If the language is broken, the systems will be too.
We’ve normalized anthropomorphism in AI:
- “AI thinks”
- “AI understands”
- “AI decides”
- “AI believes”
But none of these are true.
These aren’t harmless metaphors — they’re systemic bugs in how we design, communicate, and govern artificial intelligence. A field this confused about its own vocabulary is bound to ship defective, and potentially dangerous, products.
🔍 21 Flawed Terms in AI
Here’s a list of widely misused or misleading terms — and why they matter:
- Intelligence – Suggests reasoning or cognition. In reality: statistical pattern-matching.
- AI thinks – Projects conscious thought. Models generate based on training data.
- Understands / Comprehends – Implies semantic grasp. There's none.
- Wants / Decides / Chooses – Suggests agency. Models optimize, they don’t choose.
- Knows / Remembers – No persistent knowledge or memory.
- Believes – Misleading. Models don’t form beliefs — they output predictions.
- Learns (during use) – Usually false. Most models don’t learn live.
- AI says / Tells us – Assigns voice and authority to a tool.
- Feels / Is afraid / Loves – Pure anthropomorphism. Fictional at best.
- Sentient / Conscious / Self-aware – Baseless hype.
- Personality / Mood / Opinion – Style emulation ≠ internal state.
- Artificial General Intelligence (AGI) – Speculative, not real.
- Superintelligence – Undefined and sensational.
- Neural networks are like brains – Metaphor stretched too far.
- AI will replace humans – Oversimplified. Tasks may be automated, not roles.
- Smart – Marketing fluff. Precise terms matter.
- AI hallucination – Cutesy term for dangerous failure.
- Alignment – Vague unless defined clearly.
- Responsible AI – Sounds nice. Often means nothing.
- Bias-free AI – Doesn’t exist. Aim for “bias-managed.”
- AI Safety (as only existential risk) – Ignores present-day harms like misinformation and fraud.
🧠 Why This Matters
Every time we misuse language, we:
- Distort expectations
- Undermine accountability
- Encourage public misunderstanding
- Let flawed systems pass as “intelligent”
The result? A public that over-trusts black-box systems, a developer culture that can’t debug intent, and a regulatory environment chasing shadows.
🛠️ More on This
If you're interested in how these language problems translate into structural risk, I’ve written more here:
Language is not just communication — it’s scaffolding.
And if it’s built on sand, so is everything else.
Let’s start there.
Top comments (0)