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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Many of our world’s most formidable live players including Elon Musk, Sam Altman, and Bill Gates, as well as many cornerstone institutions of our society ranging from investment banks to the U.S. government, are betting their own economic and strategic futures on rapid near-term advancements in artificial intelligence technologies. The potential consequences of such breakthroughs are furthermore so profound that they merit close analysis in and of themselves. If achieved, such advances in AI will provide intelligence “too cheap to meter,” and through it unlock all other possible technological advancements as well as deliver unimaginable material wealth. AI pioneers such as Nobel laureate Geoffrey Hinton further believe such artificial intelligences will ultimately pose a remarkable risk to humanity, possibly even heralding the end of our time as the dominant species on Earth.1

While not everyone believes in the alternatively catastrophic or utopian transhumanist-inspired visions held by the technologists driving these developments, it is on some level a moot point, because these dizzying possibilities unlocked by AI have already captured the imaginations of the world’s most powerful economic and political institutions. Indeed, since the viral launch of ChatGPT by OpenAI in November 2022, we have witnessed three years of an escalating, almost all-of-society effort to harness the potential of AI for massive economic returns. The potential promise of AI to ameliorate or solve deep-seated problems like deindustrialization, demographic decline, or debt-funded entitlements is simply too tempting to ignore, and thus the question of AI touches on every facet of our society.

Already in June 2024, major corporations, tech giants, and utilities were set to spend around $1 trillion on capital expenditures such as data centers, chips, and the power grid to support them in the coming years.2 These expenditures were all financed with an unprecedented leveraging of financial resources from the richest people in the world, sovereign wealth funds, investment banks, and investment holding companies such as SoftBank, even with the backing of governments. So far, as of October 2025, the investment shows no signs of slowing down, with many more hundreds of billions of dollars invested. It is not just Silicon Valley software engineers or Wall Street financiers who are now asking themselves what their AI strategy is, but policymakers in Washington, party officials in Beijing, military planners in Moscow, civil servants in Brussels, and leaders hungry for development and growth from Paris and London to Abu Dhabi and New Delhi.

This immense spending has been facilitated by vast capital reallocation, which is perhaps best measured in the spectacular rise of the valuations of both publicly and privately traded companies tied to the industry. Such companies include those that are manufacturing and developing advanced chips such as TSMC, AMD, or Nvidia. It also includes companies building supporting infrastructure and data centers such as Amazon or Oracle, and of course AI labs themselves that are making mathematical and theoretical advances in AI such as OpenAI, DeepSeek, Meta, Google, xAI, Mistral, and Anthropic. It includes companies speculated to benefit from advances in AI via new hardware like humanoid robots or self-driving cars too, such as Tesla, Figure, or Xiaomi. Nearly all relevant companies span multiple categories as they try to organize the entire emerging technological stack, from silicon to inference, to their own advantage, and make deals to help finance themselves and secure further investments.

These valuations continue to reach new heights. Nvidia became the first company in history to reach a market capitalization of $4 trillion in early July 2025. By the end of July, Microsoft had become the second company to pass that milestone. Companies such as Alphabet, Amazon, Meta, and Oracle all hit their own all-time highs denominated in trillions of dollars. Is it so unthinkable now that one of these companies will soon become the first $10 trillion company? And one of the founders or executives of these companies the first trillionaire? These spectacular valuations are correlated with each other much more deeply than just a shared bet on a technological gold rush.

There are significant multi-billion-dollar cross-investments and complicated mutual ventures undertaken by these major infrastructure providers. Just last month, for example, Nvidia announced it will invest up to $100 billion dollars into their customer OpenAI, as the leading AI lab also deploys huge amounts of Nvidia systems.3 Nvidia CEO Jensen Huang then in turn publicly expressed surprise when, not long after, OpenAI signed a multi-year chip supply deal with his competitor AMD, where OpenAI was given the option to buy up to a 10% stake in the chip supplier.4 OpenAI CEO Sam Altman has stated more such deals are in the works, with OpenAI already investing in their suppliers such as Nvidia, AMD, Oracle, and Broadcom.

This brave new world of data center financing, where sellers are subsidizing buyers, has raised concern from financial observers for how seemingly circular some of the deals are. Many of the commitments that collectively run into the trillions of dollars that are now being made are paid for not with revenue but with increases in the value of publicly or privately traded stock, appreciations driven by the announcements of the deals themselves.5 The AI industry has now grown to such an extent that it is likely propping up the world’s largest economy. Without these technology-related categories, U.S. GDP growth in the first half of 2025 reportedly would have been just 0.1% on an annualized basis rather than the 4% it recorded.6 The dollar value contributed to GDP growth by AI data center buildout has reportedly surpassed U.S. consumer spending for the first time ever.7

If, absent our great bet on AI growth, the U.S. economy is already in a recession, this means Western policymakers have a strong incentive to continue supporting further investment into AI, regardless of the technical, financial, or economic fundamentals, just to continue propping up favorable economic figures lest they face severe consequences. In that sense, nearly every asset class has come to be correlated with AI. What does risk diversification even mean in such a macroeconomic environment?

Many Silicon Valley insiders and outside observers have now raised the possibility that we are in a potentially devastating global financial “AI bubble.” Amazon founder Jeff Bezos recently stated that there is in fact an AI bubble, driven by a lack of investor discernment between good and bad ideas, but that it is an industrial bubble, where society will ultimately benefit from the legitimate innovations and infrastructure created.8 It is important to add that even if he is correct in making this distinction it would be of little consolation to hasty investors who stand to lose billions. Either way, bubbles do not pop themselves, but rather just keep growing—and, of all the power players in the world today, in whose interest is it to pop this possible bubble? The answer to this question could easily be the most valuable piece of financial information in recent history.

Those seeking to protect their wealth or seize new growth opportunities will have to go beyond financial sector best practices, textbook answers, and press releases, instead pursuing investment strategies based on completely new thinking. While you might not think you’re interested in AI or that it is irrelevant for your business, family office, or policy work, this societal and economic phenomenon of AI is certain to affect you and your interests. You ignore it at your own risk—and that is even assuming it does not result in new science, advances in robotics, greater automation in manufacturing, and transformations in the military balance of power, all of which are perhaps much likelier outcomes than utopia or catastrophe.

I believe technological breakthroughs in AI and their consequences merit close strategic analysis based on exceedingly accurate rather than incomplete or misleading information. Unfortunately, with so many interested parties trying their best to shape narratives and markets—as well as mislead competitors—the epistemic commons cannot be relied upon. Because of this, I am convinced that today, more than ever, navigation-grade analysis should be made available to the hundreds of key live player decision-makers who help steer our civilization to a better future.

We at Bismarck Analysis have been closely following and advising key players in the development of artificial intelligence since our founding in 2017, five years before the AI boom began in earnest. Through our firm’s near-decade of strategic consulting and research commissioned by top AI industry insiders, many of whom have since become household names, our firm has developed unparalleled, deep expertise across technology, economics, politics, business, and strategy to deliver accurate analysis that does not overlook the implications of any of those aspects, something truly unique among the research organizations and consulting firms in any industry today.

Our confidential work for our private clients over the years has reviewed the entire semiconductor manufacturing supply chain from optics through chip design, the geopolitical dynamics of AI competition and cooperation, the likely energy fundamentals of an AI revolution and their consequences, the key scientists working on AI technology, and the financial interests and flows funding the sector. Both top investors and CEOs have relied on us to guide their investment, philanthropic, business, and political strategies.

Our more publicly-accessible work, namely Bismarck Brief, is moreover already a trusted and relied-upon wealth of knowledge and intelligence on the AI landscape. We have already examined multiple players in every layer of the AI technological stack, including energy, minerals, semiconductor manufacturing, chip design, software, hardware, and the AI labs themselves. This was not done through trend-chasing or by happenstance, but it instead came about through following a systematic, multi-year research plan aimed at mapping out and forecasting the technological landscape of one of the most important developments of our era. Over the course of the coming months, we will continue to methodically investigate the key organizations and live players relevant to AI, releasing some of these as case studies to our Bismarck Brief subscribers, which you can read at our new AI 2026 segment.

More importantly, because of the immense importance of accurate information about and deep analysis of these developments, we will for the first time ever offer an exclusive annual comprehensive report going even further beyond the scope of our Briefs, titled AI 2026: Bismarck Strategic Report. At an estimated final length of between 225 to 275 pages, this industry-grade document will feature all of the information and analysis a live player needs to navigate this new AI-driven world:

• In-depth case studies of all the world’s frontier AI labs, including both U.S. giants like OpenAI, Anthropic, and Google as well as Chinese upstarts like DeepSeek and Moonshot AI.

• Player profiles of all the key figures in the AI boom, such as Sam Altman, Demis Hassabis, Bill Gates, Mark Zuckerberg, Larry Ellison, and Elon Musk—examining their personal ideologies, backgrounds, capabilities, and goals.

• Up-to-date data presentation and analysis on data center construction, electricity consumption, and energy build-out potential in the U.S., China, Europe, and Arab Gulf states.

• List and evaluation of key AI scientists and living traditions of knowledge advancing the field, also reviewing the relative strength of different countries’ mathematical traditions to help gauge where new AI researcher talent is likely to come from in the near future.

• Analysis of key players providing capital to fund AI development, including major cloud providers like Microsoft and AWS, venture capitalists, and governments.

• Analysis of all key players in the semiconductor design and manufacturing stacks including Nvidia, TSMC, ASML, and Carl Zeiss, as well as rising Chinese domestic challengers and others.

• Overview of key domains where progress in AI is expected to have near-term transformative impact, including robotics, autonomous vehicles, and drone warfare.

• Analysis of geopolitical implications of various AI scenarios, examining impacts on the balance of power between the U.S., China, Europe, Russia, and other states.

• An opening first-principles analysis as well as a concluding letter to live players by our founder and president Samo Burja, synthesizing and summarizing key recommendations for executive decision-making and providing grounding in the big picture takeaways.

This new annual strategic report will feature our signature crisp style and analysis, extensive sourcing for further reading, and custom-made charts, tables, and graphics to aid understanding. The report will be delivered on May 12, 2026 to help set the next year’s strategic goals. Similar comprehensive reports have been previously reserved for our most valued clients such as corporate partners and high-net worth individuals, or have been very rarely released publicly such as our white papers on nuclear nonproliferation (2018) and machine tools manufacturing (2020).

In order to purchase access to AI 2026: Bismarck Strategic Report, simply upgrade or become a paid subscriber to Bismarck Brief at the new AI 2026 tier using the button below:

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Subscribers will also receive full access to our catalog of 200+ Briefs investigating the world’s most strategically relevant live players, institutions, and industries, as well as new Briefs in their inbox over the coming weeks and months—including Briefs that will contribute to the final AI report—every Wednesday at 2pm GMT sharp. To get the most value from our ongoing and methodical research, do not delay, but make sure to subscribe immediately and never miss a word. Those already subscribed at the previously-named Client tier will of course receive complimentary access to AI 2026 and do not need to take any action.

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I look forward to hearing the thoughts of our sophisticated and intelligent readership on this unprecedented and unique investigation into the economic and political landscape of artificial intelligence.

Sincerely,

Samo Burja

Founder and President, Bismarck Analysis

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Moonshot AI is a Chinese artificial intelligence startup founded in March 2023 by currently 33-year-old CEO Yang Zhilin, a leading researcher in the field of natural language processing and the development of the transformer architecture that underpins recent advances in large language models (LLMs), through widely-cited papers such as XLNet and Transformer-XL. Through its flagship Kimi series of models, the company develops both open-source and paid models that have become notable for their high performance and cost-efficiency. The company’s Kimi K2 model, for example, performed extremely well both on benchmarks and in practice for generating code as well as when it is used as an “agent” performing multiple tasks in sequence, becoming the world’s leading open-source model following its release in July 2025.1 The company has raised at least $1.2 billion at a valuation of over $3 billion as of late 2024, including from multiple of China’s tech giants, making it one of the best-funded AI startups in China.2 Moonshot employs around 300 people and is based in Beijing, with an office in Shanghai.3 In Chinese, the company’s name translates as “Dark Side of the Moon.”

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Mistral is a French artificial intelligence startup headquartered in Paris with offices in London and Palo Alto. Founded in April 2023 by Frenchmen Arthur Mensch, Guillaume Lample, and Timothée Lacroix, the company makes large language models (LLMs) and sells access to them via its interface, La Plateforme. As of September 2025, the company is valued at $13.8 billion after a funding round in which it raised $2 billion.1 In total, the company has raised over $3 billion. This currently makes Mistral the highest-valued AI startup in Europe, and its models have been well-received for their cost-efficiency. Despite its small size relative to leading U.S. AI companies like OpenAI, Anthropic, or xAI, Mistral has secured the support of Nvidia as well as the enthusiastic backing of the French government through the personal involvement of French President Emmanuel Macron. In September 2025, Europe’s most valuable technology company and a key link in the global semiconductor supply chain, the Dutch semiconductor equipment manufacturer ASML, took an 11% stake in Mistral for €1.3 billion, becoming the startup’s largest shareholder.2

Mistral initially raised around $100 million purely on the reputation of its three technical founders: CEO Arthur Mensch was previously an employee at Google’s DeepMind, while CTO Timothée Lacroix and Chief Scientist Guillaume Lample both worked at Meta. All three founders are in their early 30s. After fundraising, the company released and open-sourced a groundbreaking small model, Mistral 7B, which matched or outperformed Meta’s larger open-sourced Llama 2 models on widely-used benchmarks.3 The company followed up by offering access to Mistral Medium, a model that outperformed OpenAI’s GPT 3.5 on a number of standard benchmarks. The company has continued releasing a number of larger open-sourced and high-performing models, as well as Mistral Large, the company’s largest model, released in February 2024 and marketed as a competitor to OpenAI’s GPT-4.4

As of September 2025, Mistral reportedly had 350 employees, rapid growth compared to just 35 in early 2024.5 The company has recruited a sizable fraction of the team from Meta that built the early Llama models. Mistral was considerably constrained in its first year by a shortage of the high-end graphics processing units (GPUs) designed by Nvidia and manufactured by Taiwan’s TSMC, which are the preferred chips to develop and run AI models due to their great computing power. This did not however stop Mistral from developing globally competitive models in the meantime. The company has already gathered a series of allies and backers in both the U.S. and Europe who together can unlock more funding and access to chips for Mistral. As its “compute” resources grow, Mistral’s models are likely to become still more competitive while increasingly enjoying a home-field advantage among corporate and government customers in Europe, where Mistral can persuasively argue it is a patriotic alternative to U.S. or Chinese tech companies.

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Mistral Develops Cost-Effective AI Models Using French Talent

Mistral released its first model in September 2023, a few months after the company’s founding. From the beginning, the company used a strategy of open-sourcing many of its models, allowing anyone to download their products without paying a fee to Mistral. This made the company popular with software developers and likely helped the company win some early customers for the models it restricted to paying customers. Mistral has effectively marketed itself using its idiosyncrasies, such as marketing its products under French names like Le Plateforme and Le Chat, and emphasizing its “hacker” credentials early on by releasing new models with simple torrent links and using a logo made in Microsoft Word.6 Mistral 7B, the company’s first model, was a large language model that proved highly popular with consumers looking for a free open-source model that was small enough to run on consumer hardware: it was downloaded over 3.4 million times from the AI site HuggingFace as of May 2024.7

Mistral followed up by releasing Mixtral 8x7B in December 2023, a sparse mixture-of-experts (MoE) model that was likely one of the world’s leading open-source models at the time of its release. In mixture-of-experts models, each token—numerical representations of text processed by the model—is routed to a subset of the whole model, according to the context: this allows for a model with a large number of total parameters to be relatively cheap to “inference” i.e. generate outputs for users, since only a small fraction of the whole model is actually used per token.

The technique is widely used for cutting the costs of actually serving models to users, and it can also cut costs of model “training” i.e. initial development depending on the quality of the training process. Although Mistral’s various MoE models have typically scored very well on benchmarks that are widely used to evaluate language models, especially for their low cost of inference, mixture-of-experts was not pioneered by Mistral and the company has not advanced the technique to the same degree as, for example, the Chinese company DeepSeek did, nor has Mistral published algorithmic and architectural advances as impressive as for example DeepSeek’s “multi-head latent attention.”

Mistral’s first year, however, saw the company operating under extreme compute constraints relative to other leading AI labs. The dominant paradigm for making further AI progress is currently the “scaling hypothesis.” In layman’s terms, this states that the key factor to make AI models better and smarter is simply applying ever more computational power (“compute”) to ever-larger data sets, from more and better chips. In an April 2024 interview, CEO Arthur Mensch stated the company only had access to 1500 H100 Nvidia GPUs, “just a few percent” of the compute available to leading U.S. AI labs.8 This seems to have been as much due to the massive rise in global GPU demand leading to sudden bottlenecks in supply as due to a shortage of funding: Mensch mentioned in an August 2024 interview that the company was just about to gain access to compute that it had raised money for in December 2023.9

Mistral was even substantially more compute-constrained than DeepSeek, which by this time already had a cluster of over 2000 H800 GPUs—almost equivalent to the H100—plus a cluster of 10,000 older-generation A100 GPUs. Given that the H100 is roughly 2.5 times more powerful than the A100, DeepSeek likely had roughly four times more compute available than Mistral did at the time. OpenAI’s GPT-4 was reportedly trained on a cluster of 20,000 A100 GPUs, equivalent to the compute power of approximately 8000 H100s—five times more compute than Mistral had access to almost a year into the company’s life.10 This makes Mistral’s achievements relatively more impressive and indicates the high quality of its researchers and software engineers.

Mistral has continued to release notable models, including Mistral Medium, a GPT-3.5 level closed-source model released in December 2023, and Mistral Large, initially released in February 2024; upgrades were released in July 2024 and again in November 2024. The original version of Mistral Large was somewhere between GPT-3.5 and the original version of GPT-4 in performance level and it was also notably less censored than competitor models from OpenAI and Anthropic, willing to discuss more controversial topics. Alongside Mistral Large, Mistral also launched its free chatbot, Le Chat, a consumer-facing product similar to ChatGPT.

As of September 2025, it has released dedicated coding, speech recognition, reasoning, image analysis, and optical character recognition models, as well as a range of multimodal models for more general use cases.11 It continues to release smaller open-source models, but the company’s largest and most advanced models are almost all only available to paying customers. In October 2024, Mistral released a number of models called Les Ministraux, aimed at “edge computing” use cases such as phones and autonomous robotics.12 In August 2025, the company released Mistral Medium 3.1, its most sophisticated model to date, with multimodal capabilities. Mistral claims the model offers around 90% of the performance of Anthropic’s Claude Sonnet 3.7 at a significantly lower cost of inference.13

Progress in AI has been driven by researchers with academic backgrounds in mathematics and computer science and, much like DeepSeek has mobilized Chinese talent, Mistral is mobilizing European and especially French talent. Arthur Mensch himself studied for his PhD at the French Institute for Research in Computer Science and Automation before working as a postdoc at New York University and the École normale supérieure, one of France’s most prestigious, elite grandes écoles or “great schools.” He worked in DeepMind’s Paris office for almost three years before leaving to found Mistral, playing a prominent role in the development of the Gemini, Chinchilla, and Flamingo models.

Mensch’s work on Chinchilla was especially important for the history of subsequent large language models, because the March 2022 technical paper that accompanied the model set out that OpenAI’s original scaling laws were incorrect in some critical respects, and language models were being trained on insufficient quantities of data for a given quantity of compute. The paper, on which Mensch was third author, has been cited over 2500 times, and “Chinchilla-optimal” is now widely used as a term for describing models trained in accordance with these laws.14

Mensch is an AI optimist who consistently downplays the existential risks of AI in his interviews, and disagrees with the idea that current-generation large language models are conscious, a key difference with figures such as Geoffrey Hinton.15 In this regard he seems more aligned with the thinking of Meta Chief AI Scientist Yann LeCun, another Frenchman, who is likely personally acquainted with Mensch’s technical cofounders and may be acquainted with Mensch himself. Mensch appears to have grown frustrated with the corporate bureaucracy at DeepMind, both the technical bureaucracy and the AI safety bureaucracy, and set up Mistral to create a faster-moving organization: he has discussed the benefits of keeping teams small, at no more than five people.16 He is also motivated by wanting to prevent an AI “oligopoly” dominated by U.S. companies and to create a European AI company that can compete globally.17

Guillaume Lample received a master’s degree from Carnegie Mellon University after attending the École polytechnique, another grande école specializing in science and engineering. He was briefly a research intern at Jane Street Capital and did a number of internships at Meta while studying for his PhD at the Sorbonne, working full-time at Meta for a little over three years, including on the first generation of Llama models. Timothée Lacroix also attended the École normale supérieure before working at Meta while studying for a PhD. He also worked on the first generation of Llama models. Lacroix, in a recent interview, talks about Lample’s mastery of data cleaning for model training as a key factor in the company’s success.18

Mistral’s technical staff are disproportionately French or have a European educational background. A connection to Meta is also common, due to a history of U.S. tech companies setting up offices in Paris to recruit elite French mathematical and computer science talent. Facebook AI Research, run by Yann LeCun, announced that it was setting up a Paris office in June 2015, giving the company deeper roots in France than other big tech AI labs.19 DeepMind followed suit three years later.20 Of the 18 listed authors on the Mistral 7B paper, which included many of the company’s founders and earliest employees, 13 were French or had attended an elite French educational institution, including the company’s cofounders. Most studied at doctoral or master’s levels in France, such as Thomas X. Wang or Lelio Levaud, Head of Engineering, who has a master’s degree in computer science from the École polytechnique.

Of the five authors who did not have a French background of some kind, Albert Qiaochu Jiang, lead author on the papers for Mistral 7B and Mixtral 8x7B, has a PhD in Computer Science from Cambridge University, his master’s degree is from Oxford University, and he now leads the Mistral reasoning team. Chris Bamford, formerly at Meta, studied for his PhD at Queen Mary, University of London. Devendra Singh Chaplot was educated in India and at Carnegie Mellon University before working at Facebook’s AI research group FAIR, which is likely where he met the Mistral founding team. Gianna Lengyel, the company’s head of business development, is Italian. Diego de las Casas was educated in Brazil before working as a Research Engineer at DeepMind.

As the team has expanded over time, the preponderance of French talent has remained. Out of 99 authors listed on the June 2025 technical paper that accompanied Magistral, the company’s first reasoning model—comparable to DeepSeek’s R1—55 have predominantly French educational backgrounds, typically with master’s and doctoral degrees from French institutions.21 Of the remainder, seven were educated in the United Kingdom, and most of the rest of the staff, 25 in total, were largely educated in the United States, almost all of which are now working in Mistral’s U.S. office in Palo Alto. Of Mistral’s American staff listed on the Magistral paper, at least 11 were educated in India up to at least high school level, with some also receiving their undergraduate educations there before moving to the U.S. for graduate studies. This includes the lead author on the Magistral paper, Abhinav Rastogi, who grew up in India before studying at Stanford University and working for some years at Google and DeepMind.

Emmanuel Macron Has Enlisted Mistral to Become a European Strategic Champion

Mistral has received the enthusiastic endorsement and support of the French state, evidently through the personal intercession of currently 47-year-old French President Emmanuel Macron and his office. One of Mistral’s “non-executive” co-founders is Cédric O, who served as Secretary of State for the Digital Sector in the French government from 2019 to 2022. O is a core member of Macron’s political clique, who served as treasurer of Macron’s initial 2017 presidential campaign and was reportedly one of the first five people to join Macron’s new political party.22 Like Macron, O sees Europe as ossified in technology and business and believes incubating successful European technology companies is a way to strengthen Europe’s “strategic autonomy.”23 In his previous government role, O was a go-between for the French government and global technology executives, and has since acted as an advocate and lobbyist for Mistral in European government circles, including in Brussels. O is currently 40 years old.

The French state-owned investment bank Bpifrance has participated in multiple of Mistral’s funding rounds, including the June 2023 $100 million seed round and a $600 million round a year later.24 Bpifrance is also partnering with Nvidia, Mistral, and the Emirati government’s new dedicated AI investment firm MGX to build a 1.4-gigawatt GPU cluster near Paris.25 This cluster will presumably be used by Mistral for model training and inference, although it does not appear to be planned exclusively for the company’s use, and the size of the cluster will be smaller than some of the larger AI clusters currently under construction in the U.S., such as Meta’s planned 5 GW Hyperion cluster.26

Mistral has also won a number of government contracts, including a recent award from the Prime Minister’s Office to upgrade a chatbot used by French civil servants.27 The firm is also likely to be working with the French military, which operates its own AI research unit: Minister of the Armed Forces Sébastien Lecornu stated publicly in January 2025 that cooperation between Mistral and the military was guaranteed, and the company’s website also discusses ongoing collaboration with the Ministry of Defense’s AI agency.28 The government has explicitly endorsed Mistral as a matter of marketing: Mistral CEO Arthur Mensch was a member of the government’s Commission on Artificial Intelligence that wrote a 130-page report in 2024 on France’s AI ambitions, and is also the face of a government-led marketing campaign for entrepreneurs and investors to “choose France” to do business in.29 There are reports that Macron has gone so far as to have personal calls with key French-speaking researchers working for leading U.S. AI companies to try and convince them to come found or work for new French AI companies, presumably including Mistral.30 Mistral executives met Macron shortly after the company’s founding.31

The support of the French government for Mistral has been crucial in moderating the European Union’s overzealous regulatory ambitions for software and data collection. Alongside Meta, Mistral has refused to voluntarily comply with portions of the EU’s 2024 AI Act that do not take legal effect until 2027, and its lobbying played a key role in watering down certain provisions of the act that sought to regulate large foundation models.32 In particular, the AI Act as passed exempts all models trained with fewer FLOPs—floating-point operations per second, a measure of computing power—than OpenAI’s GPT-4 from its most onerous provisions: all of Mistral’s current and many of its future models, therefore, are likely to be outside the Act’s scope. Mistral executives have met with European Commission leadership, but it is the political heft of France as the EU’s second-largest economy and only nuclear power that carries the most weight in Brussels negotiations.33

As in the U.S. or China, the bulk of funding for Mistral’s AI ambitions will come from the private sector. Mistral has already proven an ability to raise significant funds from the U.S. venture capital sector: its 2024 funding round was led by U.S. venture capital firm General Catalyst, with a number of other U.S. firms participating, including the prestigious Andreessen Horowitz and Lightspeed Venture Partners. The French bank BNP Paribas and German media conglomerate Bertelsmann were notable European investors, alongside a number of corporate investors including Microsoft, Salesforce, Samsung, IBM, and Nvidia.34 Notably, after OpenAI, Mistral was only the second company whose models Microsoft had offered.35 Mistral has highlighted that Bpifrance, Andreessen Horowitz, and Nvidia also all participated in its September 2025 funding round alongside ASML.36

Nvidia’s relationship to Mistral is key because Nvidia ultimately controls the supply of the most powerful computer chips needed to develop and run AI models. Nvidia founder and CEO Jensen Huang appeared on stage with President Macron and Arthur Mensch in 2025 to celebrate the partnership between the three to launch the new data center near Paris.37 Huang related that he had asked Macron to call large companies in France to assist Mistral.38 Huang likely has a more than an incidental financial interest in Mistral and the development of a European AI sector. A successful strategy for Nvidia would be to back smaller customers with equity investments and preferential chip allocations in order to avoid excessive dependence on a handful of Nvidia’s biggest customers and so risk a large drop of revenue if one or more of them shift the majority of their chip usage to custom chips developed in-house with the assistance of companies like AMD or Broadcom.

But with the investment of ASML into Mistral, the startup has arguably found a partner with high free cash flow that can fund expensive AI expansion ambitions, much in the same way the high profits of companies like Microsoft, Amazon, Google, and Meta have patronized the expansions of OpenAI, Anthropic, and others. ASML is one of the best-placed companies to patronize Mistral, since it is the most valuable technology company in Europe with a market capitalization of over $300 billion as of September 2025, generated $9 billion in free cash flow in its last fiscal year, and monopolizes the global production of advanced extreme ultraviolet photolithography (EUV) machines that are used by chip foundries like TSMC to manufacture the most powerful chips.39 This means ASML will financially benefit from continued growth in semiconductors and AI and then be able to reinvest portions of its earnings into Mistral to ultimately capture more value from the other end of the supply chain, as an investor.

Notably, although it is a Dutch company, the CEO of ASML since 2024 is a Frenchman, Christophe Fouquet, and, in December 2024, the company appointed Bruno Le Maire as a special advisor to its executive board: the 56-year-old Le Maire served as Macron’s Economy and Finance Minister from 2017 until September 2024. In 2024, the company was reported to be contemplating an expansion in France, rather than the Netherlands, reportedly due to concerns at the prospect of losing access to foreign talent due to tightening Dutch immigration and tax laws.40 It therefore appears as though the ASML investment may have been coordinated with the help of the French government too.

Mistral’s Future Brightens As Compute Constraints Lessen

Mistral’s most notable customers so far are predominantly leading European firms. Its executives have repeatedly emphasized their focus on the European enterprise market, in which firms, especially those in highly regulated industries, might be unwilling or unable to use U.S. models. European regulations in the future could function as something of a barrier to entry in Europe that favors Mistral, while many large European firms, led by aging executives, are likely to follow the cues given by the French government, EU bureaucracy, and other European governments, if they signal that Mistral is the preferred AI vendor in Europe.

Political tensions between the second Trump administration and European governments have already reportedly driven European companies towards Mistral’s models as a high-quality alternative to U.S. companies like OpenAI and Anthropic: in May 2025, Arthur Mensch noted that the company had tripled its business in the last 100 days, with most of the growth coming in Europe and outside the U.S.41 Annual revenue is now reportedly over $100 million, although this is currently still far lower than, for example, Anthropic’s reported annual recurring revenue of around $5 billion.42

Mensch has also noted how European firms are more likely to be storing their data “on-premises” or in private clouds, rather than in Amazon Web Services or Microsoft Azure. In the initial pitch memo that helped the company raise money in its seed round, the founders argued that most of the value in large language models would accrue at the level of generative models themselves, given the difficulty and expense of training high-quality models, and further argued that the closed-source, paid-access via API model of OpenAI would be inadequate for firms with critical data that they would not wish to expose to a closed-source model accessed via a public cloud.43

On its website, Mistral lists Stellantis, the French-Italian automaker and world’s fifth-largest car company, as a key client, with Stellantis developing in-car AI assistants for drivers using Mistral models, while also deploying Mistral’s technology for analysis of complex component databases.44 Other clients include BNP Paribas, French IT company Capgemini, German defense technology company Helsing, the Singaporean Ministry of Defense, German software company SAP, and French medical technology company Synapse.45 Reportedly around half of Mistral’s business in total comes from Europe, with French logistics and shipping firm CMA CGM reportedly one of its biggest customers, signing a contract with Mistral worth €100 million.46

Mistral has reportedly begun to “forward-deploy” its software engineers to work closely with its customers on customized software, on a quasi-consultancy model somewhat akin to that formerly extensively used by Palantir.47 This indicates Mistral is targeting large bureaucratic organizations as customers, which tend to be very complex with high levels of technical debt in need of custom solutions. While this model slows down Mistral’s sales cycle, this business model is likely to embed a certain level of dependence on Mistral in its customers over the long term.

So far, Mistral seems to have been remarkably cost-efficient: it had not yet spent all of its $113 million seed funding over a year after the company’s foundation, despite the high costs of compute required to train and inference the company’s models.48 For comparison, OpenAI has raised almost $60 billion in external funding and was most recently valued at $500 billion, while spending billions of dollars per year.49 Mistral’s greatest constraint has been lack of funds and lack of access to enough GPUs, as the costs of building the largest and most high-performing models continue to escalate. Next-generation successor models to GPT-4 are estimated to cost over $1 billion just to train; future models beyond that are likely to see costs escalating into the tens of billions and tens of gigawatts of power, even before inference costs are accounted for.

The financial costs are likely to be much more of a problem for Mistral than the energy costs. Although average industrial electricity costs in France are roughly double those in the United States, Mistral is unlikely to be energy-constrained even if it does acquire the GPUs to begin training very large models over the next five years. The French nuclear fleet only runs at a capacity factor of around 75%, in part due to lengthy maintenance outages at older plants, but mostly because France simply has excess power supply capacity relative to demand, especially during the summer months when demand is lower and solar farms in France and neighboring countries such as Spain are producing their highest volumes of electricity.50 The French grid, therefore, almost certainly can accommodate quite a sizable increase in power demand from Mistral’s GPU clusters without building any new nuclear plants: it can simply produce more power from its existing reactors. Furthermore, there is nothing stopping Mistral simply from building or renting additional GPU clusters in the U.S.

But Mistral’s access to financial and computational resources is likely to grow substantially in the future. The company’s growing business in Europe will attract further investment from both U.S. and European investors. It has now inked a strategic partnership with ASML, a company with robust cash flows and a high market capitalization. Mistral has already recently begun to rent out its own existing GPUs and AI training capabilities, with the most likely clients presumably being less well-established European startups, under the brand Mistral Compute.51 This indicates confidence that their compute resources will significantly rise in the future.

Although it is unlikely to receive major direct financial support from the French government, another potential source of large funding for Mistral, ASML, and other European companies in the future is perhaps surprisingly the EU itself, which is less constrained in funding big-budget projects than national legislatures due to its undemocratic nature: France has been a key supporter of the so-called “European Chips Act,” a proposed EU act for €42 billion in funding for semiconductors and AI, and there is no reason in principle that further future funding projects could not be pursued.52

There is also no reason in principle why Mistral could not partner with a U.S. big tech company like Microsoft or Apple. Microsoft’s relationship with OpenAI has been slowly weakening in the aftermath of the attempted boardroom removal against Sam Altman. Microsoft could plausibly view Mistral as an important alternative LLM provider, especially if OpenAI becomes more of a direct competitor to Microsoft and other large tech companies. Media reports that surfaced in August 2025 indicated that senior executives at Apple had seriously contemplated buying both Mistral and AI search company Perplexity, but had to date not pulled the trigger on the acquisition in part due to their perception that Mistral is not a top-tier AI lab.

In many ways such a purchase would make sense: Mistral needs money, while Apple is cash-rich but has developed no cutting-edge LLMs, or even anything close to a cutting-edge LLM, and is widely perceived as having missed the boat in AI due to an old and conservative senior executive team. Additionally, the acquisition may well have faced serious political barriers given Mistral’s perceived importance to the French tech ecosystem, and that of Europe more broadly.53 Such an outright acquisition by a U.S. let alone Chinese would negate the government’s strategic justification for backing Mistral. Fundamentally Apple’s evaluation of Mistral’s research work is likely incorrect and the unrealized acquisition a missed opportunity, despite such political considerations.

With its pool of French, European, and American research and engineering talent, Mistral has already proved it can produce leading models under challenging compute constraints. As these constraints lessen, the default projection is that Mistral’s models will become that much better and may even result in new algorithmic breakthroughs. In its current configuration, Mistral appears to be a live player with the support of multiple other live players including the French Presidency and Nvidia’s Jensen Huang, successfully building up a European software alternative to U.S. big tech companies in a cutting-edge domain, in a way that Europe has at best very rarely succeeded at before. Mistral stands a good chance not just of remaining a leading global AI company, but becoming the first European big tech company.

This Bismarck Brief is part of our upcoming AI 2026 report, featuring in-depth analysis of the state of the artificial intelligence sector and technology, together with a comprehensive profile of key players. This Brief is publicly available; the rest are available to our paid subscribers only. Last week, we investigated DeepSeek and evaluated its research capabilities and collaboration with China’s rising semiconductor industry. Subscribe now to never miss a report.

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DeepSeek is one of the world’s top artificial intelligence research laboratories and is a wholly-owned subsidiary of the Chinese company High-Flyer, a quantitative hedge fund that specializes in machine learning-derived strategies. The January 2025 release of DeepSeek’s R1 large language model (LLM) brought the company global prominence: R1 performed roughly on par with the then-leading o1 reasoning model offered by the U.S. company OpenAI, but at a much lower cost of inference. DeepSeek surprised many observers who had assumed large U.S. companies had a substantial lead in the field due to access to greater quantities of more powerful, cutting-edge computer chips, and could hire what were assumed to be better software engineers and AI researchers. Through new technical breakthroughs, this relatively small Chinese company has however also now set new standards for performance in the rapidly growing AI sector that has attracted trillions of dollars in investment worldwide.

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