Meta is developing an advanced ‘agentic’ AI assistant designed to perform complex, multi-step tasks for consumers. The initiative reflects the company’s broader push to expand its AI capabilities beyond basic chat functions.
The planned assistant is intended to act more autonomously, helping users complete actions such as organising activities or managing digital tasks. Powered by a new internal model called Muse Spark, the assistant is still under development, and its rollout timeline depends on internal testing.
Meta’s strategy focuses on embedding these tools across its platforms, aiming to deepen user engagement and create more personalised digital experiences.
This marks a shift towards AI systems that can anticipate needs rather than simply respond to prompts. The move also signals intensifying competition among major technology companies in consumer AI.
The report highlights that Meta is positioning AI as central to its future growth, with a focus on making assistants more proactive and capable within everyday digital environments in the US.
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The Center for AI Standards and Innovation (CAISI), part of the US National Institute of Standards and Technology (NIST), has announced new agreements with Google DeepMind, Microsoft, and xAI to expand government evaluations of frontier AI models and support research on AI security.
According to the announcement, the agreements will support pre-deployment evaluations and targeted research intended to improve understanding of frontier AI capabilities and their national security implications.
CAISI says the updated arrangements build on earlier partnerships that were renegotiated to reflect directives from the Secretary of Commerce and the US AI Action Plan.
CAISI also says it has been designated to serve as the main point of contact within the US government for collaboration with industry on testing, joint research, and best-practice development for commercial AI systems. To date, it says it has completed more than 40 evaluations, including assessments of advanced unreleased models.
CAISI Director Chris Fall said independent and rigorous measurement is essential to understanding frontier AI and its national security implications. The announcement adds that the agreements are intended to support information-sharing, voluntary product improvements, and a clearer government understanding of AI capabilities and international AI competition.
The agency notes that developers often provide models with reduced or removed safeguards to support national security-related testing. It also says evaluators from across government may participate through the CAISI-convened TRAINS Taskforce, and that the agreements are designed to support testing in classified environments and to adapt to continued advances in AI.
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The UAE Cyber Security Council, Cisco and Open Innovation AI have launched the UAE’s National AI Test and Validation Lab, creating a national platform designed to assess the security, safety and trustworthiness of AI systems.
Hosted in Abu Dhabi, the facility will evaluate AI models, autonomous agents and applications before deployment across government and private sector environments. The initiative forms part of the UAE’s wider strategy to strengthen sovereign AI capabilities and reinforce cybersecurity protections as AI adoption accelerates across critical infrastructure and public services.
According to UAE Cyber Security Council Head Dr Mohamed Al Kuwaiti, the laboratory aims to ensure AI systems deployed across the country remain aligned with national cybersecurity policies and trusted governance standards.
The facility will conduct assessments covering model robustness, prompt injection threats, jailbreak vulnerabilities, privacy risks, data leakage, supply chain integrity and autonomous agent behaviour.
Systems meeting the required standards will receive a national certification mark intended to provide assurance for regulators, businesses and citizens. Evaluations will also measure compliance against international frameworks, including ISO 42001, MITRE ATLAS, NIST AI RMF and OWASP standards for large language models and AI agents.
The lab combines Cisco AI-ready infrastructure powered by NVIDIA GPUs with Open Innovation AI orchestration and automated security testing platforms.
UAE authorities expect the centre to scale to analysing tens of thousands of AI agents annually, supporting sectors including finance, healthcare, telecommunications, energy and critical national infrastructure as the country expands its adoption of agentic AI technologies.
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Bank of America convened its fifth Breakthrough Technology Dialogue in Singapore, bringing together leaders from business, academia, and science to discuss emerging technologies shaping the future. The event focused on areas including AI, quantum computing, energy, MedTech, and space.
The forum also highlighted the growing importance of the Asia Pacific in driving technological development and deployment. According to Bank of America, the region’s strong research base, advanced manufacturing capacity, and expanding digital infrastructure are helping position it at the centre of global innovation.
Designed as a high-level platform for discussion, the dialogue explored how emerging technologies are reshaping industries and economies. Participants also examined longer-term investment approaches and the need to connect innovation with practical use cases that can scale across markets.
The initiative reflects Bank of America’s wider approach to technology investment, combining large-scale spending with a stated focus on client and employee needs and on solutions that can be delivered at scale. The event is increasingly being presented as a global forum for shaping views on the next generation of technological change.
Why does it matter?
The significance of the dialogue lies less in any single announcement than in the way it brings together investors, executives, academics, and technologists around the sectors likely to shape future industrial and economic power. The emphasis on Asia Pacific also reflects a broader recognition that leadership in AI, quantum, and other frontier technologies will depend not only on research breakthroughs, but also on where they are manufactured, financed, and deployed at scale.
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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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The Philippines’ Department of Social Welfare and Development (DSWD), together with youth-led group Tayo ang Taya, has convened a youth consultation on digitalisation, AI, and digital resilience as part of the Association of Southeast Asian Nations’ public consultations on the responsible use of new technologies.
Held at the University of the Philippines Diliman, the ‘Ctrl+Youth: Shaping ASEAN’s Digital Future’ event brought together youth leaders, civil society organisations, student groups, community-based organisations, and youth advocates. According to the DSWD, the consultation is intended to gather youth input to help shape regional policies and frameworks on digitalisation, AI, and digital resilience.
In a video message, DSWD Secretary Rex Gatchalian said young people must be equipped not only with technical skills, but also with values that support responsibility, inclusivity, and innovation as AI and other emerging technologies expand. He added that youth perspectives would help inform the ASEAN Community Vision 2045 and support efforts to prepare young people for participation in the global digital economy.
Undersecretary Adonis Sulit of the DSWD’s Policy and Planning Group said the consultation was organised with youth organisations and the National Youth Commission to ensure that young people could directly contribute comments and proposals to a draft charter on digital resilience under ASEAN’s sociocultural pillar.
Participants took part in focus group discussions to craft manifestos on responsible technology use and digital safety. The programme also included a presentation on legislation related to digitalisation and the proper use of technology, alongside messages of support from the National Youth Commission, the Department of Education, the Department of Information and Communications Technology, and the University of the Philippines National College of Public Administration and Governance.
The consultation forms part of the Philippines’ effort to integrate youth perspectives into ASEAN’s digital agenda, with the DSWD presenting the initiative as part of its commitment to inclusive governance.
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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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Australia’s Department of Health, Disability and Ageing has published the second-year action plan for its Aged Care Data and Digital Strategy 2024–2029, setting out how data and digital reforms will support implementation of the new Aged Care Act and broader modernisation across the sector.
The plan says its central role in the second year is to support reforms linked to the new Act, including changes to critical government platforms such as the Government Provider Management System and My Aged Care. The strategy’s stated vision is to deliver person-centred care for older people while supporting a sustainable care economy through data and digital innovation.
Its second-year actions are organised around four outcomes: improving navigation and participation for older people and support networks; digitally empowering workers and providers; enabling secure data sharing and reuse; and strengthening modern digital foundations across the aged care system. The summary table on page 4 groups actions under those four outcomes and eight priorities.
Among the consumer-facing measures, the plan includes further development of the LiveUp healthy ageing tool, continued support for the Be Connected program on digital and health literacy, and additional enhancements to My Aged Care, including reforms-linked updates and consideration of translated content. The document says these steps are intended to make digital services more accessible and easier to use for older people and their support networks.
For workers and providers, the plan includes virtual nursing trials in residential aged care, work to enable ePrescribing in electronic National Residential Medication Charts, expansion of the KeepAble wellness and reablement tool, updates to the Integrated Assessment Tool, and continued efforts to improve worker digital literacy. It also includes ongoing work on advanced care planning and end-of-life care support through national resources and digital tools.
On data and infrastructure, the action plan outlines continued work on an aged care data governance framework, expansion of the Government Provider Management System as a single provider portal, further development of the Aged Care National Minimum Data Set, and wider use of the National Aged Care Data Asset through the National Health Data Hub. It also includes business-to-government connectivity work to expand APIs for provider reporting.
The plan also gives AI a defined place within aged care reform. On page 23, the department says emerging technologies, including AI, have the potential to increase efficiency, improve care, and deliver better outcomes for older people.
Planned actions include publishing the report from its public consultation on safe and effective AI use, developing a policy position to guide safe AI use in health and aged care, and promoting pilots and programs in promising areas. A separate pilot on page 25 proposes testing an AI application to generate care and rehabilitation plans for older people recovering from stroke.
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Australia and Japan have issued a joint declaration on economic security cooperation, stating that economic and technological resilience are central to national security and setting out a broad agenda for closer bilateral coordination across supply chains, critical technologies, and Indo-Pacific connectivity.
The declaration states that economic resilience is foundational to both countries’ security and that the framework is intended to strengthen strategic autonomy, indispensability, and regional resilience.
Furthermore, the declaration commits the two governments to closer policy alignment through existing bilateral mechanisms and to consultation on economic security contingencies linked to geopolitical tensions, economic coercion, and major market disruptions.
A major focus is on supply chain security in strategically significant sectors. Australia and Japan reaffirmed their partnership on minerals, energy, food, and industrial goods, while expressing concern over economic coercion, harmful overcapacity, and export restrictions, particularly in critical minerals.
The declaration also highlights cooperation on critical minerals projects, domestic smelting and metals processing, and coordination among government-backed finance institutions to support investment and supply chain resilience.
The text also emphasises critical and emerging technologies. Australia and Japan say they will deepen cooperation on research security and integrity, while promoting trusted collaboration between governments, national laboratories, industry, and academia in areas including AI, data centres, quantum, biotechnology, space, undersea cables, and telecommunications. The declaration also links advanced technologies to defence industry cooperation and supply chain collaboration.
In the Indo-Pacific, the two countries say they will work together to foster a safe, secure, and trustworthy AI and digital ecosystem, including through the Hiroshima AI Process and cooperation on digital infrastructure such as telecommunications, undersea cables, data centres, and all-photonics networks. The declaration also commits them to stronger coordination on secure undersea cables, describing them as vital regional infrastructure.
More broadly, Australia and Japan reaffirm support for a rules-based international economic order centred on the World Trade Organization, while also backing further work through the The Comprehensive and Progressive Agreement for Trans-Pacific Partnership, the Asia-Pacific Economic Cooperation, the Quad, the Asia Zero Emission Community, and other regional initiatives.
The declaration presents economic security cooperation not only as a bilateral priority but as part of a wider effort to strengthen resilience, secure connectivity, and trusted technology governance across the Indo-Pacific.
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The UN Conference on Trade and Development (UNCTAD) is convening an expert meeting to examine how geopolitical tensions, strategic competition, and rising AI-related investment are reshaping international production and global foreign direct investment.
According to the concept note, firms are operating in a more fragmented and politically influenced environment, where cross-border investment decisions are increasingly shaped not only by efficiency and market access, but also by concerns such as supply-chain resilience, technological security, and exposure to changing trade barriers.
The note also links fast-growing investment in AI and digital infrastructure to industrial policy priorities and national security concerns. It says these pressures are contributing to wider shifts in corporate behaviour, including stronger interest in geopolitically aligned and intraregional markets, intensifying competition in strategically important industries, and faster supply-chain restructuring.
UNCTAD says the meeting aims to clarify the scale and nature of these changes, assess what they mean for developing economies, and identify policy considerations for international dialogue. It also points to a more fragmented global investment landscape in which governments are relying more heavily on industrial policy, screening mechanisms, and security-related measures.
Member states are invited to submit short expert papers in advance of the session. The meeting is open to all UNCTAD member States, while international organisations, academia, research institutions, and private-sector participants may attend as observers. The session will be held in person, with a live audio stream available to registered participants.
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