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Li Ning/Text Does generative artificial intelligence (AI), known as the fourth industrial revolution, really represent a new productivity revolution?

Looking back at history, the previous industrial revolutions have brought about huge leaps in productivity. The first industrial revolution began in the 1760s, marked by Watt's improvement of the steam engine, which triggered a shift from manual labor to machine production. The second industrial revolution was characterized by the widespread use of electricity. These two revolutions greatly reduced the cost of using bioenergy, human and animal power, and in many scenarios, the marginal cost of value generated by physical labor approached zero. The third industrial revolution, also known as the information technology revolution, began after World War II and was marked by the popularization of computers and the Internet. During this period, Internet companies represented by Google reduced the cost of obtaining and disseminating information to an extremely low level.

As Baidu's former president Lu Qi pointed out, Internet companies have achieved a key transformation: turning the marginal cost of obtaining information into a fixed cost. Take Google as an example. It invests about $1 billion a year in making maps, but for each user, the cost of obtaining map information is almost zero. This model has not only changed the way information is obtained, but also profoundly affected all walks of life.

However, we need to recognize the essential difference between information and knowledge. The information revolution has reduced the cost of obtaining existing information, but this information is usually knowledge created by predecessors, such as books, knowledge points or codes. After obtaining information, the re-creation and application of knowledge still mainly depends on humans.

The emergence of generative AI has changed this logic. It has greatly reduced the cost of knowledge production. Knowledge creation is the core force driving social progress, covering everything from cutting-edge scientific research, corporate R&D, sales plans in the service industry to education and daily life planning in the family field. In essence, these activities are the process of recombining and creating existing information.

For example, with the help of AI, we can generate a high-quality copy in seconds, complete a large amount of creative brainstorming in just a few minutes, and even quickly generate pictures, videos, and music works.

In the field of scientific research, the application of AI is even more remarkable. For example, AlphaFold (a new model developed by Google DeepMind and Isomorphic Labs, two AI companies under Google) is a revolutionary AI technology that is completely changing the landscape of biomedical research, greatly shortening the scientific research cycle and reducing costs.

Traditionally, determining the three-dimensional structure of a protein requires experimental methods such as X-ray crystallography or cryo-electron microscopy, which are not only time-consuming and labor-intensive, often taking months or even years, but also costly. AlphaFold can accurately predict protein structures within hours or days, greatly shortening the research cycle.

Taking drug development as an example, ESM3 (a breakthrough generative AI model developed by AI startup EvolutionaryScale, specifically for protein design) can quickly simulate and generate new protein sequences, including antibodies and enzymes with specific functions, enabling scientists to design targeted drugs with unprecedented precision.

This capability enables researchers to bypass the trial-and-error approach that dominates traditional drug development and rapidly generate and test new protein designs, thereby focusing their research on the most promising drug candidates. The interactive nature of ESM3 allows researchers to guide the protein design process through prompts, further improving the efficiency of drug discovery. This not only speeds up research progress, but also greatly reduces costs because researchers can explore a wider range of protein space in a virtual environment before laboratory verification. ESM3's ability to simulate millions of years of natural evolutionary processes opens up new possibilities for the development of innovative drugs.

Mustafa Suleyman, CEO of Microsoft AI, predicts that the cost of producing new knowledge will approach zero in the next 15 to 20 years. He believes that these changes will mark a real "inflection point" in human history.

New Vision and New Logic

If the development and popularization of generative AI realizes the vision of the marginal cost of knowledge creation approaching zero, we will usher in an unprecedented future in which knowledge production will become extremely cheap and efficient, which will bring profound changes.

Imagine that researchers can complete experimental design and data analysis in just a few minutes, greatly shortening the research cycle that used to take months. Complex tasks such as protein structure prediction and chemical substance screening will become accurate and efficient, and scientific progress will be promoted unprecedentedly. In the laboratory, researchers will be more immersed in creative exploration rather than tedious repetitive experiments.

In the future, doctors can obtain the latest medical knowledge and the best diagnosis and treatment plans with just a click of the mouse, and tailor personalized treatment plans for patients. Every patient can get the most suitable medical service for him or her, and the diagnosis and treatment effect and efficiency will be greatly improved. The significant improvement in health level will allow people to enjoy a higher quality of life, and disease will no longer be an invincible enemy.

In the classroom, generative AI will provide customized learning resources based on each student's learning progress and interests, truly teaching students in accordance with their aptitude. Students will no longer be bound by a unified curriculum, but will be able to learn in a way and at a pace that best suits them. The potential of each child will be fully stimulated, learning will become a fun thing, and the quality of education will be comprehensively improved.

In the psychological counseling room, generative AI will provide personalized psychological counseling and emotional support. AI can keenly identify and analyze the emotional state of individuals and provide timely and effective psychological help. Whether facing stress, anxiety, or seeking emotional comfort, everyone can find inner peace and happiness with the care and support of AI.

Generative AI will be everywhere in our daily lives in the future. AI will greatly improve efficiency and effectiveness in college entrance examination tutoring, consulting services, and creative work. People will be able to complete various tasks easily, freeing up more time and energy to invest in creative and strategic work. Repetitive and mechanical tasks will be taken over by AI, and people's lives will become easier and more enjoyable.

After clarifying the future vision that generative AI may bring, we need to think about new decision-making logic. Since the marginal cost of knowledge production will approach zero, everyone from individuals to companies needs to adjust their decisions and strategies to adapt to this change.

Individuals need to re-evaluate their own values. In an era where knowledge is readily available, what abilities and qualities are still valuable? Although AI has demonstrated powerful capabilities in many fields, true innovation from 0 to 1 still depends on human inspiration and intuition. This kind of innovation requires a spark of inspiration, a moment of epiphany, and AI is currently unable to fully replicate this kind of creativity from scratch.

Therefore, we need to think about how to maintain our uniqueness in this environment. Continuous learning and self-improvement can not only help us adapt to the rapidly changing world, but also enable us to innovate and maintain competitiveness in our own fields. Innovation from 0 to 1 requires unpredictable inspiration and intuition, which is a small spark in human thinking that can ignite the entire creative process. We should cherish this ability and continue to cultivate and stimulate it.

More importantly, AI, while powerful, currently lacks a sense of direction and purpose. One of the key roles of humans in this era is to provide direction and meaning to AI. We need to decide what problems AI should be used to solve, how to use AI to improve human life, and how to ensure that the development of AI meets moral and ethical standards. This ability requires us to have a macro perspective, deep insight, and a keen understanding of human values ​​and social needs.

The field of scientific research provides us with a thought-provoking example of the profound impact that generative AI can have on the knowledge creation process.

Traditionally, scientific research is seen as a temple to create new knowledge, and the process is often time-consuming, labor-intensive and costly. Taking management as an example, research is not only about discovering new phenomena and theories, but also requires fine polishing and rigorous argumentation. As management scholars Markus Baer and Jason Shaw described in their article, management scholars are engaged in a kind of "academic craftsmanship".

However, the emergence of generative AI may have a profound impact on the value of this "academic craftsmanship". This reminds us of the impact of previous industrial revolutions on traditional craftsmen.

Before the Industrial Revolution, skilled craftsmen were highly respected and their skills were rare and precious. But with the introduction of machines and the rise of mass production, many of the once highly specialized manual skills were gradually replaced by standardized industrial processes, and the status and value of traditional craftsmen in society declined significantly.

Now, we seem to be facing a similar turning point. Generative AI can quickly process massive amounts of data, generate seemingly reasonable hypotheses and conclusions, and even imitate the style of academic writing. This makes us wonder: Will AI significantly reduce the value of academic "craftsmen"? Tasks that once required researchers to invest a lot of time and energy to complete, such as literature reviews, data analysis, and even preliminary theoretical construction, can now be completed by AI in just a few minutes.

This change will undoubtedly have a profound impact on the academic community. Just as the Industrial Revolution changed the value of handicrafts, making the skills of craftsmen, which were once unattainable, no longer scarce, will the emergence of generative AI also make some traditional academic skills lose their uniqueness? As a profession for a minority of people, is the irreplaceability of scientific research facing challenges?

These questions have triggered a series of deeper reflections: What is the most essential value of scholars in the AI ​​era? Do we need to redefine the meaning of "academic innovation"? Perhaps the role of scholars in the future will undergo a fundamental change, but how exactly will it change? Will they focus more on raising truly innovative questions and leave routine research work to AI? Or will the value of scholars be more reflected in how to guide AI in research and how to judge the reliability and significance of AI-generated results?

Going further, do we need to rethink the entire process of knowledge creation? In an era where AI can quickly generate seemingly reasonable research results, how can we ensure the depth and originality of academic research? What role will human creativity and insight play in this process?

In the era of generative AI, enterprises, as important entities in knowledge creation, are facing unprecedented challenges and opportunities. The traditional value chain of knowledge creation is being completely overturned, and core corporate activities such as product design, service innovation, and market insight may undergo fundamental changes due to the intervention of AI. In this context, enterprises also need to rethink their own positioning and core competitiveness.

When AI can quickly generate design solutions, product ideas, and market analysis, how can companies find their own unique value in the knowledge creation chain? One possible direction is to shift the focus to areas that AI cannot easily replicate, such as brand value shaping, emotional connection building, or cross-border innovation. This requires companies to re-evaluate their own advantages and invest more in humanization and creativity. These questions have triggered deep thinking about the future development direction of companies.

Strategy formulation and execution need to be based on the reality that the marginal cost of knowledge is approaching zero. Do companies need to flexibly adjust their strategies to quickly respond to market changes and technological advances? The decision-making process will be more data-driven. Will generative AI provide real-time, accurate analysis and predictions to assist decision makers in making smarter choices? At the same time, does the corporate culture need to be more open and inclusive, encouraging innovation and cross-border cooperation to adapt to the rapidly changing environment?

In this process, we need to have an open discussion: How do companies balance efficiency and innovation? In an era where knowledge is readily available, what strategic adjustments can ensure that companies stand out in the fierce competition? Will the organizational form of companies in the future become flatter and more flexible?

There are no ready-made answers to these questions, but it is these thoughts and explorations that will lead us to adapt to and master this new technological change.

How to achieve: Two-way movement of people and technology

In the future vision of generative AI, the marginal cost of knowledge creation approaches zero, which means we will usher in an era of unprecedented efficiency and innovation. To achieve this goal, the key lies in the two-way movement of people and technology.

Generative AI is not a simple tool without a clear instruction manual, but a capability and potential that needs to be mastered by humans. The role of humans is crucial in this process. Individuals need to improve their AI capabilities, which refers to the comprehensive ability to use AI to complete tasks and solve problems in the AI ​​era. AI capabilities are not just the skills to operate AI tools, but also a new thinking mode and capability structure.

In the process of improving AI capabilities, individuals need to constantly learn and adapt, and master the methods and techniques of using generative AI. For example, how to design effective prompts to interact with AI, how to deconstruct tasks and work with AI to complete complex tasks. The cultivation of this ability will enable everyone to become a "super individual" and greatly amplify their own capabilities and efficiency with the help of AI.

In addition to improving people's own abilities, the development of technology is also an important way to achieve this goal. Generative AI not only requires the basic capabilities of large models, but also needs to be continuously developed and optimized in various scenarios. For example, the emergence of Suno (an AI music generation application) has made music creation, a skill that requires professional knowledge, readily available. This technological advancement has greatly lowered the threshold for knowledge creation.

In the field of translation, Professor Andrew Ng's recently open-sourced translation project has made translation, a task with professional attributes, very easy, demonstrating the rapid application of technology in specific scenarios. In the fields of data analysis, complex medical diagnosis, and scientific research, although there is still a long way to go, the direction of technological development is clear, that is, through continuous innovation and optimization, the cost of knowledge creation will be greatly reduced.

A world where the cost of knowledge creation approaches zero

As the marginal cost of knowledge creation approaches zero, we face a series of profound and open questions. These questions may not have clear answers at present, and some are being hotly debated around the world.

When the cost of knowledge creation is zero, where will humanity go? What is the meaning of being human? In a world where knowledge is readily available, how will humans redefine their own value and existence? Will new abilities and qualities become the key to human core competitiveness? Do we need to re-examine areas such as creativity, emotion, ethics and social responsibility that cannot be completely replaced by AI?

In a world where knowledge can be easily acquired and generated, are traditional corporate organizational forms still applicable? How will organizations be reorganized and operated to adapt to this trend? Will future organizations become flatter, more decentralized, and more flexible? How will the core competitiveness of enterprises change? In an era of infinite knowledge, how can enterprises maintain innovation and competitive advantage?

More importantly, how can we ensure that everyone can benefit from this change? The reduction in the cost of knowledge creation should not be a privilege for only a few people, but should be the common wealth of the whole society. How can we design a fair distribution mechanism to ensure that the dividends of knowledge and technology can benefit everyone? This includes the popularization and improvement of education, the improvement of the social security system, and the formulation and implementation of policies and regulations. How to deal with possible unemployment and social inequality? How can we ensure that the benefits of technological progress can be evenly distributed?

As Microsoft AI CEO boldly predicted, the cost of knowledge creation will be close to zero in 10 to 15 years. At that time, we must face these profound problems and find ways to solve them. Do we have enough social mechanisms and policies to guide and guarantee this transformation?

The meaning and value of human beings, the form and operation of organizations, and the fairness and progress of society are all major issues that we need to prepare and think about in advance. The answers to these questions may not be easy to find, but it is these thoughts and explorations that will lead us to a better and fairer future. Are we ready for this change? What will the world of the future look like, and how can we find our place in this change?

(The author is Flextronics Professor at the School of Economics and Management, Tsinghua University, and Director of the Department of Leadership and Organizational Management)

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