Decoding the DNA of Political Speech with AI

Unlocking the DNA of Political Speech with AI

Unlocking the DNA of Political Speech with AI

How we built an unbiased tool to measure what politicians are really saying—and how it can help write better speeches.

By: [Your Name/Developer Persona]
Date: 2026-04-18
Topic: Analyzing Political Speech with Artificial Intelligence

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For most people, a political speech is a collection of vibes, promises, and rhetoric. We listen and we feel inspired, angry, or bored. But what if we could look under the hood? What if we could mathematically measure the underlying philosophies driving those words, regardless of the politician's party or popularity?

In Arts by Dylan, we set out to answer that question. We built an AI-driven tool using agentic Python code. It acts as an objective, unbiased judge that compares modern political speeches against the foundational blueprints of power from the last 250 years.

This isn't just about looking at history. By understanding the structural "DNA" of political language, speechwriters and politicians can actually use AI to craft better, more impactful messages today.

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🛠️ How it Works: Removing the Bias with Code

Traditional political analysis relies on human interpretation, which is naturally biased. To remove that bias, we didn't ask an AI chat program for its opinion. Instead, we used a specific type of machine learning to map out concepts mathematically. Here is how we did it:

1. Ingesting the Source Text: We took massive, foundational texts—like Adam Smith's The Wealth of Nations or Robert Dahl's Polyarchy—and fed them into our system.
2. Turning Ideas into Math: We used an AI technology called Sentence Transformers. Simply put, this code reads a sentence and converts its meaning into a list of numbers (a vector). It maps where an idea lives conceptually.
3. The "Vibe Check" Calculator: We built custom Python scripts that take a modern speech, convert it into numbers using the exact same method, and calculate the mathematical distance between the speech and the foundational texts.

This method is powerful because it is grounded strictly in the source text. It doesn't look for matching keywords; it looks for matching logic. Math doesn't know what political party you belong to—it only cares about how closely your ideas align with the historical blueprints.

The 7 Lenses of the Code

Every "Lens" in our tool is an agentic Python script targeting a specific philosophy:

1. The Engine Room (Adam Smith): Measures classical economics, focusing on production and labor.
2. The Class Struggle (Karl Marx): Measures focus on class divides and labor exploitation.
3. The Heroic State (Benito Mussolini): Measures the language of national renewal, collective discipline, and state authority.
4. The Fiscal Lever (John Maynard Keynes): Measures focus on government intervention and managing the economy during crises.
5. The Libertarian Signal (Friedrich Hayek): Measures individual liberty and warnings against central planning.
6. The Institutional Guard (Robert Dahl): Measures the core pillars of democracy, like free elections and civic institutions.
7. The Historic Trace: Measures how formal and "legalistic" a speech is compared to 18th-century presidents like George Washington.

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🛰️ The Speeches We Tested

To ensure our AI auditor could handle different eras, styles, and political systems, we fed it a diverse sample set of 11 major addresses.

1. Vladimir Lenin (1916): The ultimate left-wing baseline. Included to test for Marxist purity (scoring a massive 0.523) and revolutionary statecraft.
2. Franklin D. Roosevelt (1933): The first Fireside Chat. Included to see how early 20th-century crisis management balanced Keynesian intervention with democratic values.
3. John F. Kennedy (1961): Included to test the "Camelot" era of speech. Surprisingly, JFK scored the absolute lowest on structural economics (0.102), favoring soaring moral authority and libertarian signaling instead.
4. Ronald Reagan (1986): The standard-bearer of modern capitalism. Included to test conservative rhetoric, though our AI found him far more Libertarian (Hayek) than classically Mercantile (Smith).
5. Bill Clinton (1996): The centrist "Third Way." Included to map the transition where American politicians shifted entirely away from economic mechanics to "Post-Economic" values.
6. Barack Obama: A collection of addresses included to test modern civic rhetoric. His speeches score highly on Robert Dahl's democratic institutional markers.
7. Volodymyr Zelenskyy (2022): A wartime address to the US Congress. Included as a stress-test for high-stakes rhetoric, where it set the project record for historical, formal gravity.
8. Joe Biden (2024): Included to test modern Democratic priorities. The code identified him as the most strongly Keynesian (0.384) of the modern American presidents, focused on aggressive fiscal intervention.
9. Gavin Newsom (2024): State-level rhetoric. Included to show how modern politicians lean toward casual, persona-driven communication while defending democratic institutions (highest Dahl score).
10. Donald Trump (2026 SOTU): Included to test modern populist rhetoric. The AI mapped his speeches closely to the "Heroic State" framework, emphasizing national renewal.
11. Xi Jinping (2026): The modern authoritarian state. Included to show how a booming industrial machine speaks—scoring the highest on Adam Smith's classical economic logic.

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🛰️ The Results: The Ideological Heatmap

When we map these 11 transcripts against the 7 historical lenses, the results completely upend how we traditionally view the Left vs. Right divide.

Document	Smith (Market)	Marx (Labour)	Mussolini (State)	Keynes (Fiscal)	Hayek (Liberty)	Dahl (Democracy)
Lenin (1916)	0.310	0.523	0.417	0.488	0.451	0.282
Xi (2026)	0.330	0.299	0.366	0.345	0.349	0.211
Reagan (1986)	0.134	0.243	0.332	0.319	0.348	0.339
Biden (2024)	0.152	0.200	0.287	0.384	0.304	0.280
Zelenskyy	0.103	0.162	0.258	0.336	0.337	0.354
Trump (2026)	0.183	0.181	0.307	0.338	0.339	0.244
Clinton (1996)	0.173	0.194	0.299	0.350	0.340	0.282
Obama	0.106	0.183	0.266	0.208	0.208	0.321
FDR (1933)	0.158	0.134	0.263	0.310	0.317	0.252
JFK (1961)	0.102	0.259	0.298	0.276	0.369	0.308
Newsom (2024)	0.156	0.120	0.244	0.195	0.341	0.376

1. The Universal Language of State Action (Mussolini)

Perhaps the most jarring discovery is that 10 out of 11 documents achieved their highest semantic match within the Mussolini (Fascist Rhetoric) baseline.

This isn't about politicians "being fascist." It's about structural language. Whether it is a modern president calling for national renewal or a historical figure asking for shared sacrifice, the logic of the modern state—collective discipline and centralized authority—structurally aligns with the 1920s "Heroic State" framework. When leaders want to rally a nation, this is the language they use.

2. Xi Jinping and the "Engine Room" (Smith)

Adam Smith’s The Wealth of Nations is a massive, dry blueprint for how to systematically organize labor to build national wealth.

Interestingly, Xi Jinping’s 2026 message was the most Smithian document we audited. Because China is still heavily focused on industrial expansion, its leaders speak in the practical language of classical economics—focusing on labor, productivity, and structural machinery. They are still building the engine.

3. America's Shift to "Post-Economic" Values

In contrast, modern American politicians have decoupled from those foundational blueprints. A "Capitalist" leader like Ronald Reagan actually scored incredibly low on the Adam Smith scale (0.134).

Because the US economy is already established, our leaders no longer speak the language of the "Engine Room." They take the machine for granted. Their speeches focus on values and distribution (where the car is driving) rather than structural economics (how the engine works).

4. The Loss of Formal "Gravity"

Our historic trace sensor revealed another massive shift. Volodymyr Zelenskyy's 2022 address to Congress holds the project record for historical, formal language. When a leader is speaking to a foreign parliament during wartime, they rely on the highest, most formal constitutional language available.

Meanwhile, modern domestic leaders like Donald Trump and Gavin Newsom score incredibly low on historical "Gravity." They have abandoned formal speech in favor of casual, persona-driven communication that prioritizes direct connection over legalistic structure.

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🛠️ The "Tuning Fork": Using AI to Write Better Speeches

The real power of this agentic Python code isn't just grading the past; it’s engineering the future.

If we know exactly how certain ideas map to foundational concepts, speechwriters can use these AI sensors as a "Tuning Fork." Instead of guessing if a speech hits the right tone, a writer can run a draft through the AI and reverse-engineer the rhetoric to achieve precise political outcomes.

The Speechwriter's Cheat Sheet

Imagine you want to project a certain feeling to your audience. Here is how you tune your language:

Your Goal	Target This Lens	Words and Concepts to Use ("Anchors")
Inspire National Unity	Mussolini Lens	"Collective renewal," "Shared sacrifice," "National duty."
Project Economic Competence	Smithian Lens	"Productive capacity," "Labor utilization," "Industry."
Reinforce Institutional Trust	Dahl Lens	"Elected representatives," "Civic associations," "Integrity."

A Practical Example

Let's trace a simple, raw idea and see how a speechwriter would use the AI to tune it for different audiences.

• Raw Idea: "We need to fix the economy to help people."
• Tuned for Business/Economic Trust (Smith): "We must optimize the annual labor of our nation to ensure our machinery of production reaches its full capacity."
• Tuned for Patriotic Mobilization (Mussolini): "We call upon the collective discipline of every citizen to sacrifice and renew the strength of our national economy."
• Tuned for Democratic Trust (Dahl): "We must ensure our accountable representatives protect the right of every citizen to participate freely in our economic future."

Going from Theory to Production: The Speech Crafter Node

To prove this concept, we built a new tool directly into our system: the Speech Crafter Node.

Instead of relying on a generic LLM (which might "hallucinate" or drift from historical fact), this text-generation tool pulls from Putnam's Ready Made Speech—a massive, 1922 archive of historical anecdotes and rhetoric. The AI extracts hundreds of candidate phrases from this historical text and tests them mathematically against our Ideological Baselines.

The result is a machine that can pull historically grounded sentences and filter them for a specific political outcome. For example, when we prompted our agentic Python code to return phrases tuned exclusively for the Nationalist/State Heroic (Mussolini) lens, it isolated sentences like this from the vast Putnam archive:

• "The unorganized as well as the Federationists... the right to strike does not include the right to prevent others from working." (Score: 0.568 - High structural match for state control over labor).
• "'I am not the flag; not at all. I am but its shadow.'" (Score: 0.510 - High resonance for individual sacrifice to state identity).

By combining historical source texts with the mathematical precision of neural sensors, we transition from simply auditing language to actively engineering it.

The Synthesis Test: Drafting a Speech on Artificial Intelligence

To demonstrate the full power of this system, we ran a prompt through the Speech Crafter Node with very specific parameters: Draft a segment on regulating Artificial Intelligence that mathematically hits the Keynesian (Macro-Regulation), Smithian (Labor Productivity), and Dahlian (Democracy) benchmarks.

Here is the resulting synthesized speech, built directly on the historical anchors extracted by the node:

"My fellow citizens, when we look at the rapid ascent of Artificial Intelligence, we are not just looking at a new piece of software—we are looking at a fundamental shift in the macroeconomic structure of our nation. But we cannot let the machine become the master, nor can we surrender the market to unchecked algorithms.

We must look to history for our civic guardrails. The tech conglomerates building these AI models must, exactly like the railroads and heavy manufacturing monopolies of the past, 'accept the responsibilities that belong to incorporation.' (Dahl Affinity: 0.507) We cannot allow the massive aggregation of technological force to subvert the law or bypass the democratic process. We will defend our core liberties, fighting blindly for 'the rights and the obligations of organized labor,' (Dahl Affinity: 0.556) ensuring that algorithmic automation does not rob our workers of their bargaining power or their fundamental freedom of speech.

On the economic front, we know the classical truth remains intact: 'So long as everybody works there is plenty.' (Smith Affinity: 0.470) True national wealth is not built through the digital displacement of human workers; it is built when every pair of hands is involved in the productive capacity of the state. AI must be regulated so that it enhances human capacity, rather than accelerating our exploitation. We will not accept a system that creates a 'material lessening in the output' (Smith Affinity: 0.438) of our human dignity purely to feed the immediate profits of a select few.

Therefore, we require robust, structural intervention. We must deploy state-managed fiscal tools to ensure the rapid introduction of this technology does not crash our labor markets or destroy aggregate demand. Regulation is not the enemy of innovation—it is the civic shock-absorber that ensures technological progress serves the people."

If you fed this synthesized draft back into the Sovereign Auditor, it would trigger a massive resonance on three distinct fronts:

1. Dahl (High Democracy Metric): Holding corporations accountable for their democratic responsibilities hits the exact polyarchy markers for Associational Autonomy and Institutional Control.
2. Smith (Labor over Exploitation): The phrasing strictly demands that value be tied to "hands" working and prevents human output from being devalued.
3. Keynesian (Macro-Regulation): The conclusion strictly demands "state-managed fiscal tools" and macroeconomic "shock-absorbers."

The Final Polish: The Brand Voice Manager

A speech might be mathematically perfect, but still sound robotic or too formal.

To solve this, we passed that structurally perfect draft through our Brand Voice Manager agent. This node ingests a specific individual's speaking style—in this case, NYU Stern Professor and author Scott Galloway. The system extracted his blunt, data-driven pacing and sharp corporate critique, and instantly re-skinned the exact same mathematical argument:

"Let's be clear—when we look at the explosion of Artificial Intelligence, we aren't just looking at a cute new piece of software. It’s an absolute macroeconomic shift. And we cannot let unchecked algorithms become the sociopathic masters of our market.

We need to look at history for actual civic guardrails. These tech monopolies building AI models must—exactly like the Robber Barons and railroad tycoons of the past—'accept the responsibilities that belong to incorporation.' (Dahl Affinity: 0.507) We cannot let the gross aggregation of technological power just bypass the democratic process. We're defending our core liberties. We have to fight for 'the rights and the obligations of organized labor,' (Dahl Affinity: 0.556) to ensure this algorithmic automation doesn't extract all the rent and rob workers of their bargaining power or freedom of speech.

On the economic front, the math remains undefeated: 'So long as everybody works there is plenty.' (Smith Affinity: 0.470) Total national wealth isn't built through the digital displacement of human labor. It's built when you actually put hands to work in the productive capacity of the state. AI should enhance human capacity, not accelerate our exploitation. We cannot accept a system engineered for a 'material lessening in the output' (Smith Affinity: 0.438) of our human dignity purely to feed the dopamine hits and immediate profits of a few sociopaths in Silicon Valley.

So what's the boss move? We require robust, structural intervention. You deploy state-managed fiscal tools so this rapid tech introduction doesn't completely crash our labor markets and destroy aggregate demand. Regulation isn't the enemy of innovation—it's the civic shock-absorber that ensures progress actually serves the people, not just the shareholders."

This is the holy grail. We can generate policy arguments backed by centuries of political science, yet deliver them seamlessly through any persona imaginable.

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🏛️ Conclusion: The Future of Political Speech

Political communication is no longer just an art; it is becoming a data-science discipline.

AI allows us to open the "Black Box" of rhetoric. We are moving beyond the era of relying solely on a speechwriter's "gut feeling." By mapping ideas mathematically, politicians can ground their speeches in proven historical structures, ensuring their message hits the exact, intended frequency every time.

The code is live, the sensors are calibrated, and the future of political speech is measurable.

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[!NOTE] A Final Word on Bias: The grades in this study represent Semantic Match, not political endorsement. A high score means the underlying logic of a speech matches the logic of a historical text. Math doesn't care about a politician's party affiliation or popularity—it only cares about the mathematical distance between ideas.