September 18, 2024 • 25 mins
AI has been hailed as a transformative technology with McKinsey estimating it could add $26 trillion to the global economy. While many investors have already jumped on the AI bandwagon, not everyone agrees.
Daron Acemoglu, Institute Professor at MIT and author of books including Why Nations Fail, takes a critical look at AI and explains why the economic and social benefits may have been overstated. He joins John Lee of Bloomberg Intelligence and Katia Dmitrieva of Bloomberg News on the Asia Centric podcast.
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Speaker 1 (00:02):
You're listening to Asia Centric from Bloomberg Intelligence, the podcast
that pulls back the curtain on global business so you
can invest better across the Asia Pacific rim. I'm John
Lee in Hong Kong, and I'm.
Speaker 2 (00:15):
Kaite Dmitrieva with Bloomberg News, also in Hong Kong. We're
looking at AI this week and specifically how it might
not be this big growth driver that markets and some
analysts seem to think it might be. On the high end,
McKenzie sees up to a twenty six trillion dollar boost
to the global economy. The top economist at Goldman Sachs
(00:38):
sees it adding nine percent to US productivity.
Speaker 1 (00:41):
Yes, but not everyone is this optimistic. According to Sequoia,
AI companies need to earn at least six hundred billion
in revenue to justify current AI infrastructure spend. Seems like
we're going nowhere near this figure, and our guest is
also critical.
Speaker 2 (00:59):
I'm excited about our guest this week, John, because he's
one of the big thinkers on AI, both in terms
of the social and economic impacts. So to walk us
through this brave new world is darron a Smoglu, Institute
Professor at MIT and author of books, including Why Nations Fail.
(01:19):
He's joining us from Cambridge, Massachusetts. Welcome Deren, Hi Katia,
Hi John.
Speaker 3 (01:24):
It's great pleasure to be with you to talk about AI.
Speaker 2 (01:27):
We're glad to have you. Darren. You found in a
recent paper that AI will impact something like less than
five percent of all tasks. And you pulled back and
you looked at the effects on the US economy, finding
that US productivity would rise by only about zero point
five percent and GDP growth by zero point nine percent
(01:50):
over the next decade. So it's a lot of numbers there,
but it's overall just quite a bit lower than estimates
from many of your colleagues and economists tell us about that.
Why do you think the impact will be less than
what people think?
Speaker 3 (02:04):
Yeah, I mean, I think the bottom line is exactly.
It's a bit, quite a bit lower than other numbers
that are being floated around, and I think it reflects
a deeper issue, which is that we are very much
at the beginning of the AI technological journey, wherever that
might be, wherever that might go, and there aren't many
(02:28):
applications that can be transformative. Yet if you look at
AI what it can do right now, it can provide
a little bit of better information to a few decision makers,
and it can perform a few tasks, but it does
not have the capability to do much in any task.
In any occupation that involves a central element of interacting
(02:52):
with the real world, such as those in construction or manufacturing,
or those that involve moving things around the real world,
it cannot at the moment have a big input into
things that are social in nature, for example, psychiatry, entertainment,
things that involve many individuals coming together and using their
(03:13):
judgment or team interactions. So once you exclude all of
these occupations, there are a bunch of white colored things
that people do in their offices that require better information,
that can benefit from better processing of language or data,
and those are the things that AI can at the
moment have a moderate, non trivial, but a moderate effect
(03:36):
in helping us do better. And that's at the root
of both my numbers and the fact that we don't
have the killer apps for AI.
Speaker 1 (03:43):
Yet, Darren. If you listen to a lot of people
on AI, they say it's going to be transformative for
most industries, but you seem to take a different view,
like what percentage of jobs do you think AI will
have an impact?
Speaker 3 (03:58):
Well, I think that's a very difficult question to answer, John,
because it depends on what horizon we're talking about, and
what types of investments and what type of direction of
AI we choose for the future. So when people talk
of absolutely transformative effects of AI, I think they are
mixing two things. One is the capabilities of AI as
(04:22):
we have it today, and the second is where AI
might go, for example, something close to superintelligence. It's a
hope or nightmare of some people. And of course, if
AI is so smart that it can do many many things,
and it can be integrated with robots, it can start
driving cars and airplanes, that is a very different story
(04:42):
than the kind of AI.
Speaker 1 (04:43):
We have today.
Speaker 3 (04:45):
The other difficulty in answering that question is that there
is sometimes a presumption that somehow there's a single direction
of AI, that we are just moving towards that direction,
and the only choice we have is whether it's going
to be China or the US would gets there and
how quickly we're going to get there. The truth couldn't
be farther from that. There are so many different things
(05:06):
we can use AI, like technologies. At the end of
the day, what we're calling AI today is machine learning
large capacity for effectively processing data. We can use that
for automation, we can use that for science, we can
use that for creating better information for a variety of
different occupations. So depending on which direction we take and
(05:28):
how quickly corporate investors are convinced to jump on the
AI bandwagon, its effects are going to be different than
it's effects that are going to be more or less pervasive.
Speaker 2 (05:40):
I mean, there's certainly investors who have jumped on the
AI bandwagon. I guess the question is have they done
it too soon? Yeah?
Speaker 3 (05:50):
Absolutely, I think, Katya, that's the key question. The reason
why I wrote the paper that you mentioned, Katya, is
because I think the AI hype is very counterproductive for
two reasons. First, if you look at the narrative in
the United States in the second half of twenty twenty
three and in the first half of twenty twenty four,
(06:12):
you will see that managers in many companies, not just
publicly traded companies, but in many companies are under tremendous
pressure to invest in AI ads. New journalists, their colleagues,
management consultants are continuously on their next saying what have
you done on AI, are you falling back behind your competitors,
(06:33):
And the outcome is inevitably people throwing money into AI
without knowing what they're going to use it for, nor
having the technology be ready for doing useful things for
most companies. The second is that the AI hype, fueled
by a few tech companies and a few tech journalists
is actually cementing the current direction of AI and the
(06:56):
current structure of the industry. Open AI wants trillions of
dollars of investment. Why because they believe they have the
right business model and they want to be the leader.
They don't want anybody else to catch up, and the
best way of doing that is actually exciting investors to
want to invest more in AI. But first of all,
(07:19):
it may well be that global society is going to
be better served by a different technology, more open source,
or a different approach to AI, or even if we're
going to adopt the same approach, we may want more competition.
We may want smaller startups to sort of be the
ones that grow. So those are all things that investors
attention and focus are going to influence majorly. But even
(07:42):
more importantly from the point of view of my research,
is the direction of AI. I think that the current
emphasis on automation and using AI for manipulating users on
social media or better ads and better sort of ways
of capturing consumers is not the most socially active one.
So if we need to change the direction of AI
(08:03):
in a more socially productive direction, we may again need
to go a little bit slower and more contemplatively about
what is it that we want to do. What are
the places where AI can have the biggest social impacts?
And again the hype doesn't help that.
Speaker 2 (08:16):
Can we pull back? I'm curious, you know, we're talking
about what investors expect from AI and how much companies
are investing in it. What are we talking about? Is
it just automation? What is AI in business today? And
it seems like a simple question, Oh.
Speaker 3 (08:32):
My amazing question, because it's very difficult to get a
straight answer to that. If you talk to some business leaders,
especially in private, they'll say, of course, we're going to
automate everything. That's what we want to do, and that's
what AI is for Others, Sometimes the same people more
publicly say no, no, we don't want to automate. We
want to use AI for other things. If you look
(08:54):
at Microsoft, which is the big partner to open Ai,
they brand everything Core Pilot, and I think it's a
genuine effort for them to try to find a way
to use AI to help managers or to help decision makers.
But on the other hand, if you look at what
open AI's leaders say, they say, we're going to automate
(09:16):
to stuff. So you know, it's a real confusion, and
I think that confusion reflects exactly the same forces that
we've been talking about Katya, which is that a we
don't know where the future lies. There are many directions,
the technology is malleable, we can make different choices, and
right now we are very much at the beginning, so
we can dream. I personally think, for example, that all
(09:37):
of this talk of superintelligence and you know, AI doing
everything that humans do better than humans within twenty years, etc.
It's all misleading, but there's no way we can know
whether it is or not, because we're talking about twenty
years in a very fast changing field, and people can dream.
Speaker 1 (09:55):
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(10:16):
like what you hear, don't forget to subscribe and chairm Yeah, Darren,
there's a lot of people like an ai to building railroads.
There's a view that, like nineteenth century America, if you
build the railroads, the trains will come. Sounds like you
don't buy into that.
Speaker 3 (10:33):
Well, look, that's a very interesting debate. John perfect analogy
because I actually personally think railways were critical for US development,
and they were pretty pretty important for British development. But
you know, some of the most respectable economic historians who
work on this topic, for example, Robert Fogel, who want
(10:54):
the Nobel Prize for his work on railways, made his
reputation by arguing exactly the opposite. He said, Look, we
had other ways of shipping goods between places and transporting
other stuff and moving people around, such as canals, and
if you look at railways, that improve things. But the
marginal improvement wasn't all that big. So he argued that
(11:16):
railways weren't such a big deal. Now, I think that
railways really did change inputs into manufacturing and change the
nature of technology. So I'm not agreeing with Fogel, but
I'm just pointing out that even on something so important
that happened one hundred and fifty years ago, there is disagreement.
(11:38):
So of course there's going to be some disagreement on
AI as to whether it's going to be completely transformative
as a hub that changes everything, or it's going to
enable us to do a few things that we were
doing a little better look at search. Of course, I
think AI can help search. You said just the ideal
kind of thing, which is, you know, it's an information
processing type of technology. So when I typed something into
(12:01):
my browser, I can now get help from AI. But
is that going to be transformative? If I get a
little bit better suggestions about where to go for on vacation,
or whether I get pointed to the right journal article,
is that really going to change the nature of truism?
Is that really going to change the nature of science?
So those are the questions that I think we need
(12:23):
to grapple with.
Speaker 2 (12:25):
And what would you call transformative change? Like you sell
you some examples where you know, having this this app
maybe suggest a different kind of cover letter or you know,
suggests a different The different introduction to a paper is
one thing, but transformative changes something else transformative.
Speaker 3 (12:44):
So let me give you two examples from my vantage
point of transformative technology that I think most people will
be familiar with, and then I'll tell you what my
direction for AI that will be transformative. I think one
transformative technology was Henry Ford's car factories, because they completely
reorganized manufacturing and made it possible to first of all,
(13:08):
use electric power in a much more efficient way, at
the same time automating work and introducing very new range
of new skills and new tasks. And that really spearheaded
transformative growth in the auto industry and also in other
manufacturing industries that copied it. Another one is the Internet.
Sure there was a hypen, there was a boom for
the Internet, but if you look at the Internet, it
(13:28):
was a rather new way of bringing people together. It
was at the same time an amazing new technology for
sharing information. But it also enabled companies to often completely
new services, create new platforms, So that I think, in
my mind is transformative. So in the same way, I
think AI as an information technology can be transformative if
(13:52):
it does two things. One is just like the Internet.
It helps us create new platforms to bring people together,
to exchange formation, to exchange labor, to find better ways
of actualizing their potential. Second, it actually helps us deal
with fundamental shortages in the economy. So today we have
(14:14):
a great shortage of skilled crafts people, electricians, plumbers, better
manufacturing workers, more skilled teachers. I think these are all
things that can be helped with AI. Because why Because
AI can act as a tool that provides information to
people that's relevant, real time, context specific. So you can
(14:34):
be a better electrician with AI helping you. You can
be a better teacher with AI helping you. And the
critical thing here is that if you really want to
realize it's not automation. You're not replacing the electrician or
the teacher, but you're trying to enable them to do
better in their task and to enable them to perform new,
more sophisticated tasks. I think those are the paths for AI.
And you see something in common between these two directions,
(14:57):
I pointed out they're both about amplifying human capabilities, not
sidelining humans, but augmenting humans, creating better tasks, new things
for humans to do.
Speaker 2 (15:07):
Do you use AI?
Speaker 3 (15:09):
I used it. Yeah, I used early on chat Gipt,
and I would be lying if I said the first
time I used chat Gipt if I wasn't pretty impressed
by the way that it converses with you. But soon
I discovered that I could do most things that chat
Gipt three point five or chat Gypt four was capable
(15:30):
of in other ways that I felt more comfortable, like,
for example, if I went to sources, I could check
these things myself faster rather than you know, converse with AI,
get some suggestions, and then follow the leads. So at
the end of the day, I am not currently directly
using AI, but I'm of course aware that in some
(15:50):
other platforms that I use, AI is in the background.
So I use Google for search, and I use an
Apple phone and there is some AI in the background.
Speaker 1 (16:00):
They're an outside of chatjpt. There seems to be a
dath of any killer AI apps. Have you seen anything
getting excited over and how much patience do you think
investors will have finding the new killer app for AI?
Speaker 3 (16:17):
No, I have not seen anything that I would say
it's a great app, And I don't actually think that
chat Gipt itself isn't that great. Again, I think the
capability is there. If it was used differently, the architecture
of AI and the vast amount of processing power and
data that it has could be more useful. But I
don't find it so impressive. If you know, chat gipt
(16:40):
can write a shakespeareance on it, or can sideline teachers
and pretend that you know, all the students need to
do is actually go to chat gipt and ask questions.
Those are not to me killer apps. So I think
that's the next big task, next big challenge for the industry.
Let's find something that's both useful for businesses and actually
(17:02):
socially beneficial. That would be such a good challenge for
the industry.
Speaker 2 (17:06):
Yeah, the social benefit of it's sort of the idea
that I want AI to do my dishes and fold
my laundry and not take my job away.
Speaker 3 (17:14):
That would be great too.
Speaker 2 (17:15):
Yes, I wonder if we could move outside of the US.
I know you focused on the US and your paper,
but you do have a global outlook, and I wonder
if the impact of AI might be felt differently in
different countries, depending on where they're starting from and the
penetration of technology. What are your thoughts?
Speaker 3 (17:38):
Absolutely, I think there are a couple of issues there
we have to watch out for. The First one is
that what happens in the US and other leading AI
powers is going to influence what happens in the rest
of the world. The rest of the world is not
ready for AI. They're politicians, their thought leaders are not
focusing on AI, so by and large, AI is going
to be something that's done to them rather than something
(18:01):
in which they have agency. Still, it can turn out
to be good for them because if it goes in
a direction that really makes you know, a worker's more productive,
then it will spread to say India and Indonesia, and
it's going to make workers productive there. But I think
if it goes in an automation direction, things are reversed.
(18:23):
Many developing countries critically depend on their human resources semi skilled, cheap, active,
flexible labor. If AI starts replacing things that this word
labor does, it's going to have, you know, disruptive effects.
If AI changes the global division of labor is going
to have disruptive effects. And moreover, many new technologies create
(18:48):
a pattern of winner takes all, meaning that some countries
that are first may leave the rest behind. And that's
another concern and then the final wildcard, well, which is
we're already seeing it realize in the developing world, is
that AI is also a phenomenal technology for surveillance. You know,
a lot of the AI energy in China is going
(19:09):
to surveillance activities. That's been very well documented. And also
many of the surveillance related technologies, ranging from facial recognition,
things that can sweep the Internet, et cetera, are just
are not staying just in China. They're being exported to
dozens of countries around the world, and most of them authoritarian,
and they're using these technologies with great enthusiasm. And it's
(19:33):
not just authoritarian countries. I mean, you know, we can
debate where US should be on the spectrum, but US
invests a lot in AI related to national security apparatus,
and you know, privacy from the government has become less
secure in the United States and in some other Western
countries as well. So there are going to be other
(19:54):
politically first order implications of AI as well, and those
might matter more for the developing world.
Speaker 1 (20:01):
Okay, but it sounds like a reading between the lines,
sounds like developed, rich countries would have a bigger benefit
from AI than developing countries.
Speaker 3 (20:10):
I think right now that would be my guess. But
if I had one recommendation to developing country leaders, I
would say, this is the right time to form a
new consortium, you know, especially with leading countries such as India, Indonesia, Brazil, Turkey,
Mexico playing a leading role which is about AI, and
(20:32):
there needs to be a perspective from the developing world
and a voice from developing world, because you know, we
hear from businesses in the US, we hear from increasingly now,
which is a good thing, from some worker groups in
the US or in the Western world, but we don't
hear the perspective, the interests, the sort of the things
that the developing emerging world once and is concerned about AI.
Speaker 2 (20:56):
I wonder if if we see that happening yet, is
that happening?
Speaker 3 (21:01):
No? No, I mean, if you go to many developing countries,
there is interest within the public and some businesses, but
there's no policy. Policy makers are not focused on that.
And unfortunately, you know, we live in a polarized world
that's divided, not just within countries. It's not just within
the US or within France that there are big divisions.
(21:22):
There are big divisions between countries as well, and so
it's becoming increasingly difficult for different country leaders to work
together with each other.
Speaker 2 (21:32):
Yeah, there's you mentioned the US in China earlier. It
seems like there's this new I mean, not a new analogy,
but you know, the space race, right except take it
to AI between China and the US. Is there a
sense that you know, given the minimal economic impact that
you've computed so far, I mean, is there a risk
(21:53):
that they're spending on the wrong things.
Speaker 3 (21:56):
Well, they're spending on the wrong things, and it's maybe
actually fueling the wrong narry. You know, two arguments that
are very common in the US are First, we cannot
regulate AI because if we do, China is going to
take the leadership. And two, we have to be tougher
on China otherwise they will become the AI leader, and
we want to be the AI leader. And whoever becomes
(22:17):
the AI leader controls the world. Now, what you see
in both of these narratives is not just you know,
hyping up AI, but also deepening the tensions between US
and China, which is the last thing we want, Which
is the last thing we want in a world without AI.
And if we're gonna have AI, we want it even
less because why because AI is a global technology, So
(22:38):
if you're gonna regulate it, you need US and China
to work together.
Speaker 2 (22:43):
So if you're an investor right now, like we've been
talking about some pretty big themes, if you're an investor
and you are putting money into a company on a
bet on an AI bet, are there risks to that?
Should they be doing that?
Speaker 3 (23:00):
Investor? And of course there are risks, and it's a
very difficult thing to do because even imagine everything I
said here is right, and it's even more severe than
I am saying. But as long as investors keep on
investing in AI and Vidia, stocks are going to do
well for the next year. So I think for many
of the AI companies are AI related companies. What matters
(23:23):
is really the market's focused for the next two, three,
four years, And that's really really difficult to understand. As
John Maynard Kinges said, the stock market is like a
beauty contest. It's not what the true value of the
stocks are that's important, but what other people think the
value of the stocks, the values of the different stocks are,
(23:45):
So that's the same thing. So if other people think
in Nvidia is very valuable. If other people think that
in Vidia chips are going to be a high demand,
that's all that matters, not whether and Nvidia chips are
going to revolutionize the world or not.
Speaker 1 (23:58):
And what would it take for you to change your
mind and get more bullish on AI?
Speaker 3 (24:05):
Great question. I think if I saw AI really capably
perform more tasks than I am envisaging, if I see
you know, AI write articles or do news programs as
good as you guys, or if I see AI teach
(24:28):
students in a way that can form the same social bond,
and then those teachers the students do mentally well, they
perform well in tests and have reasonable sort of growth retention.
You know that is going to be a real shock
to me.
Speaker 2 (24:45):
So, Ron, are you working on any new projects?
Speaker 3 (24:49):
Yes, I am working on a new book which I
think also AI and all of this data based economy
raised questions about what is our relationship to technology as humans?
How does it change what we want to value and
what we want to do as humans, which is both
an economic and philosophical question, so I'm trying to explore
(25:10):
which sounds very interesting.
Speaker 1 (25:13):
It's been an intriguing discussion on generative AI and whether
they can meet very high expectations. It's been a great conversation.
Thanks Darren for coming on the show.
Speaker 3 (25:24):
Well, thank you for having me on your program, Katia
and John and it's been a true pleasure.
Speaker 2 (25:28):
I'm John Lee in Hong Kong, and I'm Katy Dmitrieva,
also in Hong Kong.
Speaker 1 (25:33):
This podcast was produced by Clara Chen and you've been
listening to the Asia Centric podcast
You're listening to Asia Centric from Bloomberg Intelligence, the podcast
that pulls back the curtain on global business so you
can invest better across the Asia Pacific rim. I'm John
Lee in Hong Kong, and I'm.
Speaker 2 (00:15):
Kaite Dmitrieva with Bloomberg News, also in Hong Kong. We're
looking at AI this week and specifically how it might
not be this big growth driver that markets and some
analysts seem to think it might be. On the high end,
McKenzie sees up to a twenty six trillion dollar boost
to the global economy. The top economist at Goldman Sachs
(00:38):
sees it adding nine percent to US productivity.
Speaker 1 (00:41):
Yes, but not everyone is this optimistic. According to Sequoia,
AI companies need to earn at least six hundred billion
in revenue to justify current AI infrastructure spend. Seems like
we're going nowhere near this figure, and our guest is
also critical.
Speaker 2 (00:59):
I'm excited about our guest this week, John, because he's
one of the big thinkers on AI, both in terms
of the social and economic impacts. So to walk us
through this brave new world is darron a Smoglu, Institute
Professor at MIT and author of books, including Why Nations Fail.
(01:19):
He's joining us from Cambridge, Massachusetts. Welcome Deren, Hi Katia,
Hi John.
Speaker 3 (01:24):
It's great pleasure to be with you to talk about AI.
Speaker 2 (01:27):
We're glad to have you. Darren. You found in a
recent paper that AI will impact something like less than
five percent of all tasks. And you pulled back and
you looked at the effects on the US economy, finding
that US productivity would rise by only about zero point
five percent and GDP growth by zero point nine percent
(01:50):
over the next decade. So it's a lot of numbers there,
but it's overall just quite a bit lower than estimates
from many of your colleagues and economists tell us about that.
Why do you think the impact will be less than
what people think?
Speaker 3 (02:04):
Yeah, I mean, I think the bottom line is exactly.
It's a bit, quite a bit lower than other numbers
that are being floated around, and I think it reflects
a deeper issue, which is that we are very much
at the beginning of the AI technological journey, wherever that
might be, wherever that might go, and there aren't many
(02:28):
applications that can be transformative. Yet if you look at
AI what it can do right now, it can provide
a little bit of better information to a few decision makers,
and it can perform a few tasks, but it does
not have the capability to do much in any task.
In any occupation that involves a central element of interacting
(02:52):
with the real world, such as those in construction or manufacturing,
or those that involve moving things around the real world,
it cannot at the moment have a big input into
things that are social in nature, for example, psychiatry, entertainment,
things that involve many individuals coming together and using their
(03:13):
judgment or team interactions. So once you exclude all of
these occupations, there are a bunch of white colored things
that people do in their offices that require better information,
that can benefit from better processing of language or data,
and those are the things that AI can at the
moment have a moderate, non trivial, but a moderate effect
(03:36):
in helping us do better. And that's at the root
of both my numbers and the fact that we don't
have the killer apps for AI.
Speaker 1 (03:43):
Yet, Darren. If you listen to a lot of people
on AI, they say it's going to be transformative for
most industries, but you seem to take a different view,
like what percentage of jobs do you think AI will
have an impact?
Speaker 3 (03:58):
Well, I think that's a very difficult question to answer, John,
because it depends on what horizon we're talking about, and
what types of investments and what type of direction of
AI we choose for the future. So when people talk
of absolutely transformative effects of AI, I think they are
mixing two things. One is the capabilities of AI as
(04:22):
we have it today, and the second is where AI
might go, for example, something close to superintelligence. It's a
hope or nightmare of some people. And of course, if
AI is so smart that it can do many many things,
and it can be integrated with robots, it can start
driving cars and airplanes, that is a very different story
(04:42):
than the kind of AI.
Speaker 1 (04:43):
We have today.
Speaker 3 (04:45):
The other difficulty in answering that question is that there
is sometimes a presumption that somehow there's a single direction
of AI, that we are just moving towards that direction,
and the only choice we have is whether it's going
to be China or the US would gets there and
how quickly we're going to get there. The truth couldn't
be farther from that. There are so many different things
(05:06):
we can use AI, like technologies. At the end of
the day, what we're calling AI today is machine learning
large capacity for effectively processing data. We can use that
for automation, we can use that for science, we can
use that for creating better information for a variety of
different occupations. So depending on which direction we take and
(05:28):
how quickly corporate investors are convinced to jump on the
AI bandwagon, its effects are going to be different than
it's effects that are going to be more or less pervasive.
Speaker 2 (05:40):
I mean, there's certainly investors who have jumped on the
AI bandwagon. I guess the question is have they done
it too soon? Yeah?
Speaker 3 (05:50):
Absolutely, I think, Katya, that's the key question. The reason
why I wrote the paper that you mentioned, Katya, is
because I think the AI hype is very counterproductive for
two reasons. First, if you look at the narrative in
the United States in the second half of twenty twenty
three and in the first half of twenty twenty four,
(06:12):
you will see that managers in many companies, not just
publicly traded companies, but in many companies are under tremendous
pressure to invest in AI ads. New journalists, their colleagues,
management consultants are continuously on their next saying what have
you done on AI, are you falling back behind your competitors,
(06:33):
And the outcome is inevitably people throwing money into AI
without knowing what they're going to use it for, nor
having the technology be ready for doing useful things for
most companies. The second is that the AI hype, fueled
by a few tech companies and a few tech journalists
is actually cementing the current direction of AI and the
(06:56):
current structure of the industry. Open AI wants trillions of
dollars of investment. Why because they believe they have the
right business model and they want to be the leader.
They don't want anybody else to catch up, and the
best way of doing that is actually exciting investors to
want to invest more in AI. But first of all,
(07:19):
it may well be that global society is going to
be better served by a different technology, more open source,
or a different approach to AI, or even if we're
going to adopt the same approach, we may want more competition.
We may want smaller startups to sort of be the
ones that grow. So those are all things that investors
attention and focus are going to influence majorly. But even
(07:42):
more importantly from the point of view of my research,
is the direction of AI. I think that the current
emphasis on automation and using AI for manipulating users on
social media or better ads and better sort of ways
of capturing consumers is not the most socially active one.
So if we need to change the direction of AI
(08:03):
in a more socially productive direction, we may again need
to go a little bit slower and more contemplatively about
what is it that we want to do. What are
the places where AI can have the biggest social impacts?
And again the hype doesn't help that.
Speaker 2 (08:16):
Can we pull back? I'm curious, you know, we're talking
about what investors expect from AI and how much companies
are investing in it. What are we talking about? Is
it just automation? What is AI in business today? And
it seems like a simple question, Oh.
Speaker 3 (08:32):
My amazing question, because it's very difficult to get a
straight answer to that. If you talk to some business leaders,
especially in private, they'll say, of course, we're going to
automate everything. That's what we want to do, and that's
what AI is for Others, Sometimes the same people more
publicly say no, no, we don't want to automate. We
want to use AI for other things. If you look
(08:54):
at Microsoft, which is the big partner to open Ai,
they brand everything Core Pilot, and I think it's a
genuine effort for them to try to find a way
to use AI to help managers or to help decision makers.
But on the other hand, if you look at what
open AI's leaders say, they say, we're going to automate
(09:16):
to stuff. So you know, it's a real confusion, and
I think that confusion reflects exactly the same forces that
we've been talking about Katya, which is that a we
don't know where the future lies. There are many directions,
the technology is malleable, we can make different choices, and
right now we are very much at the beginning, so
we can dream. I personally think, for example, that all
(09:37):
of this talk of superintelligence and you know, AI doing
everything that humans do better than humans within twenty years, etc.
It's all misleading, but there's no way we can know
whether it is or not, because we're talking about twenty
years in a very fast changing field, and people can dream.
Speaker 1 (09:55):
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(10:16):
like what you hear, don't forget to subscribe and chairm Yeah, Darren,
there's a lot of people like an ai to building railroads.
There's a view that, like nineteenth century America, if you
build the railroads, the trains will come. Sounds like you
don't buy into that.
Speaker 3 (10:33):
Well, look, that's a very interesting debate. John perfect analogy
because I actually personally think railways were critical for US development,
and they were pretty pretty important for British development. But
you know, some of the most respectable economic historians who
work on this topic, for example, Robert Fogel, who want
(10:54):
the Nobel Prize for his work on railways, made his
reputation by arguing exactly the opposite. He said, Look, we
had other ways of shipping goods between places and transporting
other stuff and moving people around, such as canals, and
if you look at railways, that improve things. But the
marginal improvement wasn't all that big. So he argued that
(11:16):
railways weren't such a big deal. Now, I think that
railways really did change inputs into manufacturing and change the
nature of technology. So I'm not agreeing with Fogel, but
I'm just pointing out that even on something so important
that happened one hundred and fifty years ago, there is disagreement.
(11:38):
So of course there's going to be some disagreement on
AI as to whether it's going to be completely transformative
as a hub that changes everything, or it's going to
enable us to do a few things that we were
doing a little better look at search. Of course, I
think AI can help search. You said just the ideal
kind of thing, which is, you know, it's an information
processing type of technology. So when I typed something into
(12:01):
my browser, I can now get help from AI. But
is that going to be transformative? If I get a
little bit better suggestions about where to go for on vacation,
or whether I get pointed to the right journal article,
is that really going to change the nature of truism?
Is that really going to change the nature of science?
So those are the questions that I think we need
(12:23):
to grapple with.
Speaker 2 (12:25):
And what would you call transformative change? Like you sell
you some examples where you know, having this this app
maybe suggest a different kind of cover letter or you know,
suggests a different The different introduction to a paper is
one thing, but transformative changes something else transformative.
Speaker 3 (12:44):
So let me give you two examples from my vantage
point of transformative technology that I think most people will
be familiar with, and then I'll tell you what my
direction for AI that will be transformative. I think one
transformative technology was Henry Ford's car factories, because they completely
reorganized manufacturing and made it possible to first of all,
(13:08):
use electric power in a much more efficient way, at
the same time automating work and introducing very new range
of new skills and new tasks. And that really spearheaded
transformative growth in the auto industry and also in other
manufacturing industries that copied it. Another one is the Internet.
Sure there was a hypen, there was a boom for
the Internet, but if you look at the Internet, it
(13:28):
was a rather new way of bringing people together. It
was at the same time an amazing new technology for
sharing information. But it also enabled companies to often completely
new services, create new platforms, So that I think, in
my mind is transformative. So in the same way, I
think AI as an information technology can be transformative if
(13:52):
it does two things. One is just like the Internet.
It helps us create new platforms to bring people together,
to exchange formation, to exchange labor, to find better ways
of actualizing their potential. Second, it actually helps us deal
with fundamental shortages in the economy. So today we have
(14:14):
a great shortage of skilled crafts people, electricians, plumbers, better
manufacturing workers, more skilled teachers. I think these are all
things that can be helped with AI. Because why Because
AI can act as a tool that provides information to
people that's relevant, real time, context specific. So you can
(14:34):
be a better electrician with AI helping you. You can
be a better teacher with AI helping you. And the
critical thing here is that if you really want to
realize it's not automation. You're not replacing the electrician or
the teacher, but you're trying to enable them to do
better in their task and to enable them to perform new,
more sophisticated tasks. I think those are the paths for AI.
And you see something in common between these two directions,
(14:57):
I pointed out they're both about amplifying human capabilities, not
sidelining humans, but augmenting humans, creating better tasks, new things
for humans to do.
Speaker 2 (15:07):
Do you use AI?
Speaker 3 (15:09):
I used it. Yeah, I used early on chat Gipt,
and I would be lying if I said the first
time I used chat Gipt if I wasn't pretty impressed
by the way that it converses with you. But soon
I discovered that I could do most things that chat
Gipt three point five or chat Gypt four was capable
(15:30):
of in other ways that I felt more comfortable, like,
for example, if I went to sources, I could check
these things myself faster rather than you know, converse with AI,
get some suggestions, and then follow the leads. So at
the end of the day, I am not currently directly
using AI, but I'm of course aware that in some
(15:50):
other platforms that I use, AI is in the background.
So I use Google for search, and I use an
Apple phone and there is some AI in the background.
Speaker 1 (16:00):
They're an outside of chatjpt. There seems to be a
dath of any killer AI apps. Have you seen anything
getting excited over and how much patience do you think
investors will have finding the new killer app for AI?
Speaker 3 (16:17):
No, I have not seen anything that I would say
it's a great app, And I don't actually think that
chat Gipt itself isn't that great. Again, I think the
capability is there. If it was used differently, the architecture
of AI and the vast amount of processing power and
data that it has could be more useful. But I
don't find it so impressive. If you know, chat gipt
(16:40):
can write a shakespeareance on it, or can sideline teachers
and pretend that you know, all the students need to
do is actually go to chat gipt and ask questions.
Those are not to me killer apps. So I think
that's the next big task, next big challenge for the industry.
Let's find something that's both useful for businesses and actually
(17:02):
socially beneficial. That would be such a good challenge for
the industry.
Speaker 2 (17:06):
Yeah, the social benefit of it's sort of the idea
that I want AI to do my dishes and fold
my laundry and not take my job away.
Speaker 3 (17:14):
That would be great too.
Speaker 2 (17:15):
Yes, I wonder if we could move outside of the US.
I know you focused on the US and your paper,
but you do have a global outlook, and I wonder
if the impact of AI might be felt differently in
different countries, depending on where they're starting from and the
penetration of technology. What are your thoughts?
Speaker 3 (17:38):
Absolutely, I think there are a couple of issues there
we have to watch out for. The First one is
that what happens in the US and other leading AI
powers is going to influence what happens in the rest
of the world. The rest of the world is not
ready for AI. They're politicians, their thought leaders are not
focusing on AI, so by and large, AI is going
to be something that's done to them rather than something
(18:01):
in which they have agency. Still, it can turn out
to be good for them because if it goes in
a direction that really makes you know, a worker's more productive,
then it will spread to say India and Indonesia, and
it's going to make workers productive there. But I think
if it goes in an automation direction, things are reversed.
(18:23):
Many developing countries critically depend on their human resources semi skilled, cheap, active,
flexible labor. If AI starts replacing things that this word
labor does, it's going to have, you know, disruptive effects.
If AI changes the global division of labor is going
to have disruptive effects. And moreover, many new technologies create
(18:48):
a pattern of winner takes all, meaning that some countries
that are first may leave the rest behind. And that's
another concern and then the final wildcard, well, which is
we're already seeing it realize in the developing world, is
that AI is also a phenomenal technology for surveillance. You know,
a lot of the AI energy in China is going
(19:09):
to surveillance activities. That's been very well documented. And also
many of the surveillance related technologies, ranging from facial recognition,
things that can sweep the Internet, et cetera, are just
are not staying just in China. They're being exported to
dozens of countries around the world, and most of them authoritarian,
and they're using these technologies with great enthusiasm. And it's
(19:33):
not just authoritarian countries. I mean, you know, we can
debate where US should be on the spectrum, but US
invests a lot in AI related to national security apparatus,
and you know, privacy from the government has become less
secure in the United States and in some other Western
countries as well. So there are going to be other
(19:54):
politically first order implications of AI as well, and those
might matter more for the developing world.
Speaker 1 (20:01):
Okay, but it sounds like a reading between the lines,
sounds like developed, rich countries would have a bigger benefit
from AI than developing countries.
Speaker 3 (20:10):
I think right now that would be my guess. But
if I had one recommendation to developing country leaders, I
would say, this is the right time to form a
new consortium, you know, especially with leading countries such as India, Indonesia, Brazil, Turkey,
Mexico playing a leading role which is about AI, and
(20:32):
there needs to be a perspective from the developing world
and a voice from developing world, because you know, we
hear from businesses in the US, we hear from increasingly now,
which is a good thing, from some worker groups in
the US or in the Western world, but we don't
hear the perspective, the interests, the sort of the things
that the developing emerging world once and is concerned about AI.
Speaker 2 (20:56):
I wonder if if we see that happening yet, is
that happening?
Speaker 3 (21:01):
No? No, I mean, if you go to many developing countries,
there is interest within the public and some businesses, but
there's no policy. Policy makers are not focused on that.
And unfortunately, you know, we live in a polarized world
that's divided, not just within countries. It's not just within
the US or within France that there are big divisions.
(21:22):
There are big divisions between countries as well, and so
it's becoming increasingly difficult for different country leaders to work
together with each other.
Speaker 2 (21:32):
Yeah, there's you mentioned the US in China earlier. It
seems like there's this new I mean, not a new analogy,
but you know, the space race, right except take it
to AI between China and the US. Is there a
sense that you know, given the minimal economic impact that
you've computed so far, I mean, is there a risk
(21:53):
that they're spending on the wrong things.
Speaker 3 (21:56):
Well, they're spending on the wrong things, and it's maybe
actually fueling the wrong narry. You know, two arguments that
are very common in the US are First, we cannot
regulate AI because if we do, China is going to
take the leadership. And two, we have to be tougher
on China otherwise they will become the AI leader, and
we want to be the AI leader. And whoever becomes
(22:17):
the AI leader controls the world. Now, what you see
in both of these narratives is not just you know,
hyping up AI, but also deepening the tensions between US
and China, which is the last thing we want, Which
is the last thing we want in a world without AI.
And if we're gonna have AI, we want it even
less because why because AI is a global technology, So
(22:38):
if you're gonna regulate it, you need US and China
to work together.
Speaker 2 (22:43):
So if you're an investor right now, like we've been
talking about some pretty big themes, if you're an investor
and you are putting money into a company on a
bet on an AI bet, are there risks to that?
Should they be doing that?
Speaker 3 (23:00):
Investor? And of course there are risks, and it's a
very difficult thing to do because even imagine everything I
said here is right, and it's even more severe than
I am saying. But as long as investors keep on
investing in AI and Vidia, stocks are going to do
well for the next year. So I think for many
of the AI companies are AI related companies. What matters
(23:23):
is really the market's focused for the next two, three,
four years, And that's really really difficult to understand. As
John Maynard Kinges said, the stock market is like a
beauty contest. It's not what the true value of the
stocks are that's important, but what other people think the
value of the stocks, the values of the different stocks are,
(23:45):
So that's the same thing. So if other people think
in Nvidia is very valuable. If other people think that
in Vidia chips are going to be a high demand,
that's all that matters, not whether and Nvidia chips are
going to revolutionize the world or not.
Speaker 1 (23:58):
And what would it take for you to change your
mind and get more bullish on AI?
Speaker 3 (24:05):
Great question. I think if I saw AI really capably
perform more tasks than I am envisaging, if I see
you know, AI write articles or do news programs as
good as you guys, or if I see AI teach
(24:28):
students in a way that can form the same social bond,
and then those teachers the students do mentally well, they
perform well in tests and have reasonable sort of growth retention.
You know that is going to be a real shock
to me.
Speaker 2 (24:45):
So, Ron, are you working on any new projects?
Speaker 3 (24:49):
Yes, I am working on a new book which I
think also AI and all of this data based economy
raised questions about what is our relationship to technology as humans?
How does it change what we want to value and
what we want to do as humans, which is both
an economic and philosophical question, so I'm trying to explore
(25:10):
which sounds very interesting.
Speaker 1 (25:13):
It's been an intriguing discussion on generative AI and whether
they can meet very high expectations. It's been a great conversation.
Thanks Darren for coming on the show.
Speaker 3 (25:24):
Well, thank you for having me on your program, Katia
and John and it's been a true pleasure.
Speaker 2 (25:28):
I'm John Lee in Hong Kong, and I'm Katy Dmitrieva,
also in Hong Kong.
Speaker 1 (25:33):
This podcast was produced by Clara Chen and you've been
listening to the Asia Centric podcast
Hosts And Creators
John Lee
Tom Corbett
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