Lex Fridman Podcast - #27 - Kai-Fu Lee: AI Superpowers – China and Silicon Valley

The following is a conversation with Kai Fu Lee.

He’s the chairman and CEO of Cinovation Ventures

that manages a $2 billion dual currency investment fund

with a focus on developing the next generation

of Chinese high tech companies.

He’s the former president of Google China

and the founder of what is now called

Microsoft Research Asia,

an institute that trained many of the artificial

intelligence leaders in China,

including CTOs or AI execs at Baidu,

Tencent, Alibaba, Lenovo, and Huawei.

He was named one of the 100 most influential people

in the world by Time Magazine.

He’s the author of seven bestselling books in Chinese

and most recently, the New York Times bestseller

called AI Superpowers, China, Silicon Valley,

and the New World Order.

He has unparalleled experience

in working across major tech companies

and governments and applications of AI,

and so he has a unique perspective on global innovation

and the future of AI that I think is important

to listen to and think about.

This is the Artificial Intelligence Podcast.

If you enjoy it, subscribe on YouTube and iTunes,

support it on Patreon, or simply connect with me

on Twitter at Lex Friedman.

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

I immigrated from Russia to US when I was 13.

You immigrated to US at about the same age.

The Russian people, the American people,

the Chinese people each have a certain soul,

a spirit that permeates throughout the generations.

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

but could you describe your sense

of what defines the Chinese soul?

I think the Chinese soul of people today, right,

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

because of the poverty that the country has gone through,

and suddenly shined with hope of prosperity

in the past 40 years as China opened up

and embraced market economy.

And undoubtedly, there are two sets of pressures

on the people, that of the tradition,

that of facing difficult situations,

and that of hope of wanting to be the first

to become successful and wealthy.

So that’s a very strong hunger and a strong desire

and strong work ethic that drives China forward.

And is there roots to not just this generation,

but before that’s deeper

than just the new economic developments?

Is there something that’s unique to China

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

Yeah, well, the Chinese tradition

is about excellence, dedication, and results.

And the Chinese exams and study subjects in schools

have traditionally started

from memorizing 10,000 characters,

not an easy task to start with.

And further by memorizing

his historic philosopher’s literature poetry.

So it really is probably

the strongest rote learning mechanism created

to make sure people had good memory

and remember things extremely well.

That’s, I think at the same time,

suppresses the breakthrough innovation

and also enhances the speed execution get results.

And that I think characterizes

the historic basis of China.

That’s interesting,

because there’s echoes of that in Russian education

as well as rote memorization.

So you have to memorize a lot of poetry.

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

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

which is creativity.

But you think that kind of education holds back

the innovative spirit that you might see

in the United States?

Well, it holds back the breakthrough innovative spirits

that we see in the United States,

but it does not hold back the valuable execution oriented,

result oriented value creating engines,

which we see China being very successful.

So is there a difference between a Chinese AI engineer today

and an American AI engineer,

perhaps rooted in the culture that we just talked about

or the education or the very soul of the people or no?

And what would your advice be to each

if there’s a difference?

Well, there’s a lot that’s similar

because AI is about mastering sciences,

about using known technologies and trying new things,

but it’s also about picking from many parts

of possible networks to use

and different types of parameters to tune.

And that part is somewhat rote.

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

can tell you a lot about cleansing the data

because AI runs better with more data

and data is generally unstructured,

errorful and unclean.

And the effort to clean the data is immense.

So I think the better part of American engineering,

AI engineering process is to try new things,

to do things people haven’t done before

and to use technology to solve most if not all problems.

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

find error tolerant ways to deal with the data.

The Chinese way would be to basically enumerate

to the fullest extent all the possible ways

by a lot of machines, try lots of different ways

to get it to work and spend a lot of resources

and money and time cleaning up data.

That means the AI engineer may be writing

data cleansing algorithms, working with thousands of people

who label or correct or do things with the data.

That is the incredible hard work

that might lead to better results.

So the Chinese engineer would rely on

and ask for more and more and more data

and find ways to cleanse them and make them work

in the system and probably less time thinking

about new algorithms that can overcome data

or other issues.

So where’s your intuition?

Where do you think the biggest impact

in the next 10 years lies?

Is it in some breakthrough algorithms

or is it in just this at scale rigor,

a rigorous approach to data, cleaning data,

organizing data onto the same algorithms?

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

Well, if you’re really in the company

and you have to deliver results,

using known techniques and enhancing data

seems like the more expedient approach

that’s very low risk and likely to generate

better and better results.

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

Now, there are a lot of more challenging startups

and problems such as autonomous vehicles,

medical diagnosis that existing algorithms

probably won’t solve.

And that would put the Chinese approach more challenged

and give them more breakthrough innovation approach,

more of an edge on those kinds of problems.

So let me talk to that a little more.

So my intuition personally is that data

can take us extremely far.

So you brought up autonomous vehicles and medical diagnosis.

So your intuition is that huge amounts of data

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

Right, so breaking that down further in autonomous vehicle,

I think huge amounts of data probably will solve

trucks driving on highways,

which will deliver a significant value

and China will probably lead in that.

And full L5 autonomous is likely to require

new technologies we don’t yet know.

And that might require academia

and great industrial research,

both innovating and working together.

And in that case, US has an advantage.

So the interesting question there is,

I don’t know if you’re familiar

on the autonomous vehicle space

and the developments with Tesla and Elon Musk.

I am.

Where they are in fact full steam ahead

into this mysterious complex world of full autonomy, L5,

L4, L5, and they’re trying to solve that purely with data.

So the same kind of thing that you’re saying

is just for highway,

which is what a lot of people share your intuition.

They’re trying to solve with data.

So just to linger on that moment further,

do you think possible for them to achieve success

with simply just a huge amount of this training

on edge cases and difficult cases in urban environments,

not just highway and so on?

I think it would be very hard.

One could characterize Tesla’s approach

as kind of a Chinese strength approach, right?

Gather all the data you can

and hope that will overcome the problems.

But in autonomous driving,

clearly a lot of the decisions aren’t merely solved

by aggregating data and having feedback loop.

There are things that are more akin to human thinking.

And how would those be integrated and built?

There has not yet been a lot of success

integrating human intelligence

or call it expert systems if you will,

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

And the integration of the two types of thinking

hasn’t yet been demonstrated.

And the question is how much can you push

a purely machine learning approach?

And of course, Tesla also has an additional constraint

that they don’t have all the sensors.

I know that they think it’s foolish to use LIDARs,

but that’s clearly a one less very valuable

and reliable source of input that they’re foregoing,

which may also have consequences.

I think the advantage of course is capturing data

that no one has ever seen before.

And in some cases such as computer vision

and speech recognition,

I have seen Chinese companies accumulate data

that’s not seen anywhere in the Western world

and they have delivered superior results.

But then speech recognition and object recognition

are relatively suitable problems for deep learning

and don’t have the potentially need

for the human intelligence analytical planning elements.

And the same on the speech recognition side,

your intuition that speech recognition

and the machine learning approaches to speech recognition

won’t take us to a conversational system

that can pass the Turing test,

which is sort of maybe akin to what driving is.

So it needs to have something more than just simply

simple language understanding, simple language generation.

Roughly right.

I would say that based on purely machine learning approaches,

it’s hard to imagine it could lead

to a full conversational experience

across arbitrary domains, which is akin to L5.

I’m a little hesitant to use the word Turing test

because the original definition was probably too easy.

We probably do that, yeah.

The spirit of the Turing test

is what I was referring to. Of course.

So you’ve had major leadership research positions

at Apple, Microsoft, Google.

So continuing on the discussion of America, Russia,

Chinese, Seoul and culture and so on.

What is the culture of Silicon Valley

in contrast to China and maybe US broadly?

And what is the unique culture

of each of these three major companies in your view?

I think in aggregate, Silicon Valley companies,

and we could probably include Microsoft in that,

even though they’re not in the Valley,

is really dream big and have visionary goals

and believe that technology will conquer all.

And also the self confidence and the self entitlement

that whatever they produce,

the whole world should use and must use.

And those are historically important, I think.

Steve Jobs famous quote that he doesn’t do focus groups,

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

what do you want?

And that really is an inspirational comment that says,

the great company shouldn’t just ask users what they want,

but develop something that users will know they want

when they see it,

but they could never come up with themselves.

I think that is probably the most exhilarating description

of what the essence of Silicon Valley is,

that this brilliant idea could cause you to build something

that couldn’t come out of the focus groups or AB tests.

And iPhone would be an example of that.

No one in the age of Blackberry would write down

they want an iPhone or multi touch.

A browser might be another example.

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

but once they see it, they want it.

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

But it also comes with, it came with a lot of success.

These products became global platforms

and there were basically no competitors anywhere.

And that has also led to a belief

that these are the only things that one should do,

that companies should not tread on other companies territory

so that a Groupon and a Yelp and then OpenTable

and the Grubhub would each feel,

okay, I’m not gonna do the other company’s business

because that would not be the pride of innovating

what each of these four companies have innovated.

But I think the Chinese approach

is do whatever it takes to win.

And it’s a winner take all market.

And in fact, in the internet space,

the market leader will get predominantly all the value

extracted out of the system.

So, and the system isn’t just defined

as one narrow category, but gets broader and broader.

So it’s amazing ambition for success and domination

of increasingly larger product categories

leading to clear market winner status

and the opportunity to extract tremendous value.

And that develops a practical, result oriented,

ultra ambitious winner take all gladiatorial mentality.

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

essentially a copycat that can be done

without infringing laws.

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

by forking the code base and building something

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

So it’s contrasted very sharply

with the Silicon Valley approach.

And I think the flexibility and the speed and execution

has helped the Chinese approach.

And I think the Silicon Valley approach

is potentially challenged if every Chinese entrepreneur

is learning from the whole world, US and China,

and the American entrepreneurs only look internally

and write off China as a copycat.

And the second part of your question

about the three companies.

The unique elements of the three companies perhaps.

Yeah, I think Apple represents

while the user please the user

and the essence of design and brand

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

that draws people with a strong, serious desire

for the product and the willingness to pay a premium

because of the halo effect of the brand

which came from the attention to detail

and great respect for user needs.

Microsoft represents a platform approach

that builds giant products that become very strong modes

that others can’t do because it’s well architected

at the bottom level and the work is efficiently delegated

to individuals and then the whole product is built

by adding small parts that sum together.

So it’s probably the most effective high tech assembly line

that builds a very difficult product

that and the whole process of doing that

is kind of a differentiation and something competitors

can’t easily repeat.

Are there elements of the Chinese approach

in the way Microsoft went about assembling

those little pieces and dominating,

essentially dominating the market for a long time

or do you see those as distinct?

I think there are elements that are the same.

I think the three American companies that had

or have Chinese characteristics and obviously

as well as American characteristics are Microsoft,

Facebook and Amazon.

Yes, that’s right, Amazon.

Because these are companies that will tenaciously

go after adjacent markets,

build up strong product offering and find ways

to extract greater value from a sphere

that’s ever increasing and they understand

the value of the platforms.

So that’s the similarity and then with Google,

I think it’s a genuinely value oriented company

that does have a heart and soul

and that wants to do great things for the world

by connecting information

and that has also very strong technology genes

and wants to use technology

and has found out of the box ways to use technology

to deliver incredible value to the end user.

If you can look at Google, for example,

you mentioned heart and soul.

There seems to be an element where Google

is after making the world better.

There’s a more positive view.

They used to have the slogan, don’t be evil.

And Facebook a little bit more has a negative tend to it.

At least in the perception of privacy and so on.

Do you have a sense of how these different companies

can achieve, because you’ve talked about

how much we can make the world better

in all these kinds of ways with AI.

What is it about a company that can make,

give it a heart and soul, gain the trust of the public

and just actually just not be evil

and do good for the world?

It’s really hard and I think Google

has struggled with that.

First, the don’t do evil mantra is very dangerous

because every employee’s definition of evil is different.

And that has led to some difficult

employee situations for them.

So I don’t necessarily think that’s a good value statement,

but just watching the kinds of things Google

or its parent company Alphabet does

in new areas like healthcare, like eradicating mosquitoes,

things that are really not in the business

of a internet tech company.

I think that shows that there’s a heart and soul

and desire to do good and willingness

to put in the resources to do something

when they see it’s good, they will pursue it.

That doesn’t necessarily mean

it has all the trust of the users.

I realize while most people would view Facebook

as the primary target of their recent unhappiness

about Silicon Valley companies,

many would put Google in that category.

And some have named Google’s business practices

as predatory also.

So it’s kind of difficult to have the two parts of a body.

The brain wants to do what it’s supposed to do

for a shareholder, maximize profit.

And then the heart and soul wants to do good things

that may run against what the brain wants to do.

So in this complex balancing

that these companies have to do,

you’ve mentioned that you’re concerned

about a future where too few companies

like Google, Facebook, Amazon are controlling our data

or controlling too much of our digital lives.

Can you elaborate on this concern

and perhaps do you have a better way forward?

I think I’m hardly the most vocal complainer of this.

Sure, of course.

There are a lot louder complainers out there.

I do observe that having a lot of data

does perpetuate their strength

and limits competition in many spaces.

But I also believe AI is much broader

than the internet space.

So the entrepreneurial opportunities still exists

in using AI to empower financial,

retail, manufacturing, education applications.

So I don’t think it’s quite a case

of full monopolistic dominance

that totally stifles innovation.

But I do believe in their areas of strength

it’s hard to dislodge them.

I don’t know if I have a good solution.

Probably the best solution is let

the entrepreneurial VC ecosystem work well

and find all the places that can create the next Google,

the next Facebook.

So there will always be increasing number of challengers.

In some sense that has happened a little bit.

You see Uber, Airbnb having emerged

despite the strength of the big three.

And I think China as an environment

may be more interesting for the emergence

because if you look at companies

between let’s say 50 to $300 billion,

China has emerged more of such companies

than the US in the last three to four years

because of the larger marketplace,

because of the more fearless nature of the entrepreneurs.

And the Chinese giants are just as powerful

as American ones.

Tencent, Alibaba are very strong,

but ByteDance has emerged worth 75 billion

and financial while it’s Alibaba affiliated,

it’s nevertheless independent and worth 150 billion.

And so I do think if we start to extend

to traditional businesses,

we will see very valuable companies.

So it’s probably not the case that in five or 10 years

we’ll still see the whole world

with these five companies having such dominance.

So you’ve mentioned a couple of times

this fascinating world of entrepreneurship in China

of the fearless nature of the entrepreneur.

So can you maybe talk a little bit about

what it takes to be an entrepreneur in China?

What are the strategies that are undertaken?

What are the ways to achieve success?

What is the dynamic of VCF funding

of the way the government helps companies and so on?

What are the interesting aspects here

that are distinct from, that are different

from the Silicon Valley world of entrepreneurship?

Well, many of the listeners probably still

would brand Chinese entrepreneur as copycats.

And no doubt 10 years ago,

that would not be an inaccurate description.

Back 10 years ago,

an entrepreneur probably could not get funding

if he or she could not describe

what product he or she is copying from the US.

The first question is who has proven this business model

which is a nice way of asking who are you copying?

And that reason is understandable

because China had a much lower internet penetration

and didn’t have enough indigenous experience

to build innovative products.

And secondly, internet was emerging.

Link startup was the way to do things,

building a first minimally viable product

and then expanding was the right way to go.

And the American successes have given a shortcut

that if you built your minimally viable product

based on an American product,

it’s guaranteed to be a decent starting point.

Then you tweak it afterwards.

So as long as there are no IP infringement,

which as far as I know there hasn’t been in the mobile

and AI spaces, that’s a much better shortcut.

And I think Silicon Valley would view that

as still not very honorable

because that’s not your own idea to start with,

but you can’t really at the same time

believe every idea must be your own

and believe in the link startup methodology

because link startup is intended to try many, many things

and then converge when that works.

And it’s meant to be iterated and changed.

So finding a decent starting point

without legal violations,

there should be nothing morally dishonorable about that.

Yeah, so just a quick pause on that.

It’s fascinating that that’s,

why is that not honorable, right?

It’s exactly as you formulated.

It seems like a perfect start for business.

Is to take, look at Amazon and say,

okay, we’ll do exactly what Amazon is doing.

Let’s start there in this particular market

and then let’s out innovate them from that starting point.

Come up with new ways.

I mean, is it wrong to be,

except the word copycat just sounds bad,

but is it wrong to be a copycat?

It just seems like a smart strategy,

but yes, it doesn’t have a heroic nature to it

that like Steve Jobs, Elon Musk,

sort of in something completely,

coming up with something completely new.

Yeah, I like the way you describe it.

It’s a nonheroic, acceptable way to start the company

and maybe more expedient.

So that’s, I think, a baggage for Silicon Valley

that if it doesn’t let go,

then it may limit the ultimate ceiling of the company.

Take Snapchat as an example.

I think, you know, Evan’s brilliant.

He built a great product,

but he’s very proud that he wants to build his own features,

not copy others.

While Facebook was more willing to copy his features

and you see what happens in the competition.

So I think putting that handcuff on the company

would limit its ability to reach the maximum potential.

So back to the Chinese environment,

copying was merely a way to learn from the American masters.

Just like we, if we learned to play piano or painting,

you start by copying.

You don’t start by innovating

when you don’t have the basic skill sets.

So very amazingly, the Chinese entrepreneurs

about six years ago started to branch off

with these lean startups built on American ideas

to build better products than American products.

But they did start from the American idea.

And today WeChat is better than WhatsApp,

Weibo is better than Twitter,

Zhihu is better than Quora and so on.

So that I think is Chinese entrepreneurs going to step two.

And then step three is once these entrepreneurs

have done one or two of these companies,

they now look at the Chinese market and the opportunities

and come up with ideas that didn’t exist elsewhere.

So products like Ant Financial,

under which includes Alipay, which is mobile payments,

and also the financial products for loans built on that.

And also in education, VIPKID,

and in social video, social network, TikTok,

and in social eCommerce, Pinduoduo,

and then in ride sharing, Mobike,

these are all Chinese innovated products

that now are being copied elsewhere.

So an additional interesting observation

is some of these products

are built on unique Chinese demographics,

which may not work in the US,

but may work very well in Southeast Asia, Africa,

and other developing worlds

that are a few years behind China.

And a few of these products maybe are universal

and are getting traction even in the United States,

such as TikTok.

So this whole ecosystem is supported by VCs

as a virtuous cycle,

because a large market with innovative entrepreneurs

will draw a lot of money

and then invest in these companies.

As the market gets larger and larger,

the China market is easily three, four times larger than the US,

they will create greater value and greater returns

for the VCs, thereby raising even more money.

So at Sinovation Ventures, our first fund was 15 million,

our last fund was 500 million.

So it reflects the valuation of the companies

and our us going multi stage and things like that.

It also has government support,

but not in the way most Americans would think of it.

The government actually leaves the entrepreneurial space

as a private enterprise, sort of self regulating,

and the government would build infrastructures

that would around it to make it work better.

For example, the Mass Entrepreneur Mass Innovation Plan

builds 8,000 incubators,

so the pipeline is very strong to the VCs.

For autonomous vehicles,

the Chinese government is building smart highways

with sensors, smart cities

that separate pedestrians from cars

that may allow initially an inferior

autonomous vehicle company to launch a car

without increasing with lower casualty

because the roads or the city is smart.

And the Chinese government at local levels

would have these guiding funds acting as LPs,

passive LPs to funds.

And when the fund makes money,

part of the money made is given back

to the GPs and potentially other LPs

to increase everybody’s return

at the expense of the government’s return.

So that’s an interesting incentive

that entrusts the task of choosing entrepreneurs to VCs

who are better at it than the government

by letting some of the profits move that way.

So this is really fascinating, right?

So I look at the Russian government as a case study

where, let me put it this way,

there’s no such government driven

large scale support of entrepreneurship.

And probably the same is true in the United States,

but the entrepreneurs themselves kind of find a way.

So maybe in a form of advice or explanation,

how did the Chinese government arrive to be this way

so supportive on entrepreneurship

to be in this particular way so forward thinking

at such a large scale?

And also perhaps, how can we copy it in other countries?

How can we encourage other governments,

like even the United States government,

to support infrastructure for autonomous vehicles

in that same kind of way, perhaps?

Yes, so these techniques are

the result of several key things,

some of which may be learnable,

some of which may be very hard.

One is just trial and error

and watching what everyone else is doing.

I think it’s important to be humble

and not feel like you know all the answers.

The guiding funds idea came from Singapore,

which came from Israel.

And China made a few tweaks and turned it into a,

because the Chinese cities and government officials

kind of compete with each other

because they all want to make their city more successful

so they can get the next level in their political career.

And it’s somewhat competitive.

So the central government made it a bit of a competition.

Everybody has a budget.

They can put it on AI or they can put it on bio

or they can put it on energy.

And then whoever gets the results,

the city shines, the people are better off,

the mayor gets a promotion.

So the tools is kind of almost like an entrepreneurial

environment for local governments

to see who can do a better job.

And also many of them try different experiments.

Some have given award to very smart researchers.

Just give them money and hope they’ll start a company.

Some have given money to academic research labs,

maybe government research labs

to see if they can spin off some companies

from the science lab or something like that.

Some have tried to recruit overseas Chinese

to come back and start companies.

And they’ve had mixed results.

The one that worked the best was the guiding funds.

So it’s almost like a lean startup idea

where people try different things

and what works sticks and everybody copies.

So now every city has a guiding fund.

So that’s how that came about.

The autonomous vehicle and the massive spending

in highways and smart cities, that’s a Chinese way.

It’s about building infrastructure to facilitate.

It’s a clear division of the government’s responsibility

from the market.

The market should do everything in a private freeway,

but there are things the market can’t afford to do

like infrastructure.

So the government always appropriates large amounts

of money for infrastructure building.

This happens with not only autonomous vehicle and AI,

but happened with the 3G and 4G.

You’ll find that the Chinese wireless reception

is better than the US because massive spending

that tries to cover the whole country,

whereas in the US it may be a little spotty.

It’s a government driven because I think they view

the coverage of cell access and 3G, 4G access

to be a governmental infrastructure spending

as opposed to capitalistic.

So that’s, of course, the state owned enterprises

are also publicly traded,

but they also carry a government responsibility

to deliver infrastructure to all.

So it’s a different way of thinking

that may be very hard to inject into Western countries

to say starting tomorrow, bandwidth infrastructure

and highways are gonna be governmental spending

with some characteristics.

What’s your sense, and sorry to interrupt,

but because it’s such a fascinating point,

do you think on the autonomous vehicle space

it’s possible to solve the problem of full autonomy

without significant investment in infrastructure?

Well, that’s really hard to speculate.

I think it’s not a yes, no question,

but how long does it take question?

15 years, 30 years, 45 years.

Clearly with infrastructure augmentation,

whether it’s road, the city or whole city planning,

building a new city, I’m sure that will accelerate

the day of the L5.

I’m not knowledgeable enough,

and it’s hard to predict even when we’re knowledgeable

because a lot of it is speculative.

But in the US, I don’t think people would consider

building a new city the size of Chicago

to make it the AI slash autonomous city.

There are smaller ones being built, I’m aware of that.

But is infrastructure spend really impossible

for US or Western countries?

I don’t think so.

The US highway system was built,

was that during President Eisenhower or Kennedy?

Eisenhower, yeah.

So maybe historians can study

how the President Eisenhower get the resources

to build this massive infrastructure

that surely gave US a tremendous amount of prosperity

over the next decade, if not century.

If I may comment on that then,

it takes us to artificial intelligence a little bit

because in order to build infrastructure,

it creates a lot of jobs.

So I’ll be actually interested if you would say

that you talk in your book about all kinds of jobs

that could and could not be automated.

I wonder if building infrastructure is one of the jobs

that would not be easily automated.

Something you could think about

because I think you’ve mentioned somewhere in the talk

or that there might be, as jobs are being automated,

a role for government to create jobs

that can’t be automated.

Yes, I think that’s a possibility.

Back in the last financial crisis,

China put a lot of money

to basically give this economy a boost

and a lot of it went into infrastructure building.

And I think that’s a legitimate way at the government level

to deal with the employment issues

as well as build out the infrastructure

as long as the infrastructures are truly needed

and as long as there is an employment problem,

which no, we don’t know.

So maybe taking a little step back,

if you’ve been a leader and a researcher in AI

for several decades, at least 30 years,

so how has AI changed in the West and the East

as you’ve observed, as you’ve been deep in it

over the past 30 years?

Well, AI began as the pursuit

of understanding human intelligence

and the term itself represents that,

but it kind of drifted into the one sub area

that worked extremely well, which is machine intelligence.

And that’s actually more using pattern recognition techniques

to basically do incredibly well on a limited domain,

large amount of data,

but relatively simple kinds of planning tasks

and not very creative.

So we didn’t end up building human intelligence.

We built a different machine

that was a lot better than us, some problems,

but nowhere close to us on other problems.

So today, I think a lot of people still misunderstand

when we say artificial intelligence

and what various products can do,

people still think it’s about replicating

human intelligence,

but the products out there really are closer

to having invented the internet or the spreadsheet

or the database and getting broader adoption.

And speaking further to the fears,

near term fears that people have about AI,

so you’re commenting on the sort of general intelligence

that people in the popular culture from sci fi movies

have a sense about AI,

but there’s practical fears about AI,

the narrow AI that you’re talking about

of automating particular kinds of jobs

and you talk about them in the book.

So what are the kinds of jobs in your view

that you see in the next five, 10 years

beginning to be automated by AI systems algorithms?

Yes, this is also maybe a little bit counterintuitive

because it’s the routine jobs

that will be displaced the soonest

and they may not be displaced entirely,

maybe 50%, 80% of a job,

but when the workload drops by that much,

employment will come down.

And also another part of misunderstanding

is most people think of AI replacing routine jobs

than they think of the assembly line, the workers.

Well, that will have some effect,

but it’s actually the routine white collar workers

that’s easiest to replace

because to replace a white collar worker,

you just need software.

To replace a blue collar worker,

you need robotics, mechanical excellence,

and the ability to deal with dexterity

and maybe even unknown environments, very, very difficult.

So if we were to categorize the most dangerous

white collar jobs,

they would be things like back office,

people who copy and paste

and deal with simple computer programs and data

and maybe paper and OCR,

and they don’t make strategic decisions.

They basically facilitate the process.

These softwares and paper systems don’t work.

So you have people dealing with new employee orientation,

searching for past lawsuits and financial documents,

and doing reference check.

So basic searching and management of data.

That’s the most endangered being lost.

In addition to the white collar repetitive work,

a lot of simple interaction work can also be taken care of

such as telesales, telemarketing, customer service,

as well as many physical jobs

that are in the same location

and don’t require a high degree of dexterity.

So fruit picking, dishwashing, assembly line inspection

are jobs in that category.

So altogether, back office is a big part.

And the blue collar may be smaller initially,

but over time, AI will get better.

And when we start to get to over the next 15, 20 years,

the ability to actually have the dexterity

of doing assembly line, that’s a huge chunk of jobs.

And when autonomous vehicles start to work,

initially starting with truck drivers,

but eventually to all drivers,

that’s another huge group of workers.

So I see modest numbers in the next five years,

but increasing rapidly after that.

On the worry of the jobs that are in danger

and the gradual loss of jobs,

I’m not sure if you’re familiar with Andrew Yang.

Yes, I am.

So there’s a candidate for president of the United States

whose platform Andrew Yang is based around,

in part around job loss due to automation.

And also in addition,

the need perhaps of universal basic income

to support jobs that are,

folks who lose their job due to automation and so on.

And in general, support people

under complex, unstable job market.

So what are your thoughts about his concerns,

him as a candidate, his ideas in general?

I think his thinking is generally in the right direction,

but his approach as a presidential candidate

may be a little bit ahead of the time.

And I think the displacements will happen,

but will they happen soon enough

for people to agree to vote for him?

The unemployment numbers are not very high yet.

And I think he and I have the same challenge.

If I want to theoretically convince people this is an issue

and he wants to become the president,

people have to see how can this be the case

when unemployment numbers are low.

So that is the challenge.

And I think I do agree with him on the displacement issue,

on universal basic income.

At a very vanilla level, I don’t agree with it

because I think the main issue is retraining.

So people need to be incented

not by just giving a monthly $2,000 check or $1,000 check

and do whatever they want

because they don’t have the know how

to know what to retrain to go into what type of a job.

And guidance is needed.

And retraining is needed

because historically when technology revolutions,

when routine jobs were displaced, new routine jobs came up.

So there was always room for that.

But with AI and automation,

the whole point is replacing all routine jobs eventually.

So there will be fewer and fewer routine jobs.

And AI will create jobs, but it won’t create routine jobs

because if it creates routine jobs,

why wouldn’t AI just do it?

So therefore the people who are losing the jobs

are losing routine jobs.

The jobs that are becoming available are non routine jobs.

So the social stipend needs to be put in place

is for the routine workers who lost their jobs

to be retrained maybe in six months, maybe in three years,

takes a while to retrain on the non routine job

and then take on a job that will last

for that person’s lifetime.

Now, having said that,

if you look deeply into Andrew’s document,

he does cater for that.

So I’m not disagreeing with what he’s trying to do.

But for simplification, sometimes he just says UBI,

but simple UBI wouldn’t work.

And I think you’ve mentioned elsewhere

that the goal isn’t necessarily to give people enough money

to survive or live, or even to prosper.

The point is to give them a job that gives them meaning.

That meaning is extremely important.

That our employment, at least in the United States

and perhaps it carries across the world,

provides something that’s, forgive me for saying,

greater than money.

It provides meaning.

So now, what kind of jobs do you think can’t be automated?

Can you talk a little bit about creativity

and compassion in your book?

What aspects do you think it’s difficult to automate

for an AI system?

Because an AI system is currently merely optimizing.

It’s not able to reason, plan,

or think creatively or strategically.

It’s not able to deal with complex problems.

It can’t come up with a new problem and solve it.

A human needs to find the problem

and pose it as an optimization problem,

then have the AI work at it.

So an AI would have a very hard time discovering a new drug

or discovering a new style of painting

or dealing with complex tasks such as managing a company

that isn’t just about optimizing the bottom line,

but also about employee satisfaction, corporate brand,

and many, many other things.

So that is one category of things.

And because these things are challenging, creative, complex,

doing them creates a high degree of satisfaction

and therefore appealing to our desire for working,

which isn’t just to make the money, make the ends meet,

but also that we’ve accomplished something

that others maybe can’t do or can’t do as well.

Another type of job that is much numerous

would be compassionate jobs, jobs that require compassion,

empathy, human touch, human trust.

AI can’t do that because AI is cold, calculating,

and even if it can fake that to some extent,

it will make errors and that will make it look very silly.

And also, I think even if AI did okay,

people would want to interact with another person,

whether it’s for some kind of a service or a teacher

or a doctor or a concierge or a masseuse or a bartender.

There are so many jobs where people just don’t want

to interact with a cold robot or software.

I’ve had an entrepreneur who built an elderly care robot

and they found that the elderly really only use it

for customer service.

And not, but not to service the product,

but they click on customer service

and the video of a person comes up

and then the person says,

how come my daughter didn’t call me?

Let me show you a picture of her grandkids.

So people yearn for that people, people interaction.

So even if robots improved, people just don’t want it.

And those jobs are going to be increasing

because AI will create a lot of value,

$16 trillion to the world in the next 10 years.

Next 11 years, according to PWC.

And that will give people money to enjoy services,

whether it’s eating a gourmet meal or tourism and traveling

or having concierge services,

the services revolving around every dollar

of that $16 trillion will be tremendous.

It will create more opportunities

that are to service the people who did well

through AI with things.

But even at the same time,

the entire society is very much short

in need of many service oriented,

compassionate oriented jobs.

The best example is probably in healthcare services.

There’s going to be 2 million new jobs,

not counting replacement,

just brand new incremental jobs

in the next six years in healthcare services.

That includes nurses, orderly in the hospital,

elderly care and also at home care is particularly lacking.

And those jobs are not likely to be filled.

So there’s likely to be a shortage.

And the reason they’re not filled

is simply because they don’t pay very well

and that the social status of these jobs are not very good.

So they pay about half as much

as a heavy equipment operator,

which will be replaced a lot sooner.

And they pay probably comparably

to someone on the assembly line.

And so if we ignoring all the other issues

and just think about satisfaction from one’s job,

someone repetitively doing the same manual action

at an assembly line,

that can’t create a lot of job satisfaction,

but someone taking care of a sick person

and getting a hug and thank you

from that person and the family,

I think is quite satisfying.

So if only we could fix the pay for service jobs,

there are plenty of jobs that require some training

or a lot of training

for the people coming off the routine jobs to take.

We can easily imagine someone

who was maybe a cashier at the grocery store

as stores become automated,

learns to become a nurse or an at home care.

I also do want to point out the blue collar jobs

are going to stay around a bit longer.

Some of them quite a bit longer.

AI cannot be told go clean an arbitrary home.

That’s incredibly hard.

Arguably it’s an L5 level of difficulty, right?

And then AI cannot be a good plumber

because plumber is almost like a mini detective

that has to figure out where the leak came from.

So yet AI probably can be an assembly line

and auto mechanic and so on.

So one has to study which blue collar jobs are going away

and facilitate retraining for the people

to go into the ones that won’t go away

or maybe even will increase.

I mean, it is fascinating that it’s easier

to build a world champion chess player

than it is to build a mediocre plumber.

Yes, right.

Very true.

And to AI and that goes counterintuitive

to a lot of people’s understanding

of what artificial intelligence is.

So it sounds, I mean, you’re painting

a pretty optimistic picture about retraining

about the number of jobs

and actually the meaningful nature of those jobs

once we automate the repetitive tasks.

So overall, are you optimistic about the future

where much of the repetitive tasks are automated?

That there is a lot of room for humans

for the compassionate, for the creative input

that only humans can provide?

I am optimistic if we start to take action.

If we have no action in the next five years,

I think it’s going to be hard to deal

with the devastating losses that will emerge.

So if we start thinking about retraining,

maybe with the low hanging fruits,

explaining to vocational schools

why they should train more plumbers than auto mechanics,

maybe starting with some government subsidy

for corporations to have more training positions.

We start to explain to people why retraining is important.

We start to think about what the future of education,

how that needs to be tweaked for the era of AI.

If we start to make incremental progress

and the greater number of people understand,

then there’s no reason to think we can’t deal with this

because this technological revolution

is arguably similar to what electricity,

industrial revolutions, and internet brought about.

Do you think there’s a role for policy,

for governments to step in,

to help with policy to create a better world?

Absolutely, and the governments don’t have to believe

an employment will go up,

and they don’t have to believe automation will be this fast

to do something.

Revamping vocational school would be one example.

Another is if there’s a big gap

in healthcare service employment,

and we know that a country’s population is growing older,

more longevity, living older,

because people over 80 require five times as much care

as those under 80,

then it is a good time to incent training programs

for elderly care to find ways to improve the pay.

Maybe one way would be to offer as part of Medicare

or the equivalent program for people over 80

to be entitled to a few hours of elderly care at home,

and then that might be reimbursable,

and that will stimulate the service industry

around the policy.

Do you have concerns about large entities,

whether it’s governments or companies,

controlling the future of AI development in general?

So we talked about companies.

Do you have a better sense that governments

can better represent the interests of the people

than companies, or do you believe companies

are better at representing the interests of the people?

Or is there no easy answer?

I don’t think there’s an easy answer

because it’s a double edged sword.

The companies and governments can provide better services

with more access to data and more access to AI,

but that also leads to greater power,

which can lead to uncontrollable problems,

whether it’s monopoly or corruption in the government.

So I think one has to be careful

to look at how much data that companies and governments have

and some kind of checks and balances would be helpful.

So again, I come from Russia.

There’s something called the Cold War.

So let me ask a difficult question here

looking at conflict.

Steven Pinker written a great book

that conflict all over the world is decreasing in general.

But do you have a sense that having written

the book AI Superpowers,

do you see a major international conflict

potentially arising between major nations,

whatever they are, whether it’s Russia, China,

European nations, United States or others

in the next 10, 20, 50 years around AI,

around the digital space, cyberspace?

Do you worry about that?

Is that something we need to think about

and try to alleviate or prevent?

I believe in greater engagement.

A lot of the worries about more powerful AI

are based on a arms race metaphor.

And when you extrapolate into military kinds of scenarios,

AI can automate and autonomous weapons

that needs to be controlled somehow

and autonomous decision making

can lead to not enough time to fix international crises.

So I actually believe a Cold War mentality

would be very dangerous

because should two countries rely on AI

to make certain decisions

and they don’t even talk to each other,

they do their own scenario planning,

then something could easily go wrong.

I think engagement, interaction, some protocols

to avoid inadvertent disasters is actually needed.

So it’s natural for each country to want to be the best,

whether it’s in nuclear technologies or AI or bio.

But I think it’s important to realize

if each country has a black box AI

and don’t talk to each other,

that probably presents greater challenges to humanity

than if they interacted.

I think there can still be competition,

but with some degree of protocol for interaction,

just like when there was a nuclear competition,

there were some protocol for deterrence

among US, Russia, and China.

And I think that engagement is needed.

So of course, we’re still far from AI

presenting that kind of danger.

But what I worry the most about

is the level of engagement seems to be coming down.

The level of distrust seems to be going up,

especially from the US towards other large countries

such as China and of course, and Russia, yes.

Is there a way to make that better?

So let’s beautifully put level of engagement

and even just basic trust and communication

as opposed to sort of making artificial enemies

out of particular countries.

Do you have a sense how we can make it better?

Actionable items that as a society we can take on?

I’m not an expert at geopolitics,

but I would say that we look pretty foolish as humankind

when we are faced with the opportunity

to create $16 trillion for humanity,

and yet we’re not solving fundamental problems

with parts of the world still in poverty.

And for the first time,

we have the resources to overcome poverty and hunger.

We’re not using it on that,

but we’re fueling competition among superpowers.

And that’s a very unfortunate thing.

If we become utopian for a moment,

imagine a benevolent world government

that has this $16 trillion and maybe some AI

to figure out how to use it to deal with diseases

and problems and hate and things like that.

World would be a lot better off.

So what is wrong with the current world?

I think the people with more skill than I

should think about this.

And then the geopolitics issue with superpower competition

is one side of the issue.

There’s another side which I worry maybe even more,

which is as the $16 trillion all gets made by US and China

and a few of the other developed countries,

the poorer country will get nothing

because they don’t have technology

and the wealth disparity and inequality will increase.

So a poorer country with a large population

will not only benefit from the AI boom

or other technology booms,

but they will have their workers

who previously had hoped they could do the China model

and do outsource manufacturing or the India model

so they could do the outsource process or call center.

Well, all those jobs are gonna be gone in 10 or 15 years.

So the individual citizen may be a net liability,

I mean, financially speaking to a poorer country

and not an asset to claw itself out of poverty.

So in that kind of situation,

these large countries with not much tech

are going to be facing a downward spiral

and it’s unclear what could be done.

And then when we look back

and say there’s $16 trillion being created

and it’s all being kept by US, China

and other developed countries, it just doesn’t feel right.

So I hope people who know about geopolitics

can find solutions that’s beyond my expertise.

So different countries that we’ve talked about

have different value systems.

If you look at the United States,

to an almost extreme degree,

there is an absolute desire for freedom of speech.

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

to the essence of what it means to be America, right?

And the same is true with China,

there’s different value systems.

There’s some censorship of internet content

that China and Russia and many other countries undertake.

Do you see that having effects on innovation,

other aspects of some of the tech stuff,

AI development we talked about,

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 as it does now

in its tech innovation?

There’s a common belief

that full freedom of speech and expression

is correlated with creativity,

which is correlated with entrepreneurial success.

I think empirically we have seen that is not true

and China has been successful.

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.

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,

but I do believe every country

shares a lot of fundamental values for the longterm.

So China is drafting its privacy policy

for individual citizens,

and they don’t look that different

from the American or European ones.

So people do want to protect their privacy

and have the opportunity to express

and I think the fundamental values are there.

The question is in the execution and timing,

how soon or when will that start to open up?

So as long as each government knows

ultimately people want that kind of protection,

there should be a plan to move towards that

as to when or how and I’m not an expert.

On the point of privacy to me, it’s really interesting.

So AI needs data to create

a personalized awesome experience, right?

I’m just speaking generally in terms of products.

And then we have currently, depending on the age

and depending on the demographics of who we’re talking about,

some people are more or less concerned

about the amount of data they hand over.

So in your view, how do we get this balance right

that we provide an amazing experience

to people that use products?

You look at Facebook, the more Facebook knows about you,

yes, it’s scary to say, the better it can probably,

better experience it can probably create.

So in your view, how do we get that balance right?

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.

So you can deny them access to further data

and still enjoy the services we have.

If we take back all the data,

all the services will give us nonsense.

We’ll no longer be able to use products that function well

in terms of right ranking, right products,

right user experience.

So yet I do understand we don’t want to permit misuse

of the data from legal policy standpoint.

I think there can be severe punishment

for those who have egregious misuse of the data.

That’s I think a good first step.

Actually China in this side on this aspect

has very strong laws about people who sell

or give data to other companies.

And that over the past few years,

since that law came into effect,

pretty much eradicated the illegal distribution,

sharing of data.

Additionally, I think giving,

I think technology is often a very good way

to solve technology misuse.

So can we come up with new technologies

that will let us have our cake and eat it too?

People are looking into homomorphic encryption,

which is letting you keep the data,

have it encrypted and train on encrypted data.

Of course, we haven’t solved that one yet,

but that kind of direction may be worth pursuing.

Also federated learning,

which would allow one hospital

to train on its hospital’s patient data fully

because they have a license for that.

And then hospitals would then share their models,

not data, but models to create a super AI.

And that also maybe has some promise.

So I would want to encourage us to be open minded

and think of this as not just the policy binary, yes, no,

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.

Finally, I think giving each end user a choice is important

and having transparency is important.

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.

GDPR today causes all these popups of yes, no,

will you give this site this right

to use this part of your data?

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.

So while GDPR in its current implementation

has lived up to its promise of transparency and user choice,

it implemented it in such a way

that really didn’t deliver the spirit of GDPR.

It fit the letter, but not the spirit.

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?

Can we have a slider that’s an AI trying to figure out

how much you want to slide between

perfect protection security of your personal data

versus a high degree of convenience

with some risks of not having full privacy?

Each user should have some preference

and that gives you the user choice.

But maybe we should turn the problem on its head

and ask can there be an AI algorithm that can customize this?

Because we can understand the slider,

but we sure cannot understand every popup question.

And I think getting that right

requires getting the balance between

what we talked about earlier,

which is heart and soul

versus profit driven decisions and strategy.

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.

I think getting that slider right in the short term

may feel like you’ll be sacrificing profit,

but in the long term,

you’ll be gaining user trust

and providing a great experience.

Do you share that kind of view in general?

Yes, absolutely.

I sure would hope there is a way

we can do long term projects

that really do the right thing.

I think a lot of people who embrace GDPR,

their heart’s in the right place.

I think they just need to figure out how to build a solution.

I’ve heard utopians talk about solutions

that get me excited,

but I’m not sure how in the current funding environment

they can get started.

People talk about,

imagine this crowdsourced data collection

that we all trust.

And then we have these agents

that we ask the trusted agent to…

That agent only, that platform,

so a trusted joint platform

that we all believe is trustworthy,

that can give us all the closed loop personal suggestions

by the new social network, new search engine,

new eCommerce engine that has access

to even more of our data,

but not directly, but indirectly.

So I think that general concept

of licensing to some trusted engine

and finding a way to trust that engine

seems like a great idea.

But if you think how long it’s gonna take

to implement and tweak and develop it right,

as well as to collect all the trusts

and the data from the people,

it’s beyond the current cycle of venture capital.

So how do you do that is a big question.

You’ve recently had a fight with cancer,

stage four lymphoma and in a sort of deep personal level,

what did it feel like in the darker moments

to face your own mortality?

Well, I’ve been the workaholic my whole life

and I’ve basically worked nine, nine, six,

nine a.m. to nine p.m. six days a week, roughly.

And I didn’t really pay a lot of attention

to my family, friends, and people who loved me.

And my life revolved around optimizing for work.

While my work was not routine,

my optimization really what made my life

basically very mechanical process.

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.

But when I faced mortality

and the possible death in matter of months,

I suddenly realized that this really meant nothing to me,

that I didn’t feel like working for another minute,

that if I had six months left in my life,

I would spend it all with my loved ones

and thanking them, giving them love back

and apologizing to them that I lived my life the wrong way.

So that moment of reckoning

caused me to really rethink that why we exist in this world

is something that we might be too much shaped by the society

to think that success and accomplishments is why we live.

But while that can get you

periodic successes and satisfaction,

it’s really in the facing death

you see what’s truly important to you.

So as a result of going through the challenges with cancer,

I’ve resolved to live a more balanced lifestyle.

I’m now in remission, knock on wood,

and I’m spending more time with my family.

My wife travels with me.

When my kids need me, I spend more time with them.

And before I used to prioritize everything around work.

When I had a little bit of time,

I would dole it out to my family.

Now, when my family needs something, really needs something,

I drop everything at work and go to them.

And then in the time remaining, I allocate to work.

But one’s family is very understanding.

It’s not like they will take 50 hours a week from me.

So I’m actually able to still work pretty hard,

maybe 10 hours less per week.

So I realized the most important thing in my life

is really love and the people I love.

And I give that the highest priority.

It isn’t the only thing I do,

but when that is needed, I put that at the top priority

and I feel much better and I feel much more balanced.

And I think this also gives a hint

as to a life of routine work, a life of pursuit of numbers.

While my job was not routine, it was in pursuit of numbers,

pursuit of can I make more money?

Can I fund more great companies?

Can I raise more money?

Can I make sure our VC is ranked higher and higher

every year?

This competitive nature of driving for bigger numbers

and better numbers became a endless pursuit

that’s mechanical.

And bigger numbers really didn’t make me happier.

And faced with death, I realized bigger numbers

really meant nothing.

And what was important is that people who have given

their heart and their love to me

deserve for me to do the same.

So there’s deep, profound truth in that,

that everyone should hear and internalize.

I mean, that’s really powerful for you to say that.

I have to ask sort of a difficult question here.

So I’ve competed in sports my whole life,

looking historically, I’d like to challenge some aspect

of that a little bit on the point of hard work.

That it feels that there are certain aspects

that is the greatest, the most beautiful aspects

of human nature is the ability to become obsessed,

of becoming extremely passionate to the point where yes,

flaws are revealed and just giving yourself fully to a task.

That is, in another sense, you mentioned love

being important, but in another sense,

this kind of obsession, this pure exhibition of passion

and hard work is truly what it means to be human.

What lessons should we take that’s deeper?

Because you’ve accomplished incredible things.

You say it chasing numbers,

but really there’s some incredible work there.

So how do you think about that when you look back

in your 20s, your 30s, what would you do differently?

Would you really take back some of the incredible hard work?

I would, but it’s in percentages, right?

We’re both computer scientists.

So I think when one balances one’s life,

when one is younger, you might give a smaller percentage

to family, but you would still give them high priority.

And when you get older, you would give a larger percentage

to them and still the high priority.

And when you’re near retirement, you give most of it to them

and the highest priority.

So I think the key point is not that we would work 20 hours

less for the whole life and just spend it aimlessly

with the family, but that’s when the family has a need,

when your wife is having a baby,

when your daughter has a birthday or when they’re depressed

or when they’re celebrating something

or when they have a get together or when we have family time

that it’s important for us to put down our phone and PC

and be a hundred percent with them.

And that priority on the things that really matter

isn’t going to be so taxing that it would eliminate

or even dramatically reduce our accomplishments.

It might have some impact, but it might also have

other impact because if you have a happier family,

maybe you fight less.

If you fight less, you don’t spend time taking care

of all the aftermath of a fight.

So it’s unclear that it would take more time.

And if it did, I’d be willing to take that reduction.

And it’s not a dramatic number, but it’s a number

that I think would give me a greater degree of happiness

and knowing that I’ve done the right thing

and still have plenty of hours to get the success

that I want to get.

So given the many successful companies that you’ve launched

and much success throughout your career,

what advice would you give to young people today looking,

or it doesn’t have to be young,

but people today looking to launch

and to create the next $1 billion tech startup

or even AI based startup?

I would suggest that people understand

technology waves move quickly.

What worked two years ago may not work today.

And that is very much case in point for AI.

I think two years ago, or maybe three years ago,

you certainly could say I have a couple

of super smart PhDs and we’re not sure

what we’re gonna do, but here’s how we’re gonna start

and get funding for a very high valuation.

Those days are over because AI is going

from rocket science towards mainstream,

not yet commodity, but more mainstream.

So first the creation of any company

to a venture capitalists has to be creation

of business value and monetary value.

And when you have a very scarce commodity,

VCs may be willing to accept greater uncertainty.

But now the number of people who have the equivalent

of PhD three years ago, because that can be learned

more quickly, platforms are emerging,

the cost to become a AI engineer is much lower

and there are many more AI engineers.

So the market is different.

So I would suggest someone who wants to build an AI company

be thinking about the normal business questions.

What customer cases are you trying to address?

What kind of pain are you trying to address?

How does that translate to value?

How will you extract value and get paid

through what channel and how much business value

will get created?

That today needs to be thought about much earlier upfront

than it did three years ago.

The scarcity question of AI talent has changed.

The number of AI talent has changed.

So now you need not just AI, but also understanding

of business customer and the marketplace.

So I also think you should have a more reasonable

valuation expectation and growth expectation.

There’s gonna be more competition.

But the good news though, is that AI technologies

are now more available in open source.

TensorFlow, PyTorch and such tools are much easier to use.

So you should be able to experiment and get results

iteratively faster than before.

So take more of a business mindset to this,

think less of this as a laboratory taken into a company,

because we’ve gone beyond that stage.

The only exception is if you truly have a breakthrough

in some technology that really no one has,

then the old way still works.

But I think that’s harder and harder now.

So I know you believe as many do that we’re far

from creating an artificial general intelligence system.

But say once we do, and you get to ask her one question,

what would that question be?

What is it that differentiates you and me?

Beautifully put, Kaifu, thank you so much

for your time today.

Thank you.

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