The Prosperous Society, Part 4: Per commercium virtus

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 4, the conclusion to the series, I outline the AI-enabled flywheel to the Prosperous Society and make the case that it results in a more differentiated, more personalized, and economically expansive new digital economy:

• AI increases productive possibility, and
• Advertising increases matching precision, and
• Matching precision increases specificity, and
• Specificity increases expressive individuality, so
• Society becomes more differentiated, not less.

But I also consider the existence of an acceptable boundary for this personalization. At what point do siloed, wholly unique digital experiences become corrosive to social cohesion? And how should that boundary inform the investments that are currently being made into AI infrastructure, such that they are put to the best possible use?

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Transcript

“The notion that security depends on winning a race with the Soviet Union for increased military production has been abandoned; the same cannot be said for the belief that it depends on winning a competition in technological innovation. Or rather, though the race is recognized as having no rational relation to security, the commitment to it has been nowise lessened. And one reason is that the race has, in fact, a deeply organic relation to economic performance. A consumer goods economy is limited in the resources it can allocate to research and development. The weapons industry sustains such effort on a vastly greater scale. This is interesting to the participant producers for its own sake. It also finances development with application to the consumer goods sector – the development of air transport and computer technology. And it is also a cover by which the cost of research and development for civilian purposes which is too expensive or too risky to be afforded by private firms can, on occasion, be conducted at public cost. Were we deeply concerned about survival, we would question the wisdom of these arrangements and we would work relentlessly to persuade as to their danger. But if economic performance is our primary concern – if production qua production is the thing that counts – then survival naturally takes second place. And so it does. Only as we get matters into better perspective will our priorities become more consistent with life itself.”

That is John Kenneth Galbraith in the final chapter of The Affluent Society. In this passage, Galbraith is extrapolating from a thesis that he accumulates over the course of the book: that the so-called conventional wisdom of maximal economic output as a matter of principle encouraged production for its own sake. That production was not tethered naturally to demand, so demand had to be manufactured in what Galbraith described as the dependence effect. Advertising catalyzed consumer demand that absorbed the products being produced in accordance with the goal of maximal output.

Here, Galbraith is taking that thesis one step further: that because the consumer economy was insufficiently large to attain maximal output, the public sector, and in particular the military, needed to be enlisted in service of that goal, with the research undertaken there creating spillover effects that resulted in new consumer products being invented that could advance the economic frontier. But these research and development efforts aimed at building weapon systems capable of eliminating humanity created obvious existential risks.

As I have noted throughout this series, we no longer live in the world of Galbraith’s affluent society, which he characterized as over-investment into private enterprise at the expense of public goods, or private affluence and public squalor. I do not think Galbraith was wrong per se. The Affluent Society was first published in 1958. The world was a different place. But his work is worth revisiting now because his arguments echo to this day. Galbraith’s ghost is still in the room. The arguments he advanced in his book seventy years ago are used now, sometimes verbatim, to protest against innovation in digital advertising and in artificial intelligence. But I do not think they are adequate in interrogating the current moment.

For one, Galbraith observed a post-World War II industrial economy that focused on mass market good production for consumers that, in large part, were buying these products for the first time. Levittown, New York, is considered the first mass-produced, master-planned suburb in the United States. Construction on it started in 1947, and it was completed in 1951. New construction methods inspired by Henry Ford’s mass production techniques reduced the cost of development dramatically and, combined with backing from the Federal Housing Administration and policies like the GI Bill, which allowed military veterans to buy homes without a down payment and at low interest rates, contributed to the rapid suburbanization of the United States.

Living in larger homes and in farther-flung suburbs, the American middle class required consumer goods like washing machines, dishwashers, televisions, and automobiles. These goods were promoted in mass market media that reached a middle-class demographic clustered in spacious, affordable homes in family-friendly neighborhoods and buoyed by meteoric wage growth and in the expanding professional economy of the world’s remaining superpower. This newfound financial stability precipitated a demographic explosion: the baby boom. For eleven consecutive years, from 1954 to 1964, more than four million babies were born annually.

But we no longer live in the society of Galbraith. 3.6 million babies were born in the United States last year, down 1% from 2024. And that is despite the overall population of the country being 80% larger in 2025 versus 1964. The median age in the United States was 28 in 1964 at the end of the baby boom; today, it is 39. The country is aging. No natural built-in demographic dividend exists. There is no population tailwind producing de facto economic growth. If we desire economic growth, and we should, I would argue that civilizationally we must, then we are forced to engineer it.

The economic structure Galbraith describes does not apply to the society we occupy. The basket of tools and technologies characterized by artificial intelligence is the clearest encapsulation of that economic growth opportunity. Unlike in Galbraith’s affluent society, our modern economy isn’t driven by mass market media and household staples. It is increasingly personalized and individuated, mediated by digital advertising targeted behaviorally to niche tastes and preferences. This is empowered by artificial intelligence. What’s more, the firms that operate the largest and most sophisticated of these digital advertising platforms are the ones investing most heavily into artificial intelligence research and compute resources. Galbraith’s economic constraints have been reversed. Our largest private firms sit at the innovation frontier and deliver technological breakthroughs with the military as a licensor.

The economic promise of artificial intelligence lies not merely in increasing production, but in increasing the precision with which human preferences, talents, and ambitions can be matched with economic opportunity. In part one, I described this as the primacy of distribution. The bulk of AI investment is currently being undertaken by the world’s largest advertising platforms. That is no accident. The distribution layer of the internet economy, which is increasingly the distribution layer of the overall economy, is the point at which AI produces the most commercial impact. As these distribution systems get ever more sophisticated, precise, and effective, the economy grows, not just through improved matching and targeting, but also through the compounded effects of digital advertising performance, lower barriers to entry in the digital advertising economy, and the expansion of production of ever more niche goods that are only viable as a result of improved distribution. This is a flywheel, but it is an interconnected flywheel across three axes: efficiency, participation, and product availability. More products for more advertisers with higher levels of adoption because they can be targeted more precisely. The application of AI to digital advertising provides for maximal commercial personalization at historically unparalleled precision.

But what are the boundaries of acceptability on individuality, and could AI be applied in ways that push beyond them? In Democracy in America, Tocqueville writes, “When all the prerogatives of birth and fortune are destroyed, when all professions are open to all, and when one can reach the summit of each of them by oneself, an immense and easy course seems to open before the ambition of men, and they willingly fancy that they have been called to great destinies. But that is an erroneous view corrected by experience every day. The same equality that permits each citizen to conceive vast hopes renders all citizens individually weak. It limits their strength in all regards at the same time that it permits their desires to expand. Not only are they impotent by themselves, but at each step they find immense obstacles that they had not at first perceived. They have destroyed the annoying privileges of some of those like them; they come up against the competition of all. The barrier has changed form rather than place. When men are nearly alike and follow the same route, it is difficult indeed for any one of them to advance quickly and to penetrate the uniform crowd that surrounds him and presses against him. The constant opposition reigning between the instincts that equality gives birth to and the means that it furnishes to satisfy them is tormenting and fatiguing to souls.”

That should invite introspection. An increasingly personalized digital landscape can instantiate a profound economic flywheel. AI increases productive possibility, and advertising increases matching precision, and matching precision increases specificity, and specificity increases expressive individuality, and therefore society becomes more differentiated, not less. This is broadly welfare-enhancing and it’s desirable. But as Tocqueville observed of democratic societies, expanding individuality also expands ambition, comparison, and restlessness. And it’s possible to apply AI in ways that usurp agency and corrode the sense of self. The thresholds between those applications must be discovered and navigated. The restless ambition that AI can instigate, as in Tocqueville’s commentary on 19th-century America, can lead society down a path of recursive, non-convergent self-definition, with deteriorating social cohesion and an eroded, if not eliminated, shared identity. What happens to a society that cannot cohere because every member’s information and entertainment diets are particular to them? The promise of AI is to instantiate the expansionary economic coordination flywheel I described without personalization giving way to atomization, solipsism, and alienation.

The flywheel of the prosperous society begins with a relatively simple observation that carries immense implications for the structure of the modern economy. When productive capability expands and the friction attached to commercial participation declines, more people participate in economic activity. More products are created and more niche preferences become commercially activated. The modern discourse around artificial intelligence often treats automation principally as labor substitution, as though the most important economic question posed by AI is how many existing tasks can be performed more cheaply by software, or even how many software platforms can be replaced in their entirety as point solutions by individual companies. But that framing is incomplete because it examines AI only through the lens of cost reduction and not through the broader effects that cost reduction has on market formation and economic coordination.

Autonomous general intelligence is not the sole exclusive economic benefit of artificial intelligence in the long term. Alongside it, and perhaps eclipsing the economic power of that outcome, is the progressive automation of the coordination layer that sits between production and consumption. And because modern commercial economies are adaptive systems, reductions in coordination friction do not merely improve the operating margins of incumbent firms. They expand participation. Lower barriers to entry invite new firms into existence. Lower operational complexity allows smaller firms to compete. Lower creative costs permit more experimentation. Lower search costs allow more products to be economically discoverable. AI expands productive possibility not simply because it automates work, but because it expands the number of people capable of participating in sophisticated commercial activity.

Digital advertising is the mechanism that binds this economic activity. The application of AI to digital advertising has and will continue to facilitate this specific preference matching. In fact, I would argue that the exercise is inchoate and essentially in an embryonic state. Research in things like generative retrieval and semantic ranking are moving so rapidly that the mechanisms being deployed now may look quaint in just a few years’ time. But while improved targeting efficiency is an obvious benefit from the application of AI to commerce, one axis that is ignored is its capacity to increase participation in the advertising market from consumers and firms alike. As I argue in Commerce at the Limit, the application of AI to the digital advertising workflow promises to automate most, if not all, of the manual effort currently required of firms to reach consumers through digital media.

That statement should not be interpreted narrowly as commentary on media buying workflows. It describes a much broader transition in the structure of commerce itself. Historically, sophisticated advertising required expertise. Effective campaign management required attribution tooling and analytics infrastructure, and creative iteration and audience segmentation and bidding sophistication that many firms simply could not operationalize. The result was that participation in the digital advertising economy favored organizations with sufficient scale to absorb those operational burdens. The hurdle to participation was quite high and it mostly favored firms selling digital goods and retail items. AI progressively lowers that hurdle height. Generative creative tools reduce the cost of asset production. Automation suites like Advantage Plus and Performance Max reduce the expertise required to manage campaigns. Upgraded recommendation systems improve merchandising precision. Increasingly, the advertising platform itself assumes the role of strategist and analyst and media buyer, abstracting away complexity that previously prevented participation.

This process matters because digital advertising is fundamentally a big economy of small advertisers. The largest digital advertising platforms derive immense value not from a relatively small collection of multinational brands, but from the enormous aggregate commercial activity generated by SMBs seeking measurable commercial outcomes through targeted advertising. The significance of this distinction cannot be overstated because it explains why advertising automation is economically expansionary. Large firms can always participate in commerce. The meaningful question is how many smaller firms cannot now because the aforementioned hurdle is still too cumbersome to clear. How much latent economic activity is excluded from digital advertising because a shop owner or a niche local service provider doesn’t even know where to start with creating an advertising campaign on Instagram or TikTok or YouTube? How much of our economy is saddled with legacy constraints?

The logical endpoint of this progression is commerce at the limit: a world in which any business can articulate a commercial objective and deploy capital toward achieving it without needing to develop institutional advertising expertise internally. Mark Zuckerberg articulated this vision explicitly when he described a future in which businesses merely specify their objectives and acceptable acquisition costs while Meta automates the remainder of the advertising process. It is understandable that that statement was viewed skeptically and cynically, but that outcome, and I view it as an eventuality through tools deployed not just by Meta but every other scaled advertising platform, is a positive destination not just for the company selling ads, but for the consumer seeing them and the firms buying them.

It is difficult to dispute the notion that targeted advertising helps finance broadly accessible services and lowers discovery costs. And that itself might be commercial by its very nature, but the revenue it generates is the hidden engine funding open, ungated access to a vast array of digital products and content. Because targeted ads command a premium, they subsidize the massive free digital utilities we use every day: search engines, social media platforms, premium products like free-to-play games, ad-supported chatbots like ChatGPT, ad-supported streaming services like YouTube and Spotify, and an even larger number of not free but substantially discounted services with ad-supported tiers like Netflix, Disney Plus, HBO Max, Paramount Plus, Hulu, and more.

If these tools relied exclusively on paid subscriptions or metered usage, they would become exclusive clubs rationed by price. Instead, personalized advertising makes this digital infrastructure universally available to billions of people who otherwise might not be able to afford it. This provides a low-income student anywhere in the world with the same access to information as an executive. Furthermore, personalization fixes the massive attention pollution of the old broadcast model by cutting through noise and slashing search costs for everyone. Instead of wasting consumer attention on brute force irrelevant billboards, the algorithm delivers a highly specific non-rivalrous informational signal directly to the people who need it. This levels the playing field for the economic long tail: the small businesses, local shops, and niche innovators who could never afford a multimillion-dollar TV campaign but who can now reach narrowly defined audiences at commercially viable acquisition costs. Entire categories of economic activity become possible only because distribution precision exists. Ultimately, personalized advertising is a macro-level benefit that keeps the digital commons free, competitive, and hyper-efficient. The consequence of enhancing this infrastructure is not merely improved operational efficiency for large enterprises. The consequence is that commercial participation itself becomes democratized.

And this is the first stage of the prosperous society flywheel. First, AI increases productive possibility. Cheaper creation permits more experimentation. Automated production reduces operational friction. Lower barriers to entry allow more firms to participate. More firms create more products. More products create more opportunities for differentiation and specialization. Economic output expands not only because existing firms become more productive, but because new firms emerge to serve unmet needs in ways that are only possible as AI improves coordination and matching. In a way, the tail wags the dog. Improved distribution precedes expanded production. But this is vastly different than Galbraith’s dependence effect in which he believed that advertising generated demand. The promise of digital advertising and the application of AI to digital advertising is to ever more precisely fulfill demand and, in doing so, allow for more narrow product categories to become viable. Call it the opportunity effect or the distribution effect. New categories are born only when they can be economically distributed.

Second, advertising increases matching precision. This has been the central thesis of this series from the beginning. Advertising is not principally a mechanism for manufacturing demand. Advertising is a mechanism for routing demand. It compresses discovery costs and facilitates economic coordination between producers and consumers who otherwise would never encounter one another. This distinction matters enormously because much of the modern criticism of digital advertising still relies implicitly on Mad Men-like persuasion rooted in the industrial mass media economy. That framework made intuitive sense in a world defined by broadcast television and newspaper circulation and geographically clustered suburban consumers purchasing largely standardized goods. But digital advertising systems do not principally operate through broad demographic persuasion. They operate through increasingly sophisticated matching infrastructure.

Third, modern ReXis architecture is fundamentally coordination infrastructure. New advancements in recommender system architecture invoke request-centric user modeling in which increasingly rich user representations can be computed efficiently and deployed at massive scale. The significance of this architecture is not merely technical sophistication. It is the increasing precision with which platforms can model consumer receptiveness and commercial intent. Recommendation systems do not merely ask whether a user might click on an advertisement. Increasingly, they attempt to understand why a specific product might resonate with a specific user at a specific moment. Behavioral targeting systems ingest sequences of historical actions and engagement patterns in order to produce richer representations of preference and intent. Request-centric ranking architecture allows these systems to evaluate commercial relevance with increasing granularity while maintaining the latency standards required for scaled advertising markets.

The practical consequence of this sophistication is that specificity becomes economically viable. And this is the key transition in the flywheel. Historically, mass market economics favored scale because scale reduced distribution costs. Broadly appealing products were economically advantaged because broad appeal facilitated efficient marketing through the small number of mass media channels that existed. So niche products struggled or were simply prima facie non-viable and thus not produced because discovering the consumers for whom they were relevant was too expensive relative to the size of the addressable market. Digital advertising has eroded that constraint, and AI enablement will accelerate that erosion. Better targeting allows increasingly specific products to find increasingly specific audiences profitably. And when specificity becomes economically viable, the structure of the economy changes.

Ads targeted at smaller, more niche, and better defined audiences can generate net new revenue in targeting inventory that might otherwise have gone unsold. This insight extends beyond advertising auctions. Better matching infrastructure creates net new economic activity because products that previously could not sustain economically viable distribution suddenly can. This dynamic becomes increasingly important when considered alongside the broader structure of retail commerce. According to the U.S. Census Bureau’s quarterly retail e-commerce sales report, e-commerce accounted for just 16.6% of total U.S. retail sales in the fourth quarter of 2025. That figure should provoke reflection because it implies that the overwhelming majority of retail spend still occurs in environments characterized by vastly less efficient discovery and merchandising infrastructure than what digital systems accommodate. A substantial portion of future economic growth will come from the migration of commerce into environments where matching precision is higher. And this transition compounds recursively. Better targeting increases the viability of niche products. More niche products allow more firms to participate. More participating firms creates more experimentation. More experimentation broadens preference satisfaction because consumers encounter products that more closely align with their actual tastes and identities. Broader preference satisfaction increases economic output because more commercial activity becomes viable. This is the collapse of the Pareto principle. That phrase should not be interpreted to mean that inequality disappears or that market concentration evaporates entirely. Industrial economies naturally concentrated because industrial production and mass media distribution rewarded standardization and scale, and large firms enjoyed structural advantages because broad products distributed through broad channels were economically dominant. But AI-enabled targeting infrastructure weakens those concentration dynamics because specificity becomes commercially sustainable. In fact, specificity could become a competitive advantage. A product no longer needs universal appeal to support a meaningful business. That is a profound change. The industrial economy rewarded homogenization because homogenization simplified distribution. AI-enabled commerce rewards differentiation because differentiation improves relevance and because relevance improves commercial efficiency. The result is not the elimination of large firms, but the weakening of the structural advantages historically attached to mass market standardization. The economy becomes more heterogeneous, and importantly, this heterogeneity is economically expansive because these sophisticated AI-enabled systems coordinate it. The prosperous society does not emerge from limitless production in the abstract. It emerges from increasingly precise coordination between highly specific and differentiated supply and demand.

This coordination dynamic also explains why AI infrastructure investment can be economically rational even at extraordinary scale. Much contemporary commentary around hyperscaler capital expenditure implicitly assumes that the economic return on AI infrastructure must materialize immediately through directly monetizable consumer applications. But that framing misunderstands the nature of the opportunity. The economic value of AI infrastructure lies not merely in present monetization, but in the future coordination capacity it supports. This is the fourth stage of the flywheel: compounding economic participation.

Performance advertising creates a recursive reinvestment dynamic in which commercial success funds additional advertising expenditure that in turn funds additional growth. The significance of performance marketing is not merely that advertising spend can be measured against commercial outcomes. It is that profitable customer acquisition produces a compounding cycle of reinvestment and expansion. Performance advertising is not static allocation. A business that reliably acquires customers profitably does not simply maintain a fixed level of advertising expenditure. It reinvests. More customers generate more revenue, and more revenue funds more advertising. This compounding loop is why performance marketing became such an enormously powerful force within the digital economy and why platforms optimized around measurable commercial outcomes became dominant.

Now imagine the size of the absolute effect when that 16.6% inches upward because SMBs that were previously excluded from digital advertising are participating in that market. And when it inches upward again because new firms emerge to serve niche tastes that were previously impractical to satisfy through blunt marketing tools. Consider how much slack exists in just the consumer economy that can be addressed with AI enablement at the distribution layer. And critically, the flywheel of the prosperous society is not merely mechanical. It is expressive, personal, and socially productive. As products become more specialized and discovery becomes more precise, individuals gain greater capacity to express preference and identity through commerce. The economy becomes increasingly capable of supporting differentiated tastes and ambitions and forms of self-definition that previously lacked sufficient scale to survive commercially.

This is one reason why digital advertising should not be understood merely as a revenue mechanism for internet platforms. Digital advertising is coordination infrastructure for an increasingly differentiated economy. The prosperity generated by this system is not confined to aggregate GDP expansion, although that’s an important consideration and it undoubtedly produces that. The prosperity emerges from increasing the fidelity and resolution of human creativity, of the human propensity for self-expression. And this is ultimately why the AI investment cycle currently underway is rational. There is immense value in unbridled personal expression. If the application of AI merely automated existing workflows while producing no expansion in participation or coordination capacity, then many of the concerns around speculative excess would be justified. But the largest advertising platforms in the world are investing aggressively into AI precisely because they recognize that increasingly precise economic coordination unlocks enormous latent commercial activity, not just through more precise targeting, but expanded participation and more expressive commercial participation from consumers. If I buy things that make me happier, I’m not just an affirmative response in a customer survey; I’m a more contented member of society. This sounds nebulous and rhetorically saccharine, but it isn’t. Consumers are willing to spend more for goods that better meet their needs. The future growth opportunity does not exist solely in improving monetization against existing economic activity. It exists in onboarding incrementally new firms and enabling incrementally new forms of commerce that historically could not participate profitably.

The prosperous society flywheel can therefore be stated relatively forthrightly: AI increases productive possibility and expands the universe of products for sale. Advertising increases matching precision within that expanded universe, better matching individual tastes with the products that satisfy them. That specificity increases expressive individuality. That individuality expands economic participation. That participation compounds economic growth. That growth funds further infrastructure and innovation. That is the flywheel.

The argument presented throughout this series is ultimately not about advertising or artificial intelligence in isolation. It is an argument about coordination and individuality and the conditions under which human flourishing becomes economically sustainable within technologically advanced societies. And while I believe the application of AI to the distribution layer of the economy can unlock enormous productive and creative potential, I also believe that this outcome is not guaranteed. The same personalization infrastructure that increases expressive capacity can also produce fragmentation and passivity and alienation if applied without restraint or wisdom.

This is why I believe the contemporary debate around AI is so frequently unsatisfactory. Much of the discourse oscillates between the extremes of apocalypticism and utopianism, not just from the perspective of human agency and self-determination, but also from the perspective of the economic sustainability of the investments being made into the compute infrastructure being built to support the application of AI. Both perspectives flatten the question into something deterministic. But technology does not possess moral direction independent of the institutional and commercial incentives through which it is deployed. The application of AI to commerce can be expansive and humanizing or corrosive and socially destructive. It can be wildly expansionary or a total deadweight loss. The distinction depends on whether these systems augment human agency or progressively displace it.

This concern is not new. Tocqueville articulated how democratic societies contained within them an underlying tension between individuality and atomization. In Democracy in America, he observed that democratic citizens are simultaneously liberated and isolated, freed from inherited structures and aristocratic hierarchies, yet increasingly susceptible to a kind of anxious restlessness that emerges when identity must be continuously self-authored. Democratic equality expands personal aspiration while also weakening the intermediary structures that historically grounded social cohesion. Individuals become more independent while simultaneously becoming more psychologically vulnerable. That observation feels remarkably contemporary.

The digital economy increasingly organizes itself around personalization. Increasingly, AI systems mediate not merely what we buy, but what we see and what we encounter and what forms of information are and become salient to us all. And while this personalization infrastructure can produce enormous welfare gains by reducing friction and improving relevance, it also introduces the risk that individuals retreat progressively inward into algorithmically curated realities that reinforce preference rather than challenge it. Individuality is not an unalloyed good. This is an important distinction because the argument presented throughout this series is not that all forms of personalization are inherently emancipatory. Human flourishing requires individuality, but individuality untethered from shared civic and cultural structures can devolve into fragmentation. A society in which every individual inhabits a unique and siloed digital consciousness may also become a society in which common experience deteriorates and social solidarity weakens. Hyper-personalization can increase expressive freedom while simultaneously eliminating the shared perspective that binds a society. And this is where the moral dimension of AI becomes unavoidable.

The flywheel described in the previous section is economically expansive because it increases the precision with which differentiated preferences can be matched with differentiated products and opportunities. But that process only remains socially constructive insofar as it expands agency and participation and creativity rather than replacing them. The distinction may sound abstract, but it becomes obvious when considered in the context of agentic commerce. Consumption is not merely transactional; it is expressive. The choices that individuals make about what to buy and what to wear and what communities to affiliate with and what aesthetic identities to cultivate are intertwined with the process of self-authorship itself. Commercial participation is not reducible to utility maximization because human beings do not experience life as optimization functions. Meaning emerges through aspiration and discovery. The act of choosing is itself meaningful.

This is why I remain skeptical of visions of fully autonomous agentic commerce in which AI systems continuously purchase products and services on behalf of users with minimal human participation. Such systems may produce extraordinary efficiency gains. They may reduce cognitive overhead and compress transaction costs and optimize consumption decisions against measurable objectives. But not every application of AI expands human flourishing merely because it increases efficiency. That distinction is central to the entire argument presented throughout this series. There is an important difference between systems that augment human intentionality and systems that replace it. A recommendation system that exposes an individual to a niche product category that aligns with latent interests can expand self-discovery. A system that autonomously resolves all acts of consumption on behalf of the user risks undermining intentionality altogether. Convenience can quietly suppress expression. And this distinction extends beyond commerce itself.

A healthy commercial society does not merely allocate resources efficiently. It creates conditions under which individuals can pursue differentiated ambitions and discover differentiated identities. Economic systems are ultimately social systems because they shape how individuals encounter one another and how they perceive possibility and how they orient themselves toward aspiration. Commercial abundance alone is insufficient if the mechanisms producing that abundance simultaneously erode the human capacities that give abundance meaning. This is why I have repeatedly emphasized throughout this series that the most economically meaningful application of AI lies in expanding productive and expressive participation rather than eliminating it. The fundamental distinction between the two is that rendering distribution more informed and capacious expands choice, and suppressing or short-circuiting the act of doing something suppresses it. This distinction isn’t exclusive to commerce and shopping; it’s universal across potential applications of AI. And a firm’s decision to pursue one strategic purpose or another dictates whether its investments in that infrastructure can be profitable. Good AI expands agency; it doesn’t subvert it. It expands participation in commerce by reducing barriers to entry. It expands creativity by reducing operational friction around production and distribution. It expands discovery by allowing individuals to encounter products and communities and opportunities that would otherwise remain invisible. It expands self-authorship because increasingly precise coordination infrastructure permits more differentiated forms of economic participation to become viable.

Bad AI progressively displaces agency. It narrows experience by optimizing excessively toward behavioral predictability. It collapses intentionality by automating decisions that constitute personal identity. It substitutes passive consumption for active exploration, and ultimately it risks reducing individuals to observers of the mechanisms that give their lives meaning. These distinctions are subtle but enormously important because they explain why I remain simultaneously optimistic and cautious about the trajectory of AI-enabled commerce.

I believe deeply that personalization can be socially beneficial. In fact, I would argue that modern digital advertising systems have already generated enormous welfare gains by democratizing access to information and entertainment and entrepreneurship. Personalized advertising funds a substantial portion of the free digital infrastructure upon which contemporary society increasingly depends. It allows niche businesses to compete against incumbents. It allows consumers to discover products that more precisely satisfy their preferences. It reduces the brute inefficiency of mass market broadcasting and improves the precision with which commercial information is routed through the economy. It undermines many of the issues that Galbraith took with the economic structure that dominated in the middle of the last century. But these systems become socially dangerous when optimization ceases to serve human intentionality and instead begins to replace it. One shortcoming around the contemporary commentary about AI is that it ignores how contingent these outcomes are upon institutional design and competitive incentives. The same foundational technologies can produce radically different social outcomes depending on how they are applied. Recommendation systems can facilitate discovery and curiosity, or they can facilitate social isolation and alienation. Personalization systems can expand expressive possibility, or they can produce algorithmic solipsism.

The moral choice therefore does not exist at the level of whether AI should advance. That is largely settled already. The moral choice exists at the level of what kinds of human capacities these systems are designed to amplify. And this brings the discussion back to infrastructure investment. Throughout the current AI cycle, enormous attention has been devoted to the scale of hyperscaler capital expenditure and to whether the infrastructure currently being constructed can justify its implied valuation. This debate is frequently framed almost exclusively through the lens of near-term monetization: how many subscriptions can be sold and how many inference requests can be monetized and how quickly AI products can generate operating leverage. But that framing misses the deeper significance of what is being built. The infrastructure being constructed today is ultimately coordination infrastructure. Its value does not emerge solely from replacing labor or generating synthetic content or reducing customer support costs. Its value emerges from expanding the capacity of economic systems to coordinate increasingly differentiated forms of production and discovery. AI infrastructure matters because it can expand the number of people capable of participating meaningfully in sophisticated commercial activity and because it can increase the precision with which human creativity and ambition are translated into economically sustainable outcomes. And this is true across both digital and physical products. When AI is attached to the distribution and discovery layer of the economy, it impacts everything, not just chatbots or SaaS software licenses.

But these investments are justified only insofar as they support the flywheel of human flourishing described throughout this series. The modern economy increasingly derives value from intangible and expressive forms of production. Software and media and entertainment and education and community formation and entrepreneurship are all becoming progressively more individualized and digitally mediated. In such an environment, coordination infrastructure becomes extraordinarily important because the economy itself becomes more differentiated. The industrial era depended upon standardization because standardization simplified distribution. The AI era increasingly depends upon personalization because personalization allows differentiation to scale economically. But again, differentiation alone is not sufficient. A civilization cannot sustain itself purely through hyper-personalized consumption loops detached from shared institutions and shared meaning. Open societies require intermediary structures capable of binding individuals together despite expanding personal autonomy. One danger posed by algorithmic systems is that they may progressively weaken those structures by mediating experience too individually and too frictionlessly. Individuals can become isolated, but more than that, they can be condemned to prescriptive roles from which they cannot escape because the systems that mediate discovery strip them of choice.

Karl Popper, in The Open Society and Its Enemies, criticized what he saw as Plato’s tendency toward totalitarianism as a function of his notion of happiness, which he interpreted really as being contented resignation with one’s predetermined position in society. Popper distinguished between closed and open societies on the basis of freedom of choice and social mobility. “True happiness,” Plato insists, “is achieved only by justice, that is, by keeping one’s place. The ruler must find happiness in ruling, the warrior in warring, and we may infer, the slave in slaving. Apart from that, Plato says frequently that what he is aiming at is neither the happiness of individuals nor that of any particular class in the state, but only the happiness of the whole, and this he argues is nothing but the outcome of that rule of justice which I have shown to be totalitarian in character. That only this justice can lead to any true happiness is one of the main theses of The Republic.”

Popper goes on to describe why choice is such an integral component of freedom. “Our own ways of life are still beset with taboos: food taboos, taboos of politeness, and many others. And yet there are some important differences. In our own way of life, there is between the laws of the state on the one hand and the taboos we habitually observe on the other, an ever-widening field of personal decisions, with its problems and responsibilities, and we know the importance of this field. Personal decisions may lead to the alteration of taboos, and even of political laws which are no longer taboos. The great difference is the possibility of rational reflection upon these matters. In our own time, many of us make rational decisions concerning the desirability or otherwise of new legislation and of other institutional changes. That is to say, decisions based upon an estimate of possible consequences and upon a conscious preference for some of them. We recognize rational personal responsibility.”

Popper then delineates between a closed society and an open society and proposes an exaggerated hypothetical of an open society taken to an extreme. “As a consequence of its loss of organic character, an open society may become, by degrees, what I should like to term an abstract society. It may, to a considerable extent, closet the character of a concrete or real group of men, or of a system of such real groups. This point, which has been rarely understood, may be explained by way of an exaggeration. We could conceive of a society in which men practically never meet face to face, in which all business is conducted by individuals in isolation who communicate by typed letters or by telegrams and who go about in closed motor cars. Such a fictitious society might be called a completely abstract or depersonalized society. Another way in which the picture is exaggerated is that it does not so far contain any of the gains made, only the losses. But there are gains: personal relationships of a new kind can arise where they can be freely entered into instead of being determined by the accidents of birth, and with this, a new individualism arises. Similarly, spiritual bonds can play a major role where the biological or physical bonds are weakened. However this may be, our example I hope will have made plain what is meant by a more abstract society in contradistinction to a more concrete or real social group. And it will have made it clear that our modern open societies function largely by way of abstract relations such as exchange or cooperation.”

Popper ends the chapter and the first volume of the book with the following. “The lesson which we thus should learn from Plato is the exact opposite of what he tries to teach us. It is a lesson which must not be forgotten. Excellent as Plato’s sociological diagnosis was, his own development proves that the therapy he recommended is worse than the evil he tried to combat. Arresting political change is not the remedy. It cannot bring happiness. We can never return to the alleged innocence and beauty of the closed society. Our dream of heaven cannot be realized on earth. Once we begin to rely upon our reason and to use our powers of criticism, once we feel the call of personal responsibilities and with it the responsibility of helping to advance knowledge, we cannot return to a state of implicit submission to tribal magic. For those who have eaten at the tree of knowledge, paradise is lost. It is an issue which we must face squarely, hard though it may be for us to do so. If we dream of a return to our childhood, if we are tempted to rely on others and so be happy, if we shrink from the task of carrying our cross—the cross of humaneness, of reason, of responsibility—if we lose courage and flinch from the strain, then we must try to fortify ourselves with the clear understanding of the simple decision before us. We can return to the beasts, but if we wish to remain human, then there’s only one way: the way into the open society. We must go on into the unknown, the uncertain and insecure, using what reason we may have to plan as well as we can for both security and freedom.”

Popper’s point here is that the prescriptive rigidity of the closed society, based on tribal norms, taboos that can’t be broken, and in some cases explanatory magic, encapsulate the restraints of a closed society. And that the open society, even in its exaggerated abstract form, at least exaggerated when Popper wrote the book during World War II, allowed for personal discretion, choice, rejection of taboos, and mobility. Popper made it clear that while the rigidity of Plato’s concept of happiness was alluring, once a society had discovered the benefits of individualism and personal choice, it simply couldn’t abandon them. This is why I believe restraint and intentionality matter profoundly in how AI systems are designed and deployed. There are applications of AI that would obviously be more reflective of Popper’s conception of a closed society that would remove autonomy, erode personal choice, and place society into cohorts or segments that are permanent and non-malleable. That is a frightening outcome, even if it is packaged under the auspices of convenience.

Some forms of friction are expressive and capture meaning. Discovery itself often requires uncertainty and exploration and experimentation. Relationships require negotiation and compromise. Artistic taste develops through exposure and reflection rather than instantaneous optimization. Human beings do not flourish merely because every system surrounding them minimizes inconvenience. A society organized entirely around frictionless optimization may ultimately become spiritually inert, even while remaining materially prosperous. And yet rejecting AI altogether would represent a profound mistake as well because the expansive potential of these systems is immense and multifaceted. AI-enabled distribution infrastructure will allow and does now allow individuals with highly specific talents and interests to participate in and contribute to society in ways that previously would have been impossible. It can lower barriers to entrepreneurship and creative production. It can increase access to education and information and entertainment for communities that previously were entirely excluded from those pursuits. It can allow more individuals to pursue meaningful forms of differentiated striving.

That last point is perhaps the most important. The prosperous society described throughout this series is not one in which technology eliminates striving. It is one in which technology expands the number of people capable of striving meaningfully and productively and specifically. A low friction economy does not need to become a low agency society. In fact, if these systems are designed thoughtfully and competitively, they may ultimately produce the opposite outcome: a society in which more individuals can discover highly specific communities and products and forms of work aligned with their particular interests and talents. That is the moral choice embedded within the current AI transition. It is the inversion of Galbraith’s affluent society. AI has the capacity to expand the number of people capable of striving meaningfully and productively with purpose and intention as a technologically buoyed open society that embraces the value of personal discretion and individuality and doesn’t surrender choice to technology merely in the name of convenience. It is a prosperous society. I am Eric Seufert. Thank you for listening.