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Traditional search engines, including Google, are rapidly losing their audience. Users are increasingly favoring neural networks, asking questions directly to chatbots and receiving ready-made answers instead of a list of links.

The trend is so noticeable that it has sparked heated discussions on social media. Let's explore why this is happening and whether it makes practical sense to replace classic search with artificial intelligence.

How the debate started

The discussion was initiated by a user under the nickname Carlos That Notices Things. He claims that Google has degraded so much that searching for information through Grok has become noticeably more convenient. This point was picked up by American blogger and political commentator Mike Cernovich. According to him, several months ago he made Grok his primary search engine, and now he only uses Google for spell-checking — for such minor tasks, Grok is slower. In Cernovich's opinion, Grok surpasses Google in every way.

Cernovich's post was retweeted by Elon Musk. Context is important: Grok is developed by the company xAI, founded by Musk himself, so he has a direct interest in promoting the product.

How neural networks can outperform search engines

AI search has several objective advantages over classic search results:

• Ready-made answer instead of links. Instead of opening a dozen tabs and manually filtering out junk, the user receives a formulated conclusion. If necessary, the system also provides links to sources.
• No ad-filled results. Classic search engines fill the top of the page with sponsored results, whereas a chatbot responds strictly to the essence of the query.
• No intrusive retargeting. After searching for a product in a traditional search engine, a person is followed by contextual ads for weeks. A conversation with a neural network leaves no such trace.
• Understanding complex and vague queries. AI can interpret a task description, clarify details, and find a solution. A regular search engine requires precise keywords. Advanced models handle multiple queries at once and consolidate the results.
• Additional scenarios. Beyond search, neural networks effectively handle related tasks — from analyzing long texts to managing large projects.

Weaknesses of AI search

Despite the clear advantages, neural networks also have serious drawbacks. The main problem is their tendency to fabricate facts (hallucinations). Critical information has to be double-checked. Additionally, AI still lags in image search and may update data with delays. The basic mode is often limited by the number of queries per day and requires a subscription, whereas Google is completely free.

What users think

Under the posts of discussion participants, a debate erupted about the feasibility of switching. Supporters describe the same scenario: tried it — and almost stopped using Google. Arguments include: no ads, the convenience of a ready-made answer, and AI's ability to find products based on a loose description. Some note that even experienced internet users have stopped using Google as their default search engine.

Skeptics offer counterarguments. Some users believe that neural network search still has many limitations and that switching search engines is premature. Complaints include false answers and incorrect information in voice search. Another grievance is the high cost and limits. A notable part of the audience points to alternatives to Grok — from Gemini and Perplexity to Claude — emphasizing that the choice is not limited to the "Google vs. one neural network" argument.

My expert assessment: AI search indeed addresses several pain points of classic search engines, but it has not yet become a universal replacement. A reasonable approach is to combine tools for specific tasks and maintain the habit of double-checking important facts. The market is only at the beginning of this transformation, and in the next year or two, we can expect many more interesting changes.