The Exchanges

Q Obviously Sierra is in a different bucket too, but like, how do you fit in within that world?

A Yeah. So we are a hundred percent focused on voice AI infrastructure. So we don't do anything at the application layer. Where we focus, you know, is the models. So we create models that are Amazing voice models are amazing for all those use cases I spoke about. So healthcare, we have medical focus models, uh, drive through voice ordering, contact center, and note taking. We then build out the inference around those models. So to handle four X, the amount of YouTube volume on a, on a day, a hundred, twenty million conversations a week and growing over a hundred percent year over year. There's a lot of infrastructure we have to build out to make sure everything scales, is available, is like super fast, is low cost for customers. So we build out a ton of infrastructure, infrastructure on inference around our models. And then we also do a lot around the orchestration layer. So if you want to build a voice agent, if you want to understand speakers, if you want to translate data, we have a ton of, a ton of software at the orchestration layer that, that companies can leverage. And then, you know, above that, it's, we have this amazing developer experience, agentic coding experience, so agents can easily build with our stuff. And when we say infrastructure, I think a lot of times people think like, oh, you're just creating the model weights, right? Like you're just creating like models…

AI assessment note: “we are a hundred percent focused on voice AI infrastructure. So we don't do anything at the application layer.”

Q Okay, so as we close out, there's one question I have to ask. This is a Brex question, because they're all about performance. Spending smarter, moving faster. I like to think that for personal performance, it's kind of who you surround yourself with. Some people say it's like the five closest people, like, who's either mentored you, who's a close friend, who's been inspiring. Who are those people for you?

A Yeah. I really put it in, like, three buckets. Um, uh, like, family, friends, and then, it's gonna sound so corny, but, like, really our investors. And, you know, on family, like, my wife's an entrepreneur, too. She's a founder, and that's been, like, a blessing. Uh, cause, you know, that, that, yeah, always able to, like, get advice from her and talk to her. Friends, you know, I've been able to, to meet, Um, over the last couple years, like, other founders that are in the same stage and phase, and, um, that, that's been amazing, and I think having, like, finding peers that you can just, like, be super open with and transparent with is super helpful, but then in, in terms of investors, like, we have an amazing group of investors that has really been along for the ride, you know, and I think about, like, Keith Block and Smith Point, That invested in our company at our, our Series C. Um, they're, you know, operators from Salesforce, they've started their own VC fund, and, like, they're just, you know, so, I think, like, Steve from Excel, Steve Laughlin, Rebecca from Insight, like, they're just always, um, uh, like, pushing the company and pushing me in, in, in great ways, and they're, they're amazing people, so that, I'm not even trying to, like, Be cheesy when I say,

AI assessment note: “I really put it in, like, three buckets. Um, uh, like, family, friends, and”

Q Did you think that this would be the reason why voice would take off, and people talking to their phones, and talking to their computers, and that kind of thing? Like, what did you think?

A Yeah, I mean, I did, which is why, you know, I, I've spent so much time on this, because we, we're actually the very first AI batch in YC, when we went through YC, and so it was Daniel Gross, who, who, if you know of Daniel now at Meta, he started the AI Batch at YC back when we went through in 2017, and it was me and like five other companies, and we got, you know, a 100,000 dollars in GPU credits. That was like our perk, which, which now seems like cute, right? Um, uh, it's like, wow, you know, that's nothing. But back then it was like, wow, you know, a 100,000 dollars of like NVIDIA K-K-Eighty usage, like this is amazing. But the, you know, the, the, the AI ecosystem back then was just like in its infancy. Like, I was going to the very first, like, TensorFlow meetups. Just to put it into perspective, like, it was, no one was really using AI in production yet. Um, but for me personally, I had gotten Amazon Echo and was, like, really into voice interfaces and, and talking to this hardware that worked well, because my experience with voice prior had been, everything was terrible. And so then I went looking to find, like, APIs that I could just in my, you know, spare time back then, like, build with, and I couldn't find anything. And all the technology kind of sucked at the time, but I felt like, okay, over the next 10 years, this is gonna get so much better, and when it does, i…

AI assessment note: “Yeah, I mean, I did, which is why, you know, I've spent so much time”

Q Of data that you have, how does that really work?

A Yeah, um, my experience has been, um, It's really, probably, like, 75% of it is, like, the data that you're training on. Like, I would say for any AI model, it's like, there's always, like, you know, like, these, these, like, step functions and, like, algorithms and architectures and stuff, but the data is just so important, and so a huge, we spend a huge amount of time just on data, trying out different data mixtures, training different models, and, um, you know, we release model updates, like, every couple of weeks, and that's the big benefit that our customers have when they're building on our platform, is, like, It's literally like constantly getting better. Um, it's not like every six months or every year there's an update. It's like there are constant improvements going out. We have a ton of different model versions. We have a ton of different APIs, which we just launched like a different API today for different use cases for dictation and like push to talk type use cases. Um, so we're constantly innovating on the model training part. So much of it's about the data. Is the quality good? Is it aligned with, like, what users want? And, you know, I'll just give you an example of that, like, for, for the speech-to-text task, like, some applications don't want to pick up the background speakers. Some do. So, like, we have some customers who are, you know, looking at police bod…

AI assessment note: “we spend a huge amount of time just on data, trying out different data mixtures”

Q How did you get it to become so efficient?

A Um, you know, years of engineering work. People like, uh, Ben at our company. We have an amazing team of engineers, of researchers that just operate so closely to customers that they really understand, like, how this stuff is being deployed. I think that's the biggest difference between us and, like, a lab at a bigger company. So, you know, we have a lot of, a lot of researchers, research engineers that will come to assembly from a bigger company. And they're so far removed from the customer. So they just create their model, they benchmark it, and then it's like, okay, I'm done. But you have to know, all right, who are the customers? How are they using this? What do they care about? What errors are, what errors like break their application and what errors like don't matter? And then how do you optimize both your models and your infrastructure for that? So we, we've just spent years and years building out the infrastructure to make this stuff work. So infrastructure that's like cross region and cross cloud and, Stuff that just will really scale, but it's a, it's probably like half our work is spent just making our infrastructure more and more scalable.

AI assessment note: “optimize both your models and your infrastructure for that? So we, we've just spent years”

Q Would you acquire those companies? Like, how would you get that?

A Yeah, I mean, a big, a big part is like, you know, we, um, We try to build a team of those experts. We have a lot of customers that, you know, help out and service those experts and give us that feedback. Uh, we partner really closely with customers. A lot of them, like, are not working in sensitive applications, so they opt in to, to, um, letting us use their data to improve models and their feedback, which is great. So it's a mix of all those things. Um, but that's, that's really where, you know, there's still a lot of opportunity to differentiate at these models. And I think for voice, A lot of people will think like, oh, voice, like what people have been telling me forever. It's like, oh, voices, you know, isn't that solved? Like, isn't that just like commoditized technology? But like, it's definitely not because there's, there's so many gaps across languages, across different, different verticals and applications and domains that there's big rooms for improvement and still.

AI assessment note: “We try to build a team of those experts. We have a lot of customers”