Blockchain is a value network, and its core purpose is to enable digital assets to circulate more effectively. On October 12, Professor Zeng Ming, Chief Strategy Officer of Alibaba, held a public lecture titled "Looking at Ten Years" at Zeng Ming Academy and Lakeside Research Center. In this public speech, which took place six years later, Professor Zeng raised a series of new thoughts on business transformation, such as "How does technological change drive changes in business paradigms? How should corporate strategy keep pace with the changes of a ten-year vision? What are the fundamental judgments about business transformation in the next decade?"
Jianle from the Wànwù Island community took notes and organized the rich and sincere learning content from this session, with some minor edits that do not change the original meaning, sharing it with you in the era of intelligent innovation and entrepreneurship, which is worth repeated reading and collection.
Full transcript of the speech:
Good morning, everyone. Thank you very much for your trust and for taking the precious time to attend this class. In 2017, I suddenly had an impulse to teach a strategic course like "Looking at Ten Years." At that time, there were two main stimuli. One was that I had been studying strategy since 1993, teaching strategy, and practicing strategy in companies. Especially during that period, it was a time of great upheaval in the internet and mobile internet, so I had some different insights to share with everyone about how to approach strategy. The second was that since 1991, I have grown up with the internet, witnessing its development over more than twenty years, and I had many speculations about the future that I wanted to share. Thus, the public lecture in 2017 came about.
The strategic public lecture in 2017 had two themes. The first theme was to redefine strategy because, in an environment that is rapidly changing, very complex, and highly uncertain, gaining momentum with the trend is the primary principle of strategy, which is very critical. When we talk about looking at ten years, "looking" means Visioning, and this process becomes very important; the more difficult the times, the more seriously we need to look and strive to see. We need both the determination to "look at ten years" and to gradually cultivate the ability to see ten years ahead. This vision determines your pattern and potential. Strategy is the iterative process of Vision and Action, a concept I have shared many times over the past five or six years, and today I will upgrade this idea because I have some deeper insights to share with everyone.
The second theme discussed last time was the great transformation of intelligent business. Online, networked, and intelligent developments have constituted the themes of corporate development over the past decade. At that time, I drew a chart based on the size of seven companies and their progress across various dimensions. Most companies today are still among the world's leading companies. The three most important development directions discussed at that time were online, networked, and intelligent.
The characteristics of intelligent business are, first, the ability to serve a massive number of users at a low cost and in real-time; second, the ability to meet the personalized needs of each user; and third, the ability to iterate quickly. Therefore, intelligent business is essentially a technological-driven reconstruction of commerce based on networks and algorithms.
At that time, I mentioned two cores of intelligent business, which I called the double helix of DNA: one is network collaboration, which involves large-scale, multi-decision, real-time interaction; the higher the collaboration efficiency, the greater the value generated. The second is data intelligence, which essentially means machines replacing humans in decision-making. It is based on cloud computing, big data, and algorithms, forming data intelligence through rapid iteration. Thus, the two core components of intelligent business are network collaboration and data intelligence. At that time, I made two judgments: one is that the future of business is in the preliminary stage of establishing an intelligent business pattern, and the second is that the future is an intelligent era, where human brains connect with machine intelligence. It is somewhat reassuring that these two judgments have generally proven correct; otherwise, I wouldn't be standing here today. Most importantly, over the past six years, I have gained many new ideas and insights on this preliminary judgment, so the core of today's sharing is an in-depth exploration of these two themes.
We will unfold this in three topics. The first is the true arrival of the intelligent era, as we have AGI, the revolution of general artificial intelligence; blockchain and Crypto have undergone nearly 15 years of brewing and development and are ready to take off; the third is XR and the metaverse. These are the three core technologies and fields we will focus on discussing in this morning's lecture. The second part will share a methodology with everyone on how to understand the actual process of technology-driven business transformation. Through this methodology, we can understand what is most likely to emerge in the next three years or three to five years. This is a very critical milestone in strategic decision-making. You need to know that aside from the long-term vision of looking at ten years, how to set the goals for the next three to five years requires a mid-term judgment. So, in the second section, I will talk about how to make this mid-term judgment. The third part will discuss some new thoughts on intelligent business.
The impact of artificial intelligence on future business
We will start the first phase of the discussion, which is the impact of artificial intelligence on future business. This chart may be familiar to everyone; it shows the significant development of artificial intelligence over the past 20 years. In the early search phase, it was called big data, and at that time, the term AI had not yet emerged. As we know, after the popularity of ChatGPT at the end of last year and this year, there are over 100 entrepreneurial teams in China focused on large models, known as the "Hundred Model War." In fact, the second phase, during the era of facial recognition, was when deep learning was first applied on a large scale in the visual field. By 2014, there were already hundreds of visual companies being established. Facial recognition, which everyone now feels is ubiquitous, was actually the first large-scale application of AI using deep learning methods. The recommendation engine behind the daily use of Douyin is also based on AI technology. Large language models, known as Large Language Models, represent a revolution in general AI. It is essentially a very simple algorithm that predicts the most likely next word following a given word. This simple algorithm has achieved a level of predictive accuracy that is sufficiently high and useful. In this sense, it seems to have mastered language. As mentioned in the book "Sapiens," language is humanity's greatest invention. Language allows us to communicate, and behind language inherently lies human wisdom, as well as the vast knowledge accumulated over the last 10,000 years, which has been distilled through IT in the last twenty years into text, audio, and video. Therefore, mastering text and language essentially unlocks all human knowledge up to this point. We still do not fully understand the operational mechanisms behind large language models. They may not think like humans, but in specific areas, they exhibit logical reasoning abilities similar to humans. This will have fundamental implications for our future.
The development over the past thirty years, from the internet to wireless internet, to sensors, digital transformation, big data computing, etc., has gradually enhanced the capability boundaries of the software world. However, it is essentially additive, a stacking process. AGI, or general artificial intelligence, connects these elements, enhancing the adaptability and autonomy of all software, moving from quantitative changes to qualitative changes, resulting in a new leap. For example, AGI can automatically program, which dramatically enhances software capabilities, representing a qualitative change. In this sense, many believe that large language models represent the first "iPhone moment" of the AI era, marking a significant transformative period.
From another perspective, the era of general intelligence can also be described as the era of robots, as AI serves as the brain, and its combination with various hardware forms different types of robots. For instance, autonomous driving vehicles are robots, especially future Robotaxis, which are essentially technology outsourcing service companies. Understanding it from this perspective provides a more fundamental insight into the impact of technology on business. When people think of robots, they often envision various flashy robots from Boston Dynamics. However, Boston Dynamics has developed for about thirty years, and it may not be as fast as Tesla's humanoid robot, which has made significant progress in just two years. This is also a breakthrough in hardware brought about by AI technology, and we can see that robots will develop rapidly in the entire environment.
In addition to ChatGPT, I want to emphasize that the other two main lines of AI and AGI development are also extremely important. One is autonomous driving, which has different requirements from ChatGPT; it must ensure safety and fundamentally addresses the interaction between humans and the physical world. ChatGPT focuses more on human brain behavior. However, autonomous driving must solve the interaction between humans and the physical world, which is why Tesla, as an autonomous driving company, has accumulated so much in robotics, as it fundamentally needs to perceive the external world. Another very important area is AI for Science, which is even more fundamental. So far, AGI can only apply existing human knowledge and cannot create new knowledge. However, AI for Science uses AI for scientific development. It is likely to create entirely different paradigms, as it may discover new chemical equations or new physical laws, propelling artificial intelligence forward significantly. Even today, projects like DeepMind's AlphaFold for protein analysis and synthetic biology, which is a relatively new field in the past few years, are also AI-driven. Many fields have already made significant progress, though they may not be widely known, but this accumulation will lead to breakthroughs in the next steps. The previous discussion provided some background knowledge that you may have heard in different contexts, and the next two slides are among the most important of today's presentation.
The difference between AI and the internet era
We have transitioned from the internet era to the intelligent era, to the AI era. So what is the essential difference between the internet and AI? The internet essentially deals with massive amounts of data, focusing on the efficiency of information flow and matching. Its core value lies in solving the problem of information asymmetry, allowing information to circulate and match as much as possible, avoiding various frictions caused by information asymmetry. However, in the AI era, the essence of AI is to process massive amounts of knowledge; it is no longer just data or information, but knowledge generated through the processing of data and information, combined with existing knowledge to solve practical problems. Therefore, it addresses the efficiency and cost of decision-making. In other words, can machines replace humans? Because so far, all decisions have been made by humans; if machines can replace humans in decision-making, it represents a qualitative leap. Its core value is essentially to create new supply. This is something I have felt deeply over the past year. Initially, we were all worried about whether AI would replace humans in the future. There are many areas to discuss, but what we see in practice today is that the first users of AGI services are those who previously could not afford human services, as human services are very expensive. Therefore, AGI services actually provide new supply.
Let me give two simple examples: online education. The previous wave of online education development aimed to use internet means to improve the teaching efficiency of high-quality teachers, which is a very typical internet effort and has made significant progress. However, online education in the AI era is about providing unlimited high-quality teacher supply to meet personalized learning needs. In principle, every student should have their own teacher, which only AI teachers can fulfill. Similarly, one of the biggest problems in the world today is the high cost of healthcare and insufficient doctor services. If AI doctors emerge, the overall health status of everyone could see a qualitative leap. Thus, AI essentially addresses the issue of insufficient supply.
In the past five years, the reason why digital transformation, online services, and industrial internet have been so challenging is fundamentally that these industries are not facing information asymmetry problems but supply shortages. For example, all those working on internet hospitals and transforming medical services face limited value because they cannot solve the core problem; the bottleneck in medical treatment always lies in the limited number of good doctors. No matter how well you match information, it will not help. Therefore, the AI era brings a completely new opportunity, as we can truly create new supply, and massive supply will create new demand.
The core capability of the AI era is to establish decision models based on decision-making scenarios. I will elaborate on this term later, as it is very important. All our decisions are based on specific scenarios. Many times, human decisions are subconscious or even unconscious. How can we make these spontaneous decisions explicit so that machines can implement them using their logic? This is a fundamental challenge, and all the difficulty lies here, especially for AI application companies and cutting-edge companies working with large models, as algorithms may pose significant bottlenecks. However, for AI applications, the core is modeling capability, understanding decision-making in real scenarios. This difficulty also arises because the decision-making methods of AGI differ from those of humans, so a translation is needed. For example, applied mathematics has become a popular elective among undergraduates over the past decade because its core is modeling. This is a very important core capability. The interesting thing about this model is that once you establish it and form a closed loop, it can continuously iterate and optimize itself; it becomes a living AI system. In this sense, all our past developments can be described as a machine era. Even the most complex mechanical systems are simple systems; they can only perform deterministic executions. However, even the simplest cognitive systems are complex systems. Therefore, AGI is developing a system that can grow organically, similar to biological systems. This will also represent a fundamental development. We need to embrace our capabilities and tendencies, along with the ability to learn and grow independently. This is the essence of AGI, which differs from the internet era. The internet era was still focused on solving relatively deterministic information matching problems, while the AI era is about building cognitive systems. This is the first point I want to share with everyone today.
To summarize, based on the 2017 public lecture, I want to elevate the discussion: the driving force of the era is intelligence. I elevate "intelligence" to a higher level, making it the dominant force of this era. The internet era was essentially characterized by online, software, and networking developments. The combination of online and software in the past 20 years has led to the rise of SaaS, while networking has evolved from PC internet to mobile internet to IoT. Its essence is connection, completing the infrastructure for network collaboration. Every new era builds upon the foundation of the previous one, so we can see the new driving forces of the intelligent era emerging on the increasingly improved infrastructure of the internet era. On one hand, there is intelligence, which we have been discussing throughout this presentation, especially the growing strength of general artificial intelligence. We do not know how powerful it will ultimately become; we only know it will become increasingly powerful. On the other hand, there are two foundational technologies supporting the development of the intelligent era: 1) the continuous enhancement of human-machine interaction capabilities, which is the XR topic we will discuss shortly; and 2) blockchain and Crypto, which enhance our ability for network collaboration.
XR: Human-Machine Interaction
Next, we will delve into XR technology, which encompasses the development process from AR and VR to XR as a whole. First, let me briefly provide some background. Ignoring the console era, which many people may not have experienced, starting from the PC era, we can think of some of the most outstanding companies today, such as Microsoft and Apple. The core invention was the GUI (Graphical User Interface), which led to all the internet revolutions we see today. From personal computers to mouse and keyboard inputs, to Microsoft's complete software system. Then came the mobile internet era, primarily characterized by touch screen inputs, along with some voice inputs. The third path began developing in the past decade, with the establishment of Oculus in 2012, which was acquired by Meta in 2014, leading to the emergence of VR headsets. Google Glass also appeared in 2014, and a series of products were launched in 2015. In 2016, there was great excitement, dubbing it the "Year of Virtual Reality," as the first-generation Oculus Rift was released, Sony launched its VR headset, and Microsoft's Hololens was introduced. There was also a very popular game called Pokémon Go, which I remember taking my child to Yokohama to catch monsters. It was the first hot game based on virtual reality, but it quickly fell silent for a while. As is familiar with the trajectory of high-tech development, there was a phase of stagnation. By 2019 and 2022, everyone was making efforts again. Magic Leap was a startup that seemed particularly promising at the time, receiving support from Google, Alibaba, and many other companies. In 2018, I visited Magic Leap to see their next-generation product nearing production. I was deeply impressed; it wasn't about distinguishing between real and fake, but rather the future where one cannot tell the difference. The effect was such that it could completely confuse your eyes because it provided real light sources, making it impossible for the eyes to judge whether what they saw was real or fake. It presented a formed image and delivered signals to the brain. That was my first impression. The second was when the founder of Magic Leap presented the first slide of the PPT, stating that they were not making glasses but rather the future of human-machine interaction. Imagine if you could just move your eyes and look at a computer to execute commands; wouldn't that be much faster and easier? Unfortunately, they encountered some final technical challenges and could not succeed. Magic Leap later transformed into a B2B company and did not achieve explosive growth as a consumer product. However, this year saw two significant releases: one was Apple's VisionPro, marking Apple's first official product launch in this field, defining many new standards and raising expectations. The second was the release of Meta's Quest 3 last week, targeting the mid-to-low end, while Apple is focusing on the high end. The two companies have chosen similar technological paths, indicating that industry standards are beginning to emerge, with both high-end and low-end options available. Additionally, Meta has launched AiGlass to further develop human-machine interaction, which, although not a virtual headset product, shows that visual interaction is once again becoming a focal point of the industry.
Returning to hardware, what is its core purpose? The core purpose of hardware is to create new opportunities for human-machine interaction. We mentioned that the earliest PC computing was through keyboard input. Mobile computing on smartphones was through touch screens, and in the era of spatial computing, the core emphasis is on visual perception. Everyone's definition may vary, and we do not need to get caught up in the details.
I want to summarize why the XR field is crucial for everyone present. What is the essence behind its technology? This represents a qualitative leap in human-machine interaction. Previously, our interactions with machines, including AI, required us to actively operate the machines; we had to input commands. However, in the future, machines will actively respond to humans. We may not need to do anything; they will naturally sense us. If we evolve to a brain-machine interface, they may even subconsciously know what we want and execute it. Thus, the future is about machines perceiving humans and taking proactive actions in the interaction interface, marking a completely different era. We will see more and more machines connecting human senses directly to the digital world. Currently, we have AR and VR glasses, wearable devices, and even clothing that resembles skin. The distance will shift from far to near, close to the skin, and eventually into the skin, with implanted chips likely occurring sooner or later. These represent a significant development trend over the next decade or two. What is the commercial significance of this trend? Starting from XR and VR glasses, we begin the digitalization of human perception and attention; humans will no longer exist independently of the digital world. We may be transforming into Digital Natives, and we might be the last generation of complete humans, while future individuals will all be Digital Natives. The definition of what it means to be human may change significantly, as we will become part of the digital world, which is very important.
The reason the metaverse was once so yearned for is that it represents a purely digital world, unconstrained by physical laws, where extreme personalization can be achieved, along with rich biological characteristics and diverse scenarios, providing endless services. This is why the metaverse excited many people; it represented a highly anticipated future. However, XR and similar devices, in addition to hardware, also require improvements in software and computing power. Therefore, matching this is edge computing, including the miniaturization of algorithms. In the future, each edge device will experience a qualitative leap in perception, computing, thinking, and decision-making capabilities. Thus, this technology and AI technology complement each other, providing an infinitely broad scenario for AI to be applied more widely. Conversely, it will also promote advancements in AI technology, as without progress in AI technology, it cannot support deeper, more complex, and real-time technical requirements. Therefore, these two technologies are entirely complementary.
Blockchain and Crypto
Next, we will discuss the development of the third technological field: blockchain and Crypto. Some friends may not be very familiar with this area, and it may be too complex to elaborate on, so I will keep it concise. You can first note down some conclusive points and digest them later. Why is blockchain technology so closely integrated with Crypto? It actually began with the first cryptocurrency, Bitcoin (BTC). In 2008, Satoshi Nakamoto published the white paper, leading to the emergence of the mining industry and Bitcoin, which gained enough consensus among people. Bitcoin now has the consensus of hundreds of millions of people, many of whom have traded or purchased Bitcoin. This is an interesting new phenomenon based on technology and algorithms, forming trust and consensus.
Blockchain technology emerged from Bitcoin, and on this basis, Ethereum was developed, becoming a universal technology platform for smart contracts. Ethereum has undergone three rounds of development. The first round was the ICO (Initial Coin Offering), which was particularly popular in 2017. Issuing tokens was the first smart contract, the simplest form of automatically issuing tokens online, forming a set of rules and systems. Token issuance was Ethereum's first killer application. Based on this, in the summer of 2020, the so-called DeFi (Decentralized Finance) emerged. This essentially used blockchain technology to recreate almost all simple financial services based on the concept of over-collateralization, replicating all simple financial services under controllable risks, which is an impressive achievement. Building on the accumulation of DeFi, GameFi began to appear in 2021, and many friends have played some GameFi games, including StepN's running shoes, which belong to GameFi games. Then there are NFTs (Non-Fungible Tokens). Each product is based on a type of smart contract, so these applications have promoted Ethereum's development in rounds. Of course, Ethereum itself is also undergoing scaling and layer 1 and layer 2 developments.
Fundamental challenges of blockchain technology
In 2022, the industry faced numerous negative events, with many crises emerging, and the entire market lacked new developments throughout the year. Therefore, many were confused about whether this field still had a future. Even some staunch believers began to waver. I think to answer this question, we must first address what the essence of blockchain is. The essence of blockchain is a value network; it is not an information network. The internet is an information network, but blockchain is a value network. Its core purpose is to enable digital assets to circulate more effectively. Another byproduct is that due to the simplicity and reliability of issuing tokens online, a series of new incentive mechanisms can be innovated through token issuance. These are the two core breakthroughs of blockchain, fundamentally breaking through production relationships. This is a technological innovation aimed at transforming production relationships.
The significant challenge this brings is that it is not a productivity tool. It is challenging to enhance user experience, so the blockchain field has been waiting for a good application that can reach hundreds of millions of users to unfold the entire system. Therefore, from this perspective, the fundamental challenge facing blockchain is the lack of technological innovations that can directly improve consumer experience. This field itself does not have such innovations. Secondly, it was originally expected to digitize traditional assets, such as various financial assets, but this has not progressed smoothly. The efficiency it improves and the value it creates are not significant enough, and there are enough traditional interests and systems to maintain, so this transformation has also not succeeded. Additionally, without new applications, there are no new digital assets. Without digital assets, having a value network to reduce the circulation of digital assets becomes meaningless, akin to a tree without roots.
What developments can we expect in this field next? One is that it will continue along the current logic, with Bitcoin continuing to serve as an alternative asset. In a certain sense, BTC will continue to gain greater consensus, or Bitcoin may play a larger role in payment, promoting the development of inclusive finance based on payment networks. This is one path of innovation along the financial main road. The second is to rely on the development of new applications. In the past two years, many innovations in GameFi and SocialFi have accumulated, and perhaps in the next six months to a year, we will see some preliminary results.
AIGC: A significant breakthrough in productivity
I believe the most valuable breakthrough will be the creation of massive new digital assets through AGI. The first breakthrough area of AGI is AIGC (AI-Generated Content), which refers to deep-level AI that creates vast amounts of content. At some point next year, there will definitely be very useful tools for converting text and voice into video. The barriers to creation will dramatically decrease from text to voice to images to video, and the space for creating new digital assets will significantly increase. Moreover, just as we discussed the virtual world, the future digital assets will increasingly become mainstream, and their importance will grow. These assets will hold value, and people will pay attention to them, leading to increased focus on their circulation and trading. Therefore, based on this, new digital assets will naturally utilize new value network technology platforms.
At the same time, I mentioned that the core of Ethereum is smart contracts, but in the future, the cooperation between machines will differ entirely from the interaction between humans. They will require more, more automated, more efficient, and more intelligent contracts to be completed directly. Therefore, in this field, blockchain and Crypto have significant development space, and in this sense, I also regard it as an important component of the entire AGI intelligent era.
Whether from the previous discussion on the Crypto field or the value brought by AGI, I believe we are about to enter an era of creator economy. On one hand, this trend is very clear: AGI will gradually replace structured human knowledge and become increasingly intelligent. On the other hand, humans, with the help of machine intelligence, have the opportunity to become more creative. Just like in the early days of the industrial revolution, there was great fear that humans could no longer obtain value through physical labor and could not survive on physical strength. However, over the past 100 years, the white-collar class, knowledge workers, and software engineers have emerged, relying on their intellectual activities to create the prosperity of the past 100 to 200 years. I can envision a relatively positive scenario where machines or artificial intelligence liberate humans from tedious, repetitive, and boring cognitive labor, allowing people to spend most of their time developing their creativity and engaging in things they are truly passionate about and can excel at. These may be the two fundamental driving forces. Based on this, the collaboration between humans and humans, humans and machines, and machines and machines will raise higher demands. In the internet era, collaboration between machines relied on APIs, which required a set of agreed-upon specifications for mutual assistance. However, with the development of AGI, all services will interact using natural language. In other words, machines will learn to communicate like humans, completing collaboration between machines, while natural language will become the communication language between humans and humans, humans and machines, and machines and machines, raising higher requirements for smart contracts.
If we look at these contents from a more macro perspective, Peter Drucker may be the greatest business thinker of the 20th century. He divided the industrial revolution into three historical stages. The first stage was the revolution of productivity, where factories replaced handicraft workshops. Traditionally, knowledge in handicraft workshops could only be passed down from master to apprentice. However, with the advent of factories, scientific management began to emerge. The second stage, starting about 100 years ago, was the management revolution, where the concept of enterprises began to take shape. Previously, there were only individual factories, focusing on production and sales. However, with management, matrix management, functional management, human resources departments, strategic planning departments, etc., emerged. Business schools were established to supply large quantities of high-quality management personnel for the management revolution, which was also very important for standardization. MBAs emerged with standardized language, representing a set of commercial training that supported the management revolution. With the development of IT from the 1960s and 1970s, we entered the software revolution, or the IT revolution, where software engineers created the most value. Following the earlier discussion of AGI's replacement of structured human knowledge, humans must transition towards developing creativity. Therefore, I define the future fourth developmental stage as the stage of the creativity revolution, where human value will be reflected in creativity.
We are about to enter a new economic paradigm. The core of the intelligent economy, which we refer to as the intelligent economy, can also be understood from another perspective as the creator economy. The three core supports are the general artificial intelligence, Crypto, and AR&VR we discussed earlier. Of course, these three development stages are different; currently, AGI is developing rapidly, Crypto is in a relatively low valley and is in a brewing stage, and AR&VR may take another three to five years to produce large-scale sales of application-level products. However, the trends of these developments are very clear.
The evolution of human civilization
If we step back from the intelligent economy and look at the evolution of human civilization from a broader perspective, the core of human development relies on two aspects: one is the development of human networks, which include language, writing, culture, systems, etc., all of which are considered soft institutional elements. The other is the very important network of tools that humans have continuously created, from the earliest fire to the use of tools, to agriculture, to physical networks, and today’s logistics networks, communication networks, and computing networks. The development of the tool network has promoted social progress and human development, and humans have invented more networks and tools, driving the development of new technologies. Thus, technological progress and social progress have produced qualitative leaps through these two networks, leading to successive developments.
If you look closely from a biological perspective, the capacity of a single human brain has actually improved very little. The progress is twofold. First, the development of the brain has gradually unfolded, and our brain development ratio is still quite low. This is why the creativity revolution is possible; we may develop many abilities we cannot currently imagine. Second, what seems more important now is the emergence of collective intelligence through social collaboration. In fact, the society we create generates greater value and accelerates development.
The advancement of the tool network driven by technological change is the main line of human civilization development. Based on this, we can make a judgment about the current state of our discussion. From the discovery and application of fire to the use and invention of tools, to the agricultural economy, which is only about 10,000 years old, and then to the industrial revolution. The first industrial revolution was powered by mechanical energy, while the second industrial revolution was powered by electricity. Although some refer to the information revolution as the third and fourth industrial revolutions, known as Industry 4.0, I personally believe that separating the information revolution as a distinct concept may provide clearer insights. Thus, we had the first information revolution with the invention of computers. The second information revolution occurred roughly from the late 1970s to the early 1980s, with the emergence of personal computers and the internet, culminating in the integration of communication networks and computing networks, leading to the explosion of the internet over the past 20 years.
The past five years and the next five years represent a transitional period as we move from the internet era to the intelligent era. I personally prefer to call it Internet 3.0. From Internet 1.0 (PC) to 2.0 (mobile) to 3.0 (the future). To clarify the concept, we can define the next decade, or even the next two to three decades, as the beginning of the intelligent era. Intelligent Era 1.0 represents the opportunities and challenges of the era we are currently in. Regardless of our position today, everyone shares a common challenge: to become a native species of the intelligent era, as this is the only way to develop and even survive. This is the macro picture I want to convey.
The basic laws of technological change driving business transformation
Next, I will discuss some prospects and technological developments for the next three to five years, which is the second part of today’s content. Sometimes, looking ten years ahead may not seem too difficult; everyone can talk endlessly about the future. However, how do you project this ten-year vision into the next three to five years? Because your strategic core is formulated around these three to five years. Ten years or even further out is the vision, that long-term foresight. How to view the next three to five years, especially during significant technological changes with such uncertainty? I have repeatedly contemplated this question, which was again triggered by the rapid rise of ChatGPT, the fastest application to reach over 100 million users. Is ChatGPT the star of tomorrow? Is it the next Google we are waiting for? This is the question I want to answer. After much reflection, I believe there is a concept I can share with you, called the emergence of native applications, or the emergence of native services.
Let’s first look at the basic laws of technological change driving business transformation. First, a major technological change often brings several waves of business transformation. Of course, during this process, it nourishes itself, and the technology itself also progresses and matures. We can see that the internet has gone through the first wave of PC internet, which we can consider commercialized from the listing of NetScape in 1993 to the emergence of Apple's App Store in 2008, which opened the mobile internet, and then to the later Internet of Things. Similarly, AI has a historical trajectory, having gone through the era of big data, then AI 1.0, and now AI 2.0, which may be AGI 1.0. Thus, it often develops in waves, continuously evolving until the technology matures and is replaced by new technologies.
From another perspective, I divide the business transformations driven by technological changes into four stages. The first stage is very early development, where there will definitely be bubbles. This is because it reveals too many possibilities, but the progress in realizing these possibilities is far below expectations. So, it is an exciting time, but this bubble will eventually burst. The internet bubble is the most memorable one for everyone. The reason the internet bubble is so memorable is that before it, especially during the stock market crash in March 2000, everyone had experienced a century of steady development in the industrial era, where linear growth was the norm. Suddenly, the internet emerged, representing exponential growth, with possibilities that seemed limitless, leading to a disruptive transformation. However, during the mobile internet era, people had the experience of the first PC internet, so there was not as much excitement. Thus, we can see that major technological advancements often come with various bubbles, then enter a penetration phase, followed by the emergence of native applications, and eventually become general technologies that are used across almost all industries, becoming foundational infrastructure. This is similar to how the internet has become the foundation of society.
Another important point is that infrastructure and applications evolve together. When we look at native applications, they typically emerge in the third stage of a technological revolution. They require time to mature to a certain extent to create entirely new value. However, at this point, they can bring in a truly massive user base, becoming national-level killer applications. For example, Google is the first native application of the PC internet. This is my own judgment, and there may be different opinions regarding the degree of native applications. You could argue that Yahoo or eBay might also qualify, but in terms of completeness, Google is undoubtedly the king of the PC era. It achieved this because it completed disruptive innovations on several levels. One was the search box. When Google's minimalist search box emerged, it was absolutely shocking. Moreover, it could return all information on the internet within seconds based on a keyword input, which was previously impossible. This represented a significant breakthrough in user experience. Such a breakthrough requires substantial innovation in underlying technology, which is what we refer to as cloud computing or, from a technical perspective, distributed computing. Today, AI computing is fundamentally based on the development of distributed computing. Thus, it opened a new era of computing, but equally importantly, it created a new business model called Pay for Performance, which is familiar to everyone today as precision marketing. It transformed advertising costs from an immeasurable metric into a precisely measurable one, allowing us to see how much we spent to acquire how many users, and charging only after the customer clicked. Moreover, the price is determined by the market; if someone competes with you, the price goes up, and if no one competes, the price goes down. Through such market pricing, it maximizes the utilization of massive clicks, leading to a significant siphoning effect, where advertising moved online, and online advertising gravitated towards Google. This resulted in over a decade of brilliance for Google, where for about ten years, all talent in Silicon Valley was drawn to Google, and all innovation occurred there, resulting in extremely high profit margins and rapid growth, culminating in a monopoly in search. This is a very typical example of a native service that opened a new era.
From this perspective, we can observe the emergence of native services brought about by technology. Google, as mentioned earlier, is the native service of the PC internet, founded in 1998 and listed in 2004. The second native service is Facebook, established in 2004 and listed in 2012. Facebook is a very typical native application of the PC era. However, when it went public, it coincided with the explosive growth of mobile internet, causing its stock price to drop by 40% upon listing. This forced Facebook to quickly transition to mobile internet. In 2006, Twitter emerged, and in 2007, the iPhone was released, followed by the App Store in 2008. By 2009, the first batch of super apps began to appear, such as WhatsApp, Weibo, and Uber. In 2010, Meituan and Instagram emerged, followed by WeChat in 2011, Toutiao in 2012, Kuaishou in 2013, Pinduoduo in 2015, and Douyin in 2016. Today, our lives are essentially defined by Douyin and Pinduoduo. These are the true kings of the mobile internet. You can observe from this history what conditions native services need to meet to emerge.
Native services in the AGI era
The next two slides are also very important for those working on AGI, as they may provide significant assistance in assessing whether you are truly at the forefront. The first is whether you are using the latest AI technology to engage in natural language dialogue, as large language models have solved the language problem. Therefore, you can engage in natural language dialogue and perceive visual space through future XR glasses and wearable devices. You can have deep, continuous interactions and communications with users. Essentially, being always online will become the default in the future. The second very important point is that once you crack the language, you unlock the totality of human knowledge, or the totality of text-based knowledge, meaning you can access all of humanity's knowledge at any time. This is what all trained models accomplish. The third point is that you need to employ a certain level of reasoning ability. In other words, it helps you make decisions. The purpose of using that technology is to achieve a qualitative leap in user experience. How can you redefine products based on scenario decisions, and can you effectively utilize large language models to leverage general knowledge? In this scenario, what specialized knowledge or skills do you need, and can you call upon the relevant knowledge and skills in real-time? Lastly, innovative interaction is also crucial, as mature hardware can better support the underlying technology.
If we view ChatGPT from this perspective, it may still be a semi-finished product. It has indeed created a new way of human-machine dialogue, while Siri only addressed the level of voice recognition accuracy. However, it cannot engage in semantic dialogue. Large language models have resolved natural language dialogue, making conversation a very mainstream interaction method in many contexts. In many situations, it is the most efficient. However, the product form of ChatGPT is quite outdated, reverting to the most primitive PC webpage format, indicating that there is significant room for innovation. The new user entry point may not necessarily be ChatGPT, as its user growth has clearly slowed down. Simple dialogue, akin to an encyclopedia, and basic writing assistance may not constitute a killer application, and no new business models have emerged. Therefore, from these perspectives, I believe ChatGPT merely sounded the horn; it is not a truly native application.
The future of Web 3.0
In the next three years, I believe the most noteworthy aspect is which entrepreneurial teams, including a few giants, have the opportunity to launch truly native applications, which will lead to an explosion in this era. Based on the concept of native services discussed earlier, I view the development over the next three to five years as follows: the next two to three years will be a nurturing period, and there may already be entrepreneurial teams working on such initiatives in some corners. We will likely see mainstream services based on AI and Crypto emerge, bringing entirely new user experiences. The emergence of such mainstream services will drive a surge of native innovative services, potentially becoming a kind of ecological entry point or platform infrastructure, similar to Apple's App Store, catalyzing the emergence of a series of killer native applications. Just as we witnessed the continuous emergence of major applications between 2009 and 2016, it is likely that in five to ten years, the original leaders will begin to take the lead, followed by the emergence of some second-generation, more native applications. Approximately ten to fifteen years from now, the first batch of leaders in the intelligent era will have established their leadership positions.
In the next three years, we will focus on identifying who these native services will be and who is most closely tied to that ecosystem. The gaming industry currently appears to be a significant breakthrough point, as history suggests that all three technologies may leverage gaming as a primary application. The promotion of AIGC in gaming is evident, while GameFi applications in the Crypto field are also significant. Therefore, gaming will undoubtedly be a major application area. However, the metaverse may emerge as a true, native super application in the next decade, integrating innovations and making digital life an integral part of our lives. However, the metaverse requires the maturity of these three technologies before undergoing another round of integration, so it is certainly not something that will happen within five years. This is my judgment about the future, for your reference.
The tipping point of intelligence: machines replacing humans
Next, we will discuss the third part of the content. With this macro perspective, how do we view specific changes in business? First, let’s look at the paradigm revolution of intelligent business. This has not changed significantly from the definition in 2007; it has just become clearer that intelligence, machine algorithms, and AI will replace humans, continuously evolving to make increasingly intelligent decisions, thereby significantly improving user experience and business efficiency. The more roles that are replaced, the more complete the roles become, and the greater the value created. However, the ultimate goal remains to provide real-time, precise, low-cost services to massive users. The successful cases of 1.0 are well-known, from the early days of Taobao shopping to the later browsing of short videos on Douyin, to the automatic scheduling of Didi rides and Meituan deliveries. The acceleration of this development is due to breakthroughs in AI that have led to a qualitative leap in machine capabilities. At the same time, an increasing number of decisions will be replaced by machines, and they will become increasingly intelligent.
This may also be a very important PPT that everyone will repeatedly apply in practice. It outlines how to achieve intelligence. In the past 15 years, everyone has struggled with digital transformation. I later realized a fundamental issue: the value created by this technology is insufficient. We are laying the groundwork for intelligence, so how do we achieve a breakthrough in intelligence? The core is the scenario, as decisions are certainly made based on specific scenarios. Therefore, who are your users? What challenges do they face in which scenarios? You need to provide a complete solution based on that scenario, offering a comprehensive service for successful intelligent transformation. Thus, the standardized products or service modules you may need to call upon are on the left. In the future, there will be no product companies; only service companies will exist. Products are merely tools and carriers that address the needs of that specific scenario.
We have always imagined scenario-based e-commerce, and I only realized today that scenario-based e-commerce is only possible today because you must make decisions based on that scenario. You need to coordinate all knowledge and expertise to provide the best service solution for a specific person in a specific scenario at a specific moment, all in one go. This is the personalization of the intelligent era, and in this sense, the C2B business model is finally established. Or more completely expressed, it is a user-driven business model, which may be C2S2B2B, where the "S" represents the intelligent platform. Consumers need this intelligent platform to directly integrate all possible resources and provide them with the most personalized, real-time solution. In this sense, we are moving towards a broad, on-demand stage, where the "S" in C2S represents the AI agent, which is the living, growing AI system that continuously learns and grows, making better and smarter decisions.
2023-2033: The decade of nurturing and exploding intelligent business 2.0
From today’s perspective, looking at the future decade from 2023 to 2033, we return to today’s theme: the decade of nurturing and exploding intelligent business 2.0. Moreover, in the next three years, we may see the emergence of native applications that will drive the development of the entire ecosystem. Artificial intelligence technology will become a general technology, empowering more and more industries to complete their intelligent transformations. The key lies in whether machines can replace humans in decision-making, and the core capability behind this is the ability to establish decision models based on scenarios. Building a living, learning, and growing AI system: the AI agent. Intelligent business will become the mainstream business paradigm.
What capabilities will future enterprises need in the intelligent era? Conceptually, we are clear that we need to become intelligent and get the intelligent flywheel turning, where one side is user experience and the other is knowledge and data. The entrepreneurial PPTs you see now generally depict this flywheel, indicating that we aim to be the drivers of intelligence. However, the real difficulty lies in the fact that this is merely a concept. We are in the very early stages of this ecosystem, and we do not know how it will unfold in the future. For instance, I have followed the autonomous driving field for ten years, and the more I follow it, the less I know how it will conclude. How will the competition in this second stage unfold? It is actually a highly complex system with too many uncertainties. Therefore, how do you embrace the future in the early stages of the intelligent ecosystem?
Intelligent strategy: look ten years ahead, think three years ahead, act in one year
This brings us back to our expertise, discussing intelligent strategy. Compared to the discussion in 2017, there is a significant change, emphasizing "thinking three years ahead." The concept of "look ten years ahead, think three years ahead, act in one year" means that looking ten years ahead is about visioning, foresight, and through today’s discussion, everyone should understand the value of such foresight. It is the premise of all your strategic decisions; you must strive to understand the various possible evolutions of the future. The second part, "thinking three years ahead," is about strategy, which starts with the end in mind. Based on the vision, you establish your positioning and development path. Acting in one year is about planning, ensuring the execution of this plan. Two points need to be emphasized: one is that, as I have repeatedly mentioned, strategy must be continuously adjusted based on rapid iterations and feedback from vision and action. You must actively engage in various attempts to understand and test whether your imagination of the future is correct, and then adjust your vision based on feedback.
The second very important point is that looking ten years ahead, thinking three years ahead, and acting in one year are not three separate tasks; they are three perspectives of one task. Whenever I encounter new inputs, I will ask what the short-term, medium-term, and long-term impacts are. Is it a one-year matter, a three-year matter, or a ten-year matter? Therefore, it is not about thinking ten years ahead when you think about ten years, and acting in one year when you act in one year. You must always consider what the short-term, medium-term, and long-term implications are. This is the essence of strategy; you need to train yourself to think from these three angles, which is crucial. A small suggestion for you is to actively apply this framework: go back and seriously consider what your three-year goals are. Can you specify them to a measurable indicator? It should not be your traditional KPIs but rather a measurement that truly reflects the essence of your innovative business.
I will reiterate: what are your three-year goals? Can this goal be distilled into a very fundamental indicator? Most students have three-year numbers that are habitual and linearly derived, and very few have brought the tension of looking ten years ahead into the formulation of three-year goals. Then, based on this three-year goal, you can backtrack to determine what you should do next year and this year. As everyone is approaching the time for annual strategy formulation, you can seriously consider whether your three-year goals are clear. If they are unclear, it presents a tremendous opportunity; uncertainty also represents a significant opportunity, indicating that we have vast room for growth, and you can create the future. If there is only a current linear derivation, it merely indicates that your growth space is very limited.
Intelligent strategy: emergence and growth
The second profound insight is that intelligent strategy is about emergence and growth. It is no longer the result of a powerful CEO's brainchild; it is a dynamic balance of short-term, medium-term, and long-term interests. Why is it called intelligent? The intelligence of this strategy lies in your proactive embrace of uncertainty, adapting to changes in the environment. Intelligent strategy is about emergence and growth, maintaining possibilities, and even creating possibilities, rather than merely pursuing efficiency. When there is no map, you must create your compass! Today, we do not have a detailed map telling us what the future will look like; we can only create our compass. This is the most significant difference between intelligent strategy and traditional strategy. The core of traditional strategy is to reduce uncertainty through relatively certain planning and execute efficiently. However, since we are in a highly complex and rapidly changing era, uncertainty represents possibilities and opportunities for creation. Therefore, the essence of strategy today is creation and innovation. In this sense, strategy is no longer just a matter for executives; it is about innovation and creation, closely intertwined with products, technology, and user experience. All these elements must reflect the principles of your strategy, and they must be organized like intelligent agents, providing feedback on whether you are doing it right. This is the potential future of strategy we envision.
However, such a strategy requires a completely different organization to implement it. I want to add that early strategies were often the result of the wise decisions of CEOs. Over the past five to six years, everyone has realized that no company can succeed without three to five co-founders. You will quickly discover that even if you have a dozen outstanding executives, they may not be able to manage it effectively. You increasingly need the entire organization to be vision-driven, strategy-driven, and future-driven.
The future of intelligent organizations
Why do we need a different organization? What will intelligent organizations look like in the future? It is because the environment requires organizations to continuously emerge with good strategic decisions and innovations. The term "continuously" is crucial; in today's environment, making one correct decision is of no use because you are entering a continuous elimination race. Our current goal may be the Asian Games, but most of the students may still be in the selection process for the city games, with several steps ahead. Therefore, being correct in this round does not matter; it merely grants you a ticket to qualify for the next round of competition. Thus, you need to establish an organization capable of continuously producing high-quality innovations and decisions. In this sense, it aligns perfectly with the context of the AI era, where the importance of simple, replicable work is declining sharply. Efficient execution will still be very important for organizations in the future, but increasingly, efficient execution will be completed by AI systems. The focus of organizations will increasingly shift towards creating unique value. Therefore, at the individual level, there will be a tremendous demand for creative talent in the future. Future talent will need multidimensional perspectives and unique expertise. Especially with the emergence of AGI, the positions of so-called narrowly defined professionals will almost be eliminated once again.
The industrial revolution marked a contraction from general capabilities to specialized narrow capabilities, strengthening cooperation and driving specialization. However, if we return to this new starting point, we will enter a new era of building general capabilities. The most creative individuals will be those with synesthetic abilities. Therefore, we need individuals with general capabilities who can also understand and leverage expertise. The sharpness of an organization, or its impact and vitality, lies in its ability to rapidly break through open-ended problems. This is the core capability of future organizations. You must continuously break through and create.
The collaboration between creative individuals and machines will be the mainstream working state in the future. I currently see some embryonic forms of this. The best organizational form for creating new jobs in the future may be tightly coupled special forces teams of eight to a dozen people. Many entrepreneurs have already noticed that a team of ten or so is sufficient. In the previous wave of mobile internet entrepreneurship, teams of thirty to fifty people were typically required to sustain a venture. However, in this wave of entrepreneurship, around ten people are generally enough, including the well-known case of Midjourney, which achieved significant success with just a small team in a very short time. However, tightly coupled teams must have corresponding foundational capabilities and support, which is very important. Therefore, this relatively loosely coupled structure can quickly mobilize internal organizational capabilities, combined with a broad, open external network for collaboration.
The evolution of organizational guiding principles
Returning to our discussion of intelligent business, network collaboration remains the core concept, and the same applies internally within organizations. From the inside out, we must transform into a networked organization. The core of management in the industrial era was hierarchical, and we must break away from hierarchical structures and move towards a networked organizational form. This is what intelligent organizations require: new guiding principles. When the management revolution began 100 years ago, we emphasized management. We spent a century learning management, and today many entrepreneurs are still learning management. Basic management is, of course, necessary, but it is just foundational. In the era of knowledge revolution described by Drucker, it became the era of software engineers, where a good software engineer could be worth a thousand average engineers. Because when an engineer sits there, you cannot tell whether they are working or slacking off.
Thus, the guiding principles of organizations have shifted from management to incentives. You cannot measure what kind of rewards to give based on output, so incentives have been prioritized. This is the equity system. Since the 1970s, alongside the internet revolution, the many irrationalities of the equity system have been fully exposed in recent years. More often than not, you cannot rely on an incentive model.
The empowerment model, which I discussed in 2017, has become increasingly necessary. It provides motivation and drive. More often than not, we have reached the second-highest level of Maslow's hierarchy: self-actualization and self-motivation. At this point, what they need is not incentives; such outstanding talent can find work anywhere and does not lack money. They need empowerment, assistance, and a platform that allows them to have greater space for expression. Therefore, empowerment will be a very important foundational capability for organizations for a long time.
However, in recent years, we have observed that although it has not permeated every aspect of enterprises, at least in the areas of strategy formulation and execution, co-creation has become a very important mechanism. This means that can the core employees within the enterprise participate in strategic discussions together? Then, dynamically adjust the strategy based on feedback and changes in the external environment, allowing the strategy to emerge. We need to create a consensus within the organization, forming a system that can continuously adjust. Co-creation requires certain prerequisites: you need the right people, and you need to share; without shared results, no one will be willing to co-create with you. Therefore, each principle is built on a solid foundation laid by the previous principle. This is the evolution of organizational principles; we are transitioning from the IT internet era to the intelligent era, moving from incentives to empowerment and co-creation.
Changes in the market value of listed companies over the past 20 years
At this point, everyone will certainly ask another question. I will first show you a chart that provides some stimulating and real insights. Market value is a decent litmus test, and you may feel a mix of emotions when looking at this chart. There is a lot of information contained within it, as we can observe the evolution of some of the most outstanding companies over the past 15 to 16 years, which can provide us with much inspiration.
You can see that from 2007 to 2017, it was truly the internet era. Companies like Google, Facebook, Alibaba, and Tencent experienced growth of about twenty to thirty times, moving from billion-dollar valuations to multi-hundred-billion-dollar companies. However, from 2017 to 2023, the previous companies only saw growth of around three times, while Nvidia and Tesla experienced growth of twenty to thirty times. This is because Nvidia and Tesla are pioneers of the intelligent era, having already begun their thirty-fold growth. Who will be on this list in 2033? It is particularly interesting to observe continuity and the phased changes over the decade.
One conclusion is that the last three slides return to the PPT from 2017. In fact, the conclusion is the same: it takes ten years to achieve greatness. If you want to accomplish something significant, you must align with a great era. Truly following this trend, you cannot achieve anything meaningful without ten years. My own experience is that the most challenging phase for enterprises that achieve greatness over ten years is from 0 to 0.1, not from 0.1 to 1. Because 0.1 indicates that your prototype has some semblance, but since what you are creating is so new, you do not even know what it should look like. Therefore, it often takes about three years to have a rough idea, and five years to clearly articulate what you want to create. I have not seen any exceptions; in the past ten years, including since 2014, when I began working with students from Lakeside, the truly successful enterprises spent at least three years in the strategic exploration phase.
So what is needed? It requires an original intention and persistence. People often ask me how to look at ten years. One is that you must strive to look and persist in looking. The second is why you would look ten years ahead. What underlies this? Why are you willing to sacrifice short-term interests to pursue long-term benefits? It is because you have a greater pursuit, a mission, a vision, and values. You want to transform the world and make it a better place; you have something different to offer others. If you lack such a motivation, your vision will naturally be limited, and you will not be able to see far or gain the support of others. The right timing, location, and people come from walking the right path and representing future trends. You can use the best and most advanced technology to solve the problems of this era. This is the true entrepreneurial spirit and the foundation for achieving greatness. All the successful companies we discussed earlier are "companies of the era." This phrase is indeed accurate. However, two fundamental points define a company of the era: you must genuinely keep pace with the development of the era's trends, and you must possess the capabilities and mindset that match what this era demands.
This is the underlying driving force behind the long-term success of those who can go far. However, such companies often face particularly challenging developmental processes. Google once considered selling, and Tencent also contemplated selling; they all faced moments when they felt they might not make it. At such times, what can you rely on? You can only rely on the power of belief, believing that tomorrow will be better. Moreover, the key in this process is that there will be a leap of faith based on belief.
Ultimately, whether it is vision or mission, do you have faith and belief in this matter? Believe in yourself; that is the only thing you can rely on. Of course, I wish everyone good luck in your endeavors!