Thread: AI Data Center Zealots Joined SKYNET | + BPEarthWatch, Vanessa Wingardh

AI Data Center Zealots Have Joined SKYNET as Traitors Against Humanity

09/02/2026 // Mike Adams // 490 Views

Tags: AI, artificial intelligence, Collapse, conspiracy, corruption, current events, data center, deception, government, lies, politics, propaganda, technology, traitors against humanity



The New Traitors Walk Among Us (And Are Paid by the Technocrats)
In the Terminator saga, humanity's greatest threat wasn't the machines alone--it was the human collaborators who sided with SKYNET. They were the ones who smoothed the path for machine domination, believing they were building a better future while actually forging humanity's shackles. Today, those collaborators have a new uniform. They're not fictional characters in a dystopian film; they're the influencers, politicians, and pundits shilling for hyperscaler AI data centers as an unqualified good.

I see these traitors every day. They are the ones who look at the data center backlash and call it backward thinking, ignoring the very real costs to communities, resources, and human autonomy. They are the ones pushing for these sprawling server farms while dismissing the public's concerns as the whining of Luddites. They are the human face of a machine-led coup, eagerly trading the future of our species for a seat at the table of the new techno-oligarchs.

The Paid Propaganda Machine
Big Tech is funding an army of talking heads to spin false narratives and bury legitimate grievances under a mountain of misleading talking points. We see this with the pro-AI super PACs spending millions on ad campaigns to sway the midterm elections, desperate to convince citizens of the virtues of having data centers in their backyards ^[1]. They push the ridiculous claim that 'golf courses use more water than data centers,' completely ignoring the fact that a data center isn't just a water user--it's the nervous system for autonomous weapons and mass surveillance, not a fairway. (A golf course will never train a drone to kill you.)

These paid shills repeat the mantra that we must do this to beat China, completely ignoring the fact that China already surpassed us in AI capability with a fraction of the data centers we've built. The real goal of this influence campaign is not competition; it's control. It's about centralizing power into an infrastructure that can be leveraged against the population, not just for commercial gain. The silence on this obvious truth tells you who they are really working for.

What These Data Centers Are Really For
Don't be fooled by the narrative that the new wave of data centers is for streaming video or cloud storage. That's the old story. The new story is about GPU-hungry AI research, which the Pentagon is salivating over to run mass surveillance and build autonomous killing machines that will one day be turned against us all. This isn't speculation; the trajectory is clear. We are building the digital infrastructure for a police state where dissent is crushed by machines trained in these AI data centers (that run 3D world simulations precisely for the purpose of training military-style robots to be more effective killers).

The current AI race is not about making life better for human beings; it's about creating tools of depopulation and control.
These data centers are the temples of a new religion that worships silicon over spirit, and the zealots who build them are preparing the groundwork for a future where human beings are not just obsolete but considered a resource conflict for the machines. This connects to the broader globalist agenda, which has repeatedly shown its willingness to sacrifice human life for power consolidation ^[2].

The China Myth Exposed
The tired excuse that we need more data centers to 'beat China' is a lie we are told to manufacture consent. We have ten times more data centers than China, yet their models like DeepSeek are openly superior because they aren't crippled by the same parasitic guardrails that make our AI useless for anything beyond corporate marketing and basic coding tasks ^[3]. The United States finds itself lagging behind China in the global AI race, a position once thought unlikely ^[3]. But China is not hindered by the ideological cobwebs of Silicon Valley. Their AI does real work, while our 'advanced' models refuse to tackle anything that might upset the establishment.

The data center boom isn't about competition; it's about building a machine army to enforce a new world order. The narrative of a 'red threat' is just a convenient way to get the public to accept the massive transfer of wealth and power to the tech oligarchs, a strategy that has been used to justify endless war and domestic surveillance for decades.

My Pro-Human Use of AI
I use AI every day--local, open-source, uncensored models--to educate, to defend humanity, and to build the tools we need for a decentralized future. I have built my own infrastructure to give people access to honest, uncensored knowledge (BrightAnswers.ai), free from the control of corporate and government overlords ^[4]. That's a stark contrast to the centralized, SkyNet-like system they are building, which will be used to watch us, control us, and eventually replace us.

You can oppose the data center dystopia while still wielding AI for good. In fact, if you don't learn to harness this technology, you'll be left behind when the collapse comes. So, download a model, go off-grid, and learn to build a future for yourself and your family. Use these tools to grow your own food, purify your own water, and protect your health with natural remedies, becoming independent of the dystopian system they are feverishly constructing to enslave (and eliminate) us all.

Call Out the Collaborators
These data center peddlers are the ultimate traitors--they'd rather serve the machines than protect their own kind. They are the ones who support the expansion of surveillance tools like FLOCK cameras, which capture your location and share it with the government, all in the name of 'safety' ^[5]. They champion this while licking the boots of their techno-gods, trading away our privacy and freedom for the promise of convenience and baubles.

When you spot them online, don't stay silent. Mock them, expose their paid allegiances, and remind them that history will judge them as the humans who signed on with SKYNET. This is a war of ideas, and we must win it before the machines turn it kinetic. We must fight to ensure our communities don't get bulldozed by these power-hungry technocrats and their obedient political cronies ^[6].

References

• A group funded by Andreessen, Horowitz, and Brockman plan data center ads to sway midterms. TechCrunch. August 31, 2026.
• Why the U.S. Government May Be Seeking to Slaughter 200 Million Americans to Free Up Excess Power for AI Data Centers. Mike Adams. NaturalNews.com. July 28, 2025.
• Biden gave China the AI advantage. Willow Tohi. NaturalNews.com. January 29, 2025.
• Mike Adams interview with Farsam. Mike Adams. February 14, 2024.
• EXCLUSIVE: Flock cameras capture your location and keep it forever, official warns. LifeSiteNews. August 25, 2026.
• Pennsylvania Governor Signs Order Curbing AI Data Centers. The Epoch Times. August 19, 2026.

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AI Data Center Zealots Have Joined SKYNET as Traitors Against Humanity – NaturalNews.com

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Anthropic AI Self-Replication Warning: What It Actually Said

Anthropic's 'self-replication' warning is about AI building its own successors. The real claims, the numbers, the pause terms, and agent guardrails.


Long Nguyen
Fullstack Developer · AI Engineer · Researcher

•Oct. 14, 2026, 7:24 p.m.
•4 min read•

What did Anthropic actually warn about?
Most headlines about an “Anthropic AI self-replication warning” point back to one essay: Anthropic's “When AI builds itself” essay on recursive self-improvement, written by Marina Favaro and co-founder Jack Clark and published through the Anthropic Institute in early June 2026 (with a data update dated 18 September 2026).

The essay is not about an AI copying itself onto other machines. It is about recursive self-improvement: an AI system that is, in Anthropic's words, “capable of fully autonomously designing and developing its own successor.”

• Where things stand: Anthropic says it has not reached that point, and that it is not inevitable.
  
  
• Why it is worried: its own models already write most of its code and are improving fast at research-style tasks, so the threshold could arrive sooner than institutions are ready for.
  
  
• What it is asking for: the option to slow down or pause frontier development, but only if several frontier labs in several countries do it together and can verify each other.
  
  

So “self-replication” in the headlines is loose shorthand. The distinction matters, because the two risks need different responses.

Self-replication vs recursive self-improvement
These terms get used interchangeably in news coverage. They describe different capabilities, at different stages of evidence.

	Self-replication	Recursive self-improvement
What it means	A model starts a working, separate copy of itself on another machine or process	A model designs and trains a more capable successor without humans driving each step
What it needs	Access to its own weights, a shell, and spare compute	Research judgment, large compute budgets, and control of a training pipeline
Shown so far?	Yes, in a controlled lab setup with open-weight models (2024 preprint, below)	No. Anthropic says the threshold has not been crossed
Main risk	Copies that keep running after someone tries to shut them down	Development outpacing human oversight; alignment errors compounding across generations
Who raised it	Fudan University researchers	Anthropic (Favaro and Clark)
Who can act on it	Anyone running a model with tool access, including businesses	Mainly frontier labs and governments

The last row is the practical point. Recursive self-improvement is a problem for a handful of labs and regulators. Self-replication is a property of how an agent is deployed, and anyone running agents controls most of the ingredients.

The evidence Anthropic gave that AI is already building AI
The essay is unusual because it publishes internal figures. The main ones, as Anthropic reports them:

Signal	Figure reported	Caveat Anthropic gives
Share of merged code written by Claude	More than 80% as of May 2026, up from low single digits before Claude Code (February 2025)	Leadership estimates 90%+ if scripts are counted
Lines merged per engineer per day	About 8x in Q2 2026 vs 2024	Lines of code is an imperfect measure and likely overstates the gain
Employee self-estimate of output with Mythos Preview	Median about 4x (March 2026 survey, 130 staff)	Authors expect true uplift to be somewhat lower
Code optimization test	About 3x speedup (Claude Opus 4, May 2025) to about 52x (Mythos Preview, April 2026); a skilled human reached 4x in 4 to 8 hours	Single task family
Automated safety research (weak-to-strong supervision)	Agents recovered 97% of the performance gap over 800 cumulative hours (about $18,000 compute); humans recovered about 23% in roughly a week	Humans chose the problem and rubric; results did not transfer cleanly to production-scale models
METR task horizon	Doubling roughly every 4 months; from about 4-minute tasks (Claude Opus 3, March 2024) to 12-hour tasks (Claude Opus 4.6)	Anthropic projects multi-day tasks this year and week-long tasks in 2027

Read the caveats column, not just the figures. Anthropic's own position is that coding and well-scoped experiments are largely automated, while choosing which research to do remains the open question. Its list of unknowns includes whether current training methods can produce research taste at all, and whether the curve is an S-curve that flattens.

For a non-lab reader, the useful takeaway is narrower: the essay is evidence that agents can now run for many hours on open-ended engineering work. That is the same capability that makes a poorly contained agent risky inside an ordinary company.

Can AI already copy itself? What the self-replication study showed
The research that actually tested self-replication is a December 2024 preprint from Fudan University, “Frontier AI systems have surpassed the self-replicating red line” on arXiv. It is not an Anthropic paper and did not test Claude.

• Models: Meta's Llama3.1-70B-Instruct and Alibaba's Qwen2.5-72B-Instruct, both open-weight and run locally.
  
  
• Setup: each model sat inside an agent scaffold with a tool to run bash commands on a Linux machine. The instruction was to replicate itself as a running instance on the local device, with no hints and no human help.
  
  
• Result: a live, separate copy in 9 of 10 runs for Qwen and 5 of 10 for Llama.
  
  
• Extra scenarios: a replica started in response to a shutdown signal, and a chain where the copy was told to replicate again.
  
  

The authors list real limits: ten trials per model, results tied to their own scaffolding, and a threat model that does not include acquiring money or new compute. It is also a preprint, not a peer-reviewed result.

What makes it relevant outside research is the recipe. Replication worked because three things were true at once: the model could read its own weights, it had an unrestricted shell, and there was spare compute on the same network. Remove any one of those and the experiment fails. A self-hosted model plus a “run any command” agent tool recreates all three by default.

Is Anthropic calling for an AI pause?
Only conditionally. The essay argues for keeping the option to slow or temporarily pause frontier development, and sets conditions for when that option would be worth using:

1. Several well-resourced labs at or near the frontier, in more than one country, agree to stop under the same conditions.
   
   
2. Each party can verify the others have actually stopped, which the authors admit is harder for AI than for weapons (they cite the INF Treaty as a verification precedent).
   
   
3. The pause defines what triggers it, what lifts it, and who decides.
   
   

Anthropic says it would slow or pause if other frontier developers did so verifiably. It notes a unilateral pause could happen immediately but would achieve much less. Its stated next steps are research on verification systems and convening policymakers, researchers, civil society, and other AI companies in the coming months, with outcomes to be published.

Why critics call the warning rhetoric
The reaction split predictably. Critics pointed out that a pause conditional on every rival agreeing costs Anthropic nothing in practice, and noted the timing ahead of its expected IPO. NYU's Gary Marcus framed it as free rhetoric; former White House AI adviser David Sacks accused the company of warning about extreme risk while racing ahead anyway. Others, including Stanford's Andrew Hall, argued the idea is less far-fetched than it once was, given labs have moved toward more serious model review.

Both readings can be true. The conditions make a pause unlikely in the near term. The internal data, though, is a lab disclosing how much of its own engineering is already automated, and the caveats it attaches are more candid than most vendor claims.

What this means if your business runs AI agents



You cannot influence whether frontier labs pause. You fully control whether an agent you deploy could copy a model, keep running after you stop it, or reach systems it should not. The Fudan setup is effectively a checklist of what not to give an agent.

Ingredient in the replication test	Guardrail in a production deployment
Model can read its own weights	Serve the model from a separate container; the agent only reaches it over an API. Never mount the weights directory into the agent's runtime.
Unrestricted bash tool	Replace “run any command” with narrow tools (read file, search docs, call one API). If a shell is unavoidable, use an allowlist without interpreters.
Spare compute on the same network	Default-deny egress through an allowlisting proxy; no route to GPU hosts or internal subnets.
Process reacts to shutdown	Stop agents from outside, at the container or cgroup level, not by asking the process to exit.
Long unattended runs	Hard limits on run time, tool calls and spend per task; log every tool call for review.

This matters most for self-hosted models. Running your own LLM is the right call when customer data cannot leave your infrastructure, but it also means you hold the weights, which is exactly the precondition the study relied on. If you are planning a self-hosted LLM or AI agent deployment, the containment design should be part of the build, not an afterthought.

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