Emma Asomba

Development Policy Specialist - Rapporteur | Research and Evaluation || European Commission | UN System ||

[Emma Asomba @Emachi]

https://t.co/lNFtJ6wb4t
@GlobaldevB

[Vinyl Records🎱✝️🇧🇷👪🗽 @j_c_domingues]

Nuyorican Soul Featuring India
Runaway - 1997

[Howard W. French @hofrench]

Excited to receive the galleys for my new book, an urban history of New York City, the story of monumental musical innovations around the last mid-century, and a profile of one of America's greatest creators, Thelonious Monk, wrapped in one.

[Emma Asomba @Emachi]

@UjuAnya Uju! Welcome back ❤️

[K.Diallo ☭ @nyeusi_waasi]

هناك قصة مشهورة في البرازيل عن امرأة تدعى ماريا سوارس، مزارعة من أصول أفريقية، اعتادت السفر لسنوات في رحلة تستغرق 35 ساعة من ولاية مارانهاو إلى العاصمة، دعمًا للولا دا سيلفا وحزب العمال. ب��د وفاتها قبل أشهر من هذا العام، قرر أبناء ولايتها مواصلة مسيرتها، فانطلق الآلاف منهم منذ أول أمس في رحلة إلى العاصمة دعمًا للولا. يستحضر هذا المشهد التاريخ الطويل للعلاقة بين لولا وقطاعات من الطبقة العاملة والفئات الشعبية الفقيرة في البرازيل. فمنذ بدايات نضاله النقابي، وصولًا إلى تأسيس حزب العمال، ارتبط مساره بمحاولات تحويل مطالب العمال والفلاحين والفقراء والمهمشين إلى قوة سياسية منظمة تسعى إلى انتزاع حقوقهم والتعبير عن مصالحهم. لولا جاب مناطق مختلفة من البرازيل، متحملًا مشقة السفر والتنقل لمسافات طويلة، للمشاركة في الإضرابات والاحتجاجات، والاستماع إ��ى مطالب الفقراء والكادحين. لم يكن هذا الطريق خاليًا من التضحيات ومخاطر القمع والتهديد بالقتل والاعتقالات. تعكس رحلة ماريا وغيرها من الكادحين تجدد الثقة في إنسان يرون فيه امتدادًا لتاريخهم الاجتماعي، عامل خرج من صفوف البروليتاريا حتى أصبح في الوجدان مرتبطًا بإمكانية وصول أبناء الطبقات العاملة والفئات الفقيرة إلى السلطة والمشاركة في تقرير مصير البلاد، دون أن يتحملوا وحدهم كلفة الأزمات الاقتصادية عبر سياسات التقشف والإفقار، ودون أن تختزل سيادة بلادهم في الخضوع لإملاءات البيت الأبيض الأمريكي.

[Lula @LulaOficial]

O povo brasileiro mostrou sua força, sua coragem e sua esperança. Ocupamos as ruas para defender nossos direitos, nossa democracia e o futuro do Brasil.

Agora, chegou a hora de transformar essa mobilização em voto, essa esperança em vitória e essa história em um novo capítulo para o nosso país.

A caminhada foi feita por milhões de brasileiros. E é com essa força, essa união e essa esperança que vamos até o fim. Até a vitória, companheiros e companheiras.

[Ana Lucia Araujo, PhD @araujohistorian]

É LULA!!!!!

[Vanessa Govender @Govender_V]

I had never heard of @ori_goldberg until his tweets began appearing on my timeline after October 2023. He was Israeli, yet he wrote of Palestinian suffering with a painful honesty that must surely have come at cost. I began following him because what he wrote and where he came from were such profound juxtapositions. Conundrums. Contradictions. How was this possible?
October 2023 changed Ori. Watching the response unfold from within, he came to name what was happening in Gaza as genocide and to describe, with unusual honesty, the moral collapse of Israel and dehumanization he saw his own people inflict. That clarity cost him the platforms and acceptance he once had among Israelis. His own society largely rejected him for it.
He kept writing anyway, about the children under the rubble, about living inside a genocidal society, about the refusal to see Palestinians as human. Until cancer took him at 50. Ori Goldberg has passed away.
“May their memory serve as the torch that lights a revolution for justice, first in Palestine and then all over the world.”
Ori Goldberg’s voice does not die with him. It should not! May it be amplified even louder. In this cruel and unjust world, may his writings be spread far and wide.
An Israeli and a Mensch. That was and will always be Ori Goldberg! Two concepts inhabiting one human being that some would deem an oxymoron. Ori Goldberg proved it is possible.

[Emma Asomba @Emachi]

@ori_goldberg This news hit me like a ton of bricks. Wow! My deepest condolences. He was one of a kind in his humanity and knowledgeable to a T. He brought so much to the conversation. May he continue his journey peacefully, with the same grace he blessed us with.

[Daron Acemoglu @DAcemogluMIT]

Seventh and last question on AI.
Can AI really help us create as many jobs as it will replace?
The AI industry offers two narratives at once.
The first emphasizes that because of AI’s capabilities, there will be a lot of job loss, and soon. Anthropic’s co-founder and CEO Dario Amodei is particularly vocal on this: https://t.co/hcOmaSZIKD
The second claims that AI will also enable lots of new jobs, so we should not fear joblessness, higher unemployment, and humans being sidelined (see, for example, Jensen Huang, https://t.co/6ZO0eGWvuX or Marc Andreessen, https://t.co/JrKsPIpTvh).
A lot hinges on this question. Sizable job displacement is costly for workers and could destabilize our already ailing democracy. Displacements from robots and cheap Chinese imports in the 1990s and 2000s in the United States, which were smaller than the projected job losses from AI, contributed significantly to declining trust in government and institutions (see my book What Happened to Liberal Democracy, https://t.co/7xaaR1eyuw, on this). Our democracy may not be able to survive another massive job dislocation.
I am worried about job loss, for two fundamental reasons. AI is currently being developed as an automation technology and in addition, I do not find the arguments about large numbers of jobs being created by AI particularly realistic. (I’ll come back below to how AI could create jobs if it weren’t developed as an automation technology).
Proponents of the job creation side fall back on four general arguments, none of which is convincing in my assessment.
First, new technologies have always created lots of jobs. True, but that does not guarantee that this time will be the same. And more importantly, this account neglects the richer details about different historical episodes. In a nutshell, automation technologies by themselves need not create jobs (though they can, depending on circumstances and how productive they are). They involve capital substituting for tasks previously performed by workers. The way they would create jobs is that the displaced workers would go and take non-automated jobs in other parts of the economy. This can happen, but it also depresses wages (or at the very least the labor share), and consequently, some of the workers may be discouraged from looking for work or may not have the skills that remaining non-automated tasks require. This, in any case, is what task-based models predict. See, for example: https://t.co/czsxeCUjjZ and https://t.co/zlb8XL7vfC.
The empirical evidence also casts doubt on plentiful job creation taking place automatically. For example, the introduction of robots led to many fewer jobs in local economies that depended on blue-collar jobs in robot-heavy industries (and this effect was quantitatively large; one more robot per thousand industrial workers reduced the employment population ratio in the local economy by about 2 percentage points and local wages by 0.42% between 1990 and 2007). See https://t.co/Lteugf7ckL.
Second, AI will increase productivity and higher productivity will lead to more jobs. But this argument suffers from the same weakness as the previous one: higher productivity will trigger job creation only in remaining non-automated tasks (unless there are new tasks, which I discuss below). If wages are depressed and workers don’t have the skills for these non-automated tasks, even with higher productivity, there will not be AI-driven job creation.
Third, AI will need a lot of supporting roles, such as people to train the AI models. This one is the weakest argument of all. If AI is indeed an automation technology, then the whole point is to save labor costs. By the same logic, the labor needed to train AI must be much less than the labor AI ultimately saves. For this reason, I see essentially no chance that there training and maintaining AI will create anywhere near as many jobs as AI replaces.
Fourth, AI is already complementing workers. This argument is based on some evidence that many workers are using AI in their jobs – see, for example, https://t.co/P70kGyTvuO. However, the mere fact that workers are using AI in their jobs doesn’t establish that new jobs will be plentiful.
The theory here is again quite clear (see the detailed treatment of this issue here, https://t.co/AbYZ2zaU49). If AI simply makes workers more productive in the tasks that they used to perform already, it will drive down task prices. If you are the only worker using AI, this will benefit you. If all workers like you start using AI, then the skills on which you depend become commodified. This is what happened, for example, to London black cab drivers, whose highly-valuable knowledge of London streets was commodified when GPS technology enabled minicabs, and then later Uber drivers, to perform the same tasks as effectively. To put it simply, it isn’t enough for AI to make workers more productive at the tasks that they are performing already.
This brings me to how AI could create a lot of jobs. Again, the task-based framework provides the answer here: new technologies create jobs, and in fact well-paying jobs, if they enable new tasks (either in the same industry or in new industries). I have argued elsewhere that AI has this potential as a powerful information technology. See, for example, https://t.co/aDd8GLNIkN. But this isn’t currently the direction in which AI is being developed. Hence, on its current trajectory I remain quite concerned about future job losses (and wage declines) from AI.
Of course, things may turn out differently. But if they do, it will be because AI becomes more pro-worker. I see this as unlikely, but it is useful to still consider the scenarios that would bring such an outcome. These are: (1) current AI technologies may become turn out pro-worker than I am anticipating; (2) in the future the industry will automatically and by itself change and focus on pro-worker applications; (3) based on AI-driven science and research, new sectors will be created where there will be need for new workers.
I’m not optimistic about any of these possibilities, and therefore my conclusion is that AI is likely to lead to more jobs being lost than gained. (But I do think there are policies that would encourage pro-worker AI; see, for example, https://t.co/yIjrtqmEeJ).
That being said, I don’t think the job loss will happen very quickly, because as I discussed in an earlier post (third question), the diffusion of AI is relatively slow. But this also implies the productivity gains from AI will be slow in coming.

[Daron Acemoglu @DAcemogluMIT]

Sixth question on AI.
Does AI need to be a zero-sum race between the US and China?
Many influential voices in tech as well as the current administration argue that it does. (See, for example, https://t.co/63jNKZIGON, https://t.co/82WRjDEklx, https://t.co/3isNlCWI2U and this from the White House, https://t.co/roDrwiWjIj).

There are many aspects of these arguments, but I would like to focus on three that have received less attention than they deserve.

The first aspect is that this framing typically assumes that there is a single direction of progress for AI, and the US and China are racing along this dimension toward superintelligence or at the very least some sort of powerful AI that will confer huge military and strategic advantages to the winner. If, on the other hand, the US and China are developing these technologies in different directions, there is much more room for coordination and even for each country learning from the other’s best practices.

My reading of the evidence is that there are huge differences in the American and Chinese approaches. The US is focused on foundation models, such as frontier large language models. China is also investing – and arguably catching up – in these models, but there is much greater emphasis in China on applications of AI (with the government setting targets for the spread of AI across industries, https://t.co/1yimDjGmzK).

This reflects several factors, in my assessment. First, Chinese AI investments started with government demands for surveillance technologies and government-supplied data to AI companies (e.g., https://t.co/GXWDWukQty). Second, China is facing a huge demographic transformation, which will create scarcity of labor in the near future. This encourages companies to integrate robotics and AI much more actively into the production process (and China accounts for 59% of all robot installations in the world, https://t.co/YQjXLWZwrs). Third, applications and integration of these technologies into the production process requires a lot of engineering. China has a large and very well-trained supply of engineers (whereas the US has far fewer engineers and a lot of engineering talent is currently going to Silicon Valley, which hasn’t focused on applications). Fourth, China was relatively late in catching the “artificial general intelligence,” AGI, bug, although now there is more emphasis on this in China as well.

Whatever the exact causes, this difference in approach implies that there is plenty for the US to learn from China as well as the other way around.

The second aspect is that the zero-sum framing ignores or downplays the crucial need for global coordination. This framing claims that the strategic and military advantage that powerful AI will create is all that matters. But the race to AGI also poses huge risks (and this is true regardless of whether this race is truly advancing toward superintelligence or just producing distorted intelligence, as I argued in my first post in this series). And these risks are global. If the US and China are racing toward AGI with no guardrails and coordination, even the follower (probably China) will have models capable of creating havoc. In this case, racing without any global coordination is a recipe for disaster.
The third aspect of the problem is that this framing narrows the scope for any kind of coordination on other global issues between the US and China – including climate change, pandemics, nuclear nonproliferation and world peace. Tellingly, climate cooperation between the US and China has now largely stalled).

Let me add that I’m saying all of this not as an admirer of the Chinese system. On the contrary, I have been a vocal critic of both the Chinese Communist Party and the Chinese development strategy since Why Nations Fail (https://t.co/NMlp4VprG7) and I have argued that the Chinese success has some of its roots in severe labor repression (https://t.co/14SDaw2Y8H).

Indeed, this argument does not ignore the fact that China has an authoritarian, repressive regime, and it is using AI for surveillance and to stamp out dissent. Global coordination and efforts to set up reasonable rules that apply to all parties do not require the counterparty to be fully democratic or ruled by angels. In fact, sometimes it is more important to set up such a global framework when dealing with dangerous adversaries (as the arms-control treaties that the US and the Soviets signed demonstrate).

In sum, my assessment is that it is a colossal mistake to frame the AI race as a zero-sum one between the US and China. It poisons the much-needed dialogue and coordination between the US and China on broader issues; it makes the AI race more reckless; and it discourages the building of reasonable guardrails and regulations.

The last point is particularly relevant, and makes me sometimes say: if China did not exist, Silicon Valley would have to invent it.

[Daron Acemoglu @DAcemogluMIT]

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 (https://t.co/WROlEDnvYx).

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.)

[Daron Acemoglu @DAcemogluMIT]

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 https://t.co/3D0lTFCwmA).

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 https://t.co/yWHpDCNgOF).

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 https://t.co/uQMWlQ02Ul). 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, https://t.co/LndeZHpj60).

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: https://t.co/VZI2JbO4Gf).

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).

[Joshua Gans @joshgans]

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.

[Joshua Gans @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.

[Branko Milanovic @BrankoMilan]

Branko Milanovic
Recommended Books
46 books Branko Milanovic recommended
https://t.co/igykGtuAqW

[United Nations University @UNUniversity]

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[Emma Asomba @Emachi]

@DAcemogluMIT The real dilemma isn’t AI profits but who owns the gains. Without redistribution, either a crash or oligarchy. Democracy can’t survive extreme inequality. So, let’s consider this perspective: Who should own AI’s upside?

[Daron Acemoglu @DAcemogluMIT]

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: https://t.co/QIGcoObcVc).

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% (https://t.co/IuYldl173r).

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, https://t.co/2D9KUL3QfM). 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?

[Daron Acemoglu @DAcemogluMIT]

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?

[International Labour Organization @ilo]

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