Gregor Robertson, Minister of Housing and Infrastructure and Minister responsible for Pacific Economic Development Canada (PacifiCan), announced more than C$17.3 million in funding for eight British Columbia technology companies to accelerate the commercialisation and adoption of AI and quantum technologies.
Through PacifiCan, the federal government is supporting projects focused on robotics, semiconductor manufacturing, AI infrastructure, and quantum supply chains as part of a broader strategy to strengthen domestic innovation and sovereign technology capabilities.
A major share of the investment will support Human in Motion Robotics, which received CAD$3 million to commercialise its AI-powered XoMotion wearable robotic exoskeleton. The company plans to integrate AI into mobility systems, expand manufacturing, and move the technology beyond clinical environments into homes and community settings for people with spinal cord injuries and neurological conditions.
Another funded company, Dream Photonics, will receive more than CAD$1.1 million to establish pilot manufacturing for optical interconnect technologies used in AI and quantum chips. The project aims to strengthen Canada’s domestic semiconductor and quantum ecosystem while creating skilled technology jobs in British Columbia.
The announcement also highlighted the rapid expansion of British Columbia’s AI ecosystem, which now includes nearly 600 AI companies. Canadian officials linked the investments to broader efforts to secure domestic compute infrastructure, strengthen AI supply chains, and position Canada competitively in emerging technologies ahead of events such as Web Summit Vancouver.
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China has launched its fourth-generation superconducting quantum computer, marking a further step in the country’s push to scale advanced computing infrastructure. Developed by Origin Quantum, the system, named Origin Wukong-180, has begun accepting quantum computing tasks from users worldwide.
The machine is built around a 180-qubit superconducting chip and integrates fully self-developed core systems, including the chip architecture, measurement and control systems, environmental support, and operating software. According to the company, the platform represents full-stack domestic capability across the quantum computing chain.
Origin Wukong-180 builds on earlier generations of the system, following the third-generation version that has already processed tens of millions of remote accesses and hundreds of thousands of computing tasks across more than 160 countries.
The company also reports milestones such as China’s first export of quantum computing services and the establishment of the country’s first quantum chip production line.
Researchers and developers view systems like Origin Wukong-180 as part of a broader shift toward practical quantum computing applications in areas such as AI, cryptography, finance, biochemistry, and engineering design, where large-scale computational power could reshape existing technological limits.
Why does it matter?
The development signals a broader shift in global technological competition, where quantum computing is becoming a strategic layer of future digital infrastructure alongside AI and advanced semiconductor systems.
As countries race to build scalable quantum capabilities, control over this technology could influence breakthroughs in secure communications, complex simulations, and financial modelling, while also reshaping supply chains for high-performance computing.
Wider global access to such systems may accelerate scientific discovery, but it also raises questions about technological dependence, standards-setting, and long-term geopolitical balance in the digital economy.
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Norway joins a group of 14 participating countries, including the USA, Japan, the UK and India. Norwegian officials said participation could improve market access for domestic companies operating in advanced technological sectors and strengthen economic security cooperation with strategic partners.
Minister of Trade and Industry, Cecilie Myrseth, said the initiative aligns with Norway’s goal of expanding cooperation with leading countries in AI and emerging technologies. Norwegian ambassador to the USA, Anniken Huitfeldt, is expected to formally sign the agreement on behalf of the country.
The move also complements broader Norwegian and European efforts to secure access to critical technologies and supply chains. The government highlighted initiatives linked to the European Chips Act and the EU Critical Raw Materials Act as part of a wider strategy to strengthen technology resilience and industrial competitiveness.
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The Government of Canada has announced plans to spin off the National Research Council of Canada’s Canadian Photonics Fabrication Centre into a commercially operated entity to expand domestic semiconductor manufacturing and strengthen the country’s AI infrastructure.
The initiative forms part of Ottawa’s broader strategy to reinforce technological sovereignty and reduce dependence on foreign supply chains in critical technologies. Located in Ottawa, the Canadian Photonics Fabrication Centre is currently North America’s only end-to-end pure-play compound semiconductor facility and has supported photonics development for more than two decades through wafer design, fabrication, and testing services.
Minister of Industry and Minister responsible for Canada Economic Development for Quebec Regions Mélanie Joly said the spin-off is intended to attract private-sector investment, support Canadian innovation, and expand the country’s role in advanced manufacturing sectors, including defence, aerospace, automotive technologies, and AI.
The government also links the initiative to growing global demand for AI computing infrastructure, where photonic semiconductors are increasingly seen as important for improving energy efficiency, heat management, and data-transfer performance in large-scale data centres. Ottawa says the future commercial entity will remain anchored in Canada while helping domestic firms scale photonic and quantum technologies.
The expected result is a stronger Canadian supply chain for advanced semiconductor manufacturing and better support for fast-growing small and medium-sized enterprises working on AI and quantum systems. In that sense, the move is less about volume chip production and more about securing a specialised domestic capability in a strategically important part of the semiconductor stack. This final sentence is an inference based on the government’s framing of CPFC’s role and Canada’s wider AI and photonics strategy.
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DeepSeek has again placed itself at the centre of the global AI race. After drawing worldwide attention with its R1 reasoning model in early 2025, the Chinese company has recently released DeepSeek V4, a new model designed to compete not only on performance, but also on price, openness and efficiency.
The hype around DeepSeek V4 is not based on a single feature. The model comes with a 1 million-token context window, open weights, two versions for different use cases and a strong focus on agentic workflows such as coding, research, document analysis and long-running tasks. In a market still dominated by expensive closed models, DeepSeek is trying to prove that powerful AI does not need to remain locked behind trademarked systems.
A model built for long memory
The most immediate difference between DeepSeek V4 and other models is context length. Both DeepSeek-V4-Pro and DeepSeek-V4-Flash support a 1-million-token context window, meaning they can process inputs far longer than those of older generations of mainstream models. According to DeepSeek’s official release, one million tokens is now the default across all official DeepSeek services.
For ordinary users, that may sound technical. In practice, it matters because a longer context allows models to work with large documents, long conversations, full codebases, legal materials, research archives or complex project histories without losing track as quickly.
That is why DeepSeek V4 is not just another chatbot release. It is aimed at the next stage of AI use, where models are expected to act less like question-answering tools and more like assistants that can follow long processes over time.
Two models for two different needs
DeepSeek V4 comes in two main versions. DeepSeek-V4-Pro is a larger and more capable model, with 1.6 trillion total parameters and 49 billion active parameters. DeepSeek-V4-Flash is a smaller model, with 284 billion total parameters and 13 billion active parameters, designed for faster and more cost-effective workloads.
That distinction is important. Not every user needs the strongest model for every task. A company summarising documents, routing queries or running basic support may choose Flash. A developer working on complex coding tasks, long-context agents or advanced reasoning may prefer Pro.
DeepSeek’s release reflects a broader trend in AI. The best model is no longer always the biggest one. Cost, speed, context size and deployment flexibility are now as important as raw benchmark performance.
Why the price matters
One reason DeepSeek attracts so much attention is its aggressive pricing. DeepSeek’s API page lists V4-Flash at USD 0.14 per 1 million input tokens on a cache miss and USD 0.28 per 1 million output tokens. V4-Pro is listed at USD 1.74 per 1 million input tokens and USD 3.48 per 1 million output tokens before the temporary 75% discount.
For developers and companies, that changes the calculation. High-performing AI models are useful only if they can be deployed at scale. If every long document, coding session or agentic workflow becomes too expensive, adoption slows down.
DeepSeek’s challenge to the market is therefore not only technical. It is economic. The company is pushing the idea that frontier-level AI should be cheaper to run, easier to access and less dependent on closed ecosystems.
The architecture behind the hype
DeepSeek V4 uses a mixture-of-experts approach, meaning only part of the model is active during each response. That helps explain why the model can be very large on paper, yet still more efficient to run than a dense model of similar overall size.
The more interesting part is how DeepSeek handles long context. NVIDIA’s technical overview explains that DeepSeek V4 uses hybrid attention, combining compression and selective attention techniques to reduce the cost of processing very long prompts. NVIDIA says these changes are designed to cut per-token inference FLOPs by 73% and reduce KV cache memory burden by 90% compared with DeepSeek-V3.2.
For a non-technical audience, the point is simple. DeepSeek V4 is trying to solve one of the biggest problems in modern AI: how to make models remember and process much more information without becoming too slow or too expensive.
That is where much of the hype comes from. The model is not merely larger. It is designed around the economics of long-context AI.
Why NVIDIA is still in the picture
NVIDIA’s role in the DeepSeek V4 story is especially interesting. DeepSeek is often discussed as part of China’s effort to build a more independent AI ecosystem, but NVIDIA has also been quick to move forward to support developers who want to build with the model.
In its technical blog, NVIDIA describes DeepSeek V4 as a model family designed for efficient inference of million-token contexts. The company says DeepSeek-V4-Pro and V4-Flash are available through NVIDIA GPU-accelerated endpoints, while developers can also use NVIDIA Blackwell, NIM containers, SGLang and vLLM deployment options.
NVIDIA also reports that early tests of DeepSeek-V4-Pro on the GB200 NVL72 platform showed more than 150 tokens per second per user. That matters because long-context models place heavy memory pressure, as well as on compute and networking infrastructure. The model may be efficient by design, but serving it at scale still requires serious hardware.
So, DeepSeek V4 does not remove NVIDIA from the story – it complicates it. The model is part of a broader push towards more efficient AI, but the infrastructure race remains central.
The chip question behind the model
DeepSeek V4 also arrives at a time when AI infrastructure is becoming just as important as model performance. MIT Technology Review frames the release partly through that lens, noting that DeepSeek’s new model reflects China’s broader attempt to reduce reliance on foreign AI hardware and build a more self-sufficient technology stack.
That detail matters because the AI race is no longer only about who builds the most capable model. It is also about who controls the chips, software frameworks and data centres needed to run it.
Replacing NVIDIA, however, remains difficult. Its advantage lies not just in its chips, but also in the software ecosystem developers have built around its platforms over many years. Moving to alternative hardware means adapting code, rebuilding tools and proving that the new systems are stable enough for serious use.
DeepSeek V4, however, sits between two realities. It points towards China’s ambition to build a more independent AI stack, while NVIDIA’s rapid support for the model shows that frontier AI still depends heavily on established infrastructure.
Open weights as a strategic move
DeepSeek V4 is also important because the model weights are available through Hugging Face under the MIT License. That gives developers more freedom to inspect, adapt and deploy the model than they would have with a fully closed commercial system.
Open-weight models are becoming a major pressure point in the AI race. Closed models may still lead in some areas, especially in polished consumer products, enterprise support and safety layers. However, open models offer something different: flexibility.
For universities, start-ups, smaller companies and developers outside the largest AI ecosystems, that flexibility matters. It means advanced AI can be tested, modified and integrated without relying entirely on a handful of dominant providers.
Benchmarks need caution
DeepSeek presents V4-Pro as highly competitive across reasoning, coding, long-context and agentic benchmarks. Hugging Face lists results including 80.6 on SWE-bench Verified, 90.1 on GPQA Diamond and 87.5 on MMLU-Pro for DeepSeek-V4-Pro.
Those numbers are impressive, but they should not be treated as the full story. Benchmarks are useful, but they rarely capture every real-world use case. A model can score well on coding tests and still struggle with reliability, factual accuracy, safety or complex multi-step workflows in production.
That caution is important. The AI industry often turns benchmarks into headlines, while real performance depends on deployment, prompting, safety controls and the specific task at hand.
More than just another model release
DeepSeek V4 matters because it combines several trends into one release: long context, lower prices, open weights, agentic workflows and geopolitical competition. It also shows that the AI race is no longer fought only in labs, benchmarks and data centres. Visibility now matters too. Tools such as Diplo’s Digital Footprints show how digital presence shapes the way technology actors and media narratives are discovered, ranked and understood. At this stage, the competition is not only about who has the smartest model. It is also about who can make intelligence cheaper, more available and easier to deploy.
That does not mean DeepSeek has solved every problem. Questions remain around independent benchmarking, safety, data governance, infrastructure and the broader political context of Chinese AI development. Still, the release does show where the market is heading.
The next phase of AI may not be defined solely by the most powerful model. It may be defined by the model that is powerful enough, affordable enough and open enough to change how people build products, services and tools with AI.
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Britain is moving to strengthen its position in the global AI race, with Technology Secretary Liz Kendall calling for greater national control over key parts of the AI stack. In a recent speech, she described artificial intelligence as an increasingly important source of economic strength, security, and geopolitical influence.
Concerns centre on the concentration of power in a small number of companies that control much of the world’s advanced AI computing capacity. The government’s strategy is intended to reduce reliance on external providers while building domestic capabilities across areas such as research, infrastructure, compute, and talent.
Plans include the development of a national AI hardware strategy to improve access to chips and other critical technologies. At the same time, Britain says it will focus on sectors where it believes it holds a competitive edge, while continuing to work with allies on standards, governance, and the international rules shaping AI development.
Officials have stressed that AI sovereignty does not mean technological isolation, but stronger strategic resilience and greater influence over how future systems are built and governed. In that context, support for domestic firms and institutions is being framed as essential if Britain is to remain a serious player in the emerging global AI order.
Why does it matter?
Control over AI infrastructure is quickly becoming a core element of national power, comparable to energy or defence capabilities.
Concentration of computing and advanced chips in a few global players creates strategic vulnerabilities, exposing countries to external decisions that can affect economic stability, security and technological development.
Britain’s push for AI sovereignty reflects a broader global trend towards technological self-determination. Efforts to build domestic capacity and shape international standards could influence global AI governance, access to critical technologies, and reshape alliances in a more fragmented digital order.
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The UK government has identified six frontier technologies as central to strengthening digital capability, economic growth, and long-term competitiveness.
Outlined in the 2025 Modern Industrial Strategy and Digital and Technologies Sector Plan, the approach prioritises AI, cybersecurity, advanced connectivity, engineering biology, quantum technologies, and semiconductors as pillars of national resilience and technological sovereignty.
Advanced connectivity and AI remain core drivers of digital transformation. Investment in next-generation telecoms, including 5G and future 6G development, is supported through public funding and infrastructure initiatives, while AI continues to expand rapidly through commitments to compute capacity, national supercomputing infrastructure, and workforce development. The strategy positions the UK as aiming to strengthen its role as a leading European AI hub.
Cybersecurity, engineering biology, and quantum technologies reflect a broader strategy linking innovation with security, resilience, and sustainability. Government-backed programmes are intended to support commercialisation, strengthen secure-by-design systems, and accelerate growth in emerging areas such as bio-based manufacturing. Quantum technologies are also being positioned for longer-term use across sectors, including healthcare, defence, and finance.
Semiconductors complete the strategy as a foundational technology underpinning modern digital systems. Rather than focusing on large-scale manufacturing, the UK is prioritising areas such as design, photonics, compound semiconductors, and specialised materials, backed by targeted funding and institutional support.
Across all six areas, the strategy reflects a wider effort to align innovation policy with economic security, global competitiveness, and more resilient supply chains.
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The Ministry of Energy of Romania and the Ministry of Finance of Romania have launched an expression of interest process to select a consortium leader for the Black Sea AI Gigafactory project. The announcement marks a new step in developing large-scale AI infrastructure.
According to the Ministry of Energy of Romania, the selected leader will be responsible for structuring, developing and implementing the project. The process aims to identify partners with strong financial capacity and relevant technical expertise.
The project is described as a strategic initiative to build an advanced AI computing infrastructure, supporting digital and industrial capabilities while strengthening integration within the European AI ecosystem.
This project will lead to the development of digital infrastructure, such as data centres, cloud facilities, semiconductor manufacturing campuses with high-availability/power utility systems, large-scale telecom facilities, or other comparable power-and cooling-intensive facilities integrating critical digital systems.
Authorities state that the initiative is intended to position the Black Sea region as a key location for next generation AI infrastructure and to expand technological capacity in Romania.
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The European Commission has approved a €211 million Italian State aid measure to support the development of photonic chips based on graphene technology.
A funding will be provided to the Italian SME CamGraPhIC, with project activities taking place in Pisa and Bergamo.
Such an initiative focuses on optical transceivers that transmit data using light rather than electrons. The use of graphene instead of silicon is expected to enhance performance and energy efficiency across sectors such as telecommunications, automotive, aerospace and defence.
The Commission assessed the measure under the EU State aid rules and concluded that the funding is necessary, proportionate and aligned with research and innovation objectives. It also found that the project would not proceed without public support, demonstrating an incentive effect.
A decision that reflects broader EU efforts to strengthen semiconductor capabilities and support advanced digital technologies through targeted public investment and regulatory oversight.
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The recent state visit between South Korea and France signals a deepening of bilateral cooperation that extends beyond diplomacy into long-term technological and cultural alignment.
Agreements endorsed by President Lee Jae-myung and President Emmanuel Macron reflect a coordinated effort to strengthen shared capabilities in emerging sectors, while reinforcing institutional ties across research, education, and industry.
A central policy dimension lies in the expansion of cooperation in AI, semiconductors, and quantum technologies, areas increasingly tied to economic security and global competitiveness.
Partnerships between institutions such as KAIST and CNRS highlight a shift towards structured research integration, enabling joint innovation and knowledge transfer.
Such collaboration between South Korea and France is positioned not as an isolated scientific exchange, but as part of broader strategies to secure technological sovereignty and resilient supply chains.
Cultural and educational initiatives complement these ambitions by supporting long-term people-to-people engagement and workforce development. Expanded exchanges in creative industries and language education aim to cultivate talent pipelines that can operate across both economies.
Rather than symbolic diplomacy, these measures serve as enabling mechanisms for sustained cooperation in high-value sectors where human capital remains critical.
From a policy perspective, the agreements illustrate how economies are increasingly forming strategic partnerships to navigate global technological competition.
Instead of relying solely on domestic capacity, coordinated international frameworks are being used to manage innovation risks, diversify supply dependencies, and strengthen regulatory alignment.
The outcome will depend on implementation, yet the direction suggests a model of cooperation that blends economic, technological, and societal priorities.
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