According to government figures, 5G services are now available in all states and union territories and in 99.9% of the country’s districts.
The number of 5G base transceiver stations increased from 140,623 in March 2023 to 562,971 by the end of June 2026.
Rural connectivity is being supported through BharatNet, which provides broadband infrastructure to village-level local government bodies and rural communities, and the 4G Saturation scheme for previously uncovered villages.
Around 24,000 mobile towers had been commissioned by June 2026 through projects funded by Digital Bharat Nidhi and other mobile connectivity programmes.
India is also continuing longer-term work on next-generation networks under its Bharat 6G Vision, which aims to support the domestic design and development of secure and intelligent 6G technologies.
By June 2026, the government had approved 136 research and development projects under the Telecom Technology Development Fund, with nearly ₹543 crore in sanctioned funding.
The projects cover areas including 6G, quantum communications, satellite and non-terrestrial networks, optical systems, an indigenous 5G core and telecom security.
A separate production-linked incentive scheme, introduced in 2021 with an outlay of ₹12,195 crore, supports domestic manufacturing of telecom and networking equipment.
Why does it matter?
India’s update links immediate connectivity goals with longer-term industrial and research policy. Rural broadband and wider mobile coverage can reduce access gaps. At the same time, investment in 6G, satellite networks, telecom security and domestic manufacturing could strengthen technological capacity and reduce dependence on imported equipment. However, district-level availability alone does not show whether users receive affordable, reliable and consistently high-quality services.
Would you like to learn more about AI, tech and digital diplomacy? If so, ask our Diplo chatbot!
The White House has announced more than US$5 billion in new federal commitments to expand the Genesis Mission, positioning AI as shared national infrastructure for scientific research across the US government.
Originally launched through an executive order in November 2025, the Genesis Mission has evolved from a Department of Energy initiative into a whole-of-government programme involving more than 15 federal agencies.
Participating agencies will contribute funding, research awards, scientific datasets and research infrastructure through the Department of Energy’s American Science and Security Platform, which provides shared access to data, computing resources and AI tools.
Alongside the funding announcement, the administration unveiled a series of National Science and Technology Challenges covering healthcare, energy, infrastructure, manufacturing, semiconductors, quantum technologies, biology, agriculture, space and national security.
Planned projects include AI-enabled biomedical research, digital twins for infrastructure, electricity grid optimisation, autonomous laboratories, materials science and semiconductor innovation.
The initiative also supports industrial competitiveness and national security through projects in AI-assisted microelectronics design, biological manufacturing, advanced materials, nuclear forensics, biological threat detection and the AI-supported design of conventional and nuclear weapon components.
According to the White House, the Genesis Mission is intended to establish a shared research model that enables federal agencies to collaborate on multidisciplinary scientific challenges using common AI infrastructure.
The administration said future phases will expand the initiative through partnerships with industry, philanthropic organisations and international collaborators.
Why does it matter?
The Genesis Mission reflects a growing shift in AI policy from supporting individual research projects to building shared national research infrastructure. By combining computing resources, scientific datasets and coordinated federal investment, the programme aims to make AI a foundational capability across multiple scientific disciplines.
Its breadth, spanning healthcare, energy, infrastructure, manufacturing, space and national security, also illustrates how AI is becoming a strategic asset for scientific competitiveness. Similar approaches are increasingly emerging in other major economies, where AI infrastructure is viewed as essential to long-term research capacity and economic resilience.
Would you like to learn more about AI, tech and digital diplomacy? If so, ask our Diplo chatbot!
Spain is strengthening its digital sovereignty strategy through the new investments in cyber-secure semiconductor design and trusted data infrastructure, with construction of Quantix’s secure chip design centre in Murcia set to begin this autumn.
Backed by a €19.6 million investment from the Spanish Society for Technological Transformation (SETT), the facility will strengthen Spain’s capabilities in secure chip design, cybersecurity and post-quantum cryptography while supporting both national and European digital sovereignty.
The Quantix centre is expected to create more than 450 highly skilled jobs and will design, validate and commercialise secure microcontrollers and semiconductor components for sectors including automotive, critical infrastructure and the Internet of Things.
It will also incorporate chip encapsulation and testing capabilities, helping reduce dependence on external suppliers and strengthening Europe’s semiconductor value chain.
During his visit to Murcia, Spain’s Minister for Digital Transformation, Óscar López Agueda, also highlighted the NEREIDAS project, a government-funded initiative developing a secure marine data-sharing ecosystem for the Mar Menor.
Supported by €1.04 million through Spain’s Sectoral Data Spaces Programme, the platform combines oceanographic, environmental, meteorological and satellite data to support applications including a digital twin of the Mar Menor and real-time environmental monitoring.
The platform currently includes 84 active datasets shared by 74 organisations, with around 90 more seeking to join. The government described NEREIDAS as one of Spain’s leading sectoral data spaces and part of a broader €400 million national strategy to accelerate the data economy across strategic sectors using EU recovery funding.
Why does it matter?
The announcements illustrate how digital sovereignty is increasingly being built through both physical and data infrastructure. Alongside investment in semiconductor manufacturing and secure chip design, governments are developing trusted data ecosystems that support AI, cybersecurity and digital public services.
Under the collaboration, Microagi will use Google[object Object],[object Object] Cloud’s AI infrastructure and NVIDIA Blackwell systems to scale model training and inference.
The Munich-based company develops software for adapting robotics models to specific commercial and industrial tasks rather than manufacturing its own robots.
Its Atlas platform fine-tunes models using data from customers’ operations and is designed to work across different hardware and AI providers.
The infrastructure will include NVIDIA RTX Pro 6000 Blackwell Server Edition GPUs and GB300 NVL72 systems delivered through Google Cloud.
Microagi will also use Google AI models and tools for processing multimodal information, including video, as it develops configurable software packages for enterprise robotics.
The company works with manufacturers and robotics providers in sectors including automotive, logistics, food and hospitality.
The agreement follows a $55 million seed funding round that Microagi plans to use, in part, to expand its computing capacity and real-world data collection.
Google described the collaboration as part of its wider effort to support the European robotics ecosystem. At the same time, NVIDIA said advanced robotics increasingly requires large datasets, accelerated computing and integrated development platforms.
Why does it matter?
The collaboration illustrates how robotics development increasingly depends on a combination of real-world data, cloud infrastructure and specialised chips. European startups may gain faster access to the computing resources needed to train and deploy embodied AI, but reliance on US cloud and semiconductor providers also raises longer-term questions about technological sovereignty, infrastructure concentration and control over industrial AI systems.
Would you like to learn more about AI, tech, and digital diplomacy? If so, ask our chatbot!
Wistron has opened a $700 million AI manufacturing facility in Fort Worth, Texas, to produce advanced NVIDIA computing systems, strengthening domestic capacity for the AI infrastructure underpinning large-scale AI.
The 324,000-square-foot facility is Wistron’s first manufacturing facility in the United States. It currently operates one production line for NVIDIA’s GB300 Grace Blackwell Ultra Superchip, with a second line planned for the upcoming Vera Rubin platform.
Wistron expects output to reach tens of thousands of boards per month during 2026. More than 500 jobs have already been created, with the workforce expected to grow to 1,000 by the end of the year.
NVIDIA CEO Jensen Huang described domestic manufacturing as central to rebuilding US industrial capacity, noting the company’s commitment to producing up to US$500 billion worth of advanced AI platforms in the country.
The facility was designed and tested using a digital twin before construction began. Wistron employed NVIDIA’s Omniverse platform together with the Nemotron and Cosmos models, Metropolis libraries and PhysicsNeMo framework to simulate production lines, optimise factory layouts and train workers virtually, allowing engineers to validate assembly processes before physical equipment was installed.
During the opening ceremony, Wistron unveiled the first GB300 Grace Blackwell Ultra Superchip assembled at the site. According to Huang, systems based on the processor contain around 1.5 million components, weigh approximately two tonnes and cost about US$4 million.
The investment is intended to strengthen US supply chains and expand domestic capacity for producing AI infrastructure.
Why does it matter?
The new facility reflects a broader shift in AI policy from focusing primarily on software and models towards strengthening the industrial infrastructure needed to support them. As governments increasingly view AI hardware as a strategic asset, domestic manufacturing capacity is becoming an important component of economic security and technological competitiveness.
The project also illustrates how AI is transforming manufacturing itself. By using digital twins, simulation and AI models to design and optimise production before construction was completed, Wistron demonstrates how AI is reshaping factory operations while simultaneously producing the hardware that will power future AI systems.
Would you like to learn more about AI, tech, and digital diplomacy? If so, ask our Diplo chatbot!
For much of the past decade, discussions on AI governance have focused on algorithms. Policymakers, regulators, and researchers have debated ethics, transparency, bias, accountability, and human oversight, seeking to ensure that AI systems remain trustworthy and aligned with fundamental rights.
These questions remain essential, but they are no longer sufficient to explain the direction of AI policy.
A new reality is emerging. Increasingly, governments are recognising that the ability to govern AI depends not only on regulating algorithms but also on securing the infrastructure that makes them possible. Data centres, advanced semiconductors, cloud computing, electricity grids, submarine cables, digital public infrastructure, and skilled workforces have become as strategically important as the AI models themselves.
The shift has been gradual rather than abrupt, yet it is now evident across national strategies, international organisations, and multilateral discussions. AI governance is expanding beyond software governance to become infrastructure governance.
AI runs on infrastructure, not algorithms alone
The rapid adoption of generative AI has often obscured a simple reality that every AI system rests on a vast physical and institutional foundation.
Large language models require enormous computing power, specialised chips, cloud infrastructure, reliable electricity, high-speed connectivity, trusted datasets, secure digital environments, and engineers capable of developing and maintaining increasingly complex systems. None of these components can be built overnight, and few can be developed without substantial public and private investment.
Image via Magnific
As a result, AI capability is increasingly determined by access to infrastructure, not just software.
Countries may have ambitious AI strategies, talented researchers, or innovative start-ups, but without advanced computing capacity, modern data centres, resilient cloud infrastructure, and skilled human capital, those ambitions become difficult to realise. Governments are therefore beginning to treat AI infrastructure as a strategic national asset rather than simply a commercial resource.
Industrial policy is becoming a pillar of AI policy
This shift is increasingly reflected in public policy.
The European Union has complemented its landmark AI Act with broader industrial initiatives, including the European Chips Act, the AI Factories initiative under EuroHPC, and, most recently, plans to develop AI Gigafactories capable of supporting the next generation of AI models. The recently adopted Digital Omnibus on AI also strengthens the role of the European Commission’s AI Office while simplifying implementation aspects, illustrating that regulation, institutional capacity, and infrastructure investment are evolving together.
The United States has pursued a different approach, combining export controls on advanced semiconductors with large-scale investment in domestic chip manufacturing and AI infrastructure. China continues to expand its national computing centres, cloud capacity, and state-backed AI ecosystems as part of its long-term industrial strategy.
Although these approaches differ politically and economically, they increasingly share the view that governing AI requires building the infrastructure that enables it.
AI policy, in other words, is beginning to resemble industrial policy.
Infrastructure has become a development issue
The infrastructure shift is equally visible in developing countries, where the conversation is moving beyond access to AI applications towards the ability to build AI capacity locally.
Throughout the World Summit on the Information Society (WSIS) Forum 2026, discussions repeatedly highlighted that meaningful digital transformation depends on foundational infrastructure. African policymakers, researchers, and technical experts argued that the continent suffers not primarily from a shortage of digital ambition, but from limited access to computing capacity.
Speakers pointed out that Africa has only a tiny share of global AI compute and data centre capacity, forcing many researchers and innovators to rely on infrastructure located elsewhere. The challenge, they argued, is no longer simply connectivity but compute sovereignty, the ability to build, train, and deploy AI systems locally.
The same message emerged during discussions on digital sovereignty. Rather than focusing solely on access to foreign AI models, participants argued that exporting raw data while importing AI services mirrors older economic patterns in which countries exported raw materials while importing higher-value finished products. Building local AI capability, therefore, requires investment in data centres, energy systems, cloud infrastructure, skills development, and trusted data ecosystems.
These discussions suggest that AI infrastructure is becoming an increasingly important component of development policy.
Infrastructure is also becoming geopolitics
The growing strategic importance of AI infrastructure extends far beyond economic development.
Competition over semiconductor manufacturing, cloud services, critical minerals, advanced computing facilities, and energy has become a defining feature of international relations. Governments increasingly view these assets through the lens of economic security, technological resilience, and geopolitical influence.
Submarine cables illustrate this evolution particularly well. Once regarded primarily as telecommunications infrastructure, they are now recognised as essential to AI systems that depend on massive volumes of cross-border data traffic. Recent international efforts to strengthen submarine cable resilience reflect how infrastructure once considered largely invisible has become central to digital governance.
Image via Magnific
Electricity is undergoing a similar transformation. As AI models become increasingly computationally intensive, access to affordable and reliable energy is becoming a key factor determining where data centres can be built and where AI innovation can flourish. This has encouraged governments to align AI strategies with broader industrial, energy, and climate policies.
In this environment, AI governance increasingly overlaps with trade policy, investment screening, industrial strategy, critical infrastructure protection, and national security.
A broader understanding of AI governance
This evolution does not diminish the importance of ethical AI principles or risk-based regulation. Questions surrounding transparency, accountability, bias, safety, and human rights remain fundamental.
Rather, it broadens the understanding of what AI governance entails.
Traditional AI governance focuses on how AI systems should be designed, deployed, and used responsibly. Infrastructure governance raises a different set of questions, such as who has access to the computing power needed to develop advanced AI? Who controls the cloud infrastructure that underpins AI services? Where are critical datasets stored? Which countries have the physical, financial, and institutional capacity to participate meaningfully in the AI economy?
image via Magnific
These questions ultimately shape who can innovate, compete, and benefit from AI.
As a result, debates that once focused primarily on algorithms increasingly encompass semiconductors, cloud infrastructure, digital public infrastructure, data centres, energy systems, connectivity, and workforce development.
From governing AI to enabling AI
The next phase of AI governance is likely to be defined less by entirely new regulatory principles than by long-term investment decisions.
Building trustworthy AI ecosystems will require governments to develop resilient digital infrastructure, strengthen education and digital skills, encourage research, modernise energy systems, expand computing capacity, and foster international cooperation on shared digital resources.
The countries that succeed may not simply be those that write the most comprehensive AI regulations. They may instead be those that create the conditions that allow responsible AI to flourish in the first place.
Image via Magnific
The global conversation about AI has not moved beyond governance. It has moved deeper into its foundations.
In this sense, the future of AI governance may increasingly depend not only on how societies regulate AI, but also on how they build the infrastructure that makes AI possible.
Would you like to learn more about AI, tech and digital diplomacy? If so, ask our Diplo chatbot!
South Korea and Saudi Arabia have agreed to deepen cooperation on startups, AI and digital transformation following talks in Seoul between South Korean Vice Minister of the Ministry of SMEs and Startups (MSS) Roh Yong-Seok and Saudi Minister of Communications and Information Technology Abdullah Alswaha.
A central topic was a proposed joint investment fund, currently under discussion between Korea Venture Investment and Saudi Arabia’s Riyadh Valley Company, which would co-invest in deep-tech companies developing strategic technologies such as AI and semiconductors.
The Korean government said it hopes to launch the fund before the end of 2026.
The ministers also discussed accelerating the digital transformation of small and medium-sized enterprises through wider AI adoption. South Korea outlined planned legislation to encourage AI deployment among smaller businesses, while inviting greater Saudi investment in Korean companies developing AI and other frontier technologies.
The meeting also highlighted broader efforts to strengthen bilateral startup ecosystems. South Korea pointed to initiatives including its Global Business Center in Riyadh and participation in startup events such as COMEUP and Saudi Arabia’s BIBAN. Both governments agreed to encourage greater investment, business exchanges and market access for companies in both countries.
Why does it matter?
The partnership illustrates how international AI cooperation is increasingly centred on investment, startup ecosystems and industrial policy rather than research alone. By combining Saudi capital with South Korea’s technology sector, both countries aim to accelerate the commercial development of strategic technologies such as AI and semiconductors.
The discussions also reflect a broader trend towards linking AI adoption with SME competitiveness and cross-border venture investment. Such partnerships could strengthen innovation ecosystems while supporting national ambitions for digital transformation and economic diversification.
Would you like to learn more about AI, tech and digital diplomacy? If so, ask our Diplo chatbot!
Germany and France have agreed to deepen cooperation on AI, AI safety and digital sovereignty following the Franco-German Ministerial Council, reinforcing their joint role in shaping Europe’s technology agenda.
The partnership centres on closer collaboration between France’s AI safety institute, INESIA, and Germany’s newly established AI Safety and Security Institute to strengthen the evaluation and secure deployment of advanced AI models.
The two governments will coordinate AI safety research, institutional expertise and risk assessments while supporting implementation of the European AI Office’s work under the AI Act. They also reaffirmed their commitment to advancing AI safety cooperation through the EU, NATO and the United Nations, positioning it as a shared strategic priority.
Beyond AI safety, Germany and France agreed to strengthen digital sovereignty by jointly shaping the forthcoming EU Tech Sovereignty Package, building on their common definition of digital sovereignty presented at VivaTech 2026.
They also called on the European Commission to reinforce the Digital Fitness Check, deepen cooperation on public sector modernisation and promote a coordinated European spectrum policy to support secure and competitive satellite communications.
The agreement builds on earlier cooperation under the Treaty of Aachen and the Franco-German economic and technological sovereignty agenda. By strengthening collaboration on AI governance, digital infrastructure and strategic technologies, both countries aim to reinforce Europe’s technological resilience and influence over global AI governance.
Why does it matter?
The agreement reflects a broader European effort to strengthen technological sovereignty by combining AI governance, industrial policy and security cooperation. Rather than treating AI safety as a purely technical issue, Germany and France are positioning it as part of Europe’s wider strategy for digital resilience and strategic autonomy.
By coordinating national AI safety institutes while supporting EU institutions such as the AI Office, the partnership could also contribute to a more coherent European approach to evaluating advanced AI systems and shaping international AI governance.
Would you like to learn more about AI, tech and digital diplomacy? If so, ask our Diplo chatbot!
South Korea has restructured its specialised intellectual property investigation system to strengthen efforts against leaks of advanced technologies, including semiconductors and AI, amid growing concerns over economic security.
The reforms establish new investigative and analytical divisions while expanding the technology police force from 27 to 61 officers.
A new Technology Divulgence Police Division will investigate trade secret theft and the leakage of advanced technologies. Its 21 investigators will include specialists in electrical, chemical and mechanical engineering alongside patent examiners, attorneys and other technical experts.
The government also plans to expand investigative authority to cover violations involving National Core Technologies and National High-Tech Strategic Technologies.
It will also cooperate with businesses, research organisations and law enforcement agencies to detect warning signs, support intelligence-led investigations and strengthen security awareness, particularly among smaller companies.
The restructuring creates an Intellectual Property Protection Standards Division responsible for investigative procedures, oversight and human rights safeguards.
Planned reforms in South Korea include clearer rules for compulsory investigations, external review through a Criminal Investigation Review Committee, stronger access to legal counsel, wider use of video recording and regular updates for parties involved in investigations.
The restructuring also reflects a broader trend towards combining specialised technical expertise with intelligence-led enforcement and stronger procedural safeguards. This approach seeks to improve both the effectiveness and accountability of investigations involving advanced technologies.
Would you like to learn more about AI, tech and digital diplomacy? If so, ask our Diplo chatbot!
Japan is moving to build what NVIDIA describes as the world’s first national AI infrastructure for physical AI, in partnership with Noetra and supported by government and industry leaders.
The initiative is designed to strengthen Japan’s AI ecosystem across manufacturing, logistics, healthcare, telecommunications and other industrial sectors.
Noetra will establish an NVIDIA Vera Rubin AI factory using 13,750 NVIDIA Vera CPUs and 27,500 NVIDIA Rubin GPUs.
NVIDIA said the facility will deliver 140 megawatts of data-centre capacity and will be based on its DSX platform.
The AI factory will support the development of open multimodal foundation models for AI agents, digital twins, robotics and other physical AI applications.
It will provide the computing foundation for Japan’s FRONTia Project, launched by the Ministry of Economy, Trade and Industry.
The project aims to combine Japan’s manufacturing expertise, real-world industrial data and international technology partnerships to develop reliable multimodal foundation models for robotics and physical AI.
Pretrained weights from Noetra’s multimodal foundation models will be made available to domestic model developers and enterprises, alongside NVIDIA software tools for agentic AI, robotics and model development.
NVIDIA said the infrastructure will support Japan’s wider AI robotics strategy, which aims to capture more than 30% of the global AI robotics market by 2040.
As the AI factory expands, it is expected to support trillion-parameter-scale model training and give organisations in Japan access to advanced AI computing capacity.
Why does it matter?
The initiative shows how national AI strategies are increasingly tied to compute infrastructure, industrial data and sector-specific foundation models. For Japan, physical AI connects AI policy with manufacturing, robotics, logistics and healthcare, areas where domestic expertise and trusted infrastructure could become strategic advantages. The project also highlights the growing role of private chip and platform providers in national AI capacity, raising longer-term questions about sovereignty, dependency and who controls the infrastructure behind industrial AI.
