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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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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Kraken has entered a strategic partnership with MoneyGram to enable crypto-to-cash withdrawals in more than 100 countries. The integration links digital asset infrastructure with MoneyGram’s global network, allowing users to convert crypto into hundreds of fiat currencies through physical and digital payout channels.
The service is intended to address one of the main barriers to crypto adoption by improving access to reliable off-ramps. Users will be able to transfer funds to their accounts and receive near-instant cash payouts through MoneyGram’s retail network and regulated payment infrastructure.
Both companies highlighted the importance of interoperability between traditional finance and digital assets in driving practical adoption.
Kraken stressed the value of connecting liquidity and compliance systems with established payment rails, while MoneyGram presented its global distribution network as a bridge between digital value and everyday financial use.
The rollout will begin across the United States, Europe, Latin America, Africa, and parts of Asia-Pacific, with plans to expand further into local bank deposits and additional payment services as the partnership develops.
Why does it matter?
The partnership addresses one of the main friction points in crypto adoption: converting digital assets into usable cash at scale. By linking crypto infrastructure with a global payout network, it strengthens the practical use of digital assets beyond trading and speculation.
More broadly, it reflects a gradual convergence between traditional financial rails and crypto-native systems, with interoperability becoming increasingly important to how value moves across borders.
It may also support financial inclusion by expanding access to cash-out services in regions where banking infrastructure remains limited or uneven.
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Western Union has announced the launch of USDPT, a US dollar-denominated stablecoin intended to support its global payments infrastructure. The company said the token is fully backed by US dollars and issued by Anchorage Digital Bank, running on Solana to enable faster and more efficient settlement.
The stablecoin is intended to function as an always-on settlement asset within Western Union’s global network. By using Solana’s high-performance infrastructure, Western Union said USDPT is intended to reduce delays and inefficiencies in traditional correspondent banking while maintaining regulatory compliance and oversight.
Integration plans include exchange availability, liquidity connections with licensed custodians, and internal settlement for agents and treasury operations, the company said. Western Union also plans a consumer service and broader digital asset access, positioning USDPT as a bridge between blockchain systems and everyday financial use.
Why does it matter?
The initiative reflects a wider shift among established financial institutions towards regulated digital assets as core payment infrastructure. By combining blockchain settlement with a global payments network, Western Union is positioning itself for real-time, digital-first international money movement.
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Meta has introduced USDC payouts for selected Facebook creators in Colombia and the Philippines, marking another step towards using blockchain-based payment rails for creator earnings. The programme allows eligible users to receive funds directly into crypto wallets using Polygon or Solana as settlement networks.
Creators receiving USDC on Polygon can move funds through supported wallets or exchanges and convert them into local currency where off-ramp services are available. The model reduces reliance on traditional cross-border payment channels and is intended to give creators faster and more flexible access to dollar-denominated earnings.
Polygon has been included alongside Solana as part of the payout infrastructure, with Polygon arguing that its network already handles a large share of global USDC transfer activity. Low transaction costs and broad wallet and exchange support are presented as key reasons stablecoin rails are becoming more attractive for recurring digital payouts.
Why does it matter?
The significance of the move lies less in crypto branding than in payment infrastructure. Meta is testing whether stablecoin rails can make creator payouts faster, more flexible, and less dependent on the frictions of traditional cross-border transfers. If this model scales, it would suggest that blockchain networks are becoming useful not only for trading or speculation, but for mainstream platform payments where speed, settlement, and access to dollar-denominated value matter.
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Brazil’s central bank has introduced new restrictions preventing regulated cross-border payment providers from using cryptoassets to settle international transactions. The measure forms part of updated rules for electronic foreign exchange services, known as eFX.
Under Resolution BCB No. 561, settlement between eFX providers and foreign counterparties must take place through authorised foreign exchange transactions or non-resident Brazilian real accounts. Use of virtual assets such as stablecoins or cryptocurrencies for settlement is explicitly prohibited.
The rule does not ban crypto trading or peer-to-peer transfers, but focuses on the infrastructure used by regulated payment firms. Stablecoin-based settlement models are expected to be most affected, as they have been widely used to facilitate faster and lower-cost cross-border payments.
The decision aligns with Brazil’s broader regulatory strategy to tighten oversight of digital assets, including AML compliance, taxation frameworks, and classification of certain crypto flows as foreign exchange operations.
Regulators aim to maintain control over cross-border capital movement while allowing crypto activity to continue outside regulated payment rails.
Why does it matter?
Brazil’s decision reflects a broader global effort to reassert control over cross-border financial infrastructure as crypto-based settlement systems grow in scale and speed.
By keeping regulated payment flows within traditional foreign exchange channels, authorities aim to preserve monetary oversight, tax visibility, and compliance enforcement in a system where stablecoins are increasingly bypassing conventional banking rails.
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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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South Korea has advanced a major regulatory shift by passing a bill that extends the Foreign Exchange Transactions Act to cover virtual asset service providers. The decision introduces mandatory registration for firms facilitating cross-border crypto transactions, including exchanges and wallet operators.
The new framework establishes formal oversight of virtual asset transfers between South Korea and other jurisdictions. Authorities will gain enhanced monitoring powers over transaction flows, with a focus on improving transparency and reducing risks linked to illicit financial activity and capital movement.
Major exchanges and service providers are expected to face increased compliance requirements, including detailed reporting obligations and system upgrades. Failure to comply could result in penalties or revocation of registration, signalling a stronger enforcement environment.
The measure aligns South Korea with global regulatory developments such as the EU’s MiCA framework and FATF recommendations. By integrating crypto oversight into existing forex law, regulators aim to accelerate implementation while reinforcing market trust and institutional oversight.
Why does it matter?
South Korea’s move reflects a broader global shift toward integrating crypto into traditional financial regulatory systems, reducing the space for unmonitored cross-border capital flows.
By embedding virtual assets within existing forex law, the country strengthens financial oversight while signalling how digital assets are increasingly being treated as part of mainstream monetary infrastructure rather than a parallel system.
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The paper links AI adoption in digital banking to customer experience, risk management, process automation, financial inclusion and regulatory compliance, arguing that these factors are increasingly important as Bangladesh’s financial sector becomes more digital.
A study that uses a narrative literature review of recent research from 2024 and 2025 and builds its conceptual model on the UTAUT2 framework, which is commonly used to explain technology adoption.
The authors extend the model by adding ethical trust and fraud prevention as mediating mechanisms, arguing that consumers are more likely to use AI-enabled banking services when they see them as useful, secure, transparent and fair.
Ethical trust is treated as a central part of adoption. The paper identifies transparency, algorithmic fairness, data privacy, reliability, accountability and digital inclusion as key factors shaping how users respond to AI in banking.
It also notes that explainable AI tools and localised interfaces, including Bengali-language systems, could help reduce uncertainty for users with lower digital literacy.
Fraud prevention is presented as a critical enabler of consumer confidence. The authors point to real-time monitoring, anomaly detection, secure authentication, biometric e-KYC and explainable fraud alerts as tools that can reduce perceived risk.
Additionally, they argue that AI systems should not only detect fraud effectively, but also explain decisions clearly enough for users to trust them.
The paper also highlights Bangladesh-specific issues, including Islamic banking, Shariah-compliant AI models, rural and urban digital access gaps, and the need for inclusive design. However, the study remains conceptual and has not yet been peer reviewed.
The authors recommend future empirical research with Bangladeshi banking users to test the model across income levels, regions, generations and gender groups.
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