5月19日，在 AMD AI 開發者日 2026 上，我很高興與AMD董事會主席及首席執行官蘇姿豐博士圍繞“AI智能體新范式”進行了一場爐邊對話。蘇姿豐博士是少數既深刻理解底層算力，又真正關心 AI 如何進入產業現場的科技領袖，這也讓這場討論有了更扎實的產業縱深。

過去幾年，大模型學會了理解和生成；但接下來更關鍵的問題是：AI 能否理解目標、調度工具、協同執行，并最終交付真實的業務結果？這背后涉及的不只是模型能力，還包括多智能體協作、企業組織變革和本地智算基礎設施。

我始終相信，多智能體會把 AI 帶向真正的生產力系統。未來的開發者，也不只是寫代碼的人，而會成為能夠編排智能體、定義結果、推動業務閉環的 DRI：Direct Responsible Individual，直接責任人。

這場對話也包含了我對多智能體時代的一些最新思考。以下為對談實錄，分享給大家。

01

2026年AI 的核心問題：

能否替代一個部門？

蘇姿豐博士：過去一段時間，你一直在談生成式 AI 正在邁向智能體時代。如今，越來越多人開始認為，2026 年可能會成為這一轉變真正落地的一年。那么站在現在的時間點，你觀察到了哪些變化，從而讓你認為智能體與此前的生成式 AI 浪潮已經有了本質區別？

李開復博士：謝謝你，Lisa。非常高興來到 AMD 上海開發者大會，也很高興能和真正“在創造未來”的開發者們坐在一起。我認為，有兩件關鍵的事情發生了變化，其中第二件尤為重要。

首先，AI 編程能力跨過了臨界點。一年前，AI 還只能輔助編寫代碼、函數等等；而現在，它已經可以端到端地交付一整套功能。這聽起來像是一個漸進式的進步，但其實不然。在座各位都知道，智能體在數字世界中的所有行為，本質上最終都會落到代碼層面。一旦 AI 的編碼能力跨過那個門檻，自主智能體就真正具備了成為現實的可能。

其次，更重要的變化在于，我們開始意識到：單一智能體的能力是有上限的。無論模型參數規模有多大，只依賴單個 Agent 的推理能力，在面對真實復雜問題時，終究會碰到瓶頸。而多智能體架構第一次打破了這個上限。負責規劃、評估、研究和執行的不同智能體，開始彼此協作、相互辯論，并在彼此結果之上繼續迭代。

這其實非常接近“美第奇效應（Medici Effect）”：當不同領域的專家被放進同一個房間時，最終產生的成果，會遠遠超過任何單一個體能力的簡單疊加。

五百年前，在文藝復興時期，人類已經發現了這一規律。直到21世紀的今天，我們第一次把這種機制帶到了 AI 世界。

從技術路徑上看，這意味著我們正在逐漸擺脫過去那種“試圖用一個模型完成所有事情”的模式。未來的 AI，不會是一個“超級大腦”的獨角戲，而會更像一個由不同智能系統協同運作的交響樂。正是基于這一趨勢，我們著手部署專業化的多智能體系統，并逐漸走向“異構智能（Heterogeneous Intelligence）”階段。不同類型的模型與算法會被組合在一起，用群體智能去解決更加復雜的問題。

2024 年最關受關注的問題是：“AI 能否完成一個任務？”

2025 年這一問題轉變為：“AI 能不能完成一整條工作流？”

在2026年，這個核心問題已經進階為“AI 能否替代一個企業的職能部門？”

以現代HR（人力資源）部門為例。當招聘 Agent 與績效 Agent 實現聯動后，系統就能夠根據員工入職后的真實績效數據，自動調整前端的人才篩選標準。從簡歷篩選、面試，到新員工入職，再到月度和季度績效自動化跟蹤，這些多智能體系統會圍繞統一的人力資源數據持續運轉升級。隨著這種能力不斷擴展，它最終會演變成一個彼此互聯的企業多智能體協作網絡，覆蓋 HR、研發、產品、銷售和市場等不同部門。

這種架構，也正在推動“One-Person Company（一人公司）”趨勢的出現。借助模塊化的多智能體框架，單個開發者或領域專家，如今已經有能力像“總架構師”一樣，快速啟動一家高度自動化運轉的公司。

在由智能體驅動的新范式下，我們實際上已經跨過了“自主執行”的門檻。AI 正在從過去被動的“Prompt-and-Response（從提示詞到響應）”模式，轉向主動的“Goal-and-Execution（從目標到執行）”模式。未來，你不再是給 AI 一個 Prompt，而是直接給它一個組織目標。隨后，智能體們會自行完成協同、執行、評估、優化，并形成完整閉環。

而這一新范式，也正在催生當前 AI 領域最巨大的商業機會：產業級 AI 轉型。新時代真正的經濟價值，不會來自只會“回答問題”的 AI 系統，而會來自能夠真正執行企業目標的自主多智能體基礎設施。

這也是零一萬物所關注的核心方向。過去一段時間里，我一直在與全球各地的 CEO 和企業高管交流，以便更深入地理解：AI 將如何重塑生產力、組織結構以及未來的領導力。

同時，我想這也會影響今天在場每一位開發者，驅使大家重新思考自己未來在 AI 時代會扮演怎樣的角色。

02

AI 轉型，為什么不能只靠 CIO？

蘇姿豐博士：在你與 CEO 們的交談中，他們是如何對待 AI 轉型的？這對開發者社區有什么影響？

李開復博士：我看到了許多明顯的問題。幾乎每個企業目前都選擇在不出錯卻價值很低的場景部署 AI。比如，會議紀要、人力資源員工答疑聊天機器人、企業內部搜索等等。這些都只是表面文章。

我很直白地告訴各位 CEO：不要只聽你們的 CIO 。典型CIO們關注的是系統穩定運行、軟件運行安全不出錯，在這一輪深入企業核心業務命脈的 AI 變革中，反而可能成為阻礙進化的舊勢力。因為 CIO 的職責，本質上是管理軟件運營，而不是重新定義公司。CIO 擅長安全地部署 AI，但并不擅長推動組織層面的真正變革。多數由 IT 部門自下而上推動的 AI 轉型，最終都會失敗。

傳統 CIO 這個角色不會消失，但它的重要性會被大幅削弱。因為AI 并不只是一個新的軟件工具，企業AI轉型絕對是是一把手工程，需要企業領袖根本性的思維轉變。

真正能夠改變公司經營結果的，往往是那些直接影響損益表（P&L）的核心業務環節。而這些領域，恰恰也是很多高管最不愿意讓 AI 介入的運營職能部門：收入、利潤、防欺詐、動態定價、供應鏈、產品上市速度，以及核心創新能力。具有前瞻性的 CEO 們正在重新校準他們公司的運營方式、組織應如何改變，以及領導方式應該如何調整。

我也經常對 CEO 們說：如果你的 AI 部署，最終沒有改變任何一個會出現在季度財報電話會議上的數字，那么你公司做的就不是真正意義的 AI 轉型，只是浪費錢打造了一個 AI 實驗室。

同樣的話，我也想送給今天的開發者。任何參與商業研發的人，都應該用同樣的方式思考問題。停止浮于表面的表演式 AI ，開始構建能真正深入業務實質的結構性引擎。

03

中國的開源生態將催生

產業AI的“安卓系統”

蘇姿豐博士：現在最讓我興奮的事情之一，就是開源 AI 社區正在涌現出大量創新，而且這個生態已經越來越全球化。你與中國的開源社區一直保持著非常密切的聯系。那么，在這個生態中，開發者和貢獻者們最近最讓你感到興奮的變化是什么？

李開復博士：開源的趨勢勢不可擋，它從根本上重寫了全球 AI 的游戲規則，其發展格局與經典的智能手機大戰如出一轍。閉源模型類似于蘋果的 iOS，追求高利潤并保持著對生態系統的強硬控制。而開源社區，則越來越像 AI 世界里的 Android。它擁有更廣泛的全球覆蓋，以及更大的用戶規模。

正如 Lisa 剛才提到的，中國開源生態之所以表現得如此出色，背后存在著很深層的結構性原因。因為硬件資源有限，中國開發者和創業公司并沒有條件依賴“大力出奇跡”的算力堆疊。在這種約束下，整個生態反而開始把重點轉向極致的工程效率，更加關注算法優化、架構創新，以及如何把底層基礎設施做得更精簡、更強大。

它就像一個充滿活力的、去中心化的學習小組，大家齊心協力為了在考試中取得好成績，每個人都在其他人公開發布的成果之上進行創造，整個群體的能力也因此呈指數級增長。我確信，這種機制將為未來帶來更多的進步與創新。

04

DRI模式重新定義技術人

人人都要把自己當CEO

蘇姿豐博士：在 AMD，我們自己的工程師也在使用 AI 智能體，加速產品設計和驗證流程。我們越來越明顯地看到：今天，一個人如果擁有合適的工具和足夠的算力，已經能夠完成幾年前需要整個團隊才能完成的工作。你就“人與智能體協作”的趨勢寫了很多文章：那么，在那些真正以這種方式創業和開發的人身上，你最近觀察到了哪些變化？

李開復博士：受限于此前的訓練，大多數開發者都習慣于在代碼層面思考所有權（Ownership）問題。比如，由一個人負責 GitHub 上的代碼倉庫和 PR（Pull request），另一個人負責值班輪換，另一個人負責某一個具體服務。這種責任邊界其實是有邊界的。它本質上只覆蓋你能夠通過鍵盤直接控制的部分，而現在，越來越多編碼工作已經開始被 AI 智能體接管。

我想和大家談談一種打破這個邊界的運營架構：DRI（Directly Responsible Individual，直接責任人）概念。

在軟件工程中，交付產品的主要瓶頸很少是代碼本身。而是所有權的模糊不清。責任分散、停滯的拉取請求以及偏離的路線圖，通常都源于：很多人只是負責項目管理大表上的某一個環節，卻沒有人真正對最終結果負責。DRI 模型改變了這一點。

我預測 DRI 模型會成為 AI 原生公司最核心的組織架構。所謂 DRI，就是由一個人，對某個跨職能結果承擔端到端責任。這不是一個職位頭銜，而是一種非常明確的責任機制。就像系統運行手冊里唯一指定的值班工程師：最終結果如何、業務影響如何，都由 DRI 責任人負責。

在這個模式下，一個人類 DRI 會處于整個智能體系統的中心。圍繞他協同工作的，是由研究、執行、合規和監控等不同 Agent 組成的專業化集群。DRI 不把時間精力花在具體執行上，而是負責整體編排、關鍵決策，并對最終的輸出契約負責。與此同時，實時數據流會逐漸取代傳統的匯報體系，業務運轉也會越來越圍繞具體、可量化的結果展開。

我認為，一個優秀工程師所具備的很多能力，和成為優秀 DRI 所需要的能力，幾乎是高度一致的。

當你編寫技術規范時，你其實是在嘗試定義可量化的商業成果；當你給系統做監控、配置自動告警時，你其實是在建立衡量結果的機制。當你凌晨兩點主動去 de-bug排查故障，而不是等別人通知你時，你展現出來的，其實正是 DRI 模式最核心的主人翁意識。

選擇 DRI 模式，也意味著你必須重新定義“什么是個人成功”。在智能體時代，一個優秀工程師的價值，不再只是由“寫了多少代碼”來衡量。這也意味著，今天很多工程師的工作方式都會發生變化。

你不再只是關注系統，而是要對結果負責。優秀工程師通常都會非常重視監控系統，使服務具備極強的可觀測性。DRI 則是把這種技術嚴謹性延伸到了他們所擁有的業務結果上。如果你是一個負責增長的 DRI，你不僅僅監控 API 延遲。你還要監控用戶激活率、轉化漏斗以及對收入的影響。你端到端地對完整結果負責。

你擁有決策權，而不僅僅是建議權。工程師通常很擅長分析，但他們常常把產出物交給產品經理或高管去做選擇。DRI 則需要自己完成閉環。你進行分析，你做決定，并對接下來發生的任何事情負責。剛開始會有些不習慣，但很快你就會進入狀態。

你會有規劃性地去配置你的智能體集群。大多數非技術的 DRI 會把 AI 智能體當作黑盒來對待。但工程師不一樣。工程師們發揮技術能力去監控智能體、評估它的輸出、識別它的故障模式，并懂得如何圍繞智能體集群建立更可靠的驗證機制。

工程師的優勢，會在這個時代被無限放大。你們擁有的不僅僅是給產品或業務的建議權，而是直接擁有決策能力。AI 正成為賦能技術人的新形態超能力。AI 正成為賦能技術人的新形態超能力。

05

智能體經濟爆發前夜

推理算力走向前臺

蘇姿豐博士：你剛才談到的這一切，背后都需要非常龐大的算力支撐。而且需要的還不只是單一算力，而是一整套能夠協同工作的全棧算力體系。那么，當開發者和企業真正開始大規模運行智能體時，底層算力基礎設施究竟需要具備哪些能力，才能支撐這一切真正運轉起來？

李開復博士：Lisa，這已經完全進入你的專業領域了。智能體 AI 趨勢底層的計算模式正在從根本上改變底層的計算模式。傳統 AI 系統所要求的更多是穩定、持續的計算負載，而智能體系統則完全不同，它具有高度突發性，而且會產生大量并行計算。一個用戶請求，可能會被拆分成20個或更多并行運行的智能體；這些結果匯總之后，又會再次觸發下一輪 Agent 協同。從本質上看，智能體經濟是“推理驅動”的經濟，而推理與訓練，其實是兩種完全不同的計算模式。

要讓多智能體協同真正具備現實可行性，系統必須滿足幾個條件：本地優先、端側處理，以及低于 100 毫秒的響應延遲。而這正是當前硬件競爭真正分出勝負的地方。我認為，在這一趨勢上，AMD 比很多公司都看得更早、更清楚。

隨著 AI 開始走向多智能體架構，我們也必須重新思考“算力”本身。未來，極致的 token 效率以及本地化處理能力會是關鍵。

06

自主企業將誕生：

數據主權與ROI成為產業AI新坐標

蘇姿豐博士：我想用今天早上大會開場白環節的一句話作為結束：AI 時代仍然處于非常早期的階段，真正精彩的部分，其實還在后面。展望未來，開發者接下來最有可能創造出的東西里，什么最讓你感到興奮？

李開復博士：未來，真正意義上的“自主企業”會誕生。驅動它的，將是跨部門、多層級協同運作的智能體網絡。下一階段的產業 AI 轉型，會同時圍繞兩個核心問題展開：數據主權，以及清晰可驗證的 ROI（投資回報率）。類似 AMD 的頭部合作伙伴，正是構建“智算主權”的關鍵地基。

對于今天的開發者來說，最大的機會，是去構建那些過去需要一個完整團隊才能完成、如今卻可以由 AI 獨立交付商業結果的 AI。AI 的角色，已經不再是“幫助營銷人員提升效率的 AI工具”，而是能夠真正承擔營銷職能的 AI Agent；不是“協助金融分析師的 AI工具”，而是能提供自動化財務分析的 AI Agent。

我曾與零一萬物的工程師緊密合作，構建了一個“開復 AI”，作為我個人的決策智能體。我們發現，在大型企業中推動 AI 落地最快的方法，往往是由 CEO 或董事長自上而下推動。因為一旦 CEO 或 CFO 真正開始使用這些智能體，他們很快就會離不開它。當管理層真正接受智能體之后，AI轉型自然會沿著企業組織結構不斷向下推進。

如果你正坐在這個會場里，帶著一臺筆記本電腦，對系統編排有所了解，并有一個大膽的想法，那么你現在會比世界上任何一家財富500強企業的戰略部門都更有優勢。

這一代開發者，正站在一個極其少見的時代窗口面前。

在這樣一個時代里，最不應該做的，就是把自己的創造力，提前鎖進一家大公司的組織體系里。

去創造屬于你自己的事業吧！

蘇姿豐博士：謝謝你，開復。

07

Cube01：

多智能體時代的智算基礎設施

李開復博士：我們一直非常重視開發者的使用體驗。這次，在 AMD 強大的硬件能力基礎上，我們進一步整合了零一萬物“萬智企業大模型平臺”的模型能力與工具體系。萬智不僅內置了多種領先模型，同時也能夠直接打通企業知識庫與核心業務工作流。這意味著，開發者可以根據不同場景，靈活選擇最合適的模型，并快速把 AI 能力真正接入自身企業研發體系。

接下來，我想重點介紹 Cube01 的三個核心能力。我們認為，這也是它在智能體 AI 開發中的真正價值所在。

• 多智能體編排層（Multi-Agent Orchestration Layer）：萬智的多智能體框架實現了毫秒級的響應速度。這款“一體機”（One-Box）解決方案允許自主工作流持續運行，并動態調用不同工具，對多個 Agent 進行實時協同編排。你的 AI 智能體將保持“永遠在線”（always-on）。

• AI 員工實體映射（AI Worker Entity Mapping）：與傳統的 AI 機器人不同，系統會為每個 AI 智能體分配一個獨立的“數字身份”。你可以為它配置真實的業務權限、數據權限以及組織角色。AI 智能體能夠精準識別業務上下文，并主動介入協作工作流。這樣一來，AI 智能體就能像人類一樣，在組織內部嵌入各自的虛擬角色。它們每一個都會成為獨立的生產力節點，并與其他智能體協同合作來完成任務。

• 算力主權與安全（Compute Sovereignty and Security）：通過與 AMD 的此次合作，Cube01 將高昂的云計算算力轉化為自主可控、安全可靠的本地基礎設施，從而為企業和組織確立了“智算主權”。過去，大家可能一直在為云計算按量付費，就像打網約車一樣。而現在，你擁有了一輛絕對安全的專屬專車。

最后，我還是想再次強調：我鼓勵所有的工程師都要帶著“結果導向”的思維去進行開發。我們非常期待看到大家借助 Cube01 蛻變成為一名能夠獨立負責結果，在 AI 時代組織里不可或缺的 DRI。謝謝大家！

以下為英文訪談內容：

Dr. Lisa Su: You have been writing and talking about the shift from generative AI to agentic AI for a while now. And it feels like 2026 is the year it is actually happening.

What are you seeing right now that tells you this moment is genuinely different from the generative AI wave we just lived through?

Dr. Kai-Fu Lee: Thank you, Lisa. It’s an absolute pleasure to be here at the AMD Shanghai Developer Day. It's great to be in a room with the people who are actually building the future.

Two things changed, and the second matters more.

First, coding got past a threshold. A year ago, AI could write functions. Now it can ship features end-to-end. That sounds incremental, but it isn't. Everyone in this room knows that everything an agent does in the digital world is code underneath. When coding crossed the line, autonomous agents became possible.

Second, and this is the bigger one: we figured out that a single agent has a ceiling. No matter how big the model, one agent's reasoning alone hits a wall on real problems. Multi-agent architectures broke that ceiling. Specialized agents—a planner, a critic, a researcher, an executor—debating and building on each other. It's the Medici effect: put diverse specialists in one room, and the output exceeds any individual. The Renaissance figured this out for humans 500 years ago. We just figured it out for AI.

Technically, we have achieved this by moving away from a single, fragile LLM trying to do everything. Instead, we deploy specialized multi-agent systems. We are also moving toward heterogeneous intelligence, which combines distinct types of AI models and algorithms to solve complex problems.

So, if 2024 was "Can AI do a task?" and 2025 was "Can AI complete a workflow?" then 2026 is strictly "Can AI run a corporate function?"

Take a modern HR department as an example. A recruitment agent that syncs with a performance agent enables the system to automatically adjust upfront talent-filtering criteria by analyzing post-hire employee performance data. From automated resume screening to interviewing, and from onboarding new employees to tracking performance monthly and quarterly. These multi-agents operate on an integrated HR data flywheel. Ultimately, these functional layers scale into an interconnected web of HR, R&D, product, sales, and marketing agents.

This architecture fuels an emerging One-Person-Company trend. By utilizing modular, multi-agent frameworks, a single developer or domain expert can now function as a macro architect to kickstart a highly functional company.

In this new paradigm powered by agentic AI, we have crossed the threshold into autonomous execution. We are moving from a passive "Prompt-and-Response" model to an active "Goal-and-Execution" model. You do not give an AI agent a prompt; you give it an organizational objective. The agents then coordinate, excute, measure, optimize, and close the loop.

This new paradigm is empowering the largest commercial opportunity for AI today: Industry AI Transformation. The next era of economic value won't be driven by systems that answer your questions. It will be driven by an autonomous, multi-agent infrastructure that executes your corporate goals.

At 01.AI, this is the exact core of our focus. I've been engaging with CEOs and senior executives around the world to gain deeper insights into how AI will shape new productivity, new organizations, and new leadership. It will also direct how developers here today to think about their role in building for the future AI economy.

Dr. Lisa Su: In your conversations with CEOs, how are they approaching AI transformation? And what are the implications for the developer community?

Dr. Kai-Fu Lee: I see many obvious mistakes. Almost every enterprise is deploying AI in safe and trivial places right now. Meeting summaries. Chatbots for HR FAQs. Internal search. All of it sub-scale. All of it cosmetic.

I tell CEOs bluntly: Don't Listen to Your CIO.

Because CIOs are here to protect the software environment, not to re-invent the company. CIOs are good at deploying AI safely, not in transforming their organizations. The bottom-up approach from the IT department often fails. The traditional CIO role won’t disappear, but it will be vastly diminished in importance.

AI is not just another software addition. The scale of AI transformation demands a fundamental leadership shift.

The functions that actually move the dial are the ones that fundamentally change a company's P&L. They are often the exact operational functions executives are afraid to touch with AI: revenue, profits, fraud, dynamic pricing, supply chain, time to market, and core innovation. Forward-thinking CEOs are recalibrating how their companies operate, how their organizations should change, and how they should lead differently.

I also tell CEOs: if your AI deployment doesn’t change a number that shows up on your quarterly earnings call, you’re running an AI Lab, not AI Transformation.

My call to action for developers is this: anyone involved in R&D for commercial organizations should think exactly the same way. Stop building cosmetic wrappers. Start building structural engines that move the needle for the bottom line.

Dr. Lisa Su: One of the things that excites me most right now is how much innovation is happening in the open-source AI community... and how global it has become.

You are very close to the open-source community here. What are you seeing from developers and contributors in this ecosystem that excites you most right now?

Dr. Kai-Fu Lee: The open-source landscape is completely unstoppable, fundamentally rewriting the global AI playbook in a dynamic mirroring the classic smartphone wars. Proprietary models resemble Apple’s iOS, which pursues high margins and tightly controlled ecosystems. The open-source community has become the Android of AI, capturing a massive global footprint and adoption.

There is a structural reason why the Chinese ecosystem has excelled so fiercely here, as Lisa pointed out earlier. Due to limited hardware resources, Chinese developers and startups couldn't afford brute-force compute. Out of absolute necessity, the community leaned into extreme engineering efficiency. The focus shifted toward algorithmic optimization, architectural innovation, and making foundational infrastructure incredibly lean and powerful. It operates like a dynamic, decentralized study group working together to ace a test, where everyone builds on top of each other's public releases. Their collective capability rises. I am sure this will lead to more improvement and innovation down the road.

Dr. Lisa Su: At AMD, our own engineers are using AI agents to accelerate how they design and verify our products. And we are seeing what one person with the right tools and the right compute can do today that would have taken a whole team a few years ago. You have written a lot about this idea... one person plus a team of agents.

What are you seeing from founders and developers who are actually building this way right now?

Dr. Kai-Fu Lee: Most developers are trained to think about ownership at the code level. You own the repo and the PR on GitHub. You own the on-call rotation. You own a specific service. But there's a ceiling on that kind of ownership—it's strictly scoped to what you can directly control through a keyboard, and coding agents can increasingly replace that.

I want to talk to you about an operational architecture that shatters that ceiling: the concept of the DRI—the Directly Responsible Individual.

In software engineering, the primary bottleneck to shipping products is rarely the code itself. It is ownership ambiguity. Diffuse accountability, stalled pull requests, and drifting roadmaps often stem from the fact that individuals only "own" items on a project management chart. The DRI model changes that.

I predict that the DRI Model will become the definitive organizational architecture for AI-Native companies. A DRI is a single human who explicitly owns one cross-functional outcome end-to-end. This is not a title; it is a clear operational contract. Acting like a single designated on-call engineer in a service runbook, the DRI is entirely accountable for the final result and the ultimate business impact.

In this model, a single human DRI sits at the center of a specialized swarm of research, execution, compliance, and monitoring agents. The DRI does not spend time on manual execution. They orchestrate, decide, and own the final output contract. Real-time data pipelines replace traditional reporting latency. Business activities are executed in concrete, quantifiable outcomes.

The skillset that makes a great engineer maps almost perfectly to what makes a great DRI. When you write a technical spec, you're defining outcomes. When you instrument a service and set up automated alerts, you're building measurements into an outcome you own. When you debug a production incident at 2 AM without waiting to be told, you're demonstrating exactly the ownership instinct the DRI model is built on.

Stepping into a DRI role means shifting how you define your personal success. In the agentic AI era, great engineering isn't measured by how many lines of code you write. This means a few things have changed in practice for many of you here today:

You instrument the outcome, not just the system. Great engineers already instrument their services obsessively. DRIs extend that same technical rigor to the business outcome they own. If you're a Growth DRI, you don't just monitor API latency. You monitor activation rates, conversion funnels, and revenue impact. You own the full outcome end-to-end.

You own the decision, not just the recommendation. Engineers are often great at analysis, but they frequently hand the output to a product manager or executive to make the choice. DRIs close that loop themselves. You do the analysis, you make the call, and you own whatever happens next. This can feel uncomfortable at first, but it becomes natural fast.

You configure your agent swarm deliberately. Most non-technical DRIs will treat AI agents as black boxes. Engineers, however, will instrument them, evaluate their outputs, identify failure modes, and build better validation pipelines around them.

This is where your engineering background becomes a genuine superpower.

Dr. Lisa Su: Everything you are describing requires real compute... and a lot of it. Not just one type of compute... the full stack, working together.

When you think about what developers and enterprises actually need to run agents at scale... what has to be true about the compute infrastructure to make that work?

Dr. Kai-Fu Lee: Lisa, this is where it lands directly in your world. The compute pattern underneath the agentic AI trend is fundamentally changing. Agents are bursty and highly parallel, not steady-state. One user query fans out to twenty parallel agent calls, collapses, and fans out again. The agent economy is fundamentally an inference economy, and inference looks nothing like training.

It requires local-first, on-device processing and latency under 100 milliseconds for multi-agent orchestration to feel real. That’s where the hardware question is being decided right now—and I think your team has read that landscape better than anyone.

The shift to multi-agent architectures requires us to look at computing through the lens of extreme token efficiency and localized processing.

Dr. Lisa Su: I want to close where I started this morning. The AI era is still in its early stages. The best is ahead of us.

Looking to the future…what excites you most about what developers are building next?

Dr. Kai-Fu Lee: What lies ahead is the birth of the truly Autonomous Enterprise, driven by multi-layered, cross-departmental agent networks. The future of industry AI transformation will be balanced by two uncompromising corporate imperatives: Data Sovereignty and Visible ROI. Partners like AMD are building Compute Sovereignty as the critical backbone to make this safe.

The biggest opportunity open to you right now is to build AI that delivers a business outcome that used to require an entire team. We aren't talking about "AI that helps a marketer do their job," but an AI agent that is the marketing function. Not "AI that assists a financial analyst," but an AI agent that delivers automated financial analysis.

I've worked closely with our engineers at 01.AI to build a "Kai-Fu AI" that acts as my personal decision-making agent. We are currently rolling out pilots with more enterprise leaders. We found that the fastest way to deploy AI in a large enterprise is through a top-down mandate from the CEO or Chairman. If you build the tools for CEO or a CFO, they get addicted. Once they are hooked, they drive the agentic transformation downward through their own corporate hierarchies.

If you are sitting in this room with a laptop, an understanding of system orchestration, and a bold idea, you are in a better strategic position right now than the strategy department of any Fortune 500 company in the world.

Don’t trade this historic position for a conventional job at one of them. Just build.

Dr. Lisa Su: Thank you, Kai-Fu.

Dr. Kai-Fu Lee: We value the ease of use for developers. On top of this powerful AMD foundation, we've integrated the brain and toolkit from the 01.AI Worldwise Enterprise LLM Platform. Worldwise not only embeds a wide range of leading models, but also connects the enterprise knowledge base directly to core business workflows. This allows developers to choose the right models and adapt them to a specific R&D use case.

Three key features that make Cube01 unique and powerful for your agentic AI projects:

• Multi-Agent Orchestration Layer: Worldwise's Multi-Agent framework enables millisecond-level response times. This One-Box solution allows autonomous processes to run persistently, and dynamic tools to orchestrate agents seamlessly. Your AI Agents will be "always-on."

• AI Worker Entity Mapping: Unlike traditional AI bots, the system assigns each AI Agent a distinct "digital identity." You can configure them to map with real business permissions and assets. AI Agents can precisely identify business context and proactively step into collaborative workflows. This way, AI agents can plug into virtual roles within the organization just like humans. Each of them becomes an independent productivity node and collaborates with the other Agents to get things done.

• Compute Sovereignty and Security: Through this collaboration with AMD, Cube01 transforms high-cost cloud computing into controllable and secure local assets, establishing Compute Sovereignty for companies and organizations. You have probably been paying as you go for cloud computing, like riding a Didi. Now you have a safe personal limousine.

Again, I encourage all engineers to develop with an "Outcome mindset." We would love to hear how you all are evolving into a DRI with Cube01.