NVIDIA’s stock has climbed nearly 200% over the past two years, minting fortunes for shareholders and unlocking billions in capital for U.S. AI startups. Chinese AI companies are advancing just as quickly, but their equity values have not achieved quite the same growth.
In 2025, DeepSeek released a model rivaling OpenAI’s offerings at a fraction of the development cost. Alibaba’s Qwen has challenged Meta’s Llama in enterprise adoption. U.S. startups now build products on Chinese open-source models drawn by token prices that, in some cases, are 90% lower.
Yet Chinese AI firms haven’t yet commanded the valuations enjoyed by Silicon Valley firms. That gap may become a challenge when founders need flexible capital. It’s precisely this dynamic—strong execution, constrained access to liquidity but high growth potential — that creates a potential need for alternative financing providers like EquitiesFirst.
“Chinese AI companies are iterating faster and shipping AI solutions in a more cost-effective manner, defying expectations,” says Al Christy Jr., founder and CEO of EquitiesFirst. “While their technical capabilities are unquestionable, their equity positions may not yet unlock the same liquidity that AI firms in the West take for granted.”
Equity-Backed Financing
Zhipu’s IPO could shift this calculation. The company completed its IPO on January 8, 2026, raising HK$4.35 billion and becoming the first major Chinese large language model developer to go public. Shares priced at HK$116.20 closed the first trading session at HK$131.50, a 13.2% gain that valued the company at HK$57.5 billion, or approximately $7.4 billion.
Strong public reception could lift valuations across the sector, potentially creating liquidity for other founders and early investors. Yet even with improved valuations, accessing capital without selling shares remains difficult through traditional channels.
“Equity-backed financing addresses a specific friction,” Christy explains. “Founders and executives have built positions in public companies, but converting that into working capital without triggering sales or losing upside has historically been difficult.”
Price Wars and Profitability
Chinese AI firms face a monetization problem that their U.S. counterparts largely avoid. Enterprise customers in China resist premium pricing for AI services. Frugal engineering culture has triggered aggressive price competition that makes sustainable profits elusive.
Bloomberg Intelligence projects Chinese AI hyperscalers will spend far less than U.S. peers through 2027, while confronting margin pressure from relentless cost-cutting.
The result: Chinese AI companies operate with discipline that Western venture capital rarely demands. They iterate rapidly, accept “good enough” performance, and design for resource-constrained environments. Financial pragmatism can become a competitive advantage, but only if liquidity holds.
Given the soaring costs involved with AI capex and the high costs of recruiting AI researchers, Chinese AI companies need to secure access to flexible credit.
Hardware Progress, Energy Subsidies
China’s semiconductor situation remains complex as well. Domestic chips from Huawei and Cambricon still lag Nvidia’s hardware for large-scale model training. Last year, DeepSeek encountered stability problems when attempting to train exclusively on Huawei’s Ascend processors, for example, ultimately reverting to Nvidia chips for training while using Chinese hardware for inference.
At the same time, inference is the commercially dominant application of AI deployment, and Chinese chips have proven sufficient so far. It’s why Cambricon reported record profits as ByteDance and Tencent increased purchases of domestically produced chips for inference-based large language models.
It may not be long until Chinese semiconductor technology achieves world-class status. National R&D investment has surged 48% since 2020, and China has the world’s largest pool of AI research professionals. Beijing’s forthcoming 15th Five-Year Plan is expected to reinforce semiconductor and AI priorities, signaling continued policy support for domestic capability.
Energy economics provides another advantage. NVIDIA CEO Jensen Huang noted that provincial subsidies have made power costs negligible for major Chinese data centers operated by ByteDance, Alibaba, and Tencent. When electricity increasingly determines the effectiveness of AI infrastructure, this subsidy will matter.
Adoption Without Valuations
Chinese models are being downloaded globally. U.S. developers increasingly rely on Chinese infrastructure for cost-sensitive applications. China’s export machine has also proven resilient: exports rose by two-fifths since 2019, even as imports stagnated. Similar dynamics could emerge in the AI race: competitive pricing and rapid iteration could lead to China becoming a services export powerhouse.
But adoption doesn’t always translate to valuations. Chinese AI firms can’t access equity markets that tolerate prolonged losses the way Silicon Valley does. Founders who hold their wealth in shares may not be able to unlock capital as easily as comparable U.S. founders would be able to.
EquitiesFirst offers financing secured by publicly traded shares, allowing borrowers to access liquidity while maintaining long-term exposure. The structure has gained traction in markets where entrepreneurs hold concentrated positions. Chinese AI is a natural category where equity-backed financing can make a difference. While technical progress is accelerating and equity positions are building, traditional credit may remain scarce or difficult to access.
China’s approach to AI development — policy coordination, cost discipline, rapid deployment — differs fundamentally from the venture-funded model dominating Silicon Valley.
The question now is whether it can sustain the development cycle long enough to matter. As more AI firms are listed and founders and other executives are awarded shares for their hard work, access to flexible capital through equity-backed structures will surely play a major role.