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.)

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).

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.

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?

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?