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Anthropic CEO Dario Amodei: AI's Potential, OpenAI Rivalry, GenAI Business, Doomerism

Alex Kantrowitz · 1:08:36 · indexed 2026-06-11

🎙 Speaker-attributed: only Dario Amodei's own words were counted (59:27 of speech) — likes/min uses their speaking time.

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66% filler
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proper use38
  1. i get very angry when people call me a doomer when someone's this guy's a doomer he wants to slow things down you heard what i just said like youquotative · filler

  2. someone's like this guy's a doomer he wants to slow things down you heard what i just said you know my father died because of cures that could have happened a few years later i understandfiller · filler

  3. i understand the benefit of this technology i'm sure you've heard the criticism from i've never said anything that that's an outrageous lie that's the most outrageous lie i've ever heard ceo jensen wang what's gottenproper use

  4. what's going to happen and that's not to say you know i think there's an incredible number of positive applications of ai right i've kind of continued to talk about that i read this i wrotefiller · filler

  5. years the models just stop getting better for reasons we don't understand or maybe reasons we do understand you know data or compute availability and then everything i'm saying just seems totally silly and everyone makesproper use

  6. been consistent on this over the years is you know there are these terms in the ai world agi and superintelligence like you'll hear leaders of companies say we've achieved agi we're moving on to superintelligenceproper use

  7. over the years is you know there are these terms in the ai world like agi and superintelligence you'll hear leaders of companies say we've achieved agi we're moving on to superintelligence or like it's reallyfiller · filler

  8. and superintelligence like you'll hear leaders of companies say we've achieved agi we're moving on to superintelligence or it's really exciting that someone stopped working on agi and started working on superintelligence so i think thesefiller · filler

  9. terms are totally meaningless i don't know what agi is i don't know what superintelligence is it sounds a marketing term it is marketing yeah it sounds like you know something designed to activate people's dopamineproper use

  10. i don't know what superintelligence is it sounds like a marketing term it is marketing yeah it sounds you know something designed to activate people's dopamine so you'll see in public i never use those termsproper use

  11. seen more progress on say math and code where the models are you know getting pretty close to a high professional level and less on more subjective tasks but i think that is very much ahedge · filler

  12. i see this exponential and i say look people aren't very good at making sense of exponentials right you know if something is doubling every six months then you know two years before it happens itfiller · filler

  13. know if something is doubling every six months then you know two years before it happens it looks it's only one sixteenth of the way there and so we are sitting here in the middle ofproper use

  14. for two years i'm not saying it will but suppose it continued for two years you know you're well into the 100 billions i'm not saying that'll happen i'm saying the situation is that when you'refiller · filler

  15. get fooled by it two years away from when the exponential goes totally crazy you know it looks it's just starting to be a thing and so that's the fundamental dynamic you know we saw thatproper use

  16. the fundamental dynamic you know we saw that with the internet in the 90s right where it was you know networking speeds and the underlying speed of the computers were getting fast and over a fewfiller · filler

  17. it would happen and so that's where i'm coming from that's what i think now i don't know if a bunch of satellites crashed maybe the internet would have taken longer if there was an economicfiller · filler

  18. look at you know swe bench growing from you know i think 18 months ago it was at 3 or something growing all the way to you know 72 to 80 depending on how you measurehedge · filler

  19. exponential is continuing and we don't see any diminishing returns but there are some liabilities it seems seems a glaring liability what do you think about that so first of all i would say even ifproper use

  20. be very high right if i think of the field i used to be in biology and medicine you know let's say i had a very smart nobel prize winner and you know i said okayfiller · filler

  21. all these things you have this incredibly smart mind but you know you can't you know you can't read new textbooks or absorb any new information i mean that would be difficult but like still iffiller · filler

  22. you can't like read new textbooks or absorb any new information i mean that would be difficult but still if you had like 10 million of those like they're still going to make a lot offiller · filler

  23. textbooks or absorb any new information i mean that would be difficult but like still if you had 10 million of those like they're still going to make a lot of biology breakthroughs like they're goinghedge · filler

  24. information i mean that would be difficult but like still if you had like 10 million of those they're still going to make a lot of biology breakthroughs like they're going to be limited they're goingfiller · filler

  25. you had like 10 million of those like they're still going to make a lot of biology breakthroughs they're going to be limited they're going to be able to do some things humans can't and therefiller · filler

  26. things humans can do that they can't but even that even if we impose that as a ceiling man that's pretty damned impressive and transformative and even if i said you never solved that like ifiller · filler

  27. ceiling like man that's pretty damned impressive and transformative and even if i said you never solved that i think you know i think people are underestimating the impact but look context windows are getting longerfiller · filler

  28. a conversation it absorbs information the underlying weights of the model may not change but you know just i'm talking to you here and we're having a conversation and i listen to the things you sayproper use

  29. a conversation and i listen to the things you say and i you know i think and i respond to them the models are able to do that and from a machine learning perspective from anproper use

  30. the gaps but it fills in many of the gaps and then there are a number of things learning and memory that do allow us to update the weights so you know there are a numberproper use

  31. we used to many years ago talk about inner loops and outer loops right the inner loop is i have some episode and i learned some things in that episode and i'm trying to optimize forquotative · filler

  32. is a way to learn the continual learning one thing we've learned in ai is whenever it feels there's some fundamental obstacle like two years ago we thought there was this fundamental obstacle around reasoning turnedproper use

  33. the continual learning one thing we've learned in ai is whenever it feels like there's some fundamental obstacle two years ago we thought there was this fundamental obstacle around reasoning turned out just to be rlhedge · filler

  34. that much about why claude is so good at code why is it so good at code and i said we don't talk externally about it i have to ask so you know every new versionfiller · filler

  35. know model that we build and you know that's why we you know i've said these things about you know we're trying to optimize for like talent density as much as possible like you need thatfiller · filler

  36. that's why we you know i've said these things about like you know we're trying to optimize for talent density as much as possible like you need that talent density in order to invent the newfiller · filler

  37. these things about like you know we're trying to optimize for like talent density as much as possible you need that talent density in order to invent the new techniques you know there's one thing that'sfiller · filler

  38. with the mission and you know i you know i you know i think there's selection effects here you know i you know are they getting the people who are most enthusiastic or most mission alignedfiller · filler

  39. and so we've been in a position you know three years ago where you know these differences were 1 000x and now you're saying you know with 20 billion can you compete with 100 billion andhedge · filler

  40. i said this before 4 5 so that 10x a year i mean you know every year i suspect that we'll grow at that scale and every year i'm almost afraid to say it publicly becauseproper use

  41. that we'll grow at that scale and every year i'm almost afraid to say it publicly because i'm no it couldn't possibly happen again so you know i think the growth at that scale like kindquotative · filler

  42. i'm like no it couldn't possibly happen again so you know i think the growth at that scale kind of speaks for itself in terms of our ability to compete with the big players okay sofiller · filler

  43. apps business but yes the majority comes through the api so you're making the most on this technology yeah i mean i would say i wouldn't quite put it that way i think we've i wouldfiller · filler

  44. i don't know 1 of consumers care about that at all right 99 are just going to be i don't understand it either way but now suppose i go to pfizer and i say you knowquotative · filler

  45. to pfizer and i say you know i've improved this from undergrad at biochemistry to grad at biochemistry this is going to be the biggest deal in the world right they might pay 10 times morefiller · filler

  46. going to be the biggest deal in the world right they might pay 10 times more for something that it might have 10 times more value to them and so the general aim of making theproper use

  47. smarter but also able to bring many of the positive applications right the things i wrote about in machines of loving grace of like solving the problems of biomedicine solving the problems of geopolitics solving theproper use

  48. many of the positive applications right the things i wrote about in like machines of loving grace of solving the problems of biomedicine solving the problems of geopolitics solving the problems of you know of economicfiller · filler

  49. geopolitics solving the problems of you know of economic development you know as well as more prosaic things finance or legal or productivity or insurance i think it gives a better incentive to kind of developproper use

  50. incentive to kind of develop the models as far as possible and i think in many ways it's it may even be a more positive business so i would say we're making a bet on thefiller · filler

  51. started getting quick adoption this was around the time that you know a lot of the coding companies cursor windsurf github augment code started exploding in popularity and then when we saw how popular it wasproper use

  52. time of this as of the time of this interview we've adjusted that particularly on the larger models opus i think it's no longer possible to spend that much with a 200 subscription and you knowproper use

  53. who make the the the the the the the the underlying applications so again without being too specific i think there are some assumptions in your question that are not necessarily correct you know i tellfiller · filler

  54. smaller models you know i think the technique you're referring to is maybe mixture of experts or something that so whether your models are a mixture of experts or not like mixture of experts is likeproper use

  55. mixture of experts or something like that so whether your models are a mixture of experts or not mixture of experts is like a way to run models more cheaply they have a given number offiller · filler

  56. like that so whether your models are a mixture of experts or not like mixture of experts is a way to run models more cheaply they have a given number of parameters it's a way tofiller · filler

  57. to find out what the truth is from you yeah look so i you know in terms of the cost of the models like one thing you'd be surprised by people you know people kind offiller · filler

  58. is from you yeah look so i you know in terms of like the cost of the models one thing you'd be surprised by people you know people kind of impute this thing to like ohfiller · filler

  59. models like one thing you'd be surprised by people you know people kind of impute this thing to oh man it's going to be really hard to get the margins from like x percent to yquotative · filler

  60. impute this thing to like oh man it's going to be really hard to get the margins from x percent to y percent we make improvements all the time that make the models like 50 percenthedge · filler

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