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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Returning back to the Anthropic compiler attempt: one of the steps that the agent failed was the one that was more strongly related to the idea of memorization of what is in the pretraining set: the assembler. With extensive documentation, I can’t see any way Claude Code (and, even more, GPT5.3-codex, which is in my experience, for complex stuff, more capable) could fail at producing a working assembler, since it is quite a mechanical process. This is, I think, in contradiction with the idea that LLMs are memorizing the whole training set and uncompress what they have seen. LLMs can memorize certain over-represented documents and code, but while they can extract such verbatim parts of the code if prompted to do so, they don’t have a copy of everything they saw during the training set, nor they spontaneously emit copies of already seen code, in their normal operation. We mostly ask LLMs to create work that requires assembling different knowledge they possess, and the result is normally something that uses known techniques and patterns, but that is new code, not constituting a copy of some pre-existing code.
Фото: Belkin Alexey / News.ru / Globallookpress.com。heLLoword翻译官方下载对此有专业解读