How the Attention Economy Reshaped Information
My day started normally: I woke up, splashed cold water on my face, and had coffee while trying to get the kids out the door. I sat down at my desk and went through my morning digital routine: email, Slack messages, LinkedIn notifications, Instagram notifications…
Hang on, my friend sent me a reel about the America’s Next Top Model docuseries. Hey, how is it 9:30 already? Wait where am I in my routine?
Ironically, what was next was to work on this blog post on the attention economy, a buzzword for a marketplace in which human attention is the main commodity.
If attention is the currency of this new economy, we are certainly trading in pennies and dimes. The content supply chain has exploded, giving creators a fraction of a second to grab any single user’s attention. In other words, in a world saturated with information, the competition isn’t about producing knowledge anymore. It’s about capturing focus.
The attention economy’s ripple effect is impacting traditional market shareholders in a variety of ways.
The Distributors - Platforms that control attention flows
Once attention became scarce, it became monetizable. Platforms learned to treat attention the same way earlier industries treated oil, land, or labor: something to extract, refine, and sell.
Social media feeds, recommendation algorithms, and targeted advertising systems are designed to maximize the time people spend looking, scrolling, and reacting. That’s why platforms don’t simply host information - they engineer engagement.
The Producers - Organizations and creators generating content
Digital platforms allow anyone to publish instantly, at near-zero cost. The result is an overwhelming abundance of content competing for the same limited human cognitive bandwidth. Economist Herbert Simon predicted this decades ago: an abundance of information creates a scarcity of attention. So when organizations feel like they can’t “break through the noise,” this is what they’re describing: a messaging environment that is structured to accommodate high frequency.
To compete in the supply chain of content generation, a new player has helped immensely: automation. Essentially, best practice for producers in this market has been to take the algorithmic analysis of social media, plug it into content generators and prioritize efficiency over accuracy.
The Consumers - Audiences allocating attention
Nicholas Carr, author of The Shallows: What the Internet Is Doing to Our Brains, writes, “The internet is an interruption system. It seizes our attention only to scramble it.”
Carr’s observation captures the lived experience of the attention economy. The system isn’t simply delivering information—it is constantly interrupting us with it. Notifications, recommendations, suggested posts, autoplay videos, and algorithmically surfaced content all compete for the same limited cognitive resource.
And while technology promises greater efficiency on the production side, it actually has an equal-but-opposite impact on the consumer side: The sheer volume of information—and the need to confirm its reliability—demands more attention, not less. Some early findings even suggest that frequent AI users end up spending more time on search engines, not less, because each generated answer becomes the starting point for further verification.
Go ahead and ask your AI helper a question. Then open a new chat and ask the same question. See if you can reliably get the same answer.
Estimated AI-Generated Web Content (%) - Source: Originality.ai web scan estimates; AI Responses Followed by Verification Searches (%) - Source: Microsoft & academic studies on AI search behavior; People Saying it’s Hard to Trust Online Information (%) - Source: Pew Research Center
For years, the attention economy has been optimized around efficiency. Platforms reward speed, frequency, and engagement. AI slop, another fun buzzword that’s popping up, is the result of the necessary speed of creation: the digital equivalent to throwing spaghetti at a wall and seeing what sticks. Algorithms take the sticky pieces to learn what keeps people scrolling and amplify it. The system works precisely because it reduces the amount of conscious decision-making required from the user.
You don’t decide what deserves your attention.
The feed does.
But generative AI may be introducing an unexpected disruption to this model.
Unlike earlier rule-based algorithms designed to optimize attention capture, generative AI often produces information that is plausible but not always reliable. The result is that users increasingly feel compelled to verify what they are reading—checking sources, running follow-up searches, and questioning whether an answer is accurate. One 2025 study found that people who use generative AI tools end up using search engines more, indicating rather than the destination, the tools are becoming a new starting point.
Interestingly, this may be forcing a subtle shift in a media environment long driven by efficiency. Instead of passively consuming whatever appears in the feed, users must actively decide whether a claim deserves their attention and trust.
This shift raises a larger question about the future of the media environment. If the logic of the attention economy has been shaped by algorithms optimized for engagement, the growing presence of generative AI exposes the distortions those systems created. The gap that emerges isn’t just technical—it’s social.
It invites us to ask what kind of information environment we actually want: Do we continue optimizing systems purely for attention capture? Or do we prioritize environments that support judgment, trust, and understanding?
This is where narrative strategy enters the conversation—not as a tool for grabbing attention, but as a framework for helping people decide what deserves it.
Because in a media environment where information is abundant and automation accelerates its production, the real scarcity is no longer attention.
It’s judgment.
