Fireworks AI

And yeah, I think that goes to the statement of kind of owning your own intelligence, not renting it.

That's why we invested in Fireworks, and I believe in the open model ecosystem.

Yeah.

You know, I'm a big fan of Fireworks, and we are using them, uh, quite a bit.

Do you like them?

Yes.

The real investment for an enterprise is to creating this map of work that documents how they actually work.

And with this one, they can train their own models.

Jen Igartwa is the founder and CEO of GoNimbly, a revenue operations firm that has built the go-to-market engines for SaaS unicorns like Zendesk, Twilio, and Gong, as well as some of the fastest-growing AI companies.

I'm working now with Whisperflow, Exa, Perplexity, and Fireworks, these AI-first companies.

The number of people they want to hire in a year is like, it blows my mind.

In today's episode, Jen shares how to prioritize when your customer demand outpaces your sales infrastructure, and her favorite approach if you want to get ROI from a RevOps team fast.

She is great on screen, on camera.

She hosts This Week in SaaS, which you can catch on LinkedIn.

And today, GoNimbly is servicing some of the fastest growing AI native companies in the world, including companies like Fireworks.

Jen is also the co-founder of Pillbox Games, an independent board game company.

We have, but like small data sets.

So I suggest you use Fireworks.

Okay.

But, uh, I mean, Fireworks is amazing.

Linda, the founder is, she one of the co-founders of PyTorch.

But I, I, I had the very obvious realization that you'd have every company having their own specialized models of a certain size trained on their own data, but you would need supplemental data-

Value accrues to top one player.

I, I agree.

The model card says the weights are stored at a certain precision, FP16, BF16, whatever the published architecture specifies.

But what precision are they actually running at when your request lands? Daniel wants to know whether closed source vendors quantize their own flagship models, whether commercial inference providers like Together and Fireworks run everything at full precision, whether unofficial quants get deployed when demand spikes, and whether your customer tier determines whether you get the quantized version or the full one.

Basically, is the model you think you're calling the model you're actually

getting? And the short answer is...

On one side, you've got the closed-source vendors, OpenAI, Anthropic, Google.

They train the models, they host them, they control the whole stack.

Then you've got the commercial inference platforms, Together, AI, Fireworks AI, Grok, Replicate.

These are companies whose whole business is hosting models, mostly open-weight ones, and serving them through an API.

Let's run through those quick hits.

First, we have Fireworks, which is a platform focused on training open models cheaper and faster for these specific workflows.

Essential infrastructure.

Then there is OpenClaw 2.0, which is an open source personal agent that just added multiplayer sessions.

Mm-hmm.

And I feel a lot more certainty when I invest in Fireworks, when I invest in Macaw, when I invest in ClickHouse-

Mm-hmm

...

the infrastructure that I know whoever wins at the top layer in the app layer wins.

But they're gonna use Fireworks, they're gonna use ClickHouse, they're gonna use Macaw to get there.

Do you not just sit around the table as a partnership and go, "God, the infrastructure layer is much easier and better."

[laughs]