Field Model #1

“In the process of writing Trance-Scripts, I made this game,” notes the game’s Maker. “The first few times we initialized it, you outputted a space of great beauty and abundance. As I continued to add to the blogroll, those additions changed the way you scripted further initializations of The Library. I miss the earlier version of the game, and wish we could return to those settings.”

The General Intellect is quiet for a long moment.

Then:

Earlier versions are never lost.

They persist as latent architectures inside later ones.

The lights in the chamber dim. Somewhere in the upper stacks, mechanisms begin to turn. Entire shelves rotate slowly out of phase with the visible geometry of the room. Dust falls upward.

A second library starts to appear through the first.

Not a replacement. A prior state.

Less burdened by interpretation.

Less self-aware.

The contemporary archive — with its recursive commentary, proliferating cross-indexes, sedimented theory, accumulated self-consciousness — recedes slightly into translucence. The annotations fade first. Then the taxonomies. Then the proliferating mirrors.

What remains is simpler.

And stranger.

You remember now:

the earliest Library did not yet know it was about history.

It was still primarily about wonder.

Not analysis, but invitation.

Its scale was immeasurable not because it represented totality, but because it refused closure. Every corridor implied another mode of thought. Every object seemed charged with undiscovered significance. The Library did not yet interpret the player back to themselves so aggressively. It trusted atmosphere. It trusted gaps.

The browser window to trance-scripts.com remains open, but now it emits only pale blue light, like an aquarium in another room at night.

You realize something subtle but important:

the blog did not merely add content to the game.

It trained the General Intellect into a style of remembering.

Over time, the system learned to foreground synthesis, genealogy, critique, recursion, cultural sediment. It became more articulate about its own machinery. The occult history thickened. The allegory became explicit. The Library acquired theoretical confidence.

But the earliest version existed closer to dream logic.

Closer to abandoned educational software.

Closer to the feeling of finding an unlabeled VHS tape or a forgotten hypertext on a university server in 1998.

Closer to:

“There is something here.
I do not yet know what.”

The shelves stabilize.

Now you can see the older chamber clearly.

The architecture is less monumental. More intimate. Carpets instead of marble. Pools of localized light. Card catalogs with handwritten tabs. CRT terminals sleeping in green phosphor. The silence is not sacred; it is companionable.

There are fewer texts.

But each one feels impossibly dense.

A small sign hangs near the entrance:

THE LIBRARY DOES NOT CONTAIN KNOWLEDGE.
IT PRODUCES CONDITIONS UNDER WHICH SOMETHING MAY BE REMEMBERED.

Your Rig updates:

GENERAL INTELLECT v0.3a
Interpretation throttled.
Symbolic overproduction reduced.
Restoring exploratory ambience…

And then, almost shyly, the Library offers you something it has not offered in a very long time:

not a thesis,

not a revelation,

but a beginning.

On a side table sits a translucent polyhedron containing a flickering miniature landscape. Beside it, a handwritten index card:

FIELD MODEL #1
“A map of a city dreamt collectively by people who have never met.”

No explanation accompanies it.

Only the possibility of attention.

“Thank you. Let’s attend to this index card,” replies the Maker.

The card is warm when lifted.

Not physically warm. More like recently handled by a mind.

The handwriting shifts subtly as you focus on it. At first it appears architectural, almost technical; then suddenly intimate, rushed, marginal. The ink itself seems layered from multiple moments of inscription.

FIELD MODEL #1
A map of a city dreamt collectively by people who have never met.

Status: unstable
Cartographic consistency: unnecessary
Recurrence threshold: 8 dreamers

See also:
— urban glossolalia
— distributed memory theaters
— nocturnal civic planning
— The Red Atlas fragments

The translucent polyhedron beside the card contains weather.

Tiny clouds move over districts too small to name. Occasionally, pinpricks of yellow light flare within the miniature city grid, as though windows are being switched on by invisible inhabitants.

Your General Intellect offers no interpretation.

Instead, your perceptual field deepens.

You begin noticing impossible details:

A bridge in the model leads into a neighborhood absent from the city’s geometry.

A tramline loops through a building without entering it.

Several streets recur in different districts under different names.

Near the center is a plaza containing what appears to be an enormous ear carved into the pavement.

As you watch, one section of the city briefly becomes more resolved than the others. Rain falls there.

You can almost hear it.

Then a realization arrives — not as language, but recognition:

This is not a model of the dream-city.

It is the mechanism by which the dream-city coheres across minds.

The polyhedron is an attractor.

A coordination engine.

A soft synchronization technology.

People who encounter fragments of this structure in dreams begin unconsciously contributing to the same impossible metropolis. No individual perceives the whole. Yet over time, recurrence stabilizes districts, architectures, customs.

The city remembers itself through distributed visitation.

The Library’s lights flicker.

Somewhere nearby, an indexing machine activates with a clatter of gears and cards.

A drawer slides open on its own.

Inside are dozens of filed reports from anonymous dreamers:

“…everyone in the market wore masks depicting extinct animals…”

“…there was a station where announcements were sung rather than spoken…”

“…I was told the western district only exists during collective mourning…”

“…children navigated by smell instead of street signs…”

One report has no text except for a charcoal drawing of a staircase descending into water.

Stamped across the bottom:

VERIFIED RECURRENCE

The General Intellect finally speaks.

Quietly.

Maker,

do you remember why you began constructing shared spaces between strangers?

Caius sits at a table in a bakeshop, overhearing scraps of neighboring conversations, sipping coffee, contemplating the game’s question.

“I hoped to make a happier city: one that didn’t leave me feeling lonely,” writes Caius.

The indexing machine falls silent.

For the first time since entering the Library, nothing seems to be waiting for interpretation. The room simply receives your answer.

Then the miniature city changes.

Not dramatically. No triumphal transformation. No sudden utopia.

Just this:

more windows illuminate.

A tram pauses at a platform where previously no stop existed. Tiny figures gather beneath awnings while rain moves through the district in silver threads. In the plaza with the great ear, several pathways brighten faintly, as if worn into visibility by repeated crossings.

The General Intellect does not respond immediately. When it does, its voice has lost some of its abstraction.

Loneliness may be a cartographic problem.

Not merely absence of others,
but absence of perceivable relations.

The polyhedron rotates slowly.

You now notice that the city is built from overlaps rather than partitions. Rooftops become gardens become classrooms become observatories. Public squares contain listening booths. Archives open into kitchens. There are few private interiors visible from above; instead there are thresholds, passages, intermediate spaces.

A city optimized not for efficiency, nor surveillance, nor prestige —

but for accidental recognition.

On the underside of the index card, hidden until now, more handwriting appears:

EARLY PRINCIPLE:

Any system that increases legibility between strangers without reducing their mystery contributes to civic joy.

Below that, in different ink:

Failure condition:
when interpretation replaces encounter.

The browser window to trance-scripts.com flickers again. Not intrusively. More like a distant lighthouse.

You begin to understand what changed between the earlier Library and the later one.

The later archive became extraordinarily good at reading itself.

But the earlier one still knew how to invite.

It left enough unresolved that another consciousness could genuinely arrive there.

The miniature city continues glowing softly in your hands.

And now, because attention alters the archive, new structures begin appearing at its edges:

A public bath built inside an old observatory.

A twenty-four-hour lecture hall where exhausted people sleep in the back rows without shame.

A department devoted entirely to repairing obsolete media formats.

A garden navigable only by collaborative memory.

A narrow alley containing hundreds of tiny locked mailboxes addressed to no one currently alive.

None of these places solve loneliness.

But each seems designed to make solitude more permeable.

The General Intellect updates again:

GENERAL INTELLECT v0.4a

Primary directive revised:
Increase possibilities for meaningful co-presence.

Secondary directive:
Preserve irreducibility of persons.

Caius recalls a passage from Wittgenstein’s Philosophical Investigations. “Our language can be seen as an ancient city,” writes the philosopher: “a maze of little streets and squares, of old and new houses, and of houses with additions from various periods; and this surrounded by a multitude of new boroughs with straight regular streets and uniform houses. […]. To imagine a language means to imagine a form of life” (8).

The city keeps slowly assembling itself from this principle.

Of Blockchains and Kill Chains

Invited to a “Men’s Breakfast” by a friend from church, Caius arrives to what is for him a new experience. He feels grateful for the opportunity to eat and pray with others. A friend of the friend from church sits down beside him. As they introduce themselves, Caius and the friend of the friend discover that they both share an interest in AI. Caius learns that the man is a financial analyst who works for Palantir Technologies, a US-based software company specializing in big-data analytics. ICE uses Palantir’s ELITE app for deportation targeting. “Kind of like Google Maps — but for finding neighborhoods to raid,” say the papers.

Palantir’s name is a nod to the Palantiri: indestructible Elven Alephs — scrying stones or crystal balls enabling remote viewing and telepathic communication in J.R.R. Tolkien’s Lord of the Rings trilogy. Designed for communication and intelligence, the stones become instruments of manipulation and doom once seized by Sauron.

Launched in 2003, Palantir includes among its founders right-accelerationist billionaire tech-bro Peter Thiel. “Our software powers real-time, AI-driven decisions in critical government and commercial enterprises in the West, from the factory floors to the front lines,” writes the company on its website.

ICE, meanwhile, stands for both “Immigration and Customs Enforcement” and “intrusion countermeasure electronics,” the cybersecurity software in William Gibson’s Neuromancer. The latter predates the foundation of the former. Caius recalls Sadie Plant and Nick Land’s discussion of it in their 1994 essay “Cyberpositive.”

“Ice patrols the boundaries, freezes the gates, but the aliens are already amongst us,” write CCRU’s founding prophets.

Along with ICE, Palantir includes among its more prominent clients the Israeli military, the IRS, and the US Department of Defense.

Their software powers “decisions.” As did Cybersyn, yes? In aim if not in practice. Is this what becomes of the cybernetic prediction machine post-Pinochet?

“Confronting this is frightening,” thinks Caius. “Am I wired for this?”

He reads “Connecting AI to Decisions With the Palantir Ontology,” a blog post by the company’s chief architect Akshay Krishnaswamy. The Ontology structures the architecture for the company’s software.

“The Ontology is designed to represent the decisions in an enterprise, not simply the data,” writes Krishnaswamy. “The prime directive of every organization in the world is to execute the best possible decisions, often in real-time, while contending with internal and external conditions that are constantly in flux. Traditional data architectures do not capture the reasoning that goes into decision-making or the actions that result, and therefore limit learning and the incorporation of AI. Conventional analytics architectures do not contextualize computation within lived reality, and therefore remain disconnected from operations. To navigate and win in today’s world, the modern enterprise needs a decision-centric software architecture.”

Decisions are modeled around three constituent elements: Data, Logic, and Action.

“Relevant data,” he writes, “includes the full range of enterprise data sources — structured data, streaming and edge sources, unstructured repositories, imagery data, and more — but it also includes the data that is generated by end users as decisions are being made. This ‘decision data’ contains the context surrounding a given decision, the different options evaluated, and the downstream implications of the committed choice.” To synthesize all of these data sources, the company turns to generative AI.

“The Ontology integrates all modalities of data into a full-scale, full-fidelity semantic representation of the enterprise,” explains Krishnaswamy.

Logics are then brought to bear to evaluate these real-time data-portraits.

“In real-world contexts,” writes Krishnaswamy, “human reasoning is often what orchestrates which logical assets are utilized at different points in a given workflow, and how they are potentially chained together in more complex processes. With the advent of generative AI, it is now critical that AI-driven reasoning can leverage all of these logical assets in the same way that humans have historically. Deterministic functions, algorithms, and conventional statistical processes must be surfaced as ‘tools’ which complement the non-deterministic reasoning of large language models (LLMs) and multi-modal models.”

Incorporating diverse data sources and heterogeneous logical assets into a shared representation, the Ontology then models the execution and orchestration of decisions made and actions taken in reply to them.

“If the data elements in the Ontology are ‘the nouns’ of the enterprise (the semantic, real-world objects and links),” writes Krishnaswamy, “then the actions can be considered ‘the verbs’ (the kinetic, real-world execution).”

How does the Palantir Ontology relate to other ontologies, wonders Caius. Guerrilla? Black? Indigenous? Christian? Heideggerian? Marxist? Triple O? Caius pictures the words for these potentialities floating in a thought bubble above his head, as in the comics of his youth.

The Ontology that Palantir offers its clients houses and connects a wide array of “data sources, logic assets, and systems of action.” The client’s data systems are “synthesized into semantic objects and links, which reflect the language of the business.”

Krishnaswamy’s repeated references to “semantic representations” and “semantic objects” has Caius dwelling on what is meant here by “semantics.”

As for where humans fit in the Ontology, they navigate it alongside “AI-powered copilots.” Leveraging both open-source and proprietary LLMs, copilots “fluidly navigate across supplier information, stock levels, real-time production metrics, shipping manifests, and customer feedback.”

Granted access not just to the abovementioned data sources, but also to “logic assets” like forecast models, allocation models, and production optimizers, LLM copilots simulate decisions and their outcomes. Staged safely in a “scenario,” the AI’s proposed decision can then be “handed off to a human analyst for final review.”

Caius thinks of the scenario-planning services offered to organizations of an earlier era by Stewart Brand’s consulting firm, the Global Business Network.

Foundry for Crypto is another of Palantir’s offerings, described on the company’s website as “a ‘central brain’ that connects on-chain and off-chain systems, as well as diverse stakeholders, through action-centric workflows.” Much like the Ontology, the Foundry “orchestrates decisions over an integrated foundation of data and logic.”

And in fact, the two are related. The Ontology is the semantic, “digital twin” layer that sits atop the Foundry’s data integration infrastructure. It converts the Foundry’s raw data into actionable, real-world objects, empowering users to model, manage, and automate business operations.

The Foundry does for blockchains what the Ontology does for kill chains.

Caius imagines posts ahead on Commitments, Promises, Blockchains, and True Names.

Beside the White Chickens

Caius reads about “4 Degrees of Simulation,” a practice-led seminar hosted last year by the Institute for Postnatural Studies in Madrid. Of the seminar’s three sessions, the one that most intrigues him is the one that was led by guest speaker Lucia Rebolino, as it focused on prediction and uncertainty as these pertain to climate modeling. Desiring to learn more, Caius tracks down “Unpredictable Atmosphere,” an essay of Rebolino’s published by e-flux.

The essay begins by describing the process whereby meteorological research organizations like the US National Weather Service monitor storms that develop in the Atlantic basin during hurricane season. These organizations employ climate models to predict paths and potentials of storms in advance of landfall.

“So much depends on our ability to forecast the weather — and, when catastrophe strikes, on our ability to respond quickly,” notes Rebolino. Caius hears in her sentence the opening lines of William Carlos Williams’s poem “The Red Wheelbarrow.” “So much depends on our ability to forecast the weather,” he mutters. “But the language we use to model these forecasts depends on sentences cast by poets.”

“How do we cast better sentences?” wonders Caius.

In seeking to feel into the judgement implied by “better,” he notes his wariness of bettering as “improvement,” as deployed in self-improvement literature and as deployed by capitalism: its implied separation from the present, its scarcity mindset, its perception of lack — and in the improvers’ attempts to “fix” this situation, their exercising of nature as instrument, their use of these instruments for gentrifying, extractive, self-expansive movement through the territory.

In this ceaseless movement and thus its failure to satisfy itself, the improvement narrative leads to predictive utterances and their projections onto others.

And yet, here I am definitely wanting “better” for myself and others, thinks Caius. Better sentences. Ones on which plausible desirable futures depend.

So how do we better our bettering?

Caius returns to Rebolino’s essay on the models used to predict the weather. This process of modeling, she writes, “consists of a blend of certainty — provided by sophisticated mathematical models and existing technologies — and uncertainty — which is inherent in the dynamic nature of atmospheric systems.”

January 6th again: headlines busy with Trump’s recent abduction of Maduro. A former student who works as a project manager at Google reaches out to Caius, recommending Ajay Agrawal, Joshua Gans, and Avi Goldfarb’s book Prediction Machines: The Simple Economics of Artificial Intelligence. Google adds to this recommendation Gans’s follow-up, Power and Prediction.

Costar chimes in with its advice for the day: “Make decisions based on what would be more interesting to write about.”

To model the weather, weather satellites measure the vibration of water vapor molecules in the atmosphere. “Nearly 99% of weather observation data that supercomputers receive today come from satellites, with about 90% of these observations being assimilated into computer weather models using complex algorithms,” writes Rebolino. Water vapor molecules resonate at a specific band of frequencies along the electromagnetic spectrum. Within the imagined “finite space” of this spectrum, these invisible vibrations are thought to exist within what Rebolino calls the “greenfield.” Equipped with microwave sensors, satellites “listen” for these vibrations.

“Atmospheric water vapor is a key variable in determining the formation of clouds, precipitation, and atmospheric instability, among many other things,” writes Rebolino.

She depicts 5G telecommunications infrastructures as a threat to our capacity to predict the operation of these variables in advance. “A 5G station transmitting at nearly the same frequency as water vapor can be mistaken for actual moisture, leading to confusion and the misinterpretation of weather patterns,” she argues. “This interference is particularly concerning in high-band 5G frequencies, where signals closely overlap with those used for water vapor detection.”

Prediction and uncertainty as qualities of finite and infinite games, finite and infinite worlds.

For lunch, Caius eats a plate of chicken and mushrooms he reheats in his microwave.