What the heck is Palantir’s “Ontology”?

I’ll be honest. I often have no idea what Palantir CEO Alex Karp is talking about.

On conference calls and elsewhere, the highly compensated, frazzle-haired executive can veer from victory laps on the company’s performance, to claims out of left field of Western civilization’s superiority, to bizarre revenge fantasies aimed at short sellers, and on and on and on.

Until recently, one of the things that I rolled my eyes at was Karp’s relentless insertion of terms best left in the philosophy grad student lounge — dialectic, determinative, ontology — into what could have been straightforward discussions of sales and profit and expectations.

(Side note: All journalists are contractually obligated to note that Karp received a Ph.D. in philosophy in neoclassical social theory from Goethe University in Frankfurt.)

Except somewhere along the line, that last word — ontology, “a branch of metaphysics concerned with the nature and relations of being,” if that helps — became Ontology, a key component of a product that’s central to Palantir’s rapidly growing business selling its software package to corporations.

As Palantir CTO Shyam Sankar said on the company’s most recent conference call, when asked what the company’s competitive advantage is in selling AI software: “Our advantage comes down to Ontology. It’s really an advantage on the AI demand side. And that has positioned AIP to be the platform that is able to capture the ever-expanding capability of the raw LLMs and turn that into business value.”

That sounds pretty important. But it’s still kind of difficult to understand what, exactly, Ontology is. The company itself is only vaguely helpful, explaining that “the Ontology serves as a digital twin of the organization, containing both the semantic elements (objects, properties, links) and kinetic elements (actions, functions, dynamic security) needed to enable use cases of all types.”

A new note out from Mizuho analyst Gregg Moskowitz, who covers the stock, attempts to explain it a bit, writing, essentially, that it’s a tool in “which fragmented data can be unified and transformed into operational knowledge.”

Goldman analysts who published a note back in March on Palantir’s tech stack did somewhat better. They call Ontology the “core technical differentiation” that “bridges the gap between the raw data across an organization (structured, unstructured, siloed, etc.) and operational decision-making.” Goldman’s analysts elaborated:

“Consider a global manufacturer: in a traditional data approach, it would have separate databases/tables for its suppliers, shipments, warehouses, and products.

However, these are just rows and columns linked by foreign keys (i.e. a column in one table that references the primary key in another table).

Palantir’s ontology instead models real-world objects and their associated relationships: 1) the ‘supplier’ represents a business partner, with direct connections to shipments; 2) the ‘shipment’ represents the physical movements of goods linked to both ‘suppliers’ and ‘warehouses’; 3) the ‘warehouse’ is a location storing products, linked to ‘shipments’ and ‘inventory’; 4) the ‘product’ is an item with attributes like stock levels, demand forecasts, and risk factors.

Thus, if a shipment is delayed, all affected products, warehouses, and suppliers are automatically updated in the ontology.”

Things are now getting a bit clearer. Essentially, Ontology is Palantir’s way of refining, structuring, and connecting the myriad kinds of data and information pipelines companies constantly use, creating a new stable foundation on which the company can run Palantir software.

You can see why this could be a pretty big advantage.

For one thing, it can take a significant investment of a company’s time to create that foundation, which is typically done by engineers employed by both Palantir — what they call “forward deployed engineers,” or FDEs — and the corporation itself.

It can take weeks and even months. That’s a process of going open kimono (digitally speaking), potentially exposing the company’s trade secrets, customer information, and any number of other sensitive areas to outsiders.

In other words, they’re not going to want to do it very often. That means that “switching costs” of getting rid of Palantir software are extraordinarily high once you have your Ontology established. Imagine the hassle of changing your bank account, except exponentially worse. That’s a pretty formidable competitive moat.

According to Goldman, there are other advantages as well. For instance, the pipelines feeding Palantir’s Ontologies are designed to work with real-time data. Most competitor programs that extract data are designed to be bulk updated at regularly scheduled intervals, Goldman says.

In theory, that should enable tools built on Palantir’s foundation to offer more real-time insight into the business, as data flowing in and out of the company is constantly updated.

And, importantly, Palantir’s forward deployed engineers continue to work with their assigned companies after establishing the Ontology foundation, developing custom tools and bits of software.

Palantir itself can then incorporate those custom tools into its own software offerings to similar companies, which — as Palantir grows its customer base — should translate into higher profits as the company sells software its engineers have already done the work of creating.

“This is an iterative process in which FDEs determine custom use cases solving problems for this one customer while the product development engineers standardize these solutions so they can be scaled across similar verticals, enabling Palantir to engage with more customers at incrementally better margins,” Goldman wrote.

Of course, that ontological layer of organized data can also serve as a base layer linking a company’s data feeds to the hottest thing in software: AI.

“Realizing value from AI in the enterprise requires the elegant integration of LLMs, workflow, and software,” Palantir CTO Shyam said on the company’s last conference call. “And that’s only possible with Ontology.”

Maybe. Maybe not. But it’s a pretty nice sales pitch. And judging from the company’s expected sales growth rate of 45% this year — it’s working.