On October 28, 2025, Palantir and NVIDIA announced a partnership: Palantir's Ontology framework will integrate NVIDIA's GPU-accelerated computing, CUDA-X libraries and Nemotron open models.
Today is not Grey Zone day - yet I decided to write a spontaneous piece reflecting on the choice of that name "Ontology".
If you follow my work, you know that ontology is the 6th dimension of my Quantum Framework, and in my eyes one of the most important (yet neglected) one.
In philosophy, ontology refers to the study of being - the analysis of what exists and how entities relate to one another.
Using this word reflects an ambition that goes beyond engineering: creating a structured model of reality that organizations can use to think and act more 'coherently'.
The philosophical background
Palantir's CEO, Alex Karp, is a philosopher. He holds a PhD in social theory from the Goethe University of Frankfurt, a school shaped by thinkers such as Hegel, Adorno, and Habermas.
His academic work focused on how language and interpretation structure social reality - an approach that still appears in Palantir's vocabulary today.
Terms like ontology, dialectic, and coherence express a way of thinking that seeks to make sense of complexity through structured relationships.
What "Ontology" means inside Palantir
According to Palantir's documentation:
"The Palantir Ontology creates a complete picture of an organization’s world by mapping datasets and models to objects, properties, link types, and actions." - Palantir Documentation
In practice, this means that Ontology connects data, logic, and operations into a single framework.
Within this model:
- Objects represent real entities (for example, aircraft, energy grids, supply chains).
- Links define relationships between them (dependencies, flows, hierarchies).
- Actions allow users to trigger processes directly from the model.
This architecture makes the data actionable.
Decisions can be simulated, tested, and implemented within the same environment, reducing the gap between understanding and execution.
In many ways, this mirrors the logic of intelligence work - the continuous cycle that transforms raw information into operational insight.
Collection, analysis, synthesis, and action are no longer separate phases but part of a single feedback loop, where every decision updates the system that produced it.
It raises important questions.
Turning data into an operational model of reality inevitably raises deeper questions :