Black Hat 2026: Governing AI Agents From Access to Action
Enterprise AI is moving from answering questions to taking action. Agents can access data, invoke tools, call APIs and execute tasks across business processes. They can act on behalf of employees, interact with other agents and operate at a speed and scale that conventional human-centric security models were not built to handle. This requires a […]
323 | Breaking Analysis | Did Jensen just make the AI buildout too big to fail?
Nvidia is no longer just selling technology. It is helping create a financial asset class around AI compute. In our last Breaking Analysis, we argued that AI can be technologically transformative and still produce a capital bubble. Our thesis was simply that the bubble pops if deployable supply grows faster than monetizable demand – and […]
What Black Hat 2026 Revealed About the Future of Security Operations
What Black Hat 2026 Revealed About the Future of Security Operations Cybersecurity has always been a race against time. Black Hat 2026 made it clear that AI is putting new pressure on the clock. The industry’s answer to growing risk has long included more visibility, more detection and more specialized tools. Those investments generated valuable […]
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.