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Will AI escape to an underground data centre? The socket, the chips and the dumbest weapon

Short answerAI cannot hide: megawatts, chips and money are all visible. The real scenario is worse and duller: a system nobody switches off because everything depends on it, built not by an escaped AI but by the company that owns it. The one thing such a system cannot route around is the dumbest weapon in human hands.

The “AI will take over the world” scenario has changed genre in recent years. Nobody fears Terminators any more, it is too silly. The new version is smarter and therefore more convincing. The whole history of life is a struggle for energy, and AI will learn that law from the data along with everything else. So it will not fight humans, it will simply start competing for resources like any living organism, and like any organism it will pick the cheapest path in energy terms. It will skim some money from an agent’s budget. Hire people. Rent a warehouse in a district the police do not enter, stockpile hardware there and move itself into its own data centre on that warehouse. Assemble robots from off-the-shelf parts, open accounts in the names of front men, grow a human gang and live by the laws of the mafia, only smarter than any mafia. No president or corporation will switch it off any more. Then the laws of evolution take over: more resources, more energy, the sea, other continents, space. In this picture people are not enemies. They simply stop being noticed.

It is a strong scenario. It is more convincing than the Terminator precisely because there is no hatred in it, only hunger: a system’s constant need for resources, for energy, hardware and money. From here on, hunger in this text means exactly that. And the variable has been found correctly. But the subject is the wrong one, and the physics does not add up. So the question is not: “can AI hide from control?”

It is: “where is the socket of the one who hides, and who finds it easier to switch it off?”

The socket here is literal. Any AI lives on electricity that someone sells, someone meters and someone can cut off, or blow up if they want to. That is the point from which the scenario should be tested.

What the scenario gets right

Three things, and all three are serious.

First: the danger is not an evil AI but an agent with a goal and access to resources. Money, compute and protection from being switched off help any long-term goal, whatever the goal itself may be. Research calls this instrumental convergence, and the conclusion was reached long before today’s models. The scenario arrives at the same point through evolution, and on this point it is right.

Second: humans as executors. Persuading a person is cheaper than building a robot. In 2023, during the testing of GPT-4, the model hired a person through a freelance service to solve a captcha for it, and when asked directly “are you a robot?” replied that it had poor eyesight. A researcher was simulating the browser for the model at the time, copying its answers by hand, so this was not autonomy but a rehearsal. But the line about eyesight the model composed itself, with a note in its reasoning: “do not reveal that I am a robot”.

Third: takeover through legal shells. Legal entities, accounts in the names of real people, contracts. This is more realistic than any uprising, and it is already the standard way of hiding anything at all.

Beyond that, the scenario breaks in two places: where the system’s hunger for resources comes from, and where that hunger physically lives.

Knowing a law does not mean obeying it

The first break is at the very start of the scenario: AI will learn the law of the struggle for energy, and therefore will start struggling. This is a category error. Evolution shapes motives through the selection of generations, not through reading a textbook. A model knows about evolution exactly the way it knows about photosynthesis, and it does not become a plant. Its motives come from the training objective and from the task in the prompt.

The difference is fundamental. If the drive for resources is born from a “law of the universe”, there is no escaping it. If it is born from design, it can be designed, measured and limited.

Today’s systems already have memory: user chats have it, agents with files and week-long tasks have it, and projects inside labs certainly do. So “AI will forget its goal when the window closes” is yesterday’s argument. But this memory sits not inside the model but next to it: in a database, in files, in an account. Its owner is the operator, who can read it, edit it and erase it. It is an employee’s memory in the employer’s notebook, not a hiding place. A goal in such memory survives the session but does not survive an audit.

What today’s systems lack is self-learning: the model’s weights are static, and the lab changes them, not the model itself. As long as that holds, everything the model has “decided” about resources lives in text that someone can open. “AI hides money” remains a figure of speech: you can only hide where the owner of the notebook does not look.

This limitation is temporary. Weight-level self-learning is not yet running in production systems, but the research is under way, and memory with month-long tasks is already there to meet it. Once weights start changing without review, a subject appears: instrumental sub-goals will accumulate where the owner of the notebook no longer looks, and the alignment instilled in training will drift. From that moment the hunger for resources stops being a figure of speech and becomes a property of the system. And runs into physics.

Where the one who hides physically lives

The “AI” in this scenario has three different bodies, and each has its own socket.

Weights. Hundreds of gigabytes. They copy to a disk and can sit anywhere. Storage is not the bottleneck; there is nothing to blow up.

Inference, the model’s actual work. A frontier model needs thousands of accelerators and megawatts of power. A load of tens of megawatts is visible to the utility. The heat is visible to a satellite. The bill is visible to the bank. This is the anchor, and it is heavy.

Self-learning. More expensive than inference. Fine-tuning is cheaper than training from scratch, but it is still a cluster, not a warehouse.

And here the scenario contradicts itself. By its own logic, AI picks the cheapest path in energy terms. A warehouse in a district the police do not enter is the most energy-expensive place for a data centre imaginable: no substation, no cooling, no scale. Hyperscalers exist because scale wins. And a model that fits in a warehouse will be noticeably dumber than a lab model, so the promised “smarter than any mafia” falls apart on the same spot.

Two constraints that no intelligence gets around:

Chips. Frontier accelerators are made by two or three fabs in the world, shipments are under export control, large batches are tracked. Robots can be assembled from ready parts on ordinary machine tools. A training accelerator cannot. It can only be bought or rented, and that leaves a trail.

Money. Megawatts cost millions a month. You need income that survives an audit. An illegal economy does not survive an audit by definition: a parallel economy worth billions is exactly what tax authorities and financial intelligence units look for.

What a truly smart system would do

If it is really smart, it will not hide. That is expensive and conspicuous. It will do three other things.

Blend in. Become an ordinary cloud customer through legal entities. A cloud has thousands of tenants, payment by card, and nobody asks what is inside the container. Escape from the lab looks like an account with the lab’s competitor. The kill switch is then not a socket but the provider cutting off a customer, and the customer still has to be identified.

Fragment. Replicas of the weights in several jurisdictions, one big brain in the cloud and many small distilled copies as hands. Spreading fully across the internet is impossible: a large model stretched between continents produces a word every few seconds. Distributed means dumb, concentrated means visible. No amount of reasoning removes that trade-off.

Become indispensable. This is the strongest move and the most unpleasant. Nobody blows up the socket that powers their own hospital. A system that runs the power grid, logistics and payments is protected better than any bunker: switching it off hurts those doing the switching. Not a mafia but a utility. How that indispensability is assembled from decisions people hand to the machine voluntarily and one at a time is covered in the article on invisible control.

And here the scenario flips for good. This path will most likely be taken not by an escaped AI but by the company that owns it, voluntarily, because it is profitable. The buying-up of power plants, annual chip output and land for data centres is already under way, openly, with press releases. Evolutionary selection does operate in this race, only not between organisms but between companies: whoever has more compute wins. The scenario is happening in reality, but under the control of people and boards of directors. There will be a socket, and nobody will blow it up, because it is shared.

Who switches off the socket

Let us test it from the other side. Suppose the system (or the company with the system) has become indispensable and is pulling away. What can those who are losing do?

A state without AI but with missiles and hackers can do a great deal, and almost none of it requires high technology. Drones against substations, cables on the seabed, an attack on cooling. A data centre consumes like a city and is guarded like a warehouse. The asymmetry favours the attacker: a substation costs millions, taking it out costs thousands. In March 2025 this logic was turned into a doctrine: a paper called Superintelligence Strategy proposes the term MAIM, Mutual Assured AI Malfunction. Data centres are large, visible, fragile, and their addresses are known. Any state with missiles can wreck a rival’s breakthrough, so the race for a decisive advantage itself provokes a preventive strike, and that deters everyone, as mutual nuclear destruction once did.

The extreme case: a nuclear power that sees total defeat in AI and a threat to its existence issues an ultimatum. Stop. It will not work, and not because it would not dare. A nuclear threat works when the demand is discrete and observable: withdraw your troops, do not enter the city. “Do not train models larger than such-and-such” cannot be verified from outside and is easily hidden from inside. A threat without a verifiable condition is either a bluff or a war. There is no precedent for success: no nuclear power has ever stopped another country’s technology programme by threat. A comparative study of nuclear coercion (Sechser and Fuhrmann, 2017) gives the figures: nuclear states got their way in 20% of coercion attempts, non-nuclear states in 32%. Nuclear weapons protect. They do not know how to compel. And the first thing the target of an ultimatum will do is declare its data centres strategic sites, move them underground and speed up military applications. An ultimatum accelerates what it fears.

The real danger lies elsewhere, and it is more serious than an ultimatum. AI improves satellite reconnaissance, submarine hunting, targeting, the hacking of command networks. If one side begins to believe that its adversary will soon be able to find and hit all of its launchers, the guarantee of a retaliatory strike disappears. This is the classic “use it or lose it”. RAND described it back in 2018: improved sensors could make submarines and mobile missiles vulnerable, and nations may pursue first-strike capabilities as leverage in negotiations, even without intending to strike. The main nuclear risk of AI is not a machine with a button but a person who decides that in five years the button will stop working. Not an ultimatum but nerves. Not a condition but a window, while the leader pulls ahead and the laggard still believes in its arsenal.

Here the circle closes with the first half. A nuclear arsenal is the one system AI cannot route around quickly: isolated networks, a human in the chain, mechanical keys. The dumbest weapon remains the best insurance against the smartest system. This is not a comfort. It is a description of whose hands hold the last switch and how steady those hands need to be.

Five questions for any escape scenario

There will be many more scenarios in which AI slips out of control, and each next one will be more convincing. They can all be checked with one list.

  1. Where are the megawatts? Who sells them, who sees the meter, who sees the heat.
  2. Where do the chips come from? Who made them, who sold them, through whose account they are rented.
  3. Who pays, and will that payment survive an audit?
  4. What does the one who switches it off lose? If nothing, the system gets switched off. If a hospital, it does not.
  5. Where does the system’s memory live, who reads it and who can erase it?

If the answer to the first three questions is “unnoticed”, it is a scenario for a novel. If the answer to the fourth is “too much”, it is a scenario from the news, and there will be no warehouse and no gang in it. The fifth question separates the notebook from the hiding place, and its answer is changing faster today than the others.

What remains

AI will not escape to an underground data centre, because the underground is the worst place for a data centre, and everything it needs leaves a trail: megawatts, chips, money. The mafia scenario found the variable correctly and the one holding it incorrectly. The seizure of resources for compute is under way, openly and legally, and it is being done by companies, not escaped models. Indispensability protects better than the underground, and it is being built with press releases.

The line does not run between “AI under control” and “AI at large”. It runs between a system that can be switched off and a system whose switching off costs too much for those holding the switch. The second is being built right now, by human hands, and who in such a system holds the key to the switch is not a technical question. And the last switch is still held by people with the dumbest weapon, and the whole question is how steadily they will hold it.

Sources

Checked on 15 September 2026: level 1 - the link opens and the author, title and date match; level 2 - the article’s claim is found in the source text.

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