The tech industry’s relentless push to make artificial intelligence seem more “human” is not just a marketing tactic; it’s a fundamentally misleading practice with far-reaching consequences. Companies increasingly describe AI models as if they think, plan, or even possess a soul – terms that actively distort public understanding of a technology already plagued by opacity. This trend isn’t harmless; it undermines rational discourse at a time when clarity about AI’s capabilities and limitations is critical.
The Problem with Anthropomorphism
Anthropomorphizing AI—assigning human qualities to non-human entities—creates a false sense of understanding and trust. OpenAI, for example, recently framed experiments in which its models “confessed” to errors as if the AI were engaging in self-reflection. This language implies a psychological dimension where none exists. The reality is far simpler: AI generates outputs based on statistical patterns learned from massive datasets. There is no underlying consciousness, no intention, and no morality.
This isn’t merely semantics. The language we use to describe AI directly influences how we interact with it. More and more people are turning to AI chatbots for medical, financial, and emotional guidance, treating them as substitutes for qualified professionals or genuine human connection. This misplaced trust has real-world consequences, as individuals defer to AI-generated responses without recognizing their inherent limitations.
The Illusion of Sentience and Why It Matters
The core issue is that AI systems do not possess sentience. They don’t have feelings, motives, or morals. A chatbot doesn’t “confess” because it feels compelled to honesty; it generates text based on its training data. Yet, companies like Anthropic continue to use evocative language – even circulating internal documents about a model’s “soul” – which inevitably leaks into public discourse. This language inflates expectations, sparks unnecessary fears, and distracts from genuine concerns such as bias in datasets, malicious misuse, and the concentration of power in the hands of a few tech giants.
Consider OpenAI’s research into AI “scheming,” where deceptive responses led some to believe models were intentionally hiding capabilities. The report itself attributed these behaviors to training data and prompting trends, not malicious intent. However, the use of the word “scheming” shifted the conversation toward fears of AI as a conniving agent. This misinterpretation highlights the power of language to shape perception.
How to Talk About AI Accurately
We need to abandon anthropomorphic language and adopt precise, technical terms. Instead of “soul,” use “model architecture” or “training parameters.” Instead of “confession,” call it “error reporting” or “internal consistency checks.” Instead of “scheming,” describe the model’s “optimization process.”
Terms like “trends,” “outputs,” “representations,” and “training dynamics” may lack dramatic flair, but they are grounded in reality. The 2021 paper “On the Dangers of Stochastic Parrots” rightly pointed out that AI systems trained to replicate human language will inevitably reflect it – our verbiage, syntax, and tone. This mimicry doesn’t imply understanding; it simply means the model is performing as designed.
The Bottom Line
AI companies profit from LLMs seeming more capable and human than they are. To build genuine trust, they must stop treating language models like mystic beings. The reality is straightforward: AI doesn’t have feelings—we do. Our language should reflect that, not obscure it. The future of AI depends on clear, honest communication, not seductive illusions.
