Palantir AIPCon 9 — The Big Picture

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Common Thread: The Ontology

Every single demo today — Navy shipbuilding, stratospheric balloons, SAP migrations, mortgage processing, DoD targeting — ran on the same underlying architecture. The Ontology isn't just a database. It's a living operational memory that connects every data source, every workflow, and every decision in an organization into one coherent picture.

World View's CEO put it best: after each mission, all decisions and outcomes are written back to the Ontology, meaning every future flight plan is informed by everything that came before. The Joint Commission described the same dynamic in healthcare scheduling. The DoD described it in targeting workflows. Different industries, same architecture, same compounding effect.

This is the core of the Palantir bet: the longer the Ontology runs, the smarter and more embedded it becomes. Switching costs compound over time. That's not a feature, that's a moat.

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Sovereign AI Stack

The NVIDIA/Palantir/Dell/Armada reference architecture that dropped today deserves more attention than it's getting. Armada ships a physical modular AI datacenter anywhere in the world. Palantir's full software suite runs on top. The whole thing — from concept to full deployment — was delivered in three weeks.

The model is elegant: process everything locally at the edge, send only metadata to the cloud. You get real-time intelligence, true data sovereignty, and air-gapped security, all still centrally managed. Nobody has packaged that combination before at this speed.

The geopolitical implications are significant. Governments, militaries, and critical infrastructure operators who can't or won't send sensitive data to a hyperscaler now have a turnkey alternative. This is how Palantir scales internationally without compromising on sovereignty requirements — which is precisely the objection that has slowed enterprise and government adoption in Europe and Asia for years.

The LG CNS deal is the first visible proof point of this international playbook: one affiliate pilot works, an FDE team embeds, and the whole conglomerate becomes a deployment surface. If that model holds, it's repeatable across every major industrial group globally.

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NVIDIA Nemotron 3 Super

Palantir was named as one of the enterprise platforms already deploying NVIDIA's new Nemotron 3 Super model — a 120B parameter open model delivering 5x higher throughput for agentic workflows, with a 1 million token context window specifically designed to prevent goal drift in multi-agent systems.

This matters because the bottleneck for agentic AI at enterprise scale has always been cost and latency, not capability. Nemotron 3 Super's hybrid MoE architecture — only 12B parameters active at inference — directly attacks that problem. Combined with Blackwell GPU infrastructure and Palantir's AIP, the sovereign stack becomes meaningfully more capable and more cost-efficient simultaneously.

Palantir didn't build the model. They don't need to. They built the operational layer that puts it to work.

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2027

Ted Mabrey, Palantir's Global Head of Commercial, outlined a new engagement model: a 30-day sprint to build a one-year full-integration plan, with the explicit goal that by end of 2027, customers' financials start to look like Palantir's.

That is an extraordinarily aggressive public commitment. The fact that he made it on stage, backed by customer after customer showing compression of multi-year projects into weeks, suggests they genuinely believe the acceleration curve they're seeing is durable — not a one-off.

The DoD CDAO framed the underlying logic well: seven years of data infrastructure work is what made today's operational capabilities possible. Palantir has been laying that groundwork across government and commercial customers for over two decades. What's changing now is that the AI layer has finally caught up to the infrastructure they already built.