The Development of an Independent AI Ecosystem: How Local Chips and Advanced Algorithms Are Driving Change in 2026

Eight years have passed since the start of the geopolitical challenge. In 2018, international barriers ignited the semiconductor industry, and Chinese companies faced an unprecedented crisis. But by 2026, the story is vastly different. The global AI landscape reflects a significant shift—from technology dominated by a single superpower to a world where multiple pathways develop simultaneously. The key question is no longer “Can we survive,” but “What is the cost we must pay for computing independence?”

The True Cost: Ecosystem, Not Just Chips

Many believe the main concern is hardware. But the truth runs deeper. A platform known as CUDA—Compute Unified Device Architecture—is actually the real barrier for Chinese AI companies. NVIDIA launched this platform in 2006, providing developers direct access to GPU computational power. Initially, it was just a simple tool. But with the rise of deep learning, it became the industry’s foundation.

Training large AI models involves massive matrix operations—and GPUs shape this process. The CUDA ecosystem has grown over more than a decade, creating a comprehensive chain from hardware to application layer for AI developers worldwide. Today, all major frameworks—from Google’s TensorFlow to Meta’s PyTorch—depend heavily on CUDA infrastructure.

This ecosystem has become an unstoppable flywheel. The more developers use it, the more tools and libraries are built. As the ecosystem advances, more developers join. By 2025, CUDA has over 4.5 million developers and supports more than 3,000 GPU-accelerated applications. This means over 90% of AI developers worldwide rely on this ecosystem.

The problem isn’t just technical; it’s structural. If you want to switch to another platform, you must rewrite all the experience, tools, and code accumulated over a decade by the world’s best minds. Who bears this cost? That’s why, during the successive barriers from 2022 to 2024, Chinese AI companies chose not to fight head-on. They took the harder route—unlocking technological independence through innovation.

Algorithmic Breakthrough: How Cost Economics Transformed

From late 2024 to 2025, Chinese AI companies collectively pivoted to a new technical approach: the Mixture of Experts (MoE) model. The concept is elegant yet powerful—rather than enabling the entire large model for each task, split it into many smaller experts, activating only the most relevant components.

DeepSeek V3 exemplifies this. With 671 billion parameters, each inference uses only 37 billion—just 5.5% of the total. For training, it used 2,048 NVIDIA H800 GPUs over 58 days, costing $5.576 million. In comparison, GPT-4’s estimated training cost is nearly $78 million—a 15-fold difference.

This extreme optimization is directly reflected in pricing. DeepSeek API input costs range from $0.028 to $0.28 per million tokens, while GPT-4’s input costs $5. The Claude Opus model is even more expensive. The practical result: DeepSeek is 25 to 75 times cheaper than competitors.

This shift has a significant impact on the global developer market. By February 2026, on OpenRouter—the world’s largest API integration platform—the weekly usage of Chinese AI models surged 127% in just three weeks, surpassing the US for the first time. Annually, Chinese models account for less than 2% of the market share, but within a year, it rose to six percent.

The reason is structural. From the second half of 2025, AI’s primary application shifted from simple chatting to agent-based systems. In agent scenarios, token usage is 10 to 100 times higher than in regular conversations. When token consumption grows exponentially, price becomes a decisive factor. Here, the extreme efficiency of Chinese models aligns perfectly with emerging market demand.

From Inference to Training: The Qualitative Leap of Local Chips

One of the biggest milestones is the transition of local chips from inference-only capability to full training capacity. This is not just incremental improvement—it’s a qualitative transformation.

In Jiangsu Xinghua, a city once known only for steel and healthy food, a 148-meter production line for local computing power servers was completed in just 180 days from contract signing. The core components are two fully indigenous chips: the Loongson 3C6000 processor and the Taichu Yuanqi T100 AI accelerator—both designed entirely locally, from instruction set to microarchitecture.

In January 2026, Zhipu AI released the GLM-Image model together with Huawei, the first state-of-the-art image generation model fully trained using local chips. In February, China Telecom completed full-process training of their trillion-parameter “Xingchen” model on a local compute cluster in Shanghai Lingang.

The significance isn’t just the chips themselves, but the signal: local infrastructure is now viable for production-grade AI development. Inference requires only inference capability—low demand. Training demands handling large-scale data, complex gradient computations, extensive bandwidth, and a sophisticated software ecosystem. This is a fundamentally higher tier of requirement.

Leading the charge are primarily Huawei’s Ascend series. By the end of 2025, the Ascend ecosystem boasted 4 million developers and over 3,000 partners. Over 43 major industry models have been successfully pre-trained on Ascend, along with 200+ open-source adaptations. On March 2, 2026, at MWC, Huawei also showcased the SuperPoD infrastructure for overseas markets, with the Ascend 910B achieving FP16 computing parity with NVIDIA’s A100.

Building such an ecosystem didn’t start with perfect chips. It began with “good enough” chips deployed at scale, driven by real business needs as a catalyst for continuous improvement. The strategic targets of ByteDance, Tencent, and Baidu for local server adoption doubled in 2026 compared to 2025.

The Invisible Advantage: Energy as the New Competitive Frontier

While the world focuses on chip competition, a more fundamental constraint is growing in the background: energy.

In the US, Virginia suspended new data center permits in early 2026. Followed by Georgia, Illinois, and Michigan. According to the International Energy Agency, US data center electricity consumption reached 183 TWh in 2024—about 4% of the national total. By 2030, it’s expected to double to 426 TWh, testing over 12% of the country’s electricity supply.

Arm’s CEO warned that AI data centers alone could consume 20-25% of US electricity by 2030. The American grid is already strained. The PJM grid, covering 13 eastern states, has a 6 GW deficit. By 2033, the US faces a nationwide power shortfall of 175 GW—equivalent to the energy consumption of 130 million households. Electricity prices in regions hosting major data centers have increased by 267% over the past five years.

In contrast, China has an annual power generation of 10.4 trillion kWh—2.5 times US capacity. Even more critical, residential consumption in China accounts for only 15% of total, compared to 36% in the US. This means China has a larger industrial power capacity that can be dedicated to AI infrastructure.

Electricity costs are even more divergent. Industrial rates in western China have reached nearly $0.03 per kWh, while US rates for major AI hubs are $0.12–$0.15—four to five times higher.

Practical implication: while America worries about power constraints, China quietly scales its computing infrastructure. According to the Ministry of Industry and Information Technology, China’s manufacturing capacity has reached 1,590 EFLOPS. 2026 is the year of mass deployment of local computing power.

Tokens as the New Digital Commodity

This phenomenon creates a new economic reality. Tokens—the fundamental units of information used by AI models—are becoming a new digital commodity produced in Chinese computing factories and distributed globally via undersea cables.

Distribution of DeepSeek users illustrates this: 30.7% from China, 13.6% from India, 6.9% from Indonesia, 4.3% from the US, 3.2% from France. Supporting 37 languages, with particular adoption in emerging markets like Brazil. There are 26,000 companies worldwide with accounts, and 3,200 enterprises using enterprise versions.

In 2025, 58% of new AI startups integrated DeepSeek into their tech stacks. In China, the market share reached 89%. In other countries, the range is 40–60%, depending on region. This distribution pattern resembles a digital version of traditional trade—technology manufactured in one region, distributed globally, creating new economic dependencies.

The Historical Parallel: How Today’s Game Differs

A comparison to the 1986 Japanese semiconductor crisis is illuminating. At that time, Japan was at its peak—holding 51% of the global market, with six of the top ten companies Japanese. But after the US-Japan Semiconductor Agreement, the US used Section 301 investigations and strategic support for Korean competitors to dismantle Japan’s position. Japan’s DRAM market share plummeted from 80% to 10%.

Japan’s tragedy was rooted in single-path dependency—better production but no independent ecosystem. When market access was cut, they had no backup strategy.

Today’s Chinese position is strategically different. It’s not defensive. Every layer—from algorithm optimization to local chip development, energy infrastructure, and global token distribution—is deliberately built to be independent. Each loss in chip competition is a direct cash cost for ecosystem building. But it’s a necessary wartime tax for establishing a truly autonomous infrastructure.

The Year 2026: Half Fire, Half Water

On February 27, 2026, three reports from local AI chip companies were released simultaneously. Cambrian—revenue up 453%, first full-year profit achieved. Moore Threads—revenue up 243%, but net loss of $1 billion. Muxi—revenue up 121%, net loss of $8 billion.

The pattern: half fire, half water. The fire is the market’s hunger for alternatives. Huang Renxun’s 95% market dominance makes it impossible for NVIDIA to monopolize AI infrastructure—and each local company’s financial report proves the market is willing to accept suboptimal technology if given a choice.

The water—losses—are the real cost of ecosystem building. Each loss is accumulated spending on ecosystem development, software subsidies, and on-site engineering support for customers. This isn’t a sign of failure; it’s wartime economics for building independence.

This transformation isn’t a celebration. It’s a brutal war report where soldiers rise while bleeding. But the very nature of the war has fundamentally changed. Eight years ago, the question was “Can we survive?” Today, it’s “How much should we spend for freedom?” Paradoxically, the cost itself is an indicator of true progress.