The AI Arms Race: Are Databricks and Snowflake Just Redecorating the Same House?
Databricks and Snowflake, once distinct in their data warehousing approaches, are now locked in a feature-matching frenzy in the AI space. The latest skirmish? SQL-based document parsing. Both companies are touting capabilities that allow enterprises to analyze unstructured data using, you guessed it, agent-automated SQL. Snowflake calls it Cortex AISQL; Databricks counters with AI Functions within their Agent Bricks framework. The core promise is identical: make unstructured data (documents, PDFs, etc.) queryable via SQL, powered by AI agents.
Feature Parity: Innovation or Echo Chamber?
It's tempting to see this as healthy competition driving innovation. But a closer look suggests something else: a convergence toward a standardized AI-driven data platform. Are they truly innovating, or just rapidly iterating on the same core concepts? The speed at which these features are being released – days apart – suggests a reactive, rather than proactive, development cycle. It's like watching two construction crews building identical houses on opposite sides of the street, constantly peeking at each other's blueprints.
Consider the underlying tech. Both Snowflake and Databricks are leveraging large language models (LLMs) to interpret and structure the unstructured data. The differentiation, then, comes down to implementation details: the specific LLMs used (often proprietary or fine-tuned versions of open-source models), the efficiency of their SQL engines, and the ease of integration with existing data workflows. These are important factors, sure, but do they justify the hype?
The Burry Signal: Are AI Investments Built on Sand?
This brings me to a broader concern: the sustainability of the AI boom itself. Wednesday, November 12, 2025, saw the NASDAQ take a hit while the Dow Jones Industrial Average soared to record highs. The culprit? Rising concerns about an "AI bubble," fueled in part by Michael Burry's (of "Big Short" fame) critique of tech companies' asset depreciation practices. Burry argues that these companies, particularly those involved in AI and cloud services, are understating the depreciation of their computing assets to artificially inflate earnings.
And this is the part of the report that I find genuinely puzzling. If Burry's right (and his track record demands attention), the entire foundation upon which these AI platforms are built – the massive computing infrastructure required to train and run LLMs – may be significantly overvalued. That has implications not just for NVIDIA and Palantir (Burry's specific targets), but for any company heavily invested in AI, including Snowflake and Databricks. Here Are Wednesday’s Top Wall Street Analyst Research Calls: AT&T, Beyond Meat, Carvana, Fortinet, Snowflake, Waste Managment and More
What if the "AI advantage" these companies are selling is, in reality, a debt bomb waiting to explode? How much of their current valuation is predicated on unsustainable accounting practices? These are questions investors should be asking, especially given the increasing feature parity between competitors.
The analyst notes also mentioned rising gold prices to $4,126.60, up 6.25% since late October. Investors often flock to gold during times of economic uncertainty. Is this a signal that sophisticated investors are hedging their bets against a potential tech correction? It's a correlation worth noting (though, of course, correlation doesn't equal causation).
So, What's the Real Story?
The AI arms race between Databricks and Snowflake isn't about groundbreaking innovation; it's about market share. And if Michael Burry's right, that market share might be built on quicksand.