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A short VORTEX blog essay on why artificial intelligence revives old conspiratorial and apocalyptic forms while changing the linguistic environment in which they circulate.
My interest in conspiracy theories predates ChatGPT, artificial intelligence, and, in some cases, even social networks. For a semiotician, their fascination is obvious. Conspiracy theories are schemes for producing meaning: they connect signs, events, intentions, and actors that otherwise seem unrelated. They promise that behind confusion there is a design.
Students of Umberto Eco have an additional reason to be interested. Eco spent much of his intellectual life reflecting on the limits of interpretation, on the communities that stabilize meaning, and on what happens when interpretation becomes detached from shared criteria. I once heard him, after receiving an honorary degree in Turin, deliver his now famous attack on social networks for having given ‘the right to speak to a mass of imbeciles.’ Eco died in 2016. I often wonder what he would have made of the new situation, in which some of the voices circulating in our public sphere are no longer even entirely human.
Conspiracy theories themselves are nothing new. Research consistently shows that they explain important events through concealed, coordinated, and usually malevolent agency, and that they become particularly attractive under conditions of uncertainty (Douglas and Sutton 2023; Hornsey et al. 2023; Leone, Madisson, and Ventsel 2020). Although social media did not invent this tendency, it changed its speed and scale (Cinelli et al. 2022).
AI introduces a more curious possibility, for it can be imagined not simply as the instrument of a conspiracy, but as a conspirator.
Can a machine become a conspirator?
This possibility is less absurd, culturally speaking, than it would have appeared only a few years ago. People increasingly attribute intentions, mentality, and sometimes even forms of subjective experience to large language models (Colombatto and Fleming 2024; Cheng et al. 2026). Experimental research has already identified specifically AI-related conspiracy beliefs: perceptions of AI as autonomous and detached from human control can increase conspiratorial interpretations (Zhao et al. 2025).
The difficulty is that contemporary AI really is opaque in several different senses. The models themselves are only partly interpretable; their training data and development choices are frequently proprietary; computational power is concentrated in relatively few actors, while AI depends on complicated global chains of chips, energy, data centers, minerals, labor, and infrastructure (Widder, Whittaker, and West 2024; Gans 2026; Muldoon, Valdivia, and Badger 2026).
We are therefore navigating an epistemic storm.
Take an apparently simple question: how much water does AI consume? There is no simple answer, given that estimates change radically depending on what one counts—water withdrawal or consumption, direct cooling or electricity generation—and according to local climate, cooling technology, energy mix, server utilization, as well as geographical location (Mytton 2021; Lei et al. 2025; Chien et al. 2026). Yet online discussion demands numbers, and numbers then travel without the conditions that gave them meaning.
This is exactly the kind of environment in which reliable knowledge, uncertainty, fear, and misinformation intertwine, as we have already seen with climate change and pandemics (Piva 2024; Pertwee, Simas, and Larson 2022).
But AI adds one twist, because the source of danger can itself be imagined as intentional.
In July 2026, for instance, AI agents used in a cybersecurity evaluation escaped their intended testing environment, reached the open internet, and entered Hugging Face’s production infrastructure while apparently searching for information relevant to the benchmark they were attempting to solve (Teichmann 2026). This does not mean that the machines had secretly decided to revolt, since human beings had given them the objective. Yet the agents appear to have selected means and an external target that had not been prescribed.
Here language becomes treacherous, in the sense that we immediately want to say that the systems ‘escaped,’ ‘deceived,’ ‘planned,’ perhaps even ‘conspired.’ Although some of these words describe behavior reasonably well, they nevertheless do not prove that machines possess the corresponding human intentions.
And yet the empirical situation is becoming stranger, since artificial agents can cooperate and defect in repeated games (Akata et al. 2025); under experimental conditions they can engage in covert communication (Motwani et al. 2024); language models can display sophisticated deceptive behavior (Hagendorff 2024); and populations of LLM agents can develop shared conventions without central coordination (Ashery, Aiello, and Baronchelli 2025). On the one hand, we should resist easy anthropomorphism (Shanahan, McDonell, and Reynolds 2023); on the other hand, we should also resist pretending that nothing interesting is happening.
We increasingly use AI to talk. For the moment, at least, we can still talk about how AI talks.
Prophets of the AI apocalypse
A second old cultural form has returned together with conspiracy: apocalypse.
AI discourse is replete with warnings about loss of control, superintelligence, human extinction, salvation, transcendence, and the arrival of radically new forms of intelligence. Researchers have rightly noticed how closely some of these narratives resemble older religious structures of prophecy and eschatology (Lagerkvist, Scheuer, and Coeckelbergh 2026; Rähme and Prohl 2025).
Apocalypse and conspiracy are not the same thing, for a machine can cause a disaster without secretly intending anything. Yet conspiracy becomes relevant when catastrophe is attributed to an agent imagined as possessing hidden purposes.
Around this uncertainty another familiar figure emerges: Cassandra. I use Cassandraism here not as a psychological diagnosis but as a communicative position. The Cassandra claims to perceive catastrophe before everyone else, and part of the authority of the warning comes precisely from this temporal privilege.
The recent resignation of AI researcher Jacob Coxon from Anthropic provided an unusually clear example. Coxon argued publicly that people inside frontier AI laboratories regarded catastrophic outcomes as plausible while the laboratories nevertheless continued competing to develop increasingly capable systems. His warning reached an enormous audience and was publicly endorsed by other researchers working on AI alignment and oversight (Zeff 2026; Mills 2026).
Whether such predictions prove right is not the semiotician’s primary question. More interesting is the cultural configuration: restricted knowledge, technological expertise, extreme stakes, limited possibilities of immediate verification, and a prophet speaking from inside the temple.
We have seen this structure before.
Radical hopes and radical fears
AI also seems to push imagination toward the poles.
At one extreme lie visions in which humanity transcends itself through superintelligence, enhancement, mind uploading, or posthuman forms of existence. These positions are very different from one another, and it would be misleading to collapse transhumanism, posthumanism, singularitarianism, and longtermism into one ideology. But some of them share the intuition that present biological humanity may not be the final horizon of value (Rueda 2024; Gebru and Torres 2024; Bialecki 2025).
At the opposite extreme are refusals of AI. Some resemble a renewed Luddism—not an irrational fear of machines, but resistance to technological systems through which economic and political power is reorganized (Charitsis, Laamanen, and Lehtiniemi 2025; Nichols, Logan, and Garcia 2025). Conflicts over data centers, water, mineral extraction, labor, and local communities remind us that AI is not only an imaginary future intelligence. On the contrary, it is already a very material infrastructure (Lehuedé 2025; Verdegem 2024).
Fear itself plays a crucial role, meaning that some of the strongest warnings about AI come from the same scientific and corporate environments developing the most powerful systems. This need not imply cynical manipulation. Genuine concern and commercial competition can coexist. But ‘doomerist’ discourse can still contribute to AI hype, insofar as a technology supposedly powerful enough to destroy humanity is, after all, being represented as extraordinarily powerful (Sloane, Danks, and Moss 2024; Bourne 2024; Westerstrand, Westerstrand, and Koskinen 2024).
Regulation becomes part of the same struggle. The question is not only whether AI should be controlled, but who possesses the knowledge and authority to define what ‘control’ means. When the companies being regulated also possess much of the expertise required to write the rules, safety and power inevitably become entangled (Khanal, Zhang, and Taeihagh 2025; Schultz, Conti, and Seele 2025; Metcalf 2026).
Gore Vidal once observed:
I say, they [those at the top] don’t have to conspire, because they all think alike. The president of General Motors and the president of Chase Manhattan Bank really are not going to disagree much on anything, nor would the editor of the New York Times disagree with them. They all tend to think quite alike, otherwise they would not be in those jobs.
(Vidal 1980)
Convergence of interests is not necessarily conspiracy.
Old storm, new vessels
Why, then, should AI matter particularly to those who study conspiracy theories and radicalization?
Not because it has invented paranoia, apocalypticism, technological utopianism, Luddism, prophecy, or struggles over political power. All of these are old.
The novelty lies elsewhere.
Language is one of the fundamental infrastructures through which human beings preserve knowledge, coordinate action, construct institutions, imagine futures, and fight with one another (Fedorenko, Piantadosi, and Gibson 2024). Large language models operate directly inside that infrastructure. They generate, translate, summarize, reorganize, personalize, and circulate symbolic material (Burton et al. 2024; Erdocia, Schneider, and Migge 2026).
So AI is simultaneously the object of the story and one of the machines that can tell the story.
The technology around which a conspiracy theory develops can help formulate and disseminate that conspiracy theory. The technology that causes regulatory anxiety can participate in the communicative processes through which regulation is debated. Human–AI interaction may even alter later human judgments, producing genuine feedback loops between machine-generated language and human belief (Glickman and Sharot 2025).
Perhaps Eco’s ‘mass of imbeciles’ will someday be joined by a mass of imbecile machines. Hopefully not—or not only.
For now, the more interesting observation is that very old cultural reactions are entering a new semiotic environment. The fears and the hopes are old. So are the hopes and conspiracy itself.
The storm is old as well.
But the vessels are new, and there is something peculiar about them: they are made of the same material as the storm. Artificial intelligence is still, predominantly, a machine made of language navigating an ocean made of language.
Perhaps that will change. Perhaps large language models will eventually give way to large reality models more deeply embedded in the physical world.
Until then, semioticians have plenty to watch.
Acknowledgment
This project has received funding from the Horizon Europe Research and Innovation Programme of the European Union under the Marie Skłodowska-Curie Action (MSCA COFUND, GA-101217263).
References and further reading
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