Writing
Essays on AI governance, asymmetric irreversibility, and the gap between what systems recommend and what people actually do.
I write about what happens when software takes over decisions that used to require human judgment — a fraud filter freezes accounts in milliseconds but takes weeks to exonerate; a clinical AI recommends but cannot be corrected — and about the infrastructure that would make those decisions accountable.
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, reconsideration, and correction
Graduated authorization states, the six-step reconsideration loop, and the conditions under which a deployment stops being authorized.
Current work
NextConsensus, Refract, and the deployment-safety stack
What's built, what's shipped, and what's still in development.
Recurring questions
- What happens when 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 →