I'm drawn to problems where the interface is only the visible edge of a much larger system.
A person trying to make a consequential decision with incomplete information. A team trying to align around something genuinely complex. A product trying to earn trust from people who have every reason to be skeptical. These are not just screen-design problems. They are orientation problems.
The work is to understand the system well enough to reduce friction without removing meaning. To create structure without false certainty. To help people see what matters, understand what is possible, and move with confidence.
Structure as Design
Narrative & Strategy
AI is changing the scale of what a designer can explore, prototype, compare, and build. It compresses distance between idea and artifact. It makes one person faster, but more importantly, it makes one person differently capable.
That is why I pay close attention to it.
The most interesting design question is not whether AI can generate more screens, copy, or code. It is how we design with systems that can suggest, summarize, simulate, and act, without allowing their confidence to become a substitute for our judgment.
As products become more adaptive, more automated, and more opaque, design has to do more than smooth the path. It has to preserve orientation. People need to understand what is happening, where the machine is making a recommendation, where uncertainty still exists, and where human choice still matters.
The future of design is not frictionless. It is intelligible.
Selected Work
A few projects and case studies that show how this thinking turns into systems, interfaces, workflows, and artifacts.

Acrylic
An AI-powered music licensing platform for helping sports teams find, evaluate, and license tracks for social content.
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Protected Work
Case studies from client work in financial services, creative licensing and early-stage startups. The decisions, the constraints, and what shipped. These are shared on request: ask and I will send the password.
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AI Workflow Experiments
Self-initiated projects built with AI agents as design partners. Each one ends with a prototype you can try and a plain account of how it was made, including what went wrong.
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