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By volume of humanoid robots shipped, the largest humanoid robotics company in the world is China’s Unitree. Founded in August 2016 in Hangzhou by currently 36-year-old Wang Xingxing, who remains CEO today, the company makes both bipedal and quadruped robots across a number of different product lines. Its G1 and H2 robots have repeatedly gone viral on social media and earned worldwide publicity thanks to events like their February 2026 display at the China Media Group’s Spring Gala Festival, where the robots demonstrated high levels of coordination and agility in a choreographed dance routine. In total, the company has sold around 10,000 humanoid robots to date, including 5500 in 2025 alone, and has also sold at least 33,000 quadrupeds.1 None of Unitree’s competitors, both U.S. and Chinese, have managed to sell anything like this quantity of robots—most have sold closer to 3000 robots in total. Unitree employs around 500 people, over one-third of them in R&D roles.2 Unitree intends to go public on the Shanghai stock exchange in 2026 at a valuation of around $6 billion.3

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In January 2024, Elon Musk’s brain-computer interface (BCI) company Neuralink implanted its first device into a human recipient’s brain, allowing a 29-year-old man paralyzed below the shoulder, Noland Arbaugh, to control a computer cursor with his thoughts and, among other things, play computer games like online chess, Civilization, and even first-person shooters.1 Since then, at least twenty additional paralyzed recipients have received Neuralink implants, allowing them to control not just cursors but computer keyboards and even robotic arms.2 While Neuralink is the most prominent BCI company, it is far from alone. In March 2026, the Chinese government designated BCIs a technological priority for the country and also approved the world’s first commercially-available invasive BCI, made by Shanghai-based company Neuracle; other such devices remain approved only for clinical trials.3 In June 2026, Mark Zuckerberg’s Meta released a study showing it could decode more than half of words typed by a subject using only noninvasive magnetic sensors.4 The further development of this technology promises not just transformative medical devices but also hands-free input to computers and new training data for AI—first steps on an envisioned path of merging human mind and body with machine.

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Gas turbines are types of internal combustion engines that convert chemical energy from natural gas into mechanical rotation that a generator then converts into electricity. Gas is the world’s second-largest energy source for power generation, behind only coal: in 2025 gas power plants worldwide generated around 7000 terawatt-hours (TWh) of electricity, compared to around 10,500 TWh for coal plants.1 Such power plants are especially important in regions such as the United States where, due to the fracking revolution, natural gas remains cheap and abundant, and also in many European countries where regulatory factors have combined to drive coal and nuclear power plants out of operation. Similar U.S. regulations mean that the recent significant increase for U.S. electricity demand, which was close to stagnant for two decades, and is driven primarily by AI data center construction, will be met through burning natural gas, driving rapidly expanding demand for gas turbines.

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Cerebras is a U.S. semiconductor company that, in 2019, made computing history by successfully creating the physically largest-ever computer chip.1 An Nvidia H100 chip, first released in 2022, is roughly 814 square millimeters large—small enough to sit on a fingertip. In comparison, Cerebras’ “Wafer-Scale Engines” are over 46,000 square millimeters in size, well over fifty times larger than Nvidia’s chips and large enough to be held with both hands. The idea behind building massive computer chips is very simple: electrical signals between transistors on a single large chip must move far less distance than signals moving across many smaller chips connected with external networking components, making a single chip not just faster and more powerful but also more economical, using less electricity. Cerebras used a novel design to circumvent existing semiconductor design and manufacturing constraints, and says its chips are twenty times faster than Nvidia’s chips at running AI models, allowing instantaneous responses with no wait-time for users; its chips have won a major customer in OpenAI, which agreed to buy $20 billion of Cerebras chips in April 2026.2

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With a market capitalization of over $2 trillion (TSM), Taiwan Semiconductor Manufacturing Company (TSMC) is the most valuable semiconductor manufacturing company in the world as of May 2026.1 The company is considered a chip “foundry” that does not design any computer chips but only manufactures them on contract for other companies, including “fabless” semiconductor companies like Nvidia and AMD. TSMC singlehandedly dominates global production of the world’s most advanced and computationally powerful computer chips and its dominance has not just held but grown in recent years: its share of global foundry revenue has grown from 59% in 2020 to 72% as of late 2025—with second-ranked Samsung dwarfed in comparison with a market share of just 7%—and its market share reaching even higher with greater chip sophistication.2 At present, the entire leading edge of artificial intelligence technology is dependent on TSMC’s manufacturing capabilities as well as its ability to scale up production quickly, and by extension so are the fortunes of many of the world’s most valuable companies including Nvidia and Apple.

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The recent growth in optimism about artificial intelligence technology has led to a boom in demand for the computing power or “compute” that these new AI algorithms require for training i.e. development and inference i.e. usage. The high compute demands for AI are met through chips like Nvidia’s “graphics processing units” (GPUs), which are capable of processing vast amounts of computations in parallel. While GPUs and their equivalents from other companies like AMD or Google can be purchased and run on a company’s own premises in what is now commonly called a “data center” or “compute cluster,” the financial costs and organizational complexities of building such data centers, maintaining them, and inevitably upgrading them with new and more powerful chips are prohibitively high even apparently for well-funded frontier AI labs like OpenAI or Anthropic. As a result, many companies and individuals choose instead to rent compute from “cloud computing” providers like Amazon Web Services (AWS) or Microsoft Azure and, alongside these large and established companies, a number of smaller and newer “neocloud” companies have arisen to meet demand specifically for remotely renting out AI compute.

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With annual profits of over $80 billion, Meta Platforms (formerly Facebook) is the seventh-most profitable company in the world and the eighth-most valuable with a market capitalization of over $1.5 trillion (META).1 Meta earns these profits from just under $200 billion in annual online advertising revenues from its globally dominant social media platforms: Facebook, Instagram, and WhatsApp. As of December 2025, Meta claimed 3.58 billion daily active users across its social media platforms.2 This is roughly 45% of the entire human population. Meta founder and CEO Mark Zuckerberg has made it a goal for the company to produce frontier artificial intelligence models at the cutting edge of capabilities, competitive with AI labs like OpenAI or Anthropic. With AI capabilities premised to a large extent on access to enough computing power or “compute” from advanced semiconductors from companies like Nvidia, Meta’s financial firepower makes it a key contender to examine in the AI race.

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Cohere is a Canadian artificial intelligence company founded in 2019 by Aidan Gomez, Nick Frosst, and Ivan Zhang. Gomez was one of eight co-authors of the seminal 2017 paper by Google researchers introducing the transformer architecture that underpins large language models (LLMs) today, and both Gomez and Frosst come from the tradition of knowledge in AI and computer science centered around Nobel Prize winner Geoffrey Hinton at the University of Toronto, which also produced OpenAI cofounder and former chief scientist Ilya Sutskever. Cohere has also tried to attract research talent by establishing its research arm, Cohere Labs, as an independent non-profit with the freedom to collaborate across academia and industry to publish its research. This move came as the major U.S. labs increasingly became closed off and stopped publishing any meaningful academic research.

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Since its founding in early 2023, the U.S. artificial intelligence company xAI has rapidly become one of the world’s leading AI labs and one of the world’s most valuable privately-owned companies. After its most recent January 2026 fundraising round, xAI is believed to be valued at more than $230 billion, making it the third-most valuable private AI lab after OpenAI and Anthropic.1 Headquartered in Palo Alto, California, xAI’s core staff as of 2025 numbered roughly 300 people.2 Founded by Tesla, SpaceX, and OpenAI founder Elon Musk, who is currently 54 years old, xAI merged with X (formerly Twitter) in March 2025, resulting in a combined corporation that is approximately 59% owned by Musk personally.3 In Musk’s own words, the mission of xAI is to “understand the true nature of the universe,” and the company plays an increasingly prominent role in Musk’s constellation of companies.4

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ByteDance, best known as the creator of the popular short-form video app TikTok, is also one of China’s largest cloud computing providers, hosts ByteDance Seed—one of the country’s largest frontier AI research labs—and owns the AI assistant app Doubao. The Doubao app is their main consumer product and had over 170 million monthly active users in China as of October 2025, meaning they have the most active users in the country, ranking above DeepSeek’s app’s 145 million monthly active users.1 ByteDance has the advantage of being able to train its multimodal models on user-submitted videos from TikTok and its Chinese equivalent, Douyin. Douyin, however, is not just a social media platform but also the third largest e-commerce platform in China.2 ByteDance’s stated aim is to integrate AI agents into every facet of how people interact with the internet and offer an immersive consumer entertainment, e-commerce, and social media experience within their own “super-app” ecosystem, something the company’s founder Zhang Yiming, who de facto still leads the company, believes will fundamentally transform user retention and satisfaction.

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With a market capitalization of approximately $1.5 trillion (TSLA) as of January 2026, the U.S. electric vehicle manufacturer Tesla is by far the most valuable auto maker in the world as well as one of the most valuable companies overall.1 Elon Musk has served as CEO of Tesla since 2008, during which time the company has, more than any other, spearheaded the rise of electric vehicles as a viable commercial product and alternative to traditional internal combustion engine (ICE) vehicles. Tesla has also spearheaded another major shift in the design of automobiles: the liberal integration of software and electronics to improve the driving experience for Tesla drivers, which, for many years now, has been expected to culminate in fully autonomous self-driving vehicles with no need for human drivers or intervention. This outcome would be tantamount to an economic revolution in personal, public, and commercial transportation, by eliminating the need for costly and rote human labor in the form of driving.

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Andrej Karpathy is a computer scientist, AI researcher, and expert in computer vision who was a founding member of OpenAI and then head of Tesla’s Autopilot driver assistance and self-driving car program from 2017 to 2022, where he was responsible for building the team and technology that today powers Tesla’s autonomous driving capabilities. Self-driving vehicles promise to be one of the most immediate and consequential areas where AI progress will obsolete human labor in the physical world. Whereas Google’s Waymo and other self-driving efforts have attempted to scale up safe autonomous taxis using multiple types of redundant sensors and extensive reference mapping of the environment, Elon Musk has instead pursued a “vision-only” approach focused on achieving self-driving AI software perceptive enough to maneuver vehicles using only cameras. Karpathy was handpicked by Musk to build this technology for Tesla and largely succeeded.

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Corporate investments in artificial intelligence, especially in the construction of a new generation of data centers built to provide computational power or “compute” for generative AI, have driven over one-third of the United States’ GDP growth in the first nine months of 2025.1 These new data centers will also substantially increase both peak and total electricity demand, after two decades in which power demand was nearly flat, and grew at well under a 1% compound annual growth rate.2 Today, the entire U.S. data center sector draws a total of less than 15 gigawatts (GW) of power, but the pipeline of new data centers under construction will, if all planned facilities right now are completed, add 140 GW of new load to the current total U.S. peak demand of 760 GW, an increase of almost 20%.3

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Ilya Sutskever is one of the world’s leading AI scientists. As of December 2025, he is the CEO of Safe Superintelligence (SSI), a startup he co-founded upon his departure from OpenAI in May 2024, where he was also a co-founder and worked for over eight years as Chief Scientist. Under his technical leadership, OpenAI, which began as a small non-profit, became the world’s leading generative AI company with a sizable lead over its competitors in both technology and paying user base, a lead it still retains today.

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John Carmack is an American software engineer and video game developer who cofounded the game studio id Software, whose game series including Doom, Quake, and Wolfenstein have been widely credited with not just popularizing but laying the technical groundwork for fast-paced video games utilizing three-dimensional graphics in the 1990s, in particular the “first-person shooter” (FPS) genre. Carmack was the lead programmer on these series and has been credited with developing multiple techniques that made it possible to smoothly render computationally-intensive graphics. Carmack’s early software work thus helped to drive adoption of specialized, high-performing computer chips called graphics processing units (GPUs), designed by Nvidia. Decades later, Nvidia’s powerful GPUs have become the standard for developing and using computationally-intensive artificial intelligence software, and, since 2022, Carmack has in turn devoted his time to running his own AI research lab aiming to build artificial general intelligence (AGI).

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It is rare for someone to be a competent researcher as well as able to navigate sometimes cutthroat corporate politics. Because he can do both, currently 49-year-old Demis Hassabis is one of the most influential technologists working on artificial intelligence today. Hassabis is currently CEO of Google DeepMind, an organization with notable research capabilities demonstrated by their many technical firsts spanning over a decade of AI research. Such research capacity is surprisingly rare in the crowded field of giant companies eagerly contending to lead the AI industry, perhaps matched by only a few other exceptions such as OpenAI and DeepSeek. For example, under Hassabis, DeepMind built the program AlphaFold to predict protein structures, for which Hassabis was awarded the 2024 Nobel Prize in Chemistry and was knighted in the United Kingdom.

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Z.ai, known as Zhipu AI until 2025, is a Chinese artificial intelligence startup founded in 2019 by Tang Jie and Li Juanzi, both professors at Tsinghua University, Chinese paramount leader Xi Jinping’s alma mater and one of China’s most prestigious universities. Alongside companies like DeepSeek and Moonshot AI, it is one of the leading AI startups in China, known primarily for the “GLM” series of open-source large language models (LLMs) with low costs of inference i.e. usage and high performance, especially on coding and “agent” tasks. The company is headquartered in Beijing and reportedly employs around 500 people, with a core research team of between 100 to 200 staff members.1 It is led by CEO Zhang Peng, an AI researcher and Tsinghua alumnus, and was one of the first companies in China to build a large language model before the viral release of OpenAI’s ChatGPT in November 2022.

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Alibaba is the second-most valuable technology company in China, with a market capitalization of nearly $400 billion (BABA) as of November 2025 and approximately $130 billion in revenues in 2024.1 Although it is primarily an e-commerce company that has been dubbed “China’s Amazon,” it today also hosts an elite artificial intelligence research lab and is one of China’s major providers of cloud computing services i.e. remotely renting out access to servers for computing and data storage, and building the large data centers to do this. Tongyi Lab oversees the development of the Qwen series of large language models (LLMs), which have become some of the most popular models in China for businesses, academics, and consumers. Almost all the Qwen models are released on highly permissive open-source licenses, allowing individuals to download them and external providers to offer them to customers without paying Alibaba. The models are also winning customers outside of China for their very low costs of inference i.e. usage and high performance: for example, Airbnb CEO Brian Chesky recently revealed that Airbnb’s customer service chatbot is largely powered by Qwen models.2

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Sundar Pichai has been the CEO of Google since 2015 and the CEO of Google’s parent holding company Alphabet since 2019. With a market capitalization of nearly $3.5 trillion (GOOG) as of November 2025, Google is the fourth-most valuable company in the world.1 With yearly earnings of some $140 billion, it is also the most profitable company in the world besides Saudi Aramco. Google’s massive revenues overwhelmingly come from its core business in online search advertising, which makes up nearly 80% of its revenues as of 2024.2 Google is also a major player in the ongoing race to develop and commercialize artificial intelligence technology. Through the Gemini series of models and others, the company directly competes with AI labs like OpenAI, Anthropic, or xAI. At the same time, Google is also a major provider of remote computing and data storage capacity i.e. “cloud computing,” which requires the building and operation of large data centers full of high-end computer chips, that are also necessary to develop and offer AI models to users. Through Waymo, the company is also a leading contender in the nascent market for self-driving taxis. As CEO, Sundar Pichai is responsible for coordinating and overseeing these myriad moves in AI.

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Microsoft is the second-most valuable company in the world as of October 2025, with a market capitalization of approximately $3.8 trillion (MSFT).1 The U.S. software conglomerate is also the single largest investor in and backer of OpenAI, the world’s leading artificial intelligence company and the most valuable privately-held company in the world with a valuation of $500 billion.2 Since 2019, Microsoft has cumulatively invested over $13 billion into OpenAI in exchange for a special albeit evolving partnership with the company, which has included provisions such as rights to use OpenAI’s intellectual property and a privileged ability to sell access to computing power and data storage to the company from its “cloud computing” platform Microsoft Azure.3 Microsoft is uniquely capable of funding big-budget investments into technology thanks to its diverse and highly profitable business empire spanning a range of basic software and hardware services, which have today made it the fourth-most profitable company in the world with yearly earnings estimated at $123 billion.4

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