Speaker 1Around the world, people are worried about AI. Not just because it might be used to hack our systems, not even just because it might take our jobs. But instead, because by making this intelligence that we've long felt unique to us abundant, we worry about what it means for our own relevance. This is not, in fact, the first time that we've dealt with these issues. And 400 years of economic history suggests that something different, and in fact quite wonderful, might happen instead. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. All right, friends, quick announcements before we dive in. First of all, thank you to today's sponsors, Robots and Pencils, CDATA, Granola, and Blitzy. To get an ad-free version of the show, go to patreon.com slash AI Daily Brief, or you can subscribe on Apple Podcasts. And if you want to learn more about sponsoring the show, send us a note at sponsors at AI Daily Brief. Welcome back to the AI Daily Brief. Today, we have a cool one for you. The original idea of Long Read Sunday was to take the most interesting big ideas that I had found that week surrounding AI, whether it was from a technology perspective or AI in society, share those writings, and then broaden out the conversation from there. It's evolved a bit from when we started. Sometimes it is more oriented, for example, towards the practical. But every once in a while, there is a good old-fashioned, big, thinky piece of writing that I get very excited to share. And this week, that's exactly what we have. There is a piece that has been making its way around AI circles called Cognitive Mercantilism. It's by writer and strategist Venkatesh Rao, and I caught notice of it when I saw a bunch of people whose perspectives I admire saying how surprised they were to find something so original and thought-provoking around AI. So, as is the style of Long Read Sunday, I will read some big, chunky excerpts from this, and then we will discuss it. And for fun, as I'm preparing this, I have Claude Cote in the background working through some illustrations to keep it a little bit more interesting for those of you who are watching. Venkatesh writes, I've been reading Joyce Appleby's The Relentless Revolution, A History of Capitalism, and it has me circling a rather delicious thought. AI panic is really a form of cognitive mercantilism. If we regard market capitalism as the first planetary intelligence, as many, including me, do, the parallels are closer than you might think. Appleby's book is a sweeping history of capitalism, from its unlikely emergence in early modern England to its eventual transformation of the entire world. Her central argument is that capitalism was not the inevitable expression of some timeless human instinct to trade and accumulate wealth. It was a historically contingent cultural invention enabled by agricultural improvements, expanding trade, and changing ideas about work, wealth, and human nature. In particular, it required escaping the assumptions of an older, essentially zero-sum worldview, in which wealth meant controlling land, bullion, and the labor of largely impoverished populations. Capitalism emerged when people began discovering that wealth could be created, rather than merely captured, and that ordinary people could be sources of demand and initiative, rather than merely labor, to be commanded as a factor of production. The parallels with our current moment are striking. As Appleby tells it, the mercantilist worldview that preceded capitalism rested on roughly four interlocking beliefs. First, wealth was fundamentally scarce and finite. It consisted primarily of land, the agricultural produce of that land, and the precious metals it contained, accumulated through mining, trade, and conquest. The goal of economic statecraft was to control more territory, maintain a favorable balance of trade, and accumulate bullion. One nation's gain was presumed to be another's loss. The resulting politics was one of treasure-seeking and beggar-thy-neighbor competition. Second, ordinary people could not be trusted with much freedom. The poor were assumed to be unruly, ignorant, and prone to disorder if left to their own devices. They needed to be governed coercively by elites, supposedly for their own good, and for the stability of society. Third, economic thinking was overwhelmingly, oriented towards supply and its control. Wealth came from controlling productive resources and commanding high prices for their output. Aggregate demand, especially demand from the poor, was barely recognized as a significant economic force. Ordinary people were primarily factors of production. They ate, worked, and reproduced, with no share of any surplus. The only real discretionary consumers were elites, who bought luxuries and commanded all surplus. Fourth, there was little reason to imagine most people doing anything other than agricultural labor. If something like 80% of the population was engaged in the production of food, it would simply be the natural order of things. What else were they good for? Remove them from the fields, and you would merely have millions of useless mouths to feed. Now replace wealth with intelligence, and mercantilism with cognitive mercantilism. The parallels are almost embarrassingly direct. First, intelligence is imagined as a fixed resource, embodied in human brains and the knowledge products mined from them. The objective of national and corporate strategy becomes attracting the best brains, controlling their output, accumulating intellectual property, and maintaining the best of the world. This is not a good thing. It is a bad thing. It is a bad thing. A favorable balance of knowledge trade. The cognitive mercantilist wants to hoard intelligence and its products like bullion. The global competition for talent becomes a treasure hunt for genius brains and the valuable knowledge species they can produce, which now includes AIs in addition to copyrightable culture and patentable inventions. Second, supposedly stupid people cannot be trusted to think for themselves. They need to be governed by cognitive elites, who know better and must regulate the behavior of the less intelligent for their own good. Democratizing intelligence is dangerous because the masses will misuse it, fall for nonsense, or unleash chaos. The assumption is that intelligence is safe only in the hands of those already certified to possess it. Third, the supply of intelligence is treated as the central economic fact. Controlling its production, preserving its scarcity, and maintaining high prices for brains and knowledge products, including AIs, are the primary concerns. The demand side barely registers. Less intelligent people are regarded as limited cognitive laborers and passive consumers of whatever knowledge products elites choose to provide. They are not imagined as significant sources of demand for intelligence. They are only intended as a means of providing intelligence to other people. Fourth, the cognitive mercantilist assumes that most people are not good for much intellectually. If 80% of humanity is occupied in low-grade cognitive labor, or simply muddling along without producing much intelligence at all, that is unfortunate but natural. Automating their cognitive work merely threatens to create billions of useless minds to occupy. This last assumption is where the analogy gets especially interesting. The agricultural revolution that helped launch capitalism began overturning the apparent necessity of having most of humanity engaged in farming. Over the following centuries, improvements in agricultural productivity reduced the fraction of the population required to produce food from a large majority to a tiny minority in advanced economies. The result was not a world full of useless mouths. It was a world in which vastly more people could do things other than farm, while enjoying access to vastly more food. I don't know how to farm. I can barely keep a houseplant alive. Yet I can obtain an astonishing variety of foods in quantities that would have seemed fantastical to an early modern peasant. My inability to produce food is economically almost irrelevant. What matters is that I can consume it, enjoy it, develop taste for it, and devote my energies to other things. The analogous possibility with AI is that most people will no longer need to be significant producers of intelligence in order to become significant consumers of it. And this is where cognitive mercantilism begins to look as quaint as its 17th century predecessor. Now, at this point, and this is NLW cutting in again, I want to go back and reference an episode I did a long time ago, all the way back in May. The episode was called "The New Jobs Aide." This episode was called "The New Jobs Aide." This episode was called AI Will Create. There's still a companion experience available online, and it was my attempt to show not precisely exactly what jobs I think AI will create, but to go beyond the tired trope that of course AI will create new jobs, which is what all of us optimists think, and instead try to actually identify some types of jobs or categories of jobs that might result. One of the big parts of my argument was that the lump of labor fallacy that runs through so much of thinking about AI job loss, that there's only so much work to be done, and if machines do it, humans won't be able to do it, and if machines do it, humans won't be able to do it, and if machines do it, humans won't be able to do it, has another interconnected piece, which is an assumption that there is no elasticity of demand. That we demand as much right now as we could ever demand. My main point is that that is crazy, and completely ahistorical when it comes to human evolution. Instead, I believe that our capacity to increase our demands is nigh limitless. In fact, in some ways it is constrained only by physical forces and universal laws, like how much time there is in a day, and how many calories we can consume without bursting. I won't go too deep into it now, but in that episode I discussed demand elasticity across six different dimensions. Price elasticity. I wanted it, but it cost too much. Access elasticity. I wanted it, but I couldn't get it. Complexity elasticity. I needed it, but the system was too confusing. Continuity elasticity. I get help occasionally, but I'd benefit from help all the time. Personalization elasticity. I get the generic version, but I'd value something made for me. And relational elasticity. I want this to be more human, meaningful, and trusted. All of these are different ways in which AI could change systems that result in more demand becoming unlocked. Okay, so with that in mind, let's come back to the next section of Venkatesh's piece, The Revolution in Desire. He continues, one of the more fascinating elements of Appleby's account is her emphasis on the emergence of new kinds of demand in Britain, well before the familiar story of steam engines, factories, and industrial capitalism really gets going. As international trade expanded, ordinary Britons began developing tastes for goods that had previously been luxuries reserved for the rich. The wealthy, tobacco, tea, sugar, and colorful Indian cottons were among the commodities that helped create a new consumer culture. People began wanting things that were neither necessities of subsistence nor traditional markers of aristocratic status, and they were willing to voluntarily work harder to get them. Appleby describes a striking increase in the number of days worked annually, from something like 250 to 300 before the Industrial Revolution. This was not simply a matter of landlords or factory owners coercing more labor out of unwilling workers. A significant part of the increase was voluntary. Driven by the prospect of acquiring desirable goods. People worked more days, adopted more productive agricultural practices. entered new trades, and sought opportunities for earning larger cash incomes. The increase in productivity generated surplus incomes, which sustained the expanding demand for consumer goods, which in turn incentivized further increases in productivity. A virtuous cycle was taking shape. Zooming forward a bit, he continues, There is an important historical inversion here. We are accustomed to thinking of the politics of labor primarily in terms of resistance to exploitation, the struggle to escape servitude, coercion, and the extraction of surplus by owners. That is certainly part of the story and a very important part. But Appleby's account suggests that another, equally fundamental force was at work, the desire to participate in an expanding universe of consumption opportunities. Modern labor politics can be understood partly as the politics of relative deprivation in a society of expanding possibilities. Once people discover that there are desirable things beyond subsistence, and that their labor can earn them access to those things, the political question changes. It is no longer merely how to escape domination, but how to secure a fair share of the growing surplus and the opportunities it makes available. This is a different kind of demand for freedom, not merely freedom for coercion, but freedom to participate in the expanding possibilities of economic life. The demand for a fair share of expanding consumption opportunities is arguably one of the defining political inventions of capitalism. And this is a much more interesting model for thinking about the AI transition than the familiar story of automation replacing jobs. At this point, it's no longer a question of whether companies are actively using AI. Using AI is no longer a question of whether companies are actively using AI. It is no longer a question of whether companies are actively using AI. 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Education, professional expertise, research, consulting, software development, and knowledge work more generally are all shaped by the high cost of cognitive production. AI is rapidly undermining that assumption. We are moving toward a world in which an ordinary person can command quantities and varieties of intelligence that would have previously required teams of highly trained specialists. Just as I can enjoy food without knowing how to farm, I can increasingly enjoy the products of intelligence without knowing how to produce them myself. The crucial economic question then becomes not how much intelligence people can produce, but how much intelligence they can find uses for. And there is no obvious upper bound on that demand. This is not simply a matter of everyone consuming more answers, reports, software, or entertainment. Abundant intelligence changes what people can want in the first place. It makes previously unimaginable projects feasible, previously inaccessible domains explorable, and previously unaffordable curiosities worth pursuing. The demand for intelligence is not a fixed appetite waiting to be satisfied. It is, itself, something that can grow as the cost of satisfying it falls. Here the analogy with the early modern consumer revolution becomes especially suggestive. Just as colorful Indian cottons and imported tobacco stimulated new desires, and new desires stimulated greater voluntary labor and more productive forms of work, abundant intelligence may stimulate a new wave of voluntary cognitive effort and open up new ways in which you can work harder. Many people who appear stupid are not incapable of thought. They are simply not particularly motivated to think, given the poor returns available to them. Why expend significant cognitive efforts when you lack the knowledge, tools, institutional access, or intellectual leverage to do anything interesting with the results? Abundant AI changes that calculation. Someone who previously had little reason to think hard about a problem may suddenly discover that a modest investment of attention unlocks enormous possibilities. They can build something, investigate something, understand something, pursue an ambition that was previously out of reach. The returns on attention rise, and with them, the incentive to pay attention. And here Venkatesh offers a different explanation than we've seen for why it feels like AI is not making us work less, but making us work more. Now this is something that I've also offered an explanation for, talking about a concept I have of the Infinite Backlog. The idea that at work, there is always a basically never-ending set of work to be done that runs off infinitely into the future. The reason that the Infinite Backlog doesn't overwork is because there is always a natural limit to the amount of work we can do. We're basically constantly calibrating what is an appropriate amount of that backlog to have taken on and gotten accomplished. Done is not some fixed natural state. It's a negotiation with your own Infinite Backlog based on your own goals or those of your employer. The reason I posited that AI and specifically agents break that paradigm is that when you can replicate yourself theoretically an infinite number of times, and those infinite replicas can work in parallel and non-stop, you can replicate yourself theoretically an infinite number of times, and those infinite replicas can work in parallel and non-stop, without ever having to sleep or eat or otherwise get distracted, it feels like all of a sudden, there is no longer an appropriate done. The entirety of the Infinite Backlog becomes unlockable, and the only constraint is your ability to imagine what is on that backlog in the first place. Now obviously, as this was my idea, I think there is something to it, but it's still in many ways only dealing with the anxiety side of AI making us work more. And many of you, I am sure, will have felt the opposite to that, which is the excitement. The "I can't make myself go to sleep because I just want to build one more thing" side of AI. And that's what Venkatesh is talking about here. He continues, "The early capitalist worker voluntarily increased annual working days from 250 to 300, because a new universe of desirable goods had become accessible. The AI-enabled thinker may similarly increase the amount of voluntary cognitive effort they expend, because a new universe of intellectual consumption has become accessible. This has certainly been the case with me. I'm suddenly willing to work significantly harder compared to even a year ago, because a lot more is suddenly within reach. We might call this a cognitive-industrious revolution, preceding and perhaps enabling a fuller AI-industrial revolution. This does not mean that intelligence becomes irrelevant as a human capacity. Quite the opposite. Just as abundant food supported the emergence of elaborate culinary cultures, rather than eliminating interest in cooking, abundant intelligence may simulate increasingly sophisticated cultures of thought. People may become more intellectually ambitious precisely because they no longer have to do all the cognitive production themselves. And if this happens, the politics of AI will likely evolve in a manner analogous to the politics of industrial capitalism. The central political demand may not be for the preservation of existing cognitive jobs, any more than the enduring political demand of industrial labor was for the preservation of agricultural employment. It may instead become a demand for a fair share of expanding cognitive consumption opportunities. Access to intelligence, the ability to command it, the freedom to pursue projects with it, and the institutional rights necessary to participate in an increasingly intelligence-rich economy may become the foundations of a new kind of labor politics. This would be the labor politics of cognitive abundance rather than cognitive scarcity. From there, Venkatesh gets into another important analogy, comparing mercantilism to Malthusianism. He notes that while they are not the same doctrine, and historically belong to different intellectual moments, they quote, share a family resemblance in their tendency to imagine human economic life as fundamentally complex and complex. And he notes that while they are not the same doctrine, they are constrained by fixed resources. The mercantilist, he continues, worries about controlling a finite stock of wealth. The Malthusian worries that population growth will inevitably outrun the productive capacity of the economy. economy, condemning most people to subsistence. Both have difficulty imagining a world in which technological innovation and expanding demand fundamentally transform the relationship between population, production, and consumption. We can now identify an analogous tendency in contemporary AI discourse: AI Malthusianism. The AI Malthusian imagines that intellectual production will expand so rapidly that human beings will become economically redundant, and the surplus intelligence will necessarily turn toxic in some way. This is a peculiar inversion of classical Malthusianism: instead of population outrunning the supply of subsistence, the supply of intelligence outruns the supposedly fixed capacity of human minds to absorb it. The result is the same gloomy conclusion: collapse. The cognitive mercantilist fears losing intelligence of scarce intelligence. The AI Malthusian fears the consequence of abundant intelligence. Both assume, in different ways, that the demand for intelligence is fundamentally limited. But what if that demand is as elastic, inventive, and historically transformative as the demand for material goods turned out to be? What if the apparent surplus of human minds is an artifact of our inability to imagine what people might want to do in an intelligence-rich world? The history of capitalism suggests that this is at least a possibility worth taking seriously. The demand side of AI. There is also a more radical extension of the argument. Now, from there, Venkatesh goes even farther into some future explorations. He brings up the idea that the demand is a possibility, and that the demand is a possibility. But what if the demand is a possibility? He basically explores the possibility of what happens if AI is not solely a supply-side technology, but also one which produces demand. Now, this section, frankly, deserves an entire second essay, because my sense is that Venkatesh is trying to articulate not AI's producing demand because they have somehow developed consciousness, but because the systems they architect and get architected around them create pathways and needs for more intelligence consumption and demand. As he puts it, although without explaining too much farther, many products and services exist primarily because the market demands it, with humans being secondary consumers. Venkatesh's final section is called Mercantilism Was Not for Merchants. An irony, he writes, in the historical precedent, is that mercantilism was not a philosophy of, by, or for merchants. It was largely a philosophy of militaristic statesmen, aristocrats, and rentier elites who viewed commerce through the lens of territorial power, unearned privileges, and accumulation. Actual merchants, entrepreneurs, and innovators were often discovering possibilities that the prevailing political imagination could barely comprehend, and steadily fought the self-limiting tendencies of mercantilism, giving birth to capitalism as we understand it. Today's cognitive mercantilists have a similar relationship with intelligence, starting with the fact that most of them are not the merchants of AI. Even business leaders of the AI sector are behaving like cognitive mercantilists, in direct proportion to their desire to be more than mere merchants. Across the political spectrum, many elites remain attached to the idea that intelligence is a scarce, dangerous resource that must be managed by credentialed custodians, kept expensive, used directly or indirectly for conquest and treasure, and denied to rivals. In the AI industry, to the extent elite political and cultural adventuring by CEOs distorts behaviors that are justified by the actual risks, it is mercantilist rather than capitalist. The prospect of its democratic abundance, in the form of global diffusion, open weights and such, is therefore experienced primarily as a threat to expertise, cultural and political authority, status, social order, military dominance, and geopolitical leverage. What gets missed is the possibility that we are witnessing the beginning of a transition as profound as the one from an overwhelmingly agricultural society to an industrial and commercial one, from a world where 80% of human brains are engaged in subsistence cognition to one in which 95% are engaged in satisfying previously unfulfillable cognitive demands. The most important consequence of the agricultural revolution was not that farming became more efficient. It was that most people were liberated from the necessity of farming, allowing entirely new kinds of economic and cultural life to emerge. Likewise, the most important consequence of the AI revolution may not be that intelligence production becomes more efficient. It may be that most minds, human and artificial, are liberated from the necessity of producing intelligence merely to survive, and become free to discover what they actually want to do with it. The cognitive Mercandilus sees a world threatened by an oversupply of intelligence and an impending surplus of useless minds. The AI Malthusian sees a world in which intelligence outruns the capacity of humanity to absorb it, leaving a growing population of economically redundant minds primed for revolutionary explosions. The Cognitive Capitalist sees the possibility of an almost limitless expansion of demand, and a world full of minds whose potential has barely begun to be explored. So that is the end of the essay, and hopefully you can see why I was excited to share it. Even for those of you who are furiously scribbling down things that you disagree with, my guess is that there is at least a piece of you who is excited to be prompted to think this deeply. And in that, interestingly, the essay is in a way not only describing its point, but also making its point. Interesting bodies of thought like this provoke responses. They encourage us to get pulled in and use our brains to imagine things that go beyond how we respond to the memo at work or how we answer the question at school, and instead let our minds roam out into the cognitive ether, exploring fuzzy, foggy places which we haven't yet had context to traverse. The question that Benkatesh proposes is whether the reason most people don't take the time to traverse those areas is because they don't have the capacity or the interest to, or because the surplus required to give them the time and justification to do so hasn't previously been available. The optimistic answer that he comes to is clearly that he believes that it might be the latter. And a world in which more people are off exploring those fuzzy, foggy areas is one with greatly increased demand, where the main condition of the time is not of having been replaced, but of having been allowed to become explorers. Now, I want to make clear that I don't believe that any of this is a direct argument or response for or against any particular AI safety consideration, at least not in this version of the essay. In other words, I don't think that there's any mutual exclusivity between being excited and even optimistic about this possibility that he's describing, while also being concerned about the transitional period where artificial intelligences are doing a bunch of the work that was previously the job of our brains. Transitions between orders, even if the order that results is a good one, are never without cost. But it looks quite different if our response is to try to deal with the challenges of the transition rather than to try to stop the transition from happening. Anyways, a lot to chew on. I will close it there. Hopefully you've enjoyed this piece. I will of course include a show notes to the original. You should go read it. I did not read the entire thing and it is absolutely worth to time. For now, that is going to do it for today's AI Daily Brief. Appreciate you listening or watching as always, and until next time, peace!
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