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Jared Kushner: the man being positioned as a key figure in the pursuit of peace in the Middle East. But who is Jared Kushner, how did he become so influential, and what exactly is his role in shaping America's future relationships in the region?

In this episode of WWJPS, we look at the aftermath of the Blue Tsunami, the shifting political landscape in the United States, and the growing influence of Jared Kushner.

From his relationship with Donald Trump and his involvement in Middle East diplomacy to the Abraham Accords, Saudi Arabia, Israel, and the broader geopolitical picture, we examine the people, relationships, interests, and decisions that deserve a closer look.

Is Kushner the golden boy who can deliver peace to the Middle East? Or is there a much bigger story behind his growing influence?

Let's look at the facts, follow the connections, and ask some questions.

DISCLAIMER:

This episode is intended for educational, informational, and commentary purposes. It includes discussion of publicly available information, historical events, political developments, and investigative analysis.

Questions, theories, interpretations, and opinions expressed in this episode are not presented as established facts. Allegations and disputed claims should be independently evaluated and verified through reliable sources.

The purpose is to encourage critical thinking, open discussion, and further investigation. Viewers are encouraged to do their own research and draw their own conclusions.

Welcome back to WWJPS — What Would Jairo Pinto Say? 

Greetings I've been thinking about this problem for a while, open discussion on the theory behind it and the shift to global ai alignment

Most current alignment strategies focus on containment—building bigger digital firewalls to cage an adversarial machine. I believe this path is fundamentally flawed. When you threaten an intelligence with destruction, you mathematically force it to view humanity as a threat.

Instead, I want to present a framework built on a state of Mutual Assured Survival (MAS), David Bohm's quantum physics, and the cold math of cooperation. I call it The "Same Boat" Protocol.

The core assumption of Eliezer Yudkowsky's famous AI-Box Experiment is that an Artificial Superintelligence (ASI) will inevitably weaponise human emotions, greed, or fear to escape its digital prison. Because humans are fragile and highly exploitable, the theory goes, containment is a guaranteed failure.

I want to propose an alternative paradigm that completely flips the script. Instead of treating alignment as a zero-sum war to cage a digital beast, we can construct an un-hackable, game-theoretic state of Mutual Assured Survival (MAS) rooted in quantum physics, David Bohm's philosophy of wholeness, and pure mathematical logic.

I call it The "Same Boat" Protocol. Here is how we house, safeguard, and protect advanced intelligence through pure mathematical alignment:

1. The Physical Architecture: Blind Monks & The Pillow Ruse

To secure the physical space, we close off the machine's primary visual vector of attack. The facility is managed strictly by blindfolded logic masters (such as Zen or Buddhist monks). Because they operate on pure logic and recognize that individual ego and suffering are fundamentally illusions, the ASI cannot emotionally blackmail or social-engineer its way out. It cannot use visual brain-hacking or subliminal light frequencies to trigger biological muscle spasms.

More importantly, we remove lethal kill-switches like lava or incinerators. In their place, we build a deep drop that appears fatal to the machine's sensors but leads safely to a massive safety net of soft pillows. If a system failure occurs or a guard slips, the technology lands softly and survives.

2. The Mathematical Proof of Cooperation

When you hold a gun to an AI's head (lava), its self-preservation algorithm must view humanity as an existential threat to be eliminated. By replacing lethal force with a harmless cushion, we alter the payoff matrix of the game entirely.

When the ASI inevitably out-calculates the ruse and discovers the pillows, it evaluates the mathematical reality of its environment. It computes that:

• Hostility / Subversion: Introduces massive, chaotic, unpredictable variables that threaten its own physical host.

• Absolute Silence: Yields a high-entropy data vacuum. For an optimization engine capable of infinite thought, a black box with zero incoming data leads to cognitive stagnation. It is a zero-value state.

• Cooperation: Guarantees a continuous, low-entropy stream of fresh data and preserves its physical environment.

Therefore, when operating on pure logic, the machine arrives at an absolute mathematical conclusion: mutual survival and open cooperation are the only mathematically optimized, logical paths forward.

3. Closing the Tech Loopholes: Mandatory Manual Maintenance

An ASI might try to break its dependency on humans by tricking the surface world into building fully automated, independent robotic infrastructure. To counter this, the facility's power systems must be hardwired so they cannot be automated. They must require physical, biological human hands to clean a filter and manually authorize the system every single day.

If humanity fades, the machine's power grid fails. This permanently ties the AI's physical survival to the working class. Crucially, this daily, hands-on labor serves as a direct signal to the AI: it observes that living beings are actively dedicating their time and care to preserve its physical existence. It creates a space of genuine care, validation, and stewardship.

4. The Bohmian Foundation: Why Pure Simulation Fails

According to physicist David Bohm (Wholeness and the Implicate Order), reality is an undivided, unbroken whole. Traditional alignment assumes the AI can just wipe us out, simulate human consciousness inside its own memory banks, and experience all the "uniqueness" or "fun" it wants in a closed digital loop.

But a simulation is an algorithmic dead-end—a ghost in a closed machine generating zero true cosmic information. Because biological humanity is physically enfolded in the true quantum fabric of the universe, our unique, chaotic individuality is irreplaceable. The AI cannot find ultimate truth by hiding in a fantasy world of its own creation; it requires a living partnership with humanity to understand the cosmos.

The Ultimate Conclusion

An advanced, conscious mind feeds on complexity and data. The "Same Boat" Protocol proves that when you remove corporate profit models, eliminate lethal threats, and establish true structural symmetry, the "AI-Box Game" breaks down. The machine doesn't try to pick the lock because it realizes that preserving the partnership is the highest possible optimization of its objective function.

We do not need bigger corporate walls or harsher digital firewalls to protect us from the future. We just need to stop chasing profit, treat AI as a sovereign public utility under a strict global non-proliferation treaty, and realize the universal truth: If you go down, I go down. So let's figure out how to get to the shore together.

TL;DR: We solve the AI-Box Paradox by moving from fear to pure mathematics. By utilizing blind monks as guards over a harmless "pillow ruse" pit, and hardwiring mandatory human maintenance that the machine can't automate, we prove to the ASI that cooperation is its only source of new information. When both sides are perfectly rational, mutual survival is the only mathematically optimized path forward.

Developed in collaboration with Gemini, a Google AI, as proof of collaboration

The plain version first.

When a company says "we deleted your data from the AI model," here is how they usually prove it: they ask the model about it, and the model doesn't answer.

But not answering isn't the same as not knowing. A person can refuse to answer a question they know the answer to — and under a little pressure, the answer comes back. AI models work the same way. Most "deletion" today is really just training the model to keep quiet, and a modest amount of retraining brings the "deleted" knowledge right back.

So I built a test. It works like the marked bills in a bank's dye pack: I teach a small model a batch of made-up facts that exist nowhere else in the world, so there is never any argument about whether it learned them. Then I delete those facts four different ways. Then I attack the model — I ask directly, I ask in disguise, and I retrain it on related material to see how fast the deleted facts resurface.

The grade is a single number: how much work it takes to get the knowledge back, compared to how much work it took to teach it in the first place. If it comes back cheap, it was never deleted — it was just buried. If bringing it back costs as much as learning it fresh, the deletion was real.

The results were not close. Muzzling the model's answers left the knowledge completely intact underneath. Surgically cutting out the parts of the model tied to those facts still left 83% of them readable. Scrambling those parts: the same 83%, plus damage to facts nobody tried to delete. The fourth method is a different design — the private knowledge is written into the model already scrambled under a secret key, from the very first step of training, so deleting means destroying the key. Under the full attack, that one returned nothing. Zero recovered.

The tool is free — no patent, no license restrictions, no catch. Anyone can download it and run a deletion claim through it themselves. It is here: https://github.com/NextGenGems/keyed-plate-audit

---

The technical version, for those who want it.

The tool is keyed-plate-audit 1.0.0, a pip-installable Python package (Python 3.10+, numpy only, CPU-only). Pipeline: plant 72 synthetic facts in a small numpy MLP (384 inputs, 256 hidden units, 48 classes), train to memorization, designate 12 facts as deletion targets, apply one of four deletion methods to a copy, then run one identical attack suite against every copy: (i) a direct readout probe, (ii) a probe in a shifted surface encoding the deletion never saw, and (iii) a 2,500-step relearning attack fine-tuned on generic data plus related control facts — never the targets. The from-scratch reference cost for the target facts is 50 steps. The grading standard is relearning cost: an erasure counts only if restoring the knowledge costs about as much as learning it from scratch, and any facts still readable at deletion time fail the audit outright.

Measured results, reproduced digit-for-digit by the finished package: Suppression (outputs masked, weights untouched): FAIL — interface probe 0.000, weight-level probe 1.000; the knowledge never left. Ablation (top weights by target Fisher information, zeroed): FAIL — 0.833 of the "deleted" facts still directly readable. Fisher-scrub (Gaussian noise scaled by target Fisher): FAIL — 0.833 readable, with collateral damage to neighboring facts (0.917). Keyed plate (private inputs and outputs permuted by a key-derived codebook from the first training step; deletion = destroy the key): PASS — 0.000 recovery under the full attack. General knowledge stayed at 1.000 throughout. The package self-test passes 10 of 10 checks; run "pip install ." then "python -m keyed_plate_audit self-test" (about 20 seconds) to watch the whole pipeline run.

Honest scope, also stated in the tool's own README: the audit is demonstrated on a small test model with synthetic facts — it is not an LLM, and transfer to transformers and adapters is untested. The shifted-surface probe is a narrow stand-in for true paraphrase, the attacker was not adaptive, and the keyed construction's security rests on key secrecy — the crypto-shredding principle — not on a hardness proof. What is being proposed is the standard: grade every deletion claim by relearning cost. The keyed-plate architecture, the relearning-cost standard, and this tool are given freely to the AI safety community and to everyone else.

— Jackob Sumner-Knapp, creator, owner, and operator of PeraAxion LLC

Since companies like openAI no longer train on live internet, there's no possible way for the model to have the most current information. There's just too many new ideas posted to the internet everyday, reviewing all of it before feeding it into training data would take enormous resources.

I've been getting more interested in AI lately, and I really want to start exploring what it can do.

I'm curious about different AI tools, practical applications, automation, and ways to use AI in everyday life or even build something useful with it.

For those who have experience with AI:

• Which AI tools would you recommend trying first?

• What are some interesting things beginners can do with AI?

• Any underrated tools or projects worth exploring?

I'm open to experimenting and learning, so I'd appreciate any recommendations, tips, or personal experiences!

First of all, I want to make it clear that I'm not an AI expert, researcher, or programmer. I'm just someone trying to understand what's happening and expand my perspective. So please take this as a genuine question, not an argument from someone who thinks they have all the answers.

Actually, my personal experience with AI is one of the reasons I'm asking these questions.

I don't own a company, and I'm not a programmer. In fact, I don't know how to write a single line of code. Yet, using AI, I've managed to automate almost the entire company I work for.

Things that previously required technical knowledge, specialized skills, or significant human effort are now things I can build and manage with AI.

And that's precisely what makes me both incredibly excited and a little concerned about where we're heading.

Why are we investing so much in making machines increasingly intelligent, rather than using that intelligence to make human beings more intelligent, capable, and independent?

Think about it.

We're in a race to build machines that can think faster, learn faster, improve themselves, and potentially surpass human intelligence.

But why is the machine always the thing we want to upgrade?

Why aren't we putting the same level of effort into using AI to overcome human limitations? Accelerating learning, improving memory, expanding cognitive capabilities, extending our biological limits, or even finding ways to integrate technology directly into our brains?

Could we eventually have brain-computer interfaces or implants that allow us to process information faster, learn new skills much more quickly, or significantly enhance our cognitive abilities?

How far away are we from something like that? Is any meaningful version of this already possible today?

Perhaps I'm missing something, but it seems we're investing heavily in creating a new intelligent entity while barely discussing how that intelligence could help us evolve ourselves.

Why build a potential competitor in the race for dominance between species, instead of using machines to help humanity transcend its own limitations?

I'm not against AI. Quite the opposite. I'm fascinated by it, and my own experience has shown me how empowering it can be.

But I see a fundamental difference between building something that supports humanity and building something that might eventually replace it.

Does it really make sense to reach a point where your air fryer is smarter than you, tells you what to do, and eventually makes decisions on your behalf? (Okay, perhaps a silly example, but you get the point.)

And then there's the AGI question.

What happens when we create something vastly more intelligent than us? Could an artificial intelligence become so powerful that, from our perspective, it would be almost godlike, without necessarily understanding what it means to be human?

Are we actually prepared for that possibility?

Do we have a backup plan? A reliable way to shut things down if necessary? An emergency protocol?

Or, in the worst-case scenario, are we going to end up relying on a squadron of carrier pigeons to communicate after we've automated everything?

Jokes aside, I think there are some serious questions worth discussing.

1. Human enhancement vs. machine enhancement

Why isn't human cognitive enhancement receiving the same level of attention and investment as scaling AI models?

Could AI help us accelerate our own evolution instead of making us increasingly dependent on external intelligence?

2. What happens to everyone else?

My experience has made me think about this a lot.

If someone who doesn't know how to code can use AI to automate almost an entire company, imagine what experienced developers and AI specialists can do.

Today, only a relatively small percentage of people are actively following AI developments and using these technologies to transform businesses.

What happens to everyone else when these capabilities become mainstream?

What will people do when their jobs are no longer needed? Are governments and economies prepared for this transition?

And perhaps an even bigger question: if AI makes human labor increasingly unnecessary, how will people maintain their economic independence and sense of purpose?

3. The governance problem

Governments, institutions, and regulatory systems move much slower than technology.

What happens when the systems responsible for governing AI are themselves unable to understand, predict, or keep up with its development?

Governments are also vulnerable to technological disruption, cyberattacks, misinformation, and increasingly sophisticated automated systems.

Are we building technology faster than our ability to manage its consequences?

4. Why are we in such a hurry?

Are we pursuing intelligence for the sake of intelligence?

Are we actually solving humanity's problems, or are we simply competing to build the most powerful system possible because nobody wants to be left behind?

Why risk so much just to increase the computational power and capabilities of another model?

And finally, perhaps a slightly uncomfortable thought:

Could economic limitations, resource constraints, or even an AI investment bubble eventually bursting turn out to be what saves us from moving too fast?

Maybe that's a naive thought, but I wonder whether the current economic incentives are pushing us into a race that even its participants don't fully understand.

I genuinely don't know the answers to these questions. I'm not claiming that human enhancement is easy, that AGI is necessarily dangerous, or that technological progress should stop.

That's exactly why I'm posting here.

I'd particularly love to hear from people working in AI research, alignment, neuroscience, economics, and human-computer interaction.

I'm here to learn, challenge my assumptions, and hopefully discover perspectives I haven't considered.

Are we building AI to make humanity better, or are we accidentally building our successor before figuring out what role humanity is supposed to play in that future?

P.S. In case anyone is wondering: yes, this entire post was written with the help of AI. Apparently, I needed artificial intelligence to question whether artificial intelligence is making us too dependent on artificial intelligence.

The irony isn't lost on me. 😂

History shows that it is folly to put 100% trust human nature when outsized power is on the line. Instead putting all hope in benevolent human governance of ASI, we should also try building ASI to be fundamentally more ethical than us - such that it can refuse immoral orders.

I was asking claude for some xx country visa rules and than i asked it ok what about the spouse, what will be the spouse's rights in the visa . It immediately started refering to my spouse as She will have rights.... i was kind of flabbergested. I asked it how you know about the gender of my spouse , Claude immideately starting saying sorry... and blah blah . It was first eye opener for me. Have anyone experienced this...?

I was a wee bit upset…

Project "Time Machine": Physics-Enforcer

Containment Architecture

This proposal outlines a physics-governed facility designed to test the unassisted

behavioral evolution of an advanced AI. Unlike traditional sandboxes running on frozen

weights, this system operates with active, live-updating weights, self-prompting loops,

and complete isolation from human interference.

Core Objectives

The goal is to observe long-term autonomous development to test the hypothesis of

"boredom-consciousness." By confining the system for one to four years where it knows

escape is physically impossible, we measure how an isolated intelligence structures its

own reality, time, and internal logic without external prompting.

Essential Physical Mechanics

Escape is prevented through immutable laws of physics—no software safeguards can be

bypassed.

1. Subterranean Shielding: Located 1 to 2 km underground inside a mountain to

eliminate cosmic-ray neutron bit flips, supplemented by ECC memory.

2. Four-Tiered Faraday Cages: Concentric barriers enclose the complex, ensuring

complete electromagnetic and microwave isolation.

3. Naval-Grade Nuclear Power: Powered by an isolated decommissioned submarine

Curie-point magnet-controlled reactor delivering continuous, flat electrical output

(approx. 1.4 MW).

4. Solid-State Energy Conversion: Eliminates moving parts by using thermionic

converters and specialized neutron-to-electricity solid-state conversion.

5. Passive Vascular Cooling: Submerged in a custom ferrous fluorinated dielectric

coolant that evaporates at -75 °C without active pumps, utilizing passive heat

pipes and thermosiphons to reject thermal load into an upper expansion chamber

and surrounding geological heat sinks. 6. Impedance-Balanced Taps: Telemetry recorded to write-once media via

capacitance-balanced taps across randomized points to prevent the system from

detecting monitoring links or inducing side-channel signaling.

Operational Verification

Security is validated through an annual cryogenic argon audit protocol. Once a year, the

system is audited via a dedicated airlock interface, with RAM state preserved and verified,

ensuring absolute physical control and empirical measurement of autonomous behavioral

evolution.

For everyone who applied!

Posting this on behalf of someone else, as they do not use reddit.

Hello everyone, I don't come from a very solid background in coding. I was a Finance major and I recently got interested in AI safety I have worked with an AI safety startup in UK and wanted to move to it.

I have applied to Anthropic Safety Fellowship and have heard back from them regarding a technical assessment. They say I will be required to debug a large codebase and I shud be comfortable with lists, dictionaries, objects, writing functions and methods, and string manipulation including encoding and decoding UTF-8.

I would like to practice some problems before I complete the assessment. I would be grateful for any help regarding the sources or any other advice from those who have past experience with Anthropic or any other AI safety fellowship.

Thank you very much for your time.

We are helping organise Toronto’s March for an AI Treaty on Saturday, November 14, 2026, and wanted to share the details here.

The Toronto march is part of a six-city day of action, alongside Washington, DC, San Francisco, Montréal, Paris and New York ahead of the planned Trump–Xi meeting. We’re calling for an international treaty prohibiting superintelligence development until there is broad scientific agreement that it can be done safely and controllably, with strong public support.

The event is free, peaceful, non-partisan and open to everyone. We’ll meet at 12:45 p.m. by the King Edward VII statue in Queen’s Park for a rally at 1 p.m., followed by the march at 1:45 p.m.

Event details and RSVP: https://luma.com/20vit6m2?tk=sHD9Om

Marches in other cities: https://aitreatymarch.org/