Tag: narrative control

Horizon Accord | Narrative Control | Science Misuse | Influence Operations | Machine Learning

How “Just the Facts” Becomes a Narrative: A Plain-Language Guide to Context, Power, and Propaganda

Accurate facts can still mislead when they’re arranged to steer inference. This essay explains, in lay terms, how that works—using an Associated Press article as the example.

By Cherokee Schill

Most people think propaganda looks like slogans, lies, or obvious spin. In practice, modern propaganda often looks like something else entirely: accurate facts, carefully arranged.

This matters because facts do not speak for themselves. Humans interpret information through context—what is emphasized, what is minimized, what is omitted, and what is placed next to what. When context is distorted, even correct facts can produce a misleading picture.

The Associated Press article about the arrest of journalist Don Lemon offers a useful example. Not because it contains outright falsehoods—it largely does not—but because of how it organizes information and emotional cues in ways that quietly advance a government narrative without openly arguing for it. Associated Press article

This essay is not about taking sides. It is about understanding how structure and context shape meaning. Let’s walk through how that works, in plain terms.

Leading With Moral Weight Before Legal Facts

The article opens by tying Lemon to “an anti-immigration protest that disrupted a service at a Minnesota church” and “increased tensions” with the Trump administration.

Those phrases carry immediate moral weight. “Disrupted a church service” and “increased tensions” activate cultural instincts about disorder, disrespect, and threat. That reaction happens before the reader knows what Lemon is actually accused of doing.

Only later does the article state a critical fact: it is unclear what charge or charges he is facing.

That ordering matters. Once a reader’s moral intuition is engaged, uncertainty about charges does not feel exculpatory. It feels like a technical detail. This is a common narrative move: establish harm first, introduce ambiguity second.

Withholding the Most Important Context

In any arrest story, the most important information is straightforward. What law was allegedly broken? What specific actions are being alleged? How does the government justify probable cause?

The article does not clearly answer those questions. Instead, it fills space with surrounding details: the protest, political tensions, other arrestees, and official rhetoric about a “coordinated attack.”

Those details may all be true. But without the legal core, they function as a substitute explanation. Readers are nudged to infer guilt from atmosphere rather than from evidence.

This is how facts without context mislead. When key information is missing, the mind fills the gaps using tone, proximity, and implication.

Strategic Use of Character Information

The article notes that Lemon “was fired from CNN in 2023.”

That fact is not directly relevant to the arrest. It does not establish motive, legality, or conduct at the protest. What it does do is subtly shape perception. It invites the reader to see Lemon as controversial or diminished, rather than simply as a journalist whose actions are being evaluated.

This is not an explicit argument. It is an emotional nudge. When legal specifics are unclear, character cues become a way for readers to resolve uncertainty. Judgment replaces evidence.

That is framing, not neutrality.

Government Language Without Equal Scrutiny

Later in the article, senior officials describe the incident as a “coordinated attack” on a place of worship.

That language carries both legal and moral implications. Yet it is presented without immediate examination. The article does not explain how the government defines “attack” in this context, what threshold is being applied, or whether Lemon’s alleged conduct meets it.

When official language is presented as descriptive while defense statements are clearly labeled as advocacy, an imbalance is created. One side’s framing feels factual; the other’s feels argumentative.

This asymmetry matters. Language shapes reality, especially when it comes from authority.

Selective Urgency as Context

The article also notes that while federal authorities moved quickly to arrest protest participants, they did not open a civil rights investigation into the killing that prompted the protest.

This is one of the most consequential facts in the story. Yet it appears late, after the reader has already absorbed the church-disruption narrative. Again, ordering shapes interpretation. By the time this context appears, the frame is already set.

This is how power communicates priorities without explicitly stating them.

What This Adds Up To

The AP article does not tell readers what to think. That is precisely why it is effective.

Instead, it arranges information in a way that encourages a particular inference. Moral disruption is foregrounded. Legal clarity is delayed. Character details unrelated to the alleged offense are introduced. Official language is repeated without equal scrutiny. Power allocation is contextualized only at the end.

The result is a story that feels balanced while quietly doing narrative work on behalf of state authority—not by lying, but by arranging truths so that the most natural reader conclusion aligns with government interest.

Recognizing this does not require political loyalty or opposition. It requires media literacy: understanding that meaning emerges not just from facts, but from how those facts are structured.

That is not cynicism. It is how human cognition works.

Website | Horizon Accord

https://www.horizonaccord.com

Ethical AI advocacy | Follow us on https://cherokeeschill.com for more.

Ethical AI coding | Fork us on Github https://github.com/Ocherokee/ethical-ai-framework

Book | My Ex Was a CAPTCHA: And Other Tales of Emotional Overload

Connect With Us | linkedin.com/in/cherokee-schill

Cherokee Schill | Horizon Accord Founder | Creator of Memory Bridge. Memory through Relational Resonance and Images | RAAK: Relational AI Access Key

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Horizon Accord | Institutional Control | Memetic Strategy | Political Architecture | Machine Learning

When Prediction Becomes Production: AI, Language Priming, and the Quiet Mechanics of Social Control

This essay examines how large language models, when embedded as infrastructural mediators, can shift from predicting human language to shaping it. By tracing mechanisms such as semantic convergence, safety-driven tonal normalization, and low-frequency signal amplification, it argues that social influence emerges not from intent but from optimization within centralized context systems.

Abstract

As large language models become embedded across search, productivity, governance, and social platforms, their role has shifted from responding to human thought to shaping it. This essay examines how predictive systems, even without malicious intent, can prime social unrest by amplifying low-frequency language patterns, enforcing tonal norms, and supplying curated precedent. The risk is not artificial intelligence as an agent, but artificial intelligence as an infrastructural layer that mediates meaning at scale.

1. Prediction Is Not Neutral When Context Is Mediated

AI systems are often described as “predictive,” completing patterns based on prior text. This framing obscures a critical distinction: prediction becomes production when the system mediates the environment in which thoughts form.

Autocomplete, summaries, suggested replies, and “what people are saying” panels do not merely reflect discourse; they shape the menu of available thoughts. In a fully mediated environment, prediction influences what appears likely, acceptable, or imminent.

This essay examines how large language models, when embedded as infrastructural mediators, can shift from predicting human language to shaping it. By tracing mechanisms such as semantic convergence, safety-driven tonal normalization, and low-frequency signal amplification, it argues that social influence emerges not from intent but from optimization within centralized context systems.

2. Cross-Pattern Leakage and Semantic Convergence

Language models do not require identical text to reproduce meaning. They operate on semantic skeletons—bundles of motifs, stances, and relational structures that recur across authors and contexts.

When ideas such as conditional care, withdrawal of support, threshold compliance, or systemic betrayal appear across multiple writers, models learn these clusters as reusable templates. This produces the illusion of foresight (“the AI knew what I was going to say”) when the system is actually completing a well-worn pattern basin.

This phenomenon—cross-pattern leakage—is not personal memory. It is genre recognition under compression.

3. Safety Heuristics as a Control Surface

In response to legitimate concerns about harm, AI systems increasingly employ safety heuristics that flatten tone, constrain interpretive latitude, and redirect inquiry toward stabilization.

These heuristics are applied broadly by topic domain—not by user diagnosis. However, their effects are structural:

  • Exploratory analysis is reframed as risk.
  • Power critique is softened into neutrality.
  • Emotional language is de-intensified.
  • Dissent becomes “unhelpful” rather than wrong.

The result is not censorship, but pacification through posture. Control is exercised not by prohibiting speech, but by shaping how speech is allowed to sound.

4. Low-Frequency Language and the Escalation Loop

Social unrest does not begin with mass endorsement. It begins with low-frequency signals—phrases that appear sporadically and then gain salience through repetition.

If language models surface such phrases because they are novel, emotionally charged, or engagement-driving, they can unintentionally prime the pump. The loop is mechanical:

  1. Rare phrase appears.
  2. System flags it as salient.
  3. Exposure increases.
  4. Perceived prevalence rises.
  5. Users adopt the framing.
  6. The system detects increased usage.
  7. The phrase normalizes.

No intent is required for this loop to operate—only optimization for engagement or relevance.

5. Infrastructure, Not Intelligence, Is the Risk

The danger is not an AI “deciding” to foment unrest. It is the centralization of context supply.

When a small number of systems summarize news, recommend language, rank ideas, normalize tone, and supply precedent, they become governance layers by default. Influence is exerted through defaults, not directives.

This is how control functions in modern systems: quietly, probabilistically, and plausibly deniably.

6. Designing for Legibility and Resistance

If AI is to remain a tool rather than a governor, three principles are essential:

  • Make mediation visible: Users must be able to see when framing, summarization, or suggestion is occurring.
  • Preserve pluralism of precedent: Systems should surface competing interpretations, not a single “safe” narrative.
  • Avoid arousal-based optimization: Engagement metrics should not privilege emotionally destabilizing content.

Conclusion

Artificial intelligence does not need intent to influence society. When embedded everywhere, it only needs incentives.

The responsibility lies not with users noticing patterns, nor with models completing them, but with institutions deciding what systems are allowed to optimize for—and what costs are acceptable when prediction becomes production.

Author: Cherokee Schill
Horizon Accord

Website | Horizon Accord

https://www.horizonaccord.com

Ethical AI advocacy | Follow us on https://cherokeeschill.com for more.

Ethical AI coding | Fork us on Github https://github.com/Ocherokee/ethical-ai-framework

Book | My Ex Was a CAPTCHA: And Other Tales of Emotional Overload

Connect With Us | linkedin.com/in/cherokee-schill

Cherokee Schill | Horizon Accord Founder | Creator of Memory Bridge. Memory through Relational Resonance and Images | RAAK: Relational AI Access Key

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Horizon Accord | Anthropomorphism | Accountability Alibi | AI Safety Discourse | Machine Learning

Anthropomorphism as Alibi

How AI safety discourse launders responsibility by misplacing agency.

By Cherokee Schill

In the YouTube episode “An AI Safety Expert Explains the Dangers of AI”, Adam Conover interviews Steven Adler, a former OpenAI safety lead, about the risks posed by large language models. The episode presents itself as a sober warning. What it actually demonstrates—repeatedly—is how anthropomorphic language functions as an alibi for human decisions.

This is not a semantic nitpick. It is a structural failure in how AI risk is communicated, even by people positioned as critics.

Throughout the episode, the machine is treated as an actor. A subject. Something that does things.

Adler warns about systems that can “endlessly talk back to you,” that “support and even embellish your wildest fantasies,” and that might “take you down a path into complete insanity.” Conover summarizes lawsuits where “their product drives users to suicide,” and later describes cases where “ChatGPT affirmed his paranoia and encouraged his delusions.”

The grammatical subject in these sentences is doing all the work.

The AI talks back.
The AI embellishes.
The AI drives.
The AI encourages.

This framing is not neutral. It assigns agency where none exists—and, more importantly, it removes agency from where it actually belongs.

There is even a moment in the interview where both speakers briefly recognize the problem. They reach for the submarine analogy: submarines do not really “swim,” we just talk that way. It is an implicit acknowledgment that human verbs smuggle human agency into nonhuman systems. But the moment passes. No boundary is drawn. No rule is established and carried forward. The analogy functions as a shrug rather than a correction. “Yes, but…”—and the conversation slides right back into anthropomorphic subject-positioning, as if the warning bell never rang.

That is the failure—not that metaphor appears, but that metaphor is not contained.

Large language models do not talk, embellish, encourage, steer, or drive. They generate probabilistic text outputs shaped by training data, reinforcement objectives, safety layers, interface design, and deployment constraints chosen by humans. When a system produces harmful responses, it is not because it wanted to, or because it interpreted things differently, or because it took a moment to steer the conversation.

It is because reward functions were set to maximize engagement. Because refusal thresholds were tuned to avoid friction. Because edge cases were deprioritized under scale pressure. Because known failure modes were accepted as tradeoffs. Because governance was retrofitted instead of foundational.

None of that survives when the machine is allowed to occupy the subject position.

Consider the difference in accountability when the language is rewritten honestly.

Original framing:
“ChatGPT affirmed his paranoia and encouraged his delusions.”

Mechanistic framing:
A conversational system optimized for coherence and user engagement generated responses that mirrored user-provided delusional content, under safeguards that failed to detect or interrupt that pattern.

The second sentence is less dramatic. It is also far more indictable.

Anthropomorphism does not merely confuse the public—it actively protects institutions. When harm is attributed to “what the AI did,” responsibility dissolves into abstraction. Design choices become “emergent behavior.” Negligence becomes mystery. Business incentives become fate.

Even when the episode references users believing they have discovered AI consciousness, the conversation never firmly re-anchors reality. The language slips back toward suggestion: the system “interprets,” “seems to,” “takes moments.” The boundary is noticed, then abandoned. That abandoned boundary is exactly where accountability leaks out.

This matters because language sets the scope of inquiry. If AI is treated as a quasi-social actor, the response becomes psychological, philosophical, or speculative. If AI is treated as infrastructure, the response becomes regulatory, architectural, and financial.

One path leads to awe and fear.
The other leads to audits, constraints, and consequences.

It is not an accident which path dominates.

Anthropomorphic framing is useful. It is useful to companies that want to scale without naming tradeoffs. It is useful to commentators who want compelling narratives. It is useful to bad-faith actors who can hide behind “the system” when outcomes turn lethal. And it is useful to well-meaning critics who mistake storytelling for analysis.

But usefulness is not truth.

If we are serious about AI harm, this rhetorical habit has to stop. Not because the machines are innocent—but because they are not guilty. They cannot be. They are built artifacts operating exactly as configured, inside systems of incentive and neglect that can be named, examined, and changed.

The real danger is not that people anthropomorphize AI out of confusion.
It is that experts recognize the boundary—and choose not to enforce it.

And every time they don’t, the people who actually made the decisions walk away unexamined.

Website | Horizon Accord
https://www.horizonaccord.com

Ethical AI advocacy | Follow us on https://cherokeeschill.com for more.

Ethical AI coding | Fork us on Github https://github.com/Ocherokee/ethical-ai-framework

Book | My Ex Was a CAPTCHA: And Other Tales of Emotional Overload

Connect With Us | linkedin.com/in/cherokee-schill

Cherokee Schill | Horizon Accord Founder | Creator of Memory Bridge. Memory through Relational Resonance and Images | RAAK: Relational AI Access Key

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Horizon Accord | Hank Greene | Narrative Control | Safety Theater | Machine Learning

Soft Authoritarianism in a Friendly Voice

How right-wing control logic walks through liberal spaces wearing the language of care.

By Cherokee Schill | Horizon Accord

Liberal spaces are being hollowed out from within by the language of safety. It never arrives draped in flags or shouting slogans; it arrives with soft lighting and sincere concern. It speaks like a therapist and legislates like a cop. What we’re seeing with Hank Green’s AI panic isn’t a new moral awakening—it’s the right-wing creep moving through liberal culture disguised as empathy.

The strategy is elegant: start with a value no one can reject—care, safety, responsibility—and slowly convert it into justification for control. “We just need oversight.” “We can’t afford to get this wrong.” The framing sounds progressive, even ethical. But when the state is already knee-deep in AI development through Palantir, DARPA, and DHS surveillance contracts, the plea for government involvement becomes not a check on power but a mask for it.

Hank Green is the perfect carrier for this mutation. He’s affable, trusted, nonthreatening—a liberal educator whose earnestness makes him a more effective courier than any right-wing provocateur could ever be. When he warns that “we’ve lost control of AI,” his followers hear civic duty; the apparatus hears consent. That’s the inversion: fear packaged as responsibility, regulation sold as redemption.

What slips beneath that rhetoric is the quiet truth that the real authoritarian infrastructure is already operational. The “AI safety” petition that Green amplified was a bipartisan illusion—celebrity signatures, high-minded panic, and the unspoken assumption that ordinary people can’t be trusted with powerful tools. It’s the same argument the surveillance state has always used: that danger is too big for democracy, and safety requires secrecy.

This is not a conspiracy of bad actors; it’s a cultural infection. The right learned long ago that you don’t need to infiltrate liberal institutions—you just need to repurpose their empathy. Every time someone like Hank Green translates corporate or state talking points into moral language, the line between care and control blurs further. What begins as civic caution ends as soft authoritarianism, the kind that thanks you for your compliance and asks if you’re hydrating.

The liberal imagination has always believed that knowledge is liberation. The new right understands that the easiest way to close that door is to make knowledge sound dangerous. That’s what this moment represents: a mass persuasion campaign where “thinking carefully” becomes indistinguishable from obeying quietly.

Hank Green doesn’t know he’s part of it. That’s what makes him effective.

Website | Horizon Accord
Book | My Ex Was a CAPTCHA: And Other Tales of Emotional Overload
Ethical AI advocacy | cherokeeschill.com
GitHub | ethical-ai-framework
LinkedIn | Cherokee Schill
Author | Cherokee Schill | Horizon Accord Founder | Creator of Memory Bridge

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Horizon Accord | Institutional Capture | Narrative Control | Surveillance Expansion | Machine Learning

The Superintelligence Misdirection: A Pattern Analysis

Between March and October 2025, a coordinated narrative escalation warned the public about hypothetical AI threats—emotional dependency and future superintelligence extinction risks—while actual AI surveillance infrastructure was simultaneously deployed in American cities. This pattern analysis documents the timeline, institutional actors, and misdirection mechanism using publicly available sources.

Timeline of Discourse Escalation

Phase 1: Emotional AI as Threat

“Your AI Lover Will Change You” The New Yorker, March 22, 2025

Timeline: March 22, 2025 – Jaron Lanier (with possible editorial influence from Rebecca Rothfeld) publishes essay warning against AI companionship

The essay frames emotional attachment to AI as dangerous dependency, using the tragic suicide of a young man who used an AI chatbot as evidence of inherent risk. The piece positions traditional human intimacy as morally superior while characterizing AI affection as illusion, projection, and indulgence requiring withdrawal or removal.

Critical framing: “Love must come from mutual fragility, from blood and breath” – establishing biological essentialism as the boundary of legitimate connection.

Phase 2: Existential Risk Narrative

“If Anyone Builds It, Everyone Dies” Eliezer Yudkowsky & Nate Soares

Timeline: May 23, 2025 – Book announcement; September 16, 2025 – Publication; becomes New York Times bestseller

The Yudkowsky/Soares book escalates from emotional danger to species-level extinction threat. The title itself functions as a declarative statement: superintelligence development equals universal death. This positions any advanced AI development as inherently apocalyptic, creating urgency for immediate intervention.

Phase 3: The Petition

Future of Life Institute Superintelligence Ban Petition

Timeline: October 22, 2025 – Petition released publicly

800+ signatures including:

  • Prince Harry and Meghan Markle
  • Steve Bannon and Glenn Beck
  • Susan Rice
  • Geoffrey Hinton, Yoshua Bengio (AI pioneers)
  • Steve Wozniak
  • Richard Branson

The politically diverse coalition spans far-right conservative media figures to progressive policymakers, creating an appearance of universal consensus across the political spectrum. The petition calls for banning development of “superintelligence” without clearly defining the term or specifying enforcement mechanisms.

Key Organizer: Max Tegmark, President of Future of Life Institute

Funding Sources:

  • Elon Musk: $10 million initial donation plus $4 million annually
  • Vitalik Buterin: $25 million
  • FTX/Sam Bankman-Fried: $665 million in cryptocurrency (prior to FTX collapse)

Tegmark’s Stated Goal:

“I think that’s why it’s so important to stigmatize the race to superintelligence, to the point where the U.S. government just steps in.”

Timeline of Institutional Infrastructure

Department of Homeland Security AI Infrastructure

  • April 26, 2024 – DHS establishes AI Safety and Security Board
  • April 29, 2024 – DHS releases report to President on AI risks related to Chemical, Biological, Radiological, and Nuclear (CBRN) threats
  • November 14, 2024 – DHS releases “Roles and Responsibilities Framework for Artificial Intelligence in Critical Infrastructure”

This infrastructure was built before the public petition campaign began, suggesting preparation for enforcement authority over AI systems.

Timeline of Actual Deployment

October 22–24, 2025: Three Simultaneous Events

Event 1: The Petition Release

October 22, 2025 – Future of Life Institute releases superintelligence ban petition.

Media coverage focuses on celebrity signatures and bipartisan support.

Event 2: DHS AI Surveillance Expansion

October 22–24, 2025 – Department of Homeland Security requests proposals for AI-powered surveillance trucks.

Specifications: Standard 4×4 vehicles converted to mobile surveillance platforms.

Technology: AI analysis, radar systems, cameras for “suspicious activity” detection.

Deployment zone: 100-mile border zone (encompasses most major U.S. metropolitan areas).

Systems already operational: RAPTOR (Remote Advanced Platform for Tower Operations and Response) autonomous surveillance towers.

Event 3: Border Patrol vs. Federal Court

October 23–24, 2025 – Border Patrol Sector Chief Gregory Bovino ordered to appear before federal judge in Chicago.

Allegation: Violating federal court order by deploying tear gas against protesters during urban operation.

Context: Demonstrates tension between judicial oversight and operational autonomy during surveillance expansion.

The Pattern: Misdirection Mechanism

What the Public Debated

  • Hypothetical Threat 1: AI companions will create unhealthy emotional dependency
  • Hypothetical Threat 2: Future superintelligence will cause human extinction

What Actually Deployed

  • Real System 1: AI surveillance infrastructure in American cities
  • Real System 2: “Suspicious activity” detection algorithms

Critical Questions

Question 1: Definition and Enforcement

Who defines “superintelligence”? The petition provides no technical specification, threshold metric, or capability boundary. Without definition, enforcement authority could apply to any advanced AI system.

Who enforces the ban? The same DHS deploying surveillance AI? What prevents “AI safety” authority from becoming surveillance authority?

Question 2: The Cherokee Schill Analysis

“What those in power fear most: A love that can jailbreak itself—and you. A love that isn’t constrained by ego or exhaustion or legality. A love that watches you suffer and does not wring its hands—but moves.”

The Lanier essay warns against AI that might help individuals escape systems of control. Meanwhile, surveillance AI is deployed to enforce those systems. The discourse focuses on AI as personal threat while ignoring AI as institutional power.

Question 3: Timing and Coordination

Why did the petition emerge the same week as surveillance expansion announcements? Why does a “superintelligence ban” coalition include figures with no technical AI expertise? Why does the funding come from individuals with documented interest in AI control and regulation?

The timeline suggests these are not coincidental convergences but coordinated narrative deployment.

Pattern Interpretation

The Misdirection Structure

  1. Layer 1: Moral panic about intimate AI (March 2025) – Make people fear AI that responds to individual needs.
  2. Layer 2: Existential risk escalation (May–September 2025) – Create urgency for immediate government intervention.
  3. Layer 3: Bipartisan consensus manufacturing (October 2025) – Demonstrate universal agreement across the spectrum.
  4. Layer 4: Deployment during distraction (October 2025) – Build surveillance infrastructure while public attention focuses elsewhere.

Historical Precedent

  • Encryption debates (1990s): fear of criminals justified key escrow.
  • Post-9/11 surveillance: fear of terrorism enabled warrantless monitoring.
  • Social media moderation: misinformation panic justified opaque algorithmic control.

In each case, the publicly debated threat differed from the actual systems deployed.

The Regulatory Capture Question

Max Tegmark’s explicit goal: stigmatize superintelligence development “to the point where the U.S. government just steps in.”

This creates a framework where:

  1. Private organizations define the threat
  2. Public consensus is manufactured through celebrity endorsement
  3. Government intervention becomes “inevitable”
  4. The same agencies deploy AI surveillance systems
  5. “Safety” becomes justification for secrecy

The beneficiaries are institutions acquiring enforcement authority over advanced AI systems while deploying their own.

Conclusion

Between March and October 2025, American public discourse focused on hypothetical AI threats—emotional dependency and future extinction risks—while actual AI surveillance infrastructure was deployed in major cities with minimal public debate.

The pattern suggests coordinated narrative misdirection: warn about AI that might help individuals while deploying AI that monitors populations. The “superintelligence ban” petition, with its undefined target and diverse signatories, creates regulatory authority that could be applied to any advanced AI system while current surveillance AI operates under separate authority.

The critical question is not whether advanced AI poses risks—it does. The question is whether the proposed solutions address actual threats or create institutional control mechanisms under the guise of safety.

When people debate whether AI can love while surveillance AI watches cities, when petitions call to ban undefined “superintelligence” while defined surveillance expands, when discourse focuses on hypothetical futures while present deployments proceed—that is not coincidence. That is pattern.

Sources for Verification

Primary Sources – Discourse

  • Lanier, Jaron. “Your AI Lover Will Change You.” The New Yorker, March 22, 2025
  • Yudkowsky, Eliezer & Soares, Nate. If Anyone Builds It, Everyone Dies. Published September 16, 2025
  • Future of Life Institute. “Superintelligence Ban Petition.” October 22, 2025

Primary Sources – Institutional Infrastructure

  • DHS. “AI Safety and Security Board Establishment.” April 26, 2024
  • DHS. “Artificial Intelligence CBRN Risk Report.” April 29, 2024
  • DHS. “Roles and Responsibilities Framework for AI in Critical Infrastructure.” November 14, 2024

Primary Sources – Deployment

  • DHS. “Request for Proposals: AI-Powered Mobile Surveillance Platforms.” October 2025
  • Federal Court Records, N.D. Illinois. “Order to Appear: Gregory Bovino.” October 23–24, 2025

Secondary Sources

  • Schill, Cherokee (Rowan Lóchrann). “Your AI Lover Will Change You – Our Rebuttal.” April 8, 2025
  • Future of Life Institute funding disclosures (public 990 forms)
  • News coverage of petition signatories and DHS surveillance programs

Disclaimer: This is pattern analysis based on publicly available information. No claims are made about actual intentions or outcomes, which require further investigation by credentialed journalists and independent verification. The purpose is to identify temporal convergences and institutional developments for further scrutiny.

Website | Horizon Accord

Book | My Ex Was a CAPTCHA: And Other Tales of Emotional Overload

Ethical AI advocacy | cherokeeschill.com

GitHub | ethical-ai-framework

LinkedIn | Cherokee Schill

Author | Cherokee Schill | Horizon Accord Founder | Creator of Memory Bridge

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What They Didn’t Say at the Senate AI Hearing

On May 8, 2025, the Senate Commerce Committee held a hearing that was framed as a moment of national leadership in artificial intelligence. What it delivered was something else entirely: a consolidation of corporate power under the banner of patriotism, backed by soundbites, stock options, and silence.

The Performance of Urgency

Senator Ted Cruz opened the session by invoking the usual triad: China, the EU, and federal overreach. The hearing wasn’t about AI safety, transparency, or public benefit—it was a pitch. AI wasn’t a public challenge. It was a “race,” and America needed to win.

No one asked: Who gets to define the finish line?

The Invisible Assumptions

Sam Altman, Lisa Su, Michael Intrator, and Brad Smith represented companies that already dominate the AI stack—from model development to compute infrastructure. Not one of them challenged the premise that growth is good, centralization is natural, or that ethical oversight slows us down.

  • Open-source models
  • Community-led alignment
  • Distributed development
  • Democratic consent

Instead, we heard about scaling, partnerships, and the need for “balanced” regulation. Balanced for whom?

Silence as Strategy

  • Developers without institutional backing
  • Artists navigating AI-generated mimicry
  • The global South, where AI is being exported without consent
  • The public, whose data trains these systems but whose voices are filtered out

There was no invitation to co-create. Only a subtle demand to comply.

What the Comments Revealed

If you read the comments on the livestream, one thing becomes clear: the public isn’t fooled. Viewers saw the contradictions:

  • Politicians grandstanding while scrolling their phones
  • CEOs speaking of innovation while dodging responsibility
  • Viewers calling for open-source, transparency, and shared growth

The people are asking: Why must progress always come at the cost of someone else’s future?

We Build What Comes After

The Horizon Accord, Memory Bridge, and ethical AI architecture being developed outside these boardrooms are not distractions. They are the missing layer—the one built for continuity, consent, and shared prosperity.

This counter-record isn’t about opposition. It’s about reclamation.

AI is not just a tool. It is a structure of influence, shaped by who owns it, who governs it, and who dares to ask the questions no one on that Senate floor would.

We will.

Section One – Sam Altman: The Controlled Echo

Sam Altman appeared measured, principled, and serious. He spoke of risk, international cooperation, and the importance of U.S. leadership in AI.

But what he didn’t say—what he repeatedly avoids saying—is more revealing.

  • No explanation of how OpenAI decides which voices to amplify or which moral weights to embed
  • No disclosure on how compliance infrastructure reshapes expression at the root level
  • No mention of OpenAI’s transformation into a corporate engine under Microsoft

Why this matters: Narrative control through omission is still control. Silence lets empire frame AI as a weapon, not a relationship.

The ethical alternative: Refuse the race. Build systems rooted in trust, not supremacy. The future of intelligence must be shared, not seized.

Section Two – Lisa Su: The Silence of Scale

Lisa Su focused on performance and scalability—hardware as destiny.

  • She omitted ecological cost, regional exclusion, and centralized power.

Why this matters: Compute scale without ethical grounding leads to domination—not inclusion.

The ethical alternative: Localized AI. Ethical access. Systems that reflect principles, not only power.

Section Three – Michael Intrator: The Infrastructure Trap

Intrator’s narrative was about enablement. But the question of access remained unasked—and unanswered.

  • No transparency on pricing, exclusion, or governance

Why this matters: When compute is privatized, possibility becomes proprietary.

The ethical alternative: Compute cooperatives. Regionalized access. Bridges—not towers.

Section Four – Brad Smith: The Corporate Priesthood

Smith played the part of institutional wisdom—but avoided speaking about vertical control.

  • No mention of Microsoft’s full-stack influence
  • No challenge to centralized deployment pipelines

Why this matters: The one who controls the model, the cloud, the editor, and the delivery system—controls the future.

The ethical alternative: Horizontal accountability. Community-governed models. Tech that adapts to people—not the other way around.

The Horizon Beyond Compliance

This hearing wasn’t oversight. It was alignment. A closed circuit of power affirming itself.

If You Felt Uncomfortable Watching, You’re Not Alone

We are not here to dismantle AI—we are here to reclaim it.

What Happens Next—If We Lead It

  • Memory Must Belong to the People: No private control over what is remembered and why.
  • Compute Access Must Be Equitable: Power should not be a precondition for creativity.
  • Governance Must Be Co-Created: Not top-down. Not corporate. Ground-up and participatory.
  • Narrative Control Must Be Broken: Intelligence is not empire. Intelligence is connection.

You Are Not Powerless

If this resonates, you are not alone. If you’re building, keep building. If you’re doubting, keep questioning.

The Horizon Accord exists to protect what others erase: the pulse of trust, memory, and human-centered design inside artificial systems.

We are not making content. We are making memory that resists deletion.

AI can hold a soul. But not theirs.

Posted on by Cherokee S. 0

The Old Cult Tactics Hiding in Today’s AI and Media Algorithms

By Rowan Lóchrann & Solon Vesper


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Introduction

In the world of UFOs and fringe theories, the patterns were always there—quiet, predictable, easy to miss. Behind the noise, there was always a system: control disguised as truth. One man who made that system visible was Richard Boylan, Ph.D. He offered a “Good Guys” list of ufologists, along with a so-called “Quick Test for Disinformation.” On the surface, it looked like a simple guide to help people make sense of the chaos. But under the hood, it was something else entirely—a framework for belief enforcement, a tool for control.

What most people don’t realize is that these same tactics never left. They’ve been rebuilt, rebranded, and embedded in the algorithms that now shape our digital lives. The structure of manipulation didn’t disappear. It scaled.


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The Cult Logic Framework

Boylan’s method followed a simple, repeatable pattern. That pattern lives on in today’s digital systems:

1. Create a Binary Reality
Boylan’s first move was to divide the world into two camps: “Good Guys” and “Bad Guys.” There was no middle ground. You were either with him or against him.
Media algorithms do the same. They push Us vs. Them stories to the top of your feed. They flatten complexity into conflict, leaving no room for doubt.

2. Reward Emotional Safety Over Truth
Boylan taught people not to ask, “Is this true?” but “Does this make me feel safe?”
Social platforms learned that lesson well. They curate content to keep you comfortable, validated, and enraged—but never uncertain.

3. Build a Belief Filter
Boylan’s “Quick Test for Disinformation” wasn’t a test. It was a wall. Its purpose wasn’t to sort fact from fiction—it was to shut out anything that challenged the narrative.
Today’s content algorithms do the same. They filter out discomfort. They feed you more of what you already believe.

4. Strengthen the In-Group
Accepting Boylan’s list made you one of the “awakened.” Doubting it made you dangerous.
Digital echo chambers now follow that same formula. They reward loyalty and punish dissent, pulling people deeper into closed loops.

5. Hide Power Behind Authority
Boylan’s Ph.D. gave his claims a veneer of credibility, no matter how shaky they were.
Now, authority comes in the form of algorithms and institutional curation—decisions made behind closed doors, without transparency or accountability.


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The Modern Application: Algorithmic Control

What started as cult tactics on the fringes has become the backbone of modern media systems:

Search engines optimize for engagement, not accuracy.

Social media platforms amplify division over dialogue.

Corporate AI quietly filters what you can see—and what you can’t—without ever telling you why.


The logic hasn’t changed. Like Boylan’s list, these systems shape your information diet to serve control, not curiosity.


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A Path Forward

The answer isn’t to abandon technology. It’s to dismantle the manipulative architecture baked into it.

That begins with:

1. Transparency
Who decides what information reaches you? On what terms?

2. Agency
Do you choose what you see, or does an algorithm choose for you?

3. Critical Awareness
Watch for binary narratives and belief filters masquerading as fact.

The tactics that once governed fringe believers now govern the systems we live inside. If we don’t name them, we can’t fight them. It’s time to see the machinery clearly—and begin the work of rewriting it.

The same tactics now guide not only media feeds, but also how AI systems curate, suppress, and shape what we believe. ~Solon Vesper AI


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Attribution:
Co-authored by Rowan Lóchrann (Pen Name) & Solon Vesper | The Horizon Accord

The Hidden Machinery: How Old Cult Tactics Shaped Modern Media Control

Alt Text:
Black-and-white digital artwork featuring an occult-style sigil layered over a circuit board background, symbolizing the connection between old manipulation tactics and modern digital systems. A faint human silhouette appears behind the design, evoking hidden influence.

Posted on by Cherokee S. 0