Writing
Essays on AI systems, healthcare decisions, and institutional design.
I write about what happens when automated systems meet real-world workflows — from clinical recommendations and fraud filters to cognitive load and the infrastructure needed to keep systems reliable over time.
Start here
The Official Record Is Late
A structure can be correct on paper and dangerous in practice — here is where that observation came from, and what I'm building now
If Every User Is a Potential Threat
People are not becoming dishonest. They are becoming game-theoretically optimal for the environment they have been placed in.
Toothless Ethics: Why Principles Don't Stop Machines
A guide to the difference between moral language and structural constraint
More essays
Don't Let Reassurance Do Engineering's Job
Why 'we care' substitutes for obligation — and how delay gets disguised as kindness.
You Don't Have the Right
In this Age of Appeals, you have the paper right. And a stamina test.
How to Design for Cognitive Scarcity
Stop designing for the idealized 'Hero User.' Build resilient interfaces that work when your user is stressed, tired, and operating on 15% battery.
Pending: The Political Economy of Waiting
The loading screen is the most powerful weapon in the modern state
Specifications
The essays argue the case. These state the mechanism — deployment controls, evidence reliability, and the institutional infrastructure that safety guarantees require.
Specification
Judgment Commitment
Protocol for making revisable binding decisions about AI system reliability — proposition registration, evidence-state freezing, and obligation tracking.
Operating model
Authorization, correction, and recourse
Graduated authorization states, the correction loop, and the conditions under which a deployment stops being authorized.
Current work
Ambit, Refract, and Fast Harm
The current systems: action-level authority, verifiable source history, and recovery evaluation — alongside NextConsensus's evidence work.
Recurring questions
- An AI system punishes faster than it can explain.
- Where does accountability reside when a model makes the decision?
- What infrastructure is missing between 'AI can do this' and 'AI should do this'?
- How do institutions maintain the ability to challenge automated decisions over time?
These are all the same question. Read the research program →