Called Exobot, the futuristic premium electric sedan and SUV will start sales in the Kingdom in early 2027.

The cars have arrived with the looks heavily inspired by the sci-fi thriller “Tron.”

Both the sedan and SUV versions of the Exobot possess “gullwing” doors that open upward.

“Ceer has also removed the pillars normally found between a car’s front and rear seats and relies instead on high-strength materials to provide structural support,” CEO Jim DeLuca said.

“We ⁠wanted to make sure the world could see what Saudi Arabia is capable of,” he added further.

Ceer’s arrival in the global automotive scene also coincides with the industry’s biggest course correction, in which Chinese automakers, armed with feature-laden yet cost-effective cars, are creating their positions around the world, challenging established ecosystems like the ones in Germany, Japan, and the rest.

Ceer is a joint venture between Saudi Arabia’s sovereign wealth fund, the Public Investment Fund (PIF), and Taiwanese contract electronics manufacturer Foxconn.

While the Kingdom’s energy wealth is there to back up the company’s R&D efforts, the Saudi economy in 2026 has been facing logistics and inflationary problems due to the Iran war.

The sedan version of the vehicle has an estimated range of 670 km, while the charging time will take some 30 minutes to reach the 80% threshold. Acceleration speed (reaching 0-100 km/h) will take 2.6 seconds, with a maximum horsepower of 850 hp.

The SUV variant will have an estimated range of 560km, while the acceleration to 100km/h will take 2.9 seconds. Horsepower and charging time will remain like the sedan version.

Talking about Saudi Arabia’s efforts to become an automotive major, the Kingdom tried to host a Jaguar Land Rover factory on its soil in the last decade, which ultimately got ‌abandoned.

Toyota declined a similar deal in 2019, citing high labour costs and a lack of local suppliers.

Taking lessons from those failures, Ceer has taken a broader approach, leaning on global partners, apart from building up a local network to keep the supply of spare parts flowing.

Foxconn has provided the electrical architecture for the Exobots, while German premium automaker BMW acted as the engineering brain.

“Originally set up as a pure EV maker, Ceer’s full lineup will include plug-in hybrids and combustion-engine models to react to shifting market demand,” DeLuca said.

Following the Exobot’s launch, Ceer will launch five mainstream models between 2028 ⁠and 2030.

Ceer will also be customizing its vehicles to meet regulatory and customer requirements set at various automobile hubs.

As per DeLuca, the company aims to expand carefully through 2034. It will first target its neighbouring Gulf and North African markets, followed by global expansion.

“We want to make sure we move very methodically. It only takes one mistake and you can kill a startup,” DeLuca told Reuters.

Image Courtesy: Ceer

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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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Similar to how a pharmaceutical factory makes drugs for patients, an AI factory makes solutions for its customers. Millions of services—from a straightforward android for your hand to a sophisticated weather report developer—are the end products of an AI factory. AI is used in many aspects of risk management and service provision.

In October 2023, Nvidia and Foxconn announced a joint project of constructing “AI factories,” a new breed of data centres that claim to have the supercomputing ability to hasten the creation of industrial robots, self-driving cars, and other autonomous devices. The project may very much emerge as a disruptive trend in the automobile industry.

The partnership was announced at Hon Hai Tech Day in Taiwan by the founder and CEO of Nvidia, Jensen Huang, and the chairman and CEO of Foxconn, Young Liu. The AI factory, which will be constructed to filter, refine, and turn enormous amounts of data into useful AI models and information, is based on Nvidia GPU computer infrastructure.

“On the one hand, you’re constructing this cutting-edge EV car, and on the other hand, we’re building this full end-to-end system. With an AI brain inside that enables it to interact with drivers and passengers, as well as autonomously drive, supported by an AI factory that produces software for this automobile,” the joint venture informed the media, while adding, “This automobile will experience life and gather additional data. The data will be sent to the AI factory, which will update all of the AI fleet’s software and make software improvements.”

The collaboration between the AI factory and Foxconn expands upon their agreement to create platforms for autonomous vehicles, which was announced in January 2023. By that deal, Foxconn agreed to serve as the major provider of electronic control units (ECUs) for the automotive industry. These ECUs will be constructed using Nvidia’s Drive Orin system-on-a-chip (SoC), a supercomputing AI platform that supports autonomous driving capabilities. Foxconn has also declared its intention to produce ECUs using Nvidia’s next-generation SoC, Drive Thor, starting in 2025.

As part of that collaboration, Foxconn announced that the vehicles it produces as a contract manufacturer will be built with Nvidia’s Drive Hyperion 9 platform. This platform includes Drive Thor as well as a collection of sensors like cameras, radar, lidar, and ultrasonic that are essential for self-driving capabilities.

Understanding The Concept

An AI factory is basically a well-organized model, which works on the basic principles of delivering success and excellence in AI deployment in an industrial environment. An AI factory combines elements like people, data, platforms, and processes to deliver the goals.

An AI factory’s goal is to create solutions for its clients.

“From a simple android of your hand to a complex weather report developer, a million services are the products of an AI factory. AI is in all from providing support services to risk management,” stated AnalyticSteps, while explaining the phenomenon.

While the Foxconn-Nvidia joint venture holds a promise of positively disrupting the automobile industry, the initiative also has to go through challenges like constantly understanding the goals of an AI factory, staying future-proof by researching, understanding and executing advanced AI technologies before the rivals and strengthening the data security front.

The AI factory approach is a method of investigating AI algorithms and logic, in which a problem is first identified by experiments and observations.

“The problems identified are then solved. Hypotheses and statistics are then developed according to the situation. The developed hypotheses often served as fundamental principles. And are important for mechanism and functioning,” AnalyticSteps explained the concept further.

A successful AI factory board needs to have a high-level and dedicated team of data leaders, as data is the basic raw material for an AI industry. Data leaders help in providing direction and control to an AI factory, apart from reviewing and validating the progress of the data collection. These specialists work collectively to achieve a single role, which is ensuring the real-time information flow and transparency of a project.

The AI factory also needs a team of experts, which will cover domains like business, data, software, and digital technology. Together, these professionals will work as a hybrid structure. While one of them gathers raw data, others search for suitable technology to implement it within the AI model, in order to execute the project within a short time span.

The AI factory works with a combination of proprietary, cloud, and open-source solutions. Even, existing AI technologies are used to develop the latest and advanced AI services. The data collected by human professionals get fed into an AI process to develop an automated service.

To make an AI factory successful, the Foxconn-Nvidia joint venture needs to reconstruct the structure and reimplement the AI algorithms, apart from seamlessly combining the five factors named data, product, process, platform, and people.

“Data is the raw material, products are AI services or final AI technologies, process is the blend of AI methodologies and intermediate technologies, platform dedicates the team of experts, and people here are the persons such as data engineers, digital marketers, or anyone who is utilizing or producing AI services,” stated AnalyticSteps

AI is making a profound impact on the manufacturing industry, offering a myriad of benefits. It significantly enhances product quality, reduces time to delivery, and boosts efficiency and overall performance.

Conclusion

AI’s role in manufacturing extends to increased efficiency and productivity, allowing predictive maintenance to prevent equipment breakdowns and repairs. This technology is not only revolutionizing the manufacturing sector but has the potential to transform it completely, driving productivity and streamlining processes, as well as offering upsides such as customization and personalization.

Job transformation is also on the horizon, as AI may replace manual roles but simultaneously create opportunities in AI system design, maintenance, and data analysis, necessitating workforce adaptation.

In summary, AI is shaping a manufacturing landscape characterized by heightened quality, efficiency, and productivity. This change may be disruptive to the current economic model but new opportunities are expected to develop.

Through its AI factory, the Foxconn-Nvidia joint venture, apart from developing and deploying AI on a common platform for its internal teams, will be able to reduce its product manufacturing time by standardizing operations across the departments. Also, the JV will have its secure common data architecture, apart from expanding its operations quickly to other industrial units.

A correctly deployed AI model brings an ocean of possibilities for any venture. A successful ‘AI Factory’ ensures that the tools, processes, and teams within a company remain transferable and replicable, thereby giving the business overall agility.

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Still, some factory workers have been operating through closed-loop management, which keeps the workers isolated inside, while for some it is becoming very hard with the rapid spread of the COVID-19. This is putting restrictions on either procurement of raw materials or shipping them off.

Foxconn Interconnect Technology, a unit of Taiwan based in Foxconn, makes data transmission equipment and connectors. The company is working in a closed loop with 60% capacity. This is just one example, there are several other companies that are operating in similar ways.

Last week, more than 30 Taiwan companies involved in making electronic parts had to suspend their production.

China is currently on a zero-tolerance policy against COVID-19 despite low cases. At Least 87 of China’s 100 largest cities by GDP are facing some sort of restrictions or the other.

The main impact is being felt on the truck transport causing long queues and further delays. Even the price for hiring trucks has shot up drastically i.e, from 7,000 yuan ($1,100) to 30,000 yuan.

Almost half of the German firms in the country are experiencing supply chain problems. To cushion the impact, China has kept the ports as well as the airports up and has encouraged the principle of working in a closed-loop.

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According to a Gulf News report, the smartphone is currently being produced in partnership with Foxconn, in a facility situated in the small town of Sriperumbudur in Tamil Nadu, just 40 km off Chennai.

The company also has plans to assemble iPad tablets there. This move is also in the works with India’s Make in India initiative. Other than India, countries like Mexico and Vietnam are increasingly becoming important for contract manufacturers supplying American brands. It is also trying to expand its production away from China and rely less on imported units which come with hefty taxes from the government.

Apple in its statement said, “We are very excited to start the production of iPhone 13 which will come with A15 bionic chipset for our Indian customers.”

After iPhone 11, iPhone 12, and iPhone SE, iPhone 13 will be the fourth model to be produced locally in India. iPhone SE was produced at the Wistron plant in Bengaluru. If one is anticipating a drop in the prices, then it is quite obvious that Apple won’t do that yet.

It further added that many of its supplier sites in India utilize solar and wind energy for their operations, meaning they are UL Zero Waste certified.

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The project’s capacity is said to produce up to 200 GW by 2030. This represents 400 GW of globally installed power capacity, and 390 GW of total nuclear power capacity in 2016.

Son said the nascent phase of the project will cost US$5bn for 7.2 GW of solar capacity. In addition, expenses for solar panels, battery storage and a manufacturing facility for panels will estimate to US$200bn, he said.

The official Saudi Press Agency said during the US visit of Saudi Arabia Crown Prince, several MoUs were signed with American-based multinational companies.

Last year, SoftBank raised more than US$93bn for the Vision Fund backed by Saudi Arabia’s sovereign wealth fund, Apple and Foxconn.

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