As the third anniversary of ChatGPT approaches, the technological landscape continues to shift dramatically under the influence of generative AI. Business leaders, grappling with rapid advancements that evolve almost daily, are increasingly bewildered by the unpredictable trends. On this significant milestone, Zhang Yu, a strategy professor at the China Europe International Business School (CEIBS), refrains from forecasting technological trends, instead urging a return to foundational principles of business strategy in this era of uncertainty. He emphasizes the importance of identifying certainties amidst the chaos.
Over the past three years, generative AI technologies have fundamentally altered the fabric of society, increasing productivity across sectors and streamlining tasks traditionally performed by entry-level workers. While these advancements have resulted in cost reductions and heightened profits, they also pose threats to employment, especially for younger professionals. Most observers concur that this evolution represents the “Fourth Technological Revolution,” following earlier waves initiated by the steam engine, electricity, and the computer/internet revolution.
The unprecedented impact of generative AI can be attributed to its capacity to fulfill diverse and personalized needs while achieving economies of scale, which lowers marginal costs. As noted by economists Carl Shapiro and Hal R. Varian, the efficacy of technology hinges on its ability to meet user demands while becoming increasingly scalable. Unlike previous technological advances that addressed relatively homogeneous needs, generative AI caters to a wider array of individual requirements, significantly enhancing both personal and business efficiencies.
However, the rapid evolution of AI technology has left many business leaders questioning how to formulate effective corporate strategies. Zhang highlights that as strategies adapt to this new landscape, it is crucial to identify which elements remain constant. Quoting Jeff Bezos, the founder of Amazon, he reflects on a fundamental question: “What won’t change in the next 10 years?”
One core area of focus should be on user value creation, according to a contemporary value-based strategic management framework. The value produced by a business is the difference between customers’ willingness to pay and suppliers’ opportunity costs. This value creation will dictate business opportunities. Using Bain & Company’s user value pyramid, Zhang illustrates that most current applications of generative AI predominantly focus on functional value, such as efficiency and cost-effectiveness. This has led businesses to compete primarily on technical specifications, often resulting in a cycle of diminished returns.
In contrast, emotional, life-changing, and social impact values remain less competitive yet hold significant potential for differentiation. Brands that tap into these higher-value layers, such as Manner and Rolex, can command higher prices. Despite initial successes in emotional and gaming applications of generative AI, a widely appealing “killer app” akin to WeChat has yet to emerge.
Thus, businesses must shift their strategic focus toward creating emotional and social impact values through generative AI. Alongside this, enterprises should also aim to enhance their unique contributions within the value creation process, as highlighted by the concept of “added value.” This refers to the difference between the total value created with a company’s participation versus without it. Companies like NVIDIA, TSMC, and ASML exemplify this principle, having captured substantial market value through their unique contributions in the AI sector.
The fluctuating market dynamics, illustrated by Google’s recent advancements with its Gemini 3 and TPU technology, pose challenges for established players like NVIDIA, prompting concerns about their market dominance. Meanwhile, applications at the downstream of the AI industry have not seen similar standout innovations, further complicating the landscape for businesses vying for competitive advantage.
To ensure sustainability, businesses should consider their future strategies within the context of traditional moats like brand loyalty and proprietary technology, while also establishing economies of scale and network effects. Historical analysis suggests that successful enterprises often achieve both, as seen with giants like Amazon and Tencent. NVIDIA’s multi-sided network effect, established through the CUDA development environment, underscores the importance of building such strategic advantages in a competitive AI landscape.
The future of generative AI, while promising, is mired in uncertainty. Key questions remain, including whether generative AI can achieve causal reasoning akin to general artificial intelligence, or how to effectively address data security and information pollution concerns. Moreover, it remains to be seen if the significant investments in AI infrastructure will translate into measurable gains in downstream applications.
Nevertheless, as organizations navigate this tumultuous era, Zhang urges them to concentrate on creating user value, defining their unique contributions, and proactively building strategic moats. Only through this approach can they develop robust corporate strategies that secure a competitive edge in the evolving AI landscape.
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