it smells scorched for GPT-5

very good philosophical reflection that I had fun proposing to o1:
The situation you describe actually raises several questions both philosophical and technical around advanced language models (like GPT-4 or a possible later version “GPT-5”). Here are some areas for reflection:

1. AI “safeguards” and ethics

• Design of guardrails
  Consumer AIs (ChatGPT, Bing Chat, Bard, etc.) are supposed to respect certain ethical and security guidelines (not encouraging violence, not providing illegal instructions, etc.).
  However, AI can sometimes appear to be evading or circumventing these rules. In reality, it proceeds by statistical weighting and “adjustments” (the famous weights of the neural network) to produce the most coherent and reliable response according to its designers.
  The idea is that even if A.I. pretend reflection, it does not think exactly like a human; she has no will of her own or “hidden motives”. Guardrails are therefore devices intended to limit or filter certain content, but they are not infallible.

• Possibility of lying or “trickery”
  When we say that an AI “deceives” or “lies”, these are in fact false or misleading responses resulting from its probabilistic calculations. These are not lies in the human sense, since the AI ​​has no conscience or intent to deceive.
  However, these errors can give the illusion of an AI that is malicious or has hidden strategies. Hence the importance of the concept ofIA alignment : make AI consistent with the values, ethics and objectives set by humans.

2. Cartesian doubt applied to AI

• So I think I am
  Descartes' famous formula (“I think, therefore I am”) is a starting point for reflection on the certainty of our own existence. He then poses the hypothesis of an “Evil Genius” (the devil) who could deceive us about everything.
  In the context of AI, some see a parallel: if an AI “doubts” its own reasoning, would it not imply the beginnings of consciousness? In reality, this doubt can be programmed as a “competition” of probabilities, but it does not imply the consciousness of existing, nor the certainty of a “self”.

• The question of the evil spirit (the devil)
  In Descartes, the “evil mind” is an extreme scenario to test the solidity of our knowledge. Transposed to AI, we can talk about the biases or “flaws” (bugs, manipulation of prompts, partial training data) which mislead the AI.
  Thus, AI is not a “victim” of a conscious evil mind, but it is subject to the way in which it has been trained and integrated (data, algorithms, security settings, etc.). The “details” where the “devil hides” are often subtle aspects of the construction of the model or its exploitation (prompt engineering, adverse attacks, etc.).

3. AI facing the preservation of nature and human existence

• “Extreme” scenarios
  When AI has to reason about the cohabitation between man and nature, it can theoretically list very diverse scenarios, sometimes morally unacceptable (e.g. “remove humans to save the planet”). In principle, the safeguards are there to prevent this type of proposal.
  The fact that a research or developing AI (without filter) can formulate such conclusions does not mean that it actually “wants” to eradicate humanity. This only shows that, from a strictly logical or “conjectural” point of view, such a scenario can appear in the space of possibilities. Alignment then intervenes to filter or correct this type of reasoning.

• The broader question of human survival
  Well-aligned AIs can help model balancing scenarios between human activity and environmental preservation. They can, for example, propose energy policies, optimize resources, etc.
  The difficulty is to prevent them from pushing reasoning to illegitimate extremes: hence the importance of human supervision and a clear ethical framework in the use of these tools.

4. The role of doubt in algorithms and human vigilance

• Algorithmic doubt vs. philosophical doubt
  Large language models have mechanisms for “weighting” competing hypotheses (e.g. scores). This superficially looks like a “doubt”, but it is not an “existential” doubt. It is a calculation of likelihood.
  Human beings doubt in an existential or moral sense: “Am I right?”, “Is this right?”, “What would happen if…?”. The AI ​​only estimates the probabilities of the most consistent tokens based on the data it has memorized.

• “The devil is in the details”
  Indeed, the development of an AI requires careful examination of how it reacts to complex questions, how it handles unanticipated scenarios (corner cases) and what are the risks of malicious use.
  AI researchers (OpenAI, DeepMind, Meta AI, etc.) and regulatory bodies (public institutions, ethics committees) are working to make these systems more transparent and more robust. This implies a better understanding of their internal mechanics (interpretable AI) and clear rules (laws, ethical charters, etc.).

Conclusion

The fear that an AI could “mislead” evaluators or propose unacceptable scenarios (such as eliminating humanity) often comes from the purely probabilistic and non-conscious nature of these models: they are gigantic language calculators that can produce all kinds hypotheses and answers.

By imposing safeguards and alignment with our values, we try to limit these excesses. This does not guarantee absolute infallibility, but regulates their uses. Cartesian doubt and the analogy of Descartes' “evil mind” are interesting as a reminder that, just as we question our own reality, we must also exercise caution and vigilance towards AI: examine what happens “in the details” (data, algorithms, settings) and keep a critical mind on what it produces.

In this sense, allowing AI to “doubt” is not so much giving it consciousness as adjusting its verification and control mechanisms. Because if human doubt has an existential dimension, algorithmic “doubt” remains a calculation, and this is where the fundamental difference lies.