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The problem is that the distinction needs to be drawn between the competence of the economists and the correctness of their analysis. Thomas Robert Malthus was an English economist who, along with Adam Smith and David Ricardo, is regarded as one of the central figures of classical economics. But he's principally known for one idea, which is the Malthusian trap, a articulated in his 1798 essay an essay on the Principle of Population. In this essay, Malthus argues that population growth is exponential, where growth in food production is linear, and that advancements in agricultural technologies that allow for more efficient production and thus higher yields, are met by higher levels of human procreation, eliminating any gains in living standards. Malthus hypothesized that this dynamic would ultimately confront an upper limit on the carrying capacity of the planet, resulting in widespread famine or war as a corrective measure. Although he made other enduring contributions to economics, mainstream economics generally rejects the inevitability of Malthus, his trap. But the more structural idea that growth, and especially growth, that experiences accelerating rates, eventually collides with constraints, is taken for granted in modern growth theory. In John Galbraith seminal book the Affluent Society, he notes that Malthus viewed poverty as a default state of the human condition and therefore wasn't concerned with alleviating it. And neither was he concerned with how the productive output of society was allocated. But Galbraith notes that this was a core focus of David Ricardo. I'm quoting from the Affluent Society quote. Like Malthus, Ricardo regarded population as a dependent variable. It regulates itself by the funds which are to employ it and therefore always increases or diminishes with the increase or diminution of capital. Advancing wealth and productivity thus bring more people, but they do not bring more land from which to feed these people. As a result, those who own land are able to command an ever greater return, given its quality for what is an increasingly scarce resource. Meanwhile, in Ricardo's view, profits and wages were in flat conflict. For the rest of the product. An increase in profits, other things being equal, meant a reduction in wages. An increase in wages must always come out of the profits. Every rise of profits, on the other hand, is favorable to the accumulation of capital and to the further increase of population, and therefore would in all probability, ultimately lead to an increase of rental. The effect of these compact relationships will be clear. If the country is to have increasing capital and product, profits must be good. But then, as product expands, the population will increase. The food requirements of the population will press on the available land supply and force up rents to the advantage of the landowner. In other words, Capitalists must prosper if there is to be progress, and landlords cannot help reaping its fruits. The victims of this inescapable misfortune are the people at large. End quote Galbraith also quotes one of Ricardo's most famous and lasting observations. Quote labor, like all other things which are purchased and sold, and which may be increased or diminished in quality, has its natural and its market price. The natural price of labor is that price which is necessary to enable the laborers, one with another, to subsist and perpetuate their race without either increase or diminution. End quote There are two relevant extensions of this line of inquiry that apply to the current question of AI's role in society and the potential displacement of all white collar work. One what happens when the demand for certain types of labor collapses because that labor can be competently replaced with AI tools? And 2Can software command any price point at all if it can not only be conjured instantaneously from a prompt, but it can meet the prompter's specific idiosyncratic needs perfectly? There is a natural and obvious tension in taking these extensions to their logical limits, which is if the demand for white collar work is mostly eliminated by software that can write software, what happens to the underlying demand for that software? Who is buying what any company produces? If the entire digital economy is absorbed into a handful of frontier model labs with this inverse Malthusian trap of infinite digital production, leading to widespread famine or war as the global workforce adjusts to structural displacement in the knowledge economy, then the global economy must shrink dramatically as a result. Who is doing the consuming? And if frontier labs at that point are merely optimizing to accumulate more and more of the shrinking global gdp, then they'll look to economize too, and they'll displace the human effort that writes the software that writes the software that writes the software, until we're left with one CEO of one frontier lab lording over the remainder of humanity. The collective action problem likely erodes far before that endpoint. In fact, there's probably some natural equilibrium where the AI tool producers make more money by not encroaching on some aspect of the economy simply to preserve a customer base than by eliminating completely. But this is actually the aspect of the AI discussion that I find least interesting or clarifying. Meandering, tedious thought experiments and fantastical projections to logical extremes tend to be jejune and intellectually immature. But similarly, it's unhelpful to be shortsighted about things like current limitations around the capabilities of frontier models, especially given the rate of improvements that we've seen in tasks like code production and other adjacent competencies. Yes, it's true that the complexity with developing software may mostly be concentrated not in de novo code generation but in software maintenance. But I find very little reason to believe that agents won't be capable of this in the future and possibly the near term future. So I don't dismiss the software writing software premise on its face. What we're left with is a question of where competition, demand and supply intersect in this new world order. Each era's economic anxiety reflects its dominant constraints food in Malthusa's time allocation in Galbraith's, and distribution in the contemporary environment. The central premise of Galbraith's the Affluent Society is that the so called conventional wisdom with respect to economic growth had grown outdated in the context of that time. The Affluent Society was first published in 1958 and that in advanced industrial societies production had become a self reinforcing system, accelerated in part by advertising, which created consumer demand. Galbraith argued that in an affluent society the primary constraint was not one of food production as with the Malthusian trap, but of the optimal allocation of resources between private endeavors and public institutions, resulting in what he characterized as private affluence and public squalor. Affluence was engendered by a feedback loop of advertising led production which enriched private enterprise but starved public infrastructure of sufficient investment. This podcast episode is the first installment in a series that I call the Prosperous Society. In this series I'll make the case that the efficiency benefits posed by AI enhanced development tools shift constraints from production to distribution, whereby human attention becomes the scarce resource in competition. This constraint serves as a natural limit on the number of products that can achieve commercial traction, channeling investments into the systems that best match consumer demand with the products that best satisfy consumer tastes and needs, which are personalized advertising platforms. These systems will capture an increasing share of the digital economy, and in fact I believe they will become the critical infrastructure of the overall economy going forward, given their increasingly critical role in efficiently routing products to consumers. But these advertising platforms will also establish a natural ceiling on the number of products that can operate successfully in a category given the need to advertise through those systems. In aggregating attention, this will force software developers to deliver increasing amounts of value to users to justify ever larger levels of advertising spend, with much of that cost being defrayed for the consumer by their own adoption of advertising monetization mechanics, which becomes an irresistible opportunity given increased demand for human attention from software developers. All of this converges to a dynamic whereby consumer surplus expands in absolute terms, even as platforms capture a larger share of monetized value. In this first episode of the series, I'll argue that distribution becomes the principal concern of software developers in an environment of decreasing production costs. You know those channels your colleagues keep bragging about? The ones getting all the credit? Yeah, they might be doing squat. Attribution makes every channel look like a hero, Even when it's a 0 incrementl tells you who's actually doing the work. It's like a lie detector for your marketing budget. Start using incrementl today. Get your demo@ Incremental.com that's incrmnt a l.com mention that you came through the Mobile Dev Memo podcast for a special 15% discount for the first six months. Part 1 the Millionaires Mall in the affluent society, Galbraith introduces what he calls the dependence effect. The concept is simple and it is destabilizing. In advanced industrial societies, wants are not exogenous to production, but are rather shaped by it, such that production creates goods and advertising creates the desires for those goods. The two are intertwined. The more sophisticated the system of production becomes, the more sophisticated the system of persuasion must become to absorb that input. Under this framework, consumer demand does not precede production it is at least in part consequence of it. Advertising does not merely inform consumers of options that satisfy pre existing preferences, it manufactures preference. In a society characterized by the abundance of consumer goods, the bottleneck is not production capacity, but the ability to stimulate sufficient consumption to clear it. This was a profound insight. In 1958, Galbraith observed a post war industrial economy in which factories had mastered scale, logistics had improved, suburbanization had reorganized consumption patterns, and mass media like radio, print, and especially television provided a national megaphone for persuasion. See my Google's Gambit and the Future of the Open Web episode of the podcast for more background on why the current AI transition is rooted in parallels to the media transition from radio to television. The television set became the conduit through which desire was shaped and synchronized. The automobile, the washing machine and the refrigerator were mass market goods marketed to mass audiences in a mass society through media that emerged that could reach large numbers of people simultaneously. But advertising in that era operated at scale and with limited granularity. It broadcasted to broad swaths of the population. It relied on repetition, emotional association, and the power of cultural norm setting. If everyone saw the same detergent advertisement and everyone saw it repeatedly, then preference formation could plausibly be attributed, at least in part, to the repetition itself on the dependence effect. Galbraith writes, quote, were it so that a man on arising each morning was assailed by demons which instilled in him a passion, sometimes for silk shirts, sometimes for kitchenware, sometimes for chamber pots, and sometimes for orange squash, there would be every reason to applaud the effort to find the goods, however odd, that quenched this flame. But should it be that his passion was the result of his first having cultivated the demons, and should it also be that his effort to allay it stirred the demons to ever greater and greater effort, there would be question as to how rational was his solution unless restrained by conventional attitudes. He might wonder if the solution lay with more goods or fewer demons. But prior to that he seems to set the stage for contradicting himself. In an earlier chapter he writes, these misfortunes did not go entirely unperceived. It was ever necessary to assert that they were part of the system. And it was also made clear by the profits of the competitive model, not without a certain ruthless logic, that to seek to mitigate the risks and uncertainties of the system would be to undermine the system itself. The race for increased efficiency required that the losers should lose. If consumers were to rule, there must be rewards for those producers who were in the path of current tastes and penalties for those who were left behind. To seek to mitigate the penalties was to undermine the incentives to separate the stick from the carrot. End quote. Well, which is it? Can advertising synthetically create demand for product ex nilo? Or do producers need to be in the quote, path of current tastes in order to avoid losing but less structurally? The dependence effect describes a world in which production precedes desire and persuasion fills the gap. It presumes a relatively small universe of goods, widely observable consumption and immediate environment that is national rather than personal, as is the modern day social media feed. It also presumes that wants can be synchronized. That presumption weakens considerably in the digital economy. The post war consumer could plausibly peruse the marketplace. Goods were visible. Retail stores were finite. Department stores curated their shelves from a limited set of commercial options. Suburban life provided social observability. One saw one's neighbors drove, wore and placed in their kitchens. Consumption was legible, but the digital marketplace is not legible. Amazon hosts hundreds of millions of SKUs. The App Store contains millions of apps, Shopify power storefronts that number in the millions. No individual can meaningfully browse these environments in their entirety. No individual can discover the long tail of digital products through casual observation either. It may be impossible to ascertain the brand of shoe a fellow rider on the subway is wearing if it's not instantly recognizable without being advertised to and digital goods in particular are also qualitatively and fundamentally different from the goods of Galbraith's era. They are niche and specialized. They target subcultures, micro communities, and highly specific use cases. They are not laundry detergents or automobiles. They are goods that cannot be marketed efficiently through broad based large audience advertising. The expected conversion rate is too low and the monetization window is too narrow. The willingness to pay is too heterogeneous. They can only be profitably exposed to consumers for whom they are specifically relevant. And this distinction matters in a digital ecosystem characterized by extreme product heterogeneity and extreme audience heterogeneity. The economic viability of a product often depends on its ability to be matched with precisely the right subset of users through advertising. The salient question is not whether advertising can create demand for an arbitrary product it is whether advertising can efficiently route existing demand to the product variant most capable of satisfying it. In a piece I published in 2020 ahead of Apple's ATT privacy policy, Does Digital Advertising Create Demand? I described this dynamic explicitly, quoting from that piece Quote because ads aren't creating demand but optimally routing demand, install activity likely won't change. It'll just be driven by an increased amount of organic search. And while that organic search may not link users with the apps in which they'll monetize, most users will continue to monetize. Demand won't dissipate with the deprecation of the IDFA and the deterioration of ad efficiency. It will just be served by different fulfillment mechanisms. End quote the core claim there is that advertising in the digital context does not function as a demand factory, it functions as a demand routing mechanism. Personalized digital advertising matches users with products on the basis of observable signals, historical behavior, contextual clues, demographic features, inferred intent, and increasingly probabilistic estimates of discretionary spending capacity. These systems operate through auctions in which advertisers bid against one another for access to users predicted to generate profitable outcomes. The mechanism is not persuasion at scale, but selection at scale. And it's optimized at the granularity of a specific user and not as in the era of the affluent society at large geographic regions or sweeping demographic profiles. If a user has exhibited behavior consistent with a preference for mid core mobile strategy games and a history of in app purchase activity above a certain threshold, then exposing that user to a new strategy game with a similar monetization profile may be economically rational. The advertising platform evaluates the probability of conversion, the advertiser's bid, and the derived expected value to the platform of the advertiser's ad. Filling that impression, the ad is served if the expected value exceeds the threshold required to clear the auction. The ad platform is not fabricating desire ex nilo it is interpreting signals to indicate a predisposition toward a category of consumption and quantifying that into an expected value. That does not mean persuasion is absent. Creative matters, messaging matters, positioning matters, branding can matter. But the economic viability of most digital products, and of products that are predominantly sold through digital channels like D2C goods, depends less on manufacturing preference and more on discovering it. This distinction explains why the deterioration of personalization reduces efficiency without annihilating demand. When Apple deprecated the IDFA with attention, my argument was not that demand would evaporate, it was that the routing would become less efficient. In a world of constrained production capacity and synchronized mass media, Galbraith's dependence effect had explanatory power. But in the current world of effectively infinite digital shelf space and algorithmic targeting, the bottleneck is different. It is not the creation of wants, it is the efficient alignment of heterogeneous wants with heterogeneous goods. What's the probability of a user discovering, through entirely random organic diligence, any given product on an infinite digital retail shelf? It's zero. To understand how personalized advertising achieves this alignment, it is useful to revisit what I called the Millionaires Mall. In a piece I published in 2024 Digital Advertising, Demand, Routing, and the Millionaires Mall. I argue that digital advertising economics are shaped by fat tailed value distributions. I write quote, the digital advertising ecosystem is even more extreme. The economics of an entire cohort of users could be defined by just a few of them. This is the millionaire's mall. The distribution of conversion value won't be normal, but fat tailed, and achieving those conversions dictates the profitability of the advertising campaign. Conversions are rare because the conditions for conversions to happen are rare, end quote. The thought experiment I propose in that piece is simple. Imagine you are standing in some nondescript, non coastal shopping mall and are told that the average net worth of the shoppers in the mall is $50 million. Two plausible interpretations of the situation are that 1 everyone is extraordinarily wealthy, or 2 most people are typical and a single billionaire is present. The distribution matters. Digital Advertising operates in a similar environment. The vast majority of ad impressions do not result in conversion. Even fewer result in high value conversion. The economic viability of a campaign can depend on a small subset of users who generate disproportionate revenue. In the piece I described it this what's more important in digital advertising is attenuating the skew of the value distribution just enough through targeting to attain profitable user economics on an entire cohort. The millionaire's mall only requires the presence of one billionaire. End quote. Targeting does not need to produce a uniform uplift across all users. It needs to shift the distribution enough that the tail contains sufficient value to justify the spend. Digital advertising is not broadly an exercise in persuasion. Digital ads don't attempt to convince the median consumer to purchase something they never previously considered buying. It is about identifying the rare consumer whose latent willingness to spend makes the exposure economically rational. The challenge comes in identifying useful relationships and representations from that latent space. The most sophisticated advertising platforms are spending vast sums of money on doing just that. When conversion optimization is layered on top of targeting, the platform effectively tells the advertiser, specify your objective and your value per objective, and we will attempt to deliver those outcomes at or below your bid price. The advertiser bids based on expected lifetime value, and the platform assumes the risk of wasted impressions and seeks to minimize it through better prediction. As I wrote in that piece, quote, an advertiser can ensure that their margin targets are satisfied with conversion optimization by submitting bids against conversion objectives that are discounted against their actual economic value. If an advertiser pays $1 for a conversion, such as a purchase that it expects to be worth $2, the difference in those values accrues to the advertiser as profit. End quote. The platform's incentive is to refine its predictions continuously. The more accurately it can identify high value users, the more budget it can capture. Budget flows towards absolute performance based on the advertiser's ROAS requirements. This feedback loop is economically expansionary. Better targeting leads to more conversions. More conversions produce more revenue. More revenue supports greater reinvestment into advertising. Greater reinvestment produces more data. More data improves targeting. This is not a machine for manufacturing arbitrary wants. It is a machine for compressing the search cost associated with matching a user to the product most capable of satisfying their existing preferences. Galbraith's dependence effect presumes that advertising manufacturers demand in order to absorb output. The digital advertising ecosystem presumes that demand is heterogeneous, partially observable, and most importantly, extant and discoverable through data. The digital storefront is too vast for persuasion alone to compress it. No one watches an advertisement for a niche organic dog food D2C brand and decides, despite not owning a dog, to buy it. Known encounters an ad for a hyper specific subscription box and develops an entirely novel taste as a consequence of a single exposure. These categories emerged because some cohort of users already possessed a latent demand for them that personalized advertising could route. Personalized advertising makes those categories economically viable. If targeting degrades, demand does not collapse. It is routed through less efficient mechanisms like organic search, word of mouth, editorial curation, and total monetization declines relative to the alternative. The product category persists. This distinction weakens support for Galbraith's dependent effect. In the digital advertising domain, production is not creating wants and then fabricating demand to satisfy them. Production is responding to the heterogeneous demand signals and advertising is optimizing the alignment. What's more, categories emerge because they're only viable because of the distribution capacity of various digital advertising channels. There is support in the academic literature for the idea that increases in digital advertising spend are consistent with more product varieties being offered. This is because the demand routing value of digital advertising creates commercial viability for those products. This has consequences for the consumer experience. First, ads become more relevant. A relevant ad is less intrusive. It aligns with existing interests. It reduces the cognitive friction associated with irrelevant exposure. In a world where ad inventory is finite and user attention is scarce, relevance reduces annoyance and product distraction. Second, personalization should improve monetization efficiency. If a platform can reliably deliver conversions at or below a profitable threshold, advertisers are willing to scale spend. That spend supports product development. It supports experimentation. It supports distribution at zero marginal price to the consumer. Many of the digital products that dominate consumer attention today are nominally free. They're subsidized through advertising. The more efficiently advertising matches demand to products, the more viable that subsidy becomes. The consumer does not pay a direct price, but they exchange value by making their attention available for targeting. As targeting improves, the expected value per impression should increase. The advertiser can justify higher bids. The platform can extract revenue while still delivering positive return on ad spend. The product developer can invest in features, performance, and user experience. The mechanism here is not coercion, and it achieves alignment across all three parties the ad platform, the advertiser, and the consumer. If Galbraith described a society in which private production generated artificial wants, the digital ecosystem reflects a society in which private production attempts to identify and satisfy idiosyncratic wants at scale. Those are two very different things. The dependence effect implied a kind of asymmetry producer shaping consumers. Personalized digital advertising implies a different asymmetry data rich platforms optimizing the allocation of attention among competing producers. In the next episode of the series, I will explore the competitive implications of that asymmetry. But for now, the key point is this. When production costs decline and production heterogeneity explodes, the binding constraint shifts. It is no longer the stimulation of demand in aggregate, it is the efficient routing of heterogeneous demand across an effectively infinite supply landscape. And personalized digital advertising is the infrastructure that performs that routing. In performing it well, it does not erode consumer welfare. It enhances it by reducing friction, increasing relevance, subsidizing access, and allowing niche products to find the users for whom they are most valuable. This is not an AI doom loop, eroding the value of software broadly and subsuming the entire economy into a handful of frontier model labs. It is the foundation of a prosperous society. Mobile game developers no longer need to pay up to 30% in major app store fees. With Xsolo Webshop, you can create a direct storefront, cut fees down to as low as 5%, and keep players engaged with bundles, rewards, and analytics. Start today@xsolo.com that's xsol lla.com or use the link in the episode Show Notes Part 2 the Primacy of Distribution if AI is deflationary for production, it is inflationary for distribution. That framing can sound paradoxical at first. When we talk about generative AI, we tend to focus on the reduction in marginal production cost. Code generation becomes cheaper, ad creative production becomes cheaper, iteration becomes cheaper, entire product surfaces can be scaffolded and deployed with dramatically less capital than even a few years ago. But trivially, when production becomes cheaper, more things get produced. When more things get produced, more firms compete for the same pool of human attention. And when more firms compete for a resource that does not scale, which human attention doesn't, the price of accessing that resource rises. In the inflationary impact of AI generated ad Creative I try to express this in straightforward economic terms. Quoting from that piece quote Generative AI is deflationary for content production, but is inflationary for distribution. Generative AI will see the production costs of increasingly complex forms of content like video approach zero. These tools will instigate an immense expansion in the volume of each content format that they perfect. The first photograph to feature a human being was taken by Louis Daguerre, inventor of the Daguerreotype process in Paris in 1838. According to the Guardian, as a result of widespread smartphone ownership, 1 trillion photographs were taken in 2014, representing more than a quarter of all existing photographs taken up until that point. Statistics like this will echo across text, animated and photorealistic video production, audio, etc. In synthetic form as a result of generative AI and as content proliferates through generative AI tools, the challenge of capturing potential customer attention becomes more acute, necessitating an increased reliance on advertising. This is inflationary. The corpus of content will grow at a much more rapid pace than the human birth rate. Organic discovery becomes ineffective as content mushrooms. This dynamic gave birth to the search ads mechanism in the first place. Generative AI will similarly create competitive friction for the discovery of all forms of content. End quote. That immense expansion is the critical part. Yes, creative becomes incrementally cheaper for existing advertisers, but critically, participation expands because more businesses can run ads. I discussed this in another podcast episode, Commerce at the Limit and if more products are trying to reach customers, and if customers still only have 24 hours in a day, then distribution becomes the locus of competition. That is the inflationary dynamic. In auction terms, this is a marginal story. The auction clears at the willingness to pay of the marginal bidder. So as more advertisers join the auction, the clearing price shifts toward whatever the new marginal bidder will pay. That movement raises average customer acquisition costs and progressively prices lower LTV products out of scalable paid distribution. When there are too many products to discover organically, the system clears through paid distribution. And in digital markets, paid distribution clears through auctions. Auctions are not metaphors, they are concrete mechanisms. And when more bidders show up to an auction for a fixed inventory of impressions, clearing prices may rise. This is where the popular narrative about AI and product creation becomes misleading. There's a tendency to think that if anyone can build software with AI, then barriers to success disappear. But that assumes production is a primary barrier. In an attention constrained environment, production is not the primary barrier. Distribution is. If AI reduces the cost of building a product from $2 million to $200,000, that Delta does not necessarily translate into higher profit margins. In a competitive market, it often translates into more budget allocated to customer acquisition. The savings migrate and potentially are competed away in distribution. And this is not conjecture. We've already seen this dynamic in mobile gaming with the advent of mobile app stores, in subscription media. In the creator economy, as development tooling improves, more products enter the market. As more products enter the market, customer acquisition costs rise and the bottleneck shifts AI will accelerate that migration across every form of content that it can produce, which increasingly is every form of content. Now this is where I want to strengthen the argument about platform rent capture, because it doesn't merely rest on increased bid density and clearing prices. It is about what happens when AI expands advertiser Participation in AI Enabled Advertising and the Invisible Retail Consumer I make two specific claims about what AI enablement does to advertising markets. One, it will improve conversion rates to the extent that every ad performs at its theoretical potential, and 2 it will increase participation by allowing any business that potentially could benefit from digital advertising to do so. Those two effects compound. If conversion rates improve, the expected value of an impression rises for existing advertisers. That increases their willingness to pay in auction markets. And if participation expands, if more businesses are capable of advertising because AI reduces operational friction, then the number of bidders rises. Higher willingness to pay combined with more participants in the auction should result in higher clearing prices. But the invisible consumer concept adds an important nuance to the story. Today, a portion of consumers are effectively excluded altogether or under monetized in digital advertising markets because their purchasing behavior is not legible through rich behavioral data. They transact offline locally and their digital footprints are sparse. That doesn't mean they lack economic value. It means the current targeting apparatus struggles to value them precisely. In that same piece I described the mechanism explicitly quote Consumers who don't frequently engage with E commerce retailers are not targetable through behavioral profiles. While these consumers may still be demographically targeted, many platforms institute CPM floors below which impressions won't be served. Consumers without rich behavioral footprints may be more economically valuable to the local retailers that are onboarded to advertising platforms as their AI enabled automation efforts reduce the barrier to participation. End quote if AI reduces the friction for small and local businesses to advertise by automating creative production, targeting and campaign management, then those businesses enter auction markets with their own valuation functions. They value consumers that E commerce advertisers might undervalue. They bid on impressions that were previously priced too low to clear. This expands the bidder base not just quantitatively but qualitatively. And when a platform intermediates a scarce resource like attention and simultaneously expands the set of buyers for that resource, it strengthens its position in the value chain. The platform is not simply taking a fee, it is operating the market in which scarcity is priced. As AI expands participation and improves performance, platforms don't need to arbitrarily raise prices. The auction mechanism does that. More bidders, better conversion and more efficient monetization These push clearing prices higher. The platform captures a share of that increased value because it controls allocation. Importantly, this does not contradict the existence of a long tail. AI will absolutely produce a proliferation of niche products, utilities and small businesses. But the number of scaled winners in any given category remains constrained by distribution economics and scale is gated by the cost of attention. This is why we won't see every local restaurant build its own bespoke version of doordash for accepting delivery orders. How would its app get discovered? How would it recruit drivers and delivery people? Again, assume every restaurant can develop a perfect, entirely functional app and backend from a prompt. I don't argue that we are not heading to that eventuality. We are assume every restaurant's app can be maintained by an AI tool cheaply or costlessly. Assume customer support, fraud detection and logistics and routing can be managed cheaply or costlessly. Provide this local restaurants app with every benefit of the doubt and you still confront the reality that if they can do it, so can everyone else. And the savings provided by AI tools in building and maintaining their app are eroded by the cost of getting their app in front of customers. Given that every other restaurant on their street is attempting to do the exact same thing. The ceiling here is the economy of scale or the network effects that explain the success of food delivery apps today. Unbundling every single restaurant into its own app depletes those economies of scale. And the fiercer distribution competition will almost certainly prohibit restaurants below some threshold from participating in independent scale distribution. Certainly some will, but some won't be able to clear the distribution hurdle and will be better positioned to remain on doordash. The ceiling in this context is not about whether software can be built, but about whether independent distribution can be sustained at scale when attention and network density are scarce. Now the obvious critique at this point is if platforms capture increasing rent as attention scarcity intensifies, does that negate consumer surplus? Does all of this just enrich gatekeepers? Given a structural reorganization around allocation, that conclusion does not follow for a number of reasons. First, because the same mechanisms that increase clearing prices also improve matching efficiency as more products enter the advertising ecosystem. Platforms have a broader selection set when predicting relevance for a given user. This assumed better match quality could increase conversion probability, retention and downstream monetization. Higher lifetime value supports higher acquisition spend which sustains the auction. But the consumer experience can improve in the process. More relevant ads are less distracting. They align more closely with intent, and when products monetize efficiently through advertising, they can subsidize access by reducing or eliminating upfront price gates. Advertising revenue, when routed efficiently, lowers direct price barriers. And there's another feedback loop worth noting. As competition for attention intensifies, products that aggregate attention, like media platforms and social media networks, face a marginal decision. They can refine attention into engagement, extracting value through subscriptions or commerce. Or they can sell attention as inventory into advertising markets in the best and highest use of customer attention. I framed this as a mechanical analytical decision based on expected value. When advertising prices rise, the opportunity cost of not selling attention rises. Some products will choose to monetize more aggressively through ads, which expands ad inventory supply at the margin. It can moderate price inflation without eliminating scarcity. But again, attention remains finite. It is simply allocated through a more complex equilibrium. Which brings the conversation back to the core thesis of this episode. When AI collapses production costs, the economic system does not dissolve into frictionless abundance. It reorganizes around the next binding constraint. Human attention does not scale with compute or with model parameters or with token throughput. There are no scaling laws to hours in the day. Distribution is the mechanism through which that finite resource is allocated. And as AI expands production and participation, distribution becomes the principal concern of software developers. Engineering becomes cheaper relative to marketing, so feature velocity becomes less differentiating than acquisition efficiency. Particularly when software can simply be cloned whole cloth from a prompt, the ability to command attention becomes the principal determinant of success for software. And the firms that intermediate attention by operating the auctions, predicting relevance and controlling the surfaces through which products are discovered are structurally positioned to capture a larger share of the surplus created by AI driven efficiency. That scarcity is not an accident. It is a structural feature of a digital economy organized around attention. And in an environment where attention is the binding constraint, the economics of distribution, not production, determine which products scale which firms capture value and how surplus is allocated across the system. If you're swimming in dashboards but still arguing about what actually drove installs, this is for you. Branch is an AI powered MMP built for growth marketers who care about signal quality and outcomes, not just reports. You can quickly answer questions like how is my TikTok spend really performing? Or which partners are driving net new users and even launch campaigns that move users from offline to app without breaking attribution. Branch's AI proactively surfaces what's working, flags issues early, and takes care of the busy work like link creation and tagging so you can move faster and spend smarter. Learn more at Branch IO that's Branch IO. If there is a through line connecting Malthus, Galbraith and the present moment, it is this. Economic systems are organized around whatever constraint is binding. For Malthus, that constraint was food. He believed that population growth would eventually exceed agricultural output. For Galbraith, writing in 1958, production was no longer the binding constraint in advanced industrial economies. Factories had mastered scale, logistics had matured, and suburbanization had reorganized consumption. What concerned Galbraith was not the ability to produce goods, but the allocation of resources between private abundance and public need. In that environment, advertising appeared as a mechanism for absorbing output. With production and persuasion interleaved, the constraint had shifted from sheer output to distribution across social priorities. Today, we are witnessing another migration of constraint. AI is collapsing. The marginal cost of digital production Code, creative design, analysis, iteration all becomes cheaper and faster. The production frontier expands dramatically. But the existence of more supply does not eliminate scarcity, it relocates it. The constraint facing software developers is no longer the ability to build, but the ability to support discovery through sufficient monetization. Human attention is finite. And in a digital economy where distribution clears through auctions, the allocation of that finite resource determines commercial success. As AI reduces production costs, more products enter the market, more advertisers enter auction systems. More creative variants compete for the same surfaces. The result is inflationary pressure. In distribution markets, customer acquisition costs will rise. In that environment, production savings are not automatically retained as profit. They migrate. They are redeployed into distribution and competed away. In customer acquisition. The bottleneck shifts from engineering bandwidth to monetization efficiency, because monetization is what supports an advertiser's bid. When distribution becomes the binding constraint, the entities that intermediate distribution, which are the platforms that aggregate and allocate human attention, occupy the pivotal position in the value chain. They do not need to manufacture demand to capture value, because they operate the market in which scarcity is priced. As AI expands participation and improves performance, those markets intensify. More bidders, better conversion, broader advertiser sets, all of these increase the value of allocation. This is not a dystopian claim. It is an equilibrium claim. And most importantly, it does not imply that consumers lose. As matching improves and product heterogeneity expands, consumers encounter products that align more precisely with their preferences. Ever more niche goods become viable. Advertising revenue subsidizes access. Direct price barriers can fall. Surplus is created not by coercion, but by more efficient alignment between heterogeneous demand and heterogeneous supply. But that supply is capped by the underlying monetization power of the product, supporting the cost of distribution. Auctions are, by definition, mutually exclusive. Only one participant can win. If Malthus worried that production would always lag population, and Galbraith worried that production would outrun socially optimal allocation. We are confronting a different imbalance. Production outruns discoverability in saturated markets, allocation systems matter more than production systems. Which leads to the next installment in the series, which relates to the narrative that I will eliminate the need for advertising altogether. The idea is that instead of browsing, consumers will delegate purchasing decisions to agents. Those agents will query APIs to discover new products and decision product adoption based on price, with product discovery becoming programmatic, automated, and abstracted from the consumer's cognizance. But even in a world of total agentic autonomy, discovery requires a catalog. The catalog is the central data structure in OpenAI's agentic commerce protocol, for instance, it sources the options that can be exposed in the instant checkout viewport. But whether a product catalog takes the form of traditional digital storefront, an API endpoint, or a machine readable commerce protocol like mcp, acp, or Google's recently announced ucp, the economic function remains the same. Someone intermediates discovery, and when someone intermediates discovery, they control allocation. They decide what is included in the catalog, and that control is economically meaningful. Even if discovery becomes invisible to the human eye, even if it is entirely abstracted away from consumers, and I don't think it will be, the scarcity problem does not disappear. There will still be more products than any system can prioritize. Equally, there will still be competition for inclusion, ranking, and prominence. There will still be mechanisms that determine which products are routed to which users. That's an allocation problem that naturally leads to advertising. So the next installment in this series will examine this proposition more directly. That agentic commerce will not obviate advertising. It may transform its interface and make it less visible to consumers, but it will not eliminate the economic function of paying for distribution within a scarce discovery environment. It.