01:37:05 · Jordi Hays, Bar Winkler
[01:37:05] S1: Do you Wonderful looks like in in five years? Because now it it sounds like there's you're building your own software, you're also doing a lot of services, one off projects. I imagine you're probably leaving some software behind, then you're maybe managing that, but, like and then you're working on a gateway. Where what what does the business look like in in five years? [01:37:28] S2: So so we don't do wonderful doesn't sell any services. Right? Everything we everything we sell is consumption and and the platform itself. And sometimes we attach use case fees to certain complex use cases, but these are all recurring. We don't charge for a single FD or DS that is deployed with our customers. As as a principle. It's very hard for me to tell you what Wonderful looks like in five years. Because five years is just a huge amount of time in this day and age. My gut is that in five years, Wonderful is an independent company, one of the largest applied AI companies in the world, mostly seen as an infrastructure. So mostly known for our AI operating system, with millions of different people around the world building their company on top of Wonderful, as an infra. I think in a similar way in a similar way to how Microsoft is perceived today, or how Microsoft was perceived a few years ago. Right? That's pretty ambitious. But I I would say that's more the north star than, let's say, Palantir.
01:37:28 · Bar Winkler, Jordi Hays
[01:37:28] S2: So so we don't do wonderful doesn't sell any services. Right? Everything we everything we sell is consumption and and the platform itself. And sometimes we attach use case fees to certain complex use cases, but these are all recurring. We don't charge for a single FD or DS that is deployed with our customers. As as a principle. It's very hard for me to tell you what Wonderful looks like in five years. Because five years is just a huge amount of time in this day and age. My gut is that in five years, Wonderful is an independent company, one of the largest applied AI companies in the world, mostly seen as an infrastructure. So mostly known for our AI operating system, with millions of different people around the world building their company on top of Wonderful, as an infra. I think in a similar way in a similar way to how Microsoft is perceived today, or how Microsoft was perceived a few years ago. Right? That's pretty ambitious. But I I would say that's more the north star than, let's say, Palantir. [01:38:26] S1: Yeah. So so so you're you end up building something net new customer, productizing something for a customer, but then you're you're fine not charging for that build out because you plan to use that same product. You'll you'll be able to resell it to other other players and capture margin that way. Is that is that right?
01:38:26 · Jordi Hays, Bar Winkler, John Coogan
[01:38:26] S1: Yeah. So so so you're you end up building something net new customer, productizing something for a customer, but then you're you're fine not charging for that build out because you plan to use that same product. You'll you'll be able to resell it to other other players and capture margin that way. Is that is that right? [01:38:45] S2: So so the the the flywheel between front end engineers or FTEs into the core product team is something that happens pretty naturally because no one wants to do double work. Right? And the the challenges between different enterprises around the world are relatively consistent, regardless of where they are geographically. So whenever there so wonderful has a library with, like, hundreds of different agents and apps that were written on the platform, and that's the first place that an FD or DS, for example, would go to find out, like, a good starting point for their own solution. Most of the bespoke stuff that we build are actually integrations on the customer's end. We don't build that much, like, bespoke features into the core product for one customer. The vision for me is to go after large use cases one by one, and I think the north star should be that Wonderful acts as a zero to one or like an agency machine for enterprises with all of those use cases we built left to their own engineers and own business stakeholders to manage on our infra. [01:39:52] S0: Did you the company? [01:39:56] S2: Founded the company in January 2025. Wow. So a bit less than two years ago.
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?