The piece Joe Lonsdale and the 8VC team put out earlier this summer keeps coming up in our conversations. Their argument is that the next wave of enterprise technology will be AI-enabled services built on the lessons of Palantir. It is a compelling frame because it focuses less on the models themselves and more on the systems around them: software, domain expertise, ontology, workflow understanding, and operational execution. That is a useful lens for understanding where SeerAI fits.
Lonsdale’s key insight is that Palantir was often mistaken for a services company when it was really creating a new kind of software platform. By working deeply with customers, mapping data and workflows, and turning those lessons into reusable products, Palantir used services as a learning loop. The result was a platform that became smarter, more useful, and more broadly applicable over time.
That lesson matters even more in the AI era. Powerful models alone do not create enterprise value. Outcomes depend on whether those models can access the right data, understand the right context, fit into the right workflows, and learn from the people who know how the organization actually operates.
For physical-world industries, this problem is especially hard. Energy, supply chain, insurance, infrastructure, disaster response, defense, and intelligence all depend on data that is fragmented across systems, vendors, sensors, maps, documents, imagery, and operational workflows. The challenge is not simply having data. It is making that data usable in context.
SeerAI’s platform, Geodesic, was built for this challenge. It connects to data where it already lives, organizes it into a reusable knowledge graph and ontology, and makes it available to people, applications, AI agents, and digital twins without requiring customers to replace their existing systems.
The physical world is inherently spatiotemporal. Assets exist in places. Events unfold over time. Risk changes across geography. Supply chains move. Sensors update continuously. Human decisions leave traces across tickets, documents, maps, databases, and workflows. To make AI useful in these environments, organizations need a living model of how those elements relate. That is the role SeerAI is designed to play. Geodesic connects distributed data sources into a federated spatiotemporal data mesh, builds the knowledge graph and semantic relationships that provide context, and runs a high-performance compute layer across space and time. Together, these capabilities help turn fragmented enterprise and physical-world data into an AI-ready foundation.
This maps closely to the Lonsdale and Palantir thesis in three important ways.
First is ontology. Successful AI-enabled companies need to map the objects, relationships, states, and actions that define how a business operates. SeerAI applies this principle to physical-world industries by connecting workflows to assets, locations, events, sensors, risks, imagery, and time. In energy, this may mean wells, pipelines, compressor stations, inspections, weather, land records, and maintenance history. In supply chain, it may mean facilities, routes, ports, carriers, inventory, disruptions, and delivery commitments. In defense and intelligence, it may mean entities, places, sensors, missions, terrain, and movement.
Second is human expertise. AI systems become far more useful when they can draw on the knowledge of the people who understand the work. SeerAI’s knowledge graph can serve as a mechanism for capturing that expertise, including which data sources matter, which relationships are important, which edge cases break workflows, and which signals indicate real risk. Over time, that expertise becomes a reusable context layer for both humans and machines.
Third is productization. Palantir’s strength came from converting difficult customer problems into reusable platform capabilities. SeerAI follows a similar logic, but with a modular architecture designed to sit above existing systems rather than replace them. Most enterprises already have cloud platforms, GIS systems, data warehouses, operational databases, analytics tools, and specialized applications. SeerAI helps make those systems interoperable and AI-ready.
The value of this approach compounds. Once an organization has a shared context layer for its assets, events, risks, documents, workflows, and external data, each new AI use case becomes easier to deploy. New workflows enrich the knowledge layer. Expert interactions improve the ontology. Each deployment makes the next one faster and more useful. This is the network effect inside the enterprise.
For clients, this means a more practical path to AI adoption. They can build on the systems and data they already have while creating AI workflows that are grounded in real operational context.
For investors, it places SeerAI at the intersection of several important shifts: AI infrastructure, digital twins, knowledge graphs, geospatial intelligence, enterprise interoperability, and the productization of domain expertise.
The Palantir lesson was that the hardest enterprise problems require platforms that understand how organizations work. The AI-era lesson is that agents, models, and digital twins will need that same understanding before they can be trusted to support meaningful decisions. SeerAI is building that understanding for the physical world.
The future Lonsdale described is coming into focus. In many of the domains where data, operations, geography, time, and human expertise matter most, SeerAI has built a foundation that can help make that future real.