MOVING MARKETS: Amid a 90-day tariff reprieve window in May, the battle for AI dominance intensified in China’s semiconductor underworld.
“Prices for AI servers have been volatile, with high-end models previously surging 15-20%. The tariff pause allows us to restore pricing where possible,” revealed a southern China chip supplier to Cnbc AI.
Meanwhile, supply channels are evolving through shadow networks. EXCLUSIVE: Cnbc AI has learned that Nvidia’s Hooper series (H200) and Blackwell models (B200) have quietly entered Mainland markets, with Hooper arriving in September 2024 and Blackwell more recently. “Sourcing paths vary across suppliers,” noted a CMC chief, without elaborating on the opaque logistics pipelines.
(EDITOR’S NOTE: Since October 2023, Washington has implemented phased bans on Nvidia chip exports to China including H100, H800, A100 and H20 models, with H20 recently added to the restricted list)
The H200, a 30% more efficient upgrade from H100 at a mere ¥200,000 premium, remains a belled cat priced at ¥3 million for the B200 variant. These premium GPUs remain critical for Large Language Model pre-training and have created a lucrative black market leasing ecosystem, with some suppliers claiming 100-unit weekly H200 availability through undisclosed channels.
SIGNIFICANT SHIFT: Following黄仁勋’s personal delivery of initial H200 shipments to OpenAI in April – as evidenced by viral social media photos with Sam Altman – within five months the technology had circumvented bans to reach a critical mass of Chinese resellers. As H100 production ended, the exclusive club of H200 suppliers shrank below ten entities, creating near-insatiable demand from cloud giants like Alibaba and Tencent.
“We’re witnessing acute H200 scarcity – recently a major cloud player approached us through six intermediaries,” shared a veteran executive whose firm processes 40% of China’s AI compute contracts. Intriguingly, formal agreements now standardize billing through abstract “P” units rather than specifying hardware models, cloaking the true infrastructure in opaque financial engineering.
UNDERGROUND NETWORKS: While early gateways utilized multi-layered reseller systems masking origin sources, our sources reveal newer strategies embedding modules as “intermediary goods”. This regulatory arbitrage has created hybrid supply chains stretching from Shenzhen warehouses to Houston financial hubs, with freight invoices listing servers as machining equipment or auto components.
EMERGING PARADOXES: This shadow IT market now confronts structural contradictions as the AI ecosystem matures. Let’s dissect the bubble dynamics through three lenses.
Ai Arms Race: From Speculation to Specialization
When Washington tightened restrictions at year-end 2023, it inadvertently spawned a chip black market ecosystem that resembled cryptocurrency arbitrageurs. Early players included overseas-educated traders leveraging personal import networks and grey-market dealers exploiting customs logistical gaps.
These pioneers created artificial scarcity that inflated Nvidia’s A100 to ¥128,000 per unit – 1280% above MSRP. By November 2024, handheld footage showcasing boxed H100 chips selling for ¥250,000 sparked a marketwide speculative frenzy that couldn’t be sustained.
BUBBLE INDICATORS: The bubble’s bursting point came with DeepSeek’s release of commercial-grade open-source models that negated the need for hyper-expensive proprietary silicon. Today, only 16 of 458 approved AI data centers are operational, with key metrics showing:
- 58% (95) of new 2025 projects remain uneconomic on paper
- 80% utilization rate disparity between H200 clusters and legacy equipment
- 43% inventory spike from restricted chip stockpiles
Contrast this with Meta suspending $16 billion in global data center projects and Microsoft mothballing two Azure locations. According to Feng Bo, Managing Partner at Changlei Capital, “When training isn’t democratized, only qualified entities continue investing in clusters. Everyone else becomes market garbage.”
SHIFTING AMBITIONS: The Great Compute Migration
What happens when bloated assets meet evolving demand? Clients ranging from Lotus Pharmaceutical to Jinji Technology have terminated ¥100+ million compute leasing contracts in 2025 alone. However, innovative models are emerging where compute providers are using venture capital as arbitrage instruments.
Our sources uncovered a compute supplier chairman whose business card lists affiliations with three AI clusters plus a VC incubator backing robotics/AI infrastructure startups. “We guarantee 100% compute demand for investee companies at preferential pricing,” he admitted under condition of anonymity.
Feng Bo terms these “M&A disguised as compute contracts,” with an alternative model seeing Compute LPs investing in venture funds for priority access to portfolio companies’ procurement budgets. This creates a capital recycling mechanism where fund investments convert to guaranteed compute revenue.
WHAT REMAINS AFTER THE STORM
“The bubble discussion misses the industrial pipeline disconnect. True adoption requires connecting three key segments of the value chain,” shared a veteran CTO following this space.
Current trends show AI demand transitioning from pre-training to inference workloads. However, fulfilling this need isn’t simply throwing more hardware at the problem:
- Technical Bottlenecks: Inferencing optimization requires engineering breakthroughs in latency reduction and throughput management, where Chinese silicon still lags
- Data Deficits: Healthcare AI development suffers from only 5% of medical data being cleansed for training, with rare disease datasets locked in institutional silos
- Ecosystem Gaps: Shortage of industry-specific L2 vertical models persists across sectors
Yet new platforms are emerging that combine hardware farms with algorithm teams deploying MaaS models. For instance, automakers like BYD are building petascale compute infrastructures for L4 autonomous driving R&D, underscoring bullish long-term demand.
The government meanwhile has rolled out “compute vouchers” – subsidies reducing corporate user costs. But Feng warns, “What the industry needs isn’t emergency treatment but systemic rejuvenation of the entire ecosystem.”
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