Valuation Volatility of AI Unicorns: The Dual Game Between Technological Evolution and Capital Markets

Capital Market Revaluation: From Scarcity Premium to Rationalization

The company’s stock price demonstrated extreme volatility since IPO, surging from HK$116.20 to a peak of HK$2,980 before collapsing to the HK$622 range within three months, erasing nearly HK$1 trillion in market value. This rollercoaster reflects evolving capital market perceptions of AI commercialization pathways.

1.1 Scarcity-Driven Valuation Bubble
During initial listing, the “world’s first LLM stock” label created scarcity premium. Investors bet on monopoly positioning from technological first-mover advantage rather than profitability. Data shows institutional holdings reached 37% with daily southbound capital inflow exceeding HK$200 million, pushing P/E ratios beyond 200x. The turning point came with July’s 19.78 million new H-share placement, triggering a 28.49% single-day price drop as markets recalculated dilution effects.

1.2 Capital Operation Chain Reactions
Two critical financing events marked valuation inflection points:

• First placement: 19.78 million shares at HK$1,588/share raised HK$31.4 billion but increased float by 4.25%, diluting EPS
• Second financing: Shares priced at HK$714 (20% discount) alongside $3 billion zero-coupon convertible bonds with initial conversion price of HK$892.50 above market price

This “discount financing-valuation reset-conversion pressure” cycle exposed capital constraints during hypergrowth phases. New capital demanding lower entry prices inevitably triggers revaluation of existing shares.

Financial Paradox in Technology Commercialization

Half-year reports reveal fundamental conflicts between technological iteration and profitability, with revenue structure shifts masking cost crises.

2.1 Revenue Transformation

• MaaS platform: HK$825 million (86.5% of revenue), +520% YoY
• On-premise deployment: Revenue share plummeted from 84.8% to 13.5%

This shift reflects industry transition to cloud-native services. However, API call volume surges had dual effects: driving 399.7% revenue growth while increasing inference computing costs by 600%, with COGS jumping from HK$100 million to HK$702 million.

2.2 Gross Margin Collapse
Overall gross margin fell from 50% to 26.4% due to:

• Cost structure changes: API business margin improved from -0.4% to 24.6% but couldn’t cover HK$2.131 billion in R&D expenses
• Diseconomies of scale: Unit computing cost decline flattened after daily API calls exceeded 1 billion
• Customer concentration: Top clients contributed >60% of revenue, creating pricing pressure

Technological-Capital Market Game Theory Model

AI valuation volatility stems from misalignment between technology maturity curves and capital expectations. A three-dimensional framework enables more accurate trend prediction:

3.1 Technology Iteration Velocity (T)

T = (Model parameter scale × Data throughput) / Development cycle

When GLM series models expanded from 100B to trillion-parameter scale, required supercomputing cluster construction pushed CAPEX to RMB 4.8 billion with 18-month upgrade cycles.

3.2 Commercialization Efficiency (M)

M = (API calls × Unit price) / (Sales cost + R&D amortization)

With industry average pricing at $0.000003/1,000 tokens, break-even requires >50 billion daily calls. This explains why companies prioritize market share expansion despite margin pressure - network effects create critical data moats.

3.3 Capital Patience Coefficient (C)

C = (Financing rounds × Valuation multiple) / Stringency of performance clauses

In public markets, this manifests as PS ratio fluctuations. When PS dropped from 25x to 6x, it signaled capital shift from growth to value investing. Zero-coupon convertible bonds represent financial innovation to extend capital patience cycles.

Sustainable Growth Framework

AI companies need triple alignment between technology, commerce, and capital:

4.1 Technology Optimization

• Hybrid computing architectures reducing GPU dependency for inference tasks
• Model compression techniques cutting parameter counts by 40% while maintaining accuracy
• Federated learning platforms expanding use cases without centralizing costs

4.2 Commercial Innovation

• Tiered pricing: Volume discounts for high-frequency clients, premium pricing for niche segments
• Vertical industry solutions raising average contract values
• Developer ecosystem incentives through revenue-sharing models

4.3 Capital Strategy

• Debt financing optimization to reduce equity dilution
• Milestone-based funding terms aligned with technological breakthroughs
• API business securitization through ABS products

The current valuation volatility of this LLM unicorn epitomizes AI industry’s transition from technological hype to rational development. For developers, understanding these techno-economic dynamics holds greater strategic value than short-term stock monitoring. True sustainable growth requires constructing virtuous cycles of “technological breakthroughs → cost reductions → scale expansion → capital validation.” This game has no final endpoint, only evolving survival rules.