AI governance
7 posts
← All writing- / 0130 September 2026
The ops debt machine: what Meta's AI agent collapse teaches about activity versus output
TL;DR: Meta's AI agents produced 220% more code. Users got 36% more features. Incidents rose 40%. Firefighting time rose 70%. Eight thousand people were let go, and the model still underdelivered. The failure was sequencing, not the technology. * Output and outcomes separated. More code did not mean more value reaching users. * Human capacity was removed before machine capacity existed to replace it. * The plan leaked before vice presidents were briefed. Morale dropped 19 points. * The corr
- / 0213 September 2026
Decision Record
TL;DR: Every AI rule written for financial services since 2024 converges on one demand. A record. Firms understand the obligation. They fail at the schema, the write path, and the retention decision. This piece is the build specification nobody publishes. * Show a decision your system made, prove what it was, why it happened, which control was in force, and who owned it. * APRA says it in principle-based language. The EU says it in Article 12. The SEC said it in 2022 without mentioning AI at
- / 0316 August 2026
Hidden reasoning is an information asset. You have not classified it.
Hidden model reasoning is an information asset you have not classified, cannot sanitise, and cannot verify. What the reasoning-trace extraction paper means for CPS 234, your retention obligations, and what counts as evidence in an AI system.
- / 044 July 2026
Your AI Policy Doesn't Fire When Your Agent Acts
I walk into firms with AI policies sitting in SharePoint. They get cited in board decks. They show up in audit binders. Then a model makes a decision, moves money, drafts client output, and the policy watches from the shelf. That gap is a wiring problem. Prose does not intercept a call. Prose does not sign an evidence record. Prose does not refuse an action because a control was not satisfied. The Difference Between a Document and a System A governance document describes what should happen
- / 059 June 2026
Most companies aren't using AI. They're performing it.
TL;DR: Organizations buy AI without defining what success looks like. They deploy tools, run pilots, create governance frameworks, but skip the step of connecting technology to measurable outcomes. The result is expensive theater that produces activity without results. Why most AI initiatives fail: * No measurable outcome defined before deployment * Procurement processes designed for deterministic software, not probabilistic AI * Missing translation layer between technical capability and bu
- / 069 June 2026
Why We Replaced Markdown With JSON: The Constitutional Stack
Prose specifications can't be verified. A constitutional stack needs a runtime, and a runtime needs structured data.
- / 079 June 2026
The oversight vacuum: why AI adoption without governance is tearing companies apart
I've watched the same pattern repeat across dozens of organizations in the past 18 months. A leadership team sees a demo. Someone in the room plays with ChatGPT over lunch. The conversation shifts from "should we" to "how fast can we" in a single meeting. The deployment begins before anyone checks what's actually under the hood. This isn't a story about technology moving too fast. This is about organizations moving faster than their ability to understand what they're deploying. The gap between