📌 Table of Contents

🔹 What Is Driving the Global AI Bubble?
🔹 Why AGI Expectations and FOMO Accelerate the Investment
🔹 The Current Situation: Infrastructure, Power, and Data Center Expansion
🔹 Negative Impacts: Energy, Water, and Environmental Pressure
🔹 Will Investments Pay Off?

🔍 Overview

This lesson explains why the world is experiencing an AI investment boom that resembles a bubble.
We explore the background, structure, and reality behind the explosive growth of AI investment, and we examine the hidden costs to energy, water, and society.

📖 Lesson Start

🗣 (S)

I keep hearing people say “AI is the next internet” or “AI is the new electricity.” Is that why so much money is flowing into AI right now?

🎓 (T)

Yes, that is one of the biggest reasons.
Around the world, major technology companies see AI as the next great infrastructure shift, just like the internet in the 1990s or smartphones in the 2010s. Because of this belief, they are making “nation-level investments” into chips, electricity, and data centers.

🗣 (S)

Nation-level? That sounds huge. How large are we talking about?

🎓 (T)

Very large.
Companies like Microsoft, Google, Meta, Amazon, and many others are spending tens of billions of dollars each year to build new data centers, expand their power supply, and secure massive amounts of GPUs.

Some governments are also supporting these projects because they believe AI will shape the future economy.
So the scale of investment is similar to building highways, railroads, or national power grids.

🔹 SECTION 1 — Background: Why AI Is Seen as “the Next Internet”

🗣 (S)

So why exactly do they believe AI is such a big shift?

🎓 (T)

There are three main reasons:

AI can automate many kinds of knowledge work
People expect AI to change how we write, design, program, research, and make decisions.
AI can be integrated into almost every industry
Healthcare, education, finance, manufacturing, transportation, energy—AI can support all of them.
AI grows more powerful with more data and more computing
This creates a cycle:
more investment → bigger models → better performance → more investment.

Because of this loop, companies feel pressure to invest “before others do.”

🗣 (S)

That sounds like a race. Is this why investors are rushing in?

🎓 (T)

Exactly.
Investors see AI as a historic turning point. This creates strong market psychology:

“If I don’t invest now, I will miss the next big thing.”
“Other investors are making money with AI. I shouldn’t fall behind.”

This fear of missing out is known as FOMO (Fear Of Missing Out).
FOMO makes the bubble grow even faster.

🔹 SECTION 2 — Structure: AGI Expectations + FOMO = Acceleration

🗣 (S)

You mentioned AGI earlier. How does AGI affect the investment boom?

🎓 (T)

Good question.
There are two kinds of AGI expectations in society:

1. Practical AGI

AI that can perform most knowledge-based labor as well as humans.
This would change the economy, because companies could automate many cognitive tasks.

2. Mythic AGI

AI that might gain consciousness, free will, or human-like understanding.
This idea spreads through media, movies, and online discussions.
Even though scientists do not agree on this, the story attracts attention and investment.

Both hopes—practical and mythic—create a sense that AI will transform human society.
This belief fuels enormous investment and contributes to the bubble-like momentum.

🗣 (S)

So people are investing because they think AI might replace a lot of work or even become something close to a new life form?

🎓 (T)

Yes.
Even if the second idea is unrealistic, the emotion behind it is powerful.
And financial markets often respond to emotion as much as to facts.

🔹 SECTION 3 — Current Situation: Explosive Demand for GPU and Power

🗣 (S)

What is happening right now? I heard that GPUs are impossible to get.

🎓 (T)

Exactly.
The demand for GPUs—especially NVIDIA’s high-end models—has exploded worldwide.
These chips are essential for training and running large AI models.

Because of this:

Data centers are expanding at record speed
Power consumption is rising to the level of entire cities
Cloud providers are signing special energy contracts just to run AI systems

Many regions in the U.S. and Europe are already facing power shortages because new AI data centers are using more electricity than local grids can supply.

🗣 (S)

Wow. So AI is changing physical infrastructure too?

🎓 (T)

Yes.
AI is not just a “software revolution.”
It is also a hardware and energy revolution, because these models require enormous amounts of:

electricity
cooling systems
water
land
rare materials for chips

This is why some experts call AI development “industrial-scale computing.”

🔹 SECTION 4 — Negative Impacts: Energy, Water, and Environmental Cost

🗣 (S)

Are there any downsides to this rapid growth?

🎓 (T)

Many. Let’s look at the three biggest ones.

1. Massive Energy Consumption

Training and running AI models uses huge amounts of electricity.
Sometimes a single cutting-edge model consumes as much power as a small town.

As a result:

Power grids are getting stressed
Renewable energy cannot keep up
Some regions return to fossil fuels to meet demand

In several areas, AI companies receive priority access to electricity over local businesses and residents. This is called resource inversion—AI gets priority, humans wait.

2. Huge Water Consumption

Data centers need water to stay cool.
Many use drinking-quality water for cooling.

In places already suffering from drought, this creates tension:

Water for residents is limited
Water for data centers continues to increase

This situation is already happening in the U.S. Midwest, Northern Europe, and parts of Asia.

3. Pressure on Semiconductor Supply Chains

The world cannot produce enough high-end chips to match AI demand.

This causes:

Factories like TSMC running at full capacity
Shortages of silicon wafers, chemicals, and rare metals
Higher prices for electronics in other industries
Slowed production for cars, medical devices, and household technology

All because AI takes the majority of available resources.

🗣 (S)

So AI development is beginning to take priority over normal human needs?

🎓 (T)

Yes, in some regions we can already see this pattern:

Electricity → AI first
Water → AI first
Land for data centers → AI first

This imbalance is one of the core risks of the AI bubble.

🔹 SECTION 5 — Will These Investments Really Pay Off?

🗣 (S)

All this investment sounds risky. Are AI companies actually making money?

🎓 (T)

Right now, most major AI companies are not profitable.
Even OpenAI, Google DeepMind, and Anthropic are spending far more than they earn.

There are several reasons:

Training models is extremely expensive
Power, GPUs, and specialized staff cost billions.
Models need constant updates
Every new version is larger, more complex, and more expensive to run.
Revenue models are not stable yet
Subscriptions and API fees exist, but they are still not enough to cover the enormous cost of development and operations.
Investment scale is massive
The world is spending trillions of dollars building data centers, power grids, and chip factories.

Because of this, many experts ask:

“Can AI companies become profitable before investor enthusiasm fades?”

This uncertainty is one of the biggest risks behind the AI bubble.

🔹 SECTION 6 — Technical Limits: Why AGI Is Not Guaranteed

🗣 (S)

Some people say AGI is very close. Others say it will never happen. What is the reality?

🎓 (T)

Today’s AI models are extremely powerful, but they still have clear limits:

1. AI predicts patterns—it does not “understand”

Current models are statistical systems.
They predict the “next likely answer,” based on massive datasets.
They do not have:

self-awareness
intentions
desires
intrinsic goals

So the “mythic AGI” idea is not supported by current science.

2. AI cannot generate concepts from nothing

AI does not create truly new ideas independently.
It recombines patterns from existing data.

This means:

AI is strong at transformation
AI is weak at “pure innovation” without human input

3. Scaling is hitting diminishing returns

Larger models = better performance
…but each improvement now requires:

exponentially more data
exponentially more GPU power
exponentially more electricity

The cost rises faster than the benefit.
This makes AGI harder than people expected.

🗣 (S)

So AGI is not impossible, but not guaranteed either?

🎓 (T)

Exactly.
There is a gap between:

Market imagination
Actual scientific progress

This gap is fueling both excitement and skepticism at the same time.

🔹 SECTION 7 — Even If the Bubble Bursts, What Will Remain?

🗣 (S)

If the AI bubble bursts, does that mean AI was a mistake?

🎓 (T)

Not at all.
History shows that bubbles appear whenever society builds new infrastructure:

Internet bubble → but the internet remained
Smartphone bubble → but smartphones became essential
Railway bubbles in the 1800s → but railroads transformed civilization

In the same way:

AI will continue to change society, even if the bubble collapses.

Why?

Because AI already provides real value:

Everyday work becomes faster
Small businesses can use AI like a digital assistant
Education, healthcare, and government will adopt AI tools
Languages, translation, and information access become easier

Even after the hype disappears, the practical benefits will stay.

🗣 (S)

So what should normal people do during this bubble?

🎓 (T)

The best approach is:

✔️ Enjoy the benefits

✔️ Stay realistic

✔️ Do not expect magic

✔️ Learn how to use AI efficiently

You do not need to “predict AGI.”
You only need to use AI as a helpful tool in your daily life and work.

🛠 Practice

👉 Practice Task:
Think about your own daily or work routines.
List three tasks where AI could help you save time, reduce stress, or improve quality.

Examples include:

Writing emails
Translating documents
Creating ideas for social media
Summarizing articles
Planning lessons or presentations
Generating images or design drafts
Searching for information quickly

After writing your three tasks, ask an AI tool:

“How can you help me with these tasks? Please give specific examples.”

Use the answers to explore practical ways AI fits into your life.

🎓 Comprehension Quiz

1. Why are companies investing such huge amounts into AI?
A. Because AI is already more profitable than any other industry
B. Because they believe AI is the next major infrastructure shift
C. Because governments require them to invest

2. What is FOMO in the context of AI investment?
A. A technical limit of GPUs
B. Fear of Missing Out, which drives investors to join the trend
C. A model that predicts financial bubbles

3. What is one major negative impact of rapid AI expansion?
A. AI eliminates the need for electricity
B. AI models can operate without chips
C. Data centers use massive amounts of power and water

4. What is one reason AGI may not appear soon?
A. AI has too much creativity
B. AI cannot generate new concepts independently
C. AI already has full self-awareness

5. What will happen even if the AI bubble bursts?
A. AI will still transform daily work and many industries
B. AI technology will disappear
C. Data centers will all shut down

📌 Summary

AI is seen as the next major infrastructure, like the internet or smartphones.
Massive investments are driven by both practical expectations and AGI-related hopes.
FOMO accelerates funding and creates a bubble-like atmosphere.
GPU shortages, power consumption, and water usage are serious concerns.
AI companies are not yet profitable; long-term returns are uncertain.
Technical limits remain, especially around creativity and true understanding.
Even if the bubble bursts, AI will continue to shape education, business, healthcare, and daily life.
The practical value of AI will outlast the financial hype.

📝 Quiz Answers

(Placed at the bottom so they do not distract during the quiz.)

Correct answers: