# Ecosystem Related Articles

HTX News Center provides the latest articles and in-depth analysis on "Ecosystem", covering market trends, project updates, tech developments, and regulatory policies in the crypto industry.

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.

marsbitYesterday 04:17

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

marsbitYesterday 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

China's AI Computing Counterattack

marsbit03/04 05:09

Who Controls Computing Power, Implicitly Controls the Future of AI: Anastasia, Co-founder of Gonka Protocol

Who Controls Compute, Controls AI's Future: Gonka Protocol Co-Founder Anastasia The centralization of compute power, not just AI models, is the critical power node in AI's future, argues Anastasia Matveeva, co-founder of Gonka Protocol. While public debate focuses on models, true power lies in the underlying infrastructure—access to GPUs, power, and data center capacity. This centralization creates structural barriers to innovation, enforces a rent-extraction model, and introduces systemic fragility. Gonka is a permissionless global network designed to decentralize AI compute. It enables anyone to contribute or access GPU resources via a programmatic, open API. Key to its efficiency is an architecture that minimizes overhead, ensuring most compute is used for actual AI workloads (primarily inference) rather than network maintenance. Rewards and governance are tied to verified compute contribution, not capital stake. The protocol addresses scalability and accessibility by allowing participants of all sizes to join without permission, with influence proportional to their compute power. It supports the emerging AI agent economy with transparent, dynamic pricing and reliable, verifiable computation. While currently not optimized for strict data sovereignty, its decentralized design avoids data accumulation, and its governance allows for future evolution to meet regulatory demands. The urgency for such decentralized solutions is high to prevent a calcified AI future dominated by a few infrastructure gatekeepers.

marsbit03/03 07:58

Who Controls Computing Power, Implicitly Controls the Future of AI: Anastasia, Co-founder of Gonka Protocol

marsbit03/03 07:58

Capital Ignition: The AI Race Behind OpenAI's Mega Financing

OpenAI's record-breaking financing round signals a fundamental shift in the global AI industry, moving beyond technological competition into a phase of heavy capital博弈. This marks the transition of the large model era into a stage dominated by capital-intensive strategies. Originally a mission-driven nonprofit, OpenAI restructured into a capped-profit entity to attract commercial capital while retaining its core ethos. Its latest funding involves key players like Amazon, Nvidia, and SoftBank, transforming OpenAI into a compute infrastructure platform rather than just a model company. The competitive landscape is analyzed through comparisons: Google relies on internal ecosystems and self-developed chips; xAI leverages social media integration; Anthropic prioritizes safety with backing from Amazon and Google; and Meta pursues open-source expansion. Two technical paths emerge—scale-first (requiring continuous capital) and efficiency-optimization (focused on cost reduction). The soaring industry barriers, including massive GPU demands and billion-dollar compute costs, may lead to a highly centralized AI structure with few base model providers. OpenAI’s commercialization through API services and enterprise subscriptions faces challenges in balancing profitability against soaring compute investments. Ultimately, this financing reflects how AI competition has escalated to a strategic national level, involving compute sovereignty and global supply chains. The next five years will determine whether AI becomes a monopolized super-infrastructure or maintains an open, innovative ecosystem.

比推03/03 04:51

Capital Ignition: The AI Race Behind OpenAI's Mega Financing

比推03/03 04:51

When Financing Becomes the Engine: OpenAI's Mega-Funding and the Capital Restructuring and Competitive Divergence of the Global AI Industry

OpenAI's record-breaking financing round signals a fundamental shift in the global AI industry, moving the sector into a capital-intensive phase. Originally a non-profit, OpenAI transitioned to a capped-profit model to sustain massive computational demands, evolving into a hybrid entity balancing mission and commercialization. Key competitors follow divergent paths: Google relies on internal resources and integrated ecosystems; xAI leverages social media integration; Anthropic prioritizes safety with backing from Amazon and Google; and Meta promotes open-source models. OpenAI’s strategy is capital-driven and enterprise-focused, depending heavily on external funding and partnerships with players like Microsoft, Amazon, and Nvidia. The industry is splitting between scale-driven approaches (requiring continuous investment) and efficiency-focused innovation. High computational costs—spanning GPUs, energy, and capital—are raising entry barriers, potentially leading to a centralized structure with few foundational model providers and many application-layer companies. OpenAI’s revenue models include API services and enterprise solutions, but sustainability depends on whether income can offset soaring compute expenses. Geopolitical factors like chip export controls and data policies will further shape competition. The central question remains whether AI will become a monopolized infrastructure or foster an open, innovative ecosystem. OpenAI’s funding moves are redefining industry boundaries and power structures.

marsbit03/03 04:18

When Financing Becomes the Engine: OpenAI's Mega-Funding and the Capital Restructuring and Competitive Divergence of the Global AI Industry

marsbit03/03 04:18

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