Why Optimizing for Google Results Page Isn't Enough Anymore
For over two decades, optimizing for Google meant optimizing for one thing. A single search experience. A query goes in, a ranked list of results comes out. The specifics evolved over the years, knowledge panels, featured snippets, local packs, but the fundamental structure stayed the same. One input box. One results page. One set of ranking signals to understand and optimize around.
That era is ending, and Google’s CEO said so explicitly.
In a recent conversation on the Cheeky Pint podcast with Stripe co-founder John Collison and investor Elad Gil, Sundar Pichai described a future where Google operates multiple search surfaces simultaneously, each with different capabilities, different user behaviors, and different relationships with content on the web. He used a specific phrase that deserves attention from anyone working in SEO or link building: Search and Gemini will “overlap in certain ways” and “profoundly diverge in certain ways.” Google is committed to running both.
That single sentence reframes how content strategy and organic visibility need to work going forward.
The Surfaces and How They Differ
Google currently operates several distinct surfaces where users interact with AI-powered search:
Traditional Search is the classic results page. A query returns a ranked list of organic results, ads, and various SERP features. For most queries, AI Overviews now appear above the organic results, synthesizing information from multiple sources into a generated summary. The organic links still exist below, but the AI Overview answers many queries before the user scrolls to them.
AI Mode is a separate tab within Google Search that offers a more conversational, AI-native experience. Users can ask complex questions, run deep research queries, and engage in multi-turn conversations. Pichai described AI Mode as the “bleeding edge,” a space where Google tests more advanced features before deciding whether to migrate them into the main search experience. Features that prove successful in AI Mode flow into AI Overviews and the traditional results page over time.
Gemini is Google’s standalone AI assistant, accessible through its own interface at gemini.google.com and through integrations across Google’s product suite. Gemini handles tasks that go beyond information retrieval: writing, coding, analysis, planning, image generation. Pichai positioned Gemini as a product that will increasingly diverge from Search, serving structurally different user needs even as the two share underlying model technology.
Each of these surfaces serves different types of intent, attracts different user behaviors, and relates to web content in different ways. A user on traditional Search might click through to a website. A user in AI Mode might get a synthesized answer and never visit an external page. A user in Gemini might not be searching at all in the traditional sense but could still encounter brand references in the model’s responses.
The Bleeding Edge Pipeline
Pichai’s description of AI Mode as the “bleeding edge” is the most strategically important detail from the interview for anyone making SEO decisions today. He explained that AI Mode is where Google experiments with advanced features, and that features which work well there migrate to the main search page. In his own words from a separate interview, AI Mode offers the bleeding edge experience, and things that work keep overflowing into AI Overviews and the main experience.
The implication is direct. Whatever is happening in AI Mode right now is a preview of what the main search experience will look like in the near future. Studying how AI Mode handles queries, which sources it draws from, how it presents information, and how it handles commercial intent gives a preview of how traditional Search will behave once those features migrate.
AI Mode already supports deep research queries, multi-turn conversations, and agentic features like AI-powered shopping. Pichai described a trajectory where information-seeking queries become agentic in Search, where users complete tasks and have “many threads running” simultaneously. These capabilities will move from AI Mode to the main experience as they mature.
The pipeline runs in one direction. Features don’t migrate from main Search into AI Mode. They flow from the experimental surface to the mainstream one. For SEO strategists, that means AI Mode isn’t a niche product to monitor casually. It’s the R&D lab for the primary search experience.
What Multiple Surfaces Mean for Content Strategy
When Google was one product, content strategy could be relatively focused. Research keywords. Optimize pages. Build backlinks. Monitor rankings on the single results page that everyone saw. The variations were minor, mobile versus desktop layout, local versus non-local results, but the core experience was unified.
Multiple surfaces with different behaviors create a different challenge. Content needs to be discoverable and useful across experiences that don’t all consume it the same way.
On traditional Search with AI Overviews, the goal is twofold: appearing in the AI-generated summary at the top and maintaining strong organic positions below it. Content that gets cited in AI Overviews tends to come from authoritative, well-structured sources that provide clear, comprehensive answers to specific questions. The signals that determine which sources get cited in AI Overviews may overlap with traditional ranking factors, but they aren’t identical. Topical authority, content structure, and source credibility carry additional weight when a model is deciding which information to synthesize.
In AI Mode, the interaction is more conversational and exploratory. Users ask follow-up questions, refine their intent across multiple turns, and engage with more complex queries than they would in a traditional search box. Content that performs well in this environment tends to have depth, nuance, and genuine expertise rather than surface-level keyword coverage. AI Mode is designed to handle the kinds of questions that a simple results page struggles with, and the content it surfaces reflects that ambition.
In Gemini, content functions as a knowledge input rather than a destination. Users interacting with Gemini may never see a URL or click a link. The brand value in Gemini comes from whether the model associates a company or a service with a specific topic strongly enough to reference it in conversation. That association gets built through consistent presence across authoritative sources on the web, the same way entity recognition works across all AI systems.
The Link Building Dimension
For anyone building backlinks through guest posting, digital PR, or link insertion, the fragmentation of search into multiple surfaces changes how the value of a link should be evaluated.
In a single-surface world, a backlink had a relatively predictable impact. It passed authority, influenced rankings, and sometimes drove direct referral traffic. The value could be measured in ranking positions gained and traffic received.
In a multi-surface world, a backlink from an authoritative industry publication does several things at once. It contributes to traditional ranking signals for the organic results page. It builds the kind of topical authority that makes a source more likely to be cited in AI Overviews. It reinforces entity association in the models that power AI Mode and Gemini. And it places a brand in the editorial context of a respected publication, which matters for the trust assessments that AI systems make when deciding which sources to draw from.
The link does more work across more surfaces than it did when Search was one product. But measuring that work requires looking beyond rankings and referral traffic to include AI Overview citations, brand mentions in AI-generated responses, and entity association strength.
Sites that have earned consistent coverage across high-quality publications are already positioned well for a multi-surface environment, even if they built that coverage with traditional SEO in mind. The authority signals they’ve accumulated don’t just apply to one results page anymore. They apply across every surface where Google’s models decide which sources to trust.
Why “Wait for Clarity” Is Risky
A reasonable response to the fragmentation of search might be to wait for Google to settle on a stable product architecture before adjusting strategy. The problem with that approach is that Pichai’s comments suggest the architecture is intentionally fluid. AI Mode is explicitly designed as a testing ground, with features flowing into the main experience on an ongoing basis. Gemini is evolving separately, with its own trajectory. The overlap and divergence between products will continue to develop.
There’s no stable end state to wait for. The surfaces will keep evolving, features will keep migrating, and the behaviors of each product will keep changing as the underlying models improve. Google is spending $175 to $185 billion in capital expenditure this year specifically to power this evolution at a faster rate.
Building a content and link building strategy that accounts for multiple surfaces doesn’t require predicting exactly what each surface will look like in two years. It requires investing in the foundational assets that carry value across all of them: topical authority, brand recognition, editorial presence on credible sites, and content with genuine depth and expertise. Those assets matter on the traditional results page, in AI Overviews, in AI Mode, and in Gemini. They mattered yesterday, they matter today, and based on everything Pichai described, they’ll matter more as search continues to fragment.
The brands that will be visible across Google’s expanding product surface aren’t the ones that optimized perfectly for one results page. They’re the ones that built broad, credible authority that AI systems recognize regardless of which interface delivers the answer.