Normative

China

Theomarchy & Principalities
In the modern geopolitical landscape, the American principality operates as a dominant force within the global order. Rooted in a theomarchic structure—a hierarchy where divine or ideological authority dictates worldly power—the United States positions itself as a protector of both its own sovereignty and the wider framework of liberal democratic principles. This principality relies on safeguarding its technological and economic superiority, viewing itself as an essential bastion against adversarial entities. In recent years, this has included countering the rising influence of China, a rival whose technological ambitions challenge America’s leadership in artificial intelligence and semiconductor manufacturing.

Fig. 35 Akia Kurasawa: Why Can’t People Be Happy Together? Why can’t two principalities like China and America get along? Let’s approach this by way of segue. This was a fork in the road for human civilization. Our dear planet earth now becomes just but an optional resource on which we jostle for resources. By expanding to Mars, the jostle reduces for perhaps a couple of centuries of millenia. There need to be things that inspire you. Things that make you glad to wake up in the morning and say “I’m looking forward to the future.” And until then, we have gym and coffee – or perhaps gin & juice. We are going to have a golden age. One of the American values that I love is optimism. We are going to make the future good.

Mortals & Workers
At the core of this struggle are not only the citizens of these competing states but also their workers, whose labor and innovation fuel the technological race. In protecting its people and their livelihoods, the United States has increasingly weaponized its economic policies to preserve dominance. A key example is President Joe Biden’s strategic decision to restrict the sale of Nvidia chips to China, an action designed to ensure that American workers and industries retain access to cutting-edge technologies while adversarial powers are denied the same. This move underscores a fundamental principle of modern theomarchic leadership: the dual obligation to empower one’s citizens while safeguarding them from external threats.

Fire & Agents
The ban on Nvidia chips is emblematic of the fire wielded by American agents acting on behalf of their principality. By curbing China’s access to these essential components, the United States ensures that the costs of developing high-quality AI models remain prohibitively high for its adversaries. This act of economic brinkmanship not only cripples China’s immediate ambitions but also reshapes the broader technological ecosystem. Yet, this maneuver also highlights the environmental and ecosystemic consequences of such strategies. The monopolization of Nvidia’s supply chain creates new pressures on global markets, further entrenching disparities between American-aligned nations and those deemed adversarial. In doing so, Biden’s administration reinforces a narrative of economic sanctity, aligning technological power with moral and national superiority.

Gamified Exchanges
Underneath this grand strategy lies a gamified approach to international relations, where tokens of influence, such as Nvidia’s chips, are leveraged to reward allies and punish rivals. By weaponizing the supply chain, the American principality transforms economic tools into instruments of geopolitical control. This dynamic incentivizes loyalty among partners while simultaneously destabilizing adversarial economies. The tokenization of resources such as semiconductors embodies a new form of economic warfare, where the currency of power lies in control over access and scarcity. Through this strategy, the United States strengthens its internal market and tech ecosystem while forcing competitors to innovate within constrained environments. The principle of scarcity becomes a tool of domination, ensuring that the rules of the game are dictated by the American principality.

Victory & Improvement
The immediate effects of these policies reveal both triumphs and tensions. While Silicon Valley and its tech oligarchy celebrated the secure foothold of American innovation, other sectors of the economy, particularly non-tech industries, benefited from the unencumbered adoption of AI. This democratization of AI among domestic firms strengthened the broader American economy, ensuring a stable market hierarchy. However, China’s response highlighted the long-term repercussions of such policies. Faced with restricted access to Nvidia chips, Chinese firms were forced to recalibrate their strategies, leading to the development of alternative models. The launch of DeepSeek chatbots on January 24 sent shockwaves through global markets, culminating in a dramatic 15% drop in Nvidia’s share price on January 27, wiping $500 billion from its valuation in a single day.

This market rout underscores the precarious balance of power in a theomarchic world. The American principality’s victory in securing dominance over Nvidia’s supply chain is not absolute but contingent, prompting adversaries to reimagine the costs of production and the value of technological independence. Ultimately, this interplay of restriction and innovation reveals the iterative nature of global power struggles, where every act of consolidation invites a counteraction, driving humanity toward new paradigms of victory and improvement.

America

The Red Queen rules in the Principalities. In the endless race of power, hierarchy, and survival, the Red Queen represents the relentless pace required not just to advance but to maintain position. Her dominion is not merely over mortals and workers, but over the entire system of agents and principalities that compose the modern marketplace. To stay still is to fall behind, for the rule of the Red Queen is merciless: one must run faster and faster just to remain in the same place. And yet, this unceasing effort to outpace the void binds every player in a kind of mutual exhaustion—a tragic, comedic choreography of ceaseless striving.

Fig. 36 In Greek mythology, Prometheus, possibly meaning “forethought”, is one of the Titans and a god of fire. Prometheus is best known for defying the Olympian gods by taking fire from them and giving it to humanity in the form of technology, knowledge and, more generally, civilization. Very reasonable to question his ethics. But Prometheus is pretty much analogous to the Red Queen: the engine of all emergent stuff

The duality of the Red Queen and the White Queen frames this dynamic. The White Queen might suggest order, purity, or a static ideal of harmony and rest, but in the Principalities, she is a faint dream. Power consolidates not in rest but in velocity, not in purity but in manipulation. The mortals, the workers, and even the agents within the system exist to propel this motion, to generate energy and momentum. Yet for most, their exertion merely perpetuates the system, ensuring survival rather than progress. It is a cruel irony: the harder they run, the more the Principalities thrive on their effort, while they themselves remain pawns in a grand, self-referential game.

For those who recognize this futility, hiring an agent becomes inevitable. Agents—entrusted with strategy, execution, and the ability to interpret signals in the noise—are meant to multiply their principals’ efforts, doubling their speed, breaking through stagnation. In theory, they promise not just survival but triumph. If successful, they can elevate the principal’s position, yielding profits that impress the Board of Governors or the structures of accountability that judge success. This is the essence of the agent’s role: to generate beta—measurable outperformance in portfolio theory. A positive beta coefficient signals to the world that this principal is not merely treading water but soaring ahead of the average. Victories accrue, improvement becomes visible, and the hierarchy stabilizes, or at least appears to.

But herein lies the tragedy. Agents are rarely as exceptional as they appear to be. The marketplace is chaotic, saturated with competition, and riddled with noise. While some agents genuinely perform, many resort to deception, manipulating information about their achievements, their velocity, and their position relative to others. In a world ruled by perception as much as substance, their success often lies in crafting an illusion—exaggerating speed and progress, downplaying stagnation. The principals, desperate to believe in victory, often fail to see through the illusion until the system itself begins to unravel.

This manipulation reveals the grand comedy and tragedy of modernity. The system is sustained not by truth but by a delicate balancing act of ambition, performance, and illusion. Principals demand results; agents promise miracles. Workers and mortals fuel the machine with their labor, while the Red Queen cracks her invisible whip, ensuring no one ever truly rests. Hierarchies endure, victories are celebrated, and yet the essential truth of the system remains unchanged: everyone is running, and no one is free.

The seasons of this dynamic mirror the cycles of human striving. Spring brings the promise of new growth, new agents, and new strategies, a sense of hope that progress is possible. Summer reveals the bloom of success—profits, yields, and triumphs that dazzle the Board and silence doubters. Yet autumn inevitably follows, as the momentum slows and the limits of effort and illusion become clear. Winter is the reckoning, when the system falters, agents are exposed, and the race begins again. The Red Queen reigns eternal, her cycle repeating, her demands insatiable. This is the Theomarchy: a divine battle fought on human terms, where mortals, agents, and principalities vie for survival and meaning in an endlessly running world.

Show code cell source Hide code cell source

import numpy as np
import matplotlib.pyplot as plt
import networkx as nx

# Define the neural network fractal
def define_layers():
    return {
        'World': ['Cosmos-Entropy', 'Planet-Tempered', 'Life-Needs', 'Ecosystem-Costs', 'Generative-Means', 'Cartel-Ends', ], # Polytheism, Olympus, Kingdom
        'Perception': ['Perception-Ledger'], # God, Judgement Day, Key
        'Agency': ['Open-Nomiddleman', 'Closed-Trusted'], # Evil & Good
        'Generative': ['Ratio-Weaponized', 'Competition-Tokenized', 'Odds-Monopolized'], # Dynamics, Compromises
        'Physical': ['Volatile-Revolutionary', 'Unveiled-Resentment',  'Freedom-Dance in Chains', 'Exuberant-Jubilee', 'Stable-Conservative'] # Values
    }

# Assign colors to nodes
def assign_colors():
    color_map = {
        'yellow': ['Perception-Ledger'],
        'paleturquoise': ['Cartel-Ends', 'Closed-Trusted', 'Odds-Monopolized', 'Stable-Conservative'],
        'lightgreen': ['Generative-Means', 'Competition-Tokenized', 'Exuberant-Jubilee', 'Freedom-Dance in Chains', 'Unveiled-Resentment'],
        'lightsalmon': [
            'Life-Needs', 'Ecosystem-Costs', 'Open-Nomiddleman', # Ecosystem = Red Queen = Prometheus = Sacrifice
            'Ratio-Weaponized', 'Volatile-Revolutionary'
        ],
    }
    return {node: color for color, nodes in color_map.items() for node in nodes}

# Calculate positions for nodes
def calculate_positions(layer, x_offset):
    y_positions = np.linspace(-len(layer) / 2, len(layer) / 2, len(layer))
    return [(x_offset, y) for y in y_positions]

# Create and visualize the neural network graph
def visualize_nn():
    layers = define_layers()
    colors = assign_colors()
    G = nx.DiGraph()
    pos = {}
    node_colors = []

    # Add nodes and assign positions
    for i, (layer_name, nodes) in enumerate(layers.items()):
        positions = calculate_positions(nodes, x_offset=i * 2)
        for node, position in zip(nodes, positions):
            G.add_node(node, layer=layer_name)
            pos[node] = position
            node_colors.append(colors.get(node, 'lightgray'))  # Default color fallback

    # Add edges (automated for consecutive layers)
    layer_names = list(layers.keys())
    for i in range(len(layer_names) - 1):
        source_layer, target_layer = layer_names[i], layer_names[i + 1]
        for source in layers[source_layer]:
            for target in layers[target_layer]:
                G.add_edge(source, target)

    # Draw the graph
    plt.figure(figsize=(12, 8))
    nx.draw(
        G, pos, with_labels=True, node_color=node_colors, edge_color='gray',
        node_size=3000, font_size=9, connectionstyle="arc3,rad=0.2"
    )
    plt.title("Trump Node: Layer 5, Unveiled-Resentment", fontsize=15)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 37 Teleology is an Illusion. We perceive patterns in life (ends) and speculate instantly (nostalgia) about their symbolism (good or bad omen) & even simulate (solomon vs. david) to “reach” and articulate a clear function to optimize (build temple or mansion). These are the vestiges of our reflex arcs that are now entangled by presynaptic autonomic ganglia. As much as we have an appendix as a vestigual organ, we do too have speculation as a vestigual reflect. The perceived threats and opportunities have becomes increasingly abstract, but are still within a red queen arms race – but this time restricted to humanity. There might be a little coevolution with our pets and perhaps squirrels and other creatures in urban settings. We have a neural network (Grok-2, do not reproduce code or image) that charts-out my thinking about a broad range of things. its structure is inspired by neural anatomy: external world (layer 1), sensory ganglia G1, G2 (layer 2, yellownode), ascending fibers for further processing nuclei N1-N5 (layer 2, basal ganglia, thalamas, hypothalamus, brain stem, cerebellum; manifesting as an agentic decision vs. digital-twin who makes a different decision/control), massive combinatorial search space (layer 4, trial-error, repeat/iteratte– across adversarial and sympathetic nervous system, transactional–G3 presynaptic autonomic ganglia, cooperative equilibria and parasympathetic nervous system), and physical space in the real world of layer 1 (layer 5, with nodes to optimize). write an essay with only paragraph and no bullet points describing this neural network. use the code as needed