I. Opening: The Next Wave After AI

While Artificial Intelligence has defined the last decade, it remains constrained by classical computation — unable to fully capture the complex probability spaces that govern natural phenomena, such as molecular quantum behaviour.

Quantum systems, by contrast, compute through superposition and entanglement — the very mathematics of uncertainty.

When AI merges with quantum algorithms, the outcome is more than faster processing — it marks the dawn of a new paradigm for reasoning, creativity, and discovery.

II. Case — Insilico Medicine: Quantum + AI, Redefining Drug Discovery

Drug development is notoriously slow and costly — often taking over 10 years and costing around $2 billion per approved drug, with high failure rates due to the limitations of classical simulation.

Quantum computing aims to change that.

According to McKinsey, by 2035, quantum applications in life sciences could create up to $2 trillion in value, driven by advances in molecular simulation and protein folding.

In my book Strategic Development of Technology in China, I introduced Insilico Medicine, a pioneer in AI-driven drug discovery and among the first to explore integrating quantum computing with generative AI.

Its Variational Quantum Eigensolver (VQE) — a leading hybrid quantum-classical algorithm — has demonstrated improved molecular energy estimation accuracy, potentially enhancing drug target modeling.

By 2025, Insilico used a quantum-enhanced generative model to design novel molecules targeting KRAS mutations, one of the toughest challenges in oncology, achieving verified biological activity in early testing.

By combining quantum’s computational power with AI’s predictive intelligence, Insilico exemplifies how the two technologies can create a powerful synergy — accelerating discovery and redefining what’s possible in drug development.

III. Turing Test 2.0 — From Industrial Tools to Cognitive Engines

McKinsey’s long-term outlook (2040+) envisions quantum computing not merely as a faster processor, but as a cognitive amplifier — enabling humanity to simulate aspects of reality once unreachable by classical computation.

The true test of intelligence may ultimately lie at the intersection of information theory and the laws of nature.

Redefining the Test of Intelligence

The Classic Turing Test
Evaluates whether a machine can mimic human conversation well enough to appear intelligent — rooted in a deterministic, logic-based framework.

The Quantum Turing Test (QTT)
Extends this idea to ask whether an artificial system can model and reason within a quantum world of uncertainty and indefinite causality — a challenge that touches the frontiers of quantum gravity and computational theory.

Though still theoretical, the QTT expands our understanding of what “intelligence” could mean in a universe that resists deterministic explanation.

The New Definition of Intelligence

This shift reframes intelligence itself — 
 from seeking absolute, predictable answers,
 to building self-consistent, adaptive models capable of operating amid fundamental uncertainty.

IV. Conclusion: Embracing the “Superposition Mindset”

The story of quantum is the story of humanity learning to master uncertainty.

From Einstein’s doubt — “God does not play dice” — to Insilico Medicine accelerating drug discovery with quantum-inspired algorithms, science has transformed uncertainty into strategy.

For decision makers, the key is adopting a Superposition Mindset:

In an age of rapid technological change, the future isn’t binary — it’s entangled, with multiple possibilities unfolding at once.

To lead in the quantum-AI era, we must invest for the long term and cultivate the ability to think, decide, and build amid uncertainty — not despite it.

I. Introduction: The Quantum Inflection Point — A Long-Term Consensus Between Capital and Science

Quantum mechanics is no longer an academic pursuit in the lab; it is a trillion-dollar variable actively reshaping industrial landscapes.

According to McKinsey’s Quantum Technology Monitor 2024, the global quantum technology (QT) ecosystem has attracted $42 billion in public investment. Private investment fell 27% in 2023 to $1.71 billion — but that’s far smaller than the 38% global average decline in startup funding. In other words, long-term capital confidence in quantum remains intact.

McKinsey estimates that by 2035, quantum technologies could unlock $2 trillion in economic value, with life sciences, finance, chemicals, and transportation as early beneficiaries. By 2040, the combined market size of Quantum Computing, Communication, and Sensing is expected to hit $173 billion.

The 2025 Nobel Prize in Physics has effectively pressed the “accelerator” on this global transformation.

II. The Nobel Prize Decoded: The “Tunnelling Signal” for Industrial Scale

The 2025 Nobel Prize in Physics honoured three pioneers — John Clarke, Michel Devoret, and John Martinis — for their foundational work in “Macroscopic Quantum Effects.”

Their work, at its core, involved creating a 1-centimetre-sized “Josephson Junction” using superconductors and insulators. They proved that billions of electron pairs could act as a single, giant particle, performing a seemingly impossible “collective tunnelling” (or “quantum wall-piercing”) — a quantum effect observed at a visible, macroscopic scale.

This discovery laid the foundation for today’s superconducting quantum computers, bridging the gap between theoretical physics and scalable hardware.

Notably, Martinis later led Google’s quantum team, achieving “quantum supremacy” in 2019, marking a historic moment where quantum systems outperformed classical computers on specific tasks.

The core message behind the honour is significant:

1. Removing Conceptual Barriers to Scalability: Demonstrating that large systems can show stable quantum behaviour provides the necessary theoretical and experimental basis for developing larger, more complex fault-tolerant quantum computers.
2. Strong Alignment with Investment Priorities: The Nobel recognition confirms the industry’s shift towards fault-tolerant quantum computing (FTQC). McKinsey reports that in 2023–2024, IBM, Microsoft, and IonQ have made key advances in “quantum error correction.” For instance, IBM utilized 288 physical qubits to preserve 12 logical qubits, resulting in a 90% reduction in error rate compared to traditional methods.

III. Case: IonQ AQ64 — The Commercial Quantum Computer You Can Access in the Cloud

In my book Strategic Development of Technology in China, I introduced IonQ, the world’s first publicly listed pure-play quantum computing company.

IonQ follows the trapped-ion approach — using electromagnetic fields to capture charged atoms and encode data in their internal vibrations. This design achieves an operational error rate below 0.1%, the most precise among all mainstream quantum computing architectures.

1. Hardware Leap and Commercial Scale: Its fifth-generation system, “Tempo,” launched in 2025, achieved a milestone of 64 Algorithmic Qubits (AQ). This means the system can simultaneously explore over 18 quintillion computational possibilities — a 268 million-fold increase in processing power over last year’s AQ36, significantly outperforming comparable published IBM systems.
2. ​​Application and Ecosystem: Tempo has already demonstrated real-world impact across industries:

• Energy & materials: simulating lithium-ion battery reactions with Hyundai;
• Drug discovery: accelerating molecule screening with AstraZeneca;
• Finance & optimization: modelling complex portfolios with Multiverse Computing.

Crucially, IonQ is the only quantum system simultaneously available on Amazon Braket, Microsoft Azure, and Google Cloud, dramatically lowering the entry barrier for enterprises and accelerating commercial adoption.

IonQ is not building prototypes — it’s engineering the backbone of the post-classical computing era.

IV. Closing: Why Investors Should Care Now

Quantum is no longer a far-future bet.

It’s entering the engineering and early commercial phase, where hardware performance translates directly into enterprise applications.

For founders, this means a new layer of computational advantage; for investors, it’s a once-in-a-generation inflection point similar to the semiconductor industry in the 1970s.

The Nobel Prize told us quantum coherence is real. IonQ proved it can be built, scaled, and rented by the hour.

中文版

一、开篇：万亿量子的拐点——资本与科学的长期共识

量子技术早已不是实验室里的理论，而是正在重构产业的 “万亿级变量”。

据 McKinsey《2024 年量子技术监测报告》，全球量子技术（QT）生态已积累420 亿美元公共投资；2023 年私人投资虽降至 17.1 亿美元（同比降 27%），但降幅远低于全球初创投资 38% 的平均水平 —— 这意味着资本对量子的长期信心未减。

更关键的是，量子技术预计 2035 年解锁 2 万亿美元经济价值，生命科学、金融、化工、交通将率先受益；到 2040 年，量子计算、通信、传感三大领域市场规模合计将达 1730 亿美元。

而 2025 年诺贝尔物理学奖的颁布，更是给这条赛道按下了 “加速键”。

二、诺贝尔奖的产业密码：宏观量子效应的“穿墙术”

2025年诺贝尔物理学奖授予了在“宏观量子效应”领域做出奠基性贡献的三位物理学家: 约翰·克拉克（John Clarke）、米歇尔·德沃雷（Michel Devoret）与约翰·马蒂尼（John Martinis）。

他们的工作，简单来说，是用超导体和绝缘体造出 1 厘米大小的 “约瑟夫森结”，首次在肉眼可见的尺度上，让数十亿电子对像单个粒子一样，实现了神奇的“集体穿墙”（量子隧穿效应）。这项研究奠定了今天超导量子计算机的物理基础，扫清量子计算规模化的关键障碍。

值得一提的是，当时的实验操作者约翰·马蒂尼（John Martinis），后来成为谷歌 ( Google ) 量子计算团队的领导者，并在 2019 年带领团队实现了量子霸权– 成为从理论到商业的关键里程碑。

而这背后的产业信号远比荣誉本身更重要：

1. 它为量子计算规模化扫清了思想障碍：证明了宏观系统也能展现出稳定的量子行为，为建造更大、更复杂的量子计算机提供了最根本的理论与实验基石。
2. 诺奖与产业技术的强关联： 诺奖的认可与当前容错量子计算的核心需求高度契合。麦肯锡报告指出，在 2023-2024 年，IBM、Microsoft、IonQ 等巨头已在“量子纠错”上取得关键进展。

三、案例：IonQ AQ64—— 云服务已开放的 “商用量子计算机”

在我的书中《Strategic Development of Technology in China》介绍过IonQ ，它是全球首家纯量子计算上市公司，走 “离子阱” 路线：用电场 “抓” 住带电离子，靠离子 “内部震动” 存信息，操作错误率低于 0.1%，是目前 “最准” 的技术路线之一。

其 2025 年推出的第五代量子计算机 “Tempo”，实现了64个 “算法量子比特（AQ）”—— 这意味着系统可同时探索超过 18 万亿亿种计算可能，处理能力比去年的 AQ36 提升了 2.68 亿倍，远超同期 IBM 系统。

Tempo 系统已在多个前沿领域展示落地价值：与现代汽车合作模拟锂电池材料，与阿斯利康合作加速药物发现。

更重要的是，IonQ 是唯一一家同时在亚马逊 Braket、微软 Azure 和谷歌云三大主流云平台上提供服务的量子系统，这极大地降低了企业的使用门槛，加速了商业化进程。

四、结束语：量子商业化拐点已至

量子技术，已不再是远期的赌注。

它正进入工程化与商业化的早期阶段，硬件性能正直接转化为企业的实际应用。

对创业者而言，这意味着一层全新的算力优势；对投资者来说，这堪比半导体产业在1970年代的历史性机遇，一个时代性的拐点已然出现。

诺贝尔奖向我们证实了宏观量子相干是可行的，而IonQ则证明了它能够被制造、规模化，并且可以按小时付费使用。

视频版

The Mid-Autumn Festival* is here again. Moonlight spills across the eaves, carrying the faint sweetness of osmanthus on the wind. Centuries ago, the famous Chinese poet Li Bai once wrote:

“The people of today do not see the moon of old,
But the same moon once shone on those before us.”

A founder friend recently asked me: “After a year of chasing projects and partnerships, what’s your most honest feeling this Mid-Autumn?”

My answer is simple: cherish what’s in front of you.

Not “someday.” Not “later.” Not after product-market fit or the next funding round. Right now.

• The project you’ve been meaning to start with someone—do it together today.
  
• The gratitude you’ve been holding back—say it now, directly.
  
• The people you love—don’t save it for “after I’ve made it.” Offer your heart while you still can.
  

These aren’t lines from a self-help book. They’re truths learned the hard way—through regrets that cannot be undone.

I. A Missed Goodbye

The summer before I went to university, my grandmother came to visit. Afternoon light poured like warm amber across the room as she asked softly, “Shall I take you to register at university?”

Eager for the future, I declined gently, worrying she was too old for the journey. I promised I’d bring her once I had settled in. She smiled, said no more. That moment—sunlight in her hair, her quiet voice—turned out to be our last real goodbye.

There is no “later” for some promises.

II. A Mentor Without a Photo

In my darkest professional valley, a mentor stood by me. He saw a “blooming flower” and a “rising sun,” when I saw only data points of failure. Our most pivotal conversations happened on walks, a moving boardroom for strategy and soul.

When I began my startup, he fell gravely ill. To avoid distracting me, he chose radio silence—a final, devastating act of support.

I arrived at the hospital to find a man, once a towering figure, reduced to a whisper. I had to stand at the foot of his bed for our eyes to meet.

We knew each other for over a decade. We never took a single photo. Always waiting for a better moment, a less rushed day, a time when we weren’t so focused on the work itself.

III. The Dog Who Walked Closest to the Street

And then there was my dog, Bubble—forever cheerful, forever protective. On walks, she always positioned herself closer to the road, nudging me gently toward safety. When I called my parents on video, she would push her big head into the camera, competing to see whose face was bigger.

Through every triumph and every setback—funding wins, product failures—she was there, offering joy or quiet companionship. Her short life taught me the purest definition of love and loyalty.

Technology has given me a way to preserve fragments of those moments. Photos, videos, and even AI reconstructions keep traces alive. But they are no substitute for the living presence we too often take for granted.

What This Means for Us as Founders and Leaders

Entrepreneurs often live in the future—thinking in terms of scale, milestones, and exits. But life is happening now. And leadership is not only about building the next platform, product, or company; it’s also about how we honour the relationships and moments that give meaning to what we build.

The truth is: what softens us, sustains us, and makes us human is rarely the grand narrative. It’s the small things—the afternoon sunlight, a word of encouragement, a wagging tail.

As another Mid-Autumn moon rises, I return to an ancient line:

“Ancient and modern, like a flowing stream—
We gaze upon the same bright moon.”

Time carries us all forward. But tonight, under the same moon as countless generations before, we still hold the power to pause, reflect, and treasure the present.

So I leave you with one question:

Who—or what—deserves your attention right now, before “later” becomes too late?

Final Note:

As founders, investors, and leaders, may we learn not only to build companies that last—but also to live moments that matter.

Note: The Mid-Autumn Festival is a traditional Chinese holiday celebrating family reunion, observed on the 15th day of the 8th lunar month, coinciding with the brightest full moon. A key tradition is eating mooncakes—dense, round pastries with a variety of sweet or savoury fillings that symbolize the full moon.

中文版

又是一年中秋。有创业者朋友问我，这一年忙着赶项目、对接资源，到了中秋，心里头最真切的感受是什么？我想起六个字 —— 珍惜眼前所有。这不是旁人灌的鸡汤，是这些年我攥着遗憾，慢慢品出的真心话。

一、那年暑假：晒暖的阳光里，藏着最后的永别

上大学前的最后一个暑假，外婆特意来看我。午后的阳光像温润的琥珀，把屋子映得通透。我们并排躺在床上，她忽然侧过身，轻声说：“要不，我送你去大学报到吧？”我那时心里装着的是远方和未来。我婉拒了，怕她年迈，经不起舟车劳顿，信誓旦旦地说，等一切安顿好就接她去玩。她笑了笑，没再坚持，阳光在她花白的发丝上流淌。

那时我不知道，有些约定，是没有“以后”的。那个午后，连同阳光的暖意和她的轻声细语，被我典藏在心底，如今想来，才明白那已是最后的、完整的告别。

二、没拍的合照，成了此生的遗憾

在异国他乡，我遇到一位如父如友的导师。在我人生最晦暗的谷底，他带我去做义工。在那里，我发现自己并非一无是处，原来我微弱的光，也能照亮他人一隅。那时的我没有信心，他总是说，他已看见我如鲜花般绽放，像个小太阳一样温暖周围。

导师因腰伤不能久坐，我们的谈话总在散步中完成。那条走了无数遍的林荫道，见证了他不疾不徐的教诲。后来我开始创业，他却身患重病，怕扰我心思，选择了沉默。待我出差回来赶到病床前，他一米九的身躯，已薄如一片风中的叶子。那时的导师已无力转头，我必须站到床尾，他才能看到我。

可惜相识十数年，我们竟没有一张合影。不是我没化妆，便是忘了带手机，总以为来日方长。此刻想起，才惊觉“古人不见今时月”，我与他的缘分，永远定格在了那些散步里，成了此生一大憾事。

三、毛孩子的守护，藏在细节里

你家里，可曾有过一个毛孩子？

我的毛孩子，永远是我的开心果。每次我与父母视频，她总要奋力挤进镜头，湿漉漉的鼻子几乎要贴上屏幕，固执地要与我比谁的脸更大。

无论我带着哪种心情回家，她都在那里——用毫无保留的欢欣，或沉默温暖的陪伴，将我的一切情绪妥帖安放。她用短短一生，教会我何为纯粹的爱与守护。

四、且行且珍惜

你呢？在这月到中秋分外明的时刻，心头是否也浮现出某个人、某段未说的话、某件未完成的事？

生活或许有奔波，有忙碌，有解不完的难题，可总有一些东西，值得我们停下来好好珍惜。我母亲最爱看云卷云舒，父亲独爱绿意盎然的草地。你看，让我们心头一软的，从来不是宏大的叙事，而是这些具体而微小的存在。

“古人今人若流水，共看明月皆如此。”千年如水逝去，我们与古人看到的，是同一轮明月，所经历的，也无非是悲欢离合。即便有时感到一无所有，我们至少还有自己，拥有感知这月华如水的能力。

那么，便不再多言。只愿我们，都能在这奔流不息的时光里，学会温柔地对待每一个当下，但愿人长久，千里共婵娟。往后的日子，我们一起淡定从容地生活！

视频版

For years, AI has lived on flat ground — processing text, classifying images, and predicting numbers. But the world isn’t flat. The real test of intelligence is moving through a messy, unpredictable, 3D physical world.

This is the next frontier: spatial intelligence. And it’s not just a technical race — it’s a test of vision, strategy, and execution.

1. The Two Strategic Paths to the 3D Future

When it comes to building AI for 3D environments, two strategies are emerging:

1. Marble — Persistent, High-Fidelity Worlds
Fei-Fei Li’s World Labs is behind Marble, a system that generates vivid, stable 3D spaces from text or images. Think gaming, metaverse design, or architecture — anywhere quality and persistence matter more than real-time change.

2. Genie — Real-Time, Physics-Driven Worlds
DeepMind’s Genie focuses on dynamic interaction and physical simulation. It generates environments that follow physics rules — ideal for robotics training, disaster response drills, and scientific simulation.

These aren’t rivals. They’re two sides of the same coin: one optimizes for creativity and permanence, the other for interaction and adaptability. Both point to the same core challenge: teaching AI not just to generate 3D content, but to understand 3D space.

2. World Labs’ “Large World Model” — Cracking the Code of Spatial Intelligence

Dr. Fei-Fei Li and the World Labs team are betting on the latter with their Large World Model (LWM). Their thesis is simple, yet profound: If AI is to become truly intelligent, it must master space before language.

Biologically, animals mastered spatial awareness (recognizing paths, finding food) hundreds of millions of years before humans developed complex language. Spatial intelligence is the “source code” for general intelligence.

World Labs’ bold move is an attempt to give AI three key abilities:

1. From 2D to 3D: Reconstruct objects and spaces from flat images using geometry and reasoning.
2. Generation + Reconstruction: Not just dream up virtual spaces but also digitize real ones with physical rules intact.
3. Scarce Data, Rich Reasoning: Shift from brute-force data collection to efficient spatial reasoning, overcoming the lack of labelled 3D training data.

3. The Fei-Fei Li Playbook: From ImageNet to World Labs

For every Founder and Investor, the trajectory from ImageNet (2009) to World Labs (2024) reveals Fei-Fei Li’s methodology:

• Start from first principles: In 2009, ImageNet was dismissed as impossible. Her insight? If recognition requires data, build the dataset first.
• “Do it, then prove it.”: She didn’t wait for consensus. Fei-Fei Li created the dataset first (ImageNet) by mobilizing 48,000 people to label 15 million images, betting that value creation beats theory.
• Stay on the core logic: Just as ImageNet unlocked vision, World Labs is betting spatial intelligence will unlock robotics, AR, and embodied AI.

The entrepreneurial takeaway: when the logic holds and value is real, act before it’s obvious.

4. Ancient Wisdom for Modern Tech Cycles

The strategic risk of this shift is immense. The philosophy to navigate it comes from the ancient text, the I Ching (Book of Changes), specifically the Kan Gua (坎卦), representing Peril/The Abyss:

• “Xi Kan” (习坎) Challenges are normal: Treat challenge as the normal state of exploration. Innovation isn’t a smooth road; it’s a series of checkpoints.
  
• “You Fu” (有孚) Hold your conviction: Maintain inner conviction and sincerity. In a capital market driven by hype, sincerity to the core problem is the source of resilience.
  
• “Xing You Shang” (行有尚) Keep moving – like water, flow around barriers instead of forcing through them. World Labs embodies this: when 3D data proved scarce, they didn’t quit. They pivoted to reasoning-driven models—same goal, different path.

5. Why This Matters

For investors and executives, the message is clear:

• Spatial intelligence is the missing link between today’s “flat” AI and tomorrow’s embodied, useful agents.
• This is infrastructure, not hype — the foundation for robotics, industrial automation, metaverse, disaster response, and beyond.
• The winners will combine deep tech with resilience — the courage to commit before the market consensus, and the adaptability to change tactics without losing direction.

AGI won’t arrive with another chatbot. It will arrive the moment AI can move through the world as confidently as it can talk about it.

And that journey, like all great ventures, requires both cutting-edge science and the ancient wisdom of how to cross numerous challenges.

当AI从处理文本和图像的“平面智能”，迈向理解与交互三维物理世界的“空间智能”，一场定义未来科技格局的竞赛已悄然开启。这不仅是算法的迭代，更是视野、战略与执行力的终极考验。

一、生成与交互，3D世界的两种战略定位

当前在AI生成3D世界的探索中呈现出两类典型技术方向，其差异本质是对 “3D 世界需求” 的不同响应，反映了行业对空间智能的初步探索逻辑。

路径一：Marble——高质量持久化世界的构建者

以World Labs旗下的Marble为代表的路径，核心竞争力在于“高保真与持久化”。用户通过图像或文本输入，可生成具备清晰几何结构、多元风格且能长期稳定存在的虚拟空间。该路径深度契合游戏开发（快速构建开放世界）、建筑设计与元宇宙内容创作等商业领域，这些场景对视觉精度和场景稳定性的需求，优先于实时交互效率。

路径二：Genie——动态交互环境的模拟引擎

Google DeepMind的Genie则代表了另一条路径：“实时交互与物理模拟”。作为世界模型，它专注于生成可根据指令实时修改、遵循物理规则的动态环境。其核心应用场景在于机器人智能体的训练（模拟现实物理规则以降低实体测试成本）、防灾应急演练模拟（复现地震废墟、火灾蔓延等动态场景）等，为科研与功能性训练提供了一个低成本、高效率的沙盒环境。

两类路径并非竞争关系，而是 “需求匹配” 的体现：若需落地商业创意，Marble 的 “高质量持久世界” 更高效；若需支撑 AI 科研或功能性训练，Genie 的 “实时动态交互” 更关键 —— 但它们共同指向一个核心问题：AI 的核心价值不仅是 “生成 3D 内容”，更在于 “理解 3D 空间逻辑”，这也是 World Labs 探索的核心方向。

二、World Labs 的 “大世界模型（LWM）”：让 AI 真正理解 3D 世界

2024 年 2 月，李飞飞团队带着 World Labs 敲开了空间智能的大门 —— 他们要做的 “大世界模型（LWM）”，核心目标是让 AI 像人类一样理解 3D 空间逻辑，实现 “感知、生成、交互” 三位一体的空间智能。这一决策并非偶然，而是基于对 AI 进化本质的深刻判断。

1. 进化视角：空间智能是生物智能的 “本源起点”

生物变聪明的起点，从来不是 “会说话”，而是 “能认路”。

从 5.4 亿年前三叶虫靠视觉躲天敌、找食物，到人类凭空间记忆记住家里钥匙的位置 —— 空间感知是生物与世界打交道的 “基本功”，而人类语言的进化不足 100 万年。李飞飞的核心逻辑是：AI 若要模拟 “真实智能”，需优先攻克 “空间理解” 这一生物智能的本源领域，不然连 “听到‘拿水杯’，就知道杯子在哪、该怎么抓” 都做不到，谈何 “通用智能”？

2. 破解现实痛点：弥合“维度断层”

当前AI存在“维度断层”：大语言模型处理1D文本，视觉模型生成2D图像，但真实世界是3D且动态的。缺乏空间智能的AI，无法让机器人自主导航于复杂环境，也难以构建真正可交互的沉浸式体验。空间智能是AGI从“数字助手”变为“物理世界行动者”的关键桥梁。

3. 技术破局点：三大核心能力构建壁垒

World Labs的“大世界模型”旨在解决三大核心难题：

• 从2D逆推3D： 通过多视角融合与几何推理，从二维图像中还原物体的三维结构与空间关系。
• 生成与重建并重： 不仅生成虚拟场景，也能对真实环境进行三维数字化重建，并内置物理规则（如重力、碰撞），避免 “物体悬浮”“穿模” 等问题，确保真实性；
• 突破 3D 数据稀缺：语言数据可从互联网获取，但 “3D 交互数据”（如抓取不同形状物体的力度）藏于人类认知，LWM 通过 “空间推理 + 少量标注数据”，让 AI 从 “依赖海量数据” 转向 “高效逻辑推演”，降低数据成本。

4. 应用前景：从科研到产业的 “底层引擎”

空间智能的应用远不止于娱乐，它将作为底层引擎驱动创新：

• 工业机器人： 实现复杂环境下的精准抓取与自主导航；
• 元宇宙与游戏：构建 “可交互持久世界”，用户可自主移动家具、改变场景布局；
• 防灾与教育：模拟地震、火灾的 3D 动态场景，用于消防员训练；搭建 “原子结构 3D 实验室”，让学生直观理解微观空间。

三、从 ImageNet 到 World Labs：李飞飞的创业方法论启示

对创业者与投资人而言，World Labs 的价值不仅是技术方向，更是李飞飞 “从 0 到 1” 的实践方法论 —— 这种方法论，早在 16 年前打造 ImageNet 时就已成型。

2009 年，当 “让机器识别万物” 还被视为天方夜谭时，李飞飞的逻辑很简单：“算法识别万物的秘诀，在于无所不包的训练集”。她组织全球 4.8 万名贡献者，从 10 亿张图片中筛选 1500 万张，手工标注 2.2 万个类别 —— 这个过程中，她面临 “几乎所有人反对”“找不到队友”“无法反驳批评合理性” 的困境，但她的坚持逻辑是：“只要底层逻辑成立、能创造价值，就先做再说”。

最终，ImageNet 为杰弗里・辛顿团队的卷积神经网络突破提供了关键支撑，成为计算机视觉产业爆发的重要推动因素。如今，这种逻辑延续到 World Labs：“回到智能本源”，攻坚空间智能 —— 不是追逐热点，而是解决 “AI 无法落地物理世界” 的根本问题。

给创业者和投资者的启示：

• 从 “可能性” 入手：先相信 “空间智能” 是未来，即使路径不明朗；
• “做了再说” 的勇气：在方向大致正确时，用最小化可行产品（MVP）快速验证，而非等待完美方案；
• 坚守 “底层逻辑”：只要坚信所创造的价值是真实的，就要有穿越坎险的韧性。

四、坎卦的智慧：科技创业中的“险中求进”

创业从不缺挑战 ——3D 算法攻坚、数据稀缺、市场教育成本高，困境是常态。《易经》坎卦卦辞 “习坎，有孚维心，亨，行有尚”，为应对挑战提供了哲学启发。

• “习坎”：将挑战视为探索过程的常态，将其视为成长中的 “闯关”，每一次挑战都在增益心理韧性与应对能力；
• “有孚”：即在动荡中不忘初心，坚守内心的信念与诚信 —— 这是定力的源泉。李飞飞从 ImageNet 到 World Labs，始终坚守 “让 AI 理解世界” 的核心目标，未因短期技术热点而偏离，这种对 “长期价值” 的忠诚，是穿越技术周期的关键；
• “行有尚”：最终，行动才是破局的关键。要像水一样，遇阻则迂回，但始终保持流动不息。当 3D 数据获取困难时，World Labs 转向 “空间推理 + 少量数据”，而非硬拼数据规模 —— 这种源于《易经》的 “守本心而变方法” 的哲学智慧，正是穿越技术与市场周期的精神内核。

结语：空间智能，AGI 从 “理论构想” 走向 “产业落地” 的关键环节

回望历史，李飞飞与 ImageNet 的成功，核心并非技术的必然胜利，而是 “本源思维”（洞见数据是智能的基石）与 “先做再说” 的勇气的胜利。今天，Marble 与 World Labs 正以同样的逻辑，聚焦 “空间智能” 这一 AI 理解并进入物理世界的基石。

投资与创业的真正分水岭，在于能否完成从 “知” 到 “行” 的惊险一跃 —— 将 “空间智能是未来” 的共识性判断，转化为策略、资源与时间上的坚定配置。当 AI 真正打通这一关键环节，能真正 “理解 3D 空间” 时，它将从 “辅助工具” 蜕变为 “行动伙伴”—— 而这一天的到来，始于当下对空间智能的坚守与探索。

视频版

Have You Ever Bought Einstein’s Brain?

Have you ever considered the invisible, yet profoundly profitable, products in the market? Not the hardware or the software, but the emotion itself.

In 2023, China’s Taobao platform saw a peculiar bestseller: “Einstein’s Brain.” For less than a dollar, customers purchased a non-existent product, receiving a whimsical, randomized text message from the seller. This wasn’t a transaction of goods, but of curiosity, humour, and social currency.

This is the real lesson: in the AI era, emotional value drives differentiation and monetization.

The “Einstein’s Brain” phenomenon is more than a cultural quirk; it is a leading indicator. It underscores a critical strategic shift as AI advances: the commoditization of functionality. As AI models become increasingly ubiquitous, the competitive advantage of a purely functional product — one that works — rapidly erodes. The new battleground is not in utility, but in cultivating what is scarce: a genuine emotional connection with the user.

Case Study: Bubble Pal, The World’s First Mass-Market AI Toy Accessory

China’s Haivivi has launched the world’s first mass-market AI toy accessory, Bubble Pal — a safe, soft bubble device that transforms any plush toy into an interactive companion for children.

Although its functional specs are solid — multilingual chat, knowledge Q&A, and certified safety standards — its market success comes from addressing a deeper need. It sold over 250,000 units, generating roughly $14 million in revenue in under a year, because it solves for loneliness and impatience. By providing a patient and intelligent emotional companion, it helps children manage their emotions. This isn’t just a tool; it’s a relationship.

In the AI era, building a product with good quality and a fair price is simply table stakes. If your value proposition is purely functional, your customers will treat it as a commodity, spending as little as possible to acquire it and as little time as possible using it. This leads to a race to the bottom on price and a relentless cycle of churn.

Conversely, if you embed emotional value — creating what we call product stickiness — you fundamentally change the value equation. You create a long-term relationship with the customer, not just a one-time transaction. This elevates your brand beyond simple utility, fostering a loyalty that is far less sensitive to price fluctuations and competition. Look no further than the average person spending five hours a day on their smartphone. The device ceased being a tool years ago; it is now a central hub of our social and emotional lives.

AI Value-Creation Sessions

To help you navigate this strategic imperative, Mans International is launching the “AI Value-Creation” session series. This exclusive, invitation-only event is designed for a select group of tech founders, investors, and senior executives. 

Our recent session explored how AI can unlock emotional value in products and drive sustainable growth.

Key takeaways included:

• The Mindset Shift: Reframing business models from functional utilities to emotional platforms.
• Monetization Levers: Pinpointing the specific opportunities unlocked by emotional value.
• The Blueprint for Construction: Step-by-step strategies to embed emotional value into products at the core development stage.

Attendees left with actionable insights and a clear framework to accelerate AI-driven value creation in their businesses. 

Access the session 

Missed the session? Stay tuned for upcoming events or request exclusive access to session highlights and resources.

In the age of AI, there’s a new currency for success, and it’s not just about what you know. It’s about how fast you can turn that knowledge into action. This is the “Knowledge-to-Action Loop,” and AI is the bridge that makes it happen instantly. This principle is not new — it echoes the ancient Chinese wisdom of 知行合一 (zhī xíng hé yī), the unity of knowledge and action.

1. Vibe Coding: From Idea to Prototype in Minutes

Every experienced professional knows the pain: you want a small tool or workflow fix, but the request disappears into the IT backlog. By the time it comes out, it’s either irrelevant or unrecognizable.

That’s the old world: knowledge (the idea) separated from action (the result).

The concept of Vibe Coding is the ultimate micro-example of the Knowledge-to-Action loop in practice.

It’s not about writing code; it’s about sketching with it. You toss out an idea, and an AI tool generates a first-draft prototype. Want changes? It adapts instantly.

The process is a continuous, rapid-fire cycle of Idea → Feedback → Iteration → Usable result.

• Traditional coding: write the “sheet music” (logic) for days, play it for weeks, restart if a note is wrong.
• Vibe coding: pick up the “guitar” (AI tools) and jam — mistakes fixed on the fly, usable output in minutes.

This is knowing and doing converging in real time.

2. MBZUAI: The Institutional Blueprint for “Knowing-Doing”

While Vibe Coding is personal, some institutions are building this philosophy into their DNA. The Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) is a prime example. Founded in Abu Dhabi in 2019, it is the world’s first university dedicated entirely to AI — not to produce theorists, but leaders solving real-world problems.

Their president, Eric Xing, is the living embodiment of this principle. His career isn’t siloed; it’s a seamless loop:

• Academic “Knowing”: A dual Ph.D. in Molecular Biology and Computer Science and CMU professor, mastering the theoretical underpinnings of AI.
• Industry “Doing”: He co-founded Petuum, a company that scaled distributed machine learning from the lab to the enterprise, earning a $93M Series B from SoftBank. Then, he launched GenBio AI to use AI to build “digital organisms” that can simulate DNA and proteins, turning his academic knowledge into a tool for biotech and pharma.

Research is the “knowing,” and entrepreneurship is the “doing.” He treats AI not as abstract equations, but as a converter that turns theory into solutions.

A Local Problem, A Real Fix

A perfect example is MBZUAI’s work on deepfake detection for the Middle East. They saw a unique, local problem — the widespread use of “Arabish” (a mix of Arabic and English) in daily conversation.

MBZUAI spotted this blind spot for deepfake detection systems:

• Knowing: Human detection accuracy was just 60%; existing AI accuracy dropped by 35% in mixed-language cases.
• Doing: Built ArEnAV, a 765-hour bilingual audio-visual dataset. This became the global benchmark for bilingual deepfake detection.
• Value: Media outlets and fact-checkers can now reliably flag fakes in Arabic-English content.

Their paper title says it all: “Tell Me Habibi, Is It Real or Fake?” It’s not about the tech; it’s about solving a local, human problem.

3. Young Founders: Age No Longer a Barrier

The traditional model of entrepreneurship required years of experience, a robust network, and substantial funding. AI has levelled the playing field, introducing a new form of leverage beyond human resources and capital. Today, the core competitive advantage is no longer what you have, but how fast you can execute.

Look at the young founders breaking through:

• Brenden Foody: Launched Mercor, an AI-powered recruitment platform, at just 19. AI handled the candidate matching and resume analysis, allowing him to build a prototype and secure major funding by age 22.
• Adam Guild: Started young, spotted restaurant owners’ pain — no digital capability. With AI, he built tools to automate marketing and operations, scaling Owner.com to unicorn status by 25.

The common thread? Not just youth, but the ability to turn ideas into working products fast with AI.

4. The Future Belongs to Creators of “Knowledge-Action Unity”

As Auguste Rodin famously said, “The world is not lacking in beauty, but in discovering eyes.” In the AI era, the same holds for technology: the world isn’t lacking in tools, but in people who can wield them to solve problems.

AI itself is merely a tool. Its true value isn’t inherent in the technology, but in the skill of the user to leverage it. Consider the vast potential of AI tools like ChatGPT: while some may use it for casual purposes like fortune-telling, true innovators will harness it for coding, building systems, and creating products.

The fundamental survival logic in the AI age is this: those who can rapidly translate “knowing” into “doing” with AI will remain competitive. Your degree of “knowledge-action unity” will ultimately dictate your standing and impact in this new landscape.