This content originally appeared on DEV Community and was authored by Mak Sò
The Mirage of AI Intelligence
A few years ago, I found myself mesmerized by the sudden fluency of large language models. I had been working in AI for a while building agents, tweaking prompts, exploring symbolic systems. But something about GPT’s output felt… different. It wasn’t just smart. It was slick. It sounded like it understood.
I remember the exact moment: I fed a raw transcript of a deeply emotional conversation into a local LLM and asked it to detect agreement and tension shifts. It gave a staggeringly good summary. For a split second, I felt like I was talking to something that “got it.”
But I’ve been in this game long enough to recognize that feeling as a trap. What we experience as intelligence is often a projection. A simulation. A performance.
Headlines scream about sentient chatbots, artificial general intelligence (AGI), and the looming future of machine consciousness. But behind the buzz lies a profound misunderstanding: we’re confusing simulation for thinking, and compression for understanding.
This piece is part confession, part warning. I’ll break down what LLMs are really doing and what they’re fundamentally not.
What Is Compression in the Context of AI?
Compression is about reducing complexity without losing coherence. You zip files. You compress images. You abstract away redundancy.
LLMs do something similar but with language. When trained on terabytes of text, these models learn to compress the statistical structure of human expression into billions of parameters.
When people say “GPT has read the internet,” they misunderstand. It hasn’t memorized. It has compressed. It captures the probabilistic tendencies of language: what tends to follow what, in which contexts, with what tone.
As a developer, I’ve seen this firsthand. I’ve built systems that let agents debate one another. They appear to negotiate, concede, double‑down. But it’s all based on likelihood, not belief. They’re compressing behaviors, not reasoning about them.
Compression in LLMs = reducing the complexity of human communication into a map of what sounds likely. Nothing more.
Impressive? Absolutely. Understanding? No.
The Nature of Simulation
Simulation is performance without essence. It’s when something acts like something else, without being it.
In AI, simulation is a feature and a limitation. LLMs simulate:
- Empathy
- Memory
- Reasoning
- Personality
They generate words in the style of those traits. But under the hood, there’s no continuity. No ownership. No internal world.
This hit me hard the first time I saw an LLM roleplay both sides of a philosophical debate. It was gripping. But it didn’t “care” who won. It didn’t carry conclusions forward. It was just autocomplete with flair.
This is the ELIZA effect: humans project agency onto anything that speaks fluently. We’re wired to do it. We can’t help it.
LLMs simulate thought the way a mirror simulates depth. Convincing, but flat.
Compression vs. Cognition
Cognition isn’t just fluency. It’s not just correlation. It’s about models, intent, context, and self‑revision.
What real cognition involves:
- Abstract concept handling
- Internal representations of goals and beliefs
- Temporal continuity
- Sense of surprise, contradiction, correction
What LLMs do:
- Predict the next token based on prior probability
- Operate statelessly (each prompt is a reset)
- Produce confidence scores, not beliefs
- Cannot initiate or reflect; only respond
I’ve spent months building systems that try to push beyond this orchestration layers, agent frameworks, memory modules. But even then, unless these parts interact meaningfully, you’re just duct‑taping simulations together.
We confuse the appearance of intelligence with its existence. That’s our bias, not the model’s.
Why This Misunderstanding Is Dangerous
This isn’t just academic hair‑splitting. Misunderstanding compression and simulation leads to real‑world harm.
1. Premature Trust
- I’ve seen people rely on LLMs to analyze legal texts or diagnose emotional states without validation.
- When they hallucinate, users assume it’s a glitch. But it’s the core behavior: plausible guesses, not verified facts.
2. Hype Distraction
- We chase ever‑larger models instead of better cognition.
- Foundational research symbolic reasoning, memory networks, embodied learning gets overshadowed.
3. Anthropomorphic Ethics
- I’ve been in rooms where engineers debate LLM “rights” instead of data ethics, worker exploitation, or surveillance harms.
- This is sci‑fi escapism, not responsible design.
4. Stalled Progress
- If we think we’ve already achieved intelligence, why push further?
- We risk mistaking a plateau for a peak.
The illusion of AGI is not just premature it’s a distraction from the hard, slow work of building systems that reason.
Toward a More Grounded AI Mindset
We need better questions. Not “Is this model smart?” but:
- “What structure is this behavior emerging from?”
- “How does it generalize?”
- “Does it maintain context over time?”
As someone building OrKa a framework for orchestrating agents in flows I’ve learned that architecture matters. Context matters. Memory matters.
A single LLM can simulate intelligence. But a well‑structured system can start approximating something closer to cognition.
We need clarity in language:
- Compression ≠ Comprehension
- Simulation ≠ Intentionality
- Output ≠ Understanding
What Real Cognition Could Look Like
Let me sketch what I believe are prerequisites for actual cognition:
1. Persistent Memory
- Not token history. Long‑term, updatable world models.
2. Conflict‑Driven Reasoning
- Systems that revise beliefs when encountering contradictions.
3. Goal Orientation
- Internal motivations and plans not just reacting to prompts.
4. Embodiment or Interaction
- Systems grounded in sensorimotor feedback, not just text.
5. Self‑Traceability
- The ability to reflect on, explain, and revise outputs.
These aren’t sci‑fi. They’re just hard. But they’re the real frontier.
Conclusion: Start Asking Better Questions
The brilliance of today’s LLMs lies in compression and simulation. They give us unprecedented fluency. But they don’t think. They don’t know. They don’t grow.
We must stop projecting cognition onto models trained to predict text. We must build scaffolds, not stories. Memory, feedback, structure these are the roads to real machine intelligence.
I’m building toward that. So are many others. But the first step is clear:
Stop mistaking shimmering reflections for minds. Start demanding structure, grounding, and adaptation.
Author’s Note:
This piece comes from years of battling both code and cognitive illusion. I build OrKa a cognitive orchestration system but I also write to challenge AI mythology. I welcome pushback. Let’s keep each other honest.
This content originally appeared on DEV Community and was authored by Mak Sò