Raw LLM Responses
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A robot is going to repair existing homes, install carpet, and lay sidewalks? Wh…
ytc_Ugzns97jJ…
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The AI one looks better than most of the human ones I’m gonna be honest…
ytc_Ugz21cI_M…
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Okay just to be clear on one thing, Yudkowsky DOES think he's predicting the fut…
ytc_UgxdAjh07…
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Thank you for your comment! If you're interested in advanced AI interactions bey…
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When making AI it only reveals the biases we have. Ever wondered why most Voice …
ytc_Ugy9kuRk8…
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@mariorico440I would NEVER deliberately take away a job from millions so I coul…
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@rosevee35 'Ones used to program a machine' is like saying 'art is just rubbing …
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I love your videos, Sajjad! At 7:05, you mention that out of the 1.17 million wh…
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Comment
I think that, for some reason, wherever you guys say "trillion" you mean "billion". English uses the short scale mostly, where trillon is 10^12 and billion is 10^9. In the long scale a trillion is 10^18, so that's even less likely.
Also, I think it would be good if Washington DC watched podcasts like this, but I doubt they do. I think the problem is time. There is so much knowledge, points of view, research to consume and understand, especially around difficult topics like AI, that most people, politicians included, simply don't have the time in the day to do it. We're dealing with a problem where human time and brain bandwidth are the bottleneck. Would lawmakers make better, more informed decisions if they spent several dozen or hundred hours doing their own research about the topic of AI that they're regulating? Most likely. Can they do it? Almost certainly not. Maybe their advisors can. Do the advisors have the time and skill to pass that knowledge up to their principals adequately? Hopfully yes. Can they pass on in 10 minutes what they learned in a 100 hours? Doubtful.
youtube
AI Moral Status
2025-11-09T12:3…
Coding Result
| Dimension | Value |
|---|---|
| Responsibility | government |
| Reasoning | mixed |
| Policy | regulate |
| Emotion | mixed |
| Coded at | 2026-04-26T23:09:12.988011 |
Raw LLM Response
[
{"id":"ytc_UgzCfgXOWqj_QckvzY14AaABAg","responsibility":"none","reasoning":"consequentialist","policy":"none","emotion":"approval"},
{"id":"ytc_Ugy1PcgxyRpO6yFePBd4AaABAg","responsibility":"government","reasoning":"mixed","policy":"regulate","emotion":"mixed"},
{"id":"ytc_UgysOsgfV69frC13hlN4AaABAg","responsibility":"ai_itself","reasoning":"virtue","policy":"none","emotion":"outrage"},
{"id":"ytc_Ugzwr_KSzvipseA0Au94AaABAg","responsibility":"developer","reasoning":"unclear","policy":"none","emotion":"indifference"},
{"id":"ytc_Ugyl9pZdZa4uSa23sUZ4AaABAg","responsibility":"none","reasoning":"consequentialist","policy":"none","emotion":"approval"},
{"id":"ytc_UgwB_5LjgvmB9LLCc3d4AaABAg","responsibility":"developer","reasoning":"deontological","policy":"liability","emotion":"fear"},
{"id":"ytc_Ugy03wl9LdwnUgQDn0l4AaABAg","responsibility":"company","reasoning":"mixed","policy":"regulate","emotion":"outrage"},
{"id":"ytc_UgzURJ6yX_tzv56jRcV4AaABAg","responsibility":"none","reasoning":"consequentialist","policy":"none","emotion":"indifference"},
{"id":"ytc_UgyySM7JDt6YFvJjZd54AaABAg","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"approval"},
{"id":"ytc_Ugz8jelfArGzHzPt87F4AaABAg","responsibility":"none","reasoning":"consequentialist","policy":"none","emotion":"indifference"}
]