The Long View: The Attention Economy Is Dead. What Comes Next?

The Currency That Built the Internet

In 1971, the economist and cognitive scientist Herbert Simon wrote that an information-rich world creates a poverty of something else: the attention of the people who must process that information. What information consumes, he observed, is the attention of its recipients. A wealth of information creates a poverty of attention.

Simon was writing about bureaucratic decision-making, but he had described the fundamental economic principle that would organize the internet for the next half-century. If information is abundant and attention is scarce, then attention becomes the valuable resource — the thing that can be bought, sold, measured, and optimized. This insight, developed further by researchers and eventually operationalized by companies like Google and Facebook, became the foundation of the attention economy: the multi-trillion-dollar system in which digital platforms compete to capture human attention and sell access to it to advertisers.

The attention economy shaped the internet as we know it. It determined which businesses succeeded (those that captured attention at scale), which content proliferated (content that maximized engagement), which design patterns dominated (infinite scroll, notification systems, algorithmic feeds), and how the digital experience felt (addictive, overwhelming, impossible to ignore). For better and for worse, the attention economy was the organizing principle of digital life.

That principle is breaking down. Not because attention has become less scarce — it has not — but because a new intermediary has inserted itself between the information and the human. AI systems now filter, summarize, prioritize, and in many cases act on information without requiring human attention at all. The person who once had to scan headlines, read emails, compare products, and navigate websites now has an AI assistant that does these things on their behalf.

If attention was the scarce resource that organized the digital economy, what organizes an economy where AI handles much of the information processing that previously required human attention? What becomes scarce when attention is delegated?

The answer to this question will reshape the internet, the advertising industry, the media business, and the relationship between humans and information. It is the most important structural question in the digital economy, and we are only beginning to understand it.

How the Attention Economy Worked

To understand what comes after the attention economy, we need to understand how it functioned and why it was so durable.

The Fundamental Transaction

The attention economy was built on a simple transaction: platforms provided free content and services to users, and users paid with their attention. That attention was then sold to advertisers, who paid the platform for the opportunity to place their messages in front of an attentive audience.

This transaction was enormously efficient. Users got access to search engines, social networks, email, news, video, and countless other services without paying cash. Advertisers got access to precisely targeted audiences at measurable cost. Platforms earned revenue that funded continuous improvement of their services, attracting more users and more attention in a self-reinforcing cycle.

Google’s annual advertising revenue exceeds $200 billion. Meta’s exceeds $130 billion. The digital advertising market as a whole generates hundreds of billions of dollars annually. This is the scale of the economic system built on human attention.

The Optimization Machine

What made the attention economy so powerful was not the basic transaction but the optimization apparatus built around it. Digital platforms did not simply display advertisements to whoever happened to visit. They built increasingly sophisticated systems for measuring, predicting, and maximizing human attention.

Every click, scroll, pause, and interaction was tracked and analyzed. Algorithms learned which content held attention longest, which notifications brought users back most reliably, which feed positions generated the most engagement. The entire user experience was optimized not for the user’s benefit but for attention capture.

This optimization produced the familiar features of modern digital life: feeds that never end, notifications that create urgency, content that provokes emotional reactions, interfaces designed to be compulsive rather than satisfying. These were not accidents or failures. They were the rational output of systems designed to maximize a specific metric — human attention — because that metric was the source of revenue.

The Content Ecosystem

The attention economy also shaped what content existed and how it was distributed. Content creators — journalists, bloggers, video makers, musicians — operated in a system where attention was the primary currency. Content that attracted attention was amplified by algorithms and rewarded with distribution, advertising revenue, or both. Content that did not attract attention was invisible.

This created powerful incentives for attention-maximizing content: sensational headlines, emotional appeals, controversy, novelty, and outrage. These incentives were not universally followed — excellent, substantive content continued to be produced — but they shaped the competitive landscape in ways that favored engagement over accuracy, reaction over reflection, and volume over depth.

The media industry was particularly affected. News organizations that had historically been supported by subscription revenue and classified advertising found themselves competing in an attention market dominated by social media platforms. The economic logic pushed toward shorter articles, more provocative framing, and content designed to perform well in algorithmic feeds. Organizations that resisted these pressures often struggled financially.

Why AI Breaks the Model

AI does not simply participate in the attention economy as another player. It disrupts the model’s foundational assumption: that information must be processed by human attention to create value. When AI can process information on a human’s behalf, the entire economic chain — from content creation to attention capture to advertising — is interrupted.

The Intermediary Effect

Consider a routine information task: comparing products before making a purchase. In the attention economy, this task might involve visiting multiple websites, reading reviews, comparing specifications, scanning prices, and evaluating options. At each step, the user’s attention is available to be captured by advertisements, cross-promotions, and engagement tactics. The user’s attention is the product being sold.

When an AI assistant handles this task, it queries product databases, summarizes reviews, compares specifications, and presents a recommendation — all without the user visiting any websites, seeing any advertisements, or giving attention to any platform. The information processing happens, but the human attention that the attention economy monetizes is absent.

This is not a hypothetical scenario. It is an increasingly common one. AI assistants are already handling product research, travel planning, news summarization, email triage, schedule optimization, and dozens of other information-processing tasks that previously required human attention on digital platforms. Each task that an AI handles on a user’s behalf is attention that the attention economy loses.

The Search Disruption

The most immediate and consequential disruption is to search advertising, which has been the single largest revenue stream in the attention economy. Google’s dominance was built on being the starting point for information-seeking behavior. When a person needed to find something, they searched on Google, and Google sold access to that moment of intent to advertisers.

AI assistants are replacing search for an expanding range of queries. Rather than typing a question into Google, scanning the results, clicking through to websites, and finding the answer, a user can ask an AI assistant and receive a direct answer. The information need is met, but the chain of attention — search result, click, page view, advertisement impression — is broken.

Google recognizes this threat. Its integration of AI-generated answers directly into search results is an attempt to retain users who might otherwise bypass search entirely. But the fundamental problem remains: an AI-generated answer that directly resolves a query eliminates the need for the user to visit the websites that search results link to, which eliminates the page views and advertisement impressions that those websites depend on.

The Feed Disruption

Social media feeds — the second major engine of the attention economy — face a parallel disruption. Feeds are designed to capture and hold attention through a continuous stream of content. The value of the feed to advertisers depends on users scrolling through it, pausing on content, and being exposed to advertisements interspersed among posts.

AI intermediaries disrupt this by summarizing social content without requiring the user to engage with the feed. An AI assistant that monitors a user’s social networks and surfaces the most relevant updates, conversations, and content eliminates the need to scroll. The user gets the information value of the social network without providing the attention that the platform monetizes.

This disruption is earlier-stage than the search disruption, but the direction is the same. As AI systems become better at understanding social content, identifying what matters to a specific user, and presenting it in summarized form, the case for spending time in a feed weakens.

The Content Consumption Disruption

More broadly, AI is changing how people consume content. A long article that took ten minutes to read can be summarized in seconds. A podcast that takes an hour to listen to can be distilled to its key points. A video that demands twenty minutes of attention can be reduced to a transcript and highlights.

Each of these AI-mediated interactions extracts information value while minimizing attention expenditure. The content creator’s work is consumed, but the consumption happens through an intermediary that strips out the attention component. The reader who would have spent ten minutes on a well-crafted article — ten minutes during which advertisements could be displayed, engagement could be measured, and attention could be monetized — now spends thirty seconds reviewing an AI summary.

What Becomes Scarce

If attention is being partially automated — delegated to AI intermediaries that handle information processing on behalf of humans — then the attention economy’s organizing principle weakens. But the economy does not stop. It reorganizes around whatever resource is newly scarce. Understanding what becomes scarce in an AI-intermediated world is the key to understanding what comes next.

Trust

In a world where AI can generate, summarize, and filter information, the question shifts from “can I find this information?” to “can I trust this information?” The abundance of AI-generated content makes trust scarcer and more valuable, not less.

This is already visible in how people relate to information. As AI-generated text, images, and video become ubiquitous, the provenance of information — where it came from, who created it, how it was verified — becomes more important than its availability. A piece of information from a trusted source is worth more than the same information from an unknown source, because the cost of verifying untrusted information is high.

Trust was always valuable, but in the attention economy it was secondary to engagement. A sensational headline from an unknown source could capture attention as effectively as a carefully reported story from a credible publication. In a post-attention economy, where AI intermediaries evaluate information quality on behalf of users, trust becomes a primary filter. The AI assistant that recommends sources must make judgments about reliability, and those judgments favor trusted sources over merely attention-grabbing ones.

This has significant implications for media and publishing. Organizations that have invested in credibility — thorough reporting, editorial standards, factual accuracy, corrections when wrong — have an asset that becomes more valuable as trust becomes scarcer. Organizations that optimized for attention at the expense of credibility may find that their distribution channels narrow as AI intermediaries deprioritize unreliable sources.

Judgment

When AI handles the information-gathering and processing stages of a decision, the human contribution shifts to the decision itself — the application of values, priorities, and judgment to the processed information. The AI can tell you which restaurants match your dietary requirements and budget. It cannot tell you which one you will enjoy the most, because that depends on preferences and contexts that are ultimately subjective.

Judgment — the capacity to make decisions that reflect human values, context, and priorities — becomes scarcer in relative terms as the information-processing work that surrounds judgment is automated. This is the skill that becomes more valuable: not finding information, not processing data, not even analyzing options, but making the final call in situations where the right answer depends on what “right” means to a specific person or organization.

Taste

Related to judgment but distinct from it is taste — the ability to discern quality, beauty, appropriateness, and resonance. In a world where AI can generate virtually any content, the ability to distinguish excellent content from merely adequate content becomes more valuable. When anyone can produce a passable piece of writing, a functional design, or a competent piece of music, the premium for work that transcends adequacy increases.

Taste is fundamentally human and stubbornly resistant to automation. AI can learn patterns of what humans have historically valued, but it cannot independently develop new aesthetic sensibilities or recognize emerging cultural shifts before they become legible in data. The curator, the editor, the designer, the critic — people whose primary value is the exercise of taste — may find their roles enhanced rather than diminished in an AI-rich environment.

Authentic Experience

If AI can summarize a book, describe a meal, or recount a journey, the information content of these experiences is accessible without the experience itself. But the experience is not reducible to its information content. Reading a book, eating a meal, and traveling to a new place have value beyond the facts they convey — they provide sensory experience, emotional resonance, social connection, and personal meaning.

As AI handles more of the information layer of life, the experiential layer becomes more distinctly valuable. The restaurant that provides an extraordinary dining experience is differentiated not by the information about its food (which AI can summarize) but by the experience itself (which AI cannot replicate). The live concert, the in-person conversation, the physical journey — experiences that require human presence and attention — become scarcer and more valuable precisely because AI makes their informational substitutes so cheap.

The New Economics

If the scarce resources are trust, judgment, taste, and authentic experience, the economic models built around attention must evolve. Several shifts are already visible.

From Advertising to Recommendations

The attention economy’s primary revenue model — advertising — depends on capturing human attention and displaying messages to it. In a world where AI intermediaries handle much of the information processing, advertising as traditionally practiced becomes less effective. The human who never visits a website cannot see the ads on that website. The human whose email is summarized by an AI assistant does not see the promotional messages in their inbox.

This does not mean that commercial communication ceases. It means that it shifts from display advertising (capturing attention to show a message) to recommendation integration (having the AI intermediary include the product or service in its recommendations). The question changes from “how do I get this person to look at my ad?” to “how do I get this person’s AI assistant to recommend my product?”

This shift has enormous implications for the advertising industry. The skills, technologies, and relationships that drive display advertising — creative design, media buying, impression measurement, click-through optimization — are less relevant in a recommendation-driven world. What matters instead is product quality, data availability, and the trustworthiness of the information that AI systems use to make recommendations.

From Engagement to Utility

Platforms that survived on engagement metrics — time on site, scroll depth, interactions per session — must redefine their value proposition when AI intermediaries reduce direct user engagement. The platform that provides the most useful data and services to AI systems may capture more value than the platform that captures the most human attention.

This inversion favors platforms with structured, high-quality data (which AI systems can process efficiently) over platforms with engagement-optimized content (which AI systems may summarize or bypass). A platform like Wikipedia, which organizes factual information in a structured, reliable format, may become more economically valuable relative to platforms that optimize for attention rather than accuracy.

From Scale to Credibility

The attention economy rewarded scale above all. The platform with the most users had the most attention, which attracted the most advertisers, which funded further growth. This dynamic produced winner-take-all markets where a small number of dominant platforms captured the vast majority of advertising revenue.

In a trust-centered economy, credibility matters as much as scale. A smaller publication with a strong reputation for accuracy and expertise may capture more value per reader than a larger publication with a weaker reputation, because AI intermediaries weight trustworthiness when selecting sources. This could partially reverse the concentration dynamic of the attention economy, creating space for specialized, high-credibility information providers that could not compete on scale but can compete on trust.

The Transition Costs

The shift from an attention economy to whatever comes next will not be smooth. The existing system is deeply entrenched, and the interests built around it are powerful.

The Media Reckoning

News organizations have already endured two decades of economic disruption as the attention economy shifted advertising revenue from publishers to platforms. The AI transition threatens to accelerate this disruption by further reducing direct engagement with news content. If readers get their news through AI summaries rather than visiting news websites, the already-struggling advertising model of digital journalism deteriorates further.

The potential counterbalance is that trust becomes more economically valuable. Organizations that produce reliable, original reporting may find new revenue models in a trust-centered economy — licensing their content to AI systems, providing verified information feeds, or building direct subscriber relationships based on credibility rather than engagement. But this transition requires surviving the gap between the old model’s decline and the new model’s emergence, and not every organization will make it through.

The Platform Adaptation

The major technology platforms — Google, Meta, Amazon, Apple, Microsoft — are not passive victims of this transition. They are actively adapting, and their adaptation will shape how the post-attention economy develops.

Google is integrating AI directly into search, attempting to maintain its position as the starting point for information-seeking behavior even as the nature of that behavior changes. Meta is building AI assistants into its social platforms. Amazon is using AI to enhance its product recommendation systems. Apple is positioning AI as a privacy-respecting intermediary integrated into its devices. Microsoft is embedding AI across its productivity and enterprise offerings.

Each of these strategies is an attempt to control the AI intermediary layer — to be the system that filters, summarizes, and acts on information on the user’s behalf. The company that controls this layer inherits the attention economy’s structural position: it sits between users and information, and it can monetize that position.

The Advertising Industry Transformation

The advertising industry, which has organized itself around attention capture for over a century, faces a fundamental restructuring. The skills, tools, and strategies of attention-based advertising — creative campaigns designed to capture notice, media buying optimized for impressions, engagement metrics as performance indicators — are less relevant in a world where AI intermediaries make many consumption decisions.

The advertising industry will not disappear, but it will transform. The new discipline might be called “recommendation optimization” or “AI intermediary marketing” — the practice of ensuring that AI systems recommend your product when users express relevant needs. This is more like search engine optimization than like traditional advertising, and it rewards different capabilities: data quality, product credibility, integration with AI recommendation systems, and the kind of authentic brand reputation that AI systems can verify.

The Long-Term Equilibrium

Predicting the long-term equilibrium of a post-attention economy is speculative, but several structural features are likely to persist.

The Attention Tax Persists, Diminished

Human attention will not cease to be valuable. There will remain experiences, decisions, and interactions that require direct human engagement. High-stakes purchases, entertainment, social connection, creative consumption, and novel experiences will continue to command human attention. The attention economy does not die. It shrinks and becomes one component of a more complex economic ecosystem rather than the dominant organizing principle.

The Intermediary Layer Becomes Critical Infrastructure

The AI systems that mediate between humans and information will become infrastructure as essential as the internet itself. How these systems are designed, governed, and regulated will have enormous consequences for what information people receive, what products they buy, what news they consume, and how they understand the world. The concentration of power in this intermediary layer is a legitimate concern that will require regulatory attention.

Trust Becomes Quantifiable

In the attention economy, attention was measured with precision — impressions, clicks, time on page, scroll depth. In the emerging economy, trust will need to be similarly quantified. Systems for measuring and verifying the credibility of information sources, the reliability of product claims, and the trustworthiness of service providers will become essential infrastructure. Organizations that establish trusted positions in these systems will have durable competitive advantages.

The Human Premium Increases

As AI handles more routine information processing, the activities that are distinctly and irreducibly human — creative judgment, aesthetic sensibility, emotional connection, ethical reasoning, physical experience — become more economically distinctive. The premium for genuinely human contribution increases, not because humans become more capable, but because the contrast with AI-mediated alternatives makes the human quality more visible and more valued.

Conclusion

The attention economy was never a law of nature. It was a specific arrangement that emerged because information became abundant while human attention remained scarce, and because the technology existed to measure and monetize that scarcity. It lasted because it was self-reinforcing: attention funded platforms that captured more attention.

AI intermediaries break this self-reinforcing cycle by processing information without requiring human attention. This does not eliminate human attention as a valuable resource, but it detaches information processing from attention in ways that undermine the economic model built on their linkage.

What comes next is not fully formed. The outlines are visible — trust, judgment, taste, and experience as the new scarce resources — but the specific business models, institutional structures, and cultural norms of the post-attention economy are still emerging.

What is clear is that the transition is underway. The internet built on capturing human attention is being intermediated by AI systems that substitute for that attention. The companies, creators, and institutions that recognize this shift and orient toward the new scarcities — building trust, exercising judgment, cultivating taste, and creating experiences that no algorithm can replicate — will be positioned for the economy that follows.

Herbert Simon identified the fundamental dynamic in 1971: a wealth of information creates a poverty of something else. For fifty years, the answer was attention. The answer is changing. The organizations that figure out the new answer first will shape the next era of the digital economy.