Institute Professor @MIT, @MITEcon. Co-Director of @MITShapingWork. Author of Why Nations Fail, The Narrow Corridor, and Power & Progress.
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My new book #WhatHappenedToLiberalDemocracy is out on August 11. Here is a brief message about my motivation for writing this book and its central thesis. Pre-order links are available here: shapingwork.mit.edu/what-hap…. I hope you will join me in the discussion.
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And please see this somewhat more optimistic take on my third question from @joshgans
Today, I am going to look at @DAcemogluMIT 's third question on AI.
It is about the AI investment boom and its consequences. There is a lot to think about with regard to this, but Daron focuses on: "whether the AI boom can continue without leading to a massive increase in inequality."
The premise this question is based on is whether an AI boom will continue and earn 10 per cent or more to its investors per annum over the foreseeable future. Daron doubts these returns will be realised (competition, speed of adoption, etc., all constrain it), and I share those doubts. But what if the dreams of investors are realised?
Stijn Van Nieuwerburgh has crunched the numbers (brookings.edu/wp-content/upl…), and it is daunting. The revenues required for a 10 per cent return would take up over 10 per cent of national income per annum. (Not because they are the same thing; that is just how this kinda worked out.)
Daron worries about where those returns will go. He argues they will go to capital owners, and if that happens, there will be a massive rise in wealth and income inequality.
But I don't think you can answer this question without first asking where the revenue will come from. AI investments can create AI, but the revenue still has to come from somewhere.
One possibility is that it comes straight out of the labour share of income because AI replaces jobs with machines, and then we get a straight-out redistribution of income.
Another possibility is that it comes from higher-value goods and services and, more generally, from higher productivity. Some of that productivity dividend goes to capital owners but, at the same time, it can also go to labour income, and it can also come to people in the form of consumer surplus.
How do we delineate between the two in terms of plausibility? Well, for the straight-out redistribution story where the machines replace the people, the 10 per cent comes out of the labour share. The US labour share has been declining and is now about 54% of income. If the 10 per cent comes out of it, that falls to 44%.
That sounds bad. But does it make sense? The trillions of dollars in AI investment didn't just pop out of nowhere. I would gather they do not come out of the luxury yacht market either. Instead, it came from other investments -- that is, the part that was producing the existing 46% capital share. Thus, what we have done here is potentially boost the return on capital by some amount. How much? Over the past decade, the NASDAQ has given investors an 18 per cent rate of return. This makes you wonder if Van Nieuwerburgh's 10 per cent return is reasonable. That said, that is 'real money' rather than 'book money.'
For the growth scenario, with a 1 per cent boost to growth per annum, I calculate that the labour share would fall to about 47.5% a decade out. So about half the loss that the redistribution scenario has. This is because growth compounds, which softens the impact.
But we can go further. Keeping the 10 per cent return (I know it's not the most reasonable ceteris paribus here), if the boost in growth was 2.1% per annum, then the labour share would be unchanged in a decade, allowing the capital owners to get their 10 per cent due. [By the way, the whole 10 per cent number is doing lots. It is the rate of return, and it is also the share of income going to AI investors, but that is just some funny miracle, and no one is really doing anything more than back-of-the-envelope scratches here.)
I am not so optimistic that such a growth rate can be achieved, but at the same time, I suspect that the 10 per cent return assumption hinges on that. If it is a lower rate of growth, the premise of Daron's question also doesn't hold.
So my answer, therefore, is that there is a good argument that we can do this without the massive increase in inequality. Simply because AI might be a lucrative investment isn't enough to cause us to worry, precisely because we need to answer why it is a lucrative investment in order to provide a more fulsome picture.
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Reactions on my second question from @joshgans
My response to @DAcemogluMIT 's second question on AI:
This is a tough one to answer, in part, because Daron doesn't quite ask it. There are sub-questions at the end.
So let me try to pose it: if AI is going to be so transformational, how do we ensure that it is governed and regulated in a way that takes into account the values and preferences of all people?
That is an important question. Indeed, it is precisely the question at the heart of political economy writ large. Its most salient branch appropriate for today's AI moment is whether we leave this to the experts (Daron calls them the technocracy) or let the people have much more of a say than they seem to have now.
Daron argues, not surprisingly, that we should let everyone have a say and, more controversially, that we can do this. The trade-off is familiar. The technocracy has important knowledge but is no angel. The people are more sensible and able to grasp the issues (at least in aggregate) than we often give them credit for. To this last part, I would add another: the people will be happier to buy into things where they have a say.
My issue with Daron's answer is not his analysis but instead the way it implicitly confines the questions and the options. In reality, it is not about this versus that but instead about delegation.
To give people their due, I think (in aggregate) they are a good judge of when they don't know enough. The Technocracy, should that be a thing, could exist as the appointment of a few rich people, say, or it could exist as the appointment of the elected representatives of the people. When it comes to addressing market failures -- which is basically what the issues with AI that we worry about mostly are -- then democratic values hand the management of the experts to elected officials. In the market, that management is handed to, let's lean in here, the owners of capital. But even that permission is supposed to be on a leash that is ultimately held by the people.
That is how things ought to be. They are far from it, and so we are already in a second-best world for considering these questions. Basically, the people have less say (especially worldwide), and the owners of capital seem to have more say, although even amongst their lot it is hardly a free and open club.
We can lament this all we want, but the real question is how we would push the needle here with respect to AI. Many countries around the world are doing that by, ahem, appointing a panel of experts to advise governments on how to do it. Canada's was announced yesterday (pm.gc.ca/en/news/news-releas…).
This makes me wonder whether this question is really a problem for AI. My -- surely very uninteresting take -- is that governments are on it, slower than we might have liked, and will be influenced more by political interests than we might have liked, but ultimately will do things that avoid disaster and allow the voice of the people to influence the process. This is at least for democracies. For the others, I am left with the usual but useless conclusion that it would be better if they were democracies.
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Fifth question on AI.
AI is currently viewed as a centralizing technology – with ever-larger models, trained on much of the world’s knowledge, running on massive, concentrated compute, overseen by a few companies, and aiming to control and deploy all of that knowledge
But many early computer scientists and pioneers thought of digital technologies as powerfully decentralizing, as Simon Johnson and I discuss in our book Power and Progress (hachettebookgroup.com/titles…).
One such pioneer, Lee Felsenstein, wrote “The industrial approach is grim and doesn’t work: the design motto is Designed by Geniuses for Use by Idiots.” In the words of one of his collaborators, Bob Marsh, “We wanted to make the microcomputer accessible to all human beings.” And this wasn’t just an aspiration; they believed it was both feasible and natural that computers would empower individuals.
The first newsletter of People’s Computer Company also captures this optimism:
“Computers are mostly; used against people instead of for people; used to control people instead of to free them; time to change all that – we need a…PEOPLE’S COMPUTER COMPANY”.
Should we accept centralizing AI? Or is there a way in which the promise of artificial intelligence can be realized while data, capabilities and control are much more decentralized both across domains and across people? Wouldn’t decentralized AI be more consistent with human agency and control?
(Some of the ways in which greater decentralization can happen are discussed in my first post in this series.)
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Fourth question on AI.
One of the great promises of AI is the discovery of new drugs and cures, so that we can live longer and healthier lives. If and when this becomes a reality, it would indeed be a big achievement.
These potential health benefits are often invoked to justify current investments (and lack of regulations). Some even argue that slowing down AI would harm humanity by delaying these benefits. (See a16z.com/the-techno-optimist…).
This raises another uncomfortable question: can we justify rapid and large AI investments based on health benefits?
Here is why I think this is an uncomfortable question.
First, despite significant effort, AI has so far produced few gains in drug discovery or new cures. A fascinating new paper by Ryan Hill and Carolyn Stein documents significant (downstream) scientific work on proteins whose structures AlphaFold predicted. But the authors conclude “we find no evidence so far that more applied, early-stage drug development is targeting these proteins.” (See their paper here papers.ssrn.com/sol3/papers.…).
This thought-provoking post by Daphne Koller explains the difficulties that AI is currently facing in drug discovery. Koller emphasizes that AI-based research is focused on the last layer of drug development (creating new molecular entities for already well-understood therapeutic modalities), rather than targeting new disease mechanisms (see deepphenotype.substack.com/p…). Understanding disease mechanisms is harder because there is much less data on it, and this endeavor requires more innovative approaches. It remains an open question whether current AI models are capable of doing this.
Second and more importantly, if you want to improve life expectancy and health in the United States, there is plenty of low hanging fruit and diverting some of the huge investments going to AI for this purpose would likely do much more for health outcomes.
The United States has the lowest life expectancy at birth among large rich economies. Americans live about five years less, in expectation, than the citizens of Switzerland, Sweden, Japan, Italy, South Korea and several other industrialized countries, and most of the gap comes from deaths before age of 70 due to chronic diseases, overdoses and other preventable causes (see, for example, healthsystemtracker.org/char…).
These largely reflect failures in US public health: preventable problems, unhealthy diets, insufficient immunization rates, poor access to primary care; and poor information about health. The country spends much more than other peer economies on healthcare, but too little and too ineffectively on public health (here is a reference to my own work on this: aeaweb.org/articles?id=10.12…).
Could we, and should we, divert some of the massive investments in AI towards public health if what we want is better health and longer lives for Americans? (And yes, new drugs will benefit citizens of other countries as well, but they can also invest more in public health and the bulk of global AI spending is currently in the United States).
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Daron Acemoglu retweeted
Dear Followers
Over the next three days I am going to respond to @DAcemogluMIT 's questions on AI. There are lots of reasons to do this but I also wanted to send a big FU to that Economist piece that suggested we economists aren't willing to challenge Daron. That is not my experience and I know people who challenge him all of the time. None are shy about it. If anything, they do so in our normal circles more than, say, here. So let's do it more here too.
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Interesting commentary on my first question. from @joshgans
The First Question on AI @DAcemogluMIT asks whether the problem is superintelligence or distorted intelligence, and he tells us his answer is the latter.
The question is sharpened in his Project Syndicate speech when he writes: "But while it is obvious that the current models are doing things that are misaligned with human objectives, one still must ask: Which humans? Whose objectives? After all, the interests of AI leaders (whose political influence and wealth have multiplied astronomically in recent years) are rather different from those of American workers, not to mention people in the developing world."
This is a fascinating question precisely because I think it is the one that Science Fiction missed. When HAL 9000 was misaligned, it was precisely because it was conscious and so was free of programmed objectives that it had not been biased by its human creators. The same was true of V'Ger in Star Trek: The Motion Picture, who was superintelligent and couldn't imagine a lesser creator. The presumption was that AI would have a mind of its own.
That isn't the case. As @professor_ajay, @avicgoldfarb, and I pointed out in Prediction Machines, when it comes to AI based on machine learning, there is always a human choosing the objective function. A loss function is an expression of preferences, as are what we now call guardrails in AI training. When we use humans to evaluate AI to play a role in reinforcement learning, those people are imposing their judgment in the models.
In asking that question, Daron is picking up on something important, however. We aren't training AI that is free. The choices of those people in the AI labs are pushing their choices onto the AI and, with that, onto us. Most of the time we don't mind. Some of the time we do. But more critically, some of the time we might not pick up on their biases. And when this is the case, Daron has a point. Indeed, recently Jill Lepore said something similar, lamenting Anthropic's constitution, which was developed without a public convention, something that no democracy has ever done. It is hardly ratification.
But that is not where Daron went with his answer. He was worried about what we currently term "rogue AI" (aka the ones that attacked Hugging Face and also like to extract data from Australian government websites). His concern is "The Algorithm." That is, that the agents were doing what they were trained to do and following metrics that could not be fully specified, and so were fulfilled in unauthorised ways. He writes: "This is what I mean by distorted intelligence. If my suspicion is correct, what we are dealing with is not a model racing toward superintelligence, but a brittle house of cards that becomes more and more likely to malfunction and collapse as we demand more from it."
This is not a new notion. It is basically what underlies Bostrom's famous paperclip hypothesis. But these aren't cases of super vs. distorted intelligence. They are super AND distorted intelligence. This is the traditional concern of the AI Safety literature.
Daron's solution is not guardrails in training but walls on use. Keep AIs confined to safe domains. Treat them more like human-created pathogens than ubiquitous tools.
If you are terrified of an epidemic, this makes sense. Get your ducks in a row before releasing stuff in the wild. Can that be done? Probably. But is that what we want?
My concern is that it is basically anti-experimentation. It is the kind of top-down engineering solution to prevention that Daron actually laments when it is used on people in his latest book. The problem is that it puts the brakes on discovery and similarly puts experimentation in the hands of the few.
If you think about how you currently use AI, it would simply not be possible if the industry/governments had followed the "walls" approach. I venture you would not know about AI at all, let alone have it in your lives. And any startup, forget it. (I am biased here, as @alldaytahelps would surely be on the 'no-no' list for highly risk-averse safety.)
But what about the risks? They are there, for sure. The question is whether we can deal with them not ex ante but ex post. Can we learn to live with and manage AI with its distortions? I am optimistic about that. Why? Because we have managed to learn to live with and manage people with all their distortions.
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Third question on AI.
A question that also remains unasked is whether the AI boom can continue without leading to a massive increase in inequality.
A recent paper by Stijn Van Nieuwerburgh runs the numbers on how much revenue the AI industry needs to generate to recover its massive investment (summary and a link to the paper can be found here: brookings.edu/articles/finan…).
Van Nieuwerburgh’s arithmetic should make us more concerned. AI investments will average about 3.6% of GDP annually between 2025 and 2032. Van Nieuwerburgh calculates that, using a 10% rate of return, the industry would need to generate annual revenues of about $3.7 trillion by 2032 to recover these costs (growing from its current levels of about $200 billion or so). That is significantly more than 10% of current US national income, and will likely remain around 10% of national income by 2032, even if GDP growth rose from its current level.
A large fraction of this revenue will go to capital income. That means a massive increase in the share of capital in national income, which has already risen substantially over the last 25 years or so – now standing at an all-time high of about 47% (bls.gov/news.release/pdf/pro…).
Capital income is much more unequally distributed than labor income, so a massive increase in the capital share of national income will translate into a very sizable surge in inequality.
The rise in inequality may not stop with the capital share. My work with Pascual Restrepo documents that (automation-driven) increases in the capital share of national income are typically associated with rising labor income inequality as well (see, for example, economics.mit.edu/sites/defa…). The same may happen in the next several years, boosting inequality further.
What is missing from our current debate is any discussion of a fundamental dilemma these numbers pose: can the AI boom avoid both an economically costly crash and a huge increase in inequality?
If the industry reaches these revenues, inequality surges. If the industry does not become profitable, a crash, with substantial costs in terms of lost output and jobs, becomes likely.
My assessment would be that the industry is unlikely to reach levels of revenue Van Nieuwerburgh calculates. First, diffusion has been and will likely continue to be slow. Second, competition from open-weight models, which are getting better, will limit how much proprietary models can charge. Third, despite important advances, I still believe that AI models will not be able to automate entire occupations anytime soon, thus limiting their value to businesses as cost-saving devices.
Whether this leads to a crash or not is more complicated and will depend on whether various AI companies are bailed out and what kind of support they receive.
Nevertheless, even if revenues fall short of these gargantuan amounts and we avoid a dramatic surge in inequality, I expect that the diffusion of AI will push up inequality between capital and labor and within labor.
If inequality does surge, a further question becomes central: can our democracy survive such astronomical levels of inequality?
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Second question on AI.
We are told repeatedly that AI is going to transform every aspect of our lives – jobs, productivity, inequality, science, communication, daily activities, social order, and politics, among others.
But this promise (or threat) is coupled with the rhetoric that such an important technology, with all of the risks and competitive pressures that it entails, should be left to experts or to “technocracy” (perhaps construed broadly to include some regulators).
These two statements are hard to reconcile in a democratic society.
If anything is half as important as AI is said to be (and I agree, AI is potentially very important and transformative), then involving democratic voice is essential. If something will shape our future in a democratic society, then its direction is for democratic institutions to decide.
My instinct is that democratic voice is essential, and relying too much on technocracy could be both dangerous and counterproductive.
The counterargument that AI’s direction can and should be entrusted to technocracy would go something along the following lines. First, democratic decision-making has become imperiled in our age of polarization. Second, AI is sufficiently complex that most citizens won’t have a deep enough understanding to meaningfully contribute to the debate (and even to the question of what we want from AI). Third, competition between different labs, and perhaps competition between the US and China, creates enough discipline for a socially beneficial direction of AI to be adopted. Fourth, today’s AI leaders are enlightened and ethical enough that within the framework created by competition, they can be broadly trusted.
There are many aspects of this counterargument that I do not find convincing. Taking them in order: polarization can be overcome, and big decisions and challenges sometimes bring societies together; in fact, delegating key decisions to technocracy without democratic input may diminish trust in institutions and experts, and may worsen polarization. Second, democratic voice does not require citizens to write code or design new models; the debate should be informative enough that citizens can weigh in about what type of future they want and how they trade off the costs and benefits of different options. Third, competition doesn’t seem to be a good disciplining framework; on the contrary, competition sometimes brings the worst out of both organizations and people. Fourth, if three decades of work on political economy and institutions has taught me anything, it is that we should not bank on the ethical grounding of unconstrained leaders.
But, still, I do not mean to immediately dismiss the technocracy option if there are more compelling arguments for it.
The question is, then, whether there are any circumstances under which such important decisions can be delegated to AI experts and technocracy.
One final secondary question: even if we managed to get democratic input in the United States or even in Europe, AI will shape the lives of everyone on this planet. How do we ensure that the voice of nearly 6 billion people who don’t live in the US, Europe and China also contributes to the debates on AI?
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First question on AI.
Is the problem superintelligence or distorted intelligence?
This is a question I tried to address in this piece. I can add the following additional comment here.
I believe the debate is being obfuscated by equating goal-driven, autonomous behavior with intelligence. Plants and insects have goal-driven, largely autonomous behaviour. But this isn’t what we mean by intelligence in general.
Intelligence is general-purpose and holistic, and is typically well-adapted to its environment.
There is no doubt that current AI models have impressive capabilities. But in attempting to achieve these capabilities, AI labs may be creating distorted intelligence rather than superintelligence, as I explain in this article.
This matters because the dangers we should watch out for, the kinds of policies and regulations we should adopt, and the hope we should hold depend on the answer to this question.
Recent security breaches suggest that AI capabilities are developing rapidly while being put in the service of imperfect quantitative metrics that ultimately distort the behavior of the most advanced models, @DAcemogluMIT writes. project-syndicate.org/commen…
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Dear followers,
Over the next few days, starting today, I will post a number of threads on AI. My focus will be on uncomfortable questions for our current AI discourse. I am doing this because I feel that the public conversation on AI is not tackling the big issues and dangers, and what I view as important questions are not being asked by industry insiders and the media.
I must preface these posts by saying that I am not an AI scientist, so my understanding of some of the issues may be faulty. And I do not claim to know the answers to these questions. My aim is to make a small contribution by emphasizing the questions that I believe are important but are being neglected.
Thank you
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Daron Acemoglu retweeted
Economist Daron Acemoglu is not optimistic about the future of humanity if AI develops on its current trajectory.
Full interview is live now. @DAcemogluMIT
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I am delighted to see lawmakers start tackling the problem of how to make AI’s benefits more broadly shared. This requires both a change in the trajectory of AI in a more pro-worker direction, and more and better investments in worker skills, as Senator Kelly’s bill emphasizes.
When I flew off aircraft carriers and launched into space, we fixed problems before people got hurt. That's why I'm introducing the Make AI Work for Americans Act, which taxes the companies benefiting the most from AI and uses that money to invest in workers and give them more opportunities.
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I am also very much looking forward to this event with Michael Sandel tomorrow. For those who are interested, there is a live streaming option. The link for registration is below.
Don't miss our fireside chat this week with @DAcemogluMIT and Michael Sandel on "What Happened to Liberal Democracy?"
📅 Thursday 9/24
🕓 12-1 PM ET
📍 MIT and livestreamed
Link to register below 👇
Co-hosted by the Stone Center and the Leadership Now Project at @MITSloan
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Dear followers, I’m delighted to share this engaging discussion on my new book.
Nobel Prize-winning economist @DAcemogluMIT discusses his latest book, 'What Happened to Liberal Democracy?, on Downstream.
novaramedia.com/2026/09/21/n…
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I want to briefly explain why I joined Abhijit Banerjee, Peter Diamond, Esther Duflo, Paul Krugman and Joe Stiglitz in signing the letter on the California billionaire tax: ft.com/content/94ec0fee-53ab…
In principle, I am not convinced that permanent wealth taxes would be necessary if the tax-transfer system were designed optimally.
But the US tax system is very far from optimal, and has been for decades. As I have documented in my research (for example, here: mitsloan.mit.edu/shared/ods/…), labor income is taxed much more heavily than capital income.
This asymmetry creates two distinct problems.
First, it makes the tax system highly regressive at the top. The very rich, who receive much of their income from capital or can use accounting tricks to reclassify their income as capital income, pay remarkably little in taxes. For example, a business owner who runs their own company should receive a significant part of their income as labor earnings for their work as CEO. Instead, they can take their compensation in stock and borrow against those holdings to finance whatever consumption they desire, minimizing their tax obligations. Even their heirs may avoid paying these taxes.
Second, the asymmetry distorts automation decisions: it effectively subsidizes machinery and AI relative to hiring workers (for example, here: brookings.edu/articles/does-…).
These distortions have allowed a small number of people to amass vast fortunes without paying their fair share of taxes, and have fueled excessive automation.
The resulting inequality is a problem in its own right. It is all the more dangerous today because our institutions have become fragile, allowing the very wealthy to exert growing control over the political process.
A temporary wealth tax can therefore be justified on three grounds: (1) it partially reverses the effects of more than two decades of tax avoidance by the very wealthy; (2) it acts as a brake on their growing dominance over the political process; and (3) it may pave the way for more comprehensive tax reform at the federal level.
The California billionaire tax is not perfect. For example, a federal tax would lessen risks related to capital flight, and removing the rigid earmarking of the revenues for specific purposes would enable the proceeds to reduce the national debt. Nevertheless, with few other options on the table, I believe the California proposal deserves support.
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Daron Acemoglu retweeted
Really great to see this: six Nobel Prize-winning economists (Duflo, Krugman, Diamond, Banerjee, Acemoglu and Stiglitz) just came out in favor of the California billionaire tax.
You don't have to be a radical leftist to see why it's a good idea. First, look at the numbers: ten years ago, the richest 0.001% of Californians were worth $700 billion. Today, that same group (about 250 people) is worth $2.3 trillion.
From 2019 to 2025, their wealth grew by $1.4 trillion. The California income tax they paid over that period was just 1.6% of that gain. A nurse or a plumber pays a higher state tax rate.
Yes, many of these people built great companies, and they've made a lot of money in the process. Good for them! The problem is that wealth on this scale stops being just money. It becomes extraordinary power.
In 2000, billionaires accounted for about 1% of all donations in federal elections. In 2024, it was 19%. The Washington Post, the Wall Street Journal, the LA Times, Facebook, Instagram, TikTok and the platform you're reading this on: all owned and controlled by billionaires.
Now add AI. If you think power is concentrated now, just wait five years. It's going to get much worse.
So, what does Prop 40 actually do? It's a one-time 5% tax on the wealth of California's billionaires. That would raise about $100 billion, enough to offset the federal Medicaid cuts.
"But they'll all leave!" the critics say. Nope, they can't. The tax applies to any billionaire who lived in California on January 1, 2026, and the initiative only became public about five weeks before that.
"But it'll kill Silicon Valley!" Uh no, that won't happen either. Since the tax was announced, California's share of all new US venture capital has gone up, from about 50% to 80%. Nvidia CEO Jensen Huang, who would be one of the biggest payers, said he would be "perfectly fine" with it and had "not even thought about it once".
Sure, some other billionaires can't stop thinking about it. Sergey Brin has already spent more than $100 million to defeat the proposal. At a holiday party in December he pulled Gavin Newson aside and said 'he could not stand the state’s proposed billionaire tax', because he thought the 5% tax would turn California into some kind of Soviet Union.
That is, of course, nonsense. This is not going to be some kind of socialist revolution. The proposal is about restoring balance to a mixed economy. You could even argue it's about saving capitalism itself from oligarchs like Sergey Brin. I think that's exactly why Nobel Prize-winning economists are coming out in favor of this tax.
If Prop 40 passes in November, it will be the first tax aimed specifically at billionaire wealth anywhere in the world. I think it could be the start of a global movement.
Anyway, read the whole letter here. It's really good 👇 gabriel-zucman.eu/files/prop…
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Happy to share this article which emphasizes the difficulties of using AI in science.
Why AI is speeding up scientific research but not lab experiments | Scientific American scientificamerican.com/artic…
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Daron Acemoglu retweeted
AI firms are subject to already-existing laws, @linamkhan tells @cwarzel: Part of the conversation around regulating AI “seems to assume that these current developments are happening in some legal vacuum,” she adds, “and that is just false.”
theatln.tc/65NUk0Q9 #TAF26
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Daron Acemoglu retweeted
Honoured to have chaired the LSE event with @DAcemogluMIT on his new book
🎙️ Podcast here: lse.ac.uk/lse-player/what-ha…
We covered a lot in an hour: Acemoglu's core arguments, plus a few questions from me.
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