China is rapidly emerging as a global front-runner in the race to integrate AI with consumer hardware.

On June 26, 2025, Xiaomi officially launched its first pair of AI glasses, positioned as “the personal intelligent device of the next era and your portable AI gateway,” directly challenging the Ray-Ban Meta AI glasses.

Core Feature Comparison: Xiaomi AI Glasses vs. Meta AI Glasses

Here’s the comparison table with prices converted from CNY to USD using the latest exchange rate (¥1 = $0.1396 as of early July 2025):

While Xiaomi lags slightly behind Meta in terms of fashion appeal and brand recognition, it demonstrates clear advantages in hardware performance, AI interaction, ecosystem integration, and pricing, especially within the Chinese market.

With more Chinese companies entering the space, leveraging lightweight design, intelligent features, high cost efficiency, and tightly integrated ecosystems, China is poised to lead global consumer AI hardware-software innovation.

China and the U.S. in Foundational AI Research: Each With Its Own Edge

According to Trends-Artificial Intelligence, the BOND AI trends report released by “Internet Queen” Mary Meeker, the U.S. and China are now in an intense phase of competition in AI. In foundational research, each country holds distinct strategic advantages.

🇨🇳 China’s Strength: Dual Engines of Open-Source Ecosystems and Industrial Intelligence

1. China is rewriting the global playbook for AI through the explosive growth of its open-source ecosystem and rapid AI industrialization:

• Scale and Quality of Open Models: As of Q2 2025, China has released several benchmark open-source models, including DeepSeek-R1 (trained at just 1/10 the cost of OpenAI’s), Alibaba’s Qwen-32B, and Baidu’s Ernie 4.5, covering a wide range of use cases from language to multimodal and code generation.
• World’s Largest Open Model Hub: Alibaba Cloud’s ModelScope now hosts over 70,000 open models and has a developer base of 16 million, making it one of the largest open-source AI communities globally.
• Affordable, High-Performance AI: Chinese models like DeepSeek V3 are optimized for cost-efficiency, offering industry-grade performance at a fraction of traditional training costs (as low as 1/10). This affordability accelerates the democratization of AI in SMEs and developing nations, enabling broader global access to advanced technology.

2. China’s Lead in Industrial Robotics: A Result of Policy Support and Supply Chain Strength

• Global #1 in Installations: In 2023, China accounted for over 50% of the world’s industrial robot installations. International manufacturers such as BMW and Tesla are already incorporating Chinese robotic solutions into their production lines.
• Challenges Remain: Despite rapid progress, China still relies heavily on imported high-end sensors and real-time control systems. However, domestic substitution is accelerating as local innovation and investment ramp up.

🇺🇸 U.S. Strength: Dual Advantage in Foundation Models and Chip Dominance

1. The U.S. continues to hold a clear edge in the development of core AI foundation models:

• Closed-Source Superiority: Proprietary models like OpenAI’s GPT‑4.5 and Anthropic’s Claude consistently lead on benchmarks such as MMLU and HumanEval, particularly in complex reasoning and long-context understanding.
• Open-Source Closing In: While Chinese models like DeepSeek-R1 have rapidly narrowed the performance gap (from 15.9% in 2023 to just 1.7% by 2025), closed-source U.S. models still dominate high-end commercial ecosystems, especially in enterprise and advanced research use cases.

2. Chip and Compute Supremacy: From Hardware Monopoly to Global Ecosystem Control

The U.S. has secured a dominant position in global AI infrastructure through its leadership in chip technology:

• NVIDIA’s Absolute Dominance: NVIDIA GPUs power over 90% of AI model training worldwide. Its hardware, particularly the H100 and H200 series, remains the gold standard for large model training.
• Infrastructure Leverage: NVIDIA-related compute accounts for an estimated 25% of global data centre capital expenditure (CapEx), underscoring its strategic role in the AI economy, from cloud providers to model developers.

Export Controls Spark a New Paradigm of “Accelerated Evolution” in China’s Chip Industry

Amid the growing U.S.–China tech decoupling, particularly around semiconductors, U.S. export restrictions on AI chips to China have triggered a powerful “crisis-response mechanism” across China’s chip sector:

• Technological Pressure: Real-world deployment demands are driving architectural innovation and improved chip yields.
• Market Pull: Rising substitution demand for domestic chips is fueling greater R&D investment.
• Strategic Loop: A dual-cycle strategy is emerging — import substitution at home, technology export abroad.

Beyond Huawei, Xiaomi has also entered the self-developed chip race — primarily to reduce material costs (mirroring how Apple’s M1 chip boosted Mac profit margins) and to mitigate geopolitical risk, such as potential sanctions or supply disruptions similar to Huawei’s.

The launch of Xiaomi’s Surge O1 chip marks the company’s first fully self-developed System-on-Chip (SoC) for smartphones, giving it core control from architecture design to functional integration. This shift reduces dependency on Qualcomm, lowers licensing fees, and improves gross margins, similar to how Apple used in-house chips to reduce reliance on Intel and unify its mobile and PC ecosystems.

In 2024, even NVIDIA CEO Jensen Huang acknowledged the shift, stating: “Export controls on China have failed. Our market share in China has dropped from 95% to 50% in just four years.”

Strategic Battleground

According to a study by MacroPolo, the think tank under the Paulson Institute, nearly half of the world’s top AI researchers received their undergraduate degrees from Chinese universities, compared to just 18% from U.S. universities. In 2019, that figure for China was only 29%, highlighting the country’s remarkable progress in cultivating world-class AI talent.

Take DeepSeek, for example — a team composed largely of graduates from top Chinese universities such as Tsinghua and Peking University. The team exemplifies a new wave of AI talent: highly educated, younger, open-source driven, and innovation-focused.

Similarly, Chinese talent is playing a critical role in Elon Musk’s Tesla Robotaxi project. Notably:

• Pengfei Duan, Tesla’s Chief Software Engineer for AI, earned his undergraduate degree at Wuhan University of Technology.
• Charles Qi, the machine learning engineer behind Tesla’s Full Self-Driving (FSD) system, graduated from Tsinghua University.

These cases clearly demonstrate that China’s homegrown AI talent is now shaping global innovation at the highest levels.

Summary

In the global AI race, China is swiftly narrowing the lead, driven by two key strengths: seamless hardware–software integration and a rising wave of homegrown AI talent. The launch of Xiaomi’s AI glasses marks not only a milestone for China’s consumer AI hardware but also highlights its unique strengths in ecosystem integration and cost-effectiveness. Meanwhile, China’s momentum in open-source models and industrial robotics is actively reshaping the global AI landscape.

At the same time, the United States maintains a firm grip on the foundations of AI — core algorithms and semiconductor supremacy, particularly in high-end compute infrastructure and closed-source model ecosystems. The core of this technological rivalry has shifted from pure performance benchmarks to a full-scale competition of ecosystems and innovation models.

Looking ahead, as China continues to export top AI talent and deepen its industrial chain integration, the global AI landscape may be on the brink of a major realignment. As NVIDIA CEO Jensen Huang put it, “Technology blockades only accelerate innovation.” In this silent war of systems and scale, China is advancing its own AI era, driven by openness and grounded in real-world applications.

中国引领AI技术与硬件一体化

在AI软硬件一体化的赛道上，中国厂商正以惊人的速度赶超全球竞争对手。

2025年6月26日，小米正式发布首款小米AI眼镜，定位“面向下一个时代的个人智能设备, 随身的AI入口”，直接对标Ray-Ban Meta AI 眼镜。

核心功能对比：小米AI眼镜 vs. Meta AI眼镜

小米 AI 眼镜虽然在时尚性和品牌影响力上略逊于 Meta，但在硬件性能、AI交互、生态协同、性价比等方面展现出强大竞争力，尤其是在中国市场，其软硬一体化能力和澎湃 OS 生态联动使其具备独特的不可替代性。

在随着越来越多的中国厂商介入，凭借轻量化、智能化、高性价比和生态整合能力，中国正在引领全球AI消费级软硬件产业。

中美在人工智能基础研究领域各有什么领先优势

根据被誉为“互联网女皇”的Mary Meeker发布的BOND AI趋势报告《Trends-Artificial Intelligence》，中美人工智能竞争已进入白热化阶段，其中在基础研究领域各具领先优势。

（一）中国的领先领域：开源生态与工业智能化的“双轮驱动”

1. 中国AI开源生态的爆发式增长，正在改写全球技术共享的规则：

• 模型数量与质量并进：截至2025Q2，中国已推出DeepSeek-R1（训练成本仅为OpenAI的1/10）、阿里Qwen-32B、百度Ernie 4.5等标杆级开源模型，覆盖语言、多模态、代码生成等场景。阿里云推出的AI大模型开源社区–魔搭社区更以7万个开源模型、1600万开发者的规模，成为全球最大AI开源社区之一。
• 低成本普惠化：中国AI大模型以“高性能+低成本”见长，例如DeepSeek V3的训练成本优化至行业1/10，推动全球AI技术下沉至中小企业和发展中国家。

2. 中国在工业机器人领域的领先，是政策红利+产业链优势的必然结果：

• 安装量全球第一：2023年中国工业机器人装机量占全球50%以上，宝马、特斯拉等国际大厂均采用中国机器人解决方案。
• 挑战：中国工业AI的短板在于高端传感器和实时控制系统仍依赖进口，但“国产替代”正在加速

（二）美国领先领域：基础模型与芯片霸权的双重优势

1. 基础模型研发：技术代差与开源追赶

美国在AI基础模型领域仍保持显著优势：

• 闭源模型领先：OpenAI的GPT-4.5、Anthropic的Claude等模型在MMLU、HumanEval等基准测试中综合性能领先，尤其在复杂推理和长文本理解上存在技术代差。
• 开源生态竞争加剧：尽管Meta的Llama、中国的DeepSeek-R1等开源模型性能快速逼近（差距从2023年的15.9%缩小至2025年的1.7%），但产业界主导的闭源模型仍控制着高端应用生态。

2. 芯片与算力霸权：从硬件垄断到全球生态

美国通过芯片技术卡位全球AI基础设施：

NVIDIA的绝对统治：其GPU占据全球AI训练芯片90%以上份额，数据中心资本支出（CapEx）占比达25%，H100/H200系列仍是大模型训练的“黄金标准”。

封锁催生中国芯片“加速进化”新范式

在中美芯片技术脱钩背景下，美国对华AI芯片出口限制。然而祸兮福所倚，美国的限制政策却激活了中国芯片产业的“危机响应机制”：

• 技术层面通过场景倒逼良率提升和架构创新；
• 市场层面刚需替代率攀升反哺研发投入；
• 战略层面形成“国产替代-技术输出”双循环。

除了华为，小米也推出了自研芯片，主要目的是降低物料成本（参考苹果M1芯片对Mac产品线利润率的提升），并规避美国制裁风险（如华为遭遇的断供危机）。玄戒O1的推出意味着小米首次实现手机核心系统级芯片（System on Chip，简称 SoC）自主化，掌握芯片核心技术，实现从芯片架构设计到功能集成等关键环节的自主可控。未来可减少向高通支付专利费，提升毛利率。类似苹果通过自研芯片摆脱Intel依赖，实现从移动端到PC端的全生态整合。

2024年，英伟达CEO黄仁勋直言：“对华芯片管制是失败的，四年间其在中国市场份额从95%跌至50%”。

（三）关键博弈点

根据美国保尔森基金会旗下麦克罗波洛智库（MacroPolo）的研究，从出身的本科院校来看，中国高校几乎培养了全球一半的顶尖 AI 研究人员，相比之下，仅有约 18% 研究人员来自美国大学。在2019 年，本科毕业于中国高校的顶尖 AI 研究人员占全球的比例还只有 29%，这一数据的大幅跃升体现了中国在 AI 人才培养方面的卓越成效。

以DeepSeek团队为例，其成员主要来自清华、北大等国内顶尖高校，团队呈现出”高学历年轻化、开源导向、创新驱动”的鲜明特质。

同样，在马斯克旗下特斯拉自动驾驶 Robotaxi 项目中，华人团队构成了核心技术力量：武汉理工大学本科毕业的段鹏飞担任特斯拉人工智能首席软件工程师；负责 FSD（全自动驾驶）开发的机器学习工程师祁芮中台（Charles Qi），本科毕业于清华大学。

这些案例清晰表明，在全球人工智能产业加速发展的浪潮中，中国本土培养的人才已展现出强劲的技术实力与创新潜力。

在全球AI竞赛中，中国正以软硬件一体化和人才红利两大优势加速追赶。小米AI眼镜的发布，不仅是中国消费级AI硬件的里程碑，更展现了生态整合与性价比的独特竞争力。与此同时，中国在开源模型和工业机器人领域的快速崛起，正在重塑全球AI技术格局。

然而，美国仍牢牢掌控基础算法与芯片霸权，尤其在高端算力和闭源模型上占据主导。这场科技博弈的核心，已从单纯的技术比拼，升级为生态体系与创新模式的全面竞争。

未来，随着中国AI人才的持续输出和产业链的深度整合，全球AI版图或将迎来新一轮洗牌。正如黄仁勋所言：“技术封锁只会加速创新”——在这场没有硝烟的战争中，中国正用开放生态和场景落地，书写属于自己的AI时代。

Have you got your ticket to the AI-native world — the “Core AI Subscription”?

This concept was introduced by Sam Altman, CEO of OpenAI, at the 2025 Sequoia AI Summit. More than just a new term, it could become the central theme of AI commercialization in the years to come.

For investors, this signals a shift: the future winners won’t be the ones merely selling models, but those building long-term user relationships and platform-level capabilities. 

For entrepreneurs, it offers clear direction: those who can create truly personalized AI assistants, and a sustainable business model around them, will be the ones to rise above the rest.

The “Core AI Subscription” is more than just a new business model; it may signal that humanity is accelerating toward a new era: the AI-native world.

What Is the AI-Native World?

The AI-native world refers to a future paradigm where artificial intelligence is not just a tool, but the foundational driving force deeply embedded in every aspect of society. It reshapes how we live, work, produce, and interact, much like electricity and the internet redefined previous eras.

In this world, AI becomes core infrastructure, pervasive and indispensable. It transforms human cognition, economic models, technological ecosystems, and social structures.

Imagine life in an AI-native world:

• You no longer use AI just to complete isolated tasks.
• AI understands your intentions, anticipates your needs, and acts before you even ask.
• It’s seamlessly integrated into your life, work, and decision-making processes, becoming an essential part of your digital existence.

Core AI Subscription: The Gateway to the AI-Native World

Sam Altman’s concept of the “Core AI Subscription” is not just visionary — it represents the key pathway to realizing the AI-native future. It refers to a highly personalized, continuously evolving AI assistant service that is deeply embedded in users’ daily lives, much like an operating system that runs across every aspect of work and life.

This service is far more than just a voice assistant or chatbot. It functions as an intelligent agent with the following capabilities:

• Personalized customization based on your habits and behaviours;
• Seamless integration with other applications and services;
• Constant learning and self-improvement over time, becoming smarter and more efficient the more you use it.

In other words, whoever owns the user’s Core AI Subscription essentially controls the “operating system entry point” to the AI-native world.

What AI Capabilities Are Required for Core AI Subscription Services?

To bring “Core AI Subscription” to life, there’s a key question we might want to explore: What kind of AI is capable of supporting such a service?

OpenAI has proposed a tiered framework for Artificial General Intelligence (AGI) — AI systems with the ability to learn efficiently, generalize across tasks, and act autonomously in complex, dynamic environments. True AGI would possess a blend of perception, cognition, decision-making, learning, execution, and social collaboration, all while aligning with human emotions, ethics, and moral standards.

Here’s a breakdown of the AGI capability tiers:

• Level 1: Chatbot — Basic conversational ability, like current GPT models.
• Level 2: Reasoner — Can solve human-level problems — mathematics, logic, coding, and debugging.
• Level 3: Agent — Acts on behalf of the user — booking travel, managing calendars, and automating task chains.
• Level 4: Innovator — Capable of invention and creativity — designing new products, writing screenplays, composing music.
• Level 5: Organizer — Manages teams, coordinates resources, sets strategies, and even runs companies.

What AGI Level Is Needed to Enable Core AI Subscription Services?

To bring Core AI Subscription services to life, the AI must reach at least Level 3 — Agent on the AGI scale. At this level, AI isn’t just passively responding to user commands — it must actively understand user needs, take initiative, trigger tools, execute task chains, and switch contexts fluidly across various scenarios.

Since 2023, Baidu founder Robin Li has echoed a similar vision, stating that “large models will usher in a flourishing ecosystem of AI-native applications.” He emphasized that AI-native applications are not simple replicas of mobile apps or desktop software — they are meant to “solve problems that were previously unsolvable or poorly solved.”

This vision aligns closely with the concept of Core AI Subscription: true AI-native products are those in which AI agents are deeply embedded in users’ lives and workflows as a systemic, always-on digital partner.

Open Evidence: Core AI Subscription in Action in Healthcare

Are there early pioneers building AI-native applications? Yes—and a standout is Open Evidence, a medical AI company founded in 2021. By February 2025, it had raised $75 million from Sequoia Capital and achieved unicorn status with a valuation surpassing $1 billion.

At the 2025 Sequoia AI Summit, co-founder Zach shared a real-world case showing how their Core AI Subscription model supports physicians:

Emergency In-Flight Medical Case

Dr. Susan Wilberg faced a medical emergency mid-flight: a 63-year-old male cancer patient on immunosuppressive therapy developed a severe rash. Suspecting shingles, she had to make a critical call—should the plane turn back? What immediate actions were needed onboard?

She turned to ClinicalKey AI, Open Evidence’s subscription-based platform built for medical professionals. It delivered instant, personalized guidance by combining:

• CDC Yellow Book protocols,
• The latest research on cancer immunotherapy, and
• Patient-specific recommendations (based on age, history, treatment, etc.).

The platform:

• Assessed the patient’s risk level given his immunosuppressed condition,
• Offered specific and timely treatment guidance,
• Helped avoid an unnecessary emergency landing while ensuring proper care upon arrival.

What Makes ClinicalKey AI a True Core AI Subscription?

Open Evidence’s AI assistant is more than a diagnostic aid—it functions like an intelligent agent that continuously learns, personalizes its output, and proactively supports users:

• Hyper-personalization: Tailors suggestions based on user preferences and patient context.
• Seamless integration: Connects effortlessly with existing medical systems and workflows.
• Continuous evolution: Becomes smarter and more efficient through real-world interactions.

Business Model & User Growth

Over 25% of U.S. practicing physicians now rely on Open Evidence daily. The system handles more than 10 real-time clinical questions per second. While the service is free for doctors, revenue comes from medical device and pharmaceutical advertising, mirroring consumer internet models, but adapted for healthcare.

To deepen value and retention, Open Evidence is embedding top physicians’ expertise, starting with gastroenterology, into its AI, creating a collective intelligence layer. This not only strengthens its data advantage but enables constant answer refinement.

The Future: AI as an Indispensable Partner

Looking ahead, Open Evidence plans to integrate broader medical reasoning, research capabilities, and workflow tools to build a fully-fledged Core AI Subscription platform, ultimately becoming a mission-critical partner to doctors worldwide.

If you’re a business owner aiming to integrate an AI subscription model into your operations, here are three essential principles to keep in mind:

1. Shift from “function thinking” to “companionship thinking.”
Don’t just ask, “What can AI do for my business?” Instead, consider, “What do my users need AI to become?” A CFO doesn’t simply need a reporting tool—they need a proactive financial partner that can anticipate risks and guide decisions.

2. Capture high-frequency “scene entry points.”
Identify must-have, recurring scenarios—such as in healthcare, legal services, or vertical workflows—and embed AI deeply into those daily user moments. Your goal is to make AI a seamless, indispensable part of how users work.

3. Build a “subscription-based emotional account.”
Offer consistent, meaningful value—like weekly personalized insights—to create a sense of FOMO (fear of missing out). When users feel your AI is essential to staying ahead, loyalty follows naturally.

By applying these strategies, you can turn AI from a one-off tool into a trusted, subscription-powered companion that users depend on.

中文版

你有没有拿到通往 AI 原生世界的门票 — “核心AI订阅服务”（Core AI Subscription）？

这个概念是 OpenAI 的 CEO 山姆·奥特曼（Sam Altman）在2025年红杉AI峰会上提出来的。

对投资人来说，这是一个信号：未来的赢家，不是卖模型的公司，而是能构建长期用户关系、具备平台级能力的AI服务商。

对创业者而言，这也是一个方向：谁能打造真正“贴身”的AI助手，并建立可持续的商业模式，谁就最有可能脱颖而出。

核心AI订阅服务”（Core AI Subscription）不仅是一个新的商业模式，更可能预示着人类正在加速进入一个全新的时代——AI 原生世界（AI-native World）。

一、什么是“AI 原生世界”？

“AI 原生世界”是指以人工智能技术为底层核心驱动力，深度融入社会各领域运行逻辑，从而形成一种全新生产生活方式与社会架构的未来世界形态。

在这个世界中，AI不再是简单的辅助工具，而是像电力、互联网一样成为基础设施，重塑人类的认知方式、经济模式、技术生态和社会关系。

想象一下，在“AI 原生世界”中：

• 你不再只是使用AI来完成某个任务；
• AI会主动理解你的意图，预测你的需求，并在你开口之前就完成任务；
• 它无缝嵌入你的生活、工作和决策流程，成为你数字生活中不可或缺的一部分。

二、“核心 AI 订阅服务”：通往 AI 原生世界的入口

山姆·奥特曼所提出的“核心AI订阅服务”，正是实现这一愿景的关键路径。它是一种高度个性化、持续进化、深度嵌入用户日常生活的AI助手服务，就像操作系统一样，贯穿你工作的每一个环节。

这个服务不仅仅是语音助手或客服机器人，而是一个具备以下能力的“智能代理”：

• 根据你的行为习惯进行个性化定制；
• 能与其他应用和服务无缝对接；
• 随着使用时间的增长不断学习、进化，变得更加智能和高效。

换句话说，谁掌握了用户的核心AI订阅服务，谁就掌控了 AI 原生世界的“操作系统入口”。

三、要实现“核心 AI 订阅服务”，需要什么样的 AI 能力？

这就引出了另一个关键问题：什么样的 AI 才能支撑这样的服务？

OpenAI 曾提出一套通用人工智能（AGI, Artificial General Intelligence）的能力分级体系。AGI 指的是具有高效学习和泛化能力的人工智能体，能够根据复杂动态环境自主生成任务、执行任务，并具备感知、认知、决策、学习、执行和社会协作等综合能力，同时符合人类情感、伦理与道德观念。

这套 AGI 等级标准如下：

1. 第一级：聊天机器人– 具备基本对话能力，如当前的GPT系列模型。
2. 第二级：推理者– 可以解决人类水平的问题，比如数学、逻辑推理、编程调试等。
3. 第三级：代理（Agent）– 能够代表用户采取行动，例如预订行程、安排日程、自动完成任务链等。
4. 第四级：创新者– 能够参与发明创造，如设计新产品、撰写剧本、创作音乐等。
5. 第五级：组织者– 能够管理团队、协调资源、制定战略，甚至运营企业。

四、从 AGI 等级看“核心 AI 订阅服务”的门槛

要真正实现“核心AI订阅服务”，至少需要达到 AGI 第三级——代理级别。这意味着AI不仅要能理解用户的需求，还要能主动采取行动、调用工具、执行任务链，并能在不同场景中灵活切换。

从2023年开始，百度创始人李彦宏也多次强调：“大模型将开启一个繁荣的AI原生应用生态。”他指出，AI原生应用不是对移动互联网App和PC软件的简单重复，而是要能“解决过去解决不了或解决不好的问题”。

这与“核心AI订阅服务”的理念高度一致：真正的 AI 原生应用，是那些能让 AI 作为“代理”深度参与用户生活和工作的系统级产品。

有没有AI原生应用领域的先行者呢？有的。

五、Open Evidence：AI 订阅式服务在医疗领域的应用

成立于2021年的 Open Evidence是一家专注于医学人工智能的公司。截至2025年2月，Open Evidence 已获得红杉资本7500万美元的投资，估值突破10亿美元，成功跻身独角兽行列。

在2025年的红杉资本AI峰会上，Open Evidence 联合创始人 Zach 分享了一个真实的应用场景，展示了他们如何通过“核心AI订阅服务”为医生提供支持。

紧急空中救援案例

Susan Wilberg 医生在一次飞行中遇到一个紧急病例：一名63岁的男性患者出现了严重的皮疹。考虑到该患者正在接受前列腺癌治疗，处于免疫抑制状态，她怀疑是水痘。在这种紧急情况下她需要尽快判断是否需要返航？飞机航行过程中应采取什么紧急措施？

Susan 使用了 Open Evidence 的 ClinicalKey AI 平台，这是一个基于订阅的服务，专为医疗专业人士设计。该平台不仅即时提供了来自CDC《黄皮书》和最新癌症免疫治疗研究的信息，还根据患者的特定情况（如年龄、病史、当前治疗方案等）给出了个性化的建议：

• 评估了免疫抑制患者的风险严重程度；
• 提供了及时且针对具体情况的治疗建议；
• 避免了不必要的迫降，并为落地后的进一步治疗做了规划。

核心AI订阅服务的特点

在2025年的红杉资本AI峰会上，Open Evidence 联合创始人Zach分享了一个真实的应用场景，展示了他们如何通过“核心AI订阅服务”为医生提供支持：更像是一个智能代理，能够持续学习、个性化定制，并主动为用户提供所需的知识和支持。

• 高度个性化：根据用户的偏好和历史数据，提供定制化的信息和建议。
• 无缝集成：与现有的医疗系统和工作流无缝对接，确保信息流通无阻。
• 持续进化：随着用户互动的增加，系统不断优化，变得更加智能高效。

商业模式与用户增长

目前，超过25%的美国执业医生每天都在使用Open Evidence作为辅助诊疗工具，平台每秒处理十多个真实的医疗问题，展现了强大的规模化能力。

尽管产品对所有医生免费开放，但公司通过医疗器械和药品广告实现收入。这种模式类似于消费互联网公司，但在医疗领域内形成了独特的用户粘性机制。

为了增强用户粘性和提升服务质量，Open Evidence 正在将全球顶级医生的临床智慧编码进系统（首先从胃肠病学开始），形成“集体智能”。这一策略不仅增强了平台的数据优势，还促进了未来的答案持续优化。

未来，Open Evidence计划整合更多的医学推理模式、科研能力和工作流嵌入功能，致力于打造一个全面的 AI 订阅服务平台，真正成为医生不可或缺的工作伙伴。

如果你是企业主，想把 AI 订阅模式融入现有业务，记住三个关键点：

1. 从 “功能思维” 转向 “陪伴思维”：别想 “我能让 AI 做什么”，先想 “用户需要 AI 成为什么”（比如财务总监需要的不是报表工具，而是能预警风险的财务管家）；
2. 抢占 “场景入口”：选择一个高频、刚需的场景（如医疗、法律、垂直行业办公），把 AI 深度嵌入用户的工作流；
3. 设计 “订阅式情感账户”：通过持续的价值交付（比如每周生成专属行业洞察），让用户产生 “不用就亏” 的依赖感。

以长期主义和构建产业协同生态为核心价值观的红杉资本近期举行了第三届人工智能峰会。此次峰会汇聚了 150 位全球顶尖 AI 领域创始人，释放了三大核心信号。

一、AI 商业模式从 “工具导向” 向 “结果导向” 转型

这一转型的底层逻辑是实现商业模式闭环，将技术转化为可量化的业务成果。过去十年，企业软件的核心价值是“提升效率”——通过SaaS工具实现流程自动化，客户为此支付“软件费用”。但AI正在穿透这一逻辑，形成全新的商业闭环：

Software as a Tool（工具）→ Software as a Co-worker（协作）→ Software as an Outcome（成果）

对用户而言，付费逻辑从 “为模型能力买单” 转变为 “为实际问题解决能力买单”。例如，法律 AI 工具已从单纯提供 API 服务，升级为直接输出法律文书，通过结果交付实现价值闭环。

二、AI “操作系统” 级入口的竞争

这场竞争的核心在于通过增强用户粘性构建平台壁垒，而粘性的根基在于两大能力：记忆能力（用户与AI的历史交互数据沉淀）与执行能力（AI调用工具的效率）。

记忆能力：从“工具”到“数字伙伴”的进化

长期记忆功能使AI能够深度学习用户的习惯、偏好及场景需求，从而实现千人千面的个性化服务。

记忆能力的关键价值让AI从“被动响应指令”升级为“主动理解需求”，形成类似人类助理的“数字伙伴”关系。例如，在医疗领域AI系统基于患者病史、基因数据和用药记录，动态调整诊疗方案，避免“一刀切”的推荐。

执行能力：从“能力展示”到“任务闭环”的跃迁

高效调用工具（如API接口、硬件设备）的能力，决定了AI能否在复杂场景中完成端到端的任务闭环。执行效率是企业采纳AI的关键门槛。例如，一个法律AI工具若能自动调用合同模板库、检索判例数据库并协调律师团队，其效率远超依赖人工操作的传统模式。

当AI同时具备深度记忆与高效执行，将形成不可逆的用户依赖, 从而构筑真正的护城河。

三、智能体经济的崛起：从工具到自主经济参与者的跃迁

人工智能正从被动工具进化为具备自主决策与协作能力的经济主体，推动”智能体经济”形成。但实现这一愿景需满足三大核心要素：

1. 持久身份：智能体的“数字人格”构建

每个智能体需拥有唯一且可验证的标识，以建立其在经济体系中的身份基础。比如，中国国家网络身份认证体系的“网号+网证”双轨制，为智能体身份认证提供政策和技术框架。

2. 行动能力：打通数字世界与物理世界

智能体需具备调用物理工具（如机器人、IoT 设备）和数字工具（API、数据库）的能力，以完成闭环任务。这里面的挑战在于需要解决工具权限管理、实时响应性及容错机制（如失败任务的自动重试或人工介入）。

3. 可信协作：跨智能体的信任基础设施

智能体间的协作需依赖可信机制，确保交互的透明性、可追溯性和责任划分。关键问题是如何定义智能体间的责任归属？比如 LOKA协议，这是卡内基梅隆大学提出的分层架构（身份层+沟通层+伦理层），为智能体提供去中心化标识与伦理决策框架。另外，如何平衡隐私保护与数据共享？

竞争优势的本质是 “数据 – 场景 – 生态” 的三位一体

根据红杉资本 AI 峰会提出的未来 AI 产业三大核心信号，AI 产业的发展趋势为投资机构和创业公司提供了关键发力点与评估维度：能否在构建收入闭环的同时，建立可持续的竞争壁垒。红杉资本指出，AI 企业的可持续壁垒最终取决于：

1. 数据飞轮：用户交互数据反哺模型优化的闭环效率；
2. 场景深耕：在垂直领域（如医疗、法律）中解决复杂问题的不可替代性；
3. 生态协同：通过入口级产品或标准协议构建跨主体协作网络。

创业公司需围绕上述维度，判断自身能否在 AI 从 “技术验证” 迈向 “价值创造” 的关键十年中，占据生态位并实现长期价值。

根据红杉资本的预测，到2026年，垂直领域智能体与混合治理框架将催生万亿级市场，而未能实现“工具→协作→成果”转型的企业将被淘汰。

With a core philosophy rooted in long-termism and building an integrated industrial ecosystem, Sequoia Capital recently hosted its third annual AI Summit. The event brought together 150 of the world’s leading AI founders and conveyed three powerful signals.

Three Core Signals for the AI Industry in the Next Decade

1. AI Business Models: The Shift From “Tool-Oriented” to “Outcome-Oriented”

This transformation reflects a deeper move toward closing the loop in AI-driven business models, where technology translates into measurable business outcomes.

Over the past decade, enterprise software has delivered value primarily by improving operational efficiency. Companies paid for SaaS tools that automated workflows — essentially, “software as a tool.” But AI is now breaking through that model and shaping a new paradigm:

Software as a Tool → Software as a Co-worker → Software as an Outcome

In this new framework, users are no longer paying for the AI model’s capabilities alone — they’re paying for its ability to solve real-world problems and deliver results.

Take a legal AI startup as an example. It has evolved from offering simple API-based services to delivering complete legal documents, shifting its value proposition from tool to outcome — and in doing so, creating a self-contained value loop.

2. Competition for AI “Operating System” — Level Entry Points

At the core of this race is the strategic goal of building platform-level moats through enhanced user stickiness. This stickiness is fundamentally driven by two key capabilities: memory (the accumulation of historical user-AI interactions) and execution (the AI’s ability to efficiently orchestrate tools and complete tasks).

Memory: Evolving from a “Tool” to a “Digital Companion”

Long-term memory enables AI systems to deeply learn a user’s habits, preferences, and contextual needs, unlocking truly personalized, one-to-one experiences at scale.

This memory capability shifts AI from being a passive command responder to an active need interpreter, evolving into a digital companion similar to a human assistant. For example, in healthcare, an AI system that incorporates a patient’s medical history, genetic profile, and medication records can dynamically adjust treatment recommendations — moving beyond one-size-fits-all suggestions.

Execution: Evolving from “Capability Showcase” to “Task Completion Loop”

An AI’s ability to efficiently call external tools (e.g., APIs, databases, hardware devices) determines whether it can execute end-to-end workflows in complex real-world scenarios. Execution efficiency is a critical adoption barrier for enterprise AI.

For instance, a legal AI tool that can autonomously pull from contract templates, search relevant case law, and coordinate with a legal team far outperforms traditional methods reliant on manual operations.

When deep memory and high execution efficiency come together, they form an irreversible user dependency, building a powerful moat for the AI platform. This is the foundation for the next generation of AI-native operating systems.

3. The Rise of the Agent Economy: From Tools to Autonomous Economic Actors

Artificial intelligence is rapidly evolving from a passive tool into an autonomous economic participant, giving rise to the “agent economy.” To realize this vision, three foundational pillars must be established:

1）. Persistent Identity: Building the “Digital Persona” of Agents

Each agent must possess a unique and verifiable identity to establish its legitimacy within an economic system. For example, China’s dual-track digital identity framework — “Net ID + Net Credential” — offers a policy and technical foundation for authenticating AI agents.

2）. Action Capability: Bridging the Digital and Physical Worlds

Agents must be able to orchestrate both physical tools (such as robots and IoT devices) and digital resources (like APIs and databases) to complete closed-loop tasks. Key challenges include permission management, real-time responsiveness, and robust fault tolerance (e.g., automatic retries or fallback to human intervention in case of failure).

3）. Trustworthy Collaboration: A Trust Infrastructure for Inter-Agent Cooperation

Collaboration between agents requires a trustworthy infrastructure that ensures transparency, traceability, and accountability. A central question: How do we assign responsibility among autonomous agents? The LOKA Protocol, developed by Carnegie Mellon University, proposes a layered framework (Identity Layer + Communication Layer + Ethics Layer) that supports decentralized identity and ethical decision-making for agents.

Additionally, how can we balance data privacy and information sharing in a world of interacting agents? This tension will define the future landscape of agent-based collaboration.

The Essence of Competitive Advantage: A Trinity of Data – Use Case – Ecosystem

In summary, the evolution of the AI industry presents both strategic inflection points and evaluation criteria for investors and startup founders: the ability to build sustainable competitive moats while closing the revenue loop.

According to Sequoia Capital, long-term defensibility in AI companies hinges on three pillars:

1. Data Flywheel:
   The efficiency of feedback loops where user interaction data continuously improves model performance.
   
2. Scenario Depth:
   Irreplaceable value in solving domain-specific, complex problems—especially in verticals like healthcare and law.
   
3. Ecosystem Synergy:
   Building a collaborative network across stakeholders through platform-level products or standardized protocols.

Sequoia predicts that by 2026, vertical AI agents and hybrid governance frameworks will drive the emergence of trillion-dollar markets. Conversely, companies that fail to evolve from tool → collaborator → outcome will be left behind.

在你到目前为止的人生里有没有什么遗憾？如果AI 可以帮你重新回到过去，再经历一遍，发现一些你没有注意的细节，帮你打开心结，你愿意吗？

故事 — Eulogy 悼念

先来讲个故事。故事的男主人公退休了，一个人过着悠闲的生活。有一天，他接到了来自一个神秘公司的电话。该公司是受他曾深爱过的前女友的家人的委托，希望他能帮忙回忆一些关于她的往事。他的前女友刚刚去世了。他犹豫了一下，但还是答应了。

不久，他收到一个盒子，叫做“回忆套件”。里面有一个小小的装置，就像一个纽扣。当他把它贴在太阳穴附近，启动后，一个温柔的女声响起——那是一个人工智能向导，专门帮助他唤起关于她的记忆。

可问题是……男主竟然什么都记不起来了。她的音容笑貌，全都模糊不清。

AI 温柔地建议他找找看有没有老照片或视频。他下楼在满是灰尘的地下室，翻箱倒柜找出了几张旧照片。但奇怪的是，每一张照片里，她的脸都被剪掉了。他震惊地意识到，他竟然连她长什么样子都不记得了。那些曾经温暖又鲜活的回忆，如今变得冰冷而沉默。

他回忆起他们最初相识时，她其实已经订婚了。可他们还是坠入了爱河，一起旅行，一起拍了很多照片——而后来，他却把照片里她的脸全剪掉了。

他还记得某个生日的夜晚，身为乐团大提琴手的她因巡演未能相伴。即便如此，她仍在晚间拨通电话送上祝福，可那时他犯了很多男人都会犯的错误，电话被和他在一起的女同事接了。结果，他们在电话里大吵一架。之后，前女友回来收拾好行李，离开了他。

其实之后男主曾经准备好戒指，还特意到前女友乐团所在地，订了一家豪华餐厅，想向前女友求婚。可惜天意弄人，一切为时已晚。

多年后，在 AI 的引导下，他发现当年她离开时在房间留下了一张纸条——但他当时完全错过了。纸条上，她坦白自己也曾短暂地背叛过那段感情，并发现自己怀孕了，没有任何爱，只是迷失了。她请求他的原谅，并写下了见面的地点。可他从没看到那纸条，于是这段感情，就在误会和沉默中，永远失去了。

这桩心事成为他生命里的隐痛，终其一生都未能释怀。

直到最后，AI 告诉他一个秘密：她不是普通的AI，而是他前女友女儿的数字化身。

在故事的最后，男主参加了前女友的葬礼。他看到一个女孩在礼堂演奏大提琴曲，琴声缓缓流淌，像是从回忆深处飘来的歌。

就在那一刻，他终于想起了她的脸，她的笑容。

泪水静静滑落，不只是为逝去的爱情，也是为那份仍然存在的温柔和记忆。因为爱，并没有真正消失，它藏在音乐里，也藏在他终于记起的那一张熟悉的脸里。他终于释然了。这是《黑镜》第七季里的一个故事叫做 Eulogy 悼念。

挥之不去的阴影：遗忘并非总是释怀

人性是复杂的，有时候，我们在很多年前一个不经意的决定，或者一时冲动做的事情，可能会让我们后悔一辈子。

生活里好多事情，我们以为早就翻篇、彻底放下了，可其实它们就像一颗种子，在我们心里长成了结。不知不觉中，这个心结会影响我们现在的生活，甚至改变我们做的每一个选择。

往事的回响：人工智能邀你直面逝去时光

在你的人生里，一定也藏着些难以释怀的遗憾吧！无论是错过的机会，还是没能说出口的话，都可能在某个瞬间突然涌上心头。如果现在有 AI 能帮你 “回到过去”，重新回顾那些重要时刻，你愿意尝试吗？

也许最后你不会得到什么实际的改变，但说不定在这个过程中，你能把憋在心里的情绪痛痛快快地宣泄出来；又或者，你能因此有勇气直面曾经的遗憾，真正地和过去和解。你心里的那个遗憾是什么？想不想借助 AI 给自己一个重新审视的机会呢？欢迎留言。

视频版

Have you ever carried a regret — something you wish you’d said, done, or understood differently?

If AI could take you back to those moments, helping you relive them, notice what you once missed, and perhaps find peace ，would you want to try it?

The Story — Eulogy

Once upon a time, there was a man who had just retired. He lived a quiet, peaceful life, enjoying his days in solitude.

One seemingly ordinary afternoon, his phone rang. The caller ID displayed an unfamiliar number, but something compelled him to answer.

A calm, professional voice greeted him. It was from a mysterious company, calling on behalf of the family of someone he hadn’t heard from in years — his former lover. She had just passed away. Her family, hoping to preserve the fragments of her past, asked if he would be willing to help piece together the memories they once shared.

He hesitated. The past was a tangled web of emotions, and opening that door again felt frightening and strangely alluring. After a long pause, with a quiet sigh and a mix of uncertainty and unspoken longing, he said yes. He had no idea that this one choice would take him on an unexpected journey — one of rediscovery, buried emotions, and long-forgotten moments.

Soon after, a package arrived: the “Memory Kit.” Inside was a small, button-like device. When he placed it near his temple and turned it on, a soft, warm female voice spoke — it was an AI guide, carefully designed to help him recall the woman he once loved.

But there was a problem.

He couldn’t remember her. Not her voice. Not her face. Everything about her was blurry, as if time had quietly erased the edges of their story.

The AI gently suggested that he try looking for old photos or videos that might spark his memory. He made his way down to the dusty basement, where time seemed to have stood still. After rummaging through boxes filled with forgotten keepsakes, he finally found some old photographs. But something was deeply unsettling — her face had been cut out of every single one.

He froze. He couldn’t even remember what she looked like. The memories that were once vivid and full of warmth now felt distant, like shadows slipping through his fingers.

As fragments of the past began to surface, he remembered how they first met — how she was engaged at the time. Still, their connection was undeniable. They fell in love, stole moments between the chaos, travelled together, and took countless photos. And yet, for reasons he couldn’t fully explain even to himself, he had removed her face from every picture.

One memory struck him with particular force. It was his birthday. She, a gifted cellist, had gone on tour with her orchestra and couldn’t be there. She had called to wish him a happy birthday. But he made a mistake that many men might make on a lonely night — his female colleague picked up the phone. The silence on the other end spoke louder than any words. They fought. Words were said that couldn’t be taken back. She returned only long enough to pack her bags — and then she was gone.

In fact, after their fight, he had bought a ring. With hope still alive in his heart, he travelled to the city where her orchestra was performing and reserved a table at an elegant restaurant, planning to propose. But fate, as it often does, had other plans. He was too late.

Years later, with the guidance of the AI, he uncovered something he had missed all those years ago — a note she had left behind in the room before she walked away. Somehow, back then, he had completely overlooked it.

In that note, she made a heartbreaking confession: she, too, had momentarily lost her way. In her confusion, she had been unfaithful, and later discovered she was pregnant. She admitted there was no love in that betrayal, only fear and loneliness. She asked for his forgiveness and left an address, hoping he would still want to see her, to talk, to try again.

But he never read the note. And because of that missed moment, their story, once full of love and possibility, was lost forever, drowned in misunderstanding and silence.

He carried the weight of regret like a shadow, following him through the rest of his life.

Until, in the final moments of his journey, the AI revealed a truth she had kept hidden: she was not just an artificial guide, but the digital avatar of his ex-girlfriend’s daughter.

In the final scene, he attended his former lover’s funeral. In the quiet hall, a girl sat on stage, playing the cello. The music drifted softly through the air, like a voice calling from the past — familiar, aching, beautiful.

And in that moment, as the notes wrapped around him like an old memory, he finally saw her face again. Her smile. Her eyes.

Love had never truly vanished. It had simply waited — buried beneath time, silence, and sorrow — until he was ready to find it again.

He finally found peace.

This is the story told in the seventh season of Black Mirror, in the hauntingly beautiful episode titled “Eulogy.”

The Lingering Shadows: Forgetting Isn’t Always Letting Go

People are complex. Sometimes, a casual decision made years ago — or a moment of impulse — can leave us with a lifetime of regret.

There are things we believe we’ve moved on from, memories we think we’ve buried. But in truth, they linger like seeds planted deep within us. Over time, they grow into quiet knots in our hearts — subtle, but powerful. Without even realizing it, these knots can shape how we live today, influencing our emotions, our relationships, and the choices we make.

Echoes of What Was: AI’s Invitation to Face Lost Moments

We all carry a few lingering regrets — missed chances, words left unsaid, paths not taken. Sometimes, they rise to the surface when we least expect it.

But what if an AI could help you “go back,” not to change the past, but to revisit those moments? To see what you missed, to feel what you buried, to say what was left unspoken. Would you take that chance?

Maybe nothing on the outside would change — but inside, something might shift. You might finally release the emotions you’ve held in for so long, or find the courage to truly face your regrets… and make peace with them.

I’d love to hear your story. What’s the one moment you wish you could revisit — and would you want AI to help you do it? Share your thoughts in the comments below.

DeepSeek的爆火让资本圈陷入一场幻觉：仿佛只要给机器人装上AI大模型，就能瞬间解锁《西部世界》般的智能。但现实是，当科技巨头们还在实验室里调教大模型的“逻辑脑”时，成功 “破圈出道” 的是那些能做出各种炫酷动作的具身机器人。例如，宇树科技的机器人会鲤鱼打挺，优必选的Walker X会打太极，甚至连北京人形机器人创新中心的“天工Ultra” 都跑完了半程马拉松。

这背后折射出当前人形机器人产业的核心矛盾：

当前人形机器人产业的核心矛盾是 “炫技级硬件能力” 与 “尚处发展初期的 AI 水平” 间的显著断层。

根据立德机器人平台的报告，2025 年中国人形机器人市场规模将达 82.39 亿元，但其中超过 70% 的订单来自工业场景（如汽车制造、3C 电子）。在这些场景中，机器人仅需 “精准执行预设动作”，尚未对 “自主决策” 能力形成广泛刚需 。

人形机器人产业发展的新催化剂

刚刚过去的 4 月份，有两件和人形机器人相关的活动格外引人瞩目。4 月 15 日，第二届中国人形机器人与具身智能产业大会在北京举办；4 月 19 日，全球首场人形机器人半程马拉松赛事在北京亦庄鸣枪开跑。

据赛事官方数据显示，此次比赛全程 21.0975 公里，吸引了 20 支机器人队伍参赛，同时还有 12000 名人类选手同场竞技，最终仅有 6 支机器人队伍完成比赛，完赛率为 30%。

冠军“天工Ultra”以2小时40分42秒刷新运动性能纪录，最高时速达12公里/小时，续航突破2小时。这场充满科技感与挑战性的赛事，不仅是机器人技术的试金石，更折射出中国人形机器人产业发展的新趋势与新机遇。

技术验证：突破实验室的 “极限挑战”

马拉松赛事堪称检验机器人技术的 “终极考场”，对机器人的长时运动稳定性和复杂地形适应性提出了极高要求，有力推动技术从实验室走向商业应用。

• 关节灵活性与动力系统革新：国产谐波电机的广泛应用成为一大亮点，通过降低电机转速，大幅提升关节输出能力。
• 散热系统的极限考验：高负荷运行下，机器人关节需在 3 小时内完成超 10 万次动作，产生的高热量对散热系统是巨大挑战。
• 开放环境适应性：面对大风、降雨、不同路面材质及坡度变化，基于强化学习的算法发挥了关键作用。某机器人通过实时感知环境数据，动态调整步态，在强风环境下依然保持稳定前行，展现出强大的环境适应能力。
• 电池续航与能源管理：机器人换电技术成为赛事一大看点，热插拔技术实现了无需关机即可快速更换电池，类似无人机的备用电源设计，让机器人续航能力得到显著提升。
• 通信系统的抗干扰能力：20 支机器人伍同场竞技，电机运行对信号的干扰、机器人之间的信号冲突等问题亟待解决。

产业链协同：国产核心部件的 “高光时刻”

这场机器人赛事不仅是前沿技术的竞技场，也成为展示中国工业供应链协同能力的重要平台。通过提升国产核心零部件的可见度与影响力，大大加速了进口零部件替代的进程。

像灵动科技、绿的谐波和柯力传感等公司充分展现了自身的实力，它们的产品在紧密相连的价值链中发挥着关键作用 —— 从动态平衡算法、力控传感器，到执行器、谐波减速器、空心杯电机、灵巧机械手、3D视觉系统，以及应用场景的数据提供商等各个环节均有涉及。

政策与资本：产业发展的 “双引擎”

在中国前瞻性的政府政策与大量资本投资的协同推动下，中国的机器人行业正经历着前所未有的增长。这种双引擎驱动的方式不仅加速了技术进步，还巩固了中国在机器人与智能制造领域的全球领先地位。

2025 年 3 月 5 日，李强总理的《政府工作报告》为中国的科技未来勾勒出了一幅全面的路线图。报告首次将 “具身智能” 和 6G 技术列为重点发展方向，同时还强调了促进商业航天事业、低空经济、生物制造以及量子技术的发展。这些领域被视为培育 “新质生产力” 的关键所在，彰显了中国致力于高科技、高效且可持续发展的决心。

在资本层面，产业基金通过 “产业运营” 模式整合生态，吸引行业内企业战略注资或提供订单，为产业发展注入强大动力。

国家创投基金：国家发展改革委宣布设立国家级创业投资引导基金，重点支持机器人、人工智能及前沿创新领域。该长期基金计划在20年内吸引近1万亿元人民币（约合1380亿美元）的投资，体现了中国对技术持续发展的强大决心。

工行科技创新基金：中国工商银行启动总规模800亿元人民币（约合110.4亿美元）的科技创新基金，聚焦半导体、先进制造等“硬科技”领域。此举与国家支持民营经济和推动科技进步的战略高度契合。

通过政策与资本的双重引擎，中国正加快构建一个融合政府引导与金融支持的协同生态系统。这不仅推动了机器人技术的快速落地，也为中国在全球新一轮技术创新竞争中抢占高地打下坚实基础。

商业化之路：技术、应用与成本的 “铁三角”

中国的人形机器人产业正在快速发展，这一进程由三大相互依存的支柱共同驱动：技术创新、多元化应用场景，以及成本优化。

中国企业在人形机器人研发方面不断取得突破，致力于实现与人类动作和互动的高度仿真。例如，灵动科技开发的算法已使机器人能够实现如持续奔跑等动态动作；而帕新科技则打造出具备触觉识别能力的传感器，使机器人能够分辨不同人类皮肤的质感。这些技术进展对于推动机器人融入以人为本的使用环境至关重要。

目前，人形机器人在中国的多个行业中已逐步落地应用。以汽车制造业为例，优必选正与一汽-大众等整车厂商合作，将机器人应用于装配线上，用于螺栓紧固和零部件安装等任务。

要实现人形机器人的广泛应用，降低生产成本是关键一环。中国制造商正凭借强大的电子和新能源汽车产业链，获得如执行器、电池等关键零部件的成本优势。例如，普渡机器人位于江苏的超级工厂年产能高达10万台，通过自动化与标准化联合生产大幅降低单位成本。而特斯拉的Optimus人形机器人中，超过50%的零部件来自中国，其中包括电机、传感器和减速器，凸显出中国在硬件制造上的全球竞争力。

中国式创新的「非对称突围」

中国正在走一条务实的发展路径——依托具有成本优势的硬件供应链抢占真实应用场景，并利用由此积累的数据反哺人工智能的演进。在最近的机器人赛事中，“天工Ultra”的夺冠不仅是捧回了一座奖杯，更是一种信号。

中国式的“非对称突围”是一种独特而扎根现实的战略：通过深厚的制造能力和以场景为驱动的快速迭代，迈向人工智能的长期领先。在这一战略中，每一颗螺丝、每一个传感器、每一条数据，都是一个更大系统的一部分，正悄然而有力地重塑智能机器的未来。

特别鸣谢以下专家的看法和专业分享：（排名无先后顺序）

• 李丰，峰瑞资本创始合伙人；
• 谌威，钛虎机器人产品生态负责人；
• 何刚，财经领域专家，《财经》杂志主编；
• 李翔，《详谈》主理人，得到 APP 前总编辑。

English version