Life ⚓️#
Machiavelli’s The Prince is among the most scrutinized works in political philosophy, and any attempt to distill its essence into a handful of fundamental ideas must be weighed against its broader themes and subtleties. The five ideas proposed—inheritance, perception, decision-making, and the three possible modes of engaging with external reality (fighting, exchanging, or sharing)—offer a strikingly efficient model. However, the question remains: does this model adequately capture the depth of Machiavelli’s thought, or does it risk reducing his insights to an oversimplified schema that glosses over the pragmatic contradictions and darker implications that define his work?
At first glance, the structure proposed here aligns with Machiavelli’s worldview in its recognition that political reality is not purely a matter of choice but is conditioned by what is inherited. This includes the political structures, traditions, and institutions into which a ruler is born, as well as the legacy of previous rulers and the material conditions of the state. Machiavelli constantly emphasizes that fortune plays a decisive role in shaping one’s opportunities and constraints. A prince does not enter the world as a blank slate but inherits a specific geopolitical and historical context. This first principle thus appears to be in step with his understanding of power as something deeply contingent on prior realities.
The second component, the idea that decision-making is shaped by what one sees, hears, and understands, also resonates with Machiavelli’s insights, though perhaps with an important caveat. He is acutely aware that rulers often do not perceive reality as it is but as it is presented to them, distorted by advisors, circumstances, and their own desires. A prince who misreads his environment is doomed, and Machiavelli repeatedly warns that an accurate grasp of political reality is a skill in itself, requiring cold calculation and an ability to see through deception. Where this summary might fall short is in failing to account for the ways in which perception is not just a passive reception of external reality but an active struggle to interpret and manipulate it.
The heart of this proposed framework lies in its categorization of decision-making into three fundamental modes: fighting, exchanging, or sharing. On the surface, these categories align with various aspects of Machiavelli’s political philosophy. Conflict is certainly at the center of his thinking; he sees war as an inevitable feature of political life, urging rulers to prioritize military strength and warning against the complacency that comes from relying on mercenaries or alliances. The idea of exchange also fits within his pragmatic realism—alliances, negotiations, and betrayals are all part of the political game. However, the notion of “sharing” is where this model diverges most sharply from Machiavelli’s core philosophy. While he recognizes the necessity of keeping subjects content enough to avoid rebellion, he is far from advocating any genuine concept of shared power. Rather, he believes in calculated appearances: a ruler must sometimes appear generous, merciful, or just, but these virtues must always be subordinated to necessity. If the ruler shares power in a way that weakens his position, he risks ruin. This tension between image and reality is crucial in The Prince, and without it, any summary risks missing the text’s fundamental cynicism.
Ultimately, this five-point summary captures certain Machiavellian themes, particularly the importance of inherited conditions and the need for strategic action. However, it may be too symmetrical and tidy for a thinker whose ideas are deeply rooted in contingency, paradox, and the brutal realities of power. Machiavelli does not propose a simple taxonomy of political action but rather a fluid and often contradictory system where fortune, deception, and necessity dictate outcomes in unpredictable ways. While the categories of fighting, exchanging, and sharing may map onto some of his ideas, they fail to capture the asymmetry between them—warfare is not merely one option among three, but often the foundational principle of survival. Moreover, the emphasis on sharing underestimates his ruthless pragmatism.
A more accurate summary might preserve the initial recognition of inherited conditions and the role of perception but frame decision-making in terms of deception, coercion, and strategic flexibility rather than a neat triad of possible responses. Machiavelli’s prince does not choose between three pathways so much as he continuously adjusts, deceives, and recalibrates based on shifting realities. His philosophy is less a fixed framework and more a dynamic process, one that recognizes that power is always in flux and that the wise ruler must be prepared to adapt without illusions. In this sense, while the proposed model offers a compelling starting point, it ultimately risks smoothing out the jagged, brutal, and often contradictory nature of Machiavelli’s vision of political survival.
Fig. 4 Veni-Vidi, Veni-Vidi-Vici. Yep, Red Queen Hypothesis all the way.#
Everything can be weaponized, tokenized, or monopolized—especially if it holds value in a system where control, scarcity, or narrative framing plays a role. But let’s get specific:
Weaponized (Turning something into a tool for power, coercion, or destruction)#
Information – Disinformation, psychological operations, or selective leaks.
Social Media – Used to incite division, manipulate public opinion, or instigate real-world action.
Law & Bureaucracy – Legal frameworks exploited to suppress opposition (e.g., lawfare).
Economics – Sanctions, tariffs, and trade barriers as geopolitical weapons.
Culture & History – Revisionism, erasure, or propaganda narratives.
Medicine & Health Data – Targeted bioweapons, differential access to healthcare, or health-based discrimination.
Science & AI – Bias in algorithms that reinforce existing power dynamics.
Climate Policy – Used as leverage against weaker nations or economic sectors.
Tokenized (Breaking something down into units for trade, validation, or symbolic value)#
Human Achievements – Diplomas, certificates, awards, and rankings.
Relationships – Social capital, follower counts, likes, and ‘networking.’
Identity – Gender, race, or personal trauma as currency in social discourse.
Creativity – Art, music, literature commodified into NFTs or algorithm-driven content.
Time & Attention – Monetized via subscriptions, ads, and engagement metrics.
Work & Productivity – Gig economy, corporate ‘hustle culture’ badges of honor.
Politics – Activism as a performance rather than a practice (virtue signaling).
Ethics & Morality – ESG scores, carbon credits, ‘green’ credentials.
Education – Degrees functioning more as status markers than learning experiences.
Monopolized (Concentrated control over something that was once distributed)#
Ideas – Intellectual property law, patents, and proprietary algorithms.
Technology – Platforms like Google, Amazon, Microsoft, and AI models.
Water & Natural Resources – Private control over essential elements for life.
Media & News – Consolidation of outlets leading to controlled narratives.
Culture & Entertainment – Hollywood, music labels, streaming services dictating what gets seen/heard.
Religion & Spirituality – Institutional control over belief systems and doctrine.
Infrastructure – Transportation, energy, housing turned into rent-seeking enterprises.
Health & Pharmaceuticals – Patents on life-saving drugs, private healthcare systems.
Security & Surveillance – Governments or corporations monopolizing control over privacy and policing.
The most fascinating things are those that exist across all three domains. Take knowledge—it can be weaponized through propaganda, tokenized through credentials, and monopolized by institutions that control access (paywalled journals, elite universities).
Show 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': ['Opportunity', 'Discovered', 'Solidified', 'Loss', "Trial", 'Error'], # Static
'Voir': ['Cool-Aid'],
'Choisis': ['Faith', 'Reason'],
'Deviens': ['Adversarial', 'Transactional', 'Hopeful'],
"M'èléve": ['Victory', 'Payoff', 'Loyalty', 'Charity', 'Distribution']
}
# Assign colors to nodes
def assign_colors():
color_map = {
'yellow': ['Cool-Aid'],
'paleturquoise': ['Error', 'Reason', 'Hopeful', 'Distribution'],
'lightgreen': ["Trial", 'Transactional', 'Payoff', 'Charity', 'Loyalty'],
'lightsalmon': ['Solidified', '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 {
('Opportunity', 'Cool-Aid'): '1/99',
('Discovered', 'Cool-Aid'): '5/95',
('Solidified', 'Cool-Aid'): '20/80',
('Loss', 'Cool-Aid'): '51/49',
("Trial", 'Cool-Aid'): '80/20',
('Error', 'Cool-Aid'): '95/5',
('Cool-Aid', 'Faith'): '20/80',
('Cool-Aid', 'Reason'): '80/20',
('Faith', 'Adversarial'): '49/51',
('Faith', 'Transactional'): '80/20',
('Faith', 'Hopeful'): '95/5',
('Reason', 'Adversarial'): '5/95',
('Reason', 'Transactional'): '20/80',
('Reason', 'Hopeful'): '51/49',
('Adversarial', 'Victory'): '80/20',
('Adversarial', 'Payoff'): '85/15',
('Adversarial', 'Loyalty'): '90/10',
('Adversarial', 'Charity'): '95/5',
('Adversarial', 'Distribution'): '99/1',
('Transactional', 'Victory'): '1/9',
('Transactional', 'Payoff'): '1/8',
('Transactional', 'Loyalty'): '1/7',
('Transactional', 'Charity'): '1/6',
('Transactional', 'Distribution'): '1/5',
('Hopeful', 'Victory'): '1/99',
('Hopeful', 'Payoff'): '5/95',
('Hopeful', 'Loyalty'): '10/90',
('Hopeful', 'Charity'): '15/85',
('Hopeful', 'Distribution'): '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 vs. Adaptation", fontsize=15)
plt.show()
# Run the visualization
visualize_nn()
Fig. 5 Veni-Vidi, Veni-Vidi-Vici. If you’re protesting then you’re not running fast enough. Thus spake the Red Queens#