金融時報對話李開復：中國AI為何能夠領跑全球C端市場？

近日，在接受《金融時報》（Financial Times）專訪時，我與記者探討了當前AI產業發展的兩個核心命題：中美AI領域的路線之爭，以及企業如何在 AI 時代真正實現“突圍” 。

美國巨頭們選擇以閉源模式押注“贏者通吃”，相比之下，中國AI領域更像是一個極具韌性的“共學小組”：在資源有限的前提下，通過開源模式和極致的工程落地能力，走出了一條高效率、重應用的破局之路。

但技術上的趕超只是第一步，競爭的重心正悄然轉向誰能率先讓AI走入廠房車間、走進企業核心生產場景。我認為，企業AI落地本質上是“一把手工程”。企業要敢于重用重塑組織的CAIO（首席 AI 官），與CEO并肩，攜手如零一萬物般懂AI的企業，以AI重塑核心業務邏輯。

以下為專訪文章正文：

在過去的四十年里，李開復博士親眼見證了中國人工智能產業的飛速發展。在產業萌芽階段，他將當時世界前沿的科研創新機制引入中國，培養出了大批高科技人才，如今，這些人才已成長為活躍在各大科技巨頭的中堅力量，也為AI產業提供了起飛的土壤。

這位國際AI專家曾主導創建微軟亞洲研究院，使其成為中國頂尖AI人才的“黃埔軍校”；隨后，他又籌組領導了谷歌中國。2009年，李開復博士創辦了著名科技創投機構創新工場，帶領團隊投資孵化了10多家AI獨角獸企業；2023年，他創辦了零一萬物。零一萬物是一家總部位于北京的大模型獨角獸企業，致力于打造性能領先的產業大模型和為全球企業打造智能體解決方案。

在與《金融時報》中國科技記者 Eleanor Olcott 的對話中，他深入剖析了中美AI領域之間的行業競爭，并闡述了為何企業必須以更積極的姿態去擁抱這場技術變革。

Eleanor Olcott：能夠介紹一下您的初創公司零一萬物嗎？

李開復：零一萬物致力于打造全球領先的AI 2.0大語言模型平臺及行業應用，助力企業AI數智化轉型，提供構建AI智能體的工具和平臺。我們基于頂尖的開源模型，會根據企業的具體業務場景挑選最優模型，以“一把手工程”為核心進行定制化開發。在AI智能體落地企業的早期階段，提供從戰略到落地的一站式服務至關重要，所以零一萬物會詳細解釋技術如何應用，并與客戶協同共創。這種深度參與決不僅是為了幫企業降本，更是為了創造實實在在的商業產出。

EO：這些企業對AI的接受程度如何？

李開復：在與銀行、保險、礦山和能源等傳統行業的合作中我們發現，相比于科技公司，這些行業在數智化轉型方面準備不足，有些企業甚至連基本的數字化轉型都尚未完成。對于這類客戶，我們會審慎評估投入產出比，建議客戶先夯實基礎，避免后續產生過高的改造成本和時間損耗。另一個問題是，很多企業在提需求時是在“看后視鏡開車”。比如他們只想做一個客服機器人，但這早已不是技術的前沿，更不是智能體最具價值的應用領域。

缺乏AI專業能力的企業必須與AI公司合作，共同制定AI數智化轉型戰略。這種轉型必須由CEO主導，而且執行難度極大。目前看來，可能只有百分之一的企業真正做好了這種準備。當這種合作意愿達成時，我們就會深度介入，要求他們設立首席AI官（CAIO），因為傳統的首席信息官（CIO）往往由于職業慣性顯得過于保守，不能勝任AI數智化轉型過程。CAIO必須具備大局觀和冒險精神，直接配合CEO重塑組織架構。如果客戶配不齊這個崗位，零一萬物可以直接派駐前沿部署工程師（FDE）。

我們的商業模式類似于Palantir，都是由顧問協助制定戰略，然后由執行人員負責實施。項目收入與最終取得的業務成果掛鉤：前期收取的戰略咨詢費僅用于覆蓋成本，核心收益則取決于為企業客戶所帶來的核心業務增量情況。

EO：那么對于另外99家不想這樣做的公司，原因是什么？

李開復：有時是因為人們僅僅將AI視為另一種軟件，有時是CEO對AI的本質缺乏基本的了解。有時人們把AI誤認為是另一種ERP（企業資源規劃）系統。或者，他們將任務委派給了錯誤的人選。如果你想制定AI戰略，CIO通常不是合適的人選，因為他們的職責是確保系統平穩運行，而不是思考如何進行轉型。

EO：目前普遍認為中國模型落后美國頂尖水平6到12個月。您認為這種差距會持續嗎？

李開復：目前，全球 AI 研究領域的突破性成果大多數源自美國。他們擁有世界上頂尖研究人員和龐大的算力資源，在模型研發上確實步步領先。但中國團隊的優勢在于極其出色的工程落地能力，基于這些突破性成果，中國團隊能夠迅速掌握同類技術，并且往往能實現更高的運行效率。這促成了我們自己的“覺醒時刻”（中國大模型公司DeepSeek2025年發布的推理模型DeepSeek-R1，以更低的訓練成本達到了OpenAI突破性模型的性能水平）。

正如“登月”最難的是第一次。一旦有人證明了路可以走通，即便不知道具體細節，成功的難度也會大幅降低。中國擁有非常強大的工程和研究底蘊，因此即便美國企業不開源也不發表論文，但中國公司通過研究這些模型的運作邏輯，已經實現了多項自主創新。可能實驗結果本身就是一種啟發，可能是通過巧妙的逆向工程、模型蒸餾，又或許是從技術的第一性原理出發，甚至可能是探尋出另一條不同的底層路徑……但最終殊途同歸，都是得到了相同的結果。

因此，中國模型往往能迅速追趕。DeepSeek發布時，中美大模型之間相差的研發周期縮短到了三個月；現在看來谷歌的Gemini已經領先，差距可能拉大到了12個月。這種差距呈現動態起伏規律，均值在6個月左右。每個人都會從已發布的優秀理念和模型中學習，因為人工智能領域吸引了眾多頂尖人才，無論在中國還是美國，情況都是如此，而且他們都渴望著彼此學習。這種學習不是單向的，比如在DeepSeek問世時，所有美國公司同樣在對它進行研究。

EO：2025年初DeepSeek發布R1模型時，OpenAI曾指責其通過“模型蒸餾”走捷徑，隨后OpenAI就表示已采取措施防止這種情況發生。我們暫且忽略竊取技術的指控，因為這似乎無法證實。但中國公司是否因為更嚴格的商業機密保護措施，而更難從美國公司那里學習呢？

李開復：OpenAI 對閉源的堅持不難理解：在投入巨資實現技術突破后，一旦開源，他們的核心成果很容易就會被他人低成本地獲取。更深層的邏輯在于，他們深信 AGI 將帶來一種質的飛躍。在那樣的未來，率先攻克 AGI 技術的公司將對全球競爭者形成降維打擊，無論是美國公司還是中國公司。因此，如果你認定 AGI 的終局是“贏家通吃”，那么對實現路徑絕對保密，就是一種必然的戰略選擇。

這些美國公司在持續籌集千億美元的巨量資金。為了支撐這種規模的估值，他們必須向投資者描繪這樣一個愿景：一旦率先建成 AGI，他們將引領世界，因此即便今日投資500億美元也依然“物超所值”，因為公司的市值終有一天會邁入50萬億美元。正是這套邏輯，讓OpenAI的故事在商業上自洽，聽起來不僅合乎情理，甚至頗具可信度。

但我認為，這個故事目前來看還有另一個版本。這場競賽并不是只有一兩個“天才少年”。在美國，OpenAI、Anthropic、Google 和 xAI 都在同臺競技，每一家都自認為是那個能解開 AGI 終極命題的“天才少年”，渴望以此實現贏家通吃，“贏得諾貝爾獎”。

而中國路徑更像是一個“共學小組”。一家公司發布模型，另一家公司就去研究和嘗試；甚至可能會去請教對方是如何訓練模型的。學習小組的所有成員都在構建開源模型并進行分享。

值得注意的是，盡管這些學習小組是由一群非常聰明的孩子組成的，但資助他們的公司卻希望每個季度都能看到利潤。這與美國的情況非常不同，因為美國公司并不在意回報，但是在中國，公司的支出是受到限制的。舉個例子，阿里巴巴不能在下個季度虧損100億美元，但OpenAI可以。因此，種種原因使得中國公司在資源有限的情況下，需要像學習小組一樣協作的方式運作，這與美國“贏者通吃”的策略截然不同。

EO：目前有一種主流觀點認為美國在AI上的領先源于地緣戰略優勢。但我認為未來也有一種可能，我們會將中國曾經的落后視為一種優勢。因為存在時間差，中國可以觀察西方如何演進，看到AI帶來的經濟和社會動蕩，并根據所看到的錯誤和陷阱選擇不同的路徑。您對此怎么看？

李開復：幾乎可以預見，未來AI產生的負面影響將率先出自美國公司。無論是被不法分子濫用，還是因程序錯誤失控，這種無意間留下的隱患其實根源是在于美國公司的運作模式。在“贏家通吃、快魚吃慢魚”的心態下，公司自然而然地會減少安全防范方面的意識。同時，由于他們的模型和技術更加先進，他們也掌握著殺傷力更強的武器。

在中國，人們普遍不認為AGI的走向會是一家公司對全行業的降維打擊。行業更傾向于相信，這將是一個領跑者不斷易主的線性發展過程。

EO：難道中國公司不想成為贏家嗎？

李開復：當然想，但大部分的企業不愿付出那種傾家蕩產的代價。中國公司更關注商業產出和盈利能力，以及構建能從模型中賺錢的產品。騰訊有微信、阿里巴巴有淘寶，字節跳動有抖音，這些巨頭都希望構建一個與其產品相匹配、能盈利且有競爭力的模型。

EO：您認為今年中國AI行業會發生什么？

李開復：企業級應用（B端）方面，我認為中國會稍稍落后于美國，因為中國企業普遍還沒有養成支付訂閱制服務費用的消費習慣。但在消費級應用（C端）領域，中國將領先美國。兩國都有大量創業公司在深耕 C 端應用，且目前時機已經成熟、模型能力也已足夠，但由于中國科技巨頭在這方面始終都有展現出堅韌的態度、也渴望追求市場支配地位，所以我認為，他們在打造爆款應用方面將遠超美國大廠。對中國大廠而言，應用開發本身就是他們研發技術的初衷，因此他們也會更專注。無論是用 AI 賦能現有產品，還是開發原生的 AI 應用，這些工作都已經成果初顯了。

在我看來，中國互聯網公司將成為 AI 應用創新的主要源頭，其動力遠超美國同行。反觀美國的標桿性應用，無論是Instagram、YouTube還是Snapchat，它們正變得非常乏味。我不認為美國的互聯網公司具備中國 C 端廠商那種拼搏精神，以及那種自我革命的果決。相比之下，像字節跳動、騰訊、阿里、美團、拼多多、小紅書這些公司，擁有極強的韌性和求勝欲。他們中的許多企業正在重金投入研發頂尖的 AI 技術、Agents（智能體）和模型，其投入力度遠超傳統的美國模式。

其次，2026 年將開啟“AI 原生設備”的元年。我們將在今年首次看到、親手觸摸、并購買到以 AI 為核心設計的原生硬件。它未必是最終勝出的終極形態，但它可能是“諾基亞時刻”、“黑莓時刻”，或者是“iPhone 時刻”。雖然尚不確定AI原生設備處于哪個階段，但這三個節點在移動通訊史上都至關重要。人類一直渴望通過語音和自然語言向設備“委派任務”，因此 AI 原生設備是大勢所趨。這意味著你只需告訴設備你想要的結果，而非完成工作的步驟。剩下的，交給智能助手去辦就好。

這一趨勢在智能體技術上已初現端倪。但它需要一個由語音驅動的交互界面，而目前來看，這種界面絕不是智能手機。手機并不是理想的載體，因為它無法做到“始終在線”和“實時傾聽”。因此，你需要一種能夠全天候運行、實時收音并捕捉全天信息的設備。它會存儲你所見、所聞的一切，并以此為基礎進行邏輯推理。

這是一個很復雜的命題，但我認為核心在于這種“環境 AI”（Ambient AI）。它始終在線、實時傾聽、擁有無限記憶，而且讓你幾乎感覺不到它的存在。

EO：回顧您在中國AI行業的職業生涯，與開始時相比，今天行業的哪些方面會讓您感到驚訝，哪些方面又基本保持不變？

李開復：我一直樂觀地相信，“AI將改變世界”。讓我感到驚訝的是過去三年AI進化的速度。我原本以為這會是一個跨越十到二十年的漫長過程，但它來得太快了，成熟得也非常迅速。當然，前路依然漫長。

回想起 1980 年代我剛進入AI行業的時候，AI 就像一堆派不上用場的“破銅爛鐵”。偶爾有成效的時候，它也會被立刻包裝成某種產品，從此不再被稱為 AI。那時候人們嘲笑我們，覺得我們這群人瘋了，居然認為機器能像人一樣思考。可現在，萬物皆可 AI。每一家 IPO 的公司都標榜自己是 AI 企業。我們見證了 AI 從“空想家的美夢”，變成了如今每個人都想參與的舞臺中心。

本文翻譯自《金融時報》報道，原文如下：

Kai-Fu Lee has had a front-row seat to the rapid growth of China’s AI industry over the past four decades, playing a central role first in building institutions that have spawned much of the talent now powering the country’s leading companies.

The Taiwanese-American computer scientist helped establish Microsoft Research Asia, which became a vital training camp for China’s leading AI talent, before later heading up Google’s operations in the country. Today, Lee heads Sinovation Ventures, a venture capital firm that invests in AI start-ups and is the founder of 01.ai, a Beijing-based AI start-up building agentic tools for companies worldwide.

In conversation with the Financial Times’ China technology correspondent Eleanor Olcott, he talks about the competition between AI’s two superpowers — China and the US — and why companies need to be more proactive to adopt the changing technology.

Eleanor Olcott: Can you introduce your start-up 01.ai?

Kai-Fu Lee: 0.1.ai makes tools to develop AI agents for companies. We build on open-source models, picking the right model for the company’s application and customising it for each customer. We believe that at an early stage of AI agent adoption, it’s essential to provide a white-glove service where we explain how technology can be applied. Together with the company, we co-create the most valuable applications that generate not just cost savings, but also business outcomes.

EO: How prepared are these companies to adopt AI?

KFL: We work with companies in traditional industries, including banking, insurance, mining and energy, which, compared to technology companies, are unprepared to adopt AI. Some of them haven’t done the digital transformation necessary for AI. In these cases, we won’t work with them because it will take too long and cost too much. The other problem is that some companies are looking in the rear-view mirror in terms of what they want. They might request to build a customer service agent, but that really isn’t where the technology or the best application areas are.

Companies that lack AI expertise must partner with an AI company to co-create their AI strategy. This kind of transformation is CEO-led, and it’s very difficult. Maybe one out of a hundred companies is prepared to do this.

When we partner with a company, we go in deep. They commit. We want them to hire a chief AI officer [CAIO], because the CIO [chief information officer] won’t do. CIOs tend to be very conservative. The CAIOs need to be bold and think big about strategy and the company organisation. They work directly with the CEO to reshape that. When our customers can’t provide a chief AI officer, we provide one for them.

Our business model is Palantir-like in the sense that we have consultants who help shape the strategy and then implementers who build it. We’re paid in accordance with the business outcome we create. We charge a set amount for the strategy development to recover our costs, but if there’s no business outcome, then we don’t get paid any more.

EO: And for the other 99 companies that don’t want to do this, why is that? KFL: Sometimes it’s because people think of AI as just another piece of software. Sometimes CEOs don’t have a natural understanding of what AI is. Sometimes people think of AI as just another kind of ERP [enterprise resource planning] software. Sometimes they delegate it to the wrong person. And the CIO is often the wrong person if you want to delegate AI strategy because?.?.?.?their job is to keep the company’s computers and software running smoothly, not to think about its transformation.

EO: The consensus today is that the Chinese models lag the leading American models by six to 12 months. Why? And do you think this will persist?

KFL: Currently, the US accounts for the great majority of breakthroughs in AI research. The US has most of the world’s top researchers and vast quantities of computing power to come up with advances in large language models. But based on these breakthroughs, talented and engineering-focused Chinese teams will quickly figure out how to build similar technologies, and often make them much faster which led to their own ‘aha moment’ [referring to China’s DeepSeek’s reasoning model released last year, which matched OpenAI’s breakthrough model at a much lower training cost].

It’s difficult for the first country to put a man on the moon. But once that has been done, and even though you don’t know the secret of how it was done, the fact that it was will make it so much easier for the second company or country to do it.

China has very strong engineering and research skills. So Chinese companies have made some inventions themselves, but they’ve also been able to figure out how these American models work, even though the American companies don’t do open source or publish papers. Perhaps the empirical result itself is enough of an inspiration. Perhaps it’s through clever reverse engineering. Perhaps it is through distilling the model. Or perhaps it is figuring out the first principles. Or perhaps, it figured out?.?.?.?different first principles, but it got to the result anyway.

So the Chinese models tend to catch up. When DeepSeek came out, it was shortened to like three months, and now it looks like [Google’s] Gemini has taken the lead, and lengthened the gap to perhaps 12 months.

That gap will shorten and lengthen, perhaps with six months as a midpoint. Everyone else will learn from every smart idea and model that’s published because the AI field has attracted many of the smartest people. That is true both in China and the US, and they’re all eager to learn. It’s not all one way. When DeepSeek came out, all the American companies studied it as well.

EO: When DeepSeek released its R1 model in January 2025, there were lots of accusations, including from OpenAI, that it cut corners by distilling its reasoning model. OpenAI then said it took steps to stop that happening. Let’s ignore the accusation of tech theft, which seems unprovable. Is it getting harder for the Chinese companies to learn from the US companies because they are taking more proactive measures to protect their secrets?

KFL: OpenAI feels that they have to keep the models closed, because after all this expensive work training breakthrough models, if they open source it, everyone will learn it much more easily. They feel so much money was put into inventing this IP; they don’t want to share it. This is understandable.

Also, they feel that the future of AGI [artificial general intelligence, a term that refers to a hypothetical future when AI has human-level cognitive abilities] will arrive as a giant step function when one company cracks it, and it will squash every other company in the world, be it American or Chinese. So in that sense, if you believe that the future of AGI is one where the winner takes all, then you have to keep how you arrived at that point secret.

The American companies keep raising hundreds of billions of dollars. They have to tell investors that by building AGI, they will dominate the world and that investing $50bn today is cheap because one day the company will be worth $50tn. So that makes the whole story work for OpenAI, and it’s an understandable, somewhat credible story.

But I think the alternate story is instead of having one genius kid or even four genius kids. In America you have OpenAI, Anthropic, Google, and xAI, each of which believes they’re the genius that will beat everyone else and win the Nobel Prize by solving the ultimate problem of AGI.

But the Chinese approach is different. The approach is more like a study group, where one company publishes a model, and the other looks at and plays with it. Maybe even talks to the company about how they trained it. All the members of the study group are building open source and then sharing it. So the study groups are formed of very smart kids who are all funded by companies that still want to show profit every quarter.

This is very different from the situation in the US, where companies do not care about returns. In China, companies are constrained in how much they can spend. Alibaba isn’t going to lose $10bn the next quarter. But OpenAI can. So, all these reasons cause the Chinese companies to behave the way they do with modest resources, learning and improving, working as a study group, as opposed to the American winner-take-all strategy.

EO: There is a dominant narrative that America’s lead in AI is a strategic geopolitical advantage. I think there’s a world in which, in the future, we see the fact that China’s been behind as an advantage. Because there is a time lag, Beijing can watch how this is evolving in the west. They can see the economic and societal disruption brought by AI and choose to take a different approach depending on the mistakes and pitfalls that they see. What do you make of this? KFL: It is almost certain that a future bad outcome from AI will come from an American company; if it’s being abused by some bad actors or by some error, a door has been left open that was unintentional. It’s just the way that they operate in this winner-take-all, run fast and break things mentality. It will cause companies to be naturally less conscious. And also they’re playing with more dangerous weapons, because their models and technologies are more advanced. In China, people in general do not believe that AGI will be one company squashing everyone else. I think people believe it’s going to be a linear trajectory where the winner will change.

EO: Surely the Chinese companies want to be the winners?

KFL: They want to, but they don’t want to pay the price; they don’t want to raise $500bn and have the company go bankrupt if they fail. The Chinese companies are more focused on the business results and on building products that make money from the models. Whether it’s Tencent’s WeChat, Alibaba’s Taobao or ByteDance’s Douyin, these companies want to build a competitive model that aligns with their products and can make money.

EO: What do you see happening this year in China’s AI industry?

KFL: I think China will lag the US in terms of enterprise adoption because of the unwillingness of Chinese companies to pay the kind of subscription fees. By contrast, China will lead the US in consumer applications. Both countries have plenty of start-ups working on consumer apps. The time is ready, and the models are good enough. But I think the Chinese giants will, by far, outrun the American giants in building great applications because the Chinese giants have always been tenacious, hungry, and monopolistic. And they see applications as the reason they’re building technology. So they’re going to be more focused. They’re going extend their existing apps with AI. They’re going to build new apps with the AI. It’s already coming out.

I think Chinese internet companies are going to be a source of this app innovation more than their American peers. If you look at the standard American app, whether it’s Instagram, YouTube, or Snapchat, they’re getting very boring. I don’t think the American internet companies have the same kind of approach to hard work, a willingness to reinvent themselves in the same way that the Chinese consumer app companies do. By contrast, the Chinese consumer app companies like ByteDance, Tencent, Alibaba, Meituan, PDD Group, Xiaohongshu, have tenacity and a desire to win and build new and innovative products. Many of them are building great AI technologies, agents, and models. They are already investing heavily in it, more so than the typical American way.

Secondly, 2026 will be the beginning of AI-first devices. This will be the year that we first see, touch, and buy an AI-first device. It may not be the ultimate thing, the format that ends up winning. It will either be the Nokia moment, the BlackBerry moment, or the iPhone moment. We don’t know which one it is, but all three moments were important in the history of mobile development. An AI-first device is needed because humans have always wanted to have a way to delegate to the device using speech and language. So this means telling the device the desired result rather than the steps to get the job done. The smart agent then?.?.?.?gets it done.

This is already happening with agent technology. But it needs to have a speech-driven interface, which isn’t a smartphone. The phone is the wrong device because it’s not always on, and it’s not always listening. So you need a device that’s always on, always listening and capturing information throughout your day. It will store everything you have seen and heard, and reason against this. So, it’s a long answer, but I think the key is this ambient AI that’s always on, always listening, infinitely remembering, and invisible.

EO: Reflecting on your career in the Chinese AI industry, if you look back at the beginning, what would you be surprised at about the industry today and what has remained largely the same?

KFL: I think an optimistic belief that AI would change the world has always remained the same. What I was surprised by is the speed at which it grew in the past three years. I thought it would be slower growth over 10 or 20 years, but it came much more quickly and matured very rapidly. We still have a long way to go.

When I started out working in the industry in the 80s, AI was always a bag of things that didn’t work. Whenever it did work, which was infrequent, it got turned into a product and was no longer called AI. People made fun of us or thought we were just a bunch of crazy people who think AI can think like humans. And nowadays, you know, everything calls itself AI. Every IPO calls itself an AI company. So we’ve gone from only the dreamers and wishful thinkers do AI, to now to everybody wants to be a part of it.

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