00:37:51 · Jordi Hays, John Coogan
[00:37:51] S0: Yeah, Ethan says my big takeaway is that both Soul, Ethan had access. So he says both Soul and Fable represent jumps over previous models and have opened a large gap with the next best AIs. People will have preferences for one or the other, but if you're doing any work where better intelligence matter, those two models are your only choices. Dean says, I think for me, the main takeaway with Soul and Fable is that I can't remember a time when the leading models were A, so decidedly ahead of everything else, and b, so distinct from one another. So Yeah. So, yeah, a lot of people are talking about how they're using the models Yeah. Together. [00:38:26] S1: It's probably a function of just tighter tighter distillation protocols, like there's less pollution. Like, also, like, not everyone's training on the exact same data anymore because it used to just be everyone just trains on the entire web corpus. And now each company has different data sets, different data brokers, different usage patterns that they can train on, and then different sort of research tastes. So go give it a
00:38:26 · John Coogan, Jordi Hays
[00:38:26] S1: It's probably a function of just tighter tighter distillation protocols, like there's less pollution. Like, also, like, not everyone's training on the exact same data anymore because it used to just be everyone just trains on the entire web corpus. And now each company has different data sets, different data brokers, different usage patterns that they can train on, and then different sort of research tastes. So go give it a [00:38:53] S0: try I on forgot to mention this. GPC 5.6 Sol's world leading in computer use. It made me use it 100x more when we lost access to 5.6. I quickly started to go insane without it. So yeah, people in the early access program obviously lost access for a couple weeks due to the DC action. But, yeah, I saw some people saying, like, what are some good uses for computer use? And it's like, anything on your computer. What do you use your computer for? They're not all gonna be great uses, but Yeah. I'm curious. I still think it's worth
00:39:26 · John Coogan
[00:39:26] S1: it's worth it's always worth, like, popularizing those, like, genuinely useful patterns. I mean, like, deep research was that in many ways, just a very like, it gave you a 20 page PDF report on the topic that you were asking for, and it did a lot of the work for you. And I still think the the come up with a good prompt idea, find the right reason to use AI is a problem for a ton of There there there are there are plenty of people that adopt the latest AI tools, immediately find what the model's good at, how to use it, how to use it profitably, effectively, not waste tokens, not waste their time. But other people need ideas. They need to know that there's a new capability. You see this with the with the chat app. You see this with the voice mode where people are like, you know, like judging a model based on hallucinations that disappeared a year ago or judging a model based on some, oh, it has a cutoff window, and that hasn't been a thing for a year, but it still burned its burned its way into, like, the memes of the online culture, and thus, people need to be updated on that. And so I'm I'm in favor of coming up with good computer use demos. The real one is just can it play Counter Strike effectively? That's the real computer use test. Can it rank me up to global elite? Can it down rank me so I can smurf a little bit? This is this is the future. This is what I want. Anyway, let me tell you about Railway. Railway is the all in one intelligent cloud provider. Use your favorite agent and to deploy web app service databases and more while Railway automatically takes care of scaling, monitoring, and security. I'm extremely excited to welcome our next guest to the show. We have Vincent Weiser from Crime Intellect. He's the cofounder and CEO, and he has some amazing news for us. Tell us what happened, then we'll dig into the news, the market, the story, the company. But first, give us the news because Jordy's already warming up over here.
00:41:31 · Vincent Weisser, John Coogan, Jordi Hays
[00:41:31] S3: Amazing. Yeah. So we've announced that we've raised a 130,000,000 and 1,000,000,000 with Prime Analytics Wow. To build the open super intelligence stack. [00:41:43] S1: It's fantastic. Congratulations. Yeah. It [00:41:48] S0: it What I what we were talking about before the show Yeah. Is is the the the the ratio between dollars raised to run rate. Yeah. Absolutely incredible. [00:41:59] S1: Yeah. Right? Much have you raised before this round? [00:42:03] S3: Just 20,000,000 total. So I think we've actually got to this run rate on, like, less than 20,000,000 in spend. That's crazy. [00:42:09] S0: Which is north of which is north of a 100. [00:42:14] S3: Yes. That's amazing. Yeah. We we doubled the pricing since we actually closed the fundraise Yeah. To a 100. So basically, yeah, on track to grow much more from here on. [00:42:25] S1: Okay. So reset me on the latest and greatest in the actual product offering. What customers are coming to you? Is it I have used a frontier model to, you know, figure out that AI can in fact do some agentic workload within my organization? I wanna cut cost and you're gonna RL an open source model for me, fine tune it, and then am I paying you for tokens? Am I are you getting me GPUs? What is the full suite of products that you offer customers these days? [00:42:55] S3: Yes. So basically, it is kind of like the full stack [00:42:58] S1: to do
00:42:58 · John Coogan, Vincent Weisser
[00:42:58] S1: to do [00:42:59] S3: training, deployment, and continue improvement of models. I think customers basically come to us for anything ranging from compute to inference to the full RL and post training stack to to train models. And I I would say it's like, with like customers coming from different buckets and categories, a lot of them are like the AI natives who are basically starting out, and and oftentimes also like being multi model. Right? Like, they they use like the frontier models. They use open models Mhmm. For different use cases. Like, good examples like RAM, for example, as one customer more in this like AI native bucket, a lot of the big AI native startups. But then we've also increasingly been able to track a lot of the new AI labs, like a lot of the Neo labs. So a lot of those are using our full stack from like large scale clusters to our pre training and post training and RL stack. But then also increasingly, like traditional enterprises and even sovereigns coming to us who want to have their own end to end stack that they can run like, on prem. So how I see this is, to some extent, it's like, there's just increasingly, I think, like a demand for having your own, like, sovereign AI stack. Right? It's like your own open end to end stack, but I think this is almost complementing, in many cases, the other, like, frontier, like, models and and stacks. [00:44:18] S1: And you're using Sovereign in not just the geopolitical sense, but also if you're a Fortune 500 company, you want to own your stack from start to finish, potentially.
00:44:28 · Vincent Weisser
[00:44:28] S3: Exactly. Yeah. I think that there was a lot of recent talk about this. Obviously, like folks like Satya and Alex Carr were like, also making making those those points and arguments over the last few weeks. And I think it's increasingly something I think where, like, a lot of, like, enterprises want to basically build this, like, compounding data mode and flywheel, where ultimately, I think the future that we are starting to see is a lot of these companies want to build basically self improving agents. Mhmm. And the way to do this, I think, is generally you you have an RL environment for use case, you scale on it, and then you ultimately deploy that into production, and then continuously improve with the user interacting with that agent. It's almost like the the Tesla autonomy, like levels, but like for knowledge worker agents. So I think like the the ramp example I think was one that people really resonated with for basically them being able to within a week and less than 50 of of training spend Wow. Outcompete a frontier model Yeah. At automating spreadsheets and and finance at a fraction of the cost of of running the cheapest models. Sure. This is like they were able to outperform Opus at a fraction of the cost of Haiku and and like 30% faster, like much more tailored to the use case. So I think in many ways, like, these, like, specialized agentic use cases are a big unlock. And I think in many ways, you still need the god like a big mega model to to do the orchestration or planning. But then I think oftentimes execution, I think, happens can happen with these specialized sub agents. So I think, yeah, those have been extremely, I think, like, fruitful and and and useful for a lot of the companies adopting and and training their own agents. Then I think the other big trend is just like an explosion in in neuro entrants that are, like, taking model training seriously. Obviously, hundreds of Neolabs really got stood up in the last probably twelve to twenty four months. So we started powering a lot of those, like, with our full stack, including with also large compute clusters. So, like, NVIDIA also joined us as part of the round, and we've been like, we've since day one basically operated large clusters. And and this is in in large part why we need so much money. Right? It's like to basically be standing up more and more GPUs.