The Confession
I've spent the last year building AI applications. I've shipped TuduBooks.ai, Floudea, and I'm working on TuduFast. I've burned through API credits, debugged hallucinations, and had more conversations with Claude than some people have with their coworkers.
And here's what I've come to believe: There is no AI.
Not in the way we talk about it. Not in the way the headlines scream about it. Not in the way that makes people either terrified or messianic.
What we call "artificial intelligence" is not intelligence at all. It's a process. A mechanism. A very, very powerful one—but a mechanism nonetheless.
The Thing We Don't Understand
Here's what strikes me: We don't actually know what intelligence *is*.
Think about that for a second.
We don't understand how we think. We've never seen a thought. We can map neural activity, sure, but the gap between "neurons firing" and "I just had an idea" remains as mysterious as ever.
We don't fully understand how the brain works. After centuries of study, the human brain remains one of the most complex systems we've ever encountered. We've made progress, but understanding? True understanding? We're not there.
So when we talk about "artificial intelligence," what exactly are we claiming to have made artificial?
A Discovery, Not a Creation
I think what happened with AI is more like a discovery than a creation.
The researchers didn't sit down and say, "Let me build something that thinks." They developed statistical methods—techniques that have been evolving for seventy years—and at some point, those methods became powerful enough to do something remarkable.
They started producing outputs that *look* like understanding.
But here's the crucial distinction: simulation is not the thing itself.
A flight simulator isn't flight. A painting of a sunset isn't a sunset. And a language model that produces coherent, helpful text isn't... thinking.
It's doing something. Something useful, something powerful, something that mimics aspects of human cognition so well that it *feels* like intelligence.
But there's no understanding in there. No memory in the way we experience memory. No "aha" moment when something clicks.
Just patterns. Very sophisticated patterns, learned from very large amounts of data.
Why This Matters
You might be thinking: "Okay, philosophy is nice, but does this actually matter? If it works, who cares what we call it?"
I think it matters a lot.
It matters for expectations. When we treat AI as "intelligent," we expect it to behave intelligently—to know things it can't know, to understand context it can't understand, to exercise judgment it doesn't possess. Then we're surprised when it confidently tells us something false, or "cleans up" our code by deleting half of it.
It matters for fear. Half the AI discourse is panic about superintelligent machines taking over. But if AI isn't actually intelligent—if it's a powerful mechanism that we discovered rather than a mind that we created—the fear calculus changes. The risks are real, but they're different risks than the sci-fi scenario.
It matters for use. When you understand that AI is a tool—an incredibly powerful statistical process—you use it differently. You check its work. You provide context it can't infer. You treat it like a very capable but fundamentally limited assistant, not an oracle.
The AGI Question
You've probably heard about AGI—Artificial General Intelligence. The holy grail. The moment when AI becomes truly intelligent, truly general-purpose, truly... like us.
Here's my honest take: AGI is a marketing term.
I don't say that to be dismissive. I say it because "general intelligence" is a concept we can barely define for humans, let alone measure or replicate. We don't know what target we're aiming for, so how would we know if we hit it?
What I see instead is incremental improvement in specific capabilities. Each new model does more things, does them better, handles more edge cases. That's real progress. That's valuable.
But the leap from "does many things well" to "is intelligent the way humans are intelligent"? That leap assumes we understand the destination. We don't.
The Powerful Process
None of this makes AI less useful. If anything, it makes it *more* useful—because you can use it without confusion about what it is.
What we have is a powerful process. A mechanism that:
That's not intelligence. But it is transformative.
The statistical methods that have been developing since the 1950s—gradient descent, neural networks, transformers—turned out to be keys that unlock something profound. Not thinking, but *pattern completion at scale*.
And pattern completion at scale, it turns out, is enormously valuable.
What I Actually Do With This Understanding
In practice, here's how this philosophy changes my work:
I never trust AI blindly. Because there's no understanding behind the output, only pattern-matching. Patterns can be wrong. Patterns can miss context. I always review.
I provide excessive context. AI doesn't "know" things the way I know them. It doesn't have my project history, my constraints, my goals. So I spell everything out, every time.
I expect confident wrongness. When AI makes a mistake, it doesn't hesitate or hedge. It states falsehoods with the same certainty as facts. That's not a bug in the intelligence—it's a feature of the mechanism. Patterns don't know when they're wrong.
I use it constantly anyway. Because the mechanism, even without intelligence, is incredibly powerful. I just use it with open eyes.
The Beautiful Paradox
Here's the paradox I've come to love: The less "intelligent" I believe AI to be, the more useful I find it.
When I treated AI like a smart colleague, I was constantly disappointed. It didn't remember our last conversation. It confidently told me nonsense. It didn't "get" things that seemed obvious.
When I started treating it like a powerful but fundamentally dumb process—one that requires careful input, verification, and guidance—suddenly everything got easier.
The tool works beautifully when you understand what the tool is.
The Invitation
If you're intimidated by AI, maybe this helps. You're not facing a superior intelligence. You're facing a very powerful pattern-matching system that can be incredibly useful *if* you understand its limitations.
If you're overly confident about AI, maybe this helps too. The systems are powerful, but they're not magic. They don't understand your business, your data, or your goals. You still have to do the thinking.
Either way, the opportunity is the same: Learn to work with this powerful process, and you gain capabilities that were impossible two years ago.
Not because you're working with artificial intelligence.
Because you've figured out how to harness a mechanism that mimics it.
Still building with AI, still checking its work, still marveling at what pattern-matching can do.
P.S. The irony of using Claude to help edit an essay about how Claude isn't intelligent is not lost on me. But then again—a very useful mechanism doesn't need to understand irony to be useful.
P.P.S. To be clear: I'm not an AI researcher. This is philosophy, not science. But sometimes the philosophy helps you use the science better.