Our Why: The Gap That’s Costing You

Your competitors are about to make decisions faster than you can hold a meeting.

You’ve built the dashboards. Deployed the sensors. Achieved “peak insight.” But when conditions change — a wildfire shifts, a supply chain breaks, a grid fails — you’re still too slow.

The gap isn’t data. It’s the time between seeing a problem and acting on it. We call this Decision Latency — and it’s costing most operations $50K–$500K per quarter.

SpatialNext is a research lab studying how to close it. We’re selecting partners for our 2026 research cohort.

See how it works below.

What We Deliver: The Decision Velocity Diagnostic

We’re not selling software. We’re not a consultancy. We’re a research lab — and we’re looking for partners.

We’re building the definitive framework for Decision Velocity: how organizations move from insight to action. To validate it, we need real operational data. In exchange, we give you a diagnostic most firms would charge $15K for.

What you receive:

  • Your Latency Score — where your decisions slow down and why
  • Your Latency Tax — the dollar cost of hesitation, calculated
  • An Architecture Map — where World Models and Reasoning Engines fit in your stack
  • A Vendor Shortlist — 3 technologies matched to your specific bottleneck

What we need from you:

  • 2 hours of your time (one intake call, one readout — 4 weeks apart)
  • 3 examples of decisions that were too slow
  • Anonymized process documentation (no system access required)

What we keep:

The right to use anonymized patterns in our 2026 State of Decision Velocity Report

This is collaborative research, not consulting. We’re selecting 3–5 partners per quarter.

Apply for the Research Cohort

Who This Is For

We work with senior leaders who own high-stakes decisions — where delay means losses.

You’re a fit if:

  • You own decisions where hesitation has a measurable cost (dollars, time, safety, mission)
  • You’ve invested in data and dashboards — but still feel too slow when conditions change
  • You’re a senior operator, not a student or researcher
  • You’re willing to share anonymized workflow examples for our research

You’re not a fit if:

  • You’re looking for a vendor to build software (we diagnose, we don’t implement)
  • You need a proposal you can hand to procurement (this isn’t a sales process)
  • You’re early-stage or pre-revenue (we need real operational data)

We’re selecting research partners, not taking clients.

Why Now

A dashboard describes reality. A Decision Engine changes it. That’s the shift which is underway.

Three things changed in 2025:

  1. LLM reasoning got commoditized. DeepSeek matched OpenAI for 1% of the cost. The “chatbot” layer is now table stakes — not a differentiator.
  2. The smart money moved beyond LLMs. Bezos, Fei-Fei Li, and Yann LeCun are betting on World Models — systems that simulate what-if in the physical world, not just generate text.
  3. The real competition moved up the stack. The question is no longer “Do we have AI?” It’s “Do we have a second brain in the room — one that reasons to what-next faster than theirs?”

The 18-month window is open.

The organizations that master Decision Velocity now won’t just have an advantage — they’ll set the tempo for their entire industry.

The rest will be explaining to their boards why they’re still paying people to stare at screens.

Apply for the 2026 Research Cohort

Blog

  • The Decision Layer Signal Scan: The Inevitable Move to Agentic GeoAI

    The Decision Layer Signal Scan: The Inevitable Move to Agentic GeoAI

    Executive Summary & Key Themes Topic: The Inevitable Transition to Agentic GeoAI and the Decision Layer. Focus: This Signal Scan analyzes the shift from reactive GIS dashboards to autonomous spatial reasoning engines. It explores the emergence of the Decision Architect as a critical leadership role and highlights the macro-economic validation of “intelligence-native” organizational structures from…

    Read more

  • Winning the ‘What-If’: The New Geospatial Superpower

    Winning the ‘What-If’: The New Geospatial Superpower

    What is this article about? This article covers the convergence of geospatial intelligence, world models, and physical AI — four interconnected developments that together define the shift from passive AI insight to autonomous spatial reasoning. Key question answered: How are AI systems moving beyond dashboards and chatbots to understand and act within the physical world? Who…

    Read more

  • Beyond Pixels: Causal Hubs and the Rise of the Simulator Marketplace

    Beyond Pixels: Causal Hubs and the Rise of the Simulator Marketplace

    Executive Summary: While physical AI dominates the headlines, the next frontier is Non-Physical World Models. By moving from monolithic LLMs to modular Causal Hubs, enterprises are building “Business Simulators” that map abstract variables like inflation and liquidity to drive Decision Velocity. The Shift to Non-Physical World Models In my last article – Beyond Next-Token Prediction: How…

    Read more

  • Beyond Next-Token Prediction: How World Models and Reasoning Engines Drive Decision Velocity

    Beyond Next-Token Prediction: How World Models and Reasoning Engines Drive Decision Velocity

    In this post, I wanted to extend the thinking I started in: Decision Velocity: The Only Competitive Advantage Left. In that article, I discussed how I have realised that my thinking around world models has been too narrow. My focus had been purely on cause-and-effect in the physical world – If I kick a ball…

    Read more

  • The Signal Scan: From Next-Word Prediction to Next-State Simulation

    The Signal Scan: From Next-Word Prediction to Next-State Simulation

    Signal Scanner for 3/15/2026 1. The $1 Billion Bet on “Internal Physics” Yann LeCun has long argued that LLMs lack a fundamental understanding of reality. His new venture, AMI Labs, just hit a $1 billion valuation to solve exactly that. By building World Models that understand cause and effect, they are moving AI from a…

    Read more

  • Decision Velocity: The Only Competitive Advantage Left

    Decision Velocity: The Only Competitive Advantage Left

    As I focus on using AI to increase decision velocity, my emphasis I realise has been too narrow. Understanding the world through the lens of Physical AI and the geospatial landscape is one part of a much bigger whole. I’ve seen World Models as a way to improve decision making about events happening, or about…

    Read more

  • Beyond Sora: Welcome to the Wild West of “Real-World” AI

    Beyond Sora: Welcome to the Wild West of “Real-World” AI

    TL;DR: While OpenAI’s Sora leads in creative video, a new class of “World Models” from companies like World Labs, AMI, and Decart is building the foundation for Spatial Intelligence. These systems move beyond “pixel prediction” to provide the structural persistence and physical reasoning required for high-stakes industries like robotics and emergency response. The world went…

    Read more

  • Why Agentic AI Fails Without World Models: The Secret to True Decision Velocity

    Why Agentic AI Fails Without World Models: The Secret to True Decision Velocity

    Executive Summary: The Simulation Edge Having the best or ‘fast’ data is no longer a competitive advantage—it’s the baseline. The real winners won’t be the companies with the best maps, but those with the highest Decision Velocity. This requires Agentic AI powered by World Models: a system that takes live geospatial feeds and runs millions…

    Read more

  • The Shift from “Book Smart” to “Street Smart” AI

    The Shift from “Book Smart” to “Street Smart” AI

    Welcome to this week’s Signal Scanner. For decades, the geospatial industry has been obsessed with the “Static Layer”—2D abstract representations and 3D visualizations that look impressive on a wall-mounted screen. We’ve become obsessed with the “liveness” of these updates, yet they remain essentially mirrors: inert reflections where the speed of the visual update far outpaces…

    Read more

  • Predicting the Next State of Reality: From Geospatial Insights to Decision Velocity

    Predicting the Next State of Reality: From Geospatial Insights to Decision Velocity

    The Shift from Insight to Agency Welcome to this week’s Signal Scanner. For decades, the geospatial and data industries have been obsessed with “insight”—the act of looking at a dashboard to understand what happened yesterday or what is happening now. But as we move into 2026, the signal is becoming clear: insight is no longer…

    Read more

A WordPress.com website

Spatial-Next

The Decision Velocity Research Lab

801.733.0723

mattsheehan7365@gmail.com

Proudly powered by WordPress