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Remember when China’s DeepSeek sent tremors through the US artificial intelligence industry and stunned Wall Street? That was last month. To listen to AI executives and investors now, you might think the world has moved on. Nvidia, the hardest hit, has recovered more than half the $630bn it lost.
还记得中国的深度求索(DeepSeek)给美国人工智能行业和华尔街带来冲击和震惊吗？那是上个月的事了。现在听人工智能高管和投资者说起这件事，你可能会觉得已时过境迁。受打击最严重的英伟达(Nvidia)已经收复了其损失的6300亿美元市值的一半以上。
The speed with which equilibrium has returned owes a lot to the assertion by the biggest US tech companies that they will spend even more than expected on AI infrastructure this year. But it also shows how quickly the investment case for AI has been rewritten. The question is how much this reflects a genuine change in outlook, and how much is just industry spin.
恢复平静的速度之快，在很大程度上要归功于美国最大科技公司断言，他们今年在人工智能基础设施上的投入甚至将超过预期。但这也表明了投资AI的理由已被重写的速度。问题是，这在多大程度上反映了前景的真正变化，又在多大程度上只是行业的自说自话。
The case for buying Nvidia stock once rested on claims such as those from Anthropic chief executive Dario Amodei, who barely six months ago predicted that the training costs for a cutting-edge large language model would soon reach $100bn. In the wake of DeepSeek, Amodei is still anticipating a huge jump in demand for AI chips — only now, it is for the completely different reason that they are needed for more complex tasks like reasoning, rather than the costs of model training.
购买英伟达股票的理由曾经基于Anthropic首席执行官达里奥•阿莫代(Dario Amodei)的说法，他在不到六个月前预测，一个前沿大型语言模型的训练成本很快将达到1000亿美元。在深度求索崛起之后，阿莫代仍预计对AI芯片的需求将大幅增加——只是现在是出于完全不同的原因，需要它们来完成更复杂的任务，例如推理，而不是模型训练的成本。
No wonder investors are feeling an acute whiplash and a greater sense of uncertainty about the sustainability of the AI boom.
难怪投资者感到受到强烈的冲击，并对人工智能热潮的可持续性产生更大的不确定感。
The Chinese company’s breakthroughs increased the risk that even the most advanced large language models will quickly be turned into commodities. This came just as model-builders were facing another existential threat: throwing ever-greater amounts of computing power into training no longer produces the advances it once did.
该中国公司取得的突破增加了这样一种风险：即使是最先进的大型语言模型也会很快变成商品。而此时，模型构建者正面临着另一个生存威胁：将越来越多的计算能力投入训练不再能产生以前曾实现的进步。
OpenAI chief executive Sam Altman signalled the obvious strategic response in a post on X this week. No longer will OpenAI release its large language models as standalone products. Rather, they will be packaged together with its other technologies, such as “reasoning”, into more complete systems. From now on, he said, the AI will “just work”, whatever task a user throws at it.
OpenAI首席执行官萨姆•奥尔特曼(Sam Altman)在本周于X平台上的一篇帖子中表明了显而易见的战略回应。OpenAI将不再把其大型语言模型作为独立产品发布，而是将其与其他技术（如“推理”）整合成更完整的系统。他表示，从现在起，无论用户提出什么任务，AI都将“直接工作”。
This is a familiar strategy in the tech industry. Moving “up the stack” — building more valuable technologies on the foundation of earlier products as they are commoditised — has long been seen as the way to defend prices and profit margins. If the cost of components that once provided a good margin collapse, so much the better: it brings down the overall cost and leads to faster uptake.
这是一种科技行业中常见的策略。向“堆栈上层”移动——在早期产品被商品化后，在其基础上构建更有价值的技术——长期以来被视为维持价格和利润率的方式。如果曾经带来丰厚利润的组件成本骤降，那就更好了：它可以降低整体成本，并加快普及速度。
This packaging of AI technologies has important implications for the direction of the whole industry. One is that, as companies such as OpenAI build more complete systems, a gap will open up at the bottom of the market for companies like DeepSeek.
这种对人工智能技术的整合对整个行业的发展方向具有重要影响。首先，随着像OpenAI这样的公司构建更完整的系统，市场底部将为像深度求索这样的公司打开一个缺口。
Anyone wanting to build their own AI-powered software will turn to large language models such as Meta’s Llama and DeepSeek’s R1 — technologies that are released in a version of open source that makes them freely available and cheap. This should open the way for many more tech companies to join in the AI boom. But former Google chief executive Eric Schmidt warned this week it could pose a challenge to the west, making the Chinese company an important global platform in AI.
任何想要构建自己的AI驱动软件的人都会转向大型语言模型，如Meta的Llama和深度求索的R1——这些技术都是以开源版本发布的，可以自由使用，而且价格低廉。这将为更多科技公司加入人工智能热潮开辟道路。但是，谷歌前首席执行官埃里克•施密特(Eric Schmidt)本周警告说，这可能会对西方构成挑战，使中国公司成为人工智能领域的一个重要全球平台。
Another implication is that AI infrastructure suppliers need to quickly adjust their offerings — and their sales pitches. Spending will no longer be so heavily skewed towards big clusters of chips for training ever-larger models.
另一个影响是，人工智能基础设施供应商需要迅速调整他们的产品——和销售宣传。支出将不再如此严重地偏向于用于训练越来越大模型的大型芯片集群。
Nvidia, which soared in value on the boom in training, still has the widest array of silicon for AI and will be working hard to optimise its chips for the many different workloads that will emerge as the market shifts. But the move beyond intensive training should lead to a wider range of technology suppliers fighting over a much more disparate market.
在训练热潮中市值飙升的英伟达，仍然拥有最广泛的AI硅产品阵容，并将努力优化其芯片，以适应市场转变后出现的各种不同工作负载。然而，超越密集训练的转变应会导致更多技术供应商争夺一个更加分散的市场。
A third implication is that the continuation of the AI boom will depend much more on the actual usage of AI, not just the massive upfront spending that has gone into building models and infrastructure. Much of the computing power that goes into reasoning is a variable cost incurred after a prompt has been entered, rather than the kind of one-off fixed costs that go into training. The AI companies need to show they can provide real value to end customers
第三个影响是，AI热潮的持续将更多地依赖于AI的实际使用，而不仅仅是用于构建模型和基础设施的大量前期支出。用于推理的大部分计算能力是输入提示后产生的可变成本，而不是用于训练的一次性固定成本。人工智能公司需要证明自己能为终端客户提供真正的价值。
None of these forces are new in an industry that was already under pressure to move faster in commercialising its technology. But the DeepSeek shock has just turned up the pressure.
对于一个已经面临加快技术商业化压力的行业来说，这些都不是新的压力。但深度求索带来的冲击只是增大了压力。