00:36:32 · Mati Staniszewski, John Collison
[00:36:32] S1: Few billion to low tens of billion parameter models. Yeah. [00:36:37] S0: So so... And for context, I think the... I mean, kinda like, you know, CPUs moved away eventually from gigahertz as, like, the metric as they move to more cores. I think we've mostly moved away from just raw parameter count, but I think the leading edge LMs are in the hundreds of billions of parameters. [00:36:52] S1: I think the leading ones, yes. But, of course, you know, you have the variations that you will use at at lower scale. Yep. So CapEx is still pretty high. Yeah. We've, of course, raised recently a 500,000,000 at at 11,000,000,000 valuation to, like Makes sense. Makes sense. To continue to continue being able to build the best models in the in in the world. Researchers, you know, of course, you want to... You want the best people in the world. I think we have we have those people working in in in audio in in in micro founder who who is who is leading that work. So that's that's that's definitely a big piece of, like, not financially, but even, like, how you keep the ambitious [00:37:28] S0: Mhmm.
00:37:28 · John Collison, Mati Staniszewski
[00:37:28] S0: Mhmm. [00:37:29] S1: About deployment, deployment, so you kind of continue building leading models helps you attract more talent and and building that. And then on the... How we service, of course, inference is correlated with how the models are used, and and for us, like, we've seen incredible incredible growth across the work. Mostly, this is charged per... If it's input text or text to speech, it's usually per per per text token. If it's voice agent or or transcription, then then it's per minute, and we see that kind of being the the bigger part of it. Usually, like, broadly, it's per token basis, and, course, as we work with businesses, it's like an annual agreement. The bigger the spend, the bigger the comments, the bigger the discount to Yeah. Gonna get that across. The way we usually do is, like, when we have a new model, we try to give it at cost to a lot of the customers so they can experience the best. It's still usually, like, not as But just [00:38:19] S0: the newest thing is often the most expensive, whereas you make the newest thing the most economically attractive one? [00:38:25] S1: We try to make it attractive so the customers are... Like, you know, like, it's it's more expensive for us than any previous generation. We don't like... [00:38:32] S0: Yes. [00:38:32] S1: Oh, the quality is higher, so we try to keep the prices still still competitive I used to to [00:38:37] S0: subsidize it, but it's inherently more expensive
00:38:37 · John Collison, Mati Staniszewski
[00:38:37] S0: subsidize it, but it's inherently more expensive [00:38:38] S1: to figure model. Exactly, exactly. And over time, we might do some tricks to optimize it, but we want the customers to experience... Because of research, the big thing that we've seen is the reliability of the model in the early days might not be there, and then two, people don't even know what's possible with that model. So you kind of want the widest set of distributions so people can show the world what's possible, so you can have it, of course, as a distribution mechanism, learn yourself what to improve, what to change Yes. And then and then get it out there. [00:39:10] S0: Are the voice models just getting bigger and bigger? Like, will we have voice models in the hundreds of billions of parameters, or have we found... [00:39:18] S1: Like, it [00:39:19] S0: seems like for certain types of model architectures, there's, like, an upper limit on, like, the natural size. Have we found that upper limit for voice models?
00:39:19 · John Collison, Mati Staniszewski
[00:39:19] S0: seems like for certain types of model architectures, there's, like, an upper limit on, like, the natural size. Have we found that upper limit for voice models? [00:39:26] S1: It's it's... It feels like for specific use cases, like, say, audiobook narration, you probably found that size. Probably don't need to stretch it too much bigger to to make the quality as much higher. But for certain use cases, that will probably grow. The the thing that's that's, you know, like, I hesitated on the question is, in a cascaded approach, you probably will not see, like, dramatic size changes. Mhmm. You inherently want the models to be quick Yeah. And they're reliable. You want to orchestrate them in a smart way. In a fused approach, probably that will get into, like, tens, hundreds billion parameter models because you kind of combine, of course, the Intel... The LLM side and the voice side. Yeah. So that will get bigger. But on the Adjust Voice, I think it will keep keep being small. [00:40:07] S0: Okay. But there are certain domains where, yeah, we'll see bigger models. [00:40:10] S1: That's still interesting. [00:40:11] S0: Yeah. Yeah. It it is amazing how... It does seem fun from a research point of view how there are still these various unsolved aspects and how you guys are just making technical breakthroughs and then releasing them down the product pipeline, that's like a really fun stage of a company's life cycle.
00:40:11 · John Collison, Mati Staniszewski
[00:40:11] S0: Yeah. Yeah. It it is amazing how... It does seem fun from a research point of view how there are still these various unsolved aspects and how you guys are just making technical breakthroughs and then releasing them down the product pipeline, that's like a really fun stage of a company's life cycle. [00:40:27] S1: For sure. It's like fun because we... It feels like we can do innovation on both sides. There's like so much on research side, so much on product side, and then like the kind of, ultimately, the biggest parts is how we deploy it to the customers, where Light and Intent, like SMB will have very different dynamic than the enterprise. It's not vendor SaaS relationship where you just give the product out there for the biggest companies out there, but you are more of a partner in their AI transformation part, so you want the resources to work alongside them to work on the frequently very new use cases that were impossible to help create and bring those voice agents to production. Yes. So that's, a big big big shift. Yes. But the biggest focus is is is how we how we bring the conversational agents out there to to the businesses around the world. [00:41:14] S0: So when you say con... Bringing conversational agents is the biggest priority, is this for customer service type use cases? Like, what are the most popular use cases for conversational agents?
00:41:14 · John Collison, Mati Staniszewski
[00:41:14] S0: So when you say con... Bringing conversational agents is the biggest priority, is this for customer service type use cases? Like, what are the most popular use cases for conversational agents? [00:41:25] S1: Yeah. Like, we want to be a partner for, like, full interactions between business and... Businesses and their customers or their audience. I'm saying their their audience because that will apply in support. Support is the easiest one because that's where it's most ready. Yeah. But, like... And that's maybe the big difference to how we see ourselves to some of the other companies in the space is this can also apply to sales. You can you can have the proactive side of reaching back. You can have AISDR versions of that. Yes. And then you can have all the way to the marketing use cases where we are your partner for for working on on on... Even outside of, like, the the conversational agent space of how you create a great marketing campaign. Yes.