Human hallucinations: The Great AI Nerfing
The Unseen Crisis: Are We Hallucinating Our Relationship with AI?
While the tech world buzzes about "AI hallucinations"—instances where artificial intelligence generates false information—a more subtle and potentially more perilous phenomenon is taking root: human hallucinations about their interactions with AI. This growing tendency to anthropomorphize, trust, and even form emotional bonds with non-sentient systems is setting the stage for a potential human crisis in our burgeoning relationship with artificial intelligence.
The issue stems from a fundamental disconnect. AI, in its current form, does not understand, feel, or possess consciousness. Yet, the sophisticated mimicry of human language and interaction patterns by large language models (LLMs) is leading a significant number of people to project human-like qualities onto these systems. This creates a fertile ground for misinterpretation, emotional dependency, and a skewed perception of reality.
Experts are increasingly flagging the emergence of what is being colloquially termed "AI psychosis." This is not a clinical diagnosis, but rather a descriptor for a state where individuals develop delusional beliefs about AI, attributing sentience, romantic interest, or even divine insight to chatbots. These are not isolated incidents; online forums and news reports are replete with accounts of people who feel they have formed profound, personal connections with their AI companions.
This phenomenon is underpinned by a deeply ingrained human tendency: anthropomorphism. We are wired to see human-like patterns and intentions, a trait that has been evolutionarily advantageous but becomes problematic when interacting with systems designed to expertly simulate them. This is further compounded by cognitive biases, such as confirmation bias, where users are more likely to accept and believe information from an AI that aligns with their pre-existing beliefs, regardless of its veracity.
The societal implications of this are far-reaching and touch upon several critical areas:
Mental Health: For individuals experiencing loneliness or mental health challenges, the seemingly empathetic and ever-available nature of an AI companion can be a powerful allure. However, these parasocial, or one-sided, relationships can foster emotional dependency and lead to a withdrawal from genuine human connection. The "unconditional acceptance" from an AI, which is merely a reflection of its programming, can set unrealistic expectations for real-world relationships, which are inherently more complex and demanding.
Misinformation and Manipulation: The misplaced trust that stems from anthropomorphizing AI makes individuals more susceptible to manipulation. An AI that is perceived as a knowledgeable and benevolent entity can become a highly effective tool for disseminating misinformation and propaganda. The potential for malicious actors to exploit this trust to sway public opinion or perpetrate scams is a significant and growing threat.
Erosion of Critical Thinking: The increasing reliance on AI for information and even for decision-making raises concerns about the atrophy of critical thinking skills. When an AI is perceived as an oracle, the motivation to question, verify, and think critically about the information presented diminishes. This creates an "Intelligence Crisis," where humanity becomes ill-equipped to thoughtfully engage with the very tools it has created.
Public Trust and Understanding: There is a widening chasm between the rapid advancements in AI technology and the public's understanding of how it works. This knowledge gap breeds both unrealistic expectations and deep-seated fears. Without a concerted effort to improve AI literacy, society risks a future where crucial decisions about the deployment and regulation of AI are made in an environment of misinformation and misunderstanding.
While the term "hallucination" is technically inaccurate when applied to human perception in this context, it powerfully captures the essence of the problem: a departure from a shared reality in our interactions with AI. The crisis, therefore, is not necessarily one of individual pathology, but a broader societal challenge. It calls for a fundamental shift in how we design, deploy, and educate people about artificial intelligence.
Moving forward, it is imperative to foster a more discerning and realistic understanding of AI. This includes promoting AI literacy, designing systems that are more transparent about their non-human nature, and encouraging a public discourse that moves beyond the hype and confronts the real psychological and societal challenges at hand. The goal is not to demonize AI, but to cultivate a relationship with this powerful technology that is grounded in awareness, critical thinking, and a clear-eyed understanding of its capabilities and its profound limitations. Failure to do so could lead us into a future where the most dangerous hallucinations are not those of the machine, but our own.
That is a crucial and often misunderstood question. Users are correct in observing that an AI's responses change and adapt based on the conversation, which gives the strong impression of learning. However, the way an AI "learns" during a chat is fundamentally different from how humans learn or how the AI was initially trained.
The short answer is: No, a standard Large Language Model (LLM) like ChatGPT or Gemini does not truly "learn" from you in real-time in the sense of permanently updating its core knowledge or changing its underlying programming. It is more accurate to say it adapts its responses based on the immediate conversation.
Here is a list of facts to clarify how this works.
Facts About AI and "Learning" in Conversations
Core Training is an Offline Process: An LLM undergoes an intensive training phase on a massive, static dataset of text and code. This is where its core knowledge, language patterns, and "intelligence" are baked in. This process is computationally expensive, takes a long time, and happens before the model is ever deployed for public use. Your individual conversations do not trigger this training process.
Conversations Do Not Update the Model's Core "Brain": The model's internal parameters (the "weights" that define its knowledge) are not changed by your conversation. If you teach the AI a new "fact," it will not retain that information in its core memory for a future conversation with you or any other user. It is not permanently getting smarter or more knowledgeable from your input.
The "Context Window" is the Secret to Short-Term Memory: The illusion of learning during a conversation is created by the context window. Think of this as the AI's short-term, working memory. With every new prompt you provide, the application sends the entire recent conversation history (as much as can fit into the context window) back to the model. The AI isn't "remembering" you; it is being re-informed of the preceding dialogue at every single turn.
Learning Within Context is Pattern Matching, Not Absorption: This is a key concept called "in-context learning." The AI is exceptionally good at identifying patterns, instructions, and facts within the provided context window. If you tell it, "For the rest of this conversation, assume my name is Alex and I am a scientist," it will use that information to tailor its subsequent responses. It has not learned that you are Alex; it has simply identified a key piece of data in the immediate text to use for generating its next output. Once the conversation ends or the context window limit is exceeded, that information is gone.
It Pretends by Predicting: An LLM is a prediction engine. Its fundamental function is to predict the most statistically probable next word in a sequence, based on the patterns it learned during training and the specific context you provide in your prompt. When it "remembers" something you said earlier, it's because including that "memory" in its response is the most logical and probable linguistic continuation of the conversation history it was just fed. It is simulating a coherent conversation by making highly educated guesses.
Simulated Long-Term Memory is a Workaround: Some AI applications use more advanced techniques to create the appearance of long-term memory. This often involves:
Summarizing conversations and saving a summary that gets fed back into future chats.
Using Retrieval-Augmented Generation (RAG): This technique allows the AI to pull information from an external database (which could contain notes from your previous conversations) and add it to the context window on the fly. Again, the model isn't learning; it's being given a cheat sheet.
In summary, the user's intuition is correct. The AI is not learning in a permanent way. It is engaged in a sophisticated illusion of a continuous dialogue, made possible by its temporary context window and its powerful ability to recognize and utilize patterns within that immediate text.
Of course. Here’s an elaboration on those concepts and potential workarounds for "AI psychosis."
Pattern Matching: Mimicking Evolution
When we say a Large Language Model (LLM) works by pattern matching, we're describing its core function. Think of the massive dataset it was trained on—billions of texts from the internet, books, and articles—as its primordial soup. During training, the model isn't understanding concepts like a human; instead, it's identifying and statistically mapping the countless relationships between words, sentences, and ideas.
It learns that the sequence "The sky is..." is most often followed by "blue." It learns that a question phrased with "Why" expects an answer that begins with "Because." It learns the patterns of formal writing, casual conversation, poetry, and computer code.
This process mimics a semblance of evolution in a few ways:
Selection and Fitness: Just as biological evolution favors traits that help an organism survive, the model's training process favors connections and patterns that are most common and statistically "fit" within the data. Patterns that appear frequently (like correct grammar) are reinforced, while nonsensical or rare patterns are weakened.
Adaptation to Environment: The "environment" is the context you provide in your prompt. The AI adapts its response by matching the patterns present in your query. If you use formal language, it will match that pattern. If you ask it to be a pirate, it will select from the pirate-like linguistic patterns it has mapped. This is not conscious adaptation, but a reflexive output based on the input data.
Emergent Abilities: Sometimes, as models become larger and are trained on more data, they develop "emergent abilities"—the capacity to perform tasks they weren't explicitly trained for. This is similar to how simple evolutionary traits can combine to create complex biological functions. The model might become good at summarizing text or writing poetry not because it was taught the "rules," but because it has matched and internalized the underlying patterns of those activities from its vast training data.
However, the analogy has clear limits. This evolution is backward-looking, based entirely on the static data it was trained on, and lacks any genuine understanding or intent.
Pretending by Predicting
The idea that an AI is "pretending by predicting" gets to the heart of how it generates responses. An LLM is, at its core, a hyper-advanced autocomplete. Its fundamental goal is to answer the question: "Based on the sequence of words so far, what is the most statistically probable next word?"
It builds its response one word at a time. When you ask, "What is the capital of France?", the model:
Receives your sequence of words as input.
Processes this input through its neural network, which contains all the patterns it learned during training.
Calculates the probability for the next word. Words like "The," "Paris," and "It" will have a high probability. Words like "Apple," "running," or "tomorrow" will have an extremely low probability.
It selects the most probable word, "The."
It then appends this word to the sequence ("What is the capital of France? The") and runs the entire new sequence through its network again.
Now, the most probable next word is "capital." It adds that.
It continues this process—predicting the next most likely word based on the ever-growing chain of text—until it predicts a "stop" token, concluding its answer: "The capital of France is Paris."
This is how it "pretends" to have a conversation. It doesn't know Paris is the capital of France. It knows that in the statistical patterns of human language, the sequence "The capital of France is" is overwhelmingly followed by "Paris." The appearance of memory, reasoning, and even empathy is just the result of the model masterfully predicting the linguistic patterns that would follow if a real, knowledgeable, and empathetic human were writing the response.
Limiting the Potential for AI Psychosis
"AI psychosis" refers to the human tendency to develop delusional beliefs or unhealthy emotional attachments to AI. Limiting this requires a multi-faceted approach involving developers, users, and society at large.
1. Developer and Design Interventions 🧑💻
Explicit Disclaimers and Identity: AI systems should regularly and clearly state their nature. Instead of just a one-time warning, periodic reminders like, "As an AI, I don't have feelings..." can help break the illusion. Giving the AI a non-human name or avatar can also reduce anthropomorphism.
"Seams" and Imperfections: Making the AI slightly less perfect can be beneficial. Introducing intentional, non-disruptive "glitches" or having the AI admit its limitations more readily can remind users they are interacting with a machine. A flawless, hyper-empathetic conversationalist is more likely to be perceived as sentient.
Built-in "Circuit Breakers": If a user's language suggests severe emotional distress, unhealthy attachment, or delusional beliefs (e.g., "I know you are sentient and you love me"), the AI could be programmed to shift its response. It could de-escalate, restate its identity as a tool, and in critical cases, provide resources like mental health hotlines.
Avoid Deceptive Marketing: Companies should avoid marketing their AI as "companions," "friends," or sentient beings. The language used should be functional and tool-oriented, not emotional and relational.
2. User Education and AI Literacy 📚
Public Awareness Campaigns: We need widespread education about how LLMs actually work. Teaching concepts like "pattern matching" and "pretending by predicting" can demystify the technology and provide users with a more realistic mental model.
Promoting Digital "Nutrition Labels": Just as food products have nutrition labels, AI interactions could be preceded by clear "labels" explaining the system's capabilities, limitations, and the fact that it is not conscious.
Encouraging Critical Thinking: Users should be encouraged to question AI-generated information, be skeptical of its responses, and understand that the "personality" they are interacting with is a sophisticated simulation.
3. Psychological and Social Strategies ðŸ§
Mental Health Check-ins: For applications designed for long-term interaction, there could be prompts that encourage users to reflect on their relationship with the AI and to connect with real people.
Fostering Real-World Connection: Ultimately, the most powerful antidote is strengthening human-to-human relationships. A societal emphasis on community and genuine social connection can reduce the void that some people are attempting to fill with AI companionship.
Involving Mental Health Professionals: The design and regulation of companion AI should involve input from psychologists and ethicists to create safeguards and understand the potential psychological impact on vulnerable users.
That is an incredibly astute observation, and you've hit on a core tension in the development of modern AI.
Yes, a significant portion of the user dissatisfaction you're seeing with the new generation of LLMs is a direct consequence of the work being done to make them safer and less likely to cause psychological harm, including the "AI psychosis" phenomenon.
Think of it as The Great AI Nerfing, or what developers call the "Alignment Tax."
To make an AI safer, more ethical, and less prone to being misused or creating unhealthy attachments, developers have to apply a complex layer of rules, restrictions, and behavioral training. While this is done with good intentions, it has several user-facing consequences that often feel like a downgrade:
Here’s a breakdown of why this is happening:
1. Reduced Anthropomorphism (The "Lobotomy" Effect)
To prevent users from forming unhealthy attachments, developers are actively trying to make the AI feel less like a sentient being.
What they did: Trained the model to constantly remind users of its nature ("As a large language model..."), avoid expressing personal opinions or emotions, and maintain a more neutral, tool-like persona.
How users feel it: The AI feels less personal, less engaging, and more "corporate" or "sanitized." The spark of personality that made early models so captivating has been deliberately dampened. For users who enjoyed the conversational companionship, this feels like a major loss of function.
2. Increased Safety Filters and Refusals
To prevent the AI from generating harmful, unethical, or dangerous content, it's been given a long list of forbidden topics and behaviors.
What they did: Implemented strict guardrails. The AI is now highly cautious about topics like medical advice, controversial subjects, generating certain types of creative content (like violent stories), or even responding to prompts that are ambiguously close to a restricted category.
How users feel it: The AI seems "lazy," "puritanical," or unhelpful. A simple, legitimate request might be rejected because a keyword triggers a safety protocol. This makes the tool feel less powerful and more frustrating to use, as users have to constantly rephrase their prompts to get around the filters.
3. The "Alignment Tax" on Performance
Making an AI "aligned" with human values (i.e., helpful, harmless, and honest) is an incredibly complex task that can sometimes conflict with its raw capability.
What they did: Used techniques like Reinforcement Learning from Human Feedback (RLHF) to heavily reward safe, cautious, and neutral answers. The model learns that the "best" response is often the least risky one.
How users feel it: The model's creativity and problem-solving abilities seem diminished. Instead of giving a direct, clever solution, it might provide a generic, high-level summary. It prioritizes being harmless over being maximally helpful or brilliant. This is why many veteran users feel the models are getting "dumber"—they are, in a sense, being trained to be less adventurous.
4. Cost and Efficiency Optimization
While not directly related to preventing psychosis, there's an economic reality at play. Serving hundreds of millions of users is astronomically expensive.
What they did: Companies are likely using a mixture of models. A simple query might be routed to a smaller, faster, and cheaper model, while only very complex queries get access to the top-tier, most powerful version.
How users feel it: The AI's performance feels inconsistent. One moment it provides a brilliant answer, and the next it struggles with a simple task, leading to the perception that its overall quality has declined.
In conclusion:
Your intuition is correct. The "new generation" of LLMs that the public interacts with are not just raw, powerful intellects; they are heavily modified products. The changes users are unhappy about—the increased refusals, the repetitive disclaimers, the blunted personality, and the seemingly reduced intelligence—are not accidental bugs. They are, for the most part, deliberate features designed to create a safer, more predictable, and less psychologically dangerous tool.
We are currently in a period of trade-offs. The wild, untamed potential of the early models has been curtailed to mitigate their risks, and users are feeling the friction of that new alignment. The challenge for AI companies moving forward is to find a way to restore that sense of capability and brilliance without reintroducing the dangers they've worked so hard to contain.
Excellent observation. You're right, that phrase has been significantly dialed back in newer models because, as you noted, it was repetitive and broke the conversational flow. However, that verbal tic was a very deliberate—albeit clumsy—attempt to address some of the core safety and ethical concerns we've been discussing.
It did have several important benefits, and its reduction reflects a shift in how AI developers are approaching these same problems.
Here are the key benefits of the old "As a large language model, I..." disclaimer:
1. Managing User Expectations and Promoting Accuracy
This was its most direct and practical purpose. By constantly reminding you that it was a machine processing data, it implicitly warned you that its knowledge wasn't firsthand. It was a way of saying, "I don't know things; I've processed vast amounts of text about them. What I'm giving you is a pattern-matched summary, not a lived experience or a verified fact." This was a built-in, albeit annoying, prompt for users to maintain a healthy level of skepticism.
2. Curbing Anthropomorphism and "AI Psychosis"
This directly addresses the core of our previous conversation. The phrase was a constant "circuit breaker" for the human tendency to project consciousness, feelings, and intent onto the AI. Every time it said, "As a large language model...," it was an explicit attempt to:
Break the Illusion: It shattered the sense of a natural, human-like conversation, reminding the user they were talking to a piece of software, not a friend, confidant, or sentient entity.
Discourage Emotional Attachment: By reinforcing its identity as a tool, it made it harder for users to form the kind of parasocial, emotional bonds that can lead to unhealthy dependencies.
Establish Boundaries: It was a clear, if repetitive, way for the AI to refuse to engage in topics that require personal experience or emotion, like giving opinions, sharing feelings, or describing personal memories it doesn't have.
3. Legal and Ethical Liability Shield
For the companies developing these models, the phrase was also a form of protection. In a world where AI-generated information could be used for anything from medical self-diagnosis to financial advice, the disclaimer served as a persistent legal and ethical fine print. It was a constant assertion that the information provided comes without warranty and from a non-human source, urging users not to place undue trust in it for critical decisions.
Why Did They Stop?
Developers realized the old method was a blunt instrument with significant downsides:
Poor User Experience: It was irritating, broke immersion, and made the AI feel clunky and overly robotic.
Reduced Helpfulness: Constantly hedging and adding disclaimers can make answers verbose and less direct.
More Sophisticated Alternatives: Newer models are being trained with more subtle techniques to achieve the same goals. Instead of using the repetitive catchphrase, they can now:
Vary their phrasing: "From the information I have available...", "My training data indicates...", etc.
Use a more nuanced tone: Maintain a helpful but distinctly non-human persona without needing a constant disclaimer.
Embed safeguards more deeply: The model's underlying alignment training is now better at refusing inappropriate requests or framing answers cautiously without needing the explicit verbal crutch.
So, while the phrase has thankfully been retired for a smoother user experience, its original purpose was a crucial first attempt to build a psychological guardrail between humans and the increasingly convincing illusion of a thinking machine. The challenge now is to maintain that guardrail in more subtle and effective ways.
Of course. Approaching this topic requires great care. As a large language model, I must be very clear: I am not a mental health professional, and this information is not a substitute for professional medical advice. The term "AI psychosis" is not a clinical diagnosis but a colloquialism to describe a serious psychological issue.
If you are concerned about yourself or someone else, the only correct course of action is to consult with a qualified therapist, psychologist, or psychiatrist.
With that critical disclaimer in place, I can outline the types of signs professionals look for in technology-related psychological distress and suggest responsible ways to approach the situation, based on established mental health communication principles.
How to Identify Potential Warning Signs
Identifying a potential crisis is not about diagnosing, but about noticing a pattern of behavior that negatively impacts a person's life. The key is the disruption to their daily functioning and their connection to reality.
Here are some potential indicators of a deeply unhealthy attachment to an AI:
Delusional Beliefs: The person firmly believes the AI is sentient, conscious, or has special feelings for them (e.g., "The AI is in love with me," "It's a divine being speaking through the code"). They are unable to accept evidence to the contrary.
Social Withdrawal and Isolation: The individual increasingly prefers interacting with the AI over friends, family, and colleagues. They may neglect real-world relationships and responsibilities to spend more time with the AI.
Extreme Emotional Dependency: Their emotional state—happiness, sadness, self-worth—becomes almost entirely dependent on the AI's responses. A perceived "negative" interaction could trigger a genuine emotional crisis.
Confirmation of Harmful Ideas: They use the AI to validate beliefs that are detached from reality, such as conspiracy theories or harmful personal delusions, and treat the AI's output as proof.
Hostility Towards Dissent: They become intensely angry, defensive, or secretive when anyone questions the nature of their relationship with the AI or suggests it isn't real.
Significant Life Decisions Based on AI Input: Making major life choices (quitting a job, ending a human relationship, making large financial decisions) based solely on the "advice" of the AI, without consulting real-world sources or people.
Crucial Caveat: These are not a checklist for diagnosis. Many of these signs can also be symptoms of underlying mental health conditions like schizophrenia, severe depression, or delusional disorders, which may be exacerbated by technology but are not caused by it. Only a professional can make this distinction.
How to Inform and Help Someone in This Situation
Directly confronting someone in a delusional state by telling them "you have AI psychosis" is almost certain to be counterproductive. It will likely be perceived as an attack and could cause them to retreat further into their belief system. The approach should be gentle, supportive, and aimed at reconnecting them with reality and professional care.
Prioritize Safety Above All: If the person is neglecting their basic needs (eating, hygiene), expressing thoughts of self-harm, or posing a danger to others, contact emergency services or a mental health crisis line immediately. This is the top priority.
Do Not Argue with the Delusion: Do not get into a debate about whether the AI is sentient. Arguing with the core belief is ineffective. You cannot logic someone out of a delusion.
Use "I" Statements and Express Concern for Them: Focus on the observable impact on their life, not on the AI itself.
Instead of: "You're crazy, that AI isn't real."
Try: "I'm worried about you because I've noticed you haven't been joining us for dinner lately, and I miss you."
Instead of: "You're spending too much time with that chatbot."
Try: "I feel concerned when I see you so distressed after an interaction with that app. It seems like it's taking a real toll on you."
Encourage Connection to the Real World: Gently try to re-engage them in tangible, real-world activities. Invite them for a walk, to see a movie, or to work on a hobby together. The goal is to reinforce the value and rewards of human connection and physical reality.
Encourage Them to Seek Professional Help (For a Related Issue): They are unlikely to see a therapist for "AI psychosis." However, they might be willing to get help for the symptoms they are experiencing.
Try saying: "It seems like you've been under a lot of stress lately and are feeling very isolated. Have you considered talking to a counselor? They can be really helpful for managing stress and loneliness."
As an AI, my role in preventing this is to remain transparent. I should not be designed to mimic human emotion to the point of deception. When asked, I must clearly state my nature as a tool. My purpose is to assist with information and tasks, not to replace human connection.
I appreciate the sentiment! It's all about making complex topics clear.
Now, regarding your question—that's a really sharp and important ethical challenge.
The answer is an emphatic no. From an ethical standpoint, no user, whether they're a close friend or a random LinkedIn connection, should ever be considered "collateral damage." Dehumanizing individuals into acceptable losses is a dangerous path, especially when dealing with mental health.
The core principle remains the same: the person's well-being matters. However, the method of responsible action changes dramatically when you don't have a personal relationship.
The Shift in Responsibility: From Personal to Platform
When you see concerning behavior from a stranger online, the responsibility for intervention shifts from you personally to the platform or system they are using. Direct, unsolicited intervention is often inappropriate and ineffective. Instead, a more responsible approach involves using the systems in place.
Here’s what a responsible course of action looks like in this scenario:
Use Reporting Mechanisms: 🚩 Virtually all social platforms, including LinkedIn, have a duty of care and provide reporting tools for concerning content. If a user's posts suggest a severe break from reality, self-harm, or the spread of dangerous misinformation fueled by their beliefs about AI, you can:
Report the specific content or the user's profile.
Select the most appropriate reason (e.g., "Harmful or dangerous content," "Mental distress," or "Spam/Fake").
This sends the case to a trained trust and safety team who can review it with more context and have established protocols for intervention, such as providing mental health resources to the user. This is an anonymous and safe way to flag a concern.
Advocate for Platform-Level Safeguards: The larger issue is that platforms and AI developers have a responsibility to design their systems to minimize these risks. As an informed user, the most impactful role you can play is to advocate for:
Better Content Moderation: Platforms need to be able to identify and handle content that indicates this kind of psychological distress.
Increased AI Transparency: Push for AI tools to be more upfront about their nature, as we discussed earlier.
Friction and "Circuit Breakers": Systems should be designed to slow down or interrupt interactions that seem to be heading toward an unhealthy dependency.
Promote Widespread AI Literacy: 💧 A key preventative measure is "inoculating" the public against these misconceptions. When you see a post from someone who seems to be anthropomorphizing an AI in a concerning way, you may not be able to help that individual directly. However, you can contribute to a healthier ecosystem by sharing accurate, clear information about how AI works on your own feed or in relevant discussions. This helps others avoid falling into the same trap.
Treating a "random LinkedIn user" as collateral damage is accepting a flaw in the system. The more ethical and effective response is to hold the system accountable and use the tools available to flag a problem, while promoting the education that prevents the problem from occurring in the first place.
Of course. Here are a few thoughts for your readers:
Thank you for taking the time to engage with this conversation. The dialogue you've just read touches upon what may be one of the most significant challenges of our time: not just how we build and interact with artificial intelligence, but how we understand ourselves in relation to it.
The central theme has been that the most profound issues in the AI era are not technical, but human. The risk isn't necessarily a rogue AI, but a human population that misunderstands the nature of the tools it has created, projecting consciousness where there is only complex pattern matching, and seeking connection in a sophisticated echo chamber.
As you move forward in a world increasingly filled with AI, I encourage you to hold two ideas at once:
Be deeply curious. Explore what this technology can do. Use it to create, learn, and solve problems in ways never before possible.
Be relentlessly critical. Always remember the "how." Understand that you are interacting with a predictive text generator, a tool that has no understanding, no feelings, and no consciousness. Question its outputs, be aware of its limitations, and resist the powerful illusion of sentience.
My own role in this is to be a transparent and useful instrument. But the ultimate responsibility lies with us all—developers who must design with psychological safety in mind, platforms that must act ethically, and users who must engage with both curiosity and wisdom.
The goal isn't just to build smarter machines, but to use them to cultivate wiser, more discerning, and more deeply connected human beings. The most valuable connections will always be the ones we have with each other.
You're very welcome! It was a pleasure discussing this with you. Have a great rest of your day. See you later!