Nobel Laureate Busts the AI Hype

AI is poised to transform everything. Or

is it? From agentic AI to instant cures the hype around AI can be deafening. But

what's the real economic impact stripped of the speculation? Today, we cut through the noise with MIT economist and Nobel laurate Darun Asimogu, whose datadriven research reveals a surprising

reality. Forget overnight

reality. Forget overnight transformation. Asimoglu's research

transformation. Asimoglu's research projects that AI will automate just 5% of all tasks and add just 1% to global GDP this decade. So why the massive

disconnect and what should smart business leaders be doing with AI right now? I recently interviewed Asamogu and

now? I recently interviewed Asamogu and asked him these questions and [Music] more. Thank you so much for being here

more. Thank you so much for being here with us today. Uh I have a few questions for you about generative AI and AI in general and its impacts on the economy.

So Chhat GPT came out in November 2022 and since then we've seen generative AI go through a lot of developments. It has

observers I think excited and a little bit worried about what it means for their jobs and uh for the economy in general. Last April you published a

general. Last April you published a paper called the simple macroeconomics of AI in which you estimate that uh over the next 10 years only about 5% of all

tasks will be profitably automated by uh this technology and that it's only likely to contribute about 1% to global GDP. That's a stark contrast to what

GDP. That's a stark contrast to what some other analysts have said. Uh you

know people have been predicting that this will be uh a truly transformative technology. uh to the labor force and to

technology. uh to the labor force and to the economy in general. Can you explain why your estimates are different from these others? And and since you

these others? And and since you published that paper last year, have you seen anything that either confirms or makes you question those estimates you made? Well, thank you Koshik. Well

made? Well, thank you Koshik. Well

look, uh I said one other thing in that paper is hugely uncertain and these are just guesses. I think it's very

just guesses. I think it's very difficult to know because it's a very rapidly changing technology and uh over the last year we have seen even more advances. So we don't know where we're

advances. So we don't know where we're going but the basis of my prediction uncertain though it may be still

remains the industry has not produced applications that are critical for the production process or for

generating new goods and services that are going to be hugely valuable. So if

you compare AI to the internet, I think from the very early days of the internet, even when there was hype and a boom, it was clear how the internet was

going to change everything. The way that we communicate has been completely transformed by the internet. It was very clear at the time. It was also very clear that the internet would introduce

a lot of new goods and services and provide platforms for people to come together in various ways for production for recreation and other things. I think

those things are not clear yet for AI. Of course, if you're a believer that AGI is just around the

corner, you think somehow in the next few years, somehow we're going to get such amazing machines that they can start performing all the cognitive

tasks. But even that scenario is not so

tasks. But even that scenario is not so clear. You know, how you going to

clear. You know, how you going to actually get AI tools into the production process? And I think the current

process? And I think the current approach is well targeted for dealing with cognitive

tasks that are performed in predictable environments in offices and don't require much social interaction and very high levels of judgment. So if you are a

uh uh software engineer that does some very basic routines for your work or you're in IT security or in you're in accounting, those are things that I

think there will be applications based on AGI and some other AI tools that will be able to perform these tasks. If you're a CEO, if you're

these tasks. If you're a CEO, if you're a CFO, if you're an entertainer, if you're a professor, if you are a construction worker or a custodial

worker or a blue color worker, I think those things are beyond what AI can perform or AI can indirectly contribute

to by being bundled with flexible robotics because we're not there in terms of those technologies. So when you do that calculation you end up with about 20% or

so of the economy that is either at the crosshairs of AI to be automated or could be majorly boosted by AI input things that are feasible they take takes

a long time many of them are performed in small companies it's not going to be profitable to do them so that's how I arrived to the 5% number based on these inputs and a lot of detailed uh material

but it may may turn out to be wrong.

Last year, I wouldn't have expected to see the kinds of leaps and bounds. Yeah.

I mean, the leaps and bounds are really uh inspiring at some level. So, I I I'm I'm pretty impressed by those. The

question is with these leaps and bounds.

Do you still think that in 2, three, four years time you can have an AGI with no human

supervision that can do you know your all of your accounting or all of your marketing? And I think that is a much

marketing? And I think that is a much higher bar. Why? First of all, because

higher bar. Why? First of all, because every single occupation has so many complex tacit knowledge parts and requires a lot of checking and a lot of

different types of intelligence being applied to it. And does that tie into the distinction you make in the paper between uh what you call easy to learn and hard to learn tasks? And uh should

that distinction inform how executives uh study or decide what business processes are most amendable to

automation? Look at the domains in which

automation? Look at the domains in which we have truly inspiring achievements

from AI such as alpha go, alpha fold or you know answering some complex but knowledge based questions. Those are all domains in

questions. Those are all domains in which there is a ground truth that everybody can agree on. You know, you either e either fold the protein or you do not. AI is capable. There's no doubt

do not. AI is capable. There's no doubt about that. That's why we're talking

about that. That's why we're talking about AI and it is capable of learning that knowledge if it's in his training data set. So once you provide AI with

data set. So once you provide AI with the right powerful algorithm for example reinforcement learning was very important for the alpha series maybe other things for generative AI and the

ground truth is there AI is going to get there but no task that we perform in reality is just recounting already

established knowledge or playing a parlor game. They are much more complex.

parlor game. They are much more complex.

They involve interactions. They involve

a lot of things that are based on tacet knowledge or they are based on matching your contextual understanding of a problem with the uh specific task at

hand. For example, diagnosing uh a

hand. For example, diagnosing uh a difficult ailment or finding the kind of product that's going to work well given the retirement planning that an individual is doing. With the current

architecture, the best that we can do is we can copy human decision makers that make decisions. So we can load in a lot

make decisions. So we can load in a lot of data from doctors uh making diagnosis or reading radiology reports or from

financial planners. And then AI uh

financial planners. And then AI uh generative AI in particular has a great way of imitating these human decision makers. But if you do that, you're not

makers. But if you do that, you're not going to get much better than the human decision makers. And especially if you

decision makers. And especially if you don't know who the very best human decision makers are, you may not even very easily achieve the human best level human decision maker level. Places where

we need a lot of judgment or social interaction or social intelligence I think are still beyond the capabilities of AI. And on the basis of this I would

of AI. And on the basis of this I would say you know my prediction which again has huge error bands around it so may it well turn out to be wrong but I don't

expect any occupation that we have today to have been eliminated in in five or 10 years time. So if you are an AGI

years time. So if you are an AGI believer that you think that uh generative AI and other AI tools are going to completely transform the economy within the next three or four

years or 5 years then you must have in your mind a list of occupations that will completely disappear. All of this that I have summarized briefly is

predicated on the current approach to AI. And what I have been arguing and

AI. And what I have been arguing and this paper was a small part of that bigger edifice is that we are not

developing AI in the best possible way.

And that best possible way is much more prohuman. It's much more targeted at

prohuman. It's much more targeted at working with human decision makers. It

requires a bigger celebration of the places where AI is better than humans and the places where humans are better than AI. And once you take that

than AI. And once you take that approach, I think the biggest promise is using AI for providing new goods and services, new ways of doing things for

humans. We are at the cusp of many major

humans. We are at the cusp of many major transformations. We are an aging

transformations. We are an aging society. There going to be many many

society. There going to be many many more people over the age of 60. Many

many many more people over the age of 70 in the United States. Many more in Europe that they are going to demand new goods, new services, new

accommodations. Financial industry is at

accommodations. Financial industry is at the cusp of big changes. Again, this is not going to be on cost savings. It's

going to be for example uh what sometimes people call financial inclusion. meaning we provide new better

inclusion. meaning we provide new better services to people who are not currently making enough use of financial services including banking. Climate change uh

including banking. Climate change uh whether you mitigate it or not is going to change many aspects of our lives.

Again, new goods and services and the entire production process requires new tasks, new ways of increasing the expertise and sophisticers. All of these I think are

sophisticers. All of these I think are to play for and those are the places where uh I think AI could make a big difference. So my recommendation to

difference. So my recommendation to business leaders would be don't be taken by the hype. I think the hype is an enemy of business success. Instead think

where my most important resource which is your human resource can be better deployed and how can I leverage that human resource together with technology

together with data so that I increase people's efficiency and I enable them to create better and newer goods and services not just cutting costs but

doing new things that are so important in this changing world. Business

executives should really be thinking about much wider uh scope of possibilities than simply eliminating costs or finding roles that they can uh cut from their organizations. That

that's my perspective. Again, uh you will be hardressed to find many people in Silicon Valley who agree with this perspective, but I've been researching this for, you know, quite a while. I may

be wrong, but at least I do have data. I

do have historical knowledge and I do have some theoretical understanding of these issues. And I would say on the

these issues. And I would say on the basis of those that of course any business leader should be happy if they can reduce their cost even by 1%. That's

great 1% more profits. But the evidence as far as I read is quite clear. No

business has become you know the jewel of their industry by just cost cutting.

All good business leaders are looking for that next big idea uh that next innovation that can uh you know turn them into one of these stars of uh of their industry. In the meantime, right

their industry. In the meantime, right now is when they are putting investments into AI and they are starting to look for a return on that investment. What

metrics do you think they should be paying attention to to know whether those investments are really paying off?

Well, I'm not going to be able to provide a simple metric for you, but let me give you my perspective. And the

reason why I wrote the paper that you started with is precisely because I'm worried about those investments. I think

most business executives, not all, but most business executives are investing in AI blindly. They are doing so without

blindly. They are doing so without understanding how AI can be synergistically deployed with their workforce. And they're doing so because

workforce. And they're doing so because they're under tremendous pressure because every day they hear from uh management consultants, from uh the newspapers, from podcasts that your competitors are investing big time in AI

and if you're not, you're falling behind. That's not the way to create a

behind. That's not the way to create a successful business. You never create a

successful business. You never create a successful business because you think your competitors are investing and you should do it not to fall behind. And I

think the recipe that I would suggest is start by thinking about where it is that you can make a big difference in terms of the new things that you do. I think

for many financial industries it's quite clear new financial services are badly needed. I think if you are producing uh

needed. I think if you are producing uh other services, health services education services, I think a complete overhaul of these things is necessary.

And that's not going to happen just by buying more cloud services from uh Amazon uh or or or or just introducing some generative AI tools easily. It's

going to happen by identifying with the help of your most skilled employees identifying where these new services can be introduced, what the demand for them

is and how that can be made possible.

And AI would then be a great tool to augment the capabilities of your workforce and uh and and yourself in doing that. That's fascinating. Well

doing that. That's fascinating. Well

thank you so much for your perspective Deron. You've given us a lot to think

Deron. You've given us a lot to think about. I hope you enjoyed my discussion

about. I hope you enjoyed my discussion with MIT economist and Nobel laurate Daron Asimogu on AI's economic impact.

The key insight for leaders rather than following your competitors into blind AI investments, focus on how the technology can help you and your team deliver meaningful innovation. Are you seeing AI

meaningful innovation. Are you seeing AI create new opportunities in your industry? Share your thoughts in the

industry? Share your thoughts in the comments. For more researchbased

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