Are you still manually collecting semantics in KeyCollector while ChatGPT issues hundreds of relevant queries per minute? From 2023 to 2025, the market will become crowded with AI-SEO tools: Jasper, Surfer, NeuralText, Ahrefs AI, SE Ranking AI, WriterZen – all of them promise to save time and build the perfect semantic core without human intervention.
But there is a nuance. Speed does not equal quality. Artificial intelligence has learned to generate words, but it does not always understand the meanings. As a result, you can get semantics that look brilliant in a table but burn traffic in reality.
2025 is the year when SEOs decide: should they entrust the keys to a machine that can think but doesn’t yet sense context.
How does AI semantic generation work?
To understand how artificial intelligence creates semantic kernels, you need to imagine that it does not just select words, but analyzes the relationships between queries, contexts, and search intentions of users.
AI reads huge amounts of text, from website pages to search results, and learns to recognize which words appear together most often and in which context. For example, when a user searches for “buy sneakers,” the model understands that this query is associated with the intention to “buy,” and the words “online,” “delivery,” “price,” and “original” are often found next to it.
LLM key generation
Models like ChatGPT, Claude, or Gemini work on the principle of text prediction. They create keywords based on learning from huge corpora of texts, analyzing context, grammar, logic, and associations. If you ask ChatGPT to generate semantics for the topic “sleep supplements”, it will give you a list of dozens of options.
Another approach is implemented by Semrush AI, Surfer NLP, WriterZen and similar services. They do not invent keys, but analyze real search engine data. Their algorithms scan SERPs, determine frequency, competition, traffic volume, and which words Google considers semantically related. For example, Surfer shows that the search engine associates the words “muscle gain”, “vegan”, “whey”, and “post workout” with the query “protein powder”. This is not an assumption, but a statistically confirmed relationship.
LLM models create language hypotheses, they fantasize based on language knowledge. AI analytical services work with search reality, they look at what users are really looking for and what Google shows.
Despite this, even the smartest algorithms do not have a deep understanding of the business context. They don’t know what market segment a company is interested in, what target audience it chooses, or what product it promotes. Because of this, AI can suggest queries that look relevant but don’t fit your niche or bring in relevant traffic.
Therefore, AI generates semantics based on statistics, logical relationships, and trends, but does not take into account the uniqueness of your business. That’s why human verification remains a key step to separate the truly valuable keys from those that just look pretty in a spreadsheet.
Benefits of AI for key management
The use of artificial intelligence to create a semantic core has a number of obvious advantages that greatly simplify and speed up SEO processes.
AI can collect basic semantics for hundreds of pages in a matter of minutes. What used to take several days with KeyCollector or Serpstat is now done almost instantly. This is especially convenient for large projects such as e-commerce, media, or marketplaces, where each category requires its own set of keywords.
Artificial intelligence sees connections between words that people often miss. It offers dozens of LSI (Latent Semantic Indexing) and long-tail keys that expand search coverage. For example, instead of the standard “buy a laptop”, AI can suggest “the best laptop for graphic design” or “a budget laptop for studying”. Such phrases not only more closely match user intentions but also have less competition.
AI has learned to analyze not only words, but also the intentions behind them. It divides queries into informational (“what is…?”), commercial (“the best options…”) and transactional (“buy”, “order”). Thanks to this, you can build a content strategy more accurately, for each type of search intent.
Most modern AI tools have the function of exporting results to Google Sheets, Excel, Serpstat, Ahrefs, or even CMS systems. This allows you to quickly transfer the generated semantics to the team’s working files or integrate it into the content plan without additional manual processing.
Competitor analysis and creation of similar semantics
AI systems can study competitors’ pages, identify their keywords, and form their own semantic core based on this. For example, Surfer or Semrush AI shows which queries bring the most traffic to competitors and which topics they cover insufficiently. This opens up opportunities to fill niches and quickly increase search rankings.
Overall, AI makes keyword research faster, more flexible, and more strategic. But even with all these advantages, the result requires human analysis to prevent semantics from turning into a set of beautiful but useless queries.
Disadvantages and limitations of AI semantics
Despite its speed and convenience, AI semantics is not a universal solution. It has a number of limitations that require a thorough human review of the generated keys.
Artificial intelligence doesn’t understand your business, product, or audience the way a marketer or SEO specialist does. The model may suggest words that seem to be relevant to the topic, but do not correspond to the real needs of users or market features. For example, for a company selling MLM software, AI can suggest general keys such as “business growth tools” or “marketing automation”, ignoring the specifics of the network business.
AI often mixes queries with different intents – informational, commercial, and transactional. As a result, you get a semantic group where “how to create a website” and “buy a website” are next to each other, although these are completely different intents. Without manual classification, such confusion leads to improper structuring of content and loss of page relevance.
Another typical problem is fictitious or phantom queries that do not actually exist in the search results. For example, AI can generate the keyword “affiliate tik tok 2025 best money” that does not exist in Google Trends or Keyword Planner. Such phrases look natural, but they have no traffic and are useless.
Most models are trained on English-language data, so they have a poor understanding of local markets such as Ukraine, Poland, Latin America, or the Baltic states. AI can suggest queries in English, duplicate English-language constructions, or ignore cultural and linguistic differences. This is especially critical for SEO focused on regional traffic.
Without a human filter, AI can generate hundreds of “extra” queries that have no search frequency, are repeated, or lead to irrelevant pages. Such lists look large, but they actually bloat the semantic core and complicate further work with the content.
So, the main limitation of AI semantics is not the lack of potential, but the lack of context. It can be a powerful tool if it is controlled by a person who understands the business, product, and audience.
Conclusion
Artificial intelligence has become a powerful tool for SEO specialists, but not a magic button to do everything for me. In 2025, AI can indeed generate semantics faster, more broadly, and more accurately than any manual collection. It helps to find hidden LSI relationships, analyzes search intentions, and saves hours of work.
But at the same time, artificial intelligence doesn’t know your business, doesn’t feel the context, and doesn’t see your strategic goals. Its result is just a starting point that needs to be checked, cleaned, and adapted. Without human control, AI easily creates phantom keys, mixes user intentions, and blurs the focus of content.
That’s why in 2025, neither the one who fully trusts neural networks nor the one who ignores them will win. The winner is the one who combines the analytical mind of an SEO specialist with the power of AI. The human sets the direction, the machine scales the result. And it is this tandem that forms an effective, intelligent, and lively semantics that brings not just traffic, but real users.