From The Attention to Intention Economy
When Perplexity launched in December 2022, it felt like magic: type a question, and an invisible researcher roamed the web on your behalf, returning with a polished answer.
Then came OpenAI’s Deep Research in February 2025, another “Feel the AGI moment”. A single prompt could summon what felt like a full-time analyst: sourcing, summarizing, and reasoning in seconds.
At first, these seemed like isolated breakthroughs. Another leap in convenience, another tool in the expanding AI toolkit.
But something deeper was happening. These weren’t just new products; they were the first visible signs of a new economic substrate forming beneath the web itself
How the Attention Economy Worked
For the last two decades, the internet has run on a single economic law: capture attention, sell access to it.
Every major platform, Google, Meta, TikTok, YouTube, X, has called it something different: watch time, engagement rate, click-through, dwell time. But they all orbit the same core question:
“How do we keep users here just a little longer?”
That pursuit built the digital economy as we know it.
The longer you stayed, the more ads they could show. The more data they gathered, the better they could target you next time.
By mastering that feedback loop, platform giants created billions in ad revenue, the business of renting micro-moments of human focus.
Advertisers were buying a chance to interrupt your scroll, to occupy a few seconds of your cognitive bandwidth.
The result was an internet optimized not for clarity or resolution, but for retention. Every design pattern, endless scroll, autoplay, algorithmic feeds, was engineered to maximize time-on-platform, not outcome-for-user.
The Catalyst: The LLM
The emergence of large language models has disrupted one of the internet’s most stable feedback loops: Search.
For two decades, Google’s dominance rested on the idea that finding was the hardest part of the web. You typed a few words, and Google’s algorithm ranked billions of pages by relevance. The better the ranking, the longer users stayed, and the more ad slots Google could sell.
That logic began to collapse when AI could retrieve, synthesize, and explain information in a single exchange. The user no longer needed to dig through ten links or skim articles; they could simply state their intent, and the system would construct an answer.
This shift reframes the purpose of “search” itself from navigation to resolution.
In the traditional model, users were explorers.
In the LLM model, they are delegators.
The act of searching becomes less about finding sources and more about verifying outcomes.
Companies like OpenAI and Perplexity have built directly on that premise, positioning AI as an intermediary that executes intent across the web.
For users, it’s a leap in efficiency; for giants like Google, it’s an existential threat.
If an agent can perform the retrieval, summarization, and even action-taking steps that once defined search behavior, then the economic foundation of traditional search, the billions made from page impressions and ad clicks, starts to erode.
The Rise of the Intention Economy: From SEO to AIO
Google’s response to this threat has been decisive.
They horizontally integrated AI into practically every product in their suite, they added in a new AI overview (in which pages impacted by the overview saw traffic declines of 30–70% almost overnight), and even introduced a new “AI Mode” alongside their search.
In late October, OpenAI showed that these actions weren’t enough to stop their ambition when they released their new ChatGPT Atlas, an AI-powered browser, which signaled them as a direct competitor to Google.
Together, these moves trace a clear trajectory: manual search and browsing, once the default model for information access, is gradually being replaced by AI systems that interpret and act on user intent.
However, what happens in a world where, instead of humans having to be responsible for how they retrieve information, AI is instead?
AI Optimized Search
For decades, users handled every stage themselves: define a query, filter results, evaluate sources, and piece together conclusions. AI compresses those steps, reassigning who does the work.
This new loop: intent → synthesis → outcome, changes the physics of the web:
Visibility shrinks.
Results become synthesized rather than browsed. Many pages that once competed for partial views now feed into a single blended output.
Agency shifts.
Instead of choosing which source to trust, the user chooses which system to trust to decide for them. Evaluation moves upstream, from individual pages to platforms.
Metrics blur.
Attention is harder to measure when users don’t click. Traffic becomes opaque; attribution fragments. The feedback loop that once powered optimization breaks down.
These shifts don’t make AI-optimized search better or worse; they make it different. It replaces exploration with inference, substituting transparency for speed.
For creators and businesses, that means the reward structure flips: success no longer depends on drawing the click, but on being the best option to the machines that now mediate discovery.
For users, it redefines participation: fewer manual choices, faster conclusions, less context. This ultimately leads to an increasingly fragmented world, where each person’s truth is defined by their system.
The Broader Pattern
The interaction between Google and OpenAI is just the headline. The same pattern is rewriting every category of software:
Shopify – spin up a store from a prompt (intent → commerce).
Cursor / Replit / Lovable – describe functionality, receive runnable code (intent → prototype).
Figma – type a layout concept, get components (intent → design scaffold).
Slack – summarize threads, draft follow-ups, propose next steps (intent → coordination).
Across these tools, the first layer of creation is abstracted: Idea → Intent → Prototype.
The system moves first; the human edits.
From Attention to Intention
The old internet rewarded whoever could capture attention.
The new internet rewards whoever can convert intent into outcomes.
In the attention era, power lived in distribution: the ability to intercept the user’s focus.
In the intention era, power lives in execution: the ability to fulfill what the user actually wants.
That means value migrates from owning eyeballs to owning outcomes, and the strategic question shifts from “How do we get more traffic?” to “How do we turn intent into reliable results?”
