livetotal likes counted 6,477livevideos indexed 12liverecord pace 13.3 / minlivehottest minute 32 likeslivetotal likes counted 6,477livevideos indexed 12liverecord pace 13.3 / minlivehottest minute 32 likes

Dario Amodei of Anthropic’s Hopes and Fears for the Future of A.I.

Hard Fork · 1:09:35 · indexed 2026-06-11

🎙 Speaker-attributed: only Dario Amodei's own words were counted (38:52 of speech) — likes/min uses their speaking time.

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70% filler
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  1. we train claude 3 7 you know more to focus on these real world tasks we also felt it was a bit weird that you know in the reasoning models that folks have offered it's generallyproper use

  2. have offered it's generally been there's a regular model and then there's a reasoning model this would be if a human had two brains and it's like you know you're like you can you can talkproper use

  3. and then there's a reasoning model this would be like if a human had two brains and it's you know you're like you can you can talk to brain number one if you're asking me afiller · filler

  4. reasoning model this would be like if a human had two brains and it's like you know you're you can you can talk to brain number one if you're asking me a quick question like what'sfiller · filler

  5. you're like you can you can talk to brain number one if you're asking me a quick question what's your name and you're talking to brain number two if you're asking me to like prove aproper use

  6. quick question like what's your name and you're talking to brain number two if you're asking me to prove a mathematical theorem because i have to like sit down for 20 minutes it'd no no nofiller · filler

  7. to brain number two if you're asking me to like prove a mathematical theorem because i have to sit down for 20 minutes it'd no no no comment brutal no comment on any relevance to itfiller · filler

  8. evolution would be the model decides for itself what the appropriate time to think is right humans are that or at least can be like that right if i ask you your name you know you'reproper use

  9. itself what the appropriate time to think is right humans are like that or at least can be that right if i ask you your name you know you're not like huh how long should iproper use

  10. or at least can be like that right if i ask you your name you know you're not huh how long should i think of it give me 20 minutes right to determine my name butquotative · filler

  11. it give me 20 minutes right to determine my name but if i say hey you know i'd you to do an analysis of you know this stock or i'd like you to you know proveproper use

  12. say hey you know i'd like you to do an analysis of you know this stock or i'd you to you know prove this mathematical theorem you know humans who are able to do that taskproper use

  13. but it's still valuable to give a bound on how long it'll think so we've kind of taken a big step in that direction but we're not to where we want to be yet yeah sofiller · filler

  14. thing called claude code which is more of a command line tool but i think also on things you know complex instruction following or just like here i want you to understand this document or youproper use

  15. a command line tool but i think also on things like you know complex instruction following or just here i want you to understand this document or you know i want you to use this seriesproper use

  16. safety so not dangerous per se and i always want to be clear about this because i feel there's this constant conflation of present dangers with future dangers it's not that there aren't present dangers andproper use

  17. a lot you know i think i said i even testified in front of the senate for things you know misuse risks with for example biological or chemical warfare or the ai autonomy risks i saidproper use

  18. 7 sonnet as we wrote in the model card we always do these you could almost call them you know trials like trials with a control where we have you know some human who doesn't knowfiller · filler

  19. wrote in the model card we always do these you could almost call them like you know trials trials with a control where we have you know some human who doesn't know enough much about somefiller · filler

  20. with a control where we have you know some human who doesn't know enough much about some area biology and we basically see how much does the model help them to engage in some mock badproper use

  21. some new threat vector that wasn't there before i think it's very important to say this isn't about oh did the model give me the sequence for this thing did it give me a cookbook forfiller · filler

  22. capable of if not if not mitigated that you know would would somewhat increase the risk of something really dangerous or really bad happening you know like put yourself in the eyes of like a lawfiller · filler

  23. know would would somewhat increase the risk of something like really dangerous or really bad happening you know put yourself in the eyes of like a law enforcement officer or you know the fbi or somethingfiller · filler

  24. of something like really dangerous or really bad happening you know like put yourself in the eyes of a law enforcement officer or you know the fbi or something there's like a new threat vector there'sfiller · filler

  25. yourself in the eyes of like a law enforcement officer or you know the fbi or something there's a new threat vector there's a new kind of attack it doesn't mean the end of the worldfiller · filler

  26. in some way there are folks who are kind of you know independent who want to analyze data you know that's maybe kind of like the prosumer side of things right and i think within thatfiller · filler

  27. are kind of you know independent who want to analyze data like you know that's maybe kind of the prosumer side of things right and i think within that there's a lot to go in termsfiller · filler

  28. they are at helping you with anything that's focused on kind of productivity or even a complex task planning a trip even outside that you know if you're just trying to make a personal assistant toproper use

  29. i guess i'm saying there's there's the market is more segmented than you think it is it looks it's all one thing but it's more segmented than you think it is yeah so i think thisproper use

  30. is you know we look at the state of the world and you know we have these autocracies china and russia and i've always worried i've worried you know maybe for a decade that ai couldproper use

  31. but but i don't want the autocratic governments to have a military advantage and so you know things the export controls which i discussed in that post are one of the things we can do toproper use

  32. the plan a it just it's just not a realistic way of looking at the world these seem really yeah i mean you know i have to tell you i was deeply disappointed in the summitproper use

  33. this essay machines of loving grace about all the great things you know part of that essay was man for someone who worries about risks i feel like i have a better vision of the benefitsquotative · filler

  34. things you know part of that essay was like man for someone who worries about risks i feel i have a better vision of the benefits than a lot of people who spend all their timeproper use

  35. a lot of people who spend all their time talking about the benefits but but in the background i said as the models have gotten more powerful the amazing and wondrous things that we can doproper use

  36. continues on it doesn't care i had a yeah so yeah i think i think it did feel that to me you know the thing i've started to tell people that i think you know maybeproper use

  37. say so what did the officials what did the company people what did the political system do and probably your number one goal is don't look like a fool and so i've just been encouraged likefiller · filler

  38. company people what did the political system do and like probably your number one goal is don't look a fool and so i've just been encouraged like don't be careful what you say don't don't lookproper use

  39. like probably your number one goal is don't look like a fool and so i've just been encouraged don't be careful what you say don't don't look like a fool in retrospect and you know aquotative · filler

  40. a fool and so i've just been encouraged like don't be careful what you say don't don't look a fool in retrospect and you know a lot of my thinking is just driven by like youproper use

  41. look like a fool in retrospect and you know a lot of my thinking is just driven by you know aside from just wanting the right outcome like i don't want to look like a foolfiller · filler

  42. lot of my thinking is just driven by like you know aside from just wanting the right outcome i don't want to look like a fool and you know i think i think at that conferencefiller · filler

  43. driven by like you know aside from just wanting the right outcome like i don't want to look a fool and you know i think i think at that conference like you know some people areproper use

  44. i don't want to look like a fool and you know i think i think at that conference you know some people are going to look like fools we're yeah so i've been i've been watchingfiller · filler

  45. you know i think i think at that conference like you know some people are going to look fools we're yeah so i've been i've been watching this for you know 10 years right i've beenproper use

  46. we are actually in the world where things are going to happen you know i'd give numbers more 70 and 80 percent and less like 40 or systems that are much smarter than humans at almosthedge · filler

  47. things are going to happen you know i'd give numbers more like 70 and 80 percent and less 40 or systems that are much smarter than humans at almost everything you know i maybe 70 80hedge · filler

  48. a few thousand people right when you know you would you would just you know you'd talk to you know just like you know super weird people who you know believe and basically no one elsefiller · filler

  49. right when you know you would you would just you know you'd talk to like you know just you know super weird people who you know believe and basically no one else did now it's morefiller · filler

  50. you know super weird people who you know believe and basically no one else did now it's more a few million people out of a few billion and yes many of them are located in sanhedge · filler

  51. focus on this issues let alone the person in louisiana let alone the person in kenya it seems that's actually a big barrier right because i think addressing the risks while maximizing the benefits i thinkproper use

  52. at all but they require subtlety and they require a complex conversation once things get polarized once it's we're going to cheer for this set of words and boo for that set of words nothing nothingquotative · filler

  53. and boo for that set of words nothing nothing good gets done look bringing ai benefits to everyone curing you know previously incurable diseases that's not a partisan issue the left shouldn't be against it youproper use

  54. you know in terms of what we're seeing in our own model we're going to be really careful you know if if we really declare that a risk is present now we're going to come withfiller · filler

  55. will tell you when there is danger imminently we have not warned of imminent danger yet think that's 60 percent of the reason really no no i think like you know i think it relates tohedge · filler

  56. warned of imminent danger yet think that's like 60 percent of the reason really no no i think you know i think it relates to this like this present and future thing like people look atfiller · filler

  57. percent of the reason really no no i think like you know i think it relates to this this present and future thing like people look at like you know the chatbot they're like we're talkingfiller · filler

  58. no i think like you know i think it relates to this like this present and future thing people look at like you know the chatbot they're like we're talking to a chatbot like what whatfiller · filler

  59. you know i think it relates to this like this present and future thing like people look at you know the chatbot they're like we're talking to a chatbot like what what the fuck are youfiller · filler

  60. to this like this present and future thing like people look at like you know the chatbot they're we're talking to a chatbot like what what the fuck are you stupid like you think the chatbotquotative · filler

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