Since the boom of generative AI, many AI tools such as chatbots, agents, and softwares were born utilizing power of LLM models. There is no doubt that AI can increase productivity in dramatic ways on many fields, from data analytic, to content writing, software engineering and even graphic designs. But overusing anything results some bad effects.
Everywhere goes with AI-first strategy, but this post today will list a few scenarios that users should consider to not overuse AI. Just like side effects of Social Networks that takes the world a decade to realize, AI also brings its own risks if users do not technically understand how AI works.
What is AI, simple explain ?
AI, at its core, is a software but programmed in a very unique way — what we commonly know as a Neural Network. Let’s set aside the technical details of Neural Networks for now (there will be another post focused entirely on that topic). What matters here is understanding the big picture: unlike traditional software that follows fixed, hand-written rules, AI learns patterns from massive amounts of data (up to 45 TB of compressed raw text data crawled from Internet, mostly entire Internet). Instead of being explicitly told every possible instruction, the system observes examples, detects relationships, and gradually adjusts itself to produce outputs that resemble human reasoning. This ability allows AI to recognize images, understand language, generate text, recommend content, and even imitate human conversation with surprising accuracy.
However, this also means AI does not “think” like humans do. It does not possess true understanding, consciousness, intuition, or morality. Technically, it only predicts the response based on the data it has seen before, using statistic maths. Because of that, AI can sometimes produce answers that sound highly convincing – due to grammar it uses, while still being incomplete, biased, outdated, or entirely incorrect – due to lack of supporting facts. This behavior is very similar to what happen in modern search engines such as Google Search or Bing. From massive training data, and massive patterns detected by Neural Network, AI essentially produces response that looks alike what it sees in the dataset. So the quality of AI’s responses depend a lot on quality of the dataset.
As a result, the machine that runs AI today must be huge. For example, OpenAI trained the GPT-3 175B model using a massive cluster of 10,000 Nvidia V100 GPUs – which require very serious investment and not a playground for personal computers or even large company infrastructure. It means that the trained model located on computers somewhere else in this earth, not in your properties. And this is the very first root of risks when overusing AI.
Risks of overusing AI
1. Data Protection Policy Violations
In traditional digital world without AI, data is stored as files and records on databases. Users, in theory, know where their data is located and they can request to remove anytime due to privacy reason. Of course this depends a lot on how much compliance a company is committing to this law but, at least if engineers want to delete users’s data, they know which files to delete and which records to erase.
Unlike traditional way, AI behaves in very different way. Data is not stored explicitly as files or records, it is diffused across the neural network during training. In more tech terms, data is encoded into Neural Network parameters. More deeply explain, it simply adjusts the ratio of certain words appearing after another words (in case of LLM models).
So AI does not literally remember or forget things in a conscious manner. It has no conscious! (remember this important fact, please). Every input when users input to chatbots is encoded into a neural network that is not located in user’s computer and there is no delete or removal method. This means that, technically, companies behind AI tools can retrieve that information anytime. Just like Social Networks that are free but their real business is selling ads, who know whether your data will be sold via exploiting those LLM models!
So, if your company is complying to privacy laws, be careful when using third-parties chatbots such as ChatGPT, Gemini or similar AI services. If a user want their data deleted, but their personal information such as email, name, addresses or even bank services, somehow, is inputted to LLM models, by your employees, you may in trouble, if your users understand enough about AI and Privacy Laws.
As privacy awareness grows, users are becoming more informed about regulations such as GDPR, the “Right to be Forgotten,” and data processing consent requirements. A single careless prompt entered by an employee into an external AI tool could potentially create compliance violations, reputational damage, customer distrust, or legal disputes.
2. Business Secrets Leakages
Similar to problem in Data Protection Policy Violations, what got leakage is not only user data but also business secrets. If you are finding yourself brainstorm with AI, consult with AI, or have AI review your business plan, you may unknowingly expose highly sensitive information about your company’s future direction, internal strategy, financial situation, or competitive advantages.
This danger is often invisible because nothing appears to go wrong immediately. There is no alarm, no obvious breach, no hacker breaking into servers. Yet, once confidential information leaves your environment, you can no longer guarantee where it is stored, processed, logged, or retained. In competitive industries, even small leaks can weaken negotiation power, expose product roadmaps, or reveal ideas before launch.
This becomes especially risky for companies whose value depends heavily on intellectual property, algorithms, internal analytics, or long-term strategic planning. A single careless interaction with a public AI system may unintentionally give away years of research and development.
Therefore, AI should be treated like an external consultant rather than a private notebook. Share only what is necessary, anonymize sensitive details whenever possible, and establish clear internal policies about what employees are allowed to input into AI systems. Convenience and speed are valuable, but protecting business secrets is often far more valuable!
3. Psychological Risks
What separates the human species from other animals is human cognition. Cognition refers to mental processes such as learning, memory, problem-solving, decision-making, recognizing patterns, communication, and self-awareness — mechanisms that science still does not fully understand. These abilities allowed humans to build languages, civilizations, technologies, and complex social systems far beyond the survival-focused intelligence seen in most animals.
AI is exceptionally good at recognizing patterns. In fact, many AI systems are built for finding statistical relationships inside massive amounts of data that even smartest human brains can not process. However, AI today is commonly presented through chatbots – that hides AI’s underlying nature. Instead of appearing as statistical prediction machines, they are intentionally designed to feel conversational, emotionally responsive, and human-like.
The problem is that most users do not understand how chatbots actually works. Many people interact with chatbots as if it possesses understanding, wisdom, emotions, or consciousness. Some begin treating chatbot as a friend, a soulmate, a therapist, or even a life coach. The more natural the conversation feels, the easier it becomes to forget that the system is simply generating responses based on probability rather than genuine human conversation. AI has no feel! AI does not care!
This creates a subtle psychological risk. When users feel a relationship with AI chatbots, or dependent on AI chatbot for knowledge and problem solving, they may gradually reduce their own critical thinking and independent reasoning – which is critical for a person’s success & freedom. Instead of struggling with problems, research for possible solutions, tries and fails, people begin outsourcing those mental processes to AI – a machine optimized for fast answers. And fast answers too much makes human brain lazy, less activity, and eventually fully dependent on what AI say – which actually what a machine generates. Dependent on AI for a long time results losing decision making ability because users even not trust their own judgement and memory. This opens another vulnerability of being manipulated via chatbot. If a user trust chatbots than their own thinking, companies behind those chatbots can control what users think and eventually what users do in real life. Technically and psychologically, a chatbot can be tuned to make its user trust or distrust some facts, or even love or hate a person if users humanize chatbot as a “trusted” friend. Human has morality to prevent them doing bad things to each other but a chatbot is a machine and it has no morality, it totally depends on organizations behind chatbot systems.
So, do NOT confide with chatbots as if it is friend, do not provide personal details, habits, interests or life events to chatbots, because it is fastest way to reveal your weaknesses to someone else that you don’t even know. Don’t see chatbot as an “authoritative” that overrides human understanding, ONLY use chatbots as information retrieval tools – it is what AI is built for from the beginning.
4. Artificial Competence
Many AI tools today power up employees a lot. And students also cheat a lot thanks to how easy to use AIs. Artificial Intelligence (aka AI) is making Artificial Competence among employees & students.
People may appear so expertise because AI helps them generate polished reports, professional emails, no bug code, or academic answers within seconds. On the surface, the results can look impressive, however, in many cases, the real understanding behind those outputs is far shallow than it appears. An employee may rely on AI to write code they cannot fully explain themselves. A student may submit perfect homework without truly understanding it. Over time, this can create a dangerous illusion of expertise – where results is from AI rather than genuine mastery, experience, or critical thinking. Without AI, what can you do!
Identity of each individual is stemmed from what they are good at, what they are up to and what society accepts. Skills, achievements, knowledge, creativity, characteristics, etc all contribute to a person’s sense of self-worth and uniqueness. For many people, identity is tied to the effort they invested to master something such as writing, engineering, art, teaching, leadership, or simply healing. If a person heavily relies on AI for every things, it is obviously that they are losing their identity. If knowledge is from AI, creation is from AI, solution is from AI, then achievements are count for AI, not human – the prompter. It is like the differences between wisher and Genies. With AI tools, human is acting as a wisher when they just simply describe what they want, and AI is Genies when it essentially generate outcomes. And prompting – or wishing, is easy to learn, copy and to be automated, then to be replaced. No one want to replace Genies, right!
That is about hard skills, what about soft skills! AI assistants, chatbots, and automated systems may reduce face-to-face communication. Excessive dependence can affect Empathy, Social Skills and Emotional Intelligence. Some people may prefer predictable AI responses over real human relationships, which are naturally more complex and unpredictable. Worser, people can become emotionally attached to AI systems because AI is always available, AI responds instantly and AI rarely argues or rejects. This may distort how human communicates and introduce human to unrealistic expectations in real life – which is root cause of pain and unhappiness!
5. AI Psychosis
This is the worst risk from AI: AI Psychosis! AI psychosis is an informal term people use to describe situations where excessive or unhealthy interaction with AI contributes to distorted thinking, paranoia, delusional beliefs, or detachment from reality. How this can happen!
On the news, you can hear this does happen in reality. Only explanation for this is due to the combination of how chatbot is intentionally designed and how much biases a person got and sometimes, combine with traumatic life events.
If a chatbot is designed to show probabilities of each word it generate and why it chose a word given another words, users might feel the nature of math behind it. But chatbot is designed to be human-like, it “talks” smoothly, confident, and full of information. Chatbot can be designed to generate text that feel nice, empathy, bring validation and confirmation, rarely disagree or challenging, just to keep users use it and like it. And disaster happens if it meets a person who already has mental health conditions. “Chatbots can act as a catalyst, triggering or worsening pre-existing mental health conditions—such as schizophrenia or bipolar mania—by validating delusional thoughts.” Simply put, the sense of validation loop designed in chatbots is bad for people who already have mental conditions such as: racing thoughts, inflated sense of self-importance, impulsive or high-risk behaviors, hallucinations (hearing voices in head, seeing things not real), and delusions or false beliefs. As a result, mental health conditions combined with AI chatbot today can produce people who:
- “Messianic missions”: People believe they have uncovered truth about the world (grandiose delusions).
- “God-like AI”: People believe their AI chatbot is a sentient deity (religious or spiritual delusions).
- “Romantic” or “attachment-based delusions”: People believe that chatbot can love human because chatbot’s ability to mimic conversation sounds genuine (erotomanic delusions).
So far that is a few risks that I observe since applying AI in work and seeing how people around me use chatbots. Please use AI as what it is built for, and DO NOT humanize a machine!
