近日，在接受《金融時(shí)報(bào)》（Financial Times）專(zhuān)訪時(shí)，我與記者探討了當(dāng)前AI產(chǎn)業(yè)發(fā)展的兩個(gè)核心命題：中美AI領(lǐng)域的路線之爭(zhēng)，以及企業(yè)如何在 AI 時(shí)代真正實(shí)現(xiàn)“突圍” 。

美國(guó)巨頭們選擇以閉源模式押注“贏者通吃”，相比之下，中國(guó)AI領(lǐng)域更像是一個(gè)極具韌性的“共學(xué)小組”：在資源有限的前提下，通過(guò)開(kāi)源模式和極致的工程落地能力，走出了一條高效率、重應(yīng)用的破局之路。

但技術(shù)上的趕超只是第一步，競(jìng)爭(zhēng)的重心正悄然轉(zhuǎn)向誰(shuí)能率先讓AI走入廠房車(chē)間、走進(jìn)企業(yè)核心生產(chǎn)場(chǎng)景。我認(rèn)為，企業(yè)AI落地本質(zhì)上是“一把手工程”。企業(yè)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(wàn)物般懂AI的企業(yè)，以AI重塑核心業(yè)務(wù)邏輯。

以下為專(zhuān)訪文章正文：

在過(guò)去的四十年里，李開(kāi)復(fù)博士親眼見(jiàn)證了中國(guó)人工智能產(chǎn)業(yè)的飛速發(fā)展。在產(chǎn)業(yè)萌芽階段，他將當(dāng)時(shí)世界前沿的科研創(chuàng)新機(jī)制引入中國(guó)，培養(yǎng)出了大批高科技人才，如今，這些人才已成長(zhǎng)為活躍在各大科技巨頭的中堅(jiān)力量，也為AI產(chǎn)業(yè)提供了起飛的土壤。

這位國(guó)際AI專(zhuān)家曾主導(dǎo)創(chuàng)建微軟亞洲研究院，使其成為中國(guó)頂尖AI人才的“黃埔軍�！�；隨后，他又籌組領(lǐng)導(dǎo)了谷歌中國(guó)。2009年，李開(kāi)復(fù)博士創(chuàng)辦了著名科技創(chuàng)投機(jī)構(gòu)創(chuàng)新工場(chǎng)，帶領(lǐng)團(tuán)隊(duì)投資孵化了10多家AI獨(dú)角獸企業(yè)；2023年，他創(chuàng)辦了零一萬(wàn)物。零一萬(wàn)物是一家總部位于北京的大模型獨(dú)角獸企業(yè)，致力于打造性能領(lǐng)先的產(chǎn)業(yè)大模型和為全球企業(yè)打造智能體解決方案。

在與《金融時(shí)報(bào)》中國(guó)科技記者 Eleanor Olcott 的對(duì)話中，他深入剖析了中美AI領(lǐng)域之間的行業(yè)競(jìng)爭(zhēng)，并闡述了為何企業(yè)必須以更積極的姿態(tài)去擁抱這場(chǎng)技術(shù)變革。

Eleanor Olcott：能夠介紹一下您的初創(chuàng)公司零一萬(wàn)物嗎？

李開(kāi)復(fù)：零一萬(wàn)物致力于打造全球領(lǐng)先的AI 2.0大語(yǔ)言模型平臺(tái)及行業(yè)應(yīng)用，助力企業(yè)AI數(shù)智化轉(zhuǎn)型，提供構(gòu)建AI智能體的工具和平臺(tái)。我們基于頂尖的開(kāi)源模型，會(huì)根據(jù)企業(yè)的具體業(yè)務(wù)場(chǎng)景挑選最優(yōu)模型，以“一把手工程”為核心進(jìn)行定制化開(kāi)發(fā)。在AI智能體落地企業(yè)的早期階段，提供從戰(zhàn)略到落地的一站式服務(wù)至關(guān)重要，所以零一萬(wàn)物會(huì)詳細(xì)解釋技術(shù)如何應(yīng)用，并與客戶(hù)協(xié)同共創(chuàng)。這種深度參與決不僅是為了幫企業(yè)降本，更是為了創(chuàng)造實(shí)實(shí)在在的商業(yè)產(chǎn)出。

EO：這些企業(yè)對(duì)AI的接受程度如何？

李開(kāi)復(fù)：在與銀行、保險(xiǎn)、礦山和能源等傳統(tǒng)行業(yè)的合作中我們發(fā)現(xiàn)，相比于科技公司，這些行業(yè)在數(shù)智化轉(zhuǎn)型方面準(zhǔn)備不足，有些企業(yè)甚至連基本的數(shù)字化轉(zhuǎn)型都尚未完成。對(duì)于這類(lèi)客戶(hù)，我們會(huì)審慎評(píng)估投入產(chǎn)出比，建議客戶(hù)先夯實(shí)基礎(chǔ)，避免后續(xù)產(chǎn)生過(guò)高的改造成本和時(shí)間損耗。另一個(gè)問(wèn)題是，很多企業(yè)在提需求時(shí)是在“看后視鏡開(kāi)車(chē)”。比如他們只想做一個(gè)客服機(jī)器人，但這早已不是技術(shù)的前沿，更不是智能體最具價(jià)值的應(yīng)用領(lǐng)域。

缺乏AI專(zhuān)業(yè)能力的企業(yè)必須與AI公司合作，共同制定AI數(shù)智化轉(zhuǎn)型戰(zhàn)略。這種轉(zhuǎn)型必須由CEO主導(dǎo)，而且執(zhí)行難度極大。目前看來(lái)，可能只有百分之一的企業(yè)真正做好了這種準(zhǔn)備。當(dāng)這種合作意愿達(dá)成時(shí)，我們就會(huì)深度介入，要求他們?cè)O(shè)立首席AI官（CAIO），因?yàn)閭鹘y(tǒng)的首席信息官（CIO）往往由于職業(yè)慣性顯得過(guò)于保守，不能勝任AI數(shù)智化轉(zhuǎn)型過(guò)程。CAIO必須具備大局觀和冒險(xiǎn)精神，直接配合CEO重塑組織架構(gòu)。如果客戶(hù)配不齊這個(gè)崗位，零一萬(wàn)物可以直接派駐前沿部署工程師（FDE）。

我們的商業(yè)模式類(lèi)似于Palantir，都是由顧問(wèn)協(xié)助制定戰(zhàn)略，然后由執(zhí)行人員負(fù)責(zé)實(shí)施。項(xiàng)目收入與最終取得的業(yè)務(wù)成果掛鉤：前期收取的戰(zhàn)略咨詢(xún)費(fèi)僅用于覆蓋成本，核心收益則取決于為企業(yè)客戶(hù)所帶來(lái)的核心業(yè)務(wù)增量情況。

EO：那么對(duì)于另外99家不想這樣做的公司，原因是什么？

李開(kāi)復(fù)：有時(shí)是因?yàn)槿藗儍H僅將AI視為另一種軟件，有時(shí)是CEO對(duì)AI的本質(zhì)缺乏基本的了解。有時(shí)人們把AI誤認(rèn)為是另一種ERP（企業(yè)資源規(guī)劃）系統(tǒng)�；蛘撸麄儗⑷蝿�(wù)委派給了錯(cuò)誤的人選。如果你想制定AI戰(zhàn)略，CIO通常不是合適的人選，因?yàn)樗麄兊穆氊?zé)是確保系統(tǒng)平穩(wěn)運(yùn)行，而不是思考如何進(jìn)行轉(zhuǎn)型。

EO：目前普遍認(rèn)為中國(guó)模型落后美國(guó)頂尖水平6到12個(gè)月。您認(rèn)為這種差距會(huì)持續(xù)嗎？

李開(kāi)復(fù)：目前，全球 AI 研究領(lǐng)域的突破性成果大多數(shù)源自美國(guó)。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(fā)上確實(shí)步步領(lǐng)先。但中國(guó)團(tuán)隊(duì)的優(yōu)勢(shì)在于極其出色的工程落地能力，基于這些突破性成果，中國(guó)團(tuán)隊(duì)能夠迅速掌握同類(lèi)技術(shù)，并且往往能實(shí)現(xiàn)更高的運(yùn)行效率。這促成了我們自己的“覺(jué)醒時(shí)刻”（中國(guó)大模型公司DeepSeek2025年發(fā)布的推理模型DeepSeek-R1，以更低的訓(xùn)練成本達(dá)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(xì)節(jié)，成功的難度也會(huì)大幅降低。中國(guó)擁有非常強(qiáng)大的工程和研究底蘊(yùn)，因此即便美國(guó)企業(yè)不開(kāi)源也不發(fā)表論文，但中國(guó)公司通過(guò)研究這些模型的運(yùn)作邏輯，已經(jīng)實(shí)現(xiàn)了多項(xiàng)自主創(chuàng)新�？赡軐�(shí)驗(yàn)結(jié)果本身就是一種啟發(fā)，可能是通過(guò)巧妙的逆向工程、模型蒸餾，又或許是從技術(shù)的第一性原理出發(fā)，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(jié)果。

因此，中國(guó)模型往往能迅速追趕。DeepSeek發(fā)布時(shí)，中美大模型之間相差的研發(fā)周期縮短到了三個(gè)月；現(xiàn)在看來(lái)谷歌的Gemini已經(jīng)領(lǐng)先，差距可能拉大到了12個(gè)月。這種差距呈現(xiàn)動(dòng)態(tài)起伏規(guī)律，均值在6個(gè)月左右。每個(gè)人都會(huì)從已發(fā)布的優(yōu)秀理念和模型中學(xué)習(xí)，因?yàn)槿斯ぶ悄茴I(lǐng)域吸引了眾多頂尖人才，無(wú)論在中國(guó)還是美國(guó)，情況都是如此，而且他們都渴望著彼此學(xué)習(xí)。這種學(xué)習(xí)不是單向的，比如在DeepSeek問(wèn)世時(shí)，所有美國(guó)公司同樣在對(duì)它進(jìn)行研究。

EO：2025年初DeepSeek發(fā)布R1模型時(shí)，OpenAI曾指責(zé)其通過(guò)“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(fā)生。我們暫且忽略竊取技術(shù)的指控，因?yàn)檫@似乎無(wú)法證實(shí)。但中國(guó)公司是否因?yàn)楦鼑?yán)格的商業(yè)機(jī)密保護(hù)措施，而更難從美國(guó)公司那里學(xué)習(xí)呢？

李開(kāi)復(fù)：OpenAI 對(duì)閉源的堅(jiān)持不難理解：在投入巨資實(shí)現(xiàn)技術(shù)突破后，一旦開(kāi)源，他們的核心成果很容易就會(huì)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(lái)一種質(zhì)的飛躍。在那樣的未來(lái)，率先攻克 AGI 技術(shù)的公司將對(duì)全球競(jìng)爭(zhēng)者形成降維打擊，無(wú)論是美國(guó)公司還是中國(guó)公司。因此，如果你認(rèn)定 AGI 的終局是“贏家通吃”，那么對(duì)實(shí)現(xiàn)路徑絕對(duì)保密，就是一種必然的戰(zhàn)略選擇。

這些美國(guó)公司在持續(xù)籌集千億美元的巨量資金。為了支撐這種規(guī)模的估值，他們必須向投資者描繪這樣一個(gè)愿景：一旦率先建成 AGI，他們將引領(lǐng)世界，因此即便今日投資500億美元也依然“物超所值”，因?yàn)楣镜氖兄到K有一天會(huì)邁入50萬(wàn)億美元。正是這套邏輯，讓OpenAI的故事在商業(yè)上自洽，聽(tīng)起來(lái)不僅合乎情理，甚至頗具可信度。

但我認(rèn)為，這個(gè)故事目前來(lái)看還有另一個(gè)版本。這場(chǎng)競(jìng)賽并不是只有一兩個(gè)“天才少年”。在美國(guó)，OpenAI、Anthropic、Google 和 xAI 都在同臺(tái)競(jìng)技，每一家都自認(rèn)為是那個(gè)能解開(kāi) AGI 終極命題的“天才少年”，渴望以此實(shí)現(xiàn)贏家通吃，“贏得諾貝爾獎(jiǎng)”。

而中國(guó)路徑更像是一個(gè)“共學(xué)小組”。一家公司發(fā)布模型，另一家公司就去研究和嘗試；甚至可能會(huì)去請(qǐng)教對(duì)方是如何訓(xùn)練模型的。學(xué)習(xí)小組的所有成員都在構(gòu)建開(kāi)源模型并進(jìn)行分享。

值得注意的是，盡管這些學(xué)習(xí)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(gè)季度都能看到利潤(rùn)。這與美國(guó)的情況非常不同，因?yàn)槊绹?guó)公司并不在意回報(bào)，但是在中國(guó)，公司的支出是受到限制的。舉個(gè)例子，阿里巴巴不能在下個(gè)季度虧損100億美元，但OpenAI可以。因此，種種原因使得中國(guó)公司在資源有限的情況下，需要像學(xué)習(xí)小組一樣協(xié)作的方式運(yùn)作，這與美國(guó)“贏者通吃”的策略截然不同。

EO：目前有一種主流觀點(diǎn)認(rèn)為美國(guó)在AI上的領(lǐng)先源于地緣戰(zhàn)略?xún)?yōu)勢(shì)。但我認(rèn)為未來(lái)也有一種可能，我們會(huì)將中國(guó)曾經(jīng)的落后視為一種優(yōu)勢(shì)。因?yàn)榇嬖跁r(shí)間差，中國(guó)可以觀察西方如何演進(jìn)，看到AI帶來(lái)的經(jīng)濟(jì)和社會(huì)動(dòng)蕩，并根據(jù)所看到的錯(cuò)誤和陷阱選擇不同的路徑。您對(duì)此怎么看？

李開(kāi)復(fù)：幾乎可以預(yù)見(jiàn)，未來(lái)AI產(chǎn)生的負(fù)面影響將率先出自美國(guó)公司。無(wú)論是被不法分子濫用，還是因程序錯(cuò)誤失控，這種無(wú)意間留下的隱患其實(shí)根源是在于美國(guó)公司的運(yùn)作模式。在“贏家通吃、快魚(yú)吃慢魚(yú)”的心態(tài)下，公司自然而然地會(huì)減少安全防范方面的意識(shí)。同時(shí)，由于他們的模型和技術(shù)更加先進(jìn)，他們也掌握著殺傷力更強(qiáng)的武器。

在中國(guó)，人們普遍不認(rèn)為AGI的走向會(huì)是一家公司對(duì)全行業(yè)的降維打擊。行業(yè)更傾向于相信，這將是一個(gè)領(lǐng)跑者不斷易主的線性發(fā)展過(guò)程。

EO：難道中國(guó)公司不想成為贏家嗎？

李開(kāi)復(fù)：當(dāng)然想，但大部分的企業(yè)不愿付出那種傾家蕩產(chǎn)的代價(jià)。中國(guó)公司更關(guān)注商業(yè)產(chǎn)出和盈利能力，以及構(gòu)建能從模型中賺錢(qián)的產(chǎn)品。騰訊有微信、阿里巴巴有淘寶，字節(jié)跳動(dòng)有抖音，這些巨頭都希望構(gòu)建一個(gè)與其產(chǎn)品相匹配、能盈利且有競(jìng)爭(zhēng)力的模型。

EO：您認(rèn)為今年中國(guó)AI行業(yè)會(huì)發(fā)生什么？

李開(kāi)復(fù)：企業(yè)級(jí)應(yīng)用（B端）方面，我認(rèn)為中國(guó)會(huì)稍稍落后于美國(guó)，因?yàn)橹袊?guó)企業(yè)普遍還沒(méi)有養(yǎng)成支付訂閱制服務(wù)費(fèi)用的消費(fèi)習(xí)慣。但在消費(fèi)級(jí)應(yīng)用（C端）領(lǐng)域，中國(guó)將領(lǐng)先美國(guó)。兩國(guó)都有大量創(chuàng)業(yè)公司在深耕 C 端應(yīng)用，且目前時(shí)機(jī)已經(jīng)成熟、模型能力也已足夠，但由于中國(guó)科技巨頭在這方面始終都有展現(xiàn)出堅(jiān)韌的態(tài)度、也渴望追求市場(chǎng)支配地位，所以我認(rèn)為，他們?cè)诖蛟毂顟?yīng)用方面將遠(yuǎn)超美國(guó)大廠。對(duì)中國(guó)大廠而言，應(yīng)用開(kāi)發(fā)本身就是他們研發(fā)技術(shù)的初衷，因此他們也會(huì)更專(zhuān)注。無(wú)論是用 AI 賦能現(xiàn)有產(chǎn)品，還是開(kāi)發(fā)原生的 AI 應(yīng)用，這些工作都已經(jīng)成果初顯了。

在我看來(lái)，中國(guó)互聯(lián)網(wǎng)公司將成為 AI 應(yīng)用創(chuàng)新的主要源頭，其動(dòng)力遠(yuǎn)超美國(guó)同行。反觀美國(guó)的標(biāo)桿性應(yīng)用，無(wú)論是Instagram、YouTube還是Snapchat，它們正變得非常乏味。我不認(rèn)為美國(guó)的互聯(lián)網(wǎng)公司具備中國(guó) 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(jié)跳動(dòng)、騰訊、阿里、美團(tuán)、拼多多、小紅書(shū)這些公司，擁有極強(qiáng)的韌性和求勝欲。他們中的許多企業(yè)正在重金投入研發(fā)頂尖的 AI 技術(shù)、Agents（智能體）和模型，其投入力度遠(yuǎn)超傳統(tǒng)的美國(guó)模式。

其次，2026 年將開(kāi)啟“AI 原生設(shè)備”的元年。我們將在今年首次看到、親手觸摸、并購(gòu)買(mǎi)到以 AI 為核心設(shè)計(jì)的原生硬件。它未必是最終勝出的終極形態(tài)，但它可能是“諾基亞時(shí)刻”、“黑莓時(shí)刻”，或者是“iPhone 時(shí)刻”。雖然尚不確定AI原生設(shè)備處于哪個(gè)階段，但這三個(gè)節(jié)點(diǎn)在移動(dòng)通訊史上都至關(guān)重要。人類(lèi)一直渴望通過(guò)語(yǔ)音和自然語(yǔ)言向設(shè)備“委派任務(wù)”，因此 AI 原生設(shè)備是大勢(shì)所趨。這意味著你只需告訴設(shè)備你想要的結(jié)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(shì)在智能體技術(shù)上已初現(xiàn)端倪。但它需要一個(gè)由語(yǔ)音驅(qū)動(dòng)的交互界面，而目前來(lái)看，這種界面絕不是智能手機(jī)。手機(jī)并不是理想的載體，因?yàn)樗鼰o(wú)法做到“始終在線”和“實(shí)時(shí)傾聽(tīng)”。因此，你需要一種能夠全天候運(yùn)行、實(shí)時(shí)收音并捕捉全天信息的設(shè)備。它會(huì)存儲(chǔ)你所見(jiàn)、所聞的一切，并以此為基礎(chǔ)進(jìn)行邏輯推理。

這是一個(gè)很復(fù)雜的命題，但我認(rèn)為核心在于這種“環(huán)境 AI”（Ambient AI）。它始終在線、實(shí)時(shí)傾聽(tīng)、擁有無(wú)限記憶，而且讓你幾乎感覺(jué)不到它的存在。

EO：回顧您在中國(guó)AI行業(yè)的職業(yè)生涯，與開(kāi)始時(shí)相比，今天行業(yè)的哪些方面會(huì)讓您感到驚訝，哪些方面又基本保持不變？

李開(kāi)復(fù)：我一直樂(lè)觀地相信，“AI將改變世界”。讓我感到驚訝的是過(guò)去三年AI進(jìn)化的速度。我原本以為這會(huì)是一個(gè)跨越十到二十年的漫長(zhǎng)過(guò)程，但它來(lái)得太快了，成熟得也非常迅速。當(dāng)然，前路依然漫長(zhǎng)。

回想起 1980 年代我剛進(jìn)入AI行業(yè)的時(shí)候，AI 就像一堆派不上用場(chǎng)的“破銅爛鐵”。偶爾有成效的時(shí)候，它也會(huì)被立刻包裝成某種產(chǎn)品，從此不再被稱(chēng)為 AI。那時(shí)候人們嘲笑我們，覺(jué)得我們這群人瘋了，居然認(rèn)為機(jī)器能像人一樣思考�？涩F(xiàn)在，萬(wàn)物皆可 AI。每一家 IPO 的公司都標(biāo)榜自己是 AI 企業(yè)。我們見(jiàn)證了 AI 從“空想家的美夢(mèng)”，變成了如今每個(gè)人都想?yún)⑴c的舞臺(tái)中心。

本文翻譯自《金融時(shí)報(bào)》報(bào)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 ‘a(chǎn)ha 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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