As the AI sector undergoes periodic adjustments, discussions about whether "AI has a bubble" have reignited. On December 5, Kong Rong, Deputy General Manager of the Research Institute and Chief Overseas Analyst at Guolian Minsheng Securities, noted in a live dialogue that every market adjustment and skepticism accompanies the industry's own progression rhythm. Since the generative AI wave triggered by ChatGPT, this cycle of "reaching new highs amid doubts" has repeatedly played out.
Kong Rong argues that the core trigger of the current "bubble" debate lies not in technological failure or dim prospects but in a "rhythmic mismatch" between the objective laws of industrial development and the subjective expectations of capital markets. As a profound transformation, AI's penetration and reshaping of industries are gradual. However, investors often expect to realize growth dividends meant for years or even a decade within a short timeframe, leading to extreme swings between optimism and pessimism. When technological progress or commercialization temporarily lags behind overstretched expectations, "bubble theories" emerge. Such expectation-driven volatility has become a market norm over the past few years, not a signal of industrial trend reversal.
How does this round of "AI bubble" discussion fundamentally differ from historical tech bubbles, particularly the dot-com bubble? Kong Rong explains that the internet's core value lies in "information connectivity," which reshaped information flow and birthed platform economies. In contrast, AI's essence is "productivity enhancement," directly intervening in production and creation processes to optimize workflows, boost efficiency, and spur innovation. This distinction means AI creates value more directly and fundamentally. Visible efficiency leaps from AI tools in advertising, e-commerce, content generation, and coding are evident monthly or quarterly. As a general-purpose technology, its breadth and depth of impact far exceed mere connectivity.
Kong Rong further highlights that leading AI firms have demonstrated clear commercialization paths and healthy financial models. Taking tech giant Google as an example, breaking down its financial reports reveals that if AI-related capital expenditures are amortized over reasonable depreciation periods, the incremental revenue driven by AI already covers or exceeds these investments, validating the ROI model. Moreover, valuation-wise, many core AI hardware companies trade at around 20x forward P/E multiples based on next year's earnings forecasts—far from extreme valuations during historical bubbles and still within reasonable growth-stock ranges.
Kong Rong emphasizes we are in the first phase of AI transforming the world, with the revolution just beginning. From code generation to scientific discovery, countless industries and scenarios await deeper integration with AI. Short-term market doubts won’t alter the long-term trend of accelerating technological iterations (e.g., multimodality, memory capabilities, personalization) and application deployments.
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