What this covers
In this bonus episode, Nobel Prize-winning economist Daron Acemoglu joins Sam to challenge some of the most common assumptions about artificial intelligence’s future. Drawing on his book Power and Progress, Daron argues that technology doesn’t have a fixed destiny — and that today’s choices will determine whether AI boosts workers or simply accelerates automation and inequality. He makes a case for focusing on new tasks that complement human skills, rather than replacing them, and warns that current incentives push AI toward centralization and automation by default. The conversation tackles productivity myths, reliability risks, and why regulation should proactively steer AI toward social good. Read the episode transcript here (https://bit.ly/4c9jFJL) .
Guest bio:
Daron Acemoglu is an institute professor at MIT, faculty codirector of the James M. and Cathleen D. Stone Center on Inequality and Shaping the Future of Work, and a research affiliate at MIT’s newly established Blueprint Labs. He is an elected fellow of the National Academy of Sciences, American Philosophical Society, the British Academy of Sciences, the Turkish Academy of Sciences, the American Academy of Arts and Sciences, the Econometric Society, the European Economic Association, and the Society of Labor Economists. He is also a member of the Group of Thirty. He has authored six books, including Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity with Simon Johnson. His work in economics has been recognized around the world, notably with the Nobel Prize in economic sciences, along with co-laureates Johnson and James A. Robinson, in 2024.
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Acemoglu argues that AI's societal impact is not predetermined; the choice between automation-centric development (which harms workers) and complementary human-centered development (which creates new tasks and shared prosperity) depends on institutional choices, regulatory frameworks, and individual agency within technology companies.
- Technology does not have a preordained destiny; different futures correspond to different winners, losers, and distributions of benefits
- Current large language models are built for automation and centralization by design and economic incentive, not because it's the only technical possibility
- Individual decisions by engineers, scientists, and entrepreneurs—combined with proactive regulation—can redirect AI development toward pro-worker, decentralized alternatives
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The current architecture of large language models may create hard limits on reliability that could be unacceptable in high-stakes domains like medicine; for example, a 1-in-1,000 error rate in nursing recommendations would be unacceptably high, as it would constitute an unacceptable casualty rate in medical applications.
“The current architecture of large language models may create hard limits on reliability, whereas in situations like this, reliability could be a very important constraint. So, for example, imagine we do this with nurses and one in a thousand time they give you the complete opposite of they what they should do and you poison the patient. I think one in a thousand seems very small, but actually in medical applications, that would be an unacceptably large casualty rate.”
Technology does not have a preordained destiny; it is shaped by human agency and institutional choice, meaning different futures correspond to different winners, losers, and distributions of productivity gains and costs.
“The main thesis of Power and Progress is that technology does, to some extent, what we want it to do. It does not have a preordained destiny that will take us in one direction or another. We have a lot of agency, a lot of choice in shaping the future of technology. And different futures correspond to different winners and losers, different benefits, different costs, different productivities.”
We cannot let AI models pretend to be doctors without having some sort of assessment that they are actually giving adequate information; we should apply tremendous barriers similar to those for quack doctors to AI models making medical claims.
“You cannot let AI models pretend to be doctors without having some sort of assessment that they are actually giving adequate information. We apply tremendous barriers to anybody becoming a quack doctor. Well, we should apply the similar standards to AI models.”
Licensing and competency standards prevent nurses from prescribing drugs or making emergency decisions without physician oversight, not because they are uniformly incompetent, but because complementary AI technology that would expand their role requires either much better-trained nurses or much more reliable AI models than currently exist.
“But as a result, we don't allow nurses to make those decisions at the moment. So, except in a few cases where you have highly trained licensed practitioner nurses, nurses cannot prescribe drugs. They cannot make emergency decisions. When a patient is having problems, they have to wait for a physician to come. So, that's the margin that we're talking about and nurse complementary technology would expand what nurses do in those domains and no, you couldn't do that unless all of the nurses become even better trained than licensed practitioner nurses or the AI models get much better.”
European colonial powers shaped the institutional trajectories of colonized nations in diverse ways, and these institutionally-determined differences are a major explanation for the huge divergence in economic fortunes across different parts of the world since the 16th century.
“The huge divergence in economic fortunes of different parts of the world since the 16th century or thereabouts. It is very much related to, for example, the fact that European powers colonized the rest of the world and shaped the institutional trajectories of very different nations around the world in very diverse ways.”
The economic incentives for building domain-specific, high-reliability AI tools (rather than general-purpose LLMs) do not currently exist because this is not aligned with the business models of leading AI corporations; the required domain-specific data does not exist and will not be created without property rights in data and proper data markets.
“The economic incentives are not there because this is not the business model of leading corporations. That data doesn't exist and it won't exist unless we have property rights in data and we have proper data markets.”
Current ChatGPT and similar general-purpose LLMs are unsuitable for professional domains like electrical work because they are: (1) not designed or optimized for the task, (2) unreliable, (3) not trained on domain-specific information about relevant equipment and standards, and (4) not trained on use cases of expert practitioners dealing with similar problems.
“Right now, today, you can an electrician, you can take ChatGPT with you, and you can ask questions, but there are several problems with that. First of all, it has not been designed or optimized for that task. Second, it's not reliable. So, a much higher degree of reliability is necessary. Third, it has not been trained on the domain-specific information that all of the relevant electrical equipment and deep understanding of the electrical laws and electronics that would be necessary. And most importantly, it has also not been trained on use cases of best electricians dealing with similar problems from which AI could learn.”
Those new tasks have generally been very good for productivity and for worker wages and employment.
“Those have generally been very good for productivity and for worker wages and employment.”
AI is fundamentally an information technology, not an automation technology; it excels at sifting through large datasets to find relevant context and information for specific tasks, but it lacks judgment and creativity capabilities that human brains naturally possess.
“AI is really an information technology. A very powerful information tech It's not an automation technology. AI is not thinking anywhere like the human brain. Instead, it has some truly impressive capabilities that the human brain doesn't have, and it lacks some of the judgmental and creativity-related capabilities that the human brain naturally has. As an information technology, what AI is very good at is sifting through gargantuan data sets and find relevant context and information for some specific task or specific context or specific application.”
Institutions—rules, laws, norms, and how society is organized—determine the effects of history and technology, and these institutional factors are a prime channel through which human ingenuity impacts economic productivity and well-being.
“My focus on institutional factors which determine the effects of history, the effects of how society is organized, the rules, the laws, the norms, and technology as the prime channel via which human ingenuity and human decisions impact economic productivity and economic well-being.”
Silicon Valley and sympathetic economists argue the productivity measurement problem is due to not accounting for how high quality new products are and Bureau of Labor Statistics overestimating inflation, with smartphones being supercomputers in your palm allowing unprecedented information access.
“Well, the people from Silicon Valley and economists who are sympathetic to that perspective would say, 'That's all measurement problem. You're just not making allowance for how high quality some of the products you're getting now is and [clears throat] the Bureau of Labor Statistics is overestimating inflation. You have in the middle of your palm a supercomputer super powerful machine that allow you to access information [20:21] like never possible before.'”
Tech companies have significant 'persuasion power'; they have persuaded society that their intentions are benign, their technology is good, and they will not misuse it excessively, even though there is substantial counter-evidence to these claims.
“Part of the reason why tech companies have so much power is because they have what Simon Johnson and I called in Power and Progress, persuasion power. They have persuaded the rest of society that their intentions are benign, their technology is good, and they will not misuse it too much. There's a lot of counter evidence to that, but we still sort of believe it.”
There is a measurement puzzle in the modern economy: the number of patents has quadrupled over 40 years, there is rapid turnover in consumer electronics and new apps, yet standard economic measures show slower productivity growth today than in the 1950s-1970s (the 'boring pre-digital days').
“We definitely do live in an age of innovation according to many measures. If you look at the number of patents at the USPTO, they have quadrupled over the last 40 years. We get an incredible array of new apps every day on our phones. We have much faster turnover of electronics in quite a significant way. I mean, when I use my iPhone that's a couple of years old, everybody says, 'Wow, you're really missing out.' And you know, when people were using rotary phones, dial phones, you know, you could use the same model for 30 years and nobody would bat an eye. So, there is a sense in which we are getting a lot of innovations, but using the standard measures of economists, we don't see much improvement in productivity. In fact, we're having slower productivity improvements today than we did in the '50s, '60s, '70s, those boring pre-digital days.”
Large language models are tools for information centralization, not decentralization; they collect and centralize humanity's information and process it in a centralized manner, leaving less for decentralized human participation, which contrasts with earlier computing hopes for distributed, garage-based innovation.
“I would also like to add whether we use technology for information centralization or decentralization is also important. In that many of the early hopes about computers were centered on decentralization. People could do in their garages things that IBM as a centralized organization couldn't do. And personal computers enabled that to some extent, not anywhere comparable to the hopes of pioneers of computing in the '60s and the '70s. But today we are going in the opposite direction. Large language models are information centralization tools. They collect all of the information. They aim to collect all of the information of humanity ultimately. And then centralize that and process it in a centralized manner that then gives you answers. And then so there is less for the decentralized human mind and human participation to do.”
Well-designed AI tools could significantly improve what electricians, nurses, educators, journalists, and academics can do by providing reliable, domain-specific information (e.g., helping an electrician diagnose unexpected equipment behavior by accessing expertise equivalent to decades of experience), but this requires specialized training on domain-specific data and best-practice use cases rather than general-purpose large language models.
“If you're an electrician and you encounter an equipment that is behaving in a way that you haven't seen before or a completely new equipment that you don't have experience with and if you have the right AI tool that can immediately and reliably give you information about why that sort of unexpected behavior is occurring or what are the things you need to know about this equipment and how it interacts with the particular type of electricity grid or the environment that it is situated in. Those are the kinds of things that regular electricians would have to work decades to get the experience in an imperfect way. So, we can significantly improve what electricians, what nurses, what educators, what journalists, what academics could do using AI in order to perform more sophisticated tasks or new tasks and acquire much better information.”
The core explanation for the productivity puzzle is not measurement problems but the direction of AI development: if automation is overdone and information centralization is pursued, the promised productivity boom will not materialize.
“I don't think that's just a measurement problem, but measurement can help understand where the bottlenecks are and also improve perhaps certain assessments of what the impact of AI is in different sectors, but I think a lot of it again comes down to what I was talking about. If you overdo automation, if you overdo information centralization, you're not actually going to get all that promised productivity boom.”
Regulation of AI must be proactive, not purely reactive; rather than trying to stop harmful corporate AI development after the fact, proactive regulation should recognize socially beneficial directions (pro-worker, new tasks, decentralization), identify why the current market tilts against them, and correct those distortions in ways that allow the market to function.
“We may need a change in the philosophy of regulation. Regulation should be not a reactive thing where oh, we try to stop whatever AI companies are trying to do. I think we need proactive regulation that helps the AI industry move in a more socially beneficial direction. And that starts by recognizing what the socially detrimental direction is and I've argued it's pro-worker, new tasks, more decentralization. It then recognizes why the current playing field is tilted against it and tries in a soft way without, you know, stopping or killing the market process trying to correct those distortions and provide a living chance to the alternative directions.”
AI technology currently operates in two fundamentally different directions: automation (which removes tasks from workers and primarily benefits capital owners) and human-complementary new tasks (which enables workers to perform more sophisticated or entirely new work, historically associated with wage and employment gains).
“I would single out in the production process automation, which is the dream of most AI models today, especially under the banner of artificial general intelligence AGI, which aims for large language models or other generative AI tools to reach levels of capabilities comparable to the best workers across a very wide range of domains. The reason why that is viewed as attractive is that just like previous rounds of software that improved cognition in different domains, that can be used for automating tasks. So, AGI is very tightly interwoven with the automation agenda. Automation is great. It gets rid of some routine tasks. It can get rid of some boring tasks. When it's applied in the physical domain such as with cranes or robots, it could remove the most dangerous tasks from the human work schedule. But, automation also doesn't benefit workers by itself. It takes away tasks from workers. It is beneficial to capital and capital owners and not so much for workers in general.”
The narrative that AI has a determined natural future and will inevitably deliver universal prosperity is simplistic and lulls people into helplessness and complacency that 'could be quite costly'.
“The narrative that there is a determined natural future of AI and we're all going there whether we want it or not. And And ultimately we're all going to become incredibly more prosperous out of that is just simplistic. And fighting against that narrative I think is very important today because that narrative lulls us into a sense of helplessness and sense of complacency that could be quite costly.”
Europe's regulatory approach to technology has made it significantly 'behind in AI and many areas of tech' because the regulatory system has been 'not very conducive to innovation' with 'too many organizations, too much interference,' illustrating the risk that excessive regulation can kill innovation.
“Look at Europe, and Europe is so far behind in AI and many areas of tech because they've not been very conducive to innovation via their regulatory system. Too many organizations, too much interference. That can be very bad.”
AI integrated with virtual reality could provide immersive, personalized training experiences that allow individuals (such as new drivers) to encounter and learn from esoteric or dangerous scenarios without real-world risk, compressing the experience-gathering process that currently requires real exposure.
“The future of technology is rich. If you integrate AI with virtual reality, you can have personalized experiences where, you know, your daughter could experience very dangerous situations sitting in front of a computer. And I can tell you from my own experience, you know, when you get behind the wheel, you think you know, and you don't.”
Individual decision-making by engineers and scientists within tech corporations significantly determines the direction of AI research; if hundreds of thousands of these workers decided to shift focus from automation and AGI to developing pro-worker, pro-human, decentralized technologies, that would be the research direction pursued.
“Hundreds of thousands of people, perhaps more, who work as engineers and scientists in these corporations, they determine the direction of research. If they decided next year that they want to work not on automation and AGI, but developing more pro-worker, pro-human technologies that will help workers and human decision-makers and decentralization, that's what we would get. So, that's an individual decision.”
Entrepreneurship currently funnels toward acquisition by large tech companies as the primary path to wealth creation ('become a billionaire'), aligning startups with corporate interests rather than fostering independent alternatives; different regulatory systems and merger/acquisition scrutiny could create different entrepreneurial dynamics.
“Right now, startups are aligned with the big companies because their dream is to be bought up by the big companies. That's the way you become a billionaire right now. Well, again, that's a choice. Different values, different priorities, different regulatory systems. Perhaps we should really be much more vigilant in mergers and acquisitions, then that could lead to very different dynamics.”
The biggest misconception about artificial intelligence is that it will completely replace humans; in reality, AI will work alongside humans, and understanding and optimizing this complementarity is central to shaping the future of work and humanity.
“The biggest misconception that people have about artificial intelligence? That it will somehow completely replace humans. I think at the end, AI will be something that works alongside humans. And the better we understand that and how to achieve that, the better we will be in shaping the future of work and the future of humanity.”
A critical mass of individuals changing their views about tech companies' benevolence and power would have effects through democratic processes, suggesting that individual persuasion can aggregate into systemic change.
“Different individuals will have to reach their own conclusion, but enough individuals, a critical mass of them changing their views would have an effect through the democratic process.”
When Acemoglu uses ChatGPT, he is simultaneously surprised by how good it is in areas where he lacks expertise and disappointed by its unreliability when he knows the subject matter; it consistently pretends to know more than it does and gives incorrect answers through excessive extrapolation.
“I use it just like other people. I sometimes ask questions to ChatGPT, and you know, most of the time I am both surprised by how good it is and disappointed that you know, if I really trusted everything I got from it, I wouldn't be doing so well... But, it's just it always pretends to know more and gives answers that are really incorrect because it's extrapolating too much.”
Employers given the choice to use AI systems gravitate toward automation because it is the path of least resistance; this reflects the current state of AI development, not an inevitable economic law.
“And that's the reason why whenever employers are given a push towards using them, the first thing they want to do is just use them for automation because that's just seems to be the path of least resistance.”
The measurement-problem argument can be partially validated by historical precedent: antibiotics were also difficult to measure precisely in early economic data, yet they produced substantial GDP gains, pharmaceutical sector output gains, and life expectancy improvements; however, AI pharmaceuticals have not yet demonstrated comparable results and life expectancy is not increasing, so the analogy is limited.
“There's some truth to that, but I think it can be exaggerated. You know, we did not measure the benefits from antibiotics that well either. But you still got amazing improvements on many directions in terms of GDP, in terms of output of the pharmaceutical sector, and lives saved. Life expectancy increased tremendously with antibiotics. Well, life expectancy is not increasing. We're not seeing any of the AI facilitated pharmaceuticals do anything yet. So, perhaps time will change that, but but we just don't have objective measures that show huge gains from AI as of now.”
The Industrial Revolution represents the beginning of a process where knowledge, science, and technical skills were systematically applied to improve production methods and goods, and this model remains salient for understanding current technological change.
“I've also been fascinated by the Industrial Revolution and how we started this process of using knowledge, science, and various skills in improving the way that we can actually start producing goods and services. That's all really salient for what's going on right now.”
Large language models' capabilities in reasoning have advanced faster than Acemoglu expected; their capabilities are 'truly impressive.'
“What has moved faster than you expected with artificial intelligence? Oh, the large language models. I mean, their capabilities, their reasoning capabilities are truly impressive.”
Acemoglu became fascinated by divergent economic, political, and social outcomes across countries and by explanations of wealth and poverty disparities during his teenage years, which framed his subsequent research.
“I got into economics because I was fascinated by what I saw around me in my very young teen years about very divergent economic, political, and social outcomes across countries, huge disparities in terms of wealth, in terms of poverty. And those interests have framed my research”
The recent rapid advances in generative AI capabilities (2021-2022) have not changed the basic trade-offs and messages in Power and Progress regarding technology's distributive outcomes; the book's analysis of how institutional choices shape technological winners and losers remains valid.
“But those advances haven't really changed the basic trade-offs and the basic messages that we wanted to convey in the book.”
AI technology is simultaneously fascinating and super promising, and super scary, reflecting a fundamental ambivalence about its future trajectory.
“I think it's fascinating. It's super promising, but also super scary.”
Daron Acemoglu was motivated to focus on technology and AI after working for over 20 years on automation and labor markets; when AI models began making rapid advances in the mid-2010s, he became concerned about implications for work, wages, and employment, leading him to invest more time in understanding AI and its societal implications.
“I've been always interested in technology as the engine of the industrial revolution, of the rapid growth process. And that brought me, together with my studies of labor markets, to focus on automation. So, I've been working on automation for over 20 years. And then, when AI models starting making rapid advances in the mid-2010s, I got worried about what that would imply from this aspect of the future of work, what it would imply for wages and employment, and that made me invest more time and resources into AI and understanding AI, understanding its societal implications, but also understanding the technology.”