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The way people interact and communicate is evolving from early text messaging (WhatsApp, WeChat)—what I want to say, to photo sharing (Ins, Snap)—what I’m experiencing, to generative creation—what I’m imagining.

Traditional social media's feed is dying, prompt is also dying, for the general public, what really matters is interactive, real-time, generative content consuming.

There are two main ways humans interact with information. One is asynchronous, such as reading books or articles written by others, watching videos and movies others created. The other is real-time and synchronous, like conversations between people or even our interactions with the world.

Before generative AI, 99% of our interactions with the digital world were asynchronous. However, with the advent of generative AI, real-time, synchronous information interaction will rapidly emerge as a new category of information services, transforming how we acquire knowledge, content, and entertainment.

思维惯性就像塞壬海妖，时时刻刻呼唤着我们，向她的方向驶去。

所以当你思考下一代信息服务的产品形态和用户价值的时候，你的目标不应该是取代现在人们正在使用的主流信息服务产品。你应该思考的是，新的技术变量能够创造怎样全新的内容叙事，和全新的信息消费方式。

新的内容平台不会是对上一代内容平台内容的再分发，而是创造一种新的内容叙事，就像电视不是可视的广播，YouTube不是把电视内容搬上互联网，TikTok也不是把YouTube搬上手机。也不需要在上一代内容平台的使用场景上做10倍提升，而是创造一种新的内容消费方式，就像Google不需要把雅虎的信息分类做的更好，TikTok也不需要把Google的搜索做的更好。

人们搜索信息的主要input是输入关键词，人们消费信息流的主要input是向下滑动，推荐算法并没有解决信息服务的个性化问题，信息的颗粒度还是太大。real-time generative media会真正实现信息服务的个性化，所以需要的不只是一个模型，而是一个real-time generative engine。事实上LLM已经在文字信息上实现了这样的体验。

If the cost of using AI models is the same as the cost of using broadband and information services, then subscriptions will not be the primary business model for AI-to-C products.