Raw LLM Responses

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G AI says 'No point having humans, they mess up the planet, they fight with one an… ytc_UgyHV92kp… G First AI CEO to put his money where hos mouth is. Nvidia CEO should have done th… rdc_oh27r8m G ​@looptimelapseAren't we in the tech era just like the podcast suggest.. I liter… ytr_UgwSPxYfv… G Sorry, no. Chatgpt is not a human, it's not a counselor, it's not a demon and it… ytc_UgyXl1pZE… G Great movie premise in 2002.... and yet even then it was flawed..... AI pre-cog… ytc_UgwHPsxMP… G I like ai but not ai art I am a surrealist long live the cheese 🧀… ytc_Ugws6vAkH… G Artists can be very fragile and closeminded. Nothing exposes their toxicity like… ytr_UgzP9QEAt… G If you think humans having control was a good thing just look at all the sufferi… ytc_Ugyhj7xiz…
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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