All-In with Chamath, Jason, Sacks & Friedberg
01:24:21 · Jason Calacanis
[01:24:21] S0: Alright. Everybody has been demanding, Friedberg, that we do a science corner on this exponential time hypothesis. I guess a layman's way of explaining it would be computers can't solve important problems quickly, but there seems to be some science and some recent innovations that say maybe this hypothesis is no longer true. Please inform audience.
01:24:46 · David Friedberg
[01:24:46] S3: The reason for doing this science corner on this OpenAI publication is just to highlight some of the benefit and value that comes out of AI. These are, like, pretty substantial breakthroughs that are happening, all the math hypotheses they've published on. This one's called the exponential time hypothesis and it starts with like the most fundamental question in computer science which is can a problem be solved with a computer? And if so, how long does it take? And that tells you whether it's actually practically computable. This is the key point of computer science, we wanna create these machines that can solve our problems. And there's a class of problems that are called p problems where you can solve them in what's called polynomial time. The bigger the problem of a particular type, the longer it takes. And polynomial time just means size cubed or size squared. It tells you how many seconds or minutes or hours it might take on a specific computer to solve that problem. And so the the problem as it gets bigger takes more time. And then there's what are called NP problems. These are problems where you can very quickly figure out if the answer is correct. So, like, you know, it can take a long time to solve a big Sudoku puzzle, but you can check the Sudoku puzzle in a couple of seconds and see if it's right. That's an NP problem. And the key question in computer science has been, do p problems equal NP problems? Meaning, can you actually solve any problem that you can quickly check-in a short period of time? And if so, then everything is computable. The entirety of the universe is computable. And that's the big question in computer science. This idea of this exponential time hypothesis, which was proposed in 1999 by a couple of guys, And they said for a specific type of problem called a three SAT problem, which we don't really need to get into, the amount of time it takes is exponential depending on how big the problem is. So the bigger the problem gets, it's some, you know, number to the nth power. So it scales exponentially in terms of how long it would take a computer to solve it, which means that they have demonstrated that there is a class of problems that can never be solved in a short period of time, and therefore that entire class of problems means that p does not equal NP. So that's been their hypothesis since 1999.
01:27:04 · David Friedberg
And what OpenAI did is they disproved that. And they disproved it in a 175 page paper, and it comes down to this one little formula, which you can pull up on the screen here, Nick, which we don't need to spend a lot of time analyzing to understand. But effectively, this little formula right here is OpenAI's paper demonstrating... The the outcome of the paper demonstrating that this three SAT problem, this particular class of problems in computer science can be solved in sub exponential time, which means it will not take an exponential amount of time. So they disproved this big exponential time hypothesis that everyone thought was real. And what that means is that p could still equal NP, which means that theoretically, every problem in the universe can be solved with a computer. The solution, it's it's being evaluated now. So, you know, it's not fully kind of verified by third parties yet. People are still trying to understand it. It's a 175 pages with a very novel set of approaches that it's taken to try and get there. They did the whole thing end to end in less than ten days. Some reports have argued they did it in a few hours. And so this was a deeply challenging question in computer science and has been for twenty seven years. Is this exponential time hypothesis real or not? And they just proved that it's not, and they did it in a few hours. So it may be esoteric for the majority of people listening to this, but it it does a couple of things. Number one, it keeps in play the idea that everything in the universe can be analyzed and every problem theoretically could still be solved with a computer. And number two, is it's amazing that OpenAI was able to do this in a few hours and deliver this sort of a result that mathematicians and computer scientists have been struggling over for twenty seven years, and this proof is so far holding. So, you know, this is amazing. And and so everyone in computer science, I think, was kinda shocked when this came out.
01:28:59 · Jason Calacanis, David Friedberg, David Sacks
[01:28:59] S0: Absolutely. Yeah. [01:29:00] S3: I was shocked in terms of how it was presented, how they solved it. It was very novel. And, it's one of these moments of humility for mathematicians, for computer science being like, holy I would have never done that. [01:29:11] S0: And, Sacks Sacks, you were just very heated about the exponential time hypothesis in the group chat for the past three weeks. So when you hear Friedberg teed up here this crisply, how does this impact your thoughts on twenty twenty eight election and the future of humanity? [01:29:29] S1: Look, I think that AI superintelligence, whatever you wanna call it, is gonna create a a lot of good things. And the fear around it... You know, maybe some fear is justified because anything that seems powerful can make people afraid and obviously the future is unknown, so you can't prove exactly what's gonna happen. But I tend to think there's gonna be so many benefits to this technology. And frankly, if we don't do it, China's going to... They're gonna be the ones that benefit. J. Cal, you're right that if The US doesn't build the data centers, they'll get built in other countries and we'll be paying to import the tokens. [01:30:03] S0: Mexico and Canada. [01:30:05] S1: Very dangerous situation. [01:30:07] S0: It's so dumb. [01:30:08] S1: Yeah. [01:30:09] S0: Crazy. Chamath, I saw you nodding there. Yeah. You have a background in computer science.
01:30:09 · Jason Calacanis, Chamath Palihapitiya
[01:30:09] S0: Crazy. Chamath, I saw you nodding there. Yeah. You have a background in computer science. [01:30:13] S2: My formal education was in electrical engineering, but I mean, I've I've worked around software engineers for so long now. This is an incredibly interesting insight. [01:30:23] S0: Alright. Another amazing episode of the All In podcast, the number one, two, and three podcast in the world in terms of the rankings. Think, wow. What a week we had. We're number one, in the livestream and obviously, four weeks running. We've hit number one. Thank you to the audience and we truly appreciate you being on this journey with us and you can always expect as candid a discussion in the world as possible, which it was very interesting in your discussion with Rick Rubin, Chamath. He was very, very taken with the podcast, and he is somebody who understands art and unique moments in the world. I thought it was very interesting that he kept pressing you on what... What's the magic here? Because he's he's kind of the Sherpa of magic in many ways. And I think the magic here is that we're candid and, we break each other's chops and we try to move the ball forward with our candidates. And you had some very nice things to say about all of us, Chamath, and I just wanted to say thank you. You are my brother and I love you.
01:30:23 · Jason Calacanis, Chamath Palihapitiya
[01:30:23] S0: Alright. Another amazing episode of the All In podcast, the number one, two, and three podcast in the world in terms of the rankings. Think, wow. What a week we had. We're number one, in the livestream and obviously, four weeks running. We've hit number one. Thank you to the audience and we truly appreciate you being on this journey with us and you can always expect as candid a discussion in the world as possible, which it was very interesting in your discussion with Rick Rubin, Chamath. He was very, very taken with the podcast, and he is somebody who understands art and unique moments in the world. I thought it was very interesting that he kept pressing you on what... What's the magic here? Because he's he's kind of the Sherpa of magic in many ways. And I think the magic here is that we're candid and, we break each other's chops and we try to move the ball forward with our candidates. And you had some very nice things to say about all of us, Chamath, and I just wanted to say thank you. You are my brother and I love you. [01:31:16] S2: Thank you. I love you too. I mean, what I said to him basically at the Corvette is we are a family, and we get together and chop it up once a week, and it's very rare to have a window into family squabbles. Yes. And I think that's basically that's basically why it works. It's kinda unique, you know. That's maybe why the Kardashians work.