Sarah Wang
speaker
1,664 appearances
13 recordings
1 series
first heard May 2025
last heard 18 Sep
Sarah Wang’s voice in public audio — every appearance, attributed to the second.
Trend
recordings per month · last 12 monthsRecordings per month over the last 12 months — 9 in all, peaking in Sep 2026 with 1.
Appearances
I'm sure they're not the ones who started it per se in this new Gen AI era, but they certainly are, I think, one of the best examples of how to integrate founders really, really well into your company.
matched
This narrative dominated the first half of 2026, which is that Anthropic, OpenAI, they're the last startups.
They're going to take over everything.
Hey guys, welcome back to the studio.
Thank you for being here.
Before we get into customer support, I actually wanted to widen out a bit.
And Jesse, I'm going to actually mention a piece that you wrote recently that went pretty viral because it's right in the middle of the zeitgeist of conversation right now on open source versus closed source models.
And then thinking machines, Kimmy K3, some very interesting open source models came out sort of right after.
And there's this really interesting debate going on around what does it mean to own your destiny when it comes to AI, especially in the enterprise?
And what does that evolution look like by use case?
So actually, since that's a pretty live topic right now, why don't we start there?
Do you think that, I mean, you guys obviously, and you referenced it, Jesse, that you have a research team, right?
You guys launched Decagon Labs, but even before the formal launch, it's always been a part of your culture.
Do you think that enterprises will get there as well on post-training open source models?
Oh, okay.
What's the timeline?
How do you guys think about what to in-house from a talent perspective versus there's also a pretty broad ecosystem right now that is, you know, could be RL as a service, evals, et cetera.
Like, how do you guys, what's your framework for, hey, this is mission critical and we need to do this best versus, yes, it'd be great to, you know, outsource this?
I just want to make this meta observation that a lot of the conversation even just now has been about things like training models, right?
We're talking about reinforcement learning.
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