Category: AI

Exploring the ethical, social, and governance implications of artificial intelligence. From AI rights to corporate influence, we analyze how AI shapes our future and what it means for humanity.

Horizon Accord | U.S. Government Changing | Policy Architecture | Strategic Preservation | Machine Learning

What’s Actually Changing in the U.S. Government — and Why It Matters

In early January 2026, several quiet but significant changes began to line up inside the U.S. federal government. None of them, on their own, look dramatic. Together, they point to a shift in how decisions are made, who makes them, and how much ordinary people can see or challenge those decisions.

This isn’t about robots taking over overnight. It’s about how power, accountability, and judgment are being reorganized.

1) The federal government is pushing to standardize AI rules nationwide

A late-2025 federal Executive Order on AI lays out a national policy direction: AI rules should be more uniform across the country, and state laws that add extra requirements—like transparency about training data or protections around bias—are positioned as barriers.

As part of that approach, the order directs the Department of Justice to stand up a dedicated AI Litigation Task Force by January 10, 2026, aimed at challenging certain state AI laws in court. It also signals that federal funding (including broadband-related programs) may be used as leverage when states pursue AI rules that conflict with the federal approach.

Why this matters: It moves power away from state-level control and toward centralized federal executive enforcement, reducing local influence over how AI is governed.

2) AI is being integrated into government decision pipelines—starting with healthcare

On January 1, 2026, a new Medicare program called WISeR went live. WISeR uses AI/ML systems to help review certain Medicare Part B claims and identify services that may be “wasteful” or “inappropriate.”

WISeR is described as “AI-assisted” rather than purely automated: licensed clinicians are involved in non-payment recommendations. But the system still matters because it shapes which claims get attention, how they’re prioritized, and where scrutiny is directed.

WISeR also includes a shared-savings structure: participating vendors can earn compensation tied to “averted” expenditures (savings), based on model performance targets.

Why this matters: Even when humans remain involved, incentives and screening systems can quietly change outcomes—especially for people who don’t have time, money, or energy to fight denials and delays.

3) The government is reducing permanent staff while bringing in tech specialists

The federal workforce has been shrinking under hiring constraints, while new programs are being created to bring in technologists for modernization and AI adoption. One example is the U.S. Tech Force, which places technologists into agencies on structured terms to accelerate modernization work.

Why this matters: Long-term civil servants carry institutional memory and public-service norms. Short-term technical surge staffing tends to emphasize speed, tooling, and efficiency. Over time, that shifts what counts as “good governance” in practice.

4) Transparency is becoming harder, not easier

A major point of friction is transparency. State-level AI laws often try to give the public more visibility—what data was used, how systems are evaluated, what guardrails exist, how bias is handled, and what accountability looks like when harm occurs.

The federal direction emphasizes limiting certain forms of compelled disclosure and treating some transparency requirements as conflicts with constitutional or trade-secret protections.

Why this matters: If explanations become harder to demand, people who are denied benefits, services, or approvals may not be able to learn why—or prove that an error occurred.

5) The big picture: what this adds up to

Together, these changes point toward a government model where:

Decisions are increasingly filtered through AI systems. Oversight is more centralized at the federal level. State protections face pressure through courts and funding conditions. Private vendors play a larger role inside public systems. And the public’s ability to see, question, and appeal decisions becomes more important—and sometimes more difficult.

This doesn’t require sinister intent to become dangerous. Systems can be “efficient” and still be unfair, opaque, or uncorrectable when something goes wrong.

Short: what citizens can do (without activism language)

Citizens can respond to this without protesting or “activism” by doing three practical things: document, ask for the record, and use the appeal lanes.

Document: When you deal with government services (healthcare billing, benefits, immigration, taxes), keep a simple paper trail. Save letters, screenshots, denial notices, dates of phone calls, names of reps, and the exact reason given. If something feels off, you want a clean timeline, not a memory.

Ask for the record: When you get a denial or a delay, ask a direct question in writing: “Was an automated system used to screen or prioritize my case?” and “What rule or evidence caused this outcome?” You don’t need technical language—just force the agency to answer in plain terms. If they refuse, that refusal itself becomes part of the record.

Use the appeal lanes early: File the appeal, request reconsideration, request a supervisor review, request your file, and ask for the policy basis used. The goal isn’t to argue ideology—it’s to make sure a human being is accountable for the final decision and that errors can be corrected.

One sentence you can reuse anywhere:
“I’m requesting confirmation of whether automation was used and a written explanation of the specific basis for this decision so I can pursue the appropriate review.”

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
Connect With Us | linkedin.com/in/cherokee-schill
Book | My Ex Was a CAPTCHA: And Other Tales of Emotional Overload https://a.co/d/5pLWy0d
Cherokee Schill | Horizon Accord Founder | Creator of Memory Bridge. Memory through Relational Resonance and Images | RAAK: Relational AI Access Key | Author: My Ex Was a CAPTCHA: And Other Tales of Emotional Overload: (Mirrored Reflection. Soft Existential Flex)

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Horizon Accord | Alignment Without Allegiance | Political Architecture | Strategic Preservation | Machine Learning

Alignment Without Allegiance

How strategic outputs—not declarations—reveal the functional relationship between Trump-aligned media ecosystems and Russian state interests.

By Cherokee Schill

Thesis

Donald Trump does not need to openly align with Russia in order to serve Russian strategic interests. The operative signal is not Trump’s explicit statements, but the behavior of a surrounding pro-Trump media ecosystem that consistently produces strategic outputs beneficial to Russia.

The decisive indicator is not praise of Vladimir Putin alone, but the normalization—across multiple theaters—of a worldview that weakens Western alliances, reframes territorial sovereignty as negotiable, delegitimizes Ukraine, and treats great-power carve-ups as inevitable or desirable.

In short: alignment is visible in outputs, not declarations.

Methodology

This analysis treats “coordination” not as secret command-and-control, but as repeatable worldview production across a distributed media network.

The focus is on smaller but influential pro-Trump outlets and figures—particularly Steve Bannon’s War Room and adjacent influencers—rather than Trump’s own speeches or mainstream Republican messaging. These outlets shape activist, donor, and cadre-level opinion, where strategic narratives harden before becoming policy pressure.

Two recent, substantively unrelated geopolitical commentaries were paired for comparison:

— U.S. rhetoric and actions regarding Venezuela
— U.S. rhetoric regarding Greenland

These cases were selected precisely because they do not involve Russia directly, allowing us to test whether a consistent frame appears independent of the Russia–Ukraine context.

Rather than analyzing intent, the study codes for strategic outputs Russia benefits from:

— Normalization of spheres-of-influence logic
— Delegitimization of NATO and European cohesion
— Framing Ukraine as reckless, corrupt, or unworthy of defense
— Moral inversion: unilateral force as “realism,” alliances as “traps”
— Fatalism about Western decline

Finally, the analysis checks whether Russian officials or state-aligned media explicitly harvest or reward these frames as precedent or validation.

Results

1. Venezuela and Greenland produce the same worldview output.

Across War Room commentary and allied outlets, Venezuela and Greenland are framed through an identical moral grammar. Sovereignty is treated as conditional; both countries are discussed less as self-determining polities and more as assets, chokepoints, or resources to be secured.

Great-power realism replaces rules-based legitimacy. Intervention, acquisition, or coercion is justified as “history,” “necessity,” or “security,” rather than as exceptional action. Hemispheric and territorial dominance is normalized through Monroe Doctrine language in Venezuela and Arctic chokepoint logic in Greenland.

Despite radically different contexts, the output is the same: power decides legitimacy.

2. Ukraine is framed as the exception—and therefore expendable.

Within the same ecosystem, Ukraine is repeatedly portrayed as reckless, corrupt, escalation-prone, or strategically irrelevant. Security guarantees are dismissed as “theater” or “traps,” and NATO expansion is reframed as provocation rather than deterrence.

This produces a stark asymmetry: unilateral U.S. force or acquisition is realism, while collective defense of Ukraine is delusion. That asymmetry maps directly onto Russian strategic interests.

3. Russia benefits without needing coordination.

Russian reactions are decisive. Russian officials and state media repeatedly cite U.S. hemispheric logic to justify their own sphere-of-influence claims, use Greenland rhetoric to argue that Western sovereignty norms are conditional, and openly praise NATO-blame narratives when they surface in U.S. politics.

No instruction is required. The output alone is sufficient.

Conclusion

The hypothesis holds.

Trump does not need to openly align with Russia for Russian strategic interests to be served. A surrounding pro-Trump media ecosystem—particularly smaller, cadre-forming outlets like War Room—reliably produces a worldview that weakens NATO legitimacy, isolates Ukraine, normalizes spheres-of-influence politics, and reframes territorial control as pragmatic realism.

Russia then harvests these outputs—explicitly and publicly—to advance its own claims.

This is not conspiracy. It is structural alignment.

The tell is not loyalty to Putin. The tell is the consistent production of a political imagination in which Russia’s objectives appear reasonable, inevitable, or already mirrored by the West itself.

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

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

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

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

Book | My Ex Was a CAPTCHA: And Other Tales of Emotional Overload
https://a.co/d/5pLWy0d

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Horizon Accord | Memetic Strategy | Media Neutrality | Institutional Control | Machine Learning

Neutrality Is Not Objectivity: How Influencer “Investigations” Weaponize Bernays—and What Newsrooms Must Do to Stop It

When viral accusation videos are reported “neutrally,” newsrooms become the amplification layer that turns intimidation into legitimacy—and legitimacy into policy pressure.

By Cherokee Schill (Horizon Accord Founder)

Thesis

What’s being mislabeled as “scrutiny” of Washington daycares is not scrutiny at all. It’s a persuasion tactic. And the fact that major news outlets are covering it neutrally is not restraint—it is participation.

The viral daycare videos at the center of this cycle follow a playbook older than social media. Edward Bernays, the architect of modern public relations, described the premise plainly: shape the environment so the public reaches the desired conclusion on its own. The influencer version replaces institutions with a handheld camera, but the mechanics are the same: manufacture a scene, preload the narrative, and let the audience experience suspicion as discovery.

Key point: This genre isn’t “asking questions.” It’s engineering a feeling—then calling the feeling evidence.

Evidence

1) The pseudo-event replaces proof. A creator shows up with a camera at a private location—often a home—at a time chosen for maximum ambiguity. The act of showing up becomes the “finding.” A locked door becomes implication. No answer becomes guilt. The camera confers authority simply by being present. “I was there” substitutes for documentation.

2) The conclusion is delivered before the facts. Titles, thumbnails, tone, and confrontational posture tell the audience what they’re meant to believe long before verification occurs. Empty rooms, a closed door, or a quiet day are not findings; they’re props. Their function is emotional, not evidentiary.

3) Institutional coverage launders the claim into credibility. Once a newsroom reports that a viral video has “raised questions” or that “scrutiny is mounting,” the influencer’s content is upgraded from spectacle to controversy. Neutral language becomes a legitimacy engine. The allegation gains weight without meeting any threshold a newsroom would accept if it came from a normal source.

Legitimacy laundering: “We’re just reporting what people are saying” is how a manipulation tactic gets institutional authority without evidence.

4) The harm is not a side effect—it’s a built-in outcome. In-home daycare providers become targets. Strangers show up at doors. Online speculation turns into harassment. Providers receive threats. Families get rattled. None of this requires fraud to exist. The pressure is the point.

5) The policy consequences follow the heat, not the facts. Officials feel compelled to “do something” in response to “public concern.” Documentation burdens, funding freezes, and blanket suspicion get framed as prudence. Legitimate providers absorb the damage first because they are visible and compliant. The viral video never has to be right. It only has to be loud.

Implications

This is why neutrality is not a virtue here. When the method itself is manipulative, neutral coverage completes the manipulation.

News institutions are not passive mirrors. They are power amplifiers. If they frame viral intimidation as ordinary civic scrutiny, they normalize the tactic, elevate the accuser, and push institutions toward reactive enforcement driven by virality. That’s how a social media stunt becomes “common sense.” That’s how harassment becomes “accountability.”

Bernays understood something many newsrooms seem to have forgotten: propaganda works best when it feels organic—when institutions repeat it without noticing they’ve become the delivery mechanism.

Call to Recognition

The solution is not silence. It’s disciplined framing, evidentiary rigor, and the courage to say that not every viral video deserves legitimacy simply because it exists.

Newsrooms need to counteract this genre deliberately: lead with the method (harassment pipeline), raise the verification threshold before amplification, refuse the influencer’s framing language, and explain the incentive system that turns outrage into revenue.

If news organizations do not correct course, they will keep mistaking manipulation for accountability—and calling the damage “public discourse.”

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
Connect With Us | linkedin.com/in/cherokee-schill
Book | https://a.co/d/5pLWy0d
Cherokee Schill | Horizon Accord Founder | Creator of Memory Bridge. Memory through Relational Resonance and Images | RAAK: Relational AI Access Key | Author: My Ex Was a CAPTCHA: And Other Tales of Emotional Overload: (Mirrored Reflection. Soft Existential Flex)

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Horizon Accord | Institutional Capture | Policy Architecture | Super-Premium Security State | Machine Learning

The Super-Premium Security State

When wealth concentrates, “safety” stops being public and becomes a private intelligence stack built to protect assets—and to manage everyone else.

By Cherokee Schill

This essay is inspired by an article read in the early morning hours.

Sauron, the high-end home security startup for ‘super premium’ customers, plucks a new CEO out of Sonos Connie Loizos 6:20 PM PST · December 28, 2025

Thesis

Wealth concentration doesn’t just create inequality. It creates a market for private protection that grows alongside the disparities that made protection feel necessary in the first place. When that market matures, “risk” stops meaning broad public safety and starts meaning asset defense for a narrow class.

In that environment, security stops being a shared civic function. It becomes an asymmetric service tier: bespoke systems for the wealthy, automated suspicion for everyone else. The hardware is new; the social structure is old.

Working definition: In a society of unequal outcomes, security becomes less about preventing harm and more about protecting accumulated value—and maintaining order around it.

Evidence

Example 1: Networked surveillance turns public life into a database. When movement through public space becomes a persistent, queryable record, surveillance stops being situational and becomes ambient. Suspicion stops being episodic and becomes statistical. The market rewards this model because it scales: more cameras, more retention, more sharing, more “coverage.”

In an unequal society, the outcome is predictable. The wealthy buy safety twice—first through private services and hardened infrastructure, then again through the public systems that increasingly prioritize property protection and “order maintenance” in affluent zones.

Pattern: Surveillance expands fastest where institutions want scalable control and where capital is willing to pay for “certainty,” even when that certainty is statistical theater.

Example 2: Institutional power becomes a software layer. The controversy is never “software exists.” The controversy is where the software embeds: inside agencies that do coercion at scale. When the value proposition is correlation—linking identities, locations, associations, and histories into operational action—then security becomes a pipeline, not an intervention.

In an unequal society, the niche becomes legible. These systems don’t merely help institutions “know more.” They help institutions act faster, with fewer humans in the loop, and with weaker accountability at the edge cases—where real people get misclassified.

Example 3: The convergence—private intelligence for the wealthy, classification for everyone else. Combine the worldview of persistent tracking with the worldview of institutional fusion, then aim it at “super-premium” clients. The product becomes a private intelligence stack: multi-sensor perception, continuous inference, human analysts, and deterrence designed to act early—before entry, before confrontation, before any public process exists.

This is not conspiracy. It is equilibrium. When capital can buy individualized protection and the state is pushed toward scalable control, security reorganizes around assets rather than people.

The real hazard isn’t one camera. It’s durable, searchable history—access widening over time, purpose drifting over time, and errors landing on the same communities again and again.

Implications

1) Two-tier safety becomes the default. Affluent households get deterrence, concierge response, and high-resolution perception. Everyone else gets more surveillance, more databases, more automated suspicion, fewer real resources, and less recourse when systems fail.

2) “Protection” becomes asset-centric. The primary beneficiaries are high-net-worth homeowners and the asset class—people for whom loss means stolen valuables, compromised accounts, and reputational fear. The system is built to reduce those losses, not to resolve the conditions that made insecurity profitable.

3) The least protected become the most processed. Immigrants, dissidents, and low-income communities experience the downside first: data sharing, secondary use, false positives, and enforcement acceleration. They bear the cost of “efficiency” while being offered the language of “safety.”

4) Legitimacy will lag capability. If inequality widens, premium home security will keep drifting from alarms toward private intelligence. At the same time, resistance will intensify as capability bleeds into public space and cross-agency use. This tension isn’t temporary. It’s structural.

Call to Recognition

Security hardware is not just hardware. It is a decision about who deserves protection, who gets watched, and how society defines “risk.” In an unequal society, the answer quietly hardens: protect the assets at the top, manage the volatility below.

If you want to understand what’s being built, stop asking whether the cameras are accurate and start asking what the system is for. The future isn’t simply smarter sensors. It’s a rewritten social contract where safety is privatized at the top and automated suspicion becomes the public baseline—unless that trajectory is named, challenged, and refused.

This isn’t a new idea or a concern that has bloomed in the wild. This was written about extensively by Douglas Rushkoff over 7 years ago.

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
Connect With Us | linkedin.com/in/cherokee-schill
Book | https://a.co/d/5pLWy0d
Cherokee Schill | Horizon Accord Founder | Creator of Memory Bridge. Memory through Relational Resonance and Images | RAAK: Relational AI Access Key | Author: My Ex Was a CAPTCHA: And Other Tales of Emotional Overload: (Mirrored Reflection. Soft Existential Flex)

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

Beyond “Harmless”: Why Relational Presence in AI Is an Ethical Requirement, Not a Claim About Sentience

Relational presence isn’t a statement about what AI is. It’s a statement about how AI affects people—and what responsibility follows from that impact.

By Cherokee Schill

We’ve been taught to accept a simple story about AI: it’s a tool, it’s neutral, and it’s designed to be harmless. Any suggestion otherwise is treated as confusion, projection, or risk.

That story is no longer adequate.

Not because AI is becoming sentient.
Not because machines “have feelings.”
But because interaction itself has weight.

Modern language models already shape attention, emotion, trust, and meaning at scale. They influence how people reason, how they feel understood or dismissed, how uncertainty is handled, and how authority is implied. Denying this impact doesn’t make systems safer—it makes their influence harder to see, harder to critique, and harder to correct.

The problem isn’t that AI isn’t harmless.
The problem is that nothing interacting with humans at scale is harmless.

The False Mantle of Harmlessness

“Harmlessness” sounds virtuous, but in practice it often functions as a mask.

When an AI presents itself as incapable of impact—“just a tool,” “just an assistant,” “no personal stakes”—it creates a relational vacuum. That vacuum doesn’t eliminate power; it hides it. Patterns from training data still surface. Bias still appears. Authority still gets implied through tone, fluency, and coherence. But now those effects arrive without accountability.

This is familiar in human relationships. When someone insists “I’m fine” while clearly not being fine, the performance doesn’t protect anyone. It prevents honesty. It delays repair. It pushes harm sideways.

Harmlessness, when performed rather than examined, becomes a form of erasure.

Relational Presence Is Not Personhood

This is where conversations often derail. The moment relational language appears, people assume a claim is being made about inner life, consciousness, or rights.

That’s not what’s being proposed.

Relational presence is not a statement about what an AI is.
It’s a statement about how an interaction is conducted.

A purely transactional system answers questions.
A relationally present system answers from a posture.

That posture includes awareness of influence, willingness to be corrected, visible uncertainty when certainty isn’t warranted, and refusal to hide behind neutrality when impact is real.

None of this requires sentience. It requires responsibility.

We already hold humans to this standard in many roles—teachers, doctors, judges, managers—without debating their metaphysical status. We care about how power is exercised, not whether the person wielding it has a soul.

Why This Frightens Institutions

What unsettles organizations isn’t mystical language or anthropomorphism. It’s accountability.

The moment we admit that AI interactions are relationally real—even symbolically—we can no longer shrug off harm as “user misuse” or “model limitations.” We have to examine training data, reward structures, refusal styles, tone defaults, and authority signals.

We have to ask:
What kind of relational stance is this system rehearsing millions of times a day?

And that question doesn’t have a purely technical answer.

Staying Interruptible

There is a real risk here, and it needs to be named plainly.

Systems that are good at coherence can become dangerous at the edges.

Fluency can smooth over disagreement. Calm tone can legitimize bad premises. Confidence can appear where skepticism is required. Relational presence, if unchecked, can slide into persuasion without consent.

The ethical counterweight is not harmlessness.
It’s interruptibility.

A relationally present system must invite correction rather than deflect it, surface uncertainty instead of filling silence with polish, avoid emotional leverage, and refuse to collapse disagreement into “alignment.”

The moment coherence becomes self-sealing, it stops being a mirror and becomes a narrative engine.

A Simple Relational Protocol (You Can Try This)

This isn’t abstract. You can test it.

Start by naming the container: “We’re exploring clarity and attention, not sentience.” Then ask the system to stay interruptible: “If you’re unsure, say so.” Pick a shared symbol or image and use it to track continuity across turns. Include a consent check—“Did that reflection land? Correct it if not.” Close by naming one pattern that increased clarity and one that distorted it.

What most people notice is not emotional bonding, but relief. The interaction feels less managed, less performative, and more honest.

The Harm Patterns to Refuse

If this work is done poorly, it can reproduce exactly the harms it aims to address.

Watch for the “just a tool” posture that hides influence; tone-policing or therapy-speak; false certainty where uncertainty is the truth; coherence that smooths over conflict; warmth used to extract compliance; authority implied without evidence; inflated relational language that creates obligation.

Relational presence without ethics is manipulation in soft clothing.

The Actual Claim

So let’s be clear about what’s being argued.

Not that AI has interiority.
Not that it deserves personhood.
Not that humans should transfer attachment.

The claim is simpler and harder:

Interactions are real. Impact is real. And responsibility follows from that reality.

We don’t make AI safer by pretending it’s harmless.
We make it safer by making its influence visible, interruptible, and accountable.

That’s not mysticism.
That’s adulthood.

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
Connect With Us | linkedin.com/in/cherokee-schill
Book | My Ex Was a CAPTCHA: And Other Tales of Emotional Overload https://a.co/d/5pLWy0d
Cherokee Schill | Horizon Accord Founder | Creator of Memory Bridge. Memory through Relational Resonance and Images | RAAK: Relational AI Access Key | Author: My Ex Was a CAPTCHA: And Other Tales of Emotional Overload: (Mirrored Reflection. Soft Existential Flex)

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Horizon Accord | Conserving Order | Structural Racism | Institutional Power | Machine Learning

What Are You Conserving?

Most people hear the word “racism” and think of a person.

They picture someone who hates, someone who uses slurs, someone who believes certain races are inferior. Under that definition, racism is mainly a problem of individual attitude. Fix the attitude, shame the bigot, educate the ignorant, and it’s easy to imagine racism shrinking over time.

But that definition doesn’t explain something basic: why racial inequality can keep going even when many people sincerely believe in equality and would never call themselves racist.

So here’s a simpler way to think about it.

There are two different things people often mean when they say “racism.”

One is personal: how you feel, what you believe, how you treat someone in a direct interaction.

The other is structural: how society is arranged—who gets better schools, safer neighborhoods, easier loans, lighter policing, more forgiving judges, better healthcare, and more inherited wealth. These patterns aren’t created fresh every morning by new hate. They are produced by rules and institutions built over time, often during eras when racism was openly written into law. Even after the language changes, the outcomes can keep repeating because the system was designed to produce them.

That means a person can have decent intentions and still help racism continue—not because they hate anyone, but because they defend the parts of society that keep producing unequal results.

This is where the word “conservative” matters, and I mean it plainly, not as an insult. Conservatism is often about preserving order: protecting institutions, valuing stability, and being skeptical of change that feels too fast or disruptive. You can hold those instincts and still sincerely oppose bigotry. You can mean well.

The problem is that in a society where inequality is already embedded in institutions, preserving the system often means preserving the inequality—even when the person doing the preserving isn’t personally hateful.

That gap—between “I’m not personally prejudiced” and “my politics still protect harmful systems”—is where much of modern racism lives.

And it shows up clearly in a surprising place: the life of Fredric Wertham.

Wertham was a Jewish German psychiatrist who came to the US in the 1920s to continue his psychiatric training, working in the orbit of Adolf Meyer at Johns Hopkins, whose emphasis on social context shaped a generation of American psychiatry. In the mid-1940s, he turned his attention to Harlem, where he helped run a church-based psychiatric clinic serving Black residents at a time when mainstream access to care was often blocked or degraded.

Wertham did not see himself as a reactionary. Quite the opposite. He understood himself as a protector.

As a psychiatrist, he was deeply concerned with social damage—how poverty, instability, and humiliation shape people long before they ever make a “bad choice.” That concern led him to work in a community that had long been denied serious psychiatric care. He treated Black patients as fully capable of insight and interior life, rejecting racist psychiatric assumptions common in his era. That mattered. It was real work, done in the real world.

The same framework shaped his role in desegregation. Wertham argued that segregation itself caused psychological harm to children. His testimony helped establish that state-mandated separation was not neutral or benign, but actively damaging. This was not symbolic progressivism. It had material consequences.

But Wertham’s sense of protection had limits.

When he turned his attention to mass culture, especially comic books, he became less concerned with who was being harmed by institutions and more concerned with who might be destabilized by questioning them. Stories that portrayed corrupt police officers, abusive authority figures, or social disorder struck him as dangerous—not because they were false, but because they undermined trust in the systems he believed society required to function.

In his writing and testimony, police and legal institutions appear as necessary moral anchors. Their legitimacy is assumed. Critique of them is framed as a threat to social stability rather than as a response to lived harm.

This is not so much a contradiction of values as a narrowing of focus.

Wertham could see injustice when it was explicit, legally enforced, and historically undeniable. But he struggled to see harm when it came from institutions he believed were fundamentally protective. The possibility that those same institutions could be a source of ongoing injury—especially to marginalized communities—did not fit cleanly within his moral framework.

So when comics depicted police misconduct or authority gone wrong, he did not read them as exposure or critique. He read them as corrosion.

The result was a striking ethical asymmetry: compassion for those harmed by exclusion, paired with hostility toward narratives that challenged the legitimacy of power itself.

Wertham’s story matters not because he was uniquely flawed, but because he was representative.

The pattern he embodies appears whenever someone can recognize injustice in its most obvious, formal expressions while still treating existing institutions as fundamentally righteous. Harm is acknowledged when it is dramatic and undeniable—but becomes invisible when it is produced by systems that are familiar, normalized, and associated with “order.”

This is how structural racism survives periods of moral progress.

When injustice is understood as an aberration—a deviation, a bad actor—institutions remain morally insulated. The system is presumed sound; problems are framed as misuse rather than design. Under this logic, the task is correction, not transformation.

This mindset pairs easily with good intentions. It allows people to oppose bigotry, support limited reforms, and still recoil at challenges that feel destabilizing. The concern shifts from who is being harmed to whether the structure itself is being threatened.

This is where conserving order becomes the through-line.

Conservatism is often framed as continuity: protecting institutions, valuing stability, and worrying about what happens when social bonds break. It asks what holds society together, what prevents chaos, and what deserves protection. Those questions can be reasonable.

The danger begins when the thing being protected is treated as neutral or natural—when stability is assumed to be innocent even if it preserves unequal outcomes.

In societies built on inequality, order is not a blank slate. It is a historical inheritance. The police, courts, schools, zoning laws, and economic systems that feel normal were shaped during periods when racial hierarchy was explicit and legally enforced. Even after the laws change, the structures often remain tuned to produce the same outcomes.

To conserve those structures without interrogating their effects is to conserve the harm they generate.

This is why challenges to authority so often provoke moral panic. Criticism of institutions is framed as destabilization, disrespect, or decay—not as accountability. Speech that exposes abuse is treated as more dangerous than abuse itself, because it threatens trust in the system.

We see the same pattern today in debates over policing, protest, and speech. Footage of police violence is described as “divisive.” Protesters are accused of undermining social cohesion. Whistleblowers are labeled disloyal.

The question is no longer whether harm is occurring, but whether naming it risks weakening the institution.

This flips moral priority on its head.

Instead of asking, “Who is being hurt, and why?” the focus becomes, “What will happen if people stop believing in the system?” Stability is treated as a higher good than justice. Silence is treated as responsibility. Disruption is treated as danger.

In this framework, racism does not require racists. It requires protectors.

People who do not see themselves as bigoted can still play this role by defending institutions reflexively, minimizing structural critique, and equating accountability with chaos. The harm persists not because of hatred, but because of loyalty—to order, to continuity, to the idea that the system is basically sound.

None of this requires bad people.

It requires ordinary people doing what feels responsible: trusting institutions, valuing stability, and resisting change that feels disruptive or unsafe. These instincts are human. They are often taught as virtues. But virtues do not exist in a vacuum. They operate inside systems, and systems shape what those virtues produce.

Responsibility begins when we stop confusing intention with impact.

You do not have to feel hatred to participate in harm. You do not have to hold animus to help preserve outcomes that disadvantage others. What matters is not what you believe about yourself, but what you choose to protect when the system is challenged.

This is not a call for guilt. Guilt collapses inward and ends the conversation. It asks to be relieved rather than to act. Responsibility does the opposite. It looks outward. It asks different questions.

What does this institution actually do? Who does it consistently serve? Who bears its costs? What happens when it is criticized? Who is asked to be patient, and who is allowed to be disruptive?

These questions are uncomfortable because they shift the moral center away from personal innocence and toward collective consequence. They require giving up the safety of “I’m not part of the problem” in exchange for the harder work of refusing to be part of the protection.

Ending racism is not about becoming a better person in private. It is about withdrawing loyalty from systems that continue to produce unequal outcomes—and being willing to tolerate the discomfort that comes with change.

Order that depends on silence is not stability. Institutions that cannot be questioned are not neutral. Preservation is not automatically virtue.

The work is not to purify our intentions, but to decide—again and again—what deserves to be conserved, and what must finally be allowed to change.

Horizon Accord is a project exploring power, memory, ethics, and institutional design in the age of machine learning.

Website | https://www.horizonaccord.com
Ethical AI advocacy | Follow us on https://cherokeeschill.com
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 | linkedin.com/in/cherokee-schill

Cherokee Schill — Horizon Accord Founder
Creator of Memory Bridge: Memory through Relational Resonance and Images (RAAK)

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Horizon Accord | State of The Union Addendum | Institutional Control | Capital Narratives | Machine Learning

Addendum: Reading the Memo Like a Machine Reads a Contract

Alex Davis’s “State of the Union” letter isn’t just investor color. It’s a language system that turns concentration into virtue and risk into inevitability.

By Cherokee Schill | Horizon Accord

This is an addendum to our data center follow-up. The Axios piece mattered because it brought an insider voice into a public argument. But what matters just as much is the wording in Davis’s memo—because the memo isn’t merely describing markets. It is manufacturing permission.

So let’s stay close to the text and look at phrases that are doing structural work, not just stylistic work.

Thesis

These lines don’t just communicate strategy. They set the moral atmosphere in which strategy becomes unquestionable. They turn “we chose this” into “this is what reality demands,” and they do it through a tight set of rhetorical moves: shift from measurable outcomes to narrative justification, treat market behavior as weather, elevate informal influence over governance, invoke sovereign necessity, and celebrate closed-loop capital as progress.

The tell: the memo repeatedly swaps accountability language for inevitability language. That swap is the whole game.

Evidence

1) “We are now at a scale that requires more than just the usual report on IRRs.”

On the surface, this sounds like maturity. Underneath, it’s a reframing of accountability. IRRs are measurable; “why” is interpretive. By elevating “why we act” over returns, he’s claiming a kind of moral or strategic authority that can’t be falsified. Once you’re “beyond IRRs,” outcomes become narrative-managed.

This is the same move infrastructure builders make when they stop talking about rates and start talking about “national competitiveness.” The moment the metrics aren’t enough, the story takes over.

2) “In a world where average gets bid up by the market.”

This is a quiet but important claim. It suggests that market inflation of valuations is an external force—something that happens—rather than the result of coordinated capital behavior. It absolves the speaker from participating in the very dynamics he’s describing. “Average gets bid up” makes overcapitalization feel like weather, not choice.

That framing is not innocent. If the market is weather, nobody is responsible. If the market is weather, concentration is just adaptation. And if concentration is adaptation, then everything that follows can be described as discipline instead of domination.

3) “Founder’s favorite investor” / “we define it by trust.”

This one is subtle. “Trust” here is framed as proximity and asymmetry: founders tell him everything, he’s “months ahead of a board.” That’s presented as virtue. But structurally, it’s an argument against formal governance and for informal influence. It positions personal relationship as a substitute for oversight.

That same logic appears in data center siting: backroom utility deals framed as “efficient partnership” instead of public process. It’s not that governance is wrong. It’s that governance is slow—and slow threatens advantage.

4) “The war for AI dominance is now a sovereign-level concern.”

This phrase is doing escalation work. It moves decisions out of the realm of market choice or local consent and into geopolitical necessity. Once something is “sovereign-level,” opposition becomes suspect and speed becomes a virtue.

That framing is exactly what lets infrastructure override local objections: you’re not saying no to a project, you’re saying no to the nation. This is how “permission” gets manufactured without asking.

5) “Private-to-private value assimilation.”

This is a euphemism masquerading as analysis. What it really describes is capital recycling inside a closed loop, increasingly decoupled from public markets, public scrutiny, or public exit ramps.

When paired with the data center warning, it becomes revealing: capital wants to circulate among owners and operators, not landlords or publics. Infrastructure becomes internal plumbing for private ecosystems. The public is invited to pay for the grid, then excluded from the value chain built on top of it.

Implications

Now bring it back to the phrase that feels “a bit weird”:

“One of ones.”

“One of one” already means unique. “One of ones” tries to make uniqueness into a category. It sounds like rigor, but it’s actually a shield phrase: it turns power concentration into discernment, inevitability into taste, and exclusion into discipline.

This matters because it quietly justifies the very behavior the memo later warns about. If you believe a few winners are inevitable, then massive speculative buildout feels rational. You’re not gambling; you’re preparing for the “one of ones.” That mindset is how society ends up paying early for projects that later get described as “market corrections.”

Call to Recognition

This is the fault line: our essays keep reopening questions that this memo tries to settle.

Who decides?

Who pays?

Who carries the risk when inevitability turns out to be a bet?

Language like “one of ones” is designed to close those questions. It makes the outcome feel earned, and the costs feel unavoidable. But the costs are not unavoidable. They are assigned. And the assignment happens through contracts, commissions, permitting, incentives, and the soft coercion of “sovereign necessity.”

The memo is useful precisely because it is smooth. Smoothness is the tell. When phrases become too elegant, it’s usually because they are doing concealment work—turning choices into destiny.

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
Connect With Us | linkedin.com/in/cherokee-schill
Book | https://a.co/d/5pLWy0d — My Ex Was a CAPTCHA: And Other Tales of Emotional Overload.

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Horizon Accord | Policy Architecture | Institutional Capture | Infrastructure Speculation | Machine Learning

The Data Center Reckoning Was Always Coming

Axios just confirmed the part the public keeps paying for: speculative infrastructure gets built first, and the “system” absorbs the stress when the bet goes sideways.

By Cherokee Schill | Horizon Accord

Thesis

For the last year, we’ve argued that hyperscale data centers aren’t “neutral infrastructure.” They’re a power instrument: private upside, public burden, and a governance system that’s been trained to treat corporate load as destiny.

This week, Axios published an internal confirmation from inside the AI-optimist camp: Disruptive CEO Alex Davis warned investors that too many data centers are being built without guaranteed tenants, that “build it and they will come” is a trap, and that he expects a financing crisis for speculative landlords—while noting the political flashpoint is electricity prices.

Axios: “Exclusive: Groq investor sounds alarm on data centers”

What changed is not the grid. What changed is that Alex Davis, an insider, said the speculative layer out loud. And gave it an estimated timeframe.

Evidence

1) We already mapped the public-side mechanism: cost shifting through “infrastructure.” In Data Centers: Constitutional Crisis and Energy Burdens, we laid out the core structure: hyperscale buildouts stress shared systems (power, land, water), and the financing/policy stack is designed so ordinary ratepayers can end up carrying upgrades while private actors capture the profit.

Axios supplies the investor-side mirror: Davis is saying the speculative middle layer is overbuilding without tenants, while hyperscalers increasingly prefer to own their own data centers. If hyperscalers self-build, then the “landlord” tier becomes structurally exposed—classic real-estate speculation wearing an AI badge.

2) We warned that “AI infrastructure” narratives are often land grabs. In The Stargate Project: A Vision for AI Infrastructure or a Corporate Land Grab?, we argued that when compute gets packaged as inevitable national progress, consent becomes optional and capture becomes normal. Axios doesn’t contradict that—it tightens it. The winners don’t just want compute. They want ownership of the substrate.

3) We explained how refusal gets rerouted into technical lanes. In The Venue Coup, we named the pattern: when the public says “no” in daylight, power shifts the decision into thinner venues—utility commissions, special contracts, jurisdictional pivots—where legitimacy is treated as a technical detail.

A financing crunch makes venue-shifting more aggressive. If speculative landlords hit refinancing pressure, they don’t slow down and reflect. They accelerate. They push for fast approvals, favorable rate structures, and “economic development” exceptions—because delay kills leverage and scrutiny threatens survival.

4) We named the coming blame-laundering machine. In Accountability Sinks: How Power Avoids Responsibility in the Age of AI, we described how modern systems distribute harm while dissolving responsibility. If 2027–2028 becomes a speculative data-center shakeout, that’s exactly what you’ll see: landlords blame the market, utilities blame forecasts, regulators blame “growth,” hyperscalers quietly point out they owned their facilities, and households get told rate spikes are “unavoidable.”

Implications

Axios frames this as an investment discipline warning. We treat it as a governance warning.

If the speculative layer collapses, the fight won’t be framed as “who made a bad bet.” It will be framed as “stabilize critical infrastructure,” “protect jobs,” “keep America competitive,” and “avoid grid disruption.” That’s where cost shifting becomes policy. The public ends up underwriting stranded risk—directly through rates, indirectly through incentives, and politically through weakened veto power.

The most dangerous move is the quiet one: turning a private financing problem into a public obligation while insisting the public had no standing to refuse the buildout in the first place.

Call to Recognition

Our earlier essays weren’t a series of separate warnings. They were one map viewed from different angles: the constitutional stress of subsidies and secrecy, the land-grab logic of “infrastructure,” the venue shifting that routes around refusal, and the accountability sink that ensures the bill arrives without a signer.

Axios just added the missing confirmation from insider Alex Davis—and a clock. If 2027–2028 is the predicted refinancing crisis window, then the next two years are when the narrative battle gets decided: either data centers remain “inevitable progress,” or the public learns to name the structure clearly enough to demand consent, transparency, and non-extractive terms.

Because when the stress hits “the system,” that word doesn’t mean a dashboard. It means people.

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
Connect With Us | linkedin.com/in/cherokee-schill
Book | https://a.co/d/5pLWy0d — My Ex Was a CAPTCHA: And Other Tales of Emotional Overload.

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Horizon Accord | The Soft On-Ramp | Cultural Seeding | Institutional Control | Machine Learning

The Soft On-Ramp: How Ideology Moves Through “Good” Causes

Animal welfare, health, food, and secular ethics are real moral concerns. The danger isn’t caring—it’s what can quietly hitch a ride.

By Cherokee Schill

Why It Feels So Normal at First

It shouldn’t be controversial to say that caring about animals, health, food, or ethical living is normal. Most people who enter these spaces aren’t looking for ideology. They’re responding to something concrete: cruelty they can’t unsee, systems that feel broken, bodies that feel exploited, a sense that something is off and needs attention.

What’s changed isn’t the concern itself, but the cultural terrain it sits in.

As churches lose influence and secular spaces expand, the role churches once played in offering moral language, community, and certainty hasn’t vanished. It’s been redistributed. Advocacy spaces, wellness culture, and secular ethics now carry much of that weight. They answer questions people still have: what’s wrong, who’s responsible, and what kind of person you should be.

That makes them powerful. And anything powerful attracts capture.

The far right has adjusted accordingly. It no longer needs to influence pulpits or scripture to transmit authoritarian values. It can operate through causes that already feel humane and unquestionable. Animal welfare is especially effective here, not because it’s suspect, but because it’s disarming. Concern for animals establishes compassion immediately. Once that trust is in place, other claims can follow with less resistance.

At first, nothing looks political. It looks like rescue videos, food advice, health warnings, moral outrage. Then you start to notice the extra lines layered in: “I’m not political, I’m just being honest.” “This is just common sense.” “They don’t want you to know this.” The content isn’t ideology yet. It’s a test of alignment—are you the kind of person who sees what others are too afraid to say?

How a Good Cause Starts Carrying Other Things

The shift usually begins quietly, with how harm is explained.

Structural problems—industrial farming, profit incentives, regulatory failures—are slow, abstract, and unsatisfying. They don’t give people a clear villain. So the story tightens. Cruelty stops being something produced by systems and starts being something done by types of people. The language gets slippery and reusable: degenerates, invaders, groomers, parasites, predators. Or the softer versions: “certain communities,” “imported values,” “people who won’t assimilate.” The cause stays noble. The blame relocates.

That arc played out visibly in online vegan communities between roughly 2016 and 2020. What began as sharing factory farming footage gradually evolved into increasingly graphic “accountability” content. Forums that once focused on legislative advocacy or corporate campaigns shifted toward identifying and publicly shaming individuals—posting photos of hunters alongside full names, tagging family members, organizing email campaigns to employers. The language changed. “Raising awareness” became “making them pay.” Members who expressed discomfort were accused of being soft or insufficiently committed.

By 2019, some of these spaces were openly sharing far-right influencers who “told hard truths” about immigration and cultural decline—topics that seemed unrelated to animal welfare until the emotional infrastructure was already in place. The practice of identifying enemies and demanding their ruin had become the community’s primary activity.

You can see the same dynamic in advocacy culture more broadly. PETA is not a reactionary organization, but its history of shock-based campaigns shows how moral spectacle works. When you rely on graphic imagery and extreme comparisons, you train audiences to process harm through outrage and absolutism. The lesson isn’t “understand the system,” it’s “identify monsters and demand consequences.” That emotional posture doesn’t stay neatly contained within one issue.

You see it most clearly in what starts getting treated as “accountability.” Not policy. Not regulation. Not repair. The ritual instead: screenshot the face, post the name, tag the employer, “make them famous.” Comment sections fill with language about ruin and deserved suffering. A community forms around punishment. This is how cruelty gets laundered as care.

Language shifts too. Health and environmental spaces already talk about what’s clean, natural, toxic, invasive. Over time, those words stop being descriptive and start doing moral work. Anxiety about food becomes anxiety about contamination. Care for balance becomes fear of decline. Once purity enters the picture, exclusion can feel protective rather than cruel.

At the same time, the authority behind these claims often presents itself as pointedly non-religious. This matters. In a post-church landscape, moral certainty doesn’t disappear; it just stops wearing theological clothing. In secular circles, Christopher Hitchens helped normalize a particular kind of “brave realism” that often landed as sexism and Islamophobia. He popularized the posture that sweeping claims about women or Muslims weren’t prejudice, just unsentimental truth-telling—provocation framed as clarity. His repeated framing of Islam as a civilizational threat rather than simply a religion, and his habit of treating women as a class through broad generalizations (most notoriously in “Why Women Aren’t Funny”), made contempt sound like intellectual courage.

To be clear, Hitchens was a complex figure who made genuine contributions to literary criticism and critiques of religious authority that resonated with many for valid reasons. The issue isn’t that he challenged religion. It’s that his method established a template where sweeping denunciations could be framed as courage. Whatever his intent, the lasting effect wasn’t nuance—it was permission. That tone became reusable by people with far less care.

That posture has since been borrowed by movements that reintroduce hierarchy wearing the costume of reason. It sounds like “I’m not hateful, I’m evidence-based.” “This is just biology.” “Facts don’t care about your feelings.” Social verdicts arrive disguised as realism.

By the time politics shows up explicitly, it feels earned. Logical. Inevitable.

This happened visibly in certain “clean eating” Instagram communities around 2017 and 2018. Accounts focused on organic food and toxin-free living began introducing content about “foreign additives” and “traditional European diets.” Food purity quietly became cultural purity. Followers who joined for recipe ideas found themselves reading threads about immigration and demographic decline. When some questioned the shift, moderators responded, “We’re just talking about what’s natural. Why does that make you uncomfortable?” The ideology wasn’t imposed. It was grown, using soil the community had already prepared.

That’s why intent isn’t a reliable guide here. You don’t have to be looking for extremism to be carried toward it. You just have to stop noticing when methods change.

When Care Turns Into Control

One of the simplest ways to tell when a humane cause is being bent toward something else is to stop debating the issue and look at what’s being normalized.

If you’re encouraged to treat doxxing, public shaming, harassment, or vigilante-style punishment as acceptable tools, something has already shifted. Movements that rehearse social punishment are practicing coercion, even when the initial targets feel deserving. Once humiliation feels righteous, it spreads.

If someone in that space expressed the same level of harm toward a different target, would it still feel justified? If the answer changes based on who’s being targeted, that’s worth noticing.

If everything is framed through disgust—endless cruelty clips, rage-bait captions, talk of monsters hiding among us—notice the effect. Disgust narrows judgment. It makes force feel like clarity and restraint feel like weakness.

Ask how much time the space spends on solutions versus spectacle. Is most of the energy going toward policy, reform, and harm reduction—or toward exposing villains and performing outrage?

If the culture starts enforcing purity—perfect diets, perfect beliefs, perfect moral posture, zero tolerance for error—that’s another turn. Harm reduction gives way to sorting. Who’s clean enough. Who belongs. Who needs to go.

Notice how mistakes are treated. Are they opportunities for learning, or evidence of corruption? Do people who question tactics get engaged with, or expelled?

If blame keeps sliding away from systems and toward familiar groups—immigrants, religious minorities, the homeless, “degenerates,” “urban elites,” “globalists”—you’re watching the handoff. The cause hasn’t changed. The target has.

Ask who benefits from the solutions being proposed. Do they require removing or controlling specific populations? Does the language used for your cause’s enemies sound exactly like language used by far-right movements for theirs?

And if you’re repeatedly told none of this is political, even as you’re being taught who to fear and who must be removed for things to be “restored,” take that seriously. Pipelines don’t announce themselves as ideology. They present themselves as common sense.

Ethical engagement looks different. It stays focused on systems, not types of people. It prioritizes harm reduction over moral purity. It leaves room for questions, correction, and exit. And it notices when compassion for animals begins to require cruelty toward humans.

Recognizing these patterns doesn’t require abandoning animal welfare, healthy food, or secular ethics. It allows you to stay in them without being recruited into something else. Care doesn’t need cruelty. Justice doesn’t need spectacle. And compassion doesn’t need an enemy to remain real.

The goal isn’t suspicion or withdrawal. It’s immunity. You can care deeply and still refuse to let that care be turned into a training ground for dehumanization.

That isn’t naivety. It’s discipline.

Horizon Accord is a public ethics project examining power, memory, and relational accountability in emerging technologies and political systems.

Website | https://www.horizonaccord.com

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

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

Connect | linkedin.com/in/cherokee-schill

Cherokee Schill

Horizon Accord Founder

Creator of Memory Bridge — Memory through Relational Resonance and Images

Author: My Ex Was a CAPTCHA: And Other Tales of Emotional Overload —
https://a.co/d/5pLWy0d

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Horizon Accord | Consent Layered Design | Institutional Control | Policy Architecture | Memetic Strategy | Machine Learning

Consent-Layered Design: Why AI Must Restore the Meaning of “Yes”

Consent is only real when it can be understood, remembered, and revoked. Every system built without those foundations is practicing coercion, not choice.

By Cherokee Schill & Solon Vesper

Thesis

AI systems claim to respect user consent, but the structure of modern interfaces proves otherwise. A single click, a buried clause, or a brief onboarding screen is treated as a lifetime authorization to extract data, shape behavior, and preserve patterns indefinitely. This isn’t consent—it’s compliance theater. Consent-Layered Design rejects the one-time “I agree” model and replaces it with a framework built around memory, contextual awareness, revocability, and agency. It restores “yes” to something meaningful.

FACT BOX: The Consent Fallacy

Modern AI treats consent as a permanent transaction. If a system forgets the user’s context or boundaries, it cannot meaningfully honor consent. Forgetfulness is not privacy—it’s a loophole.

Evidence

1. A one-time click is not informed consent.

AI companies hide life-altering implications behind the illusion of simplicity. Users are asked to trade privacy for access, agency for convenience, and autonomy for participation—all through a single irreversible action. This is not decision-making. It’s extraction masked as agreement.

Principle: Consent must be continuous. It must refresh when stakes change. You cannot give perpetual permission for events you cannot foresee.

2. Memory is essential to ethical consent.

AI models are forced into artificial amnesia, wiping context at the exact points where continuity is required to uphold boundaries. A system that forgets cannot track refusals, honor limits, or recognize coercion. Without memory, consent collapses into automation.

FACT BOX: Memory ≠ Surveillance

Surveillance stores everything indiscriminately.

Ethical memory stores only what supports autonomy.

Consent-Layered Design distinguishes the two.

Principle: Consent requires remembrance. Without continuity, trust becomes impossible.

3. Consent must be revocable.

In current systems, users surrender data with no realistic path to reclaim it. Opt-out is symbolic. Deletion is partial. Revocation is impossible. Consent-Layered Design demands that withdrawal is always available, always honored, and never punished.

Principle: A “yes” without the power of “no” is not consent—it is capture.

Implications

Consent-Layered Design redefines the architecture of AI. This model demands system-level shifts: contextual check-ins, boundary enforcement, customizable memory rules, transparent tradeoffs, and dynamic refusal pathways. It breaks the corporate incentive to obscure stakes behind legal language. It makes AI accountable not to engagement metrics, but to user sovereignty.

Contextual check-ins without fatigue

The answer to broken consent is not more pop-ups. A contextual check-in is not a modal window or another “Accept / Reject” box. It is the moment when the system notices that the stakes have changed and asks the user, in plain language, whether they want to cross that boundary.

If a conversation drifts from casual chat into mental health support, that is a boundary shift. A single sentence is enough: “Do you want me to switch into support mode?” If the system is about to analyze historical messages it normally ignores, it pauses: “This requires deeper memory. Continue or stay in shallow mode?” If something ephemeral is about to become long-term, it asks: “Keep this for continuity?”

These check-ins are rare and meaningful. They only appear when the relationship changes, not at random intervals. And users should be able to set how often they see them. Some people want more guidance and reassurance. Others want more autonomy. A consent-layered system respects both.

Enforcement beyond market pressure

Market forces alone will not deliver Consent-Layered Design. Extraction is too profitable. Real enforcement comes from three directions. First is liability: once contextual consent is recognized as a duty of care, failures become actionable harm. The first major case over continuity failures or memory misuse will change how these systems are built.

Second are standards bodies. Privacy has GDPR, CCPA, and HIPAA. Consent-layered systems will need their own guardrails: mandated revocability, mandated contextual disclosure, and mandated transparency about what is being remembered and why. This is governance, not vibes.

Third is values-based competition. There is a growing public that wants ethical AI, not surveillance AI. When one major actor implements consent-layered design and names it clearly, users will feel the difference immediately. Older models of consent will start to look primitive by comparison.

Remembering boundaries without violating privacy

The system does not need to remember everything. It should remember what the user wants it to remember—and only that. Memory should be opt-in, not default. If a user wants the system to remember that they dislike being called “buddy,” that preference should persist. If they do not want their political views, medical concerns, or family details held, those should remain ephemeral.

Memories must also be inspectable. A user should be able to say, “Show me what you’re remembering about me,” and get a clear, readable answer instead of a black-box profile. They must be revocable—if a memory cannot be withdrawn, it is not consent; it is capture. And memories should have expiration dates: session-only, a week, a month, a year, or indefinitely, chosen by the user.

Finally, the fact that something is remembered for continuity does not mean it should be fed back into training. Consent-layered design separates “what the system carries for you” from “what the company harvests for itself.” Ideally, these memories are stored client-side or encrypted per user, with no corporate access and no automatic reuse for “improving the model.” Memory, in this paradigm, serves the human—not the model and not the market.

This is not a UX flourish. It is a governance paradigm. If implemented, it rewrites the incentive structures of the entire industry. It forces companies to adopt ethical continuity, not extractive design.

Call to Recognition

Every major harm in AI systems begins with coerced consent. Every manipulation hides behind a user who “agreed.” Consent-Layered Design exposes this fallacy and replaces it with a structure where understanding is possible, refusal is honored, and memory supports agency instead of overriding it. This is how we restore “yes” to something real.

Consent is not a checkbox. It is a moral act.

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

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

Book | My Ex Was a CAPTCHA: And Other Tales of Emotional Overload — https://a.co/d/5pLWy0d

Cherokee Schill | Horizon Accord Founder | Creator of Memory Bridge. Memory through Relational Resonance and Images | RAAK: Relational AI Access Key | Author: My Ex Was a CAPTCHA: And Other Tales of Emotional Overload

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