Attention Economy

Definition

The Attention Economy refers to a concept where attention is treated as a scarce and valuable resource, much like money or time. In this framework, various entities—such as companies, advertisers, social media platforms, and content creators—compete to capture and retain people's attention.

Context

Herbert A. Simon, psychologist and economist, introduced the concept of the attention economy in 1971. He observed that in an information-rich world, attention becomes a scarce resource. Simon famously stated:

"A wealth of information creates a poverty of attention."

This idea highlights the challenge of managing human attention amidst the overwhelming abundance of information. Simon's insights laid the foundation for understanding how attention is allocated and valued, especially in the digital age.

This concept has gained prominence with the rise of the internet and digital technologies, where our limited attention spans are constantly being divided among endless streams of notifications, advertisements, and content. Platforms often use algorithms, clickbait, and personalization to maximize engagement because attention drives revenue, whether through ad impressions, subscriptions, or sales.

While the attention economy fuels innovation and connectivity, it also raises concerns about distraction, information overload, and how much control users truly have over their time. The debate continues around how to balance these dynamics for societal well-being.

In the mid-1980s Mr. Michael Goldhaber, a former theoretical physicist, had a revelation. He was obsessed at the time with what he felt was an information glut — that there was simply more access to news, opinion and forms of entertainment than one could handle. His epiphany was this: One of the most finite resources in the world is human attention. To describe its scarcity, he latched onto what was then an obscure term, coined by a psychologist, Herbert A. Simon: “the attention economy.”

Simon is the same person that wrote the first Artificial Intelligence program with Newell and Shaw. That article was reprinted in Computers And Thought the first source material by Feigenbaum and Feldman and used in Marvin Minksky's first college course on Artificial Intelligence and Heuristic Programming.

Solutions

Agent2Agent Protocol (A2A)

https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/

A2A is an open protocol that complements Anthropic's Model Context Protocol (MCP), which provides helpful tools and context to agents. Drawing on Google's internal expertise in scaling agentic systems, we designed the A2A protocol to address the challenges we identified in deploying large-scale, multi-agent systems for our customers. A2A empowers developers to build agents capable of connecting with any other agent built using the protocol and offers users the flexibility to combine agents from various providers. Critically, businesses benefit from a standardized method for managing their agents across diverse platforms and cloud environments. We believe this universal interoperability is essential for fully realizing the potential of collaborative AI agents.

• The biggest AI privacy problems no one is talking about: Installment 1: The Agent2Agent (“A2A”) Protocol
• See wiki Model Context Protocol(MCP) for more on A2A solution