01. From Chatbots to Digital Employees: The Third Awakening

The 18th-Century Hoax and Humanity’s Ultimate Fantasy

In the spring of 1770, the Schönbrunn Palace in Vienna welcomed a mysterious guest. Inventor Wolfgang von Kempelen presented to Empress Maria Theresa a machine that shocked the world: The Mechanical Turk.

It was a finely crafted mechanical mannequin, dressed in traditional Ottoman robes and a turban, holding a long pipe. It sat behind a large wooden cabinet, eyes staring vacantly at a chessboard. When the inventor turned a large brass key, the sound of meshing gears echoed, and the wooden hand slowly rose to make a brilliant opening move.

For the next 80 years, this machine toured Europe and the Americas, defeating Benjamin Franklin and even humiliating Napoleon Bonaparte. Legend has it that Napoleon once deliberately made an illegal move to test it; the mannequin paused, politely moved the piece back to its original position, and nodded to the Emperor. At that moment, everyone believed: The machine had a soul.

The secret wasn’t revealed until the 1850s, when a fire destroyed the machine: a chess master had been hidden inside the cabinet, manipulating the mannequin through a complex system of levers and mirrors.

This story might sound like a joke, but it precisely reveals humanity’s most primal desire for AI: We never just wanted a calculator that could compute; we wanted a partner who could “act,” “interact,” and even “correct” us.

Two hundred years later, modern AI no longer needs a hidden human. But the journey to this point has involved three difficult “awakenings.”

The First Awakening: The Victory of Brute Force (1997)

If a friend brags about knowing AI since the 90s, they are likely referring to IBM’s Deep Blue.

In 1997, Deep Blue defeated world chess champion Garry Kasparov. It was AI’s first highlight reel moment. But from a technical philosophy perspective, Deep Blue wasn’t “smart.”

Deep Blue relied on Brute Force. It didn’t understand “strategy” or “deception.” It simply calculated at a terrifying speed—200 million positions per second—evaluating every possible move 20 steps ahead and picking the one with the highest statistical win rate.

If you asked Deep Blue, “Why did you make that move?” it couldn’t answer. It had no mind; it only had a database. At this stage, AI was like a cold, boring accountant—precise but soulelss. It could only perform rigidly defined tasks.

The Second Awakening: The Emergence of Intuition (2016 - 2022)

What truly made AI “human-like” were AlphaGo and later ChatGPT.

In 2016, AlphaGo made “Move 37”—a move never seen in human records. The number of possible moves in Go is larger than the number of atoms in the universe; brute force cannot solve it. AlphaGo won through Neural Networks that provided something akin to human Intuition. It “felt” that a move was good rather than just calculating it.

The launch of ChatGPT in late 2022 was the peak of this awakening. Suddenly, a machine could understand sarcasm, write love letters, and pass the bar exam.

For the first time in history, humanity created a species capable of passing the Turing Test.

But at this stage, AI had a fatal flaw: it was a “Brain in a Vat.”

Imagine taking Einstein’s brain and keeping it in a glass jar filled with nutrient solution. It remains brilliant, knowing relativity and the mysteries of the universe. But:

• It has no eyes to read today’s newspaper.
• It has no hands to make you a cup of coffee.
• It has no legs to pick up your mail.

ChatGPT was that “Brain in a Vat.”

• You ask: “Book me a flight to Shanghai for tomorrow.”
• It answers: “I’m sorry, I’m just a language model and cannot access the internet or operate your credit card…”

At this point, AI was the world’s best Consultant, but it wasn’t a functional Employee.

The Third Awakening: Agents with Hands (2024 - 2026)

As you read this in 2026, we are in the midst of AI’s third awakening: The Rise of Agents.

What is an Agent? Simply put, it is AI Model (Brain) + Tools (Hands) + Planning (Prefrontal Cortex).

ChatGPT in 2023 was like this:

User: My code is broken, take a look. AI: Look at line 5, you might be missing a semicolon. (Talks but doesn’t act)

A Coding Agent in 2026 (like Cursor or Windsurf) is like this:

User: My code is broken, fix it. AI (Agent): Received.

1. (AI reads 50 code files autonomously)
2. (AI runs terminal commands to reproduce the error)
3. (AI locates the bug in utils.js and fixes it)
4. (AI runs tests, all pass) AI: Fixed. I’ve committed the changes.

See the difference? From “giving advice” to “doing the work”—this is the essence of the Agent shift.

Modern AI is no longer content with just chatting. They are granted internet access, file permissions, and browser controls. They sit at the computer like that 18th-century Mechanical Turk, clicking mice, sending emails, and completing workflows for you.

Why this matters to you

Because “using a tool” and “managing an employee” are two completely different skill sets.

• Before (2023): You needed to learn Prompt Engineering—casting “spells” to get the right words out.
• Now (2026): You need to learn Flow Engineering—acting like a Product Manager, breaking work into steps and assigning them to AI Agents.

In the following chapters, we will move beyond dry neural network theories and dissect these “Silicon Employees”:

• How do their brains (LLMs) generate hallucinations?
• How do they gain memory through RAG?
• What disasters could happen if you accidentally give them access to your company database?

Welcome to the Age of Action. Stop chatting; let them move.