Voice demos are easy to fake. Give a model a clean recording, let it read a theatrical sentence, and the result can sound impressive enough for a launch video. That is not the hard part. The hard part is making speech generation behave like an actual product: multilingual, low-latency, emotionally credible, speaker-consistent, and not outrageously expensive to serve. ...
Image generation has become good enough to be useful and unreliable enough to remain annoying. That is the normal condition of enterprise AI: impressive demos, awkward edge cases, and someone in operations quietly asking whether the model actually understood the instruction or merely produced something that looked plausible from a distance. A user asks for “a red ceramic mug on a wooden desk, next to an open notebook, in morning light.” The model produces a beautiful desk, credible sunlight, maybe even the notebook. The mug is blue. Or metallic. Or missing. If a separate vision model can look at the image and say, “That is not a red ceramic mug,” the failure feels almost rude. The system can see the problem after creating it. Very efficient, in the same way that a committee can discover a typo after approving the brochure. ...