The first great cloud battleground was infrastructure as a service. Who could provide the most reliable compute, storage, networking, and global regions? The next battleground was platforms and managed services. Who could provide the best databases, analytics, developer tools, security services, application platforms, and machine learning infrastructure? Then came AI
The next infrastructure advantage will come from optimizing complete AI pipelines, not isolated model calls. For the first generation of enterprise AI, infrastructure economics were reduced to a familiar question: How cheaply can we run this model? That question is becoming obsolete. A production AI request is rarely a single
AI Agents
How to give AI agents direction without teaching them to game the system Every agent has an architecture you can see: a model, tools, memory, orchestration, identity, and an execution loop. It also has an architecture that is easier to miss. Somewhere in the system is a definition of “better.
For much of the cloud-native era, Oracle Database was easy for modern developers to dismiss. The dominant architectural story favored small, specialized services: a relational database for transactions, a document store for flexible state, a graph database for relationships, a vector database for semantic search, a cache for speed,
The first pillar of transformation Most organizations will not fail at AI transformation because they picked the wrong model. They will fail because they tried to bolt AI onto an operating model that was designed for a different era. They will buy tools, run pilots, create dashboards, announce initiatives, and
How to choose model-and-compute routes empirically—not by model size, benchmark hype, or GPU-first assumptions. Executive Summary The infrastructure question behind enterprise AI is often framed too narrowly: CPU or GPU? SLM or LLM? Local or cloud? Those are important choices, but they are not the real
Start with the AI Control Plane framework.
1. The uncomfortable truth: the agent is now inside the blast radius The first generation of enterprise generative AI questions was simple: Can the model answer the question? Agentic AI changes the question: Can the system reason over sensitive data, use tools, remember context, and take action — without violating policy,
A blog post on personal local AI, agent-to-agent orchestration, AI ambassadors, the autonomous edge, and the civilization-scale impacts of AI literacy divides. The future will not feel like “using AI.” It will feel like being surrounded by systems that already know the context, negotiate on your behalf,
Most teams will not unlock the real power of AI by helping individuals work a little faster. They will unlock it when teams learn to work from shared context and orchestrate agents together. That is a different model of work. In the old model, collaboration meant people coordinating through meetings,
How to develop judgment and taste in an autonomous world and how to guide the next generation through it. I keep coming back to this question as a leader, but even more as a father: What do I need to teach my son if he is growing up in a
Quantum security has moved from research horizon to enterprise operating model. The 2026-to-2027 challenge is not guessing the exact arrival date of a cryptographically relevant quantum computer; it is building the crypto inventory, governance, migration factory, vendor discipline, and agent-assisted evidence loop needed to become post-quantum
AI-native transformation is not about adding more tools. It is about redesigning work for a world where people may direct portfolios of AI agents. According to McKinsey Global Institute research, the majority of companies are still treating AI like software: deploy the tool, train the users, measure productivity, and
Artificial intelligence is not just a new technology layer. It is a new economic layer. For most of the software era, companies used technology to digitize workflows, centralize data, automate repeatable tasks, and improve coordination. The economic logic was familiar: software reduced marginal costs, improved scale, and made information easier
Enterprise AI is not failing because companies lack models. It is failing because data, security, latency, cost, governance, and operations are hard to control. OCI’s advantage is that it addresses those problems as infrastructure, not as slogans. Enterprise AI is entering an uncomfortable phase. The experiments worked. The demos
Subtitle: How self-optimizing, self-healing supervisor agents will manage the runtime that manages enterprise AI across GPU clusters, sovereign data environments, and latency-first infrastructure. Enterprise AI is quickly moving from a collection of copilots into a distributed operating environment. Agents route work, call tools, retrieve data, update memory,
AI disruption will not arrive evenly. Every industry will use AI. Every function will experiment with copilots, automation, agents, and generative interfaces. Every executive team will ask where productivity can improve. But only some industries are truly primed for AI-native disruption. The difference matters. AI adoption means adding AI
