Commentary: What China’s Tech Giants Can Learn From U.S. AI Investment

The article analyzes the evolution of AI commerce by contrasting the development and capital market performance of leading U.S. technology companies—dubbed the “Seven Sisters”—with Chinese tech firms, delineating seven distinct stages of AI-driven market transformation and offering a framework for understanding the rhythm of AI business ecosystems. The key thesis is that technological innovation alone does not create lasting value in AI—successful AI businesses must effectively combine technology (the engine), business application (the converter), and capital (the fuel), each with its own rhythm and feedback cycle. [para. 1]

The piece starts by clarifying that AI technology is fundamentally different from the AI business ecosystem. For firms and investors to benefit from AI, it is essential to appreciate the timing, interplay, and patterns that shape how AI technologies become commercial successes, rather than indiscriminately pursuing hype. The example of the U.S. “Seven Sisters” serves as a case study for this process, which also holds lessons for Chinese tech development. [para. 1][para. 2]

The article chronicles the dramatic market fluctuations in 2024 and 2025, especially in response to new Chinese AI models like DeepSeek-R1. Initially, China’s “Terrific Ten” outperformed their U.S. peers with a 60% cumulative lead, leading to enormous speculation around “China’s first trillion-dollar company.” However, following the U.S. tariff “Liberation Day” on April 2, sentiment cooled; by July, U.S. firms had rebounded with 15% gains from pre-tariff levels, while the Chinese tech cohort merely recovered to their prior valuations, and disparities among top Chinese firms widened. [para. 3]

Market reactions to strong business results from Alibaba and Baidu highlighted that Chinese firms’ AI narratives and capital expenditures have not yet translated into market confidence or sustained valuation surges. This hinted at a broader challenge: can China’s tech giants replicate the U.S. model, and what are the patterns to follow? [para. 4][para. 5][para. 6]

By retracing the Seven Sisters’ history, the article illustrates that before ChatGPT’s November 2022 release, these companies faced stagnation, slumping stock prices, and dim prospects. None had positive annual growth, and some saw market values plunge over 50%. Yet, within two years, their combined capitalization soared from $8 trillion to nearly $20 trillion—over 30% of the S&P 500. This transformation occurred in seven stages:

1. Initial fears of disruption and uncertainty about AI’s impact (late 2022 to early 2023), with tech giants perceived as vulnerable to upstarts. [para. 20][para. 21][para. 22][para. 23][para. 24][para. 25][para. 26]

2. The rise of AI narratives and a capital market rally, as large U.S. tech firms articulated compelling AI strategies and started integrating AI into their businesses, triggering market euphoria. [para. 27][para. 28][para. 29][para. 30][para. 31][para. 32][para. 33]

3. Demand for financial proof, with market corrections when AI investments did not immediately translate into revenue, e.g., Azure’s slow AI growth. [para. 34][para. 35]

4. Massive infrastructure investments, validated by surges in hardware and chip company performance, e.g., Nvidia’s 500% revenue growth. [para. 36][para. 37]

5. Concerns about another technology slowdown and negative sentiment triggered by missed milestones and supply-chain disruptions. [para. 38][para. 39]

6. Industrial AI application advances, especially in intelligent agents, shifting capital market focus to software firms with demonstrated AI impact on fundamentals, as shown by Palantir’s 340% annual market cap jump. [para. 40][para. 41]

7. Disruptive efficiency gains from new Chinese models driving another market correction, but U.S. firms generally maintaining robust fundamentals despite valuation swings. [para. 42][para. 43][para. 44][para. 45]

The author distills three critical “wheels” for AI commerce: [para. 46]

- Technology must mesh with capital and business. Despite frequent breakthroughs (text, vision, inference), commercialization requires not only inventiveness but also market fit and capital support. Differences between OpenAI and Anthropic showcase how strategic definition and market acceptance, not just technical capability, drive outcomes. [para. 47][para. 48]

- Business requires storytelling, strategic investment, experimentation, and ultimately, conversion into revenue and profitability. U.S. Seven Sisters succeeded because of strong vision articulation, strategic AI investments, business model adjustment, and efficiency gains (e.g., Meta’s AI-driven advertising growth), resulting in high returns on equity consistently. [para. 49][para. 50][para. 51][para. 52][para. 53][para. 54]

- Capital acts as both a hypothesis engine and a falsification mechanism, needing forward-looking bets but disciplined by market cycles and sentiment swings (the “Gartner Hype Cycle”). Companies must communicate clearly to synchronize the three cycles, but volatility is inevitable. [para. 55][para. 56][para. 57][para. 58][para. 59][para. 60][para. 61]

Applying this to China, the current AI surge has not yet fully translated from technology (driven by DeepSeek, Alibaba’s Qwen, etc.) to durable business outcomes or market capitalization. Investment intensity and clear profit uplift lag U.S. figures, partly because China’s monetization ecosystem, especially for B2B digital services, is less mature, and future Chinese AI profitability may hinge more on consumer value creation. Only companies that articulate coherent AI strategies, undertake sustained investment, and deliver strong fundamentals will win lasting capital market support. [para. 62][para. 63][para. 64][para. 65][para. 66][para. 67][para. 68][para. 69][para. 70][para. 71]

In short, the evolution of successful AI commerce is an ongoing process of generating, testing, and refining hypotheses across technology, business, and capital dimensions—the companies that can align these “three wheels” and translate vision into solid fundamentals will emerge as the true beneficiaries in the era of AI. [para. 72][para. 73][para. 74][para. 75][para. 76][para. 77][para. 78][para. 79][para. 80][para. 81][para. 82][para. 83][para. 84][para. 85][para. 86][para. 87][para. 88][para. 89][para. 90][para. 91][para. 92][para. 93][para. 94][para. 95][para. 96][para. 97][para. 98][para. 99][para. 100]