new era is a concept often attributed to Bruce Lee: “Be water.” In a world where the cost of “execution” (doing the task) is falling, the value moves to “intent” (knowing what task to do).

  • AI provides the execution: It can write the code or the report.
  • Humans provide the goal: We define what “good” looks like. We set the standard of “taste.” We decide which problems are worth solving.

Who Wins in the AI Era?

The “winners” won’t necessarily be the people with the most technical skills, but the people with the most adaptability.

  1. The Curators: Those who can use AI to produce ten times more but have the “taste” to know which 10% is worth keeping.
  2. The Lifelong Learners: Those who don’t see their education as a finished product but as a continuous software update.
  3. The Goal-Setters: Those who can look at a complex problem and break it down into tasks for an AI to solve.

Thomas the weaver struggled because his physical skill was his only currency. Alex has a choice that Thomas didn’t have as much of: Alex can use the very tool that threatens his job to make himself more powerful.

The Horizon

We are living through a historical echo. The anxiety we feel today about “the algorithms” is the same anxiety Thomas felt about “the engines.”

If history is our guide, the transition will be messy. There will be inequality, and there will be moments of profound confusion. But if the pattern holds, we are moving toward a world where the “floor” of human existence rises once again.

Modern life is built on the ruins of the jobs of 1800. The world of 2100 will be built on the transformation of the jobs we hold today. The question isn’t whether the machines are coming—they are already here. The question is: what will you build with them?

What is one skill you have that a machine can’t replicate, and how can you use AI to amplify it?

References & Further Reading