About Me

I didn't discover AI because it was trending.
I recognized it because I'd been solving its underlying problems for decades.

My professional foundation was forged in U.S. Navy intelligence as a Cryptologic Technician Technical (aircrew), where the work wasn't about tools — it was about signals, ambiguity, trust, and consequence. You learn quickly that information is never clean, systems are never complete, and decisions still have to be made. That mindset — systems under pressure, imperfect data, real outcomes — is the throughline of my career.

When I left the military in the early 2000s, I stepped directly into the formative years of the modern internet. Before "platforms" stabilized, I was building and managing online presence for artists and creators: moderating early YouTube communities, shaping MySpace ecosystems, and designing what we'd now call social media presence management. This was before algorithms were named, before moderation tooling existed, and before "creator" was a job title.

What we were really doing was human-AI adjacency before AI:
signal vs noise, trust at scale, behavior shaping, identity persistence, reputation systems, and content survivability. Those same problems sit at the core of modern AI systems today — just with more compute and higher stakes.

Over the years, I transitioned that perspective into enterprise architecture, working across financial services and other regulated environments where explainability, governance, and durability matter as much as innovation. I've spent my career designing systems that other people must rely on — and inherit — long after the initial build is done.

My approach to AI is not novelty-driven. It's architecture-first.

I focus on:

• Where AI belongs in a system — and where it absolutely does not
• How memory, retrieval, and context actually work over time
• How to design AI that augments human judgment instead of obscuring it
• How to make AI systems observable, governable, and survivable

Lately, my work centers on applied AI systems: long-term memory, retrieval architectures, agent orchestration, and human-facing AI designed to preserve knowledge rather than accelerate its decay. I'm particularly interested in AI as a steward of context — systems that can remember why decisions were made, not just what decisions were made.

In many ways, this is a return to first principles. The same concerns that shaped my early work — signal integrity, system fragility, human trust — are now front and center again, just wearing the label "AI."

I don't see artificial intelligence as a replacement for human thinking. I see it as infrastructure — powerful, dangerous if misunderstood, and transformative when designed with restraint.

That perspective doesn't come from demos.
It comes from time, failure, and having watched entire generations of technology rise, harden, and disappear.