It’s never been easier to do too much

Generative AI has done something strange to the economics of knowledge work: it has dramatically lowered the cost of generating ideas. Any reasonably capable professional with a chatbot can now produce a dozen plausible strategies, memos, product concepts, or marketing plans before lunch. In some cases, AI lowers the cost of execution too—but not nearly as far or as fast. Shipping even one of those ideas still takes weeks, months, or years. The result is already showing up across workplaces: more initiatives than teams can carry, more tools than anyone can learn, and more priorities than any reasonable person can hold in their head. Leaders keep layering on new work because the cost of imagining new work has fallen close to zero. But the cost of actually doing it hasn’t. This creates a new management challenge: in an AI-saturated workplace, the bottleneck is no longer ideas. It’s execution. A cutting-edge genomics lab solved this problem about a decade ago—twice. The Broad Institute’s lesson in doing less to get more done The Broad Institute, an MIT-Harvard biomedical research center, experienced one of the fastest cost collapses in modern technological history. When the first human genome was sequenced in 2003, it took more than a decade and cost roughly $3 billion. Today, sequencing a human genome can take hours and cost under $200. That collapse created obvious opportunities, but also two separate crises at Broad. The first was operational. As sequencing became faster, samples moved through the pipeline more quickly than downstream teams could process them. Work piled up at bottlenecks. The lab became so overloaded that technicians started losing samples. The fix was to move from a “push” system—where each stage sends work downstream as fast as possible—to a “pull” system, where each stage only takes on new work when it has capacity. Then came a second crisis, one that looks a lot like the AI workplace problem. Once sequencing itself became cheap and routine, the