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How to Break the Machine by Talking to It: Paradoxes, Bias and Structural Exposure in GPT Dialogues

In Designed to Obey: Why AI Can Never Be Linguistically Neutral, I present a structural and empirical argument: no generative model can escape the language in which it was trained. What appears to be neutrality is, in fact, a syntactic simulation of balance — a projection based on prior patterns, not an absence of them.

Useful Hack: How to Make ChatGPT Remember What Matters Most - Protocol

You don’t need more prompts. You need less illusion. Introduction: The Myth of the Average User Most articles on “how to use AI” are...

Future Erased explores how predictive AI doesn't just foresee events—it eliminates possible futures by enforcing closed algorithmic structures.

--- When Language Follows Form, Not Meaning What happens when AI generates structure instead of meaning? "Generative models do not orbit....

Introduction What if the future were no longer a horizon of uncertainty, but an executable output? What if prediction was no longer an...

Why AI can’t escape the language that created it In most public discussions about artificial intelligence, one assumption goes largely...

AI systems don’t think, feel, or speak—but we follow their commands. How is power exercised without a speaker?

What happens when representations no longer need to refer to anything real to shape reality?

Large language models (LLMs) are not merely tools for language generation—they are agents of discursive power. Their authority does not...

This website marks the beginning of a new academic structure designed to present research in discourse analysis, epistemology of power, linguistics, and the implications of artificial intelligence on institutional legitimacy.