Newsletter #38: "Snowflake is the most consequential AI and Data company on the planet." Here's why.

Last week’s Snowflake Summit 2025 answered a question every enterprise leader is asking: “What separates companies that will thrive with AI from those that could be left behind?”

Not sure the answer is exactly what most people think…

The Summit reinforced what CEO Sridhar Ramaswamy said during their Q4 earnings call in February (link):

“Right now, Snowflake is the most consequential data and AI company on the planet, as I said on our third quarter call, our North Star is to deliver the world’s best end-to-end data platform, powered by AI, and we are making great progress every day to deliver on this vision.”

“But the point that I want to make is that, you know, all of these fit together seamlessly…the more data that is sitting in and accessible to Snowflake, the more value you can get out.”

“We call ourselves the AI Data Cloud for a good reason.”

“This is because most customers see us as the best place to get value from data, especially analytics and predictive value from data at Snowflake itself.”

Building on the quote above, I wanted to share my top 4 key takeaways that provide a way to think about the answer to the AI question on every executive’s mind.

1: What Sam Altman Said About AI Systems and What He Didn’t Say But Implied

You could see and feel the commitment to enterprise AI everywhere.

You could see it by walking the floor and interacting with the Snowflake ecosystem partners or observing the attendees watching demos, asking the kinds of questions you only ask when you’re clearly operationalizing the AI-first mindset.

You could see it by attending any number of the incredible talks, listening to Snowflake and their customers unpacking thought provoking, leading edge examples bringing to life how much value can be realized, today. More on that below.

Sam Altman’s quotes during the Day 1 Keynote:

"And I think we'll be at the point next year where you can not only use a system to sort of automate some business processes or build these new products and services, but you can really say, I have this hugely important problem in my business."

"I will throw a ton of compute at it if you can solve it, and the models will be able to go figure out things that teams of people on their own can't do."

"And the companies that have experience with these models are well positioned for a world where they can say, okay, you know, AI system, go redo, my most critical project, and here’s a ton of computing."

Oversimplifying it but the “AI System” Sam is referring to is the AI System that enterprises either have or have not built.

Those that have built it are going to experience the magic of compounding as the returns start to build upon each other.

As the system gets smarter, more useful with every interaction, compressing learning loops, creating more internal value for employees, more external value for customers / consumers etc.

Clearly Snowflake is as well positioned as any company to enable its customers to build their AI System.

Those that haven’t built their AI Systems and aren’t developing an AI Native Operating System for their businesses to operate off of are going to be at a competitive disadvantage that could become exponentially more difficult to catch up to competitors 6+ months ahead of you on their AI-first journey.

Sam Altman’s vision of throwing compute at business problems requires more than just models.

It requires the infrastructure foundation that Snowflake and the semantic layer updates are designed to provide.

2: The AI Enabled Semantic Layer: Descriptive Analytics

During the Day 2 Platform Keynote, Snowlake EVP of Product Christian Kleinerman revealed all sorts of updates.

He did so via a clever combination of user “I want…” statements that were converted into “You can” statements, thanks to the updates.

1: “I want to have a future-proof data architecture.”

2: “I want to get better economics.”

3: “I want to govern all data.”

4: “I want to integrate all types of data.”

5: “I want to deliver more business impact.”

6: “I want to get faster insights.”

7: “I want to leverage AI with our data.”

8: “I want to accelerate business growth with AI agents.”

While there was a lot to get excited about, it was the “Semantic Views” update that captured my undivided attention.

A quick step back, in case it’s helpful to define the Semantic Layer.

And of course, it’s always fun to use the “way back machine”, especially when it includes revisiting a Semantic Layer post (link) that includes a “$1 bet” because anyone that knows me, knows how much I love those… ;)

A few quotes from the keynote:

“We are very happy to introduce semantic views. What's a semantic view? It is a type of view that is geared towards that specific use case of capturing the context around the data.”

“What are the metrics? What are the dimensions? What are the definitions used by your business users, and how do those translate to physical schema?”

“But the important thing, what's really doing is the same semantic view provides context for AI use cases as well as for BI use cases. So okay, we now have a way to provide context when someone wants to chat or talk to your data.”

But having the right data foundation so it’s AI ready is just the beginning.

The competitive advantage and moat comes from moving beyond Descriptive Analytics to Predictive and Prescriptive Analytics.

Intelligence that is exactly what RelationalAI is enabling inside Snowflake…

3: AI Enabled Semantic Layer 2.0: From Reactive to Reasoning

The semantic layer foundation from Takeaway #2 enables something far more powerful than just better dashboards. It unlocks the evolution from reactive analytics to reasoning-powered decisions.

Think of it this way: Descriptive analytics tells you what happened --> your sales dropped.

Predictive analytics tells you what's likely to happen --> sales will drop 15% next quarter.

But prescriptive analytics tells you what to do about it --> adjust pricing in these specific markets, reallocate budget to these channels, launch this promotion.

This is where RelationalAI's integration with Snowflake becomes transformative.

Instead of data scientists building separate models that live outside your data platform, enterprises can now access predictive and prescriptive capabilities natively within Snowflake.

No data movement, no architectural complexity.

“'These new capabilities open up new possibilities for what customers can do with intelligent apps in Snowflake—moving from reactive analytics to reasoning-powered decisions,' said Molham Aref , CEO of RelationalAI. 'We’re proud to offer the most complete foundation for building semantics-aware, AI-native applications on top of enterprise data.'”

Capabilities highlighted during an excellent presentation by Ash Sheth of Novartis and Max De Marzi of RelationalAI.

Yet even the most sophisticated AI infrastructure doesn’t mean much without organizational commitment.

Which brings us to what might have been the most compelling talk I attended…

4: Yum! Brands AI-First Mindset Training Included CEO and Top 400 Executives In One Room...For A Week Reading / Learning Together Without Distractions

In what might have been the most eye-opening and tangible presentation I attended, “Michael Forster, VP of AI Data Strategy at YUM Brands walked through how the company is operationalizing its AI-first vision.

While there has been no shortage of interest in how enterprises can become AI-first with a lot of attention around the AI-first memos from Tobias Lütke (CEO of Shopify) , Micha Kaufman (founder + CEO of Fiverr) etc (link) and others, it has been difficult to track down tangible details from those operationalizing these efforts.

Michael definitely delivered.

It was difficult to pick the most interesting part but the way they jumpstarted the process was absolutely fascinating to me.

“So what we did is we started with our top leaders in the company, and we made them go back to school.”

“We made them read a pile of papers this big, our Harvard Business School digital mindset training. It took about 3-400 people.”

“Everybody from the CEO to all those top 3-400 leaders sitting in person together. It was a huge investment to say, why is this important?”

“They had to ingrain and dig into that material.”

“Then what we did is we took that material and we gamified it and we propagated it out to all employees.”

Such an incredible demonstration of what it takes to really make it happen, fast.

The pattern at the Summit was crystal clear.

The companies winning at AI aren’t just adopting tools, they're rebuilding their entire operational DNA.

The question about how to catch up might rapidly shift to whether or not companies that wait too long will be able to catch up.

The window to catch up is closing faster than most executives realize...