A 30-year engineering leader who’s actually hands-on with AI.

I keep complexity in the business domain, not the stack — simple code, clear interfaces, boring technology a team can actually master. I design for change, not perfection: decisions that keep options open, the irreversible ones delayed to the last responsible moment, the cost of iteration kept low. When the assumptions change — and they will — the system bends instead of breaking.

The tradeoff is real: I'll hold a decision open longer than most are comfortable with, living in the ambiguity until it has to resolve. That patience is the thinking behind the Chief Data Officer Award I earned at Target.

I build execution cultures: sprints that actually work, PRs that get real reviews, master always deployable, releases that are zero-touch. I codified it into The KHIVI Test — ten questions that tell you whether an engineering org is shipping or just busy. I've run this as CTO at two startups, where there's no slack for motion that isn't progress.

Small experiments over grand plans, fast feedback over perfect specs, teams that own their quality end to end. Product, engineering, and business aren't separate functions — they're one team solving one problem.

The best engineers I've worked with weren't born great — they were mentored well. I push engineers to find their own solutions, walk them through the tradeoffs, and build environments where they can take real ownership.

I wrote Culture Seeds to capture the patterns that build those teams: how to hire, how to set standards, how to make quality the path of least resistance.

Call it vibe coding if you like — I treat AI like any other part of the stack: deliberately. AI writes the code; I own the system. Tests stay non-negotiable, reviews stay real, nothing ships on vibes alone.

So I built a platform around it, not a prompting habit — multi-agent workflows orchestrated through Cockpit, the open-source tool I wrote for it. Same instinct as the rest of the work — optimize the system, not the parts.