Insight: Geospatial 2.0: The Full AI Stack
I thought it worth in this weeks newsletter to dive a little deeper into AI; specifically machine learning (ML), large language models (LLM’s) and reasoning engines. Much focus in the media has been on LLM’s, as money pours into companies focused here. Geospatial 2.0 does not lean on one or other of these technologies – it leans on all. Let me explain.
Machine Learning (ML): The Old Reliable
Machine learning has been around for decades. At its core, ML is about using algorithms to find patterns in data and make predictions. Think regression models that forecast housing prices, classifiers that identify land cover from satellite imagery, or anomaly detection models that flag unusual energy consumption. Traditional ML relies heavily on labeled data and lots of it. If you want to train a model to detect roads in satellite imagery, you need thousands (or millions) of carefully annotated images. That labeling process is time-consuming, expensive, and often domain-specific.
To 2.0, classic ML is like specialized tools in a workshop. You don’t build the entire house with just a hammer or a saw, but when you need a precise cut or a specific fix, those tools are indispensable. In the same way, ML models are not the operating system of Geospatial 2.0; they are task-specific instruments that handle well-defined jobs: detecting a feature in imagery, predicting a single variable, or classifying an object.
Large Language Models (LLM’s): Interpretation & Intent
If ML models are the tools, LLMs are like the apprentice in the workshop – able to pick up almost any tool, follow your instructions, and translate your intent into action. They don’t need every step spelled out in code; they can interpret natural language, infer what you mean, and route the request to the right resource.
At their core, LLMs are pattern machines trained on vast amounts of text. They don’t understand the world the way humans do, but they are extremely good at interpreting prompts, generating responses, and converting ambiguous human intent into structured queries.
If we think about this in terms of the human brain, LLMs act like our language centers. They excel at taking thoughts (or prompts) and turning them into structured language, and at interpreting the meaning of incoming words. Just like these brain regions don’t “decide” what action you should take – they simply express and interpret intent – LLMs don’t reason about the world or execute tasks. They’re the conversational interface, turning messy human requests into something the rest of the system can work with.
Reasoning Engines: From Intent to Decisions (and Action)
If ML models are tools and LLMs are the interpreter, a reasoning engine is the master builder or the brain. They take clarified intent plus structured context and decide what to do next. Unlike ML, which is narrow and task-specific, or LLMs, which are conversational and focused on interpreting language, reasoning engines are goal-directed. They weigh constraints, run through possible options, and determine the best course of action. In other words, they don’t just describe the world – they chart a path through it.
You can think of a reasoning engine as the prefrontal cortex of Geospatial 2.0. It brings together signals from perception and translation, simulates possible futures, and then selects the plan that best fits the objectives at hand. Where ML might detect a flood zone from satellite imagery, and an LLM might translate a request into “show me which areas are at risk,” the reasoning engine goes further: it recommends which neighborhoods to evacuate, how to reroute logistics, and what resources to deploy. This is where Geospatial 2.0 makes the leap from advisory insights to operational intelligence – systems that can explain, adapt, and act in real time.
When we use the word reasoning, we mean more than just raw intelligence, it is about deciding what to do. Sometimes that means responding to the present moment, like a storm bearing down on a city. A reasoning engine can take in the latest data and decide right now which roads to close or where to send emergency crews. Other times, it means thinking ahead in “what if” mode. What if the storm shifts direction? What if the river rises another meter? A reasoning engine can simulate those possibilities, weigh the outcomes, and be ready with the best course of action.
In simple terms, reasoning is both reactive and proactive. It lets systems act in real time while also preparing for what might happen next. That’s why it’s the heart of Geospatial 2.0, it bridges today’s data with tomorrow’s decisions.
Agents: Turning Decisions into Action
If ML provides the tools, LLMs the interpretation, and reasoning engines the decision-making, then agents are the ones who carry out the plan. They are the actors in the system, taking instructions from the reasoning layer and executing them in the real world or in digital environments. Agents might trigger workflows in a control room, re-route trucks in a supply chain, deploy drones to collect new data, or update a digital twin so the system can see the impact of its own decisions.
In Geospatial 2.0, agents make the loop real. Without them, insights and reasoning stay advisory — useful for humans, but not transformative. With them, the system becomes operational: adaptive, goal-driven, and continuously learning from its own actions. Just as muscles respond to signals from the brain, agents are how intelligence in the loop gets translated into movement, coordination, and measurable outcomes.
Closing Thought
None of these technologies on their own is enough. ML gives us the sharp tools, LLMs help us express and translate intent, reasoning engines decide what to do, and agents carry those decisions into the real world. Together, they form the loop of Geospatial 2.0; a system that can perceive, understand, decide, and act. That’s the shift: from siloed models and dashboards to operational intelligence that is continuous, adaptive, and embedded directly into how organizations run.
This weeks 2.0 content roundup
1) Geospatial 2.0 Lens on the New Space Race
SpaceX cut launch costs by 80% and deployed 4,000+ satellites — but satellites are just the perception layer. The real race is turning orbital data into adaptive intelligence through data fabrics, knowledge graphs, and reasoning engines.
👉 Explore the full story
2) From Insight to Intelligence
A wildfire risk map is insight. Deciding which crews to deploy, where, and when — that’s intelligence. Geospatial 2.0 is about bridging this gap with reasoning engines that turn context into real-time action.
👉 See why this leap matters
3) World Labs: North Star of Geospatial 2.0
Backed by $230M and Fei-Fei Li, World Labs is building AI that perceives, reasons, and acts in 3D worlds. Their demos — turning still images into explorable spaces — validate Geospatial 2.0’s vision: augmentation, not automation.
👉 Check out the details
4) GeoLLMs: Beyond Map-First Thinking
GeoLLMs promise smarter maps, but if they stop at dashboards, humans still carry the burden of reasoning. The future is decision-first — systems that propose coordinated actions in real time.
👉 Dive into the argument
5) Agentic Browsers & the Spatial Web
Opera, Fellou, and Microsoft are pushing browsers that plan and act for users. But the true leap comes when browsers become portals into the Spatial Web — reasoning with context and augmenting human judgment.
👉 Read what’s coming next
6) Verses AI: Volatility Meets Vision
Verses AI’s stock is volatile, but the bigger story is Genius — their spatial reasoning engine. Q2 2025 brought $300K in first revenues, but the long game is an operating system for the physical world.
👉 Get the full update