Why There Probably Can’t Be Real AGI

The Illusion of “Thinking” in AI

Every time AI gets better, the same question comes up again: are we getting close to real AGI? A system that actually thinks, reasons, and understands the world the way humans do.

As a computer science student, I used to think the answer was yes that it was just a matter of time and better hardware. But the more I learn about how AI systems actually work, and how the human brain actually works, the harder it becomes to believe that “real AGI” is even possible in the way people imagine it.

Not because AI isn’t impressive but because intelligence isn’t just computation.

AI Doesn’t Think — It Computes

When people say AI “thinks,” what they usually mean is that it produces intelligent-looking output. But under the hood, nothing resembling thought is happening.

Modern AI models don’t reason in the human sense. They don’t form beliefs, question assumptions, or understand meaning. What they do is math.

At its core, an AI model is a giant function. You give it an input, and through layers of matrix multiplications, probability distributions, and optimization rules, it produces an output that statistically makes sense based on its training data. When an AI writes an essay or answers a question, it isn’t thinking about the answer it’s calculating which sequence of tokens is most likely to follow.

There’s no internal voice. No “aha” moment. No understanding of why something is true. Just numbers flowing through a model.

Humans, on the other hand, don’t just compute responses. We reason about them. We doubt ourselves. We change our minds mid-thought. We understand concepts even when we can’t put them into words. That difference matters more than people realize.

Intelligence Isn’t Just About Scale

Another idea that often shows up in AGI discussions is that intelligence will naturally emerge if we just make models big enough. But scale alone hasn’t produced understanding so far.

Yes, larger models are better at pattern recognition and language generation. But they still don’t know what they’re saying. They predict text; they don’t grasp meaning. If intelligence were just a matter of adding more parameters and more data, we’d already be much closer to AGI than we are now. The fact that we aren’t suggests that something fundamental is missing.

The Brain’s Unfair Energy Advantage

The human brain runs on roughly 20 watts of power about the same as a dim light bulb. With that tiny amount of energy, it handles vision, language, emotion, memory, creativity, and self-awareness all at once. It learns continuously, adapts in real time, and never needs to be shut down and retrained.

AI systems, by contrast, require massive data centers, specialized GPUs, cooling infrastructure, and megawatts of electricity. And even after all that, they still don’t understand what they’re doing.

If intelligence were purely computational, this gap wouldn’t be so extreme.

Why the Brain Isn’t Just a Computer

It’s tempting to think of the brain as a biological computer that we’ll eventually replicate. But neurons aren’t digital switches. They’re analog, chemical, and constantly changing.

Learning in the brain doesn’t happen in neat training cycles. It happens continuously, influenced by emotion, physical state, memory, and environment. AI learns in isolation, then freezes. Humans learn by existing.

A child can generalize from a single example. AI needs millions. That difference is not accidental it reflects a completely different kind of system.

Thinking Requires a Body

Human intelligence is deeply tied to embodiment. We think the way we do because we have bodies because we can touch, fail, feel pain, feel boredom, and want things. AI has none of that. It has no internal motivation, no survival instinct, no curiosity unless explicitly simulated. You can assign rewards and penalties, but simulated incentives are not the same as biological needs. An AI doesn’t care whether it’s right or wrong.

And caring turns out to be a crucial part of reasoning.

The Consciousness Problem

There’s also the hardest question of all: consciousness.

We still don’t know what consciousness actually is. We can map brain regions and measure neuron activity, but we can’t explain why experience exists at all why thoughts feel like something instead of nothing.

Building AGI assumes that consciousness will somehow emerge from enough computation. But that’s an assumption, not a proven principle. A simulation of a brain is not guaranteed to be a mind. A weather simulation isn’t wet, and a brain simulation may not be conscious.

So What Does This Mean for AI?

AI will continue to get better. It will write better code, generate better text, analyze data faster, and assist humans in ways that were impossible before. But that doesn’t mean it will ever truly think.

Thinking isn’t just output. It isn’t just math. And it isn’t just scale.

Real intelligence is messy, embodied, conscious, and deeply tied to being alive.

Maybe the future isn’t about machines that replace human minds but about tools that amplify them. And once you see it that way, the idea of “real AGI” starts to feel less like an engineering goal and more like a sci-fi fantasy.