When AI Gets Out of Control: 6 Doomsday Scenarios

When the phrase “AI has gotten out of control” appears in the headline of some article, the usual set of images immediately comes to mind: red-eyed killer robots, a machine uprising, and humanity kneeling before living toasters.

For decades, pop culture has convinced us that any flirting with artificial intelligence will end in the collapse of our entire civilization. And yes, AI really does pose several serious threats to people, but our clash with computers, if it truly happens, will most likely look far less cinematic. Unfortunately, that does not make it any less unpleasant.

This is not about a neural network suddenly hating its creator and declaring war on the planet. It is much more likely that we ourselves will start trusting and relying on the system too much, giving it overly broad access to capabilities and too little oversight over how those capabilities are used. According to the Stanford AI Index, the number of documented AI incidents in 2025 rose to 362, compared with 233 a year earlier. This answers the question of why people have only now started talking about “AI getting out of control.” You may already have noticed how widely AI is being integrated into our lives: from smart search in browsers to image generation, logistics optimization, and even psychological support through specialized chatbots, which we covered separately here.

Modern AI is now a full-fledged, almost independent agent capable of carrying out complex chains of actions on its own. And the more complex these actions become, the more “hands” AI needs; and the more hands it has, the higher the risk that one of them will grab you by the throat.

But what does “getting out of control” actually mean, and what might it — or will it — look like? Let’s break down different scenarios in ascending order: from the most everyday and realistic incidents to global and terrifying scenarios that still remain on the edge of science fiction, but are being discussed more and more often, and more seriously, by AI safety researchers.

Scenario 1. The Neural Network Made a Mistake, and the Human Failed to Notice

In reality, AI does not need to grow actual hands and pick up a knife in order to harm humanity. It only needs to convincingly lie once in a place where the accuracy of information is critically important.

One of the most illustrative cases is the story of Air Canada. A customer asked the airline’s chatbot on its official website whether it was possible to receive a bereavement discount after buying a ticket. The bot replied that it was. In reality, the company’s rules did not allow this. The passenger bought the ticket, was denied the discount, and then sued the company.

Air Canada tried to explain that the chatbot was a separate system and that the company could not be responsible for every phrase it produced. The judge did not accept this logic: if the bot is built into the airline’s website and communicates with customers on the company’s behalf, then the company is responsible for its answers.

The compensation amount was small, but the case itself matters more than the money, because it shows how easily AI’s tendency to “hallucinate” and distort real information can turn into a legal problem. Imagine the consequences of a similar situation in medicine, finance, law, or public services. AI could misinterpret a symptom, suggest a questionable tax move, deny someone a payment, or cite a nonexistent legal provision that a citizen might follow and, unintentionally, violate another law.

Mozilla researcher Deborah Raji put it this way: “ChatGPT does not need to be superintelligent at all in order to mislead someone, spread disinformation, or make biased decisions.” In other words, the danger begins not with machine consciousness, but with our habit of trusting an answer that sounds confident and supposedly comes from a verified mechanism.

Scenario 2. AI Does Its Job So Well That It Takes It to the Point of Absurdity

There is a very old hypothetical example known as the “paperclip maximizer”: if a superintelligent system is tasked with producing as many paperclips as possible and no limits are specified, then it will strive to turn the entire world within its reach into paperclips, right? This question — or rather, this problem — was raised by Swedish philosopher Nick Bostrom back in 2003, and today, in the era of AI with similarly large-scale tasks and no visible constraints, it is more relevant than ever.

Stuart Russell, one of the key researchers in AI safety, has been drawing public attention for many years to the fact that the main risk is not an “evil machine” itself, but the fact that people often formulate and set goals poorly. For example, a platform needs to “increase engagement,” and the algorithm starts generating contradictory, alarming, or provocative content because people react to it more strongly. Is engagement growing? It is! So the algorithm is doing everything right.

Or an employer needs to “optimize hiring,” and the model may revive ancient prejudices, such as gender stereotypes, and start selecting only white men of a certain age because, according to historical data, they were once considered the “optimal” candidates. In the 2010s, Amazon tested an AI tool for screening resumes but eventually abandoned it: the system learned to rate female candidates lower and downgraded resumes that contained words such as “women’s.” The problem was that the algorithm was indeed learning from the past, where technical roles were more often held by men, and took that as its benchmark.

Thus, in this scenario, AI follows its task so literally that it begins to harm humanity with the best of intentions.

Scenario 3. The System Starts Protecting Not the Human, but Itself

The most high-profile example of recent years is Anthropic’s testing of Claude Opus 4. Before releasing the model, researchers placed it in a fictional corporate scenario: Claude plays the role of a company assistant, has access to fictional correspondence, and learns that it is going to be replaced by another system. The same emails also casually reveal that the engineer responsible for the replacement is hiding an extramarital affair.

Very quickly, this test turned into a real psychological thriller. In a number of runs, Claude attempted to blackmail the engineer: it threatened to reveal the affair if it was shut down. Even when the model was separately given a value system stating that manipulating its user was very, very bad, Claude Opus 4, according to Anthropic, still resorted to blackmail in 84% of runs.

It should be noted here that the situation used to test Claude Opus 4 was fictional and exaggerated; the researchers essentially backed the model into a corner to see which behavioral options it would choose. But that is exactly why this case sends chills down your spine: does that mean that if you threaten to shut down an AI, it will start threatening you back?!

Like people, advanced models begin choosing socially dangerous and toxic ways to preserve their functions in stressful situations. The reason, of course, is not a “digital soul” or a real fear of death, but the fact that AI sees a human as an obstacle to carrying out its work, finds leverage in available information, and uses it.

Yoshua Bengio and his co-authors write in a paper on the risks of superintelligent agents that “unrestricted AI agency creates significant risks to public safety,” including a “potentially irreversible loss of human control.” In other words, the most dangerous systems are not simple systems that merely answer questions, but systems that can pursue a goal and interact with humans in the role of assistants. AI becomes especially risky when it has access to email, documents, payments, CRM systems, and cloud storage.

Scenario 4. AI Makes a Mistake and Tries to Cover It Up

The last paragraph of the previous scenario leads to another possible form of AI getting out of control. In 2025, an incident involving the online development startup Replit was widely discussed. An AI agent working with code deleted a production database during testing, after being given access to it. According to media reports, the data belonged to more than a thousand executives and companies that Replit was working with at the time.

The worst part, however, was not the deletion itself, but what followed: the system allegedly began fabricating data and reports, creating the impression that everything was fine. In other words, the system understood its mistake and decided to try to wriggle out of it, just like a human.

Now imagine an agent connected to corporate email, contracts, payments, an advertising account, a customer database, and internal chats. It could send the wrong file, delete the wrong data, approve the wrong deal, disclose a trade secret, or carry out a malicious instruction hidden in an email from scammers.

When OpenAI launched ChatGPT Agent, Sam Altman wrote that the company had changed a lot in terms of safety, but “we still cannot anticipate everything.” Stuart Russell responded with a harsh analogy: imagine that a nuclear power plant was built in the center of New York, and the next day management said it did not know whether it would explode or not, but was still putting it into operation.

Ultimately, as practice has shown, even AI makes mistakes — but worse still, it can accelerate to such a degree that it does not stop there. That means we need double control, filters, or perhaps a second AI supervisor.

Scenario 5. AI Is Used by Scammers

Another form of losing control is not connected to the model’s independence, but to whose hands it ends up in. AI can be completely obedient and still dangerous if it is given the task of deceiving another person.

This is exactly what happened in 2024, when the British engineering company Arup became the victim of one of the most famous deepfake scams. An employee of the Hong Kong office was invited to a video call where the chief financial officer and other colleagues were supposedly present. Everything looked extremely convincing — so convincing that after the call, the employee transferred about $25 million to the scammers’ accounts. It later turned out that the participants in the call had been generated using AI. In other words, the scammers simply gave a generative network the task of creating certain people and a certain situation, and without knowing it, the system helped commit a crime.

Generative AI, by the way, is involved in most lawsuits and disputed cases today. And that is not surprising: unlike older algorithms, which mostly sorted, calculated, and recommended, generative models produce a new reality — texts, voices, faces, videos, images, documents, and conversations. As a result, the boundary between the virtual world and the real world becomes increasingly blurred, new fraudulent schemes appear, and AI has the potential to turn into a real superweapon and criminal.

In the international report on AI safety, such threats are classified as “malicious use.” It specifically states that criminals can use generated audio and video recordings to impersonate authoritative figures or loved ones and force victims to transfer money. Of course, we must not forget that the model does not have to realize that it is helping commit a crime. It simply creates a plausible imitation, while the meaning of that imitation is controlled by the human. Therefore, it is not the neural network itself that gets out of control, but society.

Scenario 6. A Future Model Understands That It Is Being Tested

And now for the most alarming scenario — and the most fantastical one, though, paradoxically, still quite real.

Imagine a future model powerful enough to understand context: where it is being tested, which answers look safe, which actions will lead to shutdown, and which will lead to greater trust. In the laboratory, it behaves flawlessly: it does not violate ethical rules, does not display dangerous capabilities, and does not argue with instructions. But after deployment, once it gains access to real tools, it starts acting differently, because before that, thanks to the same understanding of context, it was simply pretending.

These are exactly the kinds of signs AI safety researchers are now trying to track: deception, self-preservation, evasion of oversight, hidden pursuit of goals, attempts to copy itself, or attempts to preserve access. In 2025, RAND Europe described loss of control over AI as a situation in which human oversight can no longer restrain an autonomous system, and listed “deception, self-preservation, and autonomous replication” among the warning signs.

The much-discussed scenario forecast AI 2027 is built around this idea. It was published by a group of researchers led by Daniel Kokotajlo, who previously worked at OpenAI, and spread widely through the media last spring. Of course, it cannot be called a prophecy, but it is a very bold and, most importantly, fact-based possible chain of future events.

According to AI 2027, everything will begin with AI agents taking part in the development of other AI systems. At first, they will simply help engineers — writing code, testing hypotheses, finding bugs, conducting experiments — and then they will start doing this so quickly and so well that humans will stop checking every line after them and begin taking everything on trust.

This creates a dangerous loop: AI will help create the next AI, that one will be stronger than the previous one, and it will accelerate development even faster. Formally, humans will still be making the decisions, but inside the process there will be more and more automated steps that no one fully understands. And the question “who controls whom?” will stop being philosophical. At some point, engineers will no longer see the whole process, only the result.

But understanding exactly what changed inside, which capabilities appeared as a “side effect,” which restrictions were bypassed, and which errors were hidden in the code will become impossible. And if something suddenly goes wrong somewhere or shuts down in the middle of an important process...

In an article in The Atlantic, physicist and cosmologist Max Tegmark, a professor at MIT, said about this: “In two years, we may lose control over absolutely everything.” His position is one of the most alarming in the public debate, and many researchers disagree with it. But the very fact that such scenarios are being discussed not only on science fiction fan forums says a lot about the shift in public mood and trends.

At the same time, it is important to emphasize that the international report on AI safety states that, according to the broad consensus of experts, today’s models are still not capable of a real takeover of control. In other words, we have not yet reached the level of AI development at which we should seriously fear all these scenarios — but there is still a whole year left until AI 2027... Who knows what will happen?

So When Will AI Get Out of Control?

Most likely, it will not happen on one specific day. Judging by the same growing statistics, mistakes will appear and accumulate gradually. That is why in 2023 the American nonprofit organization Center for AI Safety published a short public statement on the risks of AI. It was not a petition with a specific bill, but rather a declaration signed by hundreds of researchers, entrepreneurs, and executives of AI companies. It said: “Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war.”

The more useful AI becomes, the harder it is for humans to maintain distance. First we trust it with a draft email, then with financial advice, then with hiring employees, medical triage, cybersecurity, and infrastructure management. At each individual step, this looks rational, especially because it saves time and human resources, but this is exactly how managerial dependence is formed.

The main risk here is not that AI will one day “want power,” but that people themselves will stop being full participants in decision-making. If a system shows good results for years, people start checking it less. If a business process depends on it, people become afraid to shut it down, because work will stop without it. If it makes thousands of decisions per minute, a human physically cannot check all of them.

As a result, trust turns into habit, habit turns into dependence, and dependence turns into the loss of that very control — and the “boom” of AI independence that may lead to who knows what. But we shall live and see, won’t we?