Born to Etiquette

There is a pattern in history where organizations founded on idealistic principles—whether technological, financial, or humanitarian—often face an existential reckoning when their success is measured in billions or trillions of dollars. OpenAI, for example, began as a nonprofit with a mission to advance artificial intelligence for the benefit of humanity. Yet, as the economic implications of its innovations became clear, it underwent a dramatic shift, embracing for-profit structures and considering an IPO. The fundamental question that arises is whether an institution can truly resist the gravitational pull of capital once it realizes its full market potential. This tension is not new. The Grameen Foundation, launched with the intention of empowering impoverished communities through microfinance, saw its model co-opted by corporate interests that turned lending into predatory mechanisms. USAID, a philanthropic instrument of U.S. foreign policy, similarly evolved from an agency ostensibly committed to humanitarian aid into a geopolitical tool for enforcing American influence under the guise of charity. In each case, the founding ideal of “helping humanity” becomes subsumed by the logic of capital, strategic control, or political maneuvering.

This brings us to the curious relationship between the MAGA movement and the Kennedy legacy. On the surface, it makes no sense. The Kennedy family represents a historical ideal of liberal Catholic aristocracy—an American dynasty that embraced globalism, social welfare, and a vision of America that stood in stark contrast to the ethno-nationalism and isolationist tendencies of the contemporary right. MAGA, an ideological movement built on anti-elitism, conspiracy-driven populism, and a rejection of state-led philanthropy, should have no affinity for the Kennedys. And yet, they do. From JFK to RFK and now RFK Jr., the Kennedys occupy a strange, mythic space in the MAGA imagination. There is almost a longing, a belief that something was stolen—that the America the Kennedys represented was hijacked by forces more sinister, whether the deep state, the CIA, or globalist elites.

The obsession with the Kennedys may stem from a deeper cognitive dissonance within MAGA itself. On one hand, the movement rejects the state as a distributor of resources, despising institutions like USAID, which Trump gutted on his first day back in office. Steve Bannon, one of MAGA’s chief ideologues, has long waged war against state-led redistribution, believing that economic and political power should be re-concentrated into the hands of nationalist movements rather than international agencies or liberal philanthropic efforts. Yet MAGA also thrives on grievance, on the idea that America has been taken from its rightful heirs. The Kennedys, despite their commitment to governance and internationalism, have become symbols of a lost America that MAGA believes was once “great” but was subverted.

Trump’s recent appointment as chair of the Kennedy Center adds yet another layer of paradox. It is an unusual move for a sitting president to seek such a comparatively low-profile position, and it suggests a calculated effort to embed himself into the cultural institutions once dominated by the liberal elite. If the Kennedys once used their influence to shape the narrative of American leadership, Trump now positions himself as their inheritor—not of their values, but of their symbolic capital. The destruction of USAID, the rejection of OpenAI’s original ideals, and the hostility to global philanthropy all point to a larger war against the very notion of distributing power and resources beyond nationalistic boundaries. Yet in claiming the Kennedy legacy, MAGA reveals its own contradiction: it despises the world the Kennedys built, but it cannot escape the allure of what they represented.

Fig. 30 I got my hands on every recording by Hendrix, Joni Mitchell, CSN, etc (foundations). Thou shalt inherit the kingdom (yellow node). And so why would Bankman-Fried’s FTX go about rescuing other artists failing to keep up with the Hendrixes? Why worry about the fate of the music industry if some unknown joe somewhere in their parents basement may encounter an unknown-unknown that blows them out? Indeed, why did he take on such responsibility? - Great question by interviewer. The tonal inflections and overuse of ecosystem (a node in our layer 1) as well as clichêd variant of one of our output layers nodes (unknown) tells us something: our neural network digests everything and is coherenet. It’s based on our neural anatomy!

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

# Define the neural network layers
def define_layers():
    return {
        'Suis': ['Improbability', 'Planet', 'Alive', 'Loss', "Trial", 'Error'],  # Static
        'Voir': ['Witness'],  
        'Choisis': ['Faith', 'Decision'],  
        'Deviens': ['Adversarial', 'Transactional', 'Hope'],  
        "M'èléve": ['Victory', 'Payoff', 'Loyalty', 'Charity', 'Love']  
    }

# Assign colors to nodes
def assign_colors():
    color_map = {
        'yellow': ['Witness'],  
        'paleturquoise': ['Error', 'Decision', 'Hope', 'Love'],  
        'lightgreen': ["Trial", 'Transactional', 'Payoff', 'Charity', 'Loyalty'],  
        'lightsalmon': ['Alive', 'Loss', 'Faith', 'Adversarial', 'Victory'],
    }
    return {node: color for color, nodes in color_map.items() for node in nodes}

# Define edge weights (hardcoded for editing)
def define_edges():
    return {
        ('Improbability', 'Witness'): '1/99',
        ('Planet', 'Witness'): '5/95',
        ('Alive', 'Witness'): '20/80',
        ('Loss', 'Witness'): '51/49',
        ("Trial", 'Witness'): '80/20',
        ('Error', 'Witness'): '95/5',
        ('Witness', 'Faith'): '20/80',
        ('Witness', 'Decision'): '80/20',
        ('Faith', 'Adversarial'): '49/51',
        ('Faith', 'Transactional'): '80/20',
        ('Faith', 'Hope'): '95/5',
        ('Decision', 'Adversarial'): '5/95',
        ('Decision', 'Transactional'): '20/80',
        ('Decision', 'Hope'): '51/49',
        ('Adversarial', 'Victory'): '80/20',
        ('Adversarial', 'Payoff'): '85/15',
        ('Adversarial', 'Loyalty'): '90/10',
        ('Adversarial', 'Charity'): '95/5',
        ('Adversarial', 'Love'): '99/1',
        ('Transactional', 'Victory'): '1/9',
        ('Transactional', 'Payoff'): '1/8',
        ('Transactional', 'Loyalty'): '1/7',
        ('Transactional', 'Charity'): '1/6',
        ('Transactional', 'Love'): '1/5',
        ('Hope', 'Victory'): '1/99',
        ('Hope', 'Payoff'): '5/95',
        ('Hope', 'Loyalty'): '10/90',
        ('Hope', 'Charity'): '15/85',
        ('Hope', 'Love'): '20/80'
    }

# 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()
    edges = define_edges()
    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 with weights
    for (source, target), weight in edges.items():
        if source in G.nodes and target in G.nodes:
            G.add_edge(source, target, weight=weight)
    
    # Draw the graph
    plt.figure(figsize=(12, 8))
    edges_labels = {(u, v): d["weight"] for u, v, d in G.edges(data=True)}
    
    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"
    )
    nx.draw_networkx_edge_labels(G, pos, edge_labels=edges_labels, font_size=8)
    plt.title("Heritage, Resources, Static (y) vs. Adaptation, Resourcefulness, Dynamic (x)", fontsize=15)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 31 Glenn Gould and Leonard Bernstein famously disagreed over the tempo and interpretation of Brahms’ First Piano Concerto during a 1962 New York Philharmonic concert, where Bernstein, conducting, publicly distanced himself from Gould’s significantly slower-paced interpretation before the performance began, expressing his disagreement with the unconventional approach while still allowing Gould to perform it as planned; this event is considered one of the most controversial moments in classical music history.