The current narrative around Google Search’s resilience in the AI era fundamentally misunderstands the transformation underway.
In fact, the graphic argues that since Google Search is seeing an increased usage, then nothing is changing, actually, traditional search might be having a sort of resurrection!
Yes, search volume is growing. Yes, AI tools are driving more queries to Google. But this growth obscures a profound architectural shift: search is transitioning from a human-facing productivity tool to an invisible retrieval layer for AI agents.
This isn’t about whether search survives, it will. This is about understanding where the search business survives and what that means for its economics.
The Productivity Spectrum Compression
In the pre-AI era (2010-2020), Google Search dominated the productivity spectrum. It was the primary tool humans used to find information, research topics, discover solutions, navigate to resources, and complete tasks.
The workflow was distinctly human-centered. Users would recognize an information gap, query Google with keywords, scan through 10 blue links, click and read multiple sources, manually synthesize the information, then plan and execute their next steps.
This 6-step process represented the dominant productivity pattern for knowledge work. Search wasn’t just a tool—it was the operating system for task completion.
The AI-Integrated Workflow Takeover
Today, ChatGPT and similar AI assistants have compressed this workflow into 3 steps: ask AI for assistance through a single conversational prompt, receive synthesized answers with integrated insights, and execute with continued AI support throughout the task.
This represents reduction in workflow steps and a productivity gain across various domains. But more importantly, it represents a fundamental shift in where humans direct their attention and intent.
The critical insight: When a user asks ChatGPT a question, they’re still triggering a search—but they’re not performing it themselves. The AI agent is.
From Search-as-Destination to Search-as-Infrastructure
This is where the mental model shift becomes crucial. Traditional analysis looks at search volume and concludes “AI is complementary to search.” But this confuses volume metrics with value capture.
Consider these two scenarios:
Traditional Search Era: The user types a query into Google, Google serves ads alongside results, the user clicks results, and potentially sees more ads, and publishers capture some attention and revenue. Value flows from Google to publishers and sometimes back to Google.
AI Agent Era: The user asks ChatGPT a question, ChatGPT’s agent queries multiple sources, including search APIs, the agent synthesizes information, and the user receives a complete answer within the ChatGPT interface. Value flows entirely within the AI platform.
In the second scenario, search volume may actually increase—the agent might trigger 5-10 queries instead of the human’s 1-2—but Google has become more of an infrastructure than an interface.
The Visibility Crisis: When Traffic Doesn’t Mean Value
This transformation creates what I call “intermediated invisibility”—traffic that exists but generates no traditional value.
In the traditional web, visibility equaled traffic, which equaled potential monetization. Publishers optimized for ranking and clicks turned user attention into advertising inventory, and more traffic generally meant more revenue.
In the agentic web, agent consumption doesn’t equal user visibility. Content is consumed by machines for synthesis, but without human attention, there is no ad inventory. More “traffic” doesn’t translate to more revenue.
Consider a practical example: Your website publishes expert analysis on enterprise software. In the traditional model, it ranks well on Google, generates 10,000 monthly visits, converts 2% to newsletter subscribers, and monetizes through ads, affiliates, or subscriptions.
In the agentic model, AI agents crawl and consume your content, your analysis informs 50,000 ChatGPT responses, but you receive zero user visits to your site, zero conversion opportunities, and zero ad impressions.
You’ve become 5x more “useful” while capturing 0% of the value.
Why Google’s Growth Paradox Is Temporary
Here’s why Google Search’s current growth actually validates rather than contradicts the disruption thesis.
Short-term growth drivers include the multiplication of AI agent searches, where every ChatGPT query might trigger 3-10 background searches. Users still bounce to Google for verification patterns like brand searches and source checking. Hybrid workflows persist for narrow tasks where traditional search excels, like navigational queries and known-item searches. And current AI systems rely heavily on search APIs during their growth phase, creating a bootstrapping effect.
But these growth patterns are structurally transitional.
The verification gap closes as AI systems improve accuracy and citation quality. The need to “verify on Google” diminishes over time. Users already trust ChatGPT for increasingly complex tasks they would have verified externally six months ago.
The narrow task exception shrinks because brand searches and navigational queries can be handled entirely within AI interfaces once agents have reliable access to business information. “Take me to Nike’s website” doesn’t require Google—it requires an agent that can navigate.
The agent-to-agent economy emerges when AI agents can directly query business databases, APIs, and structured information. The “search the public web” detour becomes inefficient. Why search when you can directly access?
The Three-Layer Architectural Future
The mature agentic web will operate across three distinct layers:
Layer 1: The Agent Interface (Human-Facing) — This is where platforms like ChatGPT, Claude, and Gemini operate. It’s where humans express intent and where value is captured through subscriptions, usage fees, and enterprise licenses. This is where the economic relationship with the end user happens.
Layer 2: The Orchestration Layer (Agent-Facing) — This handles agent-to-agent communication protocols, task routing and coordination, identity and authentication systems, trust mechanisms, and payment rails for microtransactions. This is where AI platforms coordinate to deliver user value.
Layer 3: The Infrastructure Layer (Data/Retrieval) — This includes search APIs like Google and Bing, structured databases, real-time data feeds, and business APIs and integrations. This is where search exists—as plumbing, not interface.
Traditional search is being pushed from Layer 1 (human interface) to Layer 3 (infrastructure). And infrastructure economics are fundamentally different from interface economics.
Interface economics delivers high value through direct user relationships, attention monetization, subscription revenue, platform effects, and ecosystem control. Infrastructure economics faces commodity pressure through utility pricing, volume-based fees, commoditization forces, competition from alternatives, and margin compression.
The Mental Model Shift: Task Completion Authority
The most profound change is in who completes the task.
In the search era mental model, users thought, “I need to find information to complete my task.” Search was a tool they used. They remained the task completion agent and captured the productivity gains.
In the AI agent er,a mental model, users think “I need this task completed.” The AI becomes the agent that completes it. The AI platform captures the value of relationships.
This isn’t semantic—it’s economic. When the user says “book me a flight to Tokyo” instead of “search for flights to Tokyo,” they’ve delegated the entire task. The search that happens is an implementation detail invisible to the user.
The Uncomfortable Question: Can Search Survive as Pure Infrastructure?
Google’s current business model relies on user attention for ad serving, click-through behavior to drive traffic, and query intent visibility to target advertising.
But in an agent-mediated world, there’s no user attention because agents consume results. There’s no click-through because synthesis happens in the AI platform. And intent becomes opaque because we see agent goals, not user queries.
Google’s response will likely involve vertical integration by building its own conversational AI interface (Gemini) to control Layer 1. They’ll pursue API monetization by charging AI platforms for search API access at scale, competing with Bing and specialized search providers. They’ll explore direct data licensing by selling structured data to AI platforms. And they’ll offer agent services with specialized capabilities that other AI platforms want to use.
But here’s the challenge: Infrastructure players don’t capture the economic surplus that interface players do.
AWS is enormously valuable, but it’s captured less total value than Google Search has historically, despite being “infrastructure” for much of the internet. The difference? AWS never had direct user relationships or attention monetization.
That also means Google search no longer is a consumer play, with all the brand and distribution benefits that come with it, but rather an infrastructure layer within the AI ecosystem.
Unless, of course, Google search (which is what’s happening now with AI Mode turning agentic) becomes an agent itself, but then again it won’t be Google saerch any longer…
The Spectrum Swallow: From Productivity to Personal Fulfillment
The shift goes beyond just productivity. AI chatbots are expanding across multiple spectrums.
They’re already dominant in the productivity spectrum for task completion, research, analysis, and creation. They’re emerging in the entertainment spectrum for gaming, storytelling, and creative collaboration. And they’re beginning to enter the personal fulfillment spectrum for coaching, therapy, companionship, and personal growth.
Each expansion deepens the human-AI relationship while further abstracting the underlying infrastructure. Search becomes not just invisible for productivity, but irrelevant for entirely new categories of human-AI interaction.
When someone spends an hour with ChatGPT working through career decisions, relationship advice, or creative projects, there’s no “search” happening in any meaningful sense. The AI has moved beyond information retrieval entirely.
Strategic Implications
For Google and Search Companies — The race is on to control Layer 1 by owning the conversational interface. They need to extract maximum value from Layer 3 infrastructure while they still can, transitioning to API-first business models before commoditization accelerates. The strategic imperative is to invest in agent-native services that AI platforms will pay for and to build direct relationships with enterprises for proprietary data access.
For Content Creators and Publishers — Stop optimizing for search visibility and start optimizing for agent usefulness. Consider how agents will consume, cite, and synthesize your content. Explore direct relationships with AI platforms for content licensing. Develop proprietary data or analysis that can’t be easily synthesized. And build direct audience relationships that bypass intermediation entirely, because agent traffic without human visibility has zero traditional value.
For AI Platform Companies — Own Layer 1 at all costs because the interface captures the surplus. Build or acquire Layer 2 orchestration capabilities for competitive advantage. Make Layer 3 commoditized and interchangeable to avoid vendor lock-in. Create switching costs through personalization, context, and ecosystem effects. And expand beyond productivity into entertainment and fulfillment spectrums before competitors do.
For Everyone Else — Understand that the shift from search-as-interface to search-as-infrastructure isn’t hypothetical or distant. It’s happening now through millions of daily behavior changes. Each time someone asks an AI instead of Googling, they’re rewiring their mental model of task completion.
The question isn’t whether search survives—it’s whether search companies survive the transition to infrastructure economics.
The Invisible Revolution
Perhaps the most profound aspect of this transformation is its invisibility to most observers. Search volume is growing, so search must be fine. This logic misses the architectural revolution happening beneath the metrics.
The most valuable infrastructure often becomes invisible. We don’t think about TCP/IP when browsing the web, DNS when visiting websites, or HTTPS when making purchases. These protocols became infrastructure—essential but invisible, commoditized but necessary.
Search is following the same path. It will remain essential. AI agents will use it constantly. But it will be hidden, abstracted, and ultimately captured by whoever controls the interface layer where humans express intent.
The revolution is invisible because the infrastructure remains. What changes is who extracts the value, where attention flows, and which layer commands the economic surplus.
Google’s short-term growth is real. But it’s the growth of infrastructure being consumed at an accelerating rate by the new interface layer. The paradox is that Google Search could be more “used” than ever while being worth less than ever.
That’s not a prediction about next quarter’s earnings. It’s a structural observation about where we’re heading once the transition completes. And if you’re building for the next decade rather than the next quarter, the architecture matters more than the metrics.
Recap: In This Issue!
Core Thesis
Search usage growth masks an architectural shift
Search is moving from human-facing tool to invisible retrieval for AI agents
The question is not survival of search, but where value accrues in the stack
Productivity Spectrum Compression
Pre-AI: 6-step, human-centered workflow with Google as the OS of tasks
Post-AI: 3-step flow (ask, receive, execute) inside conversational interfaces
Attention and intent migrate from Google’s UI to agent front ends
AI-Integrated Workflow Takeover
Users still trigger “search,” but the agent performs it
Agent queries multiply raw searches while removing user exposure
Value shifts from the search page to the agent’s answer surface
From Destination to Infrastructure
Era 1: Search as destination with ads, clicks, publisher traffic
Era 2: Agents use search APIs, synthesize, and retain the user
Search volume can rise while interface value capture falls
The Visibility Crisis
Agent consumption without human visits produces zero ad inventory
Publishers become more “useful” to machines but capture no demand
Traffic metrics decouple from monetization outcomes
Why Google’s Growth Is Temporary
Verification bounce-backs decline as agent accuracy improves
Navigational and brand queries get handled in-thread by agents
Agent-to-agent data access bypasses public web search detours
Three-Layer Architectural Future
Layer 1: Agent Interface where intent and economics live
Layer 2: Orchestration routing, identity, trust, payments for agents
Layer 3: Infrastructure search APIs, structured data, business APIs
Search is pushed down to Layer 3 with utility economics
Mental Model Shift: Task Completion Authority
Old model: “Find info to complete task”
New model: “Have the task completed” by an agent
Ads and targeting lose line of sight to user intent
Can Search Survive as Pure Infrastructure
Likely moves: own Layer 1 (Gemini), monetize APIs, license data, ship agent services
Constraint: infrastructure margins trail interface margins
Brand power erodes when relationship shifts to the agent front end
Spectrum Swallow: Beyond Productivity
Agents expand into entertainment and personal fulfillment
Entire categories proceed with no meaningful “search” step
Infrastructure becomes further abstracted from user value
Strategic Implications
For Google and search firms: Race to control Layer 1, pivot to API-first pricing, build agent-native services, lock enterprise data ties
For publishers: Optimize for agent usefulness, pursue licensing, create proprietary datasets, deepen direct audience relationships
For AI platforms: Own Layer 1, build Layer 2 moats, commoditize Layer 3 suppliers, expand into entertainment and fulfillment early
The Invisible Revolution
Growing search volume is infrastructure consumption, not interface health
Like TCP/IP or DNS, search becomes essential but unseen and commoditized (a critical layer but not a consumer tool)
Short-term metrics up, long-term surplus shifts to the agent interface layer
Key Takes
Usage growth vs value capture divergence
Workflow compression and agent execution
Search’s push to Layer 3 and margin compression
Publisher visibility collapse under agent mediation
Playbooks for search firms, publishers, and AI platforms
With massive ♥️ Gennaro Cuofano, The Business Engineer