Clay Bavor

speaker

1,248 appearances 2 recordings 2 series first heard Jul 2026 last heard 4 Jul

Clay Bavor’s voice in public audio — every appearance, attributed to the second.

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recordings per month · last 12 months

Recordings per month over the last 12 months — 2 in all, peaking in Jul 2026 with 2.

Appearances

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The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch · 20VC: Open Models vs Frontier Models: Who Actually Wins? | The $100,000 Token Budget Every Engineer Will Need | Why Forward-Deployed Engineers Are the Future of Enterprise AI with Clay Bavor, Co-Founder of Sierra · 4 Jul 2026 podcast

This was before all anyone wanted to talk about was agents.

And we realized, okay, this should be possible.

It's not yet possible, but we're going to have to invent frameworks for building these things.

Our own architecture is really from scratch.

Actually, our first founding head of research was the Princeton professor who literally wrote the paper on language model-based agents, the React paper.

We invented and went further down the stack than I think some companies at that point would have been willing to.

And we're not, you know, we're not doing our own pre-training.

We'll leave the capital expense there to, you know, the labs and the larger companies.

We did briefly and discarded it.

If you recall at the time, so late 22, early 23, as a startup in AI, you were kind of nobody if you weren't doing your own pre-training and building your own foundation models.

Character, inflection, adapt.

great people at these companies, but the capital expense, the ongoing capital expense to create what is effectively a highly perishable bag of floating point numbers just doesn't work for any but a small number of companies.

And so our calculus was for areas that are deeply capital intensive,

How do we slipstream behind the investments that the labs, that the hyperscalers are making, take as much as we can off the shelf while still being willing to engineer more deeply?

So today we have a set of our own proprietary fine tune models, but these are fine tunes on top of open weights models.

So we're not going all the way down to the mega cluster training runs.

And I think-

It's important that you are in control of your own destiny enough and that you don't tell yourself a story that you need to go further than you actually need to do.

I think it's a lot more complicated than that.

If you asked any software company, would you like to upgrade your staff level software engineers to principal or distinguished level software engineers?

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