A Canadian founder urging CEOs to rethink resilience in an AI-driven economy. The central idea is that traditional corporate resilience—keeping systems running during outages, cyberattacks or supply-chain disruptions—is no longer enough. As AI becomes embedded across business operations, companies also need to understand what happens when AI systems, models, vendors or automated workflows fail.
A major concern is dependency on a small number of AI providers and infrastructure layers. Companies can become deeply dependent on a particular model, cloud platform, data provider or agent framework, creating a new form of concentration risk. If an AI provider changes pricing, suffers an outage, loses access to critical infrastructure or changes its policies, businesses that have built their operations around it may have few practical alternatives.
The article's broader argument is that CEOs should treat AI as critical infrastructure rather than simply another software tool. That means building fallback systems, maintaining human expertise, understanding where AI is being used across the organization and ensuring that important processes can continue when an AI service becomes unavailable. It also means testing failure scenarios before AI becomes deeply embedded in mission-critical operations.
The broader takeaway is that AI resilience is ultimately about avoiding a new kind of organizational fragility. Companies are racing to automate processes, but automation can create hidden dependencies if leaders do not understand what sits underneath the AI layer. The strongest AI strategies may therefore be those that combine aggressive adoption with redundancy, portability, human oversight and clear recovery plans—so that becoming more intelligent through AI does not simultaneously make the business more vulnerable.
