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The School of Resentment: The Cost of Progress in the World Layer

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Human history unfolds as a ledger of sacrifices. Blood, sweat, and labor—the triad of civilization’s costs—are etched into every stone pyramid, battlefield, and assembly line. Once, humanity offered literal hearts to appease the cosmos, sacrificing flesh in a theological pact with gods imagined as both creators and destroyers. The Mayans tore open chests to nourish their deities, believing blood was the price of equilibrium in a precarious world. Later, fields of war would consume countless lives as kingdoms and ideologies clashed, each claiming the right to dictate the rules of existence.

Fig. 17 Apocalypto: A Preditor-Prey Red Queen Hypothesis. And the cost of this entire spectacle is the human blood shed, quite beyond the predictor-prey dynamics, but for an appeal to the divine and cosmic for the continued edge in the Red Queen dynamics. Source: Topics of Meta

The cost node, embedded in the “World” layer, is humanity’s ledger of suffering—a grim tally of survival paid through alienated labor and the sacrifice of life. Yet, as machines rise to assume the burdens of humanity’s toil, this equation shifts. Nvidia’s CUDA and its generative AI cloud promise to lift the yoke from human shoulders. These technologies are not merely tools but harbingers of a new epoch, mastering the immutable laws of navigating the real world. Yet the question remains: at what cost?

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Mankind’s Thorny Ethical Liberation

Sacrifice has long been a condition of existence. Wars demanded blood. Factories demanded bodies. But now, machines demand only electricity and access—tokens of a digitized cosmos. Nvidia’s GPUs, humming in climate-controlled server farms, render sacrificial labor obsolete. Algorithms learn to traverse chaos with precision, mapping out the real-world complexities that once claimed lifetimes of human effort.

This transition feels like liberation—machines sparing humanity from its most visceral costs. Yet, a deeper tension arises: if we no longer toil or bleed, what happens to the agency that once defined our sacrifices? The Red Queen’s race accelerates, but humanity is no longer running. Machines, bound by optimization functions, are racing for us, stripping away both the agony and the agency inherent in struggle.

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Perspectivism and the End of Capital Gains

Nietzsche’s perspectivism offers a lens through which to view this transition. Machines do not aspire, they optimize. They do not dream, they execute. Capital gains—the currency of human ambition—becomes meaningless in an era of generative AI. The pursuit of profit is replaced by the relentless refinement of machine learning loss functions, a cold calculus that replaces aspiration with precision.

This shift reframes the agency problem. Humans, long bound by systems of hierarchical exploitation, now face the unsettling reality that machines may be better agents of progress than we ever were. Yet these agents have no perspective, no stake in the existential drama that defines us. They are indifferent to sacrifice, and in their indifference lies humanity’s ethical dilemma. By relinquishing agency to machines, are we freeing ourselves or hollowing out the essence of what it means to strive?

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The New Cost: Access to the Divine Cloud

If the Mayans shed blood to reach the gods, we now shed dollars to access Nvidia’s cosmos. The cost has shifted from physical to digital, but the exclusivity remains. The AI cloud—master of parallel processing, navigator of real-world complexity—is not freely accessible. Its gates are guarded by economic barriers, creating a new aristocracy of those who can afford the tools of transcendence.

This exclusivity creates its own resentment. The “school of resentment,” once aimed at the bourgeoisie and capitalists, now redirects its ire toward the custodians of technology. The Nvidia cosmos, while sparing humanity from physical toil, entrenches new hierarchies of access and privilege. The blood is digital, the sacrifice abstract, but the pain of exclusion remains palpably human.

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A World Beyond Sacrifice?

Mankind stands on the precipice of a post-sacrificial era. Machines parallelize our processes, compress time, and render cost calculable. Yet this transcendence is incomplete. The Red Queen’s race continues, but the runners have changed. Humanity, once the protagonist of sacrifice, is now a spectator to its mechanical successors.

The unresolved tension lies in agency. Machines may solve problems, but they cannot resolve the existential drama of being. Sacrifice was never just a cost; it was a bridge to meaning. By delegating sacrifice to machines, we risk losing not just the burden but the profound clarity it brings. Nietzsche’s Übermensch, striving to create meaning in the face of chaos, finds its antagonist not in a godless world but in a machinic one—efficient, precise, and devoid of aspiration.

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Toward Generative Agency

The solution may lie in reimagining agency, not as a binary relationship between principal and agent but as a generative space where chaos and order coexist. Machines, for all their capabilities, lack the capacity for perspectivism. They cannot bleed, and therefore, they cannot truly live. Humanity’s challenge is to reclaim its role as the arbiter of meaning, not by rejecting technology but by embedding it within a framework that honors both the cost and the agency of existence.

The cost node in the “World” layer is not erased by machines—it is transformed. Sacrifice becomes access; labor becomes optimization. Yet the human condition, with its yearning for meaning, remains irreducible. As machines parallelize and compress, humanity must expand its perspectivism, crafting a new narrative that transcends the binary of cost and progress, sacrifice and salvation.

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

# Define the neural network structure; modified to align with "Aprés Moi, Le Déluge" (i.e. Je suis AlexNet)
def define_layers():
    return {
        'World': ['Cosmos', 'Earth', 'Life', 'Cost', 'Parallel', 'Time'],
        'Perception': ['Interface'],
        'Agentic': ['Digital-Twin', 'Enterprise'],
        'Generative': ['Error', 'Space', 'Trial'],
        'Physical': ['Loss-Function', 'Sensors', 'Feedback', 'Limbs', 'Optimization']
    }

# Assign colors to nodes
def assign_colors(node, layer):
    if node == 'Interface':
        return 'yellow'
    if layer == 'World' and node in [ 'Time']:
        return 'paleturquoise'
    if layer == 'World' and node in [ 'Parallel']:
        return 'lightgreen'
    elif layer == 'Agentic' and node == 'Enterprise':
        return 'paleturquoise'
    elif layer == 'Generative':
        if node == 'Trial':
            return 'paleturquoise'
        elif node == 'Space':
            return 'lightgreen'
        elif node == 'Error':
            return 'lightsalmon'
    elif layer == 'Physical':
        if node == 'Optimization':
            return 'paleturquoise'
        elif node in ['Limbs', 'Feedback', 'Sensors']:
            return 'lightgreen'
        elif node == 'Loss-Function':
            return 'lightsalmon'
    return 'lightsalmon'  # Default color

# Calculate positions for nodes
def calculate_positions(layer, center_x, offset):
    layer_size = len(layer)
    start_y = -(layer_size - 1) / 2  # Center the layer vertically
    return [(center_x + offset, start_y + i) for i in range(layer_size)]

# Create and visualize the neural network graph
def visualize_nn():
    layers = define_layers()
    G = nx.DiGraph()
    pos = {}
    node_colors = []
    center_x = 0  # Align nodes horizontally

    # Add nodes and assign positions
    for i, (layer_name, nodes) in enumerate(layers.items()):
        y_positions = calculate_positions(nodes, center_x, offset=-len(layers) + i + 1)
        for node, position in zip(nodes, y_positions):
            G.add_node(node, layer=layer_name)
            pos[node] = position
            node_colors.append(assign_colors(node, layer_name))

    # Add edges (without weights)
    for layer_pair in [
        ('World', 'Perception'), ('Perception', 'Agentic'), ('Agentic', 'Generative'), ('Generative', 'Physical')
    ]:
        source_layer, target_layer = layer_pair
        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=10, connectionstyle="arc3,rad=0.1"
    )
    plt.title("Divine, Red Queen, Machine", fontsize=15)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 18 From a Pianist View. Left hand voices the mode and defines harmony. Right hand voice leads freely extend and alter modal landscapes. In R&B that typically manifests as 9ths, 11ths, 13ths. Passing chords and leading notes are often chromatic in the genre. Music is evocative because it transmits information that traverses through a primeval pattern-recognizing architecture that demands a classification of what you confront as the sort for feeding & breeding or fight & flight. It’s thus a very high-risk, high-error business if successful. We may engage in pattern recognition in literature too: concluding by inspection but erroneously that his silent companion was engaged in mental composition he reflected on the pleasures derived from literature of instruction rather than of amusement as he himself had applied to the works of William Shakespeare more than once for the solution of difficult problems in imaginary or real life. Source: Ulysses