Einstein was talking about the concept of “technological unemployment,” the idea that technology will destroy jobs and lead to disaster. Will AI cause mass unemployment? Probably not, because when it comes to economics, Einstein was no Einstein. The vast majority of economists, including me, do not think that automation was an important cause of the Great Depression. Historically, revolutionary technologies do not generally cause mass unemployment. Of course, it’s always possible that AI will be different due to its rapid introduction. But the big picture is that innovation eventually makes society richer and not poorer.

As Australian organisations expand into hybrid cloud and edge environments, their attack surface is rapidly growing beyond the traditional perimeter. This byline explores how AI-first, platform-led security enables real-time threat detection at the edge, strengthens Zero Trust enforcement and unifies protection across cloud, OT and endpoints. It argues that intelligent, integrated security architectures are critical to building resilience and meeting evolving regulatory expectations in a borderless digital landscape.

Artificial intelligence has evolved from being a technical novelty into a powerful business tool with remarkable speed. Companies are embedding generative AI into customer service, software development, and finance operations to unlock efficiency gains and accelerate innovation. However, as adoption deepens, cyber security leaders are confronting a new reality: AI is not just enhancing productivity but also fundamentally reshaping cyber risk. Indeed, as AI becomes both a growth engine and a threat vector, the fundamentals of cyber security will become increasingly important. Cyber resilience will depend on embedding secure-by-design and Zero Trust principles across the enterprise.

The software development community is experiencing a near ubiquitous usage of AI coding tools as teams face pressure to generate more output in less time. While the huge efficiency gains from the tools help significantly, teams too often fail to incorporate adequate safety controls and practices into AI deployments. Industry leaders are pushing for comprehensive assessments that produce so‑called “trust scores”. These are composite metrics that integrate tool usage, vulnerability data and secure‑coding proficiency to quantify how products and teams influence software‑development lifecycle (SDLC) risk.