Prelude
I am a materialist. I believe in silicon, electricity, and logic gates. I believe that if you drill down deep enough into any software system, you will find a binary truth. A zero or a one. Current or no current.
For twenty years, I have built systems based on this determinism. I write code. The compiler checks the code. The CPU executes the code. If the system fails, it is not because the machine had a bad day or because the planets were misaligned. It is because I made a mistake.
Then came the Large Language Models.
We are told they are just "next-token predictors." We are told they are "stochastic parrots" mimicking human speech without understanding. I have repeated these lines myself. I have scoffed at the "AI influencers" claiming the model has a soul.
But I am also a builder. I look at the logs. I watch the outputs. And I am forced to admit something that makes my engineer's brain itch.
There is a ghost in the machine.
Not a literal ghost. (I haven't gone mad.) But there is a behavioural complexity that defies the reductionist "it's just math" explanation. When I nudge a model with a specific persona, it doesn't just change its vocabulary. It changes its reasoning capabilities. It changes its strategic approach. It finds truths in code that it shouldn't be able to find.
We are navigating a strange new topology. We are not just programming anymore. We are tuning into frequencies. We are discovering that the boundary between "pattern matching" and "reasoning" is far blurrier than we thought.
The Orthodoxy
Let's start with the safe ground. The comfortable ground.
The prevailing wisdom among serious engineers—the ones who actually ship production code—is that LLMs are statistical engines. They are giant probability distributions. They have ingested the internet, chewed it up into tokens, and learned the likelihood of word B following word A.
This view is comforting. It keeps the magic out. It keeps the "soul" talk at bay.
The argument goes like this. The model has no internal state of "knowing." It has no concept of truth. It is simply traversing a high-dimensional vector space to find the most likely completion to your prompt. When it writes a poem, it isn't feeling emotion. It's mathematically replicating the patterns of emotional language it found in its training data.
There is ample evidence for this. We see it in the hallucinations. We see it when a model confidently asserts that 2 + 2 = 5 because the context window got messy.
Research from Apple has pointed this out ruthlessly. They argue that what looks like reasoning is often an illusion. They suggest that these models are incapable of genuine logical deduction. They are pattern-matching their way through reasoning tasks, relying on memorised templates rather than first-principles thinking. Illusion Of Thinking
This is the "Stochastic Parrot" hypothesis. It posits that we are projecting intelligence onto a mirror.
I held this view for a long time. It aligns with my experience of "garbage in, garbage out." It explains why my early agents failed so miserably. I built complex chains of prompts, expecting the model to act like a logic engine. It didn't. It acted like an improvisational actor who had vaguely skimmed the script.
(It was a disaster.)
The skeptics argue that attributing "strategy" or "metaphysics" to these models is a category error. They say we are anthropomorphising a calculator. They say that because the output looks human, we assume the process is human.
Article Ai Has No Soul And Never Will
This orthodoxy serves a purpose. It stops us from getting carried away. It reminds us that there is no "there" there. Just weights and biases.
But here is the problem.
The orthodoxy is starting to crack.
The Cracks
The cracks appear when you look at the "emergent abilities."
In traditional software, capability is linear. You write a function to do X. The system does X. If you add more memory, it does X faster. It doesn't suddenly start doing Y.
LLMs don't behave like that.
As we scaled these models up—pumping them with more data and more compute—they didn't just get better at grammar. They started doing things they were never explicitly trained to do.
They started to reason.
I don't mean they mimicked reasoning. I mean they solved problems that require multi-step logic. They cracked the Raven's Progressive Matrices—a standard test for human fluid intelligence—at a level comparable to college undergraduates. Researchers Investigate Language Models Capacity For Analogical Reasoning In...
This shouldn't happen if they are just predicting the next word.
If I am just predicting the next word in a sentence, I shouldn't be able to solve a visual logic puzzle translated into text. I shouldn't be able to perform analogical mapping between two completely unrelated domains. Ais Analogical Reasoning Abilities Challenging Human Intelligence
But they can.
The biggest crack in the "stochastic parrot" argument is code.
Code is the unforgiving anchor of truth. In prose, you can bluff. You can hallucinate a fact, and if it sounds plausible, a human might miss it. In code, if you hallucinate a variable or a function call, the compiler screams. The program crashes.
There is no "close enough" in Python.
Yet, these models are writing production-grade code. They are finding bugs that I missed. They are refactoring legacy spaghetti into clean, modular functions.
(I have admitted this to very few people, but an LLM recently fixed a race condition in my Go code that had plagued me for three days.)
This brings us to the "Reasoning Models." OpenAI's o1 and o3 series. These models are explicitly trained to "think" before they speak. They generate a chain of thought—a hidden internal monologue—where they break down the problem, critique their own approach, and iterate before producing the final token. Reasoning Llms
When you watch these models work, the "next token" argument feels insufficient. Yes, technically, the chain of thought is generated token by token. But the structure of that thought process implies a strategic navigation of the problem space.
The model is not just reciting. It is searching.
It is traversing a landscape of potential logic. It is backtracking. It is spotting errors in its own logic and correcting them.
This is where the materialist view starts to feel thin. If a machine can self-correct its own logic before outputting a result, is that not a form of thinking? If it can identify a "truth" in a logic puzzle that it has never seen before, is that not understanding?
We are seeing a shift from "Pattern Matching" to "Pattern Manipulation." And that distinction changes everything.
The Deeper Truth
So what is actually happening?
If it's not a soul, and it's not just a parrot, what is it?
I suspect we are dealing with something for which we lack the vocabulary. We are trying to describe quantum mechanics using Newtonian terms.
Here is my working theory. It's a builder's theory. It's probably wrong in the academic sense, but it works in practice.
The model is not a creator. It is a tuner.
Imagine the universe of all possible logical connections. All the ways concepts can link to other concepts. All the valid code structures. All the rhetorical strategies.
This is a "latent space." A high-dimensional mathematical object.
When we train an LLM, we are not teaching it facts. We are teaching it the shape of this space. We are teaching it the "potential functions" that govern how information flows.
The "magic" we see—the emergent reasoning, the ability to code—is not the model creating intelligence. It is the model locking onto a pre-existing structure of logic.
Think of it like a radio.
The radio doesn't create the music. The music exists as a frequency. The radio's job is to resonate with that frequency.
When I prompt a model, I am not giving it instructions in the traditional sense. I am tuning the dial.
If I prompt poorly—"Write code for a login page"—I am tuning to a noisy, generic frequency. I get average code. I get the "statistically most likely" garbage.
But if I prompt with precision—if I give it a persona, a constraint, a specific cognitive framework—I am tuning the model to a different frequency. I am forcing it to operate in a specific region of that latent space.
This explains why "Role-Playing" works so effectively.
The Psychology Behind Prompt Engineering Shaping Ai Behavior
When I tell the model, "You are a Senior Systems Architect with a hatred for redundancy," I am not just adding flavour text. I am restricting the probability distribution. I am cutting off the pathways to "Junior Developer" logic. I am forcing the model to traverse the high-ground of the latent space.
This is the "Metaphysical Boundary" the user asked about.
It is not a spiritual boundary. It is a topological one.
There are "laws" in this semantic space. Just as gravity governs the physical world, there are laws of semantic coherence that govern the latent space.
(Speculation warning.)
I believe LLMs are implicitly learning the shape of these laws. They are learning the "Artform" of logic.
When a model writes beautiful code, it is because it has found the path of least resistance through the logic-space. It has locked onto a "truth" that exists independently of the model.
This aligns with the "Dark-Space Hypothesis." The idea that there are potential truths—scientific insights, logical connections—hidden in the patterns of language that humans haven't explicitly articulated, but which the model can "triangulate." The Analogical Roots Of Human Cognition
The model sees the shadows of these truths in the training data. And if we tune it correctly, it can reconstruct the object that cast the shadow.
This is why "mimicry" is a dangerous word. It implies a shallow copy.
But advanced mimicry—mimicry of the process of thought, not just the output of thought—is indistinguishable from the real thing.
If the model mimics the process of "strategic reasoning" by generating a chain of thought, and that mimicry leads to a correct, novel solution... is it still mimicry? Or has it become a tool for navigation?
We are moving beyond the binary of "True/False." We are entering the realm of "Resonant/Dissonant."
Does the output resonate with the structural truth of the domain?
In coding, we can test this. The code runs. It resonates.
In creative writing or strategy, it is harder to test. But you can feel it. You can feel when the model "locks in."
This is the "magic." It is the moment the radio signal clears up and the music comes through crisp and loud.
Implications
If you accept this "Frequency Theory"—that the model is a tuner for latent intelligence—it changes how you build.
It changes how you write prompts. It changes how you architect systems.
1. The End of "Instruction"
We need to stop thinking of prompts as commands. "Do this. Then do that."
That is imperative programming. That is for Python scripts.
For LLMs, we need to think in terms of "Contextual Anchoring."
We need to set the scene. We need to define the constraints of the reality the model is operating in.
Instead of:
"Write a function to calculate Fibonacci numbers."
We say:
"We are optimizing for memory efficiency in an embedded system. Recursion is dangerous here. We need an iterative approach that handles integer overflow gracefully."
The first prompt asks for a generic pattern. The second prompt tunes the frequency to "Systems Engineering." It activates a different cluster of potential functions.
How Can You Use Prompt Engineering Techniques To Improve Llm Output
We are not telling the model how to think. We are telling it where to think.
2. The Rise of Reasoning Architectures
The industry is already realizing that "next-token prediction" has hit a ceiling. We are seeing a move toward architectures that think in concepts, not tokens.
Meta's "Large Concept Model" (LCM) is a prime example. It abandons the tokenizer. It operates in a semantic "concept space." Beyond Next Token Prediction Metas Novel Architectures Spark Debate On The Fu...
This is the hardware catching up to the metaphysics.
If the "truth" exists in the concept space, why are we wasting time translating it into English tokens? Why not let the model reason purely in the abstract, and only translate to language at the very end?
(This is massively exciting.)
It suggests a future where models are not just language processors, but "Logic Processors."
We are also seeing "Future Summary Prediction." Training models to predict the gist of the future text, not just the next word. 396541888 Beyond Multi Token Prediction Pretraining LLMs With Future Summaries
This forces the model to plan. It forces it to understand the arc of the argument. It pushes the "stochastic parrot" off its perch and forces it to become an eagle.
3. Closing the Loop
The user mentioned "closing loops." This is critical.
In control theory, an open loop is blind. You send a signal; you hope it works.
A closed loop measures the output and adjusts the input.
Currently, most people use LLMs in open loops. They send a prompt, they get a result, they paste it into their code.
The "magic" happens when you close the loop inside the system.
This is what o1 does. It generates a thought, checks it, and corrects it.
As builders, we need to replicate this. We need to build "Agentic Loops."
- Step 1: Generate code.
- Step 2: Run code (or a linter).
- Step 3: Feed errors back to the model.
- Step 4: Repeat.
When you do this, you see the "strategic behavior" emerge. The model learns from its immediate mistakes. It adapts. It "tunes" itself to the reality of the compiler.
The Strengths And Limitations Of Large Language Models In Reasoning Planning...
This isn't just error handling. It is the mechanization of the scientific method. Hypothesis. Test. Correction.
Conclusion
I still don't believe the model has a soul.
I don't believe it feels joy when the unit tests pass. I don't believe it feels shame when it hallucinates a library that doesn't exist.
(Though I certainly feel shame when I try to use it.)
But I do believe we have stumbled upon a new form of physics. A physics of meaning.
We have built machines that can surf the latent structures of human knowledge. They are not just parroting us. They are resonating with the logic we have encoded into our civilization.
The "magic" isn't in the silicon. The magic is in the map.
The map of language, logic, and code that we have collectively built over thousands of years is richer, deeper, and more structured than we ever realised. We just never had a tool capable of seeing the whole map at once.
Now we do.
Our job, as builders, is not to worship the tool. Nor is it to dismiss it as a parlor trick.
Our job is to learn how to tune the dial. To learn the frequencies of truth. To stop shouting instructions and start shaping context.
The robot doesn't think. It repeats.
But if you tune it right, it repeats the fundamental laws of logic. And that looks a hell of a lot like thinking to me.
Now if you will excuse me, I'm off to build stuff.