The Prosperous Society, Part 3: The Collapse of the Pareto Principle
The Prosperous Society is a podcast series by Mobile Dev Memo that articulates an AI Bull Thesis for the digital economy. It argues that the pervasive application of AI to the digital economy will be broadly economically expansionary, leading to increased individual prosperity, expanded consumer choice, and greater human agency.
In Episode 3, I make the case that artificial intelligence stands to erode the Pareto Principle as it applies to production, as AI-enabled distribution efficiencies will enable firms to reach niche, specific audiences profitably. This will result in a wider diversity of goods being produced, creating a compounding effect on economic expansion and allowing consumers to better explore and define their own preferences and tastes.
Thanks to the sponsors of this week’s episode of the Mobile Dev Memo podcast:
- INCRMNTAL. True attribution measures incrementality, always on.
- Xsolla. With the Xsolla Web Shop, you can create a direct storefront, cut fees down to as low as 5%, and keep players engaged with bundles, rewards, and analytics.
- Branch. Branch is an AI-powered MMP, connecting every paid, owned, and organic touchpoint so growth teams can see exactly where to put their dollars to bring users in the door and keep them coming back
Interested in sponsoring the Mobile Dev Memo podcast? Contact Mobile Dev Memo advertising.
The Mobile Dev Memo podcast is available on:
Transcript
Writing in The Affluent Society, first published in 1958, John Kenneth Galbraith describes what he denotes as a new class: a group of people that emerged to pursue work for its intellectual purpose and not out of necessity, lack of optionality, or financial reward.
Galbraith notes that this new class was a product of affluence. As a society became more wealthy as a function of increased productivity, not only could the young and the old drop out of the workforce, but those of prime working age could dedicate their efforts to more rewarding pursuits rather than merely limiting their working hours. Some of these pursuits may be less oriented around production than they might have been during the industrialization process that took root in England in the mid-19th century and was soon thereafter adopted in the United States.
About the new class, Galbraith writes: “Nearly all societies at nearly all times have had a leisure class, a class of persons who were exempt from toil. In modern times, and especially in the United States, the leisure class, at least as an easily identifiable phenomenon, has disappeared. To be idle is no longer considered rewarding or even entirely respectable. But we’ve barely noticed that the leisure class has been replaced by another and much larger class to which work has none of the older connotation of pain, fatigue, or other mental or physical discomfort. And the continuing revolution in job quality being wrought by the computer is accelerating this growth. We have failed to appreciate the emergence of this new class, largely as a result of one of the oldest and most effective obfuscations in the field of social science. This is the effort to assert that all work—physical, mental, artistic, or managerial—is essentially the same.”
Galbraith characterized the new class as overly self-important, prone to pretension, and perhaps self-deluded with the belief in the grandiosity of its own influence on society. But he still recognized that the emergence of this new class was mostly a socially positive phenomenon, given its dedication to education as a self-sustaining force and its generally admirable ambitions. He notes: “Some of the attractiveness of membership in the new class, to be sure, derives from a vicarious feeling of superiority, another manifestation of class attitudes. However, membership in the class unquestionably has other and more important rewards. Exemptions from manual toil, escape from boredom and confinement and severe routine, the chance to spend one’s life in clean and physically comfortable surroundings, and some opportunity for applying one’s thoughts to the day’s work are regarded as unimportant only by those who take them completely for granted. For these reasons, it has been possible to expand the new class greatly without visibly reducing its attractiveness. This being so, there is every reason to conclude that the further and rapid expansion of this class should be a major and perhaps next to peaceful survival itself, the major social goal of society. Since education is the operative factor in expanding the class, investment in education, assessed qualitatively as well as quantitatively, becomes very close to being the basic index of social progress. It enables people to realize a dominant aspiration. It is an internally consistent course of development.”
Galbraith’s point was that the absorption of large swaths of society into this new class represented true social progress because it not only lifted that group into a new intellectual baseline as a function of its emphasis on education, but it also served as a relaxation of labor requirements afforded by society’s affluence. While laborers weren’t granted the option of working less, they were given the luxury of attaching their efforts and energies to projects that held meaningful purpose to them, even though those efforts resulted in less value being created for society and, as a result, lower remuneration.
What Galbraith held real contempt for was what he called the contemporary wisdom, or the economic dogma at the time that exalted the notion of maximal productive output as a necessity in pursuit of not just material wealth, but of social welfare. Galbraith argued that the new class threatened that model of social alignment, which he believed was supported through what he called the dependence effect, which was the use of advertising to fabricate demand. I discussed this idea in part one, but Galbraith’s contention was that the industries that produced mass-market consumer goods, like washing machines and televisions newly affordable in the post-World War II environment, used advertising to causally manufacture demand in service of the conventional wisdom, and that a society organized around this model was suboptimal because it underinvested in public infrastructure in a way that was broadly socially detrimental. He notes that the new class regime jeopardized this model because if people chose work principally for fulfillment and not as a means of consumption, then the conventional wisdom would be abandoned as a modus operandi.
Galbraith seemed to believe this affluence-driven corrosion of the machinery built to enact the conventional wisdom was inevitable. He believed that the virtue of labor for its own sake, at least so far as it was enshrined in the belief system that characterized the modern economy, had already been mostly repudiated, and that the real issue facing society was not too few jobs, but too many people. He writes: “It is a measure of how little we need worry about the danger from reducing the number of people engaged in work qua work that, as matters now stand, our concern is not that we have too few available for toil, but too many.” If this sounds familiar, it’s because it’s essentially the proto-AI-doomism argument.
But what if Galbraith’s presupposition that the marginal utility of products necessarily falls over time as society becomes more affluent simply breaks in the face of technological progress? Galbraith is probably right in the context of the 1950s. Once a household has a car, a television, a washing machine and a dryer, a dishwasher, and other consumer staples, the marginal utility of a better version of those things may not motivate a person to continue to toil away in a factory if the alternative is pursuing a more stimulating but less lucrative career while not having those things.
But the consumption options available in the 2020s are vastly more numerous and varied than those in the 1950s and 1960s. And society sits on the precipice of a dramatic expansion of those consumption options delivered by artificial intelligence.
I argue in part one that the binding constraint in an environment defined by a total abundance of choice is distribution: the ability of a firm to reach its potential customers. And I make the point that AI-enabled personalization, from the perspective of production but also the capacity of modern digital advertising platforms to match consumers with the products most relevant to them, ameliorates this challenge, enabling greater levels of commerce through more performant demand routing and allowing every individual to be exposed to the products most relevant to them. But the positive effects of AI in this regard aren’t limited to discovery and matching. The benefits also pertain to production. Companies can produce ever more niche products because they can be sure that they can avail themselves of the sophistication of these advertising platforms to reach the consumers for whom they are the most germane.
This is an important idea. In Galbraith’s model of the world, demand is catalyzed by advertising in part because consumer bases had homogeneous needs, and so too were the products that served them. The ability to reach a total addressable market defined what was produced, and since the primary advertising channels like television, radio, newspapers, and magazines didn’t support small and niche total addressable markets, the products that might best service those specific groups of consumers weren’t pursued by industry.
We obviously don’t live in that same world. The media landscape is not restricted to a handful of outlets; it’s nearly infinite. Society has already effectively reached a point where media consumption is personalized, at least in terms of how it is curated. We are moving toward an eventuality in which it is entirely derived. I spoke of this idea in my book, Freemium Economics, published in 2014, in which I described a theoretical continuous monetization curve for freemium products: “The theoretical basis of the continuous monetization curve is that a product catalog should be so complete that, at any given point in their tenure with a product, users are presented with a diverse and relevant set of potential purchasable items from which to choose. This catalog should be composed of not only static, predefined purchasable items, but also of dynamic purchasable items created specifically for the needs of a particular user. The size of the range the LTV metric can take is a function of the size of the product catalog. A small product catalog necessarily limits the breadth of values the LTV metric can assume, given that a small catalog of purchasable items doesn’t allow for a large number of combinations of purchases. Large product catalogs offer users choice. The larger the degree of choice afforded to a user, the more the user is given the opportunity to monetize. Engineering continuity in a product catalog generally requires the presence of dynamic products that are not strictly designed, but materialize as collections of customized features existing across a large or infinite number of combinations. Thus, the product catalog is not analogous to the physical catalogs that were mailed to recipients in times gone by, such as clothing retailers. In these catalogs, customers were exposed to a discrete, limited number of clothing choices and purchases were made from within that spectrum. In the freemium model, the spectrum of purchasable items approaches continuity as the customer’s ability to customize those products increases.”
This is consequential because it represents a direct repudiation of Galbraith’s thesis, which is that demand is mostly homogeneous, can be catalyzed through advertising, and can be satisfied to an extent that comes close to extinguishing it. That simply does not apply when total addressable markets shrink, possibly even approaching one, and when those ever-smaller markets can be reached efficiently through digital advertising.
I argue in part two of this series that because consumption choices are expressive acts through which individuality is manifested, the commercial value of AI allows individuals to better invoke their true sense of self as a function of consumption because that consumption is more specifically tailored to their individual preferences and tastes. In this part of the series, I’ll make the case that this dynamic allows for a feedback loop to take root, where ever more niche products are created in the first place because those products can be efficiently routed to the consumers for whom they are most appealing. Rather than stunt economic growth by directing society toward indolence or idleness, it instead instills a productive fervor because labor is rewarded across two vectors: satisfaction in addressing more specific and idiosyncratic desires of specific markets, and the ability to use the fruits of that labor to satisfy their own. AI represents the erosion of the logic of the Pareto principle, the hard floor on the productivity calculus as defined by total addressable market. AI will create an environment where total addressable markets become ever smaller, ever more specific, ever more specialized and personalized. AI does not extinguish economic ambition through abundance. It multiplies ambition by making specificity profitable.
The Pareto distribution is named after Italian economist Vilfredo Pareto, who observed in the late 19th century that wealth and property ownership tended toward conspicuous asymmetry. In one of the most frequently repeated examples attached to his work, Pareto noted that roughly 80% of the land in Italy was owned by 20% of the population. Pareto’s broader insight was that social and economic outcomes often cluster in ways that produce heavy concentration at the top and relative scarcity for the many beneath.
In the early 1940s, management consultant Joseph Juran adapted this intuition into what he termed the Pareto principle, popularizing the notion that a minority of causes frequently explains a majority of results. Juran’s principal domain was industrial quality control, not political economy. Working in manufacturing systems that were becoming more sophisticated in scale and process discipline, he argued that managers could often locate a relatively small set of defects, bottlenecks, or process failures responsible for a disproportionate share of waste, delays, and customer dissatisfaction. His phrase “the vital few and the trivial many” became an operational heuristic for factories, but it applies more broadly. In a 2002 memo, Microsoft CEO Steve Ballmer remarked that about 20% of the bugs causes 80% of all errors.
Juran’s historical moment, which overlaps in large part with Galbraith’s, is instructive here. Mid-century industrial capitalism was organized around large plants, expensive machinery, lengthy production runs, national distribution systems, and mass audiences consuming relatively standardized goods. Under those conditions, concentration was not merely common but often rational. A small number of factories could satisfy broad national demand because the fixed costs of production were substantial and the marginal costs of replication declined with scale.
Further, and to invoke Galbraith, the channels through which consumers learned of products were limited enough that only a narrow band of firms could efficiently command attention. If one wished to sell refrigerators, automobiles, cigarettes, or television across an entire country, one required manufacturing capacity, logistics competence, working capital, shelf access, and a presence in the handful of media properties through which demand could be cultivated. The famous skew of outcomes therefore reflected more than some timeless law of nature; it reflected the architecture of industrial production.
This point is worth circling because the Pareto principle is often invoked as though it were a permanent, irrevocable feature of capitalism rather than a constraint-based efficiency strategy. In a world where production requires large upfront investment and distribution requires access to scarce channels, economic value will predictably accrue to those actors capable of clearing those thresholds. And by extension, concentration emerges from the fact that scale itself is a prerequisite to participation.
Total addressable markets were historically broad as a function of these constraints. Firms did not build products for microscopic cohorts with bespoke offerings because the machinery of commerce was too cumbersome to justify it. The constituents of those cohorts couldn’t be reached efficiently. A factory calibrated to produce one million units for a national market could not economically reconfigure itself to satisfy 10,000 highly particular tastes, much less 10,000 tastes of 1,000 consumers each. The median consumer therefore became the object of production, not because the median consumer was intrinsically the most lucrative, but because the median consumer was legible to the economics of the era.
The same logic governed media. In part one of this series, I make the case that mass advertising in the 20th century operated through broad persuasion because the available channels themselves were broad. A firm purchased television inventory, radio spots, magazine pages, newspaper columns, highway billboards, and hoped that enough of the audience contained plausible buyers to justify the spend. When the media environment is concentrated, attention markets inherit that concentration. A small number of brands become familiar because only a small number of brands can afford repeated exposure. The resulting hierarchy can look like an immutable Pareto distribution when in fact it is the consequence of a cumulative feedback loop of repeated coordination advantages.
What Juran identified inside the factory thus generalized outward into the consumer economy. A minority of product lines often generated the majority of revenue because shelf space was finite. A small number of products generated the overwhelming majority of profit because serving the marginal customer through blunt channels was expensive. A minority of firms captured the majority of market share because they possessed the resources necessary to remain visible at scale and to prevent upstart challengers. Note that the American antitrust bedrock comprised of the Sherman and Clayton Acts was developed in the late 19th and early 20th centuries. These asymmetries were real, but they were historically contingent. They were downstream of constraints.
The digital economy began to soften some of these constraints long before the present wave of artificial intelligence took root. Software products and especially freemium software products introduced an altogether different cost structure. Once code is written, the next unit can often be distributed at negligible marginal cost. A product can therefore accommodate heterogeneous willingness to pay through differentiated features, subscriptions, cosmetic purchases, virtual goods, or individualized upgrade paths.
As I argued in Freemium Economics, a sufficiently rich product catalog expands the monetization surface because users encounter a wider range of offers calibrated to their own preferences and tenure states. The catalog no longer resembles a static shelf, but a wholly personalized storefront where each user’s particular tastes are served. Again, quoting from the book: “A large product catalog is the keystone of the broad monetization curve, the breadth of which allows the freemium model to monetize. It also facilitates and capitalizes on the extended degree to which users can engage with the product. Engineering the continuous monetization curve then is a matter of building a diverse assortment of opportunities with which the user can extract value from the product. These opportunities can materialize in discrete, preconfigured products or in customization options that augment and differentiate the user experience at the individual level. They can also be represented in a combination of the two, with a core set of products that can be endlessly and infinitely customized. These opportunities to enhance the experience are instruments of delight. They allow the users to whom the product holds the most appeal to engage with it to whatever extent they want, and the more opportunities that exist, the longer and higher the curve can reach. More possibilities manifest in higher levels of monetization and more varied levels of monetization, which produces the monetization curve’s long tail.”
Yet even in the digital environment, concentration persisted because discovery remained scarce. App stores rank only so many apps at the top of a category page. Search engines present only so many results above the fold. Social feeds surface only so many posts before attention dissipates. The long tail existed technically, but technical existence is not the same as commercial viability. Millions of products can reside in a database while only a tiny fraction are ever meaningfully encountered. Thus, the Pareto pattern survived the first digital era because distribution bottlenecks survived it. Infinite shelf space coexisted with finite discoverability.
Artificial intelligence changes the terms of that equilibrium because it acts directly on the problem of matching. In part one, I described modern advertising systems as demand routing mechanisms that infer latent preferences from behavioral and contextual signals, then allocate impressions through auctions that reflect expected value. The commercial significance of this mechanism is not exhausted by higher conversion rates for incumbent advertisers. It also further reduces the penalty historically imposed on specificity. A niche product with a sharply defined customer profile no longer requires national awareness in order to flourish. It merely requires the capacity to locate the comparatively small set of users for whom it is unusually valuable.
When targeting precision improves, the viable size of a market can shrink dramatically while remaining economically attractive. Or it can increase participation in these digital channels from firms that were otherwise excluded from them as a technical limitation. This is the beginning of the erosion of the Pareto principle in commercial life. I don’t mean that skewed distributions vanish mathematically from the commercial realm, nor that every market suddenly becomes egalitarian. I mean that many concentrations previously treated as natural laws were in substantial measure artifacts of search costs, media scarcity, production rigidity, logistical challenges, and coordination frictions. When those frictions weaken as a result of AI, which they are, a broader array of producers can clear the threshold of viability and revenue propagates across more categories. Consumer attention disperses across more offerings and tails thicken.
Artificial intelligence intensifies this taste matching granularity because personalization need not stop at the level of recommendation. Every interface element can be tailored. Product descriptions can be rewritten to resonate with different motivations. Creative assets can be dynamically generated for distinct cohorts. Onboarding flows can adapt to prior behavior, including the ad a consumer clicked on to reach that point. Search results can be sequenced according to inferred preference structures. Prices, bundles, merchandising surfaces, support experiences, and educational prompts can all be modulated by systems that learn continuously from interaction data. The storefront becomes eminently malleable.
And when it does, the economics of variety change again. Historically, offering many variants imposed managerial complexity, inventory risk, design cost, merchandising confusion, and diluted messaging. Under software-mediated personalization, many of those burdens can be mitigated. A catalog may appear singular to the operator while presenting itself plurally to the market. Different users encounter different emphases, different combinations, different paths through the same underlying supply. This grants firms something close to individualized merchandising at population scale.
The consequences for the long tail are substantial. Products that once failed because they appealed too narrowly can succeed because narrowness is no longer synonymous with obscurity. Cultural goods that once required mainstream gatekeepers can sustain themselves through direct audience matching. Physical goods with eccentric use cases can aggregate dispersed demand globally. Services built for unusual schedules, rare preferences, or obscure hobbies can locate enough customers to matter. Markets composed of tiny islands become navigable once navigation improves. This was the promise of the internet, and it was amplified by behaviorally targeted digital advertising. It will be amplified once again by artificial intelligence.
Part two of this series argues that consumption choices often function as expressive acts through which individuality is manifested. The economic implications of that proposition are larger than they may first appear. If consumers derive utility from being understood in their specificity, then personalization is not merely a convenience, it is an act of expression. A society in which people can more readily discover the products, communities, aesthetics, experiences, and tools that correspond to their actual preferences is a society in which revealed preference becomes more accurate because the menu of choices is more commensurate with the person choosing.
This creates a recursive dynamic. Better matching raises monetization because consumers encounter offerings that genuinely fit them. And better unit economics, accounting for more efficient distribution, attracts more producers willing to serve narrower cohorts. Greater producer participation enlarges the available set of goods and experiences. The enlarged set generates richer behavioral signals about preference heterogeneity. And those signals are aggregated and improve matching. A system once organized around average demand becomes progressively more adept at serving singular demand.
Galbraith worried that affluent societies saturated with standardized goods and buoyed by advertising would drift toward a kind of purposeless consumption while underinvesting in public goods and higher aspirations. He called this private affluence and public squalor. That critique had force in an era where abundance often meant another incrementally improved appliance marketed to a national audience through repetitive persuasion. But this emerging commercial environment marks a genuine departure. Abundance can now mean precision rather than repetition. It can mean the ability of a consumer to discover exactly the educational resource, creative tool, recreational community, or product configuration suited to their circumstances. It can mean a producer building something exquisitely useful for a population too small to have mattered previously.
The Pareto principle will continue to describe many phenomena because concentration can arise from any number of factors: talent differentials, network effects, cumulative reputation, capital intensity, and human attention itself. But its authority as a universal explanation is undermined when technology lowers the cost of specificity. We should expect some distributions to flatten, some monopolies of mindshare to fragment, some categories to proliferate, and some hierarchies to lose their monolithic stature. What erodes is not asymmetry itself; what erodes is the assumption that asymmetry is destiny. And that distinction is central to the prosperous society. Prosperity in the age of artificial intelligence is not merely more output measured in the aggregate. It is a richer correspondence between what can be produced and what particular people in their particular lives actually value. Society becomes more prosperous when it not only better activates its diverse and diffuse preferences as I noted in part two, but when it understands those preferences more fully through the iterative process of choice.
In the second part of the series, I make the case that commerce is an expression of the self in the sense that it provides an outlet to articulate preferences across a wide variety of opportunities. Note that this argument doesn’t distill down to something like the purpose of life is to consume. Commerce is art, commerce is travel, commerce is a hobby, a concert with friends, a trip to the movie theater with family. All of these things are reflections of the self and are important to retain in anchoring our identities. The point of part two is that AI’s role in expanding the breadth and variety of commerce provides more latitude in that expression.
But how do we discover our tastes, our preferences, indeed our personality or our character? I would argue that it is done through an iterative process of trial and error, exposure to new experiences or opportunities that provide intrinsic personal resonance. Increasingly, that exposure happens on digital surfaces and is mediated by algorithms.
Hegel helps us to navigate our understanding of the self. What makes Hegel especially useful in this context is that he does not treat identity as a static possession waiting to be uncovered through introspection alone. In the Phenomenology of Spirit, the self is dynamic and disclosed through contact with the world. Human beings possess latent capacities and untested inclinations. But these remain indistinct until they are activated through action. We come to know ourselves less through private contemplation than through the cumulative evidence of what we pursue, what we reject, what we persist in, and what unexpectedly animates us once encountered.
That framework maps surprisingly well onto the modern digital environment. Recommendation systems, search engines, advertising platforms, and increasingly, AI-mediated interfaces attempt to infer preference from observable behavior. They do not begin with perfect knowledge of the user; they begin with uncertainty, then refine their models through engagement data: searches, clicks, purchases, dwell time, subscriptions, dismissals, returns, repeated use, and countless other signals that function as partial disclosures of taste. In this sense, both Hegelian self-knowledge and algorithmic personalization depend on a movement from latency to actuality.
The deeper connection to this section is economic. If artificial intelligence dramatically expands the supply of content, products, tools, services, and communities, then the value of systems that can match individuals to the portions of that abundance most relevant to them rises substantially. But matching does more than allocate goods efficiently. It can also widen the process through which individuals discover who they are. The algorithm becomes commercially significant not merely because it sells, but because it reveals.
Hegel first helps us see why action is central to identity. The self becomes intelligible through manifestation. One does not inquire inward and emerge with a completed account of one’s character. Individuality requires movement, the transition from possibility to conduct. Hegel’s account therefore resists the notion that identity can be known in the abstract, without experience. We become intelligible to ourselves through the traces we leave in the world. These systems cannot infer preference from silence; they require visible acts of selection and rejection. Our digital footprints over time form a pattern through which preference can be approximated. They place individuality into daylight.
Hegel also addresses the circularity embedded in self-discovery. One must act in order to know oneself, yet one often feels compelled to know oneself before acting. This tension is familiar. We often demand certainty in advance of evidence. Hegel suggests that such certainty is unavailable because the evidence is generated through the action itself. Our digital lives increasingly participate in this loop, molded not as static representations that are inferred and held constant, but through sequences of encounters. And is through these encounters, the majority of which result in no engagement on our end, that we discover our own tastes and preferences. This is sometimes described as an echo chamber, but it’s a mirror onto the self. And what’s more, those preferences evolve and mutate over time. Further, they are sourced from adjacencies that expose us to concepts and ideas that we might not otherwise have discovered of our own volition. Even ignored recommendations help shape preference boundaries by clarifying what does not resonate.
This becomes more consequential as AI expands supply. If artificial intelligence increases the production of media, software, educational resources, niche goods, and highly specialized services, then recommendation systems gain access to a vastly enlarged possibility set from which to test fit. The user who once chose from a narrow menu can now be presented with a broad frontier of options, many of which would never have existed under prior production constraints. Self-discovery becomes richer because the field of discoverable possibilities becomes richer.
Hegel describes the self as something assembled through encounter; talent alone remains dormant absent action, and identity emerges when aptitude meets an object worthy of it and when curiosity meets an avenue through which it can be pursued. One discovers ability by applying it somewhere meaningful. One discovers meaning by testing ability against the world. Modern algorithms increasingly mediate that conjunction. They can expose an amateur musician to composition tools, collaborators, audiences, and genres previously inaccessible. They can present a person with unusual curiosities to others who share them, converting private eccentricity into social identity. Again, this was the initial promise of the internet, and it is amplified under the auspices of artificial intelligence across not just content creation, which is an obvious artifact, but through content recommendation and distribution.
I’ve made two interleaving claims here. The first is that many of the concentrations historically described through the language of the Pareto principle were downstream of technological and institutional constraints—the economic necessity of targeting broad averages rather than particular individuals. The second is that preference itself is often clarified through encounter, experimentation, and visible acts of choice, increasingly mediated through algorithmic systems that learn from behavior and respond with new opportunities, but nonetheless explore a larger opportunity set than what we might encounter in their absence. When these claims are considered jointly, the role of AI is clarified as delivering personalization as an engine of self-identity.
The most immediate commercial consequences of better matching is higher levels of engagement and better unit economics. When a consumer is presented with a product, service, experience, or piece of content more closely aligned with their actual preferences, the probability of engagement rises, the probability of purchase rises, retention often improves, and satisfaction can improve with it. This dynamic has already been demonstrated repeatedly in digital advertising markets, where superior targeting and relevance can justify higher bids, deliver stronger returns on ad spend, and result in deeper participation from advertisers. But the principle extends beyond advertising inventory. Any system that more accurately pairs differentiated demand with differentiated supply increases the value latent in both sides of the exchange.
Those improved returns then attract additional participants. Producers who might previously have judged a market too small, too diffuse, or too expensive to reach can now rationally enter it because distribution becomes more precise and measurable. Firms can build for narrow cohorts with confidence that those cohorts can be assembled economically. Incumbents can pursue specialized sub-brands, limited releases, or tailored product lines because demand can be located with greater certainty. The commercial threshold for viability declines. Markets once ignored because they were too specific become investable because specificity itself presents an opportunity.
This is where the distinction between advertising and recommendation systems begins to lose practical relevance. In a world of sufficiently advanced personalization systems, both functions converge around the same underlying task: identifying what a person is likely to value and placing it before them at the right moment, in the right context, with the right framing. Commerce becomes a continuous matching process. That process applies with particular force to physical goods, which have historically been constrained by forecasting error and uncertain demand. But AI-enabled personalization changes that calculus. The range of goods expands because the certainty of reaching relevant buyers expands alongside it through the commercial benefit of better matching and more efficient distribution. And as supply expands, the systems responsible for personalization improve in turn. A richer catalog of goods, services, communities, and content generates denser signals about what people actually value under conditions of genuine choice.
When the menu is narrow, preference data is crude because choices are constrained. When the menu becomes broad, observed behavior becomes more revealing. The user who consistently chooses one niche aesthetic over another, one learning modality over another, one form of recreation over another, communicates something more precise than the user who simply selects from a handful of mass-market defaults. Expanded supply therefore enriches the informational substrate on which future personalization depends.
This is why algorithmic systems increasingly function as discovery tools rather than mere sorting mechanisms. Commerce often serves expressive ends. It helps individuals articulate values, tastes, aspirations, and affinities. In an environment where artificial intelligence expands productive capacity and recommendation systems navigate that enlarged field of options, discovery itself becomes a mirror. Individuals are surfaced products they did not know existed, communities they did not know they would value, creative works they would not have independently sought, tools that unlock dormant capabilities, and experiences that refine their understanding of themselves. Preference is inferred, then tested, then refined. The profile becomes more accurate as the person becomes more defined, and identity is refined through that participation.
I’ve described digital advertising as supporting demand routing rather than synthetic demand creation. Many preferences are neither fully formed nor wholly fabricated. They’re nascent and contingent, awaiting collision with the right opportunity. Recommendation systems often perform the matching function that allows those dormant inclinations to become explicit. The result is a feedback loop between selfhood and system intelligence. We act and our actions reveal something about us. Systems observe those revelations and surface new opportunities. We respond to those opportunities and in responding, learn something further about ourselves. The profile becomes more accurate as the person becomes more defined, and identity is refined through that participation.
The macroeconomic implications of this process are substantial because improved coordination reduces waste throughout the system. Producers spend less capital broadcasting irrelevant messages to indifferent audiences, and consumers spend less time searching through unsuitable options. Inventory can be planned with greater granularity. Innovation becomes less dependent on appealing to the median buyer and more responsive to dispersed tastes that previously remained commercially invisible. This is a quieter form of productivity growth than the image of towering factories or dramatic automation, but it may prove no less consequential.
Artificial intelligence amplifies the flywheel further because it expands not only recommendation capacity but supply itself. AI-generated creative assets can tailor messaging to narrower cohorts. AI-assisted design can accelerate the creation of specialized products. AI-authored media can serve previously neglected tastes. AI-enabled software can produce tools for small professional communities or hobbyist groups that would never have justified bespoke development under old cost structures. Every increase in productive flexibility enlarges the universe of matchable supply, which gives personalization systems more to work with, which increases returns, which attracts further participation.
We can therefore describe a recursive commercial dynamic: better matching increases monetization, stronger monetization attracts producers and advertisers, greater participation expands supply, broader supply improves personalization inputs, and improved personalization deepens matching once again. The cycle compounds. And unlike the dependence effect, in which advertising was said to fabricate demand in order to absorb standardized output, this system is oriented toward discovering heterogeneous demand in order to support differentiated output. It does not rely upon the erosion of utility through repetitive persuasion, it relies upon the revelation of utility through relevance.
This is the synthesis of the prosperous society. In a world of abundance, consumption can become a mechanism of self-discovery because the range of available choices is wider and the systems mediating discovery become increasingly adept at understanding lives in their particularity. Prosperity is not captured merely by more units produced or more dollars spent. It is reflected rather in the richer correspondence between human individuality and the goods, experiences, and opportunities through which that individuality is expressed.
Comments: