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.
Book cover:
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:
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.
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
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.
Image source: IonQ
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.
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.
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?”
Generated by Sora 2
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.
Generated by Sora 2
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.
Generated by Sora 2
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.”
Generated by Sora 2
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.
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.
World Lab’s Marble Testing
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.
Trilobite fossil specimen
World Labs’ bold move is an attempt to give AI three key abilities:
From 2D to 3D: Reconstruct objects and spaces from flat images using geometry and reasoning.
Generation + Reconstruction: Not just dream up virtual spaces but also digitize real ones with physical rules intact.
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:
I Ching Kan Gua
“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.
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。
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.
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.
Image source: MBZUAI. MBZUAI Campus
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.
Image source: MBZUAI. Eric Xing, President of MBZUAI
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.
