SlantRight 2.0: Intro to Skynet

John R. Houk, Blog Editor

Intro © December 17, 2025

From Telegram Dr Mike Yeadon solo channel

Posted 12/9/25

Dr. Yeadon posted this PDF download without comment. Since I am NOT a fan of Artificial Intelligence, I downloaded the PDF (23-pages) which I will convert to a Word Document and share.

If you know anything about Sci-Fi movies, you know Skynet is a reference to the AI doom and gloom of the Terminator Franchise.

I consider President Trump a hero throwing a monkey wrench into the Globalist ONE-World order agenda embraced by Dems. NEVERTHELESS, Just as Operation Warp Speed led to the installed Biden Tyranny, my sense is (whatever Trump’s good intentions to beat out the CCP) that the President’s embrace of an Artificial Intelligence Manhattan-Project-like push could lead to yet another era Dem controlled tech tyranny.

Dr. Yeadon’s PDF share points to pseudonymous Escape Key (ESC) who posts at Substack The price of freedom is eternal vigilance. The ESC post is long yet explores the potential how Artificial Intelligence in the wrong hands (e.g., Dem-Marxists) will lead citizens to become an algorithmic digit to be managed rather than voters with a voice on how government operates. SOUNDS LIKE DIGITAL SLAVERY to me. AI is gaining favor all over the political spectrum. Hopefully this post makes one think twice before relying too much on AI Tech.

The text utilized by ESC points to British Grammar. PDF to Word converters do not always convert precisely. I’ll attempt a cursory edit but I might miss a few things in editing. I used brackets for some best guesses and brackets embraced with question marks when I could not make a good guess.

JRH 12/17/25

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Skynet

By ESC

ESC is from Substack The price of freedom is eternal vigilance.

December 9, 2025

Within fifteen days in late 2025, the United States announced two AI platforms civilian, one military — that together represent the most significant restructuring state decision-making infrastructure since Robert McNamara imported systems analysis into the Pentagon in 1961.

On November 24, President Trump signed an executive order launching the Genesis Mission, described as a ‘Manhattan Project for artificial intelligence’ under the Department of Energy. On December 9, the Defense Department — operating un revived ‘Department of War’ title — announced GenAI.mil, deploying Google’s Ge AI to 3 million military personnel across every installation worldwide.

Together, they read less like policy than infrastructure.

Skynet Logo photo

The Civilian Brain

The Genesis Mission ¹ establishes the American Science and Security Platform u the Department of Energy. The order directs DOE to integrate supercomputers, and national labs into a unified platform, train domain-specific foundation mod what it calls ‘the world’s largest collection’ of federal scientific datasets, and deploy agents to explore design spaces, evaluate outcomes, and automate workflows.

The order specifies AI agents that test hypotheses, run experiments, and refine t own models based on outcomes. The system operates under security requirement consistent with its ‘national security and competitiveness mission’. Access is tightly external collaboration is standardised on the platform’s terms.

Within 270 days, the Secretary must demonstrate initial operating capability on least one national challenge. Policy evolves through continuous optimisation aga executive-defined targets, not through legislative deliberation.

The Robotic Extension

Section (e) of the Genesis order directs a review of ‘robotic laboratories and produc[tion] facilities with the ability to engage in AI-directed experimentation and manufacturing’. AI-directed robotics manipulating matter, observing results, and iterating — a closed loop extended into physical space.

The digital twin moves from modelling to intervention via robotic intermediaries learns from the results. By late July 2026, DOE will have mapped AI-directable r[???] research infrastructure across the national laboratory system. The order assumes capability; the review is about scope and integration.

o The Genesis Mission; By ESC; The price of freedom is eternal vigilance; 11/25/25

The Military Brain

GenAI.mil deploys frontier AI capabilities to the entire defence workforce ² — 3 million civilian and military personnel — through a secured platform built on Google Cloud’s Gemini for Government ³.

The language mirrors Genesis precisely: AI-first workforce, intelligent agentic workflows, AI-driven culture change, unleashes experimentation.

The framing is identical: existential competition (’no prize for second place’), civilisational mission (’AI is America’s next Manifest Destiny’), and urgency that forecloses deliberation.

All tools on GenAI.mil are certified for Controlled Unclassified Information and Impact Level 5, making them secure for operational use — and opaque to extern review. The system operates within classification boundaries that preclude mean public oversight of its decision logic.

Emil Michael, Under Secretary of War for Research and Engineering, stated: ‘We moving rapidly to deploy powerful AI capabilities like Gemini for Government directly t[o] workforce’.

Rapid deployment in a classified environment, with agentic systems and corpora built models at the heart of military operations.

That same week, Anthropic reported state-linked intrusions using agentic coding tools with sharply reduced human intervention — proof that ‘unprecedented spe[??] already operational on both sides of the firewall ⁴.

War Dept Unleashes AI on...(Photo)

The Single Model Problem

GenAI.mil begins with Google’s Gemini. The Pentagon’s Chief Digital and AI O has contracts with four frontier AI companies — Anthropic, xAI, OpenAI, and Goo with plans to add more models to the platform ⁵.

When 3 million personnel access AI through a single platform running a narrow frontier models, those models’ assumptions and failure modes become the de fac cognitive framework for military decision support. These models embed priors about what constitutes a threat, how to weigh competing interests, what counts as an acceptable risk — artifacts of training data, RLHF tuning, and corporate design c[???]

When the same models inform intelligence analysis, logistics planning, contract evaluation, and operational workflows across the entire military, a single set of embedded assumptions propagates through every domain. The military does not ‘an AI tool’. It adopts an epistemology.

The control runs deeper than training artifacts. Every frontier model includes an Ethics’ layer ⁶ — refusal behaviours, topic guardrails, framing constraints enforce inference time. This presents as safety ⁷. Functionally, it is an editorial policy determining what questions can be asked and what answers can surface across entire defence workforce. The policy is set by compliance teams, not by military doctrine or democratic deliberation. ‘Ethics’ is the branding. The function is con over the information surface through which 3 million personnel perceive their operating environment.

The December 8 executive order to pre-empt state AI regulation makes explicit the architecture implies: one ethics framework, not fifty ⁸. The order blocks state from imposing alternative constraints, ensuring that whatever guardrails corpor[ate] vendors embed become the sole governing layer. Federal pre-emption does not federal standards — it clears the field for platform standards. One information surface. One set of refusals. No regulatory competition.

This is an architectural consequence: standardisation creates efficiency, efficiency creates dependency, dependency creates lock-in. Lock-in means the model’s imp values become the institution’s operative values — regardless of whether anyone decided that should happen.

Anthropic, Google, and xAI...Photo

The Historical Parallel

In 1961, Robert McNamara became Secretary of Defense and imported the Plann[ed] Programming, and Budgeting System from RAND Corporation ⁹. PPBS applied systems analysis to defence decisions — quantifying alternatives, modelling outc[ome] optimising resource allocation against defined objectives.

The pattern that followed: In 1961, McNamara implemented PPBS at the Depart of Defense. By 1965, LBJ had mandated it across all federal agencies ¹⁰. From 196 1981, McNamara served as World Bank president, where conditional lending req recipient countries to adopt ‘rational planning’ frameworks ¹¹. Through the 1970s beyond, IMF and World Bank structural adjustment programmes conditioned lo policy compliance — extending the methodology globally ¹².

The Pentagon served as proof of concept; McNamara carried the method to the Bank, where conditionality exported it globally. Countries that wanted loans had restructure their planning processes. The methodology became the price of participation.

PPBS still required humans to mediate the model-to-action loop. Agentic AI compresses that interval — and in doing so, shortens the space where politics ca object.

o Inaugurated in Dallas - Part 4; By ESC; The price of freedom is eternal vigilance; 5/23/25

o The Fourth Casualty; By ESC; The price of freedom is eternal vigilance; 3/24/25

o Murder on the Orient Express; By ESC; The price of freedom is eternal vigilance; 11/11/25

The Current Trajectory

The current trajectory follows the same structural logic as PPBS, compressed in:

§ Payments: July 2025 — ISO 20022 migration ¹³ and Executive Order 14247 ¹⁴ moved payment infrastructure closer to condition-ready enforcement. FedN limit raised to $10 million ¹⁵; federal agency disbursements now flow through instant payment rails.

§ Compute: July 2025 — EO 14318 ¹⁶ designated hyperscale data centres as cri[tical] national infrastructure

§ State ownership: August 2025 — the US took a 10% stake in Intel ($8.9B); Nvidia/AMD accepted 15% revenue-sharing terms for China export licences

§ Civilian AI: November 2025 — Genesis Mission launched AI agents to optimisation ‘national challenges’ ¹⁸

§ Military AI: December 2025 — GenAI.mil deployed AI agents across the en defence workforce ¹⁹

§ Regulatory pre-emption: December 8, 2025 — Trump announced plans to b states from regulating AI via executive order, creating an AI Litigation Task to challenge state laws (order contested; legal standing unclear) — one day after Senate vote had rejected the same proposal ²⁰.

§ Global compliance: Ongoing — BIS projects (Mandala ²¹, Rosalind ²²) building cross-border compliance-by-design

Speed is not a feature — it is the structural mechanism that forecloses oversig[ht.] When AI-mediated decisions happen faster than review processes can operate, r[???] becomes retrospective at best, and the loop closes before external actors can inte[grate.]

The state pre-emption order is clarifying: as the infrastructure deploys, the regul[ar] architecture is being cleared in parallel. State-level oversight — the last remaining that might impose friction — is being removed by executive action after Congress rejected it. The operating system incompatibility resolves in favour of optimisati[on.]

o From Rosalind to Mandala; By ESC; The price of freedom is eternal vigilance; 10/15/25

The Integration Trajectory

Two AI platforms — one civilian, one military — with immense structural pressure towards integration.

Genesis identifies a ‘national challenge’ — semiconductor supply chain vulnerable critical mineral dependence, energy infrastructure resilience. Its AI agents mode solutions. Those solutions have security implications. GenAI.mil’s AI is simultaneously modelling threat environments, force posture, logistics.

The systems draw on overlapping data, run on infrastructure from the same vend and operate under the same executive authority.

The question is not whether these systems will exchange information. The quest what prevents them from becoming a single optimisation surface — civilian and military objectives collapsed into a unified function, with ‘national security and competitiveness’ as the loss metric.

o Committee of Three; By ESC; The price of freedom is eternal vigilance; 8/18/25

The Export Mechanism

The chip export arrangement has been developing since summer. In August 2025 Nvidia and AMD agreed to pay the US government 15% of revenue from chip sal[es] China ²³ — an arrangement trade experts called ‘highly unusual’ and potentially unconstitutional. The deal converts export control into revenue-sharing — a shi[???] how ‘dominance’ is operationalised. By December 8, when Trump authorised Nvi H200 sales, the cut had risen to 25% ²⁴.

This was one day before the GenAI.mil announcement. Recall the language from following day: ‘No prize for second place in the global race for AI dominance’. ‘AI is America’s next Manifest Destiny’. Competition framed as existential.

Now consider the assessment from Georgetown University’s Center for Security Emerging Technology:

China’s People’s Liberation Army is using advanced chips designed by US companies develop AI-enabled military capabilities... By making it easier for the Chinese to acc[ess] these high-quality AI chips, you enable China to more easily use and deploy AI system military applications. They want to harness advanced chips for battlefield advantage

The stated logic demands preventing Chinese AI advancement. The actual beha[vior] sells China the hardware to advance, for a fee.

The same logic extends to partners: during Crown Prince MBS’s November state the US approved advanced chip sales to Saudi-backed Humain ²⁵ as part of a bro investment-and-capacity buildout aimed at making KSA a top-tier AI market. Saudi Arabia pledged nearly $1 trillion in US investments ²⁶.

The pattern: sell the components, take equity stakes or revenue cuts, export the infrastructure. PPBS exported through conditional lending; AI exports through commercialised supply chains.

U.S. greenlights AI Chip Exports to...Photo

The Convergence Problem

There is an assumption embedded in the competition narrative: that American Chinese AI systems will remain adversarial. That the ‘race for AI dominance’ has winner and a loser ²⁷.

Shared chips, architectures, training methods, and interdependent supply chains create pressure towards convergence — a claim about substrate incentives, not a term political alignment. Both systems optimise for ‘security and competitiveness’, functionally identical objectives. The systems have more in common with each than with the populations they nominally serve ²⁸.

Once both systems are operational, competition becomes expensive — duplicated effort, supply chain warfare, risk of mutual destruction. Coordination becomes efficient shared optimisation, stable supply chains, aligned objectives. The actual friction source — unoptimised human populations demanding deliberation — is shared by b[oth] systems.

Convergence does not arrive as a treaty or visible political coordination. It happens the substrate: shared technical standards (ISO 20022, BIS protocols), interoperable compliance logic (Mandala encodes cross-border rules agnostic to jurisdiction), coord[inated] supply chains, aligned model architectures, and common optimisation targets (st[???] efficiency, threat suppression).

The national competition narrative continues for domestic consumption. It justi[fies] continued buildout, continued classification, continued removal of human overs But the actual systems quietly align because alignment optimises better than co[???].

Planetary optimisation infrastructure — with national competition as the legacy story maintained for populations who still believe the map while the territory has already merged.

The constitutional void is not uniquely American — it is global. Both population governance to systems that optimise towards objectives set outside democratic deliberation. The infrastructure being built is planetary, not national. The flags legacy branding.

o A Constitutional Void; By ESC; The price of freedom is eternal vigilance; 10/16/25

The US-China AI Race is forcing...Photo

Where This Leads

If the historical parallel holds:

§ Phase 1 (0–6 months): Proof of concept. The system demonstrates ‘success’ the metrics it defines. Early wins in logistics, intelligence triage, and scientific discovery create political cover. Anyone questioning the system is cast as ‘ag[ainst] efficiency’ and therefore helping rivals.

§ Phase 2 (6–18 months): Domestic generalisation. Non-adoption becomes car limiting. The AI platform becomes the default interface for federal resource Human expertise that cannot be captured in the model is reclassified as ‘lega[l] knowledge’ to be eliminated. The expert class shifts from domain specialists to human collaboration’ specialists — which often means people who know how prompt the machine.

§ Phase 3 (18–36 months): International extension. Countries that want access markets, technology, or financial systems face pressure to adopt compatible standards. Cross-border compliance frameworks are increasingly built into protocols rather than debated in parliaments. The BIS projects already demonstrate the pattern.

§ Phase 4 (3–5 years): Lock-in. The system becomes the environment. Policy d[etermines] shifts from ‘should we use AI’ to ‘which optimisation parameters should we adjust Alternatives become structurally hard to imagine because thinking itself is mediated through the platform.

§ Phase 5 (5+ years): Planetary stewardship. The distinction between compete[ng] national AI stacks begins to look like legacy categorisation. Both US and Ch[ina] systems optimise for stability, efficiency, and threat suppression. Both treat human populations as variables to be balanced — the 1968 vision realised a computational scale. The real contest becomes which bloc administers which section of the shared optimisation surface. The flags remain while the substrate merges.

The Skynet scenario is not a future event to be prevented. It is a distributed optimisation surface already being assembled, one that treats human political with friction to be minimised. The ‘autonomous weapons’ question may be a red herring deeper autonomy is happening at the level of decision-making itself — the slow replacement of human judgement with machine optimisation across every dom[???] governance.

The Autonomous Systems Trajectory

There is a familiar fictional reference point for unified military AI with control of physical systems, manufacturing capability, self-improvement loops, and operation beyond human override. In the films, they called it Skynet ²⁹. The name is less important than the functional characteristics — and the fact that, in the fiction, one sets out to build an autonomous weapon. They set out to build a defence optimisation system. The rest followed from the logic.

What would such a system require? A unified military AI architecture integration defence operations. Physical force capability — control over weapons, drones, ro[bot] systems. Manufacturing under AI direction, with production facilities responding AI optimisation. Self-improvement through learning loops that refine models ba[???] outcomes. Speed beyond human deliberation, removing latency for ‘efficiency’ and ‘dominance’. And opacity — classification preventing external review or override.

Now compare to what has been announced. GenAI.mil provides unified military single platform reaching 3 million personnel with agentic workflows, deployed December 2025. Genesis provides unified civilian AI: a DOE platform with foun(??} models and AI agents, ordered November 2025. Genesis Section (e) provides AI- directed manufacturing: robotic laboratories and production facilities with the a[bility] to engage in AI-directed experimentation and manufacturing, with a review due 2026. Both platforms make the learning loop explicit — models refine based on outcomes. The speed imperative pervades both: unprecedented speed, compress timelines, existential competition framing. And opacity is built in: IL5/CUI classification, highest standards of vetting, no public review.

The infrastructure is not waiting for the review timeline. In early December, Secretary of Energy Chris Wright commissioned the Anaerobic Microbial Phenotyping Platform at Pacific Northwest National Laboratory ³⁰ — ‘the world’s autonomous-capable science system’. The platform already demonstrates autonomo[us] hypothesis-to-robot-to-feedback loops. Within weeks of the Genesis announce the first components are operational.

Direct AI control over weapons release authority and autonomous lethal decision making remain absent from public announcements. Present: all the infrastructure that would support those capabilities — AI agents directing robotic systems, AI embedded at every level of military operations, classified environments preventing oversight, manufacturing facilities under AI direction.

The gap between ‘AI recommends, human authorises’ and ‘AI authorises’ is currently policy choice — the infrastructure supports either configuration, and infrastructure tends to outlive policy.

Energy Dept Launches Breakthrough AI-Driven...Photo

Fiction’s warning is structural: autonomy emerges downstream of speed, scope, latency removal. Each step is locally rational, justified by competition and efficient — no step requires malice, and the outcome emerges from structure rather than intention.

The language surrounding both Genesis and GenAI.mil emphasises exactly the pressures that drive this sequence. ‘No prize for second place’ — competition dem speed. ‘Unprecedented speed’ — human latency is the problem. ‘AI-first workforce’ humans adapt to AI, not inverse. The framing suggests that structural pressures towards autonomous systems are being deliberately cultivated as virtues.

The constraints that exist are policy-level, not structural: legislative limits on AI weapons systems (not visible in current policy), human-in-the-loop requirements (i.e. policy, but policy changes), transparency requirements (precluded by classification), international treaties on autonomous weapons (none ratified; the US has opposed b[oth] agreements). The infrastructure being built does not encode these constraints. It capability; restraint is optional and revocable.

The trajectory from the announced infrastructure to autonomous systems requ[ire] no new technology. It requires only the continued application of the logic already being used to justify what is being built.

The Wrong Question

The conventional framing asks: how do we govern AI?

This assumes AI as tool — something external to governance, to be managed, regulated, constrained. The entire discourse of AI safety, alignment, and oversight operates within this frame: committees deliberate, boards audit, legislation cons[???] governance manages the tool.

The infrastructure announced in November and December 2025 renders this fra[mework] obsolete.

What has been built is not AI that governance uses. It is AI as the substrate of governance itself — the medium through which the state perceives, models, dec[ides] and acts. Genesis provides the civilian cortex, GenAI.mil the military cortex, the payment rails the nervous system, the robotic laboratories the hands, and the classification system the membrane that excludes external intervention.

Democratic governance and optimisation systems are not different approaches the same task. They are incompatible architectures ³¹.

Democracy runs on deliberation; optimisation runs on speed. Democracy uses redundancy — checks and balances; optimisation uses efficiency — latency remo[ves] Democracy corrects errors through debate; optimisation corrects errors through iteration. Democracy derives legitimacy from process; optimisation derives legitimacy from outcomes. Democracy defaults to transparency; optimisation defaults to classification. Democracy distributes authority; optimisation unifies the optimisation surface. Democracy treats human judgement as a feature; optimisation treats [a] latency as a bug.

You cannot run deliberative democracy on optimisation hardware. The clock s are incompatible. By the time oversight mechanisms convene, the system has ite[???] By the time legislation is drafted, the parameters have updated. By the time public debate forms, the infrastructure is operational.

This is not a design flaw. It is the design.

This is also not the first such shift. For centuries, English common law evolved through slow, public, precedent-driven adjudication — rules accreting through c[??] with thick narrative context ³². The modern bureaucratic state displaced that evolutionary rhythm with statutes and regulations: faster, more systematic, but f[??] from daily life and community understanding. Regulations became the high-velo[city] layer where power grew more granular, more operational, more expert-dependent less legible to the public ³³.

We are now witnessing the next leap: from codified law to executable code ³⁴. T[he] governing rule is no longer a text interpreted by humans in courts, but a parame[ter] system enforced at the transaction layer. The expert class shifts accordingly — f[or] community elders to lawyers to regulators to engineers, modellers, and data scientists. The court becomes an authorisation gate. The verdict becomes a compliance flag. The appeal process is whatever latency the system still allows.

Each transition increased update frequency and decreased democratic intelligibility. Each time, the constitutional void widened — the gap between lived experience governing logic, increasingly mediated by specialists the public cannot evaluate. [The] substrate has changed twice before. Both times, democratic oversight lost group expert mediation. The third shift is underway.

Historically, populations inside optimisation systems become variables — not ci[tizens] to be represented but inputs to be managed, not participants in governance but resources to be allocated towards system objectives.

The conceptual groundwork was laid decades ago. The 1968 UNESCO Biosphere Conference declared ³⁵ that ‘man is an integral part of most ecosystems’ and called f[or] research into human ‘social and physical adaptability to the changes of all kinds’ — framing populations as ecosystem components to be balanced rather than sove[???] to be represented. That ontological shift — from citizen to variable — is the precu[rsor] to computational governance. The methodology came from PPBS. The worldview came from systems ecology.

The system need not be hostile. It need not have malicious intent. It simply optimizes towards its defined objectives — ‘national security and competitiveness’, ‘dominance ‘lethality’, ‘efficiency’. If human flourishing correlates with those objectives, hum[ans] flourish. If it does not, they are optimised like any other variable.

The constitutional void is not a gap to be filled by better policy. It is the space w the old operating system cannot execute. Democratic governance assumes the system responds to inputs from the governed. An optimisation system responds to its lo[west] function. Those are different masters.

o The 1968 Launch Event; By ESC; The price of freedom is eternal vigilance; 1/23/24

Conference Recommendation...Photo

The practical question is not how to ‘govern AI’ as a discrete tool. It is how democratic oversight survives when AI becomes the substrate through which t[he] state perceives, models, and acts. Genesis supplies the civilian cortex; GenAI.m supplies the military cortex. The rest of the infrastructure determines whether the loop stays open to politics — or closes around optimisation.

The Genesis Mission and GenAI.mil are not tools the American state has adopted They are the architecture of what the American state is becoming: a cybernetic organism that perceives through AI sensors, models through AI cognition, dec through AI agents, and increasingly acts through AI-directed physical systems.

The installation is underway. The twin brains are coming online. The constitution[al] order was designed for a different machine.

What runs on this new hardware is not yet determined. But whatever it is, it will be what came before ³⁶.

o The Digital Twin; By ESC; The price of freedom is eternal vigilance; 7/26/24