324 AI & Work Evolution = Real + Rapid AI is foundationally changing the way we work. Alongside growth in physical automation (think adoption of robots and drones), we are now also seeing the rise of cognitive automation, where AI systems can reason, create, and solve problems. The ramifications are widespread. The pace of improvement in AI's cognitive ability is astounding. In the three years since ChatGPT’s 11/22 public launch, we've gone from the reasoning capabilities of a high school student to those of a PhD candidate. Professions centered on intaking large bodies of structured, historical data and outputting rules-based decisions and judgement, fall squarely in the core competency of generative AI. In this emerging landscape, a unit of labor could shift from human hours to computational power. Data centers and foundation models – in many instances – could dictate the availability and quality of certain types of labor. As a result, some tout an 'agentic future' where AI agents replace humans in many white-collar jobs. Although possible, history and pattern recognition suggest the role of humans is enduring and compelling. Technology-forward leaps have typically driven productivity and efficiency gains and more – but new – jobs. That said, this time it’s happening faster. In an extreme, entirely agentic future, humans maintain a role in the system, pivoting towards oversight, guidance, and training. Imagine facilities filled with humans teaching robots intricate movements or offices full of workers providing reinforcement learning* human feedback (RLHF) to optimize algorithms. This is not conjecture. Companies like Physical Intelligence and Scale AI, respectively, are building powerful businesses based on this view of the world. The idea of the human workforce re-configured to teach and refine machines as a primary function might sound dystopic. But it’s worth remembering historical parallels. Fifty years ago, this prospect of rows of cubicles and uniformed office workers sitting quietly in front of LED computers ten hours a day likely sounded equally dystopic. Yet here we are. Technology has constantly redefined and evolved the nature of work and productivity…AI is no different. *Reinforcement Learning = An ML approach where agents learn by receiving rewards or penalties for actions.