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We’re in a moment in history where the lines between machines and human cognition are being blurred, a little too much for comfort. Large Language Models (LLMs), like ChatGPT, have dazzled the masses with their near-human ability to write essays, churn out code, simulate conversations, and even pretend to “understand” a variety of topics. But let’s get one thing straight right now: what LLMs do is not intelligence.
And no, repeating sentences in elegant prose does not qualify as having a brain, let alone a soul.
Now, before you throw your computer out the window in a fit of frustration (believe me, I understand the urge), let’s get into the details to clarify the muddled waters of what LLMs actually represent versus what real intelligence entails.
Spoiler alert: the gap is wide, and you might be surprised at how much of it has nothing to do with raw computational power.
The Rise of LLMs: Not As Clever As You Think
Let’s start with a simple fact. Large Language Models are incredibly good at predicting the next word in a sequence. They don’t “understand” in any meaningful sense of the word, but their ability to generate coherent text from a massive dataset of human language makes them appear almost eerily intelligent. The term “large” doesn’t even begin to capture the scale. We’re talking about billions, if not trillions, of parameters. But remember, this is just pattern matching on a huge scale.
These models operate based on probabilistic learning, which means they’ve essentially learned to calculate which word is most likely to come next, based on patterns in the data they’ve been trained on. That’s it. There’s no “thought process” happening. No decision-making based on values, experiences, or abstract concepts. It’s just a massively complex series of if-then statements. That’s not intelligence; that’s an impressive parrot with an encyclopedia.
A common misconception is that because LLMs can create text that sounds intelligent, they are intelligent. Sorry, folks, just because an algorithm can mimic the prose of an expert doesn’t mean it has the knowledge, reasoning ability, or cognitive faculties of one. If you’ve ever heard a political speech full of buzzwords and no substance, you know exactly what I mean. LLMs are really good at giving you the illusion of competence.
Intelligence, On the Other Hand, Isn’t Just About Patterns
Intelligence, real intelligence, is about more than just spitting out plausible text based on statistical likelihood. It’s about understanding context, grasping abstract concepts, making connections between seemingly unrelated ideas, and having a sense of purpose. It’s about adapting to new situations, solving problems, and using intuition to make decisions. None of these things come naturally to an LLM.
Take this example: you ask an LLM to generate a recipe for a lasagna, and it’ll churn out something that reads like a solid, conventional guide to making lasagna. It’s effective in this narrow task, right? But if you suddenly tell it that you’re allergic to tomatoes and it’s a vegetarian kitchen, it doesn’t “understand” why the tomatoes need to be replaced. It will simply search its training data for a similar recipe, without a true grasp of the nuance involved. It lacks any underlying comprehension of food chemistry, human biology, or the purpose of the task at hand.
Real intelligence, however, would make those adjustments on its own. It might suggest alternatives based on an understanding of ingredients, the nutritional needs of the individual, and the cultural context in which the meal is being prepared. This is where LLMs falter: they lack common sense, a core feature of intelligence.
Problem-Solving: Can LLMs Think Outside the Box?
It’s often said that human intelligence is uniquely powerful because it can think outside the box, reason abstractly, and innovate. This is where LLMs completely miss the mark. They can only operate within the boundaries set by their training data. Their “creativity” is nothing more than a rehash of combinations that already exist in the digital ether.
You want to come up with a new business strategy? The LLM will pull from known formulas and patterns. Want to discover the cure for cancer? Well, LLMs are only as innovative as the research they’ve been trained on, no novel breakthroughs here.
Humans, however, create entirely new categories of thought. Ever heard of “thinking outside the box”? It’s an actual thing. True problem-solving is about more than regurgitating known solutions, it’s about the ability to recognize when old solutions don’t apply and create new frameworks for thinking.
For instance, the discovery of penicillin happened because Alexander Fleming noticed something unusual and asked questions beyond the conventional medical knowledge at the time. LLMs, in contrast, simply answer questions based on what’s been fed to them. If they don’t have an answer, they either fall silent or “hallucinate” one. There’s no Eureka moment in their code.
The Technical Reality: LLMs Are Not Sentient
This is a crucial point. Despite the philosophical debates and armchair discussions about AI “consciousness” or the possibility of sentience emerging from advanced algorithms, let’s be clear: LLMs are not conscious. They don’t have self-awareness. They don’t know they exist. In fact, if you ask an LLM a question about its “thoughts,” you’ll likely get a generic response that sounds vaguely introspective but is really just spitting out the same type of statistical language manipulation it uses to respond to anything else.
Real intelligence isn’t just about performing tasks, it’s about having awareness, emotions, goals, and desires. These are all fundamental to what we consider “thinking” as humans. The idea that LLMs can develop any of these is as fanciful as suggesting that a toaster can come up with new recipes. They’re good at what they’re trained for, but they have no understanding of the purpose behind the task they perform.
Let’s make one thing abundantly clear: LLMs are tools. They are incredibly sophisticated tools, sure, but tools nonetheless. And like any tool, their function is limited by the design of their creators. If you need to hammer a nail into a wall, a hammer is fantastic. But if you need to solve a complex moral dilemma, the hammer will fail, and so will an LLM.
The Political and Ethical Implications: Overhyped or Underestimated?
There’s a darker side to all this hype surrounding LLMs, especially when it comes to political and ethical considerations. In the race to capitalize on AI and machine learning advancements, many policymakers and corporate players are blurring the lines even more. They might try to position LLMs as “thinking” entities to justify their role in shaping society, public opinion, or even legislation.
But that’s a slippery slope. If people start mistaking LLMs for conscious, intelligent beings, they’re playing a dangerous game. The political ramifications are enormous.
There’s a fundamental ethical question that arises: when we begin relying on LLMs to make decisions for us, whether in healthcare, education, or governance, are we handing over the keys to human agency to a machine with zero comprehension of the stakes? What happens when an LLM provides an inaccurate answer, and the consequences are disastrous? Are we still responsible for its actions, or do we blame the tool?
Even more unsettling is the growing trend of governments and tech giants pushing for greater AI “autonomy.” Here’s the thing: autonomy requires a degree of understanding and decision-making that LLMs will never have. Their “autonomy” is limited to what they’ve been trained to simulate. They are controlled by human inputs, designed with human algorithms, and governed by human biases.
A machine that’s shaped by a flawed training set can easily perpetuate systemic issues, racial, gender, or ideological biases embedded in society, leading to skewed outputs that further entrench inequalities.
LLMs Are Impressive, But They’re Not the Future of Intelligence
So here’s the bottom line, in case you missed it: LLMs are magnificent in what they do, but they’re not intelligent. They are sophisticated statistical tools capable of producing highly convincing language-based outputs. But real intelligence, with all its complexities and nuances, is about much more than manipulating language. It’s about understanding, reasoning, creating, and feeling. Intelligence can innovate; LLMs can only iterate.
And this isn’t to say that LLMs don’t have a place in the future. Far from it. They’re powerful tools in fields like content generation, customer service, coding assistance, and data analysis. But let’s stop pretending that these tools are anything more than that. Let’s recognize their role and put them in their proper context, before they become the next great political or philosophical distraction.
True intelligence, human, animal, or even potential future AI, has depth. It has complexity. And it doesn’t stop at producing the next word. If you’re trying to solve the big problems, you’ll need more than an LLM. You’ll need true, creative, and critical thinking. And that’s something no machine can replicate… at least, not yet.
