326 | Breaking Analysis | Beyond Shared Responsibility: When AI Acts, Who Owns the Blast Radius?
The cloud shared responsibility model was initially not well understood by many customers. In fact, early adopters often believed that simply having data in the cloud meant that Amazon, or a SaaS vendor were responsible for safeguarding it. Amazon had to educate its customers and partners that security and compliance duties were split between the vendor and the client organization. In short, the vendor was responsible for securing the cloud resources but you, the buyer, were responsible for securing what you put inside the cloud; based on your policies, priorities and budget. We believe a similar but much more consequential dynamic is unfolding with respect to agentic AI. Specifically, Cloud computing divided responsibility by infrastructure layer. Agentic AI distributes authority across a chain of models, platforms, clouds, partners and customers. Our premise is the industry now needs a shared accountability model for the decisions, actions and outcomes that chain produces.
AI Observability Is Moving From Detection to Action
Why AI observability is evolving from monitoring into an intelligence layer that enables agents to diagnose and act.
Agentic Commerce Depends on Customer Readiness, Not Just Technology Speed
Why agentic commerce success depends on customer trust, AI-ready content, and orchestration—not technology speed alone.
325 | Breaking Analysis | CrowdStrike’s Post-Mythos Surge: Moat, Momentum and the Blast-Radius Test
In this Breaking Analysis we’ll dig into how CrowdStrike and Falcon are converting AI urgency into platform expansion. We’ll introduce data from a new data intelligence firm, Qualitiate, which has conducted many thousands of buyer surveys on CrowdStrike and other firms. We’ll also discuss why containing the new blast radius is a key to operational, technical and financial sovereignty in this AI era.
AI Agents Need a New Observability Model
Why AI agent observability must measure intent, experience, outcomes, and cost alongside traditional application performance.
From Vibe Coding to Governed VibeOps: Fabrix.ai Targets the Next Phase of Enterprise Operations
Fabrix.ai is introducing a multi-vendor, governed VibeOps operational intelligence platform designed to help IT, network, security and site reliability teams create operational dashboards, agents and automation workflows using natural-language instructions. The company is also adding domain-specific small language models, or SLMs, intended to improve the accuracy, performance, privacy and economics of operational AI. The announcement […]
The New Measure of Cyber Recovery: Can the Business Get Back to Work?
A cyberattack is more likely to interrupt today’s enterprise than the natural disasters business continuity programs were originally built around. Enterprises still need to prepare for fires, floods, earthquakes, power failures and other physical events. But they are more likely to experience a cyber incident that disrupts business operations by affecting employee productivity, taking applications […]
324 | Breaking Analysis | From Tokenmaxxing to Sovereign Alpha: Who Controls Your AI Economics?
The AI industry wants enterprises to measure progress in tokens, model calls and usage. But those are largely vendor-revenue metrics—not enterprise-value metrics.
Canva shows why.
On August 6th, The Information reported that Canva cut its 2026 revenue-growth forecast from 30% to 20% because its AI features cost far more to run than expected.
Let that sink in: a company generating more than $900 million a quarter – and growing above 25% – lowered its outlook because of an input cost.
Canva said it had relied too heavily on expensive third-party frontier models. The fix was not a negotiated vendor discount. It rebuilt the stack with in-house models, Leonardo.AI and task-level routing – reportedly cutting the cost of an AI task by roughly 90%. Its video and image models were reportedly 17 and 30 times cheaper than frontier alternatives.
Your CFO is not buying tokens. The enterprise wants outcomes.
AI Customer Experience Depends on Orchestration, Not Automation
Why AI customer experience success depends on orchestrating automation, human expertise, governance, and workflows.