[State of AI Startups] Memory/Learning, RL Envs & DBT-Fivetran — Sarah Catanzaro, Amplify
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How did Sarah Catanzaro’s journey from data to AI shape her investment perspective?
Those are. Light and space, the trunat Rec up.
Okay. We're here with Sarah Captain Zarrow from Amplify. Welcome.
Thank you.
First time on the podcast to be here. I know Too long. I know, I know. We've we've known each other for so long. Yeah, never made an appearance.
Uh and also made the transition from data to AI, I guess. I I don't know if if um I did. I don't know if you were always like as deep on on on AI. Um but I'll be there's a lot of sympathetical
Yeah. I've always actually kind of oscillated between data and AI. Sure. Um, like arguably I started my career in quote unquote AI. It was just more like symbolic systems back then. But as you said, I think like they're they're so symbiotic, like it it's almost hard to divorce them. That's actually what brought me into data. I was like, I want to better understand what happens when I write a SQL query. So
Yeah. Um let's briefly touch on data because I I think obviously that's that's a lot of where you and I first met. Uh DBG5 trend. That was so cool. I mean, or yeah.
How do you how
do you how do you think about the end of the modern data stack?
Okay. So so like a lot of people look at the like D B T five Tran uh merger and like talk about the end of the modern data stack. And I think that is like a fundamentally wrong take. Both of these companies were growing, you know, very healthily. Both of these companies
you funded D B T.
We funded DBT. So so like both of the companies were actually like beating their revenue targets. I think what you're more seeing is a you know IPO environment wherein companies are expected to have far more than, you know, like a hundred million revenue. And
so would you say the bar is now three hundred? No, like above six hundred. Six hundred.
Yeah. Yeah.
And the combined company is four hundred.
I believe that they'll actually be close to six hundred. I don't have the exact number. Well say
clearly just getting ready for IP.
So so so you know, basically like the merger was a way to accelerate that path to liquidity. As you might remember
and they were the presumptive winners in their categories anyway. Exactly.
Exactly. Exactly. Um, you know, I think one of the things that has actually uh pleasantly surprised me, um, and this speaks to again the symbiotic relationship between, you know, data and AI, many of the big frontier labs are actually using both DBT and FiveTran. I recall talking to folks at um thinking machines like Within weeks of the company's formation and DBT was already an important part of their stock. Certainly like training data sets need to be managed.
Why does the DBT‑Fivetran merger signal a path to IPO rather than the end of the modern data stack?
We need insight into what users are doing on these platforms. And in fact, like the way in which you would analyze interactions with an agent or analyze interactions with an LLM is even more complicated. And so, you know, while I think perhaps like uh the demand for analytics engineers, the demand for data scientists uh didn't explode in the way that some people thought. Like analytics engineers are not one third. Yeah. Uh that doesn't actually mean that the demand for the tools uh is not still like very prevalent. Well
you got what you want it. You wanted to democr democratize things. You you got it.
Yeah, yeah. I mean, I guess we democra the we we we we democratized things by uh uh perhaps reducing the need for the people. I don't know whether or not that is a good thing, but honestly I do think that like the fact that uh it is easier than ever at from a tooling standpoint for people to make data driven decisions is probably a step in the right direction. Um and I've become actually convinced that like Well, every company does need analytics engineers and does need data scientists. They probably don't need armies of them. Um, and probably having like a moderately sized data and analytics team is a good thing.
Yep. So you touched on an interesting thing I wasn't planning to ask, but this is interesting. Also I come from the data field. Data was s synonymous of analytics.
Yeah.
But you're now saying that the DPT5 trend are being used for training data. Is there any notable differences in the workloads or the requirements?
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Chapters
8 chapters
1
How did Sarah Catanzaro’s journey from data to AI shape her investment perspective?
0:00–2:42
2
Why does the DBT‑Fivetran merger signal a path to IPO rather than the end of the modern data stack?
2:42–6:49
3
What went wrong with data‑catalog products and why are they being re‑imagined as machine‑focused metadata services?
6:49–10:12
4
How are frontier AI labs using DBT and Fivetran to curate training data and power agent analytics at scale?
10:12–13:51
5
Why are $100 M+ seed rounds with no near‑term roadmap becoming the new norm and why does it worry investors?
13:51–17:20
6
What are the three competing definitions of world models and why is their market potential still uncertain?
17:20–20:44
7
How can memory management and continual learning unlock personalization and retention for AI products in 2026?
20:44–24:52
8
Is the hype around RL environments a fad, and why do real‑world logs beat synthetic clones for training agents?
24:52–28:29
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