New York Robotics has formally launched amid a surge in robotics investment, startup formation, and talent concentration across the New York Tri-State region. The announcement marks a turning point for New York’s robotics sector, which now counts more than 160 robotics startups across New York, New Jersey, and Connecticut, with nearly 100 based in New York City alone.

“Our vision is to leverage New York’s central role in the global economy to build a leading robotics hub,” said Jacob Hennessey-Rubin, founding board member and executive director of New York Robotics. “This is a place where founders, researchers, enterprises, and investors can collaborate to shape the future of robotics, while positioning the sector as a serious investment category on Wall Street.”

The milestone signals New York’s emergence as a serious global contender in robotics and embodied AI, joining established hubs such as Boston, Silicon Valley, Pittsburgh, Munich, and Zurich. Long viewed as strong in finance, media, and enterprise software, New York is increasingly becoming a center for robotics commercialization as the technology moves from research into real-world deployment.

A Dense and Expanding Robotics Network

New York Robotics was formed to address a long-standing gap in the region’s innovation landscape. While the city has deep pools of capital, customers, and technical talent, robotics activity historically developed in silos. NYR’s mission is to unify startups, investors, enterprises, researchers, and government organizations into a coordinated ecosystem capable of supporting companies from early research through large-scale deployment.

Over the past two years, New York Robotics has quietly assembled one of the most comprehensive robotics networks in the United States. The organization now engages more than 450 robotics startups worldwide, including over 160 in the Tri-State region, alongside more than 80 corporations, 20 academic institutions, 40 research labs, 300 venture capital firms, and dozens of domestic and international government organizations.

Founding members include organizations such as J.P. Morgan, New York University, AlleyCorp, EisnerAmper, and Cybernetix Ventures. The group has also hosted or co-hosted more than 20 ecosystem events, including the first dedicated robotics programming at NY TechWeek, helping elevate robotics as a visible and strategic sector within the city’s broader technology economy.

NYR’s official launch coincided with its sponsorship of AlleyCorp’s inaugural Deep Tech NY conference, underscoring the growing convergence of robotics, capital, and enterprise demand in the region.

Capital, Talent, and Commercial Pull

Unlike many robotics hubs driven primarily by academic research, New York’s ecosystem is shaped by proximity to enterprise customers in finance, healthcare, logistics, construction, and real estate. This commercial pull is attracting startups focused on deploying robots in real operating environments rather than remaining confined to labs.

“We see New York Robotics as a kind of exchange,” said Randy Howie, founding board member and managing partner of New York Robotics. “It’s a platform where startups, enterprises, investors, academia, and government can connect efficiently and translate robotics innovation into broad economic impact.”

Industry leaders echo that view. Founders point to New York’s ability to attract global engineering talent while offering access to industrial infrastructure in surrounding areas such as Long Island and New Jersey, where robotics companies can scale manufacturing and testing.

Mapping the Ecosystem

As part of its platform, New York Robotics recently released a private beta of the NYR Index, an ecosystem intelligence tool designed to map startups, investors, labs, and enterprise participants. The tool is intended to give partners visibility into deal flow, talent movement, and sector trends as robotics investment accelerates.

NYR has also partnered with organizations such as C10 Labs, an operator selected by NYCEDC for the NYC AI Nexus, to support applied AI ventures across robotics, advanced manufacturing, healthcare, and sustainability.

With robotics increasingly viewed as essential infrastructure for addressing labor shortages, productivity challenges, and demographic shifts, New York Robotics’ formal launch reflects a broader reality: New York is no longer just a consumer of robotics technology. It is becoming a place where the next generation of robots is built, funded, and deployed.

Tesla is signaling a major strategic realignment, with reports indicating the company is preparing to wind down production of its Model S and Model X vehicles while accelerating investment in autonomous driving and humanoid robotics. The move underscores Tesla’s growing emphasis on physical AI as a core pillar of its future business.

The Model S and Model X, once central to Tesla’s brand and early success, now represent a shrinking share of the company’s overall vehicle sales. As production volumes concentrate around the Model 3 and Model Y, Tesla appears increasingly willing to deprioritize its higher-end legacy models in favor of technologies with longer-term growth potential.

From Premium EVs to Physical AI

Tesla’s pivot aligns with repeated statements from Elon Musk, who has described humanoid robots as potentially more valuable than the company’s car business over time. Musk has argued that autonomous labor, enabled by general-purpose robots, could unlock economic value far beyond vehicle manufacturing.

The company’s Optimus humanoid robot is designed to perform repetitive and physically demanding tasks in factories, warehouses, and eventually service environments. Tesla has already begun testing Optimus inside its own facilities, using controlled environments to train robots while extracting immediate operational value.

Musk has framed this approach as a natural extension of Tesla’s autonomy stack. The same AI systems used for full self-driving vehicles — including vision-based perception, neural networks, and custom AI chips — are being adapted for humanoid robotics.

Billions Directed Toward Autonomy and Robots

Tesla is expected to invest tens of billions of dollars over the coming years into autonomous vehicles and humanoid robotics. Reports suggest combined spending on self-driving technology, AI infrastructure, and robotics could reach as much as $20 billion, reflecting the scale of the company’s ambitions.

These investments include expanded data center capacity, custom silicon development, and large-scale AI training. For Tesla, the goal is to create systems that can reason, navigate, and act reliably in complex real-world environments – whether on roads or factory floors.

While timelines remain aggressive, Musk has said that Optimus could reach limited commercial deployment within the next few years, initially focused on internal use cases before broader rollout.

A Broader Industry Shift

Tesla’s evolving priorities mirror a wider trend across the technology and automotive sectors. As electric vehicles mature and competition intensifies, manufacturers are searching for new growth engines beyond car sales. Robotics and physical AI have emerged as leading candidates, promising scalable applications across manufacturing, logistics, healthcare, and consumer services.

Ending or scaling back Model S and X production would mark the end of an era for Tesla’s original premium lineup. But it would also reinforce Musk’s long-held view that Tesla is not ultimately a car company, but an AI and robotics company that happens to build vehicles.

Whether Optimus can deliver on that vision remains an open question. Still, Tesla’s willingness to shift resources away from iconic vehicles highlights how seriously the company is betting on a robot-driven future.

OpenAI has quietly returned to hands-on robotics, building an internal lab dedicated to experimenting with physical AI systems, as first reported by Business Insider. According to people familiar with the work, the lab was launched in early 2025 and has since grown to around 100 employees, signaling a renewed commitment to understanding how artificial intelligence can operate in the physical world.

The effort marks a notable shift for OpenAI, which previously stepped back from robotics after winding down an earlier program in 2020. That earlier initiative produced a high-profile robotic hand capable of solving a Rubik’s Cube, but leadership ultimately chose to refocus on large-scale AI models. While OpenAI exited direct robot development at the time, it continued to back the sector financially, investing in companies such as Figure, 1X, and Physical Intelligence.

By late 2024, internal discussions reportedly resurfaced around whether OpenAI should once again build robotic systems in-house. Those conversations have now translated into an operational lab, although the current work remains deliberately narrow in scope.

A Data-First Approach to Robotics

Rather than developing a full humanoid robot, OpenAI’s lab is focused on gathering high-quality training data. Inside the facility, robotic arms perform repetitive household-style tasks such as placing bread into a toaster, folding laundry, or rearranging objects. These systems run for extended periods, generating large volumes of interaction data that engineers use to refine perception, control, and decision-making models.

The emphasis reflects a broader challenge in robotics: unlike language models, robots do not benefit from vast, readily available datasets. Physical interaction data must be created deliberately, often through slow and expensive real-world experimentation. OpenAI’s approach suggests it is prioritizing the foundations of physical intelligence before committing to a specific robot form factor.

Engineers reportedly evaluate whether each training cycle leads to measurable improvements, adjusting models and task setups accordingly. The process is incremental, but it allows OpenAI to study how general-purpose AI systems learn from repeated physical interaction.

From Robots to “Brains” for Robots

It remains unclear whether OpenAI ultimately intends to build its own humanoid robot. People close to the project suggest that the company may instead aim to develop general-purpose control and reasoning models that could power robots built by partners.

That strategy would be consistent with OpenAI’s broader role in the AI ecosystem, where it supplies foundational models rather than finished products. A robotics-focused foundation model could potentially be deployed across multiple platforms, from industrial arms to mobile manipulators and humanoid systems.

The company’s existing investments in robotics startups also point toward a collaborative rather than vertically integrated approach. By supplying the intelligence layer, OpenAI could influence the future of robotics without competing directly with hardware manufacturers.

Robotics Returns to the AI Agenda

OpenAI’s renewed interest in robotics comes amid a wider industry push toward physical AI. Advances in large language models, vision systems, and reinforcement learning have revived optimism that robots can move beyond tightly scripted tasks and operate more flexibly in real environments.

Still, the company appears cautious. While reports suggest OpenAI has discussed opening a second robotics lab, no public timeline exists for commercial products or humanoid robots. For now, the focus remains on experimentation, data, and learning how intelligence transfers from screens into the physical world.

Whether OpenAI ends up building robots, powering them, or both, its quiet return to robotics underscores a growing consensus across the AI sector: true general intelligence will not be confined to software alone.

Airbus has taken a significant step toward automation in aircraft production by ordering a six-figure number of humanoid robots from UBTech Robotics. The purchase represents one of the largest commercial commitments yet for humanoid robots in heavy manufacturing and highlights how physical AI is beginning to move from pilot projects into real industrial workflows.

The robots are expected to be deployed across Airbus facilities to support repetitive and physically demanding tasks involved in aircraft assembly. While Airbus has long used industrial automation, the move toward humanoid robots reflects a shift toward more flexible systems that can operate in environments originally designed for human workers.

UBTech’s shares surged following reports of the deal, underlining growing investor confidence that humanoid robotics is transitioning from experimentation to scalable industrial use.

From Industrial Automation to Humanoid Labor

Unlike traditional industrial robots that are fixed in place and optimized for a narrow set of motions, humanoid robots are designed to navigate complex workspaces, manipulate tools, and interact with equipment built for human hands.  For aerospace manufacturing, where production lines involve tight spaces, variable tasks, and frequent reconfiguration, this flexibility is increasingly valuable.

Airbus has been evaluating humanoid robots as a way to address labor shortages, improve ergonomics, and increase consistency in assembly operations. The robots are expected to assist with material handling, inspection, and other repetitive processes that can strain human workers over long shifts.

The order places Airbus among a small but growing group of global manufacturers experimenting with humanoid robotics at meaningful scale, alongside automotive and logistics operators.

UBTech’s Industrial Push

UBTech, best known for its humanoid robot platforms designed for research and service applications, has been expanding aggressively into industrial markets. The company’s latest humanoid systems are built to operate autonomously in factory environments, combining computer vision, motion planning, and AI-driven manipulation.

The Airbus deal signals growing confidence in UBTech’s ability to meet industrial reliability and safety requirements, which remain a major barrier for humanoid robots operating alongside human workers. Aerospace manufacturing, in particular, demands high precision, repeatability, and compliance with strict safety standards.

For UBTech, the agreement represents a major validation of its strategy to position humanoid robots as a practical workforce augmentation tool rather than a futuristic novelty.

A Broader Shift Toward Physical AI

The Airbus order reflects a wider trend across global manufacturing, where companies are exploring physical AI systems that can reason, adapt, and act in real-world environments. Unlike conventional automation, humanoid robots promise to reduce the need for costly factory redesigns by fitting into existing workflows.

As labor markets tighten and production complexity increases, manufacturers are increasingly willing to test new forms of automation that offer both flexibility and scalability. Humanoid robots, while still early in their adoption curve, are emerging as a potential bridge between human labor and fully automated systems.

For Airbus, the deployment is expected to begin incrementally, with performance data guiding future expansion. If successful, humanoid robots could become a permanent fixture on aircraft assembly lines, reshaping how aerospace manufacturing is performed.

The deal underscores a turning point for the humanoid robotics industry: major industrial players are no longer just experimenting. They are beginning to place real orders.

ASUS is scaling back new smartphone development as part of a broader strategic shift toward artificial intelligence, robotics, and advanced computing systems. The move reflects changing priorities inside the Taiwanese technology company, which is reallocating engineering talent and capital away from consumer handsets toward higher-growth segments tied to physical AI and intelligent automation.

Asus will continue to sell and support existing smartphone models in select markets, but it no longer plans to invest heavily in new flagship phone platforms. The decision follows years of intense competition in the global smartphone market, where margins have tightened and growth has slowed, particularly outside of Apple and Samsung’s ecosystems.

Company executives have indicated that future innovation efforts will focus on AI infrastructure, edge computing, robotics platforms, and intelligent devices designed for enterprise and industrial use cases.

From Consumer Phones to Physical AI

Asus has been steadily expanding its footprint in AI hardware, including servers optimized for accelerated computing, edge AI platforms, and embedded systems used in robotics and automation. These systems are increasingly deployed in factories, logistics centers, healthcare environments, and smart infrastructure projects.

The company has also invested in robotics-related research, including autonomous mobile systems, AI vision platforms, and human-machine interfaces. Rather than building consumer-facing robots, Asus is positioning itself as an enabling technology provider, supplying the compute, sensing, and control systems that power physical AI applications.

This transition mirrors a broader industry shift, where growth is increasingly concentrated in AI-driven systems that operate in the physical world. Robotics, autonomous machines, and intelligent infrastructure require high-performance, energy-efficient computing platforms, an area where Asus believes it can compete more effectively than in consumer smartphones.

Robotics and Edge AI as Growth Drivers

Asus’ robotics strategy centers on edge intelligence, where AI models run directly on devices rather than relying on cloud infrastructure. This approach is critical for robots and autonomous systems that must operate with low latency, high reliability, and strong data privacy guarantees.

The company’s hardware portfolio now includes AI-ready industrial PCs, robotic controllers, and edge servers designed to support computer vision, motion planning, and real-time decision-making. These systems are being adopted across manufacturing, smart cities, and healthcare automation.

By exiting aggressive smartphone development, Asus frees up resources to deepen partnerships in robotics ecosystems and accelerate product cycles in AI-driven markets. Industry analysts view this as a pragmatic move, given the capital-intensive nature of smartphone development and the uncertain returns in a saturated market.

A Broader Industry Realignment

Asus’ pivot comes amid a wider reassessment of consumer electronics strategies across the technology sector. As smartphones mature, many manufacturers are looking beyond handsets for long-term growth, turning instead to AI infrastructure, robotics, and intelligent systems that can scale across industries.

Physical AI, which combines perception, reasoning, and action in real-world environments, is emerging as a central theme in this transition. Robotics platforms require continuous upgrades in compute performance, sensing accuracy, and software integration, creating recurring demand for specialized hardware and systems.

For Asus, the shift represents a move from volume-driven consumer markets toward fewer, higher-value deployments. While smartphones once defined the company’s consumer identity, its future growth is increasingly tied to the machines, factories, and autonomous systems that will shape the next phase of industrial digitization.

The decision underscores a growing consensus in the technology industry: the next major wave of innovation will not be defined by screens in pockets, but by intelligent machines operating alongside humans in the physical world.

Elon Musk has once again pushed Tesla’s ambitions beyond cars, predicting that the company’s humanoid robots could ultimately outgrow its electric vehicle business. Speaking in recent public remarks and investor discussions, Musk framed robotics not as a side project, but as a future pillar that could redefine Tesla’s identity over the coming decade.

Tesla’s humanoid robot, known as Optimus, is designed to perform repetitive and physically demanding tasks in factories, warehouses, and eventually homes. Musk has repeatedly argued that the long-term economic value of autonomous labor far exceeds that of vehicle manufacturing, particularly as global labor shortages intensify and wages rise across industrial economies.

While Tesla remains one of the world’s most valuable automakers, Musk suggested that robots could unlock an entirely new market measured in trillions of dollars. Unlike cars, which are constrained by consumer purchasing power and replacement cycles, general-purpose robots could be deployed continuously across manufacturing, logistics, healthcare, and domestic services.

Robots as Tesla’s Next Growth Engine

Tesla’s robotics program draws heavily from the same core technologies that power its vehicles. The Optimus platform uses Tesla’s full self-driving neural networks, computer vision systems, and custom AI chips, allowing the company to reuse years of autonomy research. Musk has emphasized that this software-first approach gives Tesla a structural advantage over robotics startups that must build perception, planning, and control systems from scratch.

Optimus is expected to operate initially inside Tesla’s own factories, handling material transport and basic assembly tasks. These controlled environments allow the company to train robots at scale while generating real operational value. Musk has indicated that internal deployments could begin ramping before broader commercial availability.

Over time, Tesla envisions Optimus evolving into a general-purpose worker capable of understanding instructions, navigating complex spaces, and manipulating objects with human-like dexterity. If successful, this would place Tesla among a small group of companies attempting to commercialize humanoid robots at scale.

Physical AI Beyond Autonomous Vehicles

Musk’s comments reflect a broader industry shift toward what many executives now call physical AI — systems that can perceive, reason, and act in the real world. Unlike digital AI products, physical AI must meet far higher safety, reliability, and cost constraints, especially when operating alongside humans.

Tesla’s strategy mirrors developments across the robotics sector, where companies are racing to combine large-scale AI models with real-world embodiment. Musk argues that once robots reach sufficient intelligence and reliability, manufacturing capacity becomes the primary constraint, not demand.

He has suggested that a mature robotics business could eventually dwarf Tesla’s vehicle revenues, even if EV sales continue to grow. In Musk’s framing, cars may become just one application of a much larger AI and robotics platform.

Skepticism and Execution Risk

Despite the bold vision, significant challenges remain. Humanoid robots must operate safely in unpredictable environments, manipulate a vast range of objects, and perform tasks reliably over long periods. Battery life, actuator durability, and cost-efficient manufacturing all remain open questions.

Analysts also note that Tesla has a history of ambitious timelines that often slip. While Optimus prototypes have demonstrated walking, object handling, and basic autonomy, large-scale commercial deployment is still unproven.

Nevertheless, Musk’s prediction underscores Tesla’s long-term direction. Rather than viewing robotics as experimental, Tesla is positioning humanoid robots as a central business line that could redefine how work is performed across the global economy.

If Tesla succeeds, the company best known for electric cars could ultimately be remembered for something far more transformative: machines that replace human labor at scale.

NEURA Robotics and Bosch have announced a strategic partnership aimed at accelerating the industrial deployment of humanoid robots and physical AI technologies developed in Germany. The collaboration brings together NEURA’s fast-moving robotics platform and Bosch’s manufacturing scale, signaling a coordinated European push into one of the most competitive emerging technology markets.

“NEURA aims to position Europe as the global leader in one of the most significant future markets, humanoid robotics,” said David Reger, founder and CEO of NEURA Robotics. “Our mission is to set the global benchmark for physical AI and humanoid robotics, establishing a European alternative to the major platform players in the U.S. and China. The partnership with Bosch is a powerful signal that Germany and Europe are investing in next-generation technologies developed independently.”

Reger said access to real-world physical training data remains the largest constraint in robotics development. “Physical training data is the biggest challenge in robotics; no one has it,” he said. “At NEURA, we have turned this challenge into our competitive advantage, and now, with Bosch, we have the opportunity to capture, structure, and leverage real-world data.”

The partnership is positioned around a shared objective: moving humanoid robots from experimental systems into reliable, scalable tools for real-world work environments. Both companies describe humanoid robotics as a technological shift comparable in impact to the rise of the personal computer or smartphone.

Building the Data Foundation for Humanoid Robots

A central pillar of the collaboration is the joint collection of real-world physical data inside Bosch facilities. Using advanced sensor suits, the partners will capture human motion, task execution, and environmental interaction data during everyday industrial work. This type of physical training data is scarce but critical for teaching humanoid robots how to move, manipulate objects, and operate safely alongside people.

By grounding robot learning in real workplace conditions rather than purely simulated environments, NEURA and Bosch aim to accelerate deployment timelines and improve reliability. The data will feed directly into NEURA’s AI models, enabling faster learning cycles and more adaptable robotic behavior across diverse tasks.

In parallel, the companies will co-develop AI-based core software, functional robotics modules, and intuitive user interfaces designed for industrial use. This software collaboration is intended to bridge perception, reasoning, and physical action into a cohesive operating layer for humanoid robots.

From Scale-Up Innovation to Industrial Production

Bosch will play a key role in supporting NEURA’s transition from development to large-scale production. This includes optimizing manufacturing workflows, scaling embedded software, and potentially supplying robotic components such as motors and actuators. The partnership also leaves room for Bosch to support final assembly and motor production for future humanoid platforms.

NEURA enters the collaboration with a reported order book exceeding one billion euros and is actively expanding its production capacity. The company’s industrial scaling effort is led by executives with deep experience in Bosch’s own manufacturing systems, reinforcing the operational alignment between the two organizations.

The focus on industrialization reflects a broader industry shift. Customers are increasingly demanding robots that can deliver consistent performance, meet safety requirements, and integrate into existing facilities without extensive redesign.

An Open Ecosystem for Physical AI

At the software level, NEURA is advancing an open robotics ecosystem known as the Neuraverse. The concept centers on connected humanoid robots that share skills, data, and learned behaviors across a distributed network. Improvements made by one robot can propagate across the fleet through software updates, creating a continuous feedback loop between deployment and development.

Combined with Bosch’s manufacturing and systems expertise, this approach is designed to accelerate innovation while maintaining industrial reliability. Rather than closed, application-specific robots, the partners are betting on adaptable, general-purpose systems that improve over time.

The partnership underscores a broader ambition to establish a European alternative in humanoid robotics, at a time when major efforts are concentrated in the United States and China. By pairing real-world data acquisition with scalable production, NEURA and Bosch are positioning Germany as a central hub for the next phase of physical AI.

Wing and Walmart are significantly expanding their drone delivery partnership, announcing plans to add drone service to 150 additional Walmart stores across the United States over the next year. The move is designed to transform drone delivery from a regional convenience into a nationwide retail option, ultimately reaching more than 40 million Americans.

The expansion builds on years of testing and commercial operations in select markets, particularly in the Dallas-Fort Worth area and Metro Atlanta. In those regions, drone delivery has moved beyond novelty status and become a routine part of shopping behavior for many customers. According to the companies, usage has accelerated sharply, with deliveries tripling over the past six months and a core group of customers placing multiple orders per week.

By 2027, Wing and Walmart expect to operate more than 270 drone delivery locations across the country, forming what they describe as the largest residential drone delivery network in the world.

From Regional Pilots to National Coverage

The next phase of expansion will introduce drone delivery to major metropolitan areas including Los Angeles, St. Louis, Cincinnati, and Miami. These additions build on previously announced rollouts in cities such as Houston, Orlando, Tampa, and Charlotte. Operations in Houston are scheduled to begin in mid-January, marking one of the largest single-market launches to date.

The companies said the goal is not simply geographic growth, but consistency and scale. Drone delivery is being integrated directly into Walmart’s existing store operations, allowing orders to be fulfilled from local inventory rather than specialized distribution centers. This approach shortens delivery times and reduces friction for customers placing last-minute or urgent orders.

Wing’s drones are primarily used for lightweight items, including groceries, household essentials, and over-the-counter medications. Orders are delivered in minutes, often faster than traditional same-day or curbside options.

Why Drone Delivery Is Gaining Traction

Executives from both companies argue that the growing adoption reflects a shift in consumer expectations rather than a short-term experiment. Walmart sees drone delivery as a way to address immediate needs, particularly for time-sensitive purchases that do not justify a full shopping trip.

“Drone delivery plays an important role in our ability to deliver what customers want, exactly when they want it,” said Greg Cathey, Walmart’s senior vice president of digital fulfillment transformation. He pointed to strong adoption in existing markets as evidence that customers are willing to embrace the technology when it is reliable and easy to use.

Wing, which is owned by Alphabet, has spent years refining its aircraft, navigation systems, and air traffic coordination to support dense residential operations. The company emphasizes that its drones are designed to operate autonomously while meeting strict safety and noise standards, a key factor in securing regulatory approvals and community acceptance.

The Economics of Ultra-Fast Delivery

The scale of the expansion highlights a broader shift in retail logistics. While drone delivery has often been viewed as expensive or experimental, Wing and Walmart argue that high utilization changes the equation. In markets where demand is strong, drones can complete many short trips per hour, lowering the cost per delivery and reducing reliance on human drivers.

“We believe even the smallest package deserves the speed and reliability of a great delivery service,” said Adam Woodworth, chief executive of Wing. He said working with Walmart has allowed the company to demonstrate that drone delivery can operate as part of everyday retail, not just as a premium or niche offering.

Industry analysts note that Walmart’s national footprint gives the partnership a structural advantage. Thousands of stores located close to residential neighborhoods make it easier to launch drone services without building new infrastructure. If successful, the model could pressure other retailers to accelerate their own investments in autonomous delivery.

A Glimpse of Retail’s Next Phase

The coast-to-coast rollout signals growing confidence that drone delivery can move beyond pilot programs and into mainstream commerce. While regulatory hurdles and airspace coordination remain challenges, the scale of this expansion suggests that large retailers now see drones as a practical complement to trucks, vans, and gig-economy drivers.

For consumers, the promise is simple: faster access to everyday essentials. For the retail industry, the partnership represents a test of whether autonomous delivery can reliably operate at national scale.

As Wing and Walmart extend their network from Los Angeles to Miami, the question may no longer be whether drone delivery works, but how quickly it becomes an expected part of shopping in American cities.

CES 2026 made one thing clear: artificial intelligence is no longer just software. This year’s show was defined by Physical AI, a category where intelligence is embedded directly into machines that move, lift, drive, and operate in the real world. From humanoid robots and autonomous trucks to construction equipment and household helpers, robotics shifted from the fringes of CES to its core narrative.

What distinguished CES 2026 from previous years was not the novelty of robots, but their readiness. Across industries, companies emphasized scale, safety, and integration, signaling that Physical AI is moving beyond pilots and into sustained deployment.

From Vision to Deployment Across Industries

In construction, Doosan Bobcat showcased how AI and autonomy are reshaping worksites. Its RX3 autonomous concept loader illustrated how compact equipment can operate quietly, electrically, and with modular configurations, while AI-driven systems like Jobsite Companion and collision avoidance highlighted how intelligence is being embedded directly into machines operators already use.

Logistics and transportation saw similar momentum. Kodiak AI and Bosch presented a production-grade autonomous trucking platform designed for scale, emphasizing redundant hardware and automotive-grade components. The message was clear: autonomy is no longer confined to test routes but is being engineered for industrial reliability.

At the software and platform level, Mobileye’s acquisition of Mentee Robotics underscored how autonomy stacks developed for vehicles are converging with humanoid robotics. The deal positioned Physical AI as a shared foundation across cars and robots, built on perception, planning, and safety systems that can operate in human environments.

Humanoids Grow Up

Humanoid robots were among the most visible symbols of CES 2026’s shift toward Physical AI. Boston Dynamics, working again with Google, revealed a product-ready version of Atlas, highlighting industrial-grade specifications, multiple control modes, and AI-driven autonomy. The focus was less on acrobatics and more on repeatable, real-world work.

In the home, LG Electronics introduced its CLOiD home robot as part of a broader “Zero Labor Home” vision. Rather than a standalone gadget, CLOiD was positioned as a mobile AI hub capable of coordinating appliances, understanding routines, and performing household tasks through vision-based Physical AI.

These examples reflected a broader trend: humanoids are no longer pitched as distant futures, but as platforms designed to fit into existing environments, whether warehouses, factories, or homes.

Platforms and Chips Power the Physical AI Stack

Underpinning many of these robots were platform providers positioning themselves as the infrastructure layer for Physical AI. Qualcomm used CES to introduce its Dragonwing robotics platform and IQ10 processor, framing them as the “brain of the robot” for everything from service robots to full-size humanoids. The emphasis on power efficiency, edge AI, and safety-grade performance highlighted how critical compute platforms are becoming as robots scale.

Across the show floor, the same themes repeated: end-to-end stacks, simulation-first development, and software-defined robotics. Companies increasingly described robots as evolving systems, improved through data and deployment rather than fixed-function machines.

A Turning Point for CES and Robotics

CES has long been known for bold prototypes, but CES 2026 felt more grounded. The conversation shifted from what robots might do someday to how they are being deployed today. Partnerships, acquisitions, and production-ready platforms dominated announcements, reflecting a more mature phase of the robotics cycle.

Physical AI now sits at the intersection of several forces: advances in foundation models, cheaper and more capable hardware, and rising demand for automation across labor-constrained industries. CES 2026 captured that convergence in real time.

If earlier CES editions introduced robots as curiosities, CES 2026 presented them as infrastructure. As Physical AI moves from show floors into jobsites, roads, and homes, this year’s event may be remembered as the moment robotics stopped being a sideshow and became one of technology’s main stages.

According to a new market assessment from Omdia, Shanghai-based AGIBOT ranked first worldwide in humanoid robot shipments in 2025. The ranking reflects delivered units rather than pilot announcements or experimental deployments, marking an important milestone as humanoid robotics moves beyond research labs and controlled demonstrations.

Omdia’s Market Radar: General-Purpose Embodied Intelligent Robots 2026 frames humanoid robots as an emerging product category entering its earliest phase of commercialization. Instead of highly customized machines built for single tasks, the market is beginning to favor standardized humanoid platforms designed to operate across multiple environments such as logistics hubs, public spaces, educational settings, and light industrial facilities.

The report emphasizes that shipment volume is becoming a critical indicator of progress in physical AI. Each deployed humanoid robot not only performs tasks but also generates real-world interaction data, accelerating learning cycles and improving system robustness over time.

From Research Prototypes to Scaled Shipments

AGIBOT’s lead in shipments reflects a strategy centered on controlled scalability rather than headline-grabbing prototypes. The company has focused on deploying humanoid platforms that balance mobility, stability, and cost efficiency, prioritizing environments where robots can operate alongside humans under predictable conditions.

Analysts note that early shipment leadership does not guarantee long-term dominance, but it creates structural advantages. Real-world data remains one of the scarcest resources in humanoid robotics, and companies with active deployments gain faster feedback on perception, manipulation, navigation, and human-robot interaction.

Omdia also highlights that competition in the humanoid sector remains intense. Companies in the United States, Europe, and Japan are investing heavily in software-centric approaches, while automotive and electronics manufacturers increasingly view humanoid robots as an extension of their embodied AI strategies.

Humanoid Robotics Moves Toward Commercial Reality

The report situates humanoid robots within a broader transition toward general-purpose embodied intelligence – machines designed to function in spaces originally built for people. Rather than replacing traditional industrial robots, humanoids are positioned to complement existing automation by handling tasks that require flexibility, mobility, and contextual awareness.

Omdia identifies Asia, particularly China, as the most aggressive region in early humanoid commercialization, driven by manufacturing scale, integrated supply chains, and sustained investment in robotics infrastructure. At the same time, demand is expected to broaden globally as enterprises explore service, healthcare, and customer-facing applications.

While the humanoid robotics market remains in its formative stage, shipment data suggests a clear shift is underway. Leadership is increasingly defined by deployed systems rather than conceptual ambition, signaling that physical AI is beginning its transition from promise to presence.