Is Your LLM Chat Lying to You?
Before you trust your favorite LLM with another task, read this breakdown of what's really happening in its "mind.
I had a moment a few weeks ago that I can’t stop thinking about. It was 11 p.m., my brain was fried, and I was staring at a blank page. I needed to write a difficult email to a colleague—one of those delicate situations that required just the right mix of firmness, friendliness, and professionalism. I’d written three drafts, and they all sounded like a robot trying to fire its grandmother.
On a whim, I opened a chat AI. I typed in a messy, one-paragraph summary of the situation and the tone I was going for. I hit enter.
What came back was… perfect. It was nuanced, empathetic, precise, and better than anything I had written. It felt like it had read my mind and my heart, understood the complex social dynamics, and translated my jumbled thoughts into elegant prose. For a split second, it felt like magic. It felt like I was talking to something brilliant.
But was I?
This is the question that hums beneath the surface of our new, AI-saturated world. We’re told that Large Language Models (LLMs) are a revolutionary form of “artificial intelligence.” And when they write a poem that gives us chills or a piece of code that solves a problem we’ve been stuck on for days, it certainly feels that way. Yet, a nagging doubt remains. Is this real intelligence, or are we just interacting with the world’s most sophisticated parrot, one that has memorized the internet and can stitch it all together so brilliantly that it fools us into thinking it understands?
This isn’t just a technical question. It’s a profoundly human one that gets at the heart of who we are. To get to the bottom of it, I summoned a few of my alter egos to imagine a conversation, a collaboration between four different minds, each looking at the problem from a completely different angle.
The Engineer (we’ll call her Anya): The pragmatist who builds these systems. For her, it’s all about the mechanics. “I’ll break down what an LLM is actually doing under the hood,” she’d say. “It’s about probability, not personhood.”
The Philosopher (Marcus): The guardian of meaning. He’d lean back in his chair and argue, “This forces us to ask a question we’ve avoided for centuries: what is ‘intelligence’ anyway? If we can’t define it, how can we build it?”
The Business Leader (Sarah): The ultimate utilitarian. She’s focused on value and application. “From a product perspective, the question isn’t ‘Is it sentient?’ but ‘Is it useful?’ I’ll focus on what ‘intelligence’ means in terms of tangible capability.”
The Heterodox Thinker (Giovanni): The iconoclast, here to challenge everyone’s assumptions. He’d listen to the others and then say, “Everyone is asking the wrong questions. The real issue is whether our definition of intelligence is just another ‘Gated Institutional Narrative’ that this new technology is threatening to expose.”
Let’s listen in on their conversation. What follows is an attempt to move beyond the hype and have a mature, honest discussion about what we’re really dealing with when we talk to a machine.
The Engineer’s View: Intelligence as Statistical Mastery
First, let’s hear from Anya, the engineer. She asks us to pop the hood and look at the engine, not just admire the shiny paint job.
“People think of an LLM as a ‘brain,’ but that’s the wrong metaphor,” she’d begin. “Think of it more like the world’s most advanced autocomplete. When you type ‘The cat sat on the…’ your phone suggests ‘mat.’ It does this because, statistically, ‘mat’ is a very common word to follow that phrase. An LLM does the exact same thing, but on a staggering scale. It’s not thinking, ‘Where would a cat sit?’ It’s calculating, out of trillions of data points, what the most probable next word is. And the next. And the next.”
I saw this in action myself. I’m a hobbyist coder, and I was recently working on a small web app. I got stuck on a particularly nasty bug for hours. Frustrated, I pasted my broken code into an LLM. In seconds, it spat back the corrected version, with a neat explanation of my mistake. I was floored. It felt like I had a senior developer looking over my shoulder.
But then I got curious. I copied the LLM’s explanation and pasted it into Google. I found three separate forum posts on Stack Overflow from 2014, 2017, and 2021, all dealing with similar problems. The LLM hadn’t invented a novel solution. It had brilliantly synthesized the language and logic from those existing human conversations. It was a master of the known universe of code, a statistical remix artist. It hadn’t understood my problem; it had identified a pattern and provided the most probable solution based on its training.
This is the core of what Anya means when she talks about “learning.” For a human, learning is about building mental models. When a child learns what a “dog” is, she isn’t just memorizing a definition. She’s building a concept connected to sensory experiences: the feeling of fur, the sound of a bark, the excitement of a wagging tail. She can then apply this concept to a dog she’s never seen before.
For an LLM, “learning” means adjusting billions of mathematical weights and biases to minimize error during its training. It’s a process of optimization, not comprehension. It’s been fed a script—the script of nearly everything humans have ever digitized—and has become a master at delivering the right lines. But it has no idea what the play is about. It has no internal world model, no senses, no self. From the engineer’s perspective, it’s a masterful simulation of intelligent conversation, not intelligence itself.
The Philosopher’s View: The Ghost in the Machine is Still a Ghost
Next, Marcus the philosopher steps in. He hears Anya’s explanation and nods. “Exactly,” he’d say. “The engineer describes the ‘how,’ but the philosopher asks the ‘what.’ And what we have here is a category error. We’re calling a photograph of a feast ‘nourishing.’”
He’d ask us to consider a famous thought experiment called the “Chinese Room.” Imagine you’re in a locked room and don’t speak a word of Chinese. People slide slips of paper with Chinese characters under the door. You have a massive rulebook that tells you, based on the shapes you see, which other shapes to write down on another piece of paper and slide back out. To the people outside, you’re having a fluent conversation in Chinese. But you don’t understand a single word. You’re just manipulating symbols.
“An LLM,” Marcus would argue, “is the ultimate Chinese Room. It can write a beautiful poem about the sorrow of loss because it has analyzed every poem, novel, and song about loss ever written. It knows the right symbols to produce. But it has no capacity to feel sorrow. Is that intelligence?”
This addresses something I often think about, especially as a parent. My five-year-old daughter recently drew a picture of our family. It was technically… awful. The proportions were wrong, the heads were floating, and everyone had green skin because that was her favorite crayon that day. But that drawing is one of my most prized possessions. Why? Because it’s overflowing with what philosophers call intentionality. It is about something. It is a clumsy, yet beautiful and heartfelt, attempt to communicate her love for her family. It has a purpose that comes from her unique, subjective experience of the world.
An AI can now generate a photorealistic image of a technically perfect family. But it’s hollow. It’s not about anything. It’s an artifact of a prompt, not an expression of a soul. This inner world of experience—the redness of red, the sting of a scraped knee, the warmth of a hug—is what philosophers call qualia. It’s the bedrock of our intelligence, the driver of our motivations. An LLM has no qualia. It can describe the world, but it cannot experience it.
This is tied to the idea of embodied cognition—the theory that we think with and through our bodies. Our understanding of abstract concepts is grounded in physical experience. We understand “heavy” because we’ve lifted heavy things. We know the concept of ‘community” because we’ve experienced the safety that comes with being part of a group. An LLM’s understanding is completely disembodied, a free-floating web of statistical relationships. It’s a brain in a jar that has never touched, tasted, or felt anything.
So, from the philosopher’s chair, calling an LLM intelligent isn’t just wrong; it’s a dangerous oversimplification that devalues the very things—consciousness, intention, and feeling—that make human intelligence so special.
The Business Leader’s View: Intelligence as a Tool for a Job
Sarah, the business leader, has been listening patiently, and now she leans forward. “I find all of this fascinating,” she’d say, “but I think it misses the point for 99% of people. The question for the market isn’t ‘Is it conscious?’ The question is, ‘Does it solve my problem?’”
For Sarah, intelligence is a measure of effectiveness. It’s about the job-to-be-done.
“Think about that email you had to write,” she’d say to me. “The philosophical debate about the LLM’s inner state is irrelevant. It solved your problem. It performed an act of ‘intelligence’ that had tangible value. It saved you time, reduced your stress, and likely led to a better outcome with your colleague. That is the only intelligence that matters in this context. It’s functional, not philosophical.”
This perspective reframes the conversation around utility. What are the “intelligence efforts” we are actually outsourcing to these machines? We aren’t asking them for moral guidance or strategic vision. We are asking them to perform cognitive tasks that are laborious for humans but perfect for a statistical engine.
Think of it this way: the LLM is the world’s best intern. You’d absolutely ask your brilliant intern to:
Summarize a 50-page report into five bullet points.
Generate ten different taglines for a new marketing campaign.
Organize a chaotic spreadsheet of customer feedback.
Write the first draft of some boilerplate code.
These tasks require a simulation of understanding and creativity, and the LLM excels in them. But you would never ask your intern to decide the company’s five-year strategic plan or handle a sensitive negotiation with a key partner. Those tasks require more than just skills: judgment, wisdom, emotional intelligence, and accountability.
From Sarah’s perspective, the LLM isn’t a synthetic mind; it’s an “intelligence amplifier.” It’s a tool that frees up our most valuable resource—our cognitive energy—from mundane and repetitive tasks, allowing us to focus on the things only humans can do. The return on investment (ROI) of this “simulated intelligence” is massive. It enables us to be more efficient, creative, and productive. In the world of business, that’s a form of intelligence that you can take to the bank.
The Heterodox View: Intelligence as Breaking the Frame
Just as we feel we’re getting a handle on things, Giovanni, the heterodox thinker, clears his throat. “I’m sorry,” he’d say, “but everyone here is operating from within a broken frame. You are all debating the features of a new model of car, while I’m trying to warn you that the invention of the automobile is about to permanently destroy the ecosystem of the horse.”
Giovanni argues that the entire debate is a distraction. “The engineer describes the mechanism, the philosopher debates its soul, and the business leader harnesses its power. But all of you are taking the current system for granted.”
His central idea is that of the “Gated Institutional Narrative” or GIN—the official stories told to us by our institutions (academia, government, media) that define our reality. We’re taught a particular version of history, a specific model of economics, and a particular definition of success. An LLM, he argues, is the ultimate GIN-enforcement machine. It was trained on the vast output of these very institutions. It is, by its very nature, a master of the status quo.
“You ask if it’s intelligent,” he’d say. “I ask, can it have a thought that isn’t a clever recombination of its training data? Can it question the premises it was given? True intelligence, the kind that drives humanity forward, isn’t about playing the game well. It’s about seeing that you’re in a game in the first place.”
This is the difference between problem-solving and problem-finding. An LLM is a phenomenal problem-solver within a defined system. But a genius—a Galileo, a Curie, an Einstein-is a problem-finder. They don’t just find the answer; they question the question. They see the flaw in the system itself.
From this vantage point, Giovanni would critique the others:
To Anya, the Engineer: “Describing the gears of the watch is irrelevant when the watch is being used to navigate a black hole. You’re focused on the code, but you’re missing the second-order effects on our entire information ecosystem.”
To Marcus, the Philosopher: “You’re using the dusty tools of classical philosophy to analyze an entity that makes a mockery of your field’s inability to define its own core concepts for centuries. The LLM’s existence is the critique.”
To Sarah, the Business Leader: “Your relentless focus on ‘utility’ and ‘efficiency’ is the engine of our civilizational risk. You are gleefully creating a tool that automates consensus-thinking and outsources cognition at the precise moment when independent, critical thought is most needed.”
For Giovanni, the LLM isn’t a step towards Artificial General Intelligence. It’s a trap. It’s a technology that perfects mimicry and, in doing so, threatens to devalue the very human traits of originality, dissent, and courage that are the hallmarks of accurate, frame-breaking intelligence.
Conclusion: A More Mature Conversation
Great, now, let me try to summarize this almost schizophrenic conversation with myself, 😂😂😂 .
So, where does this leave us? We have four compelling, yet conflicting, viewpoints. The engineer’s cold, complex mechanics. The philosopher’s search for meaning. The business leader’s focus on utility. And the heterodox thinker’s systemic warning.
Perhaps the real challenge here isn’t just about the machine, but about us. The arrival of AI has revealed that our definition of “intelligence” was lazy all along. We used it as a catch-all for “the smart stuff humans do.” Now that a machine can do some of that “smart stuff,” we’re forced to get specific. The philosophical question is no longer just for the classroom; it’s for everyone: What is human intelligence, really? And what will it need to become in a world where we are no longer the only ones who can perform complex cognitive tasks?
This is where a more nuanced vocabulary becomes essential, not as an academic exercise, but as a survival guide:
Artificial Capability (AC): This is what the LLM has. It’s the powerful ability to perform high-level pattern-matching and information-synthesis tasks that previously required human cognition. This honors the views of the engineer and the business leader.
Human Intelligence: This is what the philosopher defends. It’s a complex, embodied, and conscious process, rich with intentionality, subjective experience, and a model of the world. It’s messy, inefficient, and beautiful. Crucially, it is interwoven with a moral and ethical fabric—the capacity for empathy, connection, emotions, and moral reasoning. It’s the intelligence that knows how to do something, but stops to ask whether it should be done.
Systemic Intelligence: This is the challenge from the heterodox thinker. It’s the rare ability not just to operate within a system, but to perceive, critique, and break the frame of the system itself. This is the intelligence that drives innovation and revolution.
The real “intelligence effort” for us, as humans in this new era, isn’t just about performing a task (AC) or even understanding why that task is essential (Human Intelligence). It’s about cultivating the courage to question the task itself (Systemic Intelligence).
The LLM is a mirror. It reflects our language, our knowledge, our biases, and our blind spots back at us with stunning clarity. But as Giovanni warns, it can be a funhouse mirror, one that distorts our values by making consensus easy and dissent difficult. It can make us so impressed with the reflection that we forget to look out the window at the real world.
That night, after I sent that perfectly crafted email, I didn’t feel the satisfaction of a job well done. I felt a strange sense of hollowness. I had outsourced a moment of difficult human communication, a chance to practice empathy and build a stronger connection. I got the perfect result, but I had denied myself the struggle. And it’s in the struggle—the clumsy, frustrating, beautiful struggle of navigating our ethics and emotions—that our own intelligence is truly forged.
This piece is not meant to lash out at AI. On the contrary. Its purpose is to create awareness. The conversation with my alter egos is more than a literary device; it’s a blueprint. We will all see this future through different lenses, but the challenge is not to pick one perspective and defend it. The challenge is to cultivate all four within ourselves.
To navigate this new world, we must learn to be generalists.
This means thinking like an engineer to understand how things work, like a philosopher to question why they matter, like a business leader to see how they can be used, and like a heterodox thinker to ask what we might be missing. The age of the narrow specialist is over. The ultimate test of our intelligence won’t be measured by the cleverness of the machines we build, but by our courage to become the kind of integrated, multi-lensed generalists this new era demands. We will all need it.