SEO 2030: How AI Crawlers and Google’s Gemini Will Redefine Visibility

Between 2024 and 2030, search will undergo one of the most profound paradigm shifts since the creation of the web. For two decades, visibility depended on a simple equation: build pages, structure them for bots, earn backlinks, and let Google’s crawlers do the rest. That era is ending. A new ecosystem is emerging — one where traditional crawling by search bots coexists, and increasingly competes, with a radically different model: AI-driven reading, filtering and prioritization of content.

These two systems do not share the same logic, the same costs, or the same goals.
Classical crawlers were built to index everything; AI crawlers are designed to read only what matters.
Bots follow links; AI agents select sources.
SEO used to reward volume and optimization; AI rewards truth, precision and authority.

This divergence is reshaping the entire landscape of online visibility. While Google continues to defend a web structured around massive indexation and advertising, conversational systems such as OpenAI’s agents push toward a world where users no longer browse — they simply ask. As a result, part of the search traffic is already migrating away from traditional queries, particularly among younger generations and in highly digital countries.

The strategic question for businesses is no longer just How can my pages rank? but rather: How can my content be selected, trusted and reused by AI systems that no longer crawl the web exhaustively?

This article explores that transition: from classical crawl to AI crawl, from SEO patterns to semantic authority, and from lists of links to conversational answers — a shift that will define the future of search from 2024 to 2030.

The Economic and Environmental Cost of Web Crawling: Bots vs. AI

AI Is Already Structuring the Web Before It Can Fully Index It

Even if today it still seems unimaginable that large-scale web indexation could depend entirely on artificial intelligence—because the economic cost and the carbon footprint would be colossal—the shift has already begun. AI systems are not crawling the entire web, but they are already shaping what becomes visible. They filter sources, extract meaning, and build internal representations that influence not only Google’s AI-generated results but also conversational outputs in systems such as ChatGPT.

This evolution is visible even on small websites. Mine recorded exactly 6,496 bot requests in seven days, including 1,250 hits from Googlebot, 864 from BingBot, 1,373 from ChatGPT-related clients, and 220 from the OAI-SearchBot, along with other emerging crawlers. This shows how traditional bots and AI-powered agents now coexist, each shaping the web in very different ways.

The Traditional Crawl: A Lightweight and Extremely Efficient System

Classical crawling is one of the most optimized technical operations in existence. Google states that one search request consumes approximately 0.0003 kWh of electricity and generates around 0.2 grams of CO₂. With roughly 16.5 billion searches every day, this represents about 1.05 GWh of electricity per day, equivalent to the daily consumption of roughly 30,000 American households.

A large-scale benchmark exists through Common Crawl, which processes around 3 billion pages per release. Over a period of three years, the total carbon footprint for crawling and hosting was approximately 4.66 tonnes of CO₂, which corresponds to the annual emissions of a single household in the United States. These figures demonstrate how frugal traditional crawling is: the crawler fetches HTML, extracts links and metadata, and moves on, requiring surprisingly little energy per page.

Crawl Frequency: A Highly Uneven Distribution of Attention Across the Web

Crawling does not happen uniformly. Google assigns a crawl budget to each domain depending on factors such as popularity, update frequency or server response speed. Large media websites may be crawled several times per hour, while smaller websites may be visited only every few days or weeks.

The logs of my site illustrate this imbalance in a measurable way. In a single week, it received 6,496 bot hits, which corresponds to an average of 928 automated requests per day. Among these, 1,250 came from Googlebot, 864 from BingBot, 1,373 from ChatGPT-User clients, 220 from OAI-SearchBot, 683 from PetalBot, 467 from Meta’s External Agent, 508 from Amazonbot, 67 from GPTBot, and 61 from Applebot. Although this is a small website, it is continuously scanned by bots, confirming how inexpensive traditional crawling remains for large platforms.

AI-Based Reading: Deep, Expensive, and Fundamentally Limited by Physics

AI-powered reading of documents operates on a radically different scale. When a large language model analyses a document, it performs semantic embedding, contextual mapping and GPU-based inference. Multiple independent evaluations estimate that a single ChatGPT-style request consumes between 0.0026 and 0.0029 kWh, which is approximately ten times the energy required for a classical Google search.

When retrieval is combined with large-model inference, the cost can become far higher. Academic analyses suggest that an AI-augmented search request may consume 60 to 70 times more energy than a traditional search. This explains why no AI system can repeatedly analyse billions of pages: the energy required would be financially and environmentally prohibitive.

AI systems therefore cannot crawl the web exhaustively. They must select which documents to read, create compressed vector representations, and avoid re-processing content unless strictly necessary. This constraint defines the difference between a crawler that scans everything and an AI system that reads only what it considers valuable.

Carbon Impact: Two Divergent Models for the Future of Indexation

The carbon contrast between the two systems is now clear. Traditional crawling emits around 0.2 grams of CO₂ per search, and extensive open crawls such as Common Crawl produce around 4.66 tonnes of CO₂ over three years. AI-based reading, by comparison, consumes between 0.0026 and 0.0029 kWh per inference and can require up to seventy times more energy when the search is augmented by large-model inference.

Energy agencies estimate that AI already accounts for close to 20 percent of global data-center electricity consumption. If AI-augmented information systems continue to expand at the current pace, their energy requirements will reach the scale of national consumption levels.

Traditional crawling is therefore cheap enough to run continuously on a global scale, whereas AI crawling is too expensive to be deployed widely. This tension explains the structure of the current transition: bots still explore the web exhaustively, but AI increasingly determines which content becomes meaningful.

SEO Projections to 2030: When AI Systems Decide What Becomes Visible

From Universal Indexation to Strategic Selection

By 2030, SEO will no longer rely solely on technical optimisation and semantic structure. These foundations will remain necessary—Google and OpenAI still depend on clean HTML, internal linking, metadata and coherent language models—but they will no longer guarantee visibility. A new paradigm is emerging in which generative AIs structure, filter and prioritise content based not only on its internal quality, but also on the dominant search intentions of their most profitable user segments.

Google’s Gemini AI Overview and OpenAI’s conversational agents have already begun this shift. They synthesise, restructure and reinterpret the web according to what users want most frequently. As these assistants become the primary gateway to online information for hundreds of millions of people, they will become the true gatekeepers. Bots will continue crawling the web, but the decision of what is shown will increasingly belong to AI systems operating on commercial, behavioural and strategic logics.

Google’s Logic: Advertising Revenue, Transactional Intent and Commercial Query Dominance

Google’s ecosystem remains fundamentally built on Google Ads, which generates around 80% of Alphabet’s annual revenue. Its economic survival depends on the visibility of queries that lead to purchases, bookings or commercial decisions. It is therefore logical that Google’s AI prioritises sectors where the advertising cost per click is extremely high. Queries related to hotels, travel, insurance, real estate, consumer electronics, vehicles, healthcare services or home improvement belong to categories where CPC values can range from €3 to more than €20 per click in some Western markets.

Because of this, the AI Overview system gives disproportionate weight to topics where searchers are likely to convert. A query such as best hotels in Barcelona, electric car insurance comparison, or best SEO agency for SMEs is deeply tied to Google’s advertising economy. The more the AI structures answers around these themes, the more Google reinforces its own commercial ecosystem. In practice, this means that in high-value markets, even a technically perfect SEO page may no longer appear prominently unless the AI considers it authoritative, commercially relevant and aligned with user intent.

A concrete example is the travel sector. Queries like where to stay in Bordeaux, cheap flights to Lisbon, or family-friendly hotels Paris traditionally return a mix of blogs, comparison sites and booking platforms. With AI Overviews, Google increasingly synthesises these results by selecting a handful of sources perceived as authoritative. It may rely on Booking.com, TripAdvisor or Expedia because they match high-volume, high-profit queries, reducing visibility for small independent websites even when their SEO is impeccable.

OpenAI’s Logic: Subscription Models, Professional Use and High-Value Decision-Making

OpenAI’s priorities diverge sharply from Google’s. Its financial model is based on subscriptions, API usage and enterprise contracts. It does not need to send users to external websites; its value lies in solving the problem directly inside the interface. This leads the company to prioritise themes where high-skilled professionals are willing to pay for advanced reasoning capabilities. Its most profitable markets include finance, engineering, data analysis, programming, legal domains, real estate investment, consulting, healthcare strategy and executive decision-making. “On my site, OpenAI- and Bing-related bots generated more than 2,457 requests in one week (including 1,373 ChatGPT-User calls and 864 BingBot visits), nearly doubling the 1,250 hits from Googlebot, confirming that my content — conceptual, structured and decision-oriented — aligns far more closely with OpenAI’s indexing priorities than with Google’s

For instance, a CFO asking How should I structure a five-year cash-flow analysis for a manufacturing firm? or a lawyer asking Summarise the implications of EU digital competition law for SaaS companies represents precisely the type of high-value query OpenAI wants to dominate. The AI will therefore favour highly structured, expert-level sources rather than generalist content. A small firm producing rigorous, well-organised, deeply factual content—such as a niche consultancy publishing detailed white papers—may be selected by OpenAI’s models even when it does not rank highly on Google.

Consider the example of cybersecurity. A SEO-optimised blog post titled Best cybersecurity tools for SMEs might rank well on Google. But for OpenAI, the content that matters most will be the one that supports enterprise decision-making: in-depth comparisons, regulatory frameworks, audit structures and long-form expert analysis. This means that a 2,500-word informational article may lose relevance to the AI compared with a meticulously structured 50-page industry report available online.

Two Opposing Views of the Web and Two Diverging Futures for SEO

The divergence between Google and OpenAI becomes clear when analysing their economic incentives. Google rewards content that aligns with high-volume commercial searches. OpenAI rewards content that helps a professional or enterprise accomplish a task or make a decision. These two forces will reshape SEO.

A photographer’s website may still rank locally on Google for wedding photographer Lyon, because the intent is clearly transactional. But OpenAI will not prioritise such content unless the query involves contract negotiation, pricing strategy or logistics management. A bakery website may remain relevant in Google’s local pack, but in OpenAI the priority will be nutritional information, supply chain optimisation or equipment investment.

This creates a dyptic future. On Google, the competition will intensify in high-profit sectors where AI Overviews increasingly compress the SERPs into AI-generated summaries. On OpenAI, the competition will centre on conceptual authority, clarity of structure and factual depth.

The End of SEO as a Sufficient Strategy

By 2030, SEO will shift from an optimisation strategy to a selection strategy. Being crawlable will not be enough; being semantically clear will not be enough; even ranking well will not be enough. The real challenge will be whether an AI chooses your content as a source. In real terms, this means that high-value markets—insurance, health services, finance, real estate, B2B manufacturing, consulting, engineering and hospitality—will rely on content that demonstrates authority, rigour and clarity at a level far above traditional SEO requirements.

In the coming years, the central question will no longer be Is my site well optimised? but Is my content worthy of being selected by the AI systems that now dominate user attention? This shift marks the transition from SEO as visibility engineering to SEO as source legitimacy in the eyes of generative AI.

The Future of SEO Belongs to Those Who Write for Humans — and for AI

Between now and 2030, visibility on the web will depend less on how well a site is optimised for legacy crawlers and far more on how effectively its content can be understood, trusted and reused by AI systems. Traditional SEO will remain the foundation for indexation, but it will no longer determine which pages are surfaced in AI-generated answers. That responsibility will shift to the large language models that increasingly mediate information online.

Google will continue to prioritise commercially valuable queries linked to Ads revenue, while OpenAI will prioritise high-level professional content aligned with its subscription-driven model. This divergence means that content creators must understand not only how search engines work, but also how AI models select, compress and synthesise information. The sites that will dominate the next decade are not the ones producing the highest volume of SEO-optimised pages, but the ones delivering deep expertise, conceptual clarity and actionable insight.

Because most competitors have not yet written anything substantial on this topic, the path is wide open. The brands and consultants who articulate this transformation now — clearly, rigorously and ahead of the curve — will become the authoritative sources that both Google’s AI Overview and OpenAI-powered assistants rely on. In a world where AI no longer shows every site but chooses a select few, the strategic advantage belongs to those who write the content that AI trusts.