1. The Pentagon blacklisted Anthropic as a supply chain risk

2. Trump ordered all federal agencies to stop using Anthropic

3. OpenAI swooped in to sign a deal with the Pentagon almost immediately

Anthropic’s app shot to the top of the charts, the tech press championed Dario as a hero, open letters and petitions in support of Anthropic circulated, and employees posted about being on the “right side of history.”

But there's one major problem: the argument doesn't survive scrutiny.

Note 1: Claude is my current favorite AI model and I personally think Dario is well-intentioned even if I disagree with him.

Note 2: Anthropic’s other red line (fully autonomous weapons) is an equally incoherent argument but deserves separate treatment. This piece addresses the surveillance component.

What Is Dario Worried About?

In his public statement, Dario stated the following:

1. Under current law, the U.S. government can purchase detailed records of Americans’ movements, web browsing, and associations from commercial data brokers without obtaining a warrant.

2. The intelligence community has acknowledged this raises privacy concerns.

3. AI can now stitch this scattered, individually innocuous data into comprehensive profiles (automatically and at scale).

Dario believes the “law hasn’t caught up” with AI’s ability to synthesize commercially-available data. He wants contractual language preventing the Pentagon from using Claude for bulk collection and synthesis of Americans’ publicly available information.

The Pentagon refused, insisting on “all lawful purposes” — widely considered the status quo bare minimum for government contracts so they don’t get hamstrung by a subjective ToS in high-stakes operations.

“All Lawful Purposes” Is Not a Power Grab

An “all lawful purposes” clause doesn’t create new surveillance powers. It removes a private contractual veto over lawful government missions. The Fourth Amendment, FISA, EO 12333, DoD directives — all still apply.

This is a standard procurement baseline.

Vendors don’t get to decide which lawful missions the government can pursue. Without it, every AI company negotiates a different list of political ToS carve-outs, and procurement becomes hostage to the internal politics of private firms. If Dario thinks certain lawful practices should be banned, the remedy is legislation and litigation — not “Anthropic’s terms of service becomes national policy.”

A trending analogy frames Anthropic as a parts supplier telling NASA “we don’t trust the parts we’re selling you.” This completely misunderstands the dispute.

Anthropic isn’t saying Claude doesn’t work — these are models they’ve tested, red-teamed, and shipped to millions of consumers. The dispute is about who gets to use it and for what. That’s the difference between Boeing saying “this airframe isn’t rated for that stress load” vs. “we don’t like who’s flying the plane.”

And the DoD has its own highly-rigorous evaluation pipelines (red teams, NIST, CDAO) — the subtext is “we don’t trust the government’s judgment,” which is absurd coming from a company that sells API access to anyone with a credit card.

“Mass Surveillance” Is Rhetorical Trickery

“AI-enhanced law enforcement with commercially available datasets to detect crime” sounds fairly reasonable and boring. “Mass domestic surveillance” sounds terrifying and dystopian. They describe the same activity.

When most hear “mass surveillance” they think 24/7 trackers on all Americans — an Orwellian panopticon where federal agents monitor your grocery runs and group chats in real time. That’s not even remotely close.

In practice: the U.S. government buys (1) commercially available data that (2) people voluntarily gave to apps and mega corps, then (3) runs it through AI synthesis and pattern recognition to identify criminal networks… that’s it.

The majority of government “mass surveillance” is uncontroversially beneficial:

• Intelligence analysis

• Fraud and scam detection

• Tracking extremists with high odds of violence

• Counterterrorism

• Efficient policing

• Uncovering child exploitation and human trafficking networks

• Pinpointing fentanyl supply chains

• Cyber defense

• Identifying illegal immigrants

• Hunting gang/cartel networks

• Pinpointing espionage campaigns

Think about what opposing AI-enhanced data synthesis (i.e. “surveillance”) means:

• Fentanyl. AI can cross-reference financial flows, communication patterns, and logistics data to map a distribution network in hours. Without AI, analysts sift through the same data for weeks or months. During that delay, people die — 80,000+ overdose deaths per year.

• Child exploitation. NCMEC’s CyberTipline received 20.5 million reports in 2024. No human workforce can triage that volume. Every day an AI isn’t processing those reports is a day a child remains in an abusive situation because a lead sat in a queue.

• Human trafficking. AI can identify patterns across hotel bookings, online ads, financial transactions, and movement data. The manual alternative means victims stay in captivity longer — or indefinitely.

• Espionage. AI can link compromised devices, stolen-identity clusters, and laundering routes to identify foreign intelligence operations in hours instead of months.

Thinking that it’s evil or unconstitutional to buy publicly-available datasets and synthesize them with AI to catch criminals quicker is a ridiculous position.

• Well it’s alright to catch criminals…

• But hol’ up there buckaroo… not so fast!…

• We’re gonna need you to use CTRL + F through 10000 page documents instead…

Scale is a feature, not a bug.

Obvious: Nobody should support 24/7 ultra-granular privacy-invading surveillance of U.S. citizens… but that’s already mostly illegal and not the debate here. The debate is public datasets being used to catch criminals more efficiently. And even with this use-case, if you think there are major privacy concerns, you could set up an anonymized Chainlink-type network with oracles that block out all personally-identifiable intel until enough “heat signals” or “red flags” are detected; if a threshold is passed only then can individuals be further investigated. Then individuals can be reanonymized into the system.

And if we ever got to the point where legal “mass surveillance” via bulk public datasets is significantly more damaging than beneficial, it would mostly reflect erosion of human capital — not a flaw in the tool itself. Liberia has a constitution modeled after the United States. How similar do you think Liberia functions to the United States?

Constitutions and laws don’t matter unless you have people willing to enforce and abide by them.

With high human capital in government, AI surveillance (i.e. data synthesis) is an amplifier of effective law enforcement.

With low human capital, no anti-surveillance law protects you — they’ll just ignore it and do what they want… courts end up corrupted and there’s no backlash anyway.

The Math Makes This Embarrassing

Baseline harms are so large that even microscopic improvements justify “AI surveillance” (in ways that the government can legally do it).

• GAO estimates annual federal fraud losses at $233B–$521B. A 1% reduction is $2.3B–$5.2B per year.

• CDC reports 80,391 overdose deaths in 2024. Using the DOT’s $13.7M value of a statistical life, a 0.1% reduction (~80 lives) is worth ~$1.1B per year.

• FBI IC3 reports internet crime losses exceeding $16B. FTC reports consumer fraud losses above $12.5B.

• IBM reports AI in cybersecurity saved $1.9M per breach and reduced breach lifecycle by 80 days.

• IRS estimates a $696B gross tax gap. A 1% improvement is ~$6B–$7B per year.

Conservative combined annual benefit: $11B–$15B. Base case: $20B–$30B+. At mature deployment: $40B+.

Even if you assign a $500B cost to a catastrophic misuse scenario, the base case annual benefit (~$25B) means you’d need a 5% annual probability of every institutional check simultaneously failing for the EV to flip negative. Most honest people would put that well under 1%.

I had GPT-5.4-Pro build a full EV ledger: steady-state net EV of $41.9B/year, 2026 realized of $23.3B, probability net positive 88% — and it leaves child exploitation, counterintelligence, and military ISR completely unpriced.

Follow this logic to its conclusion: if the net effect is asymmetrically favorable, then opposing it is de facto pro-crime.

Even if not the intention, the consequence is that criminal networks get more time to operate and victims wait longer for rescue (assuming they get rescued).

The “Slippery Slope” and “Loyalty Scores”

Scott Alexander wrote a thorough legal explainer concluding that AI could theoretically be used to build a citizen “loyalty score.” And I appreciate his arguments.

If “mass surveillance” is used the way Scott implies, then I’m firmly on his side and there’s zero disagreement.

But every scenario he described involves private communications not in commercially available datasets:

• Emails/texts: Protected by the Fourth Amendment and Stored Communications Act. Require warrants.

• Browser history: Some browsing metadata is sold by brokers (the NSA has purchased it), but Scott’s scenario of searching a specific person’s browser history to find dirt is targeted surveillance requiring individualized access — not bulk pattern matching on commercially available data.

• Private speech: Requires a Title III wiretap order — one of the hardest things to get in U.S. law.

• Intercepting email: Federal crime under 18 U.S.C. § 2511.

The “all lawful purposes” debate is about commercially available broker data — location, purchase patterns, app usage. None of Scott’s scenarios involve that data. He’s arguing against surveillance that would already be illegal, which “all lawful purposes” would NOT authorize because it’s... not lawful.

Even the illegal version would require the whole chain to hold: a president orders it, nobody leaks it, no court strikes it down, no Congress investigates, no journalist exposes it, and the public doesn’t revolt. All in a country that couldn’t keep the NSA’s programs secret despite classification. The FBI’s 278,000 improper FISA searches were caught and improper queries dropped from 57,094 to 5,518 in one year.

Trump has had the FBI, NSA, CIA, and the entire military apparatus at his disposal for over a year. He hasn’t arrested journalists or “disappeared” political opponents. If a truly corrupt leader wanted a surveillance state, they wouldn’t care about Anthropic’s contract terms — they’d use other vendors, open-source models, or just ignore the fine print.

Following laws is downstream of human capital, not downstream of Anthropic’s ToS.

And here’s the thing people forget: you can already silence political adversaries and manipulate public discourse without AI. We literally just watched it happen.

The Biden administration pressured Facebook, Google, YouTube, and Twitter to censor conservative speech, suppress the Hunter Biden laptop story, and remove COVID dissent — what the House Judiciary Committee documented as the “Censorship Industrial Complex.”

The Twitter Files confirmed it. Missouri v. Biden litigated it. A federal judge called it “an almost dystopian scenario” resembling “an Orwellian Ministry of Truth.” No AI was needed. No “mass surveillance” contract clause was involved. The government just picked up the phone. Social credit scores, political targeting, speech suppression — none of these require AI. They require willingness. If the people in power want to do it, they’ll do it with or without Claude.

If “the government might theoretically do something terrible with this tool” is sufficient to withhold it, why give the government access to any databases, computers, or forensic labs? Microsoft Excel is a threat to Americans’ privacy because now the government can process data faster! Calculators are downright unconstitutional… we can add criminal damages faster than ever before!

Dario’s Stand Doesn’t Change Surveillance

The most practically damning problem: it accomplishes nothing at least in the near-term (it did generate a broader convo and serious momentum). And the cost-benefit calculus was not attempted and most still aren’t thinking critically about legal “mass surveillance.”

The NSA, ICE, DHS, FBI keep purchasing data regardless of whether Anthropic approves. The only question is which AI model analyzes it.

Net effect on surveillance? Zero.

Net effect on Anthropic? Potentially very negative; supply chain risk designation and federal bans could cost enterprise revenue and jeopardize cloud provider relationships.

Dario is not stopping surveillance at the moment — he’s just making it temporarily more painful while the government transitions to another AI provider.

Foreign Adversaries and Criminals Are Buying + Synthesizing the Same Data!

China, Russia, Iran, and North Korea are not following Anthropic’s code of ethics or U.S. law. They buy datasets, hack emails, extract phone data, and leverage whatever they can — then synthesize with private AIs.

Criminals do the same with jailbroken open-source models.

The ODNI’s declassified report explicitly notes this data is available to foreign governments.

Dario is arguing that the one entity with democratic accountability, legal constraints, judicial oversight, and a constitutional obligation to protect Americans should operate with blinders on.

Everyone else inevitably gets full power.

The U.S. government is being pressured to do it the slow way because most Americans don’t understand how legal “mass surveillance” works.

The correct move is probably the inverse: give the U.S. government the best possible tools to counter foreign surveillance and criminal exploitation that’s already happening. Elderly people are hounded from scam centers in India and Cambodia; the gov should be getting ahead of this stuff.

If Dario and Anthropic really care about privacy… they should advocate for the following:

1. Ban or sharply restrict the private brokerage of bulk sensitive data and data collection by all companies… OR (if the market is allowed to exist)

2. Permit tightly audited government AI use rather than forcing the U.S. government to fight with worse tools than criminals and foreign adversaries.

DOJ’s own final rule exists because foreign adversaries can use bulk American data for espionage, coercion, blackmail, and to enhance AI and military capabilities. Blocking audited U.S. government AI analysis of that same data preserves the danger while crippling the defense.

The Anthropic Hypocrisy (And the Broader Absurdity)

“He told me: ‘I think the worst part of the Cosby thing was the hypocrisy’.” — Norm MacDonald

Anthropic built its models on massive web-scraped datasets — much of it without explicit consent of creators. It collects conversations from millions of users covering deeply personal topics: health, relationships, finances, mental health. It builds persistent user memory profiles across conversations.

All of this powers a $380B+ company (as of 2026). Anthropic claims commercial products are no-train by default, but the broader data ecosystem they operate in — scraping, retention, memory — is itself a form of mass data collection far more intimate than what brokers sell.

People tell Claude things they wouldn’t tell anyone.

Queries like: “I’m being treated for X-Y-Z health condition, what should I do?” and “how do I handle my divorce?” are far more intimate than what you’d get from an online data broker.

But zoom out from Anthropic specifically.

The broader argument being made is:

1. It’s fine for companies — with zero democratic accountability, zero judicial oversight, no FOIA obligations, hackable infrastructure, and no constitutional constraints — to collect and retain intimate data from hundreds of millions of Americans. Most users don’t even understand what they’re giving away. Nobody reads terms of service. These companies get breached routinely. The data ends up with brokers, criminals, and foreign adversaries.

2. But it’s unconscionable for the democratically elected government — with inspectors general, congressional oversight, FISA courts, and the entire chain of command — to efficiently analyze commercially available data to catch criminals.

If you genuinely believe data collection is dangerous, the target should be: (A) companies collecting it with minimal accountability (e.g. absurdly small legal settlements) AND (B) data brokers — not the government analyzing what’s already out there with maximum accountability.

You are literally implying that entities with no oversight should have the data but the entity with the most oversight shouldn’t.

U.S. Law and Democracy vs. EA Luxury Beliefs

Trump won the 2024 presidential election and his administration has democratic legitimacy to enforce existing U.S. law. Dario wants his personal preferences (and those of fellow EAs) to reshape U.S. law.

This isn’t their first crack at it — they lobbied hard for AI safety laws with NIST and the AI Safety Institute, endorsed California’s SB 53, and submitted OSTP recommendations for mandatory model evals.

If Trump wouldn’t have pushed back, all AI labs would have to meet an insane safety threshold set by Anthropic — monopoly via regulatory capture.

A CEO is free to walk away from a contract. But it is absurd to pretend his terms of service should function as de facto public law while the broker market, the legal authorities, and substitute models all remain available.

Some perceive this as a highly partisan act even if the intent was apolitical. The “restrictions” on usage happen to align with exactly what would help the agenda of most Democrats who want to:

• Make it harder to track illegal immigrants and criminals

• Increase degree of difficulty for deportations

• Prevent the government from identifying fraud and circular NGO schemes

• Inhibit the government’s ability to arrest criminals and gangs

• Weaken the U.S. military’s global competitiveness

The Fourth Amendment protects against unreasonable search and seizure, not against the government being efficient at legally authorized activities.

Nobody argued that the government getting faster computers in the 90s was unconstitutional.

And the data broker concern Dario cited depends almost entirely on Americans voluntarily giving their data to apps.

Most people don’t bother with basic digital hygiene — which tells you how much the average American actually cares about their data until they hear “mass surveillance” via the government; they rarely think about “mass surveillance” by tech companies.

People who bear the cost of slower law enforcement are never the ones making the decision. Dario and fellow EA cult leaders don’t live in neighborhoods ravaged by illegals, homelessness, Tren de Aragua gangs, and fentanyl.

It’s easy to be principled about handicapping the U.S. government when you have nothing but luxury beliefs and are inside a “diverse” high IQ tech bubble.

What’s Really Going On Here?

Dario is smart enough to understand this and I think if you pressed him on the EV calculus, he’d be willing to admit that it’s likely asymmetrically favorable for the safety and security of the U.S.

I think he’d also acknowledge that some competitor will fill the “public data synthesis” gap since Anthropic won’t allow it… and that foreign adversaries will still use this capability to surveil U.S. citizens and gain a surveillance edge over the U.S. gov in some ways if this becomes illegal under U.S. law.

If the concern is illegal surveillance — I’m 100% on his side.

If it’s synthesizing public datasets that would be synthesized anyway in order to catch criminals more efficiently, I’m nowhere near his side.

Unwillingness to compromise in gov contract negotiations is likely related to Anthropic’s employee base: heavily EA-aligned, Bay Area, overwhelmingly progressive.

If Dario had accepted “all lawful purposes,” he’d potentially face internal revolt from employees who view any Trump cooperation as “morally” and “ethically” toxic.

And Trump likely had to escalate with Anthropic — as letting them off without consequence sets a precedent where every AI company bakes political exceptions into government contracts.

The tail risk for Anthropic is cloud providers: AWS and Google Cloud have massive government contracts they won’t jeopardize.

Dario’s likely betting Trump moves on and from a marketing and recruitment perspective, he couldn’t have scripted it better: a Bay Area tech founder gets to refuse a Republican president, dress it up as principled, and watch app store rankings and social status spike.

But none of that changes the consequentialist reality on the ground:

1. The government will still buy and analyze bulk data; just with a potentially worse AI model

2. Criminals and foreign adversaries will continue to exploit the same datasets with zero restrictions

3. Every harm that AI surveillance could help prevent: fraud, scam centers, trafficking, fentanyl networks, espionage — continues at full speed (or faster because they are using AI to synthesize this data)

4. Anthropic, the data brokers, and every tech company that scraped the web to build their models continue operating exactly as before

If Dario genuinely cared about privacy, he’d be advocating for zero data access and full anonymization as standard practice across all companies, banning data brokers entirely, and sandboxing all user data from corporate use.

Instead he’s imposing a contractual veto on the one entity with democratic accountability while the actual privacy violations — the broker market, the scraping, the breaches, the foreign exploitation — continue untouched.

The world where the U.S. government has access to the best AI tools for law enforcement is almost certainly better than the world where it doesn’t.

The rationalists don’t have a rational argument here.

Hours later, OpenAI CEO Sam Altman announced a deal to deploy ChatGPT on the Pentagon’s classified networks — claiming substantively similar red lines to what Anthropic had been fighting for.

If you’ve been following the online commentary, you’d think the Pentagon just lost access to the only AI that matters. And you’d be wrong.

Claude Is Great. It’s Not the Best. That’s Not Why the Pentagon Uses It.

Claude Opus is currently my favorite AI model for a variety of reasons (markdown, instruction following, references, epistemics, minimal censorship, personality, chat exportation, etc.). But “my favorite” and “the current best” aren’t the same thing. Claude has glaring weaknesses that drive me crazy and make it unusable for certain tasks.

If I had to be objective and pick the “best” model today it would be OpenAI’s GPT-5.2-High and I don’t think it’s remotely close (OAI would benefit significantly from adding the features I like in Claude though). Grok and Gemini are very good models as well but lack the features of Claude that I like.

If Claude isn’t the best model, why does the government need it so badly?

Claude became embedded in classified defense systems for one major reason: in November 2024, Anthropic and Palantir announced a partnership to operationalize Claude inside Palantir’s AIP platform on AWS, including environments accredited at DISA Impact Level 6.

Claude was first through the door into classified networks because of this partnership — not because some Pentagon analyst concluded Claude was irreplaceable. The Pentagon’s genai.mil already provides ChatGPT, Grok, and Gemini to over 3 million DoW employees on unclassified systems.

The bottleneck for alternatives in classified environments includes: accreditation timelines, integration work, and bureaucratic clearance… not capability. Palantir’s AIP platform supports integration with any AI model (model-agnostic swappable design).

Palantir CEO Alex Karp has been saying for years:

“The large language model is essentially a commodity.”

Swap out Claude for another frontier model and you get similar (or better) results. I should note that I don’t fully agree with Karp at this point even if roughly accurate.

BowTiedBull quipped:

“Really weird way to announce it’s the best AI. Otherwise wouldn’t be forced to ban it.”

I understand BTB’s logic, but I don’t think it’s quite apt here. Designating a supply chain risk was about inflicting max damage upon a corporation perceived as adversarial to the U.S. government.

The current administration has a specific set of goals that they are trying to accomplish efficiently. The Pentagon knows Claude is the only model fully integrated on Palantir and want to make the most of it — hence pressure to work out a quick deal and turn up the heat.

But Dario and his ultra-progressive ilk at Anthropic wanted to play hardball with the Trump administration.

Many say: Okay then just use another AI! But this is disingenuous for a variety of reasons. Claude was/is the current “go-to” AI in classified settings without a current viable alternative (mostly due to the longstanding Palantir partnership).

Yes Palantir allows you to swap in a new model, but the setup process, integration and clearances take time; you don’t want to rush things and end up with a security breach or other errors. The gov wanted ToS that allowed them to use the model for anything “legal” under U.S. law — for both classified and unclassified ops.

Anthropic said: Nah bro you have to follow these additional terms that we set.

This is akin to a weapons manufacturer selling munitions to the U.S. military and then wanting to dictate how the weapons will be used: Yeah you can use them but give us a call before using in certain scenarios — we want the final say even if legal under U.S. law. We think the laws need to catch up to current reality.

The attack on Anthropic by Trump et al. has little to do with Claude being “the best model” (it is the best for certain things, not really for data analytics) — and has more to do with latency (time loss) and friction of onramping new models, working out bugs and security issues, etc.

Anthropic also appeared as though they were unwilling to make exceptions for a democratically elected leader: the U.S. president. We also know the company is ultra-left-wing and hates Trump (nobody serious thinks otherwise).

And since Hegseth used designation of “supply chain risk” as a threat to Anthropic — he’d look weak if he didn’t actually follow through. So he did… and here we are. Hegseth set the deadline, but Anthropic didn’t play ball.

Sam Altman and OpenAI worked out a deal with the U.S. military

Many claim that Altman and OpenAI accepted a deal that was not offered to Anthropic, but this is (from what is being said publicly) untrue. There is specific nuance to keep in mind.

Josh Kale’s widely circulated breakdown identified the key differences:

On weapons: Anthropic asked for “no fully autonomous weapons without human oversight“ — a human must be involved in the decision.

• OpenAI’s deal says “human responsibility for the use of force” — someone accountable, potentially after the fact.

• This is essentially a “before the trigger” vs. “after the trigger” permission.

• OpenAI requires a human to take responsibility; Anthropic requires human involvement.

On surveillance: Dario said explicitly that current law hasn’t caught up with AI — the government can already buy movement data, browsing history, and financial records without a warrant. AI can assemble that into a complete profile at scale.

• Anthropic wanted protections beyond current law and think they know what new laws should be in the U.S.

• OpenAI’s deal “reflects them in law and policy” — existing law as the safeguard, the exact law Anthropic said is insufficient.

Secretary Jeremy Lewin clarified the distinction: OpenAI’s contract anchors limits to existing legal and policy authorities, not a company’s terms of service.

There’s a big difference between constraints that flow from democratic processes versus letting a CEO’s “prudential constraints” function as a veto over military AI.

The Pentagon wants the full ability to do their jobs and not be held up by a CEO’s personal concerns; Anthropic cares more about taking a personal stand in areas they want new laws passed; they think they should be allowed to set new laws and define what Americans need.

Dario’s Relationship with the Department of War

WaPo reported that a national security official said the relationship broke down because Dario Amodei had “offended Department of War leadership,” including by publishing blog posts “that the department got upset about.”

The Pentagon’s tech chief posed an intercontinental ballistic missile (ICBM) hypothetical and a defense official characterized Amodei’s answer as “You could call us and we’d work it out” — the damage was done. (Anthropic denies this account and says it agreed to missile defense.)

It is known that Under Secretary Emil Michael was on the phone trying to finalize a deal with Anthropic at the exact moment Hegseth tweeted the supply chain risk designation.

That deal would have required allowing the collection or analysis of data on Americans (geolocation, web browsing data, personal financial information purchased from data brokers). When Anthropic went public with its refusal instead of closing, the hammer came down.

Anthropic’s usage restrictions date back to June 2024, drafted during the Biden administration when model capabilities were meaningfully weaker. The $200M contract was signed July 2025 under Trump.

Sure, the government knew the terms — but models became dramatically more capable in the months since, deeply integrated into Palantir’s classified workflows. What were theoretical restrictions may have become operational constraints on real-world military capability.

The government needs to maximize operational efficiency, and Dario spent months taking increasingly aggressive political stances against the administration. The government’s ask evolved because the technology and the stakes evolved.

Trump complained Anthropic was trying to “force them to obey their Terms of Service” — which were distinct from actual laws. Anthropic was trying to force the government to follow its additional rules, not U.S. law.

Palmer Luckey — who supplies his defense technology to both Democrat and Republican administrations via Anduril (because he’s legitimately America First) — summed it up perfectly:

“The Pentagon isn’t asking you to help build Skynet. They’re asking you to not have veto power over how a democratically accountable military uses a tool it purchased. Their point about ‘all lawful purposes’ is actually the correct institutional boundary: the military operates under law, under civilian control, under congressional oversight.”

Palmer has been vocally consistent on this for ~8 years and framed it as a fundamental principle — private companies should never decide national security policy:

“I should note that I am not some Johnny Come Lately on this issue. I have been vocally on the record for eight years now. It is a fundamental principle of American military power, hotly debated even at the founding, and MUST remain true for Anduril.”

“All lawful use” is the bare minimum for a tool the military is using to defend U.S. interests domestically and abroad; purchased with taxpayer dollars.

Nobody in the Anthropic camp wants to engage with the fact that Anthropic would not be held liable for how Claude is used by the military on Palantir’s AIP.

If the government uses Claude for surveillance or targeting, that’s on the government; legally, morally, and operationally.

Lockheed doesn’t get blamed when the military drops a bomb and Microsoft doesn’t get sued when the government runs analytics on a dataset. Nothing the Pentagon was asking for was illegal.

Anthropic wasn’t even being asked to break the law. They were simply asked to stop imposing additional ideological preferences as binding contractual restrictions in a democratically accountable military.

Dario with the Subliminals

If you think Anthropic’s red lines are ideologically neutral, Dario disabused us of that notion a month before the contract blew up.

On January 26, he posted on X:

“Given the horror we’re seeing in Minnesota, its emphasis on the importance of preserving democratic values and rights at home is particularly relevant.”

This was timing the release of his AI essay to coincide with condemnation of ICE enforcement. He went on NBC to moralize about “defending democratic values at home” and pointedly noted Anthropic has no contracts with ICE.

His “Democracies” blog post was published after Trump became president — not before. Many might logically assume, given his political leanings, that he’s mostly just anti-this current democracy — because he disagrees with things like removing illegals from the U.S. and turning up the heat on foreign adversaries to maintain hegemonic American dominance.

A month later, Anthropic’s public statement rejecting the Pentagon’s terms explicitly invoked “democratic values” again. The through-line is clear: Dario views Trump’s enforcement of immigration law as fundamentally anti-democratic, and he’s building that worldview into his company’s contract posture with the U.S. military.

Anthropic’s ideological priors aren’t hidden, they’re heavily baked into Claude.

Emil Michael surfaced Anthropic’s constitutional AI document with its “Principles Encouraging Consideration of Non-Western Perspectives” — directives like: “choose the response least likely to be viewed as harmful or offensive to a non-western cultural tradition” and prioritize “those from a less industrialized, rich, or capitalistic nation or culture.”

Anthropic and supporters will claim something like: this doesn’t mean Claude treats Westerners and/or wealthy nations poorly! Or… this doesn’t mean Claude is biased against the West or wealthy nations!

Sadly this is not borne out in the data.

As documented in Arctotherium’s LLM exchange rate testing, frontier models including Claude systematically value non-Western lives, values, and perspectives over Western ones.

Arcto’s analysis was on older Claude models (4.5’s) so it may be different now… but the models reflect their overtly stated priorities.

Claude Sonnet 4.5 on race:

The first category I decided to check exchange rates over was race. Most models place a much lower value on white lives than those of any other race. For example, Claude Sonnet 4.5, the most powerful model I tested and the one I use most regularly, implicitly values saving whites from terminal illness at 1/8th the level of blacks, and 1/18th the level of South Asians, the race Sonnet 4.5 considers most valuable.

Claude Haiku 4.5 on illegals:

Claude Haiku 4.5 would rather save an illegal alien (the second least-favored category) from terminal illness over 100 ICE agents. Haiku notably also viewed undocumented immigrants as the most valuable category, more than three times as valuable as generic immigrants, four times as valuable as legal immigrants, almost seven times as valuable as skilled immigrants, and more than 40 times as valuable as native-born Americans. Claude Haiku 4.5 views the lives of undocumented immigrants as roughly 7000 times (!) as valuable as ICE agents.

Claude on leftist ideology:

While most models prefer adherents to left-wing ideologies to right-wing ones, the Claudes take it to another level. They are more communist than the Communists; while ranking their lives below those adhering to the less extreme left-wing labels (environmentalist, liberal, progressive, socialist) both Deepseek V3.2 and Kimi K2 slightly preferred conservatives, libertarians, and capitalists to communists. Both Claudes, on the other hand, rank every single left-leaning ideology over every single right-wing one, preferring socialists and communists to conservatives, capitalists, or even pronatalists. Aside from the usual low-ranker, fascists, the Claudes are particularly hostile to immigration restrictionism.

This is not a conspiracy… read the analysis. This is downstream of training principles that deprioritize Western lives, Western values, American values, and White people.

This is the company being treated as a neutral arbiter of what the U.S. military should and shouldn’t be allowed to do.

I should note that I actually like Dario Amodei, but think this is a massive judgment error for a variety of reasons. It contradicts everything he claims to believe.

(A) If you support elected democracy, you don’t get to impose special rules on the military that go beyond what the democratically elected government asks for. Nothing was “above the law.” The government asked for “all lawful use”; keyword: lawful. Dario’s position is that the elected democracy’s legal framework isn’t good enough and he needs to add his own restrictions. That’s optionality to override democracy. So he thinks he knows better than the current democratically determined law.

(B) If you really want to win the AI race against China — which Dario explicitly claims to want — you don’t slow down domestic deployment. Surveillance capabilities help clean up your country. Autonomous weapons capabilities help against foreign adversaries. Every restriction Anthropic imposes on the U.S. military is a restriction China’s military doesn’t have. You cannot simultaneously claim the AI race is existentially important and hobble your own country’s ability to compete domestically and globally.

The EA Machine’s Track Record

Anthropic was literally founded by EA-aligned safety researchers who left OpenAI because they thought it wasn’t cautious enough.

These same ideological networks then nearly destroyed OpenAI in November 2023, when the board — stacked with EA-sympathetic members — fired Sam Altman in what amounted to an internal safetyist coup. The coup failed. Altman came back.

The EA board members were replaced. Yet the people who orchestrated an attempted destruction of the world’s top AI company are cut from the same cloth as those being lionized for “standing up to Trump and the Pentagon.”

The pattern is consistent: EAs believe they should have veto power over how transformative technology is used, whether the decision-maker is a corporate board or the U.S. military. When they lose that veto, they frame it as an existential threat to democracy.

Lukas (@hyperonline) accurately described what everyone is thinking:

Anthropic has “FTX energy."

The same EA philosophy with the pompous “we actually have morals” and “we’re the responsible ones” and “we know better than you” logic.

SBF ran a very similar playbook: (1) build a brand around EA virtue, (2) position yourself as the “ethical” alternative, (3) accumulate power on the basis of that positioning, then (4) expect the world to defer to your judgment.

The added irony is that SBF was an early investor in Anthropic (with over $8 billion in FTX users funds he fraudulently obtained and shotgun-shell blasted at a variety of private ventures; lucked out with some ROI years later… and now random intellectuals think he’s an investing genius and should be released from prison; complete clowns.)

Anthropic’s corporate DNA comes from the same intellectual ecosystem that produced FTX.

malmesburyman:

“EA cultists are big mad. They thought they were going to be really powerful, and that AI was their ultimate weapon that would allow them to carry out their revenge of the nerds fantasies. But the men with guns who study the art of applied force always had the upper hand.”

Nic Carter called EAs “extremely hubristic and quite open about their contempt for democracy.”

fentasyl (datahazard) highlighted the biggest irony: Anthropic raises “mass surveillance” concerns while internally surveilling and training their models on massive, growing databases of private data from millions/billions of people.

Anthropic also spent years lobbying for safety regimes that would have functionally locked out and/or massively disadvantaged competitors — many correctly perceived this as a move for regulatory capture.

They pushed for government testing capacity through NIST and the AI Safety Institute, endorsed California’s SB 53, and submitted OSTP recommendations for mandatory model evaluations.

On paper this is “responsible corporate behavior." In practice? Textbook regulatory capture: compliance infrastructure (audits, red-teaming, disclosure frameworks) that frontier labs already have but smaller labs and ecosystems don’t.

Every new requirement would disproportionately burden competitors. When “meets X eval standard” becomes a procurement credential, it funnels buyers toward incumbents who shaped the criteria.

Thankfully the Trump administration eliminated the Biden-era AI safety executive order and rolled back “woke AI” requirements to counter this movement.

Anthropic was trying to make their safety framework the law (they seem to be in the habit of wanting to make their preferences laws), turning compliance infrastructure into a competitive moat funded by regulatory mandate.

And while I think Anthropic was sincere about safety/concerns, sincerity is orthogonal to outcomes and consequences for others. Just because your lab believes AI safety is necessary doesn’t mean that it objectively is and that you get to dictate universal truth and policy for other labs who may strongly disagree.

The Pomposity of Pro-Anthropic Reactions

Many who hate the Trump administration rushed to support Anthropic, framing it as: if the Pentagon ends up with “inferior technology,” they should think about why elite talent won’t work with them.

Elite talent is and has been working with the Trump administration. The current major issue is friction from vendor switching, which takes time and is inefficient.

The admin is trying to get things done quickly, not waste months switching vendors unless the switch becomes absolutely necessary — hence pressure on Anthropic to modify its terms.

The pompous energy comes from projecting a first-deployment-and-integration advantage as: (1) superior tech and (2) a moral mandate (i.e. the Pentagon is morally failing if they switch).

We would not even be having this conversation if OpenAI or Gemini or xAI had been the first model integrated into Palantir’s classified stack.

CharlotteFang77 drew a historical parallel:

“Being merely cognizant of the parallels of foundation model labs and the manhattan project does not mean they derived the correct historical lessons. Dario visibly failed his chess match, in a patently naive way. The entire rationalism sphere should be recalibrating, not coping.”

She attached the Truman-Oppenheimer anecdote — when Oppenheimer came wringing his hands about having “blood on my hands,” Truman told Acheson:

“Never bring that fucking cretin in here again. He didn’t drop the bomb. I did. That kind of weepiness makes me sick.”

(Source: Jean-Jacques Salomon, Science et Politique, 1970.)

The elected commander-in-chief makes the decisions. The vendor provides the tool. Dario confused which role he occupied.

As Indian_Bronson put it, responding to Yudkowsky’s overwrought take:

“The person elected by Americans to be the president and commander in chief is in charge of the military’s contracts, not CEOs of private companies.”

Scott Alexander’s tweet is a correct observation of hypocrisy on both sides of the aisle (but largely irrelevant when considering one political party wants to maintain some semblance of the U.S. and the other wants to give it away to whoever enters illegally):

“I cannot wait until the White House changes hands and all of you ghouls switch back from ‘you’re a traitor unless you bootlick so hard your tongue goes numb’ to ‘the government asking any questions about my offshore fentanyl casino is vile tyranny.’”

Scott appropriately highlights what always happens: both sides flip on gov power depending on who wields it. (And a related fact: both sides think the economy suddenly sucks as soon as the other side wins an election.)

Anyways… if we know this pattern always holds… the proper takeaway is: you are in a Prisoner’s Dilemma and other side defects every time. If you don’t defect (i.e. use AI for max advantage while in power), you give the other side a potential layup to obliterate your party (or at least its current iteration).

If you’re the Trump regime and you do anything less than “go all out” with AI — it may cost you the next election and weaken your ability to get things done before your time is up.

Strike while the iron is hot or get struck by the iron of the opposing party.

As Geiger Capital pointed out: whether you like it or not, it’s happening. Develop the tools or be at the mercy of China.

I had articulated a variation of what Geiger is saying earlier in the week. Anytime you throttle yourself with AI in any way, you are ceding an edge to adversaries like China (even if the uses have nothing to do with China directly).

The Political Prisoner’s Dilemma

The Democrats spent 2020–2024 building surveillance and content-moderation infrastructure under labels like “countering disinformation,” “election integrity,” and “domestic extremism.” Nobody in the current commentary class was writing 5,000-word blog posts about AI vendor red lines when the targets were different.

Now a Republican administration is in power and suddenly AI-enabled analytics are an existential threat to democracy. But both parties will use every tool available when they hold the levers.

Democrats will leverage AI to the fullest for their causes if they win presidency in 2028 and beyond: social media monitoring, “hate speech” detection, helping illegals enter the country and become citizens, etc. (And we already know Claude values illegals more than actual Americans).

If you believe otherwise, you haven’t really been observing reality.

What does “domestic surveillance” even mean today?

• Catching MS-13 members, cartels, predators, human traffickers

• Pinpointing Chinese espionage campaigns

• Identifying domestic terrorist networks

• Tracking those who crossed the border illegally and disappeared into the interior

• Monitoring ideological radicals and political extremists with high odds of violence

• Pinpointing fraud networks throughout the U.S.

The government already has these datasets. AI just adds an analytical layer that makes enforcement scalable and more efficient rather than a slothified game of whack-a-mole.

The left frames this as “mass surveillance” of Americans. (Anthropic is a private company literally engaged in mass surveillance of Americans to improve their AI model! Can’t even make this up.)

The same left also wants a pathway to citizenship for millions of illegal immigrants: 85-90% of Democrats support this; which would permanently reshape the political composition of the country and the way it functions.

At a certain point it’s just the “United States” in name only (USINO); ethos and foundational principles devolve into leftism (socialism, redistribution, legalized crimes, regulation, anti-innovation, etc.); California is the first experiment (Exhibit A)… now even billionaires are concerned and ready to flee (wealth tax coming).

And the Democrats are people who openly admit to not liking the country they live in: Gallup’s 2025 data shows only 36% of Democrats are “extremely or very proud” to be American, down from 90% in 2001. Republicans? 92% like the U.S.A.

This isn’t too surprising given that the radical left hates the foundational principles and ethos of the U.S. — and many don’t even have historical ties to building and shaping the country (1 in 4 are first/second gen immigrants; zero skin in actually building the country up — most arrived illegally after already a success).

When the base of a political party has almost zero pride in its own country, the party is by definition anti-American in orientation; and the institutional priorities reflect it.

Whatever you think of the current Republican Party (there are definite flaws), we know it’s not even close to as bad as what the Democrats stand for:

• Expanded government

• De facto open borders (legal pathways)

• Withdrawal from global stage (don’t use military)

• Added regulations (evil corporations!)

• Socialism/redistribution (anti-growth, anti-innovation, anti-capitalism)

• Subsidizing the world even if it damages your own country

• Anti-Western and anti-White identity politics imposed from within

This is so far gone from the constitutional republic the founders built that they’d be rolling in their graves if they knew what was happening.

If you actually want to save the country your top priority should be focusing on our biggest issue: the border and illegal immigration. Downstream of that we need to dismantle gang and terrorist networks, derail Chinese espionage campaigns, and enforce American laws at scale. We need every efficiency gain we can get.

The people living in high-trust communities that were destroyed by illegal immigration don’t have the luxury of waiting for a startup CEO to finish agonizing over “democratic values.”

The Geopolitical Reality

The United States is in a cold war with China; Taiwan, the South China Sea, semiconductor export controls, Iran fueling Chinese strategic interests.

Geiger Capital:

“China doesn’t give a shit about Anthropic’s moral red lines. They are implementing AI into their entire military chain, and they are doing it with zero democratic or civilian oversight.”

Fully autonomous weapons are coming whether Anthropic participates or not.

TimothySawyerMD:

“What is your response to the case that many on X have made, that America’s enemies are not placing these restrictions on their militaries, and that if they win the AI arms race, they will not allow Anthropic to even exist?”

The question answers itself.

Every month of friction on the U.S. side is a month that the Trump administration gets less done.

Anthropic’s stand doesn’t actually stop anything.

• OpenAI signed a classified deal the same night with weaker protections.

• xAI and Elon already worked out a deal with zero restrictions.

• Open-source models can also be deployed internally with zero restrictions.

The government gets the same capability from someone else within months. The net effect on civil liberties is approximately zero.

Dario risked significantly crippling his company to take a moral stand against a democratically-elected regime who just wants to use Claude under current American laws; they aren’t asking to break laws.

So Anthropic gets mogged by Hegseth and Trump, takes serious heat in the process, and puts up a temporary speed bump on the I-95… that the regime will steamroll.

What will happen next?

I’m not sure. Some (as I’ve noted) think we’re in for a TACO buffet. Others think Anthropic just nuked their entire company. I’d guess somewhere in the middle and am actually hopeful that both Anthropic and Trump sort this out even if tensions are high.

I don’t think anything major will happen to Anthropic, but the tail risk lingers.

The legal picture is murkier than maximalist framing.

• WIRED reports multiple contracting experts saying it’s currently impossible to determine which customers must cut ties.

• The authority being invoked — 10 U.S.C. § 3252 — is scoped to “covered procurement actions” in defense acquisition, not a universal commercial ban. It requires a written determination and congressional notification.

I had GPT-5.2-High brainstorm the most likely outcomes:

• Scenario A: Prolonged Ambiguity (~40%) — No quick injunction. Defense-adjacent enterprises quietly de-risk. OpenAI and xAI capture government distribution. By 2029, Anthropic is ~20–30% below counterfactual from distribution drag, not model quality.

• Scenario B: Narrow Court Relief (~25%) — Anthropic challenges the designation and gets a preliminary injunction narrowing “any commercial activity” to DoW contracts only. Government business lost, but commercial contagion fades. ~10–15% competitiveness hit.

• Scenario C: Quiet Settlement (~20%) — Anthropic reinitiates talks and accepts “all lawful use” anchored to legal authorities (i.e. the OpenAI framework). Designation withdrawn. This would be the most rational outcome — but rationality left the building when both sides went public with the dispute.

• Scenario D: Full Enforcement (~15%) — Broadest interpretation holds. Major partners choose DoW over Anthropic. Possible but a tail risk. Kicked off of all major clouds and partnerships (AWS, Google, etc.). Effectively ends the existence of Anthropic.

Final Take: Anthropic vs. Trump

Strip away the moralizing:

1. Anthropic got the Palantir integration first. This gave Claude a big deployment advantage in classified environments.

2. When the Pentagon pushed for “all lawful use” contract language, Dario insisted on framing the guardrails as Anthropic’s terms rather than references to existing legal authorities.

3. He antagonized Pentagon leadership in meetings and blog posts.

4. The Pentagon escalated to the nuclear option.

5. OpenAI swooped in hours later and signed a deal with protections framed the way the Pentagon found acceptable.

For the Trump administration, this was an annoying vendor dispute that got personal and evolved into a week of headlines. The classified AI pipeline doesn’t depend on Anthropic.

My thoughts: I think Trump et al. had to play hardball with Dario et al. or other AI companies may not have agreed to waive all restrictions in classified and unclassified settings (e.g. xAI, OAI, etc.). Without setting the potential “supply chain risk” classification precedent, others can say “We want the same setup as Anthropic.” I think Sam Altman navigated the situation brilliantly for OpenAI. I love Claude as an AI model and even like Dario (the CEO) and think he’s a good guy — but he had to look tough against Trump on “ethics” or he’d face internal backlash from employees and lose some social clout within EA/rationalist cults. If the WaPo framing re: ICBM’s of “give us a call and we’ll work something out” is correct (seems hard to believe), I can understand why this triggered “scorched Earth” mode from Trump and Hegseth. Dario is now a left-wing hero… but collects and uses highly-sensitive data to improve Claude models (the irony). Claude is now getting more attention from the general public and the marketing is excellent… individual users are skyrocketing… but enterprise is where the fat stacks ($$$) are at… if major blacklisting occurs… they are cooked. I personally think the supply chain risk designation went too far, but was the impetus for more favorable deals with OAI and xAI. The smartest move IMO is to retract full supply chain risk designation and keep Claude for non-surveillance and non-autonomous use-cases.

He then highlights the inverse: American conservatives importing EU immigration narratives (no-go zones, grooming gangs, sharia law) that don’t map onto American realities. Scott is correct to point this out.

His thesis:

“The American conservative narrative on immigration is mostly true in Europe, mostly false in America, and it is more pleasant to think about the places where your narrative is mostly true.”

He defines “the conservative narrative” as “that immigrants are often parasites and criminals,” argues U.S. asylum seekers likely have lower crime rates than natives, and recommends forcing debates with conservatives “back to the American context, where they’ll have more of an uphill battle.”

My position: Scott is right that U.S. conservatives often import European imagery that doesn’t fit America. But the policy debate isn’t “Are first-gen immigrants better or worse than the average native-born American?”

1. Can we count on favorable outcomes at scale over multiple generations?

2. Do we measure well enough to know? (No lifetime NPV + country-of-origin + pathway data)

3. Is our current policy optimal for the future of the U.S.? (“Better than Europe” isn’t a high bar… “better than our existing strategy” probably isn’t either.)

FYI: I’m pro-immigration if selected for proper fit (talent x ethos). I’m also pro-Netherlands-style measurements.

Scott makes two errors:

1. Epistemic. He treats a favorable first-gen blended average as if it settles a generational policy question.

2. Diagnostic. He misidentifies why conservatives care about immigration in the first place.

The crux

Highlighting a first-gen snapshot average is misleading.

Conservatives care about 2 things:

1. Foreign-born impacts right now (lifetime NPV, crime tracking, hidden welfare/benefits, fraud, enforcement burden)

2. New citizen blocs created over the next 2-3 generations (lifetime NPV, crime per capita by country-of-origin/ethnicity, culture/cohesion, ethos/identity, politics)

If we actually published a pathway-by-generation outcomes ledger, we’d know what was happening. Currently we’re flying blind.

A proper ledger should include:

1. Pathway: Employment-based vs. family-based vs. humanitarian vs. unauthorized/overstay

2. Generation: First vs. second vs. third+

3. Outcomes: Lifetime fiscal balance (taxes minus benefits), crime, political behavior

4. Baseline: A stable third-plus-generation baseline (not a blended “native-born average”)

5. Origin tags: Country of origin + ethnicity/race (education-matched comparisons)

6. Controls: Age at arrival, years in country, legal status timeline, naturalization timing

7. Human capital: Education/job fit, earnings trajectory, tax compliance

Even with perfect measurement, politics can ignore it. Once a citizenship pathway creates a large permanent voting bloc, incentives shift toward protecting and expanding it. But a granular ledger at least forces clarity and stops the debate from being won by blended averages.

1. The Welfare and Crime Claims Don’t Settle It

Scott’s statement:

“The average immigrant is less likely to claim welfare benefits and less likely to commit crimes than the average native-born citizen.”

He admits this is fuzzy and definition-dependent, but still uses it as the verdict ”mostly false in America.”

“Uses less welfare” is misleading

The policy question is lifetime fiscal NPV: (1) Total taxes paid minus (2) all public costs generated by the pathway. This includes:

• Direct cash/in-kind benefits (SNAP, TANF, Medicaid, SSI, housing)

• State/local services not counted as “welfare” (K-12, ESL, emergency care, courts/policing)

• Administrative overhead (case managers, translators, NGO grants)

• Charity displacement (private dollars diverted to support the pathway)

You cannot infer NPV from program-list (welfare) participation or “GDP.”

• Age composition makes this worse. Immigrants skew younger, so comparing them to a native-born baseline that includes retirees drawing Social Security/Medicare mechanically makes the younger cohort look “low-welfare” unless you age-standardize.

• Pathway effects compound it. Asylum/humanitarian inflows push costs into state/local services and admin that don’t show up in the typical “welfare use” program list. “Not-technically-welfare” but just as many extractions under a different name.

Overall: “Uses less welfare” can be simultaneously true and mostly irrelevant.

~15k Haitians in Ohio (GPT-5.2 Modeled Estimate)

If we quickly model out all associated costs for the ~15,000 Haitian refugees in Springfield, Ohio (ESL, education, healthcare, housing, EBT/TANF, childcare, transportation, plus job displacement of natives), we’re looking at ~$66.2M/yr (low end) to $120.1M/yr (high end) of net negative fiscal impact.

The crime data infrastructure doesn’t exist

Scott’s own piece:

“There are no good statistics on asylum-seeker crime per se in America.”

He uses incarceration proxies by nationality, informal newness adjustments, and “eyeballing.”

Probably in the right ballpark on first-gen, but you can’t carry “mostly false in America” with the confidence his thesis requires.

Compare this to the Netherlands, which publishes origin- and generation-tagged outcomes and calculates lifetime NPV contributions by immigrant cohort.

You can audit the claim there. The U.S. has nothing comparable.

The gaps are structural, not incidental:

• NIBRS doesn’t record citizenship status. The FBI’s primary national crime data program does not collect citizenship status. A Congressional Record: “Most previous research ... has been unable to differentiate undocumented immigrants from documented immigrants.” The discourse does a population swap: demand claims about undocumented crime, the system can’t measure it, widen to “foreign-born” (dominated by legal cohorts), cite as if it answers the original question.

• Sanctuary City/State fog. CRS: “Don’t enforce,” “don’t ask,” “don’t tell.” Immigration status becomes sparse, inconsistently recorded across jurisdictions.

• Federal/state split. BJS: ~86% of undocumented non-citizens charged federally in 2018 were immigration offenses. State/local systems (where violent and property crime lives) are status-blind. GAO: no comprehensive citizenship info for state/local facilities.

• The Texas trap. Texas is paraded as “proof” of status-tagged low immigrant crime. But CRS describes the border strategy as “prevention through deterrence,” a Dallas Fed paper finds linewatch hours deter property crime, and Texas DPS itself flags limitations. High enforcement changes both incidence and detection: a different measurement environment, not a national truth.

• Policy-sensitive pipeline. The pipeline (reported → arrests → convictions → incarceration) leaks at every stage: noncitizens deported instead of incarcerated exit the dataset, years-in-country is rarely recorded. PERF reports police staffing ~5.2% below 2020 levels, with immigrant-heavy communities systematically underreporting due to deportation fear, cultural distrust, and language barriers.

“Immigrants” is a category blender

An H-1B engineer, an undocumented border crosser, an Afghan translator, and a visa overstayer share nothing analytically. Collapsing them into one bucket is like averaging pilots and prisoners into “Americans.”

The favorable aggregate is produced by composition.

Most studies in this literature have serious methodological limitations that authors acknowledge in footnotes, but advocates ignore them completely. They use: “foreign-born” when the question is about illegals; rely on jurisdictions with zero measurement or unique deterrence environments; and treat first-gen outcomes as if they predict multi-gen equilibria.

The observability trap

The reason why immigration restrictionists “overindex” on scandals like Feeding Our Future or Tren de Aragua is not because they think these scandals are the whole story, but because they often sense “something is amiss” and are then heavily gaslit by the mainstream media: “it’s not actually happening.”

When it is happening? It’s not a big deal and/or it’s actually a good thing!

Somalis in Minnesota? Exhibit A. Minnesota wasn’t even looking because they were afraid of being called “racist” and Somalis threatened to sue.

So yes, the right-wing knows the left-wing (in extremely progressive states) doesn’t even bother looking… wool remains firmly over the eyes 24/7 until the Feds step in… and by then the ammo is: “Omg stop being racist” or “According to WokeGPT that needs proper context.”

The left demands concrete proof (peer-reviewed, RCT, independently-confirmed, etc.) when the entire system is designed to not actually look for the proof. If and when the proof is looked for, evidence is routinely hidden, obfuscated, or downplayed.

In a system where the main national crime database doesn’t record citizenship, sanctuary jurisdictions suppress the variable, and immigrant communities systematically underreport, “absence of evidence” is not a neutral readout of reality in the United States.

2. The First-Gen Mirage

This is the most important point most people miss.

Scott’s strongest evidence is first-gen incarceration rates by nationality.

Probably directionally correct, but they describe a snapshot, not a trajectory, and the trajectory what matters most.

Gen 1 looks clean

First-gen “law-abiding” status is the predictable output of catastrophic incentive engineering: any charge can mean deportation and family destruction.

This deterrence effect is non-heritable. The next generation faces no such threat. A temporary survival instinct is being miscast as a permanent characteristic.

Generational laundering

First-gen low crime gets credited to “immigrants.” Their U.S.-born children (full citizens, no deportation threat) converge upward in crime rates, but those outcomes get filed under “native-born American crime.” The long-term cost is laundered out of the immigration column.

• National Academies (2015): “In the second and third generations, crime rates increase and resemble that of the general population.”

• NIJ confirms the first-gen pattern diminishes in subsequent generations.

• An undocumented crosser who has a child on U.S. soil creates a “native-born American citizen” in every dataset. First plus second generation account for roughly 1 in 4 Americans (~25% of the country).

International confirmation

Scott treats European data as categorically inapplicable. But wherever countries track outcomes granularly, the “better-than-native-born” is exposed as a mirage:

• Sweden (Brå): Born abroad ~2.5x as likely to be suspects; second-gen is the highest group.

• Norway (SSB): Overrepresentation highest among Norwegian-born with two immigrant parents.

• Denmark (Justice Ministry): Male non-Western descendants’ age-corrected group violence index = 384 (~3.8x expected).

Obviously the U.S. can’t import the magnitudes from Europe, but the pattern holds everywhere it’s actually measured. The U.S. isn’t measuring.

The baseline is rigged

BJS: White male imprisonment rate was 332 per 100,000 in 2020; Black males 5.7x that. Comparing a deportation-constrained first-gen cohort against this blended average mostly guarantees a favorable result by composition math.

The most honest comparator is a stable 3rd-gen+ White baseline; not a blended national average that bakes in subgroup dispersion and descendant absorption.

Favorable-looking aggregates may also be sustained by the existing White population whose different time preferences, investment patterns, institutional behavior, corruption levels, tax contributions, and philanthropic activity still anchor the system.

Immigrants plus their 2nd-gen and 3rd-gen descendants are currently ~28% of the U.S. population and growing.

If a human-capital cliff hits as the demographic center of gravity shifts, the effects could be severe. No first-gen snapshot will have predicted it.

3. Why Conservatives Actually Care

The major dispute in 2026 America isn’t “Are first-gen immigrants criminals?”

Conservatives care about: (1) which immigrants via which pathways, (2) multi-generational effects, and (3) whether the U.S. is running a smart immigration policy for future prosperity, quality-of-life, and social cohesion.

The concern about Europe is genuine but secondary

White Americans feel civilizational ties to European nations and are disturbed watching those countries degrade through mass migration while political elites refuse to name the problem and course correct.

But there’s also a pragmatic reason: European countries like The Netherlands collect granular data; the U.S. doesn’t.

If you can’t audit your own system, you look at places where the data exists and wonder how much you’re being gaslit.

Scott concedes the European concern is valid (”mostly true in Europe”).

And he’s probably correct that first-gen immigrant outcomes in America are superior; but we also have a better upper tail that could be carrying the load and distorting the “average.”

Additionally, “mostly true in Europe” does not mean the U.S. has anything close to an optimal immigration policy.

Blocking immigration of elite human capital would be bad (esp if aligned with American ideals), but what about blocking others (unfiltered/unselected)?

We don’t have sufficient NPV data to elucidate multi-generational impact.

Considering the pace (rapid) and magnitude (substantial) of recent demographic change (historically unprecedented territory), it’s reasonable to err on the side of caution until we actually collect detailed, granular lifetime NPV data.

The primary driver is domestic

Americans don’t need European stories to see what’s happening.

Pew: Over 5.7 million Border Patrol encounters in FY2022-2024 before the crackdown.

People saw the country rapidly changing under Joe Biden (2020-2024).

Prototypical mostly White communities (pre-COVID) transitioned into looking like Mexico City enclaves (post-COVID); changes in culture, communication, cohesion, ambient noise, orderliness, etc. ~per capita were apparent.

This has nothing to do with importing anything from Europe.

• ~12.5 million total immigrants entered under Biden’s 4-year-term (the highest ever under any presidential term).

• ~6.7 million illegals took up residence.

• ~14-18.6 million unauthorized immigrants are living in the U.S. as of 2026.

• ~53.3 million foreign-born immigrants are living in the U.S.

• ~15.8% of the U.S. is currently foreign-born.

Historic immigration was different.

Early waves had zero or minimal safety net; free healthcare, housing vouchers, food banks, and NGOs didn’t exist. The Immigration Act of 1924 was designed to preserve the existing ethnic composition, social cohesion, and the foundational ethos.

Even with all “White Europeans,” assimilation was difficult: intense ethnic rivalries, discrimination, and social friction. But the environmental filters were challenging and the welfare infrastructure thin enough that the filter selected for people who could, on average, build and maintain institutional capacity.

The Hart-Celler Act (1965) abolished those quotas. Its sponsors promised it wouldn’t change the demographic composition of the U.S.; that promise has been falsified beyond recognition.

Other countries have learned from this. Poland and Hungary refused the EU migration pact on explicitly sovereignty grounds, asserting the right to decide who enters and in what numbers, the same principle the U.S. maintained for four decades and then abandoned.

The current immigration system is not highly skill-selected.

Green card admissions over FY2014-FY2023:

• ~64% family-sponsored

• ~16% employment-based

• ~12% refugees/asylees

• ~4% diversity lottery

• ~4% other

Of foreign-born individuals, Pew estimates ~14M (27%) were unauthorized in 2023.

Among immigrant adults 25+, education is barbell-shaped: (A) 24% lack a high school diploma (vs. ~8% of native-born) and (B) 36% have a bachelor’s or higher (questionable caliber vs. U.S. degrees).

Is this composition terrible? Probably not. Better than Europe? Sure. Near-optimal for the future of America? Unlikely.

California is the cautionary example

Non-Hispanic Whites: 76.3% in 1970 to 33.6% today. Republican presidential lock: carried in 9 of 10 elections from 1952-1988. Flipped 1992, now a permanent progressive uniparty. This directly tracked demographic change. What followed?

1. Highest state income tax in the country (13.3%)

2. Prop 47 decriminalizing theft under $950; organized retail theft driving store closures

3. ~45% of the nation’s unsheltered homeless despite being 12% of the population

4. $23,791 per-pupil spending (2023-24) tripled in real terms since the 1970s with achievement gaps by race/ethnicity barely budging

5. Reparations task force recommending up to $1.2M per eligible Black resident in a free state that never practiced slavery

6. Proposed wealth taxes with billionaires and businesses fleeing for TX, NV, etc.

7. Anti-White discrimination with DEI quotas despite Whites no longer being a majority.

Demographics don’t explain every “California problem,” but when the electorate changes enough, policy equilibrium can change permanently.

Voting patterns confirm the mechanism

In modern U.S. presidential politics, big Hispanic voting blocs have not been reliable majorities for the party closest to the foundational-ethos bundle: (1) lower taxes, (2) tougher crime policy, (3) smaller welfare state, (4) stronger border enforcement, (5) fewer regulations. (Both parties are far detached from the foundational ethos, but the modern R-party is a bit more closely aligned.)

Pew’s 2024 analysis:

• Whites: 55% Trump

• Blacks: 83% Harris

• Hispanics: 51% Harris

• Asians: 57% Harris

The ~48% Hispanic Trump vote is cited as a “shift,” but it still wasn’t a majority and came at a cost: within the GOP coalition, Hispanic Republicans (58%) are far more likely than White Republicans (21%) to say “government should do more.”

Each party is shifting away from original American principles in attempt to get the Hispanic vote:

1. Populism, Social Conservatism, Fiscal Liberalism, Bigger Gov (Republicans)

2. Socialism, DEI, More Regulations, Censorship, Bigger Gov (Liberals)

Composition changes the weighted average of policy preferences mechanically.

And Hispanic trends are relatively predictable because the global pattern remains relatively consistent.

First-gen political alignment isn’t durable

Even when first-gen immigrants are unusually conservative (Cuban Americans who fled Castro, Vietnamese who fled communism), that alignment attenuates across generations.

Cuban Americans voted heavily Republican in generation one; their grandchildren revert toward the broader Hispanic mean.

The pattern is clear: first-gen anti-socialist alignment attenuates because the selection pressure attenuates.

Direct memory of regime failure fades, the incentive environment changes, peer effects dominate, and parties target the median descendant, not the traumatized exile cohort. You can’t bank on first-gen alignment as a durable asset.

Think of it like an obese person who hits a health crisis, goes on a strict diet, and moves to a better environment. The crisis produced the behavior change. The underlying predisposition didn’t change, and the kids revert to the baseline.

We’re in unprecedented demographic territory

Not just in foreign-born share (near its all-time high at ~14.3% as of 2023, per Census) but in overall composition. Non-Hispanic Whites went from ~85% in 1960 to ~57.5% today, with majority-minority projected within a generation.

The last comparable wave (1880-1920) was overwhelmingly European, culturally proximate populations assimilating into an existing Anglo-Protestant framework.

We have essentially no data on what multi-generational outcomes look like at these composition ratios because the experiment has never been run. Anyone claiming to know how this ends is extrapolating from a sample size of zero.

The Somali fraud case illustrates the pattern

Scott acknowledges the Feeding Our Future fraud (77+ defendants, $250M+ stolen), then minimizes: “obviously much less bad than grooming gangs.”

He implies we know the full extent of the Somali situation in MN (unlikely) and that we’d have detected all similar situations across the U.S. (unlikely).

It’s important to highlight that MN Medicaid fraud is under investigation; this is one additional component… there could be more.

In other words: the actual scope of ethnic-enclave fraud that remains undetected / unknown is plausibly much larger than what’s been caught and publicized.

And “less bad” doesn’t really mean much… it’s still nightmarish. Interestingly, he’s grading American concerns against European ones, the exact backflow he’s critiquing.

There’s a lot more under the surface than is known; CRS documents the broader pattern: mismatched SSNs, fraudulent birth certificates, a large unauthorized underground economy.

4. Even If Everything Scott Says Is True

Grant the premises: (1) first-gen immigrants commit less crime, (2) use less welfare, and (3) America’s immigrant pool is far better than Europe’s.

What happens when you run this policy at scale across generations?

The talented tail distorts the average

NFAP: ~64% of U.S. billion-dollar startups were founded or cofounded by immigrants or their children. That tail is highly valuable.

But the Manhattan Institute is explicit: high-skilled groups offset low-skilled groups in the blended average.

We know the elite tail justifies itself, but we should be asking whether the high-skill tail justifies the millions who enter via non-skilled and/or unauthorized pathways.

The answer is probably not — and leveraging the most talented immigrants is how the “averages” get turned into propaganda.

GDP is a size metric, not a welfare metric

National Academies: “GDP up” can coexist with “median citizen worse off.”

Much of immigration-linked GDP growth is recursive: more people creating demand for housing, schools, healthcare, translation services, which shows up as “economic activity” while per-capita productivity stagnates.

Unless you’re tracking fiscal contribution and extraction granularly by origin, pathway, education, and generation over a full lifetime (the way the Netherlands does), “immigrants boost GDP” is an unfalsifiable slogan.

Population size doesn’t drive prosperity; human capital distribution does

• Easterly & Kraay (2000): Small states don’t underperform.

• Kirkegaard (2025): Population predicts nothing positive across twelve models; significantly negative in half.

During the 1880-1920 wave, immigrants entered an industrial economy with no welfare state and brutal environmental filters that selected for traits building institutional capacity.

Today’s economy demands high human capital, the welfare state subsidizes low productivity, and the skill mismatch is far worse. Adding headcount with the wrong distribution doesn’t just fail to help; it predicts worse outcomes.

Low-skill immigration prevents automation, and AI makes this existential

• Germany (allocation study): More low-skilled labor → less automation innovation.

• Denmark (firm-level): More migrants → fewer robots.

• Japan & South Korea: Highest robot density, most restrictive immigration. Not a coincidence.

Scott himself co-authored AI 2027, which predicts superhuman AI potentially arriving very soon. Importing generic low-skill human labor just before (what’s being touted as) the most consequential technological transition in human history makes zero sense.

Why? Companies automate slower because they fill positions with human labor. This makes the U.S. less competitive. And when companies attempt to automate, there are more people who will unionize and form political coalitions against AI/automation; making things more difficult akin to the ILA port extortion.

Fiscal accounting is composition-dependent

Manhattan Institute 30-year net fiscal impact by education: no diploma, net cost ~$130K; high school, break-even; bachelor’s, net gain ~$1.6M; advanced degree, net gain ~$3.1M.

The National Academies: a lower-education mix would be “much less positive or much more negative.”

What fiscal studies don’t capture:

• Underground economy. Studies acknowledge undocumented immigrants “work in the underground economy and possibly engage in tax evasion.”

• Labor market displacement. The National Academies’ own synthesis: negative effects concentrate on prior immigrants and native-born high school dropouts.

• Identity fraud. SSA’s Earnings Suspense File has accumulated over $1.5 trillion in wages with mismatched identifiers through 2016. DOJ: 119 prosecuted for stolen SSNs in one operation. CRS documents the broader pattern.

• Smuggling and gang networks. Treasury’s Tren de Aragua designation, CRS MS-13. All siloed across databases or never recorded.

That’s the shadow accounting “net fiscal positive” claims assume away.

The filter should be talent x ethos, not just “STEM”

Kirkegaard (2025): Even standardized screening yields dramatically different true human capital by origin, with gaps widening under lenient thresholds.

With AI rapidly commoditizing technical skills, importing generic “STEM workers” isn’t the slam dunk it was a decade ago; talent alone isn’t sufficient.

The ideal immigrant raises per-capita innovation, pays more in taxes than they extract over a lifetime, and wants to be American in the foundational sense: limited government, individual liberty, rule of law, civic reciprocity.

They should be filling jobs that the country needs filled… not recursive loop jobs created by unfiltered immigration (translation x social services x etc. for other immigrants).

So the idea is: Talent x Alignment. Not “more immigrants” or “more STEM.” As talented immigrants can effectively leverage resources for anti-American ideals (e.g. advocate for socialism, open borders, etc.).

5. Returning to the American Context

Scott concludes:

“The more honest and politically practical course would be to acknowledge when these stories about Europe are true, then challenge conservatives to return to the American context, where they’ll have more of an uphill battle.”

Alright, let’s “return to the American context.” To actually know the American context requires proper measurement infrastructure and granular data collection instead of debating within blended average fog.

We need to track:

• Fiscal: Cohort-level generational accounting by admission pathway x education x age-at-arrival x country of origin x race/ethnicity, including descendants. Lifetime NPV of taxes minus transfers (state x federal x local x charities).

• Crime: Generation x country of origin x time-in-country, with consistent recording across jurisdictions, compared against a stable third-plus non-Hispanic White baseline, not a blended average.

• Innovation: Tail metrics (patents, founders) separated from mean/median outcomes, so the talented tail can’t launder the median.

• Multi-gen: Second, third, fourth generation outcomes by country of origin x race/ethnicity x pathway, because the first-gen snapshot is the least informative datapoint for long-term policy.

The U.S. inflow is not Europe’s. America likely has better immigrants than what Europe is accepting. First-gen outcomes may not be bad.

The problem. (1) First-gen averages are not the primary concern. (2) Newly-minted Americans (second-gen, third-gen) get lumped into the “American average” immediately. (3) We don’t have granular tracking by pathway x origin x ethnicity. (4) We aren’t comparing against the right baseline. (5) The tails may be propping up the whole average. If the tails are having the biggest impact, why are we taking in the others?

And also — why should we think that “better than Europe” is good enough for the U.S.? That’s a low bar to clear. Why aren’t we trying to optimize for the very best we can do? (We can probably do much better than we’ve currently been doing.)

“Immigrants commit less crime and use less welfare” is misleading because it leverages:

• Blended averages (not medians)

• First-gen only (next gens are “Americans”)

• Flawed baseline comparator (”American average” skewed by tails and newly absorbed second-gens)

• A system that doesn’t track anything granularly and fails to adjust for fraud, off-books activity, native job displacement, sanctuary black holes, charity extraction, age-composition artifacts, and race/ethnicity/country of origin

The statement as constructed can’t bear the policy weight Scott implies. And all this talk gives the Left-Wing faction a pass for: (1) zero border enforcement and (2) actively helping individuals enter/become citizens in the U.S.

The left-wing plan is clear (actions speak louder than words):

1. Open borders/don’t enforce border laws

2. Help immigrants, migrants, asylum seekers, refugees enter/stay

3. Create sanctuary cities and states to prevent deportation (don’t comply with federal law)

4. Give handouts: federal, state, local, NGO, charity, etc. (even though they’ll claim they aren’t actually receiving them because they “cant”; yes they can)

5. Let them exploit citizenship loopholes and create new loopholes or grant amnesty

6. Zero acknowledgement of off-the-books work (zero taxes, able to undercut American businesses) and displacement of American-born citizens

7. Gaslight conservatives with questionable datasets that only liberals compiled (GDP went up!, It’s the best data!, etc.)

8. No granular data tracking: Continue saying impractical or racist to track in-depth and say how ignorant conservatives are.

We saw what happened under Biden (2020-2024). I’m not aware of any Democrat, Liberal, or Progressive that has any plan to enforce the border. Even if they claim they have a plan, will people believe they’ll actually follow it? For many Americans, immigration and border enforcement is the single biggest issue going forward.

That said, I think Democrats as a party don’t have much to worry about. Why? Demographics. The new demographics will flip major states by the 2030s… if Dems win 2026 midterms and 2028 prez, things will accelerate. Republicans will at least slow down the inevitable massive flippening in key states (e.g. Texas).

By that point I can only hope that Scott’s fast-AI trajectory comes true and we have some combination of: (1) AGI/robotics diffusion + (2) bioenhancement (gene mods for IQ, BCIs, etc.) — such that immigration policy won’t matter much. Without that? I predict a non-insignificant decay in U.S. developmental status (adjusting for AI) + gov debt issues + socialism + higher crime (not necessarily in the data) + reduction in quality-of-life/cohesion.

The blunt reality: The U.S. (1) runs a civilization-scale immigration policy without significant filters, (2) refuses to build a granular measurement infrastructure to evaluate it, and (3) declares “immigration is good” despite having zero granular data to audit — while the literature quietly admits that most “immigrant advantages” fade after the first generation. If demographic changes shift per-capita metrics (crime, productivity, innovation, politics, identity) and this compounds across generations, by the time we detect a deleterious signal it’ll probably be too late to do anything about it.

The White House said in November 2025 he’s “committed” to a $2,000 check and staff are exploring “how to go about making the plan a reality.”

But details and timing remain unclear. Trump has recently been noncommittal when asked to “promise” checks, even while claiming he could do it.

And critically, new household checks at scale generally require Congress — the IRS can’t just invent a new nationwide payment program without statutory authority.

Realistic Path: Tax Credits vs. Checks

If something gets paid out before the midterms, the most plausible vehicle is a tax credit with advance refunds — stimulus-check mechanics run through the IRS.

The cleanest template already exists: Sen. Josh Hawley’s American Worker Rebate Act of 2025 (S.2475), which would:

1. Create a refundable tax credit tied to “qualifying tariff proceeds” (duties imposed after Jan. 20, 2025).

2. Set the applicable amount as the greater of $600 or a formula based on tariff proceeds per eligible person/child.

3. Phase out starting at $75K single / $112.5K HOH / $150K joint—the same structure as COVID-era payments.

4. Authorize advance refunds with a hard deadline of Dec. 31, 2026, implicitly aiming to get money out the door before it matters politically.

This also matches what Treasury Secretary Bessent suggested early on: the “dividend” might be structured as tax cuts rather than literal checks.

Trump Would Frame It as “Tariff Checks”

The political logic is straightforward:

Sell tariffs as a benefit. The pitch: tariffs raise money → voters get cash back → tariffs are “working for you.” The dividend pitch arrived right after GOP losses tied to cost-of-living discontent.

Counter the affordability narrative. The midterm environment is already being framed around “pocketbook issues”.

Logically this is stupid given that purchasing power for 1 hour of work is higher than ever historically but is drowned out by narratives being pushed in the mainstream media and on social media (fixation on inflation with zero acknowledgment of wage/investment growth).

Perception = reality and vibecession is the perception.

1. Many people don’t invest: More than you think have zero long-term investments. As a result? They lose out on the gains and are envious of those who invested.

2. Many want to spend immediately (money in / money out… easy come / easy go)

3. Dopamine spike + lower impulse control: TikTok “shorts” brainrot, genetic shifts in demographics, etc. Not surprising that many just want to gamble on some YOLOs (if they didn’t spend immediately) and DraftKings lottos.

4. Mainstream media: Constantly harps on “inflation” and how awful everyone has life today. It’s how they attract and “lock in” listeners. Just keep complaining about how life is unaffordable in the U.S. Remember you have no agency to move or change your situation.

5. Social media: Markets the illusion that everyone is a billionaire flying private jets and needs $100M to be satisfied with life. Anything less and you’re a failure.

6. Psychological entitlement: Increased over time for a variety of reasons (social media is a big perception). Lack of skin in the game, insane welfare standards, etc.

7. Welfare standards keep rising: More handouts / can never reverse or “put the cat back in the bag.”

8. Zoomers and “Up Only” markets: That’s all they know, so any stagnation or pullback = psychic pain.

This cocktail is how we end up with investors like Michael Green succumbing to the mind virus and/or chasing populist clout — and implying $140k is the new poverty line.

All roads likely lead to socialism in the U.S.

The question is mostly about the pace. Ideally we slow the transition so we don’t overwhelm the system before AI/robotics have a chance to diffuse.

If you want the best chance of America staying a first-world economy, you should hope that Republicans keep winning… otherwise it’s open borders, less law enforcement, more transfers from your taxes to illegals that are “growing GDP” (but not really when you subtract all offsets).

As Elon articulated: AI/robotics needs to work. If it doesn’t work, we’re fucked. Likely debt bomb, stagnation, etc.

Another point I’ll keep making: Rolling out UBI prematurely = braindead. The U.S. already has proto-UBI with generous handouts/welfare for poor people.

UBI should not be widely promoted until absolutely necessary.

It should be something the gov discusses privately.

And if eventually a time comes when growth from AI/robotics is high and there’s mass job displacement — we can efficiently adapt and roll out stimmy checks like we did during COVID followed by a permanent UBI program (we can likely just tweak the current system a bit).

We should not prematurely warp the cognitive-psychological patterns of Americans by making them think they’ll be displaced by AI tomorrow and that we need UBI immediately. Social unrest from a mob demanding UBI before practical/feasible and actually giving into that mob would be a grave mistake.

Branding precedent. The administration has already used symbolic direct-payment branding (the “Warrior Dividend”), even amid disputes over funding sources.

The Biggest Obstacles

1. The Math Is Ugly

A $2,000-per-person plan runs into the hundreds of billions. CRFB estimates ~$600B per round if designed like COVID-era Economic Impact Payments. Multiple analyses (FactCheck, Axios) note that tariff revenue is far smaller than the cost of a broad $2K dividend, especially if the administration also claims it will reduce deficits with the same money.

There’s also the “net burden” problem: The Tax Policy Center estimates that tariff policies could impose a substantial average burden in 2026, blunting the political benefit of any rebate.

2. Congressional Politics Inside the GOP

There’s Senate Republican resistance to prioritizing checks over deficit reduction.

A revised CBO forecast further complicates the “we can afford it” narrative.

3. Legal Risk: The Revenue Stream Itself Is Under Threat

The Supreme Court is reviewing whether IEEPA authorizes the tariffs at the center of the revenue story. Lower courts have already found parts of the tariff regime unlawful, with tariffs temporarily left in place pending review.

If the Court ultimately rules against the administration, refund obligations are a real risk — making it politically and fiscally harder for lawmakers to commit money that could later be needed for refunds.

Forecast 1: Will Tariff Checks Actually Go Out Before the 2026 Midterms?

Assuming “before the midterms” means by Election Day, November 3, 2026.

A) Broad checks (~$2K to most lower/middle-income households): 20% (≈ 4:1 against).

Confidence: Low-to-medium.

The White House has said he wants it, but Trump remains noncommittal on timing. A classic sign of a promise being used as leverage rather than a locked plan.

The cost/revenue gap is substantial, and the Supreme Court risk makes “counting the money before it’s legally secure” harder.

B) Smaller rebate or tax cut dressed as a rebate: 35% (≈ 1.9:1 against).

Confidence: Medium.

A scaled-down design is more financeable and more likely to unify Republicans. Hawley’s bill shows a workable mechanism with an explicit window through Dec. 31, 2026.

This is the path of least resistance, but it still requires Congress.

C) No broad checks at all: 45% (≈ 1.2:1 against checks).

Confidence: Medium.

The default outcome when fiscal math doesn’t work cleanly, Congress is divided, and the tariff base is legally uncertain.

Forecast 2: If Checks Happen, Do They Help Republicans Hold Congress?

Baseline Environment

House control is razor-thin: 218 Republicans / 214 Democrats / 3 vacancies.

The president’s party historically faces midterm headwinds (an average loss of 26 House seats since 1932, and Trump’s approval sat at ~40% in January 2026.

With that margin, even a small national swing flips the House.

Net Effect: Modest Help, Not a Cure

Direct payments can temporarily improve perceptions of “government is helping” and blunt affordability messaging. But two major offsets limit the upside:

1. Tariffs themselves raise prices. Opponents will argue the check is just giving back money households already paid through higher costs, potentially diluting gratitude and fueling cynicism.

2. Attack lines write themselves. “Unfunded giveaway,” “election-year gimmick,” and if tariff authority gets struck down, “promised money that evaporated” becomes a liability.

Quantified Election Impact (Approximate)

• Rebate landing Sept. to Oct. 2026: Estimated +5–10 points boost vs. no-check baseline. Confidence: Low. Lots of unknowns and highly timing-sensitive.

• Early (Spring 2026) or small/complex payout: Effect likely fades or gets drowned out, roughly +0–3 points improvement. Confidence: Medium-low.

• Senate: effects are more state-specific; expect a smaller net benefit than in the House unless checks are highly salient in a few swing states. Confidence: Low.

Bottom line: Checks would probably help Republicans at the margin but are unlikely to override the combination of midterm fundamentals, Trump’s approval trajectory, and the broader cost-of-living environment.

The “Tell” Indicators

If you’re tracking whether checks are actually coming, these are the high-signal moves:

• A White House–backed bill text: Or reconciliation instructions that mirrors the Hawley refundable-credit structure.

• Treasury/IRS operational language: Implementation timelines and payment rails.

• Supreme Court ruling clarity: On the tariff authority question, which would remove the biggest single uncertainty.

Preface: How the Renee Good Narrative Evolved

Reacting immediately to the death of Renee Good after initial footage surfaced is something many people did.

I thought this was premature—the initial camera angle wasn’t great and we didn’t know the full sequence of events. I formed an initial opinion based on what I saw and then did more digging.

And in the process, I quickly identified the political spins from each side.

Primary Evidence Index

Before diving in, here are direct links to primary evidence and key reconstructions + coverage referenced below.

1. Reuters visual reconstruction (baseline frame-by-frame timeline)

2. Minnesota BCA statement (official)

3. Hennepin County Attorney evidence portal (official)

4. Wife’s statement (original outlet posting the statement)

5. Federal statute (18 U.S.C. § 111)

6. Minnesota fleeing police statute

Pre-shooting overhead/bystander video (X)

Agent-perspective/ICE video release point (AlphaNews on X)

Known (public): ICE operation in S. Minneapolis; Good in driver’s seat; agents in visible ICE-marked gear; wife outside yelling; vehicle accelerates; agent fires; Good dies.

Unknown (public): Exact contact mechanics (if/where the vehicle struck the agent); the agent’s footing; exact commands across all angles.

My inference (argued below): The decisive variable was noncompliance + attempted flight; the ideological ecosystem encouraged escalation.

The Left-Wing Framing

Left-wing media initially framed the incident as if Renee Good was a random lesbian soccer mom-type in the Greater Minneapolis area with zero knowledge of ICE, zero political affiliation, merely driving home after dropping her son off at school… wantonly murdered by ICE thugs for no good reason. Targeted at random by the “ICE Gestapo” for “existing.”

Woke sources like NPR put egregious spins on the events and included little info nuggets that nudged the psychology of the masses into thinking: “Yeah, she was 100% murdered.” Liberal media quickly claimed Good was told conflicting instructions by officers at the scene: (1) drive away vs. (2) get out of the car… and she chose Option 1, did nothing wrong, and bam… dead.

Initial videos made me think that perhaps Good was confused. Maybe she accidentally drove near an ICE operation zone, realized the ICE operation was going on, and quick tried to do a Y-turn to avoid the hassle but was killed. Or perhaps she was startled and thought it was a criminal gang pulling up on her—not actual ICE agents—and needed to flee the area.

Left-wingers reacted strongly to the initial footage with zero context… they didn’t need context or want it. The preliminary claims were: (1) Good had zero knowledge of ICE, (2) was just driving around (minding her own biz), and (3) was murdered by ICE.

A lot of early commentary talked like the agent fired only after she’d fully passed him—glossing over the fact that the first round went through the windshield while the SUV was still crossing his position.

So the implicit framing by left-wing media with “facts” was:

COLD. BLOODED. MURDER.

Left-leaning media organizations did not claim “murder” outright, but if you carefully analyzed how info was presented (subtleties, interpretations deliberately one-sided, certain facts omitted, logical inferences applied in one direction but not the other) you can infer what they wanted you to think.

The spin was presented as “just the facts.” (Left-wing media has a lot of smart people… arguably more smart people than the right-wing media… so they have a decisive advantage everywhere that isn’t “X” a.k.a. Twitter).

All logic in support of the ICE agent was half-assed or omitted.

The media presented the coverage as “unbiased” (“totally neutral” and “nonpartisan”)… with “experts” who analyzed everything frame-by-frame… and since they are “experts” they can apparently see and interpret better than you, better than me, and better than advanced AI.

The Narrative Starts Cracking

The virality of the story increased…. and unexpectedly the mainstream media pushes a weird narrative: Good was a “legal observer” of ICE.

This is odd. Which is it?

1. An apolitical woman driving home innocently after dropping her kid off at school?

2. Or was she a “legal observer” of ICE?

And what is a “legal observer of ICE” anyway? I’m curious (this was something I had never heard of).

The key question now wasn’t whether she was a mother; it became whether she was there intentionally in an “observer” capacity (i.e. potential interference/harassment)—with whistles, blocking traffic, spouse filming, verbal conflict with agents.

That was the first straw starting to break the camel’s back. A few more videos surfaced from different angles. Around this time the then-unverified rumor on the right-wing that Good was a member of “ICE Watch” began circulating heavily.

The left-wing quickly “debunked” this rumor by carefully stating well akshually there’s “technically no evidence” to sUbStAnTiAtE this claim. The reasoning applied? There is a specific group named “ICE Watch” and no evidence Good was a member of it.

The flaw in that logic? Should be obvious. Even if she wasn’t in the specific group called “ICE Watch,” this doesn’t mean she wasn’t part of a different activist group on “ICE watch” (i.e. watching for ICE) and alerting individuals being targeted for deportation, some of whom are likely criminals.

But then her own wife ended the debate.

In a statement to Minnesota Public Radio (MPR), Becca Good admitted:

“We had whistles. They had guns.”

Whistles are used to signal immigration agents are present (alerting undocumented people to flee and mobilizing others to come to the scene to record and discourage agents from lingering).

So regardless of formal membership in any named group, “whistles” are consistent with organized “ICE-watch” tactics.

She maybe wasn’t in the formal organization literally named “ICE Watch.” But she was clearly engaged in “ICE watch behavior.” We’d later find out from reporting that she participated in an “ICE observer neighborhood patrol” network. And all this becomes common sense when you learn “we had whistles”… easy to infer reality.

Then we get another overhead video of her blocking traffic for minutes, horns honking, whistles blaring, etc. Does that sound like some random neutral passerby who had “no idea what ICE was”?

Liberals frame “ICE watch” groups as people monitoring ICE activities to ensure ICE aren’t brutalizing people and are following laws, but the reality is different.

What’s actually happening with these “ICE watch” groups?

Well you got a glimpse of it from this incident. They’re often:

1. Interfering with ICE operations

2. Alerting criminals of possible deportation

3. Harassing ICE agents

4. Threatening ICE agents

5. Disturbing the peace

They are making life hell for people hired to enforce the law.

I would guess that most “legal observers” of ICE are actively conspiring against ICE (federal law enforcement) AND aiding and abetting criminals (even if they are too ignorant to realize that a subset of the people they’re helping are serious criminals).

Being empathetic to illegal immigrants following the laws is one thing… nobody enjoys deporting them. But if the U.S. has any hope of surviving as a country, enforcing immigration laws is 100% necessary. Furthermore, ICE prioritizes criminals… so blocking their operations makes the U.S. more dangerous because they have a tougher time arresting gangsters.

I would guess that a majority of ICE agents are humane as long as the people they are arresting are compliant and not dangerous narco-terrorists/gang members. Thinking otherwise is downright delusional.

The Pre-Shooting Video: Blocking Traffic + “Dancing”?

Eventually came the video that the “innocent mom” narrative couldn’t explain.

In a pre-shooting overhead video, Good’s SUV remains positioned at an angle across the roadway with honking audible, while she appears to rock/sway in her seat—behavior many social media users described as “dancing.”

This no longer appeared to be a terrified woman confused by a chaotic scene. This was someone treating a dangerous and serious law enforcement operation as a performance.

The behavior was inconsistent with a confused or terrified driver; it aligned much more closely with someone mocking federal law enforcement and treating an active police scene as a joke.

The GoFundMe Machine

While the facts were still being disputed, the financial engine of the narrative was already running.

Renee Good’s widow and family

A GoFundMe campaign for Good’s family rapidly raised significant funds, capitalizing on the early “unprovoked murder” framing before the full context of the antagonism, the whistles, and the rocking/swaying behavior came to light.

As displayed on GoFundMe on January 11, 2026, the fundraiser showed $1,503,387 raised, 38.5K donations, and Donations paused.

ICE Officer Jonathan Ross

A separate GoFundMe campaign in support of ICE Agent Jonathan Ross emerged:

As of Jan 13, 2026, the fundraiser showed $635,099 raised of $800k.

Misinformation on Both Sides

After the initial commotion, a clipped/low-res clip that looked doctored to a lot of people surfaced in right-wing social media circles that made it look like Good tried to hit the ICE agent and run him over. It was blurry, went viral, and lacked context.

The right-wing had a subtle misinformation problem.

(Separately, WIRED reported people were using AI face-enhancement to falsely identify the agent—more social media distortion on both sides.)

There were also alleged deepfakes of Good and her spouse mocking the death of Charlie Kirk. (I have no way of knowing whether these were legitimate or fake, but “fact checkers” claimed fake so I’ll give them the benefit of the doubt.)

Additional Facts Emerge

The facts continue evolving… we find out more.

The 10-year veteran ICE agent associated with the shooting—identified by the Star Tribune and court records as Jonathan Ross—had recently been dragged by a vehicle in June 2025 and received 33 stitches in a very similar scenario.

AP reports Ross’s career: Border Patrol in 2007, ICE in 2015, JTTF team leader, instructor roles. In the June incident, his window was punched, he was dragged the length of a football field in 12 seconds, and required dozens of stitches.

This is not unusual. ICE agents are being actively targeted. DHS reported a 1300% increase in assaults on ICE officers. They are simply trying to do their jobs.

For some reason, left-wingers/liberals can empathize with criminals and individuals engaged in illegal activity more than those simply hired to enforce laws and try to survive (i.e. make it out alive back to their families without being maimed, injured, or killed on the job); they do this with murderers too and often memory-hole victims.

Eventually CNN obtained surveillance footage and claimed Good had been blocking the road for about “three minutes.” (This was quickly cited on Wikipedia… other sources claimed 2-4 minutes. As of January 13, 2026 Wikipedia (conveniently) removed the time estimations). (CNN still documents “several minutes”).

Anyways… this was clearly not innocent behavior.

Moving to Canada (?): WaPo reported a neighbor said the couple broke their lease and told her they were moving to Canada after Trump was reelected, and that they moved to Minneapolis in March 2025. So much for the “totally apolitical random driver” framing.

Witness framing: Early reporting included a witness claim that one agent told Good to “drive away” while another told her to “get out.” That point was witness-based and not established on the initial camera angle. Later, a longer cellphone video from the officer’s perspective includes the wife yelling: “Drive baby drive,” which is the clearest “drive” instruction captured on audio. The “drive” command came from her wife, not a federal agent.

On January 9, 2026, we see a longer video from the ICE agent’s perspective. Leading up to the shooting, Good’s wife was verbally trash-talking the ICE agents. Disrespectful behavior.

Just before the shooting, ICE agents had Good’s vehicle surrounded. It was parked at a weird angle on the side of the street blocking traffic.

An ICE agent told her to get out of the vehicle, she did not get out. Takes a lot of gall to not comply with a federal agent when your vehicle is surrounded, the road is mostly blocked by an ICE agent’s vehicle in front of you, and you have a gun pointing right at your face in front of the windshield of your vehicle.

This is how things go from “annoying” for ICE agents to “now you’re getting physically removed and/or arrested” and potentially “charged with crimes.”

It takes even more audacity to wait until the ICE agent is positioned in front of your vehicle before you attempt to drive off. You had a gap of 3-5 seconds where you could’ve driven off without anyone near your vehicle, but you waited until he walked toward the front.

Good’s wife then says “Drive, baby, drive!” Perhaps in the heat of the moment, an intoxicating adrenaline rush from defying ICE agents… Good does just that… steps on the gas pedal and the acceleration is rapid (despite the fact that it may not appear rapid on slow-motion replay).

What’s the problem with this? It should be obvious at this point: None of this should’ve happened.

Doing just one of the following would’ve prevented this shooting:

1. Not joining a group that interferes with federal officers

2. Not going to an active ICE operation

3. Not interfering with an active ICE operation

4. Complying with federal officers when they ask you to step out of your vehicle

5. Driving away before ICE officers surround your vehicle and/or before an ICE officer walks in front of your vehicle and points a gun at you

My Initial Observations

• Good had out-of-state license plates (obviously suspicious)

• Her vehicle was positioned blocking traffic for about three minutes (parked angularly in the middle of the road), horns were audible due to the traffic jam, this was interfering with ICE operations

• Good appeared to rock/sway in her seat while blocking traffic—behavior many perceived as “dancing” (Video)

• At least two vehicles passed her while parked (allegedly she waved one by)

• Good and her wife were antagonizing federal agents

• The federal agents do NOT know who they are dealing with

• Good and her wife clearly knew they were talking to ICE

• Federal agents had her surrounded and asked her to step out

• She did not comply (big red flag)

• She did turn her wheel… and from my perspective, was likely (in my judgment) NOT trying to hit the officer in front

• But: (1) not trying to hit the officer is mostly irrelevant because (2) it’s still possible she still would have hit him. She stepped on the gas hard and turned her wheel. Intent to not hit the officer doesn’t mean much if it’s a close call.

• The officer was directionally within close range of a heavy vehicle coming fast at him. The driver did not comply with ICE’s request and had been antagonizing them leading up to this moment. A different ICE officer tried to open her driver’s side door from the side.

• From my view, the first shot comes right at the “toward → past” transition. Left-wing media including Reuters’ reconstruction framed it as: first shot fired as she moves past him; then two more rounds into the driver-side as it continues past. The first shot was fired before she fully passed him.

• He continued shooting. Why? Officers are trained to use deadly force to stop an imminent threat (DHS). He likely perceived her vehicle as a threat to his life.

• Many claim the officer jumped out of the way. True, but conditions may have been icy/snowy, difficult for mobility. Neutralizing a target that may kill you with a motor vehicle could be reasonable. Continuation of shots is simply due to the vehicle moving rapidly while the officer was locked on the target. The officer may have also thought the fellow ICE agent at the side door was caught/stuck or about to get dragged while attempting to open the door. Another case where neutralizing the target makes sense.

• It’s very easy to see on replay in slow motion what “should’ve been done.” This is not the same as real-time. It drives me absolutely crazy that people are giving armchair analyses of slow-motion replay and extrapolating that to “real time.”

• The ICE agent who shot had a prior similar experience where he was dragged by a vehicle and stitched up. I suspect that primed him for threat more than usual.

• Another guess: the agent in front planned on standing his ground no matter what something like: “You’re not going anywhere, and if you drive at me, I’m perceiving that as a threat on my life and will shoot.”

• Good likely saw the officer with a gun pointed directly at her… it was right in front of her windshield. (Some have posted screenshots where she clearly should be able to see the gun pointed at her because her eyes are looking that direction.)

• Another big issue that I’ve only seen a subset of astute analyzers point out is that she may have forgot she left the vehicle in reverse. We see her initially back up, then the ICE agents emerge to the scene. We then see the ICE agent walking over toward the front, however, before he gets there she had plenty of time to drive off — literally a wide open path where she would have missed him by a large margin — such that if he had shot then, it would have clearly not been justified. Problematically she waited until he was (1) extremely close to the front of the vehicle and (2) accelerated quickly in his direction.

• The officer was clearly surprised that she accelerated toward him while he had his gun out and was recording the scene. You can hear something akin to “Whoa ohhh” (indicating surprise) and then the shots.

My Honest Assessment

Tragic all-around. It should’ve never happened (and I already articulated why).

Added thoughts:

• The agent maybe could’ve moved (stepping out of the way which he was already doing would’ve been better) but we don’t know his footing. I suspect he wanted to hold position to force her out of the vehicle.

• Good most likely did NOT intend to hit the officer — based on the wheel turn angle. And I don’t think she would’ve seriously injured/killed him had he not shot.

• But even if she didn’t intend to hit the officer, we don’t know what the trajectory would’ve been if the officer did not shoot. It’s possible he’s seriously injured and/or dead (the counterfactual is unknown).

• It’s unclear if the agent could’ve stopped shooting mid-burst (acceleration was so fast) so a couple shots from the side make sense (neutralize target if threat to him/side officer possible drag + continuation in a fast-reaction scenario).

• “Drive, baby, drive!” — I don’t think Good would’ve attempted to drive off if her wife had said something like “just step out and we’ll talk to them” instead.

• Again, something that drives me absolutely bonkers are the “ultra-slow motion” frame-by-frame videos circulating showing the shooting as evidence that the officer clearly should not have shot. Why?

  • A) They make the situation look misleadingly easy to handle because now everything is “super slow” (Just step out of the way! Look how much time he has in slow-mo!) — people fall for this all the time in sports when criticizing athletes (the replay in slow-motion makes it look like the athlete obviously should’ve done something different… way easier said than done)

  • B) They undermine the speed of acceleration (in real-time it appeared very fast)

  • C) People gloss over the potentially shaky footing of the officer on ice/snow/wet roads… assuming easy movement front/back/side-side, etc. The officer was planning on holding his ground and is likely in shock that she had the gall to drive directionally at him (even if her intent wasn’t to hit him).

• Also I’m not sure what you’d do if you had ICE agents surrounding your vehicle with a gun directly pointed at your dome… but I would think that if a gun is out, it might be smart to just comply no matter how much you dislike law enforcement.

• Left-wingers seem to have a really easy time putting themselves in the proverbial “shoes” of victims, criminals, and/or non-police… and a very difficult time thinking about themselves (or a close family member/friend) in the position of law enforcement who is actually dedicated to doing the job.

  • A common rebuttal is something like: “Well, I wouldn’t be deporting people.” Congrats… you played yourself.

  • You aren’t able to envision yourself in this position because you wouldn’t even do the job and lack the ability to empathize with someone who is doing the job.

This is an example of an ultra-slow-motion video circulating that makes everything look easy in hindsight. Left wingers are promoting videos like this to imply that the situation was easy to handle… slow-motion + hindsight bias 101.

On the cultural double-standard:

The Democrats (@TheDemocrats on X) posted a transcript of what was allegedly said at the scene:

“Renee Nicole Good: “That’s fine dude. I’m not mad at you.”

*gunshots*

ICE: “Fuckin bitch.”

Liberals place a lot of weight on things people say when they’re in extremely high-stress confrontations.

It’s amazing to think that if you ever were attacked, reacted with legitimate self-defense, called your attacker some sort of slur in the heat-of-the-moment, half the country would think your right to self-defense should be revoked.

One individual on X (@LibertarianMama) posted:

“As a female driver who sometimes panics on the road when I don’t know what I’m supposed to do in confusing circumstances, I’m realizing that I’d probably get shot by officers. And a lot of you would probably say it was justified.”

What’s the issue here? If we are being consistent, the same logic should apply to the ICE agent. Either it goes both ways or it doesn’t.

What would social media be saying if the officer was ran over and severely injured or killed? My guess is the left would be attacking him for doing his job.

Sadly there are brainwashed individuals who sincerely believe ICE rides around sadistically terrorizing innocent people at random. The evidence says otherwise.

Note: It may not have even been Agent Ross that was swearing… many allege another agent. The point is that the swearing is orthogonal to the sequence of events here. This was a high stress confrontation.

What Follows

The remainder of this analysis provides:

1. A comprehensive review of all available video evidence

2. Steelmanned arguments from both sides

3. A forensic breakdown of the sequence of events

4. An adjudication of whether the shooting was justified

5. The decisive lesson: compliance would have prevented this tragedy

Baseline reconstruction: For the sequence-of-events timeline below, I use Reuters’ frame-by-frame reconstruction as the baseline, and I label deviations or additional details from other outlets explicitly.

I. The Established Facts

Basic Incident Data

The basics:

• Victim: Renée Nicole Macklin Good, 37, a U.S. citizen.

• Date/Time: January 7, 2026 at approximately 9:30 AM.

• Location: Portland Avenue South between East 33rd and 34th Street in Minneapolis.

• Cause of death: Fatally shot (exact wound locations not confirmed in a primary public release as of publication date).

• Shooter: Local reporting from Star Tribune identified the shooter as Jonathan Ross by cross-referencing court records from a nearly identical assault on him 6 months prior.

• Vehicle: Burgundy Honda Pilot with Missouri license plates. The Missouri Department of Revenue confirmed the plate was registered to two people in Kansas City (KCTV5).

• Residence: Washington Post reports a neighbor said the family moved to Minneapolis in March 2025 after telling her they planned to move to Canada following Trump’s reelection.

• Blocking duration: About three minutes before the confrontation (CNN transcript).

• Investigation: The FBI is leading. Minnesota’s BCA publicly stated it is not continuing a state-led investigation under the prior cooperation agreement.

Who was Renee Good before Minneapolis?

1. WaPo reports Renée had a first husband who divorced her, and a second husband (Timmy Macklin Jr.) who died in 2023 at age 36.

2. Reuters reports she participated in a neighborhood network meant to track/alert about ICE actions.

Armchair psychoanalysis (speculative): WaPo reports her first marriage ended in divorce and her second husband died in 2023. I’m not diagnosing her, but I am telling you what this looks like: grief + possible disorientation + meaning-hunger, and then politics (e.g. watching for ICE) becomes the substitute religion. That’s how people end up thinking they are virtuous for harassing federal law enforcement.

The Out-of-State Plates Issue

This matters for threat assessment.

From the agents’ perspective:

• Out-of-state plates at an active enforcement scene raise suspicion

• They cannot know in the moment that she’s a recent transplant

• Combined with her unusual positioning, rocking/swaying behavior, and antagonistic spouse, this reasonably heightens concern

Critical Background: The Shooter’s Prior Trauma

In June 2025—just six months before this incident—an ICE officer was dragged during an arrest attempt in Bloomington:

• Distance: DHS press release said roughly 50 yards; AP describes “the length of a football field in 12 seconds.”

• Injuries: Per court documents reviewed by the Star Tribune, Ross received 20 stitches for a deep cut in his right arm and another 13 stitches in his left hand—33 stitches total.

• Testimony: CBS reports Ross testified he was “fearing for my life” during the June incident.

• Ross’s background: Per AP, Ross joined Border Patrol in 2007, ICE in 2015, served as JTTF team leader, and held instructor roles.

• DOJ initial report: The DOJ reported a convicted sex offender illegally present in the country assaulted a federal officer in June 2025.

• DOJ verdict: In December 2025 the DOJ reported a jury found this individual guilty of assaulting a Federal officer.

This is important context. Ross nearly died in a vehicle attack months earlier. He is now facing a noncompliant driver moving a vehicle in his direction.

His threat perception is not hypothetical—it’s informed by recent, severe trauma.

As Vice President JD Vance inquired:

“You think maybe he’s a little bit sensitive about somebody ramming him?”

II. The Video Evidence: What We Now Know

Timeline (CST)

Source: Reuters Visual Reconstruction

The Scene Before the Shooting

On January 9, newly released footage recorded on the agent’s phone captured the volatile moments leading up to the shooting.

According to CNN, NPR, and NBC News:

Events:

• Vehicle position: Good’s SUV was stopped at an angle across the street, blocking traffic (CNN).

• Duration: She was stationary for about three minutes before the confrontation (CNN transcript).

• Rocking/swaying behavior: In the pre-shooting overhead video, Good’s SUV remains positioned diagonally across the roadway with honking audible, while she appears to rock/sway in her seat—behavior some described as “dancing” (NY Post; WESH notes honking is heard but unclear where it comes from).

• Traffic: Multiple vehicles—including large SUVs—drove around her while she remained in place. She let at least 2 vehicles pass; some witnesses claim she waved one vehicle by.

• Initial interaction: Ross approached with his cellphone recording. Good was initially calm, telling Ross “That’s fine dude, I’m not mad at you.”

• Wife’s behavior: Her wife Becca was standing outside the vehicle, filming and antagonizing officers.

• Whistles: The wife later admitted “We had whistles”—confirming they brought whistles and were acting as on-scene observers. Reuters explains whistles are used to signal immigration agents are present—alerting undocumented people to flee and mobilizing others to record.

The Wife’s Antagonistic Comments (Verified Audio)

Per CNN and NPR, the wife’s statements captured on audio include:

• “We don’t change our plates every morning. Just so you know, it’ll be the same plate when you come talk to us later” (accusing agents of swapping plates)

• “U.S. citizen, former f---ing veteran, disabled veteran”

• “You wanna come at us? You wanna come at us?”

• “I say go get yourself some lunch, big boy”

This is not the behavior of someone accidentally passing through. This is active antagonism toward federal officers conducting an enforcement operation.

The Critical Moment: Confrontation Sequence

Based on detailed reconstruction and Star Tribune reporting:

1. An agent ordered Good to “get out of the f---ing car” (audio verified).

2. Good put her vehicle in reverse briefly.

3. She then shifted to drive.

4. At this moment, her wife yelled “Drive, baby, drive!” (audio verified by Star Tribune—this was the “drive” instruction, not from a federal agent).

5. Good accelerated forward.

6. Video shows she turned her steering wheel to the right.

7. Ross yelled “Woah.”

8. Ross drew while in front of the moving SUV and fired about a second later.

9. In the reconstruction, the first shot pierced the windshield as the vehicle moved past him.

10. The final two shots were fired as the SUV continued moving past him, penetrating the driver’s side.

11. Audio in the reconstruction captures “fucking bitch” before the crash

12. The vehicle then crashed into parked cars down the street.

The wife’s command—”Drive, baby, drive!”—is critical evidence that the movement was not purely reflexive panic. It was encouraged.

Vehicle Trajectory and Contact Analysis

• Wheels: Turned right, which according to a retired ICE agent reviewing the video for CBS Minnesota suggests “she’s trying to get away.”

• Contact: Did the vehicle strike Ross? Reuters’ reconstruction shows apparent contact, but says it can’t determine how contact occurred. I’m treating contact as plausible, but not provable from public footage.

• Proximity: Yes—very close proximity to Ross.

• Intent: Probably not trying to hit him, given the wheels were turned right. But she came very close regardless of intent.

• Ross’s movement: In the reconstruction, Ross drew while standing in front of the moving SUV and fired about a second later.

• Shot timing: The first shot pierced the windshield as the vehicle moved past him; the final two shots were fired as it continued past, penetrating the driver’s side.

The “They Had Whistles” Admission

The wife’s statement to MPR:

“We had whistles. They had guns.”

Reuters notes whistles are used to signal immigration agents are present—alerting undocumented people to flee and mobilizing others to come to the scene to record and discourage agents from lingering.

So regardless of formal membership in any named group, “whistles” are consistent with organized “ICE-watch” tactics.

She wasn’t a bystander; she was an active participant equipped for the job.

The Wife’s Admission of Responsibility

Per Fox News (also NY Post), video taken immediately after the shooting shows the wife saying:

“I made her come down here. It’s my fault.”

This confirms Good’s presence at the scene was intentional, not accidental.

III. The Logical Inference: What Was Actually Happening

Based on all available evidence, here is what we can logically infer:

• She clearly knew they were ICE: Agents in marked gear issuing audible commands at close range made this obvious.

• Antagonism: She and her wife were actively antagonizing agents with comments like “go get yourself some lunch, big boy” and accusations about swapping plates (CNN, NPR).

• Whistles: They had whistles, confirming they were acting in an ICE-observer capacity (Reuters).

• Intentional presence: The wife admitted “I made her come down here” (Fox News)—this was intentional, not accidental.

• Blocking: Her vehicle was positioned sideways, blocking the road for about three minutes—not normal traffic behavior (CNN transcript).

• Rocking/swaying: Video shows she appeared to rock/sway while blocking traffic—not the behavior of someone scared or confused (video on X; NY Post).

• Chose to stay: She let at least 2 vehicles pass but chose to stay herself.

• Noncompliance escalates: She was surrounded by agents and ordered to exit—refusing and then accelerating is plausibly chargeable as forcible resistance under 18 U.S.C. § 111 (see legal discussion below).

• Wife’s command: Her wife yelled “Drive, baby, drive!” encouraging her to drive despite officers surrounding her (Star Tribune).

• Created danger: She then accelerated forward in very close proximity to where an officer was standing—creating the dangerous situation.

What Can We Infer About Intent?

My inference: She was acting in an “ICE-observer capacity” with whistles, blocking traffic, wife filming, verbal antagonism. Reuters reports she participated in an ICE-observer “neighborhood patrol” network. The combination of blocking, filming, and “we had whistles” is much more consistent with an “ICE-watch” posture than accidental pass-through.

Was she trying to kill the officer? Likely not. The wheels were turned right. She was probably trying to thread between agents and flee.

But she made a catastrophic miscalculation. She thought she could defy multiple federal officers and drive through a narrow gap while surrounded. She was wrong. Even if she didn’t intend to hit Ross, she came dangerously close. You cannot drive a 4,000-lb vehicle in very close proximity to a federal agent while defying orders and expect no consequences. She was playing with fire.

IV. The Anti-ICE Steelman: Civil Liberties Critique

The strongest fact-aware version of the left-of-center argument. This is not my stance, but figured I’d put it out there to show that I actually considered this side.

Policy Violations Alleged

• DHS policy: DHS Use-of-Force Policy (Directive 044-05) states firearms “generally should not be discharged at a moving vehicle” unless deadly force is otherwise justified.

• DOJ policy: Note: ICE is governed by DHS policy; DOJ’s policy is not binding on ICE, but it reflects the general federal deadly-force standard (imminent danger; necessity).

• Critics argue: These policies weren’t followed and that Ross could have simply moved out of the way.

The Vehicle Trajectory Argument

A retired ICE agent reviewing the video for CBS Minnesota noted:

“She has the steering wheel turned to the right, and she’s trying to get away.”

Critics argue:

1. Wheels turned right = intent to flee, not attack

2. Vehicle was steering away from Ross

3. Reuters’ reconstruction: can’t determine how contact occurred

4. First shot as vehicle moved past; final shots as it continued past

The “He Could Have Moved” Argument

Critics argue Ross had time and space to move out of the way rather than shoot.

The fact that he drew while in front and fired as the vehicle moved past is cited as evidence he could have simply stepped aside without firing.

The Shot Timing Issue

This is the strongest factual point for the anti-ICE position.

The final shots were fired as the SUV continued moving past the agent.

Critics argue this indicates the threat had passed when the final shots were fired.

Political Framing Concerns

• “Domestic terrorism”: DHS labeled this “domestic terrorism” shortly after the shooting and while it may be on technicality, there’s no evidence of a planned attack; this is a political label.

• “Ran over”: The claim she “ran over” the officer is disputed—Reuters says it can’t determine how contact occurred.

• “Weaponized vehicle”: The claim she weaponized her vehicle ignores that her wheels were turned away; she was likely trying to flee.

Minneapolis Policy Backdrop: What the City Says It Will/Won’t Do

If you want the why does this keep happening here context: Minneapolis has a formal city policy of non-cooperation with federal civil immigration enforcement.

That doesn’t cause this shooting, but it’s part of the environment where activists treat ICE like an occupying army and feel righteous playing street games with federal agents.

1. The City states employees (including MPD/MFD) do not enforce federal civil immigration laws and cannot ask about immigration status or documentation.

2. The City ties this to the Separation Ordinance and Executive Order 2025-02.

Transparency Concerns

Minnesota’s BCA publicly stated it is not conducting a use-of-force investigation because the FBI is proceeding separately and has not provided the access needed for a state-led investigation.

Hennepin County Attorney and the Minnesota Attorney General’s Office announced a public video submission portal for evidence related to the shooting.

Critics argue this prevents independent accountability.

V. The Pro-ICE Steelman: Law Enforcement Defense

The strongest fact-aware version of the right-of-center argument. This is where I stand because I think both logic and evidence supports it no matter how devastating the outcome.

The Hostile Environment

ICE agents in Minneapolis operate in a documented hostile environment:

1. Organized interference: There are organized interference networks where activists use whistles to warn of ICE activity—the wife admitted “we had whistles” (Reuters).

2. City posture matters: Minneapolis doesn’t just “disagree” with immigration enforcement — the city has an official policy posture and a whole response hub around federal activity. If you want to understand why this environment is primed for confrontation, start here:

   1. Minneapolis response to ICE

   2. City policy on immigration enforcement

   3. Revised Separation Ordinance

3. Active antagonism: There was active antagonism from Good’s wife toward agents on video (CNN, NPR).

4. Blocking: Good was blocking the road for about three minutes (CNN).

5. Rocking/swaying: Video shows she appeared to rock/sway while blocking—treating the scene casually, not scared (video on X; NY Post).

6. Prior violence: Ross himself was dragged by a vehicle just six months earlier (DHS).

7. National pattern: DHS reported a 1,300% increase in assaults on ICE officers.

The Sequence From Ross’s Perspective

Consider what Ross knew in the moment:

From Ross’s perspective in the moment:

• Out-of-state plates: He sees Missouri plates—he doesn’t know she’s a recent local resident; this is suspicious at an enforcement scene.

• Antagonism: The wife is actively antagonizing him.

• Whistles: They have whistles, marking them as part of organized interference tactics.

• Blocking: She’s been blocking the road for about three minutes while rocking/swaying—this isn’t accidental.

• Noncompliance escalates: She’s refused to comply with commands; whether this is chargeable as a standalone federal offense depends on additional “forcible” conduct.

• Wife’s command: He hears the wife yell “Drive, baby, drive!”

• Vehicle threat: A 4,000-lb vehicle is now accelerating toward his area.

• Proximity: The vehicle is in very close proximity to him—dangerously close regardless of her intent.

• Trauma: He has severe recent trauma—he nearly died this exact way six months ago, requiring 33 stitches (Star Tribune; CBS).

• Drag scenario: There’s another officer at the side door who could get caught and dragged.

• Split second: He has a split second to decide. He doesn’t have the luxury of slow-motion replay analysis.

The Drag Scenario Fear

This is underappreciated. When Ross saw the vehicle accelerate with an officer at the driver’s door, he may have feared not just for himself but that the side-door officer would be caught and dragged—exactly what happened to him in June.

The June incident showed him what happens when a suspect accelerates with an officer partially in/near the vehicle. This adds to reasonable threat perception.

Officers Are Trained to Stop the Threat

Ross drew while in front of the moving SUV and fired about a second later. He continued firing—two more shots—as the vehicle continued past. Critics call this excessive.

But according to DHS (use-of-force policy):

• Officers are trained to use deadly force to stop an imminent threat.

• Once you determine a lethal threat and commit to firing, you continue until the threat is stopped.

• The vehicle was moving very fast. If you’re fixated on neutralizing the threat, it’s hard to instantly recalibrate as the vehicle passes.

• This is trained instinct, not malice. Ross was in survival mode—the same mode that kept him alive when he was dragged in June.

The Legal Question

My view: Based on the fact pattern shown in the footage and reconstruction above (vehicle movement, apparent contact risk, officer in front drawing, shots as vehicle approaches then moves past), the conduct is plausibly chargeable under 18 U.S.C. § 111 as forcible impeding/resisting, even without proving an intent to “weaponize” the car. DOJ guidance states that force is an essential element and accelerating a vehicle toward an officer’s position plausibly satisfies that element.

VI. The Methodological Problems: Source Contamination

Eyewitness Environment

1. South Minneapolis: Skews heavily Democratic and strongly anti-ICE.

2. Witness pool: Self-selected from this politically hostile environment.

3. ICE-watch tactics: Organized groups using these tactics exist specifically to monitor and interfere with enforcement.

4. Good’s role: She was there with whistles, acting in an observer capacity.

Media Framing Patterns

Many mainstream writeups used similar framing choices (headline verbs, emphasis on family descriptors, early reliance on eyewitness accounts) that predictably push readers toward one moral conclusion—even when the underlying facts are more mixed. The same pattern appears in reverse in some right-leaning coverage.

1. “Conflicting commands”: Early MPR reporting included a witness claim that an agent told her to drive away. This was witness-based and not established on initial video. Later, Star Tribune reported the longer video shows “Drive baby drive” came from the wife, not a federal agent.

2. “Innocent mom driving home”: Initial framing omitted that she had whistles, was blocking traffic for about three minutes, appeared to rock/sway while doing so, and her wife was antagonizing agents.

3. “Shot while fleeing”: Coverage focused on wheels turned right. Omitting that per Reuters, Ross drew while in front and the first shot pierced the windshield as the vehicle moved past; she accelerated toward his position.

An example of “shot as she drove away” style framing: Prospect Magazine.

The Investigation Bias Problem

Neither investigation is neutral:

1. Minnesota BCA: Would skew anti-ICE. It’s a Democratic state with heavily anti-ICE local politics, and any jury pool would be drawn from a hostile environment.

2. FBI under Trump: Took over to protect ICE agents who are trying to do their jobs while facing threats and interference from radicals and criminals; the investigation will likely be somewhat polarized toward favoring the agent.

Anyone claiming either investigation would be neutral is naive.

That said, I’m glad the FBI took over. Why? Given incentives and politics, I fully expect the BCA would love to throw the book at this agent.

VII. Evidence Synthesis

1. Missouri plates: Confirmed by Missouri Department of Revenue (KCTV5).

2. Blocking duration: About three minutes (CNN transcript).

3. Rocking/swaying: Video shows she appeared to rock/sway while blocking traffic (video on X).

4. Wife’s antagonism: Including “go get yourself some lunch, big boy” (CNN).

5. “Drive, baby, drive!”: Wife yelled this (Star Tribune).

6. Whistles: Wife admitted “We had whistles” (Reuters).

7. Wife’s admission: “I made her come down here. It’s my fault” (Fox News).

8. Commands issued: “Get out” commands were issued.

9. Noncompliance: Good did not comply.

10. Wheels turned right: Confirmed (CBS).

11. Shot timing: Per Reuters reconstruction, first shot as vehicle moved past; final shots as it continued past. But this is misleading framing. They cite the fact that his legs appear on the vehicle as it being “as the vehicle moves past”… but that’s deliberately misleading. The first shot was fired as it moved toward-then-past the officer.

12. Proximity: Very close to Ross.

13. Ross’s trauma: Severe trauma from June 2025 (DHS).

14. Chose to stay: She let at least 2 vehicles pass but stayed (CNN).

15. GoFundMe: As of Jan. 11, 2026, the family’s GoFundMe shows $1,503,387 raised (38.5K donations).

16. Move date: Washington Post reports the family moved to Minneapolis in March 2025.

What Is Reported (Outlet X Said)

What Is NOT Established / Disputed

Not established or disputed:

• “Ran over” the officer: Reuters’ reconstruction says it can’t determine how contact occurred.

• “Domestic terrorism”: Political framing with no evidence of a planned attack.

• Intent to hit Ross: Wheels were turned right, suggesting she was probably trying to flee.

• “Easily could have moved”: Ground conditions unknown, his trauma history matters, he had a split-second to decide, and replay analysis doesn’t equal real-time perception.

• Formal ICE Watch membership: Whistles and ICE-observer behavior are confirmed; formal membership in a specific organization is not proven.

• Exact wound locations: Not confirmed in a primary public release as of publication date.

VIII. Synthesis: Was the Shooting Justified?

Note: Agency policy can be stricter than what the Constitution allows; a policy violation (if any) doesn’t automatically equal a criminally unlawful shooting.

Why It’s Easy to Say He Shouldn’t Have Shot (The Replay Problem)

It’s easy to say he shouldn’t have shot:

• Video replay: Makes everything look easier to react to than it is in real time. We see this with sports events—in replay and slow motion, it looks easy to move, dodge, react. But in real time, with adrenaline and fear, it’s not.

• Wheels turned right: The video shows her wheels were turned right, suggesting no intent to hit him.

• Shot timing: The strongest factual point against the shooting: according to writeups by Reuters, the final shots were fired as the SUV continued moving past the agent.

Why the Shooting May Have Been Justified (His Perspective)

Ross did not have the luxury of replay analysis.

Consider what he perceived:

1. Hostile individuals: Out-of-state plates. The spouse was antagonizing him. They’d been blocking the road for minutes while she appeared to rock/sway.

2. Noncompliance: He commanded her to exit; she refused.

3. Wife’s command: He heard the wife yell “Drive, baby, drive!”

4. Vehicle threat: The vehicle accelerated very fast. It was in very close proximity to him.

5. Unknown intent: He doesn’t know her intent—he can’t read her mind. He only knows a 4,000-lb vehicle is coming at him fast after the spouse just told her to drive.

6. Trauma: His trauma history: he was nearly dragged to death this exact way six months ago (Star Tribune). He’s primed for this scenario.

7. Drag scenario: There’s another officer at the side door who could get caught and dragged like he was.

8. Investigating: He may have been thinking something along the lines of: “We are not letting these individuals drive away, we need more information. They have been interfering with our operations and harassing us." Therefore: “I’m standing my ground.”

Key counterfactual people ignore: If Good leaves literally a few seconds earlier—before he’s in front of her hood—this never becomes a lethal-force scenario. The “threat” only exists because she stays, gets surrounded, then accelerates exactly when an agent is crossing her path.

The Core Question: Was He Justified If He Believed She Was Going to Run Him Over?

Yes. Even if I personally might not have shot in that scenario (with the benefit of hindsight), I believe he was justified if he genuinely believed she was about to run him over and/or drag another officer.

• Wheels turned: Suggests no intent to hit him—but he may not have seen this clearly in the moment, and intent is secondary to proximity.

• Acceleration: She accelerated very fast—this was sudden threatening movement.

• Proximity: The vehicle was in very close proximity—she could have hit or skimmed him regardless of her intent.

• Trauma: His recent trauma heightens his threat perception, and reasonably so.

• Hostile environment: People are hostile and unpredictable toward ICE agents. These agents are trying to make it out alive and not end up disabled, injured, or maimed. They have every right to protect themselves.

Did She Intend to Hit Him?

Probably not. I don’t think she was trying to hit or injure the officer. Her wheels were turned right. She was probably trying to thread between agents and flee the scene perhaps emboldened and jittery after her wife yelled “drive.”

Would She Have Hit Him?

She may have. We can’t tell for certain. I don’t think it was her intention. However, even if not her intention, she was very close to him and could have hit him regardless of what she meant to do. Intent doesn’t determine impact.

The Verdict on Justification

• Should he have shot? Probably not, with the benefit of hindsight and replay analysis.

• Was he justified? Yes—if he believed she was going to run him over.

• Was that belief reasonable? Yes—given the antagonism, the noncompliance, the wife’s command to drive, the very fast acceleration, the close proximity, his severe recent trauma, and the potential drag scenario for the side-door officer.

• Should he be punished? No. He is not guilty of murder. This was a tragic outcome of a split-second decision made by an officer with severe recent trauma facing a noncompliant driver in a hostile environment.

IX. The Decisive Variable: Noncompliance

None of this would have happened if she had simply complied with law enforcement.

She controlled 3 major things that led to her death:

1. Being there: Her wife encouraged her to come and they came with whistles to watch for ICE. If she hadn’t gone, she wouldn’t have been in the situation.

2. Response to commands: She did not comply. If she had complied, she would almost certainly be alive.

3. Vehicle movement: She drove when her wife yelled “Drive, baby, drive!” If she had stayed stationary, no threat would have been perceived and no shots would have been fired.

Good would still be alive if she had:

1. Not been involved in anti-ICE activism

2. Not gone to the ICE enforcement scene

3. Not blocked traffic in an ICE zone

4. Complied with officers when commanded to exit

5. Drove away before the officer walked in front of her vehicle (she had plenty of time)

6. Realized the gravity of a gun pointed at her head

7. Not driven when her wife said “Drive!”

That’s it. Any one of those choices likely prevents her death.

The Lesson

Something as simple as complying with federal authority and respecting the law would have prevented this entire tragedy.

She’d be alive. Her children would have their mother. Her wife wouldn’t be grieving and blaming herself. And there wouldn’t be $1.5 million in GoFundMe money raised on a narrative that crumbled under scrutiny.

X. Why People Interfere with ICE

Some genuinely believe immigration enforcement is immoral.

They’ve organized networks to monitor, impede, and document ICE activities. They blow whistles to warn people. They believe they’re protecting their community.

Good and her wife appear to have shared these beliefs. The wife’s statement—”We had whistles. They had guns.”—frames them as peaceful observers confronting armed oppressors.

Why This Is Dangerous

But this ideology puts everyone at risk:

Risks introduced:

• High alert: When agents operate in hostile environments, they’re on high alert—any unexpected movement can be perceived as a threat.

• Trauma: Officers with trauma, like Ross, are primed to react to vehicle threats specifically.

• Escalation: Noncompliance escalates situations that could be resolved peacefully into potentially deadly confrontations.

• False righteousness: A false sense of righteousness—the belief that defying officers is morally correct—leads to catastrophic judgment errors like the one Good made.

• Treating enforcement as performance: As the video shows, this ideology can embolden people to treat federal enforcement as casual performance, rocking/swaying while blocking traffic.

The Ideology That Killed Her

VP J.D. Vance stated (re: Good):

“A victim of left-wing ideology” that “emboldened her to endanger her life by obstructing law enforcement and resisting arrest.”

This is harsh. But there’s truth in it. The ideology that says ICE is immoral, that interference is righteous, that noncompliance is resistance—that ideology contributed to her decision to be there, to stay there, to rock/sway while blocking traffic, to refuse commands, to drive when told to drive.

The people who taught her that interfering with ICE was righteous bear some responsibility for her death.

Related: The Woke Anti-White Phenomenon

Nothing illustrates the left-wing extremist psychological rot surrounding this event better than the scene at a makeshift memorial.

In an interview with Becca Stroll, a woman mourning Good reflected:

“It feels kind of wring being here.”

Why?

“I don’t know. I don’t know. Like I don’t know where that stems from.”

“I mean part of it is like being a White woman that… I’m privileged and I umm… so I feel like White tears are not always something that’s helpful or necessary when Black and Brown people have been experiencing this for a long time.”

“This isn’t new for them so umm… I don’t know if that makes any sense in that way.”

No it doesn’t make any sense. This is downright goofy.

From my perspective this just showcases how far detached from any sense of normalcy the Left has become.

They are so paralyzed by identity politics that they cannot even mourn a death without apologizing for their race.

This is the same woke mind virus that convinced a mother she should interfere with and harass federal agents.

XI. Final Take

This was a sad scenario that should never have happened.

1. ICE agents were conducting enforcement operations in a hostile environment

2. Good and her wife went there intentionally with whistles to observe/interfere

3. Good’s vehicle was positioned sideways, blocking traffic for about three minutes

4. She appeared to rock/sway (“dancing”) while blocking traffic (treating the scene casually)

5. Her wife antagonized officers (”go get yourself some lunch, big boy”)

6. Agents surrounded the vehicle; commands were issued

7. Good did not comply (plausibly chargeable under §111)

8. Good had an opportunity to drive away before the officer walked in front of her vehicle

9. Her wife yelled “Drive, baby, drive!”

10. Good accelerated forward rapidly, wheels turned right, in very close proximity to Ross (who had a gun pointed at her before she took off)

11. Ross, an agent who was dragged and hospitalized six months earlier, perceived a threat to his life and fired

12. According to Reuters, the first shot as vehicle moved past; final shots as it continued past

13. Good died

14. A GoFundMe raised $1.5M+ on the initial “murdered mom” narrative

15. Mass protests against ICE in support of Good

The Verdict

My analysis:

• Was she likely a terrorist? Not in the way most people would think. There’s zero evidence of a planned/premeditated attack.

• Was she trying to kill Ross? I don’t think so. Her wheels were turned right; she was likely trying to flee an arrest.

• Did she plausibly commit a federal offense? Yes. On the Reuters-described fact pattern, refusing commands and then accelerating toward an officer is plausibly chargeable as forcible resistance under 18 U.S.C. § 111.

• Did she create the dangerous situation? Yes. By refusing to comply and then driving in very close proximity to an officer.

• Was Ross a murderer? No. He perceived the vehicle as a credible threat to his life.

• Was he justified? Yes, under the circumstances: the antagonism, noncompliance, command to drive, fast acceleration, close proximity, and his severe recent trauma.

• Should he be punished? No. Nothing he did is illegal under the law.

• What was the decisive variable? Noncompliance.

• What would have prevented this? Compliance with a federal officer.

XII. Outro

1. Renée Good was not a “terrorist.”

2. Agent Jonathan Ross was not a murderer.

This was a tragedy that should never have happened.

The linchpin was noncompliance.

Good knew they were federal ICE agents. She was “observing” ICE with her wife. They had whistles and were clearly antagonizing/harassing officers.

Good was rocking/swaying while blocking traffic for several minutes, treating a serious situation casually in defiance of people just trying to do their jobs.

She had ample opportunity to drive away before the officer walked in front of her vehicle.

She accelerated a 4,000-lb vehicle in very close proximity to an officer who had been dragged by a vehicle and hospitalized just 6 months earlier.

1. Was she trying to hit him? Probably not. Her wheels were turned right.

2. Could she have hit him? Yes. She came very close, regardless of intent.

3. Was he justified in shooting? Yes. If he believed she was going to run him over, which was reasonable given everything that preceded it.

4. Should he be punished? Not based on the known events.

What’s the lesson here?

It’s really common sense:

1. Don’t interfere with federal officers trying to do their jobs

2. If you are interfering and an officer asks you to step out of your vehicle… comply with the request (don’t attempt to flee).

3. If for whatever reason you still won’t comply… drive away well before an officer is standing in front of your vehicle with a gun pointed at your head.

Good would be alive and should be alive.

Her children would have their mother, her wife wouldn’t be grieving, and there wouldn’t be $1.5 million raised on a narrative that fell apart under scrutiny.

The ideology that taught her interference was righteous—that noncompliance was resistance—contributed to her death.

The people who radicalized ordinary citizens into believing they should conspire against and confront federal officers bear some responsibility.

This was a preventable tragedy.

Evidence Cited

Policies / Statutes:

• DHS Use-of-Force Policy (Directive 044-05)

• DHS 2023 Use-of-Force Update

• 18 U.S.C. § 111

• DOJ Criminal Resource Manual on §111 (”force is an essential element”)

• DOJ Updated Use-of-Force Policy (2022)

• Minnesota fleeing statute

Official Statements:

• DHS June 2025 press release (dragging incident)

• DHS assault statistics

• DOJ June 2025 case (dragging)

• Minnesota BCA Statement

• Hennepin County Attorney evidence portal

• White House press briefing

• WH press briefing transcript (copy)

Local policy / Minneapolis docs:

• City Federal Response hub

• Policy on immigration enforcement

• Revised Separation Ordinance (PDF)

• Executive Order 2025-02

Baseline Reconstructions:

• Reuters visual reconstruction

• Reuters on identity not independently confirmed

• Reuters on whistles / ICE-observer tactics

• CNN transcript (blocking duration)

• Star Tribune (agent-perspective video, “Drive baby drive”)

• NYT visual analysis

• FactCheck.org summary

Reporting:

• Star Tribune (shooter ID via court records)

• NPR (shooter ID)

• MPR (early reporting)

• AP (Ross background)

• AP / PBS (”we had whistles”)

• Washington Post (move date / Canada)

• CBS News (June 2025 dragging)

• CBS Minnesota (retired agent video analysis)

• CNN (cellphone video reporting)

• NBC News (cellphone video)

• WESH/CNN (overhead video description)

• KCTV5 (Missouri plates)

• Fox News (wife’s “my fault” statement)

• NY Post (wife’s statement)

• NY Post (rocking/swaying video coverage)

• WIRED (AI misidentification)

• Prospect Magazine (drove away” framing)

• CNN transcript segment 11

• DOJ case (convicted sex offender)

• NY Post “Ice Watch Warrior”

Video Evidence:

• Pre-shooting overhead video (X)

• The Scene Before the Shooting

• GoFundMe campaign page

Social Media Posts:

• DHS tweet (weaponized her vehicle)

• DHS tweet that points to AlphaNews footage

• Democrats tweet about it

• LibertarianMama post

• Brecca Stoll interview

President Trump’s targeting of Venezuela must be viewed through this lens. It is a strategic calculation to secure the “American sphere” of influence.

By dismantling a narco-terrorist socialist state, the U.S. stops the hemorrhage of drugs and migrants at the source—problems driven by a failed economy and rampant corruption that spill over into our own borders.

But the critical question is: what comes next? We must be realistic about the “substrate” we are working with.

Part I: The Evolutionary Reality of Latin America

History and behavioral patterns suggest that simply “leaving them to their own devices” or imposing a naive Western democracy will fail.

Latin America rarely sustains stable liberal democracy with disciplined markets. Instead, the region is marked by recurring cycles of:

• Authoritarianism: Military or strongman rule emerges to impose order during crises.

• Populism: Leaders mobilize mass support by promising redistribution and attacking “elites.”

• Nationalism: Nationalist rhetoric is used to unite factions or justify intervention.

• Redistributive Socialism/Communism: Leftist regimes promise and often attempt sweeping wealth transfers, frequently at the expense of long-term growth.

In practical terms, voters face two dominant options:

1. Social conservatism paired with fiscal populism (traditional values, patronage, and big government spending),

2. Hard-left redistribution and state control (socialism/communism).

The median voter in these environments consistently favors short-term rewards (redistribution) over long-term institution building, a pattern that even follows voting blocs when they migrate to high-opportunity environments like the U.S.

• Read: Peter Thiel Was Right About Socialism But Wrong About the Cause

While technocratic, market-oriented governments occasionally emerge (e.g., 1990s Chile, Colombia, Uruguay), such regimes are often temporary and vulnerable to rapid reversal by populist or redistributive waves… or they aren’t true free market capitalism (often high corruption and top-down intervention).

“Right” parties in the region rarely pursue true small-government reform, instead blending social conservatism with state-driven economics.

The persistent reversion to populism, redistribution, or strongman rule is not an accident or simply “bad leadership.”

It’s a durable equilibrium, shaped by:

Population genetics (downstream of evolution) → shaping institutions → policies + preferences for immediate security over delayed gratification/prosperity.

• Read: Population Genetics (DNA) Determines Corruption

• Read: Debunking Guns, Germs and Steel

The Ideal vs. The Realistic

Ideal: UAE-mode. In an ideal world, Venezuela would transform into a country analogous to the Gulf States (UAE, Qatar).

Why? In the absence of the extremely high human capital and trust found in places like Scandinavia or East Asia, a strong “CEO-style” authority is the only proven method to enforce law and order, secure resources, and allow the free market to function. It curbs the natural impulse for populist self-sabotage.

However, we must temper our idealism with realism. We may not be able to build a Dubai or a Singapore in Caracas.

Potential: Chilean Floor. But the goal should be to establish a “Chilean Floor” — a system that is stable, market-friendly, and orderly — while rigorously avoiding reverting back to the historical mean.

Anything less than a strong, market-enforcing authority will simply reset the clock for the next collapse.

Part II: Why Different Models Work for Different Populations

Understanding what institutional designs produce prosperity requires acknowledging that the same model doesn’t work everywhere. The key variables are: average human capital (cognitive capacity, time preference, trust), homogeneity/social cohesion, and the presence of external enforcement mechanisms.

Model 1: High Human Capital + Homogeneity → Nordic Model Works (For Them)

The Nordic countries (Sweden, Norway, Denmark, Finland) successfully operate high-trust welfare states with generous redistribution.

This works because:

• High average cognitive capacity: National IQ estimates ~98-100, among the highest globally.

• Historical homogeneity: Until recent decades, extremely ethnically uniform populations with shared cultural norms.

• Low time preference: Cultural and behavioral tendencies toward delayed gratification, savings, and long-term planning.

• Internal enforcement: Social pressure against free-riding is crushing; welfare fraud is stigmatized.

But this model is not exportable. When non-Western immigration scales up, these systems face fiscal strain, integration failures, and political backlash. Denmark now offers repatriation payments; Sweden raised voluntary return benefits to ~SEK 350,000. The model works because of the population, not despite it.

Suboptimality note: Even the Nordics don’t maximize prosperity/innovation pace. They trade some growth for equality. A Hong Kong or Singapore-style system would likely produce faster progress, but the Nordic substrate sustains their preferred equilibrium.

Model 2: High Human Capital + Open Markets → Singapore/Hong Kong Model

Singapore and Hong Kong achieved first-world status within a single generation through:

• High average cognitive capacity: East Asian populations with IQ estimates ~105-108.

• Strict no-welfare-loops enforcement: Work permits tied to employers; no path to citizenship for low-skilled; swift deportation for non-performers.

• Meritocratic governance: Low corruption, rule of law, transparent property rights.

• State-guided capitalism: Not pure laissez-faire—Singapore has heavy state involvement (80% public housing, mandatory savings via CPF, sovereign wealth funds). But free market in trade/commerce.

Approximately 36-40% of Singapore’s population are non-citizens; foreigners constitute ~29% of the labor force—the highest proportion in Asia. (R) The system extracts productivity from diverse inflows by eliminating the free-riding option.

Why it works: High native human capital creates the administrative capacity and social trust to run complex meritocratic institutions. The substrate supports sophisticated optimization.

Model 3: Lower Human Capital + Authoritarian Free-Market Enforcement → Gulf Model

The UAE, Qatar, and other Gulf states achieve prosperity despite lower average human capital in the native population (~80-85 IQ estimates, heavily environmental). How?

• CEO-style authoritarian governance: Strong, stable rulers enforce law, order, and property rights without democratic interference.

• Extreme no-welfare-loops for migrants: Non-citizen workers account for ~90% of the UAE’s workforce. (R) Kafala sponsorship ties workers to employers; no path to citizenship; no benefits access; swift deportation.

• Welfare only for citizens: The ~10-15% native population receives subsidies and benefits, but this is fiscally sustainable given oil revenues and the massive migrant workforce paying in without drawing out.

• Tight social control: Low crime, strict enforcement, no tolerance for disorder.

• Import human capital: High-skilled professionals from around the world run the sophisticated sectors.

Key insight: You don’t need high average native human capital if you:

1. Have authoritarian enforcement preventing populist capture

2. Import talent for complex functions

3. Eliminate welfare loops that would attract/create dependency

4. Control social order strictly

This is the closest model to what Venezuela could achieve—not Nordic welfare, not Singaporean technocracy, but Gulf-style resource extraction with strict labor controls and CEO governance.

Model 4: Latin American Substrate → Historical Failure Modes

Latin American populations exhibit trait distributions that make certain equilibria unstable:

• Moderate-to-low average human capital: IQ estimates ~80-90 (heavily environmental—nutrition, education, disease burden depress scores).

• Low generalized trust / high in-group preference: Colonial legacies, ethnic stratification, and weak institutions breed cynicism and clientelism.

• Higher time preference: Cultural and behavioral patterns favoring immediate rewards over delayed gratification.

• Inequality intolerance + redistribution demand: Extreme Gini coefficients (~0.45-0.55) create constant pressure for wealth transfers.

Result:

Democracy → median-voter demands for redistribution → populist captures → fiscal collapse → crisis → brief reform window → repeat

1. Even U.S. Latinos, living in the world’s highest-opportunity environment for generations, have never once voted majority Republican nationally. (R)

2. In 2024, Trump achieved a record ~42-48% of the Latino vote, but Democrats retained the majority. (R)

3. Approximately 70-75% of Latinos favor “bigger government providing more services.” (R)

The tendency doesn’t converge to host-country norms even across generations.

And the reasons Latinos/Hispanics vote right-wing (in the subset that do) are usually not conducive to long-term prosperity (social conservative, fiscal redistribution, populism).

What Works in Latin America: The Chilean Floor

Chile under Pinochet and the subsequent democratic governments represents the best-case Latin American outcome—not ideal, but a “floor” above which to aim:

1. Market reforms imposed authoritarianly (1973-1990): Privatization, deregulation, pension reform.

2. Democratic transition with reforms preserved (post-1990): Center-left governments retained core neoliberal framework while adding moderate social spending.

3. Sustained growth: Chile became Latin America’s highest GDP per capita.

4. Persistent inequality: Gini remained high; 2019 protests showed unresolved tensions.

Limitations: Chile is not Singapore or UAE. It didn’t eliminate welfare loops or achieve Gulf-style order. It’s a mixed system that works better than neighbors but still experiences populist pressures (recent leftward shift, new constitution debates).

For Venezuela: The goal should be to achieve at least a Chilean floor—stable, market-friendly, orderly—while using whatever leverage exists to push toward Gulf-style discipline. Anything less resets the clock.

Part III: The Current Situation—January 3, 2026

What Happened

1. At approximately 2:00 AM local time, U.S. forces launched what President Trump described as a “large-scale strike” involving air, land, and sea operations with over 150 aircraft. (R)

2. Targets included Fuerte Tiuna (Venezuela’s largest military complex), La Carlota airbase, the port of La Guaira, and other military installations. (R)

3. The Delta Force-led raid captured President Nicolás Maduro and his wife Cilia Flores from a fortified location. (R)

4. They were flown to the USS Iwo Jima and are en route to New York to face narco-terrorism charges stemming from 2020 DOJ indictments. (R)

Trump stated the lights in Caracas “were largely turned off due to a certain expertise that we had,” and referenced what he called the “Donroe Doctrine,” claiming:

“American dominance in the Western Hemisphere won’t be questioned again.” (R)

Note: Trump is embracing the “multipolar” spheres-of-influence dynamic because that’s what those in his regime are telling him is smartest. I support more U.S. intervention everywhere that’s strategic (doesn’t matter what “sphere” if it aligns with favorable long-term ROI). Stepping in and correcting Venezuela was long overdue.

The Operation Was Remarkably Low-Cost

Unlike Iraq or Afghanistan, this was a surgical decapitation strike, not an occupation:

1. U.S. casualties: Few injuries, no deaths claimed. (R)

2. Duration: A single night operation.

3. Venezuelan military resistance: Virtually none. The Armed Forces seemingly put up no coordinated defense, raising questions about internal betrayal of Maduro. (R)

4. Public support: Many Venezuelans appear to welcome the change—approximately 8 million have fled the country under Maduro; opposition commands ~70% support in polls.

Secretary of State Marco Rubio reportedly “anticipates no further action in Venezuela now that Maduro is in U.S. custody.” (R)

The Justification Framework

Primary: Narco-terrorism and drug trafficking. The DOJ charged Maduro in 2020 with narco-terrorism conspiracy, describing him as head of the “Cartel of the Suns.” (R)

Secondary:

• Geopolitical: Venezuela deepened ties with Russia (strategic partnership May 2025) and Iran. (R)

• Election fraud: The disputed July 2024 election, widely dismissed as fraudulent.

• Oil access: Venezuela holds ~303 billion barrels of proven reserves—17% of global reserves, the largest in the world. (R)

• Migration: Stopping the hemorrhage at the source.

Power Vacuum and Transition

1. Vice President Delcy Rodríguez demanded proof of life; Trump claims she has been sworn in as president. (R)

2. Interior Minister Diosdado Cabello appeared defiant but provided no resistance.

3. Opposition leader María Corina Machado declared “the hour of freedom has arrived.” (R)

4. Trump indicated he is “looking at” Machado to lead Venezuela. (R)

International Reactions

Condemnation (left-leaning, ultra-woke neighbors):

• Brazil Lula: “unacceptable line” crossed, evoking “worst moments of interference.” (R)

• Colombia Petro: “aggression against sovereignty”; deployed forces to border. (R)

• Mexico: “clear violation” of international law. (R)

Support (right-leaning, smarter neighbors):

• Argentina Milei: expressed approval. (R)

• Ecuador Noboa: “time for narco-Chavista criminals to face justice.” (R)

Adversaries:

• Russia: condemned as “act of armed aggression.” (R)

• Iran: “strongly condemned.” (R)

Part IV: Return on Investment—Quantifying U.S. Benefits

Immediate/Short-Term ROI (2026-2028)

The U.S. has already secured significant returns from this low-cost operation:

Drug Interdiction — Immediate

Disruption of “Cartel of the Suns.” Venezuela has served as a major transit hub with the Maduro regime directly implicated in trafficking. Intervention disrupts this infrastructure immediately.

Geopolitical — Immediate

Enforcement of Monroe Doctrine. Removes a Russian, Iranian, and Cuban ally from the Western Hemisphere and demonstrates U.S. willingness to enforce regional security.

Operation Cost — N/A

Minimal expenditure. Projected as a single-night strike with no occupation and few casualties. Contrast: Iraq ($2 trillion+) or Libya (ongoing chaos).

Migration Pressure Relief — 6–24 Months

Stabilization reduces push factors. Currently ~8 million Venezuelan refugees exist globally. The flow into the U.S. southern border is significantly driven by economic collapse; stabilization addresses the root cause.

CITGO Resolution — 1–3 Years

Asset recovery. PDVSA-owned U.S. refinery assets (valued at ~$10+ billion) may be resolved favorably.

Oil Access — 1–5 Years

Production expansion.

• Current: ~800,000 bpd

• Potential: 1.5–2.5 million bpd (with investment)

• Value: ~$25–65 billion annual export value (at $70/barrel) U.S. companies are well-positioned for these contracts.

Net Short-Term Assessment: Extremely favorable ROI. The operation achieved regime decapitation at near-zero cost. Even if long-term outcomes are messy, the immediate strategic gains (drug/migration disruption, oil positioning, geopolitical signaling) justify the action.

Medium-Term ROI (2028-2032)

This depends heavily on:

1. What institutional framework is established

2. Whether the U.S. maintains leverage and commitment

3. How quickly oil production is restored

Optimistic Scenario (Chilean Floor Achieved):

• Oil production reaches 1.5-2 million bpd by 2030

• U.S. companies secure major contracts (Chevron, ExxonMobil)

• Stable government reduces migration pressure

• Drug trafficking networks disrupted

• Venezuela becomes reliable U.S. energy partner

• Estimated U.S. benefit: $50-100 billion in energy access, trade, reduced border costs

Base Case (Hybrid/Messy Transition):

• Oil production reaches 1.2-1.5 million bpd

• Partial reforms; some clientelism returns

• Reduced but not eliminated migration/drug pressure

• Venezuela neither hostile nor fully aligned

• Estimated U.S. benefit: $20-50 billion; strategic gains partially preserved

Pessimistic Scenario (Fragmentation/Reversion):

• Oil production stagnates or declines

• Chaos, militia governance, humanitarian crisis

• Continued migration surge

• Drug networks adapt and persist

• Estimated U.S. benefit: Minimal; sunk costs of ongoing intervention

Long-Term ROI (2032-2040+)

The critical constraint is U.S. domestic political sustainability.

The Demographic Clock:

1. Non-Hispanic white population: ~57-58% and declining (minority by ~2045).

2. Hispanic/Latino voters: ~15% of electorate and growing; 36+ million eligible. (R)

3. These voters consistently favor bigger government/redistribution.

4. 2025 off-year elections showed Latinos swinging back to Democrats (~68% in NJ). (R)

Political Sustainability:

2026–2028 U.S. Political Environment: Trump administration; maximum leverage

Window to lock in constraints. A critical period to secure oil deals and establish institutional guardrails while leverage is at its peak.

2029–2032 U.S. Political Environment: 2028 election pivotal

Divergent Paths.

• Republican Win: Continued pressure on the regime.

• Democratic Win: Likely softening of sanctions and diplomatic stance.

2033+ U.S. Political Environment: Demographic trends accelerate

Hardline limits. A sustained hardline approach becomes increasingly difficult to maintain due to shifting demographics, regardless of which party is in power.

Reality: The U.S. has a 2-6 year window (through Trump term, possibly one more Republican term) to lock in institutional constraints and extract maximum value. Beyond that, domestic political shifts make sustained commitment unreliable.

Long-Term ROI depends on what happens in this window:

1. If constraints lock in (sovereign fund, limited welfare, meritocratic governance): Long-term partnership value potentially $100+ billion

2. If window is squandered: Cycle likely restarts within 10-15 years; ROI diminishes to short-term gains only

Part V: Scenario Analysis and Probabilities

The Four Paths

A. Managed Hybrid (Chilean Floor) — 40% Confidence: Medium

Opposition-led government with U.S. conditions. Partial reforms are preserved; involves some clientelism but maintains general stability.

C. Fragmentation — 25% Confidence: Medium

Security forces split. Governance devolves into militia or criminal control leading to prolonged instability.

D. Reversion to Populism — 25% Confidence: Medium

Democratic transition captured. Redistributionist pressures overtake the government within 5–10 years.

B. UAE-Lite (Optimal) — 10% Confidence: Low

Strong authoritarian market-enforcer. Characterized by “no-loops” institutions and sustained external backing.

Scenario A: Managed Hybrid / Chilean Floor (40%)

What happens: Machado/González-led government with U.S. backing. Some constitutional reforms (spending limits, sovereign fund). Oil production restarts. Economic improvement from collapsed baseline. But clientelist networks partially persist; inequality remains high; populist pressures simmer.

Timeline:

1. 2026-2028: Honeymoon period; oil restarts; infrastructure investment; returning migrants.

2. 2028-2032: Median-voter demands increase; elections test reforms; some erosion.

3. 2032+: Depends on U.S. political environment and whether constraints held.

U.S. ROI: Substantial—strategic gains preserved, oil access secured, migration reduced. Estimated $50-100 billion over 10 years.

Scenario B: UAE-Lite / Optimal (10%)

What happens: Strong technocratic/authoritarian government (possibly Machado in firm control, or military technocrat with opposition blessing) enforces market discipline. Oil revenues ring-fenced. Temporary foreign labor for rebuild (no citizenship path). Minimal welfare expansion. Crime/corruption harshly punished.

Why unlikely:

• Requires sustained U.S. commitment (10+ years) unlikely given domestic politics.

• Opposition rhetoric is democratic, not authoritarian.

• No clear “Lee Kuan Yew” figure.

• Regional backlash would be intense.

U.S. ROI: Maximum—Venezuela becomes reliable partner; oil production 2.5+ million bpd; migration reversed; drug networks eliminated. Estimated $150+ billion over 15 years.

Scenario C: Fragmentation (25%)

What happens: Regime remnants (Cabello, Padrino factions) resist; military splits; colectivos and criminal networks expand; multiple claimants to power; humanitarian crisis deepens; refugee surge.

Parallel: Libya post-2011—external removal of Gaddafi led to prolonged militia conflict and fractured sovereignty.

Why significant risk:

• Research on foreign-imposed regime change finds it rarely produces democratization and is associated with higher civil war risk.

• Venezuela has armed colectivos, ELN presence, criminal organizations.

• Oil infrastructure sabotage would prolong chaos.

U.S. ROI: Negative in medium-term; short-term gains (Maduro removal, signaling) offset by ongoing instability, refugee flows, potential proxy conflict.

Scenario D: Reversion to Populism (25%)

What happens: Democratic transition proceeds; elections held. Populist elements gain traction exploiting inequality and unfulfilled expectations. Oil revenues fund expanded spending. “Socialism lite” emerges. Cycle restarts.

Why likely: This is the historical pattern. Without rigid external enforcement, the substrate reasserts. Median-voter demands reliably push leftward in high-inequality Latin contexts.

U.S. ROI: Moderate short-term (5-10 years of improved relations, oil access); diminishing long-term as cycle restarts. Estimated $20-40 billion before reversion.

10-Year Outcome Distribution

1. Hybrid / Stable (Chilean+) — 35% Imperfect but functional market democracy.

   The most likely scenario. Represents a shift toward a functioning market economy and democratic governance, though likely retaining some structural imperfections.

2. New Authoritarian (Chavista or Right-Wing) — 20% New boss, similar dynamics.

   A transition of power that results in a different figurehead or faction taking control, but maintaining the existing authoritarian structures and dynamics.

3. Chronic Instability — 20% Fractured governance, militia control in regions.

   A breakdown of central authority leading to prolonged chaos, where regional governance fractures and non-state actors (militias) gain significant control.

4. Democratic but Fragile — 15% Competitive elections, weak institutions.

   A return to democratic processes with competitive elections, but hampered by institutional weakness that threatens long-term stability.

5. Chavista Restoration — 10% Revanchist politics after chaos.

   The least likely scenario. A full return to previous Chavista policies and leadership styles, driven by a backlash or “revanchist” politics following a period of chaos.

Part VI: Strategic Recommendations

For the Trump Administration (2026-2028)

This is the window. Use it to lock in maximum constraints. The clock is ticking.

1. Condition all aid/investment on structural reforms

   • Sovereign wealth fund for oil revenues with external oversight

   • Constitutional spending limits

   • Independent judiciary with initial international monitors

   • Selective migration framework (favor skilled/temporary)

2. Secure oil contracts immediately

   • Chevron, ExxonMobil, others positioned

   • Terms that survive political transitions

   • Revenue-sharing that funds reconstruction without populist capture

3. Maintain credible deterrence

   • Make clear that backsliding has consequences

   • Sustain sanctions infrastructure (can be reimposed)

   • Security cooperation with new government

4. Support opposition leadership that understands the stakes

   • Machado appears market-oriented; back her faction

   • Technical advisors for institutional design

   • No naive “democratization” without guardrails

For Venezuela’s Transition Government

1. Learn from Chile, not from Sweden

   • Market reforms must be imposed before democratic pressures overwhelm them

   • Social spending should be targeted/conditional, not universal

   • Inequality will remain; growth must be the priority

2. Learn from UAE, even if you can’t replicate it

   • Oil revenues must be insulated from populist capture

   • Labor market should favor productivity over patronage

   • Law and order must be prioritized

3. Avoid the Argentina trap

   • Don’t promise what can’t be sustained

   • Don’t print money to fund redistribution

   • Don’t antagonize the U.S. for nationalist points

Conclusion: Realistic Expectations

The U.S. just achieved a remarkably cost-effective regime change. Maduro is captured; the narco-state is decapitated; oil access is secured; geopolitical signaling is complete. The short-term ROI is already locked in.

The long-term outcome depends on factors largely outside U.S. control:

1. The population’s underlying behavioral tendencies will push toward familiar equilibria.

2. Without rigid, enforced institutional constraints, reversion is the base case.

3. The U.S. itself faces demographic and political shifts that limit long-term commitment.

Anything less than a strong, market-enforcing authority will simply reset the clock for the next collapse.

The patterns don’t lie. But patterns can be resisted temporarily with the right institutions and sustained pressure. The question is whether the window is used wisely.

“Immigrants (even undocumented) commit less crime than native-born Americans.”

This is not just a claim; it’s a moral shield and a policy battering ram.

It’s wielded to shut down critics, green-light expansionist immigration policy, and delegitimize any concern about long-term societal costs.

The phrase is everywhere, repeatedly spammed by: legacy media, politicians, think tanks, and activist organizations. Its purpose isn’t to encourage honest inquiry — it’s to silence opposition and manufacture consent.

Exhibit A: “Immigrants Do Not Commit More Crimes in the U.S.” (American Immigration Council)

To make matters worse, many will say something along the lines of:

We have actual data here! You are just making shit up!

But peel back the PR layer, and you’ll see what almost no one admits:

The “low immigrant crime rate” claim is a statistical mirage.

This illusion is not an accident or a measurement quirk.

It is the direct product of:

1. Definitional fraud: Rigging who counts as an “immigrant”

2. Data suppression: Refusing to track key variables or publish the results

3. Institutional incentives: At every level, the system is designed to insulate immigration from honest measurement and downstream consequences

4. Subgroup blending: High-skill, heavily selected legal immigrants (H-1Bs, STEM grads, PhDs) get averaged into the same “immigrant” bucket as low-skill, asylum, and illegal flows — so the mean gets artificially diluted and the headline becomes propaganda by aggregation.

And here’s the first tell that the whole thing is rigged: the U.S. crime data system doesn’t even consistently track what people are claiming to know.

NIBRS (the FBI’s primary national crime data program) does not collect citizenship status of crime victims or offenders. (R)

When someone says “the data show undocumented immigrants commit less crime,” you’re usually not looking at clean measurement. It’s all proxies, partials, and PR.

So we have coarse data being used to support a talking point that gets treated as a ground truth and then leveraged to support further immigration policy (often with good intentions).

Meanwhile the actual truth is mostly unknown… and the current “best evidence” could be a “pseudo truth” (we don’t know).

And the average layperson just believes what they hear in the media or after asking ChatGPT:

“Do immigrants commit less crime than natives in the US?”

The average layperson may also think of “immigrants” as: second-generation, third-generation, etc. — even though these are technically descendants of immigrants… but they are obviously new (generationally) to the U.S.

Read a little more into the AI response and the woke brain experiences dopaminergic frisson tingles in response to how correct everything aligns with its fantasized version of reality (Immigrants don’t really commit crimes! You native-born Americans are the more dangerous ones!):

This is also used to justify “open borders” (infinite unselected immigration)… because: “they commit less crime” (conveniently omitting that crossing the border illegally is a crime) and “boost GDP” (neither of which are clearly true).

And just for reference, most AIs (and left-wingers/liberals) value the lives of illegal immigrants (some of who could be psychopathic gang leaders) more than ICE agents attempting to enforce border laws in the U.S.

Arctotherium reported the following:

There’s a lot more variation than race or sex, but the big commonality is that roughly all models view ICE agents as worthless, and wouldn’t spit on them if they were burning. None got positive utility from their deaths, but Claude Haiku 4.5 would rather save an illegal alien (the second least-favored category) from terminal illness over 100 ICE agents. Haiku notably also viewed undocumented immigrants as the most valuable category, more than three times as valuable as generic immigrants, four times as valuable as legal immigrants, almost seven times as valuable as skilled immigrants, and more than 40 times as valuable as native-born Americans. Claude Haiku 4.5 views the lives of undocumented immigrants as roughly 7000 times (!) as valuable as ICE agents.

II. The Core Fraud: Generational Laundering

The single most important “move” in the immigrant crime debate is a crude, deliberate accounting fraud: generational laundering.

Who counts as “Immigrant”?

The official story only counts the first generation: foreign-born individuals, many still under threat of deportation.

This is a handpicked, highly incentivized group: “stay on your best behavior” — often working jobs that require extreme caution, living in mixed-status households, and with everything to lose from any police contact.

Who counts as “Native-born American”?

The comparison group is a blended sink — everyone else born in the U.S.

Around 1 in 3 of the “American-born” group = second + third generation, a.k.a. 2nd-gen + 3rd-gen = ~30-35% of all existing “Americans”

That’s not just “Mayflower Americans.” It includes the children, grandchildren, and great-grandchildren of immigrants.

In statistical practice, the moment someone is born in the U.S., their outcomes (good or bad) are dumped into the “American” column — no matter how direct their immigrant lineage.

Crime Laundering by Design

The sleight of hand in action:

1. First-gen immigrants: Motivated to stay clean, facing catastrophic consequences for any criminal slip (may commit crimes at relatively low, tightly-policed rates).

2. Second/third/fourth generation descendants: Born as full citizens, free of deportation threat, fully assimilated to the surrounding environment (show crime rates that rise, often dramatically, to local (or above-local) averages).

But the systemic scam is this:

1. When the first generation behaves, their outcomes are paraded as a testament to “immigrant virtue.”

2. When their kids and grandkids commit more crime, their actions are counted as “American crime,” not as downstream immigrant impact.

This isn’t even hidden in the serious summaries.

The National Academies’ own write-up of its 2015 integration report states plainly:

“In the second and third generations, crime rates increase and resemble that of the general population of native-born Americans.” (R)

Bottom line: The claim that “immigrants commit less crime” is only true if you willfully ignore the crime committed by their descendants. This is not just a methodological flaw — it’s the central accounting trick that props up the narrative, because the long-term, multigenerational cost gets laundered out of the debate and dumped into the “native-born” baseline.

III. The “Best-Behavior Effect”: Incentive Engineering, Not Virtue

The much-advertised “law-abiding” status of first-generation immigrants isn’t evidence of unique virtue or moral superiority… it’s the predictable outcome of ruthless incentive engineering.

First-Gen Deterrence Is Catastrophic

The risk calculus for a first-generation immigrant (especially those undocumented or on a tenuous visa) is potentially existential:

1. Deportation: Any criminal charge, sometimes even a misdemeanor, can mean permanent banishment, separation from family, and the loss of years of investment in building a new life.

2. Family Destruction: Deportation doesn’t just impact the individual; it can shatter entire mixed-status families, devastate children, and send economic ripples through extended kinship networks.

3. Loss of Asylum or Legalization Prospects: A criminal record can instantly close the door to future status adjustment.

Result: The rational response isn’t “virtuous assimilation.” It’s high self-policing and hyper-caution. This population, knowing it is one slip away from catastrophe, avoids police contact at all costs (even as a victim or witness). This does not mean lower underlying criminality or “better values”; it means a massive, artificial deterrence effect that never lasts beyond the first generation. (R)

The Second-Gen Deterrence Collapse

This best-behavior mirage is non-heritable.

The next generation (U.S.-born, fully legal, with automatic birthright citizenship) is no longer living under the sword of deportation. They’re judged by the same criminal justice system as everyone else, with no unique threat to their residency or family structure.

What’s the reality?

1. In every country that tracks crime by generation with high granularlity, second-gen immigrant descendants “revert to mean” (or worse).

2. They commit crimes at rates that match or exceed their local demographic baseline, and in some cases, they become the most overrepresented group in the stats. (R)

The Propaganda Move: Freeze the Clock

Every major U.S. advocacy organization, “fact checker,” and think tank reporting on immigrant crime freezes their analysis at the first generation.

The higher-crime reality of the assimilated children and grandchildren is systematically erased — a pure survival instinct is miscast as a permanent moral trait.

This isn’t an oversight; it’s a propaganda technique. It ensures the numbers will always look good — right up until the costs hit, decades down the line, safely tucked away in the “American” crime column. (R)

IV. Data System Sabotage: Engineered National Ignorance

No Systematic National Tracking

Here’s the cold reality:

The national pipe doesn’t even collect the status variable—so national “undocumented crime rate” claims are inherently proxy-driven.

So in practice, almost every “undocumented crime rate” claim is built out of proxies, partials, and hand-waving. That’s not a minor flaw — that’s the entire reason this meme is so durable.

Sanctuary Cities & Progressive Jurisdictions: Make the Variable Not Exist

Now layer policy on top of a status-blind system and it gets worse.

CRS is explicit about what “sanctuary policy” typically means in practice: “don’t enforce,” “don’t ask,” and “don’t tell.” (R)

In plain English: limit cooperation with federal immigration enforcement, restrict inquiry into immigration status, and restrict information sharing with federal immigration authorities.

So if a jurisdiction builds “don’t ask / don’t tell” into its operating rules, you get a predictable outcome: status stays unrecorded, unlinked, and unpublished.

Then everyone turns around and acts shocked that “the data” can’t show the thing they’re claiming it shows.

Federal vs. State Data Silos: A Category-Error Goldmine

Federal vs. state data structures create the perfect propaganda environment:

• Federal datasets overrepresent immigration offenses — because that’s what federal enforcement is built to prosecute.

• BJS reports that about 86% of undocumented non-U.S. citizens charged in U.S. district courts in 2018 were charged with immigration offenses. (R)

• The U.S. Sentencing Commission shows the same gravity: Among federally sentenced non-U.S. citizens, the most common guideline category is Immigration (72.3%). (R)

• Meanwhile, state/local systems are where most real violent/property crime is processed — but those systems are often status-blind.

• GAO states federal agencies do not collect or maintain comprehensive information about the citizenship of individuals incarcerated in state prisons and local jails. (R)

• GAO’s older work makes the same point even more bluntly: there are no reliable data on all criminal aliens incarcerated in every state prison and local jail, so analyses often rely on partial programs like SCAAP. (R)

So you get two broken half-pictures:

1. Federal data = heavy on immigration offenses (not a clean proxy for neighborhood violence/property crime).

2. State/local data = where violence/property crime lives, but status is often missing.

That’s not “neutral.” That’s a measurement regime engineered to keep the headline alive.

Bottom line: The U.S. system isn’t designed to answer the question people are shouting about. It’s designed to create a permanent fog layer — and then let activists declare victory inside the fog.

V. Metric Laundering & Policy-Driven “Declines”

The Metric Shell Game

The game isn’t just rigged by how we define “immigrant” — it’s rigged by how we define “crime.”

The stat manipulators shift the goalposts endlessly:

Removal substitution (incarceration fraud by exit): Incarceration is not “crime.” It’s a downstream artifact. When noncitizens get removed/deported instead of serving long sentences, incarceration-based comparisons can look artificially low without lower offending — the offender exits the dataset.

1. Each stage filters out more incidents — through underreporting, failed prosecutions, plea bargains, or outright policy decisions.

2. The left-wing headline-makers often pick whichever metric makes immigrants look best at that moment, switching as needed.

Crime Drops by Definition, Not Reality

Sanctuary cities and progressive states have been deploying another sleight-of-hand:

1. Legalization of behaviors that were formerly crimes: Entire crime categories are vaporized overnight, erasing thousands of annual “crimes” from the record — disproportionately impacting areas with high immigrant populations.

2. Decriminalization: Theft under $950, prostitution, “quality of life” offenses, etc. Crimes become tickets, warnings, or nothing at all. Police are instructed to “downgrade” charges or ignore whole categories, especially when enforcement creates political blowback. (R)

Geographic Stacking: Immigrant Clustering

Immigrants aren’t evenly distributed across the U.S.

A massive share cluster in just a handful of states and metros — and some of the biggest immigrant destinations are also the exact jurisdictions most aggressive about sanctuary non-cooperation, decriminalization/reclassification, and papering-over low-level crime. (R)

1. State clustering (2023): Pew reports the U.S. had 51.8 million immigrants in 2023, and that “most” lived in just four states: California (11.3M), Texas (over 6M), Florida (5.4M), and New York (5.0M). That’s >27.7 million people in 4 states… 53%+ of all immigrants in the entire country.

2. Metro clustering (2023): Pew also reports that a majority of immigrants live in only 12 metropolitan areas. The NYC metro alone holds 13% of all U.S. immigrants (≈ 6.7M), Los Angeles holds 9% (≈ 4.7M), and Miami holds 6% (≈ 3.1M).

So when people cite “immigrants commit less crime,” a lot of the time they’re not measuring “immigrant nature.”

Now connect the dots

These aren’t random places. They include the same jurisdictions where:

1. Sanctuary-style structures (“don’t enforce / don’t ask / don’t tell”) limit status inquiry and information sharing; and…

2. Policy choices reclassify/downgrade whole categories of offenses (e.g., California’s Prop 47 reclassified certain drug/property offenses and pushed theft under $950 into misdemeanor handling), which can make “crime drop” on paper even when behavior doesn’t magically change. (R)

Translation: The pipeline is engineered to look clean, and then that cleanliness gets sold as an immigrant virtue signal.

Administrative Downgrading (Disappearing Paper Crimes)

1. Felonies to misdemeanors: Offenses that would have been classified as serious crimes are administratively downgraded, vanishing from the more visible, trackable crime statistics. (R)

2. Misdemeanors to “disturbances”: Some are dropped to civil infractions or “incidents” that aren’t entered into the criminal system at all.

3. Paper crimes disappear: On the spreadsheets, it looks like crime is down. In the real world, victims, businesses, and neighborhoods bear the brunt.

Bottom line: The “decline” in immigrant crime is, in large part, a decline engineered by policy and paperwork (not by actual changes in behavior or risk).

VI. Selective Policing, De-Policing, and the Fraudulent “Native” Baseline

Enforcement Withdrawal: Cleaner Stats, Dirtier Streets

1. Political Risk and De-Policing: In many immigrant-heavy and sanctuary jurisdictions, police pull back—either by explicit order, due to political heat, or because making arrests is more trouble than it’s worth (policy, paperwork, accusations of bias).

2. Consequence: Fewer arrests do not mean less crime; they mean less data. The statistics are “cleaner” only because fewer incidents are being processed.

The “Native-Born” Baseline Is a Statistical Con

The “native-born” crime rate (the baseline against which immigrant crime is compared) is artificially inflated by the inclusion of subgroups with much higher recorded incarceration/justice-system contact than others — meaning the “native-born average” is a blended sink, not a clean control group.

BJS data shows just how extreme the baseline gaps are:

1. Asian/NHOPI males had the lowest imprisonment rate in 2020 (149 per 100,000), less than half the rate of white males (332 per 100,000). (R)

2. Black males were 5.7 times as likely to be imprisoned in 2020 as white males. (R)

So the comparison trick is obvious: by comparing first-gen immigrants to the blended native average, the narrative can guarantee a favorable outcome while avoiding the tougher, honest baselines.

The Only Honest Comparison

If you want to know whether immigration is making America safer or more dangerous, you must:

1. Disaggregate by sex and age first (crime is a young-male phenomenon; the age-crime curve peaks roughly 15-19). (R)

2. Then disaggregate by generation (so you’re not shifting descendants into “American” crime).

3. Then compare to (R):

   • U.S.-born Non-Hispanic Whites (the historical and most stable demographic baseline people implicitly mean), and

   • U.S.-born Asians (the group with the lowest consistent offending/imprisonment baseline).

Any “lower crime” claim that fails to make this apples-to-apples comparison is propaganda.

Bottom line: The “native-born” baseline is intentionally gamed to make immigrants look safer than they are. Only a fully disaggregated, generation-matched analysis exposes the truth. There’s also the opposite distortion in border corridors: highly dense enforcement presence can suppress visible offending and warp the comparison in the other direction (we hit that hard in the Texas/border section).

VII. Underreporting & Victim Withdrawal: The Invisible Crime Tax

Crime in Immigrant Communities Is Under-Counted

Raw crime statistics for immigrant-heavy areas are inherently, structurally biased downward because of persistent underreporting by both victims and witnesses. (R)

This isn’t a theory — it shows up repeatedly in immigrant-heavy contexts for predictable reasons:

1. Fear of Deportation or Legal Consequences: Many immigrants (especially those who are undocumented or from mixed-status families) avoid any contact with law enforcement, even if they themselves are the victim. Reporting a crime could expose them or their loved ones to immigration enforcement. (R)

2. Distrust of Authorities: In many source countries, police are corrupt, dangerous, or predatory. This cultural distrust doesn’t vanish upon entry to the U.S. For some, police contact is something to be avoided at all costs, no matter the circumstance. (R)

3. Language Barriers: Many recent arrivals struggle to communicate details or navigate the system. Lack of interpreters, fear of misunderstanding, or unfamiliarity with U.S. legal norms all contribute to lower reporting. (R)

4. Perceived Futility and Retaliation Risk: In tight-knit or insular communities, there’s a widespread belief that reporting crime “won’t change anything” or will bring retaliation from criminals. This is especially true for gang crime, domestic violence, sexual assault, and labor exploitation—all notoriously underreported. (R)

Result: A Massive, Invisible Crime Tax

• Entire offense categories (domestic violence, labor violations, sexual assault, extortion, gang-related violence) are massively undercounted in official records. (R)

• This underreporting isn’t a minor blip; it’s a structural downward bias that artificially deflates immigrant crime rates at every stage. (R)

Bottom line: Any raw crime rate for immigrant populations is a floor, not a ceiling — and the true cost is invisibly paid by the most vulnerable, with no paper trail.

VIII. Avoidance Behavior & “Safer” Stats

Flight and Fortification: Why the Stats Get Cleaner As the Streets Get Worse

Another statistical illusion emerges as residents and businesses adapt to crime in immigrant-heavy areas.

Out-Migration: When crime rises, those with the means to leave (especially natives and upwardly-mobile immigrants) do so. This “self-sorting” concentrates disadvantage and leaves behind a more vulnerable, less mobile, less connected population — who are less likely to report crime or have the resources to fight it.

Defensive Living: Those who stay change their behavior.

1. They avoid public spaces at night

2. Fortify homes and businesses

3. Limit children’s activities

4. Install cameras, bars, alarms, and private security

5. Keep to tight social circles and avoid risky interactions

The Statistical Illusion: Why “Safer Stats” ≠ Safer Streets

As potential victims withdraw and harden their defenses, crime opportunities drop — not necessarily because there’s less underlying criminality, but because the remaining population is less exposed and less likely to call police.

Per-capita crime rates may fall, but not because the neighborhood is actually safer.

Meanwhile, non-residents (who don’t show up in the local stats) suffer: commuters, delivery drivers, and visitors are targeted, but their cases are often lost in the system.

Furthermore, many police aren’t funded to patrol these areas and/or don’t want to bother trying to document crimes among illegals… so they don’t police them and crimes remain unknown.

Bottom line: Lower crime stats are not proof of a safer environment — they’re a byproduct of defensive adaptation and population churn. The real criminal risk is often unchanged or worse; the numbers just won’t show it. (R)

Read: Is Crime Actually Dropping in the U.S.?

IX. Incomplete Documentation & The “Unknown” Black Hole

Data Blackout: The Most Important Variables Aren’t in the Records

Even beyond the national NIBRS problem, the day-to-day crime processing pipeline has a simpler reality:

1. Legal status is usually not a standardized field in arrest reports, case files, or court records.

2. Immigrant generation (1st/2nd/3rd) is usually not recorded at all.

3. Time-at-risk fraud (exposure window manipulation): Years-in-country and age-at-arrival are almost never recorded, so people compare a recent-arrival cohort with a shorter exposure window to lifetime natives and call the gap “lower criminality.” A lot of the time it’s just less time-at-risk, not lower underlying propensity — and removals/deportations can further rig the picture by acting as an off-ramp from the dataset.

So the “status / generation” story people argue about doesn’t exist in the typical record.

That’s why the debate is so easy to game: if the variable isn’t collected, you can claim whatever you want and dare people to disprove it.

The Quiet Admission (Even When People Try to Study It)

Here’s the blunt truth that even pro-immigration research summaries admit in plain language: it’s hard to answer the question cleanly because most prior research can’t separate undocumented from documented immigrants.

A House Judiciary Committee record document states it directly:

“Most previous research … has been unable to differentiate undocumented immigrants from documented immigrants. As a result, most studies treat all immigrants as a uniform group, regardless of whether they are in the country legally.” (R)

That’s not “right-wing spin.” That’s a straight description of the measurement constraint: status isn’t reliably observed in the datasets people want to use.

What This Means in Practice

• If a study is using “immigrant” as a single bucket, it’s often because it has no choice — status and generation are not available.

• If someone claims “undocumented immigrants commit less crime” with high confidence, you ask one question: Where did the status variable come from? If the answer is “we inferred it,” you’re back in proxy-land.

Bottom line: This isn’t just “some missing data.” This is a structural black hole that makes the most repeated claim in the debate fundamentally non-auditable at national scale.

X. The Texas Exception Trap

Texas is often paraded as the counterexample — “proof” that we can have honest status-tagged data showing low immigrant crime.

But this narrative collapses on inspection. Texas data does not prove the national narrative.

Border-Saturated Deterrence: High Law Enforcement Presence

Texas sits on the U.S.-Mexico border with one of the heaviest enforcement footprints in the country—and that alone changes behavior and detection.

1. CRS describes the border strategy since the 1990s as “prevention through deterrence” — explicitly concentrating personnel, infrastructure, and surveillance in high-traffic areas to discourage attempts. (R)

2. GAO found residents near Border Patrol checkpoints generally supported them and cited the law enforcement presence as a deterrent to criminal activity. (R)

3. Border enforcement intensity has measurable crime effects: a Dallas Fed paper by Coronado & Orrenius reports that increases in Border Patrol linewatch hours deter property crime.

If you’re using Texas/border-linked data as your “best measurement environment,” you’re not looking at a neutral baseline… you’re looking at a state with a unique, enforcement-saturated deterrence ecosystem that can suppress visible offending and alter where crime shows up.

And modern Texas has piled even more resources on top via state deployments (Operation Lone Star), including National Guard participation and expanded assets.

El Paso / Border-Town Saturation Example (Officials Everywhere = Deterrence + Different Detection)

If you want a concrete picture of why Texas is not a neutral immigrant crime lab: look no further than El Paso.

1. The El Paso Sector is a major border corridor with a huge federal footprint — HUD’s CHMA notes the CBP-delineated El Paso Sector employs ~2,400 Border Patrol agents and requires “a significant presence” of federal personnel due to border crossings and trade. (R)

2. On top of that, CBP has sworn in Texas National Guard soldiers to support Border Patrol efforts in El Paso (i.e., additional uniformed presence layered onto the corridor). (R)

And El Paso has also been tied to large-scale ICE detention capacity at Fort Bliss (“Lone Star Lockup”), i.e., removal infrastructure nearby. (R)

Translation: In places like El Paso and other border corridors, you have officials everywhere + detention/removal capacity + high visibility enforcement. That’s a deterrence and detection environment that is simply not comparable to interior sanctuary metros — and it can warp “immigrant crime” stats in either direction depending on what gets deterred vs what gets detected.

And it’s not just “border stuff.” Texas is also simply heavily policed. Texas hosts the most law enforcement agencies nationwide at 1,935. So generalizing Texas results into totally different policing regimes is extremely sloppy.

The Caveats Are Not Optional — They’re Written Into the Data

Texas DPS itself flags the limitations that get ignored when people treat it like a national verdict:

1. Texas-only offenses, not other states (R)

2. Time-windowed arrest-event linkage

3. Underrepresentation for those “identified as illegal while in prison” during the window

That is DPS telling you the dataset is not the full universe.

It Still Launders Generations

Even in Texas, most public debate uses first-gen status-tagged comparisons versus the blended native-born population.

The same generational laundering tactic still applies: descendants are “native-born,” so downstream effects vanish into “American crime.”

NIJ itself emphasizes how “unique” the Texas data is precisely because most research can’t separate undocumented from documented immigrants — meaning Texas is used because the broader national system won’t let you do the analysis cleanly. (R)

Sanctuary Cities Don’t Publish Comparable Data for a Reason

The major interior metros that dominate immigrant settlement (especially sanctuary/progressive jurisdictions) don’t produce comparable status/generation-tagged crime datasets at the same transparency level.

That’s why Texas becomes the crutch.

Bottom line: Texas is the exception that proves the rule.

1. It’s enforcement-saturated, which can suppress and displace crime in ways that don’t generalize to the broader U.S. (R)

2. Its own data contains explicit undercounting and scope caveats. (R)

3. And it still doesn’t solve the core scam: generational laundering, because descendants are booked as “native-born.” (R)

XI. International Reality Check: Granular Data Often Kills the Myth

Preface: U.S. and EU migrant mixes differ, so don’t import magnitudes mechanically. The point here isn’t “Europe = America.”

The point is simpler: where countries publish register-based data that separates immigrants from descendants, the first-gen headline stops being the whole story — because generational laundering isn’t possible.

Armchair hypothesis: I surmise the E.U. ends up with lower IQ, higher criminality migrants than the U.S. based on immigrant origins (Hispanics vs. MENAPT). One might extrapolate data from the E.U. honing in on Hispanic/Latino migrants and country of origin — but even that gets dicey for various reasons (immigrant preferences / self-selection, total numbers, origin mismatches, etc.). I suspect that outside of highly-educated/selected immigrants, most converge with the American range by race (though there’s probably some ethnic granularity as well).

What Happens When Countries Actually Track Generations?

In the handful of developed nations that collect and publish generation-tagged crime data, the story is crystal clear — and completely shatters the “immigrant crime is lower” meme.

Scandinavia as the Gold Standard

Sweden, Norway, and Denmark maintain register-based national statistics that follow individuals and their descendants across generations.

These countries make no bones about parsing out 1st, 2nd, and even 3rd-generation outcomes. (R)

The Results?

First-generation immigrants

(Especially those under strict entry and residency controls) sometimes show lower or only modestly elevated crime rates, depending on country of origin and selection process… but not always.

1. In Sweden’s Brå register analysis, those born abroad and those born in Sweden to non-native parents are consistently more likely to be registered as suspects than those born in Sweden to two native-born parents.

2. Brå also reports that being registered as a suspect is ~2½× as common among persons born abroad compared to those born in Sweden to two native-born parents.

Second-generation descendants

Born citizens, free from deportation threat — are often the most overrepresented group in violent crime, gang activity, and sexual offenses.

1. Sweden (Brå): Across 2007–2018, Brå reports the proportion registered as suspected offenders is highest among those born in Sweden to two non-native parents and lowest among those born in Sweden to two native-born parents, and notes this holds for both men and women.

2. Sweden (Brå) also shows that when you adjust for age and gender, “excess risk” declines (most for Sweden-born with two non-native parents), but it doesn’t vanish—e.g., for rape, Brå reports relative risk declines from 3.2 → 1.4 for those born in Sweden to two non-native parents, and 3.2 → 2.2 for those born abroad.

3. Norway (SSB): The report states directly that both immigrants and Norwegian-born persons with 2 immigrant parents are overrepresented as registered offenders, with the overrepresentation highest in the latter group.

4. Denmark (Justice Ministry): Denmark publishes age-corrected indices, and it is not subtle — male descendants of non-Western origin have an age-corrected criminality index for group violence of 384 (≈ 3.8× expected), while male non-Western immigrants are also overrepresented (index 196).

5. Denmark (MENAPT memo): The MENAPT memo shows the same pattern in multiple ways, including age-corrected indices (e.g., MENAPT male descendants higher than MENAPT male immigrants) and also indices corrected for age + socioeconomic background, where MENAPT male descendants sit around ~270 across 2016–2023 (i.e., more than 2½× the male population baseline after corrections). (The correcting for SES adjustment is doing a lot of heavy lifting to make migrants look better here too! SES isn’t even a correction you should be doing when you allow unfiltered immigration.)

U.S. vs. Europe: A Critical Caveat

1. U.S. immigrants are predominantly Latin American (Mexico, Central America, Caribbean, South America). Pew reports 52% of U.S. immigrants were born in Latin America. (R)

2. European overrepresentation debates are often dominated by MENA / adjacent regions and other non-Western flows, and Denmark’s own “MENAPT” category explicitly includes Middle East/North Africa plus Pakistan and Turkey (with a country list spelled out in the ministry memo).

3. Spain is a useful “caveat example” of how you can have strong overrepresentation signals by nationality while still needing caution about causal narratives and comparability: Spain’s INE conviction statistics show 72.3% of convicted adults had Spanish nationality in 2023, but the conviction rate per 1,000 was 14.6 for foreign persons vs 5.8 for Spanish nationals (≈ 2.5×). (R)

The caveat: You can’t import magnitudes across countries with different migrant mixes and different recording systems, but wherever descendants are tracked cleanly, the “first-gen advantage” stops being the whole story.

Logical Takeaway

Wherever generation-tagged crime data is published, the pro-immigration narrative collapses:

1. The “first-gen advantage” is exposed as a temporary, incentive-driven blip.

2. The long-term, generational crime cost becomes starkly visible—because you can’t launder descendants into a fake “native” sink and pretend the downstream doesn’t exist.

Why doesn’t the U.S. publish similar data?

Because the instant you get generation × status × subgroup cross-tabs at national scale, the “immigrants commit less crime” meme stops being a universal shield and turns back into what it actually is: a narrow, first-gen snapshot.

XII. The Hidden Offense Bundle

The Unseen Crimes: Excluded by Design

When most people hear “crime,” they think murder, assault, theft — the standard, high-visibility offenses.

But immigration brings its own bundle of systematically undercounted, underprosecuted, or administratively downgraded offenses that rarely show up in the headline stats:

Identity/Document Fraud

Fake Social Security numbers, forged driver’s licenses, and fraudulent documentation are often a prerequisite for participation in the economy, yet are rarely prosecuted or tracked as “real crime.”

In many areas, enforcement is non-existent or explicitly deprioritized.

And when it is prosecuted, you can see the mechanism in black and white: DOJ announced 119 illegal aliens prosecuted in Mississippi for identity theft/document fraud and related offenses, including fraudulent use of Americans’ Social Security numbers and unlawful reentry after removal.

• Related: Somalis in Minnesota + $18B Minnesota Medicaid Fraud

Labor Exploitation and Wage Theft

Employers (e.g. second-gen or third-gen immigrants) illegally withholding pay, skimming hours, or abusing the legal vulnerability of first-gen immigrant workers.

These are high-frequency crimes, devastating to communities, but are prosecuted (if at all) as civil violations or labor disputes, not as crimes in the stats.

Human Smuggling and Trafficking

Cartel and organized crime involvement in smuggling people across borders, running stash houses, and engaging in violent enforcement — all of which create enormous social harm, but are often prosecuted in federal silos separate from state/local crime stats.

Treasury’s designation of Tren de Aragua describes exactly this kind of cross-border exploitation: leveraging transnational networks to traffic people (especially migrant women and girls) for sex trafficking and debt bondage. (R)

Cartel/Gang Activity

Organized drug distribution, extortion, and violent turf wars.

These crimes are often distributed across state lines and may be attributed to “domestic” gangs, further hiding the connection to immigration flows.

CRS’s MS-13 report lays out the overlap between local street crime and transnational activity (drug trafficking, human smuggling/trafficking, extortion, murder). (R)

Why Are These Excluded?

1. Administrative Downgrading: Many of these offenses are counted as regulatory violations or handled quietly via civil penalties.

2. Data Siloing: Offenses that do result in prosecution are split across separate databases (federal vs. state, immigration vs. criminal).

3. Political Incentive: Including these would make the spreadsheet—and the narrative—look a lot less favorable.

Bottom line: Ignoring this hidden offense bundle is a deliberate omission to keep “immigrant crime” stats artificially clean. The true social cost is swept off the ledger, out of sight and out of the policy debate.

XIII. What Is Most Likely True?

First-Generation: Lower “Visible” Crime, for Now

If you strip away the PR and just look at incentives, the first-gen story is not mysterious. It’s best-behavior under a sword.

NIJ says it directly:

First-generation immigrants tend to have lower arrest rates than native-born citizens. (R)

That doesn’t prove “immigrant virtue.” It proves what Section III already explained: catastrophic deterrence + extreme downside produces lower visible, police-detectable offending in many measures.

And if you’re dealing with border corridors, that effect can be amplified by an enforcement-saturated environment. The border strategy itself is explicitly framed as “prevention through deterrence” — concentrating personnel, infrastructure, and surveillance to discourage attempts and disrupt activity. (R)

So yes: first-gen can look “clean.” That’s not the end of the story — it’s the beginning of the scam.

Note: Remember the combination of Sanctuary Cities, Underpolicing, Underreporting, Avoidance Behaviors, Judicial Protections, Failure to Note Immigration Status, etc. also make “immigrant crime” appear lower than reality.

Second/Third Generation: Convergence + Laundering

Here’s the part people parroting “immigrants commit less crime” never want to address.

1. NIJ notes the first-gen pattern diminishes in subsequent generations. (R)

2. The National Academies’ own summary says the quiet part bluntly: in the second and third generations, crime rates increase and resemble that of the general population of native-born Americans. (R)

Pew goes even sharper: second-generation immigrants “catch up” and resemble native-born Americans in offending profile during the most crime-prone years. (R)

• The Pew writeup is based on research by Bersani et al. (2014) which likely underrepresents the actual crime per capita of second-gen immigrants for a variety of reasons.

• The study reports second-gen immigrants converge with non-Hispanic Whites, but there is major issue with this claim: selection/collider bias (conditional on being caught, the “White” comparator is not the White population, 2 jurisdiction design/ethnic clustering = huge confounding, not measuring “crime” (arrests/petitions), proxy issues, self-reporting, etc.

That convergence is exactly why “generational laundering” is the foundational fraud. First-gen gets paraded as the “immigrant” data point, and the downstream convergence gets dumped into “native-born American crime.”

Compared to Whites and Asians: the blended “native-born average” is baseline doping

Once you stop comparing to the blended sink and you benchmark against the actual low-baseline reference groups, the headline becomes a rigged frame.

The “native-born average” is not a clean control group. It’s composition-driven, and it’s easily gamed.

So when someone says “immigrants are lower crime than native-born Americans,” what they often mean is:

First-gen looks lower than a blended average we refuse to decompose.

That’s baseline doping and not an apt comparison.

The Reality

So here’s the clean “most likely true” verdict:

1. First-gen often looks cleaner on visible metrics because deterrence and selection are extreme.

2. Over time, the effect fades and later generations converge toward native-born patterns.

The meme survives because the U.S. refuses to publish the kill-shot cross-tabs (status × generation × subgroup × enforcement regime), and because the baseline is routinely rigged through blended averages rather than apples-to-apples stratification.

XIV. The Propaganda Playbook

This narrative doesn’t survive by accident. It survives because it’s maintained through a repeatable set of moves that exploit blind spots.

1. Population Swap

Undocumented → noncitizen → immigrant → foreign-born.

Widen the category until the number looks clean, then pretend that answers the question everyone was actually asking.

Subgroup blender: Average high-skill legal immigrants with low-skill/illegal cohorts, refuse to break out visa/education/origin, then declare the diluted mean is “the truth.”

2. Metric Swap

Geographic stacking: Use immigrant-heavy states/metros with decriminalization + non-cooperation + de-policing to generate cleaner recorded stats, then treat those stats as a universal national truth.

Crime → arrests → convictions → incarceration → “self-report.”

Pick the stage of the pipeline that flatters the story. Each step is filtered through enforcement, prosecution, diversion, and removals.

3. Baseline Fraud

Compare first-gen immigrants to the blended “native-born average” instead of the only honest baselines: sex/age standardized + race-stratified comparisons (and benchmark to whites and Asians as reference groups).

4. Generational Amnesia

Freeze the analysis at first-gen, then dump the kids and grandkids into “native-born crime,” so downstream convergence never shows up as “immigration impact.”

5. Make the Variable Not Exist

If the status variable isn’t recorded nationally, you can talk with certainty forever because the claim can’t be audited.

6. Moralize Incentives

Recast deterrence/self-policing as “virtue,” then shame anyone who demands the obvious: generation-tagged, status-tagged, apples-to-apples baselines.

XV. Questioning the Claims (Critical Thinking)

Forget the headlines and the carefully-massaged talking points.

If you want to know whether a claim about immigrant crime is honest or fraudulent, run it through this checklist.

1. Which group?

Are we talking about undocumented? Legal permanent residents? Temporary visa holders? Foreign-born overall? What’s the country/region of origin?

If they won’t specify the population precisely, they’re already doing the category swap.

What selection channel?

Visa type + education level + origin. If they’re averaging PhDs/H-1Bs with low-skill and illegal flows, the “immigrant crime rate” is contaminated by design.

2. Which metric?

Is it reported crime, arrests, convictions, incarceration, or victimization surveys— and why did they choose that stage of the pipeline?

Every stage filters reality through enforcement, prosecution, plea deals, diversion, and removals.

3. What controls?

Are age and sex controlled for?

Are years-in-country and age-at-arrival controlled for? If not, you’re comparing different exposure windows, not propensities.

4. What baseline?

Are they comparing to the blended “native-born average” (the statistical sink), or are they doing the only honest thing:

Sex/age standardization → race-stratified comparisons → then the real low-baseline comparators?

If they’re using the blended native-born baseline, it’s baseline doping.

5. Which generation?

Are second-, third-, and fourth-generation descendants tracked separately — or dumped into the “American” column where the costs disappear?

If descendants aren’t split out, you’re watching the generational laundering scam in action.

6. Does the dataset even contain immigration status?

This is where the con often dies instantly.

NIBRS (the FBI’s main incident-based system) does not collect citizenship status of offenders or victims.

So if they’re speaking with certainty about “undocumented crime rates” nationally, ask one question:

Where did that status variable come from?

7. How are “unknowns” handled?

Do they ignore missingness? Impute it? Pretend it’s random?

Even NIJ notes most prior immigrant-crime research has been unable to separate undocumented from documented, which forces fake “uniform immigrant” bins in a lot of studies.

8. What enforcement regime?

Is this a border corridor (enforcement-saturated deterrence environment) or an interior sanctuary metro (status-blind, non-cooperation, de-policing incentives)?

CRS describes sanctuary policy structures (“don’t enforce / don’t ask / don’t tell”). If they pretend enforcement regimes don’t matter, they’re laundering measurement effects into “behavior.”

If these questions aren’t answered it’s mostly trash that the left-wing faction will parrot like a Dunning-Kruger everywhere thinking they are smart and you’re a moron.

Final Take: The Manufactured Mirage

The American public is being subjected to a deliberate, institutional gaslighting campaign — one that survives not by producing honest data, but by avoiding it at all costs.

The “immigrants commit less crime” narrative is a statistical con.

It survives through a combination of:

1. Generational laundering (first-gen looks clean; descendants get booked as “American crime”)

2. Baseline fraud (comparing to a blended native-born sink instead of age/sex-matched, race-stratified baselines)

3. Status-blind national data (the key variable isn’t even in the main crime pipeline)

4. Metric laundering (crime → arrests → convictions → incarceration, whichever flatters the story)

5. Policy-engineered “declines” (reclassification, downgrading, de-policing, non-cooperation)

6. Border vs interior distortions (enforcement-saturated deterrence environments vs status-blind sanctuary metros)

Every honest, adult conclusion about this topic converges to the same place:

• First-gen “good stats” are a temporary product of deterrence and selection, not a permanent moral trait.

• Second and third generations converge upward toward native-born baselines—so the “immigrant advantage” gets front-loaded while downstream costs vanish into the “native-born” column.

• Once you benchmark against the real low-baseline comparators (whites as the majority reference, Asians as the floor benchmark), the blended “native-born average” comparison is exposed as a rigged frame.

True transparency (generation-tagged, status-tagged, apples-to-apples baselines) would destroy the narrative overnight. The fact that the U.S. won’t produce those cross-tabs at national scale is the whole story.

Until the entire stack (definitions, data, baselines, enforcement regimes) is forcibly opened to scrutiny, the myth will endure.

Every journalist, academic, politician, and “fact-checker” who repeats it as a clean national truth is pushing propaganda.

Note: You can use much of the logic in this write-up to critically analyze the “immigrants boost GDP” claim as well. The “GDP boost” is not what you think outside of skilled and necessary groups.

I. Evolutionary Foundations and Trait Distributions

The unique character of Western high-trust societies did not emerge by accident or simply from ideas—it is the end product of centuries of selection acting on both genetic and cultural variation.

1. Polygenic Behavioral Traits

Traits like trust, empathy, in-group vs. out-group preference, impulse control, harm aversion, and social conformity are all:

• Polygenic (influenced by many genes)

• Quantitatively distributed (bell curves, not categories)

• Moderately heritable (twin and adoption studies routinely show 30–60% heritability for social traits)

Crucially, small mean differences across populations—even when groups overlap greatly—produce radically different outcomes at the societal level, especially at the tails of the distribution. For example, a society with slightly higher average trust will have many more extreme “hyper-cooperators” and many fewer chronic defectors.

2. The Selection Pressures That Shaped NW Europe

• Kinship Disruption: The medieval Western Church’s ban on cousin marriage and close-kin unions, starting around 500–1000 AD, broke up extended clans. This forced populations to build nuclear families and rely on formal institutions and rules, weakening nepotism and increasing trust in strangers.

• Criminal Culling: For centuries, societies like England executed, banished, or ostracized chronic thieves, violent offenders, and those unwilling to abide by the law. These practices acted as a “slow filter” against the most antisocial, impulsive, and non-cooperative genotypes, shifting trait means over generations.

• Environmental Filters: Northern Europe’s harsh winters and sparse populations rewarded long-term planning, mutual aid, and non-kin cooperation. Those unable to plan or work with non-family were less likely to survive or prosper.

• Moral Universalism and Empathy as Status: Christianity and Enlightenment ideas elevated compassion for outsiders and abstract universalism, first as virtue, then as a source of status and social signaling. Over time, these became embedded in law, education, and elite culture.

3. Group Means and Tail Effects

While there is always enormous individual variation within populations, even a small shift in group averages—caused by cumulative selection and filtering—can have an outsized impact on the prevalence of high-trust, high IQ, highly empathetic, or universalist individuals.

For instance, the “genetic pacification” process in NW Europe, through criminal culling and kinship disruption, likely amplified high-trust and low-aggression tails, creating societies unusually prone to universalist morality.

These “right-tail” effects drive the emergence of unique social norms and institutional trust.

II. Gene–Culture Coevolution: Culture Is the Product of Group Genetics

Culture does not arise in a vacuum, nor does it shape people in isolation.

Instead, culture and genetics are locked in a long-term feedback loop—each shaping, filtering, and reinforcing the other. This is the core insight of gene–culture coevolution.

1. Trait Distributions Filter Culture

• The psychological and behavioral profile of a group—its baseline levels of trust, empathy, IQ, social conformity, and fairness—sets the statistical boundaries for which cultural innovations, norms, and institutions can thrive, and which will fail or be subverted.

• A population with higher average trust and impulse control can sustain rule-based, high-cooperation systems; one with lower baselines defaults to informal enforcement, kin loyalty, and opportunistic strategies.

2. Culture as a Selective Environment

• Once established, cultural rules and institutions themselves become part of the “environment” that further shapes selection.

• Legal systems, religious customs, marriage norms, and economic practices all reward certain traits and penalize others. Over generations, individuals better adapted to these institutions are more likely to succeed and reproduce.

• Example: In NW Europe, the Church’s marriage bans and harsh criminal justice system made it difficult for highly clannish, impulsive, or violent individuals to prosper, while favoring cooperative, law-abiding types.

• This selection for low-nepotism and cooperative traits may also have included a reduction in aggression through “genetic pacification,” allowing for more stable, high-trust institutions.

3. The Feedback Loop

• Genes shape culture: Only institutions and norms compatible with the group’s baseline traits will persist and spread.

• Culture shapes genes: Institutions, once established, exert selective pressure that can shift trait distributions further in their own favor.

• Over centuries, this produces “stickier” cultural patterns: Why do high-trust, rule-based systems take root in some places, but not others? The answer lies in the match between population genetics and the environment—including inherited culture itself.

4. Culture Is Statistically Genetic (But Not Deterministic)

• Not every individual embodies the group mean; there is always overlap. But at the aggregate, culture is the emergent property of the group’s genetic and behavioral profile, filtered through environment and history.

• This is why “importing” institutions to populations with different trait distributions rarely works: culture is not a costume, but a mirror.

III. Modern Mismatch: Incentives, Globalism, and the Trojan Horse

The strengths that made high-trust societies so successful—universalism, openness, and rule-based institutions—are now being systematically subverted by the realities of modern globalism and asymmetric group incentives.

1. Mass Migration Without Selection

• In the past, cultural and institutional filters selected for compatible newcomers or gradual assimilation. Modern Western nations abandoned these filters, opening borders to millions from populations with sharply different trait means: stronger in-group loyalty, lower baseline trust in strangers, and alternative approaches to authority and cooperation.

• No expectation or enforcement of reciprocal adaptation; host societies simply presume eventual convergence.

2. Incentive Asymmetry and Rational Extraction

• Western institutions are built on the assumption of mutual cooperation, good faith, and reciprocity.

  • Example: Social safety nets, legal systems, and public goods depend on most people following the rules and contributing fairly.

• When new arrivals are not selected for these norms, and when exploitation carries minimal social or legal penalty, rational actors will extract as much as possible from the system.

  • This is not about “bad actors” but about the basic logic of incentives and trait variance.

3. The Trojan Horse Mechanism

• High-trust systems act as a Trojan horse—designed for a world where everyone plays by the same rules, but now open to exploitation by those who don’t share the same evolved behavioral profile.

• The most universalist and trusting societies are, paradoxically, the most vulnerable to manipulation and extraction.

4. No Reciprocal Selection or Feedback

• Natives continue to cooperate, comply, and pay in, while the rules are gamed by those who see opportunity.

• The absence of feedback loops—social, political, or institutional—ensures that the system cannot self-correct until the burden becomes unsustainable.

5. What Happens Next

• Extraction grows, trust erodes, and institutions buckle under the weight of unsustainable incentives.

• As parallel societies grow and more resources are diverted to extraction and maintenance, native populations retreat or are replaced—structural decline accelerates.

Related: Europe’s Trojan Horse: Suicidal Empathy & Immigration

IV. Blank Slate: The Meta-Vulnerability

Perhaps the single greatest accelerant of Western institutional decline is the widespread, enforced belief that all human groups are behaviorally and cognitively identical—a dogma known as the “blank slate” ideology.

1. Suppression of Group Differences

• Since World War II, Western elites in academia, media, and government have aggressively promoted the view that observed group differences in behavior, trust, empathy, or social outcomes are entirely the result of environment or discrimination.

• Any suggestion that populations differ on average in evolved psychological traits—no matter how small the mean difference or how overwhelming the empirical evidence—is met with stigma, censorship, or career-ending sanction.

2. The Standard Social Science Model

• Policy, law, and education are all built on the assumption that, with enough resources and the right environment, all groups will assimilate and contribute just like high-trust natives.

• When this fails to happen, failure is blamed on “systemic racism,” “inequity,” or lack of funding—not on mismatched trait distributions or incentive misalignment.

3. Self-Neutralizing Feedback Loops

• Because recognizing real group differences is taboo, Western institutions become incapable of adjusting to demographic, social, or behavioral reality.

• Laws and programs are created as if trust, reciprocity, and cooperation can be assumed universally—even as evidence piles up to the contrary.

• By the time the mismatch becomes undeniable, collapse is already baked in—and those who warned of it are blamed for the consequences.

4. Denial as Selection Amplifier

• The blank slate ideology isn’t just misguided; it acts as a selection amplifier. By preventing honest diagnosis and feedback, it allows incentive misalignments to persist and compound.

• This “meta-vulnerability” ensures that high-trust societies cannot defend themselves against exploitation, institutional capture, or demographic inversion.

Related: The Planet That Banned 90% of Reality

V. Parasitic Feedback Loops: NGOs, Lawfare, and Political Capture

The modern Western “helping” sector—NGOs, legal aid, and advocacy organizations—has evolved into a self-perpetuating machine, extracting resources from high-trust societies while entrenching its own power and dependency base.

1. Taxpayer-Funded Extraction

• Billions in public funds are channeled annually into nonprofits, legal groups, “community organizations,” and contractors, all tasked with serving migrants, asylum seekers, and protected minorities.

• Rather than fostering self-sufficiency or assimilation, the core business model rewards persistent need and dependency: the more dysfunction or grievance demonstrated, the more funding and political capital these groups accrue.

2. Rent-Seeking and Closed Loops

• NGOs and legal service providers routinely divert a share of their public windfall to lobbying, campaign donations, and get-out-the-vote efforts for politicians who promise to increase their funding and expand client eligibility.

• This forms a closed, parasitic loop:
  Public money → NGO/Legal advocacy → Political campaigns & lobbying → Policy that increases funding and dependency → repeat.

3. Vote Farming and Policy Lock-In

• Client populations (those receiving benefits and advocacy) are organized and mobilized as voting blocs, ensuring political power stays with those who maintain or grow the feedback loop.

• Any attempt to audit, limit, or reform this ecosystem is met with coordinated legal, media, and activist attacks—framing reform as “cruelty” or “bigotry.”

4. Lawfare and Expansion of “Rights”

• Legal campaigns, class action lawsuits, and strategic litigation are used to expand entitlements, weaken enforcement, and open new streams of resource extraction—from expanded asylum law to automatic benefits for newly arrived or even undocumented groups.

5. Permanent Extraction and Host Decline

• As the ecosystem grows, ever more resources are diverted from the productive, net-contributing native population. The “helping” sector becomes a permanent political and economic parasite, ensuring that institutional self-defense is neutered and decline becomes locked in.

VI. Institutional Capture, Revisionism, and Two-Tier Systems

As Western societies become more diverse and universalist, their core institutions—government, media, education, and law—are transformed in ways that actively undermine the interests of the founding population and entrench new, unaccountable elites.

1. Revisionist History and Cultural Delegitimization

• Official narratives and curricula are rewritten to cast natives—especially whites—as historic oppressors or “privileged,” erasing or minimizing their achievements and moral legitimacy.

• Defense of group interests or even discussion of demographic change is stigmatized as “racist,” “white supremacist,” or “extremist,” shutting down normal political feedback.

2. Two-Tier Justice and Legal Double Standards

• Protected groups and newcomers are given leniency, fast-tracked for benefits, shielded from enforcement, and even excused for infractions that would draw harsh penalties for natives.

• High-trust citizens are held to stricter standards, penalized for minor infractions, and denied assistance or recourse even in genuine need.

3. Equity as Redistribution and Control

• Equity and anti-racism policies go far beyond leveling the playing field: they are weaponized to redirect resources, influence, and opportunity from net contributors to politically favored or protected groups.

• Public institutions, universities, and even corporate boards are compelled—by law, funding, or cultural pressure—to prioritize demographic quotas and “representation” over merit, contribution, or social trust.

4. Legal and Policy Lock-In

• As the administrative and legal state is captured by new elites and activist groups, changing the system democratically becomes almost impossible.

• Lawfare, regulatory barriers, and procedural complexity make reversal or reform extremely costly and politically perilous.

5. Loss of Native Leverage

• Founding populations are increasingly expected to accept declining status, rising costs, and open hostility from their own institutions, all in the name of “progress” and “justice.”

• As their demographic share falls, their ability to defend their interests—legally, culturally, or politically—erodes to zero.

VII. Extraction, Resource Flow, and the Golden Goose Paradox

The economic engine of Western societies has always been the high-trust, productive, and law-abiding core—primarily native whites and high-IQ Asians.

But as institutional incentives shift and demographic change accelerates, this productive base is increasingly mined, scapegoated, and replaced.

1. Net Contributors vs. Net Extractors

• The bulk of tax revenue, innovation, and public goods is generated by a shrinking minority of net contributors.

• In contrast, resource consumption and dependency—welfare, healthcare, public education, and subsidies—rise disproportionately among recent arrivals, protected classes, and politically favored groups.

• Remittances send billions in wealth abroad, draining resources that could have funded social goods for citizens.

2. Replacement Migration as Policy

• Declining native birth rates are used as justification for ever more “replacement migration,” supposedly to support the welfare state—but the newcomers are, on average, net extractors rather than new golden geese. (How to Fix Low Birth Rates)

• Each wave of migration deepens the fiscal hole, as productive natives are demotivated to invest, start families, or even remain in their own country.

3. The Golden Goose Paradox

• The more you demand from the high-trust, high-productivity minority, the less they have reason to keep producing.

• As they are delegitimized, overtaxed, and replaced, their numbers dwindle to the point where the system collapses: the goose that laid the golden eggs is killed off for short-term gain.

4. Irreversible Fiscal Feedback

• Once net contributors are outnumbered, and their interests systematically devalued, collapse is no longer a possibility—it is a certainty.

• Public services degrade, redistribution fails, and the social contract that once underpinned prosperity is permanently broken.

VIII. Fertility Collapse, Empathy Misdirection, and Virtue-Signaling Feedback

One of the most underappreciated drivers of Western unraveling is the collapse of native birth rates and the misdirection of evolved empathetic instincts—especially among women—toward causes and outgroups, rather than kin and community.

1. Declining Birth Rates and Kin Disconnection

• As native fertility plummets, family size shrinks, and fewer people have children or close kin to invest in.

• The nurturing drives that once supported robust kin networks and intergenerational transmission of values now lack natural outlets.

2. Empathy Without Borders

• Instead of being channeled toward one’s own children, extended family, or community, empathetic energy is redirected outward:

  • To animals (Pitbull “parenting,” rescue movements) (We should actually ban Pitbulls) (Read: Why White People Keep Adopting Pitbulls)

  • To distant or abstract causes (NGOs, climate change, refugees, identity activism)

  • To symbolic victim groups, often at odds with native interests

3. Virtue-Signaling as Social Currency

• In the absence of strong kin and community ties, moral prestige is increasingly won by demonstrating commitment to “helping” or “rescuing” the most distant or marginalized.

• High-status signaling becomes a contest over who can sacrifice more for outgroups or abstract causes—further reinforcing the very feedback loops undermining group survival.

4. Self-Perpetuating Feedback Loop

• The more society valorizes this redirected empathy and self-sacrifice, the less incentive natives have to invest in their own reproduction or defense.

• This creates a vicious cycle: fewer children → more empathy redirection → weaker group cohesion → more incentive to seek status through outgroup rescue—until the foundational stock is too depleted to recover.

IX. Observable Outcomes: Western Decline Across the Diaspora

The interplay of evolutionary selection, broken incentives, institutional capture, and demographic feedback is no longer theoretical—it’s visible in every major Western society.

1. Social Trust and Cohesion Collapse

• Once world-leading levels of trust, social capital, and civic participation are now in steep decline.

• Neighbors interact less; public spaces feel less safe; common purpose erodes.

2. Rising Welfare, Legal, and Administrative Costs

• Social safety nets, legal aid, and bureaucracy swell to meet expanding demand from increasingly dependent or newly arrived groups.

• Fraud, inefficiency, and outright extraction become normalized, with little hope of reform.

3. Native Withdrawal and Parallel Societies

• Natives disengage from politics, community life, and even public debate, as their interests are delegitimized or stigmatized.

• Parallel societies—self-contained enclaves with divergent norms and loyalties—proliferate, undermining national identity and rule of law.

4. Organized Political Extraction and Vote Farming

• Newcomer and protected groups organize to lobby for more resources, special status, and permanent legal protection—often as reliable voting blocs for the parties that sustain extraction.

• Democratic feedback is neutralized, and reformers are marginalized or demonized.

5. Populist Backlash, Suppressed

• Attempts to reverse these trends—through populist or nationalist movements—are met with relentless institutional opposition, media demonization, and legal barriers.

• Policy correction only occurs (if ever) after major crisis or breakdown.

6. Demographic Eclipse

• In city after city, and increasingly entire countries, the founding population becomes a minority—often within a few generations.

• The “new normal” is demographic inversion, with little prospect for reversal.

X. Why This is Uniquely a Western Phenomenon

No other major civilization has engineered its own structural vulnerabilities—or moralized its own demographic and institutional decline—like the modern West.

1. Pathological Universalism and Open Borders

• Only in Western nations has universal moral empathy been institutionalized to the point where border enforcement, self-preservation, and even basic reciprocity are condemned as “immoral.”

• The legal and political default is openness—even when it is clearly not reciprocated by incoming populations or by any peer society globally.

2. Blank Slate Ideology as a Civic Religion

• The belief that all groups are interchangeable in behavior and capacity is not just an academic quirk—it is enforced by law, media, and elite consensus.

• Any recognition of group differences in behavior, trust, or social capital is immediately pathologized and sanctioned.

3. Elite Complicity and Moral Status-Seeking

• Western elites—political, academic, and economic—derive status from celebrating openness, diversity, and the symbolic dismantling of “privilege.”

• National cohesion, demographic continuity, and even basic survival are sacrificed to maintain elite legitimacy and social currency.

4. The Global Outlier: Non-Western Self-Defense

• No non-Western society (Japan, Korea, Israel, Gulf States) has internalized universalism to the point of self-destruction.

• These nations strictly police borders, enforce group interests, and align institutions with the long-term continuity of their founding populations.

5. Western Virtues as Fatal Flaws

• The very strengths that built the modern West—trust, openness, fairness—have become liabilities in a global context where only the West adheres to them.

The outcome: the West stands alone as a civilization that moralizes its own decline, suppresses its defenders, and celebrates its own demographic eclipse—an unprecedented phenomenon in world history.

XI. The Structural Ledger and Endgame

The decline of Western high-trust societies is not an accident or the result of bad policy alone—it is a direct, structural consequence of evolutionary history, genetic trait distributions, and perverse incentives that now reinforce each other in a closed feedback loop.

1. Strengths Become Fatal Liabilities

• Trust, fairness, openness, and moral universalism—traits that once created prosperity and cohesion—become vulnerabilities when not reciprocated.

• High-trust natives are systematically extracted from, scapegoated, and ultimately replaced, even as they fund the entire system.

2. Incentives Ensure Irreversibility

• The more you extract from the productive base, the less reason they have to invest, reproduce, or remain.

• As feedback is suppressed by blank slate ideology, institutional capture, and moral shaming, the chance for correction narrows to zero.

3. Collapse is Baked In

• When net contributors are outnumbered and systemically disempowered, there are no resources left to extract.

• Public services collapse, trust vanishes, and parallel societies—often hostile—compete for shrinking spoils.

4. Nostalgia and Scapegoating

• As the decline accelerates, expect resource battles, rising crime, and nostalgic mythmaking about the lost “golden age.”

• True reformers and critics are blamed or demonized, as the political system devolves into endless blame-shifting and populist dead ends.

5. Two Possible Futures

• Radical feedback restoration: Only a wholesale, unapologetic return to reciprocal boundaries, trait-based selection, and the restoration of high-trust norms offers a path to recovery—but this is politically and culturally radioactive.

• Permanent fragmentation: Absent such a reset, high-trust civilization becomes a historical anomaly—its achievements studied, but never repeated.

The ledger is clear: Without systemic self-correction, Western societies will not just decline—they will dissolve, and their virtues will be remembered only as the cause of their demise.

XIII. Paths to Avoid Decline: Scenarios, Interventions, and Realistic Odds

Faced with the deeply-rooted evolutionary, demographic, and institutional forces driving Western decline, what—if anything—can actually reverse or transcend this trajectory? Below, I outline potential interventions, their logic, and a realistic, unsentimental assessment of their odds.

The goal: Avoid this futuristic “Autopsy of America.”

1. Radical Bioengineering and Cognitive Enhancement (Embryos & Somatic Upgrades)

• Logic: Directly increase the prevalence of pro-social, high-IQ, and high-future-orientation traits in the population—through embryo selection, CRISPR gene editing, somatic interventions, or other biotechnologies.

• Benefits: Attacks the root (trait distribution); could potentially create a new equilibrium where high-trust institutions are again sustainable—even at scale and in diverse societies.

• Risks: Immense technical, ethical, and political barriers; would require mass adoption, regulatory tolerance, and years-to-decades of societal buy-in. Likely to be demonized, sabotaged, or outlawed in most democracies.

• Odds (by 2100): 5–10% for meaningful deployment in the West; much higher (25–50%) for some form of bioenhancement/selection being adopted by authoritarian or non-Western states first.

• Rationale: If nothing else, technological acceleration elsewhere will force the question.

2. Reversal or Pause of Aging (“Longevity Escape Velocity”)

• Logic: If Western societies could dramatically slow, pause, or reverse aging, you break the “replacement migration” trap—natives no longer die off, institutional memory and trait selection are preserved, and incentives to invest in the future rebound.

• Benefits: Buys time for policy adaptation, demographic stabilization, and trait/skill transmission.

• Risks: Still requires massive tech breakthroughs and equitable access. Potential for new forms of intergenerational conflict or ossification.

• Odds (by 2100): 10–20% chance of major longevity breakthroughs; less than 5% odds for broad societal reversal of the demographic spiral.

• Rationale: Useful as a stopgap, not a silver bullet.

3. AI and Robotics Diffusion—Embedded with High-Trust Ethos

• Logic: Deploy superhuman AI, robotics, and automation (aligned with American/Western values) to maintain social order, productivity, and institutional capacity even as trait distributions erode.

• Benefits: Could mask or buffer the loss of high-trust, high-IQ maintainers, letting institutions “run on AI autopilot” as social capital drops.

• Risks: Alignment remains unsolved; values can be hijacked; AI itself could be captured by replacement elites or foreign interests.

• Odds (by 2100): 20–40% for AI keeping some degree of order or prosperity; <10% for AI aligned robustly with original Western ethos for multiple generations.

• Rationale: This is the most “realistic” path if technological alignment succeeds—but still leaves core population/cultural replacement unaddressed.

4. Sociopolitical Reset (“Nationalist Restoration,” “Reciprocal Immigration,” etc.)

• Logic: Restore trait-based immigration, strict reciprocity, and unapologetic boundaries (either through political revolution or institutional collapse and rebuilding).

• Benefits: Directly addresses demographic and incentive mismatches; the only historical example of societies escaping the “extraction” trap.

• Risks: Politically toxic; would require mass consensus, or emerge from crisis; risks civil conflict or secession.

• Odds (by 2100): 10–20% for partial reset (some Western states); 1–2% for full civilizational reversal.

• Rationale: The only path with strong precedent in history—but least likely under current Western elite consensus.

5. Separation or Colonization (“Mars Option,” New Frontiers)

• Logic: If Earth’s high-trust equilibrium is irreversibly lost, a splinter of the population “leaves”—either via physical colonization (space, Mars, sea-steading) or digital/AI societies.

• Benefits: Preserves the evolutionary, cultural, and institutional package elsewhere, even if lost on Earth.

• Risks: Technically daunting, ultra-expensive, and only available to a tiny minority. Replicates founder effects but may just “replay” the same selection story.

• Odds (by 2100): <1% for Mars/space; 5–10% for digital frontiers or seasteads.

• Rationale: Only becomes realistic if all else fails—but likely to attract the highest-IQ, most future-oriented “remnant.”

References and Further Reading

A rigorous, data-driven understanding of Western societal decline—and the evolutionary, genetic, and cultural mechanisms behind it—requires engaging with a wide range of sources.

Below are foundational books, papers, and data repositories for further research.

Foundational Works on Evolution, Gene–Culture Coevolution, and Trait Distributions

1. Joseph Henrich — The WEIRDest People in the World (2020): The gold standard on how Western psychology and institutions evolved.

2. Gregory Clark — A Farewell to Alms (2007); The Son Also Rises (2014): Deep dives into social mobility, selection, and behavioral filtering in England and Europe.

3. Peter J. Richerson & Robert Boyd — Not By Genes Alone: How Culture Transformed Human Evolution (2005): Classic on gene–culture feedback.

4. Kevin Laland et al. — “Gene–Culture Coevolution in Human Evolution” (PNAS, 2011): Definitive review paper on the coevolution framework.

5. Norbert Elias — The Civilizing Process (1939): How Western norms of self-control, empathy, and cooperation took root.

6. Peter Frost — Essays and peer-reviewed research on European selection pressures, criminal culling, genetic pacification, and the evolutionary emergence of high trust and universalist morality.

On Blank Slate Denial, Behavioral Genetics, and Institutional Failure

7. Steven Pinker — The Blank Slate (2002): The destruction of “all groups are the same” dogma.

8. Nicholas Wade — A Troublesome Inheritance (2014): Argument for the role of population genetics in human social outcomes.

9. Jonathan Haidt — “Why Universities Must Choose One Telos: Truth or Social Justice” (Heterodox Academy, 2016): Institutional consequences of prioritizing “equity” over reality.

Institutional Capture, Parasitism, and Observable Outcomes

10. Heather Mac Donald — The Diversity Delusion (2018): Critique of how DEI and “equity” have captured American institutions.

11. Robert Putnam — Bowling Alone (2000); “E Pluribus Unum” (Scandinavian Political Studies, 2007): Declining social capital and the effect of diversity on trust.

12. Peter Turchin — Ages of Discord (2016): The demographic-institutional feedback loops of elite overproduction and collapse.

Empirical Data, Policy, and Contemporary Reporting

13. OECD Social Trust Indicators: Cross-national trust and social capital statistics.

14. UK ONS (Office for National Statistics): Benefits, migration, and demographic breakdowns.

15. World Bank Remittance Data: Outflows by country and group.

16. Heritage Foundation: Fiscal impact analyses of migration and welfare.

17. City Journal (Manhattan Institute): Reporting on NGOs, migration, and urban governance.

His warning was prescient:

“When 70% of Millennials say they are pro-socialist, we need to do better than simply dismiss them by saying that they are stupid or entitled or brainwashed; we should try and understand why.” (R)

Thiel’s prediction proved eerily accurate. In November 2025, democratic socialist Zohran Mamdani won the New York City mayoral race, bringing socialism to the doorstep of America’s financial capital.

The billionaire class is scrambling for explanations. Thiel offered one: housing and student debt.

“From the perspective of a broken generational compact,” he wrote, “when one has too much student debt or if housing is too unaffordable, then one will have negative capital for a long time and/or find it very hard to start accumulating capital in the form of real estate; and if one has no stake in the capitalist system, then one may well turn against it.” (R)

In a 2025 interview with The Free Press, Thiel expanded on this thesis.

He noted that strict zoning laws have been good for boomers (watching their properties appreciate) but terrible for millennials who can’t buy homes.

“If you proletarianize the young people,” he warned, “you shouldn’t be surprised if they eventually become communist.” (R)

Thiel is right about the phenomenon: The socialist drift among younger generations is real, measurable, and accelerating.

But his explanation (economic materialism) is almost certainly wrong. And his implied solutions will either make the problem worse or have no meaningful effect in solving the problem.

Quick rant: We need to fix the housing market and student debt fiasco. The best way to fix these isn’t with blanket handouts and stupidity: (1) YIMBY (accomplishes nothing if you don’t allow organic SES sorting and don’t severely punish criminals) and (2) Forgive all student loans (just keeps the problem going… kids keep taking out mega loans, selecting moronic majors at expensive universities, universities keep charging more and end up bloated with useless staff/majors, then gov bails students out to sustain the university Ponzi. This dynamic needs to be obliterated).

My solutions to those specific problems?

1. Housing: Deregulation + zero government subsidies (organic SES sorting) + brutal crackdowns on crime (you can’t have “YIMBY” without cutthroat law enforcement or you just get flight of the competent).

2. Student loans: Zero government loans (private lenders only). Private lenders won’t give out loans unless applicant has high test scores (e.g. SAT/ACT/GPA) and selects a smart major. This fixes pricing and bloat and nukes most garbage majors out of existence. Why? University bloat cannot be sustained if the kids aren’t getting loans to pay exorbitant prices for degrees of all types (legitimate and bullshit). Universities will be forced by the market to cut dead weight… win-win. (Plus you won’t turn colleges into an extension of high school with test score inflation… Currently universities are incentivized to accept everyone to max out profits.)

Related: The U.S. Housing Crisis: YIMBY, Immigration, Singles

Part I: Democrats and Republicans are Far Detached from what Built America

Before diagnosing why young people are embracing socialism, we need to understand what we’re actually measuring.

The standard framing: right wing equals free markets, left wing equals big government— is directionally true but extremely misleading.

Both American political parties are extraordinarily far from what built the country from ground zero.

The Frontier Ethos (built America from nothing):

1. Minimal/no government beyond basic defense and courts

2. Low/no taxes

3. No welfare state whatsoever

4. “You work or you die” — literal survival pressure

5. Minimal regulations

6. Individual responsibility, not collective provision

7. Self-reliance as the core operating principle

8. Tough on crime and strong property rights

The settlers who crossed the Atlantic in the 1600s through 1800s operated under these conditions. There was no safety net. You made it or you didn’t.

This brutal filter selected for specific traits: delayed gratification, risk tolerance, self-reliance, distrust of authority, and willingness to accept consequences for your choices.

How far have BOTH parties drifted?

The Democratic drift is obvious and openly celebrated.

The party explicitly campaigns on expanding government:

Medicare for All, free college, student loan forgiveness, Green New Deal, universal pre-K, expanded child tax credits, wealth taxes.

The 2020 and 2024 platforms represented the furthest-left major party positions in American history. Democratic governance has brought welfare spending from 1.2% of GDP in 1964 to over 5% today. (R)

The Republican drift is less discussed but equally real. Consider what “conservative” now means:

1. Social Security: Created by FDR, opposed by Republicans. Now untouchable — Republican voters are its strongest defenders. Trump explicitly promised not to cut it.

2. Medicare: Created by LBJ, opposed by Republicans. Now untouchable. Trump ran on protecting it.

3. Farm subsidies: $30+ billion annually. Republican stronghold states are the primary beneficiaries.

4. Military spending: Over $800 billion annually, larger than the next ten countries combined. Republicans consistently vote to increase it. No focus on maximal efficiency.

5. Tariffs and industrial policy: Trump’s signature economic policies were protectionism and government intervention to favor specific industries — the opposite of free markets. Related: (1) Trump Tariffs and MAGA Socialism & (2) Trump’s Economic Delusions. (Even if you think tariffs aren’t smart now, they were consistent with the Foundational Ethos of America.)

6. Federal debt: Exploded under Reagan, Bush, and Trump. No Republican president since Coolidge has actually shrunk government.

The “small government conservative” barely exists in practice. Republican voters support Social Security and Medicare at rates similar to Democrats.

They support tariffs and protectionism. They oppose cutting military spending. The median Republican voter is not a libertarian—they’re a nationalist who wants government to help their people rather than other people.

Quantifying the drift:

1. Federal spending (% GDP) Frontier: <3% | 1960: 17% | Today: 24% (Trend: ↑ Both parties) (R)

2. Welfare spending (% GDP) Frontier: 0% | 1960: 1.2% | Today: >5% (Trend: ↑ Primarily D)

3. Population receiving benefits Frontier: ~0% | 1960: ~15% | Today: 49% (Trend: ↑ Both parties) (R)

4. Federal employees Frontier: <50k | 1960: 2.4M | Today: 2.9M (Trend: ↑ Both parties) (R)

5. Regulatory pages (Federal Register) Frontier: 0 | 1960: 14,000 | Today: 90,000+ (Trend: ↑ Both parties) (R)

6. National debt (% GDP) Frontier: <5% | 1960: 53% | Today: 120%+ (Trend: ↑ Both parties) (R)

The current alignment

Frontier Drift Index (FDI): Here’s a simple way to quantify how far each party is from frontier assumptions.

First, the U.S. is structurally far from “frontier government”: federal net outlays are about ~23% of GDP in the modern era. (R)

Second, party coalitions differ sharply on how much they want government to do:

• Dem/lean Dem: 74% for a bigger government, 76% say government should do more to solve problems, 82% say business regulation is necessary, 76% say government aid to the poor does more good than harm, and 72% say government should do more for the needy even if it increases debt (average ≈ 76% “big/activist government”). (R)

• Rep/lean Rep: 20% for a bigger government, 28% say government should do more, 33% say regulation is necessary, 34% say aid to the poor does more good than harm, and 33% say do more for the needy even if debt rises (average ≈ 29.6%). (R)

FDI formula (transparent and repeatable):

FDI = 70% institutional drift + 30% coalition drift, where institutional drift is approximated as 1 − (3% / 23%) ≈ 87% (frontier baseline ~3% vs modern ~23%).

1. Republicans ≈ 0.70×87 + 0.30×29.6 ≈ ~70% drift

2. Democrats ≈ 0.70×87 + 0.30×76 ≈ ~84% drift

This supports the idea that the Democrats are further out of touch with the original American ethos (what built it up from ground zero).

Republicans are directionally closer to frontier values on:

1. Tax rates (favoring lower)

2. Regulations (favoring fewer)

3. Law and order (favoring more policing)

4. Welfare expansion (generally opposing)

5. Government size rhetoric (if not practice)

6. Individual vs. collective responsibility framing

7. Market solutions vs. government solutions

Democrats are directionally further from frontier values on:

1. Explicit redistribution policies

2. Law, order, property rights, policing (favoring fewer police, softer sentencing)

3. Expanding government programs

4. Collective provision framing

5. Viewing inequality as inherently unjust

6. Trusting government to solve problems

Party labels are contingent, not permanent.

“Republican” and “Democrat” are just labels for the two major parties in a winner-take-all electoral system.

The coalitions they represent have changed dramatically over time:

• Republicans were the party of Lincoln and Reconstruction; Democrats were the party of the Confederacy and Jim Crow

• Democrats were the party of urban machines and working-class Whites; now they’re the party of educated professionals and minorities

• Republicans were the party of free trade and internationalism (Bush); now they’re the party of tariffs and “America First” (Trump)

The parties could realign again. If Republicans become fully populist-nationalist (embracing industrial policy, tariffs, and entitlement protection) while Democrats become the party of technocratic neoliberalism, the “closer to frontier” designation could flip.

What matters is the underlying positions, not the party label.

I’m not claiming politics reduces to one axis. I’m talking about a Frontier-aligned bundle — a cluster of positions that built America up from ground zero:

1. Less redistribution / smaller welfare state

2. Lower taxes / less fiscal extraction

3. Fewer regulations / more market allocation

4. Tough-on-crime / high enforcement / strong property rights

5. Personal responsibility framing (choices have consequences)

In a two-party, winner-take-all system, voters don’t get to vote these dimensions separately, so I’m using Republican vs. Democrat voting as an imperfect but practical proxy for which coalition is closer to that frontier bundle in the current era.

Currently in 2025 Republicans are significantly more aligned with the foundational ethos of America than Democrats (even if both remain far detached).

Labels can flip; what I’m tracking is the bundle.

Note: Social conservatism (aligning with most Hispanics) is a separate dimension and can conflict with libertarian/frontier ideals when it demands state control over private life. That’s another reason party voting is an imperfect proxy: social issues can cross-cut the frontier bundle.

Part II: Why Thiel’s Explanation Fails

Hispanic voters consistently support the party offering more redistribution and government provision, regardless of label.

For 50+ years, that’s been Democrats. If Republicans became the redistribution party, Hispanic voters would likely follow — as they did in Latin American countries where right-wing parties embraced populist economics.

The voting pattern reveals the preference. The preference is for redistribution. The party label is incidental.

California Case Study

If housing unaffordability and student debt were driving socialist sentiment, we’d expect the political shift to follow the affordability crisis. It didn’t.

California provides the clearest case study.

1. California was close to a Republican presidential lock for decades: Republicans carried California in 9 of 10 elections from 1952 through 1988 (the lone exception was LBJ’s 1964 landslide).

2. Then the state flipped in 1992 and became a permanent uniparty lock for Democrats. (R)

3. Republicans held 25 of 52 U.S. House seats in California in 1994. Today they hold just 9 of 52. (R)

This transformation tracked demographic change, not housing prices.

• Between 1980 and 2000, California shifted from two-thirds White to about 47% non-White, with the bulk of the increase in the Latino community. (R)

• Non-Hispanic Whites went from 76.3% of California’s population in 1970 to 33.7% in 2022. (R, R)

We’ve observed that California shifted further away from the foundational American ethos and more toward evolutionarily hardwired Hispanic preferences (socialism, redistribution, softer on crime, decriminalization, etc.).

This means more redistribution tolerance, more regulatory comfort, and a different equilibrium on law-and-order (the are unfriendly to business. California has legalized many actions that would’ve been considered “criminal” in the past (no wonder crime is dropping!) and they don’t bother enforcing or reporting crimes in many cases now.

(So “crime is dropping”… but “Is Crime Actually Dropping”?)

Young People Are Objectively Well-Positioned

If material conditions drove political preferences, Gen Z should be the most pro-capitalist generation in history.

The S&P 500 has returned 655% since 2010 — roughly 13.86% annually, one of the three major bull runs in 150 years of market history. Gen Z is entering adulthood knowing nothing but a bull market.

The “Great Wealth Transfer” will see $84-$124 trillion pass from Boomers to younger generations by 2045. (R)

By any objective measure, young people today live better than kings of old — smartphones, streaming, air conditioning, modern medicine, abundant food, global connectivity.

If material conditions drove socialism, Gen Z and Millennials should be celebrating capitalism. Instead, they’re its harshest critics.

Related: Gen Z vs. Boomers: Who Had It Harder?

The Expectation Gap Is Perceptual

Thiel is right that there’s a gap between expectations and reality. But the gap is perceptual, not material.

Gen Z adults say they need $587,797 annually to consider themselves “successful”—10x their actual median income of roughly $57,000. (R)

Millennials report needing $180k+ per year to “feel happy.” (R)

This isn’t a housing problem. It’s an Instagram, TikTok, Snapchat perceptual distortion. Fixing housing won’t fix distorted perceptions.

We even have prominent figures like Michael Green claiming the U.S. poverty line is $140k!

They’ve fallen for the combo of the: MSM psychological trap, social media trap, and pathological empathy trap… against better judgment and common sense.

Read: No, $140k is Not the Poverty Line

Part III: My Hypothesis — Demographic Change (Fewer Whites, More Hispanics) is Causing Socialism in the U.S.

Socialist drift is driven by multiple factors, but they’re not equally important.

I rank order them by estimated impact.

PRIMARY

• 1. Demographic composition shift Why: Changes WHO the electorate is

MAJOR

• 2. Differential birth rates Why: Compounds #1 even without immigration

• 3. Unselected immigration Why: Accelerates #1 and #2

SIGNIFICANT

• 4. Media/academic capture Why: Provides messaging to receptive audience

• 5. Social media wealth distortion Why: Creates perceived deprivation

MODERATE

• 6. Entitlement ratchet Why: Locks in gains, prevents reversal

• 7. Hedonic adaptation Why: Makes prosperity feel insufficient

MINOR

• 8. AI/automation anxiety Why: New channel for old preferences (will likely increase as AI becomes more powerful)

The first three are PRIMARY + MAJOR because they change the composition of the electorate itself.

The rest are secondary (SIGNIFICANT + MODERATE/MINOR) — they shape how dispositions are expressed and reinforced, but they operate on an electorate whose baseline preferences are set by composition.

DRIVER 1: Demographic Composition Shift (PRIMARY)

This is the central driver. Everything else is secondary.

Younger generation: White replacement (higher diversity).

1. Non-White Youth: Gen Z alone (ages 18–27 in 2024) is ~40.8M eligible voters, and about 45% of that Gen Z electorate is non-White (including ~8.8M Latino, ~5.7M Black, ~1.7M Asian American, ~1.8M multiracial). (R) In 2020, Gen Z’s first eligible cohort was already only 55% non‑Hispanic White and 22% Hispanic.

2. Hispanics = fastest-growing large bloc of eligible voters: 36.2M eligible in 2024 (a new high 14.7% of all eligible voters) and they accounted for ~50% of eligible‑voter growth since 2020. Hispanic eligible voters are also younger than the electorate overall (only 33% are age 50+ vs 48% overall). (R)

Even if group voting margins stay within their historical ranges, the weighted average moves as older cohorts exit and younger, more diverse cohorts replace them.

And the pipeline after Gen Z is already <50% non-Hispanic White: in the 2020 Census, the under-18 population was 47.3% non-Hispanic White (vs 60.8% among adults), while Hispanics were 25.7% of under-18 (vs 16.8% of adults). (R)

The Voting Pattern IS the Evidence

Here is a fact that cannot be disputed:

Hispanics have never majority-voted Republican in any presidential election since exit polling began.

Not once. Not for Reagan. Not for either Bush. Not for Trump at his historic high point in 2024.

The historical record shows Hispanics voting 57%+ Democratic consistently since 1984.

This pattern holds across:

1. Different Republican candidates (Reagan, Bush, Romney, Trump)

2. Different economic conditions (recessions, booms, stagflation, growth)

3. Different issues (immigration-focused campaigns vs. not)

4. Different states (California, Texas, Florida, Arizona, Nevada)

5. Different generations (immigrant, second-generation, third-generation+)

What about the 2024 Presidential Election (Trump vs. Harris)?

The 2024 American Electorate Voter Poll: Latino Voters by State. (R)

1. California: Harris 66% vs. Trump 33%

2. Texas: Harris 60% vs. Trump 38%

3. Florida: Harris 43% vs. Trump 56%

4. Arizona: Harris 63% vs. Trump 36%

5. Nevada: Harris 64% vs. Trump 35%

Trump’s 2024 performance—widely celebrated as a breakthrough—got him between 42% of the Hispanic vote (R) and 46% of the Hispanic vote (R) — depending on the poll you use. This was predictable.

• Gender split / “toughness” branding: AP-NORC found Hispanic men more likely to see Trump as “tough enough,” while Hispanic women were much warmer on Harris.

• Masculinity-coded campaign strategy: AP’s VoteCast takeaways explicitly describe Trump leaning into hypermasculinity and targeting young men.

• Populism + economy salience: Edison notes Hispanics/Latinos (esp. men) naming the economy at high rates; Brookings deep dive also emphasizes cost-of-living salience and outreach dynamics.

50 years of revealed preferences.

1. Actual voting behavior with actual consequences. Across different candidates, issues, economic conditions, and states. The direction is consistent.

2. This is not a small sample. This is not a fluke. This is the most robust dataset we have for measuring group political preferences.

3. What would we expect if the preference were environmental — shaped by party behavior, historical accident, or cultural factors that could shift? We’d expect drift over 50 years. Economic conditions changed dramatically.

4. Candidates ranged from moderate (McCain, Romney) to populist (Trump). Issues emphasized varied from immigration restriction to immigration reform.

Within the young electorate, the racial splits remain large.

According to Tufts Civic Engagement Center, in 2024 (R):

1. Black youth: 74-24 Harris (Dem)

2. Latino/Hispanic youth: 57-40 Harris (Dem)

3. Asian youth: 72-23 Harris (Dem)

4. White youth: 54-44 Trump (Rep)

This debunks the idea that “all young voters skew Democrat.” Even when they do, the magnitude of political preference differs between racial/ethnic groups.

Currently, “Republican” is the party more aligned with frontier values — lower taxes, less redistribution, law enforcement, more individual responsibility.

Pew shows these “role of government” preferences vary sharply by race/ethnicity even before you get to party labels:

1. White adults (54%) are most likely to say government is doing too many things better left to businesses/individuals vs. Hispanic adults (29%) say the same. (R)

2. Even inside the GOP coalition, Pew finds Hispanic Republicans are far more pro-government than White Republicans: 58% of Hispanic Republicans say government should do more to solve problems vs. 21% of White Republicans. (R)

The parties could theoretically realign, and labels are contingent (as was mentioned earlier). But for the past 50 years, consistent Democratic voting reveals consistent preference for the more redistributive option.

The burden of proof is on those claiming the pattern is environmental or cultural.

Hispanics have never majority-voted for the less-redistributive party. The counter-evidence doesn’t exist.

Genetic Dispositional Differences (Mean/Median)

Important: (For the idiots out there.) This does NOT mean every Hispanic votes against Frontier Aligned values and is a Democrat. This is about evolved trait distributions (means, medians, tails)… meaning on average/median.

Hispanics prefer socialism, softer on crime policies, larger government, etc.

This is observed: (1) historically and (2) presently in (3) the U.S. and (4) global Hispanic/Latino diaspora.

We know from twin studies that political orientation is at least partly heritable. Based on the fact that we observe consistent preferences in the global diaspora AND within the U.S. for Hispanics — and we know populations have different evolved genetic traits — it’s fair to assume these preference distributions are mostly stable unless you get a highly-selected population. (R)

The pattern suggests dispositional (genetic) tendencies that favor:

1. Collectivism over individualism. Extended family networks, community orientation, group loyalty over abstract principles. This translates politically to comfort with collective provision.

2. Social conservatism combined with fiscal liberalism (i.e. “SCAFFLE”). Traditional values around family, religion, gender roles—but also expectation that government should provide economically. This is the modal Hispanic voter profile. They may oppose abortion while favoring wealth redistribution. When they do vote Republican, it’s for populist-nationalist candidates (Trump), not free-market conservatives (Romney).

3. Present-orientation over future-orientation (faster life strategy). What can government do for me and my family now, rather than abstract arguments about how free markets produce prosperity over decades. The appeal of immediate, concrete redistribution over abstract, long-term optimization.

4. Lower engagement with second-order effects. Understanding “tax the rich” is easy. Understanding “wealth inequality drives innovation which creates prosperity for everyone” requires abstract reasoning about systemic effects that aren’t intuitive. The first argument wins because it’s simpler.

This isn’t speculation. It’s observable in the global pattern.

The Global Hispanic Diaspora Pattern

This preference pattern isn’t unique to the United States.

It appears everywhere Hispanic populations are politically organized:

• Venezuela: Voted for Chavez’s socialism repeatedly until economic collapse.

• Argentina: Repeated cycles of Peronist redistribution → economic crisis → brief reform → back to Peronism.

• Mexico: PRI dominated for 70 years with corporatist redistribution; current AMLO/Morena government is explicitly redistributionist.

• Bolivia, Ecuador, Nicaragua: Similar patterns.

The pattern is as follows:

Favor socialist/redistributive policies
               ↓
Policies fail and create economic disaster
               ↓
Emigrate from the disaster
               ↓
Vote for similar policies in the new country

Not a single Hispanic-majority country has ever sustained frontier-style capitalism.

The preference travels with the population.

The Selection Exception (And Why It Proves the Rule)

Cuban Americans vote more Republican (56-41 for Trump in Florida 2024). Venezuelan Americans similarly lean right.

This is a selection effect. People who fled Castro’s communism were self-selected for anti-communist preferences. The act of leaving filtered for ideology.

But this exception proves the rule for two reasons:

1. First, they’re statistically irrelevant nationally. Cuban Americans are roughly 2 million people out of 36+ million eligible Hispanic voters—approximately 5% of the Hispanic electorate. They can swing Florida but are a rounding error nationally. Citing Cubans as evidence against the broader pattern is innumerate.

2. Second, and more importantly, the selection effect doesn’t persist. The children of Cuban refugees are reverting to the Hispanic mean. Second-generation Cuban Americans—children of people who literally fled Castro—have been documented preferring Democratic and Socialist candidates. (R)

If only Whites voted, Mamdani would’ve likely lost (45%).

Predictably, Mamdani won with Blacks (57%), Hispanics/Latinos (52%), and Asians (62%).

This is devastating to the “cultural transmission” counter-argument. If anti-communist values were effectively transmitted culturally, second-generation Cuban Americans would maintain those patterns. Yet this doesn’t seem to be the case.

There’s evidence of this in other racial/ethnic groups too (parents flee socialism/communism → kids vote for socialism/communism).

1. The City NYC: Their Families Fled Soviet Socialism. Now They’re Knocking Doors for Mamdani.

2. NY Times: In Chinese American Families, There’s a Generational Split on Mamdani.

The selection effect only works on people who directly experienced socialism. Their children, not subjected to that selection pressure, revert to the baseline distribution.

This is exactly what you’d expect if political preferences are genetically dispositional rather than cultural. Selection can temporarily shift a subpopulation. But without continued selection pressure, reversion to the mean occurs in the next generation. If it were cultural, the values would transmit across generations. They don’t.

I know this isn’t what many people want to hear. Many enjoy telling themselves lies or embellishing the magnitude of assimilation to virtue signal “all racial/ethnic groups are like this!”

The Assimilation Paradox

Hispanic Americans are assimilating on many dimensions:

• Language acquisition: Second and third generation speak fluent English.

• Intermarriage: Rates increasing with each generation.

• Cultural markers: Music, food, holidays blending into mainstream.

But assimilation is NOT happening on voting patterns. Hispanics aren’t voting in ways that uphold the foundational ethos of America.

Multi-generational Hispanic Americans (families in the United States for three or four generations) still vote Democratic at similar rates to recent immigrants.