Why Korea Is Putting AI Chips Directly Inside Robots
Artificial intelligence is moving into machines that do not simply display information but physically interact with the world. Robots must see objects, understand environments, interpret sensor data, make decisions and then translate those decisions into precise movements. That combination creates a very different computing problem from running an AI chatbot in a remote data center.
South Korea is increasingly treating this problem as a hardware-and-software challenge that requires specialized computing at the edge. On September 28, 2026, AI semiconductor company Mobilint announced collaborations involving LG Electronics, Doosan Robotics and Daedong to develop on-device AI semiconductor technologies for humanoid robots, collaborative robots and unmanned agricultural robots. The work is part of South Korea’s K-On-Device AI Semiconductor Technology Development Project, which is designed to connect domestic semiconductor developers with companies that can test the chips in real products.
The important idea is not simply that Korean companies want smaller AI processors. The deeper strategy is to design AI accelerators around the exact requirements of physical machines: response time, energy consumption, sensor processing, thermal limits, reliability, software compatibility and cost. Instead of starting with a general-purpose chip and adapting a robot around it, the companies are exploring a model in which the robot’s requirements help determine what the AI chip should do.
What Was Announced in September 2026?
Mobilint said it will participate in four major projects under the Korean government's K-On-Device AI Semiconductor Technology Development Project. Three of the projects directly involve robotics applications. One focuses on LG Electronics' humanoid robotics requirements, another on Doosan Robotics' collaborative robots, and another on Daedong's unmanned agricultural robots. Mobilint is also participating with LG in an AI-home project involving on-device intelligence for appliances.
The structure is significant because the semiconductor is being developed with potential deployment environments in mind from the beginning. For the LG humanoid project, the objective is to develop an on-device AI accelerator capable of helping a robot perceive and reason about its surroundings in real time. For Doosan Robotics, the work includes an AI semiconductor and software development environment intended to connect with future robot platforms. For Daedong, the target is autonomous agricultural machinery that must interpret complicated outdoor environments and perform tasks with limited human intervention.
That approach makes the projects different from a simple chip benchmark competition. A chip may achieve impressive theoretical performance while still being difficult to use in a robot because of power consumption, memory limitations, software support, sensor interfaces or heat. The Korean projects are explicitly concerned with the complete product environment.
Why LG Wants On-Device Intelligence for Humanoid Robots
Humanoid robots face an unusually demanding computing environment. A human-shaped machine may have cameras, depth sensors, microphones, force sensors, inertial measurement units, joint-position sensors and other devices generating information continuously. The robot then has to interpret that information while controlling motors and responding to changes around it.
LG Electronics has been expanding its robotics strategy in 2026. The company established a Robotics Business Center designed to consolidate robotics business development, sales and operations and to strengthen its capabilities across industrial, commercial and residential robotics. LG has also been building a robotics data factory in Seoul to generate and process data for robot learning.
That creates an architecture in which cloud computing can remain important for training and large-scale model development, while the robot itself needs local computing for time-sensitive tasks. A humanoid cannot assume that every perception or control decision will always be sent to a remote server and returned instantly.
Consider a robot reaching toward an object. A camera detects the object, software estimates its position, a model interprets what it might be, and the control system determines how the arm should move. If every stage requires a remote network request, the system becomes dependent on network latency and connectivity. Local inference can reduce that dependency.
What Does On-Device AI Actually Mean?
On-device AI means that at least part of an artificial intelligence workload is executed locally on the device rather than being completely dependent on a remote cloud server. The device could be a smartphone, vehicle, appliance, camera, industrial machine or robot.
For robotics, on-device AI can involve computer vision, object detection, segmentation, speech processing, sensor fusion, localization, motion-related reasoning and other inference workloads. Not every AI operation needs to run locally. A robot may use a hybrid architecture in which safety-critical or latency-sensitive functions remain onboard while larger training or computationally expensive tasks are handled elsewhere.
This distinction is important. The phrase “robot without the cloud” does not necessarily mean that the robot never communicates with a server. It means the machine can retain meaningful local intelligence and continue important operations without requiring the cloud for every individual decision.
Why Latency Matters More for Robots Than for Many Software Applications
Latency is one of the central reasons companies are developing edge AI hardware. When an AI assistant takes an additional fraction of a second to respond, the user may notice the delay but the physical world generally does not change dramatically during that interval. A moving robot operates under different conditions.
A robot working around people or machinery continuously receives new sensor information. A person may walk into its path. An object may fall. A tool may move. A surface may change. A robot arm may need to stop or alter its trajectory. These events make timely local processing valuable.
Local inference can reduce the round-trip time between sensing and decision-making. It can also make a system less dependent on the availability and quality of a network connection. For agricultural machines operating across large fields, industrial robots working in facilities with restrictive network environments, or mobile robots moving through changing locations, this independence can be particularly useful.
The Power Constraint Is Just as Important as Computing Performance
One of the biggest misconceptions about AI chips is that maximum computational performance is always the most important specification. Robots challenge that assumption because they operate under physical power and thermal constraints.
A data center can consume substantial electricity and use sophisticated cooling infrastructure. A mobile robot cannot simply carry a data center with it. Battery-powered machines must divide their energy budget among computing, motors, sensors, communication and other systems.
Every watt consumed by an AI processor ultimately affects system design. Higher power can mean more heat, larger cooling requirements, reduced battery life or a heavier battery. For a humanoid robot, additional weight can itself affect movement efficiency.
This is why specialized neural processing units, or NPUs, can become important. An NPU is designed to accelerate neural-network operations efficiently rather than attempting to perform every possible computing task. The goal is not necessarily to replace the CPU or GPU but to provide a dedicated engine for AI inference.
Why LG, Doosan and Daedong Need Different AI Chips
Although humanoid robots, collaborative robots and agricultural robots all use AI, their environments are very different.
A humanoid robot may need to recognize people, objects, gestures, rooms and tools while coordinating many joints. A collaborative robot may operate beside human workers and focus on industrial manipulation, inspection or assembly. An agricultural robot may need to identify crops, soil conditions, weeds, obstacles and terrain while operating outdoors.
These different environments create different computational requirements.
A factory robot may have stable lighting, known workspaces and predictable equipment. An agricultural machine can encounter sunlight, shadows, dust, rain, mud, irregular terrain and constantly changing plant conditions. A humanoid robot may face an even broader range of unstructured environments.
Consequently, the ideal AI accelerator is not necessarily identical for every robot. Memory capacity, sensor interfaces, inference precision, computer-vision acceleration, power efficiency and software support can all vary depending on the target machine.
Doosan Robotics and the Move Toward Physical AI
Doosan Robotics has been involved in Korea's on-device AI semiconductor initiative since its planning stage. In 2025, the company announced participation in a collaboration focused on AI semiconductors optimized for sectors including machinery and robotics, automotive, IoT and home appliances, and defense.
The company's role illustrates why robot manufacturers have an interest in semiconductor design. A robot maker understands the actual workload that its machines must perform. That knowledge can influence the architecture of the AI accelerator, its software stack and the interfaces required for deployment.
Doosan has also described a need for robotics AI that combines power efficiency and connectivity with the ability to reason and execute complex tasks in unstructured environments. This reflects the broader transition from conventional industrial automation toward systems that can perceive and adapt.
The next generation of collaborative robots is therefore likely to depend on more than traditional motion-control processors. AI can become a central component of perception, planning and interaction.
Why Agricultural Robots Need Their Own AI Strategy
Daedong's involvement is particularly interesting because agriculture creates some of the hardest real-world conditions for autonomous AI systems.
An agricultural robot cannot assume that the environment will remain constant. Crops grow. Weather changes. Lighting varies during the day. Soil can become wet or dry. Plants may overlap one another. Weeds can resemble crops. Terrain can contain stones, slopes and other obstacles.
A robot operating in such an environment must combine perception with physical action. It may need to distinguish a crop from a weed and then perform a mechanical operation at the correct location. A mistake is not simply a classification error on a screen; it can damage a plant or reduce the efficiency of an agricultural operation.
Local AI can help agricultural machinery make decisions directly from camera and sensor inputs. It can also reduce the need to transmit large volumes of raw sensor data continuously to a remote cloud service.
This does not mean agricultural robots will operate without connectivity in every situation. Connectivity can still be valuable for fleet management, software updates, monitoring, analytics and model improvement. The important change is that the core perception and decision functions can increasingly be designed to operate locally.
Why Sending Every Camera Frame to the Cloud Is Not Ideal
A robot equipped with several cameras can generate enormous quantities of data. Continuously uploading all of that information can create bandwidth, latency, privacy and cost challenges.
Local processing allows the machine to extract useful information before transmission. Instead of sending every raw frame, the robot could identify an object, detect an event or generate a compact representation and transmit only the information required by a remote system.
This architecture can also make systems more resilient. If a network connection becomes weak or unavailable, the robot can continue operating within the limits of its local intelligence.
For industrial environments, there can also be reasons to keep sensitive operational data local. Manufacturing facilities may not want every camera feed or machine observation transmitted externally. Local AI can reduce the amount of raw information that leaves the physical environment.
The NPU Becomes the Robot's AI Engine
The neural processing unit is increasingly important in this architecture. CPUs remain useful for general-purpose software, operating-system functions and control logic. GPUs can provide highly parallel computation and are important for training and sophisticated AI workloads. NPUs can specialize in neural-network inference with an emphasis on efficiency.
A robot may therefore use a heterogeneous architecture rather than relying on one processor for everything. A CPU can manage general software. A GPU or graphics accelerator can handle selected workloads. An NPU can execute neural-network inference. Dedicated controllers can manage motors and safety-related functions.
The challenge is making all of these components work together. That is why software development kits are important. A fast AI chip is not useful if developers cannot efficiently compile, optimize and deploy their models on it.
Why Software Could Determine Whether Korean AI Chips Succeed
Hardware performance is only one part of the equation. Developers need tools that can convert trained neural networks into models that run efficiently on the target accelerator.
Model optimization can involve quantization, operator fusion, memory optimization, pruning and other techniques. A model trained in a common machine-learning framework must eventually be translated into a form that the embedded accelerator can execute efficiently.
Mobilint's announced work includes software development environments and SDKs alongside semiconductor development. This reflects an important industry lesson: AI chips need an ecosystem.
For robot manufacturers, the ideal platform is not simply a chip specification sheet. They need development tools, drivers, model converters, debugging systems, runtime libraries and documentation. They also need confidence that software investments will remain usable as robot platforms evolve.
Quantization Can Make AI Models More Practical on Robots
Modern neural networks can contain millions or billions of parameters. Running large models directly on a battery-powered robot can be difficult. Quantization can help by representing model values with lower numerical precision.
For example, a model may be converted from higher-precision representations to formats that require less memory and computation. The exact tradeoff depends on the model and workload. Lower precision can reduce memory traffic and energy consumption, but excessive compression can reduce accuracy.
Specialized AI chips can be designed to accelerate the numerical formats that are most useful for edge inference. This can create a close relationship between model architecture and semiconductor architecture.
The future robot AI stack may therefore involve co-design: the model developers, compiler engineers, chip designers and robot engineers work together instead of optimizing each layer independently.
Memory Is a Hidden Limitation for Robot AI
AI performance is not determined only by arithmetic operations. Memory capacity and memory bandwidth can become major bottlenecks.
A robot needs to store model weights, intermediate activations, sensor information and software state. Moving data between memory and processing units consumes time and energy. If a model repeatedly transfers large amounts of information, the processor can spend significant energy on data movement rather than computation.
This makes memory architecture an important component of specialized AI chips. Efficient local memory, high-bandwidth access and intelligent data reuse can improve practical performance.
For robots, the goal is not necessarily to run the largest possible AI model. The goal is to run a sufficiently capable model within the robot's power, memory, thermal and latency budget.
Sensor Fusion Makes Robotics Different From Ordinary AI
Robots rarely depend on a single sensor. Cameras provide visual information, microphones provide audio, inertial sensors measure motion, force sensors reveal physical interaction, and depth sensors can provide three-dimensional information.
Combining these inputs is known as sensor fusion. A robot may need to determine not only what it sees but also where it is, how it is moving and what physical forces are acting on it.
This creates workloads that can be more complicated than ordinary image classification. The AI system must combine multiple streams of information and produce an output that can influence physical movement.
Specialized robot AI chips can therefore be designed around these workloads. Sensor interfaces, memory architecture, real-time processing and accelerator capabilities all matter.
Physical AI Is the Larger Strategy Behind These Projects
The term physical AI describes AI systems that interact with and act within the physical world. Robots are one of the most visible examples.
Generative AI demonstrated how neural networks can process language, images and other digital information. Physical AI adds another layer: the system must understand an environment and perform actions within it.
This creates a feedback loop. The robot senses the environment, interprets the information, decides what to do, performs an action and then senses the result. Errors can have physical consequences.
That is one reason why Korea's on-device AI strategy is closely connected to robotics. A specialized semiconductor can provide the local inference capability needed to make physical AI practical in constrained machines.
Why LG Is Building More Than a Robot
LG's 2026 robotics strategy suggests that the company is thinking about the complete robotics stack. The company has been expanding its robotics organization while developing data-generation capabilities and working with technology partners.
LG announced that its robotics data factory would be used for data generation, collection and learning, including work involving its CLOiD humanoid robot. The company has also described a strategy involving robots, core components, data capabilities and software.
This matters because advanced robots require more than a physical machine. They require training data, simulation, AI models, compute hardware, actuators, sensors, operating software and deployment infrastructure.
An on-device AI chip is one component in that larger ecosystem. But it is a particularly important component because it sits close to the robot's real-time decision loop.
Training and Inference Are Becoming Separate Computing Problems
One of the most important architectural changes in robotics AI is the separation between training and inference.
Large computing clusters can train or fine-tune models using huge datasets. Simulation systems can generate additional examples. Data factories can collect demonstrations and real-world observations. None of that necessarily needs to happen inside the robot.
The robot only needs a deployment version of the model that is small and efficient enough to execute locally.
This creates a three-layer ecosystem: powerful infrastructure for training, simulation and data processing; edge infrastructure for testing and fleet management; and specialized processors inside the robot for real-time inference.
That separation allows robot manufacturers to use enormous computational resources without forcing every robot to contain equivalent hardware.
Could Robots Eventually Run Large Foundation Models Locally?
The industry is moving toward increasingly capable models on edge devices, but the idea that every robot will run the largest available foundation model locally remains uncertain.
Memory, power consumption and thermal constraints impose hard physical limits. Larger models generally require more resources, although architecture improvements and compression can change the tradeoff.
A more realistic near-term direction is a layered architecture. A robot may run smaller specialized models continuously while calling larger local or remote models only when necessary.
For example, a lightweight vision model could monitor the environment constantly. A larger reasoning model could be activated for unusual situations. A remote service could handle long-term analytics or model training.
This type of hierarchical intelligence could provide a practical compromise between capability and efficiency.
What Makes a Robot AI Chip Different From a Smartphone AI Chip?
Smartphones and robots both benefit from NPUs, but their requirements are not identical.
A smartphone is optimized around user applications, imaging, speech, battery life and mobile computing. A robot must also deal with continuous sensor streams, physical movement, safety constraints and deterministic response requirements.
Robotics processors may therefore need stronger real-time characteristics and broader sensor connectivity. They may also need to operate continuously under varying environmental conditions.
The same AI model could behave differently depending on how it is integrated into the robot's control architecture. A robotics accelerator must be evaluated as part of the entire system rather than as an isolated benchmark.
Why Energy Efficiency Can Matter More Than Peak TOPS
AI accelerator performance is often described using operations-per-second metrics such as TOPS. These numbers can be useful for comparing hardware under particular conditions, but they do not tell the entire story.
A robot manufacturer also needs to know how much energy is required to perform a real task. Two chips with similar theoretical performance may have very different efficiency once memory movement, software overhead and sensor processing are included.
For battery-powered robots, an important metric can therefore be useful AI work per watt rather than maximum theoretical computation.
This is one reason specialized silicon can be attractive. A processor optimized around a specific workload may eliminate unnecessary computation and reduce data movement.
Privacy Is Another Reason for Local AI
Robots increasingly operate in environments where cameras and microphones can capture sensitive information. A household robot could observe private spaces. A factory robot could see proprietary manufacturing processes. A healthcare robot could operate around sensitive environments.
Local inference can reduce the need to transmit raw sensor data to remote infrastructure. It does not automatically guarantee privacy, because data can still be transmitted if the system is designed that way. However, local processing provides an architectural option for minimizing unnecessary data movement.
For companies deploying large robot fleets, this can become part of the system's security and data-governance design.
Reliability Becomes Critical When AI Controls Physical Machines
AI errors in physical systems can have different consequences from errors in ordinary software. A wrong recommendation on a screen can be corrected by a user. A wrong robot movement can damage equipment, products or surroundings.
For this reason, on-device AI does not mean that every decision should be delegated to an unconstrained neural network. Robotics systems generally need layers of control, monitoring and safety mechanisms.
An AI model may identify an object, while deterministic software checks whether the requested movement is permitted. A separate controller may enforce mechanical limits. Emergency-stop systems can operate independently of the AI model.
This layered architecture is likely to remain important as physical AI becomes more capable.
Why Korea Wants Domestic AI Semiconductor Capabilities
South Korea already has major semiconductor manufacturing capabilities, but advanced AI systems require more than manufacturing. They require processor architecture, memory technologies, software, compilers, packaging, model optimization and customers willing to deploy the technology.
The K-On-Device AI Semiconductor Technology Development Project attempts to connect semiconductor developers with demand-side companies. The objective is to reduce the distance between chip development and actual product deployment.
That relationship can be important for startups because building a competitive AI processor is difficult without real workloads and customers. Conversely, robot manufacturers can benefit when chip developers design directly around their requirements.
The LG, Doosan and Daedong collaborations provide examples of this demand-driven model.
Why the Strategy Extends Beyond Robots
Robotics is only one part of Korea's on-device AI push. The same technologies can potentially be used in vehicles, industrial equipment, cameras, appliances and other edge devices.
LG's participation in an AI-home project illustrates this broader direction. An appliance does not need the same AI architecture as a humanoid robot, but both can benefit from local inference that reduces cloud dependence and manages energy efficiently.
Once an AI semiconductor platform becomes mature, developers may adapt it to multiple product categories. This can create economies of scale for the semiconductor company while allowing different customers to customize the software and model stack.
What This Means for the Future of Humanoid Robots
Humanoid robots require a combination of mechanical engineering, control systems, perception, AI and energy management. Specialized AI chips could become increasingly important as the machines become more capable.
A future humanoid may use several levels of computing. Low-level controllers can handle motors and safety. Dedicated accelerators can process vision and sensor information. Higher-level AI models can handle language and reasoning. Remote infrastructure can support training and fleet-wide learning.
The challenge is coordinating all these layers without creating excessive latency or energy consumption.
South Korea's current projects suggest that semiconductor companies and robot manufacturers are attempting to solve this problem together rather than independently.
The Bigger Competition Is Not Just About Robots
The emerging competition in physical AI involves several layers at once. Companies are competing to build better robots, better actuators, better sensors, better AI models, better simulation systems and better computing platforms.
Control of the AI chip can influence the entire system because it determines how efficiently models can operate at the edge. A company that develops a successful accelerator may also gain an opportunity to build a broader software ecosystem around it.
This is similar to the importance of compute platforms in cloud AI, but the constraints are different. Data-center AI prioritizes enormous throughput and scalability. Robotics AI must balance intelligence with latency, power, memory, thermal limits and physical reliability.
Could Specialized Chips Reduce Robot Costs?
Potentially, but the answer depends on scale and development economics. Designing custom silicon can be expensive, and specialized hardware requires software support and manufacturing capacity.
However, if the same accelerator can be deployed across large numbers of robots, appliances or industrial machines, the cost can be distributed across a product family. A chip optimized for a known workload can also reduce the need for unnecessarily powerful general-purpose hardware.
That is one reason the involvement of large manufacturers is important. LG, Doosan and Daedong can provide real product requirements and potential deployment environments, while a semiconductor developer can provide specialized AI hardware and software.
What Could Happen to Cloud Robotics?
Cloud robotics is unlikely to disappear simply because edge AI becomes stronger. Cloud infrastructure remains valuable for training, fleet analytics, centralized management, simulation, software updates and complex workloads.
The more likely outcome is a hybrid model.
In this architecture, the robot performs immediate perception and action locally. It communicates with cloud or edge servers for tasks that do not require millisecond-level responses. Fleet data can then be aggregated to improve models, and updated models can be distributed back to robots.
This creates a feedback loop between local intelligence and centralized learning.
The Rise of the Robot Data Flywheel
Robot intelligence improves when robots encounter useful examples of the physical world. Every successful action, failure, sensor observation and human demonstration can potentially become training data.
LG's robotics data-factory strategy reflects this idea. The company has been developing infrastructure to collect and expand robotics data, including data generated by physical robots and synthetic information.
In a mature robot ecosystem, the AI chip and data system become closely connected. The chip executes the model in the field. The robot generates information about real-world performance. That information can be used to improve future models. New models are then optimized for the same edge hardware.
This creates a data-and-compute flywheel that could become a major competitive factor in robotics.
Why Agricultural Robots Could Become an Important Test Case
Agricultural robotics provides an unusually difficult test for physical AI because it combines autonomy with a highly variable environment.
A robot that works in a laboratory may perform well under controlled conditions. A machine operating in a real field must handle unpredictable lighting, vegetation, terrain, weather and obstacles.
If an AI accelerator can support reliable perception and decision-making under those conditions while staying within a practical energy budget, the same technology could have applications in other outdoor autonomous systems.
That makes agricultural robotics more than a niche application. It can become a demanding environment for validating edge AI technologies.
What Developers Should Watch Next
The most important developments will not necessarily be announcements about raw chip performance. The more revealing signals will be actual robot deployments and software ecosystems.
Developers should watch for commercially deployed robots using Korean-designed NPUs, the availability of SDKs and model-compilation tools, support for common AI frameworks, model-optimization capabilities, sensor integration, power efficiency and real-world task performance.
Another important indicator will be whether a single chip platform can support multiple product categories. If the same architecture can be adapted for humanoids, collaborative robots and agricultural machines, it could create a stronger ecosystem than a chip designed for only one narrow product.
Will Every Robot Eventually Need a Specialized AI Chip?
Not necessarily. Some robots may continue to use general-purpose CPUs and GPUs, while others may rely on integrated processors containing AI accelerators.
The appropriate architecture depends on the workload, price, power budget and required intelligence. A simple industrial machine may not need a large neural processor. A sophisticated humanoid could need multiple accelerators.
The important trend is that AI processing is becoming a fundamental part of robot architecture. As models become more central to perception and decision-making, hardware designers have stronger incentives to optimize computing specifically for those workloads.
Korea's Strategy in One Picture
The emerging Korean strategy can be understood as a chain connecting semiconductor design to physical products.
At one end are AI semiconductor companies developing NPUs and software tools. In the middle are manufacturers such as LG Electronics, Doosan Robotics and Daedong defining practical requirements. At the other end are robots and devices operating in factories, homes, farms and other real-world environments.
The government-backed development structure attempts to connect all three layers early in the development cycle.
If successful, this can shorten the path from semiconductor research to actual product deployment. It can also give Korean AI-chip developers valuable feedback that is difficult to obtain from laboratory benchmarks alone.
The Larger Shift From Cloud AI to Physical AI
The AI industry is entering a phase in which intelligence is increasingly moving into physical machines. The next generation of AI competition will therefore not be limited to language models and data centers.
Robots need intelligence where the action happens. A camera on a robot needs to understand what it sees. A robotic arm needs to respond to changing conditions. An agricultural machine needs to recognize its environment. An appliance may need to understand user behavior without sending every interaction to the cloud.
These requirements make efficient edge AI increasingly important.
LG, Doosan and Daedong's collaboration with Mobilint represents one example of this broader shift. The central idea is not that cloud computing is becoming irrelevant. Instead, computing is being divided according to where it is most useful.
Large centralized systems can train models and perform complex analysis. Edge systems can execute time-sensitive inference. Specialized AI chips can make those local workloads more efficient.
Conclusion: The Robot's AI Brain Is Becoming a Semiconductor Design Problem
The new generation of robots will depend on much more than mechanical hardware. Their ability to perceive, reason and act will increasingly depend on the computing architecture inside the machine.
South Korea's current on-device AI semiconductor projects show how manufacturers and chip developers are trying to address that challenge together. LG is focusing on humanoid robotics and broader physical AI capabilities. Doosan Robotics is targeting collaborative robots and industrial applications. Daedong brings the demanding requirements of autonomous agricultural machinery. Mobilint is developing specialized NPU technology and software environments intended to connect those chips with real products.
The significance of this strategy is that it treats AI hardware as part of the robot itself rather than as an invisible service somewhere in the cloud.
The eventual winners in physical AI may not be determined by the largest AI model alone. They may be determined by how efficiently a complete system can sense the world, process information, make decisions and perform actions under real physical constraints.
That is why Korea's push toward specialized on-device AI chips is worth watching. The country is effectively testing whether locally optimized silicon, software and robot platforms can form a practical foundation for the next generation of physical AI.
