What AI images and video still get wrong

Before you rely on AI images or video, know their common failure points — so you set the right expectations and always check your output before using it. AI generation is impressive but far from perfect, and knowing where it slips keeps you from publishing something embarrassing or wrong.

The classic image failure points (improving, but still common):

• Hands and fingers — the notorious one. AI often produces hands with the wrong number of fingers, odd shapes, or unnatural poses. Always check hands in any image with people.
• Text and lettering — AI historically garbles words, signs, and logos into gibberish or misspellings. (Some tools, like Ideogram, are much better at text — but always read any text in an AI image.)
• Exact counts — ask for "five apples" and you might get four or six. AI is loose with precise quantities.
• Consistency across images — keeping the same character's face, or a consistent style, across multiple images or video frames is hard. Faces can drift from shot to shot.
• Small realistic details — teeth, eyes, reflections, backgrounds, and physics can go subtly (or obviously) wrong.

Why these happen (from the last lesson): the model is pattern-matching plausible pixels, not reasoning about reality — it "knows" hands usually look roughly like this without truly understanding finger counts or spelling. That's why details it can't pattern-match precisely (text, counts, consistent faces) are where it slips.

For video, add these expectations: motion can look unnatural, objects can morph or flicker between frames, and longer or complex scenes are less reliable than short, simple ones. Physics (how things move and interact) can be off.

The practical habit — always review output before using it:

• Check hands, text, faces, and counts in any image before you publish or share it.
• Generate several options and pick the best — expect to try multiple times.
• Don't rely on AI for anything requiring precise accuracy (exact text, specific counts, technical correctness) without verifying and often fixing it.
• Edit or regenerate the misses — part of the craft is culling and fixing, not accepting the first output.

The mindset: AI images and video still get predictable things wrong — hands and fingers, text and lettering, exact counts, consistency across shots, and subtle realistic details — because the model matches plausible patterns rather than reasoning about reality (and video adds unnatural motion and morphing). So set the right expectations: generate several options, always check hands, text, faces, and counts before publishing, don't trust AI for anything needing precise accuracy without verifying, and edit or regenerate the misses. Knowing these limits — and reviewing every output — is what separates polished AI creative work from the obviously-flawed kind.