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Generative AI has done something strange to the economics of knowledge work: it has dramatically lowered the cost of generating ideas.<\/p>\n<\/div>\n
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\u2014but not nearly as far or as fast. Shipping even one of those ideas still takes weeks, months, or years.<\/p>\n
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\u2019t.<\/p>\n
This creates a new management challenge: in an AI-saturated workplace, the bottleneck is no longer ideas. It\u2019s execution.<\/p>\n<\/div>\n
A cutting-edge genomics lab solved this problem about a decade ago\u2014twice.<\/p>\n
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.<\/p>\n
That collapse created obvious opportunities, but also two separate crises at Broad.<\/p>\n<\/div>\n