Raw LLM Responses

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G Clearly this AI robot dint read Sun Tzu book art of war. Good job keep this secr… ytc_UgxcAAT2m… G When AI starts messing up business they'll hire you back. Technology never works… ytc_UgyPTphgk… G Right now, AI is in its infancy, but powers that be already are using it as an e… ytc_UgwQIWxgo… G How is this not the bigger news? Nvidia makes publicly available ARTIFICIAL INT… rdc_imm8vft G You're forgetting that what would we do with Ai, the things that we can't believ… ytc_UgwZBpU5v… G AI art will not replace human art. But it will definitely outproduce it and you … ytc_UgzhgNA53… G I don't believe in AI sentience. I do believe that in the future we'll have huma… ytc_Ugz9MVwis… G Why haven't such brilliant minds not invented an AI to figure out how to regulat… ytc_UgxPzqb_K…
Comment
You should also mention the objective bottleneck of LLMs: all of their learning is squeezed through the single objective of next-token prediction. Large language models are trained to predict the next word (or token) given the previous context. No matter how complex the task appears—reasoning, summarizing, coding, or answering philosophical questions—the underlying training objective is always the same: maximize the probability of the next token. This creates a structural bottleneck. Because everything must be optimized through this single objective, the model is not directly trained to “know,” “understand,” or “reason” in the human sense. Instead, it learns statistical patterns that help it continue text in a way that resembles high-quality human responses. Any reasoning ability, uncertainty expression (such as saying “I don’t know”), or structured problem-solving emerges indirectly from this next-token training objective rather than being explicitly optimized for truth or calibrated uncertainty.
youtube AI Moral Status 2026-03-01T12:5… ♥ 2
Coding Result
DimensionValue
Responsibilitynone
Reasoningunclear
Policyunclear
Emotionindifference
Coded at2026-04-27T06:24:53.388235
Raw LLM Response
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