Why AI Is Not a Bubble: A Perspective From Someone Who’s Been Here Before

Every few months, a familiar headline pops up: “AI is a bubble.”
“AI is another dot-com boom.”
“This hype will fade.”

As someone who studied computer science years ago—back when deep learning was just emerging in academic circles—and who has built multiple commercial products using both machine learning and neural networks, I can say confidently: AI is not a bubble.

AI is not new. But something very new has happened.

AI Has Existed in Commercial Use for Decades

During my university years, my thesis centered on deep learning. In industry, I later built solutions that blended machine learning, neural networks, and traditional statistical models. None of this is new. Companies have quietly used AI techniques for fraud detection, personalization, routing, scoring, forecasting, and recommendations long before the term “Generative AI” existed.

So why does today feel different? What changed?

It can be summed up in one simple statement from my professor many years ago:

“Computers cannot understand human language.”

For decades, this was true.
That barrier is now broken.

The Breakthrough: Machines Finally Understand Us

The advent of Large Language Models (LLMs) changed everything.

LLMs are not just a better algorithm.
They are a new interface for human–machine interaction.

For the first time in history, machines can interpret, generate, and reason with natural language at human-like levels. This is not “incremental innovation.” This is the removal of the greatest bottleneck in computing:

The language barrier between humans and machines.

Combine that with the AI techniques we already had—deep learning, classical ML, statistical modeling—and suddenly an entirely new landscape appears.

Which leads to an important question…

Where’s the Gap Now?

If LLMs unlocked human language, what’s next?

We now have two powerful ingredients:

1. Understanding (LLMs)
2. Action/Reasoning (traditional AI + neural networks)

But there is still a massive chasm between these two worlds. LLMs are great at interpreting and generating language, but they are not inherently designed to:

• plan long-horizon tasks
• execute sequences of actions
• maintain structured memory
• plug deeply into enterprise systems
• validate facts with certainty
• autonomously adapt strategies

This gap is where today’s “AI race” is happening.

Every company is trying to build the missing layer—the connective tissue between LLM intelligence and real-world autonomous execution. It will be the greatest innovation of this decade.

And whoever discovers it will be the winner.

Why This Isn’t a Bubble Like the Dot-Com Boom

People say, “This is just like the dot-com bubble.”

But the dot-com boom wasn’t all a bust.
Yes, many companies died—but look at what survived:

• Amazon
• Google
• eBay
• PayPal
• Salesforce

These were the winners filtered out from the frenzy. The race created the modern internet.

Similarly, today we are filtering the next generation of global platforms. The noise is loud. The bravado from companies is familiar. But this isn’t a bubble—it’s a discovery process.

A technological gold rush.

And in every gold rush:

• many try
• many fail
• a few find diamonds
• and the world is reshaped

What About Agentic AI? Isn’t That New?

Today, many startups and vendors loudly promote “agentic AI.”
But anyone with a computer science background knows:

Software Agents existed decades ago.

I studied them. Many of us did.

Agents that take instructions, perform tasks, and report back are not new. What is new is coupling software agents with LLM-powered understanding.

The concept isn’t new—the capability is.

We are not waiting on “agentic AI.”
We are waiting on the missing discovery that unifies LLM intelligence with actionable, reliable execution at scale.

That is the diamond everyone is mining for.

So… Is AI a Bubble? Absolutely Not.

What we are seeing today is not a bubble.
It is a transformation phase.

Just like the internet, electricity, and industrial automation, we are in the early, noisy, chaotic stage where:

• innovators experiment
• companies boast
• investors chase
• many projects fail
• a few breakthroughs define the next 20 years

The chatter from companies right now—the hype, the glossy marketing, the grand promises—sounds exactly like miners in the Gold Rush bragging in taverns about what they “almost found.”

Most won’t find anything.
But someone will.
And when they do, the world will shift again.

AI is not a bubble.
AI is a long-term structural technology shift, and we’re only at the beginning.

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