Transcription
I have, you know, very high conviction that like it's going, you know, the the, you know, we're going to get there within within a few years. I have a hunch that we're going to get there within a year or two. So, a little uncertainty on the technical side, but like, you know, pretty pretty strong confidence that it won't be off by much.
Guys, Daario Emodi, CEO of Anthropic, just dropped a mind-boggling interview. In this interview, Daario talked about AGI in great detail. Those who don't know what AGI is, AGI is an advanced form of AI that will have the ability to understand, learn, and reason better than any human mind. AGI is feared by AI experts because when AGI arrives, hundreds of millions of humans are expected to lose their jobs. Daario's company, Anthropic, is also in pursuit of AGI right now. And during the interview, he made a lot of shocking statements about AGI, which were kind of worrisome. I've extracted some important parts of this interview. I'm going to roll the clips now and I'll explain everything as we go.
>> Obviously makes sense like we're making progress towards AGI. I think the crux is something like nobody at this point disagrees that we're going to achieve AGI in this century. And the crux is you say we're hitting the end of the exponential. Um and somebody else looks at this and says, "Oh yeah, we've we're making progress. We've been making progress since 2012 and then 2035 we'll have a humanlike agent." And so I want to understand what it is that you're seeing which makes you think um yeah obviously we're seeing the kinds of things that evolution did or that human within the human lifetime learning is like in these models and why think that it's one year away and not 10 years away.
>> I I I actually think of it as like two there's kind of two cases to be made here or like two two claims you could make. One of which is like stronger and the other of which is weaker. So, I think starting starting with the weaker claim, you know, when when I first saw the scaling back in like, you know, 2019, um, you know, I wasn't sure, you know, this was the whole this was kind of a 50/50 thing, right? I thought I saw something that was, you know, and and my claim was this is much more likely than anyone thinks it is. Like, this is wild. No one else would even consider this. Maybe there's a 50% chance this happens. um on the basic hypothesis of you know as you put it within 10 years we'll get to you know you know what I call kind of country of geniuses in a data center I'm at like 90% on that um and it's hard to go much higher than 90% cuz the world is so unpredictable um maybe the irreducible uncertainty would be if we were at 95% where you get to things like I don't know may maybe multi you know multiple companies have you know kind of internal turmoil and nothing happens and then Taiwan gets invaded and like all the all the fabs get blown up by missiles and and you know and then now you drink a scenario you know just you could construct a scenario where there's like a 5% chance that it it or you know you can construct a 5% world where like things things get delayed for for for for for 10 years that's maybe 5%. There's another 5% which is that I'm very confident on tasks that can be verified. So I think I think with coding I'm just except for that irreducible uncertainty there's just there's I mean I think we'll be there in one or two years there's no way we will not be there there in 10 years in terms of being able to do it end to end coding. My one little bit the one little bit of of fundamental uncertainty even on long time scales is this thing about tasks that aren't verifiable like planning a mission to Mars like uh you know doing some fundamental scientific discovery like like crisper like you know writing a writing a novel hard to hard to verify those tasks. I am almost certain that we have a reliable path to get there but like if there was a little bit uncertainty it's there. So, so, so, so, so, so on the 10 years, I'm like, you know, 90% which is about as certain as you can be. Like, I think it's I think it's crazy to say that this won't happen by by by 2035. Like, in some sane world, it would be outside the mainstream. But, but,
>> okay. Next, Daario talks about AGI timelines and how AGI will be replacing white collar jobs. He talks about where we are right now with AGI development and whether we can achieve AGI by 2027. Now guys, remember AGI is the point where AI becomes equal or better than a human brain. So it's assumed that this is the point where humans start becoming irrelevant because whatever humans would do at their jobs, AGI will be able to do it 10 times better. So you're predicting that within 1 to 3 years. Um and in gerally anthropic has predicted that by late 26, early 27, we will have AI systems that are quote um have the ability to navigate interfaces available to humans doing digital work today. intellectual capabilities matching or exceeding that of Nobel Prize winners and the ability to interface with the physical world. And then you gave an interview two months ago with Dealbook where you were emphasizing your um your company's more responsible comput scaling as compared to your competitors. And I'm trying to square these two views where if you really believe that we're going to have a country of geniuses, you you want as big a data center as you can get. There's no reason to slow down. The TAM of a Nobel Prize winner that is actually can do everything a Nobel Prize winner can do is like trillions of dollars. And so I'm trying to square this conservatism uh which seems rational if you have more moderate timelines with your stated views about AI progress.
>> Yeah. So, so it actually all fits together and and we go back to this fast but not infinitely fast diffusion. So, like let's say that we're making progress at this rate. Um, you know, the the the technology is making progress this fast. Again, I have you know very high conviction that like it's going you know the the you know we're going to get there within within a few years. I have a hunch that we're going to get there within a year or two. So, a little uncertainty on the technical side, but like you know pretty pretty strong confidence that it won't be off by much. What I'm less certain about is again the economic diffusion side. Like I really do believe that we could have models that are a country of geniuses country of geniuses in a data center in one to two years. One question is how many years after that do the trillions in you know do do the trillions in revenue start rolling in? Um I don't think it's guaranteed that it's going to be immediate. Um you know I think it could be um one year it could be 2 years. I could even stretch it to 5 years although I'm like I'm skeptical of that. And so we have this uncertainty which is even if the technology goes as fast as I suspect that it will we don't know exactly how fast it's going to drive revenue. We we know it's coming but with the way you buy these data centers if you're off by a couple years that can be ruinous. It is just like how I wrote you know in machines of loving grace I said look I think we might get this powerful AI this country of genius in the data center that description you gave comes from the machines of loving grace. I said, "We'll get that 2026, maybe 2027." Again, that is that is my hunch. Wouldn't be surprised if I'm off by a year or two, but like that is my hunch. Let's say that happens. That's the starting gun. How long does it take to cure all the diseases, right? That's that's one of the ways that like drives a huge amount of of of of economic value, right? Like you cure you cure every disease. You know, there's a question of how much of that goes to the pharmaceutical company, to the AI company, but there's an enormous consumer surplus because everyone, you know, assuming we can get access for everyone, which I care about greatly. We, you know, we we cure all of these diseases. How long does it take? You have to do the biological discovery. You have to, you know, go you have to, you know, manufacture the new drug. You have to, you know, go through the regulatory process. I mean, we saw this with like vaccines and COVID, right? like it there's just this we we got the vaccine out to everyone but it took a year and a half right and and so my question is how long does it take to get the cure for everything which AI is the genius that can in theory invent out to everyone how long from when that AI first exists in the lab to when diseases have actually been cured for everyone
>> guys one thing I don't know if you've noticed or not but every time you ask these tech CEOs about AGI they start talking about curing cancer and other diseases with AGI. They try and make AGI appear like some sort of savior to humanity or something like that. What they don't tell you is the fact that millions will be replaced by AGI and several professions will totally vanish and that AGI has the potential to become the biggest disaster in human history. Bigger than atomic bombs even.
>> Let me ask you about now um making AI go well. Um it seems like whatever vision we have about how AI goes well has to be compatible with two things. One is the ability to build and run AIS is diffusing extremely rapidly and two is that the population of AIS the amount we have in their intelligence will also increase very rapidly and that means that lots of people will be able to build huge populations of misaligned AIs or uh AIs which are just like companies which are trying to increase their uh footprint or have weird psyches like Sydney Bing but now they're superhuman. What is a vision for a world in which we have an equilibrium that is compatible with lots of different AI some of which are misaligned running around?
>> Yeah. Yeah. So I think you know in the adolescence of technology I was kind of you know skeptical of like the balance of power but I I think I was particularly skeptical of or the thing I was specifically skeptical of is you have like three or four of these companies like kind of all building models that are kind of derive you know sort of sort of um uh uh like derived from the like derived from the same thing and uh you know that that these would check each other or or even that kind of you know any number of them would would would uh would would check each other like we might live in a offense dominant world where you know like one person or one AI model is like smart enough to do something that like causes damage for everything else. Um I think in the I mean in the short run we have a limited number of players now. So we can start by within the limited number of players we uh you know we kind of you know we we need to put in place the you know the safeguards. We need to make sure everyone does the right alignment work. we need to make sure everyone has bio classifiers like you know those are those are kind of the immediate things we need to do. I agree that you know that that doesn't solve the problem in the long run particularly if the ability of AI models to make other AI models proliferates then you know the the whole thing can kind of um you know can become harder to solve. I you know I think I think in the long run we need some architecture of governance right some some architecture of governance that preserves human freedom but but kind of also allows us to like you know govern the the very large number of kind of um you know uh uh uh human systems AI systems hybrid hybrid human human um you know hybrid hybrid human AI like you know companies or or like or like are like economic units. So, you know, we're we're going to need to think about like, you know, how do we how do we protect the world against, you know, bioteterrorism? How do we protect the world against like, you know, against like against like mirror life?
Okay. Next, Dario talks about how AGI is going to revolutionize humanoid robots. Podcast host asked Dario about the timelines of humanoid robots. This is such an interesting part of the interview because guys, humanoid robots are going to flip our social lives upside down. Let's listen.
>> There's very few technologies that seem to be diffusing as fast as um as AI algorithmic progress. And so that does hint that this industry is sort of structurally diffusive. So I think coding is going fast, but I think AI research is a supererset of coding and there are aspects of it that are not going fast. Um uh but I but I do think again once we get coding once we get AI models going fast then you know AI you know that will speed up the ability of AI models to kind to kind of do everything else. So I think while coding is going fast now I think once the AI models are building the next AI models and building everything else the kind of whole the whole economy will kind of go at the same pace. I am I am worried geographically though. I'm a little worried that like just proximity to AI having heard about AI um uh that that that may be one differentiator. And so when I said the like you know 10 or 20% growth rate a worry I have is that the growth rate could be like 50% in Silicon Valley and you know parts of the world that are kind of socially connected to Silicon Valley and you know not that much faster than its current pace elsewhere. And I think that'd be a pretty messed up world. So I one of the things I think about a lot is how to prevent that.
>> Yeah. Do you think that once we have uh this country of geniuses in a data center that robotics is sort of quickly solved afterwards because it seems like a big problem with robotics is that um a human can learn how to teleoperate current hardware but current AI models can't at least not not in a way that's super productive. And so if we have this ability to learn like a human should it solve robotics immediately as well?
>> I don't think it's dependent on learning like a human. It could happen in different ways. Again, we could have trained the model on many different video games which are like robotic controls or many different simulated robotics environments or just, you know, train them to control computer screens and they learn to generalize. So, it will happen. It's not necessarily dependent on humanlike learning. Humanike learning is one way it could happen. If the model's like, "Oh, I pick up a robot. I don't know how to use it. I learn that. That could happen because we discovered discovering continual learning. That could also happen because we trained the model on a bunch of environments and then it generalized or it could happen because the model learns that in the context length. It it doesn't actually matter which way. If we go back to the discussion we had like like an hour ago, that type of thing can happen in that type of thing can happen in several different ways. Um uh uh but but I do think when for for whatever reason the models have those skills then uh robotics will be revolutionized both the design of robots because the models will be much better than humans at that um and also the the ability to kind of control robots. So we'll get better at the physical building the physical hardware building the physical robots and we'll also get better at controlling it. Now you know does that mean the robotics industry will also be generating trillions of dollars of revenue? My answer there is yes, but there will be the same extremely fast but not infinitely fast diffusion. So will robotics be be revolutionized? Yeah, maybe tack on another year or two.
>> Okay, this last part is pretty interesting. The host asked Dario how historians would look back on this era. What would they write about this AI revolution that we're experiencing right now? And Daario's response was pretty eye opening for me. One thing I want to add here guys, right now if you look at how AI is advancing, the tech companies are not putting any safeguards. So in my opinion AI is going to become a disaster for humanity in the near future when somebody eventually writes the equivalent of the making of the atomic bomb for this era. What is the thing that will be hardest to glean from the historical record that they're most likely to miss?
>> I think a few things. One is at every moment of this exponential, the extent to which the world outside it didn't understand it. This is this is a bias that's often present in history where anything that actually happened looks inevitable in retrospect. And and so you know I I think when people when people look back it will be hard for them to put themselves in the place of people who are actually making a bet on this thing to happen that wasn't inevitable that we had these arguments like the arguments you know that I make for scaling or that continual learning will be solved um uh uh you know that that you know some of us internally in our heads put a high probability on this happening, but but it's like there's there's a world outside us that's not that's not acting on that's not kind of not acting on that at all. Um uh and and I think I think the the weirdness of it um I I I think unfortunately like the insolerity of it like you know if if we're one year or two years away from it happening like the average person on the street has no idea and that's one of the things I'm trying to change like with the memos with talking to policy makers but like I don't know I think I I think that's just a that's just like a crazy that's just like a crazy thing. Yeah. Um, finally I would say and and this probably applies to almost all historical moments of crisis. Um, how absolutely fast it was happening, how everything was happening all at once. And so decisions that you might think, you know, were kind of carefully calculated, well, actually you have to make that decision and then you have to make 30 other decisions on the on the same day because it's all happening so fast and and you don't even know which decisions are going to turn out to be consequential. So, you know, one of my one of my I guess worries, although it's also an insight into into, you know, into kind of what's happening is that, you know, some very critical decision will be will be some decision that, you know, someone just comes into my office and is like, Dario, you have two minutes like, you know, should we should we do, you know, should we do thing thing A or thing B on this like, you know, someone gives me this random, you know, half page half page memo and is like, should we should we do A or B? And I'm like, I don't know. I have to eat lunch. Let's do B. And and you know, that ends up being the most consequential thing ever.
>> All right, guys. That's it for today's video.