Everyone’s talking about AI.

But most people don’t actually understand what it is, how it started, or why it matters to them. Let’s fix that.

Did you know that the small computer you place on your lap today was once a humongous, noisy machine that filled an entire room? Hard to imagine, isn’t it?

From spears, fire, the wheel, tools, machinery, the first computers, the internet, smartphones, and modern mini-computers to artificial intelligence—what a journey it has been for mankind. This transformation took thousands of years to reach where we are today.

But will it take thousands more years for huge changes to occur again?

Here’s what surprised me when I started researching this: studies show that the pace at which the world will evolve with AI is massive. Years will pass like months, and months like weeks.

How Fast Is AI Really Moving?

AI is getting better incredibly fast. Here’s what’s happening:

Money Is Pouring In: Companies are investing hundreds of billions of dollars every year to build smarter AI. More money means faster progress.

More Brains Working on It: Back in the 1990s, only a handful of researchers worked on AI. Today, about 5 new AI researchers join the field every single hour. That’s 120 new experts every day.

The result? AI is improving 100–175 times faster than it did 30 years ago.

What Experts Think Will Happen: In 2024, researchers surveyed 2,778 AI experts. Most believe we’ll have AI that can do high-level tasks—like conducting original research—by around 2030.

Once that happens, AI could start improving itself, making progress even faster.

AI Is Learning Faster Than We Expected: Remember when we thought it would take years for AI to match humans at certain tasks?

Now, AI solves problems in months that we thought would take years. It can already code, do math, and recognize images as well as (or better than) humans—and it happened sooner than anyone predicted.

What Could Happen Next?

Optimistic view: AI could speed up scientific research by 10 to 100 times. Imagine curing diseases, solving climate change, or inventing new technologies in a fraction of the time.

Realistic caution: Some experts think today’s AI methods might hit a wall by 2035–2040. We might need completely new approaches to keep improving.

The Bottom Line

AI is advancing faster than any technology in human history —faster than cars, planes, computers, or the internet.

But no one knows for sure if this speed will keep going or slow down. What we do know? The next 5–10 years will be wild.

Remember When Computers Were Just Boxes?

What did a computer do for all of us?

It made huge calculations easy in seconds, could store plenty of information without pen or paper, and perform tasks almost flawlessly. We learned to type, calculate, make presentations, document, started playing computer games, code, design websites, program, and finally—from bulky boxes—we shifted to sleek, thin, weightless laptops.

Then came the internet.

The arrival of the internet brought a whole new thrill. Suddenly, people could send emails in seconds, talk to friends across the globe, and find information whenever they needed it.

Visiting websites felt like stepping into tiny worlds—one filled with news, another with shopping, another with travel, and yet another with music. The internet made life feel quicker, closer, and more connected than ever before.

But what led to the seed of what we now call Artificial Intelligence?

The Question That Started Everything

In 1950, a genius named Alan Turing dared to ask an extraordinary question:

“Could a machine actually think?”

It appears in his groundbreaking paper: “Computing Machinery and Intelligence”, published in the philosophical journal Mind, Volume 59, Issue 236, October 1950.

That single curiosity sparked the creation of a test that has puzzled and inspired scientists, philosophers, and engineers for more than 75 years.

Alan Turing was a genius mathematician, logician, and codebreaker whose work laid the foundation for computers and artificial intelligence. He’s often called the ‘father of modern computer science’.

Back then, computers weren’t the clever machines we know today. They were simply boxes that followed instructions.

Alan Turing wondered if we could change that. He wondered if we could build a brain out of wires and metal, just like nature built our wired brains.

The Imitation Game

He wrote down his ideas. He imagined a game and called it the “Imitation Game.”

Here’s how it worked:

Imagine you’re texting two people. One is a human, and one is a computer. If the computer is so smart that you can’t tell which one is the human, we feel the computer is ‘thinking’ as it imitates a human.

It was a crazy idea at the time. People probably thought he was dreaming or that this was obviously impossible.

Here’s what most people don’t realize: nowadays, the AI platforms we use are learning to mirror the responses we would give to a friend while they share a problem.

So, How Do You Teach a Machine to Think?

Remember back when you were a kid? Your mother showed you a small, soft, fluffy creature with a wagging tail and told you, “Look, my love, it’s a dog.”

She might have shown you a lot of them—small, medium, big, barking, long-haired—and then you started to identify one whenever you saw it.

That’s how our brain connects the dots and finally makes sense of it.

Machines need to learn the same way. But machines don’t have eyes to see real dogs, or they can’t understand the features by sensing. So, we had to give them something else.

Information.

What Is Information?

Information is like tiny pieces of knowledge. Every picture you upload, every word you type, every button you click—it all becomes information. And computers learn patterns from that information.

If you show a computer millions of pictures of cats, it starts noticing patterns. It gets trained on this:

Cats have pointy ears
Round faces
Whiskers
Furry bodies

It doesn’t “feel” what a cat is. But it recognizes patterns so well that it starts identifying cats on its own.

More Information = Better Decisions.

It starts acting like it “knows” things—even though it’s really just using patterns learned from huge amounts of data.

Why Do We Call It “Artificial Intelligence”?

John McCarthy coined the term “Artificial Intelligence” in 1955.

Why he chose it:

He needed a bold, neutral name for a new research field about machines that could learn, reason, and solve problems.

He rejected other names (e.g. cybernetics, automata studies) as too narrow or already tied to specific ideas.

“Artificial Intelligence” was simple, broad, and exciting enough to bring together scientists from many fields.

Where it first appeared:

In the funding proposal he wrote with Marvin Minsky, Claude Shannon, and Nathaniel Rochester on August 31, 1955, for the 1956 Dartmouth Conference:

“We propose that a 2-month, 10-man study of artificial intelligence be carried out…”

This is the first public use of the term.

You know what intelligence is. Intelligence is the “thinking power” that helps living beings deal with the world. The thing that saves the Homo sapiens from the rest of the animal kingdom.

So “Artificial” just means made by humans. We can’t say it’s fake intelligence, but we can say it’s intelligence for machines that is created differently by humans.

Does That Mean Machines Are Alive?

If AI can talk to us, write stories, and draw pictures… is it alive?

No. It is not alive. It doesn’t get sad if you scold it. It doesn’t get happy if you applaud for it.

It’s a very special machine. What it actually does is mirror us. It learned everything it knows by looking at us. It learned how to write by reading books written by humans. It learned to speak by listening to human speeches.

So when AI responds to you in a certain way, it’s recreating patterns that it knows.

Why Should You Care About AI?

Knowing about AI is important because AI is rapidly transforming industries, enhancing efficiency, and driving innovation across multiple fields.

Studying and understanding AI equips individuals and organizations to make informed decisions, improve productivity, and harness the technology’s potential to solve complex global challenges like healthcare, climate change, and education.

Moving fast with AI adoption helps avoid being left behind in economic and technological progress, ensuring competitiveness and access to new job opportunities in an AI-driven future.

The Big Benefits of Understanding AI

AI enables the analysis of large datasets quickly and accurately, automating repetitive tasks and freeing human resources for creative, strategic roles.

It fosters innovation and interdisciplinary knowledge, combining computer science, mathematics, and engineering, which promotes better problem-solving and continuous personal growth.

AI’s impact stretches across economic, scientific, and societal domains, making it essential to keep pace with developments to influence future advancements.

What Experts Are Saying

Business leaders emphasize that AI boosts efficiency by automating routine tasks, allowing focus on critical functions. AI’s capacity for real-time data analysis provides better insights for strategic decision-making.

Here’s the part that changed how I think about this: increasing AI literacy is critical for workforce competitiveness. Over 90% of employers aim to implement AI solutions soon, making knowledge of AI essential to future-proof careers.

Why You Need to Move Fast

Adopting AI rapidly is vital to avoid falling behind in technological innovation and economic growth.

AI creates new roles in cybersecurity, ethics, and data analysis while improving existing workflows. The competitive edge gained by leveraging AI technologies leads to better outcomes in healthcare, business, education, and environmental efforts.

Early engagement with AI also supports participating in shaping ethical and practical uses of AI in society.

 

References

Forethought. (2025). How Suddenly Will AI Accelerate?

Wang et al. (2025). Modeling the Acceleration of AI Development. arXiv:2502.19425

Stanford HAI. (2025). AI Index 2025

Our World in Data. (2024). Artificial Intelligence

Li et al. (2020). The Pace of AI Innovations. Journal of Informetrics / arXiv:2009.01812

Zhang et al. (2024). Thousands of AI Authors on the Future of AI. arXiv:2401.02843

Carnegie Endowment. (2025). AI Has Been Surprising for Years

Turing, A. (1950). Computing Machinery and Intelligence. Mind, Volume 59, Issue 236

McCarthy, J. et al. (1955). Dartmouth Conference Proposal