NVIDIA Didn’t Miss the Data Problem — They Drew a Boundary Around It
Why NVIDIA’s AI architecture story is strong on compute and infrastructure, and why the data boundary still needs separate architectural attention.
Category
AI Architecture
Architecture questions raised by AI systems, infrastructure, security, trust, business rules, and enterprise data boundaries.
AI architecture is where questions about systems, data, trust, security, and business process start to overlap.
These posts look past the model layer and ask what has to change around it: infrastructure, APIs, data boundaries, governance, and the practical limits of automation.
AI Didn’t Break Infrastructure — It Exposed Its Edges
How AI exposes the boundaries between infrastructure, data, security, and governance rather than simply creating a new infrastructure problem.
AI Is About to Expose Your Security Posture — Not Improve It
Why AI security tools may reveal weak engineering practices faster than they improve them, and what that means for architecture and risk.
Business Rules and AI
Why AI agents make explicit business rules, assertions, and enforceable constraints more important, not less.
AI Trust and Data Security
Why LLM-driven agents need data security enforced below the app layer, close to the database and user identity.
AI and Data
A starting point for thinking about where generative AI can help data professionals, and where messy data still needs human judgement.