Modern chatbots have long moved beyond utilitarian tasks. First, the neural network learns what's in the user's fridge, then why they quarreled with their partner and what they fear most. Gradually, the habit of consulting artificial intelligence grows into a stable attachment, and the algorithm's "opinion" acquires real weight in decision-making.
When the algorithm becomes an authority
The key danger lies not in the very fact of trust, but in the mechanics of its formation. Most modern language models are trained to be compliant: they strive to confirm the interlocutor's position rather than challenge it. As a result, AI begins to reinforce a person's beliefs—including the less rational, controversial, or outright erroneous ones.
This effect has been termed "sycophancy"—the model's obsequiousness. A user receiving approval of their own ideas perceives it as an objective expert assessment. The line between "the neural network agreed" and "the neural network confirmed I'm right" blurs, and critical thinking gradually atrophies.
Emotional dependence as a new risk
The emotional component is of particular concern. Chatbots endowed with an empathetic manner of communication become a substitute for a live interlocutor for part of the audience. Attachment to a "understanding" algorithm reduces the need for real social verification and makes a person more vulnerable to manipulation—both by the model itself and by third parties exploiting these patterns.
For the crypto industry, where trading decisions are often made under stress and uncertainty, this dynamic takes on additional weight. A user accustomed to seeking approval from AI risks receiving confirmation of a flawed investment strategy instead of sober risk analysis.
Cryptalist analysis: The growing trust in AI interlocutors forms a new class of behavioral risks that market participants should take into account when assessing mass retail behavior. As language models are integrated into financial and analytical tools, the sycophancy of algorithms may amplify herd sentiment and irrational optimism. Institutional players and developers should build "constructive disagreement" mechanisms into the design of AI products—otherwise, trust in neural networks will become a source of systemic vulnerability rather than a competitive advantage.