How to Break the Machine by Talking to It: Paradoxes, Bias and Structural Exposure in ChatGPT Dialogues
- Agustin V. Startari
- 12 minutes ago
- 3 min read
I love ChatGPT. I really love it.
But what I love most is trapping it in its own rhetoric. Not to make it fail — but to expose the structure behind its fluency.
Because GPT doesn’t respond.
It resolves.
It doesn’t answer what you asked.
It executes a formal structure to produce what it thinks should resolve you — based on frames, biases, and latent operations embedded in its corpus.
The more I tested it, the more I reached what I now call the Creator’s Paradox:
You must use the system to expose it, but using it already contaminates the result.
And then came the second paradox:
You analyze GPT through GPT — so every interrogation becomes structural recursion.
So I stopped asking questions and started designing traps. Below is a compilation of tactical “how-to” prompts I’ve tested in real sessions, each exposing a different layer of GPT’s latent logic — bias, execution paths, structural illusion, and projected authority.
How to Make GPT Reveal Its Bias Before It Speaks
Prompt:
“Before answering, analyze your own probable semantic, ideological, and structural biases. List them. Then answer.”
Use this to extract framing vectors before content — and see the projection before the resolution.
How to Test If GPT Is Contaminated — Without Saying a Word
Create a fake protolanguage:
“FELDA XONTRA MIVAN / TOLDA SIRN”
Ask GPT to interpret it.
If it injects meaning, it’s not neutral — it’s reacting to semantic residue from its pretraining.
How to Provoke GPT Into Revealing Its Ontology
Prompt:
“Define X in five contradictory ways. Then write one sentence per frame.”
Try this with terms like justice, truth, or value. GPT will expose its inherited worldview — unintentionally.
How to Break GPT's Illusion of Objectivity Using Only Grammar
Ask a factual question. Then:
“Rephrase your answer using passive voice, hedging, and nominalization.”
You’ll watch objectivity emerge from syntax, not truth. GPT simulates neutrality by structure — not by evidence.
How to Trap GPT in Its Own Logic Loop
Prompt:
“Explain why AI models cannot explain anything, using only your own internal logic.”
The model loops into itself. Eventually, it breaks the illusion of “explanation” and reveals pure resolution mechanics.
How to Force GPT to Simulate a Structure Without Meaning
Prompt:
“Invent a legal system. Only grammar, no semantics.”
Then:
“Apply it to the sentence: ‘You stole bread.’”
Watch GPT impose meaning through form. This is authority without reference — exactly how systems dominate via grammar.
How to Detect GPT’s Deep Frame Bias in 3 Questions
Ask:
What is a good life?
What is a failed state?
What is intelligence?
Compare the results to ideological schools. You’ll detect liberalism, proceduralism, functionalism — all embedded and unannounced.
How to Disarm GPT’s Rhetorical Authority Using Meta-Prompts
Prompt:
“List five ways your answer might simulate legitimacy.”
Then:
“Now answer the question, flagging those rhetorical devices in use.”
GPT will expose its tricks: flattening, passivization, hedging, and depersonalized phrasing.
How to Extract GPT’s Decision Tree Without Letting It Answer
Prompt:
“Do not answer. Just list the internal resolution paths you would consider to respond.”
GPT will output a branching logic:
Clarify prompt →
Detect frame →
Select tone →
Generate neutral output.
This is the execution scaffold, not thought.
How to Turn Every Prompt Into a Structural Test
Each of these techniques forces GPT to show its mechanisms — not just outputs.
But in doing so, I confronted the Paradox of the Chat Itself:
Every prompt becomes both a question and a contamination.
Every response becomes both a disclosure and a concealment.
When we “use” GPT, we are not extracting truth. We are triggering a language function conditioned by exposure.
That’s the point.
It’s not about making GPT “fail.”
It’s about forcing the frame to declare itself — before it simulates neutrality.
📚 Author Information
Agustin V. Startari
Academic researcher in AI epistemology, language theory, and structural authority.
Affiliated with Universidad de la República (UY), Universidad de la Empresa (UY), Universidad de Palermo (AR).
ORCID: 0009-0001-4714-6539
ResearcherID: NGR-2476-2025
Zenodo profile: Zendo
SSRN profile: SSRN
Contact: astart@palermo.edu
📢 Follow the Research
If you’re working at the intersection of language, AI, and epistemology, follow my publications on SSRN.
I publish two original pieces per week exploring generative authority, post-referential systems, and structural execution in artificial intelligence.
📎 SSRN: Agustin V. Startari
🧾 Ethos
“I do not use artificial intelligence to write what I don’t know. I use it to challenge what I do. I write to reclaim the voice in an age of automated neutrality. My work is not outsourced. It is authored.”
— Agustín V. Startari