{"id":3491014,"date":"2026-07-16T14:40:00","date_gmt":"2026-07-16T14:40:00","guid":{"rendered":"https:\/\/techingeek.com\/index.php\/2026\/07\/16\/why-ami-labs-alexandre-lebrun-refuses-to-label-his-ai-as-agi-or-superintelligence\/"},"modified":"2026-07-16T14:40:00","modified_gmt":"2026-07-16T14:40:00","slug":"why-ami-labs-alexandre-lebrun-refuses-to-label-his-ai-as-agi-or-superintelligence","status":"publish","type":"post","link":"https:\/\/techingeek.com\/index.php\/2026\/07\/16\/why-ami-labs-alexandre-lebrun-refuses-to-label-his-ai-as-agi-or-superintelligence\/","title":{"rendered":"Why AMI Labs\u2019 Alexandre LeBrun refuses to label his AI as \u2018AGI\u2019 or \u2018superintelligence\u2019"},"content":{"rendered":"
<\/div>\n
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Amidst the AI sector’s rush to label their developments as \u201cAGI\u201d or \u201csuperintelligence,\u201d Alexandre LeBrun, the CEO of AMI Labs, a venture under Yann LeCun\u2019s world model initiative, does not employ these terms at all.\u00a0LeBrun shared during a conversation with TechCrunch that their company completely steers clear of phrases like \u201cAGI\u201d or \u201csuperintelligence.\u201d<\/p>\n

\u201cWe\u2019ve never used the term AGI. And I\u2019ve observed that nobody seems to be using it anymore; they\u2019ve turned to superintelligence,\u201d he mentioned. \u201cNext time we might adopt yet another term.\u201d He remains skeptical about the new terminology too. \u201cThere\u2019s no strong definition. What does superintelligence even mean? I\u2019m uncertain. It\u2019s not particularly helpful.\u201d<\/p>\n

This is a deliberate position from a founder positioned at the forefront of AI\u2019s latest competition.<\/p>\n

LeBrun spoke with TechCrunch during his visit to Seoul last week for The International Conference on Machine Learning, where he was searching for local industrial partners, global enterprises, and researchers. Although AMI Labs has not yet released a product, it is already engaging with sectors like robotics, manufacturing, and electronics. LeBrun clarified that a world model, which integrates physics to anticipate and interact with reality, must demonstrate its capabilities beyond laboratory settings.<\/p>\n

One domain where world models are anticipated to significantly impact is robotics. Currently, robots merely execute fixed sequences, remaining \u201centirely static,\u201d and AI still seems \u201cquite limited in physical contexts,\u201d LeBrun remarked.<\/p>\n

Even the capability for AI to at least make robots \u201caware of their environment\u201d would represent \u201ca substantial change for the world.\u201d Such contextually aware AI could have, for instance, prevented a robot performing dance and kung fu at an event from approaching and injuring a child. \u201cThe hardware has progressed immensely; the advancements in hardware over recent months are astounding, yet there\u2019s no intelligence.\u201d<\/p>\n

A large language model (LLM) predicts subsequent words or text, while a world model forecasts the next state. If a glass is nudged off a table, you know it will tilt and spill; that\u2019s the insight a world model aims to encapsulate: foreseeing the subsequent condition of reality, LeBrun elucidated.<\/p>\n

He does not assert that world models surpass LLMs, which are \u201ccomplementary, not substitutable\u201d within AI frameworks that comprehend the physical realm, LeBrun stated. Drawing a comparison to the human brain\u2019s separate language and reasoning functionalities, he mentioned that LLMs will continue to be the most effective tools for language processing while world models will offer context and real-world insights.<\/p>\n

Virtually every industry that interacts with the \u201creal world\u201d could ultimately harness robotics founded on world models, LeBrun asserted, contending that physical environments are still where LLMs tend to falter.<\/p>\n

A factory robot performing repetitive actions functions adequately today, he noted. The challenges arise when \u201cyou introduce your robot to an open environment, whether at home or on the street,\u201d where it needs to comprehend its surroundings and act safely. \u201cCurrently, robots are not safe,\u201d he stated. \u201cThere\u2019s no solution to that at present.\u201d<\/p>\n

Healthcare provides a more personal illustration for LeBrun, whose former company was Nabla, an AI health startup. He compared the current AI systems to a doctor trained solely in textbooks without any practical experience. LLMs may have utility in healthcare, he pointed out, but they only address \u201c1% of the healthcare landscape.\u201d The remaining 99% relies on real-world experience.\u00a0<\/p>\n

However, according to LeBrun, a world model cannot be developed in a laboratory setting. To train on practical realities, AMI requires genuine environments and committed partners, as per the CEO. \u201cWe need access to the real world,\u201d and forming partnerships is \u201cmore efficient for us.\u201d This is part of what draws him to Asia, where the necessary robots, chips, and factories reside.<\/p>\n

LeBrun is not ready to outline a comprehensive strategy for Asia just yet. \u201cIt\u2019s premature,\u201d he remarked. However, the attraction to South Korea is rooted in two main factors. First, Korea boasts advanced industries in robotics, semiconductors, and manufacturing\u2014the sectors that the initial phase of AI barely engaged.<\/p>\n

The second draw is speed. LeBrun highlighted Korea\u2019s national initiative to invest in AI and its history of early adoption.\u00a0\u201cKorea was the swiftest adopter of the internet 25 years ago,\u201d he recalled. This combination of a solid industrial foundation alongside a propensity to rapidly embrace AI is what he considers \u201cdistinctive,\u201d and the reason \u201cwe aim to establish ourselves here from the outset.\u201d<\/p>\n

\u201cI\u2019ve been advising Alex and the team to visit Korea,\u201d JP Lee, the CEO of SBVA and one of AMI\u2019s investors in Asia, conveyed to TechCrunch.<\/p>\n

The government has been \u201cremarkably effective\u201d in sponsoring local sovereign LLM models, Lee remarked, and those already function \u201cadequately\u201d for general applications, but he is advocating for Korea to continue investing in physical AI as well. He referred to Seoul\u2019s June plan to allocate around $880 billion for chips, AI data centers, and physical AI, as one of its three designated pillars: \u201cThey ought to coexist.\u201d<\/p>\n

Lee argued that Korea\u2019s significance to foreign enterprises lies not just in hardware. Local developers rapidly adopt and tailor new tools, a phenomenon that has fostered homegrown internet entities like Naver and Kakao.<\/p>\n

Despite its immense backing and financial support, AMI has yet to offer any products. The startup, co-founded by Turing Award laureate Yann LeCun after his tenure at Meta, secured $1.03 billion in March at a pre-money valuation of $3.5 billion. There is no product available yet, nor any timeline he is prepared to confirm. \u201cWe\u2019ll make an announcement when we\u2019re ready,\u201d LeBrun stated.<\/p>\n<\/div>\n

When you purchase through links in our articles, we may earn a small commission. This doesn\u2019t affect our editorial independence.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

<\/div>\n
\n

Amidst the AI sector’s rush to label their developments as \u201cAGI\u201d or \u201csuperintelligence,\u201d Alexandre LeBrun, the CEO of AMI Labs, a venture under Yann LeCun\u2019s world model initiative, does not employ these terms at all.\u00a0LeBrun shared during a conversation with TechCrunch that their company completely steers clear of phrases like \u201cAGI\u201d or \u201csuperintelligence.\u201d<\/p>\n

\u201cWe\u2019ve never used the term AGI. And I\u2019ve observed that nobody seems to be using it anymore; they\u2019ve turned to superintelligence,\u201d he mentioned. \u201cNext time we might adopt yet another term.\u201d He remains skeptical about the new terminology too. \u201cThere\u2019s no strong definition. What does superintelligence even mean? I\u2019m uncertain. It\u2019s not particularly helpful.\u201d<\/p>\n

This is a deliberate position from a founder positioned at the forefront of AI\u2019s latest competition.<\/p>\n

LeBrun spoke with TechCrunch during his visit to Seoul last week for The International Conference on Machine Learning, where he was searching for local industrial partners, global enterprises, and researchers. Although AMI Labs has not yet released a product, it is already engaging with sectors like robotics, manufacturing, and electronics. LeBrun clarified that a world model, which integrates physics to anticipate and interact with reality, must demonstrate its capabilities beyond laboratory settings.<\/p>\n

One domain where world models are anticipated to significantly impact is robotics. Currently, robots merely execute fixed sequences, remaining \u201centirely static,\u201d and AI still seems \u201cquite limited in physical contexts,\u201d LeBrun remarked.<\/p>\n

Even the capability for AI to at least make robots \u201caware of their environment\u201d would represent \u201ca substantial change for the world.\u201d Such contextually aware AI could have, for instance, prevented a robot performing dance and kung fu at an event from approaching and injuring a child. \u201cThe hardware has progressed immensely; the advancements in hardware over recent months are astounding, yet there\u2019s no intelligence.\u201d<\/p>\n

A large language model (LLM) predicts subsequent words or text, while a world model forecasts the next state. If a glass is nudged off a table, you know it will tilt and spill; that\u2019s the insight a world model aims to encapsulate: foreseeing the subsequent condition of reality, LeBrun elucidated.<\/p>\n

He does not assert that world models surpass LLMs, which are \u201ccomplementary, not substitutable\u201d within AI frameworks that comprehend the physical realm, LeBrun stated. Drawing a comparison to the human brain\u2019s separate language and reasoning functionalities, he mentioned that LLMs will continue to be the most effective tools for language processing while world models will offer context and real-world insights.<\/p>\n

Virtually every industry that interacts with the \u201creal world\u201d could ultimately harness robotics founded on world models, LeBrun asserted, contending that physical environments are still where LLMs tend to falter.<\/p>\n

A factory robot performing repetitive actions functions adequately today, he noted. The challenges arise when \u201cyou introduce your robot to an open environment, whether at home or on the street,\u201d where it needs to comprehend its surroundings and act safely. \u201cCurrently, robots are not safe,\u201d he stated. \u201cThere\u2019s no solution to that at present.\u201d<\/p>\n

Healthcare provides a more personal illustration for LeBrun, whose former company was Nabla, an AI health startup. He compared the current AI systems to a doctor trained solely in textbooks without any practical experience. LLMs may have utility in healthcare, he pointed out, but they only address \u201c1% of the healthcare landscape.\u201d The remaining 99% relies on real-world experience.\u00a0<\/p>\n

However, according to LeBrun, a world model cannot be developed in a laboratory setting. To train on practical realities, AMI requires genuine environments and committed partners, as per the CEO. \u201cWe need access to the real world,\u201d and forming partnerships is \u201cmore efficient for us.\u201d This is part of what draws him to Asia, where the necessary robots, chips, and factories reside.<\/p>\n

LeBrun is not ready to outline a comprehensive strategy for Asia just yet. \u201cIt\u2019s premature,\u201d he remarked. However, the attraction to South Korea is rooted in two main factors. First, Korea boasts advanced industries in robotics, semiconductors, and manufacturing\u2014the sectors that the initial phase of AI barely engaged.<\/p>\n

The second draw is speed. LeBrun highlighted Korea\u2019s national initiative to invest in AI and its history of early adoption.\u00a0\u201cKorea was the swiftest adopter of the internet 25 years ago,\u201d he recalled. This combination of a solid industrial foundation alongside a propensity to rapidly embrace AI is what he considers \u201cdistinctive,\u201d and the reason \u201cwe aim to establish ourselves here from the outset.\u201d<\/p>\n

\u201cI\u2019ve been advising Alex and the team to visit Korea,\u201d JP Lee, the CEO of SBVA and one of AMI\u2019s investors in Asia, conveyed to TechCrunch.<\/p>\n

The government has been \u201cremarkably effective\u201d in sponsoring local sovereign LLM models, Lee remarked, and those already function \u201cadequately\u201d for general applications, but he is advocating for Korea to continue investing in physical AI as well. He referred to Seoul\u2019s June plan to allocate around $880 billion for chips, AI data centers, and physical AI, as one of its three designated pillars: \u201cThey ought to coexist.\u201d<\/p>\n

Lee argued that Korea\u2019s significance to foreign enterprises lies not just in hardware. Local developers rapidly adopt and tailor new tools, a phenomenon that has fostered homegrown internet entities like Naver and Kakao.<\/p>\n

Despite its immense backing and financial support, AMI has yet to offer any products. The startup, co-founded by Turing Award laureate Yann LeCun after his tenure at Meta, secured $1.03 billion in March at a pre-money valuation of $3.5 billion. There is no product available yet, nor any timeline he is prepared to confirm. \u201cWe\u2019ll make an announcement when we\u2019re ready,\u201d LeBrun stated.<\/p>\n<\/div>\n

When you purchase through links in our articles, we may earn a small commission. This doesn\u2019t affect our editorial independence.<\/em><\/p>\n","protected":false},"author":2,"featured_media":3491015,"comment_status":"open","ping_status":"closed","sticky":false,"template":"Default","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-3491014","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/techingeek.com\/index.php\/wp-json\/wp\/v2\/posts\/3491014"}],"collection":[{"href":"https:\/\/techingeek.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/techingeek.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/techingeek.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/techingeek.com\/index.php\/wp-json\/wp\/v2\/comments?post=3491014"}],"version-history":[{"count":0,"href":"https:\/\/techingeek.com\/index.php\/wp-json\/wp\/v2\/posts\/3491014\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/techingeek.com\/index.php\/wp-json\/wp\/v2\/media\/3491015"}],"wp:attachment":[{"href":"https:\/\/techingeek.com\/index.php\/wp-json\/wp\/v2\/media?parent=3491014"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/techingeek.com\/index.php\/wp-json\/wp\/v2\/categories?post=3491014"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/techingeek.com\/index.php\/wp-json\/wp\/v2\/tags?post=3491014"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}