AI isn't intelligent (yet): it doesn't understand what it's saying; it's just statistics that look like thinking.
By Iñaki Alegría Coll
A historic moment… and a dangerous misunderstanding
We are living through one of the most fascinating periods in recent history. Artificial intelligence has burst onto the scene with a speed and transformative power reminiscent of the Industrial Revolution or the birth of the internet. Today, in a matter of seconds, it can draft reports, translate complex texts, generate code, propose diagnoses, or synthesize scientific knowledge.
The feeling is clear: we are dealing with something that “thinks.”
However, this perception is based on a fundamental misunderstanding that needs to be addressed thoroughly. Artificial intelligence is not intelligent in the human sense of the term. It does not think, it does not understand, and it has neither consciousness nor intention. And yet, it is capable of producing results that make us question this assertion.
That tension between what appears to be and what actually is constitutes one of the great conceptual challenges of our time.
What Is Artificial Intelligence, Really?
From a technical standpoint, current AI systems—especially language models—are based on mathematical architectures designed to predict the probability of a word appearing based on the preceding context.
Essentially, what they do is use millions of examples to calculate which sequence of words is most likely to come next. This process, repeated millions of times and optimized using vast amounts of data, produces surprisingly consistent results.
But let's be clear:
there is no human understanding, no experience, no intention.
AI doesn't "know" what it says.
It doesn't understand.
It isn't alive.
It works in language, but not in reality.
And yet, it achieves something extraordinary: it simulates with great precision the behaviors we associate with intelligence.
The Great Illusion: When Statistics Resemble Thought
Herein lies the great paradox of artificial intelligence.
Using purely statistical models, we are able to generate responses that appear reasoned, structured, and even creative. This leads us to attribute intelligence where there is actually probabilistic prediction.
The problem is not technical, but conceptual. We confuse:
- linguistic coherence and comprehension
- fluency of thought
- formal precision combined with knowledge
AI can write about human suffering without ever having suffered itself. It can describe an illness without ever having seen a patient. It can explain an emotion without ever having felt anything.
This doesn't invalidate it as a tool, but it does require us to use it correctly.
We are not dealing with a mind, but with a highly sophisticated simulation of language.
The Decisive Role of Humans: The Art of the “Prompt”
One of the most revealing aspects of how AI works is its dependence on humans.
The quality of the response depends directly on the quality of the question. The prompt is not a minor technical detail; it is at the heart of the process. It is how humans infuse a system that lacks all of these elements with intention, context, judgment, and direction.
When the prompt is poor, the response is poor.
When it’s ambiguous, the response is too.
When it’s well-crafted, the result can be extraordinary.
This brings us to a key point:
AI intelligence is, to a large extent, a projection of human intelligence.
The machine doesn't define the problem. It doesn't decide what's relevant. It doesn't set priorities. Humans do all of that.
The Silent Danger: Stopping Thinking
As technology improves, a less visible but more profound risk emerges.
The more we rely on AI, the more we tend to delegate. We delegate analysis, decision-making, writing, and even critical judgment. This can lead to a growing dependence that undermines our ability to think independently.
It isn't an immediate problem, but it is a structural one.
If we stop questioning, verifying, and reflecting, we run the risk of becoming mere validators of answers generated by systems that do not understand what they are saying.
The question, then, is not what AI can do for us, but rather:
what we're missing out on by using it.
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