The Eastern accelerator: China’s AI reshapes blockchain

This post is a guest contribution by George Siosi Samuels, managing director at Faiā. See how Faiā is committed to staying at the forefront of technological advancements here.

TL;DR: Chinese AI chip makers are delivering 40–60% cost advantages over Nvidia. Open-source LLMs from Alibaba, DeepSeek, and Qwen now match or exceed Western frontier models. This is the biggest cost-structure shift enterprise blockchain has seen since 2017—and it’s rewriting the geography of who can afford to deploy at scale. The future won’t be Western or Eastern. It will be Singaporean: hardware-agnostic, ruthlessly pragmatic, and allergic to ideology.

For the past decade, the narrative was simple: the West owned cutting-edge artificial intelligence (AI) hardware. The West owned the software ecosystem. Therefore, the West would own the future of blockchain at enterprise scale. But that story is now fracturing.

What caught my attention in November 2025 was when a little-known Beijing startup called Zhonghao Xinying quietly released benchmark numbers for its “Ghana” AI training chip. The ASIC reportedly delivers 1.5× the throughput of an Nvidia A100 at 42% lower power. Days later, another Chinese firm announced a 3-nanometer (nm) training cluster built entirely on domestic IP. At the same time, Alibaba (NASDAQ: BABA), Baichuan, DeepSeek, and Qwen keep dropping open-source LLMs that match or exceed Llama-405B and Claude-3.5 on every public leaderboard. U.S. founders are no longer pretending otherwise. On any given week in late 2025, the top-trending fine-tuned models on Hugging Face’s Open LLM Leaderboard are Chinese.

Personally, this is not a surprise. But my read: for enterprise blockchain, this is the single biggest tailwind the industry has received since 2017—and almost all of it is coming from the East.

1. The real cost of enterprise blockchain is compute

Every enterprise blockchain deployment eventually hits the same wall. The cost of zero-knowledge proofs (ZKPs). The cost of running secure multi-party computation at scale. The cost of on-chain machine learning inference for fraud detection or supply-chain optimization. Those costs are overwhelmingly tied to specialized accelerators.

When Nvidia (NASDAQ: NVDA) H100s trade at $40,000 on the grey market and Google Cloud TPU-v5p pods are rationed to hyper-scalers, the enterprise math simply does not work outside Fortune 100 companies. Chinese ASICs and the coming wave of 3 nm/2 nm domestic training and inference chips change that equation overnight. A 40–60% cut in FLOPs-per-dollar — “FLOPs” being the metric used to measure the computational power per unit of cost of computer hardware — is the difference between a pilot project and a global rollout.

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2. Open-source LLMs as the new Oracle stack

Western enterprises spent years building proprietary oracle networks. They did not trust open models, despite even “OpenAI” starting out as open-source. That assumption collapsed in 2025. DeepSeek-R1, Qwen-2.5-Max, and Alibaba’s latest QwQ series are not only open-weights—they ship with verifiable training logs, auditable tokenizers, and built-in tool-calling that is already better than GPT-4o for structured JSON extraction.

The practical result? Companies in Singapore, Dubai, and Hong Kong are now running private instances of Chinese open-source models as the “reasoning layer” on top of Hyperledger Besu, Polygon CDK, or Canton networks. The West is still debating whether to allow frontier models in regulated environments. The East simply forked the weights and moved on.

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3. The pendulum swings east—but not forever

As I’ve learned from folks like Ray Dalio and Mike Maloney, (financial) history rhymes in ~500-year cycles. From 1400–1820 the East accounted for roughly 50% of global GDP. The 19th and 20th centuries belonged to the West. The 21st century is re-balancing in fast-forward.

Yet the most interesting outcome is not Eastern dominance. It is convergence. Singapore, Dubai, and Abu Dhabi are already building regulatory and physical infrastructure that treats Western capital markets, Eastern hardware, and global open-source code as interchangeable lego bricks. The winning jurisdictions will be those that refuse to pick a side.

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4. What this means for enterprise blockchain teams in 2026–2030

I’ve been thinking about what this means practically. In my work at Faiā, we’re seeing enterprise clients recalibrate their infrastructure assumptions in real-time. The shifts are concrete:

• Budgets will shift from “GPU rental” to “domestic ASIC pre-orders.” Companies that lock in 2026–2027 delivery slots for Chinese training and inference silicon will enjoy a 2–3 year cost advantage that is almost impossible to claw back.
• Chain-choice will increasingly favor cost-efficient, high-throughput stacks. For projects that need micro-transaction economics and unbounded horizontal scaling, BSV blockchain with Teranode offers a compelling alternative to Western hyperscaler dependencies. Where Ethereum or Polygon CDK charge cents per transaction, BSV operates at fractions of a cent—critical when you’re running millions of AI-generated state updates per day. Expect explosive growth in ecosystems (Solana, Sui, Monad, and Canton-based private networks) that ship with native tensor libraries and ZK-ML toolkits.
• Talent is already moving. The hottest enterprise blockchain conferences in 2026 will be in Singapore and Hong Kong, not Miami or Paris. That is where the new hardware is flowing, and the new models are being fine-tuned.

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Key insight: Toward a post-dualistic future

The pendulum is indeed swinging East right now. Faster than most Western observers are willing to admit. But pendulums reach their apex and reverse. The deeper opportunity is to end the swing entirely.

When a supply chain can source chips from Shanghai, capital from New York, open-source intelligence from a global contributor base, and neutral law from Singapore, we stop asking “East or West.” We start asking “does it scale, is it verifiable, and can it run at $0.02 per million tokens?”

That world is no longer theoretical. It is being built today—mostly on Eastern silicon and Eastern open-source weights—by teams who refuse to accept that technological destiny must remain geographically bipolar.

The future of enterprise blockchain will not be Western, nor purely Eastern. It will be Singaporean in spirit: ruthlessly pragmatic, hardware-agnostic, and allergic to ideology. The sooner we embrace that synthesis, the faster we get there.

In order for artificial intelligence (AI) to work right within the law and thrive in the face of growing challenges, it needs to integrate an enterprise blockchain system that ensures data input quality and ownership—allowing it to keep data safe while also guaranteeing the immutability of data. Check out CoinGeek’s coverage on this emerging tech to learn more why Enterprise blockchain will be the backbone of AI.

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Watch: Demonstrating the potential of blockchain’s fusion with AI