There’s a common misunderstanding that large language models (LLMs) like ChatGPT or Gemini are replacing search engines. They aren’t. LLMs change how results are presented and explained, but the heavy lifting of finding, organizing, and ranking the web still belongs to search engines. In plain English: LLMs are the brainy librarians inside of a giant library; search engines are the library’s cataloging system that keeps track of every book, page, and shelf.

Below is a clear look at what each does, why they’re different, and why search is not only sticking around but also growing.

What search engines actually do (and why that matters)

Search engines run a huge, ongoing pipeline that works like this:

1. Crawl: Automated bots (“crawlers”) visit web pages and take notes on what they find.
2. Index: Those notes are stored in a gigantic, constantly updated catalog (the “index”).
3. Rank & Serve: When you search, the engine looks up the most relevant pages in that index and ranks them using complex algorithms.

Google’s own documentation lays out this crawl → index → rank process in detail. If you’ve never read it, it’s surprisingly readable and shows the scope and complexity behind what looks like a simple search box. directly;

You can’t browse Google’s index directly, it’s proprietary and unimaginably large. You query it. If you own a website, you can see your slice of the index in Google Search Console’s Page indexing report, which shows which of your pages are in or out and why. Microsoft offers similar visibility in Bing Webmaster Tools, including a Sitemap Index Coverage report that flags reasons URLs are excluded.

This is the invisible machinery of the open web. It’s what makes it possible to find new content minutes after it’s published and to keep billions of pages ordered enough to be useful.

What LLMs actually do (and what they don’t)

LLMs are trained to predict and compose text. They’re excellent at summarizing, explaining, reformatting, and reasoning over information they’re given. But there are two common misunderstandings:

• LLMs do not maintain a live, internet-wide search index. The model itself isn’t crawling the web in real time or keeping a searchable catalog of every page like a search engine does. When LLMs need fresh facts, they typically consult a search engine index. Meaning they call a search engine service (UI or API). The search engine then queries its own index, returns ranked results, and the LLM fetches a few of those pages and combines them into the answer it generates for the user. Google literally calls this “grounding with Google Search.”
• “Browsing” ≠ “crawling.” What we just described is called retrieval and summarization, not operating a global crawler and index. OpenAI’s newer “deep research” mode, for example, plans multi-step lookups and shows sources. Again: retrieval plus synthesis, not running its own universal web index.

This distinction matters because it explains why LLM answers can be hallucinatory. Without a high-quality retrieval step (i.e., search), an LLM is just “guessing” based on training data that could be outdated or incomplete.

That said, ChatGPT (the product of OpenAI) now runs a real web crawler called OAI-SearchBot and maintains OpenAI’s own web index so it can discover pages and show them as cited sources in ChatGPT Search. Which again proves this article’s point: you still need search infrastructure under the LLM.

The winning combo: grounding LLMs with search

The industry term for blending search with generation is Retrieval-Augmented Generation (RAG). In RAG, the system first retrieves relevant documents from a trusted source (like a search index or an enterprise knowledge base) and then generates an answer that cites those sources. Requiring the AI search engine to cite its sources can also dramatically reduce hallucinations. The original RAG research popularized this approach in 2020, and it’s now widely used.

You’ll see this philosophy in multiple places:

• Google Gemini / AI Overviews: “Grounding with Google Search” pipes real-time search results into the model and returns answers with citations.
• Vertex AI: Google Cloud’s guidance explicitly recommends grounding model outputs in verifiable data, via Search, RAG, Maps, and more, to reduce hallucinations.

The big picture: LLMs are the presentation and reasoning layer; search is the fact-finding and verification layer. You need both.

The library and the librarian

Think of the web as a giant library:

• The search engine builds and maintains the card catalog (the index). It constantly scans new “books” (web pages), decides where they belong, and keeps the catalog current.
• The LLM is the librarian who reads the relevant pages you point to and then explains them in friendly language, weaving them into a clear, direct answer. If the librarian is allowed to cite the exact books and page numbers, you can check the work.

When the librarian doesn’t check the catalog first and just “remembers” what books might say, mistakes happen. That’s why modern AI features emphasize grounding and citations.

“But aren’t people just using AI instead of Google now?”

Short answer: no. AI usage is up, and Google Search remains massive and growing.

• Alphabet’s earnings releases and CEO remarks throughout 2025 show double-digit growth in Search revenue and healthy overall query growth, including a 70% year-over-year jump in Google Lens searches, much of which is incremental (i.e., additional to traditional text queries). That’s expansion, not replacement.
• Independent financial reporting backs this up: multiple quarters in 2025 attribute Alphabet’s outperformance partly to strength in core search, even as AI features roll out alongside it.

It’s also useful to separate revenue from queries. Revenue grows when users stay engaged and ads remain effective; queries grow when people search more, in more ways. Google has repeatedly highlighted growth in newer, multimodal behavior, like searching with your camera (Lens) or combined gestures, showing search is evolving rather than shrinking.

Why LLMs don’t (and shouldn’t try to) be search engines

1. Freshness at web scale: The public web adds and changes billions of pages. Keeping a comprehensive, deduplicated, spam-resistant, and continuously updated index is a specialized, infrastructure-heavy job. It’s what search engines were built for.
2. Transparency and provenance: When an LLM is required to cite sources, users can click and verify. This is standard in grounded systems like Gemini’s “Search grounding” and Vertex’s guidance. Purely generative answers can’t offer the same audit trail.
3. Governance and site control: Website owners monitor their presence in the index through Google Search Console and Bing Webmaster Tools, diagnosing why pages are in or out. That visibility is essential for a healthy open web and isn’t replaced by a model’s internal training data.
4. Commercial ecosystems: Search drives measurable, intent-rich traffic that businesses can analyze and optimize. That incentive structure sustains publishing and commerce broadly. The earnings results we’ve seen suggest these dynamics are holding, even as AI features appear in the interface.

What this means for everyday users

• You’ll see more answers. AI summaries sit on top of search results and often include citations so you can dive deeper. Expect more multimodal options (speak, snap a photo, or draw a circle on your screen) that kick off a search behind the scenes.
• Quality still wins. If you publish online, the fundamentals matter even more: sitemaps, clean site architecture, crawlability, canonical tags, structured data, and helpful content. Search engines need to index and rank your pages before an LLM can confidently cite them.
• Trust but verify. AI answers can be great for speed and clarity, but when it counts, click through the citations. Even OpenAI’s more advanced research features emphasize sources precisely because models can still overstate or hallucinate details.

What this means for businesses and publishers

• Search is still the discovery backbone. Alphabet’s 2025 results show search’s resilience and growth as AI features roll out; the pie is getting bigger, not smaller.
• Optimize for being cited. When LLMs ground answers, they look for trustworthy, well-structured, crawlable sources. Make sure your pages are indexable and well-labeled so they’re retrieved and cited instead of a forum thread summarizing your work.
• Expect new query types. Visual and voice-led searches are growing fast, often incrementally—meaning they’re additions to classic typed searches, not replacements. Prepare your content and product data (images, alt text, schema) to be useful in those contexts.

Quick FAQ

Do LLMs “crawl the web”?
No. The applications around LLMs may fetch pages when you ask a question, often via a search partner, but the models themselves don’t operate a global crawler and index like a search engine. Google’s own AI stack explicitly “grounds with Google Search.”

Can I see the web index somewhere?
Not directly. You can query it (e.g., with Google or Bing), and if you own a site, you can inspect your pages’ status in Google Search Console or Bing Webmaster Tools.

Isn’t AI going to reduce searches
Evidence to date suggests the opposite: search usage and revenue are growing while AI features roll out, and newer behaviors like Lens are expanding the pie.

So what’s the right mental model?
Search engines find and rank facts at web scale. LLMs present and reason over those facts. Together, they produce faster, clearer answers, with links you can check.

The bottom line

LLMs have not replaced search; they’ve changed its surface. Underneath any polished AI answer, the classic information-retrieval pipeline, crawling, indexing, retrieval, and ranking, is still doing the heavy lifting. Modern systems combine them: search grounds the answer; the LLM explains it. And if you look at 2025’s numbers and usage patterns, search isn’t going anywhere. It’s evolving, growing, and quietly powering the AI experiences we’re all watching unfold before our eyes. Reach out to SEO Rank Media if you want a partner who understands the direction search is headed and how to position your business to be at the forefront of the evolution.