Ashwin Sreenivas

speaker

310 appearances 1 recordings 1 series first heard Jul 2026 last heard 31 Jul

Ashwin Sreenivas’s voice in public audio — every appearance, attributed to the second.

Trend

recordings per month · last 12 months

Recordings per month over the last 12 months — 1 in all, peaking in Jul 2026 with 1.

Appearances

newest first · ▶ plays the moment

The a16z Show · How Enterprise AI Really Gets Deployed · 31 Jul 2026 podcast

So now this is a much more broad, open-ended exploratory task.

So we think for jobs like that, frontier models that are very smart, that can try out a lot of things, make a lot of sense.

The other reason I think it makes a lot of sense for enterprises to kind of build their cool bundle of labs is that the shape of these models is changing constantly, right?

We don't just build our set of open source models and then it's done, we can move on to our next thing and maybe we'll revisit this in two years.

You often need to train new models all the time because as the frontier changes, as the capability of the models changes,

you come up with new use cases for them.

You find new places where you're like, oh, this task seems to be getting repeated a lot because now I have this totally new frontier model or open source model that now has this capability that they didn't have before, right?

So we find ourselves constantly training new models and deprecating old ones that are no longer relevant because maybe the frontier has advanced a lot.

The open source frontier has advanced a lot and the model out of the box can do a lot of things that it couldn't do before.

So because the model landscape is changing so quickly,

Dacagon Labs is in a way a model factory of sorts, right?

We really built it to compress the time between new model coming out and useful, fine-tuned to our task model kind of popping out the other end.

Yeah.

Just because it happens all the time.

You know, in practice, we've seen that so many things relevant to model training are so tightly coupled to the use case that we have, right, that we find that we end up needing to build a lot of tooling internally.

tailor our evals to customer outcomes it's way better than just looking at like loss curves over time right we're not just saying oh can i do this one specific task we're measuring the entire system end to end we're not just saying is this model good at this task we're saying is this model working in concert with all these other models delivering the end customer outcome that we care about and because that is so unique to our setup we found that in practice we've needed to build a lot of uh

a lot of the infrastructure that we need to train these models and evaluate them.

Now, for other things like getting labeled data and measuring the diversity of our data sets, we're like, yep, these are tasks that are common across lots of companies, in which case we want to buy things from other vendors because that'll just help us get those models to production posture.

Ultimately, the only thing that we care about is how can we get the best model to production as quickly as we can.

Performance, latency, and accuracy is definitely the driving factor for most of this, right?

Showing 21–40 of 310 · page 2 of 16