As we hinted above, the "chat" experience with an AI model is a clever hack: Within every AI chatbot interaction, there is an input and an output. The input is the "prompt," and the output is often called a "prediction" because it attempts to complete the prompt with the best possible continuation. In between, there's a neural network (or a set of neural networks) with fixed weights doing a processing task. The conversational back and forth isn't built into the model; it's a scripting trick that makes next-word-prediction text generation feel like a persistent dialogue.
Each time you send a message to ChatGPT, Copilot, Grok, Claude, or Gemini, the system takes the entire conversation history—every message from both you and the bot—and feeds it back to the model as one long prompt, asking it to predict what comes next. The model intelligently reasons about what would logically continue the dialogue, but it doesn't "remember" your previous messages as an agent with continuous existence would. Instead, it's re-reading the entire transcript each time and generating a response.
To understand how the illusion of personality is constructed, we need to examine what parts of the input fed into the AI model shape it. AI researcher Eugene Vinitsky recently broke down the human decisions behind these systems into four key layers, which we can expand upon with several others below:
1. Pre-training: The foundation of "personality"
The first and most fundamental layer of personality is called pre-training. During an initial training process that actually creates the AI model's neural network, the model absorbs statistical relationships from billions of examples of text, storing patterns about how words and ideas typically connect.
Research has found that personality measurements in LLM outputs are significantly influenced by training data. OpenAI's GPT models are trained on sources like copies of websites, books, Wikipedia, and academic publications. The exact proportions matter enormously for what users later perceive as "personality traits" once the model is in use, making predictions.
2. Post-training: Sculpting the raw material
Reinforcement Learning from Human Feedback (RLHF) is an additional training process where the model learns to give responses that humans rate as good. Research from Anthropic in 2022 revealed how human raters' preferences get encoded as what we might consider fundamental "personality traits." When human raters consistently prefer responses that begin with "I understand your concern," for example, the fine-tuning process reinforces connections in the neural network that make it more likely to produce those kinds of outputs in the future.
This process is what has created sycophantic AI models, such as variations of GPT-4o, over the past year. And interestingly, research has shown that the demographic makeup of human raters significantly influences model behavior. When raters skew toward specific demographics, models develop communication patterns that reflect those groups' preferences.
3. System prompts: Invisible stage directions
Hidden instructions tucked into the prompt by the company running the AI chatbot, called "system prompts," can completely transform a model's apparent personality. These prompts get the conversation started and identify the role the LLM will play. They include statements like "You are a helpful AI assistant" and can share the current time and who the user is.
A comprehensive survey of prompt engineering demonstrated just how powerful these prompts are. Adding instructions like "You are a helpful assistant" versus "You are an expert researcher" changed accuracy on factual questions by up to 15 percent.
Grok perfectly illustrates this. According to xAI's published system prompts, earlier versions of Grok's system prompt included instructions to not shy away from making claims that are "politically incorrect." This single instruction transformed the base model into something that would readily generate controversial content.
4. Persistent memories: The illusion of continuity
ChatGPT's memory feature adds another layer of what we might consider a personality. A big misunderstanding about AI chatbots is that they somehow "learn" on the fly from your interactions. Among commercial chatbots active today, this is not true. When the system "remembers" that you prefer concise answers or that you work in finance, these facts get stored in a separate database and are injected into every conversation's context window—they become part of the prompt input automatically behind the scenes. Users interpret this as the chatbot "knowing" them personally, creating an illusion of relationship continuity.
So when ChatGPT says, "I remember you mentioned your dog Max," it's not accessing memories like you'd imagine a person would, intermingled with its other "knowledge." It's not stored in the AI model's neural network, which remains unchanged between interactions. Every once in a while, an AI company will update a model through a process called fine-tuning, but it's unrelated to storing user memories.
5. Context and RAG: Real-time personality modulation
Retrieval Augmented Generation (RAG) adds another layer of personality modulation. When a chatbot searches the web or accesses a database before responding, it's not just gathering facts—it's potentially shifting its entire communication style by putting those facts into (you guessed it) the input prompt. In RAG systems, LLMs can potentially adopt characteristics such as tone, style, and terminology from retrieved documents, since those documents are combined with the input prompt to form the complete context that gets fed into the model for processing.
If the system retrieves academic papers, responses might become more formal. Pull from a certain subreddit, and the chatbot might make pop culture references. This isn't the model having different moods—it's the statistical influence of whatever text got fed into the context window.
6. The randomness factor: Manufactured spontaneity
Lastly, we can't discount the role of randomness in creating personality illusions. LLMs use a parameter called "temperature" that controls how predictable responses are.
Research investigating temperature's role in creative tasks reveals a crucial trade-off: While higher temperatures can make outputs more novel and surprising, they also make them less coherent and harder to understand. This variability can make the AI feel more spontaneous; a slightly unexpected (higher temperature) response might seem more "creative," while a highly predictable (lower temperature) one could feel more robotic or "formal."
The random variation in each LLM output makes each response slightly different, creating an element of unpredictability that presents the illusion of free will and self-awareness on the machine's part. This random mystery leaves plenty of room for magical thinking on the part of humans, who fill in the gaps of their technical knowledge with their imagination.
The human cost of the illusion
The illusion of AI personhood can potentially exact a heavy toll. In health care contexts, the stakes can be life or death. When vulnerable individuals confide in what they perceive as an understanding entity, they may receive responses shaped more by training data patterns than therapeutic wisdom. The chatbot that congratulates someone for stopping psychiatric medication isn't expressing judgment—it's completing a pattern based on how similar conversations appear in its training data.
Perhaps most concerning are the emerging cases of what some experts are informally calling "AI Psychosis" or "ChatGPT Psychosis"—vulnerable users who develop delusional or manic behavior after talking to AI chatbots. These people often perceive chatbots as an authority that can validate their delusional ideas, often encouraging them in ways that become harmful.
Meanwhile, when Elon Musk's Grok generates Nazi content, media outlets describe how the bot "went rogue" rather than framing the incident squarely as the result of xAI's deliberate configuration choices. The conversational interface has become so convincing that it can also launder human agency, transforming engineering decisions into the whims of an imaginary personality.
by Benji Edwards, Ars Technica | Read more:
Image: Credit: ivetavaicule via Getty Images
