今年1月底，DeepSeek釋出的R1模型在科技界引起巨大反響，導致英偉達股價下跌16.79%，市值損失5900億美元，創美國金融史記錄。英偉達發言人當時稱讚DeepSeek為人工智慧的重大進步。儘管英偉達股價已有所回升，但CEO黃仁勳之前未對此事公開回應。週四，黃仁勳在訪談中首次談及DeepSeek，指出投資者對DeepSeek在AI領域的進展存在誤解，這導致了市場對英偉達股票的錯誤反應。DeepSeek因低成本高效能而受到關注，引發投資者對科技公司鉅額投入建設AI基礎設施的必要性產生質疑。黃仁勳表示，儘管R1的開發似乎降低了對算力的依賴，但人工智慧行業仍需強大算力來支援模型的後訓練處理方法，這是AI模型解決問題的關鍵環節。黃仁勳強調，預訓練仍重要，但後處理才是“智慧最重要的部分”。此外，黃仁勳對R1開源後全球範圍內的熱情表示讚賞，認為這是一件極其令人興奮的事情。“從投資者的角度來看，他們認為世界分為預訓練和推理兩個階段，而推理就是向 AI 提問並立即得到答案。我不知道這種誤解是誰造成的，但顯然這種觀念是錯誤的。”黃仁勳訪談主要環節實錄：黃仁勳：What's really exciting and you probably saw，what happened with DeepSeek.The world's first reasoning model that's open sourced，and it is so incredibly exciting the energy around the world as a result of R1 becoming open sourced，incredible.真正令人興奮的是，你可能已經看到了，DeepSeek發生了什麼。世界上第一個開源的推理模型，這太不可思議了，因為R1變成了開源的，全球都因此而充滿了能量，真是不可思議。訪問者：Why do people think this could be a bad thing?I think it's a wonderful thing.為什麼人們認為這可能是一件壞事呢？我認為這是一件美好的事情。黃仁勳：Well,first of all,I think from an investor from an investor perspective,there was a mental model that，the world was pretraining, and then inference.And inference was，you ask an AI question and it instantly gives you an answer，one shot answer.I don't know whose fault it is,but obviously that paradigm is wrong.The paradigm is pre training，because we want to have foundation you need to have a basic level of foundational understanding of information.In order to do the second part which is post training.So pretraining is continue to be rigorous.The second part of it and this is the most important part actually of intelligence is we call post training，but this is where you learn to solve problems.You have foundational information.You understand how vocabulary works and syntax work and grammar works,and you understand how basic mathematics work，and so you take this foundational knowledge you now have to apply it to solve problems.首先，我認為從投資者的角度來看，過去存在一種思維模型是，世界是預先訓練好的，然後是推理。推理就是你問AI一個問題，它立即給你一個答案，一次性回答。我不知道這是誰的錯，但顯然這種模式是錯誤的。正確的模式應該是先進行預訓練，因為我們想要有一個基礎，你需要對資訊有一個基本的理解水平，以便進行第二個部分，也就是後期訓練。所以預訓練要繼續保持嚴謹。第二部分實際上是智慧最重要的部分，我們稱之為後訓練，但這是你學習解決問題的地方，你已經掌握了基礎知識，你明白詞彙是如何工作的，句法是如何工作的，語法是如何工作的，你明白了基本數學是如何工作的，所以你現在必須應用這些基礎知識來解決實際問題……So there's a whole bunch of different learning paradigms that are associated with post training,and in this paradigm，the technology has evolved tremendously in the last 5 years and computing needs is intensive.And so people thought that oh my gosh，pretraining is a lot less，they forgot that post training is really quite intense.因此後訓練有一系列很多不同的學習模式，在這種模式下，技術在過去五年裡取得了巨大的進步，計算需求非常大，所以人們認為，哦天那，預訓練要少得多。但是他們忘記了後訓練其實相當大。And then now the 3rd scaling law is ，the more reasoning that you do,the more thinking that you do before you answer a question.And so reasoning is a fairly compute intensive part of.And so I think the market responded to R1 as 'oh my gosh AI is finished'，you know it dropped out of the sky ,we don't need to do any computing anymore.It's exactly the opposite.現在第三條縮放定律是，你做的推理越多，你在回答問題之前思考得越多，推理就會越好，這是一個計算量相當大的過程。因此我認為市場對R1的反應是“哦我的天哪，AI到頭了"，就好像它從天而降，我們不再需要進行任何計算了，但實際上完全相反。
