Zheng Quan Ri Bao·2025-11-12 16:24Core Viewpoint - The divergence in valuations of AI companies reflects a clash between the technological revolution and capital market pricing logic, highlighted by contrasting perspectives from optimistic and cautious investors [1][2]. Group 1: Investment Landscape - Major tech giants like Microsoft, Google, and Amazon are leading the current AI investment wave, supported by strong cash flows and strategic planning [1]. - The optimistic view is that AI investments are beginning to yield revenue and profit growth, creating a positive feedback loop, unlike the internet bubble era where many companies relied on financing without profitability [1]. - Conversely, concerns arise regarding the sustainability of business models for top AI unicorns, as their valuations significantly exceed revenue scales [1][2]. Group 2: Valuation Challenges - The root of valuation discrepancies lies in the friction between the technological revolution cycle and traditional capital market pricing models, with classic DCF models struggling to assess disruptive technologies still in the investment phase [2]. - There is a divide in market perceptions regarding the "moat" of leading AI companies, with optimists believing in their competitive advantages and skeptics pointing to rapid technological iterations and regulatory uncertainties [2]. Group 3: Investment vs. Speculation - It is crucial to distinguish between "investment" and "speculation," with true investments focusing on companies with core technologies and clear business models that enhance efficiency and create new products [2]. - Speculative investments chase concepts and rely on market sentiment, making discernment essential in the current market [2]. Group 4: Key Performance Indicators - Investors should prioritize tangible milestones in commercial applications over mere narratives, focusing on actual cost reductions and revenue contributions from AI technologies [3]. - Companies that can consistently translate technological advantages into financial performance are more likely to succeed in the long term [3]. Group 5: Infrastructure Constraints - The explosive growth of AI is constrained by infrastructure limitations such as energy supply and grid capacity, which could delay technology adoption and impact data center costs [3]. - These constraints serve as a critical test for the viability of AI concept stocks [3]. Group 6: Market Reflection - The ongoing valuation divergence in global AI investments represents a necessary phase for the capital market to find reasonable pricing for an emerging technological revolution [3]. - This debate encourages deeper market reflection and helps avoid blind speculation, emphasizing the importance of maintaining a balanced perspective amid enthusiasm for AI advancements [3].