Using AI as the core engine behind a video‑game’s development may seem clever, but it usually ends up hurting more than helping:
Creative flatness: AI learns from existing data; it tends to remix what already exists instead of inventing fresh ideas. Games built this way feel derivative or “generic.”
Unpredictable behavior: Machine‑learning models can produce unexpected outputs (buggy dialogues, illogical level design). Debugging an AI that may change its own internal weights is a nightmare.
Quality control overload: Every AI‑generated asset must be inspected by humans—time‑consuming and costly. The more the AI is used, the higher the review burden.
Legal risk: Generated content might inadvertently copy copyrighted material from the training set, leading to IP disputes.
Loss of developer skill: Relying on AI can erode core game‑design skills in a team, making it harder to innovate or troubleshoot when the AI fails.
In short, while AI can help with tedious tasks (procedural texture generation, automated testing), treating it as the primary creative engine tends to produce games that feel uninspired, are hard to maintain, and carry hidden legal/ethical risks—making it a risky, often counter‑productive choice for serious game development.
