This article is a follow-up of my 2019 article on the same topic, but updated in the light of today’s AI hype.
Like I mentioned then, when I first experimented with neural networks years ago, I was struck by how little “intelligence” there actually was in them. They did exactly what I coded them to do—no more, no less. Today’s systems may look more impressive, but the principle hasn’t changed: these are tools, not minds.
The Problem With the Word “Intelligence”
Humans don’t even have a precise definition of their own intelligence or consciousness. If we can’t define those terms for ourselves, it makes no sense to claim that we’ve created them in machines.
What we can say is that current AI systems don’t exhibit the qualities most people intuitively link with intelligence: self-awareness, understanding, intent, or meaning. They generate outputs by compressing patterns in data, not by reasoning or knowing.
To call that “intelligence” is to confuse statistical mimicry with cognition.
What AI Actually Does
Modern AI systems, especially large language models and generative tools, are best understood as:
• Pattern machines. They excel at finding correlations in vast amounts of data.
• Automation engines. They can handle repetitive, data-heavy tasks quickly and consistently.
• Amplifiers. They extend human capability, but only within boundaries set by training data and design.
This is powerful, but it is not thought.
The 2025 Reality Check
• Scale isn’t sentience. More data and bigger models don’t bring us closer to human-like understanding.
• Usefulness ≠ understanding. A tool can be highly practical without being intelligent.
• The real risks are human. Bias, misuse, privacy abuse—these are problems in how people deploy the systems, not evidence of AI “deciding” anything.
Why the Distinction Matters
If we keep pretending AI is a kind of mind, we risk treating its outputs as if they were grounded in meaning or truth. They aren’t. They’re grounded in probabilities.
AI is not intelligent because we don’t even know what that word would mean in this context. What we do know is that these systems are fundamentally different from human thought: they calculate, predict, and generate—but they do not understand.
Conclusion
The danger isn’t that AI will “wake up.” The danger is that humans will forget what it actually is: computation dressed up in human-like outputs. Powerful, yes. Useful, yes. But never a mind.