The Capital Market Roller Coaster: From Trillion-Dollar Myth to Valuation Collapse

In a 2023 trading session, a Hong Kong-listed AI company witnessed its stock price surge 300% within 90 minutes of opening, reaching a market cap exceeding HK$1.3 trillion and setting a record for Chinese AI enterprise valuations. However, just three months later, its stock price plummeted 80% from its peak, erasing the trillion-dollar valuation. This rapid rise and fall underscore the unique valuation logic and market sentiment volatility in the AI sector.
The company’s market cap trajectory exhibited a classic “double-peak” pattern: the initial surge stemmed from scarcity premium, while the subsequent collapse exposed fundamental issues in business model sustainability and technological implementation capabilities. At its peak, the company’s price-to-sales ratio reached 1,200x—far exceeding the 20-30x range typical for traditional tech firms. This valuation bubble’s formation and burst provide a vivid case study for the industry.

The Triple Support System for Scarcity Premium

1. Technological Scarcity Construction

The company established a differentiated competitive edge through a complete technology stack comprising “foundational models + development toolchains + application ecosystems.” Its core model achieved industry-leading performance in multimodal understanding and code generation, with over 500,000 registered developers in its community creating network effect barriers.

2. Capital Scarcity Engineering

During its IPO phase, the company manufactured supply-demand imbalance by restricting floating shares to just 2.8% of total equity. Combined with strategic investor lock-up periods, less than 15% of shares were actually tradable. This artificial scarcity became a key leverage for driving up stock prices, with first-day turnover reaching only 12%—indicating highly concentrated ownership.

3. Market Scarcity Positioning

As the first pure LLM company listed in Hong Kong, the firm filled a specific niche in capital markets. At the time, no major global AI companies were publicly traded, and only one comparable domestic peer existed, creating “uniqueness” premium. Institutional surveys revealed that 65% of incoming capital prioritized “scarce asset” status as their investment rationale.

Four Catalysts Behind the Valuation Bubble Burst

1. Technological Commercialization Bottlenecks

Despite mid-year reports showing 380% revenue growth, losses expanded to RMB 4.7 billion. Model training and inference costs grew exponentially, while commercialization remained primarily API-based with declining unit compute revenue. This “growth without profitability” paradox shook investor confidence.

2. Capital Exit Pressures

The company completed three funding rounds (including IPO) within nine months, raising HK$75 billion total. However, capital efficiency came under scrutiny: 40% went to compute procurement, 30% to talent acquisition, and 25% remained idle. This “burn-for-growth” model became unsustainable amid rising interest rates.

3. Competitive Landscape Shifts

Three industry trends emerged: leading cloud providers offered free foundational models, open-source communities outpaced proprietary model iteration speeds, and vertical-specific models began cannibalizing general-purpose model demand. These changes rapidly eroded the company’s technological moats, with customer retention dropping from 85% to 62%.

4. Regulatory Headwinds

Data security reviews and algorithm registration requirements forced the company to allocate an additional 15% of revenue to compliance. Meanwhile, interim regulations on generative AI services restricted certain high-risk applications, directly impacting commercialization potential.

Industry Insights and Strategic Responses

1. Valuation Model Reconstruction

Propose a three-dimensional valuation framework: “Technology Maturity × Commercialization Capability × Policy Risk Coefficient.” Technology maturity could be quantified through model iteration cycles and developer ecosystem scale, while commercialization capability should focus on customer retention and unit compute revenue metrics.

2. Capital Operation Optimization

Companies should establish a virtuous cycle between R&D, commercialization, and capital. For example, allocating 30% of raised funds to vertical model development in high-margin sectors and 20% to developer revenue-sharing programs to create sustainable cash flow. Gradual lock-up expiration could also prevent concentrated selling pressure.

3. Technology Roadmap Balancing

Maintain dynamic equilibrium between general-purpose and specialized models. Reference cloud providers’ “1+N” strategies: sustain foundational model iteration while developing domain-specific models for finance, healthcare, etc. Data shows specialized models can achieve 2.3x the gross margin of general-purpose alternatives.

4. Risk Mitigation Mechanisms

Establish a three-tier risk control system covering technology reserves, cash flow management, and policy response. Maintain 12 months of operating capital, deploy multi-region data centers to diversify regulatory risks, and form technology alliances with industry partners to collectively address open-source challenges.

Future Outlook: The Rationalization of AI Valuations

The AI industry is undergoing a paradigm shift from “technology faith” to “commercial reality.” Gartner predicts that by 2026, AI companies demonstrating commercialization capabilities will command over 300% valuation premiums, while those relying solely on technological narratives will face sustained compression. This necessitates building complete “technology-product-commercialization” loops to optimize balance points between compute costs, model efficiency, and application scenarios.
For developers, key focus areas should include model lightweighting, edge computing, and vertical optimization. Platform-developed model compression tools now reduce parameter counts by 70% while maintaining over 90% accuracy—reshaping industry cost structures. Developers should also actively participate in ecosystem building through code and dataset contributions to elevate their value chain positions beyond mere technology consumers.
This trillion-dollar valuation roller coaster essentially represents capital markets’ correction process for AI industry perceptions. When the tide recedes, companies with genuine technological moats and commercialization capabilities will ultimately prevail. The lessons and insights from this valuation oscillation will continue shaping the industry’s developmental trajectory for years to come.