The Ledger, every show

“And so the reason, you know, most AI note takers are using assembly as the voice infrastructure is because our models are the most scalable, right?”

“If our customers, if they're building a voice agent and you disclose up front that you're an AI, people just hang up versus if you don't, people continue.”

“And so now, you know, on a given week, on a peak week, there'll be something like over a hundred and twenty million conversations, voice conversations going through our platform, over two million hours of voice, which as of December of this past year was four,…”

“Like, you know, the most recent example is we had Cloud rebuild our whole website off of Webflow and just deployed on Vercel.”

“We've seen some customers maybe six months ago or a year ago, they'll take an open source model and fine tune it or something. And then, you know, they're now like, okay, this is like, shit, this is like really behind and it's like nightmare to maintain and I …”

“And so I don't think that means that computers are just going to be like a screen and you just talk to it, because actually we'd get tired of talking, but I think it's going to be a dimension that's added to everything, and so that over the next couple years w…”

“Like for the first time probably ever in the past year, it's like a reliable form of data capture.”

“Across humanoid robots, consumer electronics, like, you're gonna see voice as this dimension that you just, like, expect And what, the example I think about is, you know, when you see pictures of, like, kids, like, trying to, like, swipe on TVs or something, b…”

“I think over the next year, we'll see a lot of, a lot more consumer applications, hardware and software, where voice is a core dimension. So, toys, games consumer electronics”

“And our models prior, and most commercial speech recognition models trained on like, 50,000 hours”

“We've processed, ah, yeah, it's like over a hundred million audio files a month that are flowing through the API, and that's growing pretty quickly.”

“State-of-the-art automatic speech recognition still has, like, a 15% error rate on a lot of data sets”

“One stat I was just looking at before I came over here was like the amount of weekly conversations that Assembly handles through our APIs every week is up over 800% over the last three years.”

“There's, you know, almost a hundred million API calls a day coming against our API, about a million developers, a little over a million developers on the platform now.”

“One of the main problems the humanoid robots face today, because a lot of them are using our APIs If you have three people standing next to the robot, it doesn't know who to listen to. And it has a hard time disambiguating who's saying what.”

“The first model I ever trained for speech to text Was on 10,000 hours of audio data. And then now the model that we're going to release soon, that's trained on 12 and a half million hours of audio data. It's on, you know, hundreds of TPUs.”

“And so I submitted an application to YC for their summer batch. This is like summer of 20 17. And it was 30 days past the deadline. So I was like single founder past the deadline. There's no way I'm going to get in. You know, I was planning to just work on it,…”

“No institutional funds invested. It was only angels that invested”

“We've processed almost two billion audio files through our system.”

“So we trained it on, like, 60 terabytes of audio data, like, labeled audio data. So it was, I think, something like 650,000 hours of audio data.”

“We actually, I think, published like, made a contribution to Jax to make it, like, 10 times faster to train Whisper.”