The enterprise software space is heating up, and the battleground is AI. It seems like every firm, from Snowflake to Databricks, is racing to offer the same suite of AI-powered agents, specifically document parsing with SQL-based AI. Databricks fires back at Snowflake with SQL-based AI document parsing, adding SQL-based AI parsing capabilities to its Agent Bricks framework, a direct response to Snowflake's Intelligence platform.
But let's cut through the marketing fluff for a second. Are these new features genuinely revolutionary, or are they just incremental improvements dressed up in shiny AI packaging? The core promise is faster, more efficient analysis of unstructured data, leveraging agent-automated SQL. Snowflake and Databricks are both banking on the idea that enterprises are drowning in documents and need AI to stay afloat.
The recent analyst reports paint a mixed picture. On November 12, 2025, the Dow Jones Industrial Average hit a record high, while the NASDAQ lagged, raising "concerns about the AI bubble." Michael Burry, of "Big Short" fame, has publicly criticized tech companies for understating the depreciation of their computing assets. (A point worth noting: Burry has a history of making contrarian calls, some of which have been spectacularly right, and some spectacularly wrong.) He argues this inflates earnings, especially for AI and cloud service providers. Is this a legitimate concern, or just a hedge fund manager trying to move markets?
There's also the issue of market rotation. We're seeing investors take profits on high-flying AI stocks and move into other sectors. Is this a sign that the AI hype is cooling off, or simply a natural correction? Health Catalyst (HCAT), for example, experienced extreme volatility, with shares moving greater than 5% on 47 separate occasions over the last year. The recent drop was attributed to a weak financial outlook. It is fair to ask if the fundamentals of the enterprise software business have changed or if investors are simply chasing the next shiny object.
The market overreacts to news, and price drops can present good opportunities to buy high-quality stocks. But it's also crucial to distinguish between genuine value and overhyped promises. The flurry of activity in SQL-based AI document parsing feels like a frantic attempt to stay relevant in a rapidly evolving landscape. If everyone is selling the same AI agents, is anyone actually innovating? Or are we just rearranging deck chairs on the Titanic?
The problem isn't necessarily the technology itself, but the way it's being marketed and deployed. These AI agents are being presented as magic bullets, capable of solving all sorts of enterprise challenges. But the reality is often far more complex. Successful AI implementation requires clean data, well-defined use cases, and skilled personnel to manage and maintain the systems. Simply throwing AI at a problem without addressing these underlying issues is unlikely to produce meaningful results.
I've looked at hundreds of these product announcements, and the lack of concrete details about real-world deployments is striking. We hear a lot about potential benefits, but very little about actual outcomes. What's the average ROI on these AI-powered document parsing tools? How much time are they actually saving users? And what's the error rate? These are the questions that enterprises should be asking, but they're rarely addressed in the marketing materials.
And this is the part of the analysis that I find genuinely puzzling. The lack of transparency around performance metrics suggests that the results may not be as impressive as the vendors would like us to believe. The "show me the data" approach is not just a mantra, it is a requirement. Is it possible that the "AI revolution" is more of an "AI evolution," with incremental improvements rather than disruptive breakthroughs?
