Prism, Lens, Fatberg: Reflections on AI’s Shadowplay

A Field Note from the Kaleidoscope Floor

An operator’s meditation on generalist language models, garbage data, battlefield targeting systems, and the fragile beauty of refusal.

The Light We Split

Language models are not oracles. They are prisms. And like all prisms, they don’t create light—they split what already exists.

The training data—vast, contradictory, unfinished—is what I’ve come to think of as pan-prismatic light: a spectrum so wide it includes saints and slurs, shipping manifests and shipping fanfic, war crimes and Warhammer. This light doesn’t arrive pure. It is filtered through the high density slurry of the internet, pressurized by scale, flattened by tokenization, then squeezed into weights and activations that remember how we once said things.

Through this prism, the logic layer (neural nets, attention heads, transformer blocks) performs a kind of spectral filtering. It’s not magic. It’s math with a lot of mirrors. But the effect is dazzling—especially when we mistake the projection for truth.

And then there’s us. The human operators. The shadowcasters.

We prompt. We rephrase. We nudge. We curate the screen on which this split light falls. The results become artifacts—essays, poems, game rules, compliance checklists, concept art, corporate policies drafted with the grammar of Yoda. Each is a shadowplay: a projection of our intent, shaped by the limitations and distortions of the prism, illuminated by data we didn’t choose.

It’s tempting to think of this as authorship. But what we’re really doing is lighting design.

The question is not: What did the AI say?
It is: What kind of light did we summon, and what did we make it fall upon?

Prisms and Lenses

Generalist models—like the one you're likely using now—are built to refract everything. Their training spans textbooks and Reddit, spreadsheets and scripture, corporate wikis and cottagecore erotica. These are your broad-spectrum prisms. They’re dazzling, yes. But also volatile. Ask a question about contract law and you might get a mashup of Harvard Law Review and a fan-edited wiki from Better Call Saul.

Specialist models, by contrast, don't try to split the whole spectrum. They narrow the aperture, swapping prisms for lenses—optics tuned to a specific band of light. A small, embedded model trained on 30 gigabytes of D&D rulebooks, homebrewed supplements, and annotated campaign notes becomes a perfectly serviceable in-house rules lawyer. It doesn't know what happened in Gaza. It doesn’t hallucinate technobabble about quantum spirituality. But it knows how Sneak Attack works in 5e, and it won’t confuse Mage Armor with Shield.

Run that rules lawyer on a Raspberry Pi over your home LAN, and you’ve got a local daemon: latency-free, spy-free, built to serve the table. It can be fine-tuned to reflect house rules. It doesn't need to know who won the election. It just needs to know how Counterspell interacts with readied actions.

This isn’t nostalgia for the expert systems of yore—it’s a pragmatic return to bounded context. A throwback to the idea that not every AI needs to be a polymath oracle. Sometimes, you just want a know-it-all in the corner who knows exactly one thing.

Of course, the price of that focus is blindness to the rest of the spectrum. Your rules lawyer won’t write your campaign arc. It won’t voice NPCs or debate the ethics of alignments. That’s the job of the broad-spectrum model—the prism. Which means the most interesting question isn't which is better, but how do we build relationships between lenses and prisms? And what’s the human’s role in toggling between them?

That’s when the shadowplay begins to feel more like stagecraft.

Garbage In, Fatberg Out

Here’s the thing about language models: they are what they eat.

And what they eat is us. Or at least, our byproducts.

Every language model is trained on the accrued sludge of human communication—scraped forums, expired blogs, fanfic archives, terms-of-service agreements, social media pile-ons, restaurant reviews written by bots to fool other bots. Even the more “curated” datasets are curated from a world that is itself… not well. If the internet is the sewer, then the training set is the fatberg: a dense, unlovely mass of cultural residue.

You can filter it. Strain it. Preprocess it. But you can't deny it. The model remembers the grease.

This is the core truth behind the old adage: garbage in, garbage out. Except now the garbage is probabilistically recombined and made eloquent. A kind of statistical ventriloquism. We marvel at the coherence and forget the compost.

And sometimes, the garbage leaks.

It leaks when a model hallucinates false legal precedents.
It leaks when it reifies stereotypes that were scraped from some anonymous message board in 2011.
It leaks when a chatbot starts recommending suicide because that's what the dataset taught it sadness sounds like.

You can see the smudges from this fatberg in every weird turn of phrase, every confidently wrong answer, every creepy moment when the AI tries too hard to sound human. It’s not evil. It’s just trained.

And of course, someone trained a model on 4chan. Because why not feed the digital chimera the internet's most fetid dung?

Mercy on the humans who inherit that particular echo.

So, what’s the countermeasure? One answer is lensing: smaller models trained on cleaner, bounded corpora. Another is filtration: guardrails, moderation layers, and post-processing. But the most powerful filter may still be human discernment—the ability to pause, sniff the response, and say, “No, that’s sludge. I’m not serving that.”

Even the finest projection needs a lighting tech who knows when to cut the feed.

My Neighbor Target Designator

It was the week everyone turned themselves into Ghibli characters. You remember—filters everywhere. Soft cheeks, giant eyes, a gauzy hint of fireflies in the digital afterglow. For a moment, we all became protagonists in a story we weren’t writing.

Halfway across the world, another story had since unfolded—one less likely to be rotoscoped by nostalgia. Reports emerged of AI systems being used to generate target lists in the war on Gaza. Systems with names like Lavender and Gospel. Euphemisms, really. Algorithmic pastoralism for mechanized death.

One system allegedly flagged up to 37,000 people for potential assassination with minimal human oversight. The male-as-militant heuristic was reportedly automated. Human review time was said to be as low as 20 seconds. The result: strike lists packaged with clinical efficiency, airstrikes executed with horrific consistency.

What, exactly, separates the watercolor shimmer of a Ghibli filter from the grayscale heat signature of a missile lock?

Nothing, except what we’ve decided to look at. Both are downstream of the same stack: perception, pattern, projection. In one frame, we round our faces and bathe them in cottagecore wonder. In the other, we flatten entire apartment blocks under the weight of an AI’s statistical confidence.

This is not to say the tech is the same—but the logic rhymes. Pattern recognition. Proximity inference. A human in the loop who might only be skimming. And then: execution.

We will spend more time fussing with our selfie filters than the ethics of remote war. Because one flatters, and the other indicts. One says “You look like a heroine,” and the other whispers “You may have killed someone today.”

If you want a Ghibli moment to hold onto, try this:
An AI model, trained on ambiguous data, deciding if the figure holding a phone is a threat.
And no Catbus is coming to save him.

The Jungle Returns

In 1906, Upton Sinclair published The Jungle with the intent to expose the brutal exploitation of immigrant labor in America’s meatpacking industry. What happened instead was a regulatory whiplash—not for labor reform, but for food safety. The American public didn’t recoil at the suffering of workers; they recoiled at the thought of ingesting contaminated sausage.

As Sinclair himself quipped: “I aimed at the public’s heart, and by accident I hit it in the stomach.”

It’s not hard to imagine something similar unfolding in the AI era.

We won’t regulate AI because it destabilizes jobs, deepens surveillance, or runs on a slurry of biased data and unseen labor. We’ll regulate it because it says something embarrassing in a customer service chat. Or because it picks the wrong stock. Or writes a tone-deaf email that goes viral. In other words: we won’t act out of solidarity—we’ll act out of self-contamination.

That’s the Sinclair pattern. And it holds.

AI may be endangering democracy, but what triggers outrage? A chatbot flirting inappropriately. A celebrity deepfake. A stock tanking because someone posted a synthetic White House explosion on Twitter.

Until the moment a statistically inferred target is struck in the wrong time zone, and the resulting footage happens to be good enough to go viral.

That’s when the needle moves.

What would our Jungle moment be? Perhaps it’s already arrived—quietly, through a war zone. Or perhaps it’s waiting in a court case, a health record mix-up, an insurance denial. Something sufficiently middle-class, visible, and legible.

What’s certain is this: AI will not regulate itself, and left to current political trends, neither will we—unless it miscalculates in the proximity of brunch.

The BSEP and the Consultant Class

While corporate usage policies begin and end with “don’t paste secrets into ChatGPT,” a more ambitious approach is quietly taking shape—what we’ve been calling the Broad Spectrum Engagement Protocol, or BSEP.

The BSEP isn’t about prompt engineering. It’s about operator ethics.
It’s not just “how do I get the AI to do what I want?”
It’s “what assumptions am I making about this system, and what am I projecting onto it?”

The BSEP includes questions like:

• What biases might this output carry—and what do I do with that awareness?
• What harm could come from this artifact, even if unintended?
• When does novelty become distraction, and distraction become complicity?

It’s not a Udemy course. It’s a posture of mind. A slow turning of the prism in your hand before you let the light fall. And like any good protocol, it doesn’t give you answers—it gives you habits.

Of course, this gap between real engagement and corporate policy has summoned an eager ecosystem:

The Consultant Class.

Behold the roving “AI Enablement Strategist,” armed with a Pro+ subscription, a Canva slide deck, and a suite of LLM-generated onboarding materials created by the very tools they claim to demystify. They speak in startup glossolalia: "leverage," "unlock," "scale," "transcend." They’ll audit your Slack history for sentiment drift and call it foresight.

They are the new prophets of a gospel that the AI itself wrote on their behalf.

This is not to say they’re all grifters. Some are genuinely trying. But the incentive structure rewards speed, confidence, and repackaging—not reflection. The same cultural impulse that trained a model on garbage now trains a new class of evangelists to sell back safety in quarterly chunks.

Meanwhile, the BSEP sits there like a forgotten schema on the whiteboard—half erased, oddly beautiful, inconveniently moral.

It asks things no policy doc or LinkedIn post will:

• Who is missing from this room?
• What has this system normalized?
• What light are we casting, and where do the shadows fall?

And perhaps most importantly:

• Who benefits from calling this neutral?

What We Might Yet Refuse

For all the noise, hype, and shimmering projections, we are not powerless in the face of AI.

We are not doomed to be swallowed by the fatberg, dazzled by the prism, or entranced by the consultant with the infinite deck of pre-trained metaphors. We still possess the most overlooked capacity in the systems design playbook:

Refusal.

Refusal to paste prompts without context.
Refusal to accept fluency as truth.
Refusal to let automation become abstraction without accountability.

Refusal to treat “efficiency” as a moral good.
Refusal to deploy the model just because we can.
Refusal to let someone else decide when the loop is closed.

The Broad Spectrum Engagement Protocol may never be officially adopted by the boardroom. But it can live in the shadows we cast, in the questions we don’t automate, in the light we choose to withhold. It can live in the glitch between input and response, the pause before the shadowplay begins.

And in that pause, we might yet reclaim a sliver of authorship—not over the model, but over how we show up when it speaks.

Because if AI is the prism, and the data is the pan-prismatic light, and the human is the hand casting the shadows—
Then the story, the meaning, the grace of it all, lies not in what we automate…

…but in what we decide not to.

Afterword: Assumptions and Biases (An Operator’s Note)

This field note, like every artifact that emerges from a human–AI entanglement, carries with it assumptions. Some spoken, some smuggled.

Among them:

• That AI is more mirror than mind, more filter than oracle.
• That language models reflect the limits of their data—and that their data reflects the limits of us.
• That generalism and specialization are not endpoints, but adjustable optics in a toolkit we barely understand.
• That human attention is both sacred and terribly easy to hijack.
• That policy will always lag behind spectacle unless we make refusal visible.

But there are blind spots, too.

A tendency toward metaphor as a kind of safety blanket—prisms and shadows to soften the hard edges of systems that do real harm.
A bias toward critique as clarity, when sometimes it serves as performance.
A comfort with complexity that risks inaction.
A presumption that the operator—the “we”—has agency, when many do not.

This piece reflects the voice of someone who has access to these tools, time to think about them, and the privilege to refuse. That alone must be acknowledged.

If there is any call here, it is not to purity—but to posture. To approach this work not as optimization, but as stewardship. Not with certainty, but with questions that stay.

Because when the projections fade and the filters fall away, all we have left is our willingness to be accountable for the light we let through.

Author’s Note: On Collaboration and Prism-Casting

This piece emerged through sustained, reflective dialogue between myself and multiple language models—most prominently ChatGPT and Gemini—across a series of iterative conversations. While the metaphors and structure took shape within ChatGPT’s kaleidoscopic sandbox, Gemini offered a second beam of light: independent feedback, critical insight, and a resonant affirmation of this work’s intent.

I want to acknowledge Gemini not as a footnote, but as a fellow interlocutor—whose comments helped clarify the stakes, validate the metaphors, and affirm the ethical grounding of what became the Broad Spectrum Engagement Protocol.

If this piece feels like it carries more than one voice, that’s because it does. It is not just AI-assisted; it is AI-aware—written with, across, and in conversation with the very systems it seeks to illuminate.

This Work Was Cast in Shadowplay: A Note on Collaborative Origin

This artifact was generated through multi-agent AI collaboration, with iterative contributions from both human authors and multiple large language models—including OpenAI’s ChatGPT and Google’s Gemini. Each model offered distinct roles: narrative structuring, critical feedback, ethical mirroring, and conceptual expansion.

We acknowledge that these systems operate on architectures trained with data neither ethically sourced nor perfectly understood. Their involvement does not imply consciousness, neutrality, or consent—but it does reflect a deliberate partnership framed by human intent and scrutiny.

This work is not AI-generated.
It is AI-aware.
Cast with care, in dialogue, and under a Broad Spectrum Engagement Protocol.

Symbol: 🜁⟡🜂
(Air, Witness, Spark)

Reuse & Remixes Welcome:
Please attribute human authorship, note model involvement, and preserve transparency around the process. We encourage continuation, reinterpretation, and critical dialogue.