Alibaba has entered the race to build AI that powers robots, not just chatbots. The Chinese tech giant this week unveiled RynnBrain, an open-source model designed to help robots perceive their environment and execute physical tasks.\u00a0<\/p>\n
The move signals China\u2019s accelerating push into physical AI as ageing populations and labour shortages drive demand for machines that can work alongside\u2014or replace\u2014humans. The model positions Alibaba alongside Nvidia, Google DeepMind, and Tesla in the race to build what Nvidia CEO Jensen Huang calls \u201ca multitrillion-dollar growth opportunity.\u201d\u00a0<\/p>\n
Unlike its competitors, however, Alibaba is pursuing an open-source strategy\u2014making RynnBrain freely available to developers to accelerate adoption, similar to its approach with the Qwen family of language models, which rank among China\u2019s most advanced AI systems.<\/p>\n
Video demonstrations released by Alibaba\u2019s DAMO Academy show RynnBrain-powered robots identifying fruit and placing it in baskets\u2014tasks that seem simple but require complex AI governing object recognition and precise movement.<\/p>\n
The technology falls under the category of vision-language-action (VLA) models, which integrate computer vision, natural language processing, and motor control to enable robots to interpret their surroundings and execute appropriate actions.<\/p>\n
Unlike traditional robots that follow preprogrammed instructions, physical AI systems like RynnBrain enable machines to learn from experience and adapt behaviour in real time. This represents a fundamental shift from automation to autonomous decision-making in physical environments\u2014a shift with implications extending far beyond factory floors.<\/p>\n
HUGE: Alibaba just launched “RynnBrain” an open-source AI model that lets robots see, think, and act in the real world, with the aim to steal market share from Google and Nvidia. pic.twitter.com\/ULe3VcFlcE\u2014 AI Flash (@aiflash_) February 10, 2026<\/p>\n
From prototype to production<\/p>\n
The timing signals a broader inflexion point. According to Deloitte\u2019s 2026 Tech Trends\u00a0report, physical AI has begun \u201cshifting from a research timeline to an industrial one,\u201d with simulation platforms and synthetic data generation compressing iteration cycles before real-world deployment.<\/p>\n
The transition is being driven less by technological breakthroughs than by economic necessity. Advanced economies face a stark reality: demand for production, logistics, and maintenance continues rising while labour supply increasingly fails to keep pace.\u00a0<\/p>\n
The OECD projects that working-age populations across developed nations will stagnate or decline over the coming decades as ageing accelerates.<\/p>\n
Parts of East Asia are encountering this reality earlier than other regions. Demographic ageing, declining fertility, and tightening labour markets are already influencing automation choices in logistics, manufacturing, and infrastructure\u2014particularly in China, Japan, and South Korea.\u00a0<\/p>\n
These environments aren\u2019t exceptional; they\u2019re simply ahead of a trajectory other advanced economies are likely to follow.<\/p>\n
When it comes to humanoid robots specifically\u2014machines designed to walk and function like humans\u2014China is \u201cforging ahead of the U.S.,\u201d with companies planning to ramp up production this year, according to Deloitte.\u00a0<\/p>\n
UBS estimates there will be two million humanoids in the workplace by 2035, climbing to 300 million by 2050, representing a total addressable market between $1.4 trillion and $1.7 trillion by mid-century.<\/p>\n
The governance gap<\/p>\n
Yet as physical AI capabilities accelerate, a critical constraint is emerging\u2014one that has nothing to do with model performance.<\/p>\n
\u201cIn physical environments, failures cannot simply be patched after the fact,\u201d according to a World Economic Forum\u00a0analysis\u00a0published this week. \u201cOnce AI begins to move goods, coordinate labour or operate equipment, the binding constraint shifts from what systems can do to how responsibility, authority and intervention are governed.\u201d<\/p>\n
Physical industries are governed by consequences, not computation. A flawed recommendation in a chatbot can be corrected in software. A robot that drops a part during handover or loses balance on a factory floor designed for humans causes operations to pause, creating cascading effects on production schedules, safety protocols, and liability chains.<\/p>\n
The WEF framework identifies three governance layers required for safe deployment: executive governance setting risk appetite and non-negotiables; system governance embedding those constraints into engineered reality through stop rules and change controls; and frontline governance giving workers clear authority to override AI decisions.<\/p>\n
\u201cAs physical AI accelerates, technical capabilities will increasingly converge, but governance will not,\u201d the analysis warns. \u201cThose that treat governance as an afterthought may see early gains, but will discover that scale amplifies fragility.\u201d<\/p>\n
This creates an asymmetry in the US-China competition. China\u2019s faster deployment cycles and willingness to pilot systems in controlled industrial environments could accelerate learning curves.\u00a0<\/p>\n
However, governance frameworks that work in structured factory settings may not translate to public spaces where autonomous systems must navigate unpredictable human behaviour.<\/p>\n
Early deployment signals<\/p>\n
Current deployments remain concentrated in warehousing and logistics, where labour market pressures are most acute. Amazon recently deployed its millionth robot, part of a diverse fleet working alongside humans. Its DeepFleet AI model coordinates this massive robot army across the entire fulfilment network, which Amazon reports will improve travel efficiency by 10%.<\/p>\n
BMW is testing humanoid robots at its South Carolina factory for tasks requiring dexterity that traditional industrial robots lack: precision manipulation, complex gripping, and two-handed coordination.\u00a0<\/p>\n
The automaker is also using autonomous vehicle technology to enable newly built cars to drive themselves from the assembly line through testing to the finishing area, all without human assistance.<\/p>\n
But applications are expanding beyond traditional industrial settings. In healthcare, companies are developing AI-driven robotic surgery systems and intelligent assistants for patient care.\u00a0<\/p>\n
Cities like Cincinnati are deploying AI-powered drones to autonomously inspect bridge structures and road surfaces. Detroit has launched a free autonomous shuttle service for seniors and people with disabilities.<\/p>\n
The regional competitive dynamic intensified this week when South Korea announced a $692 million national initiative to produce AI semiconductors, underscoring how physical AI deployment requires not just software capabilities but domestic chip manufacturing capacity.<\/p>\n
NVIDIA has released multiple models under its \u201cCosmos\u201d brand for training and running AI in robotics. Google DeepMind offers Gemini Robotics-ER 1.5. Tesla is developing its own AI to power the Optimus humanoid robot. Each company is betting that the convergence of AI capabilities with physical manipulation will unlock new categories of automation.<\/p>\n
As simulation environments improve and ecosystem-based learning shortens deployment cycles, the strategic question is shifting from \u201cCan we adopt physical AI?\u201d to \u201cCan we govern it at scale?\u201d<\/p>\n
For China, the answer may determine whether its early mover advantage in robotics deployment translates into sustained industrial leadership\u2014or becomes a cautionary tale about scaling systems faster than the governance infrastructure required to sustain them.<\/p>\n
(Photo by Alibaba)<\/p>\n
See also: EY and NVIDIA to help companies test and deploy physical AI<\/p>\n
Want to learn more about AI and big data from industry leaders? Check outAI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, clickhere for more information.<\/p>\n
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The post Alibaba enters physical AI race with open-source robot model RynnBrain appeared first on AI News.<\/p>\n","protected":false},"excerpt":{"rendered":"
Alibaba has entered the race to build AI that powers robots, not just chatbots. The Chinese tech giant this week unveiled RynnBrain, an open-source model designed to help robots perceive their environment and execute physical tasks.\u00a0 The move signals China\u2019s accelerating push into physical AI as ageing populations and labour shortages drive demand for machines [\u2026]<\/p>\n
The post Alibaba enters physical AI race with open-source robot model RynnBrain<\/a> appeared first on AI News<\/a>.<\/p>\n<\/div>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"site-container-style":"default","site-container-layout":"default","site-sidebar-layout":"default","disable-article-header":"default","disable-site-header":"default","disable-site-footer":"default","disable-content-area-spacing":"default","footnotes":""},"categories":[1,66,67,21,576,438],"tags":[3],"class_list":["post-1290","post","type-post","status-publish","format-standard","hentry","category-ai-and-ml","category-ai-and-us","category-ai-in-action","category-artificial-intelligence","category-featured-news","category-human-ai-relationships","tag-ai"],"yoast_head":"\n