00:20:45 · Tyler, John Coogan, Jordi Hays, Rahul Sengottuvelu
[00:20:45] S4: I think if it's like... Yeah. If if it's really thin and it's basically completely flat, there's no kind of And [00:20:49] S1: you use white bread? Yeah. And you're just slicing hot dogs? Yeah. Is there someone who who had a really strong opinion on hot dog being a sandwich? What if you turn it like this? You know, instead of eating it like this, you eat it like this. No? Okay. Is the iPhone Duo a laptop? No? It folds like a laptop. Have you seen these videos? There's the keyboard. Pull up pull up this video from, techie boost deals. Apple Duo running a full MacBook Pro style screen. This is not a laptop. [00:21:22] S0: You make some good points. [00:21:24] S1: I'm getting close. Right? [00:21:25] S0: You make some [00:21:25] S1: good points. Look at the... There's another... The the the the next video. Person doing full CAD on it. Look at this. Look at this. You're telling me this isn't a laptop. I think it I think it kinda counts as a laptop. [00:21:38] S0: Can someone on the team Yeah. Just take one for the team and get it? [00:21:43] S1: Get the next one. [00:21:45] S5: Pull the next one. [00:21:46] S0: Is it still... So so my question is it is it still really hard to get? [00:21:49] S1: I don't know. [00:21:50] S0: Because obviously, you know, it's been dunked on a million billion times over the last week. And so I wonder if that's causing people to... [00:21:57] S1: That's cool. That's a laptop. Right? Fun. Anyway, I don't know. [00:22:03] S0: You Sorry. You love a you love a good circus. [00:22:05] S1: Yeah. It's it's a good one.
00:22:05 · John Coogan, Jordi Hays, Rahul Sengottuvelu
[00:22:05] S1: Yeah. It's it's a good one. [00:22:09] S0: Okay. Well Question from the chat. When is Gavin Baker on? He is on at twelve [00:22:14] S1: In one hour. Twenty Yes. [00:22:16] S0: PM Pacific time. [00:22:18] S1: So let me tell you about CrowdStrike. Your business is AI. Their business is appearing. CrowdStrike secures AI and stops breaches [00:22:27] S0: Hear that? Backing [00:22:29] S1: up the track? [00:22:29] S0: For CrowdStrike? [00:22:31] S1: Yes. Wait. Why? [00:22:35] S0: Their services are in demand? [00:22:36] S1: Oh, okay. Okay. Yeah. Everyone's backing up the truck for CrowdStrike. Yeah. I want a sound effect of CrowdStrike ing the Gong or something like that every time... [00:22:44] S5: Oh, we [00:22:44] S0: do that ourselves. [00:22:45] S1: I guess we do. I guess we do. Anyway, OpenAI has some additional announcements in the world of math. Dan Roberts says, we are announcing three new discoveries in mathematics, computer science, and physics about harmonic analysis, complexity theory, and quantum field theory. And this was a post that went out, and it had to make it through the ringer to actually have the impact that I think it eventually did. Andrew Curran contextualized a little bit more. Open eyes announced three new discoveries in mathematics and computer science, local smoothing and the Kajaka conjecture, Kakiya conjecture. The exponential time hypothesis is false. Exposed. You asked. They answered. [00:23:29] S0: Exposed for not being I do not pretend
00:23:32 · John Coogan
[00:23:32] S1: to know this. I'm not a... An advanced mathematician by any stretch of the imagination. Oh. Oh. Okay. Break it down for us. Get out the whiteboard. Explain the exponential time hypothesis. Explain the cocky conjecture. Everyone was asking. But, but people people were excited about this. There's a big long post from, Rashaab Karnawat, who says, many of you don't understand how serious this is. That's me. So let me break it down for you. In computer science, there's a famous kind of puzzle called 3SAT. You are given a long list of yes or no switches and a set of rules about how they must be arranged, and you have to find a combination that satisfies every rule. The obvious way to solve it is to try every combination, but each new switch doubles the number of possibilities. So a puzzle with a few 100 switches would take longer than the age of the universe to brute force, so it becomes exponentially complex. For decades, the smartest people in the field found clever tricks that made this a little faster, but every trick still hit the same wall. The time required kept exploding as the puzzle grew. For about about twenty five years ago, two computer scientists, Russell Impaghlazio and Ramamohan Mapatturi, working with Francis Zane, turned that wall into a formal idea. They proposed the exponential time hypothesis, which says this explosion is no longer a failure of human cleverness, but a law of nature, no algorithm, however brilliant, will ever solve three SAT without the time blowing up exponentially. Nobody could prove it, but almost everyone believed it, and it became one of the foundations of modern computer science. Researchers used it as a measuring stick. If you could show that solving some other problem quickly could... Would also crack three SAT quickly, you had shown that the problem was hopelessly hard too. Hundreds of papers across scheduling graphs, logic, and much more rest on this one assumption. Now an internal model at OpenAI claims to have broken it. It says it found an algorithm that solves these puzzles meaningfully faster than the hypothesis allows, and it has checked the proof in a computer verification system called Lean. And here's the remarkable part. The very people who propose the hypothesis, along with MIT's Ryan Williams, are now leading the public review of whether a machine has disproved their own idea.
00:25:50 · John Coogan
So what happens next? If the disproof holds, a large part of theoretical computer science has to be revisited. Every result that said, This problem cannot be solved faster, assuming ETH loses its foundation, and researchers will have to work out which of those walls were real and which were simply stopped looking which they stopped looking at. It does not mean everything becomes easy. The new algorithm is still slow in absolute terms, and this is not the famous P versus NP problem being solved, but it means our map of what's possible was wrong in a place where we were very confident about it and that AI redrew the line. If the disprove falls, the hypothesis survives and the field breathes out, but we still have watched an AI produce serious, checkable work on one of the deepest questions in the discipline closely enough that the founders of the idea had to take it seriously. Either way, something has shifted. So there... You you had a take on this of, like, okay. A lot of people are just like, okay. Cool. Math result. Right? But, like, what's the practical application? And there are some knock on effects that are interesting. Do you have anything to lay out there? Is the trend
00:27:00 · Jordi Hays, John Coogan
[00:27:00] S0: Yeah. My my continued take is that anytime you see a breakthrough like this that is, like, against a sort of an open problem that's just generally expanding humanity's knowledge, isn't exactly immediately commercial. Yep. You have to assume there's many other highly commercial discoveries happening Yep. In the background that people, both the labs and people individually, are not sharing broadly because they made a discovery and there's an opportunity to monetize it. Sure. And so I think that that is like... That any anytime there's like a big breakthrough, my head is just going to everything that's not being shared. Yeah. And that's what I'm actually like So stuff like Probably more. [00:27:41] S1: Advances in AI research, advances in bio that need to go through actual testing. [00:27:46] S0: Yeah, material science.
00:27:46 · Jordi Hays, John Coogan
[00:27:46] S0: Yeah, material science. [00:27:47] S1: Material science that might need to be patented at the very least. Advances in cybersecurity that need to be rolled out incrementally and can't just be sort of thrown over the fence. A lot of these math results, it's totally fine to just get them out into the math community, have everyone engage with them immediately, but every other industry is sort of more unique. We were talking to Doug O'Loughlin about what's the next leg up, white collar work, this type of stuff, obviously, like, the the coding agent boom has, like, fully arrived. But what's what's next? And I was wondering, like, how close do you think the models are to being able to, like, answer that Tyler Cowen Dorcache debate about, like, when will you walk into Walmart and see just, a fully AI run business? And then would that live within the... Like, it could live within the labs. Like, you could have a a big lab, say, like, go and build a business, and it could just do that. And it could do the market analysis, and then it could, like, create a new product and model it in CAD and send it to a factory and send it to a distribution center and pitch it to Walmart and get it on the shelves and, like, make the money and and run that flywheel. Like, it could do that. And if you added up a bunch of those, you might have something economically interesting. [00:29:05] S0: Yeah. I don't think CPG would be there first. [00:29:08] S1: Yeah. Yeah. Maybe maybe Doricas is is correct in the sense that it's just like not the best Yeah.