Semiconductors

NVIDIA invests $2 billion as CoreWeave expands AI factory network

CoreWeave’s long-running partnership has deepened with NVIDIA to accelerate AI infrastructure deployment, including ambitious plans for multi-gigawatt AI factory capacity by 2030.

As part of the agreement, the US company is investing $2 billion in CoreWeave through the purchase of Class A common stock, signalling strong confidence in the company’s growth strategy and AI-focused cloud platform.

Both companies aim to deepen alignment across infrastructure, software and platform development, with CoreWeave building and operating AI factories using NVIDIA’s accelerated computing technologies and early access to upcoming architectures such as Rubin, Vera CPUs and BlueField systems.

The collaboration will also test and integrate CoreWeave’s AI-native software and reference designs into NVIDIA’s broader cloud and enterprise ecosystem, while NVIDIA supports faster site development through financial backing for land and power procurement.

Executives from both firms described the expansion as a response to surging global demand for AI computing, positioning large-scale AI factories as the backbone of future industrial AI deployment.

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Maia 200 AI inference accelerator unveiled by Microsoft

Microsoft has unveiled Maia 200, a next-generation AI inference accelerator built to boost performance, efficiency, and cost-effectiveness at scale. Built on TSMC’s 3-nanometre process, the chip boosts speed, efficiency, and memory throughput for advanced AI models.

The new accelerator will power Microsoft’s cloud infrastructure across Azure, Microsoft Foundry, and Microsoft 365 Copilot, including workloads for OpenAI’s latest GPT-5.2 models.

Internal teams will use Maia 200 for synthetic data generation and reinforcement learning, accelerating AI development. Maia 200 is being rolled out in Microsoft’s US Central data centre region, with further deployments planned across additional global locations.

A preview version of the Maia software development kit is also being released, offering developers access to PyTorch integration, optimised compilers, and low-level programming tools to fine-tune AI models across heterogeneous computing environments.

The system introduces a redesigned networking and memory architecture optimised for high-bandwidth data movement and large-scale inference clusters.

Microsoft says the platform delivers significant improvements in performance per dollar, scalability, and power efficiency, positioning Maia 200 as a cornerstone of its long-term AI infrastructure strategy.

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Europe rethinks dependence on US Big Tech

Rising transatlantic tensions have reignited concerns over Europe’s heavy reliance on US Big Tech, exposing vulnerabilities across cloud services, AI, and digital infrastructure.

European lawmakers are increasingly pushing for homegrown alternatives, warning that excessive dependence on a small group of foreign providers threatens economic resilience, public services, and technological sovereignty.

European Parliament data shows over 80 percent of the EU’s digital products and infrastructure come from outside the bloc, with US firms dominating cloud and AI.

Officials warn the concentration increases geopolitical, cyber and supply risks, driving renewed efforts to boost Europe’s digital autonomy and competitiveness.

Initiatives such as Eurostack and rising open-source investment aim to build digital independence, though analysts say real sovereignty could take a decade and vast funding.

While policymakers accept that full decoupling from US technology remains unrealistic, pressure is mounting for governments and public institutions to prioritise European solutions and treat digital infrastructure as a strategic asset.

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Tata’s $11 billion Innovation City plan gains global visibility at Davos

Tata Sons plans to invest $11 billion to build a large ‘Innovation City’ near the upcoming Navi Mumbai International Airport, according to Maharashtra Chief Minister Devendra Fadnavis, speaking at the World Economic Forum (WEF) in Davos. He said the project has drawn strong interest from international investors and will include major infrastructure upgrades alongside a data centre.

Fadnavis said the aim is to turn Mumbai and its wider region into a global, ‘plug-and-play’ innovation hub where companies can quickly set up and scale new technologies. He described the initiative as the first of its kind in India and said work is expected to begin within six to eight months.

The location next to the Adani Group–developed Navi Mumbai Airport is being positioned as an advantage, linking global connectivity with the high-tech industry. The project also reflects a broader global rush to expand data centres as companies roll out AI services, with firms such as Microsoft, Alphabet, and Amazon investing heavily in new capacity worldwide.

Maharashtra, which contributes more than 10 percent of India’s GDP and hosts the country’s financial capital, is also pushing a wider infrastructure drive, including a $30 billion plan to upgrade Mumbai. State leaders have framed these investments as part of an effort to boost growth and respond to economic pressures, including unemployment.

The Innovation City is expected to support India’s ambitions in AI and semiconductors, with national officials pointing to a public-private partnership approach rather than leaving development solely to big tech companies. Alongside this, the state is exploring energy innovation, including potential collaborations on small modular nuclear reactors, following recent legislative support for smaller-scale nuclear projects.

Taken together, the plan is being presented as a bid to attract global investment, accelerate high-tech development, and strengthen India’s role in emerging industrial and technology shifts centred on AI, advanced manufacturing, and digital infrastructure.

Diplo is live reporting on all sessions from the World Economic Forum 2026 in Davos.

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From chips to jobs: Huang’s vision for AI at Davos 2026

AI is evolving into a foundational economic system rather than a standalone technology, according to NVIDIA chief executive Jensen Huang, who described AI as a five-layer infrastructure spanning energy, hardware, data centres, models and applications.

Speaking at the World Economic Forum in Davos, Huang argued that building and operating each layer is triggering what he called the most significant infrastructure expansion in human history, with job creation stretching from power generation and construction to cloud operations and software development.

Investment patterns suggest a structural shift instead of a speculative cycle. Venture capital funding in 2025 reached record levels, largely flowing into AI-native firms across healthcare, manufacturing, robotics and financial services.

Huang stressed that the application layer will deliver the most significant economic return as AI moves from experimentation to core operational use across industries.

Concerns around job displacement were framed as misplaced. AI automates tasks rather than replacing professional judgement, enabling workers to focus on higher-value activities.

In healthcare, productivity gains from AI-assisted diagnostics and documentation are already increasing demand for radiologists and nurses rather than reducing headcount, as improved efficiency enables institutions to treat more patients.

Huang positioned AI as critical national infrastructure, urging governments to develop domestic capabilities aligned with local language, culture and industrial strengths.

He described AI literacy as an essential skill, comparable to leadership or management, while arguing that accessible AI tools could narrow global technology divides rather than widen them.

Diplo is live reporting on all sessions from the World Economic Forum 2026 in Davos.

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Amodei warns US AI chip exports to China risk national security

Anthropic chief executive Dario Amodei has criticised the US decision to allow the export of advanced AI chips to China, warning it could undermine national security. Speaking at the World Economic Forum 2026 in Davos, he questioned whether selling US-made hardware abroad strengthens American influence.

Amodei compared the policy to ‘selling nuclear weapons to North Korea‘, arguing that exporting cutting-edge chips risks narrowing the technological gap between the United States and China. He said Washington currently holds a multi-year lead in advanced chipmaking and AI infrastructure.

Sending powerful hardware overseas could accelerate China’s progress faster than expected, Amodei told Bloomberg. He warned that AI development may soon concentrate unprecedented intelligence within data centres controlled by individual states.

Amodei said AI should not be treated like older technologies such as telecoms equipment. While spreading US technology abroad may have made sense in the past, he argued AI carries far greater strategic consequences.

The debate follows recent rule changes allowing some advanced chips, including Nvidia’s H200 and AMD’s MI325X, to be sold to China. The US administration later announced plans for a 25% tariff on AI chip exports, adding uncertainty for US semiconductor firms.

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Strategic pact deepens South Korea–Italy collaboration across AI and advanced tech

Seoul and Rome have announced plans to deepen cooperation in high-technology sectors, notably AI, semiconductor development and space technology, as part of a broader strategic partnership.

The agreement reflects shared interests in advancing cutting-edge technology and innovation, reinforcing economic and scientific collaboration between South Korea and Italy.

Both countries see these areas as central to future economic competitiveness and technological leadership on the global stage.

While details of specific programmes were not yet disclosed publicly, officials emphasised the mutual benefits of enhanced research partnerships, talent exchange and joint development initiatives that span emerging technologies and advanced industrial sectors.

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ASUS pauses smartphone expansion to focus on AI and robotics

Taiwan-based electronics manufacturer ASUS has announced that it will not launch new smartphones in 2026, signalling a central strategic pivot away from mobile devices and toward artificial intelligence-driven products and robotics.

Chairman Jonney Shih confirmed at a company event that ASUS will redirect research and development resources previously earmarked for phones into AI hardware such as robotics, AI glasses and commercial PCs.

The move comes amid a hyper-competitive global smartphone market and supply-chain pressures, such as rising memory costs, that make handset manufacturing less attractive than high-growth AI sectors.

ASUS will continue to support existing smartphone users with warranty and software updates, but does not plan to introduce new phone models in the foreseeable future.

Industry observers say this shift reflects broader trends in consumer electronics, where traditional phone makers are seeking growth by leveraging AI innovation and emerging device categories.

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South Korea faces mounting pressure from US AI chip tariffs

New US tariffs on advanced AI chips are drawing scrutiny over their impact on global supply chains, with South Korea monitoring potential effects on its semiconductor industry.

The US administration has approved a 25 percent tariff on advanced chips that are imported into the US and then re-exported to third countries. The measure is widely seen as aimed at restricting the flow of AI accelerators to China.

The tariff thresholds are expected to cover processors such as Nvidia’s H200 and AMD’s MI325X, which rely on high-bandwidth memory supplied by Samsung Electronics and SK hynix.

Industry officials say most memory exports from South Korea to the US are used in domestic data centres, which are exempt under the proclamation, reducing direct exposure for suppliers.

South Korea’s trade ministry has launched consultations with industry leaders and US counterparts to assess risks and ensure Korean firms receive equal treatment to competitors in Taiwan, Japan and the EU.

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Energy-efficient AI training with memristors

Scientists in China developed an error-aware probabilistic update (EaPU) to improve neural network training on memristor hardware. The method tackles accuracy and stability limits in analog computing.

Training inefficiency caused by noisy weight updates has slowed progress beyond inference tasks. EaPU applies probabilistic, threshold-based updates that preserve learning and sharply reduce write operations.

Experiments and simulations show major gains in energy efficiency, accuracy and device lifespan across vision models. Results suggest broader potential for sustainable AI training using emerging memory technologies.

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