AI and Automated Programs' Network Requests Surpass Human Users for the First Time AI等机器网络请求量首超人类

What is even more intriguing is that while the "demographics" of the internet undergoes a fundamental reversal, the financial market is placing a frantic bet and hedging on the future of AI. That research report from China Galaxy Securities about the Federal Reserve is telling a story between the lines: the market has already "extreme-priced" AI concept stocks, having risen too much and too fast, and now any macroeconomic data (such as strong non-farm payrolls) will be interpreted as bearish, triggering "correction risks." Look, this is the magical reality of the modern economy—a traditional macro report on interest rates and employment, yet its core concern is whether the AI sector's stock prices will fall. AI is no longer just a technical topic; it has become the "main trading theme" of global capital flows. When Federal Reserve Chairman Powell sneezes, AI stocks worldwide catch a cold. The revolutionary nature of technology is first reflected in the most extravagant and most authentic way through the tides of capital. The market oscillates between fear and greed, fearing both missing this "Noah's Ark" named "AI" and fearing that it is a bubble about to burst.

The backdrop to this grand narrative is the overwhelming yet somewhat comical AI application boom. Just look at the trending topics: "After using AI, the company seems even poorer," "Street stall equipment surges by 600%," "Big companies harvesting the two-dimensional community with 'electronic badges.'" What kind of picture does this paint? On one side, Anthropic and OpenAI are competing over the "reliability threshold" of models, discussing the grand "self-acceleration." On the other side, in reality, many companies have introduced AI only to see increased computing costs without profit growth, possibly even more chaos due to process restructuring. The street stall economy has unexpectedly revived under the rhetoric of "AI disrupting everything," becoming a grounded survival wisdom. Meanwhile, big companies are enthusiastic about packaging AI into various "electronic badges" (pins) and "watch scraps," selling them to the two-dimensional community—this is certainly a sign of business acumen, but it also blatantly shows that when fundamental technological breakthroughs hit a plateau, application-layer innovation tends to slide into exquisite toys or marketing gimmicks. The popularity of AI on the consumer side proves not so much its utility as humanity's endless pursuit of novelties and the capital market's insatiable demand for "AI-enabled" stories.

So, what is the real contradiction in the current AI field? It is the tension between the expectation of exponential growth in foundational model capabilities and the anxiety over the delayed emergence of killer applications. It is the gap between the "disruption" enthusiastically promoted by capital and the reality of companies "seemingly even poorer" after cautious trial and error. The founder of Spring returned to the front lines to work on AI frameworks, yet said this is "the last generation of frameworks chosen by humans themselves"—this sounds tragic but actually hits the key point: we are at the critical point of paradigm shift in human-machine collaboration. All current frameworks and tools are still designed with human developers as the center. However, with the enhancement of AI programming capabilities, the future design paradigm is likely to be led by AI, with humans transforming from "programmers" to "requirement proposers" and "reviewers." This is a fundamental role change.

We stand at a peculiar historical node: the internet traffic share of machines surpassing humans for the first time, signaling a reversal of the main and secondary in the digital world; the financial market is pricing the future of AI with high risk, with intense volatility; while what fills the market are many "scrap" applications and the confusion of "seemingly even poorer." The real revolution is not the clamor on trending topics but happens silently in the depths of data centers, in the iterations of algorithmic models, in that moment when the traffic share quietly crosses the 50% mark. It is not loud or noisy, yet it is unstoppable. Humans may be transitioning from "users" of the internet to "heritage" of the internet—a still-existing but no longer mainstream carbon-based legacy.

互联网的“硅基生命”正在反客为主。云网络安全公司那57.4%的数据，不是一个冰冷的百分比，而是一声响亮的汽笛，宣告着数字世界的主要居民已经换人了。我们亲手搭建的、为人服务的信息高速公路，正悄无声息地被改造为机器的后花园。人类用户成了互联网上的“少数派”，我们浏览、点击、消费，而机器们则在更深层地“访问”、“爬取”和“学习”。这意味着什么？意味着互联网的底层协议、流量架构、甚至内容生态，未来的优化方向将不再是人类的体验，而是机器的效率。那些为SEO设计的垃圾文章、为吸引点击而生的夸张标题，其首要消费者早已不是我们，而是搜索引擎的爬虫和AI的训练集。我们活在一个由人类设计、却主要服务于机器的虚拟世界里，这难道不是一种终极的讽刺？

更有趣的是，当互联网的“人口结构”发生根本性逆转时，金融市场正在为AI的未来进行一场疯狂的押注与对冲。中国银河证券那份关于美联储的研报，字里行间都是在说一个故事：市场已经被AI概念股“极致定价”了，涨得太多太快，现在任何一点宏观数据（比如强劲的非农）都会被解读为利空，引发“回调风险”。看，这就是现代经济的魔幻现实——一个关于利率和就业的传统宏观报告，核心关切点竟然是AI板块的股价会不会跌。AI不再仅仅是技术话题，它已经成为全球资本流动的“交易主线”。当美联储的主席鲍威尔打个喷嚏，全世界的AI股都得感冒。技术的革命性，首先在资本的潮汐中得到了最浮夸也最真实的体现。市场在恐惧与贪婪之间摇摆，既怕错过这艘名为“AI”的诺亚方舟，又怕它是一场即将破灭的泡沫。

而这场宏大叙事的背景音，是铺天盖地却显得有些滑稽的AI应用热潮。看看那些热榜吧：“用了AI之后，公司好像更穷了”、“地摊设备暴涨600%”、“大厂‘电子吧唧’收割二次元”。这描绘了一幅怎样的图景？一边是Anthropic和OpenAI在模型“可靠性阈值”上较劲，讨论着宏大的“自我加速”；另一边是现实中，许多公司引入AI后，除了增加算力成本，并未见到利润增长，反倒可能因为流程重组而更乱了。地摊经济在“AI颠覆一切”的论调下意外复兴，成为一种接地气的生存智慧。而大厂们则热衷于将AI包装成各种“电子吧唧”（徽章）和“手表边角料”，卖给二次元群体——这固然是一种商业嗅觉，但也赤裸裸地展示了，当底层技术突破陷入平台期，应用层的创新容易滑向精致的玩具或营销噱头。AI在C端（消费者端）的火爆，与其说证明了它的实用，不如说证明了人类对新奇玩意永无止境的追逐，以及资本市场对“AI赋能”故事如饥似渴的需求。

所以，当下AI领域的真正矛盾是什么？是基础模型能力指数级增长的期待，与杀手级应用迟迟未现的焦虑之间的撕扯。是资本热烈鼓吹的“颠覆”，与企业谨慎试错后“好像更穷了”的现实之间的落差。Spring创始人重回一线做AI框架，却说这是“人类亲自选择的最后一代框架”，这话听起来悲壮，实则点破了关键：我们正处在人机协作范式切换的临界点。当前的所有框架、工具，依然是以人类开发者为中心设计的。但随着AI编程能力的增强，未来的设计范式很可能由AI主导，人类从“程序员”变成“需求提出者”和“审核者”。这是一个根本性的角色转变。

我们正站在一个奇特的历史节点：机器在互联网上的流量份额首次超越人类，预示着数字世界主客易位；金融市场为AI的未来进行着高风险定价，波动剧烈；而市面上充斥的，却是许多“边角料”应用和“好像更穷了”的困惑。真正的革命不是热榜上的喧嚣，而是在数据中心的深处，在算法模型的迭代里，在那个流量份额悄然越过50%的瞬间静默地发生着。它不吵不闹，却已势不可挡。人类，或许正在从互联网的“用户”，变成互联网的“遗产”——一种尚存、但已非主流的碳基遗产。