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