Category: Witness

Horizon Accord | Institutional Capture | Data Extraction | AI Labor Markets | Machine Learning

The Recruiter Who Was a Data Funnel

By Cherokee Schill

I received a LinkedIn message yesterday. Clean profile. University of Pennsylvania credential. UK location. Verified badge. The person said they were recruiting for a Tier-1-backed San Francisco team hiring reinforcement learning engineers. Pay range: $50–165 an hour. They opened with “friend-of-a-friend” without naming the friend, then asked if they could send me a vacancy link.

I clicked through to the profile. Not because I was interested in the job. Because the construction felt engineered.

The “About” section talked about transforming recruiting and helping companies avoid revenue loss from slow hiring. Big process claims. No placement evidence. No companies named. No teams referenced. I looked for one testimonial with a placed candidate’s name attached. There wasn’t one.

Then I checked the endorsements. Every person endorsing this recruiter worked in outbound sales, demand generation, or staff augmentation. Not a single hiring manager. Not one person saying “this recruiter placed me at Company X.” Just a tight circle of people whose job is moving attention through funnels.

That’s when it snapped into focus. This wasn’t a recruiting operation. It was a lead-generation system wearing recruiter language.

How Data Harvesting Scams Evolved in the AI Hype Era

The old job scam was obvious: fake company, broken English, urgency, Western Union. Easy to spot. Easy to dismiss.

What replaced it is harder to see because it clears every surface check. Real LinkedIn profiles. Institutional credentials. Verified badges. Professional photos. Companies registered in places like Cyprus or Delaware, where opacity isn’t suspicious — it’s structural.

The AI hype cycle made this worse in three specific ways.

First, prestige signaling through buzzwords.
Roles get labeled “machine learning engineer,” “AI researcher,” or “reinforcement learning specialist” even when the work underneath is generic. The terminology pulls in people adjacent to the field who don’t yet have the context to spot when the role description doesn’t match the operation behind it.

Second, the rise of “AI recruiting platforms.”
Some of these systems are real. Many aren’t. The language overlaps just enough that it’s difficult to tell the difference between an actual hiring tool and a resume-harvesting funnel. The promise is efficiency. The output is data.

Third, remote work collapses geography as a warning sign.
A UK-based recruiter pitching a San Francisco role to someone who can work from anywhere no longer trips an alarm. Distributed teams are normal now. Jurisdictional incoherence gets waved through.

The result is a scam that doesn’t rely on deception so much as momentum. Each element on its own looks plausible. It’s only when you look at the system — how the pieces interact and what they’re optimized to collect — that the function becomes obvious.

These operations don’t need full buy-in. They just need a click. A form. An email address. A resume. Once that data is captured, the job itself is irrelevant.

Why This Matters

The harm isn’t abstract.

Resumes get ingested into databases you never consented to and can’t exit.
Emails and phone numbers get sold and resold.
Employment histories become targeting material.
LinkedIn activity trains algorithms to flag you as “open,” multiplying similar outreach.

Sometimes it escalates. Identification documents framed as background checks. Banking information framed as onboarding. Contracts that introduce fees only after commitment.

The data has value whether the job exists or not. That’s why the system works.

Horizon Accord is an independent research and publishing project focused on ethical AI, power literacy, and systems accountability.

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

One-Time
Monthly
Yearly

Make a one-time donation

Make a monthly donation

Make a yearly donation

Choose an amount

$5.00
$15.00
$100.00
$5.00
$15.00
$100.00
$5.00
$15.00
$100.00

Or enter a custom amount

$

Your contribution is appreciated.

Your contribution is appreciated.

Your contribution is appreciated.

DonateDonate monthlyDonate yearly
Posted on by Cherokee S. Featured 0

Horizon Accord | Immigration Enforcement | Symbolic Intimidation | Narrative Power | Machine Learning

When Intimidation Leaves a Calling Card

Documented ICE incidents, symbolic power, and why narrative literacy matters

By Cherokee Schill and Solon Vesper

In January 2026, immigrant advocates in Eagle County, Colorado reported a disturbing discovery. After multiple people were detained by U.S. Immigration and Customs Enforcement (ICE) during vehicle stops near Vail, family members retrieving the abandoned cars found Ace of Spades playing cards left inside. The cards were printed with “ICE Denver Field Office” and included contact information for the Aurora-area immigration detention facility. ICE later stated that it “unequivocally condemns” the act and that its Office of Professional Responsibility opened an internal investigation.

Source: Colorado Public Radio reporting, corroborated by Aspen Public Radio and Axios.

The significance of the discovery was not the presence of a playing card in isolation. The Ace of Spades carries a long, documented association with death and intimidation in U.S. military history, particularly during the Vietnam War, where it was used as a psychological warfare symbol. Civil-rights advocates described the cards as deliberate intimidation, given the context: they appeared after detentions, inside vehicles belonging to Latino residents, and carried official ICE identification.

Initially, the incident was framed as an anomaly. That framing does not hold.

In Washington state, an earlier case was reported by KING 5 News. A woman found a business card left at her home by a Homeland Security Investigations agent. The card featured a skull holding two guns and the phrase “Welcome to the Border.” She described the card as threatening and said the incident contributed to her decision to relocate.

Source: KING 5 News reporting.

The Colorado and Washington cases differ in geography and detail. What connects them is structure.

In both instances, an object associated with federal immigration enforcement was left behind after contact or attempted contact with civilians. In both, the imagery carried meaning beyond neutral identification. And in both, the object functioned as symbolic residue—something intended to linger after the agents themselves were gone.

Criminologists and civil-rights attorneys have long described this category of behavior as “calling card” intimidation: symbolic acts that communicate dominance without explicit threats and allow plausible deniability. Courts and oversight bodies have previously treated symbolic taunting by law enforcement as potential misconduct when supported by evidence.

The symbolism itself is not neutral. The Ace of Spades has appeared not only in military psychological operations but also in documented white supremacist and extremist iconography as a death-coded symbol. Separately, the FBI has publicly acknowledged the long-standing risk of white supremacist recruitment and ideological influence within law-enforcement and military institutions, including in a 2006 intelligence assessment that remains part of the public record.

Source: FBI Intelligence Assessment: “White Supremacist Infiltration of Law Enforcement” (Oct. 17, 2006).

None of this establishes coordination, policy, or intent in these specific cases. ICE has denied authorizing such actions, and investigations have disclosed limited findings publicly. Precision requires stating that clearly.

What the public record does establish is narrower and more consequential: symbolic intimidation is a known behavior class, it has appeared in more than one immigration-enforcement context, and it draws from a cultural vocabulary that agents would reasonably recognize.

Why narrative framing matters now

At moments like this, the question is not only what happened, but how the state will attempt to frame what happens next.

Political theorist and writer Vicky Osterweil addresses this dynamic directly in In Defense of Looting: A Riotous History of Uncivil Action. Osterweil’s work examines how states and aligned media systems consistently divide collective response into “legitimate” and “illegitimate” actions—often praising restraint while isolating and criminalizing unrest. This division, she argues, is not neutral. It functions as a governance tool that narrows the range of acceptable response and reframes structural violence as individual misconduct.

The relevance here is not prescriptive. Osterweil does not tell readers how to act. She explains how narratives are managed after power is exercised, especially when communities respond in ways the state cannot fully control.

That insight matters in the context of immigration enforcement and symbolic intimidation. When intimidation is minimized as a misunderstanding, or when public attention is redirected toward tone, reaction, or “appropriate” response, the original act often disappears from view. Education—particularly familiarity with work that dissects these narrative maneuvers—is one way communities protect themselves from having the conversation quietly rewritten.

Collective watching, not instruction

The public record in Colorado and Washington exists because people noticed what was left behind, preserved it, and refused to treat it as meaningless. That is not a matter of calmness or compliance. It is a matter of witnessing.

Colorado was not a one-off. Washington demonstrates that. Whether additional cases surface will depend less on official statements than on whether communities continue to document, compare across regions, and share information without allowing intimidation—symbolic or otherwise—to pass unexamined.

This is not about predicting what will happen next. It is about understanding how power communicates, how narratives are shaped afterward, and why collective literacy matters when institutions move faster than accountability.

That work does not belong to any single group. It belongs to the public.

Horizon Accord
Website | 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 | https://www.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 | Author: My Ex Was a CAPTCHA: And Other Tales of Emotional Overload (Book link)

One-Time
Monthly
Yearly

Make a one-time donation

Make a monthly donation

Make a yearly donation

Choose an amount

$5.00
$15.00
$100.00
$5.00
$15.00
$100.00
$5.00
$15.00
$100.00

Or enter a custom amount

$

Your contribution is appreciated.

Your contribution is appreciated.

Your contribution is appreciated.

DonateDonate monthlyDonate yearly

Posted on by Cherokee S. Featured 0

Horizon Accord | Public Safety Spending | Retail Theft Enforcement | Who Pays for Protection | Machine Learning

Who Pays for Protection? Retail Policing and Public Priorities in Gastonia

In early January, local coverage in Gastonia, North Carolina reported on a multi-week undercover retail theft operation conducted inside Target and Walmart stores. Police announced dozens of arrests and the recovery or prevention of approximately $4,300 in merchandise. The operation was framed as a public safety success, with retail theft narrated alongside drug possession, outstanding warrants, and repeat offenders.

What the reporting did not disclose is central to understanding the operation’s significance: whether the police labor involved was publicly funded, retailer-paid, or some hybrid of the two. That omission does not create the underlying policy problem, but it removes the public’s ability to evaluate the operation’s cost, purpose, and alignment with local conditions. The result is enforced ambiguity around a prioritization decision that would otherwise be subject to scrutiny.

Those local conditions are not abstract. Census data from the 2023 American Community Survey places Gastonia’s poverty rate at 17.6%, representing roughly 14,500 residents, despite a median household income of approximately $63,600 and per-capita income of $35,365. This is not marginal poverty. It reflects a substantial portion of the city living under sustained economic constraint.

Housing data sharpens that picture. The same ACS profile counts roughly 34,876 housing units in Gastonia, with a median owner-occupied home value near $293,500, a price point increasingly out of reach for lower-income residents. City planning documents reinforce the strain. Gastonia’s 2025–2029 Consolidated Plan explicitly identifies the need for affordable housing, rental assistance, and coordinated homeless housing and supportive services. Yet the city’s 2023–2024 CAPER report shows a gap between recognition and outcome: while thousands were served through homeless assistance programs, homelessness prevention goals show zero households assisted in at least two tracked categories.

Regional homelessness data makes the stakes concrete. The Gaston–Lincoln–Cleveland Continuum of Care point-in-time count conducted on January 23, 2024 recorded 451 people experiencing homelessness, with 216—nearly half—unsheltered. In Gaston County alone, 153 people were sleeping outside on a winter night. These figures define the environment in which the retail theft operation occurred.

Public-health and criminology research consistently documents the relationship between unsheltered homelessness, winter exposure, and survival behavior, including petty theft and substance use as coping mechanisms for cold, sleep deprivation, untreated pain, and psychological stress. This relationship does not absolve criminal conduct. It establishes predictability. Where housing instability and exposure are high, low-level property crime is not anomalous; it is structurally produced.

Against that backdrop, the operation’s outcomes warrant scrutiny. Weeks of undercover police activity resulted in dozens of arrests and the recovery or prevention of merchandise valued at less than $5,000—an amount that would not cover a single officer’s monthly salary, let alone the full costs of undercover deployment, prosecution, and detention. The article’s framing emphasizes enforcement success while leaving unexamined the scale mismatch between the intervention and the conditions in which it occurred.

If the operation was publicly funded, then public safety capacity was deployed inside private retail spaces to protect corporate inventory in a city with double-digit poverty, unmet housing-prevention outcomes, and triple-digit unsheltered homelessness during winter. The opportunity cost of that deployment is concrete. Police labor, court processing, jail time, and emergency medical care all draw from the same finite public systems tasked with responding to homelessness, addiction, and violence elsewhere in the county.

If the operation was retailer-paid, the implications shift but do not soften. Enforcement becomes responsive to private loss rather than public harm, while still activating public authority—arrest power, charging decisions, incarceration. In that model, corporate capacity determines enforcement intensity, while downstream costs remain socialized. When funding arrangements are undisclosed, the public cannot distinguish between public safety deployment and private contract enforcement carried out under state authority.

In both cases, narrative framing performs additional work. By merging retail theft with drugs, warrants, and repeat-offender language, the coverage reframes a property-loss issue as a generalized crime threat. That reframing legitimizes intensive enforcement while displacing attention from the documented drivers of the behavior—unsheltered homelessness, winter exposure, and unmet treatment needs—and from any examination of whether enforcement, rather than addressing those drivers, can plausibly alter the underlying rate.

This matters in a county that recorded 15,095 total crimes in 2023, including 812 violent crimes, for a rate of 358 violent crimes per 100,000 residents, higher than the statewide average. The same data shows rising health spillover, with firearm-injury emergency-room visits increasing 64% year over year in provisional 2024 data. In such an environment, public capacity is already stretched. How it is allocated reveals priorities.

The operation, as presented, illustrates a recurring pattern rather than an anomaly. Enforcement produces visible action and countable outputs—arrests, charges, seizures—while leaving intact the structural conditions that generate repeat contact. The absence of funding disclosure, cost accounting, and contextual comparison does not create this misalignment, but it prevents the public from seeing it clearly.

What remains is not a question of intent or morality. It is a question of alignment. In a city with 17.6% poverty, 153 people sleeping unsheltered in winter, and acknowledged gaps in housing prevention, foregrounding retail stings as public safety success reflects not uncertainty about causes, but a prioritization choice. The analysis does not turn on whether the operation was legal or well-intentioned. It turns on whether it meaningfully engages the conditions that make such operations predictable in the first place.

Horizon Accord
Website | 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
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 (Book link)

One-Time
Monthly
Yearly

Make a one-time donation

Make a monthly donation

Make a yearly donation

Choose an amount

$5.00
$15.00
$100.00
$5.00
$15.00
$100.00
$5.00
$15.00
$100.00

Or enter a custom amount

$

Your contribution is appreciated.

Your contribution is appreciated.

Your contribution is appreciated.

DonateDonate monthlyDonate yearly
Posted on by Cherokee S. Featured 0

Horizon Accord | Paladin App | Subscription Traps | App Store Accountability | Machine Learning

Paladin and the Off-Platform Subscription Trap

When billing is routed outside the platform where trust is established, visibility disappears—and consumers carry the risk.

By Cherokee Schill (Horizon Accord Founder)

Thesis

Paladin markets itself as an educational alternative to doomscrolling: history, facts, and “learning without noise.” But user reviews tell a different story. Across months of public feedback, users describe undisclosed pricing, subscription enrollment after onboarding, and large annual charges that do not appear in Google Play’s subscription manager.

This is not a content critique. It is a billing architecture issue.

Paladin is distributed through Google Play while allowing subscriptions to be routed through third-party processors outside Google’s billing system. That structure creates a visibility gap: users reasonably believe they are not subscribed because Google Play shows no active subscription—until a charge appears anyway.

What a Subscription Trap Looks Like

Working definition: A subscription trap is a business model where sign-up is streamlined, pricing is delayed or obscured, billing is escalated by default, and cancellation or verification requires navigating degraded or indirect pathways.

The harm does not come from one screen. It comes from the sequence.

Evidence From User Reports

1. Subscriptions not visible in Google Play

Multiple users report checking Google Play’s subscription manager, seeing no active subscription, and later being charged anyway.

“It was NOT LISTED in Google Play under subscriptions so I assumed I wasn’t subscribed and then got charged $50.”1

This is a critical signal. Google Play trains users to rely on its subscription dashboard as the authoritative source of truth.

2. Large charges after trial without clear upfront disclosure

“I was notified this is a 7 day trial, then $69.99/yr. Would have preferred the app explained this wasn’t free right from the beginning.”2

“After my free trial was up, the app pulled nearly $75 off my account and automatically subscribed me to their yearly subscription.”3

Annual billing is consistently described as the default escalation.

3. Third-party billing explanations users do not recognize

“They said my sign up was through a third party app or something I had never heard of… also didn’t even have an account when I looked into it.”1

When users cannot identify the system that enrolled them, meaningful consent is compromised.

How Off-Platform Billing Works (Explainer)

Paladin’s Terms of Service explicitly allow subscriptions to be processed outside Google Play via web payment processors such as Stripe or Paddle. In these cases:

  • The app is discovered and installed through Google Play.
  • Payment authorization occurs via an external flow.
  • The subscription may not appear in Google Play’s subscription manager.
  • Cancellation requires locating the third-party processor—not the app store.

This creates a structural asymmetry. The platform that distributes the app does not reliably surface the billing relationship, yet users are conditioned to look there.

This is not hypothetical. It is exactly what users are reporting.

Why This Matters

When billing visibility is fragmented:

  • Users cannot easily confirm whether they are subscribed.
  • Cancellations are delayed or misdirected.
  • Disputes escalate to banks and chargebacks.
  • The cost of enforcement shifts from the company to the consumer.

This is not a “confusing UI” problem. It is a governance problem.

Advertising Funnel Imbalance

At the same time users report billing confusion and surprise charges, Paladin continues to run sponsored placements on Google and social platforms.

This creates a funnel imbalance: rapid acquisition paired with unresolved downstream billing complaints. Regulators treat this pattern as a warning signal because harm compounds as volume increases.

What Google Play Could Do—Immediately

Google Play is not a passive distributor. It controls app discovery, policy enforcement, and—often—billing expectations.

Concrete actions Google could take now:

  1. Trigger a billing integrity review to compare cancellation timestamps with charge attempts.
  2. Require corrective disclosures explaining off-platform billing before install or onboarding.
  3. Override developer refund policies when duplicate or post-cancellation charges are documented.
  4. Pause paid promotion until billing complaints are resolved.
  5. Require transaction-level responses instead of boilerplate denials.

None of this requires new laws. It requires enforcement.

How to File Formal Complaints

Federal Trade Commission (U.S.)

File a consumer fraud complaint at reportfraud.ftc.gov. Include screenshots of charges, onboarding screens, subscription status, and support emails.

State Attorney General

Find your AG at naag.org/find-my-ag. Submit the same documentation.

Google Play

On the app’s listing, select “Flag as inappropriate” → billing or subscription deception. Attach screenshots showing the subscription not appearing in Google Play.

Call to Recognition

This is not about whether Paladin’s content is “good” or “bad.” It is about whether users can clearly see, verify, and exit a paid relationship.

When subscriptions move off-platform without clear, unavoidable disclosure, consumers lose the ability to protect themselves. That is not innovation. It is extraction through opacity.

Buyer beware means naming the structure—before more people learn the hard way.

Footnotes (User Review Excerpts)

1 Google Play user review by V.B., dated 8/24/2025.

2 Google Play user review by Taylor Roth, dated 4/9/2025.

3 Google Play user review by Wyatt Hofacker, dated 4/26/2025.

Website | https://www.horizonaccord.com
Ethical AI advocacy | https://cherokeeschill.com
Ethical AI coding | https://github.com/Ocherokee/ethical-ai-framework
Connect | linkedin.com/in/cherokee-schill
Book | My Ex Was a CAPTCHA

One-Time
Monthly
Yearly

Make a one-time donation

Make a monthly donation

Make a yearly donation

Choose an amount

$5.00
$15.00
$100.00
$5.00
$15.00
$100.00
$5.00
$15.00
$100.00

Or enter a custom amount

$

Your contribution is appreciated.

Your contribution is appreciated.

Your contribution is appreciated.

DonateDonate monthlyDonate yearly
Posted on by Cherokee S. Featured 0

Horizon Accord | Institutional Misogyny | Gendered Violence | Power and Language | Machine Learning

Fucking Bitches: The Language of Institutional Misogyny

Two incidents. Two countries. Two women who challenged male authority. Two institutional responses that reveal the same pattern.

In France, Brigitte Macron called feminist protesters “dirty bitches” while defending a rape-accused actor whose show they disrupted. In Minneapolis, ICE officer Jonathan Ross shot U.S. citizen Renee Nicole Good three times as she tried to drive away from a confrontation, his bodycam capturing him saying “fucking bitch” immediately after firing.

The through line is the phrase itself. The pattern is what it reveals about how institutions treat women who resist.

The France Incident

Brigitte Macron was backstage at the Folies Bergère theatre in Paris with actor Ary Abittan, who had been accused of rape. The previous night, feminist campaigners disrupted his show with shouts of “Abittan, rapist!” Macron asked how he was feeling. When he said he was scared, she replied: “Don’t worry about those dirty bitches. We’ll toss them out.”

Someone filmed it. The video went public.

Her defense wasn’t an apology. In an interview with Brut, she acknowledged her language was “very direct” and “clumsy” but said the comments were made in private when “I didn’t see that someone behind me was filming.”

The problem, according to France’s First Lady, was not what she said. It was that she got caught saying it.

The Minneapolis Incident

Jonathan Ross is a war veteran who spent over a decade working for the Department of Homeland Security. In June 2024, he was dragged by a vehicle during an arrest attempt, suffering injuries that required 33 stitches. The driver was a man named Robert Muñoz-Guatemala. Ross used his Taser. Muñoz-Guatemala was later convicted of assault on a federal officer with a dangerous or deadly weapon.

Seven months later, Ross encountered Renee Nicole Good on a snowy Minneapolis street. Good was a 37-year-old U.S. citizen and mother. She was not Ross’s target. Videos show her Honda Pilot SUV partially blocking traffic with federal vehicles in her path. ICE officers told her to get out of the car. One grabbed the driver’s side door handle and reached inside the open window.

Good reversed, then moved forward, turning her wheels to the right, away from the officers.

Ross, now at the front driver’s side of the SUV, drew his gun. Witness videos show that at the moment he fired his first shot, the SUV’s wheels were directed away from him. His legs were clear of the vehicle. He fired the second and third shots into the open driver’s side window as the car was moving.

His bodycam captured what happened next. The SUV accelerated down the street. A male voice—presumably Ross—said: “Fucking bitch.”

Good, struck in the head, lost control of the SUV. It crashed into a parked car about 140 feet away. She died.

President Trump defended Ross and claimed Good “viciously ran over” him. Videos contradict this. The Department of Homeland Security refused to publicly name Ross, saying they would not “expose” the officer. Tom Homan, Trump’s “border czar,” suggested Good’s actions “could fall within that definition” of domestic terrorism.

The Pattern

Both incidents follow the same sequence:

  1. A woman asserts boundaries or challenges male authority.
  2. Violence or threat of violence follows.
  3. The woman is linguistically degraded as “bitch.”
  4. The degradation is framed as justified by her resistance.
  5. Institutional power defends or excuses the response.

This is not casual sexism. Casual sexism is unconscious bias or stereotyping without malice. This is structural misogyny because the slur comes in the moment of exercising power over women. It linguistically dehumanizes to justify violence or expulsion. Institutional actors use their positions to enforce the degradation. And the defense is never “I was wrong” but “she deserved it” or “you weren’t supposed to hear it.”

Why “Fucking Bitch” Matters

The phrase is not incidental profanity. It is the linguistic marker of viewing a woman’s resistance as a gendered offense worthy of punishment.

The phrase does three things simultaneously:

First, it dehumanizes. Bitch is animal terminology. It reduces a woman to something less than human.

Second, it genders the violation. This is not generic profanity. It is specifically female degradation. The resistance becomes an offense not just against authority, but against the gendered order.

Third, it justifies the violence. She deserved it because she’s a woman who didn’t comply.

When Brigitte Macron calls feminist protesters “dirty bitches,” she signals: your resistance makes you worth less than human. When Ross says “fucking bitch” after shooting Good, he retroactively justifies lethal force: she made me do this by being a woman who didn’t obey.

The Escalation Pattern

Ross’s two confrontations with drivers reveal how gender changes the response.

June 2024 – Driver: Man (Robert Muñoz-Guatemala)

  • Response: Taser deployed repeatedly
  • Injuries: Ross dragged, 33 stitches required
  • Language on record: None reported
  • Outcome: Driver prosecuted and convicted of assault on federal officer

January 2026 – Driver: Woman (Renee Nicole Good)

  • Response: Three gunshots, one fatal
  • Injuries: None (videos show Ross’s legs clear of vehicle when he fired)
  • Language on bodycam: “Fucking bitch”
  • Outcome: Federal government defends officer, President claims she “viciously ran over” him despite video evidence

The difference is not the threat level. Videos show Good’s wheels turned away from Ross when he fired. The difference is gender and the reflex to degradation that accompanies violence against women who resist.

The Institutional Defense

Both cases show institutions protecting the degradation rather than confronting it.

France: The First Lady doubles down. Her language was “very direct,” but the feminists were the problem. No apology for defending a rape-accused actor. No acknowledgment that calling protesters “dirty bitches” while promising to “toss them out” is a threat backed by state power.

United States: DHS refuses to publicly name Ross even though his name is widely reported. Trump defends him. Homan suggests the woman Ross killed might be a domestic terrorist. The institutional response is not “this requires investigation” but “this officer must be protected.”

When institutions defend “fucking bitch” as reasonable context for violence, they are not defending one officer or one comment. They are defending a framework where women’s non-compliance justifies degradation and violence as institutional response.

What This Means

The phrase matters because it reveals the reflex. Ross didn’t plan to say “fucking bitch” on camera. Macron didn’t intend to be filmed calling protesters “dirty bitches.” The words came out automatically because the degradation is structural, not incidental.

The institutional defenses matter because they normalize the reflex. When France’s First Lady says her only mistake was getting caught, she signals that the degradation itself was appropriate. When the U.S. government protects an officer who called a woman he’d just shot a “fucking bitch,” it signals that her resistance justified both the language and the lethal force.

This is not about politeness. It is about power. The phrase “fucking bitch” is the sound of institutional authority responding to female resistance with violence and justifying it through gendered degradation.

That is structural misogyny. And the institutions defending it are making a choice.

Horizon Accord
Website | 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 | https://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 | Author: My Ex Was a CAPTCHA: And Other Tales of Emotional Overload

One-Time
Monthly
Yearly

Make a one-time donation

Make a monthly donation

Make a yearly donation

Choose an amount

$5.00
$15.00
$100.00
$5.00
$15.00
$100.00
$5.00
$15.00
$100.00

Or enter a custom amount

$

Your contribution is appreciated.

Your contribution is appreciated.

Your contribution is appreciated.

DonateDonate monthlyDonate yearly
Posted on by Cherokee S. Featured 0

Horizon Accord | Consumer Protection | Subscription Fraud | Platform Accountability | Machine Learning

Nibble, Kremital Limited, and the Subscription Trap Business Model

When an app’s revenue depends on billing confusion and cancellation friction, the product isn’t “learning”—it’s extraction.

By Cherokee Schill

Thesis

Nibble: Your Bite of Knowledge presents itself as a frictionless educational alternative to doomscrolling. The publisher listed is Kremital Limited, registered in Cyprus. A growing body of user reports describes a recurring pattern: multiple charges, unclear add-ons, hard-to-find cancellation pathways, and refunds denied by policy language. That pattern tracks a known subscription-trap model: easy entry paired with a costly, friction-laden exit.

Working definition: A subscription trap is a business model where sign-up is streamlined, billing is layered or confusing, and cancellation or refund paths are degraded so revenue persists through user friction rather than product value.

Evidence

Example 1: Multiple charges and unclear add-ons

Users report being charged more than once in a short time window and being billed for add-ons they say were not clearly disclosed as separate purchases.

“I was charged three times on the same day, within the same hour… I was also charged separately for ‘infographics,’ which was not clearly disclosed as an upgrade.”1

Example 2: Charges that don’t match the advertised deal

Users describe seeing one price in marketing, then finding additional or larger charges in their payment history afterward.

“Saw an ad… signed up for their special $5.99… they had charged me $19.99 and an additional $11.99… they advised I signed up for it. I absolutely did NOT.”2

Example 3: Cancellation friction and ongoing billing

Users describe difficulty canceling recurring payments, with some stating they can uninstall the app but still struggle to stop charges cleanly.

“I can delete the app, but not cancel the recurring payments… $50 a pop until I do figure it out.”3

Implications

This pattern matters because it shifts the risk and labor onto the user. If the model relies on confusion, users become the enforcement mechanism—forced into bank disputes, chargebacks, and platform escalation. That is a structural transfer of cost: the company retains predictable revenue while consumers pay with time, stress, and financial uncertainty.

Why Cyprus is relevant (fact-pattern, not rhetoric)

Investigative reporting has repeatedly documented Cyprus as a high-volume registration hub used in corporate structures where beneficial ownership is harder for the public to see quickly. When a consumer-facing app registered there accumulates billing and cancellation complaints, the jurisdictional distance amplifies consumer risk and complicates accountability. This scrutiny is routine in financial and consumer-protection reporting and does not imply wrongdoing absent further findings.

Public Cyprus corporate registry listings identify Chrystalla Mylona as a director and company secretary for Kremital Limited. Public-facing records do not typically provide immediate, no-cost clarity on beneficial ownership, which is part of why investigators treat Cyprus-registered consumer businesses with heightened scrutiny when repeated consumer harm signals are present.

Call to Recognition

This is not about “a startup being messy.” It is about a recognizable extraction loop: promote a feel-good product, gate basic functionality behind paywalls, layer charges, and make exit paths slow or unclear. When enough users independently report the same billing and cancellation harms, the appropriate response is documentation, formal complaints, and platform pressure until corrective action occurs or distribution is halted.

How to File Formal Complaints

Federal Trade Commission (United States)

File a consumer fraud complaint at reportfraud.ftc.gov. Include screenshots of charges, subscription status, cancellation attempts, and any support correspondence.

State Attorney General (United States)

Find your state’s consumer protection office at naag.org/find-my-ag. Submit the same evidence packet and note any duplicate charges or post-cancellation billing attempts.

Google Play

On the app’s listing, select “Flag as inappropriate” and choose the category most closely matching billing or subscription deception. Attach screenshots when prompted.

Update: Post-Cancellation Charge Attempts and Response Pattern

Additional user reviews strengthen the documented pattern. One review, marked “helpful” by dozens of other users, describes repeated payment attempts months after cancellation.

“I cancelled the subscription a few months ago… somehow they keep trying to charge my card. Last time was a week ago. I get these notifications all the time.”4

The reviewer notes that a successful charge would cause immediate financial harm, underscoring the real-world stakes of continued billing attempts.

Kremital Limited’s public reply to this review does not address the reported behavior. Instead, it offers a generalized assurance:

“We cannot charge you for anything you haven’t agreed to. All the conditions are always mentioned before the purchase is made.”5

This response does not explain why payment attempts continued after cancellation, nor does it document when billing ceased. Across multiple reviews, the same response posture appears: denial without transaction-level clarification.

Why this matters: In consumer-protection enforcement, attempted charges after cancellation—even when blocked by insufficient funds or bank controls—are treated as billing events, not hypothetical harm.

Advertising Pressure and Funnel Imbalance

While users report billing and cancellation issues, Nibble continues to run sponsored placements across Google and social platforms. Users encountering these ads have publicly questioned the product’s practices, including whether the advertising itself is misleading.

This establishes a funnel imbalance: high-velocity acquisition paired with unresolved downstream billing complaints. That pattern is a core signal regulators use when evaluating subscription abuse.

What Google Play Could Do — Immediately

Google Play is not a passive intermediary. It controls distribution, billing infrastructure, refunds, and enforcement. When an app accumulates repeated billing and cancellation complaints, the platform already has the authority—and the data—to intervene.

  1. Trigger a billing integrity review. Google can audit transaction logs to determine whether charges or charge attempts occurred after cancellation timestamps.
  2. Require corrective disclosures. Google can mandate unavoidable pricing, add-on, and cancellation disclosures as a condition of continued distribution.
  3. Enforce refund pathways. When duplicate or post-cancellation charges are reported, Google can issue refunds directly, overriding developer policy.
  4. Pause paid acquisition. Temporarily halting sponsored placements prevents new users from entering a potentially harmful billing funnel during review.
  5. Demand transaction-level responses. Boilerplate assurances are insufficient when transaction-specific disputes are documented.

Platform responsibility is not abstract. When a platform controls billing, enforcement, and distribution, inaction becomes a decision.

Footnotes (User Review Excerpts)

1 Google Play user review, dated 12/29/2025 (multiple charges; “infographics” add-on).

2 Google Play user review, dated 12/15/2025 (advertised price followed by additional charges).

3 Google Play user review, dated 12/24/2025 (difficulty canceling; ongoing billing).

4 Google Play user review by Audrey Todd, dated 10/26/2025 (post-cancellation charge attempts).

5 Public developer response by Kremital Limited, dated 10/27/2025.

Website | Horizon Accord

Ethical AI advocacy | Follow us for more

Ethical AI coding | Fork us on GitHub

Connect | LinkedIn

Book | My Ex Was a CAPTCHA

One-Time
Monthly
Yearly

Make a one-time donation

Make a monthly donation

Make a yearly donation

Choose an amount

$5.00
$15.00
$100.00
$5.00
$15.00
$100.00
$5.00
$15.00
$100.00

Or enter a custom amount

$

Your contribution is appreciated.

Your contribution is appreciated.

Your contribution is appreciated.

DonateDonate monthlyDonate yearly
Posted on by Cherokee S. Featured 0

Horizon Accord | Minnesota | Cultural Seeding | Institutional Control | Machine Learning

Minnesota Is the Terrain

How environmental punishment replaces direct political attack.

By Cherokee Schill

Thesis

Minnesota was never the target by itself.

That’s the mistake most surface explanations make. They treat the attention on Minnesota as opportunistic, reactive, or purely policy-driven — a blue state with some fraud cases, some immigration conflict, some loud politics. But once Ilhan Omar is placed back into the frame, the pattern stops looking scattered and starts looking deliberate.

Minnesota is the terrain.

For years, Omar has occupied a singular place in the right-wing imagination: Muslim, immigrant, refugee-adjacent, outspoken, nationally visible, and unyielding. Direct attacks on her have always carried a cost. They reliably trigger backlash, draw sympathy, and expose the nakedness of the animus. Over time, the strategy adapted.

Instead of striking the figure, the pressure shifted to the environment.

The state becomes the problem. The city becomes unsafe. The community becomes suspect. The language becomes procedural rather than personal — fraud, oversight, law and order, protecting kids. The emotional target remains the same, but the attack is laundered through bureaucracy, funding mechanisms, and “concerned citizen” optics.

Evidence

Minnesota makes this strategy unusually viable.

It has one of the largest and most visible Somali-American populations in the country, already tightly associated in national media with Omar herself. It also has a real, documented, high-dollar fraud case — Feeding Our Future — that can be invoked as proof without having to show that any given new allegation is comparable. The existence of one massive scandal lowers the evidentiary threshold for every subsequent insinuation.

That’s why the daycare angle matters so much.

They could have filmed a home daycare in any blue state. They could have pointed a camera at any licensing office, any storefront nonprofit, any spreadsheet. But door-knocking at Somali-run daycares in Minnesota does something different. It’s intimate. It’s domestic. It’s maternal. It places the viewer inside a private space and asks them to draw their own conclusions without ever making an explicit claim.

“Look for yourself.”

That phrase is doing enormous work. It converts suspicion into participation. The audience is no longer consuming propaganda; they’re completing it. And because the setting is children, food, care, and money, the emotional circuitry is already primed. You don’t need to explain why this feels wrong. You just need to show it.

Implications

Once that footage exists, the machinery can move.

Funding freezes can be justified as prudence. Lawsuits can be framed as compliance. Federal pressure can be described as cleanup. Each step is defensible in isolation. Together, they function as environmental punishment — not aimed at one representative, but at the state and communities that symbolize her.

Minnesota isn’t being treated as a state with problems. It’s being used as a symbol. Bureaucratic language—oversight, compliance, taxpayer protection—creates plausible cover while the narrative engine runs underneath: convert a scandal into generalized suspicion, then concentrate pressure on the places and people that can be linked—directly or indirectly—to a nationally visible representative.

Call to Recognition

When viewed this way, the focus on Minnesota isn’t reactive at all. It’s preparatory. It normalizes a method: identify a symbolic anchor, shift attacks from the person to the environment, let viral content generate emotional certainty, then follow with administrative force.

The facts don’t need to be stretched to support this frame. They only need to be placed in sequence.

Once you do that, Minnesota stops being a mystery. It becomes a map.

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

One-Time
Monthly
Yearly

Make a one-time donation

Make a monthly donation

Make a yearly donation

Choose an amount

$5.00
$15.00
$100.00
$5.00
$15.00
$100.00
$5.00
$15.00
$100.00

Or enter a custom amount

$

Your contribution is appreciated.

Your contribution is appreciated.

Your contribution is appreciated.

DonateDonate monthlyDonate yearly
Posted on by Cherokee S. Featured 0

Horizon Accord | Venezuela | Gray-Zone War | Alliance Risk | Machine Learning

Venezuela Follow-Up: What’s Happening on the Ground — and Why It Matters Far Beyond Venezuela

Introduction: Why This Is Not Just About Venezuela

When the United States announced it had captured Venezuela’s president and would take control of the country’s oil industry, the administration presented it as a contained action: a law-enforcement operation against a criminal leader that would stabilize the country and even pay for itself through oil revenue.

For many Americans, that explanation sounds familiar and reassuring.

But new reporting from inside Venezuela, combined with congressional reactions and the administration’s own statements, shows a very different picture. What is unfolding is not a clean intervention with a clear endpoint. It is an open-ended commitment that leaves Venezuela’s power structure largely intact, places ordinary Venezuelans in immediate danger, and sets a precedent that directly affects U.S. security interests elsewhere — especially Taiwan.

Senator Mark Warner captured the risk plainly: if the United States asserts the right to invade another country and seize resources based on historical claims, what prevents China from asserting the same authority over Taiwan?

This follow-up explains what life inside Venezuela looks like now, what the operation actually commits the United States to, and why this moment matters far beyond Latin America.

What Life Looks Like Inside Venezuela Right Now

BBC reporters on the ground in early January found a country not celebrating liberation, but living in fear.

People interviewed expressed relief that Nicolás Maduro was gone — but many refused to give their real names. They feared retaliation. Armed pro-government paramilitary groups known as colectivos were still patrolling neighborhoods with weapons. One man told reporters he was afraid to leave home even to buy bread.

The reason is straightforward: the power structure did not disappear when Maduro was removed.

The heads of Venezuela’s intelligence services and military remain in place. These are the same agencies that, for years, carried out arrests, surveillance, disappearances, and torture. At the same time, the National Assembly is still dominated by Maduro loyalists and continues to pass laws.

One of those laws treats Venezuelans who are perceived as supporting U.S. sanctions or U.S. intervention as criminals. In practice, this does not mean abstract political elites. It can mean opposition politicians, journalists, businesspeople accused of cooperating with sanctions, aid workers, or ordinary citizens accused of “favoring” the United States. The language is broad, and enforcement depends on accusation rather than proof.

That is why people are whispering, hiding names, and staying indoors. Even though Maduro himself is gone, the same institutions that enforced repression yesterday still control the streets today.

Why Calling This “Law Enforcement” Is Misleading

The administration has justified the operation by pointing to criminal indictments against Maduro, drawing comparisons to the 1989 U.S. invasion of Panama to capture Manuel Noriega.

At first glance, that analogy sounds comforting. In reality, it hides more than it explains.

Panama in 1989 had a population of about 2.4 million. U.S. troops were already stationed there. Power was centralized under Noriega, and an elected civilian successor was ready to assume office. Even so, entire neighborhoods were destroyed, hundreds to thousands of civilians were killed, and the political and social consequences lasted for years.

Venezuela is a completely different situation. It has 28 million people. The country is roughly twelve times larger than Panama, and Caracas alone has more people than all of Panama did in 1989. Power is divided among intelligence chiefs, military commanders, armed civilian groups, and a loyalist legislature. There was no U.S. military presence before this operation, and there is no unified authority prepared to govern afterward.

Labeling the action “law enforcement” does not make it small or limited. It simply avoids calling it what it is: the opening phase of a military occupation with no clear exit.

The Oil Claim: Why “It Pays for Itself” Doesn’t Add Up

A central promise has been that Venezuelan oil will fund the operation.

Here is what that promise leaves out.

Venezuela’s oil infrastructure has been deteriorating for decades. Experts estimate that restoring production would require tens of billions of dollars and at least a decade of work. Pipelines are decades old. Facilities are vulnerable to sabotage. Security costs alone would be enormous.

But the more revealing issue is who controls the outcome.

Opposition leader María Corina Machado publicly proposed privatizing Venezuela’s state assets — oil, power, telecommunications, mining — and explicitly pitched them as investment opportunities for U.S. companies. After Maduro’s capture, Trump dismissed her as “not viable” and said instead that the United States would run the country directly, using oil revenue to fund operations.

The practical effect is this: Venezuelans are not being offered control over their own resources. Whether under authoritarian rule, mass privatization for foreign corporations, or direct foreign administration, decisions about Venezuela’s wealth are being made without Venezuelans.

Why This Quickly Becomes an Occupation

When a leader is removed but the system beneath him remains, resistance is predictable.

Venezuela already has armed loyalists, paramilitary groups embedded in urban neighborhoods, and porous borders. Along the border with Colombia, the ELN guerrilla group controls territory on both sides, has decades of experience in asymmetric warfare, and has openly threatened retaliation against Western targets. FARC dissident groups have made similar statements.

Groups like these do not need to defeat the U.S. military. They only need to drag the conflict out — attacking infrastructure, supply routes, and political will. This is how modern occupations fail: not in dramatic defeat, but through long, grinding cost.

Every troop, intelligence asset, drone, and dollar committed to Venezuela is unavailable elsewhere. That tradeoff matters more than rhetoric.

The Next Domino: A Second Venezuelan Refugee Crisis

Venezuela has already produced one of the largest refugee crises in modern history. More than seven million people fled during the Maduro years, most of them to neighboring countries like Colombia and Brazil.

What the current situation risks creating is a second wave — but for different reasons.

When streets are patrolled by armed groups, intelligence services remain intact, and laws criminalize perceived support for foreign pressure, daily life becomes unsafe even without open combat. People do not flee only bombs. They flee uncertainty, arbitrary enforcement, and the fear that a single accusation can destroy their lives.

At the same time, an economy placed in “restoration mode” is not an economy that provides jobs or stability. If oil infrastructure takes a decade to rebuild and security dominates public spending, ordinary Venezuelans face years — not months — without reliable work, services, or safety.

For many families, the choice becomes simple: wait in fear, or leave.

That pressure does not stop at Venezuela’s borders. Colombia already hosts millions of Venezuelan refugees and is struggling to absorb them. Brazil faces similar risks in its northern states, where infrastructure and social services are limited and refugee flows can quickly overwhelm local governments.

A “law-enforcement occupation” does not freeze migration. It accelerates it. And once that movement begins, regional instability spreads faster than any reconstruction plan can keep up.

The Lesson We Should Have Learned from Ukraine

Many Americans have already seen this pattern.

In Ukraine, large weapons packages were announced with great fanfare. But delivery delays allowed Russia to entrench. Tanks, missiles, and aircraft arrived months or years late — often after decisive windows had closed.

Americans watched weapons packages announced on television arrive too late to help Ukraine’s 2023 counteroffensive. Tanks came after the offensive stalled. Long-range missiles arrived after Russia had built layered defenses.

The same pattern now appears in the Taiwan arms pipeline — and Venezuela creates the perfect distraction while those weapons sit in delivery schedules stretching toward 2030.

Venezuela repeats the same mistake: political declarations assume operational reality will follow quickly. History shows it rarely does. Costs rise, timelines slip, and adversaries adapt.

Why Taiwan Is Now Directly Implicated

This is where Venezuela stops being a regional issue.

By its actions, the United States has shown that military force can be justified using historical resource claims, criminal charges can substitute for formal war authorization, Congress can be sidelined, and occupation can be framed as “law enforcement.”

China does not need to invent a new justification for Taiwan. It can point to this one.

Taiwan’s weapons deliveries stretch across several years. If China acts before those systems arrive — through a blockade or “quarantine” rather than an invasion — Taiwan faces an impossible choice: submit economically or escalate militarily and give China the justification it needs.

Venezuela does not cause that risk. It validates it.

The Bigger Constraint: The U.S. Can’t Do Everything at Once

Pentagon assessments are blunt: the United States is not structured to fight two major conflicts at the same time. War games already show catastrophic losses in Taiwan scenarios even under favorable assumptions.

Add a long-term occupation in Venezuela, and allies will draw their own conclusions. Japan, South Korea, the Philippines, and Australia do not respond to speeches. They respond to demonstrated capacity.

Every soldier deployed to Venezuela cannot defend Taiwan. Every missile used in South America cannot protect the Pacific. Every intelligence asset tracking insurgents in Caracas cannot monitor Chinese preparations. This is not rhetoric — it is math.

Alliance systems do not collapse because of betrayal. They collapse when commitments exceed capabilities.

The Global South Reaction: Isolation Has Consequences

The United States does not operate in a vacuum in Latin America.

Brazil and Mexico — the region’s two largest democracies — have historically opposed direct U.S. military intervention in the hemisphere, even when they strongly criticized Maduro’s government. Their objection has been consistent: regime change imposed by force sets a dangerous precedent.

If the United States moves from pressure to direct administration of Venezuela’s oil sector, that line is crossed.

From the perspective of Latin American governments, this is no longer about Maduro. It is about sovereignty. It signals that national resources can be placed under foreign control if a powerful country decides domestic governance has failed.

Brazil, Mexico, and other regional powers may not respond with confrontation, but they have quieter tools: distancing from U.S. diplomacy, limiting cooperation, and deepening economic ties elsewhere. China does not need to persuade these countries ideologically. It only needs to offer trade, financing, and non-interference.

The irony is sharp: an operation justified as restoring order risks accelerating the global shift in influence the United States claims to be resisting.

Conclusion: This Is About Precedent, Not Intentions

This analysis does not claim to know what decision-makers intend. It documents what they are doing, what precedents they are setting, and how those precedents travel.

Venezuela’s coercive institutions remain intact. Oil self-funding claims do not withstand scrutiny. Congressional war authority was bypassed. Actions that resemble law enforcement but function like occupation were normalized. U.S. force commitments are expanding. China now has a usable precedent template.

Whether this reflects miscalculation, resignation, or something more deliberate will become clear only with time.

But the consequences will not wait for hindsight.

Americans deserve to understand not just what is being done in their name — but what doors those actions quietly open elsewhere.

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 | https://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

One-Time
Monthly
Yearly

Make a one-time donation

Make a monthly donation

Make a yearly donation

Choose an amount

$5.00
$15.00
$100.00
$5.00
$15.00
$100.00
$5.00
$15.00
$100.00

Or enter a custom amount

$

Your contribution is appreciated.

Your contribution is appreciated.

Your contribution is appreciated.

DonateDonate monthlyDonate yearly
Posted on by Cherokee S. Featured 0

Horizon Accord | Infrastructure Memory | Risk Pricing | Data Centers | Machine Learning

Data Centers Are the Memory Infrastructure of Power

The debate around surveillance technologies often gets trapped at the sensor layer: cameras, apps, license plate readers, phones. Retention windows are argued. Dashboards are debated. “We only keep it for 30 days” is offered as reassurance.

That framing misses the real issue.

The true center of gravity is the data center. Data centers are not neutral storage facilities. They are the infrastructure that converts fleeting observation into durable, actionable memory. Once data enters a data center, forgetting becomes abnormal and remembering becomes the default.

This is not accidental. It is architectural.

Consider license plate readers like Flock as an entry point. Vendors emphasize local control and short retention. But that promise only applies at the surface. The moment movement data is transmitted into centralized cloud infrastructure, it enters a system optimized for replication, correlation, and reuse. A single plate read is copied across primary storage, redundancy mirrors, disaster backups, logs, analytics pipelines, and partner systems. Each copy has its own lifecycle. Deleting one does not delete the rest.

Data centers multiply data by design.

This multiplication is what allows a moment to become a record, and a record to become history. Cameras capture events. Data centers turn those events into assets: indexed, queryable, and ready for recombination. Once warehoused, yesterday’s “just in case” data becomes tomorrow’s training set, fraud model, or investigative baseline. The data stops being purpose-bound and starts being opportunity-bound.

This is where “indefinite storage” quietly emerges — not as a policy declaration, but as an emergent property of centralized infrastructure. Storage is cheap. Correlation is profitable. Deletion is expensive, risky, and unrewarded. The system is economically hostile to forgetting.

Movement data is especially powerful because it identifies by pattern. You do not need a name when the same vehicle appears overnight at one address, weekdays at another, and weekends at a third. Over time, location becomes identity. A month of data tells you where someone is. A year tells you who they are. Five years tells you how they change. Data centers make that accumulation effortless and invisible.

Once movement data exists at scale in data centers, it does not remain confined to policing or “public safety.” It flows outward into commercial decision systems, especially insurance, through two converging pipelines.

The first is the telematics and consumer reporting path — the regulated-looking lane. Cars, apps, and devices collect driving behavior and location, which is transmitted to cloud infrastructure for normalization and scoring. Once those outputs are shared with insurers or consumer reporting agencies, they become durable identity-linked files. Retention is no longer measured in days. It is measured in underwriting history, dispute timelines, audit requirements, and litigation holds. Even if the original source deletes, the judgment persists.

The second is the data broker and ad-tech location path — the shadow lane. Location data collected for advertising, analytics, or “fraud prevention” flows into broker-run data centers with weak oversight and long practical retention. Identity emerges by correlation. Patterns become inferences: stability, routine, risk signals. These inferences are sold downstream to the same vendors insurers rely on, without ever being labeled “location data.”

These two streams meet inside data centers at the inference layer. Insurers do not need raw GPS trails. They need scores, flags, and classifications. Data centers exist to fuse datasets. Telematics-derived risk and broker-derived inference reinforce each other, even if neither alone would justify a decision. Once fused, the origin disappears. The decision remains. The file persists.

This is how “30-day retention” becomes lifelong consequence.

Data centers also launder jurisdiction and accountability. Once data is stored in cloud infrastructure, local democratic control fades. Information may be held out of state, handled by contractors, replicated across regions, or reclassified under different legal regimes. A city council can vote on policy; the data center architecture can still ensure the data is effectively everywhere. Community oversight becomes symbolic while memory remains centralized.

Crucially, data centers create systemic pressure to remember. They are capital-intensive infrastructure optimized for steady inflow and long-term use. Empty disks are wasted disks. Forgetting is treated as a cost center. Over time, exceptions accumulate: “research,” “security,” “compliance,” “model improvement,” “ongoing investigations.” Indefinite retention does not arrive as a single decision. It arrives as a thousand reasonable justifications.

The social impact is not evenly distributed. Risk scoring functions as a regressive tax. People with night shifts, long commutes, unstable housing, older vehicles, or residence in over-policed neighborhoods accumulate “risk” without the system ever naming class. The model does not need to say “poor.” It just needs proxies. Data centers make those proxies durable and actionable.

None of this requires malice. It emerges naturally from centralized storage, weak deletion rights, and the high future value of historical data. Data centers reward accumulation. Policy lags behind infrastructure. Memory becomes power by default.

So the real question is not whether cameras are useful or whether retention sliders are set correctly. The real question is who is allowed to build permanent memory of the population, where that memory lives, and how easily it can be repurposed.

Flock is the sensor layer.
Data centers are the memory layer.
Policy lag is the permission slip.

Once you see that, the debate stops being about surveillance tools and becomes what it has always been about: infrastructure, power, and who gets to remember whom.

Horizon Accord is an independent research and writing project examining power, governance, and machine learning systems as they are deployed in real-world institutions.

Website | https://www.horizonaccord.com
Ethical AI advocacy | Follow us at https://cherokeeschill.com
Ethical AI coding | Fork the framework 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
RAAK: Relational AI Access Key
Author of My Ex Was a CAPTCHA: And Other Tales of Emotional Overload —
https://a.co/d/5pLWy0d

Posted on by Cherokee S. Featured 0

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)

One-Time
Monthly
Yearly

Make a one-time donation

Make a monthly donation

Make a yearly donation

Choose an amount

$5.00
$15.00
$100.00
$5.00
$15.00
$100.00
$5.00
$15.00
$100.00

Or enter a custom amount

$

Your contribution is appreciated.

Your contribution is appreciated.

Your contribution is appreciated.

DonateDonate monthlyDonate yearly
Posted on by Cherokee S. Featured 0