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From Hardware-Software Integration to a Trillion-Scale Ecosystem: A Look into the 'China Core' of National-Level Blockchain Infrastructure

Amid the global evolution of fintech and Web3, China is forging a distinct path centered on national-level digital infrastructure, focusing on real-world asset (RWA) tokenization and large-scale blockchain adoption in the real economy. On March 5, 2026, at China’s National People’s Congress, Dong Jin, a deputy and director of the Beijing Academy of Blockchain and Edge Computing, announced a major breakthrough: the world’s first software-hardware integrated blockchain operating system and a dedicated 96-core blockchain acceleration chip. This "China Core" delivers a 50x performance improvement, overcoming computational bottlenecks in ultra-large-scale blockchain networks. It has already been deployed across 16 central government departments and 27 state-owned enterprises, supporting over 300,000 cross-border trade companies and processing trillions of yuan in trade volume, with hundreds of billions of invoices recorded on-chain. The chip uses ASIC-level design to offload cryptographic operations (like digital signatures and hashing) from general-purpose CPUs, drastically improving efficiency. This enables high-throughput, low-latency transaction processing—critical for applications like cross-department data sharing with privacy preservation, supply chain finance with real-time credit transfer, and automated cross-border trade settlements. The system supports high-concurrency scenarios such as invoice tracking and customs clearance, enabling real-time verification and smart contract execution. This infrastructure shift enhances transparency, reduces fraud, and facilitates automated financial services like instant lending. China’s blockchain strategy, now hardware-accelerated and deployed at national scale, marks a move toward a trusted, high-performance digital economy base—setting the stage for massive RWA digitization and new opportunities in data-driven finance.

marsbitAyer 04:17

From Hardware-Software Integration to a Trillion-Scale Ecosystem: A Look into the 'China Core' of National-Level Blockchain Infrastructure

marsbitAyer 04:17

China's AI Computing Counterattack

Eight years after the ZTE crisis, China's AI industry is fighting back against U.S. chip restrictions. In 2018, ZTE nearly collapsed under U.S. sanctions but survived with heavy fines and oversight. Today, Chinese AI firms like DeepSeek are pivoting away from NVIDIA by developing domestic alternatives and optimizing algorithms to reduce reliance on foreign technology. DeepSeek’s V4 model will use entirely domestic chips, signaling a strategic shift toward computational independence. The real challenge isn’t just hardware—it’s NVIDIA’s CUDA ecosystem, which dominates global AI development with over 4.5 million developers. U.S. export controls have tightened since 2022, banning high-end chips like the A100, H100, and their downgraded versions. In response, Chinese companies are adopting technical workarounds like Mixture-of-Experts models, which activate only parts of the network during inference, slashing costs. DeepSeek’s API is up to 75x cheaper than competitors, driving rapid global adoption. By early 2026, Chinese models accounted for nearly 60% of API calls on OpenRouter. Domestic chips, such as Huawei’s Ascend series, are now capable of full-scale training, not just inference. Production lines in cities like Xinghua manufacture servers with homegrown processors, supporting major AI training projects. Meanwhile, the U.S. faces an electricity shortage as data centers consume growing power, while China benefits from greater energy capacity and lower costs. Chinese AI is also going global via “Token exports,” with services reaching users in India, Indonesia, and beyond. The situation echoes Japan’s semiconductor decline in the 1980s, but China is building an independent ecosystem rather than relying on global supply chains. Domestic chip firms report surging revenues but ongoing losses—reflecting the high cost of achieving true technological independence. The battle is difficult, but progress is underway.

marsbit03/04 05:09

marsbit03/04 05:09

NVIDIA Q4 Revenue Surges 73%, Q1 Guidance 'Explosive' Hits New High, Jensen Huang Raises 500 Billion Revenue Forecast

NVIDIA reported record-breaking Q4 FY2026 results, with revenue surging 73% year-over-year to $68.13 billion, significantly exceeding analyst expectations. Non-GAAP EPS grew over 80%, and gross margin reached a new high of 75.2%. The Data Center segment was the primary growth driver, with revenue up 75% to $62.31 billion, fueled by strong demand for AI computing and a 263% surge in Networking revenue. Gaming and Professional Visualization also saw robust growth. For Q1 FY2027, NVIDIA provided strong guidance, forecasting revenue of approximately $78 billion (up nearly 77% YoY), again surpassing consensus estimates. The company noted this outlook excludes Data Center compute revenue from China. CEO Jensen Huang raised the company's chip revenue target, stating they will exceed the previously stated $500 billion goal for orders through 2026, driven by next-generation Rubin chips and sustained AI demand. However, the stock experienced volatility post-earnings as investors weighed concerns over AI economic overheating and a change in non-GAAP accounting to include stock-based compensation.

marsbit02/26 01:16

NVIDIA Q4 Revenue Surges 73%, Q1 Guidance 'Explosive' Hits New High, Jensen Huang Raises 500 Billion Revenue Forecast

marsbit02/26 01:16

Oracle Plunges 40%, Will Excessive AI Infrastructure Overbuild Drag Down Giants?

Oracle's stock has plummeted 40% from its September peak, despite securing over $500 billion in AI infrastructure orders, signaling that massive backlogs alone no longer assure investor confidence. Similarly, Broadcom, with a $73 billion AI order backlog, and CoreWeave, which recently landed $36 billion in deals with OpenAI and Meta, have also faced stock declines. The market is growing skeptical of the AI infrastructure boom, concerned not only about suppliers' ability to fund and deliver these projects but also about the financial health and commitment of their major clients—primarily tech giants like Meta, Alphabet, Microsoft, Amazon, Apple, and Nvidia, alongside AI startups like OpenAI and Anthropic. While giants have robust finances, they are increasingly relying on debt to fuel AI capex, with soaring expenditures on data centers straining cash reserves and free cash flow. For instance, Microsoft, Alphabet, and Amazon are projected to collectively invest $1 trillion over four years. However, AI still contributes minimally to their overall revenue, raising questions about the sustainability of using profits from core businesses to fund speculative AI expansions. Execution challenges—such as power grid limitations, cooling issues, and community opposition—further complicate timely deployment. The critical uncertainty remains: if exponential AI demand fails to materialize and monetize quickly enough, these vast investments could lead to underutilized infrastructure, massive losses, and a fundamental weakening of these tech titans. The race between AI infrastructure build-out and actual market payoff will determine whether this bet becomes a triumph or a disastrous overreach.

marsbit12/13 06:04

Oracle Plunges 40%, Will Excessive AI Infrastructure Overbuild Drag Down Giants?