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KAUSHIK VISWANATH: 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 Laureate Daron Acemoglu, whose data-driven research reveals a surprising reality. Forget overnight transformation; Acemoglu'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 Acemoglu and asked him these questions and more. (bright upbeat music)
KAUSHIK: Thank you so much for being here with us today. I have a few questions for you about generative AI and AI in general and its impacts on the economy. So, ChatGPT 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 for the economy in general. Last April, you published a paper called "The Simple Macroeconomics of AI," in which you estimate that over the next 10 years, only about 5% of all tasks will be profitably automated by this technology, and that it's only likely to contribute about 1% to global GDP. That's a stark contrast to what some other analysts have said. You know, people have been predicting that this will be a truly transformative technology to the labor force and to the economy in general. Can you explain why your estimates are different from these others? And since you published that paper last year, have you seen anything that either confirms or makes you question those estimates you made?
DARON ACEMOGLU: Well, well, thank you, Kaushik. Well, look, I said one other thing in that paper: it's hugely uncertain, and these are just guesses. I think it's very difficult to know because it's a very rapidly changing technology, and over the last year we have seen even more 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 clear. You know, how are you going to actually get AI tools into the production 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 software engineer that does some very basic routines for your work, or you are in IT security or 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 are 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-collar 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 at the 5% number, based on these inputs and a lot of detailed material. But it may may turn out to be wrong.
KAUSHIK: Last year, I wouldn't have expected to see the kinds of leaps and bounds.
DARON: Yeah, I mean the leaps and bounds are really inspiring at some level. So I'm pretty impressed by those. The question is, with these leaps and bounds, do you still think that in two, three, four years' time you can have an AGI with no human supervision that can do all of your accounting or all of your marketing? And I think that is a much 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.
KAUSHIK: And does that tie into the distinction you make in the paper between what you call easy to learn and hard to learn tasks? And should that distinction inform how executives study or decide what business processes are most amenable to automation?
DARON: Look at the domains in which we have truly inspiring achievements from AI such as AlphaGo, AlphaFold, or answering some complex, but knowledge-based questions. Those are all domains in which there is a ground truth that everybody can agree on. You either fold the protein or you do not. AI is capable, there's no doubt about that. That's why we're talking about AI. And it is capable of learning that knowledge if it's in its training 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. They involve interactions; they involve a lot of things that are based on tacit knowledge, or they are based on matching your contextual understanding of a problem with the specific task at hand. For example, diagnosing 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 of data from doctors making diagnoses or reading radiology reports or from financial planners. And then AI, generative AI in particular, has a great way of imitating these human decision makers. But if you do that, you're not going to get much better than the human 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 say, 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 five or 10 years' time. So if you are an AGI believer, that you think that generative AI and other AI tools are going to completely transform the economy within the next three, or four years, or five 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 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 pro-human. 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 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 transformations. We are an aging society. There are 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. The financial industry is at the cusp of big changes. Again, this is not going to be on cost saving. It's going to be, for example, what sometimes people call financial inclusion. Meaning we provide new, better services for people who are not currently making enough use of financial services, including banking. Climate change. 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 sophistication of workers. All of these, I think, are to play for, and those are the places where I think AI could make a big 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.
KAUSHIK: Business executives should really be thinking about a much wider scope of possibilities than simply eliminating costs or finding roles that they can cut from their organizations.
DARON: That's my perspective. Again, you will be hard-pressed to find many people in Silicon Valley who agree with this perspective, but I've been researching this for 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 basis of those that of course any business leader should be happy if they can reduce their costs even by 1%, that's great. 1% more profits. But the evidence, as far as I read, is quite clear: no business has become the jewel of their industry by just cost-cutting.
KAUSHIK: All good business leaders are looking for that next big idea, that next innovation that can turn them into one of these stars of 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?
DARON: 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 understanding how AI can be synergistically deployed with their workforce. And they're doing so because they're under tremendous pressure because every day they hear from management consultants, from 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 a way to 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 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 Amazon 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 yourself in doing that.
KAUSHIK: That's fascinating. Well, thank you so much for your perspective, Daron. You've given us a lot to think about. I hope you enjoyed my discussion with MIT economist and Nobel Laureate Daron Acemoglu 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 create new opportunities in your industry? Share your thoughts in the comments. For more research-based information from MIT SMR, check out this playlist. Thanks for watching. (upbeat music)