Aviad Almagor

Over the past two years, generative AI has reshaped how we interact with technology. Systems powered by large language models can write, summarize, analyze and converse with remarkable fluency and increasingly, with genuine depth of reasoning. What once required specialized expertise is now accessible through a simple prompt. Yet for all their sophistication, these systems remain largely confined to the digital world. They can reason about reality, but they cannot sense or act within it.

The first wave of artificial intelligence adoption was driven by excitement. Organizations rushed to deploy generative AI tools, automate processes, and explore what could be done faster with AI. Leadership teams encouraged experimentation and curiosity-fueled adoption; no one wanted to be late to the game. Now, as adoption scales, companies are facing a new reality—one where the cost of compute, environmental impact, and business model alignment matter as much as innovation.

Artificial intelligence (AI) isn’t just changing how we work. It’s rewriting job descriptions, altering career paths, and forcing leaders to consider what the workforce looks like in a world where AI is everywhere. Rather than being eliminated, roles are rapidly evolving to include AI skills. Not long ago, the introduction of large language models created surging demand for prompt engineers, experts in phrasing questions and commands to generate high-quality outputs.