Where the Economy Thrives After AI

Today on the AI Daily Brief, we're talking about the part of the economy that will thrive after AI. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. All right, friends, quick notes before we dive in. First of all, thank you to today's sponsors, KPMG, Granola, Super Intelligent, and Blitzy. To get an ad-free version of the show, go to patreon.com/aidailybrief, or you can subscribe on Apple Podcasts. If you want to learn more about sponsoring the show, send us a note at sponsors@aidailybrief.ai. Now, two other quick notes. Yesterday, if you haven't seen yet the bonus operators episode, we launched our latest free AIDB training program. This one is a Noufar Gaspar joint. She took a bunch of what I had built with Claw Camp and turned it into a more general, more extensible, adaptable, ongoing agentic operating system program. So if you have dabbled with OpenClaw or have wanted to, but want an agentic system that can slot in Claude Code or Codex or Cursor or any other model or harness that you're using, check out AgentOS. There are links to it from aidailybrief.ai. And when you sign up for AgentOS, you can actually do it with your new AIDB handle. I plan on doing a lot of these free programs. And so instead of you having to sign up with emails and passwords every time, we've created now an AIDB single sign-on with of course cool vanity handles. So even if you aren't signing up for a program yet, but you just want to squat on your favorite 3 or 4-digit handle, you can do that once again from aidailybrief.ai or directly at aidblabs.ai. But with all of that out of the way, let's talk about AI and the economy. Welcome back to the AI Daily Brief and happy weekend. Listen, it will probably surprise none of you that I have what I would call major beef with the AI jobs discussion. It is not just that I think that the labs themselves are doing an unfathomably horrible job pitching the value of AI. I've made the comparison in the past to pharmaceutical commercials. If you watch a 60-second pharma commercial on cable, which is basically the only commercials they have at this point, the first 45 or 50 seconds are spent around how this new drug or treatment is a miracle cure for some really painful, awful thing that people have otherwise had to just live with. Now, yes, at the end, there are those 10 to 15 seconds of disclosures around potential side effects, some of which can be very, very bad. But the emphasis is on the better life that you get to lead that justifies all of those risks. In AI, we're basically exactly the reverse. We spend the first 45 to 50 seconds of our metaphorical commercial, i.e., every time we get a chance to talk to anyone about AI, talking about how damn bad society is going to be after AI gets its hooks in, with only this tiny little consideration of the potential benefits, which are presented as very hand-wavy at best. Anyways, that critique is not actually the core of my frustration with the jobs discourse. The core of my frustration with the jobs discourse is one, that I fundamentally think that all of these people who are claiming that we're gonna see 15, 20, 30, 50% unemployment are just straight up wrong. And they're wrong in part because of our failure to actually try to spend even any time thinking about what jobs and employment and the economy looks like on the other side of AI and agents actually being fully integrated. To put it differently, as with any new technology, we are in a period of creative destruction. Always it is the case that the destruction is easier to see before the creation because the destruction tends to happen earlier. The creation is what gets born, and it's very hard to see the shape of the new thing before it actually starts to come into being. And yet that does not mean that we can't spend time trying to understand by following reasonable traces and first principles. What might happen in the future that would change the shape of how we think about any destruction that happens in the present. The big area that I'm interested in, and I'm working on a bunch of things around that will come to the show at some point, is the idea that in an AI world, the constraints in the economy shift from supply, how much we can make of stuff, to demand and consumption capacity, how much we can actually consume of things, which in many cases is going to be a vector of time and attention. In a world where the constraint shifts from the supply side to the demand side, the really imperative question becomes, in what areas could we consume a lot more than we are now? And then of course from there, what would it look like to consume more? What are the types of consumption that's happening? And what is the infrastructure that's needed to build or provide those new things that we want to consume? The example that I'm spending a bunch of time on is healthcare. I think we consume a vanishingly small amount of the health services that we would if our system wasn't so expensive and so reactive. There are an immense number of opportunities for preventative care, data monitoring, tracking, and a whole infrastructure that could go around them, enabled by the decreases in cost that come from widespread implementation of AI into the health system. That argument isn't done yet, but it's why I was so interested to see an essay from economist Alex Emas start to really go viral and impact the jobs conversation, at least in our little corner of AI Twitter. Alex isn't exploring exactly the same themes, but as you'll hear, there are some similar interests here. And by way of background, I think it's worth noting that Alex didn't come to this from an ideological perspective looking to justify AI. Instead, he told Fortune, "My first reaction was to be very scared. I needed to work things out carefully in order to be less scared. Not to convince myself not to be scared, just to look at history and look at people's preferences and bring these things together." So let's read Alex's essay, "What Will Be Scarce?" The Economics of Structural Change and the Post-Commodity Future of Work. Starbucks, Alex writes, is a huge company—market cap $112 billion—that sells one of the most standardized products in the modern economy. Making a cup of coffee, or even one of the fancy specialty drinks, is very easy to mechanize and reproduce. If the entire economy is soon to be automated, with labor being replaced with increasingly more sophisticated capital, Starbucks should be a canary in the coal mine. The technology for removing labor from its stores and replacing it with automated capital has been around for years. Over the past few years, Starbucks has done exactly that. In efforts to increase thin margins, management has automated more and more of the coffee-making business and instituted tightly mechanized processes for delivering it to customers. But instead of increasing automation, the opposite has happened. After trying to streamline the store experience with fewer workers and more automation, the company concluded that this had been a mistake. CEO Brian Niccol said that handwritten notes on cups, ceramic cups, and the return of great seats had led more customers to sit and stay in our cafes, showing that small details and hospitality drive satisfaction. More baristas are being hired per store and automation is being rolled back. Economics is the study of decision-making under constraints, i.e., scarcity. If advanced AI brings material abundance—if machines can produce many if not all forms of human production at very low marginal cost—does economics become irrelevant? No. We will still have scarcity, but the kind of scarcity that matters will change. Ultimately, the answer to any question about the future economics of advanced AI begins with identifying what becomes scarce. After answering that question, the rest of the analysis is pretty straightforward. In this essay, I'm going to explore what becomes scarce when automation can replicate many, if not all, human production, and what that may mean for the types of jobs that emerge. Before industrialization, it was difficult to separate a product from the person who made it. The weaver who made your skirt, the baker who made your bread, you personally knew them, and their skill and reputation were tied to the product that they sold. Economic transactions had a distinct social component that was innately linked to the consumption experience. The industrial production process changed this by breaking craft into standardized repeatable steps, performed by workers based on predetermined and regularized steps, Capitalism produced something new: the commodity form, in which a product's value lies in the product itself, detached from whoever made it. A table is a table. A phone is a phone. The screen that you're reading this essay on, or, NLW note, the device that you're listening to it through, was designed in one country, manufactured in another, using components from around the world. But none of this matters for the experience of buying and using the device. Marx described this process in intentionally loaded language. The commodity form, he argued, was built on exploitation: the ability to pay workers less than the value of what they produce. They were able to do this because the capitalist production process was based on alienation: severing workers from the product of their labor, from the process of making it, and ultimately from each other. What had once been a person's craft became abstract labor power, a factor of production to be bought and sold like raw materials. Marx saw this as capitalism's deepest pathology, But to economists and to the world writ large, the commodity form was an engine of extraordinary prosperity. If production was no longer tied to specific people, it could be disaggregated, reorganized, shipped across oceans, and scaled in ways that turned few resources into vast riches. Both things were true at once. The commodity form created enormous wealth and prosperity, but it made the human behind any specific product invisible and ultimately replaceable. This is most people's mental model of what AI will do to the economy. If a machine can produce anything a human can—write the brief, generate the image, compose the song, determine the diagnosis from a radiology scan—then the human will be replaced across all facets of production and jobs will simply disappear. Labor will be replaced with capital. David Autor and Neil Thompson push back on this in an important recent paper. They argue that AI won't simply eliminate jobs, it will reshape the economic value of human expertise. Their framework distinguishes between expert and inexpert tasks within any given occupation. When automation removes the simpler tasks, as accounting software did for bookkeeping clerks, the remaining work becomes more specialized. Wages rise and fewer workers qualify. When it removes the harder tasks, as inventory management systems did for warehouse workers, the job becomes more accessible. Employment expands and wages fall. Same technology, opposite labor market outcomes depending on which part of the job gets automated. But Autor and Thompson also consider a starker possibility: that AI advances to the point where human expertise loses its economic value altogether. Under this scenario, AI will eliminate labor scarcity and produce what Herbert Simon once called "intolerable abundance." Automation of production will no longer involve managing a workforce transition, for which we have prior episodes of Automation to rely on. We will need tools to maintain social organization, income distribution, and democratic stability without the labor market that has historically held these together. I want to consider a different scenario. One where automation can replicate human production and the commodities that it produces, a big if, but human labor does not disappear. How could this be the case? A lot of analysis takes the economy as given. There is a set of jobs and a set of goods and services produced by the economy. If the same set of goods and services can be produced by cheaper machines, then these machines replace humans and the jobs disappear. But the economics of structural change, combined with deep-seated features of human preferences, suggests something different. As people get richer, they don't just want more commodities. They want things that aren't commodities in the standard sense of the word. The social aspects of products such as the relationships, the status, and exclusivity—what René Girard calls the memetic properties of desire—become much more relevant once people's basic needs are satisfied. And the demand for these properties will bring the human element back into the production process and with it, the jobs. If this is right, then AI won't just automate the commodity economy, it will trigger the emergence of something new: a post-commodity economy where a growing share of expenditure goes towards goods and services whose value is inseparable from the human who provided them. The same economic forces that moved 40% of the American workforce off farms and into factories and offices will move workers out of automatable commodity production and into what I'll call the relational sector. By this I mean the human-intensive, provenance-rich, sometimes artisanal part of the economy where the human aspect is part of the value of the good or service itself. The economics of scarcity won't disappear, it'll just reallocate. This is not the first time this argument has been made. The goal of this post is to make the argument precise. I'll start with what we know about how economies have historically responded to massive productivity shocks—the economics of structural change. Then I'll introduce the new ingredient: a behavioral microfoundation rooted in memetic preferences that generate a desire for exclusivity and status that explains why artisanal goods, where the human element is directly tied to value, have especially high income elasticity. I'll work through a simple model that generates a clean prediction. Automated sectors shrink as a share of GDP, relational sectors grow. And I'll connect this back to the question I raised in a previous post about whether AI could lead to negative economic growth. This framework pushes further against that thesis. My claim here is narrower than the strongest version of the labor share story. I am not claiming that labor's aggregate share must rise, or even that it must remain at its current level. It may well shrink as automation progresses. The claim is about sectoral reallocation in rich economies. As AI makes commodity production cheap, spending and employment shift towards high-income elasticity sectors where human involvement still carries value. In other words, labor share can fall and the relational sector can still remain a substantial part of the economy. But importantly, the inherent properties of demand for the relational sector also ensure that labor remains a substantial share of the overall economy, i.e., it will not shrink to zero. I also want to underscore that this framework works best for the developed world, where rising incomes can fund the transition. For the developing world, where economies have been built on producing commodities for rich countries, the picture is more complicated and potentially more worrying. Section: From Farms to Factories, Too. Economics has a name for what happens when a new technology makes one sector dramatically more productive: structural change. The canonical example is agriculture. In 1900, about 40% of the American workforce was employed in farming. Today it's less than 2%. Did people stop eating? No, if anything they're eating much more. Large-scale automation made farmers, and eventually factory farms, much more productive. Agricultural production boomed and prices fell, but because people can only eat so much, the share of income spent on food went down as people got richer and workers moved to manufacturing and then to services. The simultaneous fall of prices and reallocation of labor to another sector becomes a smaller share of the economy despite serving and producing more. The less productive sector, services, where costs had not fallen and in fact have risen, became a larger part of the economy. This is known as Baumol's cost disease, and you can see the transformation for Taiwan in Figure 1 below. Another note from me and LW here: there aren't a ton of charts in this, but do check the link in the show notes to see the ones that there are. Alright folks, quick pause. Here's the uncomfortable truth. If your enterprise AI strategy is, "We bought some tools," you don't actually have a strategy. KPMG took the harder route and became their own client zero. They embedded AI and agents across the enterprise—how work gets done, how teams collaborate, how decisions move—not as a tech initiative, but as a total operating model shift. And here's the real unlock: that shift raised the ceiling on what people could do. Humans stayed firmly at the center while AI reduced friction, surfaced insight, and accelerated momentum. The outcome was a more capable, more empowered workforce. If you wanna understand what that actually looks like in the real world, go to www.kpmg.us/ai. That's www.kpmg.us/ai. Today's episode is brought to you by Granola. Granola is the AI notepad for people in back-to-back meetings. You've probably heard people raving about Granola. It's just one of those products that people love to talk about. I myself have been using Granola for well over a year now, and honestly, it's one of the tools that changed the way I work. Granola takes meeting notes for you without any intrusive bots joining your calls. During or after the call, you can chat with your notes, ask Granola to pull out action items, help you negotiate, write a follow-up email, or even coach you using recipes, which are pre-made prompts. Once you try it on a first meeting, it's hard to go without. Head to granola.ai/aidaily and use code AI Daily New users get 100% off for the first 3 months. Again, that's granola.ai/aidaily. It is a truth universally acknowledged that if your enterprise AI strategy is trying to buy the right AI tools, you don't have an enterprise AI strategy. Turns out that AI adoption is complex. It involves not only use cases, but systems integration, data foundations, outcome tracking, people and skills, and governance. My company, Super Intelligent, provides voice agent-driven assessments that map your organizational maturity against industry benchmarks against all of these dimensions. If you want to find out more about how that works, go to besuper.ai. And when you fill out the get started form, mention maturity maps. Again, that's besuper.ai. Weekends are for vibe coding. It has never been easier to bring a passion project to life, so go ahead and fire up your favorite vibe coding tool. But Monday is coming, and before you know it, you'll be staring down a maze of microservices, a legacy COBOL system from the 1970s, and an engineering roadmap that will exist well past your retirement party. That's why you need Blitzy, the first autonomous software development platform designed for enterprise-scale codebases. Deploy at the beginning of every sprint and tackle your roadmap 500% faster. Blitzy's agents ingest your entire codebase, plan the work, and deliver over 80% autonomously. Validated, end-to-end tested, premium quality code at the speed of compute. Months of engineering compressed into days. Vibe-code your passion projects on the weekend, bring Blitzy to work on Monday. See why Fortune 500s trust Blitzy for the code that matters at Blitzy.com. That's B-L-I-T-Z-Y dot com. Alex continues, the formal economics of this process were laid out beautifully in a 2021 Econometrica paper by Diego Coleman, Daniel Ashkari, and Marty Musteri. Their key insight is that demand is non-homothetic. As people get richer, they don't just buy proportionally more of everything. They shift their spending towards sectors with higher income elasticity—goods whose demand grows faster than income. Agriculture has low income elasticity—you can only eat so much. Services have high income elasticity—there's always a better restaurant, a more engaging experience, a more attentive doctor. Their framework matches the historical data well. Capturing the decline of agriculture, the hump-shaped rise and fall of manufacturing, and the steady ascent of services. The key point in Comet et al. is that the main mechanism is not Baumol's cost disease by itself. It is that lower prices in automated sectors raise real income, and rising real income shifts demand towards sectors with higher income elasticity. Baumol's cost disease then reinforces the shift when those sectors remain relatively hard to automate. The reason they may be hard to automate can be due to technical constraints, as has been the case in the past, or because the value of those sectors rests on them not being automated in the first place. E.g., the relational sector where not being automated is part of the value proposition. In other words, even if rates of automation were similar across sectors, we would still expect the relational sector to become more important if it is where richer households want to spend more of their money. How does this relate to AI-driven transformation of jobs? Coman, Lashkari, and Mestari estimate that income effects account for over 75% of the observed patterns of structural change. Price effects—the standard story that automated sectors get cheap so people buy other stuff—account for only about a quarter. The dominant force is actually pretty simple: as people get richer, they want fundamentally different things. Importantly, this is already visible in how rich households spend. In the 2022 BLS Consumer Expenditure Study, households in the highest income quintile spent about 4.3 times as much in total as households in the lowest quintile, but the ratios are much larger in categories with a strong relational component. Such as in-person dining, entertainment, education, etc. Rich households, in other words, do not just buy more stuff—they shift their spending towards goods and services where the human element, the experience, or the social meaning matters more. This is also the exact pattern Joachim Huber documents in The Race Between Preferences and Technology. Using household data on the universe of consumer spending, he shows that higher-income households spend relatively more on labor-intensive goods and services as a share of total consumption. He interprets this as evidence of non-homothetic preferences, —so that economic growth raises demand for labor-intensive sectors through an income effect even as other technological forces push in the opposite direction. If advanced AI dramatically reduces the cost of producing a wide range of goods and services, this logic predicts a structural transformation. Automated sectors will shrink as shares of the economy. The sectors with higher income elasticity will grow. The question is, which sectors and goods will have high income elasticity in a post-AGI world? Section: The Relational Sector and Desire. Here I think it's useful to have a closer look at the determinants of human preferences and desire. Economists typically model demand as if preferences are formed in isolation: the utility I get from a good, service, or experience is determined by its hedonics. How good did the coffee taste? How quickly did I get the coffee after ordering it? This makes sense when people's budget constraints bind when it comes to meeting basic needs—for food, shelter, and clothing. But once those needs are met, a different force starts to shape what people want, and even becomes dominant. René Girard called it mimetic desire, the idea that we don't desire objects only for their intrinsic properties, but because other people desire them as well. We want what others want and we want it even more when they can't have it—for status, social capital, reputation, etc. Desire is not just a relationship between a person and an object, it is also a function of what other people desire. Now here Alex goes through a number of different examples that show that this is not just theory, but put this in a bunch of different contexts and different observations from thinkers throughout the centuries. The point that Alex gets to is this: why is this memetic, relational dimension of desire relevant to the framework of Coman at all? Because it is comparative and therefore hard to satiate. Goods with this property should have especially high income elasticity as incomes rise. From there, he discusses a model that he developed with Christophe Mataraz, where, as he puts it, a person's desire for a good increases in how much others want it but can't have it. Jumping forward a little bit, he writes, the critical link to AI comes from new work with Graylin Mandel. "We find that AI involvement undermines the perceived exclusivity of a good. Objects with AI involvement are perceived as inherently reproducible and non-unique." People bid for physical art prints that varied in described AI involvement. Human-made artwork gained 44% in value from exclusivity—one copy versus many—but AI-generated artwork gained less than half that, only 21%. The mere involvement of AI made the work feel inherently non-exclusive, as if it could always be reproduced, —regardless of how many copies were said to exist. NLW note: sorry, NFTs. Coming back to Alex, though, he says, "I want to stress that this extends well beyond artists and luxury craft. Walter Benjamin wrote about this in a different context—the aura of a work of art, which mechanical reproduction destroys. But the economic logic goes beyond art. It extends to any category where the human element is integral to the value: teachers, nurses, therapists, childcare workers, trainers, hospitality, clergy, guides, and many forms of local services." In all of these cases, the human being is not just an input into the production process—their judgment, attention, memory, warmth, or presence is an integral part of the value. These are cases where, as Seb Krier put it, "provenance remains scarce even in a post-scarcity world." This matters for structural change because the memetic component of preferences is inherently income elastic. When you're poor, most of your spending goes towards necessities where the identity of the producer doesn't matter. As you get richer, a larger share goes towards goods where you're not just buying the functional product, you're buying the story, the scarcity, the feeling of having something that others want as well. This is what gives relational goods and services their high income elasticity—as income rises, the exclusivity premium becomes a larger share of total value, and that premium is something human-made goods can deliver. Section: The End of the Commodity Economy? Let's return to the commodity form. I defined it earlier: the abstraction of a product from the person who made it, The thing that made industrial capitalism possible. What happens to it when AI can produce the commodity itself? The obvious answer is that the commodity form achieves its logical endpoint: a product with no human in it at all. But the less obvious answer, the one that comes from taking structural change seriously, is that AI doesn't just perfect the commodity form. In the strict sense, it also triggers the commodity form's decline as a share of economic activity. Here's the mechanism more precisely: When AI automates commodity production, prices in that sector fall. That raises real income. If the goods and services people want more of as they get richer lie disproportionately in the relational sector, demand shifts in that direction. Baumol's cost disease then amplifies the result. If the relational sector remains harder to automate, it becomes relatively more expensive and absorbs a growing share of total expenditure. But in the context of AI automation, Baumol's cost disease is a feature, not a bug. The relative expense of human services stops being a budget problem and starts being a labor market solution. The "stagnant sector," the one that resists automation, is precisely where spending and employment grow. The relational sector gets more expensive because the commodity sector is getting cheaper, and that's what keeps people employed. What does this actually look like? Material abundance from automated manufacturing means goods are cheap. Most people's spending goes to human-led services. Today's luxuries become the baseline for future consumers. As commodity production gets automated, income and employment flow towards the sectors with high income elasticity—what I am calling the relational sector—including the arts, but also care, education, hospitality, therapy, personal services, craftsmanship, and community, where the human element is part of the value. The so-called stagnant sector absorbs a growing share of spending and jobs precisely because it can't be automated. That is where the jobs are. Admittedly, Marx would have found this outcome strange. But I want to be careful here. A product with a distinct human element is not the same thing as decommodified labor. A tailor who stitches your suit or a teacher who knows you personally may still be selling relational labor to capital. The societal relations of production may remain fully capitalist even if the human aspect of the product becomes more economically salient. So my claim is narrower: AI may reduce the commodity sector's share of expenditure and increase the share going to goods and services where the human element remains visible and valuable. That is not the end of commodification in Marx's sense. It is a shift in the composition of demand. Still, it matters for labor markets. The direction of structural change may be toward work that is, in some cases, more personal, more relational, and less interchangeable than what it replaces. In the next section, Alex shows some actual math and an equation around why he thinks memetic desire fights against the type of demand collapse discussed back in that Citrini essay, The Global Intelligence Crisis, Which again, I would point you to the main essay to go check that out. Section: The Future of Work. The durable jobs of the future won't be about monitoring AI systems or prompt engineering. Those are transitional roles in the automated sector. The durable jobs will be in the relational sector, where the human element is the product itself. Some already exist and are growing: nurses, therapists, teachers, boutique fitness instructors, personal chefs, bespoke tailors, craft brewers, live performers, spiritual guides, childcare workers, and many varieties of hospitality and care work. Others are emerging: experience designers, human-AI collaboration artists, provenance certifiers, community curators. Many haven't been invented yet, just as 6 out of 10 jobs people hold today didn't exist in 1940. The most common pushback I get when I say this is, "But not everyone is creative! Not everyone will be artists!" I think this misunderstands what's being asked. You don't need to be Picasso. You need to be the person whose involvement makes the product feel like it was made for someone, by someone. The economics of structural change tell us that when technology makes one type of production cheap, the economy doesn't collapse—it transforms. It shifts towards the things that technology can't make cheap. For AI, those things are exactly the ones where human involvement carries inherent, irreplaceable value. Alright, back to NLW. First of all, thank you to Alex for wading into what is a very fraught discussion with a thoughtful, sensible entrance into the canon. My goal in sharing this type of writing is not to be Pollyannish about the challenges we face, nor to convince you if you are nervous that you have no reason to be nervous. We are facing down immense change, even when changing for the good creates enormous amounts of stress and anxiety. Think about the kid who's all anxious even though they're going to their dream college. Change itself is, for many, difficult. The problem that I have is that the absolute dominant discourse right now is that AI-wrought change leads to something worse, to something negative. In fact, I think what's causing a big part of the political backlash is that people are doing the math and saying, "If this leads to change that is obviously negative, why the hell would we let it happen?" The big part of the failure for not just the AI industry, but anyone with an active vested interest in what that future looks like is to not spend any time actually asking what we change into. There's just this assumption that we fall off a labor cliff and you either work at an AI lab or to use the awful, stupid, anyone who says this should shut the hell up meme, you enter the permanent underclass. I believe that when we look back a decade and especially two, three decades from now, this moment of frenetic anxiety will be seen as one of the biggest misplacements of our collective energy that we've ever had. I'm excited to see people like Alex actually trying to bring some intellectual and academic discipline to thinking about what comes next, and I'm going to keep exploring that in shows to come. For now, I think that's enough big thinking for this weekend. Appreciate you listening or watching as always, and until next time, peace.