The shift from language to the world
This week I watched an interview with Yann LeCun on what comes after LLMs, and it struck me because it gave language to the problem my co-founders and I have been working on for more than five years.
We did not start SeerAI because we thought the world needed another chatbot. We started SeerAI because we believed the next major computing problem would be how to make the physical world understandable to software, models, and eventually agents.
To be clear, we did not anticipate the exact way LLMs would become synonymous with AI, or how quickly the public conversation would collapse around chat interfaces, prompts, and generative text. The progress has been remarkable, and LLMs have changed the way people experience software. But our conviction has always been that language models are a step in the evolution of AI, not the destination. The harder and more valuable problem is what comes next: grounding intelligence in real-world data, real-world systems, and real-world context.
That is why LeCun's thesis matters. LLMs are powerful language engines, but language is not the world. A model can summarize a pipeline report, but it does not understand the pipeline. It can answer questions about a building, but it does not understand the building. It can describe a disaster response plan, but it does not understand roads, imagery, flooded structures, power, passability, population exposure, and logistics as one connected operating system. The world is organized as places, assets, events, relationships, constraints, dependencies, and time. If AI is going to move from answering questions to helping people make decisions in the physical world, it needs a durable model of what exists, where it is, how it is connected, what changed, and what action is possible.
The enterprise bottleneck is context, not chat
This is the problem SeerAI was built to solve. The next frontier of AI is not simply a better chatbot. It is real-world grounding. Enterprises do not first have a model problem. They have a context problem. Their critical data is scattered across GIS platforms, cloud buckets, operational databases, IoT feeds, imagery archives, ERP systems, PDFs, spreadsheets, dashboards, and highly specialized applications. Each system contains part of the truth, but almost none of them were designed to make the whole operating environment intelligible to an AI agent.
That is why the idea of a world model cannot remain an academic abstraction. In the enterprise, a world model has to be practical. It has to represent assets, locations, events, systems, provenance, time, and operational state. It has to work with data where it already lives. It has to preserve the relationships that make the data meaningful. And it has to expose that context to agents, maps, dashboards, and decision workflows without forcing customers into another massive rip-and-replace architecture.
SeerAI's answer: orchestration, not duplication
Geodesic is our answer to that problem. We are not trying to build another data warehouse, another dashboard, or another closed application stack. We are building the orchestration layer for real-world AI. Geodesic connects distributed data sources without requiring customers to centralize everything first. It creates a persistent knowledge graph that captures relationships, provenance, semantics, and spatiotemporal context. And it provides a spatiotemporal-native compute layer that lets users reason over physical-world data at operational speed and scale.
Together, those capabilities turn fragmented enterprise data into an agent-ready operating layer. The goal is not just to let an AI system ask, "What does the document say?" The goal is to let it ask, "What is happening, where is it happening, what changed, what systems are affected, what data supports the conclusion, what has happened before under similar conditions, and what should an operator do next?" That is the difference between retrieval and operational intelligence.
Why spatiotemporal context is the core
SeerAI's core belief is that all real-world data is spatiotemporal. Buildings, pipelines, ports, satellites, fleets, storms, wells, roads, sensors, facilities, and military assets all exist in space and time. Any AI system that ignores that structure is operating from a weak abstraction. It may be fluent, but it is not grounded. It may be useful as an interface, but it cannot reliably reason over the operating environment.
This is also why knowledge graphs matter, but only if they are operational. A graph that sits off to the side as a semantic catalog is not enough. Agents need durable relationships connected to live systems, changing conditions, maps, imagery, events, and workflows. Geodesic is designed to be that connective tissue: the memory and context layer that lets agents reason across systems instead of starting from scratch every time.
What this means for the AI market
The AI industry has spent the last several years focused on model scale: more parameters, more GPUs, longer context windows, larger training sets. Those advances are real, but they do not solve the enterprise grounding problem. A larger model does not automatically know which dataset is authoritative, which sensor maps to which room, which satellite image covers which area, which maintenance record matters, or which operational action is feasible. The next bottleneck is not just model intelligence. It is the infrastructure beneath the model.
That infrastructure has to make the physical world computable. It has to connect distributed data, preserve context, reason over space and time, and make the results available to whatever agent, application, dashboard, or map the customer already uses. This is why SeerAI is model-agnostic and interoperability-first. We are not trying to replace the enterprise stack. We are trying to make it intelligent.
The founder view
From our perspective, LeCun's "after LLMs" thesis is not just a research direction. It is a market-timing signal. The first wave of AI learned from the internet. The next wave has to operate on the physical world. That world is messy, distributed, dynamic, sensitive, multimodal, and deeply contextual. It cannot be scraped into intelligence. It has to be orchestrated into intelligence.
That is the company we are building. LLMs gave enterprises a new interface. World models will require a new operating layer. SeerAI is building the bridge between the two: the infrastructure that turns fragmented physical-world data into persistent, spatiotemporal, agent-ready context.
Closing thought
The future of AI will not be defined only by models that can talk about the world. It will be defined by systems that can understand, reason over, and act within the world. That future needs a foundation underneath the model. For the physical world, that foundation is Geodesic.