Oracle APEX’s AI Bet: Generate Less Code, Deliver More Control
It’s well accepted that AI has changed the economics of software development. Code is plentiful. Leading coding agents can generate JavaScript, Python, Java and other software artifacts at remarkable speed at far lower costs. But more code does not necessarily produce better enterprise applications and outcomes.
We believe the enterprise challenge is shifting from code creation to application control. Organizations must determine whether AI-generated applications are secure, explainable, maintainable and consistent with existing governance practices. They must also decide who will maintain the generated software after the initial prototype becomes a production system.
Oracle’s new direction for APEX addresses this problem through architecture rather than through another proprietary AI assistant.
Container Security Is Moving From Detection to Attack Surface Reduction
Why container security is shifting from vulnerability detection toward attack surface reduction, memory safety, and secure architecture.
Red Hat AI and the Rise of Agentic Infrastructure: Why Now Is the Defining Moment for Enterprise AI Platforms
Enterprise AI is moving beyond experimentation toward scalable, production-ready systems. As inference, trusted data, hybrid deployment, and agent governance become critical, Red Hat is positioning its open platform as the control plane enterprises need to shift from consuming AI tokens to producing them.
Physical AI Demands a New Approach to Enterprise Wireless Networks
As AI moves beyond chatbots and into the physical world, networking becomes a mission-critical business platform. Artificial intelligence is entering a new phase. While much of the industry’s attention has focused on generative AI and conversational interfaces, the next evolution is occurring in factories, airports, warehouses, mines, and other operational environments where AI systems interact […]
The AI Bubble Won’t Burst Because AI Fails. It Will Burst If the Economics Fail.
There is an AI bubble forming. That statement usually triggers one of two reactions.
The first is that AI is obviously transformative, demand is exploding and therefore there cannot be a bubble
The second is that AI is overhyped, enterprises will eventually realize it and the entire market will collapse.
I think both arguments miss what is actually happening.
AI works. Enterprise adoption is growing. Inference demand is accelerating. AI is becoming embedded into cloud infrastructure, software development, cybersec
322 | Breaking Analysis | Forecasting the AI bubble: When scarcity turns to surplus
AI can be technologically transformative and still produce a capital bubble. Those two ideas are not in conflict.
The bubble bursting does not require AI to fail. It only requires deployable supply and capital commitments to grow faster than monetizable demand. When productive, revenue-producing AI capacity takes longer to materialize, pricing will normalize and financing will no longer bridge the gap. That’s when the capital cycle resets.
AI Success Depends on Governing Data Before Governing Models
Why enterprise AI success depends on data governance, semantic context, and trusted data before model governance.
AI Is Transforming Network Operations: Why Self-Driving Networks Are Becoming a Business Imperative
Artificial intelligence is changing enterprise networking in two fundamental ways. While much of the industry’s attention remains focused on building networks capable of supporting AI workloads, an equally significant transformation is occurring in how networks themselves are designed, managed, and operated. At HPE Discover 2026, HPE expanded its vision for self-driving networks by extending AI-powered […]
AI Productivity Requires a New Software Development Model
Why AI productivity gains depend on redesigning software delivery around agents, context, trust, and specifications.