这家由 Yann LeCun 联合创立的初创公司专注于构建世界模型，旨在预测物理世界的下一状态，而非像大语言模型那样预测下一个 token。 AMI Labs 尚未推出产品，但已开始接触机器人、制造和电子领域的合作伙伴。
当AI行业的其他公司竞相将自己的工作标注为“通用人工智能（AGI）”或“超级智能”时，亚历山大·勒布伦（Alexandre LeBrun），作为Yann LeCun的世界模型创业公司AMI Labs的首席执行官：https://techcrunch.com/2026/01/23/whos-behind-ami-labs-yann-lecuns-world-model-startup/，https://techcrunch.com/2024/12/14/what-are-ai-world-models-and-why-do-they-matter/，完全避免使用这些术语。勒布伦在接受TechCrunch采访时表示，公司根本不使用“AGI”或“超级智能”这样的术语。
“我们从未使用过AGI这个词。我刚注意到现在没人再用了；他们都换成超级智能了，”他说。“下次我们也许会换成别的东西。”他对新的标签也没有认同感。“这没有好的定义。什么是超级智能？我不知道。这不是一个很有用的词。”
这是一个尖锐的立场，来自于处在AI最新竞赛中心的创始人。
TechCrunch在上周他前往首尔参加国际机器学习会议（The International Conference on Machine Learning）时采访了勒布伦，他在那里寻找本地工业合作伙伴、全球公司和研究人员。AMI Labs仍处于产品前期阶段，但它已经在吸引机器人、制造和电子玩家。勒布伦解释道，一个包含物理知识以预测和处理现实世界的世界模型，需要在实验室之外证明自己的价值。
世界模型预计将在机器人领域产生巨大影响。勒布伦表示，目前的机器人只是执行固定程序，“完全静态”，AI在物理世界中仍然“非常愚笨”。
即便AI仅能让机器人“意识到环境背景”，那也将对世界产生“非常大的差异”。例如，这种具备环境感知的AI本可以阻止一台在公共活动中跳舞和做功夫表演的机器人：https://www.youtube.com/shorts/BojeUP0_m_w 接近并踢到孩子。“硬件非常先进；过去几个月硬件进展令人难以置信，但还没有大脑。”
大型语言模型（LLM）预测下一个词或文本，而世界模型预测下一个状态。推一下桌上的玻璃杯，你已经知道它会倾倒并洒出液体；勒布伦解释说，这就是世界模型要捕捉的直觉：预测世界的下一个状态。
LeBrun表示，他并不声称世界模型比大型语言模型（LLM）更好，在理解物理世界的人工智能系统中，二者是“互补的，而非可替代的”。他将其类比于人脑的语言功能与推理功能不同，补充道，LLM将仍然是处理语言的最高效工具，而世界模型则提供上下文背景和对现实世界的理解。
LeBrun指出，几乎每一个“接触现实世界”的行业最终都可能使用基于世界模型的机器人，他认为物理环境是LLM最薄弱的领域。
他说，今天工厂机器人重复同样的动作已经足够有效。挑战从“将机器人带到更开放的环境中，例如家庭或街道”时开始，此时机器人必须理解周围环境并安全操作。“现在机器人并不安全，”他说。“目前没有解决方案。”
对于LeBrun来说，医疗健康提供了一个更个人化的例子，他之前的公司是AI健康初创公司Nabla。他将今天的AI系统比作“只学了课本没有住院医师经历的医生”。他表示，大型语言模型在医疗中可能有用，但它们只涵盖了“医疗健康的1%”，其余的依赖于现实世界的经验。
但LeBrun表示，世界模型无法在实验室内构建。根据他作为CEO的说法，为了在现实中进行训练，AMI需要真实环境和紧密合作伙伴。“我们需要接触真实世界”，而“和合作伙伴一起做更容易”。这也是他被亚洲吸引的部分原因，因为机器人、芯片和工厂都在那里。
LeBrun尚未详细说明完整的亚洲战略。“还太早，”他说。但吸引他到韩国的原因有两个。首先，韩国在机器人、半导体和制造业等先进产业方面发达；这些硬件密集型行业是第一波AI几乎未涉足的。
第二个吸引力是速度。LeBrun指出韩国的国家计划投入大量资金发展AI，以及其早期采用者的记录。“25年前，韩国是互联网最快的采纳者，”他说。正是这种深厚的工业基础加上快速接受AI的意愿，他称之为“独特”，也是“我们希望从第一天就来到这里”的原因。
SBVA首席执行官兼AMI亚洲支持者之一JP Lee告诉TechCrunch：“我一直在告诉Alex和团队来韩国。”
李光耀表示，政府在资助本地主权大型语言模型方面“做得非常出色”，这些模型在通用任务中“已经足够好用”，但他也在推动韩国继续投资物理人工智能。他指出首尔六月计划动员约880 techcrunch.com 0亿美元用于芯片、人工智能数据中心和物理人工智能，作为其三大支柱之一：“它们应共存。”
李光耀认为，韩国对外国企业的价值不仅仅体现在硬件上。本地开发者迅速采用和适应新工具，这种模式催生了像Naver和Kakao这样的本土互联网玩家。
尽管拥有众多明星阵容和数十亿美元的支票，AMI目前还没有什么可卖的。这家初创公司由图灵奖得主Yann LeCun共同创立：https：//techcrunch.com/2026/01/23/whos-behind-ami-labs-yann-lecuns-world-model-startup/ 在他离开Meta后，于三月筹集了10.3亿美元：https：//techcrunch.com/2026/03/09/yann-lecuns-ami-labs-raises-1-03-billion-to-build-world-models/，预估值为35亿美元。目前还没有产品，也没有他会承诺的时间表。“等我们准备好了再给大家一个惊喜。”勒布伦说道。
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Kate Park是TechCrunch的记者，专注于亚洲的科技、初创企业和风险投资。她曾是Mergermarket的财经记者，报道并购、私募股权和风险投资。
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While the rest of the AI industry races to label its work as “AGI” or “superintelligence,” Alexandre LeBrun, the CEO of Yann LeCun’s：https://techcrunch.com/2026/01/23/whos-behind-ami-labs-yann-lecuns-world-model-startup/ world model：https://techcrunch.com/2024/12/14/what-are-ai-world-models-and-why-do-they-matter/ startup, AMI Labs,：https://amilabs.xyz/ avoids the terms altogether. Lebrun said in an interview with TechCrunch that the company doesn’t use terms like “AGI” or “superintelligence” at all.
“We never used the word AGI. And I just noticed that nobody is using it anymore; they switched to superintelligence,” he said. “Next time we’ll switch to something else.” He isn’t sold on the new label either. “There’s no good definition. What is superintelligence? I don’t know. It’s not a very useful word.”
It’s a pointed stance from a founder sitting at the center of AI’s newest race.
TechCrunch talked to LeBrun while he was in Seoul last week for The International Conference on Machine Learning, where he was scouting for local industrial partners, global companies, and researchers. AMI Labs is still pre-product, but it’s already courting robotics, manufacturing, and electronics players. A world model, which incorporates physics to predict and work with the real world, needs to prove itself outside the lab, LeBrun explained.
One area where world models are expected to have a large impact is robotics. For now, robots are just running fixed routines, “completely static,” and AI remains “really dumb in the physical world,” LeBrun said.
Even when AI can merely make robots “aware of the context” that would mark “a very big difference for the world.” Such context-aware AI would have been useful, for example, in preventing a robot that was dancing and doing kung fu at a public event：https://www.youtube.com/shorts/BojeUP0_m_w from approaching and kicking a child. “The hardware is very advanced; progress in hardware in the last few months is incredible, but there’s no brain.”
A large language model (LLM) predicts the next word or text, and a world model predicts the next state. Nudge a glass off the table, and you already know it will tip and spill; that’s the intuition a world model is meant to capture: predicting the next state of the world, LeBrun explained.
He isn’t claiming world models are better than LLMs, which are “complementary, not replaceable” when it comes to AI systems that understand the physical world, LeBrun said. Drawing a parallel to the human brain’s distinct language and reasoning functions, he added that LLMs will remain the most efficient tools for processing language while world models will provide context and real-world understanding.
Almost every industry that “touches the real world” could eventually make use of robotics based on world models, LeBrun said, arguing that physical environments remain where LLMs are weakest.
A factory robot repeating the same motion works well enough today, he said. The challenge begins when “you take your robot outside into a more open environment, in your household, or in the street,” where it must understand its surroundings and operate safely. “Robots are not safe right now,” he said. “There’s no solution for that today.”
Healthcare offers a more personal example for LeBrun, whose previous company was Nabla, an AI health startup. He likened today’s AI systems to a doctor trained only on textbooks and without a residency. LLMs may be useful in medicine, he said, but they cover “only 1% of healthcare.” The rest depends on real-world experience.
But a world model, LeBrun said, can’t be built inside a lab. To train on reality, AMI needs real environments and close partners, according to the CEO. “We need access to the real world,” and it’s “easier for us to do that with partners.” That is part of what pulls him toward Asia, where the robots, chips, and factories actually are.
LeBrun won’t spell out a full Asia strategy yet. “It’s too early,” he said. But the pull toward South Korea comes down to two things. First, Korea has advanced industries in robotics, semiconductors, and manufacturing; the hardware-heavy sectors that the first wave of AI barely touched.
The second attraction is speed. LeBrun pointed to Korea’s national plan to pour money into AI and its track record as an early adopter. “Korea was the fastest adopter of the internet 25 years ago,” he said. It’s that combination, a deep industrial base plus a willingness to embrace AI fast, that he calls “unique,” and the reason “we want to be here from day one.”
“I’ve been telling Alex and the team to come to Korea,” JP Lee, the CEO of SBVA and one of AMI’s backers in Asia, told TechCrunch.
The government has done “a tremendous job” funding local sovereign LLM models, Lee said, and those already work “well enough” for general-purpose tasks, but he’s pushing for Korea to keep investing in physical AI, too. He points to Seoul’s June plan to mobilize some $880 billion：https://techcrunch.com/2026/06/29/south-korean-tech-giants-commit-over-550b-to-ease-ramageddon/ for chips, AI data centers, and physical AI, as one of its three declared pillars: “They should coexist.”
Korea’s value to foreign firms, Lee argued, isn’t only in hardware. Local developers are quick to adopt and adapt new tools, a pattern that has produced homegrown internet players like Naver and Kakao.
For all the star power and the billion-dollar check, AMI has nothing to sell yet. The startup, co-founded by Turing Award winner Yann LeCun：https://techcrunch.com/2026/01/23/whos-behind-ami-labs-yann-lecuns-world-model-startup/ after he left Meta, raised $1.03 billion in March：https://techcrunch.com/2026/03/09/yann-lecuns-ami-labs-raises-1-03-billion-to-build-world-models/ at a $3.5 billion pre-money valuation. There’s no product yet, and no timeline he’ll commit to. “We’ll make a surprise when we’re ready,” LeBrun said.
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Kate Park is a reporter at TechCrunch, with a focus on technology, startups and venture capital in Asia. She previously was a financial journalist at Mergermarket covering M&A, private equity and venture capital.
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