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‘Sovereignty’ Myth-Making in the AI Race

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This piece is part of “Ideologies of Control: A Series on Tech Power and Democratic Crisis,” in collaboration with Data & Society. Read more about the series here.

NVIDIA CEO Jensen Huang delivers remarks as President Donald Trump looks on during an “Investing in America” event, Wednesday, April 30, 2025, in the Cross Hall of the White House. (Official White House photo by Joyce N. Boghosian)

In late May, US President Donald Trump made an official trip to a number of Arab Gulf States accompanied by over three dozen CEOs from US-based big technology companies that resulted in over $600 billion dollars worth of deals and celebratory proclamations by Gulf leaders, including Saudi Crown Prince Mohammed bin Salman, that their countries would now become hubs for independent, groundbreaking AI research and development in the Middle East. In what can only be described as an ironic confluence of events, G42 (the holding company for the United Arab Emirates AI strategy) was one of the partners, along with NVIDIA, at a France-sponsored event to build a European AI stack, while at the same time NVIDIA and other American tech companies were partnering with the UAE. The geopolitical era of sovereign AI is truly here.

Tech sovereignty didn’t start with AI. Initial discussions of internet sovereignty originated in China in the early naughts and 2010s. However, given the historic global dominance of US-based big technology companies, the appetite for sovereign AI — for self-sufficiency in the development of AI technologies — only began to develop in the first Trump administration’s trade war with China in 2018. Many of the chips that US technology companies relied on were manufactured in Taiwan. As China became more belligerent towards Taiwan, concerns about global AI production grew, rising out of the question of what would happen to chip supply chains in the event of an all-out conflict between Taiwan and China. During the Biden administration, increasing US chip production capacity and limiting the export of powerful GPUs to China grew to become a top national security priority. (The Trump Administration has since rescinded the framework under which these controls were put in place, but has not removed the specific restrictions limiting GPU export to China.)

This intensifying adversarial relationship between the US and China, the newer and more aggressive assertion of American AI dominance by the Trump administration, and the ripple effects of these moves across Europe and across the globe — which have manifested as a fear of being left behind in the AI race— have all heightened the way countries prioritize sovereign control of the AI stack into their AI strategies.

‘Sovereignty as a Service’ (SaaS)

Big tech companies recognize these priorities, and are themselves shaping the rhetoric of sovereign tech by, effectively, offering sovereignty as a service. This is happening at three different levels of the tech stack. Firstly, NVIDIA’s CEO has boldly declared, “Every country needs sovereign AI.” Under this imperative, the company is laying down chips and hardware infrastructure around the world, from Denmark to Thailand to New Zealand. NVIDIA describes the components comprising this global infrastructure as “AI factories,” which spin natural resources and energy into tokens of intelligence.

Secondly, cloud service providers are also getting into the SaaS game, and are offering sovereignty not just to national governments, but also private entities. Amazon Web Services, the foremost cloud service provider, offers a “AWS European Sovereign Cloud.” Microsoft Azure and Google Cloud also offer sovereign cloud to private enterprises— including “sovereign” or “sovereignty” controls to private entities, which encompass encryption and data localization.

And finally, at the model building and dataset annotation level, open-source and multi-lingual AI have also been touted as supporting digital and AI sovereignty. HuggingFace has described open-source AI as a “cornerstone of digital sovereignty,” forming the foundation for “autonomy, innovation, and trust” in nations around the world. Countries around the world are funding the development of national language models: South Korea has recently announced that it will invest $735 billion in the development of “sovereign AI” using Korean language data. Together, governments and companies alike paint advantages in the performance of multilingual AI as sovereignty wins, promoting multilingual models as bolstering economic growth, commerce, and cultural preservation.

‘Sovereignty’ for you – control for me

An expansive view of digital sovereignty is that an entity — nation-state, regional grouping, community — should control its own digital destiny. The twist with SaaS is that the “clients” are negotiating away key aspects of their sovereignty in the process.

Consider NVIDIA. What appears to be a straightforward transaction — territory, energy, and resources in exchange for the company’s chips to build out national sovereign AI infrastructure — is complicated by the company’s other business interests. The company is also in the business of providing cloud services and developing its own AI models. These arms of business are also part of its sovereign AI package deal: the company is also training Saudi Arabia’s university and government scientists to build out “physical” and “agentic” AI. Besides laying the infrastructural groundwork in India, the company is also training India’s business engineers to use the company’s AI offerings.

NVIDIA’s AI models, like its multi-lingual offerings, would benefit significantly from the cultural and language data already being transmitted through its infrastructure. Government and enterprise use of NVIDIA’s AI models through the company’s AI API and cloud opens opportunities for NVIDIA to siphon high-quality data around the world to bolster its own offerings. That the language data extracted from these countries could be used to bolster governmental and enterprise client access to high-quality multi-lingual models, like the Nemotron language models, could provide a legitimate use that justifies the company’s collection and use of that data, which could instead enrich the company’s other models.

Finally, the company’s AI models have to be trained somewhere. Governmental lock-in to NVIDIA’s infrastructure could mean that residents not only bear the costs of national AI production, but also that they bear costs of the company’s operations. Other AI companies, such as Meta, have already tried to structure data center utilities such that residents would foot the power bill. The rhetoric of “sovereign AI” — that this infrastructure is beneficial to these countries and that the countries have control over AI production — further justifies costs for residents. This leaves those dependent on its infrastructure in a position to accept an attractive myth doused in technical language and the promise of national technological leadership, which buries a reality in which they may not be sovereign over their AI infrastructure — over how and the degree to which their territory and resources are used in the production of AI for their interests or for NVIDIA’s.

Model building and data annotation: ‘Sovereign AI’ as labor and expertise extraction

By contributing their expertise to train multilingual models—seen as prime examples of sovereign AI—translators around the world are being placed in a vulnerable and uncertain position. They are annotating data for models that supplant their labor. The impacts of AI on translator roles are especially felt in Turkey, where translators have played a respected role in the country’s diplomatic history. Rather than empowering communities that speak low-resource languages, multilingual models that cover languages spoken in these communities could instead play a role in their detriment. Cohere, which focuses on multilingual models, has formed a partnership with Palantir, which supplies software infrastructure to entities like US Immigration and Customs Enforcement (ICE). Human language annotators have been told that they should aim to convert the machine-like responses of LLMs into more human-like responses. The subtle cultural and lingual nuances that aim to be captured by “sovereign” multilingual models are arguably key to the resistance of political oppression. Indeed, culturally-specific emojis and nicknames have been used to counteract censorship. Enabling surveillant entities the access to language expertise could shut down avenues for resistance and the assertion of autonomy — of sovereignty.

Finally, a number of “sovereign” multilingual models are open-sourced or built from open-source models, which have themselves been painted as supporting sovereignty. While open-source models or synthetic models can be extremely worthwhile technological efforts, highlighting only these offerings can serve to downplay and ultimately bury the ways in which these models and language data and community involvement is serving proprietary multilingual models and more targeted business interests. It is important to remain vigilant to how the rhetoric that this labor and these models are in the service of cultural preservation can serve to obfuscate less savory uses of these models, from labor supplantation to surveillance.

‘Sovereignty’ for whom?

In the 19th-century, European powers deployed build-operate-transfer schemes, or BOTs, as a tool of colonial expansion. In these schemes, private, metropolitan companies provided the capital, knowledge, and resources to construct key pieces of infrastructure — railroads, ports, canals, roads, telegraph lines, etc. — either in formal colonies, like the British in India, or in places where their government was trying to expand power and influence, like the Germans in Anatolia, the heart of the Ottoman Empire, on the eve of World War I.

Sovereignty as a service represents a modern incarnation of this colonial mode. This rhetoric is part of a whole new political economy of global politics where traditional institutional sites of power are preserved as facades but hollowed out to create commodities that are accessed by subscription from what was formerly collective property, as Laleh Khalili has written in a recent London Review of Books essay on defense contractors. In contrast to two decades ago, when the US Department of Defense would have owned the software they operated and likely developed themselves, now they run corporate software, like products from Palantir, that they pay a regular subscription fee to access (and were sued to be forced into using). This kind of subscription model enables continuous rent extraction and the ability of the corporations not only to update or fix the software remotely, but also to turn it off at the source when the governments or institutions beholden to it don’t act according to the corporation’s wishes. If we take seriously the problematic metaphor of an AI arms race, or of a “war” to control the 21st century, then tech companies, with their SaaS offerings, are acting as arms dealers, encouraging the illusion of a race for sovereign control while being the true powers behind the scenes.



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Yum China Goes High-Tech: KFC and Pizza Hut Boost Efficiency with AI!

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July 7, 2025

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AI dishes up savings and smiles at KFC and Pizza Hut

Last updated:

Edited By

Mackenzie Ferguson

AI Tools Researcher & Implementation Consultant

Yum China, the operator of popular fast-food franchises like KFC and Pizza Hut, is diving into the AI world to enhance efficiency and profitability. The company is leveraging AI technology to optimize everything from supply chain processes to in-store operations. As a result, customers can expect faster service and more personalized experiences. This tech rollout represents a significant move towards incorporating cutting-edge technology into everyday business operations.

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Background and Context

Yum China, the operator of well-known fast-food chains such as KFC and Pizza Hut, is leveraging artificial intelligence to enhance efficiency and drive profitability in its operations. By incorporating AI technologies, Yum China aims to streamline processes and optimise various aspects of its business strategies. This move not only highlights the company’s commitment to innovation but also its adaptability in an ever-evolving business landscape. For more details on this initiative, you can check the original source here.

In a rapidly changing market, such technological advancements are indispensable for businesses aiming to stay competitive. Yum China’s integration of AI is a strategic move to not only increase operational efficiency but also enhance customer experience, allowing the company to better respond to consumer needs and preferences. This adoption of AI showcases a growing trend among major corporations to harness technology for maintaining relevance and achieving business goals in a digital age.

The initiative by Yum China to embrace AI technologies is also reflective of the broader shift within the restaurant industry towards automation and data-driven decision-making. As companies look to streamline operations and improve margins, artificial intelligence offers a pathway to achieve these objectives. This transformation is crucial for building resilience against market fluctuations and for ensuring long-term sustainability of business models.

Summary of the Article

Yum China, the operator of fast-food chains KFC and Pizza Hut, is increasingly integrating artificial intelligence (AI) into its operations as part of a strategy to enhance efficiency and profitability. The adoption of AI technologies by Yum China is a significant move in the restaurant industry, aiming to streamline processes and improve customer service dynamics. By leveraging AI, the company can not only predict customer preferences more accurately but also manage supply chains more effectively, ensure food quality, and potentially increase sales figures. This strategic embrace of AI underscores Yum China’s commitment to staying ahead in a competitive market landscape where technological adaptation is crucial for business success.

Related Events

The recent initiatives undertaken by Yum China, the operator of KFC and Pizza Hut, in embracing AI technologies have sparked a series of related events across the business landscape in China. As highlighted in their recent strategies, the integration of AI is not merely about enhancing operational efficiency but also about revolutionizing customer experience. This shift is setting a precedent for other major players in the fast-food industry, encouraging them to explore similar technological advancements.

In response to Yum China’s adoption of AI, various technology firms in China are collaborating with fast-food chains to offer AI solutions tailored to the food and beverage sector. This burgeoning collaboration marks a significant trend in tech-driven partnerships aimed at bringing innovation to everyday consumer experiences. Such alliances are fostering a new era where technology and gastronomy intersect to redefine dining experiences.

Furthermore, this movement is influencing policy discussions at a governmental level, where the focus is increasingly on supporting AI development across different industries. The Chinese government’s enthusiasm for AI as a tool for modernization and efficiency is further emphasized by such corporate moves, thereby reinforcing national goals for technological advancement and self-reliance.

The ripple effects of Yum China’s AI integration are also evident in academic circles, where institutions are emphasizing AI research geared towards practical applications in commercial settings. This academic interest not only fuels future innovations but also ensures a steady supply of skilled professionals ready to meet the demands of a tech-driven economy. In essence, Yum China’s AI strategies are not just operational choices but are contributing to wider societal and economic shifts.

Expert Opinions

In the rapidly evolving landscape of the restaurant industry, particularly in China, expert opinions highlight significant opportunities for leveraging technology to enhance operational efficiency and profitability. Yum China, the operator behind fast-food giants KFC and Pizza Hut, is at the forefront of this transformation. As noted by industry analysts, the company’s strategic integration of AI solutions not only streamlines operations but also personalizes customer experiences. This move is seen as a response to the competitive market pressures and a shift towards more digital-savvy consumer preferences.

Public Reactions

The integration of AI by Yum China, the operator of KFC and Pizza Hut in China, has sparked varied public reactions. Many customers have expressed excitement about the increased efficiency and improved service that AI can bring to their dining experience. Some diners appreciate the novelty and technological advancement, which they believe could streamline operations and enhance their overall experience at these popular food chains.

However, not all reactions have been positive. Some consumers have voiced concerns about privacy and data security, as AI systems often require extensive data collection to function effectively. These customers are wary of how their information might be used or shared and are calling for clearer policies and assurances from Yum China regarding data protection.

Moreover, there is a segment of the public that is apprehensive about the potential impact of AI on employment. With AI taking on tasks traditionally handled by human workers, concerns about job displacement have arisen, leading to discussions on how Yum China plans to balance technology integration with human resource management. This sentiment is shared by many globally, reflecting a broader anxiety about the rise of automation in various industries.

Overall, while the use of AI in Yum China’s operations presents exciting opportunities for innovation and growth, it also highlights significant issues that resonate with a global audience. For an in-depth look at Yum China’s AI strategy and public reaction, the South China Morning Post provides more insights here.

Future Implications

The integration of artificial intelligence (AI) into business operations is increasingly transforming industries across the globe. Yum China, the operator of fast-food giants like KFC and Pizza Hut, is a prime example of this trend. By leveraging AI to streamline their processes, they are setting a precedent for other companies to follow. This move is expected to significantly enhance their operational efficiency and profitability, as highlighted in a detailed article by the South China Morning Post.

Looking ahead, the adoption of AI by Yum China could have broader implications for the fast-food industry both in China and globally. As other companies observe Yum China’s successful integration of AI technologies, there may be a ripple effect, prompting more industry players to invest in AI solutions to remain competitive. This could lead to a revolution in customer service, supply chain management, and even menu personalization, driven by AI-driven insights.

Moreover, the shift towards AI can potentially reshape employment dynamics within the sector. While automation may reduce certain manual roles, it also opens up new opportunities for tech-savvy professionals who can develop, manage, and optimize these AI systems. This transformation necessitates a recalibration of workforce skills and continued education for employees to adapt to a tech-driven environment, as noted in discussions surrounding similar advancements.



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Hangzhou: China’s Emerging AI Powerhouse

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July 7, 2025

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Hangzhou, the picturesque capital of Zhejiang Province, is quickly emerging as a key pillar in China’s artificial intelligence (AI) revolution. Once known primarily for its cultural heritage and as the headquarters of e-commerce giant Alibaba, the city is now transforming into a powerful AI hub, driven by visionary government policies, a dynamic startup ecosystem, cutting-edge academic institutions, and high levels of private and public investment. Its rapid evolution exemplifies China’s broader strategy to lead the global race in artificial intelligence.

Government Initiatives and Strategic Policy Support

A major driver behind Hangzhou’s AI rise is the strong backing of the Chinese government, both at national and provincial levels. The “Hangzhou AI Industry Chain High-Quality Development Action Plan” has set bold objectives: certifying more than 2,000 new high-tech enterprises, launching over 300 large-scale technological projects, and injecting an impressive 300 billion RMB (approx. US$40 billion) into innovation annually. This funding supports AI research, development of cutting-edge applications, infrastructure, and talent cultivation.

Further cementing Hangzhou’s AI ambitions is the revitalization of “Project Eagle,” a policy initiative that allocates 15% of industrial development funds to future industries, with AI being a priority. These initiatives are not only helping to establish Hangzhou as a hub of AI innovation but are also attracting domestic and international investors eager to tap into this growth.

The Rise of the “Six Little Dragons”

One of the most notable signs of Hangzhou’s AI success story is the emergence of six pioneering startups, collectively referred to as the “Six Little Dragons.” These companies represent the city’s growing diversity and sophistication in AI application:

DeepSeek – Known for its work in natural language processing and large language models.

Game Science – A game development firm leveraging AI in next-gen interactive experiences.

Unitree Robotics – Specializes in agile AI-powered robots for various industrial and consumer applications.

DEEP Robotics – Develops quadruped robots capable of complex navigation and movement, often used for security and research.

BrainCo – Focuses on brain-computer interface (BCI) technologies that merge neuroscience and machine learning.

Manycore Tech – A hardware and software AI solutions provider with strengths in chip design and high-performance computing.

These companies are not only rapidly scaling within China but are also attracting international attention for their technological advancements and commercialization potential. Their presence underscores Hangzhou’s strength in fostering both technical excellence and business scalability.

Academic Foundations and Skilled Talent Pipeline

Hangzhou’s AI ecosystem is further bolstered by a solid academic foundation. Zhejiang University, one of China’s top-tier institutions, plays a critical role in producing AI talent and thought leadership. The university houses cutting-edge research labs and has established partnerships with top tech firms for collaborative innovation.

Graduates from Zhejiang University and other local institutions often go on to found startups or take leadership roles in the AI industry. The close connection between academia and industry ensures a continuous exchange of ideas, innovation, and expertise, which is essential for sustained growth in emerging technologies like AI.

In addition, Hangzhou has invested in AI-focused education and vocational training programs to ensure that its workforce remains competitive. This comprehensive talent strategy allows the city to meet the growing demand for data scientists, machine learning engineers, and AI researchers.

Industry Collaboration and Corporate Investments

Beyond startups and academia, major corporate players are betting big on Hangzhou’s AI future. Most notably, Alibaba, headquartered in the city, has been at the forefront of this transformation. Under the leadership of Eddie Wu, the company has pledged to deepen its involvement in generative AI and has launched internal initiatives aimed at developing new AI products and services.

In parallel, Alibaba has worked to attract foreign capital to Hangzhou’s AI sector, especially in connection with the Six Little Dragons. Following Jack Ma’s involvement in a high-level business symposium with President Xi Jinping, Alibaba’s influence in shaping Hangzhou’s AI roadmap has only increased.

Other corporations and venture capital firms are also taking notice. Investment funds are flowing into AI development zones, incubators, and innovation labs across Hangzhou, helping to establish a robust support system for tech entrepreneurship and research.

Infrastructure, Challenges, and Long-Term Outlook

Despite these promising developments, Hangzhou faces several challenges that come with rapid growth. Talent retention remains a concern, as other Chinese cities like Beijing and Shenzhen compete for the same AI professionals. Furthermore, as AI technology demands powerful computing infrastructure, continued upgrades in data centers, power grids, and 5G connectivity are essential.

Additionally, navigating regulatory uncertainty and ensuring responsible AI development will be key for Hangzhou to maintain sustainable growth. The city must also remain agile in adapting to global shifts, including trade policies, technology standards, and geopolitical tensions that may impact international partnerships and supply chains.

Nonetheless, the city’s proactive governance, talent pool, and innovative momentum offer strong indicators that Hangzhou is well-positioned to become a global AI innovation hub. As China continues to push its national AI ambitions, Hangzhou stands out as a leading example of how a regional city can emerge as a technological powerhouse through visionary planning, strong public-private partnerships, and relentless innovation.



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AI is forcing the data industry to consolidate — but that’s not the whole story

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July 7, 2025

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The data industry is on the verge of a drastic transformation.

The market is consolidating. And if the deal flow in the past two months is any indicator — with Databricks buying Neon for $1 billion and Salesforce snapping up cloud management firm Informatica for $8 billion — momentum is building for more.

The acquired companies may range in size, age, and focus area within the data stack, but they all have one thing in common. These companies are being bought in hopes the acquired technology will be the missing piece needed to get enterprises to adopt AI.

On the surface level, this strategy makes sense.

The success of AI companies, and AI applications, is determined by access to quality underlying data. Without it, there simply isn’t value — a belief shared by enterprise VCs. In a TechCrunch survey conducted in December 2024, enterprise VCs said data quality was a key factor to make AI startups stand out and succeed. And while some of these companies involved in these deals aren’t startups, the sentiment still stands.

Gaurav Dhillon, the former co-founder and CEO of Informatica, and current chairman and CEO at data integration company SnapLogic, echoed this in a recent interview with TechCrunch.

“There is a complete reset in how data is managed and flows around the enterprise,” Dhillon said. “If people want to seize the AI imperative, they have to redo their data platforms in a very big way. And this is where I believe you’re seeing all these data acquisitions, because this is the foundation to have a sound AI strategy.”

But is this strategy of snapping up companies built before a post-ChatGPT world the way to increase enterprise AI adoption in today’s rapidly innovating market? That’s unclear. Dhillon has doubts too.

“Nobody was born in AI; that’s only three years old,” Dhillon said, referring to the current post-ChatGPT AI market. “For a larger company, to provide AI innovations to re-imagine the enterprise, the agentic enterprise in particular, it’s going to need a lot of retooling to make it happen.”

Fragmented data landscape

The data industry has grown into a sprawling and fragmented web over the past decade — which makes it ripe for consolidation. All it needed was a catalyst. From 2020 through 2024 alone, more than $300 billion was invested into data startups across more than 24,000 deals, according to PitchBook data.

The data industry wasn’t immune to the trends seen in other industries like SaaS where the venture swell of the last decade resulted in numerous startups getting funded by venture capitalists that only targeted one specific area or were in some cases built around a single feature.

The current industry standard of bundling together a bunch of different data management solutions, each with its own specific focus, doesn’t work when you want AI to crawl around your data to find answers or build applications.

It makes sense that larger companies are looking to snap up startups that can plug into and fill existing gaps in their data stack. A perfect example of this trend is Fivetran’s recent acquisition of Census in May — which yes, was done in the name of AI.

Fivetran helps companies move their data from a variety of sources into cloud databases. For the first 13 years of its business, it didn’t allow customers to move this data back out of said databases, which is exactly what Census offers. This means prior to this acquisition, Fivetran customers needed to work with a second company to create an end-to-end solution.

To be clear, this isn’t meant to cast shade on Fivetran. At the time of the deal, George Fraser, the co-founder and CEO of Fivetran, told TechCrunch that while moving data in and out of these warehouses seems like two sides of the same coin, it’s not that simple; the company even tried and abandoned an in-house solution to this problem.

“Technically speaking, if you look at the code underneath [these] services, they’re actually pretty different,” Fraser said at the time. “You have to solve a pretty different set of problems in order to do this.”

This situation helps illustrate how the data market has transformed in the last decade. For Sanjeev Mohan, a former Gartner analyst who now runs SanjMo, his own data trend advisory firm, these types of scenarios are a big driver of the current wave of consolidation.

“This consolidation is being driven by customers being fed up with a multitude of products that are incompatible,” Mohan said. “We live in a very interesting world where there are a lot of different data storage solutions, you can do open source, they can go to Kafka, but the one area where we have failed is metadata. Dozens of these products are capturing some metadata but to do their job, it’s an overlap.”

Good for startups

The broader market plays a role here too, Mohan said. Data startups are struggling to raise capital, Mohan said, and an exit is better than having to wind down or load up on debt. For the acquirers, adding features gives them better pricing leverage and an edge against their peers.

“If Salesforce or Google isn’t acquiring these companies, then their competitors likely are,” Derek Hernandez, a senior emerging tech analyst at PitchBook, told TechCrunch. “The best solutions are being acquired currently. Even if you have an award-winning solution, I don’t know that the outlook for staying private ultimately wins over going to a larger [acquirer].”

This trend brings big benefits to the startups getting acquired. The venture market is starving for exits and the current quiet period for IPOs doesn’t leave them a lot of opportunities. Getting acquired not only provides that exit, but in many cases gives these founding teams room to keep building.

Mohan agreed and added that many data startups are feeling the pains of the current market regarding exits and the slow recovery of venture funding.

“At this point in time, acquisition has been a much more favorable exit strategy for them,” Hernandez said. “So I think, kind of both sides are very incentivized to get to the finish line on these. And I think Informatica is a good example of that, where even with a bit of a haircut from where Salesforce was talking to them last year, it’s still, you know, was the best solution, according to their board.”

What happens next

But the doubt still remains if this acquisition strategy will achieve the buyers’ goals.

As Dhillon pointed out, the database companies being acquired weren’t necessarily built to easily work with the rapidly-changing AI market. Plus, if the company with the best data wins the AI world, will it make sense for data and AI companies to be separate entities?

“I think a lot of the value is in merging the major AI players with the data management companies,” Hernandez said. “I don’t know that a standalone data management company is particularly incentivized to remain so and, kind of like, play a third party between enterprises and AI solutions.”



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