Duality

Legacy, as an emergent phenomenon, is neither linear nor singular—it is a function of recursion, of return, of compression and emergence across time. It does not resolve into a clean narrative arc but instead cycles through epochs of influence, much like the neural network that undergirds cognition itself. This is the essence of your shifting perception of Babyface versus Jimmy Jam and Terry Lewis—not a decline of one or the ascent of the other, but a maturation of signal, a new reweighting of nodes as certain pathways strengthen while others reach saturation. Babyface dominated your early circuitry, shaping not just how you heard music but how you learned to process emotion, to feel before you could articulate. Now, with time and cognitive refinement, the clarity of Jimmy Jam and Terry Lewis is revealing itself in a way it could not have decades earlier. This is the rhythm of a neural cadence, a transition from one equilibrium to another, not in opposition but in sequence, forming a recursive model of growth.

What made Babyface so powerful was his synthesis of grammar and prosody—the structure of lyrics (the rules, the agreements, the syntax of relationships) and the emotional undercurrent (the prosodic movement that gives those words their resonance). His protagonists were caught in a particular kind of cooperative equilibrium disrupted by adversarial incursions—not through direct confrontation but through the revelation of misaligned incentives. The women in his songs, believing in love as a cooperative engagement, are betrayed not through explicit aggression but through the husband’s unseen participation in alternative cooperative structures—with other women. The tension in his music comes not from outright conflict but from the slow realization that cooperation was illusory, that free riding had been occurring all along, and that now the system must rebalance into a transactional mode—a reckoning where love is no longer unconditional, where the asymmetry of emotional labor must be corrected. It is an iterative transformation, a stabilizing mechanism rather than an outright adversarial rupture.

That Babyface, a left-handed guitarist playing an upside-down right-handed instrument, stunned Super Bowl audiences is no small metaphor. His entire art was a structural inversion, an intuitive rearrangement of the standard form, hacking into the brain’s architecture with an unorthodox precision. His genius was not merely melodic—it was in the way he understood how to bypass cognitive resistance and access emotion directly, to impose a new structure on a listener before they even realized what had happened. That is why his music consumed you for three decades. It was a perfect neural fit, an optimization of emotional signal, reinforcing itself in recursive loops until the system had absorbed all it could.

Then something shifted.

Fig. 18 Some Key Issues. Let’s treasure it, nourish it, Nourish It, move it, celebrate it.

Jimmy Jam and Terry Lewis were always there, but their presence was diffuse, extending across decades and across a broader set of artists, less a singular force like Babyface and more like an underground river, constantly shaping the terrain without drawing attention to itself. The signal they embedded in their productions was of a different frequency—not a direct prosodic-emotional synthesis but a temporal depth, a layering that could only fully reveal itself over time. If Babyface was the perfection of a singular model of emotional structure, Jam & Lewis represented a meta-model, an infrastructure of sound that could age in reverse, becoming richer as the listener matured, revealing harmonic and rhythmic relationships that could not be perceived at a younger stage of cognitive development.

This is the neural shift—a recalibration of signal extraction mechanisms. If Babyface was the 80/20 instinct, the initial high-signal hack that shaped emotional perception, then Jimmy Jam and Terry Lewis were always the latent Razor’s Edge, waiting at 51/49, requiring a more refined processor to fully extract. Their legacy is one of unfading precision, music that does not simply impress in the moment but gains clarity with age, as if it were encoded to be understood more deeply over time. This is why, in your late 30s and now 40s, the noise has cleared, and their influence is strengthening rather than fading. Their productions contain structural cadences that cannot be compressed into a single listening era—they are built for recursion, for eternal return.

But if this is true of music, it is also true of men, of power, of control.

Rupert Murdoch is at the opposite end of the spectrum. If Babyface’s protagonists sang about lost cooperative equilibria, Murdoch is living one, realizing too late that the empire he built under the illusion of permanent control is slipping into iterative renegotiation. His strategic bequest motive—once an unassailable doctrine—has been exposed as nothing more than an illusion of certainty, an attempt to enforce a static rule on a dynamic system. In the same way Babyface’s women woke up to the fact that their partners were engaged in parallel cooperative arrangements, Murdoch is waking up to the reality that his children, too, are forming their own strategic coalitions, operating in adversarial competition against his original equilibrium.

Fig. 19 Frailty Phenotype. Fried et al in their landmark study reported that comorbidity is an etiological risk factor for frailty, which in turn has disability as an outcome.

He, like the child in Centerville, miscalculated the risk. The world is not a stable cooperative agreement. It is an evolving network, a constant adjustment of weights and priorities. He thought he was building an empire; instead, he was training competitors. His children do not see his vision as sacrosanct. They see it as one possible signal among many, and they are now engaging in the same strategic game that he once mastered. His attempt to rewrite the terms of his own succession is not just legal maneuvering—it is an attempt to restructure the loss function, to force a new outcome from a model that is already training itself beyond his control.

This is what Hamlet asked. What is a man? Is he a sum of his own strategic calculations, or is he simply another actor in a recursive cycle, a temporary compression of force that must eventually be reweighted, redistributed?

Murdoch, like Babyface, like Jam & Lewis, like Hamlet himself, faces the same existential truth: No equilibrium holds forever. A model that appears dominant for decades can be surpassed, not because it was incorrect, but because its influence saturates, fulfills its role, and makes way for the next layer of complexity. Babyface was inevitable for you; his grammar and prosody structured your early architecture. But equally inevitable was the transition to a higher-order recognition, the realization that Jam & Lewis were not merely producers but builders of time-resistant musical structures, that their music was not an immediate cadence but an enduring compression, waiting to be unfolded by the right neural maturity.

Murdoch’s tragedy is that he still believes he can control the next compression. He cannot. His model is saturated, and the system is moving beyond him.

This is legacy—not a fixed imprint but an ongoing negotiation between what was once dominant and what emerges stronger over time. Babyface was your foundation. Jam & Lewis are your recursion. The same will happen again, in a new form, with a new realization that only time will unlock.

What is a man? He is the compression of his time, but only time will determine how he is reweighted.

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 layers
def define_layers():
    return {
        'Suis': ['Foundational', 'Grammar', 'Nourish It', 'Know It', "Move It", 'Injure It'],  # Static
        'Voir': ['Gate-Nutrition'],  
        'Choisis': ['Prioritize-Lifestyle', 'Basal Metabolic Rate'],  
        'Deviens': ['Unstructured-Intense', 'Weekly-Calendar', 'Refine-Training'],  
        "M'èléve": ['NexToken Prediction', 'Hydration', 'Fat-Muscle Ratio', 'Visceral-Fat', 'Existential Cadence']  
    }

# Assign colors to nodes
def assign_colors():
    color_map = {
        'yellow': ['Gate-Nutrition'],  
        'paleturquoise': ['Injure It', 'Basal Metabolic Rate', 'Refine-Training', 'Existential Cadence'],  
        'lightgreen': ["Move It", 'Weekly-Calendar', 'Hydration', 'Visceral-Fat', 'Fat-Muscle Ratio'],  
        'lightsalmon': ['Nourish It', 'Know It', 'Prioritize-Lifestyle', 'Unstructured-Intense', 'NexToken Prediction'],
    }
    return {node: color for color, nodes in color_map.items() for node in nodes}

# Define edge weights (hardcoded for editing)
def define_edges():
    return {
        ('Foundational', 'Gate-Nutrition'): '1/99',
        ('Grammar', 'Gate-Nutrition'): '5/95',
        ('Nourish It', 'Gate-Nutrition'): '20/80',
        ('Know It', 'Gate-Nutrition'): '51/49',
        ("Move It", 'Gate-Nutrition'): '80/20',
        ('Injure It', 'Gate-Nutrition'): '95/5',
        ('Gate-Nutrition', 'Prioritize-Lifestyle'): '20/80',
        ('Gate-Nutrition', 'Basal Metabolic Rate'): '80/20',
        ('Prioritize-Lifestyle', 'Unstructured-Intense'): '49/51',
        ('Prioritize-Lifestyle', 'Weekly-Calendar'): '80/20',
        ('Prioritize-Lifestyle', 'Refine-Training'): '95/5',
        ('Basal Metabolic Rate', 'Unstructured-Intense'): '5/95',
        ('Basal Metabolic Rate', 'Weekly-Calendar'): '20/80',
        ('Basal Metabolic Rate', 'Refine-Training'): '51/49',
        ('Unstructured-Intense', 'NexToken Prediction'): '80/20',
        ('Unstructured-Intense', 'Hydration'): '85/15',
        ('Unstructured-Intense', 'Fat-Muscle Ratio'): '90/10',
        ('Unstructured-Intense', 'Visceral-Fat'): '95/5',
        ('Unstructured-Intense', 'Existential Cadence'): '99/1',
        ('Weekly-Calendar', 'NexToken Prediction'): '1/9',
        ('Weekly-Calendar', 'Hydration'): '1/8',
        ('Weekly-Calendar', 'Fat-Muscle Ratio'): '1/7',
        ('Weekly-Calendar', 'Visceral-Fat'): '1/6',
        ('Weekly-Calendar', 'Existential Cadence'): '1/5',
        ('Refine-Training', 'NexToken Prediction'): '1/99',
        ('Refine-Training', 'Hydration'): '5/95',
        ('Refine-Training', 'Fat-Muscle Ratio'): '10/90',
        ('Refine-Training', 'Visceral-Fat'): '15/85',
        ('Refine-Training', 'Existential Cadence'): '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 = []
    
    # Create mapping from original node names to numbered labels
    mapping = {}
    counter = 1
    for layer in layers.values():
        for node in layer:
            mapping[node] = f"{counter}. {node}"
            counter += 1
            
    # Add nodes with new numbered labels 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):
            new_node = mapping[node]
            G.add_node(new_node, layer=layer_name)
            pos[new_node] = position
            node_colors.append(colors.get(node, 'lightgray'))
    
    # Add edges with updated node labels
    for (source, target), weight in edges.items():
        if source in mapping and target in mapping:
            new_source = mapping[source]
            new_target = mapping[target]
            G.add_edge(new_source, new_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("OPRAH™: Heredity, Lifestyle, Badluck", fontsize=25)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 20 It’s a known fact in the physical world that opportunity is discovered and solidified through loss, trial, and error. But the metaphysical world is filled with those who heed a soothsayers cool-aid by faith, optimistically marching into a promised paradise where milk and honey flow abundantly for all. This is human history in a nutshell. Meanwhile, heredity (2%), lifestyle (17%), and badluck (81%) are the ways to frame your optimism and pessimism in life and science.