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In recent months, a serious debate has erupted within the crypto community and beyond: whether to replace the familiar Google search and its counterparts with conversations with neural networks. The trend is so obvious that it requires close analysis. Users are increasingly asking questions directly to chatbots, bypassing classic search results.

Where did the turning point begin?

The starting point can be considered a discussion launched by a user under the nickname Carlos That Notices Things. He claimed that Google has degraded to the point where searching for information through Grok has become frankly more convenient. This thesis was instantly picked up by American blogger Mike Chernovich, who admitted that several months ago he made Grok his primary search engine, leaving Google only for spell-checking. Elon Musk, founder of xAI (the developer of Grok), reposted it, only adding fuel to the fire, although his interest in promoting the product is obvious.

My analysis: This is not just hype. We are witnessing a fundamental shift in consumer habits. Users are tired of advertising noise and the need to filter through dozens of links. They want to receive a ready-made, structured answer.

Advantages of AI search: why neural networks are winning

The objective advantages of neural networks over classic search results are obvious and supported by practice:

• Ready-made answer instead of a list of links: The user gets a formulated conclusion, rather than the need to manually sift through a bunch of tabs. Modern models can also provide links to sources.
• No intrusive advertising: The top of the Google page is cluttered with sponsored results. A chatbot responds strictly to the essence of the query.
• Privacy and no retargeting: After searching for a product in a regular search engine, you are haunted by contextual ads for weeks. A conversation with a neural network leaves no such trace.
• Understanding complex queries: AI can parse a vague task, clarify details, and find a solution. Classic search requires precise keywords.
• Multifunctionality: In addition to search, neural networks effectively solve related tasks, from analyzing long texts to managing projects.

The flip side of the coin: weaknesses of AI

However, it is too early to blindly trust neural networks. The main problem is the tendency to fabricate facts (hallucinations). Critical information still has to be double-checked. Additionally, AI still lags in image search, and data relevance may update with a delay. We should not forget about the cost: the free mode is limited, and full use requires a subscription, whereas Google remains free.

What are users choosing?

Opinions are divided. Supporters of the transition describe a similar scenario: tried it — and practically stopped using Google. Arguments: no ads, convenience of a ready-made answer, and AI's ability to find products based on a loose description. Many experienced internet users have already changed their default search engine.

Skeptics, however, provide strong counterarguments: limitations, false answers, and high cost. They rightly point out that the choice is not limited to the dilemma of "Google vs. Grok." There are Gemini, Perplexity, Claude, and other tools on the market.

My professional opinion: AI search indeed addresses a number of pain points of classic search engines, but it has not yet become a universal replacement. A reasonable approach in 2024 is to combine tools for specific tasks: use neural networks for analytics and idea generation, and Google for quick fact-checking and finding primary sources. And yes, the habit of double-checking important data should stay with us for a long time.