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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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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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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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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Scientists in Chinadeveloped 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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MIT researchers have demonstrated a faster, more energy-efficient cooling technique for scalable trapped-ion quantum chips. The solution addresses a long-standing challenge in reducing vibration-related errors that limit the performance of quantum systems.
The method uses integrated photonic chips with nanoscale antennas that emit tightly controlled light beams. Using polarisation-gradient cooling, the system cools ions to nearly ten times below standard laser limits, and does so much faster.
Unlike conventional trapped-ion systems that depend on bulky external optics, the chip-based design generates stable light patterns directly on the device. The stability improves accuracy and supports scaling to thousands of ions on a single chip.
Researchers say the breakthrough lays the groundwork for more reliable quantum operations and opens new possibilities for advanced ion control, bringing practical, large-scale quantum computing closer to reality.
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AI workloads are placing unprecedented strain on system on chip interconnects. Designers face complexity that exceeds the limits of traditional manual engineering approaches.
Semiconductor engineers are increasingly turning to automated network on chip design. Algorithms now generate interconnect topologies optimised for bandwidth, latency, power and area.
Physically aware automation reduces wirelengths, congestion and timing failures. Industry specialists report dramatically shorter design cycles and more predictable performance outcomes.
As AI spreads from data centres to edge devices, interconnect automation is becoming essential. The shift enables smaller teams to deliver powerful, energy efficient processors.
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OpenAI has agreed to purchase up to 750 megawatts of computing power from AI chipmaker Cerebras over the next three years. The deal, announced on 14 January, is expected to be worth more than US$10 billion and will support ChatGPT and other AI services.
Cerebras will provide cloud services powered by its wafer-scale chips, which are designed to run large AI models more efficiently than traditional GPUs. OpenAI plans to use the capacity primarily for inference and reasoning models that require high compute.
Cerebras will build or lease data centres filled with its custom hardware, with computing capacity coming online in stages through 2028. OpenAI said the partnership would help improve the speed and responsiveness of its AI systems as user demand continues to grow.
The deal is also essential for Cerebras as it prepares for a second attempt at a public listing, following a 2025 IPO that was postponed. Diversifying its customer base beyond major backers such as UAE-based G42 could strengthen its financial position ahead of a potential 2026 flotation.
The agreement highlights the wider race among AI firms to secure vast computing resources, as investment in AI infrastructure accelerates. However, some analysts have warned that soaring valuations and heavy spending could resemble past technology bubbles.
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The US administration has approved the export of Nvidia’s H200 AI chips to China, reversing years of tight US restrictions on advanced AI hardware. The Nvidia H200 chips represent the company’s second-most-powerful chip series and were previously barred from sale due to national security concerns.
The US president announced the move last month, linking approval to a 25 per cent fee payable to the US government. The administration said the policy balances economic competitiveness with security interests, while critics warned it could strengthen China’s military and surveillance capabilities.
Under the new rules, Nvidia H200 chips may be shipped to China only after third-party testing verifies their performance. Chinese buyers are limited to 50 per cent of the volume sold to US customers and must provide assurances that the chips will not be used for military purposes.
Nvidia welcomed the decision, saying it would support US jobs and global competitiveness. However, analysts questioned whether the safeguards can be effectively enforced, noting that Chinese firms have previously accessed restricted technologies through intermediaries.
Chinese companies have reportedly ordered more than two million Nvidia H200 chips, far exceeding the chipmaker’s current inventory. The scale of demand has intensified debate over whether the policy will limit China’s AI ambitions or accelerate its access to advanced computing power.
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The United Arab Emirates has joined Pax Silica, a US-led alliance focused on AI and semiconductor supply chains. The move places Abu Dhabi among Washington’s trusted technology partners.
The pact aims to secure access to chips, computing power, energy and critical minerals. The US Department of State says technology supply chains are now treated as strategic assets.
UAE officials view the alliance as supporting economic diversification and AI leadership ambitions. Membership strengthens access to advanced semiconductors and large-scale data centre infrastructure.
Pax Silica reflects a broader shift in global tech diplomacy towards allied supply networks. Analysts say participation could shape future investment in AI infrastructure and manufacturing.
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