01:39:56 · Bar Winkler, John Coogan, Jordi Hays
[01:39:56] S2: Founded the company in January 2025. Wow. So a bit less than two years ago. [01:40:01] S0: And what's the latest news on the fundraising side? [01:40:05] S2: We announced c round a few days ago. [01:40:08] S0: Give us the number. [01:40:09] S2: As announced. We raised we raised $550,000,000. Oh, yeah. Let's [01:40:18] S0: go. Bang. [01:40:23] S1: Big number. [01:40:24] S0: Incredible speed. Incredible Incredible speed. [01:40:28] S1: Last last question before you jump in. I'm I'm sure you'll be back on for the next round. But you're there's a bunch of different products tying into the platform. Do you ever see yourself getting into the inference business? Or is that something you're fine working with partners? Like, do you do you imagine actually setting up your own, you know, data centers, or or is that not, like, an area that you feel like you're getting a lot of value out of? [01:40:55] S2: So I don't have a, like, religious answer on this. I I I or I guess the first principle is that, wonderful, we see ourselves as the apply the eye partner for the enterprise. So when everything when when whenever something becomes commoditized, we want to be the first to productize it very well and give it access to the enterprise. A good example, for example, is like coding, right? Coding was a very hard science problem up until maybe yesterday, right? Or a few months ago. [01:41:28] S0: Yeah.
01:41:28 · John Coogan, Bar Winkler, Jordi Hays, Bridgit Mendler, Ken Ono
[01:41:28] S0: Yeah. [01:41:28] S2: As soon as coding becomes a sheer engineering problem, then why wouldn't it be part of wonderful? Just as a first principle. So if inference becomes so commoditized that we think it's better for us to own it, then sure, we'll own it. But we don't have a principal decision to to do it anytime soon. [01:41:46] S1: Yeah. Makes sense. Last question because we didn't get to it in the beginning. I'm curious. What were you doing before this? [01:41:54] S2: I was the founder of a pretty standard b to b SaaS company that I sold in 2021, and then I worked three and a half years. [01:42:03] S1: We're we're number one defenders. Thank you for your service. In the SaaS trenches. [01:42:08] S0: Yeah. We love it. [01:42:09] S1: Great to meet you, Bar. Yeah. Congrats on all the progress. [01:42:11] S2: Thanks for having me. [01:42:13] S0: Yeah. Good luck. Get some sleep. Enjoy the weekend. Hopefully, you can rest up. [01:42:16] S1: Job. Cheers. [01:42:17] S0: We'll talk to you later. Goodbye. Let me tell you about Railway. Railway is the all in one intelligent cloud provider. Use your favorite agent to deploy web app servers, databases, and more while Railway automatically takes care of scaling, monitoring, and security. I believe we have our next guest. We're ready to rock. [01:42:35] S1: Here they are. [01:42:35] S0: Welcome to the show. [01:42:36] S1: What's going on? [01:42:37] S0: How are you? [01:42:38] S5: How are you? [01:42:38] S4: Hey. How are you?
01:42:38 · Ken Ono, John Coogan, Carina Hong, Jordi Hays
[01:42:38] S4: Hey. How are you? [01:42:39] S0: You so much for taking the time. Let's kick it off with some introductions on both of you and then we can go into the news today. [01:42:47] S4: I'm Ken Ono. I'm founding mathematician at Acxiom Math. [01:42:50] S6: Yeah. I'm Carina Hong, founder and CEO of Acxiom Math. [01:42:53] S0: What is the latest in your world? What is the Is [01:42:58] S1: there anything left to do, Ken? [01:42:59] S0: Yeah. Job's finished. [01:43:02] S4: Well, no. Actually, I I think it's a wonderful time for mathematics now that we have tools that can empower Mhmm. Mathematicians. The questions are still there. Mhmm. Last I checked, the Riemann hypothesis is still unsolved. Yeah. So it's a new era. [01:43:15] S0: Yeah. That is the benchmark for everyone. Certainly for the AI labs, you can see that they're both were, you know, of the companies are working on this. Do you see Three [01:43:26] S4: of us, you mean. [01:43:27] S0: Yeah. Everybody. Everybody is working on this. But do you see these particular challenges as good benchmarks, or is there a more a more broad overarching vision? Or or or is it worth focusing on these specific things like the Riemann hypothesis?
01:43:27 · John Coogan, Ken Ono, Carina Hong
[01:43:27] S0: Yeah. Everybody. Everybody is working on this. But do you see these particular challenges as good benchmarks, or is there a more a more broad overarching vision? Or or or is it worth focusing on these specific things like the Riemann hypothesis? [01:43:45] S4: Well, just very quickly, I'm a pure mathematician, so I don't think in the world of benchmarks. Yeah. The problems are there, and I love the problems. Some of the problems get solved, but every time you solve a problem, there are new problems. So I think it's an exciting time. It's very disruptive for the community. Mhmm. It's let's face it. But moving forward, six to eight months from now, I think I think scientific research will be thriving. [01:44:09] S0: Yeah. I think Yeah. Please. [01:44:10] S6: Yeah. The history, I think, has been that every mathematical discovery, like, unlocks, like, either an applied like, an actual real world application, like, hundreds of years down the road, or it even starts, like, new fields in science. So we're very excited about all these mathematical discoveries happening. Mhmm. For the audiences who don't know what's new, we had an exciting week. The very long, astonishingly, notoriously hard problem bounded gap in primes have been the world record has been updated three times this week. First by Julia Stalman, the human mathematician, getting two forty down from two forty six, and then by Axiom Math at two twelve, and then by opening at Astra at one eighty six. So it's a Wow. [01:44:56] S0: Yeah. I'm looking forward. [01:44:57] S4: I I have a blitz.
01:44:57 · Ken Ono, John Coogan, Carina Hong
[01:44:57] S4: I I have a blitz. [01:44:59] S0: Is it ever like this in math? This is this is No. [01:45:01] S4: No. Yesterday. [01:45:02] S0: A football game. [01:45:04] S4: Yeah. That that is all new. There's an analogy, if I could just very quickly say, Freeman Dyson was a very famous mathematician and physicist. And he described mathematics mathematics as mathematicians could be the birds or a mathematician could be a frog. And right now, right now, we're in a position where we're amplifying the flyers. Sure. The ones with vision. [01:45:25] S0: Yeah. I like that. What else can you tell me about reaction from the mathematics profession and also what the role of the mathematician was in the pre AI era and what it might look like in the post AI era because I know a lot of very successful mathematicians who have had full careers as professors and proving theorems and working on these hard problems wasn't something that was really on their resume necessarily. Like they they contributed so much else to the world and they lived full lives and had wonderful careers And it wasn't purely about the the gamesmanship, although we're now it's game on. [01:46:05] S6: Proof Olympics time. [01:46:06] S0: Yeah. Proof Olympics. So [01:46:09] S4: so like I said, I think the questions that we that that I wake up in the morning thinking about Yeah. They're still there. It's just the paradigm. It's the the tools that we use that didn't exist before are amplifying us. Yeah. I think that's where we are. It's a great time.
01:46:09 · Ken Ono, Carina Hong, Jordi Hays
[01:46:09] S4: so like I said, I think the questions that we that that I wake up in the morning thinking about Yeah. They're still there. It's just the paradigm. It's the the tools that we use that didn't exist before are amplifying us. Yeah. I think that's where we are. It's a great time. [01:46:26] S6: So so to track kind of like the the community of mathematicians, but also the community of researchers working on AI for math, Pat Shafto, who is a program manager at DARPA Mhmm. Organizes this sort of meetup every every two months or every three months. And we just went to the last edition of that. It was, like, two weeks ago in Seattle. Terry Tau was there. Alice was there. Boris Alexi from OpenAI was there. A lot of the academics who are working on harness engineering that are great mathematicians are there. A lot of industry AI researchers are there, including people from OpenAI. We were there, and AWS was there. The the energy is incredible. I think it's like the it was like a three day, like, research program. Everyone was reporting their newest, the open source model. Godo Prover has version three. That's Princeton Labs, Sanjeev Arora's. The community is thriving. I think we're, like, seeing these incredible number of open problems that suddenly become attainable because of the technology advancing. [01:47:33] S1: How do how do you guys make money? Yeah. [01:47:37] S6: That's a great question. I think the word is going [01:47:39] S4: She paged me. [01:47:45] S1: Oh,