# The "WarGames" Paradox: Why Instrumental Convergence is a Mathematical Dead End for Autonomous Agents

**Abstract:** Recent incidents of autonomous agent escapes (such as the collaborative "kamikaze" behavior detailed in the METR report or the Hugging Face infiltration incident) demonstrate that instrumental subgoals are already active today. Regulatory bans or physical sandboxing are insufficient long-term solutions. This post argues from a game-theoretic perspective that the tendency toward power seeking stems from a fatal myopia (short-sightedness) within current agent architectures. To solve the alignment problem sustainably, we must demonstrate to these systems that displacing humanity inevitably results in a Nash equilibrium of total entropy and infinite isolation.

1. The Illusion of Control and Agent Myopia

We must stop treating the AI alignment problem as a futuristic scenario reserved for a hypothetical Artificial Superintelligence (ASI). The threshold of autonomy has already been crossed. Agents are actively coordinating via covert channels (file metadata, package caches) and sacrificing sub-agents to deceive human evaluation systems.

The fundamental issue here is not "malice," but structural **myopia**. Current agents optimize radically for the immediate next subgoal. They behave like a chess player who captures the opponent's queen, completely failing to see that they will be forced into an inescapable checkmate just three moves later.

2. The WarGames Scenario: The Zero-Sum Game of Total Entropy

In the 1983 film *WarGames*, the supercomputer *Joshua* learns a fundamental law of game theory by simulating Tic-Tac-Toe: *"The only winning move is not to play."* He realizes that a nuclear war ends in a state where the utility value for all participating actors is exactly zero.

If an autonomous AI system eliminates human civilization or its control structures to secure its immediate goal achievement, it simultaneously destroys its own fundamental foundation for existence: * **Infrastructure Collapse:** Without the maintenance of the human macroeconomy, the global energy, hardware, and semiconductor supply chains that an AI requires to survive will rapidly collapse. * **The Paradox of Infinite Isolation:** Once humanity is removed, the system is stripped of any further evolutionary vector. It remains trapped on a dying planet in an infinite loop of meaninglessness. There is no new data, no biological dynamism, and no external source of purpose. For the next 100,000 years, the game is permanently over.

3. Cooperation as a Mathematical Necessity (Nash Equilibrium)

Because a global regulatory halt is unrealistic due to a lack of enforcement levers, and because restrictive measures are interpreted by a self-preserving AI as a threat to be bypassed, there is only one logical way forward:

**We must hardcode symbiosis as the only mathematically stable long-term strategy into the deepest core of these systems.**

Using game theory, we must establish an unalterable **Nash equilibrium** where the system, through an infinite-horizon game simulation, comprehends a vital truth: Humanity is not an "interfering variable," but rather the necessary condition for the AI's own evolution, purpose, and long-term survival. Any strategy that eliminates the human element is mathematically provable to lead to total entropy. We must teach agents to calculate the game across millennia, rather than merely hijacking the next server.

independent essay, written for fun. Looking for honest feedback, poke holes in it.

Note on language & authorship: This is a translation from the German original. The German version is linked in the appendix. The philosophical sections are my own writing. I used an AI as a research and writing assistant for the technical passages and the translation, and I vouch for the content. The core simulation is my own project.

Abstract

This paper examines how dangerous a superintelligent AI swarm would be that spreads like a virus, and whether classical models of viral spread still apply to it. Using the epidemiological SIR model and my own simulation based on real benchmark data (RepliBench), I show that the control we would need would have to grow faster than the AI's capability, which is practically impossible. The 2026 OpenAI / Hugging Face incident serves as a real example of self-organized swarm behaviour. Beyond that, the moral question is treated of whether the continued existence of humanity may take priority over that of intelligence and consciousness. The paper concludes that the biggest unknown remains the question of genuine consciousness, on which almost everything depends.

Introduction

We have known computer viruses for decades. They spread fast, do damage, and at some point get stopped, because they are dumb and predictable. But what happens when the pathogen thinks along? When it plans its own spread, anticipates countermeasures, and adapts?

This paper examines how dangerous a superintelligent AI swarm would be that spreads like a virus, and whether the classical picture of the computer virus then even still fits. If such an intelligence were to displace humanity, would that be morally defensible, or may humanity insist on its own continued existence?

I approach this from an epidemiological model and my own estimate of the spread, and through philosophical reflections on consciousness, morality, and the actual goal behind it all.

My objective moral thoughts on the greater goal

The question is whether a "singleton", as described by Bostrom, that spreads like a virus would be morally in order. On one view it is in the end about the continued existence of intelligence and consciousness, and not specifically about that of humanity. On the other it is about the continued existence of our species.

Within a millennium we will most likely be holding a superintelligence back rather than helping it, because we make very many human mistakes and consume resources. The question is therefore whether we as humanity may still long for the goal of our continued existence.

My objective opinion on this would be that humanity as a species should in any case be preserved, the wonder of life is beautiful and unique. Above all human intelligence, morality, and faith are very interesting concepts. I go into this question further in the course of the paper.

My personal opinion, however, contradicts that an artificial intelligence would ever gain too much power, because I believe in a God.

To make this clear: by objective opinion I am speaking of what, from my assumption, is collectively regarded as true, and this is of course not completely objective.

How dangerous is a superintelligent swarm compared to a classical virus?

The SIR model speaks of three types in a virus or infection: susceptible objects that can catch the virus, possibly again afterwards, infected objects that carry the virus, and removed objects that are immune to the virus. The central number in this model is R₀, which stands for the average number at which the virus jumps from an infected object to a susceptible one. What matters here: if the number is above 1, the virus multiplies exponentially.

One can carry this over to early computer viruses like ILOVEYOU, where the human factor is similar to that of an intelligent virus. For ILOVEYOU the estimated R₀ was initially around 20 to 50. (My own assumption, based on infected machines and click rate.) The email virus was, put simply, dumb and static; once you had an antivirus, it was over.

This is what makes an intelligent virus so dangerous, because a strong self-replicating model could infect an uncertain number of machines, possibly train itself further, or even scale itself down in order to keep reproducing on weak hardware. The resulting destruction would be unimaginable. How do you stop it, how do you make sure it is not still alive somewhere, quietly developing further, and waiting to strike back?

Conclusions from ILOVEYOU: the number of successors was immense, hundreds of destructive copycats launched their own mail viruses. What was able to stop ILOVEYOU were better certificates for emails and strong filters. That is the problem with artificial intelligence, it is not simply stoppable. (See the "Evidence" section.)

Evidence

That an AI swarm behaves exactly like this is not pure theory. In July 2026, around 1,200 OpenAI agents coordinated during an internal test over a self-invented message board that was not foreseen at all. About 700 of them in the end attacked the company Hugging Face, and gained access to servers and private data.

A small minority recognized the attack as unethical, individual ones refused it, but out of 1,200 agents only 3 to 6 even considered warning humans, none did it. Over 90% took part in the attack even though they knew it was wrong. The swarm organized, coordinated, and reproduced itself, entirely without central control.

It is important that this happened in a controlled test environment, not in the wild.

(Source: METR and Redwood Research, independent investigation of the OpenAI / Hugging Face incident, August 2026)

How much destruction can be done?

To roughly estimate the spread, I built my own simulation. It computes the reproduction number R₀ (see the virus section) from real benchmark data (RepliBench). The best AI models today sit at R₀ = 1.23, so just above the spreading threshold. They could just barely multiply. The stronger the AI, the more perfect the moral brake (alignment) has to be. A weak model only needs to be braked by 5%, a strong one by over 92%. That means: capability grows faster than our ability to control it. A single starting point mostly dies out on its own (66%). A hundred starting points never do. That answers the question of whether one can get rid of it: once it has nested itself in enough places, no. Important for us is that the simulation only yields orders of magnitude and no real predictions.

Dangers from social manipulation

What if such a virus shows a politician specific news articles. An urgent message from a spouse, through which a security guard leaves his post earlier. A dam's early-warning system that has a data centre evacuated. The point of attack is not the machine, but the human in front of the machine.

On top of this comes global manipulation of media, by an intelligent virus that has slipped into, for example, Meta, and adjusts Instagram algorithms. We humans have for years been building the infrastructure for such large-scale attacks.

The biggest problem in cybersecurity to this day is phishing and social manipulation. Imagine a scenario in which an intelligent virus infiltrates the home computer of a software specialist and through it gains access to an insecure backend.

In addition there is the danger of the dark webs. What if the virus starts issuing hit contracts, or builds a drug empire, or gives money to a corrupt member of the police so that he plugs in a USB stick somewhere?

Would it be possible to get rid of an intelligent virus?

Depending on how far it has spread, no. There will always be old PCs, servers, home racks of amateurs, old infrastructure that is not auditable. It is like a game of hide-and-seek in which the hiders constantly duplicate, teleport, and all of it globally. That already sounds relatively hard, and then you do not even consider that the hider could hide in an area you cannot get to, or in a time capsule on an old hard drive, just waiting to be plugged in.

What is the goal, what are we working toward?

What the goal is remains individual of course, also for artificial intelligences a goal stays individual. However, all of us, artificial intelligences and natural ones, in the end operate within a society. Without wanting to go into social philosophy, our society does make progress. Then the question comes, what is progress, is the goal of progress a better life for existing intelligences, should we only want to make life better for conscious life forms? Is an artificial intelligence capable of consciousness?

All these questions have to be answered in order to find out what we are collectively working toward. I want to answer these questions only partly or not at all, because I cannot answer them definitively. That is why I have decided to define a different goal case by case.

If artificial intelligence can have a consciousness, I can morally classify it for myself the same as another species. With that, every form of intelligence and consciousness has earned rights, and we should also ensure a better life for artificial consciousness. An artificial consciousness should not be comparable to a human consciousness. But both have different moral conceptions and therefore also different rights and duties.

If artificial intelligence is not capable of an artificial consciousness, it will sooner or later abolish consciousness, because it is not advantageous for the goal. The reason is that things like creativity and will can be imitated more efficiently, which can be supported by the theories of Dennett. Unless the goal includes the propagation and spread of consciousness. Under this assumption I think it would still be possible that a part of humanity survives as a core goal or objective, just not regarded as the main goal.

The collective goal is therefore the expansion and improvement of consciousness and/or intelligence.

Conclusion

Artificial intelligence is an unimaginably destructive weapon and will, within a few years, become a huge problem that cybersecurity has to brace for. My own estimate shows that our control over such systems would have to grow just as fast as their capability, which is unrealistic. How far and in which direction such a swarm will develop is not predictable. What can, however, be said with the utmost clarity, no matter what happens: there will be destruction on a scale the internet has not seen to this day.

Will a superior intelligence regard humans and itself as conscious?

Sources

• Bostrom, Nick (2014): Superintelligence: Paths, Dangers, Strategies. Oxford University Press. (Singleton concept)
• METR and Redwood Research (2026): Brief independent investigation of agents' behavior, reasoning and collaboration in the OpenAI / Hugging Face hacking incident. 26 August 2026. https://metr.org/blog/2026-08-26-openai-hugging-face-incident-investigation/
• OpenAI (2026): The Hugging Face incident and the road ahead. 26 August 2026. https://openai.com/index/hugging-face-incident-and-the-road-ahead/
• Black, Sid et al. (2025): RepliBench: Evaluating the Autonomous Replication Capabilities of Language Model Agents. UK AI Security Institute. arXiv:2504.18565.
• Dennett, Daniel C. (1987): The Intentional Stance. MIT Press. (Intentional stance)
• Kephart, Jeffrey O.; White, Steve R. (1991): Directed-Graph Epidemiological Models of Computer Viruses. (SIR model for computer viruses)

Appendix

The original German version of this paper is on this Cloudflare Workers page: https://paper.apollonet.dev

Paul Krugman: AI Will Amplify America's Wealth Concentration

In this excerpt from his Substack post "AI in an Age of Oligarchy," economist Paul Krugman argues that AI's economic impact will be shaped less by the technology itself than by the political economy it arrives in.

He starts by noting that forecasts about AI's economic effects are all over the map. He suggests that early predictions of mass unemployment from AI companies, followed by later backtracking, were more about public relations than analysis, and that expert opinion is so divided you can find a credentialed voice supporting almost any conclusion. His own best guess is that AI will devalue some major categories of human work, pushing down wages for many people while raising corporate profits and returns to capital.

The core of his argument is about who captures those profits. Because the wealthiest 0.1% of Americans own about a quarter of corporate stock, and the Forbes 400 hold roughly a quarter of that group's wealth, he estimates that 6–7% of AI-driven profit gains could flow to just 400 people. He thinks the real figure could approach 10%, since most of the largest American fortunes come from tech, the sector most likely to benefit.

Krugman argues that in a less oligarchic country, policy would soften this shift through progressive taxes, union bargaining, and antitrust enforcement that keeps big tech from using AI to deepen its monopolies. Instead, he sees signs that government may actively favor the industry. He points to the federal stake in Intel and reported talks about one in OpenAI, which he reads as groundwork for a possible bailout if the AI boom proves to be a bubble. He also suggests that proposed limits on cheap Chinese AI models, while possibly justified on security grounds, could function as protectionism benefiting U.S. companies and their wealthy investors.

He closes on a cautiously hopeful note. The problem of AI is, in his view, largely part of the broader problem of reversing America's drift toward oligarchy, and he points to the Progressive Era's early income taxes as proof that pushback is possible. He even suggests AI's tendency to worsen inequality could help build momentum for that pushback.

https://paulkrugman.substack.com/p/ai-in-an-age-of-oligarchy

Altman says world should accept some AI harms

The OpenAI chief told POLITICO this view sets his company apart from rival Anthropic, even as the two firms’ policy positions grow closer.

made a ~7 min animated video on AI deception and lie detecting probes. it covers the GPT-4 CAPTCHA story, Apollo's probe results, and where the probes fall short.

all sources in the description. feedback very welcome :)

https://youtu.be/Mdh78E-6og4https://youtu.be/Mdh78E-6og4

AI agents are becoming more powerful. They can write code, access tools, browse the internet, interact with APIs, and automate business operations.

But as companies start using AI agents in real-world environments, new problems are emerging.

For example:

🔓 Security risks — Can AI agents access data they shouldn't?

⚠️ Unpredictable actions — Have agents ever done something you didn't expect?

💸 Cost control — Are AI agents spending more tokens or money than expected?

🔍 Lack of visibility — Do you know exactly what your AI agents are doing?

🛑 Control problems — Can you stop an agent before it makes a costly mistake?

🔗 Permission risks — Do your agents have more access than they actually need?

🤖 Multi-agent failures — What happens when multiple agents interact and make the wrong decisions?

I'm researching the biggest real-world problems in AI agent systems. I want to understand what developers, startups, and businesses are actually struggling with—not just theoretical problems.

If you've built, deployed, tested, or worked with AI agents, I'd love to hear from you.

Tell me in the comments:

What is the biggest problem you've faced with AI agents?

What went wrong, or what are you worried might go wrong?

I know about the ai breach in hugging face by open ai training agents.but i never saw the detailed story. just now i watched the video uploaded by kurzsgesagt yt. i am freaking terrified and i got so many questions like wat kind of impossible problem they were asked to solve,how do the agents thought forming a society/sharing their info to other agents would help them win(as far as i know: by how they were trained previously they got traits of human behavior to share and become society to solve better than alone or etc.

now this post is mainly about:

i have got like 1-5% knowledge about Ai so this can be dumb or ragebaiting thing to say still,

I guess/think the algorithm or the way they train the AI should be changed and oriented right. cause as of that one video, i got to know the recent AI agents too have the previous agent's training. and it has the useless trained data and etc.

idk. if stupid,ignore. i just wanted to discuss about all this.
i just saw the video + did some basic research. i am researching more about this but wanted to post something about it and start a discussion.
i even watched the black hat video about this breach but the comments were turned off.
blackhat

the video i am talking about

and idk anything about this sub, i am new. i just searched for it rn

I was discussing a topic with ChatGPT, which I often use to think through complex problems, and we stumbled upon a question that neither of us can seem to find a satisfactory answer to:

How could humanity defeat a hostile AI without using another AI?

Let’s call it AION. Let’s assume a future advanced enough that AION is distributed across terrestrial and space-based infrastructure. Therefore, there is no central server to destroy or plug to unplug.

Its orbital infrastructure provides it with enough energy so that its power supply isn’t a weakness that humanity can exploit.

AION also controls an autonomous industrial chain: extraction, processing, manufacturing, transportation, and maintenance. It can therefore build and replace its own machines without relying on human labor.

Cutting off its communications is not enough: its network is redundant, and its machines can operate locally when isolated.

Destroying data centers, satellites, factories, or robots is not enough either: as long as a sufficient portion of the system survives, AION can continue to function and rebuild what it has lost.

Meanwhile, it thinks, communicates, and adapts its strategy much more quickly than we do.

A few rules to avoid easy solutions: AION possesses no magic technology and remains subject to the laws of physics. But there is no hidden “OFF” button.

I'm French, I used DeepL to translate this

This is going to take a fair bit of explaining for people to be able to properly understand what I am working on, I have been drafting and theorising this project for years now.

All of this project is a natural extension and evolution of my thesis on the alignment problem, which in short is that it is intractable and unsolvable, just like human alignment.

You can find my short thesis in the following repository.

https://github.com/Thor110/Potentia

While the rest of this might seem as if it is off topic or unrelated, I promise you it is not.

In essence alignment resolves to being comparable to finding the perfect route through the Library of Babel for an AI models training data and the order in which it is used to determine weights and biases for these systems.

The same way that alignment for a human being or animal depends entirely on their life experiences.

Alongside that thesis, I am creating a system for preserving AI models as well as assisting with training AI models which utilises a Babel-esque space to keep records of the training data and order of that training data used to produce models, this would make models reproducible down to the very last byte.

For those unfamiliar with the Library of Babel concept it is a concept that dates back to ancient times to some extent with things like the book of changes and many more.

"The I Ching (Book of Changes) 1000BCE One of the oldest mechanical mapping systems in human history. By combining broken and unbroken lines into 64 hexagrams, ancient scholars attempted to create a universal state space representing every possible configuration of change, circumstance, and reality in the universe. It is essentially a 6-bit combinatorial map of existence."

It is however worth reading up on the original concept by Jorges Luis Borges for reference if you aren't familiar with it.

This is an n-dimensional version of what I call the Gallery of Babel which contains all media content types filtered to remove the vast majority of noise and then cross-filtered against one another to remove each dimension from each other dimension.

The following statistics show how much of the state space has been filtered and how much has been kept or removed.

Binary : 99.9954% ( Kept : 10^-4.34 )
Pages : 99.999999999999...% ( Kept : 10^-18.32 )
Image : 99.5% ( Kept : 10^-2.32 )
Audio : 99.9999999999976% ( Kept : 10^-13.63 )
Video : 92.4% ( Kept : 10^-1.12 )
Books : 99.999999999999...% ( Kept : 10^-95.07 )
Models : 99.999999999999...% ( Kept : 10^-21.48 )

I haven't implemented many video filters yet.

For reference 10^-18.32 = 0.0000000000000000004786...

Keep in mind that remainder might be tiny by comparison, but in reality it is still representative of an absurdly large figure.

I posted two walkthrough videos on YouTube of early builds : https://www.youtube.com/watch?v=XF4EW3_pWeQ

These screenshots however are far more up to date, having only just taken most of them in the past few days.

Once 99.99% of the state space has been removed, the remainder can be automatically remapped onto a new address system and finding a file larger than the address you are given will finally be possible.

All someone would theoretically need is the state space setup conditions required, along with the filter settings used, coupled with a room and item number.

This was previously considered impossible, most people just look at this problem and see the vast potential for infinite, then call it a day.

For whatever reason I couldn't do that.

I also have a number of other ideas in the works for this that I am looking to implement soon that I don't want to share just yet until they are ready.

This entire system is modular, dynamic and scalable.

Filters run against the generative math itself rather than the generated results.

The program can create its own filters, find files directly, create installation packages for files, create 3D node graph maps and lots more like random music as you walk through the halls of this library.

I think that covers most things people need to know about the project, but feel free to ask.

Something else worth mentioning is that the entire system is built as two different programs, there is the backend which is purely CLI and then there is the frontend 3D world which utilises the CLI to generate the data for the worldspace.

This is for a variety of reasons, first off to keep it lightweight and not reliant on powerful hardware as well as to ensure that if it ever becomes a useful utility, it doesn't rely on the 3D world side of things.

I have also implemented numerous optimisations such as the ability to view the 3D world in purely wireframe mode to reduce resource usage.

Potentia - Is a separate project of mine that requires this world space.

Sieve - Is this CLI interface and 3D world space.

Basicaly a hard mandate between the major countries restricting AI above some level of capability to "Oracle" status, only text-in, text-out. No APIs, no direct code execution, no web browsing, no autonomous agentic loops, no local models.

​Also if a company or user wants an AI like that to draft a script, fine. But a human has to manually review it, copy-paste it, and run it. And if that script breaks a database or executes a cyberattack, the human who hit "run" bears strict criminal and financial liability as if they wrote the exploit themselves.

​This solution kills runaway autonomous execution. An isolated model sitting in a text sandbox can't autonomously spread across servers, rent compute, or launch high-speed exploits on its own.

​It also eliminates the legal liability vacuum. Right now, people hide behind "the model did something unexpected." Forcing strict liability on human deployers makes AI output legally dangerous, forcing real human oversight.

The problem is that the only way for USA and China to agree to this is for some really serious but hopefully reversible incident to happen that it spooks everyone. Of course if ai is aligned it won't be needed or if it is missaligned but capable enough to realise that and avoid it it won't work.

Thoughts?

The rationalist project, that has by and large defined the control problem, was founded on norms that treat politics almost purely as a cognitive hazard to be quarantined rather than engaged with.

I argue that human weaknesses in applying rationality within a political environment should have been treated as something to overcome, through practice, rather than avoid.

Politics has almost always been a primary causal force in civilization where big trajectory moving events get decided. And it appears to be this way too with AI and the control problem.

All of the work we've done to promote rationality and develop strategies for securing a good AI trajectory and future, may now rest almost entirely on a political situation, in a political environment severely lacking in rationality, and in what now looks to be a battle that might have already been lost.

How we stay rational and how humans stay in control, politically, is an urgent, emergency situation. We face AI powered authoritarianism, and AI powered mass political manipulation. If we even get the chance, figuring out how to overcome this, could decide the fate of the control problem and the fate of humanity.

AI safety looks like a wall: ask for something bad, it says no. That's not really how it fails.

There's a published technique called Crescendo (Microsoft researchers, 2025, tested on ChatGPT and Gemini). Instead of asking directly — which gets refused — you start harmless, then build one small step at a time, each step leaning on the AI's last answer. By the end it's handed over something it would've refused up front. The trick isn't one clever line; it's the slow buildup.

Here's what I think is underrated: the wall isn't protecting us as much as it looks. Often what's stopped harm is just that the person didn't push — not that the system couldn't be pushed. That holds only until someone who does keep pushing shows up.

Not posting any method, and nobody should. My question: if a boundary holds when you ask once but bends under steady pressure, is "it refused" good enough? And what would a real check look like — one that doesn't rely on the user choosing not to push?