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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The United Nations System Staff College has highlighted growing interest across the UN and the wider peacebuilding community in how artificial intelligence is shaping conflict prevention, arguing that the technology can support peace efforts but cannot replace human judgement, diplomacy, and oversight.
The reflection draws on a three-part webinar series launched by UNSSC to examine AI governance, field use, and ethical risks in peacebuilding. According to the text, one message ran across all three discussions: AI may offer real value for conflict prevention, but its role should remain supportive rather than substitutive.
The piece argues that AI is already being used across the UN peace and security pillar and should be introduced only where it improves effectiveness, such as by handling repetitive tasks and allowing staff to focus on analysis, leadership, and political judgement. It also stresses that principles long associated with peacebuilding, including trust and ‘do no harm’, should apply across the full AI stack, from data and infrastructure to model design and deployment.
Examples cited from the webinar series include the use of augmented intelligence in early warning systems, where machine learning is combined with human contextual knowledge, and an AI-enabled WhatsApp chatbot used in Yemen to broaden participation in mediation, particularly among women and young people. The text presents these cases as evidence that AI can extend the reach of peacebuilding tools without replacing practitioners.
The final part of the reflection focuses on governance and ethics. It argues that while ethical AI principles are widely discussed, they need to be translated into practical, context-specific safeguards, especially in conflict settings. It also notes that risks differ across use cases such as early warning, social media monitoring, and mediation support, and says meaningful governance requires input from diplomats, researchers, mediators, and the private sector.
UNSSC says the webinar series drew between 300 and 500 registrants per session, which it presents as evidence of strong demand for more targeted learning on AI and peacebuilding. The college argues that its role should extend beyond convening discussion to turning those debates into practical knowledge for UN practitioners working at the intersection of AI and conflict prevention.
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A World Economic Forum white paper (Empowering Defenders: AI for Cybersecurity), developed with KPMG, states that AI is becoming a core capability for modern cybersecurity. The report notes that attackers are using AI to increase speed, scale and sophistication, while defenders are also adopting AI to improve detection, response and resilience.
The report describes how AI is being used across the cybersecurity lifecycle, from cyber governance and risk identification to threat detection, incident response and recovery. Case studies from major organisations highlight applications in phishing detection, vulnerability management, malware analysis, threat intelligence and automated security reviews.
WEF report also states that effective adoption depends on more than technology investment. Organisations need executive support, reliable data, skilled teams, mature infrastructure and clear governance before deploying AI in critical security operations.
The report also highlights the rise of agentic AI, where autonomous systems can detect, coordinate and respond to threats with limited human intervention. It adds that while these systems could help defenders act faster, they may also introduce risks related to accountability, unintended behaviour and over-reliance on automation.
Why does it matter?
The central message of the report is that AI can strengthen cyber defence only when paired with human judgement, structured pilots, continuous monitoring and clear safeguards. Without these foundations, organisations risk creating fragile systems instead of resilient ones.
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Panthalassa has raised $140 million in a Series B funding round led by investor Peter Thiel to advance technology that uses ocean wave energy to power AI computing systems.
According to the company, the funding will support the development of offshore nodes that generate electricity from wave energy and run AI computing onboard. Data from these systems is transmitted via low-Earth-orbit satellites.
Panthalassa said the initiative responds to increasing demand for computing capacity and constraints faced by terrestrial data centres, including electricity supply, cooling requirements, and infrastructure limitations.
The company stated that its systems operate in offshore environments and use locally generated energy to power computing equipment, with ocean conditions providing cooling.
Panthalassa has previously deployed prototype systems and said the new funding will support completion of a pilot manufacturing facility and deployment of additional nodes, with commercial operations targeted for 2027.
Investor Peter Thiel said the approach expands computing infrastructure beyond traditional locations, while company representatives described the technology as a potential source of clean energy for AI systems.
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The United Nations Development Programme has supported training sessions for members of the Parliament of Tajikistan, focusing on AI and modern digital tools. The initiative aims to strengthen legislative processes and institutional capacity.
Discussions covered AI use in policymaking, legislative analysis and public engagement, alongside topics such as strategic planning and anti corruption measures. The UNDP sessions brought together parliamentarians and staff to share international and national experience.
Officials highlighted that AI can support evidence based decision making and improve efficiency, while requiring attention to transparency, ethics and accountability. Cooperation with UNDP was described as key to adapting global best practices.
The programme includes an ongoing needs assessment to identify priorities for further development and institutional strengthening. The activities are being carried out with UNDP support in Tajikistan.
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The Government of the Republic of Kazakhstan has reviewed plans to expand AI across all sectors under the proposed Digital Qazaqstan strategy. The initiative aims to drive long-term economic modernisation through digital technologies.
Officials highlighted AI as a key tool for improving productivity, industrial safety and economic planning. The strategy also focuses on strengthening infrastructure, including computing capacity and data systems.
The government stressed the need for better data access, investment incentives and stronger private sector involvement. Measures will also target skills development and support for smaller businesses adopting AI.
Authorities said AI could enhance forecasting and policy effectiveness, but that safeguards for personal data and intellectual property are required. The strategy is being developed and implemented in Kazakhstan.
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A US Federal Reserve speech highlights the growing role of AI and emerging technologies in the banking sector and notes that they introduce new risks alongside potential benefits. The remarks stress the need for regulators to closely monitor these developments.
The speech notes that AI could affect areas such as risk management, decision-making and operational processes within financial institutions. It emphasises that rapid adoption may outpace existing oversight frameworks.
Officials said supervision and governance are important to ensure AI is used responsibly. Banks are expected to manage risks effectively while maintaining transparency and accountability in their use of technology.
The Federal Reserve said adapting regulatory approaches will be essential to address technological change while preserving financial stability. The speech was delivered as part of policy discussions in the US.
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The Academy’s Board of Governors has introduced new rules excluding AI-generated performances and screenplays from eligibility for the Oscars. The updated rules require that recognised work be created and performed by humans.
The update comes as AI technologies are increasingly used in filmmaking, including digital recreations of actors and synthetic performers. Industry tensions around AI have grown in recent years, including during the 2023 writers’ and actors’ strikes.
The move is described as part of efforts within the creative sector to preserve human authorship and artistic control as generative AI tools expand across media production.
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The UK government is stepping up efforts to better understand the structure and growth of its AI sector through an updated national survey led by the Department for Science, Innovation and Technology.
The research, conducted by Ipsos and supported by Perspective Economics, aims to gather direct insights from businesses operating in the UK AI ecosystem. The findings are expected to inform future government policy on AI and sector development.
Participation is voluntary and confidential. Respondents are drawn from senior leadership roles, including chief executives, chief technology officers, company directors, and senior members of AI or data science teams. The survey focuses on business activity, products and services, and longer-term growth plans across the sector.
Fieldwork is taking place between late April and the end of May 2026 using online questionnaires and telephone interviews. Each session is expected to last around 15 to 20 minutes, allowing businesses to contribute structured input without significant disruption to normal operations.
The initiative reflects a wider UK policy priority: ensuring that government strategy keeps pace with developments in AI innovation and commercial growth. By drawing on direct industry evidence rather than relying only on secondary analysis, policymakers are trying to build a more accurate picture of the country’s evolving AI landscape. This last sentence is an inference based on the survey’s stated purpose of informing government AI policy.
Why does it matter?
AI policy is much easier to design in theory than in a market that is changing quickly and unevenly. If the government lacks current information on how AI firms are growing, what products they are developing, and where the main constraints lie, it risks shaping policy based on outdated assumptions. Direct input from businesses gives policymakers a stronger basis for decisions on support, regulation, skills, and investment, especially at a time when the UK is trying to turn AI ambition into measurable economic capacity.
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