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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. It took a few days for researchers to digest the paper’s claims, and the implications of what it described.
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的，当时论文并没有立即敲响警钟。研究人员花了几天时间来消化论文的观点，以及所描述内容的可能影响。
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. And it had done it in China, a country many experts thought was in a distant second place in the global AI race.
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。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. 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. Maybe R1 was actually just a clever re-skinning of American AI models that didn’t represent much in the way of real progress.
一些行业观察家最初对DeepSeek的突破表示怀疑。他们认为，DeepSeek为了达到R1的结果肯定作了弊，或者捏造了数据，让模型看起来比实际更厉害。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视其内部工作原理），他们的怀疑变成了担忧。
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，这个模型似乎让美国科技行业一直做出的几个主要假设受到了质疑。
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. It’s hard to overstate how foundational this dogma has become.
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。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。
But DeepSeek’s breakthrough on cost challenges the “bigger is better” narrative that has driven the AI arms race in recent years by showing that relatively small models, when trained properly, can match or exceed the performance of much bigger models.
但是DeepSeek在成本方面的突破挑战了近年来推动AI军备竞赛的“越大越好”的叙事，它让人们看到，如果训练得当，相对较小的模型也可以达到或超过大模型的性能。
