It seems like in the last 3 years, AI has come out of nowhere and has taken over the world.
Let me tell you something that might surprise you.
You’ve been using AI for years. Maybe even decades.
And you probably didn’t even know it.
When people hear “AI” today, they think of ChatGPT. Or those fake videos of people saying things they never said.
But AI has been working quietly in your life way before ChatGPT showed up in November 2022.
Think about the last time Google Maps told you to take a different route because of traffic. That’s AI. When Netflix suggests that show you end up loving? AI again. That filter that keeps spam out of your email? Yep, artificial intelligence.
So why is everyone suddenly going crazy over something that’s been around since before you were born?
The AI You Never Knew You Were Using
Before we talk about why everyone’s obsessed with ChatGPT, let’s be clear about what AI really is. Artificial intelligence is just computer systems that can do tasks that normally need human thinking. That’s it. No robot takeover needed.
You’ve been using these AI systems for years:
Spam filters (using AI since the 1990s)Google Search (AI ranking since the early 2000s)Siri and Alexa (understanding your voice since 2011)Face ID on your phone (AI recognizing your face)Credit card companies catching fraud (AI spotting weird patterns)Amazon suggesting products (AI learning what you like)Netflix suggesting moviews (AI learning what other people like you watch)Google Translate (AI translating languages)Spotify making playlists for you (AI picking songs)
What’s different about these? They were invisible. They just worked quietly in the background.
They made your life easier without shouting “Hey, I’m artificial intelligence!”
Understanding AI: Algorithm vs Machine Learning vs AI
People throw these words around like they’re the same thing. They’re not. In my #1 bestselling book The AI Project Advantage, I break it down simply.
Think of it like cooking.
An algorithm is like a recipe. It’s a set of steps to solve a problem. “Add 2 cups of flour, then add eggs, then mix.” The computer follows these exact steps every time.Machine learning is like a chef who learns from experience. Instead of following a recipe exactly, the chef tastes the food and adjusts. Too salty? Use less salt next time. The computer program learns from examples and gets better over time.AI is the whole restaurant. It includes both the recipes (algorithms) and the learning chefs (machine learning). It’s any computer system that can do things that usually need human smarts. Some AI uses machine learning, some just uses clever algorithms. But it’s all AI.
But how does AI actually learn? It’s all about patterns. Modern AI systems are trained on huge amounts of data. Imagine showing a child thousands of pictures of cats. Eventually, they learn what makes a cat look like a cat. The pointy ears. The whiskers. The way they move. AI does the same thing, but with millions or billions of examples.
The AI looks for patterns in all that data. It might notice that emails with “FREE MONEY” are usually spam. Or that people who buy diapers often buy baby food too. Or that certain words usually go together in sentences, like “vanilla… ice…. cream”. The more data it sees, the better it gets at spotting these patterns. That’s why ChatGPT can write like a human. It learned from reading billions of web pages, books, and articles.
This matters because not all AI is the fancy learning kind. Your calculator uses algorithms to do math. That’s AI too, just a much simpler kind. Understanding this helps you see why AI has been around so long without most people noticing.
A Quick Trip Through AI History
Here’s the story of how we got here, with the key breakthroughs that made today’s AI possible:
1950s to 1960s: AI is Born
1970s to 1980s: The First AI Winter
1973: The UK’s Lighthill Report says AI promised too much and delivered too little1974-1980: DARPA cuts AI funding by millions after AI fails to translate Russian automatically1969: Marvin Minsky’s book “Perceptrons” shows early neural networks can’t solve simple problems1986: Geoffrey Hinton rediscovers backpropagation, making neural networks useful again
1990s to 2000s: AI Gets Practical
2010s: The Deep Learning Revolution
Why GPUs Changed Everything: In 2012, researchers discovered that graphics cards (GPUs) used for video games were perfect for AI. GPUs can do thousands of simple calculations at once, while regular computer chips do one complex calculation at a time. This made AI training 50 times faster overnight.
2020s: AI Goes Mainstream
2020: OpenAI releases GPT-3, shocking researchers with its abilities2021: DALL-E shows AI can create images from text2022: Stable Diffusion makes AI art free for everyoneNovember 2022: ChatGPT launches, reaching 100 million users in 2 months2023: GPT-4 passes the bar exam and medical licensing tests2024: Claude 3, Gemini, and Llama 3 compete with ChatGPT2025: Every major company rushes to add AI to their products
The ChatGPT Moment: Why November 2022 Changed Everything
So what made ChatGPT special? Why did this AI tool blow everyone’s mind when Siri had been around for over 10 years?
Three simple words: accessible creative generation.
For the first time, anyone could talk to AI and get it to write a story. Or code a website. Or explain hard stuff in simple words. You didn’t need to be a computer person. You didn’t need special training. Just type what you want and watch it happen.
GPT-1 and GPT-2 existed before, but they were research projects. GPT-3 was available through an API, but you needed to be technical to use it.
ChatGPT took that same technology and put a simple chat interface on top.
Suddenly, your grandma could use it.
But here’s my theory about why it really exploded.
ChatGPT was the first AI that scared and excited creative people.
Think about it. When AI was beating humans at chess, writers and artists felt safe. That was “computer stuff.” But suddenly, here was an AI that could write articles. It could create ads. It could tell jokes (even if they were really bad).
The people who write what you read every day were amazed and terrified at the same time.
These are the people who write news articles. They create social media content. They shape what everyone thinks about. And they all started writing about ChatGPT because it affected them personally.
Plus, ChatGPT was free. Anyone could try it. Your mom could use it. Kids were using it for homework. It wasn’t locked away in some lab or big company. It was right there for everyone.
Big Companies Panic and Throw Money at AI
Before ChatGPT, AI development was happening in three main places:
Big tech companies like Google and Amazon (using it for their own products)Universities (doing research)Small startups (solving specific problems)
Companies were investing in AI, but carefully. Then ChatGPT got 100 million users faster than any product ever. Suddenly every CEO was panicking. “What’s our AI plan?” they all asked.
Microsoft gave OpenAI $10 billion. Google freaked out and rushed to release Bard (now called Gemini). Facebook made their AI free for everyone to use. Amazon scrambled to keep up. They weren’t just afraid of missing out. They were terrified of becoming the next Blockbuster while Netflix took over.
Time for a Reality Check
Here’s where I need to be honest with you. Right now, we’re at what I call “peak AI hype.” Everyone’s promising AI will change everything. Replace whole departments! Cure every disease! Do your job while you sleep!
The truth? Most of these promises will fail when they meet the real world.
I’ve seen those “52 step n8n workflows” that supposedly run your whole business automatically. You know what happens? They break the second a customer asks something unexpected. The people selling these “solutions” online have never actually used them in a real company. With real rules. Real privacy laws. Real customers who do weird things.
But here’s the good news. After the hype dies down, we’ll find the real value. Once we stop expecting AI to do everything, we’ll discover the specific things it does really well.
Where the Real Value Is: Practical AI for Regular People
This is exactly why I wrote my #1 bestselling book The AI Project Advantage. After almost 20 years managing huge projects for companies like Deloitte, Shell, and Roche, I’ve learned something important. The best tools are the ones regular people can actually use.
You don’t need to be a programmer to use AI well. You don’t need to understand how neural networks work. You just need to know which tools solve your problems and how to use them right.
The real AI revolution isn’t in flashy demos. It’s not in complex systems that promise to replace humans. It’s in simple, practical uses:
The project manager who writes reports 70% fasterThe team leader who quickly understands what customers really wantThe manager who can test new ideas faster than ever before
What’s Really Happening in Companies
The companies winning with AI aren’t trying to replace people. They’re giving people AI tools that make them better at their jobs.
Think of AI as an invisible team member. It handles the boring, repetitive stuff. That lets you focus on the interesting work. The creative work. The work that needs a human touch.
Smart companies are treating AI like we treated Excel in the 1990s. Excel didn’t replace accountants. It made every accountant way more powerful. Same thing with AI today.
The failures I’m seeing? They’re from companies trying to automate everything at once. The successes? They’re from companies that pick one problem, solve it with AI, then move to the next one.
Getting Past the Hype
We need to stop thinking AI will magically solve everything. It won’t. But it will solve specific problems really well.
In my work with big companies, I see the same pattern. The teams that succeed with AI start small. They pick one annoying task. Maybe it’s writing meeting notes. Or analyzing survey responses. They use AI to fix that one thing. Then they build from there.
The teams that fail? They try to “transform everything with AI” all at once. They buy into the hype. They think AI is magic. When it turns out AI is just a really good tool, not magic, they give up.
Your Next Steps
The AI revolution is real. But it’s not what the hype says it is. It’s quieter. More practical. And honestly, more useful.
AI isn’t about replacing people. It’s about making people more effective. It’s about doing the boring work faster so you can do the interesting work better.
If you’re ready to move past the hype and learn how AI can actually help your work, that’s what I teach. No coding needed. No computer science degree required. Just practical methods that work in the real world.
Because AI is just a tool. A powerful tool, sure. But like any tool, you need to know how to use it properly.
And that’s something anyone can learn. Even you. Especially you.
The companies and people who will win aren’t the ones with the fanciest AI. They’re the ones who learn to use simple AI tools to solve real problems. One step at a time. One improvement at a time.
That’s the real AI advantage. And it’s available to everyone willing to learn
Creativity & Innovation expert: I help individuals and companies build their creativity and innovation capabilities, so you can develop the next breakthrough idea which customers love. Chief Editor of Ideatovalue.com and Founder / CEO of Improvides Innovation Consulting. Coach / Speaker / Author / TEDx Speaker / Voted as one of the most influential innovation bloggers.
