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Dario Amodei — “We are near the end of the exponential”

Dwarkesh Patel · 2:22:20 · indexed 2026-06-11

🎙 Speaker-attributed: only Dario Amodei's own words were counted (1:45:46 of speech) — likes/min uses their speaking time.

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86% filler
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  1. yeah i would say actually the underlying technology the exponential of the technology has gone broadly speaking i would say about as i expected it tofiller · filler

  2. technology has gone broadly speaking i would say about as i expected it to go i mean there's plus or minus you know a couple there's plus or minus a year or two here there's plushedge · filler

  3. at the exponential it is roughly what i expected in terms of the march of the models from you know smart high school student to smart college student to like you know beginning to do phfiller · filler

  4. the march of the models from like you know smart high school student to smart college student to you know beginning to do ph d and professional stuff and in the case of code reaching beyondfiller · filler

  5. you have people talking about these you know just the same tired old hot button political issues and you know around us we're like near the end of the exponential i want yeah so i havefiller · filler

  6. you know just the same tired old hot button political issues and like you know around us we're near the end of the exponential i want yeah so i have actually the same hypothesis that ifiller · filler

  7. all the cleverness all the techniques all the kind of we need a new method to do something that doesn't matter very much there are only a few things that matter and i think i listedproper use

  8. there are only a few things that matter and i think i listed seven of them one is how much raw compute you have the other is the quantity of data that you have then thefiller · filler

  9. such objective function right another objective function is you know the kind of rl objective function that says you have a goal you're going to go out and reach the goal within that of course there'squotative · filler

  10. a goal you're going to go out and reach the goal within that of course there's objective rewards you know like you see in math and coding and there's more subjective rewards like you see infiller · filler

  11. going to go out and reach the goal within that of course there's objective rewards like you know you see in math and coding and there's more subjective rewards like you see in rl from humanproper use

  12. there's objective rewards like you know like you see in math and coding and there's more subjective rewards you see in rl from human feedback or kind of higher order versions of that and then theproper use

  13. kind of higher order versions of that and then the sixth and seventh were things around kind of normalization or conditioning like you know just getting the numerical stability so that kind of the big blobfiller · filler

  14. versions of that and then the sixth and seventh were things around kind of like normalization or conditioning you know just getting the numerical stability so that kind of the big blob of compute flows infiller · filler

  15. scaling laws were one example of kind of what we see there and indeed those have continued going you know you know i think now it's been widely reported like you know we feel good aboutfiller · filler

  16. and indeed those have continued going like you know you know i think now it's been widely reported you know we feel good about pre training like pre training is continuing to give us gains whatfiller · filler

  17. you know i think now it's been widely reported like you know we feel good about pre training pre training is continuing to give us gains what has changed is that now we're also seeing thefiller · filler

  18. seeing the same thing for rl right so we're seeing a pre training phase and then we're seeing an rl phase on top of that and with rl it's actually just the same like you knowhedge · filler

  19. we're seeing like an rl phase on top of that and with rl it's actually just the same you know even other companies have published like you know in some of their releases have published thingsfiller · filler

  20. of that and with rl it's actually just the same like you know even other companies have published you know in some of their releases have published things that say look you know we train thefiller · filler

  21. trained on these data sets that didn't represent a wide you know distribution of text right you had you know these very standard you know kind of language modeling benchmarks and gpt 1 itself was trainedfiller · filler

  22. was trained on a bunch of i think it was fan fiction actually but you know it was literary you know it was like literary text which is a very small fraction of the text thatfiller · filler

  23. i think it was fan fiction actually but you know it was like literary you know it was literary text which is a very small fraction of the text that you get and what we foundfiller · filler

  24. text that you get and what we found with that you know and in those days it was a billion words or something so small data sets and represented a pretty narrow distribution right like ahedge · filler

  25. was like a billion words or something so small data sets and represented a pretty narrow distribution right a narrow distribution of kind of what you can see in the world and it didn't generalize wellproper use

  26. wouldn't generalize that well to kind of the other tab you know we had all these measures of you know how well does the model do at predicting all of these other kinds of texts youfiller · filler

  27. on the you know the internet when you kind of did a general internet scrape right from something you know common crawl or scraping links on reddit which is what we did for gpt 2 it'sproper use

  28. i think we're seeing the same thing on rl that we're starting with first very simple rl tasks training on math competitions then we're kind of moving to you know kind of broader training that involvesproper use

  29. on math competitions then we're kind of moving to you know kind of broader training that involves things code as a task and now we're moving to do kind of many many other tasks and thenproper use

  30. which is that on pre training when we train the model on pre training you know we use trillions of tokens right and humans don't see trillions of words so there is an actual sample efficiencyhedge · filler

  31. trained if we give them a long context length the only thing blocking a long context length is inference but if we give them like a context length of a million they're very good at learningfiller · filler

  32. context length the only thing blocking a long context length is like inference but if we give them a context length of a million they're very good at learning and adapting within that context length andhedge · filler

  33. know the full answer to this but i think there's something going on that pre training it's not the process of humans learning it's somewhere between the process of humans learning and the process of humanproper use

  34. of humans learning it's somewhere between the process of humans learning and the process of human evolution it's it's somewhere between like we get many of our priors from evolution our brain isn't just a blankfiller · filler

  35. somewhere between the process of humans learning and the process of human evolution it's like it's somewhere between we get many of our priors from evolution our brain isn't just a blank slate right whole booksfiller · filler

  36. have been written about i think the language models they're much more blank slates they literally start as random weights whereas the human brain starts with all these regions it's connected to all these inputs andproper use

  37. something between long term human learning and short term human learning so you know there's this hierarchy of there's evolution there's long term learning there's short term learning and there's just human reaction and the lomfiller · filler

  38. right that was the transition from gpt 1 to gpt 2 that i saw up close which is you know the model reaches a point you know i like had these moments where i was likefiller · filler

  39. that i saw up close which is like you know the model reaches a point you know i had these moments where i was like oh yeah you just give the model like you just givefiller · filler

  40. like you know the model reaches a point you know i like had these moments where i was oh yeah you just give the model like you just give the model a list of numbers that'squotative · filler

  41. you know i like had these moments where i was like oh yeah you just give the model you just give the model a list of numbers that's like you know you know this is thefiller · filler

  42. oh yeah you just give the model like you just give the model a list of numbers that's you know you know this is the cost of the house this is the square feet of thefiller · filler

  43. this is the square feet of the house and the model completes the pattern and does linear regression not great but it does it but it's never seen that exact thing before and so you knowfiller · filler

  44. a specific document or a specific skill but because we want to generalize i think of it as two there's kind of two cases to be made here all right two claims you could make onefiller · filler

  45. of two cases to be made here all right two claims you could make one of which is stronger and the other of which is weaker so i think starting with the weaker claim you knowfiller · filler

  46. so i think starting with the weaker claim you know when i first saw the scaling back in you know 2019 you know i wasn't sure you know this was the whole this was kind offiller · filler

  47. that was you know and my claim was this is much more likely than anyone thinks it is this is wild no one else would even consider this maybe there's a 50 chance this happens onfiller · filler

  48. get to you know what i call kind of country of geniuses in a data center i'm at 90 on that and it's hard to go much higher than 90 because the world is so unpredictablehedge · filler

  49. unpredictable yeah maybe the irreducible uncertainty would be if we were at 95 where you get to things i don't know maybe you know multiple companies have you know kind of internal turmoil and nothing happensproper use

  50. multiple companies have you know kind of internal turmoil and nothing happens and then taiwan gets invaded and all the fabs get blown up by missiles and you know and then now you would drink tofiller · filler

  51. one little bit of fundamental uncertainty even on long timescales is this thing about tasks that aren't verifiable planning a mission to mars like you know doing some fundamental scientific discovery like crispr like you knowproper use

  52. even on long timescales is this thing about tasks that aren't verifiable like planning a mission to mars you know doing some fundamental scientific discovery like crispr like you know writing a novel hard to verifyfiller · filler

  53. tasks that aren't verifiable like planning a mission to mars like you know doing some fundamental scientific discovery crispr like you know writing a novel hard to verify those tasks i am almost certain that weproper use

  54. aren't verifiable like planning a mission to mars like you know doing some fundamental scientific discovery like crispr you know writing a novel hard to verify those tasks i am almost certain that we have afiller · filler

  55. to verify those tasks i am almost certain that we have a reliable path to get there but if there was a little bit uncertainty it's there so on the 10 years i'm like you knowfiller · filler

  56. there but like if there was a little bit uncertainty it's there so on the 10 years i'm you know 90 which is about as certain as you can be like i think it's crazy tofiller · filler

  57. on the 10 years i'm like you know 90 which is about as certain as you can be i think it's crazy to say that this won't happen by 2035 like in some sane world itquotative · filler

  58. certain as you can be like i think it's crazy to say that this won't happen by 2035 in some sane world it would be outside the mainstream no no no this is why i'm almostfiller · filler

  59. is the world in which we do we do all the things that are verifiable and then they you know many of them generalize but we kind of don't get fully there we don't fully youfiller · filler

  60. the job of swe that's part of the job of the company but i do think swe involves design documents and other things like that which by the way the models are not bad they're alreadyproper use

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