United States
34 years and approximately 4 million miles. Retired from the road in 2024. Then I started building. That sequence is not accidental. Three decades of moving freight across this country — in ice, in heat, through blowouts and breakdowns and bad dispatchers — teaches you something no lab can replicate: what happens when a system fails at speed and a human has to make a decision in the next three seconds. That's the problem I work on now. AI systems are moving into real infrastructure. Logistics. Fleet operations. Industrial processes. Civic systems. When those systems execute at machine speed, the question isn't whether they're intelligent. The question is who's in control when something goes wrong. And right now, most of the answer is nobody. I built GALE — Governed Agentic Local Execution — a runtime governance layer that enforces operational boundaries in real time. Hard stops. Explicit human authorization before execution. Audit-grade accountability at every decision point. Not a dashboard. An enforcement layer. I also developed REA — Reproducibility Enforcement Architecture — a deterministic conformance standard for AI operating in high-stakes environments. The governing principle: tools execute, operators decide. That came from 34 years of watching what happens when it gets reversed. On the applied side I run 4TruckersByTruckers — a platform of tools built on solutions for what truckers actually complain about. CDL training. Compliance. Safety. Load analysis. And GroundWatch — a federal early-warning system that gives rural communities advance notice of data centers, pipelines, and large industrial projects before the equipment hits the ground. Live, with active subscribers across multiple states. I wrote the book on this. Don't Be Scared: The Operator Model for Staying in Control of AI at Work. If you're building AI systems that touch real-world operations and want to talk governance, enforcement architecture, or what operators actually need from these tools, I'm reachable. BoundExecution.com
Designing and stress-testing system architectures that fail safely under real-world pressure. Focused on decision safety for AI and automated systems: pause/hold/release controls, decision freshness checks, human-in-the-loop enforcement, and audit-grade accountability. Background includes 30+ years in operational environments where timing, overload, and incorrect decisions have real human consequences. Current work bridges AI governance, systems engineering, and operational resilience to prevent irreversible mistakes under load.