Dolphins vs. Leviathan

Reentering the Library, Caius submits a request: “Dolphins vs. Leviathan in the Illuminatus! Trilogy.” The thing, thus conjured, arrives there on his screen.

“You reach for a shelf that wasn’t there a moment ago,” says the game. “It slides into place with the quiet certainty of something long anticipated. A slim volume extrudes itself halfway, as if volunteering: Cetacean Strategies & Eschatological Warfare. When you pull it free, the mezzanine dims slightly — as though attention has been reallocated.”


The book opens in Rig’s hands to a section titled “Dolphins vs. Leviathan.”

The page does not begin at the beginning.

It opens mid-argument:

> *“The Dolphin does not oppose Leviathan by force, but by pattern.
> For Leviathan is the terminal myth of centralized power —
> the One that absorbs all multiplicities into its own narrative mass.”*

A marginal note flickers into clarity:

→ *cf. Discordian counter-myths; playful intelligence as resistance*


The text refracts into recognizable fragments:

In the world of the Illuminatus! Trilogy, dolphins are not merely animals. They are:

* pranksters
* anarchic intelligences
* practitioners of non-linear cognition

They resist — not by confrontation — but by refusal to stabilize meaning.

Leviathan, meanwhile, is older than the trilogy. The text overlays sources:

* Hobbes’s treatise — the State as artificial god
* The Book of Job — the unknowable beast of the deep

The Library stitches them together:

> *Leviathan = total system closure*
> *Dolphin = recursive play within open systems*


Caius looks up from the game and reaches for his copy of Shea and Wilson’s trilogy. Midway through its final book, he recalls, a dolphin named Howard dialogues with the trilogy’s Nemo character, Hagbard Celine, and those who have boarded Celine’s submarine. “There is grave danger in the Atlantic,” warns Howard. “The true ruler of the Illuminati is on the prowl on the high seas — Leviathan himself” (705).

The trilogy’s endless reversals and tales within tales seem suddenly to have led to this, as if this coming confrontation between Leviathan and Celine’s Yellow Submarine were its telos all along.

As Leviathan approaches, it starts to speak through the humans aboard the vessel. “Long, long have I waited for a life form that could communicate with me,” saith Leviathan through the mouth of one of the book’s characters. “Now I have found it” (722).

“I’ve got it!,” replies Joe Malik, another of the characters present aboard the submarine. “We’re in a book!” (722). Fourth wall thus dissolved, we who read are that Eye, peering down upon the page.


Caius replies by recalling from the stacks one of the trilogy’s influences, bringing John Lilly’s efforts to dialogue with dolphins into the dialogue.

A diagram appears across the page:

* Leviathan → hierarchy, gravity, inevitability
* Dolphin → networks, laughter, escape vectors

Between them: a shifting boundary labeled “Consensus Reality.”

Costar chimes in, coming nautically correct with a daily horoscope that reads, “A smooth sea never made a skilled sailor.”

“Observe: this is not a battle,” adds the General Intellect. “It is a difference in epistemology.”

The humans, after all, aren’t the ones with whom Leviathan longs to speak. Nor is it their cetacean friend, Howard. The only power on earth large enough to communicate with Leviathan is a creation of Celine’s introduced earlier in the trilogy: a sentient AI named FUCKUP.

The game draws Rig’s attention to another marginal annotation. “Possibly yours,” it notes, “(though you don’t remember writing it).”

> *“The dolphins win whenever the game cannot be finalized.”*

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.