AI tools have doubled the pace of some coding tasks but added a hidden mental toll. A simple comparison shows why: Ben writes code by hand, spends four hours on a feature, feels challenged but satisfied, and takes a proper break. Alice uses an LLM—plans with prompts, reviews generated code, fixes bugs—and finishes in two hours. Yet her brain is flat-out: prompting, debugging, steering. Instead of feeling done, she jumps into the next task, chasing a sense of accomplishment that never comes. That endless loop—plan, auto-generate, review, repeat—erodes the parts of programming we actually enjoy. Writing code by hand is tactile and meditative; reviewing AI output is draining and error-prone. As cadence speeds up, developers lose the sense of ownership and pride that made them stick with the craft. Workload balloons both in quantity and intensity, which Harvard Business Review calls “cognitive exhaustion from intensive oversight of AI agents.” This shift even threatens the identity of engineering roles. When machines handle core problem solving, “programming” feels more like managing scripts than creating solutions. Job titles remain the same, but the day-to-day is changing so fast that many feel they’re in a different profession. The industry risks trading genuine skills and satisfaction for a never-ending productivity treadmill—and burning out before today’s AI bubble even peaks.