Duality

Elon’s critique of chess is compelling, and I find myself largely in agreement with it. Chess, despite its reputation as a game of infinite complexity, is actually a closed system with a rigid structure that lacks true emergence. The board is static, the pieces are predefined, and the rules never change. While the combinatorial explosion of possible moves makes it an interesting exercise in calculation, it remains fundamentally deterministic. Reality, by contrast, is dynamic, unpredictable, and shaped by emergent phenomena that no grandmaster, nor even the best artificial intelligence, could fully anticipate. The distinction is crucial because it challenges the common metaphor of chess as an adequate model for strategy in the real world.

In reality, degrees of freedom are not just exponentially higher than in chess—they are of an entirely different nature. The notion of fog of war, for instance, is integral to real-world decision-making. One never has perfect information. The unpredictability of human psychology, technological breakthroughs, and sociopolitical shifts means that an apparent checkmate can suddenly dissolve into irrelevance. Elon’s analogy of vaporizing the opponent’s king with lasers from space may sound hyperbolic, but it captures an essential truth: in real life, new rules can emerge mid-game, and the board itself can change beneath the players. The invention of nuclear weapons, the internet, and artificial intelligence were all moves that fundamentally altered the structure of global power, disrupting previously stable positions. A chessboard offers no equivalent to these paradigm shifts.

Perception of the Absolute as we have now of the objects of senses
– Larry

One of the most fascinating implications of Elon’s argument is the idea that winning moves in reality might not even look like winning moves at first. Chess operates under the assumption that every move has a finite and knowable consequence, at least in theory. In reality, some of the greatest strategic victories appeared, at the time, to be mistakes or acts of desperation. The Louisiana Purchase was widely ridiculed when Jefferson made it. The Wright brothers were mocked by scientists who believed human flight was impossible. The early internet was dismissed as a niche hobby for academics and hobbyists. In each case, the prevailing strategic consensus misread the long-term impact of these moves, much as a human player might overlook a counterintuitive yet brilliant move in chess. This is the fundamental problem of using a closed-system game as a model for open-ended reality: it assumes that all variables are accounted for and that all possible moves fall within a predefined range of acceptable strategies.

This critique also extends to the way people often treat history and economics as if they were deterministic systems. Many analysts, especially in academia, fall into the trap of assuming that past trends will predict future outcomes with a kind of chess-like precision. They imagine the global economy as a well-structured board with clear rules and logical moves, when in fact it is shaped by chaotic and nonlinear forces. Black Swan events, as Nassim Taleb describes them, are the real-world equivalent of Elon’s “lasers from space” scenario—game-changing disruptions that defy conventional strategic frameworks. A player who assumes they are one move away from checkmate may find themselves irrelevant overnight because they failed to consider an entirely new axis of competition.

Vaporize the opponent’s king with lasers from space that never existed before!
– Musk

However, this does not mean that chess is entirely useless as a metaphor. It remains a useful training ground for tactical thinking within constrained environments, much as Newtonian physics remains useful despite the deeper complexity of quantum mechanics. In some domains—such as military strategy, business negotiations, or even politics—there are still moments when clear, finite calculations apply. The flaw arises when people mistake the chessboard for the world itself, assuming that rigid structures and predictable trajectories define all interactions. If anything, Elon’s point suggests that the best strategists in reality are not those who simply master the existing game, but those who recognize when the game itself is about to change.

In the end, reality is less like chess and more like an evolving meta-game where the board, the rules, and the pieces themselves are constantly being rewritten. Those who focus solely on optimization within a fixed structure may find themselves outmaneuvered by those who understand emergence and adaptation. Winning, in the broadest sense, is not about executing the perfect chess sequence—it is about recognizing when to abandon the chessboard entirely and start playing a different game.

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', 'World-Tempered', 'Ucubona-Needs', 'Ecosystem-Costs', 'Space-Trial & Error', 'Time-Cadence', ], # Veni; 95/5
        'Mode': ['Ucubona-Mode'], # Vidi; 80/20
        'Agent': ['Oblivion-Unknown', 'Brand-Trusted'], # Vici; Veni; 51/49
        'Space': ['Ratio-Weaponized', 'Competition-Tokenized', 'Odds-Monopolized'], # Vidi; 20/80
        'Time': ['Volatile-Transvaluation', 'Unveiled-Resentment',  'Freedom-Dance in Chains', 'Exuberant-Jubilee', 'Stable-Victorian'] # Vici; 5/95
    }

# 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("Veni, Vidi, Vici", fontsize=15)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 12 The absolute is the entire neural network architecture: nodes, edges, weights. As for the force driving the dynamic weights, one might say that is the absolute.