The Prosperous Society, Part 2: The Human Nexus of Commerce
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 2, I argue that the Western political economy treats commerce as a primary mechanism of individual expression, and that artificial intelligence has the capacity to make commerce more expressive as an outward representation of the individual. In this way, the personalization promise of AI should be embraced and amplified, and applications of AI that subsume commerce, or treat it as a chore, are misaligned with the core benefits of AI. By contrast, advertising aligns incentives, incorporates private value through bids, and scales across heterogeneous products and margins, making it the more durable and effective model for product discovery in an AI-driven economy.
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Transcript
One of the great promises of artificial intelligence is the potential for an intelligent system to replace human effort in tedium, to assume the rote and low-value tasks that humans may only marginally outperform computers at, such that more time and opportunity is afforded to more creative or fulfilling work. This is the generally accepted and most readily apparent benefit of artificial intelligence, and it contrasts cleanly with the Doomsday scenarios that depict artificial intelligence leapfrogging human intelligence to the extent that humans are ultimately subjugated by the superior competencies of the machines.
The central thesis of this Prosperous Society series is simple: that AI will engender an era of unprecedented human creative fulfillment, civilizational cohesion, and material abundance, principally as a function of dramatic productive and organizational efficiency gains that accelerate economic growth through deep personalization.
The first installment of this series makes the case that much of that growth will be delivered through an increase in output that necessitates more effective and revenue-performant demand routing through optimized digital advertising, which sits at the heart of the modern economy. All of the arguments in this series and, indeed, in my view, the entire premise of the transformative power of artificial intelligence, sit downstream of that notion. Thus, AI’s impact on commercial output and the ability to pair that output with consumers most efficiently capture its true value proposition. In that sense, my own views on the value and impact of AI are firmly aligned with the former sentiment: that AI will unburden humans from the mundane and empower them to pursue the profound.
But how do we distinguish between the mundane and the profound? Because the contours of that distinction color any interpretation of the impact of AI. For millennia, humans have attached moral virtue to work and commerce. From the Hebrew Bible, Proverbs 31:13 through 17: “She seeketh wool and flax and worketh willingly with her hands. She is like the merchant ships; she bringeth her food from afar. She riseth also while yet it is night and giveth meat to her household and a portion to her maidens. She considereth a field and buyeth it. With the fruits of her hands, she planteth a vineyard. She girdeth her loins with strength and strengtheneth her arms.”
And a large preponderance of Enlightenment canon is centered on the notion of the individual’s rights as a centerpiece of a functional, liberal constitutional order organized around private property and mutually beneficial trade. In the Second Treatise of Government, John Locke notes: “Where there is not something both lasting and scarce and so valuable to be hoarded up, there man will not be apt to enlarge their possessions of land, were it never so rich, never so free for them to take. For I ask, what would a man value 10,000 or 100,000 acres of excellent land, ready cultivated and well-stocked too with cattle, in the middle of the inland parts of America where he had no hopes of commerce with other parts of the world to draw money to him by the sale of the product? It would not be worth the enclosing, and we should see him give up again to the wild common of nature whatever was more than would supply the conveniences of life to be had there for him and his family.”
In the Wealth of Nations, Adam Smith makes the case that the individual impulse to improve one’s material comfort is a principal driving force behind economic progress. He writes: “The natural effort of every individual to better his own condition, when suffered to exert itself with freedom and security, is so powerful a principle that it is alone and without any assistance not only capable of carrying on the society to wealth and prosperity, but of surmounting a hundred impertinent obstructions with which the folly of human laws too often encumbers its operations, though the effect of these obstructions is always more or less either to encroach upon its freedom or to diminish its security.”
These aren’t fringe or controversial notions; they form the basis of the Western political economy framework. And if we consider the ways in which AI may realign society, but especially to society’s benefit, we must consider how and whether those manifestations and implementations of AI comply with these fundamental principles. In that framework, economic activity is not instrumental; it is expressive. The act of choosing what to produce, what to exchange, and what to consume is the mechanism through which individual preference is realized. It is the expression of personal freedom, and it becomes even more fundamental to the notion of individualism as AI contributes to a vast, dramatic expansion of productive output that can be effectively routed to individual preferences with conversion-optimized digital advertising, as I argue in part one.
It is critical to interrogate the consistency of these applications of AI with our fundamental conceptions of freedom and liberty, because one can believe that AI will have a transformatively positive impact on society without believing that every possible use case for AI is so. Again, where do we draw the distinction between the mundane and the profound tasks that AI should undertake for us? Because any encroachment of AI on the human effort and activities that render us free, render us actualized, or render us prosperous should be viewed skeptically as consistent with the Doomer narrative, no matter how loudly they are championed.
Which raises the question of agentic commerce. This is a nebulous term, often invoked without precision. My sense is that most people who use it don’t really know exactly what they mean by it. Taken literally, it should mean that a person’s AI-enabled agent makes purchases on their behalf. Less literal interpretations could mean that products are surfaced to a person through some AI-enriched sorting or classification logic. I’ve stated that agentic commerce, and by that I mean independent agents not affiliated with any given retail platform acting on behalf of a user, is a mirage. It may superficially sound appealing, but it runs counter to the economic incentives of retailers, retail platforms, and consumers.
I’ve made the case that the lighter-weight implementation that amounts essentially to a recommendation system monetized through an affiliate model will trend toward surfacing the kind of broadly appealing, lower-cost items that consumers simply don’t need recommended to them, erodes the product differentiation and personalization capabilities of AI that I unpack in part one, and delivers less commercial value than a conversion-optimized digital advertising model. More substantively, agentic commerce as conceived with an agent autonomously making purchases on behalf of a consumer is inconsistent with the fundamental principles of individual empowerment and liberty and, as an application of AI, is detrimental to a free society.
In this episode, I’ll make the case that true agentic commerce as manifested through AI-empowered autonomous purchasing agents is inimical to the Western ideals of individual freedom and the moral virtue of economic choice. I’ll also argue that agentic commerce as manifest through independent agents surfacing product recommendations from retailers and monetizing them with affiliate commissions collapses under its own competing incentives. In this way, I propose that AI’s role in the prosperous society must be mediated by positive and negative applications, benign and malign incarnations. As we delineate between them, we must invoke our fundamental conceptions of social value as rooted in the tradition and ideologies of Western political economy. Ignoring that presupposes either that we embrace the negative applications and malign incarnations of AI, which is the Doomer position, or that we reject the fundamental precepts of the Enlightenment canon, which requires substantially more work.
At first blush, the prospect of truly agentic commerce, wherein a specialized commerce agent anticipates the needs of a user and fulfills them through proactive search and purchase functionality, seems natural and intuitively appealing. Why wouldn’t consumers want agents discovering useful products on their behalf? Any time saved on the chore of shopping can be dedicated to more fulfilling and stimulating tasks or leisure.
But this mental model groups all shopping into the same bucket as a burden where a consumer aimlessly wanders the aisles of a supermarket or home improvement store, ticking boxes off a shopping list. This mental model presumes that all goods are commodities differentiated principally on price or some other entirely quantifiable factor that can be quickly and objectively evaluated with a cursory glance. This mental model reduces shopping to work, and exactly the kind of work that one would expect to be able to delegate to an AI. Shopping isn’t recreation, and it’s only personal to the extent that a personal agent needs to express a simulacrum of a consumer’s tastes and preferences. Shopping is a rote mechanical task, a chore that consumers would prefer to offload onto a machine. Anyone with that worldview must be unfamiliar with the phrase retail therapy.
In part one, I describe John Kenneth Galbraith’s conception of the dependence effect as he articulates it in his seminal book, The Affluent Society. He makes the case that consumer demand is downstream of production and is mostly a product of advertising, which has the effect of instigating and conditioning demand. My view of advertising, but particularly the modern variant of advertising that didn’t exist when The Affluent Society was published in 1958, is that it is not a demand catalyst or a demand fabricator, but rather a routing mechanism for demand. The auction system finds the clearing price for a consumer’s attention, with that person’s behavioral history as an input, and matches it with the ad that produces the greatest expected value from it.
Later in the book, in a chapter titled The Vested Interest in Output, Galbraith makes the case that businessmen had successfully infiltrated the economic decision-making establishment to coerce their view of full economic capacity as a function of output as the accepted policy wisdom. He called what he perceived as this popular axiom of maximum economic output anchored to production as the ideal organization of a political economy, the conventional wisdom, noting that John Maynard Keynes had succeeded in championing this view from the liberal camp but that little political opposition existed to the conventional wisdom from conservatives, who historically would have been naturally opposed to it. After quoting von Mises on the businessman’s prominent role in establishing the economic hegemony, Galbraith writes: “No one enjoys quite such distinction as the man who, by common consent, is allowed to look ahead and advise as to what we should do to promote or retard a particular occurrence. The intellectual naturally assumes his authority on these matters. He is likely to be gifted well beyond the businessman in erudition and oral capacity. That felicity the businessman counters by stressing his identification with production. He is not a theorist but a practical man. His is the forthright approach of the man who knows how to get things done. He has learned about life in the shop. This has provided him with a unique insight into how things are in the country at large or the world. Were anything to happen to the prestige of production, it is plain that the businessman whose mystique is his identification with production would suffer severely in his competition with the intellectual for the role of social prophet. If he wishes to defend himself in his present role, he must defend the importance of production. He can almost certainly be expected to do so.”
Galbraith summarizes this view later by stating: “The situation is this. Production for the sake of the goods produced is no longer very urgent. The significance of marginal increments or decrements in the supply of most goods is slight for most people. We sustain a sense of urgency only because of attitudes that trace not to the world of today but to that into which economics was born. These are reinforced by an untenable theory of consumer demand and by a system of vested interests which marries both liberals and conservatives to the importance of production.”
He makes the case that much of this manufactured demand is financed with consumer debt, which could accelerate unsustainably, and that the entire system of intertwined production, demand fabrication, and debt might reach a crescendo that finally breaks the American consumer’s willingness to engage, noting: “For while production does not clearly contain within itself the seeds of its own disintegration, persuasion may. On some not distant day, the voice of each individual seller may well be lost in the collective roar of all together. Like injunctions to virtue and warnings of socialism, advertising will beat helplessly on ears that have been conditioned by previous assault to utter immunity. Diminishing returns will have operated to the point where the marginal effect of outlays for every kind of commercial persuasion will have brought the average effect to zero. It will be worth no one’s while to speak, for since all speak, none can hear. Silence, interrupted perhaps by brief demoniacal outbursts of salesmanship, will ensue.”
Indeed, it is true that the debt-to-income ratio has risen across every stratum of the US consumer base since The Affluent Society was first published in the 1950s, reaching a peak just before the global financial crisis of 2008. According to research, the middle and bottom income groups both saw their debt-to-income ratios peak at roughly 1.6 and 1.4 respectively in the 2007 to 2010 timeframe, whereas the top income group saw its ratio peak at around 0.8 in the same period.
But that’s mostly a housing bubble story, not a story about the interchange between production, advertising, and debt. People weren’t buying homes in the housing price boom cycle from 2000 to 2007 because they succumbed to advertising. And US credit card debt sat at roughly 4.2% of GDP in 2025, above the post-GFC recovery level of roughly 3.8% but far below the 2008 peak of 5.1%. In the nearly 70 years since The Affluent Society was first published, it’s not clear that Galbraith’s vision for the total collapse of the American consumer under the weight of advertising-induced consumption debt is on a trajectory to materialize.
But given his cynicism toward advertising and his belief that consumption had been socially engineered to track production, I believe that Galbraith, while also distrustful of technocratic solutions to social problems, would have been sympathetic to the notion of agentic commerce. Agents are impervious to advertising. They should undertake the mechanical task of commerce with the singular goal of satisfying their consumer’s needs for the best possible value. In Galbraith’s view, agentic commerce should be the antidote to the perversion of demand by advertising and the production-consumption industrial complex. In that framework, consumption should be an optimization task, the efficient satisfaction of needs at the lowest cost.
But this would ignore the role of consumption in the expression of the individual, as a means of asserting preferences, of pronouncing one’s character and personality through things like clothes, music, travel, furniture, art, food, devices, and the experiences we choose to pursue. These are outward enunciations of the self. These are representations of the individual.
And when we seek to understand individualism, we appeal to Hayek. In Individualism: True and False, Hayek states: “The chief concern of the great individualist writers was indeed to find a set of institutions by which man could be induced by his own choice and from the motives which determined his ordinary conduct to contribute as much as possible to the need of all others. And their discovery was that the system of private property did provide such inducements to a much greater extent than had yet been understood.” He makes the case that individualism captures the behavior of people acting with limited local knowledge, what he calls peculiar knowledge, to further the aims for which they care. Rather than viewing society as a top-down prescription of rules and norms, Hayek conceives of a society as a decentralized system of interactions, with laws and norms accumulating upward. Hayek states that society can be interpreted through our understanding of individual actions directed toward other people and guided by their expected behavior.
Hayek comments that the market is the mechanism by which the individual can participate in a broader system of coordination, with prices and institutions translating those localized actions into signals that guide others. He notes: “To the accepted Christian tradition that man must be free to follow his conscience in moral matters if his actions are to be of any merit, the economists added the further argument that he should be free to make full use of his knowledge and skill, that he must be allowed to be guided by his concern for the particular things of which he knows and for which he cares, if he is to make as great a contribution to the common purposes of society as he is capable of making. Their main problem was how these limited concerns, which did in fact determine people’s actions, could be made effective inducements to cause them voluntarily to contribute as much as possible to needs which lay outside the range of their vision. What the economists understood for the first time was that the market as it had grown up was an effective way of making man take part in a process more complex and extended than he could comprehend, and that it was through the market that he was made to contribute to ends which were no part of his purpose.”
Hayek held in disdain those whom he thought had misunderstood or misrepresented the concept of individualism, believing that they viewed society as only functional when subjected to top-down moral prescriptions. He writes: “The belief that only a synthetic system of morals, an artificial language, or even an artificial society can be justified in an age of science, as well as the increasing unwillingness to bow before any moral rules whose utility is not rationally demonstrated or to conform with conventions whose rationale is not known, are all manifestations of the same basic view which wants all social activity to be recognizably part of a single coherent plan. They are the result of that same rationalist individualism which wants to see in everything the product of conscious individual reason. They are certainly not, however, a result of true individualism, and may even make the working of a free and truly individualistic system difficult or impossible. Indeed, the great lesson which the individualistic philosophy teaches us on this score is that, while it may not be difficult to destroy the spontaneous formations which are the indispensable basis of a free civilization, it may be beyond our power deliberately to reconstruct such a civilization once these foundations are destroyed.”
What do we make of this in the context of agentic commerce? In Hayek’s framework, personal tastes and preferences are embedded in dispersed, subjective knowledge that only becomes legible through action. They are data points emitted by individuals through consumption. They reflect the ends individuals choose to pursue within the constraints of what they can expect others to do, based on the limited, local circumstances they can be supposed to know. Agentic commerce represents the opposite of this: a delegated coordination machine that strips the individual of their prerogative to apply their own idiosyncratic tastes in the ways they deem optimal. Whether or not that algorithm is tuned perfectly to the individual’s tastes and preferences is ultimately beside the point. The essence of the individual is captured in the action, not the intent to act or the probability of acting in some way.
Put another way, if we see the benefits of AI to labor as obviating the need for mechanical, tedious, banal, repetitive work, freeing time for more creatively or intellectually fulfilling endeavors, why would we then also apply it to the things we do with the fruits of our labor? Why bother making work more interesting or fulfilling if the use of the paycheck is surrendered? That is dystopian. Society’s adoption of AI should enable fulfillment, not just in labor but across the spectrum of daily life. It should provide more choice, not subsume choice, and it should allow individuals to be more expressive, more latitude for people to further the aims for which they care, not less.
And if one argues that agentic commerce is not designed to fulfill big-ticket, high-consideration purchases like cars or vacations, or even clothing, but rather to ensure that household staples never run out, then it adds marginal value at best. I brush my teeth twice a day. I don’t need to train a deep neural network to understand when I’ll run out of toothpaste. Grocery delivery apps already allow for scheduled purchases. An agent adds nothing to that consumer interaction. The value proposition of an agent is to conduct background research in light of a consumer’s stated and historical revealed preferences and to surface the options that most appropriately capture their taste while also allowing them to make the final allocation decision. That’s an excellent use case, but it’s not the literal meaning of agentic commerce and it is unlikely to be fulfilled by independent agents.
My argument against the notion of agentic commerce as embodied by independent agents making purchases on behalf of consumers is normative and conceptual. I make the case that it violates the conception of the sovereign individual as rooted in the tradition of Western political economy. But my argument against the notion of agentic commerce as embodied by independent agents surfacing product suggestions that are sourced from a far-reaching web of retail platforms and monetized through affiliate links is more pedestrian and straightforward: I think the economics simply don’t work and are inferior to those presented by the advertising model.
To understand why this is the case, it is helpful to begin with the most basic distinction between affiliate monetization and advertising, which is not how recommendations are generated or how transactions are executed, but how value is measured and surfaced within a system. An affiliate model monetizes transactions ex-post, applying a fixed commission to whatever purchase occurs regardless of the underlying economics of that purchase. An advertising model, by contrast, allocates demand ex-ante, using a bid to express the advertiser’s private estimate of value for a given impression. This distinction determines not just how revenue is generated, but how products are ranked, how surplus is distributed, and whether the system can support heterogeneous outcomes across categories, price points, and margin structures.
In an affiliate system, the ranking function has no access to the advertiser’s internal valuation of a conversion and therefore cannot represent margin structure, customer lifetime value, inventory constraints, or strategic priorities that might influence how aggressively a retailer would pursue a given customer. It sees only surface-level signals such as price, historical conversion rates, and contextual relevance derived from the interaction itself. In the absence of a bid or any analogous mechanism for expressing private value, those signals necessarily dominate. And when they do, the system converges toward a narrow equilibrium that is likely to favor goods that are inexpensive, broadly appealing, and easy to convert across a wide population. This is not a claim about any specific implementation of a recommendation model, but rather a consequence of the objective function that governs the system. If a model is optimizing for conversion probability without the benefit of a value signal, then price becomes the most reliable lever through which that probability can be increased, since lower-priced goods present less friction and less perceived risk and therefore convert more readily across a diverse set of users. In a system that cannot observe or incorporate differences in value conditional on conversion, those goods will systematically out-compete higher-priced alternatives even when those alternatives produce sustainably more surplus to the retailer when they do convert.
The result is not merely a change in ranking, but a compression of the product space itself, in which differentiation is vitiated and high-margin, niche, or premium products become underrepresented because the system lacks the information required to surface them appropriately. It can infer likelihood of purchase but not the magnitude of value associated with that purchase, and so it optimizes toward what it can observe rather than what actually matters. Furthermore, it prevents new products from having any meaningful opportunity to increase their representation in the surfaced results. This is precisely the problem that the auction mechanism in digital advertising was designed to solve.
By utilizing an advertiser’s bid to evaluate candidate ads based on expected value, an advertising platform optimizes its own revenue subject to quality and other idiosyncratic scoring constraints. It ranks ads according to the amount of money they are predicted to generate for the platform. And because the bid is submitted by the advertiser based on their own proprietary calculations of revenue, an ad can be seen as the content that best monetizes the user’s attention, not in terms of counts of clicks or purchases but of total transaction volume. In an ad auction, each candidate’s rank encapsulates the advertiser’s private estimate of an impression’s value, its bid, so the winning ad tends to be the highest expected monetization of the impression, and not merely the highest conversion probability.
The significance of this mechanism is that it allows each advertiser to inject private information into the system in a way that is both scalable and incentive-aligned, transforming rank from a probabilistic to a value-maximizing exercise that aligns platform revenue with total surplus. It ensures that impressions are allocated not to the product most likely to be purchased in isolation, but to the product that produces the greatest expected value when purchased, conditional on both the probability of conversion and the magnitude of the outcome. An affiliate model cannot replicate this because it has no source of value truth and no way of incorporating advertiser-specific valuations into the ranking process. It cannot distinguish between a low-priced product with a high conversion rate and a high-priced product with a low conversion rate if the former produces a higher expected commission under a fixed fee structure, nor can it internalize differences in margin structure, lifetime value, or viral propensity that would lead a retailer to value one customer above another. It therefore defaults to optimizing on the signals that are available, and those signals are insufficient to support a high-dimensional, differentiated marketplace. The system converges, and in doing so, it narrows.
The second structural issue is the cost structure imposed by affiliate-based commerce, which is incompatible with the unit economics of a large portion of the e-commerce ecosystem, particularly among mid-market and consumer packaged goods retailers that operate on thin margins and price customer acquisition on a lifetime value basis rather than a per-transaction basis. A single-digit transaction fee may appear modest in isolation, but it does not exist in isolation and instead stacks on top of payment processing fees, platform fees, logistics costs, and costs of goods sold, all of which collectively compress margins to the point where an additional fixed percentage fee applied at the transaction level is not incremental but existential for many retailers. Many merchants operate on net margins in the mid-single digits and therefore cannot absorb additional costs imposed uniformly across transactions without fundamentally altering their pricing or acquisition strategies.
The critical issue here is not simply that the fee is high, but that it is exogenous, meaning that it is imposed rather than chosen and does not adapt to the underlying economics of the advertiser. In an advertising system, the advertiser determines the price they are willing to pay for a conversion, calibrating that price to margin, lifetime value, inventory constraints, and strategic priorities and adjusting it dynamically as those conditions change. In an affiliate system, the fee is fixed and invariant, applied uniformly across transactions regardless of whether those transactions are profitable for the retailer. This distinction between endogenous pricing and exogenous cost imposition explains why advertising can scale across categories with vastly different margin structures, while affiliate models tend to concentrate in high-margin verticals where such fees can be absorbed. It also explains why retailers can profitably acquire customers through advertising, where they control the price of acquisition, but may be unable to fulfill transactions under a fixed commission regime that does not account for their internal economics.
The third structural issue is incentive alignment, or more precisely, the lack thereof. Independent agentic commerce presupposes that retailers will willingly cede the consumer relationship to an intermediary that captures discovery, mediates transactions, and extracts a fee while also limiting the retailer’s ability to communicate with the customer or utilize the data generated by that interaction in the ways that are most valuable to the business. This presupposition does not hold in a market where the largest retail platforms derive substantial value not just from transactions but from the data and relationships those transactions create.
Amazon derives as much, if not more, value from the user-level data artifacts created by a transaction than from the transaction itself. That data is used for advertising, for recommendations, and for pricing. User-level behavioral data is mission-critical for Amazon; maintaining exclusive access to it is a strategic imperative. This observation is not specific to Amazon but generalizes to any platform operating at scale in digital commerce or advertising where the transaction serves as a source of signals that inform targeting, personalization, pricing, and monetization across multiple surfaces and time horizons. To interpose an independent agent between the retailer and the consumer is to redirect or attenuate those data flows, to limit the retailer’s ability to communicate with the customer and to reduce the long-term value that can be extracted from the relationship. Even when nominal access to the customer is preserved, it is often constrained in precisely the ways that matter most, such as the retailer may receive transactional information sufficient to fulfill the order but be prohibited from using that information for marketing or retargeting purposes, effectively severing the economic substance of the relationship while maintaining its formal structure.
Under these conditions, retailer participation in independent agentic systems is not a default outcome but an exception that occurs only when the incremental transactions delivered by the agent outweigh the value lost through disintermediation, a tradeoff that becomes increasingly unfavorable as retailers invest more heavily in their own first-party data and advertising capabilities. The fourth issue is control, which is closely related to incentive alignment but manifests operationally in the way demand is generated and managed. Affiliate conversions are, by construction, outside of the retailer’s direct control, depending on factors such as search volumes, platform algorithms for surfacing content, and the internal logic of the agent that determines which products are recommended in a given context. A retailer cannot determine when or how often its products are surfaced, cannot scale exposure by increasing spend, and cannot guarantee a minimum level of traffic or conversions, making the channel inherently unpredictable and difficult to manage.
Advertising provides a fundamentally different model of control, allowing the retailer to modulate demand through bids and budgets, to scale exposure proactively rather than waiting to be selected, and to align acquisition with business objectives through continuous optimization. This difference transforms customer acquisition from a stochastic outcome into a managed process in which the retailer can determine not only how much attention to activate, but at what cost and under what conditions. The distinction becomes particularly salient when considering new or emerging products, which, in an affiliate-driven system, suffer from incumbency bias because they lack historical conversion data and therefore are less likely to be surfaced by models that rely on past performance. In an advertising system, the auction provides a mechanism through which new products can compete on value rather than history, enabling entry and experimentation in a way that is not possible when ranking is based solely on observed conversion rates.
The fifth issue is the degradation of data inputs, which is both subtle and compounding over time. If chatbots ingest review content and preempt the monetization of those sources through affiliate links, then those reviewers are starved of income and are unlikely to write more reviews, and the chatbot loses access to a critical input that its own personalization engine needs. This dynamic extends beyond third-party review sites to the broader ecosystem of signals that inform recommendation quality, including user-generated reviews, ratings, and other forms of feedback that are typically collected at the point of transaction. If transactions are subsumed into chatbot environments that do not facilitate or incentivize such feedback, then those signals are not captured and the system’s ability to learn and improve over time is diminished.
At the same time, the data available to independent agents is inherently limited relative to that available to large advertising platforms, since it is derived primarily from contextual interactions within a chat interface rather than from a comprehensive view of the user’s behavior across platforms, devices, and categories. Advertising platforms integrate signals from purchase history, browsing behavior, and off-platform events through mechanisms such as pixels and server-side APIs constructed for high-dimensional representations of user preferences that extend far beyond what can be inferred from any single interaction. This difference in data scope has direct implications for personalization, as a system that relies primarily on in-chat context cannot match the granularity or accuracy of one that leverages longitudinal behavioral data regardless of the sophistication of the underlying model.
The sixth issue is monetization surface area, which determines the total revenue potential of the system. Affiliate models tied to contextual recommendations can only monetize interactions with a clear commercial locus, meaning that a significant portion of user engagement, including informational and exploratory queries, remains unmonetized. Display advertising is not similarly constrained and can operate across a broader range of contexts, provided that sufficient data exists to inform targeting, thereby increasing the total addressable monetization opportunity. Taken together, these issues form a coherent and reinforcing set of constraints that limit the viability of independent agentic commerce as a standalone model.
An affiliate-based system lacks a mechanism for expressing true value and therefore converges toward low-price, high-conversion goods. It imposes fixed costs that are incompatible with many retailers’ unit economics. It conflicts with the incentives of platforms that derive substantial value from owning the consumer relationship. It deprives retailers of control over distribution and reach. It degrades the data inputs required for effective personalization. It operates on a narrower and less informed dataset, and it leaves a substantial portion of user engagement unmonetized. A final constraint on the viability of independent agentic commerce emerges from the incentives of the largest retail platforms, which are not neutral participants in this ecosystem but profit-maximizing entities built around owning the consumer relationship, the transaction flow, and the data generated by both.
Any model that attempts to intermediate those components must contend not only with the economic inefficiencies, but with the strategic resistance of platforms whose interests are directly undermined by such intermediation. This is most clearly illustrated in the case of Amazon, which has no incentive to allow independent agents to route demand away from its own surfaces into an external interface that it does not control, particularly when doing so would diminish its ability to monetize that demand through advertising. Amazon’s advertising business generated $69 billion in 2025, with 22% year-over-year growth. It is deeply integrated with its retail operations, relying on product visibility within its own environment to capture high-intent demand at the moment of consideration and conversion. Any system that captures that moment externally and completes the transaction outside of Amazon’s native experience reduces the inventory against which Amazon can serve ads and weakens one of its most profitable business lines.
The strategic logic for platforms is not to enable independent agents, but to internalize their functionality, ensuring that any agentic layer sits within the platform’s own interface, aligned with existing monetization mechanisms and data flows. Amazon’s Rufus agent reflects this logic, extending the platform’s control over discovery and purchase while preserving its ability to surface sponsored products. This dynamic highlights an asymmetry in how the arguments for and against agentic commerce apply across contexts. The case for agentic commerce aligns with platform incentives when deployed natively because it increases engagement, improves conversion, and expands advertising surface area within a controlled environment. The case against independent agentic commerce arises precisely because it attempts to extract those benefits into an external layer that captures value the platform would otherwise retain. Independent agents must therefore operate in an environment where the most important platforms are either competing directly with them or engaging only on terms that limit their effectiveness, while simultaneously building their own agentic capabilities that replicate the functionality in a more economically coherent form.
A counter-argument here is that consumers will become so accustomed to interfacing with their chatbot of choice that they will simply reject any platform’s own chatbot and that platform will be forced to either open its product catalog to all chatbots to enable independent agentic commerce or risk being excluded from the new retail discovery paradigm. One could expect that a consumer’s historical record of communication with a chatbot, the chatbot’s memory, is a strong anchor to using that chatbot. It already understands the consumer’s preferences contextually. This could be true for an emergent retail platform, but I’d argue that it’s not true for the largest incumbent retail platforms, which can bootstrap chatbot context with the purchase history data they possess. This data is almost certainly more valuable in providing product recommendations than conversational context, even if specific use cases are missing. Further, e-commerce is an incredibly concentrated category. The largest retail platforms simply have more leverage than do independent commerce agents to coerce their desired outcome.
Advertising addresses each of these deficiencies simultaneously, allowing value to be expressed through bids, enabling flexible pricing aligned with retailer economics, preserving data flows and consumer relationships, providing control over distribution and attention acquisition, leveraging rich behavioral data for personalization, monetizing a broader range of interactions and, critically, preserving and concentrating leverage. For these reasons, independent agentic commerce is not simply a less attractive model or a premature one awaiting better implementation, but a structurally inferior approach that lacks the mechanisms required to support a scalable, efficient, and incentive-compatible marketplace. Ultimately, this undermines support for the independent agentic commerce use case. Advertising is simply the more effective and economically suitable model for driving product discovery.
A rigorous analysis of any nascent technological paradigm involves examining the tasks or objectives it isn’t suitable for. It’s possible to be optimistic about AI and confident that it will accrue significant efficiencies to many aspects of modern labor, that it will catalyze a wholesale evolution of the human-computer interaction model, and that it will reorganize society around more fulfilling, more intellectually stimulating work and leisure without committing oneself to the application of AI to every possible facet of life.
Galbraith’s Affluent Society saw consumption tethered to output, artificially amplified by advertising and financed through consumer debt. Galbraith observed that the conventional wisdom of seeking full economic production capacity resulted in affluence being concentrated privately to a degree that crowded out expenditure on public goods, things like public infrastructure, to such an extent that consumer spending would ultimately buckle under the weight of unsustainable debt and an immunity to advertising. But that never happened. What we’ve seen instead is the evolution of advertising into a personalized product discovery mechanism that ever more efficiently pairs consumers with relevant products to them.
Consumer debt never reached unsustainable levels; rather, consumer credit card balances as a share of GDP have remained relatively stable in the 3 to 5% range for the past two decades. The advertising-based economy similarly never collapsed. Galbraith likely couldn’t have foreseen the stunning degree of personalization that modern ad platforms have achieved; rather than inuring customers to it, advertising has penetrated every digital surface area and become one of the principal paths to sustaining what I’ve called a humanity-scale business. The promise of AI is vastly more powerful personalization, both in terms of the products made available to consumers, particularly digital products, but also the messaging used to make consumers aware of those products in the form of advertising. That personalization power is what I believe any AI-anchored growth narrative should embrace. In considering an application of AI to an existing consumer interaction or one that is being developed from whole cloth, AI is a pathway to personalization. It extracts and engages with the most robust expression of the individual, and it shouldn’t attempt to suppress or convolve it.
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