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Why DeepSeek Could Change What Silicon Valley Believes About AI
为什么DeepSeek可能改变硅谷关于AI的认知
The artificial intelligence breakthrough that is sending shock waves through stock markets, spooking Silicon Valley giants, and generating breathless takes about the end of America’s technological dominance arrived with an unassuming, wonky title: “Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.”
一项人工智能突破在股市掀起冲击波、令硅谷巨头感到恐慌、引发了关于美国技术主导地位终结的热议，这项突破却以一个低调、学究气的标题出现：“通过强化学习激励大语言模型的推理能力”。
The 22-page paper, released last week by a scrappy Chinese AI start-up called DeepSeek, didn’t immediately set off alarm bells.
这份22页的论文由一家结构松散的、名为DeepSeek的中国AI初创公司在上周发布，当时论文并没有立即敲响警钟。
It took a few days for researchers to digest the paper’s claims, and the implications of what it described.
研究人员花了几天时间来消化论文的主张，以及其中内容的可能影响。
The company had created a new AI model called DeepSeek-R1, built by a team of researchers who claimed to have used a modest number of second-rate AI chips to match the performance of leading American AI Models at a fraction of the cost.
该公司创造了一个名为DeepSeek-R1的AI新模型，构建模型的研究团队声称，他们用数量不多的二流AI芯片、以极低的成本就达到了堪与美国一流AI公司相媲美的性能。
DeepSeek said it had done this by using clever engineering to substitute for raw computing horsepower.
DeepSeek表示，它是通过用巧妙的工程技术来替代原始算力而做到的。
And it had done it in China, a country many experts thought was in a distant second place in the global AI race.
而且它是在中国做到了这一点，许多专家认为中国在全球AI竞赛中处于远远落后的第二位。
Some industry watchers initially reacted to DeepSeek’s breakthrough with disbelief.
一些行业观察家最初对DeepSeek的突破表示怀疑。
Surely, they thought, DeepSeek had cheated to achieve R1’s results, or fudged their numbers to make their model look more impressive than it was.
他们认为，DeepSeek为了达到R1的结果肯定作了弊，或者篡改了数据，让模型看起来比实际更厉害。
Maybe R1 was actually just a clever re-skinning of American AI models that didn’t represent much in the way of real progress.
或许R1实际上只是对美国AI模型进行了巧妙的换皮包装，并不代表真正取得了多少进步。
Eventually, as more people dug into the details of DeepSeek-R1 — which, unlike most leading AI models, was released as open-source software, allowing outsiders to examine its inner workings more closely — their skepticism morphed into worry.
最终，随着越来越多的人深入研究DeepSeek-R1的细节（与大多数领先的AI模型不同，它是作为开源软件发布的，让外部人员能更仔细地审查其内部工作原理），他们的怀疑变成了担忧。
And late last week, when lots of Americans started to use DeepSeek’s models for themselves, and the DeepSeek mobile app hit the number one spot on Apple’s App Store, it tipped into full-blown panic.
上周晚些时候，当许多美国人开始亲自使用DeepSeek的模型，当DeepSeek移动端应用程序在苹果应用商店排名第一时，这个模型引发了全面的恐慌。
Based on conversations I’ve had with industry insiders, and a week’s worth of experts poking around and testing the paper’s findings for themselves, it appears to be throwing into question several major assumptions the American tech industry has been making.
根据我与业内人士的交谈，以及一周以来专家们的探索和对论文结果的亲自测试，这个模型似乎对美国科技行业一直做出的几个主要假设提出了质疑。
The first is the assumption that in order to build cutting-edge AI models, you need to spend huge amounts of money on powerful chips and data centers.
第一个假设是，为了构建最尖端的AI模型，你需要在强大的芯片和数据中心上花费巨额资金。
It’s hard to overstate how foundational this dogma has become.
这个教条的根深蒂固怎么夸大都不为过。
Companies like Microsoft, Meta and Google have already spent tens of billions of dollars building out the infrastructure they thought was needed to build and run next-generation AI models.
微软、Meta、谷歌之类的公司已经花费了数百亿美元来建造他们认为构建和运行下一代AI模型所需的基础设施。
They plan to spend tens of billions more — or, in the case of OpenAI, as much as $500 billion through a joint venture with Oracle and SoftBank that was announced last week.
他们计划还要再投入数百亿美元，或者像OpenAI的情况，上周宣布通过与甲骨文和软银的合资企业，再投入多达5000亿美元。
DeepSeek appears to have spent a small fraction of that building R1.
DeepSeek建造R1似乎只花费了这些金额的极小一部分。
The obvious conclusion to draw is not that American tech giants are wasting their money.
可以得出的明显结论并不是美国科技巨头在浪费金钱。
It’s still expensive to run powerful AI models once they’re trained, and there are reasons to think that spending hundreds of billions of dollars will still make sense for companies like OpenAI and Google, which can afford to pay dearly to stay at the head of the pack.
一旦经过训练，运行强大的AI模型仍然花费不菲，而且有理由认为，对于OpenAI和谷歌这样的公司来说，花费数千亿美元仍然是有道理的，因为这些公司有能力付出高昂的代价来保持行业领先地位。
