MEXC Research Lead Analyst Sean Young rightly noted that the main mistake beginners make is perceiving LLM models as a source of trading signals, although by their nature, they are neither predictors nor real-time market analytics systems.

Language models are a tool for generating probabilistic responses, not a decision-making system with access to streaming data, risk models, liquidity, or the context of the current order book.

What AI does well and safely for traders:

* explains complex concepts (DeFi, L2, consensus, derivatives);

* analyzes whitepapers, tokenomics, and protocol documentation;

* writes code for bots, indicators, and API integrations;

* structures market metrics;

* produces high-quality sentiment analysis based on open sources, but only as an additional layer of information.

What AI cannot and should not do:

* generate trading signals ("buy now," "hold up to 120k");

* interpret market context as deeply as an algorithm with access to real data;

* account for liquidity manipulation, funding, order flow, and market microstructure;

* adapt to rapid market regime changes.

There's another risk of manipulation with mass use. If thousands of users send identical requests to ChatGPT/DeepSeek, the market will receive a uniform and consistent behavior pattern, which reinforces the herd effect.

This can lead to synchronized position entries, false trends, increased volatility in illiquid assets, and the illusion of a "working strategy" that is, in fact, self-perpetuating noise.

Bitget Marketing Director Ignacio Aguire agreed that AI models are often mistakenly perceived as reliable trading advisors, expecting them to provide instant buy or sell signals.

These modern tools are better suited to serve as assistants and are not yet reliable enough to interpret market sentiment and manage risk in real time. Until models can effectively integrate real-time data—market liquidity flows, microstructure, and execution risks (which they currently cannot do)—human judgment, risk management, and discipline remain indispensable.

One of the key risks of using such models in crypto trading is hallucinations, in which AI confidently generates plausible-sounding but factually incorrect or completely fictitious information. This poses a danger for traders: if you make a decision based on a "signal" that never existed or on a misrepresented fact, you automatically expose yourself to the risk of losses.

To filter out noise, experienced traders should treat AI conclusions as hypotheses, not ready-made solutions. They should rely on verifiable on-chain data (wallet flows, exchange balances, open interest, funding), confirm facts through primary sources (blockchain explorers, official statements), and apply their own risk filters. In other words, AI is designed to generate ideas, humans are designed to test and execute them.

CoinEx CIS PR Manager Hazel Zhao compared modern artificial intelligence to Microsoft Office, a very powerful tool. In many areas, using ChatGPT or DeepSeek can significantly simplify and improve the efficiency of routine tasks and workflows. However, most language models are unable to monitor market conditions in real time or fully process the latest macroeconomic factors.

This means that the market information they rely on is often lagging and incomplete, so beginners should not use them as trading advisors.

AI is extremely useful for compiling reviews and documentation, but position sizing, timing, and the combination of macro and micro factors still need to be assessed by the trader. The model should not, in principle, make decisions on these matters.

Recently, OpenAI, the developer of the ChatGPT chatbot, acknowledged that the hallucination problem stems from the fact that even if the language model doesn't know the correct answer, it will still produce one. Perhaps this issue will be optimized in the future, but for now, it is a clear risk for traders. Traders can and should use large models to analyze data and compress vast amounts of information, positioning AI as a way to accelerate research rather than as a replacement for human judgment. Experienced traders should maintain their own views, viewing LLM results as a draft that requires verification.