The Paradox of AI & Ad Tech: Why Technology Alone Won’t Fix Digital Advertising’s Core Problems - But a Novel, Hybrid Approach Could
Full disclosure: this research was conducted vis-a-vis OpenAI's DeepResearch agent but was edited to better align with (parent company of appeAR) ARreality Solutions' perspectives on the matter. Please feel free to message me directly if you are interested in learning more about the appeAR platform and the potential to invest.
Abstract
Digital advertising has become a cornerstone of the modern economy, driving a global market valued at nearly $680 billion in 2023 (Digital Ad Spend (2017–2028) [Updated Aug 2024] | Oberlo). Yet its rapid growth has been accompanied by profound negative externalities – side effects borne by society at large. This article critically examines why even advanced artificial intelligence (AI) will not resolve the fundamental externalities plaguing digital ad technology. Specifically, it explores how AI fails to reverse the erosion of public trust, mounting privacy concerns, and pervasive behavioral manipulation driven by algorithmic targeting. Big Tech touts AI and hyper-personalization as fixes for advertising inefficiencies, but evidence suggests these innovations often amplify rather than ameliorate problems like consumer distrust and ad fatigue. By tracing the historical trajectory of advertising and analyzing the “AI promise vs. reality,” we show that technological optimization alone cannot address issues rooted in business models and incentives. The consequences of AI-driven ad tech – from an escalating arms race for user attention to threats against democratic discourse – underscore a meta-crisis that demands more than technical Band-Aids. As various thought leaders have warned, a deeper paradigm shift is needed. In response, this article makes the case for an alternative, trust-centric model of digital advertising. We introduce appeAR, a peer-to-peer augmented reality (AR) advertising and loyalty platform that realigns incentives around social trust and consumer engagement rather than surveillance and manipulation. By leveraging genuine peer recommendations and user consent, appeAR exemplifies how the future of digital commerce can avoid the pitfalls of traditional ad tech. In conclusion, we argue that only by rethinking the advertising model – prioritizing privacy, transparency, and trust – can we move beyond the externalities that AI alone cannot fix.
Introduction
Digital advertising is the economic engine of the internet age, funding countless “free” services and platforms. Global digital ad spending has skyrocketed in the past two decades, reaching hundreds of billions of dollars annually (Digital Ad Spend (2017–2028) [Updated Aug 2024] | Oberlo). Industry giants like Google and Meta derive the majority of their revenue from advertising, making targeted ads an ubiquitous presence in modern life. This revenue model has enabled an explosion of online content and connectivity. However, it has also given rise to significant externalities – costs and harms not accounted for in the transactions between advertisers and platforms, but felt by users and society at large. Three fundamental externalities have become increasingly apparent:
Addressing these issues is not just a matter of tweaking algorithms; it requires reconsidering the business model and incentive structure of digital media. The industry’s initial response has been to throw more technology – especially AI – at the problem. If ads are annoying or mistrusted, the thinking goes, we’ll use AI to make them ultra-relevant and timely. If users are concerned about privacy, we’ll use federated learning or anonymization techniques to target them “responsibly.” If engagement algorithms are causing harm, we’ll create AI ethics panels and dial down the obvious extremes. But while these efforts may offer incremental improvements, they do not fundamentally resolve the externalities listed above. This article argues that AI will not be the panacea for digital advertising’s woes. In fact, without deeper changes, AI-driven advertising may worsen the very problems it aims to solve – increasing fatigue, deepening mistrust, and refining manipulation to a more granular level.
We begin by taking a step back to see how we got here, tracing the evolution of advertising from its early days to the hyper-targeted digital ecosystem of today. This historical context reveals that many current challenges are not new – they are the latest manifestations of tensions inherent in ad-supported media. Next, we critically examine the promises made about AI in advertising versus the reality of its impact. We then explore the societal consequences of doubling down on AI-driven ad tech, informed by insights from leading thinkers who have studied the attention economy and its discontents. Finally, we outline a path forward: an alternative advertising model exemplified by appeAR, which seeks to align marketing with trust, privacy, and genuine engagement rather than perpetual surveillance. The goal is not just to critique, but to illuminate how digital advertising can evolve in a healthier direction.
Historical Context
Advertising as a practice long predates the internet – but each technological era of media has reshaped how ads reach the public, along with public attitudes toward advertising. A look at the historical arc from print to digital highlights how today’s distrust and fatigue developed over time.
In the print media era of the 19th and early 20th centuries, advertising was relatively simple and transparent. Newspapers and magazines ran printed ads alongside editorial content, often clearly demarcated. Trust in media was bolstered by editorial standards and the physical separation of “church and state” (news vs. advertising). However, even then, the drive to capture attention introduced biases: the term “penny press” refers to cheap 19th-century newspapers that were subsidized by ads and thus competed fiercely for readers’ attention with sensational headlines. The seeds of the attention economy were present, though the scale was limited by print distribution. Importantly, ads in this era were not personalized – every reader saw the same message. As a result, the privacy intrusion was zero (advertisers knew nothing personal about readers), and any manipulation was broad rather than targeted. Trust in advertising rested on the reputation of brands and publishers; many consumers took claims in ads with a grain of salt, but there wasn’t an overarching sense of being personally spied on by advertisers.
The advent of radio and television in the mid-20th century brought advertising into the home and introduced new techniques of persuasion. Sponsors of early radio shows and TV programs often had a direct hand in content (the infamous “soap operas” were so named because soap companies produced them). This raised concerns about media objectivity, but it also gave rise to regulations and industry norms (like the FTC in the U.S. and rules against subliminal advertising) aimed at keeping advertising within certain ethical bounds. Still, business models increasingly revolved around “buying” audience attention with entertainment and “selling” that attention to advertisers. In these broadcast mediums, the reach of advertising grew, but messages were one-size-fits-all for broad demographics. Externalities started to appear in the form of trust issues – e.g. the stereotype of the manipulative Madison Avenue ad man emerged, reflecting public realization that advertising could be misleading or manipulative. Yet, audiences retained some agency: they could change the channel, and aside from crude audience research, there was no personal data tracking viewers’ behaviors across channels.
The digital revolution upended this balance. By the late 1990s and early 2000s, as the internet became mainstream, online advertising introduced novel capabilities: interactivity, immediacy, and most fatefully, targeting based on user data. Early online ads were banner ads on websites – often irrelevant and easy to ignore – but they paved the way for the attention-capturing business models that dominate today. Google’s introduction of AdWords (2000) and AdSense (2003) demonstrated the power of contextually relevant ads and pay-per-click models. Suddenly, advertising could be tightly coupled to what a user was searching for or reading about. This increased ad effectiveness while still, in theory, aligning with user intent (as Google’s founders put it, their goal was to show ads that were useful information rather than distraction). As Mark Zuckerberg later observed: “Advertising works most effectively when it’s in line with what people are already trying to do” (The Best Mark Zuckerberg Quotes for Small Business Owners). This philosophy drove the early optimism that digital ads could actually enhance user experience through relevance.
However, the pursuit of relevance quickly led to more intrusive data practices. By the mid-2000s, tracking cookies and third-party data brokers enabled profiling users across the web. Ad networks could follow your behavior from site to site, building a dossier to target you with “behavioral” ads tailored to your interests. For publishers and ad tech firms, this meant higher click-through rates and more profit. For users, it often meant a creeping sense that someone is watching. The first wave of privacy concerns around online ads emerged, exemplified by the famous scenario where Target (using predictive analytics on purchase data) figured out a teenager was pregnant before her father did, by targeting her with maternity product coupons (Unraveling the Personalization Paradox: The Effect of Information Collection and Trust-Building Strategies on Online Advertisement Effectiveness). Such stories, while anecdotal, struck a cultural nerve: they revealed how algorithms could know and act on intimate details that a person hadn’t chosen to share. Trust in advertising content was now entangled with trust in how companies handled personal data.
The rise of social media platforms in the late 2000s and 2010s supercharged these trends. Facebook led the way in turning social interaction into an advertising goldmine, followed by Instagram, Twitter, and others. These platforms mastered the art of the “attention economy,” designing feeds and notification systems that maximize time-on-site so that more ads can be shown. The business model incentivized maximizing engagement at all costs – a dynamic Tristan Harris describes as companies “fighting a race to the bottom of the brain stem” for user attention (The Eyeball Economy: How Advertising Co-Opts Independent Thought - Big Think). Algorithmic curation became the norm: instead of showing posts chronologically, platforms learned to algorithmically rank content based on what would keep each user hooked. This was a pivotal shift. It meant that complex AI algorithms were now mediating what billions of people see everyday, blending personal posts, news stories, and sponsored content into one addictive stream.
This shift has had serious implications for media trustworthiness. Users struggled to distinguish organic content from sponsored posts or political propaganda from genuine news, especially when every item is tailored to their profile. Mis/disinformation could spread under the guise of “recommended for you” content, and legitimate journalism often had to compete with clickbait for visibility. As a result, public trust in information found online declined. By the late 2010s, polls showed record-low trust in social media as a source of news or information, with 61% of Gen Z saying they don’t find social media ads trustworthy (Consumers View TV as the Most Trustworthy Advertising Channel - MNTN Research). More broadly, Edelman’s Trust Barometer and similar studies began highlighting a crisis in information trust. The advertising-funded, engagement-driven model of social platforms is frequently identified as a root cause: when what you see is determined by what will get you to click (often that means provoking outrage or fear), the information environment becomes distorted and less credible.
Consumer skepticism toward ads also grew in this period. Ad fatigue set in as the average person online was exposed to hundreds if not thousands of marketing messages per day (How Social Platforms Are Tackling Ad Fatigue - PPC Hero) (How Social Platforms Are Tackling Ad Fatigue - PPC Hero). Techniques like retargeting – those exact pair of shoes you looked at once now stalk you around the web for weeks – made ads feel eerily persistent. Surveys find that 91% of consumers today say ads are more intrusive than they were just a few years ago (Why People Block Ads (And What It Means for Marketers and Advertisers) ), and 79% feel like they are being tracked by companies due to retargeted ads (Why People Block Ads (And What It Means for Marketers and Advertisers) ). This constant bombardment led to coping mechanisms: ad-blocking software usage exploded (hundreds of millions of devices now run ad blockers by default), and “banner blindness” became a documented phenomenon – people mentally tune out areas of webpages that look like ads. In essence, users’ defenses went up, and straightforward trust in advertising messages deteriorated further.
From this historical journey, we see that eroding trust, privacy anxieties, and manipulation concerns have deep roots. The shift from mass media to personalized digital media intensified each externality: where once a skeptical viewer might distrust an ad, now they might distrust the entire feed it came from; where once an annoying ad was fleeting, now it follows the user around via tracking; where once a clever jingle might influence us, now an opaque algorithm can do so in far subtler and pervasive ways. Each era’s innovations solved certain efficiency problems (targeting, scale, measurability) but introduced new externalities. Understanding this context is crucial: it tells us that the core problems are systemic, not merely incidental bugs that AI can fix with a smarter algorithm.
The AI Promise vs. Reality
In the face of mounting challenges, the digital advertising industry’s reflexive solution has been more technology, particularly Artificial Intelligence. Over the past few years, Big Tech companies have positioned AI as the silver bullet to make advertising better for everyone – more efficient for advertisers, more profitable for platforms, and even more enjoyable or relevant for users. The promise goes something like this: advanced AI algorithms can crunch vast amounts of data to deliver “hyper-personalized” ads at the perfect moment to the perfect person. By predicting exactly what a user is interested in, AI can ensure consumers see only the ads they want to see, supposedly reducing annoyance and increasing engagement. Google, for example, touts how its machine learning systems can predict purchase intent and optimize ad bidding in real-time, eliminating waste. Facebook (Meta) has rolled out AI tools for advertisers that automatically optimize campaign targeting and creative variations, boasting improvements in click-through and conversion rates (Meta's AI Products Just Got Smarter and More Useful). The underlying narrative: if we just make ads more relevant and intelligent, the user experience will improve and the old complaints will subside.
However, the reality of AI-driven advertising has not lived up to this utopian vision – and in many ways has exacerbated the very externalities we described. Let’s unpack why the “AI will fix it” narrative is fundamentally flawed when it comes to trust, privacy, and manipulation:
1. Hyper-personalization ≠ Trust – AI can indeed target more precisely, but that doesn’t automatically earn user trust; in fact, it often undermines it. When ads become “too personal,” users frequently react with discomfort. For instance, if you have a conversation about a niche product and then suddenly see an ad for it, you might suspect your phone is listening to you (whether or not it actually is). A recent consumer study found 38% of adults consider targeted ads “creepy” (Consumers View TV as the Most Trustworthy Advertising Channel - MNTN Research). Another survey revealed that only 17% of consumers believe personalized ads are ethical (Only 17% Of Consumers Believe Personalized Ads Are Ethical, Survey Says - RMA), with the majority feeling that using their personal data for ads is wrong. These perceptions show that increased personalization can backfire. Users become more wary, not less, when every ad seems uncannily tailored to them, because it highlights the extent of surveillance involved. Academics call this the “personalization paradox” – greater personalization can increase a person’s sense of vulnerability and decrease their acceptance of the advertising (Unraveling the Personalization Paradox: The Effect of Information Collection and Trust-Building Strategies on Online Advertisement Effectiveness) (Unraveling the Personalization Paradox: The Effect of Information Collection and Trust-Building Strategies on Online Advertisement Effectiveness). In one field study, click-through rates dropped sharply once consumers realized their data had been collected covertly to personalize ads (Unraveling the Personalization Paradox: The Effect of Information Collection and Trust-Building Strategies on Online Advertisement Effectiveness) (Unraveling the Personalization Paradox: The Effect of Information Collection and Trust-Building Strategies on Online Advertisement Effectiveness). In short, AI’s ability to personalize intensifies the privacy/trust trade-off: yes, the ad might be more “relevant,” but it also feels more intrusive, leading to even less trust in the advertiser’s intentions (Unraveling the Personalization Paradox: The Effect of Information Collection and Trust-Building Strategies on Online Advertisement Effectiveness). There is a fine line between “helpfully relevant” and “creepy,” and AI is frequently pushing ads over that line.
2. Optimizing for Engagement, Not Quality – AI in advertising is usually optimized for specific measurable outcomes (clicks, conversions, view time). It does not optimize for intangibles like truthfulness of content or long-term user satisfaction. This means AI will readily learn to exploit cognitive biases and emotional triggers to get the desired short-term outcome. If outrageous or misleading content gets more clicks, a na?ve AI optimization will gravitate toward it. We’ve seen this on social platforms: algorithms trained on engagement metrics ended up amplifying sensationalism and clickbait, contributing to misinformation ( The Consilience Project | Challenges to Making Sense of the 21st Century - The Consilience Project ) ( The Consilience Project | Challenges to Making Sense of the 21st Century - The Consilience Project ). The same principle applies to ads. AI might find that a slightly deceptive headline (“You won’t believe what happens next…”) draws more attention, and without human intervention, it will favor that—even at the cost of honesty or trust. Thus, the quality of advertising can degrade when AI is laser-focused on what grabs attention, not what respects the audience. Users sense this manipulation; over time, it trains them to further distrust whatever appears in those sponsored slots.
3. More Data, More Privacy Risk – To fuel these intelligent ad systems, more and more data is required. AI thrives on data; the promise of learning your preferences implies aggregating information about you from many sources (web browsing, purchase history, location, social media activity, etc.). As one Google Ads executive put it, the industry “strived to deliver relevant ads” but in doing so “created a proliferation of user data across thousands of companies” (Google charts a course towards a more privacy-first web) (Google charts a course towards a more privacy-first web). This fragmentation of personal data has become nearly impossible to secure. Massive data breaches and leaks (from Cambridge Analytica to ad-tech databases left unsecured) have become commonplace, exposing users to identity theft and fraud beyond just ad targeting. Even in the best-case, where data isn’t breached, the aggregation of data for ads means more vectors through which companies monitor individuals. AI hasn’t eliminated the privacy problem; if anything, it has intensified the appetite for data (e.g., systems that do “predictive targeting” might ingest not only your past behavior but millions of others’ to find patterns, effectively treating consumers as lab rats in an experiment). The very efficacy of AI in finding patterns means it can infer sensitive traits about you that you never explicitly provided. For example, an AI might deduce your sexual orientation, political leanings, or health status just from your click patterns – and then advertisers use that to target you in ways you never expected (or wanted). This raises serious ethical issues that AI optimizations alone cannot solve; it edges into discrimination and predatory practices, such as targeting vulnerable groups (say, gambling ads to someone showing patterns of addiction). In summary, the more we lean on AI for ad targeting, the more personal data we end up collecting and inferencing – which heightens privacy concerns rather than alleviating them.
4. The Paradox of Efficiency – AI is making the advertising machinery more efficient, but that very efficiency can worsen ad fatigue. When algorithms micro-target the “right” users, those users might end up seeing similar ads over and over across all their devices and platforms (because the system has decided “you’re the ideal customer profile” for many brands). Rather than seeing a random mix of ads (some relevant, some not, which at least gives variety), a person could be highly targeted by the same category of ads repeatedly. This leads to a feeling of being bombarded. Indeed, 87% of people say there are more ads in general now than in the past (Why People Block Ads (And What It Means for Marketers and Advertisers) ), even though one might argue targeting was supposed to reduce unnecessary ads. The efficiency gains mostly benefit advertisers (less spend wasted on uninterested audiences) and platforms (higher probability of clicks), but from the user perspective, it’s a bit like being stuck in Groundhog Day – the same messages following you relentlessly. Over time, this contributes to banner blindness and use of ad blockers, as users attempt to reclaim a less cluttered experience. So, while AI might make each individual ad impression more likely to convert, it also risks pushing the frequency and omnipresence of advertising to a threshold where users start tuning out everything. Relevance does not equate to respect; users still resent being targeted even if the content is on-point, simply because they did not ask for it at that moment.
5. Cosmetic Fixes vs Core Incentives – Perhaps the biggest limitation of the AI fix is that it doesn’t change the underlying incentives of the digital advertising model. The model is to maximize attention and monetize it. AI will do that job exceedingly well – but that’s precisely the problem. As long as a platform’s revenue depends on squeezing more engagement and ad clicks, any AI it deploys will ultimately serve that goal, not the user’s well-being. Tech companies often speak of AI helping to show “better ads” or filter out “bad ads” (for instance, removing malware-laden ads or obvious scams, which is good). But this is tinkering at the margins. The fundamental incentive remains: show as many ads as you can get away with, target them as intrusively as needed to make them effective, and keep people glued to the screen so they see more ads. AI is simply the newest tool to pursue that incentive with greater precision. In some cases, AI might even hide the seams of advertising further – for example, native ads blended into content or AI-generated influencer avatars promoting products without clear disclosure. If users can’t tell what is an ad, they can’t consciously choose to disregard it, which is arguably even more manipulative. We reach a paradox where the more seamless and “smart” the ads become, the less agency and awareness the consumer has. This is a far cry from solving externalities like manipulation – it is making the manipulation harder to detect.
In sum, the grand promise that AI would rescue digital advertising from its excesses has proven largely hollow so far. AI excels at optimization, but when you optimize for the wrong thing, you simply get to a problematic end state faster. It fails to address, and often intensifies, the core externalities: trust erodes further when personalization crosses into creepiness, privacy is even more at risk with voracious data-driven models, and manipulation becomes more insidious as algorithms learn to pull our psychological levers. The industry’s narrative that hyper-personalized ads would be welcomed like helpful shopping assistants has not materialized; instead, consumers increasingly feel like prey in a high-tech hunt for their attention and data. As one analysis noted, digital media today is “riddled by the behavioral modification techniques of psychological warfare, as well as various forms of advertising,” resulting in mass behavior modulation and polarization ( The Consilience Project | Challenges to Making Sense of the 21st Century - The Consilience Project ). This is strong language, but not an exaggeration – the tools originally built to sell us products have been weaponized to capture our attitudes, habits, and beliefs. AI didn’t create this dynamic by itself, but it is supercharging it.
None of this is to say AI has zero positive role to play. Certainly, AI can be used to improve ad relevance in non-dystopian ways (e.g., showing me an ad for a product I genuinely need at the right time can be a win-win). It can also potentially reduce obviously bad experiences (like preventing me from seeing the same ad 100 times, or stopping fraudulent ads). But these benefits are marginal relative to the larger structural issues. As long as the prevailing model is one of surveillance-driven, attention-maximizing advertising, AI will simply be optimizing a broken system. We must look beyond AI hype and confront the deeper consequences of the path we’re on.
The Consequences of AI-Driven Ad Tech
If the current trajectory continues – with AI further entwining itself in the ad-supported digital ecosystem – what are the likely outcomes for individuals, society, and even the economy at large? Many of these consequences are already unfolding:
Exacerbating the Attention Arms Race: We live in an economy increasingly defined by competition for attention. AI-driven algorithms have become masterful at capturing and holding our gaze – auto-playing videos, endless feeds, personalized notifications timed to when our interest wanes, etc. As each platform deploys AI to maximize engagement, the competition only escalates. Netflix’s CEO half-jokingly said their biggest competitor is not another streaming service but sleep, because any hour you spend sleeping is an hour not watching Netflix (The Eyeball Economy: How Advertising Co-Opts Independent Thought - Big Think). This “arms race” for attention means ever more sensational content, more frequent interruptions, and more psychological ploys to keep us scrolling. When advertising revenue drives these platforms, AI becomes a weapon to out-engage the competition. Tristan Harris describes the result as a “race to the bottom of the brainstem” where services go lower and lower to trigger impulsive behavior (The Eyeball Economy: How Advertising Co-Opts Independent Thought - Big Think). The consequence for users is a constant assault on their attention and self-control. Human ability to concentrate, to think critically, or to simply be offline is eroded. Moreover, with AI optimizing each slice of content for virality or stickiness, the collective information diet becomes rich in sugar and salt, but poor in nutritional value. This has societal implications: shortened attention spans, reduced capacity for deep reading or sustained focus, and a populace more susceptible to knee-jerk reactions than thoughtful deliberation.
A Deeper Cycle of Distrust and Cynicism: Earlier we noted the erosion of trust in digital media and advertising. AI’s involvement, rather than restoring trust, risks deepening cynicism. Why? Because as people become aware (even dimly) that what they see is finely orchestrated by algorithms, they may start doubting the authenticity of everything online. If your social feed is curated by AI for engagement, you might wonder: was that heartfelt post from a friend shown to me because it’s genuine or because the algorithm calculated it’d keep me on longer? If news is algorithmically selected, is it really what’s important or just what’s clicky? When ads are seamlessly blended in, perhaps even generated by AI to look like normal posts, you begin to question: who is trying to sell me something, and when? This pervasive uncertainty is toxic for trust. It leads to a kind of nihilism about media – a sense that “everything is PR or manipulation.” We see some of this manifesting already in the popularity of phrases like “fake news” or the ease with which conspiracy theories take root. If people stop trusting the information ecosystem, it’s extremely hard to have a functioning society. Ironically, the more AI refines the art of influencing people, the more people (once they catch on) may resist by disbelieving even true or good messages. It’s a tragedy of the commons for trust: by overfishing the sea of attention and trust, advertisers and platforms risk killing the ecosystem that sustains them. No AI can directly solve this because trust is a social, relational construct – it has to be earned through transparency, consistency, and alignment of interests, none of which are incentivized by the current ad model.
Democratic and Civic Distortions: Perhaps the most alarming externalities of AI-optimized advertising emerge in the civic realm. We have seen how micro-targeted political ads (and propaganda) can be used to manipulate democratic processes. Cambridge Analytica’s scandalous use of Facebook data – harvesting profiles of 87 million users to target political messaging in the 2016 US election – is a case in point (Netflix 'the Great Hack' Professor: Privacy Rights in US Lag Behind UK - Business Insider). They claimed to use AI psychographic models to find people’s emotional triggers and send them tailored propaganda. Whether or not their methods were as effective as boasted, the precedent is disturbing. Political actors now routinely employ sophisticated data analytics and AI to segment voters and deliver highly specific messages that often go unchecked by broader public scrutiny (what is said to one micro-group may be completely different, even contradictory, to what is said to another). This undermines the very idea of a shared public discourse. Furthermore, when the news people consume is filtered by engagement-driven algorithms, it can create echo chambers – filter bubbles – reinforcing one’s existing beliefs and shutting out opposing viewpoints ( The Consilience Project | Challenges to Making Sense of the 21st Century - The Consilience Project ). Over time, this contributes to polarization: people not only disagree on opinions, but on basic facts, because their realities have been tailored to them. The architects of social media never set out to erode democracy, but as scholar Shoshana Zuboff noted, “ubiquitous surveillance and personalization are now a foundation for a new kind of influence power” – one that can tilt political outcomes without anyone necessarily realizing it. Daniel Schmachtenberger and others have termed this a crisis of sense-making, part of a broader meta-crisis where our collective ability to form consensus and respond to societal challenges is breaking down (The Consilience Project | Challenges to Making Sense of the 21st Century - The Consilience Project ). Advertising-driven platforms did not cause all of this alone, but their algorithms and AI optimizations have poured fuel on the fire. When clicks and shares decide which political news spreads, incendiary and misleading content often wins out over sober analysis (because outrage is more engaging than nuance). In sum, the externalities here include weakened democratic institutions, election interference, and a populace divided by incompatible information realities.
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Economic Inequality and Market Distortions: Another consequence to consider is how AI-ad tech might concentrate economic power. Digital advertising today is dominated by a few major players (often dubbed the “Duopoly” of Google and Facebook, now a triopoly with Amazon’s ad business rising). These companies leverage AI at a scale smaller firms cannot match – ingesting data from billions of users. The more effective their targeting, the more advertisers pour money into their platforms, further entrenching their dominance. This creates winner-takes-all dynamics in the ad market, which can stifle competition and innovation. Additionally, smaller publishers and media outlets find it hard to survive in an ad landscape optimized by AI, because Google/Facebook’s AI can more efficiently broker ads across the web, capturing the lion’s share of revenue through their exchanges and networks. This shift has contributed to the decline of local journalism (local newspapers can’t compete for ad dollars against the micro-targeting capabilities of the tech giants). The collapse of local news is itself a societal problem tied to the ad model: as news deserts grow, communities lose shared reference points and accountability journalism. On the marketing side, as AI targeting becomes essential, businesses with access to big data and expensive AI tools can out-advertise smaller businesses, potentially entrenching big brands over small ones regardless of product quality. One could argue this is just capitalism at work, but it’s an externality if market success starts depending more on data prowess than on creating genuine consumer value.
Mental Health and Autonomy: There is also the personal cost. We touched on attention span, but consider mental health. Social media use, which is deeply entwined with advertising, has been linked in numerous studies to anxiety, depression, and body image issues, especially among teens. Part of this is due to the curated nature of feeds (comparing oneself to idealized images), and part due to the compulsive use patterns encouraged by algorithms. If AI makes these platforms more addictive, it could exacerbate these mental health impacts. Moreover, the constant manipulation can create a subtle erosion of autonomy. When you’re continuously nudged – watch this, click that, buy this – do people become more passive in their decision-making? Some researchers worry about “learned helplessness” in the face of algorithmic decision systems: if the app always knows the best route to drive (GPS), the best next song (Spotify), the best news (Facebook), etc., do we lose the skill or habit of making choices for ourselves? In the context of advertising, if an AI can perfectly anticipate your desires, you might end up in a filter bubble of consumption, rarely discovering things outside your comfort zone or corporate-defined taste profile. While convenient, it’s a subtle form of behavioral conditioning, and over years it might influence personality and habits (for example, fostering more impulsive buying). Again, these are soft externalities – hard to measure immediately, but potentially significant over time.
Leading thinkers have been raising red flags on these issues. Technologists-turned-critics like Tristan Harris and Aza Raskin (of the Center for Humane Technology) speak about the need to realign technology with humanity’s best interests, not its weaknesses. Harris emphasizes that many of our institutions depend on “sovereign minds and ideas,” yet current advertising models “undermine our free will and democracy” by manipulating thoughts with little transparency (The Eyeball Economy: How Advertising Co-Opts Independent Thought - Big Think). Aza Raskin has publicly regretted inventing the infinite scroll that fuels endless engagement, noting that good intentions (“improve user experience”) can be co-opted by market forces into tools of overuse (Tech Leaders Can Do More to Avoid Unintended Consequences | WIRED) (Tech Leaders Can Do More to Avoid Unintended Consequences | WIRED). Professor David Carroll’s fight to reclaim his data from Cambridge Analytica highlights how opaque and unaccountable these systems have become – he calls data exploitation a structural problem that current laws barely address (Netflix 'the Great Hack' Professor: Privacy Rights in US Lag Behind UK - Business Insider). Meanwhile, sustainability thinkers like Nate Hagens draw connections between the attention economy and broader issues of resource over-consumption, calling advertising “the single most deleterious invention of the human species” in how it hijacks innate drives to fuel endless material growth (Transcript of EP 168 – Nate Hagens on Collective Futures - The Jim Rutt Show). Hagens notes that advertising increasingly employs evolutionary psychology triggers – essentially hacking our primal instincts (“you’ll be cool and respected if you buy this”) – to invent artificial desires (Transcript of EP 168 – Nate Hagens on Collective Futures - The Jim Rutt Show). This perpetual stimulation of consumption is fundamentally at odds with ecological sustainability and personal well-being.
It’s clear, then, that the meta-crisis of ad tech is not just about annoying ads – it’s entangled with crises of trust, democracy, public health, and environment. We have a system that optimizes for short-term clicks and profits, and externalizes a lot of long-term damage to society. AI, as implemented, has accelerated this system. If we do nothing, we likely get an even more hyperbolic version of today: immersive ads targeting you in augmented reality, deepfake influencers personalized to your profile, political echo chambers tailored by AI news curators, and a populace drowning in disinformation and consumerism while real problems (climate change, social inequality) struggle to get collective attention. It’s a grim prospect.
However, recognizing these consequences is the first step to averting them. We are not powerless. Society can renegotiate its relationship with technology and advertising. Just as past eras introduced regulations (like truth-in-advertising laws, privacy laws, antitrust actions) to rein in externalities, we can push for guardrails on AI and ad tech. But beyond regulation, there is a need for innovation in the model itself. That’s where alternative approaches – like the one we discuss next – come in. We need models that don’t pit profit against the public good, or at least mitigate that conflict. The next section explores what a different approach to digital advertising could look like, one designed for a healthier alignment between advertisers, consumers, and society.
The Case for an Alternative Model
Given the litany of issues with the status quo, it’s increasingly evident that tweaking the existing ad-tech paradigm is not enough. We should be asking more fundamental questions: Can we move away from invasive surveillance and manipulative algorithms as the default for monetizing content? Is there a way to connect businesses with audiences that builds trust instead of eroding it? In other words, what would a digital advertising model look like if it were aligned with user interests – such as privacy, trust, and genuine engagement – rather than against them?
One promising direction is to take inspiration from the oldest and most reliable form of advertising: word-of-mouth recommendations. Long before there were algorithms deciding what ad to show, humans relied on each other for recommendations on what to buy, where to eat, which services to trust. And to this day, nothing beats a personal recommendation in terms of trust. Nielsen surveys consistently show that people trust recommendations from friends and family far more than any advertisement – up to 90% trust in peer recommendations versus well under 50% for most forms of ads (Global Advertising Consumers Trust Real Friends and Virtual Strangers the Most | Nielsen). Even online consumer reviews from strangers are trusted by 70% of people, which outstrips traditional ads (Global Advertising Consumers Trust Real Friends and Virtual Strangers the Most | Nielsen). This highlights a crucial point: social trust is resilient. Despite all the technological changes, we still put stock in the opinions of those we know or communities we participate in.
So, what if we could harness that dynamic – genuine social trust – at scale, with the help of technology, instead of fighting against it with ever-more sneaky ads? Rather than micro-targeting people based on spycraft, what if businesses earned exposure by empowering satisfied customers to spread the word? This aligns incentives very differently: the business has to make a product or service worth talking about, the consumer shares it because they truly find it valuable (perhaps aided by some reward or recognition), and the recipient of the recommendation gets information filtered through someone they trust. The role of technology here would be to facilitate and amplify authentic peer-to-peer recommendations, not to manipulate or deceive.
In a sense, this is a return to basics with a high-tech twist. Before the internet, companies often asked for referrals and loyal customers often gave them voluntarily. The challenge was always scale and tracking (how do you reward word-of-mouth or even know it’s happening?). Now technology – if repurposed – can solve that, by connecting people who trust each other and allowing seamless sharing of recommendations, possibly with incentives like referral bonuses or loyalty points that strengthen the virtuous cycle.
Another key element for an alternative model is transparency and user control. In the current model, ads just appear in your feed without you asking, based on data collected invisibly. In a reimagined model, advertising interactions could be more opt-in and transparent. For example, imagine a system where you choose to follow certain brands or categories because you’re interested (like subscribing to a product newsletter, but more dynamic), and you can also see which of your friends use or endorse certain products. Now the discovery is interest-driven and socially contextual, rather than forced upon you. This flips the dynamic: instead of advertisers trying to predict what you want (and often guessing wrong or invasively), you signal what you’re open to, and trusted sources fill in the recommendations. Your data isn’t being traded on an open exchange; you’re volunteering your interests in a controlled way.
Privacy-first design would be a cornerstone. Invasive tracking wouldn’t be needed if the primary targeting criterion is “who do you trust and what do you explicitly show interest in?” This can be done with minimal data – perhaps just a friend graph and some self-declared preferences, all of which could be stored with user consent and even under user ownership (think personal data stores or decentralized networks). The system wouldn’t need to know your innermost secrets or follow you 24/7 because it’s not trying to predict your every whim; it’s leveraging the fact that your friends and communities often already know what might interest you, in a more organic way.
Crucially, an alternative model could change the quality of content in marketing. If advertisers know their success depends on real customers sharing their message, then gimmicky clickbait or high-pressure persuasion might not work – who would forward that to a friend? Instead, they’d need to create campaigns that people want to share, perhaps because they are genuinely funny, useful, or offer a real deal. This leans into co-creation and conversation rather than one-way persuasion. We move from “advertisements” to “recommendation-enhanced content” – something that carries the voice of peers.
Enter appeAR – a platform that embodies many of these principles. appeAR is introduced as a peer-to-peer AR-based advertising and loyalty model. Let’s break down what that means and how it aims to resolve the digital advertising dilemma we’ve discussed:
How appeAR Resolves the Digital Advertising Dilemma
appeAR is an upcoming solution designed with a fundamentally different philosophy: rather than treating consumer attention and data as things to exploit, it treats consumers as partners in the advertising process. At its core, appeAR leverages augmented reality (AR) technology to facilitate peer-to-peer recommendations and rewards. Here’s how this model addresses the key issues of trust, privacy, and manipulation:
In integrating AR, appeAR also positions itself for the emerging future of how we use devices. As AR glasses and experiences become more common, the line between the digital and physical shopping experience blurs. appeAR’s model would allow advertising to become situational and context-rich without being creepy. For example, standing in front of a shop, you could use appeAR to see if any of your friends have a loyalty recommendation for it, or if the shop has an AR loyalty program – all initiated by you. AR can thereby enhance in-person trust networks (like community recommendations pinned to real-world locations) in a privacy-preserving way.
By avoiding the pitfalls of traditional ad tech, appeAR may also avoid heavy regulatory and legal risks. Since it’s not trafficking in personal data without consent, it would be largely compliant with strict privacy laws (like GDPR) by design. Its emphasis on genuine content might keep it clear of the misinformation quagmire that plagues other platforms. And by rewarding users, it sidesteps the exploitative feel – users have more agency and stake.
In summary, appeAR’s approach addresses the “digital advertising dilemma” by changing the rules of the game: Trust is restored by making the medium of advertising a social one, privacy is protected by minimizing data collection and putting users in control, and manipulation is reduced because content flows through consensual, peer-driven channels rather than opaque AI curation. The loyalty and AR aspects ensure that there’s tangible value for participants and an engaging format that’s suited to the future. It represents a shift from the current paradigm of “algorithm knows best” to “people know best (with a little tech help)”. This is not just theoretical – early trials of referral-based marketing consistently show higher conversion rates and customer lifetime value, precisely because trust is higher ("People are four times more likely to buy a product when referred by ...) (Global Advertising Consumers Trust Real Friends and Virtual Strangers the Most | Nielsen). appeAR aims to take those principles and make them scalable for the digital age.
Conclusion
Artificial intelligence may be transforming many industries for the better, but in the realm of digital advertising it is not a magic fix for the industry’s deep-rooted externalities. Trust, privacy, and autonomy are not technological problems at their core – they are human and societal issues, tied to incentives and power dynamics. As we have argued, simply bolting AI onto the existing surveillance advertising model does not solve these problems. In many cases, it magnifies them: hyper-personalization without regard for consent breeds distrust; engagement optimization without ethical restraint undermines the quality of information and discourse; and an arms race for attention driven by AI leads to a poorer experience for all in the long run.
Our critical examination has shown that the fundamental externalities of digital advertising – erosion of trust, privacy concerns, algorithmic manipulation – are interlocking and systemic. They originate from a business model that treats human attention as the resource to be mined and sold, and personal data as the fuel to do so. AI, as currently deployed, has been harnessed to turbocharge this model, making it more efficient but not more humane. As a result, we face a meta-crisis: an information ecosystem where truth is harder to discern, users feel disempowered and spied upon, and societal polarization is exacerbated. These are costs that far exceed the immediate gains of a well-targeted ad.
It is increasingly clear that AI will not be the savior of digital advertising’s reputation. The fix must come from rethinking the model itself. We need to shift from a surveillance-centric, advertiser-knows-best framework to a user-centric, trust-based framework. In practical terms, that means developing and supporting systems where users have more control, more choice, and more benefit from marketing activities, and where businesses compete to be worthy of consumers’ attention rather than find ever-more sneaky ways to seize it.
The alternative model we explored, exemplified by appeAR, offers a hopeful path. By realigning advertising with social trust and genuine engagement, it points toward a future where advertising is not a dirty word but an integral, even appreciated part of the digital experience. In such a future, you might actually look forward to a friend’s recommendation popping up in AR when you’re shopping, the same way one might look forward to hearing a friend’s movie review – because it’s relevant, timely, and respectful of your agency. That is a far cry from today’s norm of dreading the next intrusive pop-up or feeling cynical about every sponsored post.
For businesses and marketers, this shift entails a change in mindset: from focusing on short-term metrics driven by black-box algorithms to building long-term relationships with customers. The success of campaigns would hinge not on how cleverly you targeted an unsuspecting user, but on how well you empowered your champions – your real customers – to spread the word. This might sound like a step backward to some quantitative, AI-obsessed marketers, but in reality it is harnessing the most powerful marketing force (human advocacy) with modern tools. It’s a sustainable approach because it runs on trust, a renewable resource, rather than attention, which, when over-taxed, only leads to burnout and avoidance.
To be sure, no solution is perfect or foolproof. Even a platform like appeAR will have to guard against abuse (spammy referrals, fraudulent reviews, etc.), and it will need to reach critical mass to truly compete with incumbent ad channels. But the key difference is in values and incentives: its success depends on aligning with users, not wearing them down. That is a foundation one can build on, iterate, and improve without the inherent conflict of interest that plagues current ad-tech.
In conclusion, the future of digital advertising – if it is to be sustainable – lies in earning attention rather than extracting it, in fostering transparency rather than obfuscation, and in strengthening community rather than exploiting individual vulnerabilities. AI has a role to play in that future, but as a tool subservient to humane goals, not as an autonomous driver of engagement-at-any-cost. We must remember that technology should serve people, not the other way around.
Digital advertising does not have to remain locked in a negative-sum game of cat-and-mouse with consumers. By embracing models that prioritize privacy, trust, and user benefit – as appeAR does – the industry can reconstitute a healthier social contract. Advertisers can reach audiences who actually welcome them, consumers can regain a sense of agency and respect, and the digital environment as a whole can become less antagonistic. This is not just wishful thinking; it’s a necessary evolution. The writing is on the wall for the old ways – ad blockers, regulatory crackdowns on data harvesting, and public backlash all signal that change is coming. The only question is whether the change will be forced reluctantly or embraced proactively.
As leaders, businesses, and policymakers, we should seize this moment to rethink and redesign digital advertising. Let’s shift the goal from exploiting attention with AI to deserving attention through innovation and integrity. In doing so, we can ensure that the internet – arguably humanity’s most important platform for knowledge and connection – evolves in a direction that supports our trust, safeguards our privacy, and enhances our autonomy. That, ultimately, will benefit not just consumers but all stakeholders, including the brands that rely on open-eyed consumer engagement. The sooner we start on this journey, the sooner we render obsolete the false promise that “AI will fix advertising,” and replace it with the truth that only a values-driven paradigm shift will resolve the externalities of digital advertising.
References (Chicago Style):