☀️ AI Morning Minute: Databricks
The company that makes your company’s data actually usable for AI is worth $134 billion and most people have never heard of it.
Every AI tool your company buys eventually hits the same wall: the data it needs to be useful is scattered across databases, spreadsheets, and systems that don’t talk to each other. Databricks is the company that built the platform sitting between your messy data and your AI ambitions. More than 70% of the Fortune 500 use it. You’ve probably never seen their name on anything.
What it means
Databricks is a data and AI platform company founded in 2013 by researchers from UC Berkeley who built Apache Spark, an open-source framework for processing large datasets. Their core product is a “lakehouse,” a combination of a data warehouse and a data lake that lets companies store and analyze massive amounts of data without being locked into a single vendor’s infrastructure. The data sits in your own cloud storage. Databricks provides the compute layer on top.
Over the past two years, Databricks has moved hard into AI. They bought MosaicML in 2023 for $1.4 billion to get into model training, then built Agent Bricks, a platform for building AI agents on top of company-specific data. The idea is that the best AI for your business isn’t a generic model connected to the public internet. It’s a model grounded in your own data, running on your own infrastructure.
As of June 2026, Databricks crossed $6.9 billion in annualized revenue, growing 80% year over year, with over $1.7 billion of that coming from AI products specifically. The company is valued at $134 billion and is widely expected to go public.
Why it matters
Most enterprise AI fails because the data underneath it is a mess. Databricks’ pitch is that you can’t build reliable AI on unreliable data, and their platform handles the governance, quality, and accessibility layer that makes AI outputs trustworthy. At 7-Eleven, Databricks-powered agents now automate large portions of marketing operations. That’s not a pilot. That’s production.
They signed $100 million partnerships with both Anthropic and OpenAI in 2025, plus a separate deal with Google for Gemini integration. The positioning is deliberate: Databricks isn’t betting on one model winning. They’re building the data layer that any model plugs into, which means they benefit regardless of which AI lab comes out ahead.
The lakehouse architecture solves a real compliance problem. Regulated industries can’t send sensitive data to external APIs. Banks, hospitals, and insurers need AI that runs on infrastructure they control. Databricks lets them do that, which is why financial services and healthcare are two of their strongest verticals.
Simple example
A hospital has patient records in one system, billing data in another, lab results in a third, and appointment history in a fourth. None of them talk to each other. An AI tool that only sees one of those systems gives incomplete answers. Databricks pulls all four into a unified layer, cleans and governs the data, and gives the AI a complete picture to work from. The AI doesn’t get smarter. It just finally has all the information it needed.