Bringing AI to Data: Agent Design, Text-2-SQL, RAG, & more, w- Snowflake VP of AI Baris Gultekin

**Nathan Labenz** (0:00)
Hello, and welcome back to The Cognitive Revolution. Before we get started today, a quick final reminder. If you dream of a career in AI safety research, the deadline to apply to MATS Summer 2026 program is January 18th. Listen to my recent episode with MATS Executive Director Ryan Kidd for all of the reasons that you should consider applying, and then get started at matzprogram.org.
Today, my guest is Baris Gultekin, Vice President of AI at Snowflake, the cloud-based data platform that now describes itself as the AI data cloud. Baris came to Snowflake along with Snowflake's current CEO, Sridhar Ramaswamy, as part of Neva, an AI-powered web and personal knowledge base search engine that Snowflake acquired in May 2023 Since then, he's been working at the intersection of frontier AI capabilities and hard enterprise realities, deploying these systems in environments where security, governance, and reliability are strict requirements. As you'll hear, Snowflake's core philosophy is to bring AI to the data, rather than sending sensitive data out to model providers. And in this episode, we unpack exactly what that looks like in practice. We cover a ton of ground, including the massive ongoing unlock of unstructured data, which is making the 80 to 90 percent of enterprise information that was previously trapped in PDFs and other documents queryable for the very first time. The current state of both Text-to-SQL and RAG systems, and why reasoning models have finally made natural language data analysis reliable enough for business users. The trade-offs between using frontier models versus smaller specialized models, and when to use structured workflows versus letting models choose their own adventures. How data residency requirements are shaping partnerships between cloud providers, model labs, and platforms like Snowflake. How AI coding assistants are changing the discipline of product management by enabling rapid prototyping of working features. Where Baris sees value accruing in the AI stack, and his prediction that horizontal applications will win out over narrower vertical solutions. And finally, why Baris takes the over on my timeline for autonomous drop-in knowledge workers, and what he believes will have to happen first. If you want to understand how large enterprise companies are deploying AI today, and what's really working as they mature from the early experimentation phase to the ROI at scale phase, taking all of the operational complexities and security and governance concerns into account, I think this conversation will be perfect for you. And with that, I hope you enjoy this deep dive into enterprise AI adoption and the future of data intelligence, with Baris Gultekin, Vice President of AI at Snowflake. Baris Gultekin, Vice President of AI at Snowflake. Welcome to The Cognitive Revolution.

**Baris Gultekin** (3:08)
Thank you, Nathan. Thanks for having me.

**Nathan Labenz** (3:11)
I'm excited for this conversation. There's going to be a lot to learn. I think people, we have a very diverse audience. The number one profile is AI engineer. And within that profile, people work at a lot of different kinds of organizations, from solo entrepreneurs and consultants to startups to enterprises. So some people will certainly know Snowflake and will work at organizations that are customers of Snowflake. Others probably have heard of it and don't really know too much of the backstory. So maybe for starters, just kind of give us the real quick Snowflake 101 And then I'd love to go into how AGI-pilled is Snowflake today.

**Baris Gultekin** (3:47)
Sure. So Snowflake is a data platform. We call ourselves an AI data cloud. So what that means is our customers bring a lot of their data onto Snowflake so that they can secure it, govern it, and analyze large amounts of data for various insights, dashboards, and the like. And from an AI perspective, because there is a lot of gravity to data, our customers do not want to replicate data in multiple places. Instead, they want to bring AI to run next to data. So that's a very high level of review. And AGI-pilled is an interesting phrase. For us, we're quite practical. We serve large enterprises, and the goal is to get to high-quality AI agents that create positive ROI for customers quickly. And that can happen today, and it is happening today. Super excited about where things are.

**Nathan Labenz** (4:37)
I definitely want to come back to the bring AI to data strategy that you guys have in a few minutes. But to just double-click a little bit on the before and after, because obviously Snowflake has been around for a while before, certainly anything like the AIs that we have now, were available. So what were people doing before with Snowflake, and what are the new AI use cases that have been unlocked over, say, the last, I don't know, when you would start the clock, right? Do you start the clock at ChatGPT, or do we need it? Was that not quite strong enough to actually make things work? But yeah, there's a lot of different dimensions. Maybe let's start with before and after.