The shift of risk into the interpretive layer The most consequential decisions made by autonomous systems no longer occur at the visible moment of execution. By the time an action becomes observable, accountable, or auditable, the underlying risk has already emerged elsewhere—inside the interpretive layer where objectives are translated into meaning and meaning into operational … Read more
How AI changes the meaning of compliance and risk documentation AI systems have become the primary interpretive layer through which organizations are understood. Long before a customer reads a product page, a regulator reviews a filing, or a journalist examines a disclosure, AI models have already processed, summarized, reframed, or compared the underlying information. Organizations … Read more
The failure that wasn’t about technology There are failures that reveal the limits of technology, and there are failures that reveal the limits of governance. The Monext incident in January 2026 belongs firmly to the second category. A payment processor reprocessed an archive file of Visa transactions from late 2024 and early 2025, causing thousands … Read more
The hidden risk of meaning loss in AI agent systems — and how to govern it When your organization deploys AI agent chains to automate procurement, execute financial workflows, or manage operational decisions, you assume the AI “understands” instructions. That assumption is costing enterprises millions in misaligned outcomes, compliance exposure, and operational failures that conventional … Read more
For years, digital distribution platforms were evaluated through a simple lens: visibility led to clicks, clicks led to value. That assumption no longer holds. Recent empirical evidence shows that Google Discover is undergoing a structural transformation. What was once a surface designed to distribute traffic is evolving into an attention retention layer dominated by AI-generated … Read more
Artificial intelligence is being evaluated more than ever. Benchmarks multiply, audits proliferate, impact assessments become mandatory, and entire regulatory architectures are built around the idea that sufficiently rigorous evaluation will keep systems under control. Yet something fundamental remains unresolved. Despite the growing sophistication of evaluation frameworks, the social effects of AI systems continue to surprise … Read more
The recent MIT report Mapping AI Risk Mitigations is an important document, not because it resolves the current uncertainty around AI governance, but because it makes that uncertainty explicit. The report does not introduce a new safety framework, nor does it propose a novel theory of AI risk. What it does, with notable rigor, is … Read more
When Interpretation Stops Being Optional There are moments in the evolution of technical systems when interpretation ceases to be a matter of preference and becomes a structural necessity. Not because regulation demands it, nor because ethics requires it, but because the system itself begins to enforce meaning through its operation. This note documents such a … Read more
A certain sentence has begun to circulate with the confidence of a proverb, particularly in legal and policy circles trying to tame the AI moment: without rules, there is no framework; without a framework, there is no strategy. It has the tidy appeal of institutional logic—first the law, then the method, then the plan—and it … Read more
