The market loves the engine. I’m looking at the electrical system keeping it alive. When investors talk about artificial intelligence, the conversation usually begins and ends with GPUs. We discuss accelerator road maps, training clusters, model sizes, memory bandwidth, and whichever chief executive has most recently held up a large piece of silicon under theatrical lighting. I understand the fascination. The GPU performs the glamorous work. It is the muscle behind training and inference, the component that gets photographed, named, back-ordered, and discussed as though it possesses a minor royal title. But a GPU cannot eat enthusiasm. It needs electricity—an enormous amount of it—delivered at the correct voltage, at precisely the right moment, with minimal waste, limited heat, and enough reliability that a multimillion-dollar AI rack does not become an extremely sophisticated space heater. Between the utility grid and the processor sits a long chain of power conversion, control, prote...
For most of its modern history, Accenture has operated one of the most recognizable talent machines in corporate life. It recruits at enormous scale, brings in armies of ambitious graduates, teaches them a common language of frameworks and delivery methods, places them on client work, and gradually moves the strongest performers upward. Some become specialists. Some become managers. A smaller number become managing directors. A still smaller number learn how to say “enterprise-wide transformation” without visibly needing oxygen. The model has survived mainframes, personal computers, the internet, outsourcing, cloud computing, mobile technology, automation, and every corporate trend that briefly required a new practice area and an updated PowerPoint template. Each technological wave changed what Accenture sold, but the basic human architecture remained remarkably durable: large numbers of junior people performed the labor-intensive work at the bottom, experienced managers coordinated it...