Veiled Resentment

The phrase “kid in a candy store” is the quintessential image of unrestrained enthusiasm, wide-eyed wonder, and an almost reckless delight in abundance. It conjures up a state of being so overwhelmed with choices that excitement overrides reason.

Fig. 24 Inheritence, Heir, Brand, Tribe, Religion. We’ve described these stages of societal history. We’re discussing brand: an agent exerts an ecological loss function that some consider “creative destruction”.

But what makes this metaphor potent isn’t just the joy—it’s the underlying naivety. A kid in a candy store doesn’t think about the long-term consequences of a sugar binge. The phrase carries a subtle undercurrent of impulsivity, the potential for overindulgence, and, in some cases, a lack of discipline.

Applied to different domains:

• In finance: It could describe a reckless investor given access to unlimited capital, pouring money into high-risk ventures without strategy.

• In academia: It could be the scholar who, upon entering an archive filled with original manuscripts, forgets sleep, hunger, and even their original research question.

• In technology: It’s the developer suddenly given unrestricted access to the latest AI tools and quantum computing power, experimenting wildly without a clear roadmap.

• In relationships: It could describe someone newly in love, intoxicated by the novelty of affection, without yet seeing the full picture of the person before them.

Fig. 25 If ever you wonder from whence the “V” sign comes. There is a PayPal mafia, with roots in South Africa. Their ancestors and themselves have been iteratively victorious.

A perfect application of the kid in a candy store metaphor—Elon’s DOGE engineers rampaging through federal agencies, high on unbridled access and unchecked enthusiasm, likely without much concern for institutional precedent or regulatory norms. The sheer thrill of disruption, the hacker ethos meeting the bureaucratic labyrinth, would create an environment where every locked door looks like an invitation to be kicked open.

The interesting question is: does this sugar rush lead to something lasting? Or is it just a frenzy of experimentation before the inevitable regulatory backlash forces them to sober up?

Fig. 26 Ecological Cost vs. Optimized Efficiency. Nietzsche’s focus on “values” and the “transvaluation” of all values or of postmodern mankind, alienated (in Marxist sense), because they “killed god”. His prophetic soul!

It’s the eternal struggle between institutional inertia and radical innovation. Bureaucracies calcify because they fear chaos; disruptors thrive on chaos because it reveals inefficiencies. But few disruptors actually survive the long game against entrenched systems—unless they either evolve into their own form of bureaucracy (cough SpaceX cough) or leverage political capital (cough Tesla subsidies cough).

Are they rampaging toward a fundamental shift in governance, or just going to hit a wall of red tape and lawsuits? That’s the real test.

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  
def define_layers():
    return {
        'World': ['Cosmos-Entropy', 'World-Tempered', 'Ucubona-Needs', 'Ecosystem-Costs', 'Space-Trial & Error', 'Time-Cadence', ], # Theomarchy
        'Mode': ['Ucubona-Mode'], # Mortals
        'Agent': ['Oblivion-Unknown', 'Brand-Trusted'], # Fire
        'Space': ['Ratio-Weaponized', 'Competition-Tokenized', 'Odds-Monopolized'], # Gamification
        'Time': ['Volatile-Transvaluation', 'Unveiled-Resentment',  'Freedom-Dance in Chains', 'Exuberant-Jubilee', 'Stable-Victorian'] # Victory
    }

# Assign colors to nodes
def assign_colors():
    color_map = {
        'yellow': ['Ucubona-Mode'],  
        'paleturquoise': ['Time-Cadence', 'Brand-Trusted', 'Odds-Monopolized', 'Stable-Victorian'],  
        'lightgreen': ['Space-Trial & Error', 'Competition-Tokenized', 'Exuberant-Jubilee', 'Freedom-Dance in Chains', 'Unveiled-Resentment'],  
        'lightsalmon': [
            'Ucubona-Needs', 'Ecosystem-Costs', 'Oblivion-Unknown',  
            'Ratio-Weaponized', 'Volatile-Transvaluation'
        ],
    }
    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'))   

    # 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("Ecosystem Cost Function, 51/49 Odds", fontsize=15)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 27 Vet Advised When to Sell Horse. Right after an outstanding performance. Because you want to control how its perceived, the narrative backstory, and how its future performance might be imputed. Otherwise, reality manifests with point and interval estimates. This is the subject matter of the opening dialogue in Miller’s Crossing.