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

The article distinguishes between artificial intelligence (AI) as a technology and the creation of successful AI businesses, emphasizing that understanding the rhythms and patterns of AI commercialization is essential for technology developers, enterprises, and investors. Without this understanding, participants risk failure by blindly chasing trends rather than building sustainable business models [para. 1]. The American “Seven Sisters”—the seven largest U.S. tech companies—are presented as a classic case study for understanding AI’s commercial evolution and serve as a reference point for evaluating China’s AI sector [para. 2].

Initially, China’s tech giants, dubbed the “Terrific Ten,” outperformed the U.S. Seven Sisters in early 2024, fueled by excitement over local AI breakthroughs like DeepSeek-R1. At their peak, the Chinese group’s returns surpassed the Seven Sisters by 60%. However, as global markets endured downturns and recoveries, this lead diminished to 26% by July, with sharp divergences emerging among individual Chinese firms. Stock performance indicated the market’s waning enthusiasm and growing scrutiny of fundamentals, raising doubts about whether China’s AI boom could mirror the U.S. experience [3–7].

The article then reviews the Seven Sisters' journey post-ChatGPT, showing their rise from a low point in late 2022 as each faced significant market and operational challenges. From Apple’s declining smartphone growth to Nvidia’s market cap losses and Meta’s struggles with its metaverse strategy, all seven companies experienced substantial setbacks, with market values dropping substantially before rallying to a combined $20 trillion in two years [12–20].

The ascent of the Seven Sisters unfolded through seven distinct stages: initial concern over disruption after ChatGPT (with public fear they’d be overtaken), companies finding and articulating new AI-driven narratives, a phase of financial market skepticism demanding tangible results, validated by massive infrastructure investments and breakthroughs (especially from Nvidia), followed by renewed fears as technological progress plateaued, then a broadening of AI industrial applications, and ultimately disruptions stemming from cost-effective innovations like DeepSeek’s inference models, which challenged incumbents’ fundamentals [21–56].

The article attributes commercial AI success to three “wheels”: technology (engine), business (converter), and capital (fuel), each with unique rhythms and uncertainties. Technology requires not just innovation, but alignment with business needs and capital support, as seen by the different market outcomes for OpenAI and Anthropic despite similar tech prowess. From a business view, companies must craft strategic narratives, make bold investments, pursue trial-and-error, and ultimately convert these efforts into strong fundamentals—measured by metrics like return on equity (ROE) [58–89].

Capital markets oscillate between optimism and skepticism, as reflected in cycles of market cap evaporations and rebounds. The U.S. Seven Sisters excelled by maintaining focus on fundamentals and long-term strategies rather than market sentiment [90–102].

Regarding Chinese AI, while the technology (like DeepSeek and Qwen) now rivals U.S. models, large-scale business deployment and capital commitment lag behind. China’s tech companies have not matched the Seven Sisters’ level of business or profit growth, partly due to differences in ecosystem structure and monetization pathways (China’s being more consumer-oriented, while the U.S. benefits from mature enterprise SaaS models). Sustained market favor will only go to those that communicate a compelling AI vision, make strategic investments, and show real improvements in business performance [117–150].

In conclusion, success in AI commerce arises from the effective coupling of technology, business, and capital. While each market—U.S. or China—follows its unique rhythms, the lesson is that long-term winners will be those who can continually align these three wheels, convert technological advances into business fundamentals, and adapt as the AI landscape evolves [151–172].