Multiple providers lower computing power prices, accelerating computing power universalization 多家供给方下调算力价格 算力普惠再提速

The analogy of comparing computing power to "numerical hydroelectricity" is clever, almost dangerously so. Hydroelectricity is infrastructure; its value lies in being stable, cheap, and ubiquitous, but the real money is made by those who create value using electricity and water—the appliance factories, the water companies, not the power plants themselves. The same applies to computing power. When suppliers rush to slash computing power prices to rock bottom, even approaching free, they are essentially declaring that computing power itself is no longer a moat; it is rapidly being "pipelined." So the question arises: when computing power becomes as nearly free and inevitably homogenized as air, where will these frantic price-cutting manufacturers extract their future core profits? From higher-level model APIs, bundled cloud services, or gray-area games of data monetization? This 99% drop is more like a painful pivot in their own business model, a big gamble betting everything on "we can make it back elsewhere in the future."

The sharper question is: can this cost collapse truly spawn a flourishing application ecosystem? Probably not. Much of the so-called AI application innovation today still lingers at the level of "using cheaper computing power to run homogenized chatbots, draw more refined anime avatars, or generate smoother PowerPoint presentations." The democratization of computing power solves the "affordable to use" problem, but the innovation bottleneck of "where to use it" and "how to use it well" remains unbroken. We seem trapped in a vicious cycle: the lower the computing power cost, the more resources tend to be poured into reinventing the wheel and low-level competition, vying for tiny shares in an already crowded vertical market, rather than venturing into truly high-risk, high-reward hardcore innovation. This isn't inclusiveness; it's "innovation involution" catalyzed by computing power overcapacity.

Looking back at that much-discussed Xiaomi robot arm. Against the backdrop of plummeting computing power, the story of hardware entry points suddenly becomes sexy again. When cloud computing becomes a cheap commodity, the narrative naturally extends to the terminal—smart homes, embodied intelligence, wearable devices. If Xiaomi's robot truly debuts, its symbolic significance far outweighs the practical: it marks the AI arms race spreading from "cloud alchemy" to "terminal body-making." But a lingering doubt: when our AI models are still struggling to avoid "nonsense delivered with a straight face," when robots' dexterity doesn't yet match that of human toddlers, is this hardware show a solid technical roadmap or a "PPT foresight" aimed at stimulating stock prices and imagination? Can the warm marketing of arm fist bumps mask the immaturity of core intelligence?

Then look at those hot search titles, which form a bizarre ukiyo-e scroll of the industry. "After using AI, the company seems to have become poorer"—this is a precise mockery of the blind followers. Many companies view AI as a universal key to cost reduction and efficiency improvement, only to find after heavy investment that what they've produced is "digital waste" and more complex operational nightmares. "AI self-acceleration starts," "the last generation of frameworks chosen by humans personally"... these titles exude a strong marketing anxiety and philosophical delirium. The industry is sprinting, but direction is lost. We are enthusiastic about discussing when AI will replace humans, but lack patience and sincerity regarding how AI can better serve humans today and solve complex real-world problems.

The flood of computing power has breached the dam, but look at how many new ships we have actually built that are sailing? More often, we are just using cheaper wood to replicate more similar dinghies, engaged in endless infighting in the same already murky pond. The smoke of the price war will eventually dissipate. What remains should not be just a scorched earth of cheap computing power and a pile of sensational hardware toys. The industry needs to cool down from the excitement of the "computing power arms race" and answer a more fundamental question: when computing power is no longer scarce, what is truly scarce?

算力价格跳水99%，听起来像是AI界的“双十一”狂欢，但当你盯着这份财报般冷静的降价清单时，嗅到的可能不是普惠的芬芳，而是一丝产能过剩与增长焦虑交织的血腥味。DeepSeek和小米MiMo近乎自断一臂的定价，与其说是技术红利的慷慨馈赠，不如看作是一场提前到来的、关于算力商品化残酷性的预演。

把算力比作“数字水电”，这个比喻精妙到近乎危险。水电是基础设施，其价值在于稳定、廉价、无处不在，但真正赚钱的是用电用水创造价值的人——是电器厂，是自来水公司，而不是发电厂本身。算力同理。当供给方争先恐后地将算力价格打到地板，甚至接近免费时，他们本质上在宣告：算力本身已不再是护城河，它正在急速“管道化”。那么问题来了，当算力变得像空气一样近乎免费且必然同质化，这些疯狂降价的厂商们，其未来的核心利润该从哪里榨取？是更上层的模型API，是捆绑的云服务，还是数据变现的灰色游戏？这99%的降幅，更像是一次对自身商业模式的痛苦转身，是把宝全押在“未来能从别的地方赚回来”的豪赌。

更尖锐的问题在于，成本的暴跌，真的能催生繁荣的应用生态吗？恐怕未必。当下所谓的AI应用创新，太多还停留在“用更便宜的算力跑同质化的聊天机器人、画更精致的二次元头像、生成更顺滑的PPT”这类层面。算力普惠解决了“用得起”的问题，但“用在哪”、“怎么用得好”的创新瓶颈并未突破。我们仿佛陷入了一个怪圈：算力成本越低，大量资源越倾向于投入到重复造轮子和低水平竞争中，去搏一个已经拥挤不堪的垂类市场的微小份额，而非投身于真正高风险、高回报的硬核创新。这不是普惠，这是算力过剩催生的“创新内卷”。

回看那条被热议的小米机器人手臂。在算力跳水的大背景下，硬件入口的故事忽然又性感起来。当云端算力变成低廉的大宗商品，故事自然会向终端延伸——智能家居、具身智能、穿戴设备。小米机器人若真亮相，其象征意义远大于实际：这标志着AI军备竞赛正从“云端炼丹”向“终端造躯体”蔓延。但一个疑虑挥之不去：当我们的AI模型还在努力避免“一本正经地胡说八道”，当机器人的灵巧度尚不及人类幼童，这种硬件秀是扎实的技术路线，还是一种旨在刺激股价和想象力的“PPT前瞻”？手臂碰拳的温情营销，能否掩盖核心智能的稚嫩？

再看那些热榜标题，堪称一幅光怪陆离的行业浮世绘。“用了AI之后，公司好像更穷了”——这简直是对盲目跟风者的精准嘲讽。许多企业把AI当成降本增效的万能钥匙，结果投入重金后，发现产出的是“数字废物”和更复杂的运维噩梦。“AI自我加速启动”、“人类亲自选择的最后一代框架”……这些标题散发着浓烈的营销焦虑与哲学呓语。行业在狂奔，但方向感迷失。我们热衷于讨论AI何时取代人类，却对眼下AI如何更好地服务人类、如何解决真实世界的复杂问题，缺乏耐心和诚意。

算力的洪水已经冲破了堤坝，但看看我们造出了多少真正在航行的新船？更多的时候，我们只是在用更便宜的木材，复刻更多相似的舢板，在同一个已然浑浊的池塘里进行着无休止的内耗。价格战的硝烟终将散去，届时留下的，不应只是一片廉价算力的焦土，和一堆哗众取宠的硬件玩具。行业需要从“算力军备竞赛”的亢奋中冷却下来，回答一个更根本的问题：当算力不再稀缺，什么才是真正稀缺的？