Corey Noles

We're excited to have you here.

It's an interesting time and sounds like you guys are doing some neat work.

Well, you know, so, you know, we always think about the idea that training is the hard part for these massive models.

But you and the Samba Nova team are often talking about how inference and actually running them is the real challenge.

Can you kind of explain why that is?

Especially dealing with products too.

I mean, the user just has no patience or tolerance for weight on any scale.

And, you know, I think it's interesting you talked about Volume 2 and as these things are scaling up, you know, you kept hearing these discussions for a long time about how the cost of intelligence was going down.

And something I think they're never clear about is that, like, the cost of serving an individual unit of intelligence, for lack of a better phrase, is very cheap.

But if you're dealing in product where you're serving to many, many people,

those little fractions of a penny add up in a big hurry.

Yeah.

Here's a common developer nightmare for you.

You've finally shipped your AI agent and the transcription falls apart the second someone speaks with an accent or calls in from a noisy cafe or switches language.

Does that sound familiar?

Thought so.

It's not just frustrating.

Failed calls can kill your business.

And that's exactly why Gladia built Solaria.

It's the first speech-to-text model designed to handle the messy, real-world stuff with sub-270 millisecond latency, more than 100 languages supported, more than 200,000 other developers are already building with it today,