Editor's note: Recently, in an interview with the People's Daily, Dr Kai-Fu Lee, now the CEO of 01.AI and chairman of Sinovation Ventures, shared his perspective on hot issues regarding AI. Recognized as one of the "TIME100 AI" Top 25 AI Leaders in the World and 100 Most Influential People in the World by TIME Magazine in 2013, Lee is a WIRED 25 Icon. Here's the full text of the interview.

Q: What have you been focusing on over the past two years?

Lee: For the last two years, I continued to make investments in AI and other fields on behalf of Sinovation Ventures. I also co-founded a company called 01.AI, which is really building large language models from China, and building models that work well for any language.

Q: As a trailblazer in the AI industry, you recently mentioned that the era of a new generation of AI has begun. Could you explain in detail what this era entails and why you believe it has started?

Lee: Yeah. I've been working on AI for over forty years. And for many years, AI has tried to emulate a little bit of human intelligence. But it only did one little sliver of the entire general brain that our brain has which we call intelligence.

In the last two years, we saw that it appears possible for computers to have that same general intelligence. And when I say general, I mean that it understands everything about human knowledge in a similar way to a college student. And then it can learn further and very quickly in any other discipline.

This general capability was made possible first by Chat-GPT about two years ago. And then both the US and China have seen more companies that have delivered such capabilities.

The excitement is that this capability for AI to learn from every book ever written. In the future soon, every video ever created, and everything ever spoken that it can create a super brain that learns from more data than any human can ever do in a human lifetime.

And we are now seeing glimpses where it can start to do not only generalization, analysis, writing content, but is able to do inference and make deductions, and solve very difficult mathematics problems and physics without ever having been taught to do so specifically.

This ability built on top of the general large language model with an ability to learn new things by itself and teach itself, gives us hope that we'll reach what's called AGI or artificial general intelligence which is overall smarter than any human being within the coming decade.

Q: "New quality productive forces" has been a buzzword in 2024 in China, with AI being a key player. Which AI applications do you think are good examples of achieving economic benefits? How have they impacted industries and people's lives?

Lee: The ideas of new quality productive forces I think are extremely insightful. It's the sense that productivity isn't just putting more labor, but rather using novel and new breakthrough technologies that can multiply or even exponentiate to see greater value being produced.

AI is not only a good example but I think by far, the best example of such technologies because the whole idea of AI is that it can do what humans do, think, reason, make decisions, create contents and refine our decisions, give us feedback, help us improve in any imaginable domain.

In traditional industries, a lawyer can be five or ten times more productive with AI doing much of the writing for the lawyer. An accountant can be five or ten times more productive, because AI does all the routine number crunching, leaving only the accountant to instruct AI what to do; and customer service can be handled 99 percent by machine with a higher level of customer satisfaction, with people only needed for one percent. This goes on, it goes to manufacturing companies, it goes to real estate companies and all traditional industries and service industries. We can have robots that are taught by these new AI, that can dramatically reduce human labor cost for producing goods, all of which lead to the expectations we have for this new quality value creation and productivity creation. And then the most exciting thing is in other areas that are largely viewed as new quality productivity enhancements, AI makes it a double enhancement.

I think the whole idea of thinking of and understanding that AI is all about another super smart, super big, human-like, but different from human brain. That means it's a partner that each of us as a knowledge worker can rely on, a partner who has a much larger memory, much faster processing, and much more complete knowledge. But maybe it lacks our intuition, maybe it lacks our particular kind of experience, maybe it lacks our human-to-human connections.

Keeping humans to do what humans do best and letting AI do what it does best leads directly to each of us, being able to be five or ten times more productive. So that I think is by far the most powerful technology that will lead to this new quality productivity gain

Q: AI has already been integrated into various disciplines, such as neuroscience, cognitive science, psychology, and the arts. Looking ahead, what new application scenarios do you see for generative AI? What are the potential opportunities for AI to integrate with other industries?

Lee: I think if we think generally about the coming of a new technology, let's say earlier with PC and internet, or mobile and mobile internet, and now with generative AI. Usually when we enter this new technological era, it begins with how we change the way in which we browse or look at content. Then with how we produce this new content, then with how we search and organize and find new content. Then with how we deal with richer forms of content like video, then with transactions and making money.

With generative AI, it will be no exception. This is kind of at a task level, how things will improve. And also, we can think about it as human needs. Humans have always had a need to work, communicate and learn, and entertain ourselves. Again, with PC and mobile we've seen two waves in which these technology waves changed the way we communicate, work, learn and entertain ourselves, so we're going to see AI do the same.

The answer to your question really is everything.

If you think about how did we communicate in the old days, it was person-to-person, then it was through telephone, then it was through instant messaging, then it was through internet-based phone calls, then it's through new social networks. Now I think we're gonna see a brand new way of communicating with humans and AIs together. Also, learning used to be in the classroom, then you could have virtual teachers.

If you think about your job, it's about finding out what topics to have, picking out who to interview, talking to the person about arranging the interview, preparing the questions, asking the questions, getting the answer, turning the answer into a piece of video or a newspaper article, like we're doing right now. In the future, all of this can be one step at a time automated. So I think really there is no industry that will not be touched, changed and transformed and made efficient with AI technologies.

Q: Internationally, we've seen the rise of generative AI applications like Chat-GPT and Sora. In China, there are similar models such as Yi series models. From your perspective, is there a significant gap between domestic generative AI products and international ones? If so, what are the specific differences?

Lee: Yes, there's no doubt that Americans invented most of these technologies, but the Chinese made them more efficient, more usable. I think that will be the fundamental difference.

I wrote a book back in 2018 called AI Superpowers, where I talked about the mobile internet and also the AI 1.0 era. Both of which saw the same thing, that Americans invented the mobile internet. They made the first apps on mobile internet, but the Chinese mobile internet apps beat the American mobile internet apps in usability.

AI 1.0 also, there were "the four dragons," and many computer vision companies, deep learning companies, and autonomous vehicle companies that out-execute the American companies, although American companies generally out-invent the Chinese companies.

The same thing carries over. If we look at generative AI. Clearly one could also say that two years ago when Chat-GPT came out, China was probably easily seven years behind. What has happened in the last two years is that China has learned and developed all these large language models that are very very good, very close to American top models, maybe not quite as good as the best ones, but fairly close. Yet they are so much more efficient.

The Chinese engineers really found all the ways to reduce cost and come up with new algorithms, come up with new model structures, come up with faster training, make it work on lower capability chips whether domestic or not, and really made the training process much faster. When it's faster to train, it's faster to use. Using these models called inference time, compute is also a fraction of the American costs.

We are already seeing that Chinese technologies are around six months behind the US, starting two years ago, seven years behind, now six months behind, huge progress. Cheaper to train by a factor of ten or so or more. Cheaper to infer by a factor of 30 or so. These are amazing progress made by Chinese companies and it's actually made a lot of top American researchers really turn their heads and become very impressed.

But I think the best is yet to come, and the best, the single area where China clearly outshines the US in technologies is in building applications, applications that cater to users' needs and applications that create economic value.

And I think we're now at a stage where the LLMs (large language models) are very good, Chinese or American, and very cheap, in particular Chinese. All these smart application developers, who are not necessarily AI experts, can now turn their attention to how can they build an AI app. I think now hundreds of flowers can blossom in China, with all the people, who have the capability of developing great apps, who have done it in the mobile era. Now the stage is all set for them to enter.

And I look forward to 2025 as the year when Chinese AI apps really rise up and become among the best in the world.

Q: There has been rhetoric about decoupling from China on the international stage. In your opinion, what impact would decoupling have on the AI industry? You once mentioned that Chinese companies need to find a second path distinct from OpenAI. could you elaborate on what you mean by this second path?

Lee: I think the first path taken by OpenAI is every year and a half, spend 10 times more money, train a really big model, and keep going until it beats humans. That has been called the Scaling Law, which is a model not feasible for China.

I think the category that China is in is practical, get things done, make it efficient and make it valuable, so as I described earlier the second path.

The Chinese engineers are so good at finding clever engineering solutions, and doing vertical deep integration to let the researchers, the engineers,  and the chip designers work together to make something very efficient.

I think the single sentence that describes the second approach and why it has led to a stunning result that even impressed the American researchers is the following sentence: Necessity is the mother of innovation.

The necessity is the reality that we have one-third to one-fiftieth as much resource, and we don't have access to the most advanced chips. So we have what we can, but let's make do with the best that we can do. This has been I think the strongest point of Chinese companies and Chinese engineering.

Necessities is the mother of innovation. That's why I really remember and I was very moved by the diligence, and willingness for hard work by the Chinese researchers I met in Beijing. This was in 1990, and that's one of the reasons I returned to China to work. Because I felt with people, with such work ethic we're going to make miracles happen, and that's exactly what has happened in generative AI today.

Q: To meet the needs of AI industry development, regional governments have been building and supporting intelligent computing centers. Compared to traditional ones, what characteristics should these new-generation AI computing centers possess?

Lee: The computing centers really perform two tasks. One is helping to make these models, which is usually called training. Secondly, helping these models to become used, which is called inference. And I think both of these are important workloads.

I think knowing how large the number of users are in China and how optimistic I am about the massive adoption of AI and how likely I think there will be so many great AI apps, I would be making a bigger bet on the inference than training.

In the past, training was the primary way that people wanted to see data centers used, because there weren't very many apps. Now that there are more and more apps, I'm optimistic about the future. I think the most likely way, these data centers will be used is through inference, so I think they should be populated with inference chips, and data centers that are well set up to service the people, in all of China, or at least regionally in a very efficient way.

The training data centers and inference data centers are different. A training data center isn't that concerned about massive connectivity, it's more concerned about getting all your data here and just keep training for two months. Inference is about anyone can access anywhere at any time, getting very fast. Response time is very important. Very strong networking is very important.

So, when these data centers are built, I think proportionately a very large number should focus on the inference workflow.

Q: Privacy and security have always been concerns regarding AI, such as risks posed by AI face-swapping technologies. what measures are the AI industry currently taking to address these issues?

Lee: I think privacy is not something that is the only issue with AI. AI actually has many issues, privacy is one of them.

I think those technologies will require technological solutions to catch the deepfake makers, to identify videos or pictures that are deepfaked, and those will have to be developed by technologies. Those technologies can be used even more in computing to find out if something is not original. Another mechanism would be, at the time of capture, placing an irremovable watermark. so that you know when the picture has been altered or not, These are new technologies that need to be invented.

But there are also many other worries, what about people who ask the language model how to make a harmful drug or weapon? How do we prevent those questions from being asked, and also how do you prevent criminals from using large language models to either do something terrible or to create misinformation. I think those are another set of issues that need to be addressed.

I think regulations are definitely needed that make it clear that people who use these technologies for illegal, harmful purposes, will be severely punished as a way to impede people from using these technologies in a wrong way.

What's important is to start thinking about the ways to protect a guardrail, the ways to create deterrence by significant and clear ways to punish offenders. Also, I think the laws and regulations should focus on the way that similar non-AI crimes are committed.

It is not a great idea to limit the proliferation of technologies. Because I think in the end, technology will do a lot of good. A lot of these concerns are real. Guardrails and regulations need to be put in place. They should be done on specific harmful, illegal acts, as opposed to generally slowing down the technologies because that will reduce the country's competitiveness.

Q: The penetration of AI at present has become an irreversible trend and people may be troubled or have anxieties in this process. In your opinion, are there any fields in which AI cannot replace humans, and what suggestions would you give to people like me to adjust AI anxiety?

Lee: Anxiety is normal, but AI’s proliferation and rapid continual improvement is unstoppable. First, we have to turn anxiety into proactive self-improvement, not turn anxiety into inaction and feeling of helplessness.

There are many jobs that will be around, just like we've seen automobiles remove a lot of jobs but total human jobs are not lacking in any means. Computers, mobile phones, every invention has replaced some jobs, but new ones will come around.

What are the types of jobs I think that are secure? First, people who are able to elevate themselves to be the masters of AI that would be the best job. The top jobs are gonna be around because someone needs to give AI direction. The second group would be those who can understand human strengths, focus on those strengths and work with AI. Some of the human strengths that AI doesn't have. I think one is the truly breakthrough innovation, inventing brand new concepts that didn't exist before. AI learns from data. Amazing artists and researchers can also continue to do great work, and in fact use it to teach AI, but I also acknowledge that's a small group of people.

What are some larger groups of people, types of things. I think one of the most important things which I've stated in all of my books is human connection, trust, empathy, and love. AI doesn't have emotion, AI doesn't connect with people. So I think people need to generally focus on their ability to understand other people, gain their trust and ability to communicate and convince other people. Focusing on so-called soft skills, the ability to communicate, empathize, understand, create connections, and render trust, these are uniquely human.

If we think about the medical profession, future physicians will be compassionate caregivers, but AI will be the back end that figures out what's the best drug combination. The physician has teased out what the issues are that maybe the patient wouldn't tell an AI, or wouldn't think to tell an AI, but would tell a trusting human.

And the list continues with all the other professions, that require communication, empathy, and connection. I think a lot of the service-oriented, human-to-human service-oriented jobs, I think will be what the large number of people might do.

And then lastly I'm sure AI will create a lot of jobs. Today AI has already created tens of millions of a category of a job, which you are probably not aware of, it's called labeling data for AI. That may not last forever, but similar opportunities will be created.

When mobile internet was created, now that we look back, it created new categories of jobs. People who have shops, the farmers who can now sell through top apps. The job market will change depending on the adoption of technology, and the types of jobs that are created will be numerous. We don't know what they are, but we can patiently wait. I bet in five years the number of new jobs created by AI will be ten times larger than the tens of millions of data laborers that exist throughout the world.

I have confidence that humans have the wisdom to figure out what to do.