Industry Reshaping Under the AI Wave: A Paradigm Shift from Generative to Physical Intelligence

Industry Reshaping Under the AI Wave: Paradigm Shift from Generative to Physical Intelligence

Artificial intelligence is no longer an edge technology but a core driving force reshaping social structures and value systems. With the explosion of generative AI, we are at a structural inflection point, a change that concerns not only the enhancement of efficiency but also the redefinition of human value and fundamental adjustments to social forms.

I. Redefining Social Values and Addressing Demographic Challenges

The penetration of AI demands that society confront deeper structural adjustments. Amid macro challenges such as population aging and potential labor shortages, AI is expected not only to augment human capabilities but also to "augment, expand, and substitute" existing social systems. This foreshadows unprecedented pressure on traditional definitions of professions and value systems.

More critically, the proliferation of AI forces us to re-examine the definition of "humanity." When AI provides rational solutions, human value will increasingly manifest in decisions that respect imperfect rationality, human emotions, empathy, and ethics. This requires that "human dignity" and "human perspective" be prerequisites in the design and deployment of AI technology, ensuring that technological development does not slide into an inhuman state.

II. Disruption of Industry Value Chains and Shift in Competitive Focus

The implementation of AI, especially the rapid development of general-purpose and industry-specific AI based on Large Language Models (LLMs), is reshaping existing value chains. Intermediate processes traditionally reliant on large amounts of human labor will be simplified or eliminated through AI and automation. This means the center of industry competition is shifting from mere "model development" and "infrastructure races" (such as chips and data centers) to areas with greater implementability and commercial value.

The future competitive focus will shift from "who owns the most powerful AI infrastructure" to "who can successfully transform AI into monetizable applications and business models." This implies that the ability to effectively "Run the Business" (Run the Business, RTB)—to design, implement, and operate services and systems—will become the key to long-term survival, rather than just accumulating technological barriers.

III. From Generative AI to Physical AI: Moving Towards "AIoT" and "AIoS"

The evolutionary path of AI is accelerating from the "digital space" of information processing towards the "real action" of the physical world. The breakthrough of generative AI marks a leap in interactive capabilities, but the focus of the next stage lies in "Physical AI." Physical AI is technology capable of perceiving physical laws, acting and making decisions autonomously in physical space.

This shift means AI is no longer just a virtual data analysis tool but interacts deeply with the real world through physical carriers like robots and the Internet of Things (IoT). This causes the paradigm of "AIoT" (AI of Things) to go beyond mere connectivity into a stage of real-time perception and intervention of the physical environment by AI.

IV. Dual Opportunities and Risks Brought by AI

The benefits brought by AI are immense: it can greatly enhance the productivity of both knowledge and non-knowledge labor by achieving exponential gains in data accumulation and performance through economies of scale.### IV. Dual Opportunities and Risks Brought by AI

The benefits brought by AI are immense: it can greatly enhance the productivity of both knowledge work and non-knowledge work, achieving exponential improvements in data accumulation and performance through economies of scale. For societies facing labor shortages, AI is a powerful tool to solve this structural problem.

However, this transformation also comes with risks that cannot be ignored. Biases in input data may lead to "hallucinations" and unethical decisions, and the risk of output without human intervention is increasing. Furthermore, the competition for AI infrastructure—the struggle for GPUs/NPUs, data centers, and energy—is creating a highly concentrated oligopolistic risk. In the future, successful enterprises will not only need to master cutting-edge technology but also the capability to be "operators" who can effectively manage these technological risks and convert their value into socially acceptable business models.