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Hang Seng Index up 0.86%, Hang Seng Tech Index up 1.65% 恒指收涨0.86%,恒生科技指数涨1.65%

MiniMax's stock price plunged over 15% in a single night, marking its worst performance since going public—and just as it's racing to list on the STAR Market. The scene is strikingly reminiscent of a tech upstart shouting "the future is bright" while secretly wiping away sweat. The AI industry bubble might just be quietly spilling over from these numbers. MiniMax股价一夜暴跌超过15%,创下上市以来最惨单日表现,偏偏就在这节骨眼上,它还在冲刺科创板上市。这场景,像极了一个一边喊着“未来可期”、一边偷偷擦汗的科技新贵。AI行业的泡沫,或许正从这些数字里悄悄溢出。

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MiniMax's stock price plummeted over 15% in a single night, setting its worst daily record since listing—right as it's pushing for a STAR Market debut. This scene looks remarkably like a tech darling yelling "the future looks promising" while nervously breaking into a sweat. The AI bubble may indeed be quietly leaking through these figures.

Look at this trending list: both Zhipu and MiniMax are rushing to return to the A-share market. Why? Overseas valuations might be cooling, while the domestic market still seems to offer policy tailwinds and retail investor enthusiasm to rely on. But is returning to the A-share market truly a cure? The STAR Market story revolves around "hard tech," yet these large language model companies burn through money like water, their profit models as hazy as flowers seen through fog. Are investors buying into the technology, or the grand narrative of a "Chinese OpenAI"? When technological barriers are quickly leveled, how long can the story hold up?

What’s more ironic is that Silicon Valley giants just staged a "delicious reversal" (真香). After burning billions on GPUs and stockpiling computing power, Google and Microsoft are now restricting internal employee token usage. It’s almost like a large-scale performance art: first dramatically declaring "unlimited AI computing power," then sheepishly reminding employees to "use it sparingly." Cost pressures have finally punctured the dream—turns out AI isn’t magic, it’s a business. Every token generated burns real money. When commercialization falls short of expectations, R&D budgets are often the first to be cut. Could this slow down the next wave of technological evolution?

Speaking of evolution, Microsoft’s "Skills self-evolution project"—which gained 3.3k stars in a week—sounds impressive. Training skills like training a neural network? The concept is sexy, but practical implementation still faces countless engineering pitfalls. Developer community hype can sometimes resemble a collective frenzy, and the number of projects that truly become productivity tools is likely less than one in ten. Are we falling into a new round of tech worship, ignoring the dull, slow, and meticulous foundational optimizations?

Then there’s the "26 billion USD all-Chinese AI programming company" chased by capital. Its valuation is shockingly high, as if programming is on the verge of complete AI disruption. Yet the reality is, programmers are still working 996 schedules fixing bugs, and AI-written code still requires human review. Behind the high valuation lies either investment firms' greedy bet on "AI replacing everything" or a misjudgment of real market demand? When the tide recedes, survival for these companies will likely depend not on PPTs, but on actual customer payments and profits.

Looking back at Hyundai Motor’s sales decline, it may seem distant from AI, but the core is similar. Just as traditional industries fret over supply chains, isn’t the AI industry equally anxious about its own supply chain—computing power, data, and talent? MiniMax’s stock plunge and listing dream are a microcosm of this anxiety: the urgent need for capital infusion to stay alive, while facing the cold scrutiny of capital markets.

The AI narrative is undergoing a subtle disenchantment. From "changing the world" to "computing costs," from infinite imagination to returning to financial statements. This isn’t a bad thing—at least it makes us see that even the coolest technology must stay grounded. Still, as every company rushes to cash out or raise funds, how much patience and reverence for the technology itself remains?

MiniMax股价一夜暴跌超过15%,创下上市以来最惨单日表现,偏偏就在这节骨眼上,它还在冲刺科创板上市。这场景,像极了一个一边喊着“未来可期”、一边偷偷擦汗的科技新贵。AI行业的泡沫,或许正从这些数字里悄悄溢出。

看看这份热榜,智谱和MiniMax都急着回A股。为什么?海外估值可能遇冷,国内市场看似还有政策暖风和散户热情可以依赖。但回A就真是解药吗?科创板讲的是硬科技故事,可这些大模型公司,烧钱如流水,盈利模型却模糊得像雾里看花。投资者买的究竟是技术,还是一个“中国版OpenAI”的宏大叙事?当技术壁垒被快速拉平,故事还能讲多久?

更讽刺的是,硅谷巨头们刚刚上演了一出“真香”反转。烧了数十亿美金买GPU、囤算力之后,谷歌、微软们开始限制内部员工的Token用量。这简直像一场大型行为艺术:先大张旗鼓地宣称“AI无限算力”,再灰溜溜地提醒员工“省着点用”。成本压力终于让梦醒了——原来AI不是魔法,是生意。每生成一个Token,都是在烧真金白银。当商业化不及预期,最先被砍的往往就是研发预算,这会不会让下一代技术进化慢下来?

说到进化,微软那个“一周3.3k star”的Skills自我进化项目,听起来挺炫。像训练神经网络一样训练技能?这概念很性感,但实际落地还隔着无数工程化的坑。开发者社区的热度,有时像一场集体狂欢,真正能沉淀成生产力工具的,恐怕十不存一。我们是否又陷入了一轮新的技术崇拜,而忽略了那些枯燥的、慢工出细活的基础优化?

还有那个被资本追捧的“260亿美元全华班AI编程公司”。估值高得吓人,仿佛编程马上就要被AI彻底颠覆。可现实是,程序员还在996修bug,AI写的代码依然需要人类审校。高估值背后,是投资机构对“AI替代一切”的贪婪押注,还是对真实市场需求的误判?当潮水退去,这些公司里有多少能活下来,靠的恐怕不是PPT,而是实打实的客户付费和利润。

回过头看现代汽车的销量下滑,似乎离AI很远,但内核相通。传统行业为供应链头疼时,AI行业又何尝不在为自己的供应链——算力、数据、人才——焦虑?MiniMax的暴跌和上市梦,正是这种焦虑的缩影:急需输血续命,却又得面对资本市场冷酷的审视。

AI的叙事正在经历一场微妙的祛魅。从“改变世界”到“计算成本”,从无限想象到回归财报。这不是坏事,至少让我们看清,再炫酷的技术也得接地。只是,当所有公司都在急着套现或融资时,那份对技术本身的耐心和敬畏,还剩多少?

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