Nick Taylor

Snowflake × OpenAI: why this deal feels less like a power move at the data layer: a multi-year $200M agreement to bring frontier models directly inside Snowflake's Cortex AI (Snowflake's Intelligence layer for enterprise agents) If you’re thinking in systems terms: CortexAI is Snowflake trying to make “AI on enterprise data” feel like a native database capability — not a separate product you bolt on later. The story is that agentic AI only becomes valuable in production when it can reason over governed, proprietary data (without your org turning into a compliance crime scene.) Yesterday, Snowflake and OpenAI announced a multi-year, $200m partnership to bring OpenAI models natively into Snowflake’s Cortex AI, available to Snowflake’s 12,600 customers across all three major clouds. A practical lens to read this deal --- We can 'think of modern AI' as a 'supply chain': raw material = enterprise data factory = compute + connectors foreman = evals + guardrails machines = models output = decisions + actions Most orgs are trying to buy “better machines” while the foreman is missing and the factory floor is chaotic. This partnership is Snowflake trying to own the factory floor, while OpenAI provides the best machines. Enterprises already park their most valuable asset (data + permissions + governance) inside Snowflake. If the AI runs inside that boundary, Snowflake becomes the default runtime for “AI on enterprise data” instead of being just storage/warehouse.

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Building agentic workflows? There are actually only a few key concepts to be aware of. StateGraph orchestration: A StateGraph is the agent’s blueprint. It consists of nodes that handle tasks with edges (directions) to other tasks, defining the order. Together, they provide the agent with a flow to follow. Conditional routing: At decision points, the agent inspects results and follows “if-else” statements that skip failures, handle edge cases, etc. Memory and threads: Thread memory holds recent conversation turns. Essentially, it stores the short-term memory that you are John Doe and were interested in the weather. Long-term memory stores user facts, letting the agent stay context-aware, reduce repetition, and personalize replies, such as that you have been a customer since 2018. Human-in-the-loop: Strategic hand-offs let people review low-confidence outputs, catch critical paths or errors, and feed corrections back to the model, keeping quality and alignment tight. If this still feels too abstract, I’ve included a more detailed explanation in the article below. https://lnkd.in/eAr-6YAQ

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Snowflake ’s deal with OpenAI is being talked about like another AI partnership. It’s not. This is really about where enterprise AI will live. Enterprises don’t want their data bouncing between tools, models, and clouds. They want AI inside the platforms that already hold their most sensitive data — with security, governance, and compliance baked in. By bringing OpenAI models directly into Snowflake, the message is clear: • The model matters less than the data • The winner isn’t the smartest AI, but the most trusted platform • Enterprise AI is shifting from experimentation to infrastructure This isn’t a chatbot story. It’s an ownership and control story. And it’s a sign that the enterprise AI race just entered a very different phase.

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Real transformation happens when we shift from building dashboards to solving real problems for real roles. From requests → to outcomes. From data delivery → to decision support. The future of data teams isn’t being a report factory. It’s becoming partners who deeply understand business personas, workflows, and moments that matter — and design solutions around them. This is the evolution data teams need: Less “dashboard concierge,” more “people-first problem solving.”

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We hear it all the time...We want to be a data-driven organization. But, data chaos + AI = faster chaos. Here’s the playbook we bring: 1️⃣ Get clear on what decisions matter most 2️⃣ Map where your data lives and how it flows 3️⃣ Identify gaps, inconsistencies, and manual pain points 4️⃣ Clean it, structure it, and build trust 5️⃣ Then, and ONLY then, explore AI solutions If your team is still spending hours cleaning spreadsheets or double-checking reports, that’s the first fire to put out. Better data = Better AI. That’s how you go from buzzword to bottom-line impact.

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Excited about this 3 part webinar series. We are already seeing fantastic traction with leading enterprises worldwide on building graph intelligence with Neo4j on Databricks. And we are just getting started here #graphmakesAIbetter Emory Emrich Anis Mankada Shyam Kathiresan Ryan Knight Nathan Barney

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If you haven't tried DQX for data quality checks, you should! First released almost a year ago, it quickly became a default for Databricks users. You can validate your data using json, yaml, or Python, and quarantine data that does not pass the checks. Below an example of an yaml file. Check out the latest article on Cauchy's Substack by Steven Kempers on how you can use DQX: https://lnkd.in/ecTraxxX. It is a part of our 6-part series on data engineering. Subscribe not to miss the follow-up!

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