The information was disclosed by the company during a SEC filing, which read: “There is no change to Mr. Pichai’s annual salary of USD 2 million.” The compensation package, that includes multiple stock-based incentives tied to shareholder returns, along with the performance of Alphabet businesses, also has another striking point: a significant presence of performance stock units (PSUs) with a target value of USD 126 million, divided into two equal tranches.

According to the SEC filing, these PSUs have been tied to Alphabet’s total shareholder return compared with the businesses registered in the S&P 100 index. Sundar Pichai’s payout could rise to twice the target amount, or USD 252 million, if Alphabet significantly outperforms peer companies in the American tech circle. If the company underperforms, the PSU payout could fall to zero. Also, the Alphabet CEO will further receive USD 84 million in restricted stock till 2029.

Apart from ensuring that Sundar Pichai continues to receive an annual base salary of USD 2 million, Alphabet has also included additional stock incentives tied to the growth of two of its technology businesses, Waymo and Wing Aviation.

According to the filing, the incentives will pay up to 200% of the target value to the Alphabet CEO, if performance goals for the businesses are met or exceeded. In that case, Sundar Pichai’s compensation by 2029 will reach USD 692 million.

Sundar Pichai, who became Google’s CEO in 2015, has helped the company increase its market capitalisation from about USD 535 billion to approximately USD 3.6 trillion. In January 2026, the figure, for a brief period, crossed the USD 4 trillion mark.

Sundar Pichai is not the only one getting a big pay package. NVIDIA CEO Jensen Huang is also getting USD 4 million per annum as part of his revenue success.

However, the salary would amount to less than 10% of his annual compensation. The tech maverick, known as one of the prominent faces of the ongoing AI and semiconductor revolution in Silicon Valley, sold around six million NVIDIA shares in 2025, fetching him USD 925 million. He still owns around 3.5% of the corporation, amounting to a net worth of USD 150 billion. Jensen Huang is the 10th-wealthiest individual in the world, according to the latest Bloomberg reporting.

Just like Google, NVIDIA has also seen a great 2025 with a 65% year-over-year rise and a full-year revenue of USD 215.9 billion. Though growth has been exponential in the last year, it’s important to note that for the last six months, the trajectory has more or less flatlined.

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Jensen Huang told CNBC that both parties have “reasonable perspectives” because Anthropic has the right to determine how its models are used and the Pentagon has the right to determine how technology supplied in contracts is used.

But until an agreement is reached, Anthropic could lose its USD 200 million contract with the Department of Defence.

The Pentagon had earlier asked Anthropic, OpenAI, Google, and xAI to permit the use of their AI models for “all lawful purposes.” Anthropic resisted the request the most because it was concerned that its models would be used for widespread domestic surveillance and autonomous weapons systems.

The Pentagon may use the Defence Production Act (DPA) to compel Anthropic to comply with its demands if the dispute is not settled.

US Defence Secretary Pete Hegseth has already called the corporation a “supply chain risk” and threatened to use the DPA. The San Francisco-based company, in its response, has vowed to take the legal route.

Anthropic has until March 6 to comply with the Pentagon’s request, according to Hegseth. US intelligence agencies such as the FBI and NSA have previously undertaken illegal mass surveillance campaigns against US citizens, such as the COINTELPRO project during much of the Vietnam War, the illegal use of the Communications Assistance for Law Enforcement Act (CALEA) in the 1990’s, and the use of the Patriot Act post 9/11 for covert and illegal mass surveillance.

NVIDIA and Anthropic have a strategic alliance because NVIDIA invested USD 5 billion in Anthropic’s adoption of the NVIDIA architecture.

“I hope they can work it out, but if they can’t, it’s not the end of the world,” Jensen Huang concluded.

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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.

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NVIDIA established itself as a prominent player in the computer gaming sector, with the RIVA series of graphics processors in 1997. The business rose to prominence two years later, with the advent of the GeForce 256 GPU, which provided improved three-dimensional graphics quality.

The company battled with prominent video card maker 3dfx Interactive, pitting the GeForce against 3dfx Interactive’s popular Voodoo technologies. NVIDIA’s GeForce proved to be more powerful and later in the year 2000, the company acquired assets of 3dfx Interactive. NVIDIA was chosen by Microsoft Corporation to create graphics cards for its eagerly anticipated Xbox video game console that same year. The company received the Forbes Magazine’s 2007 Business of the Year award in recognition of its explosive growth and achievement.

NVIDIA offers a wide range of computing technologies, the Quadro series of video cards for professional graphics, the Tegra series for mobile phone processors, the nForce series for multimedia functions, and the Tesla series for high-end business and scientific-research computers. These products are in addition to those for the computer gaming industry.

NVIDIA, which is considered the best in the graphics segment to date, was co-founded by a 60-year-old, Taiwanese American electrical engineer Jensen Huang on his 30th birthday in 1993. He is currently the President and CEO of the company.

Who is Jensen Huang?
• Jensen Huang was born in Taiwan and when he was nine years old his family immigrated to the United States
• He completed his graduation degree in electrical engineering from Oregon State University in 1984 and his master’s from Stanford University in 1992
• In 1993, Jensen Huang co-founded NVIDIA with Chris Malachowsky and Curtis Priem
• His company went public in 1999, where he owned 3.6% of NVIDIA stock
• In 1999, Jensen Huang was named ‘Entrepreneur of the Year’ in ‘High Technology’ by Ernst & Young
• In 2003, he received the Dr. Morris Chang Exemplary Leadership Award, a pioneer recognition from the field of the semiconductor industry, given by the Fabless Semiconductor Association
• Jensen Huang was also a National Finalist for the EY Entrepreneur of the Year Award in 2003 and was an Award Recipient for the Northern California region in 1999
• In 2007, Forbes ranked him as the 61st highest-paid US CEO, he earned USD 24.6 million that year
• In the same year, Jensen Huang also won the Silicon Valley Education Foundation’s Pioneer Business Leader Award for his work in both the corporate and philanthropic worlds
• In 2018, he was listed in the inaugural Edge 50, naming the world’s top 50 influencers in edge computing
• In October 2019, Harvard Business Review named him the best-performing CEO in the world
• In 2020, Jensen Huang was named ‘Supplier CEO of the year’ by Eurostars AutomotiveNewsEurope
• He was included in the Time 100, Time’s annual list of the 100 most influential people in the world in 2021
• In August of that year, the Semiconductor Industry Association (SIA) announced Huang as the 2021 recipient of the industry’s highest honour, the Robert N. Noyce Award
• According to the Bloomberg Billionaires Index, as of January 2023, Jensen Huang’s net worth is USD 16.3 billion
• Jensen Huang is also known for his charities. In 2022, he donated USD 50 million to his alma mater, Oregon State University, as a part of a USD 200 million donation towards the creation of a supercomputing institute on campus
• He also gave his other alma mater Stanford University USD 30 million to build the Jen-Hsun Huang School of Engineering Center

NVIDIA’s Financials

In November 2022, NVIDIA released its financial data for Q3 of its 2022 fiscal year (FY), the net income for the quarter was USD 2.5 billion, increasing 84.4% over the same period in 2021. Sales reached a record high of USD 7.1 billion, up by 50.3%. Operating income, which NVIDIA uses as a performance indicator for each of its business sectors, also increased by 91.1% to USD 2.7 billion.

The semiconductor firm publishes a revenue breakdown for each of its five key markets, which include gaming, data centres, professional visualisation, automotive, original equipment manufacturers (OEM), and others: Gaming revenue, which made up 45% of total revenue, increased by 41.8% in the third quarter. Data centre revenue, which made up 41% of total revenue, increased by 54.5% and its professional visualisation revenue also jumped by 144.5%.

The COVID-19 pandemic has improved the financial performance of NVIDIA, with the company now facing massive demand for its gaming, data centre, and professional visualisation market platforms.

Jensen Huang stated that as more businesses adopt the technology and boost their use of hyperscale and cloud computing, the demand for its artificial intelligence solutions will grow.

NVIDIA’s Recent Developments

In February 2022, NVIDIA cancelled its agreement to acquire UK-based semiconductor firm Arm Limited, backed by SoftBank Group. The USD 40 billion arrangement, first announced in September 2020, faced significant regulatory challenges in the United Kingdom.

In January 2022, the company stated that Meta Platforms, formerly Facebook, would use its technology to create its artificial intelligence Research SuperCluster (RSC). This artificial intelligence supercomputer is expected to be the largest customer installation of NVIDIA DGX A100 systems.

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