更致命的是美国电网的结构性老化。跨州输电线路审批动辄5—7年，变压器缺口高达30%，东西部三大电网互不联通，调度能力极弱。大量AI企业出现“机房建好、GPU插满、却无电可用”的尴尬局面，OpenAI、微软都曾因电网排队被迫闲置数万片算力芯片。
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The idea is that the user describes a specific outcome—something like "plan and execute a local digital marketing campaign for my restaurant" or "build me an Android app that helps me do a specific kind of research for my job." Computer then ideates subtasks and assigns them to multiple agents as needed, running the models Perplexity deems best for those tasks.
Anthropic’s prompt suggestions are simple, but you can’t give an LLM an open-ended question like that and expect the results you want! You, the user, are likely subconsciously picky, and there are always functional requirements that the agent won’t magically apply because it cannot read minds and behaves as a literal genie. My approach to prompting is to write the potentially-very-large individual prompt in its own Markdown file (which can be tracked in git), then tag the agent with that prompt and tell it to implement that Markdown file. Once the work is completed and manually reviewed, I manually commit the work to git, with the message referencing the specific prompt file so I have good internal tracking.