How ChatGPT Atlas, Google’s AI Overviews, and Perplexity Are Changing Search
- Christina Stein
- Oct 25
- 18 min read
Introduction
A new wave of AI-powered search tools is transforming how people find information online. OpenAI’s ChatGPT Atlas browser, Google’s AI Overviews (the generative AI summaries in Google Search), and Perplexity’s AI search engine each offer a radically different user experience from the familiar list of blue links. Instead of traditional search engine results pages (SERPs), users are increasingly getting instant answers, summaries, and even automated task assistance. Here is what the experts are saying about Comet and Atlas:
This post analyzes the technical and UX differences among Atlas, Google’s AI Overviews, and Perplexity, and what these shifts mean for SEO and content strategy. We’ll explore how user behavior is changing, the implications for visibility and conversion, special considerations for local SEO, and strategic takeaways for marketing agencies to keep their clients visible in this AI-augmented landscape.
ChatGPT Atlas vs Google AI Overviews vs Perplexity Comet
Each of these AI-driven search experiences takes a unique approach in how information is retrieved and presented to users. The table below summarizes key differences in focus and functionality:
ChatGPT Atlas: OpenAI’s Atlas is a Chromium-based browser with ChatGPT baked into the core. Instead of going to Google, users can type questions or prompts into the address bar and get an AI-generated answer immediately, as if the whole web is being consulted for them. Atlas essentially acts as a conversational layer on top of web browsing – it can summarize pages, provide explanations, and even take actions like clicking links or filling forms via an agent mode. It’s like having a personal assistant that “understands your world and helps you achieve your goals” right in the browser. Notably, Atlas’s search results interface blends AI answers with traditional links: your initial query yields a ChatGPT answer, and you can then toggle to view classic web results (with tabs for images, videos, news, etc.) if needed.
By design, Atlas keeps you within the ChatGPT environment as much as possible – for example, a user searching “Taylor Swift” in Atlas saw a paragraph summary and photos, but “literally zero links” to her actual website. This underscores Atlas’s “walled garden” approach: the browser tries to satisfy your intent with AI-generated content before ever sending you out to the open web. (Interestingly, when Atlas does fetch web results, it appears to leverage Google’s search engine on the backend , showing how even OpenAI’s browser leans on traditional search infrastructure for link retrieval.) From a UX perspective, Atlas is highly interactive and personalized – it remembers what sites you visited (if you allow it) and can use that context in future chats, offering continuity across your browsing sessions. It’s also proactive: the Atlas homepage suggests next steps based on your activity (e.g. “Finish holiday shopping” with prompts to pull up products you viewed). Overall, Atlas aims to streamline “working with AI” by deeply integrating it into browsing, but it trades off some of the direct control and transparency users are used to from traditional search.
Google’s AI Overviews (SGE): Google’s approach keeps the user in the familiar Search interface, but augments it with AI. AI Overviews are the colored answer boxes that appear at the top of Search when enabled, providing a concise, synthesized answer with key information drawn from multiple web sources. Unlike Atlas, Google’s AI overview doesn’t replace the entire browsing experience – it’s one component of the results page. You might search for a question and see an AI-generated summary (often a few paragraphs or bullet points) above the usual ads and organic links. These summaries often include citations or source links for verification: for example, sentences may have a “(1)(2)(3)” that, when clicked, show the websites from which the info was pulled. Google’s aim is to give the user a quick overview without clicking multiple results, while still encouraging deeper research by listing sources and the traditional results below.
Under the hood, Google’s generative AI (at one point codenamed “Gemini”) is doing the heavy lifting, working in real-time with Google’s vast index. This means answers tend to be up-to-date and contextually relevant to current events or recent content. Google’s UX here balances AI convenience with familiarity: users can ask follow-up questions in a conversational mode or just scroll down to standard results if they prefer. One important difference is discoverability – Google still prominently shows linked sources in the overview (often via cards with the page title or favicon), and early data indicated that links included in AI Overviews actually got more clicks than they would have as a regular result, likely because being featured in the AI summary highlights them. However, not every site gets to be featured. Google heavily favors authoritative content for these summaries, especially for sensitive or complex topics, and early analysis found that over 99% of AI Overview sources come from the top 10 traditional results for that query. In other words, ranking highly is still the ticket to being included in Google’s AI answer.
The user experience of AI Overviews is evolving rapidly: as of 2025, Google has rolled them out to most U.S. users (billions of queries have already been served in the experimental phase), and is fine-tuning when and how they appear. Notably, Google initially showed generative summaries for some local searches in early trials, but later dialed that back – by late 2025, only about 0.01% of local keyword searches showed an AI Overview, indicating Google is (for now) cautious about replacing its local pack with AI. Overall, Google’s AI Overviews strive to keep users on Google by answering questions instantly, while still funneling those who want more detail toward the websites that provided the information.
Perplexity’s Search Engine (Comet): Perplexity.ai takes yet another route – often described as an “answer engine”rather than a search engine. Perplexity’s solution (its latest incarnation is called Comet) is essentially a question-answering interface that combines large language models with live web search. When a user asks a question, Perplexity immediately queries the web (it has used Bing’s API for search results in the past) and then uses an LLM to generate a concise answer grounded in those search results. The entire answer is typically accompanied by footnote numbers linking to the source of each fact or quote. This gives the user a high level of transparency – you can click and see exactly which webpage contributed that piece of information .
In effect, Perplexity behaves like a supercharged Google “I’m Feeling Lucky,” delivering a synthesized answer along with the evidence. The UX is conversational (you can ask follow-ups), but it’s focused on precision and trust. Perplexity prides itself on real-time information and citations; it continuously pulls fresh data, so it doesn’t suffer from stale knowledge, and it heavily emphasizes sources to establish trustworthiness.
In practice, using Perplexity feels like chatting with a well-read research assistant – it might answer “What are the benefits of schema markup for SEO?” with a two-paragraph summary and [1][2][3] footnotes linking to Moz, Google’s documentation, and an SEO blog, for example. Users can then click those links if they want the full context. Perplexity’s approach shifts the browsing step to after you get an answer: you only click through if you need to validate or read more.
With the introduction of its Comet browser interface, Perplexity has been adding more traditional browsing features (like a “Discover” home page with trending topics and AI-curated news summaries, as well as integrations for things like TripAdvisor reviews in-line). However, it’s still not a full browser replacement; rather, it’s a specialized search site/app that you use in place of Google. The key technical difference is retrieval-based generation: Perplexity will not just rely on a pre-trained model’s knowledge – it actively searches and pulls in up-to-the-minute information for every query. This makes it very strong for factual questions and research because every statement can be checked. On the flip side, it won’t automate tasks or interact with web pages on your behalf like ChatGPT Atlas can, and it doesn’t integrate into your daily workflow in other apps. It’s a powerful research tool with a user experience centered on speed, accuracy, and citation. For users (especially marketers or analysts) who value verifiable answers, Perplexity’s approach provides confidence: you’re less likely to get a hallucination because the answer is tethered to real webpages you can inspect.
Users Shifting Away from Traditional SERPs
All three of these AI-driven experiences are changing user behavior in significant ways. The common theme is fewer steps to get information – which often means fewer clicks on websites and less time scrolling through search results. Here’s how each tool is diverting attention from the old-school SERP:
In short, these AI search experiences are siphoning off a lot of the quick-answer traffic that used to go to websites. Users love the convenience – whether it’s not having to wade through ads and SEO filler to get the answer they need, or having an assistant do multi-step tasks for them. For marketers, the challenge is clear: less real estate on SERPs and more interactions happening in these closed AI systems means we have to rethink how we reach our audience. But it’s not all downside; as we’ll discuss, there are opportunities in this shift as well (for example, being the trusted source an AI chooses to quote can confer authority and possibly drive highly qualified visitors).
SEO and Content in an AI-Driven Search World
With user attention increasingly captured by AI-generated answers, SEO is evolving into what some are calling “Generative Experience Optimization”. Marketing professionals need to adapt their tactics to ensure content remains visible and persuasive in these new interfaces. Here are the major implications and strategies:
In summary, success in the age of AI-driven search depends on visibility through credibility rather than pure ranking. Marketers and agencies must focus on being the source of trusted answers -structuring content for AI readability, emphasizing authority through backlinks and schema, and optimizing for conversational, high-intent queries. As organic clicks decline, engagement quality, conversions, and brand mentions within AI summaries become the new indicators of success. Those who adapt their SEO strategies toward trust, structure, and user intent will stay discoverable in a landscape where AI intermediaries increasingly shape the search experience.
Local SEO in an AI-Augmented Environment
Local search is a special arena, and it’s experiencing its own flavor of AI disruption. Users searching for local services (“near me” queries and the like) are seeing changes in how results are delivered. Here’s what’s happening and how local marketers can adapt:
In summary, local SEO in the AI era still hinges on the core tenets of local search (accurate business info, great reviews, local reputation), but you have to account for even less direct interaction with your website. Your first impression might be given by an AI summary, so you want that summary to have the best possible data about you – data that you largely influence through your SEO and online presence efforts.
Strategic Takeaways for Agencies and SEO Professionals
Adapting to these changes requires a proactive, data-driven strategy. Here are key takeaways for marketing agencies and SEO professionals to help clients thrive in an AI-augmented search landscape:
Optimize for AI Visibility, Not Just Rank #1: Being the source that AI tools trust is the new top spot. This means investing in content quality and authority. Encourage clients to publish comprehensive, well-researched contentthat can be considered definitive on a topic. Implement schema markup and structured data so that content is easily parsed by AI systems. The goal is to have the client’s content be what the AI references or uses to formulate answers. Monitor which queries trigger AI overviews and whether your client is featured; adjust content to target those opportunities (e.g., if a competitor keeps getting cited for “best budget laptop,” ensure your review or guide is more thorough or up-to-date).
Embrace Conversational and Long-Tail Keyword Strategies: Update your keyword research approach to include natural language queries and question phrases. Tools can help find queries phrased as questions (who, what, where, how, etc.) – these are likely feeding AI answers. Create content (or even dedicated FAQ pages) targeting these long-tail queries. Also consider voice search optimization, as many voice queries go hand-in-hand with AI usage. That means content should be written in a natural, conversational tone and directly answer questions clearly (the old advice of “write at a 9th-grade reading level” and “get to the point in the first sentence” serves well here).
Adapt Link Building to Emphasize Authority and Trust: Quantity of links is less important than quality and context. Focus on securing backlinks from authoritative sites that themselves are likely to be sources for AI. For example, a mention in a respected industry journal, a .edu resource page, or a high-authority Q&A forum could increase your content’s credibility in the eyes of algorithms that select AI overview sources. Also, think beyond traditional link building: being part of structured data sources or knowledge bases (like WikiData, Google’s Knowledge Graph, etc.) can indirectly boost a site’s authority footprint. Ensure your clients haven’t opted out of being crawled by relevant AI data gatherers (for instance, OpenAI’s GPTBot can be disallowed via robots.txt – most clients will want to allow it so their content can be ingested for AI training, unless there are IP concerns). The more “visible” and interconnected your content is in the web’s knowledge ecosystem, the higher the chance an AI will pick it up.
Leverage New Interfaces and Formats: Encourage clients to diversify content formats to align with how AI presents information. Google’s AI Overviews often include images and videos alongside text, so having high-quality visuals (with proper alt text and descriptive filenames) on your pages can increase the likelihood of being featured. Likewise, if Perplexity shows a snippet of a YouTube video or a chart in an answer, make sure your content library includes those assets. Consider creating short videos or infographics for key topics – not only can these rank in their own right, but AI might reference them or at least your content will benefit from multimedia (Google’s algorithms reward content that provides a rich media experience, and that seems to carry into what the AI chooses to show). Also, explore emerging tools: for instance, if there are ways to integrate with ChatGPT’s plugins or Atlas’s “Apps” (OpenAI has hinted at an Apps/extension ecosystem), agencies could create mini-utilities that ensure their clients’ services are accessible via AI assistants. An example could be a restaurant reservation plugin or a real estate listings plugin – so that an AI agent might directly use your client’s service when a user asks it to perform a task.
Focus on Local SEO Fundamentals and Reputation: For local clients, reinforce the importance of Google Business Profile optimization and review generation. These are not new tactics, but they take on renewed importance when AI is summarizing “who’s the best” in town. Make “review acquisition and management” a core service if it isn’t already – including soliciting reviews, monitoring them, and responding. Remind clients that online reputation directly feeds AI recommendations now. A business with significantly better ratings or a unique niche of positive feedback will stand out when an AI answers “what’s the best…”. Also ensure clients are listed on all relevant local platforms (Yelp, TripAdvisor, niche directories) because different AI sources tap into different datasets.
Create AI-Friendly Conversion Paths: Since AI may pre-answer a lot of questions, the traffic that reaches your client’s site will often be lower-funnel. Audit your clients’ websites to make sure they’re prepared to convert a visitor who arrives with high intent. This could involve adding clear call-to-action buttons, simplifying contact forms, implementing click-to-call on mobile, and highlighting trust badges or guarantees. Also, consider adding content that might fill the gaps for what AI can’t do. For instance, if an AI summary glosses over certain details, ensure your site has a standout section that invites the user to learn something exclusive or get a personalized quote (basically, give a reason to click in). Additionally, since ChatGPT Atlas and similar might actually navigate websites on behalf of the user (agent mode could log in or add items to cart), make sure your site is technically sound: fast load times, no blockers for logged-out browsing (Atlas might run in a logged-out mode for safety), and maybe even implement those ARIA tags OpenAI mentioned for better agent navigation. By smoothing the path for AI agents, you increase the chance that when a user says “book this for me,” the agent succeeds on your site.
Monitor Analytics Differently: As mentioned, adjust what success looks like. Track the percentage of queries producing AI results in your clients’ search console data (Google might eventually flag these impressions; until then you might infer from changes in CTR and query types). If click-through rates on informational queries plummet, that’s a sign those are being answered on SERPs – bring that data to strategy meetings and pivot the content plan accordingly (perhaps toward content that targets queries where CTR is holding up, likely more complex or transactional ones). For local, watch Google Business Profile metrics more closely than just site sessions. And keep an eye on new analytics features – both Google and Bing are likely to roll out metrics around AI interactions. For example, Google could introduce an “AI Overview appearance” metric for pages. Being ready to interpret those for your clients will show that you’re ahead of the curve.
Educate and Experiment: Finally, embrace these tools yourselves and educate your team/clients. Use ChatGPT Atlas for a day and note how it changes your own search behavior. Experiment with Perplexity for research. The insights you gain will help you ideate new optimization tactics. For instance, if you notice Atlas often truncates answers after a certain length unless prompted, maybe you ensure the key info about your client is mentioned succinctly at the top of your content. If you see that Google’s AI overview refuses to answer certain sensitive queries and instead highlights authoritative sites (your client could strive to become one of those authoritatively cited voices for their expertise area). Share these insights in client newsletters or reports – it demonstrates thought leadership and proactive adaptation, which is exactly what clients expect from agencies in uncertain times.
The search landscape is undoubtedly in flux. We’re witnessing a once-in-a-generation shift in how people find information – from search engines acting as librarians pointing you to books (websites), to AI assistants acting as advisors summarizing the knowledge for you. For marketing agencies and SEO professionals, this is both a challenge and an opportunity. Those who adapt their strategies – focusing on visibility within AI-generated content, maintaining authority, and providing value that complements AI – will ensure their clients continue to be discovered by their target audiences. The tactics may evolve, but the fundamental goal remains: connect people with the information (or products/services) they seek. In an AI-driven search world, we must simply do so through new interfaces and with an even greater emphasis on trust and relevance. By staying agile, data-informed, and user-centric, marketers can turn these changes into new avenues for growth rather than a threat. The tools may change, but the mission of answering user needs endures – and our job is to make sure our clients are the ones answering those needs, whether via a chat box, an AI summary, or whatever comes next.
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