The New Economics Of Attention

The Simon Framework: How the Attention Economy Was Built

In 1971, Herbert Simon wrote the sentence that became the conceptual foundation of everything we now call the attention economy: "A wealth of information creates a poverty of attention." The insight was deceptively simple: as information supply increases, the resource that becomes scarce is not information but the attention needed to process it. When anything can be abundant, the complement — the thing needed to make use of the abundant thing — becomes the new scarce resource.

Simon was writing about organizational information systems in 1971, but his insight perfectly described what would happen to the media economy three decades later when the internet made information distribution effectively free. Before the internet, the scarce resource in media was distribution — it was expensive to print and distribute a newspaper, expensive to build a broadcast tower, expensive to reach large audiences with any information. The media companies that controlled distribution infrastructure controlled the scarce resource and captured the economic value.

The internet commoditized distribution. Anyone with a server could reach a global audience. The economic value shifted, as Simon's framework predicted, from the now-abundant distribution to the newly scarce resource: attention. The platform companies — Facebook, Google, YouTube, Twitter — built trillion-dollar businesses by aggregating attention and selling access to it. The entire creator economy, the influencer economy, the media startup ecosystem of the 2010s — all of it was built on the premise that capturing attention was the prize and the business model was to aggregate and monetize it.

The attention economy spawned its own sophisticated body of practice: growth hacking, content marketing, SEO, social media optimization, viral mechanics, engagement algorithms, persuasive design, notification psychology. Every discipline was organized around the same core objective: capturing a larger share of a fixed pool of human attention. The assumption was that the attention pool was fixed — human cognitive bandwidth is indeed biologically bounded — and that the competition was for share of that fixed pool.

This framework produced remarkable economic outcomes but also significant social pathologies: the optimization for engagement over truth, the weaponization of outrage and fear as attention-capture mechanisms, the homogenization of content formats around what the algorithms rewarded, the degradation of nuanced discourse into whatever generated the fastest dopaminergic response. These were not bugs — they were the logical outcomes of optimizing for a single objective (attention capture) in a competitive market.

What AI Breaks in Simon's Framework

Simon's framework rested on one assumption that seemed so obviously true it was never stated as an assumption: that content production required human cognitive labor, which is itself scarce and expensive. If a human can write a thousand words per hour, and there are only so many humans, then the total content production capacity of the information ecosystem has a biological ceiling. That ceiling was high — billions of humans communicating daily produce enormous volumes — but it was a ceiling.

AI removes the ceiling. The total content generation capacity of an AI-equipped ecosystem is bounded only by compute, which is approaching zero cost per unit of output. In practical terms, this means the content supply curve has become effectively vertical — it can supply any quantity demanded without hitting a resource constraint. This is not an incremental change to Simon's framework. It is the elimination of the constraint on which the framework was built.

When content supply is infinite, Simon's prediction inverts. He said "a wealth of information creates a poverty of attention" — but he assumed that the wealth of information was finite, just larger than the attention pool. When information supply is truly infinite — when the internet fills with orders of magnitude more content than any human could encounter in any lifetime — the attention economy does not just become more competitive. It undergoes a phase transition. The competition is no longer for share of a fixed attention pool. It is for whether any given piece of content gets encountered at all in a sea so vast that most content will never be seen by a single human eye.

This changes everything about the economics. In the finite-supply attention economy, the game was to optimize content for the algorithms that controlled distribution. In the infinite-supply attention economy, the game is to be the content that humans deliberately seek out — not the content the algorithm pushes at them. Push distribution versus pull distribution. Algorithmic reach versus deliberate discovery. The passive scroll versus the intentional search.

What Is the New Scarcity?

The new scarcity is verified authenticity. In a world where any content can be generated at zero cost, and where synthetic content is indistinguishable from authentic content at the surface level, the provenance of content — the verifiable chain of authority linking content to a trusted identity — becomes the primary determinant of whether an audience chooses to engage with it.

This is a profound reversal of the dynamics that governed the old attention economy. In the old attention economy, the game was to produce content that matched the algorithm's preferences — which usually meant maximizing surface engagement signals regardless of intellectual quality. In the new attention economy, the game is to produce content that audiences actively trust and seek out — which requires intellectual substance, identity coherence, and verified provenance, not algorithmic optimization.

The strategic implication is stark: the entire apparatus of "content marketing" — keyword optimization, engagement hacking, format optimization for platform algorithms, viral mechanic engineering — becomes less effective as the supply explosion makes it impossible to stand out through these tactics. What becomes more effective is everything that was previously too slow and too hard: building deep topical authority over years, establishing verified identity infrastructure, producing intellectual content that is genuinely original rather than algorithmically optimized.

The Attention Economics Shift

Trust as the New Currency

If attention was the currency of the old economy, trust is the currency of the new one. The distinction is more than semantic. Attention is passive — you can capture it without the captured party actively choosing you, through algorithmic insertion or notification manipulation. Trust is active — it requires the audience to have made a prior judgment that your signal is worth their ongoing engagement. Attention can be bought. Trust must be earned.

This shift changes the competitive dynamics fundamentally. In the attention economy, the best strategy was to interrupt people's information consumption with your content — to insert yourself into their feed, their search results, their notifications — whether or not they would have chosen to consume your content voluntarily. The algorithmic platforms built entire business models around this interruptive model. In the trust economy, the interruptive model fails. Audiences with access to infinite alternative content have zero patience for content from sources they have not actively chosen to trust. Interruption becomes a trust-destruction mechanism.

The trust economy rewards pull over push. It rewards the founder who has built a reputation over years such that their audience actively seeks their new output, rather than encountering it through algorithmic insertion. It rewards depth over format optimization. It rewards consistency of intellectual character over viral moment engineering. All of these qualities — the deep reputation, the active audience seeking, the depth of intellectual substance — are the outputs of identity infrastructure rather than content marketing.

The Curation Premium

In an infinite content supply environment, curation — the selection, synthesis, and contextual framing of information — becomes one of the highest-value activities in the information economy. Not curation in the reductive sense of sharing links, but genuine intellectual curation: the act of a trusted, expert intelligence filtering the noise of infinite content supply and delivering only the signal that genuinely matters to a specific audience.

This is one of the most powerful use cases for AI identity infrastructure that most people have not yet recognized. An AI system trained on a founder's deep domain expertise and epistemic frameworks can perform expert curation at scale — synthesizing the relevant developments in a domain, filtering them through the founder's analytical framework, and delivering a curated perspective to an audience that trusts the founder's judgment. This is more valuable than original content production in contexts where the audience is overwhelmed with information and needs an expert filter more than another piece of content.

The curation premium is not limited to content audiences. LLMs themselves are fundamentally curation engines — they synthesize and select information from their training data in response to queries. A founder with a rich representation in LLM training data becomes an implicit curator for every person who asks an AI system a question in their domain. The AI's synthesis is influenced by the quality and perspective of the sources it was trained on. The founders with the deepest, most coherent representation become the hidden curators for millions of AI-mediated information interactions.

Platform Collapse and the De-intermediation of Attention

The platform companies that captured the attention economy are facing the same structural force that disrupted every industry they disrupted: their own model has been disrupted by a technology that eliminates their core competitive advantage. The advantage of the major social platforms was their algorithmic curation — they were better than any individual at identifying content their users would engage with. As AI makes every operator capable of producing infinite context-optimized content, the platform's algorithmic advantage erodes because the content supply they're curating becomes too homogeneous and too vast to differentiate meaningfully.

The more significant disruption is the LLM's replacement of the platform as the primary discovery mechanism for information. When people ask an AI assistant for recommendations, they are bypassing the platform's distribution infrastructure entirely. The AI synthesizes and recommends based on its training data, not on the platform's engagement metrics. This is not a marginal shift — it is the beginning of a fundamental de-intermediation of attention. The platforms sat between content producers and audiences. LLMs are beginning to operate as a direct channel, synthesizing expert knowledge for the audience without requiring the platform as intermediary.

The business model implications for platforms are severe. The advertising model depends on aggregating attention and allowing advertisers to interrupt it. If discovery moves to LLM-mediated recommendation, the platform loses the attention aggregation that advertising depends on. The platforms are aware of this — hence the massive AI investments by every major platform — but they are in the innovator's dilemma position, unable to fully embrace the technology that would cannibalize their core business.

New Attention Flow Architecture

The Depth Premium: Why Intellectual Substance Wins

In the new attention economics, the depth premium is the key concept for understanding where value accrues. The depth premium is the surplus economic value captured by content that demonstrates genuine intellectual depth over content that is algorithmically optimized but intellectually shallow.

The depth premium exists because of the trust economy dynamic: when audiences have access to infinite content, they allocate their finite attention to sources they have specifically chosen to trust. They choose to trust sources that have demonstrated — over time, through consistent high-quality output — that their perspective is worth the attention investment. This demonstration of trust-worthiness requires genuine intellectual substance. A source that consistently delivers shallow, generic content, regardless of how algorithmically optimized it is, does not accumulate the trust capital required to capture the depth premium.

The depth premium manifests in several measurable ways: higher willingness to pay for premium offerings from trusted sources; higher quality of inbound opportunities (partnerships, investments, speaking) that arrive because the source is recognized as authoritative; higher LLM citation frequency because the training data weighting favors semantically rich, original content over generic output; and higher audience retention and lower churn because trust-based relationships are stickier than algorithm-driven engagement.

The operational implication is that the most productive investment a founder can make in the new attention economy is in the intellectual substance of their output, not in its format optimization or distribution volume. Writing one genuinely original, deeply argued essay that changes how readers think about a problem generates more depth premium than writing fifty well-formatted posts that confirm existing beliefs in algorithmically pleasing formats.

Predictions: The Attention Economy in 2030

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