Physical AI—the embedding of digital intelligence into physical systems—is a promising but sometimes polarizing technology.

In this article, I want to explore an idea that often is framed in moral terms but is actually a cybernetic imperative: the necessity of diversity for viable systems.

From the internet and smartphones to 3D printing, recent decades have ushered in general-purpose technology that increases efficiency and collapses the cost of routine tasks.

Your social media profile headline is nothing more than a phrase on a screen—a concise summary of your skills and expertise. But although that blurb gets you noticed, the real headline for executives is in how they lead.

In 2021, container ships idled for weeks outside the Port of Los Angeles, a stark visual reminder of just how fragile modern supply-chain reliability had become. The backlog sent shockwaves across industries.

The quality systems most medtech teams are stuck with aren’t built for how they work today. 21 CFR Part 820 was authorized by the Federal Food, Drug, and Cosmetic Act of 1978, long before the software industry even existed.

A robot searching for workers trapped in a partially collapsed mine shaft must rapidly generate a map of the scene and identify its location within that scene as it navigates the treacherous terrain.

T he year 2025 has been rife with uncertainty for industrial automation OEMs and their customers. A cataclysmic shift in U.S.

A young manager told me about the day she nearly quit her job. A major restructuring had left her team reeling. As targets shifted overnight, colleagues departed and rumors spread faster than facts.

What can we learn about human intelligence by studying how machines “think?” Can we better understand ourselves if we better understand the artificial intelligence systems that are becoming a more significant part of our everyday lives?