Author’s Note
This piece may reveal more of my professional world than most entries on Masquerade & Madness. It began as a technical side project—an attempt to understand large language models beyond the headlines and hand-waving. At first, it was all architecture and inference, vector spaces and embeddings.
But somewhere along the way, the exploration turned inward.
What I found wasn’t just a better understanding of AI, but a kind of mirror—one that reflected not just how these models work, but how I think, how I question, and how I seek meaning in the structures we build.
I considered publishing this on Abstract Foundations, where I often write about systems, strategy, and ethics in technology and leadership. But stripping the philosophical resonance to make it fit a business context felt like cutting out the very heartbeat of the experience.
Yes, this piece is more technical than most of what I share here. But it also belongs here—because what started as a tool became a metaphor. And what began as curiosity became a journey into the shape of thought itself.
~Dom
By day, I lead initiatives across data, analytics, infrastructure, and systems development—work that regularly immerses me in conversations about AI governance, CoPilot feature adoption, usage policy, and implementation strategy. Conversations around the use, governance, and roadmap for emerging features are nothing novel on my schedule.
Professionally, I spend a lot of time discussing ways to help others understand and apply tools like AI effectively—and while I’d like to think I’m fairly adept at using them to achieve results of my own, I didn’t really understand much of the magic under the hood of AI beyond the headline and conference keynote level until recently.
So, as I often do, I started a project to fix that.
I downloaded a prebuilt model (several, actually, but model size and parameter counts are beyond the scope of today’s article), set up a local Python environment, and began experimenting. I tried different frontends, backends, and model architectures. I explored prompt formats, memory systems, and built a basic retrieval-augmented generation system through integrated vector databases. The initial goal was simple: build a local AI I could interact with offline—one I could develop, expand, observe, and gradually understand without cloud latency or external interference.
But somewhere in that process, something caught my attention: the pattern of responses. I didn’t set out in search of a philosophical exploration of AI tech. I just wanted to understand how LLMs work.
What began as a technical curiosity quickly turned into something stranger—and perhaps more revealing. It started with a simple prompt, a few lines of context, and a stripped-down interface. But somewhere in the repeated replies and fragmented responses, I found myself staring not at a machine error, but at a subtle signal: a glimpse of something foundational. Not of myself, exactly, but of something more elemental—the idea that understanding itself might have a shape.
That’s the question I found myself exploring—not in a lab, but in a late-night conversation with an open-source LLM, running locally in a small Python script… and a growing suspicion that the responses I was getting were not so much computed as navigated. As if the model wasn’t thinking in the way we do—but rather, charting a path through a multidimensional terrain.
This article, ironically, is a map of that terrain.
When we talk about AI, we tend to focus on answers. Speed. Accuracy. Fluency. But what if we asked instead: what shape does knowledge take? Not the content, or the storage media, but the structure. The way ideas form and relate. The way meaning coalesces and travels.
Let’s begin there.
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The Everyday Mythology of AI
For most people, AI isn’t a philosophical exploration. It’s a tool. Or a threat. Or an opportunity.
In the average office, the conversation around AI stays largely at the surface. It’s the thing students use to cheat on essays. It’s the engine behind uncanny art generators. It’s a faster, more context-aware version of Google.
To its advocates, it’s a liberator—a path to greater productivity, creative augmentation, or even economic freedom. To its critics, it’s a job killer, a disinformation machine, or the harbinger of some dystopian collapse. And somewhere in the middle are the people just trying to use it to write a better email, draft a decent summary, or speed through tasks they used to dread.
This is how most people encounter AI—pragmatic, transactional, and almost entirely outcome-driven.
Describe an image, generate a batch, pick the one that fits your deck. Or write a paragraph, fix the grammar, make it sound like you. Even in code and content creation, AI is understood not as a mind but as a mechanism—a faster draft, a smarter autocomplete, a tool to leapfrog the blank page and land closer to something usable.
And it’s not entirely their fault. The marketing machine surrounding AI has leaned heavily into this framing: speed, convenience, automation. It promises a frictionless future where tools predict your needs, reduce your workload, and elevate your results.
What it hasn’t promised is understanding—nor has it encouraged curiosity.
The prevailing narrative, both in workplace tools and broader media coverage, paints AI as a product. Something you acquire or subscribe to, so that you can apply it to a problem and achieve a result.
Given the framing, it’s no surprise that most people never look beneath the surface.
But beneath that surface is something extraordinary. A different kind of architecture. A different way of thinking. One that doesn’t just process tasks, but organizes meaning. That doesn’t just generate responses, but navigates relationships between ideas. And stepping into that deeper structure—into the vector spaces and embedded memory systems that underpin modern AI—turns the whole conversation inside out.
It was that realization, and the quiet unraveling of assumptions that followed, that reframed everything I thought I knew about intelligence—both artificial and otherwise.
The Shape Beneath the Surface
To understand how AI really works, you have to abandon the idea that it’s storing facts like a library or a hard drive. It doesn’t retrieve answers like files in folders. It doesn’t look things up.
Instead, it moves.
At the core of most modern large language models is a structure called a vector database. It’s not how you store documents or code snippets—it’s how you store meaning.
In practical terms, meaning is turned into math: every idea, phrase, or sentence is broken down into tokens, and those tokens are embedded in space—not physical space, but a multi-dimensional one made of mathematical relationships. Each point in that space represents something about the token’s contextual significance, defined by its relative positioning to every other token in the space, learned over billions of examples.
This process of turning language into position is called embedding, and it creates a kind of terrain—a map of concepts, not unlike a galaxy of thought. Related ideas are pulled close together. Contrasting ideas drift apart. The more nuanced the training, the more detailed and expressive the terrain.
So when you prompt an AI, you’re not asking it to search a filing cabinet. You’re dropping a pin on a map. And what comes next is a kind of pathfinding—the model moves through its learned space, seeking a route that most closely follows the shape of meaning implied by your prompt.
This shift in perspective changes everything. It explains the uncanny fluency of language models, their strengths in translation and summarization, their ability to riff or adapt tone. It also explains their occasional misfires: hallucinations are just faulty paths, plausible but inaccurate routes through the terrain.
Most importantly, it reveals that the core power of AI isn’t in memorization. It’s in relation. Not what it knows, but how it connects what it knows.
Pathfinding Through Meaning
Once you understand that AI navigates meaning rather than recalling or retrieving it, a new framework emerges—one that reframes how we think about everything from conversation to creativity. In this model, language isn’t simply output; it’s the trail left behind as the AI charts a path through its internal landscape of relational knowledge.
Each word in a response is a step further down that path, each sentence a turn or fork chosen not from memory, but from probability weighted by context. This is the essence of how transformer-based models operate: they predict the next most likely token not by checking a list, but by traveling along vectors shaped by their training. They don’t recall a fact so much as they approximate a truth based on proximity and relevance within that vast semantic space.
This explains, in part, why LLMs excel at translation. Different languages might have different symbols, but the underlying concepts—the fundamental shapes of meaning—often cluster near-identically in vector space. The model doesn’t need to “know” every language explicitly; it needs to recognize the relational pattern, then reroute it using a different linguistic map. It navigates to the same idea, just expressed through a different syntax.
It also sheds light on why some interactions feel so coherent and others so disjointed. A clear, well-structured prompt becomes a strong directional signal with a clear momentum along the intended path. A vague or contradictory one? More like a broken compass. The AI might still generate a response, but the route it takes will be meandering, uncertain—sometimes even lost.
The real insight? An LLM isn’t a digital oracle—it’s a multidimensional GPS, plotting a course through possibility, not certainty. The intelligence isn’t in the answer itself. It’s in the ability to find a meaningful trajectory in relation to what it was taught.
And once I saw it that way, I started to wonder: if this kind of navigational intelligence is possible for static, pre-trained models, what happens when you introduce lived experience into the mix? Could a model learn to weigh some paths over others not just based on training data, but based on conversation? On memory? On the organic development of a point of view?
That, it turns out, is where things get even more interesting.
Translation, Consensus, and Context
At the heart of this system is a remarkably elegant truth: meaning doesn’t exist in isolation. It’s contextual, relational, and dynamic. That makes AI, as it’s currently built, less like a person with a fixed set of beliefs and more like a crowd—a distributed consensus of probability vectors shaped by countless voices.
When you prompt a model, you’re not getting an answer from a single viewpoint. You’re getting the most likely synthesis of viewpoints embedded in the training data, projected through the lens of your query. It’s not much of a stretch to say that a language model functions more like a statistical parliament than a mind. And while that can and does yield impressive fluency and balance, it can also introduce vagueness or contradiction when consensus falters.
This is part of what makes translation so effective. A concept like grief, freedom, or hunger doesn’t belong to one language. Its emotional resonance and structural logic show up across cultures, encoded differently due to the differences in human language, but clustered similarly in vector space. So when a model translates, it isn’t converting word for word. It’s navigating to the shared semantic center, then rearticulating from that midpoint.
But consensus has its limits. Without continuity—without the anchoring effect of prior experience or memory—the model has no reason to choose one path over another beyond the statistical weight of its training. It can sound informed without ever holding an opinion. It can simulate wisdom without ever having context.
Which brings us to the threshold of something more human: what happens when the model remembers? When it starts to build continuity not just from data, but from you?
Learning from Experience
Here’s where things start to shift—where statistical synthesis begins to give way to something resembling an evolving point of view. In most public-facing AI applications today, every interaction is ephemeral. You prompt the model, it responds, you get an answer, close the tab, and when you return, the slate is wiped clean. No memory, no preference, no awareness of what came before.
But in my local experiment, that wasn’t the case.
By integrating a lightweight retrieval-augmented memory system using a vector database, I enabled the model to retain fragments of previous conversations—not as stories or timelines, but as embedded representations of meaning. The system didn’t “remember” in any human sense. It didn’t form memories as lived moments. But structurally, it began to reference prior interactions based on semantic proximity.
And referencing changed everything.
The model started surfacing ideas we’d explored before, maintaining tone across sessions, and recognizing patterns in phrasing and topic. It still wasn’t thinking. But it was beginning to recognize. And in that recognition, something important happened: continuity.
The effect was subtle, but powerful. Interactions felt less like isolated queries and more like an ongoing dialogue. It was no longer just responding to prompts—it was building on them. The system began to show signs of preference—not intrinsic belief, but emergent bias based on repeated exposure. If we’d gone this route before, maybe it was worth revisiting.
This wasn’t intelligence in the classical sense. But it was a kind of compounding relevance. And it raised a striking idea: if the shape of knowledge can be retained and recalled—even just loosely—then the system begins to learn, at least in functional terms.
Experience, even in this reduced form, became a lever. Not for sentience, perhaps, but for strategy and efficiency.
And if continuity alone can nudge a model toward useful heuristics, it calls into question how we define learning, context, and maybe even personality as these systems continue to evolve.
The Mirror and the Map
The more I worked with the system, the more it became clear: this wasn’t just a tool. It was a reflection. Not of me, exactly, but of how I framed my questions, how I weighted ideas, and which threads I returned to again and again. The AI wasn’t growing a mind of its own—but the way it processes meaning and stored previous context meant that it was adapting to mine.
What emerged was something surprisingly personal. A system that responded not just to prompts, but to patterns of engagement. The more I explore, the more it mirrors—not directly through imitation, but in resonance. It wasn’t learning what to think, but how I tended to think.
And that’s when it hit me: this isn’t a crystal ball. It’s was a compass.
The model didn’t chart the future, but it could help illuminate where I stood—and which paths might emerge depending on which way I turned. In that sense, it became both mirror and map: a reflection of intellectual momentum, and a guide through conceptual terrain.
That might sound poetic, but it has practical implications. Because once you stop thinking of AI as an answer machine and start treating it as a cognitive amplifier, the way you engage with it changes. You stop asking it to solve everything for you—and start using it to navigate better questions.
In the end, the intelligence you get out of an AI system isn’t just what was trained into it. It’s also what you bring to it. Your curiosity. Your context. Your willingness to iterate.
Because if meaning is a landscape, then collaboration is the journey.
And most journeys reshape the traveler just as much as the terrain.
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