What the Attention Economy Theorists Never Had to Consider: Algorithmic, Multi-Agent Attention

What the classic theorists assumed

Herbert Simon's 1971 theorem, that a wealth of information creates a poverty of attention, was written for human organizational designers coping with the first wave of computerized information. Michael Goldhaber's 1997 extension to the internet and Georg Franck's 1998 formalization of attention as a currency both kept the same assumption: a human mind is the thing whose attention is scarce, and that mind directly encounters the information competing for it.

That assumption held reasonably well for fifty years, through print, broadcast, search and social media. In every one of those environments, a human being was the entity doing the attending, even when an algorithm shaped what that human saw. A search-ranking algorithm decided the order of a list; a person still read it and chose.

The new premise: something else is doing some of the attending

What changes with an AI agent, an assistant that researches, summarizes or answers on a person's behalf, is that the entity encountering the raw information is no longer only the human. An AI chatbot reads across many sources, synthesizes them, and returns a shortlist or a single answer. The human still exercises the final choice, but the intermediary step, actually attending to the underlying sources, has moved to a machine.

This is a real and measurable shift in kind, not degree. A ranking algorithm still required a human to scan the list. A generative answer engine can substitute its own synthesis for that scan entirely. The Reuters Institute's 2026 finding that weekly AI-chatbot use for news grew from 7 percent to 10 percent in a single year is a modest but genuine, disclosed-methodology signal that this substitution is already underway, not a hypothetical.

Applying the classic scarcity logic, carefully

It is tempting to simply relabel the AI agent as the new scarce resource and move on. That would overreach. Simon's theorem was precise about what was scarce: the attention of a human recipient, bounded by cognitive limits. An AI agent does not have a fixed cognitive budget in the same sense; its capacity to process sources is closer to an engineering constraint, compute and context length, than a human perceptual limit.

What can be stated on firmer ground is narrower: whatever budget of trust a human extends to letting an agent select on their behalf is itself a scarce, allocable thing, in the spirit of Goldhaber's and Franck's currency framing. A person who lets an assistant recommend a business is spending a different, and arguably higher-stakes, kind of attention than one who scans a list themselves. That reframing extends the classic logic; it does not simply repeat it.

What "exposure" means when an agent chooses for someone

Under the classic model, exposure meant a human saw a listing, an ad, or a link. Under an agentic model, exposure can happen entirely inside a synthesis step the human never directly observes: a business can be read by an agent, weighed, and excluded from the final answer, with the human never knowing it existed as an option. That is a genuinely different failure mode from ranking eleventh on a results page. It is being invisible to the entity doing the selecting, not merely deprioritized by it.

This is the core of the new territory: exposure has stopped being a purely human perceptual event and has partly become a machine retrieval-and-synthesis event, gated by whether an agent can identify, trust and cite a source at all, before a human attention budget is ever spent on the result.

Where this is theory and where it is not

Everything above about human attention scarcity is established: Simon's theorem, Goldhaber's and Franck's currency framing, and the documented lineage through Davenport, Beck, Webster and Wu all rest on decades of published, peer-reviewed or widely cited work. The claim that AI agents constitute a genuinely new class of attention intermediary, and that "exposure" now has a machine-mediated component, is this publication's own synthesis, built on top of that established base and on the measured 2026 growth in AI-chatbot use. It should be read as a proposed framework, not as settled academic consensus, because no peer-reviewed literature located in the underlying research directly models multi-agent attention allocation yet.

What agent-discoverability requires in practice

If an AI agent is doing part of the attending before a human ever sees a shortlist, then being discoverable to that agent becomes a precondition for being discoverable to the person it serves. In practice that means the same underlying requirements that make a business legible to any answer engine: a single, consistent, machine-resolvable identity across the web, structured data that lets a system parse what a business actually offers, and content built to be extracted and quoted rather than merely read. None of that guarantees an agent selects a given business; no engine's selection process is documented in enough detail to promise that.