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大家好，这里是最佳拍档，我是大飞。
智能的成本正在趋近于零吗？答案可能是不一定。最近，李飞飞和MasterClass的CEO大卫·罗吉尔（David Rogier）在《硅谷女孩(Silicon Valley Girl)》的播客节目中直接表态，说这句话是不负责任的。
这场访谈还起了个很抓眼球的标题，叫做“AI教母李飞飞：10年后，只会剩下两类工作者”。其实这个“两类工作者”的说法是罗吉尔先提出来的，不过李飞飞表示了认同，并且把这个话题往更深的方向做了延伸：AI到底是什么，它能帮人做成什么事？它会制造哪些新的分化？而人又该怎么保住自己的主动性？今天我们就来给大家分享一下这期访谈的核心内容。
先说说罗吉尔观察到的一个很现实的变化。作为教育公司的CEO，他在自己公司里看到了一个正在持续拉大的差距。如果员工已经开始主动用AI了，他们能完成的工作量是很惊人的，而且能在工作里获得以前从来没有过的掌控感和主动性。但如果有人还在对AI抱有抵触，或者从来没接受过相关的训练，那两者之间的差距会被越拉越大。
他觉得现在整个社会对AI的认知都处在一种两极分化的状态里。一半人把AI当成神，觉得它能拯救世界，以后所有人都不用干活了。另一半人把它当成魔鬼，觉得它会取代所有工作，毁掉人类的主观能动性。这种极度两极化的心态其实不太健康，也不是我们面对技术最好的思路。与其站在两边站队，不如实实在在去搞清楚这个工具最好的一面是什么，怎么用它来真正帮到人，把两边的价值都拿过来。
罗吉尔自己就是AI工具的深度实践者。他说，要是几个月前问他平时用什么AI工具，他能列出来一串。Claude、ChatGPT这些都算。但现在他发现，自己日常用的多数应用，都是他自己用Claude Code或者Cursor这些工具构建出来的。对他来说，这是一件特别有价值的事。现在他作为CEO的整套工具栈，全都是自己量身定做的应用。比如有个叫Dividify的工具，输入他自己写过的邮件、说过的话，就能用他自己的语气生成文字内容。甚至连他的效率应用、待办清单应用，都是自己搭的。
他还给自己定了条规则：如果一个待办事项在清单上停留超过一天半，就必须处理掉，要么立刻做完，要么直接放弃，因为它其实没那么重要。要么就交给团队里合适的人去做。也就是说，你完全可以根据自己的思维习惯和工作方式，打造所有你需要的应用。这其实又绕回了主动性这个话题。现在你有能力创造出任何你想要的工作流程和工具，剩下的问题只是，你有没有去做的动力，以及去做的基础技能。而今天做一个可用的应用，成本已经从以前的几个月，缩短到一个周末就能搞定。
可能有人会问，如果一个员工过来跟我说，他想开始学用AI，我该告诉他从哪起步？罗吉尔的做法其实挺实在的。他不太推崇所谓的给仪表盘做“vibe coding”，就是只做个好看的前端界面，从来没接上真实的数据，看起来像模像样，用一小时就不行了，因为背后的系统根本没打通。他发现很多来问“怎么学AI”的人，其实自己还没下定决心，心里还在犹豫，有什么东西在拦着他们。所以他不会直接扔一堆教程过去，而是会坐下来，带着他们两三个人一组，演示一个最基础的实际任务，比如怎么用AI做深度研究，一步一步带着走一遍。
他也想过，其实看个YouTube教程可能比他亲自带效率更高。但实际情况是，只要陪着走过这么一次流程，好像有什么东西就被打开了，之后这些人就能自己摸索着往前走，还能拓展出更多用法。他也说不清到底是因为有人亲自指导降低了心理门槛，还是因为CEO亲自推动带来的重视感。但只要走完这个过程，人就像被解锁了一样，能自己主动去用了。
李飞飞很认可这个观察，她觉得这恰恰反映了现实的复杂性。这也是她一直担心的一件事。公众讨论AI的时候，观点总是极端两极化。我们当然需要看到AI好的一面，也必须警惕它坏的一面。但现在的公共讨论根本不是这样。要么就是彻底的乌托邦叙事，说AI会拯救世界，以后大家都不用工作，躺着领钱就行。要么就是彻底的恐惧叙事，说AI太糟糕了，会取代所有工作，夺走人类所有的主观能动性。这两种极端其实都相当危险。
她始终相信，AI就是一种技术。换句话说，它只是一个工具，一个极其强大的工具。但这个工具是人类可以掌握、可以用来让事情变得更好的。当然，怎么使用这个工具，我们也必须保持足够的警惕。就像我们教孩子怎么用火、用刀，再到后来怎么用互联网一样。现在作为一个物种、一个社会，我们必须学会怎么和AI这个级别的工具相处。而现在真正最重要、也最缺失的讨论，恰恰就在中间地带，那种足够细致、立足现实的讨论：这个工具到底是什么？我们怎么用它做更多有益的事？怎么避开那些已知的坑？以及作为一个文明，我们怎么和这个文明量级的工具一起往前走？
聊到这里就很自然地说到了那个流行的说法：智能的成本正在趋近于零。很多人害怕AI，根源也在这里。大家会觉得，工业革命自动化了很多体力劳动，现在AI好像在自动化很多脑力劳动。以前大家觉得上大学起码能保证一份稳定的事业，现在如果智能都不值钱了，那未来该怎么办？
罗吉尔说，他从李飞飞这里学到的很重要的一点就是，现在大家聊AI，聊的其实主要都是语言智能。用李飞飞的话说，这种理解本身是“有损的”。你没办法光靠语言学会开车，也没办法光靠语言学会投篮。所以我们现在其实还处在AI的1.0版本，整个领域也存在过度炒作的成分。AI本身没有自己的价值观，所有的价值观都是人类赋予的。
李飞飞接着补充了自己的看法。首先她觉得，工业革命其实并没有自动化劳动，它只是让劳动更高效，扩大了劳动的规模，也确实改变了劳动力市场，但它从来没有真正的“自动化”掉劳动。而且我们也不能默认劳动里是没有智能的，这个假设错得非常离谱。体力劳动、认知劳动、情感劳动，人类所有的活动都和人类智能深刻地交织在一起。人类智能对大自然来说，到今天都是一个未解之谜。我们根本没有真正搞清楚人类智能的深度和细微之处。所以任何在外面声称“智能成本趋近于零”的人，都是不负责任的说法，因为人类智能的深度远超我们现在的想象。
就像罗吉尔说的，除了我们比较熟悉的语言智能，我们还有感知智能、空间智能、身体智能、情感智能，甚至我们到现在都没搞明白，人的创造力到底是从哪里来的。每个人的创造力，来自大脑的不同部位，也来自整个人生经历的不同部分，是非常复杂的集合。所以我们必须非常小心那些过于简化的说法。
她当然认同，大语言模型和它的各类衍生产品非常强大，在商业智能、软件工程、逻辑推理这些领域都已经展现出了很强的能力，也正在帮人们完成更深入的工作。这些价值都非常重要。但这件事本身是细微的、复杂的，其中很大一部分是和人类智能形成强有力的协作，而不是取代。所以她不会用“自动化人类智能”或者“智能成本趋近于零”这种表述。她非常担心这类言论带来的误导。
这些简化的言论，也正是很多人反感AI的原因。大家看到的全是大规模裁员的头条，都是“我们不再需要你了”的叙事，负面情绪自然就累积起来了。但面对这种情况，答案从来不是躲着AI，或者干脆不去用它。
罗吉尔觉得，正确做法是拿起这个工具，去想清楚怎么更好地设计它、改进它、优化它。换个角度看，一份工作其实是一整套任务的集合。你工作里总有一些任务是你不喜欢做的，比如护士要写大量的护理记录，医生也要写病历。他从来没见过哪个护士说，自己工作里最喜欢的部分就是做记录，这根本不是她们进入这个行业的初衷。AI恰恰可以把人从这类事务性工作里解放出来。
所以回避技术是错的。回头看任何一次技术转型，最终几乎都是净增就业的。问题只在于谁能拿到那些新工作，是那些主动适应变化的人。如果你不适应，结果会非常糟糕。比如当年计算机和电子表格普及的时候，没有跟上变化的人，终生收入会下降超过五分之一。头一年的死亡率甚至会翻倍。技术转型真的会实实在在损害人的健康。这个数据听着挺疯狂的，但它是真实存在的。所以答案不是躲起来，而是推着自己去探索、去适应、去改良工具。
李飞飞完全同意这个说法，她觉得“主动性”这个词特别值得强调。还有协作、赋能这些概念，都是技术的核心意义。哪怕是AI这种被很多人吹得“像神一样”的技术，本质也应该是以人为本的。以人为本说起来很简单，但内涵很深。对她来讲，最核心的就是真正给人赋能，不管是个人、社群还是整个社会，这才是这项技术存在的意义。
Thus, we arrive at the essence of a period of transformation. A period of transformation is bound to have its losses. You will lose old habits, lose the comfort of decades, lose stability. But it is also a period full of opportunities, providing space for all the new things to come, allowing us to do better things and create more valuable things. As an individual, how do you cross from a stage of confusion and loss to one of seizing opportunities? Where is the dividing line? The answer, in fact, lies within each of us. It depends on whether you are willing to learn, embrace, and improve your skills, and maintain intellectual openness to new opportunities. This matter touches the core of everyone's growth. Whether you are a student still in school or a professional with many years of work experience, you must face this change together. The speed of change is indeed faster than any previous technological revolution, which is also one of the sources of everyone's anxiety.
So, in specific fields like education, what changes has AI brought about? Rogier believes that AI's impact on education will be enormous. Research from the past sixty years has already proven that the best way for an individual to learn is one-on-one teaching. So why are we still sitting in classrooms of 20 or 300 people? The core reason is cost. Providing every person in the world with a dedicated tutor is too expensive. Although everyone knows it is the most effective method, with AI, the situation has changed. From the practice of the past few years, AI can already provide personalized guidance at a level close to one-on-one teaching, which is much better than listening in class or simply reading a book. This has led to a cliff-like drop in costs. Previously, elementary education cost $12,000 per year, and undergraduate education cost $80,000 per year. Now, providing the same quality of teaching with AI costs only about $100.
So when will we see such changes in our education system? Rogier believes that the biggest obstacle is not technology, but institutions that fear change. But the trend cannot be stopped. We already know that using AI to assist learning can achieve the same content with 60% less time. If one school says it will ban AI and not allow students to use it, while another school allows students to use it freely, then children who use AI will far surpass those who do not. Of course, AI cannot replace everything. You still need face-to-face interaction and social connections between people. But you will begin to see a divergence among children. Children who are open to AI will learn much faster than others. The prospect of such divergence is indeed worrying, but it is not a problem with the technology itself. Rogier's judgment is that schools that cannot adapt to AI within ten years will eventually disappear because they will fall too far behind.
Li Feifei adds that the future world that these schools will face is itself changing rapidly due to AI. Therefore, schools should indeed make changes. She also firmly believes that every school and every classroom should embrace AI, and every student should embrace AI. But at the same time, we have a collective responsibility to include teachers and educational administrators in the discussion of AI, to show them feasible paths. This way, we can uphold the true goals of education. The goal of education has never been to use a tool well, nor to struggle with closed-book exams or open-book exams, nor even standardized test scores. The goal of education is to cultivate people, to enable everyone to become a meaningful contributor to their community and society, and to live a meaningful life. AI should not deprive any of these basic goals. Instead, it should help us achieve these goals better and more efficiently.
But now, many of our discussions are still stuck in polarized dualistic oppositions, such as whether AI is used for cheating, and whether AI should be removed from exams. These are not the points we should focus on. What we should really discuss is how to empower teachers with AI, how to empower students, how to reconstruct classrooms, how to redesign exams and standardized tests, and how to rethink university admissions and resource allocation. Since there is technology that can reduce the cost of education and increase accessibility, how can we provide more resources to low-income urban communities, rural areas, and the Global South? These are the truly important topics in AI and education, and we are currently missing them.
After discussing education, it naturally extends to the workplace. Many people are curious about what the workplace or companies will look like in ten years. What preparations should ordinary people make? Li Feifei's answer is very clear: proactivity. AI will give people more autonomy. A large part of future work will rely on those who know how to use these tools effectively. She gives a very specific example. One of the most sought-after jobs in Silicon Valley over the past twenty years has been product manager. Now, there are already many discussions in the industry about the changes in the role of product manager. Ten years ago, a standard product manager's job was more like a connector between users, the market, and engineers, more like a commander. They did not write code and were usually not from a software engineering background. If they wanted to create a product prototype, they had to find designers and software engineers. After getting the prototype, they would send it to users to collect feedback, and then iterate after integrating the feedback. This product management lifecycle could take several months in an ordinary company. But now it's different. The way many companies' product managers work has fundamentally changed. Many product managers now write code themselves. They don't have to wait for the entire team to build a prototype; they can use AI to help design simple prototypes, which is what is commonly called "vibe coding." This instantly shortens the entire cycle. Of course, this does not mean we no longer need designers and software engineers, but it saves a lot of time on basic tasks, allowing professionals to do more complex and valuable work. User feedback is also changing. AI can now simulate user behavior, and there are more efficient ways to reach users and form a product loop. Therefore, Li Feifei says that when she recruits young product managers now, she looks for those who are riding this wave of change. She doesn't want candidates who are still talking about textbook workflows from five years ago.
What will future companies look like? Different industries will certainly have different forms, with a lot of room for imagination. But it is certain that our workforce will be deeply empowered by powerful tools like AI. The boundaries of individual proactivity, creativity, and human capabilities will become increasingly blurred, and the threshold for doing things will become lower. In her view, this will fundamentally change the structure of enterprises. Every student today should try to imagine what role they want to play in that new structure. This work state is very much like acting as an entrepreneur in a position. Whether you are a founder yourself or an employee with an entrepreneurial spirit within the organization, it means you have to handle many things simultaneously and be responsible for the results in many aspects.
Based on his company's observations, Rogier proposes a "barbell effect" judgment. The future workplace will see a group of true top experts. When you were in school, someone always told you that if you delve deeply into one field, you can build a long-term career. But this approach is now being eroded, unless you are among the top 1%, the truly top tier. For example, an ordinary, moderately skilled copywriter can now do a similar job with large language models. But if you are the best copywriter in the world, or in the top 1%, then AI cannot easily replace you. Therefore, we will see the rise of experts. Those who achieve extreme excellence in their fields will become increasingly valuable. This rule can extend to many fields that rely on craftsmanship. On the other end of the barbell are highly proactive generalists. They can do many different things and have strong judgment and proactivity. When these generalists and top professional talents cooperate, interesting chemical reactions will occur. Both sides will feel that they cannot do what the other person does. Therefore, Rogier's guess is that the future will be a binary situation of coexistence between top experts and highly capable generalists.
Li Feifei agrees with this view. She believes that whether you are on the expert side or the generalist side, you need proactivity and the ability to use tools in unique, creative, and in-depth ways. She has already seen this in many industries. For example, designers, who inherently possess a large amount of human creativity, but some designers can use various AI tools in ways she could never have imagined. This is their craftsmanship and value.
Another point she wants to clarify is that the term "entrepreneur" in Silicon Valley is almost equivalent to registering a Delaware company, as if only starting a company is called an entrepreneur. She does not agree with this definition. In her opinion, the term "entrepreneur" is largely synonymous with "proactivity." You can be a specialist doctor, or a teacher, and still have an entrepreneurial mindset. Proactivity is the most crucial thing. Facing such advanced cognitive technology, be brave, hold onto your human proactivity, control the technology, use it, and familiarize yourself with it. Don't be afraid, don't avoid it, because this is the direction of historical progress. You don't necessarily have to become a founder in Silicon Valley. Everyone can be an entrepreneur for the craft they engage in, for the things they want to do.
Speaking of using tools, many people are curious about what AI tools are used by AI leaders like Li Feifei, and which tools have brought about transformative changes to their work. Li Feifei says the most basic ones are those everyone is familiar with, from ChatGPT to Gemini to Claude. She uses them in many scenarios, from helping her deeply research a topic to directly engaging in conversations around a theme. The uses are extensive. She also gives a very practical example. She is responsible for doing laundry at home and has to deal with piles of clothes every weekend. When folding clothes, she used to listen to audiobooks, but sometimes listening to books can be tiresome. A few months ago, she suddenly thought that she could chat with AI about a deep topic while folding clothes. This has become particularly interesting, even motivating her to fold clothes more, because she can use this time to learn what she wants to learn with AI, which is completely her own time.
Of course, in the field of software engineering, AI has brought about earth-shattering changes. In the fields of art and design, her company World Labs is also developing related models to help creators imagine 3D worlds. Profound changes are also happening in the creative field. However, she also specifically emphasizes that there is a lot of controversy in the creative field right now. Some companies position AI creative tools as replacements for human creators, which she strongly opposes. Human creativity, even at the visual level, is too vast. Even looking only at visual design, it is deeply intertwined with our emotional intelligence, the stories we want to tell, and the values that each creator carries. AI is a powerful tool to help people express creativity, absolutely not to replace creativity.
At this point, Rogier asks a core technical question: If AI cannot handle spatial intelligence, will it ever be truly intelligent? Li Feifei's answer is yes, and this is precisely what she is now fully investing in at World Labs. Many people may not be familiar with the concept of spatial intelligence. It actually encompasses several abilities that humans exhibit in three-dimensional environments. First is understanding: we can understand what is happening around us and recognize the people, devices, and environments in front of us. This is the understanding part of spatial intelligence. Second is reasoning: for example, if you want to get a bottle of water from the refrigerator, you need to determine where the refrigerator is, plan your movement path, and avoid obstacles on the way. This is spatial reasoning. Third is generation: we can imagine a living room in our minds, and those who are good at drawing can even draw it. Whether it's 2D or 3D content, humans can naturally generate spatial images in their minds. The last point, and equally important, is interaction: how we interact with things in space. For example, folding clothes, how to fold each piece, how to put them in the closet. These actions have a high degree of spatial interaction. Therefore, spatial intelligence consists of these four aspects: understanding, reasoning, generation, and interaction.
The entire industry has made great progress in this area. Overall, current AI image tools can generate many 2D images and can also help you identify flowers and plants you don't recognize. The understanding ability is quite advanced, and it can also perform some reasoning tasks. We can already complete basic graphic drawing in AI tools. In terms of generation, there are mature products in the 2D dimension. World Labs is working on 3D generation. 3D technology is very important for true robot interaction, as well as for creative work such as design, architecture, game development, and visual effects. This is also their core direction.
Are spatial intelligence and large language models two completely separate technologies? Is it that large language models have their own boundaries, and world models are another set that can be integrated? Li Feifei's answer is yes and no. They are more complementary. Think about ourselves. Take shooting a basketball, for example. The entire action happens very quickly. You don't stand there and mentally recite in language, "I'm going to shoot now." But the act of shooting itself is a highly complex intelligent moment. Language reasoning is involved. As an athlete, you will keenly realize whether the shot went in, what it means for the game, and for the current moment. Some of these processes run in a linguistic way. But at the same time, seeing the entire court, judging the positions of other players, and aiming at the basket is deep spatial intelligence. Adjusting body posture and controlling the force of movement is deep bodily intelligence. Therefore, most of the things we do in life are actually a mixture of linguistic intelligence, spatial intelligence, and bodily intelligence. For Li Feifei, they are highly complementary and work collaboratively, and spatial intelligence is a very large part of it. Think about biological evolution. It took over 500 million years for spatial intelligence to evolve to maturity, while linguistic intelligence evolved in a much shorter period. Therefore, this is a very deep, ancient, and fundamental intelligent ability that both animals and humans possess.
So, how far are we from fully mastering spatial intelligence? For example, reaching 100% human level? Li Feifei says that as a scientist, she doesn't really know what "100%" means, because science itself is constantly pushing the boundaries of the unknown. If our goal is to match human intelligence, the biggest problem is that we don't even know where the boundaries of human intelligence lie. We can never see its full picture. But if we set a more realistic goal, to match human daily abilities, average abilities, such as folding clothes, making fried eggs, playing basketball, how far are we from achieving these? The answer is still far from it, but will it take 100 years? She doesn't think so. Her goal is to see it realized in her lifetime. Many people are working hard towards it now. So the approximate timeframe is that it won't take 100 years, possibly not even 50 years, but definitely not just 1 year. Things like folding clothes involve not only intelligence but also physical embodiment, sensor technology, and hardware, so it will be more complex. But she hopes we can see this day in our lives.
Rogier also adds that he knows Li Feifei has avoided using the term AGI for many reasons, but he believes that any system approaching AGI would be impossible without spatial intelligence, as it involves understanding humans and interacting with humans and the environment in the real world. Li Feifei responds that she doesn't care about the popularity of the term. As a scholar, the academic community has always called it artificial intelligence. What does the "general" G really mean? There is no strict and clear definition in science. But regardless, it's just a word, a nickname. Intelligence itself is very complex. An AI landscape lacking spatial intelligence is, in her opinion, incomplete.
After discussing so much technology and trends, we finally arrive at the question that everyone cares about: how to cultivate proactivity? Rogier runs an educational company and has done a lot of research and observation on this issue. He admits that he has reviewed many related studies, and there are no completely mature conclusions yet. He cannot tell you with certainty what to do first and what to do second. But by breaking down proactivity, there are indeed some validated clues. For example, a person must first have a sense of security before they dare to take risks. This is a very important foundation for proactivity. Being able to experience failure and learn from it, that is, resilience, is also an important component of proactivity. Maintaining curiosity about the world is equally important. There are also some primitive driving forces, such as a strong desire to solve a problem or achieve a goal. It may not be a remarkable trait, but it can push you forward. When you have the thought of "I must solve this," it will force you to be proactive.
However, current research is mostly still in the descriptive stage of "what drives proactivity," and the evidence is not very solid. Another interesting finding is that you need to put yourself in environments that you find difficult and unfamiliar. This starts from childhood. You need to grow up in an environment where basic needs are met: love, food, sleep, psychological security. These must be in place first. Then, proactivity can be stimulated. For example, receiving extra rewards for doing something can indeed make people act. But proactivity itself, from practical experience, is much more complex. Our society teaches us in too many places to seek praise: from parents when we are young, from teachers when we are in school, from bosses when we are working. But having proactivity, having this entrepreneurial spirit that is not limited to entrepreneurs, is almost a rejection of this entire system of seeking praise.
Rogier says that when he founded MasterClass, everyone told him the idea was impossible and a bad idea. It was very difficult for him at that time because he had cared a lot about others' praise for most of his life. But he later had to realize and change: if everyone thinks an idea is good, it's probably not a good idea. If you want to have proactivity, to be an entrepreneur, you have to pursue things that others think are impossible. Now his mindset has completely changed. As an entrepreneur, when someone tells him, "This is impossible," his reaction is, "Oh, that's exactly what I want to do. I want to explore it further." So this is a fundamental shift in mindset, not something that can be achieved by learning a few skills. It is far more complex than people imagine.
Many people can relate. We are taught from childhood to be obedient and to care about others' opinions. If your views are contrary to those of most friends, and everyone disagrees when you express them, how do you cultivate the courage to stick to your own? This is also particularly important for cultivating proactivity. Li Feifei believes that this question actually gets to the core of family values. How does she raise her children and guide her students? It's actually what Rogier just said: encouragement, or even a simple sentence, "Don't be lazy." Many people in this generation often lament that there are too many internets, too much social media, too much AI, as if they are all negative influences. But Li Feifei has a contrary view. She even envies the younger generation. She feels that the world in which young people grow up is much more diverse. You browse Twitter, Instagram, TikTok, and gradually realize that this world is full of various voices. When they were young, the world was smaller and narrower. Perhaps there was only the voice of a teacher or a parent representing absolute authority. But today's young people are thrown into a world full of various opinions and possibilities from birth. This can certainly be frightening, and social media does have its problems. But this can also be a huge opportunity. You can use this reality to remind young people and tell them, "Look, your voice is truly important, because there is no single authoritative voice in this world, nor is it decided by a few people with loud voices." This can completely encourage young people and make them re-examine the era in which they live. This is an era where there are too many tools to empower you with proactivity. This is an era where your voice is more important than anyone else's voice, as long as you truly believe it. This point is very different from the growth environment of the previous generation.
Finally, for ordinary people who have no idea where to start, what is the simplest way to get started with AI? Is there a simple "unlocking" step? Li Feifei's answer is very simple. She admits that the young employees in her company may use AI more than she does. So this advice, she wants to give more to those who are not in the tech circle, such as teachers, nurses, accountants, etc. She knows that the discussion about AI is too polarized now, and people are really unsure where to start, besides anxiety. Her advice is very simple: find a young person, such as your child, nephew, or niece. As long as they are under 25 years old, most of them are already using AI. With pure curiosity, ask them to show you how they usually use AI and what they are doing with it. The key is to change your mindset, don't think of it as, "Oh my god, I have to learn a terrible new technology." You are just learning about the people you care about, your children, your students, and the future world they will live in. That world, whether you like it or not, you will enter it. Let them be your guide. You don't need to have any psychological burden, don't worry about not having learned computer science, and don't bother about which app to download. Just let the young people you trust lead you. Spend a weekend or an afternoon to see it. Once you truly understand what it is, that world will not be so scary. It can be something that empowers you. And even if you discover its problems, its imperfections, it is precisely because you understand it and know where the problems lie that your voice can be better heard.
The entire conversation is now nearing its end. In fact, you will find that after discussing so much about AI technology, future trends, and workplace changes, the core focus is never on the technology itself, but on people, on human proactivity. The more powerful the technology, the more people cannot give up their initiative. Maintaining curiosity, proactively understanding, using, and mastering tools is the most stable way to navigate technological cycles. I don't know if you have recently used AI to solve any practical work or life problems. Welcome to share in the comment section. Thank you for watching, and we'll see you next time.