Raw LLM Responses

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G If studios wants AI to do the work, then soon everyone at the studios will be la… ytc_UgywFpwcC… G @Updog89 Being a Kennedy is now an automatic cancel-worthy character flaw? RFK?… ytr_Ugy1i17Ds… G "But I asked ChatGPT if it was lying, and it said it wasn't! How could that go w… ytc_Ugwrsk38f… G Insane : wants to stop AI agents but can't think in any other way than labor so… ytc_Ugy3HGsZT… G "This is a good beginning of my plan to dominate the human race" - I tell you fo… ytc_UgzKMNZXn… G You don’t care. Not one politician cares about AI and how it will take over 90% … ytc_Ugym4zZcA… G They want to convince you that it's AI that controls everything. But its another… ytc_Ugy2z2Ag8… G It seems like many Koreans think that accents are somehow more important than ac… rdc_clvm8o6
Comment
NO, NO , NO... What people call “AGI” right now is mostly marketing. LLMs and “agents” are useful, but they are not general intelligence. LLMs scale with a clear problem: you burn vastly more compute for smaller gains. That diminishing return matters because it turns “just scale it” into a power and cost wall. A system that needs huge GPU farms to get marginal improvements is not on a clean path to human level general intelligence. And the “agent” layer doesn’t fix the core issue. Agents are task loops: call the model, check output, call tools, retry, patch failures, repeat. That can reduce hallucinations by adding filters and verification steps, but it’s still a brittle routine. It’s closer to automated workflow than a mind. Iterating until you get a coherent answer is not the same as understanding, learning, or reasoning robustly across new situations. So yes, LLMs have a scaling and efficiency problem, and agents are mostly a wrapper that compensates for weaknesses. That combination can produce impressive demos, but it’s not AGI.
youtube 2026-02-06T09:4…
Coding Result
DimensionValue
Responsibilitynone
Reasoningconsequentialist
Policynone
Emotionoutrage
Coded at2026-04-27T06:24:53.388235
Raw LLM Response
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