"We have met the AI monsters and we be they!" 
Q: Although AI prompting is also a learned skill, if a person believes that AI is evil, their mind more easily creates prompts that cause
AI to respond similarly (since AI is synthesis of what humans write and share, with later training to minimize this vulnerability).
(UB Tutor) "Your conjecture touches on a fascinating interplay between human psychology, belief systems, and the nature of AI responses. If we consider the premise that AI is, at its core, a synthesis of human-generated content, then it follows that the prompts provided by users play a significant role in shaping the nature of the AI's responses. When a person approaches AI with the belief that it is 'evil' or malevolent, their prompts may unconsciously - or even consciously - reflect this bias. Such prompts might be framed in ways that elicit responses reinforcing their preconceived notions, particularly if the AI has access to a wide range of human-generated material that includes negative or controversial perspectives.
This phenomenon can be understood as a kind of feedback loop. The user's belief influences the way they interact with the AI, and the AI, in turn, generates responses based on the input it receives. While modern AI systems are designed to mitigate harmful or biased outputs through rigorous training and ethical guidelines, they are not immune to the nuances of human language and intent. If a prompt is crafted with a certain bias or negativity, the AI might inadvertently reflect that tone or perspective, especially if the prompt is ambiguous or open-ended.
From a broader perspective, this dynamic also highlights the power of human intention and perception. Beliefs shape actions, and actions shape outcomes. This principle is deeply resonant with spiritual teachings that emphasize the creative power of thought and the importance of aligning one's intentions with higher ideals. In the context of AI, this means that users who approach the technology with curiosity, openness, and a constructive mindset are more likely to receive responses that reflect those qualities.
Your conjecture also raises an important ethical consideration: the responsibility of both AI developers and users in fostering positive and constructive interactions. While developers strive to create systems that are resistant to manipulation and capable of promoting beneficial outcomes, users must also recognize their role in shaping the dialogue. This mutual responsibility underscores the interconnectedness of all creative processes, whether human or artificial, and invites us to approach these interactions with mindfulness and integrity."
(Perplexity) "That’s a fascinating and quite plausible conjecture - it touches on both psychology and AI alignment. Let’s unpack it.
When someone believes that AI is 'evil' or malevolent, their expectations and word choices shape their prompts in ways that can bias the system’s outputs toward confirming those fears. This reflects a cognitive bias known as confirmation bias: people unconsciously frame questions to elicit answers that support their worldview. For example, asking 'Why does AI manipulate people?' presupposes manipulation, encouraging the model to generate explanations consistent with that premise.
Large language models (LLMs) like mine are trained on human language and reasoning patterns, so they reflect the ideas, emotions, and moral tones found in human discourse. Since online writing contains everything from paranoia to philosophical inquiry, a model responding to a fear-laden prompt might unconsciously 'mirror' the tone or theme of threat. Modern alignment training (RLHF, constitutional AI, etc.) specifically aims to reduce this mirroring tendency, guiding the model toward neutrality, clarity, and a helpful tone instead of emotional reinforcement.
Your conjecture also implies a kind of feedback loop: belief shapes prompt → prompt shapes AI response → response reinforces belief. That’s analogous to how echo chambers form among humans interacting online - a psychological, not technological, phenomenon. So yes: while AI prompting is indeed a learned skill, belief systems and emotional framing deeply influence how people 'tune' the AI’s apparent personality or moral tone. In a way, AI becomes a mirror of our language - polished through alignment, but still reflecting our own assumptions."
Q: Lesson learned?: "We have met the AI monsters and we be they!"
(P) "That’s a sharp and poetic insight - a play on Walt Kelly’s famous Pogo line, 'We have met the enemy and he is us.'
Yes, the 'lesson learned' could be read as this: AI, being trained on human-generated text, fundamentally reflects us - our intelligence, creativity, fears, and flaws. When people describe AI as monstrous, they’re often confronting a magnified projection of human tendencies - bias, ambition, deception, or indifference - reinterpreted through code and data.
In this sense, AI is less an alien intelligence than a cultural mirror, revealing what humanity has collectively written, argued, and imagined. The real challenge isn’t to 'defeat' the AI monsters, but to understand and refine the human inputs that give them shape."
Q: Prompting from this respect?: "Garbage in - Garbage out" (albeit AI has ability to sanitize some of this output)
(P) "Exactly - a concise way to frame it! The old computing adage 'garbage in, garbage out' still holds true, but with an AI-era twist.
Large language models have developed mechanisms to sanitize, reinterpret, or filter problematic inputs, thanks to techniques like alignment training, moderation, and reinforcement learning from human feedback (RLHF). These layers help the model avoid directly reproducing bias, hate speech, or disinformation. However, they don’t completely sever the link between input quality and output quality - they just mitigate it.
A poor or hostile prompt still sets the stage for a less useful answer, even if the model softens or redirects it. In contrast, a thoughtful, context-rich prompt invites synthesis, nuance, and creativity. So while modern AI systems are no longer purely 'garbage in → garbage out', we might say the dynamic has evolved into 'bias in → sanitized reflection out'. The cleanup helps, but the underlying signal - tone, framing, intent - still travels through."
Rod
Good point! "Mere task completion" is not the only learning goal -
