人工智能与GDP影响力的协调能力水平之路表明，人工智能的扩散将比人们想象的要慢得多。它还将...
The way to the reconcile capability level of AI vs. GDP impact is that the diffusion of AI will take much longer than people think. And it will also s...
Aaron LevieAI2026-09-09
人工智能与GDP影响力的协调能力水平，其途径是人工智能的扩散将比人们想象的要长得多。它还将以难以立即在GDP中衡量的方式显现。你可以在许多工作流程中引入世界上最伟大的超级智能，但仍受制于企业物理定律：准备数据并将其放入管道，流程再造和变革管理，就新的工作流程应该如何运作达成一致，等等。即使解决了所有这些问题，你仍然受制于现实世界的速度：等待客户对提案做出回应，获得项目许可，药物发现管道需要数年才能最终达到消费者，等等。更不用说许多积极的日常AI用例在短期内对GDP的影响完全是中性的。AI扩散将成为下一个十年的主题。好处是，在构建超级智能与现实世界工作流程之间的桥梁方面，存在着巨大的机会。安德鲁·霍：不够多的人正在认真尝试调和两个事实：- 模型现在可以解决千年问题- 美国GDP年增长率仅为1.5%。
原文
The way to the reconcile capability level of AI vs. GDP impact is that the diffusion of AI will take much longer than people think. And it will also show up in ways that are hard to measure in GDP immediately. You could bring the world’s greatest superintelligence to many workflows, and still be bound by the laws of corporate physics: getting data prepared and put into a pipeline, process reengineering and change management, aligning on how the new workflow should function, and so on.Even after you solve all that, you’re still bound by the speed of the real world: waiting for a customer to respond to a proposal, getting a permit for a project, a drug discovery pipeline taking years to eventually reach the consumer, and so on. Not to mention lots of positive daily AI use-cases are entirely net neutral to GDP, at least in the near term.AI diffusion is going to be the theme of the next decade. The upside is that there’s a tremendous amount of opportunity in building the bridges between superintelligence and real-world workflows.Andrew Ho: Not enough people are seriously trying to reconcile the two facts:- models can now resolve Millennium Problems- annualized US GDP growth is at 1.5%