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	WEBVTT

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	The following is a conversation with Kai Fu Li.

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	He's the chairman and CEO of Sinovation Ventures

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	that manages a $2 billion dual currency investment fund

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	with a focus on developing the next generation

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	of Chinese high tech companies.

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	He's the former president of Google China

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	and the founder of what is now called Microsoft Research

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	Asia, an institute that trained many

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	of the artificial intelligence leaders in China,

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	including CTOs or AI execs at Baidu, Tencent, Alibaba,

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	Lenovo, and Huawei.

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	He was named one of the 100 most influential people

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	in the world by Time Magazine.

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	He's the author of seven bestselling books in Chinese

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	and most recently, the New York Times bestseller called

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	AI Superpowers, China, Silicon Valley,

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	and the New World Order.

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	He has unparalleled experience in working across major tech

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	companies and governments on applications of AI.

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	And so he has a unique perspective

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	on global innovation in the future of AI

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	that I think is important to listen to and think about.

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	This is the Artificial Intelligence Podcast.

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	If you enjoy it, subscribe on YouTube and iTunes,

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	support it on Patreon, or simply connect with me on Twitter

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	at Lex Freedman.

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	And now, here's my conversation with Kaifu Li.

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	I immigrated from Russia to US when I was 13.

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	You immigrated to US at about the same age.

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	The Russian people, the American people, the Chinese people,

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	each have a certain soul, a spirit,

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	that permeates throughout the generations.

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	So maybe it's a little bit of a poetic question,

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	but could you describe your sense of what

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	defines the Chinese soul?

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	I think the Chinese soul of people today, right,

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	we're talking about people who have had centuries of burden

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	because of the poverty that the country has gone through

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	and suddenly shined with hope of prosperity

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	in the past 40 years as China opened up

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	and embraced market economy.

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	And undoubtedly, there are two sets of pressures

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	on the people, that of the tradition,

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	that of facing difficult situations,

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	and that of hope of wanting to be the first

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	to become successful and wealthy,

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	so that it's a very strong hunger and strong desire

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	and strong work ethic that drives China forward.

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	And is there roots to not just this generation,

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	but before, that's deeper than just

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	the new economic developments?

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	Is there something that's unique to China

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	that you could speak to that's in the people?

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	Yeah.

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	Well, the Chinese tradition is about excellence,

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	dedication, and results.

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	And the Chinese exams and study subjects in schools

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	have traditionally started from memorizing 10,000 characters,

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	not an easy task to start with.

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	And further by memorizing historic philosophers,

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	literature, poetry.

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	So it really is probably the strongest road

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	learning mechanism created to make sure people had good memory

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	and remembered things extremely well.

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	That, I think, at the same time suppresses

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	the breakthrough innovation.

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	And also enhances the speed execution get results.

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	And that, I think, characterizes the historic basis of China.

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	That's interesting, because there's echoes of that

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	in Russian education as well as rote memorization.

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	So you memorize a lot of poetry.

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	I mean, there's just an emphasis on perfection in all forms

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	that's not conducive to perhaps what you're speaking to,

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	which is creativity.

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	But you think that kind of education

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	holds back the innovative spirit that you

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	might see in the United States?

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	Well, it holds back the breakthrough innovative spirit

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	that we see in the United States.

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	But it does not hold back the valuable execution oriented,

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	result oriented value creating engines, which we see China

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	being very successful.

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	So is there a difference between a Chinese AI engineer

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	today and an American AI engineer perhaps rooted

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	in the culture that we just talked about or the education

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	or the very soul of the people or no?

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	And what would your advice be to each

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	if there's a difference?

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	Well, there's a lot that's similar,

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	because AI is about mastering sciences,

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	about using known technologies and trying new things.

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	But it's also about picking from many parts of possible networks

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	to use and different types of parameters to tune.

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	And that part is somewhat rote.

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	And it is also, as anyone who's built AI products,

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	can tell you a lot about cleansing the data.

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	Because AI runs better with more data.

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	And data is generally unstructured, errorful,

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	and unclean.

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	And the effort to clean the data is immense.

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	So I think the better part of the American AI engineering

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	process is to try new things, to do things people haven't done

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	before, and to use technology to solve most, if not all,

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	problems.

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	So to make the algorithm work despite not so great data,

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	find error tolerant ways to deal with the data.

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	The Chinese way would be to basically enumerate,

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	to the fullest extent, all the possible ways

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	by a lot of machines, try lots of different ways

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	to get it to work, and spend a lot of resources and money

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	and time cleaning up data.

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	That means the AI engineer may be writing data cleansing

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	algorithms, working with thousands of people

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	who label or correct or do things with the data.

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	That is the incredible hard work that

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	might lead to better results.

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	So the Chinese engineer would rely on and ask for more and more

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	data and find ways to cleanse them and make them work

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	in the system, and probably less time thinking

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	about new algorithms that can overcome data or other issues.

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	So where's your intuition?

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	What do you think the biggest impact the next 10 years

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	lies?

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	Is it in some breakthrough algorithms?

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	Or is it in just this at scale rigor, a rigorous approach

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	to data, cleaning data, organizing data

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	onto the same algorithms?

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	What do you think the big impact in the applied world is?

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	Well, if you're really in the company

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	and you have to deliver results, using known techniques

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	and enhancing data seems like the more expedient approach

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	that's very low risk and likely to generate

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	better and better results.

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	And that's why the Chinese approach has done quite well.

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	Now, there are a lot of more challenging startups

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	and problems, such as autonomous vehicles,

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	medical diagnosis, that existing algorithms probably

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	won't solve.

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	And that would put the Chinese approach more challenged

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	and give them more breakthrough innovation approach, more

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	of an edge on those kinds of problems.

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	So let me talk to that a little more.

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	So my intuition, personally, is that data

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	can take us extremely far.

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	So you brought up autonomous vehicles and medical diagnosis.

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	So your intuition is that huge amounts of data

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	might not be able to completely help us solve that problem.

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	Right.

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	So breaking that down further, autonomous vehicle,

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	I think huge amounts of data probably

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	will solve trucks driving on highways, which

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	will deliver significant value.

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	And China will probably lead in that.

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	And full L5 autonomous is likely to require new technologies

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	we don't yet know.

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	And that might require academia and great industrial research,

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	both innovating and working together.

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	And in that case, US has an advantage.

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	So the interesting question there is,

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	I don't know if you're familiar on the autonomous vehicle

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	space and the developments with Tesla and Elon Musk,

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	where they are, in fact, a full steam ahead

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	into this mysterious, complex world of full autonomy, L5,

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	L4, L5.

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	And they're trying to solve that purely with data.

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	So the same kind of thing that you're saying

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	is just for highway, which is what a lot of people

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	share your intuition, they're trying to solve with data.

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	It's just to linger on that moment further.

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	Do you think possible for them to achieve success

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	with simply just a huge amount of this training

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	on edge cases, on difficult cases in urban environments,

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	not just highway and so on?

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	I think they'll be very hard.

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	One could characterize Tesla's approach as kind

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	of a Chinese strength approach, gather all the data you can,

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	and hope that will overcome the problems.

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	But in autonomous driving, clearly a lot of the decisions

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	aren't merely solved by aggregating data

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	and having feedback loop.

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	There are things that are more akin to human thinking.

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	And how would those be integrated and built?

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	There has not yet been a lot of success

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	integrating human intelligence or, you know,

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	colored expert systems, if you will,

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	even though that's a taboo word with the machine learning.

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	And the integration of the two types of thinking

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	hasn't yet been demonstrated.

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	And the question is, how much can you

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	push a purely machine learning approach?

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	And of course, Tesla also has an additional constraint

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	that they don't have all the sensors.

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	I know that they think it's foolish to use LIDARS,

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	but that's clearly a one less, very valuable and reliable

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	source of input that they're foregoing, which

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	may also have consequences.

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	I think the advantage, of course,

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	is capturing data that no one has ever seen before.

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	And in some cases, such as computer vision and speech

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	recognition, I have seen Chinese companies accumulate data

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	that's not seen anywhere in the Western world,

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	and they have delivered superior results.

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	But then speech recognition and object recognition

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	are relatively suitable problems for deep learning

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	and don't have the potentially need for the human intelligence

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	analytical planning elements.

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	And the same on the speech recognition side,

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	your intuition that speech recognition and the machine

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	learning approaches to speech recognition

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	won't take us to a conversational system that

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	can pass the Turing test, which is maybe akin to what

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	driving is.

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	So it needs to have something more than just simply simple

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	language understanding, simple language generation.

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	Roughly right, I would say that based on purely machine

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	learning approaches, it's hard to imagine.

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	It could lead to a full conversational experience

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	across arbitrary domains, which is akin to L5.

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	I'm a little hesitant to use the word Turing test,

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	because the original definition was probably too easy.

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	We probably do that.

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	The spirit of the Turing test is what I was referring to.

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	Of course.

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	So you've had major leadership research positions

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	at Apple, Microsoft, Google.

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	So continuing on the discussion of America, Russia, Chinese soul

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	and culture and so on, what is the culture of Silicon

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	Valley in contrast to China and maybe US broadly?

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	And what is the unique culture of each of these three

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	major companies, in your view?

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	I think in aggregate, Silicon Valley companies,

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	we could probably include Microsoft in that,

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	even though they're not in the Valley,

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	is really dream big and have visionary goals

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	and believe that technology will conquer all

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	and also the self confidence and the self entitlement

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	that whatever they produce, the whole world should use

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	and must use.

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	And those are historically important, I think.

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	Steve Jobs's famous quote that he doesn't do focus groups.

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	He looks in the mirror and asks the person in the mirror,

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	what do you want?

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	And that really is an inspirational comment

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	that says the great company shouldn't just ask users

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	what they want, but develop something

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	that users will know they want when they see it,

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	but they could never come up with themselves.

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	I think that is probably the most exhilarating description

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	of what the essence of Silicon Valley is,

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	that this brilliant idea could cause you to build something

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	that couldn't come out of the focus groups or A.B. tests.

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	And iPhone would be an example of that.

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	No one in the age of BlackBerry would write down

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	they want an iPhone or multi touch, a browser,

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	might be another example.

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	No one would say they want that in the days of FTP,

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	but once they see it, they want it.

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	So I think that is what Silicon Valley is best at.

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	But it also came with a lot of success.

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	These products became global platforms,

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	and there were basically no competitors anywhere.

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	And that has also led to a belief

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	that these are the only things that one should do,

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	that companies should not tread on other companies territory,

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	so that a Groupon and a Yelp and an OpenTable

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	and the Grubhub would each feel,

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	okay, I'm not going to do the other companies business

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	because that would not be the pride of innovating

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	what each of these four companies have innovated.

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	But I think the Chinese approach is do whatever it takes to win.

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	And it's a winner take all market.

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	And in fact, in the internet space,

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	the market leader will get predominantly all the value

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	extracted out of the system.

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	And the system isn't just defined as one narrow category,

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	but gets broader and broader.

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	So it's amazing ambition for success and domination

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	of increasingly larger product categories

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	leading to clear market winner status

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	and the opportunity to extract tremendous value.

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	And that develops a practical, result oriented,

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	ultra ambitious winner take all gladiatorial mentality.

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	And if what it takes is to build what the competitors built,

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	essentially a copycat, that can be done without infringing laws.

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	If what it takes is to satisfy a foreign country's need

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	by forking the code base and building something

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	that looks really ugly and different, they'll do it.

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	So it's contrasted very sharply with the Silicon Valley approach.

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	And I think the flexibility and the speed and execution

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	has helped the Chinese approach.

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	And I think the Silicon Valley approach

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	is potentially challenged if every Chinese entrepreneur is

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	learning from the whole world, US and China,

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	and the American entrepreneurs only look internally

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	and write off China as a copycat.

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	And the second part of your question about the three

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	companies.

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	The unique elements of the three companies, perhaps.

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	Yeah.

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	I think Apple represents, while the user, please the user,

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	and the essence of design and brand,

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	and it's the one company and perhaps the only tech company

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	that draws people with a strong, serious desire

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	for the product and the willingness to pay a premium

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	because of the halo effect of the brand, which

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	came from the attention to detail and great respect

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	for user needs.

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	Microsoft represents a platform approach

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	that builds giant products that become very strong modes

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	that others can't do because it's

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	well architected at the bottom level

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	and the work is efficiently delegated to individuals

	17:26.640 --> 17:30.360
	and then the whole product is built

	17:30.360 --> 17:33.560
	by adding small parts that sum together.

	17:33.560 --> 17:37.760
	So it's probably the most effective high tech assembly

	17:37.760 --> 17:40.480
	line that builds a very difficult product

	17:40.480 --> 17:44.800
	that the whole process of doing that

	17:44.800 --> 17:50.800
	is kind of a differentiation and something competitors

	17:50.800 --> 17:52.480
	can't easily repeat.

	17:52.480 --> 17:54.800
	Are there elements of the Chinese approach

	17:54.800 --> 17:59.280
	in the way Microsoft went about assembling those little pieces

	17:59.280 --> 18:03.920
	and essentially dominating the market for a long time?

	18:03.920 --> 18:05.640
	Or do you see those as distinct?

	18:05.640 --> 18:08.240
	I think there are elements that are the same.

	18:08.240 --> 18:10.440
	I think the three American companies

	18:10.440 --> 18:13.880
	that had or have Chinese characteristics,

	18:13.880 --> 18:16.080
	and obviously as well as American characteristics,

	18:16.080 --> 18:20.400
	are Microsoft, Facebook, and Amazon.

	18:20.400 --> 18:21.720
	Yes, that's right, Amazon.

	18:21.720 --> 18:25.560
	Because these are companies that will tenaciously

	18:25.560 --> 18:31.320
	go after adjacent markets, build up strong product offering,

	18:31.320 --> 18:38.200
	and find ways to extract greater value from a sphere that's

	18:38.200 --> 18:39.960
	ever increasing.

	18:39.960 --> 18:43.520
	And they understand the value of the platforms.

	18:43.520 --> 18:45.600
	So that's the similarity.

	18:45.600 --> 18:53.760
	And then with Google, I think it's a genuinely value oriented

	18:53.760 --> 18:56.960
	company that does have a heart and soul

	18:56.960 --> 18:59.760
	and that wants to do great things for the world

	18:59.760 --> 19:06.040
	by connecting information and that has also

	19:06.040 --> 19:13.280
	very strong technology genes and wants to use technology

	19:13.280 --> 19:19.080
	and has found out of the box ways to use technology

	19:19.080 --> 19:23.680
	to deliver incredible value to the end user.

	19:23.680 --> 19:25.240
	We can look at Google, for example.

	19:25.240 --> 19:28.040
	You mentioned heart and soul.

	19:28.040 --> 19:31.840
	There seems to be an element where Google

	19:31.840 --> 19:34.840
	is after making the world better.

	19:34.840 --> 19:36.520
	There's a more positive view.

	19:36.520 --> 19:38.960
	I mean, they used to have the slogan, don't be evil.

	19:38.960 --> 19:43.120
	And Facebook a little bit more has a negative tend to it,

	19:43.120 --> 19:46.000
	at least in the perception of privacy and so on.

	19:46.000 --> 19:51.280
	Do you have a sense of how these different companies can

	19:51.280 --> 19:53.400
	achieve, because you've talked about how much

	19:53.400 --> 19:55.600
	we can make the world better in all these kinds of ways

	19:55.600 --> 19:59.360
	with AI, what is it about a company that can make,

	19:59.360 --> 20:03.200
	give it a heart and soul, gain the trust of the public,

	20:03.200 --> 20:08.000
	and just actually just not be evil and do good for the world?

	20:08.000 --> 20:09.000
	It's really hard.

	20:09.000 --> 20:13.120
	And I think Google has struggled with that.

	20:13.120 --> 20:15.160
	First, they don't do evil.

	20:15.160 --> 20:18.880
	Mantra is very dangerous, because every employee's

	20:18.880 --> 20:20.800
	definition of evil is different.

	20:20.800 --> 20:23.800
	And that has led to some difficult employee situations

	20:23.800 --> 20:25.240
	for them.

	20:25.240 --> 20:29.520
	So I don't necessarily think that's a good value statement.

	20:29.520 --> 20:31.840
	But just watching the kinds of things

	20:31.840 --> 20:36.440
	Google or its parent company Alphabet does in new areas

	20:36.440 --> 20:40.440
	like health care, like eradicating mosquitoes,

	20:40.440 --> 20:42.360
	things that are really not in the business

	20:42.360 --> 20:45.040
	of a internet tech company, I think

	20:45.040 --> 20:47.200
	that shows that there is a heart and soul

	20:47.200 --> 20:53.920
	and desire to do good and willingness to put in the resources

	20:53.920 --> 20:58.280
	to do something when they see it's good, they will pursue it.

	20:58.280 --> 21:00.640
	That doesn't necessarily mean it has

	21:00.640 --> 21:02.520
	all the trust of the users.

	21:02.520 --> 21:06.400
	I realize while most people would view Facebook

	21:06.400 --> 21:09.760
	as the primary target of their recent unhappiness

	21:09.760 --> 21:12.720
	about Silicon Valley companies, many would put Google

	21:12.720 --> 21:14.080
	in that category.

	21:14.080 --> 21:16.800
	And some have named Google's business practices

	21:16.800 --> 21:19.840
	as predatory also.

	21:19.840 --> 21:24.240
	So it's kind of difficult to have the two parts of a body.

	21:24.240 --> 21:28.080
	The brain wants to do what it's supposed to do for a shareholder,

	21:28.080 --> 21:29.280
	maximize profit.

	21:29.280 --> 21:30.880
	And then the heart and soul wants

	21:30.880 --> 21:36.120
	to do good things that may run against what the brain wants to do.

	21:36.120 --> 21:40.320
	So in this complex balancing that these companies have to do,

	21:40.320 --> 21:44.520
	you've mentioned that you're concerned about a future where

	21:44.520 --> 21:47.360
	too few companies like Google, Facebook, Amazon

	21:47.360 --> 21:51.560
	are controlling our data or are controlling too much

	21:51.560 --> 21:53.360
	of our digital lives.

	21:53.360 --> 21:55.400
	Can you elaborate on this concern?

	21:55.400 --> 21:58.640
	Perhaps do you have a better way forward?

	21:58.640 --> 22:05.000
	I think I'm hardly the most vocal complainer of this.

	22:05.000 --> 22:07.280
	There are a lot louder complainers out there.

	22:07.280 --> 22:11.840
	I do observe that having a lot of data

	22:11.840 --> 22:16.120
	does perpetuate their strength and limits

	22:16.120 --> 22:19.400
	competition in many spaces.

	22:19.400 --> 22:24.200
	But I also believe AI is much broader than the internet space.

	22:24.200 --> 22:26.280
	So the entrepreneurial opportunities

	22:26.280 --> 22:30.480
	still exists in using AI to empower

	22:30.480 --> 22:34.160
	financial, retail, manufacturing, education,

	22:34.160 --> 22:35.480
	applications.

	22:35.480 --> 22:39.800
	So I don't think it's quite a case of full monopolistic dominance

	22:39.800 --> 22:43.960
	that totally stifles innovation.

	22:43.960 --> 22:46.400
	But I do believe in their areas of strength

	22:46.400 --> 22:49.760
	it's hard to dislodge them.

	22:49.760 --> 22:53.280
	I don't know if I have a good solution.

	22:53.280 --> 22:57.160
	Probably the best solution is let the entrepreneurial VC

	22:57.160 --> 23:00.840
	ecosystem work well and find all the places that

	23:00.840 --> 23:04.200
	can create the next Google, the next Facebook.

	23:04.200 --> 23:08.560
	So there will always be increasing number of challengers.

	23:08.560 --> 23:11.360
	In some sense, that has happened a little bit.

	23:11.360 --> 23:15.760
	You see Uber, Airbnb having emerged despite the strength

	23:15.760 --> 23:19.040
	of the big three.

	23:19.040 --> 23:22.400
	And I think China as an environment

	23:22.400 --> 23:25.280
	may be more interesting for the emergence.

	23:25.280 --> 23:28.920
	Because if you look at companies between, let's say,

	23:28.920 --> 23:36.320
	$50 to $300 billion, China has emerged more of such companies

	23:36.320 --> 23:39.880
	than the US in the last three to four years.

	23:39.880 --> 23:42.120
	Because of the larger marketplace,

	23:42.120 --> 23:47.000
	because of the more fearless nature of the entrepreneurs.

	23:47.000 --> 23:50.840
	And the Chinese giants are just as powerful as American ones.

	23:50.840 --> 23:52.920
	Tencent Alibaba are very strong.

	23:52.920 --> 23:57.040
	But Bytes Dance has emerged worth $75 billion.

	23:57.040 --> 24:00.120
	And financial, while it's Alibaba affiliated,

	24:00.120 --> 24:03.920
	it's nevertheless independent and worth $150 billion.

	24:03.920 --> 24:08.280
	And so I do think if we start to extend

	24:08.280 --> 24:12.640
	to traditional businesses, we will see very valuable companies.

	24:12.640 --> 24:18.120
	So it's probably not the case that in five or 10 years,

	24:18.120 --> 24:20.920
	we'll still see the whole world with these five companies

	24:20.920 --> 24:22.680
	having such dominance.

	24:22.680 --> 24:26.040
	So you've mentioned a couple of times

	24:26.040 --> 24:27.840
	this fascinating world of entrepreneurship

	24:27.840 --> 24:31.080
	in China of the fearless nature of the entrepreneurs.

	24:31.080 --> 24:32.640
	So can you maybe talk a little bit

	24:32.640 --> 24:35.520
	about what it takes to be an entrepreneur in China?

	24:35.520 --> 24:38.240
	What are the strategies that are undertaken?

	24:38.240 --> 24:41.120
	What are the ways that you success?

	24:41.120 --> 24:43.960
	What is the dynamic of VCF funding,

	24:43.960 --> 24:46.480
	of the way the government helps companies, and so on?

	24:46.480 --> 24:49.520
	What are the interesting aspects here that are distinct from,

	24:49.520 --> 24:52.880
	that are different from the Silicon Valley world

	24:52.880 --> 24:55.240
	of entrepreneurship?

	24:55.240 --> 24:58.080
	Well, many of the listeners probably

	24:58.080 --> 25:03.000
	still would brand Chinese entrepreneur as copycats.

	25:03.000 --> 25:06.120
	And no doubt, 10 years ago, that would not

	25:06.120 --> 25:09.080
	be an inaccurate description.

	25:09.080 --> 25:12.320
	Back 10 years ago, an entrepreneur probably

	25:12.320 --> 25:14.840
	could not get funding if he or she could not

	25:14.840 --> 25:20.400
	describe what product he or she is copying from the US.

	25:20.400 --> 25:23.520
	The first question is, who has proven this business model,

	25:23.520 --> 25:27.200
	which is a nice way of asking, who are you copying?

	25:27.200 --> 25:29.520
	And that reason is understandable,

	25:29.520 --> 25:34.840
	because China had a much lower internet penetration

	25:34.840 --> 25:40.920
	and didn't have enough indigenous experience

	25:40.920 --> 25:43.200
	to build innovative products.

	25:43.200 --> 25:47.600
	And secondly, internet was emerging.

	25:47.600 --> 25:49.800
	Link startup was the way to do things,

	25:49.800 --> 25:52.920
	building a first minimally viable product,

	25:52.920 --> 25:55.320
	and then expanding was the right way to go.

	25:55.320 --> 25:59.480
	And the American successes have given a shortcut

	25:59.480 --> 26:02.840
	that if you build your minimally viable product based

	26:02.840 --> 26:05.040
	on an American product, it's guaranteed

	26:05.040 --> 26:06.720
	to be a decent starting point.

	26:06.720 --> 26:08.400
	Then you tweak it afterwards.

	26:08.400 --> 26:11.720
	So as long as there are no IP infringement, which,

	26:11.720 --> 26:15.080
	as far as I know, there hasn't been in the mobile and AI

	26:15.080 --> 26:19.360
	spaces, that's a much better shortcut.

	26:19.360 --> 26:23.720
	And I think Silicon Valley would view that as still not

	26:23.720 --> 26:29.200
	very honorable, because that's not your own idea to start with.

	26:29.200 --> 26:32.600
	But you can't really, at the same time,

	26:32.600 --> 26:35.160
	believe every idea must be your own

	26:35.160 --> 26:38.120
	and believe in the link startup methodology,

	26:38.120 --> 26:41.880
	because link startup is intended to try many, many things

	26:41.880 --> 26:44.240
	and then converge when that works.

	26:44.240 --> 26:46.720
	And it's meant to be iterated and changed.

	26:46.720 --> 26:51.240
	So finding a decent starting point without legal violations,

	26:51.240 --> 26:55.520
	there should be nothing morally dishonorable about that.

	26:55.520 --> 26:57.080
	So just a quick pause on that.

	26:57.080 --> 27:01.920
	It's fascinating that that's why is that not honorable, right?

	27:01.920 --> 27:04.680
	It's exactly as you formulated.

	27:04.680 --> 27:08.040
	It seems like a perfect start for business

	27:08.040 --> 27:12.440
	is to take a look at Amazon and say, OK,

	27:12.440 --> 27:14.560
	we'll do exactly what Amazon is doing.

	27:14.560 --> 27:16.800
	Let's start there in this particular market.

	27:16.800 --> 27:20.520
	And then let's out innovate them from that starting point.

	27:20.520 --> 27:22.200
	Yes. Come up with new ways.

	27:22.200 --> 27:26.520
	I mean, is it wrong to be, except the word copycat just

	27:26.520 --> 27:28.800
	sounds bad, but is it wrong to be a copycat?

	27:28.800 --> 27:31.640
	It just seems like a smart strategy.

	27:31.640 --> 27:35.800
	But yes, doesn't have a heroic nature to it

	27:35.800 --> 27:42.280
	that Steve Jobs, Elon Musk, sort of in something completely

	27:42.280 --> 27:43.880
	coming up with something completely new.

	27:43.880 --> 27:45.480
	Yeah, I like the way you describe it.

	27:45.480 --> 27:50.440
	It's a nonheroic, acceptable way to start the company.

	27:50.440 --> 27:52.840
	And maybe more expedient.

	27:52.840 --> 27:58.920
	So that's, I think, a baggage for Silicon Valley,

	27:58.920 --> 28:01.320
	that if it doesn't let go, then it

	28:01.320 --> 28:05.160
	may limit the ultimate ceiling of the company.

	28:05.160 --> 28:07.200
	Take Snapchat as an example.

	28:07.200 --> 28:09.840
	I think Evan's brilliant.

	28:09.840 --> 28:11.480
	He built a great product.

	28:11.480 --> 28:14.160
	But he's very proud that he wants

	28:14.160 --> 28:16.800
	to build his own features, not copy others.

	28:16.800 --> 28:21.000
	While Facebook was more willing to copy his features,

	28:21.000 --> 28:23.440
	and you see what happens in the competition.

	28:23.440 --> 28:27.440
	So I think putting that handcuff on the company

	28:27.440 --> 28:31.560
	would limit its ability to reach the maximum potential.

	28:31.560 --> 28:33.800
	So back to the Chinese environment,

	28:33.800 --> 28:38.400
	copying was merely a way to learn from the American masters.

	28:38.400 --> 28:43.480
	Just like if we learned to play piano or painting,

	28:43.480 --> 28:44.560
	you start by copying.

	28:44.560 --> 28:46.160
	You don't start by innovating when

	28:46.160 --> 28:48.200
	you don't have the basic skill sets.

	28:48.200 --> 28:51.040
	So very amazingly, the Chinese entrepreneurs

	28:51.040 --> 28:56.160
	about six years ago started to branch off

	28:56.160 --> 28:59.520
	with these lean startups built on American ideas

	28:59.520 --> 29:02.280
	to build better products than American products.

	29:02.280 --> 29:04.960
	But they did start from the American idea.

	29:04.960 --> 29:08.600
	And today, WeChat is better than WhatsApp.

	29:08.600 --> 29:10.520
	Weibo is better than Twitter.

	29:10.520 --> 29:12.920
	Zihu is better than Quora and so on.

	29:12.920 --> 29:17.000
	So that, I think, is Chinese entrepreneurs

	29:17.000 --> 29:18.480
	going to step two.

	29:18.480 --> 29:21.760
	And then step three is once these entrepreneurs have

	29:21.760 --> 29:23.720
	done one or two of these companies,

	29:23.720 --> 29:27.400
	they now look at the Chinese market and the opportunities

	29:27.400 --> 29:30.600
	and come up with ideas that didn't exist elsewhere.

	29:30.600 --> 29:36.320
	So products like and financial under which includes Alipay,

	29:36.320 --> 29:42.080
	which is mobile payments, and also the financial products

	29:42.080 --> 29:48.560
	for loans built on that, and also in education, VIP kid,

	29:48.560 --> 29:54.880
	and in social video, social network, TikTok,

	29:54.880 --> 29:58.640
	and in social eCommerce, Pinduoduo,

	29:58.640 --> 30:01.720
	and then in ride sharing, Mobike.

	30:01.720 --> 30:05.640
	These are all Chinese innovative products

	30:05.640 --> 30:08.720
	that now are being copied elsewhere.

	30:08.720 --> 30:13.040
	So an additional interesting observation

	30:13.040 --> 30:16.000
	is some of these products are built on unique Chinese

	30:16.000 --> 30:19.360
	demographics, which may not work in the US,

	30:19.360 --> 30:23.160
	but may work very well in Southeast Asia, Africa,

	30:23.160 --> 30:27.840
	and other developing worlds that are a few years behind China.

	30:27.840 --> 30:31.040
	And a few of these products maybe are universal

	30:31.040 --> 30:33.760
	and are getting traction even in the United States,

	30:33.760 --> 30:35.360
	such as TikTok.

	30:35.360 --> 30:42.080
	So this whole ecosystem is supported by VCs

	30:42.080 --> 30:44.920
	as a virtuous cycle, because a large market

	30:44.920 --> 30:49.400
	with innovative entrepreneurs will draw a lot of money

	30:49.400 --> 30:51.560
	and then invest in these companies.

	30:51.560 --> 30:54.480
	As the market gets larger and larger,

	30:54.480 --> 30:58.400
	China market is easily three, four times larger than the US.

	30:58.400 --> 31:01.120
	They will create greater value and greater returns

	31:01.120 --> 31:05.400
	for the VCs, thereby raising even more money.

	31:05.400 --> 31:10.000
	So at Sinovation Ventures, our first fund was $15 million.

	31:10.000 --> 31:12.040
	Our last fund was $500 million.

	31:12.040 --> 31:16.520
	So it reflects the valuation of the companies

	31:16.520 --> 31:19.840
	and our us going multi stage and things like that.

	31:19.840 --> 31:23.840
	It also has government support, but not

	31:23.840 --> 31:26.080
	in the way most Americans would think of it.

	31:26.080 --> 31:29.520
	The government actually leaves the entrepreneurial space

	31:29.520 --> 31:33.200
	as a private enterprise, so the self regulating.

	31:33.200 --> 31:36.200
	And the government would build infrastructures

	31:36.200 --> 31:39.320
	that would around it to make it work better.

	31:39.320 --> 31:41.960
	For example, the mass entrepreneur mass innovation

	31:41.960 --> 31:44.880
	plan builds 8,000 incubators.

	31:44.880 --> 31:48.360
	So the pipeline is very strong to the VCs

	31:48.360 --> 31:49.680
	for autonomous vehicles.

	31:49.680 --> 31:53.280
	The Chinese government is building smart highways

	31:53.280 --> 31:56.680
	with sensors, smart cities that separate pedestrians

	31:56.680 --> 32:01.560
	from cars that may allow initially an inferior autonomous

	32:01.560 --> 32:05.760
	vehicle company to launch a car without increasing,

	32:05.760 --> 32:11.520
	with lower casualty, because the roads or the city is smart.

	32:11.520 --> 32:13.800
	And the Chinese government at local levels

	32:13.800 --> 32:17.360
	would have these guiding funds acting as LPs,

	32:17.360 --> 32:19.400
	passive LPs to funds.

	32:19.400 --> 32:23.240
	And when the fund makes money, part of the money made

	32:23.240 --> 32:27.280
	is given back to the GPs and potentially other LPs

	32:27.280 --> 32:31.960
	to increase everybody's return at the expense

	32:31.960 --> 32:33.680
	of the government's return.

	32:33.680 --> 32:36.360
	So that's an interesting incentive

	32:36.360 --> 32:41.640
	that entrusts the task of choosing entrepreneurs to VCs

	32:41.640 --> 32:43.800
	who are better at it than the government

	32:43.800 --> 32:46.680
	by letting some of the profits move that way.

	32:46.680 --> 32:48.720
	So this is really fascinating, right?

	32:48.720 --> 32:51.800
	So I look at the Russian government as a case study

	32:51.800 --> 32:54.480
	where, let me put it this way, there

	32:54.480 --> 32:58.520
	is no such government driven, large scale

	32:58.520 --> 33:00.840
	support of entrepreneurship.

	33:00.840 --> 33:04.000
	And probably the same is true in the United States.

	33:04.000 --> 33:07.640
	But the entrepreneurs themselves kind of find a way.

	33:07.640 --> 33:11.680
	So maybe in a form of advice or explanation,

	33:11.680 --> 33:15.560
	how did the Chinese government arrive to be this way,

	33:15.560 --> 33:17.680
	so supportive on entrepreneurship,

	33:17.680 --> 33:21.520
	to be in this particular way so forward thinking

	33:21.520 --> 33:23.120
	at such a large scale?

	33:23.120 --> 33:28.280
	And also perhaps, how can we copy it in other countries?

	33:28.280 --> 33:29.800
	How can we encourage other governments,

	33:29.800 --> 33:31.600
	like even the United States government,

	33:31.600 --> 33:33.760
	to support infrastructure for autonomous vehicles

	33:33.760 --> 33:36.040
	in that same kind of way, perhaps?

	33:36.040 --> 33:36.680
	Yes.

	33:36.680 --> 33:44.440
	So these techniques are the result of several key things,

	33:44.440 --> 33:46.480
	some of which may be learnable, some of which

	33:46.480 --> 33:48.440
	may be very hard.

	33:48.440 --> 33:51.080
	One is just trial and error and watching

	33:51.080 --> 33:52.960
	what everyone else is doing.

	33:52.960 --> 33:54.960
	I think it's important to be humble and not

	33:54.960 --> 33:56.920
	feel like you know all the answers.

	33:56.920 --> 33:59.480
	The guiding funds idea came from Singapore,

	33:59.480 --> 34:01.440
	which came from Israel.

	34:01.440 --> 34:06.080
	And China made a few tweaks and turned it into a,

	34:06.080 --> 34:09.600
	because the Chinese cities and government officials kind

	34:09.600 --> 34:11.320
	of compete with each other.

	34:11.320 --> 34:14.640
	Because they all want to make their city more successful,

	34:14.640 --> 34:20.280
	so they can get the next level in their political career.

	34:20.280 --> 34:22.320
	And it's somewhat competitive.

	34:22.320 --> 34:25.200
	So the central government made it a bit of a competition.

	34:25.200 --> 34:26.840
	Everybody has a budget.

	34:26.840 --> 34:29.840
	They can put it on AI, or they can put it on bio,

	34:29.840 --> 34:32.200
	or they can put it on energy.

	34:32.200 --> 34:35.040
	And then whoever gets the results, the city shines,

	34:35.040 --> 34:38.000
	the people are better off, the mayor gets a promotion.

	34:38.000 --> 34:41.680
	So the tools is kind of almost like an entrepreneurial

	34:41.680 --> 34:44.840
	environment for local governments

	34:44.840 --> 34:47.480
	to see who can do a better job.

	34:47.480 --> 34:52.440
	And also, many of them tried different experiments.

	34:52.440 --> 34:58.440
	Some have given award to very smart researchers,

	34:58.440 --> 35:00.840
	just give them money and hope they'll start a company.

	35:00.840 --> 35:05.840
	Some have given money to academic research labs,

	35:05.840 --> 35:08.440
	maybe government research labs, to see

	35:08.440 --> 35:11.920
	if they can spin off some companies from the science

	35:11.920 --> 35:14.040
	lab or something like that.

	35:14.040 --> 35:17.080
	Some have tried to recruit overseas Chinese

	35:17.080 --> 35:18.960
	to come back and start companies.

	35:18.960 --> 35:20.960
	And they've had mixed results.

	35:20.960 --> 35:23.400
	The one that worked the best was the guiding funds.

	35:23.400 --> 35:25.840
	So it's almost like a lean startup idea

	35:25.840 --> 35:29.160
	where people try different things in what works, sticks,

	35:29.160 --> 35:30.600
	and everybody copies.

	35:30.600 --> 35:32.880
	So now every city has a guiding fund.

	35:32.880 --> 35:35.680
	So that's how that came about.

	35:35.680 --> 35:40.400
	The autonomous vehicle and the massive spending

	35:40.400 --> 35:46.080
	in highways and smart cities, that's a Chinese way.

	35:46.080 --> 35:49.480
	It's about building infrastructure to facilitate.

	35:49.480 --> 35:52.840
	It's a clear division of the government's responsibility

	35:52.840 --> 35:55.400
	from the market.

	35:55.400 --> 36:00.560
	The market should do everything in a private freeway.

	36:00.560 --> 36:02.920
	But there are things the market can't afford to do,

	36:02.920 --> 36:04.520
	like infrastructure.

	36:04.520 --> 36:08.000
	So the government always appropriates

	36:08.000 --> 36:12.000
	large amounts of money for infrastructure building.

	36:12.000 --> 36:16.880
	This happens with not only autonomous vehicle and AI,

	36:16.880 --> 36:20.840
	but happened with the 3G and 4G.

	36:20.840 --> 36:25.320
	You'll find that the Chinese wireless reception

	36:25.320 --> 36:28.760
	is better than the US, because massive spending that

	36:28.760 --> 36:30.720
	tries to cover the whole country.

	36:30.720 --> 36:34.360
	Whereas in the US, it may be a little spotty.

	36:34.360 --> 36:36.160
	It's a government driven, because I think

	36:36.160 --> 36:44.120
	they view the coverage of cell access and 3G, 4G access

	36:44.120 --> 36:47.080
	to be a governmental infrastructure spending,

	36:47.080 --> 36:49.880
	as opposed to capitalistic.

	36:49.880 --> 36:52.160
	So of course, the state or enterprise

	36:52.160 --> 36:55.000
	is also publicly traded, but they also

	36:55.000 --> 36:57.720
	carry a government responsibility

	36:57.720 --> 37:00.240
	to deliver infrastructure to all.

	37:00.240 --> 37:01.880
	So it's a different way of thinking

	37:01.880 --> 37:05.400
	that may be very hard to inject into Western countries

	37:05.400 --> 37:09.280
	to say starting tomorrow, bandwidth infrastructure

	37:09.280 --> 37:13.840
	and highways are going to be governmental spending

	37:13.840 --> 37:16.240
	with some characteristics.

	37:16.240 --> 37:18.240
	What's your sense, and sorry to interrupt,

	37:18.240 --> 37:21.680
	but because it's such a fascinating point,

	37:21.680 --> 37:25.600
	do you think on the autonomous vehicle space

	37:25.600 --> 37:30.120
	it's possible to solve the problem of full autonomy

	37:30.120 --> 37:34.040
	without significant investment in infrastructure?

	37:34.040 --> 37:36.400
	Well, that's really hard to speculate.

	37:36.400 --> 37:38.960
	I think it's not a yes, no question,

	37:38.960 --> 37:41.920
	but how long does it take question?

	37:41.920 --> 37:45.120
	15 years, 30 years, 45 years.

	37:45.120 --> 37:48.960
	Clearly with infrastructure augmentation,

	37:48.960 --> 37:52.320
	where there's road, the city, or whole city planning,

	37:52.320 --> 37:56.440
	building a new city, I'm sure that will accelerate

	37:56.440 --> 37:59.040
	the day of the L5.

	37:59.040 --> 38:01.520
	I'm not knowledgeable enough, and it's

	38:01.520 --> 38:03.920
	hard to predict even when we're knowledgeable,

	38:03.920 --> 38:07.120
	because a lot of it is speculative.

	38:07.120 --> 38:09.800
	But in the US, I don't think people

	38:09.800 --> 38:13.240
	would consider building a new city the size of Chicago

	38:13.240 --> 38:15.920
	to make it the AI slash autonomous city.

	38:15.920 --> 38:18.840
	There are smaller ones being built, I'm aware of that.

	38:18.840 --> 38:21.280
	But is infrastructure spend really

	38:21.280 --> 38:23.720
	impossible for US or Western countries?

	38:23.720 --> 38:25.680
	I don't think so.

	38:25.680 --> 38:28.920
	The US highway system was built.

	38:28.920 --> 38:31.960
	Was that during President Eisenhower or Kennedy?

	38:31.960 --> 38:33.160
	Eisenhower, yeah.

	38:33.160 --> 38:38.960
	So maybe historians can study how the President Eisenhower

	38:38.960 --> 38:42.960
	get the resources to build this massive infrastructure that

	38:42.960 --> 38:47.560
	surely gave US a tremendous amount of prosperity

	38:47.560 --> 38:50.800
	over the next decade, if not century.

	38:50.800 --> 38:53.240
	If I may comment on that, then, it

	38:53.240 --> 38:54.880
	takes us to artificial intelligence

	38:54.880 --> 38:58.080
	a little bit, because in order to build infrastructure,

	38:58.080 --> 39:00.520
	it creates a lot of jobs.

	39:00.520 --> 39:02.840
	So I'll be actually interested if you

	39:02.840 --> 39:06.120
	would say that you're talking in your book about all kinds

	39:06.120 --> 39:08.960
	of jobs that could and could not be automated.

	39:08.960 --> 39:12.000
	I wonder if building infrastructure

	39:12.000 --> 39:15.720
	is one of the jobs that would not be easily automated,

	39:15.720 --> 39:18.160
	something you can think about, because I think you've mentioned

	39:18.160 --> 39:21.160
	somewhere in a talk, or that there

	39:21.160 --> 39:24.280
	might be, as jobs are being automated,

	39:24.280 --> 39:28.160
	a role for government to create jobs that can't be automated.

	39:28.160 --> 39:31.040
	Yes, I think that's a possibility.

	39:31.040 --> 39:34.280
	Back in the last financial crisis,

	39:34.280 --> 39:40.320
	China put a lot of money to basically give this economy

	39:40.320 --> 39:45.520
	a boost, and a lot of it went into infrastructure building.

	39:45.520 --> 39:49.920
	And I think that's a legitimate way, at the government level,

	39:49.920 --> 39:55.680
	to deal with the employment issues as well as build out

	39:55.680 --> 39:58.960
	the infrastructure, as long as the infrastructures are truly

	39:58.960 --> 40:03.160
	needed, and as long as there is an employment problem, which

	40:03.160 --> 40:04.960
	we don't know.

	40:04.960 --> 40:07.920
	So maybe taking a little step back,

	40:07.920 --> 40:12.840
	if you've been a leader and a researcher in AI

	40:12.840 --> 40:16.200
	for several decades, at least 30 years,

	40:16.200 --> 40:21.040
	so how has AI changed in the West and the East

	40:21.040 --> 40:23.120
	as you've observed, as you've been deep in it

	40:23.120 --> 40:25.120
	over the past 30 years?

	40:25.120 --> 40:28.520
	Well, AI began as the pursuit of understanding

	40:28.520 --> 40:34.160
	human intelligence, and the term itself represents that.

	40:34.160 --> 40:37.680
	But it kind of drifted into the one subarea that

	40:37.680 --> 40:40.880
	worked extremely well, which is machine intelligence.

	40:40.880 --> 40:45.080
	And that's actually more using pattern recognition techniques

	40:45.080 --> 40:51.280
	to basically do incredibly well on a limited domain,

	40:51.280 --> 40:54.840
	large amount of data, but relatively simple kinds

	40:54.840 --> 40:58.720
	of planning, tasks, and not very creative.

	40:58.720 --> 41:02.480
	So we didn't end up building human intelligence.

	41:02.480 --> 41:04.760
	We built a different machine that

	41:04.760 --> 41:08.040
	was a lot better than us, some problems,

	41:08.040 --> 41:11.840
	but nowhere close to us on other problems.

	41:11.840 --> 41:14.200
	So today, I think a lot of people still

	41:14.200 --> 41:18.080
	misunderstand when we say artificial intelligence

	41:18.080 --> 41:20.720
	and what various products can do.

	41:20.720 --> 41:24.160
	People still think it's about replicating human intelligence.

	41:24.160 --> 41:26.160
	But the products out there really

	41:26.160 --> 41:31.680
	are closer to having invented the internet or the spreadsheet

	41:31.680 --> 41:35.360
	or the database and getting broader adoption.

	41:35.360 --> 41:38.400
	And speaking further to the fears, near term fears

	41:38.400 --> 41:41.240
	that people have about AI, so you're commenting

	41:41.240 --> 41:45.680
	on the general intelligence that people

	41:45.680 --> 41:48.040
	in the popular culture from sci fi movies

	41:48.040 --> 41:50.920
	have a sense about AI, but there's practical fears

	41:50.920 --> 41:54.800
	about AI, the kind of narrow AI that you're talking about

	41:54.800 --> 41:57.280
	of automating particular kinds of jobs,

	41:57.280 --> 41:59.400
	and you talk about them in the book.

	41:59.400 --> 42:01.520
	So what are the kinds of jobs in your view

	42:01.520 --> 42:04.840
	that you see in the next five, 10 years beginning

	42:04.840 --> 42:09.240
	to be automated by AI systems algorithms?

	42:09.240 --> 42:13.000
	Yes, this is also maybe a little bit counterintuitive

	42:13.000 --> 42:15.440
	because it's the routine jobs that

	42:15.440 --> 42:18.360
	will be displaced the soonest.

	42:18.360 --> 42:23.120
	And they may not be displaced entirely, maybe 50%, 80%

	42:23.120 --> 42:26.320
	of a job, but when the workload drops by that much,

	42:26.320 --> 42:28.760
	employment will come down.

	42:28.760 --> 42:31.520
	And also another part of misunderstanding

	42:31.520 --> 42:35.720
	is most people think of AI replacing routine jobs,

	42:35.720 --> 42:38.760
	then they think of the assembly line, the workers.

	42:38.760 --> 42:40.960
	Well, that will have some effects,

	42:40.960 --> 42:44.600
	but it's actually the routine white collar workers that's

	42:44.600 --> 42:49.280
	easiest to replace because to replace a white collar worker,

	42:49.280 --> 42:50.720
	you just need software.

	42:50.720 --> 42:53.120
	To replace a blue collar worker,

	42:53.120 --> 42:57.200
	you need robotics, mechanical excellence,

	42:57.200 --> 43:01.880
	and the ability to deal with dexterity,

	43:01.880 --> 43:05.640
	and maybe even unknown environments, very, very difficult.

	43:05.640 --> 43:11.200
	So if we were to categorize the most dangerous white collar

	43:11.200 --> 43:15.600
	jobs, they would be things like back office,

	43:15.600 --> 43:20.800
	people who copy and paste and deal with simple computer

	43:20.800 --> 43:25.560
	programs and data, and maybe paper and OCR,

	43:25.560 --> 43:29.000
	and they don't make strategic decisions,

	43:29.000 --> 43:32.040
	they basically facilitate the process.

	43:32.040 --> 43:34.680
	These software and paper systems don't work,

	43:34.680 --> 43:40.520
	so you have people dealing with new employee orientation,

	43:40.520 --> 43:45.400
	searching for past lawsuits and financial documents,

	43:45.400 --> 43:49.800
	and doing reference check, so basic searching and management

	43:49.800 --> 43:52.800
	of data that's the most in danger of being lost.

	43:52.800 --> 43:56.440
	In addition to the white collar repetitive work,

	43:56.440 --> 43:59.360
	a lot of simple interaction work can also

	43:59.360 --> 44:02.840
	be taken care of, such as tele sales, telemarketing,

	44:02.840 --> 44:07.280
	customer service, as well as many physical jobs

	44:07.280 --> 44:09.880
	that are in the same location and don't

	44:09.880 --> 44:12.240
	require a high degree of dexterity,

	44:12.240 --> 44:17.840
	so fruit picking, dishwashing, assembly line, inspection,

	44:17.840 --> 44:20.360
	our jobs in that category.

	44:20.360 --> 44:25.440
	So altogether, back office is a big part,

	44:25.440 --> 44:29.840
	and the other, the blue collar may be smaller initially,

	44:29.840 --> 44:32.560
	but over time, AI will get better.

	44:32.560 --> 44:36.880
	And when we start to get to over the next 15, 20 years,

	44:36.880 --> 44:39.120
	the ability to actually have the dexterity

	44:39.120 --> 44:42.600
	of doing assembly line, that's a huge chunk of jobs.

	44:42.600 --> 44:44.760
	And when autonomous vehicles start

	44:44.760 --> 44:47.400
	to work initially starting with truck drivers,

	44:47.400 --> 44:49.640
	but eventually to all drivers, that's

	44:49.640 --> 44:52.040
	another huge group of workers.

	44:52.040 --> 44:55.560
	So I see modest numbers in the next five years,

	44:55.560 --> 44:58.080
	but increasing rapidly after that.

	44:58.080 --> 45:01.240
	On the worry of the jobs that are in danger

	45:01.240 --> 45:04.320
	and the gradual loss of jobs, I'm not

	45:04.320 --> 45:06.680
	sure if you're familiar with Andrew Yang.

	45:06.680 --> 45:07.800
	Yes, I am.

	45:07.800 --> 45:10.560
	So there's a candidate for president of the United States

	45:10.560 --> 45:14.960
	whose platform, Andrew Yang, is based around, in part,

	45:14.960 --> 45:17.680
	around job loss due to automation,

	45:17.680 --> 45:21.120
	and also, in addition, the need, perhaps,

	45:21.120 --> 45:26.120
	of universal basic income to support jobs that are folks who

	45:26.120 --> 45:28.560
	lose their job due to automation and so on,

	45:28.560 --> 45:31.960
	and in general, support people under complex,

	45:31.960 --> 45:34.320
	unstable job market.

	45:34.320 --> 45:36.720
	So what are your thoughts about his concerns,

	45:36.720 --> 45:40.000
	him as a candidate, his ideas in general?

	45:40.000 --> 45:44.600
	I think his thinking is generally in the right direction,

	45:44.600 --> 45:48.440
	but his approach as a presidential candidate

	45:48.440 --> 45:52.240
	may be a little bit ahead at the time.

	45:52.240 --> 45:56.080
	I think the displacements will happen,

	45:56.080 --> 45:58.280
	but will they happen soon enough for people

	45:58.280 --> 46:00.480
	to agree to vote for him?

	46:00.480 --> 46:03.760
	The unemployment numbers are not very high yet.

	46:03.760 --> 46:07.600
	And I think he and I have the same challenge.

	46:07.600 --> 46:11.520
	If I want to theoretically convince people this is an issue

	46:11.520 --> 46:13.880
	and he wants to become the president,

	46:13.880 --> 46:17.760
	people have to see how can this be the case when

	46:17.760 --> 46:19.680
	unemployment numbers are low.

	46:19.680 --> 46:21.360
	So that is the challenge.

	46:21.360 --> 46:27.360
	And I think I do agree with him on the displacement issue,

	46:27.360 --> 46:32.280
	on universal basic income, at a very vanilla level.

	46:32.280 --> 46:36.800
	I don't agree with it because I think the main issue

	46:36.800 --> 46:38.320
	is retraining.

	46:38.320 --> 46:43.200
	So people need to be incented not by just giving a monthly

	46:43.200 --> 46:47.160
	$2,000 check or $1,000 check and do whatever they want

	46:47.160 --> 46:50.920
	because they don't have the know how

	46:50.920 --> 46:56.840
	to know what to retrain to go into what type of a job

	46:56.840 --> 46:58.640
	and guidance is needed.

	46:58.640 --> 47:01.720
	And retraining is needed because historically

	47:01.720 --> 47:05.080
	in technology revolutions, when routine jobs were displaced,

	47:05.080 --> 47:06.920
	new routine jobs came up.

	47:06.920 --> 47:09.400
	So there was always room for that.

	47:09.400 --> 47:12.640
	But with AI and automation, the whole point

	47:12.640 --> 47:15.320
	is replacing all routine jobs eventually.

	47:15.320 --> 47:17.840
	So there will be fewer and fewer routine jobs.

	47:17.840 --> 47:22.640
	And AI will create jobs, but it won't create routine jobs

	47:22.640 --> 47:24.840
	because if it creates routine jobs,

	47:24.840 --> 47:26.880
	why wouldn't AI just do it?

	47:26.880 --> 47:30.360
	So therefore, the people who are losing the jobs

	47:30.360 --> 47:32.280
	are losing routine jobs.

	47:32.280 --> 47:35.720
	The jobs that are becoming available are nonroutine jobs.

	47:35.720 --> 47:39.320
	So the social stipend needs to be put in place

	47:39.320 --> 47:42.040
	is for the routine workers who lost their jobs

	47:42.040 --> 47:46.120
	to be retrained maybe in six months, maybe in three years.

	47:46.120 --> 47:48.560
	Takes a while to retrain on the nonroutine job

	47:48.560 --> 47:51.360
	and then take on a job that will last

	47:51.360 --> 47:53.400
	for that person's lifetime.

	47:53.400 --> 47:56.160
	Now, having said that, if you look deeply

	47:56.160 --> 47:58.240
	into Andrew's document, he does cater for that.

	47:58.240 --> 48:03.240
	So I'm not disagreeing with what he's trying to do.

	48:03.280 --> 48:06.360
	But for simplification, sometimes he just says UBI,

	48:06.360 --> 48:08.760
	but simple UBI wouldn't work.

	48:08.760 --> 48:10.600
	And I think you've mentioned elsewhere

	48:10.600 --> 48:15.600
	that the goal isn't necessarily to give people enough money

	48:15.760 --> 48:19.120
	to survive or live or even to prosper.

	48:19.120 --> 48:22.800
	The point is to give them a job that gives them meaning.

	48:22.800 --> 48:25.600
	That meaning is extremely important.

	48:25.600 --> 48:28.600
	That our employment, at least in the United States

	48:28.600 --> 48:31.200
	and perhaps it cares across the world,

	48:31.200 --> 48:34.600
	provides something that's, forgive me for saying,

	48:34.600 --> 48:36.960
	greater than money, it provides meaning.

	48:38.400 --> 48:43.400
	So now what kind of jobs do you think can't be automated?

	48:44.840 --> 48:46.600
	You talk a little bit about creativity

	48:46.600 --> 48:48.200
	and compassion in your book.

	48:48.200 --> 48:50.720
	What aspects do you think it's difficult

	48:50.720 --> 48:52.320
	to automate for an AI system?

	48:52.320 --> 48:57.320
	Because an AI system is currently merely optimizing.

	48:57.360 --> 49:00.120
	It's not able to reason, plan,

	49:00.120 --> 49:02.920
	or think creatively or strategically.

	49:02.920 --> 49:05.320
	It's not able to deal with complex problems.

	49:05.320 --> 49:09.520
	It can't come up with a new problem and solve it.

	49:09.520 --> 49:12.320
	A human needs to find the problem

	49:12.320 --> 49:15.520
	and pose it as an optimization problem,

	49:15.520 --> 49:17.520
	then have the AI work at it.

	49:17.520 --> 49:21.320
	So an AI would have a very hard time

	49:21.320 --> 49:23.320
	discovering a new drug

	49:23.320 --> 49:26.320
	or discovering a new style of painting

	49:27.320 --> 49:30.320
	or dealing with complex tasks

	49:30.320 --> 49:32.320
	such as managing a company

	49:32.320 --> 49:35.320
	that isn't just about optimizing the bottom line,

	49:35.320 --> 49:39.320
	but also about employee satisfaction, corporate brand,

	49:39.320 --> 49:40.320
	and many, many other things.

	49:40.320 --> 49:44.320
	So that is one category of things.

	49:44.320 --> 49:48.320
	And because these things are challenging, creative, complex,

	49:48.320 --> 49:52.320
	doing them creates a higher degree of satisfaction

	49:52.320 --> 49:55.320
	and therefore appealing to our desire for working,

	49:55.320 --> 49:57.320
	which isn't just to make the money,

	49:57.320 --> 49:58.320
	make the ends meet,

	49:58.320 --> 50:00.320
	but also that we've accomplished something

	50:00.320 --> 50:03.320
	that others maybe can't do or can't do as well.

	50:04.320 --> 50:07.320
	Another type of job that is much numerous

	50:07.320 --> 50:09.320
	would be compassionate jobs,

	50:09.320 --> 50:14.320
	jobs that require compassion, empathy, human touch, human trust.

	50:14.320 --> 50:18.320
	AI can't do that because AI is cold, calculating,

	50:18.320 --> 50:22.320
	and even if it can fake that to some extent,

	50:22.320 --> 50:26.320
	it will make errors and that will make it look very silly.

	50:26.320 --> 50:29.320
	And also, I think even if AI did okay,

	50:29.320 --> 50:33.320
	people would want to interact with another person,

	50:33.320 --> 50:38.320
	whether it's for some kind of a service or a teacher or a doctor

	50:38.320 --> 50:41.320
	or a concierge or a masseuse or bartender.

	50:41.320 --> 50:46.320
	There are so many jobs where people just don't want to interact

	50:46.320 --> 50:49.320
	with a cold robot or software.

	50:50.320 --> 50:53.320
	I've had an entrepreneur who built an elderly care robot

	50:53.320 --> 50:58.320
	and they found that the elderly really only use it for customer service.

	50:58.320 --> 51:00.320
	But not to service the product,

	51:00.320 --> 51:05.320
	but they click on customer service and the video of a person comes up

	51:05.320 --> 51:07.320
	and then the person says,

	51:07.320 --> 51:11.320
	how come my daughter didn't call me? Let me show you a picture of her grandkids.

	51:11.320 --> 51:15.320
	So people earn for that, people people interaction.

	51:15.320 --> 51:19.320
	So even if robots improved, people just don't want it.

	51:19.320 --> 51:21.320
	And those jobs are going to be increasing

	51:21.320 --> 51:24.320
	because AI will create a lot of value,

	51:24.320 --> 51:29.320
	$16 trillion to the world in next 11 years according to PWC

	51:29.320 --> 51:34.320
	and that will give people money to enjoy services,

	51:34.320 --> 51:39.320
	whether it's eating a gourmet meal or tourism and traveling

	51:39.320 --> 51:41.320
	or having concierge services.

	51:41.320 --> 51:44.320
	The services revolving around, you know,

	51:44.320 --> 51:47.320
	every dollar of that $16 trillion will be tremendous.

	51:47.320 --> 51:52.320
	It will create more opportunities to service the people who did well

	51:52.320 --> 51:55.320
	through AI with things.

	51:55.320 --> 52:01.320
	But even at the same time, the entire society is very much short

	52:01.320 --> 52:05.320
	in need of many service oriented, compassionate oriented jobs.

	52:05.320 --> 52:10.320
	The best example is probably in healthcare services.

	52:10.320 --> 52:15.320
	There's going to be 2 million new jobs, not counting replacement,

	52:15.320 --> 52:20.320
	just brand new incremental jobs in the next six years in healthcare services.

	52:20.320 --> 52:24.320
	That includes nurses orderly in the hospital,

	52:24.320 --> 52:29.320
	elderly care and also at home care.

	52:29.320 --> 52:31.320
	It's particularly lacking.

	52:31.320 --> 52:34.320
	And those jobs are not likely to be filled.

	52:34.320 --> 52:36.320
	So there's likely to be a shortage.

	52:36.320 --> 52:41.320
	And the reason they're not filled is simply because they don't pay very well

	52:41.320 --> 52:47.320
	and that the social status of these jobs are not very good.

	52:47.320 --> 52:52.320
	So they pay about half as much as a heavy equipment operator,

	52:52.320 --> 52:55.320
	which will be replaced a lot sooner.

	52:55.320 --> 52:59.320
	And they pay probably comparably to someone on the assembly line.

	52:59.320 --> 53:03.320
	And so if we're ignoring all the other issues

	53:03.320 --> 53:07.320
	and just think about satisfaction from one's job,

	53:07.320 --> 53:11.320
	someone repetitively doing the same manual action at an assembly line,

	53:11.320 --> 53:14.320
	that can't create a lot of job satisfaction.

	53:14.320 --> 53:17.320
	But someone taking care of a sick person

	53:17.320 --> 53:21.320
	and getting a hug and thank you from that person and the family,

	53:21.320 --> 53:24.320
	I think is quite satisfying.

	53:24.320 --> 53:28.320
	So if only we could fix the pay for service jobs,

	53:28.320 --> 53:33.320
	there are plenty of jobs that require some training or a lot of training

	53:33.320 --> 53:36.320
	for the people coming off the routine jobs to take.

	53:36.320 --> 53:43.320
	We can easily imagine someone who was maybe a cashier at the grocery store,

	53:43.320 --> 53:49.320
	at stores become automated, learns to become a nurse or at home care.

	53:49.320 --> 53:54.320
	Also, I do want to point out the blue collar jobs are going to stay around a bit longer,

	53:54.320 --> 53:57.320
	some of them quite a bit longer.

	53:57.320 --> 54:01.320
	AI cannot be told, go clean an arbitrary home.

	54:01.320 --> 54:03.320
	That's incredibly hard.

	54:03.320 --> 54:07.320
	Arguably is an L5 level of difficulty.

	54:07.320 --> 54:09.320
	And then AI cannot be a good plumber,

	54:09.320 --> 54:12.320
	because plumber is almost like a mini detective

	54:12.320 --> 54:15.320
	that has to figure out where the leak came from.

	54:15.320 --> 54:22.320
	So yet AI probably can be an assembly line and auto mechanic and so on.

	54:22.320 --> 54:26.320
	So one has to study which blue collar jobs are going away

	54:26.320 --> 54:30.320
	and facilitate retraining for the people to go into the ones that won't go away

	54:30.320 --> 54:32.320
	or maybe even will increase.

	54:32.320 --> 54:39.320
	I mean, it is fascinating that it's easier to build a world champion chess player

	54:39.320 --> 54:41.320
	than it is to build a mediocre plumber.

	54:41.320 --> 54:43.320
	Yes, very true.

	54:43.320 --> 54:47.320
	And to AI, and that goes counterintuitive to a lot of people's understanding

	54:47.320 --> 54:49.320
	of what artificial intelligence is.

	54:49.320 --> 54:53.320
	So it sounds, I mean, you're painting a pretty optimistic picture

	54:53.320 --> 54:56.320
	about retraining, about the number of jobs

	54:56.320 --> 55:01.320
	and actually the meaningful nature of those jobs once we automate repetitive tasks.

	55:01.320 --> 55:07.320
	So overall, are you optimistic about the future

	55:07.320 --> 55:11.320
	where much of the repetitive tasks are automated,

	55:11.320 --> 55:15.320
	that there is a lot of room for humans, for the compassionate,

	55:15.320 --> 55:19.320
	for the creative input that only humans can provide?

	55:19.320 --> 55:23.320
	I am optimistic if we start to take action.

	55:23.320 --> 55:27.320
	If we have no action in the next five years,

	55:27.320 --> 55:33.320
	I think it's going to be hard to deal with the devastating losses that will emerge.

	55:33.320 --> 55:39.320
	So if we start thinking about retraining, maybe with the low hanging fruits,

	55:39.320 --> 55:45.320
	explaining to vocational schools why they should train more plumbers than auto mechanics,

	55:45.320 --> 55:53.320
	maybe starting with some government subsidy for corporations to have more training positions.

	55:53.320 --> 55:57.320
	We start to explain to people why retraining is important.

	55:57.320 --> 56:00.320
	We start to think about what the future of education,

	56:00.320 --> 56:04.320
	how that needs to be tweaked for the era of AI.

	56:04.320 --> 56:06.320
	If we start to make incremental progress,

	56:06.320 --> 56:09.320
	and the greater number of people understand,

	56:09.320 --> 56:12.320
	then there's no reason to think we can't deal with this,

	56:12.320 --> 56:16.320
	because this technological revolution is arguably similar to

	56:16.320 --> 56:20.320
	what electricity, industrial revolutions, and internet brought about.

	56:20.320 --> 56:24.320
	Do you think there's a role for policy, for governments to step in

	56:24.320 --> 56:27.320
	to help with policy to create a better world?

	56:27.320 --> 56:32.320
	Absolutely, and the governments don't have to believe

	56:32.320 --> 56:39.320
	that unemployment will go up, and they don't have to believe automation will be this fast to do something.

	56:39.320 --> 56:42.320
	Revamping vocational school would be one example.

	56:42.320 --> 56:47.320
	Another is if there's a big gap in healthcare service employment,

	56:47.320 --> 56:54.320
	and we know that a country's population is growing older and more longevity living older,

	56:54.320 --> 56:59.320
	because people over 80 require five times as much care as those under 80,

	56:59.320 --> 57:04.320
	then it is a good time to incent training programs for elderly care,

	57:04.320 --> 57:07.320
	to find ways to improve the pay.

	57:07.320 --> 57:13.320
	Maybe one way would be to offer as part of Medicare or the equivalent program

	57:13.320 --> 57:18.320
	for people over 80 to be entitled to a few hours of elderly care at home,

	57:18.320 --> 57:21.320
	and then that might be reimbursable,

	57:21.320 --> 57:28.320
	and that will stimulate the service industry around the policy.

	57:28.320 --> 57:32.320
	Do you have concerns about large entities,

	57:32.320 --> 57:38.320
	whether it's governments or companies, controlling the future of AI development in general?

	57:38.320 --> 57:40.320
	So we talked about companies.

	57:40.320 --> 57:48.320
	Do you have a better sense that governments can better represent the interest of the people

	57:48.320 --> 57:54.320
	than companies, or do you believe companies are better at representing the interest of the people?

	57:54.320 --> 57:56.320
	Or is there no easy answer?

	57:56.320 --> 57:59.320
	I don't think there's an easy answer because it's a double edged sword.

	57:59.320 --> 58:06.320
	The companies and governments can provide better services with more access to data and more access to AI,

	58:06.320 --> 58:13.320
	but that also leads to greater power, which can lead to uncontrollable problems,

	58:13.320 --> 58:17.320
	whether it's monopoly or corruption in the government.

	58:17.320 --> 58:24.320
	So I think one has to be careful to look at how much data that companies and governments have,

	58:24.320 --> 58:29.320
	and some kind of checks and balances would be helpful.

	58:29.320 --> 58:33.320
	So again, I come from Russia.

	58:33.320 --> 58:36.320
	There's something called the Cold War.

	58:36.320 --> 58:40.320
	So let me ask a difficult question here, looking at conflict.

	58:40.320 --> 58:45.320
	Steven Pinker wrote a great book that conflict all over the world is decreasing in general.

	58:45.320 --> 58:51.320
	But do you have a sense that having written the book AI Superpowers,

	58:51.320 --> 58:57.320
	do you see a major international conflict potentially arising between major nations,

	58:57.320 --> 59:02.320
	whatever they are, whether it's Russia, China, European nations, United States,

	59:02.320 --> 59:09.320
	or others in the next 10, 20, 50 years around AI, around the digital space, cyber space?

	59:09.320 --> 59:12.320
	Do you worry about that?

	59:12.320 --> 59:19.320
	Is that something we need to think about and try to alleviate or prevent?

	59:19.320 --> 59:22.320
	I believe in greater engagement.

	59:22.320 --> 59:33.320
	A lot of the worries about more powerful AI are based on an arms race metaphor.

	59:33.320 --> 59:41.320
	And when you extrapolate into military kinds of scenarios,

	59:41.320 --> 59:48.320
	AI can automate autonomous weapons that needs to be controlled somehow.

	59:48.320 --> 59:57.320
	And autonomous decision making can lead to not enough time to fix international crises.

	59:57.320 --> 1:00:02.320
	So I actually believe a Cold War mentality would be very dangerous

	1:00:02.320 --> 1:00:07.320
	because should two countries rely on AI to make certain decisions

	1:00:07.320 --> 1:00:11.320
	and they don't even talk to each other, they do their own scenario planning,

	1:00:11.320 --> 1:00:14.320
	then something could easily go wrong.

	1:00:14.320 --> 1:00:24.320
	I think engagement, interaction, some protocols to avoid inadvertent disasters is actually needed.

	1:00:24.320 --> 1:00:28.320
	So it's natural for each country to want to be the best,

	1:00:28.320 --> 1:00:34.320
	whether it's in nuclear technologies or AI or bio.

	1:00:34.320 --> 1:00:40.320
	But I think it's important to realize if each country has a black box AI

	1:00:40.320 --> 1:00:48.320
	and don't talk to each other, that probably presents greater challenges to humanity

	1:00:48.320 --> 1:00:50.320
	than if they interacted.

	1:00:50.320 --> 1:00:56.320
	I think there can still be competition, but with some degree of protocol for interaction.

	1:00:56.320 --> 1:01:01.320
	Just like when there was a nuclear competition,

	1:01:01.320 --> 1:01:07.320
	there were some protocol for deterrence among US, Russia, and China.

	1:01:07.320 --> 1:01:10.320
	And I think that engagement is needed.

	1:01:10.320 --> 1:01:15.320
	So of course, we're still far from AI presenting that kind of danger.

	1:01:15.320 --> 1:01:22.320
	But what I worry the most about is the level of engagement seems to be coming down.

	1:01:22.320 --> 1:01:25.320
	The level of distrust seems to be going up,

	1:01:25.320 --> 1:01:32.320
	especially from the US towards other large countries such as China and Russia.

	1:01:32.320 --> 1:01:34.320
	Is there a way to make that better?

	1:01:34.320 --> 1:01:40.320
	So that's beautifully put, level of engagement and even just basic trust and communication

	1:01:40.320 --> 1:01:52.320
	as opposed to making artificial enemies out of particular countries.

	1:01:52.320 --> 1:02:01.320
	Do you have a sense how we can make it better, actionable items that as a society we can take on?

	1:02:01.320 --> 1:02:10.320
	I'm not an expert at geopolitics, but I would say that we look pretty foolish as humankind

	1:02:10.320 --> 1:02:19.320
	when we are faced with the opportunity to create $16 trillion for humanity.

	1:02:19.320 --> 1:02:29.320
	And yet we're not solving fundamental problems with parts of the world still in poverty.

	1:02:29.320 --> 1:02:34.320
	And for the first time, we have the resources to overcome poverty and hunger.

	1:02:34.320 --> 1:02:38.320
	We're not using it on that, but we're fueling competition among superpowers.

	1:02:38.320 --> 1:02:41.320
	And that's a very unfortunate thing.

	1:02:41.320 --> 1:02:54.320
	If we become utopian for a moment, imagine a benevolent world government that has this $16 trillion

	1:02:54.320 --> 1:03:02.320
	and maybe some AI to figure out how to use it to deal with diseases and problems and hate and things like that.

	1:03:02.320 --> 1:03:04.320
	World would be a lot better off.

	1:03:04.320 --> 1:03:07.320
	So what is wrong with the current world?

	1:03:07.320 --> 1:03:13.320
	I think the people with more skill than I should think about this.

	1:03:13.320 --> 1:03:19.320
	And then the geopolitics issue with superpower competition is one side of the issue.

	1:03:19.320 --> 1:03:29.320
	There's another side which I worry maybe even more, which is as the $16 trillion all gets made by U.S. and China

	1:03:29.320 --> 1:03:34.320
	and a few of the other developed countries, the poorer country will get nothing

	1:03:34.320 --> 1:03:42.320
	because they don't have technology and the wealth disparity and inequality will increase.

	1:03:42.320 --> 1:03:50.320
	So a poorer country with a large population will not only benefit from the AI boom or other technology booms

	1:03:50.320 --> 1:03:57.320
	but they will have their workers who previously had hoped they could do the China model and do outsource manufacturing

	1:03:57.320 --> 1:04:02.320
	or the India model so they could do the outsource process or call center

	1:04:02.320 --> 1:04:05.320
	while all those jobs are going to be gone in 10 or 15 years.

	1:04:05.320 --> 1:04:14.320
	So the individual citizen may be a net liability, I mean financially speaking, to a poorer country

	1:04:14.320 --> 1:04:19.320
	and not an asset to claw itself out of poverty.

	1:04:19.320 --> 1:04:29.320
	So in that kind of situation, these large countries with not much tech are going to be facing a downward spiral

	1:04:29.320 --> 1:04:37.320
	and it's unclear what could be done and then when we look back and say there's $16 trillion being created

	1:04:37.320 --> 1:04:43.320
	and it's all being kept by U.S. China and other developed countries, it just doesn't feel right.

	1:04:43.320 --> 1:04:50.320
	So I hope people who know about geopolitics can find solutions that's beyond my expertise.

	1:04:50.320 --> 1:04:54.320
	So different countries that we've talked about have different value systems.

	1:04:54.320 --> 1:05:02.320
	If you look at the United States to an almost extreme degree, there is an absolute desire for freedom of speech.

	1:05:02.320 --> 1:05:14.320
	If you look at a country where I was raised, that desire just amongst the people is not as elevated as it is to basically fundamental level

	1:05:14.320 --> 1:05:17.320
	to the essence of what it means to be America, right?

	1:05:17.320 --> 1:05:20.320
	And the same is true with China, there's different value systems.

	1:05:20.320 --> 1:05:30.320
	There is some censorship of internet content that China and Russia and many other countries undertake.

	1:05:30.320 --> 1:05:40.320
	Do you see that having effects on innovation, other aspects of some of the tech stuff, AI development we talked about

	1:05:40.320 --> 1:05:52.320
	and maybe from another angle, do you see that changing in different ways over the next 10 years, 20 years, 50 years as China continues to grow

	1:05:52.320 --> 1:05:55.320
	as it does now in its tech innovation?

	1:05:55.320 --> 1:06:08.320
	There's a common belief that full freedom of speech and expression is correlated with creativity, which is correlated with entrepreneurial success.

	1:06:08.320 --> 1:06:15.320
	I think empirically we have seen that is not true and China has been successful.

	1:06:15.320 --> 1:06:25.320
	That's not to say the fundamental values are not right or not the best, but it's just that perfect correlation isn't there.

	1:06:25.320 --> 1:06:36.320
	It's hard to read the tea leaves on opening up or not in any country and I've not been very good at that in my past predictions.

	1:06:36.320 --> 1:06:46.320
	But I do believe every country shares some fundamental value, a lot of fundamental values for the long term.

	1:06:46.320 --> 1:07:02.320
	So, you know, China is drafting its privacy policy for individual citizens and they don't look that different from the American or European ones.

	1:07:02.320 --> 1:07:13.320
	So, people do want to protect their privacy and have the opportunity to express and I think the fundamental values are there.

	1:07:13.320 --> 1:07:21.320
	The question is in the execution and timing, how soon or when will that start to open up?

	1:07:21.320 --> 1:07:31.320
	So, as long as each government knows, ultimately people want that kind of protection, there should be a plan to move towards that.

	1:07:31.320 --> 1:07:35.320
	As to when or how, again, I'm not an expert.

	1:07:35.320 --> 1:07:38.320
	On the point of privacy to me, it's really interesting.

	1:07:38.320 --> 1:07:44.320
	So, AI needs data to create a personalized awesome experience.

	1:07:44.320 --> 1:07:47.320
	I'm just speaking generally in terms of products.

	1:07:47.320 --> 1:07:53.320
	And then we have currently, depending on the age and depending on the demographics of who we're talking about,

	1:07:53.320 --> 1:07:58.320
	some people are more or less concerned about the amount of data they hand over.

	1:07:58.320 --> 1:08:03.320
	So, in your view, how do we get this balance right?

	1:08:03.320 --> 1:08:09.320
	That we provide an amazing experience to people that use products.

	1:08:09.320 --> 1:08:15.320
	You look at Facebook, you know, the more Facebook knows about you, yes, it's scary to say.

	1:08:15.320 --> 1:08:20.320
	The better it can probably, a better experience it can probably create.

	1:08:20.320 --> 1:08:24.320
	So, in your view, how do we get that balance right?

	1:08:24.320 --> 1:08:38.320
	Yes, I think a lot of people have a misunderstanding that it's okay and possible to just rip all the data out from a provider and give it back to you.

	1:08:38.320 --> 1:08:43.320
	So, you can deny them access to further data and still enjoy the services we have.

	1:08:43.320 --> 1:08:48.320
	If we take back all the data, all the services will give us nonsense.

	1:08:48.320 --> 1:08:57.320
	We'll no longer be able to use products that function well in terms of, you know, right ranking, right products, right user experience.

	1:08:57.320 --> 1:09:04.320
	So, yet I do understand we don't want to permit misuse of the data.

	1:09:04.320 --> 1:09:16.320
	From legal policy standpoint, I think there can be severe punishment for those who have egregious misuse of the data.

	1:09:16.320 --> 1:09:19.320
	That's, I think, a good first step.

	1:09:19.320 --> 1:09:27.320
	Actually, China on this aspect has very strong laws about people who sell or give data to other companies.

	1:09:27.320 --> 1:09:40.320
	And that over the past few years, since that law came into effect, pretty much eradicated the illegal distribution sharing of data.

	1:09:40.320 --> 1:09:52.320
	Additionally, I think giving, I think technology is often a very good way to solve technology misuse.

	1:09:52.320 --> 1:09:58.320
	So, can we come up with new technologies that will let us have our cake and eat it too?

	1:09:58.320 --> 1:10:07.320
	People are looking into homomorphic encryption, which is letting you keep the data, have it encrypted and train encrypted data.

	1:10:07.320 --> 1:10:13.320
	Of course, we haven't solved that one yet, but that kind of direction may be worth pursuing.

	1:10:13.320 --> 1:10:22.320
	Also federated learning, which would allow one hospital to train on its hospitals patient data fully because they have a license for that.

	1:10:22.320 --> 1:10:28.320
	And then hospitals would then share their models, not data, but models to create a supra AI.

	1:10:28.320 --> 1:10:30.320
	And that also maybe has some promise.

	1:10:30.320 --> 1:10:39.320
	So I would want to encourage us to be open minded and think of this as not just the policy binary yes no,

	1:10:39.320 --> 1:10:48.320
	but letting the technologists try to find solutions to let us have our cake and eat it too, or have most of our cake and eat most of it too.

	1:10:48.320 --> 1:10:55.320
	Finally, I think giving each end user a choice is important and having transparency is important.

	1:10:55.320 --> 1:11:04.320
	Also, I think that's universal, but the choice you give to the user should not be at a granular level that the user cannot understand.

	1:11:04.320 --> 1:11:12.320
	GDPR today causes all these pop ups of yes, no, will you give this site this right to use this part of your data?

	1:11:12.320 --> 1:11:20.320
	I don't think any user understands what they're saying yes or no to, and I suspect most are just saying yes because they don't understand it.

	1:11:20.320 --> 1:11:30.320
	So while GDPR in its current implementation has lived up to its promise of transparency and user choice,

	1:11:30.320 --> 1:11:39.320
	it implemented it in such a way that really didn't deliver the spirit of GDPR.

	1:11:39.320 --> 1:11:41.320
	It fit the letter, but not the spirit.

	1:11:41.320 --> 1:11:50.320
	So again, I think we need to think about is there a way to fit the spirit of GDPR by using some kind of technology?

	1:11:50.320 --> 1:11:52.320
	Can we have a slider?

	1:11:52.320 --> 1:12:01.320
	That's an AI trying to figure out how much you want to slide between perfect protection security of your personal data

	1:12:01.320 --> 1:12:07.320
	versus high degree of convenience with some risks of not having full privacy.

	1:12:07.320 --> 1:12:11.320
	Each user should have some preference and that gives you the user choice,

	1:12:11.320 --> 1:12:18.320
	but maybe we should turn the problem on its head and ask can there be an AI algorithm that can customize this

	1:12:18.320 --> 1:12:24.320
	because we can understand the slider, but we sure cannot understand every pop up question.

	1:12:24.320 --> 1:12:30.320
	And I think getting that right requires getting the balance between what we talked about earlier,

	1:12:30.320 --> 1:12:36.320
	which is heart and soul versus profit driven decisions and strategy.

	1:12:36.320 --> 1:12:45.320
	I think from my perspective, the best way to make a lot of money in the long term is to keep your heart and soul intact.

	1:12:45.320 --> 1:12:53.320
	I think getting that slider right in the short term may feel like you'll be sacrificing profit,

	1:12:53.320 --> 1:12:59.320
	but in the long term, you'll be getting user trust and providing a great experience.

	1:12:59.320 --> 1:13:01.320
	Do you share that kind of view in general?

	1:13:01.320 --> 1:13:11.320
	Yes, absolutely. I sure would hope there is a way we can do long term projects that really do the right thing.

	1:13:11.320 --> 1:13:16.320
	I think a lot of people who embrace GDPR, their hearts in the right place.

	1:13:16.320 --> 1:13:20.320
	I think they just need to figure out how to build a solution.

	1:13:20.320 --> 1:13:24.320
	I've heard utopians talk about solutions that get me excited,

	1:13:24.320 --> 1:13:29.320
	but not sure how in the current funding environment they can get started, right?

	1:13:29.320 --> 1:13:37.320
	People talk about, imagine this crowdsourced data collection that we all trust,

	1:13:37.320 --> 1:13:45.320
	and then we have these agents that we ask them to ask the trusted agent.

	1:13:45.320 --> 1:13:48.320
	That agent only, that platform.

	1:13:48.320 --> 1:14:02.320
	A trusted joint platform that we all believe is trustworthy that can give us all the close loop personal suggestions

	1:14:02.320 --> 1:14:07.320
	by the new social network, new search engine, new ecommerce engine

	1:14:07.320 --> 1:14:12.320
	that has access to even more of our data, but not directly but indirectly.

	1:14:12.320 --> 1:14:18.320
	I think that general concept of licensing to some trusted engine

	1:14:18.320 --> 1:14:22.320
	and finding a way to trust that engine seems like a great idea,

	1:14:22.320 --> 1:14:27.320
	but if you think how long it's going to take to implement and tweak and develop it right,

	1:14:27.320 --> 1:14:31.320
	as well as to collect all the trust and the data from the people,

	1:14:31.320 --> 1:14:34.320
	it's beyond the current cycle of venture capital.

	1:14:34.320 --> 1:14:37.320
	How do you do that is a big question.

	1:14:37.320 --> 1:14:44.320
	You've recently had a fight with cancer, stage 4 lymphoma,

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	and in a sort of deep personal level, what did it feel like in the darker moments to face your own mortality?

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	Well, I've been the workaholic my whole life,

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	and I've basically worked 9.96, 9am to 9pm, 6 days a week, roughly.

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	And I didn't really pay a lot of attention to my family, friends, and people who loved me,

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	and my life revolved around optimizing for work.

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	While my work was not routine, my optimization really made my life basically a very mechanical process.

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	But I got a lot of highs out of it because of accomplishments that I thought were really important and dear and the highest priority to me.

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	But when I faced mortality and the possible death in matter of months,

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	I suddenly realized that this really meant nothing to me,

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	that I didn't feel like working for another minute,

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	that if I had 6 months left in my life, I would spend it all with my loved ones.

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	And thanking them, giving them love back, and apologizing to them that I lived my life the wrong way.

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	So that moment of reckoning caused me to really rethink that why we exist in this world

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	is something that we might be too much shaped by the society to think that success and accomplishments is why we live.

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	And while that can get you periodic successes and satisfaction,

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	it's really in them facing death, you see what's truly important to you.

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	So as a result of going through the challenges with cancer,

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	I've resolved to live a more balanced lifestyle.

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	I'm now in remission, knock on wood,

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	and I'm spending more time with my family.

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	My wife travels with me.

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	When my kids need me, I spend more time with them.

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	And before, I used to prioritize everything around work.

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	When I had a little bit of time, I would dole it out to my family.

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	Now, when my family needs something, really needs something,

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	I drop everything at work and go to them.

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	And then in the time remaining, I allocate to work.

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	But one's family is very understanding.

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	It's not like they will take 50 hours a week from me.

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	So I'm actually able to still work pretty hard,

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	maybe 10 hours less per week.

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	So I realize the most important thing in my life is really love and the people I love.

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	And I give that the highest priority.

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	It isn't the only thing I do.

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	But when that is needed, I put that at the top priority.

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	And I feel much better and I feel much more balanced.

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	And I think this also gives a hint as to a life of routine work,

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	a life of pursuit of numbers.

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	While my job was not routine, it wasn't pursuit of numbers.

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	Pursuit of, can I make more money?

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	Can I fund more great companies?

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	Can I raise more money?

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	Can I make sure our VC is ranked higher and higher every year?

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	This competitive nature of driving for bigger numbers and better numbers

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	became an endless pursuit of that's mechanical.

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	And bigger numbers really didn't make me happier.

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	And faced with death, I realized bigger numbers really meant nothing.

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	And what was important is that people who have given their heart and their love to me

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	deserve for me to do the same.

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	So there's deep profound truth in that, that everyone should hear and internalize.

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	And that's really powerful for you to say that.

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	I have to ask sort of a difficult question here.

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	So I've competed in sports my whole life, looking historically.

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	I'd like to challenge some aspect of that a little bit on the point of hard work.

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	That it feels that there are certain aspects that is the greatest,

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	the most beautiful aspects of human nature, is the ability to become obsessed,

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	of becoming extremely passionate to the point where, yes, flaws are revealed

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	and just giving yourself fully to a task.

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	That is, in another sense, you mentioned love being important,

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	but in another sense, this kind of obsession, this pure exhibition of passion and hard work

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	is truly what it means to be human.

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	What lessons should we take that's deeper?

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	Because you've accomplished incredible things.

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	Like chasing numbers.

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	But really, there's some incredible work there.

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	So how do you think about that when you look back in your 20s, your 30s?

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	What would you do differently?

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	Would you really take back some of the incredible hard work?

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	I would.

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	But it's in percentages, right?

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	We're both now computer scientists.

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	So I think when one balances one's life, when one is younger,

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	you might give a smaller percentage to family, but you would still give them high priority.

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	And when you get older, you would give a larger percentage to them and still the high priority.

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	And when you're near retirement, you give most of it to them and the highest priority.

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	So I think the key point is not that we would work 20 hours less for the whole life

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	and just spend it aimlessly with the family, but that when the family has a need,

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	when your wife is having a baby, when your daughter has a birthday,

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	or when they're depressed, or when they're celebrating something,

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	or when they have a get together, or when we have family time,

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	that is important for us to put down our phone and PC and be 100% with them.

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	And that priority on the things that really matter isn't going to be so taxing

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	that it would eliminate or even dramatically reduce our accomplishments.

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	It might have some impact, but it might also have other impact

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	because if you have a happier family, maybe you fight less.

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	If you fight less, you don't spend time taking care of all the aftermath of a fight.

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	That's right.

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	And I'm sure that it would take more time.

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	And if it did, I'd be willing to take that reduction.

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	And it's not a dramatic number, but it's a number

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	that I think would give me a greater degree of happiness

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	and knowing that I've done the right thing

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	and still have plenty of hours to get the success that I want to get.

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	So given the many successful companies that you've launched

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	and much success throughout your career,

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	what advice would you give to young people today looking,

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	or it doesn't have to be young, but people today looking to launch

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	and to create the next $1 billion tech startup, or even AI based startup?

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	I would suggest that people understand technology waves move quickly.

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	What worked two years ago may not work today.

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	And that is very much a case in point for AI.

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	I think two years ago, or maybe three years ago,

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	you certainly could say I have a couple of super smart PhDs

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	and we're not sure what we're going to do,

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	but here's how we're going to start and get funding for a very high valuation.

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	Those days are over because AI is going from rocket science towards mainstream.

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	Not yet commodity, but more mainstream.

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	So first, the creation of any company to eventual capitalist

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	has to be creation of business value and monetary value.

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	And when you have a very scarce commodity,

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	VCs may be willing to accept greater uncertainty.

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	But now the number of people who have the equivalent of PhD three years ago

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	because that can be learned more quickly.

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	Platforms are emerging.

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	The cost to become an AI engineer is much lower and there are many more AI engineers.

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	So the market is different.

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	So I would suggest someone who wants to build an AI company

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	be thinking about the normal business questions.

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	What customer cases are you trying to address?

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	What kind of pain are you trying to address?

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	How does that translate to value?

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	How will you extract value and get paid through what channel?

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	And how much business value will get created?

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	That today needs to be thought about much earlier up front than it did three years ago.

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	The scarcity question of AI talent has changed.

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	The number of AI talent has changed.

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	So now you need not just AI but also understanding of business customer and the marketplace.

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	So I also think you should have a more reasonable evaluation expectation

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	and growth expectation.

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	There's going to be more competition.

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	But the good news though is that AI technologies are now more available in open source.

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	TensorFlow, PyTorch and such tools are much easier to use.

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	So you should be able to experiment and get results iteratively faster than before.

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	So take more of a business mindset to this.

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	Think less of this as a laboratory taken into a company because we've gone beyond that stage.

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	The only exception is if you truly have a breakthrough in some technology that really no one has,

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	then the old way still works.

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	But I think that's harder and harder now.

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	So I know you believe as many do that we're far from creating an artificial general intelligence system.

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	But say once we do and you get to ask her one question, what would that question be?

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	What is it that differentiates you and me?

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	Beautifully put, Kaifu, thank you so much for your time today.

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	Thank you.