I was able to take part in the latest funding round for Databricks at a $62B valuation -- an investment that now makes up 6% of my growth portfolio.
At first glance, the price tag feels lofty. But take a closer look, and it becomes clear, Databricks isn’t just a player in AI’s next chapter -- it’s the stage on which the story unfolds.
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Databricks is the toolkit
The explosion of AI has created a tidal wave of data, relentless in scale and complexity. For businesses, the question isn’t if they need to harness this data -- it’s how to do it faster, smarter, and more efficiently than anyone else. That’s where Databricks comes in. At the heart of its offering lies the lakehouse architecture, a masterstroke that merges the flexibility of data lakes with the precision of data warehouses. For companies drowning in unstructured data -- images, text, logs -- and racing to experiment, iterate, and scale AI workflows, Databricks is the toolkit they can’t live without.
Numbers can often tell a clearer story than any pitch deck. Databricks is on the brink of crossing $3B in ARR, growing at 60% -- with acceleration kicking in. Its 80% gross margins speak to the profitability locked inside its model, and with over 500 customers spending $1M+ annually, it’s clear that this isn’t hype -- it’s necessity.
AI infrastructure
But Databricks is more than a data tool -- it’s AI infrastructure itself. Machine learning workflows are a high-speed game of iteration: models break, rebuild, improve. Databricks makes this chaos manageable. It streamlines the MLOps lifecycle, turning real-time analytics and experimentation into a science. For companies at the cutting edge, where milliseconds matter and AI is evolving in real time -- Databricks is the platform that fuels innovation without friction.
The company’s roots trace back to Apache Spark, a heritage that gave Databricks its unmatched edge in handling massive, complex datasets. This DNA runs deep. While other platforms struggle to scale with AI’s demands, Databricks thrives under pressure. It’s engineered for workloads that would choke lesser systems -- transforming raw, fragmented data into actionable intelligence. This is why enterprises trust it as the bedrock for their most ambitious AI and machine learning projects.
Stage 2 of AI’s evolution
We’re now entering Stage 2 of AI’s evolution, where the stakes are higher and the opportunities larger. Stage 1 crowned hyperscalers, cloud providers, and hardware giants -- the ones who laid the groundwork. But Stage 2 is different. The winners here will be the platforms that enable businesses to scale AI seamlessly, turning data into a competitive weapon and AI into an engine of perpetual innovation. Databricks is already carving out its position as a cornerstone of this phase -- indispensable, irreplaceable, and years ahead of the curve.
For investors, getting Stage 2 right is critical. The AI revolution isn’t a bubble or a trend -- it’s a decades-long expansion where demand for platforms like Databricks will only intensify. The data flow won’t slow, AI models will only become more complex, and businesses will need tools to keep up. Databricks isn’t riding this wave -- it’s creating it, piece by piece, with a platform designed to power AI’s future at every level.
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