[Latent Space LIVE @ NeurIPS] State of AI Startups 2025 — with Sarah Catanzaro, Amplify Partners
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What is Sarah Catanzaro’s background and why is she relevant to AI startups?
So Light and space, the trunat Rec up. I just have to do that.
Okay. We're here with Sarah Karen Zarrow from Amplify. Welcome.
Thank you. First time on the clock. 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 sympatical
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 D B T. 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. 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.
How does the DBT‑Fivetran merger signal the future of the modern data stack?
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
What is Sarah Catanzaro’s background and why is she relevant to AI startups?
0:00–3:39
2
How does the DBT‑Fivetran merger signal the future of the modern data stack?
3:39–7:04
3
Why did data‑catalog products fail as standalone tools?
7:04–8:58
4
How are frontier AI labs using DBT and Fivetran for training‑data curation and agent analytics?
8:58–11:56
5
What’s driving the $100 M+ seed rounds with no clear 6‑month roadmap?
11:56–18:15
6
What are the three competing definitions of world models and why are they over‑hyped?
18:15–20:23
7
Why is personalization—memory management and continual learning—the 2026 growth unlock?
20:23–23:31
8
Are RL environments a fad compared to real‑world logs and traces?
23:31–28:29
Speakers
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