Adapting Your SEO Strategy for the AI Era: ChatGPT vs Google

The "ChatGPT vs Google" dispute is not a war of substitution; it is the reorganization of the search journey.

On the one hand, ChatGPT is growing in adoption, becoming the most downloaded app in the world by March 2025; on the other, the majority of people who use AI continue to turn to Google: 95% of ChatGPT users go to Google.

At the same time, the market pendulum is swinging: Google's market share fell below 90% at the end of 2024 and market readings already put ChatGPT at ~1% of "search".

This guide, without scaremongering, shows what these figures mean in practice and gives you a playbook for performing in the SERP and LLM responses, balancing organic traffic, AI citations and conversion.

What we'll see in today's content:

• Latest data showing about ChatGPT vs Google
  • Will ChatGPT replace Google?
  • People still use Google (a lot)
  • ChatGPT vs Google: when to use each?
• LLM-SEO in practice: how to get cited and clicked on
• How to create a content framework to be cited by ChatGPT and rank on Google?
• 90-day technical and tactical checklist for implementing this strategy
• Metrics to track in the age of search with AI
• Two-tier strategy (SERP + LLM)

Happy reading!

What does the latest data show about ChatGPT vs Google?

1) ChatGPT adoption has exploded on mobile. In March 2025, the ChatGPT app was the most downloaded globally, surpassing Instagram and TikTok, driven by the boom in "Ghibli style" images. This indicates that AI has entered users' daily habits.

2) Google has lost a little share, but remains sovereign. For the first time since 2015, Google's market share fell below 90% at the end of 2024. Significant, but far from a replacement.

3) Coexistence is the true picture of current behavior. 95% of ChatGPT users also visit Google - a scenario of complementarity: conversational response (ChatGPT) + browsing and comparison (Google).

4) "ChatGPT search" is real, but small compared to the ocean of the web. Market readings have projected ChatGPT at ~1% of the search market share in 2025. Symbolic as a change of habit, but still far from Google's volume.

5) Brazil echoes the trend. National studies, such as Conversion's, show growth in AI without a sharp drop in the use of Google by those who adopt ChatGPT.

Will ChatGPT replace Google?

Short term: no. Medium term: unlikely. We adopt AI for synthesis and prototyping; we stick with Google to explore options, compare and decide (mainly in local and transactional intentions).

Strategic implication: your brand needs to perform on two stages - SERP (SEO) and LLM response (citable content, with clear data and sources).

Why do people still use Google (a lot)?

• Clickable results and diversity of sources: discover, compare, audit.
• Local and transactional tasks: Maps, timetables, NAP, policies, stock.
• Confirmation and research: read the original source before deciding.
• Habit and ubiquity: "googling" is still part of the routine.
• Practical limitations of LLMs: despite advances, they may still not be enough; and much of the living context (price/availability) is on websites.

Google has been mixing direct response (AI Overviews/AI Mode) and traditional SERP, which can fragment the origin of traffic over time.

ChatGPT vs Google: when to use each?

Use ChatGPT to understand the subject in minutes, organize ideas and draft the first version; turn to Google when you need to explore concrete options, compare prices and suppliers, check sources and make the final decision, especially in local and transactional intentions.

Table: When to use Google and when to use ChatGPT

On information journeys, many people start on ChatGPT to gain quick context and sketch out ideas; when it comes time to validate what has been understood, compare options and decide, they naturally migrate to Google.

When it comes to local and transactional intentions ("near me", price, availability), Google tends to be the first step.

Practical tip: build content that works on both stages. Open with direct answers and citable data (for LLMs) and go deeper with comparisons, evidence and traceable CTAs (for the SERP).

LLM-SEO in practice: how to be cited and clicked on

Think of two stages - and one strategy.

1. SERP (Google) - where you rank, capture intent and convert. This is where EEAT, information architecture, internal interlinking, speed, mobile experience and the fit of your page with the SERP features (Top Stories, Reviews, Shopping, Local, etc.) come into play.
2. LLMs (ChatGPT and the like) - where you are quoted. The focus is on producing self-contained fragments (short paragraphs with a central idea), verifiable data and sources linked to the right keywords. Think "thesis + evidence + source".

A quick example of a hybrid journey: the person asks ChatGPT "how to choose a CRM for small businesses?" to understand the criteria; then they go to Google to find real comparisons, prices and testimonials before deciding. Your content needs to appear at both stages.

How should I change my content to be cited by ChatGPT and rank on Google?

• Start with a TL;DR (2 sentences): deliver the direct answer to the topic (claim) + a source (evidence) - and only then go deeper.
• Turn PAAs into headlines (H2/H3) using real questions and semantic variations ("how...", "is it worth...", "how much does it cost...").
• Create "citable cells": paragraphs of 2-4 lines, one idea per block, with number + link embedded in the keyword.Insert numbers and benchmarks (always with links in keywords).
• Use tables and bullets for criteria, comparisons and step-by-steps; LLMs read lean structures better.
• Add a "How we measured" box, explaining the sample, time window and limitations (transparency increases citability and trust).
• Update dates and authorship (display last updated and create author page); use dateModified in JSON-LD.
• Create a mini glossary (entities and business terms) and anchor links to facilitate "block navigation" (human and LLM).
• Link architecture: reinforce internal links (clusters/pillar pages) and maintain qualified outputs (primary sources).

Example of a citable block (template):

In March 2025, ChatGPT became the most downloaded app globally, a sign that AI is already part of the mobile habit; even so, 95% of ChatGPT users also visit Google, which confirms a complementary use between conversational responses and browsing.

How should I configure the technical structure (Schema, meta, indexing and Core Web Vitals) to rank on Google and appear in AI responses?

• Schema.org (JSON-LD):
  
  
  • Article/BlogPosting (with author, publisher, datePublished, dateModified, image, mainEntityOfPage).
  • FAQPage (for PAA), HowTo (when there is a step-by-step), Product/Offer (if there is a price) and LocalBusiness (for local intentions).
  • BreadcrumbList for context and navigation CTR.
    
    
• Meta and internationalization: complete Open Graph/Twitter Cards; hreflang if there are language/country versions; consistent canonical.
• Indexing and control: up-to-date sitemaps; consistent robots and x-robots-tag; consider Google-Extended in robots.txt if you want to manage usage by AI models.
• Performance/UX (Core Web Vitals): aim for LCP ≤ 2.5s, stable INP and CLS < 0.1; WebP/AVIF images, lazy-load, compression and HTTP caching.
• Accessibility and context: descriptive alt text, useful captions and semantic components (header/nav/main/aside/footer).
• Data governance: visible dateModified, linked editorial policy, and periodic review (monthly/quarterly) to maintain freshness.

What common mistakes should I avoid when optimizing content to rank on Google and appear in AI responses?

• Blunt openings and hiding the answer (low citability).
• Long paragraphs without data; numbers without a source or with a generic link.
• Vague H2 ("Final thoughts") instead of PAA (People Also Ask) questions.
• Forget Schema, dateModified and authorship (loses EEAT and visibility in LLMs).
• Leave heavy images without alt text and without compression (affects CWV and retention).

How to create a "citable" content framework (H2/H3/H4, snippets and sources) to be cited by ChatGPT and rank on Google?

The idea here is to turn your post into dual-performance content, readable and citable by LLMs and competitive in the SERP.

To do this, we will combine clear answers at the top, verifiable evidence (numbers with source), semantic structure (H2/H3/H4, tables, FAQ), structured data (Schema.org) and EEAT signals (author, update date, methodology).

Goal: deliver immediate clarity for AI and actionable depth for humans, without losing speed, UX and measurement (CTR, brand searches, conversions).

How to apply this framework in practice (step by step) without turning the post into a technical manual?

1. Answer first. Open each H2 with 1-2 sentences that deliver the main idea. Then elaborate with context, examples and pros/cons.
2. Write in quotable blocks. Paragraphs of 2-4 lines, one idea per block; where appropriate, turn criteria into lists or tables.
3. Keep semantics stable. Use H2/H3/H4 as chapters/subchapters/examples and name entities in the same way (e.g. "Google Search", "ChatGPT").
4. Show signs of trust. Author with credentials, update date, data methodology and links to primary sources.

Mini-example applied:

PAA question (H2): "Will ChatGPT replace Google?"

Direct answer (2 sentences): "Not in the short/medium term. The use is complementary: ChatGPT for synthesis; Google for browsing, comparing and deciding."

Breakdown (H3): provide 1-2 figures, explain scenarios and include a pros/cons table.

Evidence: cite sources only once in the paragraph and leave the full list in the References section .

What's the quick checklist for applying this framework to your content?

• One idea per paragraph; numbers always in font.
• Data with month/year; dateModified visible.
• Glossary and internal anchors for block navigation.
• Internal links to clusters; external links only when they add value.

What is the 90-day technical and tactical checklist for implementing this strategy?

This4-phase plan is designed for marketing and content teams that need to combine SEO (SERP) and citability in LLMs.

The focus is on getting quick wins on pillar pages, structuring citable blocks (data + source) and installing measurements that connect content to business results. Treat it as a roadmap adaptable to your reality (resources, seasonality and compliance).

Days 0-15 - Diagnosis and strategy

• Map pillar pages by intent (informational, transactional, local).
• Extract PAA and autosuggest and transform into real H2/H3/H4.
• Identify data gaps: where percentages, benchmarks and linked sources are missing.

Days 16-45 - Production and rewriting

• Rewrite critical pages opening with direct response.
• Include comparative tables and checklists.
• Link keywords to sources.

Days 46-75 - Tech, UX and structured data

• Implement Schema (Article/FAQPage/HowTo/Product/Organization/BreadcrumbList).
• Strengthen EEAT (authorship, about, editorial policy).
• Optimize Core Web Vitals and UX mobile.
• Summaries with anchors; descriptive alt text; clear titles.

Days 76-90 - Measurement and iteration

• Monitor brand searches and CTR of information blocks.
• Update data with visible month/year.
• Run "citation test": do the opening paragraphs contain numbers and sources?
• Iterate FAQs according to new PAAs and service queries.

SEE ALSO THESE CONTENTS:

• How HTML, CSS and JavaScript Work Together in Website Programming
• New sales funnel for SEO with LLMs
• ChatGPT vs. Google vs. Social Search: organic traffic and SEO

Which metrics should I track in the age of AI search?

Before measuring, align your compass: this dashboard brings together the KPIs that connect SEO to visibility in AI responses. Below, you'll find what each metric measures, how to calculate it, where to track it and the recommended frequency.

• Organic SOV (Share of Voice) and citation share by LLMs
  
  
  • What it measures: your relative presence per intent cluster on the SERP and how much you are cited by AI responses.
  • How to calculate: SOV = (words of the cluster in which you appear in the Top N / total words of the cluster) × 100. For citations by LLM, do monthly sampling of prompts and record mentions/links in spreadsheet/dashboard.
  • Where to look: Search Console (impressions/positions per query), rank tracker, AI audits.
  • Frequency: monthly per cluster.
  • Next step: expand topic coverage and turn key paragraphs into "citable cells" (data + source).
    
    
• Brand searches
  
  
  • What it measures: brand memory and the effect of citations/PR on searches with your name.
  • How to calculate: track brand queries (exact variations) and the % of branded vs. generic traffic.
  • Where to look: Search Console (queries), Analytics (organic branded traffic).
  • Frequency: weekly/monthly.
  • Next step: map brand-generating content (studies, guides, tools) and replicate.
    
    
• CTR of information blocks (on-page)
  
  
  • What it measures: the ability of your modules (tables, FAQ, "direct response") to generate clicks to conversion pages or deeper sections.
  • How to calculate: CTR of the block = clicks on the block / impressions of the block on the page. Instrument with click events on internal and outbound anchors.
  • Where to look: GA4 (click events), heatmaps.
  • Frequency: continuous; review monthly.
  • Next step: test order, PAA titles and micro-CTAs on blocks.

• Engagement time / internal clicks
  
  
  • What it measures: quality of consumption (not just permanence) and progression to related pages.
  • How to calculate: use Average engagement time (GA4) + internal clicks / sessions. Combine with scroll to avoid false positives.
  • Where to look: GA4 (Engagement), heatmaps/scrollmaps.
  • Frequency: fortnightly/monthly.
  • Next step: strengthen contextual links and "next step" blocks.
    
    
• Shortcut conversions (microconversions)
  
  
  • What it measures: quick actions that shorten the journey (clicks on WhatsApp, phone, calendar, CTA above the fold).
  • How to calculate: track events (click, generate_lead, schedule) and assign shortcut conversion rate per page.
  • Where to look: GA4 (events/conversion events), CRM.
  • Frequency: weekly/monthly.
  • Next step: create visible shortcuts in the most engaged blocks.

• Freshness
  
  
  • What it measures: your ability to keep content fresh (a factor valued by SERPs and LLMs).
  • How to calculate: \# key pages updated in the quarter / total key pages and median age since last update.
  • Where to see it: editorial spreadsheet/content dashboard.
  • Frequency: quarterly (with monthly check ).
  • Next step: set up revision route (pillar pages every 6-12 months) with dateModified visible.

Do a monthly review by cluster, prioritize gaps (topics with no coverage and blocks with low CTR) and link each improvement to a business result (leads, MQLs, sales).

Image 01: ChatGPT vs Google - impact of AI on online search

Why does closing the strategy need two layers (SERP + LLM)?

If you've made it this far, you already know that in the ChatGPT vs Google debate, the user moves between conversational responses and click navigation.

Your strategy is only complete when it combines SERP performance with LLM citability, a combination that gives scale to the top of the funnel and preserves conversions at the bottom.

• Layer 1 - SERP: support EEAT, architecture and speed to capture informational, transactional and local intent, using comparatives, social proof and trackable CTAs.
• Layer 2 - LLM: provide self-contained snippets (2-4 lines), verifiable data and keyword anchors so that templates can summarize and reference your content correctly.

Choose 3 pillar pages, rewrite the initial paragraph with direct response + 1 piece of data with source, add a comparison table, implement Schema (Article + FAQPage) and set up measurement of blocks (clicks on anchors and micro-CTAs).

Those who optimize for SERP + LLM appear first in the response and finish in the click, that's how you win the ChatGPT vs Google game and turn attention into revenue.

Key learnings: SEO in the age of AI (ChatGPT vs Google): Google and LLMs have come to coexist: we use AI for rapid synthesis and Google to explore, compare and decide. The winner is the one who operates on "two fronts": ranking in the SERP and making the content citable by AI (direct answers right at the start, H2/H3 in question format, data with source, FAQ and tables). Reinforce quality signals (EEAT, Schema Article/FAQ/HowTo, clusters with internal links) and technical performance (Core Web Vitals - LCP/INP/CLS, mobile-first). Distribute multichannel (blog, social, newsletter) and measure beyond pageviews: share of voice per cluster, CTR of information blocks, microconversions and brand search growth.

In the "SERP + LLM" era, those who come out on top are those who respond quickly at the top, prove it with data in the middle and invite action at the end.

You've seen how to align SEO with citability in AI; now it's time to put it into practice with an editorial plan that generates "citable" blocks, updates data on a month/year basis and ties each page to business objectives (clicks, leads, sales).

To speed up this turnaround and create materials that perform on Google and in AI responses, follow the guide below and see how to structure your content marketing operation, step by step.

ChatGPT vs Google: SEO in the age of AI

What does the latest data show about ChatGPT vs Google?

In March 2025, ChatGPT became the most downloaded app globally, a sign that AI is already part of everyday life. Despite this, Google remains dominant: it fell to less than 90% market share at the end of 2024, but it is still widely used. Today, 95% of ChatGPT users also use Google, reinforcing that coexistence is complementary.

Will ChatGPT replace Google?

Not in the short and medium term. ChatGPT is used for synthesis and drafts, while Google remains the main tool for comparing, checking sources and making decisions, especially in local and transactional searches. This requires brands to prepare to perform on two stages: SERP and LLM responses.

Why do people still use Google so much?

Google offers a diversity of sources, clickable results, local functionalities (such as Maps and timetables), as well as trust and the well-established habit of "googling". LLMs, on the other hand, although useful, still face limitations such as hallucinations and a lack of up-to-date data on price and availability.

When to use ChatGPT and when to use Google?

ChatGPT is ideal for learning quickly, generating ideas and drafts. Google is better for comparing options, checking sources, validating news and carrying out local or purchase searches. In information journeys, many users start with ChatGPT and then move on to Google to validate and decide.

What is LLM-SEO and how can it be applied in practice?

LLM-SEO seeks to adapt content so that it can be cited by language models such as ChatGPT. The strategy includes opening texts with direct answers, creating short, self-contained paragraphs, including data with sources, using questions in PAA format in subheadings and adding tables, lists and glossaries.

How to create a "citable" content framework?

You need to structure the content in short blocks (2-4 lines), with a central idea, evidence and linked sources. Use subheadings in question format, insert up-to-date data and benchmarks, apply Schema (Article, FAQPage, HowTo) and include EEAT signs (author, update date, methodology).

What common mistakes should I avoid?

Avoid hiding the answer in the text, creating long paragraphs without data, using vague subheadings, forgetting Schema and dateModified and leaving heavy images without alt text. These mistakes reduce the chance of being cited by AI and harm Google rankings.

What is the 90-day checklist for implementing the strategy?

The plan is divided into four phases:

• Days 0-15: diagnosis and mapping of pillar pages.
• Days 16-45: rewrite with direct answers and comparison tables.
• Days 46-75: technical application (Schema, EEAT, Core Web Vitals).
• Days 76-90: measurement (CTR, brand searches, citations in LLMs) and iteration.

Which metrics to track in the age of AI search?

Some key indicators are:

• Share of Voice (SERP and citations in LLMs).
• Brand searches.
• CTR of information blocks.
• Engagement time and internal clicks.
• Shortcut conversions (such as WhatsApp or CTAs above the fold).
• Content update speed.

Why does the strategy need to have two layers (SERP + LLM)?

Because the user moves between conversational responses (ChatGPT) and clicks on the SERP (Google). To optimize this journey, it is necessary to combine classic SEO performance with citable content for LLMs. In this way, the brand appears in the response and also in the click, transforming attention into revenue.