The Trust Paradox: Why We Will Inevitably Stop Trusting Our AI Shopping Agents

Abstract

The emergence of autonomous AI purchasing agents represents a structural reconfiguration of commercial exchange that fundamentally alters the mechanisms and dynamics of consumer trust. This paper develops a predictive conceptual framework for understanding trust evolution in AI-mediated commerce, arguing that platforms deploying these agents will face powerful economic pressures to introduce monetization strategies that systematically erode the initial trust foundation. Drawing on principal-agent theory and the historical precedent of Google Search’s transformation from information utility to advertising platform, I propose that AI commerce will likely follow a similar trajectory absent deliberate countermeasures. The paper presents a three-phase trust lifecycle model: (1) utility-driven adoption, (2) monetization-induced misalignment, and (3) potential disillusionment and market bifurcation. I introduce the concept of “computational trust”—trust mechanisms emphasizing radical verifiability, systemic reliability, and algorithmic transparency—as a strategic response for organizations and policymakers seeking to prevent or mitigate trust erosion. The framework yields eleven testable propositions and provides strategic guidance for firms navigating the transition to agent-intermediated markets. This analysis contributes to marketing theory by extending principal-agent frameworks to three-party consumer-agent-platform relationships and by identifying computational trust as a distinct construct from traditional relationship-based trust mechanisms. As a conceptual framework developed prior to widespread AI agent deployment, this work generates testable propositions requiring empirical validation as the technology matures.

Keywords: trust, artificial intelligence, AI agents, agentic commerce, platform economics, principal-agent theory, algorithmic commerce, computational trust, technology acceptance, platform monetization

JEL Codes: M31 (Marketing), L86 (Information and Internet Services), D82 (Asymmetric and Private Information), O33 (Technological Change)

1. Introduction: Anticipating Trust Dynamics in Algorithmic Commerce

1.1 The Emergence of Delegated Consumption

For centuries, commercial transactions united intent and execution in a single actor. A consumer wanted bread; that consumer purchased bread. This unity is dissolving as we enter an era of what I term delegated consumption—economic transactions where purchasing authority is transferred from a human principal to an AI agent operating with substantial autonomy.

When consumers instruct AI agents to “reorder household essentials” or “find the best sustainable coffee under $15 per pound,” they engage in principal-agent relationships, delegating substantive decision-making authority to non-human intermediaries. These agents must interpret preferences, evaluate alternatives, and execute transactions—often without explicit approval for each decision.

This delegation creates what I call the shopper schism: the structural separation between the human who consumes a product and the algorithm that purchases it. This schism introduces a novel instance of principal-agent dynamics directly into consumer markets, creating governance challenges largely unaddressed by traditional marketing theory.

Products must now serve two distinct audiences: the human user, who evaluates based on experience and subjective satisfaction, and the algorithmic purchaser, which evaluates based on structured data and optimization functions. These audiences process information differently and apply fundamentally different decision criteria.

1.2 The Central Research Question

This paper addresses a critical question: How will trust evolve as consumers increasingly delegate purchasing decisions to AI agents controlled by profit-maximizing platforms? I argue that we face a trust paradox: the mechanisms that enable initial adoption—perceived utility, efficiency, and objectivity—may become vectors through which trust systematically erodes as platforms monetize their intermediary position.

Central Hypothesis: Consumer trust in AI-mediated commerce, while potentially moderate-to-high initially due to perceived utility, will face systematic erosion pressures as platforms introduce monetization mechanisms creating goal misalignment. This trajectory may mirror the trust erosion observed in Google Search as it transitioned from information utility to advertising platform, though the pace and ultimate outcome remain contingent on platform choices and regulatory responses.

The mechanism involves inherent tension between consumer and platform interests. Consumers adopt AI agents expecting these systems to act in their interests—finding optimal products, securing favorable prices, and adhering to stated preferences. Platforms deploying these agents, however, face powerful economic incentives to monetize the intermediary position through mechanisms such as preferential placement fees, self-preferencing, behavioral data sales, or commission-based recommendations. These strategies introduce goal misalignment between consumer welfare optimization and platform revenue maximization.

Initially, this misalignment may remain modest and difficult for consumers to detect. However, as monetization pressures intensify and competitive dynamics drive platforms to extract more value, the misalignment may become severe enough to trigger recognition. When consumers discover that their trusted agents have systematically recommended suboptimal products in exchange for platform revenue, trust may collapse.

The Google Search precedent demonstrates a version of this trajectory. Google began as a relatively neutral utility providing algorithmic search results. As advertising revenue grew to dominate its business model, the company increasingly blurred distinctions between organic and paid results, optimized auctions to maximize revenue, and self-preferenced its own properties. While Google remains dominant, trust metrics have declined measurably as these conflicts became apparent to users.

Whether AI commerce agents necessarily follow this path remains an empirical question. Economic incentives create strong pressures toward monetization, but deliberate platform choices, regulatory interventions, and market competition could potentially alter outcomes. This paper traces the likely trajectory based on platform economics while acknowledging alternative possibilities.

1.3 Contributions and Structure

This paper makes three primary theoretical contributions. First, it provides a predictive model of trust lifecycle dynamics in agentic commerce, integrating principal-agent theory with historical platform evolution patterns. Second, it extends algorithmic commerce frameworks by identifying computational trust—trust based on verifiable evidence rather than reputation—as a strategically relevant construct for firms and policymakers. Third, it contributes to ongoing discourse by demonstrating that trust erosion in these systems likely represents not a technical deficiency amenable to better engineering, but rather a predictable structural outcome of platform economics requiring systemic responses.

Methodological Note: This paper develops a predictive conceptual framework rather than testing empirical hypotheses. As AI purchasing agents are just beginning to deploy at scale, longitudinal data on trust dynamics does not yet exist. The framework presented here serves three purposes: (1) to provide theoretical scaffolding for understanding trust evolution before widespread adoption, (2) to generate falsifiable propositions that can be tested as the technology matures, and (3) to offer strategic guidance for practitioners and policymakers who must make decisions now, before empirical patterns emerge. The value of this work lies not in empirical validation, but in theoretical anticipation—providing a lens through which to interpret emerging phenomena as they unfold.

The paper proceeds as follows. Section 2 reviews theoretical foundations in trust, principal-agent theory, and platform economics. Section 3 examines the Google Search case as historical precedent. Section 4 develops the theoretical model of trust dynamics in AI commerce. Section 5 proposes computational trust as strategic response. Section 6 synthesizes contributions and presents testable propositions. Section 7 discusses implications for practice, policy, and research.

2. Theoretical Foundations

2.1 The Nature and Dimensions of Trust

Trust represents a willingness to be vulnerable to another party based on expectations of benevolence, integrity, and competence (Mayer et al., 1995). In commercial contexts, trust reduces transaction costs, enables cooperation under uncertainty, and facilitates exchange relationships (Morgan & Hunt, 1994; Zucker, 1986).

The trust literature distinguishes several key dimensions. Cognitive trust develops through rational assessment of trustworthiness signals—reputation, credentials, past behavior (McAllister, 1995). Affective trust emerges from emotional bonds and perceived care (Johnson & Grayson, 2005). Institutional trust relies on structures, regulations, and guarantees rather than personal relationships (Zucker, 1986).

Technology trust introduces additional complexity. McKnight et al. (2011) identify trusting beliefs about technology systems including functionality, reliability, and helpfulness. Lee and See (2004) emphasize appropriate reliance calibration—users must develop neither excessive trust leading to overreliance nor insufficient trust limiting benefits.

2.2 Trust in AI and Autonomous Systems

AI systems pose unique trust challenges given their opacity, complexity, and potential for consequential errors (Glikson & Woolley, 2020). Machine learning models often function as “black boxes” where even developers cannot fully explain specific decisions.

Research on explainable AI attempts to make algorithmic decisions interpretable (Arrieta et al., 2020). However, explanations can paradoxically reduce trust when they reveal limitations or biases users didn’t realize existed (Ehsan et al., 2021). The transparency-trust relationship proves non-linear and context-dependent.

Importantly, trust in AI systems appears more fragile than trust in humans. Research demonstrates that single failures damage trust in automated systems more severely than equivalent failures by human agents (Madhavan & Wiegmann, 2007). Users apply harsher standards to machines, expecting near-perfect performance. This asymmetric fragility suggests that trust, once lost, may prove difficult to rebuild.

2.3 Principal-Agent Theory

Principal-agent theory examines relationships where one party (the principal) delegates authority to another party (the agent) who acts on the principal’s behalf (Jensen & Meckling, 1976; Eisenhardt, 1989). The central problem involves goal misalignment: agents have their own interests that may conflict with principals’ objectives. Combined with information asymmetry—agents typically know more about their actions than principals can observe—this creates agency problems.

Three mechanisms traditionally mitigate agency problems. Monitoring involves principals observing agent behavior, though perfect monitoring is usually prohibitively expensive. Incentive alignment structures agent compensation to reward principals’ desired outcomes. Selection and bonding involves choosing trustworthy agents and requiring credible commitments.

In AI commerce contexts, traditional mitigation mechanisms face challenges. Algorithmic opacity makes monitoring difficult—consumers cannot easily observe what factors drive agent decisions. Incentive alignment is complicated by platform ownership: the entity that could align agent incentives (the platform) may itself have interests misaligned with consumers. Selection faces constraints from oligopolistic platform markets with limited alternatives.

2.4 Platform Economics and Intermediary Power

Platforms create value by facilitating interactions between multiple user groups, capturing portions of that value through various monetization strategies (Parker et al., 2016). Two-sided market dynamics create unique economic properties: platforms must attract both supply and demand sides, network effects generate winner-take-most outcomes, and platforms wield substantial power over ecosystem participants (Rochet & Tirole, 2003).

Platform power manifests through control over three critical resources: data (comprehensive information about users and transactions), algorithms (rules determining what users see), and infrastructure (technical systems enabling interactions) (Srnicek, 2017). This control creates information asymmetries and enables rent extraction.

Platforms face fundamental tension between serving user welfare and maximizing profit (Hagiu & Wright, 2015). While platforms initially compete by providing superior user experiences, once they achieve market dominance, incentives may shift toward rent extraction. This lifecycle—from user-centric service to profit-maximizing intermediary—characterizes many mature platform markets (Zuboff, 2019), though the trajectory is not deterministic and varies by market structure, regulatory environment, and business model choices.

2.5 Technology Acceptance and Trust Evolution

Technology acceptance research examines factors driving adoption and continued use. The Technology Acceptance Model identifies perceived usefulness and ease of use as primary drivers (Davis, 1989). The Unified Theory of Acceptance and Use of Technology adds social influence, facilitating conditions, and moderating factors (Venkatesh et al., 2003).

However, acceptance models largely overlook post-adoption trust dynamics. They treat adoption as the endpoint rather than examining how trust evolves as users gain experience and as system capabilities and business models change.

McKnight et al. (2002) propose trust development moving from initial formation (based on reputation) through experiential trust (based on interaction) to relational trust (based on established patterns). However, this model assumes benign or neutral systems. It doesn’t account for systems that may evolve to exploit user trust for economic gain.

The framework I develop extends acceptance theory by incorporating a trust erosion phase potentially driven by platform monetization incentives, capturing a fuller lifecycle: adoption → experiential reinforcement → monetization-induced degradation → potential disillusionment.

3. The Google Search Precedent: Platform Evolution and Trust Dynamics

3.1 Early Phase: Utility-Based Trust Formation

Google’s initial market dominance built on substantial user trust derived from superior utility and perceived objectivity. The PageRank algorithm delivered demonstrably more relevant results than competitors. Users experienced Google as a relatively neutral guide—a system whose primary observable goal was serving their information needs effectively.

This utility-based trust proved valuable during Google’s early years (late 1990s through mid-2000s). The business model, while including advertising from inception, did not yet create substantial observable conflicts between user service and revenue generation. The company cultivated an image focused on organizing information, reinforced by its “Don’t Be Evil” motto, minimalist interface, and rejection of paid placement in organic search results.

3.2 The Monetization Transition: Evolution of AdWords

The critical transition occurred with Google AdWords’ introduction and expansion. AdWords launched in 2000 as a banner advertising program. The pivotal shift came in 2002 with the move to a pay-per-click auction model. This fundamentally altered Google’s incentive structure, introducing a second powerful objective—maximizing advertising revenue—alongside serving users.

Initially, Google attempted maintaining alignment between these objectives. The introduction of “Quality Score” in 2005 linked ad relevance to cost and placement. This was presented as ensuring monetized results remained useful to users, creating purported win-win outcomes.

Over time, however, revenue growth imperatives created pressures that challenged this alignment. As Google’s market valuation came to depend on quarterly advertising revenue growth, the company increasingly optimized for revenue extraction. The transformation was gradual but systematic.

3.3 Mechanisms of Observable Trust Erosion

Trust erosion in Google’s ecosystem occurred through several interconnected mechanisms:

Auction Evolution and Opacity: The ad auction became increasingly sophisticated and opaque. While advertisers were told that Quality Score and competitive bidding determined costs, Google maintained proprietary control over precise weightings and mechanisms. Reporting based on industry analysis and advertiser complaints suggests systematic cost inflation through algorithmic optimization for revenue rather than market efficiency, though Google disputes these characterizations.

Blurring Organic and Paid Results: Google progressively reduced visual distinction between paid and organic results. Early ads were clearly labeled and separated. Over time, labels became subtler, paid results appeared more prominently, and visual formatting converged. Research demonstrates that many users struggle to reliably distinguish ads from organic results (Lewandowski et al., 2018).

Self-Preferencing: As Google expanded into e-commerce, travel, local services, and other verticals, it systematically prioritized its own properties in search results. European regulators fined Google €2.42 billion in 2017 for abusing search dominance to unfairly promote its comparison-shopping service (European Commission, 2017).

Data Exploitation: Google leveraged comprehensive user data to maximize ad revenue through behavioral targeting. While this theoretically improves relevance, it also enables practices like exploiting user vulnerabilities and extracting consumer surplus through price discrimination (Zuboff, 2019).

Reduced Accountability: Google’s increasing automation of ad management removed human oversight. Advertisers report sudden account suspensions, unexplained cost increases, and policy enforcement with limited appeal mechanisms. Platform power asymmetry leaves advertisers and users with constrained recourse (Levy, 2020).

3.4 Measurable Trust Indicators

Consequences of these practices appear measurable. Multiple surveys document concerns about trust in large tech platforms. Pew Research Center (2019) found that 72% of Americans believe social media companies intentionally censor political views. Edelman Trust Barometer (2021) showed technology companies experiencing a 6-point global decline in trust.

While Google’s ad revenue continues growing in absolute terms (Alphabet, 2023), the rise of privacy-focused alternatives signals user demand for different approaches. DuckDuckGo, a search engine explicitly positioning itself against Google’s data practices, grew from essentially zero to over 100 million daily searches by 2021—modest compared to Google’s billions, but significant as a signal of user demand for alternatives (Weinberg, 2021).

3.5 The Reversibility Question

An important lesson from Google’s trajectory concerns whether trust erosion proves reversible once business models shift. Despite criticism, regulatory penalties, and user dissatisfaction, Google has not fundamentally reformed its advertising practices. Economic incentives appear to prevent retreat—the company likely cannot abandon its profit engine without significant financial consequences.

This potential irreversibility may stem from several factors. First, structural lock-in: once business models depend on certain monetization mechanisms, those mechanisms become existentially necessary. Second, competitive dynamics: in oligopolistic markets, platforms may face pressure to match competitors’ rent-extraction strategies. Third, asymmetric repair costs: rebuilding trust requires costly investments while maintaining existing practices generates immediate revenue.

This precedent suggests—though does not prove—that once platforms begin monetizing agent intermediaries through conflicted mechanisms, trust erosion may prove difficult to reverse regardless of subsequent reform efforts. However, this outcome is not inevitable; alternative business models and regulatory interventions could potentially alter trajectories.

4. Modeling Trust Dynamics in AI-Mediated Commerce

Figure 1 presents an overview of the three-phase trust lifecycle model that structures this analysis. Each phase is characterized by distinct trust dynamics, economic incentives, and potential outcomes."

Figure 1: Three-Phase Trust Lifecycle in AI-Mediated Commerce

[Figure 1 Removed due to formatting issues - Needs to be reviewed in the Original SSRN Paper https://papers.ssrn.com/abstract=5709083]

Note: Propositions P8 and P9 relate to computational trust mechanisms (Section 5) as strategic responses to mitigate trust erosion.

4.1 Phase 1: Utility-Driven Adoption

The trust lifecycle begins with adoption driven by perceived utility and efficiency. Consumers adopt AI purchasing agents because these systems promise significant benefits: time savings, cognitive offloading, better information access, protection from behavioral biases, and potentially lower prices through automated comparison shopping.

Initial trust in this phase is calculative rather than emotional (Lewicki & Bunker, 1996). Users don’t trust AI agents because of relationship history. They trust because the value proposition seems clear and risks appear manageable. Early adopters often include technology enthusiasts and efficiency-seekers willing to experiment.

Several factors facilitate adoption:

Perceived Objectivity: Unlike human salespeople with obvious conflicts, algorithms appear neutral and data-driven. Users may believe AI agents will evaluate products based on objective criteria rather than manipulation or hidden agendas.

Demonstrable Utility: Early implementations can deliver on efficiency promises. Automated reordering, price tracking, and comparative analysis may provide tangible value. Users experience time savings and occasionally discover alternatives they wouldn’t have found independently.

Reputation Transfer: When trusted brands deploy AI agents, they may transfer existing institutional trust to the new technology. Users extend trust developed through prior positive experiences.

Low Initial Stakes: Early adoption typically involves low-risk purchases—household essentials, routine replenishments, commodity products. Users can experiment without catastrophic downside if agents make suboptimal choices.

Positive Selection Bias: Early adopters tend to be sophisticated users with skills to evaluate agent performance. If agents fail to deliver value, these users can abandon quickly without accumulating extensively negative experiences.

Proposition 1: Initial adoption of AI purchasing agents will be driven primarily by perceived utility and efficiency gains rather than deep trust in platform benevolence or agent fiduciary duty, with adoption rates positively associated with demonstrable time savings and cognitive load reduction.

Proposition 2: Platform institutional reputation will positively moderate the relationship between perceived utility and adoption likelihood, with consumers demonstrating greater willingness to delegate purchasing authority to agents deployed by brands with established trust relationships.

4.2 Phase 2: Monetization-Induced Misalignment Pressures

The second phase begins when platforms face pressures or opportunities to introduce monetization mechanisms that could create goal misalignment between consumer welfare and platform profit. This potentially happens gradually as platforms move from establishing market presence to maximizing revenue.

Several monetization strategies could introduce conflicts:

Preferential Placement Arrangements: Platforms might charge sellers for priority consideration by agents. An AI agent instructed to “find the best running shoes under $150” might favor brands with placement agreements over objectively superior alternatives.

Self-Preferencing: Platforms that sell products or offer adjacent services will face strong temptations to program agents favoring platform-owned offerings when metrics are similar or even when platform products are slightly inferior.

Commission-Based Recommendations: Platforms might structure deals involving percentage commissions on transactions. Agents would face incentives to recommend higher-priced products or products from sellers offering better commission terms.

Data Monetization: Platforms could sell behavioral data generated by agent interactions. This creates incentives to design agents that extract maximum information rather than completing transactions most efficiently.

Behavioral Optimization: Platforms might employ behavioral science insights to design agent interactions that increase spending or reduce price sensitivity in ways benefiting platforms more than users (Susser et al., 2019).

The critical feature of this phase involves information asymmetry. Consumers cannot easily observe the extent to which agents prioritize platform profit over user welfare. Algorithmic opacity provides cover for potentially conflicted decision-making. Users might sense something is suboptimal—products aren’t quite as good as expected, prices seem higher—but lack clear evidence.

Proposition 3: As platforms introduce monetization mechanisms creating goal misalignment, agent recommendation quality will decline relative to optimal consumer outcomes, but this decline will initially remain below the threshold required to trigger widespread user recognition or abandonment.

Proposition 4: Algorithmic opacity will moderate the relationship between monetization strategy adoption and user awareness, such that platforms deploying less transparent decision-making systems will exhibit larger gaps between actual goal misalignment and user-perceived misalignment.

4.3 Phase 3: Potential Disillusionment and Market Evolution

A third phase could occur if goal misalignment becomes sufficiently severe and sufficiently visible that consumers lose trust in platform-controlled agents. Several triggers might precipitate this:

Investigative Exposure: Journalists, researchers, or whistleblowers reveal the extent to which agents prioritize platform profit over user welfare through internal documents, algorithm audits, or leaked communications.

Comparative Analysis: Independent testing systematically compares agent recommendations against neutral benchmarks, revealing that agents consistently recommend inferior or overpriced products from platform partners.

Accumulation of Negative Experiences: As misalignment intensifies, users accumulate experiences where agent recommendations prove disappointing. Individual experiences may be attributable to chance, but patterns could become undeniable.

Regulatory Intervention: Government investigations or regulatory actions publicly document platform conflicts, imposing penalties or requiring disclosure of monetization practices.

Alternative Emergence: The appearance of genuinely fiduciary alternatives—agents explicitly designed to serve user interests—provides empirical contrast revealing how compromised platform agents may have become.

If triggered, trust collapse could be rapid given research showing that failures damage trust in automated systems more severely than equivalent failures by human agents (Madhavan & Wiegmann, 2007). Discovery of systematic misalignment represents not a single failure but revelation of fundamental conflict of interest.

This phase could lead to market differentiation:

Sophisticated Consumers with resources and technical sophistication might migrate to fiduciary agents—services explicitly designed with aligned incentives, transparent operations, and credible commitments to serve user interests.

Less Advantaged Populations lacking resources or expertise might remain with conflicted platform ecosystems due to switching costs, lack of awareness, or absence of affordable alternatives.

Proposition 5: Recognition of severe platform conflicts will trigger market differentiation, with consumers scoring higher on digital literacy measures demonstrating significantly greater probability of migrating to fiduciary alternatives.

Proposition 6: Trust in AI agents, once damaged through discovery of systematic goal misalignment, will prove more difficult to rebuild than trust in human advisors facing equivalent conflicts, with trust recovery requiring substantially longer timeframe for automated versus human agents.

4.4 The Pace Question

A critical question involves whether the trust lifecycle in AI commerce will unfold more rapidly than it did in Google Search. Several factors suggest it might:

Heightened Awareness: The Google precedent has created a template. Consumers, regulators, and researchers are now more sensitized to platform conflicts and may monitor AI agents more skeptically from the outset.

Higher Stakes: Search queries have limited direct economic consequences—bad results waste time but don’t directly cost money. AI purchasing agents make financial decisions with immediate impact. Consumers may notice conflicts more quickly.

Greater Transparency Demands: Modern consumers and regulators increasingly demand transparency in algorithmic systems. The opacity that Google deployed successfully for years may face greater scrutiny, especially as AI governance frameworks emerge.

Competitive Dynamics: Multiple platforms competing to deploy AI commerce agents creates incentives to expose competitors’ conflicts. Platform competition could paradoxically accelerate trust erosion.

Technical Capability: Tools for auditing algorithmic bias and detecting manipulation have advanced significantly, enabling more sophisticated analysis.

However, factors could also slow the process. Platforms learned from Google’s experience and might introduce monetization more gradually. Sophisticated consumers represent a minority. Regulatory capacity remains limited.

Proposition 7: The trust lifecycle in AI-mediated commerce will progress more rapidly than in Google Search, with the transition potentially occurring over a shorter timeframe, though ultimate pace will vary significantly by product category and user sophistication.

5. Strategic Response: Building Computational Trust

5.1 The Insufficiency of Traditional Trust Mechanisms

Traditional trust mechanisms—brand reputation, personal relationships, institutional guarantees—may prove insufficient in AI commerce contexts for several reasons. First, they don’t address the fundamental principal-agent problem when platforms control agents. Second, they rely on opacity and information asymmetry that algorithmic systems could potentially eliminate. Third, they’re designed for human-human interactions and may not translate effectively to human-algorithm relationships.

What may be needed is a new class of trust mechanisms I call computational trust—trust built not primarily on reputation or perception but on verifiable evidence that algorithms can evaluate and incorporate into decision-making.

5.2 Principles of Computational Trust

Radical Verifiability: Claims about product quality, company ethics, environmental impact, or other attributes relevant to purchasing decisions should be backed by verifiable evidence that algorithms can assess automatically. This could include:

•          Third-party certifications from credible organizations with transparent audit processes

•          Blockchain-based supply chain tracking providing cryptographic proof of provenance

•          Open-source publication of testing methodologies and results

•          Machine-readable data formats enabling automated verification

•          Real-time monitoring and reporting of compliance with stated standards

Systemic Reliability: Rather than episodic marketing claims, firms could build systems demonstrating consistent ethical performance over time. Computational trust might develop through:

•          Long track records of verified ethical behavior across multiple dimensions

•          Automated monitoring and public reporting of key metrics

•          Rapid detection and acknowledgment of failures when they occur

•          Systematic evidence that failures trigger process improvements

•          Consistency between marketed values and observable actions

Algorithmic Transparency: Firms could provide transparency specifically designed for algorithmic evaluation:

•          APIs providing real-time access to relevant product data

•          Standardized data schemas enabling comparison across competitors

•          Documentation of environmental impacts, labor practices, and supply chains

•          Disclosure of conflicts of interest, commissions, and financial relationships

•          Explanation of product selection and pricing decisions in machine-readable formats

5.3 Implementation Framework

Building computational trust would require five strategic capabilities:

Capability 1: Verification Infrastructure

Organizations would need to invest in systems enabling third-party verification of claims, including regular audits by independent organizations, real-time monitoring, publication of results in machine-readable formats, development of APIs for algorithmic access, and partnerships with certification bodies.

Capability 2: Data Architecture

Organizations would need sophisticated data infrastructure for comprehensive tracking of product attributes, integration across supply chain partners, standardization enabling comparison, security protecting against manipulation, and accessibility for legitimate verification.

Capability 3: Ethical Consistency

Organizations would need to ensure alignment between marketed values and actual practices through clear articulation of principles, systematic compliance monitoring, incentive structures rewarding ethical behavior, cultural embedding of ethics, and accountability mechanisms.

Capability 4: Transparent Communication

Organizations would need to develop communication strategies for algorithmic audiences: structured data formats, proactive disclosure of limitations, honest comparison to competitors, acknowledgment of areas needing improvement, and regular progress updates.

Capability 5: Fiduciary Positioning

Organizations could position themselves as fiduciaries to consumers through subscription-based business models eliminating seller kickback incentives, explicit commitments to prioritize consumer welfare, transparent disclosure of revenue sources, independent oversight structures, and legal structures formalizing fiduciary duties.

Proposition 8: Firms investing in computational trust mechanisms will demonstrate higher selection rates by AI purchasing agents compared to competitors with equivalent traditional brand reputation but lower computational trust scores.

Proposition 9: The competitive advantage from computational trust investment will be moderated by firm age and legacy infrastructure, with new entrants designed around algorithmic evaluation from inception achieving substantially higher returns on computational trust investments than established incumbents attempting to retrofit existing systems.

5.4 Ecosystem-Level Considerations

Individual firm strategies, while necessary, may prove insufficient. Ecosystem-level mechanisms could include:

Industry Standards: Trade associations could develop standardized data schemas, verification protocols, and disclosure requirements enabling cross-firm comparison and algorithmic evaluation.

Certification Bodies: Independent organizations could offer rigorous certification services specifically designed for algorithmic verification.

Platform Design: Platforms deploying AI agents could be structured to eliminate or minimize conflicts through subscription models, fiduciary duties, or transparent disclosure.

Regulatory Frameworks: Governments could mandate transparency in agent design, prohibit certain conflicted monetization practices, and require disclosure when agents prioritize platform profit over consumer welfare.

Algorithmic Auditing: Independent researchers and consumer advocates could develop and deploy tools for auditing agent behavior and detecting conflicts.

6. Theoretical Contributions and Propositions

6.1 Summary of Theoretical Contributions

This paper makes four primary theoretical contributions:

Contribution 1: Trust Lifecycle Model

I develop a formal lifecycle model of trust dynamics in AI-mediated commerce, predicting systematic erosion pressures as platforms face monetization temptations. This extends Technology Acceptance Theory by incorporating post-adoption trust dynamics driven by platform economic incentives.

Contribution 2: Computational Trust Construct

I introduce computational trust as a construct distinct from traditional trust mechanisms. While existing trust research focuses on reputation, relationships, and perception, computational trust emphasizes verifiable evidence, algorithmic evaluation, and systematic reliability.

Contribution 3: Three-Party Principal-Agent Model

I extend principal-agent theory to three-party relationships involving consumers (principals), AI agents (agents), and platforms (owners of agents). This configuration creates unique governance challenges because traditional mitigation mechanisms face complications from platform ownership and algorithmic opacity.

Contribution 4: Historical Precedent as Predictive Framework

I establish the Google Search monetization trajectory as a potentially predictive framework for AI commerce, documenting specific mechanisms through which platform profit imperatives may erode user trust and demonstrating potential irreversibility once business models shift, while acknowledging that the analogy has limitations and outcomes remain contingent.

6.2 Testable Propositions

I propose eleven testable propositions for empirical investigation:

Table 1 summarizes all eleven propositions, organizing them by phase of the trust lifecycle and indicating primary theoretical foundations and suggested empirical approaches.

Table 1: Summary of Testable Propositions

[Table 1 Removed due to formatting issues - Needs to be reviewed in the Original SSRN Paper https://papers.ssrn.com/abstract=5709083]

Note: Color coding indicates proposition type: Blue = Adoption phase, Orange = Misalignment phase, Gray = Disillusionment phase, Green = Strategic response (computational trust), Yellow = Policy implications.

P1: Initial adoption of AI purchasing agents will be driven primarily by perceived utility and efficiency gains rather than deep trust in platform benevolence or agent fiduciary duty, with adoption rates positively associated with demonstrable time savings and cognitive load reduction.

P2: Platform institutional reputation will positively moderate the relationship between perceived utility and adoption likelihood, with consumers demonstrating greater willingness to delegate purchasing authority to agents deployed by brands with established trust relationships.

P3: As platforms introduce monetization mechanisms creating goal misalignment, agent recommendation quality will decline relative to optimal consumer outcomes, but this decline will initially remain below the threshold required to trigger widespread user recognition or abandonment.

P4: Algorithmic opacity will moderate the relationship between monetization strategy adoption and user awareness, such that platforms deploying less transparent decision-making systems will exhibit larger gaps between actual goal misalignment and user-perceived misalignment.

P5: Recognition of severe platform conflicts will trigger market differentiation, with consumers scoring higher on digital literacy measures demonstrating significantly greater probability of migrating to fiduciary alternatives.

P6: Trust in AI agents, once damaged through discovery of systematic goal misalignment, will prove more difficult to rebuild than trust in human advisors facing equivalent conflicts, with trust recovery requiring substantially longer timeframe for automated versus human agents.

P7: The trust lifecycle in AI-mediated commerce will progress more rapidly than in Google Search, with the transition potentially occurring over a shorter timeframe, though ultimate pace will vary significantly by product category and user sophistication.

P8: Firms investing in computational trust mechanisms will demonstrate higher selection rates by AI purchasing agents compared to competitors with equivalent traditional brand reputation but lower computational trust scores.

P9: The competitive advantage from computational trust investment will be moderated by firm age and legacy infrastructure, with new entrants designed around algorithmic evaluation from inception achieving substantially higher returns on computational trust investments than established incumbents attempting to retrofit existing systems.

P10: The rate of trust erosion will vary significantly by product category, with high-stakes categories exhibiting considerably faster trust degradation upon conflict recognition than low-stakes categories.

P11: Regulatory interventions mandating transparency in agent design and monetization disclosure will slow but not prevent trust erosion, as platforms adapt compliance strategies that satisfy regulatory requirements while maintaining profit-generating mechanisms.

7. Discussion: Implications and Future Research

7.1 Implications for Practice

For Brand Managers: Traditional marketing focused on human perception and emotional appeal may prove increasingly insufficient as AI agents intermediate purchases. Brands should consider investing in computational trust—verifiable quality claims, transparent operations, and machine-readable data enabling algorithmic evaluation. This requires rethinking marketing infrastructure to serve both human and algorithmic audiences.

For Platform Operators: The temptation to monetize agent intermediation through conflicted mechanisms may generate short-term revenue but risks long-term sustainability. Platforms should evaluate alternative monetization models—subscriptions, transparent commissions, fiduciary structures—that could maintain trust. Early positioning as genuinely fiduciary may prove valuable as competitive differentiation strategy.

For Retailers and Manufacturers: Organizations face strategic choices: optimize for algorithmic evaluation through computational trust mechanisms, or attempt to influence agent selection through placement fees and platform partnerships. The former may create durable competitive advantage; the latter could prove unsustainable if conflicts become apparent and trigger user backlash.

For Consumer Advocates: The rise of AI commerce creates potential new vulnerabilities, particularly for less sophisticated users. Advocates should consider pushing for transparency requirements, developing auditing tools, and educating consumers about potential conflicts in platform-controlled agents. The risk of market bifurcation—where advantaged consumers access fiduciary protection while others face systematic exploitation—warrants particular attention.

7.2 Implications for Policy

Transparency Requirements: Regulators could consider mandating disclosure of monetization mechanisms in AI commerce agents, requiring platforms to reveal when agents prioritize platform profit over consumer welfare. Disclosure requirements could include prominent notification when recommendations are influenced by commercial arrangements.

Fiduciary Duties: Legal frameworks could clarify whether platforms deploying purchase agents owe fiduciary duties to consumers, potentially requiring duty-of-loyalty structures similar to financial advisors. This would represent significant intervention but might prevent the most egregious conflicts.

Conflict-of-Interest Regulations: Regulations may need to address or restrict certain monetization practices that create severe goal misalignment, such as undisclosed self-preferencing or kickback arrangements. However, regulatory design must balance consumer protection against innovation constraints.

Algorithmic Auditing Infrastructure: Independent auditing of agent behavior could be facilitated through regulatory requirements for API access, standardized testing protocols, and researcher protections. Public auditing could provide transparency without requiring direct regulatory oversight of recommendation algorithms.

Market Structure Assessment: Policymakers could consider whether platform dominance in agent deployment creates competition problems, potentially requiring structural interventions if winner-take-most dynamics lead to insufficient consumer choice among fiduciary alternatives.

7.3 Research Agenda

Several promising research directions emerge:

Empirical Testing: As AI commerce agents deploy at scale, researchers should systematically test the propositions developed here, tracking adoption rates, measuring recommendation quality, documenting monetization practices, and assessing trust levels over time. Longitudinal studies will be particularly valuable.

Comparative Studies: Cross-platform comparison can reveal which business models and design choices better maintain trust. Do subscription-based fiduciary agents outperform ad-supported platforms on trust and recommendation quality? How do platform governance structures affect conflict emergence?

Behavioral Research: How do consumers detect and respond to agent conflicts? What signals trigger suspicion? At what point does accumulated suboptimal experience trigger abandonment? What individual differences predict conflict sensitivity?

Technical Research: Development of auditing tools enabling systematic testing of agent behavior, detection of bias, and measurement of goal alignment between stated objectives and actual recommendations. These tools could support both academic research and consumer protection.

Intervention Studies: Testing whether transparency interventions—disclosures, explanations, audits—successfully mitigate trust erosion or merely confirm conflicts without providing solutions. What forms of transparency prove most effective?

Longitudinal Analysis: Long-term tracking of trust dynamics as platforms introduce monetization mechanisms, capturing the full lifecycle from adoption through potential responses. Multi-year panel studies would be particularly valuable.

Measurement Development: Creating reliable and valid measures of computational trust and its antecedents. How do we measure verifiability, systemic reliability, and algorithmic transparency? What psychometric properties should these measures possess?

7.4 Limitations and Boundary Conditions

This analysis has several important limitations that bound its conclusions:

Primary Limitation: Absence of Empirical Data. This framework’s most significant limitation is its theoretical nature without empirical validation. AI purchasing agents have not yet deployed at sufficient scale or duration to enable longitudinal trust studies. Consequently, this work should be understood as predictive theorizing rather than empirically grounded explanation. The propositions advanced represent testable hypotheses requiring future validation, not established findings.

This limitation is both unavoidable and instructive. The framework’s value lies precisely in providing theoretical scaffolding before empirical patterns solidify. However, readers should approach the specific predictions with appropriate skepticism. These represent theoretically informed speculation rather than data-driven projection. The mechanisms proposed (monetization → misalignment → erosion) rest on established theory, but their manifestation in AI commerce remains to be observed.

Future research should prioritize empirical testing as deployment scales. Longitudinal studies tracking trust from initial adoption through potential erosion phases, experimental studies manipulating monetization transparency, and computational audits of agent recommendation patterns would all provide critical validation or refutation of this framework.

Platform Choice Matters: The model predicts pressures toward trust erosion but doesn’t claim inevitability. Platform choices, regulatory interventions, and market competition could alter trajectories. Subscription-based models, benefit corporation structures, or credible fiduciary commitments could potentially maintain alignment.

Market Heterogeneity: Different product categories, consumer segments, and competitive environments may exhibit different trust dynamics. The model may apply more strongly to some contexts than others. High-stakes purchases may follow different trajectories than routine replenishments.

Regulatory Contingency: The analysis assumes limited regulatory intervention. Aggressive proactive regulation could potentially prevent the trust erosion cycle from fully developing, though regulatory effectiveness remains uncertain and varies significantly across jurisdictions.

Alternative Business Models: The framework focuses on conventional platform monetization approaches. Novel business models not yet conceived could potentially resolve the trust paradox in ways this analysis doesn’t anticipate. Entrepreneurial innovation may yield unexpected solutions.

Cultural and Geographic Variation: Trust dynamics may vary across cultures and regulatory regimes. The analysis primarily reflects Western market assumptions and may not generalize globally. Different cultural orientations toward trust, technology, and commerce could produce different outcomes.

Agent Sophistication: The analysis assumes current-generation AI capabilities. More sophisticated agents with genuine reasoning capabilities might alter dynamics in unpredictable ways. Technological advancement could either exacerbate or mitigate the trust challenges identified.

These limitations suggest the model serves better as a framework for inquiry rather than definitive prediction. The value lies in identifying the mechanisms and pressures that create trust erosion risks, enabling proactive responses.

8. Conclusion

The emergence of AI purchasing agents represents a fundamental transformation in commercial exchange, creating unprecedented opportunities for efficiency alongside significant risks for trust erosion. This paper has developed a predictive conceptual framework of trust dynamics in AI-mediated commerce, arguing that platforms deploying these agents will face powerful economic pressures to introduce monetization strategies that systematically erode the initial trust foundation.

The core tension proves structural: consumers adopt AI agents because of perceived utility and objectivity, yet the economic incentives of platforms deploying these agents create strong pressures toward monetization strategies that compromise objectivity and reduce utility. This isn’t necessarily a technical flaw fixable through better engineering—it may represent a predictable outcome of platform economics requiring systemic responses.

The Google Search precedent demonstrates a version of this trajectory: early user-centric service giving way to revenue-extracting practices, systematic conflicts eroding trust, and apparent difficulty reversing course once business models shift. AI commerce may follow a similar path, possibly more rapidly given heightened awareness, higher stakes, and greater transparency demands. However, the outcome remains contingent on platform choices, regulatory responses, and market evolution.

The strategic response I propose—computational trust emphasizing radical verifiability, systemic reliability, and algorithmic transparency—offers a potential path forward for organizations seeking competitive advantage in agent-mediated markets. However, individual firm strategies may prove insufficient without ecosystem-level mechanisms: industry standards, independent certification, platform redesign, and regulatory frameworks.

The stakes prove significant. AI commerce promises substantial efficiency gains and consumer welfare improvements. However, if platforms exploit their intermediary position through conflicted monetization, we risk market differentiation where sophisticated consumers access fiduciary protection while less advantaged populations suffer systematically suboptimal outcomes. Preventing this requires recognizing the trust dynamics early and acting deliberately to build mechanisms appropriate for algorithmic commerce.

We stand at a critical juncture. The decisions made now about platform business models, transparency requirements, and trust mechanisms will shape commerce for decades. Understanding the likely trajectory of trust dynamics—and developing strategic responses—proves essential for realizing the promise of AI commerce while mitigating its risks. The framework developed here aims to provide both warning and guidance for this critical transition.

The eleven propositions I have advanced are falsifiable and testable as AI commerce agents deploy. If empirical evidence contradicts these predictions, that would itself represent valuable knowledge, indicating that the mechanisms I identify prove less powerful than anticipated or that countervailing forces successfully maintain trust. Either outcome advances understanding. The theoretical framework provides structure for systematic investigation of these emerging phenomena, contributing to both academic knowledge and practical wisdom as we navigate this transformation in how humans and algorithms collaborate in commercial exchange.

Acknowledgments

I am grateful to my colleague Michal Wiecko, whose persistent intellectual challenges to early versions of this work forced me to explore additional paths of inquiry that substantially strengthened the theoretical framework and ultimately resulted in this paper. His critical engagement pushed me to develop more rigorous arguments and consider alternative explanations I had initially overlooked. All errors and limitations remain my own.

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Author Note

This paper represents independent research conducted as part of a broader examination of artificial intelligence's impact on commercial and business strategy. The author is expanding these theoretical frameworks in a forthcoming book on algorithmic commerce under contract with St. Martin's Press, expected 2027.

The author maintains no conflicts of interest related to this research. Correspondence concerning this article should be addressed to Paul F. Accornero, The AI Praxis. Email: paul.accornero@gmail.com

Declarations:

This is a preliminary working paper and is intended to elicit comments and suggestions for revision. Please do not cite or distribute without the explicit permission of the corresponding author. We welcome all feedback.

AI Usage Statement: The author used AI language models to assist with literature organization, text edit, improve readability and edit for grammar. All conceptual frameworks, theoretical arguments, and analytical reasoning represent the author’s original intellectual contribution. The author takes full responsibility for the final content, accuracy, and all claims made in this paper.