You could have, for example, America’s fighter jets, which cost hundreds of millions of dollars, and massive cargo aircraft, which require elite crews to operate. Although these weapons could turn the tide of war, they were also a message of deterrence; we have better machines, therefore we are harder to defeat.

As of 2026, all that’s changed, and new technology is defining how wars are fought and how goods are moved around the world.

The Old Ways Of War

The old ways were about creating the perfect machine. According to defence analysts, Western militaries were obsessed with building ’exquisite platforms’. This means they wanted to create technically flawless, ultra-capable, and expensive weapons to deter their adversaries.

Serhiy Goncharov, the CEO of the National Association of Ukrainian Defence Industries, which represents about 100 Ukrainian companies, told Business Insider that the West’s philosophy of fielding limited numbers of cutting-edge systems could be a serious disadvantage in a prolonged war. He claimed that those systems are good to have, but mass is key.

Gabrielius Landsbergis, the former defence minister of Lithuania, a NATO ally bordering Russia, said that while the West focused on new and expensive weaponry that takes a long time to manufacture, Russia had been ’building something cheap, that’s expendable, that’s fast’.

The F-35 Lightning II fighter jet and the C-130 Hercules cargo aircraft are some of the best examples of this policy. They are extraordinary machines and a testament to incredible feats of engineering. Both cost hundreds of millions of dollars to build and operate.

However, they have major weaknesses. They are too expensive to be lost in battle, very complicated to replace quickly, and entirely dependent on human beings to fly them.

This last point matters more than all the others because humans are organic. They get tired, they experience fear, and they need food, rest, and the psychological will to keep moving. If a C-130 is shot down, you lose an expensive aircraft, the crew, the supplies they were carrying, and the ability to resupply the troops waiting at the other end.

In a world where missiles are AI-driven with deadly accuracy, and modern battlefields are expanding their theatres into the Indo-Pacific, Ukraine, and the Middle East with dense networks of radar arrays, surface-to-air missiles, and electronic jamming equipment, these kinds of aircraft are sitting targets.

Defence planners call these areas ‘Anti-Access Area Denial’ (A2/AD) zones. To keep things simple, these are regions where your expensive, irreplaceable aircraft can be shot down as soon as they enter. The exquisite platform now turns out to be a single point of failure dressed up as a super-weapon.

Losing Is Now Part Of The Plan

Nobody wants to lose a billion-dollar piece of equipment at the first sign of trouble. So, a new, radically different military philosophy is taking hold at defence ministries across the world. They call it the ’economy of attrition’.

The idea is simple, yet counterintuitive. If assets are very likely to be destroyed in a modern war, the best course of action is not to prevent it, but to build systems that can be destroyed by the dozen while the mission continues anyway.

ALSO READ: Choking of Strait of Hormuz tests limits of war risk insurance

Instead of one colossal and invincible aircraft, it may be better to deploy hundreds of cheaper, autonomous ones. If the enemy shoots down 10 of them, the other 90 can still complete the mission. If those units cost less to build than the enemy’s resources used to destroy them, you eventually win the war of attrition by turning the enemy’s military strength into a financial liability.

Governments and militaries are building networks of self-piloting aircraft, ground robots, and underwater vehicles, all connected by a centralised artificial intelligence. These systems are capable of sustaining military operations without putting a single human being in direct danger.

Equipment is now designed to be lost in battle.

The Drones That Could Replace A Fleet Of Cargo Planes

There is a new doctrine at the Pentagon, and even at allied and enemy HQs. Militaries have decided to replace cargo planes with drones. A primary example of this doctrine in action is the Grid Aero Lifter Lite, built by a California-based startup. It is an autonomous cargo drone without a pilot, a co-pilot, or life support systems. It is built with minimal moving parts, can be assembled quickly in remote locations, and is priced at a fraction of what a single C-130 costs to procure and operate.

Aero CEO Arthur Dubois, “We’re focused on solving major problems for the warfighter, starting with contested logistics. Those same challenges of range, resilience, and operating in constrained environments also define many commercial, humanitarian, and remote operations. This funding allows us to rapidly field autonomous aircraft to deliver scalable capability into real-world operations to meet growing demand across missions.”

The rapid fielding is catching the attention of major investors. Ben Hemani, founding partner at Bison Ventures, pointed to Grid Aero’s execution speed, “What Grid Aero has accomplished in less than 18 months is rare. Not only have they already built their flagship aircraft, but they are also building a logistics capability that operates where traditional systems can’t. We’re excited to fund the next phase of their growth, as they move from rapid development to real-world scale.”

The Grid Aero approach to logistics is very different from the old method of hub-and-spoke logistics, where goods flow from a central warehouse outward along fixed delivery routes. It is replaced by what Dubois calls ’the grid’, a distributed mesh network where hundreds of thousands of nodes operate simultaneously across a wide area.

The Grid Aero Lifter is a drone carrier that features advanced self-healing capabilities, ensuring that if drones are lost, the AI instantly reroutes the remaining units to cover gaps and maintain network integrity. To operate effectively in contested military zones where GPS is routinely jammed, the system utilises visual odometry, which allows the drones to navigate by reading the landscape much like a human does. Furthermore, the entire swarm benefits from real-time learning, as every drone continuously shares its data with all other units to ensure they learn and adapt together as a single entity.

Regarding the shift toward these swarms, Anduril founder Palmer Luckey said, “You know, if I can have one guy command and control 100 aircraft, that’s a lot easier than having to have a pilot in every single one. It puts a lot fewer American lives at risk.”

Advent Of The Mechanical Foot Soldier

There are drones in the air, but there is something even more dramatic on the ground. In February 2026, a San Francisco startup by the name of Foundation Robotics delivered its first Phantom MK1 humanoid robot to Ukraine for combat testing on the front lines. These machines are somewhat similar in concept to a prototype T-800 model from the Terminator movie.

The Phantom MK1 is science fiction coming to life. The MK1 stands at 180 centimetres, weighs 80 kilograms, and is encased in jet black steel with a tinted glass visor where its face should be. It’s not a remote-controlled toy operated by someone at a comms station. It’s a completely AI-powered machine designed to replace a human infantryman. There are currently 2 Phantom MK-1 prototypes in the Ukrainian theatre.

“You should really work hard to give the US military smarter tools so that they can be more effective,” Sankaet Pathak, founder of Foundation Robotics, told TechFirst.

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He is not shy about giving the Phantom an M4 Carbine, adding, “If you’re first body in and you’re docile, then the enemies are not going to really expose themselves. So, you have to be the first body in and deadly.”

The 20 internal electric motors provide the ability to climb stairs, navigate rubble-filled urban environments, and enter low bunkers that wheeled military vehicles cannot reach. It’s also designed to emit a thermal heat signature indistinguishable from a human being, deliberately tricking enemy sensors into firing at it instead of the real soldiers nearby. In short, it’s a decoy and a grunt.

The psychological effects on the enemy are clear. The robots do not feel pain, they are not afraid of anything, and they do not get shocked or confused. A human soldier, on the other hand, would flinch if he got hit in the chest, and might drop and stop fighting. A Phantom humanoid robot hit in the chest still keeps shooting. This relentlessness is deeply demoralising to human opponents in a way that no weapon was fully anticipated to be.

However, the model is not perfect. The battery drains rapidly under the immense energy demands of 20 motors running simultaneously, while the complex joints remain highly vulnerable to damage. Foundation is already addressing these issues through the release of the Phantom MK2, which was expected to come out by the end of April 2026, featuring a waterproof chassis and improved battery life.

Foundation proclaimed that it will produce 50,000 Phantom MK-1 units by 2027. Hordes of robot soldiers might flood Ukraine by this time next year.

However, these humanoid robots also have civilian applications with a foundation aiming to sell them at $100,000 per unit as warehouse and industrial logistic workers. They can perform in conditions that are hazardous for humans, and can work almost around the clock without wages, protest or exhaustion.

Ground Robots Already Saving Lives

There is a robotic ground vehicle operating in Ukraine called the Milrem THeMIS, which is built in Estonia. It is a heavily built, tracked robot designed for endurance and heavy lifting.

Running on a hybrid diesel-electric engine, it can operate for up to 15 hours straight. It is mostly used for casualty evacuation, rolling forward under fire to retrieve wounded soldiers and carrying them to safety, which spares human medics from being shot while doing so. The vehicle has the capability to carry up to 750 kilograms, extendable to 1,200 kilograms, and can climb 60-degree slopes.

The long-term vision for these machines is expanding. “In the future, robotic systems will take over soldiers’ tasks on the battlefield,” notes Raul Rikk, the Capability Development Director at Milrem Robotics. “We envision robotic systems that can respond to verbal commands, similar to traditional military units.”

Like aerial drones, the dual-use potential is massive.

“While our current focus is primarily on defence due to the ongoing war in Ukraine,” Rikk added, “we have a strong track record of developing unmanned prototypes for civilian applications, including firefighting, street cleaning, and forest planting.”

The Rheinmetall Mission Master SP, built in Germany, operates on a completely different philosophy from the Milrem THeMIS. It is fast, quiet, and ultra-powered, leaving no heat or sound signature for an enemy to detect. It uses the Wolfpack software, which allows multiple units to coordinate autonomously so that they can surround a perimeter or scout ahead without any human giving moment-to-moment commands.

Japan’s Ground Self-Defence Force is currently testing both these technologies, pointing to a future where THeMIS handles brute, sustained work while the Mission Master executes stealth and precision.

The Kill Web

Autonomous robot armies utilise technology to integrate logistical carriers for effortless troop transport, resilient robotic foot soldiers capable of enduring heavy fire, and robot medics designed for evacuations, strikes, and reconnaissance.

They are all tied together with an even more powerful technology. In 20th-century military communication, the process worked in a straight line. A radar spots a target and notifies a command centre, which then sends a pilot to destroy the target. This was known as the ’kill chain’. Before the kill chain could be completed, the target often moved.

The kill chain is now being replaced by the ’kill web’, a decentralised AI network that connects every drone, robot, soldier, satellite, and radar into one seamless system. Software platforms like Anduril’s Lattice serve as the brain of this web by processing data from thousands of sources simultaneously and identifying threats in fractions of a second.

This autonomy extends into the deep ocean. The navies of the world are deploying autonomous underwater vehicles to protect the 1.39 million kilometres of fibre optic cables on the ocean floor that carry 99% of global internet traffic and the kill web’s foundational data. Companies like Anduril are already building for this domain with autonomous submarines like the Dive XL.

“It’s not remote-controlled by this computer. It’s doing it on the brain, on the submarine itself. If I told it to go off and perform some mission that’s months long, like, ‘Go to this target, listen for this particular signature, and if you see this signature, run; if you see this one, hide; if you see this one, follow it’, it could do that all on its own without being detected, without communicating with it,” Palmer Luckey explained.

Bloodless Wars

The singular, expensive, irreplaceable weapons of the past are giving way to a vast, self-healing, intelligent network of expendable machines, which keep fighting, delivering, and communicating no matter what is thrown at them.

The Pentagon’s traditional contractors, such as Boeing, Lockheed Martin, and Northrop Grumman, are slowly losing ground to startups that focus on the economy of attrition. The US Air Force’s autonomous wingman drone programme was recently awarded to Anduril and General Atomics, bypassing Boeing and Lockheed entirely.

Not so long ago, venture capitalists were not too keen on defence investments, but now they’re flooding into these ’new primes’ with the reasoning that autonomous defence capability and supply chains are prerequisites for global stability. It represents a massive geopolitical shift. As Palmer Luckey puts it, “I’ve always said that we need to transition from being the world’s police to being the world’s gun store.”

The drone that resupplies soldiers under fire today will be the same technology delivering packages to your doorstep next year. Similarly, the software currently coordinating robot soldiers will soon be managing warehouse inventories, while the underwater robots guarding fibre-optic cables will simultaneously protect military communications, and your ability to stream videos or read this article.

The post Ukraine might deploy robot army on Russian front in 2027 appeared first on International Finance.

Huang believes that beyond artificial intelligence itself, robotics will be Nvidia’s biggest growth market, with autonomous vehicles as the first major commercial application.

International Finance will examine how Nvidia is leveraging its AI prowess to drive breakthroughs in autonomous driving and robotics, and what it means for the company’s future and the tech industry at large.

From GPU king to AI powerhouse

Nvidia has a very underdog story. It was established in 1993 and was almost set to be named GeForce. The name Nvidia comes from the Latin word ‘invidia’, which means envy (or green with envy). In early 2006, they put everything they had into CUDA (Compute Unified Device Architecture), which began paving the way for AI’s rise. It’s important to note that there would be no ChatGPT without Nvidia.

The company was a household name in the early 2000s, being the top supplier of graphics cards that were necessary to play high-quality video games. Their top competition back then was AMD, which in the 90s had considered acquiring Nvidia. Today, Nvidia dwarfs AMD, as they are single-handedly pioneering AI, robotics, self-driving cars, chip production, and even building supercomputers. The company is considered a major source of inspiration for Silicon Valley enthusiasts. Notably, Nvidia CEO Jensen Huang was once rejected by Apple, yet he went on to become a titan in the industry and has been referred to as the “Steve Jobs of AI Hardware.”

However, the visionary cofounder of Nvidia knows the semiconductor industry can be cyclical, with data centre investment coming in “booms and busts.” To secure Nvidia’s place in the tech stratosphere long term, Huang has been scouting the next big market beyond conventional “Big Tech” pursuits. At Computex 2024, he declared that two “high-volume” products will dominate robotics in the future: self-driving cars first, and eventually humanoid robots.

These technologies are converging thanks to advances in machine learning, and both require human-like perception, split-second decision-making, and immense computing power, precisely what Nvidia specialises in.

In Huang’s words, “Every single car company will have to be autonomous, or you’re not going to be a car company,” a bold prediction underscoring his belief that autonomy is the future of transportation.

Accelerating the self-driving car revolution

Nvidia has methodically embedded itself at every level of the self-driving technology stack. Huang often describes a three-part approach: one computer to train AI models, another to run simulations, and a third inside the vehicle for real-time driving. By providing the chips and software for all three stages, Nvidia aims to be the go-to enabler of autonomous vehicles rather than a consumer-facing automaker.

“Nvidia has strategically embedded itself in all three key steps that could make every car a self-driving car,” notes Business Insider.

While companies like Waymo and Tesla build robotaxis and personal electric vehicles, Nvidia positions itself as the behind-the-scenes supplier powering those efforts.

Nvidia Drive, the company’s end-to-end autonomous driving platform, exemplifies this strategy. It includes powerful automotive systems-on-chip (SoCs), like the current Drive Orin and next-generation Drive Thor, paired with the Drive OS software and toolkits for perception and mapping. At CES 2025, Toyota (the world’s largest automaker) announced it will integrate Nvidia’s Drive AGX Orin “supercomputer” and Drive OS into its upcoming vehicles to enable advanced driver-assistance and automated driving features.

This was a major win for Nvidia, as Toyota has been collaborating with the company since 2017 on AI for self-driving and even uses Nvidia’s cloud GPUs to train its models. The Toyota deal underscores Nvidia’s “cloud-to-car” strategy: first supplying chips for AI training in the data centre, and now supplying chips and software for intelligence inside the car.

Other automakers are also lining up. Mercedes-Benz, China’s BYD, Volvo, Hyundai, Lucid Motors, and many electric vehicle startups have adopted Nvidia’s Orin-based platforms for their next-gen cars. In early 2025, General Motors struck a broad partnership with Nvidia to use its GPUs and AI software across passenger vehicles, robotaxis, and even factory automation.

GM will equip future cars with Nvidia’s “AI brain,” including Drive SoCs running the safety-certified Drive OS (based on Nvidia’s latest Blackwell GPU architecture), to enable hands-free driving and autonomy. Notably, GM is also leveraging Nvidia’s Omniverse 3D simulation platform to create virtual assembly lines and train its industrial robots, aiming to boost manufacturing efficiency with AI. This includes the company’s Drive AGX system-on-a-chip (SoC), similar to Tesla’s Full Self-Driving chip or Intel’s Mobileye EyeQ.

The SoC runs the “safety-certified” Drive OS operating system, built on the Blackwell GPU architecture, and capable of delivering 1,000 trillion operations per second (TOPS) of high-performance compute.

This partnership came on the heels of GM’s decision to wind down its Cruise robotaxi unit after safety incidents, signalling that GM now prefers to pivot toward consumer vehicles with advanced autonomy, and it’s tapping Nvidia to help make that happen.

Winning over these automotive giants could translate into huge business for Nvidia. The company projects its automotive division will reach a $5 billion annual run rate by FY2025, a fivefold increase from 2023. That’s still modest next to Nvidia’s booming data centre revenue, but the growth trajectory is clear. Nvidia’s automotive VP Ali Kani remarked that the car business is “still in its infancy,” contributing Nvidia chips to under 1% of cars on the road today, but he calls it a “trillion-dollar opportunity” long term.

Industry analysts have taken note: McKinsey estimates assisted and autonomous driving could be a $400 billion market by 2035. And after a few gloomy years when automakers dialled back self-driving investments (Ford and VW shuttered Argo AI in 2022, GM pulled back on Cruise in 2024), Jensen Huang’s confident CES showcase was seen as a “shot in the arm” for the sector.

“Nvidia has reversed that and just gave autonomous driving an absolute shot in the arm,” said one automotive consultant, noting that hearing a tech leader evangelise self-driving renewed investors’ interest in the space.

Crucially, Nvidia’s advantage is its full-stack approach. Few companies can provide the training-side infrastructure (massive AI supercomputers to train driving models) and the in-car chips to execute those models on the road.

Tesla, for instance, trains its Autopilot AI on Nvidia GPUs in the data centre, even though it builds custom chips for its cars.

This “cloud + edge” synergy makes Nvidia a natural partner for any firm aiming to deploy autonomous vehicles at scale. It’s no surprise Huang says Nvidia is “absolutely positioning [itself] as the leader for autonomous technologies, period.”

Indeed, from passenger cars and long-haul trucks to robotaxis, Nvidia’s silicon and software are increasingly becoming the standard toolkit for autonomy. As Huang put it at CES 2025, self-driving cars are no longer perpetually “coming” — “they’re already here,” citing the commercial progress of Waymo and Tesla as proof.

Building an ecosystem of robots

If self-driving cars are essentially “robots on wheels,” Nvidia’s ambitions don’t stop at transportation. The company is simultaneously assembling an expansive ecosystem for robotics and automation in other domains. Huang believes the world is on the cusp of an era of “physical AI,” intelligent machines performing tasks in the real world, and he wants Nvidia to provide the brains of those robots. At Computex, flanked by virtual humanoid figures, Huang proclaimed, “Robotics is here. Physical AI is here. This is not science fiction.”

So, what is Nvidia’s game plan for robotics? In short, to offer a full technology stack akin to its automotive approach. Simulation is one pillar: the company’s Omniverse platform creates rich virtual worlds where robots can be trained and tested safely. Built atop Omniverse is Isaac, described as a “gym” for robots, which lets developers put virtual robots through their paces to practice tasks or generate synthetic training data.

Then comes the edge hardware: Nvidia’s Jetson line of AI chips (with a forthcoming flagship called Jetson Thor) provides the onboard compute for robots to perceive and act in real time. Finally, tying it together are AI models and software frameworks that give robots their smarts.

In 2023, Nvidia unveiled Project “GR00T,” a “moonshot” effort to develop a foundation AI model for humanoid robots. In 2025, this bore fruit in the form of Isaac GR00T N1, billed as the world’s first open-source generalist model for robot intelligence.

GR00T N1 is essentially a robot “brain” that has been pretrained on vast data, not just text or images, but demonstrations of physical actions. It uses a dual-system architecture inspired by human cognition: a “slow-thinking” module that reasons and plans, and a “fast-thinking” module that executes reflexive actions.

This mimics psychologist Daniel Kahneman’s concept of thinking fast and slow. In practice, GR00T N1 can observe its environment (through sensors and cameras), interpret instructions, plan a sequence of actions, and then control a robot’s limbs to carry out complex tasks. Nvidia pretrained it on both real-world human motion data and millions of synthetic scenarios generated in simulation.

Importantly, GR00T N1 is customisable. Developers can fine-tune it with additional data so a robot learns specialised skills. Huang declared that “the age of generalist robotics is here” as he opened up GR00T N1 to the world’s robot makers.

Complementing GR00T is another key piece called Nvidia Cosmos. Unveiled at CES 2025, Cosmos is a family of foundational models focused on modelling the physical world.

Whereas language models ingest books and websites, Cosmos was trained on 20 million hours of video of humans and objects in motion. It generates highly realistic images, simulations, and 3D scenarios, for example, showing boxes falling off a shelf in a warehouse, which can be used to teach robots what to expect and how to respond in the real world.

“It’s not about generating creative content, but teaching the AI to understand the physical world,” Huang explained.

Companies are already using Cosmos: humanoid robot startups like Agility Robotics and Figure, and self-driving car developers like Waabi and Wayve, are leveraging it to accelerate their training and simulation. In essence, Cosmos gives robots common sense about physics and environments, while GR00T gives them the decision-making and motor skills, together aiming to dramatically lower the barrier to robotics development.

Nvidia’s Jensen Huang envisions a future where advanced AI chips and software power fleets of autonomous vehicles and humanoid robots, a vision already taking shape through partnerships with automakers, electronics manufacturers, and even entertainment companies.

Nvidia is backing up these platforms with real-world pilot projects to showcase what’s possible. One headline-grabbing example is its collaboration with Foxconn, the world’s largest electronics manufacturer.

In 2025, Nvidia and Foxconn announced plans for a “robotic factory” in Houston that will use humanoid robots to assemble Nvidia’s own next-gen AI servers.

This would be the first time Nvidia products are built with the help of humanoid machines, and one of the first such deployments in any electronics factory. Foxconn has been co-developing humanoid robots with Nvidia’s hardware and software, including one bipedal model and one wheeled model, and training them for tasks like picking up components, inserting cables, and performing assembly.

The goal is to have a small number of these robots operational by Q1 2026 when the Houston plant begins production of Nvidia’s “GB300” AI servers. If successful, this could herald a new era of AI-driven manufacturing.

Observers note it as a prestige project for both firms: Nvidia would solidify its position not only as a chip and server leader but as a platform provider for robotics, while Foxconn would demonstrate high-tech manufacturing innovation on American soil.

Nvidia’s robotics push goes beyond factories. The company is moving into service and entertainment robots, even magic-infused Disney creations.

In March 2025, it emerged that Nvidia, Disney Research, and Google DeepMind are teaming up on a project codenamed “Newton” to create a new generation of interactive robotic characters.

Newton is essentially an open-source physics engine that will help Disney’s robots learn complex movements with precision. Disney Imagineering’s vision is to bring more lifelike robots to its theme parks, for example, free-roaming droids like the Star Wars-inspired “BDX” robots that were previewed during Huang’s keynote.

“This collaboration will allow us to create a new generation of robotic characters that are more expressive and engaging than ever before,” said Disney Imagineering’s senior R&D VP.

In fact, Disney’s first batch of AI-powered BDX droids has already been play-tested on a cruise ship and will soon appear in Walt Disney World and other parks.

For Nvidia, this alliance is a testament to the scope of its robotics prowess, reaching into animatronics and arts, away from factory floors. It also highlights a familiar refrain: Nvidia is not making the robots or cars themselves; rather, it provides the enabler technology that allows industry leaders (be they Toyota, Foxconn, or Disney) to fulfil their AI-fueled dreams.

Towards a deep tech superpower

All these moves point to Nvidia’s evolution from a pure “Big Tech” company into something broader, a deep technology platform spanning hardware, software, and services for the AI-driven future.

“We stopped thinking of ourselves as a chip company long ago,” Jensen Huang recently remarked.

He prefers to describe Nvidia as an “AI infrastructure” and “computing platform” provider, one that now delivers cloud services, simulation software, and development toolkits in addition to silicon.

By weaving itself into the fabric of emerging industries like autonomous vehicles, robotics, and even defence (Nvidia is a partner in a European project led by Nokia to use drones and robots for critical infrastructure protection), Nvidia is staking a claim as a leader in “physical AI,” the application of artificial intelligence in the physical world.

Huang predicts that in the not-so-distant future, there will be “billions of robots, hundreds of millions of autonomous vehicles, and hundreds of thousands of robotic factories” all powered by Nvidia technology. It’s an audacious vision, but one the company is investing heavily to realise.

Financially, these sectors are still ramping up. Nvidia only recently began reporting its automotive and robotics revenues together, and they accounted for roughly $567 million last quarter (about 1% of total sales), albeit growing 72% year-on-year. The company’s present profitability still relies on data centre AI chips, but investors are keenly watching these nascent divisions.

For tech-savvy investors, Nvidia’s foray into self-driving cars and robotics represents the opening of new multi-billion (even trillion) dollar TAMs (Total Addressable Markets) over the coming decade. It pits Nvidia not against the usual FAANG companies, but against (or alongside) players in automotive, manufacturing, healthcare, and aerospace, industries hungry for AI solutions.

The company’s strategy of partnering with incumbents (rather than competing directly) could yield a sprawling customer base without massive capex on its part. As one set of analysts wrote, Nvidia’s strengths in robotics and digital twins could “scale into massive businesses themselves,” potentially driving decades of growth.

Naturally, challenges abound. Robotics, especially humanoid robots, remain harder than web or mobile tech; they involve complex mechanics, safety concerns, and huge data requirements to function reliably in unstructured environments. Sceptics point out that despite Nvidia’s impressive tools, fully autonomous robots are still in the early stages and demand significant R&D.

Even Huang acknowledges that growing the robotics market “isn’t just a matter of time” or inevitability. It will require continued advances in artificial intelligence algorithms, sensors, and perhaps most importantly, cost reduction, to see robots proliferate beyond pilot projects.

Nvidia’s strategy of open-sourcing models like GR00T N1 and collaborating widely is meant to accelerate this progress by seeding an ecosystem (much as OpenAI frameworks spurred the machine learning boom).

Huang believes that by reducing barriers and providing substantial computing power, Nvidia can do for robotics what it accomplished for AI software: make it accessible enough for an explosion of innovation.

Nvidia has gone from selling graphics chips to becoming the linchpin of modern AI, and now it’s charging into autonomous machines on our roads and in our factories. This pivot could fundamentally reshape Nvidia’s identity, no longer simply a supplier to “big tech” companies, but a superpower in deep tech, commanding influence in the next generation of industries built on AI, from smart cars to smart robots.

Huang’s remarks at a recent shareholder meeting put it best: “AI and robotics are the two largest opportunities, representing a multitrillion-dollar growth opportunity.”

If Nvidia succeeds, it won’t just be leading in AI computing; it will be everywhere that advanced computing meets the real world.

In an era when “everything that moves” is poised to become autonomous, Nvidia appears determined to supply the engines of that revolution, thereby laying the basis of its transformation as a force dominating deep tech innovation.

The post Nvidia’s vision: Chips for a robotic world appeared first on International Finance.