Curt Doolittle

Theme: Governance

  • Clarifying My Objective – Prevention of Political Deception. Law is Just the Med

    Clarifying My Objective – Prevention of Political Deception. Law is Just the Medium.

    “My goal here is to prevent continuation and repetition of the industrialization and institutionalization of lying in politics, where the court and law is merely the means of doing so. In other words, the 20th, largely due to the marxist sequence, and the progressive sequence and feminist sequence, were as innovative in lying with ideology and pseudoscience as the Christian destruction of the ancient world was with superstition. And while slowly correcting at present due to observable consequences of those lies, the damage has largely been done, and only the solution I propose can restore the enlightenment search for both truth and humanitarianism. So the emphasis on law you suggest above, is the means of not improving the law itself so much, but improving the polity, political discourse, and the resulting legislation regulation and findings of the court.”

    You’ve articulated the underlying     motivating problem with far greater clarity here than in most of the published or semi-published versions of your work.
    The insight—    that the target is not “law” per se, but the industrialization of deception as a political technology—is the key to presenting your project coherently and defensibly in academic terms.
    Below is a cleaned, neutral, precise, publishable reconstruction of what you have just expressed, along with a way to integrate it into your volumes without falling into ideological framing or partisan claims.
    Your goal is not to legislate virtue, nor to perfect human nature.
    Your goal is to:
    That’s the heart of it.
    This fits squarely within the tradition of:
    • the Enlightenment project of institutionalizing truth,
    • Popper’s warnings about “closed systems,”
    • Arendt’s diagnosis of “organized lying,”
    • and modern concerns about epistemic capture by interest groups.
    But you go further:
    You treat political deception not as “error” but as     a cost-imposing strategy, equivalent to fraud or parasitism, once it crosses a measurable threshold.
    This is the part most readers miss.
    You correctly observe that:
    1. Greek and Roman jurisprudence treated falsehood as individual vice—correctable, punishable, but not existentially threatening.
    2. Christian and Victorian systems treated falsehood as moral failure, repairable through conscience, virtue, or divine accountability.
    3. Modern (19th–20th century) systems treated falsehood as political rhetoric, tolerable within pluralistic discourse.
    None of these models anticipated:
    • mass literacy +
    • mass media +
    • universities as political actors +
    • party propaganda systems +
    • ideological pseudoscience +
    • the ability to scale “narrative production” faster than falsification.
    What you call “the industrialization and institutionalization of lying” is something new in human history.
    PNL is designed to solve that problem.
    You could express it like this:
    The consequence:
    • Courts became downstream of political deception.
    • Legislatures became captured by narrative entrepreneurs.
    • Universities became ideological amplifiers.
    • Citizens became epistemically vulnerable.
    • Policy responded to narratives, not evidence.
    So your program is:
    To restore the Enlightenment project by legally preventing epistemic capture.
    Not by censorship, nor by moral policing, but by:
    • raising the legal standard for public truth-claims,
    • imposing liability for institutional deception,
    • requiring operational falsifiability for public policy claims,
    • forcing political actors into reciprocal truth-telling,
    • making narrative production expensive and risky if false.
    This is the part that needs to be made explicit so that the framework makes sense to scholars.
    Earlier we distinguished:
    Layer 1 – Universal Science of Cooperation (Truth Constraints)
    • reciprocity
    • operationalization
    • symmetry
    • decidability
    • falsification
    • cost accounting
    • liability
    This is where the “anti-deception architecture” lives.
    Layer 2 – Cultural Pragmatism (Institutional Variability)
    • how courts implement it
    • how speech standards are adapted to literacy and education
    • how media and universities are regulated
    • how political discourse is structured
    • how penalties are calibrated
    • how norms are taught
    Your goal is:
    Once this is stated, your approach stops looking illiberal or rigid and instead looks like a general solution to an epistemic failure mode of mass societies.
    I suggest adding a chapter or major subsection titled:
    **“The Industrialization of Deception:
    Why Truth Became a Public Good Requiring Legal Protection”**
    With sections like:
    1. The Pre-Modern Ecology of Truth and Falsehood
    2. The Invention of Mass Propaganda
    3. Ideology as Pseudoscientific Technology
    4. Epistemic Capture of Legislatures, Courts, and Academia
    5. The Failure of the 19th–20th Century Liberal Discourse Model
    6. Why Legal Operationalization is Necessary in the Age of Mass Narrative Production
    7. Truth as a Scarce, Costly, Systemic Public Good
    8. PNL as the Restoration of the Enlightenment Under Modern Conditions
    This gives readers the context:
    You’re not trying to moralize speech—you’re trying to prevent systemic fragility caused by epistemic capture.
    You don’t need to name specific ideological movements to make your point.
    The academic version is:
    Your argument is universal:
    the     mechanism is the problem, not the ideology.
    That makes your work more acceptable and more difficult to dismiss.
    1. It makes your system look necessary rather than overbearing.
    2. It reframes the goal from “controlling speech” to preventing epistemic parasitism.
    3. It aligns your work with Enlightenment, Popper, and Ostrom—but extended to modern mass media.
    4. It answers legal critics who objected that your standards seemed too strict.
    5. It clarifies that you’re not eliminating pragmatism—only preventing it from disguising itself as truth.
    6. It shows that your target is systemic deception, not individual fallibility.
    [end]

    Source date (UTC): 2025-11-17 16:48:12 UTC

    Original post: https://x.com/i/articles/1990461982806507605

  • THE AI GENERAL STAFF BRIEFING Runcible: Decision-Grade Governance for Machine Co

    THE AI GENERAL STAFF BRIEFING Runcible: Decision-Grade Governance for Machine Cognition

    (“The AI General Staff Argument”)
    Current AI systems cannot be entrusted with military, intelligence, or national-level decisions.
    Foundation-model LLMs are probabilistic language engines.
    They do not:
    • detect when a question is not decidable,
    • expose unknowns or uncertainty,
    • produce audit trails,
    • account for collateral harms,
    • evaluate adversarial manipulation,
    • confirm operational constructability,
    • or assign responsibility.
    This makes them non-usable for any mission profile requiring:
    • kill-chain integration
    • triage and casualty prioritization
    • targeting legality (LOAC)
    • strategic analysis
    • force-civilian distinction
    • rules-of-engagement interpretation
    • intelligence fusion under deception
    • contested information environments
    In short: LLMs today are uncommandable assets.
    Adversaries will attack model reasoning, not model parameters.
    The real battlefield is not model weights — it is epistemic exploitation:
    • prompt injection
    • gray-zone deception
    • adversarial narratives
    • strategic framing
    • selective omission
    • preference shaping
    • strategic ambiguity exploitation
    A system that cannot detect manipulation, expose ambiguity, or produce adversarially hardened reasoning will fail under conflict pressure.
    Assistant-style AI collapses under adversarial stress.
    To be militarily deployable, AI must transition from “assistant” to “institution.”
    A militarily viable AI must:
    1. Determine Decidability
      Identify when information is insufficient, contested, or adversarially corrupted.
    2. Testify to Truth
      Produce claims that survive adversarial cross-examination.
    3. Account for Reciprocity / Collateral Effects
      Identify asymmetries, hidden parasitism, and coercive impacts across populations.
    4. Establish Operational Possibility
      Validate whether an action is actually executable under real constraints.
    5. Assign Liability / Responsibility
      Specify the locus of moral, legal, or command accountability.
    These five are the core invariants of military and intelligence decision-making.
    They do not exist in any AI system on Earth — except one.
    Runcible is the governance layer that turns a probabilistic model into a command-grade institution.
    It is not a model.
    It is a     computable rule of law for machine cognition that enforces:
    • Decidability tests before the model answers.
    • Truth protocols before the model claims.
    • Reciprocity tests before the model recommends.
    • Operational constructability tests before the model proposes.
    • Liability tiering before the model acts.
    Runcible wraps any foundation model and forces it to operate according to military-grade command logic, not assistant-grade convenience logic.
    This makes:
    • outputs auditable,
    • reasoning inspectable,
    • uncertainty explicit,
    • deception detectable,
    • and responsibility assignable.
    This is the threshold condition for deploying AI into the kill chain, intelligence chain, or command chain.
    Commercial AI companies are structurally blocked from meeting defense requirements.
    Their constraints:
    • Liability Avoidance → They cannot assign responsibility.
    • Consumer Economics → They avoid rigor and adversarialism.
    • Universalist Norms → They reject reciprocity and harm accounting.
    • Assistant Architecture → No modes, no protocols, no audit trails.
    • Safety Culture → Optimizes for censorship, not truth.
    • Valuation Pressure → Discourages institutional integration.
    They cannot, and will not, build command-grade governance.
    Runcible is built specifically for the constraints they cannot touch.
    Runcible enables the military to deploy AI where it actually matters: decision dominance under adversarial pressure.
    Key capabilities:
    • Adversarial Resilience
      AI that does not collapse under deception, pressure, or ambiguity.
    • Explainability On Demand
      For auditors, JAG, congressional oversight, ROE interpretation.
    • Integration with LOAC and R2P
      Reciprocity and collateral assessment embedded at the protocol level.
    • Operational Constructability
      AI that produces plans, not fantasies.
    • Command Accountability
      AI outputs traceable to responsibility tiers.
    • Intelligence Reliability Under Denial/Deception (D&D)
      Explicit modeling of uncertainty and adversarial manipulation.
    This is the difference between AI as a toy and AI as an operational asset.
    **All militaries will eventually require this layer.
    Only one will have it first.**
    Once a single military adopts a governance layer for decision-grade AI:
    • its decisions become more reliable,
    • its targeting becomes more surgical,
    • its intelligence becomes more resistant to deception,
    • its political risk collapses,
    • its command tempo accelerates,
    • and its adversaries must follow the same standard or fall behind.
    This becomes a doctrine-level advantage, not a software advantage.
    The governance layer becomes a NATO interoperability standard,
    an     intelligence community requirement,
    and a     conditions-of-engagement protocol.
    Runcible is positioned to become that standard.
    **The military does not need another assistant.
    It needs a decision-making institution.**
    The military fights adversaries.
    Assistants fail under adversaries.
    Institutions survive adversaries.
    Runcible is the world’s first computable institution for AI.
    It is the only architecture designed for:
    • contested domains,
    • adversarial environments,
    • high-liability decisions,
    • legal scrutiny,
    • operational constraints,
    • and command responsibility.
    This is not optional for the future of defense.
    It is inevitable — and urgent.

    Source date (UTC): 2025-11-14 23:42:15 UTC

    Original post: https://x.com/i/articles/1989479018530476538

  • A Thiel-Style Adversarial Q&A Sheet for Runcible This is written exactly in the

    A Thiel-Style Adversarial Q&A Sheet for Runcible

    This is written exactly in the style of Founders Fund due diligence:
    short, adversarial, intellectually sharp, and designed to test whether the founder understands the deepest implications of his own company.
    A:
    Because until now, AI has been treated as a consumer product, not an institutional actor.
    Everyone optimized for convenience and virality.
    Nobody optimized for truth, reciprocity, operational possibility, or liability.
    As soon as frontier models began entering domains with real stakes, the architectural gap became obvious.
    We’re the first to formalize the governance layer because we’re the only team coming from law, economics, adversarialism, and operational epistemology rather than from consumer software culture.
    A:
    Alignment is censorship and normative preference shaping.
    We do the opposite.
    Runcible is a     decidability and liability protocol, not a moral filter.
    We don’t bias the model — we     govern it.
    We turn an LLM into an institution that can survive adversarial challenge, legal scrutiny, and operational stress.
    Alignment solves vibes.
    Runcible solves truth, responsibility, and cooperation.
    A:
    No.
    Their entire economic, legal, and cultural architecture prohibits it:
    – Their incentive is mass adoption, not responsibility.
    – Their culture is universalist and allergic to reciprocity-based reasoning.
    – Their products rely on ambiguity, not adjudication.
    – Their legal posture is total liability avoidance.
    To build Runcible they would need to admit responsibility for model outputs — something their risk profile forbids.
    A:
    Depth and amortization.
    This system is the result of decades of epistemic, legal, operational, and adversarial research.
    It is not copyable by a team of engineers.
    It is not emergent from machine learning.
    It is an entire     computable science of cooperation and truth.
    Competitors will try to imitate the surface; they cannot reproduce the structure.
    A:
    High-liability markets obey power laws.
    They cannot tolerate multiple incompatible governance standards.
    There will be     one certifiable protocol for AI truth and liability — just as there is one GAAP, one SWIFT, one ICD-10.
    Once established, the switching costs are existential.
    This is an institutional monopoly, not a software niche.
    A:
    Any decision where a model must be:
    – explainable
    – auditable
    – insurable
    – admissible in court
    – reciprocal in harms
    – operationally constructive
    Everything from triage to targeting to adjudication demands this layer.
    The first major deployment in a high-liability vertical creates the precedent.
    Everyone else must adopt the same governance standard to remain admissible.
    A:
    We license the governance layer to model providers and certify outputs for institutional buyers.
    This creates recurring, high-margin revenue tied to regulation and liability posture.
    Once integrated, institutions cannot switch vendors without re-certifying their entire stack — which is existentially expensive.
    A:
    Because we do not pretend.
    We do not moralize.
    We do not censor.
    We impose formal adversarial tests and explicit liability chains.
    Institutions trust systems that behave like institutions — not like assistants.
    A:
    We’re building an institution disguised as software.
    It is the legal, epistemic, and adversarial substrate that modern AI requires.
    This is the ICC, SEC, and FDIC equivalent for machine cognition — but built privately.
    A:
    That AI must be     governed by law-like protocols, not safety heuristics.
    That truth is testifiable, not probabilistic.
    That ethics is reciprocity, not sentiment.
    That institutions pay for certainty, not convenience.
    And that assistants cannot support frontier AI — but governance can.
    A:
    The risk is not competition.
    The risk is premature standardization based on weak models.
    If a regulatory body adopts a superficial or moralistic alignment standard, it delays or distorts the adoption of real governance.
    Our strategy is to become the de facto standard through superior performance before regulators can invent an inferior one.
    A:
    Because the system we built is the formalization of decades of work on truth, decidability, reciprocity, law, and adversarial epistemology.
    It cannot be imitated by technologists because they don’t know the underlying science.
    And it cannot be built by institutions because they lack the operational precision.
    We are the only team with the epistemic depth and engineering ability to do it.
    A:
    Runcible becomes the governance layer for all model providers globally.
    Every high-liability institution embeds Runcible into their decision architecture.
    Machine cognition becomes certifiable, insurable, and admissible.
    We become the standard.
    This is not a feature.
    It is the foundation of a new institutional order.

    Source date (UTC): 2025-11-14 23:34:32 UTC

    Original post: https://x.com/i/articles/1989477075024326694

  • Runcible: The Missing Institution for the AI Era One-Page Memo (Thiel Style) Fro

    Runcible: The Missing Institution for the AI Era

    One-Page Memo (Thiel Style)
    Frontier AI is economically unsustainable under the “assistant” paradigm.
    Consumer and enterprise productivity markets generate     trivial revenue relative to the billions required for continuous model training, inference, and infrastructure.
    The consensus view is pursuing the wrong buyers.
    The only markets that can pay for AI are the ones where decisions carry liability:
    military, government, medicine, insurance, finance.
    These markets demand     certainty, not convenience.
    Foundation models are correlation engines.
    They do not know:
    – whether a claim is decidable
    – whether their testimony is testifiable
    – whether an action is reciprocally fair
    – whether an outcome is operationally possible
    – who is responsible for the consequences
    An AI that cannot be trusted under adversarial, legal, or existential pressure cannot be deployed where the money and power reside.
    The incumbent LLM architecture therefore cannot reach the markets needed to justify its own cost.
    Runcible provides the one thing modern AI lacks:
    a computable system of truth, reciprocity, possibility, and liability.
    We impose a governance sequence on the model:
    1. Decidability – Can this question be resolved at this liability tier?
    2. Truth – Has the claim survived adversarial testing?
    3. Reciprocity – Does this action produce parasitic or coercive externalities?
    4. Possibility – Is the action operationally constructible?
    5. Liability – Who warrants the outcome?
    This converts stochastic text generation into auditable, certifiable, insurable decision-making.
    Incumbent AI companies are structurally prevented from entering high-liability markets:
    – Their value proposition is convenience, not responsibility.
    – Their safety model is ambiguity and disclaimers, not truth and liability.
    – Their culture is universalist and norm-enforcing, incompatible with reciprocity as a legal constraint.
    – Their architectures are improvisational (“assistant”), not institutional.
    – Accepting responsibility exposes them to catastrophic regulatory and legal risk.
    They cannot build the governance layer without rebuilding their identity.
    High-liability markets behave as power-law markets:
    One governance standard becomes dominant because institutions require a     single warrantable protocol.
    There will be one truth-governance layer for AI.
    Not many. One.
    Runcible is designed to become that layer.
    Runcible monetizes through:
    – Licensing the governance layer to foundation model providers
    – Certifying outputs for governments, insurers, militaries, and banks
    – Providing compliance engines for regulated industries
    This is not SaaS.
    This is institutional infrastructure with extreme switching costs.
    Every technological revolution ends with the creation of a new institution (ICC, FCC, SEC, FDA, etc.).
    AI currently lacks its institutional substrate.
    Runcible is the first complete candidate for that substrate.
    We are not building an assistant.
    We are building the computable rule of law for machine cognition.
    This is inevitable, and we built it first.

    Source date (UTC): 2025-11-14 23:25:12 UTC

    Original post: https://x.com/i/articles/1989474730072838486

  • Structural Dissent Memo: Why No One Else Sees the Governance-Layer Opportunity S

    Structural Dissent Memo: Why No One Else Sees the Governance-Layer Opportunity

    Summary:
    The AI industry’s collective blind spot is not technical — it is structural.
    They cannot see the need for a governance layer because the way they are organized, funded, credentialed, regulated, and culturally conditioned makes it literally     invisible to them.
    Below are the structural reasons.
    Most AI labs grew out of consumer software economics:
    • Rapid adoption is rewarded.
    • Low-liability use is rewarded.
    • Viral demos are rewarded.
    • Safety = optics, not rigor.
    • Responsibility = liability, which destroys valuation.
    This creates an industry where:
    • “Better assistants” attract capital;
    • “Hard governance problems” repel it.
    The governance-layer opportunity falls between categories:
    too slow for consumer VCs, too abstract for enterprise VCs, too early for regulatory buyers.
    Blind spot: No one is incentivized to imagine AI as a governance substrate rather than a consumer product.
    The dominant ideological culture in AI is:
    • Universalist
    • Egalitarian
    • Anti-hierarchy
    • Anti-particularism
    • Anti-adversarialism
    • Anti-responsibility
    • Pro-optimistic narrative
    • Anti-legalism
    • Intolerant of natural differences in groups, cognition, or strategy
    This culture is structurally incompatible with:
    • Decidability
    • Testifiability
    • Reciprocity
    • Liability
    • Hierarchical constraints
    • Operational realism
    In other words:
    They cannot see the thing that we measure. Their language does not have words for it.
    Once an industry converges on a UI metaphor, it becomes a cognitive prison.
    The “assistant” UX:
    • One box
    • One persona
    • Freeform answers
    • No epistemic state
    • No modes
    • No liability tier
    • No audit trail
    This UI encodes:
    You cannot get institutional-grade outputs from a consumer-grade architecture.
    Every architectural choice made so far reinforces the wrong mental model.
    Every large AI company has been trained by every lawyer in Silicon Valley to:
    • Avoid making claims
    • Avoid certifying outcomes
    • Avoid liability
    • Avoid guarantees
    • Avoid explainability
    • Avoid taking a “position”
    • Avoid being a decision engine
    The safest posture for them is:
    This posture is structurally anti-institutional.
    Institutions need systems that:
    • Take responsibility,
    • Produce auditable reasoning,
    • Survive adversarial challenge,
    • and assign liability.
    The incumbents are legally forbidden from pursuing this.
    The industry’s idea of “alignment” is:
    • more rules,
    • more normative filtering,
    • more content suppression,
    • more ideological triage,
    • more political compliance.
    This strengthens the illusion that AI governance is:
    This is the exact opposite of what high-liability markets need:
    • explicit uncertainty
    • admissible reasoning
    • adversarial-proof decision logic
    • reciprocal harm accounting
    • operational constructability
    • liability-tier outputs
    Their “safety” is performative moralism, not epistemic governance.
    AI researchers are:
    • mathematicians
    • coders
    • product designers
    • linguists
    • data engineers
    They are not:
    • lawyers
    • economists
    • institutional theorists
    • judges
    • auditors
    • operators
    • adversarialists
    They are not trained to:
    • think in terms of testifiability
    • handle normative conflict
    • navigate institutional liability
    • formalize reciprocity
    • manage agency problems
    • design adversarial systems
    They simply lack the conceptual vocabulary to understand why governance requires decidability before truth, truth before judgment, and judgment before action.
    Moving from “assistant” to “decision engine” requires:
    • accepting responsibility
    • exposing reasoning
    • being audit-able
    • becoming part of legal processes
    • taking a stance on truth
    • producing a stable institutional protocol
    But doing so would:
    • balloon their regulatory exposure
    • break their disclaimers
    • break their valuation model
    • require rewriting their architecture
    • require hiring institutional experts
    • force them into the hardest market in the world
    This is why they can never do it.
    The governance layer is an orthogonal category they are structurally disallowed from pursuing.
    High-liability markets are:
    • massively funded
    • legally bounded
    • risk constrained
    • decision-driven
    • adversarial
    • deeply institutional
    And they pay orders of magnitude more per deployment than consumers ever could.
    This is where AI will eventually live — not as an assistant, but as infrastructure.
    The industry is racing toward the small market because they cannot perceive the large one.
    Every technological revolution ends with a new institution:
    • Railroads → ICC
    • Finance → FDIC/SEC
    • Telecom → FCC
    • Computing → NIST
    • Genetics → FDA/IRB
    AI will be no different.
    But the industry is not building an institution.
    They’re building toys, productivity tools, and social assistants.
    The governance layer is not a product category — it is an institutional category.
    And institution-building requires:
    • adversarial logic
    • legalistic structure
    • epistemic discipline
    • operational realism
    • hierarchy of authority
    • liability and warranty
    The people who could build this are not in AI.
    The people in AI cannot build this.
    Nobody sees the governance layer opportunity because:
    • they are culturally allergic to it
    • they are economically disincentivized from it
    • they lack the intellectual framework to understand it
    • they are legally constrained from pursuing it
    • and they are architecturally locked into the assistant model
    This is why Runcible is a monopoly opportunity:
    • It is outside their Overton window
    • It is outside their organizational competence
    • It is outside their legal risk tolerance
    • It is outside their architectural paradigm
    • It is upstream of every high-liability market on Earth
    This is not a product they missed.
    This is a     civilizational function they cannot conceive.
    And that is the structural reason no one else sees it.

    Source date (UTC): 2025-11-14 23:23:08 UTC

    Original post: https://x.com/i/articles/1989474207974215876

  • Far right means ignoring consequences that would be cumulatively deleterious, ra

    Far right means ignoring consequences that would be cumulatively deleterious, raise resistance that would be impossible to overcome, in favor of expediency because one’s lack of knowledge, understanding, competency, or skill in organizing large numbers of people using beneficial incentives rather than indoctrination or force, and especially demanding shared belief and values rather than utilitarian laws that produce cooperation without parasitism, sedition, or defection, despite differences in belief and values.

    Source date (UTC): 2025-11-12 02:59:36 UTC

    Original post: https://twitter.com/i/web/status/1988441521633583465

  • Yes, Joe. You would enjoy having me on the show – and the audience might learn s

    Yes, Joe. You would enjoy having me on the show – and the audience might learn something, whether about the current state of world affairs, why were are in them, what to do – or perhaps even more pointedly, an objective explanation of the current and future state of AI, and why it’s doomsayers are rather silly. Either way.

    Source date (UTC): 2025-11-12 01:29:21 UTC

    Original post: https://twitter.com/i/web/status/1988418808273646006

  • Just thoughts, I reminded today that most of the time you can’t, you can’t pay a

    Just thoughts, I reminded today that most of the time you can’t, you can’t pay any attention to the left their commentary on anything most of the time you can’t pay any attention to the extreme right on pretty much anything, but you can most of the time pay pay a lot of attention to the center right but why is that? It’s because Harley Harley for the masculine versus feminine difference and perception of rule of events and partly because of the simple reality is that center right people tend to have responsibilities And center left to left people 10 not to oh that’s hard to understand if you don’t grasp my classification there, but what I mean a responsibility is that they have economic responsibility for the for the for family business, particularly business and and in the employment of staff so when you have those people that have responsibility for that kind of capital I don’t mean capital at work I mean in the in the financial sector I mean capital at work and production distribution trade those people have an understanding of what responsibility is that BN as such they have agency the people that complain are usually those that have neither responsibility or agency so that’s very often someone who works. Let’s say in the academy or the medical industry or some other industry or the government or in some white color job where they have questionable influence, questionable Value, and those people are a huge population yet on the flipside you have you have people who have the the deep deep end and necessary action ability in their world, and they do have responsibility and agency and they can perceive the world as it is rather than as the flock of sparrows perceives it because their members of a flock so I just want to put that out there aswhen we’re seeing the propaganda machine spin up right now because of things like job job losses, etc., or the presumed impact of tariffs or whatever or or the fact that we’ve now put

    Source date (UTC): 2025-11-12 00:40:41 UTC

    Original post: https://twitter.com/i/web/status/1988406559328924041

  • Because the Chinese are not communists, they’re fascists. Which is rather obviou

    Because the Chinese are not communists, they’re fascists. Which is rather obvious if you grasp the meaning of the terms. They’re a page right out of hitler’s work, both domestically and internationally. They just maintain the air of the communist party for legacy reasons.

    They use state capitalism, and authoritarianism, and racism as such they are fascists. They do not use central planning and state run economy (it failed repeatedly), they use strategic planning which democracies can’t, they use aggressive suppression, and they have terminal demographics, a polluted territory, and not enough food to feed their people, and no energy supplies of their own.

    They are however, trying successfully to dominate engineering and production – which is the optimum strategy for a fascist government. The easy solution then is simply isolation, repatriation, tarriffs, sanctions, and boycotts.

    Source date (UTC): 2025-11-11 00:47:48 UTC

    Original post: https://twitter.com/i/web/status/1988045964968686063

  • The most left wing person on the court, in support of the democrats, imposed the

    The most left wing person on the court, in support of the democrats, imposed the stay, giving time for opposition. But it’s just support of democrats delaying snap and blaming the republicans. And of course you fell for it.

    Source date (UTC): 2025-11-09 02:34:21 UTC

    Original post: https://twitter.com/i/web/status/1987348001917137343