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Databricks isn’t chasing AI hype. Its latest funding affirms our thesis that it is trying to become the operating system for enterprise intelligence.
The CNBC interview with Ali Ghodsi makes something very clear: this isn’t about models, dashboards, or shiny copilots.
It’s about where intelligence lives inside the enterprise.
And that’s a much bigger ambition.
The $134B signal most people are missing
Databricks doubling its valuation in a year isn’t just investor exuberance.
It’s a market-level bet that the modern data stack is collapsing.
Not because it failed technically.
Because it failed strategically.
Too many tools.
Too many handoffs.
Too much friction between data, models, and action.
AI didn’t create this problem.
AI made it impossible to ignore.
“Building SAP backwards” is the real strategy
Classic enterprise software went top-down:
Apps → data → infrastructure.
Databricks is doing the reverse: Infrastructure → data → intelligence → apps.
Why this matters:
If you control the data substrate, you eventually control where intelligence is created, governed, and deployed.
That’s not a tooling decision.
That’s architectural gravity.
The quiet but radical shift
Three moves stand out:
Lakebase
Transactional databases reimagined for AI agents, not humans writing SQL by hand. A direct shot at decades-old OLTP assumptions.
Agent Bricks
Not “AI demos,” but production-grade agents wired into proprietary enterprise data, workflows, and permissions.
Databricks Apps
Natural-language-driven app creation, running directly where the data lives. No duct tape. No export layer.
Individually interesting.
Together, existential for point solutions.
Why hyperscalers should be uneasy
Microsoft, AWS, and Google still win on distribution.
But Databricks is playing a longer game:
turning clouds into compute utilities, while value migrates up into the intelligence layer.
If that happens, the cloud still gets paid.
It just doesn’t set the rules anymore.
That’s the real tension beneath all the “partner” language.
The risk no one should downplay
This strategy only works if everything integrates cleanly.
Three major product lines.
Massive GPU demand.
Open-source economics.
A customer base that still thinks in “best-of-breed.”
Execution risk is real.
There is no partial win here.
Either Databricks becomes the control plane for enterprise AI, or it becomes the most sophisticated data platform of its generation without fully owning the stack.
The takeaway
Databricks is no longer a data company.
It’s making a claim on where intelligence belongs inside the enterprise.
If they’re right, the question for CIOs and CTOs isn’t:
“Which tools do we buy?”
It’s:
“Which platform do we let shape how intelligence is created, governed, and acted on?” That’s a very different decision.
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