In recent years, LLMs have shown significant improvements in their overall performance. When they first became mainstream a couple of years before, they were already impressive with their seemingly human-like conversation abilities, but their reasoning always lacked. They were able to describe any sorting algorithm in the style of your favorite author; on the other hand, they weren't able to consistently perform addition. However, they improved significantly, and it's more and more difficult to find examples where they fail to reason. This created the belief that with enough scaling, LLMs will be able to learn general reasoning.
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根据IDC的预计，活跃智能体的数量将从2025年的约2860万，攀升至2030年的22.16亿。这意味着五年后，能够帮助企业或个体执行任务的数字劳动力数量将是现在的近80倍，年复合增长率139%；任务执行的数量将从2025年的440亿次暴涨至2030年的415万亿次，年复合增长率高达524%；Token的消耗将从2025年的5000亿激增至2030年的1.5万亿亿，年复合增长34倍。IDC的预测未必准确，但趋势非常明显，每一家企业都要为此做好准备。
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