Essays and analysis on AI strategy, governance, infrastructure, agentic systems, and the practical challenges of implementing AI at scale in complex organizations.
The barrier to a stunning prototype is near zero, but real organizations do not operate in sandboxes. Moving AI from demo to production exposes the gap between a smart model and an operational system.
Read EssayAI is probabilistic; regulated environments are not. Scaling AI is not about a smarter model — it is about the operating model that connects infrastructure, governance, and product execution.
Read EssayA chatbot answers. An agent acts. Once an agent acts, prompts are not enough — it needs identity, scoped permissions, boundaries, and audit logs, like any actor in the organization.
Read EssayFor the last year, the standard playbook was simple: connect your enterprise data to a massive API. But agentic loops are destroying enterprise budgets at scale. The realization is driving a massive shift toward Sovereign AI architectures.
A layered way to understand global AI governance, inspired by internet governance and applied to a fragmented AI policy landscape.
Areas of current research and thinking that will inform future writing.
How organizations move beyond pilots and tools to build repeatable AI capabilities that integrate with existing workflows and create measurable value.
Why agentic AI requires permissions, workflows, orchestration, evaluation frameworks, and operating models, not just smart models.
What complex public institutions teach us about AI adoption in regulated, high-stakes, multi-stakeholder environments with real constraints.
Insights from building national-scale healthcare AI: sensitive data handling, predictive modeling, NLP systems, and real-world deployment challenges.
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