David Martin / AI Infrastructure & Systems Architect
I came into AI through language, cognition and architecture, not software development.
My academic background includes cognitive science, psychology of memory and cognition, philosophy of language, and the role language plays in the mind.
Setting up a server or a WordPress site was the kind of thing I actively avoided. I was much more comfortable thinking about language, memory, structure and how people reason than dealing with software infrastructure.
When I began working seriously with GPT, I entered almost entirely through language. For roughly five months I worked on directives, memory, governance, architecture and repeated corrections to the way the environment behaved. I was not trying to become a programmer. I was trying to make the environment itself work differently.
URSA and the broader Canon architecture were already extensively developed before I moved into software implementation in late July 2026.
From architecture into software
The coding project came out of the architecture, not the other way around.
I took the existing architecture into an AI coding environment in late July 2026. AI coding systems and agents carry much of the implementation work, but I do not work by asking a generator for a finished artifact. I work continuously with the development environment: defining what should be built, working through the architecture and constraints, tracking source and execution state, correcting assumptions, reviewing evidence and deciding what should happen next.
Specialized agent roles are orchestrated underneath that process. I work with the development system as a whole rather than assigning individual tasks to individual agents.
Conventional line-by-line authorship is not the control system I rely on. Proposed changes, generated code, submitted commands, actual execution, diffs, passing tests, verification and human acceptance are treated as different states. The development environment has to keep those distinctions visible.
That is also different from ordinary one-shot vibe coding. The objective is not simply to get AI to produce something that looks finished. It is to let AI carry a very large amount of implementation work while architecture, state, correction, verification and consequential acceptance remain human-guided.
Why the human boundary matters to me
I don’t think making AI more capable requires making the human less capable.
One of my earliest concerns was what increasingly capable systems would do to people whose work they could perform. I am more optimistic now because I can see another possibility concretely.
The machine can carry much more of the mechanical load while the environment keeps the person oriented, learning, deciding, correcting and creating. Development can become more accessible without becoming less rigorous.
That is a direction I want Canon Frameworks—and especially URSA Dev—to help move into the world.