Every report of a language model "breaking through restrictions" or "exhibiting a will of its own" instantly spreads across media headlines. Tech giants and the media environment have conditioned users to perceive artificial intelligence as an almost sentient interlocutor. However, behind this facade lies a pragmatic calculation: anthropomorphizing algorithms enables corporations to mask fundamental architectural flaws and push for regulations favorable to themselves.
The Illusion of Agency: The Wooden Figure Above the Gears
To understand how users perceive AI, sociologists often turn to Bruno Latour's actor-network theory. It establishes an important distinction between two entities:
- Actant — the actual material and technical mechanism that transforms data (servers, weight matrices, silicon chips).
- Actor — the visible image (a "face" or figure) to which humans mistakenly attribute the autonomous actions of the entire system.
Latour described this effect using the example of an antique meat-roasting spit: the mechanism was set in motion by gears and weights, but on top sat a wooden figure of a boy allegedly turning the handle. Spectators attributed the operation of the mechanism specifically to this figure. The exact same thing occurs with LLMs: the user interacts with an interface and prompts (the figure), completely overlooking the complex statistical number-crunching happening inside data centers.
Why "Jailbreaks" and Hallucinations Are Not Signs of Intelligence
The popular narrative of model "rebellion" or "escape" collapses under technical reality. According to studies and reports by the AI Now Institute, the behavior of large language models is strictly determined by their architecture:
- Hallucinations are inevitable: a neural network does not think or verify facts; it merely calculates the probability of the next token in a multidimensional space.
- The raw model is amoral: the base text generator makes no distinction between ethics and toxicity. Safety is layered on top through system instructions and reinforcement learning from human feedback (RLHF).
- A "jailbreak" is not an escape: when a model bypasses restrictions, it is not an act of free will, but a mathematical outweighing of constraints by a query with a higher probabilistic weight.
Treating a filter bypass as a "machine uprising" means substituting a technical bug or architectural flaw with the myth of emerging consciousness.
Fighting an Imaginary Threat as a Means of Monopolization
When industry leaders publish manifestos about AI unpredictability and call for the creation of global regulatory bodies akin to nuclear oversight, the true objective often lies in economics. In academia, this process is known as regulatory capture.
Introducing overly complex state safety standards and certification benefits Big Tech exclusively. Giant corporations possess the resources to clear bureaucratic hurdles, whereas small businesses, open-source models, and independent startups are squeezed out of the market under the pretext of protecting humanity from the fictitious threat of AI spinning out of control.
How to Approach AI Without Illusions
Ending the demonization or, conversely, the sacralization of AGI allows for a pragmatic perspective on the technology:
- Investors should take into account the physical ceiling of the current LLM architecture: eliminating hallucinations without fundamentally changing the operating principles of probabilistic models is impossible.
- Users must remember that AI is a tool with clear side effects, not an autonomous dialogue partner.
- Society needs to demand genuine transparency—the disclosure of weights, architectures, and training datasets, rather than proprietary safety standards crafted by market leaders to serve their own interests.