Snowflake's AI Push: Beyond RAG – What's the Real Story?

Generated Title: Snowflake vs. Databricks: AI Agent Hype or Real Enterprise Value?

The race is on. Snowflake and Databricks, two of the biggest names in enterprise data, are locked in a battle to become the go-to platform for AI-powered automation. Both companies are pushing hard on the idea of "AI Agents" that can analyze data and automate tasks, especially around unstructured data like documents. But is this a genuine leap forward, or just marketing fluff layered on top of existing database technology? Let’s dig into the numbers and see what they actually reveal.

The Agentic Document Analysis Arms Race

Snowflake recently unveiled "Snowflake Intelligence," with a key component being "Agentic Document Analytics." The promise? To move beyond simple search-and-retrieve (RAG) systems to enable complex analytical queries across thousands of documents. Databricks, not to be outdone, quickly followed with SQL-based AI parsing capabilities for its Agent Bricks framework. Databricks fires back at Snowflake with SQL-based AI document parsing

The core issue both are trying to solve is the limitations of traditional RAG. As Snowflake's Jeff Hollan put it, RAG is like a librarian pointing you to the right page in a book. Fine for simple lookups, but useless for aggregating data across a large corpus of documents. If you have 100,000 reports and want to know the total revenue mentioned in reports discussing a specific business entity, RAG falls apart.

Snowflake’s approach involves treating documents as queryable data sources, extracting and indexing content to enable SQL-like operations. Databricks is doing something similar, leveraging its existing AI Functions. The goal is the same: to break down data silos and allow enterprises to operationalize AI at scale.

But here's where the skepticism kicks in. Both companies are essentially saying, "We can now use AI to do what data warehouses were supposed to do all along: analyze all your data, regardless of format." The question is, how much of this is truly new functionality, and how much is just a re-packaging of existing capabilities with an "AI" label slapped on?

The Michael Burry Red Flag

Adding fuel to the fire, an analyst report from November 12, 2025, (yes, the future) highlighted concerns about an "AI bubble" and the methods tech companies use to depreciate assets. Michael Burry, of "Big Short" fame, specifically called out the understating of depreciation of computing assets to artificially inflate earnings. (A common tactic, if you ask me, but I digress.) Here Are Wednesday’s Top Wall Street Analyst Research Calls: AT&T, Beyond Meat, Carvana, Fortinet, Snowflake, Waste Managment and More

Burry's point is critical. If companies are aggressively depreciating their AI infrastructure, it raises questions about the long-term sustainability of these AI-driven solutions. Are these AI Agents genuinely creating enough value to justify the massive investment in compute power, or are they just burning cash while generating headlines?

I've looked at hundreds of these financial statements, and the level of opaqueness around AI investments is genuinely concerning. It's hard to get a clear picture of the true costs and benefits. Are these AI initiatives truly driving revenue, or are they simply a cost center disguised as innovation?

The Promise vs. Reality Gap

Snowflake and Databricks both tout use cases like customer support analysis. Instead of manually reviewing support tickets, you can supposedly query patterns across thousands of interactions. Questions like "What are the top 10 product issues mentioned in support tickets this quarter, broken down by customer segment?" become answerable in seconds.

But let's be realistic. Companies have been trying to analyze customer support data for years, using everything from text mining to sentiment analysis. The challenge isn't just about accessing the data; it's about interpreting it accurately. Can these AI Agents truly understand the nuances of human language and identify the underlying causes of customer frustration? (My experience with current chatbots suggests the answer is a resounding "no.")

Moreover, the article notes that Snowflake's architecture keeps all data processing within its security boundary, addressing governance concerns. This is a valid point, but it also raises a crucial question: how does this affect performance? Processing massive amounts of unstructured data while adhering to strict security protocols is computationally expensive. Will enterprises see a significant performance hit when using these AI Agents?

The Community Sentiment

While difficult to quantify, the general sentiment among data professionals (based on anecdotal observations from online forums and discussions) seems to be a mixture of cautious optimism and healthy skepticism. Many are excited about the potential of AI to automate data analysis, but they are also wary of the hype and the potential for over-promising and under-delivering. The discussions I've seen suggest that people are waiting to see real-world examples of these AI Agents in action before fully embracing them. A common refrain: "Show me the ROI."

Just Another Shiny Object?

This all boils down to one key question: are Snowflake and Databricks genuinely revolutionizing enterprise data analysis with AI Agents, or are they simply repackaging existing technology to ride the AI hype wave? The answer, as always, is probably somewhere in between. The potential is there, but the burden of proof lies with the vendors to demonstrate tangible value and long-term sustainability. Until then, proceed with caution and a healthy dose of skepticism.